From cc569eea69e1c2c8d2f06ba876e08b626c61318d Mon Sep 17 00:00:00 2001
From: Jake VanderPlas <jakevdp@google.com>
Date: Fri, 5 May 2023 16:18:02 -0700
Subject: [PATCH] Add v2 notebooks

---
 notebooks/00.00-Preface.ipynb                 |   112 +-
 .../01.00-IPython-Beyond-Normal-Python.ipynb  |   128 +-
 notebooks/01.01-Help-And-Documentation.ipynb  |   273 +-
 .../01.02-Shell-Keyboard-Shortcuts.ipynb      |   123 +-
 notebooks/01.03-Magic-Commands.ipynb          |   149 +-
 notebooks/01.04-Input-Output-History.ipynb    |    94 +-
 .../01.05-IPython-And-Shell-Commands.ipynb    |   100 +-
 notebooks/01.06-Errors-and-Debugging.ipynb    |   212 +-
 notebooks/01.07-Timing-and-Profiling.ipynb    |   286 +-
 notebooks/01.08-More-IPython-Resources.ipynb  |    59 +-
 notebooks/02.00-Introduction-to-NumPy.ipynb   |   120 +-
 .../02.01-Understanding-Data-Types.ipynb      |   332 +-
 .../02.02-The-Basics-Of-NumPy-Arrays.ipynb    |   839 +-
 .../02.03-Computation-on-arrays-ufuncs.ipynb  |   415 +-
 ....04-Computation-on-arrays-aggregates.ipynb |   279 +-
 ...5-Computation-on-arrays-broadcasting.ipynb |   329 +-
 .../02.06-Boolean-Arrays-and-Masks.ipynb      |   500 +-
 notebooks/02.07-Fancy-Indexing.ipynb          |   306 +-
 notebooks/02.08-Sorting.ipynb                 |   426 +-
 notebooks/02.09-Structured-Data-NumPy.ipynb   |   217 +-
 notebooks/03.00-Introduction-to-Pandas.ipynb  |    80 +-
 .../03.01-Introducing-Pandas-Objects.ipynb    |   651 +-
 .../03.02-Data-Indexing-and-Selection.ipynb   |   901 +-
 notebooks/03.03-Operations-in-Pandas.ipynb    |   583 +-
 notebooks/03.04-Missing-Values.ipynb          |   668 +-
 notebooks/03.05-Hierarchical-Indexing.ipynb   |  1516 +-
 notebooks/03.06-Concat-And-Append.ipynb       |   632 +-
 notebooks/03.07-Merge-and-Join.ipynb          |  1190 +-
 .../03.08-Aggregation-and-Grouping.ipynb      |  1106 +-
 notebooks/03.09-Pivot-Tables.ipynb            |   642 +-
 notebooks/03.10-Working-With-Strings.ipynb    |   694 +-
 .../03.11-Working-with-Time-Series.ipynb      |  1161 +-
 .../03.12-Performance-Eval-and-Query.ipynb    |   537 +-
 notebooks/03.13-Further-Resources.ipynb       |    62 +-
 .../04.00-Introduction-To-Matplotlib.ipynb    |   235 +-
 notebooks/04.01-Simple-Line-Plots.ipynb       |   296 +-
 notebooks/04.02-Simple-Scatter-Plots.ipynb    |   173 +-
 notebooks/04.03-Errorbars.ipynb               |   124 +-
 .../04.04-Density-and-Contour-Plots.ipynb     |   150 +-
 notebooks/04.05-Histograms-and-Binnings.ipynb |   198 +-
 notebooks/04.06-Customizing-Legends.ipynb     |   222 +-
 notebooks/04.07-Customizing-Colorbars.ipynb   |   284 +-
 notebooks/04.08-Multiple-Subplots.ipynb       |   194 +-
 notebooks/04.09-Text-and-Annotation.ipynb     |   213 +-
 notebooks/04.10-Customizing-Ticks.ipynb       |   265 +-
 .../04.11-Settings-and-Stylesheets.ipynb      |   325 +-
 .../04.12-Three-Dimensional-Plotting.ipynb    |   266 +-
 .../04.14-Visualization-With-Seaborn.ipynb    |   967 +-
 notebooks/04.15-Further-Resources.ipynb       |    76 +-
 notebooks/05.00-Machine-Learning.ipynb        |    63 +-
 .../05.01-What-Is-Machine-Learning.ipynb      |   175 +-
 .../05.02-Introducing-Scikit-Learn.ipynb      |   547 +-
 ...Hyperparameters-and-Model-Validation.ipynb |   481 +-
 notebooks/05.04-Feature-Engineering.ipynb     |   307 +-
 notebooks/05.05-Naive-Bayes.ipynb             |   247 +-
 notebooks/05.06-Linear-Regression.ipynb       |   559 +-
 notebooks/05.07-Support-Vector-Machines.ipynb |   422 +-
 notebooks/05.08-Random-Forests.ipynb          |   323 +-
 .../05.09-Principal-Component-Analysis.ipynb  |   413 +-
 notebooks/05.10-Manifold-Learning.ipynb       |   401 +-
 notebooks/05.11-K-Means.ipynb                 |   346 +-
 notebooks/05.12-Gaussian-Mixtures.ipynb       |   443 +-
 .../05.13-Kernel-Density-Estimation.ipynb     |   531 +-
 notebooks/05.14-Image-Features.ipynb          |   328 +-
 notebooks/05.15-Learning-More.ipynb           |    98 +-
 notebooks/06.00-Figure-Code.ipynb             |   643 +-
 notebooks/Untitled.ipynb                      |     6 +
 notebooks/data/president_heights.csv          |     2 +
 notebooks/helpers_05_08.py                    |     9 +-
 notebooks_v1/00.00-Preface.ipynb              |   203 +
 .../01.00-IPython-Beyond-Normal-Python.ipynb  |   152 +
 .../01.01-Help-And-Documentation.ipynb        |   355 +
 .../01.02-Shell-Keyboard-Shortcuts.ipynb      |   207 +
 notebooks_v1/01.03-Magic-Commands.ipynb       |   238 +
 notebooks_v1/01.04-Input-Output-History.ipynb |   222 +
 .../01.05-IPython-And-Shell-Commands.ipynb    |   255 +
 notebooks_v1/01.06-Errors-and-Debugging.ipynb |   426 +
 notebooks_v1/01.07-Timing-and-Profiling.ipynb |   548 +
 .../01.08-More-IPython-Resources.ipynb        |    99 +
 .../02.00-Introduction-to-NumPy.ipynb         |   191 +
 .../02.01-Understanding-Data-Types.ipynb      |   830 +
 .../02.02-The-Basics-Of-NumPy-Arrays.ipynb    |  1572 ++
 .../02.03-Computation-on-arrays-ufuncs.ipynb  |  1109 ++
 ....04-Computation-on-arrays-aggregates.ipynb |   646 +
 ...5-Computation-on-arrays-broadcasting.ipynb |   804 +
 .../02.06-Boolean-Arrays-and-Masks.ipynb      |  1283 ++
 notebooks_v1/02.07-Fancy-Indexing.ipynb       |   932 +
 notebooks_v1/02.08-Sorting.ipynb              |   786 +
 .../02.09-Structured-Data-NumPy.ipynb         |   603 +
 .../03.00-Introduction-to-Pandas.ipynb        |   167 +
 .../03.01-Introducing-Pandas-Objects.ipynb    |  1563 ++
 .../03.02-Data-Indexing-and-Selection.ipynb   |  1604 ++
 notebooks_v1/03.03-Operations-in-Pandas.ipynb |  1038 ++
 notebooks_v1/03.04-Missing-Values.ipynb       |  1299 ++
 .../03.05-Hierarchical-Indexing.ipynb         |  2804 +++
 notebooks_v1/03.06-Concat-And-Append.ipynb    |  1640 ++
 notebooks_v1/03.07-Merge-and-Join.ipynb       |  3573 ++++
 .../03.08-Aggregation-and-Grouping.ipynb      |  2660 +++
 notebooks_v1/03.09-Pivot-Tables.ipynb         |  1379 ++
 notebooks_v1/03.10-Working-With-Strings.ipynb |  1407 ++
 .../03.11-Working-with-Time-Series.ipynb      |  1960 ++
 .../03.12-Performance-Eval-and-Query.ipynb    |  1150 ++
 notebooks_v1/03.13-Further-Resources.ipynb    |    96 +
 .../04.00-Introduction-To-Matplotlib.ipynb    |   532 +
 notebooks_v1/04.01-Simple-Line-Plots.ipynb    |   647 +
 notebooks_v1/04.02-Simple-Scatter-Plots.ipynb |   358 +
 notebooks_v1/04.03-Errorbars.ipynb            |   259 +
 .../04.04-Density-and-Contour-Plots.ipynb     |   331 +
 .../04.05-Histograms-and-Binnings.ipynb       |   396 +
 notebooks_v1/04.06-Customizing-Legends.ipynb  |   438 +
 .../04.07-Customizing-Colorbars.ipynb         |   572 +
 notebooks_v1/04.08-Multiple-Subplots.ipynb    |   439 +
 notebooks_v1/04.09-Text-and-Annotation.ipynb  |   446 +
 notebooks_v1/04.10-Customizing-Ticks.ipynb    |   508 +
 .../04.11-Settings-and-Stylesheets.ipynb      |   653 +
 .../04.12-Three-Dimensional-Plotting.ipynb    |   603 +
 .../04.13-Geographic-Data-With-Basemap.ipynb  |     0
 .../04.14-Visualization-With-Seaborn.ipynb    |  1799 ++
 notebooks_v1/04.15-Further-Resources.ipynb    |    97 +
 notebooks_v1/05.00-Machine-Learning.ipynb     |    91 +
 .../05.01-What-Is-Machine-Learning.ipynb      |   512 +
 .../05.02-Introducing-Scikit-Learn.ipynb      |  1587 ++
 ...Hyperparameters-and-Model-Validation.ipynb |  1176 ++
 notebooks_v1/05.04-Feature-Engineering.ipynb  |   911 +
 notebooks_v1/05.05-Naive-Bayes.ipynb          |   749 +
 notebooks_v1/05.06-Linear-Regression.ipynb    |  1397 ++
 .../05.07-Support-Vector-Machines.ipynb       |  1044 ++
 notebooks_v1/05.08-Random-Forests.ipynb       |   756 +
 .../05.09-Principal-Component-Analysis.ipynb  |  1108 ++
 notebooks_v1/05.10-Manifold-Learning.ipynb    |  1063 ++
 notebooks_v1/05.11-K-Means.ipynb              |  1007 +
 notebooks_v1/05.12-Gaussian-Mixtures.ipynb    |  1075 ++
 .../05.13-Kernel-Density-Estimation.ipynb     |  1094 ++
 notebooks_v1/05.14-Image-Features.ipynb       |   695 +
 notebooks_v1/05.15-Learning-More.ipynb        |   127 +
 notebooks_v1/06.00-Figure-Code.ipynb          |  2786 +++
 {notebooks => notebooks_v1}/Index.ipynb       |     0
 notebooks_v1/Untitled.ipynb                   |     6 +
 .../data/BicycleWeather.csv                   |     0
 .../data/Seattle2014.csv                      |     0
 notebooks_v1/data/births.csv                  | 15548 ++++++++++++++++
 .../data/california_cities.csv                |     0
 notebooks_v1/data/president_heights.csv       |    43 +
 notebooks_v1/data/state-abbrevs.csv           |    52 +
 notebooks_v1/data/state-areas.csv             |    53 +
 notebooks_v1/data/state-population.csv        |  2545 +++
 .../figures/02.05-broadcasting.png            |   Bin
 .../figures/03.08-split-apply-combine.png     |   Bin
 .../figures/05.01-classification-1.png        |   Bin
 .../figures/05.01-classification-2.png        |   Bin
 .../figures/05.01-classification-3.png        |   Bin
 .../figures/05.01-clustering-1.png            |   Bin
 .../figures/05.01-clustering-2.png            |   Bin
 .../figures/05.01-dimesionality-1.png         |   Bin
 .../figures/05.01-dimesionality-2.png         |   Bin
 .../figures/05.01-regression-1.png            |   Bin
 .../figures/05.01-regression-2.png            |   Bin
 .../figures/05.01-regression-3.png            |   Bin
 .../figures/05.01-regression-4.png            |   Bin
 .../figures/05.02-samples-features.png        |   Bin
 .../figures/05.03-2-fold-CV.png               |   Bin
 .../figures/05.03-5-fold-CV.png               |   Bin
 .../figures/05.03-bias-variance-2.png         |   Bin
 .../figures/05.03-bias-variance.png           |   Bin
 .../figures/05.03-learning-curve.png          |   Bin
 .../figures/05.03-validation-curve.png        |   Bin
 .../figures/05.05-gaussian-NB.png             |   Bin
 .../figures/05.06-gaussian-basis.png          |   Bin
 .../figures/05.08-decision-tree-levels.png    |   Bin
 .../05.08-decision-tree-overfitting.png       |   Bin
 .../figures/05.08-decision-tree.png           |   Bin
 .../figures/05.09-PCA-rotation.png            |   Bin
 .../figures/05.09-digits-pca-components.png   |   Bin
 .../figures/05.09-digits-pixel-components.png |   Bin
 .../figures/05.10-LLE-vs-MDS.png              |   Bin
 .../05.11-expectation-maximization.png        |   Bin
 .../figures/05.12-covariance-type.png         |   Bin
 .../figures/Data_Science_VD.png               |   Bin
 .../figures/PDSH-cover-small.png              |   Bin
 .../figures/PDSH-cover.png                    |   Bin
 .../figures/array_vs_list.png                 |   Bin
 .../figures/cint_vs_pyint.png                 |   Bin
 notebooks_v1/helpers_05_08.py                 |    83 +
 notebooks_v2/data.csv                         |    11 +
 184 files changed, 91323 insertions(+), 12129 deletions(-)
 create mode 100644 notebooks/Untitled.ipynb
 create mode 100644 notebooks_v1/00.00-Preface.ipynb
 create mode 100644 notebooks_v1/01.00-IPython-Beyond-Normal-Python.ipynb
 create mode 100644 notebooks_v1/01.01-Help-And-Documentation.ipynb
 create mode 100644 notebooks_v1/01.02-Shell-Keyboard-Shortcuts.ipynb
 create mode 100644 notebooks_v1/01.03-Magic-Commands.ipynb
 create mode 100644 notebooks_v1/01.04-Input-Output-History.ipynb
 create mode 100644 notebooks_v1/01.05-IPython-And-Shell-Commands.ipynb
 create mode 100644 notebooks_v1/01.06-Errors-and-Debugging.ipynb
 create mode 100644 notebooks_v1/01.07-Timing-and-Profiling.ipynb
 create mode 100644 notebooks_v1/01.08-More-IPython-Resources.ipynb
 create mode 100644 notebooks_v1/02.00-Introduction-to-NumPy.ipynb
 create mode 100644 notebooks_v1/02.01-Understanding-Data-Types.ipynb
 create mode 100644 notebooks_v1/02.02-The-Basics-Of-NumPy-Arrays.ipynb
 create mode 100644 notebooks_v1/02.03-Computation-on-arrays-ufuncs.ipynb
 create mode 100644 notebooks_v1/02.04-Computation-on-arrays-aggregates.ipynb
 create mode 100644 notebooks_v1/02.05-Computation-on-arrays-broadcasting.ipynb
 create mode 100644 notebooks_v1/02.06-Boolean-Arrays-and-Masks.ipynb
 create mode 100644 notebooks_v1/02.07-Fancy-Indexing.ipynb
 create mode 100644 notebooks_v1/02.08-Sorting.ipynb
 create mode 100644 notebooks_v1/02.09-Structured-Data-NumPy.ipynb
 create mode 100644 notebooks_v1/03.00-Introduction-to-Pandas.ipynb
 create mode 100644 notebooks_v1/03.01-Introducing-Pandas-Objects.ipynb
 create mode 100644 notebooks_v1/03.02-Data-Indexing-and-Selection.ipynb
 create mode 100644 notebooks_v1/03.03-Operations-in-Pandas.ipynb
 create mode 100644 notebooks_v1/03.04-Missing-Values.ipynb
 create mode 100644 notebooks_v1/03.05-Hierarchical-Indexing.ipynb
 create mode 100644 notebooks_v1/03.06-Concat-And-Append.ipynb
 create mode 100644 notebooks_v1/03.07-Merge-and-Join.ipynb
 create mode 100644 notebooks_v1/03.08-Aggregation-and-Grouping.ipynb
 create mode 100644 notebooks_v1/03.09-Pivot-Tables.ipynb
 create mode 100644 notebooks_v1/03.10-Working-With-Strings.ipynb
 create mode 100644 notebooks_v1/03.11-Working-with-Time-Series.ipynb
 create mode 100644 notebooks_v1/03.12-Performance-Eval-and-Query.ipynb
 create mode 100644 notebooks_v1/03.13-Further-Resources.ipynb
 create mode 100644 notebooks_v1/04.00-Introduction-To-Matplotlib.ipynb
 create mode 100644 notebooks_v1/04.01-Simple-Line-Plots.ipynb
 create mode 100644 notebooks_v1/04.02-Simple-Scatter-Plots.ipynb
 create mode 100644 notebooks_v1/04.03-Errorbars.ipynb
 create mode 100644 notebooks_v1/04.04-Density-and-Contour-Plots.ipynb
 create mode 100644 notebooks_v1/04.05-Histograms-and-Binnings.ipynb
 create mode 100644 notebooks_v1/04.06-Customizing-Legends.ipynb
 create mode 100644 notebooks_v1/04.07-Customizing-Colorbars.ipynb
 create mode 100644 notebooks_v1/04.08-Multiple-Subplots.ipynb
 create mode 100644 notebooks_v1/04.09-Text-and-Annotation.ipynb
 create mode 100644 notebooks_v1/04.10-Customizing-Ticks.ipynb
 create mode 100644 notebooks_v1/04.11-Settings-and-Stylesheets.ipynb
 create mode 100644 notebooks_v1/04.12-Three-Dimensional-Plotting.ipynb
 rename {notebooks => notebooks_v1}/04.13-Geographic-Data-With-Basemap.ipynb (100%)
 create mode 100644 notebooks_v1/04.14-Visualization-With-Seaborn.ipynb
 create mode 100644 notebooks_v1/04.15-Further-Resources.ipynb
 create mode 100644 notebooks_v1/05.00-Machine-Learning.ipynb
 create mode 100644 notebooks_v1/05.01-What-Is-Machine-Learning.ipynb
 create mode 100644 notebooks_v1/05.02-Introducing-Scikit-Learn.ipynb
 create mode 100644 notebooks_v1/05.03-Hyperparameters-and-Model-Validation.ipynb
 create mode 100644 notebooks_v1/05.04-Feature-Engineering.ipynb
 create mode 100644 notebooks_v1/05.05-Naive-Bayes.ipynb
 create mode 100644 notebooks_v1/05.06-Linear-Regression.ipynb
 create mode 100644 notebooks_v1/05.07-Support-Vector-Machines.ipynb
 create mode 100644 notebooks_v1/05.08-Random-Forests.ipynb
 create mode 100644 notebooks_v1/05.09-Principal-Component-Analysis.ipynb
 create mode 100644 notebooks_v1/05.10-Manifold-Learning.ipynb
 create mode 100644 notebooks_v1/05.11-K-Means.ipynb
 create mode 100644 notebooks_v1/05.12-Gaussian-Mixtures.ipynb
 create mode 100644 notebooks_v1/05.13-Kernel-Density-Estimation.ipynb
 create mode 100644 notebooks_v1/05.14-Image-Features.ipynb
 create mode 100644 notebooks_v1/05.15-Learning-More.ipynb
 create mode 100644 notebooks_v1/06.00-Figure-Code.ipynb
 rename {notebooks => notebooks_v1}/Index.ipynb (100%)
 create mode 100644 notebooks_v1/Untitled.ipynb
 rename {notebooks => notebooks_v1}/data/BicycleWeather.csv (100%)
 rename {notebooks => notebooks_v1}/data/Seattle2014.csv (100%)
 create mode 100644 notebooks_v1/data/births.csv
 rename {notebooks => notebooks_v1}/data/california_cities.csv (100%)
 create mode 100644 notebooks_v1/data/president_heights.csv
 create mode 100644 notebooks_v1/data/state-abbrevs.csv
 create mode 100644 notebooks_v1/data/state-areas.csv
 create mode 100644 notebooks_v1/data/state-population.csv
 rename {notebooks => notebooks_v1}/figures/02.05-broadcasting.png (100%)
 rename {notebooks => notebooks_v1}/figures/03.08-split-apply-combine.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.01-classification-1.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.01-classification-2.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.01-classification-3.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.01-clustering-1.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.01-clustering-2.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.01-dimesionality-1.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.01-dimesionality-2.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.01-regression-1.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.01-regression-2.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.01-regression-3.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.01-regression-4.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.02-samples-features.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.03-2-fold-CV.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.03-5-fold-CV.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.03-bias-variance-2.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.03-bias-variance.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.03-learning-curve.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.03-validation-curve.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.05-gaussian-NB.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.06-gaussian-basis.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.08-decision-tree-levels.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.08-decision-tree-overfitting.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.08-decision-tree.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.09-PCA-rotation.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.09-digits-pca-components.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.09-digits-pixel-components.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.10-LLE-vs-MDS.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.11-expectation-maximization.png (100%)
 rename {notebooks => notebooks_v1}/figures/05.12-covariance-type.png (100%)
 rename {notebooks => notebooks_v1}/figures/Data_Science_VD.png (100%)
 rename {notebooks => notebooks_v1}/figures/PDSH-cover-small.png (100%)
 rename {notebooks => notebooks_v1}/figures/PDSH-cover.png (100%)
 rename {notebooks => notebooks_v1}/figures/array_vs_list.png (100%)
 rename {notebooks => notebooks_v1}/figures/cint_vs_pyint.png (100%)
 create mode 100644 notebooks_v1/helpers_05_08.py
 create mode 100644 notebooks_v2/data.csv

diff --git a/notebooks/00.00-Preface.ipynb b/notebooks/00.00-Preface.ipynb
index 7d635a808..8be26e3a7 100644
--- a/notebooks/00.00-Preface.ipynb
+++ b/notebooks/00.00-Preface.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "| [Contents](Index.ipynb) | [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/00.00-Preface.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -37,27 +15,27 @@
     "\n",
     "This is a book about doing data science with Python, which immediately begs the question: what is *data science*?\n",
     "It's a surprisingly hard definition to nail down, especially given how ubiquitous the term has become.\n",
-    "Vocal critics have variously dismissed the term as a superfluous label (after all, what science doesn't involve data?) or a simple buzzword that only exists to salt resumes and catch the eye of overzealous tech recruiters.\n",
+    "Vocal critics have variously dismissed it as a superfluous label (after all, what science doesn't involve data?) or a simple buzzword that only exists to salt resumes and catch the eye of overzealous tech recruiters.\n",
     "\n",
     "In my mind, these critiques miss something important.\n",
     "Data science, despite its hype-laden veneer, is perhaps the best label we have for the cross-disciplinary set of skills that are becoming increasingly important in many applications across industry and academia.\n",
-    "This cross-disciplinary piece is key: in my mind, the best extisting definition of data science is illustrated by Drew Conway's Data Science Venn Diagram, first published on his blog in September 2010:"
+    "This *cross-disciplinary* piece is key: in my mind, the best existing definition of data science is illustrated by Drew Conway's Data Science Venn Diagram, first published on his blog in September 2010 (see the following figure)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![Data Science Venn Diagram](figures/Data_Science_VD.png)\n",
+    "![Data Science Venn Diagram](images/Data_Science_VD.png)\n",
     "\n",
-    "<small>(Source: [Drew Conway](http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram). Used by permission.)</small>"
+    "<small>(source: [Drew Conway](http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram), used by permission)</small>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "While some of the intersection labels are a bit tongue-in-cheek, this diagram captures the essence of what I think people mean when they say \"data science\": it is fundamentally an *interdisciplinary* subject.\n",
+    "While some of the intersection labels are a bit tongue-in-cheek, this diagram captures the essence of what I think people mean when they say \"data science\": it is fundamentally an interdisciplinary subject.\n",
     "Data science comprises three distinct and overlapping areas: the skills of a *statistician* who knows how to model and summarize datasets (which are growing ever larger); the skills of a *computer scientist* who can design and use algorithms to efficiently store, process, and visualize this data; and the *domain expertise*—what we might think of as \"classical\" training in a subject—necessary both to formulate the right questions and to put their answers in context.\n",
     "\n",
     "With this in mind, I would encourage you to think of data science not as a new domain of knowledge to learn, but a new set of skills that you can apply within your current area of expertise.\n",
@@ -65,18 +43,19 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Who Is This Book For?\n",
     "\n",
-    "In my teaching both at the University of Washington and at various tech-focused conferences and meetups, one of the most common questions I have heard is this: \"how should I learn Python?\"\n",
+    "In my teaching both at the University of Washington and at various tech-focused conferences and meetups, one of the most common questions I have heard is this: \"How should I learn Python?\"\n",
     "The people asking are generally technically minded students, developers, or researchers, often with an already strong background in writing code and using computational and numerical tools.\n",
-    "Most of these folks don't want to learn Python *per se*, but want to learn the language with the aim of using it as a tool for data-intensive and computational science.\n",
+    "Most of these folks don't want to learn Python per se, but want to learn the language with the aim of using it as a tool for data-intensive and computational science.\n",
     "While a large patchwork of videos, blog posts, and tutorials for this audience is available online, I've long been frustrated by the lack of a single good answer to this question; that is what inspired this book.\n",
     "\n",
     "The book is not meant to be an introduction to Python or to programming in general; I assume the reader has familiarity with the Python language, including defining functions, assigning variables, calling methods of objects, controlling the flow of a program, and other basic tasks.\n",
-    "Instead it is meant to help Python users learn to use Python's data science stack–libraries such as IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools–to effectively store, manipulate, and gain insight from data."
+    "Instead, it is meant to help Python users learn to use Python's data science stack—libraries such as those mentioned in the following section, and related tools—to effectively store, manipulate, and gain insight from data."
    ]
   },
   {
@@ -85,44 +64,31 @@
    "source": [
     "## Why Python?\n",
     "\n",
-    "Python has emerged over the last couple decades as a first-class tool for scientific computing tasks, including the analysis and visualization of large datasets.\n",
+    "Python has emerged over the last couple of decades as a first-class tool for scientific computing tasks, including the analysis and visualization of large datasets.\n",
     "This may have come as a surprise to early proponents of the Python language: the language itself was not specifically designed with data analysis or scientific computing in mind.\n",
     "The usefulness of Python for data science stems primarily from the large and active ecosystem of third-party packages: *NumPy* for manipulation of homogeneous array-based data, *Pandas* for manipulation of heterogeneous and labeled data, *SciPy* for common scientific computing tasks, *Matplotlib* for publication-quality visualizations, *IPython* for interactive execution and sharing of code, *Scikit-Learn* for machine learning, and many more tools that will be mentioned in the following pages.\n",
     "\n",
-    "If you are looking for a guide to the Python language itself, I would suggest the sister project to this book, \"[A Whirlwind Tour of the Python Language](https://github.com/jakevdp/WhirlwindTourOfPython)\".\n",
+    "If you are looking for a guide to the Python language itself, I would suggest the sister project to this book, [https://www.oreilly.com/library/view/a-whirlwind-tour/9781492037859](_A Whirlwind Tour of the Python Language_).\n",
     "This short report provides a tour of the essential features of the Python language, aimed at data scientists who already are familiar with one or more other programming languages."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Python 2 vs Python 3\n",
-    "\n",
-    "This book uses the syntax of Python 3, which contains language enhancements that are not compatible with the 2.x series of Python.\n",
-    "Though Python 3.0 was first released in 2008, adoption has been relatively slow, particularly in the scientific and web development communities.\n",
-    "This is primarily because it took some time for many of the essential third-party packages and toolkits to be made compatible with the new language internals.\n",
-    "Since early 2014, however, stable releases of the most important tools in the data science ecosystem have been fully compatible with both Python 2 and 3, and so this book will use the newer Python 3 syntax.\n",
-    "However, the vast majority of code snippets in this book will also work without modification in Python 2: in cases where a Py2-incompatible syntax is used, I will make every effort to note it explicitly."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Outline of the Book\n",
     "\n",
-    "Each chapter of this book focuses on a particular package or tool that contributes a fundamental piece of the Python Data Sciece story.\n",
+    "Each numbered part of this book focuses on a particular package or tool that contributes a fundamental piece of the Python data science story, and is broken into short self-contained chapters that each discuss a single concept:\n",
     "\n",
-    "1. IPython and Jupyter: these packages provide the computational environment in which many Python-using data scientists work.\n",
-    "2. NumPy: this library provides the ``ndarray`` for efficient storage and manipulation of dense data arrays in Python.\n",
-    "3. Pandas: this library provides the ``DataFrame`` for efficient storage and manipulation of labeled/columnar data in Python.\n",
-    "4. Matplotlib: this library provides capabilities for a flexible range of data visualizations in Python.\n",
-    "5. Scikit-Learn: this library provides efficient & clean Python implementations of the most important and established machine learning algorithms.\n",
+    "- *Part I, Jupyter: Beyond Normal Python*, introduces IPython and Jupyter. These packages provide the computational environment in which many Python-using data scientists work.\n",
+    "- *Part II, Introduction to NumPy*, focuses on the NumPy library, which provides the `ndarray` for efficient storage and manipulation of dense data arrays in Python.\n",
+    "- *Part III, Data Manipulation with Pandas*, introduces the Pandas library, which provides the `DataFrame` for efficient storage and manipulation of labeled/columnar data in Python.\n",
+    "- *Part IV, Visualization with Matplotlib*, concentrates on Matplotlib, a library that provides capabilities for a flexible range of data visualizations in Python.\n",
+    "- *Part V, Machine Learning*, focuses on the Scikit-Learn library, which provides efficient and clean Python implementations of the most important and established machine learning algorithms.\n",
     "\n",
-    "The PyData world is certainly much larger than these five packages, and is growing every day.\n",
-    "With this in mind, I make every attempt through these pages to provide references to other interesting efforts, projects, and packages that are pushing the boundaries of what can be done in Python.\n",
-    "Nevertheless, these five are currently fundamental to much of the work being done in the Python data science space, and I expect they will remain important even as the ecosystem continues growing around them."
+    "The PyData world is certainly much larger than these six packages, and is growing every day.\n",
+    "With this in mind, I make every attempt throughout this book to provide references to other interesting efforts, projects, and packages that are pushing the boundaries of what can be done in Python.\n",
+    "Nevertheless, the packages I concentrate on are currently fundamental to much of the work being done in the Python data science space, and I expect they will remain important even as the ecosystem continues growing around them."
    ]
   },
   {
@@ -133,53 +99,47 @@
     "\n",
     "Supplemental material (code examples, figures, etc.) is available for download at http://github.com/jakevdp/PythonDataScienceHandbook/. This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission.\n",
     "\n",
-    "We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example:\n",
+    "We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: \"*Python Data Science Handbook*, 2nd edition, by Jake VanderPlas (O’Reilly). Copyright 2023 Jake VanderPlas, 978-1-098-12122-8.\"\n",
     "\n",
-    "> *The Python Data Science Handbook* by Jake VanderPlas (O’Reilly). Copyright 2016 Jake VanderPlas, 978-1-491-91205-8.\n",
-    "\n",
-    "If you feel your use of code examples falls outside fair use or the per‐ mission given above, feel free to contact us at permissions@oreilly.com."
+    "If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at permissions@oreilly.com."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Installation Considerations\n",
     "\n",
-    "Installing Python and the suite of libraries that enable scientific computing is straightforward . This section will outline some of the considerations when setting up your computer.\n",
+    "Installing Python and the suite of libraries that enable scientific computing is straightforward. This section will outline some of the things to keep in mind when setting up your computer.\n",
     "\n",
-    "Though there are various ways to install Python, the one I would suggest for use in data science is the Anaconda distribution, which works similarly whether you use Windows, Linux, or Mac OS X.\n",
+    "Though there are various ways to install Python, the one I would suggest for use in data science is the Anaconda distribution, which works similarly whether you use Windows, Linux, or macOS.\n",
     "The Anaconda distribution comes in two flavors:\n",
     "\n",
-    "- [Miniconda](http://conda.pydata.org/miniconda.html) gives you the Python interpreter itself, along with a command-line tool called ``conda`` which operates as a cross-platform package manager geared toward Python packages, similar in spirit to the apt or yum tools that Linux users might be familiar with.\n",
+    "- [Miniconda](http://conda.pydata.org/miniconda.html) gives you the Python interpreter itself, along with a command-line tool called *conda* which operates as a cross-platform package manager geared toward Python packages, similar in spirit to the apt or yum tools that Linux users might be familiar with.\n",
     "\n",
-    "- [Anaconda](https://www.continuum.io/downloads) includes both Python and conda, and additionally bundles a suite of other pre-installed packages geared toward scientific computing. Because of the size of this bundle, expect the installation to consume several gigabytes of disk space.\n",
+    "- [Anaconda](https://www.continuum.io/downloads) includes both Python and conda, and additionally bundles a suite of other preinstalled packages geared toward scientific computing. Because of the size of this bundle, expect the installation to consume several gigabytes of disk space.\n",
     "\n",
     "Any of the packages included with Anaconda can also be installed manually on top of Miniconda; for this reason I suggest starting with Miniconda.\n",
     "\n",
-    "To get started, download and install the Miniconda package–make sure to choose a version with Python 3–and then install the core packages used in this book:\n",
+    "To get started, download and install the Miniconda package—make sure to choose a version with Python 3—and then install the core packages used in this book:\n",
     "\n",
     "```\n",
     "[~]$ conda install numpy pandas scikit-learn matplotlib seaborn jupyter\n",
     "```\n",
     "\n",
-    "Throughout the text, we will also make use of other more specialized tools in Python's scientific ecosystem; installation is usually as easy as typing **``conda install packagename``**.\n",
-    "For more information on conda, including information about creating and using conda environments (which I would *highly* recommend), refer to [conda's online documentation](http://conda.pydata.org/docs/)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "| [Contents](Index.ipynb) | [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) >\n",
+    "Throughout the text, we will also make use of other more specialized tools in Python's scientific ecosystem; installation is usually as easy as typing **`conda install packagename`**.\n",
+    "If you ever come across packages that are not available in the default conda channel, be sure to check out [*conda-forge*](https://conda-forge.org/), a broad, community-driven repository of conda packages.\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/00.00-Preface.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "For more information on conda, including information about creating and using conda environments (which I would *highly* recommend), refer to [conda's online documentation](http://conda.pydata.org/docs/)."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -195,9 +155,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/01.00-IPython-Beyond-Normal-Python.ipynb b/notebooks/01.00-IPython-Beyond-Normal-Python.ipynb
index 5d01277e6..ed5be0334 100644
--- a/notebooks/01.00-IPython-Beyond-Normal-Python.ipynb
+++ b/notebooks/01.00-IPython-Beyond-Normal-Python.ipynb
@@ -4,29 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Preface](00.00-Preface.ipynb) | [Contents](Index.ipynb) | [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.00-IPython-Beyond-Normal-Python.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# IPython: Beyond Normal Python"
+    "# Jupyter: Beyond Normal Python"
    ]
   },
   {
@@ -34,101 +12,25 @@
    "metadata": {},
    "source": [
     "There are many options for development environments for Python, and I'm often asked which one I use in my own work.\n",
-    "My answer sometimes surprises people: my preferred environment is [IPython](http://ipython.org/) plus a text editor (in my case, Emacs or Atom depending on my mood).\n",
-    "IPython (short for *Interactive Python*) was started in 2001 by Fernando Perez as an enhanced Python interpreter, and has since grown into a project aiming to provide, in Perez's words, \"Tools for the entire life cycle of research computing.\"\n",
-    "If Python is the engine of our data science task, you might think of IPython as the interactive control panel.\n",
-    "\n",
-    "As well as being a useful interactive interface to Python, IPython also provides a number of useful syntactic additions to the language; we'll cover the most useful of these additions here.\n",
-    "In addition, IPython is closely tied with the [Jupyter project](http://jupyter.org), which provides a browser-based notebook that is useful for development, collaboration, sharing, and even publication of data science results.\n",
-    "The IPython notebook is actually a special case of the broader Jupyter notebook structure, which encompasses notebooks for Julia, R, and other programming languages.\n",
-    "As an example of the usefulness of the notebook format, look no further than the page you are reading: the entire manuscript for this book was composed as a set of IPython notebooks.\n",
-    "\n",
-    "IPython is about using Python effectively for interactive scientific and data-intensive computing.\n",
-    "This chapter will start by stepping through some of the IPython features that are useful to the practice of data science, focusing especially on the syntax it offers beyond the standard features of Python.\n",
-    "Next, we will go into a bit more depth on some of the more useful \"magic commands\" that can speed-up common tasks in creating and using data science code.\n",
-    "Finally, we will touch on some of the features of the notebook that make it useful in understanding data and sharing results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Shell or Notebook?\n",
-    "\n",
-    "There are two primary means of using IPython that we'll discuss in this chapter: the IPython shell and the IPython notebook.\n",
-    "The bulk of the material in this chapter is relevant to both, and the examples will switch between them depending on what is most convenient.\n",
-    "In the few sections that are relevant to just one or the other, we will explicitly state that fact.\n",
-    "Before we start, some words on how to launch the IPython shell and IPython notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Launching the IPython Shell\n",
+    "My answer sometimes surprises people: my preferred environment is [IPython](http://ipython.org/) plus a text editor (in my case, Emacs or VSCode depending on my mood).\n",
+    "Jupyter got its start as the IPython shell, which was created in 2001 by Fernando Perez as an enhanced Python interpreter and has since grown into a project aiming to provide, in Perez's words, \"Tools for the entire life cycle of research computing.\"\n",
+    "If Python is the engine of our data science task, you might think of Jupyter as the interactive control panel.\n",
     "\n",
-    "This chapter, like most of this book, is not designed to be absorbed passively.\n",
-    "I recommend that as you read through it, you follow along and experiment with the tools and syntax we cover: the muscle-memory you build through doing this will be far more useful than the simple act of reading about it.\n",
-    "Start by launching the IPython interpreter by typing **``ipython``** on the command-line; alternatively, if you've installed a distribution like Anaconda or EPD, there may be a launcher specific to your system (we'll discuss this more fully in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)).\n",
+    "As well as being a useful interactive interface to Python, Jupyter also provides a number of useful syntactic additions to the language; we'll cover the most useful of these additions here.\n",
+    "Perhaps the most familiar interface provided by the Jupyter project is the Jupyter Notebook, a browser-based environment that is useful for development, collaboration, sharing, and even publication of data science results.\n",
+    "As an example of the usefulness of the notebook format, look no further than the page you are reading: the entire manuscript for this book was composed as a set of Jupyter notebooks.\n",
     "\n",
-    "Once you do this, you should see a prompt like the following:\n",
-    "```\n",
-    "IPython 4.0.1 -- An enhanced Interactive Python.\n",
-    "?         -> Introduction and overview of IPython's features.\n",
-    "%quickref -> Quick reference.\n",
-    "help      -> Python's own help system.\n",
-    "object?   -> Details about 'object', use 'object??' for extra details.\n",
-    "In [1]:\n",
-    "```\n",
-    "With that, you're ready to follow along."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Launching the Jupyter Notebook\n",
-    "\n",
-    "The Jupyter notebook is a browser-based graphical interface to the IPython shell, and builds on it a rich set of dynamic display capabilities.\n",
-    "As well as executing Python/IPython statements, the notebook allows the user to include formatted text, static and dynamic visualizations, mathematical equations, JavaScript widgets, and much more.\n",
-    "Furthermore, these documents can be saved in a way that lets other people open them and execute the code on their own systems.\n",
-    "\n",
-    "Though the IPython notebook is viewed and edited through your web browser window, it must connect to a running Python process in order to execute code.\n",
-    "This process (known as a \"kernel\") can be started by running the following command in your system shell:\n",
-    "\n",
-    "```\n",
-    "$ jupyter notebook\n",
-    "```\n",
-    "\n",
-    "This command will launch a local web server that will be visible to your browser.\n",
-    "It immediately spits out a log showing what it is doing; that log will look something like this:\n",
-    "\n",
-    "```\n",
-    "$ jupyter notebook\n",
-    "[NotebookApp] Serving notebooks from local directory: /Users/jakevdp/PythonDataScienceHandbook\n",
-    "[NotebookApp] 0 active kernels \n",
-    "[NotebookApp] The IPython Notebook is running at: http://localhost:8888/\n",
-    "[NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n",
-    "```\n",
-    "\n",
-    "Upon issuing the command, your default browser should automatically open and navigate to the listed local URL;\n",
-    "the exact address will depend on your system.\n",
-    "If the browser does not open automatically, you can open a window and manually open this address (*http://localhost:8888/* in this example)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Preface](00.00-Preface.ipynb) | [Contents](Index.ipynb) | [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.00-IPython-Beyond-Normal-Python.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "This part of the book will start by stepping through some of the Jupyter and IPython features that are useful to the practice of data science, focusing especially on the syntax they offer beyond the standard features of Python.\n",
+    "Next, we will go into a bit more depth on some of the more useful *magic commands* that can speed up common tasks in creating and using data science code.\n",
+    "Finally, we will touch on some of the features of the notebook that make it useful for understanding data and sharing results."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -144,9 +46,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/01.01-Help-And-Documentation.ipynb b/notebooks/01.01-Help-And-Documentation.ipynb
index 39879ee90..a37fc4c3a 100644
--- a/notebooks/01.01-Help-And-Documentation.ipynb
+++ b/notebooks/01.01-Help-And-Documentation.ipynb
@@ -4,29 +4,76 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "# Getting Started in IPython and Jupyter\n",
     "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "In writing Python code for data science, I generally go between three modes of working: I use the IPython shell for trying out short sequences of commands, the Jupyter Notebook for longer interactive analysis and for sharing content with others, and interactive development environments (IDEs) like Emacs or VSCode for creating reusable Python packages.\n",
+    "This chapter focuses on the first two modes: the IPython shell and the Jupyter Notebook.\n",
+    "Use of an IDE for software development is an important third tool in the data scientist's repertoire, but we will not directly address that here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b582097",
+   "metadata": {},
+   "source": [
+    "## Launching the IPython Shell\n",
     "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+    "The text in this part, like most of this book, is not designed to be absorbed passively.\n",
+    "I recommend that as you read through it, you follow along and experiment with the tools and syntax we cover: the muscle memory you build through doing this will be far more useful than the simple act of reading about it.\n",
+    "Start by launching the IPython interpreter by typing **`ipython`** on the command line; alternatively, if you've installed a distribution like Anaconda or EPD, there may be a launcher specific to your system (we'll discuss this more fully in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)).\n",
+    "\n",
+    "Once you do this, you should see a prompt like the following:\n",
+    "\n",
+    "```ipython\n",
+    "Python 3.9.2 (v3.9.2:1a79785e3e, Feb 19 2021, 09:06:10) \n",
+    "Type 'copyright', 'credits' or 'license' for more information\n",
+    "IPython 7.21.0 -- An enhanced Interactive Python. Type '?' for help.\n",
+    "\n",
+    "In [1]:\n",
+    "```\n",
+    "With that, you're ready to follow along."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "d1d2d0fb",
    "metadata": {},
    "source": [
-    "<!--NAVIGATION-->\n",
-    "< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) >\n",
+    "## Launching the Jupyter Notebook\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.01-Help-And-Documentation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "The Jupyter Notebook is a browser-based graphical interface to the IPython shell, and builds on it a rich set of dynamic display capabilities.\n",
+    "As well as executing Python/IPython statements, notebooks allow the user to include formatted text, static and dynamic visualizations, mathematical equations, JavaScript widgets, and much more.\n",
+    "Furthermore, these documents can be saved in a way that lets other people open them and execute the code on their own systems.\n",
+    "\n",
+    "Though you'll view and edit Jupyter notebooks through your web browser window, they must connect to a running Python process in order to execute code.\n",
+    "You can start this process (known as a \"kernel\") by running the following command in your system shell:\n",
+    "\n",
+    "```\n",
+    "$ jupyter lab\n",
+    "```\n",
+    "\n",
+    "This command will launch a local web server that will be visible to your browser.\n",
+    "It immediately spits out a log showing what it is doing; that log will look something like this:\n",
+    "\n",
+    "```\n",
+    "$ jupyter lab\n",
+    "[ServerApp] Serving notebooks from local directory: /Users/jakevdp/PythonDataScienceHandbook\n",
+    "[ServerApp] Jupyter Server 1.4.1 is running at:\n",
+    "[ServerApp] http://localhost:8888/lab?token=dd852649\n",
+    "[ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n",
+    "```\n",
+    "\n",
+    "Upon issuing the command, your default browser should automatically open and navigate to the listed local URL;\n",
+    "the exact address will depend on your system.\n",
+    "If the browser does not open automatically, you can open a window and manually open this address (*http://localhost:8888/lab/* in this example)."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "92286db8",
    "metadata": {},
    "source": [
-    "# Help and Documentation in IPython"
+    "## Help and Documentation in IPython"
    ]
   },
   {
@@ -35,60 +82,55 @@
    "source": [
     "If you read no other section in this chapter, read this one: I find the tools discussed here to be the most transformative contributions of IPython to my daily workflow.\n",
     "\n",
-    "When a technologically-minded person is asked to help a friend, family member, or colleague with a computer problem, most of the time it's less a matter of knowing the answer as much as knowing how to quickly find an unknown answer.\n",
-    "In data science it's the same: searchable web resources such as online documentation, mailing-list threads, and StackOverflow answers contain a wealth of information, even (especially?) if it is a topic you've found yourself searching before.\n",
+    "When a technologically minded person is asked to help a friend, family member, or colleague with a computer problem, most of the time it's less a matter of knowing the answer than of knowing how to quickly find an unknown answer.\n",
+    "In data science it's the same: searchable web resources such as online documentation, mailing list threads, and Stack Overflow answers contain a wealth of information, even (especially?) about topics you've found yourself searching on before.\n",
     "Being an effective practitioner of data science is less about memorizing the tool or command you should use for every possible situation, and more about learning to effectively find the information you don't know, whether through a web search engine or another means.\n",
     "\n",
     "One of the most useful functions of IPython/Jupyter is to shorten the gap between the user and the type of documentation and search that will help them do their work effectively.\n",
     "While web searches still play a role in answering complicated questions, an amazing amount of information can be found through IPython alone.\n",
-    "Some examples of the questions IPython can help answer in a few keystrokes:\n",
+    "Some examples of the questions IPython can help answer in a few keystrokes include:\n",
     "\n",
     "- How do I call this function? What arguments and options does it have?\n",
     "- What does the source code of this Python object look like?\n",
-    "- What is in this package I imported? What attributes or methods does this object have?\n",
+    "- What is in this package I imported? \n",
+    "- What attributes or methods does this object have?\n",
     "\n",
-    "Here we'll discuss IPython's tools to quickly access this information, namely the ``?`` character to explore documentation, the ``??`` characters to explore source code, and the Tab key for auto-completion."
+    "Here we'll discuss the tools provided in the IPython shell and Jupyter Notebook to quickly access this information, namely the `?` character to explore documentation, the `??` characters to explore source code, and the Tab key for autocompletion."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Accessing Documentation with ``?``\n",
+    "### Accessing Documentation with ?\n",
     "\n",
-    "The Python language and its data science ecosystem is built with the user in mind, and one big part of that is access to documentation.\n",
-    "Every Python object contains the reference to a string, known as a *doc string*, which in most cases will contain a concise summary of the object and how to use it.\n",
-    "Python has a built-in ``help()`` function that can access this information and prints the results.\n",
-    "For example, to see the documentation of the built-in ``len`` function, you can do the following:\n",
+    "The Python language and its data science ecosystem are built with the user in mind, and one big part of that is access to documentation.\n",
+    "Every Python object contains a reference to a string, known as a *docstring*, which in most cases will contain a concise summary of the object and how to use it.\n",
+    "Python has a built-in `help` function that can access this information and prints the results.\n",
+    "For example, to see the documentation of the built-in `len` function, you can do the following:\n",
     "\n",
     "```ipython\n",
     "In [1]: help(len)\n",
     "Help on built-in function len in module builtins:\n",
     "\n",
-    "len(...)\n",
-    "    len(object) -> integer\n",
-    "    \n",
-    "    Return the number of items of a sequence or mapping.\n",
+    "len(obj, /)\n",
+    "    Return the number of items in a container.\n",
     "```\n",
     "\n",
-    "Depending on your interpreter, this information may be displayed as inline text, or in some separate pop-up window."
+    "Depending on your interpreter, this information may be displayed as inline text or in a separate pop-up window."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Because finding help on an object is so common and useful, IPython introduces the ``?`` character as a shorthand for accessing this documentation and other relevant information:\n",
+    "Because finding help on an object is so common and useful, IPython and Jupyter introduce the `?` character as a shorthand for accessing this documentation and other relevant information:\n",
     "\n",
     "```ipython\n",
     "In [2]: len?\n",
-    "Type:        builtin_function_or_method\n",
-    "String form: <built-in function len>\n",
-    "Namespace:   Python builtin\n",
-    "Docstring:\n",
-    "len(object) -> integer\n",
-    "\n",
-    "Return the number of items of a sequence or mapping.\n",
+    "Signature: len(obj, /)\n",
+    "Docstring: Return the number of items in a container.\n",
+    "Type:      builtin_function_or_method\n",
     "```"
    ]
   },
@@ -101,9 +143,9 @@
     "```ipython\n",
     "In [3]: L = [1, 2, 3]\n",
     "In [4]: L.insert?\n",
-    "Type:        builtin_function_or_method\n",
-    "String form: <built-in method insert of list object at 0x1024b8ea8>\n",
-    "Docstring:   L.insert(index, object) -- insert object before index\n",
+    "Signature: L.insert(index, object, /)\n",
+    "Docstring: Insert object before index.\n",
+    "Type:      builtin_function_or_method\n",
     "```\n",
     "\n",
     "or even objects themselves, with the documentation from their type:\n",
@@ -113,9 +155,11 @@
     "Type:        list\n",
     "String form: [1, 2, 3]\n",
     "Length:      3\n",
-    "Docstring:\n",
-    "list() -> new empty list\n",
-    "list(iterable) -> new list initialized from iterable's items\n",
+    "Docstring:  \n",
+    "Built-in mutable sequence.\n",
+    "\n",
+    "If no argument is given, the constructor creates a new empty list.\n",
+    "The argument must be an iterable if specified.\n",
     "```"
    ]
   },
@@ -134,21 +178,21 @@
     "```\n",
     "\n",
     "Note that to create a docstring for our function, we simply placed a string literal in the first line.\n",
-    "Because doc strings are usually multiple lines, by convention we used Python's triple-quote notation for multi-line strings."
+    "Because docstrings are usually multiple lines, by convention we used Python's triple-quote notation for multiline strings."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we'll use the ``?`` mark to find this doc string:\n",
+    "Now we'll use the `?` to find this docstring:\n",
     "\n",
     "```ipython\n",
     "In [7]: square?\n",
-    "Type:        function\n",
-    "String form: <function square at 0x103713cb0>\n",
-    "Definition:  square(a)\n",
-    "Docstring:   Return the square of a.\n",
+    "Signature: square(a)\n",
+    "Docstring: Return the square of a.\n",
+    "File:      <ipython-input-6>\n",
+    "Type:      function\n",
     "```\n",
     "\n",
     "This quick access to documentation via docstrings is one reason you should get in the habit of always adding such inline documentation to the code you write!"
@@ -158,100 +202,101 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Accessing Source Code with ``??``\n",
+    "### Accessing Source Code with ??\n",
+    "\n",
     "Because the Python language is so easily readable, another level of insight can usually be gained by reading the source code of the object you're curious about.\n",
-    "IPython provides a shortcut to the source code with the double question mark (``??``):\n",
+    "IPython and Jupyter provide a shortcut to the source code with the double question mark (`??`):\n",
     "\n",
     "```ipython\n",
     "In [8]: square??\n",
-    "Type:        function\n",
-    "String form: <function square at 0x103713cb0>\n",
-    "Definition:  square(a)\n",
-    "Source:\n",
+    "Signature: square(a)\n",
+    "Source:   \n",
     "def square(a):\n",
-    "    \"Return the square of a\"\n",
+    "    \"\"\"Return the square of a.\"\"\"\n",
     "    return a ** 2\n",
+    "File:      <ipython-input-6>\n",
+    "Type:      function\n",
     "```\n",
     "\n",
-    "For simple functions like this, the double question-mark can give quick insight into the under-the-hood details."
+    "For simple functions like this, the double question mark can give quick insight into the under-the-hood details."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you play with this much, you'll notice that sometimes the ``??`` suffix doesn't display any source code: this is generally because the object in question is not implemented in Python, but in C or some other compiled extension language.\n",
-    "If this is the case, the ``??`` suffix gives the same output as the ``?`` suffix.\n",
-    "You'll find this particularly with many of Python's built-in objects and types, for example ``len`` from above:\n",
+    "If you play with this much, you'll notice that sometimes the `??` suffix doesn't display any source code: this is generally because the object in question is not implemented in Python, but in C or some other compiled extension language.\n",
+    "If this is the case, the `??` suffix gives the same output as the `?` suffix.\n",
+    "You'll find this particularly with many of Python's built-in objects and types, including the `len` function from earlier:\n",
     "\n",
     "```ipython\n",
     "In [9]: len??\n",
-    "Type:        builtin_function_or_method\n",
-    "String form: <built-in function len>\n",
-    "Namespace:   Python builtin\n",
-    "Docstring:\n",
-    "len(object) -> integer\n",
-    "\n",
-    "Return the number of items of a sequence or mapping.\n",
+    "Signature: len(obj, /)\n",
+    "Docstring: Return the number of items in a container.\n",
+    "Type:      builtin_function_or_method\n",
     "```\n",
     "\n",
-    "Using ``?`` and/or ``??`` gives a powerful and quick interface for finding information about what any Python function or module does."
+    "Using `?` and/or `??` is a powerful and quick way of finding information about what any Python function or module does."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exploring Modules with Tab-Completion\n",
+    "### Exploring Modules with Tab Completion\n",
     "\n",
-    "IPython's other useful interface is the use of the tab key for auto-completion and exploration of the contents of objects, modules, and name-spaces.\n",
-    "In the examples that follow, we'll use ``<TAB>`` to indicate when the Tab key should be pressed."
+    "Another useful interface is the use of the Tab key for autocompletion and exploration of the contents of objects, modules, and namespaces.\n",
+    "In the examples that follow, I'll use `<TAB>` to indicate when the Tab key should be pressed."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Tab-completion of object contents\n",
+    "#### Tab completion of object contents\n",
     "\n",
     "Every Python object has various attributes and methods associated with it.\n",
-    "Like with the ``help`` function discussed before, Python has a built-in ``dir`` function that returns a list of these, but the tab-completion interface is much easier to use in practice.\n",
-    "To see a list of all available attributes of an object, you can type the name of the object followed by a period (\"``.``\") character and the Tab key:\n",
+    "Like the `help` function mentioned earlier, Python has a built-in `dir` function that returns a list of these, but the tab-completion interface is much easier to use in practice.\n",
+    "To see a list of all available attributes of an object, you can type the name of the object followed by a period (\"`.`\") character and the Tab key:\n",
     "\n",
     "```ipython\n",
     "In [10]: L.<TAB>\n",
-    "L.append   L.copy     L.extend   L.insert   L.remove   L.sort     \n",
-    "L.clear    L.count    L.index    L.pop      L.reverse  \n",
+    "            append() count    insert   reverse \n",
+    "            clear    extend   pop      sort    \n",
+    "            copy     index    remove           \n",
     "```\n",
     "\n",
-    "To narrow-down the list, you can type the first character or several characters of the name, and the Tab key will find the matching attributes and methods:\n",
+    "To narrow down the list, you can type the first character or several characters of the name, and the Tab key will find the matching attributes and methods:\n",
     "\n",
     "```ipython\n",
     "In [10]: L.c<TAB>\n",
-    "L.clear  L.copy   L.count  \n",
+    "             clear() count()\n",
+    "             copy()         \n",
     "\n",
     "In [10]: L.co<TAB>\n",
-    "L.copy   L.count \n",
+    "              copy()  count()\n",
     "```\n",
     "\n",
     "If there is only a single option, pressing the Tab key will complete the line for you.\n",
-    "For example, the following will instantly be replaced with ``L.count``:\n",
+    "For example, the following will instantly be replaced with `L.count`:\n",
     "\n",
     "```ipython\n",
     "In [10]: L.cou<TAB>\n",
     "\n",
     "```\n",
     "\n",
-    "Though Python has no strictly-enforced distinction between public/external attributes and private/internal attributes, by convention a preceding underscore is used to denote such methods.\n",
+    "Though Python has no strictly enforced distinction between public/external attributes and private/internal attributes, by convention a preceding underscore is used to denote the latter.\n",
     "For clarity, these private methods and special methods are omitted from the list by default, but it's possible to list them by explicitly typing the underscore:\n",
     "\n",
     "```ipython\n",
     "In [10]: L._<TAB>\n",
-    "L.__add__           L.__gt__            L.__reduce__\n",
-    "L.__class__         L.__hash__          L.__reduce_ex__\n",
+    "           __add__             __delattr__     __eq__      \n",
+    "           __class__           __delitem__     __format__()\n",
+    "           __class_getitem__() __dir__()       __ge__            >\n",
+    "           __contains__        __doc__         __getattribute__     \n",
     "```\n",
     "\n",
-    "For brevity, we've only shown the first couple lines of the output.\n",
+    "For brevity, I've only shown the first few columns of the output.\n",
     "Most of these are Python's special double-underscore methods (often nicknamed \"dunder\" methods)."
    ]
   },
@@ -259,41 +304,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Tab completion when importing\n",
+    "#### Tab completion when importing\n",
     "\n",
     "Tab completion is also useful when importing objects from packages.\n",
-    "Here we'll use it to find all possible imports in the ``itertools`` package that start with ``co``:\n",
-    "```\n",
+    "Here we'll use it to find all possible imports in the `itertools` package that start with `co`:\n",
+    "\n",
+    "```ipython\n",
     "In [10]: from itertools import co<TAB>\n",
-    "combinations                   compress\n",
-    "combinations_with_replacement  count\n",
+    "         combinations()                  compress()\n",
+    "         combinations_with_replacement() count()\n",
     "```\n",
+    "\n",
     "Similarly, you can use tab-completion to see which imports are available on your system (this will change depending on which third-party scripts and modules are visible to your Python session):\n",
-    "```\n",
+    "\n",
+    "```ipython\n",
     "In [10]: import <TAB>\n",
-    "Display all 399 possibilities? (y or n)\n",
-    "Crypto              dis                 py_compile\n",
-    "Cython              distutils           pyclbr\n",
-    "...                 ...                 ...\n",
-    "difflib             pwd                 zmq\n",
+    "            abc                 anyio                          \n",
+    "            activate_this       appdirs                        \n",
+    "            aifc                appnope        >\n",
+    "            antigravity         argon2                         \n",
     "\n",
     "In [10]: import h<TAB>\n",
-    "hashlib             hmac                http         \n",
-    "heapq               html                husl         \n",
-    "```\n",
-    "(Note that for brevity, I did not print here all 399 importable packages and modules on my system.)"
+    "            hashlib html   \n",
+    "            heapq   http   \n",
+    "            hmac        \n",
+    "```"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Beyond tab completion: wildcard matching\n",
+    "#### Beyond tab completion: Wildcard matching\n",
     "\n",
-    "Tab completion is useful if you know the first few characters of the object or attribute you're looking for, but is little help if you'd like to match characters at the middle or end of the word.\n",
-    "For this use-case, IPython provides a means of wildcard matching for names using the ``*`` character.\n",
+    "Tab completion is useful if you know the first few characters of the name of the object or attribute you're looking for, but is little help if you'd like to match characters in the middle or at the end of the name.\n",
+    "For this use case, IPython and Jupyter provide a means of wildcard matching for names using the `*` character.\n",
     "\n",
-    "For example, we can use this to list every object in the namespace that ends with ``Warning``:\n",
+    "For example, we can use this to list every object in the namespace whose name ends with `Warning`:\n",
     "\n",
     "```ipython\n",
     "In [10]: *Warning?\n",
@@ -305,35 +352,28 @@
     "ResourceWarning\n",
     "```\n",
     "\n",
-    "Notice that the ``*`` character matches any string, including the empty string.\n",
+    "Notice that the `*` character matches any string, including the empty string.\n",
     "\n",
-    "Similarly, suppose we are looking for a string method that contains the word ``find`` somewhere in its name.\n",
+    "Similarly, suppose we are looking for a string method that contains the word `find` somewhere in its name.\n",
     "We can search for it this way:\n",
     "\n",
     "```ipython\n",
-    "In [10]: str.*find*?\n",
+    "In [11]: str.*find*?\n",
     "str.find\n",
     "str.rfind\n",
     "```\n",
     "\n",
-    "I find this type of flexible wildcard search can be very useful for finding a particular command when getting to know a new package or reacquainting myself with a familiar one."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.01-Help-And-Documentation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "I find this type of flexible wildcard search can be useful for finding a particular command when getting to know a new package or reacquainting myself with a familiar one."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.6 64-bit ('3.9.6')",
    "language": "python",
    "name": "python3"
   },
@@ -347,9 +387,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc"
+   }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb b/notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb
index f50e9fb1c..988dcea67 100644
--- a/notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb
+++ b/notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) | [Contents](Index.ipynb) | [IPython Magic Commands](01.03-Magic-Commands.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,33 +11,32 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you spend any amount of time on the computer, you've probably found a use for keyboard shortcuts in your workflow.\n",
-    "Most familiar perhaps are the Cmd-C and Cmd-V (or Ctrl-C and Ctrl-V) for copying and pasting in a wide variety of programs and systems.\n",
-    "Power-users tend to go even further: popular text editors like Emacs, Vim, and others provide users an incredible range of operations through intricate combinations of keystrokes.\n",
+    "If you spend any amount of time on a computer, you've probably found a use for keyboard shortcuts in your workflow.\n",
+    "Most familiar perhaps are Cmd-c and Cmd-v (or Ctrl-c and Ctrl-v), used for copying and pasting in a wide variety of programs and systems.\n",
+    "Power users tend to go even further: popular text editors like Emacs, Vim, and others provide users an incredible range of operations through intricate combinations of keystrokes.\n",
     "\n",
     "The IPython shell doesn't go this far, but does provide a number of keyboard shortcuts for fast navigation while typing commands.\n",
-    "These shortcuts are not in fact provided by IPython itself, but through its dependency on the GNU Readline library: as such, some of the following shortcuts may differ depending on your system configuration.\n",
-    "Also, while some of these shortcuts do work in the browser-based notebook, this section is primarily about shortcuts in the IPython shell.\n",
+    "While some of these shortcuts do work in the browser-based notebooks, this section is primarily about shortcuts in the IPython shell.\n",
     "\n",
     "Once you get accustomed to these, they can be very useful for quickly performing certain commands without moving your hands from the \"home\" keyboard position.\n",
     "If you're an Emacs user or if you have experience with Linux-style shells, the following will be very familiar.\n",
-    "We'll group these shortcuts into a few categories: *navigation shortcuts*, *text entry shortcuts*, *command history shortcuts*, and *miscellaneous shortcuts*."
+    "I'll group these shortcuts into a few categories: *navigation shortcuts*, *text entry shortcuts*, *command history shortcuts*, and *miscellaneous shortcuts*."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Navigation shortcuts\n",
+    "## Navigation Shortcuts\n",
     "\n",
     "While the use of the left and right arrow keys to move backward and forward in the line is quite obvious, there are other options that don't require moving your hands from the \"home\" keyboard position:\n",
     "\n",
-    "| Keystroke                         | Action                                     |\n",
-    "|-----------------------------------|--------------------------------------------|\n",
-    "| ``Ctrl-a``                        | Move cursor to the beginning of the line   |\n",
-    "| ``Ctrl-e``                        | Move cursor to the end of the line         |\n",
-    "| ``Ctrl-b`` or the left arrow key  | Move cursor back one character             |\n",
-    "| ``Ctrl-f`` or the right arrow key | Move cursor forward one character          |"
+    "| Keystroke                       | Action                                     |\n",
+    "|---------------------------------|--------------------------------------------|\n",
+    "| Ctrl-a                          | Move cursor to beginning of line           |\n",
+    "| Ctrl-e                          | Move cursor to end of the line             |\n",
+    "| Ctrl-b or the left arrow key    | Move cursor back one character             |\n",
+    "| Ctrl-f or the right arrow key   | Move cursor forward one character          |"
    ]
   },
   {
@@ -69,18 +46,17 @@
     "## Text Entry Shortcuts\n",
     "\n",
     "While everyone is familiar with using the Backspace key to delete the previous character, reaching for the key often requires some minor finger gymnastics, and it only deletes a single character at a time.\n",
-    "In IPython there are several shortcuts for removing some portion of the text you're typing.\n",
-    "The most immediately useful of these are the commands to delete entire lines of text.\n",
+    "In IPython there are several shortcuts for removing some portion of the text you're typing; the most immediately useful of these are the commands to delete entire lines of text.\n",
     "You'll know these have become second-nature if you find yourself using a combination of Ctrl-b and Ctrl-d instead of reaching for Backspace to delete the previous character!\n",
     "\n",
-    "| Keystroke                     | Action                                           |\n",
-    "|-------------------------------|--------------------------------------------------|\n",
-    "| Backspace key                 | Delete previous character in line                |\n",
-    "| ``Ctrl-d``                    | Delete next character in line                    |\n",
-    "| ``Ctrl-k``                    | Cut text from cursor to end of line              |\n",
-    "| ``Ctrl-u``                    | Cut text from beginning of line to cursor        |\n",
-    "| ``Ctrl-y``                    | Yank (i.e. paste) text that was previously cut   |\n",
-    "| ``Ctrl-t``                    | Transpose (i.e., switch) previous two characters |"
+    "| Keystroke                   | Action                                           |\n",
+    "|-----------------------------|--------------------------------------------------|\n",
+    "| Backspace key               | Delete previous character in line                |\n",
+    "| Ctrl-d                      | Delete next character in line                    |\n",
+    "| Ctrl-k                      | Cut text from cursor to end of line              |\n",
+    "| Ctrl-u                      | Cut text from beginning of line to cursor        |\n",
+    "| Ctrl-y                      | Yank (i.e., paste) text that was previously cut  |\n",
+    "| Ctrl-t                      | Transpose (i.e., switch) previous two characters |"
    ]
   },
   {
@@ -91,21 +67,21 @@
     "\n",
     "Perhaps the most impactful shortcuts discussed here are the ones IPython provides for navigating the command history.\n",
     "This command history goes beyond your current IPython session: your entire command history is stored in a SQLite database in your IPython profile directory.\n",
-    "The most straightforward way to access these is with the up and down arrow keys to step through the history, but other options exist as well:\n",
+    "The most straightforward way to access previous commands is by using the up and down arrow keys to step through the history, but other options exist as well:\n",
     "\n",
-    "| Keystroke                           | Action                                     |\n",
-    "|-------------------------------------|--------------------------------------------|\n",
-    "| ``Ctrl-p`` (or the up arrow key)    | Access previous command in history         |\n",
-    "| ``Ctrl-n`` (or the down arrow key)  | Access next command in history             |\n",
-    "| ``Ctrl-r``                          | Reverse-search through command history     |"
+    "| Keystroke                         | Action                                     |\n",
+    "|-----------------------------------|--------------------------------------------|\n",
+    "| Ctrl-p (or the up arrow key)      | Access previous command in history         |\n",
+    "| Ctrl-n (or the down arrow key)    | Access next command in history             |\n",
+    "| Ctrl-r                            | Reverse-search through command history     |"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The reverse-search can be particularly useful.\n",
-    "Recall that in the previous section we defined a function called ``square``.\n",
+    "The reverse-search option can be particularly useful.\n",
+    "Recall that earlier we defined a function called `square`.\n",
     "Let's reverse-search our Python history from a new IPython shell and find this definition again.\n",
     "When you press Ctrl-r in the IPython terminal, you'll see the following prompt:\n",
     "\n",
@@ -114,14 +90,14 @@
     "(reverse-i-search)`': \n",
     "```\n",
     "\n",
-    "If you start typing characters at this prompt, IPython will auto-fill the most recent command, if any, that matches those characters:\n",
+    "If you start typing characters at this prompt, IPython will autofill the most recent command, if any, that matches those characters:\n",
     "\n",
     "```ipython\n",
     "In [1]: \n",
     "(reverse-i-search)`sqa': square??\n",
     "```\n",
     "\n",
-    "At any point, you can add more characters to refine the search, or press Ctrl-r again to search further for another command that matches the query. If you followed along in the previous section, pressing Ctrl-r twice more gives:\n",
+    "At any point, you can add more characters to refine the search, or press Ctrl-r again to search further for another command that matches the query. If you followed along earlier, pressing Ctrl-r twice more gives:\n",
     "\n",
     "```ipython\n",
     "In [1]: \n",
@@ -131,7 +107,7 @@
     "```\n",
     "\n",
     "Once you have found the command you're looking for, press Return and the search will end.\n",
-    "We can then use the retrieved command, and carry-on with our session:\n",
+    "You can then use the retrieved command and carry on with your session:\n",
     "\n",
     "```ipython\n",
     "In [1]: def square(a):\n",
@@ -142,8 +118,8 @@
     "Out[2]: 4\n",
     "```\n",
     "\n",
-    "Note that Ctrl-p/Ctrl-n or the up/down arrow keys can also be used to search through history, but only by matching characters at the beginning of the line.\n",
-    "That is, if you type **``def``** and then press Ctrl-p, it would find the most recent command (if any) in your history that begins with the characters ``def``."
+    "Note that you can use Ctrl-p/Ctrl-n or the up/down arrow keys to search through your history in a similar way, but only by matching characters at the beginning of the line.\n",
+    "That is, if you type **`def`** and then press Ctrl-p, it will find the most recent command (if any) in your history that begins with the characters `def`."
    ]
   },
   {
@@ -154,36 +130,29 @@
     "\n",
     "Finally, there are a few miscellaneous shortcuts that don't fit into any of the preceding categories, but are nevertheless useful to know:\n",
     "\n",
-    "| Keystroke                     | Action                                     |\n",
-    "|-------------------------------|--------------------------------------------|\n",
-    "| ``Ctrl-l``                    | Clear terminal screen                      |\n",
-    "| ``Ctrl-c``                    | Interrupt current Python command           |\n",
-    "| ``Ctrl-d``                    | Exit IPython session                       |\n",
+    "| Keystroke                   | Action                                     |\n",
+    "|-----------------------------|--------------------------------------------|\n",
+    "| Ctrl-l                      | Clear terminal screen                      |\n",
+    "| Ctrl-c                      | Interrupt current Python command           |\n",
+    "| Ctrl-d                      | Exit IPython session                       |\n",
     "\n",
-    "The Ctrl-c in particular can be useful when you inadvertently start a very long-running job."
+    "The Ctrl-c shortcut in particular can be useful when you inadvertently start a very long-running job."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "While some of the shortcuts discussed here may seem a bit tedious at first, they quickly become automatic with practice.\n",
+    "While some of the shortcuts discussed here may seem a bit obscure at first, they quickly become automatic with practice.\n",
     "Once you develop that muscle memory, I suspect you will even find yourself wishing they were available in other contexts."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) | [Contents](Index.ipynb) | [IPython Magic Commands](01.03-Magic-Commands.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -199,9 +168,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/01.03-Magic-Commands.ipynb b/notebooks/01.03-Magic-Commands.ipynb
index e5ee9d164..a30e95404 100644
--- a/notebooks/01.03-Magic-Commands.ipynb
+++ b/notebooks/01.03-Magic-Commands.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) | [Contents](Index.ipynb) | [Input and Output History](01.04-Input-Output-History.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.03-Magic-Commands.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,85 +11,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The previous two sections showed how IPython lets you use and explore Python efficiently and interactively.\n",
+    "The previous chapter showed how IPython lets you use and explore Python efficiently and interactively.\n",
     "Here we'll begin discussing some of the enhancements that IPython adds on top of the normal Python syntax.\n",
-    "These are known in IPython as *magic commands*, and are prefixed by the ``%`` character.\n",
+    "These are known in IPython as *magic commands*, and are prefixed by the `%` character.\n",
     "These magic commands are designed to succinctly solve various common problems in standard data analysis.\n",
-    "Magic commands come in two flavors: *line magics*, which are denoted by a single ``%`` prefix and operate on a single line of input, and *cell magics*, which are denoted by a double ``%%`` prefix and operate on multiple lines of input.\n",
-    "We'll demonstrate and discuss a few brief examples here, and come back to more focused discussion of several useful magic commands later in the chapter."
+    "Magic commands come in two flavors: *line magics*, which are denoted by a single `%` prefix and operate on a single line of input, and *cell magics*, which are denoted by a double `%%` prefix and operate on multiple lines of input.\n",
+    "I'll demonstrate and discuss a few brief examples here, and come back to a more focused discussion of several useful magic commands later."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Pasting Code Blocks: ``%paste`` and ``%cpaste``\n",
-    "\n",
-    "When working in the IPython interpreter, one common gotcha is that pasting multi-line code blocks can lead to unexpected errors, especially when indentation and interpreter markers are involved.\n",
-    "A common case is that you find some example code on a website and want to paste it into your interpreter.\n",
-    "Consider the following simple function:\n",
-    "\n",
-    "``` python\n",
-    ">>> def donothing(x):\n",
-    "...     return x\n",
-    "\n",
-    "```\n",
-    "The code is formatted as it would appear in the Python interpreter, and if you copy and paste this directly into IPython you get an error:\n",
-    "\n",
-    "```ipython\n",
-    "In [2]: >>> def donothing(x):\n",
-    "   ...:     ...     return x\n",
-    "   ...:     \n",
-    "  File \"<ipython-input-20-5a66c8964687>\", line 2\n",
-    "    ...     return x\n",
-    "                 ^\n",
-    "SyntaxError: invalid syntax\n",
-    "```\n",
-    "\n",
-    "In the direct paste, the interpreter is confused by the additional prompt characters.\n",
-    "But never fear–IPython's ``%paste`` magic function is designed to handle this exact type of multi-line, marked-up input:\n",
-    "\n",
-    "```ipython\n",
-    "In [3]: %paste\n",
-    ">>> def donothing(x):\n",
-    "...     return x\n",
-    "\n",
-    "## -- End pasted text --\n",
-    "```\n",
-    "\n",
-    "The ``%paste`` command both enters and executes the code, so now the function is ready to be used:\n",
-    "\n",
-    "```ipython\n",
-    "In [4]: donothing(10)\n",
-    "Out[4]: 10\n",
-    "```\n",
-    "\n",
-    "A command with a similar intent is ``%cpaste``, which opens up an interactive multiline prompt in which you can paste one or more chunks of code to be executed in a batch:\n",
-    "\n",
-    "```ipython\n",
-    "In [5]: %cpaste\n",
-    "Pasting code; enter '--' alone on the line to stop or use Ctrl-D.\n",
-    ":>>> def donothing(x):\n",
-    ":...     return x\n",
-    ":--\n",
-    "```\n",
-    "\n",
-    "These magic commands, like others we'll see, make available functionality that would be difficult or impossible in a standard Python interpreter."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Running External Code: ``%run``\n",
-    "As you begin developing more extensive code, you will likely find yourself working in both IPython for interactive exploration, as well as a text editor to store code that you want to reuse.\n",
+    "## Running External Code: %run\n",
+    "As you begin developing more extensive code, you will likely find yourself working in IPython for interactive exploration, as well as a text editor to store code that you want to reuse.\n",
     "Rather than running this code in a new window, it can be convenient to run it within your IPython session.\n",
-    "This can be done with the ``%run`` magic.\n",
+    "This can be done with the `%run` magic command.\n",
     "\n",
-    "For example, imagine you've created a ``myscript.py`` file with the following contents:\n",
+    "For example, imagine you've created a *myscript.py* file with the following contents:\n",
     "\n",
     "```python\n",
-    "#-------------------------------------\n",
     "# file: myscript.py\n",
     "\n",
     "def square(x):\n",
@@ -119,7 +38,7 @@
     "    return x ** 2\n",
     "\n",
     "for N in range(1, 4):\n",
-    "    print(N, \"squared is\", square(N))\n",
+    "    print(f\"{N} squared is {square(N)}\")\n",
     "```\n",
     "\n",
     "You can execute this from your IPython session as follows:\n",
@@ -138,25 +57,25 @@
     "Out[7]: 25\n",
     "```\n",
     "\n",
-    "There are several options to fine-tune how your code is run; you can see the documentation in the normal way, by typing **``%run?``** in the IPython interpreter."
+    "There are several options to fine-tune how your code is run; you can see the documentation in the normal way, by typing **`%run?`** in the IPython interpreter."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Timing Code Execution: ``%timeit``\n",
-    "Another example of a useful magic function is ``%timeit``, which will automatically determine the execution time of the single-line Python statement that follows it.\n",
+    "## Timing Code Execution: %timeit\n",
+    "Another example of a useful magic function is `%timeit`, which will automatically determine the execution time of the single-line Python statement that follows it.\n",
     "For example, we may want to check the performance of a list comprehension:\n",
     "\n",
     "```ipython\n",
     "In [8]: %timeit L = [n ** 2 for n in range(1000)]\n",
-    "1000 loops, best of 3: 325 µs per loop\n",
+    "430 µs ± 3.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
     "```\n",
     "\n",
-    "The benefit of ``%timeit`` is that for short commands it will automatically perform multiple runs in order to attain more robust results.\n",
-    "For multi line statements, adding a second ``%`` sign will turn this into a cell magic that can handle multiple lines of input.\n",
-    "For example, here's the equivalent construction with a ``for``-loop:\n",
+    "The benefit of `%timeit` is that for short commands it will automatically perform multiple runs in order to attain more robust results.\n",
+    "For multiline statements, adding a second `%` sign will turn this into a cell magic that can handle multiple lines of input.\n",
+    "For example, here's the equivalent construction with a `for` loop:\n",
     "\n",
     "```ipython\n",
     "In [9]: %%timeit\n",
@@ -164,22 +83,22 @@
     "   ...: for n in range(1000):\n",
     "   ...:     L.append(n ** 2)\n",
     "   ...: \n",
-    "1000 loops, best of 3: 373 µs per loop\n",
+    "484 µs ± 5.67 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
     "```\n",
     "\n",
-    "We can immediately see that list comprehensions are about 10% faster than the equivalent ``for``-loop construction in this case.\n",
-    "We'll explore ``%timeit`` and other approaches to timing and profiling code in [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb)."
+    "We can immediately see that list comprehensions are about 10% faster than the equivalent `for` loop construction in this case.\n",
+    "We'll explore `%timeit` and other approaches to timing and profiling code in [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Help on Magic Functions: ``?``, ``%magic``, and ``%lsmagic``\n",
+    "## Help on Magic Functions: ?, %magic, and %lsmagic\n",
     "\n",
     "Like normal Python functions, IPython magic functions have docstrings, and this useful\n",
     "documentation can be accessed in the standard manner.\n",
-    "So, for example, to read the documentation of the ``%timeit`` magic simply type this:\n",
+    "So, for example, to read the documentation of the `%timeit` magic function, simply type this:\n",
     "\n",
     "```ipython\n",
     "In [10]: %timeit?\n",
@@ -199,24 +118,17 @@
     "```\n",
     "\n",
     "Finally, I'll mention that it is quite straightforward to define your own magic functions if you wish.\n",
-    "We won't discuss it here, but if you are interested, see the references listed in [More IPython Resources](01.08-More-IPython-Resources.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) | [Contents](Index.ipynb) | [Input and Output History](01.04-Input-Output-History.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.03-Magic-Commands.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "I won't discuss it here, but if you are interested, see the references listed in [More IPython Resources](01.08-More-IPython-Resources.ipynb)."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.6 64-bit ('3.9.6')",
    "language": "python",
    "name": "python3"
   },
@@ -230,9 +142,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc"
+   }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/01.04-Input-Output-History.ipynb b/notebooks/01.04-Input-Output-History.ipynb
index c8e5463fe..3e315c0c5 100644
--- a/notebooks/01.04-Input-Output-History.ipynb
+++ b/notebooks/01.04-Input-Output-History.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [IPython Magic Commands](01.03-Magic-Commands.ipynb) | [Contents](Index.ipynb) | [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.04-Input-Output-History.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,8 +11,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Previously we saw that the IPython shell allows you to access previous commands with the up and down arrow keys, or equivalently the Ctrl-p/Ctrl-n shortcuts.\n",
-    "Additionally, in both the shell and the notebook, IPython exposes several ways to obtain the output of previous commands, as well as string versions of the commands themselves.\n",
+    "Previously you saw that the IPython shell allows you to access previous commands with the up and down arrow keys, or equivalently the Ctrl-p/Ctrl-n shortcuts.\n",
+    "Additionally, in both the shell and notebooks, IPython exposes several ways to obtain the output of previous commands, as well as string versions of the commands themselves.\n",
     "We'll explore those here."
    ]
   },
@@ -42,11 +20,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## IPython's ``In`` and ``Out`` Objects\n",
+    "## IPython's In and Out Objects\n",
     "\n",
-    "By now I imagine you're quite familiar with the ``In [1]:``/``Out[1]:`` style prompts used by IPython.\n",
+    "By now I imagine you're becoming familiar with the `In [1]:`/`Out[1]:` style of prompts used by IPython.\n",
     "But it turns out that these are not just pretty decoration: they give a clue as to how you can access previous inputs and outputs in your current session.\n",
-    "Imagine you start a session that looks like this:\n",
+    "Suppose we start a session that looks like this:\n",
     "\n",
     "```ipython\n",
     "In [1]: import math\n",
@@ -63,15 +41,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We've imported the built-in ``math`` package, then computed the sine and the cosine of the number 2.\n",
-    "These inputs and outputs are displayed in the shell with ``In``/``Out`` labels, but there's more–IPython actually creates some Python variables called ``In`` and ``Out`` that are automatically updated to reflect this history:\n",
+    "We've imported the built-in `math` package, then computed the sine and the cosine of the number 2.\n",
+    "These inputs and outputs are displayed in the shell with `In`/`Out` labels, but there's more—IPython actually creates some Python variables called `In` and `Out` that are automatically updated to reflect this history:\n",
     "\n",
     "```ipython\n",
-    "In [4]: print(In)\n",
-    "['', 'import math', 'math.sin(2)', 'math.cos(2)', 'print(In)']\n",
+    "In [4]: In\n",
+    "Out[4]: ['', 'import math', 'math.sin(2)', 'math.cos(2)', 'In']\n",
     "\n",
     "In [5]: Out\n",
-    "Out[5]: {2: 0.9092974268256817, 3: -0.4161468365471424}\n",
+    "Out[5]:\n",
+    "{2: 0.9092974268256817,\n",
+    " 3: -0.4161468365471424,\n",
+    " 4: ['', 'import math', 'math.sin(2)', 'math.cos(2)', 'In', 'Out']}\n",
     "```"
    ]
   },
@@ -79,33 +60,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``In`` object is a list, which keeps track of the commands in order (the first item in the list is a place-holder so that ``In[1]`` can refer to the first command):\n",
+    "The `In` object is a list, which keeps track of the commands in order (the first item in the list is a placeholder so that `In [1]` can refer to the first command):\n",
     "\n",
     "```ipython\n",
     "In [6]: print(In[1])\n",
     "import math\n",
     "```\n",
     "\n",
-    "The ``Out`` object is not a list but a dictionary mapping input numbers to their outputs (if any):\n",
+    "The `Out` object is not a list but a dictionary mapping input numbers to their outputs (if any):\n",
     "\n",
     "```ipython\n",
     "In [7]: print(Out[2])\n",
     "0.9092974268256817\n",
     "```\n",
     "\n",
-    "Note that not all operations have outputs: for example, ``import`` statements and ``print`` statements don't affect the output.\n",
-    "The latter may be surprising, but makes sense if you consider that ``print`` is a function that returns ``None``; for brevity, any command that returns ``None`` is not added to ``Out``.\n",
+    "Note that not all operations have outputs: for example, `import` statements and `print` statements don't affect the output.\n",
+    "The latter may be surprising, but makes sense if you consider that `print` is a function that returns `None`; for brevity, any command that returns `None` is not added to `Out`.\n",
     "\n",
     "Where this can be useful is if you want to interact with past results.\n",
-    "For example, let's check the sum of ``sin(2) ** 2`` and ``cos(2) ** 2`` using the previously-computed results:\n",
+    "For example, let's check the sum of `sin(2) ** 2` and `cos(2) ** 2` using the previously computed results:\n",
     "\n",
     "```ipython\n",
     "In [8]: Out[2] ** 2 + Out[3] ** 2\n",
     "Out[8]: 1.0\n",
     "```\n",
     "\n",
-    "The result is ``1.0`` as we'd expect from the well-known trigonometric identity.\n",
-    "In this case, using these previous results probably is not necessary, but it can become very handy if you execute a very expensive computation and want to reuse the result!"
+    "The result is `1.0`, as we'd expect from the well-known trigonometric identity.\n",
+    "In this case, using these previous results probably is not necessary, but it can become quite handy if you execute a very expensive computation and forget to assign the result to a variable."
    ]
   },
   {
@@ -114,7 +95,7 @@
    "source": [
     "## Underscore Shortcuts and Previous Outputs\n",
     "\n",
-    "The standard Python shell contains just one simple shortcut for accessing previous output; the variable ``_`` (i.e., a single underscore) is kept updated with the previous output; this works in IPython as well:\n",
+    "The standard Python shell contains just one simple shortcut for accessing previous output: the variable `_` (i.e., a single underscore) is kept updated with the previous output. This works in IPython as well:\n",
     "\n",
     "```ipython\n",
     "In [9]: print(_)\n",
@@ -133,7 +114,7 @@
     "\n",
     "IPython stops there: more than three underscores starts to get a bit hard to count, and at that point it's easier to refer to the output by line number.\n",
     "\n",
-    "There is one more shortcut we should mention, however–a shorthand for ``Out[X]`` is ``_X`` (i.e., a single underscore followed by the line number):\n",
+    "There is one more shortcut I should mention, however—a shorthand for `Out[X]` is `_X` (i.e., a single underscore followed by the line number):\n",
     "\n",
     "```ipython\n",
     "In [12]: Out[2]\n",
@@ -150,14 +131,14 @@
    "source": [
     "## Suppressing Output\n",
     "Sometimes you might wish to suppress the output of a statement (this is perhaps most common with the plotting commands that we'll explore in [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb)).\n",
-    "Or maybe the command you're executing produces a result that you'd prefer not like to store in your output history, perhaps so that it can be deallocated when other references are removed.\n",
+    "Or maybe the command you're executing produces a result that you'd prefer not to store in your output history, perhaps so that it can be deallocated when other references are removed.\n",
     "The easiest way to suppress the output of a command is to add a semicolon to the end of the line:\n",
     "\n",
     "```ipython\n",
     "In [14]: math.sin(2) + math.cos(2);\n",
     "```\n",
     "\n",
-    "Note that the result is computed silently, and the output is neither displayed on the screen or stored in the ``Out`` dictionary:\n",
+    "The result is computed silently, and the output is neither displayed on the screen nor stored in the `Out` dictionary:\n",
     "\n",
     "```ipython\n",
     "In [15]: 14 in Out\n",
@@ -170,35 +151,26 @@
    "metadata": {},
    "source": [
     "## Related Magic Commands\n",
-    "For accessing a batch of previous inputs at once, the ``%history`` magic command is very helpful.\n",
+    "For accessing a batch of previous inputs at once, the `%history` magic command is very helpful.\n",
     "Here is how you can print the first four inputs:\n",
     "\n",
     "```ipython\n",
-    "In [16]: %history -n 1-4\n",
+    "In [16]: %history -n 1-3\n",
     "   1: import math\n",
     "   2: math.sin(2)\n",
     "   3: math.cos(2)\n",
-    "   4: print(In)\n",
     "```\n",
     "\n",
-    "As usual, you can type ``%history?`` for more information and a description of options available.\n",
-    "Other similar magic commands are ``%rerun`` (which will re-execute some portion of the command history) and ``%save`` (which saves some set of the command history to a file).\n",
-    "For more information, I suggest exploring these using the ``?`` help functionality discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [IPython Magic Commands](01.03-Magic-Commands.ipynb) | [Contents](Index.ipynb) | [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.04-Input-Output-History.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "As usual, you can type `%history?` for more information and a description of options available (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) for details on the `?` functionality).\n",
+    "Other useful magic commands are `%rerun`, which will re-execute some portion of the command history, and `%save`, which saves some set of the command history to a file)."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -214,9 +186,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/01.05-IPython-And-Shell-Commands.ipynb b/notebooks/01.05-IPython-And-Shell-Commands.ipynb
index 6fe0dd875..598d01517 100644
--- a/notebooks/01.05-IPython-And-Shell-Commands.ipynb
+++ b/notebooks/01.05-IPython-And-Shell-Commands.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Input and Output History](01.04-Input-Output-History.ipynb) | [Contents](Index.ipynb) | [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.05-IPython-And-Shell-Commands.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -35,11 +13,11 @@
    "source": [
     "When working interactively with the standard Python interpreter, one of the frustrations is the need to switch between multiple windows to access Python tools and system command-line tools.\n",
     "IPython bridges this gap, and gives you a syntax for executing shell commands directly from within the IPython terminal.\n",
-    "The magic happens with the exclamation point: anything appearing after ``!`` on a line will be executed not by the Python kernel, but by the system command-line.\n",
+    "The magic happens with the exclamation point: anything appearing after `!` on a line will be executed not by the Python kernel, but by the system command line.\n",
     "\n",
-    "The following assumes you're on a Unix-like system, such as Linux or Mac OSX.\n",
-    "Some of the examples that follow will fail on Windows, which uses a different type of shell by default (though with the 2016 announcement of native Bash shells on Windows, soon this may no longer be an issue!).\n",
-    "If you're unfamiliar with shell commands, I'd suggest reviewing the [Shell Tutorial](http://swcarpentry.github.io/shell-novice/) put together by the always excellent Software Carpentry Foundation."
+    "The following discussion assumes you're on a Unix-like system, such as Linux or macOS.\n",
+    "Some of the examples that follow will fail on Windows, which uses a different type of shell by default, though if you use the *Windows Subsystem for Linux* the examples here should run correctly.\n",
+    "If you're unfamiliar with shell commands, I'd suggest reviewing the [Unix shell tutorial](http://swcarpentry.github.io/shell-novice/) put together by the always excellent Software Carpentry Foundation."
    ]
   },
   {
@@ -48,24 +26,24 @@
    "source": [
     "## Quick Introduction to the Shell\n",
     "\n",
-    "A full intro to using the shell/terminal/command-line is well beyond the scope of this chapter, but for the uninitiated we will offer a quick introduction here.\n",
+    "A full introduction to using the shell/terminal/command line is well beyond the scope of this chapter, but for the uninitiated I will offer a quick introduction here.\n",
     "The shell is a way to interact textually with your computer.\n",
-    "Ever since the mid 1980s, when Microsoft and Apple introduced the first versions of their now ubiquitous graphical operating systems, most computer users have interacted with their operating system through familiar clicking of menus and drag-and-drop movements.\n",
+    "Ever since the mid-1980s, when Microsoft and Apple introduced the first versions of their now ubiquitous graphical operating systems, most computer users have interacted with their operating systems through the familiar menu selections and drag-and-drop movements.\n",
     "But operating systems existed long before these graphical user interfaces, and were primarily controlled through sequences of text input: at the prompt, the user would type a command, and the computer would do what the user told it to.\n",
-    "Those early prompt systems are the precursors of the shells and terminals that most active data scientists still use today.\n",
+    "Those early prompt systems were the precursors of the shells and terminals that most data scientists still use today.\n",
     "\n",
-    "Someone unfamiliar with the shell might ask why you would bother with this, when many results can be accomplished by simply clicking on icons and menus.\n",
-    "A shell user might reply with another question: why hunt icons and click menus when you can accomplish things much more easily by typing?\n",
-    "While it might sound like a typical tech preference impasse, when moving beyond basic tasks it quickly becomes clear that the shell offers much more control of advanced tasks, though admittedly the learning curve can intimidate the average computer user.\n",
+    "Someone unfamiliar with the shell might ask why you would bother with this, when many of the same results can be accomplished by simply clicking on icons and menus.\n",
+    "A shell user might reply with another question: why hunt for icons and menu items when you can accomplish things much more easily by typing?\n",
+    "While it might sound like a typical tech preference impasse, when moving beyond basic tasks it quickly becomes clear that the shell offers much more control of advanced tasks—though admittedly the learning curve can be intimidating.\n",
     "\n",
-    "As an example, here is a sample of a Linux/OSX shell session where a user explores, creates, and modifies directories and files on their system (``osx:~ $`` is the prompt, and everything after the ``$`` sign is the typed command; text that is preceded by a ``#`` is meant just as description, rather than something you would actually type in):\n",
+    "As an example, here is a sample of a Linux/macOS shell session where a user explores, creates, and modifies directories and files on their system (`osx:~ $` is the prompt, and everything after the `$` is the typed command; text that is preceded by a `#` is meant just as description, rather than something you would actually type in):\n",
     "\n",
     "```bash\n",
     "osx:~ $ echo \"hello world\"             # echo is like Python's print function\n",
     "hello world\n",
     "\n",
     "osx:~ $ pwd                            # pwd = print working directory\n",
-    "/home/jake                             # this is the \"path\" that we're sitting in\n",
+    "/home/jake                             # This is the \"path\" that we're sitting in\n",
     "\n",
     "osx:~ $ ls                             # ls = list working directory contents\n",
     "notebooks  projects \n",
@@ -84,14 +62,13 @@
     "\n",
     "osx:myproject $ mv ../myproject.txt ./  # mv = move file. Here we're moving the\n",
     "                                        # file myproject.txt from one directory\n",
-    "                                        # up (../) to the current directory (./)\n",
+    "                                        # up (../) to the current directory (./).\n",
     "osx:myproject $ ls\n",
     "myproject.txt\n",
     "```\n",
     "\n",
     "Notice that all of this is just a compact way to do familiar operations (navigating a directory structure, creating a directory, moving a file, etc.) by typing commands rather than clicking icons and menus.\n",
-    "Note that with just a few commands (``pwd``, ``ls``, ``cd``, ``mkdir``, and ``cp``) you can do many of the most common file operations.\n",
-    "It's when you go beyond these basics that the shell approach becomes really powerful."
+    "With just a few commands (`pwd`, `ls`, `cd`, `mkdir`, and `cp`) you can do many of the most common file operations, but it's when you go beyond these basics that the shell approach becomes really powerful."
    ]
   },
   {
@@ -100,8 +77,8 @@
    "source": [
     "## Shell Commands in IPython\n",
     "\n",
-    "Any command that works at the command-line can be used in IPython by prefixing it with the ``!`` character.\n",
-    "For example, the ``ls``, ``pwd``, and ``echo`` commands can be run as follows:\n",
+    "Any standard shell command can be used directly in IPython by prefixing it with the `!` character.\n",
+    "For example, the `ls`, `pwd`, and `echo` commands can be run as follows:\n",
     "\n",
     "```ipython\n",
     "In [1]: !ls\n",
@@ -121,8 +98,8 @@
    "source": [
     "## Passing Values to and from the Shell\n",
     "\n",
-    "Shell commands can not only be called from IPython, but can also be made to interact with the IPython namespace.\n",
-    "For example, you can save the output of any shell command to a Python list using the assignment operator:\n",
+    "Shell commands not only can be called from IPython, but can also be made to interact with the IPython namespace.\n",
+    "For example, you can save the output of any shell command to a Python list using the assignment operator, `=`:\n",
     "\n",
     "```ipython\n",
     "In [4]: contents = !ls\n",
@@ -136,15 +113,15 @@
     "['/Users/jakevdp/notebooks/tmp/myproject']\n",
     "```\n",
     "\n",
-    "Note that these results are not returned as lists, but as a special shell return type defined in IPython:\n",
+    "These results are not returned as lists, but as a special shell return type defined in IPython:\n",
     "\n",
     "```ipython\n",
     "In [8]: type(directory)\n",
     "IPython.utils.text.SList\n",
     "```\n",
     "\n",
-    "This looks and acts a lot like a Python list, but has additional functionality, such as\n",
-    "the ``grep`` and ``fields`` methods and the ``s``, ``n``, and ``p`` properties that allow you to search, filter, and display the results in convenient ways.\n",
+    "This looks and acts a lot like a Python list but has additional functionality, such as\n",
+    "the `grep` and `fields` methods and the `s`, `n`, and `p` properties that allow you to search, filter, and display the results in convenient ways.\n",
     "For more information on these, you can use IPython's built-in help features."
    ]
   },
@@ -152,7 +129,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Communication in the other direction–passing Python variables into the shell–is possible using the ``{varname}`` syntax:\n",
+    "Communication in the other direction—passing Python variables into the shell—is possible using the `{varname}` syntax:\n",
     "\n",
     "```ipython\n",
     "In [9]: message = \"hello from Python\"\n",
@@ -168,9 +145,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Shell-Related Magic Commands\n",
+    "## Shell-Related Magic Commands\n",
     "\n",
-    "If you play with IPython's shell commands for a while, you might notice that you cannot use ``!cd`` to navigate the filesystem:\n",
+    "If you play with IPython's shell commands for a while, you might notice that you cannot use `!cd` to navigate the filesystem:\n",
     "\n",
     "```ipython\n",
     "In [11]: !pwd\n",
@@ -182,24 +159,24 @@
     "/home/jake/projects/myproject\n",
     "```\n",
     "\n",
-    "The reason is that shell commands in the notebook are executed in a temporary subshell.\n",
-    "If you'd like to change the working directory in a more enduring way, you can use the ``%cd`` magic command:\n",
+    "The reason is that shell commands in the notebook are executed in a temporary subshell that does not maintain state from command to command.\n",
+    "If you'd like to change the working directory in a more enduring way, you can use the `%cd` magic command:\n",
     "\n",
     "```ipython\n",
     "In [14]: %cd ..\n",
     "/home/jake/projects\n",
     "```\n",
     "\n",
-    "In fact, by default you can even use this without the ``%`` sign:\n",
+    "In fact, by default you can even use this without the `%` sign:\n",
     "\n",
     "```ipython\n",
     "In [15]: cd myproject\n",
     "/home/jake/projects/myproject\n",
     "```\n",
     "\n",
-    "This is known as an ``automagic`` function, and this behavior can be toggled with the ``%automagic`` magic function.\n",
+    "This is known as an *automagic* function, and the ability to execute such commands without an explicit `%` can be toggled with the `%automagic` magic function.\n",
     "\n",
-    "Besides ``%cd``, other available shell-like magic functions are ``%cat``, ``%cp``, ``%env``, ``%ls``, ``%man``, ``%mkdir``, ``%more``, ``%mv``, ``%pwd``, ``%rm``, and ``%rmdir``, any of which can be used without the ``%`` sign if ``automagic`` is on.\n",
+    "Besides `%cd`, other available shell-like magic functions are `%cat`, `%cp`, `%env`, `%ls`, `%man`, `%mkdir`, `%more`, `%mv`, `%pwd`, `%rm`, and `%rmdir`, any of which can be used without the `%` sign if `automagic` is on.\n",
     "This makes it so that you can almost treat the IPython prompt as if it's a normal shell:\n",
     "\n",
     "```ipython\n",
@@ -216,22 +193,15 @@
     "In [20]: rm -r tmp\n",
     "```\n",
     "\n",
-    "This access to the shell from within the same terminal window as your Python session means that there is a lot less switching back and forth between interpreter and shell as you write your Python code."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Input and Output History](01.04-Input-Output-History.ipynb) | [Contents](Index.ipynb) | [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.05-IPython-And-Shell-Commands.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "This access to the shell from within the same terminal window as your Python session lets you more naturally combine Python and the shell in your workflows with fewer context switches."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -247,9 +217,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/01.06-Errors-and-Debugging.ipynb b/notebooks/01.06-Errors-and-Debugging.ipynb
index a7625d5ef..8b3110d37 100644
--- a/notebooks/01.06-Errors-and-Debugging.ipynb
+++ b/notebooks/01.06-Errors-and-Debugging.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) | [Contents](Index.ipynb) | [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.06-Errors-and-Debugging.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -41,11 +19,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Controlling Exceptions: ``%xmode``\n",
+    "## Controlling Exceptions: %xmode\n",
     "\n",
-    "Most of the time when a Python script fails, it will raise an Exception.\n",
+    "Most of the time when a Python script fails, it will raise an exception.\n",
     "When the interpreter hits one of these exceptions, information about the cause of the error can be found in the *traceback*, which can be accessed from within Python.\n",
-    "With the ``%xmode`` magic function, IPython allows you to control the amount of information printed when the exception is raised.\n",
+    "With the `%xmode` magic function, IPython allows you to control the amount of information printed when the exception is raised.\n",
     "Consider the following code:"
    ]
   },
@@ -53,7 +31,10 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -70,7 +51,10 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -94,20 +78,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Calling ``func2`` results in an error, and reading the printed trace lets us see exactly what happened.\n",
-    "By default, this trace includes several lines showing the context of each step that led to the error.\n",
-    "Using the ``%xmode`` magic function (short for *Exception mode*), we can change what information is printed.\n",
+    "Calling `func2` results in an error, and reading the printed trace lets us see exactly what happened.\n",
+    "In the default mode, this trace includes several lines showing the context of each step that led to the error.\n",
+    "Using the `%xmode` magic function (short for *exception mode*), we can change what information is printed.\n",
     "\n",
-    "``%xmode`` takes a single argument, the mode, and there are three possibilities: ``Plain``, ``Context``, and ``Verbose``.\n",
-    "The default is ``Context``, and gives output like that just shown before.\n",
-    "``Plain`` is more compact and gives less information:"
+    "`%xmode` takes a single argument, the mode, and there are three possibilities: `Plain`, `Context`, and `Verbose`.\n",
+    "The default is `Context`, which gives output like that just shown.\n",
+    "`Plain` is more compact and gives less information:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -126,7 +113,10 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -150,14 +140,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``Verbose`` mode adds some extra information, including the arguments to any functions that are called:"
+    "The `Verbose` mode adds some extra information, including the arguments to any functions that are called:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -176,7 +169,10 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -200,10 +196,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This extra information can help narrow-in on why the exception is being raised.\n",
-    "So why not use the ``Verbose`` mode all the time?\n",
+    "This extra information can help you narrow in on why the exception is being raised.\n",
+    "So why not use the `Verbose` mode all the time?\n",
     "As code gets complicated, this kind of traceback can get extremely long.\n",
-    "Depending on the context, sometimes the brevity of ``Default`` mode is easier to work with."
+    "Depending on the context, sometimes the brevity of `Plain` or `Context` mode is easier to work with."
    ]
   },
   {
@@ -212,36 +208,39 @@
    "source": [
     "## Debugging: When Reading Tracebacks Is Not Enough\n",
     "\n",
-    "The standard Python tool for interactive debugging is ``pdb``, the Python debugger.\n",
+    "The standard Python tool for interactive debugging is `pdb`, the Python debugger.\n",
     "This debugger lets the user step through the code line by line in order to see what might be causing a more difficult error.\n",
-    "The IPython-enhanced version of this is ``ipdb``, the IPython debugger.\n",
+    "The IPython-enhanced version of this is `ipdb`, the IPython debugger.\n",
     "\n",
     "There are many ways to launch and use both these debuggers; we won't cover them fully here.\n",
     "Refer to the online documentation of these two utilities to learn more.\n",
     "\n",
-    "In IPython, perhaps the most convenient interface to debugging is the ``%debug`` magic command.\n",
+    "In IPython, perhaps the most convenient interface to debugging is the `%debug` magic command.\n",
     "If you call it after hitting an exception, it will automatically open an interactive debugging prompt at the point of the exception.\n",
-    "The ``ipdb`` prompt lets you explore the current state of the stack, explore the available variables, and even run Python commands!\n",
+    "The `ipdb` prompt lets you explore the current state of the stack, explore the available variables, and even run Python commands!\n",
     "\n",
-    "Let's look at the most recent exception, then do some basic tasks–print the values of ``a`` and ``b``, and type ``quit`` to quit the debugging session:"
+    "Let's look at the most recent exception, then do some basic tasks. We'll print the values of `a` and `b`, then type `quit` to quit the debugging session:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "> \u001b[0;32m<ipython-input-1-d849e34d61fb>\u001b[0m(2)\u001b[0;36mfunc1\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m      1 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m----> 2 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m      3 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
+      "> <ipython-input-1-d849e34d61fb>(2)func1()\n",
+      "      1 def func1(a, b):\n",
+      "----> 2     return a / b\n",
+      "      3 \n",
+      "\n",
       "ipdb> print(a)\n",
       "1\n",
       "ipdb> print(b)\n",
@@ -258,43 +257,46 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The interactive debugger allows much more than this, though–we can even step up and down through the stack and explore the values of variables there:"
+    "The interactive debugger allows much more than this, though—we can even step up and down through the stack and explore the values of variables there:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "> \u001b[0;32m<ipython-input-1-d849e34d61fb>\u001b[0m(2)\u001b[0;36mfunc1\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m      1 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m----> 2 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m      3 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
+      "> <ipython-input-1-d849e34d61fb>(2)func1()\n",
+      "      1 def func1(a, b):\n",
+      "----> 2     return a / b\n",
+      "      3 \n",
+      "\n",
       "ipdb> up\n",
-      "> \u001b[0;32m<ipython-input-1-d849e34d61fb>\u001b[0m(7)\u001b[0;36mfunc2\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m      5 \u001b[0;31m    \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m      6 \u001b[0;31m    \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m----> 7 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
+      "> <ipython-input-1-d849e34d61fb>(7)func2()\n",
+      "      5     a = x\n",
+      "      6     b = x - 1\n",
+      "----> 7     return func1(a, b)\n",
+      "\n",
       "ipdb> print(x)\n",
       "1\n",
       "ipdb> up\n",
-      "> \u001b[0;32m<ipython-input-6-b2e110f6fc8f>\u001b[0m(1)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m----> 1 \u001b[0;31m\u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
+      "> <ipython-input-6-b2e110f6fc8f>(1)<module>()\n",
+      "----> 1 func2(1)\n",
+      "\n",
       "ipdb> down\n",
-      "> \u001b[0;32m<ipython-input-1-d849e34d61fb>\u001b[0m(7)\u001b[0;36mfunc2\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m      5 \u001b[0;31m    \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m      6 \u001b[0;31m    \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m----> 7 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
+      "> <ipython-input-1-d849e34d61fb>(7)func2()\n",
+      "      5     a = x\n",
+      "      6     b = x - 1\n",
+      "----> 7     return func1(a, b)\n",
+      "\n",
       "ipdb> quit\n"
      ]
     }
@@ -307,16 +309,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This allows you to quickly find out not only what caused the error, but what function calls led up to the error.\n",
+    "This allows us to quickly find out not only what caused the error, but what function calls led up to the error.\n",
     "\n",
-    "If you'd like the debugger to launch automatically whenever an exception is raised, you can use the ``%pdb`` magic function to turn on this automatic behavior:"
+    "If you'd like the debugger to launch automatically whenever an exception is raised, you can use the `%pdb` magic function to turn on this automatic behavior:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -343,11 +348,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "> \u001b[0;32m<ipython-input-1-d849e34d61fb>\u001b[0m(2)\u001b[0;36mfunc1\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m      1 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m----> 2 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m      3 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
+      "> <ipython-input-1-d849e34d61fb>(2)func1()\n",
+      "      1 def func1(a, b):\n",
+      "----> 2     return a / b\n",
+      "      3 \n",
+      "\n",
       "ipdb> print(b)\n",
       "0\n",
       "ipdb> quit\n"
@@ -364,7 +369,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, if you have a script that you'd like to run from the beginning in interactive mode, you can run it with the command ``%run -d``, and use the ``next`` command to step through the lines of code interactively."
+    "Finally, if you have a script that you'd like to run from the beginning in interactive mode, you can run it with the command `%run -d`, and use the `next` command to step through the lines of code interactively."
    ]
   },
   {
@@ -373,38 +378,31 @@
    "source": [
     "### Partial list of debugging commands\n",
     "\n",
-    "There are many more available commands for interactive debugging than we've listed here; the following table contains a description of some of the more common and useful ones:\n",
+    "There are many more available commands for interactive debugging than I've shown here. The following table contains a description of some of the more common and useful ones:\n",
     "\n",
-    "| Command         |  Description                                                |\n",
-    "|-----------------|-------------------------------------------------------------|\n",
-    "| ``list``        | Show the current location in the file                       |\n",
-    "| ``h(elp)``      | Show a list of commands, or find help on a specific command |\n",
-    "| ``q(uit)``      | Quit the debugger and the program                           |\n",
-    "| ``c(ontinue)``  | Quit the debugger, continue in the program                  |\n",
-    "| ``n(ext)``      | Go to the next step of the program                          |\n",
-    "| ``<enter>``     | Repeat the previous command                                 |\n",
-    "| ``p(rint)``     | Print variables                                             |\n",
-    "| ``s(tep)``      | Step into a subroutine                                      |\n",
-    "| ``r(eturn)``    | Return out of a subroutine                                  |\n",
+    "| Command       |  Description                                                |\n",
+    "|---------------|-------------------------------------------------------------|\n",
+    "| `l(ist)`      | Show the current location in the file                       |\n",
+    "| `h(elp)`      | Show a list of commands, or find help on a specific command |\n",
+    "| `q(uit)`      | Quit the debugger and the program                           |\n",
+    "| `c(ontinue)`  | Quit the debugger, continue in the program                  |\n",
+    "| `n(ext)`      | Go to the next step of the program                          |\n",
+    "| `<enter>`     | Repeat the previous command                                 |\n",
+    "| `p(rint)`     | Print variables                                             |\n",
+    "| `s(tep)`      | Step into a subroutine                                      |\n",
+    "| `r(eturn)`    | Return out of a subroutine                                  |\n",
     "\n",
-    "For more information, use the ``help`` command in the debugger, or take a look at ``ipdb``'s [online documentation](https://github.com/gotcha/ipdb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) | [Contents](Index.ipynb) | [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.06-Errors-and-Debugging.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "For more information, use the `help` command in the debugger, or take a look at `ipdb`'s [online documentation](https://github.com/gotcha/ipdb)."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -418,9 +416,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/01.07-Timing-and-Profiling.ipynb b/notebooks/01.07-Timing-and-Profiling.ipynb
index 76f0db5cb..203ca1d58 100644
--- a/notebooks/01.07-Timing-and-Profiling.ipynb
+++ b/notebooks/01.07-Timing-and-Profiling.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) | [Contents](Index.ipynb) | [More IPython Resources](01.08-More-IPython-Resources.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.07-Timing-and-Profiling.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -37,27 +15,27 @@
     "Early in developing your algorithm, it can be counterproductive to worry about such things. As Donald Knuth famously quipped, \"We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.\"\n",
     "\n",
     "But once you have your code working, it can be useful to dig into its efficiency a bit.\n",
-    "Sometimes it's useful to check the execution time of a given command or set of commands; other times it's useful to dig into a multiline process and determine where the bottleneck lies in some complicated series of operations.\n",
+    "Sometimes it's useful to check the execution time of a given command or set of commands; other times it's useful to examine a multiline process and determine where the bottleneck lies in some complicated series of operations.\n",
     "IPython provides access to a wide array of functionality for this kind of timing and profiling of code.\n",
     "Here we'll discuss the following IPython magic commands:\n",
     "\n",
-    "- ``%time``: Time the execution of a single statement\n",
-    "- ``%timeit``: Time repeated execution of a single statement for more accuracy\n",
-    "- ``%prun``: Run code with the profiler\n",
-    "- ``%lprun``: Run code with the line-by-line profiler\n",
-    "- ``%memit``: Measure the memory use of a single statement\n",
-    "- ``%mprun``: Run code with the line-by-line memory profiler\n",
+    "- `%time`: Time the execution of a single statement\n",
+    "- `%timeit`: Time repeated execution of a single statement for more accuracy\n",
+    "- `%prun`: Run code with the profiler\n",
+    "- `%lprun`: Run code with the line-by-line profiler\n",
+    "- `%memit`: Measure the memory use of a single statement\n",
+    "- `%mprun`: Run code with the line-by-line memory profiler\n",
     "\n",
-    "The last four commands are not bundled with IPython–you'll need to get the ``line_profiler`` and ``memory_profiler`` extensions, which we will discuss in the following sections."
+    "The last four commands are not bundled with IPython; to use them you'll need to get the `line_profiler` and `memory_profiler` extensions, which we will discuss in the following sections."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Timing Code Snippets: ``%timeit`` and ``%time``\n",
+    "## Timing Code Snippets: %timeit and %time\n",
     "\n",
-    "We saw the ``%timeit`` line-magic and ``%%timeit`` cell-magic in the introduction to magic functions in [IPython Magic Commands](01.03-Magic-Commands.ipynb); it can be used to time the repeated execution of snippets of code:"
+    "We saw the `%timeit` line magic and `%%timeit` cell magic in the introduction to magic functions in [IPython Magic Commands](01.03-Magic-Commands.ipynb); these can be used to time the repeated execution of snippets of code:"
    ]
   },
   {
@@ -69,7 +47,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "100000 loops, best of 3: 1.54 µs per loop\n"
+      "1.53 µs ± 47.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
      ]
     }
    ],
@@ -81,8 +59,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that because this operation is so fast, ``%timeit`` automatically does a large number of repetitions.\n",
-    "For slower commands, ``%timeit`` will automatically adjust and perform fewer repetitions:"
+    "Note that because this operation is so fast, `%timeit` automatically does a large number of repetitions.\n",
+    "For slower commands, `%timeit` will automatically adjust and perform fewer repetitions:"
    ]
   },
   {
@@ -94,7 +72,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1 loops, best of 3: 407 ms per loop\n"
+      "536 ms ± 15.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -111,8 +89,7 @@
    "metadata": {},
    "source": [
     "Sometimes repeating an operation is not the best option.\n",
-    "For example, if we have a list that we'd like to sort, we might be misled by a repeated operation.\n",
-    "Sorting a pre-sorted list is much faster than sorting an unsorted list, so the repetition will skew the result:"
+    "For example, if we have a list that we'd like to sort, we might be misled by a repeated operation; sorting a pre-sorted list is much faster than sorting an unsorted list, so the repetition will skew the result:"
    ]
   },
   {
@@ -124,7 +101,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "100 loops, best of 3: 1.9 ms per loop\n"
+      "1.71 ms ± 334 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
      ]
     }
    ],
@@ -138,7 +115,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For this, the ``%time`` magic function may be a better choice. It also is a good choice for longer-running commands, when short, system-related delays are unlikely to affect the result.\n",
+    "For this, the `%time` magic function may be a better choice. It also is a good choice for longer-running commands, when short, system-related delays are unlikely to affect the result.\n",
     "Let's time the sorting of an unsorted and a presorted list:"
    ]
   },
@@ -152,8 +129,8 @@
      "output_type": "stream",
      "text": [
       "sorting an unsorted list:\n",
-      "CPU times: user 40.6 ms, sys: 896 µs, total: 41.5 ms\n",
-      "Wall time: 41.5 ms\n"
+      "CPU times: user 31.3 ms, sys: 686 µs, total: 32 ms\n",
+      "Wall time: 33.3 ms\n"
      ]
     }
    ],
@@ -174,8 +151,8 @@
      "output_type": "stream",
      "text": [
       "sorting an already sorted list:\n",
-      "CPU times: user 8.18 ms, sys: 10 µs, total: 8.19 ms\n",
-      "Wall time: 8.24 ms\n"
+      "CPU times: user 5.19 ms, sys: 268 µs, total: 5.46 ms\n",
+      "Wall time: 14.1 ms\n"
      ]
     }
    ],
@@ -185,15 +162,16 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice how much faster the presorted list is to sort, but notice also how much longer the timing takes with ``%time`` versus ``%timeit``, even for the presorted list!\n",
-    "This is a result of the fact that ``%timeit`` does some clever things under the hood to prevent system calls from interfering with the timing.\n",
-    "For example, it prevents cleanup of unused Python objects (known as *garbage collection*) which might otherwise affect the timing.\n",
-    "For this reason, ``%timeit`` results are usually noticeably faster than ``%time`` results.\n",
+    "Notice how much faster the presorted list is to sort, but notice also how much longer the timing takes with `%time` versus `%timeit`, even for the presorted list!\n",
+    "This is a result of the fact that `%timeit` does some clever things under the hood to prevent system calls from interfering with the timing.\n",
+    "For example, it prevents cleanup of unused Python objects (known as *garbage collection*) that might otherwise affect the timing.\n",
+    "For this reason, `%timeit` results are usually noticeably faster than `%time` results.\n",
     "\n",
-    "For ``%time`` as with ``%timeit``, using the double-percent-sign cell magic syntax allows timing of multiline scripts:"
+    "For `%time`, as with `%timeit`, using the `%%` cell magic syntax allows timing of multiline scripts:"
    ]
   },
   {
@@ -205,8 +183,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 504 ms, sys: 979 µs, total: 505 ms\n",
-      "Wall time: 505 ms\n"
+      "CPU times: user 655 ms, sys: 5.68 ms, total: 661 ms\n",
+      "Wall time: 710 ms\n"
      ]
     }
    ],
@@ -222,21 +200,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For more information on ``%time`` and ``%timeit``, as well as their available options, use the IPython help functionality (i.e., type ``%time?`` at the IPython prompt)."
+    "For more information on `%time` and `%timeit`, as well as their available options, use the IPython help functionality (e.g., type `%time?` at the IPython prompt)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Profiling Full Scripts: ``%prun``\n",
+    "## Profiling Full Scripts: %prun\n",
     "\n",
-    "A program is made of many single statements, and sometimes timing these statements in context is more important than timing them on their own.\n",
-    "Python contains a built-in code profiler (which you can read about in the Python documentation), but IPython offers a much more convenient way to use this profiler, in the form of the magic function ``%prun``.\n",
+    "A program is made up of many single statements, and sometimes timing these statements in context is more important than timing them on their own.\n",
+    "Python contains a built-in code profiler (which you can read about in the Python documentation), but IPython offers a much more convenient way to use this profiler, in the form of the magic function `%prun`.\n",
     "\n",
     "By way of example, we'll define a simple function that does some calculations:"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -255,7 +238,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we can call ``%prun`` with a function call to see the profiled results:"
+    "Now we can call `%prun` with a function call to see the profiled results:"
    ]
   },
   {
@@ -269,6 +252,25 @@
      "text": [
       " "
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "         14 function calls in 0.932 seconds\n",
+       "\n",
+       "   Ordered by: internal time\n",
+       "\n",
+       "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
+       "        5    0.808    0.162    0.808    0.162 <ipython-input-7-f105717832a2>:4(<listcomp>)\n",
+       "        5    0.066    0.013    0.066    0.013 {built-in method builtins.sum}\n",
+       "        1    0.044    0.044    0.918    0.918 <ipython-input-7-f105717832a2>:1(sum_of_lists)\n",
+       "        1    0.014    0.014    0.932    0.932 <string>:1(<module>)\n",
+       "        1    0.000    0.000    0.932    0.932 {built-in method builtins.exec}\n",
+       "        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -279,42 +281,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the notebook, the output is printed to the pager, and looks something like this:\n",
-    "\n",
-    "```\n",
-    "14 function calls in 0.714 seconds\n",
+    "The result is a table that indicates, in order of total time on each function call, where the execution is spending the most time. In this case, the bulk of the execution time is in the list comprehension inside `sum_of_lists`.\n",
+    "From here, we could start thinking about what changes we might make to improve the performance of the algorithm.\n",
     "\n",
-    "   Ordered by: internal time\n",
-    "\n",
-    "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
-    "        5    0.599    0.120    0.599    0.120 <ipython-input-19>:4(<listcomp>)\n",
-    "        5    0.064    0.013    0.064    0.013 {built-in method sum}\n",
-    "        1    0.036    0.036    0.699    0.699 <ipython-input-19>:1(sum_of_lists)\n",
-    "        1    0.014    0.014    0.714    0.714 <string>:1(<module>)\n",
-    "        1    0.000    0.000    0.714    0.714 {built-in method exec}\n",
-    "```\n",
-    "\n",
-    "The result is a table that indicates, in order of total time on each function call, where the execution is spending the most time. In this case, the bulk of execution time is in the list comprehension inside ``sum_of_lists``.\n",
-    "From here, we could start thinking about what changes we might make to improve the performance in the algorithm.\n",
-    "\n",
-    "For more information on ``%prun``, as well as its available options, use the IPython help functionality (i.e., type ``%prun?`` at the IPython prompt)."
+    "For more information on `%prun`, as well as its available options, use the IPython help functionality (i.e., type `%prun?` at the IPython prompt)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Line-By-Line Profiling with ``%lprun``\n",
+    "## Line-by-Line Profiling with %lprun\n",
     "\n",
-    "The function-by-function profiling of ``%prun`` is useful, but sometimes it's more convenient to have a line-by-line profile report.\n",
-    "This is not built into Python or IPython, but there is a ``line_profiler`` package available for installation that can do this.\n",
-    "Start by using Python's packaging tool, ``pip``, to install the ``line_profiler`` package:\n",
+    "The function-by-function profiling of `%prun` is useful, but sometimes it's more convenient to have a line-by-line profile report.\n",
+    "This is not built into Python or IPython, but there is a `line_profiler` package available for installation that can do this.\n",
+    "Start by using Python's packaging tool, `pip`, to install the `line_profiler` package:\n",
     "\n",
     "```\n",
     "$ pip install line_profiler\n",
     "```\n",
     "\n",
-    "Next, you can use IPython to load the ``line_profiler`` IPython extension, offered as part of this package:"
+    "Next, you can use IPython to load the `line_profiler` IPython extension, offered as part of this package:"
    ]
   },
   {
@@ -330,14 +317,37 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now the ``%lprun`` command will do a line-by-line profiling of any function–in this case, we need to tell it explicitly which functions we're interested in profiling:"
+    "Now the `%lprun` command will do a line-by-line profiling of any function. In this case, we need to tell it explicitly which functions we're interested in profiling:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timer unit: 1e-06 s\n",
+       "\n",
+       "Total time: 0.014803 s\n",
+       "File: <ipython-input-7-f105717832a2>\n",
+       "Function: sum_of_lists at line 1\n",
+       "\n",
+       "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
+       "==============================================================\n",
+       "     1                                           def sum_of_lists(N):\n",
+       "     2         1          6.0      6.0      0.0      total = 0\n",
+       "     3         6         13.0      2.2      0.1      for i in range(5):\n",
+       "     4         5      14242.0   2848.4     96.2          L = [j ^ (j >> i) for j in range(N)]\n",
+       "     5         5        541.0    108.2      3.7          total += sum(L)\n",
+       "     6         1          1.0      1.0      0.0      return total"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%lprun -f sum_of_lists sum_of_lists(5000)"
    ]
@@ -346,51 +356,32 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As before, the notebook sends the result to the pager, but it looks something like this:\n",
-    "\n",
-    "```\n",
-    "Timer unit: 1e-06 s\n",
-    "\n",
-    "Total time: 0.009382 s\n",
-    "File: <ipython-input-19-fa2be176cc3e>\n",
-    "Function: sum_of_lists at line 1\n",
-    "\n",
-    "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
-    "==============================================================\n",
-    "     1                                           def sum_of_lists(N):\n",
-    "     2         1            2      2.0      0.0      total = 0\n",
-    "     3         6            8      1.3      0.1      for i in range(5):\n",
-    "     4         5         9001   1800.2     95.9          L = [j ^ (j >> i) for j in range(N)]\n",
-    "     5         5          371     74.2      4.0          total += sum(L)\n",
-    "     6         1            0      0.0      0.0      return total\n",
-    "```\n",
-    "\n",
-    "The information at the top gives us the key to reading the results: the time is reported in microseconds and we can see where the program is spending the most time.\n",
+    "The information at the top gives us the key to reading the results: the time is reported in microseconds, and we can see where the program is spending the most time.\n",
     "At this point, we may be able to use this information to modify aspects of the script and make it perform better for our desired use case.\n",
     "\n",
-    "For more information on ``%lprun``, as well as its available options, use the IPython help functionality (i.e., type ``%lprun?`` at the IPython prompt)."
+    "For more information on `%lprun`, as well as its available options, use the IPython help functionality (i.e., type `%lprun?` at the IPython prompt)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Profiling Memory Use: ``%memit`` and ``%mprun``\n",
+    "## Profiling Memory Use: %memit and %mprun\n",
     "\n",
     "Another aspect of profiling is the amount of memory an operation uses.\n",
-    "This can be evaluated with another IPython extension, the ``memory_profiler``.\n",
-    "As with the ``line_profiler``, we start by ``pip``-installing the extension:\n",
+    "This can be evaluated with another IPython extension, the `memory_profiler`.\n",
+    "As with the `line_profiler`, we start by `pip`-installing the extension:\n",
     "\n",
     "```\n",
     "$ pip install memory_profiler\n",
     "```\n",
     "\n",
-    "Then we can use IPython to load the extension:"
+    "Then we can use IPython to load it:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -401,20 +392,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The memory profiler extension contains two useful magic functions: the ``%memit`` magic (which offers a memory-measuring equivalent of ``%timeit``) and the ``%mprun`` function (which offers a memory-measuring equivalent of ``%lprun``).\n",
-    "The ``%memit`` function can be used rather simply:"
+    "The memory profiler extension contains two useful magic functions: `%memit` (which offers a memory-measuring equivalent of `%timeit`) and `%mprun` (which offers a memory-measuring equivalent of `%lprun`).\n",
+    "The `%memit` magic function can be used rather simply:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "peak memory: 100.08 MiB, increment: 61.36 MiB\n"
+      "peak memory: 141.70 MiB, increment: 75.65 MiB\n"
      ]
     }
    ],
@@ -426,15 +417,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that this function uses about 100 MB of memory.\n",
+    "We see that this function uses about 140 MB of memory.\n",
     "\n",
-    "For a line-by-line description of memory use, we can use the ``%mprun`` magic.\n",
-    "Unfortunately, this magic works only for functions defined in separate modules rather than the notebook itself, so we'll start by using the ``%%file`` magic to create a simple module called ``mprun_demo.py``, which contains our ``sum_of_lists`` function, with one addition that will make our memory profiling results more clear:"
+    "For a line-by-line description of memory use, we can use the `%mprun` magic function.\n",
+    "Unfortunately, this works only for functions defined in separate modules rather than the notebook itself, so we'll start by using the `%%file` cell magic to create a simple module called `mprun_demo.py`, which contains our `sum_of_lists` function, with one addition that will make our memory profiling results more clear:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -465,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -474,6 +465,25 @@
      "text": [
       "\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Filename: /Users/jakevdp/github/jakevdp/PythonDataScienceHandbook/notebooks_v2/mprun_demo.py\n",
+       "\n",
+       "Line #    Mem usage    Increment  Occurences   Line Contents\n",
+       "============================================================\n",
+       "     1     66.7 MiB     66.7 MiB           1   def sum_of_lists(N):\n",
+       "     2     66.7 MiB      0.0 MiB           1       total = 0\n",
+       "     3     75.1 MiB      8.4 MiB           6       for i in range(5):\n",
+       "     4    105.9 MiB     30.8 MiB     5000015           L = [j ^ (j >> i) for j in range(N)]\n",
+       "     5    109.8 MiB      3.8 MiB           5           total += sum(L)\n",
+       "     6     75.1 MiB    -34.6 MiB           5           del L # remove reference to L\n",
+       "     7     66.9 MiB     -8.2 MiB           1       return total"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -485,48 +495,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The result, printed to the pager, gives us a summary of the memory use of the function, and looks something like this:\n",
-    "```\n",
-    "Filename: ./mprun_demo.py\n",
-    "\n",
-    "Line #    Mem usage    Increment   Line Contents\n",
-    "================================================\n",
-    "     4     71.9 MiB      0.0 MiB           L = [j ^ (j >> i) for j in range(N)]\n",
-    "\n",
-    "\n",
-    "Filename: ./mprun_demo.py\n",
-    "\n",
-    "Line #    Mem usage    Increment   Line Contents\n",
-    "================================================\n",
-    "     1     39.0 MiB      0.0 MiB   def sum_of_lists(N):\n",
-    "     2     39.0 MiB      0.0 MiB       total = 0\n",
-    "     3     46.5 MiB      7.5 MiB       for i in range(5):\n",
-    "     4     71.9 MiB     25.4 MiB           L = [j ^ (j >> i) for j in range(N)]\n",
-    "     5     71.9 MiB      0.0 MiB           total += sum(L)\n",
-    "     6     46.5 MiB    -25.4 MiB           del L # remove reference to L\n",
-    "     7     39.1 MiB     -7.4 MiB       return total\n",
-    "```\n",
-    "Here the ``Increment`` column tells us how much each line affects the total memory budget: observe that when we create and delete the list ``L``, we are adding about 25 MB of memory usage.\n",
+    "Here, the `Increment` column tells us how much each line affects the total memory budget: observe that when we create and delete the list `L`, we are adding about 30 MB of memory usage.\n",
     "This is on top of the background memory usage from the Python interpreter itself.\n",
     "\n",
-    "For more information on ``%memit`` and ``%mprun``, as well as their available options, use the IPython help functionality (i.e., type ``%memit?`` at the IPython prompt)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) | [Contents](Index.ipynb) | [More IPython Resources](01.08-More-IPython-Resources.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.07-Timing-and-Profiling.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "For more information on `%memit` and `%mprun`, as well as their available options, use the IPython help functionality (e.g., type `%memit?` at the IPython prompt)."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -540,9 +522,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/01.08-More-IPython-Resources.ipynb b/notebooks/01.08-More-IPython-Resources.ipynb
index ad87f002d..63819e7c0 100644
--- a/notebooks/01.08-More-IPython-Resources.ipynb
+++ b/notebooks/01.08-More-IPython-Resources.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) | [Contents](Index.ipynb) | [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.08-More-IPython-Resources.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,8 +11,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this chapter, we've just scratched the surface of using IPython to enable data science tasks.\n",
-    "Much more information is available both in print and on the Web, and here we'll list some other resources that you may find helpful."
+    "In this set of chapters, we've just scratched the surface of using IPython to enable data science tasks.\n",
+    "Much more information is available both in print and on the web, and here I'll list some other resources that you may find helpful."
    ]
   },
   {
@@ -43,10 +21,10 @@
    "source": [
     "## Web Resources\n",
     "\n",
-    "- [The IPython website](http://ipython.org): The IPython website links to documentation, examples, tutorials, and a variety of other resources.\n",
-    "- [The nbviewer website](http://nbviewer.jupyter.org/): This site shows static renderings of any IPython notebook available on the internet. The front page features some example notebooks that you can browse to see what other folks are using IPython for!\n",
-    "- [A gallery of interesting Jupyter Notebooks](https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks/): This ever-growing list of notebooks, powered by nbviewer, shows the depth and breadth of numerical analysis you can do with IPython. It includes everything from short examples and tutorials to full-blown courses and books composed in the notebook format!\n",
-    "- Video Tutorials: searching the Internet, you will find many video-recorded tutorials on IPython. I'd especially recommend seeking tutorials from the PyCon, SciPy, and PyData conferenes by Fernando Perez and Brian Granger, two of the primary creators and maintainers of IPython and Jupyter."
+    "- [The IPython website](http://ipython.org): The IPython website provides links to documentation, examples, tutorials, and a variety of other resources.\n",
+    "- [The nbviewer website](http://nbviewer.jupyter.org/): This site shows static renderings of any Jupyter notebook available on the internet. The front page features some example notebooks that you can browse to see what other folks are using IPython for!\n",
+    "- [A curated collection of Jupyter notebooks](https://github.com/jupyter/jupyter/wiki): This ever-growing list of notebooks, powered by nbviewer, shows the depth and breadth of numerical analysis you can do with IPython. It includes everything from short examples and tutorials to full-blown courses and books composed in the notebook format!\n",
+    "- Video tutorials: Searching the internet, you will find many video tutorials on IPython. I'd especially recommend seeking tutorials from the PyCon, SciPy, and PyData conferences by Fernando Perez and Brian Granger, two of the primary creators and maintainers of IPython and Jupyter."
    ]
   },
   {
@@ -55,27 +33,20 @@
    "source": [
     "## Books\n",
     "\n",
-    "- [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do): Wes McKinney's book includes a chapter that covers using IPython as a data scientist. Although much of the material overlaps what we've discussed here, another perspective is always helpful.\n",
-    "- [*Learning IPython for Interactive Computing and Data Visualization*](https://www.packtpub.com/big-data-and-business-intelligence/learning-ipython-interactive-computing-and-data-visualization): This short book by Cyrille Rossant offers a good introduction to using IPython for data analysis.\n",
-    "- [*IPython Interactive Computing and Visualization Cookbook*](https://www.packtpub.com/big-data-and-business-intelligence/ipython-interactive-computing-and-visualization-cookbook): Also by Cyrille Rossant, this book is a longer and more advanced treatment of using IPython for data science. Despite its name, it's not just about IPython–it also goes into some depth on a broad range of data science topics.\n",
+    "- [*Python for Data Analysis* (O'Reilly)](http://shop.oreilly.com/product/0636920023784.do): Wes McKinney's book includes a chapter that covers using IPython as a data scientist. Although much of the material overlaps what we've discussed here, another perspective is always helpful.\n",
+    "- [*Learning IPython for Interactive Computing and Data Visualization* (Packt)](https://www.packtpub.com/big-data-and-business-intelligence/learning-ipython-interactive-computing-and-data-visualization): This short book by Cyrille Rossant offers a good introduction to using IPython for data analysis.\n",
+    "- [*IPython Interactive Computing and Visualization Cookbook* (Packt)](https://www.packtpub.com/big-data-and-business-intelligence/ipython-interactive-computing-and-visualization-cookbook): Also by Cyrille Rossant, this book is a longer and more advanced treatment of using IPython for data science. Despite its name, it's not just about IPython; it also goes into some depth on a broad range of data science topics.\n",
     "\n",
-    "Finally, a reminder that you can find help on your own: IPython's ``?``-based help functionality (discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be very useful if you use it well and use it often.\n",
+    "Finally, a reminder that you can find help on your own: IPython's `?`-based help functionality (discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be useful if you use it well and use it often.\n",
     "As you go through the examples here and elsewhere, this can be used to familiarize yourself with all the tools that IPython has to offer."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) | [Contents](Index.ipynb) | [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.08-More-IPython-Resources.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -91,9 +62,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/02.00-Introduction-to-NumPy.ipynb b/notebooks/02.00-Introduction-to-NumPy.ipynb
index e527c4355..62257da1c 100644
--- a/notebooks/02.00-Introduction-to-NumPy.ipynb
+++ b/notebooks/02.00-Introduction-to-NumPy.ipynb
@@ -2,64 +2,25 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
-    "<!--NAVIGATION-->\n",
-    "< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >\n",
+    "# Introduction to NumPy\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.00-Introduction-to-NumPy.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "# Introduction to NumPy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "This chapter, along with chapter 3, outlines techniques for effectively loading, storing, and manipulating in-memory data in Python.\n",
-    "The topic is very broad: datasets can come from a wide range of sources and a wide range of formats, including be collections of documents, collections of images, collections of sound clips, collections of numerical measurements, or nearly anything else.\n",
-    "Despite this apparent heterogeneity, it will help us to think of all data fundamentally as arrays of numbers.\n",
+    "This part of the book, along with [Part 3](03.00-Introduction-to-Pandas.ipynb), outlines techniques for effectively loading, storing, and manipulating in-memory data in Python.\n",
+    "The topic is very broad: datasets can come from a wide range of sources and in a wide range of formats, including collections of documents, collections of images, collections of sound clips, collections of numerical measurements, or nearly anything else.\n",
+    "Despite this apparent heterogeneity, many datasets can be represented fundamentally as arrays of numbers.\n",
     "\n",
-    "For example, images–particularly digital images–can be thought of as simply two-dimensional arrays of numbers representing pixel brightness across the area.\n",
+    "For example, images—particularly digital images—can be thought of as simply two-dimensional arrays of numbers representing pixel brightness across the area.\n",
     "Sound clips can be thought of as one-dimensional arrays of intensity versus time.\n",
-    "Text can be converted in various ways into numerical representations, perhaps binary digits representing the frequency of certain words or pairs of words.\n",
-    "No matter what the data are, the first step in making it analyzable will be to transform them into arrays of numbers.\n",
-    "(We will discuss some specific examples of this process later in [Feature Engineering](05.04-Feature-Engineering.ipynb))\n",
+    "Text can be converted in various ways into numerical representations, such as binary digits representing the frequency of certain words or pairs of words.\n",
+    "No matter what the data is, the first step in making it analyzable will be to transform it into arrays of numbers.\n",
+    "(We will discuss some specific examples of this process in [Feature Engineering](05.04-Feature-Engineering.ipynb).)\n",
     "\n",
     "For this reason, efficient storage and manipulation of numerical arrays is absolutely fundamental to the process of doing data science.\n",
-    "We'll now take a look at the specialized tools that Python has for handling such numerical arrays: the NumPy package, and the Pandas package (discussed in Chapter 3).\n",
+    "We'll now take a look at the specialized tools that Python has for handling such numerical arrays: the NumPy package and the Pandas package (discussed in [Part 3](03.00-Introduction-to-Pandas.ipynb)).\n",
     "\n",
-    "This chapter will cover NumPy in detail. NumPy (short for *Numerical Python*) provides an efficient interface to store and operate on dense data buffers.\n",
-    "In some ways, NumPy arrays are like Python's built-in ``list`` type, but NumPy arrays provide much more efficient storage and data operations as the arrays grow larger in size.\n",
+    "This part of the book will cover NumPy in detail. NumPy (short for *Numerical Python*) provides an efficient interface to store and operate on dense data buffers.\n",
+    "In some ways, NumPy arrays are like Python's built-in `list` type, but NumPy arrays provide much more efficient storage and data operations as the arrays grow larger in size.\n",
     "NumPy arrays form the core of nearly the entire ecosystem of data science tools in Python, so time spent learning to use NumPy effectively will be valuable no matter what aspect of data science interests you.\n",
     "\n",
     "If you followed the advice outlined in the Preface and installed the Anaconda stack, you already have NumPy installed and ready to go.\n",
@@ -73,13 +34,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'1.11.1'"
+       "'1.21.2'"
       ]
      },
      "execution_count": 1,
@@ -94,13 +58,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "For the pieces of the package discussed here, I'd recommend NumPy version 1.8 or later.\n",
-    "By convention, you'll find that most people in the SciPy/PyData world will import NumPy using ``np`` as an alias:"
+    "By convention, you'll find that most people in the SciPy/PyData world will import NumPy using `np` as an alias:"
    ]
   },
   {
@@ -109,7 +70,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -118,26 +82,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Throughout this chapter, and indeed the rest of the book, you'll find that this is the way we will import and use NumPy."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
-    "## Reminder about Built In Documentation\n",
+    "## Reminder About Built-in Documentation\n",
     "\n",
-    "As you read through this chapter, don't forget that IPython gives you the ability to quickly explore the contents of a package (by using the tab-completion feature), as well as the documentation of various functions (using the ``?`` character – Refer back to [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)).\n",
+    "As you read through this part of the book, don't forget that IPython gives you the ability to quickly explore the contents of a package (by using the tab completion feature), as well as the documentation of various functions (using the `?` character). For a refresher on these, refer back to [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb).\n",
     "\n",
-    "For example, to display all the contents of the numpy namespace, you can type this:\n",
+    "For example, to display all the contents of the NumPy namespace, you can type this:\n",
     "\n",
     "```ipython\n",
     "In [3]: np.<TAB>\n",
@@ -151,25 +109,15 @@
     "\n",
     "More detailed documentation, along with tutorials and other resources, can be found at http://www.numpy.org."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.00-Introduction-to-NumPy.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -183,9 +131,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/02.01-Understanding-Data-Types.ipynb b/notebooks/02.01-Understanding-Data-Types.ipynb
index 82b128e48..2649cc369 100644
--- a/notebooks/02.01-Understanding-Data-Types.ipynb
+++ b/notebooks/02.01-Understanding-Data-Types.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) | [Contents](Index.ipynb) | [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.01-Understanding-Data-Types.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -34,11 +12,11 @@
    "metadata": {},
    "source": [
     "Effective data-driven science and computation requires understanding how data is stored and manipulated.\n",
-    "This section outlines and contrasts how arrays of data are handled in the Python language itself, and how NumPy improves on this.\n",
+    "This chapter outlines and contrasts how arrays of data are handled in the Python language itself, and how NumPy improves on this.\n",
     "Understanding this difference is fundamental to understanding much of the material throughout the rest of the book.\n",
     "\n",
-    "Users of Python are often drawn-in by its ease of use, one piece of which is dynamic typing.\n",
-    "While a statically-typed language like C or Java requires each variable to be explicitly declared, a dynamically-typed language like Python skips this specification. For example, in C you might specify a particular operation as follows:\n",
+    "Users of Python are often drawn in by its ease of use, one piece of which is dynamic typing.\n",
+    "While a statically typed language like C or Java requires each variable to be explicitly declared, a dynamically typed language like Python skips this specification. For example, in C you might specify a particular operation as follows:\n",
     "\n",
     "```C\n",
     "/* C code */\n",
@@ -57,7 +35,7 @@
     "    result += i\n",
     "```\n",
     "\n",
-    "Notice the main difference: in C, the data types of each variable are explicitly declared, while in Python the types are dynamically inferred. This means, for example, that we can assign any kind of data to any variable:\n",
+    "Notice one main difference: in C, the data types of each variable are explicitly declared, while in Python the types are dynamically inferred. This means, for example, that we can assign any kind of data to any variable:\n",
     "\n",
     "```python\n",
     "# Python code\n",
@@ -65,7 +43,7 @@
     "x = \"four\"\n",
     "```\n",
     "\n",
-    "Here we've switched the contents of ``x`` from an integer to a string. The same thing in C would lead (depending on compiler settings) to a compilation error or other unintented consequences:\n",
+    "Here we've switched the contents of `x` from an integer to a string. The same thing in C would lead (depending on compiler settings) to a compilation error or other unintended consequences:\n",
     "\n",
     "```C\n",
     "/* C code */\n",
@@ -73,9 +51,9 @@
     "x = \"four\";  // FAILS\n",
     "```\n",
     "\n",
-    "This sort of flexibility is one piece that makes Python and other dynamically-typed languages convenient and easy to use.\n",
+    "This sort of flexibility is one element that makes Python and other dynamically typed languages convenient and easy to use.\n",
     "Understanding *how* this works is an important piece of learning to analyze data efficiently and effectively with Python.\n",
-    "But what this type-flexibility also points to is the fact that Python variables are more than just their value; they also contain extra information about the type of the value. We'll explore this more in the sections that follow."
+    "But what this type flexibility also points to is the fact that Python variables are more than just their values; they also contain extra information about the *type* of the value. We'll explore this more in the sections that follow."
    ]
   },
   {
@@ -85,8 +63,8 @@
     "## A Python Integer Is More Than Just an Integer\n",
     "\n",
     "The standard Python implementation is written in C.\n",
-    "This means that every Python object is simply a cleverly-disguised C structure, which contains not only its value, but other information as well. For example, when we define an integer in Python, such as ``x = 10000``, ``x`` is not just a \"raw\" integer. It's actually a pointer to a compound C structure, which contains several values.\n",
-    "Looking through the Python 3.4 source code, we find that the integer (long) type definition effectively looks like this (once the C macros are expanded):\n",
+    "This means that every Python object is simply a cleverly disguised C structure, which contains not only its value, but other information as well. For example, when we define an integer in Python, such as `x = 10000`, `x` is not just a \"raw\" integer. It's actually a pointer to a compound C structure, which contains several values.\n",
+    "Looking through the Python 3.10 source code, we find that the integer (long) type definition effectively looks like this (once the C macros are expanded):\n",
     "\n",
     "```C\n",
     "struct _longobject {\n",
@@ -97,28 +75,28 @@
     "};\n",
     "```\n",
     "\n",
-    "A single integer in Python 3.4 actually contains four pieces:\n",
+    "A single integer in Python 3.10 actually contains four pieces:\n",
     "\n",
-    "- ``ob_refcnt``, a reference count that helps Python silently handle memory allocation and deallocation\n",
-    "- ``ob_type``, which encodes the type of the variable\n",
-    "- ``ob_size``, which specifies the size of the following data members\n",
-    "- ``ob_digit``, which contains the actual integer value that we expect the Python variable to represent.\n",
+    "- `ob_refcnt`, a reference count that helps Python silently handle memory allocation and deallocation\n",
+    "- `ob_type`, which encodes the type of the variable\n",
+    "- `ob_size`, which specifies the size of the following data members\n",
+    "- `ob_digit`, which contains the actual integer value that we expect the Python variable to represent\n",
     "\n",
-    "This means that there is some overhead in storing an integer in Python as compared to an integer in a compiled language like C, as illustrated in the following figure:"
+    "This means that there is some overhead involved in storing an integer in Python as compared to a compiled language like C, as illustrated in the following figure:"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![Integer Memory Layout](figures/cint_vs_pyint.png)"
+    "![Integer Memory Layout](images/cint_vs_pyint.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here ``PyObject_HEAD`` is the part of the structure containing the reference count, type code, and other pieces mentioned before.\n",
+    "Here, `PyObject_HEAD` is the part of the structure containing the reference count, type code, and other pieces mentioned before.\n",
     "\n",
     "Notice the difference here: a C integer is essentially a label for a position in memory whose bytes encode an integer value.\n",
     "A Python integer is a pointer to a position in memory containing all the Python object information, including the bytes that contain the integer value.\n",
@@ -133,7 +111,7 @@
     "## A Python List Is More Than Just a List\n",
     "\n",
     "Let's consider now what happens when we use a Python data structure that holds many Python objects.\n",
-    "The standard mutable multi-element container in Python is the list.\n",
+    "The standard mutable multielement container in Python is the list.\n",
     "We can create a list of integers as follows:"
    ]
   },
@@ -141,7 +119,10 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -164,7 +145,10 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -193,7 +177,10 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -216,7 +203,10 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -245,7 +235,10 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -268,8 +261,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "But this flexibility comes at a cost: to allow these flexible types, each item in the list must contain its own type info, reference count, and other information–that is, each item is a complete Python object.\n",
-    "In the special case that all variables are of the same type, much of this information is redundant: it can be much more efficient to store data in a fixed-type array.\n",
+    "But this flexibility comes at a cost: to allow these flexible types, each item in the list must contain its own type, reference count, and other information. That is, each item is a complete Python object.\n",
+    "In the special case that all variables are of the same type, much of this information is redundant, so it can be much more efficient to store the data in a fixed-type array.\n",
     "The difference between a dynamic-type list and a fixed-type (NumPy-style) array is illustrated in the following figure:"
    ]
   },
@@ -277,7 +270,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![Array Memory Layout](figures/array_vs_list.png)"
+    "![Array Memory Layout](images/array_vs_list.png)"
    ]
   },
   {
@@ -297,14 +290,17 @@
     "## Fixed-Type Arrays in Python\n",
     "\n",
     "Python offers several different options for storing data in efficient, fixed-type data buffers.\n",
-    "The built-in ``array`` module (available since Python 3.3) can be used to create dense arrays of a uniform type:"
+    "The built-in `array` module (available since Python 3.3) can be used to create dense arrays of a uniform type:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -329,21 +325,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here ``'i'`` is a type code indicating the contents are integers.\n",
+    "Here, `'i'` is a type code indicating the contents are integers.\n",
     "\n",
-    "Much more useful, however, is the ``ndarray`` object of the NumPy package.\n",
-    "While Python's ``array`` object provides efficient storage of array-based data, NumPy adds to this efficient *operations* on that data.\n",
-    "We will explore these operations in later sections; here we'll demonstrate several ways of creating a NumPy array.\n",
+    "Much more useful, however, is the `ndarray` object of the NumPy package.\n",
+    "While Python's `array` object provides efficient storage of array-based data, NumPy adds to this efficient *operations* on that data.\n",
+    "We will explore these operations in later chapters; next, I'll show you a few different ways of creating a NumPy array."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Arrays from Python Lists\n",
     "\n",
-    "We'll start with the standard NumPy import, under the alias ``np``:"
+    "We'll start with the standard NumPy import, under the alias `np`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np"
@@ -353,16 +354,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Creating Arrays from Python Lists\n",
-    "\n",
-    "First, we can use ``np.array`` to create arrays from Python lists:"
+    "Now we can use `np.array` to create arrays from Python lists:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -377,7 +379,7 @@
     }
    ],
    "source": [
-    "# integer array:\n",
+    "# Integer array\n",
     "np.array([1, 4, 2, 5, 3])"
    ]
   },
@@ -385,21 +387,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Remember that unlike Python lists, NumPy is constrained to arrays that all contain the same type.\n",
-    "If types do not match, NumPy will upcast if possible (here, integers are up-cast to floating point):"
+    "Remember that unlike Python lists, NumPy arrays can only contain data of the same type.\n",
+    "If the types do not match, NumPy will upcast them according to its type promotion rules; here, integers are upcast to floating point:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 3.14,  4.  ,  2.  ,  3.  ])"
+       "array([3.14, 4.  , 2.  , 3.  ])"
       ]
      },
      "execution_count": 9,
@@ -415,20 +420,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If we want to explicitly set the data type of the resulting array, we can use the ``dtype`` keyword:"
+    "If we want to explicitly set the data type of the resulting array, we can use the `dtype` keyword:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 1.,  2.,  3.,  4.], dtype=float32)"
+       "array([1., 2., 3., 4.], dtype=float32)"
       ]
      },
      "execution_count": 10,
@@ -437,21 +445,24 @@
     }
    ],
    "source": [
-    "np.array([1, 2, 3, 4], dtype='float32')"
+    "np.array([1, 2, 3, 4], dtype=np.float32)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, unlike Python lists, NumPy arrays can explicitly be multi-dimensional; here's one way of initializing a multidimensional array using a list of lists:"
+    "Finally, unlike Python lists, which are always one-dimensional sequences, NumPy arrays can be multidimensional. Here's one way of initializing a multidimensional array using a list of lists:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -468,7 +479,7 @@
     }
    ],
    "source": [
-    "# nested lists result in multi-dimensional arrays\n",
+    "# Nested lists result in multidimensional arrays\n",
     "np.array([range(i, i + 3) for i in [2, 4, 6]])"
    ]
   },
@@ -493,7 +504,10 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -508,7 +522,7 @@
     }
    ],
    "source": [
-    "# Create a length-10 integer array filled with zeros\n",
+    "# Create a length-10 integer array filled with 0s\n",
     "np.zeros(10, dtype=int)"
    ]
   },
@@ -516,15 +530,18 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.,  1.,  1.,  1.,  1.],\n",
-       "       [ 1.,  1.,  1.,  1.,  1.],\n",
-       "       [ 1.,  1.,  1.,  1.,  1.]])"
+       "array([[1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1.]])"
       ]
      },
      "execution_count": 13,
@@ -533,7 +550,7 @@
     }
    ],
    "source": [
-    "# Create a 3x5 floating-point array filled with ones\n",
+    "# Create a 3x5 floating-point array filled with 1s\n",
     "np.ones((3, 5), dtype=float)"
    ]
   },
@@ -541,15 +558,18 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 3.14,  3.14,  3.14,  3.14,  3.14],\n",
-       "       [ 3.14,  3.14,  3.14,  3.14,  3.14],\n",
-       "       [ 3.14,  3.14,  3.14,  3.14,  3.14]])"
+       "array([[3.14, 3.14, 3.14, 3.14, 3.14],\n",
+       "       [3.14, 3.14, 3.14, 3.14, 3.14],\n",
+       "       [3.14, 3.14, 3.14, 3.14, 3.14]])"
       ]
      },
      "execution_count": 14,
@@ -566,7 +586,10 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -582,8 +605,8 @@
    ],
    "source": [
     "# Create an array filled with a linear sequence\n",
-    "# Starting at 0, ending at 20, stepping by 2\n",
-    "# (this is similar to the built-in range() function)\n",
+    "# starting at 0, ending at 20, stepping by 2\n",
+    "# (this is similar to the built-in range function)\n",
     "np.arange(0, 20, 2)"
    ]
   },
@@ -591,13 +614,16 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])"
+       "array([0.  , 0.25, 0.5 , 0.75, 1.  ])"
       ]
      },
      "execution_count": 16,
@@ -614,15 +640,18 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 0.99844933,  0.52183819,  0.22421193],\n",
-       "       [ 0.08007488,  0.45429293,  0.20941444],\n",
-       "       [ 0.14360941,  0.96910973,  0.946117  ]])"
+       "array([[0.09610171, 0.88193001, 0.70548015],\n",
+       "       [0.35885395, 0.91670468, 0.8721031 ],\n",
+       "       [0.73237865, 0.09708562, 0.52506779]])"
       ]
      },
      "execution_count": 17,
@@ -632,7 +661,7 @@
    ],
    "source": [
     "# Create a 3x3 array of uniformly distributed\n",
-    "# random values between 0 and 1\n",
+    "# pseudorandom values between 0 and 1\n",
     "np.random.random((3, 3))"
    ]
   },
@@ -640,15 +669,18 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.51772646,  0.39614948, -0.10634696],\n",
-       "       [ 0.25671348,  0.00732722,  0.37783601],\n",
-       "       [ 0.68446945,  0.15926039, -0.70744073]])"
+       "array([[-0.46652655, -0.59158776, -1.05392451],\n",
+       "       [-1.72634268,  0.03194069, -0.51048869],\n",
+       "       [ 1.41240208,  1.77734462, -0.43820037]])"
       ]
      },
      "execution_count": 18,
@@ -657,8 +689,8 @@
     }
    ],
    "source": [
-    "# Create a 3x3 array of normally distributed random values\n",
-    "# with mean 0 and standard deviation 1\n",
+    "# Create a 3x3 array of normally distributed pseudorandom\n",
+    "# values with mean 0 and standard deviation 1\n",
     "np.random.normal(0, 1, (3, 3))"
    ]
   },
@@ -666,15 +698,18 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[2, 3, 4],\n",
-       "       [5, 7, 8],\n",
-       "       [0, 5, 0]])"
+       "array([[4, 3, 8],\n",
+       "       [6, 5, 0],\n",
+       "       [1, 1, 4]])"
       ]
      },
      "execution_count": 19,
@@ -683,7 +718,7 @@
     }
    ],
    "source": [
-    "# Create a 3x3 array of random integers in the interval [0, 10)\n",
+    "# Create a 3x3 array of pseudorandom integers in the interval [0, 10)\n",
     "np.random.randint(0, 10, (3, 3))"
    ]
   },
@@ -691,15 +726,18 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.,  0.,  0.],\n",
-       "       [ 0.,  1.,  0.],\n",
-       "       [ 0.,  0.,  1.]])"
+       "array([[1., 0., 0.],\n",
+       "       [0., 1., 0.],\n",
+       "       [0., 0., 1.]])"
       ]
      },
      "execution_count": 20,
@@ -716,13 +754,16 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 1.,  1.,  1.])"
+       "array([1., 1., 1.])"
       ]
      },
      "execution_count": 21,
@@ -731,8 +772,8 @@
     }
    ],
    "source": [
-    "# Create an uninitialized array of three integers\n",
-    "# The values will be whatever happens to already exist at that memory location\n",
+    "# Create an uninitialized array of three integers; the values will be\n",
+    "# whatever happens to already exist at that memory location\n",
     "np.empty(3)"
    ]
   },
@@ -763,50 +804,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "| Data type\t    | Description |\n",
-    "|---------------|-------------|\n",
-    "| ``bool_``     | Boolean (True or False) stored as a byte |\n",
-    "| ``int_``      | Default integer type (same as C ``long``; normally either ``int64`` or ``int32``)| \n",
-    "| ``intc``      | Identical to C ``int`` (normally ``int32`` or ``int64``)| \n",
-    "| ``intp``      | Integer used for indexing (same as C ``ssize_t``; normally either ``int32`` or ``int64``)| \n",
-    "| ``int8``      | Byte (-128 to 127)| \n",
-    "| ``int16``     | Integer (-32768 to 32767)|\n",
-    "| ``int32``     | Integer (-2147483648 to 2147483647)|\n",
-    "| ``int64``     | Integer (-9223372036854775808 to 9223372036854775807)| \n",
-    "| ``uint8``     | Unsigned integer (0 to 255)| \n",
-    "| ``uint16``    | Unsigned integer (0 to 65535)| \n",
-    "| ``uint32``    | Unsigned integer (0 to 4294967295)| \n",
-    "| ``uint64``    | Unsigned integer (0 to 18446744073709551615)| \n",
-    "| ``float_``    | Shorthand for ``float64``.| \n",
-    "| ``float16``   | Half precision float: sign bit, 5 bits exponent, 10 bits mantissa| \n",
-    "| ``float32``   | Single precision float: sign bit, 8 bits exponent, 23 bits mantissa| \n",
-    "| ``float64``   | Double precision float: sign bit, 11 bits exponent, 52 bits mantissa| \n",
-    "| ``complex_``  | Shorthand for ``complex128``.| \n",
-    "| ``complex64`` | Complex number, represented by two 32-bit floats| \n",
-    "| ``complex128``| Complex number, represented by two 64-bit floats| "
+    "| Data type\t | Description |\n",
+    "|-------------|-------------|\n",
+    "| `bool_`     | Boolean (True or False) stored as a byte |\n",
+    "| `int_`      | Default integer type (same as C `long`; normally either `int64` or `int32`)| \n",
+    "| `intc`      | Identical to C `int` (normally `int32` or `int64`)| \n",
+    "| `intp`      | Integer used for indexing (same as C `ssize_t`; normally either `int32` or `int64`)| \n",
+    "| `int8`      | Byte (–128 to 127)| \n",
+    "| `int16`     | Integer (–32768 to 32767)|\n",
+    "| `int32`     | Integer (–2147483648 to 2147483647)|\n",
+    "| `int64`     | Integer (–9223372036854775808 to 9223372036854775807)| \n",
+    "| `uint8`     | Unsigned integer (0 to 255)| \n",
+    "| `uint16`    | Unsigned integer (0 to 65535)| \n",
+    "| `uint32`    | Unsigned integer (0 to 4294967295)| \n",
+    "| `uint64`    | Unsigned integer (0 to 18446744073709551615)| \n",
+    "| `float_`    | Shorthand for `float64`| \n",
+    "| `float16`   | Half-precision float: sign bit, 5 bits exponent, 10 bits mantissa| \n",
+    "| `float32`   | Single-precision float: sign bit, 8 bits exponent, 23 bits mantissa| \n",
+    "| `float64`   | Double-precision float: sign bit, 11 bits exponent, 52 bits mantissa| \n",
+    "| `complex_`  | Shorthand for `complex128`| \n",
+    "| `complex64` | Complex number, represented by two 32-bit floats| \n",
+    "| `complex128`| Complex number, represented by two 64-bit floats| "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "More advanced type specification is possible, such as specifying big or little endian numbers; for more information, refer to the [NumPy documentation](http://numpy.org/).\n",
+    "More advanced type specification is possible, such as specifying big- or little-endian numbers; for more information, refer to the [NumPy documentation](http://numpy.org/).\n",
     "NumPy also supports compound data types, which will be covered in [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb)."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) | [Contents](Index.ipynb) | [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.01-Understanding-Data-Types.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -822,9 +856,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb b/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb
index f9dad509a..1a429455c 100644
--- a/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb
+++ b/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) | [Contents](Index.ipynb) | [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,15 +11,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Data manipulation in Python is nearly synonymous with NumPy array manipulation: even newer tools like Pandas ([Chapter 3](03.00-Introduction-to-Pandas.ipynb)) are built around the NumPy array.\n",
-    "This section will present several examples of using NumPy array manipulation to access data and subarrays, and to split, reshape, and join the arrays.\n",
+    "Data manipulation in Python is nearly synonymous with NumPy array manipulation: even newer tools like Pandas ([Part 3](03.00-Introduction-to-Pandas.ipynb)) are built around the NumPy array.\n",
+    "This chapter will present several examples of using NumPy array manipulation to access data and subarrays, and to split, reshape, and join the arrays.\n",
     "While the types of operations shown here may seem a bit dry and pedantic, they comprise the building blocks of many other examples used throughout the book.\n",
     "Get to know them well!\n",
     "\n",
     "We'll cover a few categories of basic array manipulations here:\n",
     "\n",
     "- *Attributes of arrays*: Determining the size, shape, memory consumption, and data types of arrays\n",
-    "- *Indexing of arrays*: Getting and setting the value of individual array elements\n",
+    "- *Indexing of arrays*: Getting and setting the values of individual array elements\n",
     "- *Slicing of arrays*: Getting and setting smaller subarrays within a larger array\n",
     "- *Reshaping of arrays*: Changing the shape of a given array\n",
     "- *Joining and splitting of arrays*: Combining multiple arrays into one, and splitting one array into many"
@@ -59,7 +37,7 @@
    "metadata": {},
    "source": [
     "First let's discuss some useful array attributes.\n",
-    "We'll start by defining three random arrays, a one-dimensional, two-dimensional, and three-dimensional array.\n",
+    "We'll start by defining random arrays of one, two, and three dimensions.\n",
     "We'll use NumPy's random number generator, which we will *seed* with a set value in order to ensure that the same random arrays are generated each time this code is run:"
    ]
   },
@@ -67,30 +45,36 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "np.random.seed(0)  # seed for reproducibility\n",
+    "rng = np.random.default_rng(seed=1701)  # seed for reproducibility\n",
     "\n",
-    "x1 = np.random.randint(10, size=6)  # One-dimensional array\n",
-    "x2 = np.random.randint(10, size=(3, 4))  # Two-dimensional array\n",
-    "x3 = np.random.randint(10, size=(3, 4, 5))  # Three-dimensional array"
+    "x1 = rng.integers(10, size=6)  # one-dimensional array\n",
+    "x2 = rng.integers(10, size=(3, 4))  # two-dimensional array\n",
+    "x3 = rng.integers(10, size=(3, 4, 5))  # three-dimensional array"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Each array has attributes ``ndim`` (the number of dimensions), ``shape`` (the size of each dimension), and ``size`` (the total size of the array):"
+    "Each array has attributes including `ndim` (the number of dimensions), `shape` (the size of each dimension), `size` (the total size of the array), and `dtype` (the type of each element):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -99,75 +83,23 @@
      "text": [
       "x3 ndim:  3\n",
       "x3 shape: (3, 4, 5)\n",
-      "x3 size:  60\n"
+      "x3 size:  60\n",
+      "dtype:    int64\n"
      ]
     }
    ],
    "source": [
     "print(\"x3 ndim: \", x3.ndim)\n",
     "print(\"x3 shape:\", x3.shape)\n",
-    "print(\"x3 size: \", x3.size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another useful attribute is the ``dtype``, the data type of the array (which we discussed previously in [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb)):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"dtype:\", x3.dtype)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Other attributes include ``itemsize``, which lists the size (in bytes) of each array element, and ``nbytes``, which lists the total size (in bytes) of the array:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "itemsize: 8 bytes\n",
-      "nbytes: 480 bytes\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"itemsize:\", x3.itemsize, \"bytes\")\n",
-    "print(\"nbytes:\", x3.nbytes, \"bytes\")"
+    "print(\"x3 size: \", x3.size)\n",
+    "print(\"dtype:   \", x3.dtype)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In general, we expect that ``nbytes`` is equal to ``itemsize`` times ``size``."
+    "For more discussion of data types, see [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb)."
    ]
   },
   {
@@ -187,18 +119,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([5, 0, 3, 3, 7, 9])"
+       "array([9, 4, 0, 3, 8, 6])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -209,18 +144,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "5"
+       "9"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -231,18 +169,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "7"
+       "8"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -260,18 +201,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "9"
+       "6"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -282,18 +226,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "7"
+       "8"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -306,25 +253,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In a multi-dimensional array, items can be accessed using a comma-separated tuple of indices:"
+    "In a multidimensional array, items can be accessed using a comma-separated `(row, column)` tuple:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[3, 5, 2, 4],\n",
-       "       [7, 6, 8, 8],\n",
-       "       [1, 6, 7, 7]])"
+       "array([[3, 1, 3, 7],\n",
+       "       [4, 0, 2, 3],\n",
+       "       [0, 0, 6, 9]])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -335,9 +285,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -346,7 +299,7 @@
        "3"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -357,18 +310,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1"
+       "0"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -379,18 +335,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "7"
+       "9"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -403,25 +362,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Values can also be modified using any of the above index notation:"
+    "Values can also be modified using any of the preceding index notation:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[12,  5,  2,  4],\n",
-       "       [ 7,  6,  8,  8],\n",
-       "       [ 1,  6,  7,  7]])"
+       "array([[12,  1,  3,  7],\n",
+       "       [ 4,  0,  2,  3],\n",
+       "       [ 0,  0,  6,  9]])"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -436,23 +398,26 @@
    "metadata": {},
    "source": [
     "Keep in mind that, unlike Python lists, NumPy arrays have a fixed type.\n",
-    "This means, for example, that if you attempt to insert a floating-point value to an integer array, the value will be silently truncated. Don't be caught unaware by this behavior!"
+    "This means, for example, that if you attempt to insert a floating-point value into an integer array, the value will be silently truncated. Don't be caught unaware by this behavior!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([3, 0, 3, 3, 7, 9])"
+       "array([3, 4, 0, 3, 8, 6])"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -473,234 +438,262 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Just as we can use square brackets to access individual array elements, we can also use them to access subarrays with the *slice* notation, marked by the colon (``:``) character.\n",
-    "The NumPy slicing syntax follows that of the standard Python list; to access a slice of an array ``x``, use this:\n",
+    "Just as we can use square brackets to access individual array elements, we can also use them to access subarrays with the *slice* notation, marked by the colon (`:`) character.\n",
+    "The NumPy slicing syntax follows that of the standard Python list; to access a slice of an array `x`, use this:\n",
     "``` python\n",
     "x[start:stop:step]\n",
     "```\n",
-    "If any of these are unspecified, they default to the values ``start=0``, ``stop=``*``size of dimension``*, ``step=1``.\n",
-    "We'll take a look at accessing sub-arrays in one dimension and in multiple dimensions."
+    "If any of these are unspecified, they default to the values `start=0`, `stop=<size of dimension>`, `step=1`.\n",
+    "Let's look at some examples of accessing subarrays in one dimension and in multiple dimensions."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### One-dimensional subarrays"
+    "### One-Dimensional Subarrays\n",
+    "\n",
+    "Here are some examples of accessing elements in one-dimensional subarrays:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+       "array([3, 4, 0, 3, 8, 6])"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x = np.arange(10)\n",
-    "x"
+    "x1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0, 1, 2, 3, 4])"
+       "array([3, 4, 0])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x[:5]  # first five elements"
+    "x1[:3]  # first three elements"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([5, 6, 7, 8, 9])"
+       "array([3, 8, 6])"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x[5:]  # elements after index 5"
+    "x1[3:]  # elements after index 3"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([4, 5, 6])"
+       "array([4, 0, 3])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x[4:7]  # middle sub-array"
+    "x1[1:4]  # middle subarray"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0, 2, 4, 6, 8])"
+       "array([3, 0, 8])"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x[::2]  # every other element"
+    "x1[::2]  # every second element"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1, 3, 5, 7, 9])"
+       "array([4, 3, 6])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x[1::2]  # every other element, starting at index 1"
+    "x1[1::2]  # every second element, starting at index 1"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A potentially confusing case is when the ``step`` value is negative.\n",
-    "In this case, the defaults for ``start`` and ``stop`` are swapped.\n",
+    "A potentially confusing case is when the `step` value is negative.\n",
+    "In this case, the defaults for `start` and `stop` are swapped.\n",
     "This becomes a convenient way to reverse an array:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])"
+       "array([6, 8, 3, 0, 4, 3])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x[::-1]  # all elements, reversed"
+    "x1[::-1]  # all elements, reversed"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([5, 3, 1])"
+       "array([8, 0, 3])"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x[5::-2]  # reversed every other from index 5"
+    "x1[4::-2]  # every second element from index 4, reversed"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Multi-dimensional subarrays\n",
+    "### Multidimensional Subarrays\n",
     "\n",
-    "Multi-dimensional slices work in the same way, with multiple slices separated by commas.\n",
+    "Multidimensional slices work in the same way, with multiple slices separated by commas.\n",
     "For example:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[12,  5,  2,  4],\n",
-       "       [ 7,  6,  8,  8],\n",
-       "       [ 1,  6,  7,  7]])"
+       "array([[12,  1,  3,  7],\n",
+       "       [ 4,  0,  2,  3],\n",
+       "       [ 0,  0,  6,  9]])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -711,80 +704,82 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[12,  5,  2],\n",
-       "       [ 7,  6,  8]])"
+       "array([[12,  1,  3],\n",
+       "       [ 4,  0,  2]])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x2[:2, :3]  # two rows, three columns"
+    "x2[:2, :3]  # first two rows & three columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[12,  2],\n",
-       "       [ 7,  8],\n",
-       "       [ 1,  7]])"
+       "array([[12,  3],\n",
+       "       [ 4,  2],\n",
+       "       [ 0,  6]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x2[:3, ::2]  # all rows, every other column"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, subarray dimensions can even be reversed together:"
+    "x2[:3, ::2]  # three rows, every second column"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 7,  7,  6,  1],\n",
-       "       [ 8,  8,  6,  7],\n",
-       "       [ 4,  2,  5, 12]])"
+       "array([[ 9,  6,  0,  0],\n",
+       "       [ 3,  2,  0,  4],\n",
+       "       [ 7,  3,  1, 12]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x2[::-1, ::-1]"
+    "x2[::-1, ::-1]  # all rows & columns, reversed"
    ]
   },
   {
@@ -793,46 +788,58 @@
    "source": [
     "#### Accessing array rows and columns\n",
     "\n",
-    "One commonly needed routine is accessing of single rows or columns of an array.\n",
-    "This can be done by combining indexing and slicing, using an empty slice marked by a single colon (``:``):"
+    "One commonly needed routine is accessing single rows or columns of an array.\n",
+    "This can be done by combining indexing and slicing, using an empty slice marked by a single colon (`:`):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 26,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[12  7  1]\n"
-     ]
+     "data": {
+      "text/plain": [
+       "array([12,  4,  0])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(x2[:, 0])  # first column of x2"
+    "x2[:, 0]  # first column of x2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 27,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[12  5  2  4]\n"
-     ]
+     "data": {
+      "text/plain": [
+       "array([12,  1,  3,  7])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(x2[0, :])  # first row of x2"
+    "x2[0, :]  # first row of x2"
    ]
   },
   {
@@ -844,48 +851,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 28,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[12  5  2  4]\n"
-     ]
+     "data": {
+      "text/plain": [
+       "array([12,  1,  3,  7])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(x2[0])  # equivalent to x2[0, :]"
+    "x2[0]  # equivalent to x2[0, :]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Subarrays as no-copy views\n",
+    "### Subarrays as No-Copy Views\n",
     "\n",
-    "One important–and extremely useful–thing to know about array slices is that they return *views* rather than *copies* of the array data.\n",
-    "This is one area in which NumPy array slicing differs from Python list slicing: in lists, slices will be copies.\n",
+    "Unlike Python list slices, NumPy array slices are returned as *views* rather than *copies* of the array data.\n",
     "Consider our two-dimensional array from before:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 29,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[12  5  2  4]\n",
-      " [ 7  6  8  8]\n",
-      " [ 1  6  7  7]]\n"
+      "[[12  1  3  7]\n",
+      " [ 4  0  2  3]\n",
+      " [ 0  0  6  9]]\n"
      ]
     }
    ],
@@ -902,17 +917,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[12  5]\n",
-      " [ 7  6]]\n"
+      "[[12  1]\n",
+      " [ 4  0]]\n"
      ]
     }
    ],
@@ -930,17 +948,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[99  5]\n",
-      " [ 7  6]]\n"
+      "[[99  1]\n",
+      " [ 4  0]]\n"
      ]
     }
    ],
@@ -951,18 +972,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[99  5  2  4]\n",
-      " [ 7  6  8  8]\n",
-      " [ 1  6  7  7]]\n"
+      "[[99  1  3  7]\n",
+      " [ 4  0  2  3]\n",
+      " [ 0  0  6  9]]\n"
      ]
     }
    ],
@@ -974,31 +998,34 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This default behavior is actually quite useful: it means that when we work with large datasets, we can access and process pieces of these datasets without the need to copy the underlying data buffer."
+    "Some users may find this surprising, but it can be advantageous: for example, when working with large datasets, we can access and process pieces of these datasets without the need to copy the underlying data buffer."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Creating copies of arrays\n",
+    "### Creating Copies of Arrays\n",
     "\n",
-    "Despite the nice features of array views, it is sometimes useful to instead explicitly copy the data within an array or a subarray. This can be most easily done with the ``copy()`` method:"
+    "Despite the nice features of array views, it is sometimes useful to instead explicitly copy the data within an array or a subarray. This can be most easily done with the `copy` method:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[99  5]\n",
-      " [ 7  6]]\n"
+      "[[99  1]\n",
+      " [ 4  0]]\n"
      ]
     }
    ],
@@ -1016,17 +1043,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 34,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[42  5]\n",
-      " [ 7  6]]\n"
+      "[[42  1]\n",
+      " [ 4  0]]\n"
      ]
     }
    ],
@@ -1037,18 +1067,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 35,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[99  5  2  4]\n",
-      " [ 7  6  8  8]\n",
-      " [ 1  6  7  7]]\n"
+      "[[99  1  3  7]\n",
+      " [ 4  0  2  3]\n",
+      " [ 0  0  6  9]]\n"
      ]
     }
    ],
@@ -1062,16 +1095,18 @@
    "source": [
     "## Reshaping of Arrays\n",
     "\n",
-    "Another useful type of operation is reshaping of arrays.\n",
-    "The most flexible way of doing this is with the ``reshape`` method.\n",
+    "Another useful type of operation is reshaping of arrays, which can be done with the `reshape` method.\n",
     "For example, if you want to put the numbers 1 through 9 in a $3 \\times 3$ grid, you can do the following:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 36,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1085,7 +1120,7 @@
     }
    ],
    "source": [
-    "grid = np.arange(1, 10).reshape((3, 3))\n",
+    "grid = np.arange(1, 10).reshape(3, 3)\n",
     "print(grid)"
    ]
   },
@@ -1093,18 +1128,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that for this to work, the size of the initial array must match the size of the reshaped array. \n",
-    "Where possible, the ``reshape`` method will use a no-copy view of the initial array, but with non-contiguous memory buffers this is not always the case.\n",
-    "\n",
-    "Another common reshaping pattern is the conversion of a one-dimensional array into a two-dimensional row or column matrix.\n",
-    "This can be done with the ``reshape`` method, or more easily done by making use of the ``newaxis`` keyword within a slice operation:"
+    "Note that for this to work, the size of the initial array must match the size of the reshaped array, and in most cases the `reshape` method will return a no-copy view of the initial array."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A common reshaping operation is converting a one-dimensional array into a two-dimensional row or column matrix:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 37,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1113,71 +1154,78 @@
        "array([[1, 2, 3]])"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "x = np.array([1, 2, 3])\n",
-    "\n",
-    "# row vector via reshape\n",
-    "x.reshape((1, 3))"
+    "x.reshape((1, 3))  # row vector via reshape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 38,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[1, 2, 3]])"
+       "array([[1],\n",
+       "       [2],\n",
+       "       [3]])"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# row vector via newaxis\n",
-    "x[np.newaxis, :]"
+    "x.reshape((3, 1))  # column vector via reshape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A convenient shorthand for this is to use `np.newaxis` in the slicing syntax:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 39,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[1],\n",
-       "       [2],\n",
-       "       [3]])"
+       "array([[1, 2, 3]])"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# column vector via reshape\n",
-    "x.reshape((3, 1))"
+    "x[np.newaxis, :]  # row vector via newaxis"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 40,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1188,21 +1236,20 @@
        "       [3]])"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# column vector via newaxis\n",
-    "x[:, np.newaxis]"
+    "x[:, np.newaxis]  # column vector via newaxis"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will see this type of transformation often throughout the remainder of the book."
+    "This is a pattern that we will utilize often throughout the remainder of the book."
    ]
   },
   {
@@ -1211,24 +1258,27 @@
    "source": [
     "## Array Concatenation and Splitting\n",
     "\n",
-    "All of the preceding routines worked on single arrays. It's also possible to combine multiple arrays into one, and to conversely split a single array into multiple arrays. We'll take a look at those operations here."
+    "All of the preceding routines worked on single arrays. NumPy also provides tools to combine multiple arrays into one, and to conversely split a single array into multiple arrays."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Concatenation of arrays\n",
+    "### Concatenation of Arrays\n",
     "\n",
-    "Concatenation, or joining of two arrays in NumPy, is primarily accomplished using the routines ``np.concatenate``, ``np.vstack``, and ``np.hstack``.\n",
-    "``np.concatenate`` takes a tuple or list of arrays as its first argument, as we can see here:"
+    "Concatenation, or joining of two arrays in NumPy, is primarily accomplished using the routines `np.concatenate`, `np.vstack`, and `np.hstack`.\n",
+    "`np.concatenate` takes a tuple or list of arrays as its first argument, as you can see here:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 41,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1237,7 +1287,7 @@
        "array([1, 2, 3, 3, 2, 1])"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1257,9 +1307,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 42,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1271,7 +1324,7 @@
     }
    ],
    "source": [
-    "z = [99, 99, 99]\n",
+    "z = np.array([99, 99, 99])\n",
     "print(np.concatenate([x, y, z]))"
    ]
   },
@@ -1279,14 +1332,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It can also be used for two-dimensional arrays:"
+    "And it can be used for two-dimensional arrays:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 43,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -1296,9 +1352,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 44,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1310,7 +1369,7 @@
        "       [4, 5, 6]])"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1322,9 +1381,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 45,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1334,7 +1396,7 @@
        "       [4, 5, 6, 4, 5, 6]])"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1348,53 +1410,55 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For working with arrays of mixed dimensions, it can be clearer to use the ``np.vstack`` (vertical stack) and ``np.hstack`` (horizontal stack) functions:"
+    "For working with arrays of mixed dimensions, it can be clearer to use the `np.vstack` (vertical stack) and `np.hstack` (horizontal stack) functions:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 46,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "array([[1, 2, 3],\n",
-       "       [9, 8, 7],\n",
-       "       [6, 5, 4]])"
+       "       [1, 2, 3],\n",
+       "       [4, 5, 6]])"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x = np.array([1, 2, 3])\n",
-    "grid = np.array([[9, 8, 7],\n",
-    "                 [6, 5, 4]])\n",
-    "\n",
     "# vertically stack the arrays\n",
     "np.vstack([x, grid])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 47,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 9,  8,  7, 99],\n",
-       "       [ 6,  5,  4, 99]])"
+       "array([[ 1,  2,  3, 99],\n",
+       "       [ 4,  5,  6, 99]])"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1410,23 +1474,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Similary, ``np.dstack`` will stack arrays along the third axis."
+    "Similarly, for higher-dimensional arrays, `np.dstack` will stack arrays along the third axis."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Splitting of arrays\n",
+    "### Splitting of Arrays\n",
     "\n",
-    "The opposite of concatenation is splitting, which is implemented by the functions ``np.split``, ``np.hsplit``, and ``np.vsplit``.  For each of these, we can pass a list of indices giving the split points:"
+    "The opposite of concatenation is splitting, which is implemented by the functions `np.split`, `np.hsplit`, and `np.vsplit`.  For each of these, we can pass a list of indices giving the split points:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 48,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1447,15 +1514,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice that *N* split-points, leads to *N + 1* subarrays.\n",
-    "The related functions ``np.hsplit`` and ``np.vsplit`` are similar:"
+    "Notice that *N* split points leads to *N* + 1 subarrays.\n",
+    "The related functions `np.hsplit` and `np.vsplit` are similar:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 49,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1467,7 +1537,7 @@
        "       [12, 13, 14, 15]])"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1479,9 +1549,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 50,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1503,9 +1576,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 51,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1533,22 +1609,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Similarly, ``np.dsplit`` will split arrays along the third axis."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) | [Contents](Index.ipynb) | [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "Similarly, for higher-dimensional arrays, `np.dsplit` will split arrays along the third axis."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -1564,9 +1633,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/02.03-Computation-on-arrays-ufuncs.ipynb b/notebooks/02.03-Computation-on-arrays-ufuncs.ipynb
index 5296859e5..72621ddc8 100644
--- a/notebooks/02.03-Computation-on-arrays-ufuncs.ipynb
+++ b/notebooks/02.03-Computation-on-arrays-ufuncs.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) | [Contents](Index.ipynb) | [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.03-Computation-on-arrays-ufuncs.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -30,30 +8,31 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Up until now, we have been discussing some of the basic nuts and bolts of NumPy; in the next few sections, we will dive into the reasons that NumPy is so important in the Python data science world.\n",
-    "Namely, it provides an easy and flexible interface to optimized computation with arrays of data.\n",
+    "Up until now, we have been discussing some of the basic nuts and bolts of NumPy. In the next few chapters, we will dive into the reasons that NumPy is so important in the Python data science world: namely, because it provides an easy and flexible interface to optimize computation with arrays of data.\n",
     "\n",
     "Computation on NumPy arrays can be very fast, or it can be very slow.\n",
-    "The key to making it fast is to use *vectorized* operations, generally implemented through NumPy's *universal functions* (ufuncs).\n",
-    "This section motivates the need for NumPy's ufuncs, which can be used to make repeated calculations on array elements much more efficient.\n",
+    "The key to making it fast is to use vectorized operations, generally implemented through NumPy's *universal functions* (ufuncs).\n",
+    "This chapter motivates the need for NumPy's ufuncs, which can be used to make repeated calculations on array elements much more efficient.\n",
     "It then introduces many of the most common and useful arithmetic ufuncs available in the NumPy package."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## The Slowness of Loops\n",
     "\n",
     "Python's default implementation (known as CPython) does some operations very slowly.\n",
-    "This is in part due to the dynamic, interpreted nature of the language: the fact that types are flexible, so that sequences of operations cannot be compiled down to efficient machine code as in languages like C and Fortran.\n",
-    "Recently there have been various attempts to address this weakness: well-known examples are the [PyPy](http://pypy.org/) project, a just-in-time compiled implementation of Python; the [Cython](http://cython.org) project, which converts Python code to compilable C code; and the [Numba](http://numba.pydata.org/) project, which converts snippets of Python code to fast LLVM bytecode.\n",
+    "This is partly due to the dynamic, interpreted nature of the language; types are flexible, so sequences of operations cannot be compiled down to efficient machine code as in languages like C and Fortran.\n",
+    "Recently there have been various attempts to address this weakness: well-known examples are the [PyPy project](http://pypy.org/), a just-in-time compiled implementation of Python; the [Cython project](http://cython.org), which converts Python code to compilable C code; and the [Numba project](http://numba.pydata.org/), which converts snippets of Python code to fast LLVM bytecode.\n",
     "Each of these has its strengths and weaknesses, but it is safe to say that none of the three approaches has yet surpassed the reach and popularity of the standard CPython engine.\n",
     "\n",
-    "The relative sluggishness of Python generally manifests itself in situations where many small operations are being repeated – for instance looping over arrays to operate on each element.\n",
+    "The relative sluggishness of Python generally manifests itself in situations where many small operations are being repeated; for instance, looping over arrays to operate on each element.\n",
     "For example, imagine we have an array of values and we'd like to compute the reciprocal of each.\n",
     "A straightforward approach might look like this:"
    ]
@@ -62,13 +41,16 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.16666667,  1.        ,  0.25      ,  0.25      ,  0.125     ])"
+       "array([0.11111111, 0.25      , 1.        , 0.33333333, 0.125     ])"
       ]
      },
      "execution_count": 1,
@@ -78,7 +60,7 @@
    ],
    "source": [
     "import numpy as np\n",
-    "np.random.seed(0)\n",
+    "rng = np.random.default_rng(seed=1701)\n",
     "\n",
     "def compute_reciprocals(values):\n",
     "    output = np.empty(len(values))\n",
@@ -86,7 +68,7 @@
     "        output[i] = 1.0 / values[i]\n",
     "    return output\n",
     "        \n",
-    "values = np.random.randint(1, 10, size=5)\n",
+    "values = rng.integers(1, 10, size=5)\n",
     "compute_reciprocals(values)"
    ]
   },
@@ -95,27 +77,30 @@
    "metadata": {},
    "source": [
     "This implementation probably feels fairly natural to someone from, say, a C or Java background.\n",
-    "But if we measure the execution time of this code for a large input, we see that this operation is very slow, perhaps surprisingly so!\n",
-    "We'll benchmark this with IPython's ``%timeit`` magic (discussed in [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb)):"
+    "But if we measure the execution time of this code for a large input, we see that this operation is very slow—perhaps surprisingly so!\n",
+    "We'll benchmark this with IPython's `%timeit` magic (discussed in [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb)):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1 loop, best of 3: 2.91 s per loop\n"
+      "2.61 s ± 192 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
    "source": [
-    "big_array = np.random.randint(1, 100, size=1000000)\n",
+    "big_array = rng.integers(1, 100, size=1000000)\n",
     "%timeit compute_reciprocals(big_array)"
    ]
   },
@@ -124,38 +109,41 @@
    "metadata": {},
    "source": [
     "It takes several seconds to compute these million operations and to store the result!\n",
-    "When even cell phones have processing speeds measured in Giga-FLOPS (i.e., billions of numerical operations per second), this seems almost absurdly slow.\n",
-    "It turns out that the bottleneck here is not the operations themselves, but the type-checking and function dispatches that CPython must do at each cycle of the loop.\n",
+    "When even cell phones have processing speeds measured in gigaflops (i.e., billions of numerical operations per second), this seems almost absurdly slow.\n",
+    "It turns out that the bottleneck here is not the operations themselves, but the type checking and function dispatches that CPython must do at each cycle of the loop.\n",
     "Each time the reciprocal is computed, Python first examines the object's type and does a dynamic lookup of the correct function to use for that type.\n",
-    "If we were working in compiled code instead, this type specification would be known before the code executes and the result could be computed much more efficiently."
+    "If we were working in compiled code instead, this type specification would be known before the code executed and the result could be computed much more efficiently."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Introducing UFuncs\n",
+    "## Introducing Ufuncs\n",
     "\n",
     "For many types of operations, NumPy provides a convenient interface into just this kind of statically typed, compiled routine. This is known as a *vectorized* operation.\n",
-    "This can be accomplished by simply performing an operation on the array, which will then be applied to each element.\n",
+    "For simple operations like the element-wise division here, vectorization is as simple as using Python arithmetic operators directly on the array object.\n",
     "This vectorized approach is designed to push the loop into the compiled layer that underlies NumPy, leading to much faster execution.\n",
     "\n",
-    "Compare the results of the following two:"
+    "Compare the results of the following two operations:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.16666667  1.          0.25        0.25        0.125     ]\n",
-      "[ 0.16666667  1.          0.25        0.25        0.125     ]\n"
+      "[0.11111111 0.25       1.         0.33333333 0.125     ]\n",
+      "[0.11111111 0.25       1.         0.33333333 0.125     ]\n"
      ]
     }
    ],
@@ -175,14 +163,17 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "100 loops, best of 3: 4.6 ms per loop\n"
+      "2.54 ms ± 383 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -194,21 +185,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Vectorized operations in NumPy are implemented via *ufuncs*, whose main purpose is to quickly execute repeated operations on values in NumPy arrays.\n",
-    "Ufuncs are extremely flexible – before we saw an operation between a scalar and an array, but we can also operate between two arrays:"
+    "Vectorized operations in NumPy are implemented via ufuncs, whose main purpose is to quickly execute repeated operations on values in NumPy arrays.\n",
+    "Ufuncs are extremely flexible—before we saw an operation between a scalar and an array, but we can also operate between two arrays:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.        ,  0.5       ,  0.66666667,  0.75      ,  0.8       ])"
+       "array([0.        , 0.5       , 0.66666667, 0.75      , 0.8       ])"
       ]
      },
      "execution_count": 5,
@@ -224,14 +218,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And ufunc operations are not limited to one-dimensional arrays–they can also act on multi-dimensional arrays as well:"
+    "And ufunc operations are not limited to one-dimensional arrays. They can act on multidimensional arrays as well:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -256,15 +253,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Computations using vectorization through ufuncs are nearly always more efficient than their counterpart implemented using Python loops, especially as the arrays grow in size.\n",
-    "Any time you see such a loop in a Python script, you should consider whether it can be replaced with a vectorized expression."
+    "Computations using vectorization through ufuncs are nearly always more efficient than their counterparts implemented using Python loops, especially as the arrays grow in size.\n",
+    "Any time you see such a loop in a NumPy script, you should consider whether it can be replaced with a vectorized expression."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exploring NumPy's UFuncs\n",
+    "## Exploring NumPy's Ufuncs\n",
     "\n",
     "Ufuncs exist in two flavors: *unary ufuncs*, which operate on a single input, and *binary ufuncs*, which operate on two inputs.\n",
     "We'll see examples of both these types of functions here."
@@ -274,7 +271,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Array arithmetic\n",
+    "### Array Arithmetic\n",
     "\n",
     "NumPy's ufuncs feel very natural to use because they make use of Python's native arithmetic operators.\n",
     "The standard addition, subtraction, multiplication, and division can all be used:"
@@ -284,29 +281,32 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "x     = [0 1 2 3]\n",
-      "x + 5 = [5 6 7 8]\n",
-      "x - 5 = [-5 -4 -3 -2]\n",
-      "x * 2 = [0 2 4 6]\n",
-      "x / 2 = [ 0.   0.5  1.   1.5]\n",
+      "x      = [0 1 2 3]\n",
+      "x + 5  = [5 6 7 8]\n",
+      "x - 5  = [-5 -4 -3 -2]\n",
+      "x * 2  = [0 2 4 6]\n",
+      "x / 2  = [0.  0.5 1.  1.5]\n",
       "x // 2 = [0 0 1 1]\n"
      ]
     }
    ],
    "source": [
     "x = np.arange(4)\n",
-    "print(\"x     =\", x)\n",
-    "print(\"x + 5 =\", x + 5)\n",
-    "print(\"x - 5 =\", x - 5)\n",
-    "print(\"x * 2 =\", x * 2)\n",
-    "print(\"x / 2 =\", x / 2)\n",
+    "print(\"x      =\", x)\n",
+    "print(\"x + 5  =\", x + 5)\n",
+    "print(\"x - 5  =\", x - 5)\n",
+    "print(\"x * 2  =\", x * 2)\n",
+    "print(\"x / 2  =\", x / 2)\n",
     "print(\"x // 2 =\", x // 2)  # floor division"
    ]
   },
@@ -314,14 +314,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There is also a unary ufunc for negation, and a ``**`` operator for exponentiation, and a ``%`` operator for modulus:"
+    "There is also a unary ufunc for negation, a `**` operator for exponentiation, and a `%` operator for modulus:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -351,7 +354,10 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -373,14 +379,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Each of these arithmetic operations are simply convenient wrappers around specific functions built into NumPy; for example, the ``+`` operator is a wrapper for the ``add`` function:"
+    "All of these arithmetic operations are simply convenient wrappers around specific ufuncs built into NumPy. For example, the `+` operator is a wrapper for the `add` ufunc:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -404,25 +413,25 @@
    "source": [
     "The following table lists the arithmetic operators implemented in NumPy:\n",
     "\n",
-    "| Operator\t    | Equivalent ufunc    | Description                           |\n",
-    "|---------------|---------------------|---------------------------------------|\n",
-    "|``+``          |``np.add``           |Addition (e.g., ``1 + 1 = 2``)         |\n",
-    "|``-``          |``np.subtract``      |Subtraction (e.g., ``3 - 2 = 1``)      |\n",
-    "|``-``          |``np.negative``      |Unary negation (e.g., ``-2``)          |\n",
-    "|``*``          |``np.multiply``      |Multiplication (e.g., ``2 * 3 = 6``)   |\n",
-    "|``/``          |``np.divide``        |Division (e.g., ``3 / 2 = 1.5``)       |\n",
-    "|``//``         |``np.floor_divide``  |Floor division (e.g., ``3 // 2 = 1``)  |\n",
-    "|``**``         |``np.power``         |Exponentiation (e.g., ``2 ** 3 = 8``)  |\n",
-    "|``%``          |``np.mod``           |Modulus/remainder (e.g., ``9 % 4 = 1``)|\n",
+    "| Operator    | Equivalent ufunc  | Description                         |\n",
+    "|-------------|-------------------|-------------------------------------|\n",
+    "|`+`          |`np.add`           |Addition (e.g., `1 + 1 = 2`)         |\n",
+    "|`-`          |`np.subtract`      |Subtraction (e.g., `3 - 2 = 1`)      |\n",
+    "|`-`          |`np.negative`      |Unary negation (e.g., `-2`)          |\n",
+    "|`*`          |`np.multiply`      |Multiplication (e.g., `2 * 3 = 6`)   |\n",
+    "|`/`          |`np.divide`        |Division (e.g., `3 / 2 = 1.5`)       |\n",
+    "|`//`         |`np.floor_divide`  |Floor division (e.g., `3 // 2 = 1`)  |\n",
+    "|`**`         |`np.power`         |Exponentiation (e.g., `2 ** 3 = 8`)  |\n",
+    "|`%`          |`np.mod`           |Modulus/remainder (e.g., `9 % 4 = 1`)|\n",
     "\n",
-    "Additionally there are Boolean/bitwise operators; we will explore these in [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb)."
+    "Additionally, there are Boolean/bitwise operators; we will explore these in [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Absolute value\n",
+    "### Absolute Value\n",
     "\n",
     "Just as NumPy understands Python's built-in arithmetic operators, it also understands Python's built-in absolute value function:"
    ]
@@ -431,7 +440,10 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -454,14 +466,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The corresponding NumPy ufunc is ``np.absolute``, which is also available under the alias ``np.abs``:"
+    "The corresponding NumPy ufunc is `np.absolute`, which is also available under the alias `np.abs`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -483,7 +498,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -505,20 +523,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This ufunc can also handle complex data, in which the absolute value returns the magnitude:"
+    "This ufunc can also handle complex data, in which case it returns the magnitude:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 5.,  5.,  2.,  1.])"
+       "array([5., 5., 2., 1.])"
       ]
      },
      "execution_count": 14,
@@ -535,7 +556,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Trigonometric functions\n",
+    "### Trigonometric Functions\n",
     "\n",
     "NumPy provides a large number of useful ufuncs, and some of the most useful for the data scientist are the trigonometric functions.\n",
     "We'll start by defining an array of angles:"
@@ -545,7 +566,10 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -563,17 +587,20 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "theta      =  [ 0.          1.57079633  3.14159265]\n",
-      "sin(theta) =  [  0.00000000e+00   1.00000000e+00   1.22464680e-16]\n",
-      "cos(theta) =  [  1.00000000e+00   6.12323400e-17  -1.00000000e+00]\n",
-      "tan(theta) =  [  0.00000000e+00   1.63312394e+16  -1.22464680e-16]\n"
+      "theta      =  [0.         1.57079633 3.14159265]\n",
+      "sin(theta) =  [0.0000000e+00 1.0000000e+00 1.2246468e-16]\n",
+      "cos(theta) =  [ 1.000000e+00  6.123234e-17 -1.000000e+00]\n",
+      "tan(theta) =  [ 0.00000000e+00  1.63312394e+16 -1.22464680e-16]\n"
      ]
     }
    ],
@@ -596,7 +623,10 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -605,7 +635,7 @@
      "text": [
       "x         =  [-1, 0, 1]\n",
       "arcsin(x) =  [-1.57079633  0.          1.57079633]\n",
-      "arccos(x) =  [ 3.14159265  1.57079633  0.        ]\n",
+      "arccos(x) =  [3.14159265 1.57079633 0.        ]\n",
       "arctan(x) =  [-0.78539816  0.          0.78539816]\n"
      ]
     }
@@ -622,35 +652,38 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Exponents and logarithms\n",
+    "### Exponents and Logarithms\n",
     "\n",
-    "Another common type of operation available in a NumPy ufunc are the exponentials:"
+    "Other common operations available in NumPy ufuncs are the exponentials:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "x     = [1, 2, 3]\n",
-      "e^x   = [  2.71828183   7.3890561   20.08553692]\n",
-      "2^x   = [ 2.  4.  8.]\n",
-      "3^x   = [ 3  9 27]\n"
+      "x   = [1, 2, 3]\n",
+      "e^x = [ 2.71828183  7.3890561  20.08553692]\n",
+      "2^x = [2. 4. 8.]\n",
+      "3^x = [ 3.  9. 27.]\n"
      ]
     }
    ],
    "source": [
     "x = [1, 2, 3]\n",
-    "print(\"x     =\", x)\n",
-    "print(\"e^x   =\", np.exp(x))\n",
-    "print(\"2^x   =\", np.exp2(x))\n",
-    "print(\"3^x   =\", np.power(3, x))"
+    "print(\"x   =\", x)\n",
+    "print(\"e^x =\", np.exp(x))\n",
+    "print(\"2^x =\", np.exp2(x))\n",
+    "print(\"3^x =\", np.power(3., x))"
    ]
   },
   {
@@ -658,14 +691,17 @@
    "metadata": {},
    "source": [
     "The inverse of the exponentials, the logarithms, are also available.\n",
-    "The basic ``np.log`` gives the natural logarithm; if you prefer to compute the base-2 logarithm or the base-10 logarithm, these are available as well:"
+    "The basic `np.log` gives the natural logarithm; if you prefer to compute the base-2 logarithm or the base-10 logarithm, these are available as well:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -673,9 +709,9 @@
      "output_type": "stream",
      "text": [
       "x        = [1, 2, 4, 10]\n",
-      "ln(x)    = [ 0.          0.69314718  1.38629436  2.30258509]\n",
-      "log2(x)  = [ 0.          1.          2.          3.32192809]\n",
-      "log10(x) = [ 0.          0.30103     0.60205999  1.        ]\n"
+      "ln(x)    = [0.         0.69314718 1.38629436 2.30258509]\n",
+      "log2(x)  = [0.         1.         2.         3.32192809]\n",
+      "log10(x) = [0.         0.30103    0.60205999 1.        ]\n"
      ]
     }
    ],
@@ -698,15 +734,18 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "exp(x) - 1 = [ 0.          0.0010005   0.01005017  0.10517092]\n",
-      "log(1 + x) = [ 0.          0.0009995   0.00995033  0.09531018]\n"
+      "exp(x) - 1 = [0.         0.0010005  0.01005017 0.10517092]\n",
+      "log(1 + x) = [0.         0.0009995  0.00995033 0.09531018]\n"
      ]
     }
    ],
@@ -720,20 +759,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When ``x`` is very small, these functions give more precise values than if the raw ``np.log`` or ``np.exp`` were to be used."
+    "When `x` is very small, these functions give more precise values than if the raw `np.log` or `np.exp` were to be used."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Specialized ufuncs\n",
+    "### Specialized Ufuncs\n",
     "\n",
-    "NumPy has many more ufuncs available, including hyperbolic trig functions, bitwise arithmetic, comparison operators, conversions from radians to degrees, rounding and remainders, and much more.\n",
+    "NumPy has many more ufuncs available, including for hyperbolic trigonometry, bitwise arithmetic, comparison operations, conversions from radians to degrees, rounding and remainders, and much more.\n",
     "A look through the NumPy documentation reveals a lot of interesting functionality.\n",
     "\n",
-    "Another excellent source for more specialized and obscure ufuncs is the submodule ``scipy.special``.\n",
-    "If you want to compute some obscure mathematical function on your data, chances are it is implemented in ``scipy.special``.\n",
+    "Another excellent source for more specialized ufuncs is the submodule `scipy.special`.\n",
+    "If you want to compute some obscure mathematical function on your data, chances are it is implemented in `scipy.special`.\n",
     "There are far too many functions to list them all, but the following snippet shows a couple that might come up in a statistics context:"
    ]
   },
@@ -741,7 +780,10 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -752,16 +794,19 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "gamma(x)     = [  1.00000000e+00   2.40000000e+01   3.62880000e+05]\n",
-      "ln|gamma(x)| = [  0.           3.17805383  12.80182748]\n",
-      "beta(x, 2)   = [ 0.5         0.03333333  0.00909091]\n"
+      "gamma(x)     = [1.0000e+00 2.4000e+01 3.6288e+05]\n",
+      "ln|gamma(x)| = [ 0.          3.17805383 12.80182748]\n",
+      "beta(x, 2)   = [0.5        0.03333333 0.00909091]\n"
      ]
     }
    ],
@@ -777,21 +822,24 @@
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "erf(x)  = [ 0.          0.32862676  0.67780119  0.84270079]\n",
-      "erfc(x) = [ 1.          0.67137324  0.32219881  0.15729921]\n",
-      "erfinv(x) = [ 0.          0.27246271  0.73286908         inf]\n"
+      "erf(x)  = [0.         0.32862676 0.67780119 0.84270079]\n",
+      "erfc(x) = [1.         0.67137324 0.32219881 0.15729921]\n",
+      "erfinv(x) = [0.         0.27246271 0.73286908        inf]\n"
      ]
     }
    ],
    "source": [
-    "# Error function (integral of Gaussian)\n",
+    "# Error function (integral of Gaussian),\n",
     "# its complement, and its inverse\n",
     "x = np.array([0, 0.3, 0.7, 1.0])\n",
     "print(\"erf(x)  =\", special.erf(x))\n",
@@ -803,7 +851,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are many, many more ufuncs available in both NumPy and ``scipy.special``.\n",
+    "There are many, many more ufuncs available in both NumPy and `scipy.special`.\n",
     "Because the documentation of these packages is available online, a web search along the lines of \"gamma function python\" will generally find the relevant information."
    ]
   },
@@ -814,32 +862,34 @@
     "## Advanced Ufunc Features\n",
     "\n",
     "Many NumPy users make use of ufuncs without ever learning their full set of features.\n",
-    "We'll outline a few specialized features of ufuncs here."
+    "I'll outline a few specialized features of ufuncs here."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Specifying output\n",
+    "### Specifying Output\n",
     "\n",
     "For large calculations, it is sometimes useful to be able to specify the array where the result of the calculation will be stored.\n",
-    "Rather than creating a temporary array, this can be used to write computation results directly to the memory location where you'd like them to be.\n",
-    "For all ufuncs, this can be done using the ``out`` argument of the function:"
+    "For all ufuncs, this can be done using the `out` argument of the function:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[  0.  10.  20.  30.  40.]\n"
+      "[ 0. 10. 20. 30. 40.]\n"
      ]
     }
    ],
@@ -861,14 +911,17 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[  1.   0.   2.   0.   4.   0.   8.   0.  16.   0.]\n"
+      "[ 1.  0.  2.  0.  4.  0.  8.  0. 16.  0.]\n"
      ]
     }
    ],
@@ -882,28 +935,31 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If we had instead written ``y[::2] = 2 ** x``, this would have resulted in the creation of a temporary array to hold the results of ``2 ** x``, followed by a second operation copying those values into the ``y`` array.\n",
-    "This doesn't make much of a difference for such a small computation, but for very large arrays the memory savings from careful use of the ``out`` argument can be significant."
+    "If we had instead written `y[::2] = 2 ** x`, this would have resulted in the creation of a temporary array to hold the results of `2 ** x`, followed by a second operation copying those values into the `y` array.\n",
+    "This doesn't make much of a difference for such a small computation, but for very large arrays the memory savings from careful use of the `out` argument can be significant."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Aggregates\n",
+    "### Aggregations\n",
     "\n",
-    "For binary ufuncs, there are some interesting aggregates that can be computed directly from the object.\n",
-    "For example, if we'd like to *reduce* an array with a particular operation, we can use the ``reduce`` method of any ufunc.\n",
+    "For binary ufuncs, aggregations can be computed directly from the object.\n",
+    "For example, if we'd like to *reduce* an array with a particular operation, we can use the `reduce` method of any ufunc.\n",
     "A reduce repeatedly applies a given operation to the elements of an array until only a single result remains.\n",
     "\n",
-    "For example, calling ``reduce`` on the ``add`` ufunc returns the sum of all elements in the array:"
+    "For example, calling `reduce` on the `add` ufunc returns the sum of all elements in the array:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -926,14 +982,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Similarly, calling ``reduce`` on the ``multiply`` ufunc results in the product of all array elements:"
+    "Similarly, calling `reduce` on the `multiply` ufunc results in the product of all array elements:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -955,14 +1014,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If we'd like to store all the intermediate results of the computation, we can instead use ``accumulate``:"
+    "If we'd like to store all the intermediate results of the computation, we can instead use `accumulate`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -984,7 +1046,10 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1006,16 +1071,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that for these particular cases, there are dedicated NumPy functions to compute the results (``np.sum``, ``np.prod``, ``np.cumsum``, ``np.cumprod``), which we'll explore in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb)."
+    "Note that for these particular cases, there are dedicated NumPy functions to compute the results (`np.sum`, `np.prod`, `np.cumsum`, `np.cumprod`), which we'll explore in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Outer products\n",
+    "### Outer Products\n",
     "\n",
-    "Finally, any ufunc can compute the output of all pairs of two different inputs using the ``outer`` method.\n",
+    "Finally, any ufunc can compute the output of all pairs of two different inputs using the `outer` method.\n",
     "This allows you, in one line, to do things like create a multiplication table:"
    ]
   },
@@ -1024,6 +1089,9 @@
    "execution_count": 30,
    "metadata": {
     "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
     "scrolled": true
    },
    "outputs": [
@@ -1051,10 +1119,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``ufunc.at`` and ``ufunc.reduceat`` methods, which we'll explore in [Fancy Indexing](02.07-Fancy-Indexing.ipynb), are very helpful as well.\n",
+    "The `ufunc.at` and `ufunc.reduceat` methods are useful as well, and we will explore them in [Fancy Indexing](02.07-Fancy-Indexing.ipynb).\n",
     "\n",
-    "Another extremely useful feature of ufuncs is the ability to operate between arrays of different sizes and shapes, a set of operations known as *broadcasting*.\n",
-    "This subject is important enough that we will devote a whole section to it (see [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb))."
+    "We will also encounter the ability of ufuncs to operate between arrays of different shapes and sizes, a set of operations known as *broadcasting*.\n",
+    "This subject is important enough that we will devote a whole chapter to it (see [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb))."
    ]
   },
   {
@@ -1070,22 +1138,15 @@
    "source": [
     "More information on universal functions (including the full list of available functions) can be found on the [NumPy](http://www.numpy.org) and [SciPy](http://www.scipy.org) documentation websites.\n",
     "\n",
-    "Recall that you can also access information directly from within IPython by importing the packages and using IPython's tab-completion and help (``?``) functionality, as described in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) | [Contents](Index.ipynb) | [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.03-Computation-on-arrays-ufuncs.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "Recall that you can also access information directly from within IPython by importing the packages and using IPython's tab completion and help (`?`) functionality, as described in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -1101,9 +1162,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/02.04-Computation-on-arrays-aggregates.ipynb b/notebooks/02.04-Computation-on-arrays-aggregates.ipynb
index 53e6462fd..7684d9381 100644
--- a/notebooks/02.04-Computation-on-arrays-aggregates.ipynb
+++ b/notebooks/02.04-Computation-on-arrays-aggregates.ipynb
@@ -4,39 +4,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) | [Contents](Index.ipynb) | [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.04-Computation-on-arrays-aggregates.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Aggregations: Min, Max, and Everything In Between"
+    "# Aggregations: min, max, and Everything in Between"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Often when faced with a large amount of data, a first step is to compute summary statistics for the data in question.\n",
-    "Perhaps the most common summary statistics are the mean and standard deviation, which allow you to summarize the \"typical\" values in a dataset, but other aggregates are useful as well (the sum, product, median, minimum and maximum, quantiles, etc.).\n",
+    "A first step in exploring any dataset is often to compute various summary statistics.\n",
+    "Perhaps the most common summary statistics are the mean and standard deviation, which allow you to summarize the \"typical\" values in a dataset, but other aggregations are useful as well (the sum, product, median, minimum and maximum, quantiles, etc.).\n",
     "\n",
-    "NumPy has fast built-in aggregation functions for working on arrays; we'll discuss and demonstrate some of them here."
+    "NumPy has fast built-in aggregation functions for working on arrays; we'll discuss and try out some of them here."
    ]
   },
   {
@@ -46,31 +24,38 @@
     "## Summing the Values in an Array\n",
     "\n",
     "As a quick example, consider computing the sum of all values in an array.\n",
-    "Python itself can do this using the built-in ``sum`` function:"
+    "Python itself can do this using the built-in `sum` function:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "import numpy as np"
+    "import numpy as np\n",
+    "rng = np.random.default_rng()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "55.61209116604941"
+       "52.76825337322368"
       ]
      },
      "execution_count": 2,
@@ -79,7 +64,7 @@
     }
    ],
    "source": [
-    "L = np.random.random(100)\n",
+    "L = rng.random(100)\n",
     "sum(L)"
    ]
   },
@@ -87,20 +72,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The syntax is quite similar to that of NumPy's ``sum`` function, and the result is the same in the simplest case:"
+    "The syntax is quite similar to that of NumPy's `sum` function, and the result is the same in the simplest case:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "55.612091166049424"
+       "52.76825337322366"
       ]
      },
      "execution_count": 3,
@@ -123,20 +111,23 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10 loops, best of 3: 104 ms per loop\n",
-      "1000 loops, best of 3: 442 µs per loop\n"
+      "89.9 ms ± 233 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "521 µs ± 8.37 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
      ]
     }
    ],
    "source": [
-    "big_array = np.random.rand(1000000)\n",
+    "big_array = rng.random(1000000)\n",
     "%timeit sum(big_array)\n",
     "%timeit np.sum(big_array)"
    ]
@@ -145,8 +136,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Be careful, though: the ``sum`` function and the ``np.sum`` function are not identical, which can sometimes lead to confusion!\n",
-    "In particular, their optional arguments have different meanings, and ``np.sum`` is aware of multiple array dimensions, as we will see in the following section."
+    "Be careful, though: the `sum` function and the `np.sum` function are not identical, which can sometimes lead to confusion!\n",
+    "In particular, their optional arguments have different meanings (`sum(x, 1)` initializes the sum at `1`, while `np.sum(x, 1)` sums along axis `1`), and `np.sum` is aware of multiple array dimensions, as we will see in the following section."
    ]
   },
   {
@@ -155,20 +146,23 @@
    "source": [
     "## Minimum and Maximum\n",
     "\n",
-    "Similarly, Python has built-in ``min`` and ``max`` functions, used to find the minimum value and maximum value of any given array:"
+    "Similarly, Python has built-in `min` and `max` functions, used to find the minimum value and maximum value of any given array:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(1.1717128136634614e-06, 0.9999976784968716)"
+       "(2.0114398036064074e-07, 0.9999997912802653)"
       ]
      },
      "execution_count": 5,
@@ -191,13 +185,16 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(1.1717128136634614e-06, 0.9999976784968716)"
+       "(2.0114398036064074e-07, 0.9999997912802653)"
       ]
      },
      "execution_count": 6,
@@ -213,15 +210,18 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10 loops, best of 3: 82.3 ms per loop\n",
-      "1000 loops, best of 3: 497 µs per loop\n"
+      "72 ms ± 177 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "564 µs ± 3.11 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
      ]
     }
    ],
@@ -234,21 +234,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For ``min``, ``max``, ``sum``, and several other NumPy aggregates, a shorter syntax is to use methods of the array object itself:"
+    "For `min`, `max`, `sum`, and several other NumPy aggregates, a shorter syntax is to use methods of the array object itself:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.17171281366e-06 0.999997678497 499911.628197\n"
+      "2.0114398036064074e-07 0.9999997912802653 499854.0273321711\n"
      ]
     }
    ],
@@ -267,7 +270,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Multi dimensional aggregates\n",
+    "### Multidimensional Aggregates\n",
     "\n",
     "One common type of aggregation operation is an aggregate along a row or column.\n",
     "Say you have some data stored in a two-dimensional array:"
@@ -277,21 +280,24 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.8967576   0.03783739  0.75952519  0.06682827]\n",
-      " [ 0.8354065   0.99196818  0.19544769  0.43447084]\n",
-      " [ 0.66859307  0.15038721  0.37911423  0.6687194 ]]\n"
+      "[[0 3 1 2]\n",
+      " [1 9 7 0]\n",
+      " [4 8 3 7]]\n"
      ]
     }
    ],
    "source": [
-    "M = np.random.random((3, 4))\n",
+    "M = rng.integers(0, 10, (3, 4))\n",
     "print(M)"
    ]
   },
@@ -299,20 +305,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By default, each NumPy aggregation function will return the aggregate over the entire array:"
+    "NumPy aggregations will apply across all elements of a multidimensional array:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "6.0850555667307118"
+       "45"
       ]
      },
      "execution_count": 10,
@@ -328,20 +337,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Aggregation functions take an additional argument specifying the *axis* along which the aggregate is computed. For example, we can find the minimum value within each column by specifying ``axis=0``:"
+    "Aggregation functions take an additional argument specifying the *axis* along which the aggregate is computed. For example, we can find the minimum value within each column by specifying `axis=0`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.66859307,  0.03783739,  0.19544769,  0.06682827])"
+       "array([0, 3, 1, 0])"
       ]
      },
      "execution_count": 11,
@@ -366,13 +378,16 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.8967576 ,  0.99196818,  0.6687194 ])"
+       "array([3, 9, 8])"
       ]
      },
      "execution_count": 12,
@@ -389,72 +404,73 @@
    "metadata": {},
    "source": [
     "The way the axis is specified here can be confusing to users coming from other languages.\n",
-    "The ``axis`` keyword specifies the *dimension of the array that will be collapsed*, rather than the dimension that will be returned.\n",
-    "So specifying ``axis=0`` means that the first axis will be collapsed: for two-dimensional arrays, this means that values within each column will be aggregated."
+    "The `axis` keyword specifies the dimension of the array that will be *collapsed*, rather than the dimension that will be returned.\n",
+    "So, specifying `axis=0` means that axis 0 will be collapsed: for two-dimensional arrays, values within each column will be aggregated."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Other aggregation functions\n",
+    "### Other Aggregation Functions\n",
     "\n",
-    "NumPy provides many other aggregation functions, but we won't discuss them in detail here.\n",
-    "Additionally, most aggregates have a ``NaN``-safe counterpart that computes the result while ignoring missing values, which are marked by the special IEEE floating-point ``NaN`` value (for a fuller discussion of missing data, see [Handling Missing Data](03.04-Missing-Values.ipynb)).\n",
-    "Some of these ``NaN``-safe functions were not added until NumPy 1.8, so they will not be available in older NumPy versions.\n",
+    "NumPy provides several other aggregation functions with a similar API, and additionally most have a `NaN`-safe counterpart that computes the result while ignoring missing values, which are marked by the special IEEE floating-point `NaN` value (see [Handling Missing Data](03.04-Missing-Values.ipynb)).\n",
     "\n",
     "The following table provides a list of useful aggregation functions available in NumPy:\n",
     "\n",
-    "|Function Name      |   NaN-safe Version  | Description                                   |\n",
-    "|-------------------|---------------------|-----------------------------------------------|\n",
-    "| ``np.sum``        | ``np.nansum``       | Compute sum of elements                       |\n",
-    "| ``np.prod``       | ``np.nanprod``      | Compute product of elements                   |\n",
-    "| ``np.mean``       | ``np.nanmean``      | Compute mean of elements                      |\n",
-    "| ``np.std``        | ``np.nanstd``       | Compute standard deviation                    |\n",
-    "| ``np.var``        | ``np.nanvar``       | Compute variance                              |\n",
-    "| ``np.min``        | ``np.nanmin``       | Find minimum value                            |\n",
-    "| ``np.max``        | ``np.nanmax``       | Find maximum value                            |\n",
-    "| ``np.argmin``     | ``np.nanargmin``    | Find index of minimum value                   |\n",
-    "| ``np.argmax``     | ``np.nanargmax``    | Find index of maximum value                   |\n",
-    "| ``np.median``     | ``np.nanmedian``    | Compute median of elements                    |\n",
-    "| ``np.percentile`` | ``np.nanpercentile``| Compute rank-based statistics of elements     |\n",
-    "| ``np.any``        | N/A                 | Evaluate whether any elements are true        |\n",
-    "| ``np.all``        | N/A                 | Evaluate whether all elements are true        |\n",
+    "|Function name    |   NaN-safe version| Description                                   |\n",
+    "|-----------------|-------------------|-----------------------------------------------|\n",
+    "| `np.sum`        | `np.nansum`       | Compute sum of elements                       |\n",
+    "| `np.prod`       | `np.nanprod`      | Compute product of elements                   |\n",
+    "| `np.mean`       | `np.nanmean`      | Compute mean of elements                      |\n",
+    "| `np.std`        | `np.nanstd`       | Compute standard deviation                    |\n",
+    "| `np.var`        | `np.nanvar`       | Compute variance                              |\n",
+    "| `np.min`        | `np.nanmin`       | Find minimum value                            |\n",
+    "| `np.max`        | `np.nanmax`       | Find maximum value                            |\n",
+    "| `np.argmin`     | `np.nanargmin`    | Find index of minimum value                   |\n",
+    "| `np.argmax`     | `np.nanargmax`    | Find index of maximum value                   |\n",
+    "| `np.median`     | `np.nanmedian`    | Compute median of elements                    |\n",
+    "| `np.percentile` | `np.nanpercentile`| Compute rank-based statistics of elements     |\n",
+    "| `np.any`        | N/A               | Evaluate whether any elements are true        |\n",
+    "| `np.all`        | N/A               | Evaluate whether all elements are true        |\n",
     "\n",
-    "We will see these aggregates often throughout the rest of the book."
+    "You will see these aggregates often throughout the rest of the book."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Example: What is the Average Height of US Presidents?"
+    "## Example: What Is the Average Height of US Presidents?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Aggregates available in NumPy can be extremely useful for summarizing a set of values.\n",
-    "As a simple example, let's consider the heights of all US presidents.\n",
-    "This data is available in the file *president_heights.csv*, which is a simple comma-separated list of labels and values:"
+    "Aggregates available in NumPy can act as summary statistics for a set of values.\n",
+    "As a small example, let's consider the heights of all US presidents.\n",
+    "This data is available in the file *president_heights.csv*, which is a comma-separated list of labels and values:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "order,name,height(cm)\r\n",
-      "1,George Washington,189\r\n",
-      "2,John Adams,170\r\n",
-      "3,Thomas Jefferson,189\r\n"
+      "order,name,height(cm)\n",
+      "1,George Washington,189\n",
+      "2,John Adams,170\n",
+      "3,Thomas Jefferson,189\n"
      ]
     }
    ],
@@ -466,14 +482,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll use the Pandas package, which we'll explore more fully in [Chapter 3](03.00-Introduction-to-Pandas.ipynb), to read the file and extract this information (note that the heights are measured in centimeters)."
+    "We'll use the Pandas package, which we'll explore more fully in [Part 3](03.00-Introduction-to-Pandas.ipynb), to read the file and extract this information (note that the heights are measured in centimeters):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -482,7 +501,7 @@
      "text": [
       "[189 170 189 163 183 171 185 168 173 183 173 173 175 178 183 193 178 173\n",
       " 174 183 183 168 170 178 182 180 183 178 182 188 175 179 183 193 182 183\n",
-      " 177 185 188 188 182 185]\n"
+      " 177 185 188 188 182 185 191 182]\n"
      ]
     }
    ],
@@ -504,15 +523,18 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean height:        179.738095238\n",
-      "Standard deviation: 6.93184344275\n",
+      "Mean height:        180.04545454545453\n",
+      "Standard deviation: 6.983599441335736\n",
       "Minimum height:     163\n",
       "Maximum height:     193\n"
      ]
@@ -537,16 +559,19 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "25th percentile:    174.25\n",
+      "25th percentile:    174.75\n",
       "Median:             182.0\n",
-      "75th percentile:    183.0\n"
+      "75th percentile:    183.5\n"
      ]
     }
    ],
@@ -562,34 +587,40 @@
    "source": [
     "We see that the median height of US presidents is 182 cm, or just shy of six feet.\n",
     "\n",
-    "Of course, sometimes it's more useful to see a visual representation of this data, which we can accomplish using tools in Matplotlib (we'll discuss Matplotlib more fully in [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)). For example, this code generates the following chart:"
+    "Of course, sometimes it's more useful to see a visual representation of this data, which we can accomplish using tools in Matplotlib (we'll discuss Matplotlib more fully in [Part 4](04.00-Introduction-To-Matplotlib.ipynb)). For example, this code generates the following chart:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn; seaborn.set()  # set plot style"
+    "plt.style.use('seaborn-whitegrid')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
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KSkpSYmKievfurbFjx0oyd/5dGftf//rXBjH3u3fv1qBBgzR06FBNmjTJ+R5T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\nRU5ErkJggg==\n",
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x116102f98>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -602,27 +633,13 @@
     "plt.xlabel('height (cm)')\n",
     "plt.ylabel('number');"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These aggregates are some of the fundamental pieces of exploratory data analysis that we'll explore in more depth in later chapters of the book."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) | [Contents](Index.ipynb) | [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.04-Computation-on-arrays-aggregates.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -638,9 +655,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/02.05-Computation-on-arrays-broadcasting.ipynb b/notebooks/02.05-Computation-on-arrays-broadcasting.ipynb
index c1cae6ddf..67bd7af9a 100644
--- a/notebooks/02.05-Computation-on-arrays-broadcasting.ipynb
+++ b/notebooks/02.05-Computation-on-arrays-broadcasting.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) | [Contents](Index.ipynb) | [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.05-Computation-on-arrays-broadcasting.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,9 +11,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We saw in the previous section how NumPy's universal functions can be used to *vectorize* operations and thereby remove slow Python loops.\n",
-    "Another means of vectorizing operations is to use NumPy's *broadcasting* functionality.\n",
-    "Broadcasting is simply a set of rules for applying binary ufuncs (e.g., addition, subtraction, multiplication, etc.) on arrays of different sizes."
+    "We saw in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) how NumPy's universal functions can be used to *vectorize* operations and thereby remove slow Python loops.\n",
+    "This chapter discusses *broadcasting*: a set of rules by which NumPy lets you apply binary operations (e.g., addition, subtraction, multiplication, etc.) between arrays of different sizes and shapes."
    ]
   },
   {
@@ -51,7 +28,10 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -62,7 +42,10 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -86,14 +69,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Broadcasting allows these types of binary operations to be performed on arrays of different sizes–for example, we can just as easily add a scalar (think of it as a zero-dimensional array) to an array:"
+    "Broadcasting allows these types of binary operations to be performed on arrays of different sizes—for example, we can just as easily add a scalar (think of it as a zero-dimensional array) to an array:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -115,25 +101,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can think of this as an operation that stretches or duplicates the value ``5`` into the array ``[5, 5, 5]``, and adds the results.\n",
-    "The advantage of NumPy's broadcasting is that this duplication of values does not actually take place, but it is a useful mental model as we think about broadcasting.\n",
+    "We can think of this as an operation that stretches or duplicates the value `5` into the array `[5, 5, 5]`, and adds the results.\n",
     "\n",
-    "We can similarly extend this to arrays of higher dimension. Observe the result when we add a one-dimensional array to a two-dimensional array:"
+    "We can similarly extend this idea to arrays of higher dimension. Observe the result when we add a one-dimensional array to a two-dimensional array:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.,  1.,  1.],\n",
-       "       [ 1.,  1.,  1.],\n",
-       "       [ 1.,  1.,  1.]])"
+       "array([[1., 1., 1.],\n",
+       "       [1., 1., 1.],\n",
+       "       [1., 1., 1.]])"
       ]
      },
      "execution_count": 4,
@@ -150,15 +138,18 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.,  2.,  3.],\n",
-       "       [ 1.,  2.,  3.],\n",
-       "       [ 1.,  2.,  3.]])"
+       "array([[1., 2., 3.],\n",
+       "       [1., 2., 3.],\n",
+       "       [1., 2., 3.]])"
       ]
      },
      "execution_count": 5,
@@ -174,7 +165,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here the one-dimensional array ``a`` is stretched, or broadcast across the second dimension in order to match the shape of ``M``.\n",
+    "Here the one-dimensional array `a` is stretched, or broadcasted, across the second dimension in order to match the shape of `M`.\n",
     "\n",
     "While these examples are relatively easy to understand, more complicated cases can involve broadcasting of both arrays. Consider the following example:"
    ]
@@ -183,7 +174,10 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -209,7 +203,10 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -230,25 +227,26 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Just as before we stretched or broadcasted one value to match the shape of the other, here we've stretched *both* ``a`` and ``b`` to match a common shape, and the result is a two-dimensional array!\n",
-    "The geometry of these examples is visualized in the following figure (Code to produce this plot can be found in the [appendix](06.00-Figure-Code.ipynb#Broadcasting), and is adapted from source published in the [astroML](http://astroml.org) documentation. Used by permission)."
+    "Just as before we stretched or broadcasted one value to match the shape of the other, here we've stretched *both* `a` and `b` to match a common shape, and the result is a two-dimensional array!\n",
+    "The geometry of these examples is visualized in the following figure. (Code to produce this plot can be found in the online [appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Broadcasting), and is adapted from a source published in the [astroML](http://astroml.org) documentation. Used by permission.)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![Broadcasting Visual](figures/02.05-broadcasting.png)"
+    "![Broadcasting Visual](images/02.05-broadcasting.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The light boxes represent the broadcasted values: again, this extra memory is not actually allocated in the course of the operation, but it can be useful conceptually to imagine that it is."
+    "The light boxes represent the broadcasted values. This way of thinking about broadcasting may raise questions about its efficiency in terms of memory use, but worry not: NumPy broadcasting does not actually copy the broadcasted values in memory. Still, this can be a useful mental model as we think about broadcasting."
    ]
   },
   {
@@ -270,16 +268,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Broadcasting example 1\n",
+    "### Broadcasting Example 1\n",
     "\n",
-    "Let's look at adding a two-dimensional array to a one-dimensional array:"
+    "Suppose we want to add a two-dimensional array to a one-dimensional array:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -291,36 +292,39 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's consider an operation on these two arrays. The shape of the arrays are\n",
+    "Let's consider an operation on these two arrays, which have the following shapes:\n",
     "\n",
-    "- ``M.shape = (2, 3)``\n",
-    "- ``a.shape = (3,)``\n",
+    "- `M.shape` is `(2, 3)`\n",
+    "- `a.shape` is `(3,)`\n",
     "\n",
-    "We see by rule 1 that the array ``a`` has fewer dimensions, so we pad it on the left with ones:\n",
+    "We see by rule 1 that the array `a` has fewer dimensions, so we pad it on the left with ones:\n",
     "\n",
-    "- ``M.shape -> (2, 3)``\n",
-    "- ``a.shape -> (1, 3)``\n",
+    "- `M.shape` remains `(2, 3)`\n",
+    "- `a.shape` becomes `(1, 3)`\n",
     "\n",
     "By rule 2, we now see that the first dimension disagrees, so we stretch this dimension to match:\n",
     "\n",
-    "- ``M.shape -> (2, 3)``\n",
-    "- ``a.shape -> (2, 3)``\n",
+    "- `M.shape` remains `(2, 3)`\n",
+    "- `a.shape` becomes `(2, 3)`\n",
     "\n",
-    "The shapes match, and we see that the final shape will be ``(2, 3)``:"
+    "The shapes now match, and we see that the final shape will be `(2, 3)`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.,  2.,  3.],\n",
-       "       [ 1.,  2.,  3.]])"
+       "array([[1., 2., 3.],\n",
+       "       [1., 2., 3.]])"
       ]
      },
      "execution_count": 9,
@@ -336,16 +340,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Broadcasting example 2\n",
+    "### Broadcasting Example 2\n",
     "\n",
-    "Let's take a look at an example where both arrays need to be broadcast:"
+    "Now let's take a look at an example where both arrays need to be broadcast:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -357,29 +364,32 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Again, we'll start by writing out the shape of the arrays:\n",
+    "Again, we'll start by determining the shapes of the arrays:\n",
     "\n",
-    "- ``a.shape = (3, 1)``\n",
-    "- ``b.shape = (3,)``\n",
+    "- `a.shape` is `(3, 1)`\n",
+    "- `b.shape` is `(3,)`\n",
     "\n",
-    "Rule 1 says we must pad the shape of ``b`` with ones:\n",
+    "Rule 1 says we must pad the shape of `b` with ones:\n",
     "\n",
-    "- ``a.shape -> (3, 1)``\n",
-    "- ``b.shape -> (1, 3)``\n",
+    "- `a.shape` remains `(3, 1)`\n",
+    "- `b.shape` becomes `(1, 3)`\n",
     "\n",
-    "And rule 2 tells us that we upgrade each of these ones to match the corresponding size of the other array:\n",
+    "And rule 2 tells us that we must upgrade each of these ``1``s to match the corresponding size of the other array:\n",
     "\n",
-    "- ``a.shape -> (3, 3)``\n",
-    "- ``b.shape -> (3, 3)``\n",
+    "- `a.shape` becomes `(3, 3)`\n",
+    "- `b.shape` becomes `(3, 3)`\n",
     "\n",
-    "Because the result matches, these shapes are compatible. We can see this here:"
+    "Because the results match, these shapes are compatible. We can see this here:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -403,16 +413,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Broadcasting example 3\n",
+    "### Broadcasting Example 3\n",
     "\n",
-    "Now let's take a look at an example in which the two arrays are not compatible:"
+    "Next, let's take a look at an example in which the two arrays are not compatible:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -424,30 +437,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is just a slightly different situation than in the first example: the matrix ``M`` is transposed.\n",
-    "How does this affect the calculation? The shape of the arrays are\n",
+    "This is just a slightly different situation than in the first example: the matrix `M` is transposed.\n",
+    "How does this affect the calculation? The shapes of the arrays are as follows:\n",
     "\n",
-    "- ``M.shape = (3, 2)``\n",
-    "- ``a.shape = (3,)``\n",
+    "- `M.shape` is `(3, 2)`\n",
+    "- `a.shape` is `(3,)`\n",
     "\n",
-    "Again, rule 1 tells us that we must pad the shape of ``a`` with ones:\n",
+    "Again, rule 1 tells us that we must pad the shape of `a` with ones:\n",
     "\n",
-    "- ``M.shape -> (3, 2)``\n",
-    "- ``a.shape -> (1, 3)``\n",
+    "- `M.shape` remains `(3, 2)`\n",
+    "- `a.shape` becomes `(1, 3)`\n",
     "\n",
-    "By rule 2, the first dimension of ``a`` is stretched to match that of ``M``:\n",
+    "By rule 2, the first dimension of `a` is then stretched to match that of `M`:\n",
     "\n",
-    "- ``M.shape -> (3, 2)``\n",
-    "- ``a.shape -> (3, 3)``\n",
+    "- `M.shape` remains `(3, 2)`\n",
+    "- `a.shape` becomes `(3, 3)`\n",
     "\n",
-    "Now we hit rule 3–the final shapes do not match, so these two arrays are incompatible, as we can observe by attempting this operation:"
+    "Now we hit rule 3—the final shapes do not match, so these two arrays are incompatible, as we can observe by attempting this operation:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -457,7 +473,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-9e16e9f98da6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-13-8cac1d547906>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,2) (3,) "
      ]
     }
@@ -470,17 +486,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note the potential confusion here: you could imagine making ``a`` and ``M`` compatible by, say, padding ``a``'s shape with ones on the right rather than the left.\n",
+    "Note the potential confusion here: you could imagine making `a` and `M` compatible by, say, padding `a`'s shape with ones on the right rather than the left.\n",
     "But this is not how the broadcasting rules work!\n",
     "That sort of flexibility might be useful in some cases, but it would lead to potential areas of ambiguity.\n",
-    "If right-side padding is what you'd like, you can do this explicitly by reshaping the array (we'll use the ``np.newaxis`` keyword introduced in [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb)):"
+    "If right-side padding is what you'd like, you can do this explicitly by reshaping the array (we'll use the `np.newaxis` keyword introduced in [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) for this):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -502,15 +521,18 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.,  1.],\n",
-       "       [ 2.,  2.],\n",
-       "       [ 3.,  3.]])"
+       "array([[1., 1.],\n",
+       "       [2., 2.],\n",
+       "       [3., 3.]])"
       ]
      },
      "execution_count": 15,
@@ -526,23 +548,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Also note that while we've been focusing on the ``+`` operator here, these broadcasting rules apply to *any* binary ``ufunc``.\n",
-    "For example, here is the ``logaddexp(a, b)`` function, which computes ``log(exp(a) + exp(b))`` with more precision than the naive approach:"
+    "Also notice that while we've been focusing on the `+` operator here, these broadcasting rules apply to *any* binary ufunc.\n",
+    "For example, here is the `logaddexp(a, b)` function, which computes `log(exp(a) + exp(b))` with more precision than the naive approach:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.31326169,  1.31326169],\n",
-       "       [ 1.69314718,  1.69314718],\n",
-       "       [ 2.31326169,  2.31326169]])"
+       "array([[1.31326169, 1.31326169],\n",
+       "       [1.69314718, 1.69314718],\n",
+       "       [2.31326169, 2.31326169]])"
       ]
      },
      "execution_count": 16,
@@ -572,24 +597,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Broadcasting operations form the core of many examples we'll see throughout this book.\n",
-    "We'll now take a look at a couple simple examples of where they can be useful."
+    "Broadcasting operations form the core of many examples you'll see throughout this book.\n",
+    "We'll now take a look at some instances of where they can be useful."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Centering an array"
+    "### Centering an Array"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the previous section, we saw that ufuncs allow a NumPy user to remove the need to explicitly write slow Python loops. Broadcasting extends this ability.\n",
-    "One commonly seen example is when centering an array of data.\n",
-    "Imagine you have an array of 10 observations, each of which consists of 3 values.\n",
+    "In [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb), we saw that ufuncs allow a NumPy user to remove the need to explicitly write slow Python loops. Broadcasting extends this ability.\n",
+    "One commonly seen example in data science is subtracting the row-wise mean from an array of data.\n",
+    "Imagine we have an array of 10 observations, each of which consists of 3 values.\n",
     "Using the standard convention (see [Data Representation in Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb#Data-Representation-in-Scikit-Learn)), we'll store this in a $10 \\times 3$ array:"
    ]
   },
@@ -597,31 +622,38 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "X = np.random.random((10, 3))"
+    "rng = np.random.default_rng(seed=1701)\n",
+    "X = rng.random((10, 3))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can compute the mean of each feature using the ``mean`` aggregate across the first dimension:"
+    "We can compute the mean of each column using the `mean` aggregate across the first dimension:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.53514715,  0.66567217,  0.44385899])"
+       "array([0.38503638, 0.36991443, 0.63896043])"
       ]
      },
      "execution_count": 18,
@@ -638,14 +670,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And now we can center the ``X`` array by subtracting the mean (this is a broadcasting operation):"
+    "And now we can center the `X` array by subtracting the mean (this is a broadcasting operation):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -656,20 +691,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To double-check that we've done this correctly, we can check that the centered array has near zero mean:"
+    "To double-check that we've done this correctly, we can check that the centered array has a mean near zero:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([  2.22044605e-17,  -7.77156117e-17,  -1.66533454e-17])"
+       "array([ 4.99600361e-17, -4.44089210e-17,  0.00000000e+00])"
       ]
      },
      "execution_count": 20,
@@ -692,14 +730,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Plotting a two-dimensional function"
+    "### Plotting a Two-Dimensional Function"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One place that broadcasting is very useful is in displaying images based on two-dimensional functions.\n",
+    "One place that broadcasting often comes in handy is in displaying images based on two-dimensional functions.\n",
     "If we want to define a function $z = f(x, y)$, broadcasting can be used to compute the function across the grid:"
    ]
   },
@@ -707,7 +745,10 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -722,14 +763,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll use Matplotlib to plot this two-dimensional array (these tools will be discussed in full in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb)):"
+    "We'll use Matplotlib to plot this two-dimensional array, shown in the following figure (these tools will be discussed in full in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb)):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -741,23 +785,27 @@
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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vyQHsjCAUcgx2WVzacsMKAmsLP1j3uY3L7aC2ivr+i0d3rbTwb043VcBSH0jn/tL7RjdB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\nHIwtxPGVX5uCmie15H4fjF7PZkv34g0GhNbr2EYra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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10becee48>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.imshow(z, origin='lower', extent=[0, 5, 0, 5],\n",
-    "           cmap='viridis')\n",
+    "plt.imshow(z, origin='lower', extent=[0, 5, 0, 5])\n",
     "plt.colorbar();"
    ]
   },
@@ -767,20 +815,13 @@
    "source": [
     "The result is a compelling visualization of the two-dimensional function."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) | [Contents](Index.ipynb) | [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.05-Computation-on-arrays-broadcasting.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -796,9 +837,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/02.06-Boolean-Arrays-and-Masks.ipynb b/notebooks/02.06-Boolean-Arrays-and-Masks.ipynb
index e17269f9d..06b256964 100644
--- a/notebooks/02.06-Boolean-Arrays-and-Masks.ipynb
+++ b/notebooks/02.06-Boolean-Arrays-and-Masks.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) | [Contents](Index.ipynb) | [Fancy Indexing](02.07-Fancy-Indexing.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.06-Boolean-Arrays-and-Masks.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,8 +11,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This section covers the use of Boolean masks to examine and manipulate values within NumPy arrays.\n",
-    "Masking comes up when you want to extract, modify, count, or otherwise manipulate values in an array based on some criterion: for example, you might wish to count all values greater than a certain value, or perhaps remove all outliers that are above some threshold.\n",
+    "This chapter covers the use of Boolean masks to examine and manipulate values within NumPy arrays.\n",
+    "Masking comes up when you want to extract, modify, count, or otherwise manipulate values in an array based on some criterion: for example, you might wish to count all values greater than a certain value, or remove all outliers that are above some threshold.\n",
     "In NumPy, Boolean masking is often the most efficient way to accomplish these types of tasks."
    ]
   },
@@ -45,20 +23,23 @@
     "## Example: Counting Rainy Days\n",
     "\n",
     "Imagine you have a series of data that represents the amount of precipitation each day for a year in a given city.\n",
-    "For example, here we'll load the daily rainfall statistics for the city of Seattle in 2014, using Pandas (which is covered in more detail in [Chapter 3](03.00-Introduction-to-Pandas.ipynb)):"
+    "For example, here we'll load the daily rainfall statistics for the city of Seattle in 2015, using Pandas (see [Part 3](03.00-Introduction-to-Pandas.ipynb)):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(365,)"
+       "365"
       ]
      },
      "execution_count": 1,
@@ -68,48 +49,54 @@
    ],
    "source": [
     "import numpy as np\n",
-    "import pandas as pd\n",
+    "from vega_datasets import data\n",
     "\n",
-    "# use pandas to extract rainfall inches as a NumPy array\n",
-    "rainfall = pd.read_csv('data/Seattle2014.csv')['PRCP'].values\n",
-    "inches = rainfall / 254.0  # 1/10mm -> inches\n",
-    "inches.shape"
+    "# Use DataFrame operations to extract rainfall as a NumPy array\n",
+    "rainfall_mm = np.array(\n",
+    "    data.seattle_weather().set_index('date')['precipitation']['2015'])\n",
+    "len(rainfall_mm)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The array contains 365 values, giving daily rainfall in inches from January 1 to December 31, 2014.\n",
+    "The array contains 365 values, giving daily rainfall in millimeters from January 1 to December 31, 2015.\n",
     "\n",
-    "As a first quick visualization, let's look at the histogram of rainy days, which was generated using Matplotlib (we will explore this tool more fully in [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)):"
+    "As a first quick visualization, let's look at the histogram of rainy days in the following figure, which was generated using Matplotlib (we will explore this tool more fully in [Part 4](04.00-Introduction-To-Matplotlib.ipynb)):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn; seaborn.set()  # set plot styles"
+    "plt.style.use('seaborn-whitegrid')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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cWANA4cQaAAon1gBQOLEGgMKJNQAUrnGqJzAV3nzzzTz33IFRje3o+JfMnDlzgmcEAO9u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\nngAAAABJRU5ErkJggg==\n",
+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10b617a58>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -117,37 +104,30 @@
     }
    ],
    "source": [
-    "plt.hist(inches, 40);"
+    "plt.hist(rainfall_mm, 40);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This histogram gives us a general idea of what the data looks like: despite its reputation, the vast majority of days in Seattle saw near zero measured rainfall in 2014.\n",
-    "But this doesn't do a good job of conveying some information we'd like to see: for example, how many rainy days were there in the year? What is the average precipitation on those rainy days? How many days were there with more than half an inch of rain?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Digging into the data\n",
+    "This histogram gives us a general idea of what the data looks like: despite the city's rainy reputation, the vast majority of days in Seattle saw near zero measured rainfall in 2015.\n",
+    "But this doesn't do a good job of conveying some information we'd like to see: for example, how many rainy days were there in the year? What was the average precipitation on those rainy days? How many days were there with more than 10 mm of rainfall?\n",
     "\n",
-    "One approach to this would be to answer these questions by hand: loop through the data, incrementing a counter each time we see values in some desired range.\n",
-    "For reasons discussed throughout this chapter, such an approach is very inefficient, both from the standpoint of time writing code and time computing the result.\n",
+    "One approach to this would be to answer these questions by hand: we could loop through the data, incrementing a counter each time we see values in some desired range.\n",
+    "But for reasons discussed throughout this chapter, such an approach is very inefficient from the standpoint of both time writing code and time computing the result.\n",
     "We saw in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) that NumPy's ufuncs can be used in place of loops to do fast element-wise arithmetic operations on arrays; in the same way, we can use other ufuncs to do element-wise *comparisons* over arrays, and we can then manipulate the results to answer the questions we have.\n",
-    "We'll leave the data aside for right now, and discuss some general tools in NumPy to use *masking* to quickly answer these types of questions."
+    "We'll leave the data aside for now, and discuss some general tools in NumPy to use *masking* to quickly answer these types of questions."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Comparison Operators as ufuncs\n",
+    "## Comparison Operators as Ufuncs\n",
     "\n",
-    "In [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) we introduced ufuncs, and focused in particular on arithmetic operators. We saw that using ``+``, ``-``, ``*``, ``/``, and others on arrays leads to element-wise operations.\n",
-    "NumPy also implements comparison operators such as ``<`` (less than) and ``>`` (greater than) as element-wise ufuncs.\n",
+    "[Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) introduced ufuncs, and focused in particular on arithmetic operators. We saw that using `+`, `-`, `*`, `/`, and other operators on arrays leads to element-wise operations.\n",
+    "NumPy also implements comparison operators such as `<` (less than) and `>` (greater than) as element-wise ufuncs.\n",
     "The result of these comparison operators is always an array with a Boolean data type.\n",
     "All six of the standard comparison operations are available:"
    ]
@@ -156,7 +136,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -167,13 +147,16 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ True,  True, False, False, False], dtype=bool)"
+       "array([ True,  True, False, False, False])"
       ]
      },
      "execution_count": 5,
@@ -189,13 +172,16 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([False, False, False,  True,  True], dtype=bool)"
+       "array([False, False, False,  True,  True])"
       ]
      },
      "execution_count": 6,
@@ -211,13 +197,16 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ True,  True,  True, False, False], dtype=bool)"
+       "array([ True,  True,  True, False, False])"
       ]
      },
      "execution_count": 7,
@@ -233,13 +222,16 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([False, False,  True,  True,  True], dtype=bool)"
+       "array([False, False,  True,  True,  True])"
       ]
      },
      "execution_count": 8,
@@ -255,13 +247,16 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ True,  True, False,  True,  True], dtype=bool)"
+       "array([ True,  True, False,  True,  True])"
       ]
      },
      "execution_count": 9,
@@ -277,13 +272,16 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([False, False,  True, False, False], dtype=bool)"
+       "array([False, False,  True, False, False])"
       ]
      },
      "execution_count": 10,
@@ -306,13 +304,16 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([False,  True, False, False, False], dtype=bool)"
+       "array([False,  True, False, False, False])"
       ]
      },
      "execution_count": 11,
@@ -328,14 +329,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As in the case of arithmetic operators, the comparison operators are implemented as ufuncs in NumPy; for example, when you write ``x < 3``, internally NumPy uses ``np.less(x, 3)``.\n",
-    "    A summary of the comparison operators and their equivalent ufunc is shown here:\n",
+    "As in the case of arithmetic operators, the comparison operators are implemented as ufuncs in NumPy; for example, when you write `x < 3`, internally NumPy uses `np.less(x, 3)`.\n",
+    "    A summary of the comparison operators and their equivalent ufuncs is shown here:\n",
     "\n",
-    "| Operator\t    | Equivalent ufunc    || Operator\t   | Equivalent ufunc    |\n",
-    "|---------------|---------------------||---------------|---------------------|\n",
-    "|``==``         |``np.equal``         ||``!=``         |``np.not_equal``     |\n",
-    "|``<``          |``np.less``          ||``<=``         |``np.less_equal``    |\n",
-    "|``>``          |``np.greater``       ||``>=``         |``np.greater_equal`` |"
+    "| Operator    | Equivalent ufunc  | Operator   | Equivalent ufunc |\n",
+    "|-------------|-------------------|------------|------------------|\n",
+    "|`==`         |`np.equal`         |`!=`        |`np.not_equal`    |\n",
+    "|`<`          |`np.less`          |`<=`        |`np.less_equal`   |\n",
+    "|`>`          |`np.greater`       |`>=`        |`np.greater_equal`|"
    ]
   },
   {
@@ -350,15 +351,18 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[5, 0, 3, 3],\n",
-       "       [7, 9, 3, 5],\n",
-       "       [2, 4, 7, 6]])"
+       "array([[9, 4, 0, 3],\n",
+       "       [8, 6, 3, 1],\n",
+       "       [3, 7, 4, 0]])"
       ]
      },
      "execution_count": 12,
@@ -367,8 +371,8 @@
     }
    ],
    "source": [
-    "rng = np.random.RandomState(0)\n",
-    "x = rng.randint(10, size=(3, 4))\n",
+    "rng = np.random.default_rng(seed=1701)\n",
+    "x = rng.integers(10, size=(3, 4))\n",
     "x"
    ]
   },
@@ -376,15 +380,18 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ True,  True,  True,  True],\n",
+       "array([[False,  True,  True,  True],\n",
        "       [False, False,  True,  True],\n",
-       "       [ True,  True, False, False]], dtype=bool)"
+       "       [ True, False,  True,  True]])"
       ]
      },
      "execution_count": 13,
@@ -410,23 +417,26 @@
     "## Working with Boolean Arrays\n",
     "\n",
     "Given a Boolean array, there are a host of useful operations you can do.\n",
-    "We'll work with ``x``, the two-dimensional array we created earlier."
+    "We'll work with `x`, the two-dimensional array we created earlier:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[5 0 3 3]\n",
-      " [7 9 3 5]\n",
-      " [2 4 7 6]]\n"
+      "[[9 4 0 3]\n",
+      " [8 6 3 1]\n",
+      " [3 7 4 0]]\n"
      ]
     }
    ],
@@ -438,16 +448,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Counting entries\n",
+    "### Counting Entries\n",
     "\n",
-    "To count the number of ``True`` entries in a Boolean array, ``np.count_nonzero`` is useful:"
+    "To count the number of `True` entries in a Boolean array, `np.count_nonzero` is useful:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -471,14 +484,17 @@
    "metadata": {},
    "source": [
     "We see that there are eight array entries that are less than 6.\n",
-    "Another way to get at this information is to use ``np.sum``; in this case, ``False`` is interpreted as ``0``, and ``True`` is interpreted as ``1``:"
+    "Another way to get at this information is to use `np.sum`; in this case, `False` is interpreted as `0`, and `True` is interpreted as `1`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -500,20 +516,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The benefit of ``sum()`` is that like with other NumPy aggregation functions, this summation can be done along rows or columns as well:"
+    "The benefit of `np.sum` is that, like with other NumPy aggregation functions, this summation can be done along rows or columns as well:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([4, 2, 2])"
+       "array([3, 2, 3])"
       ]
      },
      "execution_count": 17,
@@ -532,14 +551,17 @@
    "source": [
     "This counts the number of values less than 6 in each row of the matrix.\n",
     "\n",
-    "If we're interested in quickly checking whether any or all the values are true, we can use (you guessed it) ``np.any`` or ``np.all``:"
+    "If we're interested in quickly checking whether any or all the values are `True`, we can use (you guessed it) `np.any` or `np.all`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -562,7 +584,10 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -585,7 +610,10 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -608,7 +636,10 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -631,20 +662,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``np.all`` and ``np.any`` can be used along particular axes as well. For example:"
+    "`np.all` and `np.any` can be used along particular axes as well. For example:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ True, False,  True], dtype=bool)"
+       "array([False, False,  True])"
       ]
      },
      "execution_count": 22,
@@ -661,21 +695,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here all the elements in the first and third rows are less than 8, while this is not the case for the second row.\n",
+    "Here all the elements in the third row are less than 8, while this is not the case for others.\n",
     "\n",
-    "Finally, a quick warning: as mentioned in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb), Python has built-in ``sum()``, ``any()``, and ``all()`` functions. These have a different syntax than the NumPy versions, and in particular will fail or produce unintended results when used on multidimensional arrays. Be sure that you are using ``np.sum()``, ``np.any()``, and ``np.all()`` for these examples!"
+    "Finally, a quick warning: as mentioned in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb), Python has built-in `sum`, `any`, and `all` functions. These have a different syntax than the NumPy versions, and in particular will fail or produce unintended results when used on multidimensional arrays. Be sure that you are using `np.sum`, `np.any`, and `np.all` for these examples!"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Boolean operators\n",
+    "### Boolean Operators\n",
     "\n",
-    "We've already seen how we might count, say, all days with rain less than four inches, or all days with rain greater than two inches.\n",
-    "But what if we want to know about all days with rain less than four inches and greater than one inch?\n",
-    "This is accomplished through Python's *bitwise logic operators*, ``&``, ``|``, ``^``, and ``~``.\n",
-    "Like with the standard arithmetic operators, NumPy overloads these as ufuncs which work element-wise on (usually Boolean) arrays.\n",
+    "We've already seen how we might count, say, all days with less than 20 mm of rain, or all days with more than 10 mm of rain.\n",
+    "But what if we want to know how many days there were with more than 10 mm and less than 20 mm of rain? We can accomplish this with Python's *bitwise logic operators*, `&`, `|`, `^`, and `~`.\n",
+    "Like with the standard arithmetic operators, NumPy overloads these as ufuncs that work element-wise on (usually Boolean) arrays.\n",
     "\n",
     "For example, we can address this sort of compound question as follows:"
    ]
@@ -684,13 +718,16 @@
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "29"
+       "16"
       ]
      },
      "execution_count": 23,
@@ -699,35 +736,38 @@
     }
    ],
    "source": [
-    "np.sum((inches > 0.5) & (inches < 1))"
+    "np.sum((rainfall_mm > 10) & (rainfall_mm < 20))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "So we see that there are 29 days with rainfall between 0.5 and 1.0 inches.\n",
+    "This tells us that there were 16 days with rainfall of between 10 and 20 millimeters.\n",
     "\n",
-    "Note that the parentheses here are important–because of operator precedence rules, with parentheses removed this expression would be evaluated as follows, which results in an error:\n",
+    "The parentheses here are important. Because of operator precedence rules, with the parentheses removed this expression would be evaluated as follows, which results in an error:\n",
     "\n",
     "``` python\n",
-    "inches > (0.5 & inches) < 1\n",
+    "rainfall_mm > (10 & rainfall_mm) < 20\n",
     "```\n",
     "\n",
-    "Using the equivalence of *A AND B* and *NOT (NOT A OR NOT B)* (which you may remember if you've taken an introductory logic course), we can compute the same result in a different manner:"
+    "Let's demonstrate a more complicated expression. Using De Morgan's laws, we can compute the same result in a different manner:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "29"
+       "16"
       ]
      },
      "execution_count": 24,
@@ -736,7 +776,7 @@
     }
    ],
    "source": [
-    "np.sum(~( (inches <= 0.5) | (inches >= 1) ))"
+    "np.sum(~( (rainfall_mm <= 10) | (rainfall_mm >= 20) ))"
    ]
   },
   {
@@ -752,17 +792,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "| Operator\t    | Equivalent ufunc    || Operator\t    | Equivalent ufunc    |\n",
-    "|---------------|---------------------||---------------|---------------------|\n",
-    "|``&``          |``np.bitwise_and``   ||&#124;         |``np.bitwise_or``    |\n",
-    "|``^``          |``np.bitwise_xor``   ||``~``          |``np.bitwise_not``   |"
+    "| Operator    | Equivalent ufunc  | Operator    | Equivalent ufunc  |\n",
+    "|-------------|-------------------|-------------|-------------------|\n",
+    "|`&`          |`np.bitwise_and`   |&#124;       |`np.bitwise_or`    |\n",
+    "|`^`          |`np.bitwise_xor`   |`~`          |`np.bitwise_not`   |"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using these tools, we might start to answer the types of questions we have about our weather data.\n",
+    "Using these tools, we can start to answer many of the questions we might have about our weather data.\n",
     "Here are some examples of results we can compute when combining masking with aggregations:"
    ]
   },
@@ -770,26 +810,29 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number days without rain:       215\n",
-      "Number days with rain:          150\n",
-      "Days with more than 0.5 inches: 37\n",
-      "Rainy days with < 0.2 inches  : 75\n"
+      "Number days without rain:   221\n",
+      "Number days with rain:      144\n",
+      "Days with more than 10 mm:  34\n",
+      "Rainy days with < 5 mm:     83\n"
      ]
     }
    ],
    "source": [
-    "print(\"Number days without rain:      \", np.sum(inches == 0))\n",
-    "print(\"Number days with rain:         \", np.sum(inches != 0))\n",
-    "print(\"Days with more than 0.5 inches:\", np.sum(inches > 0.5))\n",
-    "print(\"Rainy days with < 0.2 inches  :\", np.sum((inches > 0) &\n",
-    "                                                (inches < 0.2)))"
+    "print(\"Number days without rain:  \", np.sum(rainfall_mm == 0))\n",
+    "print(\"Number days with rain:     \", np.sum(rainfall_mm != 0))\n",
+    "print(\"Days with more than 10 mm: \", np.sum(rainfall_mm > 10))\n",
+    "print(\"Rainy days with < 5 mm:    \", np.sum((rainfall_mm > 0) &\n",
+    "                                            (rainfall_mm < 5)))"
    ]
   },
   {
@@ -799,23 +842,25 @@
     "## Boolean Arrays as Masks\n",
     "\n",
     "In the preceding section we looked at aggregates computed directly on Boolean arrays.\n",
-    "A more powerful pattern is to use Boolean arrays as masks, to select particular subsets of the data themselves.\n",
-    "Returning to our ``x`` array from before, suppose we want an array of all values in the array that are less than, say, 5:"
+    "A more powerful pattern is to use Boolean arrays as masks, to select particular subsets of the data themselves. Let's return to our `x` array from before:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[5, 0, 3, 3],\n",
-       "       [7, 9, 3, 5],\n",
-       "       [2, 4, 7, 6]])"
+       "array([[9, 4, 0, 3],\n",
+       "       [8, 6, 3, 1],\n",
+       "       [3, 7, 4, 0]])"
       ]
      },
      "execution_count": 26,
@@ -831,22 +876,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can obtain a Boolean array for this condition easily, as we've already seen:"
+    "Suppose we want an array of all values in the array that are less than, say, 5. We can obtain a Boolean array for this condition easily, as we've already seen:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "array([[False,  True,  True,  True],\n",
-       "       [False, False,  True, False],\n",
-       "       [ True,  True, False, False]], dtype=bool)"
+       "       [False, False,  True,  True],\n",
+       "       [ True, False,  True,  True]])"
       ]
      },
      "execution_count": 27,
@@ -862,20 +910,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now to *select* these values from the array, we can simply index on this Boolean array; this is known as a *masking* operation:"
+    "Now, to *select* these values from the array, we can simply index on this Boolean array; this is known as a *masking* operation:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0, 3, 3, 3, 2, 4])"
+       "array([4, 0, 3, 3, 1, 3, 4, 0])"
       ]
      },
      "execution_count": 28,
@@ -891,7 +942,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "What is returned is a one-dimensional array filled with all the values that meet this condition; in other words, all the values in positions at which the mask array is ``True``.\n",
+    "What is returned is a one-dimensional array filled with all the values that meet this condition; in other words, all the values in positions at which the mask array is `True`.\n",
     "\n",
     "We are then free to operate on these values as we wish.\n",
     "For example, we can compute some relevant statistics on our Seattle rain data:"
@@ -901,65 +952,71 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Median precip on rainy days in 2014 (inches):    0.194881889764\n",
-      "Median precip on summer days in 2014 (inches):   0.0\n",
-      "Maximum precip on summer days in 2014 (inches):  0.850393700787\n",
-      "Median precip on non-summer rainy days (inches): 0.200787401575\n"
+      "Median precip on rainy days in 2015 (mm):    3.8\n",
+      "Median precip on summer days in 2015 (mm):   0.0\n",
+      "Maximum precip on summer days in 2015 (mm):  32.5\n",
+      "Median precip on non-summer rainy days (mm): 4.1\n"
      ]
     }
    ],
    "source": [
     "# construct a mask of all rainy days\n",
-    "rainy = (inches > 0)\n",
+    "rainy = (rainfall_mm > 0)\n",
     "\n",
     "# construct a mask of all summer days (June 21st is the 172nd day)\n",
     "days = np.arange(365)\n",
     "summer = (days > 172) & (days < 262)\n",
     "\n",
-    "print(\"Median precip on rainy days in 2014 (inches):   \",\n",
-    "      np.median(inches[rainy]))\n",
-    "print(\"Median precip on summer days in 2014 (inches):  \",\n",
-    "      np.median(inches[summer]))\n",
-    "print(\"Maximum precip on summer days in 2014 (inches): \",\n",
-    "      np.max(inches[summer]))\n",
-    "print(\"Median precip on non-summer rainy days (inches):\",\n",
-    "      np.median(inches[rainy & ~summer]))"
+    "print(\"Median precip on rainy days in 2015 (mm):   \",\n",
+    "      np.median(rainfall_mm[rainy]))\n",
+    "print(\"Median precip on summer days in 2015 (mm):  \",\n",
+    "      np.median(rainfall_mm[summer]))\n",
+    "print(\"Maximum precip on summer days in 2015 (mm): \",\n",
+    "      np.max(rainfall_mm[summer]))\n",
+    "print(\"Median precip on non-summer rainy days (mm):\",\n",
+    "      np.median(rainfall_mm[rainy & ~summer]))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By combining Boolean operations, masking operations, and aggregates, we can very quickly answer these sorts of questions for our dataset."
+    "By combining Boolean operations, masking operations, and aggregates, we can very quickly answer these sorts of questions about our dataset."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Aside: Using the Keywords and/or Versus the Operators &/|\n",
+    "## Using the Keywords and/or Versus the Operators &/|\n",
     "\n",
-    "One common point of confusion is the difference between the keywords ``and`` and ``or`` on one hand, and the operators ``&`` and ``|`` on the other hand.\n",
+    "One common point of confusion is the difference between the keywords `and` and `or` on the one hand, and the operators `&` and `|` on the other.\n",
     "When would you use one versus the other?\n",
     "\n",
-    "The difference is this: ``and`` and ``or`` gauge the truth or falsehood of *entire object*, while ``&`` and ``|`` refer to *bits within each object*.\n",
+    "The difference is this: `and` and `or` operate on the object as a whole, while `&` and `|` operate on the elements within the object.\n",
     "\n",
-    "When you use ``and`` or ``or``, it's equivalent to asking Python to treat the object as a single Boolean entity.\n",
-    "In Python, all nonzero integers will evaluate as True. Thus:"
+    "When you use `and` or `or`, it is equivalent to asking Python to treat the object as a single Boolean entity.\n",
+    "In Python, all nonzero integers will evaluate as `True`. Thus:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -981,7 +1038,10 @@
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1003,7 +1063,10 @@
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1025,14 +1088,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When you use ``&`` and ``|`` on integers, the expression operates on the bits of the element, applying the *and* or the *or* to the individual bits making up the number:"
+    "When you use `&` and `|` on integers, the expression operates on the bitwise representation of the element, applying the *and* or the *or* to the individual bits making up the number:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1054,7 +1120,10 @@
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1076,7 +1145,10 @@
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1098,7 +1170,10 @@
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1122,20 +1197,23 @@
    "source": [
     "Notice that the corresponding bits of the binary representation are compared in order to yield the result.\n",
     "\n",
-    "When you have an array of Boolean values in NumPy, this can be thought of as a string of bits where ``1 = True`` and ``0 = False``, and the result of ``&`` and ``|`` operates similarly to above:"
+    "When you have an array of Boolean values in NumPy, this can be thought of as a string of bits where `1 = True` and `0 = False`, and `&` and `|` will operate similarly to in the preceding examples:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ True,  True,  True, False,  True,  True], dtype=bool)"
+       "array([ True,  True,  True, False,  True,  True])"
       ]
      },
      "execution_count": 37,
@@ -1153,14 +1231,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using ``or`` on these arrays will try to evaluate the truth or falsehood of the entire array object, which is not a well-defined value:"
+    "But if you use `or` on these arrays it will try to evaluate the truth or falsehood of the entire array object, which is not a well-defined value:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 38,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1170,7 +1251,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-38-5d8e4f2e21c0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/var/folders/xc/sptt9bk14s34rgxt7453p03r0000gp/T/ipykernel_93010/3447948156.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
      ]
     }
@@ -1183,20 +1264,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Similarly, when doing a Boolean expression on a given array, you should use ``|`` or ``&`` rather than ``or`` or ``and``:"
+    "Similarly, when evaluating a Boolean expression on a given array, you should use `|` or `&` rather than `or` or `and`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([False, False, False, False, False,  True,  True,  True, False, False], dtype=bool)"
+       "array([False, False, False, False, False,  True,  True,  True, False,\n",
+       "       False])"
       ]
      },
      "execution_count": 39,
@@ -1213,14 +1298,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Trying to evaluate the truth or falsehood of the entire array will give the same ``ValueError`` we saw previously:"
+    "Trying to evaluate the truth or falsehood of the entire array will give the same `ValueError` we saw previously:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 40,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1230,7 +1318,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-40-3d24f1ffd63d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/var/folders/xc/sptt9bk14s34rgxt7453p03r0000gp/T/ipykernel_93010/2869511139.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
      ]
     }
@@ -1243,25 +1331,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "So remember this: ``and`` and ``or`` perform a single Boolean evaluation on an entire object, while ``&`` and ``|`` perform multiple Boolean evaluations on the content (the individual bits or bytes) of an object.\n",
+    "So, remember this: `and` and `or` perform a single Boolean evaluation on an entire object, while `&` and `|` perform multiple Boolean evaluations on the content (the individual bits or bytes) of an object.\n",
     "For Boolean NumPy arrays, the latter is nearly always the desired operation."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) | [Contents](Index.ipynb) | [Fancy Indexing](02.07-Fancy-Indexing.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.06-Boolean-Arrays-and-Masks.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.6 64-bit ('3.9.6')",
    "language": "python",
    "name": "python3"
   },
@@ -1275,9 +1356,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc"
+   }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/02.07-Fancy-Indexing.ipynb b/notebooks/02.07-Fancy-Indexing.ipynb
index 00cc188a5..7a1b6d56d 100644
--- a/notebooks/02.07-Fancy-Indexing.ipynb
+++ b/notebooks/02.07-Fancy-Indexing.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) | [Contents](Index.ipynb) | [Sorting Arrays](02.08-Sorting.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.07-Fancy-Indexing.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,9 +11,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the previous sections, we saw how to access and modify portions of arrays using simple indices (e.g., ``arr[0]``), slices (e.g., ``arr[:5]``), and Boolean masks (e.g., ``arr[arr > 0]``).\n",
-    "In this section, we'll look at another style of array indexing, known as *fancy indexing*.\n",
-    "Fancy indexing is like the simple indexing we've already seen, but we pass arrays of indices in place of single scalars.\n",
+    "The previous chapters discussed how to access and modify portions of arrays using simple indices (e.g., `arr[0]`), slices (e.g., `arr[:5]`), and Boolean masks (e.g., `arr[arr > 0]`).\n",
+    "In this chapter, we'll look at another style of array indexing, known as *fancy* or *vectorized* indexing, in which we pass arrays of indices in place of single scalars.\n",
     "This allows us to very quickly access and modify complicated subsets of an array's values."
    ]
   },
@@ -53,22 +30,25 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[51 92 14 71 60 20 82 86 74 74]\n"
+      "[90 40  9 30 80 67 39 15 33 79]\n"
      ]
     }
    ],
    "source": [
     "import numpy as np\n",
-    "rand = np.random.RandomState(42)\n",
+    "rng = np.random.default_rng(seed=1701)\n",
     "\n",
-    "x = rand.randint(100, size=10)\n",
+    "x = rng.integers(100, size=10)\n",
     "print(x)"
    ]
   },
@@ -83,13 +63,16 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[71, 86, 14]"
+       "[30, 15, 9]"
       ]
      },
      "execution_count": 2,
@@ -112,13 +95,16 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([71, 86, 60])"
+       "array([30, 15, 80])"
       ]
      },
      "execution_count": 3,
@@ -135,21 +121,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When using fancy indexing, the shape of the result reflects the shape of the *index arrays* rather than the shape of the *array being indexed*:"
+    "When using arrays of indices, the shape of the result reflects the shape of the *index arrays* rather than the shape of the *array being indexed*:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[71, 86],\n",
-       "       [60, 20]])"
+       "array([[30, 15],\n",
+       "       [80, 67]])"
       ]
      },
      "execution_count": 4,
@@ -174,7 +163,10 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -206,7 +198,10 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -230,7 +225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice that the first value in the result is ``X[0, 2]``, the second is ``X[1, 1]``, and the third is ``X[2, 3]``.\n",
+    "Notice that the first value in the result is `X[0, 2]`, the second is `X[1, 1]`, and the third is `X[2, 3]`.\n",
     "The pairing of indices in fancy indexing follows all the broadcasting rules that were mentioned in [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb).\n",
     "So, for example, if we combine a column vector and a row vector within the indices, we get a two-dimensional result:"
    ]
@@ -239,7 +234,10 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -271,7 +269,10 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -304,14 +305,17 @@
    "source": [
     "## Combined Indexing\n",
     "\n",
-    "For even more powerful operations, fancy indexing can be combined with the other indexing schemes we've seen:"
+    "For even more powerful operations, fancy indexing can be combined with the other indexing schemes we've seen. For example, given the array `X`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -339,7 +343,10 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -368,7 +375,10 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -398,7 +408,10 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -415,7 +428,7 @@
     }
    ],
    "source": [
-    "mask = np.array([1, 0, 1, 0], dtype=bool)\n",
+    "mask = np.array([True, False, True, False])\n",
     "X[row[:, np.newaxis], mask]"
    ]
   },
@@ -423,7 +436,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "All of these indexing options combined lead to a very flexible set of operations for accessing and modifying array values."
+    "All of these indexing options combined lead to a very flexible set of operations for efficiently accessing and modifying array values."
    ]
   },
   {
@@ -440,7 +453,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -458,7 +474,7 @@
     "mean = [0, 0]\n",
     "cov = [[1, 2],\n",
     "       [2, 5]]\n",
-    "X = rand.multivariate_normal(mean, cov, 100)\n",
+    "X = rng.multivariate_normal(mean, cov, 100)\n",
     "X.shape"
    ]
   },
@@ -466,21 +482,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using the plotting tools we will discuss in [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb), we can visualize these points as a scatter-plot:"
+    "Using the plotting tools we will discuss in [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb), we can visualize these points as a scatter plot (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e332048>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -490,7 +509,7 @@
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn; seaborn.set()  # for plot styling\n",
+    "plt.style.use('seaborn-whitegrid')\n",
     "\n",
     "plt.scatter(X[:, 0], X[:, 1]);"
    ]
@@ -499,21 +518,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's use fancy indexing to select 20 random points. We'll do this by first choosing 20 random indices with no repeats, and use these indices to select a portion of the original array:"
+    "Let's use fancy indexing to select 20 random points. We'll do this by first choosing 20 random indices with no repeats, and using these indices to select a portion of the original array:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([93, 45, 73, 81, 50, 10, 98, 94,  4, 64, 65, 89, 47, 84, 82, 80, 25,\n",
-       "       90, 63, 20])"
+       "array([82, 84, 10, 55, 14, 33,  4, 16, 34, 92, 99, 64,  8, 76, 68, 18, 59,\n",
+       "       80, 87, 90])"
       ]
      },
      "execution_count": 15,
@@ -530,7 +552,10 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -553,21 +578,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now to see which points were selected, let's over-plot large circles at the locations of the selected points:"
+    "Now to see which points were selected, let's overplot large circles at the locations of the selected points (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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YsACHDx/G448/DgDYuHGjlMUTUS/jKzl3rjXu338UqqrOYcyYBJSX17hnW2s0\nDo/n7l+bbLdLe2SjRuM9s7tzYxMAsFgAs7nKo/Wu1Wrhckm20IXIi6TJWaFQ4Je//KWURRKRBKRc\nbhQKQRC8WrpGYwYuX67GkCEGVFR8CmAuGhoG4NKlJrS1JcBi2YqHHhoPtfqm19rk5uYmJCb2/Fhl\nMDueSbgKlcgLNyEhigFHj9bg1Kk4tLWpoNO54HDUwGQa0uOfm5c3BWbzCeTlTXVf02o1MJnSUFfX\nDJ1uJKqrG3HzZgpsto4kXFurRFZWNvLzvZcrFRd/hqVLH5Y0RkHw3jUs0I5noij6nVFOJAUefEHU\nizQ1WXDo0EHs2bMLhw+XoqWlxf1eV+OkFRU22GyD4XT2h802GBUVNl/FSy47eyCuXr3iEee9CgpM\nuHbtOBoa7gAAamqOYsCAoT5bqocOHcT48RN8dkN3h1qtRnu7Z1d5oAM5jh07iry8KZLGQXQvtpyJ\neoHKyvO4cKESSUlJmDLlIej1ejQ3N+HIkVK0trZi4sRc3Lyp8ztOqlB47mp1/+v7SdkNvmLFKmze\nvAnLlj2MpKRkr/d/8IMNeOGF/4evvmrG4MHjMG3aUuj1te73b9yoxtGjn2PixFyMGjU6rBi6Ulg4\nE2VlJZg3r8h9LdDGJrdu1WLatHzJYyHqxORMJHOHDh2EwWBw76TVKTnZgPnzFwLo2OyjosKG4cMH\nud+/t/U5aVI8Tp2q/99ubScmTfJ/yhMQeEJUKNRqNdau/Rb27duD9vZ2TJ06zb2URBRFnDp1AgUF\n/WC1jkBNzW1cvPg+AAP27NHAbrcjOzsbK1c+6nOGthRfIjqXQ924UY3s7IEB7y8rO4QHHnggpM8g\nChWTM5GMHT1ajn79sjB27Lgu75s+vQAXLuzGtWsVGDo0F4DnOOm0af2h0dybxPp3WV64R0D6o1Qq\nUVS0GKIo4sSJY7h6tRKNjR1d3Q8+mIf8/BlhJVqpvkTMm1eEXbs+RWtrC0aO9N06F0URJSUHkJGR\niREjRoX8GUShYHImkimXy4W6ultBd5+uWzcPv//9+1Crs7xmOYey/7QoirBYLqK6+irUai2yskYg\nO1uaoxYVCoW75Xz/JhThJFopv0QsXrwUZvMJbN/+MTIzs5CXNwVqtRpWazOOHDkMu92OvLwpQbWu\nibqLyZlIpsrLj2D69IKg79dqNVi+fAYEoQ7jxo0P+fNaWlpw6NBBOBwOjB49AomJSlgsImpqduHi\nxSacOTNQOOyGAAAbhUlEQVQKEybkhD3+fG/LeODAJowYEe9RTjiJNphzpENhNE6B0TgFtbW1KC09\nCIdDgF6vx+zZcxEXF9etsolCweRMJFONjY1IT0/3uBao63f06DH49NPtISfnmzdrcPhwKZYtW+FO\nQhMndrxXXp4Oi2UQbtyoxL59JwHkhdV1fG/LuLExAWbzBY9ywkm04Z7XfC9fP9OsrCxkZS0IuSwi\nqTA5E8mUr33pg+n6DfW84aYmC8rLj+DRR30f5NDZgs3OzkFCggElJfuQn78upM+4txx/r8NJtFIc\nFynl5DciqTA5E8nE/S04p9PpdU8wXb+h7jtdUnIQK1as8vv+vS3alJT+SE9PQF1dXchnGgdqGUt1\nLnOopJ78RiQFbkJCJBOdLThB6A+LZRAqK2973XN/QvO1c5XD4XlIQ1ccDgfUanWXCf3+DTnWr1+E\no0c/D/ozfJWTklIdVhd0Twj0MyWKBn5FJJKJ+1tsyclDceXKZQwfPsJ9LVDX74kTx2A05gX9mYcP\nl8Jkmul1PdDYdjinQt3bMpbTkYFSjFsTSY3JmSgEPXmAxP3dvhMm5KCi4ohHcg7U9XvjRjWmTp0W\n9Ge2tbX5PEjCbK5HfX0WLl+uQ1tbHM6ercSGDTnuuoY6ri1n0epOJ+oKu7WJQnB/17PZXI+2tjbY\nbLYuTykKdD4w0NGCS0i4iosXv8LXX5+Bw+HAmDFjcfhwcOcG79mzK6TEDPhvAdtsaly+XOfej7uh\nYTjM5vqAzxGRNNhyJgpBZ9fznTs1+PrrcqjVTWhtHQCVSoXm5ma4XC6MHTsOo0eP8XjO14zggQPT\nPO7RajXQaNQYNapjN7CWFkCjqUJqKrBjxzYUFS2CVqv1iqmlpQW7d3+KBx/MC3mDjPj4eFgsjTAY\nUjyu6/UC2truruvV6Zwe3e6+TnIiIukwOROFQK8XcPDgLiQkJGPq1JVISan26hL96qtz2LLlQzzy\nyGp392+wM4J93Zef/wCGDBmKAweKYbfbkZ6eDr1ej6amJjQ2NiIhIR5Llz4c1iYZM2YUYu/e3Viy\nZJnHdaMxA2fPVqKhIQ46nRMjRyZDr68BAJ+zyIlIWkzORCG4ffskHnggGwkJg6HX+55xPG7ceAwe\nPARbtnyINWvWQaFQBL3Bhr/7EhMTsXBhx97Uzc1NsNlsGDlylM/x4lCo1Wq4XC44nU6PcWStVoMN\nG3LuGV+vcdf18OFSTJ8+w6Mcm82GAweK8fXXjXA4tNDpnBgyJB5xcXEwmWYhMTGxW3ESxRomZ6Ig\nXbt2Ff369cOUKcaA9yYmJmLOnPkoKzsEk2lW0DOCA92nUCiQnGxAcrIhrDr4mtA2Z848bN26GatX\nr/UYS/Y1Uaqq6jrs9nakpd3duayk5AAcDjsMhlyMH3938lrHOcjpKC09CKVShTlz5oUVM1Es4oQw\noiB98cVp5OVNDfr+fv36obHxDoC7iW7evEzk5w/wO8M72PvC5WtCm16vx9y5C7B58yZYrf6XN33x\nxWmcO3fGfUwlAOzfvw+DBw/B/PkLYbcneNxvs6mh1Woxb14RRowYiX379khaF6K+jC1noiAIggC1\nWuM1SznQ0qrBg4fg+vVvMHjwkEiH7JO/se/09HQ88shqlJaWwGazYcCAAcjM7Ae73Y5r166gtbUN\n48aNR1HRYvezly9fRGpqKkaMGAmg6x3Ahg4dhqYmCy5cqMSYMTk9WEOivoHJmSgIt2/fRr9+/byu\nB9qXOSdnHI4e/bzHknOo6667SqAajQZz584HANTW1qKhoR5xcVoUFMxEfHy8V1lnz57F8uUr3K8D\ndclPnJiL7ds/YXImCgK7tYmCIAgd21zeL9AsbLVaDUEIfjvNUPnqpu7K/Vtx+hv7zsrKwvjxD2Dk\nyNE+E3Nrayt0Os/Z4VqtBkZjBvR6ATabGmZzvdd6br0+AVarNcRaEsUetpyJgpCamobz5895XQ80\nC/vmzRr065fVY3GFemiDVLthffPNNYwcOcrreqCehFGjxuDq1SsYPpw7chF1hcmZKAjx8fGw2Vq8\nrgfqyj19+hSWLXu4x+IK5wxkKbS3tyEpKRNNTRaUlZW6j7esqLBAEAwYP34mEhIMXl8WdLp41NXd\nikiMRL0ZkzNRkLKy+uPmzRr079/R6gs03isIAlQqVUhbXYY6hhzqoQ2hlu/v/uRkAzZv3oTc3AdR\nVLTI3eWfklKD27f749y5EthsdzBvnuchHA0N9UhNTfP1UUR0D445EwVp6tSHcOBAsXvrSn/jvZ37\naP/2t+8iPn6sz320/Ql1DDnUpVehlu/rfofDgSNHyjB48BDMnj3XYyzeaMxAWtpNPPjgA5g9ezIu\nX97vsdXnxYtfexzkQUS+SdZytlqtePbZZ2Gz2eBwOPDTn/4UkydPlqp4oqhTKBRYufJRbN68CStW\nrPI73nvixC3s3VuOceOWw24f4DXu2pVQx5BDFWr5nvtpO3HyZDOKi/fCZJoFq/UM2traoNPp3Pd4\njmlnorW1P3bu3I6HH34EdrsdGo33cjQi8iZZy/nPf/4zZsyYgffeew8bN27Er371K6mKJpKN+Ph4\nPPbY4ygtLcHp0ztQV3fN/Z7TWYcdO7ahpOQAJk9ejJSUjiQVSoK9f8zY3xhyMKdcdad8X+9futSE\nlhYV4uKGoLV1JPT6B7Bz5/Yun4+Pj0diYiKsVit27twOk2lWUHESxTrJvpZ/97vfdZ+YIwhCWJvw\nE/UGarUaCxcuxuzZdmzadACffroboujEAw9k4jvfWYqMjNuwWO7ued2Z4FpbW1FaehCC4IRSqURK\nSgLq65uQmdkPDz00DQqFwmMM2W6/icrKG6ioOIPs7DQsXDjJ3W0daFa0P6GOUd97v1JZj7a2izAa\nlwIABCER+fkzsHXr3zFgwHS0tGh8jmMXFMzEq69uxHe+84/Q6/Wh/bCJYpRC7OoQWj+2bNmCd999\n1+Paxo0bMWHCBNTV1eHJJ5/Ez3/+c0yZMkWyQInkqLS0Go2Nd49pTEmpxrRp/XD06C1YrWokJgqY\nNq0f9u/vGKueN2+e17rhmpoaHDp0COPHj8fEiRNx4sQJXLt2DdevO2EwzIBKpcbt2zfQ3HwceXmD\nMX/+fBQX34Eg3F2ipVbXYvHinluy1VnXPXtOYtq0h911NZkGYseOMygt/QpxcQnIzZ2Hfv0aYDIN\nRHt7O4qLi2Gz2dDW1oYNGzb0aHxEfUlYydmfyspKPPvss3j++edRWFgY1DN1df738u3rMjOTWP8o\n1T/UWcv+FBfXQRD6u1+r1Tcxb16mxz07d+7ApEm5GDRosMf1++v/+eeHceLEMSxd+jBGjBjps+z8\n/ARs3/4x0tONcLkmuN8zGIIf1w6X3e7AH/7wISZMWOjxM+uMs729BV99dQhAPXJzDVCplJgxwwS9\nXo/du3di0aIlfusea1h/1j8Qybq1L168iB/96Ed4/fXXkZPD7flI3sLtFr5foHXGJ08ex9ixY70S\nsy91dbfQr1+We6mWr7L1ej3Wrv0W/vrXv2DgQB0EITGo7mkpaLUa5OSkeX356IwzLi4BkycvisgX\nBaK+TrIJYa+99hrsdjteeeUVbNiwAf/6r/8qVdFEkpNqVnTndpiieB1VVRVobFR4TNC6ceMGRozw\n3knrfteuXcWQIUOxcuWjKC0t8Sj7/q02FQoFHnvscdhsZ3vs9Cp/FAoF2traPK4F2hK0tbUVSiVn\naBOFQrKW85tvvilVUUQ9TqqdtTqXDpWX10ChyAVwtyU+dKiI/v37ez3T2aWuVrdAECwwGjPwxRcV\n7kMk7PZ2j7J90Wg0cDqdEEUxokuTTKZZKCsr8Tg2MtCWoKWlB2EyzY5AdER9BzchoZgU7AEQwfLV\nEv/yywqf5z/f3dgjy72xR+f2lwCg1+vhcDgCLpd66KF8HD9+rFtxhyohIQGtrW1BH15htTbDbnf4\nPDyDiPxjcqaYFOrOWoH4Wj/scokeSbfT/Yn8zh3BY4lRfHwCWlpsAXfzysrKwu3bDd2KOxxLly7H\njh2fwGrtekJPc3MTduzYhiVLlkUoMqK+g8mZSAK+WuJarQbt7e1e93on8o7u6U7NzU1ITEwKalw8\nGrttKZVKrFmzDiUlB7Fnzy60tHgeCNLS0oK9e3ehtPQQ1qxZ5/MLChF1jQdfEEnA17jrtGkzcPjw\nIcydu8DjeufGHmq1AQaDBUbjQHz22Zfu99vb7VCpVEEdR5meni59ZYKgVCqxdOlytLe3uzdW6aTR\nqDFr1lxuRETUDUzOFBap1gnLXXfqqdfrfR4z2ZnIO9Z6JgAAXK6O1vPt27eRltZxalOg3byOHz/W\no8dRBiMuLs5jchgRSYP9TRSWUE836q26W8+JEyfhyJGygPdNnmzEyZPH8dlnuzF9egGArsfF7XY7\nlEolD5Eg6qOYnCksPX16klx0t57Dhg2HTqfD0aPlXd43aNBgfPTR3/Hgg3kBx2hFUcTWrZsxf35R\nSLEQUe/B5ExhCfV0o95KinoajVNgMBiwffvHOH3a7DH5y+FwYP/+fdi27SP85Cc/RWXlV/j66wt+\ny2pubsL77/8PMjKmoKysKaQTqYio95B0b+1wxPr+qr21/lKMOfeG+ks9tn7t2lWcPXvGfSpVY2ML\nCgtNSEy8u9fumTNf4sqVy0hMTMTQocOg0Whw82YNbt68icTERGi1Y2C1DnPf3xu3y+wNv/uexPqz\n/oH0zb5I6nGBdoWSSrQnnnW3nt7xD8TQocMA+P8DNWHCREyYMBFWazOqq6vR0tKCIUOGYerUaQA6\nDtu4V18dUiCKZfyvmmRNqgMqoqU78ScmJiEnZ6zXdam2HiUi+WJyJlmL5sQzKVrt4cQf6HMDLbEi\not6PyZlkLZqtRCla7eHEH+hzIzWkQETRw9naJGtSH1ARCila7eHEHyvL1IjIP/5XT7IWzVaiFK32\ncOLnmDIRMTlTTAllHDlaY7scUyYiJmeShdu3G3DixDG4XCIUCgUmTzYiKytL8s8JZRw5Wq12jikT\nEZMzRdX58x07YqWmpmLevCKoVCqIoohjx47i2LFyDBkyBLm5D0r2eRzPJaLegH+ZKGpKSkpgtwPL\nl6/wuK5QKDBtWj6AjuS9f/9nXscuhovjuUTUG3C2NkWF2XwCKSkpmDzZ2OV9Y8eOw7Bhw4M62SkY\n0Zz9TUQULLacKSqqq6uxcOGcoPbXHTFiFM6dOwen0wmVStWtz+V4LhH1Bmw5U8RVVJzCpEm5IT1T\nUFAoWeuZiEju2HKmiKuurvY5yaurZU6pqWlobo7dU2yIKLaw5UwR569runOZkyD0h8UyCGZzvcf7\nSiX/uRJRbOBfO5KNQMucFApFJMMhIooaJmeKOIfDAVEUva7fv6zp/tcOh6NH4yIikgvJk/OlS5cw\nZcoU2O12qYumPmLq1Idw7NhRr+tdLXOqrDyP0aPHRDJMIqKokXRCmNVqxauvvoq4uDgpi6U+Jiur\nPz7//LBX67mrZU7nzp3BI4+sjkR4RERRJ2nL+aWXXsIzzzwDnU4nZbHUB82YYcJHH30U1L3793+G\nvLypPRwREZF8hNVy3rJlC959912Pa9nZ2Vi6dClycnJ8jif6k5mZFE4IfUas1j8zMwkGQxz27PkE\nS5cuRWpqqtc9zc3N+PTTT5GXl4fRo0dHIcqeF6u/fyC26w6w/rFe/0AUYiiZtAsLFy5EVlYWRFFE\nRUUFcnNz8d577wV8LpgdovqqzMykmK//zZuNOHKkDI2NjdBoNNDr9WhpaYHdboder4fJNAsaje8j\nHXu7WP79x3LdAdaf9Q/8xUSyMec9e/a4///cuXPxpz/9SaqiqQ9TqVQwmWYBAFwuF1pbWxEfH881\nzUQU03pkhzCFQhFS1zYR0LHJiF6vj3YYRERR1yPJubi4uCeKJSIiignsOyQiIpIZJmciIiKZYXIm\nIiKSGSZnIiIimWFyJiIikhkmZyIiIplhciYiIpIZJmciIiKZYXImIiKSGSZnIiIimWFyJiIikhkm\nZyIiIplhciYiIpIZJmciIiKZYXImIiKSGSZnIiIimWFyJiIikhkmZyIiIplhciYiIpIZJmciIiKZ\nYXImIiKSGSZnIiIimVFHOwCKLLvdAbO5HjabGnq9AKMxA1qtJtphERHRPdhyjjFmcz0slkEQhP6w\nWAbBbK6PdkhERHQfJucYY7Opu3xNRETRx+QcY/R6ocvXREQUfZI1m1wuFzZu3IizZ8/Cbrfj+9//\nPmbNmiVV8SQRozEDZnOVx5gzERHJi2TJ+ZNPPoHT6cQHH3yA2tpa7NmzR6qiSUJarQb5+QOiHQYR\nEXVBsuRcVlaG0aNH45/+6Z8AAC+++KJURfcp986WHjiwCSNGxHO2NBEReQgrOW/ZsgXvvvuux7W0\ntDTExcXhrbfewvHjx/Gzn/0Mf/nLXyQJsi/pnC0NAI2NCTCbL/R4S5bLp4iIeheFKIqiFAU988wz\nWLx4MRYsWAAAKCwsRFlZmRRF9ym7dtVCELLcr9XqWixenNXFE91XWlqNxsaB7tcpKdUwmQZ28QQR\nEUWTZN3aeXl5KCkpwYIFC3D+/HlkZ2cH9VxdXbNUIfQKgmCBxZIEADAYEiAIFtTVJfToZ1ZXt0MQ\nWtyvbbZ2WfzcMzOTZBFHtMRy/WO57gDrz/onBbxHsqVUjz32GFwuF9auXYuXX34Zv/zlL6Uquk8x\nGjNgMFRBrb6JlJTqiMyW5vIpIqLeRbKWs1arxW9/+1upiuuz7p0tHalvj1w+RUTUu3B7qBjA5VNE\nRL0LdwgjIiKSGSZnIiIimWFyJiIikhkmZyIiIplhciYiIpIZJmciIiKZYXImIiKSGSZnIiIimWFy\nJiIikhkmZyIiIplhciYiIpIZJmciIiKZYXImIiKSGSZnIiIimWFyJiIikhkmZyIiIplhciYiIpIZ\nJmciIiKZYXImIiKSGSZnIiIimWFyJiIikhl1tAOg0NjtDpjN9bDZ1NDrBRiNGdBqNdEOi4iIJMSW\ncy9jNtfDYhkEQegPi2UQzOb6aIdEREQSY3LuZWw2dZeviYio92Ny7mX0eqHL10RE1PtJ1uyyWq14\n+umn0dLSgri4OPzHf/wH0tPTpSqe/pfRmAGzucpjzJmIiPoWyVrOW7duRU5ODt5//30sXrwY77zz\njlRF0z20Wg3y8wdg3rxM5OcP4GQwIqI+SLLkPGbMGFitVgAdrWiNhkmDiIgoHGF1a2/ZsgXvvvuu\nx7WXXnoJhw8fxtKlS2GxWPDBBx9IEiAREVGsUYiiKEpR0Pe//32YTCasWbMGlZWV+MlPfoJt27ZJ\nUTQREVFMkWxCmMFgQGJiIgAgLS0NNpstqOfq6pqlCqHXycxMYv1Z/2iHERWxXHeA9Wf9kwLeI1ly\n/sEPfoAXX3wRH3zwAQRBwG9+8xupiiYiIoopkiXnfv364e2335aqOCIiopjFTUiIiIhkhsmZiIhI\nZpiciYiIZIbJmYiISGaYnImIiGSGyZmIiEhmmJyJiIhkhsmZiIhIZpiciYiIZIbJmYiISGaYnImI\niGSGyZmIiEhmmJyJiIhkhsmZiIhIZpiciYiIZIbJmYiISGaYnImIiGSGyZmIiEhmmJyJiIhkhsmZ\niIhIZpiciYiIZIbJmYiISGaYnImIiGSGyZmIiEhmmJyJiIhkhsmZiIhIZrqVnD/77DP8+Mc/dr+u\nqKjAmjVr8K1vfQu///3vux0cERFRLAo7Ob/yyiv43e9+53Ht5ZdfxmuvvYYPPvgAX3zxBc6fP9/t\nAImIiGJN2MnZaDTiF7/4hfu11WqFw+HAoEGDAACFhYU4cuRItwMkIiKKNepAN2zZsgXvvvuux7WN\nGzdi8eLFOHbsmPuazWZDYmKi+7Ver0dVVZWEoRIREcWGgMl59erVWL16dcCC9Ho9rFar+7XNZkNy\ncnLA5zIzkwLe05ex/qx/rIrlugOsf6zXPxDJZmsnJiZCq9Xi+vXrEEURZWVlyMvLk6p4IiKimBGw\n5RyKX/7yl3j22WfhcrlQUFCASZMmSVk8ERFRTFCIoihGOwgiIiK6i5uQEBERyQyTMxERkcwwORMR\nEckMkzMREZHMyCI5X7p0CVOmTIHdbo92KBHV2tqKp556CuvXr8c//uM/4tatW9EOKaKsViv++Z//\nGRs2bMDjjz+O06dPRzukiLt/f/q+ThRFvPzyy3j88cfxD//wD7h+/Xq0Q4q4iooKbNiwIdphRJwg\nCHjuuefwxBNPYM2aNdi/f3+0Q4ool8uFF154AevWrcMTTzyBixcvdnl/1JOz1WrFq6++iri4uGiH\nEnF/+9vfMGHCBPzlL3/B8uXL8cc//jHaIUXUn//8Z8yYMQPvvfceNm7ciF/96lfRDimifO1P39ft\n27cPdrsdmzZtwo9//GNs3Lgx2iFF1DvvvIMXX3wRDocj2qFE3LZt25Camor3338ff/zjH/HrX/86\n2iFF1P79+6FQKPDXv/4VP/zhD/Haa691eb+k65zD8dJLL+GZZ57BU089Fe1QIu7b3/42Oley3bhx\nAwaDIcoRRdZ3v/tdaLVaAB3fqmPtC5rRaMSCBQvw4YcfRjuUiDl58iRMJhMAIDc3F2fOnIlyRJE1\ndOhQvPHGG3juueeiHUrELV68GIsWLQLQ0YpUq6OefiJq/vz5mDt3LgCguro64N/7iP10fO3RnZ2d\njaVLlyInJwd9fbm1vz3KJ0yYgG9/+9v4+uuv8ac//SlK0fW8rupfV1eH5557Dj//+c+jFF3PCnZ/\n+lhgtVqRlHR320a1Wg2XywWlMuqdeBGxYMECVFdXRzuMqIiPjwfQ8W/ghz/8IZ5++ukoRxR5SqUS\nP/3pT7Fv3z7813/9V9c3i1FUVFQkbtiwQVy/fr04ceJEcf369dEMJ6ouXbokzp8/P9phRNz58+fF\nZcuWiaWlpdEOJSqOHj0qPvPMM9EOI2I2btwo7tq1y/161qxZ0QsmSqqqqsS1a9dGO4youHHjhrhq\n1Spx69at0Q4lqurr68U5c+aIra2tfu+Jar/Cnj173P9/7ty5fbrl6Mvbb7+NrKwsrFixAgkJCVCp\nVNEOKaIuXryIH/3oR3j99deRk5MT7XAoAoxGIw4cOIBFixbh9OnTGDNmTLRDigqxj/cU+lJfX4/v\nfe97eOmll5Cfnx/tcCLuk08+QW1tLZ588knExcVBqVR22WMkm05/hUIRc/9gH330UTz//PPYsmUL\nRFGMuckxr732Gux2O1555RWIoojk5GS88cYb0Q6LetCCBQtw+PBhPP744wAQc//mOykUimiHEHFv\nvfUWmpqa8Oabb+KNN96AQqHAO++845530tcVFRXhZz/7GdavXw9BEPDzn/+8y7pzb20iIiKZiY1Z\nGERERL0IkzMREZHMMDkTERHJDJMzERGRzDA5ExERyQyTMxERkcwwORMREcnM/wcUk78ohTcyEwAA\nAABJRU5ErkJggg==\n",
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118687240>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -577,7 +605,7 @@
    "source": [
     "plt.scatter(X[:, 0], X[:, 1], alpha=0.3)\n",
     "plt.scatter(selection[:, 0], selection[:, 1],\n",
-    "            facecolor='none', s=200);"
+    "            facecolor='none', edgecolor='black', s=200);"
    ]
   },
   {
@@ -601,7 +629,10 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -630,7 +661,10 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -657,14 +691,17 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 6.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n"
+      "[6. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
      ]
     }
    ],
@@ -678,8 +715,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Where did the 4 go? The result of this operation is to first assign ``x[0] = 4``, followed by ``x[0] = 6``.\n",
-    "The result, of course, is that ``x[0]`` contains the value 6.\n",
+    "Where did the 4 go? This operation first assigns `x[0] = 4`, followed by `x[0] = 6`.\n",
+    "The result, of course, is that `x[0]` contains the value 6.\n",
     "\n",
     "Fair enough, but consider this operation:"
    ]
@@ -688,13 +725,16 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 6.,  0.,  1.,  1.,  1.,  0.,  0.,  0.,  0.,  0.])"
+       "array([6., 0., 1., 1., 1., 0., 0., 0., 0., 0.])"
       ]
      },
      "execution_count": 21,
@@ -712,25 +752,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You might expect that ``x[3]`` would contain the value 2, and ``x[4]`` would contain the value 3, as this is how many times each index is repeated. Why is this not the case?\n",
-    "Conceptually, this is because ``x[i] += 1`` is meant as a shorthand of ``x[i] = x[i] + 1``. ``x[i] + 1`` is evaluated, and then the result is assigned to the indices in x.\n",
+    "You might expect that `x[3]` would contain the value 2 and `x[4]` would contain the value 3, as this is how many times each index is repeated. Why is this not the case?\n",
+    "Conceptually, this is because `x[i] += 1` is meant as a shorthand of `x[i] = x[i] + 1`. `x[i] + 1` is evaluated, and then the result is assigned to the indices in `x`.\n",
     "With this in mind, it is not the augmentation that happens multiple times, but the assignment, which leads to the rather nonintuitive results.\n",
     "\n",
-    "So what if you want the other behavior where the operation is repeated? For this, you can use the ``at()`` method of ufuncs (available since NumPy 1.8), and do the following:"
+    "So what if you want the other behavior where the operation is repeated? For this, you can use the `at` method of ufuncs and do the following:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.  0.  1.  2.  3.  0.  0.  0.  0.  0.]\n"
+      "[0. 0. 1. 2. 3. 0. 0. 0. 0. 0.]\n"
      ]
     }
    ],
@@ -741,11 +784,12 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``at()`` method does an in-place application of the given operator at the specified indices (here, ``i``) with the specified value (here, 1).\n",
-    "Another method that is similar in spirit is the ``reduceat()`` method of ufuncs, which you can read about in the NumPy documentation."
+    "The `at` method does an in-place application of the given operator at the specified indices (here, `i`) with the specified value (here, 1).\n",
+    "Another method that is similar in spirit is the `reduceat` method of ufuncs, which you can read about in the [NumPy documentation](https://numpy.org/doc/stable/reference/ufuncs.html)."
    ]
   },
   {
@@ -754,21 +798,24 @@
    "source": [
     "## Example: Binning Data\n",
     "\n",
-    "You can use these ideas to efficiently bin data to create a histogram by hand.\n",
-    "For example, imagine we have 1,000 values and would like to quickly find where they fall within an array of bins.\n",
-    "We could compute it using ``ufunc.at`` like this:"
+    "You could use these ideas to efficiently do custom binned computations on data.\n",
+    "For example, imagine we have 100 values and would like to quickly find where they fall within an array of bins.\n",
+    "We could compute this using `ufunc.at` like this:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "np.random.seed(42)\n",
-    "x = np.random.randn(100)\n",
+    "rng = np.random.default_rng(seed=1701)\n",
+    "x = rng.normal(size=100)\n",
     "\n",
     "# compute a histogram by hand\n",
     "bins = np.linspace(-5, 5, 20)\n",
@@ -785,21 +832,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The counts now reflect the number of points within each bin–in other words, a histogram:"
+    "The counts now reflect the number of points within each bin—in other words, a histogram (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1186d90b8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -808,47 +858,50 @@
    ],
    "source": [
     "# plot the results\n",
-    "plt.plot(bins, counts, linestyle='steps');"
+    "plt.plot(bins, counts, drawstyle='steps');"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Of course, it would be silly to have to do this each time you want to plot a histogram.\n",
-    "This is why Matplotlib provides the ``plt.hist()`` routine, which does the same in a single line:\n",
+    "Of course, it would be inconvenient to have to do this each time you want to plot a histogram.\n",
+    "This is why Matplotlib provides the `plt.hist` routine, which does the same in a single line:\n",
     "\n",
     "```python\n",
     "plt.hist(x, bins, histtype='step');\n",
     "```\n",
     "\n",
-    "This function will create a nearly identical plot to the one seen here.\n",
-    "To compute the binning, ``matplotlib`` uses the ``np.histogram`` function, which does a very similar computation to what we did before. Let's compare the two here:"
+    "This function will create a nearly identical plot to the one just shown.\n",
+    "To compute the binning, Matplotlib uses the `np.histogram` function, which does a very similar computation to what we did before. Let's compare the two here:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "NumPy routine:\n",
-      "10000 loops, best of 3: 97.6 µs per loop\n",
-      "Custom routine:\n",
-      "10000 loops, best of 3: 19.5 µs per loop\n"
+      "NumPy histogram (100 points):\n",
+      "33.8 µs ± 311 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
+      "Custom histogram (100 points):\n",
+      "17.6 µs ± 113 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
      ]
     }
    ],
    "source": [
-    "print(\"NumPy routine:\")\n",
+    "print(f\"NumPy histogram ({len(x)} points):\")\n",
     "%timeit counts, edges = np.histogram(x, bins)\n",
     "\n",
-    "print(\"Custom routine:\")\n",
+    "print(f\"Custom histogram ({len(x)} points):\")\n",
     "%timeit np.add.at(counts, np.searchsorted(bins, x), 1)"
    ]
   },
@@ -856,34 +909,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Our own one-line algorithm is several times faster than the optimized algorithm in NumPy! How can this be?\n",
-    "If you dig into the ``np.histogram`` source code (you can do this in IPython by typing ``np.histogram??``), you'll see that it's quite a bit more involved than the simple search-and-count that we've done; this is because NumPy's algorithm is more flexible, and particularly is designed for better performance when the number of data points becomes large:"
+    "Our own one-line algorithm is twice as fast as the optimized algorithm in NumPy! How can this be? If you dig into the `np.histogram` source code (you can do this in IPython by typing `np.histogram??`), you'll see that it's quite a bit more involved than the simple search-and-count that we've done; this is because NumPy's algorithm is more flexible, and particularly is designed for better performance when the number of data points becomes large:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "NumPy routine:\n",
-      "10 loops, best of 3: 68.7 ms per loop\n",
-      "Custom routine:\n",
-      "10 loops, best of 3: 135 ms per loop\n"
+      "NumPy histogram (1000000 points):\n",
+      "84.4 ms ± 2.82 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "Custom histogram (1000000 points):\n",
+      "128 ms ± 2.04 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
    "source": [
-    "x = np.random.randn(1000000)\n",
-    "print(\"NumPy routine:\")\n",
+    "x = rng.normal(size=1000000)\n",
+    "print(f\"NumPy histogram ({len(x)} points):\")\n",
     "%timeit counts, edges = np.histogram(x, bins)\n",
     "\n",
-    "print(\"Custom routine:\")\n",
+    "print(f\"Custom histogram ({len(x)} points):\")\n",
     "%timeit np.add.at(counts, np.searchsorted(bins, x), 1)"
    ]
   },
@@ -891,26 +946,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "What this comparison shows is that algorithmic efficiency is almost never a simple question. An algorithm efficient for large datasets will not always be the best choice for small datasets, and vice versa (see [Big-O Notation](02.08-Sorting.ipynb#Aside:-Big-O-Notation)).\n",
-    "But the advantage of coding this algorithm yourself is that with an understanding of these basic methods, you could use these building blocks to extend this to do some very interesting custom behaviors.\n",
-    "The key to efficiently using Python in data-intensive applications is knowing about general convenience routines like ``np.histogram`` and when they're appropriate, but also knowing how to make use of lower-level functionality when you need more pointed behavior."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) | [Contents](Index.ipynb) | [Sorting Arrays](02.08-Sorting.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.07-Fancy-Indexing.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "What this comparison shows is that algorithmic efficiency is almost never a simple question. An algorithm efficient for large datasets will not always be the best choice for small datasets, and vice versa (see [Big-O Notation](02.08-Sorting.ipynb#Big-O-Notation)).\n",
+    "But the advantage of coding this algorithm yourself is that with an understanding of these basic methods, the sky is the limit: you're no longer constrained to built-in routines, but can create your own approaches to exploring the data.\n",
+    "Key to efficiently using Python in data-intensive applications is not only knowing about general convenience routines like `np.histogram` and when they're appropriate, but also knowing how to make use of lower-level functionality when you need more pointed behavior."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -924,9 +972,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/02.08-Sorting.ipynb b/notebooks/02.08-Sorting.ipynb
index 8be3373c0..07744e596 100644
--- a/notebooks/02.08-Sorting.ipynb
+++ b/notebooks/02.08-Sorting.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Fancy Indexing](02.07-Fancy-Indexing.ipynb) | [Contents](Index.ipynb) | [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.08-Sorting.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -34,128 +12,112 @@
    "metadata": {},
    "source": [
     "Up to this point we have been concerned mainly with tools to access and operate on array data with NumPy.\n",
-    "This section covers algorithms related to sorting values in NumPy arrays.\n",
+    "This chapter covers algorithms related to sorting values in NumPy arrays.\n",
     "These algorithms are a favorite topic in introductory computer science courses: if you've ever taken one, you probably have had dreams (or, depending on your temperament, nightmares) about *insertion sorts*, *selection sorts*, *merge sorts*, *quick sorts*, *bubble sorts*, and many, many more.\n",
     "All are means of accomplishing a similar task: sorting the values in a list or array.\n",
     "\n",
-    "For example, a simple *selection sort* repeatedly finds the minimum value from a list, and makes swaps until the list is sorted. We can code this in just a few lines of Python:"
+    "Python has a couple of built-in functions and methods for sorting lists and other iterable objects. The `sorted` function accepts a list and returns a sorted version of it:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "def selection_sort(x):\n",
-    "    for i in range(len(x)):\n",
-    "        swap = i + np.argmin(x[i:])\n",
-    "        (x[i], x[swap]) = (x[swap], x[i])\n",
-    "    return x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1, 2, 3, 4, 5])"
+       "[1, 1, 2, 3, 4, 5, 6, 9]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x = np.array([2, 1, 4, 3, 5])\n",
-    "selection_sort(x)"
+    "L = [3, 1, 4, 1, 5, 9, 2, 6]\n",
+    "sorted(L)  # returns a sorted copy"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As any first-year computer science major will tell you, the selection sort is useful for its simplicity, but is much too slow to be useful for larger arrays.\n",
-    "For a list of $N$ values, it requires $N$ loops, each of which does on order $\\sim N$ comparisons to find the swap value.\n",
-    "In terms of the \"big-O\" notation often used to characterize these algorithms (see [Big-O Notation](#Aside:-Big-O-Notation)), selection sort averages $\\mathcal{O}[N^2]$: if you double the number of items in the list, the execution time will go up by about a factor of four.\n",
-    "\n",
-    "Even selection sort, though, is much better than my all-time favorite sorting algorithms, the *bogosort*:"
+    "By contrast, the `sort` method of lists will sort the list in-place:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 1, 2, 3, 4, 5, 6, 9]\n"
+     ]
+    }
+   ],
    "source": [
-    "def bogosort(x):\n",
-    "    while np.any(x[:-1] > x[1:]):\n",
-    "        np.random.shuffle(x)\n",
-    "    return x"
+    "L.sort()  # acts in-place and returns None\n",
+    "print(L)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python's sorting methods are quite flexible, and can handle any iterable object. For example, here we sort a string:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1, 2, 3, 4, 5])"
+       "['h', 'n', 'o', 'p', 't', 'y']"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x = np.array([2, 1, 4, 3, 5])\n",
-    "bogosort(x)"
+    "sorted('python')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This silly sorting method relies on pure chance: it repeatedly applies a random shuffling of the array until the result happens to be sorted.\n",
-    "With an average scaling of $\\mathcal{O}[N \\times N!]$, (that's *N* times *N* factorial) this should–quite obviously–never be used for any real computation.\n",
-    "\n",
-    "Fortunately, Python contains built-in sorting algorithms that are *much* more efficient than either of the simplistic algorithms just shown. We'll start by looking at the Python built-ins, and then take a look at the routines included in NumPy and optimized for NumPy arrays."
+    "These built-in sorting methods are convenient, but as previously discussed, the dynamism of Python values means they are less performant than routines designed specifically for uniform arrays of numbers.\n",
+    "This is where NumPy's sorting routines come in."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Fast Sorting in NumPy: ``np.sort`` and ``np.argsort``\n",
+    "## Fast Sorting in NumPy: np.sort and np.argsort\n",
     "\n",
-    "Although Python has built-in ``sort`` and ``sorted`` functions to work with lists, we won't discuss them here because NumPy's ``np.sort`` function turns out to be much more efficient and useful for our purposes.\n",
-    "By default ``np.sort`` uses an $\\mathcal{O}[N\\log N]$, *quicksort* algorithm, though *mergesort* and *heapsort* are also available. For most applications, the default quicksort is more than sufficient.\n",
-    "\n",
-    "To return a sorted version of the array without modifying the input, you can use ``np.sort``:"
+    "The `np.sort` function is analogous to Python's built-in `sorted` function, and will efficiently return a sorted copy of an array:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -164,12 +126,14 @@
        "array([1, 2, 3, 4, 5])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "import numpy as np\n",
+    "\n",
     "x = np.array([2, 1, 4, 3, 5])\n",
     "np.sort(x)"
    ]
@@ -178,14 +142,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you prefer to sort the array in-place, you can instead use the ``sort`` method of arrays:"
+    "Similarly to the `sort` method of Python lists, you can also sort an array in-place using the array `sort` method:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -205,14 +172,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A related function is ``argsort``, which instead returns the *indices* of the sorted elements:"
+    "A related function is `argsort`, which instead returns the *indices* of the sorted elements:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -239,9 +209,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -250,7 +223,7 @@
        "array([1, 2, 3, 4, 5])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -263,57 +236,65 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Sorting along rows or columns"
+    "You'll see an application of `argsort` later in this chapter.\n",
+    "\n",
+    "### Sorting Along Rows or Columns"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A useful feature of NumPy's sorting algorithms is the ability to sort along specific rows or columns of a multidimensional array using the ``axis`` argument. For example:"
+    "A useful feature of NumPy's sorting algorithms is the ability to sort along specific rows or columns of a multidimensional array using the `axis` argument. For example:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[6 3 7 4 6 9]\n",
-      " [2 6 7 4 3 7]\n",
-      " [7 2 5 4 1 7]\n",
-      " [5 1 4 0 9 5]]\n"
+      "[[0 7 6 4 4 8]\n",
+      " [0 6 2 0 5 9]\n",
+      " [7 7 7 7 5 1]\n",
+      " [8 4 5 3 1 9]]\n"
      ]
     }
    ],
    "source": [
-    "rand = np.random.RandomState(42)\n",
-    "X = rand.randint(0, 10, (4, 6))\n",
+    "rng = np.random.default_rng(seed=42)\n",
+    "X = rng.integers(0, 10, (4, 6))\n",
     "print(X)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[2, 1, 4, 0, 1, 5],\n",
-       "       [5, 2, 5, 4, 3, 7],\n",
-       "       [6, 3, 7, 4, 6, 7],\n",
-       "       [7, 6, 7, 4, 9, 9]])"
+       "array([[0, 4, 2, 0, 1, 1],\n",
+       "       [0, 6, 5, 3, 4, 8],\n",
+       "       [7, 7, 6, 4, 5, 9],\n",
+       "       [8, 7, 7, 7, 5, 9]])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -325,21 +306,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[3, 4, 6, 6, 7, 9],\n",
-       "       [2, 3, 4, 6, 7, 7],\n",
-       "       [1, 2, 4, 5, 7, 7],\n",
-       "       [0, 1, 4, 5, 5, 9]])"
+       "array([[0, 4, 4, 6, 7, 8],\n",
+       "       [0, 0, 2, 5, 6, 9],\n",
+       "       [1, 5, 7, 7, 7, 7],\n",
+       "       [1, 3, 4, 5, 8, 9]])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -362,14 +346,17 @@
    "source": [
     "## Partial Sorts: Partitioning\n",
     "\n",
-    "Sometimes we're not interested in sorting the entire array, but simply want to find the *k* smallest values in the array. NumPy provides this in the ``np.partition`` function. ``np.partition`` takes an array and a number *K*; the result is a new array with the smallest *K* values to the left of the partition, and the remaining values to the right, in arbitrary order:"
+    "Sometimes we're not interested in sorting the entire array, but simply want to find the *k* smallest values in the array. NumPy enables this with the `np.partition` function. `np.partition` takes an array and a number *K*; the result is a new array with the smallest *K* values to the left of the partition and the remaining values to the right:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -378,7 +365,7 @@
        "array([2, 1, 3, 4, 6, 5, 7])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -392,7 +379,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that the first three values in the resulting array are the three smallest in the array, and the remaining array positions contain the remaining values.\n",
+    "Notice that the first three values in the resulting array are the three smallest in the array, and the remaining array positions contain the remaining values.\n",
     "Within the two partitions, the elements have arbitrary order.\n",
     "\n",
     "Similarly to sorting, we can partition along an arbitrary axis of a multidimensional array:"
@@ -400,21 +387,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[3, 4, 6, 7, 6, 9],\n",
-       "       [2, 3, 4, 7, 6, 7],\n",
-       "       [1, 2, 4, 5, 7, 7],\n",
-       "       [0, 1, 4, 5, 9, 5]])"
+       "array([[0, 4, 4, 7, 6, 8],\n",
+       "       [0, 0, 2, 6, 5, 9],\n",
+       "       [1, 5, 7, 7, 7, 7],\n",
+       "       [1, 3, 4, 5, 8, 9]])"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -429,8 +419,8 @@
    "source": [
     "The result is an array where the first two slots in each row contain the smallest values from that row, with the remaining values filling the remaining slots.\n",
     "\n",
-    "Finally, just as there is a ``np.argsort`` that computes indices of the sort, there is a ``np.argpartition`` that computes indices of the partition.\n",
-    "We'll see this in action in the following section."
+    "Finally, just as there is an `np.argsort` function that computes indices of the sort, there is an `np.argpartition` function that computes indices of the partition.\n",
+    "We'll see both of these in action in the following section."
    ]
   },
   {
@@ -439,41 +429,47 @@
    "source": [
     "## Example: k-Nearest Neighbors\n",
     "\n",
-    "Let's quickly see how we might use this ``argsort`` function along multiple axes to find the nearest neighbors of each point in a set.\n",
+    "Let's quickly see how we might use the `argsort` function along multiple axes to find the nearest neighbors of each point in a set.\n",
     "We'll start by creating a random set of 10 points on a two-dimensional plane.\n",
     "Using the standard convention, we'll arrange these in a $10\\times 2$ array:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "X = rand.rand(10, 2)"
+    "X = rng.random((10, 2))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To get an idea of how these points look, let's quickly scatter plot them:"
+    "To get an idea of how these points look, let's generate a quick scatter plot (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10be92fd0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -483,7 +479,7 @@
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn; seaborn.set() # Plot styling\n",
+    "plt.style.use('seaborn-whitegrid')\n",
     "plt.scatter(X[:, 0], X[:, 1], s=100);"
    ]
   },
@@ -492,19 +488,22 @@
    "metadata": {},
    "source": [
     "Now we'll compute the distance between each pair of points.\n",
-    "Recall that the squared-distance between two points is the sum of the squared differences in each dimension;\n",
+    "Recall that the squared distance between two points is the sum of the squared differences in each dimension;\n",
     "using the efficient broadcasting ([Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb)) and aggregation ([Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb))  routines provided by NumPy we can compute the matrix of square distances in a single line of code:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "dist_sq = np.sum((X[:, np.newaxis, :] - X[np.newaxis, :, :]) ** 2, axis=-1)"
+    "dist_sq = np.sum((X[:, np.newaxis] - X[np.newaxis, :]) ** 2, axis=-1)"
    ]
   },
   {
@@ -516,9 +515,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -527,22 +529,25 @@
        "(10, 10, 2)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# for each pair of points, compute differences in their coordinates\n",
-    "differences = X[:, np.newaxis, :] - X[np.newaxis, :, :]\n",
+    "differences = X[:, np.newaxis] - X[np.newaxis, :]\n",
     "differences.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -551,7 +556,7 @@
        "(10, 10, 2)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -564,9 +569,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -575,7 +583,7 @@
        "(10, 10)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -590,23 +598,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Just to double-check what we are doing, we should see that the diagonal of this matrix (i.e., the set of distances between each point and itself) is all zero:"
+    "As a quick check of our logic, we should see that the diagonal of this matrix (i.e., the set of distances between each point and itself) is all zeros:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -619,31 +630,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It checks out!\n",
-    "With the pairwise square-distances converted, we can now use ``np.argsort`` to sort along each row. The leftmost columns will then give the indices of the nearest neighbors:"
+    "With the pairwise square distances converted, we can now use `np.argsort` to sort along each row. The leftmost columns will then give the indices of the nearest neighbors:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0 3 9 7 1 4 2 5 6 8]\n",
-      " [1 4 7 9 3 6 8 5 0 2]\n",
-      " [2 1 4 6 3 0 8 9 7 5]\n",
-      " [3 9 7 0 1 4 5 8 6 2]\n",
-      " [4 1 8 5 6 7 9 3 0 2]\n",
-      " [5 8 6 4 1 7 9 3 2 0]\n",
-      " [6 8 5 4 1 7 9 3 2 0]\n",
-      " [7 9 3 1 4 0 5 8 6 2]\n",
-      " [8 5 6 4 1 7 9 3 2 0]\n",
-      " [9 7 3 0 1 4 5 8 6 2]]\n"
+      "[[0 9 3 5 4 8 1 6 2 7]\n",
+      " [1 7 2 6 4 8 3 0 9 5]\n",
+      " [2 7 1 6 4 3 8 0 9 5]\n",
+      " [3 0 4 5 9 6 1 2 8 7]\n",
+      " [4 6 3 1 2 7 0 5 9 8]\n",
+      " [5 9 3 0 4 6 8 1 2 7]\n",
+      " [6 4 2 1 7 3 0 5 9 8]\n",
+      " [7 2 1 6 4 3 8 0 9 5]\n",
+      " [8 0 1 9 3 4 7 2 6 5]\n",
+      " [9 0 5 3 4 8 6 1 2 7]]\n"
      ]
     }
    ],
@@ -658,14 +671,17 @@
    "source": [
     "Notice that the first column gives the numbers 0 through 9 in order: this is due to the fact that each point's closest neighbor is itself, as we would expect.\n",
     "\n",
-    "By using a full sort here, we've actually done more work than we need to in this case. If we're simply interested in the nearest $k$ neighbors, all we need is to partition each row so that the smallest $k + 1$ squared distances come first, with larger distances filling the remaining positions of the array. We can do this with the ``np.argpartition`` function:"
+    "By using a full sort here, we've actually done more work than we need to in this case. If we're simply interested in the nearest $k$ neighbors, all we need to do is partition each row so that the smallest $k + 1$ squared distances come first, with larger distances filling the remaining positions of the array. We can do this with the `np.argpartition` function:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -677,21 +693,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In order to visualize this network of neighbors, let's quickly plot the points along with lines representing the connections from each point to its two nearest neighbors:"
+    "In order to visualize this network of neighbors, let's quickly plot the points along with lines representing the connections from each point to its two nearest neighbors (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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RaLCxqUVMzFoSEp4dvjE3P4eDw358fJywsLDI1mfqU//5TR7rKYQeUalUfPbZ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\nqT788MOoZbfffrs8Ho8kKTMzU4FAIGr94sWLlZ2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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115ae2898>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -721,50 +740,17 @@
     "Although the broadcasting and row-wise sorting of this approach might seem less straightforward than writing a loop, it turns out to be a very efficient way of operating on this data in Python.\n",
     "You might be tempted to do the same type of operation by manually looping through the data and sorting each set of neighbors individually, but this would almost certainly lead to a slower algorithm than the vectorized version we used. The beauty of this approach is that it's written in a way that's agnostic to the size of the input data: we could just as easily compute the neighbors among 100 or 1,000,000 points in any number of dimensions, and the code would look the same.\n",
     "\n",
-    "Finally, I'll note that when doing very large nearest neighbor searches, there are tree-based and/or approximate algorithms that can scale as $\\mathcal{O}[N\\log N]$ or better rather than the $\\mathcal{O}[N^2]$ of the brute-force algorithm. One example of this is the KD-Tree, [implemented in Scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Aside: Big-O Notation\n",
-    "\n",
-    "Big-O notation is a means of describing how the number of operations required for an algorithm scales as the input grows in size.\n",
-    "To use it correctly is to dive deeply into the realm of computer science theory, and to carefully distinguish it from the related small-o notation, big-$\\theta$ notation, big-$\\Omega$ notation, and probably many mutant hybrids thereof.\n",
-    "While these distinctions add precision to statements about algorithmic scaling, outside computer science theory exams and the remarks of pedantic blog commenters, you'll rarely see such distinctions made in practice.\n",
-    "Far more common in the data science world is a less rigid use of big-O notation: as a general (if imprecise) description of the scaling of an algorithm.\n",
-    "With apologies to theorists and pedants, this is the interpretation we'll use throughout this book.\n",
-    "\n",
-    "Big-O notation, in this loose sense, tells you how much time your algorithm will take as you increase the amount of data.\n",
-    "If you have an $\\mathcal{O}[N]$ (read \"order $N$\") algorithm that takes 1 second to operate on a list of length *N*=1,000, then you should expect it to take roughly 5 seconds for a list of length *N*=5,000.\n",
-    "If you have an $\\mathcal{O}[N^2]$ (read \"order *N* squared\") algorithm that takes 1 second for *N*=1000, then you should expect it to take about 25 seconds for *N*=5000.\n",
-    "\n",
-    "For our purposes, the *N* will usually indicate some aspect of the size of the dataset (the number of points, the number of dimensions, etc.). When trying to analyze billions or trillions of samples, the difference between $\\mathcal{O}[N]$ and $\\mathcal{O}[N^2]$ can be far from trivial!\n",
-    "\n",
-    "Notice that the big-O notation by itself tells you nothing about the actual wall-clock time of a computation, but only about its scaling as you change *N*.\n",
-    "Generally, for example, an $\\mathcal{O}[N]$ algorithm is considered to have better scaling than an $\\mathcal{O}[N^2]$ algorithm, and for good reason. But for small datasets in particular, the algorithm with better scaling might not be faster.\n",
-    "For example, in a given problem an $\\mathcal{O}[N^2]$ algorithm might take 0.01 seconds, while a \"better\" $\\mathcal{O}[N]$ algorithm might take 1 second.\n",
-    "Scale up *N* by a factor of 1,000, though, and the $\\mathcal{O}[N]$ algorithm will win out.\n",
-    "\n",
-    "Even this loose version of Big-O notation can be very useful when comparing the performance of algorithms, and we'll use this notation throughout the book when talking about how algorithms scale."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Fancy Indexing](02.07-Fancy-Indexing.ipynb) | [Contents](Index.ipynb) | [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.08-Sorting.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "Finally, I'll note that when doing very large nearest neighbor searches, there are tree-based and/or approximate algorithms that can scale as $\\mathcal{O}[N\\log N]$ or better rather than the $\\mathcal{O}[N^2]$ of the brute-force algorithm. One example of this is the KD-Tree, [implemented in Scikit-Learn](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html)."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -778,9 +764,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/02.09-Structured-Data-NumPy.ipynb b/notebooks/02.09-Structured-Data-NumPy.ipynb
index ea4ee0bec..b9942b336 100644
--- a/notebooks/02.09-Structured-Data-NumPy.ipynb
+++ b/notebooks/02.09-Structured-Data-NumPy.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Sorting Arrays](02.08-Sorting.ipynb) | [Contents](Index.ipynb) | [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.09-Structured-Data-NumPy.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,14 +11,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "While often our data can be well represented by a homogeneous array of values, sometimes this is not the case. This section demonstrates the use of NumPy's *structured arrays* and *record arrays*, which provide efficient storage for compound, heterogeneous data.  While the patterns shown here are useful for simple operations, scenarios like this often lend themselves to the use of Pandas ``Dataframe``s, which we'll explore in [Chapter 3](03.00-Introduction-to-Pandas.ipynb)."
+    "While often our data can be well represented by a homogeneous array of values, sometimes this is not the case. This chapter demonstrates the use of NumPy's *structured arrays* and *record arrays*, which provide efficient storage for compound, heterogeneous data.  While the patterns shown here are useful for simple operations, scenarios like this often lend themselves to the use of Pandas ``DataFrame``s, which we'll explore in [Part 3](03.00-Introduction-to-Pandas.ipynb)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -59,7 +37,10 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -72,8 +53,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "But this is a bit clumsy. There's nothing here that tells us that the three arrays are related; it would be more natural if we could use a single structure to store all of this data.\n",
-    "NumPy can handle this through structured arrays, which are arrays with compound data types.\n",
+    "But this is a bit clumsy. There's nothing here that tells us that the three arrays are related; NumPy's structured arrays allow us to do this more naturally by using a single structure to store all of this data.\n",
     "\n",
     "Recall that previously we created a simple array using an expression like this:"
    ]
@@ -82,7 +62,10 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -100,7 +83,10 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -122,7 +108,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here ``'U10'`` translates to \"Unicode string of maximum length 10,\" ``'i4'`` translates to \"4-byte (i.e., 32 bit) integer,\" and ``'f8'`` translates to \"8-byte (i.e., 64 bit) float.\"\n",
+    "Here `'U10'` translates to \"Unicode string of maximum length 10,\" `'i4'` translates to \"4-byte (i.e., 32-bit) integer,\" and `'f8'` translates to \"8-byte (i.e., 64-bit) float.\"\n",
     "We'll discuss other options for these type codes in the following section.\n",
     "\n",
     "Now that we've created an empty container array, we can fill the array with our lists of values:"
@@ -132,14 +118,17 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[('Alice', 25, 55.0) ('Bob', 45, 85.5) ('Cathy', 37, 68.0)\n",
+      "[('Alice', 25, 55. ) ('Bob', 45, 85.5) ('Cathy', 37, 68. )\n",
       " ('Doug', 19, 61.5)]\n"
      ]
     }
@@ -155,23 +144,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we had hoped, the data is now arranged together in one convenient block of memory.\n",
+    "As we had hoped, the data is now conveniently arranged in one structured array.\n",
     "\n",
-    "The handy thing with structured arrays is that you can now refer to values either by index or by name:"
+    "The handy thing with structured arrays is that we can now refer to values either by index or by name:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array(['Alice', 'Bob', 'Cathy', 'Doug'], \n",
-       "      dtype='<U10')"
+       "array(['Alice', 'Bob', 'Cathy', 'Doug'], dtype='<U10')"
       ]
      },
      "execution_count": 6,
@@ -188,13 +179,16 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "('Alice', 25, 55.0)"
+       "('Alice', 25, 55.)"
       ]
      },
      "execution_count": 7,
@@ -211,7 +205,10 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -234,21 +231,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using Boolean masking, this even allows you to do some more sophisticated operations such as filtering on age:"
+    "Using Boolean masking, we can even do some more sophisticated operations, such as filtering on age:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array(['Alice', 'Doug'], \n",
-       "      dtype='<U10')"
+       "array(['Alice', 'Doug'], dtype='<U10')"
       ]
      },
      "execution_count": 9,
@@ -265,15 +264,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that if you'd like to do any operations that are any more complicated than these, you should probably consider the Pandas package, covered in the next chapter.\n",
-    "As we'll see, Pandas provides a ``Dataframe`` object, which is a structure built on NumPy arrays that offers a variety of useful data manipulation functionality similar to what we've shown here, as well as much, much more."
+    "If you'd like to do any operations that are any more complicated than these, you should probably consider the Pandas package, covered in [Part 4](04.00-Introduction-To-Matplotlib.ipynb).\n",
+    "As you'll see, Pandas provides a `DataFrame` object, which is a structure built on NumPy arrays that offers a variety of useful data manipulation functionality similar to what you've seen here, as well as much, much more."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Creating Structured Arrays\n",
+    "## Exploring Structured Array Creation\n",
     "\n",
     "Structured array data types can be specified in a number of ways.\n",
     "Earlier, we saw the dictionary method:"
@@ -283,7 +282,10 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -306,14 +308,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For clarity, numerical types can be specified using Python types or NumPy ``dtype``s instead:"
+    "For clarity, numerical types can be specified using Python types or NumPy `dtype`s instead:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -343,7 +348,10 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -372,7 +380,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -394,21 +405,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The shortened string format codes may seem confusing, but they are built on simple principles.\n",
-    "The first (optional) character is ``<`` or ``>``, which means \"little endian\" or \"big endian,\" respectively, and specifies the ordering convention for significant bits.\n",
+    "The shortened string format codes may not be immediately intuitive, but they are built on simple principles.\n",
+    "The first (optional) character `<` or `>`, means \"little endian\" or \"big endian,\" respectively, and specifies the ordering convention for significant bits.\n",
     "The next character specifies the type of data: characters, bytes, ints, floating points, and so on (see the table below).\n",
-    "The last character or characters represents the size of the object in bytes.\n",
+    "The last character or characters represent the size of the object in bytes.\n",
     "\n",
-    "| Character        | Description           | Example                             |\n",
-    "| ---------        | -----------           | -------                             | \n",
-    "| ``'b'``          | Byte                  | ``np.dtype('b')``                   |\n",
-    "| ``'i'``          | Signed integer        | ``np.dtype('i4') == np.int32``      |\n",
-    "| ``'u'``          | Unsigned integer      | ``np.dtype('u1') == np.uint8``      |\n",
-    "| ``'f'``          | Floating point        | ``np.dtype('f8') == np.int64``      |\n",
-    "| ``'c'``          | Complex floating point| ``np.dtype('c16') == np.complex128``|\n",
-    "| ``'S'``, ``'a'`` | String                | ``np.dtype('S5')``                  |\n",
-    "| ``'U'``          | Unicode string        | ``np.dtype('U') == np.str_``        |\n",
-    "| ``'V'``          | Raw data (void)       | ``np.dtype('V') == np.void``        |"
+    "| Character    | Description           | Example                           |\n",
+    "| ---------    | -----------           | -------                           | \n",
+    "| `'b'`        | Byte                  | `np.dtype('b')`                   |\n",
+    "| `'i'`        | Signed integer        | `np.dtype('i4') == np.int32`      |\n",
+    "| `'u'`        | Unsigned integer      | `np.dtype('u1') == np.uint8`      |\n",
+    "| `'f'`        | Floating point        | `np.dtype('f8') == np.int64`      |\n",
+    "| `'c'`        | Complex floating point| `np.dtype('c16') == np.complex128`|\n",
+    "| `'S'`, `'a'` | String                | `np.dtype('S5')`                  |\n",
+    "| `'U'`        | Unicode string        | `np.dtype('U') == np.str_`        |\n",
+    "| `'V'`        | Raw data (void)       | `np.dtype('V') == np.void`        |"
    ]
   },
   {
@@ -419,24 +430,27 @@
     "\n",
     "It is possible to define even more advanced compound types.\n",
     "For example, you can create a type where each element contains an array or matrix of values.\n",
-    "Here, we'll create a data type with a ``mat`` component consisting of a $3\\times 3$ floating-point matrix:"
+    "Here, we'll create a data type with a `mat` component consisting of a $3\\times 3$ floating-point matrix:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n",
-      "[[ 0.  0.  0.]\n",
-      " [ 0.  0.  0.]\n",
-      " [ 0.  0.  0.]]\n"
+      "(0, [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]])\n",
+      "[[0. 0. 0.]\n",
+      " [0. 0. 0.]\n",
+      " [0. 0. 0.]]\n"
      ]
     }
    ],
@@ -451,27 +465,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now each element in the ``X`` array consists of an ``id`` and a $3\\times 3$ matrix.\n",
+    "Now each element in the `X` array consists of an `id` and a $3\\times 3$ matrix.\n",
     "Why would you use this rather than a simple multidimensional array, or perhaps a Python dictionary?\n",
-    "The reason is that this NumPy ``dtype`` directly maps onto a C structure definition, so the buffer containing the array content can be accessed directly within an appropriately written C program.\n",
-    "If you find yourself writing a Python interface to a legacy C or Fortran library that manipulates structured data, you'll probably find structured arrays quite useful!"
+    "One reason is that this NumPy `dtype` directly maps onto a C structure definition, so the buffer containing the array content can be accessed directly within an appropriately written C program.\n",
+    "If you find yourself writing a Python interface to a legacy C or Fortran library that manipulates structured data, structured arrays can provide a powerful interface."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## RecordArrays: Structured Arrays with a Twist\n",
+    "## Record Arrays: Structured Arrays with a Twist\n",
     "\n",
-    "NumPy also provides the ``np.recarray`` class, which is almost identical to the structured arrays just described, but with one additional feature: fields can be accessed as attributes rather than as dictionary keys.\n",
-    "Recall that we previously accessed the ages by writing:"
+    "NumPy also provides record arrays (instances of the `np.recarray` class), which are almost identical to the structured arrays just described, but with one additional feature: fields can be accessed as attributes rather than as dictionary keys.\n",
+    "Recall that we previously accessed the ages in our sample dataset by writing:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -500,7 +517,10 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -523,23 +543,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The downside is that for record arrays, there is some extra overhead involved in accessing the fields, even when using the same syntax. We can see this here:"
+    "The downside is that for record arrays, there is some extra overhead involved in accessing the fields, even when using the same syntax:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1000000 loops, best of 3: 241 ns per loop\n",
-      "100000 loops, best of 3: 4.61 µs per loop\n",
-      "100000 loops, best of 3: 7.27 µs per loop\n"
+      "121 ns ± 1.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
+      "2.41 µs ± 15.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
+      "3.98 µs ± 20.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
      ]
     }
    ],
@@ -553,7 +571,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Whether the more convenient notation is worth the additional overhead will depend on your own application."
+    "Whether the more convenient notation is worth the (slight) overhead will depend on your own application."
    ]
   },
   {
@@ -562,26 +580,19 @@
    "source": [
     "## On to Pandas\n",
     "\n",
-    "This section on structured and record arrays is purposely at the end of this chapter, because it leads so well into the next package we will cover: Pandas.\n",
-    "Structured arrays like the ones discussed here are good to know about for certain situations, especially in case you're using NumPy arrays to map onto binary data formats in C, Fortran, or another language.\n",
-    "For day-to-day use of structured data, the Pandas package is a much better choice, and we'll dive into a full discussion of it in the chapter that follows."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Sorting Arrays](02.08-Sorting.ipynb) | [Contents](Index.ipynb) | [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.09-Structured-Data-NumPy.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "This chapter on structured and record arrays is purposely located at the end of this part of the book, because it leads so well into the next package we will cover: Pandas.\n",
+    "Structured arrays can come in handy in certain situations, like when you're using NumPy arrays to map onto binary data formats in C, Fortran, or another language.\n",
+    "But for day-to-day use of structured data, the Pandas package is a much better choice; we'll explore it in depth in the chapters that follow."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -595,9 +606,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.00-Introduction-to-Pandas.ipynb b/notebooks/03.00-Introduction-to-Pandas.ipynb
index 9a5487ae9..759ad4730 100644
--- a/notebooks/03.00-Introduction-to-Pandas.ipynb
+++ b/notebooks/03.00-Introduction-to-Pandas.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) | [Contents](Index.ipynb) | [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.00-Introduction-to-Pandas.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,17 +11,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the previous chapter, we dove into detail on NumPy and its ``ndarray`` object, which provides efficient storage and manipulation of dense typed arrays in Python.\n",
-    "Here we'll build on this knowledge by looking in detail at the data structures provided by the Pandas library.\n",
-    "Pandas is a newer package built on top of NumPy, and provides an efficient implementation of a ``DataFrame``.\n",
-    "``DataFrame``s are essentially multidimensional arrays with attached row and column labels, and often with heterogeneous types and/or missing data.\n",
+    "In [Part 2](02.00-Introduction-to-NumPy.ipynb), we dove into detail on NumPy and its `ndarray` object, which enables efficient storage and manipulation of dense typed arrays in Python.\n",
+    "Here we'll build on this knowledge by looking in depth at the data structures provided by the Pandas library.\n",
+    "Pandas is a newer package built on top of NumPy that provides an efficient implementation of a `DataFrame`.\n",
+    "``DataFrame``s are essentially multidimensional arrays with attached row and column labels, often with heterogeneous types and/or missing data.\n",
     "As well as offering a convenient storage interface for labeled data, Pandas implements a number of powerful data operations familiar to users of both database frameworks and spreadsheet programs.\n",
     "\n",
-    "As we saw, NumPy's ``ndarray`` data structure provides essential features for the type of clean, well-organized data typically seen in numerical computing tasks.\n",
+    "As we've seen, NumPy's `ndarray` data structure provides essential features for the type of clean, well-organized data typically seen in numerical computing tasks.\n",
     "While it serves this purpose very well, its limitations become clear when we need more flexibility (e.g., attaching labels to data, working with missing data, etc.) and when attempting operations that do not map well to element-wise broadcasting (e.g., groupings, pivots, etc.), each of which is an important piece of analyzing the less structured data available in many forms in the world around us.\n",
-    "Pandas, and in particular its ``Series`` and ``DataFrame`` objects, builds on the NumPy array structure and provides efficient access to these sorts of \"data munging\" tasks that occupy much of a data scientist's time.\n",
+    "Pandas, and in particular its `Series` and `DataFrame` objects, builds on the NumPy array structure and provides efficient access to these sorts of \"data munging\" tasks that occupy much of a data scientist's time.\n",
     "\n",
-    "In this chapter, we will focus on the mechanics of using ``Series``, ``DataFrame``, and related structures effectively.\n",
+    "In this part of the book, we will focus on the mechanics of using `Series`, `DataFrame`, and related structures effectively.\n",
     "We will use examples drawn from real datasets where appropriate, but these examples are not necessarily the focus."
    ]
   },
@@ -53,24 +31,27 @@
    "source": [
     "## Installing and Using Pandas\n",
     "\n",
-    "Installation of Pandas on your system requires NumPy to be installed, and if building the library from source, requires the appropriate tools to compile the C and Cython sources on which Pandas is built.\n",
-    "Details on this installation can be found in the [Pandas documentation](http://pandas.pydata.org/).\n",
+    "Installation of Pandas on your system requires NumPy to be installed, and if you're building the library from source, you will need the appropriate tools to compile the C and Cython sources on which Pandas is built.\n",
+    "Details on the installation process can be found in the [Pandas documentation](http://pandas.pydata.org/).\n",
     "If you followed the advice outlined in the [Preface](00.00-Preface.ipynb) and used the Anaconda stack, you already have Pandas installed.\n",
     "\n",
-    "Once Pandas is installed, you can import it and check the version:"
+    "Once Pandas is installed, you can import it and check the version; here is the version used by this book:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'0.18.1'"
+       "'1.3.5'"
       ]
      },
      "execution_count": 1,
@@ -87,14 +68,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Just as we generally import NumPy under the alias ``np``, we will import Pandas under the alias ``pd``:"
+    "Just as we generally import NumPy under the alias `np`, we will import Pandas under the alias `pd`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -112,17 +93,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Reminder about Built-In Documentation\n",
+    "## Reminder About Built-in Documentation\n",
     "\n",
-    "As you read through this chapter, don't forget that IPython gives you the ability to quickly explore the contents of a package (by using the tab-completion feature) as well as the documentation of various functions (using the ``?`` character). (Refer back to [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) if you need a refresher on this.)\n",
+    "As you read through this part of the book, don't forget that IPython gives you the ability to quickly explore the contents of a package (by using the tab completion feature) as well as the documentation of various functions (using the `?` character). Refer back to [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) if you need a refresher on this.\n",
     "\n",
-    "For example, to display all the contents of the pandas namespace, you can type\n",
+    "For example, to display all the contents of the Pandas namespace, you can type:\n",
     "\n",
     "```ipython\n",
     "In [3]: pd.<TAB>\n",
     "```\n",
     "\n",
-    "And to display Pandas's built-in documentation, you can use this:\n",
+    "And to display the built-in Pandas documentation, you can use this:\n",
     "\n",
     "```ipython\n",
     "In [4]: pd?\n",
@@ -130,22 +111,15 @@
     "\n",
     "More detailed documentation, along with tutorials and other resources, can be found at http://pandas.pydata.org/."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) | [Contents](Index.ipynb) | [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.00-Introduction-to-Pandas.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -159,9 +133,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.01-Introducing-Pandas-Objects.ipynb b/notebooks/03.01-Introducing-Pandas-Objects.ipynb
index 2e5f8f7b3..46252a7ed 100644
--- a/notebooks/03.01-Introducing-Pandas-Objects.ipynb
+++ b/notebooks/03.01-Introducing-Pandas-Objects.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) | [Contents](Index.ipynb) | [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.01-Introducing-Pandas-Objects.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,9 +11,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "At the very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices.\n",
+    "At a very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices.\n",
     "As we will see during the course of this chapter, Pandas provides a host of useful tools, methods, and functionality on top of the basic data structures, but nearly everything that follows will require an understanding of what these structures are.\n",
-    "Thus, before we go any further, let's introduce these three fundamental Pandas data structures: the ``Series``, ``DataFrame``, and ``Index``.\n",
+    "Thus, before we go any further, let's take a look at these three fundamental Pandas data structures: the `Series`, `DataFrame`, and `Index`.\n",
     "\n",
     "We will start our code sessions with the standard NumPy and Pandas imports:"
    ]
@@ -44,7 +22,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -58,7 +36,7 @@
    "source": [
     "## The Pandas Series Object\n",
     "\n",
-    "A Pandas ``Series`` is a one-dimensional array of indexed data.\n",
+    "A Pandas `Series` is a one-dimensional array of indexed data.\n",
     "It can be created from a list or array as follows:"
    ]
   },
@@ -66,7 +44,10 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -93,21 +74,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we see in the output, the ``Series`` wraps both a sequence of values and a sequence of indices, which we can access with the ``values`` and ``index`` attributes.\n",
-    "The ``values`` are simply a familiar NumPy array:"
+    "The `Series` combines a sequence of values with an explicit sequence of indices, which we can access with the `values` and `index` attributes.\n",
+    "The `values` are simply a familiar NumPy array:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.25,  0.5 ,  0.75,  1.  ])"
+       "array([0.25, 0.5 , 0.75, 1.  ])"
       ]
      },
      "execution_count": 3,
@@ -123,14 +107,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``index`` is an array-like object of type ``pd.Index``, which we'll discuss in more detail momentarily."
+    "The `index` is an array-like object of type `pd.Index`, which we'll discuss in more detail momentarily:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -159,7 +146,10 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -181,7 +171,10 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -205,32 +198,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we will see, though, the Pandas ``Series`` is much more general and flexible than the one-dimensional NumPy array that it emulates."
+    "As we will see, though, the Pandas `Series` is much more general and flexible than the one-dimensional NumPy array that it emulates."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### ``Series`` as generalized NumPy array"
+    "### Series as Generalized NumPy Array"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "From what we've seen so far, it may look like the ``Series`` object is basically interchangeable with a one-dimensional NumPy array.\n",
-    "The essential difference is the presence of the index: while the Numpy Array has an *implicitly defined* integer index used to access the values, the Pandas ``Series`` has an *explicitly defined* index associated with the values.\n",
+    "From what we've seen so far, the `Series` object may appear to be basically interchangeable with a one-dimensional NumPy array.\n",
+    "The essential difference is that while the NumPy array has an *implicitly defined* integer index used to access the values, the Pandas `Series` has an *explicitly defined* index associated with the values.\n",
     "\n",
-    "This explicit index definition gives the ``Series`` object additional capabilities. For example, the index need not be an integer, but can consist of values of any desired type.\n",
-    "For example, if we wish, we can use strings as an index:"
+    "This explicit index definition gives the `Series` object additional capabilities. For example, the index need not be an integer, but can consist of values of any desired type.\n",
+    "So, if we wish, we can use strings as an index:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -265,7 +261,10 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -287,14 +286,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can even use non-contiguous or non-sequential indices:"
+    "We can even use noncontiguous or nonsequential indices:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -322,7 +324,10 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -344,30 +349,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Series as specialized dictionary\n",
+    "### Series as Specialized Dictionary\n",
     "\n",
-    "In this way, you can think of a Pandas ``Series`` a bit like a specialization of a Python dictionary.\n",
-    "A dictionary is a structure that maps arbitrary keys to a set of arbitrary values, and a ``Series`` is a structure which maps typed keys to a set of typed values.\n",
-    "This typing is important: just as the type-specific compiled code behind a NumPy array makes it more efficient than a Python list for certain operations, the type information of a Pandas ``Series`` makes it much more efficient than Python dictionaries for certain operations.\n",
+    "In this way, you can think of a Pandas `Series` a bit like a specialization of a Python dictionary.\n",
+    "A dictionary is a structure that maps arbitrary keys to a set of arbitrary values, and a `Series` is a structure that maps typed keys to a set of typed values.\n",
+    "This typing is important: just as the type-specific compiled code behind a NumPy array makes it more efficient than a Python list for certain operations, the type information of a Pandas `Series` makes it more efficient than Python dictionaries for certain operations.\n",
     "\n",
-    "The ``Series``-as-dictionary analogy can be made even more clear by constructing a ``Series`` object directly from a Python dictionary:"
+    "The `Series`-as-dictionary analogy can be made even more clear by constructing a `Series` object directly from a Python dictionary, here the five most populous US states according to the 2020 census:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "California    38332521\n",
-       "Florida       19552860\n",
-       "Illinois      12882135\n",
-       "New York      19651127\n",
-       "Texas         26448193\n",
+       "California      39538223\n",
+       "Texas           29145505\n",
+       "Florida         21538187\n",
+       "New York        20201249\n",
+       "Pennsylvania    13002700\n",
        "dtype: int64"
       ]
      },
@@ -377,11 +385,9 @@
     }
    ],
    "source": [
-    "population_dict = {'California': 38332521,\n",
-    "                   'Texas': 26448193,\n",
-    "                   'New York': 19651127,\n",
-    "                   'Florida': 19552860,\n",
-    "                   'Illinois': 12882135}\n",
+    "population_dict = {'California': 39538223, 'Texas': 29145505,\n",
+    "                   'Florida': 21538187, 'New York': 20201249,\n",
+    "                   'Pennsylvania': 13002700}\n",
     "population = pd.Series(population_dict)\n",
     "population"
    ]
@@ -390,7 +396,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By default, a ``Series`` will be created where the index is drawn from the sorted keys.\n",
     "From here, typical dictionary-style item access can be performed:"
    ]
   },
@@ -398,13 +403,16 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "38332521"
+       "39538223"
       ]
      },
      "execution_count": 12,
@@ -420,22 +428,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Unlike a dictionary, though, the ``Series`` also supports array-style operations such as slicing:"
+    "Unlike a dictionary, though, the `Series` also supports array-style operations such as slicing:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "California    38332521\n",
-       "Florida       19552860\n",
-       "Illinois      12882135\n",
+       "California    39538223\n",
+       "Texas         29145505\n",
+       "Florida       21538187\n",
        "dtype: int64"
       ]
      },
@@ -445,7 +456,7 @@
     }
    ],
    "source": [
-    "population['California':'Illinois']"
+    "population['California':'Florida']"
    ]
   },
   {
@@ -459,24 +470,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Constructing Series objects\n",
+    "### Constructing Series Objects\n",
     "\n",
-    "We've already seen a few ways of constructing a Pandas ``Series`` from scratch; all of them are some version of the following:\n",
+    "We've already seen a few ways of constructing a Pandas `Series` from scratch. All of them are some version of the following:\n",
     "\n",
     "```python\n",
-    ">>> pd.Series(data, index=index)\n",
+    "pd.Series(data, index=index)\n",
     "```\n",
     "\n",
-    "where ``index`` is an optional argument, and ``data`` can be one of many entities.\n",
+    "where `index` is an optional argument, and `data` can be one of many entities.\n",
     "\n",
-    "For example, ``data`` can be a list or NumPy array, in which case ``index`` defaults to an integer sequence:"
+    "For example, `data` can be a list or NumPy array, in which case `index` defaults to an integer sequence:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -501,14 +515,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``data`` can be a scalar, which is repeated to fill the specified index:"
+    "Or `data` can be a scalar, which is repeated to fill the specified index:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -533,21 +550,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``data`` can be a dictionary, in which ``index`` defaults to the sorted dictionary keys:"
+    "Or it can be a dictionary, in which case `index` defaults to the dictionary keys:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1    b\n",
        "2    a\n",
+       "1    b\n",
        "3    c\n",
        "dtype: object"
       ]
@@ -565,20 +585,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In each case, the index can be explicitly set if a different result is preferred:"
+    "In each case, the index can be explicitly set to control the order or the subset of keys used:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3    c\n",
+       "1    b\n",
        "2    a\n",
        "dtype: object"
       ]
@@ -589,14 +612,7 @@
     }
    ],
    "source": [
-    "pd.Series({2:'a', 1:'b', 3:'c'}, index=[3, 2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice that in this case, the ``Series`` is populated only with the explicitly identified keys."
+    "pd.Series({2:'a', 1:'b', 3:'c'}, index=[1, 2])"
    ]
   },
   {
@@ -605,8 +621,8 @@
    "source": [
     "## The Pandas DataFrame Object\n",
     "\n",
-    "The next fundamental structure in Pandas is the ``DataFrame``.\n",
-    "Like the ``Series`` object discussed in the previous section, the ``DataFrame`` can be thought of either as a generalization of a NumPy array, or as a specialization of a Python dictionary.\n",
+    "The next fundamental structure in Pandas is the `DataFrame`.\n",
+    "Like the `Series` object discussed in the previous section, the `DataFrame` can be thought of either as a generalization of a NumPy array, or as a specialization of a Python dictionary.\n",
     "We'll now take a look at each of these perspectives."
    ]
   },
@@ -614,29 +630,32 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### DataFrame as a generalized NumPy array\n",
-    "If a ``Series`` is an analog of a one-dimensional array with flexible indices, a ``DataFrame`` is an analog of a two-dimensional array with both flexible row indices and flexible column names.\n",
-    "Just as you might think of a two-dimensional array as an ordered sequence of aligned one-dimensional columns, you can think of a ``DataFrame`` as a sequence of aligned ``Series`` objects.\n",
+    "### DataFrame as Generalized NumPy Array\n",
+    "If a `Series` is an analog of a one-dimensional array with explicit indices, a `DataFrame` is an analog of a two-dimensional array with explicit row and column indices.\n",
+    "Just as you might think of a two-dimensional array as an ordered sequence of aligned one-dimensional columns, you can think of a `DataFrame` as a sequence of aligned `Series` objects.\n",
     "Here, by \"aligned\" we mean that they share the same index.\n",
     "\n",
-    "To demonstrate this, let's first construct a new ``Series`` listing the area of each of the five states discussed in the previous section:"
+    "To demonstrate this, let's first construct a new `Series` listing the area of each of the five states discussed in the previous section (in square kilometers):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "California    423967\n",
-       "Florida       170312\n",
-       "Illinois      149995\n",
-       "New York      141297\n",
-       "Texas         695662\n",
+       "California      423967\n",
+       "Texas           695662\n",
+       "Florida         170312\n",
+       "New York        141297\n",
+       "Pennsylvania    119280\n",
        "dtype: int64"
       ]
      },
@@ -646,8 +665,8 @@
     }
    ],
    "source": [
-    "area_dict = {'California': 423967, 'Texas': 695662, 'New York': 141297,\n",
-    "             'Florida': 170312, 'Illinois': 149995}\n",
+    "area_dict = {'California': 423967, 'Texas': 695662, 'Florida': 170312, \n",
+    "             'New York': 141297, 'Pennsylvania': 119280}\n",
     "area = pd.Series(area_dict)\n",
     "area"
    ]
@@ -656,65 +675,81 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now that we have this along with the ``population`` Series from before, we can use a dictionary to construct a single two-dimensional object containing this information:"
+    "Now that we have this along with the `population` Series from before, we can use a dictionary to construct a single two-dimensional object containing this information:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>area</th>\n",
        "      <th>population</th>\n",
+       "      <th>area</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
+       "      <td>39538223</td>\n",
        "      <td>423967</td>\n",
-       "      <td>38332521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Florida</th>\n",
-       "      <td>170312</td>\n",
-       "      <td>19552860</td>\n",
+       "      <th>Texas</th>\n",
+       "      <td>29145505</td>\n",
+       "      <td>695662</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Illinois</th>\n",
-       "      <td>149995</td>\n",
-       "      <td>12882135</td>\n",
+       "      <th>Florida</th>\n",
+       "      <td>21538187</td>\n",
+       "      <td>170312</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
+       "      <td>20201249</td>\n",
        "      <td>141297</td>\n",
-       "      <td>19651127</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Texas</th>\n",
-       "      <td>695662</td>\n",
-       "      <td>26448193</td>\n",
+       "      <th>Pennsylvania</th>\n",
+       "      <td>13002700</td>\n",
+       "      <td>119280</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "              area  population\n",
-       "California  423967    38332521\n",
-       "Florida     170312    19552860\n",
-       "Illinois    149995    12882135\n",
-       "New York    141297    19651127\n",
-       "Texas       695662    26448193"
+       "              population    area\n",
+       "California      39538223  423967\n",
+       "Texas           29145505  695662\n",
+       "Florida         21538187  170312\n",
+       "New York        20201249  141297\n",
+       "Pennsylvania    13002700  119280"
       ]
      },
      "execution_count": 19,
@@ -732,20 +767,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Like the ``Series`` object, the ``DataFrame`` has an ``index`` attribute that gives access to the index labels:"
+    "Like the `Series` object, the `DataFrame` has an `index` attribute that gives access to the index labels:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Index(['California', 'Florida', 'Illinois', 'New York', 'Texas'], dtype='object')"
+       "Index(['California', 'Texas', 'Florida', 'New York', 'Pennsylvania'], dtype='object')"
       ]
      },
      "execution_count": 20,
@@ -761,20 +799,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Additionally, the ``DataFrame`` has a ``columns`` attribute, which is an ``Index`` object holding the column labels:"
+    "Additionally, the `DataFrame` has a `columns` attribute, which is an `Index` object holding the column labels:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Index(['area', 'population'], dtype='object')"
+       "Index(['population', 'area'], dtype='object')"
       ]
      },
      "execution_count": 21,
@@ -790,35 +831,38 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Thus the ``DataFrame`` can be thought of as a generalization of a two-dimensional NumPy array, where both the rows and columns have a generalized index for accessing the data."
+    "Thus the `DataFrame` can be thought of as a generalization of a two-dimensional NumPy array, where both the rows and columns have a generalized index for accessing the data."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### DataFrame as specialized dictionary\n",
+    "### DataFrame as Specialized Dictionary\n",
     "\n",
-    "Similarly, we can also think of a ``DataFrame`` as a specialization of a dictionary.\n",
-    "Where a dictionary maps a key to a value, a ``DataFrame`` maps a column name to a ``Series`` of column data.\n",
-    "For example, asking for the ``'area'`` attribute returns the ``Series`` object containing the areas we saw earlier:"
+    "Similarly, we can also think of a `DataFrame` as a specialization of a dictionary.\n",
+    "Where a dictionary maps a key to a value, a `DataFrame` maps a column name to a `Series` of column data.\n",
+    "For example, asking for the `'area'` attribute returns the `Series` object containing the areas we saw earlier:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "California    423967\n",
-       "Florida       170312\n",
-       "Illinois      149995\n",
-       "New York      141297\n",
-       "Texas         695662\n",
+       "California      423967\n",
+       "Texas           695662\n",
+       "Florida         170312\n",
+       "New York        141297\n",
+       "Pennsylvania    119280\n",
        "Name: area, dtype: int64"
       ]
      },
@@ -835,7 +879,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice the potential point of confusion here: in a two-dimesnional NumPy array, ``data[0]`` will return the first *row*. For a ``DataFrame``, ``data['col0']`` will return the first *column*.\n",
+    "Notice the potential point of confusion here: in a two-dimensional NumPy array, `data[0]` will return the first *row*. For a `DataFrame`, `data['col0']` will return the first *column*.\n",
     "Because of this, it is probably better to think about ``DataFrame``s as generalized dictionaries rather than generalized arrays, though both ways of looking at the situation can be useful.\n",
     "We'll explore more flexible means of indexing ``DataFrame``s in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb)."
    ]
@@ -844,10 +888,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Constructing DataFrame objects\n",
+    "### Constructing DataFrame Objects\n",
     "\n",
-    "A Pandas ``DataFrame`` can be constructed in a variety of ways.\n",
-    "Here we'll give several examples."
+    "A Pandas `DataFrame` can be constructed in a variety of ways.\n",
+    "Here we'll explore several examples."
    ]
   },
   {
@@ -856,20 +900,36 @@
    "source": [
     "#### From a single Series object\n",
     "\n",
-    "A ``DataFrame`` is a collection of ``Series`` objects, and a single-column ``DataFrame`` can be constructed from a single ``Series``:"
+    "A `DataFrame` is a collection of `Series` objects, and a single-column `DataFrame` can be constructed from a single `Series`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -880,35 +940,35 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
-       "      <td>38332521</td>\n",
+       "      <td>39538223</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Florida</th>\n",
-       "      <td>19552860</td>\n",
+       "      <th>Texas</th>\n",
+       "      <td>29145505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Illinois</th>\n",
-       "      <td>12882135</td>\n",
+       "      <th>Florida</th>\n",
+       "      <td>21538187</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
-       "      <td>19651127</td>\n",
+       "      <td>20201249</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Texas</th>\n",
-       "      <td>26448193</td>\n",
+       "      <th>Pennsylvania</th>\n",
+       "      <td>13002700</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "            population\n",
-       "California    38332521\n",
-       "Florida       19552860\n",
-       "Illinois      12882135\n",
-       "New York      19651127\n",
-       "Texas         26448193"
+       "              population\n",
+       "California      39538223\n",
+       "Texas           29145505\n",
+       "Florida         21538187\n",
+       "New York        20201249\n",
+       "Pennsylvania    13002700"
       ]
      },
      "execution_count": 23,
@@ -926,7 +986,7 @@
    "source": [
     "#### From a list of dicts\n",
     "\n",
-    "Any list of dictionaries can be made into a ``DataFrame``.\n",
+    "Any list of dictionaries can be made into a `DataFrame`.\n",
     "We'll use a simple list comprehension to create some data:"
    ]
   },
@@ -934,13 +994,29 @@
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -991,20 +1067,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Even if some keys in the dictionary are missing, Pandas will fill them in with ``NaN`` (i.e., \"not a number\") values:"
+    "Even if some keys in the dictionary are missing, Pandas will fill them in with `NaN` values (i.e., \"Not a Number\"; see [Handling Missing Data](03.04-Missing-Values.ipynb)):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1052,65 +1144,81 @@
    "source": [
     "#### From a dictionary of Series objects\n",
     "\n",
-    "As we saw before, a ``DataFrame`` can be constructed from a dictionary of ``Series`` objects as well:"
+    "As we saw before, a `DataFrame` can be constructed from a dictionary of `Series` objects as well:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>area</th>\n",
        "      <th>population</th>\n",
+       "      <th>area</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
+       "      <td>39538223</td>\n",
        "      <td>423967</td>\n",
-       "      <td>38332521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Florida</th>\n",
-       "      <td>170312</td>\n",
-       "      <td>19552860</td>\n",
+       "      <th>Texas</th>\n",
+       "      <td>29145505</td>\n",
+       "      <td>695662</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Illinois</th>\n",
-       "      <td>149995</td>\n",
-       "      <td>12882135</td>\n",
+       "      <th>Florida</th>\n",
+       "      <td>21538187</td>\n",
+       "      <td>170312</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
+       "      <td>20201249</td>\n",
        "      <td>141297</td>\n",
-       "      <td>19651127</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Texas</th>\n",
-       "      <td>695662</td>\n",
-       "      <td>26448193</td>\n",
+       "      <th>Pennsylvania</th>\n",
+       "      <td>13002700</td>\n",
+       "      <td>119280</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "              area  population\n",
-       "California  423967    38332521\n",
-       "Florida     170312    19552860\n",
-       "Illinois    149995    12882135\n",
-       "New York    141297    19651127\n",
-       "Texas       695662    26448193"
+       "              population    area\n",
+       "California      39538223  423967\n",
+       "Texas           29145505  695662\n",
+       "Florida         21538187  170312\n",
+       "New York        20201249  141297\n",
+       "Pennsylvania    13002700  119280"
       ]
      },
      "execution_count": 26,
@@ -1129,7 +1237,7 @@
    "source": [
     "#### From a two-dimensional NumPy array\n",
     "\n",
-    "Given a two-dimensional array of data, we can create a ``DataFrame`` with any specified column and index names.\n",
+    "Given a two-dimensional array of data, we can create a `DataFrame` with any specified column and index names.\n",
     "If omitted, an integer index will be used for each:"
    ]
   },
@@ -1137,13 +1245,29 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1155,18 +1279,18 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>a</th>\n",
-       "      <td>0.865257</td>\n",
-       "      <td>0.213169</td>\n",
+       "      <td>0.471098</td>\n",
+       "      <td>0.317396</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
-       "      <td>0.442759</td>\n",
-       "      <td>0.108267</td>\n",
+       "      <td>0.614766</td>\n",
+       "      <td>0.305971</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>c</th>\n",
-       "      <td>0.047110</td>\n",
-       "      <td>0.905718</td>\n",
+       "      <td>0.533596</td>\n",
+       "      <td>0.512377</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1174,9 +1298,9 @@
       ],
       "text/plain": [
        "        foo       bar\n",
-       "a  0.865257  0.213169\n",
-       "b  0.442759  0.108267\n",
-       "c  0.047110  0.905718"
+       "a  0.471098  0.317396\n",
+       "b  0.614766  0.305971\n",
+       "c  0.533596  0.512377"
       ]
      },
      "execution_count": 27,
@@ -1197,21 +1321,23 @@
     "#### From a NumPy structured array\n",
     "\n",
     "We covered structured arrays in [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb).\n",
-    "A Pandas ``DataFrame`` operates much like a structured array, and can be created directly from one:"
+    "A Pandas `DataFrame` operates much like a structured array, and can be created directly from one:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([(0, 0.0), (0, 0.0), (0, 0.0)], \n",
-       "      dtype=[('A', '<i8'), ('B', '<f8')])"
+       "array([(0, 0.), (0, 0.), (0, 0.)], dtype=[('A', '<i8'), ('B', '<f8')])"
       ]
      },
      "execution_count": 28,
@@ -1228,13 +1354,29 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1285,17 +1427,20 @@
    "source": [
     "## The Pandas Index Object\n",
     "\n",
-    "We have seen here that both the ``Series`` and ``DataFrame`` objects contain an explicit *index* that lets you reference and modify data.\n",
-    "This ``Index`` object is an interesting structure in itself, and it can be thought of either as an *immutable array* or as an *ordered set* (technically a multi-set, as ``Index`` objects may contain repeated values).\n",
-    "Those views have some interesting consequences in the operations available on ``Index`` objects.\n",
-    "As a simple example, let's construct an ``Index`` from a list of integers:"
+    "As you've seen, the `Series` and `DataFrame` objects both contain an explicit *index* that lets you reference and modify data.\n",
+    "This `Index` object is an interesting structure in itself, and it can be thought of either as an *immutable array* or as an *ordered set* (technically a multiset, as `Index` objects may contain repeated values).\n",
+    "Those views have some interesting consequences in terms of the operations available on `Index` objects.\n",
+    "As a simple example, let's construct an `Index` from a list of integers:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1318,9 +1463,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Index as immutable array\n",
+    "### Index as Immutable Array\n",
     "\n",
-    "The ``Index`` in many ways operates like an array.\n",
+    "The `Index` in many ways operates like an array.\n",
     "For example, we can use standard Python indexing notation to retrieve values or slices:"
    ]
   },
@@ -1328,7 +1473,10 @@
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1350,7 +1498,10 @@
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1372,14 +1523,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``Index`` objects also have many of the attributes familiar from NumPy arrays:"
+    "`Index` objects also have many of the attributes familiar from NumPy arrays:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1398,14 +1552,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One difference between ``Index`` objects and NumPy arrays is that indices are immutable–that is, they cannot be modified via the normal means:"
+    "One difference between `Index` objects and NumPy arrays is that the indices are immutable—that is, they cannot be modified via the normal means:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1415,8 +1572,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-34-40e631c82e8a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mind\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/Users/jakevdp/anaconda/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1244\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1245\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Index does not support mutable operations\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1247\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/var/folders/xc/sptt9bk14s34rgxt7453p03r0000gp/T/ipykernel_83282/393126374.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mind\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.local/share/virtualenvs/python-data-science-handbook-2e-u_kwqDTB/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   4583\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4584\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4585\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Index does not support mutable operations\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4586\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4587\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: Index does not support mutable operations"
      ]
     }
@@ -1436,17 +1593,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Index as ordered set\n",
+    "### Index as Ordered Set\n",
     "\n",
     "Pandas objects are designed to facilitate operations such as joins across datasets, which depend on many aspects of set arithmetic.\n",
-    "The ``Index`` object follows many of the conventions used by Python's built-in ``set`` data structure, so that unions, intersections, differences, and other combinations can be computed in a familiar way:"
+    "The `Index` object follows many of the conventions used by Python's built-in `set` data structure, so that unions, intersections, differences, and other combinations can be computed in a familiar way:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -1458,7 +1618,10 @@
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1473,14 +1636,17 @@
     }
    ],
    "source": [
-    "indA & indB  # intersection"
+    "indA.intersection(indB)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1495,14 +1661,17 @@
     }
    ],
    "source": [
-    "indA | indB  # union"
+    "indA.union(indB)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 38,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1517,31 +1686,17 @@
     }
    ],
    "source": [
-    "indA ^ indB  # symmetric difference"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These operations may also be accessed via object methods, for example ``indA.intersection(indB)``."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) | [Contents](Index.ipynb) | [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.01-Introducing-Pandas-Objects.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "indA.symmetric_difference(indB)"
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1555,9 +1710,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.02-Data-Indexing-and-Selection.ipynb b/notebooks/03.02-Data-Indexing-and-Selection.ipynb
index 9cce1353f..2eaf614c1 100644
--- a/notebooks/03.02-Data-Indexing-and-Selection.ipynb
+++ b/notebooks/03.02-Data-Indexing-and-Selection.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) | [Contents](Index.ipynb) | [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.02-Data-Indexing-and-Selection.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,12 +11,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In [Chapter 2](02.00-Introduction-to-NumPy.ipynb), we looked in detail at methods and tools to access, set, and modify values in NumPy arrays.\n",
-    "These included indexing (e.g., ``arr[2, 1]``), slicing (e.g., ``arr[:, 1:5]``), masking (e.g., ``arr[arr > 0]``), fancy indexing (e.g., ``arr[0, [1, 5]]``), and combinations thereof (e.g., ``arr[:, [1, 5]]``).\n",
-    "Here we'll look at similar means of accessing and modifying values in Pandas ``Series`` and ``DataFrame`` objects.\n",
+    "In [Part 2](02.00-Introduction-to-NumPy.ipynb), we looked in detail at methods and tools to access, set, and modify values in NumPy arrays.\n",
+    "These included indexing (e.g., `arr[2, 1]`), slicing (e.g., `arr[:, 1:5]`), masking (e.g., `arr[arr > 0]`), fancy indexing (e.g., `arr[0, [1, 5]]`), and combinations thereof (e.g., `arr[:, [1, 5]]`).\n",
+    "Here we'll look at similar means of accessing and modifying values in Pandas `Series` and `DataFrame` objects.\n",
     "If you have used the NumPy patterns, the corresponding patterns in Pandas will feel very familiar, though there are a few quirks to be aware of.\n",
     "\n",
-    "We'll start with the simple case of the one-dimensional ``Series`` object, and then move on to the more complicated two-dimesnional ``DataFrame`` object."
+    "We'll start with the simple case of the one-dimensional `Series` object, and then move on to the more complicated two-dimensional `DataFrame` object."
    ]
   },
   {
@@ -47,24 +25,27 @@
    "source": [
     "## Data Selection in Series\n",
     "\n",
-    "As we saw in the previous section, a ``Series`` object acts in many ways like a one-dimensional NumPy array, and in many ways like a standard Python dictionary.\n",
-    "If we keep these two overlapping analogies in mind, it will help us to understand the patterns of data indexing and selection in these arrays."
+    "As you saw in the previous chapter, a `Series` object acts in many ways like a one-dimensional NumPy array, and in many ways like a standard Python dictionary.\n",
+    "If you keep these two overlapping analogies in mind, it will help you understand the patterns of data indexing and selection in these arrays."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Series as dictionary\n",
+    "### Series as Dictionary\n",
     "\n",
-    "Like a dictionary, the ``Series`` object provides a mapping from a collection of keys to a collection of values:"
+    "Like a dictionary, the `Series` object provides a mapping from a collection of keys to a collection of values:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -93,7 +74,10 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -122,7 +106,10 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -144,7 +131,10 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -166,7 +156,10 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -188,15 +181,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``Series`` objects can even be modified with a dictionary-like syntax.\n",
-    "Just as you can extend a dictionary by assigning to a new key, you can extend a ``Series`` by assigning to a new index value:"
+    "`Series` objects can also be modified with a dictionary-like syntax.\n",
+    "Just as you can extend a dictionary by assigning to a new key, you can extend a `Series` by assigning to a new index value:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -224,21 +220,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This easy mutability of the objects is a convenient feature: under the hood, Pandas is making decisions about memory layout and data copying that might need to take place; the user generally does not need to worry about these issues."
+    "This easy mutability of the objects is a convenient feature: under the hood, Pandas is making decisions about memory layout and data copying that might need to take place, and the user generally does not need to worry about these issues."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Series as one-dimensional array"
+    "### Series as One-Dimensional Array"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A ``Series`` builds on this dictionary-like interface and provides array-style item selection via the same basic mechanisms as NumPy arrays – that is, *slices*, *masking*, and *fancy indexing*.\n",
+    "A `Series` builds on this dictionary-like interface and provides array-style item selection via the same basic mechanisms as NumPy arrays—that is, slices, masking, and fancy indexing.\n",
     "Examples of these are as follows:"
    ]
   },
@@ -246,7 +242,10 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -272,7 +271,10 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -297,7 +299,10 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -322,7 +327,10 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -347,25 +355,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Among these, slicing may be the source of the most confusion.\n",
-    "Notice that when slicing with an explicit index (i.e., ``data['a':'c']``), the final index is *included* in the slice, while when slicing with an implicit index (i.e., ``data[0:2]``), the final index is *excluded* from the slice."
+    "Of these, slicing may be the source of the most confusion.\n",
+    "Notice that when slicing with an explicit index (e.g., `data['a':'c']`), the final index is *included* in the slice, while when slicing with an implicit index (e.g., `data[0:2]`), the final index is *excluded* from the slice."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Indexers: loc, iloc, and ix\n",
+    "### Indexers: loc and iloc\n",
     "\n",
-    "These slicing and indexing conventions can be a source of confusion.\n",
-    "For example, if your ``Series`` has an explicit integer index, an indexing operation such as ``data[1]`` will use the explicit indices, while a slicing operation like ``data[1:3]`` will use the implicit Python-style index."
+    "If your `Series` has an explicit integer index, an indexing operation such as `data[1]` will use the explicit indices, while a slicing operation like `data[1:3]` will use the implicit Python-style indices:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -391,7 +401,10 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -414,7 +427,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -440,16 +456,19 @@
    "metadata": {},
    "source": [
     "Because of this potential confusion in the case of integer indexes, Pandas provides some special *indexer* attributes that explicitly expose certain indexing schemes.\n",
-    "These are not functional methods, but attributes that expose a particular slicing interface to the data in the ``Series``.\n",
+    "These are not functional methods, but attributes that expose a particular slicing interface to the data in the `Series`.\n",
     "\n",
-    "First, the ``loc`` attribute allows indexing and slicing that always references the explicit index:"
+    "First, the `loc` attribute allows indexing and slicing that always references the explicit index:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -471,7 +490,10 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -495,14 +517,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``iloc`` attribute allows indexing and slicing that always references the implicit Python-style index:"
+    "The `iloc` attribute allows indexing and slicing that always references the implicit Python-style index:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -524,7 +549,10 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -548,20 +576,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A third indexing attribute, ``ix``, is a hybrid of the two, and for ``Series`` objects is equivalent to standard ``[]``-based indexing.\n",
-    "The purpose of the ``ix`` indexer will become more apparent in the context of ``DataFrame`` objects, which we will discuss in a moment.\n",
-    "\n",
     "One guiding principle of Python code is that \"explicit is better than implicit.\"\n",
-    "The explicit nature of ``loc`` and ``iloc`` make them very useful in maintaining clean and readable code; especially in the case of integer indexes, I recommend using these both to make code easier to read and understand, and to prevent subtle bugs due to the mixed indexing/slicing convention."
+    "The explicit nature of `loc` and `iloc` makes them helpful in maintaining clean and readable code; especially in the case of integer indexes, using them consistently can prevent subtle bugs due to the mixed indexing/slicing convention."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Data Selection in DataFrame\n",
+    "## Data Selection in DataFrames\n",
     "\n",
-    "Recall that a ``DataFrame`` acts in many ways like a two-dimensional or structured array, and in other ways like a dictionary of ``Series`` structures sharing the same index.\n",
+    "Recall that a `DataFrame` acts in many ways like a two-dimensional or structured array, and in other ways like a dictionary of `Series` structures sharing the same index.\n",
     "These analogies can be helpful to keep in mind as we explore data selection within this structure."
    ]
   },
@@ -569,9 +594,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### DataFrame as a dictionary\n",
+    "### DataFrame as Dictionary\n",
     "\n",
-    "The first analogy we will consider is the ``DataFrame`` as a dictionary of related ``Series`` objects.\n",
+    "The first analogy we will consider is the `DataFrame` as a dictionary of related `Series` objects.\n",
     "Let's return to our example of areas and populations of states:"
    ]
   },
@@ -579,13 +604,29 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -598,39 +639,39 @@
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td>423967</td>\n",
-       "      <td>38332521</td>\n",
+       "      <td>39538223</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Florida</th>\n",
-       "      <td>170312</td>\n",
-       "      <td>19552860</td>\n",
+       "      <th>Texas</th>\n",
+       "      <td>695662</td>\n",
+       "      <td>29145505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Illinois</th>\n",
-       "      <td>149995</td>\n",
-       "      <td>12882135</td>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>21538187</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
        "      <td>141297</td>\n",
-       "      <td>19651127</td>\n",
+       "      <td>20201249</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Texas</th>\n",
-       "      <td>695662</td>\n",
-       "      <td>26448193</td>\n",
+       "      <th>Pennsylvania</th>\n",
+       "      <td>119280</td>\n",
+       "      <td>13002700</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "              area       pop\n",
-       "California  423967  38332521\n",
-       "Florida     170312  19552860\n",
-       "Illinois    149995  12882135\n",
-       "New York    141297  19651127\n",
-       "Texas       695662  26448193"
+       "                area       pop\n",
+       "California    423967  39538223\n",
+       "Texas         695662  29145505\n",
+       "Florida       170312  21538187\n",
+       "New York      141297  20201249\n",
+       "Pennsylvania  119280  13002700"
       ]
      },
      "execution_count": 18,
@@ -640,11 +681,11 @@
    ],
    "source": [
     "area = pd.Series({'California': 423967, 'Texas': 695662,\n",
-    "                  'New York': 141297, 'Florida': 170312,\n",
-    "                  'Illinois': 149995})\n",
-    "pop = pd.Series({'California': 38332521, 'Texas': 26448193,\n",
-    "                 'New York': 19651127, 'Florida': 19552860,\n",
-    "                 'Illinois': 12882135})\n",
+    "                  'Florida': 170312, 'New York': 141297,\n",
+    "                  'Pennsylvania': 119280})\n",
+    "pop = pd.Series({'California': 39538223, 'Texas': 29145505,\n",
+    "                 'Florida': 21538187, 'New York': 20201249,\n",
+    "                 'Pennsylvania': 13002700})\n",
     "data = pd.DataFrame({'area':area, 'pop':pop})\n",
     "data"
    ]
@@ -653,24 +694,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The individual ``Series`` that make up the columns of the ``DataFrame`` can be accessed via dictionary-style indexing of the column name:"
+    "The individual `Series` that make up the columns of the `DataFrame` can be accessed via dictionary-style indexing of the column name:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "California    423967\n",
-       "Florida       170312\n",
-       "Illinois      149995\n",
-       "New York      141297\n",
-       "Texas         695662\n",
+       "California      423967\n",
+       "Texas           695662\n",
+       "Florida         170312\n",
+       "New York        141297\n",
+       "Pennsylvania    119280\n",
        "Name: area, dtype: int64"
       ]
      },
@@ -694,17 +738,20 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "California    423967\n",
-       "Florida       170312\n",
-       "Illinois      149995\n",
-       "New York      141297\n",
-       "Texas         695662\n",
+       "California      423967\n",
+       "Texas           695662\n",
+       "Florida         170312\n",
+       "New York        141297\n",
+       "Pennsylvania    119280\n",
        "Name: area, dtype: int64"
       ]
      },
@@ -721,45 +768,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This attribute-style column access actually accesses the exact same object as the dictionary-style access:"
+    "Though this is a useful shorthand, keep in mind that it does not work for all cases!\n",
+    "For example, if the column names are not strings, or if the column names conflict with methods of the `DataFrame`, this attribute-style access is not possible.\n",
+    "For example, the `DataFrame` has a `pop` method, so `data.pop` will point to this rather than the `pop` column:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
-   "source": [
-    "data.area is data['area']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Though this is a useful shorthand, keep in mind that it does not work for all cases!\n",
-    "For example, if the column names are not strings, or if the column names conflict with methods of the ``DataFrame``, this attribute-style access is not possible.\n",
-    "For example, the ``DataFrame`` has a ``pop()`` method, so ``data.pop`` will point to this rather than the ``\"pop\"`` column:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
    },
    "outputs": [
     {
@@ -768,35 +789,51 @@
        "False"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data.pop is data['pop']"
+    "data.pop is data[\"pop\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In particular, you should avoid the temptation to try column assignment via attribute (i.e., use ``data['pop'] = z`` rather than ``data.pop = z``).\n",
+    "In particular, you should avoid the temptation to try column assignment via attributes (i.e., use `data['pop'] = z` rather than `data.pop = z`).\n",
     "\n",
-    "Like with the ``Series`` objects discussed earlier, this dictionary-style syntax can also be used to modify the object, in this case adding a new column:"
+    "Like with the `Series` objects discussed earlier, this dictionary-style syntax can also be used to modify the object, in this case adding a new column:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -810,47 +847,47 @@
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td>423967</td>\n",
-       "      <td>38332521</td>\n",
-       "      <td>90.413926</td>\n",
+       "      <td>39538223</td>\n",
+       "      <td>93.257784</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Florida</th>\n",
-       "      <td>170312</td>\n",
-       "      <td>19552860</td>\n",
-       "      <td>114.806121</td>\n",
+       "      <th>Texas</th>\n",
+       "      <td>695662</td>\n",
+       "      <td>29145505</td>\n",
+       "      <td>41.896072</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Illinois</th>\n",
-       "      <td>149995</td>\n",
-       "      <td>12882135</td>\n",
-       "      <td>85.883763</td>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>21538187</td>\n",
+       "      <td>126.463121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
        "      <td>141297</td>\n",
-       "      <td>19651127</td>\n",
-       "      <td>139.076746</td>\n",
+       "      <td>20201249</td>\n",
+       "      <td>142.970120</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Texas</th>\n",
-       "      <td>695662</td>\n",
-       "      <td>26448193</td>\n",
-       "      <td>38.018740</td>\n",
+       "      <th>Pennsylvania</th>\n",
+       "      <td>119280</td>\n",
+       "      <td>13002700</td>\n",
+       "      <td>109.009893</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "              area       pop     density\n",
-       "California  423967  38332521   90.413926\n",
-       "Florida     170312  19552860  114.806121\n",
-       "Illinois    149995  12882135   85.883763\n",
-       "New York    141297  19651127  139.076746\n",
-       "Texas       695662  26448193   38.018740"
+       "                area       pop     density\n",
+       "California    423967  39538223   93.257784\n",
+       "Texas         695662  29145505   41.896072\n",
+       "Florida       170312  21538187  126.463121\n",
+       "New York      141297  20201249  142.970120\n",
+       "Pennsylvania  119280  13002700  109.009893"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -864,37 +901,40 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This shows a preview of the straightforward syntax of element-by-element arithmetic between ``Series`` objects; we'll dig into this further in [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb)."
+    "This shows a preview of the straightforward syntax of element-by-element arithmetic between `Series` objects; we'll dig into this further in [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### DataFrame as two-dimensional array\n",
+    "### DataFrame as Two-Dimensional Array\n",
     "\n",
-    "As mentioned previously, we can also view the ``DataFrame`` as an enhanced two-dimensional array.\n",
-    "We can examine the raw underlying data array using the ``values`` attribute:"
+    "As mentioned previously, we can also view the `DataFrame` as an enhanced two-dimensional array.\n",
+    "We can examine the raw underlying data array using the `values` attribute:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[  4.23967000e+05,   3.83325210e+07,   9.04139261e+01],\n",
-       "       [  1.70312000e+05,   1.95528600e+07,   1.14806121e+02],\n",
-       "       [  1.49995000e+05,   1.28821350e+07,   8.58837628e+01],\n",
-       "       [  1.41297000e+05,   1.96511270e+07,   1.39076746e+02],\n",
-       "       [  6.95662000e+05,   2.64481930e+07,   3.80187404e+01]])"
+       "array([[4.23967000e+05, 3.95382230e+07, 9.32577842e+01],\n",
+       "       [6.95662000e+05, 2.91455050e+07, 4.18960717e+01],\n",
+       "       [1.70312000e+05, 2.15381870e+07, 1.26463121e+02],\n",
+       "       [1.41297000e+05, 2.02012490e+07, 1.42970120e+02],\n",
+       "       [1.19280000e+05, 1.30027000e+07, 1.09009893e+02]])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -907,69 +947,85 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With this picture in mind, many familiar array-like observations can be done on the ``DataFrame`` itself.\n",
-    "For example, we can transpose the full ``DataFrame`` to swap rows and columns:"
+    "With this picture in mind, many familiar array-like operations can be done on the `DataFrame` itself.\n",
+    "For example, we can transpose the full `DataFrame` to swap rows and columns:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>California</th>\n",
+       "      <th>Texas</th>\n",
        "      <th>Florida</th>\n",
-       "      <th>Illinois</th>\n",
        "      <th>New York</th>\n",
-       "      <th>Texas</th>\n",
+       "      <th>Pennsylvania</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>area</th>\n",
        "      <td>4.239670e+05</td>\n",
+       "      <td>6.956620e+05</td>\n",
        "      <td>1.703120e+05</td>\n",
-       "      <td>1.499950e+05</td>\n",
        "      <td>1.412970e+05</td>\n",
-       "      <td>6.956620e+05</td>\n",
+       "      <td>1.192800e+05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pop</th>\n",
-       "      <td>3.833252e+07</td>\n",
-       "      <td>1.955286e+07</td>\n",
-       "      <td>1.288214e+07</td>\n",
-       "      <td>1.965113e+07</td>\n",
-       "      <td>2.644819e+07</td>\n",
+       "      <td>3.953822e+07</td>\n",
+       "      <td>2.914550e+07</td>\n",
+       "      <td>2.153819e+07</td>\n",
+       "      <td>2.020125e+07</td>\n",
+       "      <td>1.300270e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>density</th>\n",
-       "      <td>9.041393e+01</td>\n",
-       "      <td>1.148061e+02</td>\n",
-       "      <td>8.588376e+01</td>\n",
-       "      <td>1.390767e+02</td>\n",
-       "      <td>3.801874e+01</td>\n",
+       "      <td>9.325778e+01</td>\n",
+       "      <td>4.189607e+01</td>\n",
+       "      <td>1.264631e+02</td>\n",
+       "      <td>1.429701e+02</td>\n",
+       "      <td>1.090099e+02</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "           California       Florida      Illinois      New York         Texas\n",
-       "area     4.239670e+05  1.703120e+05  1.499950e+05  1.412970e+05  6.956620e+05\n",
-       "pop      3.833252e+07  1.955286e+07  1.288214e+07  1.965113e+07  2.644819e+07\n",
-       "density  9.041393e+01  1.148061e+02  8.588376e+01  1.390767e+02  3.801874e+01"
+       "           California         Texas       Florida      New York  Pennsylvania\n",
+       "area     4.239670e+05  6.956620e+05  1.703120e+05  1.412970e+05  1.192800e+05\n",
+       "pop      3.953822e+07  2.914550e+07  2.153819e+07  2.020125e+07  1.300270e+07\n",
+       "density  9.325778e+01  4.189607e+01  1.264631e+02  1.429701e+02  1.090099e+02"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -982,24 +1038,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When it comes to indexing of ``DataFrame`` objects, however, it is clear that the dictionary-style indexing of columns precludes our ability to simply treat it as a NumPy array.\n",
+    "When it comes to indexing of a `DataFrame` object, however, it is clear that the dictionary-style indexing of columns precludes our ability to simply treat it as a NumPy array.\n",
     "In particular, passing a single index to an array accesses a row:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([  4.23967000e+05,   3.83325210e+07,   9.04139261e+01])"
+       "array([4.23967000e+05, 3.95382230e+07, 9.32577842e+01])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1012,28 +1071,31 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "and passing a single \"index\" to a ``DataFrame`` accesses a column:"
+    "and passing a single \"index\" to a `DataFrame` accesses a column:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "California    423967\n",
-       "Florida       170312\n",
-       "Illinois      149995\n",
-       "New York      141297\n",
-       "Texas         695662\n",
+       "California      423967\n",
+       "Texas           695662\n",
+       "Florida         170312\n",
+       "New York        141297\n",
+       "Pennsylvania    119280\n",
        "Name: area, dtype: int64"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1044,26 +1106,40 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "source": [
-    "Thus for array-style indexing, we need another convention.\n",
-    "Here Pandas again uses the ``loc``, ``iloc``, and ``ix`` indexers mentioned earlier.\n",
-    "Using the ``iloc`` indexer, we can index the underlying array as if it is a simple NumPy array (using the implicit Python-style index), but the ``DataFrame`` index and column labels are maintained in the result:"
+    "Thus, for array-style indexing, we need another convention.\n",
+    "Here Pandas again uses the `loc` and `iloc` indexers mentioned earlier.\n",
+    "Using the `iloc` indexer, we can index the underlying array as if it were a simple NumPy array (using the implicit Python-style index), but the `DataFrame` index and column labels are maintained in the result:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1076,17 +1152,17 @@
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td>423967</td>\n",
-       "      <td>38332521</td>\n",
+       "      <td>39538223</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Florida</th>\n",
-       "      <td>170312</td>\n",
-       "      <td>19552860</td>\n",
+       "      <th>Texas</th>\n",
+       "      <td>695662</td>\n",
+       "      <td>29145505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Illinois</th>\n",
-       "      <td>149995</td>\n",
-       "      <td>12882135</td>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>21538187</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1094,12 +1170,12 @@
       ],
       "text/plain": [
        "              area       pop\n",
-       "California  423967  38332521\n",
-       "Florida     170312  19552860\n",
-       "Illinois    149995  12882135"
+       "California  423967  39538223\n",
+       "Texas       695662  29145505\n",
+       "Florida     170312  21538187"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1112,20 +1188,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Similarly, using the ``loc`` indexer we can index the underlying data in an array-like style but using the explicit index and column names:"
+    "Similarly, using the `loc` indexer we can index the underlying data in an array-like style but using the explicit index and column names:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1138,81 +1230,17 @@
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td>423967</td>\n",
-       "      <td>38332521</td>\n",
+       "      <td>39538223</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Florida</th>\n",
-       "      <td>170312</td>\n",
-       "      <td>19552860</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Illinois</th>\n",
-       "      <td>149995</td>\n",
-       "      <td>12882135</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              area       pop\n",
-       "California  423967  38332521\n",
-       "Florida     170312  19552860\n",
-       "Illinois    149995  12882135"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.loc[:'Illinois', :'pop']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "The ``ix`` indexer allows a hybrid of these two approaches:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>area</th>\n",
-       "      <th>pop</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>California</th>\n",
-       "      <td>423967</td>\n",
-       "      <td>38332521</td>\n",
+       "      <th>Texas</th>\n",
+       "      <td>695662</td>\n",
+       "      <td>29145505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Florida</th>\n",
        "      <td>170312</td>\n",
-       "      <td>19552860</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Illinois</th>\n",
-       "      <td>149995</td>\n",
-       "      <td>12882135</td>\n",
+       "      <td>21538187</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1220,41 +1248,55 @@
       ],
       "text/plain": [
        "              area       pop\n",
-       "California  423967  38332521\n",
-       "Florida     170312  19552860\n",
-       "Illinois    149995  12882135"
+       "California  423967  39538223\n",
+       "Texas       695662  29145505\n",
+       "Florida     170312  21538187"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data.ix[:3, :'pop']"
+    "data.loc[:'Florida', :'pop']"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Keep in mind that for integer indices, the ``ix`` indexer is subject to the same potential sources of confusion as discussed for integer-indexed ``Series`` objects.\n",
-    "\n",
     "Any of the familiar NumPy-style data access patterns can be used within these indexers.\n",
-    "For example, in the ``loc`` indexer we can combine masking and fancy indexing as in the following:"
+    "For example, in the `loc` indexer we can combine masking and fancy indexing as follows:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 29,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1266,13 +1308,13 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Florida</th>\n",
-       "      <td>19552860</td>\n",
-       "      <td>114.806121</td>\n",
+       "      <td>21538187</td>\n",
+       "      <td>126.463121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
-       "      <td>19651127</td>\n",
-       "      <td>139.076746</td>\n",
+       "      <td>20201249</td>\n",
+       "      <td>142.970120</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1280,17 +1322,17 @@
       ],
       "text/plain": [
        "               pop     density\n",
-       "Florida   19552860  114.806121\n",
-       "New York  19651127  139.076746"
+       "Florida   21538187  126.463121\n",
+       "New York  20201249  142.970120"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data.loc[data.density > 100, ['pop', 'density']]"
+    "data.loc[data.density > 120, ['pop', 'density']]"
    ]
   },
   {
@@ -1302,15 +1344,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1324,47 +1382,47 @@
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td>423967</td>\n",
-       "      <td>38332521</td>\n",
+       "      <td>39538223</td>\n",
        "      <td>90.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Florida</th>\n",
-       "      <td>170312</td>\n",
-       "      <td>19552860</td>\n",
-       "      <td>114.806121</td>\n",
+       "      <th>Texas</th>\n",
+       "      <td>695662</td>\n",
+       "      <td>29145505</td>\n",
+       "      <td>41.896072</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Illinois</th>\n",
-       "      <td>149995</td>\n",
-       "      <td>12882135</td>\n",
-       "      <td>85.883763</td>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>21538187</td>\n",
+       "      <td>126.463121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
        "      <td>141297</td>\n",
-       "      <td>19651127</td>\n",
-       "      <td>139.076746</td>\n",
+       "      <td>20201249</td>\n",
+       "      <td>142.970120</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Texas</th>\n",
-       "      <td>695662</td>\n",
-       "      <td>26448193</td>\n",
-       "      <td>38.018740</td>\n",
+       "      <th>Pennsylvania</th>\n",
+       "      <td>119280</td>\n",
+       "      <td>13002700</td>\n",
+       "      <td>109.009893</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "              area       pop     density\n",
-       "California  423967  38332521   90.000000\n",
-       "Florida     170312  19552860  114.806121\n",
-       "Illinois    149995  12882135   85.883763\n",
-       "New York    141297  19651127  139.076746\n",
-       "Texas       695662  26448193   38.018740"
+       "                area       pop     density\n",
+       "California    423967  39538223   90.000000\n",
+       "Texas         695662  29145505   41.896072\n",
+       "Florida       170312  21538187  126.463121\n",
+       "New York      141297  20201249  142.970120\n",
+       "Pennsylvania  119280  13002700  109.009893"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1378,30 +1436,46 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To build up your fluency in Pandas data manipulation, I suggest spending some time with a simple ``DataFrame`` and exploring the types of indexing, slicing, masking, and fancy indexing that are allowed by these various indexing approaches."
+    "To build up your fluency in Pandas data manipulation, I suggest spending some time with a simple `DataFrame` and exploring the types of indexing, slicing, masking, and fancy indexing that are allowed by these various indexing approaches."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Additional indexing conventions\n",
+    "### Additional Indexing Conventions\n",
     "\n",
-    "There are a couple extra indexing conventions that might seem at odds with the preceding discussion, but nevertheless can be very useful in practice.\n",
+    "There are a couple of extra indexing conventions that might seem at odds with the preceding discussion, but nevertheless can be useful in practice.\n",
     "First, while *indexing* refers to columns, *slicing* refers to rows:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1415,14 +1489,14 @@
        "    <tr>\n",
        "      <th>Florida</th>\n",
        "      <td>170312</td>\n",
-       "      <td>19552860</td>\n",
-       "      <td>114.806121</td>\n",
+       "      <td>21538187</td>\n",
+       "      <td>126.463121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Illinois</th>\n",
-       "      <td>149995</td>\n",
-       "      <td>12882135</td>\n",
-       "      <td>85.883763</td>\n",
+       "      <th>New York</th>\n",
+       "      <td>141297</td>\n",
+       "      <td>20201249</td>\n",
+       "      <td>142.970120</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1430,17 +1504,17 @@
       ],
       "text/plain": [
        "            area       pop     density\n",
-       "Florida   170312  19552860  114.806121\n",
-       "Illinois  149995  12882135   85.883763"
+       "Florida   170312  21538187  126.463121\n",
+       "New York  141297  20201249  142.970120"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data['Florida':'Illinois']"
+    "data['Florida':'New York']"
    ]
   },
   {
@@ -1452,15 +1526,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1472,28 +1562,28 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>Florida</th>\n",
-       "      <td>170312</td>\n",
-       "      <td>19552860</td>\n",
-       "      <td>114.806121</td>\n",
+       "      <th>Texas</th>\n",
+       "      <td>695662</td>\n",
+       "      <td>29145505</td>\n",
+       "      <td>41.896072</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Illinois</th>\n",
-       "      <td>149995</td>\n",
-       "      <td>12882135</td>\n",
-       "      <td>85.883763</td>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>21538187</td>\n",
+       "      <td>126.463121</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "            area       pop     density\n",
-       "Florida   170312  19552860  114.806121\n",
-       "Illinois  149995  12882135   85.883763"
+       "           area       pop     density\n",
+       "Texas    695662  29145505   41.896072\n",
+       "Florida  170312  21538187  126.463121"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1506,20 +1596,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Similarly, direct masking operations are also interpreted row-wise rather than column-wise:"
+    "Similarly, direct masking operations are interpreted row-wise rather than column-wise:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1533,14 +1639,14 @@
        "    <tr>\n",
        "      <th>Florida</th>\n",
        "      <td>170312</td>\n",
-       "      <td>19552860</td>\n",
-       "      <td>114.806121</td>\n",
+       "      <td>21538187</td>\n",
+       "      <td>126.463121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
        "      <td>141297</td>\n",
-       "      <td>19651127</td>\n",
-       "      <td>139.076746</td>\n",
+       "      <td>20201249</td>\n",
+       "      <td>142.970120</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1548,41 +1654,34 @@
       ],
       "text/plain": [
        "            area       pop     density\n",
-       "Florida   170312  19552860  114.806121\n",
-       "New York  141297  19651127  139.076746"
+       "Florida   170312  21538187  126.463121\n",
+       "New York  141297  20201249  142.970120"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data[data.density > 100]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These two conventions are syntactically similar to those on a NumPy array, and while these may not precisely fit the mold of the Pandas conventions, they are nevertheless quite useful in practice."
+    "data[data.density > 120]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) | [Contents](Index.ipynb) | [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.02-Data-Indexing-and-Selection.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "These two conventions are syntactically similar to those on a NumPy array, and while they may not precisely fit the mold of the Pandas conventions, they are included due to their practical utility."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1596,9 +1695,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.03-Operations-in-Pandas.ipynb b/notebooks/03.03-Operations-in-Pandas.ipynb
index 6206ac790..67059ea3d 100644
--- a/notebooks/03.03-Operations-in-Pandas.ipynb
+++ b/notebooks/03.03-Operations-in-Pandas.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) | [Contents](Index.ipynb) | [Handling Missing Data](03.04-Missing-Values.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.03-Operations-in-Pandas.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,12 +11,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One of the essential pieces of NumPy is the ability to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with more sophisticated operations (trigonometric functions, exponential and logarithmic functions, etc.).\n",
-    "Pandas inherits much of this functionality from NumPy, and the ufuncs that we introduced in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) are key to this.\n",
+    "One of the strengths of NumPy is that it allows us to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with more complicated operations (trigonometric functions, exponential and logarithmic functions, etc.).\n",
+    "Pandas inherits much of this functionality from NumPy, and the ufuncs introduced in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) are key to this.\n",
     "\n",
-    "Pandas includes a couple useful twists, however: for unary operations like negation and trigonometric functions, these ufuncs will *preserve index and column labels* in the output, and for binary operations such as addition and multiplication, Pandas will automatically *align indices* when passing the objects to the ufunc.\n",
-    "This means that keeping the context of data and combining data from different sources–both potentially error-prone tasks with raw NumPy arrays–become essentially foolproof ones with Pandas.\n",
-    "We will additionally see that there are well-defined operations between one-dimensional ``Series`` structures and two-dimensional ``DataFrame`` structures."
+    "Pandas includes a couple of useful twists, however: for unary operations like negation and trigonometric functions, these ufuncs will *preserve index and column labels* in the output, and for binary operations such as addition and multiplication, Pandas will automatically *align indices* when passing the objects to the ufunc.\n",
+    "This means that keeping the context of data and combining data from different sources—both potentially error-prone tasks with raw NumPy arrays—become essentially foolproof with Pandas.\n",
+    "We will additionally see that there are well-defined operations between one-dimensional `Series` structures and two-dimensional `DataFrame` structures."
    ]
   },
   {
@@ -47,15 +25,15 @@
    "source": [
     "## Ufuncs: Index Preservation\n",
     "\n",
-    "Because Pandas is designed to work with NumPy, any NumPy ufunc will work on Pandas ``Series`` and ``DataFrame`` objects.\n",
-    "Let's start by defining a simple ``Series`` and ``DataFrame`` on which to demonstrate this:"
+    "Because Pandas is designed to work with NumPy, any NumPy ufunc will work on Pandas `Series` and `DataFrame` objects.\n",
+    "Let's start by defining a simple `Series` and `DataFrame` on which to demonstrate this:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -67,15 +45,18 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0    6\n",
-       "1    3\n",
-       "2    7\n",
+       "0    0\n",
+       "1    7\n",
+       "2    6\n",
        "3    4\n",
        "dtype: int64"
       ]
@@ -86,8 +67,8 @@
     }
    ],
    "source": [
-    "rng = np.random.RandomState(42)\n",
-    "ser = pd.Series(rng.randint(0, 10, 4))\n",
+    "rng = np.random.default_rng(42)\n",
+    "ser = pd.Series(rng.integers(0, 10, 4))\n",
     "ser"
    ]
   },
@@ -95,13 +76,29 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -115,24 +112,24 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>6</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
        "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -140,9 +137,9 @@
       ],
       "text/plain": [
        "   A  B  C  D\n",
-       "0  6  9  2  6\n",
-       "1  7  4  3  7\n",
-       "2  7  2  5  4"
+       "0  4  8  0  6\n",
+       "1  2  0  5  9\n",
+       "2  7  7  7  7"
       ]
      },
      "execution_count": 3,
@@ -151,7 +148,7 @@
     }
    ],
    "source": [
-    "df = pd.DataFrame(rng.randint(0, 10, (3, 4)),\n",
+    "df = pd.DataFrame(rng.integers(0, 10, (3, 4)),\n",
     "                  columns=['A', 'B', 'C', 'D'])\n",
     "df"
    ]
@@ -167,15 +164,18 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0     403.428793\n",
-       "1      20.085537\n",
-       "2    1096.633158\n",
+       "0       1.000000\n",
+       "1    1096.633158\n",
+       "2     403.428793\n",
        "3      54.598150\n",
        "dtype: float64"
       ]
@@ -193,20 +193,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Or, for a slightly more complex calculation:"
+    "This is true also for more involved sequences of operations:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -220,34 +236,34 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>1.224647e-16</td>\n",
+       "      <td>-2.449294e-16</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>-1.000000</td>\n",
-       "      <td>7.071068e-01</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-1.000000e+00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
        "      <td>-0.707107</td>\n",
-       "      <td>1.224647e-16</td>\n",
        "      <td>0.707107</td>\n",
-       "      <td>-7.071068e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
+       "      <td>-7.071068e-01</td>\n",
+       "      <td>-7.071068e-01</td>\n",
        "      <td>-0.707107</td>\n",
-       "      <td>1.000000e+00</td>\n",
        "      <td>-0.707107</td>\n",
-       "      <td>1.224647e-16</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "          A             B         C             D\n",
-       "0 -1.000000  7.071068e-01  1.000000 -1.000000e+00\n",
-       "1 -0.707107  1.224647e-16  0.707107 -7.071068e-01\n",
-       "2 -0.707107  1.000000e+00 -0.707107  1.224647e-16"
+       "              A             B         C         D\n",
+       "0  1.224647e-16 -2.449294e-16  0.000000 -1.000000\n",
+       "1  1.000000e+00  0.000000e+00 -0.707107  0.707107\n",
+       "2 -7.071068e-01 -7.071068e-01 -0.707107 -0.707107"
       ]
      },
      "execution_count": 5,
@@ -270,9 +286,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## UFuncs: Index Alignment\n",
+    "## Ufuncs: Index Alignment\n",
     "\n",
-    "For binary operations on two ``Series`` or ``DataFrame`` objects, Pandas will align indices in the process of performing the operation.\n",
+    "For binary operations on two `Series` or `DataFrame` objects, Pandas will align indices in the process of performing the operation.\n",
     "This is very convenient when working with incomplete data, as we'll see in some of the examples that follow."
    ]
   },
@@ -280,23 +296,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Index alignment in Series\n",
+    "### Index Alignment in Series\n",
     "\n",
-    "As an example, suppose we are combining two different data sources, and find only the top three US states by *area* and the top three US states by *population*:"
+    "As an example, suppose we are combining two different data sources and wish to find only the top three US states by *area* and the top three US states by *population*:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "area = pd.Series({'Alaska': 1723337, 'Texas': 695662,\n",
     "                  'California': 423967}, name='area')\n",
-    "population = pd.Series({'California': 38332521, 'Texas': 26448193,\n",
-    "                        'New York': 19651127}, name='population')"
+    "population = pd.Series({'California': 39538223, 'Texas': 29145505,\n",
+    "                        'Florida': 21538187}, name='population')"
    ]
   },
   {
@@ -310,16 +329,19 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Alaska              NaN\n",
-       "California    90.413926\n",
-       "New York            NaN\n",
-       "Texas         38.018740\n",
+       "California    93.257784\n",
+       "Florida             NaN\n",
+       "Texas         41.896072\n",
        "dtype: float64"
       ]
      },
@@ -336,20 +358,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The resulting array contains the *union* of indices of the two input arrays, which could be determined using standard Python set arithmetic on these indices:"
+    "The resulting array contains the *union* of indices of the two input arrays, which could be determined directly from these indices:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Index(['Alaska', 'California', 'New York', 'Texas'], dtype='object')"
+       "Index(['Alaska', 'California', 'Florida', 'Texas'], dtype='object')"
       ]
      },
      "execution_count": 8,
@@ -358,22 +383,25 @@
     }
    ],
    "source": [
-    "area.index | population.index"
+    "area.index.union(population.index)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Any item for which one or the other does not have an entry is marked with ``NaN``, or \"Not a Number,\" which is how Pandas marks missing data (see further discussion of missing data in [Handling Missing Data](03.04-Missing-Values.ipynb)).\n",
-    "This index matching is implemented this way for any of Python's built-in arithmetic expressions; any missing values are filled in with NaN by default:"
+    "Any item for which one or the other does not have an entry is marked with `NaN`, or \"Not a Number,\" which is how Pandas marks missing data (see further discussion of missing data in [Handling Missing Data](03.04-Missing-Values.ipynb)).\n",
+    "This index matching is implemented this way for any of Python's built-in arithmetic expressions; any missing values are marked by `NaN`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -401,7 +429,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If using NaN values is not the desired behavior, the fill value can be modified using appropriate object methods in place of the operators.\n",
+    "If using `NaN` values is not the desired behavior, the fill value can be modified using appropriate object methods in place of the operators.\n",
     "For example, calling ``A.add(B)`` is equivalent to calling ``A + B``, but allows optional explicit specification of the fill value for any elements in ``A`` or ``B`` that might be missing:"
    ]
   },
@@ -409,7 +437,10 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -435,49 +466,65 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Index alignment in DataFrame\n",
+    "### Index Alignment in DataFrames\n",
     "\n",
-    "A similar type of alignment takes place for *both* columns and indices when performing operations on ``DataFrame``s:"
+    "A similar type of alignment takes place for *both* columns and indices when performing operations on `DataFrame` objects:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
+       "      <td>16</td>\n",
+       "      <td>9</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   A   B\n",
-       "0  1  11\n",
-       "1  5   1"
+       "    a  b\n",
+       "0  10  2\n",
+       "1  16  9"
       ]
      },
      "execution_count": 11,
@@ -486,8 +533,8 @@
     }
    ],
    "source": [
-    "A = pd.DataFrame(rng.randint(0, 20, (2, 2)),\n",
-    "                 columns=list('AB'))\n",
+    "A = pd.DataFrame(rng.integers(0, 20, (2, 2)),\n",
+    "                 columns=['a', 'b'])\n",
     "A"
    ]
   },
@@ -495,50 +542,66 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>B</th>\n",
-       "      <th>A</th>\n",
-       "      <th>C</th>\n",
+       "      <th>b</th>\n",
+       "      <th>a</th>\n",
+       "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>5</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   B  A  C\n",
-       "0  4  0  9\n",
-       "1  5  8  0\n",
-       "2  9  2  6"
+       "   b  a  c\n",
+       "0  5  3  1\n",
+       "1  9  7  6\n",
+       "2  4  8  5"
       ]
      },
      "execution_count": 12,
@@ -547,8 +610,8 @@
     }
    ],
    "source": [
-    "B = pd.DataFrame(rng.randint(0, 10, (3, 3)),\n",
-    "                 columns=list('BAC'))\n",
+    "B = pd.DataFrame(rng.integers(0, 10, (3, 3)),\n",
+    "                 columns=['b', 'a', 'c'])\n",
     "B"
    ]
   },
@@ -556,33 +619,49 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>15.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>7.0</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>6.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>18.0</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -596,9 +675,9 @@
        "</div>"
       ],
       "text/plain": [
-       "      A     B   C\n",
-       "0   1.0  15.0 NaN\n",
-       "1  13.0   6.0 NaN\n",
+       "      a     b   c\n",
+       "0  13.0   7.0 NaN\n",
+       "1  23.0  18.0 NaN\n",
        "2   NaN   NaN NaN"
       ]
      },
@@ -616,58 +695,74 @@
    "metadata": {},
    "source": [
     "Notice that indices are aligned correctly irrespective of their order in the two objects, and indices in the result are sorted.\n",
-    "As was the case with ``Series``, we can use the associated object's arithmetic method and pass any desired ``fill_value`` to be used in place of missing entries.\n",
-    "Here we'll fill with the mean of all values in ``A`` (computed by first stacking the rows of ``A``):"
+    "As was the case with `Series`, we can use the associated object's arithmetic methods and pass any desired `fill_value` to be used in place of missing entries.\n",
+    "Here we'll fill with the mean of all values in `A`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>13.5</td>\n",
+       "      <td>13.00</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>10.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>4.5</td>\n",
+       "      <td>23.00</td>\n",
+       "      <td>18.00</td>\n",
+       "      <td>15.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>6.5</td>\n",
-       "      <td>13.5</td>\n",
-       "      <td>10.5</td>\n",
+       "      <td>17.25</td>\n",
+       "      <td>13.25</td>\n",
+       "      <td>14.25</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "      A     B     C\n",
-       "0   1.0  15.0  13.5\n",
-       "1  13.0   6.0   4.5\n",
-       "2   6.5  13.5  10.5"
+       "       a      b      c\n",
+       "0  13.00   7.00  10.25\n",
+       "1  23.00  18.00  15.25\n",
+       "2  17.25  13.25  14.25"
       ]
      },
      "execution_count": 14,
@@ -676,8 +771,7 @@
     }
    ],
    "source": [
-    "fill = A.stack().mean()\n",
-    "A.add(B, fill_value=fill)"
+    "A.add(B, fill_value=A.values.mean())"
    ]
   },
   {
@@ -686,25 +780,24 @@
    "source": [
     "The following table lists Python operators and their equivalent Pandas object methods:\n",
     "\n",
-    "| Python Operator | Pandas Method(s)                      |\n",
-    "|-----------------|---------------------------------------|\n",
-    "| ``+``           | ``add()``                             |\n",
-    "| ``-``           | ``sub()``, ``subtract()``             |\n",
-    "| ``*``           | ``mul()``, ``multiply()``             |\n",
-    "| ``/``           | ``truediv()``, ``div()``, ``divide()``|\n",
-    "| ``//``          | ``floordiv()``                        |\n",
-    "| ``%``           | ``mod()``                             |\n",
-    "| ``**``          | ``pow()``                             |\n"
+    "| Python operator | Pandas method(s)                |\n",
+    "|-----------------|---------------------------------|\n",
+    "| `+`             | `add`                           |\n",
+    "| `-`             | `sub`, `subtract`               |\n",
+    "| `*`             | `mul`, `multiply`               |\n",
+    "| `/`             | `truediv`, `div`, `divide`      |\n",
+    "| `//`            | `floordiv`                      |\n",
+    "| `%`             | `mod`                           |\n",
+    "| `**`            | `pow`                           |\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Ufuncs: Operations Between DataFrame and Series\n",
+    "## Ufuncs: Operations Between DataFrames and Series\n",
     "\n",
-    "When performing operations between a ``DataFrame`` and a ``Series``, the index and column alignment is similarly maintained.\n",
-    "Operations between a ``DataFrame`` and a ``Series`` are similar to operations between a two-dimensional and one-dimensional NumPy array.\n",
+    "When performing operations between a `DataFrame` and a `Series`, the index and column alignment is similarly maintained, and the result is similar to operations between a two-dimensional and one-dimensional NumPy array.\n",
     "Consider one common operation, where we find the difference of a two-dimensional array and one of its rows:"
    ]
   },
@@ -712,15 +805,18 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[3, 8, 2, 4],\n",
-       "       [2, 6, 4, 8],\n",
-       "       [6, 1, 3, 8]])"
+       "array([[4, 4, 2, 0],\n",
+       "       [5, 8, 0, 8],\n",
+       "       [8, 2, 6, 1]])"
       ]
      },
      "execution_count": 15,
@@ -729,7 +825,7 @@
     }
    ],
    "source": [
-    "A = rng.randint(10, size=(3, 4))\n",
+    "A = rng.integers(10, size=(3, 4))\n",
     "A"
    ]
   },
@@ -737,15 +833,18 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "array([[ 0,  0,  0,  0],\n",
-       "       [-1, -2,  2,  4],\n",
-       "       [ 3, -7,  1,  4]])"
+       "       [ 1,  4, -2,  8],\n",
+       "       [ 4, -2,  4,  1]])"
       ]
      },
      "execution_count": 16,
@@ -770,13 +869,29 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -797,17 +912,17 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-1</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "      <td>4</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>-7</td>\n",
-       "      <td>1</td>\n",
        "      <td>4</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -816,8 +931,8 @@
       "text/plain": [
        "   Q  R  S  T\n",
        "0  0  0  0  0\n",
-       "1 -1 -2  2  4\n",
-       "2  3 -7  1  4"
+       "1  1  4 -2  8\n",
+       "2  4 -2  4  1"
       ]
      },
      "execution_count": 17,
@@ -826,7 +941,7 @@
     }
    ],
    "source": [
-    "df = pd.DataFrame(A, columns=list('QRST'))\n",
+    "df = pd.DataFrame(A, columns=['Q', 'R', 'S', 'T'])\n",
     "df - df.iloc[0]"
    ]
   },
@@ -834,20 +949,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you would instead like to operate column-wise, you can use the object methods mentioned earlier, while specifying the ``axis`` keyword:"
+    "If you would instead like to operate column-wise, you can use the object methods mentioned earlier, while specifying the `axis` keyword:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -861,24 +992,24 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-5</td>\n",
        "      <td>0</td>\n",
-       "      <td>-6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-2</td>\n",
        "      <td>-4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-4</td>\n",
+       "      <td>-3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-8</td>\n",
        "      <td>0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>5</td>\n",
+       "      <td>6</td>\n",
        "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>-1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -886,9 +1017,9 @@
       ],
       "text/plain": [
        "   Q  R  S  T\n",
-       "0 -5  0 -6 -4\n",
-       "1 -4  0 -2  2\n",
-       "2  5  0  2  7"
+       "0  0  0 -2 -4\n",
+       "1 -3  0 -8  0\n",
+       "2  6  0  4 -1"
       ]
      },
      "execution_count": 18,
@@ -904,20 +1035,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that these ``DataFrame``/``Series`` operations, like the operations discussed above, will automatically align  indices between the two elements:"
+    "Note that these `DataFrame`/`Series` operations, like the operations discussed previously, will automatically align  indices between the two elements:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Q    3\n",
+       "Q    4\n",
        "S    2\n",
        "Name: 0, dtype: int64"
       ]
@@ -936,13 +1070,29 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -963,16 +1113,16 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>-2.0</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -982,8 +1132,8 @@
       "text/plain": [
        "     Q   R    S   T\n",
        "0  0.0 NaN  0.0 NaN\n",
-       "1 -1.0 NaN  2.0 NaN\n",
-       "2  3.0 NaN  1.0 NaN"
+       "1  1.0 NaN -2.0 NaN\n",
+       "2  4.0 NaN  4.0 NaN"
       ]
      },
      "execution_count": 20,
@@ -999,24 +1149,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This preservation and alignment of indices and columns means that operations on data in Pandas will always maintain the data context, which prevents the types of silly errors that might come up when working with heterogeneous and/or misaligned data in raw NumPy arrays."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) | [Contents](Index.ipynb) | [Handling Missing Data](03.04-Missing-Values.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.03-Operations-in-Pandas.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "This preservation and alignment of indices and columns means that operations on data in Pandas will always maintain the data context, which prevents the common errors that might arise when working with heterogeneous and/or misaligned data in raw NumPy arrays."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1030,9 +1173,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.04-Missing-Values.ipynb b/notebooks/03.04-Missing-Values.ipynb
index 180ca09e7..fba5edc38 100644
--- a/notebooks/03.04-Missing-Values.ipynb
+++ b/notebooks/03.04-Missing-Values.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) | [Contents](Index.ipynb) | [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.04-Missing-Values.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -37,27 +15,28 @@
     "In particular, many interesting datasets will have some amount of data missing.\n",
     "To make matters even more complicated, different data sources may indicate missing data in different ways.\n",
     "\n",
-    "In this section, we will discuss some general considerations for missing data, discuss how Pandas chooses to represent it, and demonstrate some built-in Pandas tools for handling missing data in Python.\n",
-    "Here and throughout the book, we'll refer to missing data in general as *null*, *NaN*, or *NA* values."
+    "In this chapter, we will discuss some general considerations for missing data, look at how Pandas chooses to represent it, and explore some built-in Pandas tools for handling missing data in Python.\n",
+    "Here and throughout the book, I will refer to missing data in general as *null*, *NaN*, or *NA* values."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Trade-Offs in Missing Data Conventions\n",
+    "## Trade-offs in Missing Data Conventions\n",
     "\n",
-    "There are a number of schemes that have been developed to indicate the presence of missing data in a table or DataFrame.\n",
+    "A number of approaches have been developed to track the presence of missing data in a table or `DataFrame`.\n",
     "Generally, they revolve around one of two strategies: using a *mask* that globally indicates missing values, or choosing a *sentinel value* that indicates a missing entry.\n",
     "\n",
-    "In the masking approach, the mask might be an entirely separate Boolean array, or it may involve appropriation of one bit in the data representation to locally indicate the null status of a value.\n",
+    "In the masking approach, the mask might be an entirely separate Boolean array, or it might involve appropriation of one bit in the data representation to locally indicate the null status of a value.\n",
     "\n",
-    "In the sentinel approach, the sentinel value could be some data-specific convention, such as indicating a missing integer value with -9999 or some rare bit pattern, or it could be a more global convention, such as indicating a missing floating-point value with NaN (Not a Number), a special value which is part of the IEEE floating-point specification.\n",
+    "In the sentinel approach, the sentinel value could be some data-specific convention, such as indicating a missing integer value with –9999 or some rare bit pattern, or it could be a more global convention, such as indicating a missing floating-point value with `NaN` (Not a Number), a special value that is part of the IEEE floating-point specification.\n",
     "\n",
-    "None of these approaches is without trade-offs: use of a separate mask array requires allocation of an additional Boolean array, which adds overhead in both storage and computation. A sentinel value reduces the range of valid values that can be represented, and may require extra (often non-optimized) logic in CPU and GPU arithmetic. Common special values like NaN are not available for all data types.\n",
+    "Neither of these approaches is without trade-offs. Use of a separate mask array requires allocation of an additional Boolean array, which adds overhead in both storage and computation. A sentinel value reduces the range of valid values that can be represented, and may require extra (often nonoptimized) logic in CPU and GPU arithmetic, because common special values like `NaN` are not available for all data types.\n",
     "\n",
     "As in most cases where no universally optimal choice exists, different languages and systems use different conventions.\n",
-    "For example, the R language uses reserved bit patterns within each data type as sentinel values indicating missing data, while the SciDB system uses an extra byte attached to every cell which indicates a NA state."
+    "For example, the R language uses reserved bit patterns within each data type as sentinel values indicating missing data, while the SciDB system uses an extra byte attached to every cell to indicate an NA state."
    ]
   },
   {
@@ -68,50 +47,48 @@
     "\n",
     "The way in which Pandas handles missing values is constrained by its reliance on the NumPy package, which does not have a built-in notion of NA values for non-floating-point data types.\n",
     "\n",
-    "Pandas could have followed R's lead in specifying bit patterns for each individual data type to indicate nullness, but this approach turns out to be rather unwieldy.\n",
-    "While R contains four basic data types, NumPy supports *far* more than this: for example, while R has a single integer type, NumPy supports *fourteen* basic integer types once you account for available precisions, signedness, and endianness of the encoding.\n",
-    "Reserving a specific bit pattern in all available NumPy types would lead to an unwieldy amount of overhead in special-casing various operations for various types, likely even requiring a new fork of the NumPy package. Further, for the smaller data types (such as 8-bit integers), sacrificing a bit to use as a mask will significantly reduce the range of values it can represent.\n",
+    "Perhaps Pandas could have followed R's lead in specifying bit patterns for each individual data type to indicate nullness, but this approach turns out to be rather unwieldy.\n",
+    "While R has just 4 main data types, NumPy supports *far* more than this: for example, while R has a single integer type, NumPy supports 14 basic integer types once you account for available bit widths, signedness, and endianness of the encoding.\n",
+    "Reserving a specific bit pattern in all available NumPy types would lead to an unwieldy amount of overhead in special-casing various operations for various types, likely even requiring a new fork of the NumPy package. Further, for the smaller data types (such as 8-bit integers), sacrificing a bit to use as a mask would significantly reduce the range of values it can represent.\n",
     "\n",
-    "NumPy does have support for masked arrays – that is, arrays that have a separate Boolean mask array attached for marking data as \"good\" or \"bad.\"\n",
-    "Pandas could have derived from this, but the overhead in both storage, computation, and code maintenance makes that an unattractive choice.\n",
+    "Because of these constraints and trade-offs, Pandas has two \"modes\" of storing and manipulating null values:\n",
     "\n",
-    "With these constraints in mind, Pandas chose to use sentinels for missing data, and further chose to use two already-existing Python null values: the special floating-point ``NaN`` value, and the Python ``None`` object.\n",
-    "This choice has some side effects, as we will see, but in practice ends up being a good compromise in most cases of interest."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ``None``: Pythonic missing data\n",
+    "- The default mode is to use a sentinel-based missing data scheme, with sentinel values `NaN` or `None` depending on the type of the data.\n",
+    "- Alternatively, you can opt in to using the nullable data types (dtypes) Pandas provides (discussed later in this chapter), which results in the creation an accompanying mask array to track missing entries. These missing entries are then presented to the user as the special `pd.NA` value.\n",
     "\n",
-    "The first sentinel value used by Pandas is ``None``, a Python singleton object that is often used for missing data in Python code.\n",
-    "Because it is a Python object, ``None`` cannot be used in any arbitrary NumPy/Pandas array, but only in arrays with data type ``'object'`` (i.e., arrays of Python objects):"
+    "In either case, the data operations and manipulations provided by the Pandas API will handle and propagate those missing entries in a predictable manner. But to develop some intuition into *why* these choices are made, let's dive quickly into the trade-offs inherent in `None`, `NaN`, and `NA`. As usual, we'll start by importing NumPy and Pandas:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### None as a Sentinel Value\n",
+    "\n",
+    "For some data types, Pandas uses `None` as a sentinel value. `None` is a Python object, which means that any array containing `None` must have `dtype=object`—that is, it must be a sequence of Python objects.\n",
+    "\n",
+    "For example, observe what happens if you pass `None` to a NumPy array:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1, None, 3, 4], dtype=object)"
+       "array([1, None, 2, 3], dtype=object)"
       ]
      },
      "execution_count": 2,
@@ -120,7 +97,7 @@
     }
    ],
    "source": [
-    "vals1 = np.array([1, None, 3, 4])\n",
+    "vals1 = np.array([1, None, 2, 3])\n",
     "vals1"
    ]
   },
@@ -128,49 +105,64 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This ``dtype=object`` means that the best common type representation NumPy could infer for the contents of the array is that they are Python objects.\n",
-    "While this kind of object array is useful for some purposes, any operations on the data will be done at the Python level, with much more overhead than the typically fast operations seen for arrays with native types:"
+    "This `dtype=object` means that the best common type representation NumPy could infer for the contents of the array is that they are Python objects.\n",
+    "The downside of using `None` in this way is that operations on the data will be done at the Python level, with much more overhead than the typically fast operations seen for arrays with native types:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "dtype = object\n",
-      "10 loops, best of 3: 78.2 ms per loop\n",
-      "\n",
-      "dtype = int\n",
-      "100 loops, best of 3: 3.06 ms per loop\n",
-      "\n"
+      "2.73 ms ± 288 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
    "source": [
-    "for dtype in ['object', 'int']:\n",
-    "    print(\"dtype =\", dtype)\n",
-    "    %timeit np.arange(1E6, dtype=dtype).sum()\n",
-    "    print()"
+    "%timeit np.arange(1E6, dtype=int).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "92.1 ms ± 3.42 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit np.arange(1E6, dtype=object).sum()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The use of Python objects in an array also means that if you perform aggregations like ``sum()`` or ``min()`` across an array with a ``None`` value, you will generally get an error:"
+    "Further, because Python does not support arithmetic operations with `None`, aggregations like `sum` or `min` will generally lead to an error:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -180,8 +172,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-4-749fd8ae6030>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvals1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/Users/jakevdp/anaconda/lib/python3.5/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/var/folders/xc/sptt9bk14s34rgxt7453p03r0000gp/T/ipykernel_91333/1181914653.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvals1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.local/share/virtualenvs/python-data-science-handbook-2e-u_kwqDTB/lib/python3.9/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m     46\u001b[0m def _sum(a, axis=None, dtype=None, out=None, keepdims=False,\n\u001b[1;32m     47\u001b[0m          initial=_NoValue, where=True):\n\u001b[0;32m---> 48\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m def _prod(a, axis=None, dtype=None, out=None, keepdims=False,\n",
       "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'"
      ]
     }
@@ -194,39 +186,42 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This reflects the fact that addition between an integer and ``None`` is undefined."
+    "For this reason, Pandas does not use `None` as a sentinel in its numerical arrays."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### ``NaN``: Missing numerical data\n",
+    "### NaN: Missing Numerical Data\n",
     "\n",
-    "The other missing data representation, ``NaN`` (acronym for *Not a Number*), is different; it is a special floating-point value recognized by all systems that use the standard IEEE floating-point representation:"
+    "The other missing data sentinel, `NaN` is different; it is a special floating-point value recognized by all systems that use the standard IEEE floating-point representation:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dtype('float64')"
+       "array([ 1., nan,  3.,  4.])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "vals2 = np.array([1, np.nan, 3, 4]) \n",
-    "vals2.dtype"
+    "vals2"
    ]
   },
   {
@@ -234,15 +229,18 @@
    "metadata": {},
    "source": [
     "Notice that NumPy chose a native floating-point type for this array: this means that unlike the object array from before, this array supports fast operations pushed into compiled code.\n",
-    "You should be aware that ``NaN`` is a bit like a data virus–it infects any other object it touches.\n",
-    "Regardless of the operation, the result of arithmetic with ``NaN`` will be another ``NaN``:"
+    "Keep in mind that `NaN` is a bit like a data virus—it infects any other object it touches.\n",
+    "Regardless of the operation, the result of arithmetic with `NaN` will be another `NaN`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -251,7 +249,7 @@
        "nan"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -262,9 +260,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -273,27 +274,30 @@
        "nan"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "0 *  np.nan"
+    "0 * np.nan"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that this means that aggregates over the values are well defined (i.e., they don't result in an error) but not always useful:"
+    "This means that aggregates over the values are well defined (i.e., they don't result in an error) but not always useful:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -302,7 +306,7 @@
        "(nan, nan, nan)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -315,14 +319,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "NumPy does provide some special aggregations that will ignore these missing values:"
+    "That said, NumPy does provide ``NaN``-aware versions of aggregations that will ignore these missing values:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -331,7 +338,7 @@
        "(8.0, 1.0, 4.0)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -344,7 +351,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Keep in mind that ``NaN`` is specifically a floating-point value; there is no equivalent NaN value for integers, strings, or other types."
+    "The main downside of `NaN` is that it is specifically a floating-point value; there is no equivalent `NaN` value for integers, strings, or other types."
    ]
   },
   {
@@ -353,14 +360,17 @@
    "source": [
     "### NaN and None in Pandas\n",
     "\n",
-    "``NaN`` and ``None`` both have their place, and Pandas is built to handle the two of them nearly interchangeably, converting between them where appropriate:"
+    "`NaN` and `None` both have their place, and Pandas is built to handle the two of them nearly interchangeably, converting between them where appropriate:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -373,7 +383,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -386,15 +396,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For types that don't have an available sentinel value, Pandas automatically type-casts when NA values are present.\n",
+    "For types that don't have an available sentinel value, Pandas automatically typecasts when NA values are present.\n",
     "For example, if we set a value in an integer array to ``np.nan``, it will automatically be upcast to a floating-point type to accommodate the NA:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -405,7 +418,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -417,9 +430,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -430,7 +446,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -445,13 +461,12 @@
    "metadata": {},
    "source": [
     "Notice that in addition to casting the integer array to floating point, Pandas automatically converts the ``None`` to a ``NaN`` value.\n",
-    "(Be aware that there is a proposal to add a native integer NA to Pandas in the future; as of this writing, it has not been included).\n",
     "\n",
     "While this type of magic may feel a bit hackish compared to the more unified approach to NA values in domain-specific languages like R, the Pandas sentinel/casting approach works quite well in practice and in my experience only rarely causes issues.\n",
     "\n",
     "The following table lists the upcasting conventions in Pandas when NA values are introduced:\n",
     "\n",
-    "|Typeclass     | Conversion When Storing NAs | NA Sentinel Value      |\n",
+    "|Typeclass     | Conversion when storing NAs | NA sentinel value      |\n",
     "|--------------|-----------------------------|------------------------|\n",
     "| ``floating`` | No change                   | ``np.nan``             |\n",
     "| ``object``   | No change                   | ``None`` or ``np.nan`` |\n",
@@ -461,38 +476,89 @@
     "Keep in mind that in Pandas, string data is always stored with an ``object`` dtype."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pandas Nullable Dtypes"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In early versions of Pandas, `NaN` and `None` as sentinel values were the only missing data representations available. The primary difficulty this introduced was with regard to the implicit type casting: for example, there was no way to represent a true integer array with missing data.\n",
+    "\n",
+    "To address this difficulty, Pandas later added *nullable dtypes*, which are distinguished from regular dtypes by capitalization of their names (e.g., `pd.Int32` versus `np.int32`). For backward compatibility, these nullable dtypes are only used if specifically requested.\n",
+    "\n",
+    "For example, here is a `Series` of integers with missing data, created from a list containing all three available markers of missing data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       1\n",
+       "1    <NA>\n",
+       "2       2\n",
+       "3    <NA>\n",
+       "4    <NA>\n",
+       "dtype: Int32"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series([1, np.nan, 2, None, pd.NA], dtype='Int32')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This representation can be used interchangeably with the others in all the operations explored through the rest of this chapter."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Operating on Null Values\n",
     "\n",
-    "As we have seen, Pandas treats ``None`` and ``NaN`` as essentially interchangeable for indicating missing or null values.\n",
-    "To facilitate this convention, there are several useful methods for detecting, removing, and replacing null values in Pandas data structures.\n",
+    "As we have seen, Pandas treats `None`, `NaN`, and `NA` as essentially interchangeable for indicating missing or null values.\n",
+    "To facilitate this convention, Pandas provides several methods for detecting, removing, and replacing null values in Pandas data structures.\n",
     "They are:\n",
     "\n",
-    "- ``isnull()``: Generate a boolean mask indicating missing values\n",
-    "- ``notnull()``: Opposite of ``isnull()``\n",
-    "- ``dropna()``: Return a filtered version of the data\n",
-    "- ``fillna()``: Return a copy of the data with missing values filled or imputed\n",
+    "- ``isnull``: Generates a Boolean mask indicating missing values\n",
+    "- ``notnull``: Opposite of ``isnull``\n",
+    "- ``dropna``: Returns a filtered version of the data\n",
+    "- ``fillna``: Returns a copy of the data with missing values filled or imputed\n",
     "\n",
-    "We will conclude this section with a brief exploration and demonstration of these routines."
+    "We will conclude this chapter with a brief exploration and demonstration of these routines."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Detecting null values\n",
-    "Pandas data structures have two useful methods for detecting null data: ``isnull()`` and ``notnull()``.\n",
+    "### Detecting Null Values\n",
+    "Pandas data structures have two useful methods for detecting null data: `isnull` and `notnull`.\n",
     "Either one will return a Boolean mask over the data. For example:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -501,9 +567,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -516,7 +585,7 @@
        "dtype: bool"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -529,14 +598,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As mentioned in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb), Boolean masks can be used directly as a ``Series`` or ``DataFrame`` index:"
+    "As mentioned in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb), Boolean masks can be used directly as a `Series` or `DataFrame` index:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -547,7 +619,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -560,25 +632,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``isnull()`` and ``notnull()`` methods produce similar Boolean results for ``DataFrame``s."
+    "The `isnull()` and `notnull()` methods produce similar Boolean results for ``DataFrame`` objects."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Dropping null values\n",
+    "### Dropping Null Values\n",
     "\n",
-    "In addition to the masking used before, there are the convenience methods, ``dropna()``\n",
-    "(which removes NA values) and ``fillna()`` (which fills in NA values). For a ``Series``,\n",
+    "In addition to these masking methods, there are the convenience methods `dropna`\n",
+    "(which removes NA values) and `fillna` (which fills in NA values). For a `Series`,\n",
     "the result is straightforward:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -589,7 +664,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -608,15 +683,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
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@@ -656,7 +747,7 @@
        "2  NaN  4.0  6"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -672,23 +763,39 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We cannot drop single values from a ``DataFrame``; we can only drop full rows or full columns.\n",
-    "Depending on the application, you might want one or the other, so ``dropna()`` gives a number of options for a ``DataFrame``.\n",
+    "We cannot drop single values from a `DataFrame`; we can only drop entire rows or columns.\n",
+    "Depending on the application, you might want one or the other, so `dropna` includes a number of options for a `DataFrame`.\n",
     "\n",
-    "By default, ``dropna()`` will drop all rows in which *any* null value is present:"
+    "By default, `dropna` will drop all rows in which *any* null value is present:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -714,7 +821,7 @@
        "1  2.0  3.0  5"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -727,20 +834,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Alternatively, you can drop NA values along a different axis; ``axis=1`` drops all columns containing a null value:"
+    "Alternatively, you can drop NA values along a different axis. Using `axis=1` or `axis='columns'` drops all columns containing a null value:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    .dataframe tbody tr th {\n",
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+       "    .dataframe thead th {\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
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        "    <tr style=\"text-align: right;\">\n",
@@ -772,7 +895,7 @@
        "2  6"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -786,23 +909,39 @@
    "metadata": {},
    "source": [
     "But this drops some good data as well; you might rather be interested in dropping rows or columns with *all* NA values, or a majority of NA values.\n",
-    "This can be specified through the ``how`` or ``thresh`` parameters, which allow fine control of the number of nulls to allow through.\n",
+    "This can be specified through the `how` or `thresh` parameters, which allow fine control of the number of nulls to allow through.\n",
     "\n",
-    "The default is ``how='any'``, such that any row or column (depending on the ``axis`` keyword) containing a null value will be dropped.\n",
-    "You can also specify ``how='all'``, which will only drop rows/columns that are *all* null values:"
+    "The default is `how='any'`, such that any row or column containing a null value will be dropped.\n",
+    "You can also specify `how='all'`, which will only drop rows/columns that contain *all* null values:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
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@@ -846,7 +985,7 @@
        "2  NaN  4.0  6 NaN"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -858,15 +997,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
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@@ -906,7 +1061,7 @@
        "2  NaN  4.0  6"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -919,20 +1074,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For finer-grained control, the ``thresh`` parameter lets you specify a minimum number of non-null values for the row/column to be kept:"
+    "For finer-grained control, the `thresh` parameter lets you specify a minimum number of non-null values for the row/column to be kept:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -960,7 +1131,7 @@
        "1  2.0  3.0  5 NaN"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -973,47 +1144,50 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here the first and last row have been dropped, because they contain only two non-null values."
+    "Here, the first and last rows have been dropped because they each contain only two non-null values."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Filling null values\n",
+    "### Filling Null Values\n",
     "\n",
-    "Sometimes rather than dropping NA values, you'd rather replace them with a valid value.\n",
+    "Sometimes rather than dropping NA values, you'd like to replace them with a valid value.\n",
     "This value might be a single number like zero, or it might be some sort of imputation or interpolation from the good values.\n",
-    "You could do this in-place using the ``isnull()`` method as a mask, but because it is such a common operation Pandas provides the ``fillna()`` method, which returns a copy of the array with the null values replaced.\n",
+    "You could do this in-place using the `isnull` method as a mask, but because it is such a common operation Pandas provides the `fillna` method, which returns a copy of the array with the null values replaced.\n",
     "\n",
-    "Consider the following ``Series``:"
+    "Consider the following `Series`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "a    1.0\n",
-       "b    NaN\n",
-       "c    2.0\n",
-       "d    NaN\n",
-       "e    3.0\n",
-       "dtype: float64"
+       "a       1\n",
+       "b    <NA>\n",
+       "c       2\n",
+       "d    <NA>\n",
+       "e       3\n",
+       "dtype: Int32"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data = pd.Series([1, np.nan, 2, None, 3], index=list('abcde'))\n",
+    "data = pd.Series([1, np.nan, 2, None, 3], index=list('abcde'), dtype='Int32')\n",
     "data"
    ]
   },
@@ -1026,23 +1200,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "a    1.0\n",
-       "b    0.0\n",
-       "c    2.0\n",
-       "d    0.0\n",
-       "e    3.0\n",
-       "dtype: float64"
+       "a    1\n",
+       "b    0\n",
+       "c    2\n",
+       "d    0\n",
+       "e    3\n",
+       "dtype: Int32"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1055,34 +1232,37 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can specify a forward-fill to propagate the previous value forward:"
+    "We can specify a forward fill to propagate the previous value forward:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "a    1.0\n",
-       "b    1.0\n",
-       "c    2.0\n",
-       "d    2.0\n",
-       "e    3.0\n",
-       "dtype: float64"
+       "a    1\n",
+       "b    1\n",
+       "c    2\n",
+       "d    2\n",
+       "e    3\n",
+       "dtype: Int32"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# forward-fill\n",
+    "# forward fill\n",
     "data.fillna(method='ffill')"
    ]
   },
@@ -1090,57 +1270,74 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Or we can specify a back-fill to propagate the next values backward:"
+    "Or we can specify a backward fill to propagate the next values backward:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "a    1.0\n",
-       "b    2.0\n",
-       "c    2.0\n",
-       "d    3.0\n",
-       "e    3.0\n",
-       "dtype: float64"
+       "a    1\n",
+       "b    2\n",
+       "c    2\n",
+       "d    3\n",
+       "e    3\n",
+       "dtype: Int32"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# back-fill\n",
+    "# back fill\n",
     "data.fillna(method='bfill')"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "source": [
-    "For ``DataFrame``s, the options are similar, but we can also specify an ``axis`` along which the fills take place:"
+    "In the case of a `DataFrame`, the options are similar, but we can also specify an `axis` along which the fills should take place:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1184,7 +1381,7 @@
        "2  NaN  4.0  6 NaN"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1195,15 +1392,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1247,7 +1460,7 @@
        "2  NaN  4.0  6.0  6.0"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1262,22 +1475,15 @@
    "source": [
     "Notice that if a previous value is not available during a forward fill, the NA value remains."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) | [Contents](Index.ipynb) | [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.04-Missing-Values.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1291,9 +1497,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.05-Hierarchical-Indexing.ipynb b/notebooks/03.05-Hierarchical-Indexing.ipynb
index 1122989bb..cdf373927 100644
--- a/notebooks/03.05-Hierarchical-Indexing.ipynb
+++ b/notebooks/03.05-Hierarchical-Indexing.ipynb
@@ -1,33 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Handling Missing Data](03.04-Missing-Values.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.05-Hierarchical-Indexing.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -36,18 +8,20 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
-    "Up to this point we've been focused primarily on one-dimensional and two-dimensional data, stored in Pandas ``Series`` and ``DataFrame`` objects, respectively.\n",
-    "Often it is useful to go beyond this and store higher-dimensional data–that is, data indexed by more than one or two keys.\n",
-    "While Pandas does provide ``Panel`` and ``Panel4D`` objects that natively handle three-dimensional and four-dimensional data (see [Aside: Panel Data](#Aside:-Panel-Data)), a far more common pattern in practice is to make use of *hierarchical indexing* (also known as *multi-indexing*) to incorporate multiple index *levels* within a single index.\n",
-    "In this way, higher-dimensional data can be compactly represented within the familiar one-dimensional ``Series`` and two-dimensional ``DataFrame`` objects.\n",
+    "Up to this point we've been focused primarily on one-dimensional and two-dimensional data, stored in Pandas `Series` and `DataFrame` objects, respectively.\n",
+    "Often it is useful to go beyond this and store higher-dimensional data—that is, data indexed by more than one or two keys.\n",
+    "Early Pandas versions provided `Panel` and `Panel4D` objects that could be thought of as 3D or 4D analogs to the 2D `DataFrame`, but they were somewhat clunky to use in practice. A far more common pattern for handling higher-dimensional data is to make use of *hierarchical indexing* (also known as *multi-indexing*) to incorporate multiple index *levels* within a single index.\n",
+    "In this way, higher-dimensional data can be compactly represented within the familiar one-dimensional `Series` and two-dimensional `DataFrame` objects.\n",
+    "(If you're interested in true *N*-dimensional arrays with Pandas-style flexible indices, you can look into the excellent [Xarray package](https://xarray.pydata.org/).)\n",
     "\n",
-    "In this section, we'll explore the direct creation of ``MultiIndex`` objects, considerations when indexing, slicing, and computing statistics across multiply indexed data, and useful routines for converting between simple and hierarchically indexed representations of your data.\n",
+    "In this chapter, we'll explore the direct creation of `MultiIndex` objects; considerations when indexing, slicing, and computing statistics across multiply indexed data; and useful routines for converting between simple and hierarchically indexed representations of data.\n",
     "\n",
     "We begin with the standard imports:"
    ]
@@ -56,9 +30,9 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -75,7 +49,7 @@
    "source": [
     "## A Multiply Indexed Series\n",
     "\n",
-    "Let's start by considering how we might represent two-dimensional data within a one-dimensional ``Series``.\n",
+    "Let's start by considering how we might represent two-dimensional data within a one-dimensional `Series`.\n",
     "For concreteness, we will consider a series of data where each point has a character and numerical key."
    ]
   },
@@ -86,7 +60,7 @@
     "editable": true
    },
    "source": [
-    "### The bad way\n",
+    "### The Bad Way\n",
     "\n",
     "Suppose you would like to track data about states from two different years.\n",
     "Using the Pandas tools we've already covered, you might be tempted to simply use Python tuples as keys:"
@@ -98,18 +72,21 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(California, 2000)    33871648\n",
        "(California, 2010)    37253956\n",
-       "(New York, 2000)      18976457\n",
+       "(California, 2020)    39538223\n",
        "(New York, 2010)      19378102\n",
-       "(Texas, 2000)         20851820\n",
+       "(New York, 2020)      20201249\n",
        "(Texas, 2010)         25145561\n",
+       "(Texas, 2020)         29145505\n",
        "dtype: int64"
       ]
      },
@@ -119,12 +96,12 @@
     }
    ],
    "source": [
-    "index = [('California', 2000), ('California', 2010),\n",
-    "         ('New York', 2000), ('New York', 2010),\n",
-    "         ('Texas', 2000), ('Texas', 2010)]\n",
-    "populations = [33871648, 37253956,\n",
-    "               18976457, 19378102,\n",
-    "               20851820, 25145561]\n",
+    "index = [('California', 2010), ('California', 2020),\n",
+    "         ('New York', 2010), ('New York', 2020),\n",
+    "         ('Texas', 2010), ('Texas', 2020)]\n",
+    "populations = [37253956, 39538223,\n",
+    "               19378102, 20201249,\n",
+    "               25145561, 29145505]\n",
     "pop = pd.Series(populations, index=index)\n",
     "pop"
    ]
@@ -136,7 +113,7 @@
     "editable": true
    },
    "source": [
-    "With this indexing scheme, you can straightforwardly index or slice the series based on this multiple index:"
+    "With this indexing scheme, you can straightforwardly index or slice the series based on this tuple index:"
    ]
   },
   {
@@ -145,16 +122,19 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(California, 2010)    37253956\n",
-       "(New York, 2000)      18976457\n",
+       "(California, 2020)    39538223\n",
        "(New York, 2010)      19378102\n",
-       "(Texas, 2000)         20851820\n",
+       "(New York, 2020)      20201249\n",
+       "(Texas, 2010)         25145561\n",
        "dtype: int64"
       ]
      },
@@ -164,7 +144,7 @@
     }
    ],
    "source": [
-    "pop[('California', 2010):('Texas', 2000)]"
+    "pop[('California', 2020):('Texas', 2010)]"
    ]
   },
   {
@@ -183,7 +163,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -221,9 +204,9 @@
     "editable": true
    },
    "source": [
-    "### The Better Way: Pandas MultiIndex\n",
+    "### The Better Way: The Pandas MultiIndex\n",
     "Fortunately, Pandas provides a better way.\n",
-    "Our tuple-based indexing is essentially a rudimentary multi-index, and the Pandas ``MultiIndex`` type gives us the type of operations we wish to have.\n",
+    "Our tuple-based indexing is essentially a rudimentary multi-index, and the Pandas `MultiIndex` type gives us the types of operations we wish to have.\n",
     "We can create a multi-index from the tuples as follows:"
    ]
   },
@@ -233,24 +216,14 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "MultiIndex(levels=[['California', 'New York', 'Texas'], [2000, 2010]],\n",
-       "           labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
+   },
+   "outputs": [],
    "source": [
-    "index = pd.MultiIndex.from_tuples(index)\n",
-    "index"
+    "index = pd.MultiIndex.from_tuples(index)"
    ]
   },
   {
@@ -260,9 +233,9 @@
     "editable": true
    },
    "source": [
-    "Notice that the ``MultiIndex`` contains multiple *levels* of indexing–in this case, the state names and the years, as well as multiple *labels* for each data point which encode these levels.\n",
+    "The `MultiIndex` represents multiple *levels* of indexing—in this case, the state names and the years—as well as multiple *labels* for each data point which encode these levels.\n",
     "\n",
-    "If we re-index our series with this ``MultiIndex``, we see the hierarchical representation of the data:"
+    "If we reindex our series with this `MultiIndex`, we see the hierarchical representation of the data:"
    ]
   },
   {
@@ -271,18 +244,21 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "California  2000    33871648\n",
-       "            2010    37253956\n",
-       "New York    2000    18976457\n",
-       "            2010    19378102\n",
-       "Texas       2000    20851820\n",
-       "            2010    25145561\n",
+       "California  2010    37253956\n",
+       "            2020    39538223\n",
+       "New York    2010    19378102\n",
+       "            2020    20201249\n",
+       "Texas       2010    25145561\n",
+       "            2020    29145505\n",
        "dtype: int64"
       ]
      },
@@ -303,7 +279,7 @@
     "editable": true
    },
    "source": [
-    "Here the first two columns of the ``Series`` representation show the multiple index values, while the third column shows the data.\n",
+    "Here the first two columns of the Series representation show the multiple index values, while the third column shows the data.\n",
     "Notice that some entries are missing in the first column: in this multi-index representation, any blank entry indicates the same value as the line above it."
    ]
   },
@@ -314,7 +290,7 @@
     "editable": true
    },
    "source": [
-    "Now to access all data for which the second index is 2010, we can simply use the Pandas slicing notation:"
+    "Now to access all data for which the second index is 2020, we can use the Pandas slicing notation:"
    ]
   },
   {
@@ -323,15 +299,18 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "California    37253956\n",
-       "New York      19378102\n",
-       "Texas         25145561\n",
+       "California    39538223\n",
+       "New York      20201249\n",
+       "Texas         29145505\n",
        "dtype: int64"
       ]
      },
@@ -341,7 +320,7 @@
     }
    ],
    "source": [
-    "pop[:, 2010]"
+    "pop[:, 2020]"
    ]
   },
   {
@@ -351,9 +330,9 @@
     "editable": true
    },
    "source": [
-    "The result is a singly indexed array with just the keys we're interested in.\n",
+    "The result is a singly indexed Series with just the keys we're interested in.\n",
     "This syntax is much more convenient (and the operation is much more efficient!) than the home-spun tuple-based multi-indexing solution that we started with.\n",
-    "We'll now further discuss this sort of indexing operation on hieararchically indexed data."
+    "We'll now further discuss this sort of indexing operation on hierarchically indexed data."
    ]
   },
   {
@@ -363,10 +342,10 @@
     "editable": true
    },
    "source": [
-    "### MultiIndex as extra dimension\n",
+    "### MultiIndex as Extra Dimension\n",
     "\n",
-    "You might notice something else here: we could easily have stored the same data using a simple ``DataFrame`` with index and column labels.\n",
-    "In fact, Pandas is built with this equivalence in mind. The ``unstack()`` method will quickly convert a multiply indexed ``Series`` into a conventionally indexed ``DataFrame``:"
+    "You might notice something else here: we could easily have stored the same data using a simple `DataFrame` with index and column labels.\n",
+    "In fact, Pandas is built with this equivalence in mind. The `unstack` method will quickly convert a multiply indexed `Series` into a conventionally indexed `DataFrame`:"
    ]
   },
   {
@@ -375,46 +354,62 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>2000</th>\n",
        "      <th>2010</th>\n",
+       "      <th>2020</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
-       "      <td>33871648</td>\n",
        "      <td>37253956</td>\n",
+       "      <td>39538223</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
-       "      <td>18976457</td>\n",
        "      <td>19378102</td>\n",
+       "      <td>20201249</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Texas</th>\n",
-       "      <td>20851820</td>\n",
        "      <td>25145561</td>\n",
+       "      <td>29145505</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                2000      2010\n",
-       "California  33871648  37253956\n",
-       "New York    18976457  19378102\n",
-       "Texas       20851820  25145561"
+       "                2010      2020\n",
+       "California  37253956  39538223\n",
+       "New York    19378102  20201249\n",
+       "Texas       25145561  29145505"
       ]
      },
      "execution_count": 8,
@@ -434,7 +429,7 @@
     "editable": true
    },
    "source": [
-    "Naturally, the ``stack()`` method provides the opposite operation:"
+    "Naturally, the ``stack`` method provides the opposite operation:"
    ]
   },
   {
@@ -443,18 +438,21 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "California  2000    33871648\n",
-       "            2010    37253956\n",
-       "New York    2000    18976457\n",
-       "            2010    19378102\n",
-       "Texas       2000    20851820\n",
-       "            2010    25145561\n",
+       "California  2010    37253956\n",
+       "            2020    39538223\n",
+       "New York    2010    19378102\n",
+       "            2020    20201249\n",
+       "Texas       2010    25145561\n",
+       "            2020    29145505\n",
        "dtype: int64"
       ]
      },
@@ -475,8 +473,8 @@
    },
    "source": [
     "Seeing this, you might wonder why would we would bother with hierarchical indexing at all.\n",
-    "The reason is simple: just as we were able to use multi-indexing to represent two-dimensional data within a one-dimensional ``Series``, we can also use it to represent data of three or more dimensions in a ``Series`` or ``DataFrame``.\n",
-    "Each extra level in a multi-index represents an extra dimension of data; taking advantage of this property gives us much more flexibility in the types of data we can represent. Concretely, we might want to add another column of demographic data for each state at each year (say, population under 18) ; with a ``MultiIndex`` this is as easy as adding another column to the ``DataFrame``:"
+    "The reason is simple: just as we were able to use multi-indexing to manipulate two-dimensional data within a one-dimensional `Series`, we can also use it to manipulate data of three or more dimensions in a `Series` or `DataFrame`.\n",
+    "Each extra level in a multi-index represents an extra dimension of data; taking advantage of this property gives us much more flexibility in the types of data we can represent. Concretely, we might want to add another column of demographic data for each state at each year (say, population under 18); with a `MultiIndex` this is as easy as adding another column to the ``DataFrame``:"
    ]
   },
   {
@@ -485,13 +483,29 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -504,49 +518,49 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">California</th>\n",
-       "      <th>2000</th>\n",
-       "      <td>33871648</td>\n",
-       "      <td>9267089</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td>37253956</td>\n",
        "      <td>9284094</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">New York</th>\n",
-       "      <th>2000</th>\n",
-       "      <td>18976457</td>\n",
-       "      <td>4687374</td>\n",
+       "      <th>2020</th>\n",
+       "      <td>39538223</td>\n",
+       "      <td>8898092</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">New York</th>\n",
        "      <th>2010</th>\n",
        "      <td>19378102</td>\n",
        "      <td>4318033</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">Texas</th>\n",
-       "      <th>2000</th>\n",
-       "      <td>20851820</td>\n",
-       "      <td>5906301</td>\n",
+       "      <th>2020</th>\n",
+       "      <td>20201249</td>\n",
+       "      <td>4181528</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Texas</th>\n",
        "      <th>2010</th>\n",
        "      <td>25145561</td>\n",
        "      <td>6879014</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020</th>\n",
+       "      <td>29145505</td>\n",
+       "      <td>7432474</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "                    total  under18\n",
-       "California 2000  33871648  9267089\n",
-       "           2010  37253956  9284094\n",
-       "New York   2000  18976457  4687374\n",
-       "           2010  19378102  4318033\n",
-       "Texas      2000  20851820  5906301\n",
-       "           2010  25145561  6879014"
+       "California 2010  37253956  9284094\n",
+       "           2020  39538223  8898092\n",
+       "New York   2010  19378102  4318033\n",
+       "           2020  20201249  4181528\n",
+       "Texas      2010  25145561  6879014\n",
+       "           2020  29145505  7432474"
       ]
      },
      "execution_count": 10,
@@ -556,9 +570,9 @@
    ],
    "source": [
     "pop_df = pd.DataFrame({'total': pop,\n",
-    "                       'under18': [9267089, 9284094,\n",
-    "                                   4687374, 4318033,\n",
-    "                                   5906301, 6879014]})\n",
+    "                       'under18': [9284094, 8898092,\n",
+    "                                   4318033, 4181528,\n",
+    "                                   6879014, 7432474]})\n",
     "pop_df"
    ]
   },
@@ -579,46 +593,62 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>2000</th>\n",
        "      <th>2010</th>\n",
+       "      <th>2020</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
-       "      <td>0.273594</td>\n",
        "      <td>0.249211</td>\n",
+       "      <td>0.225050</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
-       "      <td>0.247010</td>\n",
        "      <td>0.222831</td>\n",
+       "      <td>0.206994</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Texas</th>\n",
-       "      <td>0.283251</td>\n",
        "      <td>0.273568</td>\n",
+       "      <td>0.255013</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                2000      2010\n",
-       "California  0.273594  0.249211\n",
-       "New York    0.247010  0.222831\n",
-       "Texas       0.283251  0.273568"
+       "                2010      2020\n",
+       "California  0.249211  0.225050\n",
+       "New York    0.222831  0.206994\n",
+       "Texas       0.273568  0.255013"
       ]
      },
      "execution_count": 11,
@@ -650,7 +680,7 @@
    "source": [
     "## Methods of MultiIndex Creation\n",
     "\n",
-    "The most straightforward way to construct a multiply indexed ``Series`` or ``DataFrame`` is to simply pass a list of two or more index arrays to the constructor. For example:"
+    "The most straightforward way to construct a multiply indexed `Series` or `DataFrame` is to simply pass a list of two or more index arrays to the constructor. For example:"
    ]
   },
   {
@@ -659,13 +689,29 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -679,24 +725,24 @@
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
        "      <th>1</th>\n",
-       "      <td>0.554233</td>\n",
-       "      <td>0.356072</td>\n",
+       "      <td>0.748464</td>\n",
+       "      <td>0.561409</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.925244</td>\n",
-       "      <td>0.219474</td>\n",
+       "      <td>0.379199</td>\n",
+       "      <td>0.622461</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
        "      <th>1</th>\n",
-       "      <td>0.441759</td>\n",
-       "      <td>0.610054</td>\n",
+       "      <td>0.701679</td>\n",
+       "      <td>0.687932</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.171495</td>\n",
-       "      <td>0.886688</td>\n",
+       "      <td>0.436200</td>\n",
+       "      <td>0.950664</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -704,10 +750,10 @@
       ],
       "text/plain": [
        "        data1     data2\n",
-       "a 1  0.554233  0.356072\n",
-       "  2  0.925244  0.219474\n",
-       "b 1  0.441759  0.610054\n",
-       "  2  0.171495  0.886688"
+       "a 1  0.748464  0.561409\n",
+       "  2  0.379199  0.622461\n",
+       "b 1  0.701679  0.687932\n",
+       "  2  0.436200  0.950664"
       ]
      },
      "execution_count": 12,
@@ -740,18 +786,21 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "California  2000    33871648\n",
-       "            2010    37253956\n",
-       "New York    2000    18976457\n",
-       "            2010    19378102\n",
-       "Texas       2000    20851820\n",
-       "            2010    25145561\n",
+       "California  2010    37253956\n",
+       "            2020    39538223\n",
+       "New York    2010    19378102\n",
+       "            2020    20201249\n",
+       "Texas       2010    25145561\n",
+       "            2020    29145505\n",
        "dtype: int64"
       ]
      },
@@ -761,12 +810,12 @@
     }
    ],
    "source": [
-    "data = {('California', 2000): 33871648,\n",
-    "        ('California', 2010): 37253956,\n",
-    "        ('Texas', 2000): 20851820,\n",
+    "data = {('California', 2010): 37253956,\n",
+    "        ('California', 2020): 39538223,\n",
+    "        ('New York', 2010): 19378102,\n",
+    "        ('New York', 2020): 20201249,\n",
     "        ('Texas', 2010): 25145561,\n",
-    "        ('New York', 2000): 18976457,\n",
-    "        ('New York', 2010): 19378102}\n",
+    "        ('Texas', 2020): 29145505}\n",
     "pd.Series(data)"
    ]
   },
@@ -777,7 +826,7 @@
     "editable": true
    },
    "source": [
-    "Nevertheless, it is sometimes useful to explicitly create a ``MultiIndex``; we'll see a couple of these methods here."
+    "Nevertheless, it is sometimes useful to explicitly create a `MultiIndex`; we'll look at a couple of methods for doing this next."
    ]
   },
   {
@@ -787,10 +836,10 @@
     "editable": true
    },
    "source": [
-    "### Explicit MultiIndex constructors\n",
+    "### Explicit MultiIndex Constructors\n",
     "\n",
-    "For more flexibility in how the index is constructed, you can instead use the class method constructors available in the ``pd.MultiIndex``.\n",
-    "For example, as we did before, you can construct the ``MultiIndex`` from a simple list of arrays giving the index values within each level:"
+    "For more flexibility in how the index is constructed, you can instead use the constructor methods available in the `pd.MultiIndex` class.\n",
+    "For example, as we did before, you can construct a `MultiIndex` from a simple list of arrays giving the index values within each level:"
    ]
   },
   {
@@ -799,14 +848,20 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
-       "           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
+       "MultiIndex([('a', 1),\n",
+       "            ('a', 2),\n",
+       "            ('b', 1),\n",
+       "            ('b', 2)],\n",
+       "           )"
       ]
      },
      "execution_count": 14,
@@ -825,7 +880,7 @@
     "editable": true
    },
    "source": [
-    "You can construct it from a list of tuples giving the multiple index values of each point:"
+    "Or you can construct it from a list of tuples giving the multiple index values of each point:"
    ]
   },
   {
@@ -834,14 +889,20 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
-       "           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
+       "MultiIndex([('a', 1),\n",
+       "            ('a', 2),\n",
+       "            ('b', 1),\n",
+       "            ('b', 2)],\n",
+       "           )"
       ]
      },
      "execution_count": 15,
@@ -869,14 +930,20 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
-       "           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
+       "MultiIndex([('a', 1),\n",
+       "            ('a', 2),\n",
+       "            ('b', 1),\n",
+       "            ('b', 2)],\n",
+       "           )"
       ]
      },
      "execution_count": 16,
@@ -895,7 +962,7 @@
     "editable": true
    },
    "source": [
-    "Similarly, you can construct the ``MultiIndex`` directly using its internal encoding by passing ``levels`` (a list of lists containing available index values for each level) and ``labels`` (a list of lists that reference these labels):"
+    "Similarly, you can construct a `MultiIndex` directly using its internal encoding by passing `levels` (a list of lists containing available index values for each level) and `codes` (a list of lists that reference these labels):"
    ]
   },
   {
@@ -904,14 +971,20 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
-       "           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
+       "MultiIndex([('a', 1),\n",
+       "            ('a', 2),\n",
+       "            ('b', 1),\n",
+       "            ('b', 2)],\n",
+       "           )"
       ]
      },
      "execution_count": 17,
@@ -921,7 +994,7 @@
    ],
    "source": [
     "pd.MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
-    "              labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
+    "              codes=[[0, 0, 1, 1], [0, 1, 0, 1]])"
    ]
   },
   {
@@ -931,7 +1004,7 @@
     "editable": true
    },
    "source": [
-    "Any of these objects can be passed as the ``index`` argument when creating a ``Series`` or ``Dataframe``, or be passed to the ``reindex`` method of an existing ``Series`` or ``DataFrame``."
+    "Any of these objects can be passed as the `index` argument when creating a `Series` or `DataFrame`, or be passed to the `reindex` method of an existing `Series` or `DataFrame`."
    ]
   },
   {
@@ -941,10 +1014,10 @@
     "editable": true
    },
    "source": [
-    "### MultiIndex level names\n",
+    "### MultiIndex Level Names\n",
     "\n",
-    "Sometimes it is convenient to name the levels of the ``MultiIndex``.\n",
-    "This can be accomplished by passing the ``names`` argument to any of the above ``MultiIndex`` constructors, or by setting the ``names`` attribute of the index after the fact:"
+    "Sometimes it is convenient to name the levels of the `MultiIndex`.\n",
+    "This can be accomplished by passing the `names` argument to any of the previously discussed `MultiIndex` constructors, or by setting the `names` attribute of the index after the fact:"
    ]
   },
   {
@@ -953,19 +1026,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "state       year\n",
-       "California  2000    33871648\n",
-       "            2010    37253956\n",
-       "New York    2000    18976457\n",
-       "            2010    19378102\n",
-       "Texas       2000    20851820\n",
-       "            2010    25145561\n",
+       "California  2010    37253956\n",
+       "            2020    39538223\n",
+       "New York    2010    19378102\n",
+       "            2020    20201249\n",
+       "Texas       2010    25145561\n",
+       "            2020    29145505\n",
        "dtype: int64"
       ]
      },
@@ -996,9 +1072,9 @@
     "editable": true
    },
    "source": [
-    "### MultiIndex for columns\n",
+    "### MultiIndex for Columns\n",
     "\n",
-    "In a ``DataFrame``, the rows and columns are completely symmetric, and just as the rows can have multiple levels of indices, the columns can have multiple levels as well.\n",
+    "In a `DataFrame`, the rows and columns are completely symmetric, and just as the rows can have multiple levels of indices, the columns can have multiple levels as well.\n",
     "Consider the following, which is a mock-up of some (somewhat realistic) medical data:"
    ]
   },
@@ -1008,13 +1084,33 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
@@ -1049,40 +1145,40 @@
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
        "      <th>1</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>38.7</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>36.7</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>37.2</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>38.3</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>35.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>44.0</td>\n",
-       "      <td>37.7</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>36.7</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>37.1</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>36.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
        "      <th>1</th>\n",
-       "      <td>30.0</td>\n",
-       "      <td>37.4</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>37.8</td>\n",
-       "      <td>61.0</td>\n",
-       "      <td>36.9</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>35.9</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>36.7</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>36.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>47.0</td>\n",
-       "      <td>37.8</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>36.3</td>\n",
        "      <td>48.0</td>\n",
-       "      <td>37.3</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>36.5</td>\n",
+       "      <td>39.2</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>35.7</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1092,10 +1188,10 @@
        "subject      Bob       Guido         Sue      \n",
        "type          HR  Temp    HR  Temp    HR  Temp\n",
        "year visit                                    \n",
-       "2013 1      31.0  38.7  32.0  36.7  35.0  37.2\n",
-       "     2      44.0  37.7  50.0  35.0  29.0  36.7\n",
-       "2014 1      30.0  37.4  39.0  37.8  61.0  36.9\n",
-       "     2      47.0  37.8  48.0  37.3  51.0  36.5"
+       "2013 1      30.0  38.0  56.0  38.3  45.0  35.8\n",
+       "     2      47.0  37.1  27.0  36.0  37.0  36.4\n",
+       "2014 1      51.0  35.9  24.0  36.7  32.0  36.2\n",
+       "     2      49.0  36.3  48.0  39.2  31.0  35.7"
       ]
      },
      "execution_count": 19,
@@ -1127,9 +1223,8 @@
     "editable": true
    },
    "source": [
-    "Here we see where the multi-indexing for both rows and columns can come in *very* handy.\n",
     "This is fundamentally four-dimensional data, where the dimensions are the subject, the measurement type, the year, and the visit number.\n",
-    "With this in place we can, for example, index the top-level column by the person's name and get a full ``DataFrame`` containing just that person's information:"
+    "With this in place we can, for example, index the top-level column by the person's name and get a full `DataFrame` containing just that person's information:"
    ]
   },
   {
@@ -1138,13 +1233,29 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1164,24 +1275,24 @@
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
        "      <th>1</th>\n",
-       "      <td>32.0</td>\n",
-       "      <td>36.7</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>38.3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>50.0</td>\n",
-       "      <td>35.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>36.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
        "      <th>1</th>\n",
-       "      <td>39.0</td>\n",
-       "      <td>37.8</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>36.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>48.0</td>\n",
-       "      <td>37.3</td>\n",
+       "      <td>39.2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1190,10 +1301,10 @@
       "text/plain": [
        "type          HR  Temp\n",
        "year visit            \n",
-       "2013 1      32.0  36.7\n",
-       "     2      50.0  35.0\n",
-       "2014 1      39.0  37.8\n",
-       "     2      48.0  37.3"
+       "2013 1      56.0  38.3\n",
+       "     2      27.0  36.0\n",
+       "2014 1      24.0  36.7\n",
+       "     2      48.0  39.2"
       ]
      },
      "execution_count": 20,
@@ -1205,16 +1316,6 @@
     "health_data['Guido']"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "For complicated records containing multiple labeled measurements across multiple times for many subjects (people, countries, cities, etc.) use of hierarchical rows and columns can be extremely convenient!"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1224,8 +1325,8 @@
    "source": [
     "## Indexing and Slicing a MultiIndex\n",
     "\n",
-    "Indexing and slicing on a ``MultiIndex`` is designed to be intuitive, and it helps if you think about the indices as added dimensions.\n",
-    "We'll first look at indexing multiply indexed ``Series``, and then multiply-indexed ``DataFrame``s."
+    "Indexing and slicing on a `MultiIndex` is designed to be intuitive, and it helps if you think about the indices as added dimensions.\n",
+    "We'll first look at indexing multiply indexed `Series`, and then multiply indexed `DataFrame` objects."
    ]
   },
   {
@@ -1235,9 +1336,9 @@
     "editable": true
    },
    "source": [
-    "### Multiply indexed Series\n",
+    "### Multiply Indexed Series\n",
     "\n",
-    "Consider the multiply indexed ``Series`` of state populations we saw earlier:"
+    "Consider the multiply indexed `Series` of state populations we saw earlier:"
    ]
   },
   {
@@ -1246,19 +1347,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "state       year\n",
-       "California  2000    33871648\n",
-       "            2010    37253956\n",
-       "New York    2000    18976457\n",
-       "            2010    19378102\n",
-       "Texas       2000    20851820\n",
-       "            2010    25145561\n",
+       "California  2010    37253956\n",
+       "            2020    39538223\n",
+       "New York    2010    19378102\n",
+       "            2020    20201249\n",
+       "Texas       2010    25145561\n",
+       "            2020    29145505\n",
        "dtype: int64"
       ]
      },
@@ -1287,13 +1391,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "33871648"
+       "37253956"
       ]
      },
      "execution_count": 22,
@@ -1302,7 +1409,7 @@
     }
    ],
    "source": [
-    "pop['California', 2000]"
+    "pop['California', 2010]"
    ]
   },
   {
@@ -1312,8 +1419,8 @@
     "editable": true
    },
    "source": [
-    "The ``MultiIndex`` also supports *partial indexing*, or indexing just one of the levels in the index.\n",
-    "The result is another ``Series``, with the lower-level indices maintained:"
+    "The `MultiIndex` also supports *partial indexing*, or indexing just one of the levels in the index.\n",
+    "The result is another `Series`, with the lower-level indices maintained:"
    ]
   },
   {
@@ -1322,15 +1429,18 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "year\n",
-       "2000    33871648\n",
        "2010    37253956\n",
+       "2020    39538223\n",
        "dtype: int64"
       ]
      },
@@ -1350,7 +1460,7 @@
     "editable": true
    },
    "source": [
-    "Partial slicing is available as well, as long as the ``MultiIndex`` is sorted (see discussion in [Sorted and Unsorted Indices](#Sorted-and-unsorted-indices)):"
+    "Partial slicing is available as well, as long as the `MultiIndex` is sorted (see the discussion in [Sorted and Unsorted Indices](#Sorted-and-unsorted-indices)):"
    ]
   },
   {
@@ -1359,17 +1469,20 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "state       year\n",
-       "California  2000    33871648\n",
-       "            2010    37253956\n",
-       "New York    2000    18976457\n",
-       "            2010    19378102\n",
+       "California  2010    37253956\n",
+       "            2020    39538223\n",
+       "New York    2010    19378102\n",
+       "            2020    20201249\n",
        "dtype: int64"
       ]
      },
@@ -1398,16 +1511,19 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "state\n",
-       "California    33871648\n",
-       "New York      18976457\n",
-       "Texas         20851820\n",
+       "California    37253956\n",
+       "New York      19378102\n",
+       "Texas         25145561\n",
        "dtype: int64"
       ]
      },
@@ -1417,7 +1533,7 @@
     }
    ],
    "source": [
-    "pop[:, 2000]"
+    "pop[:, 2010]"
    ]
   },
   {
@@ -1436,16 +1552,20 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "state       year\n",
-       "California  2000    33871648\n",
-       "            2010    37253956\n",
+       "California  2010    37253956\n",
+       "            2020    39538223\n",
        "Texas       2010    25145561\n",
+       "            2020    29145505\n",
        "dtype: int64"
       ]
      },
@@ -1474,17 +1594,20 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "state       year\n",
-       "California  2000    33871648\n",
-       "            2010    37253956\n",
-       "Texas       2000    20851820\n",
-       "            2010    25145561\n",
+       "California  2010    37253956\n",
+       "            2020    39538223\n",
+       "Texas       2010    25145561\n",
+       "            2020    29145505\n",
        "dtype: int64"
       ]
      },
@@ -1504,10 +1627,10 @@
     "editable": true
    },
    "source": [
-    "### Multiply indexed DataFrames\n",
+    "### Multiply Indexed DataFrames\n",
     "\n",
-    "A multiply indexed ``DataFrame`` behaves in a similar manner.\n",
-    "Consider our toy medical ``DataFrame`` from before:"
+    "A multiply indexed `DataFrame` behaves in a similar manner.\n",
+    "Consider our toy medical `DataFrame` from before:"
    ]
   },
   {
@@ -1516,13 +1639,33 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
@@ -1557,40 +1700,40 @@
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
        "      <th>1</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>38.7</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>36.7</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>37.2</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>38.3</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>35.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>44.0</td>\n",
-       "      <td>37.7</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>36.7</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>37.1</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>36.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
        "      <th>1</th>\n",
-       "      <td>30.0</td>\n",
-       "      <td>37.4</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>37.8</td>\n",
-       "      <td>61.0</td>\n",
-       "      <td>36.9</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>35.9</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>36.7</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>36.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>47.0</td>\n",
-       "      <td>37.8</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>36.3</td>\n",
        "      <td>48.0</td>\n",
-       "      <td>37.3</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>36.5</td>\n",
+       "      <td>39.2</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>35.7</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1600,10 +1743,10 @@
        "subject      Bob       Guido         Sue      \n",
        "type          HR  Temp    HR  Temp    HR  Temp\n",
        "year visit                                    \n",
-       "2013 1      31.0  38.7  32.0  36.7  35.0  37.2\n",
-       "     2      44.0  37.7  50.0  35.0  29.0  36.7\n",
-       "2014 1      30.0  37.4  39.0  37.8  61.0  36.9\n",
-       "     2      47.0  37.8  48.0  37.3  51.0  36.5"
+       "2013 1      30.0  38.0  56.0  38.3  45.0  35.8\n",
+       "     2      47.0  37.1  27.0  36.0  37.0  36.4\n",
+       "2014 1      51.0  35.9  24.0  36.7  32.0  36.2\n",
+       "     2      49.0  36.3  48.0  39.2  31.0  35.7"
       ]
      },
      "execution_count": 28,
@@ -1622,7 +1765,7 @@
     "editable": true
    },
    "source": [
-    "Remember that columns are primary in a ``DataFrame``, and the syntax used for multiply indexed ``Series`` applies to the columns.\n",
+    "Remember that columns are primary in a `DataFrame`, and the syntax used for multiply indexed `Series` applies to the columns.\n",
     "For example, we can recover Guido's heart rate data with a simple operation:"
    ]
   },
@@ -1632,16 +1775,19 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "year  visit\n",
-       "2013  1        32.0\n",
-       "      2        50.0\n",
-       "2014  1        39.0\n",
+       "2013  1        56.0\n",
+       "      2        27.0\n",
+       "2014  1        24.0\n",
        "      2        48.0\n",
        "Name: (Guido, HR), dtype: float64"
       ]
@@ -1662,7 +1808,7 @@
     "editable": true
    },
    "source": [
-    "Also, as with the single-index case, we can use the ``loc``, ``iloc``, and ``ix`` indexers introduced in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb). For example:"
+    "Also, as with the single-index case, we can use the `loc`, `iloc`, and `ix` indexers introduced in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb). For example:"
    ]
   },
   {
@@ -1671,13 +1817,33 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe thead tr th {\n",
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+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
@@ -1702,13 +1868,13 @@
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
        "      <th>1</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>38.7</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>38.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>44.0</td>\n",
-       "      <td>37.7</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>37.1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1718,8 +1884,8 @@
        "subject      Bob      \n",
        "type          HR  Temp\n",
        "year visit            \n",
-       "2013 1      31.0  38.7\n",
-       "     2      44.0  37.7"
+       "2013 1      30.0  38.0\n",
+       "     2      47.0  37.1"
       ]
      },
      "execution_count": 30,
@@ -1738,7 +1904,7 @@
     "editable": true
    },
    "source": [
-    "These indexers provide an array-like view of the underlying two-dimensional data, but each individual index in ``loc`` or ``iloc`` can be passed a tuple of multiple indices. For example:"
+    "These indexers provide an array-like view of the underlying two-dimensional data, but each individual index in `loc` or `iloc` can be passed a tuple of multiple indices. For example:"
    ]
   },
   {
@@ -1747,17 +1913,20 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "year  visit\n",
-       "2013  1        31.0\n",
-       "      2        44.0\n",
-       "2014  1        30.0\n",
+       "2013  1        30.0\n",
        "      2        47.0\n",
+       "2014  1        51.0\n",
+       "      2        49.0\n",
        "Name: (Bob, HR), dtype: float64"
       ]
      },
@@ -1786,15 +1955,18 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-32-8e3cc151e316>, line 1)",
+     "evalue": "invalid syntax (3311942670.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-32-8e3cc151e316>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    health_data.loc[(:, 1), (:, 'HR')]\u001b[0m\n\u001b[0m                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"/var/folders/xc/sptt9bk14s34rgxt7453p03r0000gp/T/ipykernel_86488/3311942670.py\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    health_data.loc[(:, 1), (:, 'HR')]\u001b[0m\n\u001b[0m                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1809,7 +1981,7 @@
     "editable": true
    },
    "source": [
-    "You could get around this by building the desired slice explicitly using Python's built-in ``slice()`` function, but a better way in this context is to use an ``IndexSlice`` object, which Pandas provides for precisely this situation.\n",
+    "You could get around this by building the desired slice explicitly using Python's built-in `slice` function, but a better way in this context is to use an `IndexSlice` object, which Pandas provides for precisely this situation.\n",
     "For example:"
    ]
   },
@@ -1819,13 +1991,33 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
@@ -1854,16 +2046,16 @@
        "    <tr>\n",
        "      <th>2013</th>\n",
        "      <th>1</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>35.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>45.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014</th>\n",
        "      <th>1</th>\n",
-       "      <td>30.0</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>61.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>32.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1873,8 +2065,8 @@
        "subject      Bob Guido   Sue\n",
        "type          HR    HR    HR\n",
        "year visit                  \n",
-       "2013 1      31.0  32.0  35.0\n",
-       "2014 1      30.0  39.0  61.0"
+       "2013 1      30.0  56.0  45.0\n",
+       "2014 1      51.0  24.0  32.0"
       ]
      },
      "execution_count": 33,
@@ -1894,7 +2086,7 @@
     "editable": true
    },
    "source": [
-    "There are so many ways to interact with data in multiply indexed ``Series`` and ``DataFrame``s, and as with many tools in this book the best way to become familiar with them is to try them out!"
+    "As you can see, there are many ways to interact with data in multiply indexed `Series` and ``DataFrame``s, and as with many tools in this book the best way to become familiar with them is to try them out!"
    ]
   },
   {
@@ -1904,11 +2096,11 @@
     "editable": true
    },
    "source": [
-    "## Rearranging Multi-Indices\n",
+    "## Rearranging Multi-Indexes\n",
     "\n",
     "One of the keys to working with multiply indexed data is knowing how to effectively transform the data.\n",
     "There are a number of operations that will preserve all the information in the dataset, but rearrange it for the purposes of various computations.\n",
-    "We saw a brief example of this in the ``stack()`` and ``unstack()`` methods, but there are many more ways to finely control the rearrangement of data between hierarchical indices and columns, and we'll explore them here."
+    "We saw a brief example of this in the `stack` and `unstack` methods, but there are many more ways to finely control the rearrangement of data between hierarchical indices and columns, and we'll explore them here."
    ]
   },
   {
@@ -1918,11 +2110,11 @@
     "editable": true
    },
    "source": [
-    "### Sorted and unsorted indices\n",
+    "### Sorted and Unsorted Indices\n",
     "\n",
-    "Earlier, we briefly mentioned a caveat, but we should emphasize it more here.\n",
-    "*Many of the ``MultiIndex`` slicing operations will fail if the index is not sorted.*\n",
-    "Let's take a look at this here.\n",
+    "Earlier I briefly mentioned a caveat, but I should emphasize it more here.\n",
+    "*Many of the `MultiIndex` slicing operations will fail if the index is not sorted.*\n",
+    "Let's take a closer look.\n",
     "\n",
     "We'll start by creating some simple multiply indexed data where the indices are *not lexographically sorted*:"
    ]
@@ -1933,19 +2125,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "char  int\n",
-       "a     1      0.003001\n",
-       "      2      0.164974\n",
-       "c     1      0.741650\n",
-       "      2      0.569264\n",
-       "b     1      0.001693\n",
-       "      2      0.526226\n",
+       "a     1      0.280341\n",
+       "      2      0.097290\n",
+       "c     1      0.206217\n",
+       "      2      0.431771\n",
+       "b     1      0.100183\n",
+       "      2      0.015851\n",
        "dtype: float64"
       ]
      },
@@ -1977,15 +2172,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<class 'KeyError'>\n",
-      "'Key length (1) was greater than MultiIndex lexsort depth (0)'\n"
+      "KeyError 'Key length (1) was greater than MultiIndex lexsort depth (0)'\n"
      ]
     }
    ],
@@ -1993,8 +2190,7 @@
     "try:\n",
     "    data['a':'b']\n",
     "except KeyError as e:\n",
-    "    print(type(e))\n",
-    "    print(e)"
+    "    print(\"KeyError\", e)"
    ]
   },
   {
@@ -2004,10 +2200,10 @@
     "editable": true
    },
    "source": [
-    "Although it is not entirely clear from the error message, this is the result of the MultiIndex not being sorted.\n",
-    "For various reasons, partial slices and other similar operations require the levels in the ``MultiIndex`` to be in sorted (i.e., lexographical) order.\n",
-    "Pandas provides a number of convenience routines to perform this type of sorting; examples are the ``sort_index()`` and ``sortlevel()`` methods of the ``DataFrame``.\n",
-    "We'll use the simplest, ``sort_index()``, here:"
+    "Although it is not entirely clear from the error message, this is the result of the `MultiIndex` not being sorted.\n",
+    "For various reasons, partial slices and other similar operations require the levels in the `MultiIndex` to be in sorted (i.e., lexographical) order.\n",
+    "Pandas provides a number of convenience routines to perform this type of sorting, such as the `sort_index` and `sortlevel` methods of the `DataFrame`.\n",
+    "We'll use the simplest, `sort_index`, here:"
    ]
   },
   {
@@ -2016,19 +2212,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "char  int\n",
-       "a     1      0.003001\n",
-       "      2      0.164974\n",
-       "b     1      0.001693\n",
-       "      2      0.526226\n",
-       "c     1      0.741650\n",
-       "      2      0.569264\n",
+       "a     1      0.280341\n",
+       "      2      0.097290\n",
+       "b     1      0.100183\n",
+       "      2      0.015851\n",
+       "c     1      0.206217\n",
+       "      2      0.431771\n",
        "dtype: float64"
       ]
      },
@@ -2058,17 +2257,20 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "char  int\n",
-       "a     1      0.003001\n",
-       "      2      0.164974\n",
-       "b     1      0.001693\n",
-       "      2      0.526226\n",
+       "a     1      0.280341\n",
+       "      2      0.097290\n",
+       "b     1      0.100183\n",
+       "      2      0.015851\n",
        "dtype: float64"
       ]
      },
@@ -2088,7 +2290,7 @@
     "editable": true
    },
    "source": [
-    "### Stacking and unstacking indices\n",
+    "### Stacking and Unstacking Indices\n",
     "\n",
     "As we saw briefly before, it is possible to convert a dataset from a stacked multi-index to a simple two-dimensional representation, optionally specifying the level to use:"
    ]
@@ -2099,13 +2301,29 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2123,17 +2341,17 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2000</th>\n",
-       "      <td>33871648</td>\n",
-       "      <td>18976457</td>\n",
-       "      <td>20851820</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td>37253956</td>\n",
        "      <td>19378102</td>\n",
        "      <td>25145561</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020</th>\n",
+       "      <td>39538223</td>\n",
+       "      <td>20201249</td>\n",
+       "      <td>29145505</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
@@ -2141,8 +2359,8 @@
       "text/plain": [
        "state  California  New York     Texas\n",
        "year                                 \n",
-       "2000     33871648  18976457  20851820\n",
-       "2010     37253956  19378102  25145561"
+       "2010     37253956  19378102  25145561\n",
+       "2020     39538223  20201249  29145505"
       ]
      },
      "execution_count": 38,
@@ -2160,19 +2378,35 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>year</th>\n",
-       "      <th>2000</th>\n",
        "      <th>2010</th>\n",
+       "      <th>2020</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state</th>\n",
@@ -2183,29 +2417,29 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
-       "      <td>33871648</td>\n",
        "      <td>37253956</td>\n",
+       "      <td>39538223</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
-       "      <td>18976457</td>\n",
        "      <td>19378102</td>\n",
+       "      <td>20201249</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Texas</th>\n",
-       "      <td>20851820</td>\n",
        "      <td>25145561</td>\n",
+       "      <td>29145505</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "year            2000      2010\n",
+       "year            2010      2020\n",
        "state                         \n",
-       "California  33871648  37253956\n",
-       "New York    18976457  19378102\n",
-       "Texas       20851820  25145561"
+       "California  37253956  39538223\n",
+       "New York    19378102  20201249\n",
+       "Texas       25145561  29145505"
       ]
      },
      "execution_count": 39,
@@ -2224,7 +2458,7 @@
     "editable": true
    },
    "source": [
-    "The opposite of ``unstack()`` is ``stack()``, which here can be used to recover the original series:"
+    "The opposite of `unstack` is `stack`, which here can be used to recover the original series:"
    ]
   },
   {
@@ -2233,19 +2467,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "state       year\n",
-       "California  2000    33871648\n",
-       "            2010    37253956\n",
-       "New York    2000    18976457\n",
-       "            2010    19378102\n",
-       "Texas       2000    20851820\n",
-       "            2010    25145561\n",
+       "California  2010    37253956\n",
+       "            2020    39538223\n",
+       "New York    2010    19378102\n",
+       "            2020    20201249\n",
+       "Texas       2010    25145561\n",
+       "            2020    29145505\n",
        "dtype: int64"
       ]
      },
@@ -2265,10 +2502,10 @@
     "editable": true
    },
    "source": [
-    "### Index setting and resetting\n",
+    "### Index Setting and Resetting\n",
     "\n",
-    "Another way to rearrange hierarchical data is to turn the index labels into columns; this can be accomplished with the ``reset_index`` method.\n",
-    "Calling this on the population dictionary will result in a ``DataFrame`` with a *state* and *year* column holding the information that was formerly in the index.\n",
+    "Another way to rearrange hierarchical data is to turn the index labels into columns; this can be accomplished with the `reset_index` method.\n",
+    "Calling this on the population dictionary will result in a `DataFrame` with `state` and `year` columns holding the information that was formerly in the index.\n",
     "For clarity, we can optionally specify the name of the data for the column representation:"
    ]
   },
@@ -2278,13 +2515,29 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2298,38 +2551,38 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>California</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>33871648</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>37253956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>California</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>37253956</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>39538223</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>New York</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>18976457</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>19378102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>New York</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>19378102</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>20201249</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Texas</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>20851820</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>25145561</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>Texas</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>25145561</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>29145505</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2337,12 +2590,12 @@
       ],
       "text/plain": [
        "        state  year  population\n",
-       "0  California  2000    33871648\n",
-       "1  California  2010    37253956\n",
-       "2    New York  2000    18976457\n",
-       "3    New York  2010    19378102\n",
-       "4       Texas  2000    20851820\n",
-       "5       Texas  2010    25145561"
+       "0  California  2010    37253956\n",
+       "1  California  2020    39538223\n",
+       "2    New York  2010    19378102\n",
+       "3    New York  2020    20201249\n",
+       "4       Texas  2010    25145561\n",
+       "5       Texas  2020    29145505"
       ]
      },
      "execution_count": 41,
@@ -2362,8 +2615,8 @@
     "editable": true
    },
    "source": [
-    "Often when working with data in the real world, the raw input data looks like this and it's useful to build a ``MultiIndex`` from the column values.\n",
-    "This can be done with the ``set_index`` method of the ``DataFrame``, which returns a multiply indexed ``DataFrame``:"
+    "A common pattern is to build a `MultiIndex` from the column values.\n",
+    "This can be done with the `set_index` method of the `DataFrame`, which returns a multiply indexed `DataFrame`:"
    ]
   },
   {
@@ -2372,13 +2625,29 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2395,31 +2664,31 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">California</th>\n",
-       "      <th>2000</th>\n",
-       "      <td>33871648</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td>37253956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">New York</th>\n",
-       "      <th>2000</th>\n",
-       "      <td>18976457</td>\n",
+       "      <th>2020</th>\n",
+       "      <td>39538223</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">New York</th>\n",
        "      <th>2010</th>\n",
        "      <td>19378102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">Texas</th>\n",
-       "      <th>2000</th>\n",
-       "      <td>20851820</td>\n",
+       "      <th>2020</th>\n",
+       "      <td>20201249</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Texas</th>\n",
        "      <th>2010</th>\n",
        "      <td>25145561</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020</th>\n",
+       "      <td>29145505</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
@@ -2427,12 +2696,12 @@
       "text/plain": [
        "                 population\n",
        "state      year            \n",
-       "California 2000    33871648\n",
-       "           2010    37253956\n",
-       "New York   2000    18976457\n",
-       "           2010    19378102\n",
-       "Texas      2000    20851820\n",
-       "           2010    25145561"
+       "California 2010    37253956\n",
+       "           2020    39538223\n",
+       "New York   2010    19378102\n",
+       "           2020    20201249\n",
+       "Texas      2010    25145561\n",
+       "           2020    29145505"
       ]
      },
      "execution_count": 42,
@@ -2451,338 +2720,17 @@
     "editable": true
    },
    "source": [
-    "In practice, I find this type of reindexing to be one of the more useful patterns when encountering real-world datasets."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Data Aggregations on Multi-Indices\n",
-    "\n",
-    "We've previously seen that Pandas has built-in data aggregation methods, such as ``mean()``, ``sum()``, and ``max()``.\n",
-    "For hierarchically indexed data, these can be passed a ``level`` parameter that controls which subset of the data the aggregate is computed on.\n",
-    "\n",
-    "For example, let's return to our health data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>subject</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>type</th>\n",
-       "      <th>HR</th>\n",
-       "      <th>Temp</th>\n",
-       "      <th>HR</th>\n",
-       "      <th>Temp</th>\n",
-       "      <th>HR</th>\n",
-       "      <th>Temp</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>year</th>\n",
-       "      <th>visit</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
-       "      <th>1</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>38.7</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>36.7</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>37.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>44.0</td>\n",
-       "      <td>37.7</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>36.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
-       "      <th>1</th>\n",
-       "      <td>30.0</td>\n",
-       "      <td>37.4</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>37.8</td>\n",
-       "      <td>61.0</td>\n",
-       "      <td>36.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>47.0</td>\n",
-       "      <td>37.8</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>37.3</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>36.5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "subject      Bob       Guido         Sue      \n",
-       "type          HR  Temp    HR  Temp    HR  Temp\n",
-       "year visit                                    \n",
-       "2013 1      31.0  38.7  32.0  36.7  35.0  37.2\n",
-       "     2      44.0  37.7  50.0  35.0  29.0  36.7\n",
-       "2014 1      30.0  37.4  39.0  37.8  61.0  36.9\n",
-       "     2      47.0  37.8  48.0  37.3  51.0  36.5"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "health_data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "Perhaps we'd like to average-out the measurements in the two visits each year. We can do this by naming the index level we'd like to explore, in this case the year:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th>subject</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>type</th>\n",
-       "      <th>HR</th>\n",
-       "      <th>Temp</th>\n",
-       "      <th>HR</th>\n",
-       "      <th>Temp</th>\n",
-       "      <th>HR</th>\n",
-       "      <th>Temp</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2013</th>\n",
-       "      <td>37.5</td>\n",
-       "      <td>38.2</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>35.85</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>36.95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014</th>\n",
-       "      <td>38.5</td>\n",
-       "      <td>37.6</td>\n",
-       "      <td>43.5</td>\n",
-       "      <td>37.55</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>36.70</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "subject   Bob       Guido          Sue       \n",
-       "type       HR  Temp    HR   Temp    HR   Temp\n",
-       "year                                         \n",
-       "2013     37.5  38.2  41.0  35.85  32.0  36.95\n",
-       "2014     38.5  37.6  43.5  37.55  56.0  36.70"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_mean = health_data.mean(level='year')\n",
-    "data_mean"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "By further making use of the ``axis`` keyword, we can take the mean among levels on the columns as well:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>type</th>\n",
-       "      <th>HR</th>\n",
-       "      <th>Temp</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2013</th>\n",
-       "      <td>36.833333</td>\n",
-       "      <td>37.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014</th>\n",
-       "      <td>46.000000</td>\n",
-       "      <td>37.283333</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "type         HR       Temp\n",
-       "year                      \n",
-       "2013  36.833333  37.000000\n",
-       "2014  46.000000  37.283333"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_mean.mean(axis=1, level='type')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "Thus in two lines, we've been able to find the average heart rate and temperature measured among all subjects in all visits each year.\n",
-    "This syntax is actually a short cut to the ``GroupBy`` functionality, which we will discuss in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb).\n",
-    "While this is a toy example, many real-world datasets have similar hierarchical structure."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Aside: Panel Data\n",
-    "\n",
-    "Pandas has a few other fundamental data structures that we have not yet discussed, namely the ``pd.Panel`` and ``pd.Panel4D`` objects.\n",
-    "These can be thought of, respectively, as three-dimensional and four-dimensional generalizations of the (one-dimensional) ``Series`` and (two-dimensional) ``DataFrame`` structures.\n",
-    "Once you are familiar with indexing and manipulation of data in a ``Series`` and ``DataFrame``, ``Panel`` and ``Panel4D`` are relatively straightforward to use.\n",
-    "In particular, the ``ix``, ``loc``, and ``iloc`` indexers discussed in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) extend readily to these higher-dimensional structures.\n",
-    "\n",
-    "We won't cover these panel structures further in this text, as I've found in the majority of cases that multi-indexing is a more useful and conceptually simpler representation for higher-dimensional data.\n",
-    "Additionally, panel data is fundamentally a dense data representation, while multi-indexing is fundamentally a sparse data representation.\n",
-    "As the number of dimensions increases, the dense representation can become very inefficient for the majority of real-world datasets.\n",
-    "For the occasional specialized application, however, these structures can be useful.\n",
-    "If you'd like to read more about the ``Panel`` and ``Panel4D`` structures, see the references listed in [Further Resources](03.13-Further-Resources.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Handling Missing Data](03.04-Missing-Values.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.05-Hierarchical-Indexing.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "In practice, this type of reindexing is one of the more useful patterns when exploring real-world datasets."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2796,9 +2744,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.06-Concat-And-Append.ipynb b/notebooks/03.06-Concat-And-Append.ipynb
index 7566c851c..116ef2d4b 100644
--- a/notebooks/03.06-Concat-And-Append.ipynb
+++ b/notebooks/03.06-Concat-And-Append.ipynb
@@ -4,29 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.06-Concat-And-Append.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Combining Datasets: Concat and Append"
+    "# Combining Datasets: concat and append"
    ]
   },
   {
@@ -34,10 +12,10 @@
    "metadata": {},
    "source": [
     "Some of the most interesting studies of data come from combining different data sources.\n",
-    "These operations can involve anything from very straightforward concatenation of two different datasets, to more complicated database-style joins and merges that correctly handle any overlaps between the datasets.\n",
-    "``Series`` and ``DataFrame``s are built with this type of operation in mind, and Pandas includes functions and methods that make this sort of data wrangling fast and straightforward.\n",
+    "These operations can involve anything from very straightforward concatenation of two different datasets to more complicated database-style joins and merges that correctly handle any overlaps between the datasets.\n",
+    "`Series` and ``DataFrame``s are built with this type of operation in mind, and Pandas includes functions and methods that make this sort of data wrangling fast and straightforward.\n",
     "\n",
-    "Here we'll take a look at simple concatenation of ``Series`` and ``DataFrame``s with the ``pd.concat`` function; later we'll dive into more sophisticated in-memory merges and joins implemented in Pandas.\n",
+    "Here we'll take a look at simple concatenation of `Series` and ``DataFrame``s with the `pd.concat` function; later we'll dive into more sophisticated in-memory merges and joins implemented in Pandas.\n",
     "\n",
     "We begin with the standard imports:"
    ]
@@ -46,7 +24,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -58,20 +36,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For convenience, we'll define this function which creates a ``DataFrame`` of a particular form that will be useful below:"
+    "For convenience, we'll define this function, which creates a `DataFrame` of a particular form that will be useful in the following examples:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -131,14 +125,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In addition, we'll create a quick class that allows us to display multiple ``DataFrame``s side by side. The code makes use of the special ``_repr_html_`` method, which IPython uses to implement its rich object display:"
+    "In addition, we'll create a quick class that allows us to display multiple ``DataFrame``s side by side. The code makes use of the special `_repr_html_` method, which IPython/Jupyter uses to implement its rich object display:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -173,7 +167,7 @@
    "source": [
     "## Recall: Concatenation of NumPy Arrays\n",
     "\n",
-    "Concatenation of ``Series`` and ``DataFrame`` objects is very similar to concatenation of Numpy arrays, which can be done via the ``np.concatenate`` function as discussed in [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb).\n",
+    "Concatenation of `Series` and `DataFrame` objects behaves similarly to concatenation of NumPy arrays, which can be done via the `np.concatenate` function, as discussed in [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb).\n",
     "Recall that with it, you can combine the contents of two or more arrays into a single array:"
    ]
   },
@@ -181,7 +175,10 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -207,14 +204,17 @@
    "metadata": {},
    "source": [
     "The first argument is a list or tuple of arrays to concatenate.\n",
-    "Additionally, it takes an ``axis`` keyword that allows you to specify the axis along which the result will be concatenated:"
+    "Additionally, in the case of multidimensional arrays, it takes an `axis` keyword that allows you to specify the axis along which the result will be concatenated:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -239,30 +239,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Simple Concatenation with ``pd.concat``"
+    "## Simple Concatenation with pd.concat"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pandas has a function, ``pd.concat()``, which has a similar syntax to ``np.concatenate`` but contains a number of options that we'll discuss momentarily:\n",
+    "The `pd.concat` function provides a similar syntax to `np.concatenate` but contains a number of options that we'll discuss momentarily:\n",
     "\n",
     "```python\n",
-    "# Signature in Pandas v0.18\n",
-    "pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,\n",
-    "          keys=None, levels=None, names=None, verify_integrity=False,\n",
-    "          copy=True)\n",
+    "# Signature in Pandas v1.3.5\n",
+    "pd.concat(objs, axis=0, join='outer', ignore_index=False, keys=None,\n",
+    "          levels=None, names=None, verify_integrity=False,\n",
+    "          sort=False, copy=True)\n",
     "```\n",
     "\n",
-    "``pd.concat()`` can be used for a simple concatenation of ``Series`` or ``DataFrame`` objects, just as ``np.concatenate()`` can be used for simple concatenations of arrays:"
+    "`pd.concat` can be used for a simple concatenation of `Series` or `DataFrame` objects, just as `np.concatenate` can be used for simple concatenations of arrays:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -299,7 +302,10 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -307,6 +313,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df1</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -332,6 +351,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -357,6 +389,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([df1, df2])</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -425,8 +470,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By default, the concatenation takes place row-wise within the ``DataFrame`` (i.e., ``axis=0``).\n",
-    "Like ``np.concatenate``, ``pd.concat`` allows specification of an axis along which concatenation will take place.\n",
+    "It's default behavior is to concatenate row-wise within the `DataFrame` (i.e., `axis=0`).\n",
+    "Like `np.concatenate`, `pd.concat` allows specification of an axis along which concatenation will take place.\n",
     "Consider the following example:"
    ]
   },
@@ -434,7 +479,10 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -442,6 +490,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df3</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -467,6 +528,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df4</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -491,7 +565,20 @@
        "</div>\n",
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([df3, df4], axis='col')</p><div>\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([df3, df4], axis='columns')</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -533,7 +620,7 @@
        "0  C0  D0\n",
        "1  C1  D1\n",
        "\n",
-       "pd.concat([df3, df4], axis='col')\n",
+       "pd.concat([df3, df4], axis='columns')\n",
        "    A   B   C   D\n",
        "0  A0  B0  C0  D0\n",
        "1  A1  B1  C1  D1"
@@ -547,31 +634,34 @@
    "source": [
     "df3 = make_df('AB', [0, 1])\n",
     "df4 = make_df('CD', [0, 1])\n",
-    "display('df3', 'df4', \"pd.concat([df3, df4], axis='col')\")"
+    "display('df3', 'df4', \"pd.concat([df3, df4], axis='columns')\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We could have equivalently specified ``axis=1``; here we've used the more intuitive ``axis='col'``. "
+    "We could have equivalently specified ``axis=1``; here we've used the more intuitive ``axis='columns'``. "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Duplicate indices\n",
+    "### Duplicate Indices\n",
     "\n",
-    "One important difference between ``np.concatenate`` and ``pd.concat`` is that Pandas concatenation *preserves indices*, even if the result will have duplicate indices!\n",
-    "Consider this simple example:"
+    "One important difference between `np.concatenate` and `pd.concat` is that Pandas concatenation *preserves indices*, even if the result will have duplicate indices!\n",
+    "Consider this short example:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -579,6 +669,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>x</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -604,6 +707,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>y</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -629,6 +745,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([x, y])</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -690,7 +819,7 @@
    "source": [
     "x = make_df('AB', [0, 1])\n",
     "y = make_df('AB', [2, 3])\n",
-    "y.index = x.index  # make duplicate indices!\n",
+    "y.index = x.index  # make indices match\n",
     "display('x', 'y', 'pd.concat([x, y])')"
    ]
   },
@@ -700,17 +829,17 @@
    "source": [
     "Notice the repeated indices in the result.\n",
     "While this is valid within ``DataFrame``s, the outcome is often undesirable.\n",
-    "``pd.concat()`` gives us a few ways to handle it."
+    "`pd.concat` gives us a few ways to handle it."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Catching the repeats as an error\n",
+    "#### Treating repeated indices as an error\n",
     "\n",
-    "If you'd like to simply verify that the indices in the result of ``pd.concat()`` do not overlap, you can specify the ``verify_integrity`` flag.\n",
-    "With this set to True, the concatenation will raise an exception if there are duplicate indices.\n",
+    "If you'd like to simply verify that the indices in the result of `pd.concat` do not overlap, you can include the `verify_integrity` flag.\n",
+    "With this set to `True`, the concatenation will raise an exception if there are duplicate indices.\n",
     "Here is an example, where for clarity we'll catch and print the error message:"
    ]
   },
@@ -718,14 +847,17 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ValueError: Indexes have overlapping values: [0, 1]\n"
+      "ValueError: Indexes have overlapping values: Int64Index([0, 1], dtype='int64')\n"
      ]
     }
    ],
@@ -743,15 +875,18 @@
     "#### Ignoring the index\n",
     "\n",
     "Sometimes the index itself does not matter, and you would prefer it to simply be ignored.\n",
-    "This option can be specified using the ``ignore_index`` flag.\n",
-    "With this set to true, the concatenation will create a new integer index for the resulting ``Series``:"
+    "This option can be specified using the `ignore_index` flag.\n",
+    "With this set to `True`, the concatenation will create a new integer index for the resulting `DataFrame`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -759,6 +894,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>x</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -784,6 +932,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>y</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -809,6 +970,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([x, y], ignore_index=True)</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -877,14 +1051,17 @@
    "source": [
     "#### Adding MultiIndex keys\n",
     "\n",
-    "Another option is to use the ``keys`` option to specify a label for the data sources; the result will be a hierarchically indexed series containing the data:"
+    "Another option is to use the `keys` option to specify a label for the data sources; the result will be a hierarchically indexed series containing the data:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -892,6 +1069,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>x</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -917,6 +1107,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>y</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -942,6 +1145,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([x, y], keys=['x', 'y'])</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1011,17 +1227,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The result is a multiply indexed ``DataFrame``, and we can use the tools discussed in [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) to transform this data into the representation we're interested in."
+    "We can use the tools discussed in [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) to transform this multiply indexed `DataFrame` into the representation we're interested in."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Concatenation with joins\n",
+    "### Concatenation with Joins\n",
     "\n",
-    "In the simple examples we just looked at, we were mainly concatenating ``DataFrame``s with shared column names.\n",
-    "In practice, data from different sources might have different sets of column names, and ``pd.concat`` offers several options in this case.\n",
+    "In the short examples we just looked at, we were mainly concatenating ``DataFrame``s with shared column names.\n",
+    "In practice, data from different sources might have different sets of column names, and `pd.concat` offers several options in this case.\n",
     "Consider the concatenation of the following two ``DataFrame``s, which have some (but not all!) columns in common:"
    ]
   },
@@ -1029,7 +1245,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1037,6 +1256,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df5</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1065,6 +1297,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df6</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1093,6 +1338,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([df5, df6])</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1171,16 +1429,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By default, the entries for which no data is available are filled with NA values.\n",
-    "To change this, we can specify one of several options for the ``join`` and ``join_axes`` parameters of the concatenate function.\n",
-    "By default, the join is a union of the input columns (``join='outer'``), but we can change this to an intersection of the columns using ``join='inner'``:"
+    "The default behavior is to fill entries for which no data is available with NA values.\n",
+    "To change this, we can adjust the `join` parameter of the `concat` function.\n",
+    "By default, the join is a union of the input columns (`join='outer'`), but we can change this to an intersection of the columns using `join='inner'`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1188,6 +1449,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df5</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1216,6 +1490,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df6</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1244,6 +1531,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([df5, df6], join='inner')</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1311,78 +1611,31 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Another option is to directly specify the index of the remaininig colums using the ``join_axes`` argument, which takes a list of index objects.\n",
-    "Here we'll specify that the returned columns should be the same as those of the first input:"
+    "Another useful pattern is to use the `reindex` method before concatenation for finer control over which columns are dropped:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df5</p><div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>A1</td>\n",
-       "      <td>B1</td>\n",
-       "      <td>C1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>A2</td>\n",
-       "      <td>B2</td>\n",
-       "      <td>C2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    </div>\n",
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df6</p><div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
-       "      <th>D</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>B3</td>\n",
-       "      <td>C3</td>\n",
-       "      <td>D3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>B4</td>\n",
-       "      <td>C4</td>\n",
-       "      <td>D4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    </div>\n",
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([df5, df6], join_axes=[df5.columns])</p><div>\n",
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1419,21 +1672,9 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "</div>\n",
-       "    </div>"
+       "</div>"
       ],
       "text/plain": [
-       "df5\n",
-       "    A   B   C\n",
-       "1  A1  B1  C1\n",
-       "2  A2  B2  C2\n",
-       "\n",
-       "df6\n",
-       "    B   C   D\n",
-       "3  B3  C3  D3\n",
-       "4  B4  C4  D4\n",
-       "\n",
-       "pd.concat([df5, df6], join_axes=[df5.columns])\n",
        "     A   B   C\n",
        "1   A1  B1  C1\n",
        "2   A2  B2  C2\n",
@@ -1447,32 +1688,27 @@
     }
    ],
    "source": [
-    "display('df5', 'df6',\n",
-    "        \"pd.concat([df5, df6], join_axes=[df5.columns])\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The combination of options of the ``pd.concat`` function allows a wide range of possible behaviors when joining two datasets; keep these in mind as you use these tools for your own data."
+    "pd.concat([df5, df6.reindex(df5.columns, axis=1)])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### The ``append()`` method\n",
+    "### The append Method\n",
     "\n",
-    "Because direct array concatenation is so common, ``Series`` and ``DataFrame`` objects have an ``append`` method that can accomplish the same thing in fewer keystrokes.\n",
-    "For example, rather than calling ``pd.concat([df1, df2])``, you can simply call ``df1.append(df2)``:"
+    "Because direct array concatenation is so common, `Series` and `DataFrame` objects have an `append` method that can accomplish the same thing in fewer keystrokes.\n",
+    "For example, in place of `pd.concat([df1, df2])`, you can use `df1.append(df2)`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1480,6 +1716,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df1</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1505,6 +1754,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1530,6 +1792,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df1.append(df2)</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1596,29 +1871,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Keep in mind that unlike the ``append()`` and ``extend()`` methods of Python lists, the ``append()`` method in Pandas does not modify the original object–instead it creates a new object with the combined data.\n",
+    "Keep in mind that unlike the `append` and `extend` methods of Python lists, the `append` method in Pandas does not modify the original object; instead it creates a new object with the combined data.\n",
     "It also is not a very efficient method, because it involves creation of a new index *and* data buffer.\n",
-    "Thus, if you plan to do multiple ``append`` operations, it is generally better to build a list of ``DataFrame``s and pass them all at once to the ``concat()`` function.\n",
-    "\n",
-    "In the next section, we'll look at another more powerful approach to combining data from multiple sources, the database-style merges/joins implemented in ``pd.merge``.\n",
-    "For more information on ``concat()``, ``append()``, and related functionality, see the [\"Merge, Join, and Concatenate\" section](http://pandas.pydata.org/pandas-docs/stable/merging.html) of the Pandas documentation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) >\n",
+    "Thus, if you plan to do multiple `append` operations, it is generally better to build a list of `DataFrame` objects and pass them all at once to the `concat` function.\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.06-Concat-And-Append.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "In the next chapter, we'll look at a more powerful approach to combining data from multiple sources: the database-style merges/joins implemented in `pd.merge`.\n",
+    "For more information on `concat`, `append`, and related functionality, see the [\"Merge, Join, Concatenate and Compare\" section](http://pandas.pydata.org/pandas-docs/stable/merging.html) of the Pandas documentation."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1632,9 +1900,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.07-Merge-and-Join.ipynb b/notebooks/03.07-Merge-and-Join.ipynb
index c46383e57..b3d2059f0 100644
--- a/notebooks/03.07-Merge-and-Join.ipynb
+++ b/notebooks/03.07-Merge-and-Join.ipynb
@@ -4,47 +4,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) | [Contents](Index.ipynb) | [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.07-Merge-and-Join.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Combining Datasets: Merge and Join"
+    "# Combining Datasets: merge and join"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One essential feature offered by Pandas is its high-performance, in-memory join and merge operations.\n",
-    "If you have ever worked with databases, you should be familiar with this type of data interaction.\n",
-    "The main interface for this is the ``pd.merge`` function, and we'll see few examples of how this can work in practice.\n",
+    "One important feature offered by Pandas is its high-performance, in-memory join and merge operations, which you may be familiar with if you have ever worked with databases.\n",
+    "The main interface for this is the `pd.merge` function, and we'll see a few examples of how this can work in practice.\n",
     "\n",
-    "For convenience, we will start by redefining the ``display()`` functionality from the previous section:"
+    "For convenience, we will again define the `display` function from the previous chapter after the usual imports:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -74,12 +52,12 @@
    "source": [
     "## Relational Algebra\n",
     "\n",
-    "The behavior implemented in ``pd.merge()`` is a subset of what is known as *relational algebra*, which is a formal set of rules for manipulating relational data, and forms the conceptual foundation of operations available in most databases.\n",
-    "The strength of the relational algebra approach is that it proposes several primitive operations, which become the building blocks of more complicated operations on any dataset.\n",
+    "The behavior implemented in `pd.merge` is a subset of what is known as *relational algebra*, which is a formal set of rules for manipulating relational data that forms the conceptual foundation of operations available in most databases.\n",
+    "The strength of the relational algebra approach is that it proposes several fundamental operations, which become the building blocks of more complicated operations on any dataset.\n",
     "With this lexicon of fundamental operations implemented efficiently in a database or other program, a wide range of fairly complicated composite operations can be performed.\n",
     "\n",
-    "Pandas implements several of these fundamental building-blocks in the ``pd.merge()`` function and the related ``join()`` method of ``Series`` and ``Dataframe``s.\n",
-    "As we will see, these let you efficiently link data from different sources."
+    "Pandas implements several of these fundamental building blocks in the `pd.merge` function and the related `join` method of `Series` and `DataFrame` objects.\n",
+    "As you will see, these let you efficiently link data from different sources."
    ]
   },
   {
@@ -88,26 +66,30 @@
    "source": [
     "## Categories of Joins\n",
     "\n",
-    "The ``pd.merge()`` function implements a number of types of joins: the *one-to-one*, *many-to-one*, and *many-to-many* joins.\n",
-    "All three types of joins are accessed via an identical call to the ``pd.merge()`` interface; the type of join performed depends on the form of the input data.\n",
-    "Here we will show simple examples of the three types of merges, and discuss detailed options further below."
+    "The `pd.merge` function implements a number of types of joins: *one-to-one*, *many-to-one*, and *many-to-many*.\n",
+    "All three types of joins are accessed via an identical call to the `pd.merge` interface; the type of join performed depends on the form of the input data.\n",
+    "We'll start with some simple examples of the three types of merges, and discuss detailed options a bit later."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### One-to-one joins\n",
+    "### One-to-One Joins\n",
     "\n",
-    "Perhaps the simplest type of merge expresion is the one-to-one join, which is in many ways very similar to the column-wise concatenation seen in [Combining Datasets: Concat & Append](03.06-Concat-And-Append.ipynb).\n",
-    "As a concrete example, consider the following two ``DataFrames`` which contain information on several employees in a company:"
+    "Perhaps the simplest type of merge is the one-to-one join, which is in many ways similar to the column-wise concatenation you saw in [Combining Datasets: Concat & Append](03.06-Concat-And-Append.ipynb).\n",
+    "As a concrete example, consider the following two `DataFrame` objects, which contain information on several employees in a company:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -115,6 +97,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df1</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -150,6 +145,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -207,7 +215,8 @@
    ],
    "source": [
     "df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
-    "                    'group': ['Accounting', 'Engineering', 'Engineering', 'HR']})\n",
+    "                    'group': ['Accounting', 'Engineering',\n",
+    "                              'Engineering', 'HR']})\n",
     "df2 = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue'],\n",
     "                    'hire_date': [2004, 2008, 2012, 2014]})\n",
     "display('df1', 'df2')"
@@ -217,20 +226,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To combine this information into a single ``DataFrame``, we can use the ``pd.merge()`` function:"
+    "To combine this information into a single `DataFrame`, we can use the `pd.merge` function:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -291,17 +316,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``pd.merge()`` function recognizes that each ``DataFrame`` has an \"employee\" column, and automatically joins using this column as a key.\n",
-    "The result of the merge is a new ``DataFrame`` that combines the information from the two inputs.\n",
-    "Notice that the order of entries in each column is not necessarily maintained: in this case, the order of the \"employee\" column differs between ``df1`` and ``df2``, and the ``pd.merge()`` function correctly accounts for this.\n",
-    "Additionally, keep in mind that the merge in general discards the index, except in the special case of merges by index (see the ``left_index`` and ``right_index`` keywords, discussed momentarily)."
+    "The `pd.merge` function recognizes that each `DataFrame` has an `employee` column, and automatically joins using this column as a key.\n",
+    "The result of the merge is a new `DataFrame` that combines the information from the two inputs.\n",
+    "Notice that the order of entries in each column is not necessarily maintained: in this case, the order of the `employee` column differs between `df1` and `df2`, and the `pd.merge` function correctly accounts for this.\n",
+    "Additionally, keep in mind that the merge in general discards the index, except in the special case of merges by index (see the `left_index` and `right_index` keywords, discussed momentarily)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Many-to-one joins"
+    "### Many-to-One Joins"
    ]
   },
   {
@@ -309,7 +334,7 @@
    "metadata": {},
    "source": [
     "Many-to-one joins are joins in which one of the two key columns contains duplicate entries.\n",
-    "For the many-to-one case, the resulting ``DataFrame`` will preserve those duplicate entries as appropriate.\n",
+    "For the many-to-one case, the resulting `DataFrame` will preserve those duplicate entries as appropriate.\n",
     "Consider the following example of a many-to-one join:"
    ]
   },
@@ -317,7 +342,10 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -325,6 +353,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df3</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -365,6 +406,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df4</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -395,6 +449,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df3, df4)</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -476,24 +543,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The resulting ``DataFrame`` has an aditional column with the \"supervisor\" information, where the information is repeated in one or more locations as required by the inputs."
+    "The resulting `DataFrame` has an additional column with the \"supervisor\" information, where the information is repeated in one or more locations as required by the inputs."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Many-to-many joins"
+    "### Many-to-Many Joins"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Many-to-many joins are a bit confusing conceptually, but are nevertheless well defined.\n",
-    "If the key column in both the left and right array contains duplicates, then the result is a many-to-many merge.\n",
+    "Many-to-many joins may be a bit confusing conceptually, but are nevertheless well defined.\n",
+    "If the key column in both the left and right arrays contains duplicates, then the result is a many-to-many merge.\n",
     "This will be perhaps most clear with a concrete example.\n",
-    "Consider the following, where we have a ``DataFrame`` showing one or more skills associated with a particular group.\n",
+    "Consider the following, where we have a `DataFrame` showing one or more skills associated with a particular group.\n",
     "By performing a many-to-many join, we can recover the skills associated with any individual person:"
    ]
   },
@@ -501,7 +568,10 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -509,6 +579,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df1</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -544,6 +627,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df5</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -566,12 +662,12 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Engineering</td>\n",
-       "      <td>coding</td>\n",
+       "      <td>software</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Engineering</td>\n",
-       "      <td>linux</td>\n",
+       "      <td>math</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -589,6 +685,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df1, df5)</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -615,25 +724,25 @@
        "      <th>2</th>\n",
        "      <td>Jake</td>\n",
        "      <td>Engineering</td>\n",
-       "      <td>coding</td>\n",
+       "      <td>software</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Jake</td>\n",
        "      <td>Engineering</td>\n",
-       "      <td>linux</td>\n",
+       "      <td>math</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Lisa</td>\n",
        "      <td>Engineering</td>\n",
-       "      <td>coding</td>\n",
+       "      <td>software</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>Lisa</td>\n",
        "      <td>Engineering</td>\n",
-       "      <td>linux</td>\n",
+       "      <td>math</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -664,8 +773,8 @@
        "         group        skills\n",
        "0   Accounting          math\n",
        "1   Accounting  spreadsheets\n",
-       "2  Engineering        coding\n",
-       "3  Engineering         linux\n",
+       "2  Engineering      software\n",
+       "3  Engineering          math\n",
        "4           HR  spreadsheets\n",
        "5           HR  organization\n",
        "\n",
@@ -673,10 +782,10 @@
        "  employee        group        skills\n",
        "0      Bob   Accounting          math\n",
        "1      Bob   Accounting  spreadsheets\n",
-       "2     Jake  Engineering        coding\n",
-       "3     Jake  Engineering         linux\n",
-       "4     Lisa  Engineering        coding\n",
-       "5     Lisa  Engineering         linux\n",
+       "2     Jake  Engineering      software\n",
+       "3     Jake  Engineering          math\n",
+       "4     Lisa  Engineering      software\n",
+       "5     Lisa  Engineering          math\n",
        "6      Sue           HR  spreadsheets\n",
        "7      Sue           HR  organization"
       ]
@@ -689,7 +798,7 @@
    "source": [
     "df5 = pd.DataFrame({'group': ['Accounting', 'Accounting',\n",
     "                              'Engineering', 'Engineering', 'HR', 'HR'],\n",
-    "                    'skills': ['math', 'spreadsheets', 'coding', 'linux',\n",
+    "                    'skills': ['math', 'spreadsheets', 'software', 'math',\n",
     "                               'spreadsheets', 'organization']})\n",
     "display('df1', 'df5', \"pd.merge(df1, df5)\")"
    ]
@@ -700,7 +809,7 @@
    "source": [
     "These three types of joins can be used with other Pandas tools to implement a wide array of functionality.\n",
     "But in practice, datasets are rarely as clean as the one we're working with here.\n",
-    "In the following section we'll consider some of the options provided by ``pd.merge()`` that enable you to tune how the join operations work."
+    "In the following section we'll consider some of the options provided by `pd.merge` that enable you to tune how the join operations work."
    ]
   },
   {
@@ -714,24 +823,34 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We've already seen the default behavior of ``pd.merge()``: it looks for one or more matching column names between the two inputs, and uses this as the key.\n",
-    "However, often the column names will not match so nicely, and ``pd.merge()`` provides a variety of options for handling this."
+    "We've already seen the default behavior of `pd.merge`: it looks for one or more matching column names between the two inputs, and uses this as the key.\n",
+    "However, often the column names will not match so nicely, and `pd.merge` provides a variety of options for handling this."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### The ``on`` keyword\n",
+    "### The on Keyword\n",
     "\n",
-    "Most simply, you can explicitly specify the name of the key column using the ``on`` keyword, which takes a column name or a list of column names:"
+    "Most simply, you can explicitly specify the name of the key column using the `on` keyword, which takes a column name or a list of column names:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -739,6 +858,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df1</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -774,6 +906,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -809,6 +954,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df1, df2, on='employee')</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -891,17 +1049,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### The ``left_on`` and ``right_on`` keywords\n",
+    "### The left_on and right_on Keywords\n",
     "\n",
     "At times you may wish to merge two datasets with different column names; for example, we may have a dataset in which the employee name is labeled as \"name\" rather than \"employee\".\n",
-    "In this case, we can use the ``left_on`` and ``right_on`` keywords to specify the two column names:"
+    "In this case, we can use the `left_on` and `right_on` keywords to specify the two column names:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -909,6 +1070,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df1</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -944,6 +1118,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df3</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -979,6 +1166,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df1, df3, left_on=\"employee\", right_on=\"name\")</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1058,23 +1258,40 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The result has a redundant column that we can drop if desired–for example, by using the ``drop()`` method of ``DataFrame``s:"
+    "The result has a redundant column that we can drop if desired—for example, by using the `DataFrame.drop()` method:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1134,7 +1351,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### The ``left_index`` and ``right_index`` keywords\n",
+    "### The left_index and right_index Keywords\n",
     "\n",
     "Sometimes, rather than merging on a column, you would instead like to merge on an index.\n",
     "For example, your data might look like this:"
@@ -1144,7 +1361,10 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1152,6 +1372,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df1a</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1186,6 +1419,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df2a</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1252,14 +1498,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can use the index as the key for merging by specifying the ``left_index`` and/or ``right_index`` flags in ``pd.merge()``:"
+    "You can use the index as the key for merging by specifying the `left_index` and/or `right_index` flags in `pd.merge()`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1267,6 +1516,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df1a</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1301,6 +1563,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df2a</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1335,6 +1610,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df1a, df2a, left_index=True, right_index=True)</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1350,11 +1638,6 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>Lisa</th>\n",
-       "      <td>Engineering</td>\n",
-       "      <td>2004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>Bob</th>\n",
        "      <td>Accounting</td>\n",
        "      <td>2008</td>\n",
@@ -1365,6 +1648,11 @@
        "      <td>2012</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>Lisa</th>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>2004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>Sue</th>\n",
        "      <td>HR</td>\n",
        "      <td>2014</td>\n",
@@ -1394,9 +1682,9 @@
        "pd.merge(df1a, df2a, left_index=True, right_index=True)\n",
        "                group  hire_date\n",
        "employee                        \n",
-       "Lisa      Engineering       2004\n",
        "Bob        Accounting       2008\n",
        "Jake      Engineering       2012\n",
+       "Lisa      Engineering       2004\n",
        "Sue                HR       2014"
       ]
      },
@@ -1414,89 +1702,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For convenience, ``DataFrame``s implement the ``join()`` method, which performs a merge that defaults to joining on indices:"
+    "For convenience, Pandas includes the `DataFrame.join()` method, which performs an index-based merge without extra keywords:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df1a</p><div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>group</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>employee</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Bob</th>\n",
-       "      <td>Accounting</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Jake</th>\n",
-       "      <td>Engineering</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Lisa</th>\n",
-       "      <td>Engineering</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Sue</th>\n",
-       "      <td>HR</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    </div>\n",
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2a</p><div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>hire_date</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>employee</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Lisa</th>\n",
-       "      <td>2004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Bob</th>\n",
-       "      <td>2008</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Jake</th>\n",
-       "      <td>2012</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Sue</th>\n",
-       "      <td>2014</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    </div>\n",
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df1a.join(df2a)</p><div>\n",
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1533,27 +1768,9 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "</div>\n",
-       "    </div>"
+       "</div>"
       ],
       "text/plain": [
-       "df1a\n",
-       "                group\n",
-       "employee             \n",
-       "Bob        Accounting\n",
-       "Jake      Engineering\n",
-       "Lisa      Engineering\n",
-       "Sue                HR\n",
-       "\n",
-       "df2a\n",
-       "          hire_date\n",
-       "employee           \n",
-       "Lisa           2004\n",
-       "Bob            2008\n",
-       "Jake           2012\n",
-       "Sue            2014\n",
-       "\n",
-       "df1a.join(df2a)\n",
        "                group  hire_date\n",
        "employee                        \n",
        "Bob        Accounting       2008\n",
@@ -1568,21 +1785,24 @@
     }
    ],
    "source": [
-    "display('df1a', 'df2a', 'df1a.join(df2a)')"
+    "df1a.join(df2a)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you'd like to mix indices and columns, you can combine ``left_index`` with ``right_on`` or ``left_on`` with ``right_index`` to get the desired behavior:"
+    "If you'd like to mix indices and columns, you can combine `left_index` with `right_on` or `left_on` with `right_index` to get the desired behavior:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1590,6 +1810,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df1a</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1624,6 +1857,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df3</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1659,6 +1905,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df1a, df3, left_index=True, right_on='name')</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1758,7 +2017,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1766,6 +2028,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df6</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1796,6 +2071,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df7</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1821,6 +2109,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df6, df7)</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1880,20 +2181,36 @@
    "source": [
     "Here we have merged two datasets that have only a single \"name\" entry in common: Mary.\n",
     "By default, the result contains the *intersection* of the two sets of inputs; this is what is known as an *inner join*.\n",
-    "We can specify this explicitly using the ``how`` keyword, which defaults to ``\"inner\"``:"
+    "We can specify this explicitly using the `how` keyword, which defaults to `\"inner\"`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1932,7 +2249,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Other options for the ``how`` keyword are ``'outer'``, ``'left'``, and ``'right'``.\n",
+    "Other options for the `how` keyword are `'outer'`, `'left'`, and `'right'`.\n",
     "An *outer join* returns a join over the union of the input columns, and fills in all missing values with NAs:"
    ]
   },
@@ -1940,7 +2257,10 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1948,6 +2268,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df6</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1978,6 +2311,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df7</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2003,6 +2349,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df6, df7, how='outer')</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2083,7 +2442,10 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -2091,6 +2453,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df6</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2121,6 +2496,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df7</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2146,6 +2534,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df6, df7, how='left')</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2212,7 +2613,7 @@
    "metadata": {},
    "source": [
     "The output rows now correspond to the entries in the left input. Using\n",
-    "``how='right'`` works in a similar manner.\n",
+    "`how='right'` works in a similar manner.\n",
     "\n",
     "All of these options can be applied straightforwardly to any of the preceding join types."
    ]
@@ -2221,14 +2622,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Overlapping Column Names: The ``suffixes`` Keyword"
+    "## Overlapping Column Names: The suffixes Keyword"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, you may end up in a case where your two input ``DataFrame``s have conflicting column names.\n",
+    "Last, you may end up in a case where your two input ``DataFrame``s have conflicting column names.\n",
     "Consider this example:"
    ]
   },
@@ -2236,7 +2638,10 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -2244,6 +2649,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df8</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2279,6 +2697,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df9</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2314,6 +2745,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df8, df9, on=\"name\")</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2393,7 +2837,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Because the output would have two conflicting column names, the merge function automatically appends a suffix ``_x`` or ``_y`` to make the output columns unique.\n",
+    "Because the output would have two conflicting column names, the `merge` function automatically appends the suffixes ``_x`` and ``_y`` to make the output columns unique.\n",
     "If these defaults are inappropriate, it is possible to specify a custom suffix using the ``suffixes`` keyword:"
    ]
   },
@@ -2401,84 +2845,29 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df8</p><div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>rank</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Bob</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Jake</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Lisa</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Sue</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    </div>\n",
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df9</p><div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>rank</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Bob</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Jake</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Lisa</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Sue</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    </div>\n",
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df8, df9, on=\"name\", suffixes=[\"_L\", \"_R\"])</p><div>\n",
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2515,25 +2904,9 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "</div>\n",
-       "    </div>"
+       "</div>"
       ],
       "text/plain": [
-       "df8\n",
-       "   name  rank\n",
-       "0   Bob     1\n",
-       "1  Jake     2\n",
-       "2  Lisa     3\n",
-       "3   Sue     4\n",
-       "\n",
-       "df9\n",
-       "   name  rank\n",
-       "0   Bob     3\n",
-       "1  Jake     1\n",
-       "2  Lisa     4\n",
-       "3   Sue     2\n",
-       "\n",
-       "pd.merge(df8, df9, on=\"name\", suffixes=[\"_L\", \"_R\"])\n",
        "   name  rank_L  rank_R\n",
        "0   Bob       1       3\n",
        "1  Jake       2       1\n",
@@ -2547,22 +2920,22 @@
     }
    ],
    "source": [
-    "display('df8', 'df9', 'pd.merge(df8, df9, on=\"name\", suffixes=[\"_L\", \"_R\"])')"
+    "pd.merge(df8, df9, on=\"name\", suffixes=[\"_L\", \"_R\"])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "These suffixes work in any of the possible join patterns, and work also if there are multiple overlapping columns."
+    "These suffixes work in any of the possible join patterns, and also work if there are multiple overlapping columns."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For more information on these patterns, see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) where we dive a bit deeper into relational algebra.\n",
-    "Also see the [Pandas \"Merge, Join and Concatenate\" documentation](http://pandas.pydata.org/pandas-docs/stable/merging.html) for further discussion of these topics."
+    "For more information on these patterns, see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb), where we dive a bit deeper into relational algebra.\n",
+    "Also see the [\"Merge, Join, Concatenate and Compare\" section](http://pandas.pydata.org/pandas-docs/stable/merging.html) of the Pandas documentation for further discussion of these topics."
    ]
   },
   {
@@ -2573,35 +2946,42 @@
     "\n",
     "Merge and join operations come up most often when combining data from different sources.\n",
     "Here we will consider an example of some data about US states and their populations.\n",
-    "The data files can be found at http://github.com/jakevdp/data-USstates/:"
+    "The data files can be found at [http://github.com/jakevdp/data-USstates](http://github.com/jakevdp/data-USstates):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "# Following are shell commands to download the data\n",
-    "# !curl -O https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-population.csv\n",
-    "# !curl -O https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-areas.csv\n",
-    "# !curl -O https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-abbrevs.csv"
+    "# Following are commands to download the data\n",
+    "# repo = \"https://raw.githubusercontent.com/jakevdp/data-USstates/master\"\n",
+    "# !cd data && curl -O {repo}/state-population.csv\n",
+    "# !cd data && curl -O {repo}/state-areas.csv\n",
+    "# !cd data && curl -O {repo}/state-abbrevs.csv"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's take a look at the three datasets, using the Pandas ``read_csv()`` function:"
+    "Let's take a look at the three datasets, using the Pandas ``read_csv`` function:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -2609,6 +2989,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>pop.head()</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2661,6 +3054,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>areas.head()</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2701,6 +3107,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>abbrevs.head()</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2784,24 +3203,40 @@
    "metadata": {},
    "source": [
     "Given this information, say we want to compute a relatively straightforward result: rank US states and territories by their 2010 population density.\n",
-    "We clearly have the data here to find this result, but we'll have to combine the datasets to find the result.\n",
+    "We clearly have the data here to find this result, but we'll have to combine the datasets to do so.\n",
     "\n",
-    "We'll start with a many-to-one merge that will give us the full state name within the population ``DataFrame``.\n",
-    "We want to merge based on the ``state/region``  column of ``pop``, and the ``abbreviation`` column of ``abbrevs``.\n",
-    "We'll use ``how='outer'`` to make sure no data is thrown away due to mismatched labels."
+    "We'll start with a many-to-one merge that will give us the full state names within the population `DataFrame`.\n",
+    "We want to merge based on the `state/region`  column of `pop` and the `abbreviation` column of `abbrevs`.\n",
+    "We'll use `how='outer'` to make sure no data is thrown away due to mismatched labels:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2875,7 +3310,7 @@
    "source": [
     "merged = pd.merge(pop, abbrevs, how='outer',\n",
     "                  left_on='state/region', right_on='abbreviation')\n",
-    "merged = merged.drop('abbreviation', 1) # drop duplicate info\n",
+    "merged = merged.drop('abbreviation', axis=1) # drop duplicate info\n",
     "merged.head()"
    ]
   },
@@ -2890,7 +3325,10 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -2917,20 +3355,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Some of the ``population`` info is null; let's figure out which these are!"
+    "Some of the ``population`` values are null; let's figure out which these are!"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -3009,9 +3463,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It appears that all the null population values are from Puerto Rico prior to the year 2000; this is likely due to this data not being available from the original source.\n",
+    "It appears that all the null population values are from Puerto Rico prior to the year 2000; this is likely due to this data not being available in the original source.\n",
     "\n",
-    "More importantly, we see also that some of the new ``state`` entries are also null, which means that there was no corresponding entry in the ``abbrevs`` key!\n",
+    "More importantly, we see that some of the new `state` entries are also null, which means that there was no corresponding entry in the `abbrevs` key!\n",
     "Let's figure out which regions lack this match:"
    ]
   },
@@ -3019,7 +3473,10 @@
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -3049,7 +3506,10 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -3078,23 +3538,39 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "No more nulls in the ``state`` column: we're all set!\n",
+    "No more nulls in the `state` column: we're all set!\n",
     "\n",
     "Now we can merge the result with the area data using a similar procedure.\n",
-    "Examining our results, we will want to join on the ``state`` column in both:"
+    "Examining our results, we will want to join on the `state` column in both:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -3187,7 +3663,10 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -3222,7 +3701,10 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -3252,13 +3734,29 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -3344,21 +3842,37 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we have all the data we need. To answer the question of interest, let's first select the portion of the data corresponding with the year 2000, and the total population.\n",
-    "We'll use the ``query()`` function to do this quickly (this requires the ``numexpr`` package to be installed; see [High-Performance Pandas: ``eval()`` and ``query()``](03.12-Performance-Eval-and-Query.ipynb)):"
+    "Now we have all the data we need. To answer the question of interest, let's first select the portion of the data corresponding with the year 2010, and the total population.\n",
+    "We'll use the `query` function to do this quickly (this requires the NumExpr package to be installed; see [High-Performance Pandas: `eval()` and `query()`](03.12-Performance-Eval-and-Query.ipynb)):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -3452,7 +3966,10 @@
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -3464,7 +3981,10 @@
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -3493,7 +4013,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The result is a ranking of US states plus Washington, DC, and Puerto Rico in order of their 2010 population density, in residents per square mile.\n",
+    "The result is a ranking of US states, plus Washington, DC, and Puerto Rico, in order of their 2010 population density, in residents per square mile.\n",
     "We can see that by far the densest region in this dataset is Washington, DC (i.e., the District of Columbia); among states, the densest is New Jersey.\n",
     "\n",
     "We can also check the end of the list:"
@@ -3503,7 +4023,10 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -3533,25 +4056,18 @@
    "source": [
     "We see that the least dense state, by far, is Alaska, averaging slightly over one resident per square mile.\n",
     "\n",
-    "This type of messy data merging is a common task when trying to answer questions using real-world data sources.\n",
-    "I hope that this example has given you an idea of the ways you can combine tools we've covered in order to gain insight from your data!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) | [Contents](Index.ipynb) | [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.07-Merge-and-Join.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "This type of data merging is a common task when trying to answer questions using real-world data sources.\n",
+    "I hope that this example has given you an idea of some of the ways you can combine the tools we've covered in order to gain insight from your data!"
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -3565,9 +4081,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.08-Aggregation-and-Grouping.ipynb b/notebooks/03.08-Aggregation-and-Grouping.ipynb
index be00723d1..02c1cb588 100644
--- a/notebooks/03.08-Aggregation-and-Grouping.ipynb
+++ b/notebooks/03.08-Aggregation-and-Grouping.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) | [Contents](Index.ipynb) | [Pivot Tables](03.09-Pivot-Tables.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.08-Aggregation-and-Grouping.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -31,26 +9,24 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "source": [
-    "An essential piece of analysis of large data is efficient summarization: computing aggregations like ``sum()``, ``mean()``, ``median()``, ``min()``, and ``max()``, in which a single number gives insight into the nature of a potentially large dataset.\n",
-    "In this section, we'll explore aggregations in Pandas, from simple operations akin to what we've seen on NumPy arrays, to more sophisticated operations based on the concept of a ``groupby``."
+    "A fundamental piece of many data analysis tasks is efficient summarization: computing aggregations like `sum`, `mean`, `median`, `min`, and `max`, in which a single number summarizes aspects of a potentially large dataset.\n",
+    "In this chapter, we'll explore aggregations in Pandas, from simple operations akin to what we've seen on NumPy arrays to more sophisticated operations based on the concept of a `groupby`."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For convenience, we'll use the same ``display`` magic function that we've seen in previous sections:"
+    "For convenience, we'll use the same `display` magic function that we used in the previous chapters:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -81,14 +57,17 @@
     "## Planets Data\n",
     "\n",
     "Here we will use the Planets dataset, available via the [Seaborn package](http://seaborn.pydata.org/) (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)).\n",
-    "It gives information on planets that astronomers have discovered around other stars (known as *extrasolar planets* or *exoplanets* for short). It can be downloaded with a simple Seaborn command:"
+    "It gives information on planets that astronomers have discovered around other stars (known as *extrasolar planets*, or *exoplanets* for short). It can be downloaded with a simple Seaborn command:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -112,13 +91,29 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -217,7 +212,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Earlier, we explored some of the data aggregations available for NumPy arrays ([\"Aggregations: Min, Max, and Everything In Between\"](02.04-Computation-on-arrays-aggregates.ipynb)).\n",
+    "In [\"Aggregations: Min, Max, and Everything In Between\"](02.04-Computation-on-arrays-aggregates.ipynb), we explored some of the data aggregations available for NumPy arrays.\n",
     "As with a one-dimensional NumPy array, for a Pandas ``Series`` the aggregates return a single value:"
    ]
   },
@@ -225,7 +220,10 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -254,13 +252,16 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
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     {
      "data": {
       "text/plain": [
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+       "2.811925491708157"
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@@ -276,13 +277,16 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
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    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.56238509834163142"
+       "0.5623850983416314"
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@@ -298,20 +302,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For a ``DataFrame``, by default the aggregates return results within each column:"
+    "For a `DataFrame`, by default the aggregates return results within each column:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "    .dataframe thead th {\n",
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+       "</style>\n",
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        "  <thead>\n",
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@@ -374,7 +394,10 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -398,14 +421,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By specifying the ``axis`` argument, you can instead aggregate within each row:"
+    "By specifying the `axis` argument, you can instead aggregate within each row:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -432,7 +458,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pandas ``Series`` and ``DataFrame``s include all of the common aggregates mentioned in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb); in addition, there is a convenience method ``describe()`` that computes several common aggregates for each column and returns the result.\n",
+    "Pandas `Series` and `DataFrame` objects include all of the common aggregates mentioned in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb); in addition, there is a convenience method, `describe`, that computes several common aggregates for each column and returns the result.\n",
     "Let's use this on the Planets data, for now dropping rows with missing values:"
    ]
   },
@@ -440,13 +466,29 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -552,9 +594,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This can be a useful way to begin understanding the overall properties of a dataset.\n",
-    "For example, we see in the ``year`` column that although exoplanets were discovered as far back as 1989, half of all known expolanets were not discovered until 2010 or after.\n",
-    "This is largely thanks to the *Kepler* mission, which is a space-based telescope specifically designed for finding eclipsing planets around other stars."
+    "This method helps us understand the overall properties of a dataset.\n",
+    "For example, we see in the `year` column that although exoplanets were discovered as far back as 1989, half of all planets in the dataset were not discovered until 2010 or after.\n",
+    "This is largely thanks to the *Kepler* mission, which aimed to find eclipsing planets around other stars using a specially designed space telescope."
    ]
   },
   {
@@ -563,18 +605,18 @@
    "source": [
     "The following table summarizes some other built-in Pandas aggregations:\n",
     "\n",
-    "| Aggregation              | Description                     |\n",
+    "| Aggregation              | Returns                         |\n",
     "|--------------------------|---------------------------------|\n",
-    "| ``count()``              | Total number of items           |\n",
-    "| ``first()``, ``last()``  | First and last item             |\n",
-    "| ``mean()``, ``median()`` | Mean and median                 |\n",
-    "| ``min()``, ``max()``     | Minimum and maximum             |\n",
-    "| ``std()``, ``var()``     | Standard deviation and variance |\n",
-    "| ``mad()``                | Mean absolute deviation         |\n",
-    "| ``prod()``               | Product of all items            |\n",
-    "| ``sum()``                | Sum of all items                |\n",
+    "| ``count``                | Total number of items           |\n",
+    "| ``first``, ``last``      | First and last item             |\n",
+    "| ``mean``, ``median``     | Mean and median                 |\n",
+    "| ``min``, ``max``         | Minimum and maximum             |\n",
+    "| ``std``, ``var``         | Standard deviation and variance |\n",
+    "| ``mad``                  | Mean absolute deviation         |\n",
+    "| ``prod``                 | Product of all items            |\n",
+    "| ``sum``                  | Sum of all items                |\n",
     "\n",
-    "These are all methods of ``DataFrame`` and ``Series`` objects."
+    "These are all methods of `DataFrame` and `Series` objects."
    ]
   },
   {
@@ -582,16 +624,16 @@
    "metadata": {},
    "source": [
     "To go deeper into the data, however, simple aggregates are often not enough.\n",
-    "The next level of data summarization is the ``groupby`` operation, which allows you to quickly and efficiently compute aggregates on subsets of data."
+    "The next level of data summarization is the `groupby` operation, which allows you to quickly and efficiently compute aggregates on subsets of data."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## GroupBy: Split, Apply, Combine\n",
+    "## groupby: Split, Apply, Combine\n",
     "\n",
-    "Simple aggregations can give you a flavor of your dataset, but often we would prefer to aggregate conditionally on some label or index: this is implemented in the so-called ``groupby`` operation.\n",
+    "Simple aggregations can give you a flavor of your dataset, but often we would prefer to aggregate conditionally on some label or index: this is implemented in the so-called `groupby` operation.\n",
     "The name \"group by\" comes from a command in the SQL database language, but it is perhaps more illuminative to think of it in the terms first coined by Hadley Wickham of Rstats fame: *split, apply, combine*."
    ]
   },
@@ -599,7 +641,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Split, apply, combine\n",
+    "### Split, Apply, Combine\n",
     "\n",
     "A canonical example of this split-apply-combine operation, where the \"apply\" is a summation aggregation, is illustrated in this figure:"
    ]
@@ -608,38 +650,55 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](figures/03.08-split-apply-combine.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Split-Apply-Combine)"
+    "![](images/03.08-split-apply-combine.png)\n",
+    "\n",
+    "([figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Split-Apply-Combine))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This makes clear what the ``groupby`` accomplishes:\n",
+    "This illustrates what the `groupby` operation accomplishes:\n",
     "\n",
-    "- The *split* step involves breaking up and grouping a ``DataFrame`` depending on the value of the specified key.\n",
+    "- The *split* step involves breaking up and grouping a `DataFrame` depending on the value of the specified key.\n",
     "- The *apply* step involves computing some function, usually an aggregate, transformation, or filtering, within the individual groups.\n",
     "- The *combine* step merges the results of these operations into an output array.\n",
     "\n",
-    "While this could certainly be done manually using some combination of the masking, aggregation, and merging commands covered earlier, an important realization is that *the intermediate splits do not need to be explicitly instantiated*. Rather, the ``GroupBy`` can (often) do this in a single pass over the data, updating the sum, mean, count, min, or other aggregate for each group along the way.\n",
-    "The power of the ``GroupBy`` is that it abstracts away these steps: the user need not think about *how* the computation is done under the hood, but rather thinks about the *operation as a whole*.\n",
+    "While this could certainly be done manually using some combination of the masking, aggregation, and merging commands covered earlier, an important realization is that *the intermediate splits do not need to be explicitly instantiated*. Rather, the `groupby` can (often) do this in a single pass over the data, updating the sum, mean, count, min, or other aggregate for each group along the way.\n",
+    "The power of the `groupby` is that it abstracts away these steps: the user need not think about *how* the computation is done under the hood, but rather can think about the *operation as a whole*.\n",
     "\n",
-    "As a concrete example, let's take a look at using Pandas for the computation shown in this diagram.\n",
-    "We'll start by creating the input ``DataFrame``:"
+    "As a concrete example, let's take a look at using Pandas for the computation shown in the following figure.\n",
+    "We'll start by creating the input `DataFrame`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "    .dataframe thead th {\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
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@@ -708,20 +767,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The most basic split-apply-combine operation can be computed with the ``groupby()`` method of ``DataFrame``s, passing the name of the desired key column:"
+    "The most basic split-apply-combine operation can be computed with the `groupby` method of the `DataFrame`, passing the name of the desired key column:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<pandas.core.groupby.DataFrameGroupBy object at 0x117272160>"
+       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x11d241e20>"
       ]
      },
      "execution_count": 12,
@@ -737,24 +799,40 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice that what is returned is not a set of ``DataFrame``s, but a ``DataFrameGroupBy`` object.\n",
-    "This object is where the magic is: you can think of it as a special view of the ``DataFrame``, which is poised to dig into the groups but does no actual computation until the aggregation is applied.\n",
-    "This \"lazy evaluation\" approach means that common aggregates can be implemented very efficiently in a way that is almost transparent to the user.\n",
+    "Notice that what is returned is a `DataFrameGroupBy` object, not a set of `DataFrame` objects.\n",
+    "This object is where the magic is: you can think of it as a special view of the `DataFrame`, which is poised to dig into the groups but does no actual computation until the aggregation is applied.\n",
+    "This \"lazy evaluation\" approach means that common aggregates can be implemented efficiently in a way that is almost transparent to the user.\n",
     "\n",
-    "To produce a result, we can apply an aggregate to this ``DataFrameGroupBy`` object, which will perform the appropriate apply/combine steps to produce the desired result:"
+    "To produce a result, we can apply an aggregate to this `DataFrameGroupBy` object, which will perform the appropriate apply/combine steps to produce the desired result:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -804,20 +882,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``sum()`` method is just one possibility here; you can apply virtually any common Pandas or NumPy aggregation function, as well as virtually any valid ``DataFrame`` operation, as we will see in the following discussion."
+    "The `sum` method is just one possibility here; you can apply most Pandas or NumPy aggregation functions, as well as most `DataFrame` operations, as you will see in the following discussion."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### The GroupBy object\n",
+    "### The GroupBy Object\n",
     "\n",
-    "The ``GroupBy`` object is a very flexible abstraction.\n",
-    "In many ways, you can simply treat it as if it's a collection of ``DataFrame``s, and it does the difficult things under the hood. Let's see some examples using the Planets data.\n",
+    "The `GroupBy` object is a flexible abstraction: in many ways, it can be treated as simply a collection of ``DataFrame``s, though it is doing more sophisticated things under the hood. Let's see some examples using the Planets data.\n",
     "\n",
-    "Perhaps the most important operations made available by a ``GroupBy`` are *aggregate*, *filter*, *transform*, and *apply*.\n",
-    "We'll discuss each of these more fully in [\"Aggregate, Filter, Transform, Apply\"](#Aggregate,-Filter,-Transform,-Apply), but before that let's introduce some of the other functionality that can be used with the basic ``GroupBy`` operation."
+    "Perhaps the most important operations made available by a `GroupBy` are *aggregate*, *filter*, *transform*, and *apply*.\n",
+    "We'll discuss each of these more fully in the next section, but before that let's take a look at some of the other functionality that can be used with the basic `GroupBy` operation."
    ]
   },
   {
@@ -826,7 +903,7 @@
    "source": [
     "#### Column indexing\n",
     "\n",
-    "The ``GroupBy`` object supports column indexing in the same way as the ``DataFrame``, and returns a modified ``GroupBy`` object.\n",
+    "The `GroupBy` object supports column indexing in the same way as the `DataFrame`, and returns a modified `GroupBy` object.\n",
     "For example:"
    ]
   },
@@ -834,13 +911,16 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<pandas.core.groupby.DataFrameGroupBy object at 0x1172727b8>"
+       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x11d1bc820>"
       ]
      },
      "execution_count": 14,
@@ -856,13 +936,16 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<pandas.core.groupby.SeriesGroupBy object at 0x117272da0>"
+       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x11d1bcd60>"
       ]
      },
      "execution_count": 15,
@@ -878,15 +961,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here we've selected a particular ``Series`` group from the original ``DataFrame`` group by reference to its column name.\n",
-    "As with the ``GroupBy`` object, no computation is done until we call some aggregate on the object:"
+    "Here we've selected a particular `Series` group from the original `DataFrame` group by reference to its column name.\n",
+    "As with the `GroupBy` object, no computation is done until we call some aggregate on the object:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -928,14 +1014,17 @@
    "source": [
     "#### Iteration over groups\n",
     "\n",
-    "The ``GroupBy`` object supports direct iteration over the groups, returning each group as a ``Series`` or ``DataFrame``:"
+    "The `GroupBy` object supports direct iteration over the groups, returning each group as a `Series` or `DataFrame`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -964,7 +1053,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This can be useful for doing certain things manually, though it is often much faster to use the built-in ``apply`` functionality, which we will discuss momentarily."
+    "This can be useful for manual inspection of groups for the sake of debugging, but it is often much faster to use the built-in `apply` functionality, which we will discuss momentarily."
    ]
   },
   {
@@ -973,187 +1062,36 @@
    "source": [
     "#### Dispatch methods\n",
     "\n",
-    "Through some Python class magic, any method not explicitly implemented by the ``GroupBy`` object will be passed through and called on the groups, whether they are ``DataFrame`` or ``Series`` objects.\n",
-    "For example, you can use the ``describe()`` method of ``DataFrame``s to perform a set of aggregations that describe each group in the data:"
+    "Through some Python class magic, any method not explicitly implemented by the `GroupBy` object will be passed through and called on the groups, whether they are `DataFrame` or `Series` objects.\n",
+    "For example, using the `describe` method is equivalent to calling `describe` on the `DataFrame` representing each group:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>min</th>\n",
-       "      <th>25%</th>\n",
-       "      <th>50%</th>\n",
-       "      <th>75%</th>\n",
-       "      <th>max</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>method</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Astrometry</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2011.500000</td>\n",
-       "      <td>2.121320</td>\n",
-       "      <td>2010.0</td>\n",
-       "      <td>2010.75</td>\n",
-       "      <td>2011.5</td>\n",
-       "      <td>2012.25</td>\n",
-       "      <td>2013.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Eclipse Timing Variations</th>\n",
-       "      <td>9.0</td>\n",
-       "      <td>2010.000000</td>\n",
-       "      <td>1.414214</td>\n",
-       "      <td>2008.0</td>\n",
-       "      <td>2009.00</td>\n",
-       "      <td>2010.0</td>\n",
-       "      <td>2011.00</td>\n",
-       "      <td>2012.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Imaging</th>\n",
-       "      <td>38.0</td>\n",
-       "      <td>2009.131579</td>\n",
-       "      <td>2.781901</td>\n",
-       "      <td>2004.0</td>\n",
-       "      <td>2008.00</td>\n",
-       "      <td>2009.0</td>\n",
-       "      <td>2011.00</td>\n",
-       "      <td>2013.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Microlensing</th>\n",
-       "      <td>23.0</td>\n",
-       "      <td>2009.782609</td>\n",
-       "      <td>2.859697</td>\n",
-       "      <td>2004.0</td>\n",
-       "      <td>2008.00</td>\n",
-       "      <td>2010.0</td>\n",
-       "      <td>2012.00</td>\n",
-       "      <td>2013.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Orbital Brightness Modulation</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2011.666667</td>\n",
-       "      <td>1.154701</td>\n",
-       "      <td>2011.0</td>\n",
-       "      <td>2011.00</td>\n",
-       "      <td>2011.0</td>\n",
-       "      <td>2012.00</td>\n",
-       "      <td>2013.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pulsar Timing</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1998.400000</td>\n",
-       "      <td>8.384510</td>\n",
-       "      <td>1992.0</td>\n",
-       "      <td>1992.00</td>\n",
-       "      <td>1994.0</td>\n",
-       "      <td>2003.00</td>\n",
-       "      <td>2011.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pulsation Timing Variations</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2007.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2007.0</td>\n",
-       "      <td>2007.00</td>\n",
-       "      <td>2007.0</td>\n",
-       "      <td>2007.00</td>\n",
-       "      <td>2007.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Radial Velocity</th>\n",
-       "      <td>553.0</td>\n",
-       "      <td>2007.518987</td>\n",
-       "      <td>4.249052</td>\n",
-       "      <td>1989.0</td>\n",
-       "      <td>2005.00</td>\n",
-       "      <td>2009.0</td>\n",
-       "      <td>2011.00</td>\n",
-       "      <td>2014.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Transit</th>\n",
-       "      <td>397.0</td>\n",
-       "      <td>2011.236776</td>\n",
-       "      <td>2.077867</td>\n",
-       "      <td>2002.0</td>\n",
-       "      <td>2010.00</td>\n",
-       "      <td>2012.0</td>\n",
-       "      <td>2013.00</td>\n",
-       "      <td>2014.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Transit Timing Variations</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2012.500000</td>\n",
-       "      <td>1.290994</td>\n",
-       "      <td>2011.0</td>\n",
-       "      <td>2011.75</td>\n",
-       "      <td>2012.5</td>\n",
-       "      <td>2013.25</td>\n",
-       "      <td>2014.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                               count         mean       std     min      25%  \\\n",
-       "method                                                                         \n",
-       "Astrometry                       2.0  2011.500000  2.121320  2010.0  2010.75   \n",
-       "Eclipse Timing Variations        9.0  2010.000000  1.414214  2008.0  2009.00   \n",
-       "Imaging                         38.0  2009.131579  2.781901  2004.0  2008.00   \n",
-       "Microlensing                    23.0  2009.782609  2.859697  2004.0  2008.00   \n",
-       "Orbital Brightness Modulation    3.0  2011.666667  1.154701  2011.0  2011.00   \n",
-       "Pulsar Timing                    5.0  1998.400000  8.384510  1992.0  1992.00   \n",
-       "Pulsation Timing Variations      1.0  2007.000000       NaN  2007.0  2007.00   \n",
-       "Radial Velocity                553.0  2007.518987  4.249052  1989.0  2005.00   \n",
-       "Transit                        397.0  2011.236776  2.077867  2002.0  2010.00   \n",
-       "Transit Timing Variations        4.0  2012.500000  1.290994  2011.0  2011.75   \n",
-       "\n",
-       "                                  50%      75%     max  \n",
-       "method                                                  \n",
-       "Astrometry                     2011.5  2012.25  2013.0  \n",
-       "Eclipse Timing Variations      2010.0  2011.00  2012.0  \n",
-       "Imaging                        2009.0  2011.00  2013.0  \n",
-       "Microlensing                   2010.0  2012.00  2013.0  \n",
-       "Orbital Brightness Modulation  2011.0  2012.00  2013.0  \n",
-       "Pulsar Timing                  1994.0  2003.00  2011.0  \n",
-       "Pulsation Timing Variations    2007.0  2007.00  2007.0  \n",
-       "Radial Velocity                2009.0  2011.00  2014.0  \n",
-       "Transit                        2012.0  2013.00  2014.0  \n",
-       "Transit Timing Variations      2012.5  2013.25  2014.0  "
+       "       method                       \n",
+       "count  Astrometry                          2.0\n",
+       "       Eclipse Timing Variations           9.0\n",
+       "       Imaging                            38.0\n",
+       "       Microlensing                       23.0\n",
+       "       Orbital Brightness Modulation       3.0\n",
+       "                                         ...  \n",
+       "max    Pulsar Timing                    2011.0\n",
+       "       Pulsation Timing Variations      2007.0\n",
+       "       Radial Velocity                  2014.0\n",
+       "       Transit                          2014.0\n",
+       "       Transit Timing Variations        2014.0\n",
+       "Length: 80, dtype: float64"
       ]
      },
      "execution_count": 18,
@@ -1169,22 +1107,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Looking at this table helps us to better understand the data: for example, the vast majority of planets have been discovered by the Radial Velocity and Transit methods, though the latter only became common (due to new, more accurate telescopes) in the last decade.\n",
+    "Looking at this table helps us to better understand the data: for example, the vast majority of planets until 2014 were discovered by the Radial Velocity and Transit methods, though the latter method became common more recently.\n",
     "The newest methods seem to be Transit Timing Variation and Orbital Brightness Modulation, which were not used to discover a new planet until 2011.\n",
     "\n",
-    "This is just one example of the utility of dispatch methods.\n",
-    "Notice that they are applied *to each individual group*, and the results are then combined within ``GroupBy`` and returned.\n",
-    "Again, any valid ``DataFrame``/``Series`` method can be used on the corresponding ``GroupBy`` object, which allows for some very flexible and powerful operations!"
+    "Notice that these dispatch methods are applied *to each individual group*, and the results are then combined within `GroupBy` and returned.\n",
+    "Again, any valid `DataFrame`/`Series` method can be called in a similar manner on the corresponding `GroupBy` object."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Aggregate, filter, transform, apply\n",
+    "### Aggregate, Filter, Transform, Apply\n",
     "\n",
     "The preceding discussion focused on aggregation for the combine operation, but there are more options available.\n",
-    "In particular, ``GroupBy`` objects have ``aggregate()``, ``filter()``, ``transform()``, and ``apply()`` methods that efficiently implement a variety of useful operations before combining the grouped data.\n",
+    "In particular, `GroupBy` objects have `aggregate`, `filter`, `transform`, and `apply` methods that efficiently implement a variety of useful operations before combining the grouped data.\n",
     "\n",
     "For the purpose of the following subsections, we'll use this ``DataFrame``:"
    ]
@@ -1193,13 +1130,29 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
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+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
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        "<table border=\"1\" class=\"dataframe\">\n",
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@@ -1280,22 +1233,42 @@
    "source": [
     "#### Aggregation\n",
     "\n",
-    "We're now familiar with ``GroupBy`` aggregations with ``sum()``, ``median()``, and the like, but the ``aggregate()`` method allows for even more flexibility.\n",
+    "You're now familiar with `GroupBy` aggregations with `sum`, `median`, and the like, but the `aggregate` method allows for even more flexibility.\n",
     "It can take a string, a function, or a list thereof, and compute all the aggregates at once.\n",
-    "Here is a quick example combining all these:"
+    "Here is a quick example combining all of these:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
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+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
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@@ -1376,20 +1349,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Another useful pattern is to pass a dictionary mapping column names to operations to be applied on that column:"
+    "Another common pattern is to pass a dictionary mapping column names to operations to be applied on that column:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
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+    }
    },
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     {
      "data": {
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@@ -1455,7 +1444,10 @@
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    "metadata": {
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@@ -1463,6 +1455,19 @@
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+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1515,6 +1520,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby('key').std()</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1550,6 +1568,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby('key').filter(filter_func)</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1623,14 +1654,15 @@
     "def filter_func(x):\n",
     "    return x['data2'].std() > 4\n",
     "\n",
-    "display('df', \"df.groupby('key').std()\", \"df.groupby('key').filter(filter_func)\")"
+    "display('df', \"df.groupby('key').std()\",\n",
+    "        \"df.groupby('key').filter(filter_func)\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The filter function should return a Boolean value specifying whether the group passes the filtering. Here because group A does not have a standard deviation greater than 4, it is dropped from the result."
+    "The filter function should return a Boolean value specifying whether the group passes the filtering. Here, because group A does not have a standard deviation greater than 4, it is dropped from the result."
    ]
   },
   {
@@ -1648,13 +1680,29 @@
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1714,85 +1762,50 @@
     }
    ],
    "source": [
-    "df.groupby('key').transform(lambda x: x - x.mean())"
+    "def center(x):\n",
+    "    return x - x.mean()\n",
+    "df.groupby('key').transform(center)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### The apply() method\n",
+    "#### The apply method\n",
     "\n",
-    "The ``apply()`` method lets you apply an arbitrary function to the group results.\n",
-    "The function should take a ``DataFrame``, and return either a Pandas object (e.g., ``DataFrame``, ``Series``) or a scalar; the combine operation will be tailored to the type of output returned.\n",
+    "The `apply` method lets you apply an arbitrary function to the group results.\n",
+    "The function should take a `DataFrame` and returns either a Pandas object (e.g., `DataFrame`, `Series`) or a scalar; the behavior of the combine step will be tailored to the type of output returned.\n",
     "\n",
-    "For example, here is an ``apply()`` that normalizes the first column by the sum of the second:"
+    "For example, here is an `apply` operation that normalizes the first column by the sum of the second:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df</p><div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>key</th>\n",
-       "      <th>data1</th>\n",
-       "      <th>data2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>C</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>B</td>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>C</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    </div>\n",
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby('key').apply(norm_by_data2)</p><div>\n",
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1841,20 +1854,9 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "</div>\n",
-       "    </div>"
+       "</div>"
       ],
       "text/plain": [
-       "df\n",
-       "  key  data1  data2\n",
-       "0   A      0      5\n",
-       "1   B      1      0\n",
-       "2   C      2      3\n",
-       "3   A      3      3\n",
-       "4   B      4      7\n",
-       "5   C      5      9\n",
-       "\n",
-       "df.groupby('key').apply(norm_by_data2)\n",
        "  key     data1  data2\n",
        "0   A  0.000000      5\n",
        "1   B  0.142857      0\n",
@@ -1875,23 +1877,23 @@
     "    x['data1'] /= x['data2'].sum()\n",
     "    return x\n",
     "\n",
-    "display('df', \"df.groupby('key').apply(norm_by_data2)\")"
+    "df.groupby('key').apply(norm_by_data2)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``apply()`` within a ``GroupBy`` is quite flexible: the only criterion is that the function takes a ``DataFrame`` and returns a Pandas object or scalar; what you do in the middle is up to you!"
+    "`apply` within a `GroupBy` is flexible: the only criterion is that the function takes a `DataFrame` and returns a Pandas object or scalar. What you do in between is up to you!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Specifying the split key\n",
+    "### Specifying the Split Key\n",
     "\n",
-    "In the simple examples presented before, we split the ``DataFrame`` on a single column name.\n",
+    "In the simple examples presented before, we split the `DataFrame` on a single column name.\n",
     "This is just one of many options by which the groups can be defined, and we'll go through some other options for group specification here."
    ]
   },
@@ -1901,73 +1903,36 @@
    "source": [
     "#### A list, array, series, or index providing the grouping keys\n",
     "\n",
-    "The key can be any series or list with a length matching that of the ``DataFrame``. For example:"
+    "The key can be any series or list with a length matching that of the `DataFrame`. For example:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df</p><div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>key</th>\n",
-       "      <th>data1</th>\n",
-       "      <th>data2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>C</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>B</td>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>C</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    </div>\n",
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby(L).sum()</p><div>\n",
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1994,20 +1959,9 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "</div>\n",
-       "    </div>"
+       "</div>"
       ],
       "text/plain": [
-       "df\n",
-       "  key  data1  data2\n",
-       "0   A      0      5\n",
-       "1   B      1      0\n",
-       "2   C      2      3\n",
-       "3   A      3      3\n",
-       "4   B      4      7\n",
-       "5   C      5      9\n",
-       "\n",
-       "df.groupby(L).sum()\n",
        "   data1  data2\n",
        "0      7     17\n",
        "1      4      3\n",
@@ -2021,80 +1975,43 @@
    ],
    "source": [
     "L = [0, 1, 0, 1, 2, 0]\n",
-    "display('df', 'df.groupby(L).sum()')"
+    "df.groupby(L).sum()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Of course, this means there's another, more verbose way of accomplishing the ``df.groupby('key')`` from before:"
+    "Of course, this means there's another, more verbose way of accomplishing the `df.groupby('key')` from before:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df</p><div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>key</th>\n",
-       "      <th>data1</th>\n",
-       "      <th>data2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>C</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>B</td>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>C</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    </div>\n",
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby(df['key']).sum()</p><div>\n",
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2126,20 +2043,9 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "</div>\n",
-       "    </div>"
+       "</div>"
       ],
       "text/plain": [
-       "df\n",
-       "  key  data1  data2\n",
-       "0   A      0      5\n",
-       "1   B      1      0\n",
-       "2   C      2      3\n",
-       "3   A      3      3\n",
-       "4   B      4      7\n",
-       "5   C      5      9\n",
-       "\n",
-       "df.groupby(df['key']).sum()\n",
        "     data1  data2\n",
        "key              \n",
        "A        3      8\n",
@@ -2153,7 +2059,7 @@
     }
    ],
    "source": [
-    "display('df', \"df.groupby(df['key']).sum()\")"
+    "df.groupby(df['key']).sum()"
    ]
   },
   {
@@ -2169,7 +2075,10 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -2177,6 +2086,19 @@
       "text/html": [
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2227,6 +2149,19 @@
        "    </div>\n",
        "<div style=\"float: left; padding: 10px;\">\n",
        "    <p style='font-family:\"Courier New\", Courier, monospace'>df2.groupby(mapping).sum()</p><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2234,6 +2169,11 @@
        "      <th>data1</th>\n",
        "      <th>data2</th>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>key</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
@@ -2264,6 +2204,7 @@
        "\n",
        "df2.groupby(mapping).sum()\n",
        "           data1  data2\n",
+       "key                    \n",
        "consonant     12     19\n",
        "vowel          3      8"
       ]
@@ -2292,14 +2233,29 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2315,51 +2271,6 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>A</th>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>B</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>A</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>B</th>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C</th>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    </div>\n",
-       "<div style=\"float: left; padding: 10px;\">\n",
-       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2.groupby(str.lower).mean()</p><div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>data1</th>\n",
-       "      <th>data2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
        "      <th>a</th>\n",
        "      <td>1.5</td>\n",
        "      <td>4.0</td>\n",
@@ -2376,25 +2287,14 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "</div>\n",
-       "    </div>"
+       "</div>"
       ],
       "text/plain": [
-       "df2\n",
        "     data1  data2\n",
        "key              \n",
-       "A        0      5\n",
-       "B        1      0\n",
-       "C        2      3\n",
-       "A        3      3\n",
-       "B        4      7\n",
-       "C        5      9\n",
-       "\n",
-       "df2.groupby(str.lower).mean()\n",
-       "   data1  data2\n",
-       "a    1.5    4.0\n",
-       "b    2.5    3.5\n",
-       "c    3.5    6.0"
+       "a      1.5    4.0\n",
+       "b      2.5    3.5\n",
+       "c      3.5    6.0"
       ]
      },
      "execution_count": 28,
@@ -2403,7 +2303,7 @@
     }
    ],
    "source": [
-    "display('df2', 'df2.groupby(str.lower).mean()')"
+    "df2.groupby(str.lower).mean()"
    ]
   },
   {
@@ -2419,13 +2319,29 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2434,6 +2350,12 @@
        "      <th>data1</th>\n",
        "      <th>data2</th>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>key</th>\n",
+       "      <th>key</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
@@ -2459,10 +2381,11 @@
        "</div>"
       ],
       "text/plain": [
-       "             data1  data2\n",
-       "a vowel        1.5    4.0\n",
-       "b consonant    2.5    3.5\n",
-       "c consonant    3.5    6.0"
+       "               data1  data2\n",
+       "key key                    \n",
+       "a   vowel        1.5    4.0\n",
+       "b   consonant    2.5    3.5\n",
+       "c   consonant    3.5    6.0"
       ]
      },
      "execution_count": 29,
@@ -2478,22 +2401,38 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Grouping example\n",
+    "### Grouping Example\n",
     "\n",
-    "As an example of this, in a couple lines of Python code we can put all these together and count discovered planets by method and by decade:"
+    "As an example of this, in a few lines of Python code we can put all these together and count discovered planets by method and by decade:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2617,28 +2556,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This shows the power of combining many of the operations we've discussed up to this point when looking at realistic datasets.\n",
-    "We immediately gain a coarse understanding of when and how planets have been discovered over the past several decades!\n",
+    "This shows the power of combining many of the operations we've discussed up to this point when looking at realistic datasets: we quickly gain a coarse understanding of when and how extrasolar planets were detected in the years after the first discovery.\n",
     "\n",
-    "Here I would suggest digging into these few lines of code, and evaluating the individual steps to make sure you understand exactly what they are doing to the result.\n",
+    "I would suggest digging into these few lines of code and evaluating the individual steps to make sure you understand exactly what they are doing to the result.\n",
     "It's certainly a somewhat complicated example, but understanding these pieces will give you the means to similarly explore your own data."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) | [Contents](Index.ipynb) | [Pivot Tables](03.09-Pivot-Tables.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.08-Aggregation-and-Grouping.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.6 64-bit ('3.9.6')",
    "language": "python",
    "name": "python3"
   },
@@ -2652,9 +2583,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc"
+   }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.09-Pivot-Tables.ipynb b/notebooks/03.09-Pivot-Tables.ipynb
index 717549875..ed06e806d 100644
--- a/notebooks/03.09-Pivot-Tables.ipynb
+++ b/notebooks/03.09-Pivot-Tables.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) | [Contents](Index.ipynb) | [Vectorized String Operations](03.10-Working-With-Strings.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.09-Pivot-Tables.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,16 +11,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We have seen how the ``GroupBy`` abstraction lets us explore relationships within a dataset.\n",
+    "We have seen how the `groupby` abstraction lets us explore relationships within a dataset.\n",
     "A *pivot table* is a similar operation that is commonly seen in spreadsheets and other programs that operate on tabular data.\n",
     "The pivot table takes simple column-wise data as input, and groups the entries into a two-dimensional table that provides a multidimensional summarization of the data.\n",
-    "The difference between pivot tables and ``GroupBy`` can sometimes cause confusion; it helps me to think of pivot tables as essentially a *multidimensional* version of ``GroupBy`` aggregation.\n",
+    "The difference between pivot tables and `groupby` can sometimes cause confusion; it helps me to think of pivot tables as essentially a *multidimensional* version of `groupby` aggregation.\n",
     "That is, you split-apply-combine, but both the split and the combine happen across not a one-dimensional index, but across a two-dimensional grid."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "## Motivating Pivot Tables\n",
     "\n",
@@ -53,7 +33,10 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -67,13 +50,29 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -198,12 +197,12 @@
        "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
        "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
        "\n",
-       "     who adult_male deck  embark_town alive  alone  \n",
-       "0    man       True  NaN  Southampton    no  False  \n",
-       "1  woman      False    C    Cherbourg   yes  False  \n",
-       "2  woman      False  NaN  Southampton   yes   True  \n",
-       "3  woman      False    C  Southampton   yes  False  \n",
-       "4    man       True  NaN  Southampton    no   True  "
+       "     who  adult_male deck  embark_town alive  alone  \n",
+       "0    man        True  NaN  Southampton    no  False  \n",
+       "1  woman       False    C    Cherbourg   yes  False  \n",
+       "2  woman       False  NaN  Southampton   yes   True  \n",
+       "3  woman       False    C  Southampton   yes  False  \n",
+       "4    man        True  NaN  Southampton    no   True  "
       ]
      },
      "execution_count": 2,
@@ -216,33 +215,51 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This contains a wealth of information on each passenger of that ill-fated voyage, including gender, age, class, fare paid, and much more."
+    "As the output shows, this contains a number of data points on each passenger on that ill-fated voyage, including sex, age, class, fare paid, and much more."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Pivot Tables by Hand\n",
     "\n",
-    "To start learning more about this data, we might begin by grouping according to gender, survival status, or some combination thereof.\n",
-    "If you have read the previous section, you might be tempted to apply a ``GroupBy`` operation–for example, let's look at survival rate by gender:"
+    "To start learning more about this data, we might begin by grouping according to sex, survival status, or some combination thereof.\n",
+    "If you read the previous chapter, you might be tempted to apply a `groupby` operation—for example, let's look at survival rate by sex:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -284,27 +301,44 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This immediately gives us some insight: overall, three of every four females on board survived, while only one in five males survived!\n",
+    "This gives us some initial insight: overall, three of every four females on board survived, while only one in five males survived!\n",
     "\n",
-    "This is useful, but we might like to go one step deeper and look at survival by both sex and, say, class.\n",
-    "Using the vocabulary of ``GroupBy``, we might proceed using something like this:\n",
-    "we *group by* class and gender, *select* survival, *apply* a mean aggregate, *combine* the resulting groups, and then *unstack* the hierarchical index to reveal the hidden multidimensionality. In code:"
+    "This is useful, but we might like to go one step deeper and look at survival rates by both sex and, say, class.\n",
+    "Using the vocabulary of `groupby`, we might proceed using a process like this:\n",
+    "we first *group by* class and sex, then *select* survival, *apply* a mean aggregate, *combine* the resulting groups, and finally *unstack* the hierarchical index to reveal the hidden multidimensionality. In code:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -354,12 +388,13 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This gives us a better idea of how both gender and class affected survival, but the code is starting to look a bit garbled.\n",
+    "This gives us a better idea of how both sex and class affected survival, but the code is starting to look a bit garbled.\n",
     "While each step of this pipeline makes sense in light of the tools we've previously discussed, the long string of code is not particularly easy to read or use.\n",
-    "This two-dimensional ``GroupBy`` is common enough that Pandas includes a convenience routine, ``pivot_table``, which succinctly handles this type of multi-dimensional aggregation."
+    "This two-dimensional `groupby` is common enough that Pandas includes a convenience routine, `pivot_table`, which succinctly handles this type of multidimensional aggregation."
    ]
   },
   {
@@ -368,20 +403,36 @@
    "source": [
     "## Pivot Table Syntax\n",
     "\n",
-    "Here is the equivalent to the preceding operation using the ``pivot_table`` method of ``DataFrame``s:"
+    "Here is the equivalent to the preceding operation using the `DataFrame.pivot_table` method:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -427,40 +478,57 @@
     }
    ],
    "source": [
-    "titanic.pivot_table('survived', index='sex', columns='class')"
+    "titanic.pivot_table('survived', index='sex', columns='class', aggfunc='mean')"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is eminently more readable than the ``groupby`` approach, and produces the same result.\n",
-    "As you might expect of an early 20th-century transatlantic cruise, the survival gradient favors both women and higher classes.\n",
-    "First-class women survived with near certainty (hi, Rose!), while only one in ten third-class men survived (sorry, Jack!)."
+    "This is eminently more readable than the manual `groupby` approach, and produces the same result.\n",
+    "As you might expect of an early 20th-century transatlantic cruise, the survival gradient favors both higher classes and people recorded as females in the\n",
+    "data. First-class females survived with near certainty (hi, Rose!), while only one in eight or so third-class males survived (sorry, Jack!)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Multi-level pivot tables\n",
+    "### Multilevel Pivot Tables\n",
     "\n",
-    "Just as in the ``GroupBy``, the grouping in pivot tables can be specified with multiple levels, and via a number of options.\n",
+    "Just as in a `groupby`, the grouping in pivot tables can be specified with multiple levels and via a number of options.\n",
     "For example, we might be interested in looking at age as a third dimension.\n",
-    "We'll bin the age using the ``pd.cut`` function:"
+    "We'll bin the age using the `pd.cut` function:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -532,26 +600,46 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can apply the same strategy when working with the columns as well; let's add info on the fare paid using ``pd.qcut`` to automatically compute quantiles:"
+    "We can apply the same strategy when working with the columns as well; let's add info on the fare paid, using `pd.qcut` to automatically compute quantiles:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>fare</th>\n",
-       "      <th colspan=\"3\" halign=\"left\">[0, 14.454]</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">(-0.001, 14.454]</th>\n",
        "      <th colspan=\"3\" halign=\"left\">(14.454, 512.329]</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -619,21 +707,21 @@
        "</div>"
       ],
       "text/plain": [
-       "fare            [0, 14.454]                     (14.454, 512.329]            \\\n",
-       "class                 First    Second     Third             First    Second   \n",
-       "sex    age                                                                    \n",
-       "female (0, 18]          NaN  1.000000  0.714286          0.909091  1.000000   \n",
-       "       (18, 80]         NaN  0.880000  0.444444          0.972973  0.914286   \n",
-       "male   (0, 18]          NaN  0.000000  0.260870          0.800000  0.818182   \n",
-       "       (18, 80]         0.0  0.098039  0.125000          0.391304  0.030303   \n",
+       "fare            (-0.001, 14.454]                     (14.454, 512.329]  \\\n",
+       "class                      First    Second     Third             First   \n",
+       "sex    age                                                               \n",
+       "female (0, 18]               NaN  1.000000  0.714286          0.909091   \n",
+       "       (18, 80]              NaN  0.880000  0.444444          0.972973   \n",
+       "male   (0, 18]               NaN  0.000000  0.260870          0.800000   \n",
+       "       (18, 80]              0.0  0.098039  0.125000          0.391304   \n",
        "\n",
-       "fare                       \n",
-       "class               Third  \n",
-       "sex    age                 \n",
-       "female (0, 18]   0.318182  \n",
-       "       (18, 80]  0.391304  \n",
-       "male   (0, 18]   0.178571  \n",
-       "       (18, 80]  0.192308  "
+       "fare                                 \n",
+       "class              Second     Third  \n",
+       "sex    age                           \n",
+       "female (0, 18]   1.000000  0.318182  \n",
+       "       (18, 80]  0.914286  0.391304  \n",
+       "male   (0, 18]   0.818182  0.178571  \n",
+       "       (18, 80]  0.030303  0.192308  "
       ]
      },
      "execution_count": 7,
@@ -657,36 +745,57 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Additional pivot table options\n",
+    "### Additional Pivot Table Options\n",
     "\n",
-    "The full call signature of the ``pivot_table`` method of ``DataFrame``s is as follows:\n",
+    "The full call signature of the `DataFrame.pivot_table` method is as follows:\n",
     "\n",
     "```python\n",
-    "# call signature as of Pandas 0.18\n",
+    "# call signature as of Pandas 1.3.5\n",
     "DataFrame.pivot_table(data, values=None, index=None, columns=None,\n",
     "                      aggfunc='mean', fill_value=None, margins=False,\n",
-    "                      dropna=True, margins_name='All')\n",
+    "                      dropna=True, margins_name='All', observed=False,\n",
+    "                      sort=True)\n",
     "```\n",
     "\n",
-    "We've already seen examples of the first three arguments; here we'll take a quick look at the remaining ones.\n",
-    "Two of the options, ``fill_value`` and ``dropna``, have to do with missing data and are fairly straightforward; we will not show examples of them here.\n",
+    "We've already seen examples of the first three arguments; here we'll take a quick look at some of the remaining ones.\n",
+    "Two of the options, `fill_value` and `dropna`, have to do with missing data and are fairly straightforward; I will not show examples of them here.\n",
     "\n",
-    "The ``aggfunc`` keyword controls what type of aggregation is applied, which is a mean by default.\n",
-    "As in the GroupBy, the aggregation specification can be a string representing one of several common choices (e.g., ``'sum'``, ``'mean'``, ``'count'``, ``'min'``, ``'max'``, etc.) or a function that implements an aggregation (e.g., ``np.sum()``, ``min()``, ``sum()``, etc.).\n",
-    "Additionally, it can be specified as a dictionary mapping a column to any of the above desired options:"
+    "The `aggfunc` keyword controls what type of aggregation is applied, which is a mean by default.\n",
+    "As with `groupby`, the aggregation specification can be a string representing one of several common choices (`'sum'`, `'mean'`, `'count'`, `'min'`, `'max'`, etc.) or a function that implements an aggregation (e.g., `np.sum()`, `min()`, `sum()`, etc.).\n",
+    "Additionally, it can be specified as a dictionary mapping a column to any of the desired options:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
@@ -719,18 +828,18 @@
        "      <td>106.125798</td>\n",
        "      <td>21.970121</td>\n",
        "      <td>16.118810</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>72.0</td>\n",
+       "      <td>91</td>\n",
+       "      <td>70</td>\n",
+       "      <td>72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>male</th>\n",
        "      <td>67.226127</td>\n",
        "      <td>19.741782</td>\n",
        "      <td>12.661633</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>47.0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>17</td>\n",
+       "      <td>47</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -740,8 +849,8 @@
        "              fare                       survived             \n",
        "class        First     Second      Third    First Second Third\n",
        "sex                                                           \n",
-       "female  106.125798  21.970121  16.118810     91.0   70.0  72.0\n",
-       "male     67.226127  19.741782  12.661633     45.0   17.0  47.0"
+       "female  106.125798  21.970121  16.118810       91     70    72\n",
+       "male     67.226127  19.741782  12.661633       45     17    47"
       ]
      },
      "execution_count": 8,
@@ -758,14 +867,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice also here that we've omitted the ``values`` keyword; when specifying a mapping for ``aggfunc``, this is determined automatically."
+    "Notice also here that we've omitted the `values` keyword; when specifying a mapping for `aggfunc`, this is determined automatically."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "source": [
     "At times it's useful to compute totals along each grouping.\n",
     "This can be done via the ``margins`` keyword:"
@@ -775,13 +882,29 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -843,41 +966,49 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here this automatically gives us information about the class-agnostic survival rate by gender, the gender-agnostic survival rate by class, and the overall survival rate of 38%.\n",
-    "The margin label can be specified with the ``margins_name`` keyword, which defaults to ``\"All\"``."
+    "Here, this automatically gives us information about the class-agnostic survival rate by sex, the sex-agnostic survival rate by class, and the overall survival rate of 38%.\n",
+    "The margin label can be specified with the `margins_name` keyword; it defaults to `\"All\"`."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Example: Birthrate Data\n",
     "\n",
-    "As a more interesting example, let's take a look at the freely available data on births in the United States, provided by the Centers for Disease Control (CDC).\n",
+    "As another example, let's take a look at the freely available data on births in the United States, provided by the Centers for Disease Control (CDC).\n",
     "This data can be found at https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv\n",
-    "(this dataset has been analyzed rather extensively by Andrew Gelman and his group; see, for example, [this blog post](http://andrewgelman.com/2012/06/14/cool-ass-signal-processing-using-gaussian-processes/)):"
+    "(this dataset has been analyzed rather extensively by Andrew Gelman and his group; see, for example, the [blog post on signal processing using Gaussian processes](http://andrewgelman.com/2012/06/14/cool-ass-signal-processing-using-gaussian-processes/)):\n",
+    "\n",
+    "[^1]: The CDC dataset used in this section uses the sex assigned at birth, which it calls \"gender,\" and limits the data to male and female. While gender is a spectrum independent of biology, I will be using the same terminology while discussing this dataset for consistency and clarity."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "# shell command to download the data:\n",
-    "# !curl -O https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv"
+    "# !cd data && curl -O \\\n",
+    "# https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -888,20 +1019,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Taking a look at the data, we see that it's relatively simple–it contains the number of births grouped by date and gender:"
+    "Taking a look at the data, we see that it's relatively simple—it contains the number of births grouped by date and gender:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
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+       "        text-align: right;\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -918,7 +1065,7 @@
        "      <th>0</th>\n",
        "      <td>1969</td>\n",
        "      <td>1</td>\n",
-       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>F</td>\n",
        "      <td>4046</td>\n",
        "    </tr>\n",
@@ -926,7 +1073,7 @@
        "      <th>1</th>\n",
        "      <td>1969</td>\n",
        "      <td>1</td>\n",
-       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>M</td>\n",
        "      <td>4440</td>\n",
        "    </tr>\n",
@@ -934,7 +1081,7 @@
        "      <th>2</th>\n",
        "      <td>1969</td>\n",
        "      <td>1</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>F</td>\n",
        "      <td>4454</td>\n",
        "    </tr>\n",
@@ -942,7 +1089,7 @@
        "      <th>3</th>\n",
        "      <td>1969</td>\n",
        "      <td>1</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>M</td>\n",
        "      <td>4548</td>\n",
        "    </tr>\n",
@@ -950,7 +1097,7 @@
        "      <th>4</th>\n",
        "      <td>1969</td>\n",
        "      <td>1</td>\n",
-       "      <td>3</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>F</td>\n",
        "      <td>4548</td>\n",
        "    </tr>\n",
@@ -959,12 +1106,12 @@
        "</div>"
       ],
       "text/plain": [
-       "   year  month day gender  births\n",
-       "0  1969      1   1      F    4046\n",
-       "1  1969      1   1      M    4440\n",
-       "2  1969      1   2      F    4454\n",
-       "3  1969      1   2      M    4548\n",
-       "4  1969      1   3      F    4548"
+       "   year  month  day gender  births\n",
+       "0  1969      1  1.0      F    4046\n",
+       "1  1969      1  1.0      M    4440\n",
+       "2  1969      1  2.0      F    4454\n",
+       "3  1969      1  2.0      M    4548\n",
+       "4  1969      1  3.0      F    4548"
       ]
      },
      "execution_count": 12,
@@ -981,20 +1128,36 @@
    "metadata": {},
    "source": [
     "We can start to understand this data a bit more by using a pivot table.\n",
-    "Let's add a decade column, and take a look at male and female births as a function of decade:"
+    "Let's add a `decade` column, and take a look at male and female births as a function of decade:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1062,22 +1225,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We immediately see that male births outnumber female births in every decade.\n",
-    "To see this trend a bit more clearly, we can use the built-in plotting tools in Pandas to visualize the total number of births by year (see [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) for a discussion of plotting with Matplotlib):"
+    "We see that male births outnumber female births in every decade.\n",
+    "To see this trend a bit more clearly, we can use the built-in plotting tools in Pandas to visualize the total number of births by year, as shown in the following figure (see [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) for a discussion of plotting with Matplotlib):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
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GayRRaIZ8ff2oqChn06b13HXXSEuHI4QQohFtT4sH4K52Q38zCbEu/LW+PNztQUxmEytT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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1173ed940>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1087,8 +1253,9 @@
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "sns.set()  # use Seaborn styles\n",
-    "births.pivot_table('births', index='year', columns='gender', aggfunc='sum').plot()\n",
+    "plt.style.use('seaborn-whitegrid')\n",
+    "births.pivot_table(\n",
+    "    'births', index='year', columns='gender', aggfunc='sum').plot()\n",
     "plt.ylabel('total births per year');"
    ]
   },
@@ -1096,15 +1263,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With a simple pivot table and ``plot()`` method, we can immediately see the annual trend in births by gender. By eye, it appears that over the past 50 years male births have outnumbered female births by around 5%."
+    "With a simple pivot table and the `plot` method, we can immediately see the annual trend in births by gender. By eye, it appears that over the past 50 years male births have outnumbered female births by around 5%."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Further data exploration\n",
-    "\n",
     "Though this doesn't necessarily relate to the pivot table, there are a few more interesting features we can pull out of this dataset using the Pandas tools covered up to this point.\n",
     "We must start by cleaning the data a bit, removing outliers caused by mistyped dates (e.g., June 31st) or missing values (e.g., June 99th).\n",
     "One easy way to remove these all at once is to cut outliers; we'll do this via a robust sigma-clipping operation:"
@@ -1114,7 +1279,7 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -1127,16 +1292,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This final line is a robust estimate of the sample mean, where the 0.74 comes from the interquartile range of a Gaussian distribution (You can learn more about sigma-clipping operations in a book I coauthored with Željko Ivezić, Andrew J. Connolly, and Alexander Gray: [\"Statistics, Data Mining, and Machine Learning in Astronomy\"](http://press.princeton.edu/titles/10159.html) (Princeton University Press, 2014)).\n",
+    "This final line is a robust estimate of the sample standard deviation, where the 0.74 comes from the interquartile range of a Gaussian distribution (you can learn more about sigma-clipping operations in a book I coauthored with Željko Ivezić, Andrew J. Connolly, and Alexander Gray: [*Statistics, Data Mining, and Machine Learning in Astronomy*](https://press.princeton.edu/books/hardcover/9780691198309/statistics-data-mining-and-machine-learning-in-astronomy) (Princeton University Press)).\n",
     "\n",
-    "With this we can use the ``query()`` method (discussed further in [High-Performance Pandas: ``eval()`` and ``query()``](03.12-Performance-Eval-and-Query.ipynb)) to filter-out rows with births outside these values:"
+    "With this, we can use the `query` method (discussed further in [High-Performance Pandas: `eval()` and `query()`](03.12-Performance-Eval-and-Query.ipynb)) to filter out rows with births outside these values:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -1147,14 +1315,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next we set the ``day`` column to integers; previously it had been a string because some columns in the dataset contained the value ``'null'``:"
+    "Next we set the `day` column to integers; previously it had been a string column because some columns in the dataset contained the value `'null'`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -1166,7 +1337,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we can combine the day, month, and year to create a Date index (see [Working with Time Series](03.11-Working-with-Time-Series.ipynb)).\n",
+    "Finally, we can combine the day, month, and year to create a date index (see [Working with Time Series](03.11-Working-with-Time-Series.ipynb)).\n",
     "This allows us to quickly compute the weekday corresponding to each row:"
    ]
   },
@@ -1174,7 +1345,10 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -1190,21 +1364,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using this we can plot births by weekday for several decades:"
+    "Using this, we can plot births by weekday for several decades (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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/DBo0iNTUVN5//30WLlzImjVr2LhxIy0tLSxYsIDk5GTWrl3LwIEDWbp0Kdu2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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1179ace80>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1217,7 +1394,8 @@
     "\n",
     "births.pivot_table('births', index='dayofweek',\n",
     "                    columns='decade', aggfunc='mean').plot()\n",
-    "plt.gca().set_xticklabels(['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun'])\n",
+    "plt.gca().set(xticks=range(7),\n",
+    "              xticklabels=['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun'])\n",
     "plt.ylabel('mean births by day');"
    ]
   },
@@ -1225,9 +1403,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Apparently births are slightly less common on weekends than on weekdays! Note that the 1990s and 2000s are missing because the CDC data contains only the month of birth starting in 1989.\n",
+    "Apparently births are slightly less common on weekends than on weekdays! Note that the 1990s and 2000s are missing because starting in 1989, the CDC data contains only the month of birth.\n",
     "\n",
-    "Another intersting view is to plot the mean number of births by the day of the *year*.\n",
+    "Another interesting view is to plot the mean number of births by the day of the year.\n",
     "Let's first group the data by month and day separately:"
    ]
   },
@@ -1235,18 +1413,70 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>births</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
+       "      <th>1</th>\n",
+       "      <td>4009.225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4247.400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4500.900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4571.350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4603.625</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "1  1    4009.225\n",
-       "   2    4247.400\n",
-       "   3    4500.900\n",
-       "   4    4571.350\n",
-       "   5    4603.625\n",
-       "Name: births, dtype: float64"
+       "       births\n",
+       "1 1  4009.225\n",
+       "  2  4247.400\n",
+       "  3  4500.900\n",
+       "  4  4571.350\n",
+       "  5  4603.625"
       ]
      },
      "execution_count": 20,
@@ -1265,25 +1495,75 @@
    "metadata": {},
    "source": [
     "The result is a multi-index over months and days.\n",
-    "To make this easily plottable, let's turn these months and days into a date by associating them with a dummy year variable (making sure to choose a leap year so February 29th is correctly handled!)"
+    "To make this visualizable, let's turn these months and days into dates by associating them with a dummy year variable (making sure to choose a leap year so February 29th is correctly handled!):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>births</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01</th>\n",
+       "      <td>4009.225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-02</th>\n",
+       "      <td>4247.400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-03</th>\n",
+       "      <td>4500.900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-04</th>\n",
+       "      <td>4571.350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-05</th>\n",
+       "      <td>4603.625</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "2012-01-01    4009.225\n",
-       "2012-01-02    4247.400\n",
-       "2012-01-03    4500.900\n",
-       "2012-01-04    4571.350\n",
-       "2012-01-05    4603.625\n",
-       "Name: births, dtype: float64"
+       "              births\n",
+       "2012-01-01  4009.225\n",
+       "2012-01-02  4247.400\n",
+       "2012-01-03  4500.900\n",
+       "2012-01-04  4571.350\n",
+       "2012-01-05  4603.625"
       ]
      },
      "execution_count": 21,
@@ -1292,7 +1572,8 @@
     }
    ],
    "source": [
-    "births_by_date.index = [pd.datetime(2012, month, day)\n",
+    "from datetime import datetime\n",
+    "births_by_date.index = [datetime(2012, month, day)\n",
     "                        for (month, day) in births_by_date.index]\n",
     "births_by_date.head()"
    ]
@@ -1302,21 +1583,24 @@
    "metadata": {},
    "source": [
     "Focusing on the month and day only, we now have a time series reflecting the average number of births by date of the year.\n",
-    "From this, we can use the ``plot`` method to plot the data. It reveals some interesting trends:"
+    "From this, we can use the `plot` method to plot the data. It reveals some interesting trends, as you can see in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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yYhU6hxyIRGOCH7nMkF3wihgGW1eVYHOzGSNWL3pHXSgvVkEqyX+zKjEohYSb\nQqwWPHd/tBHf+tKWvEl+V8KqhJgLhGK4bWtVQREqU5ES//rlHbhzG5ccmEkku31hPPtGF85fTm3p\n7c2x4Fkq8L7kV08MQiYVYe/tzQCATyeiye9eyF6WkCBmC28lm20kOa9I7uzshN/vx/79+3Hffffh\nwoULaG9vx9atWwEAO3fuxPHjx9Ha2ootW7ZAIpFAo9Ggrq4OnZ2dOHPmDHbu3Ckce+LEiTlNkCAI\ngkiFZVmc7pyETCrCX36Wa6nMR4zT8QYiUCsylwzTqWUo0SvRO+ZOaR2dSyQDnOUiHImje9iF8YRI\nLjXmjwrftrWKGz+y+5HTYRIVMgCgqYCkvasJbwWRS8W4dXPVrJ4rEjHQqqVwe1NF8qUBO779i1P4\nn7OjeCmpxByQ1G0vTyORa5n6pLrN93y0ESWJUnzN1XqUGJQ40zU5wyNPEHPFlzhn1CmR5HnwJCsU\nCuzfvx/PPPMMHnvsMXz9619P8Qqp1Wp4vV74fD5otdO+MpVKJTzOWzX4YwmCIIgrZ9Tqg8URwPqG\nYpQkxKk7y7a9xx/JKaoaK3UIhKIYT2odnU8k85aLC71T05HkAkTyisoiodtdoSIZAD7zkVp8alsN\nmmsK9xdfDerLtdjQWIy7Ptowp+hukVqWEkm2u4P48fMX4QtEoFNJMTLpS2ncku97WQpUmTUo0siw\nutaAW7dMLywYhsHNLeUIR+M41XHl7bsJApj2JKuV8+xJrqurQ21trfBvvV6P9vb26Tf2+aDT6aDR\naFIEcPLjPp9PeCxZSOfCbC7suKXMcpzjcpxTOst5jst5bjzLaY5vnh0FANx6Q63QgjkcZ2fMkat3\nHEFNmTbr/DesLMWJSxZMukPYuJrz1MZYLupcU6WHOUOi3A69Cj97uQMn2ydRXcr9fW1zSUFJb1/6\n9Go88dwZ3LKluuDv5IaWStzQUpn/wEXgn75885yfazaoMGTxIhCKwmzW4he/60QoEsPf/OlGjEx6\n8eI7l+EIRNFSyS0OgokuYdUVRUvq95w+1p/93e2QSkRC+3KeP961Ai8d68MHHRbs/sSqqznEK2Yp\nfR+zZSnPjWW431hl2fQ5YzLmz1PIeyV7/vnn0d3djUcffRQWiwVerxc7duzAqVOncOONN+Ldd9/F\ntm3b0NLSgieffBLhcBihUAh9fX1oamrCpk2bcPToUbS0tODo0aOCTSMfVuvyLgFjNmuX3RyX45zS\nWc5zXM5Zl6RMAAAgAElEQVRz41luc3z37AgkYhFqzSoUJUTypM03Y47eQARxFpBLRFnnX5roeHe+\n04JNiQix1c4FOMKBcNbn7dxQjldPDKK93w69RgavO4BC9gsbSzX4yd/uBMMwBX0ny+27S0aZiGg5\nPEGcarXhvQtjaKjQYUO9AfEIZzk4fWkcZUVysCyLExfHoZCJoZWJl8xnMtvvb219MS722dDWbUFp\nDp/7tcRy/o0u9blNORLXsuD0tSwcyt/AJ69Ivueee/Dwww9j7969EIlEePzxx6HX6/H3f//3iEQi\naGxsxB133AGGYbBv3z7s3bsXLMviwQcfhEwmw549e/DNb34Te/fuhUwmwxNPPHGFUyUIgiDs7iBG\np3zYuMIEpVwitCjO5En2B2f68dKpKlFDLhWjO6nZh49P9ssRGf7Ypkr87oMhxFm2IKtFMtdKq+TF\npkjDJSQ63CG8fnIIAPClTzQLnQqB6aTKQYsHNncQ29aU5mxYstTZ3GzCxT4bWi/bcPsNKpzttsJU\npEBN6dKNZhKLB1/dIsWTXEDCcF6RLJVK8YMf/GDG4wcPHpzx2O7du7F79+6UxxQKBX70ox/lHQhB\nEARROHxDDt7TKxIx0KqkGdv6BhN+VoUs+01BLOIahJy/PIVJhx8lBhU8fLJfjnJrRp0CW1eZcapj\nctYimeDgm684PSEMWTwwFSlQV8Yltuk1cpiKFOgdc4NlWZxJtA/fstK8aOO9GqxvNAHowoXeKaxr\nMOLHL1zEymo9vvnFzYs9NGKBaeuzobxYjeKimaUs5wqfuJda3YKaiRAEQSxLrM4AAMCcqAoAcGLL\nk0EkhyKcSJbnEMkAsL6Rq5F8MdEgxBeIQF1ActgdN9VALhVjdZ0x77HETPjSdv1jLrj9kRmNUhor\ni+ANRDDpCOB0lxUyqQjrGoozvdSywaCVo7ZUi64hJ149MQgAsLmDizwqYqGxOPz44ZELePy5MylJ\nyJdHXPjvt7oRi8fn9Lr+YBRymTjF/y7Lcz0ESCQTBEEsSaZcnGAw66ejLTqVFIFQDJFoLOVYvjJC\nvmxuvotea68NLMvCG4hCW4BIrivT4Sd/uxM3rCqZ1RwIDl4kn+/mosRVaRU/GhPl0g78vgsWux/r\nG4oLysxf6qxvLEYszuJ42wQAwOEJpZQoJJYffAdPmzuEH794EZFoHPE4i2de68Bbp0fQPz43X7Qv\nGBVa1PPMS51kgiAI4tojUyRZmxBb6b7kQiPJxUUKVJrV6BxywOULI86yBZcZK7QDHjETPumyZ9gB\nYGZZvNV1RjAAOga5vxfS0W85sGGFSfi3iGEQi7NCIxViedLWZwMArKkz4PKICwd/34VTHRahxORE\nUonK2eALRqCSp17LCum4tzQbvxMEQVznWJ1BSMSMUPoNAHQqTiS7/eGU1tSCJ7mAm8L6hmL87uSQ\n4H1NritKLAx8JDmeCJJWpbXQrjSp8fj92xEMx6CSS+bVq3ktU1euRZFaBn8oii0rzfjgkgUOT0jw\ncBPLi0g0js4hJ8qMKnz17vV4/LmzeO/iOD7smhSO4XMxZkMsHkcwHINGSZFkgiCI6wKrM4DiImVK\nBJdvFpLeUKTQSDIw7Uvmu7xplSRIFhqFTAxZolKFVCJCiUE54xizXonqEs11I5ABLnr8N3evx4N/\nukGIrts9M33JHYMO/Oqdy4jG5uZXJa4NLo84EYrEsK7eCJlUjL+5ez2KNDKEwjHhujQXkTzdkjo1\nkkyJewRBEMuQQCgKbyACc5pgEiLJvjS7RYGeZABoqtLjY5srEUkIDnMGwUbMLwzDCNHRCpMaYhHd\nmnkaKnRYWWOAIbFj4vSEZhzz8vv9+N0HQ3jm1Q7yLC9h2vo5PzLfydOgleNvd2/AzS3l+LM7VkEp\nF2Pc5pv16/oEkZwaSSa7BUEQxDJkOmkvVcBOe5JTI8mFlIDjEYkY7PvESvzpx1ZgxOoV2kcTC4te\nI8eUK4jqDJ0NCU4wAYA9TSSzLItBC9e+5mS7BcU6Be7Z1XjVx0dcOZf67ZCIRVhZbRAeqynV4i/u\nXA2Aa3k/ZPEiFo/PaiHJt6TWpEWSRQyTt9Y4LVcJgiCWGJmS9oBpb2t6reTZ2C145FIxGiuKZrQM\nJhYG/rtL9yMTHIaEx96RJpKtriACoSjW1Rth0Mrx1plhsBRNXnLEWRajUz5Ul6izXqfKjCrE4qwQ\nJCgUf5ZIMpB/d42ufgRBEEsMXiSb0uwW057kLNUtroOyYUsVvuteevk3gsPAdyVME8lDE1xJsNW1\nBjRWFiEcicPpnVkrnLi28QYiiMVZGLTZPfdlxdwCcrYVLvhGIplqvufzJZPdgiAI4hqBZVn8z9lR\nFOsU2LCiOGvb5ilnFruFKrPdYjaeZGJxuHVzFcxGNVbW6Bd7KNckUokYGqV0pkie5ERyTakW/hAX\nMbTY/YI9g1ga8F5zvSZ7onB5oqPnhN2PDbN47emW1DMlbz5fMolkgiCIa4TeUTeee7MbAFBbqsVX\n/mQdTPqZiXNWV2a7hVwqhlwmnmG3mI0nmVgcKkxqbFhdBqt1bs0SrgeMWjksjgAGJtx48sgF/OVn\n1mBwgvMj15RqBAE94fBjVa0h10sR1xh89D+5pGU6ZUkieTb4gzNbUvPkE8lktyAIgrhGuJgopF9b\npsWgxYNXTgxkPM7qDECtkGS86OtU0qwl4ArJ5iaIaxW9Vo5QJIbffTAEjz+CX7/TiyGLB0adHFqV\nDKVGbtE4aQ8s8kiJ2eL0cgucXDsAJQYlGMzBbiFEkjPZLUgkEwRBLAna+m0Qixg89IWN0GtkON1p\nRSSaWvt1yOLBpCOA0kRUJR2dSgaPP5KSvBSKxCARiygJj1jSGBMC6nSiucTQpBcuX1iowFJq4M4J\ni2NuXdmIxWPabpFdJMukYhQXKTA+y0gyX90ik92CRDJx1ekeduKhp49jbGr29QwJ4nrF4w9jYNyD\nFZVFUCmk2LamDP5QFK29NuGYSDSGn73cjlicxR/vqMv4OlqVDLE4K/gzAc6TXEjhfIK4luGjjCwL\nrErybtckRLJWJYVSLobFcf1Fkpd6IxU+kpzLkwxwtiS3LwyXr/DkzGzNRID8iXt01VxmWJ0B/Ofv\nOhEMR/MfvEBc6rfD5g7iVIdl0cZAEItJJBrDibYJ/PDwefz+1FBBz7k0YAeL6UL629aWAgA+aJ8A\nwLVWPfhGN0anfLh1cyXWN5oyvg7fRprfYgQ4TzL5kYmljj5pK/5ztzRgQ6ILGx9JZhgGJQYVJh2B\nvE1F3r0whjcKPDevdY6eH8WXf3gU7QP2xR7KnBE8yXkSLhsqdACAvjFXwa/tC0TAAFDJyZN83XOy\n3YJ3L4yhrW9+ThaL3Y9vP3MKHbM4+fjkifZBx7yMgSCWGj95sQ0/e6Udbf12vHJ8APF4/rqt/Dm7\nrp678VeXaFBhUuPCZRuOtY7hqV+14r3WcVSZNdj9sRVZX0easFTEkiJLoUgMchnlaRNLG2OiPJhe\nI8OKqiLs++RK3LWzAS2NRuGYUoMS0Vgcdnf2WrouXxjPvtGFI2/3IhBavIDSfOANcN7saIzFgde7\nEE7kHyw1HN4QpBJRRiGbzLRIdhf82r5QFEq5BCLRzGpBmfI6kiGRvMzgt1g9gUieIwvjt+8PYMTq\nxcmOyYKf4/BwF6f+MfeiRrQJYrEYGHdDr5FhU5MJvmAU/RO5L+gsy+JSvx06tQzVpVydXIZhsKOl\nDNFYHP/xWicu9duxvrEY3/ri5pw+OnFCJEdjqZ5kKv9GLHUqTGpIxCLcvL4CIoaBUafAZz5Sl9J9\nbdqXnN1y8fbZEURjLOIsi97RwiOSs8UfjOKJw+dntasaZ1kMWTwFt9d+6VgffMEoyowqTDoDePn4\nwBxHu7g4vSHoNbKsZS95GsrnIJIDkaxi+BM3VOd8LonkZQa/KvbMwq+TjSlXACfbuZN70FJ4WSK+\nbWgszqJ72HnF4yCIpUQoEoPbH0F5sRrb15YBQN6dnQm7Hy5fGKtq9BAl3SQ+cUM1vrFnE+771Cr8\nrz9ag6/evT5v5EMqiGQukhyLxxGJxsmTTCx5DFo5nvjKR/C5m+uzHjNd4SJzclckGsPb50aF/+9a\nwHvU2+dGcKnfjtc+GCzo+ClXAD84dA6P/ceHOJo0xmxY7H68c24MpUYV/v7eLSjWKfD6ySFYZpnY\nttjE4nG4fWEYciTt8agUUpQZVegfdxe0QwcA4Ug8q93MVDSzxGYydNVcZggi2X/lkeQ3Tg0jzrIQ\nixiMWr0FJwY4vSHhRt8+QJYLYnlgcwULqs/Jb/MWFymwps4AEcOgrX86+S4ai+NyWvSqZ4T7/5XV\nqY0kxCIRVtUasHNDBbatLcu4XZiOWMwI7wMAoTD3XwXZLYhlgFYly3ke5Iskf3DJAo8/go9troSI\nYdA1tDAiORSJ4Y0PhwEAQxav0CUzG95ABP/wn6fRmRjPxQIsk+0DdsRZFp+6qQYqhRSfv3UFYnEW\nLx7ru/IJJPH6ySHsfvgVHHyjC7ZZtoQuBLcvApbN70fmaazQIRiOYdxWWHGASCw+58o+JJKXASOT\nXqGjFp/Fmd5MYLYEQlG8e2EMxTo5tq8rQzTGYtSa/wcZCEURCMXQXF0EiViEjgX0Jf/2vX788Mj5\ngrelCOJKeOrXF/D4s2fyRi/4m4ipSAGVQoqGCh36xtxCGaK3To/gewfPoGto+tzgb9RN1VfebU2S\nZrcQWlJT4h5xHcCXRmwfcMzoPAkAJy5xibB3bqtFbZkG/eNu4RzJxYjVi9+8149LBebnHLswBo8/\nglIDF6k8123NeXzHoAPeQAS3b62GWa9A97Az772N3+GtT1gQtqw0o7ZMi1Mdkxiaxe5vPs73WBEM\nx/D22VE8/tzZeb/nTle2KEwkz8aXzLIsotE4pJIFFMk2mw27du1Cf38/Ojs78fnPfx5f/OIX8cgj\njwjHHDlyBHfffTe+8IUv4J133gEAhEIhfPWrX8UXv/hF/NVf/RUcjsWPKk45Ayn1Q5cyU64AfvzC\nRXz7F6fw66O9AJIjyTMvDoFQFP966Bzevzie97UHJzwIR+O4YXUpGhM/yEIsF3zSXolBhaaqIgxP\nemc0NpgvTndNoq3PjvFZFhYniNlidQYwavXB7Y/kLW04xUeSdVyS0bp6I1gW6EjsqvAWpIGJ6fOp\nZ8QJtUKCCpP6iscqSYsk83kB5Ekmrgc0Sim2rDRjxOrFo784heFJr/C3cCSGy6Nu1JRoYNQpsLLa\ngFg8vy/5v17vxLefOYXfvNePg6935R0Dy7J448NhyCQifOWuFjAAzuQRyZ2JgNINq0vQXK2HPxTN\nG5ganPBCIhahvJhbGDAMg7s/2gAA+PXR3nnROizLYtjqRaVZjY0rTLC5g3mj4rOlkBrJyTRUFAEA\n+sbzi+RYnAULLFwkORqN4tFHH4VCwV3wf/zjH+OBBx7Ac889h1AohHfeeQdTU1M4ePAgDh8+jJ//\n/Od44oknEIlEcOjQITQ3N+O5557DZz/7WTz99NNzGuR80TXkwDf+7QROzSIJ7Vpl3ObDd/7jQ5xN\nnHj8Fq8/h93i6PkxdAw6ChLJ/IWlpkSDujJOJCff1LPBi2SjVo6WBi5L/1xP7ovDXOHfq4d8z8QC\n09Y/HT1Kt0qkkxxJBoC1iZJubf02sCwrXNh5sW13BzHlCqKpKtWPPFckaZ7kcIS3W5BIJq4P/vpz\n6/AnOxvg9IbxuyQ/cM+oC9FYHKvruJbVzYmdm1yWiylnAO+eH0OpQYnaUi0mnYG8lgNvIIIpVxBr\n6oyoMmuwoqoIl0dcOWv7dg45IJeKUVemFcaVK6cnGotjdMqLKrM6RQCurTNida0BbX12HG+byDnO\nQphyBREIxdBQqUdTNSdOhy3ePM+aHYXWSOapNKshlYjQX4BI5q+DCxZJ/ud//mfs2bMHJSUlAIA1\na9bA4XCAZVn4fD5IJBK0trZiy5YtkEgk0Gg0qKurQ2dnJ86cOYOdO3cCAHbu3IkTJ07MaZDzBX9z\nuzyycNmsVwOXN4Qnj1yALxjFno83gWG4kxKYjiSn2y0i0Tje+JCrCTlk8eZdYfIimS9DJRYxGMyT\noQ9MC1eDVo6tK80AgNNd8y+Sw5GYUAe2e4RE8nJhwu7H3//8JAYLWJDN6fVtPrzwbt+sq6609U17\ninvyXD/4GygfSa4v00GtkKAtUT+c31kZS/jp+Bth8zxYLYBpkRxL2C34uVJLauJ6QcQw+Mz2Wijl\nkpQdUL6O8Jo6buHaXF0EBrnF6Lut42AB3Lm9Dh9ZxyXitg/mtlxMJiKtJQmrxZZmM1hwtoVMuLwh\njNv8aKribIp8bkKupMKxKR+iMRa1ZdqUxxmGwZ9/ahWUcjGefbMbk1fYfZDXAvUVOlSbNSmP5aJ/\n3I1THZaCotmOWdotJGIRDFq5UFs5F7ztbEEiyS+88AKKi4uxY8cOsCwLlmVRW1uL7373u7jzzjth\nt9tx4403wuv1Qqud/qJUKhW8Xi98Ph80Gu5DVavV8Hrnd/UxW/h+3+P2pd0J7sDvuzDlCuJzN9fj\n9huqoVZIBcEYCHHeKm8gkuKd/ODSBJzeMBiGizbnWwkPT3LbOGXFKkglIlSZNRie9OVN3uPLvxm0\ncpj0StSVadEx4BBE/HzBrzwBiiQvJ1ovT2Fsypd3a3KuvPDOZbxyfAAvvttf8HOisTg6Bh0o0Suh\nVkjybs1OuYMQMQwMOu6CLxIxWFtvhN0dwvGL05GdsSk/WJZFd0J081GaK4W3W0T4xL2E35IiycT1\nBMMwqC3VYMLmFxaKHQMOiEUMmqs4EapSSFFdokHvmBuR6Exfciwex3utY1DKxbhhVYkQge7Mk2sz\n6UgVyetXcI1/LmVJZOfF8Kpa7vXNeiX0Ghm6h51ZRSYv/vlGKsmY9Ep86faVCIVjOPw/l3OONR+8\nt7m+ogjVJZyeG7Hm13LPvtGNf/vNJTz9UpuQK5UNp4cTu4YCE/cAQKeSweuP5PVHR6LcdZC/Ls6W\nnOnOL7zwAhiGwfvvv4+uri5885vfREdHB37zm9+gsbERzz33HB5//HHccsstKQLY5/NBp9NBo9HA\n5/MJjyUL6XyYzYUfWyhTiSinxREo+PUnbD68eWoIn7+ted4jMXOdY9+4G2XFKvzF51rAMAyKNDL4\ng1EYjWrhhsiygEItR1FiZfbW2VFIxAw+ua0Or77fD2cwitVZ3j8Wi2PM5kNtuRZlpdyNe1W9EYMW\nD4JxoL4s+7gDUe4H21BrhNmsxa4t1fjPV9txedyD22+qndN8M2FxT4tkmzsEVixGSSJhY6FZiN/m\ntcJiz82RsAnZPKEFGUtbosXzH84M485bGtBYlT9629Y7hWA4httuKMOE3Y/THRZIFFIYEo0N0nF6\nQijWK4RzBwC2r6/AqY5JvHVmBACXxe30hCCWS9Ez4oJcJsbWdRVzjnbwmM1aGPTceaBSyWA2ayEb\n5XaATAbVon+/V8pSH38hLOc5Xu25raovRueQE55wHAaDHIMWD9bUF6Oqcvq837iyBEPH+mD3R7Gu\nMfV6cCoRXPrUR+pQValHZUUR9Bo5uoadMJk0GWv6ms1a+MJjAICmumKYzVqYTBqY9Ep0DTlRXKyZ\nUZ1j4ChXjWLb+grhM1q/wox3z48iDAZVGT43q4u7B25YVZrxc/2jXRoceecyJp3BK/rcJxMBtYbK\nIhh1Cug1coza/Hlfk/ctn+myYnjSi6/csxGbV3GOhLEpL577XSf+6q710Kll8CcKDzTWFUOZp5kI\nj8mgxOVRF5RqBXTq7DaNWKKGtlYjn9PnkHM0zz77rPDve++9F9/5znfwla98RYgOl5aW4ty5c2hp\nacGTTz6JcDiMUCiEvr4+NDU1YdOmTTh69ChaWlpw9OhRbN26teCBWa3zu93KsixGEisimyuIoRFH\n3i+DZVn8y3Nn0TPigk4hxkfWlc/beMxm7Zzm6AtG4PKGUVuqxdQUtzBRSMWYsPkxNJoaUe0fdqDS\npIbDE8KwxYMNjcVortThVQBtPVasyCJ2R61eRKJxlBtUwhhNiRXexe5JaLLUWzWbtRibTMwpEoPV\n6sGqRHTs7dPD2NhgzPg8ADjeNo5Db/UgFImh0qTB3+3bDKkk+6Kkf4RbkZfolZh0BvDBhVFsT2yF\nLSRz/d6WAtfC3AYSUdqBMde8j8XtD2PY4oFBK4fDE8KPfnkOj9y7Ja8P+L1znLBtKNdCKmZwugM4\n1TqGzc3mGcdGY3HY3EE0VRaljL8mkZDnTbRHvXFVCd74cBh/ODmIUasXG1eY4LjCHS7++/P7uZun\nwxmA1eqBNXGdiISji/79XgnXwu9zoVnOc1yMuZkTuzkXOi0YGnWBZYGmCl3KOKoT5+bJi2Mo1aVG\nMl97nxOvNzabhec0VxfhVMckLnZZUF6cmmjLz7E/YQGUM6zwvFXVerx3cRxnL40jGoujfdCBO7fX\nQsQwONc1CblMjCKFWDi+poR77VOtY5BvqMDghAf+YASrE1aRzgE7RAwDtYTJ+rkqZBJ4fKEr+twv\nDzuhVUlh0MphtXpQaVLh0oADg8OOrDXb/cEIvIEI1tYb0VCuw6snBvHoz05gz21NuH1rNX79Vg/e\nPT+KpkoddrSUw2LzQSmXwOsOoFC/gTzhMe4fsudMeLYkcj9iCU2SiVziedZhi3/6p3/C//k//wf7\n9u3DoUOH8OCDD8JkMmHfvn3Yu3cv7rvvPjz44IOQyWTYs2cPenp6sHfvXvzqV7/CAw88MNu3mzc8\ngYhgSQBQUEWEDzsnBf9hcuJOPnhrykLAW0bKkqKmaqUUsTgrZIjy8A1F+DIpjZVFqElszQzlMN4n\n+5F5+DI21hxdjAAuiiaXiqGUcwK3RK9ETYkG7QP2nO0yz3RZ4QtGoddwq/03T4/keR9ubjeu4Vam\ns/UlW+z+vFtAxNVnPFGHeNIRKLgud6F0J5Jzdm2qxNaVZvSPuwvqtsVvazZX6bGiklv0ZctrcHhC\nYFmuRnIyBq0clWbuQl5hUgsljH5/issT2LCieA4zyowkETlJt1tQdQvieoO3IgxaPELXu7X1qcGa\n5kQgJz15LxKNo63fjhKDEjWl0/dC3s+cqwfApNMPsYhJuQ6sSVg1Wvts+OlvL+HFd/vQ2mtD/7gb\nFrsfa+uMKZ0DhWtN4hr1/37Thh8euQCHJ4R4nMXwpBflJlXOHW61QgJfMDpnPeIPRjHlCqK6ZDpq\nXl3Cfaa5LBdWJxd9LjOo8Cc7G/Dt+7ZCJhXhnUSDlK5h7rPjG4/ZPUEYdYVbLQCuXjaQuZJXMvx9\nZK67dAVXlz9w4AAAoL6+HocOHZrx9927d2P37t0pjykUCvzoRz+a08DmG15c8j+acZtPuFFlIhSJ\n4cjblyERM5BLxbjUzxXtLiT7/IV3+/DBpQn8w/6bCt46KBS+mUGKSE6s5qwuTsCKRQxicVZoTd03\nxp1kjRU6FKllKFLLMDSZfWUpVLZIujCYeZGcp/SL3ROCQStP2YZqrtFjaNKLgQlP1uSkSWcACpkY\nj/75DXj4px/gleMD2NFSjqIs2yh8guCGRhPePD2C1l4bogUWDHf5wvj2L05hU5MJ9392Xd7jiauD\nPxgRktpicRaTjsC8lETj4W+CK6v1qC/X4nSXFafaJ9GUsFx4AxE8+otTuHN7LW7dXCU8b8oZhEYp\nhUohQUO5DmIRg9Y+G+75WOOM68GUa7qRSDot9cUYtfpQX65DRSICxXsX1zea5m2eEklq4h7VSSau\nV8qMKsikIrQPOOD2hVFlVs+472tVMlSa1OhNVL7g7yFdQw6EwjFs3GBKuZ/xYvdUhwUf31KFTFgd\nARTrFCmid3XCb/zq8QGEEz7Zt04PC13mdm2qSHmNSrMacpkYl0ddmHQGhGvF0fOjqC/XIRSJob4s\nu4YBAJVCglicRTgSn9P5zwvhmpLpSCsfPBue9Ga9n/M6waznroM1pVqsrjHgQq8NQxaPUB3D7g4K\nvRWMWexr2dCqpAAAd57GaYIneSHrJC8HeHHJ34zyRZJPtE3A7g7h9huqsbHJBI8/UlDZk0mHH6+f\nHILNHSooQ79z0IFnXmnHobd68hYbT55HaVokGeBu5gBn2gcgCI6+MTcYAHWJguM1pVrY3aGsyXS8\nSK5KiiQX6xRgmOms3UyEIzF4A5EZ5nt+RZyt8HecZWF1BFBiUEKtkOKzN9cjGI7ht+9lT67is2GL\nixS4ZX05HJ4Qjl0Yy3p8Mu39di5K0GcvuK0lsfDwUWT+JlVoN6VC6Rp2QCYVo75ch9W1BmiUUnzY\naUEszl1Eu4edcHhCaO2drmQRZ1lMuYKC6JXLxLhxdQnGpny4cHlqxntMl3+b2er0xjUlEIsYbGwy\nodSoAn/frSnVzCphJR8z6yRTJJm4PhGJGNSUaOHwhBCLs7h1S1VGH3FzjR7haBw9Iy6MWLnqT+cT\n5/eGFakLWFOREusajOgZcWW8xwdCUbj9ESFpj6dIw+0mhaNci+S6Mi3aBxz4oN2CUqNKiFDziEUi\nNJTrMG7z43TndNnad86N4vD/XAbDcG3rc6FWcNqAb2Q0WzLtKieL5GzwATuzfvozWJcoCfvCu33g\n77p2d0goXzvbSLLuKkWSrzuRvLmZF8m5b8DH2ybAALhtSzXW1XNfbnJr2Wy8dKwfsYTwGspTJsXh\nCeEnL17E+20TePP0MJ5+qQ0ubyjncywZIsmaxInAr954a4THH0YsHkf/hBsVZrUQ1eYjxKe7JjGV\nJHr9wQhefr8f3SNOFOvkwgkGcD+wYp0ip0jmBYIx7YbPr9x7xzJvUbu8YYSjcZQk2ol+dGMFtCpp\nilhJx+nhWl/rVDLcub0OMqkILx8fyGnp4OE7JvlD0YIapBBXB363Z11iOzRf047Z4A1EMGL1YVWt\nAVKJCGKRCDesKoHbHxEizHwd8OR2ti5vGNFYHOakyPCnt9cBAF45PjhjG9OW1kgkmboyHf7t6x/F\n5ouPTzAAACAASURBVGYzpBKR8HvfMI9RZGDabjHdlpqqWxDXL3yJNKVcgu1rMuet8CXX/vXQOXz7\nmVM48PsuXLg8BaVcgqaqmVVnbtvCidO3zgzP+Js1rfxbMmtquWvbHTfW4M7EdSQWZ3HrpsqMu9R8\ngOmNhC1r4woT3P4IJux+7NpYmRLIygTvGZ6rtZDXSck7emXFKkjEDAZy1Ci2pgXsAKAlkZOUfF+3\nu4OwJZLwjRmumbnQ8ZHkPM3KIgtdJ3m5wN+Am6r1UCskGMsRSbY4/Lg86sLqOgMMWjnW1hvBAGjL\n00t9yOLBB+0WISo0nEOAsSyL/3itA75gFLt3NeJzt9QjFmdxrDV3o48Jux9ymTil6DYfSZ4WydzN\n1+OPYNTqQzgSF7rmAdM+rQOvd+Eb/3ZC8Dw982oHXjzWD7FIhLt2Ns54b7NeCZc3nLGFZygSw0tH\nuVIzhrQVYbFOgSK1DL2jrozeKL6OIy/uJWIRSgzKxOo/sy/V4QmhSCODSMSgSC3DbVuq4fSG8XbC\n85QNlmVT2ormK+WTiUKEODF7+IXspubCdntmA+895qMZAHDjas7PznsVBxJ1wKecAeF3N5WIiCRf\n7CtNamxp5jzN7Wm/H/6mksluASBl+7UyceNZP49+ZGB6W3FGW2qKJBPXIXUJkXxzS3lWy8HaeiNK\nDUpUl2hQalTh6Pkx2NwhtDQYM0Yg1zUYUWpU4WS7ZYZIE8q/6WeK5E9vq8HujzXijptqsKnJBLNe\nAblMjB0tmcX7ioRAd/sjMOrk2Hsb1xdBKZfgc7fU5527KhEYm2skmQ/KJQt+vo7z0KQ3JciWDK9F\nTEnXwRKDSvhMJGIGpQYl7J4g7J7MwbV8aNV8JDn33KKJiltSiiTnZsLuh1ohgVYpRVmxCtYciUEn\nEl1q+MLhGqUUdeVaXB515WxC8PpJbrV37ydXQiYVYTCHPePouVG09duxrsGIO26qwW1bqiGTinD0\n/FhWC0CcZWFxBFBmUKVsGfGeZN4PWWqcjiTzFge+jSMAbGwy4e6PNmBLIju/LyFeu4acMBUp8IMv\nfyRjpQj+REk/MaKxOL574DReOz4As16Bm9eneqsYhkFjZRGc3rDgJU7GkuGiUqxTIM6ycGUoFh5n\nWTi9oZQt6jtuqoFcKsZbp4dzWihGp3xwecOCP6xjliL5XNck/vqJo0u+Ic21yLgQSS6GTCIqOJIc\nicbydsHjf3flSRGRpmo99BoZznRZEYnGMTDOLWpjcVbYFeHPKXOa6L1jWw2A6WsFwN0YznRZUWpU\nZbxBpvO5W+qx7xPNaCjP7SucLel2C/IkE9czN60pxRdvb84pKtUKKb7/V9vxnb+4Ed/64mbh/N24\nIvMuj4hh8PHNlYjGWHzYmdrB1+LgheXMkqRFGjk+dVMtZFIxRCIGD31hE/5+3xaoknZtk0kObq2p\nNcKkV+LLn2vB/75nvZC4lgt+N3iukeQJux96jWxGbtXWRCm3bI3CppwBaFXSGc9bl4gmN1QUodSo\nQiAUw1ii9fbsI8nc/NMbp6UzbbeYW53k60IkR2NxWJ0BlBVz4rK8WC0IznRYlsWJSxOQSUUpJZ5W\nVOoRi7NZe6m7vCF82DmJCpMa6xuLUW3WYNzmE0zj6VxKdPC6a2cDGIaBSiHBtjWlsLmDWStp2N1B\nRKJxQQTzCJFkPupVpAQDbvU5LZKnTzaJWIQ7t9cJF43RKR9cvjD8oShqSrVZkw35C0e65WLC7seI\n1YdNzWb8w/6bMgqERsFyMXOLhl95J/us+UjcVIamJ15/BLE4m9KdR6OUYvu6MtjcoZw2jfbEZ7t9\nbRkqTGp0jzhnVUWhc9ABFtPZucT8MWH3QynndknKilUYt/sL8oy/8eEwvnfwTM6FC38hLUr6zYgY\nBjeuLoUvGMW7F8ZSPPr8tYFfEJrSftP1ZTrIJCKMJFmqXjk+gFicxR/vqJtRBzUTVWYNPrY5s0fy\nSuDtFtMd9yiSTFy/SMQifHxLVcFJ9EVqGb6xdxM+f+sKQQxmYmUNF2gZTSS3RWNxTDr8wrXDnMFu\nkY5Jr0SlObtlQqWQCjtOfMLglpXmgrtz8nYL3xxEcjgSg80dSrF28mxuNkPEMPiwcxKjUz58/9kz\nQtfCeJzL4zBn0AEbm7hFx9p6oxA57kkEOGbrSdYopWAwXcUrG5S4VwDDk17E4qzwZfM/utEMJUzO\nX56C1RnEluYSKGTTJ1VVonzTaJbo1tELY4jFWXx8cyUYhkF1qRaxOJs1GjZs8YABhCx3APjoxkoA\nwLtZEtAsdu7kS//RahIiORzhfgxqpQRqpRQ2VxAXeqegkktS3oen1KiCWMRgzOYTxllhyt6Qg//R\np5eB46N0axuLs96IeZHePmCHxeFPWTxMOmZu6fCeTt7jmen9DGktLD+2ifv8/ufsCOJxVkgISKYt\nYbVYW2/E6hoDwpF41oRCHpsrKOwg2BILEf67IOaHWDwOi92PMqMaDMOgoliNSDSe0wPPw3uJe3KU\nAfT4OAGsT9vSu2lNKQDgt+9zSaI1CY8fv81oFRLxUqMcIhGDcpMaYzY/YnHu5vj+xQmUF6tw0+rS\nvGNeSMTpHfdIJBPErDDqFPjkjTU5k73KjEowDATr5pG3L2P/P72J91rHwQAo0c8uMpqNjU0mqOQS\nrKnP3mcgG/wusz80e5HMi/1MIlmrkmFVrR7942788PB59Iy4BN3CJ0lmEsnr6ovxrS9uxh031giR\nY74gwmztFiIRA41K+v+3d+eBUZVX/8C/d/aZTCYJ2SGQsIRVUHYUTVFRcUFUQEMwuLZoF+gLtUDV\nUrEu6BuXtsKL0lpZZKnGirbVX1FBQTZxYTMgJLIECNkgmclk1vv7Y+bezExmJpMEsky+n3+EkMDz\nmMydc889zzlyF69Q5JpklluE9ol3ytWoAZ67wl4+vRN9udxuvLPlGAQBuPVK/+lw0t1esN6ATpcb\nW74phV6rlMsUpMNxJ0LUJZ86V4ukeJ1fj8Pe6SakdjPg4I9VQbObwdq/AQ0vBIlBq4IpRoPKmnrU\n1jkwcVRG0MyWVPt7uqJODv6DBdOS5BCZZCloDfaikGSlm6AQBGz99jQWrdiJuX/6Ais2HUR1rQ3n\nqq3QqBV+7d6kILmqph52hwvrNv8gBy7ynPdY/8dNPVOMyM6Iw4GSKjyxchd+s+xLv/rj6lobio5X\nIyM5BgmxWnkE6MEwPbAt9Q488dddeHvzDwAaMovSIzW6OM5VW/1uZKXDMq+9tx/f/lCB59fsxZ/f\n3Re0pl26wQt3CDNYJhnw1Csmx+vkujYpaG6USQ5SY5yRHAOny42yKiu+2HcGblHEbVdFlkW+lKQ3\nA5fU3cLhgkataPd1EUUTtUqJ5Di9fA7h++PVUCkVSE80YPSglLDDsJrjzmv6oOAX4+XyguYwyOUW\nza9JDtYkwJcUT0nv/0UnqiGKYqP2b4H694yHWqWQM8duUUSsQd2i/18mg6bJg3tOHtwLr7rWhl2H\nypCeaMDQvp4DMg0BrH/Au33/WZyprMM1w7o36s8qZViDlVt8+nUpzpvtuOqydDn7LPUVDNbhwmz1\nTM0LnNYDeB6p2OwulAQ5OSoPEkkMCJL1/vVMeq2n9hrwBNA3ju7V6O9q2FcMrDYnvvc2Rg/Xl1bK\n9AYGyVLGNjFI2yuJVq1E7vX9MHZwKq66LA1GvRq7DpVh9ceHUXbeipR4/zprqdyi8kI9vj1agf9+\ndRL/2XUcAOShKcHaZl07wpNNlm4o9h1tKL34f3tOwOkS5d6Wg7MSoFIKcqufYI6VXoDN7sKJsw3T\nGgEELdUJVHHeijf//X2TL2Jq6GEsPXH4yfAeuH5EBkrLLfjTu/tw5NQFfPNDRaNyHc8jTs/34scw\nLRelNkGB40sFb8mFRPq1dBNUcaEecUZN0At4hs+N8+ET56EQhJA1jG1J6Q2Snd5SFbvDxSwy0SWQ\nnmhAbZ0DVTX1OF1hQXbPeDzz03EXtf++QiG0+DxBTCvKLc4EaTfra8SAZBi0KowemIKR/ZNRVWND\n+Xmrz6G98OUmvh2AmluPLIk1qGGpd4YtmXQ6u2gLuBqLHY+/sRPbmugG8cneU3C5Rdw4uqfcYiVG\np0ZSnA4nymrlzJRbFPH+thJoVApMubpxgb9Oo0JSnK5Ricbxs7V4Z8tRxBrUftnnjOQYCELwTHJD\naUOQIFk6UBZkmk/RyWqoVYpG2V69VgXfska9ViWf/LxpTK+QoyOBhszxgZIqCELou0bp7zXq1XJ7\nF4k0NSewbjPQxFE9Mfv2IXj4tsFY+siV6JcRh2+PVsBmd8mdLSTSC6iipl7OEB4s8dyphiq3AICx\ng1Lx8zsuw9MPj4VKKciP4M1WB7Z8exrxRo08XlyvVWFQZjecPGcOOSTlWKknKCu/YIUoinJmscZi\nhzXMIyxRFPH3j4rwxb4z2HHwbMjPC2S2OvD1kfJLNrGxo5Lq8KWDHQpBQN4N2Zj6kz4YkpWA6dd6\nuq189rX/JMYybwYa8GSj6+qd2F9cib0BB0pq6xyI0amCXiil8oiUBD0S43QwGdQ4V+XpcFFVY0Ny\niIu9FCQXn65ByZkaZKaFrudvS/LBPWdDn2QGyUQXn3QQePf35yCKQL8Ia4XbSkMLuFZkkhODxwQm\ngwYv/XI8HpkyBIOyGg7CB+uRHEyCb5Dcwj7x0uHFUDMfgIZyiy4XJO/6vgxnKuuw9/C5kJ9TV+/E\nlm9KEWtQy50qJL1SY1Fb58B5b/eEU+fMqK61YfTAlJCN/TOSjaipa5gK5nC68X/vH4DTJeLh2wb7\nHSTTqJVIT4zBiXPmRoePpMcz6UF++Ab0SoAANGotVXmhHqXlFgzKTGg0hlIhCPIpVqVCgEalwLjB\nqRjRPxkTRwWfCCSRRuU6XW4kx+vDjrgEPD/4Feet+OZIuXwDUF0TespYKIIg+N2MBPaU1GtVMGhV\nqPIZylJZU49z1VYUe7PswV6EgiBg1MAU9EiKQVaaCSfKzKi3O/Hp16dgs7tw05hefo9dpHZj3/xQ\n4clKBgTL0rRCq82F6lr/ASznwmST9x4ul8eWBnsqEIwoilix6SD+UrhfPgTRGblFEa8V7pcz/01x\nutz4/ngVkuN1cvtCwPO9vPXKLMzPHY5JY3ohrZsBe4rO+TWPP+O94ZR6AB85dR7L/3kAf/3XIb8b\njZo6e8jT4D2SY3DzuF64fXwWACClmwEVF+pRfr4eblFEUojHhtI5hS8PnIXLLWJAr47xBim9Gfj2\nSWaPZKKLT3oP3+lNhPTt0bincntqGCbS/Ezy2SrPaO1gpWYSjVoJQRAw0HuI8bujldhxoAxKhdDk\ntFTfJFdLM8lyh4swT2ul80/qrtbdYs/3nuA41EE6wPN4vc7mxI2jezZ6XCqVXEhZSulxr1SnGkyP\ngMN7nkNoVky4ojuG9mnc67RPdxNsdlejNZ6u8NyhBav/NerV6JUWKz/ml+w75ikJGNY3eE9V6bGK\nJ6ssYET/ZPzyrqF+hw+D8V1DuHpkSWo3PVxuEX8u3I/n1n4Np8uNqlobYnSqJv+tQIMzE+Q+kMEe\n6STG6VB5od5vqtHn+07jUEkV+vYwNZm5zu4ZB7cooujEeXyy9xRidCr85Ar/9nTD+yVBgKdX7tK3\nv8bvVuyUD/K5RVEOyAHIgauUtQ9Vl2xzuLDh0x+gUgrQaZRNHgyUfHesUq6P3lfc9OAaidPlbnGL\nn0iYrY4mpxr5OlFWi71HyvHhl8fhcDbdU7r4dA2sNpc8tCcYQRBw7XBPyyXfp0fSU5mRAzydaN7d\ncgz1dhfq7S75hsbtFmGuc8jN54P93dMn9JOfMKQm6D0/N94b1VCPDU0xGhj1avnfGdjRgmR3Q5/k\npm5+iaj5pPdMqayyb0bHuAZIdBolFILQ7PcHURRxtrIOKQl6v97uoaQnGhAXo8G3RytQWVOPSWN7\n+Z0xCkatajiH1NzOFpLYGM81PdzhPalffJfqblFVUy/3Ra3w6Tzgy2x14L9fnUSsQR10vrp0eE+q\nNS064XlDDJcNkoJk6fDed95WY+OGhGgE7r2rDOzh2pBJDh6UDs5MgMst4ojPaX3p3woZJHtrkPXa\n5r0Z+o7HberODwBuGZeJG0f3lG8AzlbWobrWhoRmzl0HPMHJzIn9MTgrIehNRqJJB5vDBUu9U56a\n9PGukxAB5AT0Yg4m23vB2vDpUdTWOZBzRfdGgXycUYu+PeJQfLoGx0pr4BZFfOydbnSmwgKrzQWl\n98CTFCRLzenLqurgdLkb/fztP1aJyhobrhuRgeyMeFRcqI+ol+P6T36AQhCgUgrYfyz84Bpfb3xw\nCAtX7Ahb/tEar77zHea/9iU+/PLHiNrl7fcO3bHanNgXsI9gZSRyqUUTp7fHD02DRq3A5r2n5HWc\n9r6WrvK+Bn1vSKX2gWarAyIams83Rcpmf7HPc1o7sEeyRBAEOZssCA0/b+1N6VNu4XaLcLlFaFr4\nBkFEofm+h6tVCr/xzR2B1F62ucNEaq0O1Nmcfk/2mvp3pNgpKU6H267KiujrpOC4WwviB8BnNHWY\nTHKXPLi325tFllqfSZlZXx/vPgGrzYVbxmUGzXBm+nS4cIsijpz0DNIIV2yekeR5AZSWWyCKIvYd\nq0CMToW+PYIPA5CD5FONg+RuJl3IWmGpvkea8Gd3uFB0vBo9kmJCrk96rGLQBs+WheI7Hjdc+zdJ\nRrIRuddnY5y3C8CRU+dRb3e1+E4wMy0Wv8kdHrTExbewf/TAFCTFeQaMaDVKjB4Uun+lROqQUFZV\n523+Hrz0ROqHPTw7CT1TjNh7uBwVF6zyIbEh3uDtiPf7KD1aKqu2Ytl7B7Do9Z1+weN+bxZ4zKBU\n9E73/JyVNJFN3nu4HOeqrbh2eA8M6JWAU+XmoINXApVV1WFP0TmYrY6wXTpayi2KOH62Fk6XG4Wf\nF2PZewearJc+6JMF33XI8xiy4rwVy97bj7l/2tbopvFgSSWUCiHsUxzAc1J7whU9UF1rw5feIR6n\nK+qgVSsxoFeCXA8s3dRIdebSDUqkp8OlWuMS73CRcKNfpc/NDNNfvK0pBAFKhQCn2w27N5PPTDLR\nxWfQqRDnnX6bkWyUD812JAatqtmZ5KbqkYMZPTAVKqWAWZMGRHwGQiqzSGzxwT1poAhrkv3sKToH\nhSDghtGe+emlFY07SOw6VIYYnUrunRso3qiByaDGiTIzTp0zw1LvbLKmMC3R01e4tMKM0nILqmps\nGNK7W8jHEWmJBsToVPJIXACotztRWWNDz9TQb7wDesYj1qDG9v1nYLU5UXSiGnanW+7OEYxRL5Vb\nNP/NUOobHSqzHYx0xywN7mhp4X04vjXOmWmxcrA6dlBqRKUdMTq1nP0fNTA5ZN3T9SMz8Ku7huLR\nOy7DjaN7wi2K+H97TsrBnHRDID3az86Ih0IQsO9YJb49WoELZrvcz1kURRwoqfJMaUyLlbs1NFWX\nLJVuXJ6diKHefR4oabrkYvPehoNs4bp0tFStxQ6nS8SQrAQM6BmPb49WhD2IWFfvxNHSGvTtbkJ6\nogHfHq3Ev3b8iMdX7sJXh8thtjqw/J8H5MC1ts6OH8/Uom93U0RBpqd3qYB/7zgOh9ONs1V1SE80\nQKEQkOl9TUklNVImWWrvFhui3CLQsL6J+NXUofjV1KH4wwOj0TvMRDwpgJZunDoKlVIBp0uE3dm6\nLAoRhZfuLRWUnjB2NJ5McvOCZLmTVpiD/IFGDkjGsnk/CVs2F+iKfknolWKU36ebyySVW4R5Uut0\ndrE+yWarAyVnatC/ZxwGyRNv/Gt+bQ4XKi7Uo2eKMWQGRRAE9EqLRWVNPd7ZegxA0290KqUCPZJi\nUHK6Fhs/OwoAuDxMyyeFdxzzufNWXPA+DpBak/VMDf2CUquUuH5kBupsngNn72wpBgB5jHQwUia5\nJdmsm8f2wi3jMuWShkhIQbI01jnUYcfW8M1OZ6bGIufy7uiVasRNY3pG/HcMyeoGAZBvqIJRqxQY\n3j8ZKqUCYwenIs6oweavTmHbvjPQqpWNvsdJ8TokxeuCHuI7XWFBda0Ng7MSoFAIcoBV3ESQLHfs\niNXJN0NS2UIodfVObNt/BgmxWsQbNdh3rBIud+TTAyNR4Q3+eyQb8dCtg6BVK7Fu8w+4YA6e5f7+\neBXcoojL+iRi3OBUOF1uvLu1GHqNEj+dPBh35fRBda0Nb2w6CFEU8b13guGQIOU2wSTEanH1sO44\nd96Kv/7rEJwut1wmNOXq3rh9fBZyLg8Mkj2vvUjGuAKelkvDs5MxPDtZLssKZdSAFORcno7rRga/\nGW8vKqUAp8sNu3ckNcstiC4NqcNFc94/21KMTuV3LYjE2ermB8lA87O144em4w8PjmnxU7iIDu51\ntUyydIirb484uTwgcKqd9Kigqczo5KuyYNCq5LKGARG0b7n3xgFQqxRyy7RgtbS+pNOuUjZZmi7T\nKy10dgoArhuRAY1agcKtxThVbkbO5d3DnpyVapINLfhh69sjDtMm9JVb5EXCoFMj0aSVT4629HRq\nOFImOSlOB6Nejd7pJvzhgTHNynjfmdMHSx4ag77dIzt1rFIq8PCtgzFmUAr69jDh5rG9oNeq/A4h\ndIvVyrVaGrXnJST9zEmBrfRzEWvQIDleh5LTNWHLFKQguVusFmndDEg06XCopCps0PvlgTOw2V24\nbkQPXJGdDLPVIbesC+U/O4/j+TV7G9UWV1ywysMnfFXVeNaVaNIhKV6PaRP6wlLvxKbtPwb9+6X9\nX9anG666LB1GvRrDs5Ow5KGxuHJIGm65MhNDenfDwR+rcbT0gvzaa6oe2dct43oh1qCWy66kIHlA\nrwTccU0fueuJb7s+oHGP5IvBoFPh/psHNdkTtK0pvZlk6fXJcguiS2PMwBRkpcU2GQu0F4NPh4u6\negfcPu9Dod6TWpJJbg9G79PBcC3gnF1tLPWPZz1BQO90Eww6NRJitY27R4RpseYrOyMev79/FPp0\nN2FQZkKT3RIAoF9GHOZOGwaNSoGBvRLkuuiQnx9weK/EG+Q3dcjHqFcj5/LuEAGkxOuRe32/sJ/v\n292irfRMabhzvhSZ5NQEA1RKQe6A0RJatVKelhipIb274ZEpl+Hx/FG43dumTgq8dBol9FoVMtNi\nIQC465o+ABoyyQe9JRJDfIK+3ukmWOqdYQeQVNfa5L9bEAQM6Z2AOpuz0cAbX0XejizjBqfJQyy+\n+aE85OcDntaJR05dwGGfFnMnymqx8P92Ys1HRY0+XxqeIt2wXDu8B+KMGuw6VNaoc4XD6ca3P5R7\nbmjSTEiM0+HVOVfjV1OHyQGqQhAwaaxnuM22fWdwoKQSRr26WVmYpDg9np99JR694zLcPLYXrhmW\n7vfncj9v79qlerVQ3S2ikVopwOVyy6PqWW5BdGkM6JWA398/+pK8B14MUmxw6Mcq/OqVL/DrP23D\n/67/BvNf247Hln8ZtF65rNrqmbfQwa+Zeq0KAsK3uJO6W3SZcosfvYdppPqfHkkxqK61+TXLlu6C\nIsk4piQY8MSsUfhN7hURr2FgZgKe/dk4/OLOpqfq9PGOY5Y6I5ScroFKqUBmmDpHya3jMjF6YAoe\nveOyJmtwjfqWl1u0lO+BpktxgTDq1Xhi1ijkTex/0f/u5pJGbCbG6SEIAm67MhNPPzwWV3sDtHPn\nrbA7XDh88gJ6pRj9emYPzvIEzN8cCR3AejqENHyNVPojdV0J5sezNTDFaNDNpMWgzARoNUq5RjwY\ntyjK5T7f/tBQv7zNO1L5/+063ijDLNVaSwcrFAoBVw1JQ53NiW9+8K+B3nnwLGrqHLhmWLo8AlkI\n8nRiUGYCEk1afHngLM6b7RjSu1uznmIAnp/z0QNTMP3afkHLKJLidKi8YIVbFJtdbhENlEoFHC53\nQyb5Io3IJaLORcokf/HdaYjwZI8P/VgNs9WBqhpbo4PUbreIc9V1SOtmCHr97kgUggC9VhV2WIpD\n7m7RRfokl3gDAymgCOxdDABn5CA58kcFzf1h8HSnaPouS6tRom8PE0rO1KC61oZT5Wb0SjVGlNmJ\nM2rx6B2XRZRl65FshIDwJ/Evtl4+/1ZLW7g0+W+kxjaZrW8LUiZZGiyhUSvRPSkGBp0aRr0aZdVW\nlJypgdPlbtSlYUT/ZCgVAnYXBR98Y3d4evr6Bcnev6Po+Hm4RRFvbz6Cr3y+vsZiR1WNDVlpsRAE\nAWqVAv26m3Cmsi5ku5/qGpucWfz2hwqIoginy41d35fJf+d3R/2D7MBMMgBcNdRzY7B9f8MBPrco\n4qPdJ6BUCJg4KnzNuEIQcNVl6fKkvOaUWkQqKV4Pp0vEBbO92Qf3ooFKqYDLJcLmZE0yUVcmZZJ/\nOHUBSoWAF39+Ff7y6xz8/A5Pki/wUHlFTT2cLhFp3TpWCVkoMfrwBxOlcouWdh7pVFfOCwGBAdBQ\nj+gfJFugVSs7zOOPoX0SIYrAf3Ydh8stoncT9cgt0TPFiD//+hqMGhD6cN/FJgXkMTpVi2fLdxZS\nkJwYpPY0NcEzhVAqfwgspTHq1RiUlYDjZ2sbTfQDgGqzdGiv4ec13uipTT5y6jy+OVKOzV+dwvvb\nSuQ///Gs/xMVAOjt7aQhPW0JdKaq4TVSWVOPU+UWHCipQm2dA4O8Qfk2b29gSVVNPTRqhXyhBTxP\nb3qnm3CgpBLnvWs/UFyJM5V1GDs4NaLX3XifEokhlyBIlnobV1ywoqbODkFoqNvvCqSDew4Ha5KJ\nujKp1awIT/mnTqOCQaeSD5UHBsmdpR5ZYtCpw/aBdrjcUCqEZj+tlEQUJFdWVmLChAkoKSlBVVUV\nfv7znyM/Px95eXk4efIkAGDjxo2YOnUqcnNzsWXLFgCAzWbDnDlzMHPmTMyePRvV1aEfHUfiuLce\n2TcwSO/mCZKlg1Nut4izVVakJXacRwVSQf/Wbz0BSFb6pTkFa9Cp23TPKfF6xOhUQaflRRup0kFa\nvwAAIABJREFUH26wTH1KgmcK4c5DnoxsdpAa6tEDPX2dvwqSTa6uaehs4Wtgr3jY7C6s+e8RAJ4b\nQalLyo/ya6Hhhku6+QrVSUO6+En1y3sPn8N27/S6aRP6ol9GHPYXV/l1rqisqUeiSdfo5+rqYekQ\nRWD1x4dx3mzD25t/AADcGKaLiK+UeD1yLu+OcUNS/UpTLpYk+fBePWotnpHULb1IdkYNLeA8mWTW\nJBN1TTE+T7wH+yQkTDEaJJp0KA44VC7FUp3lfT1Gp4Ld4Q456MrpdLfq+tfkVzqdTixevBg6necN\n/MUXX8Ttt9+O1atXY+7cuSguLkZFRQVWr16NDRs2YOXKlSgoKIDD4cC6devQv39/rF27FlOmTMGy\nZctavFCgIUPm27c0xftIQDo45XlU4G5WqcWl1ivViLgYjVwfGK7vameiUAh4bMZw/PS2we29lEsu\nMy0Wj88aidtz+jb6M6nTRVlVHVK7GYJ2UZBKLrZ8U4pN20r87t59O1v4kkouLpjt8oAMaVSy9FrI\nDJJJDjW45Iz34nfD6J5QCAI2bf8Re4+UI62bAVlpsZg4uhfcoiiXhVhtTljqnUEbvV8zLB2DMhPw\nzQ8VePyNnThXbcWtV2Y22TLN1/03D8TPJg+J+PObI8mbSS6/YEVNmJHU0UqlkFrASTXJDJKJuiLf\noWWBpW29u5tgtjrkdplAQ5vazpRJBkIf3nO43C1u/wZEECQvXboUM2bMQEqKJxP29ddf4+zZs3jg\ngQfw4YcfYuzYsdi3bx9GjhwJlUoFo9GIrKwsFBUVYe/evcjJyQEA5OTkYMeOHS1eKBD8EXOsXg29\nVil3DjjjLbtI70DfYEEQ5GyyTqNs1hSbjq5XamynueNsrb7d44JOEkpJaCjBCJZFBjx381f0S0LF\nhXr8c1sJlq79Wr4YVdV6LlCBZQoDfPp233GNp8uG1Jf6eFkt4oyaRiUaCbFalJzxZAaKjlcHPdDa\np7sJt1yZieyMOIy/LA0P3DIQgiBgzBBPCYTUlq3KG7wnBhnLrFIq8Is7hyIjOQZWmws/uaI77srp\nE3Tv7UEKks9W1sFqc3apQ3tAQ7sjq3dkOsstiLomKZMco1PJk4YlfYKUXEjvS5GOpG5vUimgJUQb\nOKfrEmaSCwsLkZiYiPHjx0MURYiiiNLSUsTHx+PNN99EWloaXn/9dZjNZsTGNvzPNxgMMJvNsFgs\nMBq9dasxMTCbQ7ezaorV5sT3J6qRFKdDnM/jWUEQkJJgwLlqz0n2M83obNGWpAERWWmxXeqxb1eQ\n4nMxCRUkA8DPbh+MJ+8bhRnXZ8PudGPlh4fgcrt9Bon4B8lxMRoMyUrAwF7xmOTt11x0vBoXzDZU\n19qC1rb3STfhgsWOD7/8ES+s+wbPr/1a7iF5ptKCRJMWWrUSd+X0waJ7R+Kh2wbLNdTJCXr0SIrx\nTHh0uORDe6F6YBt0Kvw2bwR+cedlyL9xQIcpbwI8QbJCEOQSmK50aA9oaJxvtXmDZGaSibqkOKMG\nguA5+yF1HZL09pZ+BgbJ3UzaTnPOSLoJCDV62+kSoVK2/L0pbL+wwsJCCIKA7du34/Dhw1iwYAGU\nSiWuvfZaAMB1112Hl19+GUOHDvULgC0WC0wmE4xGIywWi/wx30C6KcnJ/p/7nx0/wmZ3Ydp12Y3+\nLDPNhONnayGoVaj21mwOzk5u9HntaUKsDl/sP4ObxmbK6+pI67tYonFPgQL3qI9pCG7HDeuB5DB9\nmbunx2PMsB44U23Flq9P4YsDZbDYPHWj2b2TGpVqPP+rHIiiCEEQMKxfEnYdPIvPvvPUEQ/um9Ro\nLZdlJ2PvkXK894XnkN+pcgv+VLgfTzwwBufNdgzvH/51MeaydLy35SjKauywe8vUemfEh/yaZAC9\ne138g3cXw//kjcDHO3/EoZIqXD4gJapfdxJpbwbpkKLCExwnJRmjYt/RsIemRPMeo3lvko62x+Tk\nWDzz6Hj0So31SzACgNGkh0L4Bqcq6pCcHIt6mxPVtTZcnt34vUX6uzqaFG9CVKVVBV2f0yXCaFC3\neO1hg+Q1a9bIv541axaeeuopvPLKK9iyZQumTJmCPXv2IDs7G0OHDsXLL78Mu90Om82G4uJiZGdn\nY/jw4di6dSuGDh2KrVu3YtSoUREvrLy84YS+KIr44PNjUAgCRvRN9PszoGFIwPdHy3GopBIalQIa\niI0+r73Nm345AM/ekpNjO9z6Wisa9xQo1B5NBs+hSZXojuj/wdSc3vjq+zIUfnYUcUYN1CoF6i31\nsNUFH/cMAH3SY7Hr4Fls+qIYWo0SgzJMjf6tFJ9R3ndf2w+nKy3Ytu8MHl+2HQCQGKsNub7k5Fj0\nTfME+F98c1IuLVF3wNdSJIb0jMOQnpfD7RahUAhR+7qT+O7N5T3/UOkdL2u12Dr9vqP5eyeJ5j1G\n894kHXWPaSYt7FY7yq2NxzenJ8Xgh5PVKDtXI08EDvY+0VH3Jro8SaYzZbUoT25cQWB3uiAAYdce\nLoBu9uSJBQsW4IknnsD69esRGxuLgoICxMbGyt0uRFHEvHnzoNFoMGPGDCxYsAB5eXnQaDQoKCho\n7j8HwHNa/+Q5M0b2Tw7aXirVWxN67HQNSsstGJSZ0KpCbaLmevSOy6BUKiIuOYjRqXHNsHT8Z9cJ\nmK0OpCTom/za4f2S8P4XJeiXEYdZNw0IWgaRlRYLjVqBtG4G3DA6A4Cn17FUyxzJFEqtWolvf6iQ\n+1MHq0nuTAIfMXYF0vWvTiq3UPN6SESN9UwxorTcgvLzVpRWeILkHkkdq1w1HKncwhyiDZzT6W7x\ntD2gGUHyqlWr5F//7W9/a/Tn06dPx/Tp0/0+ptPp8Oqrr7Z4cZJt3jZVE0b0CPrn0sGx7fs9nzeg\nZ/iRz0QXm+8hu0hNGN4DH+06ARFAQgRt0JLi9fjTr68JW9Ou16qw+P7RMMVooPQ+an9kyhAs+ftX\nqKypR1oTtfpqlQKDMhPw7dEKVFyox8Be8ZdsUAxdOlINnlSTrObEPSIKQgqIS8stOF3hefLUvVMF\nyZ4wNlhNslsU4XKLrUqatt0M41Y4VnoBWrUSg0IEIlJ3AakN3IBeDJKp40uO12NY30R8d6wSCabI\negVHcugz8NBqrEGDefdcjr2HyyO6gbxpTE84XG5cMywdowam8KBpJyRnkut5cI+IQpNmAJwqN+O0\ntztYZwqSG1rANc4ku7y9k1WtuP51+CDZ4XThTGUdstJjQz429bSBU8Fqc0KlFKKmDzFFv4mje+K7\nY5WXvBtLemIMbrsqsn9jQK+EFmXGqeNgdwsiikSP5IZMcmmFGbEGdadqmRkukyzNpmiTcov2crqi\nDi63iF4poQurBUFAaoIeP56tRZ90E3uCUqcxJKsbFt8/Oqp6Z1P7k8otGmqSeU0kosYSTTroNEqU\nnKlB5YX6TvckXs4kB+mT7HB5WjS1JpPc4dMLJ855TiT2TA3dVgtoqEvu38m+wUSZabFBh5QQtZQy\noNyCY6mJKBhBENAjOQYVF+ohwtPtojPRa5VQCAIstsaZZKecSW55yWCHv3JKLUnCZZIBzwlNwJOZ\nIyLqyqQ3BZdbhEIQ2O2HiELK8Ont35k6WwCeIN+gUwUtt3BKNcnRXG5x4pwZgtBQNxPKxJEZ6J8R\nj35hJp4REXUFvm8KbP9GROH4BsbdO9i04kgYdKqgB/ccF+HgXoe+eoqiiJPnapHWzdDk42iNWskA\nmYgIDeUWAA/tEVF4vpnk7k0kJDuimBCZ5ItxcK9DXz0rLtTDanPJpRRERNQ0lU8NHnskE1E40pN6\no14NUyfqbCEx6NRwON2wO1x+H5fKLVpzJqNDl1ucPOetR07tePPCiYg6KpZbEFGkYg0aDOndDSnx\n+vZeSotIbeAs9U6/Tj7Swb2orUkuLfcEyb6PAoiIKDzfTLKGmWQiasL8e65o7yW0mDSauq7egYTY\nhsFccgu4aO1uYfHWmJhi1O28EiKizsM3c6JmJpmIopjBJ5PsSy63iNaa5Hq7Z8N6TYdOeBMRdSgq\nHtwjoi4iJsRoaungXtR2t7DaPEXYOg0fFxIRRYrlFkTUVYQaTd0FMslSkMxMMhFRpJQ8uEdEXYQ8\nmjogSI76Psn1dicEgRd5IqLmUCl8W8Dx+klE0csgZ5L9yy2c0d4n2WpzQadRQRBafjKRiKir8c2c\nsNyCiKKZNGzO7g2KJU65u0WUBsn1difrkYmImkml8OluwUwyEUUxqdogcJiIw+n5vUoVpS3g6u0u\nBslERM3kd3BPzWsoEUUv6Rpnd/hnkqU+yVFbbuEJknloj4ioOfzLLTr0ZZ6IqFW03muc3Rl8LHVU\nHtxzutxwutzQa5kFISJqDt9yCwbJRBTN1KrgmeQ2O7hXWVmJCRMmoKSkRP7YBx98gNzcXPn3Gzdu\nxNSpU5Gbm4stW7YAAGw2G+bMmYOZM2di9uzZqK6ujnhhbP9GRNQyLLcgoq5CrkkOlUm+lEGy0+nE\n4sWLodPp5I8dOnQI7777rvz7iooKrF69Ghs2bMDKlStRUFAAh8OBdevWoX///li7di2mTJmCZcuW\nRbywepun3x1rkomImsf38SIP7hFRNFMpFVAqhMY1yVIm+VKWWyxduhQzZsxASkoKAOD8+fN45ZVX\n8Pjjj8ufs2/fPowcORIqlQpGoxFZWVkoKirC3r17kZOTAwDIycnBjh07Il6Y1c5pe0RELeFXbsFM\nMhFFOY1a0bi7xaXOJBcWFiIxMRHjx4+HKIpwuVx4/PHHsXDhQuj1evnzzGYzYmNj5d8bDAaYzWZY\nLBYYjUYAQExMDMxmc8QLq7d7Msl6LcstiIiaQ6nkMBEi6jo0KiVsIfokt+YaGDYCLSwshCAI2L59\nO4qKinD77bcjIyMDf/jDH2Cz2XDs2DE899xzGDt2rF8AbLFYYDKZYDQaYbFY5I/5BtJN0eo1AIDE\nBAOSkyP/us4kGvcVjXsKFM17jOa9SaJ5j9LeRFGUP5aSZIyaPUfLPsKJ5j1G894k0bzHjrw3vU4F\np9Ptt0aFN4OclmpqccI17FetWbNG/nV+fj6efvppZGVlAQBKS0sxf/58LFq0CBUVFXjllVdgt9th\ns9lQXFyM7OxsDB8+HFu3bsXQoUOxdetWjBo1KuKFlZV7gm6Xw4Xy8toWbK1jS06Ojbp9ReOeAkXz\nHqN5b5Jo3mPg3lRKBZwuN+ostqjYczR/7yTRvMdo3pskmvfY0femVAiotTn91mipswMAzldbYA5T\nchEu+I84tBYEwS874SspKQn5+fnIy8uDKIqYN28eNBoNZsyYgQULFiAvLw8ajQYFBQWR/nOw8uAe\nEVGLqZQCnC62gCOi6KdRKYOMpXZDgCeAbqmIg+RVq1b5/b5Hjx5Yv369/Pvp06dj+vTpfp+j0+nw\n6quvtmhhbAFHRNRynsMqLtYkE1HU06oVcDjdcIsiFIInKHa6RKhUCghCFI6llg7u6ThMhIio2aRe\nyVp2tyCiKCd18XH4tIFzudx+PeNbouMGyTa2gCMiaimp7REzyUQU7dRBRlM7XG4oFa27/nXYq6ec\nSWa5BRFRsym9QbJGxUQDEUU3TZDR1C6XGMWZZG9Nsp6ZZCKiZlN73xzU6g57mSciuii0QUZTO93u\nVg0SATpwkNzQ3YKZZCKi5lKrlNCoFfIhFiKiaCXVJPtmkp0usdVBcoeNQOs5lpqIqMXuyumD82Zb\ney+DiOiS03gzyTaf0dROpxsqQ+uSBB06SNaoFVC0or8dEVFXNaR3t/ZeAhFRm5BrkgPKLZTRWm5R\nb3dCz1ILIiIiIgojWLmFyyVCHa1BstXuYqkFEREREYUllVvYveUWblGEyx3V3S2cPLRHRERERGFp\n5XILTybZ5fL8NyrLLVxuEXaHG3pO2yMiIiKiMAIzyU6XCABQtfJcW4cMktn+jYiIiIgioQ7IJDu9\nmWRVKyeOdswguV4KkplJJiIiIqLQtKEyydFYbmG1OQAwSCYiIiKi8AK7W8iZ5Kgut9Cy3IKIiIiI\nQtN4yypsTimTHMUH9+pYbkFEREREEWjIJHuCZJe33CIq+yTz4B4RERERRaJRuYVbyiRHc7kFM8lE\nREREFIZUbiEf3HNG8cE9S73n4J6BNclEREREFIbcJzmwBVxUZpKlmmQOEyEiIiKiMJQKBVRKoSGT\n7JaC5DbIJFdWVmLChAkoKSnB999/j5kzZ2LWrFl4+OGHUVVVBQDYuHEjpk6ditzcXGzZsgUAYLPZ\nMGfOHMycOROzZ89GdXV1RIuSDu7pWZNMRERERE3QqJQ+meQ2KrdwOp1YvHgxdDodRFHEs88+i9//\n/vdYtWoVbrjhBrzxxhuoqKjA6tWrsWHDBqxcuRIFBQVwOBxYt24d+vfvj7Vr12LKlClYtmxZRIuq\n89Yk61luQURERERNUKsVPjXJbXRwb+nSpZgxYwZSUlIgCAJefvllDBgwwLsIJzQaDfbt24eRI0dC\npVLBaDQiKysLRUVF2Lt3L3JycgAAOTk52LFjR0SLqvPWJDNIJiIiIqKmaH0zyW1RblFYWIjExESM\nHz8eouhJXSclJQEAvv76a7z99tu4//77YTabERsbK3+dwWCA2WyGxWKB0WgEAMTExMBsNke0KPZJ\nJiIiIqJIaXwyyS653KJ1meSwqdrCwkIIgoDt27ejqKgICxYswPLly7Fr1y6sWLECr7/+OhISEmA0\nGv0CYIvFApPJBKPRCIvFIn/MN5AOx2pzQhCAjO7xULRypGBHlpwc2f+PziQa9xQomvcYzXuTRPMe\no3lvQPTvD4juPUbz3iTRvMeOvrcYvQZl1VYkJ8dCb6gEAHSLN7Rq3WGD5DVr1si/zs/Px5IlS7Bt\n2zZs3LgRq1evhslkAgAMGzYMr7zyCux2O2w2G4qLi5GdnY3hw4dj69atGDp0KLZu3YpRo0ZFtKi6\negd0GiUqKyPLPHdGycmxKC+vbe9lXFTRuKdA0bzHaN6bJJr3GM17A6J/f0B07zGa9yaJ5j12hr0J\nEOFwulFWVoPq81YAQF2dvcl1hwuiIy76FQQBLpcLzz77LLp3745f/OIXEAQBY8aMwS9/+Uvk5+cj\nLy8Poihi3rx50Gg0mDFjBhYsWIC8vDxoNBoUFBRE9G9Z6p2ctkdEREREEZGn7jldDX2SW1mNEHEk\numrVKgDArl27gv759OnTMX36dL+P6XQ6vPrqq81elLXegViDptlfR0RERERdj+9oajlIVkXhxL26\neif0HCRCRERERBHQ+oymlg/utTKT3CGDZJdb5CARIiIiIopIQ7mFGw6X1Cc5CjPJAKBjj2QiIiIi\nioBG7c0kO30yydEaJOvZI5mIiIiIIqBWBalJvtQT99oLp+0RERERUSS06oaaZKc7yjPJnLZHRERE\nRJHQeDPJNocLTiczyURERERE0Gp8yi3cUpAcpZlkBslEREREFAmtt7tFvcMFZ9Qf3GOQTEREREQR\nkDLJNrsLrqg/uMeaZCIiIiKKgE7KJNud7JNMRERERAT4ZJJ9J+4xk0xEREREXZnOp9zC6XJDEACl\nIkozyaxJJiIiIqJIBB7ca+2hPaADB8k6DYNkIiIiImpaYCa5taUWQEcOkrUstyAiIiKipmnkg3ue\nILm1pRZABw2S9VoVFELr7wCIiIiIKPqplAqolAr54J5aFaVBskHHUgsiIiIiipxOo/SUW7jdUCqi\ntNyCQTIRERERNYdWrfSUWzjd0Xtwz6BVt/cSiIiIiKgT0WqUsMndLdook1xZWYkJEyagpKQEJ06c\nQF5eHu6991489dRT8uds3LgRU6dORW5uLrZs2QIAsNlsmDNnDmbOnInZs2ejuro6okXpmUkmIiIi\nomaQM8nuNsokO51OLF68GDqdDgDw3HPPYd68eVizZg3cbjc2b96MiooKrF69Ghs2bMDKlStRUFAA\nh8OBdevWoX///li7di2mTJmCZcuWRbSoGB0zyUREREQUOZ1GCafLDYejjYLkpUuXYsaMGUhJSYEo\nijh06BBGjRoFAMjJycGXX36Jffv2YeTIkVCpVDAajcjKykJRURH27t2LnJwc+XN37NgR0aJYk0xE\nREREzSENFBHR+pHUQBNBcmFhIRITEzF+/HiIomcOttvtlv88JiYGZrMZFosFsbGx8scNBoP8caPR\n6Pe5kejdPa7ZGyEiIiKirksaKAIAyouQSQ6bsi0sLIQgCNi+fTsOHz6MBQsW+NUVWywWmEwmGI1G\nvwDY9+MWi0X+mG8gHc7ka/qgvLy2JfshIiIioi5I6xMkqy91kLxmzRr517NmzcJTTz2FF154AXv2\n7MHo0aPx+eefY9y4cRg6dChefvll2O122Gw2FBcXIzs7G8OHD8fWrVsxdOhQbN26VS7TiERycmQB\ndWcWjXuMxj0FiuY9RvPeJNG8x2jeGxD9+wOie4/RvDdJNO+xM+wtIU4v/9pgULd6zc0u/l2wYAGe\nfPJJOBwO9O3bF5MmTYIgCMjPz0deXh5EUcS8efOg0WgwY8YMLFiwAHl5edBoNCgoKIj434n2THJy\ncmzU7TEa9xQomvcYzXuTRPMeo3lvQPTvD4juPUbz3iTRvMfOsje30yX/2uV0R7TmcIF0xEHyqlWr\n5F+vXr260Z9Pnz4d06dP9/uYTqfDq6++Guk/QURERETUIr7lFqponbhHRERERNQcOrVPkKyK0ol7\nRERERETN4Z9JZpBMRERERAStuqGKWNlWY6mJiIiIiDoy3z7JbTJxj4iIiIioo/Mrt2AmmYiIiIio\nYSw1wEwyERERERGAwEwyg2QiIiIiIr8WcDy4R0REREQE/0yymplkIiIiIiJAo1JAyh8zk0xERERE\nBEAQBDmbzJpkIiIiIiIvBslERERERAGkw3vsk0xERERE5MVMMhERERFRADmTrGAmmYiIiIgIAKDV\nqAAASmaSiYiIiIg8pHILtYpBMhERERERgIZyCyXLLYiIiIiIPHqlGmHQqtDNpGv136W6COshIiIi\nImp3E0f1xLUjekCpaH0euMkg2e1244knnkBJSQkUCgWeeuopOJ1OLF68GCqVCllZWXjmmWcAABs3\nbsSGDRugVqvxyCOPYMKECbDZbHjsscdQWVkJo9GI559/HgkJCa1eOBERERFRoIsRIAMRlFt8+umn\nEAQB69atw9y5c/HSSy/htddewy9/+UusXbsWNpsNW7ZsQUVFBVavXo0NGzZg5cqVKCgogMPhwLp1\n69C/f3+sXbsWU6ZMwbJlyy7KwomIiIiILpUmg+SJEyfi6aefBgCUlpYiLi4OgwYNQnV1NURRhMVi\ngUqlwr59+zBy5EioVCoYjUZkZWWhqKgIe/fuRU5ODgAgJycHO3bsuLQ7IiIiIiJqpYjy0QqFAgsX\nLsQzzzyDyZMnIzMzE8888wxuvfVWVFVVYcyYMTCbzYiNjZW/xmAwwGw2w2KxwGg0AgBiYmJgNpsv\nzU6IiIiIiC6SiA/uPf/886isrMS0adNgs9nw9ttvo2/fvli7di2ef/55XHPNNX4BsMVigclkgtFo\nhMVikT/mG0iHk5wc2ed1ZtG4x2jcU6Bo3mM0700SzXuM5r0B0b8/ILr3GM17k0TzHqN5b6E0mUl+\n//338frrrwMAtFotFAoF4uPjERMTAwBITU1FTU0Nhg4dir1798Jut6O2thbFxcXIzs7G8OHDsXXr\nVgDA1q1bMWrUqEu4HSIiIiKi1hNEURTDfYLVasWiRYtQUVEBp9OJn/3sZ4iPj8eLL74IlUoFjUaD\np59+Gt27d8c//vEPbNiwAaIo4tFHH8XEiRNRX1+PBQsWoLy8HBqNBgUFBUhMTGyr/RERERERNVuT\nQTIRERERUVfDiXtERERERAEYJBMRERERBWCQTEREREQUgEEyEREREVGAdg+S8/PzUVJS0t7LuOhK\nS0sxcuRIzJo1C/n5+Zg1a1bIkdyd5f/B7t27MXDgQPz73//2+/jkyZOxaNGidlrVpfPGG2/g6quv\nht1ub++ltFpX+94Bned11VLh9nfdddd12p/baHrdBfP666/jgQceQH5+Pu677z4cPHiwvZd0UZ06\ndQpz5szBrFmzkJeXhyVLlsizEgKdOXMGn332WRuvsOV2796NUaNGoaysTP5YQUEB/vnPf7bjqi6O\n3bt346qrrpJjlhkzZuA///lPey+r3UU8TISaLzs7G6tWrWrvZVxUffr0wb///W/ccsstAIAjR46g\nvr6+nVd1aXzwwQe47bbb8K9//Qt33nlney+n1brS966rEwShvZfQYtH2uvN17NgxfPrpp1i/fj0A\noKioCAsXLoyKIAsAbDYbHn30UTz77LMYOnQoAOCf//wn5s+fj//7v/9r9Pk7d+5EcXExrr322rZe\naotpNBosWrQIf/vb39p7KRfdlVdeiYKCAgBAXV0d7r33XvTu3RsDBw5s55W1n3bPJANAVVUVHnnk\nETz00EOYPHkyPvnkEwDA7bffjj/+8Y9yJrazjbQO1l3vpZdewsyZM5Gbm4uPP/5Y/virr76K++67\nDz/72c9QXV3dlstsloEDB+L06dPy92LTpk24/fbbAQBr167Ffffdh3vuuQePPPIInE4n3nvvPdx7\n772YOXMmdu7c2Z5Lb5bdu3cjMzMTubm5ePvttwF4MneLFy9Gfn4+8vPzUVlZid27d+Puu+/Gvffe\ni02bNrXzqsNrzvfO4XBg/vz58iCgY8eOYfbs2e229pb685//jA0bNgAAiouLkZ+fD6DzX1skofbX\nWTt7hnrdSRnz9evX4y9/+QsA4LXXXsNdd92Fhx56CDNnzsSePXvabd2RMhqNOHv2LN555x2UlZVh\n4MCB+Mc//oEjR45g1qxZmDVrFubMmQOz2Yzdu3fjwQcfxEMPPYQ77rgDa9eube/lN2nLli0YO3as\nHCADwB133IHz58/j+PHjyM/PR25uLh544AFUVlbi9ddfx7/+9a9OlU0eN24c4uLiGn0ChvT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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117326a20>"
+       "<Figure size 864x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1329,35 +1613,26 @@
     "births_by_date.plot(ax=ax);"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "In particular, the striking feature of this graph is the dip in birthrate on US holidays (e.g., Independence Day, Labor Day, Thanksgiving, Christmas, New Year's Day) although this likely reflects trends in scheduled/induced births rather than some deep psychosomatic effect on natural births.\n",
-    "For more discussion on this trend, see the analysis and links in [Andrew Gelman's blog post](http://andrewgelman.com/2012/06/14/cool-ass-signal-processing-using-gaussian-processes/) on the subject.\n",
-    "We'll return to this figure in [Example:-Effect-of-Holidays-on-US-Births](04.09-Text-and-Annotation.ipynb#Example:-Effect-of-Holidays-on-US-Births), where we will use Matplotlib's tools to annotate this plot.\n",
-    "\n",
-    "Looking at this short example, you can see that many of the Python and Pandas tools we've seen to this point can be combined and used to gain insight from a variety of datasets.\n",
-    "We will see some more sophisticated applications of these data manipulations in future sections!"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) | [Contents](Index.ipynb) | [Vectorized String Operations](03.10-Working-With-Strings.ipynb) >\n",
+    "In particular, the striking feature of this graph is the dip in birthrate on US holidays (e.g., Independence Day, Labor Day, Thanksgiving, Christmas, New Year's Day), although this likely reflects trends in scheduled/induced births rather than some deep psychosomatic effect on natural births.\n",
+    "For more discussion of this trend, see the analysis and links in [Andrew Gelman's blog post](http://andrewgelman.com/2012/06/14/cool-ass-signal-processing-using-gaussian-processes/) on the subject.\n",
+    "We'll return to this figure in [Example:-Effect-of-Holidays-on-US-Births](04.09-Text-and-Annotation.ipynb), where we will use Matplotlib's tools to annotate this plot.\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.09-Pivot-Tables.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "Looking at this short example, you can see that many of the Python and Pandas tools we've seen to this point can be combined and used to gain insight from a variety of datasets.\n",
+    "We will see some more sophisticated applications of these data manipulations in future chapters!"
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.6 64-bit ('3.9.6')",
    "language": "python",
    "name": "python3"
   },
@@ -1371,9 +1646,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc"
+   }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.10-Working-With-Strings.ipynb b/notebooks/03.10-Working-With-Strings.ipynb
index 75c004b84..5f0a844e1 100644
--- a/notebooks/03.10-Working-With-Strings.ipynb
+++ b/notebooks/03.10-Working-With-Strings.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Pivot Tables](03.09-Pivot-Tables.ipynb) | [Contents](Index.ipynb) | [Working with Time Series](03.11-Working-with-Time-Series.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.10-Working-With-Strings.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -34,8 +12,8 @@
    "metadata": {},
    "source": [
     "One strength of Python is its relative ease in handling and manipulating string data.\n",
-    "Pandas builds on this and provides a comprehensive set of *vectorized string operations* that become an essential piece of the type of munging required when working with (read: cleaning up) real-world data.\n",
-    "In this section, we'll walk through some of the Pandas string operations, and then take a look at using them to partially clean up a very messy dataset of recipes collected from the Internet."
+    "Pandas builds on this and provides a comprehensive set of *vectorized string operations* that are an important part of the type of munging required when working with (read: cleaning up) real-world data.\n",
+    "In this chapter, we'll walk through some of the Pandas string operations, and then take a look at using them to partially clean up a very messy dataset of recipes collected from the internet."
    ]
   },
   {
@@ -44,14 +22,17 @@
    "source": [
     "## Introducing Pandas String Operations\n",
     "\n",
-    "We saw in previous sections how tools like NumPy and Pandas generalize arithmetic operations so that we can easily and quickly perform the same operation on many array elements. For example:"
+    "We saw in previous chapters how tools like NumPy and Pandas generalize arithmetic operations so that we can easily and quickly perform the same operation on many array elements. For example:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -83,7 +64,10 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -106,84 +90,54 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is perhaps sufficient to work with some data, but it will break if there are any missing values.\n",
-    "For example:"
+    "This is perhaps sufficient to work with some data, but it will break if there are any missing values, so this approach requires putting in extra checks:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'NoneType' object has no attribute 'capitalize'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-3-fc1d891ab539>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'peter'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Paul'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MARY'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gUIDO'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapitalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-3-fc1d891ab539>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'peter'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Paul'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MARY'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gUIDO'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapitalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'capitalize'"
-     ]
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
-   "source": [
-    "data = ['peter', 'Paul', None, 'MARY', 'gUIDO']\n",
-    "[s.capitalize() for s in data]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pandas includes features to address both this need for vectorized string operations and for correctly handling missing data via the ``str`` attribute of Pandas Series and Index objects containing strings.\n",
-    "So, for example, suppose we create a Pandas Series with this data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0    peter\n",
-       "1     Paul\n",
-       "2     None\n",
-       "3     MARY\n",
-       "4    gUIDO\n",
-       "dtype: object"
+       "['Peter', 'Paul', None, 'Mary', 'Guido']"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "names = pd.Series(data)\n",
-    "names"
+    "data = ['peter', 'Paul', None, 'MARY', 'gUIDO']\n",
+    "[s if s is None else s.capitalize() for s in data]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can now call a single method that will capitalize all the entries, while skipping over any missing values:"
+    "This kind of manual approach is not only verbose and inconvenient, it can be error-prone.\n",
+    "\n",
+    "Pandas includes features to address both this need for vectorized string operations and the need for correctly handling missing data via the `str` attribute of Pandas `Series` and `Index` objects containing strings.\n",
+    "So, for example, if we create a Pandas `Series` with this data we can directly call the `str.capitalize` method, which has missing value handling built in:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -197,37 +151,32 @@
        "dtype: object"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "import pandas as pd\n",
+    "names = pd.Series(data)\n",
     "names.str.capitalize()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using tab completion on this ``str`` attribute will list all the vectorized string methods available to Pandas."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Tables of Pandas String Methods\n",
     "\n",
-    "If you have a good understanding of string manipulation in Python, most of Pandas string syntax is intuitive enough that it's probably sufficient to just list a table of available methods; we will start with that here, before diving deeper into a few of the subtleties.\n",
-    "The examples in this section use the following series of names:"
+    "If you have a good understanding of string manipulation in Python, most of the Pandas string syntax is intuitive enough that it's probably sufficient to just list the available methods. We'll start with that here, before diving deeper into a few of the subtleties.\n",
+    "The examples in this section use the following `Series` object:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -239,28 +188,32 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Methods similar to Python string methods\n",
-    "Nearly all Python's built-in string methods are mirrored by a Pandas vectorized string method. Here is a list of Pandas ``str`` methods that mirror Python string methods:\n",
+    "### Methods Similar to Python String Methods\n",
+    "\n",
+    "Nearly all of Python's built-in string methods are mirrored by a Pandas vectorized string method. Here is a list of Pandas `str` methods that mirror Python string methods:\n",
     "\n",
-    "|             |                  |                  |                  |\n",
-    "|-------------|------------------|------------------|------------------|\n",
-    "|``len()``    | ``lower()``      | ``translate()``  | ``islower()``    | \n",
-    "|``ljust()``  | ``upper()``      | ``startswith()`` | ``isupper()``    | \n",
-    "|``rjust()``  | ``find()``       | ``endswith()``   | ``isnumeric()``  | \n",
-    "|``center()`` | ``rfind()``      | ``isalnum()``    | ``isdecimal()``  | \n",
-    "|``zfill()``  | ``index()``      | ``isalpha()``    | ``split()``      | \n",
-    "|``strip()``  | ``rindex()``     | ``isdigit()``    | ``rsplit()``     | \n",
-    "|``rstrip()`` | ``capitalize()`` | ``isspace()``    | ``partition()``  | \n",
-    "|``lstrip()`` |  ``swapcase()``  |  ``istitle()``   | ``rpartition()`` |\n",
+    "|           |                |                |                |\n",
+    "|-----------|----------------|----------------|----------------|\n",
+    "|`len()`    | `lower()`      | `translate()`  | `islower()`    | \n",
+    "|`ljust()`  | `upper()`      | `startswith()` | `isupper()`    | \n",
+    "|`rjust()`  | `find()`       | `endswith()`   | `isnumeric()`  | \n",
+    "|`center()` | `rfind()`      | `isalnum()`    | `isdecimal()`  | \n",
+    "|`zfill()`  | `index()`      | `isalpha()`    | `split()`      | \n",
+    "|`strip()`  | `rindex()`     | `isdigit()`    | `rsplit()`     | \n",
+    "|`rstrip()` | `capitalize()` | `isspace()`    | `partition()`  | \n",
+    "|`lstrip()` | `swapcase()`   | `istitle()`    | `rpartition()` |\n",
     "\n",
-    "Notice that these have various return values. Some, like ``lower()``, return a series of strings:"
+    "Notice that these have various return values. Some, like `lower`, return a series of strings:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -275,7 +228,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -293,9 +246,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -310,7 +266,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -328,9 +284,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -345,7 +304,7 @@
        "dtype: bool"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -363,9 +322,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -380,7 +342,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -400,35 +362,38 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Methods using regular expressions\n",
+    "### Methods Using Regular Expressions\n",
     "\n",
-    "In addition, there are several methods that accept regular expressions to examine the content of each string element, and follow some of the API conventions of Python's built-in ``re`` module:\n",
+    "In addition, there are several methods that accept regular expressions (regexps) to examine the content of each string element, and follow some of the API conventions of Python's built-in `re` module:\n",
     "\n",
-    "| Method | Description |\n",
-    "|--------|-------------|\n",
-    "| ``match()`` | Call ``re.match()`` on each element, returning a boolean. |\n",
-    "| ``extract()`` | Call ``re.match()`` on each element, returning matched groups as strings.|\n",
-    "| ``findall()`` | Call ``re.findall()`` on each element |\n",
-    "| ``replace()`` | Replace occurrences of pattern with some other string|\n",
-    "| ``contains()`` | Call ``re.search()`` on each element, returning a boolean |\n",
-    "| ``count()`` | Count occurrences of pattern|\n",
-    "| ``split()``   | Equivalent to ``str.split()``, but accepts regexps |\n",
-    "| ``rsplit()`` | Equivalent to ``str.rsplit()``, but accepts regexps |"
+    "| Method    | Description |\n",
+    "|-----------|-------------|\n",
+    "| `match`   | Calls `re.match` on each element, returning a Boolean. |\n",
+    "| `extract` | Calls `re.match` on each element, returning matched groups as strings.|\n",
+    "| `findall` | Calls `re.findall` on each element |\n",
+    "| `replace` | Replaces occurrences of pattern with some other string|\n",
+    "| `contains`| Calls `re.search` on each element, returning a boolean |\n",
+    "| `count`   | Counts occurrences of pattern|\n",
+    "| `split`   | Equivalent to `str.split`, but accepts regexps |\n",
+    "| `rsplit`  | Equivalent to `str.rsplit`, but accepts regexps |"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With these, you can do a wide range of interesting operations.\n",
-    "For example, we can extract the first name from each by asking for a contiguous group of characters at the beginning of each element:"
+    "With these, we can do a wide range of operations.\n",
+    "For example, we can extract the first name from each element by asking for a contiguous group of characters at the beginning of each element:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -443,7 +408,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -456,14 +421,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Or we can do something more complicated, like finding all names that start and end with a consonant, making use of the start-of-string (``^``) and end-of-string (``$``) regular expression characters:"
+    "Or we can do something more complicated, like finding all names that start and end with a consonant, making use of the start-of-string (`^`) and end-of-string (`$`) regular expression characters:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -478,7 +446,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -491,28 +459,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ability to concisely apply regular expressions across ``Series`` or ``Dataframe`` entries opens up many possibilities for analysis and cleaning of data."
+    "The ability to concisely apply regular expressions across `Series` or `DataFrame` entries opens up many possibilities for analysis and cleaning of data."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Miscellaneous methods\n",
+    "### Miscellaneous Methods\n",
     "Finally, there are some miscellaneous methods that enable other convenient operations:\n",
     "\n",
     "| Method | Description |\n",
     "|--------|-------------|\n",
-    "| ``get()`` | Index each element |\n",
-    "| ``slice()`` | Slice each element|\n",
-    "| ``slice_replace()`` | Replace slice in each element with passed value|\n",
-    "| ``cat()``      | Concatenate strings|\n",
-    "| ``repeat()`` | Repeat values |\n",
-    "| ``normalize()`` | Return Unicode form of string |\n",
-    "| ``pad()`` | Add whitespace to left, right, or both sides of strings|\n",
-    "| ``wrap()`` | Split long strings into lines with length less than a given width|\n",
-    "| ``join()`` | Join strings in each element of the Series with passed separator|\n",
-    "| ``get_dummies()`` | extract dummy variables as a dataframe |"
+    "| `get` | Indexes each element |\n",
+    "| `slice` | Slices each element|\n",
+    "| `slice_replace` | Replaces slice in each element with the passed value|\n",
+    "| `cat`      | Concatenates strings|\n",
+    "| `repeat` | Repeats values |\n",
+    "| `normalize` | Returns Unicode form of strings |\n",
+    "| `pad` | Adds whitespace to left, right, or both sides of strings|\n",
+    "| `wrap` | Splits long strings into lines with length less than a given width|\n",
+    "| `join` | Joins strings in each element of the `Series` with the passed separator|\n",
+    "| `get_dummies` | Extracts dummy variables as a `DataFrame` |"
    ]
   },
   {
@@ -521,16 +489,19 @@
    "source": [
     "#### Vectorized item access and slicing\n",
     "\n",
-    "The ``get()`` and ``slice()`` operations, in particular, enable vectorized element access from each array.\n",
-    "For example, we can get a slice of the first three characters of each array using ``str.slice(0, 3)``.\n",
-    "Note that this behavior is also available through Python's normal indexing syntax–for example, ``df.str.slice(0, 3)`` is equivalent to ``df.str[0:3]``:"
+    "The `get` and `slice` operations, in particular, enable vectorized element access from each array.\n",
+    "For example, we can get a slice of the first three characters of each array using `str.slice(0, 3)`.\n",
+    "Note that this behavior is also available through Python's normal indexing syntax; for example, `df.str.slice(0, 3)` is equivalent to `df.str[0:3]`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -545,7 +516,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -558,17 +529,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Indexing via ``df.str.get(i)`` and ``df.str[i]`` is likewise similar.\n",
+    "Indexing via `df.str.get(i)` and `df.str[i]` are likewise similar.\n",
     "\n",
-    "These ``get()`` and ``slice()`` methods also let you access elements of arrays returned by ``split()``.\n",
-    "For example, to extract the last name of each entry, we can combine ``split()`` and ``get()``:"
+    "These indexing methods also let you access elements of arrays returned by `split`.\n",
+    "For example, to extract the last name of each entry, we can combine `split` with `str` indexing:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -583,13 +557,13 @@
        "dtype: object"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "monte.str.split().str.get(-1)"
+    "monte.str.split().str[-1]"
    ]
   },
   {
@@ -598,76 +572,92 @@
    "source": [
     "#### Indicator variables\n",
     "\n",
-    "Another method that requires a bit of extra explanation is the ``get_dummies()`` method.\n",
+    "Another method that requires a bit of extra explanation is the `get_dummies` method.\n",
     "This is useful when your data has a column containing some sort of coded indicator.\n",
-    "For example, we might have a dataset that contains information in the form of codes, such as A=\"born in America,\" B=\"born in the United Kingdom,\" C=\"likes cheese,\" D=\"likes spam\":"
+    "For example, we might have a dataset that contains information in the form of codes, such as A = \"born in America,\" B = \"born in the United Kingdom,\" C = \"likes cheese,\" D = \"likes spam\":"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>info</th>\n",
        "      <th>name</th>\n",
+       "      <th>info</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>B|C|D</td>\n",
        "      <td>Graham Chapman</td>\n",
+       "      <td>B|C|D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>B|D</td>\n",
        "      <td>John Cleese</td>\n",
+       "      <td>B|D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>A|C</td>\n",
        "      <td>Terry Gilliam</td>\n",
+       "      <td>A|C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>B|D</td>\n",
        "      <td>Eric Idle</td>\n",
+       "      <td>B|D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>B|C</td>\n",
        "      <td>Terry Jones</td>\n",
+       "      <td>B|C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>B|C|D</td>\n",
        "      <td>Michael Palin</td>\n",
+       "      <td>B|C|D</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "    info            name\n",
-       "0  B|C|D  Graham Chapman\n",
-       "1    B|D     John Cleese\n",
-       "2    A|C   Terry Gilliam\n",
-       "3    B|D       Eric Idle\n",
-       "4    B|C     Terry Jones\n",
-       "5  B|C|D   Michael Palin"
+       "             name   info\n",
+       "0  Graham Chapman  B|C|D\n",
+       "1     John Cleese    B|D\n",
+       "2   Terry Gilliam    A|C\n",
+       "3       Eric Idle    B|D\n",
+       "4     Terry Jones    B|C\n",
+       "5   Michael Palin  B|C|D"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -683,20 +673,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``get_dummies()`` routine lets you quickly split-out these indicator variables into a ``DataFrame``:"
+    "The `get_dummies` routine lets us split out these indicator variables into a `DataFrame`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -764,7 +770,7 @@
        "5  0  1  1  1"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -779,7 +785,7 @@
    "source": [
     "With these operations as building blocks, you can construct an endless range of string processing procedures when cleaning your data.\n",
     "\n",
-    "We won't dive further into these methods here, but I encourage you to read through [\"Working with Text Data\"](http://pandas.pydata.org/pandas-docs/stable/text.html) in the Pandas online documentation, or to refer to the resources listed in [Further Resources](03.13-Further-Resources.ipynb)."
+    "We won't dive further into these methods here, but I encourage you to read through [\"Working with Text Data\"](https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html) in the Pandas online documentation, or to refer to the resources listed in [Further Resources](03.13-Further-Resources.ipynb)."
    ]
   },
   {
@@ -789,119 +795,43 @@
     "## Example: Recipe Database\n",
     "\n",
     "These vectorized string operations become most useful in the process of cleaning up messy, real-world data.\n",
-    "Here I'll walk through an example of that, using an open recipe database compiled from various sources on the Web.\n",
-    "Our goal will be to parse the recipe data into ingredient lists, so we can quickly find a recipe based on some ingredients we have on hand.\n",
+    "Here I'll walk through an example of that, using an open recipe database compiled from various sources on the web.\n",
+    "Our goal will be to parse the recipe data into ingredient lists, so we can quickly find a recipe based on some ingredients we have on hand. The scripts used to compile this can be found at https://github.com/fictivekin/openrecipes, and the link to the most recent version of the database is found there as well.\n",
     "\n",
-    "The scripts used to compile this can be found at https://github.com/fictivekin/openrecipes, and the link to the current version of the database is found there as well.\n",
-    "\n",
-    "As of Spring 2016, this database is about 30 MB, and can be downloaded and unzipped with these commands:"
+    "This database is about 30 MB, and can be downloaded and unzipped with these commands:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "# !curl -O http://openrecipes.s3.amazonaws.com/recipeitems-latest.json.gz\n",
-    "# !gunzip recipeitems-latest.json.gz"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The database is in JSON format, so we will try ``pd.read_json`` to read it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ValueError: Trailing data\n"
-     ]
-    }
-   ],
-   "source": [
-    "try:\n",
-    "    recipes = pd.read_json('recipeitems-latest.json')\n",
-    "except ValueError as e:\n",
-    "    print(\"ValueError:\", e)"
+    "# repo = \"https://raw.githubusercontent.com/jakevdp/open-recipe-data/master\"\n",
+    "# !cd data && curl -O {repo}/recipeitems.json.gz\n",
+    "# !gunzip data/recipeitems.json.gz"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Oops! We get a ``ValueError`` mentioning that there is \"trailing data.\"\n",
-    "Searching for the text of this error on the Internet, it seems that it's due to using a file in which *each line* is itself a valid JSON, but the full file is not.\n",
-    "Let's check if this interpretation is true:"
+    "The database is in JSON format, so we will use `pd.read_json` to read it (`lines=True` is required for this dataset because each line of the file is a JSON entry):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 12)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
-   "source": [
-    "with open('recipeitems-latest.json') as f:\n",
-    "    line = f.readline()\n",
-    "pd.read_json(line).shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Yes, apparently each line is a valid JSON, so we'll need to string them together.\n",
-    "One way we can do this is to actually construct a string representation containing all these JSON entries, and then load the whole thing with ``pd.read_json``:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# read the entire file into a Python array\n",
-    "with open('recipeitems-latest.json', 'r') as f:\n",
-    "    # Extract each line\n",
-    "    data = (line.strip() for line in f)\n",
-    "    # Reformat so each line is the element of a list\n",
-    "    data_json = \"[{0}]\".format(','.join(data))\n",
-    "# read the result as a JSON\n",
-    "recipes = pd.read_json(data_json)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
    },
    "outputs": [
     {
@@ -910,12 +840,13 @@
        "(173278, 17)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "recipes = pd.read_json('data/recipeitems.json', lines=True)\n",
     "recipes.shape"
    ]
   },
@@ -923,41 +854,44 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see there are nearly 200,000 recipes, and 17 columns.\n",
+    "We see there are nearly 175,000 recipes, and 17 columns.\n",
     "Let's take a look at one row to see what we have:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "_id                                {'$oid': '5160756b96cc62079cc2db15'}\n",
+       "name                                    Drop Biscuits and Sausage Gravy\n",
+       "ingredients           Biscuits\\n3 cups All-purpose Flour\\n2 Tablespo...\n",
+       "url                   http://thepioneerwoman.com/cooking/2013/03/dro...\n",
+       "image                 http://static.thepioneerwoman.com/cooking/file...\n",
+       "ts                                             {'$date': 1365276011104}\n",
        "cookTime                                                          PT30M\n",
-       "creator                                                             NaN\n",
-       "dateModified                                                        NaN\n",
+       "source                                                  thepioneerwoman\n",
+       "recipeYield                                                          12\n",
        "datePublished                                                2013-03-11\n",
-       "description           Late Saturday afternoon, after Marlboro Man ha...\n",
-       "image                 http://static.thepioneerwoman.com/cooking/file...\n",
-       "ingredients           Biscuits\\n3 cups All-purpose Flour\\n2 Tablespo...\n",
-       "name                                    Drop Biscuits and Sausage Gravy\n",
        "prepTime                                                          PT10M\n",
+       "description           Late Saturday afternoon, after Marlboro Man ha...\n",
+       "totalTime                                                           NaN\n",
+       "creator                                                             NaN\n",
        "recipeCategory                                                      NaN\n",
+       "dateModified                                                        NaN\n",
        "recipeInstructions                                                  NaN\n",
-       "recipeYield                                                          12\n",
-       "source                                                  thepioneerwoman\n",
-       "totalTime                                                           NaN\n",
-       "ts                                             {'$date': 1365276011104}\n",
-       "url                   http://thepioneerwoman.com/cooking/2013/03/dro...\n",
        "Name: 0, dtype: object"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -970,16 +904,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There is a lot of information there, but much of it is in a very messy form, as is typical of data scraped from the Web.\n",
+    "There is a lot of information there, but much of it is in a very messy form, as is typical of data scraped from the web.\n",
     "In particular, the ingredient list is in string format; we're going to have to carefully extract the information we're interested in.\n",
     "Let's start by taking a closer look at the ingredients:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -996,7 +933,7 @@
        "Name: ingredients, dtype: float64"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1011,14 +948,17 @@
    "source": [
     "The ingredient lists average 250 characters long, with a minimum of 0 and a maximum of nearly 10,000 characters!\n",
     "\n",
-    "Just out of curiousity, let's see which recipe has the longest ingredient list:"
+    "Just out of curiosity, let's see which recipe has the longest ingredient list:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1027,7 +967,7 @@
        "'Carrot Pineapple Spice &amp; Brownie Layer Cake with Whipped Cream &amp; Cream Cheese Frosting and Marzipan Carrots'"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1040,16 +980,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "That certainly looks like an involved recipe.\n",
-    "\n",
-    "We can do other aggregate explorations; for example, let's see how many of the recipes are for breakfast food:"
+    "We can do other aggregate explorations; for example, we can see how many of the recipes are for breakfast foods (using regular expression syntax to match both lowercase and capital letters):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1058,7 +999,7 @@
        "3524"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1076,9 +1017,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1087,7 +1031,7 @@
        "10526"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1105,9 +1049,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1116,7 +1063,7 @@
        "11"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1129,7 +1076,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is the type of essential data exploration that is possible with Pandas string tools.\n",
+    "This is the type of data exploration that is possible with Pandas string tools.\n",
     "It is data munging like this that Python really excels at."
    ]
   },
@@ -1137,19 +1084,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### A simple recipe recommender\n",
+    "### A Simple Recipe Recommender\n",
     "\n",
-    "Let's go a bit further, and start working on a simple recipe recommendation system: given a list of ingredients, find a recipe that uses all those ingredients.\n",
+    "Let's go a bit further, and start working on a simple recipe recommendation system: given a list of ingredients, we want to find any recipes that use all those ingredients.\n",
     "While conceptually straightforward, the task is complicated by the heterogeneity of the data: there is no easy operation, for example, to extract a clean list of ingredients from each row.\n",
-    "So we will cheat a bit: we'll start with a list of common ingredients, and simply search to see whether they are in each recipe's ingredient list.\n",
+    "So, we will cheat a bit: we'll start with a list of common ingredients, and simply search to see whether they are in each recipe's ingredient list.\n",
     "For simplicity, let's just stick with herbs and spices for the time being:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -1161,34 +1108,50 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can then build a Boolean ``DataFrame`` consisting of True and False values, indicating whether this ingredient appears in the list:"
+    "We can then build a Boolean `DataFrame` consisting of `True` and `False` values, indicating whether each ingredient appears in the list:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>cumin</th>\n",
+       "      <th>salt</th>\n",
+       "      <th>pepper</th>\n",
        "      <th>oregano</th>\n",
-       "      <th>paprika</th>\n",
+       "      <th>sage</th>\n",
        "      <th>parsley</th>\n",
-       "      <th>pepper</th>\n",
        "      <th>rosemary</th>\n",
-       "      <th>sage</th>\n",
-       "      <th>salt</th>\n",
        "      <th>tarragon</th>\n",
        "      <th>thyme</th>\n",
+       "      <th>paprika</th>\n",
+       "      <th>cumin</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1197,10 +1160,10 @@
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>True</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
@@ -1221,15 +1184,15 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>True</td>\n",
+       "      <td>True</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>True</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>True</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1262,23 +1225,31 @@
        "</div>"
       ],
       "text/plain": [
-       "   cumin oregano paprika parsley pepper rosemary   sage   salt tarragon  thyme\n",
-       "0  False   False   False   False  False    False   True  False    False  False\n",
-       "1  False   False   False   False  False    False  False  False    False  False\n",
-       "2   True   False   False   False   True    False  False   True    False  False\n",
-       "3  False   False   False   False  False    False  False  False    False  False\n",
-       "4  False   False   False   False  False    False  False  False    False  False"
+       "    salt  pepper  oregano   sage  parsley  rosemary  tarragon  thyme  paprika  \\\n",
+       "0  False   False    False   True    False     False     False  False    False   \n",
+       "1  False   False    False  False    False     False     False  False    False   \n",
+       "2   True    True    False  False    False     False     False  False    False   \n",
+       "3  False   False    False  False    False     False     False  False    False   \n",
+       "4  False   False    False  False    False     False     False  False    False   \n",
+       "\n",
+       "   cumin  \n",
+       "0  False  \n",
+       "1  False  \n",
+       "2   True  \n",
+       "3  False  \n",
+       "4  False  "
       ]
      },
-     "execution_count": 29,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "import re\n",
-    "spice_df = pd.DataFrame(dict((spice, recipes.ingredients.str.contains(spice, re.IGNORECASE))\n",
-    "                             for spice in spice_list))\n",
+    "spice_df = pd.DataFrame({\n",
+    "    spice: recipes.ingredients.str.contains(spice, re.IGNORECASE)\n",
+    "    for spice in spice_list})\n",
     "spice_df.head()"
    ]
   },
@@ -1287,14 +1258,17 @@
    "metadata": {},
    "source": [
     "Now, as an example, let's say we'd like to find a recipe that uses parsley, paprika, and tarragon.\n",
-    "We can compute this very quickly using the ``query()`` method of ``DataFrame``s, discussed in [High-Performance Pandas: ``eval()`` and ``query()``](03.12-Performance-Eval-and-Query.ipynb):"
+    "We can compute this very quickly using the `query` method of ``DataFrame``s, discussed further in [High-Performance Pandas: `eval()` and `query()`](03.12-Performance-Eval-and-Query.ipynb):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 26,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1303,7 +1277,7 @@
        "10"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1317,14 +1291,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We find only 10 recipes with this combination; let's use the index returned by this selection to discover the names of the recipes that have this combination:"
+    "We find only 10 recipes with this combination. Let's use the index returned by this selection to discover the names of those recipes:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 27,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1343,7 +1320,7 @@
        "Name: name, dtype: object"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1356,36 +1333,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now that we have narrowed down our recipe selection by a factor of almost 20,000, we are in a position to make a more informed decision about what we'd like to cook for dinner."
+    "Now that we have narrowed down our recipe selection from 175,000 to 10, we are in a position to make a more informed decision about what we'd like to cook for dinner."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Going further with recipes\n",
+    "### Going Further with Recipes\n",
     "\n",
-    "Hopefully this example has given you a bit of a flavor (ba-dum!) for the types of data cleaning operations that are efficiently enabled by Pandas string methods.\n",
-    "Of course, building a very robust recipe recommendation system would require a *lot* more work!\n",
+    "Hopefully this example has given you a bit of a flavor (heh) of the types of data cleaning operations that are efficiently enabled by Pandas string methods.\n",
+    "Of course, building a robust recipe recommendation system would require a *lot* more work!\n",
     "Extracting full ingredient lists from each recipe would be an important piece of the task; unfortunately, the wide variety of formats used makes this a relatively time-consuming process.\n",
-    "This points to the truism that in data science, cleaning and munging of real-world data often comprises the majority of the work, and Pandas provides the tools that can help you do this efficiently."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Pivot Tables](03.09-Pivot-Tables.ipynb) | [Contents](Index.ipynb) | [Working with Time Series](03.11-Working-with-Time-Series.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.10-Working-With-Strings.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "This points to the truism that in data science, cleaning and munging of real-world data often comprises the majority of the work—and Pandas provides the tools that can help you do this efficiently."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1399,9 +1369,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.11-Working-with-Time-Series.ipynb b/notebooks/03.11-Working-with-Time-Series.ipynb
index c9b4d828b..65ceb9f81 100644
--- a/notebooks/03.11-Working-with-Time-Series.ipynb
+++ b/notebooks/03.11-Working-with-Time-Series.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Vectorized String Operations](03.10-Working-With-Strings.ipynb) | [Contents](Index.ipynb) | [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.11-Working-with-Time-Series.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,15 +11,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pandas was developed in the context of financial modeling, so as you might expect, it contains a fairly extensive set of tools for working with dates, times, and time-indexed data.\n",
+    "Pandas was originally developed in the context of financial modeling, so as you might expect, it contains an extensive set of tools for working with dates, times, and time-indexed data.\n",
     "Date and time data comes in a few flavors, which we will discuss here:\n",
     "\n",
-    "- *Time stamps* reference particular moments in time (e.g., July 4th, 2015 at 7:00am).\n",
-    "- *Time intervals* and *periods* reference a length of time between a particular beginning and end point; for example, the year 2015. Periods usually reference a special case of time intervals in which each interval is of uniform length and does not overlap (e.g., 24 hour-long periods comprising days).\n",
+    "- *Timestamps* reference particular moments in time (e.g., July 4th, 2021 at 7:00 a.m.).\n",
+    "- *Time intervals* and *periods* reference a length of time between a particular beginning and end point; for example, the month of June 2021. Periods usually reference a special case of time intervals in which each interval is of uniform length and does not overlap (e.g., 24-hour-long periods comprising days).\n",
     "- *Time deltas* or *durations* reference an exact length of time (e.g., a duration of 22.56 seconds).\n",
     "\n",
-    "In this section, we will introduce how to work with each of these types of date/time data in Pandas.\n",
-    "This short section is by no means a complete guide to the time series tools available in Python or Pandas, but instead is intended as a broad overview of how you as a user should approach working with time series.\n",
+    "This chapter will introduce how to work with each of these types of date/time data in Pandas.\n",
+    "This is by no means a complete guide to the time series tools available in Python or Pandas, but instead is intended as a broad overview of how you as a user should approach working with time series.\n",
     "We will start with a brief discussion of tools for dealing with dates and times in Python, before moving more specifically to a discussion of the tools provided by Pandas.\n",
     "After listing some resources that go into more depth, we will review some short examples of working with time series data in Pandas."
    ]
@@ -52,32 +30,35 @@
    "source": [
     "## Dates and Times in Python\n",
     "\n",
-    "The Python world has a number of available representations of dates, times, deltas, and timespans.\n",
-    "While the time series tools provided by Pandas tend to be the most useful for data science applications, it is helpful to see their relationship to other packages used in Python."
+    "The Python world has a number of available representations of dates, times, deltas, and time spans.\n",
+    "While the time series tools provided by Pandas tend to be the most useful for data science applications, it is helpful to see their relationship to other tools used in Python."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Native Python dates and times: ``datetime`` and ``dateutil``\n",
+    "### Native Python Dates and Times: datetime and dateutil\n",
     "\n",
-    "Python's basic objects for working with dates and times reside in the built-in ``datetime`` module.\n",
-    "Along with the third-party ``dateutil`` module, you can use it to quickly perform a host of useful functionalities on dates and times.\n",
-    "For example, you can manually build a date using the ``datetime`` type:"
+    "Python's basic objects for working with dates and times reside in the built-in `datetime` module.\n",
+    "Along with the third-party `dateutil` module, you can use this to quickly perform a host of useful functionalities on dates and times.\n",
+    "For example, you can manually build a date using the `datetime` type:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "datetime.datetime(2015, 7, 4, 0, 0)"
+       "datetime.datetime(2021, 7, 4, 0, 0)"
       ]
      },
      "execution_count": 1,
@@ -87,27 +68,30 @@
    ],
    "source": [
     "from datetime import datetime\n",
-    "datetime(year=2015, month=7, day=4)"
+    "datetime(year=2021, month=7, day=4)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Or, using the ``dateutil`` module, you can parse dates from a variety of string formats:"
+    "Or, using the `dateutil` module, you can parse dates from a variety of string formats:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "datetime.datetime(2015, 7, 4, 0, 0)"
+       "datetime.datetime(2021, 7, 4, 0, 0)"
       ]
      },
      "execution_count": 2,
@@ -117,30 +101,31 @@
    ],
    "source": [
     "from dateutil import parser\n",
-    "date = parser.parse(\"4th of July, 2015\")\n",
+    "date = parser.parse(\"4th of July, 2021\")\n",
     "date"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
-    "Once you have a ``datetime`` object, you can do things like printing the day of the week:"
+    "Once you have a `datetime` object, you can do things like printing the day of the week:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Saturday'"
+       "'Sunday'"
       ]
      },
      "execution_count": 3,
@@ -156,37 +141,39 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the final line, we've used one of the standard string format codes for printing dates (``\"%A\"``), which you can read about in the [strftime section](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) of Python's [datetime documentation](https://docs.python.org/3/library/datetime.html).\n",
-    "Documentation of other useful date utilities can be found in [dateutil's online documentation](http://labix.org/python-dateutil).\n",
-    "A related package to be aware of is [``pytz``](http://pytz.sourceforge.net/), which contains tools for working with the most migrane-inducing piece of time series data: time zones.\n",
+    "Here we've used one of the standard string format codes for printing dates (`'%A'`), which you can read about in the [`strftime` section](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) of Python's [`datetime` documentation](https://docs.python.org/3/library/datetime.html).\n",
+    "Documentation of other useful date utilities can be found in [``dateutil``'s online documentation](http://labix.org/python-dateutil).\n",
+    "A related package to be aware of is [`pytz`](http://pytz.sourceforge.net/), which contains tools for working with the most migraine-inducing element of time series data: time zones.\n",
     "\n",
-    "The power of ``datetime`` and ``dateutil`` lie in their flexibility and easy syntax: you can use these objects and their built-in methods to easily perform nearly any operation you might be interested in.\n",
+    "The power of `datetime` and `dateutil` lies in their flexibility and easy syntax: you can use these objects and their built-in methods to easily perform nearly any operation you might be interested in.\n",
     "Where they break down is when you wish to work with large arrays of dates and times:\n",
-    "just as lists of Python numerical variables are suboptimal compared to NumPy-style typed numerical arrays, lists of Python datetime objects are suboptimal compared to typed arrays of encoded dates."
+    "just as lists of Python numerical variables are suboptimal compared to NumPy-style typed numerical arrays, lists of Python `datetime` objects are suboptimal compared to typed arrays of encoded dates."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Typed arrays of times: NumPy's ``datetime64``\n",
+    "### Typed Arrays of Times: NumPy's datetime64\n",
     "\n",
-    "The weaknesses of Python's datetime format inspired the NumPy team to add a set of native time series data type to NumPy.\n",
-    "The ``datetime64`` dtype encodes dates as 64-bit integers, and thus allows arrays of dates to be represented very compactly.\n",
-    "The ``datetime64`` requires a very specific input format:"
+    "NumPy's `datetime64` dtype encodes dates as 64-bit integers, and thus allows arrays of dates to be represented compactly and operated on in an efficient manner.\n",
+    "The `datetime64` requires a specific input format:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array(datetime.date(2015, 7, 4), dtype='datetime64[D]')"
+       "array('2021-07-04', dtype='datetime64[D]')"
       ]
      },
      "execution_count": 4,
@@ -196,7 +183,7 @@
    ],
    "source": [
     "import numpy as np\n",
-    "date = np.array('2015-07-04', dtype=np.datetime64)\n",
+    "date = np.array('2021-07-04', dtype=np.datetime64)\n",
     "date"
    ]
   },
@@ -204,22 +191,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once we have this date formatted, however, we can quickly do vectorized operations on it:"
+    "Once we have dates in this form, we can quickly do vectorized operations on it:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array(['2015-07-04', '2015-07-05', '2015-07-06', '2015-07-07',\n",
-       "       '2015-07-08', '2015-07-09', '2015-07-10', '2015-07-11',\n",
-       "       '2015-07-12', '2015-07-13', '2015-07-14', '2015-07-15'], dtype='datetime64[D]')"
+       "array(['2021-07-04', '2021-07-05', '2021-07-06', '2021-07-07',\n",
+       "       '2021-07-08', '2021-07-09', '2021-07-10', '2021-07-11',\n",
+       "       '2021-07-12', '2021-07-13', '2021-07-14', '2021-07-15'],\n",
+       "      dtype='datetime64[D]')"
       ]
      },
      "execution_count": 5,
@@ -235,28 +226,31 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Because of the uniform type in NumPy ``datetime64`` arrays, this type of operation can be accomplished much more quickly than if we were working directly with Python's ``datetime`` objects, especially as arrays get large\n",
+    "Because of the uniform type in NumPy `datetime64` arrays, this kind of operation can be accomplished much more quickly than if we were working directly with Python's `datetime` objects, especially as arrays get large\n",
     "(we introduced this type of vectorization in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb)).\n",
     "\n",
-    "One detail of the ``datetime64`` and ``timedelta64`` objects is that they are built on a *fundamental time unit*.\n",
-    "Because the ``datetime64`` object is limited to 64-bit precision, the range of encodable times is $2^{64}$ times this fundamental unit.\n",
-    "In other words, ``datetime64`` imposes a trade-off between *time resolution* and *maximum time span*.\n",
+    "One detail of the `datetime64` and related `timedelta64` objects is that they are built on a *fundamental time unit*.\n",
+    "Because the `datetime64` object is limited to 64-bit precision, the range of encodable times is $2^{64}$ times this fundamental unit.\n",
+    "In other words, `datetime64` imposes a trade-off between *time resolution* and *maximum time span*.\n",
     "\n",
-    "For example, if you want a time resolution of one nanosecond, you only have enough information to encode a range of $2^{64}$ nanoseconds, or just under 600 years.\n",
-    "NumPy will infer the desired unit from the input; for example, here is a day-based datetime:"
+    "For example, if you want a time resolution of 1 nanosecond, you only have enough information to encode a range of $2^{64}$ nanoseconds, or just under 600 years.\n",
+    "NumPy will infer the desired unit from the input; for example, here is a day-based `datetime`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "numpy.datetime64('2015-07-04')"
+       "numpy.datetime64('2021-07-04')"
       ]
      },
      "execution_count": 6,
@@ -265,7 +259,7 @@
     }
    ],
    "source": [
-    "np.datetime64('2015-07-04')"
+    "np.datetime64('2021-07-04')"
    ]
   },
   {
@@ -279,13 +273,16 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "numpy.datetime64('2015-07-04T12:00')"
+       "numpy.datetime64('2021-07-04T12:00')"
       ]
      },
      "execution_count": 7,
@@ -294,14 +291,13 @@
     }
    ],
    "source": [
-    "np.datetime64('2015-07-04 12:00')"
+    "np.datetime64('2021-07-04 12:00')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice that the time zone is automatically set to the local time on the computer executing the code.\n",
     "You can force any desired fundamental unit using one of many format codes; for example, here we'll force a nanosecond-based time:"
    ]
   },
@@ -309,13 +305,16 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "numpy.datetime64('2015-07-04T12:59:59.500000000')"
+       "numpy.datetime64('2021-07-04T12:59:59.500000000')"
       ]
      },
      "execution_count": 8,
@@ -324,71 +323,74 @@
     }
    ],
    "source": [
-    "np.datetime64('2015-07-04 12:59:59.50', 'ns')"
+    "np.datetime64('2021-07-04 12:59:59.50', 'ns')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following table, drawn from the [NumPy datetime64 documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html), lists the available format codes along with the relative and absolute timespans that they can encode:"
+    "The following table, drawn from the NumPy `datetime64` documentation, lists the available format codes along with the relative and absolute time spans that they can encode:"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "|Code    | Meaning     | Time span (relative) | Time span (absolute)   |\n",
-    "|--------|-------------|----------------------|------------------------|\n",
-    "| ``Y``  | Year\t       | ± 9.2e18 years       | [9.2e18 BC, 9.2e18 AD] |\n",
-    "| ``M``  | Month       | ± 7.6e17 years       | [7.6e17 BC, 7.6e17 AD] |\n",
-    "| ``W``  | Week\t       | ± 1.7e17 years       | [1.7e17 BC, 1.7e17 AD] |\n",
-    "| ``D``  | Day         | ± 2.5e16 years       | [2.5e16 BC, 2.5e16 AD] |\n",
-    "| ``h``  | Hour        | ± 1.0e15 years       | [1.0e15 BC, 1.0e15 AD] |\n",
-    "| ``m``  | Minute      | ± 1.7e13 years       | [1.7e13 BC, 1.7e13 AD] |\n",
-    "| ``s``  | Second      | ± 2.9e12 years       | [ 2.9e9 BC, 2.9e9 AD]  |\n",
-    "| ``ms`` | Millisecond | ± 2.9e9 years        | [ 2.9e6 BC, 2.9e6 AD]  |\n",
-    "| ``us`` | Microsecond | ± 2.9e6 years        | [290301 BC, 294241 AD] |\n",
-    "| ``ns`` | Nanosecond  | ± 292 years          | [ 1678 AD, 2262 AD]    |\n",
-    "| ``ps`` | Picosecond  | ± 106 days           | [ 1969 AD, 1970 AD]    |\n",
-    "| ``fs`` | Femtosecond | ± 2.6 hours          | [ 1969 AD, 1970 AD]    |\n",
-    "| ``as`` | Attosecond  | ± 9.2 seconds        | [ 1969 AD, 1970 AD]    |"
+    "|Code  | Meaning     | Time span (relative) | Time span (absolute)   |\n",
+    "|------|-------------|----------------------|------------------------|\n",
+    "| `Y`  | Year        | ± 9.2e18 years       | [9.2e18 BC, 9.2e18 AD] |\n",
+    "| `M`  | Month       | ± 7.6e17 years       | [7.6e17 BC, 7.6e17 AD] |\n",
+    "| `W`  | Week        | ± 1.7e17 years       | [1.7e17 BC, 1.7e17 AD] |\n",
+    "| `D`  | Day         | ± 2.5e16 years       | [2.5e16 BC, 2.5e16 AD] |\n",
+    "| `h`  | Hour        | ± 1.0e15 years       | [1.0e15 BC, 1.0e15 AD] |\n",
+    "| `m`  | Minute      | ± 1.7e13 years       | [1.7e13 BC, 1.7e13 AD] |\n",
+    "| `s`  | Second      | ± 2.9e12 years       | [ 2.9e9 BC, 2.9e9 AD]  |\n",
+    "| `ms` | Millisecond | ± 2.9e9 years        | [ 2.9e6 BC, 2.9e6 AD]  |\n",
+    "| `us` | Microsecond | ± 2.9e6 years        | [290301 BC, 294241 AD] |\n",
+    "| `ns` | Nanosecond  | ± 292 years          | [ 1678 AD, 2262 AD]    |\n",
+    "| `ps` | Picosecond  | ± 106 days           | [ 1969 AD, 1970 AD]    |\n",
+    "| `fs` | Femtosecond | ± 2.6 hours          | [ 1969 AD, 1970 AD]    |\n",
+    "| `as` | Attosecond  | ± 9.2 seconds        | [ 1969 AD, 1970 AD]    |"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For the types of data we see in the real world, a useful default is ``datetime64[ns]``, as it can encode a useful range of modern dates with a suitably fine precision.\n",
+    "For the types of data we see in the real world, a useful default is `datetime64[ns]`, as it can encode a useful range of modern dates with a suitably fine precision.\n",
     "\n",
-    "Finally, we will note that while the ``datetime64`` data type addresses some of the deficiencies of the built-in Python ``datetime`` type, it lacks many of the convenient methods and functions provided by ``datetime`` and especially ``dateutil``.\n",
-    "More information can be found in [NumPy's datetime64 documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html)."
+    "Finally, note that while the `datetime64` data type addresses some of the deficiencies of the built-in Python `datetime` type, it lacks many of the convenient methods and functions provided by `datetime` and especially `dateutil`.\n",
+    "More information can be found in [NumPy's `datetime64` documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Dates and times in pandas: best of both worlds\n",
+    "### Dates and Times in Pandas: The Best of Both Worlds\n",
     "\n",
-    "Pandas builds upon all the tools just discussed to provide a ``Timestamp`` object, which combines the ease-of-use of ``datetime`` and ``dateutil`` with the efficient storage and vectorized interface of ``numpy.datetime64``.\n",
-    "From a group of these ``Timestamp`` objects, Pandas can construct a ``DatetimeIndex`` that can be used to index data in a ``Series`` or ``DataFrame``; we'll see many examples of this below.\n",
+    "Pandas builds upon all the tools just discussed to provide a `Timestamp` object, which combines the ease of use of `datetime` and `dateutil` with the efficient storage and vectorized interface of `numpy.datetime64`.\n",
+    "From a group of these `Timestamp` objects, Pandas can construct a `DatetimeIndex` that can be used to index data in a `Series` or `DataFrame`.\n",
     "\n",
-    "For example, we can use Pandas tools to repeat the demonstration from above.\n",
-    "We can parse a flexibly formatted string date, and use format codes to output the day of the week:"
+    "For example, we can use Pandas tools to repeat the demonstration from earlier.\n",
+    "We can parse a flexibly formatted string date and use format codes to output the day of the week, as follows:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Timestamp('2015-07-04 00:00:00')"
+       "Timestamp('2021-07-04 00:00:00')"
       ]
      },
      "execution_count": 9,
@@ -398,7 +400,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "date = pd.to_datetime(\"4th of July, 2015\")\n",
+    "date = pd.to_datetime(\"4th of July, 2021\")\n",
     "date"
    ]
   },
@@ -406,13 +408,16 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Saturday'"
+       "'Sunday'"
       ]
      },
      "execution_count": 10,
@@ -435,15 +440,18 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "DatetimeIndex(['2015-07-04', '2015-07-05', '2015-07-06', '2015-07-07',\n",
-       "               '2015-07-08', '2015-07-09', '2015-07-10', '2015-07-11',\n",
-       "               '2015-07-12', '2015-07-13', '2015-07-14', '2015-07-15'],\n",
+       "DatetimeIndex(['2021-07-04', '2021-07-05', '2021-07-06', '2021-07-07',\n",
+       "               '2021-07-08', '2021-07-09', '2021-07-10', '2021-07-11',\n",
+       "               '2021-07-12', '2021-07-13', '2021-07-14', '2021-07-15'],\n",
        "              dtype='datetime64[ns]', freq=None)"
       ]
      },
@@ -469,24 +477,27 @@
    "source": [
     "## Pandas Time Series: Indexing by Time\n",
     "\n",
-    "Where the Pandas time series tools really become useful is when you begin to *index data by timestamps*.\n",
-    "For example, we can construct a ``Series`` object that has time indexed data:"
+    "The Pandas time series tools really become useful when you begin to index data by timestamps.\n",
+    "For example, we can construct a `Series` object that has time-indexed data:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2014-07-04    0\n",
-       "2014-08-04    1\n",
-       "2015-07-04    2\n",
-       "2015-08-04    3\n",
+       "2020-07-04    0\n",
+       "2020-08-04    1\n",
+       "2021-07-04    2\n",
+       "2021-08-04    3\n",
        "dtype: int64"
       ]
      },
@@ -496,8 +507,8 @@
     }
    ],
    "source": [
-    "index = pd.DatetimeIndex(['2014-07-04', '2014-08-04',\n",
-    "                          '2015-07-04', '2015-08-04'])\n",
+    "index = pd.DatetimeIndex(['2020-07-04', '2020-08-04',\n",
+    "                          '2021-07-04', '2021-08-04'])\n",
     "data = pd.Series([0, 1, 2, 3], index=index)\n",
     "data"
    ]
@@ -506,22 +517,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now that we have this data in a ``Series``, we can make use of any of the ``Series`` indexing patterns we discussed in previous sections, passing values that can be coerced into dates:"
+    "And now that we have this data in a `Series`, we can make use of any of the `Series` indexing patterns we discussed in previous chapters, passing values that can be coerced into dates:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2014-07-04    0\n",
-       "2014-08-04    1\n",
-       "2015-07-04    2\n",
+       "2020-07-04    0\n",
+       "2020-08-04    1\n",
+       "2021-07-04    2\n",
        "dtype: int64"
       ]
      },
@@ -531,7 +545,7 @@
     }
    ],
    "source": [
-    "data['2014-07-04':'2015-07-04']"
+    "data['2020-07-04':'2021-07-04']"
    ]
   },
   {
@@ -545,14 +559,17 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2015-07-04    2\n",
-       "2015-08-04    3\n",
+       "2021-07-04    2\n",
+       "2021-08-04    3\n",
        "dtype: int64"
       ]
      },
@@ -562,7 +579,7 @@
     }
    ],
    "source": [
-    "data['2015']"
+    "data['2021']"
    ]
   },
   {
@@ -570,7 +587,7 @@
    "metadata": {},
    "source": [
     "Later, we will see additional examples of the convenience of dates-as-indices.\n",
-    "But first, a closer look at the available time series data structures."
+    "But first, let's take a closer look at the available time series data structures."
    ]
   },
   {
@@ -581,32 +598,35 @@
     "\n",
     "This section will introduce the fundamental Pandas data structures for working with time series data:\n",
     "\n",
-    "- For *time stamps*, Pandas provides the ``Timestamp`` type. As mentioned before, it is essentially a replacement for Python's native ``datetime``, but is based on the more efficient ``numpy.datetime64`` data type. The associated Index structure is ``DatetimeIndex``.\n",
-    "- For *time Periods*, Pandas provides the ``Period`` type. This encodes a fixed-frequency interval based on ``numpy.datetime64``. The associated index structure is ``PeriodIndex``.\n",
-    "- For *time deltas* or *durations*, Pandas provides the ``Timedelta`` type. ``Timedelta`` is a more efficient replacement for Python's native ``datetime.timedelta`` type, and is based on ``numpy.timedelta64``. The associated index structure is ``TimedeltaIndex``."
+    "- For *timestamps*, Pandas provides the `Timestamp` type. As mentioned before, this is essentially a replacement for Python's native `datetime`, but it's based on the more efficient `numpy.datetime64` data type. The associated `Index` structure is `DatetimeIndex`.\n",
+    "- For *time periods*, Pandas provides the `Period` type. This encodes a fixed-frequency interval based on `numpy.datetime64`. The associated index structure is `PeriodIndex`.\n",
+    "- For *time deltas* or *durations*, Pandas provides the `Timedelta` type. `Timedelta` is a more efficient replacement for Python's native `datetime.timedelta` type, and is based on `numpy.timedelta64`. The associated index structure is `TimedeltaIndex`."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The most fundamental of these date/time objects are the ``Timestamp`` and ``DatetimeIndex`` objects.\n",
-    "While these class objects can be invoked directly, it is more common to use the ``pd.to_datetime()`` function, which can parse a wide variety of formats.\n",
-    "Passing a single date to ``pd.to_datetime()`` yields a ``Timestamp``; passing a series of dates by default yields a ``DatetimeIndex``:"
+    "The most fundamental of these date/time objects are the `Timestamp` and `DatetimeIndex` objects.\n",
+    "While these class objects can be invoked directly, it is more common to use the `pd.to_datetime` function, which can parse a wide variety of formats.\n",
+    "Passing a single date to `pd.to_datetime` yields a `Timestamp`; passing a series of dates by default yields a `DatetimeIndex`, as you can see here:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-06', '2015-07-07',\n",
-       "               '2015-07-08'],\n",
+       "DatetimeIndex(['2021-07-03', '2021-07-04', '2021-07-06', '2021-07-07',\n",
+       "               '2021-07-08'],\n",
        "              dtype='datetime64[ns]', freq=None)"
       ]
      },
@@ -616,8 +636,8 @@
     }
    ],
    "source": [
-    "dates = pd.to_datetime([datetime(2015, 7, 3), '4th of July, 2015',\n",
-    "                       '2015-Jul-6', '07-07-2015', '20150708'])\n",
+    "dates = pd.to_datetime([datetime(2021, 7, 3), '4th of July, 2021',\n",
+    "                       '2021-Jul-6', '07-07-2021', '20210708'])\n",
     "dates"
    ]
   },
@@ -625,22 +645,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Any ``DatetimeIndex`` can be converted to a ``PeriodIndex`` with the ``to_period()`` function with the addition of a frequency code; here we'll use ``'D'`` to indicate daily frequency:"
+    "Any `DatetimeIndex` can be converted to a `PeriodIndex` with the `to_period` function, with the addition of a frequency code; here we'll use `'D'` to indicate daily frequency:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "PeriodIndex(['2015-07-03', '2015-07-04', '2015-07-06', '2015-07-07',\n",
-       "             '2015-07-08'],\n",
-       "            dtype='int64', freq='D')"
+       "PeriodIndex(['2021-07-03', '2021-07-04', '2021-07-06', '2021-07-07',\n",
+       "             '2021-07-08'],\n",
+       "            dtype='period[D]')"
       ]
      },
      "execution_count": 16,
@@ -656,14 +679,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A ``TimedeltaIndex`` is created, for example, when a date is subtracted from another:"
+    "A `TimedeltaIndex` is created, for example, when a date is subtracted from another:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -682,22 +708,25 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Regular sequences: ``pd.date_range()``\n",
+    "## Regular Sequences: pd.date_range\n",
     "\n",
-    "To make the creation of regular date sequences more convenient, Pandas offers a few functions for this purpose: ``pd.date_range()`` for timestamps, ``pd.period_range()`` for periods, and ``pd.timedelta_range()`` for time deltas.\n",
-    "We've seen that Python's ``range()`` and NumPy's ``np.arange()`` turn a startpoint, endpoint, and optional stepsize into a sequence.\n",
-    "Similarly, ``pd.date_range()`` accepts a start date, an end date, and an optional frequency code to create a regular sequence of dates.\n",
-    "By default, the frequency is one day:"
+    "To make creation of regular date sequences more convenient, Pandas offers a few functions for this purpose: `pd.date_range` for timestamps, `pd.period_range` for periods, and `pd.timedelta_range` for time deltas.\n",
+    "We've seen that Python's `range` and NumPy's `np.arange` take a start point, end point, and optional step size and return a sequence.\n",
+    "Similarly, `pd.date_range` accepts a start date, an end date, and an optional frequency code to create a regular sequence of dates:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -721,14 +750,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Alternatively, the date range can be specified not with a start and endpoint, but with a startpoint and a number of periods:"
+    "Alternatively, the date range can be specified not with a start and end point, but with a start point and a number of periods:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -752,15 +784,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The spacing can be modified by altering the ``freq`` argument, which defaults to ``D``.\n",
-    "For example, here we will construct a range of hourly timestamps:"
+    "The spacing can be modified by altering the `freq` argument, which defaults to `D`.\n",
+    "For example, here we construct a range of hourly timestamps:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -786,7 +821,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To create regular sequences of ``Period`` or ``Timedelta`` values, the very similar ``pd.period_range()`` and ``pd.timedelta_range()`` functions are useful.\n",
+    "To create regular sequences of `Period` or `Timedelta` values, the similar `pd.period_range` and `pd.timedelta_range` functions are useful.\n",
     "Here are some monthly periods:"
    ]
   },
@@ -794,7 +829,10 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -802,7 +840,7 @@
       "text/plain": [
        "PeriodIndex(['2015-07', '2015-08', '2015-09', '2015-10', '2015-11', '2015-12',\n",
        "             '2016-01', '2016-02'],\n",
-       "            dtype='int64', freq='M')"
+       "            dtype='period[M]')"
       ]
      },
      "execution_count": 21,
@@ -825,14 +863,17 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "TimedeltaIndex(['00:00:00', '01:00:00', '02:00:00', '03:00:00', '04:00:00',\n",
-       "                '05:00:00', '06:00:00', '07:00:00', '08:00:00', '09:00:00'],\n",
+       "TimedeltaIndex(['0 days 00:00:00', '0 days 01:00:00', '0 days 02:00:00',\n",
+       "                '0 days 03:00:00', '0 days 04:00:00', '0 days 05:00:00'],\n",
        "               dtype='timedelta64[ns]', freq='H')"
       ]
      },
@@ -842,14 +883,14 @@
     }
    ],
    "source": [
-    "pd.timedelta_range(0, periods=10, freq='H')"
+    "pd.timedelta_range(0, periods=6, freq='H')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "All of these require an understanding of Pandas frequency codes, which we'll summarize in the next section."
+    "All of these require an understanding of Pandas frequency codes, which are summarized in the next section."
    ]
   },
   {
@@ -858,28 +899,27 @@
    "source": [
     "## Frequencies and Offsets\n",
     "\n",
-    "Fundamental to these Pandas time series tools is the concept of a frequency or date offset.\n",
-    "Just as we saw the ``D`` (day) and ``H`` (hour) codes above, we can use such codes to specify any desired frequency spacing.\n",
-    "The following table summarizes the main codes available:"
+    "Fundamental to these Pandas time series tools is the concept of a *frequency* or *date offset*. The following table summarizes the main codes available; as with the `D` (day) and `H` (hour) codes demonstrated in the previous sections, we can use these to specify any desired frequency spacing:"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "| Code   | Description         | Code   | Description          |\n",
-    "|--------|---------------------|--------|----------------------|\n",
-    "| ``D``  | Calendar day        | ``B``  | Business day         |\n",
-    "| ``W``  | Weekly              |        |                      |\n",
-    "| ``M``  | Month end           | ``BM`` | Business month end   |\n",
-    "| ``Q``  | Quarter end         | ``BQ`` | Business quarter end |\n",
-    "| ``A``  | Year end            | ``BA`` | Business year end    |\n",
-    "| ``H``  | Hours               | ``BH`` | Business hours       |\n",
-    "| ``T``  | Minutes             |        |                      |\n",
-    "| ``S``  | Seconds             |        |                      |\n",
-    "| ``L``  | Milliseonds         |        |                      |\n",
-    "| ``U``  | Microseconds        |        |                      |\n",
-    "| ``N``  | nanoseconds         |        |                      |"
+    "| Code | Description       | Code | Description          |\n",
+    "|------|-------------------|------|----------------------|\n",
+    "| `D`  | Calendar day      | `B`  | Business day         |\n",
+    "| `W`  | Weekly            |      |                      |\n",
+    "| `M`  | Month end         | `BM` | Business month end   |\n",
+    "| `Q`  | Quarter end       | `BQ` | Business quarter end |\n",
+    "| `A`  | Year end          | `BA` | Business year end    |\n",
+    "| `H`  | Hours             | `BH` | Business hours       |\n",
+    "| `T`  | Minutes           |      |                      |\n",
+    "| `S`  | Seconds           |      |                      |\n",
+    "| `L`  | Milliseconds       |      |                      |\n",
+    "| `U`  | Microseconds      |      |                      |\n",
+    "| `N`  | Nanoseconds       |      |                      |"
    ]
   },
   {
@@ -887,18 +927,18 @@
    "metadata": {},
    "source": [
     "The monthly, quarterly, and annual frequencies are all marked at the end of the specified period.\n",
-    "By adding an ``S`` suffix to any of these, they instead will be marked at the beginning:"
+    "Adding an `S` suffix to any of these causes them to instead be marked at the beginning:"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "| Code    | Description            || Code    | Description            |\n",
-    "|---------|------------------------||---------|------------------------|\n",
-    "| ``MS``  | Month start            ||``BMS``  | Business month start   |\n",
-    "| ``QS``  | Quarter start          ||``BQS``  | Business quarter start |\n",
-    "| ``AS``  | Year start             ||``BAS``  | Business year start    |"
+    "| Code  | Description       | Code  | Description            |\n",
+    "|-------|-------------------|-------|------------------------|\n",
+    "| `MS`  | Month start       |`BMS`  | Business month start   |\n",
+    "| `QS`  | Quarter start     |`BQS`  | Business quarter start |\n",
+    "| `AS`  | Year start        |`BAS`  | Business year start    |"
    ]
   },
   {
@@ -907,29 +947,32 @@
    "source": [
     "Additionally, you can change the month used to mark any quarterly or annual code by adding a three-letter month code as a suffix:\n",
     "\n",
-    "- ``Q-JAN``, ``BQ-FEB``, ``QS-MAR``, ``BQS-APR``, etc.\n",
-    "- ``A-JAN``, ``BA-FEB``, ``AS-MAR``, ``BAS-APR``, etc.\n",
+    "- `Q-JAN`, `BQ-FEB`, `QS-MAR`, `BQS-APR`, etc.\n",
+    "- `A-JAN`, `BA-FEB`, `AS-MAR`, `BAS-APR`, etc.\n",
     "\n",
-    "In the same way, the split-point of the weekly frequency can be modified by adding a three-letter weekday code:\n",
+    "In the same way, the split point of the weekly frequency can be modified by adding a three-letter weekday code:\n",
     "\n",
-    "- ``W-SUN``, ``W-MON``, ``W-TUE``, ``W-WED``, etc.\n",
+    "- `W-SUN`, `W-MON`, `W-TUE`, `W-WED`, etc.\n",
     "\n",
     "On top of this, codes can be combined with numbers to specify other frequencies.\n",
-    "For example, for a frequency of 2 hours 30 minutes, we can combine the hour (``H``) and minute (``T``) codes as follows:"
+    "For example, for a frequency of 2 hours and 30 minutes, we can combine the hour (`H`) and minute (`T`) codes as follows:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "TimedeltaIndex(['00:00:00', '02:30:00', '05:00:00', '07:30:00', '10:00:00',\n",
-       "                '12:30:00', '15:00:00', '17:30:00', '20:00:00'],\n",
+       "TimedeltaIndex(['0 days 00:00:00', '0 days 02:30:00', '0 days 05:00:00',\n",
+       "                '0 days 07:30:00', '0 days 10:00:00', '0 days 12:30:00'],\n",
        "               dtype='timedelta64[ns]', freq='150T')"
       ]
      },
@@ -939,14 +982,14 @@
     }
    ],
    "source": [
-    "pd.timedelta_range(0, periods=9, freq=\"2H30T\")"
+    "pd.timedelta_range(0, periods=6, freq=\"2H30T\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "All of these short codes refer to specific instances of Pandas time series offsets, which can be found in the ``pd.tseries.offsets`` module.\n",
+    "All of these short codes refer to specific instances of Pandas time series offsets, which can be found in the `pd.tseries.offsets` module.\n",
     "For example, we can create a business day offset directly as follows:"
    ]
   },
@@ -954,14 +997,17 @@
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "DatetimeIndex(['2015-07-01', '2015-07-02', '2015-07-03', '2015-07-06',\n",
-       "               '2015-07-07'],\n",
+       "               '2015-07-07', '2015-07-08'],\n",
        "              dtype='datetime64[ns]', freq='B')"
       ]
      },
@@ -972,14 +1018,14 @@
    ],
    "source": [
     "from pandas.tseries.offsets import BDay\n",
-    "pd.date_range('2015-07-01', periods=5, freq=BDay())"
+    "pd.date_range('2015-07-01', periods=6, freq=BDay())"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For more discussion of the use of frequencies and offsets, see the [\"DateOffset\" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#dateoffset-objects) of the Pandas documentation."
+    "For more discussion of the use of frequencies and offsets, see the [`DateOffset` section](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) of the Pandas documentation."
    ]
   },
   {
@@ -988,35 +1034,52 @@
    "source": [
     "## Resampling, Shifting, and Windowing\n",
     "\n",
-    "The ability to use dates and times as indices to intuitively organize and access data is an important piece of the Pandas time series tools.\n",
-    "The benefits of indexed data in general (automatic alignment during operations, intuitive data slicing and access, etc.) still apply, and Pandas provides several additional time series-specific operations.\n",
+    "The ability to use dates and times as indices to intuitively organize and access data is an important aspect of the Pandas time series tools.\n",
+    "The benefits of indexed data in general (automatic alignment during operations, intuitive data slicing and access, etc.) still apply, and Pandas provides several additional time series–specific operations.\n",
     "\n",
     "We will take a look at a few of those here, using some stock price data as an example.\n",
     "Because Pandas was developed largely in a finance context, it includes some very specific tools for financial data.\n",
-    "For example, the accompanying ``pandas-datareader`` package (installable via ``conda install pandas-datareader``), knows how to import financial data from a number of available sources, including Yahoo finance, Google Finance, and others.\n",
-    "Here we will load Google's closing price history:"
+    "For example, the accompanying `pandas-datareader` package (installable via `pip install pandas-datareader`) knows how to import data from various online sources.\n",
+    "Here we will load part of the S&P 500 price history:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Open</th>\n",
        "      <th>High</th>\n",
        "      <th>Low</th>\n",
+       "      <th>Open</th>\n",
        "      <th>Close</th>\n",
        "      <th>Volume</th>\n",
+       "      <th>Adj Close</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
@@ -1025,61 +1088,75 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2004-08-19</th>\n",
-       "      <td>49.96</td>\n",
-       "      <td>51.98</td>\n",
-       "      <td>47.93</td>\n",
-       "      <td>50.12</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>2018-01-02</th>\n",
+       "      <td>2695.889893</td>\n",
+       "      <td>2682.360107</td>\n",
+       "      <td>2683.729980</td>\n",
+       "      <td>2695.810059</td>\n",
+       "      <td>3367250000</td>\n",
+       "      <td>2695.810059</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2004-08-20</th>\n",
-       "      <td>50.69</td>\n",
-       "      <td>54.49</td>\n",
-       "      <td>50.20</td>\n",
-       "      <td>54.10</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>2018-01-03</th>\n",
+       "      <td>2714.370117</td>\n",
+       "      <td>2697.770020</td>\n",
+       "      <td>2697.850098</td>\n",
+       "      <td>2713.060059</td>\n",
+       "      <td>3538660000</td>\n",
+       "      <td>2713.060059</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2004-08-23</th>\n",
-       "      <td>55.32</td>\n",
-       "      <td>56.68</td>\n",
-       "      <td>54.47</td>\n",
-       "      <td>54.65</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>2018-01-04</th>\n",
+       "      <td>2729.290039</td>\n",
+       "      <td>2719.070068</td>\n",
+       "      <td>2719.310059</td>\n",
+       "      <td>2723.989990</td>\n",
+       "      <td>3695260000</td>\n",
+       "      <td>2723.989990</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2004-08-24</th>\n",
-       "      <td>55.56</td>\n",
-       "      <td>55.74</td>\n",
-       "      <td>51.73</td>\n",
-       "      <td>52.38</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>2018-01-05</th>\n",
+       "      <td>2743.449951</td>\n",
+       "      <td>2727.919922</td>\n",
+       "      <td>2731.330078</td>\n",
+       "      <td>2743.149902</td>\n",
+       "      <td>3236620000</td>\n",
+       "      <td>2743.149902</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2004-08-25</th>\n",
-       "      <td>52.43</td>\n",
-       "      <td>53.95</td>\n",
-       "      <td>51.89</td>\n",
-       "      <td>52.95</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>2018-01-08</th>\n",
+       "      <td>2748.510010</td>\n",
+       "      <td>2737.600098</td>\n",
+       "      <td>2742.669922</td>\n",
+       "      <td>2747.709961</td>\n",
+       "      <td>3242650000</td>\n",
+       "      <td>2747.709961</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "             Open   High    Low  Close  Volume\n",
-       "Date                                          \n",
-       "2004-08-19  49.96  51.98  47.93  50.12     NaN\n",
-       "2004-08-20  50.69  54.49  50.20  54.10     NaN\n",
-       "2004-08-23  55.32  56.68  54.47  54.65     NaN\n",
-       "2004-08-24  55.56  55.74  51.73  52.38     NaN\n",
-       "2004-08-25  52.43  53.95  51.89  52.95     NaN"
+       "                   High          Low         Open        Close      Volume  \\\n",
+       "Date                                                                         \n",
+       "2018-01-02  2695.889893  2682.360107  2683.729980  2695.810059  3367250000   \n",
+       "2018-01-03  2714.370117  2697.770020  2697.850098  2713.060059  3538660000   \n",
+       "2018-01-04  2729.290039  2719.070068  2719.310059  2723.989990  3695260000   \n",
+       "2018-01-05  2743.449951  2727.919922  2731.330078  2743.149902  3236620000   \n",
+       "2018-01-08  2748.510010  2737.600098  2742.669922  2747.709961  3242650000   \n",
+       "\n",
+       "              Adj Close  \n",
+       "Date                     \n",
+       "2018-01-02  2695.810059  \n",
+       "2018-01-03  2713.060059  \n",
+       "2018-01-04  2723.989990  \n",
+       "2018-01-05  2743.149902  \n",
+       "2018-01-08  2747.709961  "
       ]
      },
      "execution_count": 25,
@@ -1090,9 +1167,9 @@
    "source": [
     "from pandas_datareader import data\n",
     "\n",
-    "goog = data.DataReader('GOOG', start='2004', end='2016',\n",
-    "                       data_source='google')\n",
-    "goog.head()"
+    "sp500 = data.DataReader('^GSPC', start='2018', end='2022',\n",
+    "                        data_source='yahoo')\n",
+    "sp500.head()"
    ]
   },
   {
@@ -1106,45 +1183,35 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
-    "goog = goog['Close']"
+    "sp500 = sp500['Close']"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can visualize this using the ``plot()`` method, after the normal Matplotlib setup boilerplate (see [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)):"
+    "We can visualize this using the ``plot`` method, after the normal Matplotlib setup boilerplate (see [Part 4](04.00-Introduction-To-Matplotlib.ipynb)); the result is shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn; seaborn.set()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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xwxKRGG/L2DUgMU48bglj7tnbMLfJbME7/zuMk2db8Obnh3y+9tS5Fq/t62oMyEREFDR9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\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1162f5e80>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1152,35 +1219,41 @@
     }
    ],
    "source": [
-    "goog.plot();"
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('seaborn-whitegrid')\n",
+    "sp500.plot();"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Resampling and converting frequencies\n",
+    "### Resampling and Converting Frequencies\n",
     "\n",
-    "One common need for time series data is resampling at a higher or lower frequency.\n",
-    "This can be done using the ``resample()`` method, or the much simpler ``asfreq()`` method.\n",
-    "The primary difference between the two is that ``resample()`` is fundamentally a *data aggregation*, while ``asfreq()`` is fundamentally a *data selection*.\n",
+    "One common need when dealing with time series data is resampling at a higher or lower frequency.\n",
+    "This can be done using the `resample` method, or the much simpler `asfreq` method.\n",
+    "The primary difference between the two is that `resample` is fundamentally a *data aggregation*, while `asfreq` is fundamentally a *data selection*.\n",
     "\n",
-    "Taking a look at the Google closing price, let's compare what the two return when we down-sample the data.\n",
-    "Here we will resample the data at the end of business year:"
+    "Let's compare what the two return when we downsample the S&P 500 closing price data.\n",
+    "Here we will resample the data at the end of business year; the following figure shows the result:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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I/tXpWsrUde30BQghxCjkraqi5l9/QfXPfhoZh021mZg+KXVQ97EnGQf92vtr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\nSDQRrtcwNjWS2oYECk85aPBUMSs9zzfVLYQQQgwHIReQna6eA/KFk5I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jlbpqEnttLezExERctC6XbYH76H3qbKFXVbFSo7u7O1arlRdffJHVq1fz73//m969e9d4dT08PBxQPhjOnDljn6VrtVrJz8/H29sbHx8fAHuBNEEQHMdQYkWvVdOrvRdOThaeO7CYNPMR5vWfx11Rd7V0eA4hLp7WYvXq1fTu3ZtXXnmFcePGIcsy69evZ8mSJaxdu5bExEQOHTpU7biyK+ihoaHceuutrFmzhvfee49x48bh4+NDUVERWVnKxZujR48262sShGtNidVGal4xbk5qhnbz4s1jT5BuTmC4d1yDkvq0AR0AuEwxx1bhqmmxN7eRI0fy3HPP8e233+Lh4YFGo6FLly5Mnz4dNzc3goKCuO6667hw4UKNx48dO5a1a9dy1113YTAYmD59OhqNhiVLlvDQQw/h5uaGyWRq5lclCNeWn45nkldkIaadC3qtnmGh1xOquo0O+v4Nerx2XnoeHdWFGr6styoisddi0KBBfP3119W2T506tdLtihc+K5Zd0Ol0/Pvf/652/ODBg9mwYQOAWAlJEBzEbJXQaVRcyC3C2ysXiyobiOFvfeN4Nftkox5b10rHrlckErsgCG3KqUuFfH04jf6dfEkuOMGP557HV+fF2H5jUavUuDlrcNY232pG1RizIXmnsj6zg4jE3oKeeOKJlg5BEK5KRy/mE+rjgrerU6XtKdlG+5DELSd2sT3nJbydvXi86+OoVUpL+96hEc0ebyW/vALx/4WO14NH/UblXCmR2AVBuGrIsoxVktl27BLOOjUPj+hivy/XaOaLgxcBSCnex87cV/HQBvHp+A/JPZdr36/Fy+zeuAh63emwpA5iVIwgCFeRjQcvsuLH0wCUWJRZn7Iscz6niP/+etZ++6LtZ3x1nZgW9hLt3ByXQOstNwU2PQSWYtDpoX1fhz6daLELgtCqSZJMTpEZD72W8zlFle6zSTJ/nM9j18lMAKxyCXcN7MLIgufZdiwdfxfflgi5utRDcPJ7yP0bBEY6/OlEYhcEoVU7dD6XXSdrXgo6PiWXE+kFyLJMvssWDlz6jYf0H5NX5IJO7YKrcwteJAWwWUGjheiJ0Hkk6C9fYKypiK4YQRBatcxCc6337TmdRUZhMUdKPuCLMx8zsH0MLrryhTH83JybI8SaXUqAFbFwMV653UxJHUSLXRCEVq7AVF7/3N/DmRHdAjh6MZ/j6YXYZAs/577OWdNe7u11L3/r8zdUKhXdgzwwWyV6tm++ZFqN3hs82yv/NzOR2AVBaLUyCkxczC2vzujmpCHM1xUPvZbj6YX8lv8BZ017eajXYzzcd659P7VaxXVh3i0QMZB7Frw7gld7uOebFglBdMUIgtBqfXMkrdLtGyODgPKVi2Lc7+AG78eY0+vu5g6tZlmn4O0h8NvbLRqGaLELgtBqFZltuDppuDM2DF83ZTLSJeMlPk1chyyPxUMbiIc2EGdtK2mj+nWBof+AnpNbNIxWcjYEQRAqM1slzFaJvh197Ek9pSCFWd/N4rOT/6PAprTmH7upa7V1SZvd6R1gyFDKPt7wJHgEtWg4IrELgtAq7UlShjj6uCrL1SVmJzLru1kUW4v5YMwHeGnbA9UXm252xXmw4R7Yvrhl46hAdMUIgtAqHb2QD0Cojyvxl+J5dMejuDu5s2r0KsK9wtlK46o0NhkXb7jrcwjo0dKR2InELghCq2O1SUgy9O/ki16nQZIlwjzCeGPUG/YSAYM7+xHm69pyQcZ/BK6+EHkbhLWu1dBEYhcEodXJNJQgyTJWTRrgT/92/Vk3fp29QiPAoAi/lgvQZoU/PgG3AOgxvtUtqVSvPvbs7GxuuOEGkpKSSElJYdq0aUyfPp1FixYhSUohnhUrVjB58mSmTp1qX+eztn0FQRAu51JBCQmGr/nbLzP45cIvAJWSeouSJKVMwIwNMHl1q0vqUI/EbrFYWLhwIXq9Mk33xRdf5LHHHuPTTz9FlmV27NhBQkIC+/fvZ8OGDSxfvpwlS5bUuq8gCMLlyLLMpydXsa/gQ0Z1GMXA4IEtHVK5n/8NG+eAZFNKBGhbsGTBZdTZFbNs2TKmTp3KqlWrAEhISGDAAKU/afjw4ezZs4fw8HCGDh2KSqUiJCQEm81GTk5OjfuOHj26xudJTExs0AswmUwNPtaRRFxXRsR1ZdpqXJIssTplNVszthKuGcq9QfeSdDKpxeMq45tTgLOxhLTERFA3TYExR/wuL5vYv/jiC3x9fRk2bJg9scuybB9e5ObmRmFhIQaDAW9vb/txZdtr2rc2kZENK2WZmJjY4GMdScR1ZURcV6YtxpWUaeD3jF/ZmrGVXm4TuKndHHpGNc1qR406X7IMhengGQyRz4Es492E3S+NiS0+Pr7G7ZdN7Bs3bkSlUrF3714SExOZN28eOTk59vuNRiOenp64u7tjNBorbffw8ECtVlfbVxAEoSpDiZUtf6QCnZge9hJ6W1fyi60tHZZix7PKhdIH94B7QKvsU6/qsn3sn3zyCWvXrmXNmjVERkaybNkyhg8fzr59+wDYtWsXsbGx9O3bl927dyNJEqmpqUiShK+vL1FRUdX2FQRBqCi/JJ/7tz5ApvkUAL4aZTx4i456qSjmrzDgPnDzb+lI6u2KhzvOmzePBQsWsHz5ciIiIhg7diwajYbY2FimTJmCJEksXLiw1n0FQbh2JWcZOXA2h46+rlwX5k1+STYP7niA5PyzBHqPAJT6MJHBHgzu3JLDGS2Q9CN0G6useNQMqx41pXon9jVr1th/Xrt2bbX74+LiiIuLq7QtPDy8xn0FQbj2lFhtbD6kLDZ9MbeY708k8FP+Uops+Yzzf5ogXYx93xYvE7B/FfwwHx74BYJj6t6/lRETlARBaBbxKbkAOGnVZJvS+CbraSRsvD7iHfYfdyfIU8+lAhMAJdYWnvMy4H7wCb8qkzqIImCCIDSTc9nKQtSzBnfETeNPB/0AbvV7jv3H3QHo2d6TUT0CWy5AsxF+eBpKDKDRQY9bWi6WRhItdkEQHK7EaiO9wISnbzImyYcOvu6oVQ9U2sdTryPYW09SpoFhXVrgQuWFA0oXTMQI6FrzfJurhUjsgiA4TInVhrHEhsUmcdr4C7tT3ySp6BYe672QT347V2lfN2ctzloNd/QNbd4gZVkZwhgxAv72h7Kk3VVOJHZBEBziYl4x6w+cByBft5Of81bQy68P8wfOx91JX21/Z10L9Awbs+Gzu+DGhdBxcJtI6iASuyAITcxik1AB+UUWZFnmD8MGDhV+Rgd9f14Z9hruTu41HuekaYHELtvAXKj0r7chIrELgtCkPtyTjE6j5rowb6yyieTiPXR1Gclw34fxd6s5qUMzJ/aiHHDxAfdAuP/nJqv70lqIxC4IQpORZRljiQ1JLuHH40Xo1C7c6v8cTio3fFz1OFVYdNrP3YlsgxmAbkEeqNXNNHa9KAfeGwnRd8BNi9pcUgcx3FEQhCZkkcAqlbA9Zxk/576Ok1aFs9oDlUpdKakDTOkfZl/PNDqkGetIufhAz8nKAhltlEjsgiA0mfwSAz/kLOVCySFCnGPoHOBhv8+5SmJ31mrwKk3skiw7PrjMk1CQqoyAuXEBhPZz/HO2EJHYBUFoEmdyz/HcySVkmk8xOewperiNqdQSd3Wq3uXRoXTNUg+9zrHBSVb49K/wxf2OfZ5WQvSxC4LQaLIsM+f7+8gzZ3GT71PM7HUr3q5O+Lo52ffxdnWqdlzfDj50CfCwt9wdRq2Fv7wDrldPhcbGEIldEIQGOZFzgnUn1vHUgKeQJS0DPR7CUqAhVN8dd2etPanrdRpMFhs+NSR2lUrl2KR+bh8UXABNFHQY5LjnaWVEYhcE4Yr8kfEH7x15j10XduGiccWU04/begyinXMUWapMADxdypO1p4sWk8WGp0sLpJtf/gO5Z2HkB83/3C1IJHZBEOqlwFzAP376B/vT9+Oh8+KW0Lvp5jKOAqOOfWdy8HbVkVW6b8ULpb3DvNmacKlSt0yzmfwBmIvgQk7d+7YhIrELglArSZZIzk+ms3dnPHQeuOncePS6f5J/qS86yYWCChM2o4I90RTl4ObrWqmeenSIF9EhXs0X9Inv4PBncMd74Oyh/EMkdkEQrnFWycrWs1t578h7XDRc5IdJP+Cj9+GNUW9wNsvIpsyL9n29XHTodRq6t/PAs8SVyMhmLuJVVf4FyDsHliLQNOMHSisiErsgCHYWm4UtSVv44OgHnC88T4RXBAsGLcDDqXw8+v6zlVu/EQFujOiu1FFPa9ZoqzAVgN5TWZ+0391KTfVrlBjHLgiC3dmCsyzeuxh3nTuvjniVTRM2cVvn29CqlTagLMsUmqy4OGkYWbooRouvdgRweD282Q+yk5Tb13BSB9FiF4RrWqG5kM9OfEZmUSb/Gvgvuvp0Zd34dUT5RlXqJ88sLEGWZT7Zp9RQjwhwI6a9FwaTlR7BHrU9fPNp3w+63ASeIS0dSasgErsgXINyTbmsObaGdcfXUWgpZHjocGySDY1aQ7RfdKV9ZVlm7W8pVbaBWq1iaNcWnvBz/gCE9Qe/zvCXlS0bSysiErsgXGN+OvcT836Zh8lq4qaON3Fvr3uJ8ouqdf/8Ykul2wMjfJt3lEttjn8D66bDtHXQ/eaWjqZVEYldEK4B5/LPU2g2Eh3Qg2j/aMZ0HMOcnnOI8I6o89j0AlOl2z3aeeLl0gr6sLuOhVuXQ9cxLR1JqyMSuyC0YUl5Sbx/5H2+PfMt7Zx78cPUtQS6BvLc0Ofq/Rh7TmdXul21/G6zkmX4bSX0ng4u3tB/bsvF0oqJxC4IbVBidiKrDq9i+7ntuGhdiHIbT0/325BludJF0brIskxBla4YtxqqNDabjGOwbSGo1DDowZaLo5UTiV0Q2pCyxL0/fT/70vfxQMwD3BV5Fx/+kgGAJIPmChYqSsosn1rq5qxhWNeAK/pgaHJB0fDAzxBY+zUBQYxjF4SrnizL/HLhF2Z/N5svk74E4K/d/8rWSVt5tM+jeOu97fvapJoXtDCWWPlgdzIZhZX708/llCf2+4d3JjK4GVc6KmOzwOZH4MxO5XZQtLJYhlCrOlvsNpuNZ555huTkZFQqFUuWLMFqtfLAAw/QqVMnAKZNm8Ytt9zCihUr2LlzJ1qtlvnz5xMTE0NKSgpPPfUUKpWKrl27smjRItRq8XkiCI0lyRI7zu3gvcPvkZiTSDu3djiplUJbLloX+35yhdWJalupKDnLSEGxhfizudzcK9i+PSnDiJuzhjv7hTnoVdSD2QCphyAwEiJGtFwcV5E6E/tPP/0EwLp169i3bx+vvvoqo0aN4p577mHOnDn2/RISEti/fz8bNmwgLS2NuLg4Nm7cyIsvvshjjz3GwIEDWbhwITt27GD06NGOe0WCcI144ucn2JayjY6eHXl2yLOMjxiProYZlwfP5dp/ttbSYi+x2gAqLSidX2TBUGKla5A7Pi1RmdFaAmqdskbpfTtA51L3MQIAKlmue7FBq9WKVqtl06ZN/Pbbb+j1epKTk7HZbHTs2JH58+fzxRdfYDKZuP9+ZempiRMnsnr1aiZMmMCuXbtQqVRs376dPXv2sGjRokqPHx8fj6ura4NegMlkQq/XN+hYRxJxXRkRV93MkpldWbsY4jsEtVXNcdNxjDYjg30Ho1bV/i142+lCLhmUC6ATo7xwr+Hi52/njJzOKcHDWcPtPTxRqVRkGq38cKqAYR3d6ehTv8TeZOdLshK2659Y3ENJj/2/Rj9ca/o9VtWY2IqKiujXr/rarfW6eKrVapk3bx7btm3jjTfe4NKlS9x555307NmTlStX8tZbb+Hh4YG3t7f9GDc3NwoLCytdhS/bVpPIyMgGvCxITExs8LGOJOK6MiKu2hVZithwcgMfJXxEZnEmIcEhRGmjmNZnWp3HyrLMzxln8NcrLfIuXTrV2Po+YjiPv7oYAN/2HQj0cMYttxj/3Av0jAwl1Kd+Da8mO1+yDOnDwa8zPk3weK3h91ibxsQWHx9f4/Z6d3YvW7aMH374gQULFjB06FB69uwJwOjRozl27Bju7u4YjeUXWoxGIx4eHpX6041GI56eLXDxRRCuQpIs8c6f7zBm4xhe+f0VIrwieH/M+9zR9Y56P4bZJlFsttHOS2kR2mr5gl5gshDg4QzA//af45sjaZhtSnEvJ00zXhMrzoO888rF0RsXKOPVhStW529s8+bNvPvuuwC4uLigUql49NFHOXz4MAB79+4lOjqavn37snv3biRJIjU1FUmS8PX1JSoqin379gGwa9cuYmNjHfhyBOHqV2QpAkCtUnMo4xB9Avqw9pa1vD/2fQYGD7yi4YbFZqWl7qlX+t4ttuqVGGVZpshsI9SnvA/7dIYBq6Tsq23OxL5hNqy9A2zW5nvONqjOrpgxY8bwr3/9ixkzZmC1Wpk/fz7BwcEsXboUnU6Hv78/S5cuxd3dndjYWKZMmYIkSSxcuBCAefPmsWDBApYvX05ERARjx451+IsShKtRmiGNDxM+5Kukr/ji9i8Idg/mzVFv4qRp+IXLLIMZgBBvPScvFWIsqZ4wzTYJmyTjoa+cDixWpXWvu5KB7401aiEU54BGTLFpjDrPnqurK6+//nq17evWrau2LS4ujri4uErbwsPDWbt2bSNCFIS27Wz+WT44+gFfJ30NwG2db7O3yhuT1AHO5xSh06joEujOzhOZFJiqJ3ZjidKqd9Zq6ODryrkc5RtDWfEvnaNb7AWpcH4fRP8FQqtfCBSunPhYFIQWlGfKY9KWSahUKu7sfif3RN9DsHtw3QfW0/ncItr7uODurEWnUXEwJRcV4O3qRLi/GwAf/XoWAL1Ow6R+oaRkG/ni4EUulRb/cnhi3/kSHNsM4TeAq69jn+saIRK7IDSCxSaRYzRzLqeI2I4+9er//jPzT369+CsP9X4Ib703zw97ntigWPxdmq62ebHZxoGzOWQbzEQFK8MXNWo1hSYrO09kAvCP0d0qzUTV65QE7ls6auZSoQmNWoVG7eCumHEvwYD7RVJvQiKxC0IjrPjxtP3nMB9X++iTqmRZZl/6Pt4//D770vfh7ezNlB5T8NX7Mq7TuCaP652fk8rj8lWGKro5azBZbJX225F4yf6zXqeMb3cp/b/EItm3NbnMk7D7VbjtNXByhXY9HfM81yiR2AWhgarWXckvttSY2JPzk3lm9zMczjpMgEsAT8Q+wZ3d7sRV17BJeXW5kFtk/zkm1IvA0mGMLlWStNUmkZBaYL+tLW2ZV2yhO+zCaeohSPoRCi6Cb9014YUrIxK7IDSAxSaxatcZAG6MDGRHYgbFFVrDNslGZnEm7dza4efih1kys2DQAiZ0mYCzxtlhcRWZrWz4/QIAHf1cuTEyyH6fq1Plt3umocT+s7uz1r54hkqlQq1SIcly09det1mUhaavm6KseqQX81ocQVTjEoR6MFls/HwykyMX8jFZbKRkGzFblXHekcGeqFRKv7bFZmHTqU1M+HICj+x4BFmW8XTyZP349fy1+18dmtRBWXS6TNVulKrDGc/nFNt/njs0vNL1gbLrpU164fRCPLzZF9KPlAYokrqjiBa7INTDmUwjB1OUYlqFJguepa3bmFAvdBo1Wo2VH85vYOGhjaQb04n0jeS+mPuQkVGharYa5nlFyhDFQRF+9OngXem+gRG+WCWJLgEebDx4gT2nswCYMbBDpeJfABab0s2kbcoLp+4B4BMOFcoIC44hErsg1ENekdn+c7HFhnPpCJJhXQMASCnZy/eX3qJvYF8WDV7E9SHXt8iCFDlGMzqNikERvtWe31mrYVSPIHslxzKBnrUXoHJuioun2UlKP7p3B5i9pfGPJ9RJJHZBqIPZKrEvOQcAf3cnUvNNpORmcKhwE18mRXJn9zvp6zuSdi7tefyGph/hUl+yLJOUaSDM1/WyHyrO2von6yCPRnYdpR+B90bBzcsgdk7d+wtNQiR2QajDWz8pQxojgz3IKcliS/L/OFG0FatcQrfcKQC4O+sJtPVokfiyDCVsjL/AyB6BFJqsXN+l7vHwKpVSQLGui6OXa83XS2A03PB/EDWxcY8jXBFx8VQQapFXZGb/hfKhg5nq7bycMJtjxm/oqB/I3R1W8MygZwBlKGFxlTHizSU+JZcis41vDqcB0MnPrc5jJvUNBUBdS8u+LOEH1zIuv07Hv4WiHFCrYfiTYvJRMxMtdkGowYXcIjb8foHkzGTa+Xbktp7dKFR35vqgcQRI4/DUtiPQtbybwtVJQ7FZqrT+QHPJNpgr3XapYSGNqsquEdR2bfSRkV0aHpAhAzbOhT53wS0vN/xxhAYTiV0QqsgrMvPxvgT2FXzIGctuehkn8IjfQrxchtA3YCArdyqzOiu2ZvVOGiRZpsTatLM1k7OU0Th/6dO+2sgVgHPZRfaaLleirJ+9ezuPRsdYjXsgzPoS2vVq+scW6kV0xQhCBYUmCwu2r+WLzL9ztngv3TXjWHLDI/bJO3qdhgm9QwDoHeZjP05fmiirTtlvrM2HLnIup4gCk6XG+zcevFDpdlA9+8S9XHTMHNyRG7oFNDpGAMxFsHYy7ql7lNthA8QapS1ItNgFoYI3D77DztxVdPaM5P/6LcKtQEVX/3aV9okIcOfvN3at1ILWlk69r22x6MbKK7Lg7Vp7Cd8hnf24mFfMTVFBte5Tlb97I0e8/PC0Mib9hieVei+SFX1OYuMeU2gSIrEL1zxZlim2FuOk1mPL70t/z9m8Pf5v6HVOJCbWnKiqdouU1VepWj+mMSq2/vOKK7fYrZJMQmo+AL07eDMwwq/JnrdWB9dA5nEY+7xyuzBNKRFQZtZmshITaaLvAEIjiMQuXNMyijJYuncpZsnM3C4v4K4N4OE+c9DrrmyBi7IZmk3ZYi+xli9jV3VJu29OFODsobx9qxb3ajLJvyh10m95RRkfmXkcLhwASVJGu0xe7ZjnFRpN9LEL1yRZltmStIUJmyfya9pehoQM4Xia0gLu19GnjqOrs7fYbU2X2K0VkrlU5QOjsKS8Nd9k/frZSbBtIZhKKz5mn4JjW5RRLgCjl8LcrUpSF1o18RsSrjnZxdk8+uOjPL37aVxV7Zno/x/cTTeSbbTSO8y7QcMVtaXJrmwB6KZQsfVfMa/nV+mW6dGugcW0inNh37tKQgela2Xv2+VFunrfBY+fAI/SfnuR0K8aoitGuOboNDqS81K4u8ffIf961CqNfZ3PstWDrlRZi/1iXjEpOUWM6BbQ6PHsJZbyD4ksQwlJmQYOncuja6A7AO29XZjUL7T+KxzZLHDye/AMgfb9wGKC7/5P6Wrx6wxhg2DeWXBWHh9t49ZbFVqOSOzCNSHdmM7qo6t5MvZJLmbDjR7/Rl2ghQo5MSLAjaiQhrV+3ZyVfu7fzyoVIId28W/0IhXbKqxudDrDwOkMA6AUIdOqVUzuF1rj2PZKUg8pCbzjYFCp4ctHIWqCktg9g+Gxo+Adpuyr0YLGvVExC62DSOxCmybLMhtPbeSV319BkiXGdriF3Nxg1CrlT79vRx97Od6x0e0aXH/c1UmLs05tb2XbJJnGXtPU69QUFFffnlVYgq+LtuakXpiudK10ul65/fU/QOMMc38AtUbpI/cJL9+/LKkLbYpI7EKblWpIZfGvi9mbtpcB7QbwzMBFbP3TQpE5nyBPPV0C3YkJ9eLoxXzMTTBjtGLdFYut8Y/n6+qE2SpRZLbZF/Uo4+lc+gFkLVH6xENjldvbFsLp7fDEaaVP/PY3wb3COPyA7o2KSbg6iMQutFn/+uVfHM85zoJBC7g94g4+3XeeIrMNJ62aW2OC7bNJ51wf3iQXPTUVEru1CUbHFJltuOg09mGPoT4uXMgpwtt0DmdXL2WnPW/AT8/Dk6fBzR+ufwwGP6IMTwQxrf8aJRK70KZcKLyAp7Mnnk6eLBy8EL1WT3v39iSmFZBfbKGTvysTe7evdGFTKZrVtGPBLY38oJBlmQKThUAPPdnZWWhVGlydPIjI2cWE40/wY583gQHQazK06wlOpX3jQVGND1646onxS0KbIMkS/zv+P+7YcgdvHHwDgM7enWnv3h5Jkvn+aDoA42NCHFZ9sWIyb1SLXZL4IymVvCILndTpPLTvJrplbScy2IOLXn3YEfF/WDxK+8Z9w5VFoXWNrJsutCkisQut0sW8YpKzjMhy3QnyfMF55v4wlxf2vUDfwL7M7Tm30v1HS6feQxMvzlyFxVoe6xWXFiibmm+zwKtRWH96CYDIyOv4LWwul9wjiQhwp0TryeHgO3HzqnsxDeHaVWdXjM1m45lnniE5ORmVSsWSJUtwdnbmqaeeQqVS0bVrVxYtWoRarWbFihXs3LkTrVbL/PnziYmJISUlpcZ9BaGqE+mFnLxUSPd2HvZFI8bHBNM1qPbSsltObWXpvqfRqrU8O+RZJnaZWKlFbrVJ7E3KxtNFx4yBHRwav1ThQ6hqCYBqZLm8H/zTKaDWwtRPQKPD1m8OFzIC8HHVodao2dfhvmqHe+sdVEZAaBPqTOw//fQTAOvWrWPfvn28+uqryLLMY489xsCBA1m4cCE7duwgJCSE/fv3s2HDBtLS0oiLi2Pjxo28+OKL1fYdPXq0w1+YcPUoMFm4ZLBwJjuL/GKLfbw2wPncohoTe1lL/uQ5T9rp+jDQ6x4GB/aplNSPpxfw3RGlC2Z0lH+T1kmvy2Vrxvz0gjJy5b4fldvhw5XEXqp48D85u+sMN9ZQ2iDAw5nMwpL6T0oSrkl1JvabbrqJESNGAJCamoqnpye//vorAwYMAGD48OHs2bOH8PBwhg4dikqlIiQkBJvNRk5ODgkJCdX2rSmx11ZFry4mk6nBxzqSiKv+dp01cCarCK22sNJ2nUbFCXM+wXKOfZskS3yT/g2JhYncHfIPcjNsxDCb4lyJN7+N59bunvi4KH/Wa/8oP86aYyGxIPWKY7uS85WVVf58J08XYctRyuJ6nNuOf8Jqzo75EFnjjFexM3qXcC4lHFESuvco5aDS58kyWsnKKiD1XDG6wjR6uJnRa1UkJibS20NG9gCTqaTV/R6hdf59QeuNCxwTW71GxWi1WubNm8e2bdt444032LNnj71l5ObmRmFhIQaDAW9vb/sxZdsrLhVWtq0mkZGRDXoBiYmJDT7WkURc9fdH4Tm0eefx9w/Ay0VHfrGFUB8XdBo1RrOVyMiOAJzJP8OCPQs4nHmYIcHD2Zcj4+8fwKS+oew+ncWlAhP7smFMdBDOWjX+/koL/YEbInB1atgAsCs5X/4XTgIQVHiMEYcX4/7Xd5Sp+k5pkNODHh2ClNmepY9X0yqgOUYz3/96Fn//AIb1i8DNWUtNz94af48g4mqIxsQWHx9f4/Z6/7UvW7aMJ554gr/+9a+UlJTYtxuNRjw9PXF3d8doNFba7uHhUak/vWxfQTBbJRLTCugc6E5GQQkdvZ0Y3ac9nfxcOZtdRJiPCztPZHKpwIRVsvLxsY9569BbuOhceHHYi3R3G87Xh9MZHRVEmK8LQ7v421cT2n0qC23pRdLbrgtpcFKvt8JLsPVpwqSRnPfuj0nrgcpapCzm7NcZOo9S/tXDR7+eBZSVmtycxWhkoWHqvIq5efNm3n33XQBcXFxQqVT07NmTffv2AbBr1y5iY2Pp27cvu3fvRpIkUlNTkSQJX19foqKiqu0rtE0p2UZe336KV7edJNtQctl9kzIN/Hg8g/d2nUGSZXoE6An3d0OlUhHu74ZWo8ZDr6XIbOPEpWw+SfyEoe2HsnnCZsZHjKe4dOp+p9JjOvi5ctcgpWVfZLZRUGyhW5AHnQPcmv6F2qzKDM/DG5Tbei84v49+vsW4OmnIdwnjyK1bIKx/nQ+VmldMZmH1cxUT6tXUUQvXkDqbBGPGjOFf//oXM2bMwGq1Mn/+fDp37syCBQtYvnw5ERERjB07Fo1GQ2xsLFOmTEGSJBYuXAjAvHnzqu0rXF6BycLXf6YxMMIXq01Go4YugQ5YdLgJnbxUaB/JAvDd0XR7oq1Jkdla6bafa+ULm1bJym9ZX2OTY9l6NJ9RHi8wpWcUyZcs2HxN/JqUDVReZCLAo/JSb37uTk03Zv3gGrCaYMB9SrGs0zuUkS0xdypjyP9+mHCVigeAN3acqvc49s8OnAegZ3uvSh+Gg5tjRSShzaozsbu6uvL6669X27527dpq2+Li4oiLi6u0LTw8vMZ9hXIHz+XiqdfRJdAdWZbtK89v+aP8Yt/fbnRvlSMhTBYbuUVmeyGtMlVrm1SVnl+exNp7u6BWlY+EOZV7igV7FpCQncAIn38Q4TIUV40PX/2ZVu1xLndO3K+0K8NmVZI2wLEvIe0whExWbp/8Hkz5SmIHeGCXUlSrTIUPEK1GdcUzT49ezK90u86qjYJwGaITr4VZbBI/n8gE4JZewZzNNnIstaDafvnFlgbXCneU1LxiNh26aE/iPq46JvZpz6f7z112HLfFJpFlKCHUxwUPvZY+HXzIuWjAIllYfWQ17xx+B08nTxYPfIlz57s2OD4n7WV6GrNOw7m90OcuJSnvfAl+exvmpSi3LxyAxK8geJKy/x3vKQs2l1HXPnRSp1Y3aubpmOj6L0gtCDURM4Va2JEKLbVvj6TZk7qHXsuMgR2Y1DcUgJ0nMuo1C7O55BjNfHbgvD2px3byYXJsGN6uTsS096bIbCOvyFwt5gu5Raz48TQ5RjO+bk6M6xlMkKcyHX7xr4tZ8ccKRncYzaYJm7i187hanz8iwI0514fXer9GMhNYdFqpfghw8gd4bxQU5ym3T22FLY+CMUu5HdofBjwANrNy+6Zn4e9/lrfEKyb1OmjUKo5ezK+2nF19lRUnE4SGanMt9kPncik0WRnerfWvlS5JMvFncwn20jOqRyCf7DsHwHVhXvRq702AhzNWm0SAhzMp2UVkFpYQ6Nk6aoIcOFs+ZntMdBDRIeUX+/w9lG8WH+45C8C9w8Lx0OuQ5fKaLQB9O/hgsVmwSMp0+plRMxkZNpKbOt4EcNkPMpVKhZeLFiSb0nrOToL9q7greg6Z2mA6XtqG20dz4P6fIaQ3aJyUQlklBeDiDTFTIHI8uJb2ZXe5UflXphGzo8uWrjt0Pq/G9VMzCkx8uv8cN/aouWV+xV1IglBFm2ux7zyRSXxKbp2jMlrK+Zwi1h84T7ahhO8T0jGUWAny0hPg4UxsJx9uigxiVI8g+4VArUbNDaUfUiV19Fs7mtkqsT85h0KThWOpBQR76Zk1uGOlpA7QJaDyKjzv/5KMxSZRWGKl0GRlaFd//n5jV9JNSUz9Ziov7VfqovTw7WFP6kClC586WxHdMrfiXax8+A1xvwQvhiotcQCzAQ6uIcBykagQT9y6DIVJH4B3aRmBziNh9pby225+ys8OLG9RbK55kekTlwqRZdheYYWkihw+PFNo89pUYq94we5stvEyezYvk8XGOz8ncSK9kG3HLnExr5iP96ZwIl2ZrNWrvRcqlYphXQPoVcMwN23pEmuXnabeDPaeyWbP6Sx7QhrZIxA/d+dq+2k16mrD9XKLzBSUtmR9XFW89ecKpn0zjRxTDiPDRiojTPLOQ9ns0JJC+HgiPTK+Ux5TKuHWk08TnrsHnUaFf/sI6D0DvNor+wf1gvkXy8eLewQpJW1da5oG1LLKls+rzWWvDQhCPbSppkF8hZEZpy4Z6NexdbypMwpKKDbb+PZI9VEdj93Utc4hedrSVqW1rsJSDlRosthHvpzPKcbFSUOgR/WkXqbq5JrzOcW46DTkmc/yzz3/R3JBEre7RfB/nSfj1WEkiccSYEUs9L8Xxj6vdJvYzAzo7E33biGkZHvxZ+C3dOnQg/5eXuCshVv+Xf4ErbCw3JWMtCyrASMITaHNJHabJHP0Yj4d/VzJLbKQlm/CbJVavPVz9GJ+ta/cN0YGsiMxA6Be46zLFkU+l1OEi5OGUB/lQp7JYmPniUyGdfV32CzFspIQZeOtQTnXVpt02dhVQPv8g3TyUrOH3uw6mcnckw8yBg8OtYe3bnyL4V8+Cc7x0GemstDyxLfBv3TpNpUK7vkWP8APiAhwB67+0SJVrxt0C/IgyNOZPh18eGPHKVydRNVGofHaTGL/8XgGhhIrI7oHUGCysutkJiVWW4sldlmW2Rh/gXM5RZW2a9UqYkK98dTr8NDX7/SXTY8/fCGfwxfy+cfoboDSX5+YVoBNkrk1Jti+v02SUavq96FRmxyjme2l3UbTBnSg0FR5QpGXqxOYCpTFkwOUePjpRchLgb+8Q1SIJx2+Xk1AlplzQ5/ns8TNDPQYhtXJm80T/o5apYaHfq08bLDnpAbHe7Wo2p3m6+ZEbCflm+U913fCWSsSu9B4bSKxn7xUaJ/g0cHPleQspX/dUmEssSzLXMwrpr23Cz8eV1rLI7sHNnoiSFp+MesPXCC2kw9DOvvZk2lCRgkXLEpSD/bSc2NkEBfziukWpFxY7ORf/6nu2jpiNJZUTrorfjxNeIAbt18XciUvxS41r9jeQlfJNv63X7lgOdU7EdPpX9gR+jB/jQ2Fr+OUGZhPnKj2GB56Hbppr/PG6Y18dPBhXFQ+7Ah8gU7e7ZWkDpcdC94WybLMih9PV9rmUqGF7u3auuYpCFevNpHYyy5GtfdxwVmrsa+SU3GSTEJqAduOXeLWmGAOX1A+BLoGetDBr/7jk6vKKDSxbr+SAPcn55CUaWDW4E4AJOWU4OzhRid/V/7SRxmLXnXKe31VXfXHUGLF3VlrHyVTbCkffVFgsiDJMkkVaprXW24KGUe2s940GFRa+qZ9xpCzb7Jy4I/Y1E74FyaiO7+Je+98AbQaiJ0DPcaXLxox8l/2h/oj4w8W7FnA2YKzTOo6CZfCCTip3XB24ApGrZ2lhklLYT4uLRCJ0NZd9e8ySZa5VGAi0NOZm3u2A8CpNHlUHCVzKkMZgVKxS+H3lBwa41y20iIf3k1ZpizbYOZ8ThEmiw2DWcLdWcvY6HaNeg6oPm1+5wnlG0fZB5ehQos9JUuJqcax0DYLTgUpYC7tHjq7Bz4YC/kXSm//QuCP/8TTlMawrv4MuX4Eht73opGUSTu6EU/C/yWBtrRlGRoLPW6pdpXQIlmYt2seJbYS3h39LouHLGZQuPLhptO2/anyZb+vqsPwK34Al9GJETCCA1zVf1U2SWbrKSVhdw5wx0OvzNgrWymn7I0kSTLZBiU5FZos9uNTsouqdWPURZJkTBYbVpvE6QwDrk4a+nX05bbrlD7uvWeyyS2dcTkqMrDJxyQ7adUkZRjJKDSx+5Qya9JslUgpHd5ZdqG2xv77s7/Q+bspcLG0hrPGSekOKTFQbLaREjCCD/tupEAfQmwnX3Sdh6EdswSz1l05p3V0nRzOPIzFZkGn1vHGqDfYNGETQ0KGAOBXWg6hplZrWxPkqXwzk6n8Wkusyt/jdWFe9u41XSsczSNc/a7qrpgsQwlZRVb6hbnSt0P5DL+ykQXFZhtHL+bz4/EM++LCh87lAdAl0J3TGQbS8k10CXSv9thV7U/OYd+ZbPvFL29XHXlFFm7u1a708Tzwds3iYm6xvcqhjwP6TEd2D+SHhHQ++e1cpe1fHLzI7CGd7LdtNc3aDO5N6oCFhPiXXuwM6w/3fAvAT0fSOJFeBC4d6ORf3j3l7qxlYIRvtUlHFRVZinjz0Jt8kvgJ/+j3D+7peQ/dfbtX2ic6xBNJlmsc997W3HZdCO/+fKbahdCy+jER/u5o1WriU3LtI54EoSld1c2FjAJl3O+oHoGVRr9UbLGn5hXXuGL8TZHK0Lmv/kytNgQtv9hCRoHJfttik9hzOqvSiIa8IqXl373Cepz+pUmrrLvHx7Xpan6M6B7AHX3bV0sE43qWd/VcKjDZz0ONRahcfckPv0WZvFOB1SbZJ0sBRAWXTy5SqVQM6exfaymDA+kHmLRlEmsT1zK1x1SmdJ9S434qlTIaqL132+9T1pcm9LLuvzJlf4catYphXf15dFQX+4gnQWhKV/VfVabBhE6jqlY0qayP8+jFfEw1TMMf0tkPFyeNvbuiat/n6t3JfLLvnD3hG6oM9evVvnLiKzOsq7/95/7tXZuuFjjQp4MPHf3cKvW3B3vpiQwuX5HqUoHJ3u+eV2S5bIXFig5XKRlb3yqSHyV8xJwf5gCweuxq5g+cj6uu4Rej24qyX3tZw6NMWcNAp1GjUqmqXRQXhKZyVXfFuDpp6ehd+2IKhSYrxhIjId56IgLc7X3SZV0vgzv7sTXhEharDDXksiKzDTdnrb0FPrlfKGG+rmQbSjhSOhmqIm9XJwaG+xIR4E5uanITvtJy6gqvtWxS0l/6tGfToYuk5ZuQZWWBiWyDmZTsonp1M+UYzOh1GqYP7IBep65zLLUkS6hVagYGD+SuyLuI6xMnEnoFtf09ls0cbo119YW25apO7IMi/PAqybjsPpIs46nX0b+TL8lZRi7mFttbpM6l3Rbm0jdcoclSadTMql1nuCkyiGNpSovWs/TirJ+7M3fGhtq7Xioa0kVpteemVrurSVTsOy+7ltDJ342IADf7+P0uge5kG3Iuu9iFyWLjx+MZXBfmTVqBCXe9ts5ysUaLkeW/L8ciWXj2+mfp4duDHgN6NMGrujYUlRYFq2tegiA01lWd2OurrGU7sXd7LBWmwpd9FV77WwojewTy0/HqHxJlo0w6B7rj6VJ+usqm9Te3itcDKk5ucdaq7cPrwv3d2Hcmh5RsI+289DV2rZzLKeJEeqG9bz3M9/KvZ2/qXhb9uoh0Yzozo2baW+1C/e1PVobXasUFU8HB2uw7s2KrKKT0gp2TVl2ppkrFPs6qSX1gROUCYkO7+Ddpn3lDdfKrMGO1wvXRiqUL/NyUbxLH0wvZGH+hxsepenE1r8hc434Gs4HFvy7m/m3346xx5uObP+bJ/k+KpF5PFRfb8C69mC7qrQuO1mbfnRUXUg7zrXkkRm0Xr27vHVKtm6W1FGfSatQ8PLIz3dt50L1d+YicMVHK6Bg/d6dKI2cMtYzTN1ZYTFoZpVHzwiQGi4FtKdu4p+c9bLhtA70DezfBq7h22GSZT/ed49VtJ5Xa+576VtFAENq2Ntt00OsqdlPUnJSdakns7b1dcNaquef6TqhQkZxtrPR4Lc1Zq+GWXsGVtnXyd2P6wA64O2tRqVTcNagjP5/MrLUlnlVYgpNWze3XhVTrhikwF7Dp1CZmRc2inVs7vr3jW7ycq9eJF+pmk5SZ0aCMVArzFcveCY7XZhN7fUYe1FT50VmnxlmrDEcrK8rU29W7qcNziKAKY80DPJzxd3eyJ5WKjl7M53h6Id3beVRL6rsu7GLJr0vINmXTL6gfPf17iqTeCFXnUIgJSUJzaLOJvT4jD2p6k903LKLNfFX20OswWyWKzFZ7aQOLTWbHMeWCcMVvLPkl+fz7wL/ZkrSFLt5deGPUG0T7R7dI3G1J1TK9zqI2jNAM2mxiV6tVOOvUDIrwq3UfrUbNhN4htPPS892RdLq382hTk0b83ZVvHNkGM66+yq8602il7NfeI1jpo5dlmYe3P0xCdgL3x9zPAzEP4KQRJWSbglQlsbf0wi/CtaHNJnaAh0d0qXOfiNIaKJP6hTo6nGZXNswxy1Bi73IpLFHGUt8/PAKLbMBi06HT6PhHv3/gqnMlyi+qxeJti6q22NtSw0FovcRfWRvm7qxFrVJxsLTwGUCG0YqTVs3e9J1M/HIiq46sAiC2XaxI6g5QtY+9tgv2gtCULttit1gszJ8/n4sXL2I2m3nooYcIDg7mgQceoFOnTgBMmzaNW265hRUrVrBz5060Wi3z588nJiaGlJQUnnrqKVQqFV27dmXRokWoRZnSZqNSqfB102EuHbNuKLFyKjeHTM81vLPzJyJ9I7mpw00tHGXbtvVYeqXbouiX0Bwum9i3bNmCt7c3L7/8Mnl5eUycOJFHHnmEe+65hzlz5tj3S0hIYP/+/WzYsIG0tDTi4uLYuHEjL774Io899hgDBw5k4cKF7Nixg9GjRzv8RQnlQn1dSUwrAGDjsR38aFmClFPMo70fZU6vOejUYvidI5WtA1DGVMNiG4LQ1C6b2MeNG8fYsWMB5QKbRqPh6NGjJCcns2PHDjp27Mj8+fOJj49n6NChqFQqQkJCsNls5OTkkJCQwIABAwAYPnw4e/bsEYm9mem1GkosEnlFZk6lqXBTBbBk6LOMjLiupUO7JrWN8VZCa3fZxO7mpkxfNxgM/O1vf+Oxxx7DbDZz55130rNnT1auXMlbb72Fh4cH3t7elY4rLCxElmX70MGybbVJTExs0AswmUwNPtaRWktc6ZkmsrKKePnLTMCNQfwDslQklrR8bBW1lvNVVUPjysqqednFTH0RiZbMxobV5s6Xo7XWuMAxsdU5KiYtLY1HHnmE6dOnc9ttt1FQUICnp1IDfPTo0SxdupQbb7wRo9FoP8ZoNOLh4VGpP91oNNqPq0lkZGSDXkBiYmKDj3WkVhOXdwFnS8r7ebOyMuncuUujFvF2hFZzvqpoaFyhl5Jq7Ha5fWjXJpkn0dbOl6O11rigcbHFx8fXuP2yV3KysrKYM2cOTz75JJMnTwZg7ty5HD58GIC9e/cSHR1N37592b17N5IkkZqaiiRJ+Pr6EhUVxb59+wDYtWsXsbGxDQpeaLiaJsRUXYtTaHrTBoRVut2ngzejo4LazOQ3oXW7bIv9nXfeoaCggLfffpu3334bgKeeeooXXngBnU6Hv78/S5cuxd3dndjYWKZMmYIkSSxcuBCAefPmsWDBApYvX05ERIS9v15oPhVr3Hi56MhCVBdsDlVLWkQGe1Yq+SAIjnTZd/gzzzzDM888U237unXrqm2Li4sjLi6u0rbw8HDWrl3byBCFxqiY2Ad39qOnu/GaWFC6pWmrDOsVqyYJzUkMqm3j9LryX7GTVo2Hc+upUtmWVZ2uoRZdMEIzEom9jXOp0GIXsx6bT9UWu2iwC81JvNPbuIoX60QBquZTNZGLi6ZCcxLv9GuIaLE3H5VKRWSwZ4XbLRiMcM0R7/RrQFndeZ1osTerqAqJXfSxC81JvNOvASN7BKJSgV4k9mZVsZtd9LELzUkMaL4GRId4ER0ilrdrbhUvoIoWu9CcRBNOEByk4th1kdeF5iQSuyA4SMU1dVWirqPQjERiFwQHqVi6QfSxC81JJHZBcJCKqyWJPnahOYnELgjNQOR1oTmJxC4IzUDMPBWak0jsguBAo6OCCPYS5XqF5iXGsQuCA/Vs70XP9mIOgdC8RItdEAShjRGJXRAEoY0RiV0QBKGNEYldEAShjRGJXRAEoY0RiV0QBKGNEYldEAShjVHJsiy3dBDx8fEtHYIgCMJVqV+/ftW2tYrELgiCIDQd0RUjCILQxojELgiC0MaIxC4IgtDGXDWJvbVdCrBYLPz0008UFRW1dCg1ao3n68CBAy0dxmWJc3ZlWuP5Eu9JRatN7LIsk5yczL/+9S+gddWz3rp1K5MmTWLhwoW4urq2dDh2p0+fZvHixUDrOl/bt29n4sSJ9thaE3HO6k+8J69MS56vVpfYyz7VVCoV58+fZ9OmTezZs6fSfS0lMzOThx56iB9++IFHH32UiRMnAmC1Wlssporn5PTp02zYsIHff/8dAEmSWiosAFJTU3n44Yf5/vvvufPOO1vF+QJxzq6UeE9emdZwvlpVYs/Pz8disQBgNBo5cOAAkydP5j//+Q/Qci2EsricnJx48MEH+c9//kOnTp3sX5O12pYpa28wGOw/Z2RkcODAAaZOnWpv5anVLfPrLYvLarVy991388orrxATE8Nvv/0GtNz5qhgbiHNWH+I92bC4oGXPV6tZaGPVqlX8/PPPXHfddVx33XWMHTuWIUOGMHjwYObOncv777/PvffeiyzLzfrHVBZX7969iY6O5pZbbgHA2dmZmJgY8vLy8Pb2brZ4yrz99tscPHiQ6OhoxowZQ3R0NMOHD+eGG25g5syZrF69mjlz5iBJUrMmq7fffpv4+HhiYmIYOXIkAwYMAJT+zz59+gA0e0wVYxPnrP7Ee7JhcbWG89UqWuyJiYn8/vvvvPbaawwdOpTvvvuOH3/8kcGDBwPw5JNPsmHDBnJzc5v1D6hiXNdffz3bt2/n+++/B6CgoICkpKQW+QPav38/CQkJvPjiiwQEBPD5558THx/PDTfcAMD8+fNZu3YtRUVFqNXqZvv6VxbXSy+9hJ+fH5s3b2bv3r2Akph++eUXoGVaxeKcXRnxnmx4XK3hfLWKxJ6cnExMTAwBAQEMGjSI2267jTVr1mCz2QDo0aMHffv2bfYLSVXjuvXWW/nss8+w2Wz06tULnU7H+vXrgebtazx69CixsbEEBARw8803Ex0dzebNm+39w5GRkfTp04ennnoKaL6vf1XjioqK4ttvv0WSJAYPHky7du3Yvn070Px9s+KcXRnxnmxcXC19vlo0sZe9qUJDQ9m5cyclJSWo1WqGDBlCWFgY27Zts++7YMECbr755lYRV1kLYeLEifz5559IktQsiaAsrqioKP73v/8B4OfnR+/evXFxceGPP/6w7/vss88ybtw4h8dUn7gOHToEKDUtjh8/3qxf3cU5a5jW9p6sK66Wek+WJe7Wdr6aNbGvX7+ezz//nIyMDED5RJUkiZiYGDp27Mi7774LgIuLC4GBgXh5KYsA22w2XF1dHfamu9K4fH19ARgyZAhLly512Nfkzz//nK+++oq0tLRKcQ0aNIiuXbvy6quvAhAREUFxcTFubm6Acr7c3NzsfY8tGVdRURHu7u4AjBo1iocfftihb7gNGzawefNmsrKy6oytOc/ZlcTVHOesrDW7f/9+fv7550oxybLcYu/JhsbVXO/Jjz/+mLfeesveXQa0ihxWVbMk9tzcXO6++24OHTpEUlISq1evJjU1FY1Gg1qt5vjx44wdO5b9+/fzxRdfsH37dnbv3m2/sq3RaFpVXGXxeHp6OuQPKDc3l9mzZ/Pnn3+SlZXFW2+9xe+//26P6/Tp08yaNYtvvvmGvXv3smfPHlJSUuytB0eeryuN69y5c/ahZ6GhoQ45X7Isk5+fz3333ceff/5JcnIyK1as4ODBgy16zhoaV3Ocs7IPik8//ZRdu3ZRUFCASqVCrVajUqla7D3Z0Lgc/Z7Mz8/n3nvv5fTp03Tq1Il333230t9+S52vWsnN4OzZs/LChQtlWZblgoIC+f/+7/9kq9UqGwwGecmSJfK0adPknJwc+eDBg/KaNWvkBx54QN67d+81G1dSUpK8YMEC++21a9fKcXFxstFolJcsWSJPnTpVNplM8rZt2+S3335bnj179jUbV0lJiSzLspyTkyM/++yzsizLstVqlT///HP5wQcfbLHYWntcsizL33zzjTxhwgR56dKl8meffSbLsiwbDAZ54cKF8owZM5r1b7+1x3XmzBl5yZIl9u0vv/yyfOTIEdlgMMiLFi1q9rjq0uRle+XSvsB169bh4uLChAkTOHLkCN988w0+Pj5kZGSwbds2pk6diqurK6NHj6Z9+/ZNGcJVH9fevXt5/fXXWbNmDTqdji+//JItW7YwePBgBg0aRM+ePa/ZuMqYTCZeeeUVzGYzffr0oW/fvjz77LO89dZb6PV6jEYjzz77LB07dmTEiBFERUWJuErjioyMZNq0aSQnJ5OZmYnBYGDv3r1Mnz6dsLAw/vjjD2JjY0VcpXHFxsYSHR3NkSNH7BOgJk2axLJlywgMDOTkyZPNFld9Nfl3lrKvUr/++iurVq1CkiR69erF9OnTSUxMJD8/n02bNtGzZ0+++eYbQkJCgPKLEI5yNcU1ePBg/P39eeGFF3jttdfYvXs3gwcPRq/X25PntRoXQF5eHs888wxeXl7MnDmTl156CWdnZzw8PFi7di2gjGkeNGgQGo3GnjwdHdvVENesWbN47bXXOHHiBOHh4QwYMIBu3brh5eXFzz//jFartScpEZfye3z++eeRJMme1A8cOICrqytdunTB09PTHlfZxKTWoMkSe2Zmpv3nAwcO4OPjQ7t27Xj++ecB8Pb2xmAwMGfOHPz8/LDZbAwePNieQBzVB3W1xfXss88CsHjxYv7617+i1WpZuHAhLi4uBAQE2I+51uKqGJskSeTm5jJjxgy6du3KuHHjSEhIIC4ujq+++oqTJ0+i1WpJTU3Fx8fH4bFdTXF16dKF8ePHc/ToUft+oaGh9OrVizNnzlS6KCjiUn6P48eP5/Dhw/b9UlJSmDlzJsePH+fvf/+7/SKvTqdzSFwN0eiZp+np6bz55ptkZ2czatQohg8fTkREBLNmzSIkJITRo0cza9YsOnbsSEhICJ999hkFBQUUFBQwd+7cpngNbS6uuXPnEhYWRl5eHqGhobzwwgtkZmbyxBNPXHNxVY1t9OjRdO7cmaeffhpPT09AeUMGBATQuXNnbr/9dtavX8/58+exWCz8/e9/F3FViSstLY2//OUvQPmM1qioKDIzMwkMDBRxXSYuUAq0JScn07FjR2bMmGGf5NaaNLqP/e2338ZisXDHHXfw5Zdfkpubyz//+U/7MLLXXnuNxMRE3n33XcxmMxkZGRw5csTh4zmv5rhOnDjBypUrsVgsnDlzhiNHjjB58uRrMq6qsW3ZsoXs7Gwef/xx3Nzc+P3331m5ciUffPABoIxe0Gg0/P7774wYMULEdZm4rFYrJpPJPrTS0a72uGw2G3l5eTzyyCNMnDiRqVOnNkt8DdGgxL5x40b2799PWFgYFy9e5OGHHyYsLIyUlBQ+++wzgoKCmD17tn3/AQMGsGzZMkaOHNmkwbfluF566SVGjRp1TcZ1udjOnTvHunXrCAwM5O677+bzzz9HrVbj5+fHm2++SVxcnENbUG0prhUrVhAXF8fw4cNFXPWI6/XXX2fBggVER0fj5OTksNiawhX3sb/yyivs2rWLWbNmceLECTZt2sS6desAaNeuHUOGDCE1NZW8vDz7McuXLycsLKzJghZxtd246ootKCjIHhvAtm3beP7559m6dSuLFy92aPJsa3EtWrTIocmzrcW1ZMkS+vTp0+qTOjSgj72wsJApU6YQHR3NjBkzCAwM5Ouvv2b8+PFERkbi5+dHSUkJrq6u9iFzQ4cOdUTsIq42GFd9YzOZTBQWFtKrVy/uuOMOxo4dK+IScV3VcTWlK0rskiQxZswYYmJiAPj222+58cYb6datG88//zxLly7l119/JS8vr9lqNYi42k5cVxqbq6srjz76qIhLxHXVx9XUGnzx1GAwcPfdd7Ny5UoCAgJYuXIl+fn5ZGVlMW/evEpD4JqTiKttxNWaYxNxibhauwYPd7x06RJDhgyhsLCQ5557jq5du/L444+3+FhOEVfbiKs1xybiEnG1dg1O7AcOHGDVqlUkJCQwYcIEbr/99qaMq8FEXFemtcYFrTc2EdeVEXE1vwZ3xWzcuJHMzEzmzJnTqq4Si7iuTGuNC1pvbCKuKyPian4NTuxyMxf+ry8R15VprXFB641NxHVlRFzNr8mrOwqCIAgtq1WseSoIgiA0HZHYBUEQ2hiR2AVBENoYkdgFQRDamEbXYxeEq9G+fft47LHH6NKlC7IsY7VamTVrFrfcckuN+6empnL8+PFmqWwpCI0lErtwzRo0aBCvvvoqAEajkZkzZxIeHk5kZGS1fX/77TfOnDkjErtwVRCJXRAANzc3pkyZwrfffsvatWtJT08nIyODUaNG8be//Y1Vq1ZhMpno06cPoaGhPPfcc4CytOILL7yAh4dHC78CQSgn+tgFoZSfnx/Hjh2jd+/efPDBB3z++eesW7cOjUbD/fffz/jx47nxxhtZsGABixYtYs2aNQwfPpz333+/pUMXhEpEi10QSqWmptKnTx+OHDnCb7/9hru7O2azudp+SUlJLFmyBFBWpu/UqVMzRyoIlycSuyCglHDdsGEDkydPpri4mGeffZaUlBTWr1+PLMuo1WokSQIgPDycZcuWERISQnx8vH11e0FoLURiF65Zv/32GzNnzkStVmOz2YiLiyM8PJzHH3+cP/74AycnJzp27EhGRgbdunVj5cqVREdHs3jxYubNm4fVakWlUvH888+39EsRhEpErRhBEIQ2Rlw8FQRBaGNEYhcEQWhjRGIXBEFoY0RiFwRBaGNEYhcEQWhjRGIXBEFoY0RiFwRBaGP+H00XRR+nVgyyAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1186afcf8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1188,9 +1261,9 @@
     }
    ],
    "source": [
-    "goog.plot(alpha=0.5, style='-')\n",
-    "goog.resample('BA').mean().plot(style=':')\n",
-    "goog.asfreq('BA').plot(style='--');\n",
+    "sp500.plot(alpha=0.5, style='-')\n",
+    "sp500.resample('BA').mean().plot(style=':')\n",
+    "sp500.asfreq('BA').plot(style='--');\n",
     "plt.legend(['input', 'resample', 'asfreq'],\n",
     "           loc='upper left');"
    ]
@@ -1199,31 +1272,34 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice the difference: at each point, ``resample`` reports the *average of the previous year*, while ``asfreq`` reports the *value at the end of the year*."
+    "Notice the difference: at each point, `resample` reports the *average of the previous year*, while `asfreq` reports the *value at the end of the year*."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For up-sampling, ``resample()`` and ``asfreq()`` are largely equivalent, though resample has many more options available.\n",
-    "In this case, the default for both methods is to leave the up-sampled points empty, that is, filled with NA values.\n",
-    "Just as with the ``pd.fillna()`` function discussed previously, ``asfreq()`` accepts a ``method`` argument to specify how values are imputed.\n",
-    "Here, we will resample the business day data at a daily frequency (i.e., including weekends):"
+    "For upsampling, `resample` and `asfreq` are largely equivalent, though `resample` has many more options available.\n",
+    "In this case, the default for both methods is to leave the upsampled points empty; that is, filled with NA values.\n",
+    "Like the `pd.fillna` function discussed in [Handling Missing Data](03.04-Missing-Values.ipynb), `asfreq` accepts a `method` argument to specify how values are imputed.\n",
+    "Here, we will resample the business day data at a daily frequency (i.e., including weekends); the following figure shows the result:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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51bR+3HpVH6rrjLzw/kEOnSxXJIuDC7UelQqGSqEWQgjh5FQqFTOSYnjg5sHY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\nt5WdsoQQmjO5GHlyUDsUsGZTJjZt9iwS4qqcqhr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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11861cba8>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -1232,7 +1308,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(2, sharex=True)\n",
-    "data = goog.iloc[:10]\n",
+    "data = sp500.iloc[:20]\n",
     "\n",
     "data.asfreq('D').plot(ax=ax[0], marker='o')\n",
     "\n",
@@ -1245,36 +1321,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The top panel is the default: non-business days are left as NA values and do not appear on the plot.\n",
-    "The bottom panel shows the differences between two strategies for filling the gaps: forward-filling and backward-filling."
+    "Because the S&P 500 data only exists for business days, the top panel has gaps representing NA values.\n",
+    "The bottom panel shows the differences between two strategies for filling the gaps: forward filling and backward filling."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Time-shifts\n",
+    "### Time Shifts\n",
     "\n",
-    "Another common time series-specific operation is shifting of data in time.\n",
-    "Pandas has two closely related methods for computing this: ``shift()`` and ``tshift()``\n",
-    "In short, the difference between them is that ``shift()`` *shifts the data*, while ``tshift()`` *shifts the index*.\n",
-    "In both cases, the shift is specified in multiples of the frequency.\n",
+    "Another common time series–specific operation is shifting of data in time.\n",
+    "For this, Pandas provides the `shift` method, which can be used to shift data by a given number of entries.\n",
+    "With time series data sampled at a regular frequency, this can give us a way to explore trends over time.\n",
     "\n",
-    "Here we will both ``shift()`` and ``tshift()`` by 900 days; "
+    "For example, here we resample the data to daily values, and shift by 364 to compute the 1-year return on investment for the S&P 500 over time (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 30,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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PZ8zRFHy9nGnT1Mvq869NmgI9U94zn/921sO9ad/cu8S+/v4eJCRdZsrCkvPl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\niIgJKJBFRERM4A/3oO5fXZCK2gAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11864e3c8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1282,97 +1355,54 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(3, sharey=True)\n",
-    "\n",
-    "# apply a frequency to the data\n",
-    "goog = goog.asfreq('D', method='pad')\n",
-    "\n",
-    "goog.plot(ax=ax[0])\n",
-    "goog.shift(900).plot(ax=ax[1])\n",
-    "goog.tshift(900).plot(ax=ax[2])\n",
-    "\n",
-    "# legends and annotations\n",
-    "local_max = pd.to_datetime('2007-11-05')\n",
-    "offset = pd.Timedelta(900, 'D')\n",
+    "sp500 = sp500.asfreq('D', method='pad')\n",
     "\n",
-    "ax[0].legend(['input'], loc=2)\n",
-    "ax[0].get_xticklabels()[2].set(weight='heavy', color='red')\n",
-    "ax[0].axvline(local_max, alpha=0.3, color='red')\n",
-    "\n",
-    "ax[1].legend(['shift(900)'], loc=2)\n",
-    "ax[1].get_xticklabels()[2].set(weight='heavy', color='red')\n",
-    "ax[1].axvline(local_max + offset, alpha=0.3, color='red')\n",
-    "\n",
-    "ax[2].legend(['tshift(900)'], loc=2)\n",
-    "ax[2].get_xticklabels()[1].set(weight='heavy', color='red')\n",
-    "ax[2].axvline(local_max + offset, alpha=0.3, color='red');"
+    "ROI = 100 * (sp500.shift(-365) - sp500) / sp500\n",
+    "ROI.plot()\n",
+    "plt.ylabel('% Return on Investment after 1 year');"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see here that ``shift(900)`` shifts the *data* by 900 days, pushing some of it off the end of the graph (and leaving NA values at the other end), while ``tshift(900)`` shifts the *index values* by 900 days.\n",
-    "\n",
-    "A common context for this type of shift is in computing differences over time. For example, we use shifted values to compute the one-year return on investment for Google stock over the course of the dataset:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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TN8ADznlo7vBi8YV6WdsUOrwfYu3cMRHDejrLm2a0c14UPXSDcw8KKnYSeMfO\n1vr19UxmK576cLtgNjwgPRS/astpjz8n7qQunEJVyAT5I0eOoLGxETNnzsQdd9yBffv24dChQxg8\neDAAYNSoUdi2bVuQWxm62rKn/B+nLgIAKkJ0Tp8ZeTRyVg9Ea0lWX+OxmlPGVsO5ACzITkJ2mn0I\nmZZIBZ6/15pfuNTotsadSyogfb/1DHu7e6dk5Gcmev27dxwux6W6Zq+PjxThurpAcvyorq4Oq1ev\nRk1Njcsb9YEHHvBrQ2JiYjBz5kxMnToVxcXFuPvuu11+n16vR329d723tDTh/bX9/ZxQotPZh9p1\ncdpW/1uSkmNFnyvX+Wkxua8QaNdOjxidGlodZzpBrQy5v5kc7YkRmFLx9Ht1nHPWsUMi0jglbNNS\n4lBaaUBqajy0MixTDLW/l5wajBbJf78v52fOe+4dnimjC7By/QkAwN4TVV6/Xof28fjbTQOw49AF\nvPiZ51VVAPDudweRFK/F0mev9rq9/hDs94+Fs1Y12G3xhWSQf+ihh5CQkIDCwsKAZuXm5eUhNzeX\nvZ2cnIxDhw6xPzcYDEhM9O5qs7LSt6HctLQEn58Tar5xfLg/XnUAOXe0bnljdXUjKuPdE7PkPD9b\nD5x3e6yish4/bS/B6q3F7GNVlxpD6m8m1zkyGNynVDz93qYm5/HmZpPLsWbHyEhFRT102sAG+Uj4\njLVFWXm9x3+/r+enstp11E2jVmLS0Bz065yCZz6xB+qjJyvRLjFG8rUu65mOmmoDzC3O1Ri3XtUV\nhiYTVm4SHtavbTDK+vcMhfdPVbUz8TfYbeHzdNEhGeSrqqrwySef+LVBQr7++mscO3YM8+fPR3l5\nORoaGjB8+HDs2LEDQ4cOxcaNG1FUVBTwdoS7M22Yqw6F8pV1BvdlX1abzSXAA0CzMTqTxnydP+de\nmMfFuH7clQIb2JDACHSi2+sPDIdSqUBOunOk5tjZGhT16uDxedyEwK45yehf0B6j+meif0F7nD5f\nxwb57p2S0S4xBlsPXAjcPyLEheu0lmSQ79GjB44cOYLu3bsHtCE33HAD/vGPf2D69OlQKpV48cUX\nkZycjKeeegomkwn5+fmYOHFiQNsQzmJ1KjS1tK0YTih816/n1WUHhOcX2/pvjRaext6YqZHy6kbk\ndfB+TpZ4h3vxxC8f629xjmkc7kWdyoskXG7Gv1qlxN9u6Mve5w7cxmjVqKgJzZwduRw760xy/Pzn\nY7jlqq4z44tjAAAgAElEQVRBbI33JIP88ePHMWXKFKSmpkKn07HLmdatW+fXhmg0Grz66qtuj9N6\nfe88+9ehePzdbRhQaN95qrKmCXPe3YZ7JveUvJpnBDvGl1UZUFHt/kViEwjy0boEiN/rfuuRUa1+\nLSYz+5Vle9v0OkRYU4tztEnORNFEvRZ1BqNkOeiCbM9rvblLcYt6ZeDd7w66/HzVltPIzUhAPy92\nuwt39Y1Glz0j1v1eipvHF4ZFMp5kkF+0aJEc7SBtxFzJMzHgza/3A7DXte6R1w5JIkVQTNxSuEHu\nyq8WWdYjlIwXtdn1nGubJ28bhFidd2uvUxPFS5JygxHxH+4mMf5cfsXPbH9sWn+X+9dclov/rD2O\nDXvL0Dc/1e35zLScWiJAxcdq8K8HR8AGIEmvhc0GvLfKGei/dQzlh9Oa8dZqFPiMNDSbkBgX+sWl\nJMdzMjMzsWHDBrz00ktYsGAB1q1bh44dO8rRNuKDGEfi1KEz9lrUZZXO5TUl5eLz9PtOXGRvBzts\niu2cNfe939wei9Z5ZCvnr+RN1a0TZfb12fwSwSTwuBenZj8G+cVrXHch7JGb4nKf6cHvOV6F2gb3\nvzvz0fEmkTpRr2U7CNyaC9Hmi19OuD2mCoNePOBFT/7ll1/GmTNncP3118Nms+Gbb75BaWkpnnji\nCTnaR7zEDBvF6tT4npek5mlou9no/CIKdtz0ZYOdcC1M0WY+/rNDpcBRNNryh3OliMWP00tnK5yV\nH998aKRbsOYuh2wyWsC/FIzWC+S22HO8yu2xUKkQKkWyJ79lyxYsWrQI48aNw/jx4/Hmm29i06ZN\ncrSN+CizvR61DUZ8s/GUy+MKD+lX3A98eXVwa8NX13vf2wzXTNe28nUFxOTLOwNwLYpD5PHfbc6C\nM/68KOV+TuJj3QMNdy6+0UPp4tZ0RBfeWwStOmRqqMli7wn3AB9OJP9aFosFZrPZ5b5KRft7hyKN\nyIdPbL9xwDXrd+nPx/zdJFE2mw1lVQb2IsNktuL8Re8vMvzZMwonvsaKK/pnYkBhezw+fWBgGkS8\n4s8gz5QnzuYUNuLSaZwfeKELd5tzvN73350Sh6uLcl0eO3D6osjRkeHNr/YHuwltIhnkJ0+ejNtu\nuw1LlizBkiVLcPvtt+Oaa66Ro23ER2JX2J46f9zhermYzBa8+91BPP3hdqzbVQoAOHXOt9religd\ncvS1Jx+rU+PB6/uiIMt9/n784Gx/NYtIaO1FaXl1o1uiHTMHf9c1wtstc4fvhcrPtiHGA3BP1Py/\nFfta90JhLDEuPIbqAS/m5GfNmoUePXrgt99+g81mw6xZszB69GgZmkZ8JdaTZ3oRZosVZysakNch\nARdrm9FisqApgEVlrFYb3vnuAIb1yMDg7uns499uPo2dRyoA2JfhrN5ajIYmZxGc/MxEnDxX5/Z6\nSXotah0V36J1uN6f86nTxhZireMii/hHbUML9LEat42iWtOTt9ls+Icj6ZSbwc4E2QSRzO4ETgD6\nesMpTLosj/e69v8rWxnl9TEhtXmprCYMzcHYgdler2oJBV5NrlxxxRWYM2cO5s6dSwE+hGnVwtMo\nTOLdkjVH8c/PdmHX0Uo8/u42PP3RDjQHsKjM2YoG7D5aibe/PcA+9r9dZ/HjbyXsfUOz2SXA62PU\nuO+63m6vpVIq8Or9l+PdR68AEMVB3o+zFNw1vp42OyHeqa5vwaNvbcXHPxwGYF+2mJoYA7VK0aq6\nDtwEOy5mnj1WJ/x5z0qLd/kMmXgJrW29UJw4LFf6oAh15eAcpCXHCuZChKroyqCIcGqJnvym/fZs\n30PFl9ifBXKNNP/LpK7RiGVrj3t8zj3X9hL8AKmUCqiUSmg1KigU/l2SFE6Y4frHbx7g19c9elZ4\ny1LivZqGFlhtNvx2sByA/XOnUiqgUatQJ7DngJQft5cIPs7sGump2M2Q7ukY2DUNANAisjS1tT15\njVqJlATxugueGJpNOHOhHl/+eiIsL9TDZdkcFwX5CCL29jtaUu3Sk7jImacTKvLgidVqw7+/3o+N\n+84BAHYfrcQDr29ElUDJS36HocaL7Pl2CTpoNSq8M/sK9OniLOTBLdGpUiqithgOc+HUITVO4kjf\nhENRj1DHD1pWqw0qlQKJem2rcl8yBf7GtZyLBal17kyOjsnsGuSZC8W27DfGL8DjDYvVigf/tQnP\nfroTP24vwfbD5a1vgEy+3nASABCnU+OBv/RBUnzrLm6CyauJhePHj6O2ttYl6WfIkNbtdEYCh5nn\n5tu47zx2Hqlk7x845ezJ7z/pW2ZsTUML9hyvwp7jVYjRqthSl7/8XoYbxxa4HMsfohQrdsPFBHOd\nVsVb4uN875ktNpw+Xwezxeo29xnpmFEZf5XTvOXKrvj8f8fw7eZTGNQtzS+vGa248+4//HYGdY0m\nJOi10KmUqK73fbjebOG+5+3v9Uf+vdnr56vZIM8frve5KW46pup9fg5/v4kLjtU0x87WIFGvRYd2\n/r1wbaumFjO7DDI7PZ4dGQk3kkH+2Wefxa+//oqcnBz2MYVCgcWLFwe0YcS/vBmWTxQpfcvF7T1w\na1kLzfPxl+QJlafl4w6HcQOZ0IY02w+VY3if6Kq+yPQWWzvUylfvKJbDrZBIWocb5L9ab+8BxmhU\nUCoVMJms7L4f3th1pAK7jjov2ltMFpcLWm+S3zQiPXlGW99Dg7qlYfdRe+fBYrVCxVmrW1HThA9W\nH8Rdk3qifXIMlqw5iryOrpsgrd5ajOtGdsaLn/8OIPTK43JrDIRz6WfJd8qWLVvw008/ISZGel9i\nEt7qDEY0tZg9Zo6KLQUS+r4orXQmDn364xGX4Xcx3CA/rGeGYKUpRjh/8FqrrZnRfHWN7lv7ktYR\nmmPWauwjUjbYLwI8FSWqqmnC/E92YlDXNGzmVMsDgOYWC+I4n0tvLhY0josCE+8zy16Qt/EtdO+1\nvXDPK+sB2LeI5s7TL197HCfL6vDcZ7vYz+nGfefdXmPmS7+2rREBVGNwTi+KJUGGA8mxzpycnJDY\nZ5xIE0pY83U9p6HZ85d+axPeNu47h7JK4Q8Kt4fCnXvvzqvJzefNyECkYb6gPRU48sW1w/PY24He\nDjXSCS2TKymvh8ax6kVquurjHw6jqcXsFuABoNlkcXl9b76TRXvyPtSu94T7ueVfSGgdBXmELsRv\nDYMtWusMRixYvJu976+L6mCQ7MknJSVh0qRJGDBgALRa53DuwoULA9ow4ruHbuiLBUt24/oruqBf\nQXskxGrwyKItks+7dngeLtW1YPMf5yUzXkV78l50C77dLLzL3NXDOmG1o94+tycvtUuWoSm6evLF\nF+rYHAp/fekkcxKJZr22IeSGTIUYTRaX+uyhwiKwvlGlUjqDrcQyOrOHZNLmFjOOlFSz973pd4kF\neeYCwR9pHUO6p2PnkQq08BIL2yWIj/wm6UM/eY1fxW/CsByRI0OfZJAfOXIkRo4cKUdbSBvlZyVJ\nfklntte7rYm2OLKAmdueiH4RCXxhqJQK0debfVM/tlIWdyiMG+RVIkl18bEaNDSZUN8UXZuvrFjn\n3AkrUPtYNzSZQnoN8GpH8aR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YZW6ttfXABazdVYovfj0hfTDbNqtkgBJaohgMzF4SiX4O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\nKiIiclMa7sVfun79ehobG5k8ebJzbM2aNVRVVZGVlcXChQt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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x118a41160>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ROI = 100 * (goog.tshift(-365) / goog - 1)\n",
-    "ROI.plot()\n",
-    "plt.ylabel('% Return on Investment');"
+    "The worst one-year return was around March 2019, with the coronavirus-related market crash exactly a year later. As you might expect, the best one-year return was to be found in March 2020, for those with enough foresight or luck to buy low."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This helps us to see the overall trend in Google stock: thus far, the most profitable times to invest in Google have been (unsurprisingly, in retrospect) shortly after its IPO, and in the middle of the 2009 recession."
+    "### Rolling Windows\n",
+    "\n",
+    "Calculating rolling statistics is a third type of time series–specific operation implemented by Pandas.\n",
+    "This can be accomplished via the `rolling` attribute of `Series` and `DataFrame` objects, which returns a view similar to what we saw with the `groupby` operation (see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb)).\n",
+    "This rolling view makes available a number of aggregation operations by default.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Rolling windows\n",
     "\n",
-    "Rolling statistics are a third type of time series-specific operation implemented by Pandas.\n",
-    "These can be accomplished via the ``rolling()`` attribute of ``Series`` and ``DataFrame`` objects, which returns a view similar to what we saw with the ``groupby`` operation (see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb)).\n",
-    "This rolling view makes available a number of aggregation operations by default.\n",
-    "\n",
-    "For example, here is the one-year centered rolling mean and standard deviation of the Google stock prices:"
+    "For example, we can look at the one-year centered rolling mean and standard deviation of the  stock prices (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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oI9alHq/MJKtypznncf+w9cmNdc6sOW2AeZXZu0sBnLukErs1N9/ZYprcOtYg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\nX4vfgTqYSFNT4+Jwoier9nFKOB4hkoj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xs3ZDctpMWdnjUHx7vXfpu+kLe9jTvY9/ffE7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\n2X5Po0JhvhUZVn0MzpDEWnhCkBEp9B0ghERHwg2OVje48bn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OpULmkl8pLazW06fQsmeX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\nrkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11924aa58>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1380,11 +1410,11 @@
     }
    ],
    "source": [
-    "rolling = goog.rolling(365, center=True)\n",
+    "rolling = sp500.rolling(365, center=True)\n",
     "\n",
-    "data = pd.DataFrame({'input': goog,\n",
+    "data = pd.DataFrame({'input': sp500,\n",
     "                     'one-year rolling_mean': rolling.mean(),\n",
-    "                     'one-year rolling_std': rolling.std()})\n",
+    "                     'one-year rolling_median': rolling.median()})\n",
     "ax = data.plot(style=['-', '--', ':'])\n",
     "ax.lines[0].set_alpha(0.3)"
    ]
@@ -1393,104 +1423,134 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As with group-by operations, the ``aggregate()`` and ``apply()`` methods can be used for custom rolling computations."
+    "As with `groupby` operations, the `aggregate` and `apply` methods can be used for custom rolling computations."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Where to Learn More\n",
     "\n",
-    "This section has provided only a brief summary of some of the most essential features of time series tools provided by Pandas; for a more complete discussion, you can refer to the [\"Time Series/Date\" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html) of the Pandas online documentation.\n",
+    "This chapter has provided only a brief summary of some of the most essential features of time series tools provided by Pandas; for a more complete discussion, you can refer to the [\"Time Series/Date Functionality\" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html) of the Pandas online documentation.\n",
     "\n",
-    "Another excellent resource is the textbook [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) by Wes McKinney (OReilly, 2012).\n",
-    "Although it is now a few years old, it is an invaluable resource on the use of Pandas.\n",
+    "Another excellent resource is the book [*Python for Data Analysis*](https://learning.oreilly.com/library/view/python-for-data/9781098104023/) by Wes McKinney (O'Reilly).\n",
+    "It is an invaluable resource on the use of Pandas.\n",
     "In particular, this book emphasizes time series tools in the context of business and finance, and focuses much more on particular details of business calendars, time zones, and related topics.\n",
     "\n",
-    "As always, you can also use the IPython help functionality to explore and try further options available to the functions and methods discussed here. I find this often is the best way to learn a new Python tool."
+    "As always, you can also use the IPython help functionality to explore and try out further options available to the functions and methods discussed here. I find this often is the best way to learn a new Python tool."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Example: Visualizing Seattle Bicycle Counts\n",
     "\n",
-    "As a more involved example of working with some time series data, let's take a look at bicycle counts on Seattle's [Fremont Bridge](http://www.openstreetmap.org/#map=17/47.64813/-122.34965).\n",
-    "This data comes from an automated bicycle counter, installed in late 2012, which has inductive sensors on the east and west sidewalks of the bridge.\n",
-    "The hourly bicycle counts can be downloaded from http://data.seattle.gov/; here is the [direct link to the dataset](https://data.seattle.gov/Transportation/Fremont-Bridge-Hourly-Bicycle-Counts-by-Month-Octo/65db-xm6k).\n",
+    "As a more involved example of working with time series data, let's take a look at bicycle counts on Seattle's [Fremont Bridge](http://www.openstreetmap.org/#map=17/47.64813/-122.34965).\n",
+    "This data comes from an automated bicycle counter installed in late 2012, which has inductive sensors on the east and west sidewalks of the bridge.\n",
+    "The hourly bicycle counts can be downloaded from [http://data.seattle.gov](http://data.seattle.gov); the Fremont Bridge Bicycle Counter dataset is available under the Transportation category.\n",
     "\n",
-    "As of summer 2016, the CSV can be downloaded as follows:"
+    "The CSV used for this book can be downloaded as follows:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "# !curl -o FremontBridge.csv https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD"
+    "# url = ('https://raw.githubusercontent.com/jakevdp/'\n",
+    "#        'bicycle-data/main/FremontBridge.csv')\n",
+    "# !curl -O {url}"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once this dataset is downloaded, we can use Pandas to read the CSV output into a ``DataFrame``.\n",
-    "We will specify that we want the Date as an index, and we want these dates to be automatically parsed:"
+    "Once this dataset is downloaded, we can use Pandas to read the CSV output into a `DataFrame`.\n",
+    "We will specify that we want the `Date` column as an index, and we want these dates to be automatically parsed:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Fremont Bridge West Sidewalk</th>\n",
+       "      <th>Fremont Bridge Total</th>\n",
        "      <th>Fremont Bridge East Sidewalk</th>\n",
+       "      <th>Fremont Bridge West Sidewalk</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2012-10-03 00:00:00</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>9.0</td>\n",
+       "      <th>2019-11-01 00:00:00</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-10-03 01:00:00</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>6.0</td>\n",
+       "      <th>2019-11-01 01:00:00</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-10-03 02:00:00</th>\n",
+       "      <th>2019-11-01 02:00:00</th>\n",
        "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-10-03 03:00:00</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <th>2019-11-01 03:00:00</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-10-03 04:00:00</th>\n",
+       "      <th>2019-11-01 04:00:00</th>\n",
        "      <td>6.0</td>\n",
+       "      <td>5.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1498,24 +1558,24 @@
        "</div>"
       ],
       "text/plain": [
-       "                     Fremont Bridge West Sidewalk  \\\n",
-       "Date                                                \n",
-       "2012-10-03 00:00:00                           4.0   \n",
-       "2012-10-03 01:00:00                           4.0   \n",
-       "2012-10-03 02:00:00                           1.0   \n",
-       "2012-10-03 03:00:00                           2.0   \n",
-       "2012-10-03 04:00:00                           6.0   \n",
+       "                     Fremont Bridge Total  Fremont Bridge East Sidewalk  \\\n",
+       "Date                                                                      \n",
+       "2019-11-01 00:00:00                  12.0                           7.0   \n",
+       "2019-11-01 01:00:00                   7.0                           0.0   \n",
+       "2019-11-01 02:00:00                   1.0                           0.0   \n",
+       "2019-11-01 03:00:00                   6.0                           6.0   \n",
+       "2019-11-01 04:00:00                   6.0                           5.0   \n",
        "\n",
-       "                     Fremont Bridge East Sidewalk  \n",
+       "                     Fremont Bridge West Sidewalk  \n",
        "Date                                               \n",
-       "2012-10-03 00:00:00                           9.0  \n",
-       "2012-10-03 01:00:00                           6.0  \n",
-       "2012-10-03 02:00:00                           1.0  \n",
-       "2012-10-03 03:00:00                           3.0  \n",
-       "2012-10-03 04:00:00                           1.0  "
+       "2019-11-01 00:00:00                           5.0  \n",
+       "2019-11-01 01:00:00                           7.0  \n",
+       "2019-11-01 02:00:00                           1.0  \n",
+       "2019-11-01 03:00:00                           0.0  \n",
+       "2019-11-01 04:00:00                           1.0  "
       ]
      },
-     "execution_count": 35,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1529,19 +1589,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For convenience, we'll further process this dataset by shortening the column names and adding a \"Total\" column:"
+    "For convenience, we'll shorten the column names:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 34,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "data.columns = ['West', 'East']\n",
-    "data['Total'] = data.eval('West + East')"
+    "data.columns = ['Total', 'East', 'West']"
    ]
   },
   {
@@ -1553,42 +1615,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 35,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>West</th>\n",
-       "      <th>East</th>\n",
        "      <th>Total</th>\n",
+       "      <th>East</th>\n",
+       "      <th>West</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
-       "      <td>35752.000000</td>\n",
-       "      <td>35752.000000</td>\n",
-       "      <td>35752.000000</td>\n",
+       "      <td>147255.000000</td>\n",
+       "      <td>147255.000000</td>\n",
+       "      <td>147255.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>61.470267</td>\n",
-       "      <td>54.410774</td>\n",
-       "      <td>115.881042</td>\n",
+       "      <td>110.341462</td>\n",
+       "      <td>50.077763</td>\n",
+       "      <td>60.263699</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>82.588484</td>\n",
-       "      <td>77.659796</td>\n",
-       "      <td>145.392385</td>\n",
+       "      <td>140.422051</td>\n",
+       "      <td>64.634038</td>\n",
+       "      <td>87.252147</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
@@ -1598,45 +1676,45 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>8.000000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>6.000000</td>\n",
        "      <td>7.000000</td>\n",
-       "      <td>16.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
-       "      <td>33.000000</td>\n",
+       "      <td>60.000000</td>\n",
        "      <td>28.000000</td>\n",
-       "      <td>65.000000</td>\n",
+       "      <td>30.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
-       "      <td>79.000000</td>\n",
-       "      <td>67.000000</td>\n",
-       "      <td>151.000000</td>\n",
+       "      <td>145.000000</td>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>74.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>825.000000</td>\n",
-       "      <td>717.000000</td>\n",
-       "      <td>1186.000000</td>\n",
+       "      <td>1097.000000</td>\n",
+       "      <td>698.000000</td>\n",
+       "      <td>850.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "               West          East         Total\n",
-       "count  35752.000000  35752.000000  35752.000000\n",
-       "mean      61.470267     54.410774    115.881042\n",
-       "std       82.588484     77.659796    145.392385\n",
-       "min        0.000000      0.000000      0.000000\n",
-       "25%        8.000000      7.000000     16.000000\n",
-       "50%       33.000000     28.000000     65.000000\n",
-       "75%       79.000000     67.000000    151.000000\n",
-       "max      825.000000    717.000000   1186.000000"
+       "               Total           East           West\n",
+       "count  147255.000000  147255.000000  147255.000000\n",
+       "mean      110.341462      50.077763      60.263699\n",
+       "std       140.422051      64.634038      87.252147\n",
+       "min         0.000000       0.000000       0.000000\n",
+       "25%        14.000000       6.000000       7.000000\n",
+       "50%        60.000000      28.000000      30.000000\n",
+       "75%       145.000000      68.000000      74.000000\n",
+       "max      1097.000000     698.000000     850.000000"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1649,36 +1727,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Visualizing the data\n",
+    "### Visualizing the Data\n",
     "\n",
     "We can gain some insight into the dataset by visualizing it.\n",
-    "Let's start by plotting the raw data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import seaborn; seaborn.set()"
+    "Let's start by plotting the raw data (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 36,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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tj8t7zgtGo2U41IqszWJH7s3W28BIHoyFBRDKO+bx0otbqe9dTrXe8N6kSRM8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\neHt7Y8KECfDz88P8+fMxatQo+Pv7IyAgoMr7XVn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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1192479e8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1694,23 +1763,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ~25,000 hourly samples are far too dense for us to make much sense of.\n",
+    "The ~150,000 hourly samples are far too dense for us to make much sense of.\n",
     "We can gain more insight by resampling the data to a coarser grid.\n",
-    "Let's resample by week:"
+    "Let's resample by week (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 37,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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W5D7tRhaNGNdRt/jTlfNJwSDkYBD20jLYywYBiMf5FVmGHImAdbri8U0S/l7B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NKz6nnpwC4AphcNvlj73Zv70etFZtUXD1j1tnb72Hush5pyu9afutHdzPMNL3LU1cS4ns9z95cv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\nYUGtd2NoHkGcIy18PjMphsykY717rT10Q9HEdV2csuFIaOX+5LhpZJ6bQ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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118a35240>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1719,7 +1791,7 @@
    ],
    "source": [
     "weekly = data.resample('W').sum()\n",
-    "weekly.plot(style=[':', '--', '-'])\n",
+    "weekly.plot(style=['-', ':', '--'])\n",
     "plt.ylabel('Weekly bicycle count');"
    ]
   },
@@ -1727,24 +1799,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This shows us some interesting seasonal trends: as you might expect, people bicycle more in the summer than in the winter, and even within a particular season the bicycle use varies from week to week (likely dependent on weather; see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) where we explore this further).\n",
+    "This reveals some trends: as you might expect, people bicycle more in the summer than in the winter, and even within a particular season the bicycle use varies from week to week (likely dependent on weather; see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), where we explore this further). Further, the effect of the COVID-19 pandemic on commuting patterns is quite clear, starting in early 2020.\n",
     "\n",
-    "Another way that comes in handy for aggregating the data is to use a rolling mean, utilizing the ``pd.rolling_mean()`` function.\n",
-    "Here we'll do a 30 day rolling mean of our data, making sure to center the window:"
+    "Another option that comes in handy for aggregating the data is to use a rolling mean, utilizing the `pd.rolling_mean` function.\n",
+    "Here we'll examine the 30-day rolling mean of our data, making sure to center the window (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 38,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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YWIjhw4ejsLAQOTk5kMvlEIvFqKioQFpaGvbu3Yv8/HwIhUKsW7cOjz76KKqqqsBxnI3F\noDPwxhvrcfz4r2AYBrfe+nvcccdd+PjjbdDr9bj66ixIJBL8/e9/A8uy0Gg0PrVOJgiCIFxj3dTI\nGwRSKTiDAZzBACYMGtEFRYKOUjG6deuG6dOnY+rUqeA4DnPnzoVYLEZeXh4WLFiAqVOnQiwWY/36\n9QCA5cuXY/78+WBZFrm5ucjKygIAZGdn4/777wfHcVi6dKlf5E6Z8kCHT/MeHcsHDfinn35AfX0d\n3n77fRgMBsyc+Siys0dh6tTpqK6uxtixudi9eyeWLVuNxMREvP/+VhQWfocbb7zJL7ITBEEQ7eF8\njCFgzD0NWI0GQit3eKgIuELQu3dvfPLJJx2OTZkyBVOmTLGZI5VK8frrr7c7XlZWFnbs2NFuPD8/\nH/n5+X6SOrwoLy9HVtZIAIBIJMLQocNQXn7eZk63binYsGEtoqOjceVKDa69NicUohIEQXQZfFUI\nBOZ4Olang+Mix8GFChNFAJmZmSgpOQYAMBgMOH78V6Snp4NhBGDNzTNee20VlixZhhdffAlJScmW\nTofU8ZAgCCIwcAYDAHht7ucVCV6xCDWhd1oQLrnhhgk4duwInnrqUej1Btx6623o128AdDo9Pv74\nAwwaNBiTJt2Gp556DFJpNBITE1FXVwfAs8pZBEEQhPv4mnbIRJksBJyBFAKiA2677Xab7dmz57ab\nc9VVQ/DRR6YCTBMm3OzwOJs3v+N/4QiCIAjfXQZhZiEglwFBEARBeIGvWQa8IsHadU0MFaQQEARB\nEIQX+JxlQBYCgiAIgoh8LApBJwkqJIWAIAiCIDxEX1+Hlp/3AfCDhSBMggpJISAIgiAID7m0cZ3l\ntddBhSLTfupz5/wik6+QQkAQBEEQHqKvrra89tpCIDGlHTZ9/RUURf7pm+MLpBAQBEEQhA8wIi8t\nBBKp5bWquNhf4ngNKQQEQRAE4QF8hUIebwvACaRtCoH9MUMBKQQEQRAE4QFGdatfjiOQRltthb7M\nPCkEBEEQBOEBnEbrl+NYWwiMKpVfjukLVLqYIAiCIDyA1WgAAKLkZKRMud/r4zDmbodAeCgEZCEg\nCIIgCA9gtSaFIG7MWMTmjPb6OKKEBERfNcR0TJXSL7L5AikEBEEQBOEBrNbkMmAkEp+OwwgESJ+/\nAOKevcCqNf4QzSdIISAIgiAID+BdBtZpg74gkErBatR+OZZPcoRaAIIgCIKIJDizhUAg9c1CwCOQ\nSsEZDCFPPSSFgCAIgiA8gI8h8JeFgHc98K6IUEEKAUEQBEG4iep4CRQHfwHgewwBD59+yLsiQgWl\nHRIEQRCEGxhbVajctMGybV1HwBf4AkWhVgjIQkAQBEEQbqC/UmuzLfCXhcDc5IjTkcuAIAiCIMIe\no1Jhs+23GAJzt0RWr/fL8byFFAKCIAiCcIN2CoGfsgyYKLOFgBQCgiAIggh/jErb8sKMv+oQmC0E\npBAQBOEzRpUKuuqqUItBEJ0aex+/v2IILBYCnc4vx/MWUggIohNw5cO/o3zJImgrKkItCkF0Wjij\n0WabEfjnFsrHENi7JIINKQQE0QlQHDoIANCcLwuxJATReQlUJUFGbFIIrnz8IRRFhwKyhjuQQkAQ\nnQhjGHRMI4jOCmcIjI+fjyEAgKZvvg7IGm7JEbKVCYLwO6E2ORJEZ4YzGF1P8gLGSiFgxOKArOEO\nAVcIiouLMX36dADAyZMnMW3aNDz00EN4/PHH0dDQAADYuXMn7rnnHjzwwAP44YcfAABarRZz5szB\ntGnT8OSTT6KxsREAcOzYMdx3332YOnUqCgoKLOsUFBRgypQpyMvLQ0lJSaA/FkGEDRzHWV4bFaQQ\nEESg4F0G8TdORM8ZT/ntuEKZ3PI6lApBQEsXb926FZ9//jlkMhkAYPXq1Vi6dCkGDx6MHTt24J13\n3sFjjz2Gbdu24bPPPoNGo0FeXh5yc3Oxfft2DBo0CPn5+dizZw+2bNmCxYsXY9myZSgoKEBaWhpm\nzJiB0tJSsCyLw4cPY9euXaiqqsLs2bPx6aefBvKjEUTYYB2ZHOrSpwTRmeEVgsRbb4M4NdVvxxUl\nJbetoQ9dx8OAWggyMjKwefNmy/bGjRsxePBgAIDBYIBYLEZJSQmys7MhEokgl8uRmZmJ0tJSFBUV\nYfz48QCA8ePH48CBA1AqldDr9UhLSwMAjBs3Dvv27UNRURFyc3MBAD179gTLshaLAkF0ZliNGo1f\nf9W2HeK0JYLozPAKASMS+vW4wri4tjW0oVPqA6oQTJo0CUJh2xfXrVs3AMCRI0fw8ccf45FHHoFS\nqURsbKxlTkxMDJRKJVQqFeRykxlFJpNBoVDYjNmPOzoGQXR26r/4F+r/+Q/LdqjzmAmiM8MZeYUg\nysVMz2AYBhnLVwIAjGq1X4/tCUHvdrhnzx789a9/xdtvv43ExETI5XKbm7dKpUJcXBzkcjlUKpVl\nLDY2FjKZrN3c+Ph4REVFWeZaz3eHlBT35vmDYK5F+IdwP2f1ymabbYHREPYyBwP6DiKPSDhndeZH\n6JTuCRDJZf49eMoQVKd0A3SakH0XQVUIPv/8c+zcuRPbtm1DnNlEkpWVhU2bNkGn00Gr1aKsrAwD\nBw7EyJEjUVhYiOHDh6OwsBA5OTmQy+UQi8WoqKhAWloa9u7di/z8fAiFQqxbtw6PPvooqqqqwHEc\nEhIS3JKptjY4QVgpKbFBW4vwD5FwzjQttpYwfas67GUONJFw3ghbIuWcaVpN5vz6JjUEatbvx+fE\nUhgaGwP6XXSkbARNIWBZFqtXr0avXr3w9NNPg2EYjB49Gvn5+Zg+fTqmTp0KjuMwd+5ciMVi5OXl\nYcGCBZg6dSrEYjHWr18PAFi+fDnmz58PlmWRm5uLrKwsAEB2djbuv/9+cByHpUuXButjEURIMbS0\nADBFJnN6PdgQt08liM5MWwxBYG6dAqkUrEYNjuPAMExA1ugIhrPOWeqCkIWAcEYknLPzSxbCqFSi\n7+q1qFizCkaFAv03/cWrYxnVauiqqhDdr1+79wyKFgjlsSG5SHlKJJw3wpZIOWcX16yEpvw8Bv31\nbwE5/qVN69F6/FcM2PxXv/VJsKcjCwEVJiKICIbT6SCQSiGMkYERi92yEBiaGqE8eqTdeMXa1ahY\n/TJ0NdU24y0/70fZc3OgOPiL3+QmiEiE0+lsqgr6G4E0GoApeygUkEJAEBEIx3Fg9TqwOh0EYtOT\nhEAiAafTmd7TanFp0wa0njzRbt+KtWtwefMbUJedsxnXXTI1RlKfPmUz3vDfPQCA5p8KA/FRCCIi\nMDQ1QVtx0dKZMBAIok3tlFl1aFIPSSEgiAikbtcOlD03B6xSaalsxv/P6XRQHi1C6/ESXFr/art9\n9bVXAACas2cAmF0F5jEA0NXU2MwXmQN0WZVtL3gicNR9thvn5j8LVk9ppOFCxSurAABGRUvA1hCG\n2EIQ9LRDgiB8p/F//7W8FpgVAYGVQuDsKca6cJG2shKsXofK1zdYlAOgTWHg4Wsb0M0p8FS/9zfo\nqiqhKTN1rdTX1ECSlh5iqQgA0NfVBnwNQbRZIQhRLQJSCAgiguA4DmxrKxiJBJzWFC/AmF0GjDkI\nidVpnRYoMra01S1o2fcTdDXVNsoAACiPHUXryROIGTLUtE9rq+l/KvYVUFitFi37frIZMzQ3k0LQ\nhbD8hrWhyRYilwFBRBCtx3/FuWeetigDACCQ2FoIWK0OrLrV8r61VcDQYhvJba8MAACMRlxa/yo4\n1pRnzT+tsCoVOGNgur0RjjtVqs+chr428E+mhGM4gwHKo0eCdoPmAxZDVXGUFALCr6h+LYGuutr1\nRMIrVMd/bTfGBxXylgJOp7M81QOAwdxVFPCsTrqhoR41H22DoaHevDMXsieXroCjxlQNX/4LF15e\nalHOiOCiLD6Ky5vfQOVfNgVlPT4OiNXrg7KePaQQEH6B4zgoig6j8vUNKF+yMNTidFoYBylPjNg0\nxlsKFId+sTHvV739psX/6ckNXVddjebvv7UZo14JgcNZp0pWrYbWnAFCBBfeOqMuPQmYa3CkPDAt\nYOvxsT9ciOJ1SCEg/ILySBGq3iywbBuVSnqqCQBsq4NIf3NpMd5S0PjVf9D0zf8sb2svXsClDetM\nUz24oVe/3774CnVTDBwdBZKR2yA0GJuteoVwHOIn3ozEWyYFbD3G4jIgCwERwajP2OauX1j+Z5Qv\nWWQp9Un4B0eBfbxfnzc3OkJ/xZRK6ElpY2NTU/u1qDRywHBmIQCCE+FOtMHqdDC0tFhKg/OIu/cI\n6LoCs7WPM4RGIaAsA8IvsK22TzeGxkYAgLrsHGIGDQ6FSJ0So4NaAHxLVt5C4AyugxgA2chrYair\nhVGptJw7R5CFwP+0lp7Ele0fIbr/AKdz6nbtgPrMafR8YiYgYCAIYHEcAqh+dyuUhw9C3Ku3zbgo\n0b2med7CuwxC9TsjCwHhFwwtzQ7HNefOORwnvIO3EKTcnwdRcjIAgDOYLQSSjm8S+poay/mQ2vUr\niM0ZhYyXVkDaf6DDfaNSUk1rkULgd1TFx6CrvITmH3/oeN6xozibPxPnX5jXoTWB8A19Qz2Uhw8C\nAHSXK23eEyUkBnRtiiEgOgXGFsfVu+p270RT4fdBlqbzwraqEJWSgsRJt1rqnrdZCDpWCMqXLITi\n4AEAQLd770f3Rx6zvCeQmEqmdn/oEXS7+15AKAQARHXvgUFb30f8jRNM65PLwO94FGvDcTAqFFCf\nO2vZ16hUQnPxAi6uXgFtZaWLAxCuOP/CPKfvCWXygK7Npx2yFENARDIdlfO8su3vMASw3GdXwqhU\nQmC+KMmvGQkAiBlodsmYb+LuIIyORvTgNleOQGpSCIQxMUj6/e0QJZqehPjKaZYqiBpSCPyJoaUF\nraUnPd6Pzzqofm8rzj03GxdXLoem7Bzq/rHL3yJ2KVwpZwJZTEDX5zOGOEo7JCIZo1IJUXIy+ixd\n7vB9vV19fMJzOIMBnF5vuXkn/+FOpL2wCAnmqGdW1drR7jYIYmIQlZTctm2+8fMIY2SmcfNaAplp\nu9Wu8RHhGxdXvARd5SWH70ky+zrdT3/FVF5acfAXgOMA840sVDeSzgLb2vFvSBgdYIWAXAZEpMPf\nqMSpPSDtk+FwjuZ8GRSHD4LjuCBL13ngA434PumMSISYQYPBCEw/45ihwwCBez9pQXQ0GKHQklst\njI2zed++3gGvIDT/8B0avvoPAEB35QrOv7gA6jMOqh0SbtFRAGefF/+MgX9tn/oJAJoL5VAc/AVC\nuW1vez5o1KhQoLLgdYeFrAjnGFUdl+dmRIGNwxdQ2iERCli9Hpc2bUCL2afsC0ZzmVxBTLTTObU7\ntqPqrS1opQuU13B2CoE9ovh4DHr7XST9/nbTvGjn54OPP+i/8S9Im/cCopKSbN635EOb00Z5CwFg\ninjnOA6N//sv9FdqglbFrbPhTDnudve9SJ32EBiBwKS0OUBbfh5Vb78JY7NtaqihoQFXPv4Qtbt3\nQnXsKKreftPvcndmjB5Y2QIBnzqsPFoEXXVV0NcnhaCLoq24iNbjJah++y2fj8Wb2fgbUMayFUid\n+qDDua0nT/i8XleFtTQz6jh4UJ6dA0l6OnrNfhY9HpuB5Dv+2G4Ob1UQyuWWJkY275ufhHgTNG8h\n4NHXVFsisB0WSyJcYnSSmZN4621ImHiTV8c0NDag6btv0LLX1CTJlQmcsKWjv2XefRZIrC1zFa+9\nEvD17KE6BF0U6972HMeBMZuOvTqWucKawOxfk6SlQ5KWjisff9hurr6+zut1ujoWC4ELhUCakYmM\nl1ZYtjXl51H/r38CAFKnPwJpnz4u17K3EESlpiLu+lyoz5yBvvYKypcs8uozEG3wcQD22FsFkn5/\nO5r3/WRbNc8DOJa1KIBExxgdKARR3Xsg5d4pkPbt52CPNppVOpReaMToIaleX0+tz5OxuRmsTufy\n9+5P6K+ki2JoarR63b4inSfwCoEwxjbgJiWvfc1vfX29T2t1ZfiUP8ZFASJ7hPFtxVTirs91eWED\nAIGdhYARCNDj0ScQf8N4j9YmnKNzohDY0+3ue9F//eter9NRSeSuCseyDq971g9KfHwNIxRCPjLb\nZQ2Cj/5iLgcAAAAgAElEQVR3Cn/91284c8k7xc0R3iqB3kIKQRfFuhuesyhn949l+hHZ+6wTb56E\nQVvfhyixzT/tqMUr4R6uYgicIYprCxgUOGiO5Ij4iTcDMGUyWCOMjXU0PWTd2SKN6vf/hur3/gZj\nqwrqs6c92jdj+Sok//FuRKWkeLSfo3LXXZ3aXTtQNv9Z6Kou24zzlUB7PzsPsqwRAABG6N5t8qk/\nXo13F96EQen+q2ZoVAfX5UMugy4KZ1XCVnelBjIM9/pYbS4D50FslrkOSu8S7uFuDIE9vAmakbjv\nA40ZNBgD3363nanZPqqdR32qFLKrvf8b6gpwLGvx7SsOHrBJERTGx7t8GpT07g1J795o2feTR+uy\nGrIQ2NP09VcAAGVJMZJ69rKMW+KhYmSW65mhxb2HGF/crs4IdgwIWQi6KNY17Q0++vX5Pgb2LgOH\nc9VqVKxbS50QvaAthsAzCwEA9H9jC/qv3+jRPo78zs4sBOQKco21idpaGUh7fiEyV6yGJCPTYpnp\nCPvshOS77kHM1cMhHeC47DSVOW6j9fQpsFY5/vZKGG8hEMpkiBubCwCIzx3n1rEv1ihwtrIZzUrH\nxbv+c+AC9hy44PI4fRYvReyo0QAAliwERDCwLkHra6qNJe3QSdEOexO3uvQktJcqnNYsIBzTFkPg\neZCRO8qaW8dxYiFo+Pe/IB9xDUQJgW3+EsnYpwjySDMyIJBGI+PPy9w6Dqc3BXrGXT8OsaNGI2bY\n1WD+7w+o+8en0JxtXxOCFAITmgvluPTqGoh7p1nG7IMI+W2hTAZx9+7IXPMqohJtU3Kd8c4XJ1BZ\np8Jt1/XBlAntG1XJY6Lw3p5SfH24AhvznSsZ0r79EDNsOBSHDtq4doMBKQRdFGuXgbdpY4amJlSs\nXQ19rSk4yr64DU/PWfmo3fkJmKgoqI4eAQDoq6tJIfCQtsJEoet0J5S31XLPXPkKtJcrUbXlLzA0\nNKCy4HVkLHkpZLKFO84u7p4GiVoCPSViyIZnWcadpcWxWlIIAEBfZ7KEWsdM2XcP5V2aArMCLTY3\n9XKHFY+PcTj+xf5yRIuFuCUnHcP7JUMS5brEOL8+uQyIoGDtMmgtLcXFV1Z53HO99cRvFmUAMKWm\nOULSqzfSnp0HcY+eljFHbXyJjuG03mUZ+BOBTIbY0dchJW8axD162GQsaCsuhkyuSMBZ/IynKYGc\nwawQiGwDRJ1ZjshCYMLRg4/q6BFLoyjAdF0SREf7NU1Tb2Dx8Tdn8Pne80iQSxAtcf0czlv0gm0h\nIIWgi2K5SDAM2FYVNGfPoP7LLzrch+M4m4hl69exo0a7jGC3ybF1USKUsIXjONTtNjWuCWZesj0M\nw6DnjJlIvNnUP8HaYiCUyZztRsB/EeO9Zs2GKDERibf8zmZcFBfvcD5HCgHUZ86g5u/vOXyvYs1K\ny2tOq7VU8fQEjuNwoVqB8uoWXGkyxVTp9EZs+edx1Ddr8PRdw3GhWoHDpe6lmvIBjcFOGSWFoAui\nu3IFrb8dBwBLVzsA4FyYFpu//xbnns1H03ffAGhLIUx7YRF6PjnL5brW/jpKhfIM677s3sQQBApr\nJdBaOSDaY90iPObqrA5mdozs6uHo99pGRCUn24zLc0Yh6f/+gLR5L9iMW1sDuyo1297v8H0+UJPV\nab36fRmMLN7dcxIvv38Y735pqsZa26TGgN7xuO26PhiSkYjBfRJQeKwST28sxPGyjoNwQ+UyoBiC\nLoj6jKljnak8bVuqjOLQQXAsix6PP+nwaV9RdBgA0HLwF6jPnIbi0EEA7t8Ikib/H1qP/wp9ba3H\n7omujo2LJUwzNAQepDV2RTRl5wAA/dZvgvrsGbQeL/Hr8RmBAN3uugcAkPHyahga6lG5aX2XTzvk\nOA6GxoYO57DqVrAaLYwtLRDFex4YGyUSYvmjo23G9v9WjR+OVqKmoQeG90vGLTlpGD+iFziOc+k2\n4LsqBrsOgUsLwWeffdZu7KOPPgqIMERwMCpMT/Y9n3wKgG0Kk7LosMV6YE31u+9AfaoUAKA5d9ai\nDACAUOaeQhCVnIy+a16DMD4B2ouu02+INqyDQKX9+odQkvb0eGImAPJVu8LQ1AhBdDRE8QngjMaA\nriXp1Qsic8Oqxq/+C21FRUDXC2eMLc0uTe+6qiqcf2EuAIDxsPCXM6ZMGIC1M69Ht3gpfiy+jLkF\n+9Ci0iFGGgWNzgi2g86voXIZOFVT3n//fSiVSnzyySeorGwzVxoMBnz55ZeYNq19WVpHFBcXY926\nddi2bRsuXryIhQsXQiAQYODAgXjpJVNE8s6dO7Fjxw5ERUVh5syZmDBhArRaLZ5//nnU19dDLpfj\nlVdeQWJiIo4dO4bVq1dDJBLh+uuvR35+PgCgoKAAhYWFEIlEWLRoEbKyvDfJdXaMCpPpUhifYOql\nboe24iLk14xs275UgZb9+9om2O3jqe9YnJoK9dkz4AyGgLcT7SzwZt+UvGlhV5c+bsx1qP/n7qA/\nzUQS+vo66K7UQigzPfnJs0ZA0icDibfeFrA1rbMOqv/+bpfMAGne+xNq3nfcQtoapdn6CXgXo6PV\nG1HT0Ip4uQQf/LcUZyub8dIjo/DDsUr8dr4RD08eDKlEhNSEaOgNRnxaeA4D0+Jx3dAeDo/HCIVg\nJNLwyTLIyHCcEiaRSPDKK+51Ydq6dSuWLFkCvTlNZs2aNZg7dy4+/PBDsCyLb775BnV1ddi2bRt2\n7NiBrVu3Yv369dDr9di+fTsGDRqEjz76CHfeeSe2bNkCAFi2bBk2bNiAjz/+GCUlJSgtLcWJEydw\n+PBh7Nq1Cxs2bMDLL7/s6ffQpTCYi3GIYuMcasP2DYgUVj8WR3h6UxclJgEch/OLnif/ppvw35On\nZYuDhSA6hmrmO8GoVOL8gvngtBpLrQ6BNBoZS5cjbsx1AVvX2oXDddHfmTvKAGBqAMbjTQxBXbMG\nW788iWXvHsTRM3W48ZpeqG1SI6N7HKbeMhC9U2RITTA99TcqdeieGIP0FFeWVQ7aixdQ/++Og739\nidMr+cSJEzFx4kTcdttt6N/fOxNlRkYGNm/ejBdeMAW5/Pbbb8jJyQEAjB8/Hvv27YNAIEB2djZE\nIhHkcjkyMzNRWlqKoqIiPPHEE5a5b775JpRKJfR6PdLSTIUlxo0bh3379kEsFiM311RVqmfPnmBZ\nFo2NjUhM7LgZRVfFUF8PMAxEiYnoNfNpXN5SAP2VGsv7yiNHUGUwoPuDD0Mglfr9JsSnJxoaG6H6\ntQSxOaP8evzOCGcuSuRNlcJgIIiJAafVgjMa23Xr6+qoz7T1LAimQmezlv+r6kYcyX+8GzFDh6Fi\n9Yp27+kut/U08MZC0LubDC8/Nhocx+HMpWb06ibD67uKwQEY3CcB4igh0lNNCkBqQjR+Nyrd5TF5\nJa7+s91InHRrULKLXNoeL1++jHvuuQe33HILbr75Zss/d5g0aRKEVhcH65KbMpkMSqUSKpUKsVbl\nUGNiYizjcnOwmkwmg0KhsBmzH3d0DMIx+rpaiJKSwIhEkKSlo+/qtTZ9uNlWFRQHfkbNtvfRvG+v\n04wAab9+SJh0q8frJ0xo6/VOwYXuYeljELYWgtD4PCMB65LFBifVCgMBIxIhfqLpt6avb2hX8rir\nIemTAWlmX8t29KDBltc2Tdd86EnAMAwGpSdAHh2FxQ/l4OHJVyFGIvLlkACA5h++h+ZCuW8HcQOX\ntt6VK1di4cKFGDhwoM/NGwRWvk+VSoW4uDjI5XKbm7f1uMocWc3f8HklwnpufHw8oqKiLHOt57tD\nSop78/xBMNdyBsdxOKNQQNavr408px10q1P8cgCKXw4gdvDgdu8BwDWvrITQmxtUSixkr6zCrwsX\nQ8LqwuJ7cUa4yNYqMGUWJHVPRHyYyGRNU2IcVACYspOo3X8AA5+Zjai40MkZLucNAC4f+tnyOuO+\ne4N7zXn2aZxQNKPxcBGSYoQQycO3VkQgvhfrfpLJvbohrns8+OLOg2Y+DvWlSpzesMlmH6lU7LEs\nLSod6pvVSEmMATgOCzbvxZDMJORPuQbXDuvZbv57X/wGqViIvFuvcnrM1vvuxaWdnwIAanduBwQC\njP7gXUS5eW/zBpcKQWJiIiZOnOiXxYYOHYpDhw5h1KhR+PHHH3Hddddh+PDh2LhxI3Q6HbRaLcrK\nyjBw4ECMHDkShYWFGD58OAoLC5GTkwO5XA6xWIyKigqkpaVh7969yM/Ph1AoxLp16/Doo4+iqqoK\nHMchwc2a6rW1wWnHm5ISG7S1OoLVasEZDGDF0TbyRKWk2lQdtEZx6pTD8fpmLRhG5/A9V+j0JuVS\nWdcYFt+LI8LlnAGAotaUNqUwCKALE5ms0TEmC9PZNzYDAMo++xJJv789JLKE03nT19ejtcJUKrff\nhjcgjIsLumys3FSwqPr0BUjSXZuqQ0EgzhlnMNhst+gArdUaLVpAZ2xvJNdo9R7LUny2DrsLz+GO\n3L64dlAK7pvQHzoDi/MXGyCPbp/CLZcIIY+O6nCd6En/h1490nD5DbPCwrKo2HvIZxdrR8qOS4Ug\nOzsba9aswQ033ACJ1dPgqFGeC7VgwQL8+c9/hl6vR//+/TF58mQwDIPp06dj6tSp4DgOc+fOhVgs\nRl5eHhYsWICpU6dCLBZj/fr1AIDly5dj/vz5YFkWubm5lmyC7Oxs3H///eA4DkuXLvVYtq4CXyGQ\nj3bmSZv7PFoO7IfySJHTErT91m1Eyy8HULdrBwDf2n0KzK4fKlDkHqz5ewrX4j/2ra+tzeRdFVMw\n4TwAgCSzL0Rxjnt9BBq+4ZShuSlsFYJAYJ8GK7JrUiSQxTh0wTGM51k8IwZ0w4gB3WzGvthXjslj\n+mDUVe1Luk8Y2dvlMRmGgaS37fnSVl4KaMyVS4WgpMRUPOPEiROWMYZh8MEHH7i1QO/evfHJJ58A\nADIzM7Ft27Z2c6ZMmYIpU6bYjEmlUrz++uvt5mZlZWHHjh3txvPz8y0piIRzWHNnQ/tUwaiUFCT/\n4U4oi4853VeUkIio5G5O3/cEYYxpfVII3MOoNDddcbPmQ7CxVwgoBdE2WyeUZZ35UrxdrU4E3x1U\nIJOh19NzIDT/jcqvzYby2FEIpNGOg3T9EIA5NDMJQzPd65LYEfZ/N4amRp+P2REuFQJHN3AicuEt\nBIIYJxcoFwVTolJS/CIHIxRCEB1NTY7cxKhSghGLXfaLCBVCu9bXip/3IyoxCcl33eNz7FGkYh1A\nGFKFINqUftjVFALO3B1Ufm02YqwCCHvNmm157SjFMO56562JndHQooFKY0BqYjQkUULs+O4M9v1a\njdUzrnPoMvjuyCWUVyvw8OTBEHZQV8TegsFpbV20HMui8euvIB9xjU3zOG9xqRBMnz7d4Q/aXQsB\nEV7w3bOEThQCzq4srjA2DuLevZF8+x0ATLEGAGyyErxFKJdTkyM3YTUaS33zcMSRbA17vkTM1cNt\nLsZdCUNDW7lcpwp4EOALFHW1EsaWduFRztP1rFMzY8eMRfeHHvEqNbTodC1+Kr6Mx28fij7dY3HT\ntWmYPCYDMqnjW2xirAQiocBRXTgb7O+9+vo6sFqtRcaW/ftQt2sHWvbvQ+bylY4O4REuFYLZs9u0\nKYPBgG+//RZxIfKFEb7D8hYCZ08snK1CIIiJRvr8BZZtYUwM+ixeaqpy6CMCmRz68vOofH0Dkn5/\nO6IHDvL5mJ0VVq0O626CQieRz9oL5aQQIExcBl0sJZQzZ051VGjIOrdfIJV4XSdiUk46JuW0+ftT\nEjrumDhyoHeWVs25szj3zNMYUPAWGJHIUj9GV3nJq+PZ41IhGD3atmHD9ddfjylTpuCZZ57xiwBE\ncLFYCJxcoOwtBI6Q9u3nF1l4GVS/lkBTXo7+G9/wy3E7I6xGjahu/onfCASieMetd3U1NQ7HuwL6\n+raOdv6wqHlLm4Wgi7kMeIWgg+/e2iQfrkW/7OEMBqiO/wr5NSP9HoPlUiG4bFXBieM4nD17Fk0U\nQRyxsGafvTOFIHrAIOirq4Mii3XEvFHRAo7juqy/uSM4gwGcXu9Vn/ZgIXRiNeT7ZnRFrCsUcg7q\nfAQLS9EoFy4DdVkZFAd/RvKdd1sC8CIZi8ugAwuBtbLASL3v1lnT0Aq9gUWvbjIIBK6vYSXn6lB0\nqhaTx/RBz2TPrUf6K6YUcZuCSn7ApULw4IMPWl4zDIPExEQsWbLEr0IQwYMP4nPm00yd9iBkQ4fB\n0NyE2h3bIRs2PGCy2HdJNCoVEMWSO8oe/smODw4LRwTRjuMbupqZmoczGGBosLIQhLCJl7tZBtVv\nv2mqYhoXH7IaEv6E05sUgg4tBFYPIL6Ulf7uSCVOXGjAkodyIBG4Lt0tjxajX684l22QAaDnjKeg\nLD4KxS8HLGP6+jroG+otnWsB+KVZnMu9v/vuO58WIMIHo1qN5sLvATi3EAiixIgdPQYcx0Hcoydi\nhgwNmDz2cQyGhgZSCBzA31TD2ULgzLLTVRUC3jUnSe8DSXofJHpR4ttf8C4D5bGj0F6uhKSX4xx4\nvoy47orjAmWRBqfjXQbu9QDwRSHIu2WgR/P79YpDv17uXetiR49B7OgxNgpB07dfo+nbr23mXVy9\nAukLF/vU88BlBYaGhgY8++yzGDNmDHJycpCfn4+6ujpXuxFhSMu+vZbXAlnHEesMw0A2PCugTzb2\nRXYMjYHNsY1UeFOvfa5/uGIdcNpV6xHwwbvSvv3Q49HHbVoRBxt+bU6rxYWlix3Ose5zYKjvHNd3\n3k0jELsXv2HdHTIS0V68AMWhX3w6hkuFYOnSpRg+fDi+/fZbfPfddxgxYgQWL3b8R0WEN9Ypfh2l\n4gSLdkU3zG2ZiTaUJcW4tHEdgPB2GQBAXO4NEMhkyFy2An2WLkdU9+5d10LAu+bCIDOEsctz19Ve\nadfoiNO2uRN460akw1pcBu5d6xiJd9fERoUWB0/WwOhGQDZPZa0S7/+nFMVn/at8+dpO3qVCUFFR\ngcceewxyuRxxcXF44oknbAINiciBDyiUZ+eEWBIT9rnrTd99AzaEwVfhyOU3NsLYYgrMC2eXAQD0\n+NNjGPD6ZghjYyHtkwFBdAyMzc1o/OZ/oRYt6BjDrNS0depd+aIXULd7l2Wb4zhoLraVK+8s9Qra\nXAZuZniw3nWDVKn1+K7oEg785n5GjVQsQmbPWCTHua/kR3Xv7nIOF2iFgGEYVFVVWbYvX74MUQgD\nZAjv4Z9aUu7LC7EkPLZ+Z13lJVz54P3QiBIBRIrLgIevqlj7ycchliS4sDodLheYyq6HS+2ItHkv\nQD4y27Ld+N89ltct+/fh0qtrLNtsaytUvx2HsqQYDf/ZE7Ftk/mgQnd96t5mgqSlyrHwwWxcf3UP\nt/dJjpdiwjW9kZbqvsKYNu8FdH/kMUDoPGiR1fqWWuryzv7MM8/g/vvvx4gRI8BxHIqLi7FixQqf\nFiVCA9vaccphsJGkmQp5iHunWQprtPy8Dz0eeyKUYoUN9t3awt1CYE9X7VPR+tuvltfh8luL7j8A\n3E03Q3m0qN179n5no0KBSrObCgAk6emQXR24bKNAwbpRhwAA0hctQdO3X0PuRcM+a7xJmeaVLXf2\njUpKRvy4G3Dlw7/DmYrGanyzELhUCCZOnIgRI0agpKQELMvi5ZdfRlKS700biOBjVKkAodBhh69Q\nEJWUhP6vb4ZRqUD54oWWcVajjribXyCw9+WGewyBPda1/Fm9LiziVoKBtSIUTs2o7OMZOJYFIxC4\nDHjUXCiPSIXAknbowkIQ3X8AovsP8GoNnd6In0qq0KubDEMyEj3at+RcHXZ8dxb5dw/3rBaBXUyI\nMDbOUu/DVwuBS5fBgQMHMGvWLEyYMAGZmZmYMmUKjhw54tOiRGgwtqogjJGFVfEfoUzWrh5BZ0l7\n8hX7J+xIU5IYYdvzhrGp6wSM6q1KFotTXft9g4XQLmaHD/h0ld2jq4rMmDFLDIEocFUiDUYOl+tV\nqK73vElbcnw0Hpw0CAly7x7QEm6ZhJ4zn0ZcblszpoDHEKxduxYvv/wyAKBfv354++23sWrVKp8W\nJUIDq1KFjQnTGoFMhoRJt0LcOw1AW/BjV4bVqFG36xObsUirHtf72bmW15oL5aETJMjwxWLSFyyG\nKMH3nh/+QpRg+wTLy6l3kUZu3ZMhkmAtMQSBUwhipCJM/91gTLw2zeN9e3eTYUhmklvFiRzCCBCb\nMwqJt/wOokST1d7X8tQuFQKtVotBg9qazvTv3x8GO98mEf5wHAejShUWaVD2MAyD1PvzEH/DjQBM\nloyuTs0H70P1a4nNWKRZCKQZmeg151kAsDRh6Qrw5tuo1NQQS2ILIxJB3LOXZduoVIBjWRibOy5F\nb4jQUvVtvQw6mauKMd+2zfEHooQE9F1rivkIeNphv3798Nprr+H06dM4ffo0Nm7ciMzMTJ8WJYIP\np9UALBuWFgIe3qTJdpI8aF9Qnz3bboyRhkfshyeIzEWKulKNCWNLC8AwYZNyaE2fpcuQfOddAIDq\n9/6Gi6ttA8QdpbZF6rnjdO7FEPhCXZMa3x25hEtXPA+gvVijwGvbj+KnYs9cMlHmGD7rrCNGIAAj\nFgdeIVi1ahXUajXmzZuHBQsWQK1WY+VK3/suE8ElnAqlOIOvS9BZCqP4hFUb6vgbJyDp9j9EZFnn\nNoUgMp8yvUHf2ABhfDyYDtLDQoUgSgxRUjIAQF9TDW35eQBA7KjR6LduE6JS2ls1OK3GZRfUys1v\noObDD/wvsA9YKhUGsNOkRmfEpVoVmlSe34iT4qT4/dgMDOvrWZB+72eeQ/zEm5B02//ZjAskUnA+\nugxcOi/i4+OxdOlSnxYhQo/RRZfDcEBAFgIL1gF5sWPGImbQ4BBK4z3C2FiAYWCM0KdMT+FYFobG\nRkgz+4ZaFKcIY9tbLgTRMRAlJFgCfKNSu9u4eTidFoyVy4rV6QCOg0AigaG5GaqjpkDz7g8+FGDp\n3YPVaqG9VGHKqgpg3Zy0VDkeutW736Y8OgrDMj3P2IvqloLu09p/zwKpxGcLAVUY6iJY2h476XIY\nDvAug4Z/f4GEm26BKD4+xBKFDusnsnA0PbsLIxRCGBsbsWZnT9HX1gJGI6JSUkItilOE8th2Y3xj\nn9S8aRAlJCD5D3cAYFDz93ehOHQQmvPnbRqdXVj+Z3B6PTKWrUTtju3BEt1tqt55y6YTYFeAkUhh\nVNW7ntgBLl0GROeAN8PblwsOJ6xbMl9Y1rVbbLNWgZXhbNVxB1F8QpdxGWjNBbYkvdNDLIlzHCkE\njLkWgVAuR8qU+yGQRkMglVrGL61/FdrLlQAA7eXL0NfUwNDQgIurXobiYFsXvnCpaqg6djQo65RX\nt+D7I5fQ0OK5qZ7jOGzYeQzv7TnpF1kEEglYjcanc+BSIdi6dStqa2u9XoAID4IRYOMr1nnSRoWi\n09RU9xTOaLRJH7Kv0xBpCOPjwWm1KP/zi6EWJeDwrhFRcvgWb3NkcXJWnMg6s0VbYep3oD5VahnT\n11TbzG/6+iuTqT6EuIp38CcqtQEVV5Ro1XqeeccwDG4b3Qc3Z3uesugIgUQCsGy7Cqee4NJloNFo\n8OCDDyIjIwN33XUXbrnlFkQFMEiDCAyWnNwwTsGxr6Cob2hw2ru9M2OfSxxIH2gw4BU9XdVlS3W8\nzgprbvdsXwQonHDUE8NZSqu1osD/XXakqNfuNNXOGLT1fR8k9I1gKiTD+iZ5HBRozRAvYgicwV8/\nOa0W8PIe7fKXmZ+fj6+++gozZszAL7/8gjvvvBMvv/wyTp70j5mDCA6Wql1hbCFgGAbxE26ybBsa\nfPOHRSr8BVeeMwoDCt4KsTS+Y10JrzP3N9DVXkHdPz4FYArSC1ccVSp1ZiEQxba5FwyNjdCUl9t0\nSgxHNOfap+x2BQRik0LA6rwPLHRLVVer1bh06RIqKiogEAgQFxeHlStXYv369V4vTAQXzvxH4m7n\nr1DR/cGH0P2hPwEAjC1dKyiIh201KQSiuDiXdeYjAVnWNZbXnTnbQHmkrXFQOCsEjhA46W8ijGsL\n7DU01OPiymXBEcgH+NLnUT16IPWhRwK61vHz9fj+aCXUXrgMAODjr0/jlQ/bN5zyBoG1hcBLXNoi\n582bhwMHDuDGG2/EU089hZycHACATqfDuHHjMG/ePK8XJ4KHu52/wgGh+anEYK741tXgLQSRVpnQ\nGYm3ToamvAzKosOofGMD+r22MdQiBQRr106kBYI6Uzytrxf6CClhzJkb/PTOfwbiHj0DulazUoeL\nNQqMHuJdVcqxV/fA6CHdwXGczz1meIXAl9RDlxaCsWPH4uuvv8bq1astygAAiMVi/Pvf//Z6YSK4\nREJQIY8wzlSAp6ulDfEYzU1nHPl6IxFGIEDM0KsBuG6kE8nw1o/Y68aGfcps6vSHIbtmpGXbWRGl\nmKHDEHd9LoDI+T3ysQ6MJPDWtdzhPfHw5Ksgk3r3oNW3ZxwGpMX7peEcH0NQt3uX18qbUwtBQUGB\n5fW7777b7v38/HykhHGuLWELFwFBhTx8FLRRGRkXIH/DdjKFAABis3NwZdv7oRYjoBhaTApB8u13\nhlgS1yTcOBEJN07EpfWvofXkb+0aH/EIoqLQ49EnoD5zBjpzSqU9sqwRMKpUNr57zmgMWaVGXiHo\nDO42T+AtBK0nT6Du053oOWOm58fwt1BEeMJaggrD32XAm8r5H7ahuTmoqUShxuIy6EQKgVAuR8zV\nwwEAhqbOaSUwtphcXMIwtw5Y02v2M8hc86rLQkodFcdKeWAaej09x2bM14p5vmBRCJzERfiTAyeq\n8aOHvQis+bboElZtO4yaBt+rs1pnaXlb/t2phSA/Px8AsGjRIqxZs8argxPhA28hiITOX/yNkFWr\noa+vR/niBYgdMxY9/vRYiCULDhYLQSeJIeDhY0PK5j+H9Bf/jOh+/UMskX/RVV0GExUVUU+mArEY\nYrpJa+kAACAASURBVAf9C9rN66AWhlAub/eZWa02ZKmXrFYLRiwOeHpro0KLssoW+FKLaVjfJPTp\nLkeC3Hflhc8yAACBxLvrvMtv7PTp01BRf/qIh087DPcsA8AcyCQUQnPuLC6/WQDOYEDLvp/Cpgpa\noOmMLgPAtkKe0lz7vjOgr6tFxWuvQF9bi6juPfziDw43BB102hRER7e7+fKBfcFGfeY0tBfKg+Ku\n+LH4Mg6duoJbcrwvLNQjKQYD0xIgEfsur7VFhG/s5CkuswwEAgEmTpyIvn37QmK14AcfeNfZymAw\nYMGCBaisrIRIJMKKFSsgFAqxcOFCCAQCDBw4EC+99BIAYOfOndixYweioqIwc+ZMTJgwAVqtFs8/\n/zzq6+shl8vxyiuvIDExEceOHcPq1ashEolw/fXXWywchAk+NzUSsgwYhgGMRrBGo6UbG2AKahLF\nRV7HP0/hFQJhJ1MIrG+UvqRGhRvnF71g6U0fe212iKUJDNZPn/bw51UYF2dxm9gX1woW1e/9zbS+\nOvBVTu8c1xd3jgufJlaMlVXA23ofLhWC559/3qsDO6OwsBAsy+KTTz7B/v37sXHjRuj1esydOxc5\nOTl46aWX8M033+Caa67Btm3b8Nlnn0Gj0SAvLw+5ubnYvn07Bg0ahPz8fOzZswdbtmzB4sWLsWzZ\nMhQUFCAtLQ0zZsxAaWkprrrqKr/KHslwEZR26Ax9XW3XUAg6Wdohj/VFytDSOVJK+a5/PFHdu4dQ\nmsBhX0UUMPUeEcra3AKZK9fgyvaPoPh5v88KAWc0ou6z3Ygfd4NHqYN8h8ZIqe55/Hw9/rW3HLdd\n1wcjB/oWpG/dIdXbbB6XLgOGYRz+85bMzEwYjUZwHAeFQgGRSIQTJ05YUhrHjx+P/fv3o6SkBNnZ\n2RCJRJDL5cjMzERpaSmKioowfvx4y9wDBw5AqVRCr9cjLc1kuhk3bhz279/vtYydEVanAyMSRUzZ\n2G5T7kfM0GFI+sOdECWaIqDZLuK66mxphzwJt0yyvO4s59LQaJveJYqLnIBCT3AUoNd/01+QuWqt\nZVsYI4Okt+ka7GtQYeupUjT+dw/KlyzyTE7zb6bP4qU+re8Ol64ooWjV+XSM9BQ57p3QH/17+/53\nY62EGZoavepp4FKNeuONN9oWMRhw6tQp5OTkYNSoUR4vBgAymQyXLl3C5MmT0dTUhLfeeguHDx+2\neV+pVEKlUiHWqmxmTEyMZVxujniVyWRQKBQ2Y9ZruENKSvvOX4EimGvZc4kzQiCRhFQGT0h58D4A\n9wEAqtN64Nybf0WMwBh0+YOxXv2BX1C6dh2u2bQesow+qDGarDmp6SkQRMiTjlukXI30z3fj5/um\ngtFpAvrdBuvvpKmq3GY7OS0F8gj5jXmCJikO/DNn1rq1iIqPgzS1/U3M0C0edQDkYsbjc2A9XyBt\ne3DplhTjVkwAq9fjtFqN+OFXI+3aYR6t7Sksy2HFB4fRI1mGFx8Z7fVxUlJiMcBPXofEcaNQ93Ec\n9M0tAMchQQKIkzw7By6vNtu2bbPZrqio8Cnr4P3338cNN9yA5557DjU1NZg+fTr0VgEQKpUKcXFx\nkMvlUFqZGK3H+SBHXmnglQj7ue5QWxucXPeUlNigreUIfasGEEWFVAZvaeVMf6aNF6tgPFsBUXwC\nAFNXs4Y9X0LSJwPyrBF+XzdY56zsr1sBlsWZd95Dz5lPo7nkVzBRUahv7JzdHgUxMuiaWwL23Qbz\nt6a4XGez3aIF1BH4G3OF2uphU5PQHRoACgefs1Vvsh431zYBHnwP9uesuaqtj8nlk+chdsMVwxdO\nMkZJgnL+//yQyaodTtfUvuvfQM0H76H5x0LUVtZCbGx/i+9IUfPYfpyeno6ysjJPd7MQHx9veZqP\njY2FwWDA0KFDcfDgQQDAjz/+iOzsbAwfPhxFRUXQ6XRQKBQoKyvDwIEDMXLkSBQWFgIwxSPk5ORA\nLpdDLBajoqICHMdh7969yM7unME93sLqdRBEQA0CR/BlYOt270TZ/OegMQca1n22G/X//AcuvxHZ\npXDF3U0+UtWvJTj79JMAvI8SjgQEMhmMqs7R5Mg+eE0QE1kli93F3ap/fDaCrzEEfNdIADC6WcI8\nEgsS1TWpsebDIvz753K/HZOPPSpfssjS18FdXFoIFi2y9eGcO3cOgwYN8mgRax5++GG8+OKLmDZt\nGgwGA+bPn49hw4ZhyZIl0Ov16N+/PyZPngyGYTB9+nRMnToVHMdh7ty5EIvFyMvLw4IFCzB16lSI\nxWJLg6Xly5dj/vz5YFkWubm5yMrK8lrGzgbHsmBbWyEKcF3vQCGwrgvPcVCfPQtpZl+ofi0JnVB+\nRChvfxOJSu2cwWmAqTWw7pK6U7RCtr5xAZF1M/IE6+DBjuAVB9bHtEOjVYyJuxHzwVQINDoD6lu0\nSJSLEeNl2WIAiJWJcff4fkiO85/M1rFHzT8VIuWeKW7v61IhGD26zT/CMAwmT56MsWPHeihiGzEx\nMdi0aVO7cXvXBABMmTIFU6bYfhipVIrXX3+93dysrCzs2LHDa7k6M4amRnA6XcTeZOyrpOlqqgAA\nbKvpouEoAjqSYHXtA5Myli4PgSTBQWgucsO2tnZYAS8S4C0E3R/+EyTpfSJewXGGu5YPfzTYAWz7\nJrhvIQhedk5VfSve+eIEJlzTC78b3cfr40iihBjcx3HZaG+xVgg8rdbo8q/3rrvuwrBhw6BSqdDU\n1ITU1FSII6C4DdGGrroaACDu0SPEkniH0K5Kmra8HIrDBy0XY06rtXRzjETszc49Z87qtE+aQJvF\npzO4DfiMEEl6BqSZ4ZOT7m/c7YHC/936bCGwapPtbhneYFoI+vaMw+oZ1/mkDPCUXW7BB/8txemK\nJj9IZqsQWKciurWvqwn//Oc/MWvWLFy6dAmXL19Gfn4+Pv30U8+lJEKGvsasEHSPTIXAvkOj5nwZ\nqt7aYnMj5a0FkYi9vzU2x/uo5UiANz8bO0HqIe8y6GwpovYI3CxDbFEIfIwhMDS35dHbu2WcEYkx\nBAAgFQuRnipHnMw/D9rWqa+eKmYu1Yf33nsPu3btQqI5F3zmzJl46KGHcO+993ooJhEqIt1C4E7d\ni7J5zyJz1Vq3opHDDVatBiMSeZU3HIkIok03F9bLBizhRGctM22PpHdvpE5/GNH9B3Q4T2COIeA0\n3rsMjEolNBcuAAwDcBzYVjU4jnN5HQimy6BZqUWr1oDkOCnEUb6VHe7VTYZe3fwXjGrdXMtTK5xL\nCwHLshZlAACSkpI6Za3uzoyu2uRzj4pQC4G7qE+XhloEr2A1akSlpCLp97ejx2MzQi1OwOGfNpt/\nKgyxJL7DtnYNhQAwtUyWpKV3OIexxBB4byFQFh8DjEbE3zgRAND03Teo+9R1fBirNq3JBMFCcOxs\nHd7Y/Ssu1IRPyiGPtYXAUF/fwcz2uFQIBg8ejFWrVuHUqVM4deoUVq1aRSWBIwx9XR2E8tiQdR8L\nFrqamlCL4BEcy6Li1TUwKhQQREvR7e57ETf2+lCLFXCEZguBsugwdGZ3VqRiVLeCEYkgiOCS4P6E\nEYkAodCnoEJ9rSlVLmbIEMtY41f/dbkfr4QEw2Vw4zW9sWbGdRiYluDzsTQ6A7b97xS+PlzhB8kA\nUUIC0ua9AMB07fcElwrBypUrERUVhRdffBGLFi2CSCSyNB8iwhtd7RW0/LwPxpbmiOrR7i36CFMI\ntBcvQn36FACA1UVuUKSnWGeFGBoaOpgZ3nAcB/2VKxYXCGFy7wkkEp9iCAxNpvgBcY9eNuOulIw2\nl0FkxRCIhAL0SpYhLcV/GTcxQ4ZCkt4H+vo6j7rEuowhkEqleOGFF3wSjggN5S8usDReEUW4QtBr\nznNQFR+D4uABm2DCxEm3gjMa0PTdt9BdiSyFwPrp2DrNqrNjXSBL3+CZSTOcaP7+24gOZg0UAonU\nJ5eBocmUYRCVnGQzrrlQjphBg53u1xZUGHj3zZXGVrAckJoYDYGPLnSRUICbs71voez0uN26QVtx\nEUalAqJY9yr3urQQ7Ny5E2PHjsWQIUMwZMgQXHXVVRhiZcohwhgrzTDSLQTyrBHoPv1hcKyttiuM\ni0fq1OkQ9/5/9s46Pu76/uPP01xO4u5ppG2kQt2ghhaXAoUCQ4YMGDBs+zGGbR0wxmCDoRujuLS4\nFEqpUNdU0jZt4y4XObfv74/LXe5id0kuUsjz8eijl+995XN3X3l/3vJ6J2E7yR4unqVVIbPnDONI\nhhZlbj7qKU7Z15PZQ6Bd+x0AoaeeNswjGVmIFQpsDQ39DgfZ21oRyeVdHuym48d73c6VQzAUHoJv\nt5fzjw/34XD4P/seamThToOqL50PfXoIXnrpJd58802ysrL6P7JRhpzObqKfSxc2odPMw2F2eguk\noaFYKiuoeOZpEu74LeKTQCvD1uo0CCLOu4DI8y4Y5tEMHSKRiMgLLkK3a2eXboEnE66GOzFXXzvM\nIxlZCA4HACX/9yDZr73R5+3tbW1I1F319j1LEbvDHTIYggTP5Wf27KnoD5//VIxWZ+GaAO7X9T30\nJXzj00MQGRk5agychHQu6ZL42exppKPI9D4XBauzVM/V8MhQeJC27VuHfFz9wd5uEITMnvOzVbjr\nCVmEc/Zi7WMW9GDjapjVtmuHz3Vtra3I4xN+cb+dL6wDTBS169qQaLoaBK6Kjp5whwxOQuXS+EgV\nWUmBnbT1RxOiRw/BJ598AkBCQgK33norixYtQurRivXCCy/s7zhHGQJcDxsXJ3sOgYvE39yJ8fgx\nZJGR1L3/LuFnnQ14h0TMlZXDNbw+YWsPGfxcvDd9QawIRqxUjjgPQd1b/6Nlw3pEcjmaKT23eBds\nNhw6HZLEwMd+f044rNY+VWA4zGYEi6VbSWuXB6DHbY1GxArFoBtoL316gIPFTdywJIdJWVEB2efU\ncTEB2Y8nLoNACIRBsG3bNsDZe0CpVLJr1y6v90cNgpGNrdVb/1sWHfgTbjiQaDSoJ00GIPneBzqW\nezRAslSdHAaBvbUFsUJxUs5oAoE0IhJLdRXmygqCRsiDtW3XTgCEbvpLeGJtN2Sk4YHVof+54TAa\n+2QQ2HXO5FqXhyD1kcex1FR3USbt9lgm45CEC/LSI9leWMe2wtqAGQSDQUA9BCtWrBj4iEYZNjwT\n1uDklS32F892wX0V4xgubG3du0Z/KcgiIrBUlFP6p4dI+t39KMfnDOt4HFYrDpecskiEqaSYoNS0\nboXYXCWuP/frqj9EXnARjZ+uBlzaAP6HK13VNpL2rPigpGSCkpKpkb3q7hvRGYfVQuvmzVjr65En\nJHS7TiCZOyGeWXmxA64u8GRTQTWHy7QsXZhJiDIw+U/9MQhGg18/Uzw9BGGnn3nSd5XzRbBHOZK1\nqbFPtbfDhWA2n3Q104FEGhHpft22Y/swjsSJVza2IFD2xKNo13QVxDEWFVH17xcAkEWO3BnicBF5\n3gWELVwM9M1dDR0GgbSToSxWBGOpru5Wi6D2jf9St/KN9vWG5nqSiMUBVeyNClUwPjUcmSRwj2RX\nlYavUIvXNgE7+igjClcOQdL9vyfm8iuHeTSDj3LceNKe+CvK3DwEiwVhgO1XBxtBEHCYzYjkv8xw\nAXQkFgKYKwKj0jYQXO5qT9q2b3O/dj2Myp/8s7vaRawOnAb9z4n+Njmyu5tFeYs9iYLkCGYTJ+6/\nx13F4KJt2xb3a8Fm789w/aa4upVXPz/IoZLA5r6MSw1nTn48wUF9607YG6MeglHcuDwEv6SENXlc\nnLuaorub+0ii+uV/g8Pxi80fAO/KF9OJ4+4ky+HC0U33RVdzmPqPP+T4b39D84Yfvd7v3Jp7FCf9\nNQhcTZHECu/rwtX4y6HXd6mr9wwT2AdZKCpUJScnLSJgnQkHE9dvYPezWyT4oUOwceNGnn32WVpb\nWxEEwd11au3atf0f6SgBw9rUhG7nDsIWLkIklWIqK6XiqRXuC/HnUm7oL64bdNvOHYSfefaIbcSl\n2+l0kQ+0TezJjGciKDgFaYazGqa7znC2hga0369B+/WXANS9+YbX+6MGQfe4DQIfiYCdcVicBkFn\nz5lnJ1BrfR2yyI5wk+Ah+z3YHTQjQhTMyY8P+H4PlTSx5UANC6ckkR4fmHu2qD1k0LpxA7FXX+vW\nzegNnwbBE088wYMPPkhWVtaIvbkGCsFmczbnOImo/d9/MBw8gMNsIvK8C2hZv87rIfNL6MLmiStX\nouGjDxDJZIQvOn2YR9QVW3Oz+7W1vn4YRzK8iIK84719fXgEGrvOObvs3Iq6/r13etzm556b01/E\n7d9LX9vvdsgPd8oF8Pg9XL0O3Nt4iJV1NghqmwxY7Y6A9gkYDEKUcrJTwgKWUAidvkM/n90+Qwbh\n4eEsWLCApKQkEhMT3f9+bhiOHqHoNzdjOHJytdA1V1YA0LplM9BVE//nbsR1xvMGrS/YN4wj6RmD\nR5tme1trL2v+vOl8bpb/7UmMRUeHaTTOsAVAwu2/RZmXT9jCRT63EatGcwi6w6Wd37n82Reuck9x\nJw+BZmZHF1B7a6uXweYwm90Tn5irlntt9/qXhXyw7lifxtAb2wtrefXzQ9Q2BdYTkRSjZt6EBCJD\nA5cU6RmO9Febwed0eMqUKaxYsYJ58+YR5HGAadN6Fu04GRHMZrDbMRYdRTn25Gnv7Lqp2rRNNH39\nJbrdu3xs8fPGU/LUUl3lrE0egmYnfcEzBhq99IphHMnwEpyVjWriJASbDcPBA2C3U/7UCjJfeHnI\npacFux39wf1IwyNQ5uahysvHUl9H8w/O0GhQWjrhZ5xJzSsveW33SzO4/aW/uTwdHgJvgyDqokuQ\nhobS+Olq6j94j+b160j/85MIDgeCxYJi7DiS73uwy/7mT07AbHV0Wd5f4iKUWNMcAU3+GyxEYjFh\ni09HFhPr9zY+P1VBQQEAhw4d6jiQSMSbb77ZjyGOXGTt9cQnU392QRDcFrhgtdLw8YfDPKLhxzNE\nYmtq4tjtt5J0/+977ZI21Li8OMkP/h/Bmb9cWXCRVEriHXfRtnOH0yAAEATqP3iP6Muv7JOgzUAx\nFh3FodejmTrd/ZCXR8eQ9ep/EWxWxDI5DqsVWWws6gmTkMXHu+WyR+mKKz/EoetjyKCHHAKJSoVq\n4iS3voFLB8IVIugpNDo7L7Dx/pRYDSmxgdcOqdUa+GJzCbnpEczMCZy2RcwVV/VpfZ8GwcqVK/s9\nmJMJVzKTvY8uruHEYTSAvfsyG2lUFOGLzxziEQ0/3dUht2xcP7IMgk5qbL90PBPEAFp+/AGRWETM\nsuU9bBE4TKUlSMPC0e3ZDeDuwuhCJBIhkjm9FWKZjPQ/PznoY/o54E4q7GP5r6OHKgPoWjFla2ul\nZcN6r+N1pr7ZyLo9lWQnhzEpc+RqRgQHSclIDB32XAefBsHOnTt5/fXXMRgMztpph4Oqqip++OGH\noRjfkCGSyxFJpYOepRpI7K09uONEIsb89W9DO5gRQnBGJrHX3YCp+AQt69cBw5+s5sJu0GM4dNBt\ndI5mqDuRRUV3WWYoLBz049r1esoefwSJRuN0q0okBGdlD/pxfwm4Zvh9NQiEdg9Bd+W40rAwZ3Jc\nu+hYy/ofafxkFeCdqOtCZ7Sycs0RWnQWpgWoV8B3O8oprW1j2eJslIrAhQ1ClHLmTxr+3Dyfn+ih\nhx7ipptuYvXq1SxfvpwNGzaQkzO8EqODgUgkQqxUDnodayBxJaSJ5HJv7fWTQKVvMAmdOw+RWOw2\nCPzNsB1stN98TdNXX7j//iWrFHoi0WiIvvIqEKD+vbedCwf5J2vbtQPDoYOAM4QjWK3Io2OGNEzx\nc0YkFiOSyzGVFGOprUUe618c25VD0KNgl4dBYC4vcy/uTohMLBIxKTOKUJWc1AC5+VPjNAQHSZFJ\nB+8EdQhCQGWR+4LP1EOFQsEll1zC9OnTCQkJ4YknnmDHDt+tQU9GJEoVDv3J4yGwtasRdpZPDZk1\nZziGM6KQhHnEdz0ykocTz5a6Iqn0pCtxHUzCF52OZqqnu35wb4jV/36BlvU/uv92mEze58woA0Yc\nFIRgNlPyfw/4Xrkdh8Xi9Nb2lBXvoVLoWUUUs/zaLqsqFVIWnpJEZYOeG59ax/++OczxyoGJX2Un\nhzF3Qjwyqe+a/r5ysKSJXz/9I99sK/O98iDh844UFBREc3Mz6enp7Nu3j1mzZmE4idzqfUGsUmGp\nq3WLL410rA0NAKhPmYJ2TT3RV1xFcGYmspifR2fDgeCZ8GUfIeerZ6mUaNQ70AWxRwhlMD119h4S\n3ToLJY0yMASP/CZ/NV4Ek6lLyWGP67Y3NEv4zR0oUlJ7XO/8OenkpEWwdlcFxdWtCEBm4shTcB2b\nHMYLd88bFGPDX3z+Qtdddx133303//znP7n00kv5/PPPycvLG4qxDTkSlQocDsylpSjS0oZ7OD5x\nxVlD5y8k8oKLBr0P+MmENLzDIOhOknaoEQTBK2HV35veLwlPd729rQ3B4RiUc9qm7V6HfjSnI7B4\n5mNZGxv86gzpsJgRdZNQ2BvypORul5fWtLGhoIrp42IYmxJOWpyGx97Yid5k67dB8MEPxzDb7Cw/\nI/BJytIANjbqLz5HcPbZZ/Of//wHtVrNqlWrePrpp3n66aeHYmxDjqj9hlT2xCPDOg5/EAQBc2kx\n0shIZOHho8ZAJyRKFamPPD5i8kIcer1Xi+busqgHgs3uoLJBT13zyEig7C9J9//e6bq32/uckNYT\ngsNB3btvu0M2PXmMRkWGBg9Le5mgLxxGI+Kgnr1nyQ/8AWXeBGSunASRCFl4RLfrBiukJESqUMid\n8971e6uYkx/Hklk9exN8MS41nKykwfUuOBzDlwPm8ynS0tLCH//4R6655hrMZjMrV66krW1kN47p\nL5bqKvfrntyKIwVbczP2tjYUKWnDPZQRS1BSMrKY2BFROdKlwYgocAacIAgcLG7iXx8XBLwL21Cj\nzB6Lcux4IHC69MaiozSv/Y7q9pbFrv0q8/JJ+M0d7vWkv7C+H0OJtcG3RLfDZMRhNCIND+9xneCs\nbJLuusedNyVRq3sMRcSEBbNoShKpcc6EwlCVnGadGYm4/+HgCRmRAdUJ8MRitXPL337k+Y8LBmX/\n/uDzrvTHP/6R/Px8mpubUalUxMTEcN999w3F2IYcqYel6ZnBOhLR73XWTQel9t/a/SUgUakQbDYc\nnlUYw0Dn0kchQImOe4rqueHJdTz3UQFp8SEjonRpoIiVzta3jj50aeuNzmJjLo+RZup01JOnuJd7\nqlyOElj8Me70+/cD3m2xe8IlRNSbN6EzU8fFsGhKEvuON9KqH977QXfIpGKeu3Mev710wrCNwadB\nUFFRweWXX45YLEYul3P33XdTUzMwNb9XXnmFK664gksuuYSPP/6YsrIyli1bxtVXX82jjz7qXu+D\nDz7gkksu4YorruDHH38EwGw2c+edd3LVVVdx8803o22Xgd27dy9Lly5l2bJl/Otf/+rXuOJ+dSNB\naekAmMpKB/QZB5vWLT+BWEzI7LnDPZQRjcT1cBnmsEGXroY9CEr1ldy0CO5eOpGHr5vKDUvGB2Sf\nw42k/WYfqGRQT7U8h8nkriRyGR7B7aJVo8m4gSXinHPdr/3p6unqJSGL9l2i6Aq/iXppH77naD1v\nrTlCrbbjPCoqb+HHPZU06/oejnIIAi9+coCvtg7Os0EkEhEklwxrQrtPg0AikdDW1uYeZElJCeIB\nxKu3b9/Onj17eO+991i5ciXV1dWsWLGCe+65h7feeguHw8H3339PQ0MDK1eu5P333+e1117jmWee\nwWq18u6775Kdnc3bb7/NBRdcwIsvvgjAI488wt///nfeeecdCgoKOHy4702KpGFhxN90CwDmEW4Q\nmCurCEpM9Mua/iUjVjrjwvZhTixs/OwTr7+FABkEcpmE/DGRpMWF0NBiorj65FHa7Am3hyBABoFn\nw6/i/3uQ+g/eBToa8CTccRdJ9z1IcEZmQI43ipPICy8m6f7fA/4ZBK4wrcaPPjkuyfbe1D7DNEHE\nRSiRe2Ttz8qL467LJvZPfliAqWOjSYkd3ORTuyNwvRf6is8n+5133sny5cupqqritttuY9myZdx1\n1139PuCmTZvIzs7mtttu49Zbb2X+/PkcOnSIqe01yKeeeiqbN2+moKCAKVOmIJVKUavVpKWlcfjw\nYXbt2sWpp57qXnfr1q3odDqsVitJSUkAzJ07l82bN/drfLLoaEQyGcajR9Cu+SZgrt1A4rBaEMwm\nJJrRmKcvJJr2NqzDmBNiqa/DeNhbeS9Q55XgIUL13toiPt1UHJD9DicdIYPAJEjaPBrs2Fs6FO0k\nYc7kMElw8EnV0OxkQSQWu2WpHSbfv6Wt3XDzJ3SjmTYdgNA5PXtI0+NDWDw1mXBNYBJ4xWIR08fH\nkpce6XvlfvLXt3Zx+7MbB23/vvBZdjhv3jxyc3MpKCjAbrfz2GOPERXVf01orVZLVVUVL7/8MuXl\n5dx66604PCwilUqFTqdDr9ej8bD+lEqle7m6vcWtSqWira3Na5lreUVFRb/GJxKLkYaFYa2vp/6D\n9xArggk99bR+ftrBwd7mfLiNxjx94/qOtGu+QTEmY8BKdIIgoNuxneDssU4pVT8wFZ9wv1aOz8FQ\neIjg7IFJ5BaWaglRyXnifzsZnxrOnZdO4K7LJg5onyaLjRadhdgI5YD2M1Akwc7jd0nE7AeW+jra\ntnQ/OeisjT9K4HHF+P3yELS2OAW7egkDuAg//UxU+RMJSkjo03jMVjtHyppRB8uIjQhGKhYTJPev\n7v+vb+/GarPzx2sHr9Pv766YjFQyfCEDnwZBU1MTX375JS0tToWnwvba99tvv71fBwwLCyMjIwOp\nVEp6ejpBQUHUepSk6PV6QkJCUKvV6DxmdZ7L9e3uX5fR4DIiOq/rD9HRXR+q1VGRWOvbs2JrQdf2\n1wAAIABJREFUK7pdpz8Eaj+6tnZBopiIgO3zZ0tCDPWAft9eDN9+Tvqvuiqa9Ybn9yvY7dSu/YHq\nV14idEI+eY8/4tc+THrntRN3zlmMuekGGjb9RPjUqUiV/WvLLAgCa1cfIEwTxNuPn43JbCNU3f1N\n1GK1I5f5d8O77/kNWO0OVtw2d1jbu0rjI6kGgkX2fp/fru1Kvlzd5b3YMxajiI0lNmnkNrv5ueAI\nD+Y4IHXYev0trW1tmCvKUWdkEBPjp+cztneD7otNJ6is13HdubkEtV8D2jYTn2wqpqQ9tPbITTOZ\nMs4/WeU/3TQLvdFKdNTPtzzV51V/0003kZ2dTWJiYLKXp0yZwsqVK7nuuuuora3FaDQyc+ZMtm/f\nzvTp09mwYQMzZ84kPz+fZ599FovFgtls5sSJE2RlZTF58mTWr19Pfn4+69evZ+rUqajVauRyOeXl\n5SQlJbFp0ya/DZb6+q4llIKyw9vQVlHd7Tp9JTpaE5D9GI4ecUt2WiRBAdnnzxm90HGKNx0oRN2H\n76vzb1b96ku0bdsKQEvBfmrL6/3qR9Bc6vRWKWafRkOjHsZPQqu3gb7/v93tF+Xx3toiHnh+Aw9e\nPZlH1j9JiiqVGaELSYpWIwjw6BvbyUgI5dfn53ptu/lANTNyYpG05wLtO9bAxMwobjhnPEqFFF2r\nEc8Ay4mmCt4+9AnLcs8nIzyl32P2F2N7Anj1mrXIZsxFouzbDdjzd2urdRrPqkmT0e/dA4BizgKC\nEhNHr50hQiSVYmrV9fp9q4zN4HAgSUgK2O+ilInRKKQ0Neq8RH+uP3scqzeeYEZOLCmRyj4dT0r3\nz4xA4hAERDBoyYW9GWZ+TQNWrFgRsMHMnz+fnTt3cumllyIIAo888giJiYk89NBDWK1WMjIyOOus\nsxCJRCxfvpxly5YhCAL33HMPcrmcK6+8kgceeIBly5Yhl8t55plnAHj00Ue59957cTgczJkzhwkT\n+l+6IZJ0zKhc8sAjAYfVSsVTHb/FaMjAN15JRwNs+uQyBlyU/PEPJD/w+2679Xlira8HkQhp5MBj\nj6s2nOCLzSXERii5aF46k8aF8sahd6nUVeMwKindfYwls9LIHxPBby+dSHykt/vfZnfw454qiiq0\nXHtWDmaLnec/LuDm83OZPr77mdJ7hZ9TYylj1aG13DfnVwP+DL5wVYZY6+soe/xR0lc81e99WZuc\nugzRS690GwT+hnpGCQxiRTCCj5CBrT2B1FVOGAjyx0SSP6brNZcUo+aOS4avtK833l5zlB/2VPDE\njTOIjxx6T4RPg2Dx4sV8+OGHzJw5E4nHgzKhj7EbT+69994uy1auXNll2WWXXcZll13mtUyhUPDc\nc891WXfChAm8//77/R6TJ54JaK6OgsONpbbGqxkLdCTMjdIznomX9tb+NzYRujEmbNomKp//B2mP\n/bnXba0N9UjDwhDL5P0+vouZObHYHQ4mZkTRrDPz3I+fII4/BkC1/TinTY+j4Jia8jodZ0xL6rK9\nVCIma0YF26r3sOnIVcwdm8VLv5uP0eJMcmxsMVHVqPe6kQYrRGCGu2ctH/D4/UHiEdu31tcNSMLY\npm1CoglBFt1htLmSFkcZGsQKBQ5z7waBfRAMgp44VtmCts3MhDGRmK12QlS9X5dmu4W9VUW8s7qJ\nUycmcMlpGYM2tssXZXLl6VnD1u3Qp0HQ1tbGK6+8QriHepRIJGLt2rWDOrDhJGzhYndrVIfB4Hdj\njsHC1txMyf892GW5ZwOf4Wb93kqKKlq4YG46DoeAKliGOnj4W8lKPJJNrQ0NOKzWfiUW2poau11u\nqar0+cCy63R+6bj7Q0KUisvmO8vjBEEgf8yveHL3s9QbnZ6sPXX7OSNmChsLi/na+CJnJi/mnDGL\nsdkd7ryAktZyjHYDb277kfpaCRedOoaC4828sW4nEpuSVqOJmXkx/PrsUwBotbaikamRiofmGuj8\nULBUVxPUj5ClIAjYtFrkcfGIRCLCFp+OYLGM2MZldc1G3vu+iMnZUXy3o5yKej2Xzs+godnIxadl\njIjrqT+IFArsjb17Wl0GgSSABsFba44QEaLgnJne4m2f/VSMttXMG18XMj41gtsvzu91P9+UrGVP\nXQGPXn/XoJ87DmysLd2EyW7m9JTTUMqG1nj1eYWvWbOGLVu2oPgFdWdTT5pM5r9eouaN19Ht3IFd\nrxu2h6/DYuHEvd5lnuqp01FmZ6MYQN203mTFbLETERKY33V8ajgfrjvO5gM1SMQi7lk6kXGp4djs\njmHt3iWWyZCGh2PTakEQsNbX9zkzGaDVI1M94pxzafrqC/ffNq3WXV7VGcFmQzCbB6WTnkgkIjhI\nxv1T76C0rZzPjn1Dha6KnLRwNIkN/PcgrKn4nk9XSQnXBPHMb+bQ1Gri9JhzebX1RUIjrOwpauCC\nuemoNQKO7B/JjcylUlfFPrOWGm02326voE6iJTo4CqvNjkQsRjwA6Vd/P5cn5tKSfhkEDr0ewWJB\n2q7VEXPFVQEZ32ChVsgI1wTx368OE6KSs3hqEnKpGKlUjNU2fLXp/tDUamJ7YR0Wq50ZObFelSpi\nhQKHydRrF1mbwVmWGCgPwVdbS9leWMfSBV3vkZeelkFjq4lJmVF+PeArddXUGxuRB9kH/QHdZNLy\n2YlvAJgRd8qQGwQ+/XDJycnuCoNfEmKFwh1/tg9j7wbtmm+6LAuZOYuwhYsH1NDocKmWx97YwZYD\nA1OddBETruSpW2dz12UTuO3CPP737RFueHIdr39Z6HvjQWbM088SdelSAKy1/fu8lipnn4u0Pz9J\n6IJF3u/1sE+HyUjVC88DgWmco20zs+KtXXy3o9xruVIWzPiIbKQE48DBT4UVjIvIcr9/2gJ47Ean\nzsfRimY++q4aESLi4mFmbiy3/X09NTrnDC4iOIwms1P987k9L1Or2IFgl1JTI/CbZzdQP0TNk9L/\n+jQxV18DOEsH+4O13atzsoh3tdqbiMqq5HfXp/LwjbnIUgpRJdSwbHE22wtrOV7Vgs6qp6Kto+fK\ntyU/8NBPf6Gw6aiz4Zl96CV59SYrz31UwAfrjlFWp+N4lffzQqxQgCAg9CIf3hEyCMwDMC1Ow9kz\nU5iT39UzlxKrYXJWtN+z/ahgp7HfYBq8PiHvH/mEJ3c8z+4jHZ7Ib0vXsat236Adszt8eghEIhFL\nliwhKysLmYer9c033xzUgY0EXEl7w2UQWGpraPxkVZflgYiBThkbQ256BHuLGjhSpmVsSs8NRfzB\n7rBzuPUQeWnjECNFo5RT12zgaHkzR8ubSYhSDavL0+Wyt/RTdtvWnn8gjYjwSjqFnnMTWjZtRL/f\n2agkEB4ClULKhfPSaBMacQgOxJ0aJMVoQjmhA7lax/vfljIzZRZbG7awXf8NusIT/GbSjczMiWNm\nThwPb15Pg7GRc+amsmByIn/Y8ggADsE5E41VRmMw2mihmhfOeAyb3YFUIh4yd7ssKhrluBwAtN98\nReicuT6TNztja08olIYPnpBMIChrq+DNfZ/Q3CTBqC4mJjiKOmOHi10jimR/RRVSaTIvHHgbi7SF\nP874HbGqGLbW7ERrbmZV0RfYBTs2h43HZv9+SMdf3eB8mF9z1lh+Kqhmb1EDs/Pi3e+L23UFHCaT\n+3Vn3AZBgDzROWkR5KT1bgi26Mys3ljM1HHRXcSGth2qRSwWkRSt4nixFWSw4oNN3LnoDMb72G9/\nSNEksqFyM2W84V62vWY3comcKbED0xfpCz4NgltuuWUoxjEicXkIKp55isx/vzpgUZu+Yqmudr8O\nHjeeyHPPR7d7J8GZWb1s5T9NrWb2HW9kRg/Z5f5S22TghQ1f0hiyg8kxE7g+dxn7jBtYmHY6BpON\n99YWIZdJePCqUwIy7v4gi3F+RlNpSa+uy56wt7YgVqrc50Dib+/BeKyIpi8/79FgtGk7ZhQS1cAT\nQOUyCbqgMv536F32tuXx6/xrvN7XyJzH+LrxfcJM01kQEgvtz5VDTUd54M0vaTWZuHjydGKUURQ2\nHeWVgv+xdOyFWBzO2duWqu0AJKkTOGQ8js0hQiQSIZNK0Fn1NJtaSNJ0hFwqG/Q068zkDsJN0uXq\nF6xWSh99mMx//rtP29va+5xIIwZm7A423xdtp9pSBu2niKcxALCnopjisG/RSPIxS51Ki49vewaF\nVMHy8ZfxduFH1BnqSVQnUGGs4nBTEYebilBIFZS1lnNT/jWDashlJoXy6PVO5cDummuJFc4wQG/t\nrAejysAXL392kMNlzaTHe1drbarcyraGYsLbJlFU0czxEitBWXDW3CgiI0XYHLaA59M46AgJ3TX5\nFvRWPa8eWEmLeWi98z4/1fTp04diHCMSqUdZn7msdMi1zm3toZq4G24iZNYcAJTjAtPApqnVhFop\n49fn5VDXbOSNrwu5fGFWvwRpQlRyQuJaaDTAocbDNJm0/FC+EZVMRWZmOjKNmnExqWw+UI3Z6mDB\n5KHvyOcy7nQ7t6NNTSXi7CV92t7W2oo0tCP7XZU/AVFQUK8GgbWxwyAIVGZ7td4p4rWv/gAfHP2E\nY83FPDjtt4hFYqbGTqK0tZyjzcdJHWtgRsJkwoNDeOPguzhwoEtajxjIy17IJNlFfHzsc/Y1HGRf\nw0FOS5pDeVslZ6Ut5P0jq1mUtIA9dftJVDt/qwMNhfy74L8AXJp1PllBk/hiSwn1zSZSYtWDYhCI\n5R3Z3/2RMba3SxaPVEXCijodj76xA3FaGdIoyAoZS1HrkS7r6U3ORj5isbPSJVWTjNFupM7QgM0q\nJkQWTrWtkmpDLXbBzrel6ziqPUaiOp5KXTU6qx6NfHAqkrRtZj7/qZiLT8tg/d5K9hY1cP+yyV55\nQx0egp5/Q7s7h2Dg10lpTRvfbC9jbn48uek9n5f3LzsFvclKq96CwyEgFouo0xr4YO+P2IObmJ2i\nYoxqPBMmTeLfBXtYU7mGNZVrWJg8D6U0mLPTFw94rC5SNMnt/yeRGZbuzL0Sy2g2D22VW+Casv8M\nUWR0lJdYqqt6WTPw2A0GDIXOSgfJICQ0fvZTCQ+9ug1BgE0F1YAIu0PAYOq7xn5wkJQwtdPVZ7Zb\neHHff9zvPbv733xQ+V8+3VrIJzsLGK4Eb4nHA7nh4w/7tK1gs+HQ6ZB0Ur90h5R03RsEDs+GSgOT\nQADgp/3VbDrs7AiXGzmO9RWbqdRVs6rImeCYpEngjsk3ESwNplJXjUauZkrsRC7JvMBrP1+UfUGY\nIpQrx17sXjYxKpffTbmN3MhxPDb79xQe1+PAQRDO781m6nDlflz0OfuL69heWEdxdStLZqXyz48L\nOFrezEjCrneWDwcifyNQFBxv4KVPD2AwWYkOC+am83JITXR6nRRy79vxDXlXc1HmEvZbfwCgrM55\nnuWHT8RksaEQKynYK6LmuPP+4Lq0pGLnw9hVKttk0g7a59l6sIYN+6rZf7wRu0PAZLVTVqvzSoJ0\nhQGEHjwEbbt3UbfW+RnFwQMPGYRpgpgwJtKvEOX7Pxzj+Y/3u8tuwzUKgmXOMWyu38TKklf55NhX\nzI+fz7TYyQD8UL6Rb0rWusNr/cUhOHh+zyu8VPBfEtVx3Dnp1yyMXsKNT63js59KCAsKoXmkeQh+\nycgio4i/+TaqX37R3T9gKBBsNoofvNfd7W0wKhyuO3scV52ezZHyZlQKGfljInny7d0EyZ2ufU9l\nL1+s3nCC6YlnM3ZsBseai9lZuxcApbTD/bcv6H1IgfDEJGDoPQSdy0btBr3fCniuzmqeHgIAabvX\noWXDeoKzxhIya7b3MTy0+H3VYftDTloETaLJ7GrVu8sMAZotHbMIsUhMekgKh5qO0GbRoZGryY3K\nJlh2OSKRiP8deo+99fuBKwkN6jBwwhXe51j+WDWfb4eEUOcMKy8+hZjC8dRJCrEbVKyv383ccy1c\nkX0RbXqBslode4sayE4evGqcvpaMujpcDkaFR39xCDAuNRyHABKRwJTsaL7ZYUQpVXK8thGRRMQj\nsx5AhIjIYGeoY/WxLwGYlpRDlTGE7zdpMSY3EyFO4OozsrlOMo5GYxMHGg/zYdGn7t4wrgTDNsvg\n3bsWTUnicFkzH60/zp9vmoHV5uCt745y5yUT3E2FXAZBT9dA9Yv/dL+WBMBDEKqSMyvPvzLf68/x\n9rjKpGImJCey2WMC6HCI+PpTBZk5gjusYxPstJhbu1w3feF4cwlHtE4NkXpDA2MjMnGEC7x63xjE\nYhEHtopos+io1FWTqI73sbfAMGoQ+MAVx+xpFjgYmEpLvFq/dn4QBQqZVExpTRvNOjOpcRruvWKS\n83h9MAYAjBYb63ZX8NvLZjEvcRYFDYew2C3umYonWyp301IVTnp8CMkxwyesZGts9NsgcHUq7JzU\n5jnzrHn9FTTTZ3glHLpcpCKZjNA58wY0Xm2bmSNlWqYlTOK8PKfh8Xbhh2yu3kGc0ntcKZpEDjUd\n4avi77l87IVEKyOJVkZid9jZVr0LAQFZewz0sVkPcqKllBilt66/WCQmPyqHFI1T3EgqkfCn050q\nhUUVzfzj6F/YVQfxqljUbeM4a0YKi6Z0FUIaKDFXLafubadoWevmn1Dl5HqJDPWGy0Mj7qP08WBx\noqqVmkYDepOVO5/biDikkYi8QvQ2HSmaJGIUY1GoLEQFe7u5b5t4PUe1xzkvYz4ikYiKjCpW7PiJ\n7Ogkt2v+hQ9OYA9uhmhoszoNgMb2rPhWy+Ddu+QyCXcv7Uh6u+S0jC7CPS6DQPvtN6jyvBUChU6t\nfodT78XFBZnn4BAEmowtHG0pwmgU+PV5OYREmnnh0Hr3elpzc78MAkEQePfIKqIUEahlKnRWPU2m\nZmJVMU5BonZXz0151/B92XoEQeCVgv9xSuxEpsZOCtTH7Jbh//ZHOC6lu6GsNLDW1Xr9HWiX5xeb\nS5BJxSyemsRZM5za9P/5qpCDxU08ecssSmpa+XFPFZcvzPTKKVi55gg6g5Xrzh7H4VItk7OdN+Y5\nefHY7B0X9p9m3kelroaciGyywzPZXLWdjLB0/rP/HQ7VFlNRXs61Z40N6Gfyh9TH/kzTl1/Qtm0L\nlpoaZDGxPWY9e9Ky0XkTCJ3n3fWyc9mnpaqKoORk998OoxFZbCzpf35ywGPXG63sPdaARCImrr3G\ne2n2haSFpjAzbqrXuqclz8FgMzE7wbsrm0Qs4Y7JN3ktiwyOIDK4a5w1XhXLLROu63YsWUlh3B58\nI//a9xpfFH9LnkVFdnTgjQGAsAWLEOx26t97h7qVbwCQ/dobfm1r1+tBLA5Y5vpACVPLqWrQMzkr\nilMnxnPQWILepuPWCb8iN3Jcj4l/uZHjyI3saM9sdVhJ0SSRqE6gqkGPKljG0vkZbCgIpvrYRBYs\nmsZbutfc67eYh7dnQ3CW81o3FB7q8l5PSrBWmx2RSNTnyQnAl1tKqGowsPzMbBTy3h9xrg6fGqUM\npULGG18XUtVg4J7LL8YmMvPekdWcnnIaqSFxmGzeIY+y1kqS1InIJX1LNm80afmpahsAV469mHeP\nrOKNQ+/y5Lw/AU6Dwe4QSFDHcU3O5RxqPMK+hoMkawbfszpqEPjApXQ3VB4CU0kJpmJnT/vQ+QsJ\nzsoOeIZwSqyao+Ut7uY2AGfPSGHpgkz0Jht7ixrYd7yB06clkxgkpbbJwNpdFazbXcnM3FgOl2n5\ndnsZ/1y1n9z0CO64ON+ro15YUChhQU6vRlRwBOdnnAVAuDyCKlsV156VTVbS0As9BSUkosrLp23b\nFqpffhFZbCypjzzuU1LY1tqCJCSk25lp1KVLafjoAwDMFeVdDAJpWGAy3JNi1NxyQZ7XMplExpyE\nGV3WDZFruHzshQE5bk+MCUtzv775zOmIEKEzWvl2exnKICmnT0vu1828Ozon0vpbJeLQ65GoVCNG\nmTAiRMH1S5yfZXJ2NC/s2sGhFkhQx/VpjBHSeJalXk9jq4mHXttGiEpOdKiC8WnhZOtz2LvfwpU5\nN6FWyLEHNQ/ag6RVb+Gh17YxOy+OKxY5K58MJht1zQbC1UHuDpxBycmIpFKnSFcn1Vfjka5JlAB7\nihp47YtCblgynhk5fauCGpcSTpg6yK/zb29RA59sLOaKxVlMyoyiPmodZo0RuXQyCrGKG/Oudq+r\nkAbxxOw/cKjpCO8c/pgPiz6lpLWc63KvAODHip+w2q2cnjq/12O6wn1RwZFMjM5jY+VWTolxek5s\ndgc3/+1H8tIj3Z6XxvYckAjF4FfLjBoEPhAHB4NE4tXfYLCwNTdTtuJxsNsBCJk9l+AxYwJ+nAkZ\nUUzI8HYRuxppPP3uHqx2B3//zRz3Teqfq/ZjNNt46JqpfL+rnNe/KOTp22aj1Rn4tnQdX5dVc3ba\nIuSS3h+sSaExVJsqCY8UOFrezL5jDVw6P2NIb9hSDwlua20t5vJygsf0rk1u1+l6DNtEnHUO8tg4\nql54HltrC207t6PKm+C8AVosQ1pGNZQESeRckHE2wVIFYpGYVz47yL7jjSxbnEVlg55dR+rJSAgh\nPCTIy/DsD7LoGK+/bQ0NfoUN7Hr9iAkXGM02jle2MC41nA+LVrOpfYYITgOuL3y7vYxvtpVx12UT\nufHc8UwbF4tM2vEdC4LAy58dpLpR6y4HHAw0ShlP3DgDu6MjY/ZYZTOrNpzg/DnpnJLd8RupJk5C\nt2sndoMBqUdyrqn4BABR8+YQPG+he/n08bHkj4mkvE7X5zLhjMRQMhL9C7POzI1jZq4z38BoM6E1\nNyIVSxH3cM6GK8LIDBvD1NhJ7Kzdy47a3TSaGsmPzOHTE18DMCthGmpZz+ddvcEpPrQk/XQ0cjW/\nn96hRCuViHnlvvle18x7R5xaNBq5mmd2vUBu5DjOSnOKo+2t249dcDA5Jr+LLkl/GDUIfCASiZCo\nNZhOHKfimaeJvPCiQSs/NJeXuY0BGLzcgd6478rJ7tevfXGIILmEVr2F2y/OZ0xCCFdHZKM4V4pY\nJOJg8wl2Nv8EzVClq+HWib13wjsjdQHT4iZzoKGQoiI7kxN6dpMOFtJwb/e4L0NPcDhwGAxIEnqe\nZblKGpu+/ByHwYBmxkxirnTOLAKRJAVwoLgRk9nOhIxIL2/McHJG6gJMNjNryzZwKOQb7r3+ZtJD\n49l8oJpXPjuIAKTGavjTr6b53FdvdA7rmMpKfRoEgiBgN+j9zjcYTPQmK9/tKKeoooWwaLOXMaCS\nKftc037xqWOYmRNLSmz3hsSPeyo5WNzEstOz2bivigrxHspMx7h3yu1Iusnr6S8ikahLY6DuJhvQ\nkdjpMOjBwyCwt5eTpiy7Ap3M+/O8/0MRRRUt/P7qKYMualatq+OvO57FJthJ1ST3um6sMppf5S5D\nKpKytWYnJ1pKOdFS6n7/UOMRpsd1r7niEBxuD0F0cNfvCejRgK7UVbfn+3Sc05+f+JZmc4vbwzBQ\nRg0CP5Co1dhbmjEUHsRQeJDMF172K/bcVyydcgckIYFvb1xY0sSOI/Xkj5ej1ggkaxJQSDtirOV1\nOjbuqyIpRk2d1si41HD+dtts90NIqZCx+2g9kSEK6oz17u3qjPU+LfkEtdMSf3HffxirziUjsau7\ne7Dx9BAAOHwYBA6DAQSh1zwOV/mhKxHUcPiwu8IgUB4CbauZzcWHKBNbmJlwivu7HH4EVh1zlj2+\nVfghf5x5L4fLmpmTH8+l8zNQKwN/I/enBNhhNILdPqwVBrVNBrYcrOGzn0rISQvn/DlpHGjc7bVO\nZD/cwFKJuFtjoKJOx/MfFzB3QjzP3TmP0to2fthVgTmuhbK2Sip11aSEBCbXQxAENh+oYWxKGFGh\nvs9xl76A3bMUF3C0XycSpQqccgvUNBmw2hxce1bfJwxGs4231hwlIUrJkllpPte32R00tpp4b99G\nbIJzMuZvulhaaDJba3Z6LZsVP81dntiZL0+s4euStSRrEjklZkKXRF5P1uwoJyc1nKQYNQ/PvI9K\nXTX/PfgO0FG9ZbZbqDXUIyBw+7oH+N2U2xgTmubf4HtgVIfAD1wzQBe25sDX9eoK9rpj0S4C0S63\nMxEhChKjVBQ07+Yfe16itLUCramjflylkBIRoqC2yUBKrJpZubFeM1JBENh5pI61uytoNDq/hwem\n3cl9U+7w6+KNU8UQHhTGccMRrJKhT3byFLsB37khbrW78J5v3J3PD3tLM42rnW6+QBkEM/KiKQtZ\nw9qKH93Z4yMBT2NS5tDw9/f3smByItcvGU91o55/fbyffccacHTTProvBKWmuV9b63z3NrC1d9eT\nRvV80x1sRCKw2h3cvXQit1+cTzkFJKkTSAtJ4a7Jt/DsaU9wUye1yYEQGaogPlLFmPgQxGIR6fEh\nLJmdRn21c94XSC3+Q6VaXv+ykPv/vYXi6o7EQJvdQVltG9WN3g/+Dg+BwWu5o12QSKrq8KSV1bbx\nSruKYF8Jkkto1pn9Flir0xr5+/t70To6JM0TernWPeluhp8aktzjfXB7zW4EBBpNTVyfexWqHhoX\nNbQY2Xqwxq2NEKuM5pSYCYjbyw9clQ1akxbBQ+DkmV0v+jXu3hj1EPiBRO19w9ft2knEOecGbP+6\ngr1UPf+PgO2vJyxWO5sP1DApK4qCaudN9fm9r5AVNoa7TnFKVEeEKNyVB57sriugsq2Kc8ecya/P\nywXgp6pWrA4rCao4v92eYpGY7PAMttXs4tGtTxHbuIiHLzszQJ+w7/gKGVjb5Ydl4T0rnnWnQti2\nfavzvQAZBGVtFe7XCkngvVMD4Zrxl1PX1szqVSLUwW0o5E4DMilGTXmdjuc+KuAPy6eQ6WdctzuS\nfnc/lspKyp/8M3aD3uf6Lm9bX/sfBJKYcCWaYDnvrj3K9PmtfFe+llC5hr/M/aN7nQgfeTd9IThI\n6lUCCM4HntjuNNpaA1htkJsWwav3z6dFZ/Fy55ssdl77opBJWVFcfGpH/pPrGun829l8F17YAAAg\nAElEQVSNBkQyWbu+hFOnYPr4WKaPj8VitVPdqEelkHUJTfSEWCTyCnv6IiFKxZO3zObBTd9De+8l\ntdS/cuh4VSxzEmZQa6jjWHMxN+RdTX5Ujtc6giDQZtURJAlyJwdOjzsFm8OGrIfqhKjQYP547dQu\nhsWtE69nQ8VmJkfns6NmD9tqdvn9Of1l1CDwg84zwIZVHyGNiiJk+syA7N9w6KDX36l/erzLMQPB\noRItpbVtRCboONbsrGRQSBQUNZ/gREsJRdoT1BsbuTTrPK+ZHzi7qlXoqnAgkBmWTou5lSkxE7vN\ncvfFzPip7pN54SmJ7sY5Q0XcDTeh27Mb3e5dAfEQ9OYZcVWpDASj2cbOIx3hpHjVSAkXOJkRPwUh\nTmDJ/Xi1RlYpZDx2w3QUcsmAc0UkSqVTOVQkQr93D83r1xF22oIe1zdXOA2o/rRNDiRjU8JQR7fw\nbslqABTSoU0ynZARSb01jVVVm2ixBEYGt6Smlde+KGTRlKQuMuTqYOdv3hmX5kcXD4HR0KPRfLS8\nmbe/O8oFc9PdiX++ePGTAyRFqTh/brpf64OzMVubRUdGSDo35i33u713aFAIy8ZdQnlbFUe1x0hS\nJyATS7E6bHx09FMuH3sRz+5+CblYxvkZZyEgcFrSbC7NOt/nvru7XsZFZDEuIotHtjxJvbGjK2Je\n5HgONDq1Ur4uXsvZ6Yu6bOsvowaBH3R3U9ft2BEwg8DW7O0akyclDUqy3aSsKCZmRvJR0ecATImZ\n6FTKssO68k3srnN25otVRjMvcaaXUTAlZiIVuirWlK6jVl/HvoaDTIjK7dc4ssMzeHLun1DKgvnf\n10fYsHkPdy+d2K8+Cv0hZNYcVHkTnAaBDwVKV4OizsmI/tLf7Tyx2BxYm8O5LOZO5k2MD2hiWKAQ\niUTdylIH8jcVicXQHnqoW/m/3g2CMmeSV1ByV2/XUFCnNbBqwwlm5sSRl5JCSJWGVktbr3HjwWJi\nahKrqqDJGBhp6YRIFbdckEtDs4mj5c1+qVO6PQSdcwgMRsQqbw/b8coWVMEy8sZEsuLmWX6PSxAE\nZuXGOsV9/OSnqm2sOvoVAHqdmBBF3w34ZE0CyR4Nv/bW7edo83FazK2caClxrlOXyKVZ55Pkp+Jg\nXbOR7YdqyU4O6/L9uvobnJY0h/zI8WSHZ1DcWsZze15mX8OBUYNgsOlO2MRSU93Nmv3DptWCSIQi\nI3NQdAc8EYlE6K165GIZy8cv5bUDb3GgsRCZ2Om+kolltFraeGDjo4yNyOJg42FOS5rDmNBU9z72\nNTg9Gj3FwPxBLXfOGK49axy1WgNymdjdYGQoEKtUIBL59hA0+WcQRF12OaZjx9DMnIXpWBHa7751\nbhc2ML2F2iYDn24qZmZubLfZ2ycDBpONE1UtZCSGBtRA6C2J1VxWhiQ0dFBkv/1BqZAxISOSVuoQ\nkczjs3/Prtp95EQOvSDX/sM6bEdnMD55Sq/rHdUeIzNsjM/yNblMQlK0modf3446WMbzv/VW4axq\n0GM027xK/8TdeAjM5eXY21qRdcrz+GprKQ6HwG8v61vbX5FIxOQs/0NEdoeddw5/DIDUGM2SiQt9\nbOEfTSYtdYYGSlvLESFCQMAu2FmQPNfvfVisdoxmW7ee0whFGLWGepTSYMZHZgOQGZZOWFDogGWq\nRw0CPwhylZyJRO4ZiqWmGofVMuDEP+1332I6fgxJaCgpD/7fQIfaKyeqWokMCeK63Cux2C3IJDKu\nzbmCLdU7CJYGs61mF0vST+eT406L+WDjYQDWV/xEdtgYxoZnEh0c6S6bGqjhsuNwHW+tOcKM8bHs\nOFJHSoymSwx0sBCJxYhVKp85BC4lNWloSK/rRZx5NrSnQmimTCVk3qnodu9C4UPjwB+yU8KIjQhM\n+eJw8N3OcrRtZuIilQE1CHS7diCSy1FP8JZztba2YtM2ocwLTClWXzFZbHy47hj5Y4N5u/QNfmyM\n5uGZ9zEjvvcH8mAxNz+J0yYlu2fOpa3lxKvivBT2BEFwVv+EZ/ksH3bx99vndDsbX73hBFa7g7s8\nHugSVdccgtJHnbkUrq6uLu64ZIJ7THVao1O1Lyqw1SImm4l32+v7AXJTYjklKTBt5VNDnGWLpW0V\nTIrJZ09dAbHKvuWyJEWruWxB9+Xtt0z4Fd+W/sDiFG/lVI1MTaW+ul/t3V2MVhn4gTIvn7ibbibt\nL092lJ8Jgl/Zzr6of/9dYGikkb/cUsILnxwAcIsIKWXBLEo51R2DkklkiEViktQdLrCz0xaRFZ7B\nnZN/zYWZ5wCgkg78ATU+NZxlF4WRNr6NhVPiOFym9ZJAHmwkarVPD4FdrweJBFFQ3+RvgxISiTz3\n/AEZTYIgEBuhJCpUQWGpFkN7G9yRiN1hp9Go7VYmd/6kBCJCgojQDFxCWOGhAVL90otUPf+PLm11\ntbv3ONdNS2U4kErElNXp2FvrVOGrNdT72GJwkUnF7gd3la6Gp3b+k+f3vOy1jt5qwOqwIQgi6pqd\n36fdYcdgNXTZ36ebinn49e2YLPZuk/1+c3G+lzEAHjkE+q77szU1dlnm4vmPC/hmW5mPT+ikok7H\nv1btZ9cR3/fl70p/ZGftXia2hz2r9TU+tvCflHZlyLLWCpaPX8p1OVcyOyFwAlExyiiWj1+KQuqd\nXKySK7E5bO6mVv1h1EPgByKRiJAZzlhW5nMv0PDpapo+/xR7a+uAGvd5zk47q7ENBi7LuztOTzkN\nMSKywsbgEBxEK6OQiqVEB0dx7piOKoBgaTAPTLuzz+pq3aEOlrGzcTuHmo7wt1MfZfq4BCzWoUsw\nFCHC3taGuaqKoISEbtdxdUUcagElq81OYamW/3xZyJiEUDRKWZ/coUPNiZZS/rHnJc5MXeiWqnYR\nqg5ibHIYf3lrF6dPTe6zFK0niXfeTeXzz2I6fsy9zFRainLsOKxNjdR/8B4SizNbXT1peGbkUomY\nP103je01u9nTLt/vEBwBUZLrL3aHA5PFzvEWZzJxcav3Q1bb3mb30FED79Sv4+YFC1hV9AWbq3fw\n8Ix7iVV13J8WTUliclYUEZruq12Kq1tp1pm9zld3DkG77oDgUYYaf/Nt7tetBgvVDXrio1SEKOV9\n6rwaqpYzMyeWmHDfkxVXHP7CzCWEGseybX8zx1NayEgYuBicsj2UelhbRKOxiWlx/lc9eLL1YA1m\nq51TJyb4df9xqSPqrfouxoK/jHoI+oGrAqDimado+uqLfu+nZeMG9+u4628c8LgGQnpoKjfmL3fH\noOKVMdw39Xa3TrcnKZokd6+CgVBnqOdQk3MWpZAoCFJaUSqGzkYVtWsS1L/3do/rOPT6LklPQ8Er\nnx9i9YZiHrx6Cleckca82QqQDryF8mAR0V4bXanrXjQoJlzJ0gWZTMiIHNBxJCoVYfO9kwldM8y6\nt95Et3MHLQX7gY5OpcPF9LhTmBIzEYlIQoNx+LQjbHYHtz6zgb+89xPvHXFWO/xh+t1e69TrneM7\nZ+o4tKoCXtz3HzZX7wBgV+0+93rldTp+2F2BRCzqUTFTZ7Ty2aYSKhs6wgOidi+bvdVpeDjaFQpV\nEyaimdYxe65rMvLxhhMcPOEcz84j9fzm2Q18svGElxHRHRqlnKnjYvzqotrcbgCFBYWSH5tNgjqW\nhMjAhSWuzbmCWGU0IUH9nzjVNBnYfKAGbZuZFp3Z5/pzEmawbNwl/FS13f35+sqoh6AfSD10CRpW\nfUTYotP7pVxoKj4OQNrjf0Ee3/0MNVDojFZqtQZCNWKieilpTNEk8puJN/RLQa2vVOo63HRbqnfw\n9uGPiNfPIlOZx2ULMgasge+L2Ouup+yxP+Ewd3+xNe3chb2tDVlM/2e0/eXGJTnUNxuJDQ/meEsJ\nz+7+d7ez75GCy0A80HiYL0+sYVvNbuYmzuCMVOfDO1wTRHgPM8q+0jnB09rUROPnn6Iv2Oe1PBAl\nn31ly8Eath+qZcm8eNJjIrks+wKWj1/aY835UCCViPn3706luKqVZ9sz6uOUMdgdTmW+JlMz+yud\nHoPNpQVohSYvwZsD9UWcM+Z0wNl102J19Jr8mz8mkvwxkQiC4BXPVqSnYzxciPH4MaQhzvOl82+U\nmRTKH67u8OwsmJzI8coWjpY3Y7E5CAqQbHeLpRWlNBi5REZOWgQ5aYE1HqfHndKjfLG/XDhvDNPG\n6fj9K1uZlBmFSATp8SGcOb37ypnMsHS2Vu9kS/UOjDYjl4+9qM/HHPUQ9IPOGgH+SKl2xlJXh75g\nHyKpFFns4NeV1zYZeH3jOv6043E+O/5Nj9a2UqYkJ3Ksl4twsIgKds4WY5RRbKp0Jio2qw9wsLiJ\nv7271+eMYKAoUlIRq9Vu+VRPzBXlFD7+FyAwpYN9JUgu4attpTz5zh53P3uNfOgfcP7iWQr5Vcn3\nNJqakIu948sNzUY2H6imqXVgno7OHSQtFeU0frq6y3qd21MPNvXNRnQGK+mpMp499DfeO7IajVw9\nrMaAC4lYTGZSGGdprkU4PI+NB0t5auc/OaI9xiNbn2R7y48AGMXOmXmb3s5D0+4HoM5c4364v/H1\nYb7aWsqH6473erw3vznMzX9bT6u+I56tHOts4Vy+4glsLc4SSLHK9zl947k53L/sFJ/GwOYD1byw\nen8XlcTuaDa3EhrUe6LwSCAhSsVN5+ZgdwjERShJi9OgM/acS+TqqdBk6p+a7qiHoB90kTLWNkGa\n/0IYDquVkj84LzZpRMSQ3LgyEkOZcoqEH8rh29IfOHfMGYgY3rawyZoEbp90IzHB0Ty8ZQUAJkHH\nLeemExsSNiRxe4lS1aU2GkD73Rr3685yx0OFKkiGNEyMzuK8uEeyQQDw6/xrKGur5JuStQBes0yA\n4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9NLSBzs3yJ5Tui49oLl82Lb4YoVK3j00Ucxm80MGjSIWbNmIUkSN910E4sXL8Zut7Ns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\n7p2zL7/8ko8//pibbrqJ8vJybrvttl4bd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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ad10908>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1753,7 +1828,7 @@
    ],
    "source": [
     "daily = data.resample('D').sum()\n",
-    "daily.rolling(30, center=True).sum().plot(style=[':', '--', '-'])\n",
+    "daily.rolling(30, center=True).sum().plot(style=['-', ':', '--'])\n",
     "plt.ylabel('mean hourly count');"
    ]
   },
@@ -1762,22 +1837,25 @@
    "metadata": {},
    "source": [
     "The jaggedness of the result is due to the hard cutoff of the window.\n",
-    "We can get a smoother version of a rolling mean using a window function–for example, a Gaussian window.\n",
-    "The following code specifies both the width of the window (we chose 50 days) and the width of the Gaussian within the window (we chose 10 days):"
+    "We can get a smoother version of a rolling mean using a window function—for example, a Gaussian window, as shown in the following figure.\n",
+    "The following code specifies both the width of the window (here, 50 days) and the width of the Gaussian window (here, 10 days):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 39,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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eXYN18pR+zeeKoEBbKwQCGLJz4tYGY1YW9uklOLdspnPfXqwTJ8WtLamg/b1N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\nGbuLW6hrOVa7+lntV+w+ut/nlZ5GJSMqRINEIiAIAumhCwhWTr6+NBC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2N+1BQGB17BkB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IlpWu37ceX3b7Mz+2vui/0vBrfu7+pyPVpdfepXNaVt48aNNG3alOeee47i4mKGDx/OjTfeyNSp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\n1EVERDyEQl1ERMRDKNRFREQ8xP8D2G7R4lwJBEEAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118a9fb38>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1786,32 +1864,35 @@
    ],
    "source": [
     "daily.rolling(50, center=True,\n",
-    "              win_type='gaussian').sum(std=10).plot(style=[':', '--', '-']);"
+    "              win_type='gaussian').sum(std=10).plot(style=['-', ':', '--']);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Digging into the data\n",
+    "### Digging into the Data\n",
     "\n",
-    "While these smoothed data views are useful to get an idea of the general trend in the data, they hide much of the interesting structure.\n",
+    "While these smoothed data views are useful to get an idea of the general trend in the data, they hide much of the structure.\n",
     "For example, we might want to look at the average traffic as a function of the time of day.\n",
-    "We can do this using the GroupBy functionality discussed in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb):"
+    "We can do this using the `groupby` functionality discussed in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 40,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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0n1RC80xPyCJy3iTd0BUkG7uSz7AJ+e677+buu+8+7fH169ef9tjy5ctZvny5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\ngg/4f1unlzCpFZdIAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10192d5c0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1821,32 +1902,35 @@
    "source": [
     "by_time = data.groupby(data.index.time).mean()\n",
     "hourly_ticks = 4 * 60 * 60 * np.arange(6)\n",
-    "by_time.plot(xticks=hourly_ticks, style=[':', '--', '-']);"
+    "by_time.plot(xticks=hourly_ticks, style=['-', ':', '--']);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The hourly traffic is a strongly bimodal distribution, with peaks around 8:00 in the morning and 5:00 in the evening.\n",
+    "The hourly traffic is a strongly bimodal sequence, with peaks around 8:00 a.m. and 5:00 p.m.\n",
     "This is likely evidence of a strong component of commuter traffic crossing the bridge.\n",
-    "This is further evidenced by the differences between the western sidewalk (generally used going toward downtown Seattle), which peaks more strongly in the morning, and the eastern sidewalk (generally used going away from downtown Seattle), which peaks more strongly in the evening.\n",
+    "There is a directional component as well: according to the data, the east sidewalk is used more during the a.m. commute, and the west sidewalk is used more during the p.m. commute.\n",
     "\n",
-    "We also might be curious about how things change based on the day of the week. Again, we can do this with a simple groupby:"
+    "We also might be curious about how things change based on the day of the week. Again, we can do this with a simple `groupby` (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 41,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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kZWVBoVBAr9cjOTkZ+fn53Vc1EVEvY5w8BZq0dFj37YXtUF6wy6EQpWhrg3nz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\n8BD/A/9r7TmuhSCKAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119484d68>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1856,7 +1940,7 @@
    "source": [
     "by_weekday = data.groupby(data.index.dayofweek).mean()\n",
     "by_weekday.index = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']\n",
-    "by_weekday.plot(style=[':', '--', '-']);"
+    "by_weekday.plot(style=['-', ':', '--']);"
    ]
   },
   {
@@ -1865,15 +1949,18 @@
    "source": [
     "This shows a strong distinction between weekday and weekend totals, with around twice as many average riders crossing the bridge on Monday through Friday than on Saturday and Sunday.\n",
     "\n",
-    "With this in mind, let's do a compound GroupBy and look at the hourly trend on weekdays versus weekends.\n",
-    "We'll start by grouping by both a flag marking the weekend, and the time of day:"
+    "With this in mind, let's do a compound `groupby` and look at the hourly trends on weekdays versus weekends.\n",
+    "We'll start by grouping by flags marking the weekend and the time of day:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 42,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -1885,21 +1972,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we'll use some of the Matplotlib tools described in [Multiple Subplots](04.08-Multiple-Subplots.ipynb) to plot two panels side by side:"
+    "Now we'll use some of the Matplotlib tools that will be described in [Multiple Subplots](04.08-Multiple-Subplots.ipynb) to plot two panels side by side, as shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 43,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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L0xFCCBFjIvU6paiqtvfwKisbtTy8JrKybHLecSIezxk6ft6uygr23/1zbOdMpMdNtwBQ\n9+EqKl54jtzvfo/kiZPCFWpIRevnnZVl0zqEiBaNn2lXRevvclfF43nH4zmDnHe0aes6JSMzQgjN\neWqOFf/7JfTtB6BZ3YwQQgghIp8kM0IIzR1bY+a4ZKZ3H1AUzdozCyGEECLySTIjhNCc++gaM8aM\n9MBzOrMZY04OzgOlqD6fVqEJIYQQIoJJMiOE0Jx/mtnxIzMA5r55+Ox23FVVWoQlhBBCiAgnyYwQ\nQnPuo9PMjOnpJzyf0LcvgEw1E0IIIcQpSTIjhNCcp6YGXWIiOrPlhOf9TQAkmRFCCCHEqRi0DkAI\nEd9UVcVdXY0xK+ukn5njqKPZ1q2fc889d9O//wD8HfPT0tL59a9/H/Q+1q5dw7Bhw8nIyAxXmEII\nIeJUpF6nJJkRQmjKZ29BdTpOmmIGoLdaMaRnxM3IzNixZ3Pvvb/t9Ov/858Xycv7hSQzQgghwiIS\nr1OSzAghNOWpPlr8n5Fxyp8n9O1L87ateOrqMKSmdktMv1p36rtMv5l4d5vb63UKXp/a7vanc6o1\njLdt28LTT/8TVVWx21tYuvS3ZGfncM89d9Hc3IzD4eCWW76Px+OmuHg399+/lL/+dRkGg/x5F0JE\nJq/dTv2a1ThK9mIZMgTrqNEYM08enRenJ9epY+RqJ4TQVKD4P+3kkRlonWrWvG0rjtL9WFNHdWdo\n3W7Lls/40Y8WoqoqiqJwzjmTsVjM3HPPb8jIyKSw8GlWr17F5MnnU19fzyOP/Jna2hoOHCjlnHMm\nM3jwEH72s19IIiOEiEie+nrqPvgfdas/wGe3A9C09XMqX3oBU+8+WEeNxjp6DAl9+6EoisbRilOJ\nxOuUXPGEEJoKLJh52pEZfxOAUqwjuyeZ6eidKv/2WVk2KisbO33cUw3ff/LJRzz22EMkJiZSWVnB\nyJGj6N9/ALNmXcW99/4Cj8fLnDnfAVrvmJ3qrpkQQmjJXVlJzfvv0PDJx6huN3pbMpmzL8M6poCW\nXV/TvG0LLTt3UPPmAWrefB1DejpJZ43GOmo0iUOGosgNmpPIdeoY+e0QQmjKfXSNGWN6e8lM7NfN\nnOoP/IMP/pYVK1ZisVj47W/vRVVVSkr20NLSwh/+8DjV1VUsWnQT55wzGZ1OJ8mMECJiOA8eoOad\nt2ncvBF8PoyZWaRNm0HypMnoTCYATLm5pJ4/BZ/DTvOXX9K0bQvN27dTv/oD6ld/gM5iIWnEWa2J\nzYiR6C2Wdo4qwikSr1OSzAghNBUYmTlNMmNIS0NvteEsLe3OsDSxdevn/OhHCwECQ/iXXDKD73//\nJiyWRNLT06mqqqRPn37861//ZPXqVaiqys03LwJg+PCR3H//PTz66BPYbDYtT0UIEcfsxcXUvPMm\nzduLADD16k36pZdhKxiHotef8jU6swVbwdnYCs5G9XiwF++madtWmrZtoXHTBho3bQC9nsShZ2Ad\nNZqks0afsnGMCK9IvE4pqsa38boy1BWtujrEF63i8bzj8ZyhY+d94MHfYd9TTP7fl532Infw0Ydo\n2fEVA//4BPqkpFCGGlLR+nlnZUni05Zo/Ey7Klp/l7sqHs87VOesqirNX2yn9p23sBfvBsCSP5i0\nGZeSNOKsTtfAqKqK6+CB1sRm65YTRunTZ15O5pVXd2q/8fhZQ/Sed1vXKRmZEUJoyl1djSE17bSJ\nDLRONWvZ8RXOA6UkDj2jG6MT0aKoqIiHH36YwsLCwHNvvPEGzz//PC+99BIAK1asYPny5RiNRhYu\nXMiUKVM0ilaI2KF6vTR+tomat9/CdeggAEkjzyJ9xmVY8gd3ef+KopDQpy8JffqScfkVuKuraSra\nSt3771Hz5huY8wZgHTW6y8cR0UuSGdEtPHW1qBmRe0ddaEP1+fDU1WLuP6DN7RL69gVa62YkmRHf\ntmzZMlauXEnScaN2O3bs4JVXXgk8rqqqorCwkNdeew2Hw8HcuXOZNGkSRqNRi5CFiHqqqtKw7hNq\n3ngdd1Ul6HTYxp9D+vRLSejTJ2zHNWZkkHbhxSTmD6H0t/dR9sxT9Lvn1zLlLI7ptA5AxD773j2U\n/GwxlWvWah2KiDCeurrWotDTdDLzM/fNA8ARB00ARMf169ePJ554IvC4traWxx9/nF/+8peB57Zv\n387YsWMxGAxYrVby8vLYtWuXFuEKEfVUr5eK5wspf/opPPV1pFxwIXm/fYAe37s1rInM8RL69CHr\nO/PwNTVRtuwfqF5vtxxXRB5JZkTYNX9RBKpK4+7dWociIkx7xf9+xuxslARzXDQBEB03depU9Een\nKfp8PpYsWcJdd92F5biuR01NTScUmyYmJtLYGH3zxoXQms9h59Cf/0j9mg9J6NOHvPsfIOe66zFl\nZXd7LClTLsA6tgD77l1Uv/l6tx9fRAaZZibCzl5cDIDjSBnJGsciIktgwcx2pgcoOh0Jffrg2LsH\nn9OJLiGhO8ITUeirr76itLSUe++9F6fTyd69e/n973/P+PHjaWpqCmzX3NxMcnJwf5HitUGCnHf8\nCPacnVXV7Hj4AVr2f0Pa2NEMvuN2DInatkpOX/xDtv30DmreeoOe48eQMmJ40K+Nx88aYu+8JZkR\nYaV6PDj2lQDgKCvTOBoRaTzVrWvMtDcyA2Du2xfHnmKchw5iGTAw3KGJKKSqKiNGjOCNN94A4NCh\nQ9x+++3cfffdVFVV8fjjj+NyuXA6nZSUlJCfnx/UfqOx809XRWvHo66Kx/MO9pwdpd9w6E+P4a2r\nI2XKhWTOvY7aZg80a/9+Zd90Kwce/B07H36Mfkt/jcHW/o2KePysIXrPW7qZCc04Sr9Bdbla/7+i\nEtXjkZV8RYA7MM2s/cLNhKN1M87Sb2IumfnLXx5n166d1NRU43A46NWrN6mpafz6178/aduysiOU\nlOxl4sTJp9zXoUMH+e1v7+Wvf10W7rAjTlutXzMzM1mwYAHz5s1DVVUWL16M6eiifUKItjUVbePI\nk39DdbnI+n/Xkjp1WqdbLYeDZeAgMq+6mqpX/kP5v5bR84c/QdFJJUWoReq1Sr5VirDy95rXWSz4\n7Hbc1VWYcnI1jkpECk9t68iMMYiRmWMdzWKvbua2234CwDvvvElp6TfceusPTrvtZ59t5MiRI6e9\nQEDbX+pjVa9evQItmE/33Jw5c5gzZ053hyZEVKv94H9UvvQCitFIj0W3YRszVuuQTilt2gxadu6g\n+Yvt1K16n7RLpmsdUsyJ1GuVJDMirOx7WutlbGePp37tGtyVFZLMiABPdTVKQgK6IBbCTOjZC/T6\nsHc0q/zPSzR+trlTr/1Gr8Pr9Z30vK3gbLLmXNvh/f3pT4/w5ZdfoCgK06ZdyqxZV/HCC4W43W6G\nDx9JQkIC//73U/h8PhwOB/fe+9tOxS2EEN+m+nxUrniRulX/Q5+cTK8f/qTdNvpaUnQ6cm+6hW/u\n+xWVr/wHS/7giI63KyLpOgXaX6tkDE6EjaqqOIqLMaRnYBkyFABXRYXGUYlI4q6pxpieEdTdGcVg\nIKFXb1wHD6B6PN0QnbY+/ngN1dVVPPnkMzzxxD95++03OHToIPPmLWDatEs555xJ7NtXwr33/o4/\n//kfTJp0Lh999KHWYQshYoDP6eTwX/9M3ar/YerZk76/+FVUJAaGlBRyb74VfD6OPPk3vC0tWocU\n8yLhWiUjMyJs3OVleJsasY2fgCm7tWWju6Jc46hEpPA5HPiamzHk9Q/6NQl9++Es/QZX2RESeodn\nLYOsOdd2+u5UKAsr9+/fz8iRrataGwwGzjxzGPv37zthm8zMLB599EEsFgsVFeWMGVMQkmMLIeKX\np66WQ396vHWR4jOG0WPR99EnRs+i10lnDiN9xmXUvP0mFYXPkHvLopibehsp1ymIjGuVjMyIsLEf\nXVfGMmgwxuwcANwyMiOOctf4O5kFv2qzOYbrZr4tLy+P7du3AeDxePjyyy/o06cPiqLD52udIvDQ\nQ79lyZJ7+cUvlpKenoGqqgCB/wohREc4Dx6g9He/wVn6DcmTz6PXj38aVYmMX8asKzEPHETj5k00\nfCwLdodTJFyrZGRGhI19z9FkJj8ffVISBptVkhkR0JHif7+Evv0AcJTuJ3nipLDEFSnOPXcK27Zt\nYdGi7+J2e5g2bQYDBgzC5XLzwgvPMnjwEKZOncGiRTdhNltIS0ujqqoKiM8GAEKIrmn+8guO/P0J\nfA4HmbOvIW3GZVH7t0QxGOhxy0K+ue8eKl56HvPAQST06qV1WDEpEq5ViqrxLbxo7HXdVdHa47uj\n9t39c7zNTQx8/C8oOh2HH7yf5n37GfTXJ+OmZWK8fNbfFsx516/9iPJnnybn/24mZdLpu50cz+dw\nsOeHi7DkD6bPz+8ORaghFa2fd6wtoBZq0fiZdlW0/i53VTyed1aWjeKXX6fi+cJAEb3t7HFahxUS\njZ9/xpG//QVTz170/eU9Jyy4HI+fNUTvebd1nYqPb5Si23nq6nBXVmAZlB9IXMw9clE9nsAdeRHf\n/GvMGDOCH5nRmc2YcnJxHihF9Z3cjUUIIUTwVJ+P/c88S0Xhv9EnJtH7jjtjJpEBsI0tIOWCi3Ad\nPkTl8he1DkeEiSQzIiz8LZktg46tsG3ObW3JLFPNBIDHv2BmWvA1M9A61cxnt+M+OkwthBCic6pf\n/y+HXluJMTeXPr/41QnX7FiR9f++g6l3H+rXrqFx8yatwxFhIMmMCItj9TKDA89ZevQApD2zaHWs\nAUBah153bPHM8K43I4QQsaxl9y5q3nqDhOxs+t61JNB1NNbojCZ63roIxWSi/NmncVXKd5BYI8mM\nCAt7cXHruiB5eYH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Ht2eWL5+xzOew42tpCVvxv58pJxcAV7kkx0KI6Ne05XOcB0qxjZtA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fUE102J7+midaqcohqWr81i8vBwxg85Mz5+YvhYthTsZE/xXvaWHGCYve3BsSRJhN5+FzmFhVSu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\nCIIgCH2SCGYEQRAEQRAEQeiTRDAjCIIgC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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b2305c0>"
+       "<Figure size 1008x360 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -1909,36 +1999,30 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
-    "by_time.ix['Weekday'].plot(ax=ax[0], title='Weekdays',\n",
-    "                           xticks=hourly_ticks, style=[':', '--', '-'])\n",
-    "by_time.ix['Weekend'].plot(ax=ax[1], title='Weekends',\n",
-    "                           xticks=hourly_ticks, style=[':', '--', '-']);"
+    "by_time.loc['Weekday'].plot(ax=ax[0], title='Weekdays',\n",
+    "                            xticks=hourly_ticks, style=['-', ':', '--'])\n",
+    "by_time.loc['Weekend'].plot(ax=ax[1], title='Weekends',\n",
+    "                            xticks=hourly_ticks, style=['-', ':', '--']);"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The result is very interesting: we see a bimodal commute pattern during the work week, and a unimodal recreational pattern during the weekends.\n",
-    "It would be interesting to dig through this data in more detail, and examine the effect of weather, temperature, time of year, and other factors on people's commuting patterns; for further discussion, see my blog post [\"Is Seattle Really Seeing an Uptick In Cycling?\"](https://jakevdp.github.io/blog/2014/06/10/is-seattle-really-seeing-an-uptick-in-cycling/), which uses a subset of this data.\n",
+    "The result shows a bimodal commuting pattern during the work week, and a unimodal recreational pattern during the weekends.\n",
+    "It might be interesting to dig through this data in more detail and examine the effects of weather, temperature, time of year, and other factors on people's commuting patterns; for further discussion, see my blog post [\"Is Seattle Really Seeing an Uptick in Cycling?\"](https://jakevdp.github.io/blog/2014/06/10/is-seattle-really-seeing-an-uptick-in-cycling/), which uses a subset of this data.\n",
     "We will also revisit this dataset in the context of modeling in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Vectorized String Operations](03.10-Working-With-Strings.ipynb) | [Contents](Index.ipynb) | [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.11-Working-with-Time-Series.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.6 64-bit ('3.9.6')",
    "language": "python",
    "name": "python3"
   },
@@ -1952,9 +2036,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc"
+   }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.12-Performance-Eval-and-Query.ipynb b/notebooks/03.12-Performance-Eval-and-Query.ipynb
index b6e2a142b..94f175f3b 100644
--- a/notebooks/03.12-Performance-Eval-and-Query.ipynb
+++ b/notebooks/03.12-Performance-Eval-and-Query.ipynb
@@ -4,48 +4,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+    "# High-Performance Pandas: eval and query"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Working with Time Series](03.11-Working-with-Time-Series.ipynb) | [Contents](Index.ipynb) | [Further Resources](03.13-Further-Resources.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.12-Performance-Eval-and-Query.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# High-Performance Pandas: eval() and query()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we've already seen in previous sections, the power of the PyData stack is built upon the ability of NumPy and Pandas to push basic operations into C via an intuitive syntax: examples are vectorized/broadcasted operations in NumPy, and grouping-type operations in Pandas.\n",
+    "As we've already seen in previous chapters, the power of the PyData stack is built upon the ability of NumPy and Pandas to push basic operations into lower-level compiled code via an intuitive higher-level syntax: examples are vectorized/broadcasted operations in NumPy, and grouping-type operations in Pandas.\n",
     "While these abstractions are efficient and effective for many common use cases, they often rely on the creation of temporary intermediate objects, which can cause undue overhead in computational time and memory use.\n",
     "\n",
-    "As of version 0.13 (released January 2014), Pandas includes some experimental tools that allow you to directly access C-speed operations without costly allocation of intermediate arrays.\n",
-    "These are the ``eval()`` and ``query()`` functions, which rely on the [Numexpr](https://github.com/pydata/numexpr) package.\n",
-    "In this notebook we will walk through their use and give some rules-of-thumb about when you might think about using them."
+    "To address this, Pandas includes some methods that allow you to directly access C-speed operations without costly allocation of intermediate arrays: `eval` and `query`, which rely on the [NumExpr package](https://github.com/pydata/numexpr).\n",
+    "In this chapter I will walk you through their use and give some rules of thumb about when you might think about using them."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Motivating ``query()`` and ``eval()``: Compound Expressions\n",
+    "## Motivating query and eval: Compound Expressions\n",
     "\n",
     "We've seen previously that NumPy and Pandas support fast vectorized operations; for example, when adding the elements of two arrays:"
    ]
@@ -54,22 +32,25 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "100 loops, best of 3: 3.39 ms per loop\n"
+      "2.21 ms ± 142 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
    "source": [
     "import numpy as np\n",
-    "rng = np.random.RandomState(42)\n",
-    "x = rng.rand(1000000)\n",
-    "y = rng.rand(1000000)\n",
+    "rng = np.random.default_rng(42)\n",
+    "x = rng.random(1000000)\n",
+    "y = rng.random(1000000)\n",
     "%timeit x + y"
    ]
   },
@@ -84,19 +65,23 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1 loop, best of 3: 266 ms per loop\n"
+      "263 ms ± 43.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
    "source": [
-    "%timeit np.fromiter((xi + yi for xi, yi in zip(x, y)), dtype=x.dtype, count=len(x))"
+    "%timeit np.fromiter((xi + yi for xi, yi in zip(x, y)),\n",
+    "                    dtype=x.dtype, count=len(x))"
    ]
   },
   {
@@ -111,7 +96,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -129,7 +114,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -142,16 +127,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In other words, *every intermediate step is explicitly allocated in memory*. If the ``x`` and ``y`` arrays are very large, this can lead to significant memory and computational overhead.\n",
-    "The Numexpr library gives you the ability to compute this type of compound expression element by element, without the need to allocate full intermediate arrays.\n",
-    "The [Numexpr documentation](https://github.com/pydata/numexpr) has more details, but for the time being it is sufficient to say that the library accepts a *string* giving the NumPy-style expression you'd like to compute:"
+    "In other words, *every intermediate step is explicitly allocated in memory*. If the `x` and `y` arrays are very large, this can lead to significant memory and computational overhead.\n",
+    "The NumExpr library gives you the ability to compute this type of compound expression element by element, without the need to allocate full intermediate arrays.\n",
+    "The [NumExpr documentation](https://github.com/pydata/numexpr) has more details, but for the time being it is sufficient to say that the library accepts a *string* giving the NumPy-style expression you'd like to compute:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -168,39 +156,41 @@
    "source": [
     "import numexpr\n",
     "mask_numexpr = numexpr.evaluate('(x > 0.5) & (y < 0.5)')\n",
-    "np.allclose(mask, mask_numexpr)"
+    "np.all(mask == mask_numexpr)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The benefit here is that Numexpr evaluates the expression in a way that does not use full-sized temporary arrays, and thus can be much more efficient than NumPy, especially for large arrays.\n",
-    "The Pandas ``eval()`` and ``query()`` tools that we will discuss here are conceptually similar, and depend on the Numexpr package."
+    "The benefit here is that NumExpr evaluates the expression in a way that avoids temporary arrays where possible, and thus can be much more efficient than NumPy, especially for long sequences of computations on large arrays.\n",
+    "The Pandas `eval` and `query` tools that we will discuss here are conceptually similar, and are essentially Pandas-specific wrappers of NumExpr functionality."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ``pandas.eval()`` for Efficient Operations\n",
+    "## pandas.eval for Efficient Operations\n",
     "\n",
-    "The ``eval()`` function in Pandas uses string expressions to efficiently compute operations using ``DataFrame``s.\n",
-    "For example, consider the following ``DataFrame``s:"
+    "The `eval` function in Pandas uses string expressions to efficiently compute operations on `DataFrame` objects.\n",
+    "For example, consider the following data:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "nrows, ncols = 100000, 100\n",
-    "rng = np.random.RandomState(42)\n",
-    "df1, df2, df3, df4 = (pd.DataFrame(rng.rand(nrows, ncols))\n",
+    "df1, df2, df3, df4 = (pd.DataFrame(rng.random((nrows, ncols)))\n",
     "                      for i in range(4))"
    ]
   },
@@ -215,14 +205,17 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10 loops, best of 3: 87.1 ms per loop\n"
+      "73.2 ms ± 6.72 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
@@ -241,14 +234,17 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10 loops, best of 3: 42.2 ms per loop\n"
+      "34 ms ± 4.2 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
@@ -260,14 +256,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``eval()`` version of this expression is about 50% faster (and uses much less memory), while giving the same result:"
+    "The `eval` version of this expression is about 50% faster (and uses much less memory), while giving the same result:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -290,21 +289,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Operations supported by ``pd.eval()``\n",
-    "\n",
-    "As of Pandas v0.16, ``pd.eval()`` supports a wide range of operations.\n",
-    "To demonstrate these, we'll use the following integer ``DataFrame``s:"
+    "`pd.eval` supports a wide range of operations.\n",
+    "To demonstrate these, we'll use the following integer data:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
-    "df1, df2, df3, df4, df5 = (pd.DataFrame(rng.randint(0, 1000, (100, 3)))\n",
+    "df1, df2, df3, df4, df5 = (pd.DataFrame(rng.integers(0, 1000, (100, 3)))\n",
     "                           for i in range(5))"
    ]
   },
@@ -313,14 +310,17 @@
    "metadata": {},
    "source": [
     "#### Arithmetic operators\n",
-    "``pd.eval()`` supports all arithmetic operators. For example:"
+    "`pd.eval` supports all arithmetic operators. For example:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -345,14 +345,17 @@
    "metadata": {},
    "source": [
     "#### Comparison operators\n",
-    "``pd.eval()`` supports all comparison operators, including chained expressions:"
+    "`pd.eval` supports all comparison operators, including chained expressions:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -377,14 +380,17 @@
    "metadata": {},
    "source": [
     "#### Bitwise operators\n",
-    "``pd.eval()`` supports the ``&`` and ``|`` bitwise operators:"
+    "`pd.eval` supports the `&` and `|` bitwise operators:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -408,14 +414,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In addition, it supports the use of the literal ``and`` and ``or`` in Boolean expressions:"
+    "In addition, it supports the use of the literal `and` and `or` in Boolean expressions:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -440,14 +449,17 @@
    "source": [
     "#### Object attributes and indices\n",
     "\n",
-    "``pd.eval()`` supports access to object attributes via the ``obj.attr`` syntax, and indexes via the ``obj[index]`` syntax:"
+    "`pd.eval` supports access to object attributes via the `obj.attr` syntax and indexes via the `obj[index]` syntax:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -472,18 +484,19 @@
    "metadata": {},
    "source": [
     "#### Other operations\n",
-    "Other operations such as function calls, conditional statements, loops, and other more involved constructs are currently *not* implemented in ``pd.eval()``.\n",
-    "If you'd like to execute these more complicated types of expressions, you can use the Numexpr library itself."
+    "\n",
+    "Other operations, such as function calls, conditional statements, loops, and other more involved constructs are currently *not* implemented in `pd.eval`.\n",
+    "If you'd like to execute these more complicated types of expressions, you can use the NumExpr library itself."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ``DataFrame.eval()`` for Column-Wise Operations\n",
+    "## DataFrame.eval for Column-Wise Operations\n",
     "\n",
-    "Just as Pandas has a top-level ``pd.eval()`` function, ``DataFrame``s have an ``eval()`` method that works in similar ways.\n",
-    "The benefit of the ``eval()`` method is that columns can be referred to *by name*.\n",
+    "Just as Pandas has a top-level `pd.eval` function, `DataFrame` objects have an `eval` method that works in similar ways.\n",
+    "The benefit of the `eval` method is that columns can be referred to by name.\n",
     "We'll use this labeled array as an example:"
    ]
   },
@@ -491,13 +504,29 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -510,33 +539,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.375506</td>\n",
-       "      <td>0.406939</td>\n",
-       "      <td>0.069938</td>\n",
+       "      <td>0.850888</td>\n",
+       "      <td>0.966709</td>\n",
+       "      <td>0.958690</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.069087</td>\n",
-       "      <td>0.235615</td>\n",
-       "      <td>0.154374</td>\n",
+       "      <td>0.820126</td>\n",
+       "      <td>0.385686</td>\n",
+       "      <td>0.061402</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.677945</td>\n",
-       "      <td>0.433839</td>\n",
-       "      <td>0.652324</td>\n",
+       "      <td>0.059729</td>\n",
+       "      <td>0.831768</td>\n",
+       "      <td>0.652259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.264038</td>\n",
-       "      <td>0.808055</td>\n",
-       "      <td>0.347197</td>\n",
+       "      <td>0.244774</td>\n",
+       "      <td>0.140322</td>\n",
+       "      <td>0.041711</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.589161</td>\n",
-       "      <td>0.252418</td>\n",
-       "      <td>0.557789</td>\n",
+       "      <td>0.818205</td>\n",
+       "      <td>0.753384</td>\n",
+       "      <td>0.578851</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -544,11 +573,11 @@
       ],
       "text/plain": [
        "          A         B         C\n",
-       "0  0.375506  0.406939  0.069938\n",
-       "1  0.069087  0.235615  0.154374\n",
-       "2  0.677945  0.433839  0.652324\n",
-       "3  0.264038  0.808055  0.347197\n",
-       "4  0.589161  0.252418  0.557789"
+       "0  0.850888  0.966709  0.958690\n",
+       "1  0.820126  0.385686  0.061402\n",
+       "2  0.059729  0.831768  0.652259\n",
+       "3  0.244774  0.140322  0.041711\n",
+       "4  0.818205  0.753384  0.578851"
       ]
      },
      "execution_count": 16,
@@ -557,7 +586,7 @@
     }
    ],
    "source": [
-    "df = pd.DataFrame(rng.rand(1000, 3), columns=['A', 'B', 'C'])\n",
+    "df = pd.DataFrame(rng.random((1000, 3)), columns=['A', 'B', 'C'])\n",
     "df.head()"
    ]
   },
@@ -565,14 +594,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using ``pd.eval()`` as above, we can compute expressions with the three columns like this:"
+    "Using `pd.eval` as in the previous section, we can compute expressions with the three columns like this:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -596,14 +628,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``DataFrame.eval()`` method allows much more succinct evaluation of expressions with the columns:"
+    "The `DataFrame.eval` method allows much more succinct evaluation of expressions with the columns:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -633,23 +668,39 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Assignment in DataFrame.eval()\n",
+    "### Assignment in DataFrame.eval\n",
     "\n",
-    "In addition to the options just discussed, ``DataFrame.eval()``  also allows assignment to any column.\n",
-    "Let's use the ``DataFrame`` from before, which has columns ``'A'``, ``'B'``, and ``'C'``:"
+    "In addition to the options just discussed, `DataFrame.eval`  also allows assignment to any column.\n",
+    "Let's use the `DataFrame` from before, which has columns `'A'`, `'B'`, and `'C'`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
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@@ -662,33 +713,33 @@
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-       "      <td>0.375506</td>\n",
-       "      <td>0.406939</td>\n",
-       "      <td>0.069938</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.069087</td>\n",
-       "      <td>0.235615</td>\n",
-       "      <td>0.154374</td>\n",
+       "      <td>0.820126</td>\n",
+       "      <td>0.385686</td>\n",
+       "      <td>0.061402</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.677945</td>\n",
-       "      <td>0.433839</td>\n",
-       "      <td>0.652324</td>\n",
+       "      <td>0.059729</td>\n",
+       "      <td>0.831768</td>\n",
+       "      <td>0.652259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.264038</td>\n",
-       "      <td>0.808055</td>\n",
-       "      <td>0.347197</td>\n",
+       "      <td>0.244774</td>\n",
+       "      <td>0.140322</td>\n",
+       "      <td>0.041711</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.589161</td>\n",
-       "      <td>0.252418</td>\n",
-       "      <td>0.557789</td>\n",
+       "      <td>0.818205</td>\n",
+       "      <td>0.753384</td>\n",
+       "      <td>0.578851</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -696,11 +747,11 @@
       ],
       "text/plain": [
        "          A         B         C\n",
-       "0  0.375506  0.406939  0.069938\n",
-       "1  0.069087  0.235615  0.154374\n",
-       "2  0.677945  0.433839  0.652324\n",
-       "3  0.264038  0.808055  0.347197\n",
-       "4  0.589161  0.252418  0.557789"
+       "0  0.850888  0.966709  0.958690\n",
+       "1  0.820126  0.385686  0.061402\n",
+       "2  0.059729  0.831768  0.652259\n",
+       "3  0.244774  0.140322  0.041711\n",
+       "4  0.818205  0.753384  0.578851"
       ]
      },
      "execution_count": 19,
@@ -716,20 +767,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can use ``df.eval()`` to create a new column ``'D'`` and assign to it a value computed from the other columns:"
+    "We can use `df.eval` to create a new column `'D'` and assign to it a value computed from the other columns:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
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@@ -743,38 +810,38 @@
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-       "      <td>0.406939</td>\n",
-       "      <td>0.069938</td>\n",
-       "      <td>11.187620</td>\n",
+       "      <td>0.850888</td>\n",
+       "      <td>0.966709</td>\n",
+       "      <td>0.958690</td>\n",
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        "    </tr>\n",
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-       "      <td>0.069087</td>\n",
-       "      <td>0.235615</td>\n",
-       "      <td>0.154374</td>\n",
-       "      <td>1.973796</td>\n",
+       "      <td>0.820126</td>\n",
+       "      <td>0.385686</td>\n",
+       "      <td>0.061402</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.677945</td>\n",
-       "      <td>0.433839</td>\n",
-       "      <td>0.652324</td>\n",
-       "      <td>1.704344</td>\n",
+       "      <td>0.059729</td>\n",
+       "      <td>0.831768</td>\n",
+       "      <td>0.652259</td>\n",
+       "      <td>1.366782</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.264038</td>\n",
-       "      <td>0.808055</td>\n",
-       "      <td>0.347197</td>\n",
-       "      <td>3.087857</td>\n",
+       "      <td>0.244774</td>\n",
+       "      <td>0.140322</td>\n",
+       "      <td>0.041711</td>\n",
+       "      <td>9.232370</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.589161</td>\n",
-       "      <td>0.252418</td>\n",
-       "      <td>0.557789</td>\n",
-       "      <td>1.508776</td>\n",
+       "      <td>0.818205</td>\n",
+       "      <td>0.753384</td>\n",
+       "      <td>0.578851</td>\n",
+       "      <td>2.715013</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -782,11 +849,11 @@
       ],
       "text/plain": [
        "          A         B         C          D\n",
-       "0  0.375506  0.406939  0.069938  11.187620\n",
-       "1  0.069087  0.235615  0.154374   1.973796\n",
-       "2  0.677945  0.433839  0.652324   1.704344\n",
-       "3  0.264038  0.808055  0.347197   3.087857\n",
-       "4  0.589161  0.252418  0.557789   1.508776"
+       "0  0.850888  0.966709  0.958690   1.895916\n",
+       "1  0.820126  0.385686  0.061402  19.638139\n",
+       "2  0.059729  0.831768  0.652259   1.366782\n",
+       "3  0.244774  0.140322  0.041711   9.232370\n",
+       "4  0.818205  0.753384  0.578851   2.715013"
       ]
      },
      "execution_count": 20,
@@ -810,13 +877,29 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -830,38 +913,38 @@
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        "    <tr>\n",
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-       "      <td>0.375506</td>\n",
-       "      <td>0.406939</td>\n",
-       "      <td>0.069938</td>\n",
-       "      <td>-0.449425</td>\n",
+       "      <td>0.850888</td>\n",
+       "      <td>0.966709</td>\n",
+       "      <td>0.958690</td>\n",
+       "      <td>-0.120812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.069087</td>\n",
-       "      <td>0.235615</td>\n",
-       "      <td>0.154374</td>\n",
-       "      <td>-1.078728</td>\n",
+       "      <td>0.820126</td>\n",
+       "      <td>0.385686</td>\n",
+       "      <td>0.061402</td>\n",
+       "      <td>7.075399</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.677945</td>\n",
-       "      <td>0.433839</td>\n",
-       "      <td>0.652324</td>\n",
-       "      <td>0.374209</td>\n",
+       "      <td>0.059729</td>\n",
+       "      <td>0.831768</td>\n",
+       "      <td>0.652259</td>\n",
+       "      <td>-1.183638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.264038</td>\n",
-       "      <td>0.808055</td>\n",
-       "      <td>0.347197</td>\n",
-       "      <td>-1.566886</td>\n",
+       "      <td>0.244774</td>\n",
+       "      <td>0.140322</td>\n",
+       "      <td>0.041711</td>\n",
+       "      <td>2.504142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.589161</td>\n",
-       "      <td>0.252418</td>\n",
-       "      <td>0.557789</td>\n",
-       "      <td>0.603708</td>\n",
+       "      <td>0.818205</td>\n",
+       "      <td>0.753384</td>\n",
+       "      <td>0.578851</td>\n",
+       "      <td>0.111982</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -869,11 +952,11 @@
       ],
       "text/plain": [
        "          A         B         C         D\n",
-       "0  0.375506  0.406939  0.069938 -0.449425\n",
-       "1  0.069087  0.235615  0.154374 -1.078728\n",
-       "2  0.677945  0.433839  0.652324  0.374209\n",
-       "3  0.264038  0.808055  0.347197 -1.566886\n",
-       "4  0.589161  0.252418  0.557789  0.603708"
+       "0  0.850888  0.966709  0.958690 -0.120812\n",
+       "1  0.820126  0.385686  0.061402  7.075399\n",
+       "2  0.059729  0.831768  0.652259 -1.183638\n",
+       "3  0.244774  0.140322  0.041711  2.504142\n",
+       "4  0.818205  0.753384  0.578851  0.111982"
       ]
      },
      "execution_count": 21,
@@ -890,9 +973,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Local variables in DataFrame.eval()\n",
+    "### Local Variables in DataFrame.eval\n",
     "\n",
-    "The ``DataFrame.eval()`` method supports an additional syntax that lets it work with local Python variables.\n",
+    "The `DataFrame.eval` method supports an additional syntax that lets it work with local Python variables.\n",
     "Consider the following:"
    ]
   },
@@ -900,7 +983,10 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -925,17 +1011,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``@`` character here marks a *variable name* rather than a *column name*, and lets you efficiently evaluate expressions involving the two \"namespaces\": the namespace of columns, and the namespace of Python objects.\n",
-    "Notice that this ``@`` character is only supported by the ``DataFrame.eval()`` *method*, not by the ``pandas.eval()`` *function*, because the ``pandas.eval()`` function only has access to the one (Python) namespace."
+    "The `@` character here marks a *variable name* rather than a *column name*, and lets you efficiently evaluate expressions involving the two \"namespaces\": the namespace of columns, and the namespace of Python objects.\n",
+    "Notice that this `@` character is only supported by the `DataFrame.eval` *method*, not by the `pandas.eval` *function*, because the `pandas.eval` function only has access to the one (Python) namespace."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## DataFrame.query() Method\n",
+    "## The DataFrame.query Method\n",
     "\n",
-    "The ``DataFrame`` has another method based on evaluated strings, called the ``query()`` method.\n",
+    "The `DataFrame` has another method based on evaluated strings, called `query`.\n",
     "Consider the following:"
    ]
   },
@@ -943,7 +1029,10 @@
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -967,16 +1056,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As with the example used in our discussion of ``DataFrame.eval()``, this is an expression involving columns of the ``DataFrame``.\n",
-    "It cannot be expressed using the ``DataFrame.eval()`` syntax, however!\n",
-    "Instead, for this type of filtering operation, you can use the ``query()`` method:"
+    "As with the example used in our discussion of `DataFrame.eval`, this is an expression involving columns of the `DataFrame`.\n",
+    "However, it cannot be expressed using the `DataFrame.eval` syntax!\n",
+    "Instead, for this type of filtering operation, you can use the `query` method:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1000,14 +1092,17 @@
    "metadata": {},
    "source": [
     "In addition to being a more efficient computation, compared to the masking expression this is much easier to read and understand.\n",
-    "Note that the ``query()`` method also accepts the ``@`` flag to mark local variables:"
+    "Note that the `query` method also accepts the `@` flag to mark local variables:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1034,16 +1129,15 @@
    "source": [
     "## Performance: When to Use These Functions\n",
     "\n",
-    "When considering whether to use these functions, there are two considerations: *computation time* and *memory use*.\n",
-    "Memory use is the most predictable aspect. As already mentioned, every compound expression involving NumPy arrays or Pandas ``DataFrame``s will result in implicit creation of temporary arrays:\n",
-    "For example, this:"
+    "When considering whether to use `eval` and `query`, there are two considerations: *computation time* and *memory use*.\n",
+    "Memory use is the most predictable aspect. As already mentioned, every compound expression involving NumPy arrays or Pandas ``DataFrame``s will result in implicit creation of temporary arrays. For example, this:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -1054,14 +1148,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Is roughly equivalent to this:"
+    "is roughly equivalent to this:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -1075,7 +1172,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If the size of the temporary ``DataFrame``s is significant compared to your available system memory (typically several gigabytes) then it's a good idea to use an ``eval()`` or ``query()`` expression.\n",
+    "If the size of the temporary ``DataFrame``s is significant compared to your available system memory (typically several gigabytes), then it's a good idea to use an `eval` or `query` expression.\n",
     "You can check the approximate size of your array in bytes using this:"
    ]
   },
@@ -1083,7 +1180,10 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1105,30 +1205,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "On the performance side, ``eval()`` can be faster even when you are not maxing-out your system memory.\n",
-    "The issue is how your temporary ``DataFrame``s compare to the size of the L1 or L2 CPU cache on your system (typically a few megabytes in 2016); if they are much bigger, then ``eval()`` can avoid some potentially slow movement of values between the different memory caches.\n",
-    "In practice, I find that the difference in computation time between the traditional methods and the ``eval``/``query`` method is usually not significant–if anything, the traditional method is faster for smaller arrays!\n",
-    "The benefit of ``eval``/``query`` is mainly in the saved memory, and the sometimes cleaner syntax they offer.\n",
-    "\n",
-    "We've covered most of the details of ``eval()`` and ``query()`` here; for more information on these, you can refer to the Pandas documentation.\n",
-    "In particular, different parsers and engines can be specified for running these queries; for details on this, see the discussion within the [\"Enhancing Performance\" section](http://pandas.pydata.org/pandas-docs/dev/enhancingperf.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Working with Time Series](03.11-Working-with-Time-Series.ipynb) | [Contents](Index.ipynb) | [Further Resources](03.13-Further-Resources.ipynb) >\n",
+    "On the performance side, `eval` can be faster even when you are not maxing out your system memory.\n",
+    "The issue is how your temporary objects compare to the size of the L1 or L2 CPU cache on your system (typically a few megabytes); if they are much bigger, then `eval` can avoid some potentially slow movement of values between the different memory caches.\n",
+    "In practice, I find that the difference in computation time between the traditional methods and the `eval`/`query` method is usually not significant—if anything, the traditional method is faster for smaller arrays!\n",
+    "The benefit of `eval`/`query` is mainly in the saved memory, and the sometimes cleaner syntax they offer.\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.12-Performance-Eval-and-Query.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "We've covered most of the details of `eval` and `query` here; for more information on these, you can refer to the Pandas documentation.\n",
+    "In particular, different parsers and engines can be specified for running these queries; for details on this, see the discussion within the [\"Enhancing Performance\" section](https://pandas.pydata.org/pandas-docs/dev/user_guide/enhancingperf.html) of the documentation."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1142,9 +1235,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.13-Further-Resources.ipynb b/notebooks/03.13-Further-Resources.ipynb
index 16c8a8ebd..0c3a8c88e 100644
--- a/notebooks/03.13-Further-Resources.ipynb
+++ b/notebooks/03.13-Further-Resources.ipynb
@@ -1,33 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) | [Contents](Index.ipynb) | [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.13-Further-Resources.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -42,39 +14,29 @@
     "editable": true
    },
    "source": [
-    "In this chapter, we've covered many of the basics of using Pandas effectively for data analysis.\n",
+    "In this part of the book, we've covered many of the basics of using Pandas effectively for data analysis.\n",
     "Still, much has been omitted from our discussion.\n",
     "To learn more about Pandas, I recommend the following resources:\n",
     "\n",
-    "- [Pandas online documentation](http://pandas.pydata.org/): This is the go-to source for complete documentation of the package. While the examples in the documentation tend to be small generated datasets, the description of the options is complete and generally very useful for understanding the use of various functions.\n",
-    "\n",
-    "- [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do) Written by Wes McKinney (the original creator of Pandas), this book contains much more detail on the Pandas package than we had room for in this chapter. In particular, he takes a deep dive into tools for time series, which were his bread and butter as a financial consultant. The book also has many entertaining examples of applying Pandas to gain insight from real-world datasets. Keep in mind, though, that the book is now several years old, and the Pandas package has quite a few new features that this book does not cover (but be on the lookout for a new edition in 2017).\n",
+    "- [Pandas online documentation](http://pandas.pydata.org/): This is the go-to source for complete documentation of the package. While the examples in the documentation tend to be based on small generated datasets, the description of the options is complete and generally very useful for understanding the use of various functions.\n",
     "\n",
-    "- [Stack Overflow](http://stackoverflow.com/questions/tagged/pandas): Pandas has so many users that any question you have has likely been asked and answered on Stack Overflow. Using Pandas is a case where some Google-Fu is your best friend. Simply go to your favorite search engine and type in the question, problem, or error you're coming across–more than likely you'll find your answer on a Stack Overflow page.\n",
+    "- [*Python for Data Analysis*](https://learning.oreilly.com/library/view/python-for-data/9781098104023/): Written by Wes McKinney (the original creator of Pandas), this book contains much more detail on the Pandas package than we had room for in this chapter. In particular, McKinney takes a deep dive into tools for time series, which were his bread and butter as a financial consultant. The book also has many entertaining examples of applying Pandas to gain insight from real-world datasets.\n",
     "\n",
-    "- [Pandas on PyVideo](http://pyvideo.org/search?q=pandas): From PyCon to SciPy to PyData, many conferences have featured tutorials from Pandas developers and power users. The PyCon tutorials in particular tend to be given by very well-vetted presenters.\n",
+    "- [*Effective Pandas*](https://leanpub.com/effective-pandas): This short e-book by Pandas developer Tom Augspurger provides a succinct outline of using the full power of the Pandas library in an effective and idiomatic way.\n",
     "\n",
-    "Using these resources, combined with the walk-through given in this chapter, my hope is that you'll be poised to use Pandas to tackle any data analysis problem you come across!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) | [Contents](Index.ipynb) | [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) >\n",
+    "- [Pandas on PyVideo](http://pyvideo.org/search?q=pandas): From PyCon to SciPy to PyData, many conferences have featured tutorials by Pandas developers and power users. The PyCon tutorials in particular tend to be given by very well-vetted presenters.\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.13-Further-Resources.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "Using these resources, combined with the walkthrough given in these chapters, my hope is that you'll be poised to use Pandas to tackle any data analysis problem you come across!"
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -88,9 +50,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.00-Introduction-To-Matplotlib.ipynb b/notebooks/04.00-Introduction-To-Matplotlib.ipynb
index ebf07e3bd..19bf8a7bc 100644
--- a/notebooks/04.00-Introduction-To-Matplotlib.ipynb
+++ b/notebooks/04.00-Introduction-To-Matplotlib.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.00-Introduction-To-Matplotlib.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -34,21 +12,21 @@
    "metadata": {},
    "source": [
     "We'll now take an in-depth look at the Matplotlib package for visualization in Python.\n",
-    "Matplotlib is a multi-platform data visualization library built on NumPy arrays, and designed to work with the broader SciPy stack.\n",
-    "It was conceived by John Hunter in 2002, originally as a patch to IPython for enabling interactive MATLAB-style plotting via gnuplot from the IPython command line.\n",
+    "Matplotlib is a multiplatform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.\n",
+    "It was conceived by John Hunter in 2002, originally as a patch to IPython for enabling interactive MATLAB-style plotting via `gnuplot` from the IPython command line.\n",
     "IPython's creator, Fernando Perez, was at the time scrambling to finish his PhD, and let John know he wouldn’t have time to review the patch for several months.\n",
     "John took this as a cue to set out on his own, and the Matplotlib package was born, with version 0.1 released in 2003.\n",
     "It received an early boost when it was adopted as the plotting package of choice of the Space Telescope Science Institute (the folks behind the Hubble Telescope), which financially supported Matplotlib’s development and greatly expanded its capabilities.\n",
     "\n",
     "One of Matplotlib’s most important features is its ability to play well with many operating systems and graphics backends.\n",
-    "Matplotlib supports dozens of backends and output types, which means you can count on it to work regardless of which operating system you are using or which output format you wish.\n",
+    "Matplotlib supports dozens of backends and output types, which means you can count on it to work regardless of which operating system you are using or which output format you desire.\n",
     "This cross-platform, everything-to-everyone approach has been one of the great strengths of Matplotlib.\n",
     "It has led to a large user base, which in turn has led to an active developer base and Matplotlib’s powerful tools and ubiquity within the scientific Python world.\n",
     "\n",
     "In recent years, however, the interface and style of Matplotlib have begun to show their age.\n",
-    "Newer tools like ggplot and ggvis in the R language, along with web visualization toolkits based on D3js and HTML5 canvas, often make Matplotlib feel clunky and old-fashioned.\n",
+    "Newer tools like `ggplot` and `ggvis` in the R language, along with web visualization toolkits based on D3js and HTML5 canvas, often make Matplotlib feel clunky and old-fashioned.\n",
     "Still, I'm of the opinion that we cannot ignore Matplotlib's strength as a well-tested, cross-platform graphics engine.\n",
-    "Recent Matplotlib versions make it relatively easy to set new global plotting styles (see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb)), and people have been developing new packages that build on its powerful internals to drive Matplotlib via cleaner, more modern APIs—for example, Seaborn (discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)), [ggpy](http://yhat.github.io/ggpy/), [HoloViews](http://holoviews.org/), [Altair](http://altair-viz.github.io/), and even Pandas itself can be used as wrappers around Matplotlib's API.\n",
+    "Recent Matplotlib versions make it relatively easy to set new global plotting styles (see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb)), and people have been developing new packages that build on its powerful internals to drive Matplotlib via cleaner, more modern APIs—for example, Seaborn (discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)), [`ggpy`](http://yhat.github.io/ggpy/), [HoloViews](http://holoviews.org/), and even Pandas itself can be used as wrappers around Matplotlib's API.\n",
     "Even with wrappers like these, it is still often useful to dive into Matplotlib's syntax to adjust the final plot output.\n",
     "For this reason, I believe that Matplotlib itself will remain a vital piece of the data visualization stack, even if new tools mean the community gradually moves away from using the Matplotlib API directly."
    ]
@@ -57,7 +35,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## General Matplotlib Tips\n",
+    "# General Matplotlib Tips\n",
     "\n",
     "Before we dive into the details of creating visualizations with Matplotlib, there are a few useful things you should know about using the package."
    ]
@@ -66,16 +44,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Importing Matplotlib\n",
+    "## Importing Matplotlib\n",
     "\n",
-    "Just as we use the ``np`` shorthand for NumPy and the ``pd`` shorthand for Pandas, we will use some standard shorthands for Matplotlib imports:"
+    "Just as we use the `np` shorthand for NumPy and the `pd` shorthand for Pandas, we will use some standard shorthands for Matplotlib imports:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -87,24 +65,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``plt`` interface is what we will use most often, as we shall see throughout this chapter."
+    "The `plt` interface is what we will use most often, as you shall see throughout this part of the book."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Setting Styles\n",
+    "## Setting Styles\n",
     "\n",
-    "We will use the ``plt.style`` directive to choose appropriate aesthetic styles for our figures.\n",
-    "Here we will set the ``classic`` style, which ensures that the plots we create use the classic Matplotlib style:"
+    "We will use the `plt.style` directive to choose appropriate aesthetic styles for our figures.\n",
+    "Here we will set the `classic` style, which ensures that the plots we create use the classic Matplotlib style:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -115,8 +93,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Throughout this section, we will adjust this style as needed.\n",
-    "Note that the stylesheets used here are supported as of Matplotlib version 1.5; if you are using an earlier version of Matplotlib, only the default style is available.\n",
+    "Throughout this chapter, we will adjust this style as needed.\n",
     "For more information on stylesheets, see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb)."
    ]
   },
@@ -124,7 +101,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### ``show()`` or No ``show()``? How to Display Your Plots"
+    "## show or No show? How to Display Your Plots"
    ]
   },
   {
@@ -132,22 +109,22 @@
    "metadata": {},
    "source": [
     "A visualization you can't see won't be of much use, but just how you view your Matplotlib plots depends on the context.\n",
-    "The best use of Matplotlib differs depending on how you are using it; roughly, the three applicable contexts are using Matplotlib in a script, in an IPython terminal, or in an IPython notebook."
+    "The best use of Matplotlib differs depending on how you are using it; roughly, the three applicable contexts are using Matplotlib in a script, in an IPython terminal, or in a Jupyter notebook."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Plotting from a script\n",
+    "### Plotting from a Script\n",
     "\n",
-    "If you are using Matplotlib from within a script, the function ``plt.show()`` is your friend.\n",
-    "``plt.show()`` starts an event loop, looks for all currently active figure objects, and opens one or more interactive windows that display your figure or figures.\n",
+    "If you are using Matplotlib from within a script, the function `plt.show` is your friend.\n",
+    "`plt.show` starts an event loop, looks for all currently active `Figure` objects, and opens one or more interactive windows that display your figure or figures.\n",
     "\n",
     "So, for example, you may have a file called *myplot.py* containing the following:\n",
     "\n",
     "```python\n",
-    "# ------- file: myplot.py ------\n",
+    "# file: myplot.py \n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
@@ -165,22 +142,22 @@
     "$ python myplot.py\n",
     "```\n",
     "\n",
-    "The ``plt.show()`` command does a lot under the hood, as it must interact with your system's interactive graphical backend.\n",
-    "The details of this operation can vary greatly from system to system and even installation to installation, but matplotlib does its best to hide all these details from you.\n",
+    "The `plt.show` command does a lot under the hood, as it must interact with your system's interactive graphical backend.\n",
+    "The details of this operation can vary greatly from system to system and even installation to installation, but Matplotlib does its best to hide all these details from you.\n",
     "\n",
-    "One thing to be aware of: the ``plt.show()`` command should be used *only once* per Python session, and is most often seen at the very end of the script.\n",
-    "Multiple ``show()`` commands can lead to unpredictable backend-dependent behavior, and should mostly be avoided."
+    "One thing to be aware of: the `plt.show` command should be used *only once* per Python session, and is most often seen at the very end of the script.\n",
+    "Multiple `show` commands can lead to unpredictable backend-dependent behavior, and should mostly be avoided."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Plotting from an IPython shell\n",
+    "### Plotting from an IPython Shell\n",
     "\n",
-    "It can be very convenient to use Matplotlib interactively within an IPython shell (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\n",
+    "Matplotlib also works seamlessly within an IPython shell (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\n",
     "IPython is built to work well with Matplotlib if you specify Matplotlib mode.\n",
-    "To enable this mode, you can use the ``%matplotlib`` magic command after starting ``ipython``:\n",
+    "To enable this mode, you can use the `%matplotlib` magic command after starting `ipython`:\n",
     "\n",
     "```ipython\n",
     "In [1]: %matplotlib\n",
@@ -189,61 +166,57 @@
     "In [2]: import matplotlib.pyplot as plt\n",
     "```\n",
     "\n",
-    "At this point, any ``plt`` plot command will cause a figure window to open, and further commands can be run to update the plot.\n",
-    "Some changes (such as modifying properties of lines that are already drawn) will not draw automatically: to force an update, use ``plt.draw()``.\n",
-    "Using ``plt.show()`` in Matplotlib mode is not required."
+    "At this point, any `plt` plot command will cause a figure window to open, and further commands can be run to update the plot.\n",
+    "Some changes (such as modifying properties of lines that are already drawn) will not draw automatically: to force an update, use `plt.draw`.\n",
+    "Using `plt.show` in IPython's Matplotlib mode is not required."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Plotting from an IPython notebook\n",
+    "### Plotting from a Jupyter Notebook\n",
     "\n",
-    "The IPython notebook is a browser-based interactive data analysis tool that can combine narrative, code, graphics, HTML elements, and much more into a single executable document (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\n",
+    "The Jupyter notebook is a browser-based interactive data analysis tool that can combine narrative, code, graphics, HTML elements, and much more into a single executable document (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\n",
     "\n",
-    "Plotting interactively within an IPython notebook can be done with the ``%matplotlib`` command, and works in a similar way to the IPython shell.\n",
-    "In the IPython notebook, you also have the option of embedding graphics directly in the notebook, with two possible options:\n",
+    "Plotting interactively within a Jupyter notebook can be done with the `%matplotlib` command, and works in a similar way to the IPython shell.\n",
+    "You also have the option of embedding graphics directly in the notebook, with two possible options:\n",
     "\n",
-    "- ``%matplotlib notebook`` will lead to *interactive* plots embedded within the notebook\n",
-    "- ``%matplotlib inline`` will lead to *static* images of your plot embedded in the notebook\n",
+    "- `%matplotlib inline` will lead to *static* images of your plot embedded in the notebook.\n",
+    "- `%matplotlib notebook` will lead to *interactive* plots embedded within the notebook.\n",
     "\n",
-    "For this book, we will generally opt for ``%matplotlib inline``:"
+    "For this book, we will generally stick with the default, with figures rendered as static images (see the following figure for the result of this basic plotting example):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After running this command (it needs to be done only once per kernel/session), any cell within the notebook that creates a plot will embed a PNG image of the resulting graphic:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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W/n4vc2LxF9Jhy84JIaSUkvfeU0sL/+9/uhMFtoUL4fXXYYV3erMCyvHjUKcO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\n1084mcD3e7+3M16+dOoEW7ZAYiJUqwY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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c21e668>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -263,15 +236,18 @@
     "### Saving Figures to File\n",
     "\n",
     "One nice feature of Matplotlib is the ability to save figures in a wide variety of formats.\n",
-    "Saving a figure can be done using the ``savefig()`` command.\n",
-    "For example, to save the previous figure as a PNG file, you can run this:"
+    "Saving a figure can be done using the `savefig` command.\n",
+    "For example, to save the previous figure as a PNG file, we can run this:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -282,21 +258,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We now have a file called ``my_figure.png`` in the current working directory:"
+    "We now have a file called *my_figure.png* in the current working directory:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-rw-r--r--  1 jakevdp  staff    16K Aug 11 10:59 my_figure.png\r\n"
+      "-rw-r--r--  1 jakevdp  staff    26K Feb  1 06:15 my_figure.png\n"
      ]
     }
    ],
@@ -308,19 +287,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To confirm that it contains what we think it contains, let's use the IPython ``Image`` object to display the contents of this file:"
+    "To confirm that it contains what we think it contains, let's use the IPython `Image` object to display the contents of this file (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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TQGYm7yR8PMp6hIjECDjZiHPbHxnJ3kdqXm4V5BokVFspS0vWrmvbNt5J1MVo\nClhyMnDvHlu3oman75zGW+Fv8Y4hG2troGVLNvvTGG2/tB3dGnVDJfNKvKPIJiKCPZNRs384/AMX\n71/EvYx7vKPIhoYRX2Y0BSwyEujTRx3Ne4vT2KYxNl7cKFRzX2N+40UkRSDIRZzb/vv3gfPn+Tfv\nfZ2KZhXh4+SDqCRxeoz26cMWj1Nz3/8xmgKmleHDqhWrooN9B+y6sot3FNkUPgcztnY4Ofk52Hl5\nJwJcVLZqvhyiolgPUTU0732dL7t9Cb/GfrxjyMbWFmjShLXvIoxRFLAnT4BjxwAfH95JSibQJVCo\n8XsnJzb78/hx3kmUdfruaXjU9oBtFVveUWRT+PxLC9xquqFe1Xq8Y8gqKIh6jD7PKArYjh1sTVLl\nyryTlEygSyC2XdqGAr04+5EY4zBiu/rtEDNKnL0wsrNZGza1NO81RoXrwQRZ4lZuRlHAtPDQ+XkO\n1R1Qr2o9nL93nncU2RhjAQOACqYVeEeQTWws0LQpUEuc3WA0p0kTwMwMOHuWdxJ1EL6A5eUB27er\nr3nv6xwdexTN6jTjHUM2bdqwWaDXrvFOQspKaxeCItLpaG3l84QvYAcPAo6OrG+bllQ008BT8lIw\nNWVDT/TG0yZJ0tbzr+cV6AuQmSfOQkRqK/U/whewyEi6alQLeuNpV1wcm3no5sY7SelNj5mOuQfn\n8o4hm86dgaQk6jEKUAEjCvL1BY4cAZ4+5Z3EsB5lPcKfN/7kHUNWhe8jNXffKIqvky8iksR5AFuh\nAnsvRYmzxK3MhC5giYmshZFae7YZmypVgA4d2GJMkYUnhGP+0fm8Y8hKyxeCHRt0xLXH15DyNIV3\nFNnQaAYjdAGLjGSTN7R41Vho//X9eJojzi2LMbzxIpMiEeii0U/7V7h9m+1F1bkz7yRlY2Zihl6N\ne2FrkjjbIhT2GM3O5p2EL+ELmFavGgt9c/AbbLskTgfPgADWkLRAnCVuL8jOz0Z0cjT8ncVZLBUV\nxbbFMTfnnaTsRGsOUKMG0KwZsHcv7yR8qa6A7dixA25ubnBxccGcOXNe+ZopU6bA2dkZzZs3R1xc\nXJHHOn0a6N7dUEmVEegSKNSVo4MDUK8eexYmopjkGDSt3RQ1K9XkHUU2IlwI+jmxllKibHIJ0HR6\nQGUFTK/XY/Lkydi5cycuXLiAtWvXIiEh4YXXbN++HVeuXMGlS5ewePFijB8/vsjjde3KtiHQsj4u\nfbD98nYdNYVpAAAgAElEQVTk6/N5R5GNyMOIog0fZmWxBcy9e/NOUj7WltbYOnQrdFp+nvA31JVD\nZQXs2LFjcHZ2hoODA8zNzRESEoLwvzX+Cg8Px8iRIwEAbdu2xZMnT5CamvrK42n9qhEA6lerDwcr\nBxy6eYh3FNmIXMC6OHTBQI+BvGPIZu9eNgnKxoZ3EvJ3rq5ApUpsiYOxUlUBS0lJgb29/bOv69ev\nj5SUlGJfY2dn99JrCmmt+0ZRAl0CEZkozid+69bAw4fA1au8k8gvxDMEjtaOvGPIpnAiFFEnkS8G\nS0JVBUxudevyTiCPIU2HoLVda94xZGNiwvY2MuY3nhZIErB1qxgjGaIKCDDu95EZ7wDPs7Ozw40b\nN559fevWLdj9rQeUnZ0dbt68WexrCn3++efPft61a1d07dpV1rxKcavpBreaGmyBUIzAQGDhQmDq\nVN5JSFHi4tgzZFdX3klIUTp1Aq5cYUsd6pVx55jY2FjExsbKmkspOklF03IKCgrg6uqK6Oho1K1b\nF23atMHatWvh7u7+7DXbtm3DokWLEBUVhSNHjuC9997DkVdMadPpdELNOBJNejq7Q751C7Cy4p2G\nvMqMGcDjx8D33/NOIp/H2Y/x49Ef8ek/PuUdRTZDhrDZ1m+/Lc/xtPTZqaohRFNTUyxcuBC+vr7w\n8PBASEgI3N3dsXjxYvz6668AAH9/fzRq1AiNGzfGO++8g59++olzalIWVaqwq0fRu3JomQjT5/+u\nSoUqmHd0Hm49vcU7imyM+TmYqu7A5KSlqwhj9dNPbD3YypW8k5Tf2IixGN18NDo26Mg7iixu3wY8\nPYHUVG0vYH6VYaHD0LlBZ4xvVfQSHC1JS2PrK1NT5Vk2pKXPTlXdgRHjEhDA9mrTeleO7PxsbLy4\nUajnlCJ03yiKaF05rK2BFi2A6GjeSZRHBUxD9ibvxZTtU3jHkE2DBmyftsOHeScpn5jkGHjZeqFG\npRq8o8hGxOHDQr0a98KB6weQkZvBO4psAgPZjFFjQwVMQ1xquGD1udXUlUNlqPuGtlS3qI5W9Voh\nOlmcW5bCAqaRkT/ZUAHTkPrV6qNh9YY4eOMg7yiy0XoBkyQJW5O2ClXAoqPZkJS1Ne8khrPQfyE6\n2ovxvBIAXFxYV47Tp3knURYVMI0JdAlERKI4m/O1agU8esTWsmjRlbQrqFyhslDPv0QePizUpFYT\noYZ8AfZ3FiHOR0OJUAHTGNEeQBd25dDq+H1jm8Y4O/6sME1i9XrqvqFVxtidngqYxrSo2wJ5+jzh\n1rFo+crR3FScqXqnTrE1ei4uvJOQ0urYEbh2DSiiNayQqIBpjE6nQ9LkJNSvVp93FNn4+ADHjwNP\nnvBOQoxh+FBUZmZAr17aHc0oCypgGiTSFT8AVK7MtqvfsYN3EmJsBSy3IBdZeVm8Y8gmKEjboxml\nRQWMqIKxvfHU6NYt4Pp1NhRlLCZGTcTyuOW8Y8imVy/gwAEgQ5wlbsWiAkZUobArR14e7yQlk5mX\niW2XtvGOIautW9kHoJmq9qgwLD8nP6EmRVlZsf329uzhnUQZVMCIKtjZAY6OwEGNLHHbc3UPvj30\nLe8YsoqIAPr25Z1CWX6N/XDwxkGk56bzjiIbYxrNoAKmYVFJUcgtyOUdQzZaeuNFJorVfSM9nQ09\n+fnxTqKsahWrob19e+y8vJN3FNkEBrJelno97ySGRwVMw2bsn4H91/fzjiGbwgKm9nY4ekmPrZe2\nItBVnAK2axfQvr1x7s0W5BKEiCSNXDmVgKMjUKMGm9krOipgGtbXta9QXTmaNQNyc4GEBN5Jinfi\n9glYW1ijsU1j3lFkExHBLiCMkUgXIoW0NJpRHlTANCzINQjhieGa2bvndXQ6bSxqjkiMEGr4sKCA\nDTkZ0/T55zWwaoAVwSt4x5CVFt5HcqACpmEetTxgZmKGs6lneUeRjRbeeO3rt8eo5qN4x5DN4cNs\nEo2DA+8kRC5t2wL37gHJybyTGBYVMA3T6XQIcmF3YaLo1g24cIG9+dSqj0sfNKnVhHcM2Rjz8KGo\nTE3Z0pRwcT4aXokKmMa95f0WWtdrzTuGbCpWBHr2ZENaRBlUwMTUt6/4BUwnifIA5W90Op0wz4aM\nzapVwMaN4r/51CAxEejeHbh5k+0MQMSRmQnUqcMa/NrYlPz3aemzk/7JEtXx9wdiYtgbkBhWYe9D\nKl5AanoqZuybwTuGbCpVYhcn28RqGPMC+mdLVMfGhm10uXs37yTiCwuj4cNCVhZW+O7wd7ifcZ93\nFNkEBYk9kkEFjKiSGsfv3wp/C3uT9/KOIZt794Dz54EePXgnUQcLMwv4Ovlia5I4+5EEBLALwZwc\n3kkMgwoYUaW+fVlz2YIC3kmYnPwchMaHwrO2J+8osomMBHx92cQZwgS7BiMsMYx3DNnUrg14egJ7\nxbnuegEVMEHsvLwTE7ZO4B1DNg0bAnXrsjVKahBzLQaetT1Ru3Jt3lFkExYGBAfzTqEu/s7+iEmO\nQUauOPuRqHE0Qy5UwAThWdsT6y+sR16BRvYjKQE1vfHCE8LR11WcVu3p6cC+fWzCDPkfa0trtLFr\ng11XdvGOIpugIHa3LWJzXypggrCrZgfnGs7Yd30f7yiyKSxgvGf06iU9whPD0ddNnAK2axfQrh1Q\nvTrvJOqzOGAxejr25B1DNq6uQNWqwIkTvJPIjwqYQIJdg7ElfgvvGLJp0QLIyuLf3Pfyo8uwq2YH\nlxoufIPIKCzM+Pb+KiknGydUrViVdwxZ9esHbBHno+EZWsgskIQHCei5siduvH8DJjoxrk0mT2Z9\n+j7+mG8OvaQX5s80L48tcI2LA+zteachSjh2DBg5smQXg1r67BTjHUkAAG413VC7cm1cTbvKO4ps\n1HLlKErxAoA//2R7RlHxMh6tWrHnnvHxvJPIS5x3JQEAnBh3Qqh9qrp0Aa5cYa2OiDxo9qHxMTFh\nf+dquBiUExUwwYh0pwAA5uas1VGYOEtzuJIkKmAllZGbgbSsNN4xZKOW0Qw5ifVpR4T0xhtAaCjv\nFGI4dQqoUAFoIs5uMAbzeeznmHdkHu8YsunSBbh6VazRDCpgRPV8fNgH74MHyp739l+3EZGo8t01\nSyk0FOjfn+1+TYrXz70fNsdv5h1DNubmrLWUSKMZVMCI6llaspZHSu/UvPHCRmxJEGfMRZKAzZtZ\nASOv165+O6RlpyHxQSLvKLIRbRiRCpigIhIjcOevO7xjyKZfP+WHEbckbEE/t37KntSA4uPZFjWt\nWvFOog0mOhP0cxPrLszXFzh5Enj4kHcSeVABE9SWhC3YeHEj7xiy6dMH2L8f+OsvZc73IPMBTt89\nDR9HH2VOqIDNm9nzRBo+LLk33N9AaLw4D2ArVWK7D0RG8k4iDypggurv3h+bLm7iHUM2VlZAp07K\nbc4XlhAGPyc/WJpbKnNCBYSGsgJGSq6LQxd42XohtyCXdxTZiDQpigqYoHwcfXDu3jncTb/LO4ps\nlBxG3HRxEwY0GaDMyRRw9Spw+zbQsSPvJNpiZmKGZX2XoYJpBd5RZBMYCMTGAk+f8k5SflTABFXR\nrCL8nf0RliDOlKO+fYGdO1l/REOb2Hoi/J3FadUeGsrWfpma8k5CeLOyAv7xDzGGEamACUy0YcTa\ntVmD3x07DH+uINcgVKlQxfAnUgjNPiTPGzAA2CTARwM18xVYZl4mdl/ZLdQ2ID//DBw4AKxZwzuJ\ndqSkAF5ewJ07bBEzIWlpbNPYW7fYVivP09JnJ92BCaySeSWhihfAHkBv26bMMKIoQkPZAlYqXqSQ\ntTV7HhoVxTtJ+VABI5pia8uGEXfu5J1EOzZsAAYN4p1C+0aFjcK9jHu8Y8hGhGFEKmBEcwYOZB/K\nhpCTn2OYA3Ny6xZw8SJrx0XKJ68gT6g1YcHBwO7dQEYG7yRlRwWMaI6hhhGf5jyF/Q/2yM7PlvfA\nHG3axGZv0vBh+Q32GIz1F9bzjiEbGxugXTtg+3beScqOCpiRyMoT56GRrS3g7S3/MGJUUhRa27WG\nhZmFvAfmaP16Gj6Ui19jP8TdjROqRduAAcBGDTfsUU0BS0tLg6+vL1xdXeHn54cnT5688nUNGzZE\ns2bN4O3tjTZt2iicUpvyCvLQcH5DPMhUuJ27AQ0cKP8bb92FdRjURJxP++vXgUuXWOsgUn4WZhYI\ndAkUqjdiv37sQlCrw4iqKWCzZ89Gz549kZiYiO7du2PWrFmvfJ2JiQliY2Nx+vRpHDt2TOGU2mRu\nao5uDbsJNX5fOIyYLdNoX1pWGmKvxSLYTZydHjdtYh9Q5ua8k4hjkMcgRCYJsAL4v2rWZMOIW7fy\nTlI2qilg4eHhGDVqFABg1KhRCCti0xpJkqDX65WMJoTBHoOx7vw63jFkU6cOG0aUa/x+S8IW9GjU\nA1YWVvIcUAXWrwcGD+adQiy9GvdCeEg47xiyGjIEWLuWd4qyUU0Bu3fvHmxtbQEAderUwb17r56u\nqtPp4OPjg9atW+O3335TMqKm9XbujdN3Tws1fi/nGy8tKw1vNn9TnoOpQHIycO0a0LUr7yRiMTMx\nE+oZKcBmI8bEAI8f805SemZKnszHxwepqanPvpYkCTqdDl999dVLr9UVsefDwYMHUbduXdy/fx8+\nPj5wd3dHp06dDJZZFIXj95subsK7bd/lHUcW/fsDH37ImpJWq1a+Y33Q4QN5QqnEhg3sz8dM0Xc4\n0SIrK/acNDQUeOst3mlKR9F/3rt37y7y12xtbZGamgpbW1vcvXsXtWvXfuXr6tatCwCoVasW+vXr\nh2PHjhVZwD7//PNnP+/atSu6Gvnl6MhmI3EsRZznhjY2rClpeDgwYgTvNOqybh3w/fe8UxAtiI2N\nhalpLL76Crhxg3ea0lFNL8Rp06bBxsYG06ZNw5w5c5CWlobZs2e/8JrMzEzo9XpUqVIFGRkZ8PX1\nxWeffQZfX9+Xjqelfl6k7NatA1as0PZaFrlduAD4+bEPIxPVPCQgapaVBdSrByQkAHXqaOezUzX/\nvKdNm4bdu3fD1dUV0dHR+OijjwAAd+7cQUBAAAAgNTUVnTp1gre3N9q1a4fAwMBXFi9iPAIDgcOH\ngSIemRql1auBoUOpeBlSvj4fmy5u0swH/etYWrJ+mVpbE6aaOzC50R2Y8Rg+HGjfHpg0iXcS/vR6\nwNERiIhgHeiJYeglPZwWOGHL4C1oXqc57ziyiIoCvv4aOHRIO5+ddI1GNG/IkLJvr/LutneFatB6\n8CDbHoOKl2GZ6EwwrOkwrDq7incU2fj4AImJvFOUDhUwonm+vkBSEps6Xhrn751HWGIYaljWMEww\nDlatYnekxPCGNR2GNefWoEBfwDuKLCpUAP7zH94pSocKmBHKysvC0M1DhXnjmZuznm6lXRO28sxK\nDG86HKYmpoYJprCcHLbz8pAhvJMYB/da7qhXtR5irsXwjiKb997jnaB0qIAZIUtzSyQ9TMLe5L28\no8hm+HDgjz+Akg7d5+vzsersKoxsNtKwwRS0fTvg6Qk0aMA7ifEY7jUcq8+t5h3DaFEBM1IjvEbg\nj7N/8I4hmw4dgPx8oKTtMaOvRqN+tfpwr+Vu2GAKWrUKGDaMdwrjMrTpUAxrSn/ovNAsRCN1L+Me\nXH50wa1/3kKVClV4x5HFzJlsA8eff379a8dGjEXzOs0xuc1kwwdTwOPHgIMDax9lbc07DdEyLX12\nUgEzYgFrAjDYYzBGNBOjjcXNm0Dz5kBKCmDxmnZ1uQW5KNAXwNLcUplwBvbzz6yfnaF2qibGQ0uf\nnTSEaMRGeI1AWOKru/5rkb090LIlay31OhVMKwhTvABg6VJgzBjeKQhRFt2BGbG8gjxIkFDBVJz9\n5tesAVauBHbs4J1EOWfOsI4kycmAqRgTKglHWvrspDswI2Zuai5U8QLY1hDHjrFhRGOxbBnw5ptU\nvHi7n3Efeon2KlQSFTAilEqV2JqwP8SZYFmsnBx21/nmm7yTEP81/oi9Fss7hlGhAkaEM3o0sHz5\ny2vC8grysOTUEs0Mj5REeDhrG+XoyDsJGeE1AktPL+Udw6hQASPCadeODaft3//i9yOTIrHizIoi\nN0vVoqVLtbcJoaiGNR2GqKQopGWl8Y5iNKiAEQDAirgVuJt+l3cMWeh0wPjxwC+/vPj9X0/+inda\nvsMnlAHcuAGcOAG88QbvJAQAalSqgV6Ne2HNuTJ2lialRgWMAAD2X9+PFXEreMeQzciRbCZiair7\nOjktGSdun0B/9/58g8lo+XJg8GC2lxNRhzHeY7D09FKhhqnVjAoYAQC83fJtLDktzvOh6tWB/v3Z\nDD0AWHJqCUZ4jRBm7VdeHvDrr8CECbyTkOf1cOwBH0cf5BTk8I5iFKiAEQBAW7u2sDCzEKqz9vjx\nwOLFQHZuHpbFLcO4luN4R5LNli2AszPQtCnvJOR5JjoTzPGZAwuz17SCIbKgAkYAsMWLk1pPwo/H\nfuQdRTatWgG1agF7dpkhIiRCqMa9CxfSDtSEUCcO8kxGbgYc5jng5LiTcKjuwDuOLJYtA0JDga1b\neSeRz5kzgL8/a9xrbs47DRGNlj47qYCRF1x+dBlO1k7CTDXPzGQ9Ek+dYt3aRTBuHPt/+vRT3kmI\niLT02UkFjAjv/fcBMzNg7lzeScovLQ1o1AhISADq1OGdhrxOdn625p6Haemzk56BEeFNncqGEp8+\n5Z2k/JYvB/r0oeKlBbHXYuG3yo93DKFRASPCikmOwYPMB2jYEPD1BZYs4Z2ofAoKgEWLaPKGVnS0\n74grj67gzN0zvKMIiwoYEVJ2fjZCNofgQeYDAMAHHwDz5rH1U1q1aRO782rfnncSUhLmpuaY3GYy\n5h4SYOxapaiAkVfKyM3A73G/845RZqvPrkbLui3hVtMNAJtS7+QEbNzIOVgZSRIwezbw0UesVRbR\nhgmtJmDH5R1ITkvmHUVIVMDIK5mZmGF6zHQcTznOO0qpSZKEeUfn4f1277/w/Q8/BL799uUu9Vqw\naxeQn8+efxHtsLKwwriW4+guzECogJFXqmhWEf/q+C/MPDCTd5RSi0iMgJmJGXo69nzh+717s/2z\nYjTYbGTWLGDaNMCE3rGa816791Cvaj3eMYRE0+hJkbLysuC4wBE7h++El60X7zglIkkSWv3WCp92\n+RTBbsEv/fqyZcD69cDOnRzCldHhw8DQocClS2w5ACGGpKXPTipgpFjfHvoWx28fx/oB63lHKbGL\n9y/Cvab7Kxdj5+YCrq7AqlVAx44cwpVB375sFiXNPiRK0NJnJxUwUqz03HQ4LXDCibdPwN7Knncc\nWSxbxgrY3r28k7ze+fNAz55AcjJtm0KUoaXPTipg5LUeZD5AzUo1eceQTX4+4O7OOtV37847TfH6\n9gW6dGHLAAhRgpY+O+mRMHktkYoXwJ4jffEF8J//qHtG4p9/AnFxNHQoEkmSkPQwiXcMYVABI0Zp\n8GDWWmrHDt5JXk2S2KzDL78ELLTVSo8U40HmA7Rf2h43n9zkHUUIVMCI5uUW5OKDnR8gJ7/ku+Ca\nmgIzZqj3LiwiAvjrL2DYMN5JiJxqVa6F8S3HY3rsdN5RhEAFjGjegqMLkPAwARXNKpbq9/Xrx9ZV\nrV5toGBllJ8PfPwx67xhaso7DZHbvzr+C9subcO51HO8o2geFTBSKvOPzMfKMyt5x3gmNT0Vs/+c\nje99vy/179Xp2M7G//oX8OSJAcKV0YoVQO3abOE1EY+VhRU+7vQxPo7+mHcUzaMCRkqlbf22+CT6\nE2TkZvCOAgD4995/483mb8K1pmuZfn/btkBAADBdJSM6Dx4A//4327uMeh6Ka0KrCUh4kIA9V/fw\njgIASHqYhMy8TN4xSo2m0ZNSG7J5CBysHDC752yuOU7ePok+a/ogcXIirCysynycBw8ADw/WnaN5\ncxkDlsGoUYC1NeucT8SW9DAJDlYOpR76lltuQS6a/dIMc33mIsAlQFOfnXQHRkrtB78fsDxuOY6l\nHOOa4/jt4/i6x9flKl4AULMm8NVXbLq6Xi9TuDLYvRvYt49lIeJzqeHCvXgB7LFAo+qN0MdZe52i\n6Q6MlMm68+swY98MnHrnlOa2TH8VvZ7ts/X228DYscqfPzMTaNqUPZOjZ19EKclpyWj9W2scGXsE\njW0aA9DWZye1BiVlMthjMO5n3EduQa4QBczEBPjtN6BHD9b5wsVF2fN//jl7HkfFiyglryAPQzYP\nwb87//tZ8dIaugMj5Dk//wz8+ivrAK/UAuKYGNZt/swZNvuQGKesvCxYmivX8HLDhQ1YcWYFtg7Z\n+kLjay19dlIBI+Q5kgQMHAjUqwcsWGD48928CbRpw5oL9+hh+PMR9eq2ohsmtJqAQR6DFDtndn72\nSyMoWvrspEkcRDPWn1+PQzcPGfQcOh2wZAkQGQmEhRn0VMjOBt54A/jnP6l4EeB73+8xadsknE09\nq9g5tT78TwWMyCY7P9tgV257ru7BlB1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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -339,7 +321,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In ``savefig()``, the file format is inferred from the extension of the given filename.\n",
+    "In `savefig`, the file format is inferred from the extension of the given filename.\n",
     "Depending on what backends you have installed, many different file formats are available.\n",
     "The list of supported file types can be found for your system by using the following method of the figure canvas object:"
    ]
@@ -348,15 +330,18 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'eps': 'Encapsulated Postscript',\n",
-       " 'jpeg': 'Joint Photographic Experts Group',\n",
        " 'jpg': 'Joint Photographic Experts Group',\n",
+       " 'jpeg': 'Joint Photographic Experts Group',\n",
        " 'pdf': 'Portable Document Format',\n",
        " 'pgf': 'PGF code for LaTeX',\n",
        " 'png': 'Portable Network Graphics',\n",
@@ -382,16 +367,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that when saving your figure, it's not necessary to use ``plt.show()`` or related commands discussed earlier."
+    "Note that when saving your figure, it is not necessary to use `plt.show` or related commands discussed earlier."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Two Interfaces for the Price of One\n",
+    "### Two Interfaces for the Price of One\n",
     "\n",
-    "A potentially confusing feature of Matplotlib is its dual interfaces: a convenient MATLAB-style state-based interface, and a more powerful object-oriented interface. We'll quickly highlight the differences between the two here."
+    "A potentially confusing feature of Matplotlib is its dual interfaces: a convenient MATLAB-style state-based interface, and a more powerful object-oriented interface. I'll quickly highlight the differences between the two here."
    ]
   },
   {
@@ -400,26 +385,31 @@
    "source": [
     "#### MATLAB-style Interface\n",
     "\n",
-    "Matplotlib was originally written as a Python alternative for MATLAB users, and much of its syntax reflects that fact.\n",
-    "The MATLAB-style tools are contained in the pyplot (``plt``) interface.\n",
-    "For example, the following code will probably look quite familiar to MATLAB users:"
+    "Matplotlib was originally conceived as a Python alternative for MATLAB users, and much of its syntax reflects that fact.\n",
+    "The MATLAB-style tools are contained in the `pyplot` (`plt`) interface.\n",
+    "For example, the following code will probably look quite familiar to MATLAB users (the following figure shows the result):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
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DOr4O69ertmmjWrGiaq9eqps22fPxeC02bFB95BHVffdV7d5ddfPmmF8iLtLx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\nwpx7rjX+114L48fbHUEyy8z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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d658438>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -439,8 +429,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It is important to note that this interface is *stateful*: it keeps track of the \"current\" figure and axes, which are where all ``plt`` commands are applied.\n",
-    "You can get a reference to these using the ``plt.gcf()`` (get current figure) and ``plt.gca()`` (get current axes) routines.\n",
+    "It is important to recognize that this interface is *stateful*: it keeps track of the \"current\" figure and axes, which are where all `plt` commands are applied.\n",
+    "You can get a reference to these using the `plt.gcf` (get current figure) and `plt.gca` (get current axes) routines.\n",
     "\n",
     "While this stateful interface is fast and convenient for simple plots, it is easy to run into problems.\n",
     "For example, once the second panel is created, how can we go back and add something to the first?\n",
@@ -455,25 +445,30 @@
     "#### Object-oriented interface\n",
     "\n",
     "The object-oriented interface is available for these more complicated situations, and for when you want more control over your figure.\n",
-    "Rather than depending on some notion of an \"active\" figure or axes, in the object-oriented interface the plotting functions are *methods* of explicit ``Figure`` and ``Axes`` objects.\n",
-    "To re-create the previous plot using this style of plotting, you might do the following:"
+    "Rather than depending on some notion of an \"active\" figure or axes, in the object-oriented interface the plotting functions are *methods* of explicit `Figure` and `Axes` objects.\n",
+    "To re-create the previous plot using this style of plotting, as shown in the following figure, you might do the following:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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DOr4O69ertmmjWrGiaq9eqps22fPxeC02bFB95BHVffdV7d5ddfPmmF8iLtLx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\nwpx7rjX+114L48fbHUEyy8z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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d64f240>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -491,26 +486,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For more simple plots, the choice of which style to use is largely a matter of preference, but the object-oriented approach can become a necessity as plots become more complicated.\n",
-    "Throughout this chapter, we will switch between the MATLAB-style and object-oriented interfaces, depending on what is most convenient.\n",
-    "In most cases, the difference is as small as switching ``plt.plot()`` to ``ax.plot()``, but there are a few gotchas that we will highlight as they come up in the following sections."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.00-Introduction-To-Matplotlib.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "For simpler plots, the choice of which style to use is largely a matter of preference, but the object-oriented approach can become a necessity as plots become more complicated.\n",
+    "Throughout the following chapters, we will switch between the MATLAB-style and object-oriented interfaces, depending on what is most convenient.\n",
+    "In most cases, the difference is as small as switching `plt.plot` to `ax.plot`, but there are a few gotchas that I will highlight as they come up in the following chapters."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.6 64-bit ('3.9.6')",
    "language": "python",
    "name": "python3"
   },
@@ -524,9 +512,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc"
+   }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.01-Simple-Line-Plots.ipynb b/notebooks/04.01-Simple-Line-Plots.ipynb
index 03acda4e3..bbcb7d87a 100644
--- a/notebooks/04.01-Simple-Line-Plots.ipynb
+++ b/notebooks/04.01-Simple-Line-Plots.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.01-Simple-Line-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -35,14 +13,14 @@
    "source": [
     "Perhaps the simplest of all plots is the visualization of a single function $y = f(x)$.\n",
     "Here we will take a first look at creating a simple plot of this type.\n",
-    "As with all the following sections, we'll start by setting up the notebook for plotting and  importing the packages we will use:"
+    "As in all the following chapters, we'll start by setting up the notebook for plotting and  importing the packages we will use:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -56,22 +34,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For all Matplotlib plots, we start by creating a figure and an axes.\n",
-    "In their simplest form, a figure and axes can be created as follows:"
+    "For all Matplotlib plots, we start by creating a figure and axes.\n",
+    "In their simplest form, this can be done as follows (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x102e12390>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -87,25 +68,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In Matplotlib, the *figure* (an instance of the class ``plt.Figure``) can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels.\n",
-    "The *axes* (an instance of the class ``plt.Axes``) is what we see above: a bounding box with ticks and labels, which will eventually contain the plot elements that make up our visualization.\n",
-    "Throughout this book, we'll commonly use the variable name ``fig`` to refer to a figure instance, and ``ax`` to refer to an axes instance or group of axes instances.\n",
+    "In Matplotlib, the *figure* (an instance of the class `plt.Figure`) can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels.\n",
+    "The *axes* (an instance of the class `plt.Axes`) is what we see above: a bounding box with ticks, grids, and labels, which will eventually contain the plot elements that make up our visualization.\n",
+    "Throughout this part of the book, I'll commonly use the variable name `fig` to refer to a figure instance and `ax` to refer to an axes instance or group of axes instances.\n",
     "\n",
-    "Once we have created an axes, we can use the ``ax.plot`` function to plot some data. Let's start with a simple sinusoid:"
+    "Once we have created an axes, we can use the `ax.plot` method to plot some data. Let's start with a simple sinusoid, as shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
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nTHDpaQyrfn1piWzaZO1ZjlS2JUukYUAli4uTwQ1GLvSGHUi1ebO8OGrQQHUS/Ro4UEYN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\n5P4f+jwGTxP/IVYAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x107527f98>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -121,25 +105,31 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Alternatively, we can use the pylab interface and let the figure and axes be created for us in the background\n",
-    "(see [Two Interfaces for the Price of One](04.00-Introduction-To-Matplotlib.ipynb#Two-Interfaces-for-the-Price-of-One) for a discussion of these two interfaces):"
+    "Note that the semicolon at the end of the last line is intentional: it suppresses the textual representation of the plot from the output.\n",
+    "\n",
+    "Alternatively, we can use the PyLab interface and let the figure and axes be created for us in the background\n",
+    "(see [Two Interfaces for the Price of One](04.00-Introduction-To-Matplotlib.ipynb#Two-Interfaces-for-the-Price-of-One) for a discussion of these two interfaces); as the following figure shows, the result is the same:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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kkB09Ktvi0e2WLJHRNt7eqpOQXnl6AjExcq+MH+/gMRz50OLFixEUFIRFixah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\n5P4f+jwGTxP/IVYAAAAASUVORK5CYII=\n",
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nWA/weTxsPWt/32QpyT/Et2dKEejmhKcifNgOpdtGR/rCWybGNjv8oJPHl3GuDAaGQeoQ27/B8XN1xOi+PvjufLndFe2jJP8A12qVOHG1HilDgjlR+0Uk4GPmoCD8UliDyiZa8Jvcn05vwPazZUjs7Y0QT9vtpvy9lKEhqFdpcMjOFvymJP8A287egJDPw8xBtjds8n5mDw6BgQF2nCtnOxRixY4U1qDqthp/5kA35R2Jvb0R6OZkd99kKcnfh1qrx055OZKjfeHj4sh2OCYT7OGMhN5eyDh3gx7Akvv69swN+Ls6YlRf2++mvEPA52HW4GD8erXOrh7AUpK/j315lbjVosWfh7K/QLeppQ4Jwc0mNTKv0ANYcq+yhhZkXanF7MEhNjW7+1HMHBQMPg/Yfq6M7VAshlvvoAntPF+OEA9nPBFmvTXjjTUmyhdeUjG2nrGfDzp5dN/Jy8HjATMGBbEdisn5uTpiVF9f7DxfZjcPYCnJd6G8sQUni+sxPT4IfA48cP2juw9gq1F9W812OMSKGAwMdl0ox5O9vBDA0cU2UocGo06pwRGFfTyApSTfhTsLYE+NC3zIK23X9PggGBjYdeEmcq8zJQ0ob2zF9Hju3cXf8VSED3xdxNh1wT4GH1CS/wOGYfCdvBzDwz0R5M6NoWNdCfOWIi7EDbsulINh6AEsabdTXgaZWIix0bY7w/VhBHwepgwMxLHLtahTtrEdjtlRkv+Dc9cbcaOhhdN3MndMjQvClWol8m/eZjsUYgWUbTrsy63CxP4BcBTZ9gzXh5k6MAg6A4Pd2TfZDsXsKMn/wXfyMkgcBDZdq+NRTYoNgIOAj+/k9vG1lTzY3txKtGr1dnGD08dPhphAF3x/kfuffUryv9Oi0eHnnEo8E+sPZwejS+3bDFdnEcZE+WD3pZvQ6u1jpAG5v+/k5QjzkiAuxI3tUCxiWlwQ8ipu43JVM9uhmBUl+d/Zn1cFlUaP6fHcmeH6MNPigtCg0uDY5Vq2QyEsKq1X4WxJA6bFB9lsSeHH9Wz/AAj5PHzP8QewRt+uGgwGvP/++7h8+TIcHBywYsUK9Ohxd+LQihUrcOHCBUgk7cuFrV+/HlqtFv/4xz+gVqvh4+ODVatWdSzsbQ2+k7ePjbfFxRGMlRjhDU+JA76/UI6kKF+2wyEs+f5CBXg8bo8o+yNPqRgj+/jgh4sVWDK2D+cmft1hdKsOHz4MjUaDjIwMLF68GKtXr+60Pz8/Hxs3bkR6ejrS09Mhk8mwfv16TJw4EVu3bkVUVBQyMjK63QBTqWpS49S1evxpYKDd3MkA7WPmJw8IxBFFDW61aNgOh7CAYRjsvnQTT4R5wt/Vem66LGFaXCBqmttwgsOrRhmd5OVyORISEgAAAwYMQF5eXsc+g8GA0tJSLFu2DLNnz8Z33313zzGJiYk4efJkd2I3qZ9yboJh2leRsTdT4wKh0RuwJ6eS7VAIC3IrmlBSp8Kz/e3vsz8q0geuTiLs4vDgA6O7a5RKJaRSacfPAoEAOp0OQqEQLS0tmDNnDubPnw+9Xo+5c+ciJiYGSqUSMpkMACCRSNDc3PUDD4VCYVRMarXa6GMzTlegl4cDNHVlUNQZdQpWdKfNd/AZBiGuImScvIpBri0misx8TNFmW2PONn99rh5CPhDmcNuq/l0t9T6PCHbEgfxKXMxxgKOI3S4bc7TZ6CQvlUqhUt2t5GYwGCAUtp/OyckJc+fO7ehvHzZsGAoLCzuOcXR0hEqlgouLS5fnjoyMNComhUJh1LEldSoU1V/D2xP6IjIy3Khrs8XYNv/R9EoRPjp0BW4BPa3+K7up2mxLzNVmvYHBiR+OYGQfXwwZEGPy83eHpd7nuWIf7L1yGuWMOyZFsvttpjttlsvlXW43+tdWXFwcsrKyAADZ2dmIiIjo2Hf9+nWkpKRAr9dDq9XiwoULiI6ORlxcHDIzMwEAWVlZiI+PN/byJrXn0k3weMAkO/y6esedtv9MXTZ25WxJA6pvt2GyHXZT3jG4pwd8XcTYfYmbE6OMvpNPSkrCiRMnMHv2bDAMg5UrV2Lz5s0ICQnB6NGjMXnyZMycORMikQiTJ09G7969sXDhQixduhQ7duyAu7s71q5da8q2GIVhGPyYXYHBPT2s/g7WnEK9JOgX6Irdl27i+YQwtsMhFrL7UgWcHQQYE2m/I6sEfB6e6ReAb06XoqlVC1cnEdshmZTRSZ7P52P58uWdtoWH3+3qeP755/H888932u/l5YVNmzYZe0mzKKi8jeJaFeaPCGU7FNZN6u+PlXsLcb1OhZ5eErbDIWam0RmwN7cKyVG+cHLgdhmDh5nU3x9fnSjBwfwqzODQSnAATYbC7uybEPJ5mNDPn+1QWDcxtv0r+x6Ofm0lnWVdqUVTqxaTB9jP2Pj7GRDshmAPJ06OMLPrJG8wMNhz6SYSenvBQ+LAdjisC3BzwuCe7tiTQ0neHvx46SbcnUV4srcX26GwjsfjYVJsAE5crUM9xypT2nWSl99oxM0mtV2Ojb+fZ/sH4Eq1EoVVVJmSy1o0OhwuqMaEfv4QcXSm5+Oa1D8AegODvXlVbIdiUnb97v506SbEQj6SorhfcfJRje/nDwGfR102HPdLYQ1atfqOLjoC9PWTobePlHOffbtN8gYDg315VXi6jw+kYu5XnHxUXlIxhod7Ys+lSlpMhMP25VbBSyrGkFAPtkOxGjweD5P6B+Dc9QZUNrWyHY7J2G2Sl99oRE1zG8b3o7v4P5oUG4AbDS3IrWhiOxRiBq0aPX4prMG4GF8IOLiGcXdM6h8AhuHWfBG7TfJ7cyvhIORjVF8ftkOxOsnRvhDyedjHsb5J0u7Y5faumgkxNKLsj0K9JIj0d+HUZ98uk7zBwGB/XhUSe3tD5sitiQ+m4ObsgCfCPbEvl7psuGhvXhU8JA7UVXMfE2L8IC9tRFWTmu1QTMIuk3x2+S1UNqkxgbpq7mt8jD+u17dAUcntVXPsjVqrxy+KaoyN9uVs/fTuutOFeyCfG3fzdvku78uthEjAwxhaJOO+kqN9wecB+/O40zdJgMwrtVBp9DT57wF6+bSPstnHkc++3SV5hmGwN7cKCb294UJdNfflJRVjaKgn58YM27t9uZVwcxZhWJgn26FYtfH9/HG2pAF1HJgYZXdJPqe8CRW3WjE+hrpqHmZ8Pz9crVGiqJq6bLigTafHYUUNkqN8aQLUQ4yP8YOBAQ7mV7MdSrfZ3Tu9N68SQj6P1jN9BGOj/cDjgVMjDezZr0V1ULbpMJ66ah6qr58MoV4STnTZ2FWSZxgG+3KrMLyXF9ycqVbNw/i6OGJQD3fszbX9DzoBfs6thIujECPCqVbNw/B4PIyL8cPJ4no0qmx77WO7SvL5N2/jRkMLJlBXzSMbF+OPwqpmlNSpHv5iYrU0OgMOFVQjKcoPDkK7+m9vtAkx/tAbGBxS2HaXjV292/vyKiHg85AcTUn+UY377RciF7622rMTxXVoVuto2PBjiAl0QZC7E/bbeHel3ST5O6NqngjzpLLCjyHQzQn9g92wL9e2P+j2bl9uJWRiIZUVfgw8Hg/jY/xwvKgWt9VatsMxmlFJ3mAwYNmyZZg1axbS0tJQWlraaf/XX3+NGTNmYMaMGfj0008BtCfZhIQEpKWlIS0tzeJL/12pVqKkTtVxZ0oe3YQYP+RWNKGsoYXtUIgRdPr2rpqn+/pALLTvFaAe17gYf2j1DH5R1LAditGMKr94+PBhaDQaZGRkIDs7G6tXr8aGDRsAAGVlZdi9ezd27twJPp+PlJQUjBkzBk5OToiOjsZnn31m0gY8qkMF7XeiyTSq5rGNj/HHqn2F2J9XhRcSaf1XWyMvbURjixZjqZvysQ0MdoOfiyP25lZiykDbXEHLqDt5uVyOhIQEAMCAAQOQl5fXsc/Pzw8bN26EQCAAj8eDTqeDWCxGfn4+qqurkZaWhhdeeAHXrl0zTQse0cGCagwMcYOPi6NFr8sFIZ7O6Osnw6EC234AZa8OFlTDQcDHU3282Q7F5vD5PCRH+yKrqBatGj3b4RjFqDt5pVIJqVTa8bNAIIBOp4NQKIRIJIKHhwcYhsGHH36IqKgohIaGoq6uDn/9618xfvx4nD9/HkuWLMGuXbu6PL9CoTCqMWq1ustja1U65JQ3YX6ch9Hntlb3a7OpxfkKsD2nAacu5MHNid2v/JZqszUxts0Mw+Dn7DL09xOj7FqRGSIzH2t5n/tK26DWGrD96EUMCzHvAvfmaLNRSV4qlUKlujukzmAwQCi8e6q2tja8/fbbkEgkeO+99wAAMTExEAjak8OgQYNQU1MDhmHA491bzzoyMtKYsKBQKLo89typ6wCAOU/3Q7i39J79tux+bTa1VJcmbL30K8r0rngikt3V7C3VZmtibJsLq26jSlmCV5MiERkZYobIzMda3udeEQasPn4IBbdFmG/meLrTZrlc3uV2o7pr4uLikJWVBQDIzs5GRERExz6GYfD3v/8dffr0wfLlyzsS+6effootW7YAAAoLC+Hv799lgjeHg/nVCPOWcC7BW1J0gAsC3ZxwsIBG2diSg/nV4PGAMVG0boKxRAI+Rkf64rCiGjq9ge1wHptRd/JJSUk4ceIEZs+eDYZhsHLlSmzevBkhISEwGAw4e/YsNBoNjh8/DgB444038Ne//hVLlixBZmYmBAIBVq1aZdKG3E9Tixanr9Xj+QR6YNgdPF573+S3Z25A1aaDhJZMtAkHC6owMNgNPjJ6FtUdyVG++OFiBc6XNtpccTej/qfy+XwsX76807bw8PCOv+fm5nZ53BdffGHM5brl6OUa6AwMkqNpVE13JUf5YfOJ68i6Ukv1T2xAxa1W5FXcxlvj+7Idis1LjPCGg5CPg/nVNpfkOT8Z6mBBFbxlYgwIcmM7FJs3uKc73JxFOEijbGzC4d/eJxo23H0SsRAJvbxwsKDK5lZL43SSV2v1yLxci6QoX/BpweJuEwr4GN3XF0cU1dDaYN+kvTlYUIVwbwnC6FmUSSRH+6K8sdXmVkvjdJI/VVwPlUZPdzImlBzti9tqHc6WNLAdCnmA9mdRDVSnyYRGR/qCx4PNDT7gdJI/WFAFqViIJ8Jtqw/NmiX29oajiI+DHFn/kqt+uVwNvYGhGxwT8pKKMaiHu80tJMLZJK83MDhUUI2RfbypXocJOTkIkNDbGwcLqm2ub9KeHCqoho9MjP70LMqkkqP8UFB526bqOHE2yWeXNaJOqaGvq2YwNtoPlU1q5FY0sR0K6YJaq8cxehZlFndWlLOlEh+cTfIH86shEvAwkup1mNzovj7g87ix/iUXnSyuQ4tGTzc4ZtDTS4I+vjKb6pfnZJJnGAYH8qswLMwTLo4itsPhHHeJA4aEetjUB92eHMyvhlQsxLAwD7ZD4aTkaF+cLWmwmWUBOZnkr9Yocb2+he5kzCg5yq+jRj+xHnoDg8MKehZlTslRfjAwwGEbWRaQk0n+zmSdpEgaWWAud/sm6W7emtCzKPOLCXSBv6ujzUwK5GyS7x/sBj9XqtdhLsEezojyd6F+eStDz6LMj8fjITnKF8dtpMY855J8VZMal8pu0fhgC0iO9oX8RiNqm9vYDoWg/VnUwYJqPBHuRc+izCw52g9qrQFZRbVsh/JQnEvyh37rJxtLBcnMLjnKDwwDHLGRvkmuK65tf0aSRDc4Zjck1AMujkIcsIFJgZxL8gfzqxDmRbXjLSHSX4Ygdyeb6ZvkugP59CzKUu7UmP+lsMbqa8xzKskrNXqcKq5HUrSvxRYksWftfZN++PVqHZRtOrbDsXv0LMqykqN8catFi3PXG9kO5YE4leTPl7e2146PopEFlpIc7QuNzoCsK9bfN8ll9CzK8u7UmLf22a9GJ3mDwYBly5Zh1qxZSEtLQ2lpaaf9O3bswNSpUzFz5kwcPXoUANDQ0IAFCxYgNTUVixYtQmtra/ei/4NTZSp4ScUYGOxm0vOS+xvUwx3uziKr/6BzHT2LsjyJWIgnbaDGvNFJ/vDhw9BoNMjIyMDixYuxevXqjn21tbVIT0/H9u3bsWnTJnz00UfQaDRYv349Jk6ciK1btyIqKgoZGRkmaQQAtOn0OFfRgqQoH6rXYUFCAR+jqMY86w4VVCOUnkVZXHKU9deYNzrJy+VyJCQkAAAGDBiAvLy8jn05OTkYOHAgHBwcIJPJEBISgsLCwk7HJCYm4uTJk90M/65TxfVo1VJXDRuoxjy7bqu1OFVch+QoehZlabZQY97oJK9UKiGV3r1rEAgE0Ol0HftkMlnHPolEAqVS2Wm7RCJBc7PpfvvlljdBIuJT7XgWUI15dh27XAutntYxZoO3TIz4EOuuMW/UQt4AIJVKoVLdrVtiMBggFAq73KdSqSCTyTq2Ozo6QqVSwcXFpctzKxSKx47nCS8DwpO8UHL1ymMfa8vUarVR/16mNtDPEXtzyjGrN9/sd5PW0mZLelCbvztVDXdHAcTKKig4NGfBVt7n/l48bJLfxrFzOfCVdm8SmjnabHSSj4uLw9GjRzFhwgRkZ2cjIiKiY19sbCw+/vhjtLW1QaPRoLi4GBEREYiLi0NmZiamTp2KrKwsxMfHd3nuyMhIo2JSKBRGH2urrKXNU1VSLPkuBwbXQMQEupr1WtbSZku6X5vbdHpc2H4Dk/oHICY6ioXIzMdW3uc53ipskh9DiUaGkZGh3TpXd9osl8u73G50kk9KSsKJEycwe/ZsMAyDlStXYvPmzQgJCcHo0aORlpaG1NRUMAyD119/HWKxGAsXLsTSpUuxY8cOuLu7Y+3atcZenliZ0ZG+v9WYrzJ7kid3nb7WAGWbjma5sijUS4LePlIczK/G/BHdS/LmYHSS5/P5WL58eadt4eHhHX+fOXMmZs6c2Wm/l5cXNm3aZOwliRXzkDhgUE8PHCyoxhvJfdgOx24czK+Cs4MAw8O92A7FriVH++KzzGu41aKBm7MD2+F0wqnJUIRdyVG+KKxqRmk91Zi3BMPv1jF2FFHteDYlRflBb2DwS2EN26Hcg5I8MZk7w1dpYpRl5FQ0oaa5jYYNW4HYQFf4uoitcpQNJXliMiGezujrJ6OCZRZyML8KAj4PT/fxYTsUu8fn85AU5YvMK7VQa62rxjwleWJSydF+OH+9AfVKqjFvbgcLqjEszAOuzlQ73hokR/mhVavHr0V1bIfSCSV5YlLJUb4wMMARK+yb5JLiWiWu1iipq8aKDAvzhEwstLrZr5TkiUlFB7gg0M3JKvsmueTOcw8aOmk9HIR8jOzrgyOKGugN1lOwjJI8MSker71v8nhRLVo0VGPeXNrnI7ggwM2J7VDI7yRH+aJepcGFG9ZTY56SPDG55ChftOkMOG5lfZNcUdOsxsWyW9RVY4VG9vGGSMCzqjpOlOSJyQ0O9YCrk4i6bMzkiKIGDAMqSGaFZI4iDA/3wsGCaqupMU9JnpicSMDH6L4+OFJYbfXrX9qig/lVCPFwRh9f2cNfTCwuOdoXpfUtKKpRsh0KAEryxEySo21j/Utbo2zT4cTVeqodb8XG/LaQurV02VCSJ2aR0Lt9/UtrG05m6zIv10KjN9CoGivm6+KIAcFuVjMpkJI8MQuJWIiEXl44ZEV9k1xwqKAKHhIHxPdwZzsU8gDJ0b7IKW9CZZNp17E2BiV5YjbJ0da//qUt0eoNOFJYg9F9fSAU0H9da3Zn5NNhK7ibp08KMRtbWP/Slpy51oBmtQ7J0TR00tr18pEizEtiFV02lOSJ2XhJxRjUw7rXv7QlBwuq4Cji48leVDveFiRF++JUcT2aWrWsxkFJnphVcpQfCipvo6yhhe1QbBrDtNeOT+ztDScHqh1vC5Kj/KAzMDh2md06TkYlebVajVdeeQWpqal44YUX0NDQcM9r1qxZg1mzZmHatGnYsWMHAODWrVsYOnQo0tLSkJaWhi1btnQvemL17owCOcyhBabZcLVBg8omNXXV2JCBwW7wkrJfY96oJL9t2zZERERg69atmDJlCtavX99p/+nTp3Hjxg1kZGRg27Zt+PLLL9HU1ISCggJMnDgR6enpSE9Px1/+8heTNIJYr55eEkT4Sln/oNu6UzdU4POA0X2pdrytuFNj/tjlGrTp2Ksxb1SSl8vlSEhIAAAkJibi1KlTnfYPHDgQK1eu7PhZr9dDKBQiLy8P+fn5mDNnDl599VXU1FA5WnuQHOWHs9cb0KjSsB2KzTp1Q4XBPT3gLrGu9UPJgyVH+UKl0eNkcT1rMTx0Ie+dO3fe063i6ekJmax9SrVEIkFzc+chcmKxGGKxGFqtFm+99RZmzZoFiUSCsLAwxMTEYPjw4di9ezdWrFiBdevW3XNNhUJhVGPUarXRx9oqW2hzb+c26A0MvjmajTHh3Z+KbwttNqXyJg2u39JiXATPrtrNhffZQ2+Ak5CHHScK4Wd4eKI3R5sfmuRnzJiBGTNmdNr28ssvQ6VqX6xZpVLBxcXlnuOamprw6quvYsiQIXjxxRcBAMOGDYOTU3tp1KSkpC4TPABERkY+Xit+o1AojD7WVtlCm/syDFYdr0deIx+vmCBWW2izKf1y9CoA4C+j+8Pf1X5KC3PlfR4V2Yaz1xvQp09f8PkPLkXRnTbL5fIutxvVXRMXF4fMzEwAQFZWFuLj4zvtV6vVmDdvHqZNm4aXXnqpY/u7776LAwcOAABOnTqF6OhoYy5PbAyPx0NytC+yrtRZ3fqXtmBfXiX6eIntKsFzSXK0L2qb25BdfouV6xuV5FNSUlBUVISUlBRkZGTg5ZdfBgB8+OGHyMnJwfbt21FWVoadO3d2jKQpKyvD4sWLsW3bNqSlpWH79u145513TNoYYr2Sonytcv1La1fW0IK8itt4soeE7VCIkUb28YGQz2Nt8MFDu2u64uTk1GVXy5tvvgkAiI2Nxbx587o8Nj093ZhLEhs3NNQTMkchDuRXYQwV13pk+/PaZwuPoCRvs1ydRHgi3BP78yqxdFwfi1cPpclQxCIchO015g8pqqGlGvOPbF9eJaIDXOAvE7EdCumG8TH+uF7fwkodJ0ryxGIm9PPHrRYtTl9jbziZLalsasWFG7cwPoYmQNm65Ghf8HnA3txKi1+bkjyxmMQIb0gcBKx80G3Rgd+6asbF+LMcCekuL6kYw8I8sTe30uKltynJE4txFAkwOtIXB/JpWcBHsS+vCr19pOjlI2U7FGIC4/v541qdCperLdtlQ0meWNSEfv5oUGlw+tq99Y7IXXXKNpy73kBdNRwyLtqvvcsmx7LfZCnJE4sa2ccbzg4C7M2jLpsHOZhfDQPTfvdHuMFbJsaQUA/szbPs+gqU5IlFOYoEGNXXBwfyqqjL5gH25VWip6cz+vp1vwwEsR4T+vnjao0SVyzYZUNJnljcM/38Ua/S4GwJddl05VaLBqeK6zEuxt/iY6qJeY2L8QPPwqNsKMkTixvZxwdOIuqyuZ9DBdXQGRjqj+cgH5kjBvf0oCRPuM3Job3LZn9eNfQGyw4nswU/5VQiyN0JsUGubIdCzGBCjB+uVCtxtcYyXTaU5AkrJvTzR52yjbps/qBBpcGvV+swqX8AddVw1J2H6XtzLfMAlpI8YcXTfb3hKOLTxKg/2JdXCb2BwaTYALZDIWbi6+KIQT3cLfbZpyRPWOHsIMSovj7Yl1dFXTa/s+fSTYR7SxDpT6NquGxCP38UVjWjuFZp9mtRkiesmRgbgDplG85QLRsAQPVtNc6UNFBXjR2Y0M8fPF77L3VzoyRPWDOqrw+kYiF+zDb/B90W/JxTCYZp/+VHuM3P1RFDQz2wO/um2WvZUJInrHEUCTA22g978ypZXc3eWuzJuYkofxeqVWMnJg8IxLU6FfIqbpv1OpTkCaueHRCAZrUOxy7Xsh0Kq8oaWnDxxi1M7E9lDOzF+Bg/iAQ8/JhdYdbrGLUylFqtxpIlS1BfXw+JRII1a9bAw8Oj02sWLlyIxsZGiEQiiMVibNy4EaWlpXjrrbfA4/HQu3dvvPfee+Dz6feMPRsR7glPiQN2Z9/E2Gj7nfzz828jLWhUjf1wc3bAUxE+2JNzE/+cEAnBQxb5NpZRGXbbtm2IiIjA1q1bMWXKFKxfv/6e15SWlmLbtm1IT0/Hxo0bAQCrVq3CokWLsHXrVjAMgyNHjnQvemLzhAI+Jsb647CiGs1qLdvhsGbPpZsYEOyGYA9ntkMhFjR5QACqb7fhTIn5Bh8YleTlcjkSEhIAAImJiTh16lSn/XV1dbh9+zb+9re/ISUlBUePHgUA5OfnY8iQIR3HnTx5sjuxE454dkAg2nQG1hY6ZtvVmmbk37yNSf3pLt7ejIn0hcRBgN1mHHzw0O6anTt3YsuWLZ22eXp6QiZrH8crkUjQ3Nx5eq5Wq8WCBQswd+5cNDU1ISUlBbGxsWAYpmNoWFfH3aFQKIxqjFqtNvpYW8WFNjsxDHylQmw9cQVRzg+f6s2FNv/e1xcawOcBfZ2V920X19r8KOylzcOCnPDTpQqk9BHCoG0zeZsfmuRnzJiBGTNmdNr28ssvQ6VSAQBUKhVcXFw67ffy8sLs2bMhFArh6emJyMhIlJSUdOp/7+q4OyIjIx+7IUD7Lwdjj7VVXGnz9DI+Psu8Bq+gMHjLxA98LVfaDAAGA4Pj//0FiRHeGBHX776v41KbH5W9tDmN74kjm8+hiueBEMdGo9ssl8u73G5Ud01cXBwyMzMBAFlZWYiPj++0/+TJk3jttdcAtCfzoqIihIWFISoqCmfOnOk4btCgQcZcnnDQ5AGB0BsY/JxjX2PmT5fU42aTGlPjgtgOhbBkRC+vjsEH5mBUkk9JSUFRURFSUlKQkZGBl19+GQDw4YcfIicnB0899RR69uyJmTNn4rnnnsMbb7wBDw8PLF26FJ988glmzZoFrVaLsWPHmrQxxHZF+MoQ6e+CHy6adziZtfn+QgVkYiGSo3zZDoWwRCTg45nfBh+oNKZfSMeoIZROTk5Yt27dPdvffPPNjr+/88479+wPDQ3FN998Y8wliR2YFheIFT8rcKW6GRG+3K/d0qLRYV9uJSbGBsBRJGA7HMKiWYOD8cOFCjS26kx+bhqkTqzGnwYGQsjnYef5MrZDsYiD+dVQafSYGhfIdiiEZdEBrriwLAlBrg4mPzcleWI1PKVijOrrgx8u3oTWDtZ/3XWhHIFuThjc0+PhLyacJxKYJx1TkidWZcagYNQp25DJ8TIH1bfVOHG1DlPjAsE300xHQgBK8sTKjOzjDS+pA3bKud1l88PFChiY9i4qQsyJkjyxKiIBH1MGBOKIogb1yja2wzELhmGQca4Mg3u6I8ybKk4S86IkT6zOjEHB0BkYztaZP32tASV1KsweHMJ2KMQOUJInVqePnwyxQa7YKS83+4IKbNh+7gZcHIV4JpbKChPzoyRPrNKMQcFQVN7GpfImtkMxqUaVBvtyq/CngYE0Np5YBCV5YpWmDAiAs4MA35wuZTsUk/r+YgU0egNmD6GuGmIZlOSJVZI5ijBlYCD2XLqJWy0atsMxCYZhsP3sDfQPdkOkf9fF+QgxNUryxGrNGdoDbToDvpOXsx2KSVy40YiiGiVShwSzHQqxI5TkidWKCnBBfA93fHvmBgwG238A+83pG5CKhZhIS/wRC6IkT6zanGEhKKlT4WSx+ZZHs4SaZjV+yrmJ6fFBkIiNqgtIiFEoyROrNj7GH+7OIpt/APvt6RvQGRjMG96T7VCInaEkT6yao0iAmYOCcUhRjYpbrWyHY5Q2nR7fnrmBp/v4oKeXhO1wiJ2hJE+s3tzf7n6/PlHCbiBG2ptbiTplG93FE1ZQkidWL9DNCRP6+WP72TI0q7Vsh/NYGIbB5hPXEe4tQUJvL7bDIXbIqCdAarUaS5YsQX19PSQSCdasWQMPj7s1sbOysvDll18CaP+Qy+Vy/PTTT2hra8OLL76Inj17AmhfRnDChAndbwXhvBcSQrHn0k1knCvDCBvKlfLSRuSUN+GDydHg8aikMLE8o5L8tm3bEBERgVdeeQU///wz1q9fj3fffbdjf2JiIhITEwEAGzduRFxcHMLDw7Fz507Mnz8fCxYsME30xG7EBrlhaKgHvvq1BEOftZ2aLxuOFcPdWYRp8bRQN2GHUd01crkcCQkJANoT+qlTp7p8XVVVFX788ceOhb7z8vJw7Ngx/PnPf8bbb78NpVJpZNjEHr2QEIabTWr8el3FdiiPpLDqNo4U1mDe8FA4O9CwScKOh37ydu7ciS1btnTa5unpCZmsfaFliUSC5ubmLo/dvHkz5s2bBweH9nULY2NjMWPGDMTExGDDhg34z3/+g6VLl95znEKheOyGAO3dSMYea6vsqc3+YBDkIsLO3EY8FVpg9d0fHx6vgaOQhye82rr9HtnT+3wHtdlEGCO89NJLzKVLlxiGYZjbt28zzzzzzD2v0ev1THJyMtPa2tqxrampqePvRUVFzNy5c+857vz588aExDAMwxQUFBh9rK2ytzbvOHeD6bH0J+ZQfhXboTzQjXoVE/bPn5kP9uSb5Hz29j4zDLX5cd0vdxrVXRMXF4fMzEwA7Q9Z4+Pj73nNlStXEBoaCkdHx45tzz33HHJycgAAp06dQnR0tDGXJ3bsTwMD4ScV4uMjV6y61vznWcXg84DnE8LYDoXYOaOSfEpKCoqKipCSkoKMjIyOPvcPP/ywI4mXlJQgOLhzIab3338fK1euRFpaGi5cuIC///3v3Qyf2BuhgI/ZsW7Iq7iNXwpr2A6nS2UNLcg4V4YZg4Lh5+r48AMIMSOjngY5OTlh3bp192x/8803O/4+fvx4jB8/vtP+6OhobN++3ZhLEtJhdLgMuwpV+PeRIozq62N1ffP/PlIEHo+HV0b1YjsUQmgyFLE9Qj4PLz/dCznlTThUUM12OJ1crWnG9xfKkTasB/xdndgOhxBK8sQ2TY0LQri3BKv3F0KrN7AdTof/O1QER5EAC0eGsx0KIQAoyRMbJRLw8c/xkbhWq8L2szfYDgcAkF12Cz/nVmLBiFB4ScVsh0MIAEryxIaNjvTBE2Ge+L/DRbjNck0bg4HB+7vz4SUV48WnaEQNsR6U5InN4vF4eOeZSDSoNPjP0ausxvLDxQpkl93C0nF9IHMUsRoLIb9HSZ7YtJhAV8yID8Km4yW4XNX1zGtzU7bpsGZ/IfoHu2FaHNWoIdaFkjyxeW9PiISLkwhv/5DLylqwa/YVolbZhvcnRYHPt67hnIRQkic2z13igHcmREJe2oitFn4Ie/paPdJPl2L+8FAMDHG36LUJeRSU5AknTI0LxIhenli1V4HrdZapUtmq0WPprhyEeDjjH2MjLHJNQh4XJXnCCTweD/87vT8EfB5e237RImPnP/i5AKX1LVg9rR+VEiZWi5I84YwANyesnhaLS+VN+OjQFbNe68fsCmw9cwN/eyocw8NtaKkqYncoyRNOmdDPH7MHB2PDsWLsy600yzWuVDfj7e9zMaiHOxYnUzcNsW6U5AnnvP9sNAaGuOH1HdnIq2gy6blrbqsxf/M5SMRCfJI6ECIB/Rci1o0+oYRzHEUCfJE2CJ4SMeZtPoerNaZZZrKpRYv5X59DY4sGX80bTAXIiE2gJE84yVsmxpYFQwAAKV+e7naib1BpkLrxNIqqlfhPahxiAl1NESYhZkdJnnBWLx8ptr0wFAzDYNqGk/i1qM6o81yrVWLm56dwtUaJL+bG4+m+PiaOlBDz6VaSP3ToEBYvXtzlvh07dmDq1KmYOXMmjh49CgBoaGjAggULkJqaikWLFqG1tbU7lyfkoXr7yvD9whHwdRHjL5vPYt2RokceXskwDH64WI7Jn55Ag0qDLQuGYGQfSvDEthid5FesWIG1a9fCYLj3P0xtbS3S09Oxfft2bNq0CR999BE0Gg3Wr1+PiRMnYuvWrYiKikJGRka3gifkUYR4OmPXwuF4pp8/Pjp0Bc+sO46fcyqhv08JBIZhcOZaPf688Qxez7iEXr5S7H55BIaFeVo4ckK6z+gZHHFxcRgzZkyXiTonJwcDBw6Eg4MDHBwcEBISgsLCQsjlcrz44osAgMTERHz00UeYN2+e0cET8qhkjiKsSxmISf0DsGqfAi9tvQAvqQOe7uODSH8XuDqJ0KLVo7hGiWOXa3C9vgVeUgcsnxyNPw/tAQHVpCE26qFJfufOndiyZUunbStXrsSECRNw5syZLo9RKpWQyWQdP0skEiiVyk7bJRIJmpu7rhqoUCgeuQG/p1arjT7WVlGbH08QD1g3zgeny1qQWaLE/ryb2Ckv79gvFvIQ7eOIZ5/wwqgwKcTCVly5XGiq0I1G77N9MEebH5rkZ8yYgRkzZjzWSaVSKVSqu/VDVCoVZDJZx3ZHR0eoVCq4uLh0eXxkZORjXe8OhUJh9LG2itpsnJho4Hm0d83UKTVo1eghEvLgK3O0ykqS9D7bh+60WS6Xd7ndLKNrYmNjIZfL0dbWhubmZhQXFyMiIgJxcXHIzMwEAGRlZSE+Pt4clyfkkfF4PHjLxAjxdIa/q5NVJnhCusOkVZU2b96MkJAQjB49GmlpaUhNTQXDMHj99dchFouxcOFCLF26FDt27IC7uzvWrl1ryssTQgj5g24l+aFDh2Lo0KEdP8+fP7/j7zNnzsTMmTM7vd7LywubNm3qziUJIYQ8BpoMRQghHEZJnhBCOIySPCGEcBgleUII4TBK8oQQwmE8hmG6LuDBkvsN6CeEEPJgXc09srokTwghxHSou4YQQjiMkjwhhHAYJ5K8wWDAsmXLMGvWLKSlpaG0tJTtkMxOq9ViyZIlSE1NxfTp03HkyBG2Q7KI+vp6PPXUUyguLmY7FIv4/PPPMWvWLEydOhU7d+5kOxyz02q1WLx4MWbPno3U1FTOv8+XLl1CWloaAKC0tBQpKSlITU3Fe++91+VaHcbgRJI/fPgwNBoNMjIysHjxYqxevZrtkMxu9+7dcHNzw9atW7Fx40Z88MEHbIdkdlqtFsuWLYOjoyPboVjEmTNncPHiRWzbtg3p6emoqqpiOySzy8zMhE6nw/bt2/HSSy/h448/Zjsks/nyyy/x7rvvoq2tDQCwatUqLFq0CFu3bgXDMCa7ceNEkpfL5UhISAAADBgwAHl5eSxHZH7jxo3Da6+9BqC9XK5AIGA5IvNbs2YNZs+eDR8f+1iC79dff0VERAReeukl/O1vf8PIkSPZDsnsQkNDodfrYTAYoFQqIRSatIaiVQkJCcEnn3zS8XN+fj6GDGlffD4xMREnT540yXU48S+oVCohlUo7fhYIBNDpdJz+gEgkEgDtbX/11VexaNEidgMys++//x4eHh5ISEjAF198wXY4FtHY2IibN2/is88+Q3l5ORYuXIj9+/eDx+NuOWRnZ2dUVFRg/PjxaGxsxGeffcZ2SGYzduxYlJffXbCGYZiO9/ZBiyo9Lk7cyf9xkRKDwcDpBH9HZWUl5s6di8mTJ2PSpElsh2NWu3btwsmTJ5GWlgaFQoGlS5eitraW7bDMys3NDU8++SQcHBwQFhYGsViMhoYGtsMyq6+//hpPPvkkDhw4gB9//BFvvfVWR3cG1/H5d9PxgxZVeuzzmuQsLIuLi0NWVhYAIDs7GxERESxHZH51dXVYsGABlixZgunTp7Mdjtl9++23+Oabb5Ceno7IyEisWbMG3t7ebIdlVvHx8Th+/DgYhkF1dTVaW1vh5ubGdlhm5eLi0rFEqKurK3Q6HfR6PctRWUZUVFTHkqpZWVkYNGiQSc7LidvdpKQknDhxArNnzwbDMFi5ciXbIZndZ599htu3b2P9+vVYv349gPYHOfbyUNIePP300zh37hymT58OhmGwbNkyzj97mTdvHt5++22kpqZCq9Xi9ddfh7OzM9thWcTSpUvxr3/9Cx999BHCwsIwduxYk5yXZrwSQgiHcaK7hhBCSNcoyRNCCIdRkieEEA6jJE8IIRxGSZ4QQjiMkjwhhHAYJXlCCOEwSvKEEMJh/x9XQdMfuDnYiAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x107066518>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -154,21 +144,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If we want to create a single figure with multiple lines, we can simply call the ``plot`` function multiple times:"
+    "If we want to create a single figure with multiple lines (see the following figure), we can simply call the `plot` function multiple times:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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3b7o6fO18EZPGzkxERwfw8pLHA0qDo7eOolW9VmhWpxltKaIhhQmTvbk9DHUM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\n9MrqJqNScVcnJyccPHgQAHDmzBnY2trSkCEJcnJ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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1076d5cc0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -200,23 +193,26 @@
    "metadata": {},
    "source": [
     "The first adjustment you might wish to make to a plot is to control the line colors and styles.\n",
-    "The ``plt.plot()`` function takes additional arguments that can be used to specify these.\n",
-    "To adjust the color, you can use the ``color`` keyword, which accepts a string argument representing virtually any imaginable color.\n",
-    "The color can be specified in a variety of ways:"
+    "The `plt.plot` function takes additional arguments that can be used to specify these.\n",
+    "To adjust the color, you can use the `color` keyword, which accepts a string argument representing virtually any imaginable color.\n",
+    "The color can be specified in a variety of ways; see the following figure for the output of the following examples:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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xR5W7Gc6ekZFBmzZt3I4QsEfz4ObM6jnL/QVadVJRiCHXeywDQNu2bcnKynI/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rQk4I5eV+KXgeUEWM/jcDjIz88nMzNT+6NmS5ISYPli+FPzRJBpO0g2LEF36WcDoq/+Amsoo3kR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\nRo4ETp0CD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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1077b3a90>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -226,10 +222,10 @@
    "source": [
     "plt.plot(x, np.sin(x - 0), color='blue')        # specify color by name\n",
     "plt.plot(x, np.sin(x - 1), color='g')           # short color code (rgbcmyk)\n",
-    "plt.plot(x, np.sin(x - 2), color='0.75')        # Grayscale between 0 and 1\n",
-    "plt.plot(x, np.sin(x - 3), color='#FFDD44')     # Hex code (RRGGBB from 00 to FF)\n",
+    "plt.plot(x, np.sin(x - 2), color='0.75')        # grayscale between 0 and 1\n",
+    "plt.plot(x, np.sin(x - 3), color='#FFDD44')     # hex code (RRGGBB, 00 to FF)\n",
     "plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 to 1\n",
-    "plt.plot(x, np.sin(x - 5), color='chartreuse'); # all HTML color names supported"
+    "plt.plot(x, np.sin(x - 5), color='chartreuse'); # HTML color names supported"
    ]
   },
   {
@@ -238,21 +234,24 @@
    "source": [
     "If no color is specified, Matplotlib will automatically cycle through a set of default colors for multiple lines.\n",
     "\n",
-    "Similarly, the line style can be adjusted using the ``linestyle`` keyword:"
+    "Similarly, the line style can be adjusted using the `linestyle` keyword (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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JeEHBIdLSptV6zd09RIiIWwosGBcYSbojid1dd1N8spgbZt5Az+M9af3P1sJE/EhBAQcL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\nuse1DEGXQZBXEHdF3cXsu2cT5BUkbFzZ21Iikbg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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1079d9160>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -276,21 +275,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you would like to be extremely terse, these ``linestyle`` and ``color`` codes can be combined into a single non-keyword argument to the ``plt.plot()`` function:"
+    "Though it may be less clear to someone reading your code, you can save some keystrokes by combining these `linestyle` and `color` codes into a single non-keyword argument to the `plt.plot` function; the following figure shows the result:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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v3pjUvLnQEE+7mobJP05G5+WdoTVokfRKEr566iurD3GAV+REYul0wPbtwJNPqsfDhwMj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\nHj16YNeuXRg8eDCSkpLQvn3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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1077be9e8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -298,9 +300,9 @@
     }
    ],
    "source": [
-    "plt.plot(x, x + 0, '-g')  # solid green\n",
-    "plt.plot(x, x + 1, '--c') # dashed cyan\n",
-    "plt.plot(x, x + 2, '-.k') # dashdot black\n",
+    "plt.plot(x, x + 0, '-g')   # solid green\n",
+    "plt.plot(x, x + 1, '--c')  # dashed cyan\n",
+    "plt.plot(x, x + 2, '-.k')  # dashdot black\n",
     "plt.plot(x, x + 3, ':r');  # dotted red"
    ]
   },
@@ -310,7 +312,7 @@
    "source": [
     "These single-character color codes reflect the standard abbreviations in the RGB (Red/Green/Blue) and CMYK (Cyan/Magenta/Yellow/blacK) color systems, commonly used for digital color graphics.\n",
     "\n",
-    "There are many other keyword arguments that can be used to fine-tune the appearance of the plot; for more details, I'd suggest viewing the docstring of the ``plt.plot()`` function using IPython's help tools (See [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb))."
+    "There are many other keyword arguments that can be used to fine-tune the appearance of the plot; for details, read through the docstring of the `plt.plot` function using IPython's help tools (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb))."
    ]
   },
   {
@@ -320,21 +322,24 @@
     "## Adjusting the Plot: Axes Limits\n",
     "\n",
     "Matplotlib does a decent job of choosing default axes limits for your plot, but sometimes it's nice to have finer control.\n",
-    "The most basic way to adjust axis limits is to use the ``plt.xlim()`` and ``plt.ylim()`` methods:"
+    "The most basic way to adjust the limits is to use the `plt.xlim` and `plt.ylim` functions (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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F09smGPZq0QJ48klgyRLZScge8+eL7jRvb9lJnGvcOHGF4spY1GuxbRtwyy3An/8sO4nz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\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x107ab9f28>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -352,21 +357,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If for some reason you'd like either axis to be displayed in reverse, you can simply reverse the order of the arguments:"
+    "If for some reason you'd like either axis to be displayed in reverse, you can simply reverse the order of the arguments (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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xuFfAa6/J2FUjPb0fOAB8/LEMVSNyh9BQoEoVYN061Umcx2YDJk4EpkzRzoAE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\nAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1079fa160>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -384,52 +392,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A useful related method is ``plt.axis()`` (note here the potential confusion between *axes* with an *e*, and *axis* with an *i*).\n",
-    "The ``plt.axis()`` method allows you to set the ``x`` and ``y`` limits with a single call, by passing a list which specifies ``[xmin, xmax, ymin, ymax]``:"
+    "A useful related method is `plt.axis` (note here the potential confusion between *axes* with an *e*, and *axis* with an *i*), which allows more qualitative specifications of axis limits. For example, you can automatically tighten the bounds around the current content, as shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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F09smGPZq0QJ48klgyRLZScge8+eL7jRvb9lJnGvcOHGF4spY1GuxbRtwyy3An/8sO4nz\njR8vZs9WVMhOQrYoLRVT5/W+kqgtBgwAjh8XC9W5Khb1WqSnA6NHu05/5JU6dxZrbm/dKjsJ2SIn\nR8ypaN1adhLn8/AQM2fT02UnkYdFvQZms9jZKCZGdhJ5Ro927V8MPatqkLiqkSPFln0Wi+wkcrCo\n12DpUmDIEGNOq66rmBixKxJXb9SX//xHbMocHi47iTwtWwKhocDKlbKTyMGifo2KCjGcz5VbOoDY\nqi86WvwDR/qRni5ukLq5yU4ilytfabKoX2PrVuC228Q6za5u9GixiBlvmOpDSYnodhg5UnYS+R5/\nXGxqU1AgO4nzsahfY+FCttKrdOokxuh/8onsJFQXK1cCPXq41ryK2ri5iSsWV2yts6hf4cQJ4F//\nEt0OJLjyZazeuPoN0mslJgJr1gAXLshO4lws6ldYskTcIPTxkZ1EO556Cvj8c/EPHmnXvn3AuXOi\n24GEgACxQuWHH8pO4lws6r+zWkX/MVs6V2vUSOxradQNQowiPV2MzzbKptJqqbrSVBTZSZyHPwK/\n++c/xWQNLa5OKNuoUeIqhjdMtamkRKzxkpgoO4n2hIWJhflcaQMNFvXfLVkibqzQ9Tp2FJey27bJ\nTkI1Wb1a3CC9/XbZSbSnQQNgxAixOJ+rYFGHmKyxa5drr+x2M4mJHLOuVcuWicJFNUtIEEsnlJXJ\nTuIcLOoAPvhALATkyjNIbyY6WuzVeuaM7CR0pW+/Bb77DujdW3YS7WrdGnjoIeCjj2QncQ6XL+qK\nIlqg7I+8scaNxZK8K1bITkJXWrYMiI117U0h6mLkSNe50nT5or53L1BeLla1oxtLTBT3HlxpJIGW\nVVSIjaXZ9XJz/fuL5XiPH5edxPFcvqgvWwYMH+6aS+zWV/fuYuW7/ftlJyFAdIe1agXce6/sJNrn\n6SmG5mZmyk7ieC5d1MvKxMiBhATZSfShaiSBq1zGah1vkNZPYqL4f1ZZKTuJY7l0Uf/oI3EDpVUr\n2Un0w9VGEmjV2bOipc4lLerugQfEvrvbt8tO4lguXdTZ0qk/VxtJoFUffgj06SNuYFPducLQXJct\n6kVFYkOB/v1lJ9EfV/jF0Do2SGwzdKiYPX7unOwkjuOyRX35cnHp6uUlO4n+uNJIAi06eFDMF3j0\nUdlJ9OfWW8WYfiPviuSSRb2yki0de7jSSAItqhqx5eq7G9nK6GPWXbKo5+UB/v5AUJDsJPrlKiMJ\ntKa8XPSnDx8uO4l+PfqouNI5cEB2EsdwyaJe1Urn2HTbVY0k2LFDdhLXsnkz0KEDcNddspPol5ub\n+EfRqIt8uVxRP38e2LgRGDZMdhL945h151u6lN2Gahg+XFzxXLokO4n6XK6or14N9OoFNG8uO4n+\nVY0k+OUX2UlcQ3ExkJ8PREbKTqJ/bdqIJaU3bZKdRH0uV9R5g1Q9TZuK7dNycmQncQ3Z2cDgwdxu\nUS1GXWfdpqKuKAqmTZuG6OhoxMfH48Q1G1ju2LEDkZGRiI6Oxpo1a1QJqoZjx9xx/DgQHi47iXFU\n3TAlx1IUNkjUNniw2H/31CnZSdRlU1HPzc1FeXk5cnJykJSUhNTU1OrnrFYrZs6ciczMTGRnZ2PV\nqlU4p5GR/qtXeyMuDnB3l53EOMLCALMZOHxYdhJj+/e/xUijbt1kJzGORo3ExjjZ2bKTqMumol5Q\nUICQkBAAQKdOnXD4it/o77//HoGBgfD19YWHhweCg4Oxd+9eddLawWoF1q1rxJaOytzcgPh4ttYd\njauJOkbVzX4jLSdtU1G3WCzwu2KbIHd3d1T+PmD52ud8fHxQUlJiZ0z7bdkCtGpVgfbtZScxnhEj\nxO5Rly/LTmJMFy8Ca9aIfzxJXV27iobJ55/LTqIemzoifH19UVpaWv11ZWUlGjRoUP2cxWKpfq60\ntBT+/v61vpfZbLYlQr1ZLJ4YM+YizGbjTsMrKSlx2v/PK/n4AHfe2RTZ2aUID//NYceRdX7OcKNz\nW7fOGw884A2T6Rz0evpa/uwGD/ZFWpob2rQ5b/N7aOn8bCrqQUFB2LlzJ8LDw3HgwAG0a9eu+rm2\nbduiqKgIFy5cgJeXF/bu3YuRI0fW+l4BAQG2RKi36GjAbL7ktOPJYDabpZ3f6NHAhg2eDt0WUOb5\nOdqNzu3jj4ExY5z3u+IIWv7snntOTOhavNjH5pFFzj6/4uLiWp+zqaiHhYUhPz8f0b8v5pyamorN\nmzejrKwMUVFRmDp1KhITE6EoCqKiotCiRQvbkpNuREUBSUnATz8Bt98uO41xHD8OHDoE9OsnO4lx\n3XGH2M5y7VpjbJhjU1E3mUxISUm56rE2bdpU/71Hjx7o0aOHXcFIX/z8gAEDRN/6iy/KTmMcVauJ\nenrKTmJsI0YA775rjKLucpOPyHGqxqwbaSSBTFxN1HkiIoBvvgG++052EvuxqJNq/vIXsYrgnj2y\nkxjDZ5+JnY0efFB2EuNr2FCsB2WE5aRZ1Ek1JpOxV79zNq4m6lyJiaK7q6JCdhL7sKiTqhISxKJp\nFy/KTqJv58+Lxaa4mqjzdOwI3HYbkJsrO4l9WNRJVa1aAV26cGNqe61aBTz2GNCsmewkrsUIaxmx\nqJPqjLr6nTPxBqkcMTFi9rlGlquyCYs6qY4bU9vnm2+AoiLgiSdkJ3E9TZrof2NqFnVSnZeXGFu9\nfLnsJPq0bJlY54Wricqh9ytNFnVyiMREMTyMG1PXj9UqloJl14s8vXoBp0+Lmbx6xKJODvHgg4C/\nvxhrTXW3ZYvYau3uu2UncV1635iaRZ0cwmTS/2WsDEuXwqGLolHdDB8OrFghJtPpDYs6OcywYWKs\n9XnbVzR1Kf/7H7BjBzBkiOwk1LatWLlx82bZSeqPRZ0cpnlz0T+5apXsJPqwYoVYjfEG2w+QE1Xt\niqQ3LOrkUHr9xXA2RWHXi9ZERgL5+cANli7XJBZ1cqjwcODHH8XYa6rdV195wGIBQkNlJ6EqPj7A\n4MH625iaRZ0cyt0diIvjDdObWblSbIregL+RmpKYqL+NqfkjRA43YoRo7XBj6pqVlgIbN3qz60WD\nHn5Y/PfLL+XmqA8WdXK49u3F2OstW2Qn0aZVq4CHHipHy5ayk9C1qpaT1tN9IRZ1cgreMK3d4sXA\nsGGlsmNQLeLjgXXrxBWVHrCok1M89RSwcyfw88+yk2jLoUPAyZNAz56XZEehWgQEiG6YdetkJ6kb\nFnVyCn9/sXpjVpbsJNqyeLG4GcfFu7St6oapHrCok9OMHg2kp3ORryoXLwIffgiMHCk7Cd3Mk08C\nR4/qY2guizo5zcMPA97eYio8AWvXAl27Aq1by05CN9OwofjHd+FC2UlujkWdnMZkAsaMAd5/X3YS\nbVi0CHjmGdkpqK6eeQb44APt3zBlUSenio0VLfVTp2QnkauwEPjvf4G+fWUnoboKDAS6ddP+WkYs\n6uRUfn5iJMySJbKTyLV4sRjm6eEhOwnVhx6uNFnUyenGjBFFzWqVnUQOi0XMsB01SnYSqq8nnhBL\nJO/bJztJ7VjUyek6dQL+8AfgH/+QnUSOFSvEwl2BgbKTUH25uYlRXFpurbOokxTPPqvtXwxHURRg\n/nzg+edlJyFbjRwJrF8P/Pqr7CQ1s2nKw6VLlzB58mScPXsWvr6+mDlzJpo0aXLV98yYMQP79++H\nj48PAGDBggXw9fW1PzEZwpAhQFIS8P33YpcZV7Frl+h2evRR2UnIVi1aiCWls7KA8eNlp7meTS31\nlStXol27dlixYgX69++PBQsWXPc9hYWFWLJkCbKyspCVlcWCTlfx8gISEvQx7ldNaWmilW4yyU5C\n9qi60tTikrw2FfWCggKE/r6af2hoKL744ournlcUBUVFRXjttdcQExODdXpZNIGcauxYsc661sf9\nquXUKSA3VywQRfoWGiqWdsjNlZ3kejftflm7di2WL19+1WPNmjWrbnn7+PjAYrFc9fzFixcRFxeH\nESNGwGq1Ij4+Hh07dkS7du1UjE56d9ddQEiIuIwdM0Z2GsdLTxebcfv5yU5C9jKZgAkTgHffBcLC\nZKe52k2LemRkJCIjI696bNy4cSj9vXlVWloKv2t+Sr29vREXFwdPT094enqia9euOHLkSI1F3Ww2\n25O/XkpKSpx6PGfT4/nFxjZEcvItePLJ/9101x89nl+VS5eAhQtvw9q1Z2E2Xz+WU8/nVhdGPL+e\nPYGXXroNu3efQYsW2jk/m26UBgUFIS8vDx07dkReXh46d+581fM//PADJk6ciA0bNsBqtaKgoACD\nBg2q8b0CAgJsiWATs9ns1OM5mx7Pb9AgYMYM4KuvAtC7942/V4/nVyU7WwzlDA1tUePzej63ujDq\n+Y0eDaxefRumTq1w6vkV32A3bJv61GNiYnDs2DEMHToUa9aswfO/j8/KzMzEzp070bZtWwwYMABR\nUVGIj4/HwIED0daVhjhQnZlMwAsvAPPmyU7iOIoCvPMO8OKLspOQ2saOFfMOLlzQzp1vm1rqXl5e\nePfdd697fPjw4dV/T0xMRCI3XaQ6eOopIDlZrIdy772y06gvN1csN/z447KTkNpatgR69wa2bvVC\n+/ay0wicfETSeXqKG6U1tBMM4Z13xJh8DmM0powMoH//MtkxqrGokyY8+6xYX/z0adlJ1HXoEHD4\nMBATIzsJOYq3t1hvXStY1EkTWrQQhc9ofet/+xswbpy4GiFyBhZ10ozJk8XGEefPy06ijlOngI0b\nxQgJImdhUSfNuPNOsWlEDatO6NKcOWIphGuWRSJyKO5hTpqSnAz06iVm6zVqJDuN7X7+GcjMFP3p\nRM7Eljppyr33is2Yly6VncQ+c+aIewQGnG9DGseiTpozdSrw9ttAebnsJLY5e1YMc0tOlp2EXBGL\nOmnOn/8M3HOPfvcxnTcPGDwYaN1adhJyRexTJ016801gwABg+HAxDlgvfvlFrLO9Z4/sJOSq2FIn\nTercGejSRX8jYWbOFIuU3XWX7CTkqthSJ82aPl2MhBk1Sh9rkJ88KfrSDx2SnYRcGVvqpFn33Qc8\n9ph+ZplOnw48/bRY5IlIFrbUSdNSUsQQx1GjZCe5saNHgY8+Ar79VnYScnVsqZOm/fGPYlbmK6/I\nTnJjyclivXTOHiXZWNRJ8159Fdi0CTh8WJsXllu3ipmjL7wgOwkRizrpQOPGohtm2rRboCiy01yt\nvBwYP16sBc+VGEkLWNRJF55+Gjh/vgFWrZKd5Grz5gF/+pNYiIxIC1jUSRfc3ICZM3/FxInAuXOy\n0wgnTwKzZ+tndA65BhZ10o3OnS8jMlKsuy6boogRORMmiJu5RFrBok668tZbwLZtwPbtcnMsXw4U\nFwMvvSQ3B9G1WNRJV/z8gPR0IDFRrLMig9kMTJkCLFsGeHjIyUBUGxZ10p3evcViX6NGwemjYSoq\ngNhY4PnngQcecO6xieqCRZ10adYsMYvT2ZtpvPGG+O/LLzv3uER1pc3ZHEQ34eUF5OQA3bsD998P\nPPSQ44/56aei62f/fjEah0iL2FIn3erQAVi0SCx1W1zs2GN9/bXodlm1CrjjDscei8geLOqkawMH\nAqNHA08+CZw/75hjnD4NREQA77wDhIY65hhEamFRJ917+WWxBV7fvkBpqbrv/fPPYk334cOB+Hh1\n35vIEVjUSfdMJuC998R0/T591Bvq+NNPoqAPGiQWFSPSA7uK+rZt25CUlFTjc6tXr8bgwYMRHR2N\nzz77zJ7DEN1UgwZi16GgIOCRR4Djx+17v0OHROv/qafEYmImkyoxiRzO5qI+Y8YMzJ07t8bnzpw5\ng+zsbKxatQoZGRmYM2cOLl++bHNIorpwcwPmzgWefVYU5DVr6v8eiiJGuDz6qNhv9JVXWNBJX2wu\n6kFBQXj99ddrfO7QoUMIDg6Gu7s7fH19ceedd+Lo0aO2HoqoXsaPF+uvv/wy0K8f8M03dXvd3r2i\nu2XxYmD3biAmxrE5iRzhpuPU165di+XLl1/1WGpqKnr37o09e/bU+BqLxQK/K3YKbtSoEUpKSuyM\nSlR3XbqILpT588WIlc6dgWHDgJAQoHVr0fpWFODYMWDXLiA7G/juO+C118QSBJz+T3p106IeGRmJ\nyMjIer07UTruAAAGUklEQVSpr68vLBZL9delpaXw9/evfzoiO3h5iS3mnnsOWLtW7CGalCRupPr7\nAxcuALffDnTrJlZbjIgAGjaUnZrIPg6ZUXr//fdj3rx5KC8vx6VLl/Df//4Xf/rTn2r8XrPZ7IgI\nNSopKXHq8ZyN51e7Xr3EHwC4dAmwWBrAx6cSXl7//z1nzqgQ0kb87PRNS+enalHPzMxEYGAgevbs\nibi4OAwdOhSKomDSpEloWEsTKCAgQM0IN2Q2m516PGfj+emXkc8N4PmprfgGU6jtKupdunRBly5d\nqr8ePnx49d+joqIQFRVlz9sTEVE9cfIREZGBsKgTERkIizoRkYGwqBMRGQiLOhGRgbCoExEZCIs6\nEZGBsKgTERkIizoRkYGwqBMRGQiLOhGRgbCoExEZCIs6EZGBsKgTERkIizoRkYGwqBMRGQiLOhGR\ngbCoExEZCIs6EZGBsKgTERkIizoRkYGwqBMRGQiLOhGRgbCoExEZCIs6EZGBsKgTERkIizoRkYGw\nqBMRGQiLOhGRgbjb8+Jt27Zhy5YtmDNnznXPzZgxA/v374ePjw8AYMGCBfD19bXncEREdBM2F/UZ\nM2YgPz8f99xzT43PFxYWYsmSJWjcuLHN4YiIqH5s7n4JCgrC66+/XuNziqKgqKgIr732GmJiYrBu\n3TpbD0NERPVw05b62rVrsXz58qseS01NRe/evbFnz54aX3Px4kXExcVhxIgRsFqtiI+PR8eOHdGu\nXTt1UhMRUY1uWtQjIyMRGRlZrzf19vZGXFwcPD094enpia5du+LIkSMs6kREDmbXjdLa/PDDD5g4\ncSI2bNgAq9WKgoICDBo0qMbvLSgocESEWhUXFzv1eM7G89MvI58bwPNzFlWLemZmJgIDA9GzZ08M\nGDAAUVFR8PDwwMCBA9G2bdvrvj84OFjNwxMRuTyToiiK7BBERKQOTj4iIjIQlyjqiqJg2rRpiI6O\nRnx8PE6cOCE7kmqsViumTJmCYcOGYciQIdixY4fsSA5x9uxZ9OjRAz/88IPsKKpbtGgRoqOjMXjw\nYMMN/7VarUhKSkJ0dDRiY2MN8/kdPHgQcXFxAIAff/wRQ4cORWxsLFJSUiQnc5Ginpubi/LycuTk\n5CApKQmpqamyI6lm48aNaNKkCVasWIHFixfjjTfekB1JdVarFdOmTYOXl5fsKKrbs2cP/vOf/yAn\nJwfZ2dmaudmmlry8PFRWViInJwdjx47F3LlzZUeyW0ZGBl555RVcvnwZgBjiPWnSJHzwwQeorKxE\nbm6u1HwuUdQLCgoQEhICAOjUqRMOHz4sOZF6evfujQkTJgAAKisr4e7ukAFNUs2aNQsxMTFo0aKF\n7Ciq+9e//oV27dph7NixGDNmDHr27Ck7kqruvPNOVFRUQFEUlJSUwMPDQ3YkuwUGBiItLa3668LC\nQnTu3BkAEBoaii+++EJWNAAOGtKoNRaLBX5+ftVfu7u7o7KyEg0a6P/fNG9vbwDiHCdMmICJEydK\nTqSu9evXo2nTpnjkkUewcOFC2XFU98svv8BsNiM9PR0nTpzAmDFjsGXLFtmxVOPj44OTJ08iPDwc\nv/76K9LT02VHsltYWBhOnTpV/fWVY018fHxQUlIiI1Y1/Ve1OvD19UVpaWn110Yp6FWKi4uRkJCA\ngQMHok+fPrLjqGr9+vXIz89HXFwcjhw5guTkZJw9e1Z2LNU0btwYISEhcHd3R5s2beDp6Ylz587J\njqWazMxMhISEYOvWrdi4cSOSk5NRXl4uO5aqrqwlpaWl8Pf3l5jGRYp6UFAQ8vLyAAAHDhww1MzW\nM2fOYOTIkZg8eTIGDhwoO47qPvjgA2RnZyM7Oxvt27fHrFmz0LRpU9mxVBMcHIzdu3cDAE6fPo3f\nfvsNTZo0kZxKPbfcckv16qx+fn6wWq2orKyUnEpdHTp0wN69ewEAu3btkj7/xiW6X8LCwpCfn4/o\n6GgAMNSN0vT0dFy4cAELFixAWloaTCYTMjIy0LBhQ9nRVGcymWRHUF2PHj2wb98+REZGVo/SMtJ5\nJiQk4K9//SuGDRtWPRLGaDe8k5OT8eqrr+Ly5cto27YtwsPDpebh5CMiIgNxie4XIiJXwaJORGQg\nLOpERAbCok5EZCAs6kREBsKiTkRkICzqREQGwqJORGQg/wdNbw1oUJqhZwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x107508b70>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
-   "source": [
-    "plt.plot(x, np.sin(x))\n",
-    "plt.axis([-1, 11, -1.5, 1.5]);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The ``plt.axis()`` method goes even beyond this, allowing you to do things like automatically tighten the bounds around the current plot:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Tku6Eh0shO3pUtsWjWy1dKqNtfHxUJyGt8vQEYmPlXpkwwc5j2POhJUuWIDg4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\nBk/pWBptAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x107d1c9b0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -445,21 +425,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It allows even higher-level specifications, such as ensuring an equal aspect ratio so that on your screen, one unit in ``x`` is equal to one unit in ``y``:"
+    "Or you can specify that you want an equal axis ratio, such that one unit in `x` is visually equivalent to one unit in `y`, as seen in the following figure:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1076ded30>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -475,7 +458,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For more information on axis limits and the other capabilities of the ``plt.axis`` method, refer to the ``plt.axis`` docstring."
+    "Other axis options include `'on'`, `'off'`, `'square'`, `'image'`, and more. For more information on these, refer to the `plt.axis` docstring."
    ]
   },
   {
@@ -484,23 +467,25 @@
    "source": [
     "## Labeling Plots\n",
     "\n",
-    "As the last piece of this section, we'll briefly look at the labeling of plots: titles, axis labels, and simple legends.\n",
-    "\n",
-    "Titles and axis labels are the simplest such labels—there are methods that can be used to quickly set them:"
+    "As the last piece of this chapter, we'll briefly look at the labeling of plots: titles, axis labels, and simple legends.\n",
+    "Titles and axis labels are the simplest such labels—there are methods that can be used to quickly set them (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+/hw/fpxLly7h5eXFnj17GDFixE2P5efnV+TzW1FSUpLhrkXNmuDtDb//7keL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\nwmV07KgeEkdGQna248937Jg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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x107d57438>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -518,8 +503,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The position, size, and style of these labels can be adjusted using optional arguments to the function.\n",
-    "For more information, see the Matplotlib documentation and the docstrings of each of these functions."
+    "The position, size, and style of these labels can be adjusted using optional arguments to the functions, described in the docstrings."
    ]
   },
   {
@@ -527,23 +511,25 @@
    "metadata": {},
    "source": [
     "When multiple lines are being shown within a single axes, it can be useful to create a plot legend that labels each line type.\n",
-    "Again, Matplotlib has a built-in way of quickly creating such a legend.\n",
-    "It is done via the (you guessed it) ``plt.legend()`` method.\n",
-    "Though there are several valid ways of using this, I find it easiest to specify the label of each line using the ``label`` keyword of the plot function:"
+    "Again, Matplotlib has a built-in way of quickly creating such a legend; it is done via the (you guessed it) `plt.legend` method.\n",
+    "Though there are several valid ways of using this, I find it easiest to specify the label of each line using the `label` keyword of the `plot` function (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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HtIWRtIWRtIVRXFxctrY3KchnzpxJWloaU6ZMQVEUChUqxNy5c03Z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\nXAghNO7/ADRl1uEb30mBAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x107d7ca90>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -562,41 +548,44 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As you can see, the ``plt.legend()`` function keeps track of the line style and color, and matches these with the correct label.\n",
-    "More information on specifying and formatting plot legends can be found in the ``plt.legend`` docstring; additionally, we will cover some more advanced legend options in [Customizing Plot Legends](04.06-Customizing-Legends.ipynb)."
+    "As you can see, the `plt.legend` function keeps track of the line style and color, and matches these with the correct label.\n",
+    "More information on specifying and formatting plot legends can be found in the `plt.legend` docstring; additionally, we will cover some more advanced legend options in [Customizing Plot Legends](04.06-Customizing-Legends.ipynb)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Aside: Matplotlib Gotchas\n",
+    "## Matplotlib Gotchas\n",
     "\n",
-    "While most ``plt`` functions translate directly to ``ax`` methods (such as ``plt.plot()`` → ``ax.plot()``, ``plt.legend()`` → ``ax.legend()``, etc.), this is not the case for all commands.\n",
+    "While most `plt` functions translate directly to `ax` methods (`plt.plot` → `ax.plot`, `plt.legend` → `ax.legend`, etc.), this is not the case for all commands.\n",
     "In particular, functions to set limits, labels, and titles are slightly modified.\n",
     "For transitioning between MATLAB-style functions and object-oriented methods, make the following changes:\n",
     "\n",
-    "- ``plt.xlabel()``  → ``ax.set_xlabel()``\n",
-    "- ``plt.ylabel()`` → ``ax.set_ylabel()``\n",
-    "- ``plt.xlim()``  → ``ax.set_xlim()``\n",
-    "- ``plt.ylim()`` → ``ax.set_ylim()``\n",
-    "- ``plt.title()`` → ``ax.set_title()``\n",
+    "- `plt.xlabel`  → `ax.set_xlabel`\n",
+    "- `plt.ylabel` → `ax.set_ylabel`\n",
+    "- `plt.xlim`  → `ax.set_xlim`\n",
+    "- `plt.ylim` → `ax.set_ylim`\n",
+    "- `plt.title` → `ax.set_title`\n",
     "\n",
-    "In the object-oriented interface to plotting, rather than calling these functions individually, it is often more convenient to use the ``ax.set()`` method to set all these properties at once:"
+    "In the object-oriented interface to plotting, rather than calling these functions individually, it is often more convenient to use the `ax.set` method to set all these properties at once (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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WZvBgMXapWzfRGcResUCUwcyZot/0d98Bjz0mO406vR748ENxyt6tmzijILIkBw8CAQFA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\nVLeflpZWsYGtWHZ2tuwIFoN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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x107f67cc0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -610,22 +599,15 @@
     "       xlabel='x', ylabel='sin(x)',\n",
     "       title='A Simple Plot');"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.01-Simple-Line-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -639,9 +621,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.02-Simple-Scatter-Plots.ipynb b/notebooks/04.02-Simple-Scatter-Plots.ipynb
index eaf6c4249..7a1b94424 100644
--- a/notebooks/04.02-Simple-Scatter-Plots.ipynb
+++ b/notebooks/04.02-Simple-Scatter-Plots.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) | [Contents](Index.ipynb) | [Visualizing Errors](04.03-Errorbars.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.02-Simple-Scatter-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -30,19 +8,20 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Another commonly used plot type is the simple scatter plot, a close cousin of the line plot.\n",
     "Instead of points being joined by line segments, here the points are represented individually with a dot, circle, or other shape.\n",
-    "We’ll start by setting up the notebook for plotting and importing the functions we will use:"
+    "We’ll start by setting up the notebook for plotting and importing the packages we will use:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -56,24 +35,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Scatter Plots with ``plt.plot``\n",
+    "## Scatter Plots with plt.plot\n",
     "\n",
-    "In the previous section we looked at ``plt.plot``/``ax.plot`` to produce line plots.\n",
-    "It turns out that this same function can produce scatter plots as well:"
+    "In the previous chapter we looked at using `plt.plot`/`ax.plot` to produce line plots.\n",
+    "It turns out that this same function can produce scatter plots as well (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ewhg9kGesAIbX0KMH8owVwPAagh7IM1YAw2sIeiDPWAEMryHogTxjBTC8hslYIM9YAQyv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\nTkSuQmCC\n",
+      "image/png": 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Qk+KYYSKlER3yVqsVmZmZAIBx48ahra3Ns+/EiRMYP348YmNjodFooNVqcfr0aa9zsrKy0NLSEmD12S8cTlIcM0ykNKJH1zgcDiQlJXm2o6Oj0dPTg5iYGDgcDmg0Gs8+tVoNh8PhVa5Wq9HZ2enz2jabbdD1uFO/sD/XkSqXyxXWdhoMBrz//vteZaGuT7jbHA5sszKEos2iQz4pKQlOp9Oz7Xa7ERMT43Of0+mERqPxlMfHx8PpdGLo0KE+r63X6wddD61W2+95QF+5P9eRKpvNpoh2fh/brAxss3+sVqvPctHdNQaDARaLBQDQ2tqK1NRUz7709HRYrVbcunULnZ2dOHfuHFJTU2EwGNDc3AwAsFgsyMjIEPvxHuwXJiIamOg7+SlTpuDIkSPIz8+HIAiorKzEnj17oNVqMWnSJBQVFaGgoACCIGDZsmWIi4vDwoULUVpaiv379+OBBx7Apk2bAm5AX/9vWVkZLly4AK1WC5PJxH5hIiIAECLMsWPHRJ976tSpINZEGthmZfhhm2trawWdTieoVCpBp9MJtbW1YapZ6PB79s9A2cllDYgkhstJkD8445VIYjhsmPzBkCeSGC4nQf5gyBNJDJeTIH8w5IkkhsOGyR8MeSKJ4XIS5A+OriGSIKm9go7Ch3fyREQyxpAnIpIxhjwRkYwx5ImIZIwhT0QkYwx5IiIZY8gTEckYQ56IKATq6uqQkpKCqKgopKSkoK6uLiz14GQoIqIgi6TloEWFvMvlQklJCa5cuQK1Wo0NGzYgOTnZ65gNGzbg008/RU9PD/Ly8pCbm4tvv/0W2dnZnlcFTp48GS+88ELgrSAiiiB3Wg5aEiHf0NCA1NRUvPLKKzh48CCqqqqwcuVKz/5//vOfuHDhAsxmM7q6uvD0008jOzsbp06dQk5ODlatWhW0BhARRZpIWg5aVJ+81WpFZmYmACArKwtHjx712j9+/HhUVlZ6tm/fvo2YmBi0tbXh5MmTmDVrFpYsWYJLly4FUHUiosgUSctB3/VOvrGxEXv37vUqGzZsGDQaDQBArVajs7PTa39cXBzi4uLQ3d2N5cuXIy8vD2q1GqNGjcLYsWPx+OOP48CBA6ioqMDWrVuD2BwiovAzmUxeffJA+JaDVgmCIPh70uLFi7FgwQKkp6ejs7MTRqMRTU1NXsdcu3YNS5YswaOPPopFixYBABwOBxISEhAdHY2bN2/imWeewaFDh7zOs1qt/dbKHiyXy4X4+HhR50oV26wMbLP0NDU1YcuWLejo6MDIkSOxbNky5OTk3PGcQNp848YNZGRk9CsX1SdvMBjQ3NyM9PR0WCyWfhd2uVyYM2cO5s6di1/96lee8pUrV+LJJ5/EU089haNHjyItLc3n9fV6vZhqwWaziT5XqthmZWCbpUev16OkpMSvcwJps9Vq9Vkuqk/eaDTi7NmzMBqNMJvNWLx4MQBg48aNOHHiBPbt24fPP/8cjY2NKCoqQlFRET7//HMUFxejoaEBRUVF2LdvH188rCCRMmaYSGlE3cknJCT47Et//fXXAQDp6emYM2eOz3NramrEfCRJWCSNGSZSGs54pZC705hhIgothjyFXCSNGSZSGoY8hVwkjRkmUhqGPIWcyWTqNyw2XGOGiZSGIU8hV1hYiOrqauh0OqhUKuh0OlRXV/OhK9E9wFUo6Z4oLCxkqBOFAe/kiYhkjCFPRCRjDHmiCMKZwRRs7JMnihB3mhlsMBjCWTWSMN7JE0UIzgymUGDIE0UIzgymUGDIE0UIzgymUGDIE0UIzgymUGDIE0UIzgymUODoGqIIwpnBFGy8kycikjFRd/IulwslJSW4cuUK1Go1NmzYgOTkZK9jFi5ciG+++QZDhgxBXFwcdu3aBbvdjuXLl0OlUuGRRx7B6tWrERXFf2eIiEJFVMI2NDQgNTUV9fX1ePbZZ1FVVdXvGLvdjoaGBtTU1GDXrl0AgHXr1mHp0qWor6+HIAg4fPhwYLUnIqI7EhXyVqsVmZmZAICsrCwcPXrUa//ly5dx/fp1vPzyyzAajfjwww8BACdPnsSjjz7qOa+lpSWQuhMR0V3ctbumsbERe/fu9SobNmwYNBoNAECtVqOzs9Nrf3d3N+bNm4fZs2fj2rVrMBqNSE9PhyAIUKlUA57Xx2aziWqMy+USfa5Usc3KwDYrQyjafNeQnzlzJmbOnOlVtnjxYjidTgCA0+nE0KFDvfYPHz4c+fn5iImJwbBhw6DX63H+/Hmv/ndf5/XR6/V+NwTo/cdB7LlSxTYrA9usDIG02Wq1+iwX1V1jMBjQ3NwMALBYLMjIyPDa39LSgldffRVAb5ifPXsWo0aNwpgxY/Dxxx97zpswYYKYjycikpW+1UfT0tKCvvqoqJA3Go04e/YsjEYjzGYzFi9eDADYuHEjTpw4gYkTJyIlJQW5ubl48cUX8dprryE5ORmlpaXYtm0b8vLy0N3djezs7KA1hIhIivpWH7Xb7RAEwbP6aLCCXiUIghCUKwWJ1Wrt9z+DweJ/75SBbVYGpbQ5JSUFdru9X7lOp0N7e/ugrzNQdnKQOhFRGIV69VGGPBFRGIV69VGGPBFRGIV69VGGPEUcvueUlCTUq49yFUqKKHd6zylXZyS56lt9NBQPm3knTxGF7zklCi6GPEUUvueUKLgY8hRR+J5TouBiyFNE4XtOiYKLIU8Rhe85JQoujq6hiMP3nBIFD+/kiYhkjCFPFGKc3EXhxO4aohDi5C4KN97JE4UQJ3dRuDHkiUKIk7so3BjyRCHEyV0UbqL65F0uF0pKSnDlyhWo1Wps2LABycnJnv0WiwU7d+4EAAiCAKvViqamJty6dQu//vWvkZKSAqD3NYJPPfVU4K0gilAmk8mrTx7g5C66t0SFfENDA1JTU/HKK6/g4MGDqKqqwsqVKz37s7KykJWVBQDYtWsXDAYDRo8ejcbGRsydOxfz5s0LTu2JIlzfw9WysjJcuHABWq0WJpOJD13pnhEV8larFfPnzwfQG+hVVVU+j+vo6MB7772Hd999FwDQ1taG8+fP4/Dhw9DpdFixYgWSkpJEVp1IGji5i8LpriHf2NiIvXv3epUNGzYMGo0GAKBWq9HZ2enz3D179mDOnDmIjY0FAKSnp2PmzJkYO3Ys3nrrLWzfvh2lpaX9zrPZbH43BOjtRhJ7rlSxzcrANitDSNosiLBo0SLh+PHjgiAIwvXr14Wnn3663zG3b98WnnzySeHmzZuesmvXrnl+Pnv2rDB79ux+5x07dkxMlQRBEIRTp06JPleq2GZlYJuVIZA2D5SdokbXGAwGNDc3A+h9yJqRkdHvmP/85z94+OGHER8f7yl78cUXceLECQDA0aNHkZaWJubjiYhokET1yRuNRpSWlsJoNGLIkCHYtGkTAGDjxo2YOnUq0tPTcf78eTz00ENe561ZswZvvPEGhgwZguHDh+ONN94IvAVERDQgUSGfkJCArVu39it//fXXPT9PmzYN06ZN89qflpaGffv2iflIIiISgZOhiIhkjCFPksXVHYnujqtQkiRxdUeiweGdPEkSV3ckGhyGPEkSV3ckGhyGPEkSV3ckGhyGPEmSyWRCYmKiVxlXdyTqjyFPklRYWIjq6mrodDqoVCrodDpUV1ff04euHN1DUsDRNSRZ4VzdkaN7SCp4J08kAkf3kFQw5IlE4OgekgqGPJEIHN1DUsGQJxKBo3tIKhjyRCJEwugeosFgyJMihGK4Y2FhIdrb2+F2u9He3s6Ap4jEIZQkexzuSErGO3mSPX+GO3KCE8lNQCH/t7/9DcXFxT737d+/H8899xxyc3Px4YcfAgCuXr2KefPmoaCgAEuXLsXNmzcD+XiiQRnscMe+O3673Q5BEDx3/Ax6kjLRIV9RUYFNmzbB7Xb32/f111+jpqYG+/btw+7du7F582Z0dXWhqqoKOTk5qK+vx5gxY2A2mwOqPNFgDHa4Iyc4kRyJDnmDwYA1a9b43HfixAmMHz8esbGx0Gg00Gq1OH36NKxWKzIzMwEAWVlZaGlpEfvxRIM22OGOnOBEcnTXB6+NjY3Yu3evV1llZSWeeuopfPzxxz7PcTgc0Gg0nm21Wg2Hw+FVrlar0dnZ6fN8m8026AZ8n8vlEn2uVLHNd9d3Q7JlyxZ0dHRg5MiRWLZsGQwGg9d1Ro4ciS+//LLf+SNHjgz73zG/Z2UIRZvvGvIzZ87EzJkz/bpoUlISnE6nZ9vpdEKj0XjK4+Pj4XQ6MXToUJ/n6/V6vz6vj81mE32uVLHNg6PX61FSUnLHY958802vUThA7x3/m2++Gfa/Y37PyhBIm61Wq8/ykIyuSU9Ph9Vqxa1bt9DZ2Ylz584hNTUVBoMBzc3NAACLxYKMjIxQfDyRKJzgRHIU1HHye/bsgVarxaRJk1BUVISCggIIgoBly5YhLi4OCxcuRGlpKfbv348HHngAmzZtCubHEwUsnMsXE4VCQCH/2GOP4bHHHvNsz5071/Nzbm4ucnNzvY4fPnw4du/eHchHEhGRHzgZiohIxhjyREQyxpAnIpIxhjwRkYypBEEQwl2J7xtorCcREd2Zr2HpERfyREQUPOyuISKSMYY8EZGMySLk3W43ysvLkZeXh6KiItjt9nBXKeS6u7tRUlKCgoICzJgxA4cPHw53le6JK1euYOLEiTh37ly4q3JP/PGPf0ReXh6ee+45NDY2hrs6Idfd3Y3i4mLk5+ejoKBA9t/z8ePHUVRUBKD3jWVGoxEFBQVYvXq1z2XcxZBFyB86dAhdXV0wm80oLi7G+vXrw12lkDtw4ADuv/9+1NfXY9euXXjjjTfCXaWQ6+7uRnl5OeLj48NdlXvi448/xmeffYaGhgbU1NSgo6Mj3FUKuebmZvT09GDfvn1YtGgRfv/734e7SiGzc+dOrFy5Erdu3QIArFu3DkuXLkV9fT0EQQjajZssQv7769SPGzcObW1tYa5R6E2dOhWvvvoqAEAQBERHR4e5RqG3YcMG5OfnY8SIEeGuyj3xj3/8A6mpqVi0aBFefvll/OIXvwh3lULu4Ycfxu3bt+F2u+FwOBATI9/XUGu1Wmzbts2zffLkSTz66KMAgvu+DVn8DTocDiQlJXm2o6Oj0dPTI+tfELVaDaC37UuWLMHSpUvDW6EQ+/Of/4zk5GRkZmaiuro63NW5J7755htcvHgRO3bswBdffIGFCxfir3/9K1QqVbirFjKJiYn43//+h2nTpuGbb77Bjh07wl2lkMnOzsYXX3zh2RYEwfPd3ul9G/6SxZ38D9evd7vdsg74Pl9++SVmz56N6dOn45lnngl3dULq3XffRUtLC4qKimCz2VBaWoqvv/463NUKqfvvvx9PPPEEYmNjMWrUKMTFxeHq1avhrlZIvf3223jiiSfwwQcf4L333sPy5cs93RlyFxX1XRzf6X0bfl83KFcJM4PBAIvFAgBobW1FampqmGsUepcvX8a8efNQUlKCGTNmhLs6IVdXV4fa2lrU1NRAr9djw4YN+NGPfhTuaoVURkYGPvroIwiCgK+++go3b97E/fffH+5qhdTQoUM9b4+777770NPTg9u3b4e5VvfGmDFjPG/bs1gsmDBhQlCuK4vb3SlTpuDIkSPIz8+HIAiorKwMd5VCbseOHbh+/TqqqqpQVVUFoPdBjlIeSirBL3/5S3zyySeYMWMGBEFAeXm57J+9zJkzBytWrEBBQQG6u7uxbNmyfu/nlavS0lKsWrUKmzdvxqhRo5CdnR2U63LGKxGRjMmiu4aIiHxjyBMRyRhDnohIxhjyREQyxpAnIpIxhjwRkYwx5ImIZIwhT0QkY/8HJFvsjqv7vpYAAAAASUVORK5CYII=",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bdaae48>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -91,21 +73,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The third argument in the function call is a character that represents the type of symbol used for the plotting. Just as you can specify options such as ``'-'``, ``'--'`` to control the line style, the marker style has its own set of short string codes. The full list of available symbols can be seen in the documentation of ``plt.plot``, or in Matplotlib's online documentation. Most of the possibilities are fairly intuitive, and we'll show a number of the more common ones here:"
+    "The third argument in the function call is a character that represents the type of symbol used for the plotting. Just as you can specify options such as `'-'` or `'--'` to control the line style, the marker style has its own set of short string codes. The full list of available symbols can be seen in the documentation of `plt.plot`, or in Matplotlib's [online documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.markers.MarkerStyle.html). Most of the possibilities are fairly intuitive, and a number of the more common ones are demonstrated here (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Ijo5GSUkJLC0tER8fjyFDhrT7vsjISNjb2yM4OFgvQTvEcQAtzWVUHB2H4dCh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\nmTw9PXH8+HGMGzcOtbW1+Pb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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bf7e470>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -113,11 +98,11 @@
     }
    ],
    "source": [
-    "rng = np.random.RandomState(0)\n",
+    "rng = np.random.default_rng(0)\n",
     "for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']:\n",
-    "    plt.plot(rng.rand(5), rng.rand(5), marker,\n",
+    "    plt.plot(rng.random(2), rng.random(2), marker, color='black',\n",
     "             label=\"marker='{0}'\".format(marker))\n",
-    "plt.legend(numpoints=1)\n",
+    "plt.legend(numpoints=1, fontsize=13)\n",
     "plt.xlim(0, 1.8);"
    ]
   },
@@ -125,21 +110,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For even more possibilities, these character codes can be used together with line and color codes to plot points along with a line connecting them:"
+    "For even more possibilities, these character codes can be used together with line and color codes to plot points along with a line connecting them (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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YAwcOhJOTE3JycuDj48M6DhEYmUyGHj16oE+fPnBwcEDnzp2h0WhsYuTas54dUy/0x49W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\nTwghIkeFnhBCRI4KPSGEiNz/Aak6QmZ56l4lAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1023b1940>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -154,21 +142,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Additional keyword arguments to ``plt.plot`` specify a wide range of properties of the lines and markers:"
+    "Additional keyword arguments to `plt.plot` specify a wide range of properties of the lines and markers, as you can see in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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YTCam1C0wtHLZ4XAwJXKBoaczMV83Id4XCQkJOHv2LFOd12QywWazYdasWRN6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\n9QIJCQlBaCEhFOgJx8LCwlB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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e7a6208>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -188,31 +179,34 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This type of flexibility in the ``plt.plot`` function allows for a wide variety of possible visualization options.\n",
-    "For a full description of the options available, refer to the ``plt.plot`` documentation."
+    "These kinds of options make `plt.plot` the primary workhorse for two-dimensional plots in Matplotlib.\n",
+    "For a full description of the options available, refer to the [`plt.plot` documentation](https://matplotlib.org/3.5.0/api/_as_gen/matplotlib.pyplot.plot.html)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Scatter Plots with ``plt.scatter``\n",
+    "## Scatter Plots with plt.scatter\n",
     "\n",
-    "A second, more powerful method of creating scatter plots is the ``plt.scatter`` function, which can be used very similarly to the ``plt.plot`` function:"
+    "A second, more powerful method of creating scatter plots is the `plt.scatter` function, which can be used very similarly to the `plt.plot` function (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e8a18d0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -227,24 +221,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The primary difference of ``plt.scatter`` from ``plt.plot`` is that it can be used to create scatter plots where the properties of each individual point (size, face color, edge color, etc.) can be individually controlled or mapped to data.\n",
+    "The primary difference of `plt.scatter` from `plt.plot` is that it can be used to create scatter plots where the properties of each individual point (size, face color, edge color, etc.) can be individually controlled or mapped to data.\n",
     "\n",
     "Let's show this by creating a random scatter plot with points of many colors and sizes.\n",
-    "In order to better see the overlapping results, we'll also use the ``alpha`` keyword to adjust the transparency level:"
+    "In order to better see the overlapping results, we'll also use the `alpha` keyword to adjust the transparency level (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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BbCa7IqzNRpMroxfBVGbwSBij4aZYlvNlzs/PMTm3xAOnDuNyOxg42EVXX4vJ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\noNX2QiLxruOqO6TSSGi1NHIlx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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e8fce10>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -252,14 +249,13 @@
     }
    ],
    "source": [
-    "rng = np.random.RandomState(0)\n",
-    "x = rng.randn(100)\n",
-    "y = rng.randn(100)\n",
-    "colors = rng.rand(100)\n",
-    "sizes = 1000 * rng.rand(100)\n",
+    "rng = np.random.default_rng(0)\n",
+    "x = rng.normal(size=100)\n",
+    "y = rng.normal(size=100)\n",
+    "colors = rng.random(100)\n",
+    "sizes = 1000 * rng.random(100)\n",
     "\n",
-    "plt.scatter(x, y, c=colors, s=sizes, alpha=0.3,\n",
-    "            cmap='viridis')\n",
+    "plt.scatter(x, y, c=colors, s=sizes, alpha=0.3)\n",
     "plt.colorbar();  # show color scale"
    ]
   },
@@ -267,24 +263,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice that the color argument is automatically mapped to a color scale (shown here by the ``colorbar()`` command), and that the size argument is given in pixels.\n",
+    "Notice that the color argument is automatically mapped to a color scale (shown here by the `colorbar` command), and that the size argument is given in pixels.\n",
     "In this way, the color and size of points can be used to convey information in the visualization, in order to visualize multidimensional data.\n",
     "\n",
-    "For example, we might use the Iris data from Scikit-Learn, where each sample is one of three types of flowers that has had the size of its petals and sepals carefully measured:"
+    "For example, we might use the Iris dataset from Scikit-Learn, where each sample is one of three types of flowers that has had the size of its petals and sepals carefully measured (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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xWSboCmOzWimUimQX80xcnOTQA/dt4Ce3eRaTSZwbmPrp9PmYWVjARgWn8+qT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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1107c79b0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -296,7 +295,7 @@
     "iris = load_iris()\n",
     "features = iris.data.T\n",
     "\n",
-    "plt.scatter(features[0], features[1], alpha=0.2,\n",
+    "plt.scatter(features[0], features[1], alpha=0.4,\n",
     "            s=100*features[3], c=iris.target, cmap='viridis')\n",
     "plt.xlabel(iris.feature_names[0])\n",
     "plt.ylabel(iris.feature_names[1]);"
@@ -306,8 +305,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "A full-color version of this plot is available in the [online version](http://github.com/jakevdp/PythonDataScienceHandbook) of the book.\n",
+    "\n",
     "We can see that this scatter plot has given us the ability to simultaneously explore four different dimensions of the data:\n",
-    "the (x, y) location of each point corresponds to the sepal length and width, the size of the point is related to the petal width, and the color is related to the particular species of flower.\n",
+    "the (*x*, *y*) location of each point corresponds to the sepal length and width, the size of the point is related to the petal width, and the color is related to the particular species of flower.\n",
     "Multicolor and multifeature scatter plots like this can be useful for both exploration and presentation of data."
    ]
   },
@@ -315,28 +316,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ``plot`` Versus ``scatter``: A Note on Efficiency\n",
+    "## plot Versus scatter: A Note on Efficiency\n",
     "\n",
-    "Aside from the different features available in ``plt.plot`` and ``plt.scatter``, why might you choose to use one over the other? While it doesn't matter as much for small amounts of data, as datasets get larger than a few thousand points, ``plt.plot`` can be noticeably more efficient than ``plt.scatter``.\n",
-    "The reason is that ``plt.scatter`` has the capability to render a different size and/or color for each point, so the renderer must do the extra work of constructing each point individually.\n",
-    "In ``plt.plot``, on the other hand, the points are always essentially clones of each other, so the work of determining the appearance of the points is done only once for the entire set of data.\n",
-    "For large datasets, the difference between these two can lead to vastly different performance, and for this reason, ``plt.plot`` should be preferred over ``plt.scatter`` for large datasets."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) | [Contents](Index.ipynb) | [Visualizing Errors](04.03-Errorbars.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.02-Simple-Scatter-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "Aside from the different features available in `plt.plot` and `plt.scatter`, why might you choose to use one over the other? While it doesn't matter as much for small amounts of data, as datasets get larger than a few thousand points, `plt.plot` can be noticeably more efficient than `plt.scatter`.\n",
+    "The reason is that `plt.scatter` has the capability to render a different size and/or color for each point, so the renderer must do the extra work of constructing each point individually.\n",
+    "With `plt.plot`, on the other hand, the markers for each point are guaranteed to be identical, so the work of determining the appearance of the points is done only once for the entire set of data.\n",
+    "For large datasets, this difference can lead to vastly different performance, and for this reason, `plt.plot` should be preferred over `plt.scatter` for large datasets."
    ]
   }
  ],
  "metadata": {
+  "jupytext": {
+   "encoding": "# -*- coding: utf-8 -*-",
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -350,9 +345,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.03-Errorbars.ipynb b/notebooks/04.03-Errorbars.ipynb
index 094ae9c89..8c92f56fc 100644
--- a/notebooks/04.03-Errorbars.ipynb
+++ b/notebooks/04.03-Errorbars.ipynb
@@ -4,40 +4,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) | [Contents](Index.ipynb) | [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.03-Errorbars.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualizing Errors"
+    "# Visualizing Uncertainties"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For any scientific measurement, accurate accounting for errors is nearly as important, if not more important, than accurate reporting of the number itself.\n",
+    "For any scientific measurement, accurate accounting of uncertainties is nearly as important, if not more so, as accurate reporting of the number itself.\n",
     "For example, imagine that I am using some astrophysical observations to estimate the Hubble Constant, the local measurement of the expansion rate of the Universe.\n",
-    "I know that the current literature suggests a value of around 71 (km/s)/Mpc, and I measure a value of 74 (km/s)/Mpc with my method. Are the values consistent? The only correct answer, given this information, is this: there is no way to know.\n",
+    "I know that the current literature suggests a value of around 70 (km/s)/Mpc, and I measure a value of 74 (km/s)/Mpc with my method. Are the values consistent? The only correct answer, given this information, is this: there is no way to know.\n",
     "\n",
-    "Suppose I augment this information with reported uncertainties: the current literature suggests a value of around 71 $\\pm$ 2.5 (km/s)/Mpc, and my method has measured a value of 74 $\\pm$ 5 (km/s)/Mpc. Now are the values consistent? That is a question that can be quantitatively answered.\n",
+    "Suppose I augment this information with reported uncertainties: the current literature suggests a value of 70 ± 2.5 (km/s)/Mpc, and my method has measured a value of 74 ± 5 (km/s)/Mpc. Now are the values consistent? That is a question that can be quantitatively answered.\n",
     "\n",
     "In visualization of data and results, showing these errors effectively can make a plot convey much more complete information."
    ]
@@ -48,14 +26,14 @@
    "source": [
     "## Basic Errorbars\n",
     "\n",
-    "A basic errorbar can be created with a single Matplotlib function call:"
+    "One standard way to visualize uncertainties is using an errorbar. A basic errorbar can be created with a single Matplotlib function call, as shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -69,14 +47,17 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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OuM7pdLrs3xjWB9/Rz4oos2mkTU2Bfckll+jBBx/0qiyIqaTeEIQdbBppw6Pp8FS5gz2JNwRhh9ErQMdxIn8FSGAHLMpnbyCJbBppQ2Aj0TiBQrLnCpDARugITaA6BDbgAicXhCnWgc2XC1IwT1lyrCEIjMNGrNk0xhaYCoGNWLNpjC0wlVh3icQd4TM1m8bYAlOhhY1Y4ylLxAktbMSeLWNs/cBVmH/CeBk0gT1OXA7uuNQj7thP9hq9me04jjo6OiZcvfX29vo2nSxdIoisuLxcttTb32G3sG5mE9iIJIbjIcrCmjKYwEYkMRwPURbWzWz6sBFJlYbjEd6IgjBuZtPCRiQxHA+YjBY2IsttC4aWN+KOwAYsx4kqOegSAQBLENgAYAkCGwAsQR82AkE/K1A7o8D+4osvtGbNGg0ODqpQKOj222/XRRdd5HXZAADjGAX2E088oW9961tasWKF/v73v6u7u1svvfSS12UDgEDYcgVoFNgrVqxQY2OjJGlkZESnnHKKp4UCAEw2ZWDv3LlTPT09E363ceNGzZ07V0eOHNGaNWu0bt26ksuaTjGYz+d9m54wqqjzZLlcTpL5ceT1erzYPvs5PiodV37VecrAXrp0qZYuXTrp9wcPHtTPfvYz/fznP9eCBQtKLtva2mpUqEwmY7ysrajzZKlUSpL5ceT1erzYPvs5PiodV7XUOZ1Ol/2bUZfI3/72N91222166KGHdP755xsVCgDgjlFgP/DAAxoaGtJvfvMbSVJTU5MeffRRTwsGAJjIKLAJZwAIHk86AoAleNIRsWfLGFtgKrSwAcASBDYAWILABnyWzWb18ccf8+Z31Iw+bMBHfX19eu+99+Q4jjo6OvTYY4/F8iGSJArj3ggtbMBHvb29chxHkjQ0NKT9+/eHXCLYjMAGfNTe3q76+hNfs8bGxrLTOADVoEsEkRWH4XgLFy7U3Llzlc1mtX37drW0tIRdJFiMFjbgs+bmZs2ePVsLFy4MuyiwHIENAJYgsAHAEgQ2AFiCwAYASxDYAGAJAhsALEFgA4AlCGwAsASBDQCWILABwBIENgBYgsAGAEsQ2ABgCaPpVXO5nLq7u3Xs2DE1NDRo06ZN+sY3vuF12QAA4xi1sHfs2KELL7xQ27dv149+9CNt27bN63IBAL7CqIW9YsUKjYyMSJIGBgY0a9YsTwsFAJhsysDeuXOnenp6Jvxu48aNmjt3rm644Qb99a9/1RNPPFFy2UwmY1SofD5vvKytqHN85XI5SSe+D0mp83jU2Tt1xWKxWMsKPvjgA910003as2fPhN+n02nNnz/faJ2ZTCZxb5amzvHV3t4u6cQrz5JS5/GoszuVstOoD3vr1q3atWuXJGnmzJmaNm2aUcEAANUz6sO+5pprtHbtWr3wwgsaGRnRxo0bvS4XAOArjAL79NNP12OPPeZ1WYBYisPb3xENPDgDAJYgsAHAEgQ2AFiCwAYASxDYAGAJAhsALEFgA4AlCGwAsASBDQCWqHnyp3LS6bQfqwWA2Cs3+ZNvgQ0A8BZdIgBgCQIbACwRqcB2HEcbNmzQtddeq66uLn300UdhF8l3hUJBa9asUWdnp5YsWaI33ngj7CIF4rPPPtN3vvMdffDBB2EXJTBbt27Vtddeq6uvvlo7d+4Muzi+KhQK6u7u1vLly9XZ2Rn7/XzgwAF1dXVJkj766CNdd9116uzs1C9/+Us5juPZdiIV2Hv27NHQ0JCef/55dXd369577w27SL7bvXu3Wlpa9Mwzz+gPf/iDfv3rX4ddJN8VCgVt2LBBM2bMCLsogdm3b5/effddPfvss3rqqaf0z3/+M+wi+eqPf/yjhoeH9dxzz2nVqlV66KGHwi6Sb7Zt26b169fryy+/lCTdc889Wr16tZ555hkVi0VPG2GRCux0Oq1FixZJktra2vT++++HXCL/XXHFFbrtttskScViMRFv79m0aZOWL1+uM844I+yiBOZPf/qT5syZo1WrVunmm28ee21YXJ177rkaGRmR4zgaHBzU9OlGU+9bYfbs2XrkkUfGfv7LX/6iBQsWSJK+/e1v6+233/ZsW5H6FAcHB9XU1DT287Rp0zQ8PBzrnT1z5kxJJ+p+6623avXq1eEWyGcvvviiTjvtNC1atEi///3vwy5OYD7//HMNDAxoy5YtOnTokG655Ra9+uqrqqurC7tovkilUjp8+LB+8IMf6PPPP9eWLVvCLpJvFi9erEOHDo39XCwWx/brzJkz9cUXX3i2rUi1sJuamnT8+PGxnx3HiXVYj/rkk090ww036Mc//rGuvPLKsIvjqxdeeEFvv/22urq6lMlktHbtWh05ciTsYvmupaVFl156qRobG3XeeefplFNO0X/+85+wi+WbJ598Updeeqlee+01vfzyy7r99tvHugzirr7+ZKweP35cs2bN8m7dnq3JA/PmzdPevXslSf39/ZozZ07IJfLfp59+qpUrV2rNmjVasmRJ2MXx3fbt2/X000/rqaeeUmtrqzZt2qSvf/3rYRfLd/Pnz9dbb72lYrGof/3rX/rvf/+rlpaWsIvlm1mzZulrX/uaJKm5uVnDw8MaGRkJuVTBuOCCC7Rv3z5J0t69e3XxxRd7tu5INV8vu+wy/fnPf9by5ctVLBYT8XLfLVu26NixY9q8ebM2b94s6cRNjCTdkEuC7373u3rnnXe0ZMkSFYtFbdiwIdb3K1asWKF169aps7NThUJBP/3pT5VKpcIuViDWrl2rX/ziF/rtb3+r8847T4sXL/Zs3TzpCACWiFSXCACgPAIbACxBYAOAJQhsALAEgQ0AliCwAcASBDYAWILABgBL/A+dCDgfDDPEXgAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bf54080>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -95,25 +76,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here the ``fmt`` is a format code controlling the appearance of lines and points, and has the same syntax as the shorthand used in ``plt.plot``, outlined in [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) and [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb).\n",
+    "Here the `fmt` is a format code controlling the appearance of lines and points, and it has the same syntax as the shorthand used in `plt.plot`, outlined in the previous chapter and earlier in this chapter.\n",
     "\n",
-    "In addition to these basic options, the ``errorbar`` function has many options to fine-tune the outputs.\n",
+    "In addition to these basic options, the `errorbar` function has many options to fine-tune the outputs.\n",
     "Using these additional options you can easily customize the aesthetics of your errorbar plot.\n",
-    "I often find it helpful, especially in crowded plots, to make the errorbars lighter than the points themselves:"
+    "I often find it helpful, especially in crowded plots, to make the errorbars lighter than the points themselves (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bec4d30>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -129,8 +113,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In addition to these options, you can also specify horizontal errorbars (``xerr``), one-sided errorbars, and many other variants.\n",
-    "For more information on the options available, refer to the docstring of ``plt.errorbar``."
+    "In addition to these options, you can also specify horizontal errorbars, one-sided errorbars, and many other variants.\n",
+    "For more information on the options available, refer to the docstring of `plt.errorbar`."
    ]
   },
   {
@@ -140,10 +124,10 @@
     "## Continuous Errors\n",
     "\n",
     "In some situations it is desirable to show errorbars on continuous quantities.\n",
-    "Though Matplotlib does not have a built-in convenience routine for this type of application, it's relatively easy to combine primitives like ``plt.plot`` and ``plt.fill_between`` for a useful result.\n",
+    "Though Matplotlib does not have a built-in convenience routine for this type of application, it's relatively easy to combine primitives like `plt.plot` and `plt.fill_between` for a useful result.\n",
     "\n",
     "Here we'll perform a simple *Gaussian process regression*, using the Scikit-Learn API (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) for details).\n",
-    "This is a method of fitting a very flexible non-parametric function to data with a continuous measure of the uncertainty.\n",
+    "This is a method of fitting a very flexible nonparametric function to data with a continuous measure of the uncertainty.\n",
     "We won't delve into the details of Gaussian process regression at this point, but will focus instead on how you might visualize such a continuous error measurement:"
    ]
   },
@@ -151,11 +135,14 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "from sklearn.gaussian_process import GaussianProcess\n",
+    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
     "\n",
     "# define the model and draw some data\n",
     "model = lambda x: x * np.sin(x)\n",
@@ -163,36 +150,37 @@
     "ydata = model(xdata)\n",
     "\n",
     "# Compute the Gaussian process fit\n",
-    "gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1E-1,\n",
-    "                     random_start=100)\n",
+    "gp = GaussianProcessRegressor()\n",
     "gp.fit(xdata[:, np.newaxis], ydata)\n",
     "\n",
     "xfit = np.linspace(0, 10, 1000)\n",
-    "yfit, MSE = gp.predict(xfit[:, np.newaxis], eval_MSE=True)\n",
-    "dyfit = 2 * np.sqrt(MSE)  # 2*sigma ~ 95% confidence region"
+    "yfit, dyfit = gp.predict(xfit[:, np.newaxis], return_std=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We now have ``xfit``, ``yfit``, and ``dyfit``, which sample the continuous fit to our data.\n",
-    "We could pass these to the ``plt.errorbar`` function as above, but we don't really want to plot 1,000 points with 1,000 errorbars.\n",
-    "Instead, we can use the ``plt.fill_between`` function with a light color to visualize this continuous error:"
+    "We now have `xfit`, `yfit`, and `dyfit`, which sample the continuous fit to our data.\n",
+    "We could pass these to the `plt.errorbar` function as in the previous section, but we don't really want to plot 1,000 points with 1,000 errorbars.\n",
+    "Instead, we can use the `plt.fill_between` function with a light color to visualize this continuous error (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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E0NnZmXAru6OjA/X19cjPz8ftt9+e0uP3EkHhTgjJOoIgyDs+DrWlzXEcGhsb8fnnn+Pa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\niMKdEEKyEIU7IYRkof8PKWVUtmMej+IAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e55bba8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -203,7 +191,6 @@
     "# Visualize the result\n",
     "plt.plot(xdata, ydata, 'or')\n",
     "plt.plot(xfit, yfit, '-', color='gray')\n",
-    "\n",
     "plt.fill_between(xfit, yfit - dyfit, yfit + dyfit,\n",
     "                 color='gray', alpha=0.2)\n",
     "plt.xlim(0, 10);"
@@ -213,31 +200,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note what we've done here with the ``fill_between`` function: we pass an x value, then the lower y-bound, then the upper y-bound, and the result is that the area between these regions is filled.\n",
-    "\n",
-    "The resulting figure gives a very intuitive view into what the Gaussian process regression algorithm is doing: in regions near a measured data point, the model is strongly constrained and this is reflected in the small model errors.\n",
-    "In regions far from a measured data point, the model is not strongly constrained, and the model errors increase.\n",
+    "Take a look at the `fill_between` call signature: we pass an x value, then the lower *y*-bound, then the upper *y*-bound, and the result is that the area between these regions is filled.\n",
     "\n",
-    "For more information on the options available in ``plt.fill_between()`` (and the closely related ``plt.fill()`` function), see the function docstring or the Matplotlib documentation.\n",
+    "The resulting figure gives an intuitive view into what the Gaussian process regression algorithm is doing: in regions near a measured data point, the model is strongly constrained, and this is reflected in the small model uncertainties.\n",
+    "In regions far from a measured data point, the model is not strongly constrained, and the model uncertainties increase.\n",
     "\n",
-    "Finally, if this seems a bit too low level for your taste, refer to [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), where we discuss the Seaborn package, which has a more streamlined API for visualizing this type of continuous errorbar."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) | [Contents](Index.ipynb) | [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) >\n",
+    "For more information on the options available in `plt.fill_between` (and the closely related `plt.fill` function), see the function docstring or the Matplotlib documentation.\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.03-Errorbars.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "Finally, if this seems a bit too low-level for your taste, refer to [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), where we discuss the Seaborn package, which has a more streamlined API for visualizing this type of continuous errorbar."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -251,9 +231,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.04-Density-and-Contour-Plots.ipynb b/notebooks/04.04-Density-and-Contour-Plots.ipynb
index 3fea071b4..ec5e37d1e 100644
--- a/notebooks/04.04-Density-and-Contour-Plots.ipynb
+++ b/notebooks/04.04-Density-and-Contour-Plots.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Visualizing Errors](04.03-Errorbars.ipynb) | [Contents](Index.ipynb) | [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.04-Density-and-Contour-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -34,15 +12,15 @@
    "metadata": {},
    "source": [
     "Sometimes it is useful to display three-dimensional data in two dimensions using contours or color-coded regions.\n",
-    "There are three Matplotlib functions that can be helpful for this task: ``plt.contour`` for contour plots, ``plt.contourf`` for filled contour plots, and ``plt.imshow`` for showing images.\n",
-    "This section looks at several examples of using these. We'll start by setting up the notebook for plotting and importing the functions we will use: "
+    "There are three Matplotlib functions that can be helpful for this task: `plt.contour` for contour plots, `plt.contourf` for filled contour plots, and `plt.imshow` for showing images.\n",
+    "This chapter looks at several examples of using these. We'll start by setting up the notebook for plotting and importing the functions we will use: "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -63,14 +41,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll start by demonstrating a contour plot using a function $z = f(x, y)$, using the following particular choice for $f$ (we've seen this before in [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb), when we used it as a motivating example for array broadcasting):"
+    "Our first example demonstrates a contour plot using a function $z = f(x, y)$, using the following particular choice for $f$ (we've seen this before in [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb), when we used it as a motivating example for array broadcasting):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -82,17 +63,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A contour plot can be created with the ``plt.contour`` function.\n",
+    "A contour plot can be created with the `plt.contour` function.\n",
     "It takes three arguments: a grid of *x* values, a grid of *y* values, and a grid of *z* values.\n",
     "The *x* and *y* values represent positions on the plot, and the *z* values will be represented by the contour levels.\n",
-    "Perhaps the most straightforward way to prepare such data is to use the ``np.meshgrid`` function, which builds two-dimensional grids from one-dimensional arrays:"
+    "Perhaps the most straightforward way to prepare such data is to use the `np.meshgrid` function, which builds two-dimensional grids from one-dimensional arrays:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -107,21 +91,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's look at this with a standard line-only contour plot:"
+    "Now let's look at this with a standard line-only contour plot (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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du8jPz4/279/Pqe/evTvFxsbSlStXqGXLlvTs2bNi96kEAF26dImcnZ156xUKhVolnqur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N69ezdjxbOgoCDaUUgGVZYmS5cu1Yhokozap6nZxu7duxEUFKSRczGBl5cXfv31V8b7jxo1CuvW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\nYWE0YsQICgsLE/vxbcKECVRYWEgtWrSgbdu2SS3C5n+QHA0bN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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bed1320>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -136,23 +123,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice that by default when a single color is used, negative values are represented by dashed lines, and positive values by solid lines.\n",
-    "Alternatively, the lines can be color-coded by specifying a colormap with the ``cmap`` argument.\n",
-    "Here, we'll also specify that we want more lines to be drawn—20 equally spaced intervals within the data range:"
+    "Notice that when a single color is used, negative values are represented by dashed lines and positive values by solid lines.\n",
+    "Alternatively, the lines can be color-coded by specifying a colormap with the `cmap` argument.\n",
+    "Here we'll also specify that we want more lines to be drawn, at 20 equally spaced intervals within the data range, as shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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GL7/8kssuu4z4+Hj27NlDYWEhW7duJSEhAb/fT1NTE1arlZqaGkwmE36/n7a2NgDC4QgR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NP/SMvXHyCknIu39BCSBEhepKYaZcHdWosgSxi6MxPViToyMhKn06nRwbKysiguLubcc8/V7svz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S1fvpw33niD559/XlSy+nFfu3Ytzz77LHfdddeM8+LMM89k165dghL478SxxUkgEMDj8YiK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\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J7XaTkpIiiguUlJQwePBgUYAgGaLRKI2NjZSXl1NXV0d9fT11dXVC61qdVJlZVV9EdaXE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\nXv33k7YAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e2dd048>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -167,30 +157,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here we chose the ``RdGy`` (short for *Red-Gray*) colormap, which is a good choice for centered data.\n",
-    "Matplotlib has a wide range of colormaps available, which you can easily browse in IPython by doing a tab completion on the ``plt.cm`` module:\n",
+    "Here we chose the `RdGy` (short for *Red–Gray*) colormap, which is a good choice for divergent data: (i.e., data with positive and negative variation around zero).\n",
+    "Matplotlib has a wide range of colormaps available, which you can easily browse in IPython by doing a tab completion on the `plt.cm` module:\n",
     "```\n",
     "plt.cm.<TAB>\n",
     "```\n",
     "\n",
     "Our plot is looking nicer, but the spaces between the lines may be a bit distracting.\n",
-    "We can change this by switching to a filled contour plot using the ``plt.contourf()`` function (notice the ``f`` at the end), which uses largely the same syntax as ``plt.contour()``.\n",
+    "We can change this by switching to a filled contour plot using the `plt.contourf` function, which uses largely the same syntax as `plt.contour`.\n",
     "\n",
-    "Additionally, we'll add a ``plt.colorbar()`` command, which automatically creates an additional axis with labeled color information for the plot:"
+    "Additionally, we'll add a `plt.colorbar` command, which creates an additional axis with labeled color information for the plot (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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JBrLobG0HG3efh48dqrqVH7NEBC7xADdD1wlslCkKATeBFSJLudmoAgtcXtFAnlELMAts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\nuXA8TEFtIykAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e63ffd0>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -208,25 +201,26 @@
    "source": [
     "The colorbar makes it clear that the black regions are \"peaks,\" while the red regions are \"valleys.\"\n",
     "\n",
-    "One potential issue with this plot is that it is a bit \"splotchy.\" That is, the color steps are discrete rather than continuous, which is not always what is desired.\n",
+    "One potential issue with this plot is that it is a bit splotchy: the color steps are discrete rather than continuous, which is not always what is desired.\n",
     "This could be remedied by setting the number of contours to a very high number, but this results in a rather inefficient plot: Matplotlib must render a new polygon for each step in the level.\n",
-    "A better way to handle this is to use the ``plt.imshow()`` function, which interprets a two-dimensional grid of data as an image.\n",
-    "\n",
-    "The following code shows this:"
+    "A better way to generate a smooth representation is to use the `plt.imshow` function, which offers the `interpolation` argument to generate a smooth two-dimensional representation of the data (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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thwGGvjLbR40sprGYxuiVlISVhDWCHwsV1oJ+27bsdrsDj1cpM3PMRNpsNgdh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\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10eec17f0>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -234,21 +228,20 @@
     }
    ],
    "source": [
-    "plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower',\n",
-    "           cmap='RdGy')\n",
-    "plt.colorbar()\n",
-    "plt.axis(aspect='image');"
+    "plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower', cmap='RdGy',\n",
+    "           interpolation='gaussian', aspect='equal')\n",
+    "plt.colorbar();"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are a few potential gotchas with ``imshow()``, however:\n",
+    "There are a few potential gotchas with `plt.imshow`, however:\n",
     "\n",
-    "- ``plt.imshow()`` doesn't accept an *x* and *y* grid, so you must manually specify the *extent* [*xmin*, *xmax*, *ymin*, *ymax*] of the image on the plot.\n",
-    "- ``plt.imshow()`` by default follows the standard image array definition where the origin is in the upper left, not in the lower left as in most contour plots. This must be changed when showing gridded data.\n",
-    "- ``plt.imshow()`` will automatically adjust the axis aspect ratio to match the input data; this can be changed by setting, for example, ``plt.axis(aspect='image')`` to make *x* and *y* units match."
+    "- It doesn't accept an *x* and *y* grid, so you must manually specify the *extent* [*xmin*, *xmax*, *ymin*, *ymax*] of the image on the plot.\n",
+    "- By default it follows the standard image array definition where the origin is in the upper left, not in the lower left as in most contour plots. This must be changed when showing gridded data.\n",
+    "- It will automatically adjust the axis aspect ratio to match the input data; this can be changed with the `aspect` argument."
    ]
   },
   {
@@ -256,21 +249,24 @@
    "metadata": {},
    "source": [
     "Finally, it can sometimes be useful to combine contour plots and image plots.\n",
-    "For example, here we'll use a partially transparent background image (with transparency set via the ``alpha`` parameter) and overplot contours with labels on the contours themselves (using the ``plt.clabel()`` function):"
+    "For example, here we'll use a partially transparent background image (with transparency set via the `alpha` parameter) and overplot contours with labels on the contours themselves, using the `plt.clabel` function (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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V9Sl330m1D4A333yTFStWIMsyo0eP5vbbb6dPnz707NmTunXrctVVV+H1eunduzfTpk1j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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10eeec5c0>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -290,26 +286,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The combination of these three functions—``plt.contour``, ``plt.contourf``, and ``plt.imshow``—gives nearly limitless possibilities for displaying this sort of three-dimensional data within a two-dimensional plot.\n",
+    "The combination of these three functions—`plt.contour`, `plt.contourf`, and `plt.imshow`—gives nearly limitless possibilities for displaying this sort of three-dimensional data within a two-dimensional plot.\n",
     "For more information on the options available in these functions, refer to their docstrings.\n",
     "If you are interested in three-dimensional visualizations of this type of data, see [Three-dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb)."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Visualizing Errors](04.03-Errorbars.ipynb) | [Contents](Index.ipynb) | [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.04-Density-and-Contour-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "encoding": "# -*- coding: utf-8 -*-",
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -323,9 +313,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.05-Histograms-and-Binnings.ipynb b/notebooks/04.05-Histograms-and-Binnings.ipynb
index 1873ff7c5..c3c611c40 100644
--- a/notebooks/04.05-Histograms-and-Binnings.ipynb
+++ b/notebooks/04.05-Histograms-and-Binnings.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) | [Contents](Index.ipynb) | [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.05-Histograms-and-Binnings.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -34,14 +12,14 @@
    "metadata": {},
    "source": [
     "A simple histogram can be a great first step in understanding a dataset.\n",
-    "Earlier, we saw a preview of Matplotlib's histogram function (see [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb)), which creates a basic histogram in one line, once the normal boiler-plate imports are done:"
+    "Earlier, we saw a preview of Matplotlib's histogram function (discussed in [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb)), which creates a basic histogram in one line, once the normal boilerplate imports are done (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -50,21 +28,25 @@
     "import matplotlib.pyplot as plt\n",
     "plt.style.use('seaborn-white')\n",
     "\n",
-    "data = np.random.randn(1000)"
+    "rng = np.random.default_rng(1701)\n",
+    "data = rng.normal(size=1000)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1023e5a58>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -79,22 +61,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``hist()`` function has many options to tune both the calculation and the display; \n",
-    "here's an example of a more customized histogram:"
+    "The `hist` function has many options to tune both the calculation and the display; \n",
+    "here's an example of a more customized histogram, shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bfc0588>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -102,7 +87,7 @@
     }
    ],
    "source": [
-    "plt.hist(data, bins=30, normed=True, alpha=0.5,\n",
+    "plt.hist(data, bins=30, density=True, alpha=0.5,\n",
     "         histtype='stepfilled', color='steelblue',\n",
     "         edgecolor='none');"
    ]
@@ -111,22 +96,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``plt.hist`` docstring has more information on other customization options available.\n",
-    "I find this combination of ``histtype='stepfilled'`` along with some transparency ``alpha`` to be very useful when comparing histograms of several distributions:"
+    "The `plt.hist` docstring has more information on other available customization options.\n",
+    "I find this combination of `histtype='stepfilled'` along with some transparency `alpha` to be helpful when comparing histograms of several distributions (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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TIr1goKc5r9eL/9d+DooMeLxR5OdN3g7M330aZa4rMKmcFXXLMkaqV0EUJ37N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\nOhGRTjDQiYh04v8DF2mFWN13xe4AAAAASUVORK5CYII=\n",
+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e666358>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -134,11 +122,11 @@
     }
    ],
    "source": [
-    "x1 = np.random.normal(0, 0.8, 1000)\n",
-    "x2 = np.random.normal(-2, 1, 1000)\n",
-    "x3 = np.random.normal(3, 2, 1000)\n",
+    "x1 = rng.normal(0, 0.8, 1000)\n",
+    "x2 = rng.normal(-2, 1, 1000)\n",
+    "x3 = rng.normal(3, 2, 1000)\n",
     "\n",
-    "kwargs = dict(histtype='stepfilled', alpha=0.3, normed=True, bins=40)\n",
+    "kwargs = dict(histtype='stepfilled', alpha=0.3, density=True, bins=40)\n",
     "\n",
     "plt.hist(x1, **kwargs)\n",
     "plt.hist(x2, **kwargs)\n",
@@ -149,21 +137,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you would like to simply compute the histogram (that is, count the number of points in a given bin) and not display it, the ``np.histogram()`` function is available:"
+    "If you are interested in computing, but not displaying, the histogram (that is, counting the number of points in a given bin), you can use the `np.histogram` function:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 12 190 468 301  29]\n"
+      "[ 23 241 491 224  21]\n"
      ]
     }
    ],
@@ -178,45 +169,51 @@
    "source": [
     "## Two-Dimensional Histograms and Binnings\n",
     "\n",
-    "Just as we create histograms in one dimension by dividing the number-line into bins, we can also create histograms in two-dimensions by dividing points among two-dimensional bins.\n",
+    "Just as we create histograms in one dimension by dividing the number line into bins, we can also create histograms in two dimensions by dividing points among two-dimensional bins.\n",
     "We'll take a brief look at several ways to do this here.\n",
-    "We'll start by defining some data—an ``x`` and ``y`` array drawn from a multivariate Gaussian distribution:"
+    "We'll start by defining some data—an `x` and `y` array drawn from a multivariate Gaussian distribution:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "mean = [0, 0]\n",
     "cov = [[1, 1], [1, 2]]\n",
-    "x, y = np.random.multivariate_normal(mean, cov, 10000).T"
+    "x, y = rng.multivariate_normal(mean, cov, 10000).T"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### ``plt.hist2d``: Two-dimensional histogram\n",
+    "### plt.hist2d: Two-dimensional histogram\n",
     "\n",
-    "One straightforward way to plot a two-dimensional histogram is to use Matplotlib's ``plt.hist2d`` function:"
+    "One straightforward way to plot a two-dimensional histogram is to use Matplotlib's `plt.hist2d` function (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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DjRs3+uqtAGCCJiIzUSQfZ6GqKoqKivDwww/j+uuvP6VvjMViQUtLS8/FfgZc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\nAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1106647f0>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -224,7 +221,7 @@
     }
    ],
    "source": [
-    "plt.hist2d(x, y, bins=30, cmap='Blues')\n",
+    "plt.hist2d(x, y, bins=30)\n",
     "cb = plt.colorbar()\n",
     "cb.set_label('counts in bin')"
    ]
@@ -233,51 +230,66 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Just as with ``plt.hist``, ``plt.hist2d`` has a number of extra options to fine-tune the plot and the binning, which are nicely outlined in the function docstring.\n",
-    "Further, just as ``plt.hist`` has a counterpart in ``np.histogram``, ``plt.hist2d`` has a counterpart in ``np.histogram2d``, which can be used as follows:"
+    "Just like `plt.hist`, `plt.hist2d` has a number of extra options to fine-tune the plot and the binning, which are nicely outlined in the function docstring.\n",
+    "Further, just as `plt.hist` has a counterpart in `np.histogram`, `plt.hist2d` has a counterpart in `np.histogram2d`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(30, 30)\n"
+     ]
+    }
+   ],
    "source": [
-    "counts, xedges, yedges = np.histogram2d(x, y, bins=30)"
+    "counts, xedges, yedges = np.histogram2d(x, y, bins=30)\n",
+    "print(counts.shape)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For the generalization of this histogram binning in dimensions higher than two, see the ``np.histogramdd`` function."
+    "For the generalization of this histogram binning when there are more than two dimensions, see the `np.histogramdd` function."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### ``plt.hexbin``: Hexagonal binnings\n",
+    "### plt.hexbin: Hexagonal binnings\n",
     "\n",
     "The two-dimensional histogram creates a tesselation of squares across the axes.\n",
     "Another natural shape for such a tesselation is the regular hexagon.\n",
-    "For this purpose, Matplotlib provides the ``plt.hexbin`` routine, which will represents a two-dimensional dataset binned within a grid of hexagons:"
+    "For this purpose, Matplotlib provides the `plt.hexbin` routine, which represents a two-dimensional dataset binned within a grid of hexagons (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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IAm67DkomBUuICTTW4yUjdnRPiNQ6BysJkxB00Qhldf2k4vmLYiglseckhOyz43GUrfy3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\nRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e666550>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -285,7 +297,7 @@
     }
    ],
    "source": [
-    "plt.hexbin(x, y, gridsize=30, cmap='Blues')\n",
+    "plt.hexbin(x, y, gridsize=30)\n",
     "cb = plt.colorbar(label='count in bin')"
    ]
   },
@@ -293,7 +305,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``plt.hexbin`` has a number of interesting options, including the ability to specify weights for each point, and to change the output in each bin to any NumPy aggregate (mean of weights, standard deviation of weights, etc.)."
+    "`plt.hexbin` has a number of additional options, including the ability to specify weights for each point and to change the output in each bin to any NumPy aggregate (mean of weights, standard deviation of weights, etc.)."
    ]
   },
   {
@@ -302,24 +314,27 @@
    "source": [
     "### Kernel density estimation\n",
     "\n",
-    "Another common method of evaluating densities in multiple dimensions is *kernel density estimation* (KDE).\n",
-    "This will be discussed more fully in [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb), but for now we'll simply mention that KDE can be thought of as a way to \"smear out\" the points in space and add up the result to obtain a smooth function.\n",
-    "One extremely quick and simple KDE implementation exists in the ``scipy.stats`` package.\n",
-    "Here is a quick example of using the KDE on this data:"
+    "Another common method for estimating and representing densities in multiple dimensions is *kernel density estimation* (KDE).\n",
+    "This will be discussed more fully in [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb), but for now I'll simply mention that KDE can be thought of as a way to \"smear out\" the points in space and add up the result to obtain a smooth function.\n",
+    "One extremely quick and simple KDE implementation exists in the `scipy.stats` package.\n",
+    "Here is a quick example of using KDE (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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nEZKeR6tsfaiyLBRazz2brV/6AWBXYsecTjEZD/yxxx7DeeedBwBYuHAhhoeH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\ntLSwatUqPv744ySfaYDhE7jZLUaXiCRjIQIscX6FY7PZ8Pl86vL09DR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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e36a198>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -342,8 +357,7 @@
     "# Plot the result as an image\n",
     "plt.imshow(Z.reshape(Xgrid.shape),\n",
     "           origin='lower', aspect='auto',\n",
-    "           extent=[-3.5, 3.5, -6, 6],\n",
-    "           cmap='Blues')\n",
+    "           extent=[-3.5, 3.5, -6, 6])\n",
     "cb = plt.colorbar()\n",
     "cb.set_label(\"density\")"
    ]
@@ -353,28 +367,22 @@
    "metadata": {},
    "source": [
     "KDE has a smoothing length that effectively slides the knob between detail and smoothness (one example of the ubiquitous bias–variance trade-off).\n",
-    "The literature on choosing an appropriate smoothing length is vast: ``gaussian_kde`` uses a rule-of-thumb to attempt to find a nearly optimal smoothing length for the input data.\n",
+    "The literature on choosing an appropriate smoothing length is vast; `gaussian_kde` uses a rule of thumb to attempt to find a nearly optimal smoothing length for the input data.\n",
     "\n",
-    "Other KDE implementations are available within the SciPy ecosystem, each with its own strengths and weaknesses; see, for example, ``sklearn.neighbors.KernelDensity`` and ``statsmodels.nonparametric.kernel_density.KDEMultivariate``.\n",
+    "Other KDE implementations are available within the SciPy ecosystem, each with its own strengths and weaknesses; see, for example, `sklearn.neighbors.KernelDensity` and `statsmodels.nonparametric.KDEMultivariate`.\n",
     "For visualizations based on KDE, using Matplotlib tends to be overly verbose.\n",
-    "The Seaborn library, discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), provides a much more terse API for creating KDE-based visualizations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) | [Contents](Index.ipynb) | [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.05-Histograms-and-Binnings.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "The Seaborn library, discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), provides a much more compact API for creating KDE-based visualizations."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "encoding": "# -*- coding: utf-8 -*-",
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -388,9 +396,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.06-Customizing-Legends.ipynb b/notebooks/04.06-Customizing-Legends.ipynb
index ada12a045..4f0ddad24 100644
--- a/notebooks/04.06-Customizing-Legends.ipynb
+++ b/notebooks/04.06-Customizing-Legends.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.06-Customizing-Legends.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -36,26 +14,26 @@
     "Plot legends give meaning to a visualization, assigning meaning to the various plot elements.\n",
     "We previously saw how to create a simple legend; here we'll take a look at customizing the placement and aesthetics of the legend in Matplotlib.\n",
     "\n",
-    "The simplest legend can be created with the ``plt.legend()`` command, which automatically creates a legend for any labeled plot elements:"
+    "The simplest legend can be created with the `plt.legend` command, which automatically creates a legend for any labeled plot elements (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "plt.style.use('classic')"
+    "plt.style.use('seaborn-whitegrid')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -67,17 +45,22 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c21e9e8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -87,7 +70,7 @@
     "ax.plot(x, np.sin(x), '-b', label='Sine')\n",
     "ax.plot(x, np.cos(x), '--r', label='Cosine')\n",
     "ax.axis('equal')\n",
-    "leg = ax.legend();"
+    "leg = ax.legend()"
    ]
   },
   {
@@ -95,21 +78,24 @@
    "metadata": {},
    "source": [
     "But there are many ways we might want to customize such a legend.\n",
-    "For example, we can specify the location and turn off the frame:"
+    "For example, we can specify the location and turn on the frame (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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nrF+v1B13KNWzZ7ZPL1sm+RxQql8/pU6ccM/HOuPGMmdkKDV3rjSR2GzSnn7unEXBeYjWCfv4caW6d5eT59FH8/w2r5X52gbht95SatOm657++WelmjaV8Js3l589xZkyb92qVGysxNeqldOtPJZhwnZWWppSFy/Kv3/5RakpU9SZ0+lXvmu33nq1+dtKOZX5/Hm50w0MlGaSNWu8HJgHaZmwMzKUmj1bqSJFlAoJUWr06Hx1OHi9zOfPK1Wlilz1hwxRqReS1JgxckdZooS0IHq6ButsmdPSpH+nSBFp6Zk40fUOf29hwnaHIUOUAtS60PaqbOAJNWaM9Nf4gtzKvG2bjFQBlPq//5OmHN1pmbDnz5c/QrNmSsXF5fvtlpT5wgXpTQTUn6ExKhY7VI8e3huV4WqZ4+OVat9efu2tWyv1119uCsyDPJWw/WbeXWoq8LLtfQzCZNyVsh5/Fa6LkY3WaDP+s2FDYNcu4IUXgClTgEaNZKYZeUli5goMXboA06fLuOhbbrE0pDyLisIXTafg/tC1CExJxqaIe/Hl1IsoXtzqwPKmfHkZBz59OrB9O3DHHcCKFVZHZQ2/SNjHjgFt2gDvT7DBNmgQMrbuQHCZ4jJif8kSq8PLs7Aw4IMPgFWrgJMnZfLCl19aHVUBpxQwfjxQqxaQkCAzPJ54wqeWSLiZlBRg0CCgd2/gUqM2sO3ZjULfLAKioqRsiXosBWSzya99zx5Z4uGBB4BXXpHJaf6kwCfsH38E7rwTsNtlxtbkyUBYwzrAjh3A228D7dtbHWK+tW0L7N4tMy0fewx46ilZgoLcLDER6N4dGDpUbmk0Wwzj8GGgeXPgk0+AYcOAtWuB8nWLS+0FkLVL6tSRdUw0Ub06sHkzMHAgMG4c0Lq1rGXiLwp0wv74Y/mDRkcD27bJNNgrwsOBV18FQkOBc+eADh1k/rgmypcHNmyQXDJ1qiTx06etjqoAOXQIaNIEWLxYMsPChVIr1URWRcU05SZy7FiZzX6dW2+VVZyaNgWWLbMkTmeEhspFaM4cqYg1bCg1b39QIBN2Whrw7LPAM89IHt6xQ1bLy9Hff8tfvHFjYPlyb4XpsqAgySVffikXpIYNgX37rI6qgHjlFSA+Hli5Uq6KPrRSXm4+/1yWGSlRQs79zp1zeGHjxvKCW26RF737rjSTaOKxx2RtHqWAu+7SqnXTaQUuYV+8CDz4IDBxIjBkiPwRc10bp04dYOdOaafs3Fmq5hrp0UNqVMnJUin89lurI9JYaqr8f/p0qb7de6+18eRDRgYwciTw+OPSFLJlCxATk8ubypeXk6d7d7nj3LrVK7G6S716cs2pU0f6g995R6trTr4VqIR9+LBcaVetklumCRPy0TdUrpz0/HfsKFXziRM9GarbNWwoJ26NGkCnTpJvKB+UAsaMkWUYk5NlPeqqVa2OKs+SkyXnvv22dM59/72soJcnYWHSwfPjj3LFB7TKemXKSPNgjx7AiBHAgAEFtzOywCTsffvkXPvrL+C776RTIt8iIqRK/tprwKOPuj1GT6tQQVbfvO8+4MkngTff1Op7Z53UVMlyr70mvVqajADJcv68/M0XL5YBLdOmObFcqc0m1XIA2LhROlmPHnV7rJ6Sdc0ZORKYOVP2fUhKsjoq9ysQCXvrVjnXlJKE5dJdbGAg8MYbctlOSwNef12+EZqIiAC++UZui19/HXj6aSA93eqofFhKCvDIIzJiYtQoYPZsrUaDHD8OtGgh34F584CXXnJDc3tqqgzyb9JEq8H+NpvcJE2aJF1R994r69kXJNon7FWrZJRSsWLSAVG3rhsPvmOHNIq1bg2cOuXGA3tWcDDw2WcylGvKFNkkweGwOiof9cwzMkJi0iS5JdGoc/GPP4BmzYBff5UE1a2bmw58zz3SPJKSIm2M27a56cDe8fTTMqhnxw6pyMXHWx2R+2idsBcskAH0NWtKsq5Wzc0f0KSJVFf375dtNjT6y9tsMpTrww+Br7+W0TKXLlkdlQ8aNkxq1U8/bXUk+bJ3r+TSs2eBdeukScSt6tWTXstixSSBHzjg5g/wrEcekXb8+HgZtfjrr1ZH5B7aJuypU6WToVEj6SssU8ZDH9ShA7B6tbTnZVVnNPL888AXX0iz5H33adW64zmXLgH/+Y+0odWoIdMANbJli9QfAgKkCbDxPzboc5OqVaWm/fzzeRhu4ntatZLzPjlZmo3277c6ItdpmbA/+khm93XoIE0iRYp4+AObN5du6IAAqdJo5l//kruR7dul+SghweqILHThAtCunSzKYrdbHU2+ZfXRlCwp+yPWru3hDyxXThqGAwKkR1+jCTaArDuycaP8u0UL/SfYaJewJ0yQ71qXLnKrHx7upQ/OmjbWsKH8rFGbNiC3iEuXymTOVq2AEyesjsgC585Jttu2DZg/H6hf3+qI8mXDBrnWVKggSahyZS8HMHIk8NBDMjNHI7Vry41CeLic+5o1yV9Hq4Q9bpz0gj/6qHzfvN6ZnzVWavp0wDC0q6F17CiTag4dktqGRk3yrjtzRtpid+0CFi3Sbtjm2rXy96taVZoAy5a1IIhPPpGM9/jj2g30r1FDknZWk/yPP1odkXO0Sdhvvy2zhbt3l6nYli6L2rYtULiwtC9s325hIPnXpo00Ix09Ku2gf/5pdURe8r//AQcPyjj7HOdq+6ZVq652rm/YAJQubVEgERGyrmmHDjLQf+pUiwJxTuXKkqgrVJA7lbVrrY4o/7RI2G+8IXdj//qXdKD9YxEbb6tSRao5xYtL8t6yxeKA8qdZMxlZcPas1LQPHbI6Ig/KmmreqpVcnTp2tDSc/Pr2W5m5esstwPr10nZtqdBQaYvs2FFq2Vm/X02ULy/NSTVrAvffLyNJdOLTCVspmXw2ejTQpw8wa5YPJOsslStL0i5VSi7XJ09aHVG+NGggCSAxUZL2wYNWR+QBR4/K8LT58+XnYsWsjSefvvlGmozr1JELrM9sOFCoEPDVV8CaNXKrm5FhdUT5UqqUnPu1a8u6Qxqt9+Z8wjYMI8AwjE8Mw9hiGMYPhmHUcGdgSsm6AGPGyKzhmTN9cMZwxYqStD/+WM4CzdSrJ7fYly8XnGFPV/z9txTqr7+kWqWZr76SjuI775Rbd5+71hQqJIuVOBxSVZ00yeqI8qV4cbkI3n771QEMOnClht0ZQKhpmk0AvAJgglsigiTrYcNktceBA6WpLMBX7wXKl5e2GkCS94YNloaTX3XqSNiArHuk0Vr2OfvzT0nWJ0/KGPqsNTI0sXChzFps2FDC9/iwVVcEBEjv/+DBMrZdI0WLyk1CgwYyG3jBAqsjyp0rabAZgO8BwDTNrQDcMkZKKeC990ph/HiZfDZlig8n62tlXWU6dpRLt0Zq15Z2vZAQaerdvdvqiFxw5oz0pmZNAcxafU4T8+bJhLCmTaV9Ndelga0WEiJXmIceAp57TiZJaKRwYenUbdpUNjiZM8fqiG7OlVQYDeDaeXPphmG43MJstwOzZxfHc8/JCqfaLO1gs0ljWI0acou4erXVEeVLTIwk7YgIWTplxw6rI3JS0aKyieH69dqNs547V27WmjeXFSe12eAmJESqpw8/LJMkJk+2OqJ8iYqSfSpatJBJr599ZnVEObMpJ9ffNAzjAwBbTdNcmPlzvGmaFbKet9vtKtyJWS1KAbt321CvntInWV8j8OxZVOrXDyF//IH4//wHiXffnaf3ORwOhIaGeji63B05Eoy+fSvh3LlATJv2N+64I9ljn+XOMof89htsaWlI8fGdzHMq87Jl0Xj11XKoXz8Jkyf/jfBwDdfFTU1Fmbfewtnu3ZFSq9aVh33l3M5NcrINzz5bAZs2RWL06GPo2vWc08dypcxJSUmIjY3NPvsppZz6LyYm5uGYmJhZmf9uHBMTs/La53fu3KmctX//fqff6xMSEpS6806l+vXL81t8qcyHDytVs6ZSkZFKbdzouc9xW5n/9z+lSpZUqk4dpdLT3XNMD8muzLNmKWWzKdW6tVKJiRYE5Sk//qiU8q1zOzfJyUp17KgUoNTEic4fx5UyZ+bObPOuK00iSwA4DMPYDOBDAC+4cKyCpVgx6XycNk1+1mz7i6zBLxUqyKby69dbHdFN7NolDe8hIbKCvxYdHlfNmgX07SsTmpYv9+JSC562ZIn0JYwZY3Uk+ZI1zLxzZ+lH/eADqyO6QU6Z3NX//LqGfa0jR5SqXVupr7666ct8sczHjyt1661KhYYqtWqV+4/vcpm3bVOqSBGlKlVS6rff3BOUh11b5pkzpWbdtq1SSUkWBuUJaWlK9e6tFKBODhqkVEaG1RHly+XLSj36qNS03303/+/3xRo25UVkpIzL6tpV1rDQSOnScqNgGDI12uc2950wQToZf/xRtvbSyPTpQP/+shbVN9/IFlcFSmCg7OLTvz9KTpkiG/xqtF9dcLAsgdGzJzB8uOxt4QuYsD0tOlrGZzVpIuO15s2zOqJ8KVlSmkTq1JGRW0uXWh0Rrn7xZ82SnSu8vmyda6ZNk6U42rWT32eBS9ZZAgOBadNwtls3Wblt1y6rI8qXoCBZmDBru71Ro6y/5jBhe0PWuKG77pJxW5qtKVysmMy2u/NOWeTO0huF77+XttHz5yXTlStnYTD5N3duUQwcKOsnLVkibaYFWkAAjr/2GrB5MxAba3U0+ZZ1o/DEE8Bbb8kCdFYmbSZsb4mMvLqde9OmVkeTb0WKyNDyxo2vrpjodUuWyEpIiYnaLToEyMzdt98ug06dpGOrwCfrLDbb1W1xVq6UCTYarT8SECCzrf/v/4D33pOh5lYlbV9ZSsk/RERcnVSQknJ1RXpNREfL9+2BB+RGITVVbhe9Yt48oFcvmUe8cqWPz9e+nlLShDt2LNCx43ksXlzY2uWBrbR5s0xhT0yULOhzCwRlLyBAlksJDgb+/W9Zf2fSJO8PSmIN2yoTJsiYufHjrY4kXyIjpfOxTRsZjuaVdewXLAAee0zWhfX5xTWul5EBPPusJOsnnwTGjj3qv8kakN67kSNlNbcePaTiogmbTTa1fvllWTJj4EDv3yiwhm2Vl14Cfv4ZGDpUthvr08fqiPIsPFzGDHfpIkno8mUPbzreuLH8fiZN0mqgcloaMGCA9I2++KJcmzXbfNz9bDYZm12kiHwHzp6Vk0mT9iGbTfpPQ0JkU5XUVO+uJMqEbZWQEFk8olgxYPx4lD10SGqSPrPg982FhkqTcteuwDPPSNJ+wZ1TpzIyro6rqlxZen40kpwsoS9dKhtwjBql0bo43vDii7LG6Y4dslSrRmw26YAsVEjW609NBWbP9s5XV4/sUFAFBspa2iVLIuqjj2TtZo3GExcqJCNGevYEhgwBTp+WE9nlxHT5MtCvn1zQwsJkUSGNnD4tfaNbt0pz7eDBVkfko/r0uXpnefCg/K0rVrQyonwZNUratIcPl0UiFy70/IJdbMO2ms0GvPEGDi1fLslaKeDCBaujyrOQENnQ5YkngHfekc5Il5olL16UXs25c+Wes0sXt8XqDX/8IaM3s/b6ZbLOg4wMGS/atKnsvamRV16RcfVr1sho06NHPft5TNg+Ij1rs77335cBz6ZpbUD5EBQkJ+3bb0srxn33SdNkvmXtErNunTQMvvqqVu0Iu3bJ/KhTp2TcumY3BtYJCJAZKoB0LK9YYW08+TRggDTD//abdLfs2+e5z2LC9jXNmkkNu3FjrXavsdkkv86dK3sSN20qtc18iY+XpL1smTSJaGTRIvnTFSoEbNok/6Z8uP12YNs2WQfhwQdl7JzV0wrzoX17WSEhLU3usA4c8Ey7PBO2r2nSRE7ccuVkoYmZM62OKF969pSRd8ePy/4Ba9bk4U1Z+5I1aSLbe3Xo4MkQ3SojQ9oyu3aVPTK3bweuWQqa8qNcOdlFo1Mn2dQyPd3qiPKlXj3pt2jcGDhxwjPdg0zYvqhqVZlg0Lq1jJvTbCxYixbS+V+unMwLGj8+h8pSWpr02NStK5NhAJlcpImLF6WJ/a23ZCGn9etlwSxyQUSEJOsVK6St7dQp4Ngxq6PKs0qVZPWEFi0SPXJ8JmxfVbiwzFBZvx7I2kXl0iVrY8qHGjWkaeThh2WoeffuN4R/7Bgq9e8vM0oGDJCZOBrZu1c2yV2xQkaCTJ+u3eg03xUQcHUzyyeekKrrxo3WxuQjmLB9WVCQVFcBuWxXreqDa5zmLDJShpaPGyd7C8TGyp6d2LABqFcPYb/8IgNYp02T4SYaUEqSc8OGwLlz0uQzeLBWfaN6eecdqby0aSMnkkZrkHgCE7YuqleXNob775dNZhM9c8vlbjab1LDXrZOQmzQBvv3kMFTRovhj/nzZ9VQTZ89KG/2TT8pGuXv2yGY35EG33irtaw89JGPo2rTx/Ng5H8aErYuaNaUz8sUXZdGcrB4uTbQssgcH3lyITp2A+xc+jvZl9uD3sNpWh5Vny5dL7li0SIYvfv8926u9JjpaZqV8+ilw4kQBXkA8d0zYOgkNlXHa69YBDgewe7fVEeUuOVnm7zZogMh3XsWiL1MxYwawaWchdOpUDe+/79tbXp4+LYsEduokmzns2CHDFzXbOlJ/NpusNrZ3r+wydPmyrHd66JDVkXkVTzsdtWolJ+6AAfLzvHnSWOxL41aVkr2vateWxX66dQO2bYMtJBj9+wP79wNNmiTi5ZdlxdTNm60O+HppacDEiXJjM3++7DiyY4fc2JCFslZZ+vlnYM4c4Lbb5PzSaNU/VzBh66pwYanmKSW3it27y2wVX+lNP3hQtp6OiJBOxjlzZLGfTBUrApMmxeOrr4CTJ2WyQZcu1k/wVEr6de+4Q5ZFrV9f2qpHj9amX9Q/1K8PxMXJrc9rr8mEm1mzrO+UPHJEJiJ4CBO27mw2aVCdNk1mCbZsKYOfrViTwW6XaikgX6Bly6TZpmXLbF9us0mSPnhQKklr10o7cd++np3em52sRN2wofTrJifLaoSrV0tM5IPKl5c7yzVrgBIlrp57gHV3m6++Krt6eGhHJCbsgiAwUJpHfv1VZqns3CmNr4DsfehweO6zU1OlJ655c6n1vPHG1cWrHngAeVmtPyJC1rT/7TdZqnXhQrnTvf9+YNUqz054u3BBFkzM+ryEBJlceuCA3CBwuJ4G7rlH2qu++07uOs+ckemm770nf1BPUUra8rp3zxyvCql5bNmSp/PeGS4lbMMwHjIMw4rd/Sg7YWGyKPzff8ssSUDu5cuUkal4a9e6t61v9WqgbFmZl33kCPDBB5J1syY95FOpUsBHHwGHD8vGJNu3y81CpUoyomvbNvck7+Rk2VOxRw8ZKfnMM/KrmzVLmmT69fPY9408xWa7OmwnIUFq38OGyfnZqZN0RCQluf45SgG//CKdGjEx0pb3/fdSWQLkZK1SxfXPyYHTE94Nw/g3gPsA7HFbNOQe1w576txZBhAvWiRt3eHhMv0wa3W0c+ekPTynqqRSMpTql1+kmWXjRsl0PXrIDMy2bWX7rvbt3bbtRvHisj7H0KEyk3DWLBkcM26cPNe2rTRdxMZKzbho0ZzDT0+XC8CBA5LwN26U9R4cDjlWjx5yc9KwoVtCJ19Qs6aMpNq7V87zefNkXOaff8pmGNu3S6Wmbl2gWrWbn7cXLsi5n5goJ15GhkxmO3tWKkWvvCId6pGRXimaKyuUbAawFMBA94RCHtGihfw3ebLUsNesuX69jrp1pdevTBmp4gYHy+JLI0ZItitS5Po55TVqSBUVkNrEvHkeC71QIbm2PPywVJpWr5a73vXrpcKUJTxcOjGjo6VjMDBQWoLOnpVrTdZNRUCAdCY+9RTQsaM0rWuywQ85o04daSIcN06aTCpXlscnTJB2N0BOllKlZDTT2rXy2MsvS//LiRNyIgFSOYmLk9cvXChNLuXKeb1INpVL47xhGP0B3Lj5U1/TNHcYhtESwFOmaXa/8X12u12FO7n/nsPhQKgme7y5iyVlVgpF585F8PHjCDx9GkFnzsCWloZLd92FM/37AwBKfvQR0ooXR0rNmkiJiUF6sWJu+3hXynzqVCD27w/Dn3+G4PjxIBw7Fozk5ACkptqQng5ERmYgOjodxYqlo1q1FFStehk1a6YgKsraUQQ8t61nczhQ6NAhFDJNhBw+jKCEBCAtDcfefRcAUGLiRBT6/XeklSiBtFKlkFKjBhwxMUgrXz7Pn+FKmZOSkhAbG5vtPWOuCftmckvYsbGxTh03Li4OtfxsjUqW2T+wzP7BlTLb7fYcEzZHiRARaYIJm4hIEy51uZim+QOAH9wSCRER3RRr2EREmmDCJiLSBBM2EZEmmLCJiDTBhE1EpAkmbCIiTTBhExFpggmbiEgTTNhERJpgwiYi0gQTNhGRJpiwiYg0wYRNRKQJJmwiIk0wYRMRaYIJm4hIE0zYRESaYMImItIEEzYRkSaYsImINMGETUSkCSZsIiJNBDnzJsMwCgOYAyAaQAiAIaZpbnFnYEREdD1na9hDAKwzTbMFgD4APnZbRERElC2natgAPgSQcs0xHO4Jh4iIcmJTSt30BYZh9Afwwg0P9zVNc4dhGGUArATwvGmaG699gd1uV+Hh4U4F5XA4EBoa6tR7dcUy+weW2T+4UuakpCTExsbasnsu1xq2aZozAcy88XHDMOoAmA/gpRuTdZZatWrlM1QRFxfn9Ht1xTL7B5bZP7hSZrvdnuNzznY61gawCEA30zT/51RURESUL862Yb8LIBTAvw3DAIDzpmk+6LaoiIjoH5xK2EzORETex4kzRESaYMImItIEEzYRkSaYsImINMGETUSkCSZsIiJNMGETEWmCCZuISBNM2EREmmDCJiLSBBM2EZEmmLCJiDTBhE1EpAkmbCIiTTBhExFpggmbiEgTTNhERJpgwiYi0gQTNhGRJpiwiYg0wYRNRKQJJmwiIk0wYRMRaSLImTcZhhEB4EsARQFcBvC4aZpH3BkYERFdz9ka9gAAdtM07wYwB8BQ94VERETZcaqGbZrmR4ZhBGb+WAnAObdFRERE2bIppW76AsMw+gN44YaH+5qmucMwjPUA6gBoa5rmnmtfYLfbVXh4uFNBORwOhIaGOvVeXbHM/oFl9g+ulDkpKQmxsbG27J7LNWHnxjCMWwB8a5pm9Wsft9vtKjY21qljxsXFoVatWi7FpRuW2T+wzP7BlTLb7fYcE7ZTbdiGYQw3DKNX5o+XAKQ7FRkREeWZU23YAD4FMDuzuSQQQF/3hURERNlxttPxBIB2bo6FiIhughNniIg0wYRNRKQJJmwiIk0wYRMRaYIJm4hIE0zYRESaYMImItIEEzYRkSaYsImINMGETUSkCSZsIiJNMGETEWnC5fWwc2K32z1zYCKiAs5jGxgQEZF3sEmEiEgTTNhERJpwdscZjzAMIwDAZAC3A0gB8IRpmr9ZG5VnGYYRDNnBpwqAQgDeMk1zmaVBeYFhGKUA2CEbOB+wOh5vMAxjOIBOAEIATDZNc6bFIXlM5nk9G3JepwMYUJD/zoZhNAIwzjTNloZh1AAwC4AC8AuAp03TzHDH5/haDbszgFDTNJsAeAXABGvD8Yp/AUgwTbM5ZBefSRbH43GZX+apAJKtjsVbDMNoCaApgLsAtABQ0dKAPK8DgCDTNJsCeBPA2xbH4zGGYQwFMANA1jbpHwAYmfmdtgF40F2f5WsJuxmA7wHANM2tAOpbG45XLAIwKvPfNgBpFsbiLe8D+ATAUasD8aL7AOwFsATAcgArrA3H4w4CCMq8a44GkGpxPJ50CECXa36OBbAx898rAdzjrg/ytYQdDeD8NT+nG4bhU8027maa5iXTNC8ahhEFYDGAkVbH5EmGYfQBcMo0zVVWx+JlJSAVkEcBPAVgrmEY2Q7dKiAuQZpDDgCYDuA/lkbjQaZpfoXrL0g20zSzht9dBFDYXZ/lawn7AoCoa34OME2zwNc4DcOoCGADgC9M0/zS6ng8rB+AtoZh/ADgDgCfG4ZRxtKIvCMBwCrTNC+bpmkCcAAoaXFMnvQCpLwxkD6p2YZhhObynoLi2vbqKADn3HVgX0vYmyBtXzAMozHkFrJAMwyjNIDVAIaZpvmp1fF4mmmad5um2cI0zZYA9gDobZrmcWuj8or/AmhnGIbNMIxyACIgSbygOourd8tnAAQDCLQuHK/andlnAQDtAfzkrgP7WnPDEkjtazOkPbevxfF4w6sAigIYZRhGVlt2e9M0/aZDzh+YprnCMIy7AWyHVJSeNk0z3eKwPOlDAJ8ahvETZFTMq6ZpJlock7e8CGC6YRghAOIgTZ1uwZmORESa8LUmESIiygETNhGRJpiwiYg0wYRNRKQJJmwiIk0wYRMRaYIJm4hIE0zYRESa+H/pxFXEmSRv7QAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c21e9e8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "execution_count": 4,
@@ -118,7 +104,7 @@
     }
    ],
    "source": [
-    "ax.legend(loc='upper left', frameon=False)\n",
+    "ax.legend(loc='upper left', frameon=True)\n",
     "fig"
    ]
   },
@@ -126,21 +112,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can use the ``ncol`` command to specify the number of columns in the legend:"
+    "We can use the ``ncol`` command to specify the number of columns in the legend, as shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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UinD2sf8DwLQwno+IiIJQ9HQHiMgMAGef+CMACmCgqk7JPWYggCxVTcnvXMnJ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\niBzDxE5E5Jj/B+vIEdfZxz8dAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c21e9e8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "execution_count": 5,
@@ -149,7 +138,7 @@
     }
    ],
    "source": [
-    "ax.legend(frameon=False, loc='lower center', ncol=2)\n",
+    "ax.legend(loc='lower center', ncol=2)\n",
     "fig"
    ]
   },
@@ -157,21 +146,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can use a rounded box (``fancybox``) or add a shadow, change the transparency (alpha value) of the frame, or change the padding around the text:"
+    "And we can use a rounded box (`fancybox`) or add a shadow, change the transparency (alpha value) of the frame, or change the padding around the text (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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EKmZOmQKcf77bEXmIqucGKL7+GrjrLlt8e8stnO4YfdassXluDzxgb9dRThV4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\nRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c21e9e8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "execution_count": 6,
@@ -180,7 +172,8 @@
     }
    ],
    "source": [
-    "ax.legend(fancybox=True, framealpha=1, shadow=True, borderpad=1)\n",
+    "ax.legend(frameon=True, fancybox=True, framealpha=1,\n",
+    "          shadow=True, borderpad=1)\n",
     "fig"
    ]
   },
@@ -188,7 +181,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For more information on available legend options, see the ``plt.legend`` docstring."
+    "For more information on available legend options, see the `plt.legend` docstring."
    ]
   },
   {
@@ -197,27 +190,32 @@
    "source": [
     "## Choosing Elements for the Legend\n",
     "\n",
-    "As we have already seen, the legend includes all labeled elements by default.\n",
-    "If this is not what is desired, we can fine-tune which elements and labels appear in the legend by using the objects returned by plot commands.\n",
-    "The ``plt.plot()`` command is able to create multiple lines at once, and returns a list of created line instances.\n",
-    "Passing any of these to ``plt.legend()`` will tell it which to identify, along with the labels we'd like to specify:"
+    "As we have already seen, by default the legend includes all labeled elements from the plot.\n",
+    "If this is not what is desired, we can fine-tune which elements and labels appear in the legend by using the objects returned by `plot` commands.\n",
+    "`plt.plot` is able to create multiple lines at once, and returns a list of created line instances.\n",
+    "Passing any of these to `plt.legend` will tell it which to identify, along with the labels we'd like to specify (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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QDnhxTyv/rCzA09O6DoCPDXoM6y+tR5NO2fHkX1th9QMsR+rxx1Ww8avVAnv3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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d779518>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -226,31 +224,36 @@
     "lines = plt.plot(x, y)\n",
     "\n",
     "# lines is a list of plt.Line2D instances\n",
-    "plt.legend(lines[:2], ['first', 'second']);"
+    "plt.legend(lines[:2], ['first', 'second'], frameon=True);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "I generally find in practice that it is clearer to use the first method, applying labels to the plot elements you'd like to show on the legend:"
+    "I generally find in practice that it is clearer to use the first method, applying labels to the plot elements you'd like to show on the legend (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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QDnhxTyv/rCzA09O6DoCPDXoM6y+tR5NO2fHkX1th9QMsR+rxx1Ww8avVAnv3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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11025d908>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -258,14 +261,14 @@
     "plt.plot(x, y[:, 0], label='first')\n",
     "plt.plot(x, y[:, 1], label='second')\n",
     "plt.plot(x, y[:, 2:])\n",
-    "plt.legend(framealpha=1, frameon=True);"
+    "plt.legend(frameon=True);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice that by default, the legend ignores all elements without a ``label`` attribute set."
+    "Notice that the legend ignores all elements without a `label` attribute set."
    ]
   },
   {
@@ -275,26 +278,43 @@
     "## Legend for Size of Points\n",
     "\n",
     "Sometimes the legend defaults are not sufficient for the given visualization.\n",
-    "For example, perhaps you're be using the size of points to mark certain features of the data, and want to create a legend reflecting this.\n",
+    "For example, perhaps you're using the size of points to mark certain features of the data, and want to create a legend reflecting this.\n",
     "Here is an example where we'll use the size of points to indicate populations of California cities.\n",
-    "We'd like a legend that specifies the scale of the sizes of the points, and we'll accomplish this by plotting some labeled data with no entries:"
+    "We'd like a legend that specifies the scale of the sizes of the points, and we'll accomplish this by plotting some labeled data with no entries (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Uncomment to download the data\n",
+    "# url = ('https://raw.githubusercontent.com/jakevdp/PythonDataScienceHandbook/'\n",
+    "#        'master/notebooks/data/california_cities.csv')\n",
+    "# !cd data && curl -O {url}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+EGzevJm0tDTWrVvH448/PuD9dYVCQtpGIoZJp4d4m3zs/fAIlnHxWC1m6isb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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117d74cc0>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -310,7 +330,7 @@
     "plt.scatter(lon, lat, label=None,\n",
     "            c=np.log10(population), cmap='viridis',\n",
     "            s=area, linewidth=0, alpha=0.5)\n",
-    "plt.axis(aspect='equal')\n",
+    "plt.axis('equal')\n",
     "plt.xlabel('longitude')\n",
     "plt.ylabel('latitude')\n",
     "plt.colorbar(label='log$_{10}$(population)')\n",
@@ -327,18 +347,16 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The legend will always reference some object that is on the plot, so if we'd like to display a particular shape we need to plot it.\n",
     "In this case, the objects we want (gray circles) are not on the plot, so we fake them by plotting empty lists.\n",
-    "Notice too that the legend only lists plot elements that have a label specified.\n",
-    "\n",
-    "By plotting empty lists, we create labeled plot objects which are picked up by the legend, and now our legend tells us some useful information.\n",
-    "This strategy can be useful for creating more sophisticated visualizations.\n",
+    "Recall that the legend only lists plot elements that have a label specified.\n",
     "\n",
-    "Finally, note that for geographic data like this, it would be clearer if we could show state boundaries or other map-specific elements.\n",
-    "For this, an excellent choice of tool is Matplotlib's Basemap addon toolkit, which we'll explore in [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb)."
+    "By plotting empty lists, we create labeled plot objects that are picked up by the legend, and now our legend tells us some useful information.\n",
+    "This strategy can be useful for creating more sophisticated visualizations."
    ]
   },
   {
@@ -348,26 +366,31 @@
     "## Multiple Legends\n",
     "\n",
     "Sometimes when designing a plot you'd like to add multiple legends to the same axes.\n",
-    "Unfortunately, Matplotlib does not make this easy: via the standard ``legend`` interface, it is only possible to create a single legend for the entire plot.\n",
-    "If you try to create a second legend using ``plt.legend()`` or ``ax.legend()``, it will simply override the first one.\n",
-    "We can work around this by creating a new legend artist from scratch, and then using the lower-level ``ax.add_artist()`` method to manually add the second artist to the plot:"
+    "Unfortunately, Matplotlib does not make this easy: via the standard `legend` interface, it is only possible to create a single legend for the entire plot.\n",
+    "If you try to create a second legend using `plt.legend` or `ax.legend`, it will simply override the first one.\n",
+    "We can work around this by creating a new legend artist from scratch (`Artist` is the base class Matplotlib uses for visual attributes), and then using the lower-level `ax.add_artist` method to manually add the second artist to the plot (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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vvEG3b98u9s0wGfz9/WnKlCl0/PhxsrGxKbZt0RkyJo/TarX06aefUocOHYpF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\nqjRp0iTZp0FOSkoie3t76t+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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117f1e3c8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -383,14 +406,12 @@
     "                     styles[i], color='black')\n",
     "ax.axis('equal')\n",
     "\n",
-    "# specify the lines and labels of the first legend\n",
-    "ax.legend(lines[:2], ['line A', 'line B'],\n",
-    "          loc='upper right', frameon=False)\n",
+    "# Specify the lines and labels of the first legend\n",
+    "ax.legend(lines[:2], ['line A', 'line B'], loc='upper right')\n",
     "\n",
-    "# Create the second legend and add the artist manually.\n",
+    "# Create the second legend and add the artist manually\n",
     "from matplotlib.legend import Legend\n",
-    "leg = Legend(ax, lines[2:], ['line C', 'line D'],\n",
-    "             loc='lower right', frameon=False)\n",
+    "leg = Legend(ax, lines[2:], ['line C', 'line D'], loc='lower right')\n",
     "ax.add_artist(leg);"
    ]
   },
@@ -399,24 +420,17 @@
    "metadata": {},
    "source": [
     "This is a peek into the low-level artist objects that comprise any Matplotlib plot.\n",
-    "If you examine the source code of ``ax.legend()`` (recall that you can do this with within the IPython notebook using ``ax.legend??``) you'll see that the function simply consists of some logic to create a suitable ``Legend`` artist, which is then saved in the ``legend_`` attribute and added to the figure when the plot is drawn."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.06-Customizing-Legends.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "If you examine the source code of `ax.legend` (recall that you can do this with within the Jupyter notebook using `ax.legend??`) you'll see that the function simply consists of some logic to create a suitable `Legend` artist, which is then saved in the `legend_` attribute and added to the figure when the plot is drawn."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -430,9 +444,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.07-Customizing-Colorbars.ipynb b/notebooks/04.07-Customizing-Colorbars.ipynb
index 6620f4a49..5de7df641 100644
--- a/notebooks/04.07-Customizing-Colorbars.ipynb
+++ b/notebooks/04.07-Customizing-Colorbars.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) | [Contents](Index.ipynb) | [Multiple Subplots](04.08-Multiple-Subplots.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.07-Customizing-Colorbars.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -35,8 +13,8 @@
    "source": [
     "Plot legends identify discrete labels of discrete points.\n",
     "For continuous labels based on the color of points, lines, or regions, a labeled colorbar can be a great tool.\n",
-    "In Matplotlib, a colorbar is a separate axes that can provide a key for the meaning of colors in a plot.\n",
-    "Because the book is printed in black-and-white, this section has an accompanying online supplement where you can view the figures in full color (https://github.com/jakevdp/PythonDataScienceHandbook).\n",
+    "In Matplotlib, a colorbar is drawn as a separate axes that can provide a key for the meaning of colors in a plot.\n",
+    "Because the book is printed in black and white, this chapter has an accompanying [online supplement](https://github.com/jakevdp/PythonDataScienceHandbook) where you can view the figures in full color.\n",
     "We'll start by setting up the notebook for plotting and importing the functions we will use:"
    ]
   },
@@ -44,19 +22,22 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "plt.style.use('classic')"
+    "plt.style.use('seaborn-white')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -68,24 +49,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we have seen several times throughout this section, the simplest colorbar can be created with the ``plt.colorbar`` function:"
+    "As we have seen several times already, the simplest colorbar can be created with the `plt.colorbar` function (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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BsHGB0C+kAtk7H+JWerTAcgM96mvWc5NFy490DI1j7CcO2irUFo8O4mxrMLYxtVw+cdKu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nx4w76IT9F7Kq15yXS87v2/CvzVlxz7gwVDSDsvSgYZm3XZyMaULCEbsEvAhgPbl89oy0dASiwf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\naZ5hrjPevFxyai8r0OrNl+u86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FpupNB+SjXOz0VNEqeTOZuQfOezhx16r56XoPI9zmPEyhy6ljqxyV6P79/f1dILRHAngrANRa31tKebgeUEr5fAB/D8CjVpseBuBhpZR3AvgzAD9Sa13rQ8lbDTljXcfPlMg4dNAO1AqLwqZ4X7dsKbV1QOwsronHwrgumMCUzEKluLK0rq1lrv6UdDNCcwTnFJwLObntHU6YzNgZBKnp2GLL0TmstOyiGBnBh7YdcP7rZQDOkBf3kR0gtNsB3Ef/j0spi1orx8LfD+CNtdaPrf5/EMD7aq3/spTyNAC/AOBvrpP5Vh9O5wFO9ciqXLjRgPMeK7YB7TBCO48rW5QvtvXUWgvILXWmZWLAcj1pJ1cyc2Etk2dLlfH/Vn25utW6H5ksYIXhQj+2KL8SlVNmjI8RR5dhxKnF7L8bnuD1EYxk/107BA6U1Njhb3IMraUYo0zGPgngQfR/X8gMAJ6Gs4T12wA+s1q/E8BPTS7synbixloXIqhyc8rArYf1gOrKo/97HllJz5GWglXNlTnqJMIyBbvOgrbUmCM1979VV/xfiUBJaQrR6bGubIoBxodzchzC9tTYFBV/vTDSc35srh3Y2fH2TViP0LRMK7sbwJMAvGE1hvZ+3llK+UIAt9ZaP0ybfwnArwJ4A4BvA/C+dcu8VUIDznrhDIgZKB1ItUFHwwktF5fNKZzMM/M2VVluX5RZJ0qi3NwpHJG5MmXb+JpGrFWXWRtkTqfljFwnbGHE5b2rGOnhRTGSqbPASPxXIovzNz3L2XN8Jr87ATy2lPJuAHsAnl5K+VEA99Zafx3AVwP4f+Sc5wN4TSnlhwD8ewDPWLfMWws5eXlycv4OcBcSjHrXKQBtlTHb1lvPiCYjGQ0jGKiqyFw+rTK1tk2xXh270LGlilqhn3bWdTAyWt51bR2MOJIexQgAS2pxbGBk02NoU3FTa70K4Adk8wdp/+9gORPK5/whgG9dr5RnbetvrOWGAa4BTZVEBsCLAnPUWg07SiAjYV5rLEnTysB7UfIataltMkUZzRg5a3EN2cs9oy42TWgtRb/J8HZTtvWQ83p1vnXTvSzwZ/m5/DcFnHWv7bIIMsvrcxkjLq8WRi5ToV0mLkZt6x9JAc572nXCp+tZuSOgGhlIz9Jy+6aE1I7wrmenaymE7Dg9Z8SprYuRkfQ2bbuCkZnQtmDZYLlbjgx883Fu31RrEcaU8SAe2G+NGbUGubM0e+NGmxwzGiWjKW2pYaTLY3ScqXccH5PlN9VGMOLaI5sl5vHBi2Bkk5MCcVP4jWRbe/Qp1vkHnH+shLfFOXxu2Dq3J6hlhNAiFQaVu82EZ6ROTq7dAOry1lk/3tb6z2WaMpnSs6wutd5bbcNtqbegxH4m/lhmM8MOO64cGelp+acS2yYwwvt45jbqYl2M8PZNWDySldms0MgYkO7hbQWsbudtse6Wuu4sUzOOLJREYpu7Qz0AqiDTG0IVkG5767goiyM4pxbcf7WWCssIrOWY9BeqRNNXc3fSu/9THWJr3dkIRlq3rIxghPPQJwFGMTK/bWMLxqBkwOrjHyMgzu7oB8bCDOdlY7sDJe/j7fyIDz+GEqBloDky0bQceN3+TLW5zqV5u3L0VFlLYWdqLHvbSHyhS/PK2l3xMkp0F8GI1lFLiWVOxj3+FU6NiY0JLMqi7cXnOCxsktB6IWfPKW7DtvpNAQVq/M9ebzwC4Eh7aljRUjYZeBRc/JYJPl8/apt1GkdakUdGZlqGjODctbWspcrcr0VCcc18o6Y+1uSIVEms99rrjABdufkaR1VIhg1edwTDDo7XWa3qNyRc3q22Z+xtyrQfuf27Zlt/HxqDc3/fv5pYgdwKOXpLNadYnKrJwKpKLJbqyfUtEGrqdRmojtCy1/GMhqK6ru2TLXuEFm2k9RHEEg/WA9duInZ5a378BuDsVecZwbUwMuL4ehhxziQjs6tXz79a/erVs2+ayfDH64EJ903UTdlNR2illAMArwHwlQBuBfDTAP4NgNcCOMHyhWzPqrVeLaW8CMATARwBeE6t9Z4s3Uydua8pKYj5XMCP1UQenF/PHID0x4TEYQMDVcfL9C0YTADuxZTaIVihcR6ZmnPpZI8Mtcx1+pY6dmTCnyNkc999cETmnFeGkdbbK7Kfu96LYsThJa6PlZnihEP0jEi0zlpfsNqExfcQbiTrKbS/BeDjtda/XUr5YgC/v/q9oNb6jlLKKwE8uZTyRwAeDeAbADwEywdNv94l6Ly9AtN9WMKBtQVazkPzZsvCCKdsWuMgAUj2wDyFriToypGB1X09SUHsQt/WtfDStY/WX0uN8XoQfHTmaEOnxJwS0bw5bf2uZfZxGiCfTLieGMnwwXUTGNHbK5gAwxgrrXFV/m36bRutMbRNkuemrEdobwTwptX6Hpbq62EA3rna9hYA3wGgAnhbrfUEwB+XUhallC+ttX7UJcoeXxUakxmDV9VcphRcB+Q8nbXCMwZQpKEkxj8GVHg3VnGuLByeZoosvpqk+13o0VJrIyrNdXZXz6rGmMiUrDRdVi2t47IwU8mthRFdb11jDx+MC8UIE5gqMyVO/iYCk1mUkcnC4dNhIb7fsCnLlCLv3zVrElqt9dMAUEp5EJbE9gIAL1kRFwB8CsAXYvlSt4/TqbH9HKFlHl+9Ln+h3AE188i90KJlWbjGAFXQKUkpUGN8pKXONG9VXkxm7gvlmVrrTRZk5tqop9CinhaLRZqXphtt50itF27qF+x5v1OP7hq4LKP4yNqKMaLOTjESjo+xq6SoZcqwGUv+GM2m7KYjNAAopTwEy1eCvKLW+vpSyh20+0EAPoHzL3WL7daysRdHZkx0LWJzIO2FEmEa9jjgKLHxG2WBs/I7SI3DrYxoWyB1ZBbrAeDWT8vPHbLlybUes/biH19DkBqbU2WOyBxO3Md5HT4US05JbgojqqpZkbGz43z4NhUeRxxxeJo3t606u00ZC4Zs/65Zb1LgywC8DcB/V2v9rdXm3yulPKbW+g4AjwfwdgD3ArijlPISAF+B5VsqP+bSDHOe142LuFAjI7YAJK+PhpxZ2MleUxUZA0jTZ8AyyEfK4cIKJbPWuFpGbtoRWm0T16Rhnwv/uG2i3tz3IXSCpBWaZioxw0RGbD2lNoIRF3IyibfUmabNM5/q8LR8GTbckITicxN2Myq0nwDwRQBeWEp54WrbswH8fCnlFgAfAPCmWutxKeUuAO8BsA/gWa1Es/Bl5HuGjth6YB2peCU1JioXZkbn1BtmdTwkCIDJbH9/357nFJUbJ3EhqCM/va2ECQ1ov6rItQ/feuCIJNSGa2cmNQ2xoizuXHV8rVlOh5frhREXzjNGmLxdXasyY4enJOicrSO3TRNa1GNmm8xrU9YbQ3s2lgSm9mhz7IsBvLiXIQPHeU8XZrSm6FtgdUDl/1moyccxoek+16AMPv6wBXdQN87RGp9Rj9wKQ7PxNlVpHDJp3TCpafjGEzdXr149E15m4aM6MK1rPo/DmCxkzMLeKKtzjOtgRK+Hy83hYdTp3t7Z8bLsy0ycjvvgTdSDe7qEidTdg3aZ3xS4ERXadTEFkwsjFbTZOIqOjTjZ3gopNORxykzBqmGmAr0VQmSdKNLNwOrGzZjcgsyY1Nz3MXXsR03JjNuI1UcMzDOpqTdnRaYKlzt2Cx9cllaoqxiZ4vSmYESdnnN2jBVnrE553G3E6bWcHY+TbsKi7lr7d8124uF07jDA2RmuVqipA789EnGmKkFBquGnpuOUBp+nKsN57eyXjZUwYQWJHR4enhtba4WeXF7XNtrBuAPqjbFKTHEe30Qay0ijdX4LJ669HZlljrFFaC2MOAUdGGEiycZJVZ3FuitjZhk+Yn3TkwKzQhswrSQHVCUvBqsSnfPCQP46GbWM0BioWQjCaXAYwst4hrOlzDSdFrkpYTGZZSqNgc/PmWbgVzKLa4jwKNYPDg7O1QsTWZCYPvbVIzEtg1Pv2a+n4td1eo7Q2GkxfrLxslh36qyHV64rxqZzfJuydRRaKWUfwCsAPBTA/QCeUWu9l/b/HJYfI/7UatOTARwAeD2AzwPwJwCeXmv9DNawnVFomdIaAW0LrEqeai1CC3C2CCDA6kLOXniTlUPDCA0ZecyMySz+O4XmxtGycC+WSmZxbnbnP/9YnbmwiOtU24CdkrYrl7Hn4BQjrOjZ+U3FiJLZCEaYyFidZRMBbKyoe45ukyFnVp6OPQXAbbXWR6w+Y/dSLEkr7GEAvpPvgiil/DyA19daX1tKeT6Avwvg5euUdyuEpoB14YCqNAVu5oEZpA4kvO7Ul4YUzuPy+XxNqs7cYG+vw/RCTw07g9g07Mxu7VB1pOqK647VWCyZvLO2dWTGpDiq0qIsqhh77e+GJ1rk2CK0FkYYG6qguLwtZ9cLgx1GMqzwGOkmrPcsZ7LvkQDeCgC11veWUh4eO1bq7a8D+CerW8JeXWt9zeqcv7867C2r9RuH0ID8W5VKbiPAXTeUYKCGMmOF1goxnbLsqbOWt8vCmmzwNwjr8PDw3MymIzVNa1ShMUFnRMj1HGQW52XjZaomsvQUH07JTyG5Kao5w0hsH3V6XGZ1dnp9mi+bKjUeRmi16brWw2yy73YA99H/41LKYvX19M8H8AsAXgbgCoC3l1J+V86Jp4zWsq0RWpgDF4NRj8nILvO8PcA6MmNrjSFwGhxCqDpjRdojt8zr8n8NJVWhMaG58bMeYXMnZaJuhZlHR0dnCI3VWEakIyqt5RwyZa/79Z7FzOFNwYiqc3eOa092elwevdbMMhV/PRTamoSmTw3tr8gMAD4D4OdifKyU8ttYjrXFOX+JzlNGPdvapECLyDJ11vPEmeftNUrmfQMcDNieAmt1vsy0sysRjBCbmyxgNTeq0LjOHJG5+5wizNTJg2zcbjTU7NVny7GN4GOEzBgPzuFxHURb9sqtk0Q9fIQ5XDinsSlbk9DuBvAkAG9YjaG9n/Z9NYB/Xkr5L7C8Af+RAF63OucJWL6W7PEA7lq3zDun0BxgRwDdAmvkM1IW7myhtGIdyJ8GGCmzK0NrENn9dJCfCY0Vmo6zuUH5LOSMX3QQvqOdH76P4/ipCSY3zSsLP10ZWm2kDlDLPdoWo4TmysDXxNsZJ9qWqtqV2JiA1TIi1fQ3aWsS2p0AHltKeTeAPQBPL6X8KIB7a62/Xkr5pwDeC+AQwC/XWv91KeWnAbyulPJMAB8D8D3rlnnrs5zZNgdQ54FHgOqIzQEkCIqNSU2VywjZcp6RXmYOlI4QVJllN9G6dU2L64LLq2F/2NHRERaLxZnOyIQWKs3llZHYiFJcx7mNYMS1UQsjag4fDnNRnmzCyPUFVz8tZzeifKeYa/+sjGG11qsAfkA2f5D2/yyAn5VzPgLgcRcpa9jWv5zeGkdgIPC5rWNHwRpeVteV1Pi/kpkCU9N3+cY1q2VjLr0xE1VuHI5mt304MnN1k3nnuOYszGRizTrcFHWWOamM7EYIUNsow8oIRhw+OFzXOu4RX2aO2JwK3pT1ytPaty3basjpOn+LlDJQZue1CK1lSmK8rVcu52ldObJ8ed0RQu9JAJ0VVWJTdcYdwE3CqAWRRRsoqfWILK5NCVXJLfJ2zqxFBiOk5q6x10aZo1OsOLJj55flNYUcWs5vk4QWCre1f9dsqyEnL2N9hDB6+9wxmi8Dz6kRF4a1wsyRcuj1al7634E1G5fK1NmUkJOvmW/Z4LoLdaZ5uzGzFpFNsR4RtZyJLtfFiDOHISYwJpgpDq+n4HkZ65sON7Vs2f5ds61PCgAesLHO23pk0UtX940agzVTaq6c6+bFy1hnwLoZz1ZIqvv58SfNJ8rKkyGcHqsNNwGgCjELNzVvZ9qxXV1n2zMCVJuCkRbB9RyelmUUw85aYfsmSW0mtAHjSmgBNtuWkcVF1FGAkY/RcQ8Ga6s8Let5e13PAMvElpFXRnJ6L1rLVI3FuiNYViN6A2h2be7ae3XWUzijzmYdjLTKyg4vK5+WY+R6tRzZ9uuh0mZCW8N6XtYpN/0/UukjnhU4H2rGNiW9rGwX8byZtVSOho4Z2WQkNzXvLKRskRiXd8RGOntP/fTScun2MOKO6RFYqxw9Mh0ht02HmZr/1FnObdvWCW3EMlLLjm2BWokqtl1PYABjarSnZFr7WmrOkaFLQ+thb+/smFkcH+FmKy1nU9UZl6P1f8r5UxVaq374+MzprVPelmV1Nyu0pe3UNMU6FdTyxlP/j+Yx6oWvZ4O3ZrNawG6RSe+/5pndFMz7N32zZ2bXq+4vSkoOn5vGxfVyxq0wfURcbMO2fh/a9TxnHeuNlfTO24YxaUxVP844BN90mS7TRkPP60U0WVnW2b8NuxEV2g0RcgKX1ynWle3b6rTA+Xu13PoU20WgrmM6NppdF4eLN8u1b8JuRELbSsg5Eg6tk9bUEGrd/DLS20T6o9YbrB0dWL9Inq4MkaY+nna97XrV/UUH33vjlnrcOna96ncOOa+TOVD0jgXOD9jqfndOL81NWS/NVhjk9rXG9fjXUy16vHs0zT0UnpV1pNw9uyhZZbPYut6bHMqw05uk2fRAfWt9kyQTbT1Sll2xrRNaCwwtUMS+XqjQOyYjy1b+rVlD/R/LkcHxlqrKZuVab5wIQMa1x3qvDFN+Yb3HpqaAf3RCw7WBa4us3ZXYexhp5aP7R67pIhHEus5hit2IIeelE5rOgmX73DYGkgOjsxHvm5GR7s/2jQ7EjwCU1zNiUDILsuI3OWRvdNB8tEyZOms9G6lv5miR29SOONLpM1Jr4WSqQutFB6MEl5HuiE1xeJuwmdDWtEzttIA6otAy8Or+Xj5ZGVtgXQewXD7t8EpkI2Sjb37Y29s787ET18ljmb16R1+YmH3BPjs/u8YR66lg3p61mxIcr2s9uDZsYdHtHzlXj+lZy+Ft0mZCm2A9MDoQuHNbnlW36/9RMuqpgN4vu14ul/vvyERJgoklXrCoHyPReucbY0PFhbn03Ve2WkqtpQindAJ9+mCkPfRaR9SX1v06GNGle01TVkY93w1PtJzBrNCu2VYV2oj6UZBEJfObVKcoIJe/lsGVyZVNjx0FbGYK0pbyii/yRD3wQ+KsziLfIDD+RqbmGf/5x2TG30llgnMf+R0ltkytuc6dYcPtc084cP6jmFnHuen5I85tCoYzhxftvimbXx80aNx47MV4v4Kz9ajNFHC6kNOlm/302Uj+r2XKANvzwK3flStXTj9IwsQRZKZEBuD0DbORfqxrGTXkZFXoPh/IJKYKcpTYXBu5bVn78DU7MnPqrKXYMoz0nJtiInvd+QgpZ+acglPUm7JZoU2wrDFVeSlIQ51FZY7M3Gm+rW0tAnPb3DmZRwbas50OkFl4yeos6ou/fQn47yZyZ3Z14UhNVRqrMvd1eyWzdYiN67Wn0hyp6Xlcr/qWYZdvtm2kzVvkm2FK83SOJtZ7v03aLpJWy7Y+KTCihHo/VnAXLUdWJvW6Dpw9wI4S74hCU3I7OTnBwcHBaRr6daY4PyPkzPuzUlssFufCzCtXruDg4ODMfyZAR8i9DpgRitZjEDrvD7Jyrzu6iI0SmSq0TK1lhOcilmgTZ9dLnXHarf27Zlu5bSPr6Oppe7cdhPGtCpwPd+IRbzwC0lGSbakFLXurszsy4DGzk5OTMzOXrND460zx2myuC673KEscr6TGv4ODAywWC1y5cuWU5JTEsuWokhh1Yg4jTGpxPZnTi/oYwciIwxvFgQtHe9ZyDHrrzCZsHUJbfR39FVh+b/N+AM+otd5L+58L4Kmrv/9HrfXvlVL2APy/AP7tavt7aq0/vk6Zd06hAWeJTY/Tlw6GOXDHeXF8qwy6dEBted8WoB2IM8vIhP+zKrt69SoWi8UZtcLGX2fi8nA59LxMoUW+TGaqzGKbIzdtmxFyG1E0zgmqA+EJE/1NxYiuj+DC4cSp+yn4cKS2bUID8BQAt9VaH7H6LudLATwZAEopfw3A0wB8A4CrAN5VSrkTyw8Q/6ta65MuWuatEVo2pc0AjcYPYw/sLBqV1RkDtRXeZN53CiBHBoM1X702Xm8pJVVnHG5qffA3Bk5OTs44i1Y5VBU6QlssFmcUm4ac2VharHO+rbZx2AgnpmNivGSHoiqGcZJhxLWZYoS/5zDi4FokneEjys9tpLfsjCjfKbbmLOcjAbwVAGqt7y2lPJz2fRjA42qtxwBQSjkA8FkADwPwH5ZS3o7l19OfW2ut65R5K4TGioV/SmQcPqgiYwswMJnxOpBP1TsVqF6z5WmVLFrjaJpf2BQii99iscDJyQkWi/NNGEALMuOOn31PQMujHYZJTRVaEKwLP5mAM5WWtWksW0qGsZOps4w4p2LEEWyGk5aja11PhhVH1hlONmVrEtrtAO6j/8ellEWt9ajWegjgY6sQ82cB/F6t9UOllP8AwD+otb6xlPJIAL8C4OvXKfNO3FjrFFos1fO0ABukpmMio2ENkL+yOiOyDMiOGEdJzSkbp84cmQHXwkxHZhyqals4ZaaTD0xgBwcH6Zha66cdUTurOjyuw9iuBMa3pahxSKoh5ghGtN0yjKhSc1/fGlHyrvxcRkdkHFJv0tZI75MAHkT/92utpzNUpZTbALwGwKcA/NBq8+8COAKAWuu7Sil/tZSyV2udPJMzRGillAcDeB+Ax64yfi2AEwB/AOBZtdarpZQXAXjiav9zaq33uLQyqa0EwOosOihw/rEUBiX/dyFcmHpiLRN3KAaorgeI9XuXvHTfqcxsVJ2dnJzY8TKXRoQjSmZcjiAITkNJrafSIvzkDpaRmLaPs0yJKTZG01MVNgUjLUJzCi1w0RuuiDR5e2xTnGjZnGPYkTG0uwE8CcAbVmNo748dK2X2LwD8dq31f6FzXgTg4wDuKKU8FMCH1yEzYIDQVnHuP8YytgWAlwF4Qa31HaWUVwJ4cinljwA8GsvBvocA+FV0JKOSWKgJp84AnHrgOJfDqqlg1XLoMlNoDNRsPfO+sU0t6+iZB9bbFFip7e/vn97SwR8hDlJzSseZG8zXe8x6EwRZ6Ok646iCdk6v17ZOmanimYoRdXiZig+HNqrUMoxkWHFOLnN069iahHYngMeWUt4NYA/A00spPwrgXgBXsOSJW0spj18d/+MA/iGAXymlhCD6vnXLPKLQXgLglauMgeUA3jtX628B8B0AKoC3rVj1j0spi1LKl9ZaP5ol6siMG5TVWS8NR2QO7NoA6gXVc2aqS0MKJTZHiI4w1ZRA4vwALIdOAVyuI1VUqh55/Iyvky3qSMlHFRoTF4+dtUgtI7MWNhxZcLjJP8VKFmq6c0cwom3nlDxjgMlMiY3Pc87TmZIZrwc+WgQ01dYZQ6u1XgXwA7L5g7R+W5LcE6eWz1mT0Eop3wfgo7XW3yylBKFxbPspAF+I5UDgx+nU2H6O0BQQDFD2vBmRRWdWkGaEBoyNA2SkkxFaBlrnoTU9Z6oUAqhRP6zEsvOUyIJsVJ2NKrRIl+/8z8hKlZtuy0hN28epUOdMmJi43Nqeuo3rlut7xBQjGaFlWHAqLhuS0LZRR8MKja91BxTaVq2n0P4OgJNSyrcD+FoAvwzgwbT/QQA+gfMDgbE9NQdWngBwgGUwcWNmhAbkbylQz8tLDctUJajqaYFWVVukyeZCCQ6PGOwM2CD+qCsO2zXM1NlNLkNs507D5XIqLRvfU5Wmx/JsJxOnenuu8yD2uNZMiXCbBjEqCTlsZSrNqTNeb6lINxTRUmyZmufyZCEnY2MmtIbVWh8V66WUd2ApJX+2lPKYWus7ADwewNuxjI/vKKW8BMBXYDmz8bEsXQZqNDp3Sq1IBVKcF2mwkgHO37CZhRQKHM5jqkprjZ04deRMAasdUq+BQ61Y5/Jx2JqpM05b1Y4Lb7gj8ToTV3b7RmuiQPHRIg99rEvPjXw4DGMnwdfnrn1djLSUmDpCR2Q63JFhQ0mMy7EDkwJbtXVu2/gxAK8qpdwC4AMA3lRrPS6l3AXgPVh+eOVZrQSYzByxKWCzxuMO7DofcL7SHVg1H0dmCtyWYtN707JflKcVZjqPG9fADkDL5cruvH9GaFqHWreO1DTk7JFYr8NEPTBhx1CEC5fZ2amCD4xEGRgzWf2ug5FW+MnOLxuqaDm8KJuOIfLjbpskNK2LG8GGCa3W+hj6+2iz/8UAXtxLx3neaFTg2mxmkBqDVNWZ62TuvrVeo7gyuTKOeGOeXeT/7IGdOQ8cy+xYDcd6ROYIrVceLRvXOS9HwlDd3mojJvX4udt3uJ1YsTuMRF3pNWl6znoY4bqP8mcYOTo6sgrOKWmHD8UJcI3I5pBzCxaNwF7GAevo6OjU+6iic57Kef4RwLbAymGAA52SmVtmpBLG18GdmMsWlgGbVW6LzEYIzdVdlmdLra2r0riMfJNwYCawEbhgjHBbRV6ZGr4oRjK8TMWIU3xZe7Aq43LF87ybVGi9MblNkuembOvvQ9MGZBLjpQJUw4asg2TrXA5e6jb1wICfpnehhe5rqTQli2wfD4w7z643z7pxmRaptcJOR0ZOrfVUG8+aZu2jdR/trvcjOoy4sHJdjDhcaH2q0+JtbhwtIzcNORkrHD63sJI9PbKOzQptwBSkvHTH8ZiSqoHM207xvJGX5svbnQdlsDJx8Xor9HOhj/7UA8b2yCPqRsuVqYesczobUWmO2KLcmXJzz3TqAH2GEeAaUagyY4y4PDaFkVZ9unZgxxb73Cx51m6u/Z0Ka+1b12ZCGzTXscPD8n71uK6TuA4X/3mp65GP+++IwBGGU2oK5BF1Bpz1wBw68bXFfu7ELSLLrqdVB66+WnWsJOYITolG2zQrD1+HPqsZ2yNdVmO87iaJLhsjPYJz/7VNXJ0HTnjseCa0SzYdk+KBWh0n0zGyVsgDnG+AaNwpCo3LF8sstMi8sfuv+9iYrHSWM/ZzXUQaOmjuyqrX4q7ZWUZssZ6p5Ey9OYJz6kzLx9gIVaoOTzGi9zNmGOHOvwmMqEN2jk8xNAUjbqw18gtcbHpcaxdJq2VbnxQArg2Kc6cOALSAGudO8bgj5eL1Fklk6ihTcg6oXE4lMwaqEphLM1vX6xm1EbXG6/x/CuGpI9LxUg7PuU6cGtNQXp3aRTDSqs9NYsTlx/XKafP6pgltVmgTTAHAjcUgBc6TloLSKbF1KzsDVG9wPQMy/1fwunI6JebAq2Gp5te7ninWIjau+1GyU8LLTEmdz1Mic/lm5XTXNcVcnTrVxtcQx2Rkx9u4LV05mbj13sxNTgqEsm7t3zXbyqSAhlTO2wI4Q3BhzsOOhA7Z9qyT63YuQ8s7t/YrUF354vqnpKnl1bKPXqOWpbW9RXJuW08t8bmMEd7G9cIYye5Pu9Ewose0ysc303Ka8xjaFkw7to4XhTFQw3r/2aY2bmvQ3oG6RRxO3WXnuWuKY7hu3LmuzC0lNlWlTQG0u90k+99TSutixKV1o2HEpcMqldeBs3UzK7QtGTda6xVBarviFdYN31q2bjh0Pcqyjm26bWaMnLURfMxjaFuw3rjO1BBpdP861mu0KYpg5Njs3Ozeo6llu6it2wajSvIi2y5SvnVt3TYYxc3otsDHPMu5BWuN+7hxhOsxTuRs3bEjXc/Gilr5ZIPXvbGqbGxo6jiRs9E6daGTtlUWOrvj+X+r7adOgkwhxsymOiW37tpMw8hWmq2xyE0S2hxyDpgbBM1me1rH81LXp44rtTwe7x9ZZpMbPdDrTF02mK55rntbQguMvfrrOaLWZEi0Y3RgndXV83SwPMPD1MmSbWJEby2JtHqOrzXbfz0U2hxyDhoDdeRxHT6Wt8e6W+q65j9FjfVAxCTkftmLCR2A+af3cmV3vY8QnsuPt4+osdYMXXaLSpzn7qEL49sQON8eRhxONoGR2LYpjOg+9+NZXYeL0ZvK52c5t2AK1BZg9W2vLVDHsjdj5Kwn6UdA2QPd1avnv27Ox+gyQoh1ic5di663LAvzRogktrH64nsOe4P8moZr99Y+LQv/j+tw19myEYz0cJI9MRF15PCh/xUrmuambB1CK6XsA3gFgIcCuB/AM2qt99L+ZwL4u1h+DOWna61vLqV8CYDXA/g8AH8C4Om11s+sU+athZxKZu4Roh6QI70WuWm+zjKP68CZ3QHPoROTUJyvN8JqnpyOAyunOUpy7jrcNWdt5OpSFVZGLvxYEhNZtG+8IjzSiPvJOK/sbnpOs3XHfUa+7rp0Xe2iGNEbgvnBem4vfkpC83cY0edjd0ChPQXAbbXWR6w+Y/dSAE8GgNUHhX8EwMOx/FjKu0op/yeA/wnA62utry2lPB9Lwnv5OmXeesipANXXFOsxLTXnPLGClP9nII3lCFgDUAHECK14QJUHxJ1KizycMtvfP/tNTD5O38+vZdPraF13r65aCtmRDLcnh5SRDm8PonMYydLMHOIIRlqktg5Gej99/RVjhNOL7dkN5o7MdH0HxtAeCeCtAFBrfW8p5eG0778EcHet9X4A95dS7gXwn6/O+furY96yWr9xCC1MQZZ9KUcVm/PCDqyjYYUDTuvHwFQiY6BqnnxMKBbNL0DpgMrkFnm6MDW7Br7WKQotIzPAf4qN2zHIPiyIrJefc2ZKZLx9Cj4cRlr42BRGou0YI8D5x5dc2ziSdLjY9CxnK70kvL0dwH30/7iUslh9PV338Vfj7pNta9nWHn1yoHTrjuBaYWpPpY143xY4damkxuBkkuHysOmgvaq01tfInSprqTRHZplCa9Uj13tcP6/H9fILDPf2rn0rwn0Ix6mBTAVmuHFK0eGjp9JaGMlw4sayeHvk2SOcHlZUoSsudkCh6Rfg9ldk5vbpV+P+EgNfjGvZToScvbd4Os/cG2dT4Ea+aqNhhJP5TCoOhKxOGKg8oxX7tDNkX0xigtPOpKGJU2juml37uCUTlyMaJrUgNv7gDXc2JcARjCipZe/mz5S9rnM+GT5GMeIwofXETkCJR4/V8mjeisfAxg6Mod0N4EkA3rAaQ3s/7bsHwM+UUm4DcCuA/xTAH6zOeQKA12L5Jbm71i3zTryC26k1R2yZR+4RW+SXWc/z8pLDh4w4Od1Yto6LY7RTsAfWT8Np53FldT+93qxtXBvpWGDUOYdWTOAATj90rOlGWo5ctd1aZJZ9QjBT9etgpEVoLVXmnFLm+EK1cv3qGJsjTCazTX+Xk51ktt/YnQAeW0p5N4A9AE8vpfwogHtrrb9eSvl5LAlrH8BP1lo/W0r5aQCvW82AfgzA96xb5q29glvVWYDOkVn2cdZWiMFEGflmFkCJ9VaoqWHmYrFoEhqHX6PEpp2i9Z1LR2yuo0XanEerjbiteF1JjNuA606JLUgMOD/4HfXZwoojLF32FFtGautixKlkXr969eopyShpOnLsOUfFpn6tPvCwKVtHodVar2L5/V62D9L+VwF4lZzzEQCPu0hZw3ZmUsCFmQrUXhjqwowpYM1A6giNva2T+dGpFbDuOP5l37Rsfesye4d/psxGQa8dLEhc6zdTgpyOji0ymek9aS3iUafGnw3MVL0S2lSMxPW4unVOxZWdlRPXkdZpNnzB52UY7SmqdWyTBHkZthM31qoH1tCiB9bss3EjgM3AqrdKsDdkkLrwIX76ymi+dlcOpwxbXx/PiG0k7ORr17bRdoptQUSh0GLZI7OoA64/R7SuLEpKSmzHx8dnHF42uaRf4WJiA87eMDwFI/GVMjdh4/Ch6bK65ZBeTdVhCwebsjVDzq3aVr8p0CKzAGrmfXVb/FoeOPINU6ACOHf/F5MS3wyp30XkNJlU1Os6wLpwt/flJF3Xe9R4yWm7dVc3joSVyDj01I+YqEX9saLjetcyOJXTc2YZTgJHjI1RldZTZ3HtHPYxmWVf7mIF766Vy6JlyMLOmdC2rNB4XcNHBa0DriM89sbq1Z25cRFWZfGL8ZAYfGVjRaaAjR+PqcR1azlc+OnAy2TmlIFTaI7YprQRj4kpofE+Dh9ZnXG4yWSfqToth4aNWcipQxWtkHRdjESdszqLdAMjro05jai3IERWZ87xOYfknNcmSaaHlV0MR3dmDE3JLLt9I4js8PAQV69ePV0qqTnAhgWQwhQgjtB0VtMZh5nRYbPxID3PjYfoNlVp6p0dCfbCzshfy6Rl5c7ERKYhLBMXkzeHVXw9LUXXU/E8CaCOLsgslk7Js/OLPAMfwPl7BKP8TGSszmKCKNLisVV2eooPJn12es607hw2NmUzoQ1aC6zqfR1YDw8Pz4DWKTjn1SPvMO6kDIoAXaR3cHBwms7BwcG569EQwgE1I0JHLi1Sc3eIt4gQ8OrP5e+UEROaU2SsalSBcsjN42gaamYdJ3MGztFxeOkIjbGioaeGmz2MKJG1wsU4l9U7k5q7vp6yU0xwG8+EdsnGXlDDiRGwMpk5sI6OkXBjZMQRHTS8rfOcDNYgNJ0MUEJtAZbXHYg55MmeHHChiJJZC4yurEFoHCZmIdrJybWxo6iH7JpaCo3LkKk0xUkoN8aGqnjGyQhGOOR0Di/UmY7V6nUyTvTXI0TFiQs13ZjkRYxVdrZ/12zrb6wdCS003IxfFnYqqTmQhDERBTjYA7fOZ3Bl4UPP80Y6mp4LQV24pmGoC0MyMhshk9ay1b5x/azSOOwEkC5dfiNYCUw4Ne8mBxgfvfbR+laH585nQlT1rvhyUUQLbxp28nJTNiu0CdbywApWFz44oOpYmqbN+QJ+9ig8sLsZMiyOieMzEh5VZ1wWRz4OtBqCOm+dhZpZyBll5PAyysu3WzhlxoqEw0sOPTN10cOHtqUO6mu7s8PLlLxLl9sqwwi3d29cVZVZ1F3L4fXwEeuubecxtB2YFHCNqo2uCs2NmbhJgfhFXiMKLVQOk6B24MPDwzNg1UHenjrjtLUsmffNFJvb7iYGOP1Ydxbbo8xRF1H+EQKK81pE5jqoSzPDRqbiQ6E5MmPsRDrrqPiRx950TFbVmMOJXrMrB5NWr14vajOhDVoWQjjAOlLLAKvjKT3vC5z3dicnZ2+YPTw8xMHBAQ4PD0+PZ68bYO2RmSpFLUMLrKO/VqjpyMwBMq5biYxNyUfVGF+r62yuTFMw08KFjrs6MlNSU8fn2ifDiLapw4eOu7UU2qi1MLAp6ym+eQytYRlgdSDXzWi6O8XV+zrARGeKjqeP54QFqXGYyUA9Pj4+nbKfEkZwGXQbd3ZHUpk6y0LNjEB6hMLExp2Xx7uygf9eObK8RxyebuthxBGeG5CfipG4NYPDyr295VtG2OGxSms5vAwrI+26aYXG13+j2M68bUMbWtcdSJ03Zq+t5Bj5An78jDunmpt2Z4BqWKHg1NDCWU99qQpb58fXnpFpq4yhMuJYJrb4KRlz3WXXnNlUJZ+pM6fmHM4iT64fh5Gwo6Oj0zeKcFtpXjok0SIwVfHOCanavR4h56zQ1rCWpwqV1RpT0zCz54HZ9vbysRE9LgDL3jbKMNXzOhAzOLLQjMGaqbNWGOJIbaR9MlXnOpbmq2No2gFdftl6RmwOC0paLjRVQuxhJHubRezX8HIEF3xtTGQOIw4D67TpqN20hFZK+XEAfwPALVh+0eWdWL6M7QTLF7Q9q9Z6tZTyIgBPxPKLLs+ptd7j0uOG5G0OrBw+utsyVI05ldYLJ1RR6P4AbOQZY2aunE5F6HW3zAFWy5oNrPM5Duzrgl9JzakDVmY9dZilpXnGMsOJU/QOM+oYs0mBHkayenPqnQk0iI2HJlrk1rMWsW2S1HrpbZpAN2Fdii2lPAbANwH4ZgCPBvAQAC8D8IJa67cA2APw5FLK1632fwOApwL4R6OF6BGbU2kKxJ5Ky8ZUWt5awxLuHHpu5on5mtJGIAUVyxZBMZBat2aMkNvoT8vXSyesl9YUjDjn4HCiTk5x4I5vjde66CALdTUyaI3VZaqshZXMQfG2TdkoLnbJRjTjd2L5Gt07AfwGgDcDeBiWKg1YfqXl27H8csvbaq0ntdY/BrAopXxplqg2nuv0meJRknFAVaA7wnGgY3C6fHVfptD0OrP/apmS0W0MKjeLmKXVI7Zs+8g5br+7lljvhSwZRvi/ksfoUiePXLvyser4Mqy58vRwMqrKWvuup0K7kQhtJOT8EgD/MYDvAvCfAPh1LD98EK3AX275OJ0X2z+aJdwCLAMAOP8dz+yXqSoHnmiQ2Ma3HHB6GkpkANVrc9eXWQaOnvJpHa/ptkgvyqd14vbzMkurVbaRY8N0oD7WM2fXw0mmwnsY4bLwREeGQ4cTZ1MwEpYp+utBMLtIWi0bIbSPA/hgrfUBALWU8lksw84w/XKLbj9jWYNlgAXaHyVu/Ua8IXdOHiu7evX820NVGeg2zat1vS3LyIDXW+rNbe+RGf93xBb7HZmNKLkp1qqzHn6m4CP7tYwxwjgLZ+fGV6NcWV5ZnkqCzglcT8KJh+9b+3fNRgjtXQCeXUp5GYAvB/D5AH6rlPKYWus7sPxKy9sB3AvgjlLKSwB8BZYq7mO9xB3B6H/exgPt2bktkuGO6Dpui5gysnKAHLkutUy1tFRWFs61CC5LX+tGj9MyO2LTc7LrCxuZKWPHFv9Z+TgVx/+Z5PR8/p9hJLsGR24jWOhd5zp2PQiu54xG8yqlfB6AXwHwYCwjt++ttX5UjvlZLIetFgD+Sa31VaWULwbwISwnHgHgzlrrz7Xy6hJarfXNpZRHYfkJqn0AzwLwhwBeVUq5BcAHALyp1npcSrkLwHvouI1aRiaO5HSGMTufzTWQAyl7ziyUyPKYausAtHdOS6E5Reb+u86uaV7mOIsOrvNQRXZcHJv9d2p0RM0pYer2iyj4y1RpmyI0AD8I4P211heXUp4K4AUAnh07SynfCuCraq2PKKXcCuBfl1LeBODrAPyzWusPj2Y0dNtGrfV5ZvOjzXEvBvDiXno9ZTVyDtAH6zoWXlfzdOpPz3MkOtvljsNkZNFS/j1ycqH4SP78v4eLXcTNBu9DeySAO1brbwHwQtn/HgC/v1o/AXAFwCGWk48PK6W8E8CfAfiRWuuftjLa+o21s822LctU5mzXbKozKqV8P4DnyuaPALhvtR6ThadWa/0sgM+WUg4AvA7LkPPTpZQPAnhfrfVfllKeBuAXAPzNVv5bIbRsMHqqvI3ZJv4/RaVlabbGmbKy8p36rfR3yUYUx6byuSwbGYfM9k0Z1xzJn/+PDgOsY9erftcJOWutrwbwat5WSvk1XJswtJOFpZQvAvAmAO+otf6D1ebfBvCZ1fqdAH6qV+bde3ahYRnBuFfkZHfTO2LKZg31nDCW2us+/jEauqwD1inhTS8s4/WRsZ/WOZuy3uRCtL9ud/9HiG509tZhUPdnkzijdpHxt6mm1+p+g3Y3gCes1h8P4C7euZo0+C0Ar6m1/s+065cA/Fer9W8D8L5eRlsPOR0oHAnFtkxBZf9VhfD/XkO1gNwqx0jZnPUGmbP13kC2pqX1MoXIRo4bOT5sRFFz2/M2foY1trWcXuTFbRj3k7Xy1nYP06c0OE9X/t72Tai0TZLcOgotsV8E8LpSyrsAPADgewCglHIHlqrsmwH8NQDPLKU8c3XO0wE8H8BrSik/BODfA3hGL6NLJ7QpjatA7r3ry4Eva2BHVO71PHEM591L5yIeGBhTWNkgd4+seL+SXo/IXKfRWT9Htq3JFWdcZz1FFduc0xv5xUsGWuUYwZlbMum21E2GEffeOVePuxRyOqu1fgbAd5vtMdl4D4CXJ6d/61AmK9uqQlOv5oDSIzN9tzvf7OdudMyIU4EX6/yJuKx8GUjd9WXWIo+MGDKCUdJSwmqRnpvNdXmNKMWW8hvZzubqdh2nxzdLu/bpYSSW/FbgFslx+dzwxBSMhLWentikbYrQLtO2RmgZmekxGUj4K9wBUrfku7hZlUT6CvweWB2JjYY9vMysN26lJKI3dzoyUyLrWYtMM4LTMvbKDfTDzZ7D0/bQd8bxj50eEwtvV4wwGWV4cK87z0jVpcX5jLSJ+585vYvaTGhrWsv7OpUUZMZEpY8pATh9/jJTIwwoVWRBbNwp3AeInaqbCtjsMaqwLKxr7esBu0VuLfJqhZUj5XN5tMo7xeHxx39d2KcKXsuYYSTDo1NsjBfe11KM7lozG3V4m7CZ0AbNgbTnfZlEgsyYaIB80Dnr6Jy/AtJ9lZw9sm5zgF3nNo5eWBlKgscHlTRGlJgLrbQMmbLS/LLX42TP0o6QblZvIw4v2o1f2MnfiYj6C8swothUBei+j6rrrOB6Yape60i7uPbYpO0iabVs6yFnrGdk4AiF3wjaerYzzmmFOFnIqSELEyoD2YUcLcDqtYdl78VScuBjQp3u7e2dCz3XtUxFZSQ1qtgc2bk8R4lMwzp1eO5NwuH4+KPAUzHilLwjOMVFj8wytdZrn+tNZrNCm2A9kKps59dfhyrgEGKxWODo6Og0bX3FC6uaOIbLkam0+Gq6AlY7U4vUdJDY2SgRcLoZSbgBaJcfl0c7xAhZ6bvktHNlD4Br+pmNkMCVK1dOP0rCbRflY3WmaY9gRPOLtlwsFtbhOQeo5zpHF/lp201tl03ZTGhrWguw7HU1jGCL//HBClUsbmYoGsR5e/W8i8XilNiykLQHVmd6LRkp8ASHI7i4nhawlcCy8uiyR2Q9gnMhXavj9QjM1TfjhH9MZursHEacygfaGGGH534jOOnhJQvrWyr+ojYT2qC5RnQqjQkllNnBwcGZBmSFxu92VwCE15uq0ILMeoDtTQ7oPjUXhsU2JTElM1626jnyZWLLiE+XPTJrvVCxpzrXwYyqd52tzNK9nhjhXw8nmVobJYmsHjfxgga+9pnQJljW+d0sEXvf+Bai2v7+te9khqJx4Zieo2VxIQOD9eDg4BzR6eyWC0FdvmwZCbDSyUJOd30avgSRxXktayk0VUHZV7b4FT4tImuVPyMydXocZmq4qfURGIn/Ixhx5VB8OMxkY7ItIsuIxDkTxcYmCS3K2tq/a7b1SQEHEgUbgxXwIUsAlTus60ijZXCADdDquoJ1dHKArUUgwPmwk0PNKXWsYaemoUTD+WcdKCPelorjY3tl1/oM5+bIzDm86HgOI/wKbjfW5urQzWQyHg4ODk7/h/NzKn6E2Hr4YGxMUbw3q20t5GyFaAzUIDT+MnkAlsEWg8JBiNqhgPYMlo7H6PhZgFNB7MjMAVjLyzaihji0jE7JM3WaXtRbEBgrUS6HdgBHaKq2MuJitdb60LNu06Wrrx5OgowODg7OtXE82sRkphNM62DEjZMFmcUvU2gZubn2HMFJL9Rex+aQc8CctHbg5DGRWKrnVcAHSEPBuM6UlUdDRTcG4hSaA2wWJun1s7XGmJw6i04axObGfZjMYj8TW8t6nUbr130OMFNqLdWctW3L2cU6h5ealkYASmYtMuA2bJGa4oNJrfVzCs2pM20X5+w2GXJqOW4E24lJgczzsspQsO7v7+Po6OjMmAg/GaAKAcgJjUmNCY1By2B142ej5KbmyhTXwfXA6kzLHf8jPQ4tWaVxflMIDfBf3Yp199Vy1wY9clPLnAMrsljnMrPj29/fx+Hh4el57h7GUYwoqTmFpoSWHTN1WCJT7hp2zpMCW7QMrKzKnNfNQBDelzuZApUbXMMwBqwSmoaf2QyWklkLuGEKUh4P4UmA1tgZqzANKTlPTq9ljsyyDqVklk0UuGGAUWzwpJCSWvwCK3z9MRShROa+1ar4GMGIG5qIIRLFRS/0jHRb5sYldfJlUxbla+3fNdsJhcZKQhVZy2PGeaoKYpzNjdeoMWA1nGAgZmrMzWox6PV/Zo40eHKEw0y1ESJz4UxGatzBM2JzJJWRWGvp1FkWbjqcsEKLc1TFcpjpvqge1zkFI5mSb+EkU/CRhl53r024LsNZzQrtkk3HcNjbqTpxCi0Ay4ojO98N9rqQi8e4HNicZ+2Fmj2FxsbjhRzGBTHpOIlaXC+Hl3Gdem167fxfiSULOx2hqUNRxZZNFKiCdqQbhM6KrDVeFksde1R1ljm8Fka4PVXJ9/DRus1HceLaxSkz7gtxzZuymdAGzYVQ7H1HvDZ7WB5nyjod4J/1BNoqTZWWI7Ys9GTPm5FZWBZ29t6oyudx3kwUcT4T4mjIqapAy9kjNqfiXNrOGCOqzPjHY2YOH0xs6jQi/3UwwvjIcMIhaGt/Dx9K6FqnM6EtbetjaAzQaDBnKquz0MZ5/h5YgfydVwpW9syjSo3JTU07p/O+R0dHWCwWZ853RMb/ncOIa+Z0XGjD6fP2rK5HiE3VGp+vpuXmn46bhQVh8E2zXAcOHzEsEfhqYYQJrYeRzOkpPtTh9YhN25wdHo+jfS7bVm7b0EYM8PJ4CMt+DjP1tgwH1JYC4HXt2NlPwwJHXG7MLQsjMsA6oIaK4A7Hv+hoDPRYajji8laS1HVHuEpmjtz4d3R0dI70XNjXwkrm+Fy9suPLiHgqRrJ8RkhtnckBNVf3fH8dcPYtIpuwWaFNMAYph0h6jIKUQwc3U5WFSr2ycH68rmMmI79e6Kmm5VYyC4u0FNiZIuPy83XqujPt4K5DAWdv58hCTF26dmq1O9+iEcfHSwjY4TGZj2LEEdoIRlqkNjpUoaqf8RGOissW1+EEQVg8gL8Ji/R7ddKz1VedfgXAg7H8Luf31lo/Ksf8CwBfguUHhv+y1vr4UspXAXgtgBMAfwDgWbXWJmPvxCynIzRWZVn44O4lyoDaAmyL0BigmVpT4nKA1etQC6AyqbGxMmFSi3MA/94uHoNrqTPNS5dOsWmdqwLjpSM1Pr/XJq1JAHZ4jBMmwYtgROtMcZKFjkpYPXWmpObUszoUbcMdVWg/COD9tdYXl1KeCuAFAJ4tx/x1AP9ZrZUb4WUAXlBrfUcp5ZUAnozl9zlT20rIGSAb8QDRAdj79jyuApQbWUOVMB0j4XUHVgVu5pV76qxHZO74qDsdlI760bK769J65rrhunJE1iI3Jitdz4guU2tOqbl9EV4zkfXwwfkpCVwEIxk+ovyZI3SOz7U9EzdfZ6TTw88U2yChPRLAHav1twB4Ie8spXwZgL8C4DdKKX8FwD+stb4ZwMMAvJPO+w7sGqGFtVRLbGPS487iQp9eGDGq0HTZ+vWUm5LcVMAqEUd6US8abmbl5GvSa3bm6iojM0dK7l6v2N4iMK5zHo7g+uGbZ1mNqXp3pJZdR+u6tb6mYMSprxbJtQjEYV7LNuIQp9g6hFZK+X4Az5XNHwFw32r9UwC+UPbfAuClAH4OwBcDuLuUcg+APVJs7rxzthPfFOCQc2/v7FtEs/DBhSxTwoisTFwut67gc/8z8Cpxh2m5A7BxbBBYLFsdROtVr0GvtWWuLp0DcWrIjak5wusRG9c71wVjhLHiynFZGNE6z5zMCEY4ryDyuB518PF/0wptHau1vhrAq3lbKeXXADxo9fdBAD4hp/07AK+stR4B+LNSyu8BKACYud1552xrs5wuhORQlAHpgDoC0CyU4LKoMSC5vLyeeWcHykzRqXGZNXSKDsCKlQeHo9wZEes1tepA60jrMqv3bDzNbVOC4/SjTOzMFCsOIw4fWVn5GqdiRCMJretMHTsyy8iuhQ92KlEHvL7JSYEoa2v/oN0N4AlYflD48QDukv3fDuCHATyhlPIFAL4GwAcA/F4p5TG11nesznt7L6Otz3KyGnFhgW5XhRDrbhnW88AKIKdmMqJogbml5LhsPPbFY2RxDh/D6iQjLS0z78+uWU3rzBEaLzMl5LZng/OufI7MNA/ex2Vplfd6YcSpyp4jbDlBNa6DOCfW9/Y2/7aNDdkvAnhdKeVdAB4A8D0AUEq5A8Cbaq1vKaV8ZynlvViqsp+otX6slPJjAF5VSrkFS4J7Uy+jrSm0MJbSQE5ULUBmIJ3auK1O74gtW7ZA7dLW64j1AGqUTWe2MgXWyqdHZK0y8X9X/y0VpyTjnFRWzh5GppDXjYQRLrNGMGE8i72/v7/x2zZaeBnFUq31MwC+22x/Hq0/x+z/EIBHD2Wysq1PCrB3Ac4Czs1OttZb29Yp3+h/p4RGgM7lVaWq4USEmK08R8u+jvXqeUpbuTCTj2Nc8LYWRlqYuJkxwufH7PEmbRPYuUzb6qRArGvjhPVi9E2Acqr1GjjbPxritZ7d7NXHtsDXaodMAY2GeExiUzGyDXwA7Xa4iOIZwci2Zzm3bVsnNPc/27au9Yhgk+MOUzvRFPWwSbBOHTO6iE1Ja4pD+FzASHZstn2TIeeNaF1CK6UcAHgdgK8EcAzgmQCOYB5JKKW8CMATV/ufU2u9J0s3C5kymT5CghNmXYbNAbkV0mTrWXg1JY0svZE0N2UjxDK1PUdILJupvREwMoqXTWBkk05vg7Ocl2YjCu0JABa11m8qpTwWwM8AOIA8klBK+SMsB/C+AcBDAPwqgK93CepAaCxHB7t1P2/L/k+xKYDLxoV0sJoHtnl8TNNw6a076N0baxy1ERLhpT5uxcvRSZJssNyl15oMuR4YaRHUZWNExxC3fR/atm2E0D4EYFFK2QdwO5YPj34jzj+SUAG8bXVn7x+XUhallC/Vh1ADqEpk2Xa3LdaB9gCrrvesRwa9WTa9vSC28X+erdS845fdAtH6z3mNXMuIZQQwQlLcrvw/xsKcd+djs1ti3PapGFmX2DIiU0JzROYG9ZWQMoy4ds8wMo+h9e3TWIabH8TyafjvAvAo80jC7QA+TufF9jOExpY9tuPuxxkBsltyXpll4VtGWs5Lnpz4B6FDsWhZsptBW4/u6HYuW0ay6xDbKJFlBJa1If+Y2FohZ5ZeDyMtBecwMmUMraec3CNX2Y3GUReKQcYIpx/HZtiYCa1vzwXwm7XWHy+lPATAb2P57FVYPJLwSVx7vIG3nzMHwtbjINnd1ZkyGA0zMo+b3U+VgVKJjIEan0tjsDkycUB1jwuN/EbCVFcHDqAZibmbRjNyyR7v0TIoCfGxGRYcbloYyVSbu+ZWfWVqfQQj0ZaRj3N8Ll/FiOYVL7fclN2shPYXWIaZAPDnWI6fuUcS7gVwRynlJQC+AsB+rfVjmlgGRve2Ct6u6xmAgb5ac9ZSZY4sHPkEke3v75+CFsCZfa48Dpycvnv20eWdKbUeqTnrqbKMxKJN+MkG7rxxnD7SlDkexYLiQl8QMKLk+Do0v5ZlGNH/2TcLVJWxgwvHp2TH5tpfMTKPofXt5QBeU0q5C0tl9hMAfhfySEKt9Xh1zHsA7AN4VitRBpgCdX/fv47HATgjMkceDiSt8MH9VxJhQEUHdS9oPDk5OSW6zDTt7K0V7v+Icsuu17VNqz5bhMHtFB0zrv3k5NrHTXSsUXGheTlcjBBci8hGHZ/WWc/p6Xv6GCPs8DR9/q6sy9NhUF/DtMnbS1RJu/27Zl1Cq7V+GsB/bXY92hz7YgAvbqWnYGPQ9d7Tn3lc3h55uGVmWWdv/RhA/OaH7G2z4Zm5vK0wUj8Jx52iRWhO6a2j0jICyMJHVV7RsaNegtQiHS2jU2jOyfGSCc69a8zhLVNmm8JIEIpTUIELJZ3sVenOuO0dRuaQc0umYYJ+17L1HUPtRJxeFl70rKXGHEFEp4x8MjCx145O3ZrlzJRYBuCWcltHpWXqzP2yrx5xfurhmeyjDl1ZNA9VZYoZzt8Rb6Sp/3udVtuI26qFEVam8Z+X/AEcxUAYYyUbm+Pf0dHRfGPtNjJ1pMTrSm5KaNky64SxzZlTLgwsVT/RYd2YWXRWBioDPOtAOsDsSIo/NKLEpqFpptZGVdoImcU1uzaKNuR841NzkbYSXy9/HYLgL5Nnb4KNfC4LI7EMImKM8I8dX4TgfFzUjcufFbzDR095T7FZoQ2Yyn31vExmrY/3ThkA1nzVsjCCB64BpCCN/ZyHe/8/p6Xl6XleJjL3nv4sDFVS4+tdl9BcOyiRafqsyqLOWqSmQxGxZDLrvZ8/U/VTMaJlU4cR7RpjZ+zo4j9fu7aB1kVLwbdwMs9ybkmhZSDTcELX+ZgsxMi87qj3Ba55XB3cVZC2PHwAncNS14mykDbW3bv4+RuXWRjq0tIO0yM0bSenjPUX9abpc91Fmi01kWHDfdR5KrFdFCMaTnOdZ0QWx8X5i8UCR0dHZ5wA103LnEKfx9CWtrWQU0PMDJyjwG2BtQVYBSlwNhwKcI4QGc9SsTJTz+3MEZsD7vHxcVOxaRia3fLB165tE3UQ/0c/y+acg4ZaQWQuFNYy6Pmu/Ufw4TDC16c4GcEI12Okx6qJ03aKC4BVdKxkW/hQlRbYmEPOLZkCzIUQ2azWFLC2QooslNBfdEIGHafNgGVllqlGBi6XQ9WUU16ZUuNtrXE2HvdxdZCpM24jbS8Nr90n55jUuPOzynFfdRpRhazcVM3puXxtUzHiFBrjQ9V4NuOtGImfKrRYar5ZyDkrtC2/sdaNcfRUQPaBVgVtpqKcZYBxhBagy0DK4Vb81wHfbDaUVZQO+oYyYyJTAtNjIh1VaSMhZ9Qjk0Lc/BnrBwcH58JMToOJX0N2VsFcjsiX2zALHx0ueuEn58HE1sKIklqGD6feVZ2pUwl8cD+Isjmn13J2mya0G9F2QqGxd81CCgUtH5t54cjHLYGxpwLiHA4XMyLQEFUVmv7P0ugpNSW0CEEPDw/t/uzOdb0WF25qW8S1hRpbLBa2PtwkQCyzUNNhw2EitjlF37ovTRW8U/KZQmthhB2e1ic/DaA/xopeb+sWoB4+NmWzQhs0BWwvvOh5YEcWI2AF2k8FMECjMwLe60ZZgLOvSL5y5crp4O8ISFtjJC7kjHuPdN2Fn9oR3LUomcU1sOPhyZKMmJjI4sehprvWFl6y9u6p+otiJCM0dnaZg3BK1JGaEndmLXxwO2/SNkFapZTPA/ArAB6M5UsrvpffwlNKeRyA50eWWH6Y+GsA3AbgzQD+7WrfL9Za/3krr524bSMjt0yttcCqoO15GaAPViYzB3gdQ2LPG2GFjru1ytEKKXR8LEiMSS0LPUcmBlilBYnxc4ZxW03kH/eXaRpxvVHeqAsls6wDZu3oVGMLGy1Sc4Q2ihF2dvGfcdJrWyZQLl9WHiVW5xR2WKH9IID311pfXEp5KoAXAHh27Ky1vhXAWwGglPI/ALi71vqBUsozALys1vrS0Yy2GnLGMlNnmWfuARbovwOLTcGiYHXHxnp4YKfOMmLtKUXX6Z03VjV2dHSEw8PD0+06+6Wkptfj2iTIKMisp6r4WlWZZapMl1yX2r49JZ85wczpXRQjqtCyc/V+RF72HLA6n5aCbyndqbZBQnskgDtW628B8EJ3UCnlKwD8bVx7MezDlpvLk7FUac+ptX6qldHW7kOLpQNri6xGPTDQDzeBa9411jXkZG8KXLujm0MDVmWscNwgcYvMXBlaKo3DTCYzJjoltZEwT9VQKLWMiOJ6+RxXXr7fKnMYXAZdtpSa296bQBolNEdgipHsvCAtxoTDhisDr2dk5nCyTSulfD+Wrxxj+wiA+1br8Z5EZz8K4OW11vtX/+8B8Eu11veVUn4SwIsA/Pet/Lem0FohRY/cnBceVUQKEiYzBWur06lnjbIoubkOpOXgNEcAqwrNkZmSWqSlt2/0FJpTWNxp4tijo6PTdQ0xWWFGfqomempP65EVeG9YIghZz1sHIw4nzpwSi/KwU+ShiB4+1BHETyd9NmXrKLRa66sBvJq3lVJ+DdfelWjfk7h6I/Z3AfhJ2nxnrTWOvRPAL/TKvDVCY1OQOrJreWG3f8T7OjJTYwDyrQkOWApWLocqiZY5habhhRv0dxMFfI6SJOAnBaKMfN1cnoODAxweHtrzODzNBv9HlSK3k1NqTp05vHDYuQmMaHlZrbu2jKXDRobvlrn6Y5zsoN2N5bdJ7sHy/Yl3mWO+BsAHa61/Sdt+s5Tyw6uPLX0bgPf1MtrafWgZsFpg7YWZIwotK1MAQb1vKBTg7KzV6FhZD6CqXrIfH+durtXwM5vl7Ck0VZZ8vUHoh4eHODg4OKMu+CZSR2q9WdERjHBbZ8MQbl8rvYtiJBxCYMRhgtOIulEVP2KZcs/U80VtHYWW2C8CeF0p5V0AHgDwPQBQSrkDy3cp3gOgAPi/5bwfBPALpZRDAP8OwH/by2irCq3nWXugHvlFPpqvhjtxvPO+TGou1GTiGyHYlmUTAuqFHZllN9yqSlNi4evjdont7s5/DZWcKlMyy0i7Z5n6duFjNu7q8MPYUOKJtg7LMBL4iHV2FBqqM07inMyJq7l60ihh0+NnmyK0WutnAHy32f48Wn8jgDfK/n8F4JsHiwtgB2Y5Y72lbtzgb+Z9W2DVPJ3HdY3E5zNIW9eQEepIeML/3U8Vl5JWFpq60ERDT1ePJydnnwQIAuNjI5+4veP4+Pj0pttemJmRWtahes6N26rlDLVtta1GMTKCgykOWNPUulKFfb0U2o1oW/9yunbwFkG5/S0SaYE1/o8oBEeyTG46u+nuOXPKgs0BlPcxmfG6PuKkxNaaFHDXziokq7+4Xg6dlGh1QmCUyDJiyZyd+42EmtomUzDinFlsZyWvaj4ru7t+Zw4Trfq9qGUOZaSs27Kth5yxbAE4trmB9RZQe2AdMe3Yrmw8HqLX0wNF5MHLWM9CNDcu5shDx91cCKvmBqdjWzzpoM9oqjprjQfGtek1Zx3RhZUZPkbJS4+P/7zkdnDt1yL8KWXiY0ctU2ezQtuBkLPnudjrunNHz9dzAQ9W7lh8rnrbHom68mn+mbXCiRZpKdnp8UyAnD6XN8jKKWPg/Jtm3f1lLbUwRUEwmWmd9hyaLi9KLi2scBpxHE8acRmy6+HtPQWvy1mhXbOduW2D16cAU8/XdFvHKFmpxXZeunJofi0S7Vmm1lrElg26u+2tkJPrwd1/Fmnt7Z1/46wj1ozgeqoss9G6zhxa5nxaHXe07VoOr+eAtWwjefEy1jdJZmG7SFot2+qznG78rHV8iyxGvG+PtPgYVS4MWC3fKHm19ik4Y9lSOS58dCSWkUvWAU5O/Mc9oh4cyemSy59dm7v2Xp1lGGkpNz6v1X5Z+yhGsnJm+0cx3LOszkbr8nPBtq7QWjK8BbYRIGbp635WJkpssS1TaFqWrGyt2U3OJ9ueqZyR+9fCRgeQ+Vr52jV/Xud8MvU3es0jamlEnbXSGzmG942oeC6Dnr9OGaaoNV3fhLWUa+zfNds6oY1YKzTIwJOpODYG4bred13rgaEVlmX7smN7ZNSyUGdKXjybp3mtc00tW6ejt9KagqdeWZXMesR20fL3lNnnukLry4brYCMeeMq+7JjeOesAq6UCNtnxetaazeqFk259nTxbZdBJiett16vuL0pCo+dviuQ+1+2GUGiXaW4MbddNwz23PsWy8OpGs6ljmzfDNW/S5pBz0NbpaJdFMKNh0+j2yzB3S4uuT7FNADUr02Wajo2ODPpfr7L28LGLDvRGJLSthJyZXZToemNKowPVvXJlpLdu+i3LQNOaZGgB0U24ZHnpf83TlYHPiRuhL8M2VfdZOptIb9PjXLOyPG83RMjpBrPjf2sQ13nnESK6HqbjTb3ZvZbSYiBn7/fSbbzPzeS6cwDYJzP0eVm3zZnbP3Umz/0f2d5qf1VnU9LPJlimTLxMsRFHtcm8ZoU20VpgaM3MjY4V9UClabpfln/ruE2AOSMnt0+XjpCU/DTddX+uzI5cR210QsPV8xRs6DE9wtJ8R47PypGVvXUO26zOvF26QuNGcqplnbAxPKwz9r6tjjI6braOwhshtynqLMhJyYpfTaNvBYlfPG8ZaWmZ+FhNPyM0flUP4L+tqdczSnatELDlTPjae+3bw8iIU8yINCPE3vVl5cy2zwptaTsRcvbA0Pq5cEGB2SKzkfy0jJmXzs6bYk7hKHlk7/dScuE3pJ6cXLv7n99bliks91rz7CWK2bL1ah9eZjYSprfaIP4rRlyaF8FItp9vZG6dm11bZpkK5n2fq7Y1QnMdfpQoMpIB+t6Wic+VpddBenfm9wDbKlOmXnrExcor1oPAOL943Y/Wj751VfNwH+1lwuLXoLe+7+BIbcQyUsjaiLeP4EG3T8HIFMLKzlVsuPv7HEYyh7cpmxXaRJsCCt3GYVXPu7bynlKO3r7WMZpnZgzS7AWFTCShuPQzc0pmYe5V2Fln0S/U61eU+BglPSU2vr5eCMp15R6p6tW3Pns6Wvdq62DEnRdlapVhSvkyhzeHnDtwH1pL8fCYUICz5SGn5q3bekBtPeTdKo9ua3ngWG/9nDrTz8zFf5dPq4NFOTS8dB/01S+q6/Ys5ORrbLVPTxWN/jRdzS8jU7etV4bsBZecjnMqnGZWBld/rTr+XLSthpytjp8BR1+kyIPgakyCrYYeBauCdmS7WutxoQygjsha6izKsVgscHR0dCZ9HTvjMroQRlUakxb/stC0FXZqvlnbtOqbHR7/Yj9w9l3/rs6nYoQd3CiZ9pyhI3JXPyOObpN2oxHk1icFppKGHgPkgO2BlI9rlcV53OydZCMAZxu5QVbVmBIZv7s/9kVei8Xi3Pv/dbDa5anjdazAFosFFovFad5KbtnXyjOCy9rDbe/VqTo+JT+XpoanWd6tMkzFiXvFUhybYSFrq1mhXbPLJLQrAHDffffh4ODgzO+WW2450zG4s7AacKFO793xow3cA6wC1b3PXz/+y5+We+CBB3B4eHjm98ADD5x+JDh+/P/4+Pj0GE7z8PAwJaWoHwDnbs/ofU+ATUkUwLl2iP2sWOKNufw2Dv6+AX/l/fDwEIvF4hweAhP632Hj4ODAhsJu8kKJOq5zUxhx32/gOslwwh+KDly0cKIfltb1lZ3/VNdE+8hHPtKsm4985CMXzWLjdpmE9uUA8PrXv/4Ss/zctiCgTdvJyclp55ptZ+3LAfxfa577SQB/8bSnPe2LBo79i9XxO2GXSWi/A+BbAPwpgONLzHe22T6X7AqWZPY76yZQa/3zUspXAbh94PBP1lr/fN28Nm17vdBjttlmm+1Gsa0/yznbbLPNtimbCW222Wa7aexSxtBKKfsAXgHgoQDuB/CMWuu9l5F3y0opBwBeA+ArAdwK4KcB/BsArwVwAuAPADyr1nq1lPIiAE8EcATgObXWe7ZU5gcDeB+Ax67Ksstl/XEAfwPALVi2/zt3sbwrHLwOSxwcA3gmdrxuZ/N2WQrtKQBuq7U+AsDzAbz0kvLt2d8C8PFa67cAeByA/w3AywC8YLVtD8CTSylfB+DRAL4BwFMB/KNtFHbV8f4xgL9cbdrlsj4GwDcB+OZVeR6yw+V9AoBFrfWbAPwUgJ/Z4bLO1rDLIrRHAngrANRa3wvg4ZeUb8/eCOCFq/U9LL3uw7BUEgDwFgDfjmX531ZrPam1/jGARSnlSy+7sABeAuCVAP5k9X+Xy/qdAN4P4E4AvwHgzTtc3g+t8t3HcmbvcIfLOlvDLovQbgdwH/0/LqVs/SmFWuuna62fKqU8CMCbALwAwF6tNaZ+PwXgC3G+/LH90qyU8n0APlpr/U3avJNlXdmXYOm4vhvADwD43wHs72h5P41luPlBAK8C8PPY7bqdLbHLIrRPAngQ51trPcoOvkwrpTwEwNsB/NNa6+sB8LMnDwLwCZwvf2y/TPs7AB5bSnkHgK8F8MsAHmzKtAtlBYCPA/jNWusDtdYK4LM42/l3qbzPxbKsX43lOO/rsBz30zLtQllna9hlEdrdWI5ToJTyjViGIlu3UsqXAXgbgP+x1vqa1ebfW43/AMDjAdyFZfm/s5SyX0r5j7Ak5I9dZllrrY+qtT661voYAL8P4L8B8JZdLOvK3gXgcaWUvVLKXwXw+QB+a0fL+xe4prz+HMABdhQHs7XtssK+O7FUF+/Gcqzq6ZeUb89+AsAXAXhhKSXG0p4N4OdLKbcA+ACAN9Vaj0spdwF4D5ZO4FlbKe15+zEAr9rFstZa31xKeRSAe6gcf7ij5X05gNesynELlrj43R0t62wNm58UmG222W4am2+snW222W4amwltttlmu2lsJrTZZpvtprGZ0GabbbabxmZCm2222W4amwltttlmu2lsJrTZZpvtprH/H6dlKQh6d3wjAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c21c630>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -110,36 +96,42 @@
    "source": [
     "## Customizing Colorbars\n",
     "\n",
-    "The colormap can be specified using the ``cmap`` argument to the plotting function that is creating the visualization:"
+    "The colormap can be specified using the `cmap` argument to the plotting function that is creating the visualization (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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7LIfYKEEsSeNm3c9prAX+pe17M3BxM/K9qmGQ2rcEubjKmluBVzmx5WztV62d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\nd/ZDSNni/s+n+hOxDBAjHwOOkA6bI5Vex9ShSrU/ei67vvd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9K2/g9u4UksEwlF34HQAZyAGgZMDPQH4L7LMS2yoCcwZ2Dq58A3m1XENtOYe9cnPhU4zP86TSDAQbCSHUyoEM0IFcxsASEbCj7S3Es1F1zIxSyyHQY11ineJIygHsnetuZNDri8fu2ajdU0xHuMkhh3er8ixgz5Y6JCuYk+tuxyZAACD1XYk21BB/2b6wxWEj0GfC7AVpAoz7KMytc82iIN1do+TC23vlEWBjPOYu2l4ZW5EM9s4sL2mhgnSAyI7owgvgzWVvrrwOgVuBHeDB6PRtFF13r2MeURfBvLd43Zgdidn3GrPzfAh0CygiEVCNcXrB6YSgG/H11KrPvGHfPXFrTE6VlmSw0pGkJyXrSs66z/XDQH86jS781iXqiEZjHAnB5HCt0BDAumPnvRpWPjjYzXoVGBhY3PgqD84F6rpnSy0CLCq8glplNhSLv57qnsUyY/Yxds9xYt4TTqfs0hvYHWQuzL54gspHQe0VOBUBfinArsLsAQ4ATBVcxGvgNlouM7uBuwf8UYlg74+b665uuyXmbH/tQR5c+tTP3g0EKQp0M46tp8b0hJ3RLcSz46ZZBNUNVqCr58dlSQhHOnK3K0+YeHyl8/pymNc8P2TyS/oR9IQDKZgrTxXYyOWxKs8Ws4vy6cNB3TImfdHDBXg6udU24QngRZhVAW7xW621DaRYCVISVdTVZ+6mRaH0LpgJ73Qq6Xy02G61Ee4xtokL0bumrvaCFqNZ7SvQAM8gcCVsVMCQV1drCBcKSXdyaffWPUbm6OsCZFY3sBuTW10NwMbqErMb6GsDvkxbJds1KG189r4UEGoAOZE8o4F9dN1FJ6RbTnTDNw2Rah5sMwvz7LjXkRW79+Dt8zfO7J0naLIiqPeXdSMeR0KwcQslvJZ/lRFVL6/rDYhWTIC3FQd5FmZVMDGuV3PPShOiCVCOo6EIQz3vEKTtvV5xhBMGMMdY3eL0ZsEHVx6DIGN9nOWAvbrLT8rS2okEoOBUBPBsMT1Jt1JlBXeRt9osw+t5AkgW3561k4XUJdRL68ZwF1IAvwa77EXxItM3Zq8OrNiV2BcxUq4bBvItMLrlaVn7Gonm+tHriemEHZsM3lVHHOxjLie69Hb+1OlIux5M/rMQy1k99mbsVXSESNp8VZ4B7LGx2AW4+7G77XIsQjc3vhcimjB7gQJIx8saNUGOwnRBjiCfdQOZhY6s3n4TBLli1jgyzV5gsXYzwBeShFMNTN72yoC1hRCuRPbZnvWoGLDt2NxtVzZuwDeQJ9A3Ro+sL6zuGXlX3pVcGpsTg4lQCnBCkYtsUT+4ue8j0NEB/v3qSEzIRnn3OtIY3T6XqCf5uiu9kN4akvCOhN0B1ZP9BbrxgDEWaUwpQJd3kNsvcNVoxay1vOXELV4fhYiJIF2gq5Jd7LHhoyCtR2DG9Ab0k04MScju2UqQVj/tKtckHYDCTZhaQwBVchvmDUFdfAX7rmx+NcMSQG4eg1xpjfYIcmh9eMLsuzJ/6loLoG+TTlaffTZOVBnj9fiYUmdWLRBXvqjVMzFGwHvbFsTQzoDfs/mH0JHYExN15NQB3Vg9Ah7ImXlmbhUwQ0vaY0OENngKRYzdqjxjP7uA3ABhJVe1oBKjlmypSwFKqU1gBvxbFnsmzChE+xyPU5/5hOXtNxuNQmzHxVxpc61zHQzozEAlAYbV6Spoar+tsDic1XW3cQrGgNTcdQIGwAMhhl8A3sBut41Ar+wufgZ7jOd99FwCujF97Z55IhhLyrG2r7VTUaUGBPBUWdpM2d3IgNl1JId5eQ/cBvtKR0ag9259JgICOkYv2QPsDHKvI7ZZ37qNW0AhQDFwfUnMbspuo7pkGiVgawJU/tKHNpclgt7iNjveNp4KsdbbFjvWC3ChybkR5PkzGsgtcWRAL0EBbOtB3hez2DZTkvWZMuU4bdf2iqC3RCeRzE5jYAc0X4CgrHfIyZqsAQHmunssb11AMtADGpNbt5oz+ZX9OzMK/eSIlrvIMlEAdToCHbZuOiLhDol+BAa3pJzrxfvVkQz4BcsHkPc6EslgSzpG6ollSVUWL44UE4hvNRRC3RmPL43ZrTRGrzLJdRzpSBp72hDPNnqInE1mlnrN6vNGmCdhblhxY1A7LjEJl+MwE2Ts3ouCrOqhkVrrvfnyAHSo7KmIxldlRCIHfbEwiDwfsCODnfQ7ueL82WNJ7YbA8AHsFQ5QZk/gXQ3w1buCHPjRxfff9KIhkibwsQOUutGJIG/8seV9LPkn7cd36EjP6u9bR1zmaMwdQzv7HAlhxeqAdqWqjjADV33hyQDPLPi5Hsxe8TxuPIw9OQmzmuUkBXaFuqoivL3aUNgsSLlSHo11y30f6jRx1awuxooG7sbugUFj3GUGIArdv5vf31jPWN2EKYZOvyNndWEzDvXwRJaAhcIz5Gcr8yqkEnUmD6pBeJElJOvM66jZEERAW5xuoHeW7+TAIfQgoEDc+Q0kC7Iok+9VX/VVHanM2pMz6oipwExH7tGPVje8vY7EbtgG+O6z/02QBWvPi7YxquS4TEeqAr2qsV2VZxhUowpHkHnSLCNVCMTChBXcBBpfCilECdxxyCYQE0prIUYj0DNb/JjeuAp1juBunzsBR7BFARur99abWbPnrK5ZSLgY+A3sRDrQpt1XAQ8/RjvOz9KM6+KZZ+1VF23qLrx+RpRH36tgyTj/zgAfr9cXI4JaXZlNR/bO44uvBUdwP1VHena/pSPRAESjeq+OmFcY8zmzXIol6WrQEQN8AcD6yu/1pbnxYr3VGivgiV2pI+g35H5zYLTO0c200j/yrRF06XN3vmfGKDg7HwE+CL47F4uBWOporCngve7+iqoB2+7h19fzrW48qTMPbH6UP+iBt2L5Vuee+aOsOpAP3gBG2RQS2ZuOoIgO9Doiow3t3X7XkQjksb5zHbm366197s6/rY70+kGg4W+taAdN0xEjBSbpsQDb4Jp5eaax8Z0wiZpCCJMZg1OwypQUCEACvJVeZrNM71G9YundtniuTa5Ac4EPHgHmSiB1hM4TJ78Tpvc6sQI4g9xZumjcNvdMeiDd3RwD6GNTDkzfZ/A70EUPwf9mnpiz+q90RB7pF4eO9N+XoBczHekBLvWz64kHaCP/qurSHoYXP74kNz66ljaHObOPBrNiU+YWc1uQhRSFNyrl/cKb13Fs8QiSmWDj+b5rq3enB4sdqhtBD8iz7OlvnMVLODcCvXdHh0d6UrkFkJk7PnOVa/t9Ng7x78Q9fzcd6XXgJehIPPdUHQGcFAA0HdlZvDbTkZfpxkOSV0FOAKgpSAnngHUShQeH/dMpqz5pYC6Y2WyyR4AbDDK7UMuA3fkba+3eH6hNVg7jCkhDeHBQzVcdySUaxfbcEx15UQk6wpoZAXeP37U8xV2N5UgBP43SA2NmmIe1d+Uv3+JeT/4TAO/uFTztXrcB8FnSkZnhPNKRF9f1BuSGHjPEwd2Z/O29VvJ9lVlMN7i1w/fzMONIUWYx8exePVPNrvmubupRueXCtt/hwKgfGPzZNZ+iI+vr/dLXkRfnxrfEUkxQ6Hd97BKzzvF8PBd/1z6/g/XvXxNMS+4cJalCEgrkySKJQdcAnXUHDUNWu3vNFOUwCfUOrmzflrM8BTDLSLN/Du6nxZqzRFa81mp8wL068mnpB3CfjszAONORCutHf3cd2V9SNr4XoryalwXXZ6wL5ey3nKOpMFfZ0nvKymrGDHP8zrp+7DdRoLGLaQPAIB0ENs8+twEgyF2JffdR6oLEeoDIyjN4CuOv+ph7gzsMMumy0Nb/X9vvLcaU31Vm9IOAeiIwnchdjmsd6QGfvYTbXsWqPE1HfNls+w03nYgr+agc6d105MUNqrESgX4quW/RBNcLrUwE3Fv9ng3iuVWZZ2rJrXA4LwIj/TsZl24CNMESW7+wvbAA7Q+GvJ02EX4UYi9ADtfm+JnH8QZx0JE/y9Oz0av264fh2vdx4MiOMDFJGxsQR/vBx7rzvLvJyhZAfqQj2VD0YxFo0I+jZ5yVe3QkG+UjHXHZJx3hrCN9N+Y9OvKimB3wxo9zs9lxHHnWC85HiiEpDjBafeDY/bSSLHWDiFtdIA4GIf0be7fYR235O9rUfn8NAkWlBvhZwi0K0dZUa2+WdQCP33Ooj9XNfmfPtxpIMoszB8+otd2cNWfDhh38OupRZvfqXmihNtce1EPaOuNRkIEedSQOP/40dWQVDjmYvS2ZKRncqCOmH1FHds7EYDoS3m1JyTjTkfZatx5HEnhRr7hSaOxeiDZNz1bQ9kSEUye4ZOmRBQ2Y2xdctTvqNTBh+NwsMxz0Bui9jU2m9mbX1dYaqJCRTZWgJltjVUpalNi5ZiHaG1w2y8teM8BtKudmEDAy/srVv0dOgLN2PL41HtxfClLGCqBHdcBLV5KvGW/FXXc0YJ8oE4GTxVxHttK79pnlmwG73RzSbrYPOuLAjjriTG2v824KXGP0a0V78cl0RAZP2aCqUUecwV32/kqx68mqPE+CDq4cEehbkZf7e5BH8Cfmx4RR9B69IFeZ2BTb2r6BJn/eQ0OzztFtIJcBD2hxqb2CSIwGdJldJtwo1SPE+Z0Qa3WQJ9Ab00eXrs7f/psB/dZbXhHgtnf3PO/HVzkFxMwOeuj3u77AIVORIVmWPrYmBe+JnM17HYnnjQDs+1s6ErXibXUkvw0YvD2WSUOrgnrTvRn89pqqjYPVIbC9jtisQCa/qCNt3v7qk4WsyiHYz+fzA4DvAvBrAXwE4L8H8A8BfLdW50cBfPFyudTz+fytAL4BMkXaN10ulx9ZXTe6XzOg+2SNLkA754YiMAgy0wDqNpqy3mG7Y2bTBNdYUgV4UtfTvt+V2f0tNAW5j3oQkLMwvDEOM4d31EWQBkhRmBHobS43PReBP5ueK7n+EwNwq/RMDmB4j9umYyLSVVRJ2GojAlV7hVNZPISSpVADCqnyUro3DTrSQL/Qkfj2WNSR+G5/8kasLk/UkUYAg44EUjD5NIMt9zHVKDrppl+JmhcYdaQP96KO7AHgph/ixr99zP57APz05XL5T8/n878E4O/r9i2Xy+UHzufzdwD4xvP5/E8AfAHAVwP4KgB/CcBvnF0wjwv2+CsK8WFzQfYgb1P5ILwz3I5FaFGAyR2dCDRaTPssgO8SItAsKtn0S4Sin3dGiBMhb+yFOwA60QYJu09j9uDi1ZqBfp2Afj7hprpy+wj4e935W+47UZyhh4eJFmtlXdlH12DThT5k7oKwWsuG9mKLQT3WJ7J6JIPT5mze60icrbXXkY1cHz4tHTHQF52HwWaTMR0x0BNBp4d1UjDPkEk9H9Un05Xo+bmHOerEu2Tj/yKA72ntIKz96wH8oJ77PgC/A8AFwPdfLhcG8BPn8/l0Pp+/8nK5/OTsorGbzeOvEegPQYBtdg/qwa77TpBEWZBH/arxpQkTYBTmruwrAtUpoJqrRroMsryWK0UBbtdnST7V8EJLdtP0rzjM3DoB+rXWYTJF2wzgI8tHRriP3Wes7iDPoG/v7W9h6WRVTC5ZwYjEfW8GsWjMzpxd+aAnBlwjgwj0hwD2mY5sTR/8jbLoKQD36cjwUs2hjtgEE3MdkbGtemEFPLOGNLuwewR5um+QYzP4Eeh7ffts/OVy+XkAOJ/Pn4eA/lsAfLuCGgB+DsBXAPhyAD8d/tTOD2BPggzxt8V9PdC3Ulr3y6ZCN6ENlpw8pjch9v3zs2KNGadLZkabEPGk8XmccKEwt/3Oorx5KJdeS11PZk1ELeoRwdhWRuUR6D57asW+c5pNtWf5e+Zdm5X+rax+P5uHzxbiPJ0kn2EbUNrqfERCX9XmKoB0y1mbR2e+JW8nDB+BfiqlATsem340XSlZJ1KX7p06MiMFA3nUEQM5KTHYnP9UIRNk1qB3lcH2rj55YrInBM8VIQHe5Hzdq8wANB9bDeCOBN35fP4qAN8L4M9dLpe/cD6f/0T4+vMAfgbAz+pxf35aYgxWdDsZu3dAt3jMtmS5SxZqXg3T4rNbTyjF3EmLvSrH7LsIkOGTLhDXJlDUKkt4d4A3699iMVB7XqlXSKyF+0UXrQf6vsvx9bpm+d6tj7E89PlmScJWErOPbC5uOzUmt3n+Zas4nRzo1igEZy33jvw11ZWeOKt7gs705KT6UYhw6nQjxvHvT0c03ID3eGzwBGkkgAR6nXYtNIfIRUG+kR8fdZ0BWUcYPs+fAJ3bnHqzcitB968A+H4A/+Xlcvnrevrvnc/nr71cLj8A4OsB/A0APw7gT5zP528H8GsAlMvl8lNH1/bkCyHGZA3YKshTJ7yNSgJ579o3FsIYf65Kylzr3lamsUERJkAqZrULrpXFPJSiGds6FebOnAzcuh6WXY8Mz1Ogm2Dt3Arss3Xv3g7sFNa+i5u777IUk7L5BjSgE2MngKoNtmGZT68weAL42E6NFFRnoo6Y/pxUP+S4DETwrjqSxilo0xXzBsMMOT3YqbLE36U2UkCBLANfOA2uMRKIhBCF1DN63NrYC/X4VuUWs/9RAL8CwB87n89/TM/9IQB/5nw+vwHwYwC+53K57Ofz+YcA/DBELl88umh0t0Uo2U0zVyxa6RO5FZ8lZOhAkEf9qRpFd4D3ecyIRbBtrjMTJFcQCDsLwK/w7kETpuVgNqI2OSQwTg3FCYxorJ5iM4vLrjUB3/dZ4Gx7Zl+vvgP6zJVvCk8Z7CDLvvvS2nkBBDGOcSFOA7Yt4GDTZG8lj10AfCSaFQvzGggsVm+uvJOBGIDSwrwS9r2OlKAT9+sIpfYyjw3wJGOvI1euLTeBWhopNMAzoRJ0bUPJzpfWXrFOudutyVHrE5O5tq3KrZj9D0HA3ZcvTH77bQC+7eh68gB+XIJC9V1sp+LCM6CbsMV6e6w/A3tZCDHev8Wv8Tiwu4E9gp5YRzXVAipK38UYvYBr9e5Edc/2xmgYgD7UpQO9x+HZlRdwj8Dn9tvqIO/AvgJ8BLp9FiaX0TCVKmy1Ui6Sdd+2Tetem5ub/FXUFgbsOqIusuCmQLe4PraPueCWz4mJ3EgGMZ+zqb5s4W+ijtj14xDblY5EW9jrSIEl4ZydKW1Fu2GFaa9VXH4m0ow9tXEGV+LuHZEZIWRDEwdYeZK2vlPX26dSfHijDZtFS9SVToC9EE/FLXUxl7IDey/IGdCtuN6HOcYxB3s1Qaoe24xPrOuzbZCRT7Y006bsbi+DAObRjHPCWV2qbqN71sfpNQF/33dw9X0Eu7nyxvRyr4UrP3HhK9XmwnPhBvaiS9LwVjpW1yfVRRZLkaW7SpzXPcT5R/ohVwqeH2Ud6cmgHVNO/t4C+z06MiOE2umI6QdZxY0MIMeVKjYAtciKu2YAZYlun3NwVnwJrrXn99bM/mmW9EJDAHZkRTlfkCx3EGJUitkeuJ2E6dl9BvYaBFmdsHCFvpqo0pEVVaFLMRlgOCjaKAiGD6QxQbbBNUGA0U13gDvQ615lqyO7z9z59tCzsnDhpVtNVkBt9bQFOtqk/3E1VV9eu62qWsbnTHXqqxJ0xLy2jUZ274Ge5/LPoI/6d6+OrAihnwQTVYyhHKMBnqmCVVdYeyMIMgJzY9alnOY6kuqRDE3ueREdWf7580wlHRvVR8KhjXKKVttGSFniJXkAnUD78dl2/bcF+wByaGAVAC9dSQ7ySt6nXsmME4dRXTeE2e8ZCfjRZe+BvmL3xugzdp+UWWKOK6MWceON3X0l1RwKCMjRgL7vsiyTP497MGs9caDbcNve80t6EvamFxHg70oIHI8VaCDKnp/pQwI69OUfC1dcN2qRVVx6b88+xxLbynprcpjngF+VZ12yuYG8CZSSxU4xWRBgWQh0Fb/f56KJ8HpBGsjdWmfAcwWYxHJbXf2+7OHGMI87D/UAXJDunlX0K5N6Jj4DPbJ7AnoHeKk0y34QjLyAy0QASXxOVeJ2YoJl9gt7IFIg7qkDXVbbHddINxc+dgP6s9s74M6+gQxALSOfhlmb1xfIIOpJz+pHhPAUHfHRf6wJPNGLXkdknIV4fbWwhnyxLub9Waw+6kYNupH72zHoxNH4iWedqaaNgbb+T0C32K1SmnvfrHkHfLPUdl7u4f2pWAhTdQsqM5icoiAN5NlaizDzGukUNrHcVzIlswkibwvTBZn7yaegacbgNruDK1B3ze5UB3pUjkZ1RbZC4FrAZUOtzuplK2ulImDfaQD6vrOvyYfgsRwoJ+DxugN0tWWQ955fzO3MCAGmex3Yb+lIDPNQWdZBIPmxLUppbrv1Ml0hROCebE98/uxeDx50pR8h+a5db59aEWstxUAZhWmjnVr/qArsHstdVGqk90EQZipNiJolFcOdXPgG8uDCcxAmU2SbUHewvNaI7MWsigHdjqPlngLeGHySgY+ARw0gr1dn9ANmd6EUoGwA7cC2tSwwAGH5ri2J1N2nilop9PdHAwZ3QdPzd/oRRrm1rjP4Flk9eoC9nqzCPdMLu+6TdESz6hHkDEpE0OvJFojAjvdABBQIwZ75yPtj+GCpyOpHtvNZF3YEgiADOKXbxIdKRssX17QmmoPertNGfsGtd184CJEJAIt1Jsg+xemDtUYDvMXsV8T6qfLpdVyoR/Gh96Hm4a9I1rtl21fufK3AvstmIK97BvqSnRXoRPI70nhlq2A8YMeODZv2Ooj7XnftYquawQ/JwDx8Nw/ZZfZhqHP9iHG799JE7y8youvKHeyOt9MRM14Exg7NpFeJvQ3wUU+or1Orq+pq96wzoKe6sI/g64nhRWXjidxqR0EayI3RozCHhNENdp8JE5hYbaAJEaRgUjeMzV/rAN+zelQyqYf0w4u75q7hCtxAFFgWZAZ57QA0xubxOAF9v7obf4vZAQc6aUdw2XJ9FfAuU2nsUosYoNIbqjnD39ITCga7WFsiACWAJnlWN9g9EoF5Dwb4qXwmOsJKBpuRgo2nMP3p9CSy+66TdVAjACeEo2Le0Ah0Trqxeo5nZ/YkSHKgb9oAkdV7qx3Z3bpkqBNmuy6wQLtZa7HeZG4myBedVEE0cIOCMiIpXfzXFM/qPBHmzBDnmL3LYiubgzGCXJm9AX1/VBd+d1ZPgF+48brckgSmNW+t2R5QtUPZ2bzKsW610lj3qKAhLOh1Yq0v1ABlLvwoh9hd14F+oiPGsJjpiHkhAegbqdzYBsSJGx9j9amegFN4upt+ojduY5usjOMM8C8O7PZg6RzQBGkC8t9T+30W6jwbv3Lj7ZLc9hqDQidUADcrGwFvwpxZbKY2QDSzDtxz8Vhs3SZmtdO5LgEzY/WUbTdWr9cM9Aj4ntntnkQA9szsHau7QAoq5TidKjV2R2BxqRulZ+mfcVYaaBG8utamCia4bqw8wCMdKSqopY5QyFOofG02XPP4GuDJ9deMQwMzrB7c9j0hpOYNn2N79V7geDxvS+CZl38CsiCBTpCtISg1WHTt+1Bg1sUSWd1ubfAjosbuJkSGs3llvWaw2OAYHnB7JiILUbjVvz1nfOYJ4PsJIueCnA9GsQEzMlKO10DvY3e7YS+YyOxTARZgF4PApQijF23HEGbI4BsMz2KtNmf1sXGawda2a75TYGX7rukDMsiTbpSgU52OxLs3HdGzllsooEQKIMyZfbJJF2IMV6NezL0/K7G5MpvHdvehx315VjfelH4lyGTF+0azhkrWfsy6rqw20LE7y+9rE68Cn9T9Yr9+tNqFSBlOf9dbaOQEzFHpLXgvSFbX3Vz45MZHVjdA90Cv+7uDnYr8PRHAoUtO60KV8gi7sPmzuXGLPRCxxFg6nouucQnHQ7tHPemYvbHtREd6EYm3Lua/dN5fjXqhoDdymOkswJ3+RoIz03K7xEFRg34cUPuzL/8UhTQTZCzRRbNrOMizC21CjBa8LwTV9dDQ0Wrbq6ls9WKeCtGVJzJOZ2TInnusSC+fxn4TQQ4AarHjhNVb33o8Z3H4yo2Hg72bOy4DncWwaB88FZrWb8ZGTy0OBvf6ev1YuvIIMlqQwU0dUZD3pGBANzKAkYHpDUnzZf3Izx8NTKG8j2VcEjsbz3va9tkTdICDFbghSOSGQyekZqUXQuxdRDbwQgRqbBMbv4IayAvNE2oJ9OTeSaz/vSVabT+XY9016LtutdTVxv6dgR5AStIxkGbhqDt0YjTV3AB0i/3rri+1ZONjWx+G5Gc8botZTse/88+DboQNgdXbNd9SR1pXLKN5dRw9wEAGhumBFCaEALj3d09pHukE4C+K2TOI19/5ORek/SayaQT8VOCUr9Nf22JHAymM1TVzCvtOLffKPZuVMelCy99mBsxuWv7dHPQCvI61IygDONP3Q+kG/veGYuIZJJBP6t8brtXzzooPrNE2RE5mRbne8roKZb1Ix41scok6YsafVTdYGZ3jvQ8MWE8I8+dd//2qnZIX9ZLA3pcYt1vJVjCc6wzF8LtgsVP/+oGbZpKyGNyaKt3XpMk5uxqZxeKxGEf2z/W2pXfj7Zx/F1i6B3IC6wS0Q8nvog/dDlPAb3MD1D3DvWUW6gCZdVsCt303+f3sfAR20JHV75mdVGJ23X9Pra0b+dxJBlb7PtTz553+WatX3N9TnuI9fJAys2wrQebfuGXugTgwPFFq4GxQKCnE0T1X52aln7l0ptD5zSbbT6Q5+24G3p7VY4yejjm79Yffd8ZEE4bLusbqPJHVrQzu/BNAMfPujnRk1n3X34uC0kQvAQc6uKrrraRtX/okrh/fbssXBfaVa3P4N51lPv5trzS0/M4uuPIwjhTM//x98PqiHMl1ydrI58cY4Ymfx3uMfcJPoJ73UHpjfvS7/PnoegtDfwchzP7uXZeL7su9TfwsYO9Z7anWzcq9f3YPMOM136Y6K1fsQ5TM8HV+/KQLvuXfxUtM4vQPXg4YNZ6bMfD8cm8v3E/z2veWF8HsR5MYHJV7/+wpyhbyTE+ry50Z5k+jJEWhMj9+0gVfhFq8e7lhcFpY0T4/l1X6MOVZpLp6h5nfAmaJ1G7+dp00OoqN42Efwh7e763Mxp3liAiI1oBNxuCJbDIEsOM9cvJrHvd+mmWVIJz9Ln8+uh6mI/5m9+jPzAzJ7C2/dzE097bvs2fj+7J6MWQ4N/yGWzeJfU96XjLpllAfhZyBLCcaUyMf36rHveXWpA0xqTR+OfluBfD2YoturH3lNs3ODMB2rTGtnL8DUmfxbVc17+/5G+A2MI+asvVGqextdlfCXEduSTTqSLw+h4pw2Pr693V9qle7yjPdA/hnB3sNjWYlWlKfszv/XWPYIEQZ7+B9oDbyLQuzuw7mYG4COxDkyv1LyWt7zjvMwip34cMvYy9CZlAGJkAtkKlOe9BOlq/xm3V/P9s6wE/qc5QMvVWWnl8EDfhQP+Q3DvR0kiLQ/TFWWO91JG2pTnLBabfo7Hkw6s3R51jexnB+cLD3vUWr7/wc+3DF4EIzhcalMPkEAgj1vwT4yfXRru3vkyd25yzIYfRaqH8EdW+1jwQ/E9q864fmwCJbbWMGeM6bAZ2AIRl3xOzTaxfviuoYPhqp/jnvVVKbl85Jgbsuygz4YaNAHGRLRet89dBpwiLKu7ok9zuwetu33wU9wVzWTmIZ6Pe68Kt2SuHSQVs+O7MD8rDtrS9wW88qfi8NZGOUQ2OTTyxgQjR2j8KcufBy7ehFZKttwjUBrVyuKGgT5NsIE8hA8XNrkDvYMYKxbEpdgd3LZu6QAJ02t7INieHvV4ze5qnbJvXwzQE/PttTY/nkeXXASroRNpgBB+lot6AbqitHYV68V4rfgaAXnPRk6YW2unLTJ/upmdx73Hprtt6Q3irPBnYf0O/nrBGMIX2FEfutfw/yOcDi66nxjaT2ZhKAPFrd7heuiyi4/FYWT4SZNqwF2WfpZy7qyN6+j1Z7DXKg6KumKCcHMvP4Pro0LoAAdOpu3r/P3m/NGFAA9GrrLv0E5eTA6tnLOiCEgdnjdFI+1VghSq8036UjbCSQdSR5fnCiqFEP7BqTZ2yA57yPxfrm0xh/2PFICLPyrMwe4/XGpHDA2LkyEWINlthZPwizAV1dvObseeFwYEI0F60BH9kqx8kSkxDDb/t73Gu1++SLTa3UAx4EmfW2UJvxlSsDRV35GlgdEOAXFXXdnd0xqfAtsBvjl5OzeqiLrQc3NUzhFsOY905JzX2P1avBmEZCmDlOSU/MAMPmJfC/eYqORH2IgBe96IhhsvX1eh/e31Q/FuVZwJ4EiNk4amN5Z8n2LjGzJ5ZVgFCBmjAj4KPVFvUJ92n3m1vtKLwaDI3/zTxmr6H+7TmT0o5tkuZC61zfdFwWDFoIVAlcNrQlRlND65xx9poqNqB/GaZ341fMXjZg2wAFtwE8sryDvmeeeLtRMY3NR32YE0Lz6FQn2kauK20mWNUNYjSP7x4daey70BHTk9rpCQcxRANVJ4QGve7RcKaZl9QTQjnoTH82ZmfOgrUG3ZmxBeu3td9zc9+jdQREoDt88j8DPOt3hXSV1pnR4yzQHui1chKmCScKtblsTYBZkCvQ94Uwji7MgnQgcWFdXFHeJS9cdOVYhq6XPLI2IIxus8X200ED8KmkS8fkPdC3tsij1al9bu49JsDPL7DYM85KbNcgLm97henShbfEXmVsxZl9r/6OPIObi3yPjiRgTskge3/Rjc/egNQ/egbplskGZ4/vmBDmbQm8gASdWLvoDosAdmZd6J7GZDICu4OwqzB3c9N0b95Ac8+0AcnA76eaoCJAPe5yYaYZXtmF5cag27frHFvtWMb43AVZisxQGmN1m8216Hpr+/qi+qCTl10SbXTMboDfHhrQo9tetpI/t22se1TWOClFrxPjueASq/e0M+M0AXnt9KSBXnnbyMBmI7IFFU1HrE55rIXXI+pH3vfM3rnyiETAyRDkv5mLby7W0J7lhcXs7uoKkGOD7cw4IVs9Wdx+bbFRxBXbO+tdGT4XGHdKFVozCrRqq2ew6756fXpm5/B519fK94kgZyVNjKn7QqP7K+zIzWU3kIMha7BxEQZiWZopGRYicd2N0c2lbxqsv+4XiDCQG+gN6NuGbVPAbyWxep+wc29kZKJbeuLKT04IpkN6flf96Akh6QmUUYqEd0YGpF7BoCOdsFJy1j5PdKRn9pikM/3eg677tRbKMegKNe+PaA70FwX2vhjorfvEGqJUwokymHwdLQwW2wBflPFAKlCWY1o0qPz5LBEXhFl7q82dULkJ0uPJwPjqP6TehEUxF9ddY/Y50IvOf25srsspbdvWgL6F6QarXFASdru57uzJu3vmjTewB0bvgR5ZvX1XeqCPGfpm3Ka+s+lH72lxIwVvf2qE0OtJBLm9nm860ozPU3WEswdoyye3NfomzF67rRksraLleuwZD/WDZoB3/Vh5kM/a9WaVyiBzwIjV5rm1DsJkybRoVlUazeYAI3VdFf9DsXujE6Czx8juJtDm2rff5HhtD89ov4/3jcXcWjvOzB6BrgINcXp24fcGeCLS9eIVtLfWegMc5IAAvDF8cM/Vbd+2DWUrbZ+z8hHw0Y2HzxDc3XbUEQZAwQvUGB1ouuGbs3qtAJU5q5uOMATcRRXDgN7Xozk/BzrSg970OOrJHgy96XaO+93L9GcfRdP0Ax4WbRvp+nrHHtOzgL11uTUXzay1Z9wzawJFOtOztTaWlxE1GeCaVWnhrZ7qSxRmGjQxEWISoMbu+3Qz4fIgyJkQI8h9Nl1nxdm26eKKZRvTr5Vq2xORJPKqTPuMGjLw9zC75gVi+NBc9y0n5bZtU3af1TcwO1xxV7PS2Cy/Fdml9xwJBbDl3A7Mo1uwuoV3pkL36IjphH3uk2096GOOp/f+mguPnNHve3zSXJ/aTpkQshtvXt80Z4NnzsYD8QFZ3XBGYWoWm7iCuGBXujZhRsC36Z2DELk1CpasHutyBPZRiG653UK7exYTMpWBaxC+PPOx1S4k65rtgyAdOMwCrmkxY1HJp3gupEsz+TTPvsC6/53VoR+407vrvTtvx9tWlgYquvZ98hFw4NekG8rqmlRrYV7w+qKeUEcE1ulguvE2OnIPIfThXgT5VfXkWrOuuOFynbhFBpkQkHSi1vcQs5/P518F4P8C8HWQJcy+W5/9RwF88XK51PP5/K0AvkG//6bL5fIjs2sZIOQ4CDM1YIjPqmRNqag7P7HWbRE95iRQaaCngT3Wa2a5k0BZ1ve6cl2yu7lvtXv2WYl1NTdttgnQzcXZumsQaKfE6gZ6c/nZHrg9P2clIczBfsDuAviC6HmMAA9KSq7Ecz3xEIlVD3ZibBSTYjGOB2ji+RXC4MKDn6Yj94J95gVeO3BH3bBQ1rpsW8iHUU9iWxkhpMVSQluvyk2wn8/nBwD/E4Bf0FN/CsC3XC6XHzifz98B4BvP5/M/AfAFAF8N4KsA/CUAv/HoupZNjcISi62xFBOoCsgrA1euQC2DtS6WYa1uA95FkFK3OdijEK1OO0u/7bHVdoXoC0GFhSy8XohmuTd125l9SSWEGB37AatzALs99Kx0YI+DeSLDR3bfFOzb5kCfsjzNn/OIkSq7vkjoRMrmRgD6IMV1JOZyTF+cCJwY3kZHek9w5cpf274u2X2v3HpwVjrSxEK5roV6AiCsnD3gPmb/dgDfAeCb9fOvB/CDevx9AH4HgAuA779cLgzgJ87n8+l8Pn/l5XL5ydVFW+OAsFfgpKtgRsu9AyADOdCEuQVr3QsSmIPdjvs6AK7ztwRpe7PW12ouWm0G66p75rkQeyseS2S8rRCkl8KFKXsEZgeIGPsuDN+WT9aVWbgKq1umvq0cEx667/ZpoOsBvwC7xOilGaQIejuXMvPG8HDl7YsTQeh2Y2huR9x205FrRfP6hNEd8EVzOdHbs7X6rB5P0ZEYsx/pSPPuAtCznoTut45AjBxmJRLCVqT3yVx504+3Zvbz+fx7Afzk5XL5a+fz2cBOCmoA+DkAXwHgywH8dPhTOz+A3R4O8D52ZuBadWVLZnlJC1XGekcBAmCq4KIsp/FLD/aaXERuSaFZiUC3+h1ZbYvRr1yxV2A3YTLjWmty1yr6rpi1IGPMLhs3wYowC04nAN287kS2lyRcVYCXWny9NWV1W5ZpBXSvi2u/AdzAn2P3nDC0fQ/09p1m47cAfCC7qH2Phbe/kEIBsJGwO7H0suxKCG1AYKngKtc/LXSkJwQA71VHrgrsyOR70J+Y8zHj79gY9cSSzkYI1XI7ROCCdwc7gN8PgM/n828H8G8D+J8B/Krw/ecB/AyAn9Xj/vyy2EsMe5W1zM0ikmI8CvOqgOdK2EjGyMkaa/7AhaTBjC3sWIrHpNTOeIlKHxs8CtAGAu1BSI3dJwLcaxxg498DM0GOIDfLvanVdkZvGq2sDgW6MHxRQ2igN3BHVp/F7FKPrPVrV3629exOyaU3F96Brp4MRsBLG7HmOajlRszr2RkgZXW3ezravRaRV2GcqOi66GNSribXPecsjnQkAt1kGQeFOXNnt93IwM7F4716X7yRQpILHOjeoyHjLwoTNri3t016Z6wcgv1yuXyNHZ/P5x8A8AcB/A/n8/lrL5fLDwD4egB/A8CPA/gT5/P52wH8GgDlcrn81Oq6xnBuCYW8W/KmE+YGtBisUkVlBXdx0BPCWt2BzSPgASSh9syWLDaym9XAXuGg5yque81W3AE/9skvyFTrZn3QjKrAYEaw1u66A1Xbq2DfGTK6Ttu2snYNUlscMi62GJojtcFRks4VDdktnyTkTqfx3KbPs1ncXvJ1BzlwPhbgy/ZY5flPRYB9Vf0QMFecCoErgamiEOFKUEKgsKx277rP2+FIR4wEmis+AXvMxu88koFjIfZCODFE3TAyaAQANCIoTVfeEuyL8kcAfOf5fH4D4McAfM/lctnP5/MPAfhhiEZ+8egCvVvrmW2z8C7MawVYlZ9LwaYDIjYQ6u6gJ7hQJekljTWsGxeE6u6Z1gs+VJcDm5vXYfteoCbEyozHkIjZK6d+Yh8m7KAzIRbSt7d0SKzF7fKzgmuL8kt7NmFzbsxelcl9HffYFeh7eWZOz2518ePJSLcA0Dj2ve8W7LPyW5eVL5RHgB3pSYvLSRRL9IPU7AkbSMZdSIEBcM2eH4Gajjgh5NF770tHcg+Bs3rMzvdkkIhhmTVt0m86YqS2qRuPLRLDWO4G++Vy+drw8QuT778NwLfduo6PKDMghGGu1YVpgN+IwPZAtaoAIQ+sCYud4ccU123LruK6TtFFCxn0INg12APTq+Aed83Qq/vpcdu6Dma1XXgMJoIbage8MXqtjFK4gf4Y5Hlwz5GH4fegdNyDPoLdE3FdLN9tJf5dkFGfVYmEYJsMo9bWIIgvbjrCOqqwgXzUkUKEK0I/dSCCd9URGyo9A3uvIy1hV1l1xJ935QEaITB1DG/aoUvGvms2/r2XyjoHmIKBSGJ3FBemAb4SFOhicU2gVyiLE0lMBrfYpQO7XG0tTO4EaaCMwmO4e1VZ3fzKQYgIjA632g14eYvF8w3yLEzcLHT+aUElVoPmm4yf1xF9+5zNnwL2yOj2ee3Ko4F8YHnKgLeY3bveMH3zzUYPCCsCxJq3MXCqgKrmb6OOFG0/GypMmt8pn4KORDKY6Yj1yqx0xMigJfiCERnkwVEGAnRAQl5xc0Q3ru/ZjX8vpbm2JA8uRQEeXHvJNEIGVBRC3T1Lbe7MFWhjrWPmPQoUmHfzNLdWhWlCk+84CZDhrqUNlbXETAR47Fax857Em7eHKb6PHZBy0lapykZ71VbSBF7P5tuWP9calLQD+iwbnxJVHbv7OHffz1je+tLNfY9A34Ih6PvXo3wqy1iLxujaFbeHATNLHVGQX40IVCd6HbFmPtKR2ES9jlj7pTfYJjqyV+02DDpiQLd+ds/t+A2jrjTdUI/IjjfvmBF9eR9u/PsqcaCJDIqQ+GtvT0ZA4TaEmxm4EuNU9HVGELai3gBccC3Di6CgesXV+GsrsYGjEE1wHIRZta5miVvCzgSGDHpL7jXjxpZl9roxe3KS5PElixyNdAVoE/eNiw0tlli0MUPN7noGeB6Vtep2sxJBbp97do/7rbgcYhdb7Bc2oLfn7PZJR0BJR0Q/qD0rF+/F6XXEemc+DR3piQATHTESMENlHqzt4znXm1FHZjIRg4oEcECT2gCuLwnsgLqsaq1lzDtwAjXAV1a3jMTVOZEn74gYJ9YuqpiIM6bRe/TCjOesDJnWcL7GYxNCcLO8bzWD3ISVsvbVex5WJVpu8+urxZMaozHbstJi8HbLuDcAuCfSM/nKfV8OqsGxO9/qhQzwnIDT4y5W39rvaZBJZWfcyO5XsBg88t/V0kheuuSqs53piulICc/yoXWkGX64jlxr+A5ZR3oZGSEg9LNLPo6AEjw+ImwvyY13F1KFBDTAF6AtWGICLATsBBTLUkOsuCUsiNwq22crs0zrUZ2AnIgBXGixzt7tkgV9DWBnltF0Pfgj8KyQMpS4w2gPTwyg2HcAs4Neuh+DxxHYXOo9ybyHe/ZdO32JTGdHA9ADuAf2bDF5cO+RB9U0Vp/EyprCEQNZxT2/gpuOyAhDbvpBUUd2duN/oCPlwH3vyz06Ej/PdCSPklvriP1NLEQ2pMDa0NrGQjzpiXlRbjygDx8QbYBnkjg8gj66eoAPcyzK6GIAnN3tnN5pGKxxVCLzxkETvQWX42yJY5JlOJe+nxdJGrHMtGOTczBANrGHvvllL/zE6/cDPOTJs8J4vY9BnuoUENC3bXSFI7jlc5fMw8QwNANBCYxW1yIZKdhoS1Qb9io6Uisj9ZmTk4PU08EPYKEj+dytstIP4P3qyOx+0uaiIyga+un3MpGLJiq39cM8Y4LOAV9V2NwAHOM4bkkJ+Y6bYsTvgagwmeGfVi8/niVLTEDxt1F49ncmwPQZrgz2nYGhggF2wDe2b0qhiZkA/A0+JXLP6rPneQrQY5m59lkGHdhDl1YEuP3eY2c6lFMFAGPJoCNEMs+eg5zDfQ3Ari8APnUdifoRzxu47TczHcm/zQZDnmGuIwDjtNkIS/H2Tm87gu7TKKbohQLgVaCSTBGB7OBkpd0Ky0Mmd33iuvdlJdiV/veDG7JVt3OjOzc93/5uHbcX8nnu5QmN2QCAUNo1qV0r3rMf8nv0LKvfWVm11crtjQzfn+tBL0+Tv4svpQAheWkgCToS8xgzHVmB+ReDjvQy7YvpCNi6aOM1RUdenBufHsbiLv1IkIyqld5OmaVu3w8SCn/7RMt9lECbxbgjqOJ3o1ew+jzEkBQUXhnefuQ5YFrW+YjBn0ruR+zXJ7P69u5j8Qy+43h5qSOsWfaFjvT60d9LL7is863yIXRkdg/z9AqR6ocvbhF15EUl6KysH+5YG0fhvJ1r+q7lSOhvW6JObkuUjeff0jt/7+Vt3eJVedWRXO7Rj9NLitkZxy4nsM4U31Jq/hSEejSqCpgr+KrPdnb6FjsenT8aVw6MXtH7KKsEo5WVVzFT/Hu9kpesI2+jH8BcR+7RD/nb2fVk//jSVoQ5SiTFUUr+XY5v4jkrKyv6ttnnWGaxqBzPz7eegj42PegK9ARSvl52Ue03NPxdf92nGJxVWcaq3Rc5YUXDb5hlAEzvwlofufx+fu8+tgVGHVmRxz068tSk5T3gu6Ujhfy5ko6wy62/Tco5LHSE6MWNoJN96geG9xPbb2YJqJj9jtey7/t7xHLUr3zLyvbJph601o1jWef4Xr38XoRq9e7fxMtdRtIVGYGdu5P65Na8Trdi4qNYdQ6K+P3Y1mPGmRJQK/t4d4svZRAYt4TtLKPNnMG90pGeEF6yjtTUM2G/P9YRqUMG+ExHXpQbD2QhxsEgcXAI4N0WzB3Yo6E4ykov7s/cTbAYSjzbZ5lnfcnxvHX9eHeTbDtb94kLdCbMCPQSruFjC7ISxPN9ncYxB2u3PgKtL7OEUm09Avob7faxc/2YgtinXMK8g4VF8Q3w6b536EgE9vvUEX/9+P3oiAH9SEeq9aHzHORxhGivH/FeDy+J2QEMQoyLJ/YAjxPvA27FGbkrKxmIGxZ8VmYucARzEmY4Z68byquV7J/hryGasCukyySWKMhtAvKtOMAT6MOxKUGZ1jc/E8LvbpWcIGMANle7nPNRYtTJxxd22KsMkqrEOkAIsiBDVaPH1rU2hgamI+1ln05H2ss+K7LAqCNHIeStco+O9IOO+tGEUUe2JEP4+xB8RAhZR+T9A9eRx5eUja8daA3otfrLAG265mgEEJRLvwfcneNw7d6ix3OzsnLHSie4CLh+mKgPAdX3jEkZoLpQAHkf2b/0ewpYfdIKUwKbYUXGe4cXTBCATqMSRDfyKSyvVQ7tNrK5T3XsLG3zEvgQUR9PYQZgr4xKAnRFufYbZ++i15Ek9w7kvY7YFE/RCNhzPKeO2AxEvY5Yf7mMhSFtM+9Wi/ec6cgp6gS9QDde3gMOI4iqTQ7hTO7v+WaA1zq+7NG7cP1xunf4nBMpo6Xuj/u3u4gc5DJO26eS2vQFhTaDSgDHTB7RwvdCPJUMchOs7Q3cck9z9bISAgH06b7+afXSx5Qt4ca3repT/aUcm5DBQGxvrcl4fwG6jJjU6aG79jAdiWQQdaSd7/TmHh3p4/t31ZF+n16lDToiusNJR2RFIwBVX/5ST4mCoHLOJgO9HReR/4tK0MXSrDayENukEAH4zGEmzmi92/EoSHuX2z6vSnN1iwszC9CPd0J7h3srpLPF2FtdAobehbZJEU2YtpKV9ZWaxW7gngjR53DL7r0BfAsKVigwDVz5gGwElnLpZaTH9k6/s6SDrUJmCbKVVc0zLzpJQ7tyGwInQEfVUXENeO4V9GRgutF0hfMEIW3iiBD65Vl71jpyy5V/io7QQkfkRSZKOmLXtPaU1WTH+0cdiUDfCvz1YciU7KvyPEs2qyD3Toi2vzbLzUnQcUIGF6QahN2P5T5zdo9CXblmc8Ghm6hBp0EqBGJ9h1rnebcJAe19bhZPVZiua4/cpZKTNqdCOG2ymq1P7eQgNxff4v0e9KTXtzHoccRVe+4A+9gH3eJchHZEYFPYnhrots2nAm/TghNkxuBwF1SN/cNoN3tDrQ8hejKIIB+JodeT2sA9m8En6sO9OrJi9Thzz0xHzLtrQ9ftnXTkz4WcEHqTbLkaj9ddL0xHHl6cG5+sLKZAT5Z7r0mANudaz/Lz/TG7R+HZfg14n2tNzvl8a2yz4FopCJMuALRBX2qh5t7GOmR3nNwt64D+UCjE8c7cbfZUBfVqKqb0zAt+N9A3w9y6vrh9ZvYZWiSEkbYuTDqvu4DergjEpYS5GT5ze/dOOB4iuI4wZ6Bf95qIIG4R4HNPMOviSj/u1ZHZ5JvG5ra1xRz082krDfBE4vTYApXW3mMXnuuI6UAE+mnmFmh55iWbAzNHJq+64EL1iRTr8HnN8qs4flWOLXZJYK+V2gSLvgSTbD1vE3FL0JU29a+6r0MdAuDNajdXjQTkBTiVouDOII9gtxlVt+JgT2+i3XjbLDI5NzmFz2xdZ9zY3qaNIgUphZlU5F4qixCv8i7PukMy9ha3R28iJuQaswegR32Y6UivJ2+jI0exep5x1z2/fjUcm2k3k0LFaSso7JNPmH5EQsgJwZizQQL6RvTSXnHNjdxiLoT5uTqhGbPvux/3TH/E8sA9YB/deBEeT5ncQH86GdjHuYKIGKTT/NbKoE0sd2GGvb3WF5u/jQ6AflJ232gEuwE8Tpnsx+F5DyWE1mZioBzwNs1WZeCk8bkB30C+A7LEuy74gYo2Txwqg3XiBQrGbbbMcMyq92QQgW46cb3WBPKe6VdTa7+tjvRuew92WZvPiIH0HkIKLgtuYRerXswIwfQiTgASyWAjCfnW71S8iAQdhnisB7YJMVpsd+1ncZoEydlFuy1IUkTEz3kWVW4zqW6bM/rpFIGuyzFBYtJKNoOusN/s3lGQEoeZxfYtAv2kv3fQF0/cGau32M5i+PCs4fNcLroP/edt+is4wJmhIGdQZeyw1XbFYJ+KLutlWeYA8tK2US51AcieDEwPZBvBvwL7U3Uku+9ZR3qA9667EcFmK53ovP87ycdKDC55ngIjhB7w0TjaRlFPVCdW5RljdrgwARdeJ6TrtSbgzwRpLtywUukdwlwJ0pYoroNrVpr7LsdABDqRC5OqLdHjTM48Z3UXpBuayOAR6CeN105U2nGz8hQy8PYsTwC6ywiN1e2zTZ7RZnplWVDRRgiSdrdJcxRca21TPHPhNtEms08lFZ+9L+ZNxOmWDbSmE/tecb1G4K/ceQF40pGadeOpYC+lyEy2SgB9vD7qhx0DQEE5eXKToGSy9Phsn+Xd68iRz/Ysk1ds0FhMXb84/3q02BHoJsTRete2NLEJr9aagJ6TdH48CBFo65lVqr5aKRF4K2AWoFs8JtezWN2tdluKSadPcss9B3oUJBG0L92stgnXLfdGxY/NhaPSjazL+/acmPdC9G0jn53dI/iJpefMQF9UYVEq9krS36aAR5VUPDOJl9OMWpyByO+bB9SErj3OvTeuJxnoU1LY6wD2lY70bdDrSAQ765SupRQxZgH04vmxrr0W9QNNRwzkMlGoredndfFZiFoCnzK7E8W8TmlksCrPNnmFM0bvYo2s3QuxWW8T4gTsPbM/xWobwKmIQMnAWlhBPwM4dCmm2mL+vTgQfIZQfW5kNy0m5wy0G8VBNDk+N6BHdj8C+y2g65kmE9k7wHuwR9BbMo64gKD+elF5kPUra4jC3JJRRRXfZh9qbZOMM5rn17vimeGzSy/f7Uk30n7i/d2rIz0hCNBrAz0rwJ3V0fTFPD8KRCALeGaQ91WhZhhNV8Jgq0AGB178c8fsQZmApcV2wZpQ9wb0ursAzVqvhOn37VbtnFjsAeyFwboAA087S31V1VIqStnkWXSed+s2sh6ICHQTpAnRB890btsC6LbyylPAnp4fEWrZdT8CO6owkjVDBemS2txW3K1UxZMrusCHvjNwIps1uL+7yyhuvefXJ+Ry7F6TfkSQD+Fe58rH/ZGO2NLVVF1HsOVrRFZvBLD7Cj4WthayYcfr7j8rpiMer3tC17riVuWZ+9mDILtESgb8BOh7nVruXphHcfsqFuvds7IVv87WSyNbaiI3WjYyqo9/81+Pnw38MQ530JcUr/dA70HvijEHe1QNq94K7JU1OA8g9xgdQNVliWoR0BNkQU4Eo1Vk3v+9+h1pAXpmJBC0eoRwbtpbE3Sj3w+x+wHYVzpSiqx5b2Ge5DFER4oSQeHYK1Ow7yGfEwA/w8JszsBovHsdKR27r8qzrAgjexdk7ANdZd1nQF9Z7xm7gyvajQGAwlqdVJLFNkEmF2/jJMD2p0QqSKDWaL0liWdDOPvnHq9jCj9hY8CZXcc/t6RM63Ol9nd5DXR37fr4vd3bZNNk5PsI9sbollVXdkeh1BnBZPO66z3Zu5dk87XWV7rZh3mRBOahnudwBt1o3uEY8t2lI2QDBtaeX2R1q7e4+txW2hU9obHuhXHqCCEbnF5PPCwihG45M0iL8qwz1bTGRrTaOYPaZ9sT0A9cNYkLdhGgocuEGQupv0wFTBI/RfesbGVq8YEI9JLZPDyLs+L491bi65IlbXSw+e/j6qj3uvJkN5wKCINHYuC2PbHk3ozRDfBMMj6bqy1NJWuvVVN8eFKOlM3dGGX9SG82Rn1JuhJd+T2HdQuWbwm6tsaY6shMPw50xNa8t/0WllAtKNj3velJ7/XtO+u6fO41RUIQMWR9cbn7OxFkJEBOCKvyjCPoItO5AHpX3qy2CetIiAZ41B3YFegcBGo3tNJoTlPfVIBtA9cCLpvHbppB37Bhxy7WneS+UEZ3xXPL3QBfWJ/5HsBn9zvF6yUyfLbm0eLf48o3leiVgzWpxtDXcTnNY99cePSMzrqcsOUf+qRh3GwiBwf4oB82ZBcI3pG0ny9PncnBdOCWB9hAPtORXj69jpQTUAjMmy44Ke47LcZQVKqgSth3KCnYqEsPW82AHelHFJP1YhRAVzCGD5F+SV1vsZggYzzmjBiFGZi7c8miMFGrA72xuo7NioCPJQKdSH5DBdiqCBQ5/tqwNZATkSpY3gZBYnTxZq58Y3QVpLlo1Fg8A7wN9jlg9/aevV4fM8BHmahWGbCYqNH7roN+WpwOBbgmF72TQl4MqiTsvrMopa22a/WzwEGMw9ggrQ7sx5kMsueXErYzoO9RP66qbHsG+S0dqTtQNumG2LYG0A0bahj9T0Q6lBAotbQRhNFAZe8vE8Iq1DPDbvdog3uIbg6Dfp633rohs31c1jeIxeA96BPQ9yDEamAP1npmtYEMdFKJlE1+f1KDlH5ODnK12lSpMXp8BrfY+fO0CitBAj4iLiRkeld/xe597G6xs913Jh/50hNjBGH3TfdHrG4KV+wYrohxAofWzx5Af6w32X3vvT8OWx+fD0DfH50IIuBTA9zQEdOpQAobNhlbYXrRyKB2OtKT2kgIt4oZawL0PQlb0PKFMnssx9a7HgqzAX1/DECvc8DH0hIvKkQDOVc5viIBnohQNckShcmDAHtDtn7umWyiIC3TasxO7TcO2CN27wHfrm1/38uB0JSvgBrowe7GQwHfs7r9rSsh4RoMQHumVu850GPb1a4tI1i8zetIBr2O1B24Bv2IevK2OhJ+y9rNaB5f0pNKKLUEvTZXPjxDeMZZca+vlyuh/7cqz7vW21KQ2Q2eCdOFWEegD4CfuGmk71k1q80O8tjgBvgoTBUel1xHi8V4Irj4DviqLAUZ3PLG5PDjHBP3fbETwANAcOMN+AZUc91lkg1uCTVbdNLidom3A6tPto1kUgt5jlER7XdHemKsl3WlNj1pvSYTctgbEQS9MF1JbvwTdWR4kNJWU22DbTqCKlw6Y8VTcPenerYePbUQtr0kN74vM0GmOE2FGbvBokBFgNcR6HZ+abX3hdXmUZh7acK0rjlzzZrlngoyH7dnXsTrVnpBEkjjeFqCKrr1kqH165TixqGFC+1m+b4tEx/iaW6j/TxZVwKz2/2N3WuoU3brPTw5itOt1K7NxnYNwK/ehRaJQV6nDDqSvD9+mo6UzT/HQkV1RPIUzeOb9KP3Oi7P5uGeP+/YNkYEUXTUQiRMJ6iM5S6wn8/nbwbwHwJ4A+DPAfhBAN8NacofBfDFy+VSz+fztwL4BggfftPlcvmR2fXiQ8ZzI0A65oyxmVnlPhkXgX7Tjdf7E4Xj8JsWQKswyyYgL87gZogw1NtjMn/G2zFZD3oTZGTAnNn2zapsibjsITgDzFi91dEGukCnSlKgE5zhQWjMzswN6NHwVGRj1Z4DOXQo7ZlyO+RxCTM96bPZoUtN5dHc96gbpjd3h3pBR+Ln9BuN5yuBScfJBzIotTTPY0YC0Qu8rSHaxl0bNsN/8HcTM5XL+Xz+WgC/BcC/C+ALAL4KwJ8C8C2Xy+W3Qa7/jefz+d/R778awO8G8GfvqDeAbM1nFjAOkGmb9YuakGZAH9z6/nczlz9cd/K5Ca1G4d2y3MdA7/uZLRNv5xwwzsq2RQA1d7793ejqgfr++3nfvgO366uHXKwBeaFd7e+jN6JJOvv+KaV3eTMrZj0ZWX2iBwM5HGxTHZvolHblzYZrI4E7E0L/TEeh3jBOP36HA4HgDrAD+J0A/gGA7wXwlwH8FQC/HsLuAPB9AH47gN8K4PsvlwtfLpefAHA6n89fubpo7R5slsjqGyxubSDEIJQI0iDU4TzfJ8gU948jr+Zbfob8TMeNndh7+C6DzH/bWfb0OxqAfgRUDwmyN2DX9v7/7GH0fenpel3i6Kg3ILeVs7vte/c3elbDSy0xkZaM9nUO1l4/ZnrR54H6JJ8Swqw+M0KIz3lUVv3nsdfjFrPf48b/SgD/OoB/H8C/AeB/B1Aul4vV8OcAfAWALwfw0+Hv7PxPri6chIaJW2NCBCaC7FyvBGCeAHw1qMbmR5IXbz2tTP731r9aT0CpYB0xNRig7tl6Yc76Tq2sukx6QfY/Sy48QqzcAb4HevvbWOdWd25MbO3lXoW8ww4iHwbb6ho2onBFb+7osdx6diCSQajn1IvKYdVSR44M/pGOWD9j3fWj6gVTd/05Oa0AHQ2ZPdut0ualC1WMXtSq3AP2nwbwjy6XyycALufz+UsQV97K5wH8DICf1eP+fCqsCjKez7/pwTIKcmG1h/NBEIALFEADOim4EftQOf99+NuZVe4VcPZc9xYDbwJEJ8iRQakxevoNehB2383QTuRyIknQESiPcTcjQOMGIBsHWo+Um5Ujo7hqz7muTLrVlnqyiNkj0G2f9KqMf8uZEGK9sp7kut9qhyT/tr+/Ye9x4/8mgN91Pp/pfD7/qwB+OYC/rrE8AHw9gB8C8LcA/M7z+VzO5/O/BmH/n7p18f45R4acJLmcgro/7izz1Bhw/j5WZKYIw/ddXYCewObPceN7ABm86fz8dz2Q7ceN0cPFesMwAD/E6Q2o1F1/qMfcbVzG8RiN2NGLG1aaZ1R7D5AHQKVSG5o6g8+jHvTMPvtuVNY7jMV9bP2U383KLVYH7mD2y+XyV87n89cA+BGIcfgigH8M4DvP5/MbAD8G4Hsul8t+Pp9/CMAPh9+915Ia40iQ7fxEOMNxsNo06T+dCDnV4y1Y6CllDZz1ueie9/Hz4N6ne9GhwjX3nNXNv/F87s5/+mUkhOyNhS/v/8ysXp/qCOn1YqiHmc6sQrvbOZ1bZRb+3Mvud3W9XS6X/3py+guT330bgG+7db1V0urouaeNckuQb1MG5t/yvezcQNWyvYt1fh+Flh+ecA1696bMLueHATzQG+LOM0s/DJ7bgpHbd/YwvCCE/rqz+txozw+hN/e48a/ltfzSLLMBMr+Ey7M87eg+5v09fyMnS/+jp1Zkfq5VKFy/jaTyiQzy32ESGz+tOk8pKx7gGz8a3N3ZNe4kmaOf5Ujnw3k7SU+S/PqEQ/fdCvir6y0rkH/T6nNDF94l3Lm3fX9RmbbUIHHolY1gap/j1gu1syzxbaZ0s5K/X9XjrV3l4z9c5SDv+xtOx+374FHOMsFHriS3v7NkWL7n8m8+Bfd0Psa+75nojLN/ef/nXkfacdhmfzsZb2D1im82rp7lqMQM/dHgm1l5drAf9RvHz3HkUOpgTH+8Aixl4cyAHn8zA3r43TB4ZOogdII+agQtR0zra6IHwHWb/djA7OdGUPeAT2Mc2rn+2mO9VvWdntcL5DHvtxW2dUWWqBdz3XgyIQAT+a++C+f7666MC0ZdiH9+z+/uKVE/VuWDvwhz/4MjCVEO0FxlThbWGp3DngFbaK2bBTbdcCbMqTLMrfkwfPE9uPL3DMLJA5AUYIw2y4yMh+E2lTOgWLUEsyaemN0Q9d5AY2YOBgJ+LL8dN3S/Yw6dJ/p3xwOM4vE85MvnFoSwNObsmw2QuVtHop5Mrq/TV63eX3gKu/djE2adQHwHyK0861tvbmgjYALQFdzyZRYqt8bd0GYP6V8/tF41ZrSRTn27RmHZW01UgHLy65dNv6ehHrOXUdplO2N1NLCkGkK1OOC4CTSy8aw0Ro9AZ7RBMRU6R3sEPCYdCzN25/y5dS/ZffttUtFV3e9xR3tw9IZ1TggTQJqeNGNeAjFst3XErhF1ZbjP3ADNw49+f5sd4pRdVnrvb1aeDez+8kcGeiytgSYAQ1ErWikDnRmg6kKpQEM9IXeNTC1zAPeEFW4BfWW1fX/cLr317l1eXzlksZFcJAKdDei6FzyMI+gGVmarQ2b1ym1UaAgB5kNDmxI2Fz4Ogjlui77NiLx9B0IIICeSWYL3ysCmZNCAzmLIrTSiCGSQ3mfHVA9GgiAlCEr1WunH4IncKP300tH76wlhVZ79fXbg2Gqb4GpbaYXC7K8bQL0gI7N3ggQUDXazIEwTmoE9bjoRJcrm97dt4p71rtotWfpLQbrHaLUNtA3wGNlUlkyGTB3FgE0nhQR0VUQFcOwDT0wfgc5x738b3fa+2Hc+BZnH7PZ9fObVdSLjzXpxYtu3FVkKy1uR5sq32WU6/UiVPdCRFSE0j88+y/dtRSEjquQRZv3o8xDA8bsCHDzAGE4B2r4HFvRZwJ4EGM71VrsXZC21AT1Z7tkLDFbuESRwDPZmvZEEOQpzFGSfbLxVRia3GX3CI0zidQEThcfVmWU6wDPEIFjbJ3XpmV2bLwFe6ygM75+tjpH121RLCM/htzmM22NbFcqAmOpGIAOTSSlF7rGFodIAhnBvpSMrZp/qyEnIoHidbEskNeh48FIAHGmIhXpiROP5qAPr8mzM3oNgZHP9HF7YTta72hpsOtMnMIKdSN9qovD9Nv6md8v6TVm9LflT5sL09bpnhivfsi89yN1NFrDszNjA6Ocu4wAyGJCV3Y3ZI+DNvLK680OGPRzUDvCV4fOotXu7617Dscfz7hFEtz+6pVFssW1K12axLePa6L23ZbMJ2WotTUfijWzW4afqyMDmUUdK0pHeIM2YPZHDHcSQ9MTaM+nH9M8APLMbnyZBDGDJ+9E9a0Llgp0ZOJ1kbpxYTIgU3koCMMTs9tuVILcN2B5GgJcsSBNmKWtBEo5dtAoDFQXmDOzI7qrPNgbpNM+MrUCmfu4Bz7ooQ2B3K6YnkdXtuAZgR2aPrD7b9ma43AAg3ueIiuB1nANE2rtOgFVLBXHQkW2bXfw+HZkxOxGwPUzJoPSgL7m+KxJoz9ud65O3cbppl5O197otX0jMfuy+2yqZEeRxzbUdyMKcCXH5rjIyq0c3zeL0Qti2DWWbC9Nc+BHot5+9j3ujO2Yg21nWRzNDsPW/7ZjdQF8gc70Xy8cr0KspDvNQxxQqtHtwUjBbm48T04/bHt3/4GLWTgx9SQQwIYSeFAZCYGF1aJ0Ll+T2NmWb6UjP7PfoSEcGU++vUCKvXk/KDX0xozojhJ0ZJzg5rMoz9LMjTFUUk1jZ8sVF7W3day7c1tSylTiYWVdq0YtXahNETl9PvIvZTwPQowBnwsyKNxdkFHBfDFRmwZNrHNw0Wdee9I0znjM7uM3DUVoIJJuBvrV75xa2uuhBZPW8RwB2Njx2LQ4KaMd7CAPMcNynN5EQMOhJNZnocl3jApwa536KOlK2grIVOZ7qyajfjdjgxu2eEgnBZHSrPZ+3nx0RCFmQuVECuzOlNbUAYMcuK3JQGGzDW1jeh7F8qyll5GVpH7HanVU2IQZhOtPnjYiwbcF4YQT4TKhuvcXldkFyBy6W3sVCzupC5Q3k0tMo/joru5PakubGL+TSGB1yYErE3X6vHsdnVhdvZE/HvWK6UlbcXgElG9IYPnHSEWZfhy2SAVQGSUd2A/sdOtJc+TLVkej12T6CftQRdFuO12f60cIqprb0d1s6LWyr8oz97PI0znzWQIx9d6Awo1luA3lj9lAsG1upohIE7NYdZ/PV9cI0oJcck/VCS5ZbBWvHIlBqCpctdvBYyHMUs+KZVkvCCZg2EibfmVGqrqTC2leujC7DCKgBvoB03W9nd7a6MOsU0NYGox/f3O3mbYzsbm78LHbfO/DH38WehIp1Rj5OvZQnwRRDWusIILZFOLvwvOqMwYOO2IAs0w/gPh1p+ZkQ1gXdSCHf1rO663Zi9ol+xIyKYTj1fCCEec1/Wpdn63rLQBitd7PWXFEKYdvCOtibXYd8vwNMatV16t5adTIG3uRvZpZ7MuIpdp9EN75350WQo9Jt22jBY317q526VMJmrD6wZhU28yWTkQDPZP23kn1nddtJQZ7q0zFBPyCGkUHuHoaDPmbpjdGvlbFXZ3hj+Qpn8n5YsO2tfpEQCH1OxL2+Ulj1A1P3PY7CZBKXphRdZLGYjthD368js4TczJ2P+tB7gERxuW1/3lmJhGAxu4272Bko2s6r8qxj453tMIClVtkzx3022YPFtimEC9r87ghKxYv11dueQj/6RJhmrZ3lrb5jTBYN2T2CbCACKQNSEKSweVF2p8IgriCWeVtjnG4+uk2oYowh2Xc/Pq6LtZeDvXfhI+jNjd+rgXq+5RduPNaf64nLp8++NyZnpONtI8xmjml/W0ubz927brnpiDz3XEdMPw4BP2H3bUEICfRYGOKgG1lPLIwi9fwyEazKMzF7J8AAiChQZt9vm9GXx1+yMg+58MKE/FSzIIHMWn1d5EMG+xG7bypIs9r9/kiQscyixL0Cu65+WiqjbNQEunPV7iSAJnG6gdxdd/gYkXBsn/uyYvYV2LkpHuPa9lXYXfc7c/NS9gpca07y2X6UTSaEyOa2trkZW8DXT8vZdLR112z5rsbqBvB7dWQB9lXIZ/qxBdDL596F77AwyaYYARghiBtv5AAlBHqZMTuQQWBumoM7ghxAmCuOaMOuAtz3vQmyra+lYJ8JMQozsnovzFLCiLmO3UsDexZkH7ubcvaClOxrttyWZXdgUbPglVmXjtbsu1nvUoFawOoKIsTpcYWWCPaKCHLuGKR36UewAx6/t+43zkC/VlZAh244Y30bL3BTNzIItqKroBK3dt42KMAJzEV7X6s+nw5t1vzPLTJYgf2WjrQ+9EAEYoxG933G8pvpSTBqR8VzJ9TyI8Luriur8ixgb657x+YbgBrADkjM7q6VHJMu+7vvYqlNkMzcwB6zsf3LGbGshBnBngEfWTxb79Mpx+qbAlxismNB9sNNhcVl4FepLMljQ4mCXBpTX21R0Ec2n4F99DDm7RKBbp97sFs9DdgGdPdCPBPvbmb8+zmri0z6PEOftM35HNcNYN8ZAvoyeH5RP9oyYsCTdWTlAcYYfeuIYeX5bYEQmpw6XTFCgDI5M+NadeFMlt6ZQvNeDSsfHOyWnCog7BAm2hTgVRNPZqmZq4Je/gIAiFhZHSAqbc1rY/Jb7pnt+66OqSufXLW5lV4JsRRXzqisM0F6gkrc3K0Q9ipttZEBn9q87dcA+MqQqcupimHpXPjI5uERDw2P6fwR2G0wh+2NuR34jGut+TeB5WM3Y7ynrdfe9MRYnbnpCRc0MtCWg3t9vvf122/rSA5fDnRk4sob2MckbQb+6dS59FFPmm6M+R3zguJ4hisDmxpTqkApEh5dXxqzywOxP5y6k8buHnu5MOXBndUlGcdtY11FtcTJ+Z/gorU9ZRbpk0Iz0FscFt20LTF67m2YFRNiznqjsTuqKnQRoRngNxC4Sv9xId9vzXuK4Ha33WoxYxD55TxR18YBJPAiALpiZ+Baa3Lf94q22Qi6dq+FfhaI295CoAIUzjrCTDidIrObfvCgI9I7A9eROrL6U3RESAGHemI6Eb2+tqmOmJ4cjaKLhOBdnCGXwZBu1Zc0gi4WicVUcHABnppbBhjga2UQ8WCtxUp71088BkypjgVpx0TjvhdkM0pDYi64bCuBThIvaTSZtkETYjVPiGWQjLpuKN6TWIkTyG1/1TCCdLRiG7XYxel9jTjUy48d4KxswsggH5iec9KusX/tB4GMelEIuvQxB2IgFDJ2R3PnjdFPJ+mC2/fs+RXtmfHltDkdu8G5F+y3dWQEfNaPSAZCduFacIIYdaUP9dA8QEUJri8rGx8bKrO7CRLV+onRhGYC3HcdZJOstYG8JiFGwFuZJej8PjPAeww+A/vg1ndbvBaAxPR98SSLChEGdmrC3MhcWQE1EwW3nQUkkO92a98B8Pm5vW3CcXCv2zvpCnB7KcfALcN5M/CvAeCWsNsro8IZPinvTFNUR0oghUpox9is/sboMbxzUJuOREJ4qo5YO92jIyt2t+0UvzvQlawb3BGChjqNEERP9rpOfT7rTDUbkWaSVXgVOLVek4JKrArrrlkpZqUJ+86dpaYkSOA48WIlJ2CQjksQwpE7b9Y6CjCy+pEbb0pvm1nrnVnf8pFSAVf2agzICmxjdm7gJkidSAfAk7KGPN+xfAyEgIObbTAM4qg5MQs5Hg+uO/sAmyt3o+swzx5L/VkZS2L0FrPrb04ooMrq5gubl7J1OoKJjqzA/jQdme1bYjYMlb5HR2ZhXgnyYWaAfNDVXjVBrTNpxb71x5fF7H0Myw1IABrgiTQmAbBbXFKoue/MjG2L1vvYYh8Js3fR4nEUntRhjNMisImggizJYrcuNxpH0AEiRGJhPmKNyaHB2O5uGxc1CoWxkbntkoktRLgiDC8FS9shPFNg9Vk9rC5AZncHurv4O7MfG4j12ON0c+f92Fg99rXPZWKeiXYtYlRYHRDX2FxkszUdKaUP756uIysP0L4T/cBUR0xPoo70OmM6slHutYmvQ3sdxcDv1evThkoH2c3K87z1pm6nsfswaMmmjDOmqgwmUSCJ1VaWei7E2YL3PbPlsKEH/Gi9TYCkf7tNhOrZVn/uvlTW+7AMitgrBODNG6PwWZ+9SOJuU2Mp+QCtH0ls2+JAe75w3HsX9qnXkxi3G4PbuZZI1HPxpRdmNDZnHoEeY3nPMI/xewMPo3mAgCgtQbwc8YLIdaTkvM2Rjsz0Q57b7p/rc4+ODJ6gGd5GAp59jzqytWtYHD9z5UVPiKnNo9reUSjy/ePBC+3POlNNAwEjAV5cFBkeWgkN4CUCfNM4pmVU74/DZnWR/fh5SMaQJ1S2CfAT6I3pyYVsgpxNYtEL090zMXqtg4nFcFyJcSo6nDa4vuJJZLD37uGRFx9bqrF8ADZgsaMm6WygjMXtAeTtN4Hp47x0oyxUJwAfLKQ33OGAbzqiw0Utj1NMB4q/Tz+GdR9GRwzsBnQ3xsEQNKAfD6oRj88IgdvISdMN218PRis9jxsPd9GKuiGkgLfYnMhicM3ggrCV8YUMcWtVcHCX8B73PdWpc9V8hcws2NgfWtpnz6AmA9AJesXuAiSdATYIswCAurzSHhreMDdA78WUhFudi4VGiPWeP+tRacCOLmRM3JkM2Jnek0gOcosz7e/jeXP9B3nAXGVuOoIqbN7riBGDvdRidTmF+n0IHUnnotyBJ+gIDaCv6sVZOxurXyFkJ0lbMYQvKhtvYNGhDyhF4q69ancSJOlQ2tBR0rgLSZD2SNH9G9z3Toi9TGc6H4Ul+5jUogR+E15vBIzNo4CbQJsS59K8dxXmXhlMOjyUXKAGXFMIe8tJwM+prsUSc5SfLXJ3b3x6XemBEdu69f0iyib3BTdjAD/ng2p4uK7Vc2efVacEd0Ym6cg6YoNu7tWRqAdP1ZF+8kvZz3UknQs6Ugp5z0inI4UwdeGZtaeFhcVhxo7ENTZDcH2RbjxEGW0ixNbtBqAGoBvbAV2c1blmci5/tnLLbvdNGy14FJjss0GIyZqVANtnZOsf61wgwkTV0IVIXtlVgRrona05DcIwYPs5xsjmAeQ32qRn2xnLy2c/b+C27yPA0/lkBObMKtV1HTFScHYbdYQVCP395vX/dHQkGlbqzkdP6x4dmZECs/Z8BB2xsI5IBjOtyjMl6ERAYBnpxWRC0HevwzHCWGATJODuJLB2x+70zlLd8ufRikeBxd/MBJ6se8foc0EK4EsnUBtW7CB2wMd7lxAeWenZ/E4PPtQpf+5BBLgsZoYgMnc2BOGZJ3I60pHSruvvdtu10v0x1vUXk45YiYSw298EYxp15MV1vQGqhCQKEHOP8RVME6IMjxzBDYzCk797H/XLn4cM9sCa+e+iK9a70b1AkwsbBAoFfQH0nP2Kk3dQwvnZ9Y9mtL23zLrHsjs8/202CnaOJ3/jx0Tydw3wwKGOuH4Y6Pt6/hLVEf37qCMvzo2PD2KxqD93dHFjxWdxzKdRu+NyCzcrYN36uxb/qksWH87G1WTlmrm+q5t8ug11lOBageoW2OxRNgoAPtSRl6EfwLGsjwzv+9CRF5WgIxxbQCui2O/OSH6tdXkfVv5trzUDyso4X29e+/6bH733DLwfb8DKUy61MlifVR1ZGdKVjjzu8/PAHWA/n88PAP48gF8LMSB/ALIkw3dDtOtHAXzxcrnU8/n8rQC+Qb//psvl8iOr68bGXXULxSTSPa7pLYG9TZkJ5iiDW9N5/2Llsk6vsQpZML9ef6/Z376vMgNdD9Akz8U4/FlWuy8zHRnudVC3l6IjQ94jHL9vHXnXsfH/HoDT5XL5Lefz+esA/HEADwC+5XK5/MD5fP4OAN94Pp//CYAvAPhqAF8F4C8B+I2zC1rXERCSFxjjFv9NPu+fwzU7C/8uxDRLPgFRALHnoEsKxeSU1qnC+kk9cxqFOe1hCEmv1bDOWWZ5Vfd3Af4AogmAYxJqD5/buHzNQZjMmHNCawRq1hED9UxH+nP5d/N6z57r3jKC9/l0pNeBd43Z/28Ap/P5XAB8OYBHAL8JwA/q998H4HcAuAD4/svlwgB+4nw+n87n81deLpefjBfzPuIMchtZ1HddFOoHh8wBv2KKp1jyWePK+R5cUYCUstH2nXUX2qg4ZkimHfPsc+snRugnDoLsQR/X4k7nJ9nyFXvcKqsE1Mr4rroh5bcyEMimtC7ELdNmy0lHmcaRgHaPqCN5KvKn6cjMsNxTRm/Kzuf2HUFOaW78qCN27khHeqAf6ci7MvvPQ1z4fwTgVwL49wF8jYIaAH4OwFdADMFPh7+z8wnssWwTAUbhDecoK0RUhsFTGBR1/YBj95KdNxDT0FXEDcw+H1jsOzahk0hQ+4JdmEA2DjMh2rvfEdxx9KDVfTAGqe7xOe9j+lX30jDmoPsuDh6xSSdIxwbEmW6JkD8v4u4ZEdyrIzOv4EhHbrH8quchdg/PdKQGHZrpiI0sBOY6YkTd60heYNN15F0TdH8YwF+7XC7ffD6fvwrA/wHgTfj+8wB+BsDP6nF/figmgCjENlsH+kn45F1m/xvvm4wME13+mZtpJQp4aalb/6yzsw8L9SGZNuOKg9YEKIpaSYCOIlb8WtleYhvKTIi1hgkaq08e0Y8SG4TOE9Zvz5wfOn6cJ8Ec2PKbcVhoGiCivxEZcgK+9ZebvAEGyGdK3QII430KIemEze3XPhe//y0diZ7H+pn9+B4dSW75oCNxCK/rB7HmG8qcFHodmQE9DkE2Hdnf0Y3/5xDXHQD+GSRe/3vn8/lrL5fLDwD4egB/A8CPA/gT5/P52wH8GgDlcrn8VH8xASOSoIiAE1ECfdsHRdpKZ+XhAs8u36igt0oPCmbuLDWFhhXgRsFeVZjtXXQVKIq82CIvbYgwCZRQ5gYjA91eITWhNqZHx/qsbwFiBLoZB7tPfNajcs+LH/GloAbMoqPe2N/AKypLWZJKxk3YXHsSn/LA7pGNo4604xL0pfvdvToS7/guOhI9QGZ7t58S+G3iDmaPx/cKoGZSQKX27kPvfkUdaTrRfX5XZv/TAL7rfD7/EITR/yiA/xPAd57P5zcAfgzA91wul11/88MQWX3x6KJxFZheiFtxkA/CjeAPyibuoJQoyBTLT+oRm6YGYRqoBgZVS70pcG22VAovrBjgrypMQMa529DGVWnegQK9raFWfVKIxvgB4BH48/3I9Ha/WVkBvLVtBHyhDPbqINwKtRGBW5iAok2rpQYy0mvK50x0xAy+6ciMHKIHMLyIEp+r3TN/HuQSju1VX2vLlY6ITFxHtuIz9MiEE3ZlaqSAqjpC+aY2G5Abd7hRqXm6r3eK2S+Xy88D+E8mX31h8ttvA/BtR9eLA/6jBe6BboI9FZuvKwPcXik1YZpgARFajAVvGe0GhBhLIwozvrcNBbsKlXTKZ3LQF9gLKnpjtd4GCGZuwz+zIJXZkYVoM8CYBY/GwNy3pgjd1Fzxvf/0rDfYfQ50tNd2HeQ0nLP3HMT4+ayw9roqETWQkCpvL6KoIxHoZkROwaAYsKOONN3CSAwO8vevI2aQN+KmLxuh6Yh4fRxiBQU5jzoyC/d6+fc68q5u/KdSDOAG7FMhnLYsxAR8FeDWsboLVVkA+TPgAu2TdinREsZRt0SZJcXgIC+sr6Nq48qMpwZwuRJAsNfYKgNM8lwyr/tciC2eC4wehVjr+Nk3n3Qzzr02Y3m7l983K8fMfY9MHvc2Q0s/4WJcyafNQKTvXxPJ66kIL7YY1GNV/D3woCsToD+UEeSe+LUZexzYKb8TdMbv+3Y6YqveCMDtO0JRmZmONHKrHqqYztQqHuCu9ynksbuxuZGBGf6oE9e9vqwXYYAoiJiAkYfvhWgCPBVKAjSBEvyd4Ab0APajRAwwS7i4ACPzbmzdJW7B28QJVefJq85IelFUdV9NYW3LbprVxUCZkzBVBdms+e4gt88R7JnVeQn2FbvP4nNjcTsuhdrkn6tJFjm46yJ4n5CDyJNbxeIf/5nrCaL80YD+sLnHdyol6URk9g+lIydYjkVe1LF5+GRxD5JJSYKONIA3HeA2dXYhJFeeJ/oRZ+k1oBtJrMqHf589HYf4rrlqDnQTornyM4ES1LJ3AjUhOruvi1hkOU4zqarw4kQLTZB6zR2Arkug713DGZ2RXNoSWL1/5TQCMlrsCHQD+b77fOgOfE5gz3u/vt0L4Zn7covVj+dJF0aX65NIvFiMKm59YbR4lvSYzSMy3QiG0djddMSAfrJ9oaQTvY54HB/eI3+Cjlgz9Tpik2aajmydjsT9biGe6oisMaqyIU8w2qQcQx1MB0Ps3vTECKEy9ncZLvtplCjIEjaJydCAbkI8BaGNgs2CtASPxGd+z1W2dUxYaRYVhFNzz9w1K5WSIInNmlbsTBA6R5sYsu7O6la/Qj7pRHPNOBga9sRbm7wxAF22GdhrcuPHedhkA4/PHgsF7XdGH2PzGdjjmmuyaq4u3gCR0V5tfjzJ0q8Q5rqhci5h64C+HehGIgYC+tj9bXSE4Svsmgvvs+VmHdm5gpiAWkFFkrZbQbpeZfcATUdyWjCXngzaOnqqA6vybG+9mSAjq0fBmZBPQYgnmjF96GMNjFQmQpyJ0oYrRrbziRGADRnctp4WaRbe5kbT1QjA6lpyEyDajKvNhZ/VgwNb8CjQCPTrNTN8ZPqe4blyAvotsEegt7bU5bB2Qjueuu0MCMiV0WHrrekst9oGDSzqOdnAlKgf8VgMZSaDGdA3Kgegz3rRTw7Rd8Xd0hEOOlL08x50pHW91gICA6XgWivC4jVglnkEI+FZKYPnl934RhDmae6uB6vyfO+zh/5Pc2G8K0UTdhMhxmSdMT4dCDIKsQdac2vl2yZMT4bId4XRlsMt6mZT0dFKpapQAZQCRpVlkwu3BRHiACIgzyCT6hEAby54n4zLDN+79LVZdwN5rVUSvrq+WQ/0dYKOGrNXkhVzqRC46Bz+RYyAu+6sgKe00KL2PYKotCmQZS5BH7cA+MCUph/BUzP9sHORDCLQT0GHtkYOrg9RVyKr36sjLif/zKygBtr8gQSZwpq4gkDYuTb9uNYKm/efC+Ok7XBl83g51UuzA35fM5LwHM1eMyGsyrPNVAN4n6oBtbfYg0CLCLBfR63fzwS5YlQgCw9Amwsunidlc4K6sayNWkWhmeS3Mpe7rq4J618NrvyS2bMwZZJNJCFerw5w39dgBCrqXgew9+x+lxtvhrNb5JILA8rwvDFkAc4R4LKKjz23PoMtaFFDclOTdLY2SPbIDKBBP6yfnTAA/VSy95eSdAuwHwE9ygZwNgcyIXivAstCoApe1AIq6uIVDe9Iuh1NVzfSWYJJ1kcwRh8JwQFvnp8lAXuPblWetetNLLWDnALAnd1LAroZAqI4Pe8c7PcIEhjBHgUogANMkGSW3AQIP2btOqnE6s5rYoZ0JBmZR8PLOeBqMDQt4zph9xi77wrwCPR+f48rP3PhK1XE5au5SDtADRM2S8QBzuSlWzaZNfHEqEVd99DefenBeQo5j+j9RTfddKV5iERt5J7pxhHYj/RjpiNNP4KuxL0kbJ0UuGpvQiFhdV2qa7fpwKskcI8Iwdfby8nXvmdmVZ4xZs+Da0gt9dbtLa6PQDchlolA34cgo+AqO8hrE6T6XAZysApzVCrJDNtiiqMgbEBNL8go0N59r7UDegD7vu8D2M2Vv4fdZ8m5WsSN58KopTbQl62o+76FKxizSxtEI1UKtxizf86jQT7UPDvrSkMDdIzR4wAc9wbuI4T3BXbL49gE/2KgoNNci3fgiUddnpvDfP+0bocYWjoR9TqyfoZnmUraj6PVhWdJO3aPgjVwRyH2TLCKzValF2SdCNBAzqAmSBRZjupEBUwVrPuq7G6WeyuEsttssDeEqfuZezbtblOA147de3d+yu7AaH9i+EMSlwtLFXBVVt/ccJRt9FF8Ic7S6ltKSByW7MHM9SQmcgMwO+9vM7edvPcmAt0H59CnAvY60REhAkRHR/SkQJfXZl22Szy83tuzz7GYd95IYDCaDvxVeVY3HnBBxsSKszsldjfh90KcgTxm5qMwo0ytWYaEC2VBEqsrX9xyi+fKOBFpP2m02BrjImda/ZlHlo/xur/dhuCuzVz5PTP4vmZ3A7z4kLoH8hCx1mhFx/IX1OqsLvGos/kqmy+xemb0PpMcDY4bWQbgsvOcjnZbAoNutFGWEzKInt+KEFweCv7ueW7pSIvXJ0TQsu4VjQjGAWUKdAP/hBAi0O2z1aMngCMP6dn62YEsSANltNrRNcsgz0BfufJFUX5kuVW/kiB9BhHfN+GBBdz6mUkU0Ni8ZY9JMtZXFuGZGz8DeuWZIOfxehoi2zN7x/IR8Ki7aF0DfB1p1Uf8AFRk2zYZwsmMwgVgSL8xIJ/bn2jWflfXf+KNbJsmH1lWa5HnnSunjWqLk1fEPn+iXlfcE+j1YuXKmzd5j44wyQETzXUkEIEBngltxJ7rCDVduSJ7tujqUFIdon6MA6aiXqzKsy7ZbF1SZlEbC4MGZo8DO25Z7sgIUZh62EoEugmyd8vW7hmnCRh6A0WqCuamWYIOk3pYiYI0q91b8OYKV2f2GdBtj1oF6LuBfc/MHsHW3J/igK9XoJyAbWv1OwQ5VVD11XbHQT72rJxM3gzzmQFjvO5vP/ZdcZEMZuFeKU4ET9cRlRGNOmJEAE3AFRO+6gkHHbF/G5EuSZ4JwZ595f3pnZqu2LLUBvpVeTawx2IN315rNSEE97ekbQT6YaIOIfEUi57iYLEJDOkd5bZaTQ2jmqK15rBFy31FNlCAdUGZUI/bIwqyB4tnXjUe7z5H8DeQ16uD3Bi+xewdE0Sg296Mw1bBvM3XZiOCjSEutYR6jyP4sjtvfe239cMBTyqDzOpRZ6IXeFei7k4dMRew15He8zPQW32EGPytNgr37wlhBvSkH+ZZxDYMerIqz9PPDhdS71JRG2yTARO3NmyzA3rP6rNYLAq0WUmyTKkLlBma7gSSMDtW741SNEzyPMfJnxoElgfVIIHC4u7aATyCPLrzqDtwfZS9bXcx+56BXrbg9lfg5M1CZO9fC5sTUasLVRkjP47R93Y/0hHfLNRDC/MiYHpWn7G7fbY3JnvARx3pAd/rCPU6UpX6OyKwz7ZZXVvSlghXGKsfg9t1RcdeBH2ZGdSVuj0rs/eCtEwrgTQh4wZh3EaBDla7MQFS5sWalkxocKCbQHeQzAPWWWw3RpnZa7sX5X1gpHKvQKdxGYJgeWD1ujvop0CPgDewR8BHJbd4nUh+U0LX2hUCeCqoOsrIQM4l1I9H971XzHoH8IGQ0zEmbnGwe4D9NvMAmyyU9YHgzgcdsZY40pHm8VlCLiTnop4MhJD0IxOCG7exTWbN1JyzDvgvDuxJt4IgY+Y6J/KiW5wFGgHelsOFMnuw2H0jMCDxWHDjWYW4RcATWv9otNiDcpmAEetgbGGx2LpNennO3d+QXQ+gb0Nja1XXXbc9gJ5ZXPoVs1vDtuGCJVbEf7OLMWjLJxuzV0KpJdRpXn8b7hmfc1Zi4qo/b+288v5aPN6RQVwzPRLBXToCnXQDtsCmuvMEFE3UNSJgToRQ0YM617933yM+okGM7Zh1xAghJ/Ziefbln4a5vBNgqAkkbtFVG0KBIMRmKeXCgyAbwwchUozTe2HqH0RFIjUA9lwt6QI3TEBW3JkC97PFRgCkGDewe3PjA+gF2Nc1q0c3foayBnbdA2Fv1rco4MUtpUJD3Waeyfhc4/2tzeJ4DDOczXjav+782vvzGHnopoXL03Qi1QdBRwjtzTSGu+8gYAeBAsADbv0ZTD+6+kd9WAFV2isf9xn5vn378qxufAkNTOlzFMTBBgzCbEbBLOWB1QbcPQPQhFiCiJuF7qx2b3wqQU1G55XgWIBAdMeyBZ8J0ka/SZ85Eui9a40zsN8Z7Jppqto3Zy5+JXAJTF6kLtYXPybn/HnHufjnWtpASO7+NsYOx6u/LUH+bmyfxuyiDa4jNVCETZMdPcDoUdSOFCjVKxq0zjocFGf1hX4syrN2vQEuqJUgrcxceXfngpsWBIkg0KVCQERQJVgHFPBkwlxY7aXhiQKEM1V8zlulj8XmGW1uwDfQNzd9AHcdwQ+MgCdS7e7AXrYMdPu7Kn4jlzKt20zx7onRvTou51hFy+ms9CMxOmJo2MXJdr0DHSGTB5HmNhzwBTYTrAKXIDPSwHM6lvJxXcgz6UYDc+j5YW4cZ4OVVuVFdL1Fd3cpSPRCEpRFIZEx+QLos64VVmGVAHg7bgA2gR7oafJEQINA7y1RiH5uDvQB9HvH2ikhx5NzXScaA23aHRvrGbOQXNxotCz93PhEIxSVMT/j3c0i7aj7aORz4i7KYK4nKXE7AfpcVjZ8xhd9sF+Z7sQYfeXCJ8A3dtdnCs92q9il+5DoVns+S9fb7Hj2Wc5RatjUcHpyEHDcyAU4bUeiBngCeQuGzw3oTUHyFmP2mIPoBXfkqs1i2ZUA8/cGMku4xWx7cOcjwx+58ZCXZtIKBnXPjN42uV7vYczczP45Z887KzFrDmTX14rLOYJ25nVFA5CBvgrz7GJRR8zz457dzTm0+wXdaHVq+6eTwawd+/NHiH92Zo+xrX3uBQTMDcPwO+oEGQ3L5BoAlM2oxeEgFaZhPgAdnGP1KNhBqP1zYQ10K6vBT70bb+f8O8YA8BmgZ78bSmnP2pidjMVJ/2bL1x4APbqTK0DPzq9Cnd4DPAQoMNcdmn+30jOpJJKOtN/6V+71md6ZBzABfXyG/Hw4/Dwr9xpN4AWA/Z6yiqXid5G9eyD2DG/FrLX1ozYd179rwg1AX9XjqeWeUVJWx/HLxXcz8MYBMbG7bdX11r+uFZk8/q19rtxmnrgVM85Y/Z7Sy+0poEg4D+wev0sDayY60t/LWB3cyX9CCOOzHHt/t0pvVP349t/eShR/KmVpud8aOvb3T/x9ZIojiwIk4xKZu//77E282/McliPhLlkb+fxTUXePcWk/zf3rH6r0Sdyj3+XPR9cL7v/BNYBRB3uDcm99nlLubd9nAXtfnmrdrNz7Z09px5VFfsklx2x1fvykC77l3720koz5+ut7AfmLvTwL2PvXGg9e1Dks9/7ZU5jFEslPvdbbZpjfR0lKSmV+/KQLvrta0A2gfZBy4Oamfn9YpHIsvKd0Gz71b9/l2veWF8HsVvhu+DogY2x7669nSaNbsTFPrh1D2OM6vrsAl2xzmJmiNWD7t9r6v3vS59ksNTk0+tBsedTHf+/fj9cb9W32W2AkiuEz57f83pbogHnoeFReXIJu9vBrNkVWegZs7kP7ipk1cWL5k7WAmnVfWPlZNe6VVf8a52zShn7wiB/3IPPz7bsZgNsLLeybda3Z9/0Lq9EYtOvG7oXuPtpZnN9bWGXT73i2SRkB2H9/8LdwPWHmNrurfXdLR6YcENDfvILwYw7brP7vUo7a9lZbPjvYa2g0K63blhk+QX+XEDYBwoUIPbZsqQ1djMLsi/19O+63KEhl+Px97A5DWNVlfK6nlt4VXgGKTIEbQEsHzrCx/U4z6j07RzA3Y9Fv3W8mde5Z/SkMv5q9JoEQPOjH8Hu4nthn8dbu15EI2KaXyPJPv02JyexhJK9A/8XneooRmxnOW+WDgz27QeN3vetrAk1uFHljw6y1Aq10ljtaa0ZWuiiI9rrlhN2TkBeCnJXZ0MZVWbHdLOs/2+Td8w6ENsotMnuBDn0tSmu3Jq+I2wTo7f5IHkes/+x57mV3m5fOSYF9+D/mhJA2k71Yw+T5tX1k9EXXW7xf0hE9F+s0K31+oJ0fnnfZFAes7u+HHKH/2ZkdsMYKwMP4dpQ0pK3rzW2iv/Z+MXlD2htJFTY1lMc2M5fQlSZb7TaxhP1uIQjmeJ251b63ZGa0c2uQUwJb8XHsDegFvrzs5t3ozPr91lVAmbpsI7uXbcr6Yz18a11XlJ/NnuuocBenrYHkhNBvRgaVZTah6PmR6ki5FeZFHeHJJBJ2HwRd6Darv9TVruP3eEr/h7XIzHU/MpzPBnZnPT9njeDuMOXvzUI3l13nAVNrX4JrBovISI0D2cQUdq9wTT0RBRjfympM3j53G+aCjGpjzzFzUR3UIwii1V6DHChFZoLNjK7HrdHNCJDTXqxcq0hx42DXMPDbcdmAbWv3tvqkFWSGevsz3vNCUKu2MSncmC4JYWB2ao9qXgLA6WUWIwUbF3FLR9yoRyKSk2YIplswCHI51yt7zriPpU3AGXQlDwYaQ6e+PCuzx3i9AQkOmNSw3Va19YsKtIE+CNPGLssv5Wa9IKMrZveKgDcXLc3q2W3RGDjQg4HonveoRODb9FvZTfONCrUFG7gyUGQ22OXssbDGfEuwN9B77G6rxVhd5qDPzxc/R+WNxYAZH6EGY2qEUJl0CejuEYOeUNMnUkORAU/vpCNIOgK4nsxCPjf68bme5gGucjl92/blWcCeBIgMFo99BEi27hpNNijIwWgTTZQgTApWW9w0j+e9LmtmbwLs6rZ6puaRdHcxZWjPPLPcSYDUWess1FKKL7ZIJCvWFELhItfejsBuY9wD2GOJWfhyGtk8snpb0bU46K0+A7N3Soq5yxld9+j9LQmB3YVvemLenH6uGuPRQkfoCTrSmLsjAyeBOTlF/bBlwJtH0PSCGzHMRDcazQUhLMqzMbsJKsVk2gh9g41/54IVCUApQq7XZpiBCpwodLHEi/kuArIGATLLem2Z1TFsDF3LPdYdI7APEzDoXr3sjksAkTF6LTIlVCkyp7vUcwPKQlvqjvaqqr3Q0r7vsvktTp+472UT9i4O7vZZw4uSQJ+fpa/WqlReE4LN3T5z4UHU9ASqBztkurF6pCM91IOOpPkBAZnRNTA7t+Pg9YW/9Rjf99FgtFt2VRjHLswJwYC/Ks+eoGPFqjWKNcLOjA0izMjsJjhfpYXaudrEJcI0IYLt9URgJktr3Gyl836vWaD91lxM/bdzFmS02rEcDRWeCbIUWXyisXkpbTkmYmH3nVkAmS80eVVVAZ9uOsm490DfHlA2YfNtc9Abw9vW19vBr6w0STCZTkx1BU4Iph87MzbN7SQSkCSOJHIhK8duBW1ewV5H2ixDc6wH95sDqJHIoGd206nYtWt/v9tv4TroBDjXhfHc6D0d5UI+JNg3APj//vlP4c1G+ORU8PFW8KUHwkcb4WHbcCLCm43wUAoeSsGpFDyUsCa7Tha4meLA5gfHoEAFAKwhtAIrt9EcBA6fe8DXimCx4WtiqyHYK+OxMj6pFdda8VgrHivjsVZ8cq34eK/4+Mr4ZGd8fK26MR53OX687ni8Mq5XmQ56f9QZYx938CMDewVdd5S9gh4rtlqB6y7nd9nLNNI7NtZ1umsF9n7OeFWv5fvspA5S6FPHJt4ATgA2EBO2WlC2AuKCbZfjsm8oe0G5biinDWUj4LGAtw38QKingvqwYT8VXLcCOumKMw8E3uR8PVH7/pOHgo83wpdOhDcn0Yk3qhOiH4SHsuGhiDExHckLiMR5Ct3biDMa4y10pAYdAcu682bUOeiEAXuvjGtl1Q3GY91xVX153EU/HnfGx1fbVEd2xidNPyoerxX7VaYN36+Met3Bj1XWiL7uwJf+RcJbLB8S7L8aAP7mn/3mD3jLX3qlAPjouSvxhLLr9slzV+SzV341gP8nnviQYP+7AH4bgH8KX1XstbyW1/J+ywYB+t/tv6D3OW73tbyW1/Jyy4t66+21vJbX8umVV7C/ltfyGSkfJGY/n88FwJ8D8OsAfAzgP79cLj/+Ie59VM7n8wOA7wLwayF5r/8ewD8E8N2QxOuPAvji5XKp5/P5WwF8A2S1s2+6XC4/8kx1/lUA/i8AX6d1ecl1/WYA/yGANxD5/+BLrK/qwZ+H6MEO4A/ghbft25QPxez/EYDPXS6X3wzgvwXwJz/QfW+V3wPgpy+Xy28D8LsA/I8A/hSAb9FzBOAbz+fzvwPgCwC+GsDvBvBnn6OyqpT/E4Bf0FMvua5fC+C3APh3tT5f9YLr++8BOF0ul98C4L8D8MdfcF3funwosP9WAH8VAC6Xy98B8Bs+0H1vlb8I4I/pMUGs9a+HMBAAfB+A3w6p//dfLhe+XC4/AeB0Pp+/8kNXFsC3A/gOAP+vfn7Jdf2dAP4BgO8F8JcB/JUXXN//W+9bAHw5gMcXXNe3Lh8K7F8O4F+Ez/v5fH720XuXy+XnL5fLz53P588D+B4A3wKALpeLdVH8HICvwFh/O//Byvl8/r0AfvJyufy1cPpF1lXLr4QY9f8YwB8E8L8CKC+0vj8PceH/EYDvBPBn8LLb9q3KhwL7zwL4fLzv5XK5fqB7H5bz+fxVAP4GgP/lcrn8BeRXiz8P4Gcw1t/Of8jy+wF83fl8/gEA/zaA/xnAr5rU6SXUFQB+GsBfu1wun1wulwuALyED4yXV9w9D6vpvQvJKfx6SZ+jr9BLq+tblQ4H9b0HiIpzP598Ece+evZzP538FwPcD+G8ul8t36em/p/EmAHw9gB+C1P93ns/ncj6f/zWIsfqpD1nXy+XyNZfL5QuXy+VrAfx9AP8ZgO97iXXV8jcB/K7z+Uzn8/lfBfDLAfz1F1rffw5n7H8G4AEvVA/epXwoV/p7Iaz0tyGx8e/7QPe9Vf4ogF8B4I+dz2eL3f8QgD9zPp/fAPgxAN9zuVz28/n8QwB+GGIgv/gstR3LHwHwnS+xrpfL5a+cz+evAfAjoR7/+IXW908D+C6txxuIXvyfL7Sub11eR9C9ltfyGSmvg2pey2v5jJRXsL+W1/IZKa9gfy2v5TNSXsH+Wl7LZ6S8gv21vJbPSHkF+2t5LZ+R8gr21/JaPiPl/wcQJqQMdhQGtgAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d902588>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.imshow(I, cmap='gray');"
+    "plt.imshow(I, cmap='Blues');"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "All the available colormaps are in the ``plt.cm`` namespace; using IPython's tab-completion will give you a full list of built-in possibilities:\n",
+    "The names of available colormaps are in the `plt.cm` namespace; using IPython's tab completion feature will give you a full list of built-in possibilities:\n",
+    "\n",
     "```\n",
     "plt.cm.<TAB>\n",
     "```\n",
@@ -153,27 +145,30 @@
    "source": [
     "### Choosing the Colormap\n",
     "\n",
-    "A full treatment of color choice within visualization is beyond the scope of this book, but for entertaining reading on this subject and others, see the article [\"Ten Simple Rules for Better Figures\"](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833).\n",
-    "Matplotlib's online documentation also has an [interesting discussion](http://Matplotlib.org/1.4.1/users/colormaps.html) of colormap choice.\n",
+    "A full treatment of color choice within visualizations is beyond the scope of this book, but for entertaining reading on this subject and others, see the article [\"Ten Simple Rules for Better Figures\"](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833) by Nicholas Rougier, Michael Droettboom, and Philip Bourne.\n",
+    "Matplotlib's online documentation also has an [interesting discussion](https://matplotlib.org/stable/tutorials/colors/colormaps.html) of colormap choice.\n",
     "\n",
     "Broadly, you should be aware of three different categories of colormaps:\n",
     "\n",
-    "- *Sequential colormaps*: These are made up of one continuous sequence of colors (e.g., ``binary`` or ``viridis``).\n",
-    "- *Divergent colormaps*: These usually contain two distinct colors, which show positive and negative deviations from a mean (e.g., ``RdBu`` or ``PuOr``).\n",
-    "- *Qualitative colormaps*: these mix colors with no particular sequence (e.g., ``rainbow`` or ``jet``).\n",
+    "- *Sequential colormaps*: These are made up of one continuous sequence of colors (e.g., `binary` or `viridis`).\n",
+    "- *Divergent colormaps*: These usually contain two distinct colors, which show positive and negative deviations from a mean (e.g., `RdBu` or `PuOr`).\n",
+    "- *Qualitative colormaps*: These mix colors with no particular sequence (e.g., `rainbow` or `jet`).\n",
     "\n",
-    "The ``jet`` colormap, which was the default in Matplotlib prior to version 2.0, is an example of a qualitative colormap.\n",
+    "The `jet` colormap, which was the default in Matplotlib prior to version 2.0, is an example of a qualitative colormap.\n",
     "Its status as the default was quite unfortunate, because qualitative maps are often a poor choice for representing quantitative data.\n",
     "Among the problems is the fact that qualitative maps usually do not display any uniform progression in brightness as the scale increases.\n",
     "\n",
-    "We can see this by converting the ``jet`` colorbar into black and white:"
+    "We can see this by converting the `jet` colorbar into black and white (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -184,13 +179,14 @@
     "    cmap = plt.cm.get_cmap(cmap)\n",
     "    colors = cmap(np.arange(cmap.N))\n",
     "    \n",
-    "    # convert RGBA to perceived grayscale luminance\n",
+    "    # Convert RGBA to perceived grayscale luminance\n",
     "    # cf. http://alienryderflex.com/hsp.html\n",
     "    RGB_weight = [0.299, 0.587, 0.114]\n",
     "    luminance = np.sqrt(np.dot(colors[:, :3] ** 2, RGB_weight))\n",
     "    colors[:, :3] = luminance[:, np.newaxis]\n",
     "        \n",
-    "    return LinearSegmentedColormap.from_list(cmap.name + \"_gray\", colors, cmap.N)\n",
+    "    return LinearSegmentedColormap.from_list(\n",
+    "        cmap.name + \"_gray\", colors, cmap.N)\n",
     "    \n",
     "\n",
     "def view_colormap(cmap):\n",
@@ -211,14 +207,17 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11656ceb8>"
+       "<Figure size 432x144 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -235,22 +234,24 @@
    "source": [
     "Notice the bright stripes in the grayscale image.\n",
     "Even in full color, this uneven brightness means that the eye will be drawn to certain portions of the color range, which will potentially emphasize unimportant parts of the dataset.\n",
-    "It's better to use a colormap such as ``viridis`` (the default as of Matplotlib 2.0), which is specifically constructed to have an even brightness variation across the range.\n",
-    "Thus it not only plays well with our color perception, but also will translate well to grayscale printing:"
+    "It's better to use a colormap such as `viridis` (the default as of Matplotlib 2.0), which is specifically constructed to have an even brightness variation across the range; thus, it not only plays well with our color perception, but also will translate well to grayscale printing (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1165767f0>"
+       "<Figure size 432x144 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -265,50 +266,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you favor rainbow schemes, another good option for continuous data is the ``cubehelix`` colormap:"
+    "For other situations, such as showing positive and negative deviations from some mean, dual-color colorbars such as `RdBu` (*Red–Blue*) are helpful. However, as you can see in the following figure, it's important to note that the positive/negative information will be lost upon translation to grayscale!"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noQtQSOgCFPoDAVPczXaO6jAAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1165caf60>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
-   "source": [
-    "view_colormap('cubehelix')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For other situations, such as showing positive and negative deviations from some mean, dual-color colorbars such as ``RdBu`` (*Red-Blue*) can be useful. However, as you can see in the following figure, it's important to note that the positive-negative information will be lost upon translation to grayscale!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c20e908>"
+       "<Figure size 432x144 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -323,38 +298,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll see examples of using some of these color maps as we continue.\n",
+    "We'll see examples of using some of these colormaps as we continue.\n",
     "\n",
-    "There are a large number of colormaps available in Matplotlib; to see a list of them, you can use IPython to explore the ``plt.cm`` submodule. For a more principled approach to colors in Python, you can refer to the tools and documentation within the Seaborn library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb))."
+    "There are a large number of colormaps available in Matplotlib; to see a list of them, you can use IPython to explore the `plt.cm` submodule. For a more principled approach to colors in Python, you can refer to the tools and documentation within the Seaborn library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb))."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Color limits and extensions\n",
+    "### Color Limits and Extensions\n",
     "\n",
     "Matplotlib allows for a large range of colorbar customization.\n",
-    "The colorbar itself is simply an instance of ``plt.Axes``, so all of the axes and tick formatting tricks we've learned are applicable.\n",
-    "The colorbar has some interesting flexibility: for example, we can narrow the color limits and indicate the out-of-bounds values with a triangular arrow at the top and bottom by setting the ``extend`` property.\n",
-    "This might come in handy, for example, if displaying an image that is subject to noise:"
+    "The colorbar itself is simply an instance of `plt.Axes`, so all of the axes and tick formatting tricks we've seen so far are applicable.\n",
+    "The colorbar has some interesting flexibility: for example, we can narrow the color limits and indicate the out-of-bounds values with a triangular arrow at the top and bottom by setting the `extend` property.\n",
+    "This might come in handy, for example, if displaying an image that is subject to noise (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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OiYpYGd9flNO4iMqlJxNVxj7JREN2dOnCC6yULAFUAtKJAwIypDjwrEsj3Uu4\ndkEh+ajrZyUB+x+7B0u5QaIIBbupaKxiMMuSTSIC5lKJY4s5lHCDDjJpkRsLKRu69se061qXDFbI\nPE8eECXK5YNysW4eLJ+9u9R1R8mShXKLn3dQONAsyvNEuo6SKrtcUdLHkTmX7Z03H8C5+7ePf1Ha\nhAijctnNmKhTJM2YqaaBjV0xwciG0X2TSNOVU9vXqCMY1WGZzsefaZA2CTFOPeMZMBuxFaaocpiQ\nb+nAyDwfhE/1PFj1c1Rla4f2ToI766MN7gF3zBgwAIiiF6AWP0DV6MqVvga8vADqutQFQ0FzOA0G\ndb0SmUTXIXZjdcHksa6rdQaBVgGggOlB1BDZEUaxMPMDQxLdNRPZ3QcHrL5pJYwGDq4e6d1iiRQo\nLCOVBGsJluuZsMNw815u3dD4wiLzS6EYuXVGtej1IZSLO4ldPIBzaQEomSjlp/aIR2I591LlwptL\nvaFVEh0lsPzYx6NbxvSQH6lXTQETM1KNC69ABwlAVi6yGw70ccVZDkQJZmgAhw+dxOaNbhCHi6cR\nELqAzQuYrIBMjGetLJLcgln52KjGtF6eNgkTCwufFf2Tnz+I51372FoAueqkeMhI7JlPS0ZKdDqg\nJHWLH1GIEGAufHC5EDAQENFV174l4QYRaCmgDUMLQiEE6MRRdNZvqLPnwrndelmB9R0FJanUc1ZY\nGM3IIgAVQNQ4XUsPesvUGVHskxs8oEAmx3yaulg4H1QeMtVX08D4yYp8PzdgLyIWqqZrrXH+ZReA\n+y0pcyYQBwpH9F+rGf19CuSMA1HtZsdJG5AC4IaFNr4e4i+pICs17m2IuARQ3shSy74xlQ5x17nt\nHdlSTvoAvgg4ZZaQyFCmJUp7cOtQcQBqkMIeB6CGyaT3uMZAdddNpushuaGaMkzXxnLZKdXqGWAa\nBwHURLoeFpNW91WuOkaqmeKg98At6D9wy6rqjGRtA7i+QSW4dwJL4YwroWBCYgmGBSzX+zOiKpYm\nDIdXx4/i+Nz6yrj6+CmTKpgwSq1hWMMk2nEm6jjZY5lgUUukygGpwEp0lABMgbSjnXEVhMQzFDLg\no8HuoA6cSHjQIR2rU+Tg1OIZF2wHZ8vl8y0AnHXONhRZDpICNi8gtMLhhRwb0xyyMLBZAbYWNjcu\npQJQC0YvTx8mDZbVtCNCClz3ggtB2ifT9CPwVCfB3sTHRiUalHZASQci7VZsVNopXXkgAfaMlCTP\n8MfhGxSNk5cmAAAgAElEQVRGYRKErZjHxDNQ89u2oF/YRv/k59MTBO113S8cCzWga7+M0/XxBx7A\nlrP3lAHisa4TP+rSAefE6V0JdKJEmx1ZMVExi1rqvE3f5XQvHiwXGtDJ1ANjxrnzJhnR/kjKGQGi\nasCpYV/agFSTGg+otmlgATdNS5ldd9L2tPnD0fgiaQAoAMADXwV2nd9yrslsEpPArUdyPG6zrJ7q\n+EGN3HlJGO8bmCmgHVANlbZecsz+BoAaNYplErfiVLoeAqYG6h6yXTbQzyS6HlXfuH2twozPP5jh\n8h3TpylodkJze5+Eub1PKv+f+Ne/WEl1h8IcU0S0C8CDLWUOAIhHZaxJIrszVeKYISKfcogdy6QF\nYBT88HYAXH3UuWSHFloS+oVAoiwyvRXrvUHNClsGoRvLOHLoMDZu3wqgMq6xUQ2/IblnMKxuWhfp\nR2Y5I9rx6QxSJZB2Okil+xBLPBN18/v+BJf94Gv8vHBtHyihz3GGlYUErJ82RWmQNXjl730ZH/yB\nJ9U6dCEEtM4gpIDJCghdYGeiYDINLgxsUbiA8sLNEVgbzef7v9rclmH6Fg+mpJIgpXx28ijJp3as\nFCUdl2E77QJJB0g6HkD5uB4SzqtAVLopLQNxDy+i7lAKl3pBS+Hm+PMpDOJ4zpDMMlHevVsIZMoi\nLfxAA2ORmzCKrw6imrqOP+i2nP8Yp2Ppcj458CRLvc8lPi+UdNngA0PV8VPpVKMxhWdP/fPb8ozn\n/RyJCkBKgq1Pqqo0vvUdN+AjP/X0Cd6UFiExOs/dzJ23ehlgIdA0pNXL3cZWxBLibupGlydGu8MY\nh+ZmQSgBVCm2cDTxKGkEjF+0RdfZjOZQ+BpoGnTpTWHSB9vTJuMAVDM3FQG0fALc3VBDVGPZRbTr\nehiYapO2mIJJhDCYib7tyPiZCIxke4VNlOh1TYSn7eysiu5ZpTuv+aBcD+C1AN4B4HsBfLjlmBsB\nnO/jqe4H8CoAr15NI850CcZHEsESwMI/yz45ZelW9yBLCvdBp4wzgqlnJAojS4MaG1VrGZvP3T3A\nTHSUQK+wJXgKDImbakbUsqXHGcnTEBcTjGpw8/ig8if/wGtK408gLB0+gmTn1vrzHdIACAUiH2bA\nCpSkAFv86U9fA876vqgACwGWCuzjj2SSOTdeYaBiFsoDJzbWT8LLrUxHML6BiQpsVDxnXwBPwVXn\nQFS0nnZKNgpKuT7aj84LixACn/iN9+Han/r+agCB17UBlzrXwiVHZU0RoA7ZwAm5ISjhdJ0bi8Io\n9EvAbMayUEHKyYP9epg4WkdxUVoSEindryCkSuKcTSkOLxVIJPnYKPc8BPdzURRIVYIw4XR4XkEC\nupMCJq/i34QC2ICSFH/1C88HZ72VvzRedyOZqNMsfcvpC6LYDjfYaDeuwCDPEgzauHmBywlkbQ4W\ng/ktqOiBVadWZ2s9/rdXWEeLt5Qv/04VM9UYwQVUAKbZqTTv3zS5osaByRb3XQygDNeH+tfKdTdU\np+mdAHc2tJaLdc1F7ihjTAac22Qi0LS8AHTXNXQ3HECFkYRNXU+bn2q10uaSnESGJKz7VQD/k4i+\nD8DdAF7hy54F4HeZ+VuZ2RDRjwP4O1QpDtbMf3imSTCqjiXgkqkIrLeVBGZRsgiSCEowcu/eKSQj\ntwK5ciOyMg+eCmN9Qk4MsBPWMooD9yE5+2zM+fOHCXD5vnvROWdf5dqTokz6qXz8TIjXShTh/333\nn+Ndb3hFyUyFqUxkCfiA9Tu2Dj7fjVgoF/huQWxBSceXER5A+azgSoOKHNb/UpFBFjnYuFgoW5gK\nPAUGagwTFX5JuAmFpXIginTi4nZ8nJYDSp6NCqPxktS5o3SC2w4X2H/WnGurdIAq3PHn/ORrYfz6\nn33/z+Jlv/v/gcjlKWSEjOq+XYUFlAT3M0ilXaoLQcgFQQuXQ8qlurDolsDJuWuLFiYqxEWV8VB+\nvaOF+y47egTJ9m2lrrWPZ9MNXR/pGaRK4I477sZljz+vHI0ZdK1SHbnzqNF3+qfcJ1Zltg4wsx2c\nAWMFQiRmKQ5OhYQkmrE0gVSQJlsRyrZJ8/gYQNXcNrqzIpdNcuOHgWd8++qzWAMDbBTggQrb+nlD\nRxJzx8161kKGxD1xyzlkq++reTzXANQw/VlGCaBCOQwp2ybjwFXtuLl1WM4tunrwWtuqbwNQk4m/\n2vDRMCzL/AplynmrRiWse15L2fvhckqF/x8FcOFUJ/4GlJBokwg4+MARnLVzCyAYDCDxoclkfIZr\n4QLGe+TcQLkHS0YxcuOMrGWGsc49ZHxagPBMlwb2wnN9fE7lyiMA4sLzyuzZDrCFxY22C/mBwjQl\nL733X5CqVyER8UTEaCTabLzanmphOENYTgfCDkwR4IBUlIgTUoGKHFxkLki+yMGmAy4ykLUgU0Ba\nA1gDMIONqSXmjPu5amqWkOk8mruvnJbEAyitAanLxJDQfmoaD6agXTD5/rO6PqhclIxLSR2GD28i\nfOcf/BpyWw0gYM88ut7Jf0xbhlzXdayiJeSCkUsBHQCyYhTGj+Jr6BrskvfWdO1v+YCuiSDmduHB\nBx7E2Xt2lsBZC+fiUxF4DgDrqZecVwWUU6XrMPVL6Nvq35Q+xYGQsAtHINI5BzKDrqcEUkQzJuqU\nSPiCazOYtYSbjd+2Y5rHTysEIL/vLui95w7sU8/89qHHNKVgQNU6oxZDGm1zI/TcNgYGwJQFDTIR\nq8h+PS5YvA08lTIlcBsFhC0D8tDtMDv3l2VrbRkiK9V1DKBGHTqpSxcAzMIRSJ/13RXyD3BzFOMq\ngdS0TNRM1kbI6zUY1X27tzrgY52Pz2lHQBBDWUARQ1ln6HJrYZUDUsYbz8I4w2q5mirE+Ie9bfoq\nx8xWBlCIaFJZ794TRDj5lX/H9ic/MQJVrg0v+O13lXmhHOhCxUYJlKPzAOAfb3kQz3v8DiDESZbJ\nFxXAFsxVolwC4dO3HsJVF2wDZOaGxBc5UCSgPANbi3seXsJj1ncdcLJuVB6MmzeOALcdaGHeUb1H\nPgicyulnRA1EQcgyaNyxUkmVYNO78FgoQOiKhYrcec07XgaWE4GJHVvndR2AlPBTwmgBfOYt78Cl\n//kNyK2FUQJF0HVgnQJIhmOdRukaCIDdnS7oestjd5eZ8gWFtAU0oOvg8gvtd6A6PC+VrgUBv/uz\nv4rX/xef3UQIwHpX3vqtzrXnUyu7kXtFa1vHSgDfQ/cP79vGJfwlog0A/gguflMC+DVmft90DXVy\nxoCoIG3GdZwrZ9gtX0GI9aD4eCa557G1zUMN6tIxYG5TSyO4DqBGNmSIcY1farZYzA3WJ42HcA0T\nlPUsIRUNw9/SpjaxJCacjndQ17HLjHftB9hljU/keLAzia7HqaEZ3zSJW7cpNQA1sjECrWzihEIz\nEPWoSjA6lisDy3AuO7IEEi5piQM0EkowEnask2WB3DKMFCUTEdx3tgU8xYwUUPWH4VkNbEJgLaQg\n9O9/AOv2nIWFCy7EukQ6oOTdONoHK8sSOAH3fOoGnP/sK6ttohp1+OyLq4wX7IfEh+zlLDTCtNoE\nt31+rgskKUgpoCgAVYBMDk46IGuQ9AXEvHTAiS3IWjgqxoDBVScfwFTtwmM2KrgVXTxTAFT/+8uH\n8PKnnV0BqcBGSQmQ9G5GD6SCW9L/P5QJ7OiK8jyljuGstg1gCuRGElg3HYsAcPDgYezetQ2GGde+\n/c3IDeMvP/5FfMuznzpU12H0ZqVrtx6HUwqqdB1AcmCkwhQuYbuW1TyI2sfJEVDTdWCgmromAD/0\nax5AkfC6FpGuJQhhyjGaeqaKsTFRQ2zLhAl/fwzATcz8UiLaBuA2IvojZp4S8Z3uIGrIEK42IBWk\nHtQ72nhOouJjvQKbOi23ycczSRo3r5A/TxuAcq0cctBwwNRRGD7SjgTWpY0vJWaMn6p5WOsCj1td\nZCqBnAV0a51Njr8uQwHUkBi4WNdhEEBZF6GMO1sLXZcVDXnmJmF3RpYYmt5gmK6nB0JySnfeTNZG\niABiZ3wYXFpbgp/jjAECQ4JgrDNizIREOubJ2haDisqIhl+geu7L+SSj5+YrN9+FSy85r9weXqGN\n5+yFFISNO9Y1Jhd2bQ8shCSCECgBlAjXFn5rQeUxE8VlH+kAFMBkACJcun+vSzsjLEgULr7TgyU2\nBns78yUDRWwc4+SBFJsIOPl4q+r8lYvNsRlUbYtcev/XNZvxNzcdxYuetBPx8Hz2o8wckHLr7P+D\nJJgEdnTIf5ASwtsedO2Cr+u6FsLFhQoCzt273emTARYELYHveOHTW3XdBMq1eKjYhRkxggDQtX1k\nqlPqMoCfcoABVcCpqet4guwAoESk69aHXAjntyx17YGVFYCZEkSNGZ03YtDXJAl/GUBIergewMOr\nAVDA6Q6iRmRuokap1jI07Gh/3AS4YnNHuQdoTKB7LCGJ56q5gFFuHSKgv+SyWo9LXUAr/yrgf/8U\n6JJrhu7XFH/6TmiwQ7lsEUgmTybZ1HXbiDd7322QZw8Px+GsB+jOyPP0TTUYgI4eBG/ePVUbm1Im\nJR1ZwdrEQlXVzZioR1tKoxo+BQRBePecYPe1b9i5eCrGwYEpKwNQqoMkhnPvhPcgyAA773+vvmy/\nA08Lx4ANm8t2BQgQcI8AlYxZluVIOkm5Xfjt8TxqgYWKz+X+uBF3VZepAM9AQRiQdewSrMGBm+7E\n3ov2uYIh+Fx51onZg6sQ9OXzQ9XYp8aXU9Qn/P5/ei9+4D0/5f5IWQNXIIEXPXVDFOPkXXQBAAYA\nFeW6qgCWwEff8bt4wZteP1LXJMgDJqfrEjwxwLmBVLKm6zs/+g845/nPHqrr5SNH0dm8aaSuv/Tv\nd+IpTzgfXQBf+6uP4fyXPL8GskSLrgNjJcRwXR9azLB3fToA2MoPZiHBlitds6iYv2mExiXbHNq3\nTZLw970ArieigwDWAXjldI2s5DQHUZgIvNSM7HDcNXjcSuxMSxuCe6nZjmGj0YIc7xtsTOVkRjPK\n/RSkDHgOWa2p+iqqSdnJrpyFqgOoxtdmi+TWZedtr6xx74YBqGG6zisA5Dp+d574qkYBKADVyKAR\nEgAUgPEAKutNVCfgAdSUup5WZiDq0ZXwpc8lgHZG1hJATH7sh2OiHFhyjFWvsEiUAJgc4+Trc0SE\nKwsJoBz9Nb4dBAI2b61wBNxrfMO/3IZnXnZhGU8D3+a0m0YGF+UIwvD/Z975Afz6G19Tbq+fDSj9\nV3AgAUTOJccCTBKHjy9g2/oO9jzxIsfS+XQFzLaaOD28L81foD4yeYh8/2+9uXqLShdf9csxqBIC\nd95/HOfu2VIDVQPgygPEF7zxR2qsV9B1CZaDU8vr+sTBQ1i3a0epayW0G70XLpUJj3vRc0bqurtz\n69hrvvqy/Qg6ePy3X1cHyr5M0HV24iSSjetLl/IwXQtBJYAa0DURGMLfVuXAcUPX00hbss3lgzeh\nd/9NAIDs8F0AcMFUlQMvAPAlZn4OEZ0H4GNE9ERmXpiyvtMYRMVulXiS3TFimceCmNppRuwbV0sS\nASi+9TPARc+c6JwbE/IBlyNyCNUaEoEk5tYRY8OPA8ZfyepFDpyCVohSvbTpegiDtNLa18TdF2RC\nADUMPFF/Edw2tUuk63EJQ0eJnDjQbianRNiCyI1ss951x0QghmeeXOxIUHFIvJkVOVL/bDFTuc9X\nGj3DVBIxQ0MboqLVqjf8BDzn8seV63/zc2/Fi9/1Fv+/MqShHvLGl4ASQBVFgVSryrgSlWdlABAE\ngnAAibhknLZu2QwwO6Pr71Vgn7gVLHHNbcDA6IEyzfjPqC/hFkAFAGfv6QBKwrFmVL2HkTvQHTu8\nXwtAqqnrzXt2euYQ1W/Qe2jXBLoeBR1H6bpqW9X0zuYNURzVcF2Hj9amW+/mH/pBXPw7v+NbSg1d\nOwaxMNN5ydztrt/j+b2XYH7vJQCAw/2TyA7feUfLoZMk/H0dgLcDADPfSURfA3ARgC9M1VicziCK\nCNndtyPZt7/a1jZiLRYeTIMw9jRTNm+gnjYANYYJmirfxTDWaZisoYtoXN4unHgI2DA46SR7I7Ii\nUDXQ7kYvMIU8IrBiwvvdCqAaspp8KKdbLpVvNhFC4LZPfg4XXHuFezeobiRjfFwZUiDdMFdtb4Do\naQN1m9KsRRDh5e/+JedCi/rP+G2PDW6oQ8YAqiwYgRO2fuRu9E6E98O2bGusV3hisP+c5E7Ujmq+\nD42+TJHALZ+4ARd9y5XV8RQAYQCeNMhqoQouB1BNCYNxuq6355HUdbkv2jlO1+E3rF/8u79XPwnT\ngK6VGsy3OFGbiUamaBnRt02S8PduuHQtnyainQD2A7hrqoZ6OX1BFFAHUG0Sv3CB1VnF13ss95zo\n4zEb0jWp61GVCf3Sx/oWm9KV+bAHPKctAAoIL+vKO4V6jHdLj3SaS9+4SWbb5IM3HcarHr/tlJ17\nluLg0ZcLn3XFwLYQ/9Q06kvLfcx1m/3NtMNBgA/81WfxXS8eP+0GjfjX3DMxLm8BGgDq/fUoT88E\nHyLN+/LO9/8dfv57nz9Z+3zbmmNILrzu2YP3m8REPVfb65YVBqmSE9646XU9qUyqvnAt/3r7vXjS\n/rNby1zx3W/F5/7oLSN1Pe31ELWk6KkVaN88LOEvEf2w282/A+CtAN5HRP/mD/t5Zj4yZVMBnOYg\naiWy1l/eKwVQ1D8JTtcP39876SfynUJaYoX6hUWqhoCefHwQdVMmAVC0fBzc3Vj9X9EZVi7TqlQs\nHIZdd+oAyqQyDEABOKUACsAsJupRltrdb8S5ydqHgPPtrE8FYPNo8+oY5O+57smA6U9c/pZP3IDH\nPe/K+saI9f7Ub/0xrnn9d8c7ay9o061YhjYBwMnD4PXbEPMdBx86hrO2DY5Ytsx48NavY8dF503c\ndgD4qe95IfpjrPa/fPB6XPaql5YNE0QDaGygz+EGE9MktJoniXQ9pwjgEAg/4iOwoev/+R/fhu98\n95tHXsuqpO3DuhYy4v5fesEe37ZBF+Zn/+gtURB8dVj1O/3oYBIEMcy2YXTf1pbwl5l/O1q/Hy4u\nas3kGwZEAWg8oKvA9da4kRgrOXWyrkmdAKiCwKcGUEAr2k9HxUUlc8P3TXK6bAmczJWjDIPw3OaV\nV8YW3F9e1TQA7fVyyYS5oPZql53fMrEROtxjbOusFHCs3rU4/hSrS1EwS3FwGkg5uoxrvxT+Mzf2\no3puOXbocIvxbdvm5Pihw9i4MwLpbSEAtTghwsXXPBnIlgb6msPHF7Ft83pc+0OvAJs8ihWq4p/i\numzDoFoGeH4rEEYUukvDti0bkfnM4/GbygxsvOBc9E0991WodRIi+q5//CzOe9bTsdzP0U2rAOWL\nv/Ml6JuqAvIjjOO70+bKWjpyDOu2bvajtONYobhpK9f14nIP850EN/z71/HMx+8rr/GVv/rTQH+x\n/F/emBb5ifdcj9/86ZdWGybQ9cD2WKd+4VLHw3Vdxnj5/yEJbGj11BaY6m7Hlt2nlZy5IGqNRjG1\nylRp5auHPWApWjyC7vyWVnA1sbQY08JymcE9yAf+5SC+67L2EWXj7hIVfbCqmDf2IIyoopinfnBJ\ngDo+/mcYsBnHnA3RdWjTpHH2bbJyAAWM7tgGv9omllUCp3pVp1tX800mPog65DIisIsDirfFeY5C\n+bAOREP8QxRye6B1UzZt6gD9xmCj+NkSDYMY/xK5UcdK4Qt//nE89TteAC4y9x5HKQA4NqqN/o1R\nGdR4PbT+8//+dTzl4nNKg2sB/OrvXI83/OBLIxDmjTE3GI6Wy37wo5/AjuueW/4/68orsJQzIBWW\nC38vUTUxBFy3BVQD1TyHBGfM082bYJirXFCNWKGb/uYf8PgXXF3qivwUNfEzQDGA8tvXKQD5Mp55\n4Q5QvjSVrt/7+ufUdd3sQ8boGnHwfBlU79MThID6WjKLStehtbF+OdL7qtx5UzJRj4acWSBqGuC0\nloHVcRuG+YLhnjG2BpgP2amjF6GlvFg6CttkeUYY1CaAYgCvvmz3yu5M0Qc8cIoB1DAZC8QmOeew\nIfxtAOp00HVTJgI5/gv02P3gzXvWsN6VyTQdDRHtB/AhoCT5zgXwi8z8G1GZawF8GFUw5p8z81tX\n3eBvRGEG2Pgh31VCSbduqm1sawCrMrgNYMVR0txaMHbdkdLKlIYgaSYsnVjE3OYNCIA/ZJ0OxyQk\nkC8u4akvuwYo+i5nTznc3w1fDxmqIYBP/daf4Oof/Q+NXFfs8yL5qUt8F2iZcenj9iG3Pg+WZ5d+\n+vtegl5hHdCK6nF/BwGV+tzHUFz+LWAA8899NpbyMKo3uuToFoQpcBzRwoMjz6hKJBrnTyI/eTSz\nizMUzG5areg8j7/u2ki/Zm113ewHV6LrFuBU13Vj5CFJB56ExCfe+wE85yf/g9O1kG50pZAIXUOb\nrkNKjljX00jb6Lz6/tMLRE3dexPRXiL6eyK6iYi+QkQ/6bdvJqK/I6LbiOhviWhjdMybiOgOIrqF\niFYQBQiEYbDHP/GX7e3pL0bI39YfyrWUeISGb1d2z50D5xyaLKylXXZuMzITP3CjHxJuLJOI5cYi\n08FtQ5bhlVZDWIe1x97+ufqGSV6AMOR5XJkJdX0sG3/KiWTMOQ8uVokAedNZZWc4Uk4BgAKcO2/U\n0ibMfDszP5mZLwPwFACLAP53S9FPMfNlfjljAdQp7cP8M1waUusSTX7s3e8DbA4yGajIQHkflPfx\n2Q/8Fajog4oeqFgG5Ut+WQb1F0E9t/DSSfDSCfCiXxaOgReO++WEX/z/xeNVuaWT4KWToP4i5uek\nq9Of454v/htEvuTP3QcVGbR0DDWZDCiX3IEEDxRCpvFrXv8a3HLXQXfZDaNaMGCsY89zyygskBn2\ni0VmGP2C0TcWPb/eMy5fVr+wWMoNFjO3nOwbLPQNFvoFjlz6bJzoFzjplxPRshDK+eOWc1dPr7Do\nl+djHH/oSNQW1x43ObBrZ2EZ//DO/99NBAyfTRyoXFYc6ToGUF7XsLnXde50HfRd9MfoeqHS9fIC\nfun9/zRe1wstul5eAPWXvK6r81S67oGKDChc+37lDz8BsjlgMjz3x15V6dqaUte1R7yha9ui6+ne\nyxKzty6nm9C0uWiIaBeAXcz8ZSJaB+CLcCnWXweXSv2dRPQGAJuZ+Y1EdDGAPwbwNLj8DR8HcAG3\nNICIeHkxoihHgaFJgJIv0zNAdyWeuiFuJgZGarNkt0dqfITbZ8hxk2hqJOiZSgIpMSjjyI6Ru4fp\njW05L+HEx7SUW6tvFQbQtwLpkOeG+gvgdB1Gu/FWrutw7rm5OXBzPPQYISK+6p1/P7LMP//8c0bW\n6wHCLzLz1Y3t1wL4WWZ+yUradDrKqerDiIiXF06AbAGYojKqbABbgExRGV0b1t0vjAFM4Zhs6wxy\n3u9DSYEb7zyMp+7b6D/zPWCPkw415MF7D2PHPj+vnZBujriwHk2DAiHcRMAqBcGiZwTSxM0Vh8A4\nSeWSJwoJMjlsug4gCVbal1FlVm7DYRJd1zT3H9XUJszu0uC2M6rpTnITpkDxU59Ex9TcgyP0GsZz\nCCKkAijYjfYSQDn5cpgfTpCbiDcwUPEUKG6am/occmGaL0kE5Y+BLWq6XnzoYazbvA5g43QdQEhT\n19YCRV7TdW09THFTTrg84rM5zm0lnMuViAAh8bmvHcXTL9jhdS3dlCpKA0T4/Ic/jae9/NkoM7OX\nuhYo5w8MU+H4iZshlNdzpetyyhqgpuvzd2xYUf9FRD+74+kvf9fe5/3g0DL3fvS/4aEvfOQnmPm9\nk9Z7KmVqdx4zPwDgAb++QES3wHUsLwNwrS/2fgCfBPBGAC8F8EE/T83XiegOuJTsDapiJY0Ybojb\ntLYiAAWMTvLYzGcSGcMw3eboJKHDwUmbjCSEJgRO0+CrgclroviH+LxtgKpBOE8uTQA1yrUXuzhO\ngRCAjrDl6Zu6dgAKOPL5L2Hj0y51HfiAvofreqmwmGv4/52h4JHBleNkDVIcvBLAnwzZ9wwi+jJc\nIrufY+abV3uyR0NOaR/GXLptyLt06qDKTbrrgJUBisIFbgejWuTOiFoDYS2sKfCU7cIxDYEhAEYm\nndy2WcEuHEMwrCSqOeFq6zpxU7XIHlhKpCRAsgPYAiwUiJVLISMsGAyWiZvnThCIbZRPyTETZcBx\nBKCKeH64CFQZy579sY7RsPX1+Bh3uf53xCsfHn0hqHTLBYA0JwwKUlBCQPhJeCURtCRoKWDAHiy5\n9ksBuFmFGfATR4dkzmUTIhcdscW6Leudbk1RgacAtKwB8qzScUPX7J+P8PxMqmt/wSWNE+v38l0J\n7OKJuq5VAgiBy198BVD08dUv3gE8Zi/O2bkZIknd8b5aAjldg2s9LTPju3/hD/C+t77OeS1QAaiT\niz2knelSBLn5JUf0X6eXN29tYqKI6BwAlwL4LICdzHwIcJ0UEYUpvvcAuCE67IDfNlragNIKwVMp\n4x7CSaWRETcGVa0s1ZBhom0B5+LEIdiNZ9WLDWnGsI5kLSFF08nI1D4nYGhL89m3zOVcTXUhDLR0\nAl0fzQQ2J3b81A+20seq3rkJdb3l8ktH19Oiawa5YdC1bf60q/T7N5PVHf3ql3Dsq1+e6Fgi0nCA\n4Y0tu78I4DHMvERELwTwF3AJ685oWfs+zNEwFFgjD6DI5BFzkTvwVGTgwhldzrO6cW0a2cBUeFdx\nfS65gYsqY1wCUxHYJ9LasxIKnCnPRGmQ9OtsQSpxc9lFzzuRnwePyS3W1oJC/v5dv42rf+aHBsBS\nEa9bRu5dZ4YrEFX+t24C5pK1itmOAKZGeFDCu1NOouwnV969PsVDiwZaWgiy0FJAEaCEgLYE7afx\n0hBgclnHTy4uIemk2JAqEBiG3fyHHAKsm7FpQdeRS488iEJR4Dm/dD0+8QvPBRd5pWtTVOxjQ9fH\nlmt2q0wAACAASURBVC02JQywHanrMnxEuKzrEF7XSjvmKQBnpZ1+ZR+kNKByQGmcf+m5gFIAWbAt\nyn7T6dpUuvYjGgNgfv9bXxcxUij1p9MU+ZRuERI0cnTx6RYTtWoQ5Wnw/wXgp/zXXPPOrS1NwBYP\n9gk70qraoQZ1rUDTqHqpDo6CkWXmxig/Dr1QfVvDxNuNZyGeDqbt5pWApb/gaPWo3LCpZDhqwqTT\nzYQRKrU6WtqkvvgRmKe8pN62iFKfWlr0ukUXQ25KozPz55347C25uAbqHabrJkBuBc2Duo51kPsR\nl4eXC2zrrv7bpslEbd1/Gbbuv6z8f/ffvm/U4S8E8EVmfqi5I55jipn/hoj+GxFtWW3CukdTTkkf\nFkZhhfghU5TG9Ek/+n7826+/Apz1HXjKeuDcA6kAqIocNgCrIvfG2RlSNgZsLNg/m9bU35PweRKA\ntBACkAJCBneNBEnt3XgJSHsAJSRYJaC0A9PrQ8/NgawGtE+34N1pRAIMApF0/Vz0nj7r534YWWHL\neMoAmgrrjGxuLHLr4o9u+9CHsffbv7XclhtGL89BQiA3gGEHrJxrzwEpwLmNzBADHWaskIJAVLnt\nlCDckRWOeRICWhCUtJBE+Mfnvggv+qe/RiIZhXVzFmopYAnodrtQwl0HwQGoMLVYxURxg3EMbrwK\nNHPWB/I+Pv7z18AuLdR0zQFI+V82hWP6rMEGY1D0Kl2zsRXAia671yswN5+AhABJAZLSA+aKdSp1\n7YEyVALWCUgnzo3MHZCqmH0GQLYAkwB5d2p8ViZyAwcs4/2fvw+vfsoevOv3rsdPvPYlpc6nEsJI\nJuo0w1CrA1FEpOA6n//BzB/2mw8R0U5mPuRjDh702w8AiNOfts1rU8pb3/a20vJfc/XVuOaaMIQU\n4wHUMPA0zYivmgxhk8JXUcRakP9K4ZphnowXKQFUUbivg0ji57IJoGrHjogbKI33kHxY9thhiE3b\nWutoe7aLp7xk4KqaxMvAlRNV961NWvZNrOuy7hXqull/y7RCrbr2rMNKdF1YdlN1edHCdV033/gZ\nfOpT/xhqnVpWOQz41Rjiygvvtl+/HC6u8kwGUKekD3vrr7y9ZCOufcZTce3ll5VuvBJAZT1wb8kB\npaxXGdTAUHgja7McbC1sYWGLwhlSD5yGAakAoEgItyhRGlghJYSWIKU885Q4xkInoMQBP6kTcJ+8\nu9zfKz8cnk2BMK1LzaiG14McEMl6fbDSjqGw7kOhX1hkltEvDHa97MVYyk3ERFnkFshzg9xaFMYx\nVqYEYVwDUEWvB0rqE9VKIWAfeAB691ll7JMSBCVF6bKTZN26cNuu/Nj1WM6tY72EKOOuUiUgGCAm\nkHWvfJjz0MLPDeyuHLXg8hJAZQ5A9Xvg3IEozvrgvF+CZs77XtdB9wWsMbDGwuaT61oCyE4UDkAR\ngZT0uhYQWjlQpbTTtQdOpHx8njFA4rw4bDUo9QwmCGwEQMbFTEUDZe594Ah2bt/sR1gCr3nqHuSG\n8fT9m/Bb7/pl9FgOBbrjRAiCHJXiYFQOKaLrALwHVcbyd7SUeRaAdwPQAB5i5mdP1VAvq/3k/QMA\nNzPzr0fbrgfwWgDvAPC9cMOhw/Y/JqJ3w1Hg5wP4/LCK3/LmN7cwEXWlEFt8/O4FPG+fAxM9A3Si\nuXsYqIaQrolw3bg3jay1g2xFE0hFjEeLh6cuIwCUb81gC0eAJ7c/6vRA1azpkdDGrUPZqtAGgm+7\nv4jKfx61bdz1+fqEe3ubLa3tHwBQbeAp0vXXFwnnRNPTjZqCZajE55xG10UGNGYjD9KWpgIArrnm\nGlxzzTXlfX77r/zKytrsJRnRCY0SIpqDm1vqh6Jt8bQJ30FErweQA1iGi506k+WU9GG/+KY3AEVW\njnCjwhtUAJRn3qD28IN/8EX89qv2A1kPf/CZA3jtpRudkc0ynDQSaW/RAaisABsDk1csFBvjfpsU\ncWCBA5CSEiQERKIgpKjWtXLz3yW5YyasN6hpp2YwAfi8QX4REsQWxra71dmzUCJJkNsqUDw3DkD1\nCoOssOj50XlZYZFbNxovM9b99wAqK9x5CsvI/XCvAKTyLIcqBhlnuWEL5FJWBZALgpISWhJSJZD4\nRQuGygxsJ/HuQgErY5c6IEiCmCE9WGA/vD/IfUd7OHuDqJhHjvJ/2QKHHj6J7al1YKnfc2A578P2\neyUTZfs92LyAzY37LQxe/8kF/ObTk1LXJ3oG6xSDm9lMh+laeaBc03XudF1kQJECuihH35WjSV1l\nAAn0lvpIN2wEsQHHgJkZZ+/agr5nHIO+DQNPecZVeNIVV5ajK3//19/Z9nqMlZGekiG7yCG/9wJ4\nLoCDAG4kog8z861RmY0A/iuA5zPzASJa9dQRU4MoIroSwGsAfIWIvgR3h38BruP5UyL6PrjJ/l4B\nAMx8MxH9KYCb4TrgH20bmReksIxauEijKLHFYoESQMFadBrlh37LTwWs2liolsDx8DkWMxX9xfqE\ns54BikFIc19rk+P/LfvbQdXw6xQmBw8x8m3Hxg82A1jKLOaTwaBoaq6bwo34wODz30qYROedCEC1\n6POcea7tSwUGb+JQmVDXxpQjYQDAHrobYue+CkjJlgD5Zr0mB6SbqPOBhQy71iWTN3OErHQi7iDM\nvARge2NbPG3Cf4XrhM54OZV92MHDx7FnY8dVaW3JQiHPHAuRZ/jLL9yD337lBeD+Mjjr4XufMA/b\nW4TtZyj6OXReoMgLmKzwxtUZ2MBOjHLp1Zgo6RkorTwzoUE9AZlo2ERBpAWkziGNAesCZA1ECG4m\nwle/fCcuuOISb6ANHrjjbuy86DwI+PnRopgpefJBYG67Cyr3LjzLQGacUe0XBv3CoucBU1jPrcVy\n5vZlhUXmgVZWOADlwFQVG8XMwIEDMGdVIWkhkFwKNxpPRSAqVQZKOvCUKolECdzz1a/hkidcAM4t\nUulAUnwXCS4cwfYyyPkuhHUx5u5ZcL97N3eAvFfFSlpbxbwVBXZ0CdyPAFTWg+0ve6DcL3XtQFSl\n6/dcyshOLpZ6To1FthJdK+kXBVISMlGwia503bE4fudBbNz/GAivv1tvvAOPu+qJ3vWn0O1q5z62\nEpBc62cDllt48DCSrVthLOOWOw/g3HN2+9QVFr1iRLzeCHFM1PD+a0SIyOUA7mDmuwGAiD4IN0jk\n1qjMdwH4M2Y+AADMfHiqRkaymtF5n0bMaNbleUOOeTuAt09SvwoZzqqjo1Wn9PnQ+gGjOiJAub8E\npNNMixKzUG0xMKgZWDYWFILjksaUJ6NiZYbkl2reCfIjZ4DqgZZFD0a5EYXjUlcwAFbJQOB4/Dtw\njK8zgKmuFrAcsVItV2QZEA0wcffxPvZtHDVyY1DXpViLE0ZigzTt+4dtW5FMqOsAlK3j+sXOfWWx\nQx/5KHa+5Loa89jq4pMadvEEaH4Ddq1LylKrlWlB1DeTnMo+bPeW9S6hbZlTzAcLF5lbsh5e/LhN\nsL2lEkTZfg+ml8H0c7dkBWyWochycGFgc4N77zmKs3bM10AUm8HnnVoMq9SqAlOJ8oZbQxYGnGqw\ntZDWAGAsFxZdP0T+vEvOBhc5qMgBIbHr3N0uyFnU2SpmRr5uO6xhWIQkmyGQ3DNLxuWEmjtwG45v\nO8+DKIPlzODiz/0FPvOEFyHLA+AKrJQDULkHVMFNZDftAJbz2nULcjFQUlLNnadVAFACiTLoaIld\nj92HpcyU8VslA4UqFYIyFioNOfUYzIQ//sin8dqXXdV4MGx9sW60JRcBNPdLAPX//O09eMsz1qPo\nZbBZDtPz+s5zmCwH5wa2MDBZUeq31HWgfcbougRRQdeZgtEFVGFgEwW2Fut3b3Bt8td84aX7nCtZ\nKdzwwU/jma9+ASCiRKFOyeV9sgDmtm/FkQOHkO7YjgseuxuZcbrOCtvIfbgCGRMTNSJKYg+Ae6P/\n98EBq1j2A9BE9A8A1gH4DWb+H9M11MmZlbHcS+0eNgDUQs4unX6QpkGNAZT397cxJGURoMUnFRlZ\nEuhZQkdwzWASoTSuQOTqWTwGzA9OvFldj8uRFD9+8TtTMvcNAAUARnWGgieLMZMWx3U3tg8M2G+A\nKYYn3/4Pe+8ebrlR3Yn+VlVJ2vuc0y+73W7bjdvGNjYGbMAGY14GhzAGkgAJdyYhj5lJAuRBhsnr\nS2a+ZHJvEu5HZjKZXPKEkO9mSHKHIclkyCQESOLhYQIOD9uA3waM8dvd7X6evbekqnX/WFVSSVva\ne5992nYben2fztGWSqWSllTrp99atWpBuz0FoGw5NQouSJeut2rbzSbOzCfGM949gMaH4QZb5+q6\nca74d0vXp3/7NTPO1jr36ta6mkoJC/hCZ8gsPZ+Ux0+IGRSCjq0ffZVP6piofFIBqHJ9gmu/cgwv\nOpUFTOWF/C9KOM9QnLZmkB9eh7MMtg7OMkgbuLwJJsi7rpUmD6AUrI5cO7mBSwu4LMGnHkjwwj2Z\nN9QMAyAbKAmGDsHJOgGXBe7/8oM485kXVO9fO8A5v+tW8LkXVYS8Y0i8kxNXXkiwedgDqFFhcWxS\nYlw4fOLiV2E0KjEuHcZ5zUSF+Kh8fQSdpFWaA3bTfR35gHKlZJi8CrFPHkRNPJAqLaO0DtYGdosB\nGGGyiKCIYZSTeCrrBJCB4MD47te8sAJUIODYTZ/F2sXPED0HxjHoOjCP+RjOM1G/8IItKEcT2HGO\nsgLN8t/lJWxewBW2ct/GupbwqzaIInzutv143jN2iq6NgjIeLCfGs1AGKpHYOl0mgBPXoAkB6x4w\nQ0ns1JWvfWEECrleWsIAVnfvQu7nOiydDBLQXKBwy/ZBNMedt6kPRAPguQCuBrAK4FNE9Clmvmsz\nFZ74EisvNpQdDFQFoNjJ15NJGkU6Y4BmnFo8blFn0eHmGUQAoHQOpgcQAJgNoIDuJJM9MhUjFeJ0\nwm80O7h5hnXfeoGdK8nU9uTow5is7eoEUw0XX2T3AzBlRHFPfaJNrddFdR1o5fWjUMNVoIO/6dI1\n1g8BK9umNvNga/WlVdXe5dIjBcsETU3QfNyFNhdYfpKJeqKFW8yErYKHq0DiEGScT1COcthxjhfu\ncCjXJ7CTAuOjY1ApAMoWJWzhYHMLtuxjo9i7diZgO21YlVIgBaRbMkxGOXSiQKaEThVMlkIVBq6w\neO6qQzmS9/mhkcOZBCQh51AYGq8TIElw1vln1DE0zJIKwbnqWU3OfzrGJTfYnbKVoTzEQwUAtT4p\nMcot1vMSo9y7+HKL3DqUpYMtHZxzcKxhx2U1Iqz9HbX/a3dh57nnC4AiAVHaKGz/6N/gwCu/A4lW\nyBKNMlGe0dKtXHchJYIsuSUocki1qmJ/proUZqxe8lxhHaPpfcIouzaQ4skY5VgAVLE+weH9RzAw\nJIBqIoMIbFHC5t26vnffCGftaOYvJE245ClbMTmcQ6cKpRIQpdMSOtVwRQKVG2jPNn79SIq9p3DV\nf2s19lP4+ESbRSIDDYpCRnGGa+rMWB7Acp0stbAOx0qFyZIpy7sCyx+98wY8eucNAIAj99wKABd0\nHHofgLOj310DP+4FsI+ZxwDGRPRxAJcC+AYHUdGb0msaGvk6ZL0kgwQS8GY2YZIabi5PZx4ugK1p\nQAy1MTXU/N3JRvl6NoqoGy69NoBq1df3+M7y8p06TJpx8766ydquRp2NMWjcTMfQvqIOXqfaNhXs\n7VzjgEV0rYYraAOoTvAUZLDWvy+uA6gBVcudp/sYqT5dLyGbdemdBFFPsAQL7/qAVO3mKccCoIrR\nBHY0QTmWbfm4gCkL7D9qsWol8Lgyrk6MqhjX7qdFWChCsV5CJTJSS6cKNtW446jGRdskvqqijACc\nOiTYUQ6lFHQwqibxU4NILiOKwWHXZUP6BYYfXcc+ENwxcmsxCbFQhcV6Lq68Y7nFeu4w8qxUXlgB\nT1ZAlLUOQuYJW8YdQaBbd5+DYmIBklF6pAjOOtz/wmugJyWcFvBUWgXr64i7ipAKQdyAjETVAfGp\nVnCKGm6///bBT+MHXn25v2CHMi+QhI8rG0bc5ZWuUeQoJznsyDNQ4wkyYhTHJhXrWI4tbG57dT06\nkiNPpj3QQdcS8+ZBVKKgMw2dOZhUXINgxu4hYMc+IN/fJwqpL8oEXCYgNwCH2K5Y14G58jfOVQDK\nx8Cx6LnwSVOXEaLp/mvnhc/FzgslRUt57BCO3HPbnR2HfgbA+US0F8ADAL4bMtI4lg8A+C0i0gAy\nAFcA+I2lGurlSQKiOsRNgyZZr9+KxKed7bzIOXEz137lCK5+6pZ6Q2QQHQjbEj9TdT4GpYO6vpbh\nZO4DA7OciLUsklCT655Lcne0y86yyDPYlMqL1Wpm8KHH5w9AKmad5l3h1Gg5pbr10qtrH2gJgvJu\nt87zNUbZ0Vzdt+8HhTgAmgbN8jum4DrAcec9nm4pM8DjY6DB6tS+jYqexYaelMdHohFbcK5OqBkA\nVJnDTcRtV05kvRznFSulJjnyUYnhuEThDar1xjW43hquHi9KE/632o6r6bAYV0W1Yc019qcKe4cj\nFOsGo/Ucq9sFUBTWYQXw5Q1UkmPEGqtJBrKSw4jKEkgD8OoHUhIPhTqZpg0pC4SRCgBqEgOpcYFR\n4VDmFmVphXmzDq5kWA+mmMUNFVx6ejxCmQozE+JolCKUPmFjWch/ax1MooXB8gyUi/o3rQg697FU\nJJnMC00oHHmA4PPBoQZS3/PqFwAoq3tgEg3KJ9IPW5+ZvCzAhYApO57AjsQ9az1QLsc57j9U4FSe\n4FBukK2PYCfTunYeRJ6RaUwOT6p7LXMIk09n4Nm3VEMZBZso6NLBlAxXOiTtgUJKXLykFchoKJNI\nnrDUgovc2zWOvARNXccDBYOujz5yAHbL1krXy4hWNHN0cd9sDMxsieitAD6COsXBrfHoYma+jYg+\nDOALACyAd292xoUTF0R1Wf4Zxo9GB8HD7WjAiy6jumDQcQNAxceRQkiIT8xA0hMg7Q1nOzZqSsq8\nd3qZPmnESLWoow0BKGAhd1QXmJoFpLrEMaDg70mlq1knnaOneAQfuJt9WjbA3CfQjK8nuHW7gVQE\nFzcRy8TAcQFQwPIpDk7KcZLo652YJUbGM1ExI2UnwkD89Z/fiG+9+qm1q+fYBMVI2KdivUQ5EReP\nKwRcuNJVLh523HTnEfAi9QhyJcwE6WBYCZwxTnGM3CqwczADAzuSGboTpVBOcpDWUFryDA23pAL4\n0gGoLISdCIaVgGOPHsbKLhk4E4ypA1fklnN1FusQLxNG5eWF9W48+T8qHIpJCVs6lIVFWXgWyjNS\nzjFcKexQzUYlwEQGmQQQRX50ntUO2iiwYyhWXiWBgZL7pQh1Uk5FSAuF1DikTuKmnPZsmlEYHzmG\ndNsa/scP/gze+N4WeVGlhGBhnZykn6jyfeUTiW0ryjoWapyjHE1wistRrJdIxyMUE4tyHOk6l5go\n5+8BGJ2uW4p1XThsO3s71h85CrbsF1fFQVXH+Rxiyug6ZsqnXSA/CILsADCuN/cigytQWVhGsmM7\nxrmt4uCWkS4mKpZZJDszfwjAha1t72r9/nUAv75U4zrkxAVRHdJ17w792buw7Q1vEqMcGc0pozon\n6NivNM/UZQzZIR6x1TauDfbFG1oBENG54nrjFAMtN9Gh3GFLahotaza7udU19nUcAMycLqEt7aGk\nCuIarXJ1wgfOtso12CiW7L4ls2ROBuYDKMT3MEaMLrp/UWfgy4xv/gwGz3jecdF1SKA5S9cNILUw\nAzVfNuvKA066804UCYCDqylbbMVMuLzA7/zfH8IP/eiL8MpXXoDy6KgysMWoRDkuUY5KlLn8t4WD\nnfhg4yIAi2gqFH/O8MRpIigF6FSjnPj4mJKFnWjMqC0B0qQU8h27sJYflQD0QqahmcBgJcvBbgBy\nEiBPRkaLrW5fm4qVAYByPMEtdz+Ep5x9Ru3miXI/jfMaQE08mCompQdPFmUuQMpaJ6DRChvlLMNZ\niclqZ0sPc8aRnxdPaQVbOChDMInGlZeegevv2NfoN8MoPEnESUiNRlY4lMahcITSEc47ZQ1feeQo\nTtu2Cgfg9X/4n/vn7nMRWC5zyeXlE6a6vEQ5EdbRToqKiSrXRdc33XEATzttRUCUj38T115T195R\n3K1rI7ref+c+0bdl6NLCWVMDRy2gOSReJaOhsgQqyaHSQRWz50pJdUFVPNT0Rdd5orzr09UJUpdm\noohmxu/qJT9SHyt5UoGoSiKEu+0NfrbnhrFdAEB1je6qd7bqaQ1zDwY2jMSDGFdHSnJutAynJJMU\nIyuTOM5+CMR1pLAtU9PB433gaE6ZjYCn9jEBTFkGHj6WY9dqWuMIqufSWy8cVpLWw0+S3Xfm9C/z\nQE+cO6vtjouuqxdAbUDX+3mIU9Uk2h1Ac6Tr3nraQGw+IJrpad0EmjrROppvWonioeANKvss0bYo\n8ea3XYXi2LgKKHYBMPn/5bhEMSpgc+uZKScMDTMK70KxPP1BVY0wcwRtSySaKsYqsBIA/CMro9ge\nthZ79P2wW1eg8xIuEeYkywQEkrUVGCR2aE9I6y9YRupmGS48bw8OTUofhyQxMiX7rOVlnROqioGy\ncm0BQJWFhS1qV54rCzg/xxw7C9eaS45IMm2TD4ZXJoU2umKhPnbj/Tj68L3Yec5Tq/IT7WAKB6Md\n/unDf4dXv+EaTEol8/hpoGTgtn3rWMuSahRydKUNPVf57DzQZOfAZY71Q0eRwGcgL0o4D6AknYXz\nOi4FQI2EdSzHAp6K3Fa6Lh17tm9a10SMxBHes+VsvPXIVytdTwqHFVebedIKNCrFlZeUUGkBVSQy\n+jMtoTzwI2eh4eoYOGCqH62fOfaTRaPK4xUYx2VEeWawT77h5s57zGWWJWkYzIiZ6C2DOQY1kmIS\nueqaoKlRd2RcVWWQOxiITbh5GtV0bIuvsLDcyIbN3NXRbUwcM5JyDJsMsWu1Pznn0JBQupqO1+WK\nKN0LtGbqum/bDDmVRh4Pef21WCeGj5GaxUbNct9uQDajt5PuvCdeauPj3SHWZ4j28+FJAk0nQ9rz\nEnZcoBiXEhM0tsJATUrYiZXt41JiilxtVCWrk/+I8+dVAApmDBTBEMMQoWQgczLKqzFEnmRuvUIT\ndq5NYAsSAJUWcDaBzUvoshT2zBYgG2W4Zky9X1V4JoSZcJ6Fsj74uCjrjOQhhUFeBvbJ4sgjjyBZ\n2VaxUeLaK+HKHGwL2AhEsYvcTGFAh6pBlHYW7FIol2DHg7fj8LlPx9quPSgLK7FEhYVShFwr5KXD\n5a98Oe667St41iVPQ27FHWWdqtpuHQOaUM1eF76bOOr3PetYZQJ3DsOhQX54AlulLwiJNa0AptxW\nrGMxinTuc2qVHpRY1AAq1jWR/M+J8QP7v4yxIiTOIrEMkxmU41LKaoUiB5QuUSbk80eVcEnhE7mG\nuRlLieGzVphHRHGhLXtcxb+hZqPCQIJyWRClhBGctf9EkhO4p+0wIbbsKTrt2pHtDkciFI4eSnLq\nNKS7Y502EKez8DGRrBfdgXvdp+re2QZQulifPnbOAmBq3qPCNOO24tPHrQ62e1PAbUEKpq3rWG47\nkG8YQDXb4KbbEcfYdbZx+auuqrPFzHKLiglzhvUsJ+VxkhBU7iwe/MTnq+zlrrTg0k1lqra5hfUj\ntOzY4h+KLRWAyi1jZBljFy3WYWQd1v2+kWXcf8ZeWK7LjBz7Mg65dxOVEwEtduJQTkq4XIKYXV6n\nVHCFZ1MaoMBGnoDm+/XZm78qW/2zbH3sURjBFZJultZFAErSGDifsyld2+5deAxbOpxy101wxRi2\nGKPMR7D5GHYy8ss6bD6SZbwuv/2+0YEHfXk55pHTz5P4qiIAs3DOOjt6aR3OOOdsFD47uvNANQDB\n+NqA1tsex78BHuAJIHEBONk6iaYrBCjF+i4nNYD6tNmGkXVNfVuHseOGrmXdNXS9biWFxCS3KMaF\nD1R3wmSOJt5N6HVdWDjr2+TBMkJ6hpC1viOwfMrz4YHTwVvvqOY6XNadF5iovuVE675OYBAVJEL5\n7Wk02mU6ZIsq6+N7T+Eai3ZFvyGfYrYcbnlIgEqTFdn4AzTlCvNC/pHtdNP1nPLwRObpKpNmdvZF\nWmVc3on2Yxp5VLplLrGSzmy2nazhnDJdwoyLdvQ8Ky1dx2BpPNWm/t8EgCbH+tvZI339SkWM6+k8\nXcvIrE7oZLzUYy9V8HXQrLM4/YWXSIyMlelbXFnCFVGG6sJWwcQS++Tw4vX9sLlFbhnjyKBOYsMZ\nDKlfBl/7CsZ+28gyJk6WsRVDnIfYqokYz1vGSTWcXvJRNedxc0XpwZ8HhNV707zmyy4+t1oPQeUO\nwFN3DLxh9ZMM2zoTeW6j4HH/3xYOo8MHURYlHjzzPJTFBLaYwOYTuHxcASdXTGA9wLKF/52P8eKH\nboHJhnB5vH8i7JY/x/6v3FkBNQ7z94VRhD65Z0jCabkeyg+ISj/2mVub+gZQxWmFuCjPPrqQdTwk\nTS29jss67qmcyGL91DcXrx/A2DJyFp1NenQt61JmPzQmjpEzqmejLNkPSrDg6tmS8371noM1WI6m\nEmqA5RbjeOe99SwpLtwMoHIpr154gTCkHjQv9e7Q7P7rpDtvSalu26yI/xnMRJfkX70N6TlPm9o+\nURkyN6nrm+HGA4CLd7WmdZFCwJzYp4V8XraEi8Aj7f86+NSnzDhAZGtCTYA194g63qlQ9fQj7dY5\nZigiDFvuovZovVja9TCA1Kef6JKF0kzO1PVs8JVDI4VtlmfGQAFTVzyVzqD+Xc+HuICuvehNprZY\nVDYxAfHdAA5BVFowc3vaBBDROwG8CsAxAP+KmW9cvqXf4BKMKgdU4Q2sH7YuSwAsXI28s8HgFRZ5\n4bN9O0bBXK0fKRwSJW+Ljd6HL2zbjeccfgiaAEvyzrISn49ygCKGLqzkFTIK5yfrsEUCXSiwfM9t\negAAIABJREFUlelguLQ+KLpmo+S/6xi0EwAj1S4fX2Ry6DBuHw0rd9j6J/8R9tLLvLvHg5jA9kSM\n1EWnJrh1f46ymMAVE7gihytziYtyZdWeuB+wxQRmsIJr186CysdgHSbOreOlnMpgFWHrWefCOgdt\nSZgvjoCUn17GcZj7LwAFyX3FILz08qd36zuATUlqVevaOdhwL0vr3bgS91QNFCgcyrIGvLnXdci7\nZFnacH+2ht2To3Jd8MHxBJiiwJgAxyT6hoyIJgAqt7CGQEbDpham1Dhr54rot7QC5iNdg7mOf4v6\n0wv27Gz0riFVhPPuvgp4Op7yZiwqmgipnhVYvlS1j5k8aUDUlLQMZwmFBFaGpreNaE+sTBeAAiAA\nqn38TCBVG8ZGvMzUOT1o2kjAkDb1YQBw6lM6XXnNuHpeKtFjV7zT4vCgQ/JRY97AeNTeQhLHGrSE\nABQOmCLvFoiLagKojrJzQDMAyVBssvl6DMe2df4YdwSbYJscgJcx86NdO4noVQDOY+YLiOgKAL8P\n4AXLnuybRjyYqg1UPR+asw5fPKJwfmmxdsYO7L/1QQFUubjcCgYmLQCVO4YiIPdf/XGPcNGjDyAn\ngvbGlePHjgjKsewLqRI8C+SsnNMM6naFpQaCFg03T8S2tcMCLDPUli1wo6KKkzFXvAB2PfeMj8Rn\nCQsVAshlOP9tx6iKg3JF4QFUDusD86vRjoivTcEVOUh58OT7D5JkSiClYUuZhNmWDlo7WK2qNtik\nNv6ldZg8/AhW9uz2MVGLfNgJSxeAZAU8ravYHltY2FJG3+3PCat5DahsYTvBcu7dY6UHK6eMjiCP\nTqsgcbCaUE2QHHStGTDM0LmFNQo6E10/tG+EswbGM6K2pWsfDxWU6oKO++8BMyrGjr2uiyVjoual\nODjRmKgngTtvjviH23jeRYamz4tdqvffV0znaMonk6ltm4qv2aRs9JmZ55TaqLSPjzuUTo9biB9I\nuhi6zTSkqYNE9TRgxjHLnKdTTNaT2DO4b+bVMX00ReedN4H0PNmEO0/GSPTLawG817fxegDbiOj0\nTTX2G1bE8FRMjtevuPRcY7k4y8GWceir+8TQFpKp2vq4nJJRsRLhf8nA35zxtGpfWO4zw8bvIhzL\ndc6mwnnQEvJOhcVyBaCqyY1jFm0ZIaoYiwBSgpGV6Vt87if2oMrK/XLWBzzbJoAKgGpqsR5shcB9\nV4JdCEq3fmRfWSWudBGI46hdIR6KTj219sYyyy3oei3DwAEgAss18AyMmejdgks57zZbSMoGD2ZL\nB5TefdjWacESXF60tuetbaU/tgzPjV+szykWppI5ZTXxrsymrqvJrF00gCBc14w+yTFjt5nIgAIA\nx46Nlg730EpSHPQtJ9rI4yc/iJoHETpimGI5KxlPHZJmfQk0Z5xr5j4Hzsf1l1FPm2/fP+qvo0MW\ngQez7k7o2NLiaJXvo7ucp7M/e+2G2ndc5Ij44O891teBz3DrbSqwvMNl4WbE11XlN9ZzNABpxHZt\n9msr9ROu9i0zhAH8HRF9hoje1LG/PVP6fX7bSWmLC0lgA6MK/5XvwVTERoXUAyGJJnsmSoa3c8O4\nhv+FY7z83tsqpiIY0lMm65ELyAf5eqOaOxnlFeqpzhuSMrowxUjcNofR+iSK9+l+r37l9/9ntc6Q\nD4E6KJs9Q+H8lCvNMiH7uqw7YaB8gDNbSWdQAShbVqkOXJnjmV+/Dc7K6EEZXSbHuiKHDeCqKHDe\nzddJHa5O1hnAm4tBFEcuKQ+e2i6sReWzn7gTbBkfvKdo3euQJLW+9xXA9SxUANC2AsH19qDr5no8\n5Yp/boAKPN/w8LGKbazbEgGoAPh6wPJUj8RAUdrq3txfpBXeTgcZ7JLJNucFlp9gGOrJC6I+13Y2\nzIG9QrFulk2a5SbsomTq/ZR2ZSVvHnPhqZtnbhZlMOKOYGzWqtZ0dRDKZ0LXl1+96fZtSNgBW3YC\nAPas9g95fSyE8mPtxsydHFofuGe6njnnmZU/a71Y8ssfm2KiXsTMzwXwagA/TkQvXroRJ6VXLnzv\nAQCowIrzc+AxPJjwLI0NYCdJPJCqlwCs6pxRqH6XzBXQKhrHoVGHszX7xOyNe/gdMmQ7xmBgmv2d\nnX42f/FHXifX5H/HXYl19YjfMrA9Acw4IM4kHnJAcZWXKk5rYCugFNZvOn1vw8XX3h9ca3dccHnl\nYquynvs2cbgnTtBdM55ng5SKj4UaPfAQLrvyqQAY15zRjAENOg75nJxzkf7Qoy9Zz6PnIgRxV799\n2QO5nTr2WdsGVbbz6rpD3jAA7/md6/y2kBg2GnzTI6YnFQEzsP+RAxu7b16Un/alb5nj6ruGiG4j\nojuI6OdmlHseERVE9J1LNTKSJ21M1GU7mr/nGaso5/SGz1W6euh+Q2whcTEt4QXas6zMA0mLXN28\nL6mNhGw16kUHKl+2MqAz2/exEljV3Fvt2KxgUE6ndZglXfridKWr6Mxa7Clnb/CY2bLSMdHootLu\naO78/Kdx1w3Xzz2OmR/w/x8hor8E8HwA10VF7gMQj2zomin9pMQS4oeiUVu3fe92jB89LLsjN0pl\n5BrBzICd5H5+svbik20ievfZjx6FxGdOSCNhF5UnWAZuOTzBc3ZMG1Y9qPs0ruJhuq5r4x+lhf8w\nsIF98ud0MZhydUyTACoXASq/+HvKge0gajIopKpyEjsV7msIkjc1A9bqEMsKWMEDruk+U/RTW5VK\nojYMTz8N5eGDDebxy8eAs6wD21DcAygfwP6/X/bP8bx/eJ/cI3/eWbpmACsrGfL1iR/5K7rdYpQf\nWUg+Xql2NQS2MZ5vka3DD/3IC2t3Xn2hrd9RyEG8GaGt7PNoAdtPbRnpBUXNjYnq204KwG8D+BYA\n9wP4DBF9gJlv6yj3DgAfXqqB7fYej0qePDLdGTxKa8DDX515VK/3o2c4euc8bsdJHo+gukVav3DM\nznFu76qZXW0FoFod/KyUJZtq4YZ1/dg9G7G0maeLLr8S3/amf1stXUJEK0S05tdXAbwSwJdaxf4K\nwA/4Mi8AcJCZH3rsruQbRFpWOBh/jlweFZjxDFB4ZhneoKJOaBjcwB/Z+0y0YU4cBmwZMM5WCRFj\nuWAtrcdu+JM5yyiOjaeNKSCgZoHnvWlcm0HZSqupUVtV8s/azntmzFUgrhFIXiW1jNoYsViN6WCc\nE1bLjyxsZzmP29AGU46bA3TsvGufN/MCgKcOQpnWfIcQXb30o++v1gHAejdoQfXsFW3wMjo2aRzD\n8cJcDaNxDGG/eq6D+9xvLoJt+XT4SwXOqv/TeQY3IjTXndfbYz8fwJ3M/DVmLgC8DxLD2ZafAPDn\nAB5eupGRfJOBqGnZwUeBXefOL+glPBpfG59YJN5NDx5t/KZNuy6nRd172/xCG5LHEBC2OoqlhsVu\nFgwXHR1O65o309nMkkSpmUuPnA7gOiK6AcCnAfwvZv4IEb2FiN4MAMz8QQBfJaK7ALwLwI89Jhfw\nzSqtxyHk32kXCVu+5e42xu2pNtRhpN/68x3nTJeZ8SzO2jdLwmFhCHwlrffxvpuur4w+2GEwabvT\nQztmjaytEFjn7rPuvT0UbNznuLhrAYK66kU/Grs3uw3GBwX3IiDg1zjbOz6ub1usMoeO28NosFFt\nufvWe6c3tsJSxpPct7e3mg2LpjnuvH4Q1Y7XvBeteE0iOhPA65j593CcDNAJDaJ4dHR+ocdZwl3f\nO6izp/OB+5eito/nk3fp7rVm1UukOJglqcvh9lyEJD9yHGtd7PrL+7688ao3yIB1tmSzLFrSFQfX\nlMcq8WUY3t63dAkzf5WZn83Mz2HmZzHzO/z2dzHzu6Nyb2Xm85n5Umb+/GNyAd8Acujh5WJCYunT\nFUX/F3nTq6/3UvqtNzx6d7Xv3qMT7F8vQBt0obSZmw1J69CzLr1CXJBEACmMqxxsrXao5Vzcr3L3\n4r49F+Ksu2/uqLNenznH5yLScUtuv+XBDXsQiKgBFiiao3SqLACzMh1PG5cP08M0dtDsLu6cp++Z\n285B1j8N2LIyP9nmpqr/TQBxrNSmO+ATGkTR0AMDj+LvPjR7Ko/5r/T0/VovNw9k6JQzm2/iolo+\n0YYZoD+YOfcJOIt0C4Dj41YcLxg3bc46r/Hb3f3F3rIHyzBR8QKP9vqhanXpqzkBdQhIcOas5aQ8\n9rJt1ykAgPzhhwAC/tfN+3Fwv0+SGM31FiRZy8Sw6Xo+TkAMu8+VKeuojf0omTZiU8YzWo8l1LFn\nLcPONalHaQIpqtpQVyoWN37v+56jeKtMhCxL2K4VNQt1GPV6MmG5gjAfnuwM25ptJKUAPwFxfAKl\nNP5W7wGIcP+5z5puJABqvRfk2xxLmwH51E13tSvp7EguvHh3o62fvVVGHFMLISsCvjjc0Wieis7b\nvm2xlOujCnBTWMhfR3VN4k5tXGdjvafPnBtwPA1sNtPF6I7A8q994Z/w93/0Tvz9H70TX7/tCwBw\nQceh9wGIA1O74jUvB/A+IvoqgDcA+B0i+o7lW/tkCSxXCmCHc7alvcNr6zC/bmF4RbfYnxXzjWVQ\nFgmdV9QfXL6S6KVfgI0eNmh9VDqWtv31Fx7Gt12yq/c4dc6zevdtS9yiBBewsm3BgovIifUczXDZ\nnZTHWdLTd8MdPoBvf+ZOuCMHYY8cauwnrUBaoTw2qR4j8nOEBYNYrm0FHT5UAymWhJlbbQHb8dIL\n4+jBl2cfld8W2EiCB03hR2hPvD7rOVqC7Q4GXBNVwIWoBWCU9oBErl4pBafqhJnkLKAU2Llm+0j5\nxJqqnohYa79de2Dm6/Dn7GLfwtyS5O9ZuH/NaweuvPR8SNSS3L7RpMCQgT/59NfxxmduqYvqCDAr\nwuUXn4bJEYljUlquTyuALPDyXQr3HyPJKk9NXVe9e4+uw3por/YAijANAJXuAKBRW+sdVOmhLW0c\nHMCyps1NzdLV3osuuxIXXXYlAGD90KO459Yv3Nlx6GcAnE9EewE8AOC7AXxPXICZn1qdh+j/hYQs\n/NXSjcUJzEQtOXdhryw0lcgCtWxoX08n4xpk/PGVRR/ePqDUt32RWhe+mmqS3ekvMQAzAZQc5gt2\nxEcsFNTfN5H17JMucYzv78aRW/o4TTA8T5Zx552U4yfXf63l9vbGvXp2FYG0FgDljXlggZSmijEw\n3hAOjh1pAKBgJJv/ZUmm9sULGutKK39eVTFQFH7Hxj/qV+4ph/Jh2yN9j5dWwkaZmFKhupsM94GU\ngvIAKDBQshgPjFr//aK0mdrWuUSMUQzkNNXpP5pu9iaIePhzN6LLuTbMEpDS+L4XnSP3RwUmLQIp\nEXh0FFg/+a+JcPie/VM6auta2Ck/1Uu0L4n0rFrPhPK6DueCqgE0adVkQP19D8/svbfXYUZH9h3s\n13tTrUuHKmgiJKp/6XO5MrMF8FYAHwFwM4D3MfOtcUxn+5ClGtiSE5aJ2nBHH6ZTmdquahdgNQXH\nkkHXbeVtwIW3bwLsHBBGhcNgE0PXD45LbMvk+EVYp1llnjCvTjWqcZPPsNJe56Ge1tV26dqWnRNZ\nq/FhuMHW5rGxND/Pp9syFXAQySCKVztOEwzPk5MuuydWrti7BciPdEwB5YEK1cBFGBYxYqoCMwKk\njGWkimABHC0djKJ6NJ4LHAhDRc8dUe0GMopgvLENBjcYYVMZcVUbVCXnRQWoBEC958ajeMtLT8GB\nkcPZ2yYAzU4BUjETqglOrvvwx/HMl1xZbasYIU0Qbxw1AY82IFdCa8lT5TjM3Ucgbo40q1gmpUE6\nAZkESicCrkwAVzLti9ZKACTVbWjnUdOqGZcUZPflz+697qg18je4Rqm+n8ozf4lRyJWqQLMhFwEi\nhvU6YhUC+72u1bSuR44xTBQSr+fEH1s/A+GZCvoWgBf+NwBz8xKw56K91XVtOe2U+hppurcjBPzY\nD3bmCs3uv2ZVy8wfAnBha9u7esr+4HINbMoJy0RVMpdZoOmv+z6audq+hHI3E6hNCjt96pVhMv2Q\nbkS2D6LJiPtOt/Fqe4Q764tfjq4HOrBh89vRX6III5QflKDyh0Y8pYOxbTWg8+2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OAAAg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\nczUA4OGDB3DjjTfe3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tWFRj5Q6A0KLGfl+ZczjZXMGvZerlGs9CzQC6CPy0WbABbefu5T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SUHqXTKFrALCUQLtCCtJ96wbQGZfVGKixrjIdqDL3YAuYoqH1lHK8Yvl3WJix68KzATtCWRPI\nGR8DVQg7EQBUPoQbDTwzlcMORyV4snkBV9jyvXS4t2Dr8LQnnYzi0GKrrokIpKg0qOFPe1ZCpQl0\nlsDlCVRmkPQM7p9dh53uMNj1m2FOeN+/3I5Xf99F+PCvvQfX/vfXC8serFmNjeJw+XAMXPgH7xJW\nwjNPQ2Mx9AZ2ZISJGhpZHsDT4m23w+7cBfP878fiYo7cOO8OrP6Cy7ApTVeeVoReopAkMxgu5sgS\nhSzRyE7cifWvfx0WC4t+oko3neO6N0ETgaCgqbqm7Ixz61OUc1X3BOYqmcAYD5JGJYDifIjX/No1\ncMMFAU+jHDb3es4NnKnrmq2DM6JvTIgVUlqN6VqYqKTSdZrAjgok/QwqM7h360XYuffrgHP44G//\nJV7+ay/zSQfi7n3cSevAHLIEXU3PZYFNZnzl1rtxzhknwzlg6wkbMDQWhRN9r0TEnXdke+cR0QZ0\nNBVvbDfWkJyZ8+Z2sRzbICqWSUXHmgAqWl/arNXWFZrESvh1ioBDhcN8GgIuKyD1jNPXy/uzn1A3\nppHRDfK1hxdw/pa5+ik63gMtbp/5TVOBp7ZvpMuR2JpC7ZmpSXZ/qfUdZ4tP3Dhgt64nLlvW6eP9\nG8HjEZjSrijHRGdcFrau66em3/a4qEyrqQsJTitHi4k6LlNKAO9lcUXJwmPjWYnSveMBlGcqzDCH\nHQozYQY5XFHAFqYysP6PrQV7xqSrOn3NqKoKRKlMDKsrUjGw/njb7X24faBwxtZG3pvS+PGrTgGK\nHNe95VU+Q9BFrIS/ZP/3Jx+/Hte+8Km+sKa4dgobWCdG7l09Q/83KARALeYWo8JguP1k5AODkXEo\nPICKXXs2lEBokVAkM3bhDfxrogi9VKGfOmSJQn9uHdzQwGYa/YYlJCJox0gsI1GVS1JHdffq+g56\ntqW+Q8KAuPCGNV274QB2VAh4GhUwwwIuzytdFwYuACgPpuB42bqmNADm1P8lYOeQOIcdd30WrtcH\nscNLf+77xLVI9QQEScDRpZ4lBEIkxL9dcPZO5JZL9gqwNgAAIABJREFUN15h5W/l7rzJMZ0rJAZ+\nES1NxeMNOhqSXwfgA5MO/NgBUZHEZuj2Aw5nznr3UBvrsETdoaXsOwPjgKcLULEHUEFiIFVz9Ywb\nU86HQqUDOH/LXGeNpqFxOJRbbJ4Vt9mYi6/lBlsOF9MFrOKmvOEcRBWlC1RgqR0qdJxvsAg1E7Vr\nabJQ8chr7RBWoGuvv4dzwpaMx+KharqOlwb6ugmmaBpdizy8aLFltj0OLYxgUDj0ksnFN6cVexQq\nlh+XZYrPzqNw7xlTxjqxd/E873/fjI9cfQL62sEMR7DDHGaQw+YF7CiHHRU1t14wrmylSStb12lw\nlJZYKHklGCbMzGYlM2HTBKqXIiky7zZinDKTiZGHf6BSGshHIJ2ArYkKgbpy/mQAg0cPQs/PgwG8\n/OorkVtfXNOhZCUKx7jxptuxZ/9BXHTZ+SWAGhYWCyODxZEpwVRuXO3POGFhOLjzOgxpmJOUIqhE\nQSlCohQSYsz0UuRGYWQc+omS8gq1Y8lrqA+VEJAqQmEBrbTE/qAqKslMGB46jJm+xIaFeKjAOEpy\nQF7GQMUAKoDl8DqNrk1hQQx8/NAcrplfgJ2Zgx4stOpapwlsqkuXretnsHmBpN/zx3TQ1pUAwCkN\nFQp0mgRsCkkqUKLnd/7F9Xjjy581ZkcYKL+P//WBf8KPvuQ5KBxjaF0tEH45ws5N7LiwwsDyq9He\nVLwpcUPyWQCtCTSxPDZAVPyD4bohPXMeYKRVIGdtnzGEMWbcR1/8P+hd8dzOU5f+bv+pPGI+ALIZ\nHDLArI4KabYwSzWxBaDH46KGKkNX+Hx8Fb1ElYaW8yGQCvAidnAg6Qwe1mPsG1iROMikxMy1WKnc\nyBMdIFQ4RW0VYiAVs1FNgFUDUE2J9T72g1xC14Ckl9cWyFi3ZB4EjtWZqZ+Ta4DKg6kGaC4/FyMg\n63emJXYBqFiatnA1ujvWMli+66QGNnx5glDCwBTgYgRXFPj7l5+MRU5h9u+BHeYoFkfizhsVYmS9\nYXW5gc0tbGFhC1eCp09/+SFc9bjNY6cnJdlapAlKk4+PIeTOQWcaex9x2LR5Dv94P+PKExlbWVLz\nA9OREPkq1pLdxUUKpCkG+wrMbk4AlmBkZgdyDg8eHmHHvGQEB6MaXGTWCvDJrcM5556GnYXFYmFx\n6P7dGK3biEFusXawF3vtGjy4ew/S+XXi6jMOxjg462A9gLLGlaCnzUZLnUlxZaqCoLSC8YBqcXAY\n82tm0bMKNtPY+/BebD9RMqb3PbIfJ+04QepEkRTmTDVJkV6uSLfmlNKfnwVCo12u+uNxqAFVeDZq\n5GPdPNtoFgLrOIIZ5sJGBRA1KuCM8/qudC1gyuF5GGF0EMDBEQzElQegoWsDnWnoTMMVCZx10Kmw\nUOyq7xAkut778AJOOGW71IIqfDaf6UkdKXb4+Wuf7F240S0efSeWGbOzPc9COUkiMCubg5ZkolY2\nt23paCpeHbejIflSB35sgKg4e6pcVN3RZWXgcl3T5dPNOJUAKv51TMjPD4aZM4E88zrEBURMRWxc\nmwxFAFCN+JiZKRiIMZdPWjXs3Te0WN9PSgDV9QywHCZ0rASST+sNI011BayUX7cks7fwKGhu/djy\nqj9grOuOwTaAdO2bcW48KH5s/2XqehIDFT6nvbpOlwLTLbImUzg0cpjL1KrB77HWpPO7VuJGtM6C\nfQ0oOxyC8iE4HyEdHIAZjoSBGo5gFsWw2rzwLIVFMTQ+NophjQUbMahPPnU9ioWipm8BEcGoKgFU\nwagWDjq3mMs0zGCE5611SIyFGbhqPtXKu8SkLAKbDDAFyBjMzK2pA0Qvp550Qsk+AcJWB1eeYXHh\nFY6RO4fc+UDy9ZswGBksjAy++q39WLe9DzW31rv1BEA462CNg3MO1rAwUcylIY0NKikqXxUFJspC\naQVtFbTWODwyKKyCdYz+2nVYzC2ICHNr12KQ2yqrzzFyy0iVQ6EILlFwTNh/1z3Yesausd/nwcMD\nrMsq9y2TFvBkC1+2IgeKUZ2BGshnE4Hm3fcfwsb5HlxhYT2QYutjhFiudyldq8RCparUtc4kpop7\nKcrALr8viKCUwqZNs+IN0VpKIDgHtgXIZXVde+RaAlkPlJmBl17zPfin67+CSy89r9T1SiSA5Vhu\nHi3ga7mwbreNFgHgzOZ+EwoF/2rbaVr2bzYk/xgRvZSZ/3zSeB8jIKpFOtmpZhZWtyF2ILSaq7F9\nxosqEnPD/cMYOl3VoGoGERcDYEKphi6ZBnQzM9Z7p34bJ7PSjNAurFFS/dH5Y9feJLdeG4ACMLHB\ncmdX8yaAajBlrfsA4MP7QGs2YhEZZsdiBqfQ9RiQisGTvK/BwWj7fUODjf0EbXCTGZjLjow772g0\nID4uy5QYbDhXsVDWQLGFMzlcXsB6FkKAlDesg2BwLczIwuYWZmjgjIMrHGxh8Wh/HeYP7QciUBGE\ngkENbp6E4HIHm1ronkZihNmIWYlP7yE8eydVQcrJCDpJwabAPV+/C6dccq6kxEfX1OZCD2nvcbq7\nKdkJCR4f5uLGG+QGi7nFuu3bsZhbDHKLIrcwRkCULUEUy7V791tw6zVFKSpZOG2UuLa0AE6XaNnf\n6RqrVLaDUcI+JSTlD4wmFE4y94xjpJqx7pSd43MyM9bO9YB80bv0HDBaqJIIvPvWjgQYxwyU8aDZ\njgqYgcH62RTFQlGyjkHXwaUHZjx4KMfWuRQf1Sfgh+2eVl3rVIuuM42kL/um1pXB4IDMQAKwRddK\nJ2BrQb78AmX9OoBih0984VY8+0qpsxu+h+DSs8x40hXnY6EwMD4uaiXiMB7zdl42i/My8VoccAbf\nMsPbx++77kLBRNTVVDyWZkPyv4I0JH+Mgqg2y98aRNwFkjrYpzhVfqnn/dEi0JtF6coBasZTMu8q\nA9ongwpeRBDCuRYANQ1v0y7h5jWOo3587ezTUgCqs+dWQ4Y+jiA+V2zui0f3I12/oTznGIvVsgwA\n7ti7iNM3tbj0lgoObwCoVrDcVdpizUYAwCzGky4eHALb+lXw+IATzJAZ03VnwHjXhXrZ2E98iYdx\nl+6R5I6OM1HfaeHyj5il7lJw8VhTuvZCYLEbBdapgBmMUCyMYEcCnIqhd+XlEitiC8nQmzmwR572\nedzFQZqwb2RxwlwGlRAoUdCphU61gCdTgRFAfvtXrQHsiMqsLpcaqExcUjvP3Fa2oCmb6LZcsaS6\nCzPBkKnPuKoyeVXmwOHee3ZjZsNGDAtbB1BF+PMgqghMlPPYrT722nV7FooUwWoHpQlOKzz6wG5s\n2LETHPaN5gutCPrQASRbNyPVClniUDglZRU0+5Y0qsxIi+ftr9x2Hy4+dSPKzDyOqsuXFcmlDpQd\nRbrOBTTbQe5Bs4EZGpiB9br2rtvcgygj7lYwsAFAvlDgajyA3F+zgCiFz/Q24pl0oARQ/zycw7Pt\nQNy08UNxGYguZRBclkClHvBlPZCzwkRZCySufJB9zhPOwcE9+5Bt2uh1zmV5CGPjYpsrj4myDOQT\n2IMVTm1/C+CVAN4G4EcA/E3LNvcAeCIR9SENyZ8JaUg+UY5dENUi7aapnZEa23ZJo9ySkdWbYNwp\nLmHQYVzZAaMB0I8y7SYZ2Qnun7sPjLBzXdUHbu9igU2zaWeAZRd4ci0rCsulay6WuApwzxe362Kk\nknUVwxRPM+F9F2SsAahvfxXYdT7Qsa1xjKTy55bLxwDUsnSN2ve+rV+/n2ZQlKNZUtdtWZttOl2i\nxMORgD9dWTzH5T9IPNVBDIQYmdCuRSqRF1JEMzfCQo0CgJLX4MIrjevQCDMzshUzYaIClI3fgCbC\nPAH5Yg6lFVSi4HoazjMaaQioLqljmfMoKtCoUo299+zBttN7FYDyhTgp8X38Gm69WBxLOxfjGH/6\nof+D77/mWWWm3T13P4C1mzfh4NCgpxX2Fg5FblHkBsSMwoMHY8St5YwwTwKkQlyPAJdicAjpzLxU\nY4eUdVBa/rRWcAljZsM2FLkBsy7nuRFJDKdWBD07j7Sw6CUauXHoJRXwC21nAsMWecRw0dknAfki\nAODjb30/XvSm63y1cVNVHfe6vnXPCKemxicQeAbSA6hiMdLzyMKMTAWgvDtPWt+Mz+GhN6DShKeM\nHkaeKuhUwxnGU3v7UQx0NLczSBGMErZRJVqyNUcFVJpDZxXwIw8Gyce+hZlp/oSNGEUOF/YuvZJ1\n9DpfKRPFmAyUVjg/vg0tTcXjQsETGpJPlMcUiCqloyZU9Z7BxQiU9sbXRdt0qyNinoB2QxgZyLG0\n9lgCgCrdPL5NyARpO1YAUGFYTQAVX2EbgGoDT0FS3T6esE8MpiYBqWbg+dQS9HPKhQA70GgB8DFn\ncA6HDGE+YSTE9e3DgNqOVVs2SdeNfcZ07e+FaXTdkGm2PVp8kc2Pg6hjQuJ4KN/mIzSdtUWUhZX7\ngOK8gBlaLBzKoYyrQNTIwAzFvWMKKwaeQ6sNOVUVVyjxhIqA1BISxUgKK1lZhsE9QQTyQABAkWSz\naQXrQZTODFxmsHnznIzZOV8w1Hk2qvv+CiBDAo6lIOa1P/wcHMotjBN33oYtm3FwYDAqHPYODXLP\nPlnjUIyq96YQBkqAlAE7A2eNBG6XD7SA8UBG+UB4pzRUksEqhYR1yUAFkTYpFiNFSLVDXjiMEofc\nWIyMQt86WK1qzZIZE5h9dviBX/kxIF/ArZ//Gs664CQPoHJ8/Ov78X0nKZwxxygWTBU87l14lX4D\nG2VgcguXi66NYxQtug7lHADxSCSWkAZdpw7OVKURYgllEGyqoXIBULqXwhkLZXJx6bmqV2P5kNqY\nRzmEOUDYwbIBtUPZxmclwi0PBfX1y581vYvuWS3Ly0LB/vNbsMw2VUcmAONoyqQvrMtdAywBoCpU\nPd0YOp62YuarK0arXHZkzGXbUaYBUPmUTwV/duP9rfvXxsDAgaGZeJzVXC736jWy5pPKfTBSvdq6\nGmQ7Aro2oNXrOgb5+WD8HFNml6wmwa6sdtzxd1yOvpRAI8TJOCeuXGdrVaql0GJw8YjrTjsu46CK\ngfyZocEotxhYxqJlDKzDwDKGjrFgXe114BgDK58H1mFoGMXQH2+xMtp2aGGHnvUKBR9zA1cUZZX0\nsiyDMZ5Rk2rmbW69stgmon53zGWxzFCyoKwb5SuWC2CyMAE4xO9zg3133gSbL8LkA5jRAsxoETYf\nwgwWYEf+NR/i0Xu+gWIk29l8INvk4hqMj31436MC1ApfPd04jIpqbIVP0S+ZKK769oVKSSWEKEGG\n2Imzn3COfEcedF595kyp6//Zf7LPtCxEB7mFGdoSTBUDAzOSz0HXCx26XnSyfOh1vWgdbl2zFQPD\nyEfVvWMiRtOOAptparp2hfH1x0w5brYWj9zzUKVrf62DvKj07b8PceVy7W+lvfMsSyJC19+xFu15\nDDNRywE5Ha6dliy9VT37R+zT4K47MXPqaePHHwsGWn6WVpBJ9+C0aDwAoKzBNnXt/bJLd3QeJ2ak\n1vaqW6cZHxWOv7KIL0xEYD03Kt/XdS3vDzmNeWXbwc0UkoRGxbGukznMmIXxMU6j62w8maCtKvkR\nwtilHI+J+g5LzbCyMCc2cunZqhq1K4y4a/JgVI0wTgMBVGYkLp7ciMHMG8xEzFAAgPaMrfbp+pYB\nQwzLBJcDPQAP7x9i66ZZkLaS2ZUSbK5h0wK6n8J6o6/7rnLjOeNju9zEuTTMW4FNKEsdREUzi6gG\nVABQ1rK476yDLeS9yQu4Isfclp0wucT2uCIXMAeUrwBASqO/fivsaAhOErCz0Enmt8sAJL5TEyGZ\nmStLJ4SA9+BqDAU+nWdWAssSX1up4ri0TVjIviimj4tyEeP4k4/+M/KyGn0U/5QLuPmcncflw0cw\nNK4EDZN0LWwUl7resu8BDAiwLLnNGYD3XvFyvO7rHwZpBZt6fScEnQrbWOq6MAKUI9ft5h2bomuT\n15ksxSBMr7E7z39Pha1A8kpkSXfeMTa1HftMFDxgmGgUp/hWJwGoWk2XyX7+8kYKAKorDusIarr0\nZrexTBNO2ebCmwZGdm3TxkjFH5oBm/Vt25a3gJDOE4gU/9KVKCHbzqtqUm39IbbqetJ9wRWAOoq6\n5sbraiW4QLr+ViJE9ENE9DUiskR0SWPdLxHR7UT0DSJ6Tsf+G4joE0R0GxH9v0S0zMaVjx2RmmPy\nPUvskbA3bMVFUrnyCnDZ1sVKavvIohhIDJTxjEJuPBNhGSMnbVQGEUMxKt87LBqHoZWChyPPXgS2\nYmQdRrnFOqUErPnMPzsKdYnEoLJ1pZuxAn8eGJQlZbrnA8eeePOxMtJUOAAVKyUOfJC59bWgnJGY\nJ1s4D6AMXDGCKYaw+RA2z2GGi7DFCLYYCRtVDIV1KkaeeRrAFUPY0UAYqnxQrje5gfGAzRkpmSBu\nQofcV00Xd1RVHT3E+ljrqvmPx3/yQd8ElCwUe+bR2QCaLd7//i/B2RA47r/zkYUdSQLB5QseQFnG\nQSap7j6ma1fqehCtC7qWfbz+c4tXfe5PPCA3MHmVpGCNACg2DZbaWYnbLKvSY3xuRrA9daDsIrC3\nciYKk5moYwxFPQZAlMTZlEZrQsAsFUPcPYgCfctDdMCCSWBp0vopjH2XOaSH71xiv6Vlmp5EY4AH\nkA7yy5DWq+oYsyPqjIdi1DP/uOVdLGNFMH28Aw7tQ/r062TZRMZRPtfIt4m65gnrG2NsXD8ND45t\no3Z/o+M84/t3yWpceUBkvDr+Vig3A/gBAJ+JFxLRuZBAzXMBPA/Ae6j9ZgitF84G8C+Q1gv/uYV9\nLBFzCUICE+XKPwFRN922F3+wb52wUqbK0Cq8cRw5Rs4eRHnjOvJGdegq5iJsOwz7WecZjcrImsKX\nTcirAGYXAtYj8FT26vOg4MNv/1g1BzPqjJtfFN/jjrkKfvfA5F8/8VnPOkUAyjKslbitcrkZwRQj\nuGIEV+SY3f8gnMlhi6H8mRw2l3U2H8IUI1gzKkGWAKtcSkmYHM5IEUtnBBRZWxXytIyy/EJw6Qm7\nUmfWuHLitas7Zup8YPa/fvJWOOtw15178cqXPx5cVAxUKGFg/PvCeBDkZP9BaJNT0zXXde31G96H\n+yQvde18pp/P8PSgyeYON35ldwXmfdD+b/zkHwOjgQ/grxpRt4mAJ5TxUSXwdFxrDr2snwz7WLqO\nv2MLQj0mQFSHxPRpkKSHXTNTGMJ4/+Wcq3NZdfzOulSAsDVbTqvGvEx/Vzhyqgj3HxrV10WnJVf3\nGodVTo2n1U97zq7PE/cdY8aaW1RfwME2fBfiK0JR0fmNk0+YD6YDvZNkGboGM9g3iI7FbT93fN+2\n++IoPlFNmoRW6ulj5tuY+XaM37lXA/gQMxtm/jaA2wFc0XKIqyEtF+Bfr1nZSB6D4tmokKUX+qGx\nc2UbjvN2rcOPz+z1mXdcuXoYPpC8Moyjlif0kWek8siwNrcvoqd8AWscATr2xR2rcTlfWwgeCL74\nDdeU7xG6p0UAqvyLXE/le29YL3/6U3D7TV+HcVKmQICUVDa3hrH14MMe8BQCfgoBQQf7s7Dhc5HD\n5UMPqkZ+uxFcPhJwZSLwVBTlZ2usGHnPQEmrOw+qHI/152NMfvAofMPiV73jb6v5CkDIyGRr8aSn\nnQG2DiefOC8xSNZKBfKg40LGcseBoS9SKYA3BkqTdB0+j7E2ka7ZMmzuwP66w/uLztlU6t8aaTPz\n3979MgGDYa5zgXWsfwdJNAswR4AKAqbMCmMvl4qJWu0D5pGWxy6I6hJnox91B0pZSbzMEvu4wwfH\nlpV0Zqe7aPnDCLJjvte5zq4wBqtLmsOMWa52bBBNJlMeea1uARsdDFNts3j7lvijleh696j7+3u0\nqO6p9jpk8cQzQWi8qOeRlKPERHXJDgD3Rp/v98uaUmu9AGCs9cJ/LhHDU0vJh7AVtUB/Y8seaQcP\nFyV4Oqz7MA418FM4jgxtYx1XxrdgyOfwPoqtkTR0KXUQ2sgcXvAsjS/A6azD4bwCeiWLVmP4p/8W\nXABTHqDsfNy5ZbwRc6hCLq1d7u9vklgiKzE6zhoPgCpA5UwOZwVksX91ppBlvtWKs0W5jq2Bc0Ze\no3pTtZYy0Z/zrzUXZYvnPyVhsN/14ssAMFxIHHAOh/aKTeAmaC6cuDqLAKZk2Un9tMy6LBhltffC\n1cFvl67D+8+uO7HUecjgNL76+xftnNezi1rKeB2zAOoA+EOAuVwEjxEEcVcXhzDvV0zUSsEOY/L8\ndaylxTwmQBTvvmPS2vpHVfUoG2sHA6wMQJX7dt8Vam6+9pmKYRRAXAdTpbuqq1zUYByQtck0VzLp\nPg5PDuGG/493NUeAxPj+UzGw6ACDj5gk2mw60LUc2Z6Z8S/Df16fdnxRFaqbeOzREjQQhT5cq5Tm\n09tNo8P40OGHy7/O8xN9koi+Gv3d7F9feEQGVpdj7JnyCAsLM33Tp26QDwyf9eTBVDCs7KuHW4e5\nVEk6vmX0Fw6LASwNiBguw/9/e+cfbMlx1ffP6e6Ze9/+0kralbS7klaS5V0JhIxlkF3CDi7/kOXg\nGIoqiAOVGJLwRwiBkCpiO5Uq/kowqUowBEIlQIwNMWA7xBEugYXjSvTDwtj6gSTL0q4trVZaSStp\ntdqVtPv2vXfvyR/dPdMzd+7Pt++9u2K+VV137vzqM3Nm+nzn9OnTZdxIYTz75W/0XC0nRnglGOWl\nvrJCaaRjvV1jQgZzTywefOg5Nklp/KO8RWKgEXj6r+4tR2tFj06Ml+n3K14pDftoP53SpY9PZbAc\nvDnL9INXR3vLnlT1AiHqLdNbWiwIk/ZWwj7LBanSfo/+SkyNsFLWV5C3kjSlXqh+ICD9pEsvEsI6\nvvPAAcBnTI/Yev7mKlkOJOXXnt0CvV7QczkvXiRNKXEqSFVBnEo91nUdPThveelIMaqwBwWR0p7y\n5qUTRX2FF7QgeCEgfkS7+djhpO3Q1PPou/NOPnowkE2v61kw3hM1X83GOUGiZNcbADjz2IPDdxpx\nY4dt0SFG+mS/udvr/xw+VTvBkG4eQhfU2KSPzZLpwmAX0aSYfNReuRzDV4Y1EHHb2Ufi0cqHTETc\nHxzQusONTq8w9L42ZAkfcZIR/ye/G3We3BmSk6s4czIf4mpQ777b5zbzwws7izK0ftX3qur1Sfme\n8PtnI6o7AlyW/L80rKvjqIhcDDBi6oXXD8Jz+Kb3vAWA+//vw+WmSEziKL2QBDPOjdbv9ctRYVaS\nnFBlAsjte3dXRmxFkvXoeRcFY0xpTFW58/w9yf7+iz5240UvkAaP2HXX7AheCYJxDd07kUT1hg80\n3/O2twysix9r4GXq90sC48MRNTHuPU+aQkxOY4m5onorgJb/i+290ptVP6bvu/Q2545iDr6alzYS\nvKAtxr3zN/y9v+P37GsRC0W/z+HvvBDWl0zsFy56pdBxJMz9SC4DOS49LxREev/73hGWS11HohyX\n474rqpVnpqcU3k4CiYy6jXJVyH30QtViRfdfPrztANiy/40D+p4WSkgqOqTMF4U6R0hURL7/TQPr\nIuEZZZpKp0X19g9LGLfNNAdgv/vy6ee+W29M8oDVn+1s+RSLyYitWYn+9N8dE3RnJZ7FAjUBj/dq\nmTqGdJNJb1Cvs/ol+wOyz9ervQ7deekNuBX4kIjkInIlcDXw1w3HxKkXYPjUC68/qPeIf/ctN5Wj\ntvDder/xhPVGrTBgVd30VFkOU7REotTH/z7/xJHSQFKWq44f9esiUQp28q3Hni5GTxW2MxpSHazb\nyzPCbM3g6Y2GtZd4gIDQrRc8Gv1eiMPqB0LVL0Y1FiXGHPX77Hr5xUHCpUlm8zTEozhOOXlqCZTq\nHHxahmFocp/qDuRK4s7Kluo9uezKC0ui1k+8UuGE/Z5ym7uwzEZOmaQ31XVP4aE/v2NA10vGFkSr\nOKYopa7j/fZyKEd7zl93vOcJqa+GVAxceHH99c/KIkfUKttCfx3D2645c0SdWySqPmoLhhOeZkx3\n90/3ZoxZGaLlp06caVy/Krz07NhdzIuHR25fzjZV5sWb5C7NkjV24BzPPLbqcwCcbxs8U8Ma+JXq\nfHmzvgCj5l38Ut1juQFY6o8us0BEfkREngLeBnxRRP4cQFUfAT4LPALcBvyshgdERH4nSYfwq8B7\nReQx/LxUH1/NNZ5T6Cv5qePF30ik/sXe5TKGsFd6BDR6oah9mUdDVXv/0n9RvcfzhQGjm/pWeqEr\nLSVQhUesISj4wPOnJ3rv63vUZY11AiwcebIkb1o2nSkRuvjYkYIchY0QtgE8sy3M45YSsJiCQTXx\navXp1wfdxHufELphctc/Pu7/ky/WLrzf3HgmBKayOij4lqUXyrgxSgIV6yxkTLZH2P5gShdNi5Yx\nRBq8UQA7G+YNjffzx/+05iDu12ulcSR2Id8qvFD+OsYl25z+3KPSs9T2u0VEHhWRAyLykUnOfU6R\nqIhp7PfKN/5i5noW7GwPwul+M/m67LzhweAz44JdY3fp77h85PaG3I8TY1Yy1VeQ3ftmr3hWhOR7\nE2GSa1OlJ1VP2PsuH9I1OQIr9/7l1MeMwlp4olT1C6p6maouqOouVX1/su1XVPVqVb1WVW9P1v+M\nqt4Xll9S1feo6n5VvVlVX171hZ6DGDl7QG3TvS8venJQdZYUu0WS1ITtSbb8ichPMHyPLw3mYNa+\nsm/n9F3NZcJNrRC4iNN79lZkbMoJePTC3Ykcg92IO99wTTxB3GmMVJKwSa3s3i8Iy2jS8K0XT/Hm\nv/8BJoXO4rWL944qGRo495B1lTguytvTT9hWv6c8e/n3VI797I9OOd5DlXt/5RPTHTMC0fM3NMXB\nbM1XY3qWFCJigN8E3gd8N/APROSacSc+J0nUqEFNK7Vh/O77bin/rCaofAosmMm1fDY8OqvFRB8N\nQ+ScZq689BSrIW7rhgmvzeoKf/n06gLC3VuDBlnYAAAVtklEQVTeu6rj61iLFActzg7qswekOHT9\nTZX/N5xXEpcXzniPa5guuMAkT+kk76mEl/KqfEzMYYopPA7jJJCYa27U6OKGbS9859Gx+5g6+YrC\niFR2NyPuU9pWX7tj01T2ZJY5RW1yTCVXYv3cE5zLZzZvPnDX4Yf4Vw82hE1MgTd/9F+u6vgUfR2X\nbHP6c45Iz5LiRuCgqj6pqsvAH+PTsozEOUmimhC/INyIhJIqq3tQzhZSN/FME/auAwbIXU3OWeSe\n9VKXZpmdaIo0D01ft004olsa6hH6qrz30um/0psI9Kwzn9cxqhFaWoWrvcXZgRhT+V1BECNc8dBX\nEevX2ZAFw4ggAju6GUakKMU+o+pJlutvREoYIoEy1hvrKENleyA5lWS/DV9D6RpbkxfA1o6JdZNk\n/fCEyoT7I0iYWDglWfHelecxxT7pu2XChMT+vM3tgoRJmIv/NZkBXK2+gTZmyLk/fWzPgKwAUiPT\nTR+WhpJMmQZdp5Oc2/I2xnmlK+TrJy/5fow1mKReY4Wewn+6vtcoY7WmOkLNIoXst9/+1eG7T4ge\no73oq425GoF6qpanaU7VUsHrhkRN9KU1bJ/+8C+vp2YJbxkjS/3l1L/58gyVVLHl2EFf9QT7jorn\ngfLlmxZr9TDl1PUz3Uv07Kuj4+akKXi9AXvk1cb1Xp+zkMrBY7Ixo/cmxTrniWoxCp4JFbnBCmMV\n9C/WeL1XsntEkuSNoBXBhcx3sVjx6xX/7qUlbju0fWdY9tXFOdZidcaKN+Zp3ckz2GRYc1uV/7d/\nbHIvREH+jFSIy/Wf/g/hlJ4wIaa4X8aY8l4Z668/kiJjkhKPMxjr/L7WUpIwU5xfxNctDcwlpqaJ\naotlGMa9TR++6NlCVsR75OM9NtZfnzVBp1R1ZEV4Je8Wy1aqehZ80stItGyQNe5fPg/CHx39eqmH\nhCRbKUlznTzD8ItPfWOC190tN98U5Jy5Hbv7CU6xlAyk6Ck8pYt8TV/mHj3OY7wGMBBgvM7pWQrM\n8QTE6wgz/DZcNnF4S8NDM6E3RN70nkkracRLp5fRC/3Q0vpXWHOF/uVqckgMayw23F92/BnYfolf\nFjNVx/iuLdNnah/EbHdgo+hK22U3ZwjGPW0TxJjSaElJaIwV7zEwAj2tGFQnSi/81+CH6BgZIMbR\n4O4/+SLWSGHYSmMcDHLwTHgSIog1SPwfDasxA22ZmmjC4Z997hOVeJ1hb4o13qPmai6VpVOv8PBP\nfQRZXA7eKPHeI2tLD5SxSGinxTqfQkBMpUstkqjqMU0lXKuURE7iPYnktdIQNnjbhpEEY8FaMAZM\n9KTBXXc+wY3XX4QYg3GezBlrEhkMVjSUlEgpFywvsizR5yR0DSwmjXfc10mq55SIBUJY6DbUGZ63\nOoGScN/jNVR0P8Km1T++6x7HSaCq91wmCxzgVa6lzL24my676fIwJ9lDl0N66ncajl1tTMQRIA0g\nHpaqpYLXjSdqqPdnQpfrZHWMOFe9/ibvVpGPKe67OmoS/ScXZMMtZlpDnXekX1njvrY2AstpwPb5\nu4fsNaKxO6u6luHbKtvn4ybGjMbDSot1gGkYTyyRqJjSMxKJjDWYgsx4I5snBCgLBCQLxSXGNt0v\nD9syEb9/MKRZ4p2IhjoSp8KgBkLx7cePF3LGbjxvWL1xHeepFrzXyZg6OTGFZ+3qnZsRI2zJXEFm\nvKF3hSfJ2AwxDuuywrgblyM2K71NxiI2C/uHY23mz2McJh5rPREzxnfVSfyNcibFy+plr1/qZO2k\n8LmXdnhSaA3veOcbivtpAlEWI4gLxMZWCa5LiXNN131KwuT17v/HZyI91hbPQPU5i0TZxOcwJcyT\nQkqvaEXvJhDmyc9UwdMsvv1+TgyMwltBuZ+TPMnpwURk02GYaF8HrhaRvSKSAx/Cp2UZidcBiZrF\nA9RwzMqY9APTGuTUu3WWp2EBkN5y+YhNOOJsVg9r/bBKfEPT7Y+u9xHnPHlmfPBqprV9RpIXhlzg\nDBd9NvQ1ojGKcU+y+Mrq6xmCtjtvHiA1z0jZRVUQKWsw1nrvhBWMM2XJTEF+HBTGMBMpjGYeSo9y\nuTCopiReuanunxvBuVBfNOLOegPvLG/cd2FhXMVIeJ5n/0Aw4g2rCSU/9G0Ov3QaY4T+ps1Yawrv\nmzGC2KwgTp4EOWyWY7LcEyGbeSLlcl/CcrnOHx+PNc5hbfhvBLE+u/g7r7u48MzYhgJlN6gxJYF6\n7IlnEjU3fHAZw4/tOI7dvK3sbiwIS0lkTNC52JIcZ0Khqyzq3AzqNy53ElJdnCOeJ5BpY8BmBnEG\n46T0StkqkfK6DmQ5dqmGa9Kw/FyYzuYbv/uZRL8lIRWirmdrR1X17vPIOEA1fOJRXmEnOXG07zQY\nlp5FRHaJyBdDvT3g54DbgW/i5wMdMZu8x7nbnRdduSITdO1EV6jHK33LVlObndqNSD/QaLybH5CR\nkszeT1w7jQxk305vgxGpBq+Pk2tKqGpBlEaNT6lfbvp3W8fBKy/CluGTCvcQbJy6Z2JiU7takfB3\nwjswja6b9Dlw0YPHZtYrS7tbB7adLbTdeXOIGPicGlUbDVsgMFkgT5mh5wwus+RhRFKvD2rgwKbt\nXPHqcUwSM5JOBuu9QL4dyBLDmkdSFTwXkagZZ7CZxWQlubPOha6+4KUouneigZWBZ73ulYixOVGW\nSEwyazD79uNeW0q6E6MnKsjVc6jNMG4FVcWE9kyCYdeenwRZNSSHFB+IjjGFNyoSK5PlCdlyXq5w\nbXcdeIG8mzWSqBhcHuPSUuy/cjcHv/JVrnv3WwvdFoRZvLftO8eXuapzmkOPH2P3zoWSNDuLcYLN\nLD3Xw2b+/uuKkvVXWA566wvkIVbKaDndT8wnFWVKu/9SshXJV2YEm1v/XAVdi/X1x+ewquuS7EdC\nmGr3kgu3cboP3/dPf4LTy0l3KoO6nhVPs/j2EyzftY8tWKTwQp2iN5MXSlW/AHyhYf2zwAeS/38B\n7J/m3GMtk4j8nogcFZEHk3Xni8jtIvKYiHxJRM5Ltn1MRA6KyLdE5OZk/Q0hyOuAiEydVKKwCSPY\nbTRqeubUyK62rTaQLxn3dSUDRvD/3X3P4DGjuvXq6PdC3XZqUjVu77vuvGPovrM8zvVG8dWlXvG1\nUUeTVszzjxfnueOOOxCB08tJru8hBCp2dxZeroaYtVoWl2RxCLEZoetCp41kaVDXvaNPAtB7cXyi\n0wG5wu/JMRkvV9u12o7OK7GhbZhISUAov/ILw+Usxvr/NrPYzLDYU0xYNpkPOs+N0A0G8brFE3Ss\n90DE0rWl8YzbuolB7QSj+u3eKb8cDLfNbWFgjbPY3GAyFwy+N7ImxvdYy9EDPkTk+MIlFO9MEusl\n/j75Vy6EHhtTeiecEZwNXjJrsKHb0jrhtSMPY6xgrfFeMdfBuk7hgbJZF+PysK7jyVGWY/IONu9i\nsg4uC9tcjsk6mLwTjsnpLS16EpHbwusVu/KyENwfjb8zwvHPfp7Qm8nffO1urAi/9anbCtW+8V03\n1bXtEcjm1TsWwFiuuuZijAvesMz5+5x5IuWJbKmDzJa6LPRb+98N+s3D8tO6GNabYn0mid4jcQrX\n/szJRWxueeHlRUzu5Sl0bdO4Mds8YwRw61fuKx/v6GUUiq5ST5Znb8Tq3qjVeKHWGpN83n8Sn3wq\nxUeBL6vqfuArwMcAROS7gB8HrgXeD/wXKS3ubwP/RFX3AftEpH7OIRBYPsOAMatknq59EXUaosGH\neTKk1hCk5eQLA7vfcfc96FJzTqCKaRpWX3wo05ipMRM1xqtr4gdpLXffdWd5TMPOBSmaoDun6fHf\nmldfqLF8MYljujMQvIXMYI49OaSWOmEZUcGIbjwVy2JT1oIhur7jq18bXldlfSB3F+9FAbsjJDpt\n0PU4nrItrw3Prv2uFm13XgUb04YF4v7C4aMJebJgHU8cfD54ASwms5jMYXJvULduy7G5wXZKktN1\nJTGKv11r6Nrwa4RN1peuERas4XSvz4I1dK0hD90+B1dO0xewucV1PFErvBS5xUYClVnEBbKXuSLQ\n+JJrL0eM5byTTxUP65nXfFLPgdghQxGw7WwZG2WNYG0w7sYUXqFThx/GZRbrJHhMHCbrYLOuJ1J5\nB5sv4LIuNl/AZl1cvgmXbwrLXUzW9eQr62IDgXJZB5N16W47PxA07+kzzi+bQKCcNeTOkDlLZg07\n3n+z/2hEePDrfuj+z334h7jj3/3GwLVq0LcfDUglwNyY2EVb3k/bsQV5sh1T6Nt1HBdcej6vZR2v\n4wl0/bQu0rHlfh2Jy57ImNyf33UdNrfs3bUVmxl279oSPKHx+XOF1/HYUiDOUbG1C/7gu24IowP9\n+sIDJaVnLBsY6TcdYmzUEv2zFQu1Jhh7lap6F3C8tvqHgU+F5U8BPxKWP4jvR1xR1UPAQeDGMNno\nVlWNYyw/nRwzHlln8A1N44CSbT0Eesv0GBxRMt7zVMXytnrmVk9DpJPMoTfOC1WT4cSZ/uC+Q7xr\nqTdilqScwxLHxX7rOj1Iy8AxNBOzYluYl66yz5DJdPsXxizFQ3JRjSRPpkxMXFtfyqOVaWxmQ4Nn\naor4q9V6klaLNtlmiY1owzR5lndesascqWV84PTV1+3xXoDMIc6TF5s5TjnH0RNnOJxt9USnawvj\nF4nUgpWKgV2wnjSlpWuFrZsXvPdCCPt4ArN5U4brWk/SOhaTG1zHeS9U7gpZTDCuYl1BAr947xFP\nDPIO0Xx0NlfnFDXROxEIlBEQVZwVcmfIraUTCEvuSjIjxnezuazs7nJ5js06uHzBe5jyDqa7gO0s\neCIV1tt8AZN1/fpO1xOurIsL613mAnkST9SswWUmeMEMCy8fI3fGe8qCtyw/f3uSViDmu4If/Lc/\nP6jw0FZo8DxGgizB65gSFZzf7vXrCo+gC/pYPPYKe2S58EClxLlJ19bgdV7TdW4F13VkXReeIVOS\n5dxWdF3Ga3nid+EmW+i8sJvDcmyFR90GL1TqjVoNojfqNp6fWy8UzB4TdZGqHgVQ1edEJLKNPcA9\nyX5HwroVfOKqiAmSWAng+7qHe07CPgFKMF42w51+CV3YThE7VRwSaMIE2WazCssYHSsztHuphvM6\n47I0hfMlcUdQkpN4xWkqA0MZm1S/XSbsN+oOjkMTGavwCRiI0TrrJCLRY1F3/WKTfdR1kDhYYISu\n+03fETPoWrWPNKXKCPEa4zBRaN8U+FvobZoW69CGQTQ+gqDJ6DbjHMZabDBk/cxhFhbYvrzCpt1b\nufWOQ1xx3c7qmYzQXVyh1/epDpwqPZVicuIUVmALPaxNRuxlBrNsyBYcrpOW6PFyhaEvup4ym8Ry\nWT5w417/cSQhWGfgQ8P/xKlF4ui8zFpsT0uCkhmyZU+qllYM/RDgbjNDX8H1FegBPqdTL4zA668s\nhUmJewNJcmMiThNG6UkIJLfO4JytEKjo7XK5pesM9pKL6biS2OXWFIlCM+u7JyMpCP6ZajtEuBfi\nRzF6XTtwWYjJWgpdtI6sk6HLOVmYkNh1LFpMwOxP9dZ//zPc80v/FUOfngq5KssqYd7E8ppPbtlO\n9uIxNplydF+h68yQdV3h0XRdh1so/5ss6tsH8NvcIc4H7EsI6l9Z7pExGP8ZtV4QqCR+LAtdefmq\nP2S9Nwq4C5hLLxRQzlc0qgB7gQeT/y/Vth8Lv/8Z+Ilk/e8CP4q/Abcn698O3DqiPm1LW9riyyTv\naO39OTTBeQ9Ne95zubCObdhGPy9tacs8lbPw7uYb3X6MKrN6oo6KyMWqejS4ueO0z0eAy5L9YrKq\nYesboaob3BnSosW5C1W9YqNlOAewZm1Y2361aHH2oKpL4/faOEwzbjxtGG4Ffiosfxj438n6D4lI\nLiJXAlcDf62qzwEnROTGEKT5j5JjWrRo0WKt0bZhLVq0OOsY64kSkc8A7wQuFJHDwC8DHwc+JyL/\nGHgSP5oFVX1ERD4LPAIsAz+rwR8H/HPg94EucJv6fAwtWrRosaZo27AWLVqsGTa6P7HW93kL8Chw\nAPjIBtR/KX648zeBh4CfD+vPx2cxfQz4EnBecszH8CN4vgXcvA4yGuA+QjzGvMgGnAd8LtT1TeCt\ncyTbLwIPAw8C/wPIN0o24PeAo1Tjc6aWBbghXM8B4BNr/dy1ZWL9blgb1rZfq5ZtLtuweWq/wvnb\nNiy9HxstQHJDDfBtfABoBjwAXLPOMlwCfG9Y3hIeiGuAXwX+dVj/EeDjYfm7gPvxHr0rgvyyxjL+\nIvCHSSM0F7Lhv9B/Oiy70CBtuGzAbuBxQnAi8Cf47psNkQ0fkPy9tQZoalmArwHfH5ZvA963Xu9J\nW4bqdkPbsLb9WrVsc9eGzVv7Fepo27CkzNPceTcCB1X1SVVdBv4Yn8tl3aCqz6nqA2H5VTxzvpQp\nc8qslXwicinwd/EjhiI2XDYR2Qa8Q1U/CRDqPDEPsgVYYLOIOGABHxC8IbLpPORda7FW2NA2rG2/\nViXbPLdhc9N+QduG1TFPJGoP8FTyf8I8LGsDEbkCz7b/CrhYk5wyQJpTJpU55pRZK/wa8Ev4oaMR\n8yDblcCLIvJJEblPRP6biGyaB9lU9RngPwKHQz0nVPXL8yBbgoumlGUPM+UsarHGmJs2rG2/psZc\ntmHnSPsFf4vbsHkiUXMDEdkCfB74hfBFp7Vd6v/XQ6YfAo6GL81RQ6jXXTa8q/YG4LdU9QbgNfy0\nGvNw37bjv5L24l3jm0XkJ+dBthGYJ1lanGNo26+ZMJdt2DnafsH8ybNmmCcSdQS4PPk/MpfUWiG4\nTD8P/IGqxiHMR0Xk4rB9kpwya4EfAD4oIo8DfwS8S0T+AHhuDmR7GnhKVb8R/v9PfIM0D/ftPcDj\nqvqSqvaA/wXcNCeyRUwry0bI2GI8NrwNa9uvmTGvbdi50H4xgzyvmzZsnkjU14GrRWSviOTAh/A5\nW9Yb/x14RFV/PVk3VU6ZtRBKVf+Nql6uqlfh781XVPUfAn82B7IdBZ4SkX1h1bvxo1s2/L7h3eBv\nE5FuyO/zbvzw9Y2Urc1Z9PrEPLRhbfs1m3zz2obNY/sFbRtWYqMj29OCHx78GD747KMbUP8P4Cds\negA/ouC+INMFwJeDbLcD25NjPoYfcbAuw3BDnT9IObplLmQD3oQ3Ig8Af4of2TIvsv1yqOdBfNBj\ntlGyAZ8BngHO4BvIn8YPD55KFvw0JA+Fd+XX1+O5a8tE+t2wNqxtv1Yt11y2YfPUfoXzt21YUuJQ\nwxYtWrRo0aJFixZTYJ6681q0aNGiRYsWLc4ZtCSqRYsWLVq0aNFiBrQkqkWLFi1atGjRYga0JKpF\nixYtWrRo0WIGtCSqRYsWLVq0aNFiBrQkqkWLFi1atGjRYga0JKpFixYtWrRo0WIGtCSqRYsWLVq0\naNFiBvx/yiMZc7t4oy0AAAAASUVORK5CYII=\n",
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d93be48>"
+       "<Figure size 720x252 with 4 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -372,15 +352,15 @@
     "plt.subplot(1, 2, 2)\n",
     "plt.imshow(I, cmap='RdBu')\n",
     "plt.colorbar(extend='both')\n",
-    "plt.clim(-1, 1);"
+    "plt.clim(-1, 1)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice that in the left panel, the default color limits respond to the noisy pixels, and the range of the noise completely washes-out the pattern we are interested in.\n",
-    "In the right panel, we manually set the color limits, and add extensions to indicate values which are above or below those limits.\n",
+    "Notice that in the left panel, the default color limits respond to the noisy pixels, and the range of the noise completely washes out the pattern we are interested in.\n",
+    "In the right panel, we manually set the color limits and add extensions to indicate values that are above or below those limits.\n",
     "The result is a much more useful visualization of our data."
    ]
   },
@@ -388,33 +368,38 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Discrete Color Bars\n",
+    "### Discrete Colorbars\n",
     "\n",
     "Colormaps are by default continuous, but sometimes you'd like to represent discrete values.\n",
-    "The easiest way to do this is to use the ``plt.cm.get_cmap()`` function, and pass the name of a suitable colormap along with the number of desired bins:"
+    "The easiest way to do this is to use the `plt.cm.get_cmap` function and pass the name of a suitable colormap along with the number of desired bins (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ja4kShBC65/tvMrdtrMjF6YHE+EOdO8dXKjI7245/vG8THn+D7W5BKQ3beSqcGRiYYk8D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\n9ZTSxwEgqDmK6qxZDCmlAwC+DqAXgghOUUpfAlAqUcqvEoD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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x116d71940>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.imshow(I, cmap=plt.cm.get_cmap('Blues', 6))\n",
-    "plt.colorbar()\n",
+    "plt.colorbar(extend='both')\n",
     "plt.clim(-1, 1);"
    ]
   },
@@ -431,24 +416,26 @@
    "source": [
     "## Example: Handwritten Digits\n",
     "\n",
-    "For an example of where this might be useful, let's look at an interesting visualization of some hand written digits data.\n",
-    "This data is included in Scikit-Learn, and consists of nearly 2,000 $8 \\times 8$ thumbnails showing various hand-written digits.\n",
+    "As an example of where this can be applied, let's look at an interesting visualization of some handwritten digits from the digits dataset, included in Scikit-Learn; it consists of nearly 2,000 $8 \\times 8$ thumbnails showing various handwritten digits.\n",
     "\n",
-    "For now, let's start by downloading the digits data and visualizing several of the example images with ``plt.imshow()``:"
+    "For now, let's start by downloading the digits dataset and visualizing several of the example images with `plt.imshow` (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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CLnkOdb0Z/VWuy+VJk8mknRhBXNk1SMKH3kyr1bJPVsy0Wi0DXQVc4P4iVp5sW/RCNBq1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\nqeThGw5bqoA40HCPU6mUE4F5jnBaXV31Wtfr62tbXV11RssVCjjRKKPX/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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bb38d30>"
+       "<Figure size 432x432 with 64 Axes>"
       ]
      },
      "metadata": {},
@@ -471,24 +458,26 @@
    "metadata": {},
    "source": [
     "Because each digit is defined by the hue of its 64 pixels, we can consider each digit to be a point lying in 64-dimensional space: each dimension represents the brightness of one pixel.\n",
-    "But visualizing relationships in such high-dimensional spaces can be extremely difficult.\n",
-    "One way to approach this is to use a *dimensionality reduction* technique such as manifold learning to reduce the dimensionality of the data while maintaining the relationships of interest.\n",
+    "Visualizing such high-dimensional data can be difficult, but one way to approach this task is to use a *dimensionality reduction* technique such as manifold learning to reduce the dimensionality of the data while maintaining the relationships of interest.\n",
     "Dimensionality reduction is an example of unsupervised machine learning, and we will discuss it in more detail in [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb).\n",
     "\n",
-    "Deferring the discussion of these details, let's take a look at a two-dimensional manifold learning projection of this digits data (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) for details):"
+    "Deferring the discussion of these details, let's take a look at a two-dimensional manifold learning projection of the digits data (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) for details):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "# project the digits into 2 dimensions using IsoMap\n",
+    "# project the digits into 2 dimensions using Isomap\n",
     "from sklearn.manifold import Isomap\n",
-    "iso = Isomap(n_components=2)\n",
+    "iso = Isomap(n_components=2, n_neighbors=15)\n",
     "projection = iso.fit_transform(digits.data)"
    ]
   },
@@ -496,31 +485,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll use our discrete colormap to view the results, setting the ``ticks`` and ``clim`` to improve the aesthetics of the resulting colorbar:"
+    "We'll use our discrete colormap to view the results, setting the `ticks` and `clim` to improve the aesthetics of the resulting colorbar (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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YbNmU7EoUavuj0QjMRg2utOQvNcUcDOu+2MIDj/+bV9/9sM/9p8+ZzgiXAyUc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\nAXcC2UP1OVL3Ygg5R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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1139266d8>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "# plot the results\n",
     "plt.scatter(projection[:, 0], projection[:, 1], lw=0.1,\n",
-    "            c=digits.target, cmap=plt.cm.get_cmap('cubehelix', 6))\n",
+    "            c=digits.target, cmap=plt.cm.get_cmap('plasma', 6))\n",
     "plt.colorbar(ticks=range(6), label='digit value')\n",
     "plt.clim(-0.5, 5.5)"
    ]
@@ -529,28 +523,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The projection also gives us some interesting insights on the relationships within the dataset: for example, the ranges of 5 and 3 nearly overlap in this projection, indicating that some hand written fives and threes are difficult to distinguish, and therefore more likely to be confused by an automated classification algorithm.\n",
-    "Other values, like 0 and 1, are more distantly separated, and therefore much less likely to be confused.\n",
-    "This observation agrees with our intuition, because 5 and 3 look much more similar than do 0 and 1.\n",
-    "\n",
-    "We'll return to manifold learning and to digit classification in [Chapter 5](05.00-Machine-Learning.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) | [Contents](Index.ipynb) | [Multiple Subplots](04.08-Multiple-Subplots.ipynb) >\n",
+    "The projection also gives us some insights on the relationships within the dataset: for example, the ranges of 2 and 3 nearly overlap in this projection, indicating that some handwritten 2s and 3s are difficult to distinguish, and may be more likely to be confused by an automated classification algorithm.\n",
+    "Other values, like 0 and 1, are more distantly separated, and may be less likely to be confused.\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.07-Customizing-Colorbars.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "We'll return to manifold learning and digit classification in [Part 5](05.00-Machine-Learning.ipynb)."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -564,9 +550,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.08-Multiple-Subplots.ipynb b/notebooks/04.08-Multiple-Subplots.ipynb
index e06195cfc..149372e18 100644
--- a/notebooks/04.08-Multiple-Subplots.ipynb
+++ b/notebooks/04.08-Multiple-Subplots.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) | [Contents](Index.ipynb) | [Text and Annotation](04.09-Text-and-Annotation.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.08-Multiple-Subplots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -30,20 +8,21 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Sometimes it is helpful to compare different views of data side by side.\n",
     "To this end, Matplotlib has the concept of *subplots*: groups of smaller axes that can exist together within a single figure.\n",
     "These subplots might be insets, grids of plots, or other more complicated layouts.\n",
-    "In this section we'll explore four routines for creating subplots in Matplotlib."
+    "In this chapter we'll explore four routines for creating subplots in Matplotlib. We'll start by importing the packages we will use:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -57,28 +36,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ``plt.axes``: Subplots by Hand\n",
+    "## plt.axes: Subplots by Hand\n",
     "\n",
-    "The most basic method of creating an axes is to use the ``plt.axes`` function.\n",
+    "The most basic method of creating an axes is to use the `plt.axes` function.\n",
     "As we've seen previously, by default this creates a standard axes object that fills the entire figure.\n",
-    "``plt.axes`` also takes an optional argument that is a list of four numbers in the figure coordinate system.\n",
-    "These numbers represent ``[left, bottom, width, height]`` in the figure coordinate system, which ranges from 0 at the bottom left of the figure to 1 at the top right of the figure.\n",
+    "`plt.axes` also takes an optional argument that is a list of four numbers in the figure coordinate system (`[left, bottom, width, height]`), which ranges from 0 at the bottom left of the figure to 1 at the top right of the figure.\n",
     "\n",
-    "For example, we might create an inset axes at the top-right corner of another axes by setting the *x* and *y* position to 0.65 (that is, starting at 65% of the width and 65% of the height of the figure) and the *x* and *y* extents to 0.2 (that is, the size of the axes is 20% of the width and 20% of the height of the figure):"
+    "For example, we might create an inset axes at the top-right corner of another axes by setting the *x* and *y* position to 0.65 (that is, starting at 65% of the width and 65% of the height of the figure) and the *x* and *y* extents to 0.2 (that is, the size of the axes is 20% of the width and 20% of the height of the figure). The following figure shows the result:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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GYAcAyxDsAGAZgh0ALEOwA4BlCHYAsAzBDgCWIdgBwDIEOwBYhmAHAMsQ7ABgGYIdACxD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\nBwDLEOwAYBmCHQAsQ7ADgGUIdgCwzP8AxZhVoScunCkAAAAASUVORK5CYII=\n",
+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bd88240>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -94,21 +75,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The equivalent of this command within the object-oriented interface is ``fig.add_axes()``. Let's use this to create two vertically stacked axes:"
+    "The equivalent of this command within the object-oriented interface is `fig.add_axes`. Let's use this to create two vertically stacked axes, as seen in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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1A4qLaV4gJgZo0kS/x1AqgXfeAY4dAxYvBpYtAxo1AjZs0O9xmGmSfVIozcWF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\nu7u7HkM3P7VrAzt2UNE8V1e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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e296748>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -138,25 +122,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ``plt.subplot``: Simple Grids of Subplots\n",
+    "## plt.subplot: Simple Grids of Subplots\n",
     "\n",
-    "Aligned columns or rows of subplots are a common-enough need that Matplotlib has several convenience routines that make them easy to create.\n",
-    "The lowest level of these is ``plt.subplot()``, which creates a single subplot within a grid.\n",
-    "As you can see, this command takes three integer arguments—the number of rows, the number of columns, and the index of the plot to be created in this scheme, which runs from the upper left to the bottom right:"
+    "Aligned columns or rows of subplots are a common enough need that Matplotlib has several convenience routines that make them easy to create.\n",
+    "The lowest level of these is `plt.subplot`, which creates a single subplot within a grid.\n",
+    "As you can see, this command takes three integer arguments—the number of rows, the number of columns, and the index of the plot to be created in this scheme, which runs from the upper left to the bottom right (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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EuP/++0V9fb1tu8ViEePGjRMJCQkdHuNNufrcBUq1tbUICgrqcn99fT3mz5+P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\nYII=\n",
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CCKEDUuyFEEIHHL5w3GazsXr1akpLSwkMDCQjI4Pw8HBt+ptvvslHH30EwAMPPMBTTz2FUopRo0Zpr/gaOnQoCxcu7JwtEC5pzvXYsWMAVFRU0K9fP2265OqdJFfRGofFPi8vj/r6enJzcykqKmLdunVs2rQJaHrP4ocffsh7772Hv78/KSkpxMbGEhwczODBg9m8eXOnb4BwTXOuWVlZjB07ls2bNzNixAhAcvVmkqtojcPDOAUFBcTExABNv/GLi4u1aX379mXr1q0YDAb8/PxoaGiga9euHD9+nLNnzzJt2jRmz57NP/7xj87bAuGSa3MFKC0t1f4tuXovyVW0xmGxt1gsGI1G7bPBYKChoQGALl260LNnT5RSrF+/nkGDBtG/f3969erFnDlz2LFjB3PnzmXRokWdtwXCJZKrb5JcRWscHsYxGo1YrVbts81mIyDgx8Xq6upYtmwZISEhvPDCCwBERERgMBgAGD58OOfOnUMphZ+fn7vHL1wkufomyVW0xuGefWRkJPn5+QAUFRVhMpm0aUop5s2bx8CBA3nxxRe1L0xWVhZvvfUWACUlJdxyyy3yxfEw1+YK0L9/f+3fkqv3klxFaxzu2cfFxXHw4EGSk5NRSrF27Vq2b99OWFgYNpuNw4cPU19fz4EDBwB49tlnmTNnDosWLWL//v0YDAYyMzM7fUOEc5pznT9/PgDz5s2TXH2A5CpapW4As9msTCaTMpvNN2L14hruzEJy9RySq2/qSBZyU5UQQuiAFHshhNABKfZCCKEDUuyFEEIHpNgLIYQOSLEXQggdkGIvhBA6IMVeCCF0QIq9EELogBR7IYTQASn2QgihA1LshRBCB6TYCyGEDkixF0IIHZBiL4QQOiDFXgghdECKvRBC6IDD1xLabDZWr15NaWkpgYGBZGRkEB4erk3ftWsXO3fuJCAggCeeeIIHH3yQqqoqnnvuOa5cuULv3r3JzMwkODi4UzdEOKc512PHjgFQUVFBv379tOmSq3eSXEVrHO7Z5+XlUV9fT25uLgsXLmTdunXatMrKSnbs2MHOnTvZtm0br7zyCvX19WRnZxMfH88777zDoEGDyM3N7dSNEM5rzjUrKwuAzZs3a9MkV+8luYrWONyzLygoICYmBoChQ4dSXFysTTt27BjDhg0jMDCQwMBAwsLCKCkpoaCggLlz5wIwatQoXnnlFaZPn64t19jYCMCZM2fcuS3CCZ999hmDBg3SMigtLdWmSa7eS3L1bc0ZNGfiDIfF3mKxYDQatc8Gg4GGhgYCAgKwWCyEhoZq00JCQrBYLHbtISEh1NTU2PVZWVkJQGpqqtMDFp1HcvVNkqvvqaystDuc3h4Oi73RaMRqtWqfbTYbAQEB151mtVoJDQ3V2oOCgrBarXTv3t2uz4iICHJycujVqxcGg8GpAQv3yM7OZtCgQcTExFBZWcmCBQskVx8gufq2xsZGKisriYiIcHpZh8U+MjKSTz/9lPHjx1NUVITJZNKmDRkyhP/4j/+grq6O+vp6ysrKMJlMREZGsn//fhITE8nPzycqKsquz6CgIIYPH+70YIX7PPDAA3z66aekpaVx8eJFBg4cqE2TXL2X5Or7nN2jb+anlFJtzdB8dv/rr79GKcXatWvJz88nLCyMsWPHsmvXLnJzc1FKMXfuXMaNG8f58+dZvHgxVquVm266iZdffplu3bq5NEDROSRX3yS5itY4LPYd5cqlm57G0TZkZGRQWFhISEgI0PSn9LXHRj3J0aNHeemll9ixY4dd+759+9i4cSMBAQFMnDiRKVOmtNmP5OpZJNcfSa6tUJ1s7969avHixUoppb766iv1+OOPa9POnTun4uPjVV1dnbp06ZL2b0/T1jYopVRycrK6cOHCjRiaU7Zs2aLi4+PV5MmT7drr6+tVbGysqq6uVnV1dSoxMVFVVla22Zfk6jkkV3uS6/V1+h207b10MzQ0VLsUzNO0tQ02m43y8nJWrVpFcnIyu3fvvlHDdCgsLIwNGza0aC8rKyMsLIwePXoQGBhIVFQUX375ZZt9Sa6eQ3K1J7len8MTtB3lyqWbnqatbaitrSUtLY0ZM2bQ2NhIeno6ERER3HXXXTdwxNc3btw4Tp061aLdlRwkV88hudqTXK+v0/fsXbl009O0tQ3BwcGkp6cTHByM0Whk5MiRHrm30xZXcpBcPZ/k2kRybdLpxT4yMpL8/HyA6166WVBQQF1dHTU1NdqlYJ6mrW34/vvvSUlJobGxkatXr1JYWMjgwYNv1FBdMmDAAMrLy6murqa+vp4jR44wbNiwNpeRXD2f5Cq5XqvTD+PExcVx8OBBkpOTtUvBtm/frl0KNm3aNKZOnYpSigULFtC1a9fOHpLTHG1DQkICU6ZMoUuXLiQkJHDnnXfe6CG3y549e6itrSUpKYklS5Ywa9YslFJMnDiRPn36tLms5Oq5JFfJ9Xo6/dJLIYQQN548z14IIXRAir0QQuiAFHshhNABKfZCCKEDUuyFEEIH2lXsjx49yrRp01q079u3j4kTJ5KUlMSuXbsAuHLlCvPnz2fq1KnMnj2bqqoq945YuI3k6rskW/FTDi+9fP311/nwww8JDg7WvhwAV69eZfz48ezevZvg4GBSUlL4wx/+wJ49e7BYLMyfP5+PPvqIr776ihUrVtj1eeXKFYqLi+VlCDfQzp07ycvLo2vXrqxcuZKIiAiCgoIkVx+wc+dO/u///g+DwcD7779PUFAQ4PrPrOTqOa59eUlzru3l8Kaq5ofxPP/883bt1z6MB9AexlNQUMBjjz0GNL3PMjs7u0WfxcXF8oozD5KamkpOTg7Dhw+XXH1McXGx9uIRV7OVXD1P88+rMxwWe2cfxuPofZYAvXr10gbct29fpwYs3OfMmTOsWrWKsrIyLRPJ1Tf87W9/45lnntEyAdezlVw9x5kzZ0hNTbXLtb1cflyCo/dZNrf99H2WgPanYN++fenXr5+rQxBuEBgYCPyYieTqG86cOQNgd9jF1WwlV8/jyuE0l6/Gae1hPM3vswSu+z5L4dkkV98l2eqb03v2jh7Gk5KSwuLFi0lJSaFLly68/PLLnTFu4WZ/+ctfCAoKklx9kPzMCqDzX0t4PWazWZlMJmU2m2/E6sU13JmF5Oo5JFff1JEs5KYqIYTQASn2QgihA1LshRBCB6TYCyGEDkixF0IIHZBiL4QQOiDFXgghdECKvRBC6IAUeyGE0AEp9kIIoQNS7IUQQgek2AshhA5IsRdCCB2QYi+EEDogxV4IIXRAir0QQuiAFHshhNABh68ltNlsrF69mtLSUgIDA8nIyCA8PByAEydOsHbtWm3eoqIiNm7cyJAhQxg3bhwmkwmA2NhYHn300U7aBOGK5lyPHTsGQEVFhfYyacnVe0muojUOi31eXh719fXk5uZSVFTEunXr2LRpEwB33303O3bsAODjjz+md+/ejBo1is8//5z4+HhWrlzZuaMXLmvONSsri7Fjx7J582ZGjBgBSK7eTHIVrXF4GKegoICYmBgAhg4dSnFxcYt5amtr2bBhA8uXLweguLiY48ePk5aWxtNPP825c+fcPGzRUdfmClBaWtpiHsnV+0iuojUOi73FYsFoNGqfDQYDDQ0NdvPs3r2bhx56iJ49ewLwy1/+kqeffpq3336b2NhYMjIy3Dxs0VGSq2+SXEVrHBZ7o9GI1WrVPttsNgIC7I/+7Nmzh8mTJ2ufR44cqf3pGBcXx9///nd3jVe4ieTqmyRX0RqHxT4yMpL8/Hyg6YRO80mcZjU1NdTX13PLLbdobStWrGDv3r0AHDp0iMGDB7tzzMINrs0VoH///nbTJVfvJLmK1jg8QRsXF8fBgwdJTk5GKcXatWvZvn07YWFhjB07lu+++47bbrvNbpmFCxeybNky3n33XYKDg+XPQg/UnOv8+fMBmDdvnuTqAyRX0Sp1A5jNZmUymZTZbL4RqxfXcGcWkqvnkFx9U0eykJuqhBBCB6TYCyGEDkixF0IIHZBiL4QQOiDFXgghdECKvRBC6IBPFfuzZ88yYsQIzGaz1nbgwAGmTp3Kr371K4YNG8b06dMpKipyeR2HDh0iJSWFYcOGERMTw5o1a+zuWOyIkpISIiIi2LBhg1378uXLyczMdMs6vJE35jpp0iQGDhzY4r+nn35am0fvuYJ3ZltVVcWKFSu47777iIyMZNq0aS3G54nZ+lSxX7NmDQ8//DC33347AIcPH2b27NnU1NSwYMECnnzySU6ePElaWpr2CFhnHDp0iJkzZ3L16lWee+45EhISyM3N5bHHHsNms3Vo7A0NDSxdupSrV6+2mPbkk0+Sm5tLSUlJh9bhrbwtV6UUZWVlxMbG8rvf/c7uv/T0dG0+vecK3petxWIhNTWVjz/+mJSUFP793/+ds2fP8uijj/L1119r83lktu6/7N+xzrhJ4/Dhw2rQoEHq9OnTWltCQoIaPXq0qq2t1doqKytVdHS0mj59utPreOSRR9SDDz6oLl++rLW9/fbbymQyqc8++6xD48/KylKDBw9WJpNJvfbaay2mL1u2TKWnp3doHdfj6TffeGOuJ0+eVCaTSf3xj390OK9ec1XKO7N95ZVX1MCBA9Xhw4e1tnPnzqkhQ4aoRYsW2c3bGdnKTVXAm2++SVRUlPbMj3/+85+UlJTw0EMPERwcrM33i1/8gujoaL766iun+q+rq+Omm25iypQpBAUFae333HMPcP1HybZXaWkpmzZtYt68ea3OM3nyZP7617961p7Cz8Abc/32228BGDBggMN59ZoreF+2Sin++7//m9GjRxMdHa219+rVi+eff57hw4fbze9p2fpEsf/hhx/49NNPiY2N1dqMRiN//vOfmT59eov5L168iMFgcGodXbt2Zdu2bTz++ON27SdOnADg1ltvdX7g/Hj45v777+c3v/lNq/MNHTqUvn37kpOT49J6vJG35vrNN98APxb72traVufVY67gndmeOnWKs2fPct999wFNxb/52H9qaipTpkyxm9/TsnX4IDRvcODAARobGxk9erTWZjAYuOOOO1rMW1JSQmFhIb/+9a87tM6Kigq++OIL1q9fj8lkIi4uzqV+Xn/9dcrLy8nOzm7x3PGfio6Otnuioa/z1ly/+eYbQkJCyMzM5H//93+pra3l9ttvZ8GCBTz88MMt5tdbruCd2ZaXlwNw8803s379enbt2oXFYiEsLIylS5cyZsyYFst4UrY+UewLCgro1q2bdpKnNVarlcWLFwMwZ84cl9dXXV2tBRscHMyKFSvo2rWr0/188803bNy4kVWrVtG3b19OnTrV5vwmk4k9e/ZgNpsdbqsv8NZcv/32W6xWKzU1Nfzud7/j0qVL/Nd//RfPPvssV69eZcKECXbz6y1X8M5sL126BMB//ud/EhAQwPLly/H392fbtm08+eSTbNu2Tdvrb+ZJ2frEYRyz2cxtt92Gn59fq/NcvnyZJ554gpKSEubMmaMdt3OFn58fr776KuvXr2fAgAHMmDFDex54ezU2NrJkyRKioqJa/PnXmuYvi6NfCr7CG3MFmDJlCqtWreK1114jLi6OiRMnkpuby+23387vf/97Ghsb7ebXW67gndnW19cDTUX/3XffJTExkQkTJpCTk0P37t15+eWXWyzjSdn6RLGvrq62exXbT126dImZM2fyxRdfMHHiRBYsWNCh9fXo0YPx48drQd96661OX1O7bds2SktLWbhwIVVVVVRVVWl7DpcvX6aqqqrFpWHN23jx4sUOjd9beGOuACkpKaSmptq1BQUFkZCQwPnz57UTuM30lit4Z7bdunUD4F//9V/p0aOH1t69e3fGjBnD8ePHW1y/70nZ+kSx9/f3b/Wa2QsXLpCenk5hYSFJSUmsWbOmzb0JZwUFBTF69Gh++OEHqqqq2r3cgQMHuHr1KpMnT+bee+/l3nvv5ZFHHgGafhHce++9nD592m6Z5m109kSVt/LGXNvS/M7Xn56w1Vuu4J3Z9unTB/gxx2v17NkTpZRHZ+vwmL3NZmP16tWUlpYSGBhIRkYG4eHh2vSMjAwKCwsJCQkBIDs7W7uB4cqVK/Tu3ZvMzEy7S6nc7eabb+aHH35o0W6xWJg1axYnTpxg+vTpLF261OV1lJWVMXv2bGbNmtVir81qteLn50dgYGC7+1u8eLG2J9/s/PnzLFq0iISEBCZMmECvXr3spldXVwNN29tRzbk236hSUVFBv379tOmSq2u5nj17lpkzZ/Jv//ZvPPXUU3bTvvvuOwC7/8+gv1zBO7O98847CQwMbPGXGTQdpunatWuLXwTuzLajHO7Z5+XlUV9fT25uLgsXLmTdunV2048fP87WrVvZsWMHO3bsIDQ0lOzsbOLj43nnnXcYNGgQubm5nbYB0HQJ1blz51ocC33xxRc5ceIE6enpHfrSAISHh1NTU8POnTu1Y3fQ9MO0d+9eoqOj2/yz9KciIiK477777P6LjIwEmo7z3XfffS1OIJ09exZw/TLPazXnmpWVBcDmzZvtpkuuruXap08fLl26xHvvvYfFYtHaT58+zfvvv8+IESNa/BLXW67gndl269aNMWPG8Nlnn2mX10LT+Yd9+/YxduzYFnvw7sy2oxwW+4KCAmJiYoCm60aLi4u1aTabjfLyclatWkVycjK7d+9uscyoUaP4/PPPO2PsmpEjR3L58mW7AMrKyvjggw/o3r07d999Nx988EGL/5qZzWY++OADu+dz/FRAQAArVqzg66+/Ztq0aeTk5JCVlcWkSZPw9/dn5cqVTvXniqKiIsLDw93yxbk2I7C/wURy7ViuL7zwAmfOnCE5OZm33nqL7OxsJk+eTEBAAC+88EKL+fWWK3hvtosWLSI0NJT09HQ2bdrE1q1bSU1NJSgoiGeffbbF/O7MtqMcHsaxWCx2v/0MBgMNDQ0EBARQW1tLWloaM2bMoLGxkfT0dCIiIrBYLISGhgIQEhJCTU1N520BEBMTg7+/P0eOHOGuu+4Cmp6xAU0nelrbQ0hISADgyy+/ZOnSpWRmZrZ5eVRCQgJdunRh69atZGZm0q1bN0aOHMmCBQvo37+/Nl97+3OGzWajqKiI8ePHu6U/ydV+fnfmGhsby8aNG/nDH/7ASy+9RFBQEPfccw/PPvtsi7tq9ZgreG+2/fr1Y9euXfz+979n27ZtKKUYPnw4zz//fIvl3J1tRzks9kaj0e4Ms81mIyCgabHg4GDS09O143sjR46kpKREWyYoKAir1Ur37t07afhNevbsyZgxY/joo49IS0sDmq6ISElJadfyiYmJlJSUtOv43fjx4x2G50x/1+rXr1+rt3AfOnSICxcuMGnSJKf6bI3kas/ducbGxtrdHdoaPeYK3p3t7bffzmuvveZwPndn21EOD+NERkZqd4AVFRVhMpm0ad9//z0pKSk0NjZy9epVCgsLGTx4MJGRkezfvx+A/Px8oqKiOmn4P5o5cyaFhYWcPHnS6WXPnz/Pvn37iIiIcMtY3N0fwJ/+9Cfuv/9+bS+oo67NFbDby5Fcf57+QL+5gmT7s3P0pLTGxka1cuVKlZSUpKZMmaK+/fZb9cYbb6i8vDyllFKvv/66SkxMVElJSeqdd95RSjU9pW7mzJkqKSlJPf7448pqtbrtyW1tmTt3rlq+fLnTy5WWlqpPPvnEbeNwd38nT55U//Iv/6KOHj3qtj6bc50wYYIymUzq4MGDkuvP3J/ec1VKsnVWR7LwmUccK6XU6dOnVXR0tCovL3drvzfakiVLVEZGRqf07Q2PwpVcnecNuSol2TqrI1n4xLNxmt1yyy3aSR5f4mlvvPm5Sa6+S7L9+fjEHbRCCCHaJsVeCCF0QIq9EELogBR7IYTQASn2QgihA1LshRBCB6TYCyGEDkixF0IIHZBiL4QQOiDFXgghdECKvRBC6IAUeyGE0AEp9kIIoQNS7IUQQgek2AshhA5IsRdCCB1w+PISm83G6tWrKS0tJTAwkIyMDMLDw7Xpb775Jh999BEADzzwAE899RRKKUaNGsUdd9wBwNChQ1m4cGHnbIFwSXOux44dA6CiooJ+/fpp0yVX7yS5itY4LPZ5eXnU19eTm5tLUVER69atY9OmTQCYzWY+/PBD3nvvPfz9/UlJSSE2Npbg4GAGDx7M5s2bO30DhGuac83KymLs2LFs3ryZESNGAJKrN5NcRWscHsYpKCggJiYGaPqNX1xcrE3r27cvW7duxWAw4OfnR0NDA127duX48eOcPXuWadOmMXv2bP7xj3903hYIl1ybK0Bpaan2b8nVe0muojUOi73FYsFoNGqfDQYDDQ0NAHTp0oWePXuilGL9+vUMGjSI/v3706tXL+bMmcOOHTuYO3cuixYt6rwtEC6RXH2T5Cpa4/AwjtFoxGq1ap9tNhsBAT8uVldXx7JlywgJCeGFF14AICIiAoPBAMDw4cM5d+4cSin8/PzcPX7hIsnVN0muojUO9+wjIyPJz88HoKioCJPJpE1TSjFv3jwGDhzIiy++qH1hsrKyeOuttwAoKSnhlltukS+Oh7k2V4D+/ftr/5ZcvZfkKlrjcM8+Li6OgwcPkpycjFKKtWvXsn37dsLCwrDZbBw+fJj6+noOHDgAwLPPPsucOXNYtGgR+/fvx2AwkJmZ2ekbIpzTnOv8+fMBmDdvnuTqAyRX0Sp1A5jNZmUymZTZbL4RqxfXcGcWkqvnkFx9U0eykJuqhBBCB6TYCyGEDkixF0IIHZBiL4QQOiDFXgghdECKvRBC6IAUeyGE0AEp9kIIoQNS7IUQQgek2AshhA5IsRdCCB2QYi+EEDogxV4IIXRAir0QQuiAFHshhNABKfZCCKEDUuyFEEIHHL6W0GazsXr1akpLSwkMDCQjI4Pw8HBt+q5du9i5cycBAQE88cQTPPjgg1RVVfHcc89x5coVevfuTWZmJsHBwZ26IcI5zbkeO3YMgIqKCvr166dNl1y9k+QqWuOw2Ofl5VFfX09ubi5FRUWsW7eOTZs2AVBZWcmOHTv44x//SF1dHVOnTuX+++8nOzub+Ph4EhMT2bJlC7m5uUyfPl3rs7GxEYAzZ850zlYJhw4cOMDFixdZsWIFqampbNq0iREjRgCSqzeTXH1bcwbNmTjDYbEvKCggJiYGgKFDh1JcXKxNO3bsGMOGDSMwMJDAwEDCwsIoKSmhoKCAuXPnAjBq1CheeeUVuy9PZWUlAKmpqU4PWLjXJ598AkBJSYnWJrl6P8nVt1VWVtodYWkPh8XeYrFgNBq1zwaDgYaGBgICArBYLISGhmrTQkJCsFgsdu0hISHU1NTY9RkREUFOTg69evXCYDA4NWDhHi+99BIxMTEMHz6cyspKFi5cKLn6AMnVtzU2NlJZWUlERITTyzos9kajEavVqn222WwEBARcd5rVaiU0NFRrDwoKwmq10r17d7s+g4KCGD58uNODFe7Tp08fgoODCQ8PJzw8HKWU5OoDJFff5+wefTOHV+NERkaSn58PQFFRESaTSZs2ZMgQCgoKqKuro6amhrKyMkwmE5GRkezfvx+A/Px8oqKiXBqc6DySq2+SXEVr/JRSqq0Zms/uf/311yilWLt2Lfn5+YSFhTF27Fh27dpFbm4uSinmzp3LuHHjOH/+PIsXL8ZqtXLTTTfx8ssv061bt59rm0Q7SK6+SXIVrXFY7DvKlUs3PY2jbcjIyKCwsJCQkBAAsrOz7Y6NepKjR4/y0ksvsWPHDrv2ffv2sXHjRgICApg4cSJTpkxpsx/J1bNIrj+SXFuhOtnevXvV4sWLlVJKffXVV+rxxx/Xpp07d07Fx8eruro6denSJe3fnqatbVBKqeTkZHXhwoUbMTSnbNmyRcXHx6vJkyfbtdfX16vY2FhVXV2t6urqVGJioqqsrGyzL8nVc0iu9iTX6+v0O2jbe+lmaGiodimYp2lrG2w2G+Xl5axatYrk5GR27959o4bpUFhYGBs2bGjRXlZWRlhYGD169CAwMJCoqCi+/PLLNvuSXD2H5GpPcr0+h1fjdJQrl256mra2oba2lrS0NGbMmEFjYyPp6elERERw11133cARX9+4ceM4depUi3ZXcpBcPYfkak9yvb5O37N35dJNT9PWNgQHB5Oenk5wcDBGo5GRI0d65N5OW1zJQXL1fJJrE8m1SacXe1cuBfM0bW3D999/T0pKCo2NjVy9epXCwkIGDx58o4bqkgEDBlBeXk51dTX19fUcOXKEYcOGtbmM5Or5JFfJ9VqdfhgnLi6OgwcPkpycrF0Ktn37du1SsGnTpjF16lSUUixYsICuXbt29pCc5mgbEhISmDJlCl26dCEhIYE777zzRg+5Xfbs2UNtbS1JSUksWbKEWbNmoZRi4sSJ9OnTp81lJVfPJblKrtfT6ZdeCiGEuPHkefZCCKEDUuyFEEIHpNgLIYQOSLEXQggdkGIvhBA6IMVeCCF0QIq9EELowP8DQu3prLVV+e4AAAAASUVORK5CYII=",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bec31d0>"
+       "<Figure size 432x288 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -174,22 +161,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The command ``plt.subplots_adjust`` can be used to adjust the spacing between these plots.\n",
-    "The following code uses the equivalent object-oriented command, ``fig.add_subplot()``:"
+    "The command `plt.subplots_adjust` can be used to adjust the spacing between these plots.\n",
+    "The following code uses the equivalent object-oriented command, `fig.add_subplot`; the following figure shows the result:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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q6+vx85//3O51LleoVCr87ne/Q19fH0pKSrBu3Tq89tprNn8uOnLz5k1s2bIF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\niYgkw8FORCQZDnYiIsn8H1BDee8pcOnKAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10be83ac8>"
+       "<Figure size 432x288 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -209,34 +199,37 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We've used the ``hspace`` and ``wspace`` arguments of ``plt.subplots_adjust``, which specify the spacing along the height and width of the figure, in units of the subplot size (in this case, the space is 40% of the subplot width and height)."
+    "Here we've used the `hspace` and `wspace` arguments of `plt.subplots_adjust`, which specify the spacing along the height and width of the figure, in units of the subplot size (in this case, the space is 40% of the subplot width and height)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ``plt.subplots``: The Whole Grid in One Go\n",
+    "## plt.subplots: The Whole Grid in One Go\n",
     "\n",
-    "The approach just described can become quite tedious when creating a large grid of subplots, especially if you'd like to hide the x- and y-axis labels on the inner plots.\n",
-    "For this purpose, ``plt.subplots()`` is the easier tool to use (note the ``s`` at the end of ``subplots``). Rather than creating a single subplot, this function creates a full grid of subplots in a single line, returning them in a NumPy array.\n",
-    "The arguments are the number of rows and number of columns, along with optional keywords ``sharex`` and ``sharey``, which allow you to specify the relationships between different axes.\n",
+    "The approach just described quickly becomes tedious when creating a large grid of subplots, especially if you'd like to hide the x- and y-axis labels on the inner plots.\n",
+    "For this purpose, `plt.subplots` is the easier tool to use (note the `s` at the end of `subplots`). Rather than creating a single subplot, this function creates a full grid of subplots in a single line, returning them in a NumPy array.\n",
+    "The arguments are the number of rows and number of columns, along with optional keywords `sharex` and `sharey`, which allow you to specify the relationships between different axes.\n",
     "\n",
-    "Here we'll create a $2 \\times 3$ grid of subplots, where all axes in the same row share their y-axis scale, and all axes in the same column share their x-axis scale:"
+    "Let's create a $2 \\times 3$ grid of subplots, where all axes in the same row share their y-axis scale, and all axes in the same column share their x-axis scale (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1023b3828>"
+       "<Figure size 432x288 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -251,22 +244,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that by specifying ``sharex`` and ``sharey``, we've automatically removed inner labels on the grid to make the plot cleaner.\n",
-    "The resulting grid of axes instances is returned within a NumPy array, allowing for convenient specification of the desired axes using standard array indexing notation:"
+    "By specifying `sharex` and `sharey`, we've automatically removed inner labels on the grid to make the plot cleaner.\n",
+    "The resulting grid of axes instances is returned within a NumPy array, allowing for convenient specification of the desired axes using standard array indexing notation (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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hISRJQmZmJlauXOnw+N7eXjQ0NGDSpEkYM2aMm0+JvOnGjRuwWq2YPXs2IiIi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\nvvmiAAAAAElFTkSuQmCC\n",
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FT0T0FJk9s4+KisKpU6cQHx8PQRCQnZ2NgoIC+Pr6wmg04uzZszAYDDh58iQA4MMPP0R6ejpWrVqFEydOQCaTIScnx+EHQkREj2e27F1cXJCZmWky5u/vL/75559/7vJ5u3fvtnFqRERkL/xSFRGRBLDsiYgkgGVPRCQBLHsiIglg2RMRSQDLnohIAlj2REQSwLInIpIAlj0RkQSw7ImIJIBlT0QkASx7IiIJYNkTEUkAy56ISAJY9kREEsCyJyKSAJY9EZEEmL1TldFoxObNm3HlyhXI5XJkZWXBz89PXF5UVIQvvvgCrq6uWLJkCaZMmYKGhgZ89NFHuH//PgYPHoycnBwoFAqHHggRET2e2TP7kpISGAwGqNVqrFy5Erm5ueKy+vp6FBYW4osvvsB//vMfbN++HQaDAfn5+YiOjsahQ4cwbtw4qNVqhx4EERE9mdkz+/LycoSHhwMAgoODUVlZKS67ePEixo8fD7lcDrlcDl9fX2i1WpSXl2Px4sUAgIiICGzfvh0pKSni89ra2gAAN27csOexkBU6MujIxBbMtedgrn2TLbmaLXudTgelUik+lslkaG1thaurK3Q6HTw8PMRl7u7u0Ol0JuPu7u5oamoy2WZ9fT0AYN68eRZPmByjvr7e5O05a7cBMNeehLn2TdbkarbslUol9Hq9+NhoNMLV1bXLZXq9Hh4eHuK4m5sb9Ho9PD09TbYZGBiIgwcPwtvbGzKZzKIJk321tbWhvr4egYGBNm+LufYczLVvsiVXs2UfEhKC77//HjNnzoRGo4FKpRKXBQUF4V//+hdaWlpgMBhQXV0NlUqFkJAQnDhxArNmzUJZWRlCQ0NNtunm5oYJEyZYPFlyDFvP/Dow156FufZN1ubaTxAE4UkrdFyN88svv0AQBGRnZ6OsrAy+vr6YNm0aioqKoFarIQgCFi9ejOnTp+PWrVvIyMiAXq/HM888g23btmHgwIFWTZCIiGxntuyJiKj345eqiIgkgGVPRCQBLHsiIglg2RMRSQDLnohIAlj2REQSwLInIpIAlj0RkQSw7ImIJIBlT0QkASx7IiIJYNkTEUlAt8r+p59+QlJSUqfx0tJSxMbGIi4uDkVFRQCA+/fv4/3338fcuXORlpaGhoYG+86YiIgsZvZXL/fs2YOvvvoKCoVCLHQAePDgAWbOnIkjR45AoVAgISEB//73v3Hs2DHodDq8//77KC4uxoULF7BhwwaTbd6/fx+VlZW8GUIP8PDNENzc3GzaFnPtOZhr32RLrmZvXuLr64u8vDysXr3aZLy6uhq+vr4YNGgQACA0NBTnzp1DeXk5Fi1aBKD9/rP5+fmdtllZWclbnPUwBw8etPkGFcy152GufZM1uZot++nTp+P69eudxq29/ywAeHt7ixP28fGxaMJkXzdu3MC8efPETGzBXHsO5to32ZKr2bJ/HHP3n+0Ye/T+swDEl4I+Pj4YMWKEtVMgO7LHy3Pm2vMw177JmlytvhrH398fNTU1aGxshMFgwPnz5zF+/Hjx/rMAurz/LBEROZ/FZ/bHjh1Dc3Mz4uLisGbNGixcuBCCICA2NhZDhgxBQkICMjIykJCQgP79+2Pbtm2OmDcREVmgW2U/YsQI8Uqct956SxyfOnUqpk6darKuQqHAjh077DhFIiKyFb9URUQkASx7IiIJYNkTEUkAy56ISAJY9kREEsCyJyKSAJY9EZEEsOyJiCSAZU9EJAEseyIiCWDZExFJAMueiEgCWPZERBLAsicikgCWPRGRBLDsiYgkgGVPRCQBZu9UZTQasXnzZly5cgVyuRxZWVnw8/MDAFy+fBnZ2dniuhqNBp999hmCgoIwffp0qFQqAEBkZCTmz5/voEMgIiJzzJZ9SUkJDAYD1Go1NBoNcnNzsXPnTgDA2LFjUVhYCAD4+uuvMXjwYEREROD06dOIjo7Gxo0bHTt7IiLqFrNv45SXlyM8PBwAEBwcjMrKyk7rNDc3Iy8vD+vXrwcAVFZWoqqqComJiVi+fDnq6ursPG0iIrKE2bLX6XRQKpXiY5lMhtbWVpN1jhw5ghkzZsDLywsAMHr0aCxfvhwHDhxAZGQksrKy7DxtIiKyhNmyVyqV0Ov14mOj0QhXV9N3f44dO4bZs2eLjydPnoxJkyYBAKKionDp0iV7zZeIiKxgtuxDQkJQVlYGoP0D2I4PXTs0NTXBYDBg6NCh4tiGDRtw/PhxAMCZM2cQEBBgzzkTEZGFzH5AGxUVhVOnTiE+Ph6CICA7OxsFBQXw9fXFtGnTcPXqVQwfPtzkOStXrsS6devw+eefQ6FQ8G0cIqKnzGzZu7i4IDMz02TM399f/HNQUBDy8/NNlo8cOVK8SoeIiJ4+fqmKiEgCWPZERBLAsicikgCWPRGRBLDsiYgkgGVPRCQBkiz7mzdvYtKkSaitre1y+caNG5GUlGTTPmpra7Fs2TKEhYUhLCwMq1evRkNDg8k669evR05Ojk37ob85I9fubI+52p8zsj158iTmzp2Ll156CePHj0dKSgo0Go3JOr05W0mW/ZYtW/Dmm29i5MiRnZYdPnwYRUVFNm3/zp07mD9/PjQaDRYtWoTU1FSUlpYiNTUVBoNBXG/p0qVQq9XQarU27Y/aOTrX7m6Pudqfo7M9e/Ys0tLS0NTUhBUrVmDp0qW4du0aEhMTcfHiRXG93pyt2S9V9TXnzp3Dd999h5KSEpPxtrY27Ny5E59++qnN+9i3bx9u3LiBY8eOiV9Ae+mll5Camoovv/wSc+bMAQAMGzYMb775JnJycrB//36b9ytlzsi1u9tjrvbljGyzs7MxdOhQFBUVQaFQAADefvttzJw5E5988gkKCgoA9O5sJXdmv2/fPoSGhpr8lk9LSwveeecd5OXlISYmBkOGDLFpH8XFxQgLCzP5pvHLL7+MUaNGobi42GTd2bNn44cffuiVZwo9iTNytWR7zNV+HJ3t3bt3odVqMWPGDLHoAeC5557DxIkTceHCBZP1e2u2kir7P//8E99//z0iIyNNxltaWqDT6fDJJ59g69atnX7V0xJ3795FbW1tlz/+FhAQgKqqKpOx4OBg+Pj44ODBg1bvU+qckaul22Ou9uGMbJVKJb755hukpKR0Wnbnzh3IZDKTsd6araTexjl58iTa2trw+uuvm4wrlUp8++23NpcB0P5BEoAuzzS8vb3R1NSEpqYmeHh4iOMTJ04Uf1mULOeMXK3ZHnO1nTOylclkeP755zuNa7VaVFRU4NVXX+20rDdmK6kz+/LycgwcOLDThzwuLi52K4SO3/5/+OVghwEDBgBov7PXw1QqFW7cuPHYKw3oyZyRqzXbY662c1a2j9Lr9cjIyAAApKend1reG7OVVNnX1tZi+PDh6Nevn8P2IQiC2XUe3X/HX+Tr1687ZE59nTNytQZztd3TyPbevXtYsmQJtFot0tPTERYW1mmd3pitpMq+sbHR5BaLjjBw4EAA7e8pPqpj7NE5dDy+c+eOQ+fWVzkjV2swV9s5O9u//voLCxYswI8//ojY2FisWLGiy/V6Y7aSKnsXFxcYjUaH7mPYsGEAgPr6+k7L6urq4OnpKf6D0KFjTo9+EETd44xcrcFcbefMbG/fvo3k5GRUVFQgLi4OW7Zseewrit6Yrdk3vYxGIzZv3owrV65ALpcjKysLfn5+4vKsrCxUVFTA3d0dAJCfn48HDx7go48+wv379zF48GDk5OR0+R62sz377LP4888/HboPT09PjBgxotNVNwBw6dIlBAYGdhpvbGwU50eWc0au1mCutnNWtjqdDgsXLsTly5eRkpKCtWvXPnH93pit2TP7kpISGAwGqNVqrFy5Erm5uSbLq6qqsHfvXhQWFqKwsBAeHh7Iz89HdHQ0Dh06hHHjxkGtVjvsACwxbNgw1NXVoa2tzaH7eeONN3DmzBlUV1eLY6dPn8bVq1cxc+bMTut3XMHT8aqALOOsXC3FXG3nrGwzMzNx+fJlJCcnmy16oHdma7bsy8vLER4eDqD9+tLKykpxmdFoRE1NDTZt2oT4+HgcOXKk03MiIiJw+vRpR8zdYpMnT8a9e/fw66+/WvX82tpaHD161Own8GlpaRg0aBBSUlJQUFCAXbt2Yfny5QgICEBMTEyn9TUaDfz8/HrVX5yexFm5Woq52s4Z2VZXV+Po0aPw9PTE2LFjcfTo0U7/Pao3Zmv2bRydTmfyAYlMJkNraytcXV3R3NyMxMREpKamoq2tDcnJyQgMDIROpxOvI3d3d0dTU5PjjsAC4eHhcHFxwfnz5zFmzBiLn3/u3DmsXbsWOTk5Xf5GRwcvLy8cOHAAOTk52LFjB9zc3BAZGYnVq1dDLpebrGs0GqHRaLo846fucVaulmCu9uGMbM+ePQug/cPZx53VP3yS1luzNVv2SqVSvHYcaD/QjutbFQoFkpOTxffjJ0+eDK1WKz7Hzc0Ner0enp6eDpq+Zby8vDB16lQUFxcjMTHxseuVlpZ2OT5r1ixotdpOhd2V0aNHY8+ePWbXO3PmDG7fvo13333X7LrUNWfm2p3tAczVXpyRbUJCAhISEro9p96ardm3cUJCQsRvimk0GqhUKnHZ77//joSEBLS1teHBgweoqKhAQEAAQkJCcOLECQBAWVkZQkNDHTR9yy1YsAAVFRW4du2axc+9desWSktLu/yQ1VpffvklXnnlFavOWuhvzLXvYrb2Ybbso6KiIJfLER8fj5ycHKxduxYFBQX47rvv4O/vj5iYGMyZMwdJSUmIiYnBCy+8gCVLlqC4uBjx8fG4cOHCE/9FdrbQ0FBMmTIFu3fvtvi5DQ0NyMjI6PKr1daora3F8ePH8cEHH9hle1LGXPsuZmsnwlNQW1srqFQqoba29mnsXvjjjz+EiRMnCjU1NU9l/x3WrFkjZGVlPdU52DML5tqOudofs21nSxaS+iG0DkOHDhU/lHmaeusdb3oq5tp3MVvbSeobtEREUsWyJyKSAJY9EZEEsOyJiCSAZU9EJAEseyIiCWDZExFJAMueiEgCWPZERBLAsicikgCWPRGRBLDsiYgkgGVPRCQBLHsiIglg2RMRSQDLnohIAszevMRoNGLz5s24cuUK5HI5srKy4OfnJy7ft28fiouLAQCvvfYali1bBkEQEBERId4KLDg4GCtXrnTMERARkVlmy76kpAQGgwFqtRoajQa5ubnYuXMngPb7MX711Vc4fPgwXFxckJCQgMjISCgUCgQEBGDXrl0OPwAiIjLP7Ns45eXlCA8PB9B+hl5ZWSku8/Hxwd69eyGTydCvXz+0trZiwIABqKqqws2bN5GUlIS0tDT89ttvjjsCIiIyy2zZ63Q6KJVK8bFMJkNraysAoH///vDy8oIgCNi6dSvGjRuHUaNGwdvbG+np6SgsLMTixYuxatUqxx0BERGZZfZtHKVSCb1eLz42Go1wdf37aS0tLVi3bh3c3d3x8ccfAwACAwMhk8kAABMmTEBdXR0EQUC/fv3sPX8iIuoGs2f2ISEhKCsrAwBoNBqoVCpxmSAIeO+99/Diiy8iMzNTLPhPP/0U+/fvBwBotVoMHTqURU9E9BSZPbOPiorCqVOnEB8fD0EQkJ2djYKCAvj6+sJoNOLs2bMwGAw4efIkAODDDz9Eeno6Vq1ahRMnTkAmkyEnJ8fhB0JERI9ntuxdXFyQmZlpMubv7y/++eeff+7yebt377ZxakREZC/8UhURkQSw7ImIJIBlT0QkASx7IiIJYNkTEUkAy56ISAJY9kREEsCyJyKSAJY9EZEEsOyJiCSAZU9EJAEseyIiCWDZExFJAMueiEgCWPZERBLAsicikgCWPRGRBJi9U5XRaMTmzZtx5coVyOVyZGVlwc/PT1xeVFSEL774Aq6urliyZAmmTJmChoYGfPTRR7h//z4GDx6MnJwcKBQKhx4IERE9ntmyLykpgcFggFqthkajQW5uLnbu3AkAqK+vR2FhIf73v/+hpaUFc+fOxSuvvIL8/HxER0dj1qxZ2L17N9RqNVJSUsRttrW1AQBu3LjhmKOibuvIoCMTWzDXnoO59k225Gq27MvLyxEeHg4ACA4ORmVlpbjs4sWLGD9+PORyOeRyOXx9faHValFeXo7FixcDACIiIrB9+3aTsq+vrwcAzJs3z+IJk2PU19ebvGKzdhsAc+1JmGvfZE2uZstep9NBqVSKj2UyGVpbW+Hq6gqdTgcPDw9xmbu7O3Q6ncm4u7s7mpqaTLYZGBiIgwcPwtvbGzKZzKIJk321tbWhvr4egYGBNm+LufYczLVvsiVXs2WvVCqh1+vFx0ajEa6url0u0+v18PDwEMfd3Nyg1+vh6elpsk03NzdMmDDB4smSY9h65teBufYszLVvsjZXs1fjhISEoKysDACg0WigUqnEZUFBQSgvL0dLSwuamppQXV0NlUqFkJAQnDhxAgBQVlaG0NBQqyZHRET20U8QBOFJK3RcjfPLL79AEARkZ2ejrKwMvr6+mDZtGoqKiqBWqyEIAhYvXozp06fj1q1byMjIgF6vxzPPPINt27Zh4MCBzjomIiJ6hNmyt5U1l272NOaOISsrCxUVFXB3dwcA5Ofnm3yW0ZP89NNP+Oc//4nCwkKT8dLSUnz22WdwdXVFbGws5syZ88TtMNeehbn+jbk+huBgx48fFzIyMgRBEIQLFy4I//jHP8RldXV1QnR0tNDS0iL89ddf4p97micdgyAIQnx8vHD79u2nMTWL7N69W4iOjhZmz55tMm4wGITIyEihsbFRaGlpEWbNmiXU19c/cVvMtedgrqaYa9cc/g3a7l666eHhIV662dM86RiMRiNqamqwadMmxMfH48iRI09rmmb5+voiLy+v03h1dTV8fX0xaNAgyOVyhIaG4ty5c0/cFnPtOZirKebaNbNX49jKmks3e5onHUNzczMSExORmpqKtrY2JCcnIzAwEGPGjHmKM+7a9OnTcf369U7j1uTAXHsO5mqKuXbN4Wf21ly62dM86RgUCgWSk5OhUCigVCoxefLkHnm28yTW5MBcez7m2o65tnN42Vtz6WZP86Rj+P3335GQkIC2tjY8ePAAFRUVCAgIeFpTtYq/vz9qamrQ2NgIg8GA8+fPY/z48U98DnPt+Zgrc32Yw9/GiYqKwqlTpxAfHy9eullQUCBeupmUlIS5c+dCEASsWLECAwYMcPSULGbuGGJiYjBnzhz0798fMTExeOGFF572lLvl2LFjaG5uRlxcHNasWYOFCxdCEATExsZiyJAhT3wuc+25mCtz7YrDL70kIqKnj79nT0QkASx7IiIJYNkTEUkAy56ISAJY9kREEsCyJyKSAJY9EZEE/D9adJdy0iZr/gAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1023b3828>"
+       "<Figure size 432x288 with 6 Axes>"
       ]
      },
      "execution_count": 7,
@@ -287,25 +283,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In comparison to ``plt.subplot()``, ``plt.subplots()`` is more consistent with Python's conventional 0-based indexing."
+    "In comparison to `plt.subplot`, `plt.subplots` is more consistent with Python's conventional zero-based indexing, whereas `plt.subplot` uses MATLAB-style one-based indexing."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ``plt.GridSpec``: More Complicated Arrangements\n",
+    "## plt.GridSpec: More Complicated Arrangements\n",
     "\n",
-    "To go beyond a regular grid to subplots that span multiple rows and columns, ``plt.GridSpec()`` is the best tool.\n",
-    "The ``plt.GridSpec()`` object does not create a plot by itself; it is simply a convenient interface that is recognized by the ``plt.subplot()`` command.\n",
-    "For example, a gridspec for a grid of two rows and three columns with some specified width and height space looks like this:"
+    "To go beyond a regular grid to subplots that span multiple rows and columns, `plt.GridSpec` is the best tool.\n",
+    "`plt.GridSpec` does not create a plot by itself; it is rather a convenient interface that is recognized by the `plt.subplot` command.\n",
+    "For example, a `GridSpec` for a grid of two rows and three columns with some specified width and height space looks like this:"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -313,24 +319,28 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "From this we can specify subplot locations and extents using the familiary Python slicing syntax:"
+    "From this we can specify subplot locations and extents using the familiar Python slicing syntax (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10f438128>"
+       "<Figure size 432x288 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -349,21 +359,24 @@
    "metadata": {},
    "source": [
     "This type of flexible grid alignment has a wide range of uses.\n",
-    "I most often use it when creating multi-axes histogram plots like the ones shown here:"
+    "I most often use it when creating multiaxes histogram plots like the ones shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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sbNzWYBSNRhkeHmZmZoaFhQXa29uJRqMsLS3h9/txu92kUimRh1e6Nbce9BqN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Icffoju7m7ExcUNv85Hw/JWoaFDJe7u7li0aNGEv3/p0qWTnOi+RYsWISoqatzba7VatLW1Ka46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\nsbyJiFSI5U1EpEL/A5Irq324HXUvAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ef598d0>"
+       "<Figure size 432x432 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -374,19 +387,20 @@
     "# Create some normally distributed data\n",
     "mean = [0, 0]\n",
     "cov = [[1, 1], [1, 2]]\n",
-    "x, y = np.random.multivariate_normal(mean, cov, 3000).T\n",
+    "rng = np.random.default_rng(1701)\n",
+    "x, y = rng.multivariate_normal(mean, cov, 3000).T\n",
     "\n",
-    "# Set up the axes with gridspec\n",
+    "# Set up the axes with GridSpec\n",
     "fig = plt.figure(figsize=(6, 6))\n",
     "grid = plt.GridSpec(4, 4, hspace=0.2, wspace=0.2)\n",
     "main_ax = fig.add_subplot(grid[:-1, 1:])\n",
     "y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)\n",
     "x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)\n",
     "\n",
-    "# scatter points on the main axes\n",
+    "# Scatter points on the main axes\n",
     "main_ax.plot(x, y, 'ok', markersize=3, alpha=0.2)\n",
     "\n",
-    "# histogram on the attached axes\n",
+    "# Histogram on the attached axes\n",
     "x_hist.hist(x, 40, histtype='stepfilled',\n",
     "            orientation='vertical', color='gray')\n",
     "x_hist.invert_yaxis()\n",
@@ -402,22 +416,16 @@
    "source": [
     "This type of distribution plotted alongside its margins is common enough that it has its own plotting API in the Seaborn package; see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb) for more details."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) | [Contents](Index.ipynb) | [Text and Annotation](04.09-Text-and-Annotation.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.08-Multiple-Subplots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "encoding": "# -*- coding: utf-8 -*-",
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -431,9 +439,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.09-Text-and-Annotation.ipynb b/notebooks/04.09-Text-and-Annotation.ipynb
index 621eeaed3..edd9be619 100644
--- a/notebooks/04.09-Text-and-Annotation.ipynb
+++ b/notebooks/04.09-Text-and-Annotation.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Multiple Subplots](04.08-Multiple-Subplots.ipynb) | [Contents](Index.ipynb) | [Customizing Ticks](04.10-Customizing-Ticks.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.09-Text-and-Annotation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -43,7 +21,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -61,19 +39,33 @@
    "source": [
     "## Example: Effect of Holidays on US Births\n",
     "\n",
-    "Let's return to some data we worked with earler, in [\"Example: Birthrate Data\"](03.09-Pivot-Tables.ipynb#Example:-Birthrate-Data), where we generated a plot of average births over the course of the calendar year; as already mentioned, that this data can be downloaded at https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv.\n",
-    "\n",
-    "We'll start with the same cleaning procedure we used there, and plot the results:"
+    "Let's return to some data we worked with earlier, in [Example: Birthrate Data](03.09-Pivot-Tables.ipynb#Example:-Birthrate-Data), where we generated a plot of average births over the course of the calendar year. We'll start with the same cleaning procedure we used there, and plot the results (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# shell command to download the data:\n",
+    "# !cd data && curl -O \\\n",
+    "#   https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
+    "from datetime import datetime\n",
+    "\n",
     "births = pd.read_csv('data/births.csv')\n",
     "\n",
     "quartiles = np.percentile(births['births'], [25, 50, 75])\n",
@@ -87,22 +79,25 @@
     "                              births.day, format='%Y%m%d')\n",
     "births_by_date = births.pivot_table('births',\n",
     "                                    [births.index.month, births.index.day])\n",
-    "births_by_date.index = [pd.datetime(2012, month, day)\n",
+    "births_by_date.index = [datetime(2012, month, day)\n",
     "                        for (month, day) in births_by_date.index]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
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Gj1hIRUVRuTIpD64Ykwn47DPgr38Fhg2jiHFFhfJr/fILRWVGjaJkPzmuXgXOnDE32xg8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\njEVEREREADXGIiIiIiIA/B/rXnxzXnFSGQAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1144abdd8>"
+       "<Figure size 864x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -118,22 +113,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When we're communicating data like this, it is often useful to annotate certain features of the plot to draw the reader's attention.\n",
-    "This can be done manually with the ``plt.text``/``ax.text`` command, which will place text at a particular x/y value:"
+    "When we're visualizing data like this, it is often useful to annotate certain features of the plot to draw the reader's attention.\n",
+    "This can be done manually with the `plt.text`/`ax.text` functions, which will place text at a particular *x*/*y* value (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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GIcaRI0DXrkCvXsD+/brXvXGDBJ9Qbq1pU5p4wZDjpKZSZDs4mDzYq1eT3YOm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\n+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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114c827b8>"
+       "<Figure size 864x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -158,7 +156,7 @@
     "ax.set(title='USA births by day of year (1969-1988)',\n",
     "       ylabel='average daily births')\n",
     "\n",
-    "# Format the x axis with centered month labels\n",
+    "# Format the x-axis with centered month labels\n",
     "ax.xaxis.set_major_locator(mpl.dates.MonthLocator())\n",
     "ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))\n",
     "ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
@@ -169,9 +167,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``ax.text`` method takes an x position, a y position, a string, and then optional keywords specifying the color, size, style, alignment, and other properties of the text.\n",
-    "Here we used ``ha='right'`` and ``ha='center'``, where ``ha`` is short for *horizonal alignment*.\n",
-    "See the docstring of ``plt.text()`` and of ``mpl.text.Text()`` for more information on available options."
+    "The `ax.text` method takes an *x* position, a *y* position, a string, and then optional keywords specifying the color, size, style, alignment, and other properties of the text.\n",
+    "Here we used `ha='right'` and `ha='center'`, where `ha` is short for *horizontal alignment*.\n",
+    "See the docstrings of `plt.text` and `mpl.text.Text` for more information on the available options."
    ]
   },
   {
@@ -180,33 +178,35 @@
    "source": [
     "## Transforms and Text Position\n",
     "\n",
-    "In the previous example, we have anchored our text annotations to data locations. Sometimes it's preferable to anchor the text to a position on the axes or figure, independent of the data. In Matplotlib, this is done by modifying the *transform*.\n",
+    "In the previous example, we anchored our text annotations to data locations. Sometimes it's preferable to anchor the text to a fixed position on the axes or figure, independent of the data. In Matplotlib, this is done by modifying the *transform*.\n",
     "\n",
-    "Any graphics display framework needs some scheme for translating between coordinate systems.\n",
-    "For example, a data point at $(x, y) = (1, 1)$ needs to somehow be represented at a certain location on the figure, which in turn needs to be represented in pixels on the screen.\n",
-    "Mathematically, such coordinate transformations are relatively straightforward, and Matplotlib has a well-developed set of tools that it uses internally to perform them (these tools can be explored in the ``matplotlib.transforms`` submodule).\n",
+    "Matplotlib makes use of a few different coordinate systems: a data point at $(x, y) = (1, 1)$ corresponds to a certain location on the axes or figure, which in turn corresponds to a particular pixel on the screen.\n",
+    "Mathematically, transforming between such coordinate systems is relatively straightforward, and Matplotlib has a well-developed set of tools that it uses internally to perform these transforms (these tools can be explored in the `matplotlib.transforms` submodule).\n",
     "\n",
-    "The average user rarely needs to worry about the details of these transforms, but it is helpful knowledge to have when considering the placement of text on a figure. There are three pre-defined transforms that can be useful in this situation:\n",
+    "A typical user rarely needs to worry about the details of the transforms, but it is helpful knowledge to have when considering the placement of text on a figure. There are three predefined transforms that can be useful in this situation:\n",
     "\n",
-    "- ``ax.transData``: Transform associated with data coordinates\n",
-    "- ``ax.transAxes``: Transform associated with the axes (in units of axes dimensions)\n",
-    "- ``fig.transFigure``: Transform associated with the figure (in units of figure dimensions)\n",
+    "- `ax.transData`: Transform associated with data coordinates\n",
+    "- `ax.transAxes`: Transform associated with the axes (in units of axes dimensions)\n",
+    "- `fig.transFigure`: Transform associated with the figure (in units of figure dimensions)\n",
     "\n",
-    "Here let's look at an example of drawing text at various locations using these transforms:"
+    "Let's look at an example of drawing text at various locations using these transforms (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1144abd30>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -227,30 +227,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that by default, the text is aligned above and to the left of the specified coordinates: here the \".\" at the beginning of each string will approximately mark the given coordinate location.\n",
+    "Matplotlib's default text alignment is such that the \".\" at the beginning of each string will approximately mark the specified coordinate location.\n",
     "\n",
-    "The ``transData`` coordinates give the usual data coordinates associated with the x- and y-axis labels.\n",
-    "The ``transAxes`` coordinates give the location from the bottom-left corner of the axes (here the white box), as a fraction of the axes size.\n",
-    "The ``transFigure`` coordinates are similar, but specify the position from the bottom-left of the figure (here the gray box), as a fraction of the figure size.\n",
+    "The `transData` coordinates give the usual data coordinates associated with the x- and y-axis labels.\n",
+    "The `transAxes` coordinates give the location from the bottom-left corner of the axes (here the white box), as a fraction of the total axes size.\n",
+    "The `transFigure` coordinates are similar, but specify the position from the bottom-left corner of the figure (here the gray box) as a fraction of the total figure size.\n",
     "\n",
-    "Notice now that if we change the axes limits, it is only the ``transData`` coordinates that will be affected, while the others remain stationary:"
+    "Notice now that if we change the axes limits, it is only the `transData` coordinates that will be affected, while the others remain stationary (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1144abd30>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -265,7 +268,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This behavior can be seen more clearly by changing the axes limits interactively: if you are executing this code in a notebook, you can make that happen by changing ``%matplotlib inline`` to ``%matplotlib notebook`` and using each plot's menu to interact with the plot."
+    "This behavior can be seen more clearly by changing the axes limits interactively: if you are executing this code in a notebook, you can make that happen by changing `%matplotlib inline` to `%matplotlib notebook` and using each plot's menu to interact with the plot."
    ]
   },
   {
@@ -274,28 +277,29 @@
    "source": [
     "## Arrows and Annotation\n",
     "\n",
-    "Along with tick marks and text, another useful annotation mark is the simple arrow.\n",
+    "Along with tickmarks and text, another useful annotation mark is the simple arrow.\n",
     "\n",
-    "Drawing arrows in Matplotlib is often much harder than you'd bargain for.\n",
-    "While there is a ``plt.arrow()`` function available, I wouldn't suggest using it: the arrows it creates are SVG objects that will be subject to the varying aspect ratio of your plots, and the result is rarely what the user intended.\n",
-    "Instead, I'd suggest using the ``plt.annotate()`` function.\n",
-    "This function creates some text and an arrow, and the arrows can be very flexibly specified.\n",
+    "While there is a `plt.arrow` function available, I wouldn't suggest using it: the arrows it creates are SVG objects that will be subject to the varying aspect ratio of your plots, making it tricky to get them right.\n",
+    "Instead, I'd suggest using the `plt.annotate` function, which creates some text and an arrow and allows the arrows to be very flexibly specified.\n",
     "\n",
-    "Here we'll use ``annotate`` with several of its options:"
+    "Here is a demonstration of `annotate` with several of its options (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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XvLg+CSYnwtyXn59u+Nx7r9WVeC+f7yMXQrjW+++/T1aW5y/L6E4kyIUQLrNr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\nAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114622cf8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -303,8 +307,6 @@
     }
    ],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "fig, ax = plt.subplots()\n",
     "\n",
     "x = np.linspace(0, 20, 1000)\n",
@@ -323,23 +325,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The arrow style is controlled through the ``arrowprops`` dictionary, which has numerous options available.\n",
-    "These options are fairly well-documented in Matplotlib's online documentation, so rather than repeating them here it is probably more useful to quickly show some of the possibilities.\n",
-    "Let's demonstrate several of the possible options using the birthrate plot from before:"
+    "The arrow style is controlled through the `arrowprops` dictionary, which has numerous options available.\n",
+    "These options are well documented in Matplotlib's online documentation, so rather than repeating them here it is probably more useful to show some examples.\n",
+    "Let's demonstrate several of the possible options using the birthrate plot from before (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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UgRkI573atdOIjpaUa+5JhKG44AI5P/9c0rnJlVdKZPXkSflp0yb0NrfeKvmv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\nI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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117960208>"
+       "<Figure size 864x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -361,8 +366,8 @@
     "            xytext=(10, -40), textcoords='offset points', ha='center',\n",
     "            arrowprops=dict(arrowstyle=\"->\"))\n",
     "\n",
-    "ax.annotate('Labor Day', xy=('2012-9-4', 4850), xycoords='data', ha='center',\n",
-    "            xytext=(0, -20), textcoords='offset points')\n",
+    "ax.annotate('Labor Day Weekend', xy=('2012-9-4', 4850), xycoords='data',\n",
+    "            ha='center', xytext=(0, -20), textcoords='offset points')\n",
     "ax.annotate('', xy=('2012-9-1', 4850), xytext=('2012-9-7', 4850),\n",
     "            xycoords='data', textcoords='data',\n",
     "            arrowprops={'arrowstyle': '|-|,widthA=0.2,widthB=0.2', })\n",
@@ -390,7 +395,7 @@
     "ax.set(title='USA births by day of year (1969-1988)',\n",
     "       ylabel='average daily births')\n",
     "\n",
-    "# Format the x axis with centered month labels\n",
+    "# Format the x-axis with centered month labels\n",
     "ax.xaxis.set_major_locator(mpl.dates.MonthLocator())\n",
     "ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))\n",
     "ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
@@ -403,28 +408,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You'll notice that the specifications of the arrows and text boxes are very detailed: this gives you the power to create nearly any arrow style you wish.\n",
-    "Unfortunately, it also means that these sorts of features often must be manually tweaked, a process that can be very time consuming when producing publication-quality graphics!\n",
-    "Finally, I'll note that the preceding mix of styles is by no means best practice for presenting data, but rather included as a demonstration of some of the available options.\n",
+    "The variety of options make `annotate` powerful and flexible: you can create nearly any arrow style you wish.\n",
+    "Unfortunately, it also means that these sorts of features often must be manually tweaked, a process that can be very time-consuming when producing publication-quality graphics!\n",
+    "Finally, I'll note that the preceding mix of styles is by no means best practice for presenting data, but rather is included as a demonstration of some of the available options.\n",
     "\n",
-    "More discussion and examples of available arrow and annotation styles can be found in the Matplotlib gallery, in particular the [Annotation Demo](http://matplotlib.org/examples/pylab_examples/annotation_demo2.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Multiple Subplots](04.08-Multiple-Subplots.ipynb) | [Contents](Index.ipynb) | [Customizing Ticks](04.10-Customizing-Ticks.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.09-Text-and-Annotation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "More discussion and examples of available arrow and annotation styles can be found in the Matplotlib [Annotations tutorial](https://matplotlib.org/stable/tutorials/text/annotations.html)."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.6 64-bit ('3.9.6')",
    "language": "python",
    "name": "python3"
   },
@@ -438,9 +436,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc"
+   }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.10-Customizing-Ticks.ipynb b/notebooks/04.10-Customizing-Ticks.ipynb
index b3b6a820c..9c8c824c7 100644
--- a/notebooks/04.10-Customizing-Ticks.ipynb
+++ b/notebooks/04.10-Customizing-Ticks.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Text and Annotation](04.09-Text-and-Annotation.ipynb) | [Contents](Index.ipynb) | [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.10-Customizing-Ticks.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,13 +11,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Matplotlib's default tick locators and formatters are designed to be generally sufficient in many common situations, but are in no way optimal for every plot. This section will give several examples of adjusting the tick locations and formatting for the particular plot type you're interested in.\n",
+    "Matplotlib's default tick locators and formatters are designed to be generally sufficient in many common situations, but are in no way optimal for every plot. This chapter will give several examples of adjusting the tick locations and formatting for the particular plot type you're interested in.\n",
     "\n",
-    "Before we go into examples, it will be best for us to understand further the object hierarchy of Matplotlib plots.\n",
-    "Matplotlib aims to have a Python object representing everything that appears on the plot: for example, recall that the ``figure`` is the bounding box within which plot elements appear.\n",
-    "Each Matplotlib object can also act as a container of sub-objects: for example, each ``figure`` can contain one or more ``axes`` objects, each of which in turn contain other objects representing plot contents.\n",
+    "Before we go into examples, however, let's talk a bit more about the object hierarchy of Matplotlib plots.\n",
+    "Matplotlib aims to have a Python object representing everything that appears on the plot: for example, recall that the `Figure` is the bounding box within which plot elements appear.\n",
+    "Each Matplotlib object can also act as a container of subobjects: for example, each `Figure` can contain one or more `Axes` objects, each of which in turn contains other objects representing plot contents.\n",
     "\n",
-    "The tick marks are no exception. Each ``axes`` has attributes ``xaxis`` and ``yaxis``, which in turn have attributes that contain all the properties of the lines, ticks, and labels that make up the axes."
+    "The tickmarks are no exception. Each axes has attributes `xaxis` and `yaxis`, which in turn have attributes that contain all the properties of the lines, ticks, and labels that make up the axes."
    ]
   },
   {
@@ -48,68 +26,78 @@
    "source": [
     "## Major and Minor Ticks\n",
     "\n",
-    "Within each axis, there is the concept of a *major* tick mark, and a *minor* tick mark. As the names would imply, major ticks are usually bigger or more pronounced, while minor ticks are usually smaller. By default, Matplotlib rarely makes use of minor ticks, but one place you can see them is within logarithmic plots:"
+    "Within each axes, there is the concept of a *major* tickmark, and a *minor* tickmark. As the names imply, major ticks are usually bigger or more pronounced, while minor ticks are usually smaller. By default, Matplotlib rarely makes use of minor ticks, but one place you can see them is within logarithmic plots (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "plt.style.use('classic')\n",
-    "%matplotlib inline\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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iRGbetM3z2xOQpBFNrCeQmScjYvemw3uAZzPz+cFkTrC2/XNmsPd/LCJeB9wbEb8LPNZk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\nEZCkGWYRkKQZZhGQpBlmEZCkGfZ/oG4thqPtPXkAAAAASUVORK5CYII=\n",
+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10346a438>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "ax = plt.axes(xscale='log', yscale='log')\n",
-    "ax.grid();"
+    "ax.set(xlim=(1, 1E3), ylim=(1, 1E3))\n",
+    "ax.grid(True);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see here that each major tick shows a large tickmark and a label, while each minor tick shows a smaller tickmark with no label.\n",
+    "In this chart each major tick shows a large tickmark, label, and gridline, while each minor tick shows a smaller tickmark with no label or gridline.\n",
     "\n",
-    "These tick properties—locations and labels—that is, can be customized by setting the ``formatter`` and ``locator`` objects of each axis. Let's examine these for the x axis of the just shown plot:"
+    "These tick properties—locations and labels, that is—can be customized by setting the `formatter` and `locator` objects of each axis. Let's examine these for the x-axis of the just-shown plot:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<matplotlib.ticker.LogLocator object at 0x10dbaf630>\n",
-      "<matplotlib.ticker.LogLocator object at 0x10dba6e80>\n"
+      "<matplotlib.ticker.LogLocator object at 0x1129b9370>\n",
+      "<matplotlib.ticker.LogLocator object at 0x1129aaf70>\n"
      ]
     }
    ],
@@ -122,15 +110,18 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<matplotlib.ticker.LogFormatterMathtext object at 0x10db8dbe0>\n",
-      "<matplotlib.ticker.NullFormatter object at 0x10db9af60>\n"
+      "<matplotlib.ticker.LogFormatterSciNotation object at 0x1129aaa00>\n",
+      "<matplotlib.ticker.LogFormatterSciNotation object at 0x1129aac10>\n"
      ]
     }
    ],
@@ -143,9 +134,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that both major and minor tick labels have their locations specified by a ``LogLocator`` (which makes sense for a logarithmic plot). Minor ticks, though, have their labels formatted by a ``NullFormatter``: this says that no labels will be shown.\n",
+    "We see that both major and minor tick labels have their locations specified by a `LogLocator` (which makes sense for a logarithmic plot). Minor ticks, though, have their labels formatted by a `NullFormatter`: this says that no labels will be shown.\n",
     "\n",
-    "We'll now show a few examples of setting these locators and formatters for various plots."
+    "We'll now look at a few examples of setting these locators and formatters for various plots."
    ]
   },
   {
@@ -155,30 +146,37 @@
     "## Hiding Ticks or Labels\n",
     "\n",
     "Perhaps the most common tick/label formatting operation is the act of hiding ticks or labels.\n",
-    "This can be done using ``plt.NullLocator()`` and ``plt.NullFormatter()``, as shown here:"
+    "This can be done using `plt.NullLocator` and `plt.NullFormatter`, as shown here (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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bxqZMoa1YZPcri6LoTheIjhhsLzvVCTvd9Eg7XZOiazpe4Jax7E7X8+h2ed2u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\nfSTZcMbpliVeyErWYhqLLlN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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x110a2b7f0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "ax = plt.axes()\n",
-    "ax.plot(np.random.rand(50))\n",
+    "rng = np.random.default_rng(1701)\n",
+    "ax.plot(rng.random(50))\n",
+    "ax.grid()\n",
     "\n",
     "ax.yaxis.set_major_locator(plt.NullLocator())\n",
     "ax.xaxis.set_major_formatter(plt.NullFormatter())"
@@ -188,7 +186,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice that we've removed the labels (but kept the ticks/gridlines) from the x axis, and removed the ticks (and thus the labels as well) from the y axis.\n",
+    "We've removed the labels (but kept the ticks/gridlines) from the x-axis, and removed the ticks (and thus the labels and gridlines as well) from the y-axis.\n",
     "Having no ticks at all can be useful in many situations—for example, when you want to show a grid of images.\n",
     "For instance, consider the following figure, which includes images of different faces, an example often used in supervised machine learning problems (see, for example, [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)):"
    ]
@@ -197,14 +195,17 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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FtOS93/oRP/qtH/GNv/5Nqlklaa81gJQR8zNxSnlsz6aOk/OBnz18wHs/+oDF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S5tk+s6TG9msoyqhbSOmtQ7slGsNWtEccij/hHttgzQ6+06vVMBeXbPJKFOb2dKGdY1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MqlqjPZxx+/Ye21dbdpiW2NxumI91QpzXu+5LBO9hvwf0/t6g1O7PaQiv5E2qDZVF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\ns7I4JVpHKCj4E5+mEf/76nxFlqQMp0OSKCFYhh3CGQQ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eh6YqKK6La1usNiGb+YY8zdFN\nA0dRyJKcLMmEmaGcmsRBzOsXZ6RhKnbfhmMUrebDj77NuO+jqxqaWgvqIArvf+td/upP9mmUisF4\n0mFYF6cLOapXMGyz25I3bVMExaqmqWu8gctgNmAwHbC+XFFXDYauduJC4VEmFMH+2Gd5vmT7eBtd\nF4vDmbQRUlQFaum1J9G3TSO0Sy1d0u2LwBhvYn7vH/8T7v/WPVzTlIuqdScq1HWddz96j8/+4TMO\nt4ZinLyJ8SeiHEQ+/JZtdroo3bgGyldyV81yLQaDHkPXRdc0zsIrbNkrLOWLW9W1XG9xOwZWXdcs\n4xhL17vdvJ5tk/u9jtW0XG1IwlTw5RvRktANHbWByWQotEirUPS4NK1b8Sgk6sTznI5ACkhpipjK\nFWkhvPTqWiqsxd1rm8GGIdAeruvQsyyYKURpyiaMCRaBMGwFXMvC0nXqpmHgiNK262kh1lAsXYD9\nLYmRaRGWhiSGNnWD69lYtsmw12Ps9yiqivP1CgDV0AWx4A0Mj6aqHGzP+MJ4IqfYQq2dK0I9b0md\nktt3uwa8qqk4Pacr0XVTSAzSMCUKY/Ik71aF+HfHpH9/UBL20Jr8t8pwNiRexzQNbPcHmKrGcrGh\nLIpOQSsEhDWhbYqxqJV1N89ybUDI+m+98zYf/P7XcG1LoiMETI0GsrLk3e++yxefPWA5v+LWe/dE\nk90UC3/tA2w5lrDykQ1SRVW72ryuawbDAUeHO9iGweVqIxG8ufwccOPGPru39qAp2D44INhEwhlV\nKl3bm4QilOaKpoppVZp3W+6lNGhsGoEOLbOSuqo4fv+Yi9MzJntDju8fCVRHVaGrCgWCiHD34IBv\nfPcjHv/yAfu3j+gNxci8tf5ubWk6/y1D2k/lQqA2mA05vHtAU9UsThcCFew7XfPUHwuVdJWX7Bxt\n88UvnqAg/Pta4Wgpt+6ronXHEIaeIFxO/ZHeKaafPvgSVVO4+4073NzewnecTnxnyPWHrCjYvbPL\n8nzJ/GTO8fvHZFEqPNU8sc+XZ7lcpgXFuD5lW5tpVVPRLb3D5DRNQ7AOMS2DSqWbUGmq2mFJWuBZ\n33E6zs/1fIpuiuYYBjRN99wpKNIBRsGzLCzD4HS+FBbUEjuTFQWOYQhGV11jOxa9vkea5p27y3R3\nwmDcJ40z8awaQoltmQb0BfvKtMxuZ9DUdXzbxtA0fNtGQejjKon+MHVR7ihNg20YtMwwVS6OtyC5\ndku/5W3pmkpV9rAtC11TcS0LW04NNVXlYrMhSjJhgqFe0w4suUCclyVT32c4GwptnVzRSZOMWOq/\nWsqB27NxPIG+tW2xYVDWAkzoWhYM+5ydzZmnCyopYO22f3/ToJQnGV7PYaG2zB6feDPsUloB9Gok\noL9VsSognUebWpxCKGLc2Rv2uHx5yfJsyde+9zW2D2Z4klxZy+1kVVHoWeICP/zeR/z8b39KlmTM\nDmY0dYPTuz412gaqooqMQogmBbjN9V38kSd/do2uC61JGmWMdoSAazDo8d4Ht/nlzz5jsj2mKEvW\n64DZaIBjimZgq5RtZJ3eBQt5jZqmddYx/sinyHI03eHqZI7lmHz0H/8uuqYJqqJ8aBRFoShLHNfm\ng99+j6ef/YrV4pL9e/vdaVpoon9n2IaY8EmgWV3XmJiYlkl/2md3d0ISpB0NYbo/7XRjo3GfYGso\ngsOtfYpb+xSK2N86W6w6wwPh56aQx1nHKBIlMN31Xr68YHW+5Ob7t7lxuMPOYNChPdQ2OBjCqbbM\nSu59eI8HP3jAxbMzPvjofcqyJElzsn5GuAjJEjFpbCS1sy21NF3F8no4tommiD5KWVSsNiFu36NR\n6FTObUDUNQ0VUc7ZhnG9qyl31VpOkC6ZUIqi0PQabNPsAHYtDWARiRULwW0Sz1Yb0Bp5mOiahtdz\nKasKx7MFUcAy0XoKkZ93yBERNHVMXWhz2uDdZiaOKYiflmEQ5xkXmhjXt9fWSOGmZejXpMemFtoi\nucBeVhV60+CYBqri4dsWoHTBuQUhlnVNlKZi4b1ufg1/0wL7DF2nrMUkbrw94up0jqZpjIY+yrBP\nmouMR5N+hZZx/TNMKcCMsgxVVfAsG11Vyccl68VGWLQXFU31lYNSzmh7JFL3MGNyNCOeCHj/JkmI\nUoEQsVyTqqylk6qMiAqYjoWuC+cTwzaJ1hFPHzxmejBj99YOfddh7HkC8iU3oQH6rotu6MwOtnj7\nw/d5+eg5w1GPw7eOCINYsHnmGyFUizMBuDcN0KSsX2vwxz6e55AWBZZhsDUYsHg1x3TMjsdj6Trv\n3LvNw4fPqcKM3f0tFqsNoSQVhGlGHEs2jNzDKouKIhW7UqZloFsG3sAT8gBTJ97EnD0749kvn/K9\nf/YHTHfGkheldBmhrgnImKFr7B7scveDt/nVzx5w/O5txjtjoTiW3lpN3YAjSiu1VrssyBt6DKZ9\nLMMgVTPZKPbZOtri4Q8fAnAwnZAECeurNU8/e8F7792msjWWcUwQRjiejWboKNKrTriQiLLDtE0h\nAowyLl9d8vqLE/Zu7fP21+8xG/SxTfMaVSwPlKHrYpsGl2GC5Vrc++Y9vvjkc5784gkf/Pa7zMY6\ncZqx9N1uSbdddAUJaCtKDMvAsSyGrsvM7/Pk5SkNMNweSuMBEZRSCRqr6xokvlhTVUwJWlMVhRo6\nFpGiKJhSj2PIbKojeuY5aVkSxAlZloutd8fCtSxBKG3LploE0fYwrMqKnmOLazcMyqrqAGjtOoyl\n610202ZKHf1RbaFrohncwvsFpVUo9zVV/EMjiJYtt6o1o28JCo5his8gf69v26KsliVaKOUNrSFC\nC0Fs+UqmplFW4jNOxwMhEK1qbEMs71Z1TZCmHV9M17Qu+JqaqFRceZi3Ja8lD9NKWpK1z+9vHJTS\nOKUsK8bbIy5Or7h/54hyZ8JqE7KMImiEkyYKwnCyaaRZqYLjOeiGRllUGKZOHCQ8/DcPsByH9777\nHropON+GPG1bZIKiKHimycD3CBYBO8c7lHnJs89e4E+HHB3vks9KlpM+4TIUfSKRsHW7UIomTP3a\nVHLq+0RJShAnTPbGlLmkRlYlg8mQt+/c5PWrc7750ftUiMZoXgqQXCWdemuQbhvipRWqVXEq66aO\nbulURcVmvuH0ixO+8f1vsH+8S16WXTloyJvYYmBc02LQ97j3zXeZn8/5+d/8lIMb+2wdzTBsg83V\nRozko1Rwn1xxfbqpd83sMM0om5rZ4Yz9nSlBKNYBAHaGQy49IWRVNJVf/PIxN792ixt72yhNQ1qJ\nPlAmV4RayoGmixcwCRLOn52zvlyxfWObw/uHzCZDAXiTwailK4qsRZWQMcEycn2Xw7dvMD+Z8+Dj\nX3F094CbB7tsj4acrVZiApXmZJHszVmCK2SaBkNX9IlOFgvOVysme2MUVREWP/L6oiQlkkuzhq6j\nSP6RIU/wlmJp6zqFKQSHlrwHLRnT1HWBIqkqgiRlE8ein6cquK4tGOa2jSmxyW0J58jMvMhLNknC\ntNejZ9tY8mfXdc1GTu3KquoC5ptQvFbKUEipgQJUuSjf4izHMemChi4DWJtnaDLgGm8ECE32bApJ\nTTA0rYMwRploiGd50V2frgk2vdFyvw2jSxDGvs+Z75AmqcjqTRPXNPEdRzT+M0HveDP71FQV3bbJ\nyoIkLzrMkaqqnfr9K5tRFnlJEibsH2yhFDU/+OEv+J2PvkbPc9gkQlilGzqqVKYqmorSAJpCz3VI\n84IizVlerHj6iy+xHZc737iNYYvI61oCQlbWNVVdiembomDoGgPX5bUqFn63jrbojXxBKCgr3nrr\nmN2bQ174c8Ig6vaJmka4m4omuE3PsvHkg316scAZehR5TrLZyBc6xdA0PvzoA/7qL3/Ey+envH3/\nFq+WC/KqElY4htaVFrpUHDuug2XoZGVJlgtoWV1UnDw+4dlnz7n7zbvce/82UZZ1mFWzlSHImyew\nrBp9x2G2NeL+N97j2YNn/Nm/+FP+6X/1n3Lj9gGr6YYkEMCuIhcnnKZr3cpCtInFC+z36N/3iJKU\n55+86jCn5+s1nnTuVRQFb3/EL374gAcobB1t8fYHd7gxnXZY1aquuVhv+PL0jLNnAsfRNA07t3YY\n7Yzx+x5a27OjkZwt0ZNoEbCV1LIlQUJpC4+zg7cOiFYhDz/+nAc//IzdW3u8/8Fd3t7bE7C0KOpI\nlWleUAOvF4tu18t0LXJpYNnaNIHY+A8du4PqV4oiYPvyhWzXQ9qMqOVlyzOMnm2LXp/8/FEm+iZZ\nIlZvbNPE0NRrBr2ioNc1tmEw8HsslhvCdcxmE5EMh4xkcDBl36rNjK7CUJRpcuzeHsBtYGnLzEqa\ntIJYFtc0DVUC3trdO0v2tkD02gxVmobKoJUVBY4pKLGpRCvX9TUiOAlj0jinN/QwdMHcb3lfjqET\nphmaquLaJsOBz+uX5ywXG2b9Pr5ti4ypafBt+5rVLr/bqhFLw6osq9usTNVUYfNV1Z0c5zcOSmUh\nJPTLTcj+3X3OXpzz93//Cbfev8X+ZIxnWWzimLgQ7hOKIsaBhiZuwCaIOHt6xtmzMyzX5ugd0cwF\nUaK1NwTJZylkahrlOamk6KEolEUlxvq7Y04ev+by9RWD2YDv/PYHeAf75GXJxXpNUQn8gqFp5EXJ\nfBNwcn5FJYFoAFmUdylkkGZYS+b8QAAAIABJREFUuoHlGfzu9z/kRz/8JZ989pjf/+b7rJOEqyDo\n8K5JlmNYpmh0lwWGIXjl/nBAkRc8+uUTTp6ccPzujS4glXlJryeCb+sw0ZYRrcuJZ1s4tlgavf31\nO6wvZ/yv/82/4J/91/8lk62RwLHYFsEykEumBpZn4foujm0x831oGl6cXXIi4W/tdPHxs1dMx0MM\ny6DMS0zH4uDuAT/58x/z/NELfvIXP8VybYbbQ/oTAXATHBxRQm0fb0tYnivUxpoYTdu6jm0aaKpG\nkCSsk4R1HHO2XImmqWvTNKK5mwQxft/j7v1jprMxT3/1gk9/+Bl/9yc/wO2Lyak38GSDXxwqds9m\n/86+2EGsKuJITMpaZ11kuRcFCYGXEDhOlxVZ0gFEUZSOs21oWvfitNl0m6203GlBWNwQLkIa5OeQ\nWUTrENJmhaauCwcY26RZRWyu1szHA8ae92vZcBsYp71e5+Kjqioa1/D9tqeT5Bl5cW1Wuo4jDE2l\nrIwuyFSSid2WnK2xQMvbFuWS6F8VEmPblq7LKOJqviKJs677b0pb7mtnF4NMKwmzjL7t0O95nOka\ny+WGcCelZ9v4to2uKOhtmfZG9tZUFarMPE1NI81zMql3aw1Z28nwbxyUqkJYruRpzjoImR7OaDSF\nH/zpP2A7FoPZgP07+ww9j93+gJ4tVkBeLZecXcw5eXyCosDu8Q7eqIc38KjKkv7QZx1F5EUheMi6\nLnoDjcBCnC6WnM+XIHGsSSg0T7s3d/A/vMfJl6ecPz3jv/u/f4w3cAXAzRHj5jTOqMoSRVXxR0I/\npOsaeZIJ1kuWd6rSJEoJZYO2Z1l89N2v89OfPeK//2//Jd/7499ldzQkqSuSssA09M5cT2kafMfG\nUHUuFytePD1hc7Xh6P4h28c7xLkQ3rXuEHlVkhS56GXoOsjgWzXC6UNVBZGxzEtmBzP+4D/7T/iH\nP/kBe3f3ufH2kRCH7o5B/v42lY7znCevTklCQVdsmgbDNrqRue6YXKxWlHl5DeBSFca7Y8J1hD/2\nsV0LQ5INNEPDsk0Goz62Z+H2HExNJ05SlvM1q/Ol2D7fj8jKEl1dEqYZV4sVl+cL4nWMoiqdEFI4\nJwu+1s72hLdvHfH2zUNe//aSV09PWS82pLEI3qpUMpu2gT/uU9cNRZSK/pFcTyiLEuqmm7AWeUEY\nxiwd4dDiGAZRlmHLUklVFJHdqNeuN61FUoOY8mYyU0uLnLNn5xRZIYwo22mXLFGESLTuGPSVPEg1\nXSVLcsEfHwrM8uANVHAbiJAtijddRtZJgqaqrKKIIEzEtFr2lMIswzZMenbrh9NgaGIiLKZvFYaq\ndSYLrc6oDVi5NDLIq4pNmvL61TnrVYDbd2nn8q0hQC0DraGJZ6stbT3LEoLiVcR6HeLJ8tSUgVp5\no3dUtdcmf3+QZcR5ThglZHEqRKlyP/ArBaUkTDr+bp7mOI440UdbI86enrGebzh/foEtFzwdX2gV\nVE0gHHZv7Yqa3rEwbJMkikmygnATc7a8wPUdwjhhPYwpypIoSTk7nzM/W5LGqVhtGPs4PYfVxZIy\nLTg83GFra8JmFbB/a5cLudGcyyisSLKkP/ExbUvIE1aJdPu8Xq4EKPOCNM9ZhGF3Ao53RxzcO+BP\n/ud/RX/cZ+tom8HWAH/Yw7JNavn3wk0sXtClsCs+fvuI4XRIXuQUmSAqVHI6sgoisrwgL8ouDU+K\nnLysOF+tubhaiQaqqmA5gsF962u3iTcxP/urn6Mbesc7am2FFBAW6paBKQVrQLcUCjDp+8RxytIU\nawStyG3raAvjYiWYTQMPxxEByO05+H0P33WwdNEHW4YRlxcLXj9+zcnTV9RVw8GdI9aHG0zbIN7E\nzE8WLM4WxEFIUeb0h0Pe+vA+mqF1ELX1dsjAdfEsi3s7O9yYTrncbAgSIWQs0pw0E/cDGuK12G4v\nC0EvrCW6pc2e2/vY1DVJmpNYmehJJsLpoy3b2oxCk4GozcyLqiLOMuFA0jSswogsFkp90zIoi4pN\nFOOaJtu+L3qAhkFRlgRpwiqMSdMMRRU8pDTJiGQPycpzPNvuGs7IzK1VM5eVaFWEWUqU5ayimDhK\nKLK8vTRUFNIi7156MeWiK/3rpsFQ6WiY7fSwkKVoXpadrVIQxgRBjOXYmHJ0rwBpmrGS38nQc/Eb\nB0vXCRVFSEZiobuqqoowSQnl9U19v2vit0G3KIquAijKknUcs4liklgsSaMo1/jirxKUikwKH3VN\nnPiF4KR4AxGAdEPHckwsV0RU27WE+rrn4nkOtm0KvULTMA9Cirxkdb6SaISE/qTPamfN+WxAXVWE\nK1HuLc8XVHWJYZjs3d5juDUijTJWV2t2d6cMez12BgOOdre4vLMmDhPBaEZ8xqIoxUmbFRSZAIDR\nNJRlSVVeI3NRIMuLbjrmOw51LU7HG+8eS5eIksXJgvXlWqJCLAG18x1c22J4YxfHsfAcm6womK9L\nNP1aQZ0lGWtlg6qqXPU9VlGMaeiESUoUxbw+uWT+ei7Qpj2HOEzY2x4yGPaIgpj5+ZL56ys2843o\nrzgWliMQEqZECVdFJa6tqDqGEYhMdzzwWY/7XFwsaOoGy7HYurHF7HCGaRsMfJ+B5+DbTndatmUN\niJR8ebnmxa+e88kP/5Y8T3l3+S32Lo/RNJU8K9gs1py9esnL578iDJdsbd1A03T27+6LiWVeslps\n2JoMO/NHQ9fZGgzYHgw6r71NmjIPAoIoIQgiIbytaynAvb62NtNoTSxKaTmlKmpnaVSXZbca0u4K\nKorSvdCt2LN1lVkvA2zXxjDF1LFYRyRBTJWXomSRZdk6jjmZLzg9vWR9tZb3xCSVymjPtsmqCjXP\nafnvbfnXIMq2qq5ZxXFnmlrkBWmUdsppED27qhalXfvii2sWMVlBEZM42SBvnXKAbiqqKgpZVXFx\nsaCua+yejaqp3SoVgGWbrD0HpaFzGpqHISfzBc+fn7K6XAnkShiTF7L3lqbd59E0DUOWcKJvlbGM\nYzZxQioNKcqilLbxFXX5FXtKTd0IlKlM++umQdd1BrMhjZx2ub6D59rYllDgOo5Nz7E7zVHVNGyS\nhDROWV2uefrpEz7++78jTSPuvPMBe4dHQn/SiOXS0xevePbFQ4Jgiev5fJB8xK13b1MWJcEyIAhj\neo5Dqapi2rFlUE3rTi8TpilBFLMKIrEmIeFaLQO5qqrrL1Smoa3/V1kJDYblmOSpkMebtvFGMLCE\nmtdzGA56jDwP17Ko6ppFFHXjZN3QCZYBr371inAVdjQBb+Axn62wXZsszVieLrl4cUGw2GC5FltH\n2+imTrSKuHn7gMlkyHRrxOpgJvoq0klDgS4NztNC2PSUYiNeURWJwoBPf/GY7b0pWV50MHdVUzvV\nraHrOJaJZ9mdQK8d97Yne1lWrC5XLC6vSJKQy8sXlD8pyOMC2/FFPyYKWC0uubx8wXp9xWp1wXS2\nJ9XhKqquEgUxcZqjazquaaLJdF9RxJCERiiUB64r7omukTkZ4VqRDHiBINE0jVqV7iJ1JfbYbLGi\nZFZC7BpnmXQWVrog25Zv5RtBCfm7l1EkSASeLaB/D5+TJRn+qE94MBM+ddIU9HK54tmXrzl9dk4a\npfgTn+nBlDIr2AQxs35f2BDJzKH9p5ZZTNvQX8cxUZaR52W3Z1gWVSc0zooSQxq/aqqKLqU2tdzv\n01Whv9OU64leWV+7SYPopc2TlPOLuTiMFYVgvuHTH/yS+eUpg/GUrf1dhrMhZVaQ5DmGobNcB3z5\n+CVPfvYFdVOxf/uQJEoIo5ih55LJPlwt71kj+21hmhKkCUEi3oM8zQUssRIo4rqsqLWvKAlQNEni\nKysxeapqVFPAx/2BJx4wR+hJeraN84ZwrR3zp1IjEW1iLl5c8PLJM16+fMTV1WuiaEURfwfH89F1\ng7qqOX31nC+++Jg4DnCcHo7bw+/3cSUrfLOJGA78Ttla1NcW07oMVIoClZwO5Ukm2MFx1gWnDqMq\nxZ6K7NTlZYltGmwfbjHZHtPUNY4t7Ghs28SxbTzbEs1pQ/QxamAVRWRFQZJkEl5W8/LRC/7mz/8V\nl+cnjKfb3LzzLlv7e6wuVyIIlhWXry559ewJi8szRpMZqvZ1RjtjNosNxXGFa1kM+z6O65DLfkpd\ni75buApJo7RTfzf1tb9e++fLn3/J/8/em/xqmqV3Qr8zvOM333tjysjIqsoqZ5WxLdrGdquRWo1A\nCIkFO/4HWLCg2fAPgJoFC8SKJUj8C2bXiEZAW5Tabbfd5ZqysnKKiBt3+IZ3PCOL5znn+6LAgypZ\n+m4iIzLi3u99zznPeYbf8OazNyT29owAo55tdLwPkK3MLrjJWSa5k2SrbHafkND46KPfxKtv/Qak\nVhj6AdZ4FEWFsqzx7e/9AJABb77+jBDYx0fc/vIW65sNZCFxfDhhHCe0dZX9wpLlM3C+3YHkdUca\nR0VVom4d3GxhAUY8py4LuIFqYAuNURpUmjWoS6LngKdVibeWSqgImoj6ELC/P2L/7oArJfH1z7/C\nH/+v/xSvv/4FPvr2J/jtf+sP4b3H8dQBAbh9fYef/dmP8Zd/+i/Qd0f8/X/472PzZEOKi1PS06J+\nYfD+vaBk2C6JjDItLLdFxp60xmOMeQhjnMPEGcilyWetCTbhZUCICpUmkDLhlfK2zhitw6lHt++h\nChKY+/xff45/9cc/xC9+8WdYrnb4wb/x+/jok4+pb3zooLTC/df3+Jf/+x/jlz/7Ga6fPcXTD5/D\njDNRyvhcJ9PPBKS2nmhiEw8DvPUw40y6X7MjXJRUWcTu1w5KVVPBjCbXs3a2FKUZWi+0yCmfEoJx\nPFSPpkyJxpGOMqXbPeCBm5sP0bYbXF+9wDgMxG1rlqjqBk+efoinTz/C69efoixrjH2Hu9fv8IQb\ntcfHE6anV6iKItt0p0WLWsNcWLgIEFWiauvMqUogrrT5QwiwzpE4O7u3Xu82kAIoFHm3tVXJDb4C\nShIAMkEZAi/GOBvMk8Hx/oDDuwP+9P/8If7iz/45uu4Rt293mPoR4+m3sFiy2aAU+OIXP8GPfvR/\nYRw7vHz5G3jy4iUqViM8nnoW3iJ8DHhSoqVEk7SUAvVZUt2fN/RIzrp2ttjfUvrdrlo0yyaTI4tS\nw1qXyxgBGmensm2YZxwnsty5frpD3TbwxuM7v/Ux1k82uP38Db76+ZcQgnSlt0+3WG5X+PDVb+Dx\n7h2kVDgdOgglUS8qTP2Erh+x264xGIOWwZeJj5YCUrK2nnkwQdkfZVtSnBUqU2QJjBR2zqOPM3wM\nWJSUvaZRdwBQX2RJFdt4KykRDXnCdY8dirqAmSyLwN3C/nSCFBpCKJxYj+v+9R3+1f/9x/jyi59A\n6xJvP3+L7/z2d+H54rXeZxfk1HyOMaII5PYc8iGm5w0+wE6GhfddptBEts82zsLL9+EApbUUbL2A\nlmyQySqaioGUIVI5lUXyJHnkBR/w4sOP4JxF1dRQWuPLn34O7wKaRQOlJX70wz/Dj/7ih3j16gco\ndEMyuL3GOFEPLiUALgQ4hi6k9TNcmcQQswpnKuHpvH3DRvdis0D30L13iKMPsCFingwWqxZWeVjv\nYLxCtBZKCBiu1UOMeOg6WOczQ79pVvjN3/oDPPv2cyzWC9x+eYsvf/Y5rJ1RNQ2evHiGf3DzH+Lz\nn/4c3jvEGDD2A7rHDvWixnDo0U8Tlm2DwRg6sNy49CGQvXeImK2D4YWmDEIiBi57+HZOJUpgH7eS\nuVeKpy0A410EmXCSg67IeKoEEutnanKOpwEPrx9x9+Ud+tMJbbtGUZT48MMfYLHY4P7da0zDiKKo\n0S6X6PsD+u4ApTXJPRzIYUMXGnsWHksOuyEEJG/RUis0bY2u6NGsWzjnYU/0rEKSSmYKfGkjkpHm\nnD3XiEhcYBynfHnUXIqmgFsqDVUqMqN89RQ/evsGp8MRL777AT76zW+TVO79EWVFt3VRVNjd3MBM\nFof9O8xTD4E1mgWROqeZ/NIgBMmOcABMQMI0LZqN5Sb3xQHlrF9KAcv9ECoHzpO4oirQ91PuNaUs\nECBMEMA+dWndL4JGv+9RLypcv7jGH/w7/xBX18+hCoXj/hFf/fxzDKcbshESAh+8/C5ubl5hc72F\nEgWO90cqu4cJvZmze3A6M8mc9Wx46jAZC2ctW5SFXMIF/rzOe4yGrOcrHRGVgpICji+Omt2KjfMU\nXME4JyEYYmAIG8Xg5sM76n9dvbjC9tkWn/zeb5I6gg94/ekb0sLvJrSrFpvdDX7/H/y7+O7v/AYp\nYXgyIvCOgv9kLVrORCfnILlagUD2Q3Qs0esdZUxmJjUELb+hHG73cAIEEWh1pQEoShsdWbY442A1\nub4mASwlJWwIGMcRJ64xlZBY36yxebLGF5/+HEoWeFV9G08/eo7FeoWqqklCl3sv1aLGb/7uv4m7\nr+5wf/cGlkfsRVVg6gngNixaSCFQl4Rspg3oeONRSu+MI9WCEBBFhFDUQ0ikR0SiViQ0muPg2w8j\n1cpcfkoGqEXe3D4GzNz0O7GLLgA457C/fUR37NA0S/zu3/v3cP38GZ6+eorjwwlffvoZnJ0BRCw3\nS3z83d+B1iWG4YiqaimIzoSGPt4f8OTVE+pzSYlCq5wRelCDXhcaZjT8XkiiJPiA2y+/zgdCKUXW\nPhe3VTLl9N7DWoFTP8AFj4YPT/LuWjcNTtOExarF7/6jv4dXP/goo3KbVYOn33pGUxXWLRKCOI5X\nT27QtEtcv7gmve5FnbFOPkRUhcJhoMlWbwwmS7bQnlU001dRF7ATOcA6Y9myJ+Zm8DTMqCFglc0l\nbFEXMM6+l4k572E4a7L8Pjv2vY8ANk8pe337y1s8/egpVrsN/vA/+LdRlBqHuwO++MsvoKTCcrPE\n9QfX+NZvf5sQ7GWBR54Uxy7ixcfPMTMUxEeigQDItJBUEo+W3WxO49mlxDoW36dMfzyNCIuKyp2W\nDAZgHRGIlcLIF/+mbc/gS94byTpMCIF20eCDj57h9aev0R960ji7XuOD773MInVVW2N/u4eUAqub\nNV589zmKimy3FpsFHt/uc2IyOVKvSAoRmvdRBDCyycHY0XoZNly1hvtKeL+98GsFpcfbPW5eXpOl\nS0HNUWGoRi6ExsgH4QGkKbysaygp880QY8SuXdCf1TW+/3uf4Hh/ZFdWQ1SEbYunHxFIz1nSA7K8\nMEJJLFcbLDebvMF1STSGbp6wrGqMhsaok7WYuC43kzk/ZKFJ53iY4WZHriv8gi03gAUALwRccPBa\nQYDsyDdti8kY1FrTDR8jPPdd+nnGaZ7xxdt3cMZitVlifbUmwbTPvoL3Ds9efohPfv8TLDYLHO8P\nUEri4c0DHd5SY/fsGsvtH2D/7gHDcEJVNYQZ0hrzQL5iCBHb5QI0bxH53drZnE0YZpcnTGY2uL39\ngoMu9TWS+D4pXKrMYg8+wAnaUMZ6IBoCHwIopIRSlDFO1qJdN7jR15gHKjXqZZ1dUEkjmzIzXWgs\ntguCMaxbcncpSSGgbipIJnfHGJmT5XIwpVvVkMsJe4ghUqZEmYQnnBn3JZJcK0SEdgQHEYLG5ulg\nGucyDWXmgDRxuZpAji+e3eD7f/gJ/vU//xHe/OINdKHwwfde4vqDa2yebrHYLGHGGUVTYrVdkXXY\nbLMsT/fY4eblDV0OPIUrmYvn+MB205T7LSGmAZKAjfTckZ9/7OiZnHNQhrTJZm7Wp+x9YjjBumnR\nc6tEAARGDoF+dl0jmR58/7c+higUvvzpl6QMefuIdtNi92yH5WZBGuUNJQRFqbG/PeD4cMTrT1+z\nZ5/C1fMrsqvyHo6TEMW948lZDLNhK3G6CL2gQULaH85aCs/fVLok/XszknyqZ+2dsirRHwcst0ua\nNHBPwDBHhhqVVLO3FQEqI4BX33mB9j/+RwiIeHxDLq5aKzTLGttnu3xbEDeH5DJUoUjKtaVRONFa\nZE7nUy1rrc/pbyLMktg9ufWmTU9iWjzhGKkeFkJA8jg9Mf6jiTDlWSKj0qTTJELAaEwu2ZqmwmK7\nxpIb4vbv/wDDqcfbr6lknfoJ7apBu2px8+ENO7t4qELDjPNFj6fA7tkOmycbNEtCOh/vjigKnVG5\n1nlM08z9sZm0kLJUCzX23eyyX3tah+SmOw8zFusWAjzRYjnV9C6cJhSuZsQ29SYo04hC0K3uPOoV\nafQ0SzIHgKDG6symjt4Rgn65XZJeektKoEWhM54mCafNlmk6PsJOPPoP5K4cPDVMA4v6pVI7feZp\nmCC1JD6VEMBI30tpBV8WmJw98yGFIHmVFLm4/2KtRaEUvvfbH6OsSxzujsT5tBZSSxJ8e7rB6bGj\naSJTn06PHfoDOa+srlZ4/vFzUl0EjfGVEJitzRnMaA1m6zCaGQKCdasVEJAnb2YyGPojXyjk9ZYg\nCzJQz5MuENIMj9yPs5KVHfndJh0mxxPlddPgk0++hesX16Q7/8Ut+uNAmU5RQOczVp4b7o7K2mbV\nYvt0i6sPrlDUJaxzMEphmGfGTDmMxmKcDeycTCgJRJkUOaeenGukLLIszq8dlBLFI004QtQoygJm\nJEU5O9ssAYoIDFyfp4OcGNEFk/wi2xKZkciaSSDNGodVpJs2hICBgYneedLobmsSmyoUilKjqMmE\nL2nBeOdhLaWIbib+FeEwuL5NkAD+fOkzzgPzi5RgWRBiRXlH2JPZupwaV1rTZlMSzgfeIBG7xYJv\nYoVSF2i//22UZYGHuz10oTF2Y7ZT1mWBxXZJ72CivomuNIrqhpvF5CtXVmVG0jvrcOoHkmg1FjPj\nWSw/ZxKxD0wcHrsBZj5Le6QGaAr2zrhsJAgBCgIuwEt6HilkFmNzzmeoRIyEtpZKoqwKNDUh0BfL\nhvolSmKeqDkdEVGWJW32VYOKkew+hNyPG41h7XZaAwjA/QrOKjXynT03Ti+/JtZpIuGxgl1oI108\nXL6EGOFjwGQNBAAHEuVPZFGASqumLPHBd15g85TG4/dvHlBXJdqmhl1QVlTWJVtmV1iC9MTrZY2S\nAzQ5M1MQTzwwAEz3IFChmSzpVgv6/M6dL2I7WVhDWWcSRpQXDroxIdtjhI4U/BLuCwD8BX4p9XQd\nT+aqokBVFtBS4smHT7A4DmirCkpJ9P2IsinQtA0AgXpJHnnL3RJ1W2fX6eBDnrIl4u7sHIylBr2Z\nSG7HzgQFSCj8eSS5FCH/fzAOSKNrO1vWMqrZp4urZT7gjm8ywe4aAPGeEtwfIEg/0TQ0hLAomBGu\ntYZZ1CiqImsGjR1B061xedM1C/JKV1rSz48xE0DnkQBaVJpQz4FkJThVns89h8ugZGcLL30WiPOs\njidTsHTuQlM45o2ekKsSgmUpZHZPbasKz14+Qcn63qfHI6RS2YZnsW6hCp0pPEWhIZRE1VTk4FHS\n78Gf1fDNVZQ0GXKOavR5NFmdgSyJAiAEToc9PE8gPWcVMYTc8A48dk5rG0IghQVeO6kIcEil3lkz\nWmuNoijQNBVWNfH52qoiyyE+iP04oh8nGvMLUmrQSiNEkg9x3p8Bjzwy9ixnEbjZK3IwcnDcWLXG\nZmAoQAcPILlfMxt46yCl4lWKuekrudQGIv8bAlimMbu+mBoFntaln7G+WmO7WqJdNOi7Ec26RVXT\nZSF4f8dIKhkJPAyQwenEAxilFKwjLlmMAd7TM2oQMNm78N7epEYyrR1l/QG6KPNZA5DJ6+4CW1cy\nTEBJwUhwyv5Soz9BMDSXtYVSWG0W2C0XGA35MjaMvWuaMnNeiZlQomyrTEb3xsNFwBSUBIysNJsm\nbs6cgcvOOFqfBAdAJAmWbxKUYqQDnn5YcttMpDrBTU4hBKKkiGxni+AC2k0L7qkiRALhSUmRv102\nRAdhXFMiMUpF2jLHpqJeifNk+si0FaVl1jYaR2KQB+5rJORuKgEj6AV6Hhc76zMOJKX0ZqSmbYwR\nRUVSFJGxM5GzKh88rBO54Zw0cByTI633KIKmUag/40mEFPDe5xH/LGaUZYFYaNTLhlUVBLyhwFBU\nBTsSUzbinAM88uLagpxHhRSZcQ1BuCPPzyYgME1jDqTkeqER3FnHJgYK0ilTSj73KWBLKTD1E6q6\nglSC3DDKgj47iFtouxl9VWKzXTEHb8bsLNq6xrJtCbPFvCfnyc8+ZWvp55jJ0NqwBrgArbPgz2NN\nKt/c+e9weZKsw5IXXWqkVnWV7o/3pnepD1KyumK2S2LBtcmanFGWVQGvJcq2QtMQqLSsCqJKFVQi\nHSSVi9mlA9S7TNpGs3OI3Fdy3uPU9SjKgg8sZezeeZIZsWR1HbjZnYJSVjP1NFRK+cVsLTeMSS3S\nsKIpCeVJtEUJ6z0mbuqn4UjBMiXgv1drIvo677FaNES2bRoM84zTYsAs50xLSpAg7zyC5AAkCFE/\nTSTqRlAbGngE53MyYwYDayhwqSi/eaaUXx5opE4vzkOVnEaXNOYXgoiJ3gWE4CGlwtQRFN2xeh+E\ngNbgqC6wKCus6pr8reYZdVlCCYHRWizbBsZYnMaJ+lUX6OWkH40ImHi+cRINIXDGQH5vlgMSw/cz\nnZkBe46EuNKIODmySilyqhwjYJI5IjdpQyDnisiZYFL2S+RGAFg0NfGQtIfWCnBsd1MXqJcNNPeo\nuscuI7Ujb8bgfW7mpvdrZ5s/Y+DnpQMcM98txoChP8Jz8zjZ9aQNFUOEtyTfm1DfUUW+GSkboe9F\npQmBKCNGAbwbDO5fP+D2i7c47u+hdIHnL19itWpx/+4Bj/s7bDc7vPrut3Dz0RM0rABgJ8JvOeNo\nGsPmkIlTGQL1kqpFjaopyX2EsWSezSYSgj04lme+UGdMSgDeOIRSQ0IiBO4vKp6a8toYznwXF7Ib\n1rlMii61RlNXEIxR2yxaCADPrnZ5GhtBE8AIZP6Xsw5aqxxIBY/GvVJU5kca8Vtr2XPQ5cGDMxxU\nWZIlrZ2UIo/VS1ECkeAk8kRMAAAgAElEQVQMAYBOQSL4PDEWoBLNOZ/7WPR9ZG54J2vuUmssawq4\n3nuo9RrXyyVKrXHUGuP1Fnf7Y5ZIJjOQkJveJEVC+u4xRDhHMtPOegg2SE09zGkYcwUC4Bx0f92g\nBIDHxg5Vo7hsAtzsoBoFZ11+aD/5fDNJRT5vRVFgGqk0KZsSAqRHHAGcpgk/+8nnKKsCuydb1FWJ\n06nH7Zd30KXG5skai0WLolCw1qFjO+6kNplG2977PHoUAPWb2F4mIX5FBKf0hFG63NT+ImjpUudx\neAoIaVGklHCW+1I+oGwrPvAxywITg5tNDJREpUkLvCkKnHQBUShSbaxKHMcxZyt25oDAgTVtTqkU\npJPnnhHrgntHjVEhuIHPQSwEj67bQ6siP18ObiklZ4yPYLOETPYMwDwanB5OGE8DAJHfx/HxgLu3\nb9AdHzFPE7QuoXWJ7m6A9TOmaQAi8Fbd4i/+xZ+iamo8efoKzz9+jiW7pTrrmNhNPK+M21FUZi/4\ngqiaig6oDxnvYiZqH4B7jqnnlEpNQMBaBzkZSEVmkTaYnMWY2aJsSli2Mgoh7QVQw1snOgfh0KSQ\nWDcNdm2bSbVtVbGImkCpSQxtshZDTyqbqfyiz8RlHCtWSCkzty1PHicDNzuiYVgKzmY2MDO7zyTo\nQ4iIhQakxGyoh+uFxGymnPULviwnxncp9lorC51lcwqtsK6bs7bSPGMyBkpKbJoGDUMoCqXwbLuF\nVgqPpx7jQAKD3hKSPoaIKMiA0xlL8SBVNJLWgeAnNHUb+xExBiipEWPIZfavHZSSYpybHeqmzuB+\nbx0829LQbUWN25wuz5acDxrSgXGrBt42ONkTfvzVT/DZn3+GH//Fn8BZi6Ks8eTpS6w2W/SnE8a+\nBwAslmSL9N3f/S6evLqBnS2mYcpKjM4Q6pduFEodlVJsIV4StGC29Fk9ZVaeS8gkVKZLhRhV3tze\neqDg2yUE2Jk2S1kVsDwhSZlUQojPhcZYGBSadWk0uXssqgq11ljwjbRqGpL/5RupUCTwtmgrPD6e\n0O072CRilnhOoMkFpfHMe2JLGyEuyjguk+3sYKYRztNtFHzIuCNvaZwutWS7aZtVAGliRj2Ebt/h\ny59/iq4/Yho7OGcw8q/WztC6hJQkaj+OJ1hr4L2BUgWqsoF1FsPrA3756V+i/JMG6/UN6rrFcrmF\nUgWVVpHY/2VVYbFZZIcMV5fQZTiD7mbD2B2Wp2VdqKQcmmynAoP3Ag84iDvHoMrZsQYTGEKROGm0\nx4uqwHqzou+vC9RFgWGe8e54xOv9Hpu2xUfX12jLEpO16OYZldZ4tt1imGc88PR2GMgllzJ2DjKM\nSA+e7dWZcxgslaWWD7U1dHamqT+3FiYDqRU9syOWfVHR+7OeMrI0KfPOZwUFpTWkoiDVLIiDCk06\n7Pddh9fvHvDpn3+K4x1N+bbPtvjwey+hywL7d3vsb/e4fnmD3ZMt2qZCXZfo+5FaE2bmy5GwflNP\nRqqE6q/gJRA5S4ox8llll90Ycmb7jYISab8Inmh5FELDGZ891Mu6zLdtejnzMOP0cIQzlK4WpYZk\n88S3X3yN29dfouseURQltK5QROC4f8DD3S2MmaCkQlnV6Lojfv6zf4n/45/+L3j1rU/w/NVLlDX1\ns+xEPYCpn7IpQVJkJEcTJtlad54eMiYp+ICyouZh2pxK0/QnpZmioECXehMmUMM5gUYJT3IW61dK\nol22KBtyq3iyXGJRVrh9PODHn36O+7cPKKoSL18+w6kbsO96fP6Tz1EvG6x2axRlgXbVYJIS42kk\nhO9MPmNmMvQs3PhNwEXJzf5wkfVN/YgY5FndjzdB6j+ZiSZIwQUMx5Ea7NyHkUrlG2579RRlXeN0\n0uhOe4RQY7O5xmK5Q1U1ePmtj2BmQtsf948w84h2sUaIAX13QLtYwAcHM824e/cVM8QD6rqFEBJl\nWaMsa9TVAmbeYnNzhc3NhriKnM0AhNiOPnLm688TR87+iGJCwTT1PQo2XYwhIsrzgT89nni/gJvd\nhKepF2SdHQG8HiYc70+4f32Puy/eodv3WGxafOd3voN62eCrn36F47sDdi+u8PJ7H+C733tFBgIx\nYpICxvJQCJG03OfE/rc8sDnbdHvu8zlrASa+T8OQMwlrLAoBhKDzPs0MBG6rpAx/nmb0+57gMJzd\nF5oGRBHA1I043h/w5hdvMA20B8qaWhX72wPuvrqHmQymbsQ80AClrEs8//g5Xn3/FeqmIg5pgt2E\nOeO0rLHvGWlaQ60AsiCb4JwhYxFBOLsQEi/h1wxK1HuRsNZl9wnvKMVOmIhMcuRbYh5m7G/3uH9z\nh8PhHbx3GMcTxrFHCGe4fV0tIDVg/QQ3GExjD2sNiaTrAtYaaF3Ae4vPPv0LfPHLn6CuF6iqBkKo\nHHG1LrBa79C0LeTVOlMrMkqb6/w0ag4XfZ9EPxFCMC7EI/lWFFVJzWaAdKTZ481bj6mnwFFUZFhQ\nVgXs7DIT/0/fPeDtL9/gy5+SvfE8D6jrBZ69eIWyrtDtTzgeH1AUJdabHW4+vMHNy6cEzDNEj0mH\nsD/0XGZQUKkXJD9R1VXO3CJjeI77A2erLHPBxFqlZZ52zaxKubpewU4my4ZIRbfdzYdPcP3BFeq2\ngTUGp4eOVS+pj6KkwvpmjWbVkLnAQH732ydbBO+xvz1kqsTbz96iO3QYB3JXqeoWmp09aFxMgNR0\nsaVLYBqm/FxpcJEujEuRtHmYsw+cEAJFXfCejXm6mMolMxmY0aA7dHQAexKkI6VNi6EbMHUzbr/+\nEoBEVdWQUsF7i5/9yc+htMwZ15d/+QX+8od/jn9WFPjkD36AF995Ca11hrFkvB1bx8cQUS/JXixP\n6VgrO2UbZjIYhy5nSolaQ5gtUoIQkvZY5MDsjMPUTxhOAw7vDjg9HNE99sQ3m8ne3ZgZxk48Jbd8\n6Tewlizpt1c36E8nEpcLkSbDzuPh7S0++8lP8JMf3uDpR8+w2Kyg2VsuxvMgIllMUR8wckZO0JWx\nGxG8g1YFYgRC9FDqGypPSkXSWG62sKNFWZVwcNQI5KZwcZF1UAYxYOpnQAaoUsIbmpY07QJ1vUC7\nWKJuWlzfvMDNqxsUZYGhG/BwewvnKIoPpwFj30MIhXkc80v23sGYCVVd08+NgIDEzLYxi80yl1Y0\nPcMZn5RqdMaH0MbwkIqgDGksThv+3LNIjdY06RmOA/a3ewxdjxAYTFqXiNGjP3Y4Ph5w//YNTqdH\nBO+gdAEpFcqywd3ta8zzCGdnRAB13WK/v8Xnv/wxyrLGzbPnWO22aJqWm9uOfo4PqOqaTBsZL+I9\nlXnWOARuSHbHPZwzecJBZSC9oySnOg8GgOApKgNHlYRUCsvdErogYb3NzRp2tlhuV2jvWjo03Yip\nm3C8IwKrYhPHEALe/OIN/0yCWADA5skWu+c7zCNle7rSPF2k3kxknJKAYFfVKe895xx7hSEHpTS5\nS9i4qR+zCaLk5raZTH4egVSKx2yceLw/ons84fHuDuPQYZoGGDNR30NpTGOHql7AOTq4db3EPI4s\nGUIj7YgI5wwOh1u8+6MvsVpdYXm1xGq74aAroHUBAQJIEiCXjD4DN6KDp89kJkNa+D1dyrmfaT1U\n4REDqzWOBIWoUi8T9B7mcYadLMq6xNXzKyx3K0z9hLvXbzENA5WksmbuWUBRFow8B3y0MK7HNPUo\nyxKAgOPsZxhJvPD1V7/E55/9FJv1DTa7a1RNwwaiGkVdoGqpBI8hwgfH5TUNM7r9AUnxgYZJ4psT\ncr0jQ74QQ1aCTHB0upEJNCclRe+iLKAKjSevbvDx1Xegy4IRnaQ+GGOkPgYHs3bdol7UuPnwBi++\n8wJCAFVbYX97YBKf5bRyOjfMlEbdVtBVAQTqE8wDlVLVgtJVSotDbhAnPA6hdBUe397l56Pg5OAF\nlUZSSQjvIR1lUIGbroGJhd2+w93X73IZagw1Ap138N7Ce55GSo2mXaHQFYydYO2EeR7gHGlMCykx\nzyOsnWHMhNPpAY+Pb6B1ibpeoChKkLuIQF23WCw2aBYLMpvkA5ooGBER82jQH08InrBBACCkRHQh\nTxIFqCwgGyVK7wnFXkKXjHrmm5xkUc7N27SOx7sjrCV1yBB5CCCokV2UJaqaVEbpogL1nzQNO3Sp\nUbc1hBIoCk2Ho5swjdQnHLuRM6SA4CP1v3A5OfQMSKSg23dHCkqseRW4TBdCQDHyPzV+HcMGNjcb\n7J7ucPX8Go9393i4vSVH3qZm0N8G7WqFdrUE4FHWFeZ+xjzNaJZkDWXMjBAViqJGCB73d1/jzRuD\n5XKH7e4azXKJoihRVoR8R4yI6xZCSrg0sUoAWO5N9qdTnlwDwDxMGdSrlM+ofMHMGkTkNUzo+bqp\nyMhhtOj2H+F4f6IsUkrmo1HLZbFZACBn39PDCcf7I1ZXK8zDjOM9mVXod4y+FgHOGSzXK+ye7FBW\nFe2nECEE4deo/SH4kqQz2z2cMJsBSpXvZbffuNFN5Q1F6JQqSgaF+YqyBOk8Ak+BVKGxudmgqApc\nf3CFdtVmR5ThOGDqRgo0zDjuDz1ODyfENDGIMSvipduuKAuorUK1qCAuJkJFfUYlu9kxgDDCcE2c\nAk5MKG1L6aWzBu/evAYADKcB7arNL02yA26q+5MzirMUmMZuwunhhHmcUZYNmsahqlp47/L3qKoG\nZVViudng6YfPUdYVhq5nKY6Ra3BKg9OfdccjTzmJSR2CR1nWHEwARAFjZkjNLrl82CJLdwQfMXQd\nutOB8C04qyDQZAZMN+EyzjNw0ZENcwhnQGnVVu9xB5MzcVmXGcZgxhlD18MYyl4FBBt+Flgs1lCa\nZHtrdk1ebpfkbMyk3ZTuS01ZR6YEGZvhAFKSz50QIjevUymTfPuOx3u0iyUK7m2mRriUEmY2hNaX\nkp2VG9RL8sarFzXmwaDfd3h4+4ipm3LDuT8SpqhhalCzanB6OMFOBqubNe3j04Th2KM7nGDMjOsn\nz1HUGuvdFjcfPCO6CQ9i7ER0ldwnu+D4GeZ/TsOIcezOrQSQk5AYZj7EAipdsJZKuIKdYpbbJZRW\nWG+XaFlHfJxnLLZkxkDqnRHL3TJPr1N2m5KO5WZBdvDGoV5UDA59RuesokRksV3SzyoUvCE5FGvO\njI7Ut00JyP7+AYA4t0Y4iH1zRLeSNLkSdLjHboQqKHMSUqBwBfGv+GYSAiyPW9MB4FEhpdF0QOxk\ncLw/Yjj0WZHOmglSUVlizISqaqG0RrOoM70BXCalm7zkX1WhgYYyhWmYMPdz7r/QNEwympmmIv2h\nx2FPmVKy8CmqAqJI/QqGCZhEcgUjqnWmWLz6/kdYbpZQWkKXBWMyaESqtM66UqnvkpCtidOnNMEp\nxm7E2I3oDx0bMdKBK8oC7XIBCLJyngaaNpZVCYQzqNWxRbqzDsf9HuPYg4baaWNbCAjEKPKoWmlq\npg96hNY6//sYIjd96bJJ/cIYSK6mrAqsb9Yk7L9b4Xh/gLGGMlpWYhAQZJ7INuNlXaFqa7SbBaqm\nIlQzY6wsl1ouASi9h+dsxtkz0JZuZdqDaeqWSpi+P+B4eGBdIBp0RDYWEP1EWlpNBSkl2jWRhFfX\nK7L/mg3qBWXcp8cT3EyE0dXVijJtR418M5EKQwwky0sXokYdapQt0TSkVlhsFqR3zo4wwZ3lPhJI\nNwNGjcXYjfCGiOnd4ZD7qcjlG2l8Tf2Y8WleeepjFhplIzJ3MwVpQm1fDDdAGmczewEmHF+SEzmw\nPA4NAjoa+hQa7UZjuaP3IJRA3daUkWm6EFVDjth61jnjS6Vz6nH13TH3D2k7JprMNyzfkn6SVCrX\n/EpTiXN6OGGxWRCXqtQocxQUqFpk7FAC4ElFh7+sy0ygnMYZ09DD2hnOGk5vLYrihKKoYcYVhBJY\nbZeoFzWaZYNm0eS6lAImMawTHOH80mkzeZ+a2CQG9nD/Fl1HZGDHLzM3WgWYoHsuXT3rfatCEy+t\nKbHarXD98pr4d5awJmM/YjqNGWFtZmZHm8QLog0dVcQ0kGWQVBLrqzXWuzWB8vxZl0az7XnVVJgH\nKlEhBPeF5lyaxBhJB/zhAdaO5z0ATrHVmZAaBaG+i6ZE/9iTxZGWWUq4qAuGV5DssVAiO/8qpdCs\nWqiSstfkDR8jkXmlJvjIcBpYEC1yuVahbCouAQHvLHEHmXOXJn4hRG7cc7M4UHakCw2ICDPS+LtZ\nNu8xCo7HeyqTqxJjQe9f8a1fLWLOCgBww5+0jCCoZ0rgS4K0DIee15pwPnNPz5Wcj4MjnWulFepl\nQ41ynnTWjNyPMSAGyhCqRYXozjSalE3YycIYApXOw4ShP1LppmWmYfSHPgcB70bCBCqJylUo24iK\nqThRRYQ6YJ4NfCT7bOqBBrTrBvOoMA2kbDn3MwXbQJQXM5rs8ustlXYll9l2tkTt0gVVJ4r2iQjn\nnl6qKOxMDX0zkib3w+07Xh95sRsBeTGg+rWDklIKMfPcaHTe7Umhr2CyqZREAbnUsyb4vOUN7/NI\nWmkai26fbNEsG5zuj+iPDaZ+wDQyHid4AAJFUWXLH6kVqrbOfQln6DZMzOqM2Qghlx7eJtdXkTfx\n2A24e/d13qTTOENwv0QVKlNmEp4i0SNSaakLkuBYbZcEPxBnh4rAYM2xm/IEkmgUVNM747Ifm+UD\n3S4b6KrI2COA+wRNCaUkPABdSMQ6ZJ5bopRIhj4EF7h0e6RMSKmMCr48kJE5bhCkKGoni/4wUMN9\nUefskII5yb3oQkEtqYyhMi+gqSpcLYmEnMweE+VmZlmYfp7w+mGP4TRklHjKDJNpqGQcWTqkaQ0v\nwZFKK5hpziV6wZlyuoCUKkjD6vE266BTZkfTrhAouBKdhgLscBzowmFKUdmUWF+v80F8eP2QByPB\nB0zDCAEJIUBOLTvy0UtqEombSWU1XYAEUZA0sWOslS40zEyH1s4W80S/HvcHTPMArcv3eGH9voeU\nkmRgtEIYAoNBRZaKIaXIM/zDOw8LploxS6CsSrQr0h6bFiTXPI8TFHU1siplwe8vCQVWTUmefOuW\nMlDOHqVm/B9XHmlKnIYg42nENA7w3kHrkku2ACEkIt7XEP+1glLqk6SJB41fHfZvHrG+XqOo2TrG\nn7WvdUFwfM10kpRxSClQ1SXKpsTmZoN6UUEKSaNOIbIyIklDTOgPAyFirUNR0WbUpc79EjOYvOAJ\nPJYBkKmnIgXAzVqhBI7dHYbhkJ9v2Hdc3kQs1i27t3jUizqTjqUUAJcFEEDNvZ7xNOQMEjFyKUep\n9eB6TP0IOzscHx8QgsdsRhgz0VTNGlRVg9XqClprNC0BCJfbJcmzMJUkl0/cXBIMOfAzZVla00bf\n391jHDt6fn/GgbgkUAfkSaQQQLRAu2ow9iMe373DlXyaN7ezDgVnhVS6UKMy0WkCSPhOK4UVu6UO\nxrzHTVs1DcSNwGnBFs/diP40XEjFEMo6pf6JAJwONXGnQs6AvfcoyjJTX5KxqGRh/b4/YH9Xo6qb\nzDkUgr6PkAKLzQKSR/apIR6YTpOav0IIhOc0rDHGcJ8pomwKzKOhybNx0CV9fqEED3YUuaBUBEdI\nWCrBVCfPB9kxONVMhnSyIjCcenTdIw0/AESloTX3yx4eWZ9KQa0X0NzvLOuS9a9NLttkIk7LM+iy\nbmsEF7LLkOdWyumRGtspe5+GGbqg7DcBjlOSQaocJQNeGfrjkEtBqSRlmKcBZqDz+/DwFjEGlGXN\na8TDBkU9zW/cU6KmG2OROFWr2gr98YjD3QHr6zWqRc12wyGTIO1kMfsJMVKXv1nSopVViRgBXShs\nlgts25bsrIXIcg9KkN/VYAyGmSy/cw+CbcCP/YT+2GehMccgydQPIYIueWcFELeqXtSwbsY8j6hr\n+lzH4yNUSc3jaZhRlBoxAlM3QZVnpxPBKX6IgW9vg0bXDDsQBOjjBaCJSI3dsyu6lb2HmcfcK1BK\no64XEEKySgJ5tgnun0ilskFAwqI4QyUVuJymdFvAKQdrDB4f3sKYkRDTzM0CgILdYtNGSOh78gmT\nWKyX8M7j+LDPkzwAMNJgfbPJAwMzGXQhQhYK7aJmfSCbLa+7cSIRMgZi9sbABfJNK3hCZK1D1Vbc\nuKbs0RsPX3hM3UTCflw+z7PDNCQBN6CoNNZP1rkfNw8zB9qAsl4hxIj94R3KqkFRPkfV1gR25UCe\nsijvGOdzAS2Qmpq5VUts/+3TDaKP6PYdY5ooC0i90nRRpDaG0hKanWAiU5gIp8UkVRcARjkPx4Ey\ne+thjMH+4Q7T1ENKnZvcqTWxf7wlbXJFZNxm2TCNw6Lgnxdc4IkqOODS91gsW2gpWeOd+W1KYdu2\nOaM9DAOdsWFC0klz1mHqJkJwszNO8DEzI4Inud2z5hkBdueefBcPj/eYp4EDK3ExQzjDUQCaSn+j\noJT4Rqk3QTgHjatnT3D/+h0ODwdsIKArDW051eaGlmInFCEE+6WVaOqKAlDw2bQuSWomnRY6PESI\nfLqu0VYVyaUah/5ItknlMMMvfZ7ipEMXQswHiygYnjliNLWLgSAF4L+/3m3RHQhun7Sb6JbwwGRY\nB4mie9mUKFjTmICYEnSSY7YtUppunKIscPPyBn52pLRpLMZ+wtB1iD5CKp3VDiLYO6sqqKEfIyDO\nzP2UhUIAwUUebdMN3K5a3L99i+PxHql2FxCZRvN+QOIaX5xdTaWSWF9vMHY9Hm/vMA8LLK/WKOsS\nx4cjlttl5mzZskBRaSwXLZJmkHUO3TDl7HWYSBKkLCi4e3sW3EtNXiqrQ562mdHksi2tl52J2hKC\nR7NYYPf0GQcVOjzzKfHDaIRe1wvs929xd/cl6rbGGjviWnK2YibDelkh702pyERyuVlgtVyQh51z\naKoKaybsjtYg+Ih5mkn5YtngdOoxz8R5mxgYmfqBiVZCkA865N6Q5K4ZTf7/zlEGfTjccoAjXhhw\nLt/uH14zX/NMfK0XNTAYyIXEcCQEd+rVNssmY7TKpkRb11BSYFnXmSguhEBdFGiqCpu2ZYUEIiVD\nsMZVCLg/HNH1I6aBhhieyzTvAsq6zMTqEAKmfsY8zjgdDhj603s9I6qyAmK8DLjfMFOKrEOTCZCS\npnHr6zWkFHj9yy/x+M5hvdvxQRCInhqWBWOaIqiHUliPUzfkA5zsgd08k5mgMRhYxJ6wHQKTljDW\nY5yIflIvapjJUI3MpYwu9VmkPDkmxIix73nMr7kHVPDCUJ8LAF58/BI/+9Mf4/j4mNGoAFDVFZeC\nDjHyGLtQKCtakMR+l4UiGYhFhXbVEModwO7pFpv1AtvFgkjB3sNYh+OxJx6XAMZxxjTN6A49mQM6\nn1HyqVGf8EGJw+a9Z5oFYYu6wxFvXn9GSF2lIIUk7JA4ByUKSEn1gLlwpczoaakkrl/cwEwzHu7u\nMI4DNrsdEIGC6TvOOLjCwowKUinSD9IaAOs9ARlO4YzDKInGMZ7Ibl3xCDsyNWgaZkwdTWkm9rMj\nBPQMaw2MmTHPPTbbG+xubgAktQQA3NhP+xOIKIsKRVHieLzHl58Dz+23cf38KWURsyVL8xCzJlAq\nd4qSeqCFplK0myfUTGJNhg2XDi9SCMzbDSbnMM4zDqcOXT/BWnLrobWliVfkUi4F36mbuKc0YRoG\ndN0BzpmM49EcNNOFYsyE+/vXZyzbPGNzs0OzINkfXWjM0jA1Z6YsVJ4J3q4oMEwRUgzZPlwrBasV\nCkFnIbvqcv9JCQGtNW62GzjeO1Si0QWNSJdHmpCOpwFjP2H/cI/utD/HDR54/WqpluAu3ygopaAm\nU1YAtk+2HlcvrjFPE7789BdwzsG5HWF+6hKawYZ2RWNsO1mMYsyLVi9rXK2WpOQoJcnLDoR70IVG\nYGJq0gIauynrG5nJ5KZtEsiyk2WCo8ul3jh2KIoSV0+folk1WZSebiRapGbRYntzhdeffwHnHJar\nDfHmBJkhxhhRXJB1LbOwdUn65KTzVKCoSpRVid1ykb3oEgk3ib9Z79E2NRxvgBDp9ppmi8eHA/aP\nJ0zdiOE0Zp1mavyfS+c00k29us8+/dEFiZNvInFeuBhCLgHO5FyRJ40xkuyp0hpXz5+g7zrc3X6N\nse+wGwmn0m6o0SmV5OzX5KZ3algrJXP5mcwJpBLExwohqzKOpwHdvsN4GnHadyzkZ2GtQYyBqUaE\nzWnbNda7HYq64IwdPH0MZzeTQAEaWqAqWwzDCW/e/AJSklnC1dOnOUAIITBzFp/H/tZDKoduGBEj\nYDxNWpuihOYmPjjbHMyMShfZe61gx9uYoRYKcz/l0vDSx208DZgHg7Ef0J+OfPGkqkBkbFI6uADR\neea5h/cO1hoM/QlD32O92WG5W6BZtShGg7IhAf/hOKBdk9QKofapFWC9hwRQlwVWNVFd0h6drcVp\nmtDPM7SUqMsSlsvy5DKdsm0B6isJPkf9oUd/6HDcP6Dr9hxYCc1OfSSilCRJHSHk30gx+VsFpVQn\nKyURaRfnm1wogc3NFo/vVjjs7zD0R6w311ht16gXJBHaH3qoKwnvQGhqZlQ7a/FQks4S9Wpi9mQD\ngDCFzKAOnuRxnSE0tlQS82QwHAeio3QjxtOAECLmccI8jTB2RlFUuH5+g3a5ON/21r6HLlWFwuZm\nh/39PR7v3+UNYSaD1W6VxfWDCnnioQsyLmhftES1uCgLrPeo2PrGcmpsWHHTOofeGPQjNbtL9nNL\n4+UlIk3deIIUHEv8Os8YHZdLummacX//Gg8Pb/JC05SNUd4XGRKALNORLpfgAzfTSayLAnSN9XaH\nw/4O726/wDj2GMcO26trrHZLCM5wExUnNU4haHpXNSXTXyhoWkP9CaUlJgadHu9JDcGMBt3xgHkm\nekff7xnXRnzHul5id/MkwwykkhDxzAlL5TdZcNGB0UWJsmzw+PgGb99+hmE4YBhOuLp+hsV6Bbsi\nCyYhZR7rG1YsjXVJICgAACAASURBVBEYBxI1W60Wef/HGNFbC2MtDuMALRWWdU0ARWNwGqiBn4M0\n6H14QxNnayzsaBitPuB4eID3HsvVFs7ZHNgzgRrntSvKGs5beG8xjkd4bzBNHfrTEavjFqstTbCT\nSYN8IFpIvagZMU4E9JlFA01TYt22aMqS2iQxUl/QWiSuYKpYqBz1mIaJzx6Rwaeeen/Hdwd0hw6P\nD7c4Hu+hlEZRVDmYpuegs0bgT7rA1HsB+NcKSp5Z6YTGZK0ZKVhbhcTu19sduuMRj49vMc8D+m6N\nxXKD9W7DPZ5EY9DZSYPsVyzhYtJh49tMakklIItgQZxdK9p1i7qt0O8JCZ6C0jyMLKHhME0dFosd\ntjc7NMtFvsE9Y0XohZ3HklVdYXd9g8PjPY7He3jv0DRLeOuxvlpl0KeUAhY2N6DtRBo9iESunMW5\n5xZ8wG61zKlxbwz5wjEuBYLsztP3mpkDpVhWRJeagkZqqEoW2DMW8zTi4f4Nbm+/gFI61+pCyNw3\nEKl8o4clAKs4p9NJrC59UaNdoVm2WK22GMcj9vu3mKce+4e3uL55iaKs6PBIoCwL1quiNauaihjp\nnBmEJDtik6mDgTVz7qc4azFNHTd5VQ5GZdlgvb7Gcr3OOuHpM8cYISL1iE5cKjhrYN2MullCKY2q\natC2G/T9HsNwwjyPGPoTrq5eYHN9heV2iRgi9ZK2S4zcbzSToQlUVaCpqa1gvadyxzmcpgla0eFK\nbrDWe3hWVO3fPNJEigGW/YHK9O6xw3DocTru0fcHhBCwWl2haRfwMTEOznsxZRQAsFruEAKh/L33\nMGZmStKM7vSIw36N1XqL1WGHxWZBGKFxxuZ6DcmAYK0VqXoI4kG+q46YrSXycCBp5MGYDBJ2IaBn\nsGa373B4R0wDrUl3ezwN2L87YH93hxiRCfdanxv9IQYo5lpmra5IaG6aMn7DoJTS87TZVaHyWBqg\nrKFZLrBYr3A83eN4uMc8jzgeHzD019h0TzD10xkRygeWauIiByiyZZGoFzUUY2vsnASkXCZqJurA\n/Vf3GE8jxn5E3+9hzIR5JoTyYrHD5nqHekEcsYTUToJtQgBl2eSgS5y5BuvtFd58/Rn6HpimHuPY\nw8wTlus12lUDVWhq6BcEJBu6AaurFawxiKfIY+OC8UPk01azqiZlSj5jdAC8B4EIPmaAZOL7kbzI\nQNIbMwXoaZzw9s1neHh4ixCowV8UZV4fcs09u5AmtH1yPEmiaoJJqkrrjIJv1oRHWm+u0XUHDP0R\n09xjGE+Y54n1kmpIpYEYUDUrIDKwNhFQhYQPLjfnQwwodIV5HpgjGDAMJ6aWhNyf0Eqjqloslzu0\niyXKpkJVlxi6MZeOSAFWCLx98wsAwFdf/xRSSmw2Tzio1VgutxSMhj2Oxwc4Z3E83OH6+AF2V8+x\n3KwZCU/DC13SVM6XBbz1OBQnFFWBsaS+kgBNExtWGT0lqyTnMXQDHt8+wgxJVpn2rZ0tHt884vR4\nwGF/h2nuECOwWGyw3Kz54kp6FDE91ntBabnawboZxpTZnYY81IhD2Q8H7Pe3aJoV1usrbK9vsLne\nYuqIpFw2FYE3PWXxy+0SANA3I8qqRLfvsxZVUWqGUnCpOVIA6h47lA1dRv1hwLsv3+HwSBf3crnl\nTEjnWEEZV5n3nxAiG8pqXfKe/etxSuKvwwwIIf76Nvnfff3d1999/d3Xr/kVY/z/TJn+xkzpP/nP\n/wluPrzB9YtrrG/WeHqzw3axgBSAlmS2VyoFrWiaVmmdB34pPUx6wVJwA5lBeAX7VCVzvvQJE0gv\nRrKHSXW39T77WE0MJ7CeUunAfRTnCWpg2CxyHg2OD0eWeB2JQ8YNyP/uv/7H+J/+t39GqNm6wpat\nkqqLJqa4+DxVUZD/vGIqQIwwniRCfQhZNjVphHvvM9whNQkna8mIIMmYpnfFz5/e3ewcJmNgvCMB\n/oEmVkkuN4YIM80YO6r59+/26Pd9JjM76/A//4//FT0fq1Quqwq7xSJrMyfeWwSoyVkUKLRCIUlB\ns2FTymSLlCy2S7ZKEkLAeQfLlBEAgGBLau5V+Eh8uMhzshBidrcZDbni9mZGP5AKhJlsnrIlp9X+\nMOB4d0DPSGw7GZwOB/zRH/0P+E//8X+DJx/e4ObVEzx/+QRPd1uaNMVIbitliUJrtGWJtizz+7Xe\n5/UFyPXY8TMVSmVrMM9rmZ1uLxvS3DMJMeb3Y7zHnBrF3iHEs6OyD5HW1Dkc+wHjMOHxdo/+0GM4\nDpiHOSPV//t/8l/gP/sv/1s2LN0RA2LVZAONHSPqF3VN9l7cq01nJZlSSn6eUlPrpOC9LMWZHqO4\n0Z72YHpGwe8xXj57Os+pZ4mzBnjgUjuZFRjeB9000X/nPe3xH/3e7/2VMedvVb5N3ZTxJLOxcLVH\nVWgYf2HtwuPENFaUQsDxoUuBSfKDp4dJCysvDmcKWoJR3oVQ8IIOPbwnK5dw9vSi/475ZSUfKut9\nRs5O3ZS1noM7WwwBREUJUmBk/EYKlIVi0jEEtBA5MBEgTaJQZ+5Vci+pLg6lAOC47NVMzg0xouBN\noxU9s+TvHXHGFLlADqeBG+Zn40LqNwUZmFoTOZ23WeA9lXGXziU+EPrbOGpipiZ/ld63SEHVo4gq\nI9kF/z8BwEsJcKCWgiRX05cPSfWRgK9Byuw8q/nvp+93eVEpyZfURY8h9dNiqmeQKBMMi/BE6E3N\nfWdJEM9Olrhf3kNpnU0clDx/9/zZpcwHLB3AEELW3077/nIqpi7eSfp9CAFekMfaxYlBQt+n05yM\nBixflsY5GtSciJZBekpndYRUvYzHEUWlYdYtlZeOzA1iaqUohZL3q+S9CZ70xhgBDlTJfUfw/kz/\n9le/Ltcp/T7tS1z+mtYqBSm+2NLPTUOegQOwCwGWk4lLY42/6utvBZ5MUzEiMhoMZqZNFwJmZwkx\nyh9ESZknbHkhY4Tih7x0WUgPkyK05E0hBGCcz0EudfE9T7hSRI4c0NLL84EyKesc3VYjNZXNNGfh\nqUtOE71okEOpo0lZMutLljVSIoupp1ukUDrfpAKAj2dOmYDgIES+6iY179NCs+yL5AOcNn4K3hFA\ndBYx0iaLiHzLhjRwypO5RNGYh5l6JAwXQEQeRAjBUhkxwkiL0ZLTr+GDWV8ikQMHRA4IaT3T50/B\nLKHuL4NqQuOLdAFxEE+ZZvpK0Lmz51rkn3EGvmY9JXfmVWX10JCwWvR83nrmkhnMs8FoLDekz5eh\nEoALPvPz0ld+LiHg8x4K+RnBn0NK+f5lenE4I84igu99XymAAAhmFSTvNes9xtlkDtwlwz5ZFKXD\n3x97In9PFnZ2qNtktHkOgi4EFHx+1MUaxRjfC0bvDTUu/xvnYUjKHC+DUfyVvw/+OWmdwRfNpfFm\nes4ckDxpYDmudNK7/Ku+/sagVLJAVVaTM0nJkD58clDVUsLHAMMCZxE0VQggeIm8uJ0uP1QqexRH\n78i/DyHA4RyAfAgw6eECMel9oEwmHaoYYt6INk/0OFvgsifZMJ0XiMstj4wwd4pMKCsGz6XPGC8+\nSzqMUgogvg9ULBJuhWH+lyVs4M2uOYVOGdSlm2uiiGgpoaWiZ4hJi4Ya/yGQ40lqqiZnWaKnnL8H\nTeQ8T+cApzUF0YuvS431dMtVktDNkj/nZXqeSgQA+b8j+CDzM6b/n7LAdCGlzS6FgBLnd5CwLVlL\nKLsyu6yyQKqhETH6HAR0QdPcZOrgvMtBkdbOoxIkmu9CyIcwf36kIHLOZtPhSrtUcgaesw0A4PIv\n8KFz3iPgvMapDPT+vObpArbcZL9cx3Ome2YkDCfGKHG7ISZcWSDXHAHg0tYrSJmxfCkpyGVoet6L\npCAF5XDxe8Gl6mWmmloQaY+nf3/ZXsl7G4Dh95G+dzqz6e9840wJfIMlfzQhkDOBdIOkzeZDhECA\nFB6SsyCAb0e+pSK/yIQtiaDIagBIhrqnqJ9SvuSpZrksm52DDyH7tQtB5n/OnzcrIpUCxOTWGb3s\n3XnR0gOSOH2Ar/hAIaXuLDDPL9JyOWC4X1DoAhKUKcWLdJ3hXEh/kNLaVHY6HjUHWtm8uPlzpXfO\nvRzHCG4A720gCLCX3Tkg/+odJKTIkyvvaHwdI94LAPl7SsH9LpFLYaUoMEpBo/AUYEQMZOfDN5Rn\nrp5Pz8vrbxi4FzgLzsGPn8Extcga+//ymE+gx4RLiiD2f4zn2zspm0a+qM6oZFZvdA6l1nw5BLhI\n6ogpc82ZesoWvUfg0uy8ghxc2WEGgkqotFecD7mHNBoLyxczIjJoNl1sxjp2JqELfzgMWV89yeSm\nzzZNAyDOQmya+0JKSdoXIQDOwSkFrzXtTyEok+V1DXypSFAAvrxUUuBKa6IThjAEnHNb5OCU9rJN\n2RwH8WQRfnlRAYQeF+xTd6kM8DdNz/5WOCUqB/yZo6XJhrmQEqO1GK3NUbJkBGn6gJo1vo2joKSV\npODFz2kdqSBm5UfQRg6cOtMNHGCch7EWkyP0rfXEoUqSJOnfJ0VGIUgCREgycUysbADZQCC9b12o\nTCj+1cwyobHTgqRmtvMeZYhgUnvOHgHKuNItZNPmAdjX3Wfg3XmR6KAlyQ7nPWbrcBxH7PsB/akn\nBHNE1uZ2LFFLVJ4Kw2nMqgJZkwlk1JkoFYEb0pEztcsGJzUoI04ceATATfmQm6jpUkhW7JdZUN5s\nkZq+l1NdJS+bw8iZRT/POHQ9htOIoRvOZUk4uxirgi2J+M/IOFTmkbNLpFEWdiuUzusx2hnW61wi\nNkWRb3znPQpu2McYUWqVWwMpA0nZXQjkDBJjRDdNcPG8TyZrMcwzhnmmfiu3CBInM3EupWK3HOug\nWXJZKoW5Jxlg9LwZLwwvCk0+iUlHSReKL9gIYwyO44hlXaPiC0SAgr8QAo4zXhNCbm6nr5kvxfQe\nUrmWGv253LvIltIZM1yaSeBswJrKWQ50EuDKKaIpCswMPgW46Y+//utv1VNK9ArwB/Heo2My5nEa\ncxo+W7qV6MPSg6+bGkowP6wosm1y6jekjT+7sy2z9R7dPDPymdQhzWyzbctwHEivJjVFecOurlZZ\nczqZ8wlB2VKzajB1xIa+fGprHfvDg51FA5SQKFm3Jt2iafI3GZM3clWWkABnboS1kXz7u0A0E5vK\nCSHgQ8wZHXjhqVw4l4azI1dgY10Ghw6nIT9T9KxxLki/qWxKeOdRL2vMw5wPdna/jZTtqVJnhG8q\nmXyMiBxoJmPQzzP6ecYwGUzjBDPO5LQxG5bgCNCFQrNusbpaYbls82TncmJJPYaA2TrY4PPzWe/z\nOk7DnHlxYzei23dAjJBMaq5qQrYH6zOZezyNOeMljXCwNO9Zr8jHgPuuwzQbDP2I5JLsWWajbEgX\nqKorrFYLtFVJtJGiQFUUeShBlwplgP08U5bD06N912cxs9SgTjy+ZB+flDGdcVjuFlhuVygqDe8D\nikLDsblCvSDQ6XDqz/ZeHEDqRZsJ3oiE0QvDBDMRMt1ODvdSYLEgI8mq0KgKsuJOGldpOv7/kPZm\nP5JkV5rfZ9f21bdYM7N2sqlmEz0PM0IPJECv+r/1ojdBAqQRe2F1VeUWi3u4u+13MTM9nHOvRxI9\nTaorAKKKVZUebu5m557lO7/vzxv1r0swex8OrxrTvhB03bxrKY12JWzoB9xKoGBv7b7t69oEIeBK\nip6DS+P7L/38xaAUJuGr0o0euLof0A8SzamBUYRjsEwkaytjjEGap4izGOWuRJolCKMAVZYhi2NM\n04Q0jqAMjVCpLNPQ2kBrkrcPdY++HdCfWUDIjHDJ43FLLhCeBz8KsNqtkFUpjJ6QZPEFfsUPJzyw\nk+zspkceqEwIIy4HhM/Z0YLZ49JimlAPA7SmgNKPtEhL6x/eZZ8vi1EUGeIoRBJGCHwfURhQoBM+\nZ0Cams5au0DU9iP0qMjGqpcUdEdFDKJTi+7cwTG34SEtMiR57Hg380RrCiQCnPhwo+uJksj1vpYg\nQBQG0POEU9/D9wSkVmiGEXXToTm3kB0ZI9B0j5xPbKDzhOeQs8WmQLWrEKUR4iRCmiZuarosC4ZB\nXnoMryy7ZS+hRonu3GPoBhip0dcDzocTtKLPMYwirK7WxLTyPIRxgDAKGO7HJpt2P4wRynbp9Vi3\nGLsRp6eT80ILw4AEv5rME/2QriFbZai2tBKVZjHKPIPwBNKImv9SEyFzwYJmoADXnYm13ry07u/V\nQNfXnlpopVg4yr2YKES1K7G527qgk+QJ4JHw2K792IPVbg8AQBQTbiWIaK2pPtD9MemJ2EyjduaW\nvi8QpoReWe1WqKrCJQJlliKLIpfd2ux/5kBrp4ejUqjHEee2Q3NqHa5EK+MOvGVZHIrG5yqlqHLs\nrtZY+PmqsgxJGMJME9Io+qIvrDkz+1VByRe+Y/popXE+1DgtZ7LZOdRu/yyIAjoBeMcKAIa6hx8G\n6M4t8nWBtEgx5pJsZpYFJXOS+5E2vq2R5djxNnUnUb/Ul1OJJ01jP5J1EDj1jmPEaYKQFeKTnnjn\nioKSLV+MJI8xT4hLihxHfGPzzpsxaEcyR5BKo+kHNG2PrusxMAvHKAqaaqCHyBoNpmWKfEVwdTL7\ni5CltLnts/lh2w3uS5Id6Y6sfsoGpKEd2MGlR9/26NsOmn27fD/EartFVuQE40ojB+O3DXynfgaQ\nsP2VLWmV0njuRyeL6M4dbe2zS0lXd85n7uI8C0K6MKf6NQI1qzLEaYw+Hr6AnEm2TYLnudeXPTmC\n6FGjrzv07QA9KozDgL4j3veykGHlPM1Ii8xBzuxENwgDSDm68lT4F9rB+elMO1mnDvWhRnMk2D+B\n92ip1w8JNigCH13dYagHFNsCaZ5iKEcEcYgwoIy36wfHTZK9RN/07q82y1MjrZRMhrwApSTHHeEL\nxFGKBcDYU3AUAdFHtaRVCyGIZz7zZ+p5cGx3AGR37lPQOu/PmPjQUiM5Rfu+QL4pKKOfZuZChaj3\nNaodBds4i9GWOYqUnHJttaK53LNcLLs644FKfqudMsqwW5B06BXPJ5JAEAXIVhnk9RrTTNjmeaZe\nYxKTBXgSho6lJNlFx/s35Aj/v4IS+dZT2j3UvbOaMbxs2RwbnB6PFJTiyIGvyMVWO4rk2EkUmwJD\n2iOrckRJCClplcKyvCkTGtxN3B47VzYO3cATQO0sjciQMUUYhY52qQZ1ae54FndBp4/F4/ruhCKu\nTuALmGWBUhqHU42T18JojYGDRHdq0dU9mheyqxG+Tw1RZst4gk41zeXl2I2IsxhJnqBLIpbw02pO\nX/cuvbWAdTlI+lzZZmjsBjYm7KDU4IiX9FCG7ia0QkK1XDRO9ppsz8NqrqQ2UJI+19lQX8oiTIdm\ncMB3NdB3a6dBy0LZ4GQMlon91cJLUJrMBJUq4nBzyWL91ZaZVnusANIeNpM2VB6OEvNsHJs9zyvq\nWUYRgih0nnWqJ042laJkhDgvl+b8PM+Q3YjjTLQIevAp8LWnBkLQa3rCc4wlahz7hG9tB+TrHGOX\nEf87DqkfyFO/ZVlcy6A9ti44GYaeTQwXxLK4XcQoJhtyn5vTWmmIiQIOTRE9jGwFhWVhhLMHEeDC\nv+YSiwSygwtoQzOg73oAM/JjyQerYHswH03YoHlpUG5L5KscYzuiLVKkWYwkjlwvDRyUpFQYmZnk\nwaPt/1Prgt9kCZWddG0TExgEhgKTPUwJLRxfpnrw3AFvnYw9AF7yaxndjO/Qo10Y9d1NS0EhQpIl\npPPpBxityAzRoyZ3nKRIshwja0nSMiOeza5EyDeHHxLHuG8oAIwdoXDbI+FdPeFBDiPGYcDQN1Bq\ncJvoeb4GQM4mUZMgihPyHstihFPAf15gMbT1bCdANpvwOKWVhnZ97BjasnHUQFbG3bnn9JyFgny6\nBWHIfR1mfBt6EOOBbI7jPEGSJY41NDQDjNaYzIyxHdCeO34weigpIccR40B7d1oTZyhNC0RRgiCM\nEASh2z73A0EBgx8ce6oTcvQyPSKF8ww9UsZptVoWJm+0dg9n3wwY+wFaSQx9B2MkyRuCEHleoFit\nkORkIRWlEWxaHGexs272BcH92nOP7tyiPVNw6NuOlnKNglIjBVzuacQxkThpwhQgZLjepEkxrkYK\nXCGXDFanRHuDM5TUbp9vAWFTsjKHUaTnUkpCqQHyecA8TwjDEFEUIysqDG3pMt9lAcowQJBRT2vo\nqH0wND36ZqA+X91DjfIy8ZxmKDWg71poQ1l/GMaI45TujziCH4SI0+SCzeUD35aey0z3ufAE/pwY\nYIOe4WAmmNQgR4npaL/niafGQBBEiOIYq+0G1a5CuS1RbktUuxKmypClyUVbaAzGUaGvO6iRXXqb\nnrVTF3NPS+KgElsgThPHOfc8D2M7EliuypCvc5gyI2TxK8mB71PCEv7aTClf5VRODOoyteKmvB4V\nhm7EMAxQcoBSkh1HyQ8sDCOEQYQsX2G9vYLsRhRb8tzK8hTXmzW5onQ99ssJ7ZE4O/WhRt8MXB7R\nlydHCSl75u7Q7x8GcvaUckQUxUjiHFGcIklzRFGEJKWTLy2pCef5wn2INij5/gXGRYS92VktG0l9\nkL4d0NUdl1EKSg608T3NjMuIkeUl8rJEmmcwmfWgm9mjznOGB/M0Q71ItMcW9YFKjb5tMQ5kUknX\n02Mc6QGelxlDXyMIYwRBiDQt2aySbvgkydnSiZW6rPoGT2B9cdEYARcKo1GGDoFzR5nMqUN3btA0\nR3RdDaUILBYEET1ck0G3kKur0RPSIqXgzr93tSnx9u0N4jBEOxD6t3mhE/vw+IyurWGMdgHJXiNp\npIAsI1utcYwQ9hHiLkWaFYiSxPVY7CGyLDOmiTLoIAxcFjyzYzMWcpeVg4QcB4xDB2M02vbEi9YN\n4jiF74dI0wLrzQ1W7RaTnlBuSqw3Fe52a8zLgsO5wYOZyVX31KJ5qTF0ZEVNwR+YJwOpRmgtsSwz\ntFHoOuLAT0Yhy9eI4wRpRrbXcZIgzXMU25Ky+MCDJ0I3bbTXSy4uLHBcBPwATsTsgV1tZE9Z/dg4\ng9AwpF5c/lhhvb3G5voKuzc7iECgWBXY5DmqNMXM08T9DPZw69lUgB2WPXJ/sWtKvu8DEWWash9x\neK6RpAmu72/heR6yVQ6jDU5PJ8Jd36xRbkskWYIgDOCHFJDC4N8PO38xKGVVhuRIFj8cITBPM+qX\nBseHI8ZhQNeeME0T+r6GHDtoo9C2RyRxhqLcIjIEtxKeD6MnvP3tW7y53uHdbotlAY5pCs/zyGnk\nI9XgYz+y+AqAAGMpMvh+gKwskJUZ3v/4IxQTCYahxhA1KMstpsmgA50YWVvgxr9HktNJbvU9VqAW\nBzQyXkDXJbsRRk8YG+L/NKeG6uu25t/TYBga98BW1Q5GaywzsEwLtDTIdMYBi3zPrt9c4Xa9hjYG\nn9MDjNJ4eTji9EQb5NNknJnlNBn3sAxDg2Ve0MqBMjoGZgkh0PdUJmRZhc3uFlmeueaplRYAQMqT\nD+ELRzgYe8qKmhe6Nj1qDG2H02mPw+ETxqGFmTTCMEZZ7rBaXWOaNAXDNCUy4nwRZSV5gtubLb67\nvUHg+6iHAaMxeH7/TA4mfQcpL9A6z/MQBBGm6QxjZrZi75EkBbK0xCh89H2Duj6gLHcoirWzKxe+\nByEubi1hHCJOI/58BMxkLtfWNGibE7SWGIcWTXuEL3w09QEqyZBlK6RJDiVHtOeaPepivLvZ4c1m\ngwVAGsckX6g7PP78yKx0Yk0HQejQPEEQIs0KZHkJM0k8fn6Ptj3ifN4jOD0iTStst/cIwxhC+IiT\nBPDeoljnCILQkRusISoF4MsE2Zbj5/0J9enIiJ6ODEuhMI49w+BGBMHAinofL/sFYz9gMhrX766w\nq0q8225RxDE8IdCOowsS9rtJisStuxhlEHaj85aLsxhZmeHp5yecjgeMC4l2j/sDwpcIV29uyEcu\n9KF+eYbsJHZvduTem1A/61dnSkYbfPrTJ+cZLoQHJckfazIGfXuGJxZ8/9vf4fHTB3x+/zOlz8KH\nED7StEBRbCk7CWhSkuQJtkWBMkndOLIvMhSrAlmZks/ZSI1Sm2Gc92eMo0JW5vj299+j2lUYmhHv\nf/pHViwTGmGzvcNkNA6Hz4z0MMjPNCWyvS5HXgR4142soYIoxHSmxv15fybn3mXhFYYRw9CgaQ5M\nehSI4wzr9a1Dh8QplTUA2NLYw3q3wnd3t7gqiLI5LTP6rsfh84FWeNjdhAIONQJXqxvsbq7xp38S\nqOsDlBpgjEaSZNhu76C1xMPDT5hnWrfIsgJFlSNKqadD9j6sHVpIi2LMTP2aJITfkfGBklSeNqcG\nwEyZSRNj6GsIIZAkGe7uv8HtN7c478/YPzxhXiLkqzU5ZSwLsirD9maD3WaFVZa5tZSb9QoPN7RE\nSq65Kyg1ou9rRGGC9foGSo0Yxw6AB2MkfD/EenMLKXvsnz9w784gihMUSUWmoiAHE1ueWgSzEMLx\nnIwyNL3sWig14O7N12jbM1uma4QR9T2SOEOcFBDCh++Hzim2yjIUCTlx6GnCsSpQbSuUm5LIkiCJ\nR8QmjvJIGXy13uLd33yFSU8Yhg5NcwCwwBjNBhEhhqHBNBnEMkPgUw+22kWIkhgioKa9PTDlIMnp\npkjhgfR7Skks84zzeY8sL/D973+Hvm3g/QvQtifqZ4UxsbGyCvM8QakRWikEcYh1TmYdSRS5fiPA\nEgGfXF/Ixp3cdOpjjedfnjG2I6pdha9//zWubrb4Y/pPhGL+8At++Zc/IUkKVNsNkjzB+fkMEQjk\nZQY5SHIcTkLkaYyUJQu/Kih5AE6HPYpNSYTELMHYjkiKFKf9EW13wpuvvsX11zfkNHqqcTh8RFXt\nkCQ5A60yKCkRBD7WNytsdyvEdvlVEHeoSFKsqgLrmw1kr1wPxpY/ZCVTIOWTOsli3H17h7Y+Q/gE\nCaOH+YZWqd9b+AAAIABJREFULDwPtOpJJYtRBqKkG/e1bxgAzFjYuy50EyQ7/jw9v0AEwNtvvkWa\nZVg+kHAuCCJsNnf46odvAeHh6cMDuqZh66jUYWRXuwrrNEXGHmnbokB9tcHz9oA4i7BaXREWd5pw\nOj/D8wTK9QrbuyuM3W8BAE3zgjCcsN3eo1rvuAnfcspO4C+P3/+ykH7J7faBVk3CwKfFXG46WvWz\n9fWKue9VlCt0TQ0pe5r0Xa0RJvTQpBk7sLAbbLrKcHV/hd3dFnkcI+Dl0DgIUKUpbr6+wec/fcbp\n6Ux7bJNh260A5WoDrUYcXj5jGBoIkdIBVq4RJymOx0c0zQs8T2C9vkGw25ATsdIMhbO7fQJDOyBj\nJ+J5mhEmIaQcIWWHJM1w9eYWldxgHAYc9h9RlTvESY6qukKWVZgMmZZev7vC5mbttu6XZUEWRajS\nFMUqR7Ep6PDh6aTnk0bKyJLgcnmOMCRH3pvbd/AWH3VzABagqrZYra8Iads38P2A+q/se2d7ctYY\nEiBHnTiLsUpXQBJhnhcEfgh4HgI/wO7qHtfvbjBPV5i0h8PjA7SWSBLKvpIsQ9dQ2RxnKfIiQ5HE\niPjZs3uJge8jj2NsiwJjHENPE7Z5Ds/z8BRHxIpie7XNboW77RrNb+/R1S1WO2IqpWWGu+/uUG5L\nnB6O6JueXGRS6jmnCREb8iRBxQH/PxyUsjJDlpeU+fiEEc2qHHk3Yn21RVYWyCtygtjebvCb3/8e\n26cb+H7EUzGaCOQoUG5K3H53i+2qQhSQHmgGkIYRisQgTQhKtbpZIVvRZrRkL6nrLGEsB00qTs9n\n5FWOr377He7VV27cmxbUPyo3pTMWiJLI2SlbuLo3sIiMed1RGCCOKSsLIsoK9ahRbdZIigT5Oic/\n9aJCfT7AGIOy3EKEPpZpQbVeu6lLxuLC63fX2OQZrTkIASME8iTB7XaNp5sNNndblz36gY/tzbUz\nI5CjxO7NFeIsgRxIDhCGEYKQUv/vf/MHFiXOKDclyRDS2OlXXMCdZ4S+wC4nC/CXtEORp7h9c8X9\nQGuvQ86xspPEaGa4fxD47LkWYHu7QxiHWN+ucf/dHba3W+RFiiy+nHxxGEIajTgMUaQJNrdrnJ7W\nGHvJfnqZO4nffPM9bt58TY11LQn0liSIogTfffcH7k0qxGlKE7k4cCVGFFFWaLPfeaIHI0piaGXQ\nn3usr7bweUyelim++eFv8Obtt5CjRBhH7CpDU6urtzu8+c1bXJelo0VYNXsg6L/JSmKVb++2VJay\nlCNf5W7YYLRB5Ee4/eYW2/ud23GzE2K7dEtuLjOSMkVWZMhXGcIkIkEx20dNmia8vk8iS+F7GNoV\nwjjC7uYW2/stPOGhXBV4+5u32N5u2ciU7vG+7nF8SGC0we7+CrtNhTQi9jjATtDGOO2Su24WRc7L\ngjSNsXu7Q1qlmPSEtunxHguiNMH9D2/w5oe3bFrpo6hyJGmE23fXTqYRhQG2RYEqy5DH8RdCzv9w\nUNrebHD37T26ukOcxLh5c4XNpsLqqsLmduPG235A/OY4j3H99Q00u9aqkfCxURrh/rt7XL27gh+R\nWjoKAqfw1GbCKEn3k5Upgl0FPyQZ/unphIEJhMDiLI9E4OP63TUAuLG/xcYuuKhX4yxGnMZu6jGx\nXQ39kHJ3V5bYlSVWZY7hboe+HXGuWyilXXAjqyCQlqgZMC+M0vAmFJsCfuBjfb3G3Xd32N6skReZ\nU7AHvg9ojUAIcspIIiRF4t6z4smYfY++T8rm9c2aEL7eq+1z7u3Zf2ZtseEBSz9D+J4TWwKk6A6D\nAJs8x5v12hEGm3HE59MJp46cVMjWeSTqIAs3taJl3ygh2+1yU+Lumxvcv7lBxloUIYTDw+aehySM\nEPqS1hvCAGmZIS0zGK3JSGCa3dpFVqYorWc9W2AvWFDq0rm4RGnkhJRkse6jKLYA4ILk2A4o1wVW\nmxLX9ztsbtaXxW+WTFS7yskgrJcdAKxv1rj79hab2/UFw/NqJ0wamjZb0aX1ruubHufnM8Z+dPt5\n9jtK8gRJ4bmJE1EpF7cK5AkPYRQgKVKnXicvP1JQ2x+7jxelMbIqRxhHaI4NDReuV+4QvfnqGtYc\nUoQ0IbeW854Q2NxuLgJXfm27UqNZo5fwGo4NGha1kkQRTKQxGpr69g1NtMttiTiOkOWpU/YnQYCI\nVeTTsiAJAse1soHuVzvkrsocq+s14ixBWqXYFDlWN9dobnf4sHlG1/S88jCx3a/APC0snAOKTYkw\nDlFuCmzuNuS2isVBtnwh3HKtVBoeqFkbRuSBFjHbu697+oJenTSE57gsedqbDx6D2Fh9nBapA/Kr\nUblACsAxdNIowjrL8M3VFRYAvZT46fkZx7ohMz51QYSQZXOAgb3HhC9cA/Dm3RWubjYoWHZvlcEe\nqOlsg7Bdi/ByslU22mCIelgLKMEur8viuV4DTUHg9pECtj9K8oRvQB4Z+8KxyIXnUdrsgGdELZjE\nDD1PtPM1L04SQFMYEpmmZYoiLJGkMVarAkkaIylSVHmG2BoccnPW7rJlfBKHvn95IAOfnFZjcrpY\n5pmXWu3G/OwWh22Q9n0BzxPs0BpAsBTFPlFZVtH3F9ADdxYCRZbgu/tbmNsZYRahObZuDcRqqhYs\nCKKAzVE3SPMEq5s18iqDCHwXXDmWudUSpbRjzFvQWhAF8ANyd7HTsMlMMMzuss7NX6BNFjhH3SRP\nkK0yBCHJOLTU0L52/609fCczIYojFNvCIaWNImNPq8/zPN77ZImBEFQ6rW+o/E7LlCQIrwLCNF2A\niAAdnIEgmKHHZbgt7dqyQNP3GKWifU7eTQRAjX+GHpp5hpgIO5QwWC+Loi+IG78aXTLNM61PbAqs\ntpT+2Zq73ZDflRwktDRQI/U2kjzB6mqFtEhQljlxq0O6SWcOHppXLoTnYdQa9TBAMqvbTh+MoR5I\nFEdABVdzk4raYGwHp3+iL5+4zyS9D1FtKxpDxhfVc5RETkEL0PsoU9odsoumC+/rZHGMI1rSD/UX\nVSs8mkoWmxJpkaIoMmQZubfkWULrJa8mGop34eIwRMD7Px67tAZhABEIRH5E2qf5Yo8+sXuE0cbZ\nFgFAEPgQoY80J+GoCAQmPcETcBmrfXiNzWT5lLL2TpbPFAcRtkWB2POhkgSqovWXNIlRFhllB1H4\nRePXvg+LNQEIjdFLiT6Okcc0EAHAezzgB8WjZvS8OHtzrTRdoyQhoj3FoyRiEaOgzI/vY5JwzJhn\n4wJHklJ5nmfUu5vnGeuqYB2WYgb8jDiNkOYJZR1FiixL4HPAe01msAwgxYYBvaTVDhuQ7IPoeZSp\nBWGIMCFdlQcPxhjIbsTQjVD9xc5c+B78MEBeUeYYxaFTzdOEjSzprfegFYZqqal8LFJkRQpgQb2v\nyWV3YrpkHCKIQx69+y6DtuN9P/AdPmTh59qufTgd0St8i13WDn0fqyzDJs9hpjVGrdFJiXYcCWPE\ntBD7ZwOmdqZRRM9UFLnlX+ACFfxVQWk0Gru3V7TflCUusIS+j5uqgrcA/TBSBNUGcRQhjkLkWYI8\noR0faQwkR+OZCQBSk4HhaAx6JXFqO1LI8lqD59GO2sQfjvCFq5fjJCKr73lBN0iMI8H2baYkfIE0\nT1CUGXxPwCwzRqk4paWH1uEhlKIH7lVaOXNNvclzd33DyFvgZkIchyjSFHFEjfE4vPB6iC20cD0d\nuiywZ1+tmQkBVqlOnwmfjIEPsdADmZWknp6XxZkGWNCZ3Ri3wXYyE+QinVAU3oWYaLEZknEdURC4\nxc0oCFAlCbsVX74XC2CrUtIimZkCvl3QfEVocafeNF2cVsMsQxpGlC2xwp/KFo/XRmh3Ks5ilyXK\nXjp/egDsFhw5C3ZrsOAHPsyknQ5omRZSTocBkjQGQAiO+/UaSRCiH0b0/Qg9U1Ap0wRxEjvJhNTa\nUSogLnwogAJ6ww40DrkyWXtruE/BDwVZeqcxMtsknxdIqdD2A5T1wROC7p08RRzT9EtPE3opoTTR\nGWw2B1AmbFe8lmVBFIXIkhhlluK8qWgjwmJCwhBJGiPmg1tqjb4NXK9Q+MIFIgtXHDlLsjwkD+QT\nN7n7+PL9BkGAPI6x4aBlF+ntcrkFNNqF/ISzLGs6Yl/vNT3iv/fzF4PS6bnG1f3uchPyhxAFAXZl\niTJN3U0/8AZ9wtvW8IBulDDsumEv0DKYijjGeRjI5YPRBkIIZ+Fsd4OsCWIYh0jTGGkc8cMVYrOi\n1xqlcg+g8DykcUzq1lfbzrT06HEmQTclgcGI0RQa45p9nufhpqpwXRaYOMOQmoiWgjEYIffEFF/7\n6y/R7hWZacLIwSHwhdOETNOEMKY03GYtnkd8oKRIkOQp4jx2vvcAW2BLhelVj8mm91T+eK5nYP+M\nZZVHvM8lhIdw9uEH4CAV0XcTXfATFvpmyzLNh4qDoS0z5oVe227/+0IgmAl0Fvq+KxnzKkNWprQm\nYREcaYCMRa0RN2XthFTymgsANyW112n//LLMtGoE2pwv1oXzAxSecFlulabEOre21ACiMEQgPPRS\nOXyML8QXNkdxGCKPY7cPRg4jHOSlxjwtTrRJU1sKSGWaIk9iF5AXkByjlwoTr8ZEfuB265aFfNeE\nEOiFhLIrKK9/OKmwuJk0ipCXMe53Wyj2MByUJipFRPek1BovbesCgP3eXmc0JBOh79Vas0rG1the\nr10XcYwqLu/sZ2SHAY7Pjss073U2pJcLfPGv+fmLQenzT5+xvl25DIm8rijq+Z6HPCbsRzrNyOPY\nBQX7ZmdrE+1d+NyW2hgFgZvC5Uly2eeS2jXKw5imLjFv/Sd8s0dhiCwMuem7QCf0u7WZYOYJvicu\nXCaPGseTTyAtOxkAaEFVcmDRnE0suID0fRFSCRQBSwIAC4QnviDu2b6K4L+fpgl6pj0iayW0cHZJ\nD0yELM+QrwtaOj7UmMzEQsAEUUw9gDiPCYfis+ZoXoj0qSdoRZ5qQzdQuq49zOqSw9jrGweJ0CeD\nB1tWav5cCEdrLvxxvplcQJ0vsgI76l+WBaOZ0UuJVkoEbCYasOrXIiqwLPRwVzmq3QoA0Ly0X6BV\n0iqjJm/gu31C+90rydxx5lx5gpZAhe8jDC/TPs1ZTJxEmJYZeuLrcSUm9dRmNn2wVErFrQFfCGit\noaeZmOHc6LYPVej7iJOYBgGK1oO0Gim48zpTEAbOOCOLYmr4citAvSKYamMwMRrHvM5ObGbLA4zl\nCzKq53pLg1KINQW8dZ4jCUnBnsTmFf11cQHYUigBKuujwHeHk93af70zaZE79vcCdMjGvu8a4Pb5\nEPy+RRB84SJs+8Tg+GCWiyHIn0Pg/ns/fzEoNYcG/bmnxT49QSXGmdcJIQhwz+WOvfktIJz+WQDh\nGcjJOKCUvWipNQbeJauyFDOIPW1tuZcFGNoB/khMmYmDXOD7rtyKo8g1t4XHPBrDHwM/fD5sQ252\n6wdqoFNS9hJDOmKMY8S80UwPTUDM7jDgU4IU1B4Y6mY07GdrTx/bq+k5Y/Q8D3EYQHiC03RqEsZB\niGJTQGmNNmhpHaKXzhAxVQl8nwJdEPocwBmLyzf3OBCYzi4yz/PMfB8FpS64ET0q6CSmMiUMYKbw\nctNwqbfgcvJZwiYx1w2kNo6/PmqNl66jcmOaEPoCZpqRRBGmmYP1NKMZBuLycL/uVKRuiqdHDSM1\nJLsE281/IQTLMiKoJEIoFfSgOYuaGPhHN77vh86NVY0a3bkjvlAUQU8zAsG89SAgUwJ+3q3Zg+Tv\n1TKCRkVT1TAIXUY/sAsHHbwUlIZwJLUy7y7KQWKaJ4RJiDmjzNwB75YLQ54cfhaYySDwKHDY3o5l\nDtnFXDKCeMUc4g0Kw4SMnn3oPM9zgS/g51AIAW0MSRiEcH8WoB3WMAxdmRXws2ozomkhhxKtNUal\niJMUx0h5ambvfZtF22cYuJh9ANRbtP9+4oAkOBOdcWkn/KqgZDejiyqDVgpSa6RRRL2DIIDiE9FG\ne0HvApMQrqTRxmAY5StFNV3YoevQydEhS7MowhAQWsIPA2QrwnN05w7n5zOUVBDgqQBPkSzSEwDM\nRHwYeBeMrTSamqWvpj3WSRQg11CpNPnE85fsRGX8mkEY8g4ZM5YnQyUnp/d2dHrue/SS1nFCzhzC\n2YcXEEr21PfkZGEMgsBnTlBI4+OM9CTtscX+0wFBFKC6WmFOJyRpjEmwEHSxGejCW+qUhi+MLFaj\ndku2AKCVwdCPiMIAmYkwcrkgPFuqUValtIZhIJu9pp5vUGkM2mHAS0sBKQpDFAmNxQ2Ii52ySlpP\nE87DgF5J6sdoDT/y3U0chAHkKHH4/IIkT1FuCgoIUfBFE9mNzgG3r2h5XR6ox0Hf34T6UKPclnQg\ncO/ONviziLIU28SmoEMlTy/JQt0wbtbaP7VSUuNe0/2bhCF0EqPhjCXOEreYLnuJ/fs9YT7uDNR6\nQh7HQBKztRZRDZShvp0dpHig8ld41NfUWpMrC8sy6D1zj3OaaZAkCTGSzCEGpRznnT9c+gt/zsLz\n3ITaoluiIHASAMFBzd5L1nXHuqEoY3DmA6riXtG/lV35QmAGHGM/4KCuORMM+N8DcABAPf377Mm/\nGJSC0Ed7bqH0DvO8YBgVkiiisguXEZ9NCfXM3mvTBDkZdHJE29PeE/jBCrhX8dK2lDLyREdw+mqh\n/1iodo/TGOf9mcoczgqyPMVVVWIUAgHjUG2QoA/gQtJbFkAphbEfmVtEiA76wmlbXmYpdDIxP/sS\n9WdtoWIz42zZLYWnM+04oh9GnNoOvVLIs4T26ZaZudKc3UwEVhvYvtvjPTUsjK1YFva593B8OlKj\ndFRYXa3opEzsdJGySa0ZmjYqGGkge/J4nzSNpGfeDVMD8YSkUlBJ4hqdZhaAMQhm4a4LAJShUmZQ\nEvVAwK+XY4363EKPGvk6R3q9ds3uQZIPPWlrmNIJoBlHQseymLNn9IsNJGM34vOPn6HfXmF1Nbvy\nnJrIhB6ZJuJWaWWgJWE7tKQGa5KS2yv9N5RpzkX2JVDMGEg7HZwv9MtOSdRtj/3xTCpptnSyeN9m\nGDBqTb2oaeJG/+ze1+syZJ5ndHWL4+MLiSnfjlhvKsgiRxIGCIXvGNautcGlGED6vEFKDP1Isgx2\nfQYoUM+Gpm9DN0D2EsW6IKcdzyNTjld6qokzXGUMOaaMigYzWYIwDgBQBhhzSWnbCbbknnlQEgcB\nQm6/tOPoDqYijh3vnJaEF8xc8gvPgw84px57zfpV0JvnmRBBf4E++Ve5mbSnDpPUiBPKkJwzxzxf\nPiCW5VtsbCcl6q7D0/MR0zQjzmNMoyJecxw7/CYFtRm9VDg1DQ4PLzg/0e6MlpRWT5oAWs1Ly7gO\nQG0rLPOMIs+4N0KlzAz22GLNiBwl5mlx+IWxHahc4vItiAIonu7MRe7cRMhv3XfpvLJN34kImd0o\nUXc9jqcGh/0JRhlUVxXWZYGZofreQiVNwIFTT55DzkplMHTEo2qPDaFmo4DlFRpd00E8eoSk3VTI\nVpmbZGAhtxZLMhha4vuM7QCtyI5ncSUru4Io44K/4yx7l8Y4cNHldOOIehhwPDV4/rTH4eGFdhaz\nBML30MQhVExyjuPLGXIgL7miyrFZlwijAKPSGEYCop2fTth/eEZ9aKgZzQrp7kSUADUolNuS9hO5\nJ2cRzGokycDIpArS7IRIsis+5wiboaTGwnt+tmGrOTDP/CBMM5WWL8ca+4cXjN2I9c0KyzxDi0s2\nMCjlHlJlDA7nBqf9Gc/v99BKOxvxZV6gpML5+QzZS/hcpvXtgG5TES44uixRW7MBgDDQRk+YjMHY\nS0YPj+ibAbJnfvviAaxXkz1NV2fbt+T3aXtSNilQ04RupGVy2RPo0A98+IK2JwatkbAUx+fs6eL8\nMrtyyxo7KGNwHCV6pWhn7hVuNxDCaZJscmLXc+znbVsarz3fXjfG/0NBiVhGEkPdIc0SGJC54ah4\nBA9Acdpug9KgFfYvZ+wfj6gPNVbX1OgcmgF9EsG7vRjkaW4wH48NPv38gMefHtGdO/i8N2PT6r7u\n3b7aZGaCbdU9VlcVkpT2thY+sa1ie5ln9A1NfdRAJ4clPNrXyvIUp8OZXCb4GkbOjkJD+hUB1kZx\nWlsPA47HGk+fDzh8PmAyE/JVTkjUrscCgtJpZVCHJL9flQUC36cbpu0xDhLHz0c8/Osjjk8vWOaF\nhKUT841DGufWB9rkL9YlslXmAG5qVM5eSQ4SijnmE9/oTtjDJY8xE5Q2zJsmR2Esi+sH2iGGVBp1\n2+F8avD8YU/LlcJDdbVCWhJq+On9M/mtDaS2VwPp04pNgd39Fgmv+uhRozk2ePjpMz796wcMXYs0\nLRAnGYp1jqzKYJTBy+cDxm5AsSkQRKGDommpoAbNhwgF62UikJjTQQGOe7XwYdQMA53Yngfj08PS\nK4VRSjw/n/D40wPO+zOSPEVaJOjOPf2ua0lN/ix1D1Hfj3j+uMfnHz9j/+lAxgW+z3heQkWf92cS\n1CbUk2wONbU81gWKTYEwChEy0tZaX83TBDkQ5JB6iiP7FF7uTc/34E0eo4QlB2kFnSaIAnZoNpdB\nhTIGdT/g8fEFh097ygJZC+b6PFzi2mHToBQOTYPnuqH1oFeDEMMBrusG+J4HGUXUT+57ZFGEIo4R\nOib/zBNm3033rNHE68Bk3Yh+VVBauFl2OjTYvrsGzEQjyGl2jg+2ZCJpgMb53OKRT8Y4IzHb6fGI\n/ccD5mlG+02L5naDOKWJ2TiMeP6wx6d/+YT9pyc0Z8JNJFmGolwhSTPM04Q4J7FbktF6RntqMbQD\ninXBD6zvxGazmZlIKMlaelAXVbY2NIUD8O5mh9PhTPJ5ZkS/DkRuxL4sGLXBMI44nRo8vX/Gy+cX\nAMD2fotyW8Iojc8/PxJPeX+GGqm0WV2t6WFNYyit0TcDunOHz3/6jI8//oLTyx6e5yHPKyRpjiRP\nYaSBWJGmqjk26E4dim2JrCQFsNETgepZqWyUdiTEZaHNdPtjxZdN12NUChH3WRbOCm2DdZpmDIN0\npfLh4wFhHGDz7hq7tztEcYShHfDwrw94/PkRx8cDhm6kvk8QIHlMcHo6IckS2PWIvu7x8vSM8/EZ\nWmsQyC0inzpWKmup8PKZMheiktLwQg1kS07vn3ou0zRDBL5bxfA8Wisae9rjs8vVvv9K2GkmjKPE\n+dji6ZcnHJ+OpCkqU3p/D0ecn07I1wWaH1pUm5LFrTPO+xqPPz/i47/8gpf9M8ahI3FplqOoNuQ4\n4lFADmPqD+rQd/yl8lyiWOdIywyeBweko3USugdt5q4G5b5P+2ObyWpU0Axh6/sBfpGhU+oLw4ZB\nKRwOJ+w/7KmqCGmvcmxHBGGAKkuRch+pVwqh7+PYdvj0tMe5bmmFB6wjSyPA9zAOCp7wkMa08TBq\njeemwUvToExTsrqPaN1Is3DSTuGUMV946Jl5huSy+FcFJfuh9N0Ab14wjRrdREhZFdNFkFyA9mKG\nXuL4eMTLwxFxGmFzt4HwBZ7fP+Pjj+9Rv5zw9P4RN1/fIq9yuvlGhdPTCYfPexwPTwxyo9dbjAcs\ngtXLgfsyY8aNtscW+26Psi+RFDS10oxDJZk/PagXwLt23m8AcLNaIeD1k7ru0AcDBI9AITws1mmE\nxXD1S43zc439h2csC3Dz7Q2u3l0hSiKoQWL/4YDPP37G44cPtJwZpyiqCuvrFQdoalB3pw4vT3u0\n9RnGKPh+CE8IBBFNVyiAjggiWrXp6x6nhyPUoAhatwD9uYM1L7SW5La/0HW1Czy2wT/xqWWVyXbC\naculyUyQw0icp+cz7L7Y1dsrFOucqIihj53c4fx8RpwmCILQgf+maUZzILAbWW1P3LRekCQFkgRI\nEg46M5Wgfkjw+74ecHw8QUmNvMohfNr+t4JKKxVxAHj7F1tudyPqY4NwkG4JnNZXBB04Z9pTe3mg\ng2R9s0Z1tULL5fOHf/0JC4Dj0xGrq5UjDnTnDsenIx4/fYAx2hkWyHFEGAyINwmSNIEf+jRZ1LR8\nm5UZXj6/YP9xj+7UodyVyMqUJAXcL7MaMy1p+GJXU15dnMt45aDQHBsSlIKGNlEcQkVMtZhmjIPE\n/vMB9UtNxNh5wfn5jKEZ0BwbGKnR3O+Q5LR1oJTGy/MJj++fcPi4x+HzM6QcEIYxdnc3uP76Gqvr\nFa7vd8gTYnznTLv40+MjPj3s0ZQDtqsSZRwTe8r3nbTALjQv3PjWrBn71T0lq5Ae2xHH/dnVtUEU\nwuQJffEMb1dSoTk0ePl8wDwvuPvu1i1Srq5XyIsC3alH89JC+D6OD0ciBvLpMZsFWbZCyBhNksqn\nyMoMYRIijELesdPuwUqLFEPb4/h4RKkLWvxk1xNrIkAOq5qVsRZmRXX0w+nElMgFfdM7LKmFpdn7\nw3CWdXo+ozk2kIPCzTc3uPv2DmmRstBTYHW9wnl/RrnefuFLd3w60efJbr3kFDEjz1coig1g2VEh\nrT1M7OiSgKD98MCs6w5WaWnZQfZ7AsC6nglNvQdA/Ti7yLpgwcTqYDfO5WVji/EdugH1Sw1jDDbX\nG9x8fYNqW7qpUZYm8N9cYZ5m5FXmXGWUJGywGrXT1rhG/zxjngoYu6ohmB5pyDSBFm5jDC052JCj\nCR0UhsWKFFy5WS0V5smOpbn3MWrUhxp+GNDAwPNcUJqmiUifzzW6c4e77+7IiSUJkVUZqt0K1WqD\nru7QvDTElvfYcsvMUOOILK3osAojFvP6hHMpSHnvC98dAFZ1n5YpmkNDn6c9NLA4x1+7gWCvx7Yc\n7LVN5sIhN8qgfWnp2nxy0InTiAW2ZGzQHhucn86YzYyMM7PuHOLh50/YPzzgvD/h9ps7ZFXmdua6\nc4fT4xFGT9jc7uB5HlEKPCAtU2xuNsiz1ElCQt9HkSR4u93ix/EBz48v6IcR17s1tjyJt/pAcNm8\ncCnvkqOqAAAgAElEQVRoy8JfLQmwN/BkJrz/pw/Iqwye71GAmGc3JaMPl7zKxl7i+t01yl1F1kLa\noFgVuP7qhk0RFzdlsZ15Wv+IkYqUges0iQn5IbUK2gWAlgaeJyECgpaJoABOLTXkuSczTzOZ/IFH\nzDOdJn7gY8LklLL/+//2f0IrmnIJn7b1Jz0hiAM6iaQm7ZTUF3eVnkwQrt9dIckTXp6lG/Hq3RWt\ns4Q+DGuwrFedXcK1CvV8lbM+BZiXy5Kw/dHSIIpnx4AKggB93UHyDekJojNMjo9E35fRCqPsXDD1\nPLBi2XPB2mjCkXgslZhYUT3U5F2WVRnKXUmLsGbCEsBtzxdZguiHN4gzmopK9nDzACS8W7bMM+So\nqD8kFbTdbWNe0MxWXHYpOS1TCF+gq3tCL/OBsgDwBDAbyoi6umFS5cgPrIAnFjIq4NLNHoT23p3n\nmcgO3YC0SLF9syWbIwBRGmNzt8HYj2RltdAm/2QmiGkGfIEkz5wHW5RECOMQC8jWyi4M27WVeZ4B\n3hvOq5x7TA3ac8u7l6H7/O3zZTQZq3q+cJnvnz9/8ECwtDh0QwdTpNDSQAR0356fzxi6AXlFJT7g\noVgXWO022H96wvPHZwzNSI16/pzChBykV9drrK5XdMDyvblZl8hiRsSwnm1kXVOVpvj+zR1+XB5Q\nvzROmlOCF31f9ZjtZoCVQvzqRrcVX3nCQ1d3tJ2cRtCxAXg5FgucBZMaJPJVjuqqon82U3MyiAKs\nb9Y8CSIciHXwsJvk1ozA40XHeV5ck3ABXM+EIPIeIiEQZcRtEkKgO7Uw0ribxPZeLJvaLjguy+JU\ns88fnml9pUhd43LsRyztgiihrXZyc1FuAhRnCapdBT/wKeOxfOzQR5rGePube4RJiPbUwjAqw+FW\nrBbHTAhCgpYtCz14wvcvDGYOFnKgtRI7dbRusVho/Gr34YyauFyaMYwdlCLJg9bG9WAscbM795j0\nhLRM3aLysixO4OgHRD0QvnBanDiNEPB6j8VQ3FQVXt52eDmc8PJwRJInbk/LKIM4T6hxrBPIkff9\nrBhQXxxTZCddRmfNRGlZ2XevZf/M8fiItnnBxGXUMtN12UPIg5VB0G6g9yqjFMLD+mYNLMSktnt5\nxTrH9n5LRpWc6chBYuGDZmL1N3gNiH4x4Ec+kxqECzJ6pOXZICQvtrSgUtuvyUxhYfSOfV2b/c3z\nDG+hTPrPxYm00+i5cpISAPJWTLIYYRzRhJL5V0EY8hCKGt3FunBYFMoqKej7oc8QNmKA5VVOdlNp\ngiQKnfrb9lZfr5QEvKj77ZtbvPcFuqbHnndKLT9MTRddlt278zi4/aqghOXik+bNNIkL4gCmGx2H\n2g9pErBMVMoVmwIeaNpmS6Eo8LF7S5Cwvu5IEZuMkAM1CgVrjeghufx6EfiI4tBNkYQQ8Hyy4ra9\nFN8nZXJSpMQSZ5+1KfDhTTMWM7tmtXWOcA6hoNcYO4mQ+wiULRnH97F7ZjNDwYKY9EXW8iaMIiRp\niDSJSWWbZaiKHOe6w/lYo34+I0wi9yDaHTXKNCPKCJaF+hWLwALfiSHNYNAeG8DzkK8IYpYUkVuV\nmZrJlQPUGzIY+gYWF6vHi7QBJBp2/STPY0tpfi9ykI7zTa4WA313cYglDnmnbmbSAODBw67IQc5k\ntMrQsx+aCAQNE4Sg78MXmDzP9VKWhSdDUqM9NtRMLTI6vXNSVmt+eOxgQqsRXXsiIqWwDsCLCzwz\nB3PZS8RZjMlQlmkz+ZQhbYaz3yRPkeQJ8nWBkPt2Qzs4PRQt4FKwDgIyFQ3Zkflyb5LF1jKDyJ8A\nJkWDhUsfDFx2kU7PZ7DfpCfnQE2lGq1B2Ub36+VVO8CYzPRFr22ZF9enFEK4sldJXujFQsQJbZxI\n1B6gu/ud85YjEGKGIifSQhgESBlhYm3W53mG4WY2QNlTGoZ4e3OFfXhG03RokxhbnjQDl3Ul4ZFd\nty3lfl1Qsh8Oe4/ZD5Pq1Q7ltgQ06ZlseeQB6OoeAJBkMbyYsp08TpFmCYaOUuWh6SG5XHIurN5l\nJSTg8sIGhTAO2ROLMoih6blkm1DtKuo9xYGzPSZtyMLTqMmdzByN6APgG0QNEnpMXCCa1OR6I0J4\nzr7ID+jfnfZnQl4I8mIDqAehFriJXZElUPrikqIkT1AGCbMYns4xYXCZ3Yh/nqgHNk0T2qaGUQZZ\nTtOdrMqRlRmE8DC0o7tRiWXF2Aw5uMBuwXF+ICCED88HIy3oxvR94U7/iXsocpDojh1Ub5Ew1ESt\nbentXQwvJ0PXpfrLdHNoicg4M05m0oYeHG62a03X6AkBoxSGbiDiJJdGxYZO9h7UR7MZ8jgOrmyz\nQde+H88TmIx2TXTK+PwLsiY2gOc515qEKQUkYvURxjmSPEZ3jtGeWnjeyH0vtoAXHsQsKJtdpi+W\nhH1ffFF6yZGGKt25RXfqXGadr3OIgAKT5naA0UyA4ICK5d/OI9zytTbwfI/9B61RJ1EYbGms2JsN\noMw6X+XO3j6MQyJQJBHyiv45AMK6pAmKJGH0jHAZsSUpYKFVlEFrTONIAtNBIQwDpGlCZAKpUGaT\nWzy3GiqLRLHB7d/7+auCkj1p7CmrBoWsypzy2sK5wih0DWM1aHYjJciUGhVlUp7nmEgL4DIY22ew\no17r0eb7vjMEtKI7zwPUQDqW0/MRWhOULIgClHmJkCl+tglsS4bJEBTOe2UcME0Xy2etNKFR8hgL\nq5OpjxAStpYfUDvN6M891sc1yk2BIL5MoQDwCTY7J1V741kmk/Ap8xrajl1RBZkzTjOa5gWPjz9h\nHHtI2SGKUvzN7/6zK3OTnIJnzwRFi3wRnoCShJa1ehir9/E8ykj9yKeyipu1fhg4RIgfCCzTzG4g\nNdrmjPAXH3GWIk7of3aNYTKTyyzHbmRdWcyY2B5aURltSY9CCCdTqJsXPD/+glF2mOcJUZTiq69/\nh3newbPMpWlxEylrKCplD20k2yy9yiJe3eTztBDKWJGAUvjCDRCMIYv5/twjLVN4LNz0Q/ocMC+Q\nAxkrjt1IwlOe/FEgUcwJ/3K4MJmJrYhIkqIGib5t0ZxrtOcztte3SIsEUbJBtspoclqTCenYjy7D\ntVby9nWXZXHNcJet8/BlmamHa/TE1QyXsP0I2UnHq7J9sHyVO4tvywO3SnYAF0EoZ55W6vMayuZ5\nHryFhMXdKPHycsbz+2dESYTd/RYps+mtet7q3zwuT42Z/mKW9NcFpT+L3h7ASmtqSGtJJEZbnwpf\n0K7annzR9EjpOW1newCPM4eOzO2WGTCaMwTulVjwVX08oaxWpJDuBvflaCXRdzXq8wFd2yBNC+Rl\niepqRSlsEsHTBqGaXL/BAtRs1uN++G9tlhLGNOWzhgO2zAg424NH7GM1KDx8+AXm/x7JNjxJsVpf\nISsLyvI4eDz89ADhC1S7Cgv3MgAgXxUQvo/z4cR1fwbwQ/T+wx/x44//F8IwxtXVV3jz9ntc3d+i\n2BTIV8QKH7uRjS2Nw13M08QmltJlnFho9C4Y1yImgTChkmxig4EFC6I4cs3zvukhfIHD8yMeHn7E\nOPaoyi2+/f4PuHlzj3JXwWjDNzc5pKZlhptvbrDMC/Yf9+QMsilY77Sn/pkmnlXbv+CX9/+NXrfa\n4e3b36IoCQpo7cFpQZkFoVwujkPHI/kLL8p9jTyR1EojiANkZcYTLgpSxaaAkZqNRTvULzWODy9Y\nlhlmMoiTBGEcQXiCrMUVl72cdVGWEcBnx181KmRFhnme0RxrJBlB+Zr6iPNpj9PpmZlPC6Lof8Q8\nfw3f93lSS706wcH9NdTNthheX5cjpXpwhhbg+9qihW0mNHYjmpeay2nfVQtxFkMsJI+Ypxk61G6t\naZnJQt7zPDRRgGMYIuTSz7ZIrDsPWamNOD6f8fL4guf3z8jKDFppvPn+HlVZuKAmWB5gt0BeN+1/\nVVCiNw033QEoSg/NgNXNiqh+dgSaXxwlAKA9Nnj45SPMpCDlgCRJsdndIk5j+L7A/uMeFXf8p2mG\n6SXydYFyU2CaZvz8T/+MNM2wvtlAM095nmd8/vATHh9/wjA0yNIKq/UOxYrKGmIjJ+xsO3K9PfGN\nNUNwFvXFacR/VYOiEiIKEKWkO7I3ZbbKSFAmPGqELwu6pkZ7OOPzh5/Rtie8e/db/OG//AOKVY44\nT7B7s0NX9/CEh7/9h7+FHwZ4/vCMZVlw9faKfqcklGpWZnj+8My9AoPb229xe/st3n37A7bX10jL\nBFmVkeDUTM7r3e7NLQCbgTZkg82Sh4Ctcqzi2epX6FQ27sbzeAk6yRNs77aUaYgZ5XqFx0/vcTw+\n4OXwgJu399jebclCh4WOALC+2eC3v/8WywJ83JEd0nq3wvNnEobOZkZXd9BSYxw6hEGCq3fv8Ob+\nN7h79zVWuy2yKkeSJdDKoGHFPhEfBcaxR983mOcJQgRfNIFtpmQfGjlIFKucAIO9BBYgzROIMsPQ\nDjg/13j++ISf/vk96nqPut7j/s1v8N1vf49yQwaRyzSjut3yWL/G2Enc/XCHMApxfCD90fZ+A3h0\nqK6v1zi/nLH/02c8Pv6Ivm8Rxylub7/D7dfvsL5ZI63o+bDDHs0L1ZOe2JacmuxWcOh5HhZvYeX9\nzPokQHYSxbZgXRkNNNKCX3uacPh0wPHxCUPXIi8r540XJRd2lc+Ta7sgb/uLYRJ+0U4JooAGEYba\nA+2xxfH5BXKQiOOU2V8JyUJGBa9cXKN74e/HLnhPExlU/GrypBUPuixjAfyI7IyruXLZkuwl0jx1\np9L2zRZBGKA9N3h5esanj/8M4fv4h//lf8Vus0NWZuibHtdvr/DV334FoyYcH4/Y3m1RbArIQeL9\nH3/B+naDclvifDjRpE74MFohz1Z49+5/wO2bt3jzwzt89TdfYXWzRponWDz64sgJlsomqwuxfJ7p\n1UiW0mL6UNVIdjtxFiCMIsysp5kNOXAUqxzLV9coVgW2d1ssy39CfTrjx//nn/Dy9IT6+ILNzYZc\nYMoM27sNlNS4/eoGSRzBW4Cu6ZAWqdvmf/r4GevNDg8fPwCLh9vb7/Ht93+H9fUOWZEhiHyGdBFB\nYdKk71GDxGJmmoIuC+TYox9qxwwCyI1GDdJB8u1U0g8DgtWzsnricsue5PCA7d0WQfT3mOcJDz9/\nwvt/fI/m2GD/ce8sm4tNgePjERkLHj1B+qT21EF4Hg6fXvDhH3+BlCOM1lBKwvci/N3f/c9Yra+w\n2m2Q8AMVMMZV9hLtqUVX9zw1BYa+Q9/XsFnS66Bkf+zhSKr9yQlax35EuSsRpTG29xRwo5Q8Afu+\nxuHwGXGc4T/913/A/Xf3WGagPbX4+m+/xv3ba+yfjjg8vuB3f/8DhBD4+MsjPCGwuloxYO+J2w4z\npOyQpiXu73+Dm7t3+PpvvsXd92+wvl4jX5FEYBiGi+u0NRwAS0PM7PjqC2t9qKdL353ne1Cs/YmS\niKicPXmyZVVGh+WpxdD2aOsT2rpG2x4hhMDV9VuU6zWJkqcF119fk6ccZ2zVVUWei4vE/gNlu9VV\nhb7tnS037Tn6ePubr/DuN29x9eYKUUR93DAik4BACMfgH7XGoDURTaeJaBt/IVX663pKnqUI2f9L\nKV9f97TQuFDaqCuFkAVpN1/dYHW1wvXX1zh8+h5//D/W+OlP/w19e4aR1wivCE2Srwpc3e0gZnCz\n74K4GDuJp58fMU8z1ejHGuvtDt/+8AcU6xyb2y3yVY71jdVYJAjDgCT7dgG3l5h4MuFx+kty/y8b\n61jo9/d173zswzgEWBFvtCHtUhJhe79DklOWUm5LZEWKP/xPf4/3f/wFz7/s8fGfP0KNCtWuQnfu\nkOQxfvp/fwE8YP9hj5fPLyi3BeZpQXNoMRsqsa7v3pAoL40QpRHiJCKFt/fKHMEji2olFfUTiEGC\ncRig9QilqEFre0rFOkcLYOhGN13zhY8oW1iv4kFrA2+kfkkQEvlyNa0x9uQ7Vq4LfPf77/B3/7XB\n8fGI7tyhOdQY2v+vvS/rkSy5zvvixl1zq6zq6q7pGbZmKFsQJBOwX/ym328bth8sgYBA0Rpyeji9\n1JLb3WIPP5wTkVlja0RzAIEPFUCzye5idd28ESfO8i0z9p93GA4j/ul//Abv//E9hBAY9j2sttjc\nbmCVI1iDD6jKFnXV4ebVGzQdYWRIfF/mX1SeM8KZJ1fOGhwOn+G5J+WjfabImN+jiFko7fhwwN0v\nvyAAH5fb3brD5vaKP5cVXr97jXfvv8b66gr98YjxMEJNOjvJLJYtXl8TmLc/DPCsl+Stw+MPD9h9\nekJRSHz47ndoPy+x2V7j669/hdV2jZu3N7i6vcL13TX1HFMPJ0S2DtO5hwjuswYmlSfuW2of5DOX\n+z0FpsOI7d01qrbCeJxw9ZoUOKs33PMSAqfHLZG19YzT6RFV1WJ1tUHdEI3n7us73P7ilsxe74+k\nw3+7wTRQzzS1HbplC6Ms6gUpbC63S7x+fY2b1SojvMNFxppI+QM3wyelkVyF0zn7WUEpZUkxUOc9\n4ZKqpsLUT+jWLXltaeJ0NYsW3brD9s0Wcz9h82pDD//VLd7+r68xHkf8/jff4v0/vcfp+IDHH+6h\nxhntqsP9+3t468ilk3Eq09Qj+Fe4+4sv8fbrr1B3RC9pl/TvJIwRkDAeoEmIJ0skypI8i8+LXFpe\nZkqXfSUBYOonhBiwlitSEZgNUT4qibKUbHezyDd2WUp8+dUb3LzaYvxPMzd6ia2fHDjKmsrGbtXh\n5u0N4VS8x93Xdyjk24x1cc6RsgBrcAPIzU01KSpJRgXNk7e0mbWeL/huEaQNSPZSVrs8oIiRJGJk\nLTMy3GsLC4G5mFFIwe+QQKFpIlRXJd5+eYuvvnxN05cQsKgpqFWShPD244iHTzsqdyZFQNfZ4o0i\nyy2boAv82ScCa7ownPUwE0ERrD6X68NwpCyJdbLolJ4nrFlXmy+YdKP3Tz1W10u4yWA4EEF2sWmB\nmw2h72+JQrO9u8YPv/0DrHL47tffUdDQCruPO7z/y+8hCoHf/s/f4p///p+xulqh3/fYfX5E3TbY\n3t7g3b/792gXLdY3Kx6UtOhWBDdol23G2RVCAAx2TYBVISj7SVrcjilSlytP5TgwFZL2ytxPF5pj\nA7Z3W6y2K55Qk/eh1Rar6zWO9wc45wh+wzgpZwhljoqgMf2+p8m6FHj7yy+wvd1ifbXM8JnkbLJq\nGqKd8LtLShrp59SOhCCVtRhnlWEwaa/mAdSfGpSc9Xkkmrr+KWI7YzHsR1zfVVlCQq81YT+WLSIr\nPbbLBr/4q6+wuVkzLeHsBxd8IKPIZYtu1UKNdIPoyeCbX32Dqq2ZhEqHOgnFlQ3pLBXF2ZaHRux0\nkyUwG3C2JvIunMfLXItTE5+EuGKMGY08n2YWYBPZJ23qZ1ILrMkFNU0rwOPOlkuQuqvJcYRBZzFG\nGB79rm/WpFhoPT3nbHLD0hrL9TtxCwFkHEvgelxDY+TJTbpZp+GE5O7h/fMXXrcNllsifT6Oj1CD\nAi4Cd3oOyyUBeZSVZG21JbxQzXKvUhQZuSsAVCz7KoSAdQ5SknNu1VRcNp/lbdVMUIHpOGWftBTU\nQwiIAQiBRuTjcczZlbUKx+MjjJ5ZXpjG/0iQgIvJWy7leKxLWSrx0vpdj8VmgbatsehanlhSA/jd\nX7/DzRc3VOZqC2csDQeKAt2G+pR/+3f/gSVSaPL8l7/6K/p+q5ZlepP3Ie/N9kxHiSHCcjCOMUL1\nivovOJ+r1GcijBHtTVEIIDynEMXIziSe+m5VW0PK4qyasV3j5uYKwQX0e7KdX2wWuP2KpF4ucYCL\nzYKVNXwu4+qmQt01eHW9wde3tyilhLakAS6A7K6bsEo5FjCFpCrLnDUVrJ/GI9vcM/vZmVLCBaXJ\n2GVjuJAF1DhjPNW5RzIcBrSrFl3XoO0azKAPuOkabG43qAbF4DFPhoSgcWTD1kfJCFENM4y2maOT\noAEQQN1UNGETZ68tLz3MrFHWFclCsDiY4NIzRpEPuRoV9Dzn/y14KpgQ5evrNR5+eER4CNhGciSN\nIUBbGrValq1NVJuUfSS/rGlSZH3D/C9SH4y50Vw1FTv6lqgDno/1ueEZQ0RAyEj5hDHx3ufDY5TO\ntt1FUfKEkdNkXkVJI/Gr11cwitj488DPzlljVdMkzjvCNCWwYFWXGaGbpC6S+YCxAYdxpECFiBCI\nPuCty3zG9CvyZ1CWNECw2iLAX2DSaLolI/W49KQoqDmD4/EBp9MTksOHiCnqIL8/+gsA4QxdqWp6\njuPjEVevr2C1xeH+QMj9G0kyPD6glKRNTv5t5KmWYCoQImuI3375CmoiuIBVpEiZKENCgLBBgvp1\nJTMMsq+dp3F4OpRJ3QGIWWYneM/SOlRupSD0Y9xSBgBHosP0ux6bV2uoUWH/aY/NzQZdU+P69oqh\nETxR3hPKu12RamaCC5Q1qZ9SWU8ZVF2Rc0khyGJJssQQhMiqBEnqJsWD5MpSlSVdlHzJCQbOpqY3\nqWj+TJWASxmF9KGcNwM1yPpdT5lBKaEYGLlYdmi7Jo8lk9a14ZIqlYVNR/WtVjrjlQAqD5NDRSIy\nFrE4Y514ZJ8M/9JEzbGKgB41AxGp9PEhkSufv/h48Z+FLLgXcgWjLJ4+PkEIkUFmSQaiWTSoHTnS\npoay8z7fIG1bQ2mbUb0hhMyf89x0T8DJGCPK5sxBq5oqB//IjPLgPSSD3FIz3jOC25gZRSEhZUnP\nGEIOxAD355oSjW+wvlkTTodxOHmTLznbY3pFa1rEBb2fsqRDl4iUgh0typJuUGsNIZidZ7qL5Wzj\nbMZYFEXGxHjn2MWFfrY8bi7oRiVwqYG1BtN0wn7/Gc4qbqBTmREj4YYu96Pgs5sGMum9T/3E5qEV\nhl2PE08Mu0WLioX0a77dSd/Io0g9roqwPgKACxFSEl+srEjjq2Aiegghi75RLGNgJWPlSF+cgLpW\nW6hJMWjSkcNyUkEwBsZoGDM/O3OpP4g0lPHJkotG/ONpQtM1ONwfsLpeoWYrsuwrxzxDNc4ERZBF\nNgdNpVwhBWRBYNrkWTgagwA8t9rmfZBMO7NtUzwbUbqLP0tZkrf0rNa6vPf+pfVH9ZQAZIToGRyY\nxrAUuPr9gPXNmrr/xxGLFaWGKW1znjzRF8sFxn7M+AxbWAjhOYWP+RALIDdBnSCaQcFkxKRP7T19\n2N77M6csBNjZYB4VvHMkUxIivCF7Ij3NUGo6v3guSdOtnZDRd9/cYR5mMv1zHssrqtW10vRvrhgx\nzEqKSde4YneHSwF1EcQz4Ch448pKZiRuKMJFkIv5Vsk+byUFLqWoYW2sgVJko1OWFTe30wXy3Am1\nKApG8i5yxkRYs8iZHriME7yBDWOK5LNbLgHfwsWzAVQKihAAwvHlnx0g80hCGp83KNlmsYQrZ6dJ\nc91oMj7QesZ+9wnTRCVIiedW5Hl/XmRK6W8vs/kYI4b9gMVmAcSI3acd1q/WNIHkG78QAm3bQHdc\nThsHFyNk5aCRyg5S70xZ+6U8SjCBaDQx8ITXwzucrcJY2ymEkO3ZnXGIrAaQUPRWU0DSesp7OQW9\npM8uGTmeepACIpfkwQc8fP9Aw4nrNZqOybSlzLpmMSlnJLuqC1WDoiBVz0QRSQhsxJj/LAm0We9J\nh5vLtoTcJguueEZvp4yZ1U/VqDAexp+MOf9qUMoawOEiQ7rYkMT6p5QUkayex8PABFfCPaSHEILK\nwbprct8n8YDoe8ac8iXXhcxZixEFzsBHF13WoZGVRMlSpIk8qyeSwU0fitEGapyh5gnO0Vgzb2Du\nQSSQJXjc+sUv7wiMth8ACCw2C1RFxXrYLUTXABUgCwFRkCSvTf0ABomlYCmrErUQCL6kZmMiwlbn\nn1FWRMgta+rl6FGjbitWAyA1Rj1raKWg1QjnDNp2mSENRL1IAeNcwgXnUXKvY7VdYe5nPH14yjdW\nwqm0i4b6X8pAjxpVXcH5kI0aHMtXEDEz5E2eTCJTYM/SLxejbc9ARl9XcI1jAKF9Nn3zysAqA60m\nHA6f0Q97xMiE6vjcdujHKz3z5e/p8yRDhh7dqsN4HHB8OKJdtKhY8CyB/dpFy84w8VmAK4qC6B0Q\ntJ+5SqAMkvsqRQGApJ2DJykVx6L9dUtcRTWqjOgnSR0mps8Kapqg9AStJxSCg8LF55cYBjlICZmp\nWN6RXG4IAYf7A/X16ooCJw8RyopK58Cyyyl7TH29dO6S/nbyowueKCM1n0Ebkoigg2Lfvx87nmjn\n4BjBTUMNOpdqmNHvB+zv9z8Zc/6oTImCUsgBQXBzOfIUIaWs/WGgqcy6w3ggLI6sJEQtICKgWfRf\nsMlkyZG1aqp8q1ATM+bMLCGqHfAsYCWtmzS6T72YBJp0xsIqC4gINdJBnqYBWk0QokBdt+dDyzdF\njDSaDyFCSIH1qw2++OYOH3/3EeOBspLVdgWjDMbTRM1oIVAW/CKdhTHmGQ0isdirBQHXyoLkbL0n\nB5EYSeMobVhVlZypnAN2ZN7hPMyYhwnTcCK7nrJGWZbQeubMSECI4nnQZfmNJLVR1iXWN2sSNzuM\nUCM1nYuCekVN0cDMBpOcSNaCS9KUvqf9UJYSwZz3RNPUNErn3gQioFhi2Fq6POZhzhMy7xyMliib\nKk/jnHHQSmEcjzgeH2Dt8zQ/ZXaykOfMPf8dkDLE1EAOTA9x1iHyxMqHgN3HHTY3lC01VXUuPSRd\nFuB+Z1mVjGuLlBWlpOxHGCkhziVWIUS2vioZjJqkdKyxuZ+UepFq1lDzDKUGzPOAGAPadk37kkvx\nSz5o4MY3ARHP6WGmYokCuw87dCvCDCZwa2RpnKIuck+prM7c0rTP3EW7RlsLUVWIrHkuwGYagcxn\nQ4zZvLViYcTZkI2YtjaX74lqdHo6kfDdh8efjDl/XFDKKTIJZ4EpGynCpiWlxDwQs3w8jViORMeO\n1e4AABGoSURBVIkQiwZCFNDa5JtVFALBctSuS1QN+aOVSU6Ta1ej6caRViIm4KMLED5kDhi9OGo2\n6kljHlWWdvDWYeonzHMPpUYUhURd18+CErHn4/nf5V4Aje9fIcaIj99+RL/riSojaPhTVpL0nhgw\n5quIll/0MKucusuSzDabqkLLdlTOewxaQTsPrRNhl1QYqzWhka02JCrPpGU9aQz9CbMaUVUNZ0aC\ng5zPE0Ta0LS5JKt1ykYy34v6gFdvtrDaEffKcdNZEKo78q1e1iUB41qyohYA6rbNDqpHMcFwadKw\nKy1ZGonshGFDwKyoF2jLMkutFpImYGogsfyUoamRSLfOUaO7LBtGcRc5IIToAX/en5eZUw5ICfrh\nqIfXtR2u3lzheH8gNcnP1PSuE5k6hDzdTd+zrEoUHSPgU1BGzMHJsfa1NcTxq5Kdkw+om5qJumzt\npen9TccJapyzgNvUj5jnAUqN8N6h61ao6iY/a3rGy32aqhchiFaTvi4RzofjgNPjCavtklUu+N2W\n8tn3LdncVQrxDIWdrLd9CGQQkDJgnjL7QF5/dRpUXRh9JmcUrU1Wex0OAw73B+w/7bH/vP+/LpQf\nr/9vRPfzQBRz0IrgKZOPmPsJAgLDjsq4sF6gLHhawUx2Zz33UATaiiZpy7rGqiV8DGn5OpjGYtQa\ns9K5uU0vwOVbLEELrKLp33gcoUfC8QzHAeN4hDEz9XGkhJRVfobMzhYUVBNxN8myFGWB5XaFm7c3\nuH//gNPTCSFE2AURjrtlB7si3Zi2qlCzZfW6a9HPip1WyLG3khIdO9XuxxGab7e0ioKg/snnrCgL\n6Imar+NxRL8/Qs0jyrJmxU/JlIuCA1JgnfLz9yXC7IyG7bGllOiWLRbrBa7vrrH/vGMXjSn3oRKP\nq5AF6fXUNUpZ5MlLw5pKFXObCiFgQ0DBQaEqy4xtmdmAM5cghWD0dkUZlLIMETGY+h5KT7BWwzlL\nE8VIPS/KLEI+jFKeyatZ8yuNnV0AQszDAe/IFfnV21coCoH9xz3293tsbjdoO5oylUUBURF7Px1a\nWRRZxmNRVeiaGgUEtCcDy5l98VRpsmtJKqXzvwtAVAL9vsfx4YjhMJCHmzJwzuJ0eoIxE7x3qKoW\nVdWirrtnwSOGkAcREGzt5AIZCzBsgOR8DFY3r+Gtx+7jE1bXS+allsR0iESJSZlpKSWqosiu04u6\nztbcacyfxNpSxlZfBK2klZScVCat8XjqsT/2UCPBHsbjiMcfHsnNZtej7Rpcvdn+vKCE9NCUkfM0\nhW7nPOrGeeztrc+NxN3HHdpVi/XVClUn0VRl9pmytc8N09TXSfiXspAIMmI2BFE31kFEagw3bQ0h\nAMtqffOgMu9mOAwksn8cCaxnDQ6He/hc6lQohESMHjE+x1kEvimJgkJ9JTWSSp9RJmNShv2A/ec9\nQSAU9UQigO3NBl1dIRZkT7OsG0hRZCcNFwKqSABUZy1Oinh5yeEWAuRmEnRm/Cc4wHSaMB4H9Mcj\ns+pbrutTNlEixhoAQSQII0LPJyWx140yRJguC7SrDqutwbAfyKSAm9vjYeQM8cwfpM+ItsK6ayFb\n8jFbMJBOs5WOvYAiGAbPneaZmrF8S5dVCVdamNkiRJZF8QHTMGPuZwynHtYqOGfOgEgO1kmqJEt4\n+DPqOZFKYzyL98V4Bsiep3YRX3zzBaYTlRL7z3usr1ZYrjo07IF2LAoozfSPQkA7R2WLlGjKikCe\nPF3SxkIb0mYq6xJNXVMWHAJbe9HPoidNrjSHEeNpoAmcGtH3TznDraoabbtAVdU54NKzhnMZlwi7\nnChEl9RGAQTCqFVNhc3NBrtPO8oG1wss1h3adolF18BYB2MsAqhU09xCaXjiloJSsnUHqB8sOYAl\nV+VkWZ94bKPW2A0jDscex8cjrLY4PZ6w+7zD599/htUG3WqBzasNrm43Py8oOc1AJ864ktgZALjA\nOAVxnow4BtHd/uIWH/73B+w+PGG5WUK83qJlbFFb16i5Lu1HuqEXDTms+hghA5nquYQBKgpARrr9\nUhoPItkmoOVwGHB6PKLfnTD1A4yh3gSl/jQyL4oCsqwgODClTU0TOG4SB1L1q7sGapqxYGGwdNtY\nbTEdR5weT1CMqg6JurJZQS4lSueAskRTVewSHGmMLiWs9ximGfOkYFnDO+Lc7LY6aYnHXIcfH484\n7Q+wRqGqGlRVA+csYtTwPmGxOEvgsi2BKZPci1GGGp51CUhkCQujCOzqnYc3PsMF6lON5WYBa88u\nKe5qmRubMYTc3PTcx5FSwoWA2RjsTz2mcUbwMTdj03BjHucs3EaAyhH98QRnTe6XxOg5A0wN/DMQ\nNe8JgJUbz72Vy0sm+pj7clIma/UCb969xodvP+J4f8Dh1YY0uLj8WHcd9Ua0hjEOXga2xBbZAj5l\nCIENR6kdUeQmsfOegaMGRhFW6unDE/b3e6hxgtIjhuHAcI4Sdd2grjtIWeaMECC6TTp6EWecYIZQ\nMIg99VqrqkZVVajbmgYaw4zD/YH+rqmx6BosWoLpzLOGmjUiyAE4goJtCAEJ2lheBKFCUBM8nT8P\nEnBLhp27YcDj7oDDwwHHxyPmQeH+u3scHvYIMWC1XWF9s0G36rC6Wv28oJRUCgM3W9OLp+zmjJ9I\nv9Hh8ug2C1y93mI8jnj4niRnpSRjxvaCQUxqlRKlJN8qHwNmA24IB/KoLwS8J4xO4HTeGYfpOOH0\n1EONM05PPY6PJ4ynAdPU88hf0HSK+wxpAxRSZu5bggSAJ3tpgidLydbQlLYuGc4f+ev7XY+pn4hk\nyhQKdXeN8NpDdQ2assSybQlubywKkDStsRbHQ4+JlTuLQkCwMYOeiDxKEyiD+TThtOvRHw8YTkcU\nokBV1iiKEoDNUADvKfAmmH+8mFRBAItVl9nk6WoVEFhtl8T+FoImQoxhCY6E9s1sCGE/k4jbeLuB\nutXYbFeQRYGmqdFWFYx30NZBxAjvAoZ5Rr/rs5VQwQFBTSpLrpAPH/X/Dk97aEXUHsJrpZIh7SvK\n/FJ5mRreaVHmQP89hMA4IsanOZ+zKikLOEe9ws3NGqenHrsPTyREeE3mCCljIGpVgOQeoY8Rg1J5\n7J2doUsJrwPMpCH4R1KzwngaMR4nzD0FhsP9AcPpgGkaoNQAaxXKsqGpaFmjLJtchocLqV9coL4j\nOHvCOfCSJDUJ9znWli8rsiGbx5k86WoKVKqt0bVNZgKkIUwK8IazwtTXXXH/MI33Y4yMUWWnYa3J\n8VlrPO2PePq8x+MPjzg8HDDsqGqRZYGr7RaLzQLdqkO3pl8/KyhlTWveyGlTXK40+YgUaVhTmrAR\nQMTp6ZTJiOFqCd8RGDJ5SkEIGOcxa8PSDQEVGwUYzeNT5/iQ0J8FF9A/ndAfBho17nocdwdM48Ac\nMJEb2t57lLKElBVkWUKWJcwFzooOAHJfKaX69aLhG5RkUJPhZMJIqYFQ408fn2AN9bP0pLC+2ZCL\na0vPPysDM2tqZjNRODmeeutQNXUuP5Nppp411DhjGnoMwxFSlvmzIrhPUg2Q3H+RKMsasizhL7hF\n3pE7cc19QWccykpiuV0iiqTBTiPslMkUsgA8WWsbTfSQuZ8xnUZqTN9OqJsa9aJBw6h2pUzmbalJ\nZVmV5NAhSwk9MyxjJJttrTTUNGEcjs8a9PT+qESjvlJq3FOmc0Yk0cAhXmRJ9Gdn3R7qOZF7TcWy\nIUIKggecJhweDqi53+aXHr5t6Pa35HyTDv9ppJ4k2XtVTBtiACt7siVJm3lQTPsYMRxGnB6PODw9\nYZp6aD3n/pGUFJCkpMsylaUJA5jAmXT+zllgnoYjQgTGmJUFTo/3EMU3hEqf6TK12mJ/v2fMGWPB\njM2icgk/pYyBFgIzK05G/pSXTXO2TIpkfulCQK8U+nnGqDT604jd/R4P3z/g6cMT+l2P4DxW21Xm\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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10dca2b70>"
+       "<Figure size 360x360 with 25 Axes>"
       ]
      },
      "metadata": {},
@@ -215,7 +216,7 @@
     "fig, ax = plt.subplots(5, 5, figsize=(5, 5))\n",
     "fig.subplots_adjust(hspace=0, wspace=0)\n",
     "\n",
-    "# Get some face data from scikit-learn\n",
+    "# Get some face data from Scikit-Learn\n",
     "from sklearn.datasets import fetch_olivetti_faces\n",
     "faces = fetch_olivetti_faces().images\n",
     "\n",
@@ -223,14 +224,14 @@
     "    for j in range(5):\n",
     "        ax[i, j].xaxis.set_major_locator(plt.NullLocator())\n",
     "        ax[i, j].yaxis.set_major_locator(plt.NullLocator())\n",
-    "        ax[i, j].imshow(faces[10 * i + j], cmap=\"bone\")"
+    "        ax[i, j].imshow(faces[10 * i + j], cmap='binary_r')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice that each image has its own axes, and we've set the locators to null because the tick values (pixel number in this case) do not convey relevant information for this particular visualization."
+    "Each image is shown in its own axes, and we've set the tick locators to null because the tick values (pixel numbers in this case) do not convey relevant information for this particular visualization."
    ]
   },
   {
@@ -240,24 +241,29 @@
     "## Reducing or Increasing the Number of Ticks\n",
     "\n",
     "One common problem with the default settings is that smaller subplots can end up with crowded labels.\n",
-    "We can see this in the plot grid shown here:"
+    "We can see this in the plot grid shown here (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Nyxde8+mn7yZZ/g+4B3iy5Znzd7QP8GZmNngDXf7Pf7F786fNfJzbvPxJPp/ct1CamdmI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\nAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10dc82e10>"
+       "<Figure size 432x288 with 16 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -269,23 +275,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Particularly for the x ticks, the numbers nearly overlap and make them quite difficult to decipher.\n",
-    "We can fix this with the ``plt.MaxNLocator()``, which allows us to specify the maximum number of ticks that will be displayed.\n",
-    "Given this maximum number, Matplotlib will use internal logic to choose the particular tick locations:"
+    "Particularly for the x-axis ticks, the numbers nearly overlap, making them quite difficult to decipher.\n",
+    "One way to adjust this is with `plt.MaxNLocator`, which allows us to specify the maximum number of ticks that will be displayed.\n",
+    "Given this maximum number, Matplotlib will use internal logic to choose the particular tick locations (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10dc82e10>"
+       "<Figure size 432x288 with 16 Axes>"
       ]
      },
      "execution_count": 8,
@@ -305,7 +314,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This makes things much cleaner. If you want even more control over the locations of regularly-spaced ticks, you might also use ``plt.MultipleLocator``, which we'll discuss in the following section."
+    "This makes things much cleaner. If you want even more control over the locations of regularly spaced ticks, you might also use `plt.MultipleLocator`, which we'll discuss in the following section."
    ]
   },
   {
@@ -314,25 +323,30 @@
    "source": [
     "## Fancy Tick Formats\n",
     "\n",
-    "Matplotlib's default tick formatting can leave a lot to be desired: it works well as a broad default, but sometimes you'd like do do something more.\n",
-    "Consider this plot of a sine and a cosine:"
+    "Matplotlib's default tick formatting can leave a lot to be desired: it works well as a broad default, but sometimes you'd like to do something different.\n",
+    "Consider this plot of a sine and a cosine curve (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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P/p2v/WhhrPPc9OwJrFtnHE4gJgbo2BE4eTL/+1I1ef/2G/D668a+x35+sh9p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\nTn1sgejMg5O3hrT1aYtDbx0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mCoQxxlje8e3xGnDt2jW1Q9Asbhtl3DbKbKFtivT2+CJ5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\neTPGmBX6f/hhxZChns1rAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1152f8a58>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -354,21 +368,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are a couple changes we might like to make. First, it's more natural for this data to space the ticks and grid lines in multiples of $\\pi$. We can do this by setting a ``MultipleLocator``, which locates ticks at a multiple of the number you provide. For good measure, we'll add both major and minor ticks in multiples of $\\pi/4$:"
+    "There are a couple of changes we might like to make here. First, it's more natural for this data to space the ticks and gridlines in multiples of $\\pi$. We can do this by setting a `MultipleLocator`, which locates ticks at a multiple of the number we provide. For good measure, we'll add both major and minor ticks in multiples of $\\pi/2$ and $\\pi/4$ (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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dvniYVlGtnIbeT/kxoccErzP06em3di20b59s6ENDjeFxl6EHqFOqDssGLKN8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\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1152f8a58>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "execution_count": 10,
@@ -387,21 +404,24 @@
    "metadata": {},
    "source": [
     "But now these tick labels look a little bit silly: we can see that they are multiples of $\\pi$, but the decimal representation does not immediately convey this.\n",
-    "To fix this, we can change the tick formatter. There's no built-in formatter for what we want to do, so we'll instead use ``plt.FuncFormatter``, which accepts a user-defined function giving fine-grained control over the tick outputs:"
+    "To fix this, we can change the tick formatter. There's no built-in formatter for what we want to do, so we'll instead use `plt.FuncFormatter`, which accepts a user-defined function giving fine-grained control over the tick outputs (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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9MvWxK3wXKnhWAAAkpycjbGkYVp5bmeNjHOm92bMnsH69aQiGe/eADh2AU6eU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\nTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1152f8a58>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "execution_count": 11,
@@ -420,9 +440,9 @@
     "    elif N == 2:\n",
     "        return r\"$\\pi$\"\n",
     "    elif N % 2 > 0:\n",
-    "        return r\"${0}\\pi/2$\".format(N)\n",
+    "        return rf\"${N}\\pi/2$\"\n",
     "    else:\n",
-    "        return r\"${0}\\pi$\".format(N // 2)\n",
+    "        return rf\"${N // 2}\\pi$\"\n",
     "\n",
     "ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))\n",
     "fig"
@@ -432,9 +452,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is much better! Notice that we've made use of Matplotlib's LaTeX support, specified by enclosing the string within dollar signs. This is very convenient for display of mathematical symbols and formulae: in this case, ``\"$\\pi$\"`` is rendered as the Greek character $\\pi$.\n",
-    "\n",
-    "The ``plt.FuncFormatter()`` offers extremely fine-grained control over the appearance of your plot ticks, and comes in very handy when preparing plots for presentation or publication."
+    "This is much better! Notice that we've made use of Matplotlib's LaTeX support, specified by enclosing the string within dollar signs. This is very convenient for display of mathematical symbols and formulae: in this case, `\"$\\pi$\"` is rendered as the Greek character $\\pi$."
    ]
   },
   {
@@ -443,50 +461,43 @@
    "source": [
     "## Summary of Formatters and Locators\n",
     "\n",
-    "We've mentioned a couple of the available formatters and locators.\n",
-    "We'll conclude this section by briefly listing all the built-in locator and formatter options. For more information on any of these, refer to the docstrings or to the Matplotlib online documentaion.\n",
-    "Each of the following is available in the ``plt`` namespace:\n",
-    "\n",
-    "Locator class        | Description\n",
-    "---------------------|-------------\n",
-    "``NullLocator``      | No ticks\n",
-    "``FixedLocator``     | Tick locations are fixed\n",
-    "``IndexLocator``     | Locator for index plots (e.g., where x = range(len(y)))\n",
-    "``LinearLocator``    | Evenly spaced ticks from min to max\n",
-    "``LogLocator``       | Logarithmically ticks from min to max\n",
-    "``MultipleLocator``  | Ticks and range are a multiple of base\n",
-    "``MaxNLocator``      | Finds up to a max number of ticks at nice locations\n",
-    "``AutoLocator``      | (Default.) MaxNLocator with simple defaults.\n",
-    "``AutoMinorLocator`` | Locator for minor ticks\n",
+    "We've seen a couple of the available formatters and locators; I'll conclude this chapter by briefly listing all of the built-in locator and formatter options. For more information on any of these, refer to the docstrings or to the Matplotlib online documentation.\n",
+    "Each of the following is available in the `plt` namespace:\n",
     "\n",
-    "Formatter Class       | Description\n",
-    "----------------------|---------------\n",
-    "``NullFormatter``     | No labels on the ticks\n",
-    "``IndexFormatter``    | Set the strings from a list of labels\n",
-    "``FixedFormatter``    | Set the strings manually for the labels\n",
-    "``FuncFormatter``     | User-defined function sets the labels\n",
-    "``FormatStrFormatter``| Use a format string for each value\n",
-    "``ScalarFormatter``   | (Default.) Formatter for scalar values\n",
-    "``LogFormatter``      | Default formatter for log axes\n",
+    "Locator class      | Description\n",
+    "-------------------|-------------\n",
+    "`NullLocator`      | No ticks\n",
+    "`FixedLocator`     | Tick locations are fixed\n",
+    "`IndexLocator`     | Locator for index plots (e.g., where `x = range(len(y)))`\n",
+    "`LinearLocator`    | Evenly spaced ticks from min to max\n",
+    "`LogLocator`       | Logarithmically spaced ticks from min to max\n",
+    "`MultipleLocator`  | Ticks and range are a multiple of base\n",
+    "`MaxNLocator`      | Finds up to a max number of ticks at nice locations\n",
+    "`AutoLocator`      | (Default) `MaxNLocator` with simple defaults\n",
+    "`AutoMinorLocator` | Locator for minor ticks\n",
     "\n",
-    "We'll see further examples of these through the remainder of the book."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Text and Annotation](04.09-Text-and-Annotation.ipynb) | [Contents](Index.ipynb) | [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) >\n",
+    "Formatter class     | Description\n",
+    "--------------------|---------------\n",
+    "`NullFormatter`     | No labels on the ticks\n",
+    "`IndexFormatter`    | Set the strings from a list of labels\n",
+    "`FixedFormatter`    | Set the strings manually for the labels\n",
+    "`FuncFormatter`     | User-defined function sets the labels\n",
+    "`FormatStrFormatter`| Use a format string for each value\n",
+    "`ScalarFormatter`   | Default formatter for scalar values\n",
+    "`LogFormatter`      | Default formatter for log axes\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.10-Customizing-Ticks.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "We'll see further examples of these throughout the remainder of the book."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "encoding": "# -*- coding: utf-8 -*-",
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -500,9 +511,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.11-Settings-and-Stylesheets.ipynb b/notebooks/04.11-Settings-and-Stylesheets.ipynb
index bc8b6bcde..b17fc8143 100644
--- a/notebooks/04.11-Settings-and-Stylesheets.ipynb
+++ b/notebooks/04.11-Settings-and-Stylesheets.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Customizing Ticks](04.10-Customizing-Ticks.ipynb) | [Contents](Index.ipynb) | [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.11-Settings-and-Stylesheets.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,10 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Matplotlib's default plot settings are often the subject of complaint among its users.\n",
-    "While much is slated to change in the 2.0 Matplotlib release in late 2016, the ability to customize default settings helps bring the package inline with your own aesthetic preferences.\n",
-    "\n",
-    "Here we'll walk through some of Matplotlib's runtime configuration (rc) options, and take a look at the newer *stylesheets* feature, which contains some nice sets of default configurations."
+    "While many of the topics covered in previous chapters involve adjusting the style of plot elements one by one, Matplotlib also offers mechanisms to adjust the overall style of a chart all at once. In this chapter we'll walk through some of Matplotlib's runtime configuration (*rc*) options, and take a look at the *stylesheets* feature, which contains some nice sets of default configurations."
    ]
   },
   {
@@ -45,16 +20,16 @@
    "source": [
     "## Plot Customization by Hand\n",
     "\n",
-    "Through this chapter, we've seen how it is possible to tweak individual plot settings to end up with something that looks a little bit nicer than the default.\n",
-    "It's possible to do these customizations for each individual plot.\n",
-    "For example, here is a fairly drab default histogram:"
+    "Throughout this part of the book, you've seen how it is possible to tweak individual plot settings to end up with something that looks a little nicer than the default.\n",
+    "It's also possible to do these customizations for each individual plot.\n",
+    "For example, here is a fairly drab default histogram, shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -69,17 +44,22 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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k75N8F3gb8Ld91zND7wQ+33cRekGefNeIJJuBLcBj/VYyXUnOS/INYBF4uKqeONW6Mw/3\nJA8neXLZY2/33zcDVNXfVNUrgDuB9826nmlbbfu6df4aOFpVd/VY6mkZZ/ukedJNydwLfOCk2YGz\nXlWdqKrXMpoFeH2Sq0+17szPUK2qN4y56l3AvwM7Z1fN9K22fUneAfwB8LtnpKApm+Dn14LDwCuW\nPd/UtekskWQdo2D/TFXd13c9s1JVP03yReB6YN9K6/R9tMwVy57ewGiOrBlJrgf+CnhLVf2873pm\nrIV59yeAK5JcnmQ9cBNwf881TVto42d1Kp8C9lXVbX0XMm1JXp7kgm75V4A3AKfcWdz30TL3Ar/K\naEfqIeA9VfX93gqasiQHgPXAD7umr1bVzT2WNFVJbgD+EXg58GNgT1W9qd+q1qb7hXwbvzz57iM9\nlzQ1Se4CBsBFwBKwo6o+3WtRU5TkWuBLwF5GOxoL+FBV7e61sClJ8hpgF6P/N88DPltV/3DK9T2J\nSZLa0/vRMpKk6TPcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lq0P8BE4CFs153l+0AAAAA\nSUVORK5CYII=\n",
+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1023b1a90>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -92,21 +72,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can adjust this by hand to make it a much more visually pleasing plot:"
+    "We can adjust this by hand to make it a much more visually pleasing plot, as you can see in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c2bea20>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -115,10 +98,11 @@
    ],
    "source": [
     "# use a gray background\n",
-    "ax = plt.axes(axisbg='#E6E6E6')\n",
+    "fig = plt.figure(facecolor='white')\n",
+    "ax = plt.axes(facecolor='#E6E6E6')\n",
     "ax.set_axisbelow(True)\n",
     "\n",
-    "# draw solid white grid lines\n",
+    "# draw solid white gridlines\n",
     "plt.grid(color='w', linestyle='solid')\n",
     "\n",
     "# hide axis spines\n",
@@ -144,7 +128,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This looks better, and you may recognize the look as inspired by the look of the R language's ggplot visualization package.\n",
+    "This looks better, and you may recognize the look as inspired by that of the R language's `ggplot` visualization package.\n",
     "But this took a whole lot of effort!\n",
     "We definitely do not want to have to do all that tweaking each time we create a plot.\n",
     "Fortunately, there is a way to adjust these defaults once in a way that will work for all plots."
@@ -154,38 +138,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Changing the Defaults: ``rcParams``\n",
+    "## Changing the Defaults: rcParams\n",
     "\n",
-    "Each time Matplotlib loads, it defines a runtime configuration (rc) containing the default styles for every plot element you create.\n",
-    "This configuration can be adjusted at any time using the ``plt.rc`` convenience routine.\n",
-    "Let's see what it looks like to modify the rc parameters so that our default plot will look similar to what we did before.\n",
+    "Each time Matplotlib loads, it defines a runtime configuration containing the default styles for every plot element you create.\n",
+    "This configuration can be adjusted at any time using the `plt.rc` convenience routine.\n",
+    "Let's see how we can modify the rc parameters so that our default plot will look similar to what we did before.\n",
     "\n",
-    "We'll start by saving a copy of the current ``rcParams`` dictionary, so we can easily reset these changes in the current session:"
+    "We can use the `plt.rc` function to change some of these settings:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "IPython_default = plt.rcParams.copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can use the ``plt.rc`` function to change some of these settings:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -193,6 +162,7 @@
     "colors = cycler('color',\n",
     "                ['#EE6666', '#3388BB', '#9988DD',\n",
     "                 '#EECC55', '#88BB44', '#FFBBBB'])\n",
+    "plt.rc('figure', facecolor='white')\n",
     "plt.rc('axes', facecolor='#E6E6E6', edgecolor='none',\n",
     "       axisbelow=True, grid=True, prop_cycle=colors)\n",
     "plt.rc('grid', color='w', linestyle='solid')\n",
@@ -206,21 +176,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With these settings defined, we can now create a plot and see our settings in action:"
+    "With these settings defined, we can now create a plot and see our settings in action (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d680da0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -235,21 +208,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's see what simple line plots look like with these rc parameters:"
+    "Let's see what simple line plots look like with these rc parameters (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Ty4bjMo24fJmxvhCv/zzMT58O0XYxSiQM+UWCY/faeeBXnTQctG+Kh35ZLIsP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\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11004e940>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -265,10 +241,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "I find this much more aesthetically pleasing than the default styling.\n",
+    "For charts viewed onscreen rather than printed, I find this much more aesthetically pleasing than the default styling.\n",
     "If you disagree with my aesthetic sense, the good news is that you can adjust the rc parameters to suit your own tastes!\n",
-    "These settings can be saved in a *.matplotlibrc* file, which you can read about in the [Matplotlib documentation](http://Matplotlib.org/users/customizing.html).\n",
-    "That said, I prefer to customize Matplotlib using its stylesheets instead."
+    "Optionally, these settings can be saved in a *.matplotlibrc* file, which you can read about in the [Matplotlib documentation](https://matplotlib.org/stable/tutorials/introductory/customizing.html)."
    ]
   },
   {
@@ -277,30 +252,29 @@
    "source": [
     "## Stylesheets\n",
     "\n",
-    "The version 1.4 release of Matplotlib in August 2014 added a very convenient ``style`` module, which includes a number of new default stylesheets, as well as the ability to create and package your own styles. These stylesheets are formatted similarly to the *.matplotlibrc* files mentioned earlier, but must be named with a *.mplstyle* extension.\n",
+    "A newer mechanism for adjusting overall chart styles is via Matplotlib's `style` module, which includes a number of default stylesheets, as well as the ability to create and package your own styles. These stylesheets are formatted similarly to the *.matplotlibrc* files mentioned earlier, but must be named with a *.mplstyle* extension.\n",
     "\n",
-    "Even if you don't create your own style, the stylesheets included by default are extremely useful.\n",
-    "The available styles are listed in ``plt.style.available``—here I'll list only the first five for brevity:"
+    "Even if you don't go as far as creating your own style, you may find what you're looking for in the built-in stylesheets.\n",
+    "`plt.style.available` contains a list of the available styles—here I'll list only the first five for brevity:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['fivethirtyeight',\n",
-       " 'seaborn-pastel',\n",
-       " 'seaborn-whitegrid',\n",
-       " 'ggplot',\n",
-       " 'grayscale']"
+       "['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background']"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -313,13 +287,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The basic way to switch to a stylesheet is to call\n",
+    "The standard way to switch to a stylesheet is to call `style.use`:\n",
     "\n",
     "``` python\n",
     "plt.style.use('stylename')\n",
     "```\n",
     "\n",
-    "But keep in mind that this will change the style for the rest of the session!\n",
+    "But keep in mind that this will change the style for the rest of the Python session!\n",
     "Alternatively, you can use the style context manager, which sets a style temporarily:\n",
     "\n",
     "``` python\n",
@@ -332,14 +306,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's create a function that will make two basic types of plot:"
+    "To demonstrate these styles, let's create a function that will make two basic types of plot:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -363,43 +337,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Default style\n",
+    "### Default Style\n",
     "\n",
-    "The default style is what we've been seeing so far throughout the book; we'll start with that.\n",
-    "First, let's reset our runtime configuration to the notebook default:"
+    "Matplotlib's `default` style was updated in the version 2.0 release; let's look at this first (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# reset rcParams\n",
-    "plt.rcParams.update(IPython_default);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's see how it looks:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Fc52jN7k6VR0SFEL7Wu1ZsXdFoecGBZm9uItDksy8hHnc2PhGQoNDrW/8zBkY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\nGGNMXBk5ciTFxcXU1ro/haW9ctJz2F66napzVU7qt614wjF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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11004ec18>"
+       "<Figure size 1100x400 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -407,31 +364,35 @@
     }
    ],
    "source": [
-    "hist_and_lines()"
+    "with plt.style.context('default'):\n",
+    "    hist_and_lines()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### FiveThiryEight style\n",
+    "### FiveThiryEight Style\n",
     "\n",
-    "The ``fivethirtyeight`` style mimics the graphics found on the popular [FiveThirtyEight website](https://fivethirtyeight.com).\n",
-    "As you can see here, it is typified by bold colors, thick lines, and transparent axes:"
+    "The `fivethirtyeight` style mimics the graphics found on the popular [FiveThirtyEight website](https://fivethirtyeight.com).\n",
+    "As you can see in the following figure, it is typified by bold colors, thick lines, and transparent axes:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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KNd4lIxdNMsLVq6puBXW9ite5RHikIoBTlz3EkC4bADRzK2z1b3Dz5YmXQEoK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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1101a9390>"
+       "<Figure size 792x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -447,24 +408,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### ggplot\n",
+    "### ggplot Style\n",
     "\n",
-    "The ``ggplot`` package in the R language is a very popular visualization tool.\n",
-    "Matplotlib's ``ggplot`` style mimics the default styles from that package:"
+    "The `ggplot` package in the R language is a popular visualization tool among data scientists.\n",
+    "Matplotlib's `ggplot` style mimics the default styles from that package (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Upo8/rT2stiiN+tb410n294VobPBTtSS64uOjkUBSCCFEQnh6fXzzfw7y7pFB7rtwLufM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\nSocTIe18D2zxtUm1GD0lkoQQWTG1GsLRQ/IUbrZAzM6Vp2wiG5VKhS1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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11006f160>"
+       "<Figure size 792x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -480,24 +444,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### *Bayesian Methods for Hackers( style\n",
+    "### Bayesian Methods for Hackers Style\n",
     "\n",
-    "There is a very nice short online book called [*Probabilistic Programming and Bayesian Methods for Hackers*](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/); it features figures created with Matplotlib, and uses a nice set of rc parameters to create a consistent and visually-appealing style throughout the book.\n",
-    "This style is reproduced in the ``bmh`` stylesheet:"
+    "There is a neat short online book called [*Probabilistic Programming and Bayesian Methods for Hackers*](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) by Cameron Davidson-Pilon that features figures created with Matplotlib, and uses a nice set of rc parameters to create a consistent and visually appealing style throughout the book.\n",
+    "This style is reproduced in the ``bmh`` stylesheet (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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aGNtRa5cD0DmBzhHg7mxLC8IjlZ0uN1+YDRrHlr3HkEYT0QX5dvdxn8x21Hpb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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11118d128>"
+       "<Figure size 792x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -513,24 +480,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Dark background\n",
+    "### Dark Background Style\n",
     "\n",
     "For figures used within presentations, it is often useful to have a dark rather than light background.\n",
-    "The ``dark_background`` style provides this:"
+    "The `dark_background` style provides this (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Dv0e1J4NAWUmYadOH1vlcxM1bWH3tLC7/sQtF+QVMhNhgdO7cHpqa6ggIiBdL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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1114bef28>"
+       "<Figure size 792x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -546,24 +516,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Grayscale\n",
+    "### Grayscale Style\n",
     "\n",
     "Sometimes you might find yourself preparing figures for a print publication that does not accept color figures.\n",
-    "For this, the ``grayscale`` style, shown here, can be very useful:"
+    "For this, the `grayscale` style (see the following figure) can be useful:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+++/x22+/4cSJE4yOlg2mdY5AxyjqsWPHcPHiRVy8eLHz8fPnzyMvLw87duxg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\niy/w/fffQyQSISAgAGvWrLHYLusvv/wS33//PQB0+zvGPHHUaDQ4ffo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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1114beef0>"
+       "<Figure size 792x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -579,25 +552,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Seaborn style\n",
+    "### Seaborn Style\n",
     "\n",
-    "Matplotlib also has stylesheets inspired by the Seaborn library (discussed more fully in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)).\n",
-    "As we will see, these styles are loaded automatically when Seaborn is imported into a notebook.\n",
-    "I've found these settings to be very nice, and tend to use them as defaults in my own data exploration."
+    "Matplotlib also has several stylesheets inspired by the Seaborn library (discussed more fully in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)).\n",
+    "I've found these settings to be very nice, and tend to use them as defaults in my own data exploration (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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VFa/PO18zcn+ROGATCO9S9szk/pM4b6mf8l6+B4GQXS6sr78KktQ9CxngH3RiOVsQBEEI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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111a0f278>"
+       "<Figure size 792x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -605,33 +580,27 @@
     }
    ],
    "source": [
-    "import seaborn\n",
-    "hist_and_lines()"
+    "with plt.style.context('seaborn-whitegrid'):\n",
+    "    hist_and_lines()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With all of these built-in options for various plot styles, Matplotlib becomes much more useful for both interactive visualization and creation of figures for publication.\n",
+    "Take some time to explore the built-in options and find one that appeals to you!\n",
     "Throughout this book, I will generally use one or more of these style conventions when creating plots."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Customizing Ticks](04.10-Customizing-Ticks.ipynb) | [Contents](Index.ipynb) | [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.11-Settings-and-Stylesheets.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "encoding": "# -*- coding: utf-8 -*-",
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -645,9 +614,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.12-Three-Dimensional-Plotting.ipynb b/notebooks/04.12-Three-Dimensional-Plotting.ipynb
index ffdc3d875..bd580cde2 100644
--- a/notebooks/04.12-Three-Dimensional-Plotting.ipynb
+++ b/notebooks/04.12-Three-Dimensional-Plotting.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) | [Contents](Index.ipynb) | [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.12-Three-Dimensional-Plotting.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -35,14 +13,17 @@
    "source": [
     "Matplotlib was initially designed with only two-dimensional plotting in mind.\n",
     "Around the time of the 1.0 release, some three-dimensional plotting utilities were built on top of Matplotlib's two-dimensional display, and the result is a convenient (if somewhat limited) set of tools for three-dimensional data visualization.\n",
-    "three-dimensional plots are enabled by importing the ``mplot3d`` toolkit, included with the main Matplotlib installation:"
+    "Three-dimensional plots are enabled by importing the `mplot3d` toolkit, included with the main Matplotlib installation:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -53,14 +34,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once this submodule is imported, a three-dimensional axes can be created by passing the keyword ``projection='3d'`` to any of the normal axes creation routines:"
+    "Once this submodule is imported, a three-dimensional axes can be created by passing the keyword `projection='3d'` to any of the normal axes creation routines, as shown here (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -73,17 +54,22 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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w+tvf/oYXXngBO3bswJtvvomRI0di4sSJePzxxzU9Rzabxdy5c/Hee++hb9++qK+vx4wZ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\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bfc8780>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -97,36 +83,41 @@
    "metadata": {},
    "source": [
     "With this three-dimensional axes enabled, we can now plot a variety of three-dimensional plot types. \n",
-    "Three-dimensional plotting is one of the functionalities that benefits immensely from viewing figures interactively rather than statically in the notebook; recall that to use interactive figures, you can use ``%matplotlib notebook`` rather than ``%matplotlib inline`` when running this code."
+    "Three-dimensional plotting is one of the functionalities that benefits immensely from viewing figures interactively rather than statically, in the notebook; recall that to use interactive figures, you can use `%matplotlib notebook` rather than `%matplotlib inline` when running this code."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Three-dimensional Points and Lines\n",
+    "## Three-Dimensional Points and Lines\n",
     "\n",
-    "The most basic three-dimensional plot is a line or collection of scatter plot created from sets of (x, y, z) triples.\n",
-    "In analogy with the more common two-dimensional plots discussed earlier, these can be created using the ``ax.plot3D`` and ``ax.scatter3D`` functions.\n",
+    "The most basic three-dimensional plot is a line or collection of scatter plots created from sets of (x, y, z) triples.\n",
+    "In analogy with the more common two-dimensional plots discussed earlier, these can be created using the `ax.plot3D` and `ax.scatter3D` functions.\n",
     "The call signature for these is nearly identical to that of their two-dimensional counterparts, so you can refer to [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) and [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) for more information on controlling the output.\n",
-    "Here we'll plot a trigonometric spiral, along with some points drawn randomly near the line:"
+    "Here we'll plot a trigonometric spiral, along with some points drawn randomly near the line (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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VxowZ0+H3CgsL1ewXl8vFvn37WLZsGZIkcfHFFzNw4EDVHSdSHo0yB451gUOq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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e2fd898>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -150,7 +141,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice that by default, the scatter points have their transparency adjusted to give a sense of depth on the page.\n",
+    "Notice that scatter points have their transparency adjusted to give a sense of depth on the page.\n",
     "While the three-dimensional effect is sometimes difficult to see within a static image, an interactive view can lead to some nice intuition about the layout of the points."
    ]
   },
@@ -158,18 +149,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Three-dimensional Contour Plots\n",
+    "## Three-Dimensional Contour Plots\n",
     "\n",
-    "Analogous to the contour plots we explored in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb), ``mplot3d`` contains tools to create three-dimensional relief plots using the same inputs.\n",
-    "Like two-dimensional ``ax.contour`` plots, ``ax.contour3D`` requires all the input data to be in the form of two-dimensional regular grids, with the Z data evaluated at each point.\n",
-    "Here we'll show a three-dimensional contour diagram of a three-dimensional sinusoidal function:"
+    "Analogous to the contour plots we explored in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb), `mplot3d` contains tools to create three-dimensional relief plots using the same inputs.\n",
+    "Like `ax.contour`, `ax.contour3D` requires all the input data to be in the form of two-dimensional regular grids, with the *z* data evaluated at each point.\n",
+    "Here we'll show a three-dimensional contour diagram of a three-dimensional sinusoidal function (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -187,24 +181,29 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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UCt544w1uvvlmxo4dyz333NOh5+A59+348ePCIXEB4PdHumcjWwndIQ10Rx6u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BIREKZcjFODadJDoODuwmazSQGKMjH9xQxXt+5qrwUAUZkuqQNmt9o0Ry+uVEPwGLr7jJkvpVzU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\ndKcxRlJI3H///fj4xz8OALj//vvx1ltvxSvVEwyfz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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e6a7fd0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig = plt.figure()\n",
     "ax = plt.axes(projection='3d')\n",
-    "ax.contour3D(X, Y, Z, 50, cmap='binary')\n",
+    "ax.contour3D(X, Y, Z, 40, cmap='binary')\n",
     "ax.set_xlabel('x')\n",
     "ax.set_ylabel('y')\n",
     "ax.set_zlabel('z');"
@@ -214,21 +213,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Sometimes the default viewing angle is not optimal, in which case we can use the ``view_init`` method to set the elevation and azimuthal angles. In the following example, we'll use an elevation of 60 degrees (that is, 60 degrees above the x-y plane) and an azimuth of 35 degrees (that is, rotated 35 degrees counter-clockwise about the z-axis):"
+    "Sometimes the default viewing angle is not optimal, in which case we can use the `view_init` method to set the elevation and azimuthal angles. In the following example, visualized in the following figure, we'll use an elevation of 60 degrees (that is, 60 degrees above the x-y plane) and an azimuth of 35 degrees (that is, rotated 35 degrees counter-clockwise about the z-axis):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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xiWYwGOZVdIWjvoXPd+H7WKVSEY1G5QdbNKkymcw8KSEej8ubkDqEX1hsrtDp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\nFQBQX1+PrKwsjBw5EhMnTkRmZ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9CozYCYtRKAZapB/j2/J7gyq1cDtJzVUqbG4hOBzuVy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\n4Cp/LRfd5HNUd8S1tp41iFfz292Jzngk/FhQEibTP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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e6a7fd0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "execution_count": 7,
@@ -256,31 +258,36 @@
     "\n",
     "Two other types of three-dimensional plots that work on gridded data are wireframes and surface plots.\n",
     "These take a grid of values and project it onto the specified three-dimensional surface, and can make the resulting three-dimensional forms quite easy to visualize.\n",
-    "Here's an example of using a wireframe:"
+    "Here's an example of using a wireframe (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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SKVpB5K+//kKv1zNr1iyWLl1a5Hb8/PyYNGkSAQEBXLlypdDyM2fO0KpVKwYOHIggCOzY\nsUNadu/ePTIyMqSuvgC1atVi2rRpjBkzptDj9j8NUUiLijG2tLSUxDgvL4/s7GwpxlgsIG+YAGIo\nxmlpaaSkpJCenk5qaippaWkmMf4bMInuO4wothqNxkhsc3NzSU1NJS8vDysrK+kCfR1s376dXr16\noVKpaNCgAXXr1mX9+vVG6xw4cICuXbsyYMAAHjx4wNmzZ42Wp6enExUVRbt27ZgyZQrz5883Wp6Z\nmcnly5dp2rQpcrmc77//nm+++YaMjAwg3wpu1apVoZvHuHHjsLKyYtmyZa/lWN83npbwIUZZiKGC\nzxJj0cIuaBmLYmwYEmcS49eLSXTfQcS6CMWJrVarxcbGBhsbmyJ9qi+CoXtBEAQ2b97MkCFDpOVf\nfPEFixYtMrJ2Dx48SNeuXVEoFEyePJmFCxcaCeTZs2dp1KgRVlZWjBo1iosXLxIWFiYtDwwMpF69\neqhUKgAaN25M69atJTH19/eX/LmGyOVy2rZtyw8//MCdO3de6bjfFV6HmD2vGAuCgEajKVKMDaMr\nwNilYRLj14tJdN8hDMVW7MAg1o9NTU1Fp9OhVquNxLYon+zLEhYWRnZ2Ns2aNZPea9iwIbVr12bD\nhg1AftrutWvXpHbr/fr1Izo62igK4cyZM7Rt2xbIj2qYOnUq8+bNM1oufl7km2++4ffffycmJoYz\nZ84UKboAd+7coXXr1syZM+e1HPO7wpuIPCgoxnK5HEtLyxeyjE1i/Poxie47gBgyJBYKF8U2Ozub\ntLQ09Ho9arWaEiVKFArUf1UMRXvz5s1SrVtDDK3dw4cP07FjR2miTqlU8umnn7Jo0SJpfXGSTMTH\nx4e//vqLoKAgaXlBUXVycmLixIl88sknWFpaUrly5SLHGx4ezsSJE4mKisLf3/91nIJ/Ha/DTfEs\nMU5MTCQ1NdUkxkVgEt2/EfFxLysri7y8POk9UWwFQXim2L4uS1ev17NhwwZcXFwKLWvcuDHu7u5s\n3LiRgwcP0qVLF6Plw4YNIyoqirCwMO7fv09KSgp16tSRlltYWPD5558zb948UlJSuHnzJg0bNiy0\nn4kTJxIREUH16tWLHGNubi7Xr1+nQYMGzJkzh+nTp0vn7U1QXHLEP5XXKcbi9yIm6Jgs4/9hEt2/\nAUPLVoy1hPykBFFsbW1tsba2fu2WbXHjCQ4ORq1Wc/To0SLX+eKLL/j+++/x9/fnP//5j9EyKysr\nJk2axOLFi/H396dVq1aFrOXBgwdz9+5d1qxZQ6NGjQqlHUN+inK1atW4detWkWO4evUqlStXRqVS\n0blzZ8qVK8dvv/32kkf9bvA2hf1l9/UsMS6qYhsg1ZcA4yJBBYX73ybGJtF9ixiKraGFptPpSE1N\nRSaTSWL7vKmer2rpihfhgQMH6N+/PydPnuTJkyeF1mvatCmlS5emXLlylCpVqtByHx8fQkJC8PX1\nLeSvBVAqlcycOZM1a9bQsmXLYseTnJxMbm4u586dK7QsLCyM+vXrS+P+/vvvWbRoUZHjNfHmEcW4\nqIptgJEYF1UkyDDG+FlibBif/L6LsUl03wJFia0gCFKwO4CtrS0qleql8+pf9Ud44MAB+vbtS+fO\nnY1iZg1xcXGRwrrAWPBVKhXjx48nICCg2Emwfv36kZycXKgWg0h6ejpxcXF89tlnLF68uNDy8PBw\nPDw8pNc1atSgX79+fPfdd899nP9m3oZVbVikp6iEj+ep2FaUGGs0mmLFWLyu3peKbSbRfYOId+6i\nxDYtLQ0zMzNsbGyKzNd/Xl71IpLJZERHR5OSkkLDhg0ZNmwYGzduLHLd27dvo9PpiIyMNHpfjCdu\n0aIFWq222DFpNBr0en2xVcUuX76Mu7s7H374ITdu3ODSpUtGywuKLsDMmTM5cOAAERERz3vIJt4w\nRYn7s1KhX0WMk5OTSU9PJy0tjTNnzrB27dq/6cifD5PovgGKqmWr1+ulOglmZmZFdmn4uzh69Chd\nunRBLpfTpk0bkpOTCQ8PN1rn8ePH3Lt3j+HDh7N582bgf4Vc0tPTyc7OJjQ0lEqVKrF9+/YiHwMv\nX75M9erVCQwMLFQMHf4nqubm5kyePJklS5ZIy8RJNMMJOgA7OztmzpzJtGnT3gsrpyD/tsk6gPPn\nz1O7dm3UajV2dnbY29vj4OCAk5MTFSpUYP369ahUqucWYzHpw8zMjLi4OBITE//uQ3wqJtF9jRQU\nW7GYdUZGBunp6SgUikJi+zqiD15lGzKZjMOHD0vVwuRyOcOGDZPickUCAgJo1qwZPj4+bN++Xfrx\nC4IgWS3h4eF069aNbdu2SRa+uF52djYhISE0aNCAAQMGFGmNGFqyw4YN4+LFi1IK8dWrV6lSpYrk\nLzSkRYsWhIaGcvDgwZc6B/8W3pbAF7efO3fu4OPjw8iRI3ny5AmRkZE8efKEhw8fEhISQrly5Zg2\nbRo7d+5k0KBBJCcnP5dlDJCdnU2XLl1YvXo1fn5+bN68mbCwMDQazXOPe9SoUTg4OFC3bt1i15k0\naRKurq54eHgUMkyeF5PovgaKEludTkdGRoZUJLw4y/Z1Jje8DE+ePOHKlStScRmAIUOGsHPnTqMK\nY2fOnKFly5a4uLhQpUoV9u/fL0UgiDG7ISEhfPDBB9jY2BAcHFyoeEt4eDh16tRh6NChbNiwgaSk\nJKkillarJSIignr16gH5ERETJkyQajuEhYUVci2I3Lhxg7p16/LNN9/84+syvI+kp6czZ84cWrdu\nTc2aNfn0009p27YtlSpVQqFQkJKSQr9+/Rg+fDh9+vThq6++QqvVUqdOHU6dOmW0rYJuCvE3qFKp\n+PHHH/Hw8ECtVrN//36GDh1aZN2P4hgxYkSx0TsAhw8f5tatW9y8eZPVq1e/dFcTk+i+Anq9nuzs\nbLKysorsP6ZUKrG1tS2y/1hBXjUC4WU/f/ToUVq2bGnUhaFSpUrUrVuXAwcOSO/5+/vTqFEjMjMz\nGTJkCLt27TKqZJaUlMSDBw+oWbMmgwcPlixlw+ItkZGRNG3alDp16lC/fn0OHTokxX6mpKRw+/Zt\nXFxcpMmRYcOG4efnR3R0dJH+XJGrV6/SqlUrHBwcJNfHq/I+uiqexts8HkNLd/fu3Xh4eBAfH09g\nYCCff/45Gzdu5KOPPgLyb5j16tXj9u3bLFmyhA8++ICFCxdy5coVcnJy6NWrF/3793/mzVQul+Pm\n5oZKpWLkyJHs2LGDqKgoKdrleWjRogV2dnbFLvf19WXYsGFAfleT1NRUHj58+Nzbl8b6wp8wgV6v\nN3p8FtvHFOw/9jxi+zoe915FdA8ePEi7du0Kve/j48PGjRvJy8sjOjqa+Ph4GjZsiK2tLQMGDCAg\nIIBHjx5J64eGhlK/fn0UCgV9+vThxIkTRr61jIwM7t69i7u7OwBjxoxhzZo1KBQKLCwsuHXrFtWr\nV6dkyZKSmKtUKoYPH86iRYu4dOkS7u7uUklLwxTpiIgIHBwcGDVqFHPnzmXPnj1cvHjxtdU1eJO8\nbZ/u29zXyZMnGTduHO3atWP16tU4OTkRHBxMRkYGbdu2xdfXl/bt22Nubk5MTAwPHjwgPDwcNzc3\nqlevTmxsLN26dePo0aP07NmzyH0UPH/p6elSf77XTVxcHBUqVJBely9fnri4uBfejqlzxAtQVJcG\nQRDIzc2Vmhi+TNFwUTTf9oSKRqPhyJEjeHp6FlrWtWtXPvnkE27cuEFERAQtWrSQ/Kk2NjZ07dqV\nnTt3SsVxgoODadSoEQAlS5bE29ubHTt2MH78eAAiIyNxd3fH3NwcgI4dO/L5558TEhJC48aNiYiI\noH79+tKsdFxcHAEBATg6OvLLL7+g0WhITU0lICBAesTbv38/Dx48QC6XExQURMmSJdFoNIwePRqd\nToe1tTUtW7bEy8uLpk2bSpN0Jt4sgiDg5+fHxIkTcXR0lKxagLVr1zJ8+HC++OILDhw4QLNmzWja\ntCklS5ZEEARGjx7NxYsXGT58OMuWLaNMmTJUqVKFgIAABg8ezKZNm4wShgpeN2lpaVJn6XcVk6X7\nHBiWVxQFV6vVkp6ejk6nQ6FQoFarsbCweOmMn7/DvXD+/HmcnZ05ffq09J7hcfXu3Zv9+/cTGBhY\nKPZ26NChRo/ywcHBNG7cWHotWsriuERLWMTMzIwxY8bw66+/AvmTaKmpqVSoUIGyZcvi7e3NlClT\nmDNnDiVKlADgt99+47PPPmP69On8+eefqNVqRo8eLW3/7NmzTJ48GZ1Ox48//ohCocDW1pY7d+4w\nceJE7O3tsbe3p1KlSnh6ejJ79mxOnz79QpMt7ytv86Z+9OhRJk6cyIoVK0hKSpJu6omJiRw6dAhf\nX19u3rzJvn37CAwMZMiQIWi1WoYOHcquXbtwd3fn/v37KJVKqY5Du3btOHDgAO7u7iQkJBS779TU\n1Ke6CF6F8uXLG0Xd3L9/n/Lly7/wdkyiWwwFC4eLYpuXlyeFSFlZWUlpkO9j2M+xY8fo3bs3ISEh\nPHnyhPT0dNLT0yVf9IgRI9i4cWORBWpatWpFSkoKUVFR6PV6QkNDpXoKgiDQokULcnJypFjbsLCw\nQhb1hx9+yLFjx/Dz82PHjh34+vrSuXNn7OzsyM7O5ueff+bjjz+Wbirffvstjx8/xtbWFnNzcypX\nrkyvXr2Qy+VcunSJ3Nxc1qxZQ/fu3bly5Qq7d+/m2LFj/Pnnn9y8eRMLCwvKly+PWq3m3r17rF+/\nnu7du2Nvb4+trS1ly5alefPmLFiwgPnz55Oamvp2voh/EIcOHWLy5Mls3ryZlJQU2rVrJ1XEmzt3\nLnl5eXTp0oWdO3dy5MgROnTogJWVFQMHDiQoKIiZM2eybds2Fi1ahEajITQ0FH9/f/bu3Uu3bt2Q\nyWT89NNP0v6KsnRfxb1QVLdmkR49ekgx7BcuXKBkyZI4ODi88D5MLdgLIJ50w95U8L8q/WJ5PDFk\nJTs7G71ej7W19UvvMzU1FWtr65eujZueno6FhcULPzp7enqyfPlyvv/+e7p168awYcOMrHVBEKhb\nty4JCQk8fPiwUALH3LlzefjwIePGjaNfv35ERERIyR8lSpRg6dKl3L17l+XLl9OgQQM2b95MzZo1\nyc3NJSIigtOnT7N48WJycnKQyWRERkZSqlQpOnToQEpKCi1btmTFihV06dKFqKgoLCwsEASBTZs2\n4enpybx589i1axdly5ZFq9XSqlUrdDodM2fOxMPDA5VKRcOGDQkLC6N06dLMnz+fli1botfrWbVq\nFREREZw6dYoVK1YQFhaGn58fUVFRyOVysrOzJf/06NGjady48Ru5sYrxzMX1kntdiHGuRYXcvS72\n79/PlClT2Lp1K/Xr12fMmDF06NCBIUOGcO3aNby8vJg7dy7//e9/EQSBxo0b8+WXX7J48WJcXV05\nceIEkZGRWFpa0r17d+Li4jhz5gz29vYAPHr0iEaNGqHT6bh27RolSpQodP46d+7MmTNnXirZaPDg\nwfj7+5OYmIiDgwNz5sxBo9Egk8kYM2YMkF+U6ciRI1hbW7Nu3ToaNGhQ3OaK/bGYRPf/I1q2Wq3W\nSGw1Go3UIlsMfTK8+HJyciT/4cuSlpYmxR6+DC8junfv3sXLy4vIyEh27drF8ePH+fPPPwutN3z4\ncM6dO8fNmzcLLYuJiaFly5Z8/fXXnDt3jjVr1hiJ7oMHD2jSpAmBgYF4enrSq1cvDh8+TEpKCkql\nEo1Gg7W1tSQGMTEx9O/fn0qVKjFv3jwmTJjA3bt3sbKy4vLly6SmphIZGYmzs7PUNWPIkCEcO3aM\ncuXK8eTJEzZv3sy8efOIjY2lZs2a7Nmzhxo1aqDX67l+/bp0McbHx9OgQQPq1avHoUOHpKeaWrVq\noVAocHFxkQTYzs4OtVpN3759GTx4sNFkyqvyTxHdnTt38umnn0ouAJ1OR40aNQgKCsLKyoqWLVvy\n6NEj4uLiUCgUnD17lgkTJpCXl8fIkSOl9N4ZM2YwYMAArl69yubNm2nfvr3RfjZt2sSsWbOYPXs2\nH3/8sdH5EwSBLl26EBAQ8C48eRY7gH+9e6GowuGQL7ZpaWnk5eU9tf/Y3x1n+6JjEOOH9+3bR9u2\nbbGzs6M4lR9pAAAgAElEQVRTp06cPn2a9PT0QuubmZmRmJhYZAnFKlWq4Obmxu7du6VJNEMcHR1p\n0qQJw4YNIzc3l+3bt5Obmys9SVSsWJEqVarg6OiIVqule/fumJubs2TJEsmS6Ny5M+fOnSMtLQ25\nXM6xY8eM9iEIAl999RUPHz4kMzOT0aNHM2LECC5evMjFixcJDAzE3t4eV1dXI+vHyckJCwsLPDw8\npApaJUqUwMXFhbJly3Ly5EkOHz5MuXLliI+PJykpiQULFtC4cWPi4+Ol1O73vfjK6+D27dtMmTIF\nT09PPDw8EASB8PBwnJ2dcXBwYPTo0VSuXJlOnTpJT3Pz58/n0aNHfPPNN4wfP57169fTqVMn2rVr\nR8mSJWnUqFEhwYX8GPKKFSuyaNEiKRutqGvyXeZfK7pFFQ4HpC4NeXl5lChRAhsbm6daoH93Rtnz\nIoqtmIYcEBAgpf7a2tri5eXFkSNHCn0uMjKSihUrGk22GdK/f38uXrxYpOg+evQIf39/QkJCJLdN\nmzZtWL9+PRYWFtStW5fhw4eTkJAglZf86quvjLpifPTRRwiCgIWFBU2bNmXJkiVG5+rq1as0a9YM\nnU6HXC5HrVbj7e2NSqUiKyuL7777jnv37pGUlFRobNnZ2UYVym7fvi11G/71118ZPXo0sbGxmJmZ\n8ejRIzp16oRMJqN79+5kZmYa9RcTU1PFRI93rfjKm5pIy8jIYNCgQbi7u0tZjQAnTpygY8eOzJ8/\nn/T0dOzt7SURvXDhAufOnWP9+vX079+fHTt2UKVKFcaOHcvUqVO5evUqX331VZH7k8lkrFu3jqSk\nJHbs2GF0XO/S+X4a/zrRNRTbjIwMqcqXKLYv2n/sXRDdp31ep9NJBXZEgTUzM+P06dN07NhRWq9X\nr17s3bvX6LNiwsKgQYMKLRNp06YNGRkZODo6Gr2fnJxM27ZtpXOo1+txc3PDysqKgwcPMmXKFKKj\no7G2tqZixYpkZ2fTr18//vvf/xpZ1dOmTUMmk9GsWTNq1KhBcnKylKWUnZ1NXFwce/fuRSaTMX36\ndFQqFW3btuXYsWM4OjoSHR2Nk5MT8fHxPHjwQNruzp078fb25vjx41L/t/Hjx1OtWjWuXr3KyZMn\ncXd3p2/fvvzwww80btyY06dPIwj57ec9PDwk94jYpbeoIt9iSUJRjAt+T+9z7QVBEBg3bhyenp6k\npKTQpEkT6f1Tp05hbW3N1q1bWbduHX5+frRv356cnBxGjhyJu7s73t7eCILA/PnzuXnzJps3byYj\nI4N69eoVWeRexNXVle7duxdK+87JyXnjbprXwb9GdIsqryiTycjLyyu2/9jz8C64F6DwXV6v10ti\nK9bpFUtHBgYGUq1aNcqWLSut361bN44fPy7dhCDfIvH09KRv377s37/fKD5Z5NatW5QuXdoofTI9\nPZ327dtz//59IP8cVatWjTNnzpCdnc3u3bvp27cva9euZdasWdy9excnJydCQkKws7Nj4cKFACQk\nJEgZTQsXLsTX1xdra2tp+Y0bNyhfvjyrVq1i1KhR9OrVi9TUVObMmcPo0aMpWbIk5cuXJy0tjfbt\n20uWvCAIbNmyhTFjxuDu7s6SJUto1aoVFy5c4D//+Q99+/alSpUqBAUFsWDBAkqXLs3FixfJycmR\nLuy0tDRq1aolxWg/b5Fvw1oUBRM93iRvQtyXLl1KXFwcX3zxBQkJCdSsWRPITy2/fv06q1atYuPG\njcTHx1OmTBkqVKjAt99+i1wuZ/jw4QDMmzePpKQkTp06Rd26dfnhhx/44osvnrnvDRs2sHnzZqPj\nSk1NfedjdOFfILrFFQ4X03efpyXOm+Z1FSKH/4mtYVH0gnV6jx07RqdOnYw+W6ZMGerXr8/x48el\n9c6dO0fz5s2pWrUqDg4OBAYGFtp3REQEnp6e7N69G8ifGPLy8uLevXvScQmCwO3bt+nQoQNnzpxB\nqVTi7e1NTk4Ow4YNIy8vj59++onbt28zfPhwNm7cyPnz5xk0aBAqlYrevXtTo0YNevToIVmiV69e\n5eLFizx48ABLS0s++ugjqlevTl5eHvXr12fYsGHExMRw+fJlKSJDtIwiIyNJT08nJiaGmJgYVq9e\njbOzM+PHj2fmzJl88MEHbNiwgf79+9OrVy8pqmP8+PGo1WpUKhWWlpakpqbSsGHDIm9G4rktqsi3\nYcEW8fdpKMbvqovCkCNHjvDbb7+xZcsWIiIiaNiwoXT9iDe3b7/9loYNG3Ly5Enat2/PmTNn+PPP\nP8nMzMTb25vHjx+zYsUKhg0bRrVq1Rg2bBiVK1cuVEmuKAxdCuLf70NiBPyDRfdpYpuamoogCFI7\nnFcR23fFvaDX68nKypJiS59WFH3r1q2ULl260Pu9e/dmz5490uvz589LnYGLcj9AflJDr169CA0N\nJTExkenTpxtFdHh4eFCmTBkqVqyItbU1mZmZ1KhRg5SUFDp16sTJkycxNzcnJCSESpUqMXfuXH76\n6SeGDBlCaGgoNWrUkCyoWbNmcf36dfR6PStWrOCHH36gcuXKlClThmrVqiGTyWjTpg3+/v5kZGTQ\nrVs3nJ2dKVu2rBSz26JFC0aOHEliYiJ79uxh1qxZaDQaoqKipGPdt28fWVlZ/Prrr0RFRTF58mQm\nTJiAhYUF69evl6JbqlatSmxsrFETzuf5rgwLtigUCpRKpeSiUCgUz3RRGEbX/B3cvHmT8ePHs2HD\nBhwdHQslxvzwww/Uq1dPylY8ceIEXl5ejBs3jsmTJ2Nvb0/lypWZNm0acrmccePGERwcjL+/v5TB\n+LwYim5KSsobSwF+nfzjRLe4wuGiIAmCcf+xv1swXxXDOhCGx1ZcnOL9+/dJTU3l8uXL0nviMfTo\n0YMjR45ICSHh4eGSn04U3YLHGhERQZMmTWjXrh1//vknGzduxMbGBrlczqeffioVucnJySE8PJyu\nXbsyY8YMfHx80Ov1XL58GZVKxdq1a+nRowfx8fGcPHmSpKQkXF1duX//vlSvwcHBgYkTJ2Jra8u+\nfftIS0vDzc3NqGdbmzZt8PPz48aNGwiCQP/+/Tl27BidO3emUqVK9O3bl9TUVLp160ZOTg5fffUV\nZmZmxMbGMnbsWJycnNi6davUAeH//u//OHz4MCdOnMDX15e2bdvyxx9/IJfLuX37NjVq1CAiIoI/\n/vjjpb4/UTSetymkYR8y0UXxPFEUr8u9kJKSQt++ffn666+l30ZQUJD0t6+vLwkJCSxYsADI9+1f\nuXIFX19fvL29SU5Oxtvbm/379xMcHIyzszMuLi6MGjUKtVpNjx49XnpsJkv3LVNU4XBRbA39moaC\n9C5YqS+7jYKWrbm5+XP1Vjt27Bht2rTh2LFjhSwmJycn3N3d8fPzIzQ0FDc3N6m1jru7OyqViosX\nL0rrJyYmkpycjKurK71792bJkiXIZDIeP36Mg4MD586dw9nZmSNHjpCRkUFOTg5Pnjxh/fr1/PXX\nX9Ixp6SkkJqaysqVK8nIyOD333+nWrVqpKSkkJmZibOzs7TPiRMnkpWVhV6vR6fTcf369UKie/r0\naW7cuEF8fLyUejxnzhwePHjAsmXLePToEQqFgilTpnDz5k1q1aqFm5sb7du3l9oRmZmZIZPJCA4O\n5siRIwwdOpSYmBhGjRpFpUqV2Lt3r1Qr2czMrNjZ9leloBg/T7cFww69r9NFIdZGSExMxMfHB8h3\nJ4WFhdGwYUOSk5P59NNPpcgUgNOnT1O5cmXCwsL49ttvOXbsGM2aNWPatGk0bdqUnj17Mn/+fMzM\nzBg1atQLP3WafLp/A0XVshUD9A3F1srKijt37rBt2zbJ5/Q6rdS3VTtBLCeZmpqKXq9HrVa/UJGd\no0eP0rdvX0qUKCEVYTbcv+hiMHQtiOv06tULX19f6b3Q0FDq1KmDXC6nQYMGPHz4EEEQKFu2LD17\n9iQkJISbN28il8vp27cvTk5OTJo0ia1bt5KUlESdOnUoXbq0ZOmJxyFGXGRnZ2NpacmZM2d49OgR\ngiCgUqmws7NDEASpp5qXl5c0JkdHR8qUKUN2djZXr16VykEGBwdLlmL9+vVZtWoV3t7enD17losX\nL3Ljxg0OHjyIXC6nTJkyNGnSBK1Wy/Xr12nVqhU1atSgV69e3L59G3d3d8aOHYuFhQX37t2jdOnS\nxMXFFenzfhM8rfWN6KIAjFwUGo1Gikd/WTHesmULkZGRRvGzly9fxsXFhZIlSzJ79mw8PDxo0qSJ\ndPPft28ft27d4tdffyU9PZ3bt2/j6+tLt27duHjxIlWrVmXLli2kpKTw4YcfvvC5KCi6JvfCG+RZ\nYmtmZoatrS0ajYaOHTvi5OREhw4d2Lp1K3369OHjjz+WHslfBVEw3sbssyi2YqSFOPn3vPvXaDT4\n+/vTsWNHunTpwuHDhwut07NnTw4cOEBAQADNmzc3WiYKsrivS5cuSZakWHgG8v3mq1atki6GRo0a\nsWfPHsaOHcvOnTvZtGkTtra2xMXFkZWVxW+//YZcLicnJ0eydMqUKUPVqlUlF4CrqyvlypWjdu3a\nJCUlSdsuyrqvXbu2VCRn8ODBuLi4MHv2bLZs2UJ2djaVK1eWuhZ/+OGH6HQ6yVpUq9UcOXKEffv2\nSQVXlEolH374IUFBQURGRtK1a1fKli3LBx98QOPGjXn48CEymYwRI0Y813f5pniai8Lwd/IyLorr\n16/z5Zdf0qpVK6MaGsHBwTRp0gR/f3/8/f2pWrWqFO6l1+vZv38/H374IQ0bNuTYsWPUrFmTc+fO\n0bdvX2QyGUuWLKF///7UrFmTKlWqvNDxFhyryb3whhDFNiUlhezsbKOWOAX7j8nlciZPnizVZq1Q\noQIfffQRs2bNYu/evTRt2pTIyMi/fYb4aaIpim1KSkohsX1Rzp8/j6urK2XLlqVz586S6Bruv2LF\nilSsWJGzZ88aWZAAHh4eaLVaoqKigHzRrVevHiEhIQQGBlKqVCm0Wq30Pfj4+CAIAoGBgdSsWRMf\nHx9OnjzJvHnzcHZ2pmfPnjg4OPDBBx9QoUIFdDodWq0WS0tLIiIicHV1lUo69u/fH0dHR6lItRgX\nHB8fj6OjI59//jn379+XztWTJ09ISUnh6tWrCIJAWloagwcPBmDv3r0sXryYtLQ0mjRpYpTC3alT\nJ6pWrYqZmRk1a9Zk4cKFDBw4kNTUVORyOXl5eXz11VfcvHkTT09PLC0tKVGiBCVKlCA+Pp5ffvnl\nhb6TtxGnK1rGZmZmRi4KlUr1TBdFXl4emZmZ+Pj4MGfOHO7du2dUTD4oKAgPDw8mTZrEsmXLCA8P\nx9PTE5lMxurVq9Hr9cyfPx/I7zh97do1li9fzokTJyhdujS1atUiOjpamnR72eODfNF9UxXGXifv\nneiKF6ZYJ+Fp/cd27txJWFgY+/bt4/r16/z3v/9l0aJFbNiwAWdnZ6ytrRkwYIDUEuZleRPJDYKQ\nX6A7JSVFStgoTmyfd/+GoWLNmzcnOjq6yDJ5zZs3Ry6XF0p4EF0MYoRDaGgoHh4eTJkyBTMzM5KS\nkhAEgaFDh9K7d28qVaqEmZkZ5cuX59q1a4SFheHo6Ej58uXx8/Ojbt261KxZk4CAAB4/foxCoZAq\ntslkMo4fP46FhQUVK1ZkzZo1lClThsTERL7++muqVasG5E+utWzZkt9//506derg7OxMWFgYer0e\nS0tL2rZty/Xr17l9+7YkFuIEXtOmTYmKipKSI+bPny9NqGk0GmrVqsXp06dZu3YtAwcOpGHDhtjZ\n2eHj48OoUaMICgoiNDSU8ePHo9FokMvlb8y3+6oUFPenuSgM64BotVo+++wz3Nzc6N27t1QXWbwG\ng4ODpZrIbdu2JTIykgYNGpCbm8uiRYvw8vLC3Nyc3NxcKXSsbdu2bN++nZiYGD7//HOCgoKKLVL+\nIsdksnTfEHK5XBJejUZTbP+xuLg4pk6dytq1awkMDKR9+/YMHTqU8PBw0tLSiI6OJiIiQuqtJaZ/\nvgyvw70gft5QbPPy8l4oO+5ZHD16VJp0UiqVRgkDhtja2pKXl1fkMYlRDA8ePCA7OxsHBwciIiKQ\ny+VYWFhgZ2fHhQsX6Nq1K3v37qVy5cpoNBo2btzI8OHDuX37Njdu3ECpVDJnzhypK0BmZqZUwFxM\nJkhJSaFEiRKS/zEzM5Pc3FyuXbsmtVxPS0sjJCQES0tLLC0tGTVqlFRDolatWmzZsgWVSsXVq1e5\nffs2AKVKlUIQBHbs2MGTJ08wMzPDycmJsmXLsmfPHmmiqFy5cqxcuZIZM2awdOlSAgICaNCgAQqF\ngrt373L06FEqVqwoxagqFApycnJeW8ugvwNDMbawsODw4cOcP3+eFStWEBcXJ5XBzMvL49atW6Sm\npnL06FHmzp3LpUuXqFq1KtbW1vz6669YWlpKYio2Il26dCnh4eEkJCSwbNkytm7dSrdu3V6qYFRB\n0TX5dN8QYlaPYdB5wcczvV7PiBEjqF+/PiNHjmTq1Kn89ddfLFu2jD59+qBQKJgwYQJqtZpz586h\nVqvp2rWrUSPGF+F1WbqGdR9edypybGwsDx8+NPLHde7cmSNHjhT6fHR0NDY2NkV2O23SpAkpKSns\n37+fBg0a8N133wFIkzidO3fm1q1btGvXjtDQUCpUqEDHjh3x8/OTst0yMzMZMWIEHh4eLF26FEtL\nS27cuGEUOyz2bIuPj+fs2bP07NmTpKQkRo8ezciRI0lNTZXCqLKzs1m/fj3Tp08nNjZWisrQarXI\n5XLS0tL4+OOPGTBgAAqFgtzcXMqWLSv9btRqNV26dCEyMhKVSsW2bdtQKpWsWLECtVrNiBEjKFGi\nBCNGjCApKYnmzZsTFhZG7dq1sbCw4PLly/Ts2VNKsZ45c+YzvzORt+Xaehk3xu3bt5k2bRrr1q3D\n1taWyMhI6tevL/mLw8PD0ev1LFiwAHt7e4KCgvD09CQpKYmlS5cil8vx9PREo9GwYsUKOnbsKE24\nubi40LlzZ3755ReaNm36Wo7xVWvpvi3eO9EVC2w/rQiNaN3a2tryyy+/UKNGDUaNGsW4ceNYv349\nGzduJCYmhpycHARBICkpiYcPH/L555+/1JheRXRFH7VOpzMqsvOilu2z9n/06FE6dOhg5J7w9vbm\n1KlT0uO1yLlz5/D29ubQoUOFtiOXy+nRowe7d+/G09OTdevWYW5uLpVrrF69OjKZjMuXL5OZmUl0\ndDQHDx7k/v37dO/enWrVqqFUKlGr1cTGxiKXy6lbty43b97ExsaGXr16odfrJd+c+D2fPXuWMmXK\ncOTIESkGt27dupJrY/ny5fj4+HD8+HEEQaBEiRLcuXOHefPmMWPGDFq3bi09fmZnZ+Pk5CQVybG3\nt8fb21uKXTY3N5eqjyUmJkohiGPHjuXatWtERUWxdetWKTPu9OnTjBkzBjMzM3Q6HampqUZJJs/i\nXay9oNFo6Nu3L5988ok0WWrYrRlg9erVODo60r9/f8zNzQkNDcXLy4sffviB//znP6SmpuLu7s6p\nU6d48uQJQ4cOJSEhgcDAQKZOncqSJUuQy+UMGjTopcZY8EaSkZEhhTi+y7x3omvo8ytOaLZu3cqG\nDRvYtGkT9+7d48aNG4wZM4YFCxbg7u5Ojx49OH36NNWqVaNUqVIAZGVlsWvXrlcKcn/R9XNzcyXL\nViaTvbQb4Xku2m3btlGrVi2j98qUKYO7uzsXLlyQxn/v3j1yc3MZPHhwkaIL+REOoaGhJCQkoNVq\nqVChAo6OjlhaWkoW4OzZsxEEgXv37rFp0ybWrFlDZGSklDm2ceNG7t+/z+3bt2nRogWbNm1i6NCh\nLF++HI1Gw4MHDzAzM8Pe3p5u3bohCALx8fFYWVlJF9bEiROJiopi6tSpnD9/nvj4eLRaLQqFgrp1\n61K2bFk2bdrEoUOHWLBgAeHh4ZJ//MaNGygUClQqFU+ePKFatWrSpGpSUhIrV65k3LhxmJmZcevW\nLQBpAvLSpUtUq1ZNqh984cIF6tati5OTEyqVCplM9tI38HeFWbNmERMTYxSVEh4eLonunTt3CA0N\nZcaMGdK1GBQUJLloWrRoQZMmTbC0tJRqZbRt21aaaOzUqROrV6+mQ4cO5ObmSll3L1Ius6DoCoLw\nt6XyvwjvneiKFCe6sbGx3Lhxg27duhEbG8uUKVMoXbo0Xl5exMXFsX37du7du8fcuXNRq9XodDqp\n+HdqaioTJ040SgB43rE8L4Zim5ubi7W1NSVKlHgla+dZlnZ2djYXL16UfJqGdO7c2ahG7blz52jW\nrBktWrTg1q1bRpW5RFq0aEF6ejq7du1CJpMRGxtLw4YNadGiBadOncLb25vQ0FBsbW3p3LkzTZo0\n4eHDhyQkJBAWFsYHH3zAvXv3cHZ25vz583h6erJ//34GDRqEra0tffr0QRAEXFxcePLkCWfOnKFU\nqVJs3bqVvLw8srKykMvlNGvWjPPnzzNx4kSsrKzo27cvCoUCnU5H48aNuXv3Lrm5uSiVSvbs2cOt\nW7ck37NWq8XKyorMzEzq1q0rhbiJCRTdu3dn3Lhx5OXl4e/vLx37jBkzyMjIID4+no4dO+Lj44NG\no+Hs2bOMHj1aigh49OgR+/fvf+nv9HXzIu6FgwcPsm/fPlxdXaWqXXq9nsjISGkyUhRbsVKd6NZZ\ns2YN48eP58qVKzRr1ozjx4/z6NEjqUbFmjVraNq0KXv37kUulzNmzBisra0xNzeX5muKi6J4mhj/\n3RFIL8J7J7riD6c4odm1axfdu3dHLpczePBgbGxsGDRoENHR0fz88880aNCA0qVLM3r0aE6ePImf\nnx+ffPIJgBSuZNi99HnH9KwvXYyPTEtLIycnB2tra6lW75uO8z116hS1atXizJkzhZZ16dJFeiSH\n/xW5USqVdOzYschYXrFpYEZGBlZWVpiZmZGdnY2joyNubm7s3LkT+F/XYDFszM3NTUoPFme/o6Ki\niIuLo0WLFlLVM9G6Etvbp6Wl4ePjw+7du0lOTsba2hq9Xo+/v7/UeHLMmDHEx8dTr149LCwspHY7\nXbt25dChQ8yZM4esrCy6d+/Oo0ePqFatGg4ODnTq1ImMjAz279+Ps7Mzp06dYtOmTUyfPl2qHWH4\n9FO9enVsbW1Zvnw5AIsXL8bS0pLPP/+cPn36kJWVhVKpRKlUsnjx4md+N++aWNy7d49JkyYxbNgw\nKasM8i1bGxsb7O3tOXbsGJGRkZQvX17yoQYFBVG1alUuX77MmDFjCAwMpGnTpsybN4969erRsmVL\n1q5dS6lSpejSpQuLFy9GEARatmyJTCZ7ahSFGNIm1i4WrWKxVKaYoQjvpqumIO+d6ML/ZliLKvzx\n559/0q9fP77++mvUajXXr19n5MiRUrB8QVxcXJgxY4ZUJevhw4fcvHnTqNrW84znaXdgUWzFZpYF\nu1C8yXq6kN+7auDAgWRkZEiPyiJ16tRBo9Fw48YNID+WV0yKMIzlhf9VMDt79iwWFhZAfqRD3bp1\nCQkJIT4+Xipoo1QqefDgAV26dMHCwoLw8HBUKhVdunShSpUqODk5ERsbS61atdi6dSv9+vWTKmwF\nBwdLhcPF0ogtWrRgx44dUjqxq6sr06ZNw8XFBT8/P3x9fZHJZNy9exdBENizZw8KhYJGjRrh6urK\nxx9/LB2fg4MDCQkJxMfH8+OPP5KamsqAAQO4deuWVCayXLlyAAwcOJCwsDCjSVYvLy+2b98uJeX0\n7duXGzdukJSURO/evaXKapcvX36u4jRvQyiex9LNy8tjxIgRTJo0iaysLGrXri0tE10Lubm50g3G\nsLvzhQsXuHPnDrNnz0av13Pt2jUeP35MXl4eCQkJNGrUiBUrVkhteezs7OjZs2exczNFhbQVVYtC\nr9ezcuVKKlSoQExMDB9//DE///yzUW2R5+HIkSPUqFEDNzc3vv/++0LLT58+TcmSJWnQoIHRBPLL\n8F6KLhQtNLdu3eLevXskJyezc+dONmzY8Ewfj7idLl268OmnnwL5scBjx44tskXN846lOLF9Wsru\nm7B69Ho9hw8fplu3bnTo0IETJ04UGnvHjh05cuQIiYmJxMbGShaOt7c3p0+fJisry6jOQ1RUlBSW\nVbNmTerUqYOLiwsHDx7E3NwcLy8vqYGnGJFw4cIFEhIS6Ny5MwqFgnLlyqHVarG3t+f+/ft07dpV\nqrB16tQpyXev0WjQ6/XcvHmTkiVLolarsbW1xdvbG09PT8LCwvjxxx8pVaoU1apV49GjR5ibm5OY\nmEibNm24evUqACEhIUD+b2TWrFkkJSXh7e1N+fLlcXNzY+3atVSpUoUrV64wefJk6fx4e3sjk8mM\nJsbatGmDUqmUUqJ9fHyQy+VMmjSJadOmSe4PvV7PgQMHXvt3+qb49ttvsbOz47///S9XrlwxmgOI\niIjAw8ODFStWSHG6hkkSR48excbGhj59+kjp4YsXL+aTTz6RJh9r1qxJiRIl2LhxIwqF4qVicw2z\n7sT/P/vsM86dO4erqyt16tQhKiqKoKCg596mXq9n4sSJHD16lCtXrrBt2zauXbtWaL1WrVoRGhpK\naGgos2fPfuGxi/yjRHfXrl20bduWKVOmsG3bNqmL6LO2IzJ//nzJIn7w4AE///zzC4/lZcT2VS2d\np1m6wcHB2NvbU6VKFTp16lSoxxjkC8uRI0cIDAykcePG0mReqVKlqFmzptS4Ua1WY21tLZV4VCqV\nhISEoFAosLW1JScnhz/++IPIyEg0Gg15eXk8fvwYjUZDeHg4Dx8+lKzouLg4lEolwcHBDBo0CAsL\nC5RKJQqFguDgYKpXr46rqyuCkN/Dbu/evTg4OJCYmEhOTg5dunTh8uXLzJ49m4SEBNLT0xk3bhyQ\nP4tdq1YtIiMjuXTpkjTJI54rjUYDwNChQ4H8oustW7YkOTkZvV4vnU+ZTEaFChVQKpWsXLlSOl+1\na9ibX3cAACAASURBVNemZMmSLF++HEEQcHR0RBAEjh8/zqpVq7CxsaFy5cpAftuf94GjR4/yxx9/\nsHr1auRyOVFRUYUs3fLly/Pzzz9Lk5Ki6D558oTY2FgpySgoKAh7e3sUCgVqtZr69evzyy+/ULNm\nTZydnalUqRJxcXG0adPmlcZsaL3L5XKcnJyYOHEiv/zyywu5CIODg3F1daVixYoolUoGDhxoVGPE\ncH+vg/dSdAvOWIrs2LGDCxcuMGfOnKe2+yi4LcNtiBenXq9n9uzZRU4+FYUY+pWenk5WVhaWlpYv\nVIzmdSZYGHLgwAG6desGQPv27QkICCgUItayZUsiIyM5efIkzZs3N0rQ6NixI2fOnJGy4fR6PXfv\n3gWgbt266PV6Lly4QHBwMP369aNcuXJcvHgRuVxOmzZt2LdvHxEREdjZ2dG+fXspvCw5ORnIv2AH\nDBggjeXy5cuUKFFC6soASEJ88+ZNMjIy0Ol0eHh4UKpUKQ4dOoS5uTlXr16VHkkBGjduTGxsrFSn\nNzk5GUtLS9q0acOyZcuwsLCQbi4xMTF88sknUqcLsatFcHAw6enp1KtXj4SEBC5dugTkW/cPHjwg\nPT2d6dOn065dO+mR98SJE+h0OqKjoyVf9tNcDG8jDfhZ+4mLi2PChAmsXbuW0qVL8+jRI7RarZSR\nKAj5jSZ9fX35+OOPcXFx4fLly1Ikw4IFC7C1taVVq1ZA/lNNREQEs2bNIiAgAGtra+rVq0dUVBTX\nrl3Dw8ODzp07v1D36mcd06skRsTFxRl1eHZ2diYuLq7QeoGBgXh4eNC1a1fpCepleC9FFwoXmvnr\nr7+4desWrVu3ZuTIkS+0HUOxqlq1qmSl6PV6vLy8ColUQcSatpmZmVhYWGBrayt1K3gbPG0/+/fv\nlxoGli5dmho1anDu3DmjdVQqFV5eXhw7doxGjRqRmpqKRqORHhcPHToknSMx20omk+Hq6kpubi4R\nERHY2toyevRoMjMzuX//PiqViqFDh+Lr60tQUBBmZmZSNty1a9coX768FD52/vx5aSwBAQGUKVNG\nmtGuU6cOWq2WvLw8kpOTcXd3x8HBAblcTv369QkODqZGjRqUKlWKuXPnIggCbm5uVK9enWnTpmFl\nZcW0adOk7S9atIgHDx5Qp04dIiMj8fPz4969e/Tq1Uvys9+5c4fIyEi+/PJL3NzcuHz5MjY2Nqxe\nvVo6jyqVipIlS7Jz50727t2Ll5eXVI5y2rRp6HQ6bGxsyMvLK3S+3yVyc3Pp2rUrffv2larKiVau\n+LuKjY0F8oveTJkyhVu3/h975x0YVZ29/c/MpPfeIb0nQAJGSughVBFpi4CIXaTZXV1/ImvBwoKU\nVcCCdESatNBJo4USUkhIIb0QSO+Zycy8f+S9380QVEDc/fG+e/4hTO7c3Htn7rnn+5zzPM91bGxs\nsLGxobGxkW3btgnlMZVKJSrd6Oho4uPjuXz5MvPnzycpKYnAwECSkpKYMGHCHz7225Pun0kB7t27\nN0VFRVy5coV58+b9oeN/KJPunRpQknj2ihUr7inZ3anClJpoWq2W2tpaRo4cecf3Sp31trY2Yfp4\nv8n2z5hgyMnJob6+nvDwcPHaiBEj7tgkHDRoEAUFBYSEhIjJCj09PQICAtDT0xMiN//4xz8Erpac\nnIy5uTlGRkbo6+sTFhZGenq6+HnEiBEkJycTGxsr3HShw8FXEl5xc3Pjhx9+EMeRkJCAXC6noKCA\n6dOns2TJElG9WlpaEhoaKq7TzZs3cXZ2xtbWFicnJ6Ft+9prr7F69WqeffZZVCoV+/btQy6XM3Dg\nQBISEpDJZDQ3N7NkyRJeffVVrK2tefLJJ5k/fz4eHh5UVVWxcOFCjhw5QmlpKXPmzKGxsZGff/5Z\nVDgODg7k5+ejVqsJDAwkMDCQbt26ERwczLFjx4SqF8CqVase6Od6r/FbTd633nqLyspKpkyZIl6/\nHc+9ePEibW1tfP755xgbG5OcnCyghXXr1mFlZUVUVBTQYYWk0Wj48MMPqa+vJzMzk5CQEGpra5HJ\nZEycOJGLFy8ycODAB3qOfyTpurq6UlRUJP5fUlKCq6urzjZmZmaYmJgAHQ1mlUrVxWH6buOhTLpS\ndE5US5YsITMzU9BH72cfUri5ueHt7S1+f+7cOR3jxfb2dhoaGmhqahLi4VLl/SDO5UG9/8CBA4wd\nO1ZH+vB2XFeqIq2trZHJZNjb23eZrJDGrqCjEaXVagkICKCuro7GxkYMDQ3F37l8+TJKpZKxY8di\nbGzMsGHDSEhIIDAwUDTVLl26RHl5OXl5ecyYMYNr164JFbWzZ89SVVXFhQsXmDZtGoMGDcLDwwPo\nuLHMzMy4efMmRUVFpKSkcPPmTZRKJcXFxfj7+9Pe3o6/vz/u7u4cP36cHj16CAGc/v378/HHHwuf\nNTs7OxYtWkSvXr04fPgwo0ePFrb0n376qRi2HzBgAD4+PgQHBxMVFcWuXbu4fv06jz32GO7u7iQl\nJREUFIRCoaCkpERMXUgNyePHj//HLXag64ro+++/59y5cyiVSuHOAXTBc3/44QecnJzESkXCc+vq\n6sQDRaKXf/vtt1hZWTF06FDi4+ORyWS8++67gr22b98+fHx8fnWa6F6ic6X7RyjAjzzyCLm5uRQW\nFqJUKtm+fXsXB4uKigrxs6TNLBGr7jX+n0m6wH19kL+W7KQuv/S7mTNnUlJSQkNDA42NjYKObGRk\nJIRa/pNxp/NYu3Ztl5GcPn36UF5eTmFhoTgXiXVlZWUlcMvOMXr0aA4dOkRmZqbQM3BycsLLywsr\nKyvq6uoEfz4hIQG1Ws0TTzwBdBAp2traiIyMZMWKFULTuLq6moaGBlasWIFarSY6OpqFCxdiZWVF\nRUUF4eHhotqYMGGCGJxPTEzEwsKCxYsXM3v2bDw8PMjIyKClpQVLS0ssLS158803efPNN1m6dCmP\nPfaYaJytW7eOpqYmqqurmThxImVlZRQXFwvBpLCwMIKDgwkODuaXX36hurqapqYmXF1duX79OsuX\nL8fQ0JCXX34ZR0dHGhoaGDFiBMePHycgIIAbN27Q0tLCSy+9hFwuJy0tTTwIkpKS7vi5/bsw3dvj\n9OnTLFmyhM8++wwnJycdwZnOlW5JSQlnz55l7ty54jilpPvPf/6TIUOGUFdXh6+vLxqNhqNHjzJ1\n6lRkMplI1hEREYLEkpiYeE/w32/Fg0q6CoWC1atXEx0dTXBwMNOmTSMwMJC1a9eKRujOnTsJCQkh\nLCyMV199lZ9++um+j/uhTLoPar71t/bh7OyMi4uL+H9rayuzZ88W4uidhXb+zOO438jPz6e6upqs\nrKwuvxs8eDAHDhwQUwd6enokJCQwZMiQO6qODRw4kGvXron5RXNzc4qLi0lNTRVyjgsXLmTWrFnE\nx8djaGgoJkcyMjLQaDRs2bKFgoIC3nnnHaE/O2vWLMrLy+nZsye1tbWUlZUJ94mpU6eKvz9ixAg0\nGg2GhoZC2ergwYPExcXR3t5OTU0NMpmM3NxcwsLCMDY2JjMzE3t7e3H+enp6REdHo1arOXbsGJ9/\n/jnt7e2cP3+eyspKnnjiCfT09PD39wcQx2dkZISHhwe1tbV4enpiZmaGvb09FhYWxMTEMGDAAI4e\nPYq/vz85OTkMGzYMrVaLubk5bW1tfPTRRwB3nP38d8Xtib24uJjZs2ezdu1aGhsbdapclUpFTk6O\neO29997DyMhIwAcSM6179+6sW7eOESNG0KNHDxQKBUeOHBEOzxqNhsTERObMmcPWrVvFcWg0mvvW\nWrj9nDrHH1UYGzVqFFlZWeTk5PDXv/4VgJdeeokXX3wRgLlz55Kenk5ycjJnzpwRnnD3Ew9l0pXi\n1wgS9xK/lew6L8NVKhWlpaWsXr36T6lMHjS88PPPPzNx4kSSkpJoaWkRxIb6+nqioqJITEwUcphN\nTU1kZGQwe/bsOyZdQ0NDhg0bJizMW1payMrKErO03bp146mnniIiIkKIymi1WlauXMnWrVvR09Nj\n0aJFwiVWrVYjk8nEUn7OnDlUVVXh6OjI4MGDUavVLF26VEySSAQJW1tbURVbWlry6aef0r17d0xM\nTAgMDOTWrVuEh4ezYsUKvvjiC5555hlxzFZWVuzdu5cFCxYINpqzszNnzpwhPT2dSZMmAR2N1Jyc\nHPr06UNqaiobNmzAwMAAT09PSkpKRJLav38/ra2txMTEUFhYSGVlJdbW1gQFBXHs2DEx533t2jXk\ncjmnTp36XwExNDc3M2PGDObNm0dUVBQZGRnCbRk6FOZcXV0xMTHhxIkTYuTO3d0d6Jj0sLS0ZMuW\nLTz++OOUlZWJnsEXX3yBgYEBvr6+HDx4UJAtvv76a3r16sXq1asJDAy872X5naJzpfswaOnCQ5p0\n/4wKs/N+1Go1jY2N2Nra6iy7bty4werVqzl37twd9/G/KXbs2MGMGTMIDQ3lxIkTOtbsY8aM4dSp\nU0I969y5c/Ts2ZPBgweTn59/R72FqKgoQYiQmEJfffUVxsbGREdHc+zYMYEDNjY2MnPmTLZu3Yqz\nszNBQUFi7jEjIwNra2taW1tFM2Xs2LFAB1ustLSU0NBQ/v73vzNjxgw++OADTp8+Tbdu3aipqUGh\nUHD06FGhgdu7d2/UajUe/1cwPSQkRDDQ9uzZQ1NTE4BwGelsqTNw4ECqq6sxMzMTlZ2NjY2QLoQO\nwkBSUhLe3t5iVjglJQULCwscHBw4ePAg/v7+nDlzhqCgIOzt7Tl37hzjx49Ho9Gwc+dOfHx8BG35\nbsVcHmRIla5Wq2X+/Pn4+PiwYMECoGOSJCAgQGwr4bltbW28+eabzJ49m5CQENEXuHLlCoGBgaxf\nv5633nqLy5cvEx4ezvnz5ykqKiIiIgKZTMayZcvw8vLi5s2bZGVlERkZSWVl5R9yiLjTOUnxsMg6\nwkOadKV4UElXCinZdrb9WbZsmfh9S0sLw4cP5+mnn6a2tvaBH8eDqnQzMjKoqakhPDycyMhITpw4\nIYgNkiOE1AACiI+PZ+DAgULY/E4ECqlTa2ZmRnt7O25ubmLOctKkSbS0tLBp0yb09fVxdnbmxIkT\nbNy4kbKyMp566ikuXrxIVVUVGRkZqFQqAgIChJiKNJVQVFREfn4+Y8eO5fHHH+fs2bPk5eXxxRdf\nMHDgQJ253ZEjRxITE0Nubi6GhoYYGBig1WpF8nzttdd0HId9fHzw9/ene/fu4pwkKMHR0VHnXH19\nfcnPz6dnz55MmTKFp59+WjSB5s+fj6mpKenp6fj4+PDaa6+RkZHBL7/8QmBgIKWlpQQHB5Ofn4+p\nqSlXr14VhotLly7tIuYCHd+7B+na+2vx6quvcuHCBZ3V2u2VroTnrlq1Cn9/f0xMTHSaaikpKcI+\nSXLpCAsLY/ny5YSEhNC7d2/y8vLIyMhg3LhxrF+/XvQMNBqNmBn/o3H7tfpv0v03xYOsMG83tJSW\n3pLVNHQsc3fs2MHQoUOZNm1al338mfoJdxtarZatW7fy2GOPodFoGDNmDHFxcV3o0CNGjBDJtbMJ\n5ciRI3UmNaSQGgeSuMirr74qoIvw8HDGjh1LbGws7e3t+Pn5sWDBAubMmUNrayvTpk1j6NChohlX\nX1+v4ygLHYlbEjWRbkx7e3s2bdqEgYEBp0+fFrbr9vb2uLi4EBMTw6VLl+jRo4fQvZVsfAwNDend\nu7eoiAoLCwWEIIWk7lZQUKBz7X18fCgqKuLJJ58kLS2Np556isOHD1NcXMykSZMEndrb2xs9PT0W\nLFjA8ePH8fT0JDMzU4zl9ejRA61WK6Y2zp49KzQEjI2N7+jaez8Sh78XWq2WdevW8csvvxAdHS1G\nn5RKJQUFBfj6+opt09PTcXR0ZNWqVXz22Wekp6cTGhoqfn/+/Hkhp1lRUUFzczNKpZILFy7Q2tpK\neHg469atE8p+P/zwAw4ODpw8eRIPDw+dh94fjc4F08PiGgEPadJ9UPCChHNK+5KS7e3usqNHjwY6\nBsnVajXV1dXExcWJJfPtRI3/VEiGnT///DMzZszA3NyciIgIbty4IdhWUkiQQG1trTBZhH8Jm9+u\nO3E7A+f555/n9OnT+Pn5YWBgQHR0NJWVlcjlcn744Qfefvttbt26JaY8Hn/8cfbs2UNaWhrNzc1d\nHlrSja9Wq3WSgFKppLGxkf79+wtzRUnTVtL0HThwIAUFBQA6n92pU6eQyWTo6+tTW1srSCKd963V\narGwsBDaDNAxMtjS0sLkyZPJyspi6tSptLW1oaenJ5pKUtK9fv26UCTbvXs3165dExMNoaGh2Nvb\ns337dgwMDFCr1ezevVtHzAXQce2V8Os7SRzer326pGfcr18/HZGa3NxcunXrJlYc0FHp7t27l1de\neQUPDw+d8TGtVsvly5eZPn06Tk5OXL58mbCwMFauXMnzzz9PWloavr6+bNu2jaqqKm7duoW1tTUu\nLi64ubl1GcP6I3E7vNDW1qZzHv+b46FMulLcb6KTkm1dXZ24AaQv+53iu+++Ez8bGhqyf/9+evbs\nyfz588USEf4zla5EP1ar1SiVSq5du4a+vr6gQSsUCoYNG9ZF6KZv377k5uZy4MABHnnkEQEVODo6\n4u3trcMSk6pM6Lh2vXr1Qk9Pj6ysLAYPHoxWqxVVszSvKsEzGo2G5uZmRo0axfnz57l69SoKhUIH\nR4QOOjCgk4ygo3HTrVs3li9fLpKNhYUFaWlpuLu74+rqKsbgJNsfgEOHDlFTU4O9vT1KpRKZTNal\nSZiZmQl0iLLv2rVLvC5VokZGRkyePJlNmzbR2tpKc3Mz27dvZ+DAgSQnJ+Pi4sL169eRyWTMnDmT\nlJQUsrKyCAkJoaamBgcHB9zd3blw4YKAPZYvX/6rn6VEOrmTxKEkBnR7Vfx7WrO7d+9myZIl/PTT\nTxQXF+tMKtwOLVRXV1NdXU1+fj4LFy5EpVKRnZ0ttjl9+jQqlYr33nsP6Ji39vPz48CBA0RGRuLq\n6kpMTAy9evXC39+frVu3YmxszPXr12loaBDY/YOIO43aPQyyjvCQJt37rXRvT7aWlpaYmJj87pyt\nra2tGIGSsENLS0vkcjlffPGFzjHdb9xP0u1M0lAoFBgZGbFx40ahjCXFndTFDAwMGDx4MNu3bxec\neSluhxg+/PBDALF869u3LzU1NTQ3NzNu3DguXrzItm3bkMlkWFhYAB3XOjs7G2dnZ7Zs2YKZmRkR\nERGo1WpcXFy6XK9Tp04JbLbzsWZlZeHv709ycrLYb0xMDNOnT6epqQmtVkt4eDgtLS24urqSkpJC\nVVUVr7zyCjJZhw4rdODAn376qWgGQgd7ytTUlPDwcPbs2SO27dxYnTFjBj/++CODBg3CwMCA+fPn\nk5CQQJ8+faipqRFSmePGjRPKaWfOnGH48OHU1NSIRClRyzuvGO5mRlcqCvT19UWDT6qKJV2PX6uK\nY2JieOutt/jpp5/w9PQkJycHPz8/se/MzEydh5/kebZ06VKMjIzIycnB1dVVNJOXLFmCt7e3gEuS\nk5MpLi7mySefJDs7W9g3eXt74+fnR15eHtevX8fU1JT29nYdfd4HGf/pFea9xkOZdOHelvRStdW5\ngy8lW2lfv7efFStWAIibNjExkYaGBtasWUN2dvafJlhzp5CSbWNjIwYGBuIB0NraKhxuO0dUVBQn\nT54USUWK6OhoQcns/LdHjRqlUxVKDTdpqd7S0iIq4ZCQEObPn09QUJCQVJTJZKSnp6NQKJgwYQKr\nV69GrVYTHBxMe3u7zhJXigsXLuDg4ICxsTGbNm0Sr2dlZeHn58c333wjdIhTUlLo378/JSUlOrCJ\nt7c3qampvPnmm9jb22NpaUljYyPQ8Zn37NmTr776SmxfWFhI9+7dUSqV2Nvbc/bsWaBj+qKlpUUk\nipaWFgYMGIC9vT1hYWHMnTsXNzc30tPTKSoqQq1WC3cMNzc3vvzyS6Kiorh27RrZ2dkEBgaSlZUl\nPNR+jShxLyGT3Vn428jICD09PRITE3nllVdYv349vr6+5OXlYWdnh6GhoaiKMzMzdSrdVatW4eTk\nJNwg0tLSBJ6bnZ3NxYsXBUSj1Wq5dOkSiYmJzJ07lwsXLggXlNLSUiorKxk6dKggD93pQftH4r+V\n7n8oZDLZb84+dk62UnUqdfBv38/vJbxJkyaJD9XExEQsc0eMGCHGb/4ovPB7IU1XNDQ0CGKDRNKQ\nyWTs2rWLsLAwzp8/r3Msbm5uODk5dWGb9e7dm7q6Oh1dVOhgrd26dYvCwkJhKwSIFcKlS5c4ePAg\nzs7OfPXVV/j5+VFRUYFCoaC4uJiqqiri4+MxNTVlxIgR2NnZceDAAerr6wF0lrjQcd2Ki4uxt7en\nubmZ+Ph4QbvMzs6me/fuHDt2jNDQUPz9/YX7hNT03L17N3K5HHt7e06ePElqaqpwhzAxMcHV1ZX6\n+noeffRRvvvuO8rKyoRuRr9+/cjJyWHixIkCYigoKMDKyoqioiIxPldZWYmxsTEuLi7s2LGDgwcP\ncujQIezs7CguLkZfX5/BgwfTvXt3Ll26hJubG0lJSRgYGPD444+Tk5MjzleyI3/QIcETx44dY9q0\naaxbt45Bgwahr69Pbm4u/v7+OlXx1atX8fb2FtKbiYmJOo3jznjup59+iqurKxEREUCHw4RSqWTU\nqFF0796dCxcukJKSwrPPPsu5c+eE5VVraytFRUVCRP5BReekq1Qq/7Bi2b8zHuqk+2uwwO3JtvO4\n1J3ibqvUMWPGAAgrmFu3blFcXExNTQ179uz50zDdzsQGSVhHmq7o/P7vv/+e119/HZVK1cUhQmru\ndI6MjAzMzMxITU3VeV0ulzNixAiOHDkiKkOFQiGcGMrKykhISMDT05NNmzaxaNEiSkpKUKlUREVF\ncejQIeLj42lpacHPz4+FCxeyYsUKzp49i4GBgQ6PHRDyh7W1tfj6+vLII4+wbds2oCPpFhYW4uHh\nQUhIiKjCAgICaG9vp62tjf3796PVdugYZ2VlCcqqk5MTbW1tDB48GB8fHxISEnjmmWf45JNPSExM\nRC6XExERQXZ2NhMnTuSXX36hvb2d3Nxc3N3dycvL48iRI/Tt25fY2FiBKffp04ddu3ZRVVWFkZER\neXl54ho3NDQQHBzMypUr8fX1xcnJCW9vb2QyGWZmZujp6d2TK8m9hFarZcWKFSxYsACFQiFYZDKZ\nTGCzUlUsl8spLy/Hx8cHlUrFvHnzcHR0JCwsTPiSpaamEhQUREpKCqdPn6aurk5ABOfPn0elUrFw\n4UKqq6spKysjPz+fwMBA9PX1GTZsGKdOnSIwMLALw/BBx5+tMPag46FNup0TjpSstFotLS0t1NXV\n6Yhu3617xO+F1FCTbnC5XM758+f56KOPWLRokdCIvd/zuf0YtFrtb8IinSM5OZlbt24xatQohg8f\n3uXGjoqK6vLa4cOH6du3L0eOHOnytyWIQVIAk7rvI0eOZNSoURQUFHD9+nU++eQTCgoKMDQ0xNPT\nUySvc+fOoVKpcHFxYezYsVRXV5OXl4ehoWEX488tW7ZgaWlJWVkZUVFRODo6snHjRtRqNTk5OZw8\neRInJyf8/f2F5xp0NDWNjIw4f/485ubmJCYmYmRkRFJSEubm5iiVSlpaWnj88ceZMmUKFy9eZOHC\nhRw+fJgtW7ZgY2ODn58fubm5eHp60r17d2JjY7l+/TrBwcHk5uZy+PBhnnzySaHY1tzcDHSsBkaO\nHElBQQH79u0T1/j69esYGhqSlpZGYGAgMpmMW7du4ezsTHt7O2q1msrKSuHv9aCWxEqlkrlz5/Lz\nzz+zdOlSgoODdfadnZ2tg99mZ2fj7e2NiYkJa9euxcbGhurqasLCwoTgUXp6On5+fkLnQq1W4+Dg\nQHt7O9u2bcPNzY2QkBAuXbqEhYUFTz/9NJcvX0alUjFp0iQxUtarV69fteW537hd1lHqJTwM8dAm\nXfgXrqvRaGhpaRFKVRYWFkJ0+273czdJ19raWjxR5XI5np6eaDQa9u/fz6hRowTP/o+G9PCora3V\neXj8WqUOsH79eqENcSeHiMjISNLS0kQCb29v5/jx4zz77LN3nMuNiooiISGBiooKMcJkYGDAkCFD\nGDJkCCqViuDgYKZOnUpiYiLt7e2Eh4cTHR3NhQsXsLe3x8/PTyx5p06dikqlQqVSUVFRQW5urjjX\nI0eO4OHhgbe3N0OGDOH69esoFAr27duHsbExNTU1NDQ04O/vLzDGK1euMHDgQDHb6+DgQHl5OT16\n9ODgwYNUVVWJ/YSFhTFhwgRUKhWXLl3i7bffJiYmBi8vL3x9fcnJyUGr1TJ58mS2bt2KXC4nODiY\n7OxsEhMTGTlyJH379qW6ulqnSp82bRqOjo5s2LCBL7/8EicnJ5ydnbl27RrvvvsuKSkplJWVkZmZ\nKeamJft4yX3jQTSBqqqqePzxx6mtreXw4cNUV1cL4of0N7KysnSSbmZmJoGBgWRnZ7Ny5Urefvtt\nrKyssLGxQU9Pj9raWiFfmJWVRWhoqLj2ra2tnD59mmnTptHS0kJsbCzV1dU8/fTTHDp0CCMjI86e\nPYulpSXXr1+/Z6PXu4nbk+7DMqMLD3HS7Zwo6+vr7yvZ3mlfvxfSuIxGo6GkpAS5XM6PP/7IX//6\nV/bv33/P9u2dj0Gj0Qh79vb2dszNze/qfGpqati3b5+gWA4bNozExEQd8XVjY2P69u3LqVOngI7l\nYffu3XnssccoLCzkxo0bOvu0sbER5owajQZfX1/RBJNEZL788kvkcjnx8fEolUoGDhyIqakpbm5u\nGBsbC6IC/KsB6e7uzuTJk4VjcGZmJq2trTg6OhIUFETfvn1JS0tj2rRprF+/HkNDQ2bMmEF2djb+\n/v4YGxvj6upKTU0NQ4cOxdXVFa1WS0FBgU4jz8zMjKKiIhQKBY6Ojnj8X3nIf/7znzzzzDM0C45s\nbAAAIABJREFUNjZiZ2eHpaUlZmZmlJWV8cQTT3DkyBG8vb3x9vYmOTmZHj16YGNjQ8+ePTEyMiI/\nP198V4YMGcKtW7cwNjbm5MmTjB8/XlCLQ0JCyMvLo7Kyku3bt6NUKjE1NUWlUiGXy4WWr7e3N337\n9hVC4fcaKSkpDBs2jIiICDZv3oyZmVkXaq9GoxGYrhSZmZn4+/vzyiuv8O6771JbW6vTVJOaaB9/\n/DHvvPMOWVlZ9OrVC0NDQ44dO4ZSqeSZZ55BX1+fmJgYwsLCsLW15eLFi0yfPp29e/cKN4YHOZ8r\nRef79b/wwr8p2traqK2tRavVYmJicl/JVop7Sbrz588XPzc1NdGvXz9UKhXfffcdzz//PJMmTbpn\nYROtViuWnm1tbToi4ncTmzZtIjo6Woy12djYEBgYqDNrC7oC5ocPH2bkyJHo6ekxfPhwTpw40WW/\nneESExMTPD090dfXZ/v27ejr65OamkpDQwOZmZno6ekRHBxMS0sLarWaqqoqfHx8xHVNSEjAzMwM\nY2Njpk6dyo4dO9Bqtezbtw9HR0c0Gg1BQUGYmJjQo0cPPD09OXv2LJWVlYwZM0Zo/QJCs2H37t0C\nT5Xef/PmTZqbm6moqBB0Y6na9vPz4+LFi8TGxmJgYEBSUhKNjY34+vqSnZ2Nq6srDg4O6Ovrc+XK\nFa5du4aBgQGHDh3C2NhYEEaqqqqAjpWPnZ0d7e3tfP7557i7u7NlyxY0Gg1RUVHCMVmpVLJlyxYq\nKyuF+FBubq6wR/rLX/7C8OHDu2h6/FZUV1fzxhtvMGrUKEJCQli8eLFYCUljdlIUFhZibW0tqmzo\nwPOlh9ILL7zA1atXdei+6enpWFtbU1JSwvTp00lNTRUMu88//xxbW1scHBzQarXk5eXxxhtvkJaW\nRltbG1OmTOHGjRvi4SOJKklY8YNi2z0IWcf/RDy0SVcul4vE9FvL7ruJe0m6crlcCJx3DkmPs7Ky\nkg0bNtzVvqQZzs7uE+bm5veEf7W2tvLJJ590EW+/02yulHS1Wq0Q7YaOudzbt4V/6S3I5XJKS0vp\n168fZWVllJSUEBAQwP79+4mLi8PNzQ2tVktwcDCtra2UlZVRVVWFvb29sNzOzMykW7duVFVVERYW\nhlwu5+LFi+zfv1800aRKS/JsMzc3x9ramvr6evz9/cVN1qdPH2QyGRcvXsTW1la8HhAQQFNTE+3t\n7bi7u/PWW2/pVHwBAQH069ePVatWYWRkRO/evVm8eDF+fn6kpKSwefNmKisrOXv2rMCUy8vLeffd\nd/nss88EO27o0KFC+rOsrIzW1laee+450tPTBctNT09PYKNyuZzdu3fr+HBptVomTJjApk2bhNPF\nk08+KWQQfy00Gg0bNmwgIiICrVZLdHS0+ByluL3SvR3PhY6kum/fPlavXo1cLufq1atdKt3U1FTe\ne+899PT0SElJoWfPniQkJFBVVSWsfX788UchmylZoW/btg19fX0qKyuZPXu2IHhI16O9vZ3W1lYd\n2rPEtrvb+/B2eOG/le6/IQwMDNDT0/uPiM0sXbpU/HzhwgUsLS3RaDSsXr0aHx8f3n//fTEf+mvR\nmdhgZGQkBNjvtbHy/fffExAQQEZGhs453MmWJzAwELVaTUJCAmVlZWL8Jzo6uothZVFRkajYvby8\nqKio4PHHHxfnPm7cOI4dO0Z8fDzGxsY4OjoKd9/w8HCMjY2prKzExMSElJQU9PT0sLKyorKykvz8\nfCZOnMiaNWsoLy+nvLyckpISMUo2aNAg4uPjaW5uRiaTdancJJpwaWkp48aNE9csJiYGc3NztFot\nixcvFjoQUgQEBNCtWzfOnDlDU1MTX331Ffv27ePSpUt89NFHLF26lMbGRmQyGba2tsJcc/r06UKg\nR6vVUlhYiLOzM5aWlkRERKBQKNi8eTOtra2CRt7c3Ex9fT3e3t6o1WoqKirENEHn74B07NHR0YSF\nhfH666/rWMdIodV2mFwOGzaMzZs3s2vXLpYtW0ZBQYHOOdbV1VFfX4+bm5t47dq1azrXr76+nhs3\nbvD666+La3m7Rc/Zs2eRy+VMmjSJ2tpabt26hbe3N8uXLycwMFDIOX777bf06tULrVbLyZMnGTdu\nHNu2bcPJyQmtVis+H4lpaGBg0IX2rFAoRHO6M8FDqorvRHvunHT/W+n+m+M/kXRHjx6tMyM4dOhQ\nVCoVJ06coE+fPjg4OAim2u2hVqtpaGigoaFBEBskCvK9nkdTUxNffvklS5cuJScnRyx7ocOCpLi4\nWAerlclkREVF8e233xIVFSXgGKnplZCQILaVWGjQMeer0Wh45JFH2LBhA2ZmZvTu3Rt/f3+OHTtG\nc3OzuKljY2OFW8T58+eRy+UcP35cGEsOGjSIo0eP8uSTT3Lo0CEGDBiAhYUF9fX12Nra0traSq9e\nvbh69aqgWJ85c0YnaUiwi0wm4/jx4/Tr14/Zs2fT2tpKfHw8crkcCwsLcnJydHQc/P39KSsrQ09P\nD1NTUywsLHByciI9PR2tVsvgwYPx9vamR48eZGdnY2trS//+/YXwenh4OFqtFgMDA6ytrWlpaeHq\n1atAxxzrjRs3sLS0xM7ODrlcjp2dndADeP7559m/f784FkNDQzH1AB205by8PObOncvf/vY3oCOx\npKam8uGHH9KzZ08mTpzItGnTOHr0KD179kSj0XR5IElkks6rPwkPl+Ljjz/GyMhIQGUtLS0UFxeL\na9XS0kJJSQmLFi0SDhghISGkp6cLlw4J2y8oKGDChAnExcWhVCoJCAigtbWVyspKAgICcHBw+NXv\nrwT7SGy7zrTnzlXx7WJAkhNI557Of5PuvyE6U4H/TCHzXwupSoSOpZrEvNm9ezdarZbvv/9ex769\ns2ykVPV1dp+4n1izZg0DBgzgkUceITIyktjYWPE7PT09hgwZckeIIS4uTvhdQcf5Dx8+XIeFJmnW\nQseNbGlpyZo1a4SbRmhoKCNGjKCoqIjq6mqhpB8XF4efnx/29vZcvHhRzDAbGhqSlZVFRUUFK1as\n4OrVq2i1WgwNDXFxcSEwMBATExMUCoVwVFYoFEyfPp0zZ87g6ekpxF7y8/OxsrJiz549XL9+nWnT\npvE///M/yGQy3nnnHeRyOZWVlWRnZ3epdDMzM0Uzq3///uTl5WFhYYGBgQHLly+nqKiImTNnolAo\nqKur49KlS1RXV3Px4kWam5sxMDAQ7DN/f3/hAL1v3z6ampqYPn06o0aNQibrcDn29fVFLpejUql0\n9CXa2tooKyvj+vXrFBYWsnDhQl555RV69OhBYmIizz33HOHh4cyYMQOtVsu3336LWq3mueeeE98Z\nyWqo89L69iR8+2vnz59n8+bNREVFiYfXtWvX8Pb2FgSDlStXYmhoKBxvU1JS6NGjB1999RUvv/wy\nV69epVevXqxduxZzc3P69evH2rVrkcvlnDlzRmC2EgRxL3E3VbF0v1dWVhIaGkpsbCxbt25l586d\nYhLlbuPw4cMEBATg5+f3q+4eCxYswNfXl169enHlypV7Pqfb46FNulI8CH+y+0m6a9asET9Loid1\ndXWoVCry8/OZNWsWf/vb3+6K2HA/x1BfX89XX33F//zP/wB3xnCjoqK6jI7169ePmzdvdtFbGD58\nODExMUAHblhZWSkmQm7evImbmxvffvsttbW1tLS04OzsLCq6uro6RowYIcgi0syuu7s7AwYMID8/\nHzs7O2xsbHj11Vepqalh1apVNDc3s3PnTsrLy/Hz8xNVj0KhoLa2FktLS2bPns2NGzfo1q2bEHvJ\nzMzE3d2dFStWYGVlJSYMFi5cyNGjR3F0dCQtLY3CwkK8vLzEOXp5eVFSUoKFhQU1NTWUlpby9ttv\nc/78eZqbm9m1axc2NjZER0fT0NDA0KFD2bBhA5GRkWi1WqEsVlpaKpw3FAoFzc3NqNVqFixYwKJF\ni7C3t0etVmNiYsKPP/6o0wNwcHDQUcMaM2YMYWFhNDU1sW3bNjZu3IiLiwt79+5lzZo1pKamsnjx\nYkxNTfHw8NDB+2+HDaTXbp9ckDDdyspKnnnmGSIjI3XsZjoL3zQ1NfHPf/6TAQMGiO9oamoqLi4u\nnDx5ksjISBwdHZHJZPz88880NTXh5OREbGwsERERwgaqtbW1i5TmH4nOVbGkOWFra8uePXtwcXHB\n2NiYzZs367ga/15oNBrmzZvHkSNHuHr1Ktu2bePatWs628TExHD9+nVycnJYu3YtL7/88h8+l4c2\n6T4oecf73UdgYKCoFLRarWAdSTdUW1sbSUlJIpH9FrGhc9ztcaxYsYJhw4aJGywqKqqLJcyIESM4\nefKkzmsSBi1ZqksREhJCc3MzOTk57NmzR7w+ZMgQ5HI5FRUV1NTUUFVVhZ6eHt7e3nz33XcYGBgI\n6CE+Pp7+/fuzfv16kpKShA6Al5eXYIVJS2Q7OzueeOIJFAoFbW1tnDx5UiiEnTp1CgsLC9FYVCgU\nnDt3TlQ9+fn5uLi4UFhYSG1tLd26daOpqYmXX34ZhUIh5nGdnZ11EpykU1BTUyMq3ZKSEpycnHBz\nc+Pdd9/Fy8uLnJwczM3NCQgIoLa2lj59+ghtiejoaIqLi7G0tOT555/vklABQXc+d+4cMpmM1tZW\nsY27u7tQPYOOUT4TExOuXr3KqVOn2LVrF88//7wYv5K2u33OFu7cILs96UrVsLm5OS+88AKTJ0+m\nqalJB7/tPLmwcuVKbG1tGTZsmPh9SkoKKSkpPPPMM2RnZxMWFsaWLVsIDQ2lR48e7Ny5k27dumFt\nbY2DgwMtLS2Ympr+IR+x3wrpHlEoFPj4+KBUKvnwww/Zu3cvV65cuevVY1JSEr6+vri7u6Ovr8+0\nadOEXKsUv/zyC7NmzQLg0Ucfpa6urguj8l7joU26UjxIHdt73U9nqbri4mL09PRQqVT4+fnxww8/\n8Prrr/Phhx8K6OG34l5ghoyMDJYtW6ZjnOnl5YWlpSUpKSnite7du2NnZycUuqDDxmfYsGEcOHBA\n52/LZDJGjRrFoUOHWLZsmTje6OhosTR+6aWXCAgIIDo6mri4OCoqKlAqlaI6jYuLo76+nitXrvDq\nq6+yd+9eSktLkcvlmJmZCZ+tSZMmERcXx5QpUwSmPXHiREaOHMn27dvZvn07vr6+1NbWcunSJfz9\n/fn666+FYE9OTg7l5eViZtfOzg5TU1OsrKzw9fXl1q1bXL16FS8vL5qamgQOGBcXR0NDA1qtVtj2\n7Ny5E5VKRe/evTEwMKCmpoZz584RHBzM5s2bMTAwIDc3VyTXcePGodFouHXrFjk5Oejp6QkY5rPP\nPiM2NpacnBzMzMyIiYnh0KFD3Lp1S1zrvLw8jI2NcXZ2BqC8vJx58+ZhbW0ttpFw2Y8//lgk7F+r\nan8LSoCOeVw/Pz+WLVtGc3Mz77//fpemmTS5UFpaypo1a7CyshJEiJaWFvLy8jh16hRz5swRGrrr\n1q3Dx8eHPn368OOPP9Le3k5eXh6tra3o6+szdOjQX/3+PojofL80Njbe1/RCaWmpzkSJm5sbpaWl\nv7mNq6trl23uNf6bdLl/daLOEENDQwNtbW14eXmJ7nNsbCxmZmb8+OOPd30cv3cuKpWK5557jmef\nfVYYN0oxbNiwLnBC5ymGhoYGjh8/zuuvv87Bgwe7/C0pGaenp4uku3btWnFs7e3tODg44Ovri6Wl\nJa2trVhbW9Pe3k5zczO7d+8W/Pzg4GBMTU3RarWUl5cjk8kEQUEa7fLy8kJPT4+bN2/y+uuvs2/f\nPj777DP27t2LQqHAzc2No0ePMmDAAKytrTl06JCAbwoKCmhqahIEDGn5CR2QU319PV5eXhgZGaFQ\nKCgpKRG2OQqFgjlz5pCcnIy7uztHjx7Fz88Pa2trysvL2bdvHxqNRiSQ7Oxs8YCTuvZJSUnExcVR\nVFQkjsHd3Z3nnnuOq1ev4u7uTmJiohDmkQR5pMq3s8jQc889p/M5ZGZmkpGRQUhIiCCR3KnSvb2q\nbWxs5ObNm+I6S+8zMzPju+++Y/369VRXVyOTyXQaXFISXrx4Mc8++yw5OTki6V69ehVLS0smTpyI\no6MjycnJqNVqzMzMuHHjhliJlJeXU1xcTHl5OW1tbbz++uv8WXE7fVqtVt/1TPv/hnhok+6DhBfu\ndz/W1tZiPlaj0WBiYkJbWxv19fU4OzuTkJCAi4sLH330kaDf/tFjWLJkCY6Ojnz00UekpaUJAoNM\nJmPo0KFdKL2dk+6BAwdE483U1JTk5GS0Wq2opiIjI7l06RJtbW2CEVdWVoZCoaBPnz4cOXIEfX19\nvL29hcWPNLYXGBhIa2srR44coaysjO7du5OcnIyNjQ0qlYqqqiqRDI4ePUq3bt04efIk1dXVqNVq\nmpubCQ0NZd68eUJmsVevXpw7d47Q0FAWLlzIypUrBfPsmWeewc7OTqdClJpsEyZMQKPRoNFohJzi\nxIkTaW9vFwLrXl5e9O/fHz8/PzZv3oy7uztlZWW89957QmRHX18fd3d3rKysxGSInp4ezs7O7N+/\nn8TERNzd3XnppZcwMjIiNTWVF154gcrKSsLCwkhPT8fJyYmxY8diZmYmDDmlY5PidqnHK1eu4OXl\nxbvvvsvXX3+NVqvtkmAlam/nqjYnJwcfHx8dklBycjLx8fGiCZqenq6jy3Dr1i2USiU3btwgLi6O\nyZMnY25uLjRzL1y4QF1dHfPnz0elUpGRkcGpU6d48cUXhbKYJHspieGYm5sLEf0/Izon3T9y77u6\nuuqM55WUlODq6tplm85swTttc6/x0CZdeLA2OfezH5VKxZNPPin+39jYSFFREV5eXqhUKjw8PDh+\n/DiOjo68+OKLf/gYL168yHfffcc333yDiYkJkZGROs2zfv36kZqaqmOaGRkZSUpKCvX19ezYsUOo\nPY0dO5Z9+/ZRV1cnRnAcHR3FzQYduLSkLvb0009TVFREXV0dvr6+xMbGMmDAAMrLy7G2tqampgZ/\nf3/MzMwoLi7G3d2d48ePi9EmSbkLYP/+/YwbN459+/ZhYmKCu7s769evBzowtI8++giNRsO1a9fI\ny8sjNDSUxx57jIqKCrZv305rayvPPPMMhoaGOsmrqKgIa2tr3njjDaDD6UCr1TJlyhRKSkr45ptv\nsLa2Fuc0ceJEbt26RWJioo7bsKQ1ITlqqNVqUeFptVpCQ0OJiYlBq9VSVFTElClTCA0NRaPRkJCQ\ngLW1NefPnxfjTn/5y18wNjamqamJvn37Ah24tdQUW7VqlTiHmpoaamtreeeddxg6dKho4BUUFOjQ\nqm/cuIGhoaHO53U73NDa2srRo0cZM2aMwGjvZEQZFBTEe++9x/vvv8/169d1PNF27dqFn58fPj4+\nZGRk4OzsTGpqqrg2p0+fFjh5ZWUl+vr6XeaR/x1xP6vVRx55hNzcXAoLC1EqlWzfvr0LZXn8+PFs\n3LgR6MDoraysuhiZ3ms81EkX/jOVbnt7O/X19TQ1NenM40qTFP369aO8vJz6+nq6detGfn4+Bw4c\n0NFUvddjKC4uZuLEibzxxhsCD+wsNi418fr37y/0FaCDvvvoo49y4MABTp8+zbhx42hvb2fYsGEc\nPHgQExMTzM3NxbF3dlaYOXMmlZWVPProo9jY2AAdmKSPjw9xcXHY2dmh1Wpxd3dHLpdTUFBAZmYm\nhoaGmJubc/z4cczNzQkODiYrKwt3d3caGhpITExkwYIFpKSkYGxszMiRI9m0aRO5ubmkpqYSHByM\ni4sLpqamNDY24u/vj0KhYO7cufzwww8EBARgZ2dHc3OzDlVZGhGTqL/5+fnMmTOH+Ph4Fi9ejFKp\nxNHREaVSSWtrK2PGjOH8+fMMGTKEM2fOoNFo+PTTT+nXrx8vvPACWVlZNDU1CXJAZmam6NbX1tai\nUCgYP3485ubm9OrVC4VCwcWLF/H39xcYf0lJiUieVVVVzJkzRzQPpSTaWa9j48aNwodNJpPxyiuv\nsHz5ctzc3AStGH4dz5WqYZVKxaxZs2hpaeGDDz4Q29wJzzUyMqK5uVmH7gsdeG5ycrIoGJKTk9HT\n02PWrFmkpqZia2vLqFGjOHXqFLa2tmRnZ6NUKv9UaAG6Vrr3Cw8qFArBJA0ODmbatGkEBgaydu1a\n1q1bB3RMl3h6euLj48NLL73E119//YeP/6FOup1Vxh7Evn4v6d6J2GBmZiY47ZKVdllZGXK5nOLi\nYgoLC1m3bp2YsbyfYygqKiI6OpqgoCCdJCOZS3Y+/zspjI0YMYL169czfPhwZDIZDQ0NDBgwgLKy\nMioqKnS+wFLnHTrGyLRaLePHj+fy5cu4ubkJxlZ5eTkHDhxAq9Wyfv161Go1kZGRfPLJJ7i7u1NT\nU0NGRgbl5eVMnjyZhoYGXFxcOHbsGI8++iguLi54eXnR3NzM8OHDCQ8PZ/HixUyYMIHy8nK8vLx4\n//33AYS27hNPPEFVVRWDBg1CqVRSXV2toxssJd28vDzxmWzdupUhQ4ZQUVFBWloaBgYG2NnZkZ2d\njaWlJf3798fDw0MIoevr6/PUU0/x2muvERQURF5eHvr6+kyZMkXo9sbExIhqeObMmTQ3N9O9e3ds\nbW2Fo4K3tzft7e2iEpXGCSUdYEBAI9K5QIdaXFhYmPhMpk6dSmpqapcl7W+Ni6nVal566SWamppw\ncXHRgWBur3TT0tK4cuUKS5YsQaFQkJaWJpKu9L2dPHky0AGDFBUV8dxzz5GUlMSNGzd48sknyc3N\npW/fvrS3t2NhYUHPnj27fIcfZHROtI2NjcJO6H5i1KhRZGVlkZOTw1//+lcAXnrpJZ2V6erVq8nN\nzSUlJUVg+n8kHuqkCzwQbEfaz6/t4/eIDW+++abYVi6Xc/r0aUG59fb2pqGhAQ8PD5KSkpg7d+49\nPSQKCgqIjo5m7ty5vP/++2IEDcDT0xMbGxuSk5PF8UtJt/O5DB8+nKSkJMaOHSsMI01NTRk5ciSH\nDh0S26WlpYljk2xzDA0NCQsLIyEhgUcffRQjIyPi4+MJDw/n6NGjGBoaUlxcjJ2dHRYWFqLqiYuL\n49FHHyUtLY3IyEj09fU5f/48+/btE0s4FxcXGhoaCAsL48UXX+Tw4cP85S9/oaCgAA8PD8rLy3Fy\ncuKDDz6guLiYXbt2YWRkRGFhIXl5eXTv3p3KykrxIJKYV+np6fTq1QulUikq7djYWNLS0oSwuuRV\nNmHCBHJycgS219LSQt++fZHJZKLaUavVTJ06lZ9++olFixYRGRkJdFRK/fv3x9TUlICAAMzMzIRw\nkUTDLS0tJSsrS4j1rFmzRqxUJAdjgC+++EII/HRenhsbGxMUFCSSshS/1ljz8/Pjtdde4+bNm8ye\nPVuMgkmNUMk+SIrY2FgCAgKEiFBqaiqhoaE0NTWxfPly3N3dRVI7deoUvXv3FrrDRkZGtLW1CdEb\nSVb03xkPm5Yu/D+SdP+sWd27JTa89dZbOv9XKpVERETg4ODArVu3OHLkiJhJ3bx5M+PHj+8ipXj7\nMWi1Wnbu3ElkZCQvvfQS8+bNExKAnUdWbvcz8/HxQU9PT2gxSF5fUpXV+fjHjRsnRsc6JxnowHcT\nExMB6NatG2lpaTzyyCM0NTVx4sQJCgsLkcvleHl5ERcXx9ixYzl27BiPPPIIBQUFnDx5kqCgILp1\n60Z1dbXAbU+cOCEcOCROvZ2dHba2tqjVahQKBfn5+Xh6epKamkrfvn3x9PTkqaeeYtWqVSgUCuLj\n47l48SJBQUH06NFDjMlJo1ZpaWlkZWUhk8kYMGAA27ZtIzc3lytXrnDjxg3Cw8PFEPyYMWOIi4tD\npVJhb2+vYyIpKZ6pVCpiYmJobW1l7969/OMf/wDQsUry8fERJAkfHx9SUlLQ19dHpVLx9ddfC+2G\nrVu3iu9LeXm5mCL45ZdfWLZsGa6urjqVKMDNmze5du2ajp7H7Um3tbWV0tJSNm7cSFpaGtu2bSM7\nO1sHSsjLy8PR0VHofBQUFFBWViaYWFVVVTQ0NODu7s53331H9+7d6devH9DxMCorK+O1116jubmZ\nvLw8XnjhBTZs2IC7uzuXLl0SFfafHQ+z7gI85En3zyJI3ItjA3RUtxLmKe1HghjKyso4evQoAwcO\nxMrKSjCkwsPDefXVV4VVjfRelUrFqVOniIyMZNmyZbi5uYn36OnpERUVpZNko6OjOXLkiM61kCpY\nSZd327Zt9OzZ846ww9mzZwWk0HnyobGxkcbGRoyMjMjNzSU0NJTa2lqcnJyENZG1tTU+Pj7Exsby\nxBNPEBoaKmQdjxw5gpGRERERERQUFNCnTx8OHDiAv7+/SDRFRUUYGxuTlJTETz/9xKBBg1i3bp2o\ndNPS0hgxYgQ1NTVCa9jQ0JAnnniCXbt2ERAQQFhYmJhDlirdffv2cfPmTSIiIkhISGDixImYmZkJ\nemqfPn1EpWtlZYWZmRlGRka0t7djZ2en812wtrZGLpezf/9+qqur6du3L1ZWVmi1WpqamoTco7u7\nO9XV1eK788svvzB8+HA8PDz4+eef8fLywtHRkbq6OmbMmCHwcKkyLCsr4/LlyzQ0NODj4yMeSCqV\niqKiIkJCQoTwOXSFF3JzczE3Nyc2NpZdu3Zhbm5ORkaGzqRCZzxXq9Xy/PPPY21tLeAASWOhqamJ\nlStX4uHhIUxEf/zxRwwNDYmKihIP41mzZpGYmCgMR+Vy+Z9GiOgcD7PCGDzkSVeKB5V0pdnMe3Fs\nkOLvf/878C/lqNjYWNRqNUZGRhgYGKBQKGhsbMTJyYmUlBTCwsIoKChg/vz5uLu706dPH0JDQ3Fy\ncmL69OksWLCAxMREZs6cycGDB8XfGT16tA7EMGDAAK5duyYG8FUqFQMGDODo0aPi2Lds2cJrr72m\nc9MCgjd//PhxIWMoRUtLC+bm5vj7+5OYmEhkZCQ5OTkEBARQX1/P5MmTcXV1xcXFhYzbiPCKAAAg\nAElEQVSMDAYMGMDUqVPJyMhg1KhRNDc3U1BQQEREBIWFhfj7+2Nvby+W1tCRaEJCQjh48CC7du1i\n0aJFHDt2jOzsbNzd3UlPTyc6Opq2tjaam5sFRjp37lwuX76Ml5eXSLpVVVVCuS07O5t58+YxfPhw\nHBwc8PPzE42vgIAAgoKCRKUrifFIn3vnVYxMJhPXVRr2P3PmjEjyVlZWosqWtALkcjlNTU3o6+vT\nu3dvXFxcMDIyEk0mKZFKUwz29vaiiTlixAiqqqpwcXERIi979uwRdNX169ej1WoFRVvqoqvVaj79\n9FOam5vZu3evKACk8TApOss3bty4kYqKCgErwL+EyyVDy4KCApF0161bR0REBDKZjB9++AF3d3dK\nSkpoaGgQ35s/G8uV4nYB8/9Wuv/GeFCVrlarFbYvKpXqrh0bOsezzz4rfpY66/3798fHx4e6ujqO\nHz9OUFAQSUlJTJo0Ca1WKzRlP/74Y6BDfKO0tBQzMzNCQ0ORy+WMHj2aw4cPi2pYYoNJs7WGhoYM\nGTJEVKlSYyo5OZm2tja2bdtGv379mDRpEjdu3OhiWClBDBcuXECr1aJQKERVaGZmhq+vLwkJCQwa\nNIjc3FwyMzNRKBTk5eVhZmaGUqmkd+/eGBsbM378eKqqqtDX10dfX5+EhASRdCWYQZrgaGlpoaWl\nRbjwenp6CludvLw8DAwMMDQ0xNHRER8fHxoaGpg9ezbFxcW4uLigp6dHeno6YWFhXL58maysLHx9\nfRk9ejRyuZyPPvqInj17YmFhwdatW4mIiKC+vh4/Pz8hVVlZWcmbb77JO++8I869s1KbSqUiNzcX\ne3t7tmzZwvfff09dXR2zZ88GOjDp06dPAwjxdDs7O1xdXTl69Ci+vr5CsvDSpUtUVVVha2vLpUuX\nRPKT3g8dlbqPjw9mZmaYmppiYmLC+vXrsba2ZvTo0RQXF3P58mWuXLkinDyqqqqYOnUqycnJvPDC\nCyIRNzY2UlFRoaM9IVW+xcXFfPjhhwwYMECHpJGamoqvry+rV6/mjTfeEDY9ly9fpqysjClTpqDR\naIiLi2Pq1KksW7YMe3t7IQKzaNGiu75f/mh0hhf+W+n+B+J+k650Q9TV1aFWq9HX178nx4bOITGO\n4F/c+7y8PDQaDZaWluzYsYPhw4djYWFBVFQUqampxMXFiTnOoqIiTE1N0dfXZ+zYsaK69fb2xsbG\nRtin29raEhISQnx8PNCBiw4ZMoSDBw8ik3XY1NjZ2dGnTx9iY2P55ptvxJjSY4891oVbPmbMGI4c\nOcLmzZvFNenevTsKhYKamho8PDy4fPkyffv2JSMjg8bGRpydnTl16hTt7e2UlZUxePBgoKNylslk\nnDx5kjFjxlBZWYmvr6+wcvfy8qKxsZErV65QUFAgZmirqqpE80jyUsvPzxfzopWVlXh5eQnboC++\n+AKZTMaWLVswMzOjrq6Oy5cvc+PGDSorKwkPD0cul9OrVy9KS0uprq4WDy2J7OHr68vf//53goOD\nBQFCImlIn19OTg5ubm7Y2NigVqt57LHHGDhwIDdv3hTVqZQ0v/76a+zs7MRI2rFjx/D19aWqqorm\n5mZhB29kZMS5c+dEcr906ZJQ90pPT9eBDMrLy7ly5QphYWGYmZkxc+ZMduzYQV5eHv7+/qSkpDBk\nyBA8PT3x9fWld+/eQgw8IyNDiAh1hheCgoJYsGABr7zyCqWlpWJSAToq3YyMDEaMGIFSqcTb2xtj\nY2NWrFiBkZER/fr14+TJk7S2tjJt2jQOHz6Mt7e3EG2X8N8/O26HFzpPZzwM8f9t0pUcG1paWsTN\n8EdkFgFRsba1tQlr8/z8fNra2qioqMDb2xs9PT1Onz7NBx98gJ6enugCSzKMWq2WsWPH6kwVdE7C\n0AExHDp0SODOklyjRqPREcXesGEDarVaDMZPmDChS9Lt1q0b3bp1E/vXaDTo6emJf4uLiwkICKCk\npIS2tjaMjY0ZNmwYLS0tYgxryJAhQEen3tzcXCxbtVotFRUVFBQUkJKSwvjx45k5cyYbN27k7Nmz\nQpNCYo5BR+VuamrKzp07CQ0NFaw7yeL76aefZv369YSEhDB9+nSWLFlCr169+OWXXygpKcHHx0dU\nb05OTshkMiZNmsSVK1cwMzPjzJkzXLlyBTc3N37++Wc+++wzMjIy8PDwwNjYGCMjI3JyclCr1cTE\nxIj/Nzc3884772BsbExrayseHh5cvXqVuLg4bt68yfr167Gzs8Pc3Jzi4mLOnTuHs7MzhYWFKBQK\nsrOzsbGxoaamhrNnz4qRNY1GIwRwWltbRRMPOlySAwMDBUQwa9YsduzYQWZmJkqlkilTpvDBBx/w\n5ZdfkpmZKex0lEolly9fxv//sHfmUVXV6/9/nQnOAQ6gMquAoiAIAgKiiKg44DyVww1Ns8m0W1nZ\nYKONlqXXzMoyc8oGcyI1FUUlFRSQQXAAmSdlkJnDePbvD377c6Hpdstb3+66z1qs5ZLD5+yzz97P\nfj7P8x48PGhubsZoNFJTU8ONGzeIi4ujsrKSRx99lNTUVHGumpqayMnJYd++fTz99NMkJyfj7+9P\ndna2sJ/v168f69atw8rKipSUFExMTLh58ybt7e0MHz78d907/078cJD2P/TCHxi/pb3Qmdig1WqF\nlurtkIicNWuW+LednR1GoxE7OzssLS2xtbXl6NGjVFRUcOjQIRYvXoxKpRKT44kTJ4oWQVhYGBkZ\nGUJ/tXMfV5IkwsPDOXz4sJBe7N+/Pz179hS0XuhIuidOnGDJkiXiPI0cOZKsrCyKioq6HPfYsWNp\nbGwUfUl5m6vVaklISGDEiBGsXr2a7t27YzQahbZtQUEBbW1tDBgwQDgqWFtbo1QqSUpKwtfXlw0b\nNtDa2kp0dLRIunv27OHs2bM4Ojry5ZdfEhwcLLSAc3Nz8fLyIjY2Fh8fH9avX8/DDz9MTU0N165d\nIzg4GE9PTxoaGnjqqac4ePAg9vb2XLhwgZ49e1JdXS0qZIVCga+vL66urtTX19PU1ISPjw933303\nV65cwdfXl969ewumldw7fuihh3B3d+eDDz7g1q1b9O3bF4VCgYmJCXl5eRiNRlGpt7W1ERoaSmho\nqGiZyAyuxMRE9Ho9Wq0WLy8vNm7cSF1dHRcuXBCYWqVSiZmZmfgu5O/GaDSyY8cOunXrJlAKrq6u\neHp6smfPHk6fPs3BgweZPXs25eXlNDc306dPHyEGnpWVxaBBg0Slm56ejrOzM6tWrWLt2rUUFBQI\nRpskSVy+fBm9Xs+UKVOEKaefnx8bNmxg1KhRBAQEkJ+fT1JSEuHh4WzZsgWDwSD6uW+88cbvLlp+\nTfzwHv1fT/dPil9DkPg5x4bbiYBQKpUCnynztUtKSgRj6ciRI3h7e1NWVkZhYSEjR47k/Pnz1NbW\nMmHCBGJiYmhpacHU1JTRo0cLlIIMFcvNzRU0XNmNWK4QIyIifmQu2dTU1AXMbWJiwsSJE7s4GMA/\nQfoy4N/Z2ZmwsDBqa2sFrvPgwYP06tWLsLAwEhMTcXFxEWgK+Rzm5+fT1NQkXIcXL17Mzp07sbW1\nFUO5Xr16ERgYSHx8PP3792fbtm08/vjjpKSkUFVVRV5eHsOGDaOmpobKykqio6O59957hS5vnz59\n0Ol05OXlUVZWxtNPP83hw4eRJIkNGzZQXl7eBYfq5+dHYmIi5ubmWFlZCYGa0tJSIft45coVYTUv\nI1EuXLiAj48PdXV1fPrppygUCmxsbIQrw8aNG7GyskKpVFJWVsapU6eEU8ewYcOws7Pj2LFjWFpa\nYjAY0Ol0DBkyBK1WS0NDA7du3erS0pDPoazDcPr0aaFl7OHhQU1NDa+//joXLlygpqaGqKgoUQHL\nA7DOSS8jIwMfHx9Ba75+/Tq1tbUsWbIEPz8/0tPT8fHxEV5lsbGx1NTU8Nhjjwnas4uLC3v37sXB\nwYGgoCA2b96Mi4sLnp6eJCcnC5ifRqP5EWb4Px3/g4z9SSGf+F+qUn+tY8PtQkC8+uqrAGIg19ra\nSn5+Pu3t7bS2tmJlZUWvXr04cuQIDz30EAqFgsceewxHR0fc3NyIi4sDOnqtnVsMo0eP5ttvv8XM\nzAxLS8sfoRjkpCt/hueff56wsLAuEo7QwerqrJcLiNfI21xzc3Pc3d1xcnLCaDSyf/9+vLy8aGtr\nY9iwYcTGxjJ9+nRaW1u7APmvXbtGVVUVDz74IIWFhUydOlVUmVOnThWvW7hwoSA+tLa2MmbMGIYP\nHy40BmQhobVr1zJ//nysrKzw8/NDoVCg1+tJS0vjgQce4OWXX8bS0lLgV0NCQkR/UQ5fX1+Sk5OF\nh5upqSlpaWk4ODiQlJREeXk5V69eJTk5mWHDhuHs7ExJSYnoo4eFheHr60uvXr2Iiori1KlTKBQK\nDAYDAQEB+Pv7d8E+l5aWkp2dLR4YdXV12NrakpyczIQJE4QAd7du3QQho7NCWn5+PgDbtm1j/vz5\nZGZmcuLECfz9/SksLBSDss6fMT09vYuTr1y5dv4/2dHkiSeeEENIPz8/4cqwe/duBg0aRO/evamu\nriYrK4uYmBhmzJhBeno6Xl5efP7559TX11NcXEy/fv2or6+npaWFcePG/S467r8TP3yf/yXdPyl+\nD7Hhl9b4LcfRWTBD7k8aDAbOnz/PgAEDSExMpKWlhe+++47AwECUSqUQX54wYYJoMUyYMIETJ05w\n69Yt6urqmDRpEidPnhQ37Q+T7tChQ8nPz+fGjRucOnWKa9eu8corr7B79+4fsdNSU1O7aLwmJCR0\n+QzZ2dlYW1tja2uLnZ0dJ06cEFqjWq0WX19fXFxcaGtr62KAeOHCBTw8PLC0tBQEDT8/P27dusWU\nKVPE60aNGkVbWxvXrl1j4cKFXT5Pbm4utbW1DB8+nJs3b4qqVX6f7OxsLCws+Pvf/87Fixd59NFH\nRSI6duwYFhYWXQTafX19hSVPVVUVZmZm9OrVi/3791NTU4Ofnx9mZmbodDpefPFFGhoaKCoq4urV\nqxgMBvEQDQwMJCUlhZ49e2JlZUViYiIDBgzg0qVLaDQaHnnkEfR6vbCU6TzYc3BwoKGhgZEjR7Js\n2TIsLCxob2+nT58+XXZaMmssJiaGI0eOcO7cOZqbm8nIyOC7777j4YcfxmAw4OzsLERYgC56CdDR\nWzc1NRVY4JSUFL7//nueeeYZIbKTlpYmIF5ZWVlcvnyZ5cuXo9Vqhb7Gl19+ydKlS8Xg09vbm9ra\nWg4fPkxVVZWgM69Zs+a2aVr/u/G/9sKfFD8kNhgMhl9NbPipNX7PcQBCyKSiooKGhgbxu2HDhqHV\nasnLyxMKWG5ublhZWfHII48QERHBsWPHMBqNWFhY4Obmxvnz57G2tmbSpEmcOXOGxsZGoCNxJScn\nCwqsRqNh5MiRHD16lOeee45XXnmFoKAgVCqVQD5AB6103LhxosWQm5srgO0KhYK+ffvSs2dPbt68\niUqlEtoB2dnZqNVqkpOThVCMubm5wP62tLSQmZlJWFgYJ06cIDQ0lHXr1onjk88DdEzsFQoFCQkJ\n3HXXXUDHQ+b48ePk5uaKai8sLIz169cLai10EDgCAwMxMTHB3Nyc5uZmlixZglarZf369bi5uXV5\niLi4uNDc3Ex5eTl+fn7k5OTg5OTErl27xMDQw8MDHx8fBg8eTHt7O21tbbz44os4ODgI8fKAgACx\nU/L39+f06dNUVVVhYWGBtbU1WVlZPPbYY/Tp04fExER69eqFvb09kiQJN+To6Ghyc3NRKpVUVVVx\n8uRJTE1Nsba2Fhhe6JgNWFpaotPpCA0NZfv27Xh4eLB792769evHmDFj2LVrl1CHk0kNcnTG55aU\nlLBw4ULUarXwPIN/JmpJknj88cdRKpVi4JqcnCyGu/JcYvfu3QwdOhQ3Nzfs7e3FYFWlUglNZXk3\nJ6ux/Sfih5WuPAj/K8VfOul2rhI6ExvkAdOvJTbIa9wuVtuqVavE/6lUKtzc3KivrxeJR6bPnjp1\nivHjx1NZWYnRaOTixYsYDAYuXrwIwNSpUzl+/LjYjvr7+wsFsZ+Sdhw7dixbt24VE3uFQsGcOXOE\nELYc06dPFyiGTz75BOjYrkqShF6vZ8SIEcTFxZGWlsbVq1fp0aMHV69eZfDgwRw+fJhJkyaRnJxM\nUFAQX3zxhUBRVFVVMWvWLKKjo1myZAnJycnExcXh4+PD5s2bBcvq8OHDqNVqNBqNSLCOjo64uLhQ\nVlbGxYsXuXDhAqtXr8bOzo6dO3eSk5ND3759OXLkCAEBAXz88ccCLpSamkpgYCDJycn4+Pj8qHJX\nqVSUlZVhNBrp1q0bp0+fJjo6mgULFmBtbS3oskqlkgkTJmBqasrZs2cZN26cWEdmXVVWVhIaGkp7\ne7vQnggJCSE+Pp6goCBmzZqFp6cnNTU1gpChVqt55JFHhMOtXBBIUoezcGeRIfmYP/30UxwdHYWX\nndFo5JtvvkGlUgmbpkOHDmEwGMjLy+vSU5XtdxobG4mMjGTGjBk4ODgI0oRcDLj+f6GfoqIi3N3d\nhcbCxYsXxUMkISFBPLjq6uowGAwEBQUJcafx48djbm4uKvX29naam5t/5N57uxLxT7Uxfu09/n8l\n/lpH+zMhP11/K7Ghc9yOC2PmzJni3zLZABDVpaWlpYAkhYWFYWVlxaRJk3jttdcIDQ0lNjYWc3Nz\npkyZ0sXh4Yd93p9qMSQlJbFq1SpxIc6ePZtvvvmmy6BxwoQJnDt3jurqaj7//HMAsRvIzc3lypUr\nHD58GIPBwNixYxkyZAitra2cOXOGtrY2PDw8yMvLY+zYsQQEBBAVFcW5c+eADhxxcXExw4YN4957\n7xWuECdPniQ/P19oNyiVShYsWMBrr70mPl9wcDA6nY6bN28SFBSEl5cXr7zyCm+++SZZWVkEBARw\n6dIlunfvzrvvvotCoWDDhg1ER0cTEREBdNBjCwsLRWKrq6sTguwFBQUCN7tz507CwsJobGzEzMyM\nU6dOUVdXx/jx40VfWGaNQUd7o7W1laKiIvz9/XF1dcXc3JyysjICAwMpKirC19cXb29vdDodgYGB\n1NbWCj2JiRMnUlZWxnvvvYckSUL8ftasWahUKqGKptPpaG9vZ9iwYcIaBzpcfPV6PTk5Ofj6+rJo\n0SK2bt3KlStX6NevXxfZR7kHu2TJEjw9PfH29u4yUJXdfevr63nuueeYOHFiF9Hx06dPC/fbxMRE\nKioqeOCBBzhz5gyFhYUUFBSINsX69etRKP7p4KvVaoWVutxqkSRJJOKGhgYMBoPAE8sP4l8bt0vA\n/M+Mv3TSlaUIW1tbxZDlt9p23A7hHPnvlUqlALm3trZy7do1unfvLrRYZfyjXLXV1dWRmppKZGQk\nxcXFArXg7e1Ne3t7F3EWWTwbOpLnsWPHxNZ73bp12NradtnKe3p60qNHjy7MJ71eT1hYGN9++60w\n2aurq0OhUFBZWUm/fv3Q6XQolUo++ugjIXM3ZcoUGhoahOjJxIkTWbJkCTt27ODLL7/E2tqa06dP\nM3LkSDGggg5HVR8fHz788EPq6uooLCxEoVDwwgsvkJOTw4kTJ2hsbBQVlSRJPPbYY0DHtj4kJIQL\nFy4wcuRIqqqqeOONN2htbeWtt94SvWKZACEPruTdwrVr19Dr9UJHwsXFBWdnZ65evSr0JMzNzfH2\n9uaOO+6gT58+tLS00NLS0mXLfv36dTQajbAZKi4upmfPnl2MJi0sLPD29iY9PV20ZRQKBfb29qKl\nEB8fLzzlXFxcKC0txdraWjAMZS0HGU8sJ8uvv/6aCRMmiJ7u1KlTSUtL49SpU12OEzoq3fj4eG7c\nuMHatWsF7VwOubXw5ptvMmrUKG7dukVAQADQwWQrKSkRMofnzp0jJyeH6dOnc+3aNaZOnUp8fDwt\nLS2YmJjg4OAA/LgClXcY8vBSTsQ6nU7sqmR3ZzkRNzc309ra+m8n4j9igHc74y+ddOVEK1eSt2O9\n25F0ocOkEBBtj0mTJqFQKMjMzKSsrIzW1lYaGxvJz88X9tZPP/00ubm5JCYmUlVVhUKhYNKkSYK4\n4O7ujpmZmaBduri4YG9vT0JCArt27SIhIYEVK1aI6lWOn2oxzJgxgw8//BCgC3ROq9UyYMAA6urq\nGDZsGI6Ojpw4cQITExMSEhL4xz/+wcmTJ0U/esyYMZSXl3PixAmcnZ3ZtGkTaWlpjB49mjNnzgjK\nsEKh4JNPPsHHx4e2tjYcHR0xNzfnmWeeYc2aNWJAKFelvr6+Ynv67LPPcuPGDVG9yw7EM2fOxGg0\n0tbWxp49e7CxsSEyMpKbN2+KXvPVq1cFYeDOO+/knXfeoaGhgaSkJG7cuIFKpaK4uJhPP/0UT09P\n5syZQ7du3QQZQI4zZ85gb2+PWq3mwIEDGI1G8vPzGThwoBim5ebm4uzsTE1NjdiR9O/fnxs3bpCU\nlERgYCD79+9Hq9Xi4+ODWq3m3LlztLa2YmZmJj4/dLjyWltb06NHD1pbW9m/fz9ubm4CxaHVahk8\neDC7du3qMkRrbm4mOzubmJgYPv/8c0xMTH6kA5uamkqPHj344osvePXVV0lKShJJd8OGDaLvX1tb\nS25uLnPnzuXKlSsolUrx/0AXRMqvvT9kzWJTU1PhhGxmZiZaL7LbRuf2ROc+cefkLtsv/dXiL510\nAYFDlL+Q3xO3M+lOnDixS89ZfoJ7e3uTkpKCq6srBoNBVIZWVlZcunRJJGsZxvVDNtpPtRg+//xz\nnnrqKbZs2cLs2bM5depUFw2BO++8k3379okqSl5XFmuxsbFBqVSi1WqZNm0aH330EZIkicl9TEwM\nvXv3pqCggBkzZmBnZ4dGo+HVV19FpVIxevRoDAYDly9fJisri5UrVwqK84ABA8jMzOSDDz7A19eX\nwYMHC4WuhQsXEh4eTkVFBTExMYJw4O3tjZmZGaampsJJWKPR8NZbb6FUKiktLcXCwoKioiKKioro\n3r07dXV19OnTh8zMTB588EG++OILKisrOX/+PDU1NZiamrJw4UJGjhxJU1MTsbGxnDt3Djs7O5yc\nnDAxMeGFF16gsrJSJPfOzspnz54VguDbtm1j2bJlNDU10bNnT1JSUhgwYADnzp1DqVRiZ2cnHmTj\nxo1Dq9Xy7rvvEhQUxNmzZ2lqasLNzY2cnBwmTJggbII6m0XGxsaK6jQmJgY3Nzdu3LghEAeSJJGR\nkUF2djb9+/cXf7d9+3aMRiNfffUVtra2tLW1kZGR0UWMJi0tjQMHDrBy5UpMTEwoLi7G09OT5uZm\nPvzwQyF2Hx8fD8DSpUvZvXs3ZmZmHD58WJwf+VqVj+e3VJydWxMmJibodDoBY5O//859Yrk/fP78\neY4cOfK7dReqqqoYP348Hh4eRERE/KyXoaurK76+vvj7+zNkyJDf9Z5/+aQLf4yQ+W/5+6CgIPH/\nR48exdnZmcuXL+Pg4EBNTQ0WFhbs2LGDkJAQ9Ho9R48eZfz48UKwBX6ZnQYdKIYdO3bwwgsv4O3t\njZWVFREREezevVu8pk+fPmJwJ4fMLoMOHVWZcbV06VJKSkpQq9UEBwcLBwhnZ2dMTEyIj48nMTGR\noKAgdu/ezSeffCJUymbOnCnoufv37ycgIABPT0+mT5/Orl27uO+++0hMTMTCwoJ3330XR0dHwsPD\nmT59Om+88Qbff/890CH5KA9qFAqF8J3LzMxEr9fTu3dv/Pz8SEpKIj09nT59+qDVamlqauLcuXM8\n8MADKBQKFi1axK5duwSU7+TJk6jVaqZMmcLFixc5e/YsDg4OgiTxySefMGfOHGG5vWPHDqCj9SK7\n6up0OnJycpg/fz5Dhw4VTLxRo0YJycPq6mrhlFFbWyvaFZcvXyY7OxvX/y/Q7u7uzuTJkzEajdTW\n1opBlkwJluFy33zzDbNnzyY1NVUkz6SkJFEdX7lyRRzvSy+9xJgxYwR64dq1azg6OgqqbF1dHQUF\nBSgUChYvXkxycjKDBg1CrVazadMmTExMuOOOO8R6jo6O9O/fn+joaLHrMhqNaLXa3+0V9kshtydM\nTEy69IllI9TMzEzWr18v7qtp06aJ8//vxOrVqxk7dizXrl0jPDycN9988ydfp1QqOXXqFMnJyT8y\nEv134y+fdH8NQeLfWet2Jt3169cDHS2G6upqFi5cSH19vSBFGAwGUamUl5cLZtWUKVMoKCjg9OnT\naLXaLi6/oaGhXL9+ndLSUqqrq3nttddQKpUMHz5cvP/8+fOFgI0csvOBHNnZ2eJYm5qaxHTe29ub\nlpYWLC0tqampIT4+HlNTU7Kysli8eDHPPPMM/fr1w93dnVdffZUVK1ZQV1eHjY0Nly9fFoOonTt3\n0q9fP5ydnXnsscf45JNPhFWNWq3G19eXdevWsX79enbs2EF+fr4QIW9ra+PixYvi+HJycqipqUGl\nUtHQ0EBpaSkDBgzg8uXLFBYW0rt3b+rr6ykqKhKUWAsLC+Li4mhtbcXT05MxY8YQExMDwD333ENz\nczMJCQmi31tXV8fHH3/M8uXL6dmzJ0qlkq+++oqdO3cSHx+Pv7+/QCSo1WpaWloYNmwYN27coKGh\ngdmzZ3Pu3DnS0tJoaGjA2tqaoUOHkpCQgJeXF01NTSQmJtLa2sqiRYu4evUqgYGB5OXlie223F+X\nIVCNjY00NjZy5MgRZs6c2SXp7t+/n9GjR2Ntbc22bdt49dVXWbNmDSNHjuyCiU5JSemiJBYXF4ck\nSaxdu1bACQMCAqisrGTt2rU0NzczdOhQjEYjJ06cYM6cOcLWKTg4GIPBANDlPeD3eZX92pDXV6vV\nLFiwgDVr1nDfffcRExPD3Xff3WWn8GvjwIEDLFy4EOgg7fxQ/lQOSZJuiy0Y/BckXTluJ+TrdoQk\nSQKGJEdNTY3YtjU1NWEwGNBoNHzwwQeoVCoqKirIz89n8uTJmJmZsXDhQurq6m3nKZoAACAASURB\nVITADSDcVnfv3k1ERAQBAQEsW7aMbdu2ifcJDw+nuLhYDLKgY0p+8OBBMbCRK2no6JGWlJQQEhLC\nzp07RXJTKBQcOXJETPNffPFFMjMzRYW1YsUKJk+eLLR3MzMzGT9+PPHx8ahUKtra2nB1dcXd3Z2Q\nkBCef/55NBoN1dXV9O7dW5Ai4uLiBGZ20qRJzJs3j2+++UYMW/bu3SsMM8eNG4dOp6OtrY2UlBSu\nX7+O0Whk2LBhLFq0iG7dunH27FlaWloEvblv374MGTKEU6dO0dDQIEwkLS0tuXnzJjdv3mTTpk2M\nHDkSZ2dnvv/+ezw9PYVx4cqVKwkICBBU2qCgIGFfdPHiRRQKBT179qS+vp5//OMfKJVKmpqaiIiI\noLi4WIjByD3Qbt26UV1dzcCBAzl37pwQPndzc8PCwkIMQqOjowWJRqPRcOvWLaHqtX//fvr27Yuf\nnx9VVVXs3buX48ePc+XKlS7bX1mlTL4mX3nlFdzc3ARaQU66b731FuHh4VhZWeHk5MSePXswGAw8\n9NBDvPrqq/To0aPLg/yNN9740fX+R8RPKYz169ePO++8E3d39397vbKyMlGxOzg4UFZW9pOvk1tF\nQUFBAmb5W+Mvn3Rvp3bC7ap0W1tbBaZRVuCCDtUoFxcXYc7Yu3dv7O3t2b17N15eXnh6enL8+HGx\nbe3RowcrVqxg4sSJnDhxQoDhvb29eeWVV5g0aRLvvPMOixYt4osvvhAAe7Vazbx587oM1JycnBg0\naJBwj+hMBba3t6esrIwpU6bw3nvvYTQaGTx4MN988w1RUVE0NjZy9913U1hYiEql4sqVK3zzzTds\n3rwZW1tbBg0aRFFREe3t7eTk5LBjxw4iIyPJy8sTtuvLly8nLi5OWPV07p2p1WrxMNi7dy9z585l\n3759aDQabt68KdoiI0eOZOPGjWi1Wo4fP05qairXr1+nvLycUaNGce+993Lz5k1WrVolLNVNTEyI\njY3lypUrODk5kZaWBiAoyHl5ebi5ubFhwwaWL1/OuXPn8PDwYO7cubS0tHDo0CGqq6vZuXMn7e3t\nmJubExAQQGxsLH5+fhQVFdGtWzeuXr1KcHCw0ERoaGhg8ODBBAYGYm1tjVqtJiYmBgsLC15++WX6\n9++PpaUl8fHxQlCnoqJC+LNBhybE119/zezZs4V3mSwmZGJiwo0bN8jMzKR37944OjqiVCqprKzs\ngtntnHQ3btxIQUEBf//738Xvk5KSsLGx4euvvyYwMJDg4GDa29t55ZVXcHJywsLCgr179xIeHi7w\nz2ZmZgK18MPr/z8dne/PX6ulO27cOAYNGiR+fHx8GDRoEFFRUT967c99hrNnz3Lx4kUOHz7Mxo0b\nf1MrQ46/fNKV43ZVqb9nDXn7UV9fj6mpKZaWlmzatEn8vrKykhkzZiBJEnZ2dqjVau68804UCoXA\nQB49epSePXui1+tpaGjg1KlTJCQk4O7uTlRUFA8++CDvv/8+7e3tok3h5uaGt7d3lwFbZGQku3bt\nEnAy+CeK4dKlS4JNJB+XJEmYm5vT1NSEQqHggQce4IMPPhDwnnnz5vHdd98xYsQIamtreeqppxg8\neDB79uwRSmIjRoxg5cqV7Nu3j7vuuov8/HxcXV2BDk0BeYtmZ2fXxY9tzZo1DBw4EFNTU4qLi3n9\n9dfp27cvR48eZfHixZiZmWEwGHj77bextbVlz5495Obmil5rVlYWISEhaDQaTExMqK6uFkMZhULB\n119/zfLly3FwcODUqVMolUrMzc2prq7Gw8MDKysr4TBx+PBhRo0axZw5c4AOqFZtbS3+/v40NDRg\nNBpJSUkRFkmWlpbY29sLK3PokGCUCTshISFUVlZiMBi4fv06U6dORa1WC0EfhUJB7969kaQOi6dF\nixYJ2KEkSZw+fZopU6YIbC10PDC9vb35+OOP6d+/P8eOHSMrK4uvv/5aaAlDB0vw2rVrQn953bp1\nqNVqQbgoKSmhtbWVTZs28cgjj5CRkcHQoUPZs2cPkiQRERHBF198gVarFYNgQLgD34575reEnBir\nq6t/VdKNjo4mLS1N/Fy6dIm0tDSmTZuGvb29aOvcuHHjZ1sUsuOJra0tM2fO/F193f8l3duwRmed\nB+ggP8jT6969ewsgOXR8sW1tbZw8eZLi4mIhEK5Sqbh16xZnz55lwYIFODg4UF5ezh133ME999xD\nTU0N9957Lz169CA9PZ077rhDDHoAFi1axPbt28Xxe3t7Y2try+nTp8VrZsyYwbFjx1iyZAnQIQak\n0+kEuH7nzp0Cgjd9+nQKCgrQarXY2Njg7OwsYGlqtZrY2Fi2bdvGhAkTaG5uRqlUkpGRwYABA9Dr\n9dja2lJSUkLv3r0xGo08++yzBAcH8/333+Pi4sLHH38sLH0+//xzWlpa8PDwYNu2bezbtw8HBwdW\nrlxJamoq1dXV9OnTB09PTxQKBQMHDiQoKIimpiZKS0tpaWmhX79+REZGMnr0aIxGI4WFhdTV1TFw\n4ED8/PzYu3cvqampfPHFF0Khrb29HVtbW65evYqdnR0mJiYcP34cU1NT4fC8cuVKAOLj4zExMcHO\nzo6zZ89SUlKCk5MTt27doqKigoMHD5KYmCjYkDY2NmRkZAiMsaOjI0ajkSVLlghIVFRUFBYWFpSX\nl6PX69HpdAIxICc4a2trLC0tSUlJwdfXl4yMDD7++GNSUlJQKpVs2rQJU1NTmpqa2LhxYxePMnkA\nKl87r776KiYmJjg7OwMdVW7fvn1JS0tj6dKlglW3evVq7O3tCQkJYf369TQ1NXXx1+uMWvjh/fOf\njs7thbq6ut8tYD5t2jS2bt0KdIgMTZ8+/UevaWxsFKJKDQ0NHDt27EfY6H8n/vJJ989sL/yUzsNP\nURJlzrtSqWTv3r3C/LBv375cuHCBhQsX0r17d5qbm4XNuSyKsm/fPuEAYGZmxosvvoi1tTX33Xcf\nW7ZsETfnjBkzSE1NFcpV0FHtdm4x2NjY4OPjIzy+oEN3t7W1ldbWVlJSUsjPz8fd3Z2mpiY0Gg2F\nhYX4+/tz6dIlsrOzmT9/PpaWlmRkZLBhwwaWLl3Kl19+iY2NDQsWLCAjIwODwSA+p6mpKfv376ey\nspLXX3+dCRMmCIWrHTt2sGrVKhYvXsylS5cICwtjxowZPPTQQ0RFRZGTkyOm1QsWLOhyTh944AEs\nLCyQJImhQ4cydepUhgwZwrZt21AqlcI8MjQ0FDMzMwYOHMi3335Lfn4+S5cuxcHBAVNTUzIzM4Xz\n8JQpU8jJyeH8+fOMHj0aLy8vlEol8+fPp6mpiRdeeIE9e/ZgbW2Nh4eH2Cm0tbVx9uxZgQo4ffo0\nAwYMID09nYCAAOHMDB1Sk3q9nmXLlhEfH09tbS3Xr18nMDCQ/v37s2PHDlQqlaiaZdTKxYsXOX78\nOBMnTkSv17N582Z69+6NnZ0diYmJ2NraUlZWJphtgEAmLFq0iGXLlolzJd8ziYmJFBQUCKGfsrIy\n0tPTsbOzIzMzk6amJtRqNb179xYDNBsbmx/h4v8ohbEfvtevrXR/KZ5++mmio6Px8PDgxIkTghRS\nWloqhoU3b94kNDQUf39/ca39Hqv5v3zSleOPTLoyrVF22/2hzsMP19i4cSOAcAnw9PQU7CkTExN6\n9epFbW0tRqNRqGK9/fbbODk5oVAoBG3W0dGRvXv3Av+kzMr9Tq1Wy+zZs9m1a5d43zlz5nDw4EHx\nlG5ubhYaBHI8+eST5ObmYm1tjZOTEw4ODnh6elJcXCyIHR4eHixduhR7e3siIiJwdXVl3LhxNDU1\nERAQwOHDhwkKCiI8PJy6ujpmzpzJc889h7OzM+3t7axcuRInJyeGDh1Ke3s748eP5+bNm6xZs4bT\np0/j6OiIjY2N6IXOnTtX0EdlpmBnajV04IwbGxuRJIljx44REBDA22+/zfbt2+nTp49AZzg6Ogro\nka+vLyEhIURHRwsd2IKCArKysmhpacHMzIyZM2eybds2IiMjefzxx2ltbeXWrVuCtuzq6oqdnR0R\nERH069cPT09PqqurRbvG1NSU7du3o1arSUlJQavV4uHhQWlpKQqFQph16vV6lEqlkEcMDw+nW7du\naLVagUuFjhbBwoULycnJoVu3bsyZM4dFixYRFxcnTCWjoqJEhSZLOEJH0s3Pz6dnz548+uijnD9/\nvgu1+bvvvsPc3Jw777yT8+fPM3jwYNasWcO8efOwt7dn69at9O3bV4j0AF36wX90/PC+qqur+90K\nY927d+f48eNcu3aNY8eOifUcHR0FVr5Pnz6kpKSQnJzMpUuXRGL+rfGXT7o/FL25HWv9Usg2P83N\nzZibm6PX67uwYn5qDUtLyy5KSBkZGZSVlaFWq2lsbOTcuXNMnDgRJycnzp07x/Xr1zE3N+fmzZto\nNBrOnz/PBx98ILCJ8vvcd999fPrpp2LdhQsXdunj2tvbM3z4cPbu3Ssm67Ifl1qtFpRpWUg7Ozsb\nOzs7BgwYwMGDB9FoNDg6OrJ//37S0tL48MMPKSwsxNXVlczMTAC2bNlCTU0Nw4YN49lnn2XVqlUc\nOHAAKysr6urq2L17N/X19SxfvhyFQkF+fj4PPfQQPXr0oKKiglGjRvHVV1+J4UxTUxMPPPCAMJg0\nGAxCNKhzWFlZ0a1bNyRJws/Pj8OHDxMdHc1rr73G5s2bRbIuKSnp8nejRo0SVZv8MNq4cSPPP/88\nly5dIiIiAo1GI9hX0GEYGh4ejoWFBUqlUoDoL1++zGuvvUb//v1RKpVCQ0EWAY+JiWHVqlVoNBqB\n+Dhx4gRDhgwhOTkZV1dX0TOXYYPyQLFzgklPTycsLIx3331XwMdiY2MJCwtDkiQOHDjAwIEDBeFG\n7u0fP36c0tJSNm7ciEKhIC4uTviYVVVVce3aNd544w2USiXx8fHodDqcnZ2pr6/H3d2dsrIyUlNT\nBSMM4NFHH/3R9f1HVrrw1zalhP+CpCvHfxqnK9t7NzQ0oNPp0Ov1XXq1/2qNhx9+GED0blUqFSEh\nIeh0OqKiopg3bx46nY4rV64wbtw4vvzyS/z9/QkLC2PLli0EBQVhZmbGpUuXhLjM3/72N06cOCEG\nAT4+PkL/FjpuhjvvvJMdO3aQnZ3Ne++9J/p5ss3Ovn37aGtrY8qUKRiNRiorK3F3d2fHjh1YW1uj\n0+koLi7GysqKUaNGUVhYiJWVFbm5uTz77LO89dZbODg4kJWVhV6vZ+HChTz33HPCefi5556jtbWV\nefPmAR1SkrLrhFKp5ODBg2RnZ1NVVYWXlxdPPvkkZmZmZGRkiD5iW1vbj87pwYMHBbzns88+Y+PG\njURGRtKrVy98fX3FA0VmlUmSxLlz59ixYwfNzc2EhoaiUqlQqVQ8/fTTeHh4cOPGDQIDAwUQPz8/\nX1iqv/TSS6ICHTt2rKBCOzg40KNHD8zNzbG1tSUyMhKdTse6devQ6/W8//77wu1BqVSyatUqvvrq\nK/bu3UtlZaVIaPfddx+XL1+mpqZGHLtsdJqXl8eMGTO4ePEiGo0GDw8P4uPjCQ0NJTU1VaAWhg4d\nyksvvcSqVavYsmULRUVFfPbZZ+j1eiorKyktLWXgwIFIksTChQuxsrJi4sSJQAd+NykpiZUrV3Lm\nzBlKSkqYM2cON2/eFMfTp0+fP1XR64fJ/a9oSgn/RUn3P9VeMBqNAjyv0WiwsrISOgH/znG8/PLL\nQEdVKVdhWVlZKBQKLl++zOjRo6moqEChUHD16lU+//xzsQ09cuQIVVVVwvl1wYIFtLW1YWVlxfTp\n04WgtUKhIDIyki1bttDc3Ex1dTXjx48nPT2dBx98kGeffVYQBKCD2ii7CE+ZMkW4Gciav2+88Qa5\nubn4+fnR1NREbm4uOTk5ZGVlcf/99zNmzBhKSkqorKzkwIEDwrTwnnvuQa/Xo9fruXXrlrCcLy8v\np66ujrfffpu2tjZ27NghYG4NDQ0cOnSI77//noqKCg4fPsytW7cwMTFBkiQGDRpEeno69fX1PPzw\nwyxbtgytVou1tTVLly6lf//+aDQa2tvbmTBhgqiCk5KS2LdvH6NHj+a+++6joqKC6dOnCwsftVpN\nc3Mz99xzDzqdrguqIiEhQfRWBwwYIHY2I0aM4ObNm/Tv35/Y2FgKCwtpbW2lsrKS/fv3o9FoGDx4\nMA8++CBTp04VO6ERI0agVCp56qmnaGtro7GxERMTE4YOHYpSqaR///5YWFgImKGM121tbSUsLIz9\n+/czc+ZMYaXTo0cPDhw4wIwZM7hw4QJDhgxh1KhR5ObmsnLlSgIDA/Hy8kKhUHD+/HkCAwNRq9V8\n+umnXLlyRaAQmpubSU5Oxt3dnSFDhvD999+Tl5fHmTNnBDMM6CJZ2jn+6EpXjv9Vun9S/KcGaTJg\nXa5QrKysfpVj8M8dh1KpxMnJCeggI7S1tZGUlMTDDz9Me3s7CQkJzJo1C3t7e8rLy9FoNJibm5Oa\nmsqECRPYtWsXwcHBhISEUFZWxhNPPAHwo4Ha9OnTiYmJobCwEAsLC7p164aLiwu3bt1i6tSpoi0C\nHdjDnJwcrKys2Lp1KyqVitbWVuF+8cILL6BSqcjLy2PZsmUsWbKEzMxMUlJSWLRoEU8++SRPPPEE\nVVVVuLi4MHDgQBQKBRqNBktLSyoqKgS77Pr16xQXF2Nubs65c+f46KOPMBgMaLVa0Rt/7rnnUCgU\nfPfdd7S1tbFs2TI8PDzQarUUFBQwbtw4+vfvT319vTB8nDx5MhkZGSxcuJBHH32UZ599lrS0NMrK\nygTJ47XXXmP58uVMmTKFBQsW8P7773P16lW8vb0FoF6hUDB69Gg++eQTxo0bx9dff01cXFwX/LD8\nusbGRiF+/t133wmpQnd3d5qbmwkLC+PUqVNEREQQHR1Nnz590Ov1nD9/nurqakaNGoW7u7u4lpYu\nXYparRbypBUVFahUKpqamkQ7aNOmTezbt49p06YRGxsr3Jb379/PtGnTSEhIwN/fn3nz5gmH5c56\nC3JrISUlhTfeeAMXFxfRPrlw4QKSJPHiiy+SkZGBUqkkJCSExMREFAoFzc3NKBSKLiLof0b8MLm3\ntbX95G7z/3r85ZMu3B5ZRnmdzmLoRqPxVztPdF7j545j7dq1QEdlISde2ejwvffeY968eRgMBoYO\nHUpNTQ2pqalcuHCBu+++m88++4whQ4Zw5coVwsLC2LdvH9u2bSMwMBBLS0uio6MxGAxYWFgwZcoU\nvv32WxQKBZs3b6ayspK6ujoBg5KrZE9PT9ra2oRmwbx584TC1vLly8nPz0ej0Qj9gLa2NtLT0xk7\ndizR0dFUV1eLabgkSSxfvhyj0UhycjKXL1/GxsYGOzs7bGxsmD59OkuXLqWuro6vvvqK0tJSnnji\nCfbu3cv8+fOFzJ+npyf19fVERkZy3333UVhYKLzaZPxzVFSUEEAxGo3o9XouXrzIoUOHuPfee1Gp\nVOh0OmHxc/XqVV577TW2b9/O3LlzOX36tHA9tre3F6y/3bt3Exoaip+fH5s3b2bPnj1C/0FWZIMO\n7K6fnx9lZWXExcXh5eWFTqejoaGBXr16MWbMGBISEggICKCxsZGSkhKmTJkiMLh33HEHjY2NYqiq\nVqsxGAyijXPlyhXGjh2L0WgUOrlfffUVarWa/v37c+rUKdEXbm5uxsHBgaqqKlauXElmZiZPP/00\nVlZWHDp0SCBr4uPj8fHxYeHChbzxxhtcvnxZMOU2btyIg4MDwcHBHDlyRMCiOic1WUfkp+KPqnR/\nSkv3z6iwf3fI6lw/8/OXiObmZqmhoUG6ceOG1NTU9Jt+DAaDVF1dLZWUlEhlZWVSfX39b1qnsrJS\nqq6u/tnfKxQKCZBMTU0lpVIp2dvbSz4+PpJOp5MaGxslGxsbydfXV4qIiJC0Wq3Ut29fKSEhQXJz\nc5OOHj0qmZmZSbt27ZIGDRok2draSidPnpTeeecdadKkSVJlZaVUWloqRUdHS+7u7tIXX3whOTg4\nSPHx8dJbb70l3luj0UhjxoyRLC0tJUAKDQ2VJk+eLO3cuVPS6XSSXq+XrK2tpYiICMnU1FSKioqS\nHnjgAUmv10uA9Oabb0q2trbS2bNnpYEDB0qurq5SaWmpNGLECGn06NFSt27dJIVCIc2dO1cqLCyU\ngoODpYCAAEmpVEqmpqbS66+/LnXr1k2KiIiQevXqJSmVSkmn00l9+/YVx6TRaKRu3bpJSqVSCggI\nkIYOHSqZm5tLa9askZ566inpnnvukezs7KSZM2dKSqVScnR0lNzc3KS1a9dKmZmZkrOzs5SWlib1\n6NFDGjNmjBQUFCT17t1bsre3lxQKhaRQKCQbGxtJoVBI1tbWkoWFhTR16lQpMDBQUqvVkkqlkgDJ\n0tJSnDczMzPJ3d1d6tGjhzR//nxJq9VKgOTi4iINGTJEMjMzk2xsbKQLFy5ILi4uUlRUlGRmZib5\n+flJW7ZskaZMmSINHDhQ0uv1kkKhkCwtLaVly5ZJ8+fPl8zMzCS1Wi0BEiDt379fAsR7A9LDDz8s\n3bhxQ7KwsJByc3Olp556SlqyZIn01ltvSebm5tKCBQska2tr6eDBg5KdnZ10//33S5MmTZJKS0sl\nMzMzafLkydJ9990nRUVFSUOGDJFqa2ulixcvShqNRlqzZo1UW1srubq6SiYmJpKJiYl4X0BKTk6W\namtrf/KnoqJCKi8v/9nf366fyspK6ebNm1Jtba1UU1MjDR8+/M9OPb8UP5tX/1fpgqDtyjCd3yuG\n/kvHIVcMsn5oeXk5vXr1wmAwcPXqVebMmcPly5dZvXq1qLTPnz/PPffcw65duxgwYAA2NjZUVlby\n5JNP8re//Y3hw4dz7tw5qqqqhDRiQ0MDS5Ys4bPPPqNfv37MmTMHSZLQaDRCC1auHBMSEnjmmWf4\n4IMPaGpq4vHHH0elUnHq1CmCg4MZM2YMb775Jv369UOlUrFy5UpsbGyIi4ujvr5eVNtPPPEEZ86c\nEdja+vp6Ro8eLTQSZIlGGWscGBiIhYUFvr6+zJs3Dzc3N0pLS4mPjyc9PZ1vv/0WLy8vli9fjpWV\nFQ8//DAxMTE89thjBAYGEhISQkhICNAB9E9LS+PBBx/EysqKiooKXF1dmTRpklgrPT2dbdu24ezs\njKWlJR9//DFLly5Fr9djYmLCiBEjWL58Oc8//zwLFixApVLx0UcfMWzYMNRqNd7e3pSXl1NdXd3F\n8DM/P5+0tDRMTU3RaDSkpqZiMBj48MMPkSSJyspKfHx8iI+PZ9GiRTQ0NKDVaqmtrSUjI4OvvvoK\nLy8vIW6uVCpZunQpOp1O0LqhAzuamJiIm5sblpaW7Nmzh4KCAl544QWBcXZzcxO7oblz5xIbG8us\nWbOwtramqKiI119/nVOnTjFq1ChaW1tZvHgxSqWSyMhILl26RF5eHqNGjRIIDuig/f4QPfJnxQ8r\n3b9i/FckXfhnsvt3voz29naBSJBpu7frOH4uPvvsM+CfVGGNRkNaWhoqlYr7779fGDWWlZUxdepU\nMjMziY+PZ/78+Rw4cAB/f38uXLjAXXfdxZUrV4iIiGDatGmMHz+e7du3Y2FhwZ49e6iursbS0hJ3\nd3daWlp46KGHAITLRkVFBRqNRlBzk5KSOH/+PMHBwWzcuJGHHnpIJOfi4mLmz5+PWq0W7YLc3Fye\nfPJJSktLOX/+PI8++ijz5s3DzMwMSZKwt7dnypQpbN26ldLSUkpKSggLC2PTpk3U1taSlJTE2bNn\nCQkJoXv37gQHBwtdBB8fH3r16sX169fp168f/v7+pKen8/TTT5OVlcWZM2coKCjAycmJt956C3d3\nd86fP09dXR2NjY2kp6fTr18/gXEtKyujpaUFtVrN6tWrmTZtGn5+fkRERPDqq69SUVHBHXfcQW1t\nLTNmzGDFihWkpqYyZMgQpk+fzpw5c9Dr9bz++usUFRVRW1tLeno6vXv3xsHBQQjcyC4m999/P5WV\nlRw5coQBAwYgSRJNTU3odDp8fHwEXlsWMVepVPTq1YsePXqIB5NOpxP6x3Jf9/jx48TGxuLr68v9\n999PTk4Orq6umJmZ8dZbb3HgwAGmTp3KqVOnGDduHCdPnmTWrFmUlZVRWlrKm2++SXt7OzExMYSE\nhLB69Wra29sZOnQoBw4cICwsDBMTEwwGAwaDQSS4f4VLlf6E9oJ8Pv+K8V+VdH9tdKbtqtVqrKys\nBG33dss7/jDc3NwEZtfU1JTm5maB9b148SJ6vR5LS0s+//xzNm7cSGNjI9HR0Tg4OBAWFkZTUxNx\ncXHcc889HDhwgFmzZqFWq4mLi2Pz5s2sWLGCd955h5iYGEJDQ3n++efR6/VER0cDHQM9eXIeHh6O\nJEnMnTuXJ554QlTCM2fO5KOPPsLe3p7w8HAGDx4srMnr6upoaWnhrrvuYs+ePYKZtXfvXu68805O\nnjzJU089RXh4OJGRkfj7+4u+pAwXa29v5/7778fKyop//OMf5OXlERQURHNzs4C/QYf8pJubGy4u\nLgJBsm7dOp588kkyMzNJSkpi/vz5hISEcP36dXQ6HSqVqkvSHThwIBqNhk2bNgnrmR49euDj4yO+\nAw8PDwYOHChk/Wpra7l06RL33nsv0GG/rlQqhbwmdNgDNTY20rdvX0xMTATqY8WKFURGRgr8bWpq\nKjdu3BAoho8++kg4L7u7uzNz5kxaW1s5efIk5eXlAiGRk5NDz549AYQDdE1NDZ999hl79+6luLiY\nefPmERAQgJeXF7a2thw6dIiJEyfy/fffi75/3759yc/Px8/Pj3nz5rFy5UquXbuGJEls3rwZMzMz\nioqKePvtt2lvb2fy5MnC2umXsLl/RnROureDjfZnxX9F0v21BAnpJ2i7sihK57X+k0kXEMgD2ber\nqKiIBx98EEnq0MKVB1XW1taMHz+esrIycnNzmTNnDsnJySQlJeHs7ExIAMj5hAAAIABJREFUSAjZ\n2dnU1NQwduxYSktLOXHiBLGxsXh7e/Pee++RmprKihUraG9vF1jmzmwvKysroqOjsbW1xWg0Eh8f\nz4ULF4iMjKS8vJxbt25hYWEhoFwjR44UFGAZmvX++++Tl5fH2rVrcXJyoqSkRLQiOp97WQBnxYoV\nlJeXs2XLFiRJoqioiD59+jBo0CAuXbok/kaudBUKBT4+Ply6dInw8HCCg4M5efIkeXl5rFy5El9f\nX9LS0lAoOuxeZJEXmZTi5eXF9u3bWb16NUuXLiU1NRVPT0/hQuDv709LSwtlZWVcv35dcPFlDKvc\nruksKLR161bmzp1LWlqaMHAcOnQoLi4urFu3Dk9PT7p160Z8fDyPP/44ZmZm5Ofns3//fmExVFdX\nR3R0NG5ubtTU1Ai3YujAc8tOyZ2jurqaCxcuYDAYWLhwocB4nz9/np49e1JaWoqzszPl5eUUFhay\nYcMGTE1NiYiIYNy4cdTU1CBJEjNnzkSj0ZCYmIi9vb2wvpFbHPI17Obm9i9NJP+oSrdz/FXhYvBf\nknTl+DmChPQvaLud449Ius8++yzwTxfjpqYmKisrUavVXLp0idTUVEpLS6mtreWFF14AOvRvp02b\nRk1NDS0tLeTn57N48WK2bNmCTqdj7969jBgxguzsbOLi4oCOXtyuXbsE9tRoNHYRtI6JiaG+vl4I\n4CgUCsLDw7l69apgth05cgRXV1fWrl1LaGgoixYt6kK9zMvLw9HRkYaGBuF8XFhYKCQd5aisrESl\nUvHMM88QFRXFl19+iVarpbi4GFtbW0xNTfHx8REtBuiodGWfss6/e/nll6murmb58uWYm5szaNAg\nUlJSaGhoQK1Wk52dLeBrarWa8PBwqqurSUlJESQE2autsbERT09PkpKSmDx5Mt988w0bNmygZ8+e\n4nNqNBp8fX0pLCykpKQEg8HAV199JXRpJ0yYQHR0NEOGDCE2Npbi4mJR+Wq1Wl566SWysrI4ffq0\naGEplUqqq6u5deuWEIFvbW2luLiYvn37AgjHhM5hNBppb2+ntLQULy8vTp48yR133MGhQ4eYNWuW\ngJM98sgjqNVqFi9ezMSJEzlw4ADz58/H2toac3NzfH192bNnDwqFAg8PD1paWtDpdIIaLF/DX375\nJQqF4raZSP6e+GGl+3spwH9W/Fck3V/C6v4r2u5PrfWfvoiUSmUXrytJkti7dy9BQUFdcIeTJ0+m\ntLSUHj16sHv3brKysrj77ruxsLBg/fr1fPrpp2RkZGBmZsbDDz/Md999x913381dd90lWGt2dnai\n+lepVEJj1s3NjaamJuzs7Fi3bh319fUolUpOnjyJqakp7e3tmJmZ8e2333L06FHuvvtucnNzcXBw\noLa2lvr6eqqrq7lx4wYODg6Ym5sLHHNnHV05Ll++jNFoJC8vj8bGRpF8cnJy6NOnD4CoZuWQ2wsA\ngwYNEkn3o48+QqVSkZycTGtrKy4uLmRlZWFiYoKpqSlXrlwRVjcA/v7+SJIkkllBQQE+Pj6YmZmh\n1+sJCQkRUn+yQNCwYcOor68XGNzAwEBcXFw4evQo+/btIyAggOTkZMaPH094eDhHjx4lKCiIxMRE\n9uzZw+zZswkLCyM2NlYch6+vL3V1dQwZMgStViuE0nv16iUGWi0tLWRmZqJQKAgMDOziawcdW/61\na9cybdo0Dh48SHh4OHq9nqioKMaNG8exY8c4ffo02dnZfPbZZ5w9e5bhw4dTXV3N6NGj+e6777h1\n6xYbNmzg008/RavVsn//fmpraxkwYAD19fViJ6hSqYTWb2f/sh+aSLa3t9PS0iISsWwiebujc9L9\nX6X7fyQ6J8y2tjZqa2v/JW33l9b4vcfwS/HRRx+J45QHWj169KBXr14UFRUJGb81a9aICj0kJISP\nP/6YgoICoqKihKNEdXW1uLk/+OADJk+ezMSJEzl+/DivvPKKeE9HR0fy8/NRqVRkZmaKakseJtrY\n2PDkk09y8eJFnnjiCRYsWMDw4cOFTkNJSQkDBw4Uw7KCgoIu9uLff/8969evJz09ncOHDxMTE0NV\nVRXFxcU88MADdO/enYMHDwqKLXT0eeWk27m9cOvWLVpaWoS+qZx0s7Ky2Lp1K4MHD+bEiRMkJyfT\nvXt3XF1duXbtGvX19ZSXl+P6/3V8AXQ6HY2NjWRmZpKQkEC/fv3EcApg4MCB5OXlERwcTGFhIfb2\n9oSGhqLVakUP3MfHB4VCwaFDh/j000+ZP38+R48eJTQ0lH79+tHQ0ICTkxPp6ens3LmTyMhIRo4c\n2SXpfvvtt0CHs8eoUaNYsmQJzz33HKampmzZsgUTExMaGxvFfKEzI07ui0OHGP706dP58ssvmTNn\nDqdPn8bKyorDhw+TkZFBcHAwZmZmDBgwgKtXr1JaWsqsWbNISEjg5s2beHl5ERAQwNGjR7GwsGDC\nhAkolUquX79Onz59xPW7aNGiLtd0e3u7oGR3TsSysaRCoRA7N9nN93Ym4s5/X1NT87+k+38h5C9d\nHrqYmJj8S9ruT63xRyRdf39/cUM1NTVhbm5OXFwcBoNBIApMTU2JjIwUlutOTk7MnTuXe+65h9ra\nWu666y7CwsJYvXq1kBYE2LVrFxYWFsyaNauLtUh9fT2SJFFfXy+q39mzZzN27Fj8/PxYsWIFzz33\nHI6OjmRlZXWxP5GTkdFoxGAwYGJiQnJyMpIkMWvWLDw9PXn88ce5dOkSkiRRW1srxGDk/umgQYPQ\naDRdqtbOSXfAgAHk5uYK7zg3NzfxvXl6epKbm8vf//53QkND8fb25rHHHuPdd98Va6am/j/2vjss\nqnPrfs0wlBmGIlUUBESkOzRpggVsFBWx9xprNMXkWhKTmKhRb0yUGDVGscSCsaDGgiiiglItIEgV\nkQ7SZ4Y6sH9/cM97Ieq9KSb5Pr/fep55Hp1yzjDnPfvss/faa6UiOzsblpaW3e5mfvjhB6iqqsLT\n0xOHDx/uZlkOdLIDbGxscPnyZVZLdXd3h0AggKqqKkQiEQYPHozy8nLcunUL+fn5TCfYwsKCOSLH\nxsbC2NgY7e3tcHZ2ZpkudzzXr1/PXCZGjx6NyMhIBAQEoLS0FHw+H/v374eOjg6rr7a0tKCpqQna\n2tqMzgh0Ko/p6+sjLS0NpaWlWLJkCQoLC3Hq1CmMHTsWJiYmGD9+PM6dO4fAwECcO3cOZmZmCA4O\nho6ODt566y1UVVWhrKwMQqGQKZ21tLQwWyQA+PLLL9nwTEtLS7fAya3zruLmABjzQigUsotHVzff\nl9mq/xZw66G+vv7/lxf+TnANtPb2djQ1NTE91V8ztvuybf0VQRfoNEjkwI0ct7a2QiKR4NmzZ2hq\nasKmTZsgEokgkUhQXV2N8PBwzJgxAy0tLXj//fcBdKqLiUQijB07FlKpFDweDwsWLIC2tjbrQItE\nItTV1QEATExMsH79eowcORLjx49HTk4OlJWVmYMs8O8mFtCZYWRnZzOXXD6fj82bN+PDDz+Ejo4O\n1q1bhydPniA5ORmLFy+GjY0NvvnmG7z77rsQCoXYsGEDzMzMEB8fjwkTJsDY2Bj37t1DW1tbt/KC\nqqoq+vXrh8zMzG77517T0dFBSUkJdHV1YW9vj6VLl+LBgwdITk5mzbTMzMxudjV37txhvmGurq64\nfv06Yy50hZOTE3744QemINZ1GwCYUJBIJIKXlxdu376NUaNGsfFwrvnJ4/Fgbm4OuVwOQ0NDCAQC\nZGdn48KFC6ivr8fChQtx8+ZN+Pr6Ijo6GjY2NqipqWGOBBs2bICvry8SEhKgr6+P9vZ2dty6wsfH\nB3K5HDExMWhsbMSxY8dQVVWFHTt24OzZswgJCcHZs2fh5OSEyspK/POf/4Smpiaam5sRHByM7du3\ng8fjYefOnbh27RqTpXz+/DkAwNnZGerq6hCLxawHwt0pcoGYC56ceBHXU+GcnLlgrKysDDU1NRaI\nf2mrzgVirrH5qvOna3lBKpX+/0z374RCoWB1KK758Hu7qX9l0O0qINLc3AxtbW1W7+UI8QMGDMDO\nnTsxZswYmP3L5HHFihWwtbVFdHQ0jh49Ch6Ph6FDh6J3796YM2cOFAoFPD09mZMF0BnUeTwebG1t\nmWGlra0tPvzwQ3z55ZfIzMzsFnRzc3NhaWkJhUIBmUyG/Px89OvXDzExMXBzc0NBQQEmTpyIadOm\nwdfXl9Voc3Nz0bt3b8yaNQuLFi3C+vXr8d5774HP5+PAgQNwcnJCeHg4Ll68yOQkDQ0N2a2og4MD\nUlNTu9VzOzo6kJOTg6qqKgQHByM/Px82NjZQU1PD2rVr8dlnn0EikSA1NRWZmZmwtbUFAKYn8PHH\nH8PFxQVEBKlU+kJzCgCsrKxw//59jBo1Cnw+HyUlJS+8x9HRkVml37p1C6NGjWLNupEjR+Lu3bso\nKipCa2sr1NTUoKSkhEGDBiEqKgobN25ES0sLZs2aBVNTUxQVFcHIyAjJycnQ1dVlFxhfX1/cvHkT\nvXv3RktLC/r06YPVq1eDx+N1a/xymharVq1Cz549ERERgbfeegt1dXWoqqpC79698eTJE2ar/tVX\nX4HP58PT0xO6uroICwuDiYlJN50P7sICgDE4uDXNaRILhUKWzHBi61wJpqWlBS0tLd1U4V4ViAUC\nQbdArKSkhI6Ojv8YiLsG3f+tCmPAGxJ0BQIBmyr6o/grGmncUAZnAMlBJpOhqqoKOTk5sLKyAo/H\nQ48ePXD48GFYWFhAoVCgvLwc6urqTIBk3bp1SEtLg7u7O+tCr1mzBklJSd2aMJaWliAiaGtro6Sk\nBOnp6SgrK4ORkREkEgnEYjF0dXUBgDXKtLW1WY0xMzMTiYmJWLduHXbt2oXDhw+juLi4WwmipaUF\nR44cwY0bN2Bra4sZM2aw29Xc3FzY2dnh448/xokTJ5Cfn4+AgAA8ffoU1tbWzA3Y2toaKSkpyMrK\ngomJCeRyOerq6rBs2TIMHToUDQ0NyMrKgpWVFQBg5syZKCoqgkwmw6NHj7pluleuXEFDQwOmTp0K\nJycnVg5JSUl54Zjk5uZCKBQiPz8fdnZ2OH36NHutra0NMpkMQqEQ2traSExMRHJycjfTUV1dXejp\n6cHe3h6PHj1CW1sbVFVVMWzYMJw/fx6NjY1wc3ND79694efnh0uXLmHYsGGIiIhgNu7t7e0wNTWF\npqYmwsLC4ODggObmZsycOZNNyHXFsWPHEBERAW9vb0RGRuKdd95BREQExo8fj/379zM6Xnh4OC5f\nvgwNDQ2EhIRgypQpkMlkCAsLw5EjRyCVShm/Geh0K37ZBBo35NHY2Ag1NTWoq6tDVVUVQqEQYrGY\nWT39sqzwy9LEbwnEHPOIU1xrbGzEjh07mL/cH8Hp06dhb2/POPKvAjfk0r9/f2zduvUP7RPAm6G9\n0NHRQc3NzVRXV0fV1dW/W3/hdWyjqamJSktLqamp6YXXGhsbmT5CbW0tNTY2UkFBAZtv5/F4pKSk\nRAKBgE6cOEE6Ojqkrq5OH3zwAU2ZMoU0NDTop59+IkNDQ9LQ0CBPT0/atWsXmZub0/Hjx8nJyYlu\n3bpFYrG429y8q6sr+fj4kLKyMmlra1OPHj1ILBaTWCyme/fu0alTp2j48OEkl8tJJpNRdHQ02dvb\nU3V1NclkMrp9+zapqqrShAkTqKamhuRyOcnlcjIxMaH09HSSy+V05swZsrCwICMjI9q2bRvJ5XLa\nv38/TZgwgQoLC0lDQ4NkMhnbh5aWFm3YsIF4PB6Fh4ezbV66dIk8PT3JycmJfv75ZyorK6N//OMf\n5OXlRWfPnqUBAwaQtrY21dXVse0dPHiQ3NzcyMTEhHr16kWpqanU0NBANjY2dOrUKZLL5ZSRkUEG\nBgZkYmJCPXr0oMrKSrbPiooK0tXVJZFIRJ6enrRhwwaSSCQklUrp+fPnVFZWRnV1dWRtbU0WFhak\npaVFbm5u7PPco2fPnhQcHEzu7u70888/k1wup8ePH5OSkhI5OjrSgQMHSC6Xs9/32rVr1KdPH3Jy\nciItLS0qLi6m0tJSmjdvHtnZ2dH27dtp+vTptHXrVrpy5Qppa2tTz549ux1bsVhMLi4uNH36dFqz\nZg1paGiQjo4O8Xg8CgkJIQsLCyoqKiJNTU1SV1cnd3d3MjU1JX19fcrKyiIej0f6+vqkrq7Otrl9\n+/YX/rb6+noqLy+nyspKkkqlL7z+qodMJiOpVMrOq8rKSiorK6OysjKqrKykqqoqqqmpYY/q6upu\nj5qaGqqtraWamhoqLS2lwsJCeuutt6h///6krq5O5ubmtGjRot8VN7KysignJ4eGDRtG9+7de+l7\n2tvbycLCggoKCqi1tZUkEgllZmb+ms2/2doLHDhJuz+C11Fe+CXoXxkCVwLR1NSEiooK2tvbGWOB\ne5+2tjbjEvfq1YuNY968eRM2NjascWVtbY2kpCQEBQVh+PDh2LdvH1JTUzFixIhuDsC6urrw9PRE\nQUEBzMzMoFAoEBUVBblcDoVCgc8++wyRkZGws7NjGV1OTg769+8PNTU1REdHIyQkBD169MC6detY\nF10mk6GyshI//vgjJBIJlixZgq+++go6OjpMD8HR0REPHz5kTbmu1D4HBweoqqrCzs4OK1asYNYo\ndnZ2ePToEfLy8mBnZ4f09HQcOnQIhw4dgru7O7Kzs9GvXz82diuVShEQEACZTAY9PT1UVVXB3Nwc\n4eHh0NbWZgMOpqamkMvlsLe3Z463HMLCwjBkyBBGA5szZw4qKiqY+aOGhgbjAZeVlUFfXx96enrd\njjHnO5eVlYWhQ4cyG6XCwkLw+Xzk5uZizJgxADr1NzjTzvLycgQGBsLExAQ5OTnQ1NSEt7c3srKy\nEBAQAB8fH1y7dg0SiQQymQxr167tVmaQyWRIT0/H8+fPUVVVBT6fDxcXFyxatAi9e/fGpEmT8NNP\nP6FPnz7g8Xhwd3dHVVUVZs6ciSlTpkBZWRl9+vRhbhpKSkpYtGhRt7Xb1NTULbv9LULmXFmEq+ty\ntE2xWMwa3F1LE79s1gFgwyQAoK6ujq1bt6JPnz549uwZrly5gilTpvzq79MVVlZW7A7wVUhKSoKl\npSVMTU2hrKyMqVOn4vz5879rfxzeiKD7Z2nqvo5ttLa2sgYZpwHb9ZZKoVDg6NGj7LNcl3ry5MmY\nOHEihEIh9uzZgyVLlqCyshK3b9/Ghg0bUFlZCRUVFfj7+6O5uRlxcXFQUlKCv78/O4FEIhEaGhpw\n/fp1eHl5wd3dHRKJBAcOHGC3k0OHDsWPP/6Iffv2YcKECfjmm29w48YN9O7dGydPnsTChQuxa9cu\n1NbWorm5Gbdu3cInn3wCFxcXdHR0oLGxERs3bgQA+Pn5sdovAPTv3x/l5eVITU2FpaVlt99owIAB\niI+Ph7OzMyIiIvD222/j7NmzUFVVhVgsBo/Hg6qqKhYsWIBdu3axYQUtLS306tWLjUsLhUIoKytj\n7dq1yM/Ph4aGBurq6vDFF19g/fr1jLzP4/Ggq6sLbW1tzJ8/n9UsW1paEBoailWrVsHU1BTq6uoQ\nCoUYM2YMLl26xOqX3333HZYtWwYHBwdUV1e/UPMNCwvDokWLUFVVBVtbWxZ0Dxw4AFNTU5iZmTEd\nYyUlJfj6+rILTUdHB/z8/HDjxg3weDx2/LhJssTERKSnp0MoFOL58+cYNmwYY74AYBddIyMjODo6\nory8HBs3bmQNtd27dyMrKwsTJkzAoEGD2FRmbm4u1NXVce/ePbYeR4wYwYIqV88nIqYJ8TrwskCs\nqanZLRBzpQmurqtQKJCSkoLc3FycPn2a8dOtrKzg6+v7Wr7Xy1BSUsIayABgbGz80nr/b8EbEXSB\n16up+zq2wY15NjY2QiQSMQdVTp+VC1itra1wcnJindjW1lYMHDgQcrkcu3btQltbG3R1dXH9+nV0\ndHTg4sWLyMrKYsaQWVlZuHjxIqZMmYL+/fvjwoULADpP7ODgYBAR03jw8fGBs7MzDh8+DC8vL4hE\nIkyfPh0ikQghISGYN28e2tvbcevWLezZswfz589HVVUVFixYgI6ODixcuBALFy5EaWkp5s+fj6Cg\nIGzZsgVBQUGMV6qtrc0caZWUlODg4ID4+PhutV+gM+g+fvwYVlZWcHBwwNGjR/Hee+8hJiYGZmZm\n0NfXx4oVK+Dv74/AwED2Oe4ugfudOVrX+PHjoaqqCoVCgePHj8PW1hYDBw5EY2Mjq1FzDBc/Pz8U\nFRUhIyMDx44dg4ODAyQSCWsWqaioYNq0aczksbS0FNevX8fs2bNhZ2fHJgI5/zWpVIqzZ89i9uzZ\n8PX1RU1NDbKzs5GXl4eoqCjGQOi6rkaOHImjR4/CxcUFly5dYmwGhUKBCxcuwNTUFI8ePYKhoSFs\nbW3x7bffIjAwEFevXsX777/frX9x6dIl2NjYYOfOnUhJScHUqVOxdu1aNDU14d1338XTp0+hoqKC\nr7/+mtmrx8bGor29nV3gOOzdu/eF7Pa36En/EfwyEHMNTz6fD1VVVURERCAkJATLly+Hubk51q1b\nh9ra2v+4zREjRmDAgAHs4eDggAEDBjDO9N+BNyboAv8zMl0us5LL5cxBQUlJqdstP7egVVRUmGUM\n17hpbW3FkydPwOPxIBKJ4OnpiWfPniE+Ph5FRUV49uwZJk6cCIVCgXHjxsHBwQHq6up4/vw5cnNz\nQUQwMDCASCSCgYEBFAoF3n//fdy9exc+Pj4oKipCR0cHpk2bBqCzSSSXy1FbWws/Pz+89957ICLo\n6+tDW1sbtbW1+OabbxAcHIyUlBRMmDCBNey4hhWPx4OzszOuXbv2Qkbr6OiIjIyMF4KuRCJBSUkJ\nzM3NWZPp3LlzePfdd9Ha2oqmpiZkZ2dj06ZN3T7HBYSXHTeOXfDVV1/h888/ZxNnmpqaUFVVRXV1\nNSoqKtDa2orJkyfj+++/x/bt2/HOO++goaEBNTU1zKPMzc0Nra2tSE1Nxb59+zB58mRoa2tDIBBA\nLBYjMDAQERERADobMj4+PjAyMsLIkSMZw2Pr1q0YNGgQWltbAYCxRoDOYPDo0SOsWLEClZWV0NXV\nRVpaGrKyspCQkICQkBBmreTn54fr16/jgw8+QF5eHiwtLdkwAkfX4uRJ9fX1ER8fzy506enpCAoK\nwrhx41BRUYHU1FS0trZi+PDhUCgUKCkpYUGXs2ySSqUgol89UPS60TXoc351169fx6NHj3Dw4EGU\nlZVhw4YN6Nmz50uZKF1x7do1pKWlscejR4+QlpbGSj3/Db1792b8dwAoLi5mQkS/F29M0P27M91f\n1m05XiPXoQU6b2W5cVuObcEteC8vL0b2bm5uhrW1NUpKSjBo0CDo6+tDQ0MDPXr0gEQiQd++ffHz\nzz9DX18fBQUF+Omnn5iKGNBJp1m2bBnOnj0LdXV1Fkiqqqpw+/ZtNqigrq6OkpISGBsb48GDB9DQ\n0MCOHTtQVVWFS5cuQSQSITMzEw8fPoSlpSWam5vh5OSElJQUVvfl4OLigqSkpJcG16Kiom7BmIhg\nbm6OpqYm9OnTh/0WTk5OOHv2LB49eoTy8nIcPnz4Bfm+2tpalJWVvfQYcNY6pqamsLe3Z8/zeDxm\nqJmVlQWxWIy33noL4eHhMDAwgJOTE3g8HtLT06GpqYmMjAwoFApMmDABJ06cwMGDB5k0ZlFREerq\n6jB27Fhm4RMWFsY4135+frh16xZ8fHxw8eJFiMViTJs2DWPHju2WXVVXV7N1MHHiRPz0008YOHAg\n9u/fD19fX/j7+7Ogy90F2djYYPjw4YiMjISJiUm36TEiglAohJ+fH/bt24fKykrU1tZizZo1KCoq\ngr+/P5YvX46Ojg6cPHkSJ0+eZOuSu/v69ttv0djYCKFQ+Idol38EXUsaGhoaaGxsxJIlS3Dp0iVE\nRUVh5MiR0NXVxfDhw7F69epuk3p/BK865wcOHIi8vDw8e/YMra2tCA8PZ3ZWvxdvTNAFfp+m7qu2\n8WtBRKxu29bWBg0NDfD5fDb+SERsIXV0dEAsFr9yaINr7jQ2NqKgoACqqqr4+uuvMWzYMIwcOZJx\nWZ2dnREbGwsVFRXIZDIMHToUurq6aG1tZVk1R6UaO3YsLl++DA8PDyxZsgTvvPMOswsXCATIyMiA\no6Mja6r99NNP6Nu3L6ysrODu7o7Hjx8jLy+PBTEHBwekpKQgMzMTffr0YbxMJycn5OTkvJDpOjg4\nQCaTMQoS91tw9uetra3dfgsdHR0AnYHglxTA+vp6NDc3Iycnp9udA3ccHj58CB6Ph+Li4hdef/jw\nIVxcXNDe3o7S0lIYGhoCANzd3aGpqYmWlhbU1NTA09MTKSkpaG1tRWBgII4dOwaJRAITExM0NTUh\nPj4eZmZm0NXVRU5ODiIjI/H8+XMMHz4cQKfWhYWFBaqqqtDS0oJbt25h2rRpGDNmDCv9AMCRI0fY\nKO64ceNw7tw5RiWbNGkSXF1dUVBQgG+//RZhYWFobW3Fxx9/DCcnJ0RERODp06eQyWTw9PSEWCxm\nE4y5ublMFU0kEiEmJoaJu9+6dQtDhw6FQCBATU0NlJWVGR/c398fampqbCT9z9JPeBV+WdIQCoW4\nefMmxo4di5CQEBw6dOi183LPnTsHExMTJCQkICgoiDVdy8rKEBQUBKCzRLZr1y6MHDkSdnZ2mDp1\najddj9+DNy7ovo5t/NrF9qq6LRcsmpqamGgKF+T+00XBw8MDPXv2ZJ9VUlICj8dDXFwcnj59irCw\nMGRlZSEiIgKmpqbw9/eHsrIyVFRUIJVKoampifb2dgwfPhzLli2DkZERQkJCcP/+fdTX18PY2BjG\nxsaMlA8AGRkZsLe3h6GhIQ4fPowlS5bA1dUVQKeObHJyMh4/fowBAwYwkRaFQoH8/HxYWVkxT7l+\n/fqhvLwcxsbG3QjyXMOHU/TieL/FxcXo0aNHN4GbyspKjB07FmKO/EG8AAAgAElEQVSxGEKhEBMn\nTmT8TAAsu9bX10deXl63366wsBDNzc3w8fHpVq7h8PDhQzg7O8PR0RF3797FxYsXYWBggPv377N6\ntIuLCzw8PPDw4UOoq6vD1dWV6Q4DQGJiInr16gVPT0/ExcXB398fW7duxZw5c7pxaEeMGMG0dw0M\nDGBtbY1BgwahsLAQhYWFaG1txYkTJzBv3jxER0fD3d0d6urqUFFRQXl5Oby9vbFu3Tq0trbi5MmT\nKCgogKenJ6Kjo7F7927cvHkTRkZGMDY2xrJly9gUplwuR0pKClpaWsDn8+Hm5gahUIjg4GBYWVnB\n0NCQ6XJwx4QrfezZs4fJnHIlJ64Wzon+/FmB+JdJSWtrK1atWoVDhw7h8uXLGD9+/J+SdQcHB6Oo\nqAhNTU0oKyvDlStXAHRqlHBNTgAYPXo0srOzkZub+18F3X8N3pig+zoZDMB/tgPhRNA5fQeubsvR\nWrpa/XCNCIFAAIVCwRazXC5nY5RdaW5dmQxSqRRmZmZoa2tDWloaPD09sWbNGpSVlcHLywvBwcHQ\n0NCAtrY2VFVVoaqqChUVFcTExCAlJQUbNmyAra0tKisrkZKSgu+++w7JyclwdnZGYmIigM6gW1NT\ng8LCQowePRpPnz6Fk5MTAMDNzQ0JCQnM7Zf7fW1tbZnTMEeM79evH+tyc/oMDQ0NePz4McRiMRIS\nEtjrKioqyM7OhqmpKdNgkEqlCAkJQWBgIFpaWuDt7Y2ePXvi7bffZseCG3zgqGhdsXv3bqioqGDp\n0qXQ19dnrggcHjx4ABsbGza8sGvXLmzYsAF5eXnIzs5mrhlubm5ISkoCAMTGxkJDQwPPnj2Dmpoa\n7t69ixEjRmDIkCFISkrCqFGjcO/ePYwfP77bMXVyckJ+fj50dHTYlJ9AIEBAQADOnz+Pc+fOMQul\n58+fo7S0FFOmTEFERASbYqupqcGRI0dQVlbG2AklJSVITk6Gjo4OKisrUVpaij179kAikbAslWuw\nSiQSLFiwANnZ2QA6m1HPnz/HmjVr0NTUBENDQ3bODB8+HHp6ei+wCTQ0NNigw58RiLmSHHchFgqF\nSExMRGBgIHx8fPDTTz9BX1//d237fzLemKDL4XXwbF+1De4WqCvf9r/VbTlLHk44hVvMXDbc2toK\nmUzGTlpnZ2cYGRmxfWZmZqK6uhoKhQKTJ09GTEwMgM6s79ChQ7h58yaKi4uhqqoKNzc3+Pr6orW1\nFc3NzTAzM2ONvWXLlqFXr17sVooLLCkpKQgPD8dXX32FrKwsPHjwgAVdR0dHZGVlwdLSslv32sjI\niGX1HDihnpqaGqbqpq6ujry8PPTu3ZtJO0qlUsYtdXBwwMOHD9Hc3IypU6fC0dERY8eOhbW1NVxd\nXeHs7IysrCymyJadnc2CLseLBTrrowcPHsT48ePh4eGBp0+fokePHjh9+jQr/zx48AAODg5wd3fH\nzZs3IZPJMH78eMycOROHDh1CUlIS3NzcIJFIkJubC7lcjt27d2Pp0qWIiIiAQqFAdHQ0/Pz84O3t\njcTERNTW1oLP53eTPCQiPH78GCoqKqitrWVlhra2NowaNQrnz59HeHg45s6dCxUVFfj6+iIqKgpT\npkxBcnIyiAiDBw/G/v37ERgYiIaGBkgkEpiammLo0KFMM4GI8O677+Lu3bt48OABeDwe5s6diwsX\nLkBLSwsWFhaMwnf+/HmkpaWBz+fD1NQUYrEYlZWVaG9vh5KSUreR3654Ga3rdQViTpiKY1B0dHTg\n008/xY4dOxAREYEZM2b8LTXlvwJvTNDtmun+0QEJoHum27Vuq1AoXsm3/TV1W+DFxcwFKM40sqsy\nmEgkgkgkgpKSElJTU/Hw4UP07dsXbW1tyMzMxOTJk6GkpIQPPvgAsbGxSEhIgJqaGgYOHIjp06dj\n5syZUFNTg5GREWQyGXJzcxEcHIzS0lIcOHAAtbW1iIiIQHBwMDIyMpCens60fkUiEfT19Vn9k4NQ\nKGRZPYe8vDzo6uqyUVuuPldQUAAHBwc8fvyYMQnU1NSQm5vLasazZ89mpPe0tDTY2dnBxcUFDx8+\nxPHjx7F161bEx8ez8V9HR0ekpqayfX/00UfQ19fHqFGjoK+vDx0dHcyaNQtffvklpFIpnjx5AhUV\nFfTt2xeurq5IS0vD+++/Dz6fj7lz5+LYsWO4f/8+Bg4cCFVVVdjb2+PixYtITEzEypUrYWJigkuX\nLiE9PR1eXl6MJ7xv3z4MGTIE586dY3QzNTU1nDlzBubm5ujbty8SExNRV1eHxsZGeHl54dGjR4iP\nj0dAQAAUCgVGjRqFqKgopKamor29HePHj8etW7cYswAAuxtYuXIl9u3bh2nTpmHRokXIyMhg48FE\nhNOnT0NPTw8ymQwpKSmQyWRoa2tjlkJfffUV8vPzWUkK6Cwr/JZa6R8NxFx2y/kSikQipKWlITAw\nEFZWVjh//vwfZgf8T8fvs7z9H4xXuUf8FvxSl7exsRFEBJFI1C2z7aofyr3+e1yEOfEQri44bNgw\nzJ49G0eOHEFjYyPs7OwYHezBgwe4e/culi5dik2bNrEBgoMHD0KhUGDv3r1YtmwZM3y8evUqux22\nsLBgrAVTU1OsXr0a7u7ucHFxAQDmSszxbAGw0klXtLS0oKqqimVKQKd2Qd++fXHv3j1IpVIoKytD\nLBYjLy8PkydPxv79+9nfKhAIkJubCzc3NwgEApSVlTFZxbS0NFhaWjIBGkNDQ3z33XeYPXs2BAIB\nrK2toaOjg9TUVHR0dCA2NhYxMTEgIibZOHDgQHR0dEBbWxsXLlyApqYmc8x49OgROjo6mCtz3759\nYWZmhrKyMhZ8Bg4ciH379mH27NkQiUSYNGkSvv/+e7i7uzM2hY2NDeLj47Fp0yZs3LiR+YilpaWx\nSTklJSW4uLiwTFZDQwN9+vSBtrY2hEIhmpub4ebmhpUrV+LGjRusiccF+dzcXEyYMAEXL15kGstN\nTU1MUEgikUBHRweDBg2CgYEBUlNTkZOTA21tbWRmZgLoZFPI5XLo6upi7dq1EIlEbPrL0tISM2bM\n+M3r9ZfgFMa60ss4TnR7ezva2trYOcKtgRs3bsDKygoRERFITEzEsWPHmMj8m443MtN9HUG3vb29\nW92Wc5zomuF15duKxeLfbdv+MnCNDaDTdWHDhg1oa2vDkSNH4OPjA7FYDCMjIxgZGUEkEiErKwvv\nvvsuevbsCQ0NDZw7dw7l5eUQCoW4f/8+oqOjkZCQAHd3d1y6dAlPnz6FiYlJN/pLz549megNh5dJ\nCz579gy6urqsXgh0ljs4hTCu+8zn85GTk4MRI0bgyZMnjF/b3NyM4uJivPfeexCJRJg5cya7e8jJ\nyYGzszPMzMygpaWFJ0+eYMiQIZgwYQKKioqgp6fHMubMzEysXLkSn3/+OaRSKczNzaFQKDBgwAAk\nJyfj448/xvbt21nJpK2tDWvXroWnpyezNAI6R4+7HlcHBwfcu3cPixcvBgBMnDgR8fHxGDJkCHtP\nXV0djIyMMGzYMBQUFDBR9mPHjsHe3h6urq7o27cvLCwscPXqVUYPrK6uRkdHB6uFc8pera2tOHDg\nAAwNDWFjY4Pdu3fj+PHjmDZtGiZNmoT9+/cjMjISffv2xenTp6GlpYXx48ejpKQER48eRWJiIgwM\nDODt7Q11dXVMmzYNEyZMwPjx4/HgwQMUFhaisbERFhYWzOjy+vXrv3N1/nd0zYhFIhGjdnGuE4cP\nH4a/vz++/vprtLe344cffvhL2RJ/J96YoMvhjwZd7hZILpeDx+P9qrrtbxFJ/y348ccf2XfatWsX\ndHV1sXXrVjg7O0NJSQnV1dUoLi7Gt99+Cz6fj3nz5uHw4cMwNjZGYWEhJkyYgBEjRuDKlSsoLy/H\ngQMHQERYvnw5PvnkExQXF3cLJL8c4gA6GQUFBQXdnsvJyYGrqytSUlJYKSEzMxMSiQT6+vrIz88H\n0MmpbWlpgampKSwtLZGRkQGg00Ghvb0d7u7uzO6c+zvT09Nhb28PPp+PgQMHIj09HWKxGOPGjYO6\nujr27t2Ljo4O2Nvb47PPPoOVlRW0tLRgZ2eHxsZGyOVyeHp64uHDh/Dz84OOjg4iIyPh6OiIffv2\nwdjYGBMnTmSOt0BnAG1vb2d17qysLPB4PFZbNzIy6lbTLiwsRGZmJkpLS9HR0cE4u21tbfjpp5+Q\nl5eH+fPnY9asWSguLkZ0dDSamppw9+5daGlpISMjg8luchdXHR0ddveRkJDA3uPq6srqzj/88AOW\nLl2Kmpoa3Llzh/nqPXnyBNu2bUNsbCy2b9+OdevW4cSJE3B1dUVeXh6MjY1RX18PZWVlxhZZvnz5\nC/oRfwa4pnNbWxtTIDt48CAbKS8oKMCHH34IY2PjN7aG+0v8/6D7L3St23JEc1VV1ZfWbdvb27t5\ngv1Z8Pf3x1tvvQWgcwZcS0sLPB4PJiYm2LZtG1RUVDBv3jwIhUL07dsX8fHxOHPmDGpqamBvb49T\np05hzJgxMDc3x8iRI1FWVobQ0FB89NFHGDZsGCtdcODcBDg8f/6cCVZzz1dVVaGtrQ3e3t5ISkqC\nVCpFU1MTcnNzYWNjA2dnZyaTl5uby9x8OcZBeHg4VqxYAScnJ3z++efw9PREQkICgM5pH6FQyDrW\nzs7OuHfvHoBO9kFQUBD27duHjIwMmJiY4MaNG9i+fTsePXoEGxsbdpz69euHJ0+eoK6uDqtXr0ZG\nRgZ69+6Nbdu2YevWrfDx8WFBt6OjA3fu3MHSpUvZYMjRo0dhbGzMLgZZWVlQV1dnPNtvvvkG8+fP\nh6WlJe7cucMEw69cuQJdXV10dHQgKCgIkyZNQkJCAuzs7HD9+nUcOXIEc+fOhYeHB6KiopCfn49t\n27axi3paWhpGjBgBFRUV6OrqwsjICOrq6nBxcYGFhQXu3r2LkJAQLF68GFu2bEF0dDRWrVqFjz/+\nGPHx8XBycsLatWtRUFCAkSNHYseOHQgNDUVBQQFaWlpY+cXOzg7btm37E1bsv8GdTzKZjOn1Pnv2\nDOPGjYNCocD169dhY2MDAwMDNrjxfwVvTE33j5QXflm35QQ2uForl839kbrt78WOHTuQmJiItLQ0\n1NbWQllZGU+fPoWSkhL69euHiIgI7N69GwsWLMDSpUvB5/MRFRWFHTt24Pvvv8e4ceOY0SQR4Ysv\nvsCWLVtgZWUFkUiE1NRUDBgwAESEvLw8CIVCFBcXw9jYmAmba2pqMotvLtPcvXs3SkpKEB4eDoVC\ngba2NkycOBF6enooKCiAtrY28vPz2bCEvb09vv/+ezQ3N2Py5Mksy7K3t0dpaSmqq6tZlsvB1dWV\nKTqlpKRgyJAhCAwMxJw5c9Dc3AxjY2PG9eWE1Dliv62tLVJSUtCrVy/w+XysXr0awcHBsLCwAJ/P\nh1QqRXFxMWpra6GtrY2VK1fC1tYW69evR0hICAQCAaKjo+Hs7IwrV64gODgY0dHRuHz5Mk6dOoV7\n9+5BLBbjypUr+OKLL/Ds2TNmC7Ro0SIoKSlBU1OTCRKdPn0a169fxxdffAFNTU1cuHABBw4cQEBA\nALKysjB27FiEhoZi//79eP/99zF//nwmWKSpqYk+ffogPz8furq6mD9/PjZs2IDRo0dj2bJlCAsL\nw4MHD3D58mW8/fbbCA0NxYIFC3D79m1oaWnBz88PLS0t+PnnnyEWi5lp6Z8FjjJIREz7+cCBAwgP\nD8d3333H2DF/BTi3jvT0dPD5fISFhcHd3f0v2//L8H860+3o6Ojmp8bVbbsGWo7ixKle/RXCH7/E\n7du3oa6ujtraWkgkEigUCsydOxePHj1iNu3cFFJoaCgbsBCJRKitrcXYsWORmpoKFRUVLF++HMnJ\nySgsLER7ezuOHDnCKFW9evXCwIEDcefOHcYN1tDQQGVlJRYtWoTFixejqKiIGWQqKSkhKysLt2/f\nRv/+/XHv3j3MnDkTZWVl+P777/HJJ58gLi4OH374IbZv346ioiLcunULtbW1TIRcIBDAxcUFycnJ\nLwRdJycnZGRkoLW1Fffv34eLiwvGjRuH1tZWaGpqorS0FAqFgk3VAf9u1Lm5ueHRo0d4+PAhJBIJ\nEhISsHLlSrS1taGxsRHu7u6Ijo7GjRs34O3tDaFQiEmTJuHkyZNYvXo1hg8fzsZwr169ioCAACxd\nuhQfffQRpk6dCkNDQ4wePRqXL19Gc3MzRo0ahTt37uDZs2eYPn06+xtmzpyJnJwcXL58Gd7e3jAw\nMEBAQAAuX77MAv+SJUuwYMECREZGoqSkBHK5nDU7T5w4gY6ODkYnS01NhbKyMluHQqEQXl5e4PP5\nePz4MfNV27NnD5qamlBXV4fTp0+zEWQu+PwZ9dOu2a2SkhLU1dVRXl6OSZMmoaysDDExMX9pwAWA\nd955BwEBAcjMzERqauofniZ7HXhjgu5vyXS78m35fD60tLS61W0FAgHrynNGe0pKSkxUpKGhgY3k\ndrUmed2gf6nmNzU1ISUlBXw+H8nJyRCJRNDT02Nye7du3YJAIECPHj2watUqPHz4EPfu3UNTUxM8\nPDzg5eWFjo4O2NraIjY2Fjo6Oujo6EBISAhOnToFb29v/PDDDxgwYADc3d1x+/ZtbN68GZ9++imy\ns7NhZ2eH/v37IyYmBkKhkLkE29jYIC8vjzXoevbsicWLF6Ourg6nTp1Cnz590NzcjEOHDkEsFkOh\nUEChULzgY8Y1ttLT07uVO8RiMUxNTREfH4+ysjLY2Njg008/hYmJCVpaWthk3bNnz5hFDwc3Nzck\nJyfjzp07qK+vh729PdOi0NTUZIyOuLg4eHh4QCaToby8HEDnnY+7uzvu3buH4uJipKamYvDgwQgM\nDERubi6mTZuG9vZ2mJubo6WlBaWlpaysMH369G7sjyFDhjABGe47coMybm5uePz4MUJCQqCtrY3p\n06dj165d2L59O3bu3ImioiKEhoYiNjYWQqEQS5Yswb59+3Dx4kXY2Njg2rVr2LBhA86cOQOpVIr3\n338f+fn52Lt3L95++23MmTMH8+bNY+fGqVOnIBKJWKLxqgGd3wNONa+lpYVN1504cQIzZ87E+vXr\nsXnz5temk/Br0dDQgNjYWKaLIRAImK3U34k3Juhy+E88XS6IdeXbcrfe3Ge6krY5eTmOf8mdsJwy\nGDcCy1GEXue4JFc/bmtrY/5V9+/fB5/Ph0wmQ21tLZOEfPvtt6GhoYHPPvsMFhYW8Pf3R3JyMgQC\nARYvXszkE4ODg3HlyhUUFBSgqakJW7ZsAQBs374dCQkJuHLlCo4cOYIff/wRFRUV0NDQwNmzZ7F+\n/Xrk5OSgra0NSUlJkEgkaGxshLOzM5KSkhAfHw9PT08AncwELS0tuLi44MmTJ9i9ezfKy8uxefNm\nKCsrw9vbmyllcXB3d0diYiIbSe4KV1dX/PzzzxgwYABOnjyJ06dP4/jx4wgLC0NZWRlOnDgBS0vL\nF3QaBg4ciKSkJFy9ehUKhQIHDx7Et99+i6qqKgCdxo53795FfHw8RowYgbKyMty+fRsTJkzA/v37\noaysDIlEgtDQUHh4eEBJSQlHjhyBtbU1zpw5A7lcDjU1Nfj7+yMyMhK3b99mdjtdwTUEBQIB4uLi\nAACbN29GQEAAjh07hpkzZ7JgtHz5chw4cAB6enqYNGkStm/fjtLSUnz55ZcsgEZERGDjxo1obm5G\ne3s7QkNDoa+vj9LSUsyaNQuGhob4xz/+gd27d8PAwABhYWEgIsTExCAgIOAFEXHgxQGd3xqIOfF7\nJSUliMViVFdXY/bs2UhLS0NMTAy8vLz+libZ06dPoaenh3nz5sHZ2RmLFi16qULdX403Kuhy4sgv\nC3icTkJzczOTiwP+rbBERMwMj3v9l/xUbh9dTfo4t1SOIsXVhzly+G9dwFzGwI1Gdv0elpaWCA0N\nZReWxMREVFRU4NKlS1BSUsLOnTtx//59tLS0gIiwcOFC5Obm4vDhw5gzZw78/f1x5coV3Lx5E0OG\nDIGenh4kEgkePHjA6sW6urpob29HZGQkGhsbceHCBTZVduPGDRgZGcHU1BQCgQASiQSxsbG4ceMG\nrl+/DhcXFzg6OkJJSYmN3I4ZMwZKSkoICgrChx9+CHNzc/B4PCxevJhpobq5ueH+/ftMz6ErnJ2d\ncffuXfTu3Rtr1qzByZMn0bNnT3h4eCAwMBA///zzS919TU1N0dbWhsrKSnz99dewtrZGSEgIayDZ\n29szbq6RkRE2btyIlStXYvXq1Th06BB4PB5GjBiBqKgojBo1CnV1ddi/fz82bNiAH3/8kf3Go0eP\nxunTp1FYWAgXFxccOnSo27Hu6OhAeno6Uzo7ffo0Tp8+jU2bNqGlpaUbt9XY2Bh8Ph92dnbg8XiY\nNm0abGxsEBcXx0aGgc7JvBkzZuDx48fo1asXGhsb8dFHH+H8+fM4fvw45s2bB2tra/zzn/8E0Kk7\n4ebm1m0N/yc3By454e7q5HL5S+/quLXKnTOqqqq4cOECJk2ahOXLl2Pnzp3/VXrxzwRn3rl8+XLc\nv38fIpGIJRp/K/6Tlw/9L0NLSwvJ5XIqLy/v5ktWVVVFZWVlVF9fT42NjSSXy0kqlTL/Js4Hq6am\nhvlu/dHHq3yhnj9/TjU1NVRfX99tXzKZjGpqaqisrIx5k71q2xs3biSBQEA8Ho8GDBhAAGj8+PGU\nnJxMDg4OZGpqSuHh4cwLTUdHh22zT58+NHr0aAoNDaWDBw+SsbExqaurk7q6OpWUlJBMJiM/Pz/y\n8/OjIUOG0OLFi8nV1ZV5twkEAlJVVSWBQMB83QQCAX322WcUHR1N5eXltHfvXrK0tKT58+dTdXU1\n1dXVkVQqpdu3b1PPnj1p9uzZtHTpUjIxMaGoqCiSy+VkYWFB5ubm3f7OhoYGioqKInV1ddLT06PD\nhw93e72wsJB4PB4FBQW99HcyMjIiTU1N9v/8/HzS0dGhjIwMksvlZG1tTb6+vhQXF0c9e/ak58+f\nk1wup7Fjx9L27dvp5s2bpKSkRI8fP6aPPvqIQkJCqLa2lgICAujrr7+m58+f09OnT0lZWZl69OhB\nZ86cIWdnZ/rxxx/Z8Tt+/Dg5OjqSp6cnzZkzh3r27ElffvklffXVVzR8+HDS09OjkpISksvldPjw\nYbK1tSVjY2Oqq6sjuVxOc+bMIR6PR6amptSjRw/y9PQkKysrkkgkdOTIEerXrx8VFhaSp6cnqamp\n0dGjR0lFRYUAkJqaGuXm5v7uNSyTyaihoYFqa2upqqqKKioqqLS0lMrLy9m/MzMzqbS0lIqLi2na\ntGk0b948qqur+7tDARERlZeXk7m5Oft/bGwsBQUF/VW7f2VcfaOCbmtrKzU2NlJZWRk1NTVRbW0t\nC6Zdgy334IJcVVXVawu2v2cBc0G5oqKC6uvrf9X2CgsLmQHlgAEDSElJiSZNmkRGRkbUq1cvamho\noDNnzhCPxyMPDw/2ualTp5KKigoZGRmRt7c37dq1i0QiES1evJikUilVVlbSV199RXp6enTmzBn2\nucOHD5Ouri7t2bOHqqqqqK6ujhoaGkhPT4+8vb1f+G4CgYD27t3b7aJTVFREAoGAvv76a6qvr6fT\np0+ToaEhrVmzhnx8fMjR0ZH9VpyBZ15eHgGgWbNmvfR3UFZWJrFYTHFxcd2ev3HjBgkEAhowYEC3\n59etW0eTJ09mQXfEiBHk5+dHO3bs6PZZMzMzunz5MikpKVFcXBwZGBhQUlISyeVyunz5MllbW7ML\npUAgIENDQ6qurqajR4+Svb09lZSUUFlZGdnY2FB4eDjt2rWLXF1dic/n0/3798nS0pKioqJo1qxZ\n9MEHH5BUKiVbW1s6c+YMDR48mMLCwigpKYl0dHTIwMCAVFVVycjIiNTV1SkrK4s2bdpEqqqqtGLF\nCqqvryczMzOaNm0aM5d0cnL6U9Yxt0ZKS0upsrKSvvjiCxIKhdSjRw8aNmwY7dixg3Jycv7uUMAw\nePBgys7OJiKizz77jP7xj3/8Vbt+ZVx9YyhjXUFETLhbQ0ODTZhx4EZ3+Xz+K8sIrxu/HPUFwDiw\n7e3trCwil8uZDCT3/pcxJnR1dVFWVobp06fj559/Bp/Px6lTpzB8+HCUlJTgxIkTCAkJgUgkQkpK\nCtzd3SGXy1FRUYGOjg6Eh4fD1dUVHR0deP/992FtbQ2ZTAZVVVWMHDkSH3zwQbdb0lGjRqGmpgYD\nBw7sJixuZGT0QoOE08RVU1Nj0o70L11hrpTT3NwMLy8vXLt2DStXrkRqaioMDQ2ZWI9AIEBTUxOC\ng4PB4/GYHGFXFBQUQCAQYNSoUZgzZw7u3LkDDQ0NVFVVYfbs2dDX10dJSQmIiNUUV65cCYlEgpSU\nFBQXF6OxsRFKSkqYM2cO2y7XFNy1axfMzc2xY8cOeHp6sibf4MGDGaWsoaEBAoEAurq6UFNTQ3Bw\nMLZu3Yo7d+4wQfBhw4ahuroaK1euxODBg7F+/XqoqKjAw8MDpqam8PT0hLm5OfNEA4DPP/+csRSm\nTJmCtrY2JkwzevRouLm5MSrW+fPnUVpaihMnToDH42HLli14++23f/3i/JXgSmdcQ0omk6GgoADj\nxo3DW2+9hfz8fKSkpLBhmP8JCA0NxYwZM5gGxcGDB//ur/Rm1XS5ui3wb6EY4OV1W66W9VcE3F+C\nCzrcAubqaRoaGqy5B7zY4PhlXY3P5yM8PBwRERFMkDouLg6FhYVYsWIFDAwM0NjYyJpvSkpKrPZm\nbm4OALh16xY0NTWRnZ3NJobS09Ohra3NhhaAztFfoVDYTd0L6GyccV5hHJ48eQKhUMiEWoDOi05W\nVla3fWlqasLMzAxnzpyBUChEXl4e7ty5Ax6Ph6dPn8LPzw+WlpawtLTs5ozB4ebNm3B2dkZdXR18\nfHzwzjvvoL29HQsWLEBQUBBkMhl69OjRTQZSQ0MDa9euxcX6ncoAACAASURBVKpVq9CrVy82jsw1\nlehf9cwlS5bgxo0bCAgIwKVLl/Dhhx92+1uWLVuG7777Dl9++SV69eqF6upqZGZmgsfjYfXq1diy\nZQu2bNmC9evXQywWIyoqCqampqirq8PVq1cxZcoUyOVyaGpqYsqUKVi/fj0++OADEBFGjhyJkpIS\n3L9/H6tWrcL69evh7e3NdIZnzJiBGzduwMnJCVZWVigqKoJCoYCxsTGKiopee8ClLgJGQqEQQqEQ\nd+7cwZgxYzB8+HAcP34cvr6+WLhwIfbu3Yvg4ODXuv8/AolEguTkZDx8+BBnz55lLhx/J964oMtl\nVr/MbjllI06I5e/yfmptbYVUKn2lGtl/UyB7GVvCz88PaWlpTFRcQ0MDra2tWLhwIQoKCrBlyxZY\nW1ujV69ekEqlsLS0xNWrVyGXy3H06FFMnjwZkZGR7HtERUXBx8enm9X0nTt34Ojo2M1yprGxEcXF\nxSgpKUFlZSV7Pj4+Hi4uLi/M9sfGxsLLywuxsbEA/t3QqaqqQmNjIzQ0NDBz5kzs2bMHY8eOxdtv\nv43evXvDy8sLkZGRaGpq6taUjImJQXBwMJKSkrB582akpqZi1qxZaGxsxODBg+Hm5sbYBV0xd+5c\nFBQUoKOjg2XlAJjeRltbG2xtbaFQKJCWlgaFQvFC5sZJMWZmZmL//v1YsGAB9uzZAwAYN24cysvL\nQUQYMWIE6urqsHnzZhw9ehTt7e0QCAS4c+cOxGIxxGIxHB0dUV9fD3Nzc0ilUly/fh3V1dVQVVXF\n8+fPUVxcjHfffRdnzpzB8ePHsW3bNlhYWEBDQwOPHz9mKnPZ2dmv3V3hl/Y5CoUC69atw+7du3H+\n/HlMnTr1L2UmdHR0wNnZ+Q9b5vydeKOCrqqqKgQCAQQCAeMhyv8lWkP/mo7hJOj+anAndEtLC8vC\nf82gxa9lS2hoaODq1atwd3dHdXU1xGIxDh48iGPHjmHu3Ll48OABUlNTcf78eZSVleHjjz9GdnY2\nIiMjmRlleno6iAhRUVFYunQprly5gra2NgBAXFwcJk6ciJs3bzLBlHv37sHe3h5+fn7dAltCQgL8\n/f1RWFiIiooK9nxsbCzGjh2LsrIyVFRUsN/k2rVrGDZsGIYOHYqAgAB88cUX8PX1xeLFi5Geno7A\nwECoqqqyQQmZTIa6ujrExMRg1KhR6NOnD7Kzs7FixQpcuHABn376KRISEuDt7c0YG12hrKwMkUiE\nJ0+eYPXq1Th16lS3i7K6ujpOnjyJ4cOH49atW3B3d3/BiUJNTY3Zxbu5uWHBggU4e/YsampqmMIW\nd3y3bdsGf39/mJqaoqKiAmpqanj69Cm+//578Hg87Nu3DyEhIdi+fTtycnIwffp0eHh4ICkpCRkZ\nGfDy8sK4ceNgamqKTz/9FMHBwUhNTUVUVBTMzc2Rl5eHDRs2/L6F+Qp0zW450Zr79+8jMDAQEokE\nZ8+e7ab7/Fdh586dL3Cy/7fhjQq6S5YswYQJE7Bjxw4cOnQI7733HpqamhgZXSaTvVZC+K8BNxLZ\nNcv+o2PEXa3HOcdbrkRx7tw5pKenY/Xq1dDW1sa6detgZmYGHo8HQ0NDODo6IiYmBi0tLRgxYgQT\nIx8zZgwuXryIjIwMqKioYPDgwTAzM0NcXByICLGxsRg9ejQcHR0RHR0NoDO4enh4vBDYEhISMGjQ\nIAwZMoS9l9M4GDJkCLy8vHDjxg3I5XKoqKjg5s2b8PX1RWNjIyIiIhAeHo709HQsW7aM6dz6+/sj\nJiaGZf+cM0OvXr3g7u6O8+fP4/PPP8dbb72FVatW4fbt2/D09ISXlxeePHnSLfg/ePAAxcXFbJuP\nHj1CUVERK690dHTg2LFjyM7Oxrhx49DU1IQDBw50OwZ3795FRUUF9PX1ERYWhp49e8Lf3x+HDh1i\nPnNtbW0IDw/H0aNH8cknn2DNmjUICQnB999/D7lcjs2bN2Pfvn1obGxEaGgoYmJiEBQUBD6fjyNH\njqBv376wsbFB//79ceHCBfj4+EBNTQ0XL16EqqoqvvzyS8TFxUFLS+u1Wun8Uhua/jU+vnHjRoSH\nh2P+/Pl/y2RmcXExLl++jIULF/7l+36t+E9dtj+7vfe60dHRQXFxceTk5ET6+vo0YcIE8vDwoFmz\nZlFoaCglJSVRdXU1VVdXd2MPcDSuhoaG18Zi6EoBq6qqIqlU+qeyI17FloiIiCB9fX3W1e7Zsyd5\ne3uTm5sb8fl8srKyIpFIRBKJhIyMjGjs2LHk4+NDe/bsofHjx9PQoUNp48aNpKurS1FRUbR69Wqa\nOHEiyWQyGjVqFB07doyePXtGmpqaVF1dTUVFRaShoUH19fX07bff0pQpU0gul1NiYiL17duX6urq\n6NNPP6W5c+cyWp26ujr16tWLhg8fTrq6ulRXV0cVFRXk4+NDampqVFxcTOfOnevGwti4cSMtXryY\n5HI5HTp0iHr06EHr1q2jmpoa8vf3J4FAQPn5+VRRUUHjxo2jXbt2UUNDA0mlUrK2tmYMAzs7O/Lw\n8KBNmzaxbZ87d44MDAxo/PjxVFNTQ3Z2dqSjo8MYElKplPr06UMSiYQePHhAenp6dPfuXYqLiyMT\nExPq27cvXb58mfbu3Uu6urr06aefUkREBJmamlJFRQXJ5XJasGABubu7k5qaGm3dupVu3LhBWlpa\npKKiQgYGBrRp0yYKCwuj3r1705MnT8jPz480NTUJAAUFBVFlZSXV19dTTU0NPX/+nMrLy6m0tJQq\nKiqoqqqKamtrGS3yt6wZjjVSW1tLMpmMkpKSaNCgQRQaGkrt7e1/6/k9ceJEevDgAd28eZPGjBnz\nt36XX4H/G5QxIqLIyEjauXMntba2EhGRQqGgjIwM2r9/Py1cuJC8vLxo2LBhtGrVKgoPD6f8/PxX\nLlyOX/pbA159fT3jMjY0NPylwfZVgT8yMpKMjIwY39bY2Jh0dXWpX79+NHXqVLKysmK8W3d3d5o+\nfToFBQWRmpoa9e7dmywsLMjDw4P69OlDAEhZWZn4fD6NGDGCNmzYQLa2tnTs2DE6ffo0DR06lORy\nOWVmZpK+vj5JpVL65z//STNmzKDy8nK6ffs2WVlZUVxcHDk4OJCamhpFRkaSXC4nd3d3RlX78MMP\nSSKRUP/+/SklJYU0NDSosLCQ5HI5+fr6Unh4OJWVlZGPjw8JBAJ69uwZyeVy2rlzJ6mrq9PGjRup\nvr6edu/eTYGBgVRWVkabN28mVVVVOnnyJFVUVFBSUhJpamqSlZUV+92GDh1KGhoaVFBQQHK5nOLi\n4kgkErELyLfffkuqqqp0/fp1ksvldODAAfp/7Z15WFN39offCxEEBQVBqQwuCFhEUIFEtCpU6zaK\n22gttWWsUkftjFpxnanruONSbetedera1k61LuO4IGiVgGiltFocsSKi0CJuKBpC7u8PvfcXEFwD\nCXjf58kfwAPfk3Bzcu5ZPsfLy0vMzs4WPT09xVdffVW8c+eOuGnTJtHW1lbs2bOn6O7uLu7atUs+\n48qVK2LNmjVFNzc30dHRUXRwcBDHjBkjenl5ibt37xb79+8vCoIgtmrVSmzWrJloY2Mj9uvXT25b\nK+1RVm/4b7/9VqxfurTfvXXrlty+ePv2bfHmzZvirFmzxLCwMPGXX34x87taFHfv3i1+8MEHoiiK\n4uHDhyuy3/Z5KdOvCuLjb0mqnKqwKIryOpOEhAR5qqtBgwYEBwfTunVr/Pz8sLKykpXvgUfauErL\nC0uFLqmgJwk2mwO9Xi9via1evTrW1tbcv3+fRYsWsXfvXvLy8nj33XfZsGEDS5Ys4fz588ybN4/C\nwkLc3d25ffs2/fv3Z9u2bTg4OJCcnCwXKdu3b4+bmxu2trb07t2b5ORkOVfs6OiIp6cn8+fPR6PR\nEBgYyOrVq1m4cCE9evRgwIABxMbG8u677+Lo6EhgYCABAQFyTnLt2rUcPXqUdevW0axZM7Zv305C\nQgLz5s3D09OT999/X85vHjlyhMGDB6PRaMjLy6Nt27aMGDGCrl27MnDgQGJiYpg8eTLdu3enRYsW\n7Nq1i/DwcGxtbUlNTZWLrevXr2fq1KkcOnSIunXr4ufnJ6/FkZgwYQKrVq0iJSWFkJAQPD09i6l1\nffDBB2RkZHDy5Emsra3ZuXMnAwYMYN26dYwePZq8vDyOHz+Oh4eHLFiUlZVFWloaRUVF2NjYcPPm\nTby9valfvz5paWkYDAZu3LiBh4cHq1atKtbC97QYb3AoeT1LD0klTrpmz58/z5gxY+jatSvjxo2r\nUFW9svj73//Opk2b5FZCaZHpF198YW7TyqLMN/5L53RLw2AwkJGRQUJCAlqtlpSUFHn9S3BwMCEh\nIdSrV6/YBWxlZSU7YamgpdPpsLGxMVuxTnoukuO3s7NDpVKVasvJkydZtWoVO3bs4P79+9StW5fd\nu3cTFxfHihUr+PTTT9myZQtffPGFvGVWo9GgVqtl/YO0tDRZovHs2bP07NmTO3fuMHjwYA4ePMjt\n27epXbu2vHOsTZs2JCYmEhAQgF6vR61WEx8fz+LFi2XtBkkLeNWqVcydO1d2bPv37+edd97B39+f\nKVOmMGnSJG7evMmQIUMYN24cR44cITo6mi1bttClSxfOnTvHxYsX6datGx9//DExMTHk5OTg5+dH\ns2bNmD17NoAsBdmyZUvq1KmDjY0Nly5dIjk5+RHH1KhRI9zc3MjLy2P16tV069ZNfj1PnTpFWFgY\nI0eOpFGjRsyYMYNBgwYRHh5OVFQUUVFRrF69WhYjz83NZejQoQwfPlwWj2/YsCFOTk6yAxw3bhwd\nOnR4RFfiRRBFUb6OJWcLDwql27Ztk+U+16xZY3YJxLKIj49n0aJFsr6xhaI43WdBfNir+cMPP6DV\natFqtWRkZODi4oJaraZ169a0bNkSGxsbrly5grOzs9xlIDnispxdedqs0+m4f//+Mzn+3NxcJk2a\nRHp6OmfPnqV9+/ZyL/O9e/cYNGgQe/bsIS8vj6CgIE6dOkVKSgpWVlYEBwfzyiuv4OzsTO3atfn0\n008JCAigRYsWXLlyhV9//ZWMjAzu3bsHQN++fZk6dSo+Pj5kZGTw2muvIYoiGRkZxaKpiIgIrl69\nyptvvsnIkSPl7x86dIg+ffrg6+vLhQsXWLZsmSyjKIoirVq1IjAwEDc3N+bMmQPAiRMn6Nu3L9Wr\nV5d1BbZt2yYv35Q4fPgwvXr1QhAE1q1bR9++fWXHJAkijR8/ns2bN9O4cWNOnz4t3/FkZWXRqVMn\nhg0bxtKlSxk9ejQxMTGEhoaSmprKwoUL6d69O7t37yYyMhIPDw80Gg3bt2/H2dmZadOmERERUWFt\njMbXirSt+vTp0yxatIjc3FwKCgo4c+YMI0aMYNGiRRVi07NQ2Z2u+e8bLBBBEKhevTpt2rSRIzBR\nFMnJyUGr1RIXF8fMmTO5ePEi1apVY/z48bRt25bGjRvLDlsaSpAiJZVKhZWVVbk4YimVIAjCMw98\nuLi4yEsj8/LyOHjwIHv27GHnzp1Ur16ds2fPEhERwfbt27l69SqDBw9m06ZNBAUFsWvXLgYMGIDB\nYECr1WJtbc2lS5cIDw+nffv2NGjQgBo1ahAaGsobb7yBvb09YWFh9OjRg2HDhuHt7S0PiBjTu3dv\nhg0b9kibVmhoKK6urvz88880bdqU119/Xf6ZIAhERkYye/Zsjh8/Lv8fbGxssLe3Jzc3F1tbW5yd\nneUNCsZIAylFRUVUq1ZN/r8Zb809cOAA1apVIzMzk88++4x33nmH+/fv07t3b6KiohgzZgyFhYVM\nmzaNNm3asH//fmrUqCG3yG3bto26deuSmZlJZmYmM2bMYMSIERXaMy6J1ACywPjmzZvZsGEDH3/8\nsRzdSmp8lkhoaGixNVOVDSXSfQ5OnjxJ165diY6O5o033uDkyZNotVrOnTsnr1fRaDQEBwfj4OAg\npyQMBkMxJ1zWiO/TUp45ZEnEPCEhgePHj/P999/LEfCoUaPo27cvhw8fZvPmzWzfvp3w8HCWLFnC\nl19+yd27d1m7di0Gg4GRI0eiUqk4fPgw8fHxODg4sGnTJtasWcNvv/2GKIrF0hQAa9asYcKECaSk\npNCgQQP0ej1ff/018+fP5/fff8dgMPDBBx+wdu1aYmJiGDBgAPBgKeSIESPkDRjHjh1j+PDhfPTR\nR7i6uvLnP/8ZZ2dn0tPTi71O69atY/r06cCDZaPOzs507NiRBQsWUKNGDaZNm8Z3332Hl5cXrq6u\n/Pjjj+Tl5eHg4ICVlRVt2rRh1qxZxMbGMnz4cOrUqUNubi4+Pj5cu3ZNFoy3tramV69eRERE0KlT\nJzlHXhGIoihv5bW1tcXGxoacnBw+/PBDPD09mTNnTrHxboUXRkkvmBKDwUBOTs4jzeHiQ80HSWM2\nMTGRvLw8GjdujFqtJiQkhKZNm8oFHKmoYVygK6tIV/Ic6fZQml4r71SGKIqcPXuWr7/+mtzcXE6c\nOEF6ejpOTk5kZ2fL2rNeXl707duX9u3b4+XlxZIlSzh48CDLly8nISGBjRs3otfrGT58OFlZWVy9\nepU7d+4wZMgQoqKi8Pb2plOnTjg6OqLRaGjUqBHz58/H1dWVqKgooqOjadSoEe+++y5qtZqoqCia\nN2/OkiVLGDZsGLm5uYSHh1OzZk3mzZvHhg0bCAsLY//+/QwePFheXLl+/Xrq1KnD+PHjOXr0KIGB\ngfK009atWwkICCA+Pp4mTZqQn5/PH//4R7799ltiY2O5ffs27du3x8rKipycHEJCQvDx8WHDhg3U\nqlWLiRMnMnDgQFQqldw7K8ljuri4PNX/2JQYr8+Rhmq+/fZbli1bxoIFCwgNDa1Qey5fvkxkZCQ5\nOTlYWVnx/vvvM2rUqAo7v4JQnK65MBgMpKeny0W61NRUrK2tadGihZwfdnFxKVakM3bAUjQsvSkk\nkRxA3mhhLm7evEliYiJbtmzh3r17pKWlkZmZiZeXF7/++it6vZ7Zs2fz+uuvU79+fTnv+dVXX1Gv\nXj2WLVtGSkoKQ4cO5U9/+hNbt27FycmJrKwswsLCOHDgAM2bN2fOnDmEhoYyaNAgAgIC6NOnD126\ndOH777/H1dWV6dOn8+WXX3Lnzh3++c9/MnPmTOrWrcs333xDkyZNuHjxImFhYWzatIlbt24RFRWF\nIAjUqlULX19f3n//ff7617+SlJSEk5MTAwcOxN3dnePHj3Pu3Dm6dOlCfHw8b775JpcuXSIlJUWO\nGF1dXcnOzqagoIBx48YxduxYBEGgoKCAatWqYWtrKxfrjPPDj/sfmwrj6FbK81+/fp3o6Ghq1arF\nwoULzbJJITs7m+zsbFq2bEl+fj5BQUHs3Lmz2DaRKoDidC0F8aHojpSSSEpKIisrCzc3N9RqNRqN\nhoCAAFQqVbFoWEpDFBUVyUUhS+yQyM/PJzU1lQMHDvDDDz9QVFTExYsXyczMpFatWvz++++4u7vT\ntGlTOcKXtgrrdDp5RLegoIC6dety/fp1wsPD6dGjB1OnTpVb12JiYjh69Chbt25l5syZrFmzBniQ\ni3R0dMTa2pqFCxfSq1cvOnXqxKBBg4iIiODnn39m7Nix/Prrr9jb28udA127dsXBwYGrV6+SmZkp\ni/VIyy59fX3p3LkzAQEBBAQE4O7u/sjrL70uRUVF8utSGpITlh6SiFHJO54XTT0VFBRgMBjkkfP/\n/ve/zJ07lxkzZtC9e3eLWXnep08f/va3v9GpUydzm2JKFKdryYiiyOXLl+VOiVOnTqHT6WjevDmB\ngYHcuXMHnU4n77uSWtZK5oYrIsUgRU7PmtbQ6/VkZWWh1WpxdnYu1rokieX06dMHJycnRFHk1q1b\nxMXFsWPHDnbt2kVBQQHe3t64u7tTo0YN7OzsiI2N5datWwiCQI8ePRg8eDC+vr7s3buXDRs2kJqa\nio2NDbVr10YQBPLz82nevLlc8HR1deXMmTPcuHGDsLAwXnnlFfnh4uKCu7v7U+31epHXReJx/bRP\n6g8vSWFhIQUFBXJ0e/v2bSZPnkxhYSHLli0rJvJjbqS7kJ9++kkeSa8iKE63sqHT6fj666/56KOP\n0Ov18u6woKAgWrduTVBQEHZ2do8U6Z6kw/u8FBUVyfulKjqtIYqirAEhLVXMz88nMzMTW1tbRo4c\nKXdtGDulzMxMuXUrODiYBg0amPyDyTiifFx0+6wYfygZP4w7Yko64pKRtrW1NUePHmXKlClMmDCB\n/v37W0x0Cw/uisLCwpgyZQq9e/c2tzmmRnG6lZGpU6fSoEEDhgwZgiAIXLt2jcTERBISEjhx4gS3\nbt3C29tbzg17eXkBvFCRriRS65VOp5Or3uZ645YsINrY2JTplMr7DqC0fGlF3GkYDzYYf9gKgiAP\n6Dg5OaHT6Zg+fTpXrlxhxYoV1KtXr1xte1b0ej09e/ake/fujB492tzmlAeK062KFBUVkZaWJhfp\nzpw5g62tLYGBgXJ+uHbt2hgMBvR6/SMFHCkXW5azkJyKlZWVXPU2F08TaUtOSXJIZbXpPe45Pw0l\n86XmLGZKfbdSAXbWrFl88cUXcuvie++9R7t27XB1dTWbjaURGRmJi4sLixcvNrcp5UXVdbqffPIJ\ny5cvR6VS0aNHD8vY9mkmytKV8PDwkJ1w8+bNS9WVMHZK4sPNFlKhzByC78bPSYq0n6cXWXy4Jqgs\n7QFjR/w0f6uio9vHYbw+x87ODp1Ox9y5c0lLS6NPnz5cvHiRpKQk+vfvz9ChQ81mZ0mOHTtGhw4d\n8Pf3lz8A58yZU2ysugpQNZ1uXFwcc+bMYe/evahUKnJzc4s12Ss8XlciKCiIkJAQ3NzcikWI0kYH\nGxubcp2kexLSpJ21tTXVq1c3SaQtKT2VdMRPmh4s2etqzuhW+lAsLCyUPxR//PFHxo4dy6BBgxgx\nYoRZ70oUgKrqdAcOHMhf/vIXOnbsaG5TKg1l6UrY2Nhw7do1AgICWLx4MdWrV6+wIl1pNhYUFFRY\npP2ktIQU4dra2lpEdCt9ENnZ2aHX6/n44485cuQIK1eurPCFkPv27WPMmDEYDAaGDh3KxIkTK/R8\nC6ZqOt1WrVrRu3dv9u3bh52dHTExMQQHB5vbrErHjBkz+OSTT4iIiMDe3p6TJ09y9+5dXn31VblI\nJ7VZSUUcaXvFixTpSiJFoNJgQUVM2j3OFqloJxptE36etISp7CkZ3aalpTFmzBh69uzJ2LFjKzz6\nNhgM+Pj4cOjQIerXr49arWbbtm1Vbcjheam8gjedO3cutmpFegPMmjULvV7P9evX0Wq1nDhxgjff\nfJMLFy6Y0drKSdu2bRk+fHixCrder+fnn38mISGBZcuWFdOVUKvVqNVqebWNTqd75iJdSUoWp8yp\n4VpShct4U7AUDUutWRUhamSsjSytz5EWQ65YsUJuJ6xokpKS8Pb2pmHDhgC89dZbVXGyzORYvNMt\nbfW2xMqVK+nXrx8AarUaKysrrl27Rp06dSrKvCpB586dH/meSqWiRYsWtGjRguHDhz+iK/H5558X\n05Vo3bo1r776KlZWVnKxCZ4cGZaUpLS3tzfr7btxl0RJxTZBEIpp25ZMS5jiw8eY0oqIGRkZjBo1\ninbt2hEbG2vWImdWVhYeHh7y13/4wx9ISkoymz2VBYt3uo+jT58+xMbGEhoayrlz5ygsLCxXh7to\n0SLGjx9Pbm6uRU31VASCIFC7dm26dOlCly5dgOK6Eps3by5VV8LV1bXUyFByRvfu3XsuSUpTU1p0\n+yRHKWkoG9ttnIJ5lg+fkhQVFcnyoNKk1r/+9S82bdrE0qVLUavVL/iMFcxFpXa67733HkOGDMHf\n3x9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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e6a7da0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig = plt.figure()\n",
     "ax = plt.axes(projection='3d')\n",
-    "ax.plot_wireframe(X, Y, Z, color='black')\n",
+    "ax.plot_wireframe(X, Y, Z)\n",
     "ax.set_title('wireframe');"
    ]
   },
@@ -289,24 +296,29 @@
    "metadata": {},
    "source": [
     "A surface plot is like a wireframe plot, but each face of the wireframe is a filled polygon.\n",
-    "Adding a colormap to the filled polygons can aid perception of the topology of the surface being visualized:"
+    "Adding a colormap to the filled polygons can aid perception of the topology of the surface being visualized, as you can see in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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MdpKuItCNa3A0lJSPFh5QRQjIiAjm/HHYyqSobKIyhimKUP0Qrv57rMjN5+24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\nSxtKjM6gu3TpUm699VaefPJJN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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ecb7860>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -321,25 +333,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that though the grid of values for a surface plot needs to be two-dimensional, it need not be rectilinear.\n",
-    "Here is an example of creating a partial polar grid, which when used with the ``surface3D`` plot can give us a slice into the function we're visualizing:"
+    "Though the grid of values for a surface plot needs to be two-dimensional, it need not be rectilinear.\n",
+    "Here is an example of creating a partial polar grid, which when used with the `surface3D` plot can give us a slice into the function we're visualizing (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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qsdzpWLOKXoiiiHa7Ta/Xo1wub2jZzno/ZulY9H2ftbU14jhmYWGBer0+INzl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\n4Q348Pm8KD4ZxaugeGQ8Xg+yT8bjVZA8Ium4Rqw/TqI/zi8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h7+D4wjUlpEsVErPKiC2p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Oe5+A+EY6R7pOP/t3c/L61cUQDHvxfShX3SUlDMThpIIUkDGvwFllTsJoLVjQXduPAfkFDEgFVw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\nde+rhRf0HQAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10eae2128>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -363,8 +380,8 @@
    "source": [
     "## Surface Triangulations\n",
     "\n",
-    "For some applications, the evenly sampled grids required by the above routines is overly restrictive and inconvenient.\n",
-    "In these situations, the triangulation-based plots can be very useful.\n",
+    "For some applications, the evenly sampled grids required by the preceding routines are too restrictive.\n",
+    "In these situations, triangulation-based plots can come in handy.\n",
     "What if rather than an even draw from a Cartesian or a polar grid, we instead have a set of random draws?"
    ]
   },
@@ -372,7 +389,10 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -387,24 +407,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We could create a scatter plot of the points to get an idea of the surface we're sampling from:"
+    "We could create a scatter plot of the points to get an idea of the surface we're sampling from, as shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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RDqs89aCXXVui3P9HD8VFRn75gMwp14+gaWcHP32ijX27gtgMUW652c78540E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\nit1uZ+nSpXzzzTe8/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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e7947b8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -417,25 +442,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This leaves a lot to be desired.\n",
-    "The function that will help us in this case is ``ax.plot_trisurf``, which creates a surface by first finding a set of triangles formed between adjacent points (remember that x, y, and z here are one-dimensional arrays):"
+    "This point cloud leaves a lot to be desired.\n",
+    "The function that will help us in this case is `ax.plot_trisurf`, which creates a surface by first finding a set of triangles formed between adjacent points (remember that `x`, `y`, and `z` here are one-dimensional arrays); the following figure shows the result:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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bhnS3imyVUhP/zr22xDkJ8gLc/i1fGuzslGwP+kGJk8/j3P9NQ3ksLbx4rPTpKE1CQF33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\n0gV9RwbD9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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ef33e80>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -457,19 +487,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Example: Visualizing a Möbius strip\n",
+    "## Example: Visualizing a Möbius Strip\n",
     "\n",
-    "A Möbius strip is similar to a strip of paper glued into a loop with a half-twist.\n",
-    "Topologically, it's quite interesting because despite appearances it has only a single side!\n",
+    "A Möbius strip is similar to a strip of paper glued into a loop with a half-twist, resulting in an object with only a single side!\n",
     "Here we will visualize such an object using Matplotlib's three-dimensional tools.\n",
-    "The key to creating the Möbius strip is to think about it's parametrization: it's a two-dimensional strip, so we need two intrinsic dimensions. Let's call them $\\theta$, which ranges from $0$ to $2\\pi$ around the loop, and $w$ which ranges from -1 to 1 across the width of the strip:"
+    "The key to creating the Möbius strip is to think about its parametrization: it's a two-dimensional strip, so we need two intrinsic dimensions. Let's call them $\\theta$, which ranges from $0$ to $2\\pi$ around the loop, and $w$, which ranges from –1 to 1 across the width of the strip:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -482,16 +514,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now from this parametrization, we must determine the *(x, y, z)* positions of the embedded strip.\n",
+    "Now from this parametrization, we must determine the (*x*, *y*, *z*) positions of the embedded strip.\n",
     "\n",
-    "Thinking about it, we might realize that there are two rotations happening: one is the position of the loop about its center (what we've called $\\theta$), while the other is the twisting of the strip about its axis (we'll call this $\\phi$). For a Möbius strip, we must have the strip makes half a twist during a full loop, or $\\Delta\\phi = \\Delta\\theta/2$."
+    "Thinking about it, we might realize that there are two rotations happening: one is the position of the loop about its center (what we've called $\\theta$), while the other is the twisting of the strip about its axis (we'll call this $\\phi$). For a Möbius strip, we must have the strip make half a twist during a full loop, or $\\Delta\\phi = \\Delta\\theta/2$:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -510,7 +545,10 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -527,24 +565,29 @@
    "metadata": {},
    "source": [
     "Finally, to plot the object, we must make sure the triangulation is correct. The best way to do this is to define the triangulation *within the underlying parametrization*, and then let Matplotlib project this triangulation into the three-dimensional space of the Möbius strip.\n",
-    "This can be accomplished as follows:"
+    "This can be accomplished as follows (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Qy7z93T75wtbujzPVCSYe9vGaDV76zlMUnSyZrM/YsRrFPs2bNcWLr1oEzSym6MGUBSxZ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\nRfQT2Thl1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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e69c358>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -555,9 +598,10 @@
     "\n",
     "ax = plt.axes(projection='3d')\n",
     "ax.plot_trisurf(x, y, z, triangles=tri.triangles,\n",
-    "                cmap='viridis', linewidths=0.2);\n",
+    "                cmap='Greys', linewidths=0.2);\n",
     "\n",
-    "ax.set_xlim(-1, 1); ax.set_ylim(-1, 1); ax.set_zlim(-1, 1);"
+    "ax.set_xlim(-1, 1); ax.set_ylim(-1, 1); ax.set_zlim(-1, 1)\n",
+    "ax.axis('off');"
    ]
   },
   {
@@ -566,22 +610,16 @@
    "source": [
     "Combining all of these techniques, it is possible to create and display a wide variety of three-dimensional objects and patterns in Matplotlib."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) | [Contents](Index.ipynb) | [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.12-Three-Dimensional-Plotting.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "encoding": "# -*- coding: utf-8 -*-",
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -595,9 +633,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.14-Visualization-With-Seaborn.ipynb b/notebooks/04.14-Visualization-With-Seaborn.ipynb
index 21817be21..76f31337c 100644
--- a/notebooks/04.14-Visualization-With-Seaborn.ipynb
+++ b/notebooks/04.14-Visualization-With-Seaborn.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.14-Visualization-With-Seaborn.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -30,160 +8,38 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Matplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users will admit it often leaves much to be desired.\n",
-    "There are several valid complaints about Matplotlib that often come up:\n",
+    "Matplotlib has been at the core of scientific visualization in Python for decades, but even avid users will admit it often leaves much to be desired.\n",
+    "There are several complaints about Matplotlib that often come up:\n",
     "\n",
-    "- Prior to version 2.0, Matplotlib's defaults are not exactly the best choices. It was based off of MATLAB circa 1999, and this often shows.\n",
-    "- Matplotlib's API is relatively low level. Doing sophisticated statistical visualization is possible, but often requires a *lot* of boilerplate code.\n",
-    "- Matplotlib predated Pandas by more than a decade, and thus is not designed for use with Pandas ``DataFrame``s. In order to visualize data from a Pandas ``DataFrame``, you must extract each ``Series`` and often concatenate them together into the right format. It would be nicer to have a plotting library that can intelligently use the ``DataFrame`` labels in a plot.\n",
+    "- A common early complaint, which is now outdated: prior to version 2.0, Matplotlib's color and style defaults were at times poor and looked dated.\n",
+    "- Matplotlib's API is relatively low-level. Doing sophisticated statistical visualization is possible, but often requires a *lot* of boilerplate code.\n",
+    "- Matplotlib predated Pandas by more than a decade, and thus is not designed for use with Pandas `DataFrame` objects. In order to visualize data from a `DataFrame`, you must extract each `Series` and often concatenate them together into the right format. It would be nicer to have a plotting library that can intelligently use the `DataFrame` labels in a plot.\n",
     "\n",
-    "An answer to these problems is [Seaborn](http://seaborn.pydata.org/). Seaborn provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas ``DataFrame``s.\n",
+    "An answer to these problems is [Seaborn](http://seaborn.pydata.org/). Seaborn provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas.\n",
     "\n",
-    "To be fair, the Matplotlib team is addressing this: it has recently added the ``plt.style`` tools discussed in [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb), and is starting to handle Pandas data more seamlessly.\n",
-    "The 2.0 release of the library will include a new default stylesheet that will improve on the current status quo.\n",
-    "But for all the reasons just discussed, Seaborn remains an extremely useful addon."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Seaborn Versus Matplotlib\n",
+    "To be fair, the Matplotlib team has adapted to the changing landscape: it added the `plt.style` tools discussed in [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb), and Matplotlib is starting to handle Pandas data more seamlessly.\n",
+    "But for all the reasons just discussed, Seaborn remains a useful add-on.\n",
     "\n",
-    "Here is an example of a simple random-walk plot in Matplotlib, using its classic plot formatting and colors.\n",
-    "We start with the typical imports:"
+    "By convention, Seaborn is often imported as `sns`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "plt.style.use('classic')\n",
     "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
     "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we create some random walk data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Create some data\n",
-    "rng = np.random.RandomState(0)\n",
-    "x = np.linspace(0, 10, 500)\n",
-    "y = np.cumsum(rng.randn(500, 6), 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And do a simple plot:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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H3PzFzWwq28SW8i2UmEoIVASSGCx5ESgVSqakTOFPa/5EdnW2X/pXairF7DLz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\nSKoIUuBLJBJJFeH/OyKinWS1qMsAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1023b2cc0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the data with Matplotlib defaults\n",
-    "plt.plot(x, y)\n",
-    "plt.legend('ABCDEF', ncol=2, loc='upper left');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Although the result contains all the information we'd like it to convey, it does so in a way that is not all that aesthetically pleasing, and even looks a bit old-fashioned in the context of 21st-century data visualization.\n",
+    "import pandas as pd\n",
     "\n",
-    "Now let's take a look at how it works with Seaborn.\n",
-    "As we will see, Seaborn has many of its own high-level plotting routines, but it can also overwrite Matplotlib's default parameters and in turn get even simple Matplotlib scripts to produce vastly superior output.\n",
-    "We can set the style by calling Seaborn's ``set()`` method.\n",
-    "By convention, Seaborn is imported as ``sns``:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "sns.set()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's rerun the same two lines as before:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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hJGWVk9eYtpea8HujHDvQm7fdUT692tTOSitqjYq25kGe+fcPee2FY+OOa28e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KqqJJ3z+NReVWDndvyvvYL/1Zen3Cc+NDjn2rlBqeCra2tZadEzGI3jDsdIi2JOXjHbdgLkGJop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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11a0d73c8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# same plotting code as above!\n",
-    "plt.plot(x, y)\n",
-    "plt.legend('ABCDEF', ncol=2, loc='upper left');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Ah, much better!"
+    "sns.set()  # seaborn's method to set its chart style"
    ]
   },
   {
@@ -194,31 +50,34 @@
     "\n",
     "The main idea of Seaborn is that it provides high-level commands to create a variety of plot types useful for statistical data exploration, and even some statistical model fitting.\n",
     "\n",
-    "Let's take a look at a few of the datasets and plot types available in Seaborn. Note that all of the following *could* be done using raw Matplotlib commands (this is, in fact, what Seaborn does under the hood) but the Seaborn API is much more convenient."
+    "Let's take a look at a few of the datasets and plot types available in Seaborn. Note that all of the following *could* be done using raw Matplotlib commands (this is, in fact, what Seaborn does under the hood), but the Seaborn API is much more convenient."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Histograms, KDE, and densities\n",
+    "### Histograms, KDE, and Densities\n",
     "\n",
     "Often in statistical data visualization, all you want is to plot histograms and joint distributions of variables.\n",
-    "We have seen that this is relatively straightforward in Matplotlib:"
+    "We have seen that this is relatively straightforward in Matplotlib (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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MxM/IYDTLsjQwMPkJRJzOmKLRqFze7D7do9GoXJZb0ejZT4gSHYnqU2cWBoCsIsBhtIGB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\ncDis9evX6/rrr1d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+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11a191080>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -230,58 +89,31 @@
     "data = pd.DataFrame(data, columns=['x', 'y'])\n",
     "\n",
     "for col in 'xy':\n",
-    "    plt.hist(data[col], normed=True, alpha=0.5)"
+    "    plt.hist(data[col], density=True, alpha=0.5)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Rather than a histogram, we can get a smooth estimate of the distribution using a kernel density estimation, which Seaborn does with ``sns.kdeplot``:"
+    "Rather than just providing a histogram as a visual output, we can get a smooth estimate of the distribution using kernel density estimation (introduced in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb)), which Seaborn does with ``sns.kdeplot`` (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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EaRaTY+j29qBYUwRJvPOr8I+ODqK3P4zqchHLFpszVGHu0GhErF6uRyQKnK+X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\nkEzgkiRJkpSGZAKXJEmSpDT0/yrJsHWX4UslAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11a195a90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
-   "source": [
-    "for col in 'xy':\n",
-    "    sns.kdeplot(data[col], shade=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Histograms and KDE can be combined using ``distplot``:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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TzDM4YDA5UfwDx6V7KJAnT67M0Qmx8ZSUwI8dO8ZDDz3EgQMHeP755+ccv3Tp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\nH+Bt7gzy0999mvT2NosJp91MhttGcY4Ll8OCZsTwB0PYrCb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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11acced30>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -289,59 +121,31 @@
     }
    ],
    "source": [
-    "sns.distplot(data['x'])\n",
-    "sns.distplot(data['y']);"
+    "sns.kdeplot(data=data, shade=True);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If we pass the full two-dimensional dataset to ``kdeplot``, we will get a two-dimensional visualization of the data:"
+    "If we pass `x` and `y` columns to `kdeplot`, we instead get a two-dimensional visualization of the joint density (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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OZPnO1SSmHAcgxCeIcX1GM6hLfxztGu+xVhqq2HFgL78d3MLpglQAlHIlvcO7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7/q8iXmAa4097JYLBhMBsID+79k6a2z1huuaa0n0FM/qD55ubgzKSBqUNvYqrqljj2lB9ldcrB3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\nYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11aebdbe0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
-   "source": [
-    "sns.kdeplot(data);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see the joint distribution and the marginal distributions together using ``sns.jointplot``.\n",
-    "For this plot, we'll set the style to a white background:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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VTaLLkRUDiQi8QJO6DEkKBwD8eLBYcCXyYiCRV/Omruhc3vq5PcHQAeHQANi6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\nSBH+H3jVAOxlSfUyAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ad139b0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -349,62 +153,54 @@
     }
    ],
    "source": [
-    "with sns.axes_style('white'):\n",
-    "    sns.jointplot(\"x\", \"y\", data, kind='kde');"
+    "sns.kdeplot(data=data, x='x', y='y');"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are other parameters that can be passed to ``jointplot``—for example, we can use a hexagonally based histogram instead:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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PsX///pc096U7JAAwdQWm0fhf4bPPORfjRw7j6OQkhoaH8R8//AHuurv+cfirXvUafOLe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\ngxFAo9Xh7t27XFzJz7y5SivH85/4DMJdnqoVCsKEXggv3rgwNRgNxl8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pSxWWim0e7LdR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\nKwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11543ac18>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with sns.axes_style('white'):\n",
-    "    sns.jointplot(\"x\", \"y\", data, kind='hex')"
+    "We can see the joint distribution and the marginal distributions together using `sns.jointplot`, which we'll explore further later in this chapter."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Pair plots\n",
+    "### Pair Plots\n",
     "\n",
-    "When you generalize joint plots to datasets of larger dimensions, you end up with *pair plots*. This is very useful for exploring correlations between multidimensional data, when you'd like to plot all pairs of values against each other.\n",
+    "When you generalize joint plots to datasets of larger dimensions, you end up with *pair plots*. These are very useful for exploring correlations between multidimensional data, when you'd like to plot all pairs of values against each other.\n",
     "\n",
-    "We'll demo this with the well-known Iris dataset, which lists measurements of petals and sepals of three iris species:"
+    "We'll demo this with the well-known Iris dataset, which lists measurements of petals and sepals of three Iris species:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -470,7 +266,7 @@
        "4           5.0          3.6           1.4          0.2  setosa"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -484,21 +280,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Visualizing the multidimensional relationships among the samples is as easy as calling ``sns.pairplot``:"
+    "Visualizing the multidimensional relationships among the samples is as easy as calling ``sns.pairplot`` (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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VAW1dnR5yXyjixd90ovtLdSEBDBcMpWiFW+gw3LZYuIUMwy98KGrrYc6niUeS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\nqkIzN7UlPwmtkMybNw933nkn1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f+kx2Mh3fuE9zERUWwFmqd6nnlFyB4Pls2UbFghiaFA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\nEHv//fcZY4x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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ae9d160>"
+       "<Figure size 806.85x720 with 20 Axes>"
       ]
      },
      "metadata": {},
@@ -506,30 +305,55 @@
     }
    ],
    "source": [
-    "sns.pairplot(iris, hue='species', size=2.5);"
+    "sns.pairplot(iris, hue='species', height=2.5);"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Faceted histograms\n",
+    "### Faceted Histograms\n",
     "\n",
-    "Sometimes the best way to view data is via histograms of subsets. Seaborn's ``FacetGrid`` makes this extremely simple.\n",
-    "We'll take a look at some data that shows the amount that restaurant staff receive in tips based on various indicator data:"
+    "Sometimes the best way to view data is via histograms of subsets, as shown in the following figure. Seaborn's `FacetGrid` makes this simple.\n",
+    "We'll take a look at some data that shows the amount that restaurant staff receive in tips based on various indicator data:[^1]\n",
+    "\n",
+    "[^1]: The restaurant staff data used in this section divides employees into two sexes: female and male. Biological sex\n",
+    "isn’t binary, but the following discussion and visualizations are limited by this data."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -607,7 +431,7 @@
        "4       24.59  3.61  Female     No  Sun  Dinner     4"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -619,16 +443,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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AbgeayWSyrXMiInIqu3fvBgAcOXIEwO0/FBZj9QKLhoYGrFixAsnJyXBzc4NK1fFOkJ0f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\n4jGsiIhI8f4/8A46ouL3SiQAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11c3a3fd0>"
+       "<Figure size 432x432 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -646,23 +473,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Factor plots\n",
+    "The faceted chart gives us some quick insights into the dataset: for example, we see that it contains far more data on male servers during the dinner hour than other categories, and typical tip amounts appear to range from approximately 10% to 20%, with some outliers on either end.\n",
+    "\n",
+    "### Categorical Plots\n",
     "\n",
-    "Factor plots can be useful for this kind of visualization as well. This allows you to view the distribution of a parameter within bins defined by any other parameter:"
+    "Categorical plots can be useful for this kind of visualization as well. These allow you to view the distribution of a parameter within bins defined by any other parameter, as shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11c6f0518>"
+       "<Figure size 436.35x360 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -671,7 +503,7 @@
    ],
    "source": [
     "with sns.axes_style(style='ticks'):\n",
-    "    g = sns.factorplot(\"day\", \"total_bill\", \"sex\", data=tips, kind=\"box\")\n",
+    "    g = sns.catplot(x=\"day\", y=\"total_bill\", hue=\"sex\", data=tips, kind=\"box\")\n",
     "    g.set_axis_labels(\"Day\", \"Total Bill\");"
    ]
   },
@@ -679,23 +511,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Joint distributions\n",
+    "### Joint Distributions\n",
     "\n",
-    "Similar to the pairplot we saw earlier, we can use ``sns.jointplot`` to show the joint distribution between different datasets, along with the associated marginal distributions:"
+    "Similar to the pair plot we saw earlier, we can use `sns.jointplot` to show the joint distribution between different datasets, along with the associated marginal distributions (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+Le0cYmEV4TpYnEcf12UNC5Yrarr3M7roTpbqSrSQ8JVN8yqXt5GfZREaTQgg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\n/lzbSmNLqEt0dq7bRnG+M6lsH0VRcF5ezgpeLkhdw+UMSvK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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11d25ef98>"
+       "<Figure size 432x432 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -704,28 +539,31 @@
    ],
    "source": [
     "with sns.axes_style('white'):\n",
-    "    sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='hex')"
+    "    sns.jointplot(x=\"total_bill\", y=\"tip\", data=tips, kind='hex')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The joint plot can even do some automatic kernel density estimation and regression:"
+    "The joint plot can even do some automatic kernel density estimation and regression, as shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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SUhIFQlG+9fg/CUfjABgNOuZNzsag+pha6MBigsb6GjbW1wzqfK2tTZgcLmyyB58YJcGA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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11c3b0550>"
+       "<Figure size 432x432 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -733,29 +571,46 @@
     }
    ],
    "source": [
-    "sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='reg');"
+    "sns.jointplot(x=\"total_bill\", y=\"tip\", data=tips, kind='reg');"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Bar plots\n",
+    "### Bar Plots\n",
     "\n",
-    "Time series can be plotted using ``sns.factorplot``. In the following example, we'll use the Planets data that we first saw in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb):"
+    "Time series can be plotted using `sns.factorplot`. In the following example, we'll use the Planets dataset that we first saw in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb); see the following figure for the result:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -827,7 +682,7 @@
        "4  Radial Velocity       1         516.220  10.50    119.47  2009"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -839,16 +694,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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VFRVFozwAAGCAqISgb37zm9q+ffsd7fHx8Vq3bl3kCwIAAMZh2wwAAGAkQhAAADASIQgA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\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ade0dd8>"
+       "<Figure size 720x360 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -857,8 +715,8 @@
    ],
    "source": [
     "with sns.axes_style('white'):\n",
-    "    g = sns.factorplot(\"year\", data=planets, aspect=2,\n",
-    "                       kind=\"count\", color='steelblue')\n",
+    "    g = sns.catplot(x=\"year\", data=planets, aspect=2,\n",
+    "                    kind=\"count\", color='steelblue')\n",
     "    g.set_xticklabels(step=5)"
    ]
   },
@@ -866,21 +724,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can learn more by looking at the *method* of discovery of each of these planets:"
+    "We can learn more by looking at the *method* of discovery of each of these planets (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
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Pf/5j/SciIiIiIiIiIlIWBoOBvn37snbtWq5c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\nJxg4cCARERFMmTKFmjy9rz3jlu/pp58mIiLCYW2oDPaM25AhQ3jooYc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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bd8d668>"
+       "<Figure size 1624.47x360 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -889,8 +750,8 @@
    ],
    "source": [
     "with sns.axes_style('white'):\n",
-    "    g = sns.factorplot(\"year\", data=planets, aspect=4.0, kind='count',\n",
-    "                       hue='method', order=range(2001, 2015))\n",
+    "    g = sns.catplot(x=\"year\", data=planets, aspect=4.0, kind='count',\n",
+    "                    hue='method', order=range(2001, 2015))\n",
     "    g.set_ylabels('Number of Planets Discovered')"
    ]
   },
@@ -898,45 +759,68 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For more information on plotting with Seaborn, see the [Seaborn documentation](http://seaborn.pydata.org/), a [tutorial](http://seaborn.pydata.org/\n",
-    "tutorial.htm), and the [Seaborn gallery](http://seaborn.pydata.org/examples/index.html)."
+    "For more information on plotting with Seaborn, see the [Seaborn documentation](http://seaborn.pydata.org/), and particularly the [example gallery](https://seaborn.pydata.org/examples/index.html)."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Example: Exploring Marathon Finishing Times\n",
     "\n",
     "Here we'll look at using Seaborn to help visualize and understand finishing results from a marathon.\n",
-    "I've scraped the data from sources on the Web, aggregated it and removed any identifying information, and put it on GitHub where it can be downloaded\n",
-    "(if you are interested in using Python for web scraping, I would recommend [*Web Scraping with Python*](http://shop.oreilly.com/product/0636920034391.do) by Ryan Mitchell).\n",
-    "We will start by downloading the data from\n",
-    "the Web, and loading it into Pandas:"
+    "I've scraped the data from sources on the web, aggregated it and removed any identifying information, and put it on GitHub, where it can be downloaded\n",
+    "(if you are interested in using Python for web scraping, I would recommend [*Web Scraping with Python*](http://shop.oreilly.com/product/0636920034391.do) by Ryan Mitchell, also from O'Reilly).\n",
+    "We will start by downloading the data and loading it into Pandas:[^2]\n",
+    "\n",
+    "[^2]: The marathon data used in this section divides runners into two genders: men and women. While gender is a\n",
+    "spectrum, the following discussion and visualizations use this binary because they depend on the data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "# !curl -O https://raw.githubusercontent.com/jakevdp/marathon-data/master/marathon-data.csv"
+    "# url = ('https://raw.githubusercontent.com/jakevdp/'\n",
+    "#        'marathon-data/master/marathon-data.csv')\n",
+    "# !cd data && curl -O {url}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -996,13 +880,13 @@
        "4   31      M  01:06:32  02:13:59"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data = pd.read_csv('marathon-data.csv')\n",
+    "data = pd.read_csv('data/marathon-data.csv')\n",
     "data.head()"
    ]
   },
@@ -1010,14 +894,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By default, Pandas loaded the time columns as Python strings (type ``object``); we can see this by looking at the ``dtypes`` attribute of the DataFrame:"
+    "Notice that Pandas loaded the time columns as Python strings (type `object`); we can see this by looking at the `dtypes` attribute of the `DataFrame`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1030,7 +917,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1048,15 +935,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1072,51 +975,51 @@
        "      <th>0</th>\n",
        "      <td>33</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:05:38</td>\n",
-       "      <td>02:08:51</td>\n",
+       "      <td>0 days 01:05:38</td>\n",
+       "      <td>0 days 02:08:51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>32</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:26</td>\n",
-       "      <td>02:09:28</td>\n",
+       "      <td>0 days 01:06:26</td>\n",
+       "      <td>0 days 02:09:28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>31</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:49</td>\n",
-       "      <td>02:10:42</td>\n",
+       "      <td>0 days 01:06:49</td>\n",
+       "      <td>0 days 02:10:42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>38</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:16</td>\n",
-       "      <td>02:13:45</td>\n",
+       "      <td>0 days 01:06:16</td>\n",
+       "      <td>0 days 02:13:45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>31</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:32</td>\n",
-       "      <td>02:13:59</td>\n",
+       "      <td>0 days 01:06:32</td>\n",
+       "      <td>0 days 02:13:59</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   age gender    split    final\n",
-       "0   33      M 01:05:38 02:08:51\n",
-       "1   32      M 01:06:26 02:09:28\n",
-       "2   31      M 01:06:49 02:10:42\n",
-       "3   38      M 01:06:16 02:13:45\n",
-       "4   31      M 01:06:32 02:13:59"
+       "   age gender           split           final\n",
+       "0   33      M 0 days 01:05:38 0 days 02:08:51\n",
+       "1   32      M 0 days 01:06:26 0 days 02:09:28\n",
+       "2   31      M 0 days 01:06:49 0 days 02:10:42\n",
+       "3   38      M 0 days 01:06:16 0 days 02:13:45\n",
+       "4   31      M 0 days 01:06:32 0 days 02:13:59"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1128,16 +1031,19 @@
     "    h, m, s = map(int, s.split(':'))\n",
     "    return datetime.timedelta(hours=h, minutes=m, seconds=s)\n",
     "\n",
-    "data = pd.read_csv('marathon-data.csv',\n",
+    "data = pd.read_csv('data/marathon-data.csv',\n",
     "                   converters={'split':convert_time, 'final':convert_time})\n",
     "data.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1150,7 +1056,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1163,20 +1069,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "That looks much better. For the purpose of our Seaborn plotting utilities, let's next add columns that give the times in seconds:"
+    "That will make it easier to manipulate the temporal data. For the purpose of our Seaborn plotting utilities, let's next add columns that give the times in seconds:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1194,8 +1116,8 @@
        "      <th>0</th>\n",
        "      <td>33</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:05:38</td>\n",
-       "      <td>02:08:51</td>\n",
+       "      <td>0 days 01:05:38</td>\n",
+       "      <td>0 days 02:08:51</td>\n",
        "      <td>3938.0</td>\n",
        "      <td>7731.0</td>\n",
        "    </tr>\n",
@@ -1203,8 +1125,8 @@
        "      <th>1</th>\n",
        "      <td>32</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:26</td>\n",
-       "      <td>02:09:28</td>\n",
+       "      <td>0 days 01:06:26</td>\n",
+       "      <td>0 days 02:09:28</td>\n",
        "      <td>3986.0</td>\n",
        "      <td>7768.0</td>\n",
        "    </tr>\n",
@@ -1212,8 +1134,8 @@
        "      <th>2</th>\n",
        "      <td>31</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:49</td>\n",
-       "      <td>02:10:42</td>\n",
+       "      <td>0 days 01:06:49</td>\n",
+       "      <td>0 days 02:10:42</td>\n",
        "      <td>4009.0</td>\n",
        "      <td>7842.0</td>\n",
        "    </tr>\n",
@@ -1221,8 +1143,8 @@
        "      <th>3</th>\n",
        "      <td>38</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:16</td>\n",
-       "      <td>02:13:45</td>\n",
+       "      <td>0 days 01:06:16</td>\n",
+       "      <td>0 days 02:13:45</td>\n",
        "      <td>3976.0</td>\n",
        "      <td>8025.0</td>\n",
        "    </tr>\n",
@@ -1230,8 +1152,8 @@
        "      <th>4</th>\n",
        "      <td>31</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:32</td>\n",
-       "      <td>02:13:59</td>\n",
+       "      <td>0 days 01:06:32</td>\n",
+       "      <td>0 days 02:13:59</td>\n",
        "      <td>3992.0</td>\n",
        "      <td>8039.0</td>\n",
        "    </tr>\n",
@@ -1240,22 +1162,22 @@
        "</div>"
       ],
       "text/plain": [
-       "   age gender    split    final  split_sec  final_sec\n",
-       "0   33      M 01:05:38 02:08:51     3938.0     7731.0\n",
-       "1   32      M 01:06:26 02:09:28     3986.0     7768.0\n",
-       "2   31      M 01:06:49 02:10:42     4009.0     7842.0\n",
-       "3   38      M 01:06:16 02:13:45     3976.0     8025.0\n",
-       "4   31      M 01:06:32 02:13:59     3992.0     8039.0"
+       "   age gender           split           final  split_sec  final_sec\n",
+       "0   33      M 0 days 01:05:38 0 days 02:08:51     3938.0     7731.0\n",
+       "1   32      M 0 days 01:06:26 0 days 02:09:28     3986.0     7768.0\n",
+       "2   31      M 0 days 01:06:49 0 days 02:10:42     4009.0     7842.0\n",
+       "3   38      M 0 days 01:06:16 0 days 02:13:45     3976.0     8025.0\n",
+       "4   31      M 0 days 01:06:32 0 days 02:13:59     3992.0     8039.0"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data['split_sec'] = data['split'].astype(int) / 1E9\n",
-    "data['final_sec'] = data['final'].astype(int) / 1E9\n",
+    "data['split_sec'] = data['split'].view(int) / 1E9\n",
+    "data['final_sec'] = data['final'].view(int) / 1E9\n",
     "data.head()"
    ]
   },
@@ -1263,21 +1185,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To get an idea of what the data looks like, we can plot a ``jointplot`` over the data:"
+    "To get an idea of what the data looks like, we can plot a `jointplot` over the data; the following figure shows the result:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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GG2/EsGHDMoxa2JvpqRYqhmHgu9/9bhBSveCCC/Cxj30MSqlA6PHFL34Ru3btwtKlS4N5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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b646ac8>"
+       "<Figure size 432x432 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -1286,7 +1211,7 @@
    ],
    "source": [
     "with sns.axes_style('white'):\n",
-    "    g = sns.jointplot(\"split_sec\", \"final_sec\", data, kind='hex')\n",
+    "    g = sns.jointplot(x='split_sec', y='final_sec', data=data, kind='hex')\n",
     "    g.ax_joint.plot(np.linspace(4000, 16000),\n",
     "                    np.linspace(8000, 32000), ':k')"
    ]
@@ -1303,15 +1228,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1330,8 +1271,8 @@
        "      <th>0</th>\n",
        "      <td>33</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:05:38</td>\n",
-       "      <td>02:08:51</td>\n",
+       "      <td>0 days 01:05:38</td>\n",
+       "      <td>0 days 02:08:51</td>\n",
        "      <td>3938.0</td>\n",
        "      <td>7731.0</td>\n",
        "      <td>-0.018756</td>\n",
@@ -1340,8 +1281,8 @@
        "      <th>1</th>\n",
        "      <td>32</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:26</td>\n",
-       "      <td>02:09:28</td>\n",
+       "      <td>0 days 01:06:26</td>\n",
+       "      <td>0 days 02:09:28</td>\n",
        "      <td>3986.0</td>\n",
        "      <td>7768.0</td>\n",
        "      <td>-0.026262</td>\n",
@@ -1350,8 +1291,8 @@
        "      <th>2</th>\n",
        "      <td>31</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:49</td>\n",
-       "      <td>02:10:42</td>\n",
+       "      <td>0 days 01:06:49</td>\n",
+       "      <td>0 days 02:10:42</td>\n",
        "      <td>4009.0</td>\n",
        "      <td>7842.0</td>\n",
        "      <td>-0.022443</td>\n",
@@ -1360,8 +1301,8 @@
        "      <th>3</th>\n",
        "      <td>38</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:16</td>\n",
-       "      <td>02:13:45</td>\n",
+       "      <td>0 days 01:06:16</td>\n",
+       "      <td>0 days 02:13:45</td>\n",
        "      <td>3976.0</td>\n",
        "      <td>8025.0</td>\n",
        "      <td>0.009097</td>\n",
@@ -1370,8 +1311,8 @@
        "      <th>4</th>\n",
        "      <td>31</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:32</td>\n",
-       "      <td>02:13:59</td>\n",
+       "      <td>0 days 01:06:32</td>\n",
+       "      <td>0 days 02:13:59</td>\n",
        "      <td>3992.0</td>\n",
        "      <td>8039.0</td>\n",
        "      <td>0.006842</td>\n",
@@ -1381,15 +1322,22 @@
        "</div>"
       ],
       "text/plain": [
-       "   age gender    split    final  split_sec  final_sec  split_frac\n",
-       "0   33      M 01:05:38 02:08:51     3938.0     7731.0   -0.018756\n",
-       "1   32      M 01:06:26 02:09:28     3986.0     7768.0   -0.026262\n",
-       "2   31      M 01:06:49 02:10:42     4009.0     7842.0   -0.022443\n",
-       "3   38      M 01:06:16 02:13:45     3976.0     8025.0    0.009097\n",
-       "4   31      M 01:06:32 02:13:59     3992.0     8039.0    0.006842"
+       "   age gender           split           final  split_sec  final_sec  \\\n",
+       "0   33      M 0 days 01:05:38 0 days 02:08:51     3938.0     7731.0   \n",
+       "1   32      M 0 days 01:06:26 0 days 02:09:28     3986.0     7768.0   \n",
+       "2   31      M 0 days 01:06:49 0 days 02:10:42     4009.0     7842.0   \n",
+       "3   38      M 0 days 01:06:16 0 days 02:13:45     3976.0     8025.0   \n",
+       "4   31      M 0 days 01:06:32 0 days 02:13:59     3992.0     8039.0   \n",
+       "\n",
+       "   split_frac  \n",
+       "0   -0.018756  \n",
+       "1   -0.026262  \n",
+       "2   -0.022443  \n",
+       "3    0.009097  \n",
+       "4    0.006842  "
       ]
      },
-     "execution_count": 29,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1404,21 +1352,24 @@
    "metadata": {},
    "source": [
     "Where this split difference is less than zero, the person negative-split the race by that fraction.\n",
-    "Let's do a distribution plot of this split fraction:"
+    "Let's do a distribution plot of this split fraction (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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XkrRjxw4tXLhQ9fX1WrBggaqrqyVJZ8+e1cGDB1VXV6edO3dq69atsm175FsA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\nAAxBqAMAYAhCHQAAQ/w/HaCoKnHAZlAAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bb754a8>"
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1426,15 +1377,18 @@
     }
    ],
    "source": [
-    "sns.distplot(data['split_frac'], kde=False);\n",
+    "sns.displot(data['split_frac'], kde=False)\n",
     "plt.axvline(0, color=\"k\", linestyle=\"--\");"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1443,7 +1397,7 @@
        "251"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1458,21 +1412,24 @@
    "source": [
     "Out of nearly 40,000 participants, there were only 250 people who negative-split their marathon.\n",
     "\n",
-    "Let's see whether there is any correlation between this split fraction and other variables. We'll do this using a ``pairgrid``, which draws plots of all these correlations:"
+    "Let's see whether there is any correlation between this split fraction and other variables. We'll do this using a `PairGrid`, which draws plots of all these correlations (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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r1y6+/OUv8+yzz/LMM8/geR4/+7M/y7Fjx7jnnnt47rnnuPvuuzl69OhaT9Uw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UUul6OtLUoPuH37dl5//fXyMXfccQcAW7ZsYffu3dPZndkn8LBP7cX71X9dWAMQeNgf/ILUvpdI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\nRCKRSCSS3/h/L/d2XtJO7wgAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11aeb5908>"
+       "<Figure size 769.975x720 with 16 Axes>"
       ]
      },
      "metadata": {},
@@ -1490,24 +1447,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It looks like the split fraction does not correlate particularly with age, but does correlate with the final time: faster runners tend to have closer to even splits on their marathon time.\n",
-    "(We see here that Seaborn is no panacea for Matplotlib's ills when it comes to plot styles: in particular, the x-axis labels overlap. Because the output is a simple Matplotlib plot, however, the methods in [Customizing Ticks](04.10-Customizing-Ticks.ipynb) can be used to adjust such things if desired.)\n",
-    "\n",
-    "The difference between men and women here is interesting. Let's look at the histogram of split fractions for these two groups:"
+    "It looks like the split fraction does not correlate particularly with age, but does correlate with the final time: faster runners tend to have closer to even splits on their marathon time. Let's zoom in on the histogram of split fractions separated by gender, shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 26,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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v8lV9XyIvYxAT1bg/vGDNhs+cM6n4sURhxbQmVW/rElA4+MHwwQCDmKhSGMRE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\nrbfeKv7hH/6hWuWVpZwxdHd3i61btwo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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bdb3a58>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1525,23 +1482,26 @@
    "metadata": {},
    "source": [
     "The interesting thing here is that there are many more men than women who are running close to an even split!\n",
-    "This almost looks like some kind of bimodal distribution among the men and women. Let's see if we can suss-out what's going on by looking at the distributions as a function of age.\n",
+    "It almost looks like a bimodal distribution among the men and women. Let's see if we can suss out what's going on by looking at the distributions as a function of age.\n",
     "\n",
-    "A nice way to compare distributions is to use a *violin plot*"
+    "A nice way to compare distributions is to use a *violin plot*, shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 27,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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MTAoLCykrK+OOO+6gqkoLLshH6+zs4I03XuO5535NQ0MdiRk5LLztTlx5RePa3R4eE8vs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\niMUp7EVERP7/DXMAAPmmNk33GAG5AAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11d2aeeb8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1549,7 +1509,7 @@
     }
    ],
    "source": [
-    "sns.violinplot(\"gender\", \"split_frac\", data=data,\n",
+    "sns.violinplot(x=\"gender\", y=\"split_frac\", data=data,\n",
     "               palette=[\"lightblue\", \"lightpink\"]);"
    ]
   },
@@ -1557,22 +1517,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is yet another way to compare the distributions between men and women.\n",
-    "\n",
-    "Let's look a little deeper, and compare these violin plots as a function of age. We'll start by creating a new column in the array that specifies the decade of age that each person is in:"
+    "Let's look a little deeper, and compare these violin plots as a function of age (see the following figure). We'll start by creating a new column in the array that specifies the age range that each person is in, by decade:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 28,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1592,8 +1566,8 @@
        "      <th>0</th>\n",
        "      <td>33</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:05:38</td>\n",
-       "      <td>02:08:51</td>\n",
+       "      <td>0 days 01:05:38</td>\n",
+       "      <td>0 days 02:08:51</td>\n",
        "      <td>3938.0</td>\n",
        "      <td>7731.0</td>\n",
        "      <td>-0.018756</td>\n",
@@ -1603,8 +1577,8 @@
        "      <th>1</th>\n",
        "      <td>32</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:26</td>\n",
-       "      <td>02:09:28</td>\n",
+       "      <td>0 days 01:06:26</td>\n",
+       "      <td>0 days 02:09:28</td>\n",
        "      <td>3986.0</td>\n",
        "      <td>7768.0</td>\n",
        "      <td>-0.026262</td>\n",
@@ -1614,8 +1588,8 @@
        "      <th>2</th>\n",
        "      <td>31</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:49</td>\n",
-       "      <td>02:10:42</td>\n",
+       "      <td>0 days 01:06:49</td>\n",
+       "      <td>0 days 02:10:42</td>\n",
        "      <td>4009.0</td>\n",
        "      <td>7842.0</td>\n",
        "      <td>-0.022443</td>\n",
@@ -1625,8 +1599,8 @@
        "      <th>3</th>\n",
        "      <td>38</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:16</td>\n",
-       "      <td>02:13:45</td>\n",
+       "      <td>0 days 01:06:16</td>\n",
+       "      <td>0 days 02:13:45</td>\n",
        "      <td>3976.0</td>\n",
        "      <td>8025.0</td>\n",
        "      <td>0.009097</td>\n",
@@ -1636,8 +1610,8 @@
        "      <th>4</th>\n",
        "      <td>31</td>\n",
        "      <td>M</td>\n",
-       "      <td>01:06:32</td>\n",
-       "      <td>02:13:59</td>\n",
+       "      <td>0 days 01:06:32</td>\n",
+       "      <td>0 days 02:13:59</td>\n",
        "      <td>3992.0</td>\n",
        "      <td>8039.0</td>\n",
        "      <td>0.006842</td>\n",
@@ -1648,15 +1622,22 @@
        "</div>"
       ],
       "text/plain": [
-       "   age gender    split    final  split_sec  final_sec  split_frac  age_dec\n",
-       "0   33      M 01:05:38 02:08:51     3938.0     7731.0   -0.018756       30\n",
-       "1   32      M 01:06:26 02:09:28     3986.0     7768.0   -0.026262       30\n",
-       "2   31      M 01:06:49 02:10:42     4009.0     7842.0   -0.022443       30\n",
-       "3   38      M 01:06:16 02:13:45     3976.0     8025.0    0.009097       30\n",
-       "4   31      M 01:06:32 02:13:59     3992.0     8039.0    0.006842       30"
+       "   age gender           split           final  split_sec  final_sec  \\\n",
+       "0   33      M 0 days 01:05:38 0 days 02:08:51     3938.0     7731.0   \n",
+       "1   32      M 0 days 01:06:26 0 days 02:09:28     3986.0     7768.0   \n",
+       "2   31      M 0 days 01:06:49 0 days 02:10:42     4009.0     7842.0   \n",
+       "3   38      M 0 days 01:06:16 0 days 02:13:45     3976.0     8025.0   \n",
+       "4   31      M 0 days 01:06:32 0 days 02:13:59     3992.0     8039.0   \n",
+       "\n",
+       "   split_frac  age_dec  \n",
+       "0   -0.018756       30  \n",
+       "1   -0.026262       30  \n",
+       "2   -0.022443       30  \n",
+       "3    0.009097       30  \n",
+       "4    0.006842       30  "
       ]
      },
-     "execution_count": 35,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1668,16 +1649,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 29,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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zz/36eiiwOioOg4MDi6J3X1ZWgvv378HLPxAxmbNfV2YjFonwteVrEOXnj6qq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\nhBBCCOMo2BNCCCGMo2BPCCGEMO7/A2wATbzbo2s4AAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bd81c18>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1689,7 +1673,7 @@
     "women = (data.gender == 'W')\n",
     "\n",
     "with sns.axes_style(style=None):\n",
-    "    sns.violinplot(\"age_dec\", \"split_frac\", hue=\"gender\", data=data,\n",
+    "    sns.violinplot(x=\"age_dec\", y=\"split_frac\", hue=\"gender\", data=data,\n",
     "                   split=True, inner=\"quartile\",\n",
     "                   palette=[\"lightblue\", \"lightpink\"]);"
    ]
@@ -1698,16 +1682,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Looking at this, we can see where the distributions of men and women differ: the split distributions of men in their 20s to 50s show a pronounced over-density toward lower splits when compared to women of the same age (or of any age, for that matter).\n",
+    "We can see where the distributions among men and women differ: the split distributions of men in their 20s to 50s show a pronounced overdensity toward lower splits when compared to women of the same age (or of any age, for that matter).\n",
     "\n",
-    "Also surprisingly, the 80-year-old women seem to outperform *everyone* in terms of their split time. This is probably due to the fact that we're estimating the distribution from small numbers, as there are only a handful of runners in that range:"
+    "Also surprisingly, it appears that the 80-year-old women seem to outperform *everyone* in terms of their split time, although this is likely a small number effect, as there are only a handful of runners in that range:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 30,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1716,7 +1703,7 @@
        "7"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1729,21 +1716,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Back to the men with negative splits: who are these runners? Does this split fraction correlate with finishing quickly? We can plot this very easily. We'll use ``regplot``, which will automatically fit a linear regression to the data:"
+    "Back to the men with negative splits: who are these runners? Does this split fraction correlate with finishing quickly? We can plot this very easily. We'll use `regplot`, which will automatically fit a linear regression model to the data (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 31,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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5xMfKpiraGn14XCpVHg/ORfyBQQLiCjM6KzB6Ymh1OvlmU1vZKt/pGl2zuzsV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ULs7PNj/9FO7D/aiR538qU+AcBabcS0eWX4j7IwujWFstnnyMIApmk0cKsqVADTdXp8z1SCQwEf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\nb79dhMNh0dLSIm644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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11e0dfba8>"
+       "<Figure size 720x360 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -1751,33 +1741,26 @@
     }
    ],
    "source": [
-    "g = sns.lmplot('final_sec', 'split_frac', col='gender', data=data,\n",
+    "g = sns.lmplot(x='final_sec', y='split_frac', col='gender', data=data,\n",
     "               markers=\".\", scatter_kws=dict(color='c'))\n",
-    "g.map(plt.axhline, y=0.1, color=\"k\", ls=\":\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Apparently the people with fast splits are the elite runners who are finishing within ~15,000 seconds, or about 4 hours. People slower than that are much less likely to have a fast second split."
+    "g.map(plt.axhline, y=0.0, color=\"k\", ls=\":\");"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.14-Visualization-With-Seaborn.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "Apparently, among both men and women, the people with fast splits tend to be faster runners who are finishing within ~15,000 seconds, or about 4 hours. People slower than that are much less likely to have a fast second split."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1791,9 +1774,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.15-Further-Resources.ipynb b/notebooks/04.15-Further-Resources.ipynb
index 4aed29225..7bffc3a4a 100644
--- a/notebooks/04.15-Further-Resources.ipynb
+++ b/notebooks/04.15-Further-Resources.ipynb
@@ -4,78 +4,44 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "# Further Resources\n",
     "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) | [Contents](Index.ipynb) | [Machine Learning](05.00-Machine-Learning.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.15-Further-Resources.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Further Resources"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Matplotlib Resources\n",
-    "\n",
-    "A single chapter in a book can never hope to cover all the available features and plot types available in Matplotlib.\n",
-    "As with other packages we've seen, liberal use of IPython's tab-completion and help functions (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be very helpful when exploring Matplotlib's API.\n",
+    "A single part of a book can never hope to cover all the available features and plot types available in Matplotlib.\n",
+    "As with other packages we've seen, liberal use of IPython's tab completion and help functions (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be very helpful when exploring Matplotlib's API.\n",
     "In addition, Matplotlib’s [online documentation](http://matplotlib.org/) can be a helpful reference.\n",
-    "See in particular the [Matplotlib gallery](http://matplotlib.org/gallery.html) linked on that page: it shows thumbnails of hundreds of different plot types, each one linked to a page with the Python code snippet used to generate it.\n",
-    "In this way, you can visually inspect and learn about a wide range of different plotting styles and visualization techniques.\n",
+    "See in particular the [Matplotlib gallery](https://matplotlib.org/stable/gallery/), which shows thumbnails of hundreds of different plot types, each one linked to a page with the Python code snippet used to generate it.\n",
+    "This allows you to visually inspect and learn about a wide range of different plotting styles and visualization techniques.\n",
     "\n",
-    "For a book-length treatment of Matplotlib, I would recommend [*Interactive Applications Using Matplotlib*](https://www.packtpub.com/application-development/interactive-applications-using-matplotlib), written by Matplotlib core developer Ben Root."
+    "For a book-length treatment of Matplotlib, I would recommend *Interactive Applications Using Matplotlib* (Packt), written by Matplotlib core developer Ben Root."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Other Python Graphics Libraries\n",
+    "## Other Python Visualization Libraries\n",
     "\n",
     "Although Matplotlib is the most prominent Python visualization library, there are other more modern tools that are worth exploring as well.\n",
     "I'll mention a few of them briefly here:\n",
     "\n",
-    "- [Bokeh](http://bokeh.pydata.org) is a JavaScript visualization library with a Python frontend that creates highly interactive visualizations capable of handling very large and/or streaming datasets. The Python front-end outputs a JSON data structure that can be interpreted by the Bokeh JS engine.\n",
-    "- [Plotly](http://plot.ly) is the eponymous open source product of the Plotly company, and is similar in spirit to Bokeh. Because Plotly is the main product of a startup, it is receiving a high level of development effort. Use of the library is entirely free.\n",
-    "- [Vispy](http://vispy.org/) is an actively developed project focused on dynamic visualizations of very large datasets. Because it is built to target OpenGL and make use of efficient graphics processors in your computer, it is able to render some quite large and stunning visualizations.\n",
-    "- [Vega](https://vega.github.io/) and [Vega-Lite](https://vega.github.io/vega-lite) are declarative graphics representations, and are the product of years of research into the fundamental language of data visualization. The reference rendering implementation is JavaScript, but the API is language agnostic. There is a Python API under development in the [Altair](https://altair-viz.github.io/) package. Though as of summer 2016 it's not yet fully mature, I'm quite excited for the possibilities of this project to provide a common reference point for visualization in Python and other languages.\n",
-    "\n",
-    "The visualization space in the Python community is very dynamic, and I fully expect this list to be out of date as soon as it is published.\n",
-    "Keep an eye out for what's coming in the future!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) | [Contents](Index.ipynb) | [Machine Learning](05.00-Machine-Learning.ipynb) >\n",
+    "- [Bokeh](http://bokeh.pydata.org) is a JavaScript visualization library with a Python frontend that creates highly interactive visualizations capable of handling very large and/or streaming datasets.\n",
+    "- [Plotly](http://plot.ly) is the eponymous open source product of the Plotly company, and is similar in spirit to Bokeh. It is actively developed and provides a wide range of interactive chart types.\n",
+    "- [HoloViews](https://holoviews.org/) is a more declarative, unified API for generating charts in a variety of backends, including Bokeh and Matplotlib.\n",
+    "- [Vega](https://vega.github.io/) and [Vega-Lite](https://vega.github.io/vega-lite) are declarative graphics representations, and are the product of years of research into how to think about data visualization and interaction. The reference rendering implementation is JavaScript, and the [Altair package](https://altair-viz.github.io/) provides a Python API to generate these charts.\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.15-Further-Resources.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "The visualization landscape in the Python world is constantly evolving, and I expect that this list may be out of date by the time this book is published.\n",
+    "Additionally, because Python is used in so many domains, you'll find many other visualization tools built for more specific use cases.\n",
+    "It can be hard to keep track of all of them, but a good resource for learning about this wide variety of visualization tools is https://pyviz.org/, an open, community-driven site containing tutorials and examples of many different visualization tools."
    ]
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -89,9 +55,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.00-Machine-Learning.ipynb b/notebooks/05.00-Machine-Learning.ipynb
index caff9877c..889866b91 100644
--- a/notebooks/05.00-Machine-Learning.ipynb
+++ b/notebooks/05.00-Machine-Learning.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Further Resources](04.15-Further-Resources.ipynb) | [Contents](Index.ipynb) | [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.00-Machine-Learning.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,43 +11,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In many ways, machine learning is the primary means by which data science manifests itself to the broader world.\n",
-    "Machine learning is where these computational and algorithmic skills of data science meet the statistical thinking of data science, and the result is a collection of approaches to inference and data exploration that are not about effective theory so much as effective computation.\n",
-    "\n",
-    "The term \"machine learning\" is sometimes thrown around as if it is some kind of magic pill: *apply machine learning to your data, and all your problems will be solved!*\n",
-    "As you might expect, the reality is rarely this simple.\n",
-    "While these methods can be incredibly powerful, to be effective they must be approached with a firm grasp of the strengths and weaknesses of each method, as well as a grasp of general concepts such as bias and variance, overfitting and underfitting, and more.\n",
-    "\n",
-    "This chapter will dive into practical aspects of machine learning, primarily using Python's [Scikit-Learn](http://scikit-learn.org) package.\n",
+    "This final part is an introduction to the very broad topic of machine learning, mainly via Python's [Scikit-Learn](http://scikit-learn.org) package.\n",
+    "You can think of machine learning as a class of algorithms that allow a program to detect particular patterns in a dataset, and thus \"learn\" from the data to draw inferences from it.\n",
     "This is not meant to be a comprehensive introduction to the field of machine learning; that is a large subject and necessitates a more technical approach than we take here.\n",
     "Nor is it meant to be a comprehensive manual for the use of the Scikit-Learn package (for this, you can refer to the resources listed in [Further Machine Learning Resources](05.15-Learning-More.ipynb)).\n",
-    "Rather, the goals of this chapter are:\n",
+    "Rather, the goals here are:\n",
     "\n",
-    "- To introduce the fundamental vocabulary and concepts of machine learning.\n",
-    "- To introduce the Scikit-Learn API and show some examples of its use.\n",
-    "- To take a deeper dive into the details of several of the most important machine learning approaches, and develop an intuition into how they work and when and where they are applicable.\n",
+    "- To introduce the fundamental vocabulary and concepts of machine learning\n",
+    "- To introduce the Scikit-Learn API and show some examples of its use\n",
+    "- To take a deeper dive into the details of several of the more important classical machine learning approaches, and develop an intuition into how they work and when and where they are applicable\n",
     "\n",
     "Much of this material is drawn from the Scikit-Learn tutorials and workshops I have given on several occasions at PyCon, SciPy, PyData, and other conferences.\n",
-    "Any clarity in the following pages is likely due to the many workshop participants and co-instructors who have given me valuable feedback on this material over the years!\n",
-    "\n",
-    "Finally, if you are seeking a more comprehensive or technical treatment of any of these subjects, I've listed several resources and references in [Further Machine Learning Resources](05.15-Learning-More.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Further Resources](04.15-Further-Resources.ipynb) | [Contents](Index.ipynb) | [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.00-Machine-Learning.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "Any clarity in the following pages is likely due to the many workshop participants and co-instructors who have given me valuable feedback on this material over the years!"
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -83,9 +46,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.01-What-Is-Machine-Learning.ipynb b/notebooks/05.01-What-Is-Machine-Learning.ipynb
index 1dd061dae..b5a952528 100644
--- a/notebooks/05.01-What-Is-Machine-Learning.ipynb
+++ b/notebooks/05.01-What-Is-Machine-Learning.ipynb
@@ -1,33 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Machine Learning](05.00-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.01-What-Is-Machine-Learning.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -42,12 +14,11 @@
     "editable": true
    },
    "source": [
-    "Before we take a look at the details of various machine learning methods, let's start by looking at what machine learning is, and what it isn't.\n",
-    "Machine learning is often categorized as a subfield of artificial intelligence, but I find that categorization can often be misleading at first brush.\n",
+    "Before we take a look at the details of several machine learning methods, let's start by looking at what machine learning is, and what it isn't.\n",
+    "Machine learning is often categorized as a subfield of artificial intelligence, but I find that categorization can be misleading.\n",
     "The study of machine learning certainly arose from research in this context, but in the data science application of machine learning methods, it's more helpful to think of machine learning as a means of *building models of data*.\n",
     "\n",
-    "Fundamentally, machine learning involves building mathematical models to help understand data.\n",
-    "\"Learning\" enters the fray when we give these models *tunable parameters* that can be adapted to observed data; in this way the program can be considered to be \"learning\" from the data.\n",
+    "In this context, \"learning\" enters the fray when we give these models *tunable parameters* that can be adapted to observed data; in this way the program can be considered to be \"learning\" from the data.\n",
     "Once these models have been fit to previously seen data, they can be used to predict and understand aspects of newly observed data.\n",
     "I'll leave to the reader the more philosophical digression regarding the extent to which this type of mathematical, model-based \"learning\" is similar to the \"learning\" exhibited by the human brain.\n",
     "\n",
@@ -63,22 +34,23 @@
    "source": [
     "## Categories of Machine Learning\n",
     "\n",
-    "At the most fundamental level, machine learning can be categorized into two main types: supervised learning and unsupervised learning.\n",
+    "Machine learning can be categorized into two main types: supervised learning and unsupervised learning.\n",
     "\n",
-    "*Supervised learning* involves somehow modeling the relationship between measured features of data and some label associated with the data; once this model is determined, it can be used to apply labels to new, unknown data.\n",
-    "This is further subdivided into *classification* tasks and *regression* tasks: in classification, the labels are discrete categories, while in regression, the labels are continuous quantities.\n",
-    "We will see examples of both types of supervised learning in the following section.\n",
+    "*Supervised learning* involves somehow modeling the relationship between measured features of data and some labels associated with the data; once this model is determined, it can be used to apply labels to new, unknown data.\n",
+    "This is sometimes further subdivided into classification tasks and regression tasks: in *classification*, the labels are discrete categories, while in *regression*, the labels are continuous quantities.\n",
+    "You will see examples of both types of supervised learning in the following section.\n",
     "\n",
-    "*Unsupervised learning* involves modeling the features of a dataset without reference to any label, and is often described as \"letting the dataset speak for itself.\"\n",
+    "*Unsupervised learning* involves modeling the features of a dataset without reference to any label.\n",
     "These models include tasks such as *clustering* and *dimensionality reduction.*\n",
     "Clustering algorithms identify distinct groups of data, while dimensionality reduction algorithms search for more succinct representations of the data.\n",
-    "We will see examples of both types of unsupervised learning in the following section.\n",
+    "You will also see examples of both types of unsupervised learning in the following section.\n",
     "\n",
-    "In addition, there are so-called *semi-supervised learning* methods, which falls somewhere between supervised learning and unsupervised learning.\n",
+    "In addition, there are so-called *semi-supervised learning* methods, which fall somewhere between supervised learning and unsupervised learning.\n",
     "Semi-supervised learning methods are often useful when only incomplete labels are available."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
@@ -88,9 +60,9 @@
     "## Qualitative Examples of Machine Learning Applications\n",
     "\n",
     "To make these ideas more concrete, let's take a look at a few very simple examples of a machine learning task.\n",
-    "These examples are meant to give an intuitive, non-quantitative overview of the types of machine learning tasks we will be looking at in this chapter.\n",
-    "In later sections, we will go into more depth regarding the particular models and how they are used.\n",
-    "For a preview of these more technical aspects, you can find the Python source that generates the following figures in the [Appendix: Figure Code](06.00-Figure-Code.ipynb).\n"
+    "These examples are meant to give an intuitive, non-quantitative overview of the types of machine learning tasks we will be looking at in this part of the book.\n",
+    "In later chapters, we will go into more depth regarding the particular models and how they are used.\n",
+    "For a preview of these more technical aspects, you can find the Python source that generates the following figures in the online [appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb).\n"
    ]
   },
   {
@@ -100,9 +72,9 @@
     "editable": true
    },
    "source": [
-    "### Classification: Predicting discrete labels\n",
+    "### Classification: Predicting Discrete Labels\n",
     "\n",
-    "We will first take a look at a simple *classification* task, in which you are given a set of labeled points and want to use these to classify some unlabeled points.\n",
+    "We will first take a look at a simple classification task, in which we are given a set of labeled points and want to use these to classify some unlabeled points.\n",
     "\n",
     "Imagine that we have the data shown in this figure:"
    ]
@@ -114,8 +86,7 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.01-classification-1.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Classification-Example-Figure-1)"
+    "![](images/05.01-classification-1.png)"
    ]
   },
   {
@@ -125,15 +96,15 @@
     "editable": true
    },
    "source": [
-    "Here we have two-dimensional data: that is, we have two *features* for each point, represented by the *(x,y)* positions of the points on the plane.\n",
+    "This data is two-dimensional: that is, we have two *features* for each point, represented by the (x,y) positions of the points on the plane.\n",
     "In addition, we have one of two *class labels* for each point, here represented by the colors of the points.\n",
     "From these features and labels, we would like to create a model that will let us decide whether a new point should be labeled \"blue\" or \"red.\"\n",
     "\n",
-    "There are a number of possible models for such a classification task, but here we will use an extremely simple one. We will make the assumption that the two groups can be separated by drawing a straight line through the plane between them, such that points on each side of the line fall in the same group.\n",
-    "Here the *model* is a quantitative version of the statement \"a straight line separates the classes\", while the *model parameters* are the particular numbers describing the location and orientation of that line for our data.\n",
+    "There are a number of possible models for such a classification task, but we will start with a very simple one. We will make the assumption that the two groups can be separated by drawing a straight line through the plane between them, such that points on each side of the line all fall in the same group.\n",
+    "Here the *model* is a quantitative version of the statement \"a straight line separates the classes,\" while the *model parameters* are the particular numbers describing the location and orientation of that line for our data.\n",
     "The optimal values for these model parameters are learned from the data (this is the \"learning\" in machine learning), which is often called *training the model*.\n",
     "\n",
-    "The following figure shows a visual representation of what the trained model looks like for this data:"
+    "See the following figure shows a visual representation of what the trained model looks like for this data."
    ]
   },
   {
@@ -143,8 +114,7 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.01-classification-2.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Classification-Example-Figure-2)"
+    "![](images/05.01-classification-2.png)"
    ]
   },
   {
@@ -155,8 +125,8 @@
    },
    "source": [
     "Now that this model has been trained, it can be generalized to new, unlabeled data.\n",
-    "In other words, we can take a new set of data, draw this model line through it, and assign labels to the new points based on this model.\n",
-    "This stage is usually called *prediction*. See the following figure:"
+    "In other words, we can take a new set of data, draw this line through it, and assign labels to the new points based on this model (see the following figure).\n",
+    "This stage is usually called *prediction*."
    ]
   },
   {
@@ -166,8 +136,7 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.01-classification-3.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Classification-Example-Figure-3)"
+    "![](images/05.01-classification-3.png)"
    ]
   },
   {
@@ -178,12 +147,12 @@
    },
    "source": [
     "This is the basic idea of a classification task in machine learning, where \"classification\" indicates that the data has discrete class labels.\n",
-    "At first glance this may look fairly trivial: it would be relatively easy to simply look at this data and draw such a discriminatory line to accomplish this classification.\n",
+    "At first glance this may seem trivial: it's easy to look at our data and draw such a discriminatory line to accomplish this classification.\n",
     "A benefit of the machine learning approach, however, is that it can generalize to much larger datasets in many more dimensions.\n",
     "\n",
-    "For example, this is similar to the task of automated spam detection for email; in this case, we might use the following features and labels:\n",
+    "For example, this is similar to the task of automated spam detection for email. In this case, we might use the following features and labels:\n",
     "\n",
-    "- *feature 1*, *feature 2*, etc. $\\to$ normalized counts of important words or phrases (\"Viagra\", \"Nigerian prince\", etc.)\n",
+    "- *feature 1*, *feature 2*, etc. $\\to$ normalized counts of important words or phrases (\"Viagra\", \"Extended warranty\", etc.)\n",
     "- *label* $\\to$ \"spam\" or \"not spam\"\n",
     "\n",
     "For the training set, these labels might be determined by individual inspection of a small representative sample of emails; for the remaining emails, the label would be determined using the model.\n",
@@ -200,11 +169,11 @@
     "editable": true
    },
    "source": [
-    "### Regression: Predicting continuous labels\n",
+    "### Regression: Predicting Continuous Labels\n",
     "\n",
-    "In contrast with the discrete labels of a classification algorithm, we will next look at a simple *regression* task in which the labels are continuous quantities.\n",
+    "In contrast with the discrete labels of a classification algorithm, we will next look at a simple regression task in which the labels are continuous quantities.\n",
     "\n",
-    "Consider the data shown in the following figure, which consists of a set of points each with a continuous label:"
+    "Consider the data shown in the following figure, which consists of a set of points each with a continuous label."
    ]
   },
   {
@@ -214,8 +183,7 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.01-regression-1.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-1)"
+    "![](images/05.01-regression-1.png)"
    ]
   },
   {
@@ -228,8 +196,8 @@
     "As with the classification example, we have two-dimensional data: that is, there are two features describing each data point.\n",
     "The color of each point represents the continuous label for that point.\n",
     "\n",
-    "There are a number of possible regression models we might use for this type of data, but here we will use a simple linear regression to predict the points.\n",
-    "This simple linear regression model assumes that if we treat the label as a third spatial dimension, we can fit a plane to the data.\n",
+    "There are a number of possible regression models we might use for this type of data, but here we will use a simple linear regression model to predict the points.\n",
+    "This simple model assumes that if we treat the label as a third spatial dimension, we can fit a plane to the data.\n",
     "This is a higher-level generalization of the well-known problem of fitting a line to data with two coordinates.\n",
     "\n",
     "We can visualize this setup as shown in the following figure:"
@@ -242,8 +210,7 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.01-regression-2.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-2)"
+    "![](images/05.01-regression-2.png)"
    ]
   },
   {
@@ -253,7 +220,7 @@
     "editable": true
    },
    "source": [
-    "Notice that the *feature 1-feature 2* plane here is the same as in the two-dimensional plot from before; in this case, however, we have represented the labels by both color and three-dimensional axis position.\n",
+    "Notice that the *feature 1–feature 2* plane here is the same as in the two-dimensional plot in Figure 37-4; in this case, however, we have represented the labels by both color and three-dimensional axis position.\n",
     "From this view, it seems reasonable that fitting a plane through this three-dimensional data would allow us to predict the expected label for any set of input parameters.\n",
     "Returning to the two-dimensional projection, when we fit such a plane we get the result shown in the following figure:"
    ]
@@ -265,8 +232,7 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.01-regression-3.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-3)"
+    "![](images/05.01-regression-3.png)"
    ]
   },
   {
@@ -287,8 +253,7 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.01-regression-4.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-4)"
+    "![](images/05.01-regression-4.png)"
    ]
   },
   {
@@ -298,15 +263,15 @@
     "editable": true
    },
    "source": [
-    "As with the classification example, this may seem rather trivial in a low number of dimensions.\n",
+    "As with the classification example, this task may seem trivial in a low number of dimensions.\n",
     "But the power of these methods is that they can be straightforwardly applied and evaluated in the case of data with many, many features.\n",
     "\n",
     "For example, this is similar to the task of computing the distance to galaxies observed through a telescope—in this case, we might use the following features and labels:\n",
     "\n",
-    "- *feature 1*, *feature 2*, etc. $\\to$ brightness of each galaxy at one of several wave lengths or colors\n",
+    "- *feature 1*, *feature 2*, etc. $\\to$ brightness of each galaxy at one of several wavelengths or colors\n",
     "- *label* $\\to$ distance or redshift of the galaxy\n",
     "\n",
-    "The distances for a small number of these galaxies might be determined through an independent set of (typically more expensive) observations.\n",
+    "The distances for a small number of these galaxies might be determined through an independent set of (typically more expensive or complex) observations.\n",
     "Distances to remaining galaxies could then be estimated using a suitable regression model, without the need to employ the more expensive observation across the entire set.\n",
     "In astronomy circles, this is known as the \"photometric redshift\" problem.\n",
     "\n",
@@ -320,9 +285,9 @@
     "editable": true
    },
    "source": [
-    "### Clustering: Inferring labels on unlabeled data\n",
+    "### Clustering: Inferring Labels on Unlabeled Data\n",
     "\n",
-    "The classification and regression illustrations we just looked at are examples of supervised learning algorithms, in which we are trying to build a model that will predict labels for new data.\n",
+    "The classification and regression illustrations we just saw are examples of supervised learning algorithms, in which we are trying to build a model that will predict labels for new data.\n",
     "Unsupervised learning involves models that describe data without reference to any known labels.\n",
     "\n",
     "One common case of unsupervised learning is \"clustering,\" in which data is automatically assigned to some number of discrete groups.\n",
@@ -336,8 +301,7 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.01-clustering-1.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Clustering-Example-Figure-2)"
+    "![](images/05.01-clustering-1.png)"
    ]
   },
   {
@@ -359,8 +323,7 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.01-clustering-2.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Clustering-Example-Figure-2)"
+    "![](images/05.01-clustering-2.png)"
    ]
   },
   {
@@ -371,10 +334,10 @@
    },
    "source": [
     "*k*-means fits a model consisting of *k* cluster centers; the optimal centers are assumed to be those that minimize the distance of each point from its assigned center.\n",
-    "Again, this might seem like a trivial exercise in two dimensions, but as our data becomes larger and more complex, such clustering algorithms can be employed to extract useful information from the dataset.\n",
+    "Again, this might seem like a trivial exercise in two dimensions, but as our data becomes larger and more complex such clustering algorithms can continue to be employed to extract useful information from the dataset.\n",
     "\n",
     "We will discuss the *k*-means algorithm in more depth in [In Depth: K-Means Clustering](05.11-K-Means.ipynb).\n",
-    "Other important clustering algorithms include Gaussian mixture models (See [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb)) and spectral clustering (See [Scikit-Learn's clustering documentation](http://scikit-learn.org/stable/modules/clustering.html))."
+    "Other important clustering algorithms include Gaussian mixture models (see [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb)) and spectral clustering (see [Scikit-Learn's clustering documentation](http://scikit-learn.org/stable/modules/clustering.html))."
    ]
   },
   {
@@ -384,7 +347,7 @@
     "editable": true
    },
    "source": [
-    "### Dimensionality reduction: Inferring structure of unlabeled data\n",
+    "### Dimensionality Reduction: Inferring Structure of Unlabeled Data\n",
     "\n",
     "Dimensionality reduction is another example of an unsupervised algorithm, in which labels or other information are inferred from the structure of the dataset itself.\n",
     "Dimensionality reduction is a bit more abstract than the examples we looked at before, but generally it seeks to pull out some low-dimensional representation of data that in some way preserves relevant qualities of the full dataset.\n",
@@ -400,8 +363,7 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.01-dimesionality-1.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Dimensionality-Reduction-Example-Figure-1)"
+    "![](images/05.01-dimesionality-1.png)"
    ]
   },
   {
@@ -412,10 +374,10 @@
    },
    "source": [
     "Visually, it is clear that there is some structure in this data: it is drawn from a one-dimensional line that is arranged in a spiral within this two-dimensional space.\n",
-    "In a sense, you could say that this data is \"intrinsically\" only one dimensional, though this one-dimensional data is embedded in higher-dimensional space.\n",
-    "A suitable dimensionality reduction model in this case would be sensitive to this nonlinear embedded structure, and be able to pull out this lower-dimensionality representation.\n",
+    "In a sense, you could say that this data is \"intrinsically\" only one-dimensional, though this one-dimensional data is embedded in two-dimensional space.\n",
+    "A suitable dimensionality reduction model in this case would be sensitive to this nonlinear embedded structure and be able to detect this lower-dimensionality representation.\n",
     "\n",
-    "The following figure shows a visualization of the results of the Isomap algorithm, a manifold learning algorithm that does exactly this:"
+    "The following figure shows a visualization of the results of the Isomap algorithm, a manifold learning algorithm that does exactly this."
    ]
   },
   {
@@ -425,8 +387,7 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.01-dimesionality-2.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Dimensionality-Reduction-Example-Figure-2)"
+    "![](images/05.01-dimesionality-2.png)"
    ]
   },
   {
@@ -439,9 +400,9 @@
     "Notice that the colors (which represent the extracted one-dimensional latent variable) change uniformly along the spiral, which indicates that the algorithm did in fact detect the structure we saw by eye.\n",
     "As with the previous examples, the power of dimensionality reduction algorithms becomes clearer in higher-dimensional cases.\n",
     "For example, we might wish to visualize important relationships within a dataset that has 100 or 1,000 features.\n",
-    "Visualizing 1,000-dimensional data is a challenge, and one way we can make this more manageable is to use a dimensionality reduction technique to reduce the data to two or three dimensions.\n",
+    "Visualizing 1,000-dimensional data is a challenge, and one way we can make this more manageable is to use a dimensionality reduction technique to reduce the data to 2 or 3 dimensions.\n",
     "\n",
-    "Some important dimensionality reduction algorithms that we will discuss are principal component analysis (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)) and various manifold learning algorithms, including Isomap and locally linear embedding (See [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb))."
+    "Some important dimensionality reduction algorithms that we will discuss are principal component analysis (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)) and various manifold learning algorithms, including Isomap and locally linear embedding (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb))."
    ]
   },
   {
@@ -454,7 +415,7 @@
     "## Summary\n",
     "\n",
     "Here we have seen a few simple examples of some of the basic types of machine learning approaches.\n",
-    "Needless to say, there are a number of important practical details that we have glossed over, but I hope this section was enough to give you a basic idea of what types of problems machine learning approaches can solve.\n",
+    "Needless to say, there are a number of important practical details that we have glossed over, but this chapter was designed to give you a basic idea of what types of problems machine learning approaches can solve.\n",
     "\n",
     "In short, we saw the following:\n",
     "\n",
@@ -470,27 +431,17 @@
     "  \n",
     "In the following sections we will go into much greater depth within these categories, and see some more interesting examples of where these concepts can be useful.\n",
     "\n",
-    "All of the figures in the preceding discussion are generated based on actual machine learning computations; the code behind them can be found in [Appendix: Figure Code](06.00-Figure-Code.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Machine Learning](05.00-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.01-What-Is-Machine-Learning.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "All of the figures in the preceding discussion are generated based on actual machine learning computations; the code behind them can be found in [Appendix: Figure Code](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb)."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -504,9 +455,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.02-Introducing-Scikit-Learn.ipynb b/notebooks/05.02-Introducing-Scikit-Learn.ipynb
index 8d3ecb877..60b614cce 100644
--- a/notebooks/05.02-Introducing-Scikit-Learn.ipynb
+++ b/notebooks/05.02-Introducing-Scikit-Learn.ipynb
@@ -1,33 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -42,14 +14,14 @@
     "editable": true
    },
    "source": [
-    "There are several Python libraries which provide solid implementations of a range of machine learning algorithms.\n",
+    "There are several Python libraries that provide solid implementations of a range of machine learning algorithms.\n",
     "One of the best known is [Scikit-Learn](http://scikit-learn.org), a package that provides efficient versions of a large number of common algorithms.\n",
     "Scikit-Learn is characterized by a clean, uniform, and streamlined API, as well as by very useful and complete online documentation.\n",
-    "A benefit of this uniformity is that once you understand the basic use and syntax of Scikit-Learn for one type of model, switching to a new model or algorithm is very straightforward.\n",
+    "A benefit of this uniformity is that once you understand the basic use and syntax of Scikit-Learn for one type of model, switching to a new model or algorithm is straightforward.\n",
     "\n",
-    "This section provides an overview of the Scikit-Learn API; a solid understanding of these API elements will form the foundation for understanding the deeper practical discussion of machine learning algorithms and approaches in the following chapters.\n",
+    "This chapter provides an overview of the Scikit-Learn API. A solid understanding of these API elements will form the foundation for understanding the deeper practical discussion of machine learning algorithms and approaches in the following chapters.\n",
     "\n",
-    "We will start by covering *data representation* in Scikit-Learn, followed by covering the *Estimator* API, and finally go through a more interesting example of using these tools for exploring a set of images of hand-written digits."
+    "We will start by covering data representation in Scikit-Learn, then delve into the Estimator API, and finally go through a more interesting example of using these tools for exploring a set of images of handwritten digits."
    ]
   },
   {
@@ -69,8 +41,8 @@
     "editable": true
    },
    "source": [
-    "Machine learning is about creating models from data: for that reason, we'll start by discussing how data can be represented in order to be understood by the computer.\n",
-    "The best way to think about data within Scikit-Learn is in terms of tables of data."
+    "Machine learning is about creating models from data: for that reason, we'll start by discussing how data can be represented.\n",
+    "The best way to think about data within Scikit-Learn is in terms of *tables*."
    ]
   },
   {
@@ -80,11 +52,9 @@
     "editable": true
    },
    "source": [
-    "### Data as table\n",
-    "\n",
     "A basic table is a two-dimensional grid of data, in which the rows represent individual elements of the dataset, and the columns represent quantities related to each of these elements.\n",
     "For example, consider the [Iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), famously analyzed by Ronald Fisher in 1936.\n",
-    "We can download this dataset in the form of a Pandas ``DataFrame`` using the [seaborn](http://seaborn.pydata.org/) library:"
+    "We can download this dataset in the form of a Pandas `DataFrame` using the [Seaborn](http://seaborn.pydata.org/) library, and take a look at the first few items:"
    ]
   },
   {
@@ -93,13 +63,29 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -184,10 +170,10 @@
    },
    "source": [
     "Here each row of the data refers to a single observed flower, and the number of rows is the total number of flowers in the dataset.\n",
-    "In general, we will refer to the rows of the matrix as *samples*, and the number of rows as ``n_samples``.\n",
+    "In general, we will refer to the rows of the matrix as *samples*, and the number of rows as `n_samples`.\n",
     "\n",
     "Likewise, each column of the data refers to a particular quantitative piece of information that describes each sample.\n",
-    "In general, we will refer to the columns of the matrix as *features*, and the number of columns as ``n_features``."
+    "In general, we will refer to the columns of the matrix as *features*, and the number of columns as `n_features`."
    ]
   },
   {
@@ -197,17 +183,17 @@
     "editable": true
    },
    "source": [
-    "#### Features matrix\n",
+    "### The Features Matrix\n",
     "\n",
-    "This table layout makes clear that the information can be thought of as a two-dimensional numerical array or matrix, which we will call the *features matrix*.\n",
-    "By convention, this features matrix is often stored in a variable named ``X``.\n",
-    "The features matrix is assumed to be two-dimensional, with shape ``[n_samples, n_features]``, and is most often contained in a NumPy array or a Pandas ``DataFrame``, though some Scikit-Learn models also accept SciPy sparse matrices.\n",
+    "The table layout makes clear that the information can be thought of as a two-dimensional numerical array or matrix, which we will call the *features matrix*.\n",
+    "By convention, this matrix is often stored in a variable named `X`.\n",
+    "The features matrix is assumed to be two-dimensional, with shape `[n_samples, n_features]`, and is most often contained in a NumPy array or a Pandas `DataFrame`, though some Scikit-Learn models also accept SciPy sparse matrices.\n",
     "\n",
     "The samples (i.e., rows) always refer to the individual objects described by the dataset.\n",
-    "For example, the sample might be a flower, a person, a document, an image, a sound file, a video, an astronomical object, or anything else you can describe with a set of quantitative measurements.\n",
+    "For example, a sample might represent a flower, a person, a document, an image, a sound file, a video, an astronomical object, or anything else you can describe with a set of quantitative measurements.\n",
     "\n",
     "The features (i.e., columns) always refer to the distinct observations that describe each sample in a quantitative manner.\n",
-    "Features are generally real-valued, but may be Boolean or discrete-valued in some cases."
+    "Features are often real-valued, but may be Boolean or discrete-valued in some cases."
    ]
   },
   {
@@ -217,17 +203,17 @@
     "editable": true
    },
    "source": [
-    "#### Target array\n",
+    "### The Target Array\n",
     "\n",
-    "In addition to the feature matrix ``X``, we also generally work with a *label* or *target* array, which by convention we will usually call ``y``.\n",
-    "The target array is usually one dimensional, with length ``n_samples``, and is generally contained in a NumPy array or Pandas ``Series``.\n",
+    "In addition to the feature matrix `X`, we also generally work with a *label* or *target* array, which by convention we will usually call `y`.\n",
+    "The target array is usually one-dimensional, with length `n_samples`, and is generally contained in a NumPy array or Pandas `Series`.\n",
     "The target array may have continuous numerical values, or discrete classes/labels.\n",
-    "While some Scikit-Learn estimators do handle multiple target values in the form of a two-dimensional, ``[n_samples, n_targets]`` target array, we will primarily be working with the common case of a one-dimensional target array.\n",
+    "While some Scikit-Learn estimators do handle multiple target values in the form of a two-dimensional, `[n_samples, n_targets]` target array, we will primarily be working with the common case of a one-dimensional target array.\n",
     "\n",
-    "Often one point of confusion is how the target array differs from the other features columns. The distinguishing feature of the target array is that it is usually the quantity we want to *predict from the data*: in statistical terms, it is the dependent variable.\n",
-    "For example, in the preceding data we may wish to construct a model that can predict the species of flower based on the other measurements; in this case, the ``species`` column would be considered the target array.\n",
+    "A common point of confusion is how the target array differs from the other feature columns. The distinguishing characteristic of the target array is that it is usually the quantity we want to *predict from the features*: in statistical terms, it is the dependent variable.\n",
+    "For example, given the preceding data we may wish to construct a model that can predict the species of flower based on the other measurements; in this case, the `species` column would be considered the target array.\n",
     "\n",
-    "With this target array in mind, we can use Seaborn (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)) to conveniently visualize the data:"
+    "With this target array in mind, we can use Seaborn (discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)) to conveniently visualize the data (see the following figure):"
    ]
   },
   {
@@ -236,34 +222,40 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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1ICc4Ty+XT+7pp2qjJejc1eV0Y3d24+r28OQbR4lSyFidMnxDL0NOWnQqn5gO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3/+E4B169axaNGiVucvLS1l4cKFrFq1iiuuuKJFedpTTOZEdoXvf7PV0ehaSUUt2YU1JMXqCQkM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\nK2P16tVs27YNSZJ48cUXOX78OA0NDTz77LOMHz++K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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1170288d0>"
+       "<Figure size 516.75x432 with 20 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "%matplotlib inline\n",
-    "import seaborn as sns; sns.set()\n",
-    "sns.pairplot(iris, hue='species', size=1.5);"
+    "import seaborn as sns\n",
+    "sns.pairplot(iris, hue='species', height=1.5);"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
-    "For use in Scikit-Learn, we will extract the features matrix and target array from the ``DataFrame``, which we can do using some of the Pandas ``DataFrame`` operations discussed in the [Chapter 3](03.00-Introduction-to-Pandas.ipynb):"
+    "For use in Scikit-Learn, we will extract the features matrix and target array from the `DataFrame`, which we can do using some of the Pandas `DataFrame` operations discussed in [Part 3](03.00-Introduction-to-Pandas.ipynb):"
    ]
   },
   {
@@ -272,7 +264,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -297,7 +292,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -323,7 +321,7 @@
     "editable": true
    },
    "source": [
-    "To summarize, the expected layout of features and target values is visualized in the following diagram:"
+    "To summarize, the expected layout of features and target values is visualized in the following figure."
    ]
   },
   {
@@ -333,8 +331,8 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.02-samples-features.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Features-and-Labels-Grid)"
+    "![](images/05.02-samples-features.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Features-and-Labels-Grid)"
    ]
   },
   {
@@ -344,7 +342,7 @@
     "editable": true
    },
    "source": [
-    "With this data properly formatted, we can move on to consider the *estimator* API of Scikit-Learn:"
+    "With this data properly formatted, we can move on to consider Scikit-Learn's Estimator API."
    ]
   },
   {
@@ -354,7 +352,7 @@
     "editable": true
    },
    "source": [
-    "## Scikit-Learn's Estimator API"
+    "## The Estimator API"
    ]
   },
   {
@@ -371,7 +369,7 @@
     "- *Inspection*: All specified parameter values are exposed as public attributes.\n",
     "\n",
     "- *Limited object hierarchy*: Only algorithms are represented by Python classes; datasets are represented\n",
-    "  in standard formats (NumPy arrays, Pandas ``DataFrame``s, SciPy sparse matrices) and parameter\n",
+    "  in standard formats (NumPy arrays, Pandas `DataFrame` objects, SciPy sparse matrices) and parameter\n",
     "  names use standard Python strings.\n",
     "\n",
     "- *Composition*: Many machine learning tasks can be expressed as sequences of more fundamental algorithms,\n",
@@ -392,16 +390,15 @@
    "source": [
     "### Basics of the API\n",
     "\n",
-    "Most commonly, the steps in using the Scikit-Learn estimator API are as follows\n",
-    "(we will step through a handful of detailed examples in the sections that follow).\n",
+    "Most commonly, the steps in using the Scikit-Learn Estimator API are as follows:\n",
     "\n",
     "1. Choose a class of model by importing the appropriate estimator class from Scikit-Learn.\n",
     "2. Choose model hyperparameters by instantiating this class with desired values.\n",
-    "3. Arrange data into a features matrix and target vector following the discussion above.\n",
-    "4. Fit the model to your data by calling the ``fit()`` method of the model instance.\n",
-    "5. Apply the Model to new data:\n",
-    "   - For supervised learning, often we predict labels for unknown data using the ``predict()`` method.\n",
-    "   - For unsupervised learning, we often transform or infer properties of the data using the ``transform()`` or ``predict()`` method.\n",
+    "3. Arrange data into a features matrix and target vector, as outlined earlier in this chapter.\n",
+    "4. Fit the model to your data by calling the `fit` method of the model instance.\n",
+    "5. Apply the model to new data:\n",
+    "   - For supervised learning, often we predict labels for unknown data using the `predict` method.\n",
+    "   - For unsupervised learning, we often transform or infer properties of the data using the `transform` or `predict` method.\n",
     "\n",
     "We will now step through several simple examples of applying supervised and unsupervised learning methods."
    ]
@@ -413,10 +410,10 @@
     "editable": true
    },
    "source": [
-    "### Supervised learning example: Simple linear regression\n",
+    "### Supervised Learning Example: Simple Linear Regression\n",
     "\n",
     "As an example of this process, let's consider a simple linear regression—that is, the common case of fitting a line to $(x, y)$ data.\n",
-    "We will use the following simple data for our regression example:"
+    "We will use the following simple data for our regression example (see the following figure):"
    ]
   },
   {
@@ -425,17 +422,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11acab828>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -469,16 +471,16 @@
     "#### 1. Choose a class of model\n",
     "\n",
     "In Scikit-Learn, every class of model is represented by a Python class.\n",
-    "So, for example, if we would like to compute a simple linear regression model, we can import the linear regression class:"
+    "So, for example, if we would like to compute a simple `LinearRegression` model, we can import the linear regression class:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -492,7 +494,7 @@
     "editable": true
    },
    "source": [
-    "Note that other more general linear regression models exist as well; you can read more about them in the [``sklearn.linear_model`` module documentation](http://Scikit-Learn.org/stable/modules/linear_model.html)."
+    "Note that other more general linear regression models exist as well; you can read more about them in the [`sklearn.linear_model` module documentation](http://Scikit-Learn.org/stable/modules/linear_model.html)."
    ]
   },
   {
@@ -518,9 +520,9 @@
     "These are examples of the important choices that must be made *once the model class is selected*.\n",
     "These choices are often represented as *hyperparameters*, or parameters that must be set before the model is fit to data.\n",
     "In Scikit-Learn, hyperparameters are chosen by passing values at model instantiation.\n",
-    "We will explore how you can quantitatively motivate the choice of hyperparameters in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb).\n",
+    "We will explore how you can quantitatively choose hyperparameters in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb).\n",
     "\n",
-    "For our linear regression example, we can instantiate the ``LinearRegression`` class and specify that we would like to fit the intercept using the ``fit_intercept`` hyperparameter:"
+    "For our linear regression example, we can instantiate the `LinearRegression` class and specify that we would like to fit the intercept using the `fit_intercept` hyperparameter:"
    ]
   },
   {
@@ -529,13 +531,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+       "LinearRegression()"
       ]
      },
      "execution_count": 7,
@@ -568,8 +573,8 @@
    "source": [
     "#### 3. Arrange data into a features matrix and target vector\n",
     "\n",
-    "Previously we detailed the Scikit-Learn data representation, which requires a two-dimensional features matrix and a one-dimensional target array.\n",
-    "Here our target variable ``y`` is already in the correct form (a length-``n_samples`` array), but we need to massage the data ``x`` to make it a matrix of size ``[n_samples, n_features]``.\n",
+    "Previously we examined the Scikit-Learn data representation, which requires a two-dimensional features matrix and a one-dimensional target array.\n",
+    "Here our target variable `y` is already in the correct form (a length-`n_samples` array), but we need to massage the data `x` to make it a matrix of size `[n_samples, n_features]`.\n",
     "In this case, this amounts to a simple reshaping of the one-dimensional array:"
    ]
   },
@@ -579,7 +584,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -605,10 +613,10 @@
     "editable": true
    },
    "source": [
-    "#### 4. Fit the model to your data\n",
+    "#### 4. Fit the model to the data\n",
     "\n",
-    "Now it is time to apply our model to data.\n",
-    "This can be done with the ``fit()`` method of the model:"
+    "Now it is time to apply our model to the data.\n",
+    "This can be done with the `fit` method of the model:"
    ]
   },
   {
@@ -617,13 +625,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+       "LinearRegression()"
       ]
      },
      "execution_count": 9,
@@ -642,8 +653,8 @@
     "editable": true
    },
    "source": [
-    "This ``fit()`` command causes a number of model-dependent internal computations to take place, and the results of these computations are stored in model-specific attributes that the user can explore.\n",
-    "In Scikit-Learn, by convention all model parameters that were learned during the ``fit()`` process have trailing underscores; for example in this linear model, we have the following:"
+    "This `fit` command causes a number of model-dependent internal computations to take place, and the results of these computations are stored in model-specific attributes that the user can explore.\n",
+    "In Scikit-Learn, by convention all model parameters that were learned during the `fit` process have trailing underscores; for example in this linear model, we have the following:"
    ]
   },
   {
@@ -652,13 +663,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 1.9776566])"
+       "array([1.9776566])"
       ]
      },
      "execution_count": 10,
@@ -676,13 +690,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-0.90331072553111635"
+       "-0.9033107255311146"
       ]
      },
      "execution_count": 11,
@@ -695,6 +712,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
@@ -702,12 +720,12 @@
    },
    "source": [
     "These two parameters represent the slope and intercept of the simple linear fit to the data.\n",
-    "Comparing to the data definition, we see that they are very close to the input slope of 2 and intercept of -1.\n",
+    "Comparing the results to the data definition, we see that they are close to the values used to generate the data: a slope of 2 and intercept of –1.\n",
     "\n",
     "One question that frequently comes up regards the uncertainty in such internal model parameters.\n",
     "In general, Scikit-Learn does not provide tools to draw conclusions from internal model parameters themselves: interpreting model parameters is much more a *statistical modeling* question than a *machine learning* question.\n",
-    "Machine learning rather focuses on what the model *predicts*.\n",
-    "If you would like to dive into the meaning of fit parameters within the model, other tools are available, including the [Statsmodels Python package](http://statsmodels.sourceforge.net/)."
+    "Machine learning instead focuses on what the model *predicts*.\n",
+    "If you would like to dive into the meaning of fit parameters within the model, other tools are available, including the [`statsmodels` Python package](http://statsmodels.sourceforge.net/)."
    ]
   },
   {
@@ -720,7 +738,7 @@
     "#### 5. Predict labels for unknown data\n",
     "\n",
     "Once the model is trained, the main task of supervised machine learning is to evaluate it based on what it says about new data that was not part of the training set.\n",
-    "In Scikit-Learn, this can be done using the ``predict()`` method.\n",
+    "In Scikit-Learn, this can be done using the `predict` method.\n",
     "For the sake of this example, our \"new data\" will be a grid of *x* values, and we will ask what *y* values the model predicts:"
    ]
   },
@@ -728,9 +746,9 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -744,7 +762,7 @@
     "editable": true
    },
    "source": [
-    "As before, we need to coerce these *x* values into a ``[n_samples, n_features]`` features matrix, after which we can feed it to the model:"
+    "As before, we need to coerce these *x* values into a `[n_samples, n_features]` features matrix, after which we can feed it to the model:"
    ]
   },
   {
@@ -753,7 +771,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -768,7 +789,7 @@
     "editable": true
    },
    "source": [
-    "Finally, let's visualize the results by plotting first the raw data, and then this model fit:"
+    "Finally, let's visualize the results by plotting first the raw data, and then this model fit (see the following figure):"
    ]
   },
   {
@@ -777,17 +798,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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cLiMwwMzc6YMZN7QHgQHmS34feveuAH5sJ+rdu7LTfL/87Wd/ofZ+GHEqkP/whz/w/PPP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\n6dJERKSTUCC3wjAMPttXwr+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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11c4d9b70>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -797,13 +823,14 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
-    "Typically the efficacy of the model is evaluated by comparing its results to some known baseline, as we will see in the next example"
+    "Typically the efficacy of the model is evaluated by comparing its results to some known baseline, as we will see in the next example."
    ]
   },
   {
@@ -813,29 +840,29 @@
     "editable": true
    },
    "source": [
-    "### Supervised learning example: Iris classification\n",
+    "### Supervised Learning Example: Iris Classification\n",
     "\n",
     "Let's take a look at another example of this process, using the Iris dataset we discussed earlier.\n",
     "Our question will be this: given a model trained on a portion of the Iris data, how well can we predict the remaining labels?\n",
     "\n",
-    "For this task, we will use an extremely simple generative model known as Gaussian naive Bayes, which proceeds by assuming each class is drawn from an axis-aligned Gaussian distribution (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) for more details).\n",
+    "For this task, we will use a simple generative model known as *Gaussian naive Bayes*, which proceeds by assuming each class is drawn from an axis-aligned Gaussian distribution (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) for more details).\n",
     "Because it is so fast and has no hyperparameters to choose, Gaussian naive Bayes is often a good model to use as a baseline classification, before exploring whether improvements can be found through more sophisticated models.\n",
     "\n",
-    "We would like to evaluate the model on data it has not seen before, and so we will split the data into a *training set* and a *testing set*.\n",
-    "This could be done by hand, but it is more convenient to use the ``train_test_split`` utility function:"
+    "We would like to evaluate the model on data it has not seen before, so we will split the data into a *training set* and a *testing set*.\n",
+    "This could be done by hand, but it is more convenient to use the `train_test_split` utility function:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "Xtrain, Xtest, ytrain, ytest = train_test_split(X_iris, y_iris,\n",
     "                                                random_state=1)"
    ]
@@ -856,7 +883,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -873,7 +903,7 @@
     "editable": true
    },
    "source": [
-    "Finally, we can use the ``accuracy_score`` utility to see the fraction of predicted labels that match their true value:"
+    "Finally, we can use the ``accuracy_score`` utility to see the fraction of predicted labels that match their true values:"
    ]
   },
   {
@@ -882,13 +912,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.97368421052631582"
+       "0.9736842105263158"
       ]
      },
      "execution_count": 17,
@@ -918,15 +951,15 @@
     "editable": true
    },
    "source": [
-    "### Unsupervised learning example: Iris dimensionality\n",
+    "### Unsupervised Learning Example: Iris Dimensionality\n",
     "\n",
     "As an example of an unsupervised learning problem, let's take a look at reducing the dimensionality of the Iris data so as to more easily visualize it.\n",
-    "Recall that the Iris data is four dimensional: there are four features recorded for each sample.\n",
+    "Recall that the Iris data is four-dimensional: there are four features recorded for each sample.\n",
     "\n",
-    "The task of dimensionality reduction is to ask whether there is a suitable lower-dimensional representation that retains the essential features of the data.\n",
-    "Often dimensionality reduction is used as an aid to visualizing data: after all, it is much easier to plot data in two dimensions than in four dimensions or higher!\n",
+    "The task of dimensionality reduction centers around determining whether there is a suitable lower-dimensional representation that retains the essential features of the data.\n",
+    "Often dimensionality reduction is used as an aid to visualizing data: after all, it is much easier to plot data in two dimensions than in four dimensions or more!\n",
     "\n",
-    "Here we will use principal component analysis (PCA; see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)), which is a fast linear dimensionality reduction technique.\n",
+    "Here we will use *principal component analysis* (PCA; see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)), which is a fast linear dimensionality reduction technique.\n",
     "We will ask the model to return two components—that is, a two-dimensional representation of the data.\n",
     "\n",
     "Following the sequence of steps outlined earlier, we have:"
@@ -936,16 +969,16 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
     "from sklearn.decomposition import PCA  # 1. Choose the model class\n",
-    "model = PCA(n_components=2)            # 2. Instantiate the model with hyperparameters\n",
-    "model.fit(X_iris)                      # 3. Fit to data. Notice y is not specified!\n",
-    "X_2D = model.transform(X_iris)         # 4. Transform the data to two dimensions"
+    "model = PCA(n_components=2)            # 2. Instantiate the model\n",
+    "model.fit(X_iris)                      # 3. Fit to data\n",
+    "X_2D = model.transform(X_iris)         # 4. Transform the data"
    ]
   },
   {
@@ -955,7 +988,7 @@
     "editable": true
    },
    "source": [
-    "Now let's plot the results. A quick way to do this is to insert the results into the original Iris ``DataFrame``, and use Seaborn's ``lmplot`` to show the results:"
+    "Now let's plot the results. A quick way to do this is to insert the results into the original Iris `DataFrame`, and use Seaborn's `lmplot` to show the results (see the following figure):"
    ]
   },
   {
@@ -964,24 +997,29 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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wa3u/dhBgQsq5nW53WGBDez/2Xs+af+RumKCczNxD8lZ5QW+ohFKpgNbPGyGBLTCodwiG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\nIiIiWWKCIiIiWWKCIiIiWWKCIiIiWfK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+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11c6676d8>"
+       "<Figure size 444.75x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "iris['PCA1'] = X_2D[:, 0]\n",
     "iris['PCA2'] = X_2D[:, 1]\n",
-    "sns.lmplot(\"PCA1\", \"PCA2\", hue='species', data=iris, fit_reg=False);"
+    "sns.lmplot(x=\"PCA1\", y=\"PCA2\", hue='species', data=iris, fit_reg=False);"
    ]
   },
   {
@@ -992,7 +1030,7 @@
    },
    "source": [
     "We see that in the two-dimensional representation, the species are fairly well separated, even though the PCA algorithm had no knowledge of the species labels!\n",
-    "This indicates to us that a relatively straightforward classification will probably be effective on the dataset, as we saw before."
+    "This suggests to us that a relatively straightforward classification will probably be effective on the dataset, as we saw before."
    ]
   },
   {
@@ -1002,11 +1040,11 @@
     "editable": true
    },
    "source": [
-    "### Unsupervised learning: Iris clustering\n",
+    "### Unsupervised Learning Example: Iris Clustering\n",
     "\n",
     "Let's next look at applying clustering to the Iris data.\n",
     "A clustering algorithm attempts to find distinct groups of data without reference to any labels.\n",
-    "Here we will use a powerful clustering method called a Gaussian mixture model (GMM), discussed in more detail in [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb).\n",
+    "Here we will use a powerful clustering method called a *Gaussian mixture model* (GMM), discussed in more detail in [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb).\n",
     "A GMM attempts to model the data as a collection of Gaussian blobs.\n",
     "\n",
     "We can fit the Gaussian mixture model as follows:"
@@ -1018,15 +1056,18 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "from sklearn.mixture import GMM      # 1. Choose the model class\n",
-    "model = GMM(n_components=3,\n",
-    "            covariance_type='full')  # 2. Instantiate the model with hyperparameters\n",
-    "model.fit(X_iris)                    # 3. Fit to data. Notice y is not specified!\n",
-    "y_gmm = model.predict(X_iris)        # 4. Determine cluster labels"
+    "from sklearn.mixture import GaussianMixture      # 1. Choose the model class\n",
+    "model = GaussianMixture(n_components=3,\n",
+    "                        covariance_type='full')  # 2. Instantiate the model\n",
+    "model.fit(X_iris)                                # 3. Fit to data\n",
+    "y_gmm = model.predict(X_iris)                    # 4. Determine labels"
    ]
   },
   {
@@ -1036,7 +1077,7 @@
     "editable": true
    },
    "source": [
-    "As before, we will add the cluster label to the Iris ``DataFrame`` and use Seaborn to plot the results:"
+    "As before, we will add the cluster label to the Iris ``DataFrame`` and use Seaborn to plot the results (see the following figure):"
    ]
   },
   {
@@ -1045,23 +1086,28 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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elWKRGhdPwtSraz3m6NtLuQf2O9cd+vZq1j0G8/uD0OL3BNCAAQO0YcMGXXnl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\n6tru5al3TjBOAwPc8ZgAuum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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11a932828>"
+       "<Figure size 1164.75x360 with 3 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "iris['cluster'] = y_gmm\n",
-    "sns.lmplot(\"PCA1\", \"PCA2\", data=iris, hue='species',\n",
+    "sns.lmplot(x=\"PCA1\", y=\"PCA2\", data=iris, hue='species',\n",
     "           col='cluster', fit_reg=False);"
    ]
   },
@@ -1072,9 +1118,9 @@
     "editable": true
    },
    "source": [
-    "By splitting the data by cluster number, we see exactly how well the GMM algorithm has recovered the underlying label: the *setosa* species is separated perfectly within cluster 0, while there remains a small amount of mixing between *versicolor* and *virginica*.\n",
+    "By splitting the data by cluster number, we see exactly how well the GMM algorithm has recovered the underlying labels: the *setosa* species is separated perfectly within cluster 0, while there remains a small amount of mixing between *versicolor* and *virginica*.\n",
     "This means that even without an expert to tell us the species labels of the individual flowers, the measurements of these flowers are distinct enough that we could *automatically* identify the presence of these different groups of species with a simple clustering algorithm!\n",
-    "This sort of algorithm might further give experts in the field clues as to the relationship between the samples they are observing."
+    "This sort of algorithm might further give experts in the field clues as to the relationships between the samples they are observing."
    ]
   },
   {
@@ -1084,7 +1130,7 @@
     "editable": true
    },
    "source": [
-    "## Application: Exploring Hand-written Digits"
+    "## Application: Exploring Handwritten Digits"
    ]
   },
   {
@@ -1094,8 +1140,8 @@
     "editable": true
    },
    "source": [
-    "To demonstrate these principles on a more interesting problem, let's consider one piece of the optical character recognition problem: the identification of hand-written digits.\n",
-    "In the wild, this problem involves both locating and identifying characters in an image. Here we'll take a shortcut and use Scikit-Learn's set of pre-formatted digits, which is built into the library."
+    "To demonstrate these principles on a more interesting problem, let's consider one piece of the optical character recognition problem: the identification of handwritten digits.\n",
+    "In the wild, this problem involves both locating and identifying characters in an image. Here we'll take a shortcut and use Scikit-Learn's set of preformatted digits, which is built into the library."
    ]
   },
   {
@@ -1105,9 +1151,9 @@
     "editable": true
    },
    "source": [
-    "### Loading and visualizing the digits data\n",
+    "### Loading and Visualizing the Digits Data\n",
     "\n",
-    "We'll use Scikit-Learn's data access interface and take a look at this data:"
+    "We can use Scikit-Learn's data access interface to take a look at this data:"
    ]
   },
   {
@@ -1116,7 +1162,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1144,7 +1193,7 @@
    },
    "source": [
     "The images data is a three-dimensional array: 1,797 samples each consisting of an 8 × 8 grid of pixels.\n",
-    "Let's visualize the first hundred of these:"
+    "Let's visualize the first hundred of these (see the following figure):"
    ]
   },
   {
@@ -1153,14 +1202,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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INHDCJCIi0mDJGqbq93Gp2PeCBQsMbdLvz6rfr+0qbF1LWh/Kz8/XalOtbVi1rqI6dunz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\nB22bU+lfehBX9QCrdKKHRHoQXfXAu12nE6j6RLo+qe+lwgdOPfzdFOn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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11acadd68>"
+       "<Figure size 576x576 with 100 Axes>"
       ]
      },
      "metadata": {},
@@ -1187,10 +1239,10 @@
     "editable": true
    },
    "source": [
-    "In order to work with this data within Scikit-Learn, we need a two-dimensional, ``[n_samples, n_features]`` representation.\n",
+    "In order to work with this data within Scikit-Learn, we need a two-dimensional, `[n_samples, n_features]` representation.\n",
     "We can accomplish this by treating each pixel in the image as a feature: that is, by flattening out the pixel arrays so that we have a length-64 array of pixel values representing each digit.\n",
     "Additionally, we need the target array, which gives the previously determined label for each digit.\n",
-    "These two quantities are built into the digits dataset under the ``data`` and ``target`` attributes, respectively:"
+    "These two quantities are built into the digits dataset under the `data` and `target` attributes, respectively:"
    ]
   },
   {
@@ -1199,7 +1251,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1224,7 +1279,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1260,11 +1318,11 @@
     "editable": true
    },
    "source": [
-    "### Unsupervised learning: Dimensionality reduction\n",
+    "### Unsupervised Learning Example: Dimensionality Reduction\n",
     "\n",
     "We'd like to visualize our points within the 64-dimensional parameter space, but it's difficult to effectively visualize points in such a high-dimensional space.\n",
-    "Instead we'll reduce the dimensions to 2, using an unsupervised method.\n",
-    "Here, we'll make use of a manifold learning algorithm called *Isomap* (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)), and transform the data to two dimensions:"
+    "Instead, we'll reduce the number of dimensions, using an unsupervised method.\n",
+    "Here, we'll make use of a manifold learning algorithm called Isomap (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)) and transform the data to two dimensions:"
    ]
   },
   {
@@ -1273,18 +1331,18 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(1797, 2)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1797, 2)\n"
+     ]
     }
    ],
    "source": [
@@ -1292,7 +1350,7 @@
     "iso = Isomap(n_components=2)\n",
     "iso.fit(digits.data)\n",
     "data_projected = iso.transform(digits.data)\n",
-    "data_projected.shape"
+    "print(data_projected.shape)"
    ]
   },
   {
@@ -1303,7 +1361,7 @@
    },
    "source": [
     "We see that the projected data is now two-dimensional.\n",
-    "Let's plot this data to see if we can learn anything from its structure:"
+    "Let's plot this data to see if we can learn anything from its structure (see the following figure):"
    ]
   },
   {
@@ -1312,24 +1370,29 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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J83zHi8Pt/bHzz3x+0ueGJxx9qD/RP5xEYWh1nIHkIMUcfWFyIc53kkiFOI45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MnVcyQ6SWzie3iFVzFuEzq+FPG7EYnV6sFtiHHqkV+wtd8CPlxYqNf+FUZXVze/+7v/bt5o+b1g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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11acadb00>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.scatter(data_projected[:, 0], data_projected[:, 1], c=digits.target,\n",
     "            edgecolor='none', alpha=0.5,\n",
-    "            cmap=plt.cm.get_cmap('spectral', 10))\n",
+    "            cmap=plt.cm.get_cmap('viridis', 10))\n",
     "plt.colorbar(label='digit label', ticks=range(10))\n",
     "plt.clim(-0.5, 9.5);"
    ]
@@ -1341,11 +1404,11 @@
     "editable": true
    },
    "source": [
-    "This plot gives us some good intuition into how well various numbers are separated in the larger 64-dimensional space. For example, zeros (in black) and ones (in purple) have very little overlap in parameter space.\n",
+    "This plot gives us some good intuition into how well various numbers are separated in the larger 64-dimensional space. For example, zeros and ones have very little overlap in the parameter space.\n",
     "Intuitively, this makes sense: a zero is empty in the middle of the image, while a one will generally have ink in the middle.\n",
-    "On the other hand, there seems to be a more or less continuous spectrum between ones and fours: we can understand this by realizing that some people draw ones with \"hats\" on them, which cause them to look similar to fours.\n",
+    "On the other hand, there seems to be a more or less continuous spectrum between ones and fours: we can understand this by realizing that some people draw ones with \"hats\" on them, which causes them to look similar to fours.\n",
     "\n",
-    "Overall, however, the different groups appear to be fairly well separated in the parameter space: this tells us that even a very straightforward supervised classification algorithm should perform suitably on this data.\n",
+    "Overall, however, despite some mixing at the edges, the different groups appear to be fairly well localized in the parameter space: this suggests that even a very straightforward supervised classification algorithm should perform suitably on the full high-dimensional dataset.\n",
     "Let's give it a try."
    ]
   },
@@ -1356,19 +1419,19 @@
     "editable": true
    },
    "source": [
-    "### Classification on digits\n",
+    "### Classification on Digits\n",
     "\n",
-    "Let's apply a classification algorithm to the digits.\n",
-    "As with the Iris data previously, we will split the data into a training and testing set, and fit a Gaussian naive Bayes model:"
+    "Let's apply a classification algorithm to the digits data.\n",
+    "As we did with the Iris data previously, we will split the data into training and testing sets and fit a Gaussian naive Bayes model:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -1379,9 +1442,9 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -1398,7 +1461,7 @@
     "editable": true
    },
    "source": [
-    "Now that we have predicted our model, we can gauge its accuracy by comparing the true values of the test set to the predictions:"
+    "Now that we have the model's predictions, we can gauge its accuracy by comparing the true values of the test set to the predictions:"
    ]
   },
   {
@@ -1407,13 +1470,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.83333333333333337"
+       "0.8333333333333334"
       ]
      },
      "execution_count": 30,
@@ -1433,8 +1499,8 @@
     "editable": true
    },
    "source": [
-    "With even this extremely simple model, we find about 80% accuracy for classification of the digits!\n",
-    "However, this single number doesn't tell us *where* we've gone wrong—one nice way to do this is to use the *confusion matrix*, which we can compute with Scikit-Learn and plot with Seaborn:"
+    "With even this very simple model, we find about 83% accuracy for classification of the digits!\n",
+    "However, this single number doesn't tell us where we've gone wrong. One nice way to do this is to use the *confusion matrix*, which we can compute with Scikit-Learn and plot with Seaborn (see the following figure):"
    ]
   },
   {
@@ -1443,17 +1509,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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a1qB4uaKMWzKSj6f1w9HJkXFLRvJak2rk8737C6i01DT2bT5A0bLP5Xr+vVQ+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\nLzLMGdy6nsSyT5eSeieVln1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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11f230ef0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1462,7 +1533,7 @@
     "\n",
     "mat = confusion_matrix(ytest, y_model)\n",
     "\n",
-    "sns.heatmap(mat, square=True, annot=True, cbar=False)\n",
+    "sns.heatmap(mat, square=True, annot=True, cbar=False, cmap='Blues')\n",
     "plt.xlabel('predicted value')\n",
     "plt.ylabel('true value');"
    ]
@@ -1474,9 +1545,10 @@
     "editable": true
    },
    "source": [
-    "This shows us where the mis-labeled points tend to be: for example, a large number of twos here are mis-classified as either ones or eights.\n",
+    "This shows us where the mislabeled points tend to be: for example, many of the twos here are misclassified as either ones or eights.\n",
+    "\n",
     "Another way to gain intuition into the characteristics of the model is to plot the inputs again, with their predicted labels.\n",
-    "We'll use green for correct labels, and red for incorrect labels:"
+    "We'll use green for correct labels and red for incorrect labels; see the following figure:"
    ]
   },
   {
@@ -1485,14 +1557,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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x0a8+sjs0JX/sZ+01zLr6OgBAdUM1AKCmvgbBjmBrorLAor8uQnR4NNKT0u0O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\nIiIiDZazZI8fP+7RpsqSldqlCj5Lly71aNu3b5/V0JSsVM2RsqikbDA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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x121637240>"
+       "<Figure size 576x576 with 100 Axes>"
       ]
      },
      "metadata": {},
@@ -1520,8 +1595,8 @@
     "editable": true
    },
    "source": [
-    "Examining this subset of the data, we can gain insight regarding where the algorithm might be not performing optimally.\n",
-    "To go beyond our 80% classification rate, we might move to a more sophisticated algorithm such as support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), random forests (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)) or another classification approach."
+    "Examining this subset of the data can give us some insight into where the algorithm might be not performing optimally.\n",
+    "To go beyond our 83% classification success rate, we might switch to a more sophisticated algorithm such as support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), random forests (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)), or another classification approach."
    ]
   },
   {
@@ -1541,31 +1616,21 @@
     "editable": true
    },
    "source": [
-    "In this section we have covered the essential features of the Scikit-Learn data representation, and the estimator API.\n",
-    "Regardless of the type of estimator, the same import/instantiate/fit/predict pattern holds.\n",
-    "Armed with this information about the estimator API, you can explore the Scikit-Learn documentation and begin trying out various models on your data.\n",
-    "\n",
-    "In the next section, we will explore perhaps the most important topic in machine learning: how to select and validate your model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >\n",
+    "In this chapter we covered the essential features of the Scikit-Learn data representation and the Estimator API.\n",
+    "Regardless of the type of estimator used, the same import/instantiate/fit/predict pattern holds.\n",
+    "Armed with this information about the Estimator API, you can explore the Scikit-Learn documentation and begin trying out various models on your data.\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "In the next chapter, we will explore perhaps the most important topic in machine learning: how to select and validate your model."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1579,9 +1644,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb b/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb
index 3edcada26..af5479981 100644
--- a/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb
+++ b/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb
@@ -1,33 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) | [Contents](Index.ipynb) | [Feature Engineering](05.04-Feature-Engineering.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -38,20 +10,19 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
     "deletable": true,
     "editable": true
    },
    "source": [
-    "In the previous section, we saw the basic recipe for applying a supervised machine learning model:\n",
+    "In the previous chapter, we saw the basic recipe for applying a supervised machine learning model:\n",
     "\n",
-    "1. Choose a class of model\n",
-    "2. Choose model hyperparameters\n",
-    "3. Fit the model to the training data\n",
-    "4. Use the model to predict labels for new data\n",
+    "1. Choose a class of model.\n",
+    "2. Choose model hyperparameters.\n",
+    "3. Fit the model to the training data.\n",
+    "4. Use the model to predict labels for new data.\n",
     "\n",
     "The first two pieces of this—the choice of model and choice of hyperparameters—are perhaps the most important part of using these tools and techniques effectively.\n",
-    "In order to make an informed choice, we need a way to *validate* that our model and our hyperparameters are a good fit to the data.\n",
+    "In order to make informed choices, we need a way to *validate* that our model and our hyperparameters are a good fit to the data.\n",
     "While this may sound simple, there are some pitfalls that you must avoid to do this effectively."
    ]
   },
@@ -62,11 +33,11 @@
     "editable": true
    },
    "source": [
-    "## Thinking about Model Validation\n",
+    "## Thinking About Model Validation\n",
     "\n",
-    "In principle, model validation is very simple: after choosing a model and its hyperparameters, we can estimate how effective it is by applying it to some of the training data and comparing the prediction to the known value.\n",
+    "In principle, model validation is very simple: after choosing a model and its hyperparameters, we can estimate how effective it is by applying it to some of the training data and comparing the predictions to the known values.\n",
     "\n",
-    "The following sections first show a naive approach to model validation and why it\n",
+    "This section will first show a naive approach to model validation and why it\n",
     "fails, before exploring the use of holdout sets and cross-validation for more robust\n",
     "model evaluation."
    ]
@@ -78,9 +49,9 @@
     "editable": true
    },
    "source": [
-    "### Model validation the wrong way\n",
+    "### Model Validation the Wrong Way\n",
     "\n",
-    "Let's demonstrate the naive approach to validation using the Iris data, which we saw in the previous section.\n",
+    "Let's start with the naive approach to validation using the Iris dataset, which we saw in the previous chapter.\n",
     "We will start by loading the data:"
    ]
   },
@@ -88,9 +59,9 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -107,17 +78,17 @@
     "editable": true
    },
    "source": [
-    "Next we choose a model and hyperparameters. Here we'll use a *k*-neighbors classifier with ``n_neighbors=1``.\n",
-    "This is a very simple and intuitive model that says \"the label of an unknown point is the same as the label of its closest training point:\""
+    "Next, we choose a model and hyperparameters. Here we'll use a *k*-nearest neighbors classifier with `n_neighbors=1`.\n",
+    "This is a very simple and intuitive model that says \"the label of an unknown point is the same as the label of its closest training point\":"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -132,16 +103,16 @@
     "editable": true
    },
    "source": [
-    "Then we train the model, and use it to predict labels for data we already know:"
+    "Then we train the model, and use it to predict labels for data whose labels we already know:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -165,7 +136,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -196,7 +170,7 @@
     "\n",
     "As you may have gathered, the answer is no.\n",
     "In fact, this approach contains a fundamental flaw: *it trains and evaluates the model on the same data*.\n",
-    "Furthermore, the nearest neighbor model is an *instance-based* estimator that simply stores the training data, and predicts labels by comparing new data to these stored points: except in contrived cases, it will get 100% accuracy *every time!*"
+    "Furthermore, this nearest neighbor model is an *instance-based* estimator that simply stores the training data, and predicts labels by comparing new data to these stored points: except in contrived cases, it will get 100% accuracy every time!"
    ]
   },
   {
@@ -206,11 +180,11 @@
     "editable": true
    },
    "source": [
-    "### Model validation the right way: Holdout sets\n",
+    "### Model Validation the Right Way: Holdout Sets\n",
     "\n",
     "So what can be done?\n",
-    "A better sense of a model's performance can be found using what's known as a *holdout set*: that is, we hold back some subset of the data from the training of the model, and then use this holdout set to check the model performance.\n",
-    "This splitting can be done using the ``train_test_split`` utility in Scikit-Learn:"
+    "A better sense of a model's performance can be found by using what's known as a *holdout set*: that is, we hold back some subset of the data from the training of the model, and then use this holdout set to check the model's performance.\n",
+    "This splitting can be done using the `train_test_split` utility in Scikit-Learn:"
    ]
   },
   {
@@ -219,13 +193,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.90666666666666662"
+       "0.9066666666666666"
       ]
      },
      "execution_count": 5,
@@ -234,7 +211,7 @@
     }
    ],
    "source": [
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "# split the data with 50% in each set\n",
     "X1, X2, y1, y2 = train_test_split(X, y, random_state=0,\n",
     "                                  train_size=0.5)\n",
@@ -254,8 +231,8 @@
     "editable": true
    },
    "source": [
-    "We see here a more reasonable result: the nearest-neighbor classifier is about 90% accurate on this hold-out set.\n",
-    "The hold-out set is similar to unknown data, because the model has not \"seen\" it before."
+    "We see here a more reasonable result: the one-nearest-neighbor classifier is about 90% accurate on this holdout set.\n",
+    "The holdout set is similar to unknown data, because the model has not \"seen\" it before."
    ]
   },
   {
@@ -265,20 +242,20 @@
     "editable": true
    },
    "source": [
-    "### Model validation via cross-validation\n",
+    "### Model Validation via Cross-Validation\n",
     "\n",
     "One disadvantage of using a holdout set for model validation is that we have lost a portion of our data to the model training.\n",
-    "In the above case, half the dataset does not contribute to the training of the model!\n",
-    "This is not optimal, and can cause problems – especially if the initial set of training data is small.\n",
+    "In the preceding case, half the dataset does not contribute to the training of the model!\n",
+    "This is not optimal, especially if the initial set of training data is small.\n",
     "\n",
     "One way to address this is to use *cross-validation*; that is, to do a sequence of fits where each subset of the data is used both as a training set and as a validation set.\n",
-    "Visually, it might look something like this:\n",
+    "Visually, it might look something like the following figure:\n",
     "\n",
-    "![](figures/05.03-2-fold-CV.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#2-Fold-Cross-Validation)\n",
+    "![](images/05.03-2-fold-CV.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#2-Fold-Cross-Validation)\n",
     "\n",
     "Here we do two validation trials, alternately using each half of the data as a holdout set.\n",
-    "Using the split data from before, we could implement it like this:"
+    "Using the split data from earlier, we could implement it like this:"
    ]
   },
   {
@@ -287,13 +264,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.95999999999999996, 0.90666666666666662)"
+       "(0.96, 0.9066666666666666)"
       ]
      },
      "execution_count": 6,
@@ -317,13 +297,13 @@
     "What comes out are two accuracy scores, which we could combine (by, say, taking the mean) to get a better measure of the global model performance.\n",
     "This particular form of cross-validation is a *two-fold cross-validation*—that is, one in which we have split the data into two sets and used each in turn as a validation set.\n",
     "\n",
-    "We could expand on this idea to use even more trials, and more folds in the data—for example, here is a visual depiction of five-fold cross-validation:\n",
+    "We could expand on this idea to use even more trials, and more folds in the data—for example, the following figure shows a visual depiction of five-fold cross-validation.\n",
     "\n",
-    "![](figures/05.03-5-fold-CV.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#5-Fold-Cross-Validation)\n",
+    "![](images/05.03-5-fold-CV.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#5-Fold-Cross-Validation)\n",
     "\n",
-    "Here we split the data into five groups, and use each of them in turn to evaluate the model fit on the other 4/5 of the data.\n",
-    "This would be rather tedious to do by hand, and so we can use Scikit-Learn's ``cross_val_score`` convenience routine to do it succinctly:"
+    "Here we split the data into five groups, and use each of them in turn to evaluate the model fit on the other four-fifths of the data.\n",
+    "This would be rather tedious to do by hand, but we can use Scikit-Learn's `cross_val_score` convenience routine to do it succinctly:"
    ]
   },
   {
@@ -332,13 +312,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.96666667,  0.96666667,  0.93333333,  0.93333333,  1.        ])"
+       "array([0.96666667, 0.96666667, 0.93333333, 0.93333333, 1.        ])"
       ]
      },
      "execution_count": 7,
@@ -347,7 +330,7 @@
     }
    ],
    "source": [
-    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn.model_selection import cross_val_score\n",
     "cross_val_score(model, X, y, cv=5)"
    ]
   },
@@ -360,7 +343,7 @@
    "source": [
     "Repeating the validation across different subsets of the data gives us an even better idea of the performance of the algorithm.\n",
     "\n",
-    "Scikit-Learn implements a number of useful cross-validation schemes that are useful in particular situations; these are implemented via iterators in the ``cross_validation`` module.\n",
+    "Scikit-Learn implements a number of cross-validation schemes that are useful in particular situations; these are implemented via iterators in the `model_selection` module.\n",
     "For example, we might wish to go to the extreme case in which our number of folds is equal to the number of data points: that is, we train on all points but one in each trial.\n",
     "This type of cross-validation is known as *leave-one-out* cross validation, and can be used as follows:"
    ]
@@ -371,24 +354,24 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
-       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
-       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
-       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
-       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
-       "        1.,  1.,  1.,  1.,  1.,  0.,  1.,  0.,  1.,  1.,  1.,  1.,  1.,\n",
-       "        1.,  1.,  1.,  1.,  1.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
-       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
-       "        1.,  1.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
-       "        1.,  1.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
-       "        1.,  1.,  1.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
-       "        1.,  1.,  1.,  1.,  1.,  1.,  1.])"
+       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "       1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1.,\n",
+       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "       1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "       0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1.,\n",
+       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
       ]
      },
      "execution_count": 8,
@@ -397,8 +380,8 @@
     }
    ],
    "source": [
-    "from sklearn.cross_validation import LeaveOneOut\n",
-    "scores = cross_val_score(model, X, y, cv=LeaveOneOut(len(X)))\n",
+    "from sklearn.model_selection import LeaveOneOut\n",
+    "scores = cross_val_score(model, X, y, cv=LeaveOneOut())\n",
     "scores"
    ]
   },
@@ -409,7 +392,7 @@
     "editable": true
    },
    "source": [
-    "Because we have 150 samples, the leave one out cross-validation yields scores for 150 trials, and the score indicates either successful (1.0) or unsuccessful (0.0) prediction.\n",
+    "Because we have 150 samples, the leave-one-out cross-validation yields scores for 150 trials, and each score indicates either a successful (1.0) or an unsuccessful (0.0) prediction.\n",
     "Taking the mean of these gives an estimate of the error rate:"
    ]
   },
@@ -419,13 +402,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.95999999999999996"
+       "0.96"
       ]
      },
      "execution_count": 9,
@@ -445,7 +431,7 @@
    },
    "source": [
     "Other cross-validation schemes can be used similarly.\n",
-    "For a description of what is available in Scikit-Learn, use IPython to explore the ``sklearn.cross_validation`` submodule, or take a look at Scikit-Learn's online [cross-validation documentation](http://scikit-learn.org/stable/modules/cross_validation.html)."
+    "For a description of what is available in Scikit-Learn, use IPython to explore the ``sklearn.model_selection`` submodule, or take a look at Scikit-Learn's [cross-validation documentation](http://scikit-learn.org/stable/modules/cross_validation.html)."
    ]
   },
   {
@@ -457,18 +443,18 @@
    "source": [
     "## Selecting the Best Model\n",
     "\n",
-    "Now that we've seen the basics of validation and cross-validation, we will go into a litte more depth regarding model selection and selection of hyperparameters.\n",
-    "These issues are some of the most important aspects of the practice of machine learning, and I find that this information is often glossed over in introductory machine learning tutorials.\n",
+    "Now that we've explored the basics of validation and cross-validation, we will go into a little more depth regarding model selection and selection of hyperparameters.\n",
+    "These issues are some of the most important aspects of the practice of machine learning, but I find that this information is often glossed over in introductory machine learning tutorials.\n",
     "\n",
     "Of core importance is the following question: *if our estimator is underperforming, how should we move forward?*\n",
     "There are several possible answers:\n",
     "\n",
-    "- Use a more complicated/more flexible model\n",
-    "- Use a less complicated/less flexible model\n",
-    "- Gather more training samples\n",
-    "- Gather more data to add features to each sample\n",
+    "- Use a more complicated/more flexible model.\n",
+    "- Use a less complicated/less flexible model.\n",
+    "- Gather more training samples.\n",
+    "- Gather more data to add features to each sample.\n",
     "\n",
-    "The answer to this question is often counter-intuitive.\n",
+    "The answer to this question is often counterintuitive.\n",
     "In particular, sometimes using a more complicated model will give worse results, and adding more training samples may not improve your results!\n",
     "The ability to determine what steps will improve your model is what separates the successful machine learning practitioners from the unsuccessful."
    ]
@@ -480,23 +466,23 @@
     "editable": true
    },
    "source": [
-    "### The Bias-variance trade-off\n",
+    "### The Bias-Variance Trade-off\n",
     "\n",
-    "Fundamentally, the question of \"the best model\" is about finding a sweet spot in the tradeoff between *bias* and *variance*.\n",
-    "Consider the following figure, which presents two regression fits to the same dataset:\n",
+    "Fundamentally, finding \"the best model\" is about finding a sweet spot in the trade-off between *bias* and *variance*.\n",
+    "Consider the following figure, which presents two regression fits to the same dataset.\n",
     "\n",
-    "![](figures/05.03-bias-variance.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff)\n",
+    "![](images/05.03-bias-variance.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff)\n",
     "\n",
     "It is clear that neither of these models is a particularly good fit to the data, but they fail in different ways.\n",
     "\n",
     "The model on the left attempts to find a straight-line fit through the data.\n",
-    "Because the data are intrinsically more complicated than a straight line, the straight-line model will never be able to describe this dataset well.\n",
-    "Such a model is said to *underfit* the data: that is, it does not have enough model flexibility to suitably account for all the features in the data; another way of saying this is that the model has high *bias*.\n",
+    "Because in this case a straight line cannot accurately split the data, the straight-line model will never be able to describe this dataset well.\n",
+    "Such a model is said to *underfit* the data: that is, it does not have enough flexibility to suitably account for all the features in the data. Another way of saying this is that the model has high bias.\n",
     "\n",
     "The model on the right attempts to fit a high-order polynomial through the data.\n",
-    "Here the model fit has enough flexibility to nearly perfectly account for the fine features in the data, but even though it very accurately describes the training data, its precise form seems to be more reflective of the particular noise properties of the data rather than the intrinsic properties of whatever process generated that data.\n",
-    "Such a model is said to *overfit* the data: that is, it has so much model flexibility that the model ends up accounting for random errors as well as the underlying data distribution; another way of saying this is that the model has high *variance*."
+    "Here the model fit has enough flexibility to nearly perfectly account for the fine features in the data, but even though it very accurately describes the training data, its precise form seems to be more reflective of the particular noise properties of the data than of the intrinsic properties of whatever process generated that data.\n",
+    "Such a model is said to *overfit* the data: that is, it has so much flexibility that the model ends up accounting for random errors as well as the underlying data distribution. Another way of saying this is that the model has high variance."
    ]
   },
   {
@@ -506,11 +492,11 @@
     "editable": true
    },
    "source": [
-    "To look at this in another light, consider what happens if we use these two models to predict the y-value for some new data.\n",
-    "In the following diagrams, the red/lighter points indicate data that is omitted from the training set:\n",
+    "To look at this in another light, consider what happens if we use these two models to predict the *y*-values for some new data.\n",
+    "In the plots in the following figure, the red/lighter points indicate data that is omitted from the training set.\n",
     "\n",
-    "![](figures/05.03-bias-variance-2.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff-Metrics)\n",
+    "![](images/05.03-bias-variance-2.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff-Metrics)\n",
     "\n",
     "The score here is the $R^2$ score, or [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination), which measures how well a model performs relative to a simple mean of the target values. $R^2=1$ indicates a perfect match, $R^2=0$ indicates the model does no better than simply taking the mean of the data, and negative values mean even worse models.\n",
     "From the scores associated with these two models, we can make an observation that holds more generally:\n",
@@ -528,28 +514,27 @@
    "source": [
     "If we imagine that we have some ability to tune the model complexity, we would expect the training score and validation score to behave as illustrated in the following figure:\n",
     "\n",
-    "![](figures/05.03-validation-curve.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Validation-Curve)\n",
+    "![](images/05.03-validation-curve.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Validation-Curve)\n",
     "\n",
-    "The diagram shown here is often called a *validation curve*, and we see the following essential features:\n",
+    "The diagram shown here is often called a *validation curve*, and we see the following features:\n",
     "\n",
     "- The training score is everywhere higher than the validation score. This is generally the case: the model will be a better fit to data it has seen than to data it has not seen.\n",
-    "- For very low model complexity (a high-bias model), the training data is under-fit, which means that the model is a poor predictor both for the training data and for any previously unseen data.\n",
-    "- For very high model complexity (a high-variance model), the training data is over-fit, which means that the model predicts the training data very well, but fails for any previously unseen data.\n",
+    "- For very low model complexity (a high-bias model), the training data is underfit, which means that the model is a poor predictor both for the training data and for any previously unseen data.\n",
+    "- For very high model complexity (a high-variance model), the training data is overfit, which means that the model predicts the training data very well, but fails for any previously unseen data.\n",
     "- For some intermediate value, the validation curve has a maximum. This level of complexity indicates a suitable trade-off between bias and variance.\n",
     "\n",
-    "The means of tuning the model complexity varies from model to model; when we discuss individual models in depth in later sections, we will see how each model allows for such tuning."
+    "The means of tuning the model complexity varies from model to model; when we discuss individual models in depth in later chapters, we will see how each model allows for such tuning."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
     "deletable": true,
     "editable": true
    },
    "source": [
-    "### Validation curves in Scikit-Learn\n",
+    "### Validation Curves in Scikit-Learn\n",
     "\n",
     "Let's look at an example of using cross-validation to compute the validation curve for a class of models.\n",
     "Here we will use a *polynomial regression* model: this is a generalized linear model in which the degree of the polynomial is a tunable parameter.\n",
@@ -566,7 +551,7 @@
     "$$\n",
     "\n",
     "We can generalize this to any number of polynomial features.\n",
-    "In Scikit-Learn, we can implement this with a simple linear regression combined with the polynomial preprocessor.\n",
+    "In Scikit-Learn, we can implement this with a linear regression classifier combined with the polynomial preprocessor.\n",
     "We will use a *pipeline* to string these operations together (we will discuss polynomial features and pipelines more fully in [Feature Engineering](05.04-Feature-Engineering.ipynb)):"
    ]
   },
@@ -574,9 +559,9 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -592,7 +577,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
     "deletable": true,
     "editable": true
    },
@@ -604,9 +588,9 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -631,7 +615,7 @@
     "editable": true
    },
    "source": [
-    "We can now visualize our data, along with polynomial fits of several degrees:"
+    "We can now visualize our data, along with polynomial fits of several degrees (see the following figure):"
    ]
   },
   {
@@ -640,14 +624,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
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4vA6cPhdOrxOdGRpaW3F6/ducPieOM753ep3YvU48Z1wbvRQGrR6zrrM33nnK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\nsKmpQPsAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d3faa90>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -657,7 +644,7 @@
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn; seaborn.set()  # plot formatting\n",
+    "plt.style.use('seaborn-whitegrid')\n",
     "\n",
     "X_test = np.linspace(-0.1, 1.1, 500)[:, None]\n",
     "\n",
@@ -678,11 +665,11 @@
     "editable": true
    },
    "source": [
-    "The knob controlling model complexity in this case is the degree of the polynomial, which can be any non-negative integer.\n",
-    "A useful question to answer is this: what degree of polynomial provides a suitable trade-off between bias (under-fitting) and variance (over-fitting)?\n",
+    "The knob controlling model complexity in this case is the degree of the polynomial, which can be any nonnegative integer.\n",
+    "A useful question to answer is this: what degree of polynomial provides a suitable trade-off between bias (underfitting) and variance (overfitting)?\n",
     "\n",
     "We can make progress in this by visualizing the validation curve for this particular data and model; this can be done straightforwardly using the ``validation_curve`` convenience routine provided by Scikit-Learn.\n",
-    "Given a model, data, parameter name, and a range to explore, this function will automatically compute both the training score and validation score across the range:"
+    "Given a model, data, parameter name, and a range to explore, this function will automatically compute both the training score and the validation score across the range (see the following figure):"
    ]
   },
   {
@@ -691,14 +678,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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NdiGAkhKp6RHxLheMn6+EO7sdXH37h644IopKXDueQiFmg726Gqivb3rVOcO3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\ni4iIWMgPEUMpSKY0y3EAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118d05f98>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -706,13 +696,17 @@
     }
    ],
    "source": [
-    "from sklearn.learning_curve import validation_curve\n",
+    "from sklearn.model_selection import validation_curve\n",
     "degree = np.arange(0, 21)\n",
-    "train_score, val_score = validation_curve(PolynomialRegression(), X, y,\n",
-    "                                          'polynomialfeatures__degree', degree, cv=7)\n",
-    "\n",
-    "plt.plot(degree, np.median(train_score, 1), color='blue', label='training score')\n",
-    "plt.plot(degree, np.median(val_score, 1), color='red', label='validation score')\n",
+    "train_score, val_score = validation_curve(\n",
+    "    PolynomialRegression(), X, y,\n",
+    "    param_name='polynomialfeatures__degree',\n",
+    "    param_range=degree, cv=7)\n",
+    "\n",
+    "plt.plot(degree, np.median(train_score, 1),\n",
+    "         color='blue', label='training score')\n",
+    "plt.plot(degree, np.median(val_score, 1),\n",
+    "         color='red', label='validation score')\n",
     "plt.legend(loc='best')\n",
     "plt.ylim(0, 1)\n",
     "plt.xlabel('degree')\n",
@@ -726,9 +720,9 @@
     "editable": true
    },
    "source": [
-    "This shows precisely the qualitative behavior we expect: the training score is everywhere higher than the validation score; the training score is monotonically improving with increased model complexity; and the validation score reaches a maximum before dropping off as the model becomes over-fit.\n",
+    "This shows precisely the qualitative behavior we expect: the training score is everywhere higher than the validation score, the training score is monotonically improving with increased model complexity, and the validation score reaches a maximum before dropping off as the model becomes overfit.\n",
     "\n",
-    "From the validation curve, we can read-off that the optimal trade-off between bias and variance is found for a third-order polynomial; we can compute and display this fit over the original data as follows:"
+    "From the validation curve, we can determine that the optimal trade-off between bias and variance is found for a third-order polynomial. We can compute and display this fit over the original data as follows (see the following figure):"
    ]
   },
   {
@@ -737,14 +731,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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YOnUq/vSnP3n8OpMp2pu3CzlKjlNtQys27y1CbJQB35s/ClHhesVq8QS/pzzD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\nRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d3ecdd8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -779,7 +776,7 @@
     "## Learning Curves\n",
     "\n",
     "One important aspect of model complexity is that the optimal model will generally depend on the size of your training data.\n",
-    "For example, let's generate a new dataset with a factor of five more points:"
+    "For example, let's generate a new dataset with five times as many points (see the following figure):"
    ]
   },
   {
@@ -788,14 +785,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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S3XffbWmhvGrt2tnKyoquYR4//oSkTh0+XKBQaHtP93WyLeHYHcSYMAYA3mC6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\nS2ytoeovSZdcconmz5+v884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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11944a278>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -814,7 +814,7 @@
     "editable": true
    },
    "source": [
-    "We will duplicate the preceding code to plot the validation curve for this larger dataset; for reference let's over-plot the previous results as well:"
+    "Now let's duplicate the preceding code to plot the validation curve for this larger dataset; for reference, we'll overplot the previous results as well (see the following figure):"
    ]
   },
   {
@@ -823,14 +823,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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aUFofqx+afmZ7OCkUghdecFFYCOefn3xv5t8/Ae++9gxqjWZ6t++USXMTkOHA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\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119451f98>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -839,13 +842,19 @@
    ],
    "source": [
     "degree = np.arange(21)\n",
-    "train_score2, val_score2 = validation_curve(PolynomialRegression(), X2, y2,\n",
-    "                                            'polynomialfeatures__degree', degree, cv=7)\n",
-    "\n",
-    "plt.plot(degree, np.median(train_score2, 1), color='blue', label='training score')\n",
-    "plt.plot(degree, np.median(val_score2, 1), color='red', label='validation score')\n",
-    "plt.plot(degree, np.median(train_score, 1), color='blue', alpha=0.3, linestyle='dashed')\n",
-    "plt.plot(degree, np.median(val_score, 1), color='red', alpha=0.3, linestyle='dashed')\n",
+    "train_score2, val_score2 = validation_curve(\n",
+    "    PolynomialRegression(), X2, y2,\n",
+    "    param_name='polynomialfeatures__degree',\n",
+    "    param_range=degree, cv=7)\n",
+    "\n",
+    "plt.plot(degree, np.median(train_score2, 1),\n",
+    "         color='blue', label='training score')\n",
+    "plt.plot(degree, np.median(val_score2, 1),\n",
+    "         color='red', label='validation score')\n",
+    "plt.plot(degree, np.median(train_score, 1),\n",
+    "         color='blue', alpha=0.3, linestyle='dashed')\n",
+    "plt.plot(degree, np.median(val_score, 1),\n",
+    "         color='red', alpha=0.3, linestyle='dashed')\n",
     "plt.legend(loc='lower center')\n",
     "plt.ylim(0, 1)\n",
     "plt.xlabel('degree')\n",
@@ -859,12 +868,12 @@
     "editable": true
    },
    "source": [
-    "The solid lines show the new results, while the fainter dashed lines show the results of the previous smaller dataset.\n",
-    "It is clear from the validation curve that the larger dataset can support a much more complicated model: the peak here is probably around a degree of 6, but even a degree-20 model is not seriously over-fitting the data—the validation and training scores remain very close.\n",
+    "The solid lines show the new results, while the fainter dashed lines show the results on the previous smaller dataset.\n",
+    "It is clear from the validation curve that the larger dataset can support a much more complicated model: the peak here is probably around a degree of 6, but even a degree-20 model is not seriously overfitting the data—the validation and training scores remain very close.\n",
     "\n",
-    "Thus we see that the behavior of the validation curve has not one but two important inputs: the model complexity and the number of training points.\n",
-    "It is often useful to to explore the behavior of the model as a function of the number of training points, which we can do by using increasingly larger subsets of the data to fit our model.\n",
-    "A plot of the training/validation score with respect to the size of the training set is known as a *learning curve.*\n",
+    "So, the behavior of the validation curve has not one but two important inputs: the model complexity and the number of training points.\n",
+    "We can gain further insight by exploring the behavior of the model as a function of the number of training points, which we can do by using increasingly larger subsets of the data to fit our model.\n",
+    "A plot of the training/validation score with respect to the size of the training set is sometimes known as a *learning curve.*\n",
     "\n",
     "The general behavior we would expect from a learning curve is this:\n",
     "\n",
@@ -882,8 +891,8 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.03-learning-curve.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Learning-Curve)"
+    "![](images/05.03-learning-curve.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Learning-Curve)"
    ]
   },
   {
@@ -905,9 +914,9 @@
     "editable": true
    },
    "source": [
-    "### Learning curves in Scikit-Learn\n",
+    "### Learning Curves in Scikit-Learn\n",
     "\n",
-    "Scikit-Learn offers a convenient utility for computing such learning curves from your models; here we will compute a learning curve for our original dataset with a second-order polynomial model and a ninth-order polynomial:"
+    "Scikit-Learn offers a convenient utility for computing such learning curves from your models; here we will compute a learning curve for our original dataset with a second-order polynomial model and a ninth-order polynomial (see the following figure):"
    ]
   },
   {
@@ -916,14 +925,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+vOrO/a4869cr85nPyQqH7HEmTFMyLcmyZJimva727fn9ZmG/ZR+rbLZwTCik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\ncPKYnE9+qb/wa4DRo0cPffjhhxowYIAkKTMzUyt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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1194c3f28>"
+       "<Figure size 1152x432 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -931,20 +943,22 @@
     }
    ],
    "source": [
-    "from sklearn.learning_curve import learning_curve\n",
+    "from sklearn.model_selection import learning_curve\n",
     "\n",
     "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
     "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
     "\n",
     "for i, degree in enumerate([2, 9]):\n",
-    "    N, train_lc, val_lc = learning_curve(PolynomialRegression(degree),\n",
-    "                                         X, y, cv=7,\n",
-    "                                         train_sizes=np.linspace(0.3, 1, 25))\n",
+    "    N, train_lc, val_lc = learning_curve(\n",
+    "        PolynomialRegression(degree), X, y, cv=7,\n",
+    "        train_sizes=np.linspace(0.3, 1, 25))\n",
     "\n",
-    "    ax[i].plot(N, np.mean(train_lc, 1), color='blue', label='training score')\n",
-    "    ax[i].plot(N, np.mean(val_lc, 1), color='red', label='validation score')\n",
-    "    ax[i].hlines(np.mean([train_lc[-1], val_lc[-1]]), N[0], N[-1],\n",
-    "                 color='gray', linestyle='dashed')\n",
+    "    ax[i].plot(N, np.mean(train_lc, 1),\n",
+    "               color='blue', label='training score')\n",
+    "    ax[i].plot(N, np.mean(val_lc, 1),\n",
+    "               color='red', label='validation score')\n",
+    "    ax[i].hlines(np.mean([train_lc[-1], val_lc[-1]]), N[0],\n",
+    "                 N[-1], color='gray', linestyle='dashed')\n",
     "\n",
     "    ax[i].set_ylim(0, 1)\n",
     "    ax[i].set_xlim(N[0], N[-1])\n",
@@ -961,8 +975,8 @@
     "editable": true
    },
    "source": [
-    "This is a valuable diagnostic, because it gives us a visual depiction of how our model responds to increasing training data.\n",
-    "In particular, when your learning curve has already converged (i.e., when the training and validation curves are already close to each other) *adding more training data will not significantly improve the fit!*\n",
+    "This is a valuable diagnostic, because it gives us a visual depiction of how our model responds to increasing amounts of training data.\n",
+    "In particular, when the learning curve has already converged (i.e., when the training and validation curves are already close to each other) *adding more training data will not significantly improve the fit!*\n",
     "This situation is seen in the left panel, with the learning curve for the degree-2 model.\n",
     "\n",
     "The only way to increase the converged score is to use a different (usually more complicated) model.\n",
@@ -982,30 +996,28 @@
     "## Validation in Practice: Grid Search\n",
     "\n",
     "The preceding discussion is meant to give you some intuition into the trade-off between bias and variance, and its dependence on model complexity and training set size.\n",
-    "In practice, models generally have more than one knob to turn, and thus plots of validation and learning curves change from lines to multi-dimensional surfaces.\n",
-    "In these cases, such visualizations are difficult and we would rather simply find the particular model that maximizes the validation score.\n",
+    "In practice, models generally have more than one knob to turn, meaning plots of validation and learning curves change from lines to multidimensional surfaces.\n",
+    "In these cases, such visualizations are difficult, and we would rather simply find the particular model that maximizes the validation score.\n",
     "\n",
-    "Scikit-Learn provides automated tools to do this in the grid search module.\n",
-    "Here is an example of using grid search to find the optimal polynomial model.\n",
-    "We will explore a three-dimensional grid of model features; namely the polynomial degree, the flag telling us whether to fit the intercept, and the flag telling us whether to normalize the problem.\n",
-    "This can be set up using Scikit-Learn's ``GridSearchCV`` meta-estimator:"
+    "Scikit-Learn provides some tools to make this kind of search more convenient: here we'll consider the use of grid search to find the optimal polynomial model.\n",
+    "We will explore a two-dimensional grid of model features, namely the polynomial degree and the flag telling us whether to fit the intercept.\n",
+    "This can be set up using Scikit-Learn's `GridSearchCV` meta-estimator:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
-    "from sklearn.grid_search import GridSearchCV\n",
+    "from sklearn.model_selection import GridSearchCV\n",
     "\n",
     "param_grid = {'polynomialfeatures__degree': np.arange(21),\n",
-    "              'linearregression__fit_intercept': [True, False],\n",
-    "              'linearregression__normalize': [True, False]}\n",
+    "              'linearregression__fit_intercept': [True, False]}\n",
     "\n",
     "grid = GridSearchCV(PolynomialRegression(), param_grid, cv=7)"
    ]
@@ -1018,16 +1030,16 @@
    },
    "source": [
     "Notice that like a normal estimator, this has not yet been applied to any data.\n",
-    "Calling the ``fit()`` method will fit the model at each grid point, keeping track of the scores along the way:"
+    "Calling the ``fit`` method will fit the model at each grid point, keeping track of the scores along the way:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -1041,7 +1053,7 @@
     "editable": true
    },
    "source": [
-    "Now that this is fit, we can ask for the best parameters as follows:"
+    "Now that the model is fit, we can ask for the best parameters as follows:"
    ]
   },
   {
@@ -1050,15 +1062,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'linearregression__fit_intercept': False,\n",
-       " 'linearregression__normalize': True,\n",
-       " 'polynomialfeatures__degree': 4}"
+       "{'linearregression__fit_intercept': False, 'polynomialfeatures__degree': 4}"
       ]
      },
      "execution_count": 20,
@@ -1077,7 +1090,7 @@
     "editable": true
    },
    "source": [
-    "Finally, if we wish, we can use the best model and show the fit to our data using code from before:"
+    "Finally, if we wish, we can use the best model and show the fit to our data using code from before (see the following figure):"
    ]
   },
   {
@@ -1086,14 +1099,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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MvPDWQby6+Sh6HG4smpEesNoYzEQjEM7Tfk6XB63tPYPCt6U/fG3d6O51X/B12tgoCF0u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\niENcDI95JCIi3zGYg+B0fTu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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118bc2c88>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1106,7 +1122,7 @@
     "plt.scatter(X.ravel(), y)\n",
     "lim = plt.axis()\n",
     "y_test = model.fit(X, y).predict(X_test)\n",
-    "plt.plot(X_test.ravel(), y_test, hold=True);\n",
+    "plt.plot(X_test.ravel(), y_test);\n",
     "plt.axis(lim);"
    ]
   },
@@ -1117,8 +1133,8 @@
     "editable": true
    },
    "source": [
-    "The grid search provides many more options, including the ability to specify a custom scoring function, to parallelize the computations, to do randomized searches, and more.\n",
-    "For information, see the examples in [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) and [Feature Engineering: Working with Images](05.14-Image-Features.ipynb), or refer to Scikit-Learn's [grid search documentation](http://Scikit-Learn.org/stable/modules/grid_search.html)."
+    "Other options in `GridSearchCV` include the ability to specify a custom scoring function, to parallelize the computations, to do randomized searches, and more.\n",
+    "For more information, see the examples in [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) and [Feature Engineering: Working with Images](05.14-Image-Features.ipynb), or refer to Scikit-Learn's [grid search documentation](http://Scikit-Learn.org/stable/modules/grid_search.html)."
    ]
   },
   {
@@ -1130,31 +1146,21 @@
    "source": [
     "## Summary\n",
     "\n",
-    "In this section, we have begun to explore the concept of model validation and hyperparameter optimization, focusing on intuitive aspects of the bias–variance trade-off and how it comes into play when fitting models to data.\n",
-    "In particular, we found that the use of a validation set or cross-validation approach is *vital* when tuning parameters in order to avoid over-fitting for more complex/flexible models.\n",
+    "In this chapter we began to explore the concept of model validation and hyperparameter optimization, focusing on intuitive aspects of the bias–variance trade-off and how it comes into play when fitting models to data.\n",
+    "In particular, we found that the use of a validation set or cross-validation approach is vital when tuning parameters in order to avoid overfitting for more complex/flexible models.\n",
     "\n",
-    "In later sections, we will discuss the details of particularly useful models, and throughout will talk about what tuning is available for these models and how these free parameters affect model complexity.\n",
-    "Keep the lessons of this section in mind as you read on and learn about these machine learning approaches!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) | [Contents](Index.ipynb) | [Feature Engineering](05.04-Feature-Engineering.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "In later chapters, we will discuss the details of particularly useful models, what tuning is available for these models, and how these free parameters affect model complexity.\n",
+    "Keep the lessons of this chapter in mind as you read on and learn about these machine learning approaches!"
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.6 64-bit ('3.9.6')",
    "language": "python",
    "name": "python3"
   },
@@ -1168,9 +1174,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc"
+   }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.04-Feature-Engineering.ipynb b/notebooks/05.04-Feature-Engineering.ipynb
index 7315fb277..507d74241 100644
--- a/notebooks/05.04-Feature-Engineering.ipynb
+++ b/notebooks/05.04-Feature-Engineering.ipynb
@@ -1,33 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) | [Contents](Index.ipynb) | [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.04-Feature-Engineering.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
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@@ -42,13 +14,13 @@
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-    "The previous sections outline the fundamental ideas of machine learning, but all of the examples assume that you have numerical data in a tidy, ``[n_samples, n_features]`` format.\n",
+    "The previous chapters outlined the fundamental ideas of machine learning, but all of the examples assumed that you have numerical data in a tidy, `[n_samples, n_features]` format.\n",
     "In the real world, data rarely comes in such a form.\n",
     "With this in mind, one of the more important steps in using machine learning in practice is *feature engineering*: that is, taking whatever information you have about your problem and turning it into numbers that you can use to build your feature matrix.\n",
     "\n",
-    "In this section, we will cover a few common examples of feature engineering tasks: features for representing *categorical data*, features for representing *text*, and features for representing *images*.\n",
-    "Additionally, we will discuss *derived features* for increasing model complexity and *imputation* of missing data.\n",
-    "Often this process is known as *vectorization*, as it involves converting arbitrary data into well-behaved vectors."
+    "In this chapter, we will cover a few common examples of feature engineering tasks: we'll look at features for representing categorical data, text, and images.\n",
+    "Additionally, we will discuss derived features for increasing model complexity and imputation of missing data.\n",
+    "This process is commonly referred to as vectorization, as it involves converting arbitrary data into well-behaved vectors."
    ]
   },
   {
@@ -60,8 +32,8 @@
    "source": [
     "## Categorical Features\n",
     "\n",
-    "One common type of non-numerical data is *categorical* data.\n",
-    "For example, imagine you are exploring some data on housing prices, and along with numerical features like \"price\" and \"rooms\", you also have \"neighborhood\" information.\n",
+    "One common type of nonnumerical data is *categorical* data.\n",
+    "For example, imagine you are exploring some data on housing prices, and along with numerical features like \"price\" and \"rooms,\" you also have \"neighborhood\" information.\n",
     "For example, your data might look something like this:"
    ]
   },
@@ -71,7 +43,10 @@
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@@ -99,7 +74,10 @@
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@@ -113,11 +91,10 @@
     "editable": true
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-    "It turns out that this is not generally a useful approach in Scikit-Learn: the package's models make the fundamental assumption that numerical features reflect algebraic quantities.\n",
-    "Thus such a mapping would imply, for example, that *Queen Anne < Fremont < Wallingford*, or even that *Wallingford - Queen Anne = Fremont*, which (niche demographic jokes aside) does not make much sense.\n",
+    "But it turns out that this is not generally a useful approach in Scikit-Learn. The package's models make the fundamental assumption that numerical features reflect algebraic quantities, so such a mapping would imply, for example, that *Queen Anne < Fremont < Wallingford*, or even that *Wallingford–Queen Anne = Fremont*, which (niche demographic jokes aside) does not make much sense.\n",
     "\n",
     "In this case, one proven technique is to use *one-hot encoding*, which effectively creates extra columns indicating the presence or absence of a category with a value of 1 or 0, respectively.\n",
-    "When your data comes as a list of dictionaries, Scikit-Learn's ``DictVectorizer`` will do this for you:"
+    "When your data takes the form of a list of dictionaries, Scikit-Learn's ``DictVectorizer`` will do this for you:"
    ]
   },
   {
@@ -126,7 +103,10 @@
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     {
@@ -135,7 +115,7 @@
        "array([[     0,      1,      0, 850000,      4],\n",
        "       [     1,      0,      0, 700000,      3],\n",
        "       [     0,      0,      1, 650000,      3],\n",
-       "       [     1,      0,      0, 600000,      2]], dtype=int64)"
+       "       [     1,      0,      0, 600000,      2]])"
       ]
      },
      "execution_count": 3,
@@ -156,7 +136,7 @@
     "editable": true
    },
    "source": [
-    "Notice that the 'neighborhood' column has been expanded into three separate columns, representing the three neighborhood labels, and that each row has a 1 in the column associated with its neighborhood.\n",
+    "Notice that the `neighborhood` column has been expanded into three separate columns representing the three neighborhood labels, and that each row has a 1 in the column associated with its neighborhood.\n",
     "With these categorical features thus encoded, you can proceed as normal with fitting a Scikit-Learn model.\n",
     "\n",
     "To see the meaning of each column, you can inspect the feature names:"
@@ -168,17 +148,17 @@
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     {
      "data": {
       "text/plain": [
-       "['neighborhood=Fremont',\n",
-       " 'neighborhood=Queen Anne',\n",
-       " 'neighborhood=Wallingford',\n",
-       " 'price',\n",
-       " 'rooms']"
+       "array(['neighborhood=Fremont', 'neighborhood=Queen Anne',\n",
+       "       'neighborhood=Wallingford', 'price', 'rooms'], dtype=object)"
       ]
      },
      "execution_count": 4,
@@ -187,7 +167,7 @@
     }
    ],
    "source": [
-    "vec.get_feature_names()"
+    "vec.get_feature_names_out()"
    ]
   },
   {
@@ -207,7 +187,10 @@
    "metadata": {
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     "deletable": true,
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@@ -234,7 +217,7 @@
     "editable": true
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-    "Many (though not yet all) of the Scikit-Learn estimators accept such sparse inputs when fitting and evaluating models. ``sklearn.preprocessing.OneHotEncoder`` and ``sklearn.feature_extraction.FeatureHasher`` are two additional tools that Scikit-Learn includes to support this type of encoding."
+    "Nearly all of the Scikit-Learn estimators accept such sparse inputs when fitting and evaluating models. `sklearn.preprocessing.OneHotEncoder` and `sklearn.feature_extraction.FeatureHasher` are two additional tools that Scikit-Learn includes to support this type of encoding."
    ]
   },
   {
@@ -248,7 +231,7 @@
     "\n",
     "Another common need in feature engineering is to convert text to a set of representative numerical values.\n",
     "For example, most automatic mining of social media data relies on some form of encoding the text as numbers.\n",
-    "One of the simplest methods of encoding data is by *word counts*: you take each snippet of text, count the occurrences of each word within it, and put the results in a table.\n",
+    "One of the simplest methods of encoding this type of data is by *word counts*: you take each snippet of text, count the occurrences of each word within it, and put the results in a table.\n",
     "\n",
     "For example, consider the following set of three phrases:"
    ]
@@ -257,9 +240,9 @@
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    "execution_count": 6,
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     "deletable": true,
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+    "tags": []
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@@ -275,8 +258,8 @@
     "editable": true
    },
    "source": [
-    "For a vectorization of this data based on word count, we could construct a column representing the word \"problem,\" the word \"evil,\" the word \"horizon,\" and so on.\n",
-    "While doing this by hand would be possible, the tedium can be avoided by using Scikit-Learn's ``CountVectorizer``:"
+    "For a vectorization of this data based on word count, we could construct individual columns representing the words \"problem,\" \"of,\" \"evil,\" and so on.\n",
+    "While doing this by hand would be possible for this simple example, the tedium can be avoided by using Scikit-Learn's `CountVectorizer`:"
    ]
   },
   {
@@ -285,7 +268,10 @@
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    "outputs": [
     {
@@ -315,7 +301,7 @@
     "editable": true
    },
    "source": [
-    "The result is a sparse matrix recording the number of times each word appears; it is easier to inspect if we convert this to a ``DataFrame`` with labeled columns:"
+    "The result is a sparse matrix recording the number of times each word appears; it is easier to inspect if we convert this to a `DataFrame` with labeled columns:"
    ]
   },
   {
@@ -324,13 +310,29 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -385,7 +387,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "pd.DataFrame(X.toarray(), columns=vec.get_feature_names())"
+    "pd.DataFrame(X.toarray(), columns=vec.get_feature_names_out())"
    ]
   },
   {
@@ -395,8 +397,8 @@
     "editable": true
    },
    "source": [
-    "There are some issues with this approach, however: the raw word counts lead to features which put too much weight on words that appear very frequently, and this can be sub-optimal in some classification algorithms.\n",
-    "One approach to fix this is known as *term frequency-inverse document frequency* (*TF–IDF*) which weights the word counts by a measure of how often they appear in the documents.\n",
+    "There are some issues with using a simple raw word count, however: it can lead to features that put too much weight on words that appear very frequently, and this can be suboptimal in some classification algorithms.\n",
+    "One approach to fix this is known as *term frequency–inverse document frequency* (*TF–IDF*), which weights the word counts by a measure of how often they appear in the documents.\n",
     "The syntax for computing these features is similar to the previous example:"
    ]
   },
@@ -406,13 +408,29 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
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        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -469,7 +487,7 @@
     "from sklearn.feature_extraction.text import TfidfVectorizer\n",
     "vec = TfidfVectorizer()\n",
     "X = vec.fit_transform(sample)\n",
-    "pd.DataFrame(X.toarray(), columns=vec.get_feature_names())"
+    "pd.DataFrame(X.toarray(), columns=vec.get_feature_names_out())"
    ]
   },
   {
@@ -491,11 +509,11 @@
    "source": [
     "## Image Features\n",
     "\n",
-    "Another common need is to suitably encode *images* for machine learning analysis.\n",
+    "Another common need is to suitably encode images for machine learning analysis.\n",
     "The simplest approach is what we used for the digits data in [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb): simply using the pixel values themselves.\n",
-    "But depending on the application, such approaches may not be optimal.\n",
+    "But depending on the application, such an approach may not be optimal.\n",
     "\n",
-    "A comprehensive summary of feature extraction techniques for images is well beyond the scope of this section, but you can find excellent implementations of many of the standard approaches in the [Scikit-Image project](http://scikit-image.org).\n",
+    "A comprehensive summary of feature extraction techniques for images is well beyond the scope of this chapter, but you can find excellent implementations of many of the standard approaches in the [Scikit-Image project](http://scikit-image.org).\n",
     "For one example of using Scikit-Learn and Scikit-Image together, see [Feature Engineering: Working with Images](05.14-Image-Features.ipynb)."
    ]
   },
@@ -511,9 +529,8 @@
     "Another useful type of feature is one that is mathematically derived from some input features.\n",
     "We saw an example of this in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) when we constructed *polynomial features* from our input data.\n",
     "We saw that we could convert a linear regression into a polynomial regression not by changing the model, but by transforming the input!\n",
-    "This is sometimes known as *basis function regression*, and is explored further in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb).\n",
     "\n",
-    "For example, this data clearly cannot be well described by a straight line:"
+    "For example, this data clearly cannot be well described by a straight line (see Figure 40-1):"
    ]
   },
   {
@@ -522,17 +539,22 @@
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-    "editable": true
+    "editable": true,
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    },
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     {
      "data": {
-      "image/png": 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+      "image/png": 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YflNSW9LtVf9mLVwt6eOSVorzm5L0WUm/csZsdaxZmbnqWLNLJN1re0ibfzB8IyIesf03kpYi4mFt/gHzz7Zf0OYbyLdXPFPZuf7c9i2S3izmumMAc3WUYL3KzFXHel0s6aHi9cc+SfdHxOO2/1iq5vnIR8gBILmspz4AAAVCDQDJEWoASI5QA0ByhBoAkiPUAJAcoQaA5P4fw0dF396M+vcAAAAASUVORK5CYII=",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114836550>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -553,7 +575,7 @@
     "editable": true
    },
    "source": [
-    "Still, we can fit a line to the data using ``LinearRegression`` and get the optimal result:"
+    "We can still fit a line to the data using `LinearRegression` and get the optimal result, as shown in Figure 40-2:"
    ]
   },
   {
@@ -562,17 +584,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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eqqzT6bBhwzQLCwf3FjNMTDTodDoGtWrTbrdpt9sDP67qjnqa3o761UdY445axXBHrdWg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\nH1pY4+tXGp0xAAAAAElFTkSuQmCC\n",
+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1143bf2b0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -592,7 +619,7 @@
     "editable": true
    },
    "source": [
-    "It's clear that we need a more sophisticated model to describe the relationship between $x$ and $y$.\n",
+    "But it's clear that we need a more sophisticated model to describe the relationship between $x$ and $y$.\n",
     "\n",
     "One approach to this is to transform the data, adding extra columns of features to drive more flexibility in the model.\n",
     "For example, we can add polynomial features to the data this way:"
@@ -604,18 +631,21 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[   1.    1.    1.]\n",
-      " [   2.    4.    8.]\n",
-      " [   3.    9.   27.]\n",
-      " [   4.   16.   64.]\n",
-      " [   5.   25.  125.]]\n"
+      "[[  1.   1.   1.]\n",
+      " [  2.   4.   8.]\n",
+      " [  3.   9.  27.]\n",
+      " [  4.  16.  64.]\n",
+      " [  5.  25. 125.]]\n"
      ]
     }
    ],
@@ -633,8 +663,8 @@
     "editable": true
    },
    "source": [
-    "The derived feature matrix has one column representing $x$, and a second column representing $x^2$, and a third column representing $x^3$.\n",
-    "Computing a linear regression on this expanded input gives a much closer fit to our data:"
+    "The derived feature matrix has one column representing $x$, a second column representing $x^2$, and a third column representing $x^3$.\n",
+    "Computing a linear regression on this expanded input gives a much closer fit to our data, as you can see in Figure 40-3:"
    ]
   },
   {
@@ -643,17 +673,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114819c50>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -672,7 +707,7 @@
    },
    "source": [
     "This idea of improving a model not by changing the model, but by transforming the inputs, is fundamental to many of the more powerful machine learning methods.\n",
-    "We explore this idea further in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) in the context of *basis function regression*.\n",
+    "We'll explore this idea further in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) in the context of *basis function regression*.\n",
     "More generally, this is one motivational path to the powerful set of techniques known as *kernel methods*, which we will explore in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)."
    ]
   },
@@ -686,7 +721,7 @@
     "## Imputation of Missing Data\n",
     "\n",
     "Another common need in feature engineering is handling of missing data.\n",
-    "We discussed the handling of missing data in ``DataFrame``s in [Handling Missing Data](03.04-Missing-Values.ipynb), and saw that often the ``NaN`` value is used to mark missing values.\n",
+    "We discussed the handling of missing data in `DataFrame` objects in [Handling Missing Data](03.04-Missing-Values.ipynb), and saw that `NaN` is often is used to mark missing values.\n",
     "For example, we might have a dataset that looks like this:"
    ]
   },
@@ -696,7 +731,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -716,11 +754,11 @@
     "editable": true
    },
    "source": [
-    "When applying a typical machine learning model to such data, we will need to first replace such missing data with some appropriate fill value.\n",
+    "When applying a typical machine learning model to such data, we will need to first replace the missing values with some appropriate fill value.\n",
     "This is known as *imputation* of missing values, and strategies range from simple (e.g., replacing missing values with the mean of the column) to sophisticated (e.g., using matrix completion or a robust model to handle such data).\n",
     "\n",
     "The sophisticated approaches tend to be very application-specific, and we won't dive into them here.\n",
-    "For a baseline imputation approach, using the mean, median, or most frequent value, Scikit-Learn provides the ``Imputer`` class:"
+    "For a baseline imputation approach using the mean, median, or most frequent value, Scikit-Learn provides the `SimpleImputer` class:"
    ]
   },
   {
@@ -729,17 +767,20 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 4.5,  0. ,  3. ],\n",
-       "       [ 3. ,  7. ,  9. ],\n",
-       "       [ 3. ,  5. ,  2. ],\n",
-       "       [ 4. ,  5. ,  6. ],\n",
-       "       [ 8. ,  8. ,  1. ]])"
+       "array([[4.5, 0. , 3. ],\n",
+       "       [3. , 7. , 9. ],\n",
+       "       [3. , 5. , 2. ],\n",
+       "       [4. , 5. , 6. ],\n",
+       "       [8. , 8. , 1. ]])"
       ]
      },
      "execution_count": 15,
@@ -748,8 +789,8 @@
     }
    ],
    "source": [
-    "from sklearn.preprocessing import Imputer\n",
-    "imp = Imputer(strategy='mean')\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "imp = SimpleImputer(strategy='mean')\n",
     "X2 = imp.fit_transform(X)\n",
     "X2"
    ]
@@ -761,7 +802,7 @@
     "editable": true
    },
    "source": [
-    "We see that in the resulting data, the two missing values have been replaced with the mean of the remaining values in the column. This imputed data can then be fed directly into, for example, a ``LinearRegression`` estimator:"
+    "We see that in the resulting data, the two missing values have been replaced with the mean of the remaining values in the column. This imputed data can then be fed directly into, for example, a `LinearRegression` estimator:"
    ]
   },
   {
@@ -770,13 +811,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 13.14869292,  14.3784627 ,  -1.15539732,  10.96606197,  -5.33782027])"
+       "array([13.14869292, 14.3784627 , -1.15539732, 10.96606197, -5.33782027])"
       ]
      },
      "execution_count": 16,
@@ -801,9 +845,9 @@
     "With any of the preceding examples, it can quickly become tedious to do the transformations by hand, especially if you wish to string together multiple steps.\n",
     "For example, we might want a processing pipeline that looks something like this:\n",
     "\n",
-    "1. Impute missing values using the mean\n",
-    "2. Transform features to quadratic\n",
-    "3. Fit a linear regression\n",
+    "1. Impute missing values using the mean.\n",
+    "2. Transform features to quadratic.\n",
+    "3. Fit a linear regression model.\n",
     "\n",
     "To streamline this type of processing pipeline, Scikit-Learn provides a ``Pipeline`` object, which can be used as follows:"
    ]
@@ -814,13 +858,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "from sklearn.pipeline import make_pipeline\n",
     "\n",
-    "model = make_pipeline(Imputer(strategy='mean'),\n",
+    "model = make_pipeline(SimpleImputer(strategy='mean'),\n",
     "                      PolynomialFeatures(degree=2),\n",
     "                      LinearRegression())"
    ]
@@ -832,7 +879,7 @@
     "editable": true
    },
    "source": [
-    "This pipeline looks and acts like a standard Scikit-Learn object, and will apply all the specified steps to any input data."
+    "This pipeline looks and acts like a standard Scikit-Learn object, and will apply all the specified steps to any input data:"
    ]
   },
   {
@@ -841,7 +888,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -849,7 +899,7 @@
      "output_type": "stream",
      "text": [
       "[14 16 -1  8 -5]\n",
-      "[ 14.  16.  -1.   8.  -5.]\n"
+      "[14. 16. -1.  8. -5.]\n"
      ]
     }
    ],
@@ -860,6 +910,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
@@ -867,29 +918,19 @@
    },
    "source": [
     "All the steps of the model are applied automatically.\n",
-    "Notice that for the simplicity of this demonstration, we've applied the model to the data it was trained on; this is why it was able to perfectly predict the result (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for further discussion of this).\n",
-    "\n",
-    "For some examples of Scikit-Learn pipelines in action, see the following section on naive Bayes classification, as well as [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), and [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) | [Contents](Index.ipynb) | [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) >\n",
+    "Notice that for simplicity, in this demonstration we've applied the model to the data it was trained on; this is why it was able to perfectly predict the result (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for further discussion of this).\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.04-Feature-Engineering.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "For some examples of Scikit-Learn pipelines in action, see the following chapter on naive Bayes classification, as well as [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) and [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -903,9 +944,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.05-Naive-Bayes.ipynb b/notebooks/05.05-Naive-Bayes.ipynb
index f5d492a42..c7eb85078 100644
--- a/notebooks/05.05-Naive-Bayes.ipynb
+++ b/notebooks/05.05-Naive-Bayes.ipynb
@@ -1,33 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Feature Engineering](05.04-Feature-Engineering.ipynb) | [Contents](Index.ipynb) | [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -42,12 +14,15 @@
     "editable": true
    },
    "source": [
-    "The previous four sections have given a general overview of the concepts of machine learning.\n",
-    "In this section and the ones that follow, we will be taking a closer look at several specific algorithms for supervised and unsupervised learning, starting here with naive Bayes classification.\n",
+    "The previous four chapters have given a general overview of the concepts of machine learning.\n",
+    "In this chapter and the ones that follow, we will be taking a\n",
+    "closer look first at four algorithms for  supervised learning,\n",
+    "and then at four algorithms for unsupervised learning.\n",
+    "We start here with our first supervised method, naive Bayes classification.\n",
     "\n",
     "Naive Bayes models are a group of extremely fast and simple classification algorithms that are often suitable for very high-dimensional datasets.\n",
-    "Because they are so fast and have so few tunable parameters, they end up being very useful as a quick-and-dirty baseline for a classification problem.\n",
-    "This section will focus on an intuitive explanation of how naive Bayes classifiers work, followed by a couple examples of them in action on some datasets."
+    "Because they are so fast and have so few tunable parameters, they end up being useful as a quick-and-dirty baseline for a classification problem.\n",
+    "This chapter will provide an intuitive explanation of how naive Bayes classifiers work, followed by a few examples of them in action on some datasets."
    ]
   },
   {
@@ -61,7 +36,7 @@
     "\n",
     "Naive Bayes classifiers are built on Bayesian classification methods.\n",
     "These rely on Bayes's theorem, which is an equation describing the relationship of conditional probabilities of statistical quantities.\n",
-    "In Bayesian classification, we're interested in finding the probability of a label given some observed features, which we can write as $P(L~|~{\\rm features})$.\n",
+    "In Bayesian classification, we're interested in finding the probability of a label $L$ given some observed features, which we can write as $P(L~|~{\\rm features})$.\n",
     "Bayes's theorem tells us how to express this in terms of quantities we can compute more directly:\n",
     "\n",
     "$$\n",
@@ -91,14 +66,18 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns; sns.set()"
+    "import seaborn as sns\n",
+    "plt.style.use('seaborn-whitegrid')"
    ]
   },
   {
@@ -111,8 +90,8 @@
     "## Gaussian Naive Bayes\n",
     "\n",
     "Perhaps the easiest naive Bayes classifier to understand is Gaussian naive Bayes.\n",
-    "In this classifier, the assumption is that *data from each label is drawn from a simple Gaussian distribution*.\n",
-    "Imagine that you have the following data:"
+    "With this classifier, the assumption is that *data from each label is drawn from a simple Gaussian distribution*.\n",
+    "Imagine that we have the following data, shown in Figure 41-1:"
    ]
   },
   {
@@ -121,14 +100,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
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/EkTvg4do3DDjw71ffDSOMvMXs/PwGeLikyhTNJCX+nShdo3M660CvDpkAJHR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\nYbOnE37lKobvT2CvaNCjcOvpWgya9e4jncsuK1f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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x116167550>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -148,8 +130,8 @@
     "editable": true
    },
    "source": [
-    "One extremely fast way to create a simple model is to assume that the data is described by a Gaussian distribution with no covariance between dimensions.\n",
-    "This model can be fit by simply finding the mean and standard deviation of the points within each label, which is all you need to define such a distribution.\n",
+    "The simplest Gaussian model is to assume that the data is described by a Gaussian distribution with no covariance between dimensions.\n",
+    "This model can be fit by computing the mean and standard deviation of the points within each label, which is all we need to define such a distribution.\n",
     "The result of this naive Gaussian assumption is shown in the following figure:"
    ]
   },
@@ -160,14 +142,13 @@
     "editable": true
    },
    "source": [
-    "![(run code in Appendix to generate image)](figures/05.05-gaussian-NB.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Gaussian-Naive-Bayes)"
+    "![(run code in Appendix to generate image)](images/05.05-gaussian-NB.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Gaussian-Naive-Bayes)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
     "deletable": true,
     "editable": true
    },
@@ -175,7 +156,7 @@
     "The ellipses here represent the Gaussian generative model for each label, with larger probability toward the center of the ellipses.\n",
     "With this generative model in place for each class, we have a simple recipe to compute the likelihood $P({\\rm features}~|~L_1)$ for any data point, and thus we can quickly compute the posterior ratio and determine which label is the most probable for a given point.\n",
     "\n",
-    "This procedure is implemented in Scikit-Learn's ``sklearn.naive_bayes.GaussianNB`` estimator:"
+    "This procedure is implemented in Scikit-Learn's `sklearn.naive_bayes.GaussianNB` estimator:"
    ]
   },
   {
@@ -184,7 +165,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -200,7 +184,7 @@
     "editable": true
    },
    "source": [
-    "Now let's generate some new data and predict the label:"
+    "Let's generate some new data and predict the label:"
    ]
   },
   {
@@ -209,7 +193,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -225,7 +212,7 @@
     "editable": true
    },
    "source": [
-    "Now we can plot this new data to get an idea of where the decision boundary is:"
+    "Now we can plot this new data to get an idea of where the decision boundary is (see the following figure):"
    ]
   },
   {
@@ -234,14 +221,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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BStMIUMYwjB4md3HO7+lDPBafhHGWUvZphywrkJUNuGUO6lYRFEV5sL5MCAGB2cDLtDDE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hAAAg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\nqVV54uXPoVGvE3b6ub0dGb1kjdb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tFLpwYHT9F+kHKTLKGAwC0iihvrxIIAY0jRJhnJ/r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\nCiussMIKK1xnWAXnFVZYYYUVVrjO8P8DYfQSJAwZ8HAAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118a16d30>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -262,9 +252,9 @@
     "editable": true
    },
    "source": [
-    "We see a slightly curved boundary in the classifications—in general, the boundary in Gaussian naive Bayes is quadratic.\n",
+    "We see a slightly curved boundary in the classifications—in general, the boundary produced by a Gaussian naive Bayes model will be quadratic.\n",
     "\n",
-    "A nice piece of this Bayesian formalism is that it naturally allows for probabilistic classification, which we can compute using the ``predict_proba`` method:"
+    "A nice aspect of this Bayesian formalism is that it naturally allows for probabilistic classification, which we can compute using the `predict_proba` method:"
    ]
   },
   {
@@ -273,20 +263,23 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 0.89,  0.11],\n",
-       "       [ 1.  ,  0.  ],\n",
-       "       [ 1.  ,  0.  ],\n",
-       "       [ 1.  ,  0.  ],\n",
-       "       [ 1.  ,  0.  ],\n",
-       "       [ 1.  ,  0.  ],\n",
-       "       [ 0.  ,  1.  ],\n",
-       "       [ 0.15,  0.85]])"
+       "array([[0.89, 0.11],\n",
+       "       [1.  , 0.  ],\n",
+       "       [1.  , 0.  ],\n",
+       "       [1.  , 0.  ],\n",
+       "       [1.  , 0.  ],\n",
+       "       [1.  , 0.  ],\n",
+       "       [0.  , 1.  ],\n",
+       "       [0.15, 0.85]])"
       ]
      },
      "execution_count": 6,
@@ -306,11 +299,11 @@
     "editable": true
    },
    "source": [
-    "The columns give the posterior probabilities of the first and second label, respectively.\n",
-    "If you are looking for estimates of uncertainty in your classification, Bayesian approaches like this can be a useful approach.\n",
+    "The columns give the posterior probabilities of the first and second labels, respectively.\n",
+    "If you are looking for estimates of uncertainty in your classification, Bayesian approaches like this can be a good place to start.\n",
     "\n",
     "Of course, the final classification will only be as good as the model assumptions that lead to it, which is why Gaussian naive Bayes often does not produce very good results.\n",
-    "Still, in many cases—especially as the number of features becomes large—this assumption is not detrimental enough to prevent Gaussian naive Bayes from being a useful method."
+    "Still, in many cases—especially as the number of features becomes large—this assumption is not detrimental enough to prevent Gaussian naive Bayes from being a reliable method."
    ]
   },
   {
@@ -326,7 +319,7 @@
     "Another useful example is multinomial naive Bayes, where the features are assumed to be generated from a simple multinomial distribution.\n",
     "The multinomial distribution describes the probability of observing counts among a number of categories, and thus multinomial naive Bayes is most appropriate for features that represent counts or count rates.\n",
     "\n",
-    "The idea is precisely the same as before, except that instead of modeling the data distribution with the best-fit Gaussian, we model the data distribuiton with a best-fit multinomial distribution."
+    "The idea is precisely the same as before, except that instead of modeling the data distribution with the best-fit Gaussian, we model it with a best-fit multinomial distribution."
    ]
   },
   {
@@ -339,7 +332,7 @@
     "### Example: Classifying Text\n",
     "\n",
     "One place where multinomial naive Bayes is often used is in text classification, where the features are related to word counts or frequencies within the documents to be classified.\n",
-    "We discussed the extraction of such features from text in [Feature Engineering](05.04-Feature-Engineering.ipynb); here we will use the sparse word count features from the 20 Newsgroups corpus to show how we might classify these short documents into categories.\n",
+    "We discussed the extraction of such features from text in [Feature Engineering](05.04-Feature-Engineering.ipynb); here we will use the sparse word count features from the 20 Newsgroups corpus made available through Scikit-Learn to show how we might classify these short documents into categories.\n",
     "\n",
     "Let's download the data and take a look at the target names:"
    ]
@@ -350,7 +343,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -397,16 +393,16 @@
     "editable": true
    },
    "source": [
-    "For simplicity here, we will select just a few of these categories, and download the training and testing set:"
+    "For simplicity here, we will select just a few of these categories and download the training and testing sets:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -432,14 +428,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "From: dmcgee@uluhe.soest.hawaii.edu (Don McGee)\n",
       "Subject: Federal Hearing\n",
       "Originator: dmcgee@uluhe\n",
       "Organization: School of Ocean and Earth Science and Technology\n",
@@ -461,7 +459,7 @@
     }
    ],
    "source": [
-    "print(train.data[5])"
+    "print(train.data[5][48:])"
    ]
   },
   {
@@ -472,16 +470,16 @@
    },
    "source": [
     "In order to use this data for machine learning, we need to be able to convert the content of each string into a vector of numbers.\n",
-    "For this we will use the TF-IDF vectorizer (discussed in [Feature Engineering](05.04-Feature-Engineering.ipynb)), and create a pipeline that attaches it to a multinomial naive Bayes classifier:"
+    "For this we will use the TF-IDF vectorizer (introduced in [Feature Engineering](05.04-Feature-Engineering.ipynb)), and create a pipeline that attaches it to a multinomial naive Bayes classifier:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -499,7 +497,7 @@
     "editable": true
    },
    "source": [
-    "With this pipeline, we can apply the model to the training data, and predict labels for the test data:"
+    "With this pipeline, we can apply the model to the training data and predict labels for the test data:"
    ]
   },
   {
@@ -508,7 +506,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -524,7 +525,7 @@
    },
    "source": [
     "Now that we have predicted the labels for the test data, we can evaluate them to learn about the performance of the estimator.\n",
-    "For example, here is the confusion matrix between the true and predicted labels for the test data:"
+    "For example, let's take a look at the confusion matrix between the true and predicted labels for the test data (see the following figure):"
    ]
   },
   {
@@ -533,14 +534,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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vYtbUBez8cTcAyedSSE29TMnSN173V/DD4V+oE1gN3yJF7ctsNhtZOdlk5WZj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\nkBERkar9AwXLSNBV/8O+AAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118a3d1d0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -551,7 +555,8 @@
     "from sklearn.metrics import confusion_matrix\n",
     "mat = confusion_matrix(test.target, labels)\n",
     "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
-    "            xticklabels=train.target_names, yticklabels=train.target_names)\n",
+    "            xticklabels=train.target_names, yticklabels=train.target_names,\n",
+    "            cmap='Blues')\n",
     "plt.xlabel('true label')\n",
     "plt.ylabel('predicted label');"
    ]
@@ -563,11 +568,11 @@
     "editable": true
    },
    "source": [
-    "Evidently, even this very simple classifier can successfully separate space talk from computer talk, but it gets confused between talk about religion and talk about Christianity.\n",
-    "This is perhaps an expected area of confusion!\n",
+    "Evidently, even this very simple classifier can successfully separate space discussions from computer discussions, but it gets confused between discussions about religion and discussions about Christianity.\n",
+    "This is perhaps to be expected!\n",
     "\n",
-    "The very cool thing here is that we now have the tools to determine the category for *any* string, using the ``predict()`` method of this pipeline.\n",
-    "Here's a quick utility function that will return the prediction for a single string:"
+    "The cool thing here is that we now have the tools to determine the category for *any* string, using the `predict` method of this pipeline.\n",
+    "Here's a utility function that will return the prediction for a single string:"
    ]
   },
   {
@@ -576,7 +581,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -601,7 +609,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -625,7 +636,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -640,7 +654,7 @@
     }
    ],
    "source": [
-    "predict_category('discussing islam vs atheism')"
+    "predict_category('discussing the existence of God')"
    ]
   },
   {
@@ -649,7 +663,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -687,47 +704,37 @@
    "source": [
     "## When to Use Naive Bayes\n",
     "\n",
-    "Because naive Bayesian classifiers make such stringent assumptions about data, they will generally not perform as well as a more complicated model.\n",
+    "Because naive Bayes classifiers make such stringent assumptions about data, they will generally not perform as well as more complicated models.\n",
     "That said, they have several advantages:\n",
     "\n",
-    "- They are extremely fast for both training and prediction\n",
-    "- They provide straightforward probabilistic prediction\n",
-    "- They are often very easily interpretable\n",
-    "- They have very few (if any) tunable parameters\n",
+    "- They are fast for both training and prediction.\n",
+    "- They provide straightforward probabilistic prediction.\n",
+    "- They are often easily interpretable.\n",
+    "- They have few (if any) tunable parameters.\n",
     "\n",
-    "These advantages mean a naive Bayesian classifier is often a good choice as an initial baseline classification.\n",
+    "These advantages mean a naive Bayes classifier is often a good choice as an initial baseline classification.\n",
     "If it performs suitably, then congratulations: you have a very fast, very interpretable classifier for your problem.\n",
     "If it does not perform well, then you can begin exploring more sophisticated models, with some baseline knowledge of how well they should perform.\n",
     "\n",
-    "Naive Bayes classifiers tend to perform especially well in one of the following situations:\n",
+    "Naive Bayes classifiers tend to perform especially well in the following situations:\n",
     "\n",
     "- When the naive assumptions actually match the data (very rare in practice)\n",
     "- For very well-separated categories, when model complexity is less important\n",
     "- For very high-dimensional data, when model complexity is less important\n",
     "\n",
-    "The last two points seem distinct, but they actually are related: as the dimension of a dataset grows, it is much less likely for any two points to be found close together (after all, they must be close in *every single dimension* to be close overall).\n",
+    "The last two points seem distinct, but they actually are related: as the dimensionality of a dataset grows, it is much less likely for any two points to be found close together (after all, they must be close in *every single dimension* to be close overall).\n",
     "This means that clusters in high dimensions tend to be more separated, on average, than clusters in low dimensions, assuming the new dimensions actually add information.\n",
-    "For this reason, simplistic classifiers like naive Bayes tend to work as well or better than more complicated classifiers as the dimensionality grows: once you have enough data, even a simple model can be very powerful."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Feature Engineering](05.04-Feature-Engineering.ipynb) | [Contents](Index.ipynb) | [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "For this reason, simplistic classifiers like the ones discussed here tend to work as well or better than more complicated classifiers as the dimensionality grows: once you have enough data, even a simple model can be very powerful."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -741,9 +748,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.06-Linear-Regression.ipynb b/notebooks/05.06-Linear-Regression.ipynb
index ccecf6292..aef8483fd 100644
--- a/notebooks/05.06-Linear-Regression.ipynb
+++ b/notebooks/05.06-Linear-Regression.ipynb
@@ -1,33 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) | [Contents](Index.ipynb) | [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.06-Linear-Regression.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -42,11 +14,11 @@
     "editable": true
    },
    "source": [
-    "Just as naive Bayes (discussed earlier in [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) is a good starting point for classification tasks, linear regression models are a good starting point for regression tasks.\n",
-    "Such models are popular because they can be fit very quickly, and are very interpretable.\n",
-    "You are probably familiar with the simplest form of a linear regression model (i.e., fitting a straight line to data) but such models can be extended to model more complicated data behavior.\n",
+    "Just as naive Bayes (discussed in [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) is a good starting point for classification tasks, linear regression models are a good starting point for regression tasks.\n",
+    "Such models are popular because they can be fit quickly and are straightforward to interpret.\n",
+    "You are already familiar with the simplest form of linear regression model (i.e., fitting a straight line to two-dimensional data), but such models can be extended to model more complicated data behavior.\n",
     "\n",
-    "In this section we will start with a quick intuitive walk-through of the mathematics behind this well-known problem, before seeing how before moving on to see how linear models can be generalized to account for more complicated patterns in data.\n",
+    "In this chapter we will start with a quick walkthrough of the mathematics behind this well-known problem, before moving on to see how linear models can be generalized to account for more complicated patterns in data.\n",
     "\n",
     "We begin with the standard imports:"
    ]
@@ -55,15 +27,15 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns; sns.set()\n",
+    "plt.style.use('seaborn-whitegrid')\n",
     "import numpy as np"
    ]
   },
@@ -77,13 +49,13 @@
     "## Simple Linear Regression\n",
     "\n",
     "We will start with the most familiar linear regression, a straight-line fit to data.\n",
-    "A straight-line fit is a model of the form\n",
+    "A straight-line fit is a model of the form:\n",
     "$$\n",
     "y = ax + b\n",
     "$$\n",
     "where $a$ is commonly known as the *slope*, and $b$ is commonly known as the *intercept*.\n",
     "\n",
-    "Consider the following data, which is scattered about a line with a slope of 2 and an intercept of -5:"
+    "Consider the following data, which is scattered about a line with a slope of 2 and an intercept of –5 (see the following figure):"
    ]
   },
   {
@@ -92,14 +64,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
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Yg81VA0A8sDSoQ6GQNm7cqPfee09JSUnasmWLbr75Zis/AnHGhN4sW4cCiGeWBnVFRYXa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\nSUVORK5CYII=\n",
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115e58cf8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -120,7 +95,7 @@
     "editable": true
    },
    "source": [
-    "We can use Scikit-Learn's ``LinearRegression`` estimator to fit this data and construct the best-fit line:"
+    "We can use Scikit-Learn's `LinearRegression` estimator to fit this data and construct the best-fit line, as shown in the following figure:"
    ]
   },
   {
@@ -129,14 +104,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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G4PcuFbCllbV6a2WetuScUHCQXT+/pbfGD+2h4KCGm2i09tL12VXogX6PFs1DUAPwew0F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\nrUWLFunBBx/8Qb8Ef/TGG2/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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115f34470>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -164,7 +142,7 @@
    },
    "source": [
     "The slope and intercept of the data are contained in the model's fit parameters, which in Scikit-Learn are always marked by a trailing underscore.\n",
-    "Here the relevant parameters are ``coef_`` and ``intercept_``:"
+    "Here the relevant parameters are `coef_` and `intercept_`:"
    ]
   },
   {
@@ -173,15 +151,18 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model slope:     2.02720881036\n",
-      "Model intercept: -4.99857708555\n"
+      "Model slope:     2.0272088103606953\n",
+      "Model intercept: -4.998577085553204\n"
      ]
     }
    ],
@@ -197,7 +178,7 @@
     "editable": true
    },
    "source": [
-    "We see that the results are very close to the inputs, as we might hope."
+    "We see that the results are very close to the values used to generate the data, as we might hope."
    ]
   },
   {
@@ -207,12 +188,12 @@
     "editable": true
    },
    "source": [
-    "The ``LinearRegression`` estimator is much more capable than this, however—in addition to simple straight-line fits, it can also handle multidimensional linear models of the form\n",
+    "The `LinearRegression` estimator is much more capable than this, however—in addition to simple straight-line fits, it can also handle multidimensional linear models of the form:\n",
     "$$\n",
     "y = a_0 + a_1 x_1 + a_2 x_2 + \\cdots\n",
     "$$\n",
     "where there are multiple $x$ values.\n",
-    "Geometrically, this is akin to fitting a plane to points in three dimensions, or fitting a hyper-plane to points in higher dimensions.\n",
+    "Geometrically, this is akin to fitting a plane to points in three dimensions, or fitting a hyperplane to points in higher dimensions.\n",
     "\n",
     "The multidimensional nature of such regressions makes them more difficult to visualize, but we can see one of these fits in action by building some example data, using NumPy's matrix multiplication operator:"
    ]
@@ -223,14 +204,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.5\n",
+      "0.50000000000001\n",
       "[ 1.5 -2.   1. ]\n"
      ]
     }
@@ -252,9 +236,9 @@
     "editable": true
    },
    "source": [
-    "Here the $y$ data is constructed from three random $x$ values, and the linear regression recovers the coefficients used to construct the data.\n",
+    "Here the $y$ data is constructed from a linear combination of three random $x$ values, and the linear regression recovers the coefficients used to construct the data.\n",
     "\n",
-    "In this way, we can use the single ``LinearRegression`` estimator to fit lines, planes, or hyperplanes to our data.\n",
+    "In this way, we can use the single `LinearRegression` estimator to fit lines, planes, or hyperplanes to our data.\n",
     "It still appears that this approach would be limited to strictly linear relationships between variables, but it turns out we can relax this as well."
    ]
   },
@@ -268,12 +252,12 @@
     "## Basis Function Regression\n",
     "\n",
     "One trick you can use to adapt linear regression to nonlinear relationships between variables is to transform the data according to *basis functions*.\n",
-    "We have seen one version of this before, in the ``PolynomialRegression`` pipeline used in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) and [Feature Engineering](05.04-Feature-Engineering.ipynb).\n",
+    "We have seen one version of this before, in the `PolynomialRegression` pipeline used in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) and [Feature Engineering](05.04-Feature-Engineering.ipynb).\n",
     "The idea is to take our multidimensional linear model:\n",
     "$$\n",
     "y = a_0 + a_1 x_1 + a_2 x_2 + a_3 x_3 + \\cdots\n",
     "$$\n",
-    "and build the $x_1, x_2, x_3,$ and so on, from our single-dimensional input $x$.\n",
+    "and build the $x_1, x_2, x_3,$ and so on from our single-dimensional input $x$.\n",
     "That is, we let $x_n = f_n(x)$, where $f_n()$ is some function that transforms our data.\n",
     "\n",
     "For example, if $f_n(x) = x^n$, our model becomes a polynomial regression:\n",
@@ -291,9 +275,9 @@
     "editable": true
    },
    "source": [
-    "### Polynomial basis functions\n",
+    "### Polynomial Basis Functions\n",
     "\n",
-    "This polynomial projection is useful enough that it is built into Scikit-Learn, using the ``PolynomialFeatures`` transformer:"
+    "This polynomial projection is useful enough that it is built into Scikit-Learn, using the `PolynomialFeatures` transformer:"
    ]
   },
   {
@@ -302,15 +286,18 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[  2.,   4.,   8.],\n",
-       "       [  3.,   9.,  27.],\n",
-       "       [  4.,  16.,  64.]])"
+       "array([[ 2.,  4.,  8.],\n",
+       "       [ 3.,  9., 27.],\n",
+       "       [ 4., 16., 64.]])"
       ]
      },
      "execution_count": 6,
@@ -332,7 +319,7 @@
     "editable": true
    },
    "source": [
-    "We see here that the transformer has converted our one-dimensional array into a three-dimensional array by taking the exponent of each value.\n",
+    "We see here that the transformer has converted our one-dimensional array into a three-dimensional array, where each column contains the exponentiated value.\n",
     "This new, higher-dimensional data representation can then be plugged into a linear regression.\n",
     "\n",
     "As we saw in [Feature Engineering](05.04-Feature-Engineering.ipynb), the cleanest way to accomplish this is to use a pipeline.\n",
@@ -343,9 +330,9 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -362,7 +349,7 @@
    },
    "source": [
     "With this transform in place, we can use the linear model to fit much more complicated relationships between $x$ and $y$. \n",
-    "For example, here is a sine wave with noise:"
+    "For example, here is a sine wave with noise (see the following figure):"
    ]
   },
   {
@@ -371,14 +358,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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2ZFm/NAYKQcCHR6t4rK0b4T1tN5SRsRaikInhqf6AUcQrf1iBjN9bvwrWnlbNi8AHR6vw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\nILz+aSlaOwewacU0q2e9nb1D+O93zuB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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1188dcd68>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -404,7 +394,7 @@
     "editable": true
    },
    "source": [
-    "Our linear model, through the use of 7th-order polynomial basis functions, can provide an excellent fit to this non-linear data!"
+    "Our linear model, through the use of seventh-order polynomial basis functions, can provide an excellent fit to this nonlinear data!"
    ]
   },
   {
@@ -414,7 +404,7 @@
     "editable": true
    },
    "source": [
-    "### Gaussian basis functions\n",
+    "### Gaussian Basis Functions\n",
     "\n",
     "Of course, other basis functions are possible.\n",
     "For example, one useful pattern is to fit a model that is not a sum of polynomial bases, but a sum of Gaussian bases.\n",
@@ -428,8 +418,9 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.06-gaussian-basis.png)\n",
-    "[figure source in Appendix](#Gaussian-Basis)"
+    "![](images/05.06-gaussian-basis.png)\n",
+    "\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb)"
    ]
   },
   {
@@ -449,14 +440,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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PRUbePGR+5cqVWLly5egqG6Gqeg8ERnWjMi8EgMJh1uzZsgfn6HEkuwp7ThTj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\nC2FzAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118997668>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -505,7 +499,7 @@
     "editable": true
    },
    "source": [
-    "We put this example here just to make clear that there is nothing magic about polynomial basis functions: if you have some sort of intuition into the generating process of your data that makes you think one basis or another might be appropriate, you can use them as well."
+    "I've included this example just to make clear that there is nothing magic about polynomial basis functions: if you have some sort of intuition into the generating process of your data that makes you think one basis or another might be appropriate, you can use that instead."
    ]
   },
   {
@@ -517,8 +511,8 @@
    "source": [
     "## Regularization\n",
     "\n",
-    "The introduction of basis functions into our linear regression makes the model much more flexible, but it also can very quickly lead to over-fitting (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for a discussion of this).\n",
-    "For example, if we choose too many Gaussian basis functions, we end up with results that don't look so good:"
+    "The introduction of basis functions into our linear regression makes the model much more flexible, but it also can very quickly lead to overfitting (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for a discussion of this).\n",
+    "For example, the following figure shows what happens if we use a large number of Gaussian basis functions:"
    ]
   },
   {
@@ -527,14 +521,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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wFKgaZwvjLLtep+Urd87jK3fOi8LIUo9GUbhhQQ4vfNDM17/zATb/ANXVq1Oi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\nm2VK3411nZ2dSElJ6f2a4WxQX+Tk5ODQoUMAgOPHj/MksGH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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115f34f98>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -561,7 +558,7 @@
    },
    "source": [
     "With the data projected to the 30-dimensional basis, the model has far too much flexibility and goes to extreme values between locations where it is constrained by data.\n",
-    "We can see the reason for this if we plot the coefficients of the Gaussian bases with respect to their locations:"
+    "We can see the reason for this if we plot the coefficients of the Gaussian bases with respect to their locations, as shown in the following figure:"
    ]
   },
   {
@@ -570,14 +567,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ObJyqyoA+BiYO68OgaBP9woMJMQScUzHybD39i6rz9+OWq4aw4f0M3th2jN8ljZGEyh7k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\n9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7uM8uokDjQKh+ahjMuI14/U2YDOV7zCUCuJPazBmdFAGWtQWpUHeuSQMAyKHqEl7r/qGNAZASGY7cRBn+d6AS7+4pC3b3nKI1GgeWzx2NhzYVUZZZADirdcXhEgQuHhM/6A6F85WirgMiAQ/ZHkwdjU6yjX5K6jswIS20956ca7YFGk9+f9JX7+w0vcmC+z8+gtXfFuNYTTuevHo8pGGD662dRjQOjEuRw8oCEeECMOhbJoN4z531mpo2HT78vRI1bTqw6EoUCPVNnor6DoxOjICA7/7LLitWCpGAh9NDYJ2mrEkDhqFA42/hQj423liAv10yCl8eqcHlL+zC1hP1g2qj7+AKe4PEi7+cgUTEx67lFyPajTRU4j5HG9M0BrO9jpwzoZDC6QrLslDUqXBpXt8TNV3h8xiMSpChpH4IBJpmNVIixS6zFIl3+DwGf78sF9NHxuGxr07grg8LMXNUHB7/07hBcZYUBZpuvjpSg6e+V6BJbUBEmAA7TzeF7BvbYNZ76J+98nu3vi+UEwWaVAYoNUa3U5u7G50UgT1nmwPQq4FV1apDeszQXYMbDKZlx+D7B2bgg98rsOHn0/jji7tw6/Qs/P2yXEhEwXu7H7JTZ1arFatWrcLChQuxdOlSVFRUuLyeq9DcpLbtYlcZzENiuiYUuJsAEMqJAsUeVATobUxSBBo6DGjThnbl7JpWHVKjJMHuxpAn4PNw6/Rs7PjHbFyXn4a3dpfh6lf2BDWhZMgGmm3btsFoNOLTTz/FsmXLsG7dOpfXD/cKzcHkzrqNgBfa56fba5x5sIeGk5vIJQSE7vSZ0WxFg0qPtOjQ/bAQauJkYXhm/kR8cNv5UGqMmL9xb9CqgQ+6QPPXv/4V27Ztg8Xi276BwsJCzJw5EwAwefJknDhxwuF1XCVhqtAcPL1TNqPEQvTeOG62ApUt2qD0zx8UdR1IjRIjUuL8DBpnuA2eoZwQUNeuA8sCqRRoBtyMUXHYfM+FkIoEWPLm7yhtGviRzaBbo/nnP/+JL774Ai+//DJmzJiBBQsWICsry+N21Go1ZLKuRTA+nw+z2QyBoOevXNOmwz8/KwID27nlvcVLBVAoFB7//FCj1+uD+nuODgfeujoZAHDz55V96jkBwIZtp1FW24Bb82Mg5AemhEmg7kNReRNSI4Retc2yLGQiHn5XVOK8qIH74OPPe1FUZ+u3paMJCkXo7QkK9uvDH9bMicODP9Tgpjf34oV5KYgIG7ikjEEXaEaMGIF//vOfUCqVeOqpp3DVVVfhvPPOwwMPPIApU6a43Y5MJoNGo7H/22q19gkyHKPVdsBQGJ8Hg6Vr+kws5OORq8YjL2/oJwQoFArk5eUFuxsAgCbNOadf+7K4HTVaPl69MR9xsjC//+xA3Ae9yYIaVRn+nJ+JvDzvpv/GprSj0cAO6N/In/fiuLoKQB2mTxqDjNjQW6cZTK8Pb+UBeCshDQtf34ebv6iGzmRBqhd71AoLCz3+2YNu6mznzp148MEHcfPNNyMvLw87d+7EunXr8Pjjj3vUTn5+Pnbt2gUAKCoqQm5ursvrWQDPzJ9on76hvTPB42zRPzVKjP8smoyj1W1Y+Po+NKpC46TPMw1qWKysV4kAnNFJtppng2lvhCeq23RgGCApcmgcdxCqalp14DGMvZbjQO1RG3Qjmm+++QaLFy/G+eef3+Px+++/36N2LrvsMuzZsweLFi0Cy7J4+umnXV6fGiV26zwIEnjL547uUz2bq8xw9eRUJEeKcfPbB7Dkzf3Y9NcL3Cq5H0xdZ9C4X+Ost9ykCKj0ZtS2692qGTfY1LTqkCQPh0gw6D7bDivP/XgK5l7z0gOxR23QBZr169c7fPyyyy7zqB0ej4cnnnjCrWupvMzg0t9pg9OyY/D2Lefh5ncO4L7/Hcb7t03zaLf9QCuu64BYyEemD2ewjOksRXOqviMkA011qzYk+z3UBKtg8KALNAPNmzlKEnj9jS4vGBGLNX8Zj39+fgyTn/gZGoN50NZEU9R1YHRSBPg+nMHCBRpFnQpzxoTeIWg1bToUZEYHuxvDnrNag4HeozZ4PwYOkD0r5wy6NybiHhGfBz6PgdpgHrQ10ViWRUm9yqf1GQCICBciPUZs3/gZSixWFnUhOuU31Djas8ZjgH9c7noN21fDPtCQ0PXcj6dgcTLfPFjUtuvRrjNhrA/rM5y8JLl9vSeUtGgMsFhZJFMiQNA52rNmZYEnv1Mge+X39n2Fzmw5XufVz6VAQ0JWKBxQV1zbeQZNiu8l/vOS5ShvDr1D0Bo7bGWdEuQUaAYD7myosnVXYtVVY8EAUGqN/c4KsCyLV3ac9epnUqAhISsUDqg7WdsOhulaY/FFXrLt+IpQO9q5vt2Whp5IgWbQWf/z6T4b1Z3NChRWtOJkrXcjago0JGQ5mm9mACy7LLDzzZ4oru1AdpzULwdRcSdzhtr0WYOKCzT+32BLfOPJrMB/d5xFtBcllADKOiMhrHcadJREiFatCRqjOcg963KytgNTMqL80lZatBiyMEHoBZoOAxgGiA9AJQfim/6y0L46UoPnfjxlv+bKCclwXKzLNRrRkJDWfb758GOXYfrIWDz34yk0dx73EEztWhNq2nQY54f1GQDg8RiMSYoIuUDT2KFHnCxsUO91Gq4czQoI+QyWzx2Nr47U4OHNx3sEol8UDV79HPrLkyGDYRj8+8/joTNZ8OzWkmB3x56KPDbFt9Tm7samyKGoU8HqqOroINXQoadps0GqexYaAIR1Vm5IkIfhuR9P9ajOAaDPUSruokBDhpSRCTLcNiMbmw5Vo7CiNah9OVlrO/tjrI97aLqbmBYFtcEclFLv3mroMCAxghIBBituVqB83ZXYs3IOsmKluOWdg06PTvEGBRoy5DwwZxSS5OFY9fWJPvtsBlJxbQcSIsIQH+G/T/Pces+Ryja/tRlojSo9pTaHiDhZGD796wX447gkv7ZLgYYMOdIwAR67aixO1nbgf/tdH+EdSEer2zAh1T/rM5zsWCnk4QIcqWpz6/qvjtRg+rrtbm3GCwSj2YpmtRFJFGhCRoxUhJcWT8H6BZMQ3qsIan8n4TpDgYYMSfMmJAU1MaBda0Jpkwb5fq7vxeMxmJwRjSOV/U8Ldl/MDVaJnqbOe09rNKHnuoI0rLuu79Ep3qBAQ4YkLjFAb7Zi5RfHB/wcl6LqNgDAlPQov7c9OT0KpxtU0Bhcp3E7Wswd6BI9DR20WTOUdc/q9KUuJAUaMmSNTJBhxR/HYJuiAf87UDmgP/tIZSsYBpiQ5t+pM8C2TmNlgaP9TJ8524znz0Xe/jR2Bhp/rlOR0EOBhgxpt16YhZmj4vDEt8U47MZ0k78cqWxDbkIEIsK920ntytTMaPB5DPaUNru8LsHJdJVENHBnxTerjba+UKAZ1ijQkCGNx2Pw4sLJSJSH4473DuFsY+DTgq1WFkVVbX6rCNBbRLgQU9Kj8NsZ14HG0UFrfIaBxcoOWGHOls5AEz3IT0ElgUWBhgx5sbIwvHvreWAY4LrX9mJvr5FA98ysmz+v9Hmx/HSjCu06U0AP+poxKg7Ha9rRqjE6/PrZRhUOlStxUW58j8Xcu2ePgMFsxS8l3u3w9lSLxoBIsRBCqgowrFGtMzIs5MTLsPnu6bjlnQNY8uZ+XJefhlunZ+FMgwqPfHnCvmjeqDHj4c3HAcDrhc89Z1sAABeOjPNP5x2YlRuPF7edwY5Tjbg2P63P19f/dBpiIR8brp+E2G41xixWFu/vK8eu0024amJKwPrHadEYESuj0cxwR4GGDBsZsRJ898AM/GfbGby7txxfHK4GjwF67+nkMrO8DTT7SpuRFSsJ6ImSk9OiEC0R4uHNx7Fs09Eex1gXVbVhy4l6/O2SUT2CDADweQxmjIrDb2eawbIsGMb746Xd0aI2IJamzYY9CjRkWJGIBHh4Xh7umT0SW07UYWXn6KW3mjYdVHoTflE02qtDd38zd8ZssWL/OSWumhTY0cI3R2uh0pth7oyS3B4ZlmWx6VA1YqUi3DErx+H3XpATix+O16OmTYe0aElA+9miNmJEvCygP4MMfhRoyLAUKRFi0bQMvLz9rNN03+nrtkNvssJosRUS5N7MAefTagfKlFAZzLgoNz4wHe/03I+n7EGGozNZ8NCmo2ABrP7TWMicnIEzqXNvz7Hq9oAHGqXGiGnZNKIZ7miFjgxrjsqki4V8PHRZLrRGiz3IcHQmC5ZtOuowYeCrIzW44/1DAIB/f3syoDvwne2RYQEwDFymVY9JkkPE5/W7D8dXFisLpdZIU2eEAg0Z3rqXSWcAJEgFWHvtBDxwyag+IwaOhWX7lHL56kgNVn5xDJrOtOG6dn1Ay724Oq6aZYENP592+nWRgIe85Agc7axeECitWiNYFn3WicjwQ4GGDHvdy2y8Nz/DPi3m6tja3qVcnvvxVJ+zOgJZ7sXRSKw7ZyMezsS0KJyo6QjouTbKztRryjojQzrQ/Pzzz1i2bFmwu0FCVH/l0bq/mXty9ro/cCMxvpOsMVcjHsB2gJraYA5oORqumGkMTZ0Ne0M20KxZswbr16+H1erdiXCEtOtMLr/e/c3cWYmV/t7wffGXKalYf/0kh2tMy+eOdvm9oxJsmWCBrJTAVQWIo6mzYW/IBpr8/HysXr062N0gIay/IMG9mbMsizgHgcadN3xf9V5j4kq597cHaGRnoDnTqApY3+xTZzSiGfZCPr35s88+w3vvvdfjsaeffhrz5s3D/v37+/1+hUIRqK6FFL1eT/cCPe/DkgkybNitg8XBFJpYyMDc0YjjJ9vxUVErTtZ2YHa2FMWNBjRpzIiXCnBzfjRGh3dAoegIaJ9HhwNvXZ3c7RH3fmZ0OB+HTtdgZrzjMja+PidKypVgANRVlKKRF9iNoYFGrw/fhHygWbBgARYsWOD19+fl5fmxN6FLoVDQvUDP+5CXB6Sm1GD1NyfR1jmNFi0R4uYLsrDpUBX+saXWXlng+qlpWHftRPBC6A11dEobmoxWp393X58TTMlxxEi1GD9urNdtDBb0+uhSWFjo8feEfKAhJJD+MiXV4TTU/83KwTdFtahq1WJaVgxmj44PeDkXfxuZIMPXRbUBK0WjVBspEYAAoEBDiFdkYQIsOT8j2N3wyYh4GVR6M1o0xoAs2LdoDJTaTAAM8UBz/vnn4/zzzw92NwgZlDJibOVnqpTawAQatRF5KXK/t0tCz5DNOiOEuJbeGWgqldqAtN+iofIzxIYCDSHDVHp014jG34xmK9p1JsRKaQ8NoUBDyLAlFvERHxGGKqX/qwO0aqn8DOlCgYaQYSwjRhKQqTOuKgBNnRGAAg0hw1p6tDgwgUZjq3NGlZsJQIGGkGEtI0aCunYdTBb/1gS0j2ho6oyAAg0hw1p6jARW1v9VpluozhnphgINIcNYoFKcW9QGCHgM5C5O+iTDBwUaQoYxLtBUt/p5RNNZfiaUar+RwKFAQ8gwlhARBh5jO3ran1o0VOeMdKFAQ8gwJuTzEB8Rhvp2f6/RGOjAM2JHgYaQYS4pUuz/EQ1VbibdMCzb38noQ5c35yoQQshwV1BQ4NH1wzrQEEIICTyaOiOEEBJQFGgIIYQEFAUaQgghAUWBhhBCSEBRoCFkEPnoo4/w0EMPAQBWrFiBjz76KMg9IsR3lHVGyCBzzz33QC6Xw2g0YsOGDcHuDiE+o0BDyCBTVFSEhQsXYvPmzRg3blywu0OIzyjQEDKIGI1G3HjjjbjuuuvwxRdf4MMPP4RIRDvsSWijNRpCBpHnn38es2fPxsKFCzFz5kysX78+2F0ixGc0oiGEEBJQNKIhhBASUBRoCCGEBBQFGkIIIQFFgYYQQkhAUaAhhBASUBRoCCGEBBQFGkIIIQElCHYHgomOciaEEM95epTzsA40gOc3bKhSKBTIy8sLdjeCju5DF7oXXehedPHmAzpNnRFCCAkoCjSEEEICigINIcOUyWLFg58cgaKuI9hdIUMcBRpChqmyZg2+KqrFz8UNfm+7SWXAre8cQLPa4Pe2SeihQEPIMFXZogUAVCm1fm97f1kLdpxqwv5zSr+3TUIPBRpChqnKzgBT1er/QFOl1AEAKpQav7dNQg8FGkKGKS7QVLfq/N42F7y4URMZ3ijQEDJMcVNmde16mC3WgLRdQYGGgAINIcMWN6KxWFnUtev92nZN5yipooWmzggFGkKGJauVRaVSi7HJcgD+XaexWllUt+og4DGo69DDYLb4rW0SmijQEDIMNakNMJitmD4yFoB/12kaVQYYLVZMTo8Cy3YlBpDhiwINIYPcG7tKsa+0xa9tctNm52fHgscA1X5MceZGR9NHxnX+LJo+G+4o0BAyiBnMFjyz9RQ+3F/h13a5bLCceCmSI8V+HdFwiQAzR9kCTXkzJQQMdwNevfmaa66BTCYDAKSlpWHhwoV46qmnwOfzMWPGDNx3332wWq1YvXo1Tp06BZFIhDVr1iAzMxNFRUU+XUtIqDnbqIbFyqKsyb+jgkqlFgwDpEaLkRot9usaDTdVNj41ErIwgX305C97zjZj7RYFPr/rQoQL+X5tmwTGgAYag8EAlmXxwQcf2B+7+uqr8fLLLyM9PR133nkniouLUV1dDaPRiE8//RRFRUVYt24dXnvtNTz++OM+XTt27NiB/HUJ8VlJnQqArVyM1cqCx2P80m6VUotkeTjCBHykR0uwt7TZL+0CtqmzRHkYwoV8ZMRI/J55tvN0E07UdKC4rgP5GdF+bZsExoBOnZWUlECn0+G2227DTTfdhIMHD8JoNCIjIwMMw2DGjBnYu3cvCgsLMXPmTADA5MmTceLECajVap+vJSTUlNTbCl7qTBY0qPyXglyh1CIjVgIASIsWo96P2WFVSi3So21tZ8VJUOHnEU1poxoAUFxLxUBDxYCOaMLDw3H77bdjwYIFKC8vxx133AG5XG7/ulQqRVVVFdRqtX16DQD4fH6fx7y51hGFQuHPXzFk6fV6uhcYfPfh0Nl68BjAygK/FiowOVnsl3bPNXbgvFQJFAoF+HoVWBbYXXgSKXKh/Rpv70VZYwfGJ4ZDoVBAyupQ2aLBiZPF4PtpNKaosdVP21tcgYLIgVn/GWzPi1AzoIEmOzsbmZmZYBgG2dnZiIiIQFtbm/3rGo0Gcrkcer0eGk3XcNtqtUImk/V4zJtrHaFT82zoBEGbwXYfqjfX4MIRcdh9thkWSSzy8jJ9blNntKBVdw4Tc5KRlzcK7WEtwJ4miGKSkTcq3n6dN/fCZLGiWXsO47KSkJc3GvmqSnx24jiiUrKQ1jnK8YXBbEG9+hwAoFbHH7C/1WB7XgTToD9h8/PPP8e6desAAA0NDdDpdJBIJKisrATLsti9ezemTp2K/Px87Nq1CwBQVFSE3NxcyGQyCIVCn64lJJQ0qw1oUhlwUW48xEI+zvkpIYBb+E+PkfT4X39kntW16WFlYZ86y+xs2181zypatLCyQEJEGErqO2Cxsn5plwTWgI5o5s+fj4cffhiLFy8GwzB4+umnwePx8I9//AMWiwUzZszApEmTMGHCBOzZsweLFi0Cy7J4+umnAQD//ve/fbqWkFByqt6WCJCXLEdWnBRlzWq/tMu96Wd0BoEkeTgEPMYvxwVwQSwtxjbFx60DVSi1uNDn1rvWZ66amIK395ShrFmNkQkRfmiZBNKABhqRSIT169f3eXzTpk09/s3j8fDEE0/0uW7y5Mk+XUtIKOFOvhyTHIGcOClO1rb7pV0u3ZgLNHweg5Qo/+yl4YIVN6JJjhRDxOf5rbhmaZMt0Fw5MRlv7ynDydoOCjQhgDZsEjJInapXIU4WhjhZGHLipahq1cFo9r3KcqVSC1mYADFSkf2xND/tpalq1YLPY5AcGQ7AFsTSYsR+S3EubdIgNUqMCamREPF5KKZjqEMCBRpCBqmSehXykm2f1rPjpLB0FsL0VaVSi/QYCRimKwssPVripxGNDilR4RDwu95aMmMkfh3R5MRLIRLwMCpRRinOIYICDSGDkNlixekGFcYkdQUawLZx01eVSi0yYnqmSadFi9GkMkBv8m0vTVVr1x4aTmasFJVKLVjWt4V7lmVR2qjGiHjb1oWxyXIU13b43C4JPAo0hAxC5S1aGMxWjE6ypeXnxNneXH1NCLBaWVQptfb1GU5X5plvI48qpc5BoJFAbTBDqTH61HZDhwEaowUjEmz3YlyKHC0aI5pUBp/aJYFHgYYQP2jVGH1+I+2OyzjjRjSREiFipSKfRzTc8QB9A41thFPlw/SZ3mRBs9qAtOieo6XMzsyzch+nz7hEgBHxttHd2JRIAMBJWqcZ9CjQEOIH//f+Idz9oecb2Zwpqe8An8dgZEJXhYvsOKnPe2m4NZ70XoGG20zpy3EB1a2O286IkXb+bN/6zgWakZ1TZ2M616/8uU6zvaQBG3465bf2iI1bgcZsNvf4d0cHfYIghFPdqkVhRSsOV7b6vMbBUdSpkBMn7VGdODtOinM+jmi4PTSZsdIej8fLwiAS8HxKCOCqNqf3Wv9JjxGDYeBzQkBpoxoRYQLER4QBAOThQmTESPwaaN76rQwvbT+L2jY6rM2fXAaapqYmlJWVYcmSJSgvL0dZWRlKS0tx2223DVT/CBn0tp6oBwCYLCyOVrX5pc2S+g6MTuq5PyQ7XoomlQEqvcnrdiu44wGiegYDHo9BWpRvKc72igO91mjCBHykRIp9rg5Q2qRBToKsR7bc2GS531KcjWYrDle2Auj6mxL/cLlh8+jRo3jvvfdQVlaGxx57DIBtg+SMGTMGpHOEhIIfjtchI0aCSqUWhypacX5OrE/tqfQmVLfqsHhaRo/HuYSA8mYtJqRFetV2lVKLlEgxRIK+nzHTYnxLca5SahEm4NlHHN1lxEhQ7uNemtImNS4Y0fPejk2R48fieqgNZsjCfNt/fqK2HXqTFXwegy0n6nDbjGyf2iNdXP5lLr30Ulx66aXYuXMnLrroooHqEyEho65dh8OVbfjH5bn48kgNCitafW7zdEPPRABOTuci+LlmtdeBxraHxnEF6LRoMY5Xt3nVLmCbOkuLFvcYcXAyYyXYpmjwum21wYy6dr09tZkzNlkOlgVO1XegIDPG6/YB4ECZrSr0jedn4P3fK9DQoUeiPNynNomNWx8BEhISsHr1ahgMXWmEa9euDVinCAkV3BTLFROSUaXUYevJep8PKFN0HnY2JrlnxfGMGAkYBj4lBFQqtbh4dLzDr6VHS9CqNXk9Oqhq1fZJBOBkxErQrDZ63TZ3wmifQJNiu0fFtb4HmoNlSuTESbH0giy8t68CP56sx00XZPnUJrFxKxlg5cqVGDduHObNm2f/jxACbDlej9GJERgRL0NBVjTadSZ7dpS3Suo7EBEuQEpkz0/T4UI+UqPEXqc464wWNKkMfVKbOVxasrd7abofeNZbJpd55uU6jT3jLKFnEkNyZDiiJEKf12msVhYHy5WYlh2DkQky5CbK8MPxOp/aJF3c+mgRFxeHBQsWBLovhISUxg49DlYo8bdLRgEAzsuyfaI+VNGKUYneF3osqbNVBHA0BZUTL/M60HCL9Rm9Ms449k2bSh3GJDk+v8mZdp0JHXqz02k5bi9NRYvGPgrxRGmTGnweY0+V5jAMY68Q4ItTDSp06M32v+EV45Px8vYzaFIZHK45Ec+4NaJJTU3FG2+8gd9++w27d+/G7t27A90vQga9rSfrwbLAlROSAQBZsRLESkU4WK70uk2WZXGqXuX0jT4nToqyZo1XZVcqeh0P0Bs3ovEm84wbBTk73Cyz23EB3ihtUiMzRuIwiWFcihwl9SqYLd4XHOX+ZtOybYFm3oRkWFngp2LKPvMHt0Y0JpMJZWVlKCsrsz9GmWdkuPvheB1GJsjsoxeGYVCQGe1TQkBNmw4qg9m+GbG37Dgp1AYzmlQGJHi4UN37eIDeYqUiiIV8rzLP7HtonASaiHAhYqQir/fSlDZqkNNrfYYzNkUOg9mKsmaN1yPJ/WVKJEeG24NtbqIMOXFSbDlejxvO9/1U0+HOrUCzdu1alJWVobKyEqNHj0ZCQkKg+0XIoNakMuBAmRL3XTyyx+NTs6LxU3GD11MuJVwigJMRDVdc81yzxuNAU9V5PEC0ROjw6wzD2I4L8GLU0VUVwPHUGYDOFHDPp/0sVhZlzRrMHuM4iWFscmcpmtoOrwINy7I4WKbEH3Ji7dOVDMPgiglJ2LjznF9LCw1Xbk2dffjhh1i9ejVeeOEF/Pjjj1izZk2g+0XIoPZTcT2srC3brDsu86mwwrvps5J621pD782aHC7F2Zt1GkfHA/SW7uVemiqlFhFhAkSKHQcxwDa16M2IprpVC6PF2ifjjMMdG+BtQkBFixaNKoN92oxzxfhkWKwsfvbT9BlXPWI4civQfP/993jnnXcQERGBm2++GUePHg10vwgZ1LYcr0d2nLTPXpfxqXKIBDwcKvfuDaWkXoX0GLHTFGBus6W3gSbTybQZx9sD0KpadUjrJ4hlxEpR2+b54W1dxTQdBxohn4fRiRFeJwQc6LU+wxmXIkdGjAQ/HPc90KgNZtz+3kEsefP3YXmGjluBhmVZMAxjfxKJRKJ+voOQwWfTwSo8/YPC5/NLlBoj9p1rwRXjk/q8sYYJ+JicFoVDXq7TlLhIBABspWKyY6U452EKtf14gFjXgSY9WgKV3ox2nWdlbmypzc6nzQDbAWhW1vP06dJGbg+N42w5oKsUjTd/2wNlSkRLhPZinRxu+mzP2WaoDL7VsPvo9wq0aU0IF/Lx1w8PoU07vKbj3Ao0V111FW644QZUVlbijjvuwKWXXhrofhHiNxYrizXfFeOfXxzDG7vO4RdFo0/t/VxcD4uVxbxe02acgqxonKhph87o2ZuT3mTBuSY18pxMm3G8Ka7ZqLIdD+BsQyXHnnnmwToNy7KobtX127a3mWelTWrEyUSIkjj/gDs2RQ6lxoiGDs/PpjlYrsTUrBiHm2znjU+G2cri9yrv67TpTRa8+ds5zBwVh7dvOQ/17Xr87ZMiWKzD58A2twLNjTfeiCeffBIrVqzAsmXLcPvttwe6X2SAlbUasftMc7C74Xdaoxl3fViIt3aX4ZYLs5AdJ8WzP5b49CL/4Xg9MmIkGOdkP8jUzGiYrSyOeljO5WyjGlYW9sPOnMmJl6KyRetROm9/GWecrgPQ3F+naVYboTNZ+h/RxHq3adN2fLPjaTOOvUJAXbtHbTd06FHRosX52Y6rCkxMi0RqlBi7K7yvxvDJgUo0q4247+KRyM+Ixuo/j8PO0034z7bTXrcZalwGms8++wwAsH79enz99ddQKBT44YcfsGHDhgHpHBkYPxyvw4Pf1+DG/7cfr/561u9H4xrNVp/2OHirsUOPha//jl8UDVj9p7FY/edx+Mflo3G6QY3Nh6u9arNda8Kes824YkLfaTNOQWY0AHic5qzoXMx2ltrMyY6TwmxlPQoG7gYab6oDODuHprc4mQgSEd/j4pqlTRqn6zMcbq3M0/UPrr4Zt1GzN4ZhcMX4JByu1aLDi6rZBrMFr+86h2lZMfZiq0umZeD6qWl4aftZ/Fzsff23UOIy0CQlJQEAcnJykJ2d3eM/MrCa1Qb8+9uTmP/aXpyo8exTmzMsy+LVX8/ino8OY2SsCFdNTMazW0/h8W9O+m1Yf7iyFbOf24Hpz2zHxp2lXr1YvVFS34G//HcPSpvUePOmqbhluu05O29CEialReKFn097dXbMT8X1MFtZzBvveNoMAKIkIoxMkOGQhxs3T9WrECbgIcvJzn1O9+Ka7qpUasFzcDxAb5FiISLCBB5NnXGncjrbrMlhGMaW4uzBiEbZeXKpq/UZwLZPJzNW4nHm2cFyJSQivtPRKWDLLDRbge1eTLluPlyDunY97pvTlQbPMAyeuHo8JqZF4qFPizxebwtFLgPNzJkzAQDZ2dlQqVS45pprsHv3buTm5g5I53xhtVqxatUqLFy4EEuXLkVFRUWwu+SVdp0Jz/94CrOe3YH391WgtEmNa17dg9d3lsLqQzAwmq1Y8cUxPLv1FK6enIK1lyfjpUVT8NdZOXh/XwXu+ajQp0O8WJbFu3vKsPD1feDzGYyIl2HdlhJcuHY71nxXHNCDpXaebsL81/bBwrLY9NcLcEleov1rDMNgxR/HoLZdjw/2ef6c2HKiHqlRYkzsp3ry1M6Nm578jUrqVRidFAF+PwU5szuPC/CkuGaVUotkJ8cDdMcwDFKjxR6Nlrig1PsIZ0cyYyUerdFwb8IjElyPaABblpg3I5qCzGgI+M7vy5T0KMRK+B7XPjNbrHj117OYlBaJmaPienwtXMjHazcWQCjg4a8fFEJjMDtppX/nmtT4v/cO4Z6PCrH5cPWgTDRwa43mySefxOzZswEADz74IJ5++ulA9skvtm3bBqPRiE8//RTLli3DunXrAvazTBYrTjeo8N2xWmz4+TRWfnEM3xythdqHJ4/OaMFrv5Zi1rM78MqOs7gkLxE//30Wti+bjTljErB2SwmWvr0f9e16j9tu15pw89sHsOlQNR64ZBReXDgZIj4PPB6Dh+flYdVVY/FTcQNueGs/Wr3YrKYxmPHAJ0VY/W0xLsqNx3f3zcT/7vgDvrt/BuaMScA7e8sx69kd+PunRX5P9fzf/krc9u5BpMdI8NW90zE+tW9AuHBkHGblxuOVHWc9yq7q0Jvw25kmh9lmvU3NikGH3oyzHnxaLanv6JMu7UiMVIQoidCjFOdKpbbfaTNOeozEoxTn6lYtYqUiSN2oypwZK0WlUut2AO59fLMrY5PlKG/Run0wXJvWiFMNKkxzMm3G4fEYzMiU4tfTTR69pr85WosqpQ73zRnl8PmSGiXGy4unoLRJjX9+fszjKWurlcV7e8sx76XfcKCsBYfKW/HQpqMoWLMNC1/fh7d+O4cKH88A8he3KgMIhUJkZNgOYUpPTweP51Z8CqrCwkL7iGzy5Mk4ceKEw+t+OF4HebgQkWIh5GIB5OFCyMVCh58qLVYWFS0anG5Q43SDyv5fWbMGJovtScJjAGmYAJ8crEKYgIeLcuNx5cRkzBmTgIhw55vZOEazFZ8crMTL28+iSWXAnDEJWHZ5LsaldL1hbryxAB8fqMIT353EFf/ZhWeum4jLxyW5dV8qWjS49d2DqFJqseH6Sbg2P63PNbfNyEZSZDge/LQI123ci/dundbv/DvnTIMKd31YiLJmDZbPHY27Lxphz+YZnxqJlxZPwfK5o/HOnnJ8crASXx6pwcxRcbhzVg5mjIzr9w3cGauVxTNbS/D6rnO4eHQ8Xl6S77Ic/T/njsZVL+/Gxp2lWPHHMW79jF8UDTBZ2D6bNB2Z2rlOc7BciVw3dqs3qQxoVhv7TQTgZHfWPHNXRYsWl4xxr6JHWrQYe842u/3GV6W07aFxR2asBEazFfUdeqT0M40H2NZnwgQ8t67lEgJK6lVO11y6O1TeCpYFznOSCNDdjEwpvlZ0YEdJI/40KaXf6y1WFv/dcRZjkiJc3vfpI+Ow4o9jsHZLCSb9Fok7Z43ot20AqG3TYfnnR7HnbAsuyo3Hs/MnIl4WhuM17dimaMDPxQ1Y870Ca75XYFSCDJeNTcSlYxMxOS3K5REWFiuLFo0BjR0GNHToUd+hh8ZgxvVT011m/fXHrUCTkpKCDRs2YPLkyTh27FhIlKBRq9WQybo+BfH5fJjNZggEPX/lez467PD7xUIGESI+pCIepCIetCYrqttNMFq6XnxJMgEyo0SYMjYSGVFCZEaJkB4phIDHoLjRgN0Vauwub8ZPxQ0Q8hgUpIoxI1OKP6RLIRX1DNYWK4sdZWp8WNSKBrUZ4xPDsWJGCsYlhAPttVC01/a4fooceOnKFDyzqxF3flCIebkRuOO8WIS7mBo52ajHE9vrwQJ46tIk5IlVUCgUAAC9Xm///wCQLQDWXJKIJ3Y04M8v78KTlyZhRIzrkiq/nlPjP/uaEC7g4enLkjEpyYRTp0ocXrtgJIM/pqdhy2kVvlK0Yun/O4CcaBGuGxeJWdkyCDw4z0VvtuL53U3YU6HBVaPluGuaDFXnzrj8Hj6A2dkyvP3bOVwYZ0KcVODwPnS3aW89YiV8hGvqoFC43sTHsiyiwvnYfrQc+fL+RwdHam3XSIytUCj6H6XGCM04Wqdy2tfu9CYrmtUGhFvUbl0vMqqgNVrQ2K4F48b1pQ1tGBUb5lbbjNr2e/52RIGJSf0Hj6Jz9UiJEOC0k+dRd0KNbbSx/fBpyHT9Hwy3tbAFAh4QrqmHop/1l+wIIFrMx6d7T2OkqP810t/K1Sht0uDhixKcvgY4M+NZ/JYpxbotJYgwt2NysvP7wrIstpWqsfFAM6wscP8FcbhilAzKmjIoAYgAzEsH5qXHo14Vhd+rtPi9SoONO0vx6q+liA7n4/x0CUbFhaFdb4FSa0GL1gylzoJmrRmtOgt6DzaFPAaJjAq5cd5XsXa71tnHH3+MnTt3YuTIkbjnnnu8/oEDRSaTQaPp+sRntVr7BBkA2PrgTLRrbSXOO3SmznLnnf+rM9v/f6yQj0vHR2BUggyjkyIwMkEGicj57Rs3FlgA2yftI1Wt+P5YPbacqMPvu5sg4rdg5qg4XDEhGZflJWLfuRas/+kUzjSqMT5VjmevH4NZo/r/dJ8H4KKpFqz/6TTe2HUOp1qt+M+iKQ6ni74uqsEjP5UjNVqMt285z14zi6NQKJCXl9ez/Txg8lgVbnn7AFb+1IDXbszHzFF9600ZzVY89X0x3tvXiKmZ0XhlST6SIt2rwzVtMrDSbMHXRbV4c9c5PLe7CW8ebkNqlBgxUhFipSLEysKc/H8R9CYr/u/9QzhWrcFjV43FbdOz3B4VPZGQiUs2/IofKlmsvTbP6X0AbDu7Cz8qxw3nZ2Dc2LFutX/+CC0UdSqH7fW2p/kcgHpcPm0cYmX9v6Cn1AnwS+lpZI4Y5fJ5CNiSDIByFIzJQl5e/5/Gq6z1wMEWtJn5mN1P3y1WFs3aMvylIAl5ef2PDGWJWuDnekAWh7y8jH6vb/yuHuPSY926hyzLImZLPZRWiVvXn92+B5PTozF5wrh+r1UoFLhqUho+L6zu956zLIu///gbcuKl+L+5U/tdcwOAjSNycc1/9+C5PS349v4ZDpM2mtUGPLz5OH4ubsK0rBg8v2CSyw24eQAunmb7/+1aE3493Yifixuw81QTtp6x1dSLkgiRGBGOhGgJJmSGI0kejkR5GBLk4Ujs/P/xsrAea1iFhYX9/j69uXyGHj9+HBMmTMDBgwcxcuRIjBxpy5w4cODAoK/enJ+fjx07dmDevHkoKipymsDg6bkbnuLxGBRkxqAgMwaPXpmHouo2/HCsDltO1OOXkkYwDMCytl3Pr96Q79b8f3dhAj4emZeHmaPisGzTUVzz6h6s+OMY3DY9GzweA5Zl8dIvZ/HCttOYlh2D128sQLTU/SFwbmIENt8zHbe8cwC3vnMQz86f2GO6raZNh3s/Ooyiqjb834xsrLhiDIQuFlad/Q7XT03H/Pw07DzdhC0n6tCsNqJFY0R5iwYtaiO0LjY/ioV8vH5jgdvTh5yMWAluOD8TH/xegdtn5GCkiwXn7SWNMJqtTjdpOjI1MwY/nmxAo0qPhAjXgVdRp0JCRJhbQQaAfV9JWbOmx7SqI+6mNnO4adIGdf/rEfUdepgsrNOqzb0lR4ZDwGPcqnlmMFtQqdTiz5NT3WrbfjaNG5lnWqMZJ2raceesHLfaBoArJiThg98rsPNUk8vp018UjSipV2H9gkluBRkAkIUJsHFpAf7yyh7c82EhPv3rBQgX8u1f33qiDo98eQJqgxn/mpeH22Zku902AERKhLh6ciqunpwKo9mKhg494iPCevyMQHIZaH7//XdMmDAB33//fZ+vDfZAc9lll2HPnj1YtGgRWJYdFAkMPB6D/Ixo5GdE419X5uFodTt+UTQgK1aKqyenuMx86c/MUfHY+uAs/PPzY1jzvQI7Tzfh6WsmYMPPp/HlkRpcm5+KtddOQJjA8ydWUmQ4Nt11Af76fiEe2nQU9R163H3RCOw604wHPzkCk4XFazfku7V24QqPx+DiMQm42MGctt5kQYvGCKXaiGaNAUq1Le21XWfCFROS+n2zdea+OSPx2aEqPP/jKWxcWuD0ui3H65AQEYaCjGi32y7I6txPU97a770pqe9wWkjTEW5E6kmg6a/OGYfLHnMn0FQr+6/a3J2Az0N6jHvFNStatLCyrkvP9DY2RY5395bDZLG6/MBzpLINZivr1voMZ1pWDGKlIvxwot7p35NlWby84yzSY8S4enL/o8fuRsTLsP76Sbjzg0Ks/uYk1l03Ee06E1Z/cxJfHqnB+FQ5Nlw/2a01P1dEAp7ba67+0m+gueOOO5Camor77rtvoPrkFzweD0888USwu+EUwzCYnB6FyelRfmszRirCmzcV4KP9lVjzfTFmPbcDLAv84/Jc3HvxSK8X2gFAHi7Eu7edh+Wf2VKid51uwv4yJXITIvDajfn97tz2FXeMcX/7QDwVJwvDHbNy8OK2Mzhc2QpHrWuNZuw41Yjrp6a7XEjtbXxKJMIEPByqcB1ozBYrzjSqccuFWW63ze21KXMjxZmrrBzl5HiA3iLChYiSCFGv6j97i9tD4+6IBrCNrCrcOC6gtNF1MU1HxibLYTRbca5J4zJwHyhTgsd0ba51h4DPw+XjEvFNUS30JovD0cDus804WtWGtddO8OqD4+XjknDfxSPxyo6zCBfysfVEPZrUBvztklG4b85Ij2cLBguXgaa1tRUPPPAACgsLexx6BtiqBZDBh2EY3PiHTPwhJwbrttj2yLiTJeOOMAEfLy6cjOTIcLy+6xyunZKKNdeM73eNYLD7v5k5+PD3CqzbUoLVM/uODnaUNEFvsuIKF5s0HREJeJiUHtXvxs3yFg2MZqtbqc0csYiPlMhwt2qeVbRo+j0eoLe0aDEaNW4EGqUWDAMkR7l/Nk5mrASHK1vtxXqd4VKbczwc0QC2UjSuAs3BciXykuWQu5EJ2t0V45Px8YEq7Drd5HCq9uXtZ5EcGY5r892b7nPk75fl4lhNO97dW46RCTK8cVMBJqZFed3eYODyHWLVqlUwmUyorKzEwoULB6pPxA9GJkTgrZun+r1dbq/NbTOykRAR5tMoabCQhQnwwCWjsOrrkziYI0Lvtf4fTtQhVirqU0beHVMzo/HGrnPQGS0QixxPWyr6OezMmZx4mVuBplKpxagEz6Zb0qMlOF7Z0u91Va1aJMnDPZqSzYixVYhu05pcrheWNmmQGiX26INMTlzn2TS1HbhmiuNrjGYrDle2YvG0/pMRertgRCwixUJsOVHfJ9DsP9eCA2VKrP7TWK+mqDl8HoNXlkzBzycbcOXE5AFbRwkkl+Owp556Cueddx4SEhIwbdq0Hv+R4S1RHj4kggxn0XkZyIiR4J3Dyh7ld3RGC3aUNGLu+CSPFl85U7NsBTaLqtqcXlNS3wEBj8GIBPc/uQOde2ma1C73u1itLKpadf0eD9BbeowEjRpzv3tpqpU6j6bNgK7imv1VCLAV0/Tsngj4PIxJisBJFxuBT9S2Q2+y9rtR0xEhn4fLxyZiW3EDDOaeCSqv7DiLOJkIi7wIYL3Jw4W4riBtSAQZoJ9Ak56ejgsuuAD79u3DjBkzevxHyFAiEvDwj7mjUd5qxNdFNfbHd55ugtZowZVeJjrkZ3AFNp1Pn5XUqZATL/X4U3B2nBQderPLo4YbVQYY3TgeoLe0aDGMFhZNKtdl96tatUhzMxGAk8UdF+Bi1zrLsihtVHu0PsMZl+L6bBqukOZULwINAMybkAyVwYw9Z7uqnRdVteG3M824Y2bOkAkO/uQy0GzYsAH79u3D/PnzsXv37h7/ETLUXDUhGSNiRFj/02n7p9UtJ+oQLRE6LSPfnyiJCKMSZC4PQuvvsDNnsu3FNZ2/YXuaccbhRilVLmqeGcwW1HfoPR7RcEHPVeZZQ4cBGqPFrRpnvY1NlqNNa0Kdk/JMB8uUyImXIj7Cuw2IF46MRUS4oMfJm69sP4soiRA3/CHTqzaHOrdSGJYtW4YXX3wRDz/8MH766aeQLVBJiCs8HoPbCmJQ06bDh79XQm+y4BdFI+aOS/Ip9XxqVjQOOymw2aE3oaZN1+/RAI6M6Cyu6SrzzNM9NBx3jguobdODZfs/HqC3cCEfSfJwl4Gm6/hmz6bOgG4JAQ6mz6xWFgfLlV5Nm3HCBHxclpeIn07Ww2i2ori2A9sUDbhterbLskfDmVuvnkceeQRpaWmoqKhAXFwc/vWvfwW6X4QERX6KBDNGxuGV7Wew5UQd1Aazz/uDpmbaCmyeaexbYNO2ax/I82JEkxothpDP9Dui4TFwq1ZYd1zJf1dVnLmqzf0deOZIRqwElS5SnD0pptnb6CQ5GAYON26ealChQ2/2KrGjuysmJKNDb8a+cy34769nEREmwM0epKcPN24Fmra2NsyfPx8CgQD5+fmwWgf+ECtCBsqKP45Bq9aEf315ApFiIS4cEetTe1Ozugps9lbS+WboyWZNDp/HIDNWijIX59JUtmjcOh6gN7GIj6hwvstzabgg5M3mv6xYCcpdjWga1YgIE3g1vSULEyArVupwRNPfQWfumjkqDlIRH6/9ehY/HK/DTRdmIlLsWar0cOL2s6+0tBQAUF9fDz6fFrvI0DUhLRJXTUyG1mjBZWMTfd4klxEjQZwszOGJmyX1KsjDBUh2szZcb9lxUpfn0nhyPEBviTKB6xFNqxZCPoNEued9z4yVokllgNbouPpAaZMGOQkyrzMbnZWiOVCuREpkuFtn57gSLuTjkrxE/H5OiXABH7dNp8MgXXHrFfToo4/ikUceQXFxMR544AGsXLky0P0iJKiWzx2N1CgxFp6X7nNbDMNgamY0DjnIPCupV2FMstzrN9ScOCkqWrROT0StVOp8CjSuzqWpUmqREiX2Ku2b61OlkxFTaZPaq/UZztgUOSqVPY9fZlkWB8uUOC87xi+p+fMm2PbR3HB+hts16oYrt1aucnNzsXHjRlRVVSEtLQ0xMb4NOwkZ7DJjpdizco7f2puaFY2tJ+vR2KFHQucIwGplcapehet82EWeEy+F0WJFbZuuzxSW1mhGs9rg8R4aTpJMiH1V7bBYWYfBpKrV8z00nMzYrsyz3hl3aoMZde16r1KbOWOTO8+mqVPZ12MqWrRoVBl8Xp/hXJKXiH/Ny8P1U33/MDLUuTWi+eGHH7Bo0SJs3LgRCxcuxNdffx3ofhEypHA1tbqnOde06aA2mDEm2fsK4vZjnR0kBFQpbdNevoxoTBYWDR2O04SrlVq3i2n2lhnTuWnTwV4aLovOp0DTmXl2srbr7JgDnWtkvmScdSfk83DHrBxEullDbjhzK9C899572Lx5M1599VV8+eWXeP/99wPdL0KGlHEpkQgX8nCovCvQKHxIBOBwVZzPOTgy2tvUZk6izDbh4SghQGMwo0VjtGeneSpSYivc6SjF2Z5x5mGlhO4SIsIQKxX1SAg4UKZEjFTk8jgIEhhuBRqGYSCV2v7oMpkMYWE0H0mIJ0QCHialRfVYp+FSm0f7UPY9TiZCRJjA4bHO3GjB10DjKCHAl4wzTmaMxOEaTWmTGnweg4wY7wMNwzAYm9IzIeBguRJTM6OHVOmkUOFWoElPT8e6deuwbds2PPPMM8jI8L2WDyHDzdSsaJys7bBnWpXUq5AZK4HUh01+DMMgJ17qMNB4ejxAbwky2/c5SgjwZQ8NJyNW6nREkxkj8Tglu7exKXKcaVDbD/qqaNH6bX2GeMatv+TChQsRGRmJvXv3YvPmzbjhhhsC3S9ChpypmTGwdCuwqajv8OhoAGecpThXKrUeHw/QnYjPIFEe5mREwx145tuIpqZNB5Ol57680kaNX843Gpssh9FiRWmT2r5/hgJNcLgVaNauXYsrr7wSq1atwueff45169YFul+EDDn2ApvlrdCbLChv1mC0H44Sz46TobZdB72pZzXhSqXWnt3lrfRoicM1mqpWHcRCPmI9OBa8t8xYCSxWFjXdApnFyqKsWeNxJWtHxnUrRXOgTAmpiG/PRiMDy61AIxQK7dNl6enp4PFC85Q3QoIpUiJEbqKtwOaZBjWsLJDnjxFNvBQsaztAjWM/HsDHI3vTosUORzRVSi3SosU+rXc4Oi6gulULo8XqU8YZJztOhnAhD8V1HThYrkR+ZrRPNeuI99y66ykpKdiwYQO2b9+OF198EQkJfc90J4T0ryAzBocrW+1pt76kNnNy4voe6+zt8QC9pcdIUNfed3qrqrXvvh1PcaOtym4BsquYpu+Bhs9jMDpJjr2lLTjVoPK6AjfxndtTZzExMdi5cydiYmKwdu3aQPeLkCHpvKxoqPRmfHO0FmIh3+cRB9AtxblbQoCvGWectGgxrCxQ363kPsuytj00PpZxSYgIQ7iQ16Pm2dlG76s2OzI2WQ5FXQdY1vf6ZsR7bqW7hIWF4ZZbbglwVwgZ+qZm2t7s9pa2YFJapFflW3qThgmQKA/rkXnm6x4ajv1cms7EAgBo15mgMph9HtEwDIPMmJ6ZZ6WNGsTJRIiSeL/20x23cVPE52FSepRf2iSeowlLQgZQeozYXpHYm8POnLFlnnVt2qzy8niA3hwdF8BVHPB2s2Z3vY8LsB3f7L8Nldzi/6T0SDr5Mogo0BAygLgCmwC8OuzMmZx4WZ8RTUqU58cD9JYcFQ4e03MvTZU9tdm3IAZ0bdrkjl22FdP0X6DJS45AmICHC0fE+a1N4jk6Do6QAVaQGY0tJ+r9OqLJiZOiVWtCq8aIaKnIp+MBuhPyeUiOFPca0fi+h4aTGSuB3mRFo8oAIZ+HVq3Jb+szACARCfDD32Yi1ceRHfHNgAUalmUxa9YsZGVlAQAmT56MZcuWYfv27fjvf/8LgUCA6667Dtdffz30ej2WL1+OlpYWSKVSPPPMM4iJifH5WkIGg2vz09CmNdkLbfoDlxBQ1qLpDDQ6XJrnn+zQtGhxj7001a06RIqFkIf7XkySS3Eub9aA17leNcLPtcj8OUIi3hmwQFNZWYlx48Zh48aN9sdMJhPWrl2Lzz//HGKxGIsXL8acOXPw7bffIjc3F/fffz++//57vPrqq1ixYoXP18bF0fCZBF+MVIR/zB3t1za7imtqMDoxAs1qg19GHIBtLWbP2Wb7v6tava/a3Jv9uAClFtbOM3W8Ob6ZDG4DtkZz8uRJNDQ0YOnSpbjjjjtw7tw5lJaWIiMjA5GRkRCJRCgoKMDBgwdRWFiImTNnAgBmzZqFffv2+eVaQoaq9BgJBDwGZc1q+xqKP6bObG2L0aDSw2C2VR6oUmqRFuWftrmD0ypbtChtUiNMwPM5gYEMPgEZ0Xz22Wd47733ejy2atUq3Hnnnbjiiitw6NAhLF++HA8//DAiIroWRKVSKdRqNdRqtf1xqVQKlUrV4zFvr3VEoVD47fcOZXq9nu4FQvs+JMoEOHquHrGwPdctHQ1QKNr7+S7nuHvB06nAssBvhSeREiFAlVKLSfECv92neAkfx8vqoDOzSIkQ4PSpEr+060+h/LwYDAISaBYsWIAFCxb0eEyn04HPt6UXTp06FY2NjZDJZNBoujJlNBoNIiIiejyu0Wggl8v9cq0jeXl5/vmlQ5xCoaB7gdC+D2P2q1HTpoNVEgOgAbMLxvm0H4W7Fx1hLcCeJoiikxGXFAGjpQyTR6UhLy/LL/0eldyBVp0J7ToTxqfHDsr7H8rPC38rLCz0+HsGbOrslVdesY9ySkpKkJycjBEjRqCiogJtbW0wGo04dOgQpkyZgvz8fOzcuRMAsGvXLhQUFPjlWkKGsuw4KcpbNKho0SIiXIBIsX9OfkzrnIKratV2pTb7YQ8NJzNWgnNNGlQptbRwP0QNWDLAnXfeieXLl2Pnzp3g8/lYu3YthEIhVq5cidtvvx0sy+K6665DYmIiFi9ejBUrVmDx4sUQCoVYv369X64lZCjLiZdBb7LiQJkSGT4cD9BbkjwcQj6D6lYdpCLbW4a/kgEA27HOaoPtjB5/pjaTwWPAAk1kZCTeeOONPo/PmTMHc+bM6fGYWCzGSy+95PdrCRnKuMyzUw0qXDE+yW/t8nkMUqJsKc6Szt31/qgKwMnodpQBjWiGJtqwScgQkdNtNOCvjDMOd1yAVCRAfESYX8u5dD8zJ4dGNEMSlaAhZIhIiAiDVGQLAP7aQ8NJj5agunONxteqzb1xQTE1SgyJiD77DkUUaAgZIhiGQXbniMDXkzV7S4sWo1ltxOkGtd+DmEQkQEJEGI1mhjD6+EDIEJIdJ8OJmg6/T51xwaVZbUCan0c0ALD6z+PsVa3J0EOBhpAhZHyKHL+eavT77vruwcWfqc2ceROS/d4mGTwo0BAyhNw6PRvX5KdCyPfvrHj34OLvqTMy9NEaDSFDiEjAQ0JEuN/bjZOF2c+2CcSIhgxtFGgIIf3i8RikRYvBY2yHoRHiCZo6I4S4JT1aAoPJ6vdpOTL0UaAhhLjlocty0ao1BrsbJARRoCGEuGVSelSwu0BCFI2BCSGEBBQFGkIIIQHFsCzLBrsTweLNAT6EEDLcFRQUeHT9sA40hBBCAo+mzgghhAQUBRpCCCEBNewCjdVqxapVq7Bw4UIsXboUFRUVwe5S0JhMJixfvhxLlizB/Pnz8csvvwS7S0HX0tKCiy66CKWlpcHuSlC9/vrrWLhwIa699lp89tlnwe5O0JhMJixbtgyLFi3CkiVLhu3z4ujRo1i6dCkAoKKiAosXL8aSJUvw+OOPw2q19vv9wy7QbNu2DUajEZ9++imWLVuGdevWBbtLQfPNN98gKioK//vf//DWW2/hySefDHaXgspkMmHVqlUIDx/eJVb279+PI0eO4OOPP8YHH3yA+vr6YHcpaHbu3Amz2YxPPvkE9957L1588cVgd2nAvfnmm3j00UdhMBgAAGvXrsWDDz6I//3vf2BZ1q0PqMMu0BQWFmLmzJkAgMmTJ+PEiRNB7lHw/PGPf8Tf/vY3AADLsuDz/Xc8byh65plnsGjRIiQkJAS7K0G1e/du5Obm4t5778Vdd92F2bNnB7tLQZOdnQ2LxQKr1Qq1Wg2BYPjtcc/IyMDLL79s//fJkycxbdo0AMCsWbOwd+/eftsYdndNrVZDJpPZ/83n82E2m4flE0gqtZ1oqFar8cADD+DBBx8MboeCaPPmzYiJicHMmTPxxhtvBLs7QdXa2ora2lps3LgR1dXVuPvuu7F161YwDBPsrg04iUSCmpoaXHHFFWhtbcXGjRuD3aUBN3fuXFRXV9v/zbKs/bkglUqhUqn6bWPYjWhkMhk0Go3931ardVgGGU5dXR1uuukmXH311fjTn/4U7O4EzRdffIG9e/di6dKlUCgUWLFiBZqamoLdraCIiorCjBkzIBKJkJOTg7CwMCiVymB3KyjeffddzJgxAz/++CO+/vprrFy50j6FNFzxeF1hQ6PRQC6X9/89gezQYJSfn49du3YBAIqKipCbmxvkHgVPc3MzbrvtNixfvhzz588PdneC6qOPPsKHH36IDz74AHl5eXjmmWcQHx8f7G4FRUFBAX777TewLIuGhgbodDpERUUFu1tBIZfLERERAQCIjIyE2WyGxWIJcq+Ca+zYsdi/fz8AYNeuXZg6dWq/3zPsPspfdtll2LNnDxYtWgSWZfH0008Hu0tBs3HjRnR0dODVV1/Fq6++CsC28DfcF8OHu4svvhgHDx7E/PnzwbIsVq1aNWzX72655RY88sgjWLJkCUwmE/7+979DIhneB7+tWLECjz32GDZs2ICcnBzMnTu33++hygCEEEICathNnRFCCBlYFGgIIYQEFAUaQgghAUWBhhBCSEBRoCGEEBJQFGgIcWLz5s14/vnnvf7+pqYmrF692q1rp0+f7vXP6a6trQ3ffvstAOCNN97AsWPH/NIuIb6gQENIgMTHx7sdaPzl1KlT2L59OwDgzjvvxMSJEwf05xPiyLDbsEmIJ4qKinDzzTdDrVbj/vvvx+zZs7F161Z89NFHMJvNYBgGr7zyCgDgwQcfBMuyMBgM+Pe//42IiAg89NBD2LRpE1544QXs378fZrMZl19+Oe68806HP6+4uBhPPvkk+Hw+wsLC8OSTTyIlJQWvvvoqtm3bBovFgsWLF2PRokVYv349Tpw4gba2NowZMwZr167Fxo0bUVJSgk8//RRHjhzBvHnzcMEFF+Dhhx9GdXU1LBYLbr31VsybNw9Lly7FmDFjcObMGajVavznP/9BamrqQN5eMkxQoCHEBbFYjDfeeANKpRILFizArFmzUF5ejjfeeANisRirVq3C7t27IZfLERUVhWeffRZnz56FVqu1ly4BgG+//Rbvv/8+EhISsHnzZqc/79FHH8VTTz2FvLw8bNu2DevWrcNdd92FXbt24bPPPoPFYsGGDRugUqkgl8vxzjvvwGq14sorr0RDQwPuuusufPLJJ1i4cCGOHDkCAPj0008RExOD559/Hmq1Gtdeey3+8Ic/AAAmTpyIf/3rX3jhhRfw/fffOw2AhPiCAg0hLhQUFIBhGMTGxiIiIgJtbW2IjY3FihUrIJVKce7cOUyePNkegO655x4IBALcfffdPdp57rnnsH79ejQ3N9uPqXCksbEReXl5AIDzzjsP69evR1lZGSZOnAg+nw8+n4+VK1fCZDJBqVTioYcegkQigVarhclkcthmaWkpLrzwQgC2orIjRoxAVVUVAFvdKgBISkpCc3Ozz/eLEEdojYYQF44fPw7AtrCv1WohFArx0ksv4YUXXsCaNWsQFhYGlmWxf/9+JCQk4O2338bdd9+NDRs22NswGo3YunUrNmzYgPfffx9ffvklampqHP68hIQElJSUAAAOHjyIrKws5OTkoLi4GFarFSaTCbfeeit27tyJuro6bNiwAQ899BD0ej1YlgWPx+tz4uGIESNw6NAhALYjIU6fPo20tLRA3C5CHKIRDSEu6PV63HTTTdBqtXjiiScgk8mQn5+PhQsXQiAQQC6Xo7GxEXPmzMFDDz2Ejz/+GGazGffee6+9DZFIhMjISFx//fUIDw/H9OnTkZKS4vDnrVmzBk8++aT9ILqnn34a6enpmDlzJhYvXgyr1YrFixdj0qRJeO2113DDDTeAYRikp6ejsbERGRkZOH36NN599117m9dffz0ee+wxLF68GAaDAffddx9iY2MDfesIsaOimoQQQgKKps4IIYQEFAUaQgghAUWBhhBCSEBRoCGEEBJQFGgIIYQEFAUaQgghAUWBhhBCSEBRoCGEEBJQ/x/+xDS1houLxgAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118ad2c18>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -613,8 +613,8 @@
    },
    "source": [
     "The lower panel of this figure shows the amplitude of the basis function at each location.\n",
-    "This is typical over-fitting behavior when basis functions overlap: the coefficients of adjacent basis functions blow up and cancel each other out.\n",
-    "We know that such behavior is problematic, and it would be nice if we could limit such spikes expliticly in the model by penalizing large values of the model parameters.\n",
+    "This is typical overfitting behavior when basis functions overlap: the coefficients of adjacent basis functions blow up and cancel each other out.\n",
+    "We know that such behavior is problematic, and it would be nice if we could limit such spikes explicitly in the model by penalizing large values of the model parameters.\n",
     "Such a penalty is known as *regularization*, and comes in several forms."
    ]
   },
@@ -625,15 +625,15 @@
     "editable": true
    },
    "source": [
-    "### Ridge regression ($L_2$ Regularization)\n",
+    "### Ridge Regression ($L_2$ Regularization)\n",
     "\n",
-    "Perhaps the most common form of regularization is known as *ridge regression* or $L_2$ *regularization*, sometimes also called *Tikhonov regularization*.\n",
-    "This proceeds by penalizing the sum of squares (2-norms) of the model coefficients; in this case, the penalty on the model fit would be \n",
+    "Perhaps the most common form of regularization is known as *ridge regression* or $L_2$ *regularization* (sometimes also called *Tikhonov regularization*).\n",
+    "This proceeds by penalizing the sum of squares (2-norms) of the model coefficients $\\theta_n$. In this case, the penalty on the model fit would be: \n",
     "$$\n",
     "P = \\alpha\\sum_{n=1}^N \\theta_n^2\n",
     "$$\n",
     "where $\\alpha$ is a free parameter that controls the strength of the penalty.\n",
-    "This type of penalized model is built into Scikit-Learn with the ``Ridge`` estimator:"
+    "This type of penalized model is built into Scikit-Learn with the `Ridge` estimator (see the following figure):"
    ]
   },
   {
@@ -642,14 +642,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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4aejX3YcHpoXJ3O2twJIlY4G6sbWeFaX4+3YgLuUclZU1JH6XQVYrXgxDtA4J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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1189f3390>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -681,15 +684,15 @@
     "editable": true
    },
    "source": [
-    "### Lasso regression ($L_1$ regularization)\n",
+    "### Lasso Regression ($L_1$ Regularization)\n",
     "\n",
-    "Another very common type of regularization is known as lasso, and involves penalizing the sum of absolute values (1-norms) of regression coefficients:\n",
+    "Another common type of regularization is known as *lasso regression* or *L~1~ regularization* involves penalizing the sum of absolute values (1-norms) of regression coefficients:\n",
     "$$\n",
     "P = \\alpha\\sum_{n=1}^N |\\theta_n|\n",
     "$$\n",
-    "Though this is conceptually very similar to ridge regression, the results can differ surprisingly: for example, due to geometric reasons lasso regression tends to favor *sparse models* where possible: that is, it preferentially sets model coefficients to exactly zero.\n",
+    "Though this is conceptually very similar to ridge regression, the results can differ surprisingly. For example, due to its construction, lasso regression tends to favor *sparse models* where possible: that is, it preferentially sets many model coefficients to exactly zero.\n",
     "\n",
-    "We can see this behavior in duplicating the ridge regression figure, but using L1-normalized coefficients:"
+    "We can see this behavior if we duplicate the previous example using L1-normalized coefficients (see the following figure):"
    ]
   },
   {
@@ -698,14 +701,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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1lctWXOVzvGHXCT7acISwAC+eTRjhULMupGivA0ZjJT4B1dwweyu+QSZUFQ3c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\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1194f3208>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -714,7 +720,7 @@
    ],
    "source": [
     "from sklearn.linear_model import Lasso\n",
-    "model = make_pipeline(GaussianFeatures(30), Lasso(alpha=0.001))\n",
+    "model = make_pipeline(GaussianFeatures(30), Lasso(alpha=0.001, max_iter=2000))\n",
     "basis_plot(model, title='Lasso Regression')"
    ]
   },
@@ -726,7 +732,7 @@
    },
    "source": [
     "With the lasso regression penalty, the majority of the coefficients are exactly zero, with the functional behavior being modeled by a small subset of the available basis functions.\n",
-    "As with ridge regularization, the $\\alpha$ parameter tunes the strength of the penalty, and should be determined via, for example, cross-validation (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for a discussion of this)."
+    "As with ridge regularization, the $\\alpha$ parameter tunes the strength of the penalty and should be determined via, for example, cross-validation (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for a discussion of this)."
    ]
   },
   {
@@ -742,16 +748,13 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
     "deletable": true,
     "editable": true
    },
    "source": [
     "As an example, let's take a look at whether we can predict the number of bicycle trips across Seattle's Fremont Bridge based on weather, season, and other factors.\n",
-    "We have seen this data already in [Working With Time Series](03.11-Working-with-Time-Series.ipynb).\n",
-    "\n",
-    "In this section, we will join the bike data with another dataset, and try to determine the extent to which weather and seasonal factors—temperature, precipitation, and daylight hours—affect the volume of bicycle traffic through this corridor.\n",
-    "Fortunately, the NOAA makes available their daily [weather station data](http://www.ncdc.noaa.gov/cdo-web/search?datasetid=GHCND) (I used station ID USW00024233) and we can easily use Pandas to join the two data sources.\n",
+    "We already saw this data in [Working With Time Series](03.11-Working-with-Time-Series.ipynb), but here we will join the bike data with another dataset and try to determine the extent to which weather and seasonal factors—temperature, precipitation, and daylight hours—affect the volume of bicycle traffic through this corridor.\n",
+    "Fortunately, the National Oceanic and Atmospheric Administration (NOAA) makes its daily [weather station data](http://www.ncdc.noaa.gov/cdo-web/search?datasetid=GHCND) available—I used station ID USW00024233—and we can easily use Pandas to join the two data sources.\n",
     "We will perform a simple linear regression to relate weather and other information to bicycle counts, in order to estimate how a change in any one of these parameters affects the number of riders on a given day.\n",
     "\n",
     "In particular, this is an example of how the tools of Scikit-Learn can be used in a statistical modeling framework, in which the parameters of the model are assumed to have interpretable meaning.\n",
@@ -764,13 +767,15 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
-    "# !curl -o FremontBridge.csv https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD"
+    "# url = 'https://raw.githubusercontent.com/jakevdp/bicycle-data/main'\n",
+    "# !curl -O {url}/FremontBridge.csv\n",
+    "# !curl -O {url}/SeattleWeather.csv"
    ]
   },
   {
@@ -779,13 +784,35 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "counts = pd.read_csv('FremontBridge.csv', index_col='Date', parse_dates=True)\n",
-    "weather = pd.read_csv('data/BicycleWeather.csv', index_col='DATE', parse_dates=True)"
+    "counts = pd.read_csv('FremontBridge.csv',\n",
+    "                     index_col='Date', parse_dates=True)\n",
+    "weather = pd.read_csv('SeattleWeather.csv',\n",
+    "                      index_col='DATE', parse_dates=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For simplicity, let's look at data prior to 2020 in order to avoid the effects of the COVID-19 pandemic, which significantly affected commuting patterns in Seattle:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "counts = counts[counts.index < \"2020-01-01\"]\n",
+    "weather = weather[weather.index < \"2020-01-01\"]"
    ]
   },
   {
@@ -795,16 +822,19 @@
     "editable": true
    },
    "source": [
-    "Next we will compute the total daily bicycle traffic, and put this in its own dataframe:"
+    "Next we will compute the total daily bicycle traffic, and put this in its own `DataFrame`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -820,16 +850,16 @@
     "editable": true
    },
    "source": [
-    "We saw previously that the patterns of use generally vary from day to day; let's account for this in our data by adding binary columns that indicate the day of the week:"
+    "We saw previously that the patterns of use generally vary from day to day. Let's account for this in our data by adding binary columns that indicate the day of the week:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -850,17 +880,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
     "cal = USFederalHolidayCalendar()\n",
-    "holidays = cal.holidays('2012', '2016')\n",
+    "holidays = cal.holidays('2012', '2020')\n",
     "daily = daily.join(pd.Series(1, index=holidays, name='holiday'))\n",
     "daily['holiday'].fillna(0, inplace=True)"
    ]
@@ -872,33 +905,36 @@
     "editable": true
    },
    "source": [
-    "We also might suspect that the hours of daylight would affect how many people ride; let's use the standard astronomical calculation to add this information:"
+    "We also might suspect that the hours of daylight would affect how many people ride. Let's use the standard astronomical calculation to add this information (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8, 17)"
+       "(8.0, 17.0)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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bA7KOZTzixaW5DKpgMKjweZN7zgBOa0GvQrSWI621w7MXFwEoQGvDXlycW0YV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\nmtn3L/D6+npkZWWhoKBA+hi0SGluQ0ND6O7uhsvlwoEDB3D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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1196796d8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -931,24 +967,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "# temperatures are in 1/10 deg C; convert to C\n",
-    "weather['TMIN'] /= 10\n",
-    "weather['TMAX'] /= 10\n",
-    "weather['Temp (C)'] = 0.5 * (weather['TMIN'] + weather['TMAX'])\n",
-    "\n",
-    "# precip is in 1/10 mm; convert to inches\n",
-    "weather['PRCP'] /= 254\n",
+    "weather['Temp (F)'] = 0.5 * (weather['TMIN'] + weather['TMAX'])\n",
+    "weather['Rainfall (in)'] = weather['PRCP']\n",
     "weather['dry day'] = (weather['PRCP'] == 0).astype(int)\n",
     "\n",
-    "daily = daily.join(weather[['PRCP', 'Temp (C)', 'dry day']])"
+    "daily = daily.join(weather[['Rainfall (in)', 'Temp (F)', 'dry day']])"
    ]
   },
   {
@@ -964,11 +998,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -987,17 +1024,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1012,8 +1065,8 @@
        "      <th>Sun</th>\n",
        "      <th>holiday</th>\n",
        "      <th>daylight_hrs</th>\n",
-       "      <th>PRCP</th>\n",
-       "      <th>Temp (C)</th>\n",
+       "      <th>Rainfall (in)</th>\n",
+       "      <th>Temp (F)</th>\n",
        "      <th>dry day</th>\n",
        "      <th>annual</th>\n",
        "    </tr>\n",
@@ -1038,7 +1091,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-10-03</th>\n",
-       "      <td>3521.0</td>\n",
+       "      <td>14084.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -1049,13 +1102,13 @@
        "      <td>0.0</td>\n",
        "      <td>11.277359</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>13.35</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-10-04</th>\n",
-       "      <td>3475.0</td>\n",
+       "      <td>13900.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1066,13 +1119,13 @@
        "      <td>0.0</td>\n",
        "      <td>11.219142</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>13.60</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>56.5</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.002740</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-10-05</th>\n",
-       "      <td>3148.0</td>\n",
+       "      <td>12592.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1083,13 +1136,13 @@
        "      <td>0.0</td>\n",
        "      <td>11.161038</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>15.30</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>59.5</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.005479</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-10-06</th>\n",
-       "      <td>2006.0</td>\n",
+       "      <td>8024.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1100,13 +1153,13 @@
        "      <td>0.0</td>\n",
        "      <td>11.103056</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>15.85</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>60.5</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.008219</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-10-07</th>\n",
-       "      <td>2142.0</td>\n",
+       "      <td>8568.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1117,8 +1170,8 @@
        "      <td>0.0</td>\n",
        "      <td>11.045208</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>15.85</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>60.5</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.010959</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1126,24 +1179,24 @@
        "</div>"
       ],
       "text/plain": [
-       "             Total  Mon  Tue  Wed  Thu  Fri  Sat  Sun  holiday  daylight_hrs  \\\n",
-       "Date                                                                           \n",
-       "2012-10-03  3521.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0      0.0     11.277359   \n",
-       "2012-10-04  3475.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0      0.0     11.219142   \n",
-       "2012-10-05  3148.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0      0.0     11.161038   \n",
-       "2012-10-06  2006.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0      0.0     11.103056   \n",
-       "2012-10-07  2142.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0      0.0     11.045208   \n",
+       "              Total  Mon  Tue  Wed  Thu  Fri  Sat  Sun  holiday  daylight_hrs  \\\n",
+       "Date                                                                            \n",
+       "2012-10-03  14084.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0      0.0     11.277359   \n",
+       "2012-10-04  13900.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0      0.0     11.219142   \n",
+       "2012-10-05  12592.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0      0.0     11.161038   \n",
+       "2012-10-06   8024.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0      0.0     11.103056   \n",
+       "2012-10-07   8568.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0      0.0     11.045208   \n",
        "\n",
-       "            PRCP  Temp (C)  dry day    annual  \n",
-       "Date                                           \n",
-       "2012-10-03   0.0     13.35      1.0  0.000000  \n",
-       "2012-10-04   0.0     13.60      1.0  0.002740  \n",
-       "2012-10-05   0.0     15.30      1.0  0.005479  \n",
-       "2012-10-06   0.0     15.85      1.0  0.008219  \n",
-       "2012-10-07   0.0     15.85      1.0  0.010959  "
+       "            Rainfall (in)  Temp (F)  dry day    annual  \n",
+       "Date                                                    \n",
+       "2012-10-03            0.0      56.0        1  0.000000  \n",
+       "2012-10-04            0.0      56.5        1  0.002740  \n",
+       "2012-10-05            0.0      59.5        1  0.005479  \n",
+       "2012-10-06            0.0      60.5        1  0.008219  \n",
+       "2012-10-07            0.0      60.5        1  0.010959  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1160,24 +1213,28 @@
    },
    "source": [
     "With this in place, we can choose the columns to use, and fit a linear regression model to our data.\n",
-    "We will set ``fit_intercept = False``, because the daily flags essentially operate as their own day-specific intercepts:"
+    "We will set `fit_intercept=False`, because the daily flags essentially operate as their own day-specific intercepts:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "# Drop any rows with null values\n",
     "daily.dropna(axis=0, how='any', inplace=True)\n",
     "\n",
-    "column_names = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun', 'holiday',\n",
-    "                'daylight_hrs', 'PRCP', 'dry day', 'Temp (C)', 'annual']\n",
+    "column_names = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun',\n",
+    "                'holiday', 'daylight_hrs', 'Rainfall (in)',\n",
+    "                'dry day', 'Temp (F)', 'annual']\n",
     "X = daily[column_names]\n",
     "y = daily['Total']\n",
     "\n",
@@ -1193,23 +1250,26 @@
     "editable": true
    },
    "source": [
-    "Finally, we can compare the total and predicted bicycle traffic visually:"
+    "Finally, we can compare the total and predicted bicycle traffic visually (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Ts2UcfWNIWkbXtchrilAauGIpoAS4QqFYcGqZ0IPUSqU6nHU05VzJlJaZKVRgE4LmdHSm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PYvuUgHUicN17a0JmvgILXslElv1eskpB38jF4CBgN3HImFTo3V3snk2m9ph8BOtYkj8j04It/D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\nknkuDQ357TeRqvGYqPYwzqjcGGPWMm6j0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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1195dc208>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1227,40 +1287,43 @@
     "editable": true
    },
    "source": [
-    "It is evident that we have missed some key features, especially during the summer time.\n",
-    "Either our features are not complete (i.e., people decide whether to ride to work based on more than just these) or there are some nonlinear relationships that we have failed to take into account (e.g., perhaps people ride less at both high and low temperatures).\n",
+    "From the fact that the data and model predictions don't line up exactly, it is evident that we have missed some key features.\n",
+    "Either our features are not complete (i.e., people decide whether to ride to work based on more than just these features), or there are some nonlinear relationships that we have failed to take into account (e.g., perhaps people ride less at both high and low temperatures).\n",
     "Nevertheless, our rough approximation is enough to give us some insights, and we can take a look at the coefficients of the linear model to estimate how much each feature contributes to the daily bicycle count:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Mon              504.882756\n",
-       "Tue              610.233936\n",
-       "Wed              592.673642\n",
-       "Thu              482.358115\n",
-       "Fri              177.980345\n",
-       "Sat            -1103.301710\n",
-       "Sun            -1133.567246\n",
-       "holiday        -1187.401381\n",
-       "daylight_hrs     128.851511\n",
-       "PRCP            -664.834882\n",
-       "dry day          547.698592\n",
-       "Temp (C)          65.162791\n",
-       "annual            26.942713\n",
+       "Mon              -3309.953439\n",
+       "Tue              -2860.625060\n",
+       "Wed              -2962.889892\n",
+       "Thu              -3480.656444\n",
+       "Fri              -4836.064503\n",
+       "Sat             -10436.802843\n",
+       "Sun             -10795.195718\n",
+       "holiday          -5006.995232\n",
+       "daylight_hrs       409.146368\n",
+       "Rainfall (in)    -2789.860745\n",
+       "dry day           2111.069565\n",
+       "Temp (F)           179.026296\n",
+       "annual             324.437749\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1283,11 +1346,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -1309,73 +1375,69 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "              effect  error\n",
-      "Mon            505.0   86.0\n",
-      "Tue            610.0   83.0\n",
-      "Wed            593.0   83.0\n",
-      "Thu            482.0   85.0\n",
-      "Fri            178.0   81.0\n",
-      "Sat          -1103.0   80.0\n",
-      "Sun          -1134.0   83.0\n",
-      "holiday      -1187.0  163.0\n",
-      "daylight_hrs   129.0    9.0\n",
-      "PRCP          -665.0   62.0\n",
-      "dry day        548.0   33.0\n",
-      "Temp (C)        65.0    4.0\n",
-      "annual          27.0   18.0\n"
+      "                effect  uncertainty\n",
+      "Mon            -3310.0        265.0\n",
+      "Tue            -2861.0        274.0\n",
+      "Wed            -2963.0        268.0\n",
+      "Thu            -3481.0        268.0\n",
+      "Fri            -4836.0        261.0\n",
+      "Sat           -10437.0        259.0\n",
+      "Sun           -10795.0        267.0\n",
+      "holiday        -5007.0        401.0\n",
+      "daylight_hrs     409.0         26.0\n",
+      "Rainfall (in)  -2790.0        186.0\n",
+      "dry day         2111.0        101.0\n",
+      "Temp (F)         179.0          7.0\n",
+      "annual           324.0         22.0\n"
      ]
     }
    ],
    "source": [
     "print(pd.DataFrame({'effect': params.round(0),\n",
-    "                    'error': err.round(0)}))"
+    "                    'uncertainty': err.round(0)}))"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
-    "We first see that there is a relatively stable trend in the weekly baseline: there are many more riders on weekdays than on weekends and holidays.\n",
-    "We see that for each additional hour of daylight, 129 ± 9 more people choose to ride; a temperature increase of one degree Celsius encourages 65 ± 4 people to grab their bicycle; a dry day means an average of 548 ± 33 more riders, and each inch of precipitation means 665 ± 62 more people leave their bike at home.\n",
-    "Once all these effects are accounted for, we see a modest increase of 27 ± 18 new daily riders each year.\n",
+    "The `effect` column here, roughly speaking, shows how the number of riders is affected by a change of the feature in question.\n",
+    "For example, there is a clear divide when it comes to the day of the week: there are thousands fewer riders on weekends than on weekdays.\n",
+    "We also see that for each additional hour of daylight, 409 ± 26 more people choose to ride; a temperature increase of one degree Fahrenheit encourages 179 ± 7 people to grab their bicycle; a dry day means an average of 2,111 ± 101 more riders,\n",
+    "and every inch of rainfall leads 2,790 ± 186 riders to choose another mode of transport.\n",
+    "Once all these effects are accounted for, we see a modest increase of 324 ± 22 new daily riders each year.\n",
     "\n",
-    "Our model is almost certainly missing some relevant information. For example, nonlinear effects (such as effects of precipitation *and* cold temperature) and nonlinear trends within each variable (such as disinclination to ride at very cold and very hot temperatures) cannot be accounted for in this model.\n",
+    "Our simple model is almost certainly missing some relevant information. For example, as mentioned earlier, nonlinear effects (such as effects of precipitation *and* cold temperature) and nonlinear trends within each variable (such as disinclination to ride at very cold and very hot temperatures) cannot be accounted for in a simple linear model.\n",
     "Additionally, we have thrown away some of the finer-grained information (such as the difference between a rainy morning and a rainy afternoon), and we have ignored correlations between days (such as the possible effect of a rainy Tuesday on Wednesday's numbers, or the effect of an unexpected sunny day after a streak of rainy days).\n",
     "These are all potentially interesting effects, and you now have the tools to begin exploring them if you wish!"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) | [Contents](Index.ipynb) | [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.06-Linear-Regression.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.6 64-bit ('3.9.6')",
    "language": "python",
    "name": "python3"
   },
@@ -1389,9 +1451,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc"
+   }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.07-Support-Vector-Machines.ipynb b/notebooks/05.07-Support-Vector-Machines.ipynb
index 31cf9508b..e9f088f4a 100644
--- a/notebooks/05.07-Support-Vector-Machines.ipynb
+++ b/notebooks/05.07-Support-Vector-Machines.ipynb
@@ -4,29 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) | [Contents](Index.ipynb) | [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.07-Support-Vector-Machines.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# In-Depth: Support Vector Machines"
+    "# In Depth: Support Vector Machines"
    ]
   },
   {
@@ -34,7 +12,7 @@
    "metadata": {},
    "source": [
     "Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression.\n",
-    "In this section, we will develop the intuition behind support vector machines and their use in classification problems.\n",
+    "In this chapter, we will explore the intuition behind SVMs and their use in classification problems.\n",
     "\n",
     "We begin with the standard imports:"
    ]
@@ -43,17 +21,18 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from scipy import stats\n",
-    "\n",
-    "# use seaborn plotting defaults\n",
-    "import seaborn as sns; sns.set()"
+    "plt.style.use('seaborn-whitegrid')\n",
+    "from scipy import stats"
    ]
   },
   {
@@ -67,24 +46,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As part of our disussion of Bayesian classification (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)), we learned a simple model describing the distribution of each underlying class, and used these generative models to probabilistically determine labels for new points.\n",
-    "That was an example of *generative classification*; here we will consider instead *discriminative classification*: rather than modeling each class, we simply find a line or curve (in two dimensions) or manifold (in multiple dimensions) that divides the classes from each other.\n",
+    "As part of our discussion of Bayesian classification (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)), we learned about a simple kind of model that describes the distribution of each underlying class, and experimented with using it to probabilistically determine labels for new points.\n",
+    "That was an example of *generative classification*; here we will consider instead *discriminative classification*. That is, rather than modeling each class, we will simply find a line or curve (in two dimensions) or manifold (in multiple dimensions) that divides the classes from each other.\n",
     "\n",
-    "As an example of this, consider the simple case of a classification task, in which the two classes of points are well separated:"
+    "As an example of this, consider the simple case of a classification task in which the two classes of points are well separated (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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gkfH8fCA/vye8vAqOcUt5DpVNJhj55cndu3fxxhtvYPjw4Rg4cGCZnpOUlGHM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\nYAAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11602a4a8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -92,7 +74,7 @@
     }
    ],
    "source": [
-    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "from sklearn.datasets import make_blobs\n",
     "X, y = make_blobs(n_samples=50, centers=2,\n",
     "                  random_state=0, cluster_std=0.60)\n",
     "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn');"
@@ -103,24 +85,27 @@
    "metadata": {},
    "source": [
     "A linear discriminative classifier would attempt to draw a straight line separating the two sets of data, and thereby create a model for classification.\n",
-    "For two dimensional data like that shown here, this is a task we could do by hand.\n",
+    "For two-dimensional data like that shown here, this is a task we could do by hand.\n",
     "But immediately we see a problem: there is more than one possible dividing line that can perfectly discriminate between the two classes!\n",
     "\n",
-    "We can draw them as follows:"
+    "We can draw some of them as follows; the following figure shows the result:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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x97PhNXEy5tLmAqQH8501Bzf+8nek/rAdgZkZKDE0RErASAz67B8wNjZu/Qm6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\nQ5kwYQIeHh7ExsayZ88eevfurVXcewoODiYjI6NUuz3NBdx7HGA/c1G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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1188979e8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -144,32 +129,35 @@
    "source": [
     "These are three *very* different separators which, nevertheless, perfectly discriminate between these samples.\n",
     "Depending on which you choose, a new data point (e.g., the one marked by the \"X\" in this plot) will be assigned a different label!\n",
-    "Evidently our simple intuition of \"drawing a line between classes\" is not enough, and we need to think a bit deeper."
+    "Evidently our simple intuition of \"drawing a line between classes\" is not good enough, and we need to think a bit more deeply."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Support Vector Machines: Maximizing the *Margin*\n",
+    "## Support Vector Machines: Maximizing the Margin\n",
     "\n",
     "Support vector machines offer one way to improve on this.\n",
     "The intuition is this: rather than simply drawing a zero-width line between the classes, we can draw around each line a *margin* of some width, up to the nearest point.\n",
-    "Here is an example of how this might look:"
+    "Here is an example of how this might look (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ly5cjKioKMpnMY9a0H1cFhUIJrVYLlcq38eOeQMJMDBi++ugYHnL0yXVFDUB7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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11889a710>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -184,7 +172,7 @@
     "    yfit = m * xfit + b\n",
     "    plt.plot(xfit, yfit, '-k')\n",
     "    plt.fill_between(xfit, yfit - d, yfit + d, edgecolor='none',\n",
-    "                     color='#AAAAAA', alpha=0.4)\n",
+    "                     color='lightgray', alpha=0.5)\n",
     "\n",
     "plt.xlim(-1, 3.5);"
    ]
@@ -193,34 +181,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In support vector machines, the line that maximizes this margin is the one we will choose as the optimal model.\n",
-    "Support vector machines are an example of such a *maximum margin* estimator."
+    "The line that maximizes this margin is the one we will choose as the optimal model."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Fitting a support vector machine\n",
+    "### Fitting a Support Vector Machine\n",
     "\n",
-    "Let's see the result of an actual fit to this data: we will use Scikit-Learn's support vector classifier to train an SVM model on this data.\n",
-    "For the time being, we will use a linear kernel and set the ``C`` parameter to a very large number (we'll discuss the meaning of these in more depth momentarily)."
+    "Let's see the result of an actual fit to this data: we will use Scikit-Learn's support vector classifier (`SVC`) to train an SVM model on this data.\n",
+    "For the time being, we will use a linear kernel and set the ``C`` parameter to a very large number (we'll discuss the meaning of these in more depth momentarily):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "SVC(C=10000000000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
-       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
-       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
-       "  tol=0.001, verbose=False)"
+       "SVC(C=10000000000.0, kernel='linear')"
       ]
      },
      "execution_count": 5,
@@ -238,14 +225,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To better visualize what's happening here, let's create a quick convenience function that will plot SVM decision boundaries for us:"
+    "To better visualize what's happening here, let's create a quick convenience function that will plot SVM decision boundaries for us (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -272,7 +262,8 @@
     "    if plot_support:\n",
     "        ax.scatter(model.support_vectors_[:, 0],\n",
     "                   model.support_vectors_[:, 1],\n",
-    "                   s=300, linewidth=1, facecolors='none');\n",
+    "                   s=300, linewidth=1, edgecolors='black',\n",
+    "                   facecolors='none');\n",
     "    ax.set_xlim(xlim)\n",
     "    ax.set_ylim(ylim)"
    ]
@@ -281,14 +272,17 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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9vQWXTxzDqVvJiOzeE0kBgdYOi9gJrVaLBw/uG565lk4fMwzDeRepVCqwcOHv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\nAH6sncTGlP8LAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119058128>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -305,24 +299,27 @@
    "metadata": {},
    "source": [
     "This is the dividing line that maximizes the margin between the two sets of points.\n",
-    "Notice that a few of the training points just touch the margin: they are indicated by the black circles in this figure.\n",
-    "These points are the pivotal elements of this fit, and are known as the *support vectors*, and give the algorithm its name.\n",
-    "In Scikit-Learn, the identity of these points are stored in the ``support_vectors_`` attribute of the classifier:"
+    "Notice that a few of the training points just touch the margin: they are circled in the following figure.\n",
+    "These points are the pivotal elements of this fit; they are known as the *support vectors*, and give the algorithm its name.\n",
+    "In Scikit-Learn, the identities of these points are stored in the `support_vectors_` attribute of the classifier:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 0.44359863,  3.11530945],\n",
-       "       [ 2.33812285,  3.43116792],\n",
-       "       [ 2.06156753,  1.96918596]])"
+       "array([[0.44359863, 3.11530945],\n",
+       "       [2.33812285, 3.43116792],\n",
+       "       [2.06156753, 1.96918596]])"
       ]
      },
      "execution_count": 8,
@@ -338,24 +335,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A key to this classifier's success is that for the fit, only the position of the support vectors matter; any points further from the margin which are on the correct side do not modify the fit!\n",
+    "A key to this classifier's success is that for the fit, only the positions of the support vectors matter; any points further from the margin that are on the correct side do not modify the fit.\n",
     "Technically, this is because these points do not contribute to the loss function used to fit the model, so their position and number do not matter so long as they do not cross the margin.\n",
     "\n",
-    "We can see this, for example, if we plot the model learned from the first 60 points and first 120 points of this dataset:"
+    "We can see this, for example, if we plot the model learned from the first 60 points and first 120 points of this dataset (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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5RaW+A4rF04j/oigKp14fx6Dz57KmDZkCNaKj2Hv7FpXyuUPIs+5BpZatOe7j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\nc+fOasiQw622jx0rbdggXf9n8PTpcB09+qiSkoZ06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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119060cc0>"
+       "<Figure size 1152x432 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -389,7 +389,7 @@
    "metadata": {},
    "source": [
     "In the left panel, we see the model and the support vectors for 60 training points.\n",
-    "In the right panel, we have doubled the number of training points, but the model has not changed: the three support vectors from the left panel are still the support vectors from the right panel.\n",
+    "In the right panel, we have doubled the number of training points, but the model has not changed: the three support vectors in the left panel are the same as the support vectors in the right panel.\n",
     "This insensitivity to the exact behavior of distant points is one of the strengths of the SVM model."
    ]
   },
@@ -404,14 +404,21 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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aZqe7u4vS0jKuuOJqv3HJySnv+Tgnn7yFu+56gE9+8lrOOec8duz4FOvXb/Ab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\n",
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba29c3a8344b463a8848a4a7bfec4da8",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11907e7f0>"
+       "interactive(children=(IntSlider(value=10, description='N', max=200, min=10), Output()), _dom_classes=('widget-…"
       ]
      },
      "metadata": {},
@@ -420,35 +427,38 @@
    ],
    "source": [
     "from ipywidgets import interact, fixed\n",
-    "interact(plot_svm, N=[10, 200], ax=fixed(None));"
+    "interact(plot_svm, N=(10, 200), ax=fixed(None));"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Beyond linear boundaries: Kernel SVM\n",
+    "### Beyond Linear Boundaries: Kernel SVM\n",
     "\n",
-    "Where SVM becomes extremely powerful is when it is combined with *kernels*.\n",
+    "Where SVM can become quite powerful is when it is combined with *kernels*.\n",
     "We have seen a version of kernels before, in the basis function regressions of [In Depth: Linear Regression](05.06-Linear-Regression.ipynb).\n",
-    "There we projected our data into higher-dimensional space defined by polynomials and Gaussian basis functions, and thereby were able to fit for nonlinear relationships with a linear classifier.\n",
+    "There we projected our data into a higher-dimensional space defined by polynomials and Gaussian basis functions, and thereby were able to fit for nonlinear relationships with a linear classifier.\n",
     "\n",
     "In SVM models, we can use a version of the same idea.\n",
-    "To motivate the need for kernels, let's look at some data that is not linearly separable:"
+    "To motivate the need for kernels, let's look at some data that is not linearly separable (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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k5IeYMePsbaXrWLPmDGr1WgIDw+wWmxCNkSRuUSG9Xs/mf36A8uc1uKWmUhga\nhuOESfR9/Hf2Dk3UEoPBwOHD68nJ2YReryQsbAJJSet54IGzFvuOGnWJBQv+y9Ch79ghUiEaL0nc\nokK/vPUGY//3Kd43C1K1XPv1GFuLS+j/3Av2DE3UAp1Ox48/PsaoUT8SGmqceOPEiW85fDgMa40t\nCgW4uCTaNEYhhAwHExXIzckmaNXKW0n7hibl5TgsW0K5lRmVRP22Y8dcpk9fRmhoOaWlsGEDXLpU\niE53rsJjyspkNSkhbE0St7Dq7OGDdLx+zeq26IvnSUm5buOIRG0zGLahVsPx47B6NfTsCaNHQ0yM\nni1bLL8qjh71JDR0uh0iFaJxk6ZyYVVARBRJHio0BfkW27S+frT09rFDVKIyioqK2LnzY5yc9gNQ\nWtqV3r2fx93d/a7HOTiUUVYGCQkw6bYpqIcNgy1b9Hz3nQ8TJmTh4AAbN0YDz9CrV/dafCdCCGsk\ncdtIbk42u975C+779uJQVkpx+zhaPvcC4W3a2js0q5pGRbOm13102vCLaWUgMC4ukNK3H51UKnuF\nJu6ipKSEn3++n0cf3c7N5ZLLy7fw9dd7GTFiGS4uLnc5NpYtWzYwdKjltgEDYMGCEDZt+gi9vpyu\nXcfIWG4h7EQStw2Ul5ez+cEHeHTP7lvPJs6fY82xwygXLickKtqe4VWo+98/5uviJ+m/bw9RpaWc\ncHdnb5/+DHr3H/YOTVRg9+65PPjgraQNoFTCzJnbWb36SwYMeKbCY3v1+j1ffbWIoUOvWN3u5lbA\nffdNqOmQhRC/kSRuG9izeCFTb0/aN4y+eJHvP/+MkPfrZiLUBDdhzLLVHN+zi72nThDRpRvjYjvY\nOyxxQ1ZWOvv3f4Kr6yl0OhXu7iOBQ7i5We7r5gYKxaG7nk+l8qR//7kcPjyaTp3KLLYXFjY3/X9J\nSQkZGen4+2twdnau7lsRQvwGkrhtoPz4MTwr2OZ2LsGmsVRFu5730a7nffYOQ9xGq73M8eNTmD79\nBA43fhFeu7aKuXMrbr3R6e6dYNu378mqVRNo02aJ2Q+AAwf8adLkccrLy9mw4Y94e68jNPQ6Bw+G\nkJs7miFD3pQJeoSwEUncNlCmUmMAs2fFN+nUFaV0ISp29OiHzJx5wqysSZNyhg5NYu9eBT16GMy2\npaQ44OFh5eG1FSNH/pdly8Jwdt6Ck1MORUUtCA5+lHbtBrN27YtMmTKXm/3cOnW6QF7ex6xcqWf4\ncFlTXgiSLaSOAAAgAElEQVRbkMRtA21nPsT2hd/TLyPdrFyrVOIyfKSdohL1mZvbEavl3boV8+ab\nHQgIOEl0dCkAFy44s3XrdMaMqdzzaaVSybBhfwb+bFaem5tDkyY/cWfndLUafH1XU1j4+j17rgsh\nqk/GcdtASEQkeX+aw9qQUMowrmm7x9uH9Y89Rc8p0+wdnqiHDAbrzdIGA7RsOZLExOUsXPgkCxc+\nQWLiMsaO/RcKhbU2H3MZGRlcvHjB6gQ7a9b8nTZtrI/fj45O5Pr1q7/tTQghqkRq3DbSfdpM8kaP\nZfmi+eiLS2gzZhzDIyLtHZaoBdevX+LXX/+Jq+sxDAZniot70afP/9VobbSwsCt6/RHT8+2bduzw\no337aQQGhgJ973qOsrIykpIuYTCEkZ6exf79LxMZuQuNJoddu1rj4DCTPn2M89IfOrSSbt2+JDER\nwqysKXL4sB9lZe9y9WoCer0LRUU96dfvDdys9ZQTQlSLJG4bUqs9GSgLdDRoqalXSUiYyowZp01l\nOt1BvvwynnHjVtRYB67evd/gq69+Zfr0Paam6/h4FVrtc7RpE3rP47dt+wSF4nvatj1DQoIXe/Y4\n8sormdyslMfEnOTSpbfYt8+L7t2nk529iOHDi1iwAHQ6zOYuv3QJHB1LmTFjmamsvPwQX355mgkT\nluJw568LIUS1SOIWogYdPvwvZs48bVbm6Aj337+V7duXcN99NfNoRK32YvjwVaxd+y0Gw1H0ehVh\nYVPo37/LPY/ds2ce3br9haZNSwDIyMhh1iy4syU9MrKYffuWANNxcTE2g48bB4sXQ3Q0tGgBp0/D\nzz978/bb2WbHKpUwYcJmDhxYRffu42vkPQshjCRxC1GD3NxOWyRAAF9fKC09BNRcnwYXFxf693/i\nNx9XWLjElLQBMjKgV6+KrmGcjKWkJAg4jocHTJ8OV6/CqVMQFARRUb5AtsWxgYF6Cgr2ApK4hahJ\n0oYlRA3S6Twq3FZeXvM9rg0GA9nZWRQXF1f6GFdX805k3t6Qnm5939LSYADU6vu5dOnWFKchIXDf\nfbBnTxweHtb7ahgMoNPJ1LhC1DRJ3ELUIKVyCBkZllXuffu8aN68ZlfSOnjwBzZvHsK1a+2Jj+/A\nTz89RlZWxj2PKylpYvb6vvvgl18s97t61QkXl4kAdOs2hUOH/szy5S1ITobDh92YN28AMTFf4OAw\nhGzLCjdbt2po3/7hKr03IUTFpKlciBrUp8/D/Pjjr3TpsoT27QswGIwJLCvrJfr0aW22b1FREXl5\nefj7+//mDlzHjv1EaOiLjBiRe6MkB4NhCV9+eY1x49bedeiXi8tErl7dT0iIcVpTBwfo3x8++MCb\n/v3LCAoq4PDhaIqLpzFw4GzTcf36PUNp6eOcOXMMLy9/hg+PAiAsrCXLlp2ia9dltG9fgE4HGzcG\nUVLyOu3aWemCLoSoFoXBYDDcezfbSEvLs3cIdZ5Go5b7VEn2vFfnzh0jMXEt4Exs7EwCAoJN2woL\nC9my5RX8/bfi759FcnIUSuV0+vR5qtLn37hxCtOmrbMoT0lx4MiR+XTpMuqux2/Z8g+cnRfQseN5\ntFoVp07dR+fOH1JWpicrS0vz5rEWq3+lpV3j8OE5uLvvw8GhnIKCOJo1e5Ho6E4AnD8fz6VLPwFu\ndO48Cx8fv0q/n/pC/v4qR+5T5Wg06iodJzVuIWpB8+ZxNG8eZ3Xbhg2PMXv2GtOQqh49fiU5OYHd\nu53p1euRSp3f1TXZanlQkJ7c3Hjg7ol7wIA/UFz8NBcunKJ582giI70A4zPzCxd+Ys+eN3B2vkpp\naQiOjuPp2nU2Bw5M58EHD9/W+e4y69Yd58qVFYSGNqNZs1iaNYutVPxCiKqTxC2EDZ09e5Tu3Tdz\n53Dupk1L2L17MVC5xF1S4m+1PD8fnJzuPY4bwNXVlZiYjma1o82b36d//w8ICro5c9pVUlKO8MUX\nm3n55cMWPeaHD0/k++//S2ho3VzhToiGSDqnCWFDSUn7adeu0Oo2D48k9Hp9pc7j7DwGrdbyd/fq\n1W3p3v2BSsej1WpJTk7GYDBQXFyMm9uS25K2UVBQOV5e+60uFwrg5nax0tcTQlSf1LiFsCGNpiVJ\nSU6Eh1uud11cHFDpTmq9ez/Khg3X8fdfSJ8+10hNVbJ9e2datny3UutjX7x4lHPn5hAZeYCysjLO\nnImjqGgsfftesLq/Wp2HwWA5SQtAWZmscNfY5OXmsPOdv+C+fy+OZWUUtoul1XMvEt66jb1DaxSk\nc1o9I50+Kq8u3iuDwcDataOYPXunqay8HFasgPT0SAICQikqak/Xri/i66u55/lyc3OIj9+Al1cT\n2rXrWamFRHJzczhyZCCTJ581K9+zx5fExBKmTSuwOGb5cjXe3s4MHGg+3OzSJRdOn55Lly5j73nd\nhqAufqZsraysjLWTx/Dont1mTbarmjUjcuEKgiMiftN9Ki4uxsnJqVGu517VzmnSVC6EDSkUCrp2\n/YxvvhnIsmXOLFoE33xjHJLVrNklJk7cyYwZn3LgwASysiqYFQVjz/RffnmbQ4ceoqxsIdev76j0\nJCz793/O+PFnLcp79swkKcmXO3/KGwyQm9uPkpL3WbEiiuJi43zlGzYEsW/fS3ZJ2teuXSU5OYk6\nVO9oNPb+sIipdyRtgLHnzxP/+X8qfZ5jv/zExomjOd6pLXu6xfHT758iJyuzZoNtoKSpXAgbCwwM\np0mTGfj7H6BTp1JTuVYLP/4I48fDjBnxzJ//EcOGvW1xfHFxMevX38/s2TtQ3vgLLivbzFdf7WP0\n6KU4Oztz7VoSiYm/Eh3d4cZKYbcolZdNx90pIiKcr75qxqBBu4mIKOXSJWc2bepFnz4f4+uroaho\nNKtXL0WnK6ZTp4l06GA+5OvatctkZKTQvHk7i+FkNeHs2X0kJr5Ns2YHcHEpZ9Omjmg0vycu7u69\n6EXNKT92lIoejrieTajUOU7u2I7XC88yOOPWj1NDchJfJiczdvkaWZjmHiRxC2FjBoOB9PQvGTrU\nvCkxMBDUasjONk5D6ub2q9Xj9+z5klmzdpglXycnmDVrK0uX/peysiPExGyhb98cjh/3Yf/+wQwZ\n8h9TIi0rC0Svx2JJUICMDCV+fj348ccYfH39CQnpyNixfU1N8G5ubvTvP8viOK02mUOH/kDr1ruJ\njs6/bQKXl0lOPsfp03NxcUmnqCiEzp1/h0YTBEBOThb793+OUnkdnS6E7t2fRK22nhYyM9NJS3uS\n6dNvdYaLi9vPrl3Pc/FiKFFR1offiZpVplJhAKw9lNFV8G93p6vffcX0DPMWJQUwbu9u9q9ZRfex\nMr/93UjirkV6vR6FQlGp546i8cjLyyUo6LTVbb16wc6dMGQI6HQVdTI7iLXKrLs7XLv2Ba++etmU\nlHv3zqJHjx/4/ntXRo0yNmN27vwka9cuYcyYJLPjDxxQEh29i+HDt5GfD2vWxODmdt89P78Gg4ED\nBx5j9uy9prLw8Atcu/YeCxYkExf3CzNmaG/sC2vXriIr6wvAgFb7JFOmXESphLIyWLVqCaGhX5gm\ndTF71wf/y7Rplj3Y77svlQULviEq6l93jVPUjJiZD7Fz0Xz6ZJr3d0hRKnEZNqJS53BNvGS1PECv\nJ/94PEjivitpj6gFJ7ZuZsMDE9nboTXbe3Tkp+efITfHymTOolFycXGloMD6YiQZGeDjA4WFoNf3\nsbqPwXAroet0kJdnTIipqdC5s9aUtK9dg9WrYeNG8PVdT16ecXpUX19/vLw+ZcGCbiQkKElOhq+/\n9iE/v5zhw41DwVQqeOCBk5w48TRlZZY94G938OBaRozYb1EeGFiGh8cy+vXTmsoUChg9+hKJie9x\n8eIcJky4aGo5cHKCSZPOcf78HKvXcXK6arWVAMDZ+ar1DaLGhUZFk/XGm/zUJIRywADs9fJm/aNP\n0mtK5Va/K/O1PqteCeAQEFBjsTZUVapxGwwG3nrrLRISEnB2dubtt98mLOzWnMRbtmzhs88+Q6lU\nMnHiRCZPnlxjAdd1Cfv34vzsk0xPvfVlpb94gS+TExm3bLU8uxG4uLiQmdkLg2GJxfCq3buhZ09H\nvvtuDOPGPW31eDe3oVy5spR9+ww4OYGnJ2RmQnIyTJ5cisEAy5dDcDCMHg0lJbBmjZadO+czYsTv\nAGjTpg+tW2/g7NnjuLmBh8csBgzIsrjWuHEJzJ0bR2zsn+nadYrVeHJzzxIUZDn+/NgxGDbMsoc6\nQEjIfhwcSqxua958P9euXaVJkxCz8rKyoAqHpJWWBlo9l6gdPWc+RM6YcSxdNB9DcQmtx45nRGRU\npY93GjEK7a7tBOp0ZuWropvTfcZDNRxtw1OlxL1p0yZKS0tZvHgx8fHxvPvuu3z22WcAlJeX8957\n77FixQpcXFx44IEHGDhwIL6+vjUaeF2V9M1cs6QNxmaN8bt3sn/Nj3QfO8E+gYk6pVev9/nyy2uM\nGbOboCA9hYWwYIEvRUV9OHFiGhMnDjU1UZeUlLBv31JKS7Np23Y0PXpM4tNP3+W1186bNZlfvqxg\n6VIV7drl0acP3Ky4uLrC5Mmwbt3HpKdPwt/fuEGhUNCyZXu8vFxIScm3Gqe/P7Rvfxlf3xc4cyaC\nVq26Wezj7R3D1auOhISYfwlX9BwdwMFBj4uL9Zq8h0cZhYWWPeQ7dHiCdeuWMWKE+XSvBw74ERHx\noPULiVrj5eXNoCefqdKxvR96hPVXLhO8ZCH9U7VkKhRsaB9L2Fvv4O5e88vfNjRVStyHDx+md+/e\nAMTGxnLixAnTtgsXLhAeHo5KZVyHt1OnThw8eJChQ4fWQLh1n+tF6xNYBBgMFMQfA0ncAvD29mXc\nuLUcOLCKvLzjKJXBDB8+ExcXF7P94uPXkZX1J0aMOIu7O+zZ83e2bh3JwIFZFs+5w8IMqFTOZGTc\nStq3Gzo0hUWL5jJkyOtm5c7OzuTktOTq1VT27wel0tgE37cvnDwJnTtDUFA+c+f+12ri7tRpKKtX\n9+SRR3aa1Yb9/JzZuNGfhx66ZnHM5ctdcHLKols3yw54J0/G0b+/Ze0tICCY1NT/MH/+23TqdBhn\nZx2HDsXi6fksnTt3tXzDos5SKBQM+9McMp96lqXr1uChCWTQkGHSIllJVUrc+fn5qNW3Bo4rlUr0\nej0ODg4W2zw8PMjLq9xA/KoORq9LHAKtT5pRBqjCQ2rkPTaE+2Qrdf1ejRo1s8JtOTk5FBe/yuTJ\niaayXr2ySE2dT5cu1o9p1UrHkSPBwHWLbQ4O4OlZZPWe6HQ9OHFiF+PHG1AojIl71Srjc/LevY2T\nxCQmbmPXrlE4OuZTVtaW2NjnadasAwBjxy5h8eJnCA3dikaTRUJCDC4uD9OsWTAHD/6eLl2MPYiN\ny5w2pV27N8nOvsyvvz5P+/a3muiPHvUjLOwlAgKs907u3380BsMoEhJOUFhYyuTJHWz+ZV/XP1N1\nRWXuk0ajpmXr52wQTcNSpcStUqkoKLj17Opm0r65LT//VrNbQUEBnp6VGyLQEGYk0g8YSuqmTQTc\n8exmbUQUcROnVfs92nPmppKSErKyMvHz88fJyckuMfwW9X2Wq02b/mWWtG9q3RrOnXOgfXvL58rp\n6b54efUAFlhsy8yE8vLWFvfEz88DJ6f1DB16azITR0eYMAF++MH4euFCeP31TNzdd9zY4wjr128n\nI+P7G8OwVAwa9C3p6elkZ2fRpUuE6TNy/nwoCxZ8h7NzGsXFIbRr9yTBwVEEB3fmxAl/FiyYh7Nz\nCqWlTQgLe5DWrXve89/Nzy8CgIwM68/Qa0t9/0zZitynyrHpsp4dO3Zk69atDBs2jGPHjtGiRQvT\ntujoaJKSksjNzcXV1ZWDBw/yyCOVW/GoIej90CP8kpRI2A+L6JueRh6wrnUMgW/+FZWqfv5SLysr\nY+OcP+K94ReCtVr2h4ZSOGoMg179ozRt1apMqxOltGoFH37oTvv25s+lS0shJ2cQbdo8xM8/b2bE\niBTTNr0eli27jzFjLDuYHTmyg169rI8ZDwyEXbuMzeV3PnocOjSJBQs+JSrqS1OZv78//v7mK5c1\na9bBVDO/U9u2vWnbtrfVbUII66qUuAcPHszu3buZOnUqAO+++y5r166lqKiIyZMn89prrzF79mwM\nBgOTJ08moBLd+7OystDpFPV+vlqFQsHwt/5G2lPPsHjNKtz8/Og7ehzKiqaqqgd+ef1lpn33NaZH\nqufOkv3Rh6zR6Rn2x7fsGFnDplZ3Qqt1IDDQsmZdVKTn+++hWzeIioL9+xXs3dubMWPeYPv2ZwgK\nymb5cuO+ubnulJSMZPDgf5CZmc6xY/OAEsLDR9KiRQfKy0txdrY+dajBAEuXwr8qGCLt6nqyht6t\nEKKy6swiIx9++CHp6dn4+2sIDAwiMDCQwMAgNJqAetEsayu2boLKyswguU93Bt3RUx5gZVQ0Xbbv\ns+hQVVfU9+Y6vV7PypXjefzxrWa9s9evd6Zp01JatzZ2HktKgnbtYNeujhQVxfDgg99brPf9zTeD\n8fYehkr1PgMHpuLgAMePu7N//2RmzfqC1avjmDTJclKYTz6Bpk1h5Eis1v6XLu1Kv36bavid1131\n/TNlK3KfKsemTeW1oWXLljg4JJKWlopWe6uJT6FQ4OvrZ5bMAwICa2UeZGHp0onjxFpJ2gDRyUmk\npFwnPDzCtkE1Eg4ODgwdOp95897Ew2MXjo6F5OS0wMlpD0OHGuc4j4kx/peTA/n5R0hNPUdZGRaJ\nOy5uFwkJ+xk/3jgJS1kZpKYWolJ9x5IlkXh7P8vRo6/TocOtiYLWrXOhd+8SIiKMk7gMH25+zpIS\nKCnpW5u3QAhhRZ2pcYOxc5pOpyMjIwOtNoXU1BRSU42JvLS01Gxfb2/vG8ncmMgDAgJNQ9Aaspu/\nZAsLCzm2ZROuahVxvfvV2rNm7bWr5PTtQS8rM7/9HBJKm10H8fCwPguYvTXEX/1arZbc3A707Hnr\n+fa6dcZE3b+/sff3xo3G2dd63/boODsbvvjCWObmBufPG6dVVath504nzp9/kOjoSVy9Oh8npzTy\n8oJQKlcxa5bx3333buMMbYMHG6914YKSjRtHMHr015Va/7uhaIifqbtJ02o5tmg+lJcRPXIMUZVc\nb7ux3aeqqmqNu84lbmsMBgNZWZlotVpSU7VotSlotVqKigrN9lOp1KZauTGhB+Dp6dWg5grXaNQs\n++u7OH39Jf0SL5GvULA9No4mb7xJTN8BtXLN1Y89yMOrVprNj1sGLJg1m5Efflwr16wJDfXLY8OG\nEUyfvguAAweMSbp5c/N9du2C0FCIiDC+3rIF4uIgPx+++w7++EfzGciysxWsX/9XBgwwDs3Jz8/j\n3Ln2DBlyaz7qzEzjPOrFxaDVvsbUqf9Xrb8tvV5PSUkJrq6u9eZvtKF+pqzZOfd/eH78DwakanEA\nDqtU/Dp1BiPffv+e/16N6T5VR4NO3NYYDAby8nLRarU3audatFqtaT7mm1xd3cySeWBgID4+vvXm\ni+JO53dvxm/adFrc8aNlddNwWm/cjrdPzc9Ql5+Xy5bnnyZm+1Za5ebyq68f5wYNYeg//l1nn29D\nw/3yOH58E46OT9O373VWrjQuA3ong+HWEqF5efDzz8YEX1BgHKOdmmqsobdufeuYJUvuY8CAn02v\nN2yYxPTpGyzOvWxZS+A1SksziYsbjUZjOd2owWAgPn4LGRnb0OlcaNduJsHB4YBxWOHmzX/Cw2Mz\nKlUWmZlReHjMoGfPh6p7a2pdQ/1M3eni6VMwZijdcnLMylMdHdn1z0/o/cCMux7fWO5TddX7Z9y/\nlUKhwNPTC09PL5o3vzUcraCgwJTIb9bOk5ISSUpKNO3j7OxMQEAggYGBBAQYE7qfn1+96NF+fcEC\netyRtAFGJiex5KsvGPzS/9X4NVVqT8Z89T1XExPZcfokUXEdGBPcpMavIyqnXbtBXLq0gvnz51JQ\nsApIt9hHoYDLlx1YuVJv6tjWoQNobpsfaPdu+PVXaN/e+NrJyfxxSGTkK/z001lGjEg01c737VNz\n7lwhjz32EL6+sG3bexw6NIVhw/5m+jFcXl7OqlWPMWzYGgYPNs6dvmPHV5w//yq9ez/JunVP8eCD\ny7jVwp7O+fPH2bvXkR49Kp6QRtjO+UXfM/2OpA0QoNNRumEd3CNxi9pVbxN3RTw8PIiKiiYqKtpU\nVlxcbEriN5+ZX716hStXLpv2USqVFj3a/f01da5HuzItzWq5I+BYwbaaEhIRQcjNtldhV5GRMURG\nfsT69Urgc4vtpaVw5Yo3zz2XSUqK8Zm25o5J/Xr1Mi5GcjNxFxVFm21v3rwrWu1PzJ//P9zckigo\n8CYnZxuvvXZrrvABA1JJS/uUbdvC6NfvSQC2bv03s2YtN437Viigb98Mtm9/j/37m9Ox4y/c+Vi8\nWbMiDh78HpDEXRc4FVpWDm5yzLc+r72wnQaXuK1xdXWladNwmjYNN5WVlZWZerDffHaelpZKSsqt\nqSIdHBys9mi3Z/NwaXi41fIiQBHdzLbBCLtr1+53/PzzBkaMMF/feMUKaNs2k+JiOHgQRo2yfryT\nk7FZfedOB0JDH7PYHhgYxrBhbwOwdev/mDJlnsU+Go0ene5nwJi4HR23WUzWAtCnTyZ/+9snjBpl\n/Yvfw+MSOp2uXrR8NXSOcR3JnfcNd855aQCKWra2doiwoUaRuK1xcnKiSZMQs6UDdTod6enpN3qz\na00JPT09jZMnj5v28/HxuZHEg240twfarGd1m6eeYve6X+iVlmpWvrRde/rPetgmMYi6o0mTSJKS\n3uaLL2YRHFyOXm+sbffubVzZ66OP3ImNLaSgwLjG9p1ycoxJ/vLlKKZPt77+9006XarFwiY3OTnd\n6sTm6Gi5shcYa96eniquXHEkNFRnsb2kxF+Sdh3Rc+p0Fi3/gcd27zTrlLqsZSs6/e5Zu8UljBpt\n4rbG0dHxRs36VmcbvV5PVlbWbR3gjDX0M2dOc+bMrQkr1GpPi05wKpW6xjvBtenenc0ff8qi/35C\n8PF4ip1dSO3andg//0XGtjdSOl0J06eX4+ZmfH37yMCuXR1ISHiB69e/ZPZs85quXg/OzjB0KKxe\nff89r+Pl1ZHr1x0JDrZMusXFkab/LyqKAfZZ7HP5shMtWsxg48Y0Hn54r9m2oiIoKhp8zxiEbSiV\nSoZ8v5j5772N24F9KMpKKWofR/vnXyLgjnXShe3V217l9mQwGMjNzbEYnpafbx6/m5u7RTL39vap\nVjK/vbdmdnYWTk7OdXYctb01lp6tycnnMRj60rmz5XtdubI53brt4/TpLeTnv8TIkUkolcZe5WvX\ngr+/JxcvjmDcuI/u+TkyGAysXDmWxx7bZvbj4MABfwoKvqJt2/4ApKQkkZAwmUmTzpj2KS6Gb74Z\ny8SJ87h69RzHjv2e/v0P0LRpGfv2+XDq1EhGjvx3nZ8auLF8pqpL7lPlNLrhYHVRfn6+2aQxWm0K\n2dnmPXVdXFzMerQHBATi7+9f6QlU5A+i8hrTvVq9egYPP7zaLKEWFMDy5c8zfPhfAMjLy2X//q+A\nTJKTSykpOUpcXCLR0WlcuBBGdvYohgz5612bq/Pycti58w1Uqp24uBSQlxeDv//jxMWNNNvv+vVE\nfv31X7i5HUenc6WsrA8DBrxoSswGg4Hjx3eSmnqONm0G0qRJRM3ekFrSmD5T1SH3qXIkcddRRUVF\nZs/LtdoUMjMzuP22K5VKNJoAs9q5v7/Gau1D/iAqrzHdq/z8XLZufY5mzbYSFZXFyZNNuH59NMOG\nvWc1Ea9Z8yzTp3/H7f0sjYn+WYYPf/uu1zp58iBJSaeIjR1ESEjjajZtTJ+p6pD7VDmSuOuR0tJS\nU4/2m7Xz9PQ0dLet4e3g4ICfn7/Fgiuhof6N5j5VV2P88khN1ZKScpGIiDZ4enpZ3SczM4PLl7sx\naFCqxbbVqyOJi9uH280H5rdJTDzFrl1TGDYsiSZNYNs2B44ebcsjj2xqNP0rGuNnqirkPlVOo5uA\npT5zdnYmJCSUkJBQU5mxR3uaqYk9NTXVNETtxAnjPgqFgrCwYDw8vM16tLtbG3sjGqWCgky02l2k\nph6iS5cZeHn5WOyTnHyKtm0tkzZAVFQyWm0KERGRZuU6nY59+ybwwgvXTGVjx+rp2fNXPv10CE8/\nvaNm34gQokKSuOsIY492YzP5TXq9nszMzNuSuZaCgmySk69x+vQp036enp5mHeACA4Pw8FDV22ld\nxW9nMBhYu/YlYmMXM21aHuXlsGHDZ5SXv24xG1lISAsSEvwIDs6wOE9SUjAxMQEW5Xv3Lmf69GsW\n5RoNhIcf59q1xHrznFqI+k4Sdx3m4OCAv78//v7+xMS0BcDfX8X585fN5mhPSUnh3LmznDt31nSs\nu7uHRY92Ly9vSeYN1M6dXzN27Ff4+ekB49rZI0ZcZcuWt7h+vS/BwU1N+2o0gezdO5DevX8wW/6z\npARSU4fRtatl7/Ls7AR8K5gG39dXx7lzeyVxC2EjkrjrGYVCgbe3D97ePrRs2Qow1rYKCvLNZoHT\nalO4dOkily5dNB3r6upqWgL1ZkL39fWttSVBhe2Ul683Je3b9e+fxsKF3xIc/Gez8kGD/s1330FU\n1GZatszg5MlAkpOHMXToe1bPHxTUhaQksDZxX3a2Ay1btquR9yGEuDdJ3A2AQqFApVKjUqmJjr61\nvmNhYaFZj/bU1BQuX04mOTnJtI+Tk5NFj3Y/P/86P55WmHN0tN4RSKEAR0fLKUbd3d0ZPXouaWla\nrly5QlhYBLGxfhWev1OnocydG8prr10xWw40IQGSk9syYkTbar8HIUTlyLdzA+bu7k5ERKRZR6PS\n0lKzSWOMTe3XuXbtqmkfR0dHqz3ane9cGULUGcXFLYHdFuXp6QpcXDpWeJxGE0ibNs3u2gPYYDCw\nY52YWHEAACAASURBVMfXhIW15N//Tqe4uJi2bSEjw4HExNZMmbKyJt6CaORyc7LZ+99PcDl5Ap2b\nO65DhtFz4v3yeM8KSdyNjLOzM6GhYYSGhpnKysvLTT3azWvoWo7fmKJdoVDg6+trWgb1Zo92a8OG\nhO3FxDzN2rXbGTXqgqlMp4Nly/oxbtzkap17zZrnGTfuW/z8jCNHDQb49ttgNJr/MHy4TFMqqi9D\nq2X/jPuZEX+Um90urq/5kbVHDjH6nb/bNba6SMZx1zO2Gh+p1+vJyMiwWNu8pKTEbD8vLy+zDnAB\nAcY52uuCxjaWNDHxJGfPfoyrazx6vStFRT3p0+dP95zK9G736dy5o6jVw2nXznKZx/nzH2To0E9q\nJPb6orF9pqrqt96ndf/3B2Z+/SV31q3j3T0oWL2O5u3jajbAOkLGcYsa5eDggEajQaPRAMaORwaD\ngezsLIs52s+eTeDs2QTTsR4eKose7Z6eXtLkVcsiImKIiPiyRs+ZmPgT06ZZX5vZ1fVYjV5LNF7u\n8ccskjZAbGEBC9asarCJu6okcYtKUygU+Pj44uPjS6tWxjV5DQYD+fl5ZsPTtNoULl68wMWLt5pt\nXV3dCAgIuC2ZB+Hj4yM92us8ZwwGsPaby2CQPg+NXXpqKke+nYsyJxtl67b0nDqtSh1b9RV8DxgA\nZKlXC5K4RbUoFArUak/Uak+aNTPv0X6zRp6WZkzmyclJFj3aby64cnN9c39/WZO5LomNncmWLZ8z\ncGCaWXl5ORQV9bRTVKIuOLp2Fbo/vsoD167hAOQASxfPp8+3C39zE3Bxl27oDu7nzr/8/V5etJo0\npaZCbjDkGXc9U5+fsZWUlJhq5DfnaM/ISEevvzX+2NHREX9/jUWPdicnp998vfp8r2zpXvdpx47/\n0qTJu/ToYVzpLiMDli4dwPDhCxvddLvymTIqKSlh34BeTLpt0icw1pDnPTCDBxd+/5vuU35+Hhum\nT2HG3l3c7JFxys2dY8/8nsEvv1Zzgdcx8oxb1HkuLi6EhTUlLOzWLF5lZWUWPdpvLsByk7FHu59Z\nMg8ICGw0C1vYW58+T5GUNIAFC+ajVBbg7NyVceMmS8tII7bvx+UMvyNpAygAj/17f/P5VCo1o5b+\nyM/zv0N/5DB6dzeCxk1kcM/7aiDahkcSt7ArJycngoObEBzcxFSm0+lMPdqNzezGznAZGemcOnXC\ntJ+3t7fpefnNGeFUKpU93kaDFx7ekvDwv9o7DFFHlOblUtFAUMeSYqrSkOvs7Ey/2Y/B7MeqF1wj\nIIlb1DmOjo4EBAQQEHBrsQuDwUBWVqZFj/aEhDMkJJwx7adSqU218tato1EqPaRHuxA1rMOYCWz7\n6EMGpFmuMlfULlb+3mqZJG5RL9xsLvf1/f/27j2+qfr8A/gnbZP0kvSaJi23QilggQIWUJhcZD9x\nooggMGhZqzCdPzedG7gx57xsgzFFvExl3nDgDUHUCerPCTKqIggUudPSO4XeL9CmDU3anN8faU4b\nkkIobU5O8nm/Xnvtxfec9Dz97qxPzjnPeb4xSE4eDsCWzBsbG5ySeUFBPgoK8nH48H40NbUgODjE\n4fU0vd6A6Oho/nEh6iadXo/sRZkoe/kF9LFYxPGdfftiwC8fkjAy/8DETbKlUCgQHh6B8PAIDBky\nVBxvampCZWUFLBYjcnMLUVlZgZKSYpSUFIv7qFQqsaLd3g0uJiaGz22J3PSTPz6O7wYnofnTT6Bq\nOI/mgYkYes99SEwZLXVoPo9V5TLDqlb3dZ6rCxcudOoAVylWtHc+/YOCgpwq2nW62G5VtMsJzyn3\nyWmujEYjDu34D0KjojBm8o0e7Zkgp3mSEqvKiS4hODgYAwYkYMCAjnUpLRaLWMFufz2turoKFRXl\n4j4BAQEuK9rVarUUvwaRW756bjW072zA9NLTOB8QgK9GjUHfx/6C4ZOnSB0a9QBeccsMv8m6rztz\n1dbWhpqaGlRVOS64YjabHfaLiooSm8bYe7Rfrie4t+I55T45zNX3H25Cym8eQMJF6wp8mDgYqTu+\n9shaAnKYJ2/AK26iHhAYGNh+ZW0Qx6xWK+rr6x1aulZWViIn5yRyck6K+2m14Q5X5QaDAVptOIvg\nyKOM//7IKWkDwKzCAmz51zrc9OBvJIiKehITt49oaWnBmTOnEROjQ2RklNTh+JSAgADExMQgJiYG\nw4ePAGCraG9oOO9U0Z6fn4f8/DzxsyEhoU7JPCqKFe3Ue1S1tS7HlQACXLy+RfLDxC1zgiBgx+pV\nCPl4C64pyEeJLhbfTLkRNz79LLThEVKH57MUCgUiIiIRERGJoUOHieNGo7H9NntVezKvQHFxEYqL\ni8R91Gq1Q0W7Xm+ATqfjgivUI0z9BwAH9jmNNwAI6nSuknwxccvcf196HtOffRqx7f2+k2uqYf3o\nA7zZ3IQ73npf4uj8j0ajgUaThMTEJHHMZDI5VbSfOVOK0tLT4j5BQUGIjdU7XJ3Hxuq7tdIS+bf+\ndy3B91/vwvW1NeKYAGDL2HG4ZUG6dIFRj+FfBRkTBAH45CMxadsFALg+axfyjhziOrZeICQkBAkJ\nA5GQMFAcM5vNqK6uckjmVVWVKC8vE/ex3aLXOS24wop2upTkH03CoTX/wMbX/wnD8WMwBQejZsKP\ncP2TK3z+1UZ/wcQtYy0tLQgvK3O5baSpGRsP7Gfi9lIqlQp9+/ZD3779xDFbRXu1wzPzqirboivH\n2lu029ZEd65o97dVuujSxtw6E7h1Js6fPweVSo2QkK46i5McMXHLmFqtRqPBANRUO23LDQ7BgDHX\nShAVdZetot3WxS2lvfuU1WpFXV1dp2RuuzI/efIETp48IX42PDxc/Kz96jwsTMMiOD8XEREpdQjU\nC5i4ZUyhUKD1tlmoP3EcUZ1exxcAfHvDJNyROk664KhHBAQEQKfTQafTOVS0nz9/zqEArrKyEnl5\np5DXaanF0NAwhx7tBoMBERGRTOZEMsfELXM3LVuObU1NiNn6MUaVnsbpiAicmjQVU595QerQqJco\nFApERkYhMjLqoor2Rodn5pWVFSgqKkRRUaG4T3BwsLgEqr0ILiZGno1jiPwVO6fJTFcdiYxGI4pO\nnoC+f38Y4uIliMz7sHtTR0V7RwFcBerq6hx6tEdGahAaGuFQ0a7TxbKi3QWeU+7hPLnHo53TWlpa\n8Lvf/Q61tbXQaDT4+9//jqgox6YfK1euxMGDB8U2kGvXroVGo+lWkHR5Go0GKeOvkzoM8jJdVbR3\nfj2tufkciopKUVZ2VtzHdos+tj2R68WErlKpJPgtiKizbiXujRs3YujQoXjggQfw+eefY+3atXj0\n0Ucd9jl+/DjWrVuHyEgWRxB5E5VKhX79+qNfv/4AbN/6y8vrnSra7a+r2dnWRI8Wl0G1J3RWLBN5\nVrcSd3Z2Nu69914AwJQpU7B27VqH7YIgoKSkBI8//jiqq6sxb948zJ079+qjJaJeERQUhLi4eMR1\nesxitVpRW1srJnP7f9fWHsfJk8fF/SIiIsQCOHsy98RCFkT+6rKJe8uWLdiwYYPDmE6nE297h4WF\nwWg0Omxvbm5GRkYGFi9ejNbWVmRmZiIlJQVDhw695LG6e7/f33Ce3Me5ck9X82QwRGD48ETx34Ig\noL6+HhUVFSgvLxf/c/ZsMc6eLRb302g0iI+PR3x8POLi4hAfH4/ISN+oaOc55R7OU+/pVnHagw8+\niF/84hdISUmB0WhEWloatm3bJm63Wq0wmUzi8+3Vq1dj2LBhmDVr1iV/LosZLo9FH+7jXLnnaudJ\nEAQYjY0XLbhSgYaGBof97BXtHVfnBkRHR8uqRzvPKfdwntzj0eK01NRUZGVlISUlBVlZWRg3zvF9\n4aKiIvz2t7/FJ598gtbWVmRnZ+POO+/sVoBE5N0UCgW02nBoteFIShoijjc3N7ffYq9CVZUtmZ8+\nXYLTp0vEfZRKpbjgSueK9sDAQCl+FSJZ6FbiTktLw/Lly5Geng6VSoU1a9YAANavX4+EhARMmzYN\ns2fPxvz586FUKjFnzhwMHjy4RwMnIu8WGhqKQYMSMWhQx632lpaWTs/MbQ1kysvLcPbsGXGfwMBA\nsaLd3tJVrzewzzZRO77HLTPu3oKqPFOKg6+/guAzpbDodEhclIkkP+tbztt17pF6niwWC2pqqjsV\nwNkq2ltbW8V9bBXtMQ7J3GCIQ3BwsEdjlXqu5ILz5B6P3ion73Zq//eo/+UvkFFSBHsp0J5PPsK+\nvz6F6+YvkDQ2oosplUrEx/dBfHwfcaytrQ21tbWorKxAdXVHA5na2hqcOHFM3C8yMtKhol2vj2O/\nCPJ5TNw+qPjZp7GopMhhbGJdHT546Tm0zpnLjljk9QIDA9sTsV4cs1W0111UBFeJ3Nwc5ObmiPtp\nNFqHq3KDwYDw8AifqGgnApi4fc758+cQd/CAy21TT57AgV07Mf6mmz0cFdHVs98uj46OQXLycAC2\nZN7Y2OCUzAsK8lFQkC9+Njg4xKEAzmCIQ3R0NJN5Jy0tLSjKP4UonR4Gg0HqcOgSmLj9Df9QkQ9R\nKBQID49AeHgEhgzp6BPR1NTk1DimpKQYJSXF4j4qlUqsaNfr7RXtOr+saN/5whqoNr2HUfl5OBUW\nho2JSRj9wEO4cfZcfrnxQkzcPiYiIhLlqeOAr7Y7bctKHoFJU6dJEBWRZ4WFhTlVtF+4cMGhR3tl\nZQXOnj2DM2dKxX0CAwMRG6t3KIKLjdX7dEX7t+vX4Yan/4a+Fgv+DUDb1ITHjh5G1X1L8J/X/omk\nv65C0jiug+BNmLh9UOKy5dian4/bOxWnfRejQ9ivf+vwfDvvyCEUb34fgSYTgsaNw4/mp/H5N/ms\n4OBgDBiQgAEDEsQxi8Ui9mS3J/Pq6ipUVJSL+wQEBIgV7ddckwilUgO93uDxivbe0vLxFvSzWPAl\ngBsA2KsKBgDIyN6PjUt/jf7bs6BWq6ULkhzwdTCZcfc1i6ryMhx87Z9QnzkNc4wOQzIWI3HESHH7\nrpeex+Bnn0Zqe7vaegCbpv4Yt7610WcWjeArKe7hPDlqa2tDTU1N+9V5hfj83Gw2IyxMjaamFgBA\nVFRU+zPzjqtze7dIOflm/GjcWVKEjwHMcbG9GcBnTz2LGxff4/bP5DnlHr4ORg708X1wyxN/dbmt\nrKQYcS8+JyZtAIgCcE/WTmx85inc8tiTngmSyAsFBga2F7IZAIwCYGvjXF9fD4ulETk5hWIRXE7O\nSeTknBQ/q9WGX1QEZ4BWG+7Vz4nNffoAJUXo6sl+KIDWTncgSHpM3H7o2PvvIr2+3mk8CID6++88\nHxCRlwsICEBMTAxiYwciLm4gAFtFe0PDefEWu/12e35+HvLz88TPhoSEOiXzqCjvqWhXz5mHM9n7\nYTGbXW6vCAiAdmSKh6OiS2Hi9kOK1jZ09ScjsFO3KiLqmkKhQEREJCIiIjF06DBx3Gg0oqqqo6Vr\nZWUFiouLUFzc0VtBrVaLSTw21pbUdTqdJAuuTLr759h5/hwq3nwdP5SX4dpO2wQAWyfcgNkz7/B4\nXNQ1Jm4/NGDGrTj22lqMNDU7bTON9q+2qEQ9TaPRQKNJQmJikjhmMpmcKtrPnClFaelpcZ+goKD2\nivaO5jGxsXqPFIz++KFlaPnfB/DlG6/g0KdbMaAwH01hYaibeAOm/fUpr7k7QDYsTpOZnir62Prw\nQ5j99nrEtP/PLwDYNGIkUt79APo+fa/653sDFsi4h/Pkvp6cK7PZ7FTRXlNTjba2NnEf2y16ndjS\n1X67vTcrvO1NbdTq4G4fh+eUe1icRlfk9tXPY9foa2H5ajsCm5thSh6BmElTcGjNU1DX1sLUvz9G\n33M/4hMSLv/DiOiKqVQq9O3bD3379hPHbBXt1Q5d4KqqbIuudBYdHd1+Rd6xJGpoaGiPxGVvakPe\ni1fcMtNb32T3vvc2+jz5KMaeOwfAdgX+fwMSELH2dQy9bkKPH88T+K3fPZwn90kxV/aKdvvzcnsh\n3IULFxz2Cw8P77Tgii2hazRaSW5z85xyD6+4qdvMZjPML/9DTNoAoABw6+kSvPf8Mxj63hbpgiPy\nc/aK9piYGAwfPgKAc0W7/eo8L+8U8vJOiZ8NDQ0Tr8jtt9sjI6P4zFrmmLgJB3Z8iR/n5brcpjuY\nDaOxERpN974ZElHP67qivdHhmXllZQWKigpRVFQo7hMcHAy9vqMATq83ICYmRpKKduoeJm5CQEAA\nrF1sExQKoMuXx3xD+ekSHNu8EbBYMOi2WUgaNVrqkIi6RaPRQqPRuqxo73jfvAKlpadx+nSJuI9S\nqRQr2u3JXKeLZQtkL8X/VQhj/2c6diYPx/yTJ5y21Y4dD41GI0FUnvHfF59D3EsvIK2+DgoAh1/7\nJ7YtSMPMVc/wdiL5hJCQECQkDERCwkBxzF7Rbr/Fbr86Lys7K+4TEBAAnS7WYcEVvd4AlUolwW9B\nnTFxE5RKJTQPLcO3f1qOSTU1AAArgH8PTsLg3z8ibXC96NShHzDk+WdwbWNHEc3oJiPi16/D7jGp\nmLRwkYTREfUeVxXtra2tqK2tcXhmbn9d7ehR2z62NdGjxWVQ7VfovrK+gVwwcRMAYNyd81E0fCTe\neetNqOvqcGHAAIy/95eIiY2VOrReU7J5I9IbnStf9VYrWr78AmDiJj8SFBQkFrHZWa1W1NXVOVSz\nV1VV4uTJ4zh58ri4X0REhEM1e3DwECl+Bb/BxE2iQdckY9DfVvfqMaxWKw59k4Wm2hpce/Mtkha9\nBbroHCdua27yYCRE3sl2u1wHnU6HEe2rCwqCgPPnzzlVtJ86lYtTp2xFrl98oQagdOgCZzAYEBER\nyUdQPYCJmzwmZ89unH7yT7jx8A+Islqxq19/nF+UiZuWLZcknsBrx6Lx3bdw8VcHAcCF5JGuPkLk\n9xQKBSIjoxAZGYVhw64BYEvmnSvaTabzyMsrQmFhAQoLC8TP2ivaO67O4xAdHc2K9ivEBiwyI9fG\nBkajEQemT8H8gnyH8VK1Gj888wImLkjv8WNebq4sFgu2LZiDe7792mFJw43DRyL1g08Q7cOPCTqT\n6zklBXfnKv/APhS8/AJCjx1DW7Aazdf/CJMf/zO0vdCRrK2tDXv//SFM+XkIHTIM198xB4GBXS3S\n6Rn2eWpubhaTuW1t8wrU1dU57KtUKh2el9sr2qX+HTyBDVjIq33/1pu486KkDQD9W1rw7daPgV5I\n3JejVCpx81vv493VqxCyby8Ura0wjbkW1z60zG+SNvW8ouPH0HTfYvystFQcs+bm4I2CPMzasrVH\nE1JZUSEO/vJezMrejxgANQoFtr7xCsb/cx3ivKBdcWhoKAYOHISBAweJYy0tLU4LrpSXl+Hs2TPi\nPoGBgS4r2pVKpRS/htdh4iaPUFRXo6uXSNS1NR6NpTONRoMZf14p2fHJ9+S+thYZnZI2AAQAmLv7\nG3z94WZM+mlajx3rhz/+Houz94v/1gkClhzYhw2P/h63vrOpx47Tk9RqNfr3H4D+/QeIY62trZ0W\nXOmoaK+srBD3sVW0x4jJ3H51HhwcLMWvISkmbvII5dBhqAcQ5WKbqb/0VwZEPSXExZ0lAIgBYD5y\nGOihxF129gyu2bvb5bakPd+isrISBoOhR47V24KCghAf3wfx8X3EMavVitraWrFpjH3BldraGpw4\ncUzcLzIy0qGlq14f59O9JwAmbvKQifMX4qN31mPJ/n0Ofdj2R8egb+YSyeIi6mmtEa6fY1sBtIaH\n99hxztXWon+T67cfdI2NqD1XL5vE7UpAQABiY2MRGxsLIAWArQju3Ll6p4r23Nwc5ObmiJ/VaLQO\nV+UGgwHh4RE+U9HOxE0eERQUhInr3sZbTzyKmL3fIbS5GVUjUxB9z324dvIUqcMj6jHKn9yKqp07\noO+0rjYAfBkXj9S77+mx4yRdk4y9Q4Yh0cU6A0eSh2PS4CQXn5I3hUKBqKhoREVF45prkgF0rB9e\nVVXlkMwLCvJR0OnuR3BwyEWvp9kq2uWYzJm4yWNi4+Jx66tv4sKFCzCbW5DCNX/JB03OuBtfFORj\n0PvvYlJ9HVoAfJ6YiLBHHoNOr++x46hUKlgXZaJ41V8wsKVFHC8MDkbAoky/6TNuXz88PDwCSUkd\njV+ampqcKtpLSopRUlIs7qNSqdoL3/TtV+dx0Ol0Xl/RztfBZIav7riPc+UezpP7rmSuKs6U4ujW\nj6HUhuP6eQt6rS3onvffRdOHH0BVUQZzfB9o5y/E9fMX9sqx3OWt59SFCxecerTX1tagcxoMDAxs\nX3AlTkzosbH6Xqlo7+7rYEzcMuOt/4fwRpwr93Rnnpqbm/H9uxvQVlkJTcooXHf7bNk30RAEAT/s\n3IHao0egHTwY1902y+l34jnlHjnNk8Vi6VTRbkvm1dVVaOv0qCMgIMChot3+etrVVrTzPW4i8oic\nPbtR/vBDmJV3CsEAqhQK/HviG5j25juIjI6WOrxuqa+pwc5778KMPbuRYLWiWqHAp2PH49oXX0Ff\nH3xWTB2USiX69OmLPn36imNtbW0uK9praqpx/PhRcb+oqCjxFrv96jwsLKzXY+YVt8zI6Zus1DhX\n7rmSebJardj+k2n42eEfHMYFABt+mobbXnq1FyLsfe/eMQMP7dnttPL8v6bciJlbtor/5jnlHl+c\nJ0EQUF9f51TRfuGCyWE/rTbcqaJdqw13WQTHK24i6nXZO3fgpiOHnMYVAKK+2w2LxSK77lY1NTVI\n+n6vU9IGgNG7v0VhzgkkXjPc43GRd7E3gImOjkFysu18EAQBDQ3nnSra8/PzkJ+fJ342JCTUKZlH\nRXX/7hQTNxG5rbGqEjFd3KQLNjXBbDbLLnFnbXwH461tLrf1b2vFgbKzTNzkkkKhQEREJCIiIjFk\nyFBx3Gg0dmrrakvoxcVFKC4uEvdRq9VYseLJbh2XiZuoXcP5czjw2TaEReswbvrNXv9KiBRSZ9yG\n/676K27u1IrSrjZ5hEee7/U0TVsbTgMY62Lb14GBSL1uoqdDIpnTaDTQaDRITBwsjplMJqce7d3F\nxE0EYPvTf0P0e29hTlkZzgPYMWo04h/7M0ZM/bHUoXmVyKhoVC5IR8XafyCutVUc3x8dg9h7/lfC\nyLov6aabceKZv6PQ3ILETuNVAE4kDMIUH2+fSZ4REhKChISBSEgYeNU/i4mb/N5377+HKf94Fv3M\nZgBALIC0I4fx0e+XonHH19Bqe65NpS/4yaNP4Jt+/WH+bBtUdbW4kJCA+MwlGHOjPL/kDB6ZgqNz\n5iJ/03v4AYASQCuASo0Gk59+TuLoiJwxcZNfqygpwemnVuCO9qTd2e1Fhdiyfh1uevC3HomlMOck\nqkuKkTxhIsIjIj1yzO5QKBSYcvfPgbt/LnUol2W1WlFcXIiQkFCHBSwudvvzL2PnwEEI2LkDaGyA\nKWkoRvz8PiTfMMmD0RK5h4mb/FZNZQVOZC5AQqd1gDtTwrYcaW+rKCnBgeW/Qeqe7/Ajkwn74/ug\ncvaduOXJlbLso+wtDnz0ARpeeRkjjh5Gk0qN/1x3PYY89mckjhrjtG9gYCCmL1sOLFsuQaREV4aJ\nm/zW/peeR8bJE/ioi+31AFTJvVtNLAgC9j90P5Z896049pPyMtS/8jL+ExWD//nNsl49vq86/u03\nMDzyMGbU19sGTM24Puu/2FReBv0X//X5ZR/Jt8m7RyHRVQjJOQkFgGEA9l20TQDw0fjrMbGXez7/\nsPMr3Lxvr9N4lCBA8fm2Xj22Lzv77gak2pN2J3NO5WLPm69LEBFRz+EVN/kta/urSyMBZAPYAiAC\nQAuAY0lDMfvNd3p9haXaUzno16k6uzNVdWWvHtuXBZeXuRxXAQjq4tEIkVxc1RX39u3bsWyZ61t5\nmzdvxty5c7Fw4ULs2rXrag5D1CuCpt+C6vZFJMYCmAfgBgAxWi2mvb4eMQZDr8fQd+w45HaxUMGF\nfv17/fi+qiUuzuW4BUDrJYrUiOSg24l75cqVeO45169K1NTU4O2338amTZvwxhtvYM2aNbBYLN0O\nkqg3TE7PwLa7luBwmO15pwBgb4wOZx5+BIkjRnokhuHXTUDW5BthvWi8RK1GyPw0j8TgiwwLf4Yj\n4c6v8X0yOAkTfn6vBBER9Zxu3wdMTU3F9OnTsWnTJqdtR44cwdixYxEUFASNRoOBAwciNzcXI0d6\n5o8hkTsUCgVuf+pZ5C+6C+/93zZAqcKotJ8hxcNXZNNfeQNv/fF30H+9C7H151CclATFgnRMzVzs\n0Th8yagbf4y9f1mF3NdfQerxo2hSqXB03HUY9Kcn+V4+yd5lE/eWLVuwYcMGh7FVq1ZhxowZ2Lfv\n4pIeG6PRCK22Y9WT0NBQNDb61kox5DuSRo1G0qjRkh1fow3HbS++iqamJjQ0nMdkvYHtVnvAhPQM\ntC1IR+7RIwjRanEzl+ckH3HZxD1v3jzMmzfvin6oRqOB0WgU/93U1IRwF7etLtbdJc78DefJfXKa\nK1usrp/NeubYvikubkqP/jxfnquexHnqPb1SMjtq1Cg8//zzMJvNaGlpQWFhIYYMGXLZz/na+q29\nwRfXue0tnCv3cJ7cx7lyD+fJPV6xHvf69euRkJCAadOmISMjA+np6RAEAUuXLoVKperJQxEREfkl\nhSB0sbiuBPgN7fL4TdZ9nCv3cJ7cx7lyD+fJPd294mbnNCIiIhlh4iYiIpIRJm4iIiIZYeImIiKS\nESZuIiIiGWHiJiIikhEmbiIiIhlh4iYiIpIRJm4iIiIZYeIm6gGCIKCurhYmk0nqUIjIxzFxE12l\nA1s246vbpqNq/CgcmzAGn99/D+praqQOi4h8VK+sDkbkL374/FP0+8NSzGhosA00NkL4cDPeqCjH\nHR99CoVCIW2ARORzeMVNdBWq330LKfak3U4B4PY9u7H/823SBEVEPo2Jm+gqhJwpdTkeZ7WiZV1h\nRAAABmNJREFU8dhRD0dDRP6AiZvoKrTodC7HjQCUfft7Nhgi8gtM3ERXQTVzFiqCnEtFto5IwYSf\nLpQgIiLydSxOI7oKk+++B9srKhC9eSMmnz2DqqAgfDt2PIat+DtUKpXU4RGRD2LiJroKCoUCNz/y\nGBofeAjbv9qByD7xuGX8BFaTE1GvYeIm6gFabTgmzb5T6jCIyA/wGTcREZGMMHETERHJCBM3ERGR\njDBxExERyQgTNxERkYwwcRMREckIEzcREZGMMHETERHJCBM3ERGRjDBxExERyQgTNxERkYwwcRMR\nEckIEzcREZGMMHETERHJCBM3ERGRjDBxExERyQgTNxERkYwwcRMREckIEzcREZGMMHETERHJCBM3\nERGRjDBxExERyQgTNxERkYwwcRMREclI0NV8ePv27fjiiy+wZs0ap20rV67EwYMHERYWBgBYu3Yt\nNBrN1RyOiIjI73U7ca9cuRK7d+9GcnKyy+3Hjx/HunXrEBkZ2e3giIiIyFG3b5WnpqbiySefdLlN\nEASUlJTg8ccfR1paGj788MPuHoaIiIg6uewV95YtW7BhwwaHsVWrVmHGjBnYt2+fy880NzcjIyMD\nixcvRmtrKzIzM5GSkoKhQ4f2TNRERER+SiEIgtDdD+/btw+bNm1yesZttVphMpnE59urV6/GsGHD\nMGvWrKuLloiIyM/1SlV5UVER0tLSIAgCLBYLsrOzMWLEiN44FBERkV+5qqryi61fvx4JCQmYNm0a\nZs+ejfnz50OpVGLOnDkYPHhwTx6KiIjIL13VrXIiIiLyLDZgISIikhEmbiIiIhlh4iYiIpIRJm4i\nIiIZkTxxb9++HcuWLXO5beXKlZg7dy4yMzORmZkJo9Ho4ei8x6XmafPmzZg7dy4WLlyIXbt2eTYw\nL9HS0oJf//rXWLRoEe677z7U19c77ePv55MgCHjiiSewcOFCZGZmorS01GH7zp07MW/ePCxcuBAf\nfPCBRFFK73LztH79esycOVM8j4qLi6UJ1EscPnwYGRkZTuM8n5x1NVdXfE4JElqxYoUwY8YMYenS\npS63p6WlCfX19R6Oyvtcap6qq6uFmTNnChaLRWhsbBRmzpwpmM1mCaKU1r/+9S/hxRdfFARBED77\n7DNhxYoVTvv4+/n05ZdfCn/4wx8EQRCEQ4cOCffff7+4zWKxCNOnTxcaGxsFs9kszJ07V6itrZUq\nVEldap4EQRAefvhh4fjx41KE5nVef/11YebMmcKCBQscxnk+OetqrgThys8pSa+42e/cPZeapyNH\njmDs2LEICgqCRqPBwIEDkZub69kAvUB2djamTJkCAJgyZQr27NnjsJ3nk22OJk+eDAAYPXo0jh07\nJm4rKChAQkICNBoNlEolxo4di/3790sVqqQuNU+AbQGlV199Fenp6XjttdekCNFrJCQk4OWXX3Ya\n5/nkrKu5Aq78nOrRBixdYb9z93RnnoxGI7Rarfjv0NBQNDY29mqcUnM1TzqdTlw2NiwszOk2uD+e\nTxe7+FwJCgqC1WpFQECA07awsDCfP4+6cql5AoDbbrsNixYtgkajwa9+9StkZWVh6tSpUoUrqenT\np+Ps2bNO4zyfnHU1V8CVn1MeSdzz5s3DvHnzrugzISEhyMjIgFqthlqtxoQJE5CTk+PTf2i7M08a\njcYhSTU1NSE8PLynQ/MqrubpwQcfRFNTEwDbHHT+owH45/l0MY1GI84RAIdk5I/nUVcuNU8AcNdd\nd4lfEqdOnYoTJ074beLuCs+nK3Ol55TkxWldYb9z94waNQrZ2dkwm81obGxEYWEhhgwZInVYHpea\nmoqsrCwAQFZWFsaNG+ewneeT4xwdOnTI4UvL4MGDUVJSgoaGBpjNZuzfvx9jxoyRKlRJXWqejEYj\nZs6cCZPJBEEQsHfvXr87j1wRLmrAyfOpaxfPVXfOKY9ccV8J9jt3T+d5ysjIQHp6OgRBwNKlS6FS\nqaQOz+PS0tKwfPlypKenQ6VSiSvW8XzqMH36dOzevRsLFy4EYHsM8+mnn8JkMmH+/Pl45JFHsGTJ\nEgiCgPnz50Ov10scsTQuN09Lly4V795MnDhRrK3wZwqFAgB4PrnB1Vxd6TnFXuVEREQy4rW3yomI\niMgZEzcREZGMMHETERHJCBM3ERGRjDBxExERyQgTNxERkYwwcRMREcnI/wN2k1m8sjO/owAAAABJ\nRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11909f048>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -456,7 +466,7 @@
     }
    ],
    "source": [
-    "from sklearn.datasets.samples_generator import make_circles\n",
+    "from sklearn.datasets import make_circles\n",
     "X, y = make_circles(100, factor=.1, noise=.1)\n",
     "\n",
     "clf = SVC(kernel='linear').fit(X, y)\n",
@@ -471,14 +481,17 @@
    "source": [
     "It is clear that no linear discrimination will *ever* be able to separate this data.\n",
     "But we can draw a lesson from the basis function regressions in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), and think about how we might project the data into a higher dimension such that a linear separator *would* be sufficient.\n",
-    "For example, one simple projection we could use would be to compute a *radial basis function* centered on the middle clump:"
+    "For example, one simple projection we could use would be to compute a *radial basis function* (RBF) centered on the middle clump:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -489,21 +502,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can visualize this extra data dimension using a three-dimensional plot—if you are running this notebook live, you will be able to use the sliders to rotate the plot:"
+    "We can visualize this extra data dimension using a three-dimensional plot, as seen in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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nrnAkzWNAb2/6yFK388tf3o+HHvo1VFXFmDFj8LWvfR0XXHBe9F37o0KNSFse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\nkJQsgUAgEAgmQVKyBAKBQCCYwP8DzZfIxLKdt6oAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1188cc780>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -513,16 +529,12 @@
    "source": [
     "from mpl_toolkits import mplot3d\n",
     "\n",
-    "def plot_3D(elev=30, azim=30, X=X, y=y):\n",
-    "    ax = plt.subplot(projection='3d')\n",
-    "    ax.scatter3D(X[:, 0], X[:, 1], r, c=y, s=50, cmap='autumn')\n",
-    "    ax.view_init(elev=elev, azim=azim)\n",
-    "    ax.set_xlabel('x')\n",
-    "    ax.set_ylabel('y')\n",
-    "    ax.set_zlabel('r')\n",
-    "\n",
-    "interact(plot_3D, elev=[-90, 90], azip=(-180, 180),\n",
-    "         X=fixed(X), y=fixed(y));"
+    "ax = plt.subplot(projection='3d')\n",
+    "ax.scatter3D(X[:, 0], X[:, 1], r, c=y, s=50, cmap='autumn')\n",
+    "ax.view_init(elev=20, azim=30)\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.set_zlabel('r');"
    ]
   },
   {
@@ -531,33 +543,33 @@
    "source": [
     "We can see that with this additional dimension, the data becomes trivially linearly separable, by drawing a separating plane at, say, *r*=0.7.\n",
     "\n",
-    "Here we had to choose and carefully tune our projection: if we had not centered our radial basis function in the right location, we would not have seen such clean, linearly separable results.\n",
+    "In this case we had to choose and carefully tune our projection: if we had not centered our radial basis function in the right location, we would not have seen such clean, linearly separable results.\n",
     "In general, the need to make such a choice is a problem: we would like to somehow automatically find the best basis functions to use.\n",
     "\n",
     "One strategy to this end is to compute a basis function centered at *every* point in the dataset, and let the SVM algorithm sift through the results.\n",
     "This type of basis function transformation is known as a *kernel transformation*, as it is based on a similarity relationship (or kernel) between each pair of points.\n",
     "\n",
     "A potential problem with this strategy—projecting $N$ points into $N$ dimensions—is that it might become very computationally intensive as $N$ grows large.\n",
-    "However, because of a neat little procedure known as the [*kernel trick*](https://en.wikipedia.org/wiki/Kernel_trick), a fit on kernel-transformed data can be done implicitly—that is, without ever building the full $N$-dimensional representation of the kernel projection!\n",
+    "However, because of a neat little procedure known as the [*kernel trick*](https://en.wikipedia.org/wiki/Kernel_trick), a fit on kernel-transformed data can be done implicitly—that is, without ever building the full $N$-dimensional representation of the kernel projection.\n",
     "This kernel trick is built into the SVM, and is one of the reasons the method is so powerful.\n",
     "\n",
-    "In Scikit-Learn, we can apply kernelized SVM simply by changing our linear kernel to an RBF (radial basis function) kernel, using the ``kernel`` model hyperparameter:"
+    "In Scikit-Learn, we can apply kernelized SVM simply by changing our linear kernel to an RBF kernel, using the `kernel` model hyperparameter:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "SVC(C=1000000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
-       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
-       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
-       "  tol=0.001, verbose=False)"
+       "SVC(C=1000000.0)"
       ]
      },
      "execution_count": 14,
@@ -570,18 +582,28 @@
     "clf.fit(X, y)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's use our previously defined function to visualize the fit and identify the support vectors (see the following figure):"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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if4KlpRXZbm5QS+I+b21DlwGDtBCV0BKalLg1Gg3vvvsucXFxmJiY8P777+Pm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4AAAQ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\nAwBgI/8FOMqL16Jtqf8AAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118bb3e80>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -611,21 +633,24 @@
     "\n",
     "Our discussion thus far has centered around very clean datasets, in which a perfect decision boundary exists.\n",
     "But what if your data has some amount of overlap?\n",
-    "For example, you may have data like this:"
+    "For example, you may have data like this (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+8yHWIf25vW0LNR56Hrylugctxo0vp8gLysrKwnLB3PtJ+C7vzAyOzJ+L5rkJ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\nzPbxxx/D3d0db7/9Nm7evIkZM2Zg/PjxomOZbM+ePVAoFNi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GUHMhsbxcXsOHi7shXPnYjh2t37/vPqlQWDfiJTdXbghnnQXNm0fvl3/+eRg8WGq2ZGRISGlamoQMHnxw+H3vuUfsqWnLunXwt79JGOXcuXDssdHZoSgNQGfYjZ1QCF55RYpGVTWF+PHHxB3v6aftFzpLS0XoYuGMM+Df/6aiVSsR/ObN5QnhgQdkvaBNm4YtoublSTTIV1/B44/Dyy9LXHbfvuH327JFBNsqVDIUEhfOoEEaGqgkBJ1hN2ZCIQlfmzOnOm55+XIpZ/vf/yamOe1PP9mXyg0GZRZ6yCGxhToOHszq7t3pXloqY/XoIS6MeDj44Mgz6pq89ZbMyMM1Pdi5Ez7/XG4qiuIgOsNuzLz6am2xBhHTwkKJaknELPD448PXny4vh2uusc8MjURamvi7jzkmfrGOhbIy+5j7KtLS7MMGFSUOVLAbM1Om2GcE7tghC49OM2yYLP6FIy1N0sC9SJ8+kQW7pEQWWRXFYVSwGzPhZnlpaYkpqpWfDx99FF60jYl9hu02XbpIuKHd75edLXVaDjggqWYpTQMV7MbMKaeIv9WK0tLEzQIPO0wWF+2SYcrLpWmFV3n2WUmIqvn7paeLiPfrJ+GIVoRC0RWlUhQbVLAbM2PGWPt5c3KkOt1++yXu2CNHyqJb3f6TubniP491BhoKRXZJJJqMDCkatXOnJPq89ZbUIFm5UopO1Y1WWbtW4sOzsuTv0bOnhP6FIxSS+PlFiyLXTlGaDCrYTmMMWcuXw5tvyhfVTQ46CN5+W1LUq0LhsrLgwgsllC2RtGghCSwnnijHbNZMhOzKK6VJQUP54gs49VS6HXmkiF7fvpJo4yZZWVInZsAAOadWMeTr10vSz+zZMrs2RoT4/PPhP/+xHnf2bGjXTppfnHGG1C+fNq1htgUCkrh0++3S5MFuLUPxFBrW5yQrV8Jvf0vH9etFVEpL4YQTpHTnXnu5Y9PJJ0tDiIULZUbYs6fELCeDjh3Fn/3zz7B9u/wcLoLEjoULZbEvEKgu/jRnjkSkLFggLphU5d57pQ553aeC4mLpNXnuubW7/3z4oXTOqVvBcMwYueH97neRj7lgAfTvL6GPhYWyrjB6tEwiTjop/t9JcQ2dYTvFnj3yZVi9Gn9xMezaJdEC8+fLDMxN/H4RtwEDkifWNWnTRkQ1FrEGERurRcqiIkkHT2Vee83eb71tm7SVq8mtt1qXmw0EZFukUMyiIhHrXbtErEH+3b0bzj5brlPFs8Ql2Nu3b+fUU0/l+++/d8oe7zJ9enW7qpqUlcG338ojfWPHGHHB9O8vHYMuuwy+/jq+MYuLxY9rd7z//je+8VMNu98VROC3bw+//8yZ9jeIUEhS7xXPErNgl5eXM378eLKdLIvpZT780D5ULRiUzDcvsXu3uHi2b5eY7br9Kq0YNUrqc7z7rtyknn9eKt+9+mri7Ez1FPCBA+1rfe+zT/1a6eF6UYZCkXtVrl5t768uKoJVq8Lvr6Q0MQv2pEmTGDp0KK1bt3bSHu/SunX9iIgqMjLc82E3lD17JPmldWspQ7rPPvJq2RJuusk+YmHBAinlWlMsgkG5iV12Wexx1zk50KuX9TafT4o+pTK33WYdLZOTA//8Z/3u9cOGWYdi+v3SezKSW+mgg+w/k5trX+JW8QQ+Yxo+RZk1axY///wzV199NQUFBUyYMIHOnTvX+szixYvJdaijSUlJScrP5LNWrKDjsGH4LRrJhrKzWfXxx5hYfbgJpNa5NYaOQ4eStXIlfot6IKGsLAJHH816iwiTNrffTsvXXsNnEXIXzMtj0z33sKdfv5hszFm6lPYjR9Y6twYwubn8+MIL+PfsYd+HHyb3q68IZWaye8AAto4aRXCffWI6nlNUnduM9etpPXkyzT74AIJBSrp1Y8uNNxKwqOWStmMHnQYNIm37dvyVN8dQRgYmJ4cfZsygvEOHsMf0FxXR5bTTSLOYZQdzc1kzbx4hm2qJXvieVdGYbQ0EAvTu3dt6o4mBSy+91AwbNswMHz7c9O7d21x00UVmy5YttT6zaNGiWIa2ZPny5Y6NlVBuusmY3FwTkgd1Y9LSjMnJMebll922zJZa53bePGPy88V2u1denjGvvGLMxRfLZ/PzjRk61Jg+fez3yc015vHH4zP0s8+MOeEEE/L75byefroxS5ca8+abco5rHi893Zi2bY3ZujW+Y8ZJves2GDSmvDzyjjt2GHP33cYcfLAxBx1kzJgxxvz0U/QH/vBDY5o1k79V1fnPzzfm/fcbZm8K05htDaedMQl2TYYPH27WrFnToIM2FC/9ccz775vdffoYc9RRxvz+98Z8/bXbFoWl1rn9y1/CizUY4/MZk5VljN9f/Z7fb0x2trys9snJMWbZMmfsXbasWvRCIWMOOMD6mFlZxtxyiyPHjNlWN6/bXbuMeewxY66/3pipU43ZuTPiLl76njVmW8Npp8ZhO83pp7OhTZvUaRHWELKzZYEsXPq0MfVLi4ZC8l56uvhka3rZsrKkst6RRzpjY0ZG9SLed9/Z10MpLYUXX4y9/rbXad4crrjCbSsUh4k7Dnv69On1/NeKRxk82L72SCSMEeHu2lUWvVq0kBvAmWdK5l4iCAbtF3pB63YojQ5NnGkqbNok9T3y80WUTzpJ+i3WpGtXSR23WxyNFFJmjITzffaZdHBZtQreeCNxhfy7dbMvMJWRAb/9bWKOqyguoYLdFNi6VULjpk+XsLuKCvjkE+jfn7wPP6z92QcflI40Rx1V7SLx+aSB78SJcPTR9sc55hgp23rEEVLr48ADE/t7paVJB5u60Ug+n9x0Uj0LUlEaiAp2U2DyZPH11nURBAK0vfPO2j5nn0+q6S1dKlmG5eXi6tiwQfpBPvCA9Uw7N1fEM9lcdpn0kezYUeq3ZGRI3ZGFC6F9++TboygJRAW7KfDSS7YJL2m7djUs++3kk6WI0KGHikBmZkoa+ptvulfj+uKLpTLixo2SlTl3rrh3rDBGOvG0by/+7333hbvu0hKmiifQKJGmQJi0cuPzRZd2XpPTTxdf9dat8vO++8ZhnEP4fLD33pE/d801Una0KvNy2za4/35xEb3zTv3Mw1j56itavPmmdKg/88zYF3MVpQY6w24KnH++bT0Lk50tXcxjYd99U0Oso2XtWnGf1E2TLy4Wwf7oo/iP8csvUlL3+OPZ7557pFRq27bOjK00eVSwmwLjxkl0SN3ZY04Om2++WRbvmgJvvGG/LRCQyJZ4Oe88aW4cCJAWCEgRre3bpc/jhg3xj680aVSwmwLt28si3Jlnykw7I0OqxE2fzu7zznPbusRTViYiumGDfXsxY+KP2/7mG1mstfKHl5eL71xR4kB92E2Frl2ldnRxsQhK8+Yy416xIolGlAEBoAXgkK84Eo88Im2yQATZbnExP1/afMXDsmX2TyulpRKfrihxoDPspkZOjmQhOrW4FhWbgSFAM2A/oB0wFam51xBKSE//CYiyP+GTT4o7aPdueVX5rn3AKOBd4CHgsEzpiBNvqdZw3Xx8vsTHpSuNHhVsJcHsAY4GZiEz7DLgZ+BG4K4oxyhFFHZvOnc+H9gbGA7sst8lFJJa1FZ1uA3wNdAXuBr4MgQfXhk+zT0aTjvNPhs0Jweuvjq+8ZUmjwq2kmCeBrYDdf3DAWASsDuKMS4CngQC+P3FiIDPBE4BbEISN20K379wYeW/mUBmBWRdCTyG3GBiJC0NXn9dOsRXpcz7/ZJUdN110n3Hi+zZI66l44+XWPt//KO6X6SSVNSHrSSYlxBxtiID+Bg4J8z+y4B5QN3GtGXAWuAd6/1zc8PHl9frrVEOjAZuAKYBBWFsCsPxx0si0rRpFM6ZQ/4hh8BVV0navhfZtk3KEWzZUv20snQpPPywtL3zSielRoLOsJUEE2lOECmkcC71Z+dVFAL/sd7UqpWIpJWvPhP4ndVOpcjN5SogjqbJbdrAhAmsf/JJaZvmVbEGGDMGfvqptmspEID162V9QEkqKthKginAYjpbSRBxa4QjE/vL1A+Eab30xBMSDVMzyzAb2B/4S7hjFiPumiZOKAQzZkhIYl3KyqSYWKo3QW5kqGArCWY40BER3prkIqIYqe9nuBKpOcAl9pu7dYOvv5bFvo4doevBcHseLAFahTumAZZGsMuKIPAWcA1wPdnZy2h4JEwKUVZmLdZVlJTYx7UrCUEFW4mdUCiKGVYO8BkiYi0RF8jhwPOV70WiPeJbrivsucC5QAR3w4EHir/1hx/gu1Vw65fQoqPFeHXZPwrbarKn0pYhwBTgETp0GAkMwnZhNNXJygofiti5c9PJkk0RVLCVhrNggTRAyMiQ17nnwsqVYXZoDjwI7ED80V8DFzTggPcBTwGHEwrlAJ2BycALNDwBpyuyWPk20MFm/zzgugaOOxr4FvGrA4QqI1reQQTcg/h8cPfd9euNg7x3zz3Jt6mJo1EiSsOYPx/69au9CPXWW9K95osv7MuaxoUPGAoM5bvvVjjQL9OH+M7fA05AEnGKKt+vcrNc0IDxSoAXkUXLugSAB4A/RzfUihVS88QYqT9y2GENsCMBFBTA5s1wxx3VBcSCQRHrIUPcta0JooKtNIxrr62fjGKMxOXedhvMnOmOXTHRGfgemI5Eo+wF/B44nobN3H+J8PnNkYcIBmHECHj1Vfm/MTBhgrQ5e+45d10PN94If/qT3JR9PqmJbjXrVhKOCrYSPTt3wvLl1ttCIWlikHS2AP8AXgeygBGI6EYrKPnAnypfsbIP4b2LHSIP8eCDMGuW1HqporxcGhhPmgS33hqHfQ6Qlwf9+7trg6I+bMXLrAUOBf6K+MUXATchi39xZCw2mEzgCsSdUpc8IAqx/dvfrNPoAwFpy5YK4XNbtshN+aOPPN6RPohXo3dUsJXoadlSQuWs8PlgwICkmgN/RBYya/qOA8Aakh9HfT9wBjKzzwSyCYUygcuJmDUZDIoY2rFzp1T7c4uKCrj8cujQAYYNk0XmNm2kQ4+nmA0chmTY5iDZUz+7alFDUcFuLBQWwgcfSAnPRM5+Hn3U2n+Znw/33pu449ZjJ/AJYBUHXIrUHkkmmUjW5WfAvcD9rF37JlIOMII/PC1NMjPtyM+XEDu3uP56eOEFibvetUtqi2zfDhddBF995Z5dDeLfyGLycmR2XYosFPdGbvreQAXb6xgDd94JrVvLAlW/fjL7ef31xBzv1FPh3Xcl3drvF7E580z49FP72XdC2EP4JRi3ihMdiVQiHE15eQNiuf/8Z+tKf1VV/pJaDrcGu3ZJmVord01JifTDjJXCQqnRPndubd+945QD11O/pk0FsmD8zwQe21lUsL3Ogw/CX/8qF/zu3dWzn2HDREQTwUknSQeb4mL50s6ZI53Tk0o7wqal85tkGeIMt90Gp5wii3s+n7zy8qQ/5IQJ7tm1cqX97D4Uiv0amzRJJhmDB8tMvXVrmDo1djvDshjrJzGoDsn0BirYXqaiQuJh7Rar7rgjscfPzLRt7pt40pCCIHkW23KAO5NrTrxkZsLbb4tf+Prr4bo/w1tjYU4eZA1GQg9d8GPvtZd9l56q7Q3lX/+Cu+6qnmTs3i2z7RtvTNCTYaQFRrvtRcBtQGtkctAT22JjSUIF28usXx++1sPnnyfPFle4DhiLCHRzpKNNK8RfeWp0Q8yfLzWe09OlhvWwYXJe3cDnk6eXB+6GBz+FU/4GvtmISFwN9EB890nk4IOl/6cVeXlwTTTlBWpgDIwfbz/J+EvYqlwx0hv7dYRsJCmrLmXAyUiG7lbkZrm08rPuuVBUsL1Ms2bhBTs/P3m2uIIPuAOJxX4dSTffAlwc3e7//a/4/D/9VCI1SkulOl2vXrBxY4Jsjoa7gG+o7YcvRMIYxyTfnBdflLZyNV0jVe6ayy5r2FiFhZI5acfy5QkIYcxEShnUXSxPR+rbWN10XgZWIS6TmgSQ0NEo29Q5jAq2l9lnHzj2WOsFqexs+OMfk2+TK+QDpwMnEnUumDHSWKDuTC8YlIW2++5z2MaG8Bj1hQJk1vcC9vXBE8SRR0rK/Jgx0Ls39OkDTz0lLpyGusSys8O3Yqvy4TvO5cCzwMGI7GUihbkWIy3n6vIs9qKcDryfABsjo4LtdZ56SuKja85+cnOhSxcYO9Y1s1KedevgZ5sY3PJyl1Psw/SqJIh9B58E0rathG0uWgTvvSd1RGJJl8/IgIEDrYU+M7PhM/YGcREyay6ufL2ILF5bEcZvD0jkSfJRwfY6Bx8sj5FjxkihoN69YfJk8V83epdIHASD4WdyrtZ57hxmW0vEV+9hHn5YQk9rhjHm5kpizt13J8GAcE0xqhiEfXmDMqJeI3EYrSXSGGjTRmY/0SauGCOLbc88w/4bNshsaehQ+47frrEZeBRZdMsF/oDPd7QzQx90kCSrWC1+pafDeec5c5yYGI+0KatrWy6S5u5STLZTtGkD33wjT4cvvSQukoICKX6VZxX14wYjkJIHZdR2QeUiPm8rN0riUcFuaoRCMHy4FBUKBGhuDHzyiazcL1gA+ze0cH+iWAUch4hWVTjb13To0BHptxjnzcXng7//XYSiZtJGVfxzQqIVomU4ssB4P5JGbRDRuAKpu90IaNECbrhBXilJPvA5Is5vIDPyPGAc0qjZHdQl0tR47jkR66Ki6tX4wkLYtEnEK2UYgYSw1Yw9LiIraw3wd2cOcdFF4qs+5BCZVaenS9bmggX2oWxJoSr6ZQOyADkV+IGo0twVB2kDvIpch+uQJ74xuPk30Bl2U+Ohh0Ss6xIMSh2STZtkgclVfga+xCqhwe8vRUTsFmcOdc458ioslAUxN2t21GNvwvasVJJEDnE/0TmEzrCbGps22W/LzAwfI5s0diGugHDbHcbtAkuKEgUq2E2NcC2nyspcdgVU0Qm7x07x4hyXTGM8zhrgv8iagJJYPgf6I9m2+yOLx87WZVfBbmrcdpt1edTsbLjkElkMcp1MJJusvp3GZCNfBCU8m5B+lUcimZ89kBvdTy7a5AbrkRIGnZFmF5NJTCXHt5DkrXcRn/fGymMdh8/nXNx8TIJdXl7O2LFjufTSSxk0aBDvvfeeYwYpCWLnTim68+WXUow+Oxvy8wllZ0s43+mnwz8TUSOhAqnFECkRoS63Io1rs6muE7IXP/0kXwIlHEGkyfAXSILIrsp/FyP1MbzcLaYhLAeOQLrWrwVWIIu5vYHdDh4nBIykfhhmCfADLVu+4tiRYlp0nD17Ni1btmTy5Mns3LmTCy64gDPOOMMxoxSHee45uOIKiXctKxNf7UEHwdVXs3nLFtoOGQKHHurwQcuRL8c/ELFOQ8LVHkBCpiLhR8LaxlEdxncshYWrHbazMfIOEtFQV5grgG1ImNoFiND8Fzm/rUhLOyqJNiaDPyDCXHPxuhiJ+JiENJpwgiXYp7EX07LlLMCZUgcxCXb//v3p168fAMYY0tzs6KyEZ9kyEeu6zV1Xr4YZM9j52GO07d49AQe+BCnGVHPW8QxycS8g+oe7FsCZzprWIAxSv2MS8D+gPXAzcCmpG2K3AHvf6R7gU+Qp5VTk0b0IyKZLlxASMnllMoxMMFuRa82qkFQp8DTOCXYJ4a5nn8+5srgxCXZeZTZSYWEho0aN4rrrrrP83IoVK2I2rCYlJSWOjZUMUsnetnfcQfPS0vqXU3k5oc8/x6xahdOWZmauplOnNypD8GpSSjD4LT/99CRFRSfHNHayz+1++91Ly5az8PurbnhfEwpdzs6db7N5821h93XrOthrrzL23TcTv7++GyoUymDr1iDNmg0gJ+d7fL5g5ZZi/H4Iha5j3bq9KClJdkOKhhHp3GZk/MRBB/lt60wFg7tZtcqZv43Pl0PXruWWxwqFMti162S2b3foOjAxsnHjRjNw4EAzc+ZMy+2LFi2Kdeh6LF++3LGxkkFK2dujhzESXFH/1aKF+d/UqQk46IPGmCxjDDavK2MeObnndoUxJsdY/w45xpiVYfd27zrYYIzJNtZ2Zxtj5hn738tvjBmadIsbSuRzW2GM2dvYX4P9HbboXmNMbp1j+IwxLcyqVfMaNFI47Yxp0XHbtm2MHDmSsWPHMmjQIGfuHEpi6NTJvshRRQXlbdok4KBp2LsLfNSPsa5q03QX4jZxotZwBTAXeAlYGeMYr2Bfla0ccLOiXzj2R3ymuVT/HXyVP9+NuKkybfYNIYt1XicNmIB1AadcnO9INA64BynOlYec32OBz6io2M+xo8Qk2NOmTWP37t1MmTKFgoICCgoKKCmxqt+ruM7111sXdfL7oWNHSg85JAEHDVc4KRcYUuPnxUiJyyuQRcprgbbAR3Ec/+PKMS6sHLcXcAYNT7gpxj6ioqJyuxP8gDQsaGgkTTiuA+YAA4HDgN8iIWc3Ah2xvxH5kJrR0fIzcDsSeXEq0srMndKj9bkGEeZmSKRRPnKtvQoc4/CxfEij363AMiSc8DPA4fWhmJ8A4pjWN5SUcjFEQcrZO2GCMTk5xqSniyskP9+Y1q2NWb06gbaOMvUfEXONMecaY0KVnyk2xrQy1o+szYwxO+uNGtne/xlj8izGyzLGnNHA32GeMSbfxr78yu32RLZ1oTGmuxH3RDNjTHNjzGRTfX4SSU9jTJqp/3vlGmPmRznGcmNMS1Pb/ZVnjDnZGFPqsL11jtyg67bYGLPAGLPUGBNMjEFhaOh3zHGXiOIx7rgDFi+Wmtm//z088gj88IM0OUgYDwOPAAchLpB2yCPqa1Q/pr+G/WwsCDwXw3H/YTNmKRId0ZCMv1OR2WndlPWsyvfjqYm8Cpn1r0Bm6nuQELQ7gL/FMW60vI48hVSFWGYQCmUhSUknRjlGAfLUUrtAlzw1PeaMmY6QjbgnjsLruYJa/Kmp0L073H9/Eg/oQ+Jg/xDmM2uw91cHiOxL3YH4kbchmXz9EFG2cy2kI4+rXSOMW4UP8YNfXXmcdMQVMgipoBdPWN99WLtUAoifeRT1bxRO0h74HhHuT4B9WLv2OLp0OSvK/f8HfIt12FwASVb5swN2KjVRwVZcpCOyQGOVKpxDeGGdAfweEc0SxDe+L5J+7MNaSKj8TEPIR/r7/RNJRtkPZzq+zEGeIuxYicwIE0kmkrYuTYvLyxsSerajcn+7tasdcVmmWOPt5wPF41yE/SXoQzIjrfgOSQUuRmZzIUT0/4fMyu1aO+Uiqdmx0AzognPtucKV6wxi/zukCgdjvyDrA45Poi1NBxVsJQq2AhOBAYhQLnBo3FzgTUQEqwQqp/L/r2LfhukRrN0eQWQWfB4yc69yWWQhM+VZSLhXKjAS8a1a0Q65OaQyuUhEj9WNJQdws2NP40VdIkoEFgN9EIGsSsGdgfh1Jzsw/klICNQLyOy4CzKzDtcz7yvsZ3dpSAjbnxA/80ZkVn019h2y3eDPSAjcD1S7FfyIiP8b59PeFyGLmd8ileuuJ/5GshORJ5wnkZtiCJGUZ5AwP8VpVLCVMIQQ8dtd570AIobn4kz36BaIwEZLVyTG1coHbIAOyCP5KfGbljCqegY+DPwLOad9kJmp04W4HkcEugT5+32L+NBvQWKoYyUNaZI8Afld8pCSrioriUJdIkoYPsU+2SSALMS5gV0EhQ9ZFPRK+dV8RKDXIgkoL+C8WG9FGvdW+fpBbmoBZIYcqfrhWiRS5vswn9kbcZedQrVY/wQsBLbEZLVijQq2EobN2F8iBmkS6wZHAX9F3AdVKdb5wD5I6dBUraLnBq9gfz6CSASMFZsRAT4cCWM8AonPDtNiDpAbTx/EtdUPCR+8EGfrTzdd9NlFCcMR2Ce2ZFA/vXcnEn/7HCIGg5DZXesE2HYN4pJ5FhGRY5HwtNRolpo67MQ+Lr0ciWGvS1UDhLXUTsFfiKw5fIe1dJQjLpH1lftV+ebfQlpnfYLeTONDBVsJQ1fEvfAJ9b/0GYhrooptyELTFqq/qA8g/tNFiF/ZaToQnw+2KXAMchOzinXPx9rP/w6yWFt3YTeIuFjeRNY26vJ65fa6+5UiC8UL0HC/+FCXiBKBqkI5uciiUjNkkfA1JO28ir8gM92aiRSlSALFtUmxVLGiD3Jjq1sh0Y/8HS+02GcB9n0P9yA3cCvmhtmvFCnKpcSDzrCVCLRCvmhfAV8ifuK+1C/P+TzW7pMgUiWulMSmWivW+IB5iHvqC+TvVg50Q+LSrf4mrSrft+qUkgnsZXOsFkjkiFX0TibRtYZTwqGCrUTJkZUvOyKV1y1BBdst9gU+RCI9ViMz7nBlPy8G7Lrp+JH2b1YUIMW3rGqkhJDMViUe1CWiOES4uhdtkXrEirt0Rhb/ItVoPgDpd1g3i9GH9Ne0S2o6Aqk/nlfn/dzK8Zwr5N9UUcFWHKKqw0ldqr6sNaMDtiLdZX6DZCE+jfXjd6xsRBoCBCJ9ULHlBuBlanetMcD7yJOWVXQJwENIlNCJiPCfBfyncjwlXlSwFYc4C8nY25vqDh/NkXTomkWcvkdmePchae/zkTTtk4i/g8saJKysc+W/+yIdVuzS2NcgGZaHA6chJVRDNp9tiryK+LtrVj4MIPH3N9vs4wMuQP6u64H/IgufihOoD1txkCGIn3IJsvDUi/qLk79HIkdqCmMRki79dyRdOhZ+QULGfqkcu8qnPhXJ1nyizuc/QOK4S6kW9EVID8iZ6FzGIJmXVgvJ5ZXbnkTjqpNLU78qFcdJB45G4rfrivVWpOaE1Sy2GJgWx3GfQIS/7tgBpMjSzzXeCwJDKz9fc/ZdhMwIZ8dhR2OhgvA9Jmve6JRkoYKtJJGd1I8HrklDm+TW5A3sXSpZSLGoKj7D3r9dSHw3jsZCBtApzPaq1m9KMlHBVpJIB8I/Qv8mjrHrRibUpeaC6I4IdtgtqDU17sF+IfnuJNuigAq2klQygbHYi8CEOMYeiH1zAoMsKlbRG/vH/Uy8uUhmkFofDyOLv0606LoEyVKtOq8+xOV1C/ax2EoiUcFWksxtSDOBbCQzrjnQEgnti7Zbd112IGJvlWGXgdQzqZm00w5JybYqFJVF7RopXmAXEhVzBhK98Wck5nk4UvcjVl/zQqSEbs2yrCAuIy2b6gYq2EqS8SOdajYCLwL/h3z5L45jzKmIf9yKNOB8i/f/haRrV9048hGXzbPIIuXNSPxwuEa5qcIwpGxAEfLkEEAiOZ5Hzms7JISyoVxROWbNsL4KqlvGKclGw/oUl2iFFL13gpnYp8ZnIjPF0+u8n4WI82SkTsreyGz0EkSky5EbQRuklkqqZun9BLyHvYsnUPk6A4mLjraJ8CakjKoVVWF9D0dtpeIMOsNWGgGRLuNwjXf3Q5J+dgD3I8JfFXu8B+m5OCReAxPIaqKr0VKBZCBGSynhz1u4kD8lUahgK42AAsI3Ljg2ijH+hnWoXwUyQ18Xg13J4ACiE88ixG0SLe2xr//iR2qKKMlGBVtpBPwR8dPWTdTJRWpbRDMDDdfbMAv4MSbLEk8XJLU+3GwY5Hfo2IBx/chNzCqiJwe4swFjKU6hgq00AvKRWs+XIz5aP1I98GVgZJRjdA2zrZTwSSRuMxNx7YSLRfcDlzVw3GFIhE1bRLizkPP6HnBYw81U4kYFW3GQXUg1twVYR1fMQr7wOcij/P045wtthdRi3l157KXAOQ3Y/yasZ5MZSMhc+zjtSyQdkEJWU5DFxXSqnzYykPM9FTnnDWUYUuzpW+QpYynRuZiURKCCrThACBG8Nkh8c19kVvZ2jc/cj/iav0IW9n5CSqwOIDUq5J0GjKd+J/YuSEGoVCcH+B3SpmsdktxyAXAd8DUNn13XxI+4U9rEY6DiABrWpzjABCTBooTq8Lo9SJzzx8gM8E7qh94VI8Wg3gHOToahEbgZuBRxpVQlo/TFe/OadqiPuXGigq3ESTHwINYRFsVIPYoLsb/UCpFElVQQbIADgTFuG6Eolnht6qCkHGuwv4wM0mG7bhH8ukTqB6koCqhgK3HTAusi9zW398E+xTsfbc6qKNGhgq3ESXuk5ZdVudIc4CrEhz2U+lEYGchC1qBEGpgkfkR8+X9EQuEK3TQmJtLSdgLXA/sgN9J+SLikkiqoYCsO8CySFVczcSUX6aJ9deXPTyK+4WaIkGchUQwLkMiMhvA+kk6+PxJi9jLhXS6JZipwKNKn8imk4WwHsrLCJeOkGjvo2HEQEhq4neruO6chkSdKKqCCrTjA4cByJITscEREHwU+olqM05Awvu2I33s7IrR7N+hIrVo9C5yHiMhGJMpkJNJMNx7KkNTtb2iY+H+D3IiKqY4pLwJ+4cADryQ1Qhaj4SHS07dRPy4+gDw1uHlDVKpQwVYcoh0wCYn5XYCIqFVKeEblZyN1iLFiK61bP0T9iJQiJNIklhKiIDeX1shs8jgkUuSdKPf9J3bJP37/buSm5QWew++3S2Lahn3lPiWZxCzYoVCI8ePHM2TIEAoKCli3LlWL4yiNh9cxxu6SLUFEu6E8jiSZ7EJix4uQpJ6LkKJPkVhN+JrZ/4vBJjcIl3Hqj7BdSRYxC/bcuXMpKytjxowZjBkzhvvvv99JuxTFgkJ8PrvuKSHEl3wr9s14rfa5HesY8kDltkgcRfhmtIdEaYvbnIMxdrHy6YiPXnGbmAV78eLFnHzyyQD06NGDb775xjGjFMWaU4hco/khpFlBNG2xNiKzajs+jWKMa7AW7DQqKtoCx0QxRiowjlDIavE3F1lM1Ry7VCDmv0JhYSH5+fm//pyWlkZFRQXp6dVDrlixIj7rKikpKXFsrGTgJXu9ZCvkcsABh5GX93UYf2sJweDXbNo0lT17wtds9vt3cvDBFfhtpi0VFZmsXh353OTn/5X99x+LMT78/lJCoWyCwVasXv0IaWkrI+6fOvyLDh0mkp39LeAnGGzG1q3Xs2vXKUBqXSNeum6dtDVmwc7Pz6eoqOjXn0OhUC2xBujevXvsltVgxYoVjo2VDLxkr5dsBVi5chrduj0AvIpdwk5aWoADDvgIaUYbiWOQmXTdKIhM0tNHRnluuiOFl14FNpOWdhRpaX1JS/vOU+d2xQrIzV0C/AIE8Pvb0a6dn3bt3LasPl66bhtq6+LF9ovnMbtEevXqxUcfyQr40qVL6do1XD1hRXEGY/KQ5r0PEb7LTLTNc59AYshrujWykRjv2xpgWTNgBFJAqj/eDsDaCynF6uXfoXES8wz7rLPO4pNPPmHo0KEYY5g4UbsoK8lkIHCjzbZ8YHCU43RHSr5ORrqkZyKlSK9F0uoVJXWIWbD9fj933XWXk7YoSgNohyR0/IvaUR6ZSLp8Q+qTtEdisR91zDpFSQT6zKN4mL8jdZ/3QYQ6GxiO+KTr9ndsDJQh7cCuBu4GFrlrzq+UIglEhyOulEuQBCrFaTRWR/EwfsQtcgMSnpdL+JhoL/MysrBZWuO9u5BQx/9g3d4sGZQh1RiXUv2k8zIwG/g/tLu6s+gMW2kE+BF/c6qIdQj4iObN/0Ps6fI1+Rhpr1Za5/0KJPX9WgeOESvPU1usQX7/AGKzV2qpeAMVbEVxlK+R/ofn0qbNXcCpSDbkT3GMeQf2qeEVSNRMuASgRPIY1pmiIGn+qeK2aRyoYCuKY+xBCkitB/aQllaEiNa3iNsg1op3kWbpacR3Q4iHcDcKP16sC57KqGArimM8T323BUhM+EakjncsNI+wvQL3Opr3w36BtxTolURbGj8q2IriGJ8hM2oryhBfbyxcib1/3oc0MG4Z49jxcj3WZXRzkRrlLZNqTWNHBVtRHGN/7IU1E6m5HQs3ICFzVoWvOiPdfNziQGTh81Ak87Q5ItbXIslIipNoWJ+i1ONLJCzNAOcCR0e530jgYaxrnBgkOzMWcpHZ+/PAI8AmJNlnFBLz7PbXuAfip18D7ESyR2NpUKFEwu2/tKKkEBXAEKTbTEnlew8g5VpnETlssAswEalBUor4rjMqXy8gKfOxkoXcEEbGMUai6eK2AY0edYkoyq/8FRHrABI/HEJ80u8hmYXRcB3wCTCCoqJjkHrZXwHnO22s0gRRwVaUX7HqFwnSweZRog/L6wE8yf/+9+/KMTs7YZyiqGArihBCms3asYdqN4miuIMKtpIiGOBtqhf5rgN+SOLx/UgRKTuaIcWlFMU9VLCVFMAgpVIHA28i6cxTgCOAD5Nox/VYF1HKQbrX+JJoi5NsQTIhY820VFIFFWwlBZgLzKB20kl55c+DiL57TLzchHSLyUW+Gn4kPO0MouugnmosRPzp7ZEIjg5IGzPFq2hYn5ICTMU+Q7AUScw4PQl2pCOCtgQpDWqA84DfJOHYTvMVUr+k5iLqeqREKzSswYOSKqhgKynA5jDbfMD2ZBlSSc/Kl5e5HYluqUsAGANciHddPE0XdYkoKcApWNejAKnBoQWEGs772PusNwM/J9EWxSlUsJUU4FqsswizEf/xQck1p1EQ7uE5RONsodb4UcFWUoD9gTlIY918pIBQNtAXeMlFu7zMxdiL9hHA3km0RXEK9WErKcJxyKLYAiSB5SgkqkGJjQnA68AvSI0UEJ91LrLIq3gRnWErKYQfOAGpu6FiHR9tkfrblwN7IYk/FyKhftFWH1RSDZ1hK0qjpS2SgDTFbUMUh9AZtqIoikdQwVYURfEIKtiKoigeQQVbURTFI6hgK4qieAQVbEVRFI+ggq0oiuIRVLAVRVE8gibOKI2QCmB25SsbuASpCKjlRBVvo4KtNDJ2ACcB/wMKEZF+DinmPwu95BUvoy4RpZFxDbAGEWuQmtBFwHvAP9wySlEcQQVbaUQUIbPoMottAeDvyTVHURxGBVtpRPwCpIXZviVZhihKQlDBVhoRrQm/sKidaxRvo4KtNCKygKuAHIttucBfkmuOojhMTEvme/bsYezYsRQWFlJeXs4tt9xCz55e7zKtNA4mAj8AbyMLjn4giHQKH+KiXYoSPzEJ9tNPP81xxx3HiBEjWLt2LWPGjOG1115z2jZFiYFM4FVgJTC38ufzkGL+iuJtYhLsESNGkJkpXZeDwSBZWVmOGqUo8dOt8qUojQefMcaE+8DMmTN55plnar03ceJEjjzySLZu3crll1/OrbfeyjHHHFPrM4sXLyY3N9cRI0tKSsjOznZkrGTgJXu9ZCt4y14v2Qresrcx2xoIBOjdu7f1RhMjK1euNGeffbb54IMPLLcvWrQo1qHrsXz5csfGSgZestdLthrjLXu9ZKsx3rK3MdsaTjtjcomsWbOG0aNH8/DDD9Otmz52KoqiJIOYBPuBBx6grKyMe++9F4D8/HymTp3qqGGKoihKbSL6sGNl8eLFiRhWURSl0WPnw06YYCuKoijOopmOiqIoHkEFW1EUxSN4QrD37NnDVVddxfDhwxkyZAhLlixx26SIzJkzhzFjxrhthi2hUIjx48czZMgQCgoKWLdundsmRWTZsmUUFBS4bUZEysvLGTt2LJdeeimDBg3ivffec9skW4LBIOPGjWPo0KFccsklrFq1ym2TIrJ9+3ZOPfVUvv/+e7dNicjAgQMpKCigoKCAcePGxT2eJ9pveC0V/p577mH+/Pl0797dbVNsmTt3LmVlZcyYMYOlS5dy//33p3SkzxNPPMHs2bPJybEq7JRazJ49m5YtWzJ58mR27tzJBRdcwBlnnOG2WZbMmzcPgJdeeomFCxfy0EMPpfR1UF5ezvjx4z2RNFNaWooxhunTpzs2pidm2CNGjGDo0KGAN1Lhe/XqxYQJE9w2IyyLFy/m5JNPBqBHjx588803LlsUnvbt2/Poo4+6bUZU9O/fn9GjRwNgjCEtLVyNbnc588wzufvuuwHYuHEjzZs3d9mi8EyaNImhQ4fSunVrt02JyMqVKykuLmbkyJH87ne/Y+nSpXGPmXIz7Eip8GPHjuXWW291ybra2Nl69tlns3DhQpesio7CwkLy8/N//TktLY2KigrS01PukgCgX79+bNiwwW0zoiIvLw+Qczxq1Ciuu+46dw2KQHp6OjfffDNz5szhkUcecdscW2bNmsVee+3FySefzOOPP+62ORHJzs7mD3/4A4MHD+bHH3/k8ssv55133onrO5Zy387BgwczePDgeu9/99133HDDDdx000316pa4hZ2tXiA/P5+ioqJffw6FQikr1l5k06ZNXHPNNVx66aWcd955bpsTkUmTJnHjjTdy8cUX8+abbzpWB8hJXn31VXw+H5999hkrVqzg5ptvZurUqey7775um2ZJp06d6NChAz6fj06dOtGyZUu2bt1K27axV470xDdUU+Gdp1evXsybN4+zzz6bpUuX0rVrV7dNajRs27aNkSNHMn78eI4//ni3zQnL66+/zubNm7nyyivJycnB5/Ph96emp/T555//9f8FBQVMmDAhZcUa4JVXXmHVqlVMmDCBzZs3U1hYGLe9nhBsTYV3nrPOOotPPvmEoUOHYoxh4sSJbpvUaJg2bRq7d+9mypQpTJkyBZBF01RcKOvbty/jxo1j2LBhVFRUcOutt6aknV5k0KBBjBs3jksuuQSfz8fEiRPjforVTEdFURSPkJrPPoqiKEo9VLAVRVE8ggq2oiiKR1DBVhRF8Qgq2IqiKB5BBVtRFMUjqGAriqJ4BBVsRVEUj/D/LZ3GOBHoFUwAAAAASUVORK5CYII=",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1197ec860>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -642,26 +667,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To handle this case, the SVM implementation has a bit of a fudge-factor which \"softens\" the margin: that is, it allows some of the points to creep into the margin if that allows a better fit.\n",
-    "The hardness of the margin is controlled by a tuning parameter, most often known as $C$.\n",
-    "For very large $C$, the margin is hard, and points cannot lie in it.\n",
-    "For smaller $C$, the margin is softer, and can grow to encompass some points.\n",
+    "To handle this case, the SVM implementation has a bit of a fudge factor that \"softens\" the margin: that is, it allows some of the points to creep into the margin if that allows a better fit.\n",
+    "The hardness of the margin is controlled by a tuning parameter, most often known as `C`.\n",
+    "For a very large `C`, the margin is hard, and points cannot lie in it.\n",
+    "For a smaller `C`, the margin is softer and can grow to encompass some points.\n",
     "\n",
-    "The plot shown below gives a visual picture of how a changing $C$ parameter affects the final fit, via the softening of the margin:"
+    "The plot shown in the following figure gives a visual picture of how a changing `C` affects the final fit via the softening of the margin:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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fnqtxsTHs/GU2ltQU7rl/CBWqVC3QeO2hpL9/sqN6sU31Yi2nbYtcJyKGDBnC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7oVMn2yDPf//BgAGqCFfXru4bmxBCCHGnzp+HZs0gJgYSE0GrhXnzVOvMjz923Jpb5L5HHoGffoLk5JuX/fgjHDgAhw7ZdCwToiCTzAYh8qMNG2D6dHUQMhrVZYmJcPUq9Ozp3rEVJS+84DibJDkZnnpKtlQIIYQoWB5+GC5cUHMKAJNJHec+/1yy9lzl+HFYtco20AAq2+HiRbW4JEQhIcEGIfKjGTMgKcn+cpMJTp5UUW+R9zZvdn7dmTNw/brrxiKEEELcjbNnYdcuyMiwvy4pSWVTiry3fr3zDJLERPjhB9eOR4g8JMEGIfKj06edX+flpQoVirznmc1OM7NZ0hyFEEIUHOfPg4+P8+vPnnXdWIoyHx+1fcUZnc51YxEij0mwQYj8qE4d5wei1FRVqFDkvV69wMPD8XWNG0NAgGvHI4QQQuRUVJSaQzhTo4brxlKUdeniOLsEwN9f1XMQopCQYIMQ+dEzz4Cvr/3lXl7QtClUrOj6MRVFU6dCSIhthoNWq4IMs2a5b1xCCCHEnQoPV4WNHWU36HSqTpHIexER8Mor9hkMOh20aaN+hCgkJNggRH5Uty7MmQN+furE1ttbRbvvuQe++cbdoys6ypSBffvgscegWDEIDoaHHlJdKmrXdvfohBBCiDuzYIHKzPP3vzm38PWFd9+F++5z9+iKjhdfVO1H69dX87yKFeHtt2HlSukIIgoVaX0phNkMW7eqoknFikGPHuqk0t0GDoTu3dWBJyYGmjRR7arkIORapUqpwM+cOe4eiRBCiIIkLk4dw69fVyf4+eEYHhgImzapoPnmzerfPXtCaKh7x1UU9eihfoQoxCTYIIq2q1fh/vvhn39UyyFvbxg9Gj77TLWHcrfgYBgyxN2jKDjOnYOPPlKVnkNDYdQoePBBd49KCCFEUbNkCYwYoer+pKWpbZBVqsBvv+WPE/uGDdWPuDWzGX75RW2fvHwZWrSA8eOhQgV3j0yIfE+CDaJo69ULDh9WgQZQEwKA4cOhVi21bUEUDLt3Q9u2qviV5XPculUFk6ZMce/YhBBCFB3796tAQ3LyzcvS0lTb6t694Y8/3DY0cYfMZhg6FFasuNmS/MABmD9fBSDCw907PiHyOanZIIquf/5RWycsgYbMUlPhgw9cPyaRM2Yz9O0LCQk3Aw2gJga//UbgmjXuG5sQQoiiZdo022ORRXo67NgBJ0+6fkwiZ9assQ00gPock5LUvMNodN/YhCgAJNggiq5jx9S2CUeMRlUYUBQM+/dDdLTj65KSCPnqK9eORwghRNG1f7/zk1AfHzX/EAXDnDm2gYbMkpPR7d3r2vEIUcBIsEEUXaVLO58MaDSyF68guXbNtj1lFp7Xr7twMEIIIYq07OYP6elq/iEKhsuXnV+n0eARG+uyoQhREEmwQeSOtDTVkvGZZ+Cdd1ShvvyuTh3V2tBRZWg/P5gwweVDEjlUu7ba+uKIpyeGBg1cOx4hhBC547//4K234Nln4bvvICPD3SO6tQkTQKezv1yjgfLlpXVyQdKqlfMs2LQ0UmrUcO14hChgJNgg7t5//0FUFAwbpuocvPIKVK4MM2e6e2TZ02jghx8gLEz1mQa1Ou7nB889J/2mC5LwcOjXT312WXl7EzN0qOvHJIQQ4u68/z5UqwavvabqIAwZouYX58+7e2TZa90ann5aHZM8PNRl/v7qWPXDD24dmrhD48Y5Djb4+kKHDqSXKuX6MQlRgEg3CnH3unWDCxfAZFL/thRFmjQJ7r03f7dWqlIFTp+GZctg40aIiFBBE4lUFzyffqpWvL79Vu2JNZvVRG/pUtIiI909OiGEEHdiyxZ49VVISbl5WUICGAzQsyfs3Om+sd2ON96A/v1h4UK4ckUFIPr3d5zxIPKv0qVVu9LevdXvn0aj5rmdOsHixXDmjLtHKES+JsEGYSs5WW2H2LMHSpWCgQMhuxO1/ftVVWVLoCGzlBSYPl31ms7P/P3hscfUT3534gS89x5s2ABBQTBqFAwa5DzFryjx8VG/a++9p35/9Xpo1kytKh054u7RCSFE0Xb8OHz1FcTHqxPvLl1urvo7Mm2abetIC6NRtZA8dgyqVs2z4eaKmjVVdkZ+ZzLB8uXw8ceqRkHjxmrBqE4dd48sf7j3Xjh7Fv78U9WIqlMHypZ196iEKBAk2CBu+vtvNQFITobERHXy9uqrqhJv48aO7/Pvv84L85lMUnE5N23fDh06qCCOZc/q+PGwaBH8/rsEHCxKllQ/Qggh8ofnn4ePPlLFETMyYP589T29ebPz+/zzj8pQc8TbW80/8nuwoSAwm+Hhh+Hnn292XTh9Wm33WLoUund37/jyC61WLWAIIe6I1GwQiskEnTvD1asq0ACq4F5KCowejffp0zdve+GCShszm1WtBmfFmjw8ZDtCbjGb4ZFH1GeT+f02GGDvXhVwEEIIIfKbVatUDafk5JvHr4QEOHUKHn305u1SUlSmZFyc+nf16o4LOINKY4+KyttxFxVr19oGGkDNCQ0Gld1q2RorhBA5IMEGoWzaBDExjlcR0tMJWbpU3aZGDXWAr1YNypW7mcboKBXS2xueeirvx14UHDqk9nw6kpQEs2e7djxCCCHE7XjvPdsTWYv0dNi4Ec/z51UxxbAwqFsXihdXNRmGDXNc9NfTE+rVg0qV8nzoRcKnnzr+fCzWrXPdWIQQhY4EG4Ty77/O0xUzMvA9eFBlPhw5olYfkpPV/rVBg2DMGLV3LTBQ3d7PT1XpnTFDTQjE3YuPd75dBW6uBAkhhBD5SebMyKx8fIh86SW1XTMpSWXvpaaqlfYnnoApU9R8wtdX3T4gACpUUIWARe6IiXF+ndks8wshxF25q5oNPXv2JCAgAIDSpUvz1ltv5cqghBtUquQ8XdHLC88rV1RKXVYGg+p/ffw4rF4Nu3ap1YmHHlKrEyJ33HOP81RGT09o29a14xFCiDwic4tCpnJlOHfO8XUpKej++sv++JaergoVRkSorRXLl6uT4nvvVV0AtLJWlms6dIAdOxwX40xPV++5EELkUI6DDampqZjNZr788svcHI9wl+bNVXDAYLDvLOHlhefVq87ve+6cinx366Z+RO4LCoLRo9XqT9agj48PPPece8YlhBC5SOYWhdCkSepkNuuxy9tbbck8ftzx/RIT4ccfVb2iCRPyfJhF1uOPq60uKSm2Ga6+vvDAA1C+vNuGJoQo+HIcGj569CjJyckMGzaMQYMGsW/fvlwclnA5jQbWrFFtLv39b16m0UDfvpizS+E3m6UTgiu8+y6MHKkmAMHBKp20bFn1uVWu7O7RCSHEXZO5RSHUsSO8+KI6dnl5qcs0GrXlsl07zNllKeh0rhljURYaClu2qJpcOp2aX/j6Qt+++b91uRAi39OYzc426mfv2LFj7N+/n759+/Lvv/8yYsQIfv31VzxvnJTu2bMHnQsPEikpKfha9vSJHPM+cYLy/fqhSUtDeyPDweTri8nTE4/kZDRGo83tzUBy/fr8t3ixG0ZbeNzJ7682IQGfY8cwBQSQWrWq8+0vwkq+H/KWvL95KzffX4PBQIMGDXLlsfLCreYWIPOLgqrEa68RvHIl2vR0QM0fzD4+aDIy7OYWACY/P85+8gkGSeO/K3fy++t96hQeMTGkVayIsVixPB5Z4SDfD3lL3t+85Yr5RY63UVSoUIFy5cqh0WioUKECer2e6OhoIiMjrbepXr16Th/+jh05csSlz1doPf64Ks6UKQalTUkBLy80Pj5qX6WldZVWi8bfH93ChfLe36U7/v1t3PjWt8nIgK1bVYuxhg2hRImcD7CAk++HvCXvb97Kzfd3z549ufI4eeV25hYg84sC5+BB1QLzRqABQANoUlMxabVo/PxsawbodGjbtqXckCESUL9Ld/T7e7u3u3AB9uwBvR6aNXPckayIkO+HvCXvb95yxfwix9soVqxYwdtvvw3A5cuXSUxMJDw8PKcPJ/KDa9dg506HXSm06ekqbX/ECFUAUq9XRSB374batV0/VpG9tWvVlphu3dR+1/LlYehQm4meEELkNzK3KKQWLXJa5Njs46OOTw0bqvpEUVHwzjvw/fcSaMhvUlPVnCIqCh59FLp0gZIlYcMGd49MCJFP5TizoU+fPrzwwgs8/PDDaDQa3nzzTZs0x1yXmAg//ABXrqg+zK1by0EotyUmqs4GzroepKTArFnqR+Rfhw+rHuVZi3F9/bUqJjlnjnvGJYQQt+DyuQWoVfcNG9Q+9e7di3QWWJ6JiQEHWyUANOnpULEifPKJiwcl7tjjj8PKlWo+mJKiLktIgK5d4a+/oEoV945PCJHv5PgI7u3tzbRp03JzLM6tWgUPP6yCC2lpqhhh6dKwbp2KqIrcUaqUmmw5aHFpBjS3k7ov3O+dd9TqQ1bJyfDFF6pVaUiI68clhBC34NK5RUoK9O6tAg0mk0oFHz8eXnoJ/vc/14yhqGjTRrWvTEy0v87LC5o2df2YxJ25ckV9hpYgQ2ZpaTBtGsyd6/pxCSHytfzfqPjkSejfH5KS1EEqLU39+c8/Kn1L5B5PT3j5ZYfVn82+vjB5shsGJe7Y1q1OV5Dw8VGZD0IIUdQ99RSsX68CsampKtCekgJvvqlaLorc06eP6nKQdW+/tzepUVESbCgIDh5UcwhHMjJg82bXjkcIUSDk/2DDzJmO95kbjao3819/uX5Mhdn48WpFx99f7Z0MDITwcM6//75MBgqK0FDn16WnZ3+9EEIUBUlJKtPL0SqtwQBTp7p+TIWZry9s367qMvj53Wyv2L49Z+bNk22xBUGxYs4XMkDV8xJCiCzyf7Dhr7+cF7Xz8ICjR107nsJOo1H9sK9cgZ9/Vqs+Fy+S2Latu0cmbte4cSpY5EjZsrdfbVoIIQqr8+ezr6B//LjrxlJUlCkDf/6pVsh//BFOnICff8YUHOzukYnbUbeu84CCv7+aewghRBb5P9gQFQXabIZZqpTrxlKU6HTQooVahSjCLY0KpEcegVatbAMOPj4qU2XpUveNSwgh8ouIiOy780iRyLwTFQX33Sfzt4JGo1E1GwIDVe00C39/uP9+tVVGCCGyyP/BhrFjVaqdI8HB6oRYCHGTh4daNfr8c2jfXgWMnnkGjhxRKxNCCFHU6fXQqZMqTpiVvz88/bTLhyREvteoEfz9N0yYAA0aQMeO8OWX8O232S8MCiGKrDzuJ5UL6tdXhQlfekntFUtPVxMBb2+V5l9UvtzMZli7VrWGunQJmjeHJ5+EChXcPTKRH3l4qFUGWWkQQgjH5s+HZs3g4kVVeFqjUVl9XbvCY4+5e3Suc+2aaon800/q9T/2GPTt6zgQI0Tp0qrrlRBC3Ib8H2wAtSrbrRt89hlcuAD33gsDB6q08KLAbFa9jZcuVUWtAPbtg3nz1OSgdWt3jk7ciePH4ZtvVFGytm3VZyeFsYQQwvXCwlR3nu+/V4sX/v5qG1rTpkXne/nECTWnSkq6WSxzxw6YPRt+/9159wGRv6Snww8/wN69aovQww9D8eLuHpUQQhSQYANAlSpFN5K6bp1toAHUgSU9Xa0+XLpUcOsqGAyqv3lAgLtHkrsOHoTXXlP92319VXAsLk5VP8/IUD8zZqhijevW2d/fbIaNG9X+yIwM6N4dOncuuJ+zEELkR15e6jjat6+7R+IegwfD9evqOGyRlAR79sCsWao9aEFkMkF8vJpbeBacqe4tGY3w6adq/nDlClSrBsOHwyuvQEKC+vH1hRdeUAGjIUPsH+P6dVi0SAUmKlSAYcNU8WghhMgDRWQPQgE3d65toCGz1FT44w+XDidX7N2r6m0EB0NICNSpo07MC4MtW9RK0cqV6qB+8SJ88IGaIKSkqOABqLTdAwdg1Cjb+6enQ5cuKpV3zhyVwfLggyqTZ+pUFbQQQggh7salSyqokDnQYJGcrIINBY3RqI6TYWFqZT8oCEaOVCfhBZ3ZDL17w7PPqizJ2FjV3WPECDXPsLzGlBT1M3asqq+Q2Z9/QrlyquvYokXwxhtQvrzaslwQ55JCiHxPgg0FwaVLzq/TaNR+y4Lk4EFViXrr1pur/AcOqBPs9evdPbq7YzarVQKDQf3dIiPD9t8Wqanw3XdoExNvXjZtmjroJyXdvI/JpB7z1VehVq3sfydux4ULqq2sBC6EEKJoun7dtquAo+sLmhEj4M031djT0lTQ5IsvoGXLm4H+gmrDBrW1xWCwvdxsdjy/SEuDjz+2/XeXLiooYXkMk0nd96+/VPbkK6/c3RgzMtQc7+hRx2MSQhQ5EmwoCO67z/m+ybQ0FZEuSF54wf5gCWpSMGGCy4eTq/79F86du7P7eHnheeWKOjBfuaLSI5OTHd/WaFQrGDlNbT17VrXFjIpS9SJKlFApmJa9ukIIIYqGChUcZzVYNGjgurHkhtOn1ZbTrPOL1FQ4eVLVuCrIFi92nuXqSEaGqkkCak6xZEn27V5TUuD99+2zIW7X/PmqXkSzZqoLVvnyqrC5EKJIk2BDQTBmjOOq0D4+6oSxUiWXD+mu/P6784j30aMFe7U9JeXO6yqkp6PbsUNVeC5bFi5fzv72RiN89132kwZHkpLU9o6tW9U44+PVn199Bf3739ljCSGEKNh8fVVXK53O/jqdTmXSFSRr1zov7JmYCCtWuHY8ue1OAg2g5iIVKlDqqafUdtXHH7/1dpL0dNU2+059+SWMH68yShIT1VjPnIEePdTWDSFEkSXBhoKgVCn47TcoWRICA9UeRF9faN9edTYoaG7VrrQgtzOtXDn7tNSsfHygVi2Kv/ee2tqQmnp79zOZbv+2FkuWqECO0Wh7eXKymqQdP35njyeEEKJge+MNGDpUzSmCgm7+LFyoVqgLEg+P7LuIFPRCkd263VkxbW9vWLeOgHXr1HzhdraRZGSoDMs7YTbD8887z1h96aU7ezwhRKFSgM/qiph771Up8KtXq6I+R46olMCC2MWhSxfnAYW6dVVApaDy9FTFqRytFHl5qeCCZcITEKDqLxw/jvZOtzFERqo2bXfil1+cr4xotbBp0509nhBCiILNwwNmzlSr0IsXw7ffQnQ09Ovn7pHduc6d7YPpFgEBBT+Dr29fCA+3z3T18VHBIsvcyddX/fTvD7GxaO+kVoW/v9pqeScuXYKYGOfXb9t2Z48nhChUCniYt4jRaqF5c3eP4u699ZbK1EhIsN0vqtPZFjMqqEaNUqsrL72kovoZGao91fz5ajLwzTdqBaB9e/Xvdu3u7PF1Opg8+c77wAcFOb9Oq73z4IUQQojCITxcrZwXZKVKwRNPqC4amVfZfX1V/YkOHdw3ttzg46O2JDz2mJpDeXmp4Mrw4aoo5urVsHu3qsX08MOqWPWdbL2wzAMeeujOxuXn5zzIA+r9F0IUWRJsEK5XqRLs2qUKRf70kzpItWkDb79d8IpdOjNypJoA/PuvOhCXLHnzuswphfv2ZV+xOSBAFQH18lIrUBkZ8Nprjntn38rQoarWg6PJR0aGyjgRQgghCqp334Xq1WHKFPjvPyhWTLWAfOGFgr1F0yIiAlatUrURrl5VARZLJmXfvurHwlGtLwtfX9V6PCZGzVEyMlTB0O+/d5yZmR29XmXfbtliP5/x8oJHH72zxxNCFCqF4JtXFEiVK6tiTSkpqiDR2rX5J9CwbBnUqKH2O0ZGqm0RaWl3/jgeHqrrQ+ZAQ1a1azvPKPD1VfsgY2Phhx/U+3XlCjz33J2PBVQx0S5d7J9Pp4MPP8w+8+F2SJsrIYQQ7qTRqBX9U6fUQkZ0tArQO+vo5UpXrqjMx+BgdXxv0wa2b8/ZY4WEqHlUdoGBQYOyz1g8dkxtyV22TGVMHDqU84Ljc+aoTM3MdTF8fFSWxd2203TW2lMIUSBIsEGIzF5/XaUoHjmigiCXLqlgQ6dO2bcIyymtFj79FFPWNENvb3WQHjtWrTq0awf33393Wx00GtUWbNYsVRujRAn1mL/8onqT50R6unp/IiLUawkIgDJl4IEH4OefZYIghBBCXL0K9erBggWqE1RqKvzxh9pOmVftIR98EGrXxpQ10KLTqcKgwcFqQaRzZ7jnnrt7rho14MABldFZqpRqezlxosreDAvL2WMeOqTmEl5eKogRFqbqXE2ceOctxoUQbiPBBiEsrlxRWzmyVlROTlbbPtasyZvn7d6dM59+Ci1bqpUAvV5tw9i9W/09N2m1arXjr7/g4kU1ybnTYlAWZjP07KmCDdHR6rKkJDUJWL1a7fscPFgCDkIIIYq2999XAYesLasNBtWSMi+Ok56esH49V4cPh+LF1fyidm1VCPTZZ3P/+cqVg9mz1Rzg9GlVW6pYsZw91oED0LSpmksYjWqx59o1OHwYZsxQW2V27szV4Qsh8oYEG4R7mEwqfXDNmpsnqu62erXz1liJiapidx5JbthQdYNISVF7MT/6CEJD8+z5csX27WplJjnZ8fVJSapGxC+/uHRYQgghirArV+DXX9XWgLzISMyJr75yvh3z4kW1AJAXfH25NnasytJMSYH9+9UiQX73zDNq3uVIerq6rl8/WcwQogCQYINwvQ0bVB2Djh3V6nfZsir1LmvE39XS07OfmKxZo1Ie76S6c2H23XeO+2pnlpSkVjqEEEKIvJSerrZBliun2j526AClS8PGje4emSrA6Examuo09s03rhtPfmYyqXnirVy7Bnv35v14hBB3RYINwrWOHYOuXeHyZdX6Mi5ORduXLlUtq9zp/vuzDzYYjbB1qyrwJG5/xejKlbwdhxBCCDFmjJpLpKSouUVCgsoaeOAB+Ocf946te3fnmZOgxjxkiNomIG4vY8HDQwUchBD5mgQbhGu9844qjJSVwQBffKHaMLlLuXKqN3V21Z1TUtTqgzvHmV90737rFlne3nDffa4ZjxBCiKLp2jVVi8DRtr60NHjvPdePKbMXXrh1gee0NJg+3TXjyc+0WpXpcSspKaoGhRAiX5Ngg3CtzZtVhoAjPj6q+rA7zZunKh1rNM5v4+MD//7rsiHlW61aQcOGqoWXM97e7s9YEUIIUbgdOOC8vWVGhqqJ5E7lyqk6R7VqOb9NRkbe1W4oaKZNy34xw88PevdWXbWEEG5hNBqJiYnh9OnTXLp0yentssnpEiIPZFeZOCND9Y52Jw8PePVVWLUK9uxxfJu0NDnAgQrI/Por/O9/MHeuyk4xm9WEz9tbtcH85hs1yRJCCCHySkhI9nURctoVITdVr64yOFu2dFzvSKNRLSMFNGoE69fDU0/Bjh1q26ZWq4IMRiN06waffebuUQpRqKWnpxMbG0tcXBxarZaKFSsCsGTJEi5fvkxCQgLmG1ue6tWrR+nSpR0+jgQbhGuNHav2JDoqsliiRPZRf1d65hkYMcJ+nB4ecO+9qsClUFkN06apFNWkJJXKeuCA6qTRtKmaHAghhBB5qU4dCA93PLfw91dzj/ygXj1VtPKff+zrEvj5wfjx7hlXftSkCWzbprZLGI2qGOT169CgAZQq5e7RCVHgpaSkEB0djUajwWg0UrNmTQC+/fZbTp48iSFTULRMmTLWYENgYCD+/v4EBwej1+vR6/WEhobyj5PaOBJsEK41YIAq4LR5881Jgbe3Wg1fujT77Quu1L8//PYbLF+u9oCaTGqlvlgxWLLE3aPLf7RaCAxUP3e6MmM2w/nz6s/SpfPP74AQQoiCQaOBr7+Gdu1U9qGlzaS/v6ob1L+/e8dnodHADz+o7IbkZDUP8vBQ86Cnn5YaR45Ytmq2bHnn9zUYVJHQ8HAICsrdcQmRj5nNZgwGA7GxscTGxpKcnEzDhg0B+OWXX9i/fz+pqalER0cTHh5OUFCQNdhQrFgxvL290ev1NgEFi+7du9/RWCTYIFzL0xN++kml18+eDbGxqp3khAlQpoy7R3eTRqNS9EaOhEWL1Dg7dYK+fbOvUSDuzO+/qwriZ8+q97x4cZg5E7p0ubPHuXJFrX6ULi3ZFEIIURQ1bqwyJ6dPVyn4xYqp7lF9+qgT+vyiWjU4fVoVtNy0SR33hg2De+5x98gKj9RUFbxZuFB99hkZauvF3Ll3tl03LU0thoSEQKaTLSHczWQykZCQQFxcnHWrQ/PmzdFqtWzYsIFt27aRnp5uvb1Wq6V+/fpotVoiIiKoU6cOer2eq1ev0qBBA5tgQps2bXJ1rBJsEK7n4aFWGfLLSoMzGo1K42vSJO+eIyMDPv2Uiu+/r9p01akDL7+sii8Wdlu2wIMP2u5d/fdfFdD5/nvVI/1Wdu+Gxx+Hv/9WQYbgYNXxZNCg3Bnj0aMqkFG9uloZEUIIkX+VLVswOjoEBKhASF620j5+HKZMofLPP6sMj4ED1RbR/FC/Iq/16gUbNth2J/nhBxWM2r8/+zakoLZtvP46zJihMlszMqBNG5g/P3e2cBgMqhior6/aWiOLJCKLjIwM4uPjrYGE2NhYmjRpgk6nY8eOHaxZswZTlhb0derUISgoiMjISBo1amSTlRAcHIz2xu+ZJcMB4MiRI5TK421JEmwoSDIy4IMP1IE0OlrVDZg0CUaPli+qgshkUpH2TZvwsZxwr1unKlbPnAlDh7p3fHlt4kTHRbKSk+HZZ1Xth+wcPaoO/omJtvcdPVptyRg8OOdj+/tv6NdPrT55ealVkt69VbcSP7+cP64QQuRHR47A88+ror+ggr3vvAM1arh3XCJn9u5VixbJyXgajapd97Rpahvo3r2FO+Bw4AD88Yd9G9S0NDhzRmXX9uiR/WOMG6eyWjPPUX77TWXPHD2qtozmhNkMU6fCW2+pgIfJpAJBCxbAAw/k7DFFgZSWlmYNIlgCCvXq1SM0NJRDhw7x7bffWosvAmg0GqpUqYJOpyMyMpLmzZvbbHMIDg7Gy8sLgGrVqlGtWjV3vTQ7EmwoKMxmlQr42283v/zOnlUnbPv2qZMgUbD88ota3c96wm0wqHaR/frdui93QWU2qwrTzvz9t5ooZHdiP3my42CFwaD+XwwcmLMg3LVr0KKF2jqTuYDXt9+q/bUrV975YwohRH51+LAq6JuYePM77+ef1Qnbtm2S3l8QDR9uG4gHFTS/dAnefhvefdc943KFdeucdyZJTLx1sOHSJbX9IjXV9nKjUc0LFi3KecHRDz5Q73/muUtiosro3LBBBTNEgWc2m0lJSbHJSoiNjaVmzZqUKVOG//77j4ULF9rcx8PDg7JlyxIaGkqJEiVo1aqVTTAhKCgIjxvbwcqWLUvZsmXd8dJyRIINBcWOHbaBBguDQe37e+45qFLFPWMTObNggf1kwMLDA9auhZ49XTsmV/L0vFnEKyuN5tZ7bNeuVasCjiQmwqlTUKnSnY9r/nxV/yFrpfCUFLXqd+oU3KjIK4QQBd4zz9gGGkD9PTFR7Xv/7Tf3jU3cuUuXVMDekdRUdbJcmIMNPj7O5w8aDeh02d9/0yZVsDNrsAHUnPu773IWbEhPhylTHHdMSU6G115Ti1Ai3zObzSQlJdlkJcTGxhIVFUW1atW4fv06H330kc19vLy8iIiIoEyZMoSFhdG+fXubrITAwEA0Nwqkh4WF0bp1aze8srwhwYaC4ttv7VPCLIxG+PFHlXoucubUKZUyunatWk0fPlztpbzVQeluOAs0gJroOfu8CwONBrp3VwftrAEDjQbatlUH++xkd73JlPNCnr/95vy99/SEP/+UYIMQonAwmVSh3qzBVYsNG9QcIz8VWCxIzGbV1Wr6dLhwQdVleuEFaNYs754zOTn7z8vRSXRh8uCDKoDmiJ+f6oqWHR+f7K/P6bzwzBkVcHDEbFZbaEW+kTkjwRJQKFmyJA0bNiQjI4P333/f5va+vr4EBwcDEBQURMeOHW1qJvj5+VmDCf7+/rRo0cLlr8ldJNhQUGRkOJ8MmM1qMiByZs8eaN1arVxbUu9eegk+/1x9+efVVoZu3WDrVsdbAdLTVSp/YfbuuyrdMSHh5vvu4aHe7w8/vPX9BwyAjz92nB1RoYLqTJET2e1ltRShFEKIwsLZ3MJyXXbXC+fMZhgy5OYWPIBz51SnjLysy1S2rDqOOppbaDRqvlOYlSqlsn0/+MA2i0Cng65db130u31753Nqf/+c14MKCHC+vcNyvXCZ69evc/36dZvMhJCQEGtGwdy5czFk+j/k7++P342tvV5eXnTv3p2AgABrQMEnU5DK09OTpk2buvT15GcSbCgounVT6d2OVsO9vFRbxqLk6FGVibBxo1rhDg1VB9G6deHJJ+9sS8ngwfbva3IynDihTmaffz5Xh27zvG++qVYZMh/YdDpVjLAA7cfKkQoVVL2RKVPUZMxsVr/nr7wCUVG3vv/zz8OyZapYqiXgoNGolYu7qWHy2GMqldFRqiOoiYgQQhQGWq2q17B1q+PrmzS5deX+wsRohK++Usf+K1dUW0qNBoKCVIC7f//bz5rbutU20ADqOGcwqDT83r3V4+Y2Dw9VgPDJJ+0DDn5+qstCYTd5MtSurYoxnjoFJUrAU0+p7lU3Vped8vdXgYqnn7Z9//z8oH79WxeXdKZ4cTVH3bnTPoDn66syakWuiYmJ4dq1azaZCd7e3nTv3h2A5cuXc/HiRUAVXwwKCsIz03ddt27d8Pb2Jjg42Kb4okX9+vVd92IKuLs6gly7do1evXqxYMECom7n5EDkXJs26ktq9261Am/h56eqRtep47ahudzGjapqb9aTdFC1LRYuVFkJffve+rH+/RdOnnR8XXKyCvDkVbAhKEiNd9AgTNu3o/XxUa/n8ccd76c8ckQV8ipZUk0Ob3XALAjKloVPP1U/dyosTLWOevNNVWE7NVX9P7FMMnKqQwe1+vHTTzcniVqtmgx88cWtUyyFEHdN5hcuNG2a2rrm6MR02jT3jMkdTCZVJ2n9+pvf/f/9d/P6bdvgvffUn7eT4bZwoePsAlABnJ9/hocfvvtxO/LYY+r1vPACxuRkPMxmKFNGzWmyHh9TU2+2iWzaVJ2YFwZ9+qifnBg5EsqXV4GZQ4cgJEQFiMaPv7vg24IFagtNcvLNRRI/P7U109nWD+FQTEwMly9fttnukJGRwaOPPgrAmjVrOHbsGKCKLwYHB1OyZEnr/Tt06IBGo0Gv1xMYGGgtvmhRvXp1172YQi7H/2PS09N55ZVX8M3pvmhxZzQaVU9g0iT1ZZWerk5+xo1TRWWKCpMJHnnE+QE8I0P9DB6sThozTwjS09WXu05380Q9MVFlhmQO4GSWXV2F3FC2LPzxByc2b6ZKsWLq4JZ128bVqyqSvnevGqvJpFL9f/yxaAWZHAkPV3thc7OvukajVrZWrFDbOS5fVhWiJ02S91sIF5D5hYs1aaJq1UyYoAK4oL7rPvxQnXwWFatWqZNuZ1ltSUkq4/F//1PbICzMZnWdj486Rltcv+58C4rRqLYQ5qURI2DoUP799VeiatZU2YRZff21up1lTpSaqjo5zZ5dtDJaHOnYUf3kpho11KLRBx+oBQ1fXxUYGj48b2uEFUDXr1/n/Pnz7Nu3j1OnThEbG0t8fDyPP/44Hh4e/Pnnn+zcuRPAmoEQEhKC2WxGo9HQunVrWrRogV6vJyAgwFovwaKCo/8PIk/k+JvknXfeoX///nyakxVJkTN+fvDRR+pLKj5enUgXtaJNe/dCXNytb6fVqvTFYcPUHsnx49VEwmxW+/neekutKFSunP1juGhvozEsDBxFUc1m6NwZ9u9XwRJL4cLERDW206dBr3fJGIsUrVa1Hu3Xz90jEaLIkfmFGzRrptK7LQH2orh//NNPb73AkJamMic//vhmYPrFF+H8+ZuFjz/8UM0zOnZUi0SOghdmMzRvnicvw4anJ2lRUY4DDdu3qzlS1sWbr75S88ssBfBELilVSmUMFaWsIQdiY2P577//7Do6DB48mODgYA4fPszvv/9OdHQ0ZcuWRa/XExISQlpaGn5+fjRp0oR69eoRHBxsU3zRIjIy0k2vTGSVo2DDd999R7FixWjZsqVMBtzB0zP7InaFWULC7QVYUlPVXv5r16BhQ5UdYNly8d9/KoocGwujR6vK0FOm2B9wfX1VoUh32rVLbZ9wVME4LU2laT71lOvHJYQQeUDmF25WFIMMFrGxt3e7pCSVYTh3ripEmHnu8P33sGWLWr1+9FGVeZqcbNt1ydcXWraEmjVzcfA58MYbjrNEDQaV2TB5sqy2ixxLSEjg+PHj1iCC5c8+ffpQpkwZzp49y8qVKwEICAhAr9dTsmRJTDf+r9SpU4cqVapw8eJF6jjIKg0NDXXp6xE5pzGb77zM8COPPIJGo0Gj0XDkyBHKly/P7NmzCQ8Pt95mz5496Fz4JZWSkiIpl3kov7y/2vh4KrdqhfYWrZuMOh3nZ8zAb/9+QufNQ+ugY4HR359/tm7F7OVF6Ny5hM2fj1mrRWM0khESwsW33sLQqFFevRQbzt5f/bJlFH/nHaevN759e85n6eUr7OWX39/CSt7fvJWb76/BYKBBgwa58lh5QeYXRU9+eX9DP/mEsPnzbzm/SCtVilM//0zl5s3xcJC1YPLx4eqIEVwbMwbP8+cp9eyz+B49itnLC01aGgn338/FyZMx36hsn9ecvb+VW7TAMybG4X2M/v78t2QJqXdSbLuIyi+/v65i2aaQkpLC8ePHSUhIsPlp2bIllStX5vz586xcuRKtVktAQAABAQEEBQVRp04dwsPDSU1NJTk5mYCAAJvCjFkVtffX1Vwxv8hRsCGzgQMH8tprr9kVcNqzZ49LJzRHjhyRYh55KF+9v08+CZ995rxug4eHqn1w/DjUqqUyAxwJClJdByypjMnJqhCQTqf21bmwAKPT9/ebb9R+Pkd7Oz08YNQo272jwqF89ftbCMn7m7dy8/119bH5bsj8omjIN+/vlStQtWr2GQ46nVr1r1gRunRRW1odqVlTzScs/vsPLl1SnZbCwnJ12Lfi9P2tVg1uFNCz4+urimdnKqgnHMs3v7+5wGw2YzQa8fT0JD09nd27d9tkJcTGxtKyZUuaN29ObGwsM2bMwNPT09r+MTg4mHr16lGmTBkyMjJITEwkKCgIrVab4zEVpvc3P3LF/KKIV38R+YLJpA7yOt3ttYGaPl1tJZk9W51wW4IOlpWuqChV5VmrVT/ZyXy9nx+4KJPhtnXt6vw6b29V2EkIIYQQ9pKSVJ2niIhbFzyMiIDNm1V7y5MnVV2F1FRV9NHXV21nfOEFGDRIdaTITta5R7ly6ic/GTdOFT7OunCj1UK9ehJoKITMZjNpaWn4+PhgNpvZunWrXc2EevXq8cADD6DValm7dq21+KJer6dMmTKUuNGtJCgoiGeffRZ/f3+7egkAnp6e6KWmmCAXgg1ffvllboxDFFXz58PLL6uVBJNJZRnMnZt94UYPD1Uk87XX1MpBcLC6/9mzUKWK6oNs8eijqnWRo24TWq2q55Cf+fnBsmWqjWd6uvrRaNTlzzwj3RGEEIWWzC9Ejl2+rGoy/fKLOtb7+Khj5osvZr8IUauWmlccO6ZqPkVGqg4dGo1qrWw5eWrUyPnj+PmpuUd+N3IkrFypWnBbtoP4+amFmwLyf89oNPL7779z9OhREhISCAgIoFKlSnTq1Cnb1PzCymg0kpKSgv+NrmZ//vknly9ftgYU4uPjqVy5Mv3790ej0bBjxw6MRiPBwcGEhYURFRVl7dLg4eHBpEmT8PHxcRhMsGyPEOJWit7/RJF/fPihOvBnjqpv3KjacB06dOuoelCQqqCdndGjVQbExYu2RRZ1OtXZI3ObqvzqgQdUN4oPP4Q9e9TqyBNP3Pq1CyGEEEVNUpKaR1y4cPO4n5ysulBdunR7Ww+rVr35d0edHLy81DF59GjbOYyXl8qQGDny7l6DK3h5wZo18MMPMG+e6sTRrZvKmMznRcivXr3KggULmDNnDqGhodx7770EBgYSHR3NihUrGDVqFI8//jgjRowoVF0J0tPTSUpKsmYM7Nq1izNnzlizEhISEoiMjOTxxx8H4NChQ8TFxREcHEypUqWoWbMmJTPNrcePH59tUEZqJYjcIMEG4R6pqSqjIWv6nsmkJgrTp8N779398wQHw+7dKqjx1Vcqw+Gee+DNN9VJfEFRqZJqtSWEEEII55YsUR2osnZxMhhUvaeXXoIbqeB3ZdAgCA1V84tDh1RWwIABan4RHHz3j+8Knp7Qu7f6KSC2bNlCnz59eOCBB/j6669p5GD764EDB5g9eza1a9dm0aJFdO7c2Q0jvXMpKSnEx8cTEREBwP79+zl27Jg1mJCUlIS/vz/PPfccAP/99x/nzp1Dr9dToUIF9Ho9YZlqggwbNizbeglFMftDuJ78lgn32LfPeQHGtDSV2pcbwQaA8HAVtZ83T+3BdGHhx3zDZII//oAzZ9RWk6ZNi+b7IIQQonD79tub2wKy8vJSx8L+/XPnubp0UT9FdW4BqubW77+rv99/v5pz5ZGNGzfSt29fFi9eTIcOHZzernbt2syePZuBAwfSq1cv5s2bR7du3fJsXLfDbDZjMBiIjY2lRIkSeHh48Pfff7N//35rzYSUG1t+X3rpJTw9PYmOjuby5cvo9XqqVq2KXq9Hr9dbO0L07t3b4RYHi7spzChEbpFgg3APLy91cM7u+rxQFCcDBw9C586qarbJpN6DkiVV+mT58u4enRBCCJF7vL2zvz4v5hdFcW5hNquCmR9+qDIkNBq1WPT00zB1aq6/J2fOnKFfv34sW7aMtm3b3tZ9mjVrxo8//kjXrl35448/qFGjRq6OKTOTyURiYiKxsbEUL14cHx8fTp48yfbt263BhPQb2TZPPvkkxYoVIykpidjYWIKDgylXrpy1o4NF+/btad++vdPnzC7QIER+IcEG4R516qiUQ0ctHX19YfBg14+pMEpKgtatIWsv7RMn1OUnT6qCm65w7RqsW6cmIO3a5fs9oUIIIQqgQYNgwwbH2Q0ZGWr1Xdy9OXNU/YusBbg//FDVucjlblkzZ87k0UcftQ80mM2wcyf8849aQGne3CbQ0bhxY8aPH8/06dOZN29ejp/faDQSFxdHXFwc4eHhBAQEcO7cOVauXMnatWuJi4vDZDIBMGTIEMqXL09GRgYGg4GIiAgqV65sDSZYCjg2atTI4TYQIQoTCTYI9/DwUNsa+vdXhZssvL3VqvuYMe4bW2GydKlaacjKZFIBiDVr8r52hdkMr7wC779/c0UpPV213Hr11aK5IiSEECJv9Oyp6j7t22c7v9DpVD2F22mxLW5tyhTHAR2DAd54I1eDDcnJySxcuJDt27fbXvHffypz88yZm3OJsDDVhaR6devNhg8fTrVq1XjvvfectmNMS0uz1kYoVqwYoaGhXLt2je+//57Y2FgSExMx38jI7dmzJ3Xq1MHDwwOTyUTp0qWpVauWtUVk8eLFAahatSpVMxcbFaIIkmCDcJ/u3dXJ7ssvq6i0n59akXjlFZkM5JZdu1SFaUcMBjhwIO+DDfPmqValKSm2KyDvv686awwdmrfPL4QQoujw9IT161Xdp1mzVGvs6tVVu+zu3d09usIhNVV19nDm3DmVRZJLBQhXrFhBgwYNqFSp0s0LjUZo1Uq1Pb+RUQCoAEirVioQ4ecHQEREBPfffz8zZ86kT58+BAUFUbJkSQwGA4sXLyY2NhZDpoLl7dq1o2XLlnh7e+Pp6UlUVJQ1KyFzMCEyMpLevXtTPVNgQwhhS4INwr1atlTFmkTeKF1a9RdPTbW/ztcXbhww89TkyfZdR0BNCF5/XYINQgghcpevr1rIePlld4+kcPL2Vu+xo2M7qCySXNyiuW/fPtq1a2d74S+/qAxNkwkzkATEAnFmM35JSVRcvhzzoEHMnTuX69evk5KSwooVK8jIyKBhw4aULFkSX19f/P39KVmypDWQEBwcbO3oEBgYyOACvK334sWLXL58mfT0dGvHCulAIVxNfuOEKExiYmDVKnUif999qvbFm286vq3ZnPftrlJT4eJF59efOZOrqx9CCCGEyGVmM+zYAXv2qHaf3bqp+cWCBfaLGT4+8NhjubpFMjY2lrJly/Lff/8RFxeHh4cHNffvh8REPgfOARmZbl/FYKDirl1oBg8mMjKScuXKYTKZMBgMjBgxgmI3akZptVoeeeSRXBtnfpCWlsZ3333HrFmzOHz4MKVLl8bLy4tr166Rnp7O448/zogRI4iMjHT3UEURITN8IQqLmTPhuefUaoLRqA70rVqpVNKJE1WdhIwMtSLh4aHqOeT1dhVvbzXxyLxvNrNcXv0QQgghRC66ehU6doRjx9TcwstLbVv49FPYsgVOn765XdPfHypVUt0o7kBGRgbx8fHExsYSGxuL0Wi0Fk78+uuvOXz4MOfPn+f69esAFC9enJoREeDnR1mDgZKAHgi2/Gmp/wU8+OCDN17GVUqUKEGpUqXu+i3Jr3777TcGDx5M9erVGT9+PN27d8crU/eV/fv3M3v2bGrUqMHw4cN5++238ZA5mMhjEmwQojBYt04VXMxaFfqPP6BECbUaMWsWHD8OtWurApwVKuT9uDQaVYdj4UL7QpU+PmoLhRSIFEIIIfKnnj1VC+0bbRut84wRI2D7dvj7b7V4ATBggLp9lvajaWlp1vaPsbGxJCUl0bp1awBWrVrFnj17bG6v0+mswYYKFSpQu3Ztrl69yqOPPnqzPWRiIkyYgMMmmFotDBxoc9GRI0coXbr03bwT+dry5ct54oknWL58Oa1atXJ4mzp16jBnzhymTp1Knz59GDBgAF999ZUEHESekmCDEIXBm2863juZkgLLlsGMGfDxxy4fFgDvvAObN6tiTZbK1f7+Kthxh6sfQgghhHCRY8fUYoUl0JBZWhrMmoV59mxSHnzwZjBh715iY2Np164dXl5erF+/nk2bNtnc1cPDg+bNm+Pl5UXFihUJDAxEr9dbAwlBmbIuGzduTKlSpbjnnnsoXrw4gYGB6oqQEPjiC7WdIyNDjdHDQwU6ZsyAMmUyDTWNhQsXsn79+rx4l9xu8+bNjBs3jt9//53atWvf8vahoaH8+uuvPPDAAzz99NN8+OGHLhilKKok2CBEYXD4sPPrvL3h33+hTh2XDcdGcDDs3QvLl8NXX6lMhkcegT59VHaDUBOla9fUtpYb1bOFEEIIt/r7b8yeniQCcdwowHjjz5YZGQTt3cvOnTtZvXq1zd28vLxo0qQJISEhVKhQAW9vb2sBRr1eT0BAAJobWY01a9a85TBKlSpF27ZtWbx4MaNHj755Rd++ULcufPKJ6q5VtSqMGwdZHnPlypXUqFGj0HaNeO655/jkk08cBxoSEtTCU1iYTSapj48P3377LVWqVGHcuHFUrlzZhSMWRYkEG4QoDCIj4fJlx9elprqm60R2fHxUSmOWtMYiz2RS2R3TpqnPyWyGXr1U/Y0bBayEEEKIvGIymYiPj7ducbD82ahRIyJLleKI0cjyLPfxA2oDQeXLU758eTp27GjTGtLPz88aTKhQoQIVcmHb5hNPPMHgwYPp1auXtfUkAJUrq0wGJ+Li4nj99deZWkgzKffs2cOlS5fo1auX7RXHjsHo0aquhlYL4eHw1lvw6KPWm+j1eoYNG8acOXOYNm2ai0cuigoJNghRGDz9tDqoWLYpWHh6QosWqm6DyH9Gj4bFi223wKxYAbt3q1UaX1/3jU0IIcQtGY1GEhIS8PPzwycfZuuZTCauXbtmE0iIjY2ldu3aVKpUiYsXLzJv3jyb+wQEBFC1alUiGzWiVEQED/z7r00BRh9QBZ6ffJLixYvbnvznkVatWjFo0CC6du3K6tWrre0psxMfH0+PHj1o164dPXr0yPMxusPs2bMZNWqUbd2Fs2ehSROIj1eLGADnzsHIkapg94gR1puOHDmSRo0a8cYbb6DT6Vw8elEUSLBBiMLgkUdgzRr4/nt14mo2Q0CA2tO4aJG7RyccOXdO7TfN2jYsPV21C12xwmYFQgghRP6QlJTEV199xezZszlw4AD+/v4kJycTHBzMoEGDGDVqlMvS0s1mM9HR0dYggiWgULlyZerWrUtycjIfZ6rZpNFoCAoKsmYbhIaG0q1bN5uaCZ6Z2lEH//QTje+7T6XiGww36yI8/bRazHCh119/nbS0NJo1a8YHH3xA586dHRY3NJlMrFu3jmeffZamTZsyY8YMa6ZFYbNmzRpefPFF2wvffffmXDAzg0EVEx861NpyvEKFClSqVIndu3dz3333uWjUoiiRYIMQhYFWC19+Cdu2qeBCXBw88AD06yer4/nVunWqhVjWYAOoKtvffCPBBiGEyEdMJhNTp05lxowZtGzZkrfffpt27drh4eGB2Wzm1KlTfPrppzRv3pyGDRsyf/58St5owXg3Ll68aA0mWAIKJUuWtJ4czp07F6PRCKjii8HBwdbOCzqdjp49e1q3OAQFBaHVaq2P7evrS4MGDZw/ec2aqr3lokWq2HOJEvDYY6qzlYtpNBrefvttGjRowOTJkxk3bhwjR46kadOmBAUFkZCQwJ49e5gzZw5+fn489dRTDB48+LYDDWazmWvXrhETE4OHhwdhYWGq80U+FhMTQ3h4uO2FP/zguKgnqBpRhw6pWhc3REREEBMTk3eDFEWaBBuEKCw0GmjeXP2I/C9T72uHsrQOE0II4T5Go5FBgwZx+vRp9u7dS7ly5Wyu12g0REVF8c477/D666/z7rvv0rRpU3777TeqVKmS7WNfvHiRq1ev2tRN0Ov1dOnSBYCvv/6a2NhYAGuxxYiICOvz9uvXD39/f4KDg22KL1qur3O3BaKDglThxXHj7u5xcknfvn3p27cvu3btYt68efz666/ExcURFBREVFQUCxcupFmzZrcdZEhMTGTJkiXMmjWL//77j/DwcIxGI1euXKFp06aMGTOGbt262WR85BdarRaTyWR7YXbzC5PJ7nqTySTtL0WeyX//a4QQoijo2FGtMDgSECDFNIUQIh+ZMGECly9fZt26dfjdomuQr68vr7zyCpGRkXTu3Jnvv/8eo9FoE0zw9PRkwIABAKxevZozZ84A4Ofnh16vJzQ01Pp4PXv2tAYZMhdftKhatWouv9qCoVGjRjRq1OiuHuPjjz/m1VdfpVWrVrz//vu0a9fOmvmRmprKt99+y7Rp03jyySeZP38+HTt2zI2h55qwsDDOnz9vm4HxyCNqK4WjzEm9HmrUsLno/Pnzt1UDQ4ickGCDEEK4Q2govPyy6kaRuUCkn59Kb7yxoiWEEMK9du7cyY8//siBAwccBhouXbrE+fPnbWompKSkMGbMGP7++29r7QBQxRcttREsHnjgAbRaLXq9Hm8HWW1ZsyjE3TObzUyaNImffvqJnTt3UqlSJbvb+Pj4MGDAAAYMGMAff/xB//79+eCDD6xBovygZ8+efPnll7z11ls3L5wwARYuVF3KMm+n8PODTz+1aYF54MABrly5QsOGDV03aFGkSLBBFH5XrsBrr8HSpZCWpgoaTZkCdxkNF+Kuvfiiatv1+utw4oRacRg7FiZOVEW4hBBCuI3ZbEaj0fDBBx/QvXt3duzYYZOd8ETlyvhMmcLB/fvZ6u2NtmFDgrp0ITgsjMjISIxGI8899xw1atRg7ty5lC5d2mEqfgnpGOVy06dP55dffmHr1q2EhITc8vatW7dm/fr1tGnThoiICNq3b++CUd7aqFGjaNmyJa+99trNbijFisHevfDqq8QuWcLl5GRS7rmH4BdeoHSHDjYnf7Nnz+bxxx/H61ZbO4XIIQk2iMLt6lWoVw+io29Gd9euVX2HV62Ctm3dOz6RcyaTShH09bWJ0hc4ffuqHyGEEC5jvlGpX6PREBMTw7Fjx+w6Ojz22GNotVp+/vlnRo8ezc6dO62FFovv3YvxlVcgOZl7gcbp6QT++SfapCTYutVad6dkyZLcf//9rF69mtGjR7vxFQuLa9eu8cYbb7Bv3z7HgYa0NFV4O0tgqEaNGixcuJAnn3ySw4cP54sOF1WqVKFOnTrMnz+fsWPHAqoGw+9//cWsCxdYbzZTonx5fNPTuf7UU5jGj2fEiBGMGDECk8nEsmXLOHz4sJtfhSjMJNggCrd331UBh6xVeQ0GePxx+Oefgn2iWhTFx8Pzz8Pnn6sJQUQEvPQSjB4tn6UQWcTHxxMTE4PBYCApKcmmCr0QhZnJZMJsNuPh4UF8fDz79++3yUqIjY3loYceolKlSly5coU1a9bg4+NjDSaULVsWT09PfvnlF9q2bcsrr7yCv7+/OsFMSoJRoyA5GYBAy5OmpsKRI6p1caZU+0GDBvHhhx9KsCGfWLhwId27d7ffnrJhAzzzDOzfr4IN7dvDjBmQqSZG586dee6559i4cSOtW7d26bid+fDDD2ndujWVK1cmIiKC/v374+fnx5gxY1iyZAn+/v7W2x46dIg5c+ZQs2ZNPDw8eP7553OlY4oQzkiwQRRuS5aoE1JHLlxQ7ZwqVnTtmETOWbbBHD9+s/DRxYvw3HPqs3zvPfeOT4g8YjabSU5OJikpiaSkJEqVKoWXlxenT5/myJEj1sstQYUnnngCX19fduzYwdatW62P061bNze+CiHuXFpaGitXrmTLli1cv34db29vihcvTp8+fbjnnnvw9vbGYDCwc+dOm0BCfHw8Xbp0oX79+iQlJbFu3Tp0Oh16vZ6wsDAqVapEYKAKE0RFRTFp0iR8fX3tVqujo6MpV64cAQEBNy9cv95u1dsqKUkFwzMFG0qVKsXVq1dz+60ROWAymZg9ezZfffWV7RVr1kDPntYAEiaTuqxxY7UlISoKUJkwY8aMYdasWfkm2FC9enVWrFhB9+7dycjI4NNPP6V///4OMy9q1arF6NGjWbt2LSaTiQ0bNjBhwoSbWzCEyGUSbBCFm7Nq/6Ci1s76EIv86dtvVVAha4VlgwFmzlRBhxvtwIRjly5dYt26dcTExKDVagkNDeX++++3qXwuXCc5OZnLly9bgwSWgEHTpk0JCQnh0KFDfP755wQGBlrTvgHGjBlDREQEV65c4eDBg+h0Ovz9/QkNDaVMmTLW29arV4+oqCj8/f3R6XQcP37cXS9ViDty7tw5Zs6cyRdffEGVKlWoUaMGISEhJCQksHv3bmbNmkXp0qV58cUX6d69Oxs3brQWXyxdujTBwcEUL14cgOLFi/Piiy86LL4I4OXl5XTPusO2gLeaO2RZ5PDw8MBoNN7eCxd56tixY5jNZho3bnzzQrNZ1UuyBBoyX56YqOp+ffml9eKHH36YiRMnumbAtyk8PBytVmv9P3HmzBmGDRtGeHg4AOnp6axatYpZs2Zx6NAhXn/9dR577DH69evHY489xpdffnlX20LMZjObN2/m+PHjJCYmEhAQQJUqVWjZsmW+2G4i3EeCDaJwe+ABdYBwdJDX6cBB9WGRj339tTrwO+LpCb/9plo+FURms9oi4udn3eubew9tZtOmTcyaNYu1a9fSrl07ihcvjtls5sKFC4waNYoePXowZswY2wmYuG0mk8maeWAwGChWrBhBQUHExMSwfft2u2BCjx49qFy5MmfPnrVbYfPz86NWrVqEhISg1+uJioqiatWq+Pv7W3/0ej0AjRs3pkmTJk7HFRYWJi3NRL6UkpJCWqaT8nXr1nHt2jViY2M5evQon332GU2bNmXdunVUr16dt99+G51OZ93mMGnSJE6dOsWHH37IypUr+fzzz22zDzLRarVOAw23EhISwp49e2wvvO8+51mTOh307m1zUXR0NMWKFcvR84vcFRMTQ4kSJWxPgC9dgvPnHd/BZFI1vjIJCQkhPT2dlJSUPBzpnfnf//7H//73P55++mlrMK58+fKACqYlJibSpEkTxo4dS+/eva2ZDEuWLKFWrVps376dZs2a3fHzxsbG8sUXXzB79mw8PT1p3LgxgYGBJCQk8OGHH5Kens7o0aMZPHiw9bglihYJNojC7aWX1Gp4QoLt5TqdSrmXiv8Fy62i4wU1ev7ZZ/DKK6qQqUYD3brBxx9DZORdP3RKSgpDhgzhr7/+Yty4cXz66ae2/bhRE+GFCxfSt29fOnfuzMyZMx1WTC+KjEYjZ8+etdumULVqVSpVqkRMTAzz588nOTnZJvOgW7duNGjQgLS0NA4dOmQNEoSFheHv7289KSpdujSDBw+2Zib4+fnZrKKWLl2a1q1bU716dYfjkxUjkR+ZzWaSkpJISUmxBrs2bdpkbQ8ZGxtLamoqUVFR1pZ7x48fx2g0kpyczMKFC5kwYQKDBw+m4o2tjs8//7zD3/fBgwczaNAg+vfvz/fff5/r311t27bl2WefJSkp6ebe97AwGDMG5syxbV3s6QkhITBkiM1jLF++nA4dOuTquETOZf6uBtRxN+tlWa938Bj55fv3/PnzbNiwgc8//xyAhg0bsmDBAj6bOpWksWNJ++kngjUaPE6fVtt8MgXe/Pz8GDt2LLNnz77jYMO2bdvo1asXbdq0Yd68ebRo0cLmPTGbzWzdupVZs2bx1ltv8d133+UooCHyr4yMDOLi4rKtByWzSVG4RUWpqtCjRsGuXSq4EBYG77xjs59SFBD9+8PvvzvObkhPh/vvd/2Y7tY778DkybYT1h9+gO3b4fBh1Q4zh9LS0ujWrRshISHs378fX19fh7cLDw9n4sSJjBo1ir59+zJgwACWLVtWqIoJZs088PX1pUSJEphMJlavXm0TSEhKSqJBgwa0a9eOtLQ06wTOws/Pj9DQUCpVqoSfnx81atSwblOwBBUsqaslSpRg0qRJTsel0+moUKFCXr50IXKdyWQiISGBpKQka3G5bdu2cfLkSWvNhIyMDMLCwhg3bhwAFy5cIDY2Fr1eT7ly5dDr9URERJB+Y0vC6NGjycjIoEaNGnzwwQcMHTrU5jmdndj5+PiwePFiunbtyuTJk5k8eXKuvtZy5crRvHlzli5dyvDhw29e8d57atve22+rrX0mE3TsCLNnQ6C1ZCSxsbF88803HDlyJFfHlR8lJibyzTffcOrUKRITEwkMDKR69er07NnT6fHH1UJDQ7l06ZJtsKB4cShXTtWDysrDAx580OaimJgYvL29802dg3nz5jFgwABrDRIA4uPRNG5MwKVLN7cUX7wIEyaohY0XX7TedMiQIbzxxhtER0dbj123smnTJnr37s2iRYvo3Lmzw9toNBpatGhBixYt+PXXX+nRowfffPMNrVq1yulLFS6WmppqUwvH8qfl74k35uMNGjRwWmhUgg0id50/D8uWqQ4QTZpA167Oiyi5yj33qIBDbCykpKiDSj6JRos71KuXOjn/+2/bug06nTqA3uZBMt9ISrIPNICaGFy/DvPmqToUOfTkk0+i0+lYunSp/Z5jB4KCgvjxxx+5//77eeWVV5gyZUqOnzuvZZ4onj17lvj4eJuAQbFixWjatCkAH330EdevX7dZzapduza9evVCq9Vy7NgxvL29rZkH5cqVo1SpUgD4+voyaNAgaxBBp9PZBGH8/Pzo2rWrC1+5EHnPaDQSFxdHXFwc5cuXR6PRsGfPHg4ePGgtvmgymfDy8uLFF19Eo9EQFxdHcnIyERERVK5cGb1eb7N1oH///g6fK/NJ+I8//kiJEiXsAg2YTCrQ/Mcf6kT+oYdsijt7eXkxa9Ys7r33Xl544QX8/Pxy9f0YM2YMkyZNYvDgwTdrO2i1MGmS6l5w5QoEBYGDbRzz5s2jU6dOlChRIlfHlJ8cO3aMmTNnsmTJEu677z7q169P6dKlSUhI4PPPP2f8+PEMHTqU0aNHW1P73aVKlSp4enrabhvQaGDWLJVVmLlug1arft9ee83mMZYsWZKviu2uXr2a999/3/bCefPg2jX72mUGA0ydCk8+af19LVasGK1atWLDhg3069fvls/333//0bdvX5YuXUr79u1va4ydOnVi6dKl9OvXjx07drj990DcLDx94cIFuyCC5c/kLHVMPD09CQ4OJjg4mCpVqqDX6wkODqZEiRKcO3fO4fNIsEHkHkuBPrNZnQgGBkJoKGzeDKVLu3t0d7VCLPIJLy/YtAlefhnmz1cZDuXKqe0yjz3m7tHduW3bnAfjkpNVjYocBhvOnTvH8uXL+ffff+0DDXFxsGWLWrFp2RIytcXy8fFh2bJl1KxZk+eee85uy0VeyZx5kJSUhNlstqZPb9myhQsXLtgEE8LDw60nJKtWreLKlSuAWknx8/OjSpUq1se+5557AGyyDzLvHX366aedjkuj0VjHIURhkZaWZp1Qli9fHi8vLw4dOsSOHTuIjY0lMTHRGpybNGkSfn5+pKamYjQaKVOmjHWCmfn/kbPVzTsxa9Ys+/aQMTHQurUqDpyYqI4Dkyerk/xMAVHLlozly5czePDgux5LZh06dODjjz9m1KhRzJ8/3zbLwtMTnKzo/f7777z//vts2rQpV8eTnyxZsoSnnnqKUaNGsX//fsqUKWN3m3/++Ye5c+fSqFGjbFfCXUGr1Vq7Sdik9LdrB2vXqmPuzp0q0NC1q8pgyXRibDabmTVrFvPmzXP94J24du2atRiq1fLl9gUvLTw91RygUyfrRcWLF+fatWu39XwfffQRgwcPdhxoOHAATp1SwcDatW2uateuHUOGDOGjjz7igw8+uK3nEjlnNptJSEiwy0rI/Of58+dtslm8vb3R6/Xo9Xq773q9Xn+z/a8DEmwQeWvnThXhz1wsJyFBRVB79IDdu902NFHIBATA9Onqx2gs2HU3bjX2u3htn376KY888ghBQUE3LzSb1ST97bdv7tnMyFDZIjdSnQFKlixJhw4dWLRoEU888USOnt9kMlkzAC5fvszVq1dt6h6YTCbrytCKFSs4fPiwTeZBSEgI48ePB+DixYtcuXLFuj2hXLlyNgfHnj174uHhgU6ns8s8AGjTpk2OXoMQBZHZbCYlJcU6oSxTpgz+/v6cOHGC9evXExsbiyFTNtXo0aOtJyqenp5ERUVZJ5bBwcHWlfxmzZrl6X7rkydPcvDgQXr16mV7xaBBcOzYzYKM6enqZ8YMaNoUunSx3nTMmDG8/fbbuR5s0Gq1fP3113To0IFBgwYxb968W24LWLFiBWPGjGHFihVUrVo1V8eTX3zxxRe8/PLLbNiwgZo1azq9XeXKlXn//ffp1asXPXv2ZOHChTzwwAMuHKmtIUOGMHnyZE6fPm27ja1FC7WF0WRS2Q4OTqpWrVqFl5cXLVq0cOGIs6fVau3rUNwqqzjL/MJsNt/W1kmDwcAXX3zBrl27bK84c0Zlhpw4oZ47I0MVYf/xR7UodMOoUaNo1KgRU6ZMQafT3fL5hHNGo5H4+HingYS4uDi7LjiWIrthYWFERUVRsWJF6tSpY/3Od9T+925JsEHkjunTbQMNFkYjHDmi0t5r1HD9uEThVpADDQDNmjkvSqXTqUl2DqSnpzNv3jzWrVtne8XcufDuu+r/aub/r5MmQalSqsf4DWPGjGHUqFGMGzcOjUZjl3lgMBioVq0aHh4e/PPPPxw8eNAm8yAlJYWXXnoJrVbLzp07rdXcLZkHmYMglStXJjQ01CbzIHNV+b59+2b7eiNzoZCmEAWFpfiiZUJZsmRJQkJCOH/+PD/88ANxcXGkZtpmNmDAAKpUqYKXlxc6nY6SJUtaV6qCg4OtWx1q1apFrVq13PWy+Oeff6hTp47tPvjLl9X2CUedH5KSVKA0U7ChUaNG/PPPP3kyvoCAAH7//XeGDx9OuXLleOyxxxg5ciTlMp1IJScns3z5cmbNmkV0dDRr1qyhXr16eTIed9u9ezcTJ07kjz/+cFrANqtmzZrx/fff0717d/7880+ioqLyeJSOhYSEMHnyZB544AE2b95s37HHyUn3gQMHGD58OMuXL883xSFB1aG4cOGCbVDrkUdg3z77bZqggilZgiXnz5+/rSKmy5cv595777UN0mRkqCzJ8+dtu78dPqw6t5w4oTKSgAoVKnDvvfeybNkyhg0bdicvs8hJT093WishNjaWhIQEuyBTYGAger2ekiVLUqNGDZvver1eb9eV58iRI7f9/zenJNggcsfRo+rLyxEvLzh5UoIN+c3Ro7Bunfp8unXLlc4H4g75+sK0aareROYJgbe3OvnPUtH8dp0/fx4vLy9qZP4/ZzbD669bn8fyv1ULxBkMnH3uOQylSlmDCUlJSZw+fZrk5GQOHDjAmjVr7A5qTz/9NEFBQSQlJXHt2jWbzAN/f39rdkOLFi1o0qSJ08yDOnXq5Oh1ClEYWYovWiaWERERREZGcv36dRYvXkxcXBwZmfZhd+3alYYNG+Lr60tISAgVKlSwmVxaTqTKlStnc2Kc31gKCto4e1Z9T2au0ZPZyZM2/7S03MsrOp2Or776iuPHjzNnzhwaNGhAeHg4wcHBJCcnc/bsWe69915efvllOnfufFu1cgqqadOm8cILL9ifqKSlwc8/q5XuypVV0cxM70PTpk0ZPnw4n3zyiVtT6Z944gmuXLlC8+bN+eGHH6hWrVq2t//tt9949NFHmTlzJq1bt3bNIG/Tgw8+yOLFi22z+IYMgY8+UtuPMgfrLN3YMtU1uXTpElu3bmXJkiW3fK5du3bRsWNH2wtXrVJ1prK2mTca1eWrVqmaWzd07NiRPXv2FPlgQ+YMNEfBhKSkJJvba7VagoKC0Ov1VKxY0S6QEBQUlC87ieW/EYmCqXp1tU/LUcAhPV11hRD5Q3q66sTx00/q31qtKhQ0caJKsReuNWIElCih6k4cOqTqJwwZoj6LTLUUbsVkMmEwGDAYDPz999/4+voSHx9PUFAQFy5cYMvatSRFR5MEGIBkYChQFvgXWHnyJPzyizXzwN/fn+DgYGJjYylVqhT33XefXccFSxu4unXrZhsZDwkJyfHbI7JnNBptumjc6u/3F8SOLYWMpVWYZUJpmTimpqYye/Zsa/FFi5YtWxIZGYmfnx8lSpSgatWqNhNMy/+v0NBQHn74YXe9rLsWEBBgHygoU8Zx1qRFlrlFQkKCfcAiD1SpUoUPPviAqVOncvr0aWJjY/Hz86NkyZL2e+cLoUuXLrF69Wpmz55te8XOndC5s5pnpKWpwHlgoFrYyHQyP3LkSBo2bOj2VPo33niD0qVLWwPiY8aMoVOnTtYgUXJyMl9//bU1U2Xp0qW0bdvWbeN1ZtiwYVStWpX333//5vFWp4MdO1Rb7c8/V/VOataEN96w667x2Wef0bdvX5saLM7ExcXZ327XLvsW8xYJCer3IlOwISQkhNjY2Nt+fQWR2WzGYDBk28khJct3m6X4ol6vp1q1aja1EoKDgwkMDCyQXcIk2CByx4QJql1f1nQtDw91gJGshvzjxRfVqkPWCdwHH6gD0UMPuWdcRVm3buonE5PJhCkjA09PT1JTU/nnn3/sTiAbNmxIVFQUZ8+e5bPPPrPe9+rVq1y7do2zZ89Ss2ZNMjIyiI6LQwdEAP43fixT8irAmIAA/J97Dj8/P+vB7H//+x9+fn6EhIQ4LPolcp/RaLT5nA0GA4cPH+bChQsOgwdZJysWWq3Wmkni7+9PZGSk7I91kdTUVJvJpU6ns+5n/+ijj4iJibG5fZ06dahYsSLe3t5UqFCBwMBAmxUrS5FWX1/fW24pKsgqV67MgQMHSEtLu5nqW7w4tG8Pv/1mv5XC318FyTPZtWsXlStXtrksPT2dH374gYULF/Lvv/+SlJREUFAQtWrVYtSoUbRs2TLHKfGW1rdFzeeff06fPn1sTzrj41X76fj4m5elpqqT3DZtVKbDjVT68uXL07RpU5YvX86QHGbw5ZaRI0cyaNAgvv76a15//XUeeughihUrhtFo5Pr167Rt25bXXnuNjh075ttMlYiICLp06cJHH33Eq6++evOK4GD48EP140RcXBxz5szhhx9+uK3n8vX1tT/uhIerDCRHxyMfH7tOYcnJybneMcbVTCYTiYmJDjs4WP60tPW18PHxsX6vlytXzi6YkF3xxYJMgg0idzRurPZOOupGcZtfYMIF0tJgzhzHFYqTklQ7JAk25AlL5kHmE8WQkBBKlSpFSkoKP/zwg83JZHJyMu3bt6dFixYYDAZWrFgBYJN5YDngh4SE0Lp1a+uJpdFo5PPPP7fWMihbtixjx49Xha++/dauFZafjw9+w4fbZFJcunQJo9FoW2BS3DFL8CBrAMFZ9oGj4EF0dDTFixe3fr46nY7IyEi7TJPMf3dW5MlSO0PkjKX4YuYJplarpXHjxoBqc3j+/Hmb+1SsWNEabKhZs6bN6pVer7euxGs0Gh7MsuJYlERFRVGrVi2+++472zaZixbZd6Pw8FCLHFnazs6ePZvHH38cUBkk77zzDrNmzaJSpUqMHDmS2rVro9PpiI+PZ/PmzYwcORIPDw9eeOEFHnnkEde92ALu8OHD9llSS5bYp9GDmhMmJalFjh49rBe3bNmSw4cP5+1Ab5Ofnx9DhgxhyJAhxMXFcf36dTw9PSlWrFiBCdJOnTqVZs2aUbNmTfr06XNb90lJSaF379706NGD+vXr39Z9SpUqxbFjx2wv7N9fLWQ5otFAloyr48ePU9JJB5f8InP7X0dZCXFxcTYZaKC2Wen1epv2v5m/629VVLawynGwwWg08tJLL3H69Gk0Gg2vv/66TasxUQSNG6cKzC1bBlevQpMmaiKQD/cPFVmXLzuvrQF2+1+FcyaTifT0dGsxs7///puEhASbE8gyZcrQrFkzzGYzU6ZMsTswNW7cmFKlSuHl5cXVq1fx9/cnIiLCesJYtmxZAIKCghgzZgz+/v42mQcWAQEBdntIO3TowIoVK6wTb0Dt39y+Xf3/tGQh+ftD2bKqnkMm8+fPZ8CAAfl2JcddHAUPsvt7qpO95pbMA8tnXbJkSYdBA51Ox9mzZ6lbt26hXPHIyt1zC7PZbF2tskwuT5w4Yd0mtHTpUo4fP25zn4iICGuwoXbt2lSvXt1mkpm52Gm7du1c9loKojFjxjBjxgzbYEOxYqrQ3W+/wcaNEBQE/fqp1nqZnDx5kl27dvHtt99iMBjo27cvaWlp/Prrr9b2t5nVrVuXcePGsWHDBkaPHs3+/ft55513isT/s7sVFxdnH4jet08FFRwxGFSx8EzBhqCgIE7mwzlH5myigqRcuXL89NNPPPDAA1y8eJHRo0dnu3///PnzPPTQQ5QtW5YZM2bc9vM8+uijtGjRgqlTp94s5hoZqQrFP/OMym4wmdQWXV9feP99m9awqampLFq0iM2bN+f0peaK9PR0p7US4uLi7IovajQaa9ZZ6dKlqVWrll0GWtbii0LJ8Vnghg0bAFi2bBk7duxg+vTp9nu3RNFTqpT6shH5U7FijlceLLKkuhUlmTMPLCeMnp6e1qJRP//8M1euXLHJPKhSpQoDBgwAYPXq1SQkJNhkHkRERADqINW2bVu8vb1tTiItkzUPDw/Gjh3rdGweHh7Wx7pdY8aM4ZlnnmHEiBE3J88REaouxOefq6CgpycMHKiqVmdKaczIyGDu3LmsWrXqjp6zIMrIyLitjIPcDB5kl3ngSHR0dJE5AcrruYXRaLQWX7RMLOPj4+natSsajYZVq1axd+9em/sYDAbMZjMajYbatWvbFWDMnA7cpEmTXBtrUdS9e3eef/55Fi5cyNChQ29eodWqQoNZC9PdkJ6ezpgxYxg9ejTe3t707NkTvV7P559/bm3d6Yjlu3nbtm107NiRyZMn26ahC4cCAgLsitdRoYLzVHo/PzU/zCQpKcla90fkjnr16rF582YGDx7Me++9x8iRIxk+fLi1jojZbGbjxo3MmjWL3377jSeffJJXX331juoAVK5cmbp167JixQrbbKBRo9Qi44wZqgNd9eoq+yhLxsSKFSuoU6dOngaRs7b/dZSdYMiy7Vur1Vq/0ytWrGiXlRAUFCSLLzmU42BD+/btrStpFy5ckFRbIQoCf3/o3RtWrLDf/6rTwfjx7hlXHjAajaSkpFgnM6dPn7YGCywnj5ZJKag9qGfOnLF5jMjISGuwITk5GY1GY5POnjkAMHToUHx8fBxmHgAu78ndtm1b0tLS+Oqrr2wnBIGB8MQT6seJmTNnUq5cOerWrZv3A81lmYMHtxNAcEfwQDh3t3OLjIwMh5PLzp074+fnx6ZNm9i4caPNfQICArj//vvx9fWlZs2aREZG2qxYnTp1yvrZurM1ZFHg6enJqlWraNWqFX5+frYZDk6kpqYyaNAgvLy8eOWVV5g7dy5xcXGsWLEi20BDZqGhofz00080bNiQTp06SdDoFipUqMD+/fttjy2DB2dfZLp3b5t/7t+/n0aNGuXRCIuuSpUqsXXrVvbu3cvs2bOJiorC09MTX19fYmNjiYqKYsyYMcyfPz/H527jxo1j4sSJdO3a1TYLpF49+OILp/eLi4tjypQpvP322zl6XgtL+19nWQmxsbF2x3YvLy/r93pkZKRNrQRLBlpBLL5YENxVfrunpyeTJk3it99+46OPPsqtMYmi7MoV+OQT1SlBp4Phw9VeL0lNyj2ffKLSHc+cUftfNRr1Xrdpk+0JqLtZqu5nrWtgSV/+888/+fvvv60nksnJyfj6+vL8888Dqif44cOH0Wg01hPE0NBQ6+M3btyYe+65x+5k0uJWeyAtverzC61Wy9dff0379u0JCwuzb1XlxNdff827777L1q1b83iEtyevggd6vV6CB/nU7cwtjh8/bjOxbNOmDWFhYezfv98mI0ej0Vjbs/r5+VG1alVryqtlopk51TgqKooo6Z7kVlWrVuW3336jS5cubNy4kSeeeMJhEcaMjAx++ukn3nrrLcqWLcs333yDh4cHM2fOZM6cOfYpzevWqa1k//2nVluffhoyBY9KlCjBhAkT+OSTTyTYcAtDhgyhefPmTJ48+eY+9MhIWLAAhg1TGZRpaSrTwcMDVq60qQl07do1Vq1axfTp0930Cgq/+vXrM2/ePObMmUN8fDzJycnWTKy7PbZ17dqVNWvW0KNHD3788cfb6gCTmJhIz549adu2Ld27d8/2tpnb/x47dozo6Gi7wEJGltpTvr6+1gBC+fLl7dpC6nQ6Oaa7icactXF6DkRHR9OvXz9+/vlnayGVPXv2uLSoSkpKSpEtvOEKrnh/vU+fpvzDD6NJSUF7Y9Xd5OdHSpUqnPn8c8yWvWGFkMt/f9PTCfzjDwLWr8fs7U18164YGjZUgQcXsWQe+Pr64uHhwdWrVzl//jzJyckkJyeTkpKCwWCga9eu+Pj4sG3bNrv0ZlCVpL28vNi7dy///fcffn5+Nj+1atUiNTUVo9GIVqvFx8enSEWv9+7dy/jx4xk8eDAPPfSQ00nB9evX+fLLL/n++++ZPXs2VatWve3nuJPf34yMDFJSUkhOTrYWRMz698w/aVkzcG7QarU2n7Ovry9+fn7odDrr3zP/+Pj4FNiJRm5+PxgMBho0aJArj5XXHM0tQM0vFixYAKjAREBAAG3btqVkyZLExsZy6dIlAgMDCQwMzJXVKplf5C1n7+/Vq1dZunQp33zzDRUrVqRt27YEBweTkZHBuXPn+OGHH4iMjKR///506dIFrVbLjh07mDp1Kj/88IPN//fw99+n2NKlaJKT0QAmDw/w8uLCW2+RkCkQGxsbS8eOHfn1118LTcvevPr9HTFiBN26dbM7cfS8eBH9t9/i/e+/pFSpQlzv3hgzBfYBFixYwIkTJ3jzzTdzfVyuVlS/H4xGI5MnT+bQoUO88MILNGjQwOEx1mw2s3fvXt58801q1qzJq6++aq2Nk5CQ4PAnMTHRWuMq40ZXLj8/P4KCgqzf7Vl/fArxOUJecsX8IsfBhu+//57Lly8zcuRIEhMT6d69O7/88ot1wHv27HHphObIkSPZ9nkXd8cl7++996pevFl/Jf38VF/gQlwLojD8/ppMJoerzTVq1CAwMJATJ06wadMmm8wDULUFIiIi2LFjB6tXr7bJPNDpdPTp04eAgADOnj3LpUuX7FaibydKXxje37tx/PhxXnnlFdasWUO/fv3o378/ERERmM1mLly4wOLFi1m1ahUPPvggb7zxxh21uczIyOCvv/6idOnSd515kF13hax/L0qZB7n5++vqY/OdutXcAtRrKF68OMHBwQQEBOT570FR//7Ia7d6f9PS0vj+++/ZvHkz169fx9vbm+LFi/PQQw/ZbfV67LHHuOeee5gwYcLNC//6C5o3d9yFSadTGZWZVt0feeQRWrZsyahRo+7yleUPefX7+8svv/DEE0/w559/En4H9Z5Onz5N8+bN+fHHH2nYsGGuj8vVivL3g9lsZv78+UybNg0vLy/GjBlDo0aN8PHx4eLFi2zfvp0vv/yS1NRUunTpQsOGDYmLiyMxMdFh8cWsWxv0ej2XL1+mUaNGt70lStwZV8wvcryNokOHDtZWQRkZGbz44otFMrIncsmFC7B/v32gAdQEYfbsQh1syI8s2xa8vb3x8fEhMTGRw4cP2508tm3blvLly/PPP/+wdOlSu8cJCwsjMDAQrVaLh4cHJUqUcLhVoW7dutxzzz34+vo6XIksU6bMHZ0Ei5uqVKnCsmXLuHTpEvPnz+ell14iJiYGrVZLaGgo3bt3Z/r06YSGhpKRkUFcXNxtb11ITU0lOjrabrKZNXgQEhIiwQNxS7c7tyhdurQbRifcwdvbm379+tGvX79b3vbChQv0zlIbgM8+s69RZOHhAT/+aNOar3Llyly8ePFuhlwkPPDAA2zdupUuXbrwyy+/EBYWdsv7nDlzhk6dOvHiiy8WikBDUZS1/e8999zD9OnT2bp1K5999hlTpkwhOTkZHx8fihUrRrNmzahUqRJ6vR4vLy8qVapkE0wIDg7OtvhiWlqaBBoKuBwHG3Q6HR9++GFujkUUEUajkdWrVzN37lwOHTpEfHw8/t7elE9PZxjwEOCX9U5xca4faCFjSVvLesJYunRpSpcuTVxcHN9++6318pQbFaW7du1Kw4YNSUhIsMs88Pf3t0anIyMj6dKli93JpKVSe8WKFamYpVVZZpIClzcyMjJsPvNu3brRrl07u+DB4sWLrcEDRyzBA8tnGxISYv37lStXqFWrls3vRUHetlCYXL9+nejoaOvnnN8XBWRuIe5GfHw8K1as4IUXXuDKlStkZGQQkpJCa6ORMUDdrHdIT4fr120u8vX1JSYmxkUjLtgsLZ2bNm3K1KlT6dGjh8P2f8nJySxbtoyXX36Zp59+mnHjxrlhtOJ2WOaKjjo4WP6edXujl5cXkZGRTJgwwS47ITg4mMDAQJkPFGF3VSBSiDthNpuZO3cu77zzDuHh4dZ+2kFBQRhiYjhQty5zjEaeA0YArwHeoOoI3CgCKG6yZB4ABAYGYjab2bFjh10woUaNGjRt2pSUlBSmTZtm9zitWrWidOnSeHl52WQeWE4ky5UrB6he8hMnTnS6bSEoKEgqS7uAJXhwO1sWcho8cJR9kF3w4MiRI9auHSJvpKWl2Xy2FSpUwMvLi5MnT3Lw4EG7z/7pp5/Gz8+PPXv2sGXLFuvjdOvWzY2vQoi8cf78eZ599ll27tyJTqdjzpw5lC1bFk9PT659/DHfvfsu3dLTKQu8CbSy3FGrtZtfxMbGotfrXfsCCiiNRsNbb71F06ZNmT59OuPHj2f48OHUq1ePgIAA4uPj+fPPP/n8889p0qQJixYtom3btu4edpFmMpmIj493GkzIrvhiSEiIXevf3Co6KQovCTYIlzCZTIwdO5bt27ezfPly+5PS8HDKPfss3T74gFMGA08BnYHvgUA/P3jlFdcP2g0SExNJTEy0OXEMCAiwtltbsmQJMTExNpkHtWvXplevXmg0GtatW0dGRobNiaIl/czX15euXbvanUxaMg90Oh2DBw92OjYPDw+XFn0tKu4keJCUlOS0YKLl88mN4IHIW5ZsII1GQ1JSEufPn7f7nFu1akWxYsU4cOAAn332mV2xOkutk9jYWE6fPm39fMPDw23+nzZo0IBq1apZrz906JBLX6sQee3w4cN07tyZoUOH8uKLL3LhwgWaNm1qvb74xInUmDOH569d4zugH/AB8Ii3N9xzD2RK5zebzaxevZr333/f5a+jIOvevTvdu3fn8OHDLFiwgC+//JKEhASCgoKoVq0aO3fuzDazUeQey1ZIR+0gY2NjSUhIsBZftAgICCA4OJgSJUpQrVo1u04Oknkq7oYEG4RLTJo0iUOHDrFp0ybnfX1ffx0SE6k4Zw7feXvzeHIyfc1mVn3+OV4FsA2VJfMgLS3N2mLx4MGDXLlyxeakQq/XW9t6ffHFF0RHR9s8ToUKFazBBj8/PyIjI21OGiMiIqy3ffrpp53ufddoNLJH0gXyInhgqXngKGhg+bcED9zHbDaTnp5u8/lGRESg1+u5du0amzZtsvnMk5KS6Nu3L1WrVuX8+fN89dVX1sfy9PTE39+fhg0bUqxYMcLCwrjnnnuoXr263e8DqGBCdgUfQ0JCCk1VfSGyOnfuHJ06deLNN99k4MCBXL58mWrVqvHee+/dzE4ICoLNm/Hs0YN+589TS6OhXWIiwdWr03X1apvH27ZtG8nJybRr1871L6YQqFmzpsMMSpF7UlNTHW5tsPyZmJhoc3tL+1+9Xk+5cuXsshKytv8VIrfJb5fIc5s2bWLFihXs2bPHeaABVDrj9Onw8st47NzJXC8vOr/1FrMuXGC864abLYPBQHx8vM1JRUZGBi1atADgt99+48iRI9Y2fgB6vd5aGXvfvn2cPn0anU5nPXEICAiwPn779u0xmUw2K9SWzAOAXr16ZTu+zLcVuSM9Pd3pFoU7DR5kLZiYXdcFCR64j9lsxmw2o9VqycjI4NSpU3afdY0aNahatSrXrl1j9uzZdmmnllonGRkZ/Pvvv9bPNzw8HH9/f+uJUJkyZRgxYoRNJlLmz71kyZI0b968yFY7FyI7o0ePZtSoUQwcOBCA4sWL07lzZxYuXMhTTz1184bVq8PRo7B/PzUuXOD7lBS6PP44/3l745/p8WbOnMno0aOLVHtkkX+YzWaSk5OdZiXExcVZO3lZeHh4WIMHlStXtquZEBgY6LT4ohCuIMEGkec++eQTnn76aYoVK2Z7xY4d8M47cOAAlCkDTz0F3bpBsWLQqROewGu+vgwdOpQnnngi1w/+RqPRZhVSq9Vy+vRp64lF5pPKsWPHotVqWbduHXv27LF5HC8vL5o3b45Go8Hf35+SJUvanDQGBgZab9u/f388PT3tTiKPHDkCQNWqVXP1NQp7lhXoWwUNLH+/3eBBsWLFsu22IMED93GUeeDv70+pUqUwmUz88MMPdp//vffeS/v27cnIyHCYeWDphuDv70/jxo3tPnPL913x4sVtT3qy8PPzo1SpUnn7BghRCJ0+fZrt27fz9ddf21w+afx47m/fnpazZtFQq4XOnVU3qzJloG5dqFuXJkCLRYv46quvGDFiBKAyC//8809mz57t+hcjigRL8UVnhRfj4uLs5hze3t7WwEGZMmXstji4ov2vEHdDgg0iT128eJG1a9fy6aef2l7x+ecwdqxqa2k2w8mTsGsXDBkCM2dab9asWTP8/PxYv3497du3v+Xzpaamcv36dbsTxnvvvRd/f3/279/Pxo0bbTIPAJ555hkCAwM5c+YMW7dutcksiIyMJCMjA29vb+rXr0+lSpVsTioyb1to1qxZtuOT9j25L2vwwNHfT5w4oQqR3tjW4kjWbQuhoaESPMinzGYzRqPRmvp56tQp4uLibD77sLAwWrZsCcAHH3xAQkKCzWNYap1otVrOnz+Pt7e3TeaBpTCqj48Pw4cPt372WTMPfH196dChg4teuRDCYu7cuQwePNi2llBSEnUef5x5aWl0PXGCb4Hmp0/DwoWwdSvc2JIIqu7JpEmTGD58OAsXLuTFF19k/fr1UhxS5JjRaCQhIcHpFoe4uDiMRqPNffz8/NDr9YSGhhIVFWXXyUGKL4qCToINIk+tWLGCBx98kODg4JsXxsXBmDEq0JBZUpKaEAwZAg0bWjMP+vXrx6JFi2jfvj3Xrl1j3759dieUvXr1onTp0hw9epSVK1faPKxWq6VGjRrWk4WSJUva7Xm3FL9p0aIF9913n9Mv9lKlSskqZB67neBB5r/fKvPA8vmWLVtWggf5lNlsJi0tzeazhZuZPhs2bLArohgZGcljjz0GwK+//sqVK1cAFdDLXBgVoEmTJtbMI8vnnnlLV3Zt2DQajTWLQQiRf3z11Vf8+uuvthfOmAHHj/NgWhq+QA+gc3o6Y9PTaTx4MJpMmYlt2rTh3Llz3HfffVy8eJGNGzdKdqHIVnp6urVjgyV4cOjQIbZv305cXBzx8fHWAsAWgYGBBAcHU7JkSapXr26zzSE4OFiKL4pCT4INIk9dunSJSpUqWf+dkZFB9KJFGDQakoAkwABUBUoDl5OT+fqppzB06WLNPDh+/Dhnz54FVA/tzJkHluCB5cSifPnyPPTQQ04zDypVqmQznqxkX1vuy4vggU6nIzQ01ObkMWsAydvb2/q5HzlyRPa8u5Bl24Kl3/qFCxe4fPmyzedsMpno3bs3AMuXL7duJbLQ6/XWiX9sbCwGg4GAgACKFy+OTqcjLCzMett+/frh6emJTqdz2OPdUlNFCFE4mM1mLl26RFRUlO0V8+fDjblDR+Ao8DnwCBD411/c06cP/uHhxMXFsXXrVlJTU2nRogWvvvoqvr6+rn0RIt9JTU3NdotD1uKLWq2W5ORkqlSpQvny5e2yEqT4ohASbBB3ISMjw3qi6OvrS0hICGlpaTaV13ft2oWfnx87duygSZMmJCQkMHf1akhNtT6OFghGBRt8zWZKmUzo6tSxnjTu27ePRYsWASqY8PLLLztdgbZ8uYu846rgQda/Zw4eCNdylHlgMBioXbs2Hh4eHD16lD179th8/kajkZdffhmtVsvevXvZvXs3cDPzIHMtk1q1alG6dGmbzzxz4dSePXtmO77MgQchROFn2UpltzUxy3apUOAZ4Clgs68v/zZsSFJQEEFBQUycOJFJkybRqlUrCTQUAWazGYPB4LTwYmxsrM32WlA1eizzyipVqtgFE4KCgjh27JgsZgiRDQk2CDuXLl0iMTHR5sQiISGB6tWrYzabmTlzJomJiaRmChg0adKEzp07o9Vq2bZtm/WkITAwEJPJZA0ABAYG8tBjj+G/YQP+RiM6wBewnEIG+/vTe9gwVdDphpMnT1pPJuRkM/dJ8KDosQQPPD098fDwICYmhn///dfus+7evTtBQUFs27aN3377ze5xoqKiCAoKsgYiMmce+Pv7YzKZ0Gq13HfffbRo0cJp5kHNmjVd8bKFEIWEVqslICCA69evW1tLA9C8OaxapWpBZb490Mrbm1ZPPw2ZvoNiYmKkRkMhYTabrfUSnAUU0tPTbe7j4+NjU3wxaycHf39/macIcZck2FCIZc48MJvNlCxZElB9pK9evWpzUlGyZEn69OkDwJIlS2yKqWm1WmtldY1GQ/ny5a0V2S0nkOHh4YCKAmfOPKhatSpPPvmkNR3a09OT6r17w0cfqW4UmQIWaLUQGAgPP2zzOn766adbFl4s7BISEjh37hyJiYkEBgZSunRpm5XfzBytQGcXQMh68LWwpKVnLpiYNWggwYP8wRI8yPr5VqxYEb1ez/nz59mwYYNdy9Zhw4ZRtmxZzp07x48//gioyteWz9Xyu1G+fHnuv/9+u8/c8jtYu3btbFd2sm15K4QQOdCsWTN+/vlnBg0adPPCV1+F338Hg8H2xjodPP+8TaDh3LlznDx5klqZikYWNWazmXPnznH16lUAihUrRtmyZfPlsdxoNBIfH+80mBAfH29XfFGn06HX6wkPD6dy5crWIIIloJB5m60QIm9IsKEAunbtGtevX7c5YfTw8KBNmzYAfPPNN5w4ccIm8yAyMpKRI0cCcPToUa5fv249cQgJCbEGIkClLFuCCTqdDl9fX44ePWq9vlu3btmOL/MXd5s2bcjIyGDz5s3cd999N2/0008waBCsXg2+vpCWBjVrwvLlalJww9WrV1m1ahXTp0/P2ZtVgJnNZnbt2sXHH3/Mjz/+aO2OkJCQwLVr12jRogX3338/JUqUsDnBzC54kPlkMSwsDJ1OZ11tDg4OJiAgwHobCR64jyV4AGrlJSUlhb///tthe8bKlStz9uxZFixYYPc4ffr0Qa/XW3t3BwYGUrx4cWuwwJJxVLVqVSZMmGBXWNFCCqMKIfKbMWPGMHXqVNtgQ/368N13MHSo2lKh1UJGBkycCJMm2dx/3rx5DBgwwGngvjBLTEzkq6++YtasWVy8eJESJUqg0Wi4fPkyISEhjBkzhoEDB971ttSMjAyA26pbYCm+6KyTQ0JCgk3xxYSEBA4dOsT169dJT08nMDCQ8uXL88gjj1C/fn2Cg4MdZtIJIVxLgg1ulJGRYXPyYClcePjwYU6cOGFzUmEymay92tevX8/hw4etj6PVagkPD7cGG0qXLk1gYKDNCmTmlcVhw4ZlO66KFSvm2mvUaDSMGTOGjz76yDbYEBgIK1fCxYtw4gRERoKDwo3z5s2jR48etmmSBZyjzIPDhw9z9uxZ6+UXLlxg9uzZxMbG0qBBA0aOHGnT3isxMZH9+/czZcoUIiIiGD9+POXKlcu220Lm4MHVq1dZuHAh8+fP59SpU4D6rKpXr87jjz/OwIEDpUJyLnKWeRAWFkbZsmVJSUnhm2++sclCycjIoH379rRo0YKUlBSHmQeWiVxoaCgdOnSwyzixTKJLly5t7SXviI+Pj3zeQogC5YEHHmDcuHHs3LmTxo0b37yiY0c4dw727VNdr+rWBX9/m/umpKQwb948h9vDCrvPP/+cZ555hvvuu4/33nuPdu3aodVqAXWs2rhxI7NmzeLll19mypQpjB079rYf22w2s3XrVmbNmsUvv/xiLagYEBBA586dGTBgAJUrV3bYEtLShchCq9USFBSEXq+nYsWK1qyEEydO8MUXX7B161YGDBhAixYtCAwMJCkpiX379jFo0CDuueceJkyYQNeuXXPvjRNC5IgEG/JAfHw8V65csVuF7NSpEz4+PmzdupVNmzbZZB4AvPjii3h7e3Px4kVOnDhhk3ng7++P2WxGo9Fw33330aRJE5u2fplXoJs2berql5ytIUOG8PHHHzN37lxrdoVVZKT6cWDz5s1Mnz6dTZs2uWCUOWc5ibzdmgeOMg+io6OJjIzE39+f5ORkPvjgAzp06MCjjz5KUFCQ0+CByWTitddeY9q0aWzcuPGWLfoMBgNPPfUUy5cv58EHH+SLL76gfv36eHt7k5KSwrZt25g9ezYvv/wyw4cPZ+rUqQ5Xuos6S/AgIyMD/xuT2H379pGQkGDzmZcvX56WLVsC8Pbbb9u1xGrcuDFly5bFy8uLlJQUu8yDcuXKAWobQnaZB/7+/kV+q5EQomjx8PDgjTfeYMCAAWzfvt26nRNQGQ316zu8n8lkYvDgwbRp06bI1Yt59913mTNnDps3b6ZGjRp212s0Glq3bk3r1q05efIkXbp04dKlS7zxxhvZPq7ZbGbt2rU89dRTGAwGHnzwQT755BNAdRO6cOECu3fvZvDgwXh7e/PAAw9Yt/np9XoiIyPtii8GBgZagyAWc+bM4fXXX+eVV15h6dKlNoWGAR566CFee+01vvvuOyZMmMDWrVuZOnWq3eMIIVxHgg3ZsGQeZK5roNPpOH/+PLt377Y7iXzssceIiIjgyJEjrF692vo4Hh4e6HQ6UlJS8PHxISIignr16tmdPFraLrZv35727ds7HVfx4sXz/LXnpqCgIFavXk2rVq1IT0/Ptqe9xdq1a3n00UdZvHgx1apVc8Eob8qN4AHgsK6Fs3oHZ86coXbt2sTFxdG0aVNefvllxo8ff8uxWiZbAQEBdOnShS1bttgdfC1iYmLo3LkzVatW5cSJE3bZIr6+vrRt25a2bdty/vx5RowYQffu3fnuu+/w8/O78zeygElNTbUpjGowGPD29rbu5/3+++9t2jcajUaqVKnCgAEDAJVxFB8fb5N5YAkuaDQaOnfujLe3t13mCajPMbvMA61WK0XMhBAii0GDBnHy5ElatWrF6tWrrQFaZ1JTUxkyZAiXLl1izZo1Lhpl/rB48WJmzZrFtm3bbLbOOhMVFcWWLVto3rw5JUqU4NFHH3VaeHHLli2sWrWKbt26UbVqVTQaDWfOnEGv11OsWDEqVKhAly5dCAoKYvv27bz44ovMnj3bWivsdsyfP593332XLVu22Lc8RdXgmDdvHj/99BPXrl3DaDTy0UcfsXnzZpYtW3bLxRghRN4oUsEGS2ZASkoK586dsztxbNiwIaVKleLUqVMsW7bMrur+wIEDiYqKIjEx0WHmgSUNuXr16tZVakeZB5UrV6Zy5coufe3uVqlSJbZs2UKXLl1YtmwZY8aMoXfv3jap2yaTiXXr1lkPht999x0tWrS46+d2VfAg69+9vLxuu+bBpUuX0Gg0zJo1i/r16zsJNKQDsYAesF3dnjhxIjt37mT+/PnW7TaZpaam0rNnT+69915mzJhxy3GVKlWKH3/8kUcffZSBAweyfPnyArUyYDabSU1NJSUlxXqS/s8//3Dp0iWbz9rHx4d+/foBqjDqmTNnbB6nRIkS1mCDRqMhMDCQEiVK2BVGBXj88cfx8fFxmglik+YrhBAiV7z++uuEhIRQr149HnnkEUaPHm23an/16lUWLFjAnDlzaNiwIb/++muRaneZmprKs88+y+rVqx0GGozG68TFxRIXh13NhD59+vDss89y/vx5mxoIlro/ly9f5tdff2Xu3Lk0atTImp3g7P2tVasWjRo1okOHDoSFhdG6detbjv/gwYP873//Y/PmzXaBhuPHj/P888/zxx9/8Mgjj/Dxxx8TeSNj9siRIwwYMIBq1arRsWNH3nrrLapUqXIH75wQ4m4V6GBDenq69UTx33//JTw8nLCwMJKSkvj999/tTiY7dOhAw4YNuX79OosXL7Y+jqVln6Vjgl6vp379+nYnj5YTi6pVq1pv60jQjR7OwlaFChXYv38/q1atYtasWTz11FM0btyY4OBgkpKSOHjwIP7+/owdO5Yvv/zSYdEms9l8x60a3RE8yAmj0cjcuXNZuXJllmuSgYnAAsAEeAAjgLcBFazRaDQ888wzDBkyhPHjxztMPdTpdEyfPv22X4OnpydffPEFzZs359tvv6Vv3745fGVm4A/gmxvj7wncj2pGdvvS0tJISEiw+XyTk5OtAamtW7dy8OBB6+duNBrx9fXl+eefB2D//v0cOnTIJrsg8+9Ys2bNaNCggV32icWDDz6Y7fiKYpExIYTIDyZMmEDv3r2ZN28e7dq1o2zZstYtalevXmXnzp306NGDZcuW0ahRoyJX/Hj58uVUrlyZgIAAdu/enSmgsJfY2EUkJl680S00FOiMRlORwMBA63y4QYMGADz66KPWbQ5eXl4YjUYqVqzIihUrss3Izapu3bosWrSIYcOGceLEiVsuZsycOZMnn3zSLlCwdetqevd+iOeeq8qiRWMICBgN3CxmXKFCBWbPns2nn35K06ZNue+++/juu+9k26EQLpSvgg2Wnuxms9muQKLBYKBcuXLUrVuX1NRUpk2bZpN5EB0dja+vr7VI4smTJ60nDJYq/hEREQCEhYXx2GOPWU8msmYeFCtWjE6dOrn2xRcRXl5e9OrVi169evHPP/9w+PBhrl+/bk0jj4qKwmAwsG/fPqftG50FD7y8vOyCQ86CB/mx28Ivv/xCZGQk9W32mZqBTsBOICXT5XOBv4GbaaBNmzZFp9Oxfv16m4O+2Wxm1qxZfPbZZ1kO6CZUAGMacAGoCLwI9AHU++Lj48PEiROZPXt2DoMNaUBXYBtgKf60BLO5Fqmpv5CUZEKv1+Ph4cGFCxc4efKk3Wc+ZMgQfH192bRpE1u2bLF7hsaNG+Pt7Y2npydBQUHWrKKsWxm6du3Kgw8+6DTzwNXbdYQQQuSeMmXKMHnyZF566SW2bNlCdHQ0GRkZhISE0KRJk0JVaDozs9lMSkqKww4OcXFxHD9+nG+++YYmTZqwZMkSQC2yBQXFotfPJCoqDb0e9HoIDo5Gr/+GoKBVeHi0sz5HUFAQEydOZOrUqTbzpp9//pmSJUs6CDT8C0wGfrzx757Ay0BZ6y06depEaGgoa9asoXPnzk5fX1xcHMuXL+fIkSM2lx86tIiePYewaJE3nTrtBg4CH6DmNf2tt+vVqxdPPfUUs2fPplatWvTo0YP169cX6ZanQrhSngYbMmceJCUl4e3tbd1Pt3r1aq5fv25zUlG9enV69uyJRqNh+fLl1pNKS+aBpQWPt7c3DRo0sDlxvHDhAo0aNQJUatfTTz/tdFxeXl6UKVMmL196kWUpnHe7WQdJSUnWivqnTp1i586d1sfy8vKyOWmMiIhwWO/A8veC3uJozZo11pT+m7YAe7ANNIDKdtgK7ALU771Go+Ghhx7i119/tTnwr1+/Hh8fH5o3b57lMYYA3wKWfuT7gKHAX8Cb1lv16NGDCRMmcOTIEapXr+50/BkZGcTFxWX5jGdSu/ZmgoNTOH4c1q+HpKREDIbdGI2dgU6MGTOGiIgIzp07x7p162wyD4KCgqy/HzVq1HBY98ISPGjSpAlNmjRxOr6ilDIrhBBFlbe3N23btnX3MHKN2WwmKSnJYTDB8vesBce9vLysGQhlypTh4sWLPP/884SHh1vbXGu17VELAllZsin3WC9p164dZ86c4cqVKzZ1w2bNmsWYMWOy3P8foDGQABhvXPY5ar6xC7i5DWLMmDHMmjUr22DD119/TYcOHShRokSm9ySZAQOG8v77Zjp1srx2y5/DgJZYMhx8fHwYOnQoCxYs4N133+W9995jwIAB7N+//64WnAwGA7GxscTExJCeni7FtIVwIk+DDVOnTrX5d4UKFRg8eDCg9qinpqbaZB5kLt4ydOhQfHx8HNY80Gg0dOzY0eaxzWazpDHnAUvwIC4uzmGdi+yCB1llzjwICAigePHit2zVWJTExMRw7733Zrn0Z25mBGSVDKzGEmwAiIiI4J9//rG51YoVKxg8eHCWg+pubAMNFknAdMzmUaSmRpCUlISfnx8DBgxg8eLFdOzY0SbLxLI9qWLFipw6dYqvvvoqy+N9TcmSKQQHg7c3BAer5iP+/hnodLvw979KQMBB4AXq1atHvXr1nB6wS5YseVtFrYQQQoiCwmQykZCQ4DCIYPkz67zK19cXvV5PSEgIFSpUsOvkoNPprMf8P/74g9DQUAdZk9l1+tqPmmOo4tCWFusxMTHWYENCQgJbtmzh+++/z3Lfp4G4G89hkXHjsueA76yXPvTQQ4wdO5akpCRrweSsTp48Sd26dW0u27z5HdLTYeBAR/dIR82LIoEBwAjq1avH8uXLAVVU9K233mLLli3WblG3y2Aw8PXXXzNr1iwOHjyIXq+31ofq3r07Y8eOpWXLlvkqa1YId8vTYEO7du1sTiIz1zEYOnRotveVk4q8kdPMg+joaNu2UthmHkjw4O5ZthDZ8kLVNjA5ugdZC0VatiJlFh0dTdu2bTGZTJmyiT4hKSmZpCQoV079xMXB0qWQlJSBwTAao1EFMbp27UqZMmU4cOCANUvC8tkGBwfj6am+RiIjI+nZs2emz9wPf//XuXE15curn0yjBU4Cp4Ff8fKagapFIYQQQhQOlqw/Z1kJ8fHxmEy2x3h/f3/0ej0lSpSgatWqNsEEvV5vU1z7VhzPLUDNIYwOLs98/U1Z5xdXr14lLCwsS9agEfgV20CD9RGAVTeuUyfjOp2OYsWKERMT4zTYkJSURKlSpWwu++ST7xkzBhyf02fw//buO8ypamvg8O9kes/M0MvQmyAKIqCCoggCSlFRuoV7ASmi2C8qdgWVa/0GFHu5qFjBLjZEvYiIKMgVxEKTMuj0Psn3x5pkkkwSpqRMMut9nnmUnOScnTOZnH3W3nst+LPyZzvwCImJ91BQIAM3hmEwe/ZsMjMzaxxssFqtPProo9x2220MHDiQ2267jbPOOouIiAi2b99Oq1ateO6555g1axaRkZE8/fTT9OvXr0b7Virc+TXYUNuIoaq9+ixbcOUteHDw4EF69eqlwQM/atKkCfv27XN59DxkDaLzDASrFYqLIyksPI2IiGzMZjNWq5WvvvqK/Px8XnvtNfvv/ODBg0RERFBaWsojjzxSuYet2DoDp58uwYaqmQdWEhJakZBwFvHx8bRt25YNGzYQHR3NTTfdZA8uuEpKSuK4445zebQNsPco79xS+f7mA+OApl6frZRSSjUUJSUlbnMl2P6bl5fn9HzDMEhOTiYlJYWMjAynIEJKSoo9+aKvpKSkkJ2dTVFRkUMZawMYSdXNv6tB2BJQgwRMDh486JT3ory83F6yvUoF7gdHbCxUJboWkZGRHvulIPkiHM9hYWEh77zzM48/Hg/kezkWyOyM/eTmLic5uWoZxsUXX8yNN97ock7cs1qtXHPNNXzwwQd89dVXbqtZpKSkcPnllzNv3jxeeeUVRo4cycqVK2uVNFOpcNWgEkSqquCBp0CBr4IHtZ15sH37di0X5Gdjx45l9uzZTJo0iaKiIvvvOTn5VI4/fh1QyNNPw19/QWFhFBUV3YEP6d37AOeddx5Wq5XVq1dz4YUXsnfvXvvMg7S0NLKysoiJiXGYedCB+Pi5JCQU2GcexMXBpEkgHYzLgBPsbTt8+DBpaWkeAw2eXV/547pcwx0T8Cowu5bHUEoppXzParVSVFTkcVaC7SbeUUREhD2A0Llz52qzEpKSktzcpPuPLYfFyy+/zCWXXOKw5T6kUlQ+VQECA0gAHnLax+rVq+nduzdNmjSxP5aWlsaRI0dcZjxEA72RHFDu9MUx0GCxWDhy5Aipqake23/MMcc4LdHMysrCbE4nJcWKLP10FyxxVMaXX66nR4+b7I+YzWaSk5M5cuSI0xJud+69914+/vhj1q9fby+l7Yktd1arVq04//zz+fDDD6stAVGqsdFgg58dLXjgLpgQiOCB8i9bdmhb9QxbYqPNmzdz4MABp99/amoqxx13HEOGDCE7O5tbbrmF9g7rDbp0mczxx48E7qdp0/2kp6cRHz+RhIQJJCQk2S/+H330ERkZGWRmZjqtFywvL+eFF15g5syZDjMPOgD3AztwThAVC5yMY6DBarXy2muv8eCDD9bhTMxFZlE8W/nvMjxP2ywBsutwDKWUUqr2rFYr+fn5XoMJjpXPQG7ebcGD1q1bO81KMJvNJCYmNrg1+3PmzOH22293CTZ0RfI33Qi8jdy0D0cSRPd0er27RJBpaWlkZGRUq4AlfYvRyKwCR3HAvU6PfPjhh3Tp0sWeAN6d888/nwULFvDrr7/SsWNHSkpKKpduvA2cgQRLipH+RXX5+fDiixZ++MF5+XZsbGy1xJqu9u/fz+LFi9m6detRAw2OBg8ezOLFi7niiiv4/PPPa/w6pcKRBhtqyTF4UJMlCzUJHjgmTDxaqUYVXLm5uWRnZzv9nsvLy+2Zr9977z22bdtGYWGhfQ2m2WzmyiuvBGDbtm3s2bPH/js1m832kqyGYTBz5kw+//xz7rrrLpKSkoiPj3eYTTCfc85x366KigqWLFnCnDlzqnVyLrjgAq6++mp27txJly5dKh+NAL4AZiGlqSKRIMA04EGn169fv56ysrI6Zvc2gOVIZuu3kUzUr+N+pkMc4LmaRO3lIqW2nqF793ygO1KK63wfHkMppVRDZbFY7NdtTwGFigrnAHhcXJx9VqAt+aJjMCEuLq7BBROOZuTIkcybN4+PP/6YoUOHOmzpArzi9bXffPMNW7du5bzzznN63DAMezUJ52DDUGSW4lzgYOVjLYBM4HT7sywWCw899BATJ060z25wN+MjLi6OSy65hOXLl3PvvfdiNpv5+++/sVq7YRi7kUTZPyMzNQ5Ve/2LL8KQIQm0adPe/pjVauXvv/8+agBhxYoVTJw4sVrOCJkRcgPwLd26RQEXAvcAVfnmpk2bxk033cS2bdvo2bMnSjVWjT7YUNvgQUFBQbULk010dLQ9OJCUlOQ2eOD4by2TEzy2mQe2322bNm0wmUz88ssv7Ny5s1rAaMGCBZhMJtatW8e3337rtK/4+HhOP/10DMMgPT2drl27Ov2ek5KS7M+dPHlytQSOgL1+9MKFC/nkk0944IEHuO+++2rUobFarVxxxRUA9movjmJjY7n00kt56KGHePTRRx22mIGXkZvyQ0jmZucETVarlQcffNBtEKN2OiI5GUqATyv/6/h3FAW0w7EjUj9FwEDgV6CkMonUT8BFwL7KtiillApltuSL7io4ZGdnk5ubWy05YmJiImazmZYtW9KjR49qORNqk3wxVERERPD4448zZcoUPvnkE4455pgave7XX3/l3HPPZdmyZW7Py5QpU1i4cCG7du2iU6dODltGIdffPZX/bostKeTevXtZsWIFy5YtIysrix9++IHFixdjGAYXX3wxs2fPdhgYEXPmzKF///6ce+65DBgwgJSUFDZu3Ej//v2RWRSjK48xHcfBjF274LbbDFatut5pfxs2bCA9Pd3r8o2ysjIef/xx3n//fZctbyPBBZm5YTJVAP9BEmP+CMgAUlRUFDNnzmTZsmUufS+lGpewCzZYrVZKSkpqlTCxtsEDT7MPNHgQfIWFhWRlZVX7HQ8ePJjExEQ2b97Mxx9/7DTzAOCqq64iOTmZ/fv38/3339t/t7ZpkhUVFZhMJvr160ePHj2cZpw45jGQC59n7gINjmJjY3nrrbcYNmwYM2fOZOnSpU5VXFzl5ORw+eWX8/PPP/PBBx94nP2yYMEC+vfvzymnnMIkSczgILnyp7oHHniAn3/+mWeeecZru2suBvgSOBcZiYhClnGciIyE+Gq06AXgD6rqbtsUAv8C/oFrYEUppVTDUlJS4nVWQn6+c4JAW/JFs9lMu3btqs1KcKyg1NgMHz6cpUuXcvrpp/P8888zbNgwr4MI69atY+LEidx6662ce+65bp+TlJTEokWLGDt2rJucBgaQYf9XYWEhc+bMYfXq1Zx33nkkJSVxyy23MHfuXAB+++03Hn/8cQYNGsSAAQN4+umn7QkpO3TowHPPPce4ceN488037dUknPtcE4DDyDXexM6dFYwYUcjNN1/IKafc7NTuzMxMZs+e7bVP9t1339GkSROOPfZYh0etyIxQ1yUi5cgy0PtxXCoydepUTj/9dA02qEatwX/j1jR48Msvv5CSknLU4IHtBjE5OZmWLVt6zXmgwYPgcZ15kJ6eTkJCAocPH+bbb7+t9jmYMGECbdu25ZdffuH111932ldMTAx9+vQhMTGRlJQUunbtWm22iS0b8eDBgzn11FM9tsuWe8Gf0tPTWbduHbNnz6Z9+/ZMnDiR2bNn06tXLwzDwGq18sMPP5CZmckrr7zCueeey6effkp8fLzXdr/zzjsMHz6cvLw8ZsyY4bWTYbVaueuuu3jiiSdYt26d0+yM+msHfIeUpPoDmcbZyesrau8FPCeljETqi4/08TGVUkrVlC354qFDMvXd3ewEb8kXu3bt6hRECEbyxVAzZcoUmjVrxpw5c0hMTGTOnDlMnjzZXnayqKiIl19+mczMTA4dOsSyZcsYO3as131eccUV7N69m9NOO4133nnHbcLFnJwczjrrLLp06cL69eu58MILGT9+vD3QABJQuOeee7j11ltZtGgRJ510Ep988ol9fyNHjuTpp59mzJgxTJw4kddff5377rvPpSz7PA4cOJsnn7yThx9+gzvvvJcZM5xzTRw6dIg1a9bwwAMPeH1fR44coVWrVi6P/gzkeHhFKfASjsGGli1bcuTIEa/HUeFtz549PPbYY7z11ltkZWVhsVhIS0tj+PDhzJkzh27dugW7iX4X8GCDr2ceOCZM7NSpkwYPGriysjL2799f7Xfds2dP2rVrx4EDB3jhhReqzTwYP348vXr1oqCggC1btth/p6mpqbRp08Y+va9Dhw5MmzbNvt115kHHjh3p2LGjx/Y1lHWYiYmJPP/88+zbt48VK1YwYsQIsrKySEpKIi8vj+bNmzNz5ky2b99e4wDIsccey7p16xg3bhzLly9nzpw5TJo0yam2dXZ2Ns8++yzLli0jNTWVr7/+mpYtW/rpXfao/PEHb7XDa7JdKaVUfVitVvLy8rwmXywrK+Pw4cP2G8bo6Gh78KBt27bVZiU0xOSLoWbYsGH8/PPPrF271p74MT4+HsMwKCgoYNiwYSxatIiRI0fWKHBjGAZLly7l/vvvp1evXpx33nnMnTuXE06QRNNlZWWMHz+eNm3aEBERwcknn8yiRYtYsGCB2/3FxMSwZMkSUlNTGTVqFOvXr7fP8Bw1ahTffPMNmZmZlJeX0717d+bPn09aWhr5+fl8//33fPjhh1x44YV89NFn9O7d22nfJSUlXHjhhcyaNcupjKc7FovFzWfNgvcZmM5lPw3DcOrLqsbjl19+4dprr+Xzzz9n6tSpPPXUU7Ru3RrDMDh06BCvvPIKp512Gr169WLJkiX2v5dQVFpa6vFeHcCwui5m85FNmzZx+PDhOgcPvOU6cBc82L59Oz16+OvGRbmeX9uIhO13Gh8fT9OmTSktLWXt2rXVfu8nn3wyJ598MtnZ2dWqGsTGxjJ8+HD69u1Lbm4un3/+ebXfdYsWLZxuisNNTT6/RUVF5OXlkZycXJmJuW4sFgsffvghmZmZrF+/ns6dO5OYmEhubi67du1ixIgRzJkzh0GDBjlcaG1fE6HSyXsESd7kbnZDLHAA8Jz9WtWOfv/6ly/P76ZNm0K6UwOBfw/6+XavoqLCY/LFnJwcj8kXXZc2HDlyhL59+2I2m4mNjdVggo8d7fNrsVjIycnBarWSkpJSr5khhw4d4qmnnmL58uVER0fTokULDh48yO7du2nRogWzZ8/m0ksvdZmNYMVd38JqtTJ58mR69uzJTTfdVG17fn4+Z5xxBllZWZx55pmkpqbSsWNHJk6c6La6RW5uLuPHjyclJYWXXnrpqO/zq6++4vLLL2fTpk0Oj1Yg+a0Ou3lFFLLE4hH7I7///jsnn3wy+/fv93os5Vkofv9+8803jBkzhquvvprZs2eTmJjo9nklJSW8+OKLXH/99TzzzDOcffbZAW7p0c+vbaZ5aWmp/e/q448/Jisry/5dX1hYSL9+/WjZsqXba7NfZzZ89913umwhBO3Zs4e8vLxqAYMePXpgtVr597//TUFBgVO0tn///owaNYqIiAh++OEH++80LS2Ntm3b2i8sSUlJ9pkHtuc4fuEnJyczevTogL/nUBAXF2df7lEfJpOJESNGMGLECP788092795tD2J07NjRqY42bAOuBT5COgRnIBmfj3Oz54bkEmApkgzSsRpMPFIZQwMNSinlTVlZmddZCXl5edWSLyYlJZGSkkKrVq3o0aNHtcCCu7xC27dv9+MMOnU0JpPJa6LE2mjWrBk33HAD1157LT/99BN//fUXc+bMYcmSJcydO9ehv2dBqlMsBvYDacDlSL4F+YwYhsH111/PmDFjuOGGG6rl2khMTLQHBN5++20uu+wyxo0bVy3QcODAAZ544gmWL1/OmDFjePjhh2sUUDnhhBPYu3cvO3bsoGvXrpWPRgAPADNxHswwAYmAcyLKlStXMmrUqKMeS4WPn3/+mTFjxvD4448zZswYr8+NiYlh+vTp9OzZk9GjR/PGG29wyimnBKilwlb+t6ioyH6vtm7dOvbu3Wv/vi8pKaF9+/b20rm7du2irKyMlJQUe/nfNm3aeFwy5Ndgw8KFC/25e+WF68wDwzDIyJBEPZ9//jmHDx92CiS0adOGiRMnArBq1Spyc3Pt+4qNjbUn/TEMg169ehEZGekUMEhLSwNkPeUNN9zgsV0REREuGYtVMLVs2dJLJ28rcBJQQNXMho+AU5CcB33938A6SwK+Aa4EXsNqtWAYTYFbkE6CUko1bsXFxV6DCQUFBU7PN5lM9uSLtpKQjkscGnPyReUsIiKCY489ls2bN5Ofn+8SaACYjXNupSPAEiSB9AfYZjocf/zxtGnThnfeecdt7ojIyEiWLVvGjBkzWLZsGd27d2fgwIH2fs2ff/7Jf//7Xy644ALWrFlDnz59avwebDeCy5Ytc8nvMAVJMH0t8DtWKxjGMOBhoCpfRUVFBcuXL+eNN96o8TFV6Lv00ku59dZbjxpocDRgwACeffZZJk2axK+//urT71Fb+d+CggJ7Cdevv/6anTt3kpOTwy+//EJqaippaWnMny+V2g4cOEBubq5T+V/H2UgzZ7rvRwcl2KB8x2q12qcWHjhwgL/++stpaUpkZCTDhw8HJJK6c+dOp5kHLVq04LLLLgNg9+7dZGdnk5CQYJ954HjDeeGFF9qDCbaZB7bSjABnnXVWIN6yCrprcQ402BQAVyF1phuyZkg5qjJ27Piebt36ETrLQFRwfI4k99qBJC29BjjT6yuUaoisViuFhYVey0IWFxc7vSYyMtIePOjevbvb5ItHq6iklKM1a9YwceJEl0DDLuA5oNjl2UXAV8hgxmn2R6dMmcLq1au9Jqrs27cvK1as4L777uOzzz6z3/Skp6ezcuVKlyoZNTdr1ixOOOEEbrzxRpdZn+Mqf/L4+edf6d69+mzP1157jZYtW9K3b90GZiwWC5999hnff/89OTk5xMfHk5GRwdixY70mBFfB891337Fv3z5mzJjhsiUXeAz53JcD5wFXYCuTCpIAtW3btrz99tuMGzeuxse0lf/NycmhQ4cOGIbBd999x5YtW8jJySE3NxeLxUJkZCQ33ngjhmGQl5dHSUkJLVq0IC4ujt69e9sHjUHuA31Jgw1BYpt54BgwsNU93rJlCzt27HBaxmC1WrnuuusAWL9+PVu3brXvKzY21ini1LlzZ5o3b+60TMWxmsC0adO8ts1dJmHV2FipWjrhznok87L7UpsNSxQWSyIaaFDe3Y/MfLGNtP0CfIHk/rjZ04uUCgpb8kVvZSHLysqcXhMTE2MPHmRkZNiDCLbHEhISNF+C8qmsrCw3Sbnf9vKKQmAVjsGG2lR0MJvNtbpRO5r27dszc+ZMxo4dy0cffeTmJj8Jq7V6P2jz5s3MmzevTrMasrOzeeaZZ1i2bBkxMTGcfvrpmM1msrKy+Pzzz7n88suZNm0as2fPdljeoRqCZcuWcdlll7kE1/5Cyrv/SVXJ1KXA48BGoL39mXPmzCEzM9PpM1xSUmL/bm/fvj3R0dFs27aNr7/+muzsbKfyv9deey0JCQn27/6MjAyngLFt4No2OA2ByYmhwQYfsa1bNAyD7Oxse8UFx2DC6NGjiY2NZd26dXz66afV1jouXLiQ6Oho/vrrLw4cOOA08yAhIcH+IRkyZAiDBg1ym/MA4MQTTwzY+1aNlRXPgQilQs1eJKDgOtJWCNyNTJv1XMVGKV9zl3zR8f9zc3OrJV+Mj4+3T3ft0qVLtWUOmnxRBYP7ig6e+g9W3FV08FMu+xq56667+Mc//sEZZ5zBm2++edQKYGvXrmXy5MksX7681uvvt2zZwjnnnMOgQYN48sknOeWUU6qdv99++43HH3+cU045hSVLljB9+vRavyfle1arlZUrV7Jr1y6XLbcifYxSh8dKgDKs1tkUFb1m/14fMWIE8+bN45tvvmHz5s1kZ2c7lf+dOXMmrVq1wjAMoqOjq5X/tVXmGzBgAAMGDPDvG64FDTZ44DjzwBYwaNeuHQkJCezevZsNGzZUS6A4e/ZsmjVrxo4dO3j33Xft+4qNjSUhIYGSkhJiY2Np06YNgwcPrpYg07ZG5/TTT+f000/32DbnqVxK+YMBnAp86mF7fyAmcM1Ryq9W4bnzWwGsBG4MXHNU2CsrK/M6K8E1+aJhGCQmJtoTcbkucUhJSXGbfFGpYEpLS+PAgQMuj44EPOV0SwTOdXrk4MGDTlO8A81kMvHUU09x22230aNHD0aPHs2cOXMYMGCAPRBQUlLCq6++SmZmJrt37+all17ijDPOqNVxtmzZwrBhw3j00Ue9TmPv0KED99xzD5dccgkjR46kuLiYOXPm1Os9qvrLy8vDMAyaN29uf0ySLz5HdnYp2dnQqhWkp8P+/fDGGxZycj6ktPQupJIJTJw4kVatWpGbm0tiYqLTd71j3oRjjjnGPhs+FNQp2FBWVsbChQvZt28fpaWlzJ49m6FDh/q6bT5ltVqxWCxERERQVFTEb7/95jTroKCggIEDB9K2bVt27drFCy+8UC2SOnXqVDp37kxJSYnbmQe2coTHHHMMGRkZHmcedOzY0c20MqUamvuBwVQvHxmPZGNWKlzkICMN7pQh0yBVIIRi/8Kd4uJij7kSsrOzKSx0/l41mUz2JIsdO3Z0m3yxPiUJlQqGUaNGMXHiRO666y6HfB/dkTXrb+Lcv4gFegPOf+8rV65k3rx5AWitZ4ZhcOutt3L55ZfzzDPPMHXqVPLz80lPT6egoIC///6bAQMGcO2113LOOefUOsFfdnY255xzzlEDDY66devG2rVrGTRoED169PA6SKn8o6Kiwr6czTGxY3Z2Ns8991xl+d88+/NHjpRgQ1yc/LdTpwhSUgZhNrfHbDaTnp5OTEwMqampnHlm+OSLqlOwYfXq1ZjNZu677z6ys7MZN25cwDsDFovFqdrCzp07SUtLo3nz5uTn5/Pee+85zTooLCzk7LPPpl+/fuTk5PDKK6/Y9xUXF0dCQoJ9qkpaWhqDBw+uVp7TVhqoS5cudOnSxWPbEhMTPdZUVSp09EUS5l0J/LfysX5IoGFgkNrUUOUA7yPT5IYAbYPaGlVbJyMjavlutiUhs3xq6mvgQeBX4FhgQeV/VU00hP7F0diSL3oLJpSUOAevoqKi7MGDli1bOgUTzGYziYmJmnxRhZ0TTzyRtLQ0PvjgA0aOHOmw5TngHuDfQB4SaJgB3IWUkRRbt25l586dPs3DUB/p6elcffXVLFiwgP379/P333+zZ88eBgwYQHp6ep33+8wzzzBo0CAPgYbvgC1AC2AYjrduHTt25N5772Xx4sUabPADW/JF2/e62WymU6dOlJWV8eijj5Kbm2sfmLYtfauoqCA+Pt6h/G93UlJ+wmwGW57S1FSQAoBNgRE45hT766+/PCQ0zQeeBl5C/kYmAxchlVEatjoFG0aMGGGvSGC1Wn0SbbdYLFRUVBAVFYXVauWnn36qtkyhY8eOnHDCCZSUlLB48WKnmQeHDx8mMTGR5s2bYzKZOHjwIPHx8TRp0sQeNLBVXGjSpAmzZ88mISGBuLi4au1PTU2t9fQnpcJTPyQZpG0te2wQ29JQPYQkEYxCpuKXA5OAFUhNbtXwnQlkADuRmQw2kUi26LNruJ97gDuRJFBWYDPwMpIIaoqvGhvW/NG/qC2LxUJeXp69k7llyxZ++eUXe0DBXfLF2NhYe/CgXbt2TokXzWYz8fHxmi9BNTqGYTBnzhyWLl3KWWed5RBQiwBuQpanFQBxuLteLl26lBkzZhAVFRWwNteEyWSiTZs2tGnThsjIyHoFGiwWC5mZmTz11FMuWw4Bo4D/Vf47AulnvAkMsj9r/PjxXHXVVezcudPrQKiqrqSkxClQHBcXx7HHyuDAI488Ui0xaa9evejUqRNRUVF07tyZhIQEp+/69957jw8++IBRo0Yxfvz4ylc9DIzB/SzhO3AMNPz444+UlpaSkZHh8tzDyPLlQw77+Q4Z/NsApNbzTPhXnYINCQkSRcnPz2f+/PlceeWVbp/nukwhJiaGzp07A/DWW2/x999/O808OPbYYznvvPMwDIM333zTfjG3zTywJWWJjo7m1FNPtc86iI+PZ9++ffTr1w+QJEneplxFRkY6ralRSh2NBhncextZe1qMc3LBl4GWyCiNavhMSCnXSUid9xhkWcWJwCvU7FK5A+k4FDk8VoF0DGYgAQuzrxoctmrav6iP8vLyoyZfdCwdffjwYdq3b09KSgrNmjWrlpTLbDbbl1Eq1ViVlpaSlZVFYWEhKSkppKenYzKZmDJlCk899RQ33HADS5YscQm6Gcissur+7//+j6+++oqlS5cGpP3B8tlnnxEbG+smmeQo4AecA+AgOS92IH0MCXROnz6dxx57jPvvv9/v7Q0Vttx7jt/vhmEwcKDMzH3yySfZs2eP02vat29vDzb06tWLiIgIp+/65ORk+3NHjx5d7Zhz584lMzOTUaNGOTw6FHgKmIP8Lg0kCeodgHNyz2XLljFz5kw3wbVrgH04fxYKgT+QgF1mjc5JsBjWOqZ4/fPPP5k7dy6TJ092iN5U2bRpU7UoXevWrTn3XEn8snr1asrKyoiPjycuLo7Y2FiaNWtmz2Xw119/ERMTQ2xsbI1GNoqLi/Vi70d6fv1Lz69/+ev8tm9/IXFxW91uq6iIZ8eOrwiN8qD1E06f38jI/URH76OsrBVlZa1r/LqmTR8kPf0pDKO82raKijgOHlxITs75dWqTL89vYWEhJ5xwgk/25S816V94qzNfWlpKfn4+ubm55OXl2X9sjxUWFlZLvmgrEW37SU5OJjExkeTkZCIjI53KRyvfCqfvj4bI3+f3p59+4qWXXuK9994jNjaWuLg48vPziY2N5YILLmD8+PFERUVx6aWX0qdPH66//np71nx3ysvLeeKJJ3j11Vd5+umnadu2YS9LrO/5ffbZZ9m3bx8LF1YlzYyN3Ua7dhdhMhVVe77FEs2RI/8kK6tqUPXLL7/kqaee4sknn6xzOxoqT+fXarVSUFBg/37Pzc2lrKyMk046CYB33nmH3377zek1aWlpTJ48GZBZBGVlZU7f+/WdgVZUVMTQoUNZuXIl7dq1c9laTmzsTxhGOcXFPbFanf8GcnNzGT58OG+99ZbLgHgF3bqdgMlUijsWSyw///xdndsciP5FnWY2ZGVlMX36dBYtWmT/pbpz0UUXOVVbSExMtI9a+LqmZyDqhDZmen79S8+vf/nv/P7ucUtEhEGPHmbA9YITfsLr81vX91GBLKGpLiKijFatomnVqm779uX53bRpk0/24y817V+YzWaPlRxcky/aRqfatm1bLVeCbbTK26BGeH2+Gx49v/7lr/O7e/dupkyZwu7du5k1axaPPvqo003Sli1bWLZsGWPHjmXChAmsX7+emTNnMnz4cKZPn86sWbPo0KGD/fkHDx7kiSee4LHHHqNjx458++239uXPDVl9z29sbCwdOnRw2cd/ccxd4chkKqVp019p2rTq+bm5uZSXl4fV35EtB8K3335L06ZN7d/vY8aMwTAM1qxZU+16lpiYSPfu3e3lUnNzc6vNQLMFE/x1rm6++WZuuOEG1q1b5zQTQrjP31RWVsbZZ5/NJZdcwpAhQ1y2FiH9C/dMpmJ69OiGp8/L0QSif1GnYMPy5cvJzc0lMzOTzEyZurFixYpqkZGGVONTKaV8Lw33SQVBprs17HV0ypdOQZbPuPs8xCL5T9TR1LR/8dhjj9n/Pyoqyt6hbN26dbVKDklJSZovQSkf2r59O8OGDWPBggVceeWVboN1xx13HMuXL2fJkiVceumlTJgwgbfffps9e/awfPlyTjzxROLi4khOTiY/P5+cnBwuvPBC3nrrLfr06ROEdxUc8fHxHD582OXRFnjO+RSBaxLqwsJCr7O9GqLy8nJ7kNgxaDxixAgSEhJYv349n376KYcPH6Zp06b28r9FRUXEx8fTq1cvWrZs6bH8b7BKQy5YsIBdu3Zx5plnsmbNmqMu28/Pz2fChAnExcV5WDIUC7QC9rjZBtCFugYaAqVOwYabbrqJm266yddtUUqpEDMPuAXndfogX60jANeotgpfE5FEoQVIckibSCT5pGYKr4ma9i8uvPBCewdTky8qFTgHDhxg1KhR3HXXXVx88cVHfX5KSgqrVq3ioosuYsqUKbz66qv8+9//5p577uHgwYPk5ubaE7zHxcUF4B00LBkZGXz22Wcujw5DkkG6EwNc5vTItm3b3CQVDK6ysjKOHDlSbQbakCFDaNasGT/++CNvvfWW/fm28r+FhYUkJCTQrVs3kpOTOXz4MP369bMvabPp0KGD08yYhsIwDB599FFuueUWevXqxaWXXspll11mTxNg8+eff9pn8owcOZLMzEwPJVMN4Dakv+kuyeSdfnkfvlSnYINSKtRVIJFQ7aDXzxXAB0g2YNuIdgLQBKlAoBqPOKRyy2hgLzL6VI7UjX8T/VvzrWCNWinV2N19992MGzfOQ6DBivQvnG8vIiIieOqpp+jfv7+9DGZMTEyDu0EOhjFjxjBv3jx+//132rdvX/loJHLdGIlcR4qRa0oMEtTua3+91Wrlscce4+GHHw5ksykvL+fgwYPVyv8OHDiQDh06sGfPHp577jn78yMjIzGbzRQVyeBMhw4dOO+885xmoDmW/23RogUtWrRg+/btpKWlBfS91ZdhGNx+++1cdNFFPPbYYwwYMIDu3bvTunVrTCYThw4dYtOmTUyYMIF33nmH44477ih7vBQ4CNxOVRCqHElC7q5casOiwQalGpX3gOuBrcgX1nnA/UDNE+EpR9FIsOE94FlkhsN5yCh3aE1pVL7QBdiOlLzcA3Sl7jkglFKqYcnPz+eFF15gy5YtrluQUpZPIKOvbYCbgX9iC7TGxMRw5ZVXkpmZyciRIwPY6oYtPj7eflN6zz33OGwZhJRjfgzJ4dAWmdHQ1+n169evp7y83M1a//qpqKhg37591fLiHHfccfTu3Zvs7GxWrFhhf35MTAxms5mSkhIAWrZsyQUXXGDPj+M6A832eDjr3Lkz9913H7fffjtffPEFR44cwWKxkJqayimnnEJKSkot9nYDMBf4AvmbOhUZ3Gr4NNigVKPxMlJmxzYNqxRYBXwC/Ag0C1K7Ql0EcE7lj1IG0hnse7QnKqVUSPnPf/7Daaed5lIhogwYjARaSyof2wMsAH4D7rY/c8KECVx77bUuo/hq9uzZnHzyyVxyySV069bNYUsLZKmme8XFxdxwww1cfvnltV5KZrVa+e2336otc+jcuTODBg2ioqLCqapgfHw8ZrPZXhbYbDYzadIkj+V/4+Li6NmzZ63aFK7i4uIYPny4D/aUhJREDS0abFCqUagALqf6eq8KIBv4N7A4wG1SSimlVKh4++23mTZtmsujbwC/UBVosCkAHgCuxDaYER8fz9ixY3n33XeZM2eOn1sbOrp06cKSJUsYOXIka9eurba+353i4mImTZpEmzZtuOyyy9w+57fffuPIkSNOwYSWLVsyatQoDMNg1apVFBUVYRgGSUlJpKSk2PMGREdHc9FFF9kfd0y+CLIswjkwopR7GmxQqlHYRvUkhjalwCtosEEppZRSnhw5csRNOcqVeK7KFAl8BEyxP9KiRQuOHDninwaGsOnTp1NcXMygQYO49957GT9+fLXZAiAzEtavX88NN9xAamoqCxYs4KOPPrIHFBITE5k8eTIAH3zwAQcOHCAiIoLk5GTMZjOJiYn2fU2dOpW4uDhSUlLcVhSpSdBDqaPRYINSjYIV7wnqrF62KaWUUirU/f3336xatYo9e/ZQWFhISkoKxx9/PKNGjfKQCd+Z1equr3C0/oPzdq0c49mcOXPo3r07S5Ys4aqrrmL8+PF06dIFkEDPzp07+eKLL0hOTmb+/PkkJyfzySefEBUVZV/O0KxZ1ZLY888/n5iYGBITE52SL9q0bq35upT/abBBqUahF5LFOM/NtmjggsA2RymllFIBsXnzZh599FFee+01RowYwTHHHEOLFi3Iyclh8eLFzJ07l1mzZjFz5kynm1VX6enp/Pnnny6PTgQ+xv3shnKkjGOVAwcO0KdPn3q+o9BlsVgwDAPDMNi7dy+//vqr0zKHvLw83nvvPXbt2sX111/P+++/T1lZGXFxcTRv3pwrrriCa6+9FpPJRE5ODpGRkR7L/zZt2jQI71ApZxpsUKpRiAAeAmbgnLchAkgBrg5Go1SNHAKygPZohYtw9wNwI9Jxj0Qqm9we1BYppUKX1Wrl2Wef5dlnn2X+/Pns2LHDbTBh8+bNZGZmcvzxx/Pmm2/Sv39/t/s7++yzWblyJRdc4DhAcR5wD/Azznkb4pHs+c3tjxQVFfHWW2+xcOHC+r+5Bqq8vByTyYTJZOLAgQNs377dKZiQm5vLlVdeSXJyMr/99huffPIJCQkJmM1mWrRoQbdu3SgvL6dLly688MILmEwmt8spgFpWM3BVgeTaiEH6Fyp8WYBMpPrcAaSyyfXAPwJydA02KNVoTAaSkS+Y/yGBhrFIcsjmXl6ngmMPcDHwFTL7pAKYCdxLVZ1lVT/ZwPPARqAdcuFtH6S2bAROR4KBtmnHLwBriIx8BS2hqZSqrcWLF/Pqq6+yYcMGMjIyPD6vT58+rFixgjVr1nD22Wfz3nvv0a9fv2rPmzJlCgsXLmTv3r20adOm8tFoYD3wL+BpoBiponATUqqxyssvv0z//v1DOhdAaWkphmEQFRVFVlYWX331Fdu2bbMHFPLy8vjHP/5B27ZtOXjwIOvWrSM5OZmUlBQyMjJISUmxL2kYMGAAAwcOJCrK/TU9Pt5fAwxPIL+vIuRGtG3lY4P9dLzGaCPSv8gDRgDnIn8rwTAFWE3VYOMvSOLW75Dk8f6lwQalGhVbicZS5M+/+ho+1RDkAf2Bw0iQwTZa9Dgyy+H5ILUrnGwEzkSm+RYinYClVM0ACrQ5SPZ2RxVADk2bPgIMDXyTlFIh65133mH58uU899xzXgMNjkaPHs0TTzzB2LFj2bp1K6mpqU7bk5KSmDx5Mg888ABLly513AI8CjyC9C9iqu27tLSUhx56iDvvvLOub8nvrFYrRUVF9tkEubm5fPXVV/ZZCdnZ2RQVFTF+/Hh69epFfn4+W7ZsoXPnzpjNZvt/bUkYe/bsSa9evdwmXwSqVXgIjKeBK3Ce5boDuSH+CjguCG0KJ1Zk4OJlJPBmAV4FFiLnN9CDe9/iHGiwKQCeJipqNP4ezNBgg1IN1vfIaEESMAZI9frs2glWdFXVzHNALnKz6agQWAXcBdSs86jcKUdqVec6PFZa+d8rgNOArgFsz9/AFg/bKkhK+iiAbVFKhYMlS5awdOlSN8smipCbj0PIjeVgHBNIjx07llWrVvHMM8+wYMGCavu98cYbGThwIH369GHq1KkuWw3cBRosFgszZswgIyODkSNH1ut91YfVaiU/Px/DMEhMTKS4uJiPP/7YaZlDaWkpw4cP5+STT6aiooJNmzZhNptJSUmhdevWmM1mmjeXG8aMjAwuu+wyjjnmGLfHq0nSzcCyIDMaXG88QT4XtwBvBrJBYeg/SIU3x3OcjwwaXQy8H+D2vIoEPdypICnpY8C/f5MN7a9AKUU+ElzYgFwYIpCpiA8Cs4LXLBVA7qLQNlHAFziWEhNbkJGlHUBvYD7QxV8NDHEfUL0mvE05MoPk/sA1hwq8VYsxDEvgmqKUCnk//vgju3btYuzYsfzyyy8OWz4AxiPfN6XI9aQtsBZoZX/W3Llzufjii7niiiuqVTFo2bIl7777LsOHD+fw4cPMnz/f48g9QG5uLtOnT+fw4cO89957bqsi+IrFYiE3Nxer1UpqaipWq5U1a9bYgwk5OTmUl5czcOBARowYQWRkJNu2bSM5OZm0tDQ6dOiA2WymQ4cOAJjNZhYuXOixgobJZAqx6hq7cZ8oHGRE/jM3jxcDLyElTg1kSe4E3AWVFMgMSddZigBlyPk9BHhOwup7Zci9hDsWDKPc7y3QYINSDc4/kKlWrjdDVwE9gUEBb1Fw/Q3choz2FwJ9kJH9M4LZKD9L9LLNAOJcHvs/4Fqk81gBfA08iZyz8f5oYIjbgwQV3CkDdgWwLQDpyEyVX9xsMygoGEhSUoCbpJQKWY899hgzZsxwyQXwB5LM0TGQXQLsRGZ6fW9/dODAgSQkJPDpp58ydGj1JVw9e/bkyy+/ZNKkSTz88MNcdtllTJ8+3an6wdatW8nMzGTlypWcf/75vPjii8TE1O8Gtby83B4wsM0ueO+99zhw4IC9koPFYqFHjx5MmDABwzDYt28fUVFRtGjRgu7du9tnKABERq7juuveADYjCS0vRkb3zUA4lumMo/qMSUeuv59s4CTkmmm7gV4PLEH6Gck+bl842OdlWwySoDGQwYZRyACKu2oxMRQU+P+eQoMNSjUoh5FRbXejrkXAYuDtgLYouPKAE5ELnW2a+3+B0cAzhG/JzkuRESh30fEKYLjDv38HrsF5mlxZ5c9FSNmx+mSsDkc9kBlD7sQCxweuKYAEkB5EPs9FLtviOXz4Cg02KKVqbNu2bdx8880uj2biPshajgQ6vwUkKaRhGAwePJitW7e6DTYAtG/fnq+//pqNGzeybNkyOnXqRFJSEvHx8faSjDNnzmTr1q32m/ujKSkpIScnh+LiYnueiY8//pjffvuN7Oxs8vPlhql169bMmCG5dbKzswFo166dfbmDLRABMHv2bA9HewW51tqCLyWV5+htJPjgLegfqpoDxyDvz1U0MM3lseuBX6nqf4H0S35BlmP8nx/aGOq6I7MX3Ckl8EtgzwB6IcFEx35iHDCY4uKefm+BBhuUalBsZYjcra+yAlsD25ygexzYj/OFDqRzMBsZpfE8fTN0jULW0a7DeRQqHklg6NgJehbPU+RMyHq9wJQ3Ch2nIp2uQqqfOxPBSRB5NvK7ugKZ6mpF1lNnUlISjp1epZS/5OTkkJzsOuq8ierXUhsD2I4t2ACQnJxMbm6uh+dXOfHEEznxxBPJzMwkKyuLgoICzGYzTZo0cVpeYUu+aAsadO0qeXG++OILtm3bRk5ODkVFEmxNTEzkmmuuASQAER0dTdeuXUlJScFsNpOenm7f76RJk47axurKkT6E63LFUmRkegVQPV9FeHgCyUtURNUsh2jkmvgvh+dZkWTU7j4zpciAz6N4WwLYOC1EAneun61YZEDBHOD2GMgyqauoSi5u6+csRoJJ/qXBBqUalFZ4XksOULPRgfDxLNVHem1KkS/0AYFrTsCYgDVIsOVhpAJFb+BmpDyiI3fBGJtiZLaMcma7+J4OHEH+5mzTR1/Hce1yYI1CEjVlIZdnW1LY7UFqj1IqFCUmJlJQ4DozrgMSnHc3jd7A9XuvoKCAtLS0Gh8zJiaGlJQUrFYrhw4dokmTJgBs3LiRb775hpycHEpL5VplGAY33ngjkZGRGIZBUlISbdu2tc9MMJvN9v2OGjWqxm2ouW+R2X/uFCE30uEabOiLBJ7uQGZQxgBTkaWYjr/vUrz3R23BCr2VdHYWsvT3ZqQvV4H83Q0ElgepTQnAY0h/8i9k6WbgEsXrJ0SpBqUdciHYQPUOQQLeL35W4CPkC+UwMAQpp9fC562schBJGnQQOAEYiySc8hVPnQGQzpG37aEuEvn9zTnK8wYAL+J+yUUcgV8SECraI7kZPgK2AS2Rz6+/6prXlAE0PeqzlFLKk/bt2/PDDz9w2mmnOTw6F7lWuAvgJ+IayP7hhx8YPHhw5b+OUFGxnNzcd8jJiSc7+xy6dZtBXFwCW7du5ZNPPiEnJ4eKiqp+S5s2bUhNTSUmJoYmTZrQqVMnp2CCLVHkoEGDGDTIdd24FfgUCQrHIyPCvqwQVIb3Eflw7luAnMujldCOQQJQez1sz0BvIz25BlnG+iYyw+E0JN9YsMUgfZ3A0k+JUg3OSiQhTw5yA2lCpl9NAs738BorsvbwVapuOjci68A/RQIBvvY0VTfCxUiJziSkUkJHHx3jfKQqgLvougXHKZ/+Y0VKJMYgv4eGZiKyrrIQaatNBHLTOtzdi2rpv8jv4SegExL0CocEnSZkFOKsYDdEKaV8Zvr06cydO5d58+Y5PHo8Mpp9M3IzXY7cyEcBaygrqyAn5y9ycnL4/vvv+fHHHzn77LPZtesjVq8eR25uKVarLefDOi699G3atfuA+Ph4WrVqxTHHHGMPJKSkpNiXcfTu3ZvevXvXovW5wJnIjK585FblLmAm8AC+mbbfD8/LD2MIXGLlMuQ9piDXo4ZmEXAl1ZcExAO3+mD/BcBTyEySMmAccDnhEXBvhnxmlQYblGpwbFnpXwI+RKa1XYT35QJv4xxoAAkAFCMXzV/x7bq6bcgoiWNuibzK45+N3JT64njzkWlnrqV74oHb8f/N/+vI1MI9yI38UOARGlZJyXgkt8MoZPq9gZyrDsC71L8DswyJ0hch52A78AlwHZK1WymlVENy2mmnYbVa+fzzz+3JEouLi8nJmUp29jFkZ79ARkY+LVsO4cCBkTz//BoKCtbYX//+++8zduxYYmJiSEq6ivbtCzGbISUFzGYwm8tISfkaeJ6OHS+hY0dfDTCAlPj+gapBhvLKnyeQgZgJPjhGHHKzvAjnG2kTMmgyz81rfCkHWUP/H+R6nYj0Na6jYQUd/onksFiCBKVsJVOvRyp31Ecu0q/dTdXvYAfS5/gG6cOocKDBBqUapDhkpsKlNXz+/+F+Gj3IDegmfDsL4GHc5wmwIDfmG4H+PjhOs8p9zUZucE3IOvY78X/SwxeQTo9jR+RD5H39gNQmbyh6IAGlr5DyZl2R2Sz1DfgcQjpErglLC5HEQpNpWIEXpZRqfKxWK4WFhWRnZ5OTk0N2djaTJ0/muuuuY+nSpSxevJjiYsfv8S6ceeaZtGw5iMTEfLp164bZbMZsNrN3714efvhhVq5cCfxKs2a7OPdcd0ctRILvl/jwneQgU8/dzWYsQK47vgg2AFyNzCi4GSmxbUFm7C3Hv6UJy4BTkJKjtn7UX8isk98J3rp+dwwkKDMf6YOBDLqkenpBLdwF/Ibz77oEOT+zkP6WCgcabFAqLPzpZVsEvk8SuB3PtZpNyAXEF8EGkOj2+8hUw0KgCf6P/FcgN9muUwetSIfnXqST1ZAYSAfmFB/u81U8BywqkDWft/vweEoppVxZLBby8/Odggmpqan06tULi8XC4sWL7ckXbQYMGMCOHTtYuHAhCxYsoHnz5k45ExISEgBJJjlmzBhASmbOnDmTxx9/nA4dOiAjzFF4TtTs677Fn5XHc1eRC2QU3Jf+CUxHBmUSKn/87Q1kUMBdla1ngJuANgFoR22k4fulJU/jeYns58jMB9eKKioUabBBqbBwMrJ0wV0N7WLg2FruLxe56L2JTNO/FEmeZ/vK6IGMorsLONim8PtaIoGre/0znjtXZUhnoaEFG/whG8/ZqMuQSg5KKaXqo6KigpycHHsgIScnh7i4OAYMkOWTDz30EDk5OU6vOfbYY+nVqxcmk4mTTz6ZuLg4p5wJsbGxDB06lAkTJnD33Xdzxx13cNJJJ9kTMzrKzc3l+eef5/bbb2fp0qWMH2+7seyG93KZJ9bh3X6HlEz8BUmIPQ/oXLmtJd6TM2bU4XhHY8K/MxlcvYoMnrgTgYzoTw9cc4LG0zkAOQ/5aLAhPGiwQamwcBXwHNWDDbHIWv7aRMn3IrMScqga2f8MWYbxIVIuZz4yqu16Q25ClhfUpQPSkETgOXkUNKw1lf40AFnS426JTiJwamCbo5RSIaisrMweSLAFEwCGDh0KwNNPP83evVVZ/w3DoGPHjvZgwymnnILJZHIKJkRHV5WuGzJkiNvjRkdHc9ddd7Fx40Zuuukm5s2bx4wZMzjmmGOIj48nOzubTz75hJdeeomhQ4eyZs0a+vd3nJWYgqzNf47q1/s4YGEtz8RSJE9CMXKN/S+wAkmMPabyeOOQgL5roDsByRUQ6rzdehlI/6Mx6IMMWrmTADQPYFuUP2mwQamw0AWZhTCRqlGBMiSj83O13NelyFp9x1kLBUjuhIeRZIE9gUwklwJUVaNIBt7Bt8kog6ErsibRdRkFSLBlUmCbEzRnILNUfsZ5tCkCOT9uF/IqpVSjUlJSwt9//+20zKGoqIhx48YB8MYbb/DTTz/Zn28ymWjRooU92HDSSSdRWlpqDySkpKQQEVF10+kcAKgdwzC4+OKLueiii9i4cSPPPPMMX375JYWFhaSkpHD88cezdetWWrVq5WEPDyMJoF9Drn+26/tT1K7S1Q5kiYDjEomyyp/JwAEkiP0YUpbYsRpFFDAD3+VrCKZJwBrcj+xXACMD25yguRNJKO4axIpHkk83lqBL+NNgg1I+ZUUuqGVAdwL7JzYMuVh/hiQ76kftS1D+hVQ2cLc8ohBJRHlN5b8vQS6KK4GDSKdjLNIpCHUG0uG5AOcLYSRyk32NuxeFIQNJCnUhMgIVg0yp7UVVx1MppcJbcXExR44ccQom5OTkcMEFFxAZGcmnn37Kf//7X/vzIyMjMZvNVFRUEBERQb9+/ejevbs9mJCUlOS0nKFnz541aEUukgi4ObLcoHYMw6B///51CFxEAy8iyRn/iwQEzkCuB7XxJJ5zPRnIgMlUZNBiA1K2+2Pk5vMCZBAgHIwCjkOWkzj2L+KRstKBXNIRTKcjeRtmU/W5KEdmy8wNVqOUH2iwQSmf+QiJvB9GptlHI4kE/V01wVEUEnSoq78r9+FpjebfLv9ujtRgDkdnI4kpb0BmdUQjoyp3Edwa0OXIiEgSgYn8N0U6fb8ha2zbET6dPqWUkmDCoUOHnJY5ZGdnM3r0aMxmM5s3b+aDDz6wPz82NpaUlBSKiopISkqid+/etG3b1l7RIT4+HsOomuFXv9KQJcDlyNLFaOT6PACpmBTIRIJtqV8Vpj/xnI+hFOdkkwYS0DijHsdrqCKAtcDdSJnHv5EZhDcD04LYLpBZrAYS+AiECcB5SCLSUmQJbyCSdKpA0mCDUj6xAVln6Drtfj4S/Z8a6AbVURu8L4GobaLJUHcqntcUBlohsl71KSTgEId8vhYRmK/yDmjda6VUKCopKWHfvn1OsxKys7M588wzadOmDbt27WLVqlX25yckJGA2mykpkbwB3bp1Iy0tzZ4zITY21mn/rVq18rIMob4mAh8gyw9sSxDWIwGHnQTuxrC+TgJex30OoGjg+IC2JrhikUpODaWa05fAFcCWyn/3QZbPDAzAsaPwbRUt1dBosEEpn7gJ9+v7C5GR8SmERh6DGOQG9kGqv594pN6yCjwLkn9jM1WdzVLgfiSfwstBapdSSgVfWVkZv//+e7VlDieffDI9evTg0KFDPPec5C8yDIOkpCT7MgeAdu3aMXXqVPsyh6go5+WAaWlppKWlBfx9ybLMD6i+rr0CSeL8H6R8YyiYCtxI9WBDBDLQMSTQDVIAfA0Mx7nPtxEYiizLDfWE3yrYNNiglE986WXbESSnQYsAtaW+bkfa/AxVyaAqgIeQi48KvLXAj1SvPV6EJJrajpQjVUqp8FNRUcHOnTudggnZ2dkcd9xxDBw4kKKiIl588UUAIiIi7EkWbXkRmjdvzsUXX4zZbCY5Odkp+SJAYmIinTt3rnbc4FuH54GKAiQhc6gEG5KQ93M2VUsyK5DymuGQWDpULcDzYNl1yDJKpepOgw1K+UQs1UcebCyV20NFBLAcyQb8BdL2MwmdqZrh6C0816SuAN6l5sGGn5CZOB8iuUXGAXcguRhq61vgNrp0WQ+YgVnIzBj9rCilauenn36qVtGhc+fODB8+HMMweOWVV7BYLERFRdlnIMTHy3dNYmIi06dPx2w2k5iY6JR8EaQEZIcOobgMLAHPuXkM5AY+lPQCfkcGaPYigYY+wWxQI1eKzGLwZB3Sh61JuW8Lklj7fmA/0BoJVsyg9oGkUiSfxaN06ZKFzK5YBAyq5X5UQ6DBBqV8YgryJeua/MhA1lWaa7CPA8iNYysaRoS/JVKFQAVfJPKZsLrZVpu63N8Dg5ERMdu+/gO8DWyidjkZ3gXGA8VERlqBbGRWzCtInotQCrAppfzBarXaEyVu376dw4cPOwUTmjdvzrHHSi6gDz74gJycHGJjYzGbzaSmptqXLphMJmbNmkVSUhJxcXFOyRdt2zMyMgL75gJiFJKjx514pFS1d4ZRgtzgp9MwghMGetPYUBh4728ebbujfyDXf9ssiV3IrImNwIpatKkcWdaxESgkMhIkAfuXwONIf1uFEg02KOUTtyCjz4eQzNEgf17xyCwBb9bRseN0JMpvIBUeHkLKSCoFclP/JO4TaxnAmBruZy7VZ0jY1v7eiAQeaqIC6eS6zuYpQnJIPANcVsN9KaVCla20I8DPP/9cLQljfHw8M2fOBOCrr75iz5499uSLLVq0oE2bqmoKl1xyCfHx8cTEuC+p2Lx5c/+/oQYnBcmh5DrVPQEJRAzx8tpS4Aa6dl2OjEyXA6ORPkm6H9qqQk8Ukgjb3VIJA7npr0mw4Uckd5Rrn6AQKZt6FTWfffk6MmvSdWlHIVIm83x0MCO0aLBBKZ9ogowa/xspR1UGnAP8C2jv5XUbgZHExDh+qf4BTAJWIWsblRoEnIZ0CFzrcl8E1KSsWj5SXsodC1LjvKY24XnZUCHwBBpsUCr0lZaWEh0dDcCuXbv49ddfnYIJFRUVXHfddQD8+OOPbNu2jeTkZFJSUsjIyKBp06oywRMmTCAmJqZa8sXt27cDkJqaGqB3FWpmIssN7kRu6pojwYeL8H4jOBl4F5PJ8bt6NfBD5X6i/dJaFWoeRKpBOM54NIBE4L4a7uM1PJdMLwPeoObBhqdwP7Bia9dnwIga7ks1BBpsUMpn0pDOwJ21eM0NuE/MUwRcjQYblDCQYMB9SDmqw0j27uuRSH9NVOC9Y1pRi/YU4X0Np7vPtFKqIbFarRQVFREbG4vJZOKPP/5g+/btTsscioqKWLhwIdHR0ezatYsNGzbYyz927twZs9lsXypxzjnncO6551ZLvmiTmJgY4HcYTk6r/Kmp/yFL3VyDwqXIevo30WWSSvRGKlLcgCxXADgLWELNAwSleO5DVFA147cmPAUabLR/EWo02KBUUK33su1XZB28OSAtUQ1dFLCw8qcuUoAuSIJId4bUYl998TyKEY0GyZQKPqvVSn5+PrGxsURFRbF//342b95sr+SQk5NDaWkp8+bNo0mTJhw4cIDvvvvOHkxo06YNKSkpWK0y2nn66aczbNiwavkSbGJjdWpzw/GJl235yLJPDTYom15I7qa6Ggk8gvtE1vGV22tqNJ5nT5YgszBUKNFgg1JBFYnnmzbbdqV85QGk+oTrRTweuKcW+0lC1mA+gPMogwHEAVfWuYVKqZqxWCzk5uYSGxtLbGwsWVlZfPnll9WWOUybNo1OnTqRk5PD1q1bMZvNpKen06lTJ1JSUoiLiwPgxBNPpH///h6DCa7LH1RDFoPn2We272mlfGUQUlVkI84lumOB/kii9Jr6J1LRogRZ4mkTD0xDlhGpUFKvO5ktW7Zw//338/zzz/uqPUo1MucCL+F++tlAZM2cUr4yHEm+NB/YjazP7An8HzJboTbuQDqsSyqTxFUAxyLJIVv7qsGqkdL+BZSVlZGTk0NMTAxJSUnk5uaydu1aezAhNzcXq9XK2LFj6dOnD6WlpezcuROz2UzLli3p0aMHZrOZJk2aANC9e3d69PA8Ldq1XKQKZecgCYHdiQemBrAtKvwZwPvIIMTzSN/CQBJJ30ftKqylAf8FLgY2UlERiazMmgfc5cM2q0Cpc7BhxYoVrF692h4RV0rVxd3A+1it2RiGLeBgQjJNPxq8ZqkwNgKpGLEdKSP1LjAdSSY2D6hpkjYDqWBxDb//vpZOnfogZVuVqp/G0r8oKSkhOzubyMhI0tPTKS0t5a233rLPSsjPlynJQ4YMYciQIURERLB7925SUlJo3769fblDu3btAGjVqhXXXHONx+N5mrGgwlFz5Pt5Mc6zz+KBM6hd/gelasJWfe0hJCHkE8A7SH/jOmBYLfbVEfgCOMjvv2+kU6eh6Gyc0FXnYENGRgaPPPKIPQuxUqouMoDvyc6+itTUj5EZDiOBW5Hs0978DjyLJHs6CVl/Ge+3lqpwshvpbOZStYznbqQW9iagqYfXuRNDaWlHNNCgfCUc+hdWq5XCwkJycnIwDIOWLVsCsGrVKo4cOUJ2djbFxTLduG/fvowZM4aoqCgOHz5MUlISzZs3x2w2k5KSQuvWMlMoISGBK6+8MlhvSYWcm4GeFBXdSFzcXqAZssRtNkdPFvwuMlIdjwSi+/i3qSqM3I/0J2xBrt+Br5AElDfXcl/NKS3thAYaQludgw1nnXUWe/fu9WVblGqk2nDgwG2kpr5Si9f8H3ANsp6tFPgPcC0SCe7u+yY6+QyZyvYj0BIpwTUV79UJHGUDHyPT7M5ApsypwLoc+Bvn5TvFwAFkNOzxYDRKKSA0+hdWq5W8vDx7XoT27dsD8NZbb7F3716ys7MpKysDoFOnTkybNg2QUpLJycm0bdvWHkxo3lzWIBuGwZw5c4LyflS4Oo/ff+/hdfmMs7+BwUgJ7nzkup4JTACepHbT4WsrD6m29DSSV2gYkhC5ay328R1SiSMDSSSos3kC6w+kIluxy+OFSABiGt7Lwatw5Nfsc7bayYFQXFwc0OM1Nnp+/as25zcm5n+0b38NJpPjl3k+VmsBZWVnsmvXh/jrAms2v0Tz5vc6HPsgFstl5OW9zP79R1+Xl56+nCZNHsNqla8ewyjnyJFLyMqa77c2g35+HRlGKd26veewbMdRGRbLC/z884Ja7VPPr3/p+a3O3+ejoqKC/Px88vLyyMvLsz/+xRdf8Pvvv5Ofn09FhfwNpaWlMXnyZAD27dtHSUkJzZo1IykpiaSkJMxms729fftWz42SlZVFVlaWX99PQ6afb/+qzflt3foKEhN3YDKVVT5iAQqxWF7iwIEu5OSM80sbTaYC2re/kKio/ZhMUibRYnkBWMUffzxFcXFvr6+PjDxI27aXER29G6vVBFipqDCzZ88ySku7+KXNNvr5rZKW9iRNm1bgLv2LxVJBVtbDHDkyo1b71PPrX4E4v34NNtQ8klp/27dvD+jxGhs9v/5Vu/O7FCir9qhhWImOzqFHj7/xT2mgbOBeXCPWJlMRKSmfk5KSBZzq5fXPI2v4SnCsudy06Qs0bdoHmOXj9lbRz6+jXLwFdkym0lqfKz2//uXL87tp0yaf7CfY6ns+bMkXs7OzycvLo08fmSb+2Wef8d1335GXl2cv+ZiTk8O5556LYRgcOnSItLQ0+6wEs9lMamqqPQmj/h3Unn5/+FfNz282sA53/QuTqYhWrf5Dq1b/8nHrbO4A/sSxb2AyVQCFdOhwG55LNoMERMYi5cKrgugREUV06jQdGW1P8H2TK+nn11EM7j4/ACZTGc2axdCsmfYvGpJA9C+0rp5SIWcn7qtXgNxE7sY/wYa38fyVUYjkj/AWbLgV50RVNgXA7cBMdMpjICQh+RX+8LBd1+aq0FdcXGwPJmRnZ9OnTx+io6PZsGED69ato6CgwOn5PXr0IDY2luTkZDp06OAUTDh48KD9eaedpon1VLg6CETheMPvbJ8fj/0U1afe2/xe+dPew/aPkSWArv0ia+U+X0T6F8r/BiNV1PLdbEvEex9Rhat6BRvatGnDK6/UZp25Uqr+eiPJdsrdbLNQu/WNtVGAc81jR1ZkxNwTCzLq4MlBZI2mJrj0PwO4B6ll7Rr8iUfWVSoVXEfrXxQUFNirNmRnZ3PssceSlJTEjz/+yDvvvGNPvmjTvn17mjdvTmpqKt27d7cHEmxBhZiYGECWObgudSgpKdFKDqoRaIX7foVNRz8eu8jLtgik/+HJZi+vLwA2oMGGQBmO5MvYifMMhyigDXBWMBqlgkxnNigVci5HEjW5dgoigA5A9TXBvjEYCSq4k4hU0fDEhOdoN8iFKKbuTVO1NAkJDl1PVQApGkk8WpvyVEoFx3333ef0b1uOhLS0NHr37l0tmJCQINOou3btSteu/grIKhXKkpBrw0qqzzKIR5I1+svpwCu4H9CIALzlXWgGxOK+fxGFVkoKJBPwOVLB5AukX1cCDEISmdc0kbgKJxpsUCrkdEWWLFyMfHEXIx2Bpki5Kn+NwB0DnIxcSBwDHZFACjDxKK+fDjxG9Sma0ciFKcI3zVQ1NAsYA6xC1rNOQ34XSjV8I0eOdAooxMbGAtC6dWt7qUilVG09iixZ+Aa5VttuE64CzvPjcW8C3sR9kGMR3q9N5wFzPWyLAC6tb+NUrTQBPgA+RKqWnQr0D2qLVHBpsEGpkHQBMgL9GnAIWWc/HP9Gje8D1uM8u8EABgIvcfQlEHcCnwC/UTUlMgEZdbjfpy0NLQXAC8ioTiRy038h/r3xLweuQNbJxiCjSf+qbMdwPx5XKd8YMGBAsJugVBiKR67TGyv/GwecC7T14zEPItc814GSWCSf09GqIyUjCainIVP3y5AgQwxSptufyz8auq3AI0iCzWOA+UBPPx/zR+QzcwDp05Qg/dUXkdkzqrHRYINSIcsM/CNAx1qLJHh0nZUQhUxhrMlIYhLwLXJT/SIStJhY+RPnq4aGmMPAACRgZAvArEcyc89BAjFn4/tcFtcBzyCjSLaRpDykg/Bf4FgfH08ppVToOLHyJxDOp/oaf5DBkzOo2WzN85B8Vo8AW4BOwDwad8LjJ5Flt6VI8syvkaDMxcBxyEDR8T4+5hFkyW2Oy+MfIr+jj3x8PBUKNNiglKqBxbivJFGKLN04jCzjOJoYZPRhmpttxcgSjVJkuUZ6nVoaWq4A9uLcySoEdgDXIEEYK7LWcbSPjpkHLMd9Qq1iJHnkf3x0LKWUUsqTHcB3uC+XWAz8G7lBronOwEMetv2CjLg3B04i/Ctf/YkEWxyXpVQg1/3lSN/CAE4A1iBLYX3hCaQP56oE+BLYDmgZy8ZGM3UopWrgf162xSBLI+rjGSRYcSEwFclafDWeq1+EgxLgdTzVpJalDnlI0quJSKfMF7YjM1LcsSAzK5RSSil/24nnJYMWJEBQH9nAmcish0uQagjtkABHOFuJ54TeIEGHQiQ3xxQfHvcTPFcGiag8nmpsNNiglKqBdl62lSDBgbr6GEnulI9USMhFovHLkRkV4aoA750BR6XAgz46birey5ul+eg4SimllDcZeA64G8hyiPoYg1RFKEL6FvnAHqT6RVY9992QHaD6sld3SpA+2B4fHbcZnmeNmND+ReOkwQalVA1cgyRzdBUJnEL9SkvdgvslGoXAvXjuiIQ6M5LYqibKgU0+Om4XoL2HbfHAbB8dRymllPLmWCSg4O52JI6jJ4f05gfkuuluWn8ZsKIe+27o+iHlxmsiBvjZR8edgfccU5qAujHSYINSqgbGAf9ELv62EpVJyKjEi/Xc9xYv28qB/fXcf0NlQsp91ST5o4Gca1/5DxLocJy+moAkq5zuw+MopZRS3ryJ5FKw3RxHIZUobgQG1WO/3+H5NqcIWFePfTd045BrfE1u88qAlj467mBkWYbj4FQE0s/5DxLYUI2NJohUStWAgUzj/ydSHjEbKWU0Bs/r/2sqBZna6E4Zvktc1BDNR4IpDyPnMR/3SyviKp/rK8chpbAeRuphJwMzkdwQellQSikVKB2BX4FXkSUPzYCLkFl49ZGO55ttE767wW6IopFzOQrYR1VySFcG0AHflcM0kCWwY5HKIPuRqiZXo4khGy/tVSqlaqEXvs+jMAupgOB6IYxA1lWafXy8hsQAliClKD9DKlPcikz7LKSqVviVyIiBL7WuPPYSH+9XKaWUqo1YJDn0VB/uczie8wfEIgH2cNYRSQi9EdgFvIVUnihGkm8mIAMZr/v4uAYS5Bjl4/2qUKXBBqVCSjlSHvII0Bcp9RTqrkEugD8hSRNBLoDJhPeaSkfpSK1xkBrYzyK/51bAP2jctcKVUkr5336kPGECcAZyQx7KYoBXgHORWZK2/E8JyCDHwCC1K5AMoH/lzySkGsRTwCGkSsc0ZEmsUv6jwQalgioXeIUmTb5DRq7Pw/Oats+B8Uj2YAMZ/R4CrKLmiYAaojik3OIryEWwBOkc/JPwntXgiRm4ovJHKaWUqovNwLukpx9Grqe9PDyvDBnlfwlZzmcgy/lWABMC0E5/Go4kinwQGeFvDcxDZk02RrbAg1KBo8EGpYLmI+SmGpo0KQCeBy5HyhAd5/Lc34GzqRr5t/kUWWf/th/bGQjR+H4KpVJKKdXYlAEXIH2MEpo2BXgcGcx4lqokzzbXIMH+4sofm+lI2etQnwHQCckfoJQKBq1GoVRQHEICDQVAAYYBkhzwCBKJdy33+BDuyzfZaiT/5reWKqWUUipU3A58iOT9qcAwbMkB30BG+B3lIzMY3JWfLgLu9F8zlVKNggYblAqKZ5DswO4UAe+4PPYl1QMQNtF4Lx+plFJKqfBnQUbx3VUeKATud3lsF54rSlmBb33XNKVUo6TBBqWCYhvO0xUdFQO/uDzmrUSTFWjqi0YppZRSKmQV4H6Wgs1BnMsrp+N+1qRNE180SinViGmwQamg6I7nTM+xSN1jR3ORDMruJAEn+ahdSimllApNCXivItEU53KQbYDjcX87EI/kkVJKqbrTYINSQXEpnus/RwGjXR4bBkzBOeAQgwQaXkf/lJVSSqnGzgTMRqo8uYoHrnTz+AtAmstrEpAKWf/wcfuUUo2N3qEoFRQtkOzP8VRd4BOAFOADJA+DIwNYDqxGyl8OQjJI/w8YEID2hpoc4D9I4qufg9yWYLICryLZxFsDQ4G1QW2RUkopf7od6SMkAAZWq1H5/8OBa908vxOwA7gNOBWpfPUCkjtKi9ZVtwmp7vEa7nNjNBYHkH5oe2Q27g3A4WA2SDVQ+i2iVNCcA+wGXuTIkU2kp5+G1LT2tFzCAM6o/PGXYiRj9S/IxeM8JCASSpYDVyHlvSyAhTZtBgLv4X16aTiah5Q6s5VM3Q/8F+mMXh2sRimllPKbGGTQ4mtgNUeO/EWTJjOAE728JhUJRLgLRvjKTuAtJNn1CKCPH4/lD38Bo4AfkUB+JGAlMfFeoEcwGxYEfwD9gFyqcn48gCQ/34QMbiglNNigVFClA/M5dGg76enBvlh9S1XZzQIk6DEPeBc4OYjtqo3PkJto59GGhISvkffyROCbFDTfIxd+12RhhcBNwFSgeWCbpJRSKgAM5Lp9MocPb6dJk2D2L6zAHCTwXYEMAtwJnI4sA3WdydlQnQ9sxjWhZuvWVyGzBrsGoU3BciXwN85V1UqBLOA64MUgtEk1VLqMQimF3JwPRy4e+UjnIB9ZjjASyPPTcXcADwOPAr/5YH934S4Tt8lUglz8cnxwjFDxAlDiYZsJ6eQppZRS/rQCeA7pZ5QC5ch1+hPgRj8dswx4E1iKXOu8VdyoiZ3ABrf7MYxy4MF67j+UlANv4758ewWydNPqZptqrDTYoJRCLsZlHraVAyt9fLwKYBpwHHA9MnXzGGT0oz4XqR+9bIvGNwGNUJGL+84AyO86P4BtUUop1TgtwX05ziJk2aOnvkddbUGm8V8E/Au4BGiFTO+vq21I8u7qJNiwsR77DjXlyOwUT8rQYINypMEGpRQStfd081kI/OTj4y1GAhzFLj/PAsvqsd8WXraV0riWDZwJJHrYFo0kAlNKKaX8aY+XbeXIjEpfKUaWNBxGZmSWVf73CHJNLPD8Uq9a4ukGWxJwZtRxv6EoFujiZftx6O2lcqSfBqUU0A7PiSkjgfeREli+iN5bkKmN7kY6CpFARF0twN37sFgikKodLeux71AzDqmp7pqaJwapq94/wO1RSinV+HgL8pch+YMexDfLHF/H8/LBcqQKWF30B5q43WK1xgDz67jfULUE98nD46lfH06FIw02KKWAC/D8dVCOlI98BhgC3FrPYxXgPQfEXuo+BW8aUrZLSn6JBCoq0pEcBo1JNPAV8juLBZKRQMMYpDKH4fGVSimllG9cheeqVhXAR0juhs7ArnoeazueZ2nmA1vruF8DqaRhpuq9mIB4/vprCnBaHfcbqsYCjyEBmMTKn2ZIP/Gs4DVLNUhajUIphVwo1iDlOC24n3Vge/y+yuf1q+Ox4pGb3nIP29Oo+42wCXgJ+BxZkpEDnM2uXX3o3r1NHfcZylogHbk/kbKX7fA0OqOUUkr53nwk8P0ekqfB3XKEQmQJxETqN4MyAxlscLdcIg5oX4999wZ+BZ4GvkCur//k8OF4mjTKy+pUYBKSz8IAeqJj2Mod/VQopSqdhqytXIIEE2I8PK+Y+uVViACmI6PtruKQEpX1YSCj+U8jUyr/gdUaV899hrqWwAlooEEppVRgRQCrgE+R0tSellVYkBvX+iRyvtDLNgOYXI99A6QiMzXeQPpBJ9Rzf6EuAgnCHIveUipP9JOhlHJgRm72x+Mp87J0CLwlfKqJxUgSIccEholIXfCF9dy3UkoppRqWE4F7kSV+nkQDh+pxjBTgNWR2g21AIxaZUfkykF6PfSul6kKXUSil3DgOz6WNYoGT6rn/eGRa5YfI7AMTMiJxOppLQCmllApXffGcm6kE6FrP/Z9F1XKHn4DuwKV4r1allPIXDTYopdw4HugFbKZ6DexI4DIfHMMEjKj8UUoppVT4W4jkEnLNDRUHTECWKtRXM+B6H+xHKVVfuoxCKeXBO0AfZBZCApCETEF8j8ZVQlIppZRSvtEfWEFVvyIRmTE5gvrlg1JKNUQ6s0Ep5UETYAPwPfADMlJwJvq1oZRSSqm6mwyMQ5ZS5iH5mjoFs0FKKT/Ruwal1FEcX/mjaq8C2Af8iCS+OgXP9cZDwefAA8h62GORzOJ9g9oipZRSoSgeCTiouikEtgIHkZKexwa1NfWTg8x2eRnJ2zUFqVqWFMxGKR/RYINSSvlcBXB35U9x5WNRlT+LgcuD1K76uA3JJG5bZ7sNKf+VCQwIVqOUUkqpRuR/wD+BLyv/bSCDGd2At5DAQyg5iFQqyQKKKh/bBjwEbAxWo5QP1Tlng8ViYdGiRUyYMIFp06bxxx9/+LJdSikVwmYCd1AVaABJtFkI3IDUHA8l/wOW4JzQy4J0DGZjMmUHo1EqDGnfQimlPPkDCe5/6fCYFani8SMwCCgNQrvq4yrgT6oCDSB9jb3Av4LSIuVbdQ42rF27ltLSUl5++WWuvvpqFi9e7Mt2KaWUz8TG/gTMAkYBd1G/Ot5H8zvwItWreNgUAjf58fj+8Cye308EyclrA9kYFca0b6GUCh2FpKS8BowBJiIJtD2VDfeFu4F8D9usyHKEN/x4fF+rAF4Fyt1sKwNewH2JVBVK6ryMYtOmTQwePBiA448/nq1bt/qsUUop5TuLaNfuPiTabwE+Q0bpP8I/0/8/4uhx3J3IRTbCD8f3h8O47wwAlGAy5QSyMSqMad9CKRUa/gQG0rx5FlWz/t5BZheswT8r1VfjPZiRj8x6mOCHY/tDKdIX8qQIDTaEvjrPbMjPzycxMdH+74iICMrLPXVGlVIqGDYASzGZiqm6QBch2a/H4P0iV1cRyBpKb2IJrcrDpyLlydyJpbi4dyAbo8KY9i2UUqHhH8B+IiIclxfmA+vwXwnPow1QRAFN/XRsf4gF2nrZ3p3Q6ispd+ocdktMTKSgoMD+b4vFQmSk8+62b99e95bVUnFxcUCP19jo+fUvPb/+0bLlPaSkFGO4ufevqChg795nKSw8yafHjIjoTOfO5Zg8XB+t1giys8/hwIH/+fS4/mQYvenUKZbIyEIMo2pUxWKJpLS0FX/91YvCQv38+ktj+n6oSd8CtH8RTvT8+peeX98zmbLp0mUtJpO7QGghpaX3sWvXmT4/brNmw0hNfcHDccFiMfHrrydRVhY6v+/k5Nm0bHlL5aBQFYsllv37Z+vn188CcX7rHGzo27cvn376KaNGjeL777+na9eu1Z7To0ePejWuNrZv3x7Q4zU2en79S8+vv+TgacphRIRBu3axgK/Pew/gGqREZJHLNhOG0Y7U1OWkpqb5+Lj+tgE4F/gFGT0pxWQ6kdjYV4mNzdLPrx/58vth06ZNPtmPv9SkbwHavwgnen79S8+vP+wEYvCUyyg6Os9P53wJkhfiENWXF8RgMt1J586+D3L4Vw+kT3EzVTM3LJhMS2jTZg55efr59adA9C/qHGwYNmwYX375JRMnTsRqtXL33XfXuXFKKeUfA4CvcJ+d2YL/6lLfiZShuh34FbmAtkCyLs/A85KEhqwjsAUpSbUH6AJ0qtyWFaxGqTCjfQulVMPXBu+5BI7x03GbAZuRJNP/QapQxAP9keTXvp2pGTgLkCTeXyPLUE8C4oLaIuU7dQ42mEwmbr/9dl+2RSmlfGwu8CjVgw1RQG/8F2wwgIsqf8JNz8ofpXxP+xZKqYYvDrk5Xo5zSWiQm/+b/XjslsCTlT/hJB4YGuxGKD/wR6pUpZSqozzgfaAAyejcuZ77awuspqLiXCIiQBJCWpGb5TX13LdSSimlQsMPwCYgDTgLSU5YH4uBg1gsr2IyRSGDDOWVj4+o576VCh8abFBKNRBPAZcjSw4sSGBgFDJVMKYe+z2DHTvW0aPHHmSd43HACfVsq1JKKaUavmyk+tS3SGUDExIYeAkYWY/9RgEv8Ouvl9C5814keDESSKlXa5UKNxpsUEo1AF8ggQbX6YjvAfOBx+q5/2jgnHruQymllFKh5QIkwbHrcsrxwPdI/qG6KytrDYRaUkalAkeLlyqlGoA7qR5oAKnm8BxSVUIppZRSqqZ2AetxnyS6FHgwoK1RqjHSYINSqgH4wcu2aKSig1JKKaVUTW1F+hDulCMzHpRS/qTBBqVUA9Dcy7ZSpNyTUkoppVRNtUByQHnSNlANUarR0mCDUmHJCnwFZAKvAcXBbc5RXQkkuHk8AugHtA5oa5RSSinlTi7wIlL28ccgt+Vo+gPpHrYlILmilFL+pAkilQo7B4HhyFpFC/JnbgCvAsOC2C5vLkJKUX6AlL0E6QgkAi8Eq1FKKaWUsnsamIv0K8qRvsXJwJu4HzAINgNp2xBklmQRMs4aC8wEzghWw5RqNDTYoFTYORv4CekIOBoHbAcyAt2gGjAhwZC1wDNIQshRwDQgKXjNahD+Bt5FZqcMAToFtTVKKaUao6+BecgNu6MvgH8gpSQbouORwZcngc+RZZszgYFBbFNDYEXKgW5GlqqOpH5lxpVyT4MNSoWVzUhAwTXQQOVj/wcsCWiLas5AZl401NkXwfAIcB3yVW2p/BmDzPaICmK7lFJKNS6LqR5oACgB3gIOA00D2qKaS0eupdcFuyENxGEkuPA/JOgQgQz6rEL7YMrXNGeDUmFlG57/rEuRKLYKDe8DNyAzGvKR0qDFyHKTa4LYLqWUUo3PD8iNqTsxwC8BbIuqn9HI77MA6VvkITNKzwX+CGK7VDjSYINSYaUVMkPAHRPQPnBNUfV0B9IJcFUErPCwTSmllPKHVl62lQItA9UQVS8/Vv6UudlWBjwa2OaosKfBBqXCyml4TtIUgyR2UqFhu5dtEcDeQDVEKaVUo7cA9/0LE3AsOpgRKrYifQh3SoGNAWyLagw02KBUWIlAptknA3EOj8UBNwN9g9QuVXve1r6WAU0C1RCllFKN3vnABCTgYJtBmYBcq14OVqNUrbXwss1Ew0wirkKZJohUKuz0A34DnkKyR7cBZgC9gtkoVWvzkWRWrsslIpFyXWkBb5FSSqnGygCeAP6JlMD8GymzPZmGWfZSuXcaUlY8z822WHQGrPI1DTYoFZbS0CSCoW4WkiTyUySJE0iHLh0JJCmllFKBZAAnVf6o0GQCVgNDkVmSRcgM2GhkgGNA8JqmwpIGG5RSqkGKRDoEHwPPIhUpxgATqVoio5RSSilVG/2AXcjAxZdAa2AmcHwQ26TClQYblFKqwTKAMyt/lFJKKaV8oQkyk0Ep/9IEkUoppZRSSimllPIpDTYopZRSSimllFLKpzTYoJRSSimllFJKKZ/SYINSSimllFJKKaV8SoMNSimllFJKKaWU8ikNNiillFJKKaWUUsqnNNiglFJKKaWUUkopn9Jgg1JKKaWUUkoppXxKgw1KKaWUUkoppZTyKQ02KKWUUkoppZRSyqc02KCUUkoppZRSSimf0mCDUkoppZRSSimlfCoy2A1QSoWqcmA18AWQCkwGOge1RUoppZQKdXuBF4ADQH/gfCAmqC1SStWNBhuUUnXwJ3AKcBjIB6KAe4AbgFuC2C6llFJKha7lwALAApQCicBVyMBGlyC2SylVF7qMQilVBxcAu5FAA0AZUAzcC6wNVqOUUkopFbK2IIGFYiTQANLPOAScDViD1C6lVF1psEEpVUu/AZuACjfbCoH7A9scpZRSSoWBh6kKMjiyAvuBbwLbHKVUvWmwQSlVS7vxvnZyV6AaopRSSqmwsQP3Axkgtyy/B64pSimf0GCDUqqWOgAlHrYZQPcAtkUppZRS4aEnEOFhmwVNQq1U6KlXsOGjjz7i6quv9lVblFIhIQNJDhnlZlsccG1gm6OUCivat1CqsZoPRLt5PAIZ6Ogb2OYopeqtzsGGO++8k6VLl2KxWHzZHqVUSHgJ6IpkiTaA2MqfO4BTg9gupVQo076FUo3ZMcDjSH8irvKxJKAN8DbS31BKhZI6l77s27cvZ555Ji+//LIv26OUCglNgB+AT4CvgWSkQkWrYDZKKRXitG+hVGM3FRgBrELKa/cBRuF5eYVSqiE7arBh1apVPPvss06P3X333YwaNYoNGzb4rWFKqYbOBJxZ+aOUUjWnfQullGdNgNnBboRSygcMq9Va56K1GzZs4KWXXuKBBx6otm3Tpk3Ex8fXq3G1UVxcTGxsbMCO19jo+fUvPb/+pefXv/T8+pcvz29hYSEnnHCCT/blL976FqD9i3Cj59e/9Pz6l55f/9Lz61+B6F/UeRlFTfTo0cOfu3eyffv2gB6vsdHz6196fv1Lz69/6fn1L1+e302bNvlkP8Gm/YvwoefXv/T8+peeX//S8+tfgehfaOlLpZRSSimllFJK+VS9ZjYMGDCAAQMG+KotSimllGrktG+hlFJKhQed2aCUUkoppZRSSimf0mCDUkoppZRSSimlfEqDDUoppZRSSimllPIpDTYopZRSSimllFLKpzTYoJRSSimllFJKKZ8yrFar1R87Dpda3koppVQ4OeGEE4LdhHrR/oVSSinV8LjrX/gt2KCUUkoppZRSSqnGSZdRKKWUUkoppZRSyqc02KCUUkoppZRSSimfCrtgw0cffcTVV18d7GaEDYvFwqJFi5gwYQLTpk3jjz/+CHaTwtKWLVuYNm1asJsRdsrKyrj22muZPHky48eP5+OPPw52k8JKRUUF//rXv5g4cSKTJk1ix44dwW5SWDpy5AinnXYau3btCnZTGi3tW/iW9i0CQ/sW/qF9C//SvkVgBKpvEenXvQfYnXfeyfr16+nRo0ewmxI21q5dS2lpKS+//DLff/89ixcvZtmyZcFuVlhZsWIFq1evJi4uLthNCTurV6/GbDZz3333kZ2dzbhx4xg6dGiwmxU2Pv30UwBeeuklNmzYwAMPPKDfDz5WVlbGokWLiI2NDXZTGi3tW/ie9i38T/sW/qN9C//SvoX/BbJvEVYzG/r27cutt94a7GaElU2bNjF48GAAjj/+eLZu3RrkFoWfjIwMHnnkkWA3IyyNGDGCK664AgCr1UpERESQWxRezjzzTO644w4A9u/fT3JycpBbFH6WLFnCxIkTadasWbCb0mhp38L3tG/hf9q38B/tW/iX9i38L5B9i5AMNqxatYpzzjnH6eeHH35g1KhRGIYR7OaFlfz8fBITE+3/joiIoLy8PIgtCj9nnXUWkZFhNcmowUhISCAxMZH8/Hzmz5/PlVdeGewmhZ3IyEiuv/567rjjDkaPHh3s5oSV119/nbS0NPtNmfIv7VsEjvYt/E/7Fv6jfQv/076F/wS6bxGS30IXXHABF1xwQbCb0SgkJiZSUFBg/7fFYtGLlwopf/75J3PnzmXy5Ml6wfKTJUuWcM0113DhhRfyzjvvEB8fH+wmhYXXXnsNwzD4+uuv2b59O9dffz3Lli2jadOmwW5aWNK+ReBo30KFOu1b+J/2Lfwj0H0L/WZXXvXt25dPP/2UUaNG8f3339O1a9dgN0mpGsvKymL69OksWrSIk046KdjNCTtvvvkmBw8eZNasWcTFxWEYBiZTSE6Ya5BefPFF+/9PmzaNW2+9VQMNKixo30KFMu1b+Jf2Lfwr0H0LDTYor4YNG8aXX37JxIkTsVqt3H333cFuklI1tnz5cnJzc8nMzCQzMxOQpFmabM83hg8fzr/+9S+mTJlCeXk5Cxcu1HOrlDoq7VuoUKZ9C//SvkV4MaxWqzXYjVBKKaWUUkoppVT40DkpSimllFJKKaWU8ikNNiillFJKKaWUUsqnNNiglFJKKaWUUkopn9Jgg1JKKaWUUkoppXxKgw1KKaWUUkoppZTyKQ02KKWUUkoppZRSyqc02KCUUkoppZRSSimf0mCDUkoppZRSSimlfOr/AdylfBTBvts7AAAAAElFTkSuQmCC",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119ed0eb8>"
+       "<Figure size 1152x432 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -689,7 +717,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The optimal value of the $C$ parameter will depend on your dataset, and should be tuned using cross-validation or a similar procedure (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb))."
+    "The optimal value of `C` will depend on your dataset, and you should tune this parameter using cross-validation or a similar procedure (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb))."
    ]
   },
   {
@@ -707,7 +735,10 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -731,21 +762,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's plot a few of these faces to see what we're working with:"
+    "Let's plot a few of these faces to see what we're working with (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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MISKFWAk+vdFS+MaZAjX4AgDkggyg5BaR18xI6fq7ubMx11oDhWofSknG6Zhh3AKAknPd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\nxZQS4kbDrmmu3bcKnBk5NzpIPfWUSvsDhgAloo9RNg7gJsMlJyGCBB1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Ah6oM6D08HrT/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Z0jpVGWHZRlJxpM5xysKWGtgbUGUhI0oHUGrTNI2dvi6f6BEfQNDqLR30BeyyOpyjkHVwb4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\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8LHWmYUTGkPNr7z1gSReWLOIs/bK07woABkF7W0vQCoACX54XBOUxIzkfFRCPVmJTJpKK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\nAZiF6OjoYBWJRCLW6Wk0GqSmpuLbt28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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11906eef0>"
+       "<Figure size 576x432 with 15 Axes>"
       ]
      },
      "metadata": {},
@@ -753,7 +787,7 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(3, 5)\n",
+    "fig, ax = plt.subplots(3, 5, figsize=(8, 6))\n",
     "for i, axi in enumerate(ax.flat):\n",
     "    axi.imshow(faces.images[i], cmap='bone')\n",
     "    axi.set(xticks=[], yticks=[],\n",
@@ -764,8 +798,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Each image contains [62×47] or nearly 3,000 pixels.\n",
-    "We could proceed by simply using each pixel value as a feature, but often it is more effective to use some sort of preprocessor to extract more meaningful features; here we will use a principal component analysis (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)) to extract 150 fundamental components to feed into our support vector machine classifier.\n",
+    "Each image contains 62 × 47, or around 3,000, pixels.\n",
+    "We could proceed by simply using each pixel value as a feature, but often it is more effective to use some sort of preprocessor to extract more meaningful features; here we will use principal component analysis (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)) to extract 150 fundamental components to feed into our support vector machine classifier.\n",
     "We can do this most straightforwardly by packaging the preprocessor and the classifier into a single pipeline:"
    ]
   },
@@ -773,15 +807,16 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
     "from sklearn.svm import SVC\n",
-    "from sklearn.decomposition import RandomizedPCA\n",
+    "from sklearn.decomposition import PCA\n",
     "from sklearn.pipeline import make_pipeline\n",
     "\n",
-    "pca = RandomizedPCA(n_components=150, whiten=True, random_state=42)\n",
+    "pca = PCA(n_components=150, whiten=True,\n",
+    "          svd_solver='randomized', random_state=42)\n",
     "svc = SVC(kernel='rbf', class_weight='balanced')\n",
     "model = make_pipeline(pca, svc)"
    ]
@@ -790,18 +825,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For the sake of testing our classifier output, we will split the data into a training and testing set:"
+    "For the sake of testing our classifier output, we will split the data into a training set and a testing set:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "Xtrain, Xtest, ytrain, ytest = train_test_split(faces.data, faces.target,\n",
     "                                                random_state=42)"
    ]
@@ -810,7 +845,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we can use a grid search cross-validation to explore combinations of parameters.\n",
+    "Finally, we can use grid search cross-validation to explore combinations of parameters.\n",
     "Here we will adjust ``C`` (which controls the margin hardness) and ``gamma`` (which controls the size of the radial basis function kernel), and determine the best model:"
    ]
   },
@@ -818,21 +853,24 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 47.8 s, sys: 4.08 s, total: 51.8 s\n",
-      "Wall time: 26 s\n",
-      "{'svc__gamma': 0.001, 'svc__C': 10}\n"
+      "CPU times: user 1min 19s, sys: 8.56 s, total: 1min 27s\n",
+      "Wall time: 36.2 s\n",
+      "{'svc__C': 10, 'svc__gamma': 0.001}\n"
      ]
     }
    ],
    "source": [
-    "from sklearn.grid_search import GridSearchCV\n",
+    "from sklearn.model_selection import GridSearchCV\n",
     "param_grid = {'svc__C': [1, 5, 10, 50],\n",
     "              'svc__gamma': [0.0001, 0.0005, 0.001, 0.005]}\n",
     "grid = GridSearchCV(model, param_grid)\n",
@@ -847,14 +885,14 @@
    "source": [
     "The optimal values fall toward the middle of our grid; if they fell at the edges, we would want to expand the grid to make sure we have found the true optimum.\n",
     "\n",
-    "Now with this cross-validated model, we can predict the labels for the test data, which the model has not yet seen:"
+    "Now with this cross-validated model we can predict the labels for the test data, which the model has not yet seen:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -866,21 +904,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's take a look at a few of the test images along with their predicted values:"
+    "Let's take a look at a few of the test images along with their predicted values (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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RjcKTHWqWE2XtSwnBOUWYWgMw0Iy8T8a4MwyMMTKWo+7RbWIGyNMcwhP2PQqH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mAqC8ElAKX0HlxjLQM8yGl4Rx8IydxQJJsRosTMQ+w6MUbuge77is9qv/RLv4Qf+ZEfAQBMJhPE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+65B9/4xjfwvve9D9dffz2+8Y1v7HmdaITNsJWgA8uAu5RUcFc4lpIEvZCoO9xhkKrVnrgt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\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1ssu9XC977SuZLWcsDpcTOzaygnoXSB5nMeV5RXkucMcz3/gjzk5vUVVrjBmI44w8XwhD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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118d01320>"
+       "<Figure size 432x288 with 24 Axes>"
       ]
      },
      "metadata": {},
@@ -910,7 +951,10 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -920,15 +964,17 @@
       "                   precision    recall  f1-score   support\n",
       "\n",
       "     Ariel Sharon       0.65      0.73      0.69        15\n",
-      "     Colin Powell       0.81      0.87      0.84        68\n",
-      "  Donald Rumsfeld       0.75      0.87      0.81        31\n",
-      "    George W Bush       0.93      0.83      0.88       126\n",
-      "Gerhard Schroeder       0.86      0.78      0.82        23\n",
+      "     Colin Powell       0.80      0.87      0.83        68\n",
+      "  Donald Rumsfeld       0.74      0.84      0.79        31\n",
+      "    George W Bush       0.92      0.83      0.88       126\n",
+      "Gerhard Schroeder       0.86      0.83      0.84        23\n",
       "      Hugo Chavez       0.93      0.70      0.80        20\n",
-      "Junichiro Koizumi       0.80      1.00      0.89        12\n",
-      "       Tony Blair       0.83      0.93      0.88        42\n",
+      "Junichiro Koizumi       0.92      1.00      0.96        12\n",
+      "       Tony Blair       0.85      0.95      0.90        42\n",
       "\n",
-      "      avg / total       0.85      0.85      0.85       337\n",
+      "         accuracy                           0.85       337\n",
+      "        macro avg       0.83      0.84      0.84       337\n",
+      "     weighted avg       0.86      0.85      0.85       337\n",
       "\n"
      ]
     }
@@ -943,21 +989,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We might also display the confusion matrix between these classes:"
+    "We might also display the confusion matrix between these classes (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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bv9lhmNe5e3vWx30HQNb1bDZt2Eavvv8HwONPP0bLtk+xcuMSPotdwFMtmhst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\nl9i6dSu5ubnaY3o5WzDNqS1btow2bdowZcoUtm3bpjoWixcvZvPmzeT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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119354160>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -966,8 +1015,10 @@
    ],
    "source": [
     "from sklearn.metrics import confusion_matrix\n",
+    "import seaborn as sns\n",
     "mat = confusion_matrix(ytest, yfit)\n",
-    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
+    "sns.heatmap(mat.T, square=True, annot=True, fmt='d',\n",
+    "            cbar=False, cmap='Blues',\n",
     "            xticklabels=faces.target_names,\n",
     "            yticklabels=faces.target_names)\n",
     "plt.xlabel('true label')\n",
@@ -981,48 +1032,41 @@
     "This helps us get a sense of which labels are likely to be confused by the estimator.\n",
     "\n",
     "For a real-world facial recognition task, in which the photos do not come pre-cropped into nice grids, the only difference in the facial classification scheme is the feature selection: you would need to use a more sophisticated algorithm to find the faces, and extract features that are independent of the pixellation.\n",
-    "For this kind of application, one good option is to make use of [OpenCV](http://opencv.org), which, among other things, includes pre-trained implementations of state-of-the-art feature extraction tools for images in general and faces in particular."
+    "For this kind of application, one good option is to make use of [OpenCV](http://opencv.org), which, among other things, includes pretrained implementations of state-of-the-art feature extraction tools for images in general and faces in particular."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Support Vector Machine Summary\n",
+    "## Summary\n",
     "\n",
-    "We have seen here a brief intuitive introduction to the principals behind support vector machines.\n",
-    "These methods are a powerful classification method for a number of reasons:\n",
+    "This has been a brief intuitive introduction to the principles behind support vector machines.\n",
+    "These models are a powerful classification method, for a number of reasons:\n",
     "\n",
-    "- Their dependence on relatively few support vectors means that they are very compact models, and take up very little memory.\n",
+    "- Their dependence on relatively few support vectors means that they are compact and take up very little memory.\n",
     "- Once the model is trained, the prediction phase is very fast.\n",
-    "- Because they are affected only by points near the margin, they work well with high-dimensional data—even data with more dimensions than samples, which is a challenging regime for other algorithms.\n",
+    "- Because they are affected only by points near the margin, they work well with high-dimensional data—even data with more dimensions than samples, which is challenging for other algorithms.\n",
     "- Their integration with kernel methods makes them very versatile, able to adapt to many types of data.\n",
     "\n",
     "However, SVMs have several disadvantages as well:\n",
     "\n",
     "- The scaling with the number of samples $N$ is $\\mathcal{O}[N^3]$ at worst, or $\\mathcal{O}[N^2]$ for efficient implementations. For large numbers of training samples, this computational cost can be prohibitive.\n",
-    "- The results are strongly dependent on a suitable choice for the softening parameter $C$. This must be carefully chosen via cross-validation, which can be expensive as datasets grow in size.\n",
-    "- The results do not have a direct probabilistic interpretation. This can be estimated via an internal cross-validation (see the ``probability`` parameter of ``SVC``), but this extra estimation is costly.\n",
+    "- The results are strongly dependent on a suitable choice for the softening parameter `C`. This must be carefully chosen via cross-validation, which can be expensive as datasets grow in size.\n",
+    "- The results do not have a direct probabilistic interpretation. This can be estimated via an internal cross-validation (see the `probability` parameter of `SVC`), but this extra estimation is costly.\n",
     "\n",
     "With those traits in mind, I generally only turn to SVMs once other simpler, faster, and less tuning-intensive methods have been shown to be insufficient for my needs.\n",
     "Nevertheless, if you have the CPU cycles to commit to training and cross-validating an SVM on your data, the method can lead to excellent results."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) | [Contents](Index.ipynb) | [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.07-Support-Vector-Machines.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1036,9 +1080,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.08-Random-Forests.ipynb b/notebooks/05.08-Random-Forests.ipynb
index f567f238e..913fada4a 100644
--- a/notebooks/05.08-Random-Forests.ipynb
+++ b/notebooks/05.08-Random-Forests.ipynb
@@ -4,29 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.08-Random-Forests.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# In-Depth: Decision Trees and Random Forests"
+    "# In Depth: Decision Trees and Random Forests"
    ]
   },
   {
@@ -34,10 +12,11 @@
    "metadata": {},
    "source": [
     "Previously we have looked in depth at a simple generative classifier (naive Bayes; see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) and a powerful discriminative classifier (support vector machines; see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\n",
-    "Here we'll take a look at motivating another powerful algorithm—a non-parametric algorithm called *random forests*.\n",
-    "Random forests are an example of an *ensemble* method, meaning that it relies on aggregating the results of an ensemble of simpler estimators.\n",
-    "The somewhat surprising result with such ensemble methods is that the sum can be greater than the parts: that is, a majority vote among a number of estimators can end up being better than any of the individual estimators doing the voting!\n",
+    "Here we'll take a look at another powerful algorithm: a nonparametric algorithm called *random forests*.\n",
+    "Random forests are an example of an *ensemble* method, meaning one that relies on aggregating the results of a set of simpler estimators.\n",
+    "The somewhat surprising result with such ensemble methods is that the sum can be greater than the parts: that is, the predictive accuracy of a majority vote among a number of estimators can end up being better than that of any of the individual estimators doing the voting!\n",
     "We will see examples of this in the following sections.\n",
+    "\n",
     "We begin with the standard imports:"
    ]
   },
@@ -45,14 +24,17 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns; sns.set()"
+    "plt.style.use('seaborn-whitegrid')"
    ]
   },
   {
@@ -66,21 +48,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Random forests are an example of an *ensemble learner* built on decision trees.\n",
-    "For this reason we'll start by discussing decision trees themselves.\n",
+    "Random forests are an example of an ensemble learner built on decision trees.\n",
+    "For this reason, we'll start by discussing decision trees themselves.\n",
     "\n",
-    "Decision trees are extremely intuitive ways to classify or label objects: you simply ask a series of questions designed to zero-in on the classification.\n",
-    "For example, if you wanted to build a decision tree to classify an animal you come across while on a hike, you might construct the one shown here:"
+    "Decision trees are extremely intuitive ways to classify or label objects: you simply ask a series of questions designed to zero in on the classification.\n",
+    "For example, if you wanted to build a decision tree to classify animals you come across while on a hike, you might construct the one shown in the following figure."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
-    "![](figures/05.08-decision-tree.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Example)"
+    "![](images/05.08-decision-tree.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Decision-Tree-Example)"
    ]
   },
   {
@@ -97,23 +77,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Creating a decision tree\n",
+    "### Creating a Decision Tree\n",
     "\n",
-    "Consider the following two-dimensional data, which has one of four class labels:"
+    "Consider the following two-dimensional data, which has one of four class labels (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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jQJ0sHpMKry8RiF9j3b6ayX4jI4z27sYpLJQoVzfSe/Si+xczSrprerOzs+di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\nx7fzqr/V11bxzvBMfMrHmrU3CuX0fOsIc568/7Ib7hU2icuOcdckLm3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s7KmtVrN6RSaL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VODe/cl6vOFZHTqVO/arvsPMKdny9T63bgnnb+fOkb+8GEoBgNeWzdwt0bP45NubYnVO4HsI25l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\nZiA+eeokxooKIsMj0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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11616a908>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -133,15 +116,15 @@
    "metadata": {},
    "source": [
     "A simple decision tree built on this data will iteratively split the data along one or the other axis according to some quantitative criterion, and at each level assign the label of the new region according to a majority vote of points within it.\n",
-    "This figure presents a visualization of the first four levels of a decision tree classifier for this data:"
+    "The following figure presents a visualization of the first four levels of a decision tree classifier for this data."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](figures/05.08-decision-tree-levels.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Levels)"
+    "![](images/05.08-decision-tree-levels.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Decision-Tree-Levels)"
    ]
   },
   {
@@ -163,7 +146,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -175,14 +158,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's write a quick utility function to help us visualize the output of the classifier:"
+    "Let's write a utility function to help us visualize the output of the classifier:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -207,8 +190,7 @@
     "    n_classes = len(np.unique(y))\n",
     "    contours = ax.contourf(xx, yy, Z, alpha=0.3,\n",
     "                           levels=np.arange(n_classes + 1) - 0.5,\n",
-    "                           cmap=cmap, clim=(y.min(), y.max()),\n",
-    "                           zorder=1)\n",
+    "                           cmap=cmap, zorder=1)\n",
     "\n",
     "    ax.set(xlim=xlim, ylim=ylim)"
    ]
@@ -217,21 +199,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we can examine what the decision tree classification looks like:"
+    "Now we can examine what the decision tree classification looks like (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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LE2ruPffcCJgBWhnkrVxPcJcgHBooUWksMpmMmf/+C5u+W4FZajoqdzfCn1lg\n0OU1giA8PkTybUXmMXFUr4dlBZQdPUnssMH07NG1VYahoz77hvE/r+X+YGdqTBzRJibcvRTPzKjt\nVfFkX7/BXltrxs+fVW9fPt5e+Dz7oDCCp4c71179JVtWrsUnPYNb3l44L52PW7WKUNVJOXm1lhG5\naDRkyWS4V5tqcDGoIzMq19aOnDudNQeOYhufyAQq1/JKIB0/zZp/fsaCv/+xST8PQ7KztWH2bww3\nLHznTiZHl/0Pi+RU1G6udFowi5A2NAlNEIT6iY0VWpHOvPYzSt3VG9g8+yqrnn+NzMwsvV9TceI0\n1Z8y+lWoKNh/GPPzMTU+CLhqdSiPn2ly/yOmTWTS6v/htnklUyK/Y9iU+gv4u4X2I/Xh7Rd9vDg4\ndzqHHB24AWwI6kjH135ZNRRvY2XF9K8/JsvHq6qIBtyrp2wZG092fgHaasPSTypJktj/3gfM3bmf\nqVeuM+vICXL+/CG3b9f9CEAQhLZFJN9WZD5qBJnVnu/eAHyAQK2WxRdjOfL5t/q/qLb2ri0ynQ6p\njufMkknznj2bmprg7e6GicmjB04GDRvMhcXzOODiTDIQFeiP1+u/YsHvXqPnxhVURP6PGau+pc/g\ngTXOs7W2wjWo9tZzBTl53Ji6kG2LfsmxHfuaFXt7cf5cDOGxl2u0jbqbw5nN240UkSAITSGGnVvR\npKcXsN/WmlNHTpIRE0fP4hLCq32/+u45+qLq3xvVzVtVw71ZwPVrSahNTRgN3H9immRuhsvYkU3q\nOz3zLlqdFr8Ono0+Z8arv6Dg6fmk385kSscATCvvhJ0d7HF2eLikxAM+ERO4dOYiIcXFAGQCTioV\ng1UquHaDg59+RWb/3ni4uzbpNbQX9a0QfFxm1AvCk07x/vvvv2+QK2WmGuQybU1gt2C6TBxDWuxl\nJlRbugMQH9yJrpPH6fV6nQf1Z3NJKdcqVBwrL6dIreHpMiVDS0r5ztKc5ODO3OgciPqZhQx/xJBx\ndcUlpax9+30sP/2KijVR7L9wCa/Qflg1srqShbk5bi7OKBSNH2jx8vPhdnBHTuskomUyVHn5TIKq\nLeL8leVEuzgR/IRufNGhgwfbTp2jV9bdqrZoVxd6/vY17OxsH3GmIAgG4+lf77dE8jUQlaMDV06f\nx69MiQw45uSIy8vP00HPRRIUCgXdw0LRde9Cx43bGKzTIeNe0grVaEmaOJoZf3kX/+DG7/e65aMv\nmb97P16Gdr37AAAgAElEQVQaDZ5aLb3SM9iem0+PVt4XtYOPN11Hj0DpaE/I/sM1togrBHKmjCew\njuHpJ4FMJsNlQB925ReQJFdwuWsQXr/+BUHdgquOKSoppaC4BBur5lcgEwShBR6RfMWws4GEhPYj\n9btP2bJ1F5JOIiRiPIGB/o88p7iklB3//gLLS5fRWVhgOjacyc8uatTQokwuQ1fXYc0YlrS4mlRj\ncoAMsLx2o8n9NNfQUcNZ0S+EpedikHNvR6NPOnjSLzeXgqJiHPRwp6fT6diy7Hs4ehKZWk153xCm\n//ZVLMzNGz7ZSDp4eTK3jtnfWq2W9R9+isuRE1iXlXGwZ3eGvfs63j7eRohSEIS6iORrQH5+Pvi9\n+kKjj9/+90+Yt/tAVeLLSkphv4M9YxtRz7drcGd+7t2LoLMXqoZqj7g4ETJ1YpPj1jjUXlurecTz\nWn2Ty+XM/uQDNv+4irz4Kyiu3OAPGXeQf/oNO9dvo+Nf3qF7n5Ztbr9jeSSjflxVtbGBOjWNTXIZ\n8957s+UvwMB2/LSGiE3bqkYKBp8+x5p/L2P+5x8aNS5BEB4Qs53bKI1Gg+3FuBq/IHetltITjVse\nJJPJmPTBe6yfOoEtXYPYOGwwtv/3DgEBfk2OxW/ONE47OVR9fcnWBveZLSvoL0kS2/73M1FLX2Lz\n4l+y8fNvH7mEyNbGmpmvvoCjgz3PFBVhxr1PjlNvZ3BleWSLYgHQnblQY0chU8DiQmyL+zUGKTaB\nh/fxsU+8SoVKZZR4BEGoTdz5tlFyuRztw2tkAamOtvq4uDgz9/13WhxLv6GDuPrFv9i8fQ9otXSc\nMJqwFk502vnTGoZ8/QPOunuzdksTrrJF0jHztV898jzTjDu1227XbmsqnVntn6vO1LSOI9s+bR31\npCtsbTBtYGmYIAiGI+582yi5XI5q+GBKq7UlWFvRYcIYo8QT3DWI6W+/yvR3XqenHmYYq4+frkq8\nANaA/NS5Bs9T1fHcUuXbuC37HsVp7CiSqhVFyZPLkIeHtbhfY+g6ZxpHXF2qvr5tYoLJpLFN2nlK\nEITWJT4Kt2Ez33iJ7Xb2cPESOgsLPKeMZ1AbTAh3Mu5w4ue1mGbnou0YwMRnFzU4UUmqa+JXI5KD\n3/RJfHngCE+rVCiAdXIZJg1MXGuMERHjOSKDuP2HQa3GfPAApi6a0+J+jaFrz26YfPYhUVHbkZUp\ncRwykCkTjfOhTRCEuonk24YpFAqmvbDE2GE8UmFRMUdfe5e5lQVD1AePsvp6Eks/+eCR51mOCCPr\nQizulc95iwDCQhu83q2DR3hBpeIIoAXm6SR2HzuF7qXnWnxnN3zKeGjk2ue2rnOXznT+/RvGDkMQ\nhHqI5Cu0yOENW5lZrVKXKTDwxFkSEq7Srdqa04eNXzCLPTod5YeOg06LfNAApj7/VIPXM72bixlQ\n/T7OPjObUmU5ttaNK/rxMEmS2Be1A2XsZTT2dgxdMAd3jyezcpYgCIYhkq/QIrriklp/RG4qFQnZ\nOUD9yVcmkzFh8VxYPLdJ19ME+qGJPlrjmrmBfi0qJLH+318wZm0UTpKEBEQdO83Q/36Em5tIwIIg\ntA4xA0NokaDRI7j00B1ndKA/oUMG1nNGy0x87ilWDh3EDTNTCoGNAX4EvfhMs2sa5xUW4bYnGqfK\nWskyYEZKKsciN+ovaEEQhIeIO1+hRbr16MKBV18gasNWHO9mkx3oT5eXnsOslZbpWFqY8/Tn/yD2\nUjwX7mYzZcQQzM1qb93YWDl5+bgXFtRokwGKouIWRioIglA/kXyFFhs9dzraWRGUlCmxs7E2yM46\nvUK666WfTn4+bOgSRNeEq1VtmQo5dn1D9NK/IAhCXcSws6AXCoUCe1ubx25LO7lcTvc3X2Zdj67E\nmpiwz82VE4vnEj5prLFDEwShHZNJ9W0Mqm8XDxvkMoLQHJIkcTMjExcH+2bPmhYEQaihz4h6vyWG\nnQWBe7OvA7w8jR2GIAhPCDHs/JjQarVE7z7A9shNFIjJQG2KSq2mVKk0dhiCIDxGxJ3vYyAvL5+t\nb/2R6TGXsQH2rlyL629/zYARba/U5JNEkiQ+/zSGawc7oCuzwbFHHC//3gsvDye9Xuf8xTQ2ryil\nOMMWe79C5v3CiW5BHo0+v6SsjPyiErzdXR+7Z/KC0F6J5PsYiP5hFUtjLlftyzvpThYbf1xN/+FD\nxJtpNZlZ2dxMvklIn15YWjy6trQ+rFoTS+bKpbhJjvcajsIy2ef8/VP9Jd/8omJ++JMpbrd/iSPA\nDfjq1vd8/LOqwSVWkiTx6acXubGnIxT4Y9HtAk+/ZUvPbi3fiEJofSVlZXyz7Co515ywdCpj0nxr\n+vfxNXZYgp6I5PsYME27zcMp1jb9NhUqVYMbGDwJJEli/Udf4L1zH50Ki9nv44XTr54hrJU3E0g+\nb4XF/cRbKfuyLxcKr2Nh0bi1x95yc+xMH9zFFqkzSddVVH29dUMSLrf/VOMc++vz+XbHPxk9qdMj\n+96/LYXMVS/ipnO71xAzgGX/+oxXv1Y3+0Pb3btF7FlRQEWWI1beeUx+xgV7+4d3Dxb0Ydk7abgc\newcLFEjA9zHbyf3iIj5++h1ZEVpPt0d8TyTfx4DayxMJaiTgYi/PFhWXaE+OHTjC0HWb6aDVARCR\ndpttX/+IcuSw1r0Dtiyq1VRhK3HeZQaKRuyda2V2C+5erPoPej/xxrr1oUx17w7nlvVeXNABiqrz\nJHQk2/TD2iH8kf1fStiD7/3Ee9/Voewqt8DJs/bWjA1RV5Rz6N3ddIl/BQtAQuLTpO8ZuyKixZta\nlBQUkHIsBs+eHXHx82lRX+1B9q1UdOd6IK/2e/fInsKGPZEM/u0kI0YmNIVIvo+5Ec8u4ufLicy4\nnIg1cMDNFe8lC8SQc6X8mLiqxHtfaNptYi7GMnjwgAbPT72VTszu/ZhY2xA+czLWlo2rEz1imoK1\n547gnjUcAKU8B89RWrprHEHT8PlX8AUu1mjL69mXsjQfupTbAuAzZSxrIrfje2tG1TF3umzjmZGz\nMCl/dBWxRKvaCVFrn0OI2SCsK/tviuj1xwiMf7DNogwZfuenUbo7lgGjhjW5v/sOrd1Pyvcy3LLH\nkmIbR1bELma+NeeJ/vs2LzQhXVP79+tYbl71tyE83kTyfQy4uDiz4PsvOLRrH+WFRQyaNBZXZzH0\ndJ/M1YUKoPo97g17WwIC/Ro899iOffDxl0wtKEQFRG3bzYj//A1Pz/onNGm1Wo4cOExeQQY9v/Pk\n7I+R2JVY4trfnPA5M+o9rzmsbewY+WFXTv+wHlWmGeY+5Uz6xWBMGlG+c/D8gew8tg2fWxHAvQ8H\nDuMKsbZu3pt3ebEKU2p+MDGX7CnJb/rs+wuHT3HzSBYVFFIW7Ylv/jgAPIpDKVznzqUhZ+jdiC0m\n2yv/TsFE94qEC12r2nJsY+g7rqMRoxL0SSTfx4SpqQljp040dhht0ui501lz6BjzL13GHLijkJM+\ncSxD3N0eeZ4kSdyN3MDMgkLgXvKef+0Gm1asYeY7r9d5TnZ2Drve/jMRsfFYAZs67iXoD18yvPdk\n/b6oagK7BRH4UVCTz/Pw8WbSl3Bq7UY0hXJc+1gxfMasZsfRb9JAdkYexCtndFXbbe9dLJw4skn9\nHPh5D3lfBuGgGkIWe/AnvMb37TX+pJ+PofcTPJlfJpMx6f0RHPhsDeXXLDBx0hI0y4Xg3s0fYRDa\nFpF8hceelaUFc776iP3rt6C9m41j757MGT28wfMqVCpsMrNqtZvcqd123+Hvfuap2Piq5+8Lkm7x\n2dcfwdetl3xbwsPHm+lvNf35bl3cPDzp8fZNLv+8EU2GFSa+JQx8PgArK5tG9yFJEqnbyvBR3buD\ncyaIu1ymA/2qjimnEAc/UWXMw8eLRR81bctN4fEhku8ToFSpJCM7F39PD0xN2+ev3MrSgslL5jXp\nHHMzM4r8fCHvwa5GOkATUP9yDvO09Fozz91Sk5p03cdZ/3GD6TdWQqNRY2Ji2uTnsjqdDm3Bg4mC\njgSQzmmsccMeH8op4m7YBiZOfkrfoQtCm9I+34mFKjuXr0a2YSv+d7LYHuiP5wtLGTQ23NhhtQky\nmYzOLywl6h+fMjE1jTyFnD39ejP7+SX1nqOq41lwrpcfDT9dbj9kMhmmps2baa9QKLAILoHsB21d\nmE7a+GWYe3bE3teSiRFPYdKI2eKC8DgTf+HtWFzMZfy++5nulaUPuyalsP2zrykJG4iNlRjWAwgJ\n7UdQ5P84vC8aBxcnnh404JF3c4OeXURkwhVmXEvCDNjewZ2iZ14xXMDtwOg3B7OnfBWml7qhsSzG\ndOhNnnv/lUZNInuSSZKklxng6TdTyEhJo9eg/lhYivcBYxHJtx1LPnaSaQ/VHB6VkcnxQ8cZK7bM\nq2JpYc74iAmNOtbLuwPTl39F9LZdFBVlYfXmC3hU9IHyVg6yHeng78vT3/qQeScVC4sOODoNrffY\n89EnSYnOQmYi0X1KJ7r07WnASNuGvJwcdv1zH8p4G+Q2WrwmmDL+2abPMdDpdKz9SyS6/d2wLQ0h\nzvsgIb92pv/Ywa0QtdAQkXzbMVMXZ5RQY3FIqrkZ3o94pik0zNLCnIlzplOkziTWzYXsNGNH9PiR\nyWR4dvB/5DGH1x4g+z8B2Ffcm/Z8IfocFf93jpDh/Q0QYdux/a/78DiyEFnlbIPilNuccDvEkCnh\nTern6Ob9WG+ZguW9QqX4pk/l0tebCAlXNfsxgtB8YlejdmzUjMms79UNbeXXZcDZEWF07RpszLAE\noVFu7ijGvuJBCU3Xgv4kbH6yPumUFBeiveRZlXgBbDVe3D5e2OS+8hLKqxLvfTbJvbl542qL4xSa\nTtz5tmMW5uZM/+JfbFu1HnnWXRSdOrJw3nRjhyUIjaIpqqNCV9GDcotJ8Ve5uD4eTZEJjr1kjHlq\nEgqFotY5jzNTUzMk84pa7XJzXR1HP5q5m4QWDYpqb/tlrkl4ePd7xFlCaxHJt52zs7Vh+q+eMXYY\ngtBkVt3KkFKlqrs+DSpse6gBuHU9iWNv3qFD1mwAVNGlbExfz9w/zDdavK3B3MIS2+H5qDaUYca9\nyVF3Hc/Qb+qjN9Woy4iFo1h9bBVecfMwxYICsxu4TivB1tZB32ELjSCSryAITaZWqzi97wgKEzkD\nRg1vlaVBk94ax+ayVUgXfJFM1FgMucOsl+5V6Dq/MZYOWQ+qdZlhzd1DrpS8VoiNrb3eYzGmme/M\nYY/bdnIuyZHbaOg9PaBZE8+sbWx56ptZHNm4F2W2luDQDvQaMq0VIhYaQyRfQRCa5Nb1ZPb88Sye\nVyOQ0PJjt3VM/nAYHXz1uxuRvZMTSz9dSGFBLgqFSY2kqlXWHpJWlNmiVJa22eSbFH+VMz9eRpVp\nhoVPBcN+OZAO/g1PflQoFEz6hX6SpIWlFeMWT9FLX0LLiOQrtFlF6kxjh9Cg9CzJ2CEY3PFvLuJ/\ndUHV1/4Jizn6zTrmfdA6WwHaOzjXavMa5MCdnbex1XhVtWl7JOHqNrBVYmip4qJCDv3+Kr5plbtC\nxcOO1FU8s8JTrG9+Qhks+T4Ob6RC25GuqyCvZ19jh/FI9xNvW9viTaksZfvH2ym7bIXcRkvAJHuG\nzmza5gePUp5ae1lKeWor7ptch8ETR7ArbRu3dp2BIitMu+Yx/u361wsb28nNR/BKq3nH6ZEYwZn9\nRxgycXQ9ZwntmcGSrzrkyVqbJzRfokbJtTQfeAxWlRgr8WrUao5vjaYks5xOYQEE937wDHDT/23G\nZc9CHCo3Yr8Tf53zDifpN0o/xRTMPNVwo442A5v4QgTa57Ro1CrMLRq3B7Ox6HRSjeVCADLk6HRN\nn7UstA9i2Flos9raHWVbUVGu5KdX1uJ5fi7m2HBxZTzJz2xj4gsRKJWlVJxzR86DJTcO5Z1JPhBH\nv1H6uX6/JZ05nrQd74yJSEjc9tlO+NPd9dN5EykUChSKtp14AQbPGMbGDTvxuR1R1XYnaBvjx+l3\n/2fh8SGSryC0MelxV7i24zYm1jLC5g7H3t4RSZKI37sO3blDnE01wfv8fzDh3vCvc3l3bm/MoHhB\nAaYmZiCv4zl0y0sCV+nSvyeeq7w4GbUNmVzG3BkjsLVr/UlOF6JPkXQgE5kMOo/3ImTogFa/pr7Y\n2zsS9hd/zv+0EVWGCea+Ksb/coCoLPUEE8lXENqQi3/dSuZXY3EvnokOLat2b6LXsm6UbviOF5Z/\niZdWSynjKaHmm7b13WBOZMTjF9IL1eA7aLc+KKaQY52IzRRHrlgU1zinJSML9g5OTHhmarPPb6pj\nUYe4829vHJRDALgSHU/FH44zcEJYs/rTqNWc2HGIkpwy+ozvg6dP65dcDe7bneC+xhkhgHtzAczM\nLFqlEMne5TvJ2K9CKpdj06ecqW9GtPlHAcYmkq/QZmi1WmLOx5BlaQI2rTNzti26kniV6+cv0bFP\nL5KivHEvvndHJ0dB5+Q53P70D6gOXeOf2vcxowgdhzGnCAvsqvoo9TnCU+7lWBSk0Oc3GjZYfEDu\nZS8UNkp6TCpmWG9vKKj5ED3WrQ9lKt/HYng/ZUcBHsoH4+ZOpd25vnUTAxu3H0YNxYWFRL62mQ6X\nZmOGDftWniDw18kMnRmuv4DbkOSEaxz97BLaa07gXIL/DEtGLRqnt/4Pb9hP6Zf98NJ6AqBN0rBZ\nvY5577evgif6JpKv0CYk3Uji+OGNhPS2xT5fxdEDR/AZ+j7W1nYNn/wYO/yfVQzbdpQIpZI4CwvK\npNE4UnM/4etnYUDFtqrnuEVcIYslWMn/iJOuF5luW+i99BYWFt4AmJubseiNgMqzzYDaQ8I5+YBb\nK74wPdMW175b05U07w7u4IoD+F1airyytL1nYRjXVkcxKELd7pb96HQ6ov9+Ed/4yqVhBZD95RUu\nd7pAj1D9rCbIOFaKS2XiBVBgQvE5K71tgdheieQrtAlnju9k3IQOALgDAQE6dmz9nuGj3jBuYK0o\nK+48U7Yeoke5CoCe5eW8JNvLck7jSCgAKsqwKw+pMYHKji5YYkXuC1vI906h59iBWNiN5VRTLu4A\nXYr0f8ebm3WXIz8fRZ1thnUnLWOXTsTMrOXLkKy6l6G7pqtKmFo0WPVo3j6OqjsmWD20p4xphhcF\nBTm4uHrWc9bj6UbCZWwTQmu0OZV3IelwlN6Sr6yOz0AyxZO3/r2pRPIV2gS5vASq7bgil8uxsMgz\nXkAGUHr6GD0rE+99vSU1GQ4fY1awjFLucs18Cy7ywFrnpvoG8KsX/vzgzqIN7CdcWlrMpl9H43dt\nPjJkaPaqiLyyiqUfL21x35PfnExUySrU5zyR5DosQu8y69fNmyls5aertcGA2jcNR6c+LY6zrbF1\ncEBlmQ1lD/6GJCTklvpb4hQ4zoXUk9dxUHYG7n1gdAhTibveBojkK7QJErXvjlRqKyNEYjhW/QaR\naG5K14oHa2SvWJihtu9MbsF1LHFkaMW7nFd9g5pyTLEAIM8qgdDXp7S5N7fj6w/jc2121XpWE8ww\nPx5KUmIiHbt2bVHf1tY2LP7XIkpLi5HJZFhZ2dT4fl72XU5tPgnAoOmDcXKtf0x99NJxrIxdgdOZ\nCVhp3cnw2EuvZz3a3Y5IAJ7evkjDDqPZ0wuTyv9j6b7bmDp3iN6uMXB8GFrVEVL2xKErl+HQT2L6\nCzP11n97JZMkySDjA7kVSYa4jPCYOn74EBXlMXTr4YIkSew9eBcvtxfx9u1G4sWLSJJEt75921zC\naYkrFsUo353B+B3H6aRScd3MlJ/Gh1O87WsceXCnokXN5a4f4W7ZFYWljk5TXJs9y7c+kiRxaO1e\n7hwvR6aQCBjnzKBJw5rUx7bPt2DxY827USUFeHx8kYGjRugz3Bqunr/M8T+m43Xn3uyr2567Cfur\nN8H9etR7jiRJxBw7Re6dXAZOGIqdXfvd2UetVrH3h50UX5Fj6qwmdGE/vAP9jR3WE2FI79o1yO8T\nyVdoM65duUbC5XPk6HSYBy4gsNiWze/uxT52ODLkFPQ8zNS/j8bNs+nP5dLTErmZtAkz82JUFQ4E\nd1uEq5txZ1RfsShmUMEW1PGF3IqJw7G7J9kjR7BrgAVexQ/uTHToUD0XRcQrrVeQYff326n47yCs\ntPfuGAvNb9Dh3TSGTG180rweF8+5X+lwKQupaksN2MiSNZOa/dxXkiQORu7hzuF7e9p6Djdn1MLx\nNT6ErX49CtfDs2qclz1iAws/FXdfgnE9KvmKYWehzQjqEkRQl6DK8pLuHPjHbnwvLqkaxrSPWcKB\nz9ew4MO5Teq3tLSYtJvLmDzVHbAG1Gza8G9GjPqkVbbCa6qQvr0I6duLInUmsd5uKEedRbWld9X+\nrekBm5kxv3XrFmce1OClfTBUa1/RiZQ9sQxpwlLezj27k/7KPlLWp6DIckMXmE7oix1bNOHqwM+7\nKfm8P+5adwCKz2exT7OLcUsnVR2jzqr9O1Rnta9Zy0L7Y/x3HkGoR3mSRa16uBXJTV+4H3shitHj\nXGq0jR5rw4Xze+jbf3KLYmwNw/82lduB+ylIkDBxUhOxaBBOLq566VutVhEduYfiGxJmHlpGPjUa\nG1s7dBW1h/MlVdOH+EcuGMuw2WqKiwtwcAxt8WOCjGgVnpWJF8Ba686dQ2qoNofLwr8CrtQ8z9y/\nDcxAE4RHEMlXaHN8ynWc3/kvlLI0VIytugMEMPHPR9c5oUn9aRNuo1DUTAJmZgrUzilN7qu69Cyp\nVYpUKExMGLekdfZcXf37SFwPLMAOC3ToiDz9M0u/m4tt33K0SWoU3LtjrKAIpwHNS5wmpqY4Ounn\nwwLaOj4UaGp+PezFUHamr8T18r1nvtk9djPpxaY9rxYEQxPJV2hTbpy9ROpzb/HitRR0wDeKw9zS\nbsGWEHJcdjNtYj694uOb1GcHF1cORJ9gyOiAqrYTe1NZPKAr5k3sqzonXQWxbnCFewm4XFnGjs92\nUBJvjsJWS9BUtxoTo67HxZOakEzv8AG4uHs0+7rNdfVSHJZHw6pmTcuR0yF2Dsc3RzP1zWls1qyn\n6LwFMoWE81At0543zDPT0pIizMwt6qxz7DhQiyquFDOsAVBRilNozWUyHXx9eGb5PC4ePQHAlGHz\n2uXMZaF9EclXaFOS/vVfFl5Lqfr6Ne0t/hHwDLYjn2bptA4E+DS9MICdqwd5+VpObLuEZKZGVm7G\nGP8xuFq3bMKVtzqTPHcZ1yqrNm78vyhc9izErrIgRkpcHJZ25+k+qA+Rf16Fyb5QHCumsuPbw3g/\newmv5/S33KMuOp2OnOwM7B1cMDe3IDP1NraqMTWOMcOKshwV5uYWzPtT65cDvHn1Bhc2xKEtlWPq\nW0ZBjBxdojvYleI2QcPkl6ZWDVWXlhRhYiUjMeRzbIsCMTE1w2mwhikvTq/Vr0KhoH948+52r166\nTMKuG8hk0GNSEJ16dmvRaxSExhDJV2hTLJNrb+Lb3VJJxCu9W9Rv/6Bu9A9qvTfVkuJCVGc61KhE\n5VTSk6t7NlFSWIz1jklYS/eGYjvkhZP2037sZxW0Wjyxxy5w7qsUTJMCUbvF4jNLxpA5Q1nzzV58\nMx4MaWdbxTAwvHOrxVHdzSvXOfLGbTwz781MLiWbQo7SjVFQBCU/3uG4bzRDI0aRkXKLnW+ewztl\nOj1QkCLfT2GXOEZOmKLXu9pze09y7QMLXIvu3eWf3n2G4j+fpU/447NjkvB4EslXaFOUgb4Qf7VG\nW4V3ByNF03gSEkh1PCOVIDuxqCrx3ud0tz9p505Cf/1c/2byBW6nbcHcvJjiEmuuftaDjumV9XzT\nIe+bRG71SibkNUfi/heFWXInVB1u4TtHTqdu4/UTRAPOb7hclXgBrHHFBHM0VGCCOTZaTzLPnCI/\nLJutn0cRkPJaVTnJQN1YEhKK2PenCzy9yrfBrfgOrztA6u5idEo5tr0rmPJ6BObmFrWOu7IxE/ei\nBzG5FQwkYcNGkXyFVieSr9AoyUnJxF48hQw5A4eE49mhdZ5Zdvzdi6yLi2fazQx0wLaOAfR/bnGr\nXEufbG0dMOl/G93+B/WH860SCRrTgcLsAoopxpwHE7MKHOMI6tkJyKZInYmdafN/niXFhdzN+oFJ\nEe6AI6f2Z1GUXnPXGqfyrtw4EkXE69PoPUpDVuYtXFzCDbrtm6649tuNGbaoUWKCOTp0JCUmUDrT\nCfvCySSwAVe64c69YhlyTPG8Pp7Tew8zdPLYeq9zcucRcj7pjGeFPwDaK2o2K9fXucuOpqB2THW1\nCYK+ib8yoUGnTxynMP8Mgwa7IklaTh37mW49pxDcwpKBdenUvxd+Kz7k4J5LyOQypk4eh4V5ywvz\nG8LMP09lp906SuMtkNtq6TzVmd7Dh6NRq/np8Eqcjk3FWnIl1yIex1nZOLj3ItasDzlXL+LimEpO\nPmRk1V/z5opFMVZmt2q1x53dztzRD5ZSufuaE2OZjJXSuapNi5oCFzW37ConmDlBikpDFwOuyHHp\na0rxvjwsJaeqtgJu4s9wAC7afU63pJewrKzx7UIQl1mHG92RIUNLBRIaFCaPHnZOPZiHc0V41dcK\nTCk+Y4NGo6m1rtsyuAzpmlS1pE1CwipYqY+XKwiPJJKv0KCUpHOEj7o3bCqTyRgc5smxw0dbJfkC\nWFmYM3G24TZq1xdrG1vm/LF2ARATU1Oe/nQpZ/YdJe9mIX0HBxLUa/q9zRDKu3MlwJcb8Midhq5Y\nFAPg7S6jq0nNu1W1vTmS9CBhBAQ5oAtfg3pXD0yxRELiepdInnkrGCvbe+cnapSkZ92qmqltCOFz\nx7M5fSNp+22Ql9hC9zT8u+nIT9+MwlaDZ44zlkcca5zjTCeySSCbBFzpTmb37Uwc3UCRlbpWSMnq\n3t5uwutj2ZT7M6bnuyPJdGgHJDLrtYgWvEpBaByRfIUGyWUVtRvrahPqJZfLGTS+7lKNjU1+XQLl\nBDwn3wIAACAASURBVEu1h4mHjRrNjqhlhI/2qmrrMCGfW102Y33VClNXNaHPD8XKtqwqcXc1sQR3\nZdVM7ebKzc7m4H8PU55ijpmbigFLexLYLajOY2UyGTPenE3Fy0qUylIcHGvWp97yySYkpBqFVUps\nb1LkcxEPWQgm/nFE/HJ4g1XJAke7cOvoDewrOgGgoQLb0NI6J2rZOznxzLLF3Mm4hUwmw8WlF8nX\nElF7eGJn79gmKqAJ7ZP4yxIapNXV3EFGkiR0krWRohEeZmtrQ7/QGRw5dBC5vBydzpKg0bPxVIRV\nJfZ7d85ler2uJEls+t0u/C4uxaEyYUYnbMVphTMOTs71nmduYVnns+awhWFsPr4R3+RZyJBRrLiN\nzxyIePX/mhTXwAlDUZUf4ubOWHTlcmx7q5j56qPXLHt28CXm8Dm2v3KWvORSFPLLmJmb4TBIzaTf\njcbZvf5dkgShOUTyFRoUNnwye3etpVdvGyrKtSTEVzBj7i+MHZZQTefgIDoHP7jjPF+Qy6HP93Kz\n1JRB08LA89Gzg5sj9tQZnC+NRYYMFWWkcBBduo51H//ECx+80eT+XDzcmfnNMI5HRqEplOE10JHQ\ncbXX9DbG0OnhDG3CqWq1ivNfpKFJdqAzw7DRuYMSpGiJHdrVLPlsQbPiEIT6iOQrNMjX34//Z++8\nA6JK03z9nIrknJOAmBADoqAgScyhzaG124476c7O7M7Optlwd3Z2Z+/M7s7Mzt07s9PT0z0dtFtb\nbXNWUBQUsyRBBBQByaGAotI594/qhq4mQyGo9fxXp875vq+g6rzne8PvfeXNH3L7xm0cXdS8/o3I\n56q13/PGg4LHfLL9EUH3tiIg58DekwT8xJXJ23q2zZMkiRPvHOHJeROiTobT7A7W/tVL2NkP3EtZ\nr9MhF9XoaKOQA0SxFQVqmk+UccjnAGu/P3SFLA8vb9b86fAM7kgoKcjH5cE8qrmJE91a0gICxru+\ntLdrcHR8OrFxGy8Gffc7smHjK8hkMubMm0PUzOk2wzvOOfrvZYTeexUFKmTICa5eRfl7j3s9987u\ndPS/iyeoaAMh5etwPbiFQz87PKh5ohfGUzftNGWcZwYvdzVrd5PCaDrkSVNjvdU+02jjGxhIh2s5\nEmLPN+11KBS2Lkk2rIvN+Nqw8ZzR8aiXXWtVT4EJAE2mEQexWwBEjoLWG/YMps23QqFg6Y9j0Qbc\n72rI8CVOTRFUV/QsixqveHj54LiiEgc8eczVruM6WnFN1vQq0GHDxkiwGV8bNp4zHCPaeh6c0Eft\nqqKnkZUpGbR3I2RSOAnfnkWHYLnLbZlwg4hp0wc1xnhh419tYfq/GjEkXSMv4lc8id+L8vvprP/L\nTWO9NBvPIbaYrw0bzxkb/3Yqv8h7B/8bW5GjoirsGJO+NbHXc40hTdyRfYhadAVgAkl4Jhp7Pbcv\n4lelsvf2p7SeisClLYL64EvM/LY3KtWzIY7yJYIgEL8ylfiVqQOem5t1g6KTj5AkiFgcQHRyXL/n\nt7eb67RtcWMbX2IzvjZsWJmqhw/Jy7rDpOgphE2d8tTnDwz15fVzDhx+9yLeGjmvrlhJmasBsCzq\nvXOiENd9C4gQZwKgo43imf+XP/v+Xw5pPkEQ2Pr3L1P9egVVZQUsn7d4WLKV2UcyuX+wHmOzHPup\nWlb8YDFunn2XLA0HSZK4nn6ZuvtNBM/yJyouZsg5DDmnsij7V1c8NGZN6JLzRXT81QUS1vas4+7U\ndrD/nz9Hf9UfAOW8ajb+0zrs7QdXqldZXs7NI7dBkIjbEIdPwPjXObcxOGzG14YNK/HoYT7Hf7sL\nuwurCOhcy1X7PK6v/oTNP3r6ZSoKhYKoVUlfEfAw9DineE8b3pqZXa/VOOHSGDmoeG9v+AcF4x80\nvDaN+Tm3ePgzD/zbzQZMKpU41LKL1/57+7DG6w1Jkvjwb/6Iy9mVOIn+FClLubdhD5v/ZmitFIsP\n1+CrWdj12q1jCqVH8klY2/Pco786itfJ7V3drsTTJo657GPT320ecJ7bF65z9yed+DdsQELi+LEz\nLPjXJqbMebbc+TZ6xxbztTHuqG1opqru2cmUBbhfdJmq8v/COXsFgZ2JCAh4amcgHFxA7tUbY728\nXpH0PX/+gkGOJPaS8dsLWm07l0+cpbTw3ojXUnT6IZ7tM7rXgYBwYzJ1tZUjHvtLbmRkdRleAFdD\nOMbD0ykvKh7SOKKmp1KWsbX3W2l7np1Fm0kZctryBueOz/ukAv8Gs+61gEDgk6Xc2j20tdoYv9iM\nr41xQ1tbOx+++2tyNDe42HqK/z79MQ0tLWO9rEFRW3MWQ5MC79Yki+OuhnAqblvPgFgT/yUSbYon\nXa9FROxmN6NQDlxWc/3MFXZvTqf5RwlcfVPko7/5EKNxaLHiryLIe+62JbkBudx6zrm64sYuw/sl\nntqZlNwu6uOK3nGcrsNE92cVEXGM6j2hTe5k6nFM4Ty4hxtDXc/Pbqi1OSufF2z/SRvjhuOH9pC2\n1A253Nz1RoqW2Lv/EDtSlg5w5djioHpErbqRyFh7zjpew7M9tuu9VnkljtEd3d2EvkaH/uk1Nvg6\nC1+PYXdJJhW72zE1K2jiIbIsE+9s/5jARCdWfPMlZLKez+dGo5E77zwhpNIsouHVGYX+VBgZM0+x\nePuqYa0lavUUcs7k4NNk/tuJmBDiHuDhuWD4H/BrBM0O4J7qAW767uSzGpcrLEmIHtI4q7+/mn0t\nn6K/6geSgCKmig0/6L0RyKS13pTnFeDREQlAk8M9Jq4ZXBxbHa6F0u7XEhLqibaOS88LNuNrY1Do\ndDoEQUClsr5M4ZcIQityuddXXgvgqcIwy0od50eBacC0QPhM5U7oJAHHjZ/Q/KkzbvpptMoeIt90\nmO/tXNFrUk+hceSNDUaCIAg4Bbvg0BqLi2ECAJJBIrdwN2JhGsc5zOpvm9WmnlQ/QqlU4enlR3Vl\nGfYlUy3GUuFIa3HPXd5gmTQjEu2Pb5C37wDGZhlOkXo2f289APlXbnPjvQfoHqlQBeqJfiOMmQvn\nDHmOGXExFG3YQ91hDV4ds6hxuYLn9hr8gvrPVP46ajt7dvx0O+1trUiShJNz39nRcSsXona+RsmZ\nAyDB5CUBzE5KHNQ8KX+6gOO1H+Oel4woGGiJzmTjd1cMaa02xi8242ujX9ra2jm070Ps1C1IEhiM\nnmx6+XWUg3BNDhVR7DmmxLOhLLRq3Rb27f49M9fqeTj1pxRl2ZO8fgGrt/dueL+OKIpcPHCGhlwd\nCncjC3ck4untPeB1I6Uxo5UQ3YSu1wICTvgDAg3ZUL++hiP/dBbFrUgkpQ4WnGHN3y5H738LqiO7\n148Jle/wjS/AzMQYZibGWBxrb2vlyr88JqTyiwSlGrhWdYwJnzbh6ureyyj9s+mvt1K+/j4ltw6z\nJCF6yIb3qzg6uQzqvNmJ85idOG/I4weEBPPm+9vIv3EThVLB1Fmv2NTlniNsxtdGvxz9fDepaY7I\nZGbXqF5n5Mjne9mwZYdVxjcYDJw9cRy9vpnGhk5u3dASHeMHQElxI0HBs6wyz2jj4GDPzre/x5Pq\nWqZO1/MnPwga0vWf/XQP9vtX4oI7EhIHLu9lyztpuLp7DHzxSFD2jD+KGJB9cWs4/YsMAq++Ym7z\npwfT2Vgy/A4QsNmBht/n46mdjgEtlTP28vIrvaT7jpDsoxcJrLTc7QU9Wc6VQ0dYtnN4PZ9DJ08i\ndPIkayxv1JHJZMyYN349PzaGj834vgA8qa7h0cNHzJg1A3v7ocnkCTQjk3UnqajUCkzGBqut7eP3\nf0Nqmgt2dkqMRi8OfFZKW5sHCoWM8ImJzIoZWjxurPHzH3rruYb6GjrPhuCBeScnIBBSspnMTw92\nuX17Q6fT8flnHyNIDUgIqNSBrNu0rdc4bV8EvhRE/fmbeLWY3bgGOtHSiEnQ4ZUg8OS4g0V/XTkK\nOu6pWf/7ldyLvsv9zM+x91Syc8OmYdX2DoSdsx1NaC3kK4104uT8bAl42LDxdWzGdxxiMBg4dnA/\nJlMjkqhgcmQsM2fPHvI4kiSx75OPcHKqJXiCE0cPpBMYHEd8Uu9N3Xsdo9eviHW+Nndu3mbGTCV2\nduYbq0IhZ9WaEMrLA0lbtswqczwLNNTUYN8SaHFMhgxDc/8uxoP7dpGwUIFSaRZe0Gg6OH74c1av\nM4s/aNs07PmX43Tk2aNzMVDzeifTvhlvMUbo/Cj8/7mI/IP7qC9uQWOsIcBvEqqEiyx7+yU+yDoI\nX5NolruaM32nzp7J1NkzGU3mL03m/U/3MCHv1a6HgMppn/P6qo2jOq8NG6ONzfiOQz7b/R4JiSrU\nanNM6e7tdFQqNVMjpw1pnOxLl5kytR0fX7MbNzHFiYsZV9Bq4wa9A/bwmkx11SP8A8xu59IHzQSF\nWMcVXPn4MbNmO1kcc3RS09H+bJQXWYvwKZFkTj6Ie3F417FWRQUhsQNkxUoNKJXdXglnZzs6tdVd\nr7P//gwTT72GxxcVhXX3ijnvfZtFGywf5GYlzWVWUu+uzYh17lSW5OPRYRZ2qPG8zJzN4b2eOxoo\nlErW//sSMt79DN1jNXZBeta9lfrMSVfasPF1bMZ3nNHaosHJqQW1ultGbuZsby5nXhmy8a2rfUj4\nfMsylqgZbty5dZv58fMHNcbSFau4mJ5OaeZ9QEbwhBhiF1in/GPBwoVknH2HBQndn/VeQT3Tpg+v\nXOVZRaFQsOAvJpP9qz3YFU9D71WF1+oO5qUN1Ne2N/eyeXfYoWlHeS0Q2VfOcdVOJu/IVZLXmqit\nqcfoPrCbeOH6FPL8b3L/3OcIComEtZGETZ08hE9nya2Ma+R+9Bh9tQK7MD3zvxlJxMz+v9defr5s\n+vvBNTdob2sl87MMDG0S0xdPIXza1IEvsmFjDLAZ33GGXq9HqerpbhSEoUv+KRSO6HXNqNTd/+bH\nFW3MmD2hn6t6kpSaCgwsNj9U3D3c8PGN5UJ6DiETVFRXGXBymkLElGcjGcaaRMbOZOrHUVRXlePm\nvmBQAvz29sG0NLfg6mY2opWPW/H2NWtJCzIBSd4z+7jBWMjeXVl4ewtUN5ioVyQwddYr/c4TNX8O\nUfMtS3skSaKzswM7O4dBZ+DWVldx51/bCKz/wmVcDRm1ewnZFW6VnWxtdTWHvpdJcMlG7FCSvfcG\nlX+WTuJG63x36+uq6dRqCQwOs2Ud2xgxz6TxvXPrDtVVlSQkJuLs8nx1CfHy9qS+zrK8pqpKg69/\n1JDHSl2yjE8/+jWLl/iiUiuor2ujqckD/0D/gS8eBkajkdPHjqLTNyJJalLSVuDh2X+2bkJyCnp9\nPI8eVjIrxg8HB+sn7TwryGQyAoMG79Jds2Ezp44dQdNaAQj4+k8ledEiAOwdHRDjCzAdMnQlK1U7\nZzJ9UTWpaaEARAHXb+ZQVRlHQODgH3iun84m98NqpEo3ZMHNzHozmOiUgUtprh+5RkC95W7ev2QV\nOWcusnDVkkHP3xeXP8gmtKRbp9mnLYb7ew4Qv86EXN5TEnKw6Dq17P2HfUhZk5HrHdBHf8rKf0zC\nNyhw4Itt2OiDZ8r46vV6dv3xN8yYqWL6dEdOH/stgSHxzE9YOPDFzxBLV27n3KnPUMg1mEQ5Hp5T\nWLZqcIX5X8XBwZ5tr36P9DMnMRra8fSKZMuOoY8zWHZ/8DsSk+2xt1chSSJHPv89G7d9Fyen/ju4\nqFQqIiaFjdq6xoLqyiouXzyJIHQCjqQsXo2nl3U79AiCwPLVfZfbJPzzSkqcP6ctT4XWRYdzfA6J\nqebGBwaDidxr9fj42ZFXkD5o49tYV0vuf3QQVPeFG7gZbv38GBPnNOPi4tbvtQo7GSJGi8xlvdCG\nvbPDoOYeCH1tLzXhNW50atsHXZPbGyf/5zjeZ3cg//J2eW0WZ3/xKTt+sWXYY9qw8UwZ31PHjrJo\nsStqtflHlpAUQMb5bObGzUeheKY+Sr/4+vmw/bX/ZZWxHBzsWbV2vVXG6o8HJaWEhhqxtzcrYAmC\nwKI0Xy6cO/1U5h9PaLWdnDnxIUtXBAEqJEni0P53ee3tH45oBzZUlCo16//CbCTv2WkwVXfSUJ9L\nbZnI+Z9MxLX422gdyqiPPEdcvHFQv6GcY1cIqLM0+AHVy8g5fpzF21b3e23ChmQ+OXiICWXmNUlI\n1M8+wdqF/bu9+6Pg+m2KTpeDXELn2oAJY7eRBISwOhxG2EO3rVCFw9duldriF9dDY8M6PFMWy2hs\nRq22lDf0D5BT9fgJIaFDEzWwYV1qqqrx9bO8ISlVCoxG62rRGgwGThw5iNHQgISKWdELiZg8/ASg\n0eDC+bMkpfp2vRYEgfgED7IyL5OYktTPlaPL1HlzyH73Kg/ejyCo2Pxw59zhj/v1aM7tOsGy19YM\nOIaDuz2ttKOm26DpaMHNw6mfq8w4Ormw/D/mkP3BZ+ifqFCHdLL5W6uGVJf8VbKPZPLw5+54tpk1\nplvc0ymO+m/8ilZiZ/ChNuws8f9ryojjs3K3nu0YFe7DbyJhwwY8Y8ZXwB5RNFj8WOtrTcQljL4M\n33C4duUKD8vuAiKu7hNYvGz5c5uoMSd2Lgf3XiIlrduFWFbaRGi4dd3cez7+A4nJatRq880+58ph\nVKothISGWHWekWDQ61CpLHe4jo5KHj5sG6MVmREEgbTlWyj5G8u1KbGnqXBwnXYWrEzm/X17CM3b\niYCAhERt9GFWpw1u9xoUHsrmH4cOceW9c/9gA/5t3TXrgU2pqGc3MusvtTTX3WR54iqrJHJFb53C\n1VsZ+NelANDoUMDEdf272G3YGIhnqqXgoqWrOHmsmo4OPQCF+XU4u03Dzm781fzlZGej77zGwiQH\nFiY54e1Vxi9/9nNy7+SN9dJGBTs7NZOnLSL97BPuFdZyObOKpuYgZkUPXRykL+rrGvDw0HSFHQBi\n5/txPeeC1eawBnHxSVy7WmNxLOvSExampIzNgr6Cf4APiqA6i2MSEirvwekyK5UqNv9yJe3b99GQ\nfIj2HZ+x9Zdrn6o7/UuMTb10XGqSM2XmDOLSUqxWCzxlznRSfxNE+479tG0+QOR/tlstg9rGi8sz\ntfN1cXVhxxt/Rsa5c+i0GqZNX8WkEdQcjiZlJTeRy+upeFRLQ30bTs52rF4bTnPTBd7/3Wm2vvrt\n5y6zd868ecyOiaGyopq4BM8hS1kORHtbB45OPW/yAiMT9O+N6qonXLtyCQcHZ5IWpfbazSkrM5Pa\nJ2WAiqRFy/DwNMtD+vh64x+0kPNns5DEdiTJiZnRy3F0tE5i0UhQq9VEvdFOyb/ew1U7FRETjyL2\nsX7H4N3h7l6erP/LsVeYsp+sRSqTupSvREw4TNWNylzBEeEE//DpiYvYeP55powvmG8ey1auHOtl\n9IskSRQX3WPHztnYO6g4fiSXZSvNCkEuLvYEBomcPPo5G7Zsf+prq6+r52L6KQT0OLv4s2jp0mHH\n3HpDJpMRPGF0SjBCQoO4mC4x5Su6CY8ft+IfZF3956yLF2ioz2FerB8d7S18/N4vWLf5GxZlUwf3\nfUJoaDPz450RRT3HD/+O5avfxsvb3BKxU6tFITcRPsWNx491VFSUMtOKXoCRsPWH8VyNyefCscs0\nyxzZunHpgJnK45GlP0jlcMvHKG9OR5TrYX4xm7+3npbmBpQqNQ4OA8ehR5PCW3cov/GQoKgAouJi\nntuQk43h8cwZ32eBq1lXWLNuGg6OarRaPR6elqU2crkMpNanvq7GhiZOHHmXxUsDEQQlLS0V7N31\nPttefWtY42k0baSfOYFo6sDJ2dfqhvzrCIJAYupGzp85jJ1dB3q9HDePySxfPXzFraLCQu7eykQm\n0yOKTixbtYGH5ddITTPXQjs6qVmxOpD0M8fYuO1VwPy5JbECP3+zMpdMJiNtSSBnThxCpVZh0Dfz\n+FEJi5dNxs/flaBguF9UQUFeAZFRkX2u5WkSlzodl0QtxRXBuHQ+m7Xynj4+vPHbHVRXPkRC4vzv\nCvlt2n4knYIO9RMmLfNl099tHZNKiH0/24PpwBw89OsoUpaRu+JjXv4nW0tAG93YjO8oUFtbxbx5\n5huanZ2StraerjBJsq5LdjBkpp8mbUlA1w3A1dUeV9cn1Dypw9dvaElrHR1a9u3+fyxZ7odCIael\npYJPPvw9O17/5mgsvYvQsFBCw76HTqdDqVSOyNhXV1ZTkHuUxGR/wAFRFNnz8f8Q+LWNuyAICEJH\n1+uGukY8vXrWlD4sy2fnm7ORydyBeZw6no+zsx2OTmomTfEk50ruuDG+Q0WjaUapUGFnP/au86/j\nHziB/T//DM8j2/HFHOft1LZSevAMp/yOseqb1m912B+lhfcwHJqOl96sNuZqCKP1hEDe8hvMWGBr\nD2jDzDOVcPWsMCt6Lnl3awHzjdvV1Z47Nx8DYDSaOHemkgULlz71dUnoexgrH181dbW1Qx4r4+xp\n0pb4olCYY7Curvb4+3fwsPyhVdZ6+8YN9uz6DZ/t/i/27HqPluYWWppbMRrNJR5qtXrEu+yr2RnM\nj/frei2TyZgaqaKkRGNxniiKiFK30QkJDaLioWWpSc6VMpYsD7dY06IlU7mWUw5Am6YTB0fXEa13\nuBgMemrKH6DTdg752oaaWj743ifsW3OX3esu8dlP92AyWT/GPlJar6tQ0J1gZYcLAnJa7j79nWbJ\nzWK8tJbdnlwMoVTmV/dxhY0XEdvOdwCK7xVjMolMjRx8veCE0AkU5kVwJbuYiAgnRNGOJzXudF5R\nIJPZ89KG74yJLKa3dyg1T/Lx9euOhRUVatnyypQhj6XXt1loRgMEBjtR8bCCCaHd2tGiKHJo/x70\nnY9BkJAkd9ZuerXfZKyS4hJqnlwkOcW8G79XWM37v/tnJk/xRdMm4OEVyZLlw2u+cD0nh0fl+YDA\nk6p6BMHP4n17ewW+/pFcvviIuHg/Wlt0ZF9uZvP2b3WdI5PJmDF7CefPnGbKNHvqanXk3tUzY+bX\n6pyVckSThEFv5EJ6Izvffm1Yax4Jd28fQdtxlshwiYIjeuo8oob0tzvx8/P4Z27vSmrSf9bOaZ9j\nrHh7eI3sRwtB2bv2udz56dfjTomN5JLjTTraNWhpREBGh6yOFVEznvpabIxfbMa3DxobGjm8/30m\nTVGgkMv44PdHWb7mFfz8/Qa+GFi+ei0tza3cLypm0dIpuLiOfVwtPmkhB/dVUF5eiZengocPTUyf\nuXhYMbHgkMk8rrhGUHC3bN+dW42sXh9jcd7eXR8xZ64BFxdzDNVoNHFo/y62vdJ7nLmxoYmD+z7g\nldfMDwQGg4lH5Y1se6U7qaqk+AH5uflMnzF9SGu+mH4ehTyf+ARzVnJ+rowTRwtYsbrbFVyQp+XV\nt7bR0txKVuZFXN3ceOObCT122TOjZxM5I4rC/CLCJ9sRENxJdtYR0pZ0i71cz3mMVuvNnTsubH99\nG0plL/KHo0hbUwNqTrN0uQ8AEZMh724RZaXTCQsPHfB6URTpzHfqMrwAKhypvzNaKx4+filyOgrq\ncMD8wNbMI/QOdczYOLDmtLWZMCmCY3P+E/fMlwjFXIdsELUUnT/IzPlPfz02xic249sHZ04cYNlK\nn67d7oQwuHDuEFtfGXxM09XNhblx4yfGIwgC6zdvR9Oqoa6ugQVJIcN23cbEzuPgvhKqKh8TEGjH\ng/taJoQnWJRPlRQX09iQh4tLd/9fhUKOQE1vQ1JdWcX5Mx/i59ft1sy7W8nc2FCL8yIme3D1yp0h\nG9/qyrskp3p1vZ4+w4eS++1cSK9DQIdJdCJl8RYEQcDN3ZWVL/Wv+KRQKCgpvovR8ICQEGcelVXw\nyUdNBE9ww2BQETJhPqvXpwxpjQNRaNQyTdF3iZpG08b+9BzqBROdVSJvrPCyeD9qpg85V6/RGeLb\nxwjdCIKA4Gzk6/8uufP4czsve3s1Z+1OcP9ILdpmHYogDWv/fCWTZoxNjN1DNRFPuj1KSuxpybZH\nFMVRTUrsj7qaasryi4mcNxsn57EJgdjoxmZ8+0Ama0MQLF2jMkHTx9nPFs4uzlZxe6/b9DItza08\nrnjM+q0RPWphb9+4iKtrz/pY6F2QIfvSWdKWBHG/qIbC/GqmTffH3cOB+vo2i4xxk0lEJht6wppA\nTxekh6cLm7d/f1DXS5JE9qXLNNRV4ekdgCCTERLSgLd3EFezywgNd6f0QT2OTvEsX/3SoMIUk4Mr\nuFcRPKTPUdiHZGd+wQN+f6eKjsRFSKKI6tofWPEEAgO7/9caTSetDuadf/EA8wqCQNByJZr/qcTZ\naM5Cq/W4wpz1E4e03qeBIAgseXUlS14d65V8gdjL/940dpnOh361n5ZD/rg1R5Pve5XwN2Ukb0kb\ns/XYsBnfPhGlnkZDksafktZY4+rmgqtb77sLQdDj6mbPo4eNhEww18g2N3ZgZ9+HDregA+yZNMWX\nWzceceZUAQa9SMWjDsLCPFGqzF/XC+erWbn2W72P0Q+i5IIkSV1G0Wg0AYPfAXz03m+JniMjPNyJ\nuto8Dn9ewo7XpnPsSC7LV03H3l5FxORG9u89zoo1A2fYTlPYU2jUMjV88DshmbI7nt7W0sqFX19C\nVyXHY66STMUTtGnLEAABMHzj23z4u7/jL96YhEqtwGg0cTJdw6Jv/BUyhQIQmTpAmdGyt1aR5ZtB\nZVYOMjuReWsnM3nW0DwOLyJByS7UXqrA2WB+wDFhxDm2fUx2vXk5N9F/Eo2/PsK8tppllL57htlL\nG3F167/lp43Rw2Z8+2Ba5AJuXDtPzDyzey73Th2hEfPHeFXPGIIL0TFO3L5Zwf2iGgRBoLy0k7/9\n8b/3erpS6Y5OZ5aPjI4xazWfO1PLX//jdzh19BAmUwuSpCZ1yau4uQ/dbbZ89VYOH/gAL28dkgka\nm+zZuG1wNc63b9wicjp4+5iT1bx9nFiyfAL79txg87aYLsnL4BAPYuMCqa2px8fXq78hR4S2wLPm\ncQAAIABJREFUvZ1PVp4m5PpOVMjRvNdMw8Jfwlc2M4Ig8GjSIs7lmhD0DYhyDxJf+86QY/zxq1Og\n/4ZFzxQtrY1czcsmIjCC8AlDTzYcDAvXpnKu7RSPT19D7JThPLuTdX/+dEuevqQ85zFuesvwl39d\nCrcyTpOybsWQxtLrdRz/zRHaClQoXEzM2BTG9PnjQzzmWUOQJKn3NEEr06B78DSmsSoPyx9y6/pl\nJElixqw4IiZHjPWSngo3cnJ4cP86gmAAXFm1buuwpDA7OrTs3fUO4eESjk4KCvI6SEjZRPjE3mX6\nDAYDn374DkHBOjy97Dh/5jHOLs64uCgRJUfmzl/KxIiRuzzr6xqQy+W4ewxe1enIwQPMm9fR4/hP\n//kkP/rH5YDZLX0lq5Smxg6MRnfmzEtlblxcn2N+6T4eyP3bG9fePYXXf65DQbeH5pFdFpeOmlCE\ndRcqBxw9z8tLN/c5zkA73+eNkzfOclhWi25+DEJpGTPyKvnTJW+MWRz2aZB5+CzN/zsOu694eers\nb5PwgQMTJg3tnrbrR7vwPLGtqydzjccV4n7lNGax9fFO/Oy+v1c242vDgvzcPGqqzhEZZW78bjKJ\nnD/byqtvfnfYY5Y+KEPT2saMWdMHdZMrLyun7EE5dbVXSUruNiRnTj1m6ys/6FVnebQpKbrP40dH\nmBrp03Ws+F4DxUX2zI8X8fJ24kJ6MdOj/PHyNhu0B/ebUKhiiIuP73XMQqO2y/AO1Qge+s9DOHxs\n2Se5gwaOffOXmL6/A0QRt9MX+d6UJUwIeDE1iS/sPUf58VbEDhmOUZ2kfSeZ/11+Ft2ihV3nmBoa\n2XG7nrR5T7/u/mlhNBr54/c+wi97K2qcaJc9Qbf2LFv/cWjytq0tjexfm0dQS4rFcc3m/Wz40YvV\ns3uw9Gd8bW5nGxYUF15nQYJn12u5XIanp5b6uga8vD37ubJvwieGWbweKOMzNCyU3Ds3SVjob3E8\nIdGHzIwLpC1dMqx1DIWy0nIqKx4TEzsXe3s77OztuZr9EL1ez/QZAeTdreRaTiN/9+Of8+lH7+Ln\nV4eu09hleAEmTnIn88KdPo3vlwxn9xm6wJ/ivQ9w03d7AupDMvn3bd8n8/xl5DIFaQmv99vZ557d\n85FA2Bv3TlzB8ItJBOjMDx7ifRPv1vyK9l8mWtz05J4e3DAWEPgc/y0A5v/PS+QeOE39QxMuMx2Z\nuXw194aYQNqiaQRDz+9Tk2h8rr9LIyG+n5wSm/G1YUFvbhC5XMBkGly/1/54/KiCzPRDyOQaRJMC\nL9++xTJkgoAoSny1U51oEpEJo9u6zmQysfuDdwgO6cQ/wIkj+7MIjUjk8aMHvPbWPJ5Ut3DpYgmT\np/gyJ0ZBY0MTL+/8E65fvU57+95eRhwdkYdZ8bE8fu0QlYdKsasNpX1iLtHf8cfVzZPVCeNLAGMs\nqD/bwgRd945fhhzXoikIBfcgqXvnK3Z24ip7uvXXY4FCpSJ628iym129femIyULKnN9V+91oX4zP\n4vHZT328YzO+zxkaTRulJaVETI4YVgu70PAoSh9cIXyiOR4qSRI1NYohaT/n3rlD/t1MZDItouTA\nnLmLmTRlMudP72HpCj/APPaj8jKu51xjbmxP4YHElMWcOPwbkhd1u52PHi5m51sbhvyZhsKZkyeI\nT1Dh6GTejSal2pN+7hJKpSegws/fFT9/89OsRqOnpcUseVlSdA61WrDY1Xd06FGrB66nHS6rvrOW\ntldbqH1SSUjoKhRDFPF4nuO9ub3c2lQKGQn1ItllDxHCJmBqayPwSAZvLPkTVJ22SobB4P0PKzj1\nH5+gLXRA4SYSutqZ5PlpMHTl0hcem/F9jjh97DDt7UWEhtlz6shJXN2jSFs2tPaLc+bO5WJ6KxfS\n7wIGJMmV1et2Dvr6psZmigpOkbIogC+N7LnTn2MSNxAabrlrDQl140pWQa/G18XVmbCIFHZ/+An+\nAU7o9SaSUoI5tO99Xv/GD0atO4xO24Cjk+WNODRMSWWlM1WV9QR8pWb2UblIYuoEDu77hORFAXS0\ne3HiaB6OTmo6OgwoFKHseH10+946ObsOSjDBaDRy6dA5mot1OIUqSN60GKVydGPnJpMJvb4Te3vH\ngU+2MpOWBVCUUYBHhzkRyIge+7hGXl68k5jC69wtuIaPyonFS7855IeWFxlPb2+2/2zbWC/jucBm\nfJ8TysvKkclKWJBgjpP6B7hx83oh1ZXR+Af6D3C1JUmpi4BFw1rH5cx0FiRYSnAmJPly/dodnJx6\nKiOJknmXWFRYTFlpCXEL4ruykJ9UPWTbK3Ms4sNGQxP5uQVEzRytWlM7TCadue3jF9Q8MbBs1Qoy\nM85wv/g+ajuJ9jY74pPXm1WgMCAIchyd1Kx6aSYGg4mH5Y34+KUhl4+um3wwSJLErr/9GM+zm3HE\nGR0dfHh5F2/8+rVRy/I9cOUImWI9HY52+DV28OrERCJCJo/KXL0xOykW/T9cpvjIfsR2GS4zjWz8\nrvlBaPa0ucxm/CjP2XgxsRnf54S7t64TG+djcSw6xocb166wOvDpZSLKBRmiSbQwXkaDCQ9PDyor\najHojV1iGdeu1hA9dz0fvfdbwicamT7dhQvn3sHdcw7JixYjYephHBwcFXR0tI/a+lMWr+DA3t+Q\nttgPlVrBo/Jm5IpQnJ2dWLlmPSaTic5OnYVL38s7lJonhV0NK5RKOeWlRuKTxocSVP61mzheSEWN\nedeuwgHPrDVcP3eJ2CVJVp/vWt4VTkxygTBzI4Eq4N2Dp/m34ElPtZ9t7PIEYpc/tels2BgSNuNr\nRTSaNi5lpCPIZCQvSuu3c4+18fTypaG+AE+vbhdfzZM2/PynPrU1ACQuWszh/f/NosXdsdpLmQ3s\neP01RHEhJw4fwCQ2I4lKZsxeSVnJA2LnK3D9ovHE/PgALl28SUdHAlGzYsm9c5gZs7rjzbduatj+\nWkyPea2Fq5sLW3Z8j4yzpzEYOpgQGs+a9XO63pfL5T1i6QtTkjm4r4rSkgpc3eRUV0NM7Mped5Vf\ndnnSdVaik0xUt4cwMeGHo+oCvlVeiq/BcqfnKPlwrzIdl1HIUj3bWgzxlka9euYkzlZeIzhimtXn\nMxoM3Mi5gEbXwcSAMFpam4mYEoWTq7vV57JhYyj0l+1sq/O1EoV5+dy9dYT4RH9EUSTzQg0JydsG\n1T3GGoiiyPu/+yVpSz2ws1PS0aEn43wLb3zjz6y225AkiVvXb1D5+CERkyOZNr33G+mDkgfczDmH\nIGgRRQcSklYSGBzY67kH933E/AWW66ur1aA3xBETO4esixd49PAmMpkOk9GRefHLiZg0ySqfx9p0\ndGhpbmzGP9Cvz7/5kYP7mDq1GWdn84OZXmfk5HFXktN+MCprumenwUNZy4kFlQTUpXQdr3W5zsIM\nBROmW393/t6e/WTFJ1r+De7c5qezp+MdGGDVuVqamvi3PQeoTU6lPTsbuaMjdjNnYpefzxJ7FetX\nLbPqfDZsDIV4Vd9Jjbadr5W4czuDlEVfahbLWbw0iMwLpwkL/8ZTmV8mk/HKm9/l/KmT6PQt2Km9\nefXNV61qeD9677dEzYB5sS48uH+GfZ9eZ9O2nkr2EyMmDlqJSqVyQadr7JJnBHhY1k58srk2OD4p\nmfgv2rKNdxwc7AdUAutsr8bZuXtHplIrsLOvGNV1+QT7EfKje5T/8jguj2ajCcgn4DsaJkwfnXrp\nlQsXcDvzEtqFiQCIOh2RT6rxDrT+fPtOnaN+1RoMxcWow8JQf/FgZpg/n5M3rpNQ/QSfQbYBtWHj\naWIzvlZCJnTwdZF+Qei9+8xooVarWfHS6OjHXs3KZna0gI+v+Ulu4iQPdPp6ysvKCQ0LHfa4i5Yu\nZ9f7/0VyqgdOznaUlTYhSsF4eD6fLsPe3EySODpJT9fPZnE7vYq7zgITN3iw5eY0ym7dJ2TGVFzc\nBy+tOVT8AgP4QWw0Ry9m0CYIhCgVbNn58qjM1SAICIKAoaIC58WLLd4zzYkh++pV1q7rvzWkDRtj\ngc34WglR6llTK0lD10Mer9TVPmZerKULZVqkJ7du5I7I+NrZqdn59p9z8fx52tuaCZ2YzILEmSNb\n7DjGy2cy1VWl+AeY/5bNTVpE0fpx+UufZ/Dk5yFM6DQLSpQeLUb/X7kseLV/tS1rERoexnfDwwY+\ncYR4SxLFoojM2RljYyMKj6906SkvY/LE0V+DDRvDwWZ8rcTsOamknztCwkJfTKJI5oVakhc9P/Vw\nPn4hVFfd6jIaAPm5dUTNGrlLWKlUkrbsxYjNLVqyjPSzp3lQcp820Ui9MZKIJVtBN/C1DfVVFOYf\nRMBEUEgKE77IJu6NsmPN+HV2l4u5tU/mwe47LBgv/W6txJaVS3nw8V4qF8SjOXMGl5UrkTs7Y2ps\nJKq4iGlvDb5G3cbzzfn0i9ytqUMJLJoxjWnTx7YZhC3hyoq0t3eQmX4euVxBYmoqdnZjo5qTe+cu\nJUW3kRCIjllImBWe/iVJYtcf32HyZAMhoW7cK6ynsdGHDVuGJs5uw5JCo5bHNQP/BJ+U3Ud4eIq0\nZH8EQeD23Toe6qMIn5vQ6/kn1+YRlmfp6q2au5/XLj9/DQREUSTr0mXq6psAkUYRwjzcSUlJfK67\nFfWGJEnsPXiE3PZOQGKmkwOb165+qiVe45HPDh/jpH8wMn+z5oHizh2+FejDrNmj62WzJVw9JRwd\nHVi+emwbn2ZmpCOZ7nY1R7h143M0mjRmzp41onEFQeCVN75J7p1cblwvYUrkfFIWD73FYllpOdey\nTyIIHYiSPdExi5g8dXR6qj4LTFPYM633RHALPrtwlcSU7kzh2TO9aT6fx5I+kpiKEzQY84zIv/iJ\nmzDgFPd8agDKZDIWJiWO9TLGBZ8ePMLZyZHIXM35J6eamuDQUba84HHvK80aZHO6xYaMs2ZxLjNj\n1I1vf9iM73NG1ePbJKd218VGx/hwMSN72Ma35H4Jd25kgmDAwd6XZavXMGNW3+7O/tDpdGSmf8rS\n5UGAWZAi49xBVOqXuXblLDKhHVFUEz03hYjJT08N6VlAkOkBy7wCmdC3r/rN/5PAbzR/oDLDHwQZ\nDin1bPhp700sxgM6rZZdh45RIYITIitmzSCyj1I2G32T267tMrwAMnd37rZp2TKGaxprJEmio5ed\nfwdj6w2wGd/nDAFDz2OCflhjlZeWU3D3IAmJfoCSNk0dn+3+I1tfeXNY42VmXGBhkqUKV3yiL3t3\n/z92vBaFIJgznC+mf467x5/g6eXR2zAvJJLo1KMVo0l06vN8e3s7/uK9xdxua0amDEFt9/QEX4bD\nL3ftoWTxMgSF+ZZUevUqf+1gT0hY6Jiuqz/aNRqUKhUq9fhpytBbAGPk/ciebQRBIMRooPQrx0St\nlnDl2Jo/m/F9zvj6DdncCtBlWGPdzLlAfGJ3jaSTsx12dlVoWjU4uwy9I44kSXz9ATQ/r4rFyyZY\nxKQSkvzJyjzPmvWbhrXuL7lz8yb3CrKQy/WYTA4kJK0mKCRo4AvHIctXb2T/p79n0lQ5DvZK8u62\nsTB14P2M2k6NTDm+De+TisfcDwhGpui+Henj4jiTdZG3wkLHbF19UVNVzTsnz1Lh4opKr2e2IPHW\ntk3jIq463U5NukaDzNn8+xRbW5nhMH4eDsaKt1Ys4XfHTvDQ0wulwcB0bTtbto+tP8BmfJ8zUhav\n59Sx3YRPlKHXizx+rGDTy38yrLEEmQmwbAzg6Cinra1jWMY3KTWFz3b/isVLu4Ocd27WEb7Jy+I8\nmUxANPVswjAUqqueUF52juTU7jjPqRO7efXNH/ZIwil9UI5ep2PKtMnj4gbaG84uzrz+jR9QVFhE\nR7uWHW/MfG6SiXSdnYj29nz90xjG6f/i9yfP8mipWTRaB2S3tuJ97CRrV68Y24UB2ze8BJ8fJk9r\nDknMcLBjqxXivZIkcezUGe42tSKXJOJDAklc+HTK1qyBj58v//DWTloaGlCq1Tg49e01elrYjO9z\nhn+APzvf/gGlD8pRq9SkLR++nJ+7xwTq60rw8u7Wi66uFli8Yng9atVqNQsSN3Eh4wwyoR1JcmD1\n+jfJuXqcJcu645k3r9cSEzuyMq1r2ReIjbNUNoqZ58K1K9eIi48DQNOqYf+ePxAWLqFSyfnoD0dY\nvHw7AUHWlUC0JlOmPX/JaSEREwnMuEzNV2VDS0qY/xTqhIdKZ0cHFY6WN26Ziwv5za2MjrzN0JDJ\nZLyycZ3Vx9176ChnQiMQoswPyg/KyjBeuERq8kKrzzWauHp6jvUSunihjW/unbsU5F1CJnQiik4k\npa7G38ras2OBIAhMjBj5jSs5bRFHDtRTkF+OvT20tKpJTB1ZM/vepCftHRzIOH8SuawdUbRj4uQE\ngkIGkQLcLz13TSajiE7XnaR08uh+li736NpBhoVD+rmDvLzzOyOc28ZQEASBby9bxAfnTlMpk+Ms\nQXKAL7NjFoz10nqgUKlQ6Q09esfnlz2ksKCQaZHPZ5LYDU07gle3h0oKCyPrYgapY7imZ50X1vg+\nqa6hpOgUySn+fBkTPXHsI157+y+fG3feSBEEgZc2bkWv19Op1eHiOnRX82AwG+T/ZdUxI6bMIv3c\nxyxa3L1TvJJVil9A9w1EEDTIZJYylnJZm1XXYWNwBIYE86PXdoz1MgZEoVAQo1ZwobEBhYd5F9Vx\n4wbyhQv5/Obd59b46nvJ5OqZ2mljKLywxjcn6wLz4y3dknPnuXI95zqx82PHaFXjE5VKhUpl2fIu\nMz2d2pr7SBIEBE0jPnFodZYFefk8KM7H0dmdpNRUFArrfhX1uk4cHJScOVmAUilHpzMSv3AiDx50\n73wlqWcbP4nRa+1n4/ngtc3rufyTn9EaFAyShDo8HPWkSdRXVY710kaNiYjcMZkQ5OYcELG9nSl2\ntt/KSHhhjW9vwl6CAJL4VAS/nmnOnz6Jm3sZCZPNO+FHD+9yIV1PcmqaxXkdHVpOHv0cpFYkSU3s\ngjSCJ4Rw5PN9uLtVMS/OA43mIX985xe88ub3raoIFjljGkUFp1iyPLzrWH19O+4e3dnOkVHxXL96\nmrlx5hj2vcIGgoJnD3qOzk4d9XX1SCIETxipm/z5xmQykZV5mWZNG4uSF+LoMrwM/PGAIAhMCQ+j\nKGWRxXFv6fkt6vmTTev4n30HKRHkyEWRKKWcrdtGVo3wovPCyktWVz0hJ/tj4uZ3735PHq9k51s2\nt3Nv3Cu4R1HhbdQqRxoaiklbYpl0dTGjiS07/tTi2B/f+RWLl7mjUJifljPOVRKXsI3c23uJnd+d\nhazTGcjLdWfVWusmiuRkZ1N6/yLTpjtTVdmBRuPF5u2vWWQ0Pyp/xM3rmUiSyJRpc4iMmj7guDVP\najh64CPqasuZFR2AUqWg8rGClWt34u3jNeD1T5NCoxaZcsKYrqGlsZGf7TnAk4VJyJycsMvKYufk\ncGLnzRnTdY2E8tIyfp2eSUtSCshkOGVe4FtxMUybZv0mGSPBaDDw4f5D3DeJKCWJ+T6erFy6eOAL\n++BLczFeqwLGG/3JS76wxhfg7q3bFOZfNjdqNzmQmLKmz6bvLzKnjx1BbVfKlKmedHYa2PfpbV7a\nEIWLS3fXpswL9Wze/mddr4sKimhoOEV4eLdQhskkcvRwC/MXyPH1s9z5XMmWWLfJ+qr/Op2O/LsF\nBAYH4evnPfAFg2DX+7/GYKhiyfJI5HLzg5okSWScb+flnd+yyhzWYjwY399/+hlXE5Itbti6Tz7B\nO8APX0liS3wsoeMws3kgdJ2dnE+/gCiKpKUmY+fQs7PZWPPbj/dwfUECsi+FQKoq2aJpZklaypiu\n60XBpu3cBzOjZzMzevBuxheRzk4dra33WDjbvFO1s1OyfWcMZ04WsGxlFAAGvRFBZpnC39zSjKuL\nZUxILpfh6eVGyf1qC+Or0XTi6Dg6Dz1qtZo586KtNl5bWztOLlraNYouwwvmnYBM1mq1eZ4n6pD1\n2CkZAgNpjoujVa3mv08e5/8EB6FQKsdohcNDbWfHihXjtxuXJEkUilK34QUICOT6xfv0rghu42ny\nQhtfGwNTV1OPl7el0IZMJqOhHjIvPEaSQBQ92bDVsrtRzLwY9nx8gbQl3TWR9wobiJq5jJonj7mS\ndZN5cX5UPGqhuEjGjtefjW47KpUSg174Qjmsx7tPfT3PAh6SSKkkWRhgsaMD4YskvsaERDIzMpkW\nOZVjl7PRSgLTfbxITU0aqyWPG0wmE+npF6lobmGCuxspqUn9hsUkSeKzw8e4pWnHKEm0aNqs8q2U\nvvb/szFybMbXRr8EBPlx+aLI1K9UUOh0BiZNjWXFmpcAes1UVigUxMSuJv3cadTqTvR6JX7+M5ga\nOZWpkVNpbIjhalYWIaEx7Hxr4DjreEGlUoHgh7unnju3KpgVHQzAvcI6goKtt8N+nti4KJkHh47R\nmJqGYGdHe1YWCh+frpu5ADQ2NvDTjCw6kpIQBIFbNTU83neQVzdZXzDiWUGSJP7jDx9QFJ+IfGoU\nmU1NXP/DB/zl26/3aQgPHTvJ6dAIhC/EJDoOHUKp13c96IjV1czxHrzQRGbWFU48KKdJkOErmtgY\nPZMZM56d3+t45oWO+droG1EUyb6URVubBgcHe6orrxI734cn1W3cKxTZ/tq3e5QfAdy8fo0HxTcA\nAzK5O6vXbQbMRut5eXKWJIlTR49QXpZPW2sL7p6+zJu/iJnRI2vbOBpYI+Z74OhJrjQ1o0UgVDTx\n9prluHoMremFXqfj3PkM6hubuNSkQVzXbVTdThwjzNGeW0mW2cN2mRf5j/Wrxn1TiIHo7OggPeMi\n9mo7ElMSkcvlPd5/98BhSiUZKkRi3d3YsHo5Odk5/E5h19WDFkCsrOS7MpHoPpLVfrz7Mx4npXS9\nlvR6Ot9/n4Apk1ABsZ7uvLRicF6m6seV/PjabUzzuksvHc+e4WfbNjzz/5OnhS3hygZgNhoXzmfQ\n3PQYhcKRRUtX4OBg3+O8psZmDux9hwXxbjg5qci6VEPYxCQ0Gg1+AYFEzez9yTfvbi611eeJjDI/\nWRv0Ri5e6GTH6+MrCelFYqTG93z6RXY7uiIEmJXfJEli4plT/O2bw0+OKywo5PDNuzTKZPiYTGxL\nTmBPdg6FCZZuZvHWLf4jLhp3H+skyo0Fubn5vHvzDm0Lk6CzE6/Mi/xw/Wq8fbu7e/3yj7vIT03r\nqqGVamrY3FhLS1sbZ+b1VPladi2bTetf6nW+n+zay6NkS90pl/Tz/OerW4e89t37D3I+Nt4yXKDV\n8vKDIhYvt0WNB0N/xtdWU/MC8elHf8DH+z7zFwjMmtXKpx/9Gq22Z4P186cPs3K1Px6eDqjUClLS\nAikrvcri5Uv7NLwARYXXuwwvgFKlwNlFQ2uLZlQ+j43R53ZNbZfhBXNiWbmzC+2a4f9Pp0VO469f\n2crPtm/mL17dRmBIMOEO9ohfG9O/vhY379Et3Rrtvcfnt+7SkbYEmVqNzNWVhlWr2XMuo+t9k8lE\niULZZXgBBF9fbtc3MDcqEgoKLAfMvUtsPw3g53h5ID150j1+WxtRquFFF+0UCjBY6lhJbW04O499\nU4LnAVvM9wXh8aNKvH1a8fA0P3ErVQrSlvhw4explq+xfIoWZO0IguUTm0ym7dFP9usIvXQTVSrA\nYHi2hegMBgNXs67i7OzEzOhZz437fDAoevmscpPJ6opkL61aTtXHe7jj6IzOzQ2/slJ2Js4ftb/1\n4ZOnyaxpoE0mI8hk5LVFSQSFBFt9nlrB0sUsCAJ1gmWWvNDLA4AcgfBJESzKK+DCjevop0xFVVhA\niiD22+N41bLFiCdPc72oEJMEU+1UvDzMuPnytBQy935O2xcdnCRJwv9KFrHfemtY49mwxGZ8XxAe\nlZcTHGJpUNVqJTp9zx2MaOoZzxFF9YDiIwFBU6l4dIvgEFfA/GOtr1Pj6eVBR4eWgtx8JoSFjjsh\niv4ovldETvYh5sW60d5u5P3fnWXjtm/g6vbsKjQNhcRJEykoLMA0LRIAsbOTSH0navue4YqRIJPJ\n+M7Ol2ltbKS1qZnARQlWNbyiKJKRfpHypmYMdfXkREYhSzMnyJUD/3PyBD9561WrG3sPyUT11455\nfkUJSyaTMR2J652dyL6IowplZcQFm70NL69/iaU1teTlFxCVEIvnIFzwa5YvZeRNBMHByYm/WJzC\nwYsZNMlk+Igi27ZueKEePkcTW8z3BaG9vYPjh/4vicnd9bTVVa3oDNHMj7eMK1VXVnHm5Ickp/qi\nUim4kVODt98C4uITBpzn3KkT1NUWIghGTKIzy1ZuIT/3DjXV15gW6cKjh+106vzZsGV4IvqSJHH3\n1h3q6+pYkLiw15i1Nfn0w1+TktadXCSKIlmXJDa9/NqozmstrJFwdTXnOhn3S9EKMiYq5by8fo3V\nd76jzX+++0fyYxcg9/DA1NKC5swZXDduRBAEJKOR1oMHSXWyI2V+LNMGoXLWG20tLew7eZYGQcBX\nENi0ahl38+/xx9KH6BfEIxkMuGSc5weLUwieENJ1ndFoZPeBw9w3GFEDCUEBz1yrvrFCkiQ+PXiE\nO+1aRCBSqeDVTet6JLWNFbaEKxsAXL6QQWVFDtOmO1PxqB1tpx8btuzo9Um2o0PLxXNn0Rt0zJu/\nEP8Av15GHBhNq4bTx35LQlJ33PBxRQuCLJaY2HlDGkur7eSTD3/DzFlqvLwduJpdy6Qpi5gzb2jj\nDIW9u/6NpBRLd2TWpVY2bH022g6OB4Wrseburdv8V4cR2YTuv4OhthZDRQV2UVG0HD6My/LlyJ2d\nke7fJ625npf7SGjqC6PBwD+8+wF1q9YgyGRIRiOBx4/yT99+m6b6Bs5eykKtULIsbXwpYdU9qeHk\n0eNMmhxBXOLCQe1qOzs6yLuTS1h4GJ5fSRwbC/YePMLJiKnI3dwAc/34wlvXeWPrxjGuDEqHAAAe\nLUlEQVRd15fYFK5sAJCQnIJWO5+C3ALmLQjp1/3r4GDP8jUjd17duHad6BjLusKgYFdyrpQM2fie\nPnaIJcs8USrNT7XJqYGcP3uR6LlzR80VJoo9mz2YRJuYxmDpaGtDJpONqcEpKX+EMHe+xTGljw+d\nd+/SnpWF60svdalACZMmcfFKPSsbG4dUTnXufAY1KYuQfxGaERQKHi9I4GrWFeYnLGDLEI350+C/\n33mPi80aHBYv5pzBwAf/5xf8y9uv4dlPktu5jEwOVtbQNn06qqs3ieto480xbLCQ19bRZXgBZA4O\nFOqNY7aeoWDLdn7BsLe3IyZ2zlOLu4aGh/Gw3FJ2UavVo7YfesxUlDRdhvdLPD0lGhuaRrTG/giZ\nEMOdWzWAWZv6wvlK5sYNX5j+RaG9tZWfvfchf37iHH9+5BS/+uPH6HW6gS8cBebHRCO7c9vimKkg\nn6lNDTg9rrCUXwS0YeGUPygb0hxN7e3InCyzgAUPD2obGi2OtWs0nD99jtL7JUMa39oU5OZxUdOB\n6/btKH18UAYGYnjlVd77/Eif13S0tXGgupbO5GQUXl6Ic2K4HBZBTnbOU1y5JQK9PHQ/IyFpm/G1\nMaqEhoVSXe1EU2MHYFbHOn+2npS0oRsw0aTuURrS3CyNavJTfFIykyM3ciVbzo3rDixd9U3CwkNH\nbb7RoOpRBWdPnaWxrv6pzfmHQ8e4v3gZ4oJ4jAkLyUtK5aN+buyjSUBIMEtEA/KrVzFpNMhv3mCR\nppl/+vPvsjF6BqavlTg5F91jcuTQuhMlzY1BfvOmxTHVlWySF3bnU2RcvMRfHznFrtAI/u3RE37x\nhw8xmUzD/2Aj4FZxCTJPS4+UIAiU6vuuTLhx7Qba2ZYqbrLAQPIrq0ZljYMh2t0Fsb77ey1qNMy0\nt15r0tHE5na2Meq8vPNtLpxPp6ioCpXKg+2vvYxaPfQfSHLaCo5+/ntSF/uhVivJz6vDx3fmqCf/\nhIWHPnMG90ve23uWHLcJmKZOZV/mVZYoZWxcs2LU5y1HhvCV7HhBpaLMNHa9sje/tIrF9Q3k5uYx\nPW5OV6xy6ZI08t//iHtTpkHIBJQ5V1np44m9o+OQxg8ICWb9/QecTk+n2dMTj/o61oQFd7mu9Tod\nBx9WoktdZN7xTJlCXkAAJ0+fZdUYNGfwd3NDrOvpMfKS9b1tDAsLRX7vAUTN6DomarV4WrEP91BZ\nu3IZ0rFT3My7i4TAdAc1W8ehi783bAlXNp4pzIlgZ9DrtUTOiCFicsRYL2nccuVWAT/scEAW2t2u\nT3bjOj+JjcbHf3gJdIPlRx99Sl1qmsWxCRcz+Pvtm0d13uFScDeX0vJHJMTPx93LE51WS3nJA4LD\nQnFwGryohNFopLmuHncfb4uM26LcPH6q0aEKCbE4Pzr70v9v776jo7qzBI9/X5VyRIEgkBCSkAki\nJ+VIECIag21sQ9vG3faE493unjO9s9Oz2zvdO9PdZ+ec3Z6eM9NtT3scxhhjcjIGlCUEApNBgBAI\nIUCAsoRKVaV6b/8QBgrFUqiSxf38p6dX793Sgbr1+73f717+4uU1A/U2es1isfCTX/2GB1HT8YyJ\nAU3DePAgf5uwgClTp3T5un/5dDOnZsxGHxiIajQy5tBBfrFpIy59+DL9PJDVzkI8h3634wjbo63L\nAGqaxupTxaxcvcLm67W1tZGTnUd9czMpsdEEjhnd5bn7Dx1hl7cfhLQnG13pVd5w0ZEU37Fc4oPb\nd3Bxc8U3oPcF/wfTgcOZHLxfR0NYGN4V5Sz09mLN8v6NTpsbGvjrw7m0xTx5/5rZzKLTxax/qfdF\nMDRNY/POPZxuNmBSFCI0C++uXW3zSB3AbDKxZfNWss9dpFWvx2dCKGHeXvxgYTJB48Z2+hpN08jJ\nzqO0to5AF2eWL04b8D3fw4kkXyGeQ3uzj/ObwEj0/k8ltatX+dnYkUROfsGmazXU1fHrLdu5n5KG\nzssLp+LjrB/pR0pS1/tR8/MLOVF5B72iEBc+gfkL5ln9/t6du/zrgUPcGhuMk9HI5Loa3t/wKs6d\nNOwYDA/uVnG0+CQTQsYxc077s8zqqnv8/OhJ1AVPmglw7hx/NymM0Ijwft3v8x27yRoZhC4sDNVg\nYFTmYf7nxvU2Jc7d+w+yJyQM3aPpbM1iISo7k5+81bd98+fPnuP31Q1ok5484x5z8AC/fOcHUkxj\nAMhWIyGeQ8uT5/PlH7ZSnrIMna8v6r17zKq4TmRaz8VSnrX9m0yqV6xC/+gD2RIdw/6sTJISui45\nmpgYT2I31/yPw9ncWboMPaABl0wmNu/cy5t22KO595vD7G8x0TZvAVplJS988BF/tekH5BcVY5kf\nbb1gdsYMiooL+51833hpNdNPn+H08aP4ubqQ/tYbNncHutDQhG7mky1Qil5PmU7f5367RVfL0GKt\nv0BVhk/k5rUyJkTKI53BJMlXiGFKp9PxN++sILfgJrevXCJyVACxb27o07WqFaXDh3u9ry/N9fX4\n2Nhe8Du3dM/UPXZx4eYAz8NlZufxbdV9ABaMHU1KciItzc18U9eEJSERBVBCQrgyYgSHDmcSEjQG\n9e5d9E81k7DU1TFmhN+AxDNj9ixmzJ7V59d39oHt1I/JS+WpUpff0ZlMuNpp9sGRjh0/wf4r16hT\n9IzSLLw8fzZTpti2yr0/JPkKMYzp9XoWLUrt+cQejAKuqqrVCma/hga8nipwYCtPTcP07DF14Lbe\n7D90hF0jAiGxfQFR6a0KDIezGOPrQ/MLk3B+6ly9tze3HraQsXQJ4X/8iBv+S9C5uaEajQTn55I0\niM0EWpqb2XbgEFWahr+msnZRGn6BnT//njc6kDM7d+IyZw4uoaHtW2tcnfs8Rbx47mxOnzyBeV57\nwRvNYiG8opygpf3/NzOU3btzl09u3aUtrX3L4y3gg8Pf8NvwMLstHpN9vkKIHq1buojR+/ZiqatD\ns1hwLixgRURoj802upMwyh8qbj7+2fn0KRZHdb3S1lbH7tfA2Ce1zAkZz7F7D4iIjMDj+nWrc1WD\ngTGuriiKwn/btJGVJeeZfayQjPOn+fmmjf16n93RNI3ffPoFedFxlMYncSw+mV9v391pQZKcvAJ2\n3qvBY8kStKZGzH/4N5aUnOOtV17q8/0nRITzXlgIL+TlEJSfy4Kj+fz0jVf685a+FzKPHsccbV31\nrDE+kbycfLvFICNfMaCKi4qoKD8LigU3tyCWrXpx0D64hP14+fryqz/bRH5uAbVlV0lblNTv1cmr\nli5hVNFxThbm44TGwlnTiZxk20Kw7pg6GQ2aFAUfPz+SnRWOlJTAlClYamsZX5DHsh+2N8twdnHh\nxZXLBiyO7hQXHed2TBy6R9uSFEWhJnUhh7NyrPb/moxGdlXcwZiSig5wnTYdS8h4jn/5BavSF/dp\ntfN3Zs6czsyZ03s+cRhxcdJDWxs4P5n/0AyGQW/U8jRJvmLAnDh2DLPxJAlJ7c/Hmptq2fnVZta+\n2rfnjGJo0el0JKcmDeg1Y2Kjiem4+2hAhCtQVVlJ66VLKIqC24wZRDwqIvHq6hXMvVLKyeKjBPn7\nkfhn71Bxo5yjZ84xwsOdxQtT7bLqurq2DiXKeu+v4u5Ok6HV6tj1K1epmxjJ0xHpfX25HRbBhzv2\n8F82vjbosQ4nGWnJFOzYx8NF7VvxNE1jdFEhMe++bbcYJPmKAXPzxrnHiRfAy9uNNtPtPq/EFKI/\npoWGUFhaivfixaCqmA4cIDYh+vHvJ06KZOKkSAD2HTrCnjYFLToe9eFD8j76jL9dvxafbp5pNzc0\noCgKnj59L2+akhTP13u/wZic8viY/vQpkubNsTpv3PgQPI7k0RYc/PiYZjajKArX5OmhzTx9fPhx\nUiy7crOo0+kZpVpYv3a1XWfpJPmKAaMoHVdOKoqGqqpDpr+meH4cvn4T1++qbOn1uK5cycG8bKKm\nW/frNZtMHLlfi5bSvshI5+lJ9bIV7DyUxZudPE992NjI77ft4rqvP4qmEtnYwPuvreux2MSlCxfJ\nvXgZgMQpLzBtxnQ8fXzYMHECO48cptbPD9+GBtJDxjJ2vHUbS+8RI4hXNA6XleESEYHa2krj/v34\nZGTgfPxYX/9Ez7UJ4WH8ODys5xMHiSRfMWC8fUKoq72Nn397+zhVVWmz+EriFTYzGgz88audlKJD\nr8EMVyfeeuUlm0Ym9UrHc2s7GSXW3X9A48iRPP2vVNHpqO1wZrs/7d7PtUXp7X17gcttbXyyax/v\nvtZ16czCouN81mjAEt8+bX+m5BIbjh4jMS6GmOj5LJg/l+b6ejx9u/7/smHtaiwff8bXp07ByJH4\nrFgBLS3M8bRtr7AYGmS+QgyYxRnLKL3qS07WXXKzK8nJamHlGnneK2z379t2cTYukWoXV6o0jUPG\nNr7YvqtXr83OK+C9X/6WyhvlNB46RFNODpqmoWkaY7SOW5kCgsbgX1VldUwzmwnSd/7xWKHorZtG\nODlRrna/1zazrBxL1JMRtzplKlnXn6z01ul0+Pj79/hF9c23NvKXC2YzWw8RxcdYVXGd178njQSE\nNRn5igGjKAqr1r7q6DDEEGcyGvlk+27KVA0XTSNmzCiWLU6zOqdUVWjYuxefpUvRe3tjaWjg4ObP\neX3dmm7XD1y+eIkPT53HJSMDv6AgAMy1tTTu3ctEncJrazrWtNbr9ayODGNLXh7GuDi0+/cJPXWS\nNV2UbHTXVBqePdbDe37YSczNfWw8mxgfS2IXRcoeNjWRnZOPl4cHiSmJj5N5S3MzJRcuETExghFd\n7CEW9iXJVwjRZ5qmkZmZw5XaejzQWJmcQOCjdn2dMZtM/K//+y/cf2U9yqPVxDsqb+GRm09K8pNi\nlIZbN/FatgK9d3ttXL2vL5Zlyzjz7WlmP7MY6WkFly5j8fbG+VHiBXD298erpYX//dO/7LoUZlwM\ns6MayM0rJGj0KGa/t6nLJJ8wdgzbbtyAsPbnhUrpVZInhHR67neCVQs1Ty081DSNkE5G4f1x+vRZ\nPrpwGUN8AprBwMEPP+Gv167i+JmzHKip5+HkqbjmFpFAGxvW9r6ZgxgcknyFEH32py3bKJoyDd3k\naWiaxrmDmfz39DRGddLxqORSCR8UneDO6CB8ntrGowSHUJyfS8pT5wYAD0ZZJ3Hn8aHc+PZ4t8lX\nr4Gmdlz416LXYzYau10U5eXry/Je7O9NX5iC99FjnCjMQwHiwsOYN7/rmADeXJnBP2/bzY2xwWiK\nQtjtW7y5dmCni3edL6E1bWF7yUwXF6qXr+CTHXso9Q+gLSEJJ8ASGEhOWRmzz50nasbztbd3qJHk\nK4Tok+aGBr51ckEXGAi0P3ZoSlvIvtx8NnXSHGHryTM0LVqCkp3d47XfXreGfzh3DucZMx4f0509\nQ8yc7usiL46ex95/+mfa4uNxerQFSDUasbi5kZWdS8aypba8xS7FxcUQZ8P5PiNG8Hc/fJMHt++g\naRqjMtJ6flEX8vILKa68A0DM+GAS4mPRNI37z4zqFUXhWuVtzEuXWU1wKxERnD5+VJKvg0nyFUL0\nSX11DQb/AKsayYqi8LCLZ5lVig5Fr0dtaUEzmR5PO2uVt5gfZD1Sjpz8Aukll8ktLsY0aRKuJZdY\n6OrUYQvOs4JDx7N46mSyCwpQ9HpQFDSzGe+UFCgv7c/bHRAju+iT21sHDmex08sXElMAuFJejiEr\nl8VpyQSoFu49c/4YX18qysshIuLxMbWxkdHeXv2KQ/SfrHYWQvTJ2LAJjL5ZbnVMrasjckTnPUz9\naF8R7J2eTlNmJk2HD2PZ+iUvNdWRmtKx+eBra1bxj4nR/Kimit+kxrPumSlho8FAY11dh9e9uXE9\nQS5O+KSn47NkCb7LlzOiqJC01OS+vdEuNNTUcGDfAS5fLBnQ63an6N4DCHmqItaECRTeaU+5KyZN\nxLmwAE1VUQ0GfL7ez7uvv8zUa1dRG9qXiKkGA5bNm9lzu4qffrqFT7buwE4t3cUzFM1Of/kaY5k9\nbiPEkNHaaiQvKxOT2UhsfDIBgX1rvdcfJW0GdM6hg3b98+cv8lnxKe6FR+BeW8Nco4EfvvZyp4uV\n8gqL2FxdT9ucuWCx4JmTxU+TEwgNsy0+VVX58IuvOKd3xujqxvi6Gn6UvpCgp0aVV69cZeeJ09Tq\ndASqKmtj5hE+MaKbq9rm4JFsdtc1Yl4QDeXlTCm9wo/f3jDoe9p/9vlW6pKtOw4F5mbz60fNEGof\nVJNZcBQPVxcWpaXg6uaGqqocOZLNzaZmSs6co37jD9B7PNqL39jI0tLLvLx6+aDG/byKc+n8iyhI\n8hViUNypvMORg5+SlDoaZ2c9x49WETYxjdnz5tk1jsFOvtCeDG+XXcc3MAAfv+773t6+WUH2yVO4\n6vVkpCbh5etr8/127NnP/sgp6LyffLCNP3SQ/7Fpo83X6gvDw4f8bM9BWhOf1Lm2NDQQun8PjWPG\nYlYUwjULP1qzEk/vrj98++JfP9vCqcRkFKf2J4aa2cyCogLefb13nYj+66dbaElbaHUsOC+HX7ze\ndYEQ0XfdJV955ivEICjMP8iSjCd1eOMSx5KbVWj35GsPOp2OkMiJvTp3XOh4NoSO7/nEbpQ+NFgl\nXoBKTy+MBkOPJR4HwpWLJTQ90w/YcOYM5UuX4+TfPrtxwWLhg+17+EkXe4X76p11qzF+uYNSVzfQ\n4AVzK292sritK5194Dt3ckwMPkm+QgwCndICWHfFURSDY4IZZtw7Wc/lZjKhd7ZPGgmLCMM95yht\no58sElMNhseJF0DR6ynTOw14UxFXd3d+8tYbj/v92tr4fZa7KzkNDei+m3EoLyd2XFD3LxKDQhZc\nCTEINK1jf1VV83BAJMPP4hlRuJz69vHPWnU189xdcXKyz1jCNyCABEVFvXYNAEtTE25Vdzuc56Sq\ng9bNy8XV1ebEC7Bh3YssL7vK+LwcwvNyeMNiJDU5YRAiFD2RZ75CDIJ7Vfc5sPsjEpIDcXN14mjh\nPaZMS2fGrO73qQ40ezzzdYTLJZfJPHsRowJRfr4sWbzQ7m0rL547z+nS64z09sTNzY3PNSe0R1t6\n1KYm4s+eYtP6dXaNSQwtsuBKCAdoa2ujICeP1tYWElJS8fLqOBoebMM1+Q5F+QVHKay4jQmY4uHO\n2lXL7NofVgw9knyFeE5J8hXCcWS1sxBCCNGJNrOZ/QePcLu1lZFOelYtXWyXVfOSfIUQQjyXNE3j\n/3z0GddSF6Lz8EA1Gjn/8ef84t23B71gijyQEEKIAdBmNlN3/wFqJ12VxNB09tRprs2cje5RxS+d\nqyu3k1LIyc4b9HvLyFcIIfpp36EjZN6rpdHPj8DqB6yNmsSC+XMdHZboQcXtuyizrAvf6Hx9uX/l\n4qDfW0a+QgjRD9eulLIHJ5pTU9HNmkXtosX8Z0kprS0tjg5N9CAhNhqn4uPWBy9dZMG0qEG/tyRf\nIYToh6LzF9GmWn9YP4yNoyCv0EERid7yHxnIiyO8cc/NxXTrFi5HC0k3thDxQu/KpfaHTDsLIUQ/\neLk4oxqN6J6uOHXvHmPGjHJcUKLXli5KJdVgoPxaGcEZCwe8GUZXJPkKIQZMm9nMh1u2c1ltLx8w\n1UnHO6+utUvpx6qKW7h7euAbENDna5Rdvcb5iyXMmj6VCb1sQZixKJXC/9xKfcYyFEVBM5sJu3ie\nae9t6nMcwr5c3d2ZNH2aXe8pRTaEGMbsXWTj37/4iqIFsY9HgWprK4nfFvOWDZ13bFVZcYs/Hs6m\nMjgE5xYDU+preH/DepxsbLTwwedfcmL0WJg8GS5dJK62mrd7WR6y5kE1u7JyqUNhrF7H2hVLcXVz\n68vbEcOIFNkQQtjFNYtqNf2qc3PjirltwO9jbG3l3KkzjAsex8dZeVSlZ+AEaMAFo5Gtu/fz+roX\ne329S+cvUBwSihL+aLQ7NYrCy5dJvFLKxEmRPb4+YGQg7wziFwwx/EjyFUIMmM4+UAb6Q6aw6Dhb\nr5XTOGMm+rMltJrbeLoekc7VlRs2JvwL166jzI+1OqZMnszZE0W9Sr5C2EpWOwshBsw8vxFoVVWP\nf9bu3CE60M+ma9y5VcmWHbvZu+9rjAbrHshmk4ltV2/QkpqGU0AAzJqFiY7djDyx7WlaZHAw6q1b\nVse0G9eZMjHcpusI0VuSfIUQA+bF5emsrb3PhLwcwvJyeLmxlhVLl/T69dl5Bfzy23McmR/LrsnT\n+PlnX/Lg3v3Hv79+5Sq1kU9GooqioPfwwFxe/viY64li0mdNtynuWfNmM/3SBdQ7dwBQKyuZdf0a\nU+28CKc/6h5U01Rf7+gwRC/JgishhrHvU1cjTdP4m8++pDZtodXxeQW5vPdo4VNjXR0/yyrAEh3z\n5HVtbUR8tQXv8eNxBhbPnUV4L1cqP3v/k8dPcu3uXSYFBzNn/px+vR97qauu4XfbdnF95ChUowm3\nkkv81fq1TIma6ujQnnuy4EoIMeQZDQbqO+kmU/vUtLKPnx8xFjP5t2+jGzcOzWTC//A3vP/uJrx8\nfft1f0VRmB8zn/nA5YuX+GLHboIDA0hIjEdROk5tDxV/2v8NlctX4vooRi02ll99/DG//4tAAkbL\nXuOhSqadhRBDgqu7OwEtD62OaarK6Gfy3luvvMS7qpHoY4WknzvF3294td+J92mffrWTf7pfT9aC\nOD72CeAf//AnLBbLgF1/oJWZLVZfDhRnZyyhoXydf9SBUYmeyMhXCDEkKIrCmqjJfJqdjSE+HrWh\ngXFFR3nljZc7nBsdG030IMRw/24Vha4eKI+eK+sCA7menMqRzGzSlywahDv2n5PJSIe13ZqG2T5P\nFEUfSfIVQgwZ8+fNYdqUSeTk5OE3YgTRf/6OXad8L10qoW3SJKspQZ2PD3cam+0Wg62WhIeyrawM\nl4j259yGc+dwMZlInDEYX0/EQJHkK4QYUtw9PclYnuGQe8+cOYMvc4toi36SuCw1NYQH+Dsknt5Y\nuXQxlu272FtYQItFJdDNhTUL5sn+5CFOVjsLMYx9n1Y7DxXb9h7gkKZHnT0brbycqKuX+fHbG9Dp\nZImMsE13q50l+QoxjEny7Zu7lbc5fuIUkRETiJph255hIb4jW42EEMIGQcHjeDF4nKPDEMOYzKMI\nIYQQdiYjXyGEEIPOYrGQl5NPY/NDUpPi8PGzreb3cCPJVwghxKCqr6nht1/t4n5SCoqHB4e+zmLj\nxFBiFsxzdGgOI9POQgghBtXWQ1k8WL4SnY8PipMTpqQk9pRcxU7rfYckSb5CCCEGVY1O36FYSo2b\nO2aTyUEROZ5MOwshhpXWlhY+2bWPChQ8NI2FE8OIiZ7v6LCeawGqhTJNs0rA/sZWnF1cHBiVY8nI\nVwgxrPzui684EZ/E/aQUypNT+bj+IRfOX3B0WM+1tYtSCdi/D0tzM5qq4lx0lBWR4UO6W9Rgk5Gv\nEGLYqK+uoTRgJIpe//iYJSqK3II8pk2f5sDInm8BIwP5h3c2kpmVQ1OLgdTkOAJGjXR0WA4lyVcI\nMWyoFgua3olnx1Pa8zvAGjKcnJ1JT1/s6DCGDJl2FkIMG/6jRzHh3l2rVbRKWRmx4WEOjEqIjmTk\nK4QYVt5fs4L/2P8Nt3ROeGgqyeOCmDs/xtFhCWFFGisIMYxJYwUhHKe7xgoy7SyEEELYmSRfIYQQ\nws4k+QohhBB2JslXCCGEsDNJvkIIIYSdSfIVQogh6s7NCs5/exqLxeLoUMQAk32+QggxxLSZzfy/\nTzdzeWwIbQEBBH7yBW8vmEPUtKmODk0MEBn5CiHEELN93wFKUhaiTJ+O89ixNCxJZ/PJ0891/9vh\nRpKvEEIMMRUmCzpXV6tj90b40Vhb66CIxECT5CuEEEOMj6Z2OObV3Iynj48DohGDQZKvEEIMMSsT\nYvHMPIKmtidh7cYNEkd44+Ts7ODIxECR2s5CDGNS2/n7q/ZBNftz8jFoGnMnhDJ3wVxHhyRs1F1t\nZ0m+QgxjknyFcBxprCCEEEIMIZJ8hRBCCDuT5CuEEELYmSRfIYQQws4k+QohhBB2JslXCCGEsDNJ\nvkIIIYSdSfIVQggh7EySrxBCCGFnknyFEEIIO7NbeUkhhBBCtJORrxBCCGFnknyFEEIIO5PkK4QQ\nQtiZJF8hhBDCziT5CiGEEHYmyVcIIYSwM0m+QgghhJ1J8hVCCCHsTJKvEEIIYWeSfIUQQgg7k+Qr\nhBBC2JkkXyGEEMLOJPkKIYQQdibJVwghhLAzSb5CCCGEnUnyFUIIIexMkq8QQghhZ5J8hRBCCDuT\n5CuEEELYmSRfIYQQws4k+QohhBB2JslXCCGEsLP/D0GUmwEOOcg9AAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118b21e80>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -246,21 +231,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you're running this notebook live, you can use the helpers script included in [The Online Appendix](06.00-Figure-Code.ipynb#Helper-Code) to bring up an interactive visualization of the decision tree building process:"
+    "If you're running this notebook live, you can use the helper script included in the online [appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Helper-Code) to bring up an interactive visualization of the decision tree building process:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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QuXrzxPNTKC4s4cDxg0yf2Lx2cu9RDVGxU1tt4/j+bayYW9K0PMnDTUGgVwEK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\nVZJV3OI2c0lWcafb2XdgH4YxPlZXmtW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WaMBPi2ZoKHVerjdHo2LAIJlocFhb/IHOhXjd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+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27908facf15245bab6735b3bdfa456d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118894860>"
+       "interactive(children=(Dropdown(description='depth', index=1, options=(1, 5), value=5), Output()), _dom_classes…"
       ]
      },
      "metadata": {},
@@ -279,32 +271,32 @@
    "source": [
     "Notice that as the depth increases, we tend to get very strangely shaped classification regions; for example, at a depth of five, there is a tall and skinny purple region between the yellow and blue regions.\n",
     "It's clear that this is less a result of the true, intrinsic data distribution, and more a result of the particular sampling or noise properties of the data.\n",
-    "That is, this decision tree, even at only five levels deep, is clearly over-fitting our data."
+    "That is, this decision tree, even at only five levels deep, is clearly overfitting our data."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Decision trees and over-fitting\n",
+    "### Decision Trees and Overfitting\n",
     "\n",
-    "Such over-fitting turns out to be a general property of decision trees: it is very easy to go too deep in the tree, and thus to fit details of the particular data rather than the overall properties of the distributions they are drawn from.\n",
-    "Another way to see this over-fitting is to look at models trained on different subsets of the data—for example, in this figure we train two different trees, each on half of the original data:"
+    "Such overfitting turns out to be a general property of decision trees: it is very easy to go too deep in the tree, and thus to fit details of the particular data rather than the overall properties of the distributions it is drawn from.\n",
+    "Another way to see this overfitting is to look at models trained on different subsets of the data—for example, in this figure we train two different trees, each on half of the original data."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](figures/05.08-decision-tree-overfitting.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Overfitting)"
+    "![](images/05.08-decision-tree-overfitting.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Decision-Tree-Overfitting)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It is clear that in some places, the two trees produce consistent results (e.g., in the four corners), while in other places, the two trees give very different classifications (e.g., in the regions between any two clusters).\n",
+    "It is clear that in some places the two trees produce consistent results (e.g., in the four corners), while in other places the two trees give very different classifications (e.g., in the regions between any two clusters).\n",
     "The key observation is that the inconsistencies tend to happen where the classification is less certain, and thus by using information from *both* of these trees, we might come up with a better result!"
    ]
   },
@@ -317,16 +309,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
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xItv2fMKidZvve6+C1mtoXHQ2jI3GNq5+cKPGTeBU+m6mT2wZYr9VaMPbP6Jb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\n",
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f280b0c9da354fa395d5313c4f860380",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118c4ca58>"
+       "interactive(children=(Dropdown(description='random_state', options=(0, 100), value=0), Output()), _dom_classes…"
       ]
      },
      "metadata": {},
@@ -353,24 +352,27 @@
     "## Ensembles of Estimators: Random Forests\n",
     "\n",
     "This notion—that multiple overfitting estimators can be combined to reduce the effect of this overfitting—is what underlies an ensemble method called *bagging*.\n",
-    "Bagging makes use of an ensemble (a grab bag, perhaps) of parallel estimators, each of which over-fits the data, and averages the results to find a better classification.\n",
+    "Bagging makes use of an ensemble (a grab bag, perhaps) of parallel estimators, each of which overfits the data, and averages the results to find a better classification.\n",
     "An ensemble of randomized decision trees is known as a *random forest*.\n",
     "\n",
-    "This type of bagging classification can be done manually using Scikit-Learn's ``BaggingClassifier`` meta-estimator, as shown here:"
+    "This type of bagging classification can be done manually using Scikit-Learn's `BaggingClassifier` meta-estimator, as shown here (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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bZE4OqCl77rkVMAG0Mshes5nAoADsGihR2VZkMhlz//1Xtn33MyZJKahcXRj9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TCxqZEP33z1ucUmLp6/QP6FSwAkTnmJaTOm4+HtzboJ4Ww/fIg6X198GhvJDBnHuFDrNpZeo0YR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\nVwghhLCz/w+BfjfvUVnKbQAAAABJRU5ErkJggg==\n",
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173/eChe+X9XvDlccvRQPPabCKYWNGT//3cg957YdSnj9hHPggveqHcNwE4nCHfeqHUJT8/Cf32VcM76N7vMvqy9ooWcnhPJu//VX5SmOZ27+D5z/HvUD03QVm/R5lVLXn36hPEePAV+9QoUynDxSf24KBcDHLsvP0guhtvzvemvPbdEo/PhXcPY74O2XqtDDhefBeW9Sjw2F1Bo+fIlqlS3VHyMlfOGbqvLhc19TIYZsVnnG/74Trr952N4mR9a87hzH37hlZJ4vElWe9bMvqrDO9p1ql3XvQ8P3HCtWwTnvgp/8Gn55LVz4vrEXRHIZVcZ3eKG9w9kg6Dp88kNqGz2e2bVblYdlMtAVrhUCjNyMsL29yrvOPl111915rwpDbN2ukmlTp8DnP6ESV6C21T//oaqR3d2kRFw+/RFobFDPc8e9Svoylcq9dy3KALe3w9c+Dx//IOzcrWLgXV12fY3D2bYT/vIPsArj0Wm4+Ta47D3D9GY5cNA8VXlRKHg0e8bIPN+9D6n3sPd3LpVWiczeY4gGi5Twle8VX0iu+oWKq4/377PLsDC+je4Ry8B0mB47dTJUVoz+egbKU88XhyikVMmh51+GlavhQ++DD+amKMw/QJVMlcKy4Ge/UzPOBFBRoZJ8s2eq+z5yBaxdX1xWl0qp8TSXXKRmthXObfP74KpvwWe+4vCkUnm4Tp/DUMf+tLWr9U6bol6DlOqic+OtytideYoSPG9uUa+pa2bcFR8f2vOWoqm5ONkI5U9i7o/dTWoSSiGGActfLd1x57JPMb6N7uRG+OilqtzKslXiSdPgB18fu2kMAyEUdG606CKVgj//Ay5+h/Lo+uP6m1QdalfZV7oNvvgtuPVvasv9xibnOmZQxiSVLl1ZcPzRqnW30MBIqdTeXl6ZP3rHMHpKzQbDH6+Hv/5TNWZkTVXbO/8AuPn2nue58VYl73n26ao0beoUFWYZyITlgXDkMvj3HfkXEyF6dhlDpaLCOQ4upZq64bJfML6NLqiZaCefoGT7QkHl/UyUL+ipJ6iYYF9omgojlFPy9q/biz0xy1TbYknfnmdDnar5LYUQaprvV7+nqiVsW10ITjtJba03bYEb/q0ufKBGwn/+Ez3HP/WcWl8mq5oSzjmjdEXGyytVCVwm03MBeWm58v6tXh51OgOvb1AlgZ/8UOm1DxfHHqlCLS++ot5Lv0/FwPvafQyEcKWafdd7XpuuQ3394IaYukxIxr/RBZWZ7y87Px55cXn/GX7TVAasHJy2vllTbU2PPkIZyYRD4snvUwpi/e0OTj4err9W1QxHovCmU3u2vJ/6sErGrVgFkxpUJ1yXUb3uJuW5dnmor62Bl1bAt690fp77HuqZytuFk5YvqOfYvlNNvxhpNA1+/gOVSHtxOUxphHPOHN5Y63e/Cj/5jaqKsCw45gj41pUTY+fmMixMXGnHicB7Ptz/CBzDUIM2DyvYwrZ3qIaDaZOVwM+f/66kGgXKq+2Nx6OqHjwe5R12hRg0TXlWn/loj7TjcJNMwekXFF8QvF74z99V/L2QH/9KVT8UXpCchlh6vUq1bLxXqQwUKdXfWAkjuYws+5S0Yzk8/JiKlbZ3wonHqK1pYfJosHRtf7vaUrOm0kKoqSqutW0pIwFjmjnN2t/33PaH6+BvN6p4ZzJXhdBloLpskqH3JLd6x3FPOUFJHTbWq/jnEcsG+goHxu49zobD61HrcDK6F7xZJcwKB1aedqIai947vHHBOfuewQV1gXG92/2Sfc/o3v5fVQPZ9YO+63547mW4/e9DEzvv6ITv/UTFloVQ8doF8+GvNyjD6fHCFR9TW/Aujj5ctdlaDpn/3mze2vP/L7yi6l97xzsL8eQm+FJw3nRGeda3Xjd688imNBbXUYMynHNnOx+z4ED4yufg6txEZ9NUKmdf/iy87yL4z90qTHLGKcp4P/K4qid2FcNc9gH2vfDCm97m7GHOnK5ijIPNRL/vo0oMpks/QdeVN9b77fP74Nc/6vEu97YoZbF4XHmsXo/yTgu31YsX9Hi637pKDU/sj3AFRGLFt+u6Gn0+mnPk/vR3Jfzelcjz+5Uy2ve/1vdxmZxAeUN9cdx063YlkN51TtOE731NJVJdXMY7fYQX9r2AUmu78+3bdsAnv6TGywyUTVtg09Z8wRrLKo4/ptJw0396/t1QD3feAFd+Fj50iaqFnT61RyKyaxT4Fb2qAAy9/21nwA/nn+OsvGVZ8Peb4W3vH9xrHQwffj98/+uq5OrQJara4LtONb8FeL1KvMYpUfXl76rPMp5Qf+kMfOv/nOtcXVwmEPue0Z3XhwJVOgN/vmHg5+yM9C8t2EUsnv/vQEDFJT98qWoGqK1Wcc45s+BNpylBmd5JtAverBoACvF4VOw04FcVBVd8HJb2UWa0ZRt89POqJXg0OO1E+MMv4G+/VcpkQ0kQdUbUha7woqYb8NxLQ1qmi8tYs+/FdL/2efjEF51rVqWEHTudj2vLeVXTpxZ7mosOKhaEcSLgh3NLtIt+4RuqFrUr1uz3wwcuLp7fdegS+OzHVCuvrqlE3dmnq+aB5hb130OXqDV+4VPwwU85l5KBCmM8/Hh+nLlcsqZqY66pHv32VI+hqjQKEagL0mtrVRx58UH9CwW5uIwz9r2YLihtge/+GF55NT9+6jHgorflF/XH4qoh4MVXlHdWFYYff0dpGvTm8afVlrdkcsujyrOu+laxV7zmdfjQZ/M7ukA91yN3OHuFySRs3aG68qqrSr/WG26B3/659LpOPh6u/s7AkoiPPAHfu1rFny0Tzj5DNU6MpoH7/Dfgmed76neFUFoRwQBEYz1CP7/5ESxaMHrrcnEphz5iuvum0QXltV7yMdXtlUwpz7IqrLRhe5ePffm78PhT+cX5oSA8cKuSjuzNq6uV8SwUI/d5lYd9fonxMHfeB1f/qtj79njgwdvK67CLxtQ4nxdehjmz4X3vVF45qMqKT35JVS441bkesVQJkpfDth1w0eX5zQt+H1z6bvjoZeWdYziIJ+AbP1SyngJVNpZMKR2I3q+xukq9h67H6zKe2O/qdEEZzhv/BA8+qnRgD5qn2ll7G9JMBv73ZLERlVK1ap59ev7tBy9SExW2bsvX9w2F+lahOmC28+0Bf4/SV18kEvDej6jwQiajPPh77lfx4HlzleH52Q/gA59Uou+9yWTglZWqnbacEfL3PuQw4SKtRgmNptENBeEXP1QXm0xG7Ugu/nDxRSWbVZ/vcOkjuLiMMPteIq03XbPNvv4FeMcFxZ6rLZ2lI6V03q4LAb/8P5g2VRnMYEBVKPzu6r4TbYsXqDhs3rRaH3z2o+r/n35edWitf8P5+HseVGVwXWuyLOX1/fqPPY+Z3KjGDDnW5wpVfVEOmYxz67LTqKTRoLJCCfEIp1Y8cl1dZSY5XVzGAfuup1sOfh8sO7g49mtLOPFY52NmTIM7blBDJS1LKWP1l6kXAn51lZpWe/8jypC8712wcD688zLlnVq22kafeSp858v5ybyVrxXHg6WEdevzb6uvc15LKqXqlMvhzFOV8Hrv5JzHA2edVt7xI8XM6SrEsHlb/mcVCsISN6brMnHYtz3dcvj+19SPORhQYQK/T43z7qttWAg4cK7qrCq3NMrjUXPLrr9GxVePOQJ+92clWJ5IqhhqKq1amJ95If/YBQfme8ldFHZ8GYZzja+uK2NV1joN5VlCTnDdUNUbnxniYM7h4FdXKTU2v0/tNKZOhmt+Wn45n4vLOGD/9nRBKXzdcQO8ukbVtC47ZOjTbMvlsaeK46fJlNIfOP7ontsueLMaSW5a6vFdYt5OAyFLVTGUI8Td2gaXfVp10EGPIMvFby8OzYwFU6fAv/+mkn2WpWqdXf0ClwmG6+mC+uEeuliNKh8tgwtK1LoQw4DqgmqGcCXc9Cc1XPKAOaoR4W+/hSULi48/eHGxIfIYSgeiP+68r1gEPZOBP17X/7GjhRBKMW3ubNfgukxIXE93LLn0YiVzWDiRwamZob5Otdf2xze/CB/8tDKemYwKa7z13PL0aPc0O3vKpVqrXVxcBoxrdMeSt5wNnZ1KhjKRVDHKb36pp/52MMydDffcDA/9T0lbHnN4+c0Dxx6pKiV6D07UtL4HV7q4uAyIfbc5YrBEo8rbDPQx2ma4sW2lC1HOnLSRXsfnvqbG6SSTaj1+v1JAc9LFdRk3rEmXWRLoMiosOub9Je9zjW4XO3apduDX31ClW8cfo5SyJsLU4eHEtpWozIpVMGWSEuUpEefevbaCh341lz0bQjTMTXDmZzcxbcngBHY6dvl48i8z2bk6zNRFUU68fBs100rPfMskNVo2B6manCZUmx+HjmSbBrWGicqK1jS7DlpGIlPGnD2XUeGDC0q37rtGF1Qm/Lx3K/3brk4zj0cJZ//mx2O7tuFg2w64+37V2Xb6KcWjgQZBy+YAf3zf4WSTGl3qNB6/xeXXLWfS/HjfBxfQscvH7999BJmkhrQ0hG7jDdh87OaXqJ5aLObzwr+m8tCv5qLrEjOrseTsZt7yzdfRdGVwu4xQKWRGkF7nR6sx8c4oMZttApHIzGRBanCiRFLC9pth858F2U4IL4FF35BUzOv/WJfSHLe0dI2CG9MFNdgxFs9v7c1m1RSHjs6+BWfGO089B1d+W5WaWTbcfi9cdGFPN9wgefr6GZhpQW85MDMtePQvDZz7g40DOtdjfzus2+ACSEsjk4RH/1LPGV95Oe+xu1fV8dCv5mCmdLqK7VY/UE94VhNHvO91dtjK4M6MFAgW5Wh5ClZ9TSBtkCaEF8PSX0o8A7BZdgbiW8FXD96aAb3UcceOW2HDrwV2Sn2OnSslL14mOP5uibd6bNe2r+IaXVDiKk5oQtXNTlSja9vwnR/nd5elUnDTbfDOC5zjtK+tVZMrpFQjdA5e5Hjq1q1BpJ1/NZdSY+vuBu6tdz6mFFs3Tu02uN3nsjQ2vDGVTH3+dOOmR6ZipvObIcy0wXN3L6T5c+r2UtvsTBus/FKPgQHoXCVZ+33BIVeX3vB1r0nC7nth3VUCpDLak94Ei74l0YYwCWos2fyX/PcDBHZW0nQfzLx4zJa1T+MaXYDDlzrPMWtsUJoGE5XWtp5Gh94YBqxaXWx0b/4P/PoPPWVjd9+vGjDe8478xz3+DG9vugKLCGt5K0/yTdJUoXksZi/byCHNy2lpp0+PszfJxYLtr0mk2fPjF4akYXGg6PhYStDpcA7D9Pb7XM2PFd8ms4Lm/0mkDaLEjjDVBGu+J2h9jpz8Q8869zwkCc6AueOgYW8wmA6DOOw0pFtKaF24DBm3OQKU0tf3vw4+X64dOKgaEq7+zsQuwC85yFHC5AKDG08o4fRUWoVZbKn+/zd/zJ+Gcdd98NXvUrVnObVs5Gh+y+Uci9eXpqI2y1sui7DIN4uldT4OaV6OufsGx791/p6E26z3S4wQCI/6kQuPRA/C7A8U/+innC/RCjqiNZ9k6gVlGIhSD+njUNuEFz8oaH0ekPnhFAA7Ldhx28T9jtQcCWj5b4AWgLpjXYM7Uriebhenn6R0Z599UbXYHneUMsJOZE3VwrtildICOOeM8iQaRxu/D951Ifz7jp4Qg8ejxGMOKQgBbNysPOB0QeLKyI1SPzQ3Gug3f8oLVxikqda38fbzrmfW5xbhC6kdQ9gzmUUADhGMNemt9NZT8zfCsf+WbL1B0LlKUrUEZr5P4nfYZNQsg7kflmz6IwgPyCzUnwizL+3/7Wg4BV7/Sf5twiNpOLG0l9v2AmQjgF3asMp+hj2PZxZ8WfL8KoGdllhp0Dww6TSoKaOB0WVwuEa3N1XhYg3dQrJZJWT+xmZVy+r3wx+vh3/+QYUjxhuf/Sg01sONt0E6BaefrEIGhR78lEmQdehGy2Z7Qiy27ajh4NHSzJ+1EkIHDXqZvgaYf0V53tWcD8L0d0ii6yEwVf2V9Rx1cPCPJK99HRDKWFYeqGKypci206cnLLySKeeW9/zjkcBUOPG/kj0PQ7oZqpdB1aED2+DZWYhtBE8VBKaM3Fr3FVyjO1Duexje2NQzBSKVUobpmr8qScbxhqapkezvfWffj2uoh1NPhMee7vF2fT446TglCtR1rpnTVQlabwxj1EfmeMJQe8TAj2s8BU5+VBJdq4xEqI85pqC239JJSlhINC9UL4VJZ0pWXimIb1RGa+6HJf5JA1/bWKEHoPYoWP0twRvXgNBh6nlw0JcleonNXhctT+eqQSz1PlUfCof+TGLsZ+XtA8GN6Q6Up58vHrtjWfD8y86Pn0h872vwofepBNuUyfDB98IPvp7/mK9eobx7PffVCfjhsEOVLvEEQfcpY9mfwQUV+pj3aYnmk2heiRaQaEHJQVdKjrlZMv8KycsfETQ/DPFNgp13wHMXCTJliLqNF6Sl4tbtLwO2QGYFu++FNd/t293NtMHKLwrMiMCKC+y0oH0FrL1q4sa4RwPX0x0oM6Yp1a7CSQoTucqhC48Bl1+i/kpx9OFwwx/gltthb6uKhb/p1JL70Ui2iR12mumaj7BnYrYSz3ofNJwk2fskGEFoPIPuut4VnxNYKXJJNsASWEnJtn9BxVzYdbdA98OMiyS1R47ZS+iT9pch2wHS6vkM7bRgzyOShXEwSqQrmh8tvk1mBHselCz5wcTOQY8krtEdKO+4AG6+Pd/o+nzw0Q+M2ZJGnbmz4CufK+uhO+w0rzYuo+X15dTXbGWRb9bIrm0AdPVilmMcgjNh1nuLb49tpMfg5rAzgl13QbYT7KQqvWp5WnDg5yQzLxrysoedbCeOI+8FYCVKG13pMNVJ3TFMC9tHccMLA2VyI/z1N6pFOBRUgyF/8l01CcIlj0i2iZZ21azwxpwLaGkfH8IsmTZY+QXBI0cKHj1WsOYHOW91EIQXU1xy5ZWk93YZXADVgPDGrwV2CY35kaJjBaz6umDFFYI9DzmPBKw5XFWBFOJrBG996XM3nEKRgRUeSUPpjY8LrvaCywjQFVJoaYc35lzQrQuwzh/lmI47AXiu+oJB6QWYCehcBd5qqJg/8B+3lPDcuwTxLXQ3Y2heSf2JcOhPB+6ixbfCC+9TRluaAs2raoytDNiJ/MXpAckx/1LNFKPBzjth3Y8EdhqQAj0gmfQmWPyd4te54zZ4/SdqvUIHBBz+e1W+1xd7HlYJODQVG644AA77ncQzQZs4hwtXe8FlROmt6tVlbHcdtIyZ/sUsGKQHKSXE1isjW7UYNC/svl8ld4SufuCh2XDYtQPTCIisgeRO8rrf7Ixg7xOSTPvAtRRCs+CYWyTbbhTE1ktqjpDUHQMvfbj4aiCl0msYDewsrP9ZfouvlRQ03S+ZfZlad2+mvx3qjpO0PAm6HxpOpSw9iklnQP3xksga8FQro+vSN30a3f1NIm+iEvZMdvysRiJxVfg8TopeieqZLIgMTvUKIL0XXv64ILUL0JQ3u+CrkjXfUxnyLmJvKN2EQ39WvoeaacMxqKYZqgliMAI2gSlw0Bfy11B3HLQ+K7uNnuaXzPmgRB8lmeZ0izK8hQgDoq8XG11Qr2PGuwb+XHrAbaYYCH0a3eyhbpxyvONZ+ZKjnOHUXOIKoKUd6msYUgVBKcnERHhwBlaasPmvsONWpfg19XzJ0kMfZe5vv8StG35Fwj4O2evrufo7FCV7pCnY+7hEyvLDDNVLnetu9QBD3vZHN8Da7ws6V6s64rpjJcndYARg5sWSxjP6Pt5MKN2D4VAu89Y6vyddO4TxRDYKzY+AGYX6E8or5ZvIuOGFCYplJTDNCELLYtieInGZdXNmssurxq4nqmeizdjO9NWr+9y9lDLIa9Jbe+KzQ/BgAfS2CKFX1hJ6YDIbXjwdO63czi3X2XSa03mGv7CHpRS6o0I4J4EGmgr2VCqved1V6nwiJwd88FWyZCtwOWQ64KUPCswYgCDbAa3PwoGflcx8d9/HWklY/W3RLcgTnKE65yrLGGtXCt0Hsz8o2fJXFVYApVFRcxhUzh/8ebvIRmHTHwTNj6oLzOzLJJPPGvh5oq/DSx8S2Lnmijd+B3M+JJn7oaGvcbzSZyKtNT0wXVSXkUdKSbTzJRLxDQihI22TUKqK5wKnMjNSOuuxLbyaM6eVnnTsWflSn0Z3sImvvLXf/kuO/dFXkLqBmTRIUM/feJIo07oegWPtEqAHJXZWqYJ1Ibzqh77kewNPgCV3wd7HlH7DpNOVZyhtFe/1VClDMhC23QQbfpUf/gDwNUpOeqDv9a36mqD5ERVbVqhE1In3S/QhTHCSEvY8CFv/KbCSMOXNklnvY8gylNKCZy8SJLb1fB6aX5Z1gSnkmXcK4m9A789d80mO+48su717POIm0vYhUsltJBIbARspbRAQ93USNAc2raGQHXaalibncq5dBy2DIZY6+XZu5tAffx0tkwWy6IBBgrdwOf/k/tyj+o4RHHK1ZM13VKxSmqp1dcFXBlcUGpgKM9/T8+/W5+C1bwjMOGBD42mw+Luq1bccMq25KoECsg7Sib2x0qoCoPfFBAS2KWl5Wl0QBosQMPksmHzW8BbOtj4Hqd35a7ZTgo3XqiaQckM9ZgISm6HocxfqOaa/bdiWPK5wje4EI5nYUByU1CQNvIbk6EGft+3gw9i1x/nHOZRxMF3UPPHfIpOqYzGXhwEb5ziBxPBkERUeDv2ZpGYZ1D8sSWxR3qivD30haasuK6Oyf88u3QIrrsjP9Df/T+KpESy4sjyDVXesqmCwemuua5Lao6BzNTTdK0DAlPMk4V4yFdJCvfxCbNWYMB5J7nBWVjNjaqpGf3oNXWheldgrPJfQ1c5jX8U1uhMO521LdpPBK18S1B0jmfne8rbHUlqkUzuw7TQH+qawcFqxYV1rJtmxZxvrGJrhtQNB0PSi2y08lPJwQ/peDvlziNBiQ9WOoqoM+pvftfdJJTpuRgANZr1PcsAnSifb9jxEUZG/nRbsulOy4Mq+n6uL8BL117FcCaLrfvVXuUCqmGVup7DjNsFBX5RMf7v6txGEygUQWSvz5COlpSogxiPhJTh+ZIEp5RtcUJ/ltLfBztt7qjzQJEYQ6o8flqWOS9yOtAlGMHSgcg96YSY8bPj1kXS+KthyneC5iwWmg5e01uxxw8xshOam/9DZ/iydHS+zd8/dbOpcwVozmffXxVA93dbT34YsyFRlCbCCyyj+BdvoHpN5V1VTcUig2+CWQ3wrvHqlINMisDPKe916g2DHv0sfY2ecW1od1cUcSGyHp84VRFbTXeJWd6zk6JslW6/PedC2+rNTgtd/mu8RH/x/Em8t6CGJHlLCOgu/IfHVlf+6R5OqxdBwomr2AECXaH615oEy//OSGe9WMXs0pU9x5HUTd/xRObiJtAlILPIqsehqhNCw0jbr/3IMr/30dLqMl+aX6stcoOa4zh9l/oztLDQCtO69n2ymJe9+W+q8mr2ENNV5tw/V4HZRsep55n/1vXj37kZaktW8i7usP2LRky3SKyT1xynvtKoM4TIzrrLoTQ+qsi//FGh7niLR8cAMyQl3OX/V41uVMljvJJgwJI2nwSE/7t+QvPRhQfsr+c+p+SQLvyl5/UcCM1bQmRaSHP4HSVWv6UJ2VgmmmzEVqx7vAy+lDc3/g+aHBZ5amPEOOeRSr4GU/o133ETaPkZF+BCCxhyCX/k/PH+6l4XSZAHn81+uJUk9dkqwbV2WuD9/qGPQuw0QSGkXGVxQO+xqbSt77Oq823uP1imkyyAndqitc6YFGk6RNJ6qyrHiW1X9ZeVBEDv4aF65ewOeliayWgVPfzCM3AOkAU0lrQ6/pjxjC+pH+vJHBdENSt0KILFdOsZIrWTxbV2EZimPa/3PQHgBGwLTnJN00srFLQM9a2h/mSLRGyQktjg3KMgsRVMxNM/E2lILTSX5Jp0+fEm6fcXg9odrdCco4Y9+A+/dDyOkSpkfxJ3UsZ4/sAIRtKk4fjfBqS1MzwuyCRYaAdTmRgfyMxhCE9RXxakKbC9rDTv2SNYxk0nPVvLKJwTSVA0LzY/ArsMh3QbxTSoxIjRVfVB3jCDboMYLHHWDZPvNSgg7OANmXyr7jddKCZ0r1ZZeGOr8MtPr12oVD1QUHsmkfhoTZrxLZfk7VoC3TgnZCAHpVtjyN0HbC8rgJnequWmhWaq6oXKhMpiFQjZCV6+p7thcZ1q6p1a24ZTSScDIGtj6D0F6LzSeLpn2toHFSV3GP254YQIi2jqomXMcIp3/S09Twd+NR9jTcAST746ihZR3O32SMra9iXSoWt/ehlcIL42T34bQiq/FKqEmHcebt50XJr6xwE3RZE4bsNeW2y856UFZVk9/Pik8njXYyTjPfvBoYm/4QORisTL/ObqeWxgqUQPK+B3x54FPM8hG4Jm3CbKRrvKo/DpiPSipORJanqQgnKGe68T7VWxy059h110CIaDxTRJfrYoXN5ySr1XQ8lRuRHxOoEbzSyrnw5F/G1rjhsvo44YXxgFSSrKZZtKpXWh6gEBgNtogK99FeyfoDtklTWPuKU0c+HXw6pWQgnXMZMeebTApmWd4K6sOAySJ+BuAjeGppbrmWEeD24VT6ZiU8LDTtdmGwgSZ0KDlCQY0U0zTWggF/4YQFq/99kSir+u9mgjASbzVW6OMbGQ1+Keq1t/BbF13/EfFWHvqUfNPYiWg5fHi20FVJHTp0M77BMz7hKT1OVWaJm3lNW/6E8z7lGpYAFh3dX7Zmp0SxN6QtD2vPGaXfQPX6I4SkY7nSSa35FLiOrHISmob3oTHM/CMiT1nBrIyhEjkByp1T5rOH9eSqV7dfVupHjQhNMLVR1JZdTggEQMpEcg7j6qpHKnxNH7/fQiRQgjYfs8S7ExhWluALpXDrqut/qJvSkKzh64xEF1b3GFW9Nwlbm9/RXXQdWXhpQ2vfbPAqFrwxm9g8jmqeiG5o/hMdka1yrpGd9/BNbqjQDbTSjK5uVcVuIWUFp3tz1HfeM7AT6hpRP/2c8Lv+KhyNS0LNI3UT7/ByYsHNo5VlLlvXWgE2OHdxraCDq1EZiYHfKKS139Kt0ERhtre21mZt/WXNtSfNKDlYejbu71UI+TQ8qVJGk4ATw0YFTDtQknF3PLOLaWqdGi6T6AH1bG9dQmqDpXsfYI8QzlYUk3kdBnyER4lND7pdPBPVp1eefcbylvflzL7+zuu0R0FMuk9jmotZrYVKSViEL8m87TjaF/7P3z/uRfSGTLnn4F9wOxhWG1pCrUb1ppJ1m9XWqyesGTTn1UXWP1JMOu9kte+LohtknmJtIHGc6UMIISyVvM/9CzLv3UuVrLH8mteOOCTgxOHWf9zwY7bwE4CGuy8Q7D4u5LJb1L3T7sAtt0AmVaZC2nkPkONopK0PDRJw0n5nXCecInxNr00dud/UY2Ht1OgvGiJnYJVXxas+xEs+YEqp3OZ2LiJtFEgmdhMpON5ZEG1vRBeJk0dhIDpOKErudaFU8w3viVXMrZgcEIrXs8L+AIPo2EhJay95gRev/ZErKQHzQ+LvyeZfGbP41PNsOsOSO0R1J+gjJ9T5CS5C565UBTEhwFdia2EcvnCbAS23wytzwtCcyC8UBLbADvvFAUesHofhAHhhbDsN8XTE1Z/W9D0AN0hC2GoMMgxt/ToFbS/ojQglMebvzbNJzn6xvI9eZexw02kjTH+wAyinS/nG11hEKpcXPqgCcBCI8DCaT3/fmhncbvwUOOqmeyRtAWamKK9jkxnqVu2E39jlPjWOqQt2fOQYPKZyuBF1sFLl6vSNTsjaLoPao6Apb8qFmGJrFZb+yIhHwteukxw4n2qbtgThrkfgbkf6bm4SFtJOe59XCJN0HwqabbwG5LgTGeBcFD3e+sEO26T2BlVvbDgy/lrqzksNyjSIV5sZ5T+cLl6EC7jE9fojgJCGNQ1nEOk80Uy6SaE8BKqXEwwNAzCpuOI6ZME68sr8R0AgjYOgMginjvqQGSvxJbMCFoel6Rb1BZ93VUCq9dcMispaH8pl/0/Jv+sgemq3tbp+ayUGrdeSuFLaEp/t/NVFY/1T4HGU+hXkUzzwIGfkRz4mb4f5yQmo+4QZNtdgzvRcY3uKKEbIWrqThnrZUxY0js94EF1r/VCeNR4H189dK4pPs5KQcfKYqMbXqj+OlcUa/hKCzKtfa9HCKg6GKoOGf4EV8OpsOcBWdTlpvkljWcooxvfCk33AzZMepPzbDIzpsIj/sm4db7jCNfougwLti1pvddP2y0+NoQF0946vFNvKw9NOo4JR0JorppkIGRx1a7mU+pXThx+jeTJ8wTZtmLDW9uHSma6RamYtTyt4sVT3qzCBIOZfyYtZTybHhR4q2HGuyULvyKJbxLENvS8IOGBhpOh8dTcgM7v5DoAgS3XqyTcjHeox9pZWPtDFV5BU1UdS74viy48XaSaYev1gs7XVCfe7Esl/kkDfy0u5eEm0iYw6qOzB11jO1xk0nt44ANV7HjgQKyEF2HYCEOw7DeS2mEYs7ctvJrpkwQvXz2XyI8DSs9AqI6zRd+UTDlXzVvbeK3Im/ILEs0Hp/zP2SBaaSUys+qrAmwVMxUGzLpEMu+TzmuREp59uyC+je5yuKGMcF/xBUHbs7mROjn9iSU/VNoVkbXQ+gxofqg9XHnmVhoePy0/jAIqyXbyw6oTbsOvBdtuyi910/yS4++URZoPqWZ49p0CK6FauIWhRsgf+y+Jf/jnmu43uIm0fQwpJYnYOmLRV5HSRNdDhGuOwecb/V+JZcXZ9Phadtx/SXcplzQ1pAlrvw/H3zl8McjKi7MsWOKn6X6B0NT4mS5lq7YXCg0ugMBX72xwt/5DGWlQhrT+REl4IdQfp8R5StG5ClJ7yKs/tjOCvU9KMh0MeBx86zO9jKMtsFOw7kfQeKqkahFULco/Jr4Zx54MzaMSibVHoMrgCmqLpa1G93R1v3Wx5foegwvqv1ZCsuU6MeipHC5940Z6JiDJxCZi0ZVImQUklhWjvfV/mGZpNbARW0t8My2vTEU61K0mdsBaLco6f7RPpbJyCXq3UXkgHPhpybxP5ksJVhygSrDykSR3wurviDy1r71PwBvXCKyk+rNTgpYnBZXz+za4oOqQnYye0KD5UXXeXXerWHJ/RNaWeI620lMjfHU4hlnsbI+ITqH4DuTU0Rx6SyKrKLpYSVPQuaqPhbsMCdfTHQWktInH1pGMbwAkgeBcQpWLBx0WSMTWFNX8Im2S8Q05TYXRQ8oMoekxNMMu+lEbYZv5c3s6yrblBHMObK+k6QGIbxZULVGqW/3V8C40AjApyTZWO9YDz3yvZPu/C0Vp1H93/VcZ3/hWgZ0ELVDsCdopwbabof74vr276mXO4ubSgtd/qs6vBwRv/BaOvrFvIfLgLOcknB6iZHzY1wD1J0LLU72Uy7yS6qU9pWoNp0DzwzLPmGqGur2Q8CI1tSLP8OqSykXFj3UZHlxPdxSIdDxPLLISy4piWTFi0dV0tD014PPYdppIx0uYptO0Q4ltObgyI4zPP4PJJ2/B3xhF8/RYIz2Q5ZivtbHIE1D1vEaA6ZMEVqvg6bcK1l0t2Pp3wepvC168TOC8dImub6eabZhmtPscTgSmklMRc7jfFLS/JMjsVYLimb3Or8XuQ3O3C08lLPyamu6g+SR6QCI8qtbWTgpAec+ZVtj4u77LGmoOh9ABymh2ofmVF99XtcHB/6emLXiqJUZYyT8u/UXPORZ8WTVd6MGeSRTzPikdKxxmXZoLv+g9UyCMAMy5zA0tjBT7dSJNSpt0ehfZdAuGUYk/MKtPla0uzGwn0cgKspkWDKOKiqqleL31jo+1rRTNTf+hWFlbp37S+Rhl6g1KadPSfDdW17jaAoQwqKo5AX9gelnnG04ina/QsWMrr/3sFHY9dBCeqgxHXplk4SXpPE9urZnkpY9OofOWGsj2WBXNLznoS7Jg+muaUOgf6FoLtrDRgUDoAHaFlrDm+Vl4/1FJqgkaTpJMOlN5yo+fIci0llu/lV+xoAUkC74smXZBeUcndyvFNOGBlqcEe/9X/Ly+SZKT7u/beFlJVX2w5yGBJwyz3q+SaENFSuh8VVVaVC+lT487uQu2XNdTvTDnsok9/nw80Fcibb81ulLatLU8QjbbqvaLwkATHuoa34zeR+2PZcZpab4nF0/NIXTq6t+Ex1v8zc5m2mhreSj/8YAQHqrrTi47+ZVKbqOz/ZnisELu+f3+mVTVHDcoHYfhwDQjZNJ70fUgXt+kkkI6/1g0k85NxbGEhlNknrfm9z2I1/sSQvTW+zVoaj6Zp047HjIq9qgHJOElcPjvpcra31g4zhwKDSwAuvJONV9O2/ZUOPgH0rFl2EpDdC14qp077Db8SrD1huLYaHiJ5Oh/uB7j/ohbveBAKrmVbLalp/1HmtjSIhZZSVVNiYJGIBF7HVnYMiQtopFXqa0vdlEMI4x08EyltPAY5cs6mma0+Hlz1NWfhWeMZ1YbRhjD6H8EccX0bJHRFYYkOSvNtvCG7tsWsBJRMNlCSpPMyr3IvK4zCDa8jh5fzeLPBYm/cRqtz3t74q5GTjqmcGq9AUt/LTGjarpwqdbdPY/C6m8KEOqrUjk/p6vQ66VOf6dk+y0Cq9dzaH6Z1zoc26gEdjpXqQ62Az8tqT+h37fLZR9kRIxuNtOKaUbxeGvL+iGOBenUDod+S0k6vavP47JmB07be8sxzgpCM6gMH0408nLP8wmdisqD0XrNYclm20kmNoGU+IOzi8IVHm8dQuhFnq5uVI25wR0IR36tnd0v+rGTvcILHqh8r0q5d0252NlkFE4TAgTJbflKZ4f/6C5mvHk1RiiLlIKTr3+FvevfTceaOdgZ5ZlmI7Dic+rtl6YKCXjrlXpXcLZqzXUyuum98NrX8jV1I2ska74vOPQnPQY1MBUO/6Pk9R8r7VvfJJj3GUnDier+VDO8cKkqzUIKYlFY+UUVhy1XJ9fOwIbfCHbdodqXG09VsdtCUR2X8c+wGl0pLdpaHsXMtqAGIEoCwTmEq48es21vKXQ9RE6jL/92re+2Iq9vspJqzLMIAq+vsdQhhCrm4/HWkoxvBGwCwbl4fT0tP4n4JiIdz3efMxHfQEX4ECp6CeJ4vZPweBvIZPbm3DYBQqOq+qiyXu9ooOqH1xKPrcG2M3h9kwhXH4Vh9FQaTD8lxcH/3Mbar0wiu8NP1Xw46EuScEMF6zI9Uy7i1jymyNfQRW+RII2dTxzS/c/wgc3MOO81dJ/Jrofns/vR+fgbo8x65yP4Tr+c3iGF42+X7P4vJHcLdt8DqZ0Ags4V8Mon4Ig/SaqW5L+e5scoikpIU7D3f7JI37ZqMRz19+JQgpWCLdflyrh6tfXaacEb16pR7eWw+juC5kd7FMr2PKS61o6+qVjMx2V8M6xGNx5dnZsy22OQUskt+PzT8AeGsSd0GAiG5pOIr0f2FjkVOhXhQ0ofBARDB5KMb8CyEnSNKxCaQUVl38d5vfWOyTYpLaKdL5BvxC1ikVcJBud1e8NCCGrqTiWV3EIquR1NDxAKHYTh4Opks+3qMcLAH5yVu8CMPPHYGmKRV7tfSybdROve+2mYdCFarwTliW+2qH/TRnbskcyM9FxYFqQq2eZV5WFrG+fS0pyiXr6BEDaG5iVcfQxTPpyl7UWJTAlqDtmJtATPfeqdND15AFbCh+Y1Wf+X4zj89xmqDu7ZSfgnw5zLYd3VXRuO3mNxYNMfBct+XTDQsvRgiH6REjb9USWo7CzFc9yAdFP/5wHlqTc/Qp4MpTQFiW2SyBryRrm7jH+G1egmE5sp3BNKaZJMbB41o2uaMWKRlWQyzRhGFZXhQ/A4GDvdqKC2/kwinS+SzbSj60Eqwkvx+ac5nLUHTVPJtmRiE9nMHgyjlmBo3qDnnTmXf6mJDtlsGz59St5tgeBcAsHSgqqx6Opehk8jGl1JTe0p+PwDmygxGOLR1eR//hKkRTq1rWjNC40AOyjRAQAs9ARZO2kxL28/kwrvRk6dXIMQgklvi9AUT5C5Jkg2VkPbqzNoemJedzecnTEgA2t/KDnm5mIvMrGlOOEFQo1uL6DhVHj9ZwWP9EgaT+tf5Kbp/pzBLTV1QlNDLcsh2+GsCYzWvzCPy/hjWI2u0JzicMpQjQaWlaRlz38BVSmQseK07t2Fx9tAuOrwIuPr8dZR13D2gJ9H0zyEKg4C+mlfKgNdC+Z72zmktAfsoVpWklhkJT0hExskdLY/Q8Pktw0qxCOljWVG0TRf0YXFNGNYZhTDU42uB5CyuBVKSgvbKqMAttTzo2Phz1t74BSTZcdIYAa7bpyPnS22SNH1zuerO1bS/gp5cVphSOocBG58dWraxWtfU56rNJWy2MKv9x8S2Hajk8FVVRSaX9XGzvtUeaGFwDSlv1D4NsqsUjlzmVgMq9ENVSwqnpAg9FHTjY1FV9NlcHuTzeylteUhautO7zP2Ohhs28x5vS14PNUEQvPQ+hNW7YWm+wgE5+SGVnZdsXS8vkYMz8CSkNnMXoTQioy4bWewrQS6UZ4RV8cLMunddLQ9jZrpZuPzT6e69nhA0Nn+DKnk9tzzWQQrFmB46nPx/N4IvGWWxa3zR0sO0nRGoNUvQ/hkUXVCqdzitLfDztsh1SSxksoAGhUw50POBrDhRDj5UUl0vdJVCPS9EerGdmwDFnhrJTPfJ5n+DsoeXSR0OPiHkhWfz438sZQwz4FXyCKtB9uE1qeV9GN4kWrAcGO+44thNbr+wGwsM0Y8tjo3+0snXH2kY/3qSJBKbCp9p7SIRpZT13DWsD2fbWdobb6vO76bSunEY2v7rfXtOjYWWUkquQ3Q8XqnYJptkEs+9hdbdkIrmQSUiDIuBJl0M50dz2GZEYTwdms7dJFO7STa+Qq6UUkquZ2uAZugWpOdg50SrQzNw3X+KEHvtpIdZ6VoPMVgzS9BpG2kqSoiNL9k7secjagRVO25TfcrAfKK+ZKp5/dtADXPwOOmU86TbPx9YbuxJNMGb/xWJZnnfrD889Udq8SD9jykNBQaTyuuGc5G4cVLBak9SotBM1Tb8tJfScro+XEZJYY3vCAEFeGDCVUuwrbTaJq/7GmzQ8W2M0UNCIWUip8OlkR8fa+EGiAtbJkmHn2NcHXpgJ2UkraWhzCznXSFAjKZJBUVi6gIH5r3ODUevfR7mMm0kIy/gZQmPv9shAggZYweY6kTCM7pN8RjmlHaWh/p9radQgVgkUxsRNcrcYwjFanZAgiSic15lRh9sWOPzBsBVBqJEJ3oPi+TbrGRf7BofbACO6yx4IOCKb2GLEsJu++Bbf9U7cZTzpXMfj9Me+vINS7MfA+0vwytz0vUWynovijZsPEaqFkqqRmAVIa/EWa9t/T9m/8sSOzoaQ6xstC+XLLnQaX56zI+GJHrnxA6uj6wjWIpulp1zWwHhlGFzz+tDyPUa2KrAx5P+c0I5ZBJ76bY+Nik+0lLZzPNWGaUvHI1aRGPrSVUeTAgiXS+nCsxs/B6J1FVcyx6QctwIv4Gkc4Xuw1lKrmF3mIvQvMSDB7Yp9ecybQQi6xSVScl58T0WuaAZ4HbZcV0F6QqIbWYbeHVeQMvuyoaehMSe6io+BuIOELYLKlsxLrmKNA8rN8+gykFYjgbr4Wt/+iJsW7+M3S8AoddM3JGV/PAsl9L1l0N229yeL8s2HWXoOaw4VtD86PF3Xh2UtD0kJLBdBkfjGvBGylN2vY+QGfbU8QiK+hsf5rW5nux7WKPVtO8udpXJ4MgEMKgsurwYV2fblQ5Pl9/DSGWGXO8XUobaWdo2/swyfh6ukuvMnto3ftgXqw2kdhKpOM5xwYPkN01vJVVS0tepDLpPbS1PEQmvRMpyxHL0fAHZhAMHYQQ5V2vhTD6rQhxIpGZmVdOBhCxYWfGYoFxG7rWiS5MNGzCNDM5usLxPFYStv49P6llpwXtyyG6wfGQYcUI9XGBGuZYq7NPIXM1ySODnYWMioq5lMm4NrqJ2Aay2Y7uxJyUJqYZJR5zFiKtrjkej6cO0Lv/DE8dgdCB1DWeO+yebqhiYbE8o9CpqFzifEAOj7cBJ8kLTfORTu8hmy2WwbJlmkzOg86k9xJpf7rvxUmLeNRhaFgvop3L+/Fuc6PChQchDAxPNcGKBVhmHF0vpxVKw++f2WcibVt4dd5fV0w36N3GtvBq1vmj2MBP2+HC3fBYeyuZoo5Au0SHoTIIjvq3OiS2lfEShkjj6RLH65MG0y4YXkvVeErugpuHIL6lPH3fgSCl2kE8drLgibMFT5wl2Pvk8D7Hvsq4Dq+nUjso3r5bpFPbqXTYMmu6n7rGszHNGFJmMYzqEe2EM4xKahvOItq5HDPb1q041l/i0PCECYQOIJXYlLug5LrLao4hGnnV+SBpY+cEa+OxNTi1IhdiDyHGLYSBpgWoqT8tVzLmJ5vtoG3vQ7nn7stgCHy+6VSEl/T5XkyfJLrDCF10/duOa+z96SQid1diGgvY8u4o5ic7wGOViG44GRzwNaqEUuG7JbNq/M1IE14Acz8q2XgtPYsQcMDHlQbucOKfAsILheF4IdSQykGWkjuy8zbY0msHkdkLr14Jx9zs3FLdGzsLbS8qofbao8uv4thXGNdGV9cDDgVgoGl9x4vLlUscDjyeGmrrT3O8z8x2Eo+tw7IS+APTCQTndnvG4aoj8ftnkEpuRdO8BILzMDxhIh0vlXgm2d06XF7dq04gMLvftWcye4qO8wdm4A/M6o6fG0alCvW0PIhzAi0fw1NLdd2J/SZRd+REzQsFyQFe/JAgskp1YWlozPtTNb4dHpb/NIVeYFxtKUiKWnY0F9frah7VZrz2qtzkBKmUyaacz6jJF879EEy7UNLyJKAp3QQjpLzFpLC50d/Ky544AsGR2SAXp+rwD2ITWr3UOavhqYbhLiDaekNxHbKdhZ23C+Z/rvQFOb4VXrpcdHve0oTF35dMPnN41zeeGddGN1SxiFRqe/62UehlZ8LHkkx6D22tj3avPZtpIpnYTG39mQghEELg808p6hTz+2cQj6+l8KfjD8zuTk76AjPIZtspbQA1PN5aQv3I/1dWHU5by4O5si8J6OhGBVU1xxaFTbLZjvJeOGBmW2lreZja+jP6NbxOBje2ESKr89tejZTGzHtDrPx6kN/UHMen5DOAhheBlEGs2HuZKZ1DHlPfAqE5kh23CqykZMq5kvqTyn45w4KvDqa9Vc1Xe/VKQdsL6oKw520pXvlGHNUkJ3nBE6ddWHw+OfB5d4EpMPsyyZbr1QVGGCqMsvh7w6/PYMYdbrSgv4lRr14pVMinlw7F6m9C3VH7j3jPuDa6Hm8tNbWnEOl8EcuMoushKquOGPYGh5Eg0vFi3sVCSgsz20YmvRufv7SLFQovIZXekSdWLoQX2zZJJbfh888gVHEQqeQ2zGwHpQxvde1p/Sa7PN5a6hrPJR5bi2VG8PqmEgzN7za46XRTbuJFHI9R56zlWwIz20Y6tQN/YGbZx3SRasIxDmob4G/Reb5mNuuYxhfTaaZZYSxrBv1lpaoOhqqDxzbbIy148TJVR4stsNNQc3uQpakGXvypiuObAt4w0rQKkzo58J/nAR9TQzab/ycwQpIpb2ZExqlPOhN23CrzqiX0AEw6s/R7nOmA+CbyDC6oC0PrczB5+EroxzXj2ugC+PxTaPC/ZayXgW2nSad2I4SOzz+13/lmptlZdJuUJtlsa0mjm043Ee1cjm2lMIxKhBYkm2lCyjSZ9A4ymSYCgVlU1RxLXcPZxKOriEVfoyhiKQTSTkAZyS7DqHRUKkundtHe+jhdRj1dalJiCaQ0SaebBmV0w4uL45IAUpOkZ5l4JJyUrmdypqaMYMf4oe0lUKXZBR78f0O88r0WrKAyWDoQE9agjC6oRo6qxSN7gZn3CUnHKz2aFdKCKW9RMdpSaB5KFReVnAm3LzLuje54IJnYRmf7072Schq19Wf0qWOr6yEsq6A0TBgly8ky6WbaW//X7R2bZgboyH+QNEkmthAMLcLwVOIPzs0Z3QKkhWUlHBXIyiUaWU458dvS6Bj64LSUvdUw79OSN36nJBGFpryh8PcSnCcqOD5WTaXUaXoQNl4jukfSHPSl/pM4/dEmTJ7xxIgJi0PNIAssP2KYarv6itAYCQ0r2PN+T7PLbyUfC4wKOPomScdySO5UGhD9vfdGCOpPgJanJbIrdCQkmoeydYX3BVyj2w+2naWzo0t/oOf2jrYnqJ90QcnqiIrwMjrbn6HHcGnoegCf33mGWSy6qqzmBLBpbbkXpIVuhHPGvTiQFouuGpKymOUYtCsfIQSBUI+ymJSqZbhQl2KdP+oY1531Pqg5QrLnAYHwSKacC9sPsmmQXiqlzp6HYPW3e5I5rc9IXrhEcPxdxXoE5bJeT/Gr4B5sJCbwpDfGMZkQl6Sd598NlJojnCcJJydb2LUWHgkguDzZgDHcRbwjgBBQcxgD6qpb8j3Ja98UtDytfkzBGUpUaJQ0scYF+4TRtawUifg6splWPN56QqGDBi21WEgmvQeBhizw+iwr2aeITCA4C03zEo++hmUn8fumEwovKRmWKNUwUYzsNs5qWoXzj7PUJIty8Xjrch13velqZVWCOAgtp8BSXBtaWX00muZFSotIxws52U+JYVRRVXsCHk91rmRsG+twrmAIL4DwAudt8hvXFmTPpcDOSHbfowz2YPi7v4WM6Hm+DJJnvXFOy4YH7XnuFVke9kbYrKcxgoLwv73U/LCC+pV+NfFXgyN+CJWZWoSEw8wgNYMMK0wEjAo1MSMbVbuYvgZm7qtM+E/XshK0NP8XaWcBm0x6D8n4Buoaz0MfBsOrpgM7/fBlv4mqwuoE20rR3voE6dTOnDbuvFzHmI7XN5lkordmAvQY1P7ic8WFQoZnaN/myqrDadt7f64Lzlat3UYVFZWHkE5tR9N8BILziEaWk07tpHcRqqYFCATUXjPS8RLJxJbu+02zg7a9D9I4+W1qNPs0WGtuZ9se59dYqqQsUyhmhuo027UnQ9I/8E6ALJJmrdgNlUie8ndyqD3w71KLMLnR6FRlj10f0eI0xj+jLF1eweEbKgiemSVeCdNzn/UePUlhEd8+yTDWDI9HjqN0aG/CG914dHW3wVXY2HaGRGwNlVUD2PeUwOttzClu9f5Banh9k/NmnPVHt8iNGQEkUlok4uux7TTVtcdRET6EdGoHtp0BLIQw0PUKghULScbXY8sslhV3bvsVnh7NPwRC6FRWLRvS6/Z4qqmfdD6J2AZV2eCfSiA4ByH0vDHvVTXHEel4PqeWJvF466mqOb5bYjKZUCOK8rFJpXYQCM4G6Da+hSgNhm1s88LMyGIWpCrJdMD6vwksO/fae3n6ekhywFstGucOvMbVlhLfLkGqoFPQ0AQL63UWBAZ+zt+0JMn2tv+5pZoCVhwe4+LzfIT1cd0U6jICTHijm8nsxelHrW4fOkJo1DacSUfbU5hZ1VPq80+lqua4AZ0nm2lRRjPPI7VIJbdg20eg6wHqJ51PMrEZM9uB19eAPzALIXSCoQMA6Gh7hlRyc/45hEFVzXFYZoRMugnDqFLatsPQIKJK9Jb2+RhN81Bde0IuZivzxvKU6hKTyJKKcFJaZLPt6JqfhUYFC6fBQztV5YSVhOffoxJnqlSp6/wCvUJSfaxFw5sHl/zThODsygD3RhNkckvWgUpNsNg/uNDC1kzpjkBDCLZkshwSKP/C7TL8xG2bDekslZrGXK8xKrMcJ7zR9RjVmNl2Crflhqd62J7DMCqpbzyHdHov6eQ2hNCxrMSAxMptO0mpehlpZ0Hzomne3ESKYqRtUhleSjbTjG2n1LZfCAL+mfj909WXZQybRoTQiwrwhdDxeOvJFl4AJfh8xWVzycRWIh3Pooaa2nh9jVTXntx9/+57VQVAT22o+q93ks2ia1PUn2MxFCXRN1cGCWqCB6NJkrbkkICXd1ZVoA/yhzjFY9CZdja8NpI6o++yw/HG5kyWeyMJ2iybQ/xezqwMENQmrqf+ZCzJTR0xdCGQSGp1nS82VI/47mPCG91QeAmp1LZeXVVKUSxUMbwGKBHfkGvRVV51PLaWcPWRBEPzyjpeN6odmws0zYvWhwxmNtNKZ/uzmGYnQmj4g/MxjErMbDs+/9RxN/CzkKqa42jb+0Av0SJJZdVhRQlI04zR2fFMXvgkk95DtPMlQOlsbNuYxUoWe4Z2StBw7tArdoUQnFoR5NSK/mVJ20yLJtNiikenRnc2nm+rCvHTvR3dnnMXBjDbYzDNM3F+fmtSGX7X2tn9WnZlTV5KpvnWpBo8E3A0RYtpcVNHjCyQzYWU9pgWf2+P8qn6kW2NmzifegkMI0xdwznEoqvIZtvweOqoCB88rPoLtp0l0vkShRN7I50v4g/MLthS5yOlJBZZQTy2jvyEl4YQgqra40tuaWwrTVvLw91bcSktkvG1dF1YkolN+APTu2Oo4xHDqKRh8oWkU7uQdgavf4qj1nI6uc1BH9DOaQQfwrbwanyHHEDyLokdz3+/Kg4evRYJW0puaI/xXCKFIQRZKTk+5Oe91RVFn+Mcr4cvN1Rzd2ecjRmTDBKvEBwT9HNBeOgTmtO2RBOMitG7pSOWd/EwgXbL5pVkmqODEy8rtjJZLGVqA6tSmdzUmxEUyhqxM48ihqeK6toTRuz8ZrbDsWxMoGGa7Xi9DSWPTae2kYi/Tr7B1vAHZ1MZXtbnWJ9UcivSUU2sJyaaSu7A491QMixRiG2niXa8TCq1Tc2vy4mcj6TRVsm3wXvkZ04LstZMMuXDm3nx+gUkt2rYSYEwJJoX5v+4HC3g4eG5RJrnk6k8D+nZRIoDfR5H4zPT6+GTDdXDuoa9psWf2yJsyZgI4IiAj0tqKvFpI2coms3iC1taSnZmy28NB+WEtFs2AU0QGMPQhEcIFQ4ruM6PRsBnnzC6I42adFtiYm8/imeJ+AaHsIKNbSb6n6MmM7mqhL5QI3TKMbqqguKRnGaDDdIkHluLbSepqjkW04wQj6wma3bg9TYSqlzU7xqHC19gJtHIyoJbNfw5pbSFRoC1/iRHPZVg+x89tD5gEJxnM+uzGUIHjZ6mwlPxVFG4ICPV7aPh8VlScnVzB5223W0vXk6msZB8tG7ktsWTPTrbs/mG1ydgxgBCJOvTGf7cFiVm20gJhwd8XFpbOSbhicMCPv7VmV8b7wGODflHPJk2bvaktp0lGlnB3j130br3wVzt5/hANyrw+SaTfx3U8fmn9Dth10msHFQGvz98vqmUkxkSZV6fzWxrThOityG3SCY2k0m30Np8L8nkRsxsK4n467Q2/xd7uNWvS2AYFVTVHIMQBkJ46JqIXDjtw6iEOV/IcsSDSRZdkx5VgwtQypkcrZTYunSWlMz/9pjAy8kMnQ7e6HDx7uoKvKInFewB6nWdZWVWX8Qsm1+3dNJu2WSlWvMryTT/7ii3KWh4qdA1PltfRZWm4RXK+zw04OWi6pGXhR0Xnq7ywB7sHtRoEaG9rZVw1VHd5VJjTXXdSUQjK3N1pxAIHkBlryGSpQiG5tGZbc3v/xQGgeAc0ukmNM1XUmzd460lFFqYm5QhcSrBEsIgWGZowbKSJVqNbWKRFQUeuY1tZ0nE1w9qMvFgCATn4Pcr2UpN92MY40/d+tRQgM2ZbJ636xVwSsXo7AiStnS8YEvgX50xPjJC3u58n5evNtTwQCxBq2lxqN/LKRVBjDK9wpeT6aJVZ4GnEyneUzM2n/N8n5erp9TSatkENUFolMId48LoZtK7HQc1xiLLc8LfY58dFUInXHUY4QE2XPgDs8lmWkjENyCEjpQWXk8jkY4Xc3FUia5XUFN/RncHnZntJJnYjMQiEJiNZgSJdS7PGUV1jDqXJBhagD9QnsqLoZf+cit93kIsstlWx8dLKbHtFLadQteDaNrw1JsKzcDrKx0jH01sKdmYMTGlZJ7Pg0cIDgt42ZEN8kA0gS4ElpScVRFk6SjV2y7weTBLOPfLkyObBJruNbi8dnAiRlkpsR3WbUpGPHHVF5oQNIxy6d64MLqmGXGMmdp2isKuo4mGEIJw9ZEEQgeRiK0lm20nk9kFkCtzUzKQne3PUFt/GsnEFjo7nu3WNEjEXiffw1Vi4xWVhxAIHdjvaPXeaEYpb0zHMMIOs9l0PJ5isZdMpoX2lseQsiv0IPAH5uTCA+MmYjUkmrImP9vbmdeh9pZwkMMDPi6oCvGmygAtpk2DoeEfxYRQha5xZMDHcyWy76Pxa7Gl5KFYkgejCZK2ZKHfy3uqK/qsOz4k4OM/nfkiShpwsN87Lpyq0WRc/EI8nlrHH6uuh/aJH7G0TTraHiOZ2ISZdRANQJJJ71alaR3P50IAXT92J0EZi2Ry84AMLqjBl15vsaK1EBrh6qNyWhI98pWa5iEYmp/3WNtK07b3oV4GV60/ldxM3ElmcgIipeR3rRE6bJuUlN1/t3TG+XpTG9e1RfAJwQyvMaoGt4sLq0JF3pIOLPF70UbBgN0bTXBXJE7ElmRRZVZXNXd0V3M40WjovLO6AgPwC4Ev52FeMkahhbFkXHi6Hm8DHm8DmUxzzuAoBauwg7j2RCSR2JxrAe470WGaUfoXt1FIhzH05VBVeyIdrY+RzbbnxgZ5qKk7GY+3Rk2RiK7OtSE3EqxYVKQvkUpuLfE6JIn4hlGL/44krZZNq+X8WZnAC4k0HiGI2zZ+TePkkJ9Z3tHTJqw1dN5fU8E/2vO7qS4dggHblM7yUjKNV8CxQT+THKoS1qYy3N4ZY3NBFYNElY+tTGY4IugcZrGlZJ7Xw5WN1bRbNpWaxrxRarsdb4wLoyuEoKbuVFKJzSST29B1P8GKBcM+Mn2syHZfTEqhKZ1dO9sdcugbrew4biF6bmKyZcaQ0kQ3qrq/+IZRSVXNMX0e39eEYeea4vFBVkpeSaZpM23m+Tx9/uB1p+mOvc8FPB5PdW/ln0+keH91JUeHRq9J4NhQgEMDPjams1ToGrM9xa/HluoTKUx2tZoWK1MZPAKW+X08GkvyQCxBVqqt70OxJB+uDefFqQs70goxpaSzxIVqcybLb1s6SefWM8Uw+HR9eL80uDBOjC6oLW4gdACBcVKtUC6WlexW2PL7Z+SVkFlWgs72Zx10aXuj9AmkNGlre9Thfg2heZVnK9QMA90IE6pcMqR164Ps2PP5pxGLrMRpBHx/04fHig7L4od7OkhKVa5kCBVL/Eht2HE7XqPrTPcabM2YJS8jvSPsGQk3dcY4Mugble19F0FN42CHBJ4lJbd2xng8nsKUMMdj8IHaSqZ4DJ6OJ/lnuyrTEgJuljEsevYuFmBJuL49yiG9whX/6YyXNLhd5zrIQRgoKyW/2NtJslfoYUfW5A+tEb7cuG84VQNl3BjdiUg6tZP2tie6f4HRzuVU1RxLIDhblcHtfah4ZE8XQidcfRQ+32RSyV05jYFCT0EnGDqQivBSLCtKNtOKYVTi8TaOmZfg8VRTET6YWORVeruDhqeOivDSMVlTf/y7I07EtrsNaEbCa6kMq1MZR6MF8Mm6Kn7X2sm2jFnW0KKMlHRYNrWDzIRbUrImlSFuSxb4PVSX0HMoh9s64zwRT5HNfTybsyY/3tvBtxtr+Ge70hsA+vTmM1LSZtnU515PqXALqJK5E0N+ppcISRSWuNnAloxJ1LKp3A+lLV2jO0iktOloe7oobNDZ8Sw+/3TMbHtOWaz4m+31TaEyfCger6oMSCU34RQn9XhqCFUuQgiBx1MzZuEWKW3isXUk4xuQ2ASCc6lvPF/pKcgMXv90vH3MixtrVqczRR5rWsKrfRjdKl3ja401tJkWD8cS/C+mtBbMXGNC4aclYFAGZGfWZFM6y52ROGmpvi22lLy9KsTplf0L7xRiS8nj8WSeVypR2/+HY0n0nF5E/+eBil5JwgO8Hl5NZfK+zQZqx3B2ZZC5PueYtnp8P/Ga/QzX6A6S7lbaAgQaZrYV287iWLwjdMLVR+cJ8gjhLBGZzbayt+lOhBBUVB5CqHJRv+uSUpJJN2Fm2zE81Xh9U4bsFfeM21GmJh5dg5lpo6b+1CGdd7So0DTidr6ZNIDqMoxkraHzrupKzg+HaDYt6nSNa1sjbM6Y3R6jV8BZFcEBtbNmpeS3LZ28kcliyuJv0r8642QlnFYZwFvGedemMjwaS5K0bccwgC27Cg+djZ+BShICeIHjQ378vdrv3lEVYn06S1aq+XFeAVMNg4/UhftskFjgK/5ua6j24dHwclenMtzeGafFspjr9fDOqhBTxljdzTW6g0TTfY4tvhJbdZl5ahxrjzXhQ9fzW4dDFQvIZJqcp0LkBmLGoivRjYo+R5pLadG29yFMswMpbYTQMIwqahvO7He0UClsK00ysYnC1uF0ugnTjI7LrrFCzq0McEOBSpYuBMcPIPEV0DRmeZWR+GxDNfdHEjyfSOHTBGdUBDm2RNa+FPdGEmxIZymVlpTAHZE4TyZSfL2xuk/d2sdjSW7p7Hl9pfzKk0N+VqUyNJtW96fpARb6PNQaOi8lVVXGKSE/Zxd42ZM9Bt+fXMsT8SR7shaL/F6ODPryDO4riRS3dcaJ2jazvB4ura6g3qOSZr9riaAGP0mCQiOgCX7d0skJIT/LRqhWd20qwzW9kn+rUhk2pLN8f3LNkMI3Q0XIUuIAQGt642iuZcLR1vIImfQeegyShsdTS13j2QAk4huJdLyQu0+pGtXUnYbX11h0rnhsPbHI8u6ZZE4/G4+3kbqGN5VcTzy6lmhkBfmbX52K8CFUDFLgPJttz9PD7UIID9W1Jw1p4vBAWWsm0TyDq9p4PJbk7kiCiG0z02NwSU2FY5nXXtPi1s4Y69NZanSNC8IhDh2BbrOv7m6lxeq/2sMDnBMOcn4JKUhTSq7Y1Vo0ZgiUNypzHu4RAR/vqq5AopJkr6UyaMAxQT/vrq4YskLZw9E4/+pM5N2mAz+cXEudoWNKyZaMyYuJFE8lekSDvAJODgV41whoHvyouZ2NmfzvrQGcXRnkgqqhS2v2xXHe0s6I6+kOgerak4h2vEgyuQUAf2BGXm1xMHQAPv9k0smdkJM3LDVtIlQxn2BoHradpqPtGbKZ4oqH/mpzU6ltFEcbLVLJbYM2uobh3PYppTVmMWZbSlakMqxMpqnWNU4KBfqdwnByRYCT+9FHiNs2P2xuJ2GrTXjMtvhjW6SofGo4KFezIItK+pUyuhHLxna4QPuAowM+Xktlido2K1IZXt7dyoVVIT5dX9W9SxsOD9OWktsKDC6ob+K/O2N8rK4KQwime3R+Hk/lefcZCf+LJTmrMkjVMIcbWs3ii5qJs0zlaOIa3SGgaR6qao8jLI8FnL/Auh4iWDG/6HYnhNDQ9QDB0Fwi2eaCml0df3BOP+tx3i4PRZ5RDbk8vNfUDAlCp6JiybCNuR8IXd1ir6czpKXyph6JJbmivpoDSiRzyuXZeIpsgYJXRsIdnfFhN7qnhvzcFum7DAtUqKBa08hI6RjbDeuaY9uvFBCxJZ22jUWP9u/tkTgL/V7HSoPBErMlpVR1N/fyNJtN2zGR5xGCXVmTKn1ws+hKcZDPwwsFQjs+AYsGOfNuuNj/6jVGANXZNXwxKX9gNj7/DEBXsVihZA7708wNVS4CUeDxCZ1QRf8JuL4Ihg6kruFNBEIHEgjOo7budCrCBw/pnINlfSbbbXBBeVNpCf/siPZ7rJSS9ekMt3TEuCcSp7XA42k2LUcj2JYLA0gp2ZDO8kw8RdMAxbsLOaUiwEkhPx6UIfALwXmVATwFXyOJqrL43K4Wbu2IYRdOKxaCt4RDeHsd5xUqgfV6Olu07zElvJzoW/T9uXiKL+9u5WM79vKDPe1s7mPAJkBIKy0uWt0rFl1vaFgOYRBTSiaNgOjMhVUhQprofm98AiYbBkcNMP4+3Lie7jhECEF17QmY2U6y2TYMowpPGSVZXm8DVdXHE428jG3F0fQgleEjHGPIA8XjraPKWzfk8wyVTWmzu/60Nzuy/W8Zb+mM8UROhNwA7osm+ExdVXdR/wK/l2cSqW6DDsrTPMDnIWVLfrq3gyZTGVsbFQ+9xGFMTzloQnBRdSUXhEN0WDZZKflvNEFICDWVAkk2F481AST8L56kRteKSsneVBlkkqHzcCxJypYcE/RxckWAL+1uLUoNaJBnoAtZkUzzj45o98Vna9bkZ3s7+O6k2pIhHF0Izq0McFc0WXTf9qzJG+ks83wegprGqRUBHutV0uYVKt482PrmvqgzdH4wuVZdJE2L+T4Phwd8ZYd2RgrX6I5jDE8Vhmdg+qiB4EwCwZljKpc3ktQbGh4hSBd4TFX9CM80ZU0ej/XEE7sM2d/ao1w1uRYhBIf6vczyeNiazZKWKollCMFFVSHujsTZmTXzttEvJFIs83tL1vqWImLZPBpLsjWTZa7PwxKfl5+1dJLJhTYEyjgWViFkJDwcSzrW7x4a8BUl/E4M+nk4lsyLoeoCjupjwsXdkUSRt29KeDye5G1VpZNd51dVsCad5Y2CxFUWJZDzGZ/6Hr+jKsQMr8H/YklsqZoqThjB9umQpnFmmfXOLyZS3BdNkLAlywI+zg8HR2TasWt091H2RYMLsDTg49bOOKbVM7HOK+Ct4b5/WBszpuNMrA7LJiklQSHQheDzDVW8kkyzNp2lXtc4IRQgrGu8lEwXxS3TEl5MpgdkdDssi+/uaSeVi4OuS2e5jwRmr6U5NV90kSmjsaGLt1SF6LRtXkik0QR4heDCcJAb2qNsyJiENcFbwkGOCfXE/DsckkwWaghlf5TyINt7dbOJ3GDOY8bZMMtHYwlu69Xq/FgsyZpUhm9Pqhn21m7X6I4TbDtDMrE5p/DVgD8wC1EYn3XBIwRfb6zh9s4Yq1IZKnWN8ypDHN5PnK5G1xwTGEbOGHWhC8GRQT9HFhgFv8MPTwDBAUqP3hdRGrRdZqivyHDhNcIAjhyAgTeE4LLaMBdV28RsSUDAN5vaSeQ86r2W5B+5cTnHhAJsy2SJORh1D3BIGcmnQ/1eNqWzZArWXM6xY4mUkjs7E0XTjlstmzXpLEuGef2u0R0HWFaS1uZ7se0MqsRrM/HoGuoazkb0Md59fyWsa1w6wAkGC3xKz2CvaeV5yGeUOXLmTRUBbuzMb7DwCDixYmAe2xuZ4uRWKQJAKvc8oNS53jqI+tKgphHU4JFogizF1Rl3RxIcEwpwc0fMUeDHBhY5dJYVclJFgOcSaZpMk7QEnxBU6xpnDaKdeTQxIU+QpwtbSvaOQHmZ+4seB8Sir/aakgFSmphmlETiDUIVC8Z2cfsImhB8uaGaWztjLE9m8GuCMysCnF7mbLPjQn46bJv7ogksCUFN8P6aSqYNsPSqXtfZ5pD089AraZYjQc8P9PKaMEsDPZ1br6UyPBVLgoCTQoGSZVBx22ZDOkulptFqOVdndObm6GwrUZEhgReTKU6p6Nt4eoXgq43VrExl2J7JMsVjsCzgG5NpvwPBAOp0rahZRQiY4x1+E+ka3XFAJtWE03SIdGq3a3SHkQpd4wO1YT4wiGOFEJwbDnFWZZCkLQlpAk2IvA62al3jrf10sJXSPjgh5Cesa7wQT7HH6lFEM1G6Ca+m0izLhVDu7IzzYCyR19765sog5xY0UDwdS/LPjh6h86BQk297G14BHJirb67VdXY7eHY2qt72lDLeJ10IDgv4OGyUZsYNB0IILqmp5Hetnd06GD6hJnHMHgFxerdOdxzgrG0rJoSuwf5CxLJ5Np5iVUp5yVpucsQP9rTxSjJDzJbsyFpc0xrhpYTz2Po7OmMsTznXvO4xLc4Lh6g39KItvg2sT6vjYpbNA9H8+GNGwn8jCRJ2z5GtpsU/O5SMY0pK0hI6bBufEPhyjqdXQEAI3p0LWby9KuTYaOEBZuc8+sI64X2FRX4v32is4dRQgKMCPi6rCfORQQ7h7A/X0x0HVFQeQlu6md45ayF0gq6XO+K0mRav5IY8HlaiXvT+SJw7Igl0VJjCEHBlQzWrUxlSBTbIBv7WFuWIgkTc8mSaex3qWEF11XWFKaZ6DNY4NDV0jc/ZZZoYDl1dhhA0ZS3m+pQftSKVoRDV2iz5dF2Y19NZanWNo0P+7tHjhwZ8vLs6xM0d8W5/XEdJVnqE4Eu7Wumwbep1jXMrg6xIZXg9naVCE5xXGeT4URpDP1JM8Ri8u2b4NSAKcY3uOMDra6Cm/lRincsxzSgeTy2V1YfnyT+6DD8vJVL8tS3abWD+0xnn8tpKDs8ZTFNKftfSyWs5L9OCbgWZP7VFmO0xHBNPGZRObu947/3RRElFWR04I2ewTqsI8Hg8hd2rHdkr4PxcSVyjoTvq4WalzBsl7gHHErmuAZalytxOqwhykM/LPZ1xdpsWh/g9zPZ6+Et7T8NEi2VzfUesu7oiZUlu7FQTKE6a4IZ3NHDDC+MEn28ydY3nMGnqu6htOGOfmQ83XslKyXW5KQpm7i8L/K091m3U7osmWJt2Dgfsylo0luii0qAo6522S2/Ljwn2eNi1hs7XG6tZFvBSo2ks9Hn4Qn01c3KxxWpd5+igr6jt9/iQP0+f9vCgryhU4EElBPuq4Tal5KFoghWpDHsti6cTae6JFjdMQHHjxj3RYtEbl2Jco+uyX7ItYzrGL0XuPoCn46mS5V0CZeicfkBKpFtnUzrLulSGrJSc0kfXVeFzTPYYfLyuiqun1vH5huqiqQzvr6nkHeEQjbpGjaZxUtDPxQWlZCFN4zP1VVRrKnlmAIcEvFzUj4TinZ1xXkioRpCMVKI55bRYg2qg+H1rJ21jrOI13nHDCy7Dim2niUVeI53eia4FCYWX4PNNHutlFRHWNSyHDb8lJeGcx1jKI9GAwwM+KnWdS6or+GdHrGfqgoCjA35+sreTmG0jUMNEP1ZXiR9Vd9sbAzVFYSBYwEvJNJ22xJKSJxMp1qWzHB70EdI0jgj4qNQ15vu8/HhKLa2WTVAT3bHbvniyQHqx92suZ9bzK8kMb6Q7uGpK7bgvFRsrXE/XZdiQ0qK1+T4S8dexzAiZTBPtrf8jldwx1ksrosHQmevx5HkdBmoWWFds9KSQ31EcZqHPwyU1qrLkhIoAX2ms5uSQn2ODPj5ZV8XmTIY2yyYtVeVAUkquaY3w8bqqvOfTUKOEjhug9sATsSRbsibp3OictIQdpsVdkQT/7ozx1aZWNubCIpoQNBh6WQYXwHS4EAmgQhP4hFIT83b/u/h4CaSk3Z2cdCnG9XRdho1UcluuyaOXTyQtopFX8Aemj9m6SvGp+jA3dcR4MSd1eGTQx8W9tt9nVgbZbVq8kEhjCIFVYmDkLK+newpFp2XTZBbLigsEWSRXNlbz30iCZtNikc/Dm8MhAgMUVXk5mS4ZY+1SYPtLW4Qf5oR8BsJSv4+Xkun82SMCrqivot2y2W1azPQYzPcarM2Y/LKls+gcpixPq2F/xTW6LsOGaUaKxvoAWGaJMfRjjF/TuKw2zGUlVDP1nHbB26ts2i2LyYbR71ib0uZTogvBHK+HT9UPTDmukMoyjHSbZRO3JRX6wIzuxTUV7Mya7LUsBGr68VvDIaZ7PUwHeqsoL/Z7meUx2FrQyaYLOHAEmgr2FVyj6zJseDy1CGEUGV7DUz02CxomwrrWHeftj0pdY47XYGPGzIuB6ggWDHGyRRdnVgZ5LZ3pc+qEBoOaexbSNL41qYbNGZNO2+YAr6fP135JTQU/3duJJSVZVCfXUr+PuSPQPruv4L4zLsOGzz8Nw6gma7bnJhsLEBrhqiPGemmjysfqqvhdayfbc3KSFZrGJ/sZVT4Q5vk8fLCmkn91xmm37O4qjN51vccH/YNOZAkhiiomSjHL6+EHk2t5Np6k05Yc7Pey0OeZ0NKiWSnZmjGp0ASTR2BcuzsN2GVYkdIiEd9IOrUD3QgRCi0YsBB7KYYyDXgsaDUtsrlRNCNhhKRUEpFRy+afHTFWpzJ4heDkkJ8LqkLoE9jwjRUrkmn+0qZGP1lIZnoMPl1fVXYisou+pgG7RtdlwjDRjO5okZGSV5Jp2k2bA30eDvAaE9rTHCs6LZuvNrXmjYMygEMDXj5WNzDHwR3B7uKyj9JmWvxfcwcpaZOVSpR9qd/Hh2orXcM7QJYn00UNMyawPJkZ1vFXbp2ui8sE5pbOGBFb1QTbqC6ylak0a0q0L7uUppRJHe5Ll2t0XVxytJkWd0fi3NQeY01KeTdjSdK2ifZT77omlS2qCU5LJXLuMjCWOYgA6ajuw+HcNbjhBRcX4I10ll+0dGBLtaV8OpHkyICfS2uHpmkcsWz+2RFlVTKDIQQnhfy8tSrUZyVDwrb5a1u023BO8eh8tDbsmEmv0ARJK9/seoCqQZSL7e+EdY2P1ob5c055zpKSOV6D9w6z3KNrdF1cgOt7SReC8hZfSKY4LRNgxgBrTm0peSWZYUUixap0lpSU2KhSpEfjSRK25P19GPO/tEVZk8p0d4XtzFr8ZG8HV0+pK6pIOKcyyM0Fs9t0ITgu5EosDoZDAj5+PtXL9qxJhablyWUOF67RddnvyUrJHgdlLCnVIMlSRteUktfTWbJSssDnwa9pSCn5bUsn6zNZ0g7RiayEZxMpLqqucGxeSNg2q3sZXFD1txmpxrUvLpiFdkLIj4nknkiCqC2Z7TF4X01l2c0cLsUYuc7BETv/iJ3ZxWWCYKBGrBdOhNWFGt3uxK6syU/3dnSXF9lIPlYbxiNESYPbm5SUFCveKqNcKjCQdogxCyE4tSLIqf0MjRwKcdtmeTJN3LI5KuinZgS8v/0J1+i67PcIIXhzZZC7o/HubbqO6iQ72GHKrpSSa1sjRAuEyX/fFuGsimCf7bmQaysuEXOt0tWWtnBApJXzpkeb1akMv2np7Pa8b40kOC7g47K6kZkftj/g7kFcXICzKgO8q6qCBl0jpAmOCvr4amONY1dXu2XT6hCO0BBkpMRbIknmQbXoXt5PDe1H68JUaILeJtYCrmuLOo7qGSlMKbmmtbNIZP2ZZJoXSwzfdOkf19N1cUF5uydXBDi5jBlfHiFKzjs7yOfhpWQa05LdxsqDmtpwoM/LkQFfv/HWaR6DKxuq+e6e9u7bbFQZ2H86Y1xUPTpTordmTMwSL/SBaJIjgwPTAXZRuEbXxWWAVOoaB/o8rO81tVegvNhFfi9fb6zhjkicVckMlbrGueEgh5UYBFmKFQ4i4FngmXh61IxuUCt9cSlWDHYpF9fourgMgo/WhflDa4QNuc6vRkPn43VhdCGo1IWaLDGE2aKlpoyNpjT4FI9Bra7RWtCgIYBT3JK0QeMaXReXQRDSND7fUE3ctslKSbU+vBn9IwI+7o0m6N33YKCmW4wmX22o5gfN7XTkkoYCWOr3csIARwy59OAaXReXITBQyb9ymewxuLiqgps6YxgILCSzPAbvKpj6O9JUGTo/mVrP7qzJrqzJDK+n5Oh5l/Jwja6LyzjlxIoARwR9bMmYVOsaU0ZAULtcpniMUX9+W0qeiCd5Kp5GF3BqRYCjh1kHYSxwja6LyzgmoGksdKgV3h/4W1uUV1I9Qzh3tEfZmTF5e/XwaiGMNm6drovLKJK2JetSGXZkzDFXMRvPtJpW0dTjjIRHYkkS9sSeNOx6ui4uo8QriRR/bY8iENioMT5X1FdT6eokFNFkWhhCFDWD6ELQZtkERyiWPhpM3JW7uEwgIpbNn9ujpKXSXchI2JW1uL49OtZLG5dM8+iO3XcWkoZhrhQZbVyj6+IyCqxwGAVjAatSGSw3zFBEta5zcoUfb683zSvgLeHQoEbLjyfc8IKLyyigCWf1MMHwj4PZV7ioqoL5Pi9PxVMYwMkVgSJpy4mIa3RdXEaBZQEfN3XE8m4zgGUBL9oEL4EaKYQQHBbwDbiFerzjhhdcXEaBkKbxqboqKjSBTygFsQN9HtUu7LJf4Xq6Li6jxEK/l59NqWOXaREUglq3s2u/xDW6Li6jiCYE08ews8xl7HE/fReX/ZyMlKxPZ9GB+T6Po3C7y/DhGl0Xl/2Y11MZftsaQY2/FHgEfKGhmmmuNz5iuIk0F5f9lKyU/LY1QkpKUrmmjagt+V1Lp9uiPIK4RtfFZT+lS4C9kE7bpsWa2PoG4xnX6Lq47KcYAnAYuyNl130uI4FrdF1c9lPmeT34hZbXEacBM7wGNRNc32A84xpdF5f9FE0IvtBQxWRDx4PKqs/1Gnyyrmqsl7ZP46YoXVz2YyZ7DL43uZY200IXgipXZnLEcY2ui4uL2x03iriXNRcXF5dRxDW6Li4uLqOIa3RdXFxcRhHX6Lq4uLiMIq7RdXFxcRlFhHSbrF1cXFxGDdfTdXFxcRlFXKPr4uLiMoq4RtfFxcVlFHGNrouLi8so4hpdFxcXl1HENbouLi4uo8j/Ay9SWUmT2OasAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118c0a748>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -398,22 +400,25 @@
     "For example, when determining which feature to split on, the randomized tree might select from among the top several features.\n",
     "You can read more technical details about these randomization strategies in the [Scikit-Learn documentation](http://scikit-learn.org/stable/modules/ensemble.html#forest) and references within.\n",
     "\n",
-    "In Scikit-Learn, such an optimized ensemble of randomized decision trees is implemented in the ``RandomForestClassifier`` estimator, which takes care of all the randomization automatically.\n",
-    "All you need to do is select a number of estimators, and it will very quickly (in parallel, if desired) fit the ensemble of trees:"
+    "In Scikit-Learn, such an optimized ensemble of randomized decision trees is implemented in the `RandomForestClassifier` estimator, which takes care of all the randomization automatically.\n",
+    "All you need to do is select a number of estimators, and it will very quickly—in parallel, if desired—fit the ensemble of trees (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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bZE4OqKh477kVMAE0MshZu5mg4EDsmihRaSgymYx5//4r2775EZPEFJSuLox5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\nP8ctGsamB3K3AAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118cfeb38>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -441,23 +446,26 @@
     "## Random Forest Regression\n",
     "\n",
     "In the previous section we considered random forests within the context of classification.\n",
-    "Random forests can also be made to work in the case of regression (that is, continuous rather than categorical variables). The estimator to use for this is the ``RandomForestRegressor``, and the syntax is very similar to what we saw earlier.\n",
+    "Random forests can also be made to work in the case of regression (that is, with continuous rather than categorical variables). The estimator to use for this is the `RandomForestRegressor`, and the syntax is very similar to what we saw earlier.\n",
     "\n",
-    "Consider the following data, drawn from the combination of a fast and slow oscillation:"
+    "Consider the following data, drawn from the combination of a fast and slow oscillation (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Od48kVn2+ZclvCJsfeSKAeJ4yqGsI1XK7frklduULRZRMKHvdCy0gb79pRWC7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\nAZmIiEgCDMhEREQS+P8p5hEpezc9PwAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10babb0b8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -483,21 +491,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using the random forest regressor, we can find the best fit curve as follows:"
+    "Using the random forest regressor, we can find the best-fit curve as follows (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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lef4veKfTSXdHC3YpjtFqRpFlLPkMl21sxmIvaJEV1pCn5kk/8ryPyMQ4Nkcd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W0LsZgM9Id6AFBdbnSDg6V60GP3Xd6iza0OhULE4zEWGwxgMECFK3RNlb/QBx6OlH8xhlxymECd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\nQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118cfe0f0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -523,7 +534,7 @@
    "metadata": {},
    "source": [
     "Here the true model is shown in the smooth gray curve, while the random forest model is shown by the jagged red curve.\n",
-    "As you can see, the non-parametric random forest model is flexible enough to fit the multi-period data, without us needing to specifying a multi-period model!"
+    "The nonparametric random forest model is flexible enough to fit the multiperiod data, without us needing to specifying a multi-period model!"
    ]
   },
   {
@@ -532,24 +543,27 @@
    "source": [
     "## Example: Random Forest for Classifying Digits\n",
     "\n",
-    "Earlier we took a quick look at the hand-written digits data (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb)).\n",
-    "Let's use that again here to see how the random forest classifier can be used in this context."
+    "In Chapter 38 we worked through an example using the digits dataset included with Scikit-Learn.\n",
+    "Let's use that again here to see how the random forest classifier can be applied in this context:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['target', 'data', 'target_names', 'DESCR', 'images'])"
+       "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -564,21 +578,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To remind us what we're looking at, we'll visualize the first few data points:"
+    "To remind us what we're looking at, we'll visualize the first few data points (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Hl946Ie3vrVjRTqXjaOSeNH36dC+r8R6f+IiIyFHY8RERkaOw4yMiIkdhx0dERI7Cjo+I\niBwlqLW1tdXIhlJCUJqIGgBcLpfb5VoSSksiSqksK9KeGq1GKWGlpc2k4+FtCk1K72mp1LKyMrfL\nR44cKW6jTWwtJRJ9nZiUjmVaWpq4TUREhLjOjknH3ZESglp7ay+1G6XdP7R2JbUBrW3fipEJ06U6\ntImopVQy0H4mJDdyjX300UfiOjPvEXziIyIiR2HHR0REjsKOj4iIHIUdHxEROQo7PiIichR2fERE\n5CiGJ6mWhgxokW8p0qvFb7XhEVJ028gkvt4wMkmuVDtgzeS5gDzhcEpKiriNNFGuVqM2sbGvhy1I\njEy+3F4mbNai8WvXrnW73Mh1Ccj7rA1Z+SnDiaTY//79+z3eRpvMWRsSYEXsXzpe2vVuZJiRN0Mu\njDByvqQ2Ik32D/ju/sAnPiIichSPn/j27t2Ll156CQBw++23IycnB7179za9MF/btGkTtmzZgttu\nuw19+/aFy+VCeHi43WWZYv/+/VizZg2uXr2Ku+66C8899xy6dOlid1mmWbx4Mfr169cuXndihoqK\nChQWFiI4OBidOnVCTk4OBg4caHdZpti8eTO2bt2K1tZWxMTEYMmSJe3mSdpblZWVWLRoEQ4fPmx3\nKaZZuXIl9uzZg9tvvx0A0KdPH+Tm5tpclfc8euK7cuUKFi5ciPz8fBQXF2P48OFYvXq1VbX5zHvv\nvYfXXnsNxcXFKCsrQ3JyMpYuXWp3WaY4d+4clixZgvz8fOzevRuxsbEBcc4A4MSJE5g2bRr+8pe/\n2F2KaU6ePInVq1ejsLAQZWVlyM7Oxpw5c+wuyxSffPIJNmzYgG3btmHLli2IjY3FunXr7C7LFKdO\nncKqVatgcCKsduvIkSPIy8tDcXExiouLA6LTAzzs+FpaWgAAFy9eBAA0NTWhY8eO5lflY9XV1Rg2\nbBiio6MBAGPGjEFVVRWam5ttrsx77777LgYNGoS4uDgAwKRJk7Bz506bqzLH66+/jvT0dNx///12\nl2KasLAw5ObmIioqCgAwcOBA/POf/wyItjhgwAC8/fbb6NKlC65cuYKvv/5a/dujv2hqasLChQux\nePFiu0sx1ffff4/q6moUFhZi6tSpePLJJ1FXV2d3Wabw6KvOzp07w+VyYeLEiYiIiMC1a9fwyiuv\nWFWbzwwaNAibN2/G2bNn0atXL7z11ltobm7Gt99+i+7du9tdnlfOnj2Lnj17tv27Z8+eaGxsRGNj\no99/3fnUU08BAP72t7/ZXIl5YmJiEBMT0/bvFStWIDU1FaGhhnNo7UpISAgqKyuRk5ODsLAwzJo1\ny+6SvOZyuTBp0iT069fP7lJMVV9fj2HDhmH+/PmIiIjA5s2bsWDBAhQXF9tdmtc8upo+/fRTFBQU\ntH1ltmnTJixZsgQVFRVt/4+WNpJSStpvfePGjRPXaWk0TyQlJWH27NmYPXs2goODkZ6ejoiICHTo\n0OEn/TxtkmojaSgzk03SVy8hISFt/60lNI3ss5Zgay+kY6wlzo4ePSquk86z0b9fNTU1YdGiRaiv\nr8err7560zotUSklFY1M1gzI9WvX+a1SnaNHj8YvfvEL7N69G48++uhN7UVLaBqh3T+04/hTbdmy\nBaGhoUhLS8OXX35p6DOka0y7L5pR+63c+FX0/v37MXDgQLzyyivYt29f2zcSWh1Sotaq1LonPPqq\n89ChQxg6dChiY2MBAFOmTMFnn33m97O7NzY24p577sH27dvx5ptvYsyYMQD0hucvevXqhfr6+rZ/\nf/XVVwgPD0enTp1srIo0Z86cQUZGBjp06IDi4mJ07drV7pJMUVtbe1Pw4ze/+Q3q6ura/nTij8rL\ny/Hxxx8jLS0Ns2bNwuXLl5GWloavv/7a7tK8dvz48Zseaq678Zdmf+VRx9e/f3+8//77+OabbwD8\nkPCMi4vz+1RWfX09MjMzcenSJQBAQUEBHnjgAZurMsfw4cNx7Ngx1NbWAgC2bduG1NRUm6siSUND\nA6ZOnYoxY8bghRdeQFhYmN0lmaa+vh5PPPFE2y/K+/btQ0JCQlti0B+VlpZi586dKCsrw/r169Gx\nY0eUlZWhR48edpfmteDgYCxfvhynT58G8MNTX2xsrN/f7wEPv+q89957kZWVhczMTISFhSEiIgIF\nBQVW1eYzCQkJmDlzJh588EG0trZi6NChePrpp+0uyxSRkZFYvnw55syZg+bmZsTFxWHVqlV2l0WC\nN954A3V1daisrMTevXsBAEFBQSgqKvL7byCSkpLw8MMPIzMzE9euXUNkZKT4jk5/FRQUZHcJprnz\nzjuxdOlSZGdn4+LFi+jWrRv+8Ic/2F2WKTz+i/nkyZMxefJkK2qx1ZQpUzBlyhS7y7BEcnIykpOT\n7S7DMitWrLC7BNNkZ2cjOzvb7jIsk5GRgYyMjHbzslQzxcTE4MMPP7S7DFONHTsWY8eOVTMJ/ogz\ntxARkaOw4yMiIkcJag20qQaIiIgUfOIjIiJH8el0ENLrgrRBq9ofwX0dqzXyWhRp3fjx48VtfD0A\nXBuEKg181mrUBjebPUBZo9UotUWj++VL2uQB0rnUBpVr+2XWJBFmkO4FCQkJhj7v5MmTbpf/lNcq\neUp7VdqyZcvcLi8rKxO30e4fVjh//rzb5c8//7y4zfVU8o9pASDtnl5aWup2+ejRo8VtJHziIyIi\nR2HHR0REjsKOj4iIHIUdHxEROQo7PiIichSfjuOT0mNactPXUxsZScyZnTz19T5ryT0pyarVqB1D\naZ0VSTot+eZu1nkAmDZtmrhNe3ndkpH9MsqXycdbkabNGjVqlKHPs2LfpOvFyGuctHPp6+HXN75V\n40ZPPvmkuM3QoUM9/jlSElQj1abhEx8RETkKOz4iInIUdnxEROQo7PiIiMhR2PEREZGjmD5Xp5bo\nO3DggNvleXl5ZpdhmJZWlOatNDsJ6mtaSlCaW1NLvmlJVl+mAaXzBcjnbOPGjeI22nyLVuyXlGLU\n0n5z5851u1yrXTtOVpHSj9q1pO2DJCUlRVxnxTmT2r52jKW0sJH2e6vtjJISmkZSmJ9//rm4rqSk\nRFw3a9Ysj3+WhE98RETkKOz4iIjIUdjxERGRo7DjIyIiR2HHR0REjsKOj4iIHMX0Saq1iXynT5/u\ndrk0WSxgz2S4EilCrA1NkIYzaEMIpNi2VcdCipYDQLdu3dwu1yZzloZAAMYm7TZK2y/t+Eu02svL\nyz3+vFsxMimzkcs5KChIXGfVJNVSG5k3b55Xn/tj2nAG6fhaQRt+IB1L6doDgPPnz4vrfHmNaaRh\nCz//+c/FbX71q1+J6yorK90u146ThE98RETkKOz4iIjIUdjxERGRo7DjIyIiR2HHR0REjsKOj4iI\nHMX04QxalN1IVHnw4MHiOin2bySq/lNosW8zSRFsq+LXI0eOFNdJQwK086x9nj8zMgTFipnytXYo\nxdy1iLt2vqR1Rt6U8FNosX+pzWlv1IiPjxfXtZc3pDz++ONul2vHwpdDMczWt29fcd3zzz8vrpsw\nYYJpNfCJj4iIHIUdHxEROQo7PiIichR2fERE5Cjs+IiIyFFCzf5AI2mvuXPnGvpZUhrKm1SnNrGx\ny+Vyu1xLWEnJMWnyasC6VKoR0r5pNfpz4kyjnTOp3VsxeXVERITHdWgpXK3N+3qSeC0Fa6SW9jLJ\nvZbQlCb21yb892e//vWvxXWLFi0S1zHVSUREZBA7PiIichR2fERE5Cjs+IiIyFHY8RERkaOw4yMi\nIkcxfTiDFmU3EtOXhiwAwNq1a90u1yafvVW8WZvMV4qKazF3KSpu1SS/GqkW7ZhI27SXCX41Wkxf\ni5dLtH2uqKjweBujUXutvRkZPqEdp/Y0tMbI8Tpw4IC4Tjo3VgyBMHIctTaqrZN+lhUTpmuTSksT\nppeUlIjbaG3RTHziIyIiR/H4ie/48ePIzc3FpUuXEBISgmXLlmHAgAFW1OYz5eXlKCoqanvdy4UL\nF1BXV4d33nkHkZGRNlfnvb179+Kll14CANx+++3IyclB7969ba7Ke5s2bcKWLVtw2223oW/fvnC5\nXAgPD7e7LK/t378fa9aswdWrV3HXXXfhueeeQ5cuXewuyzSLFy9Gv379MH36dLtLMU1FRQUKCwsR\nHByMTp06IScnBwMHDrS7LK9t3rwZW7duRVBQEPr06YNnn302IO6JHj3xXb58GVlZWZg5cybKysrw\nyCOPYMGCBVbV5jPjx49HeXk5ysrKUFpaih49esDlcgXECb5y5QoWLlyI/Px8FBcXY/jw4Vi9erXd\nZXntvffew2uvvYbi4mKUlZUhOTkZS5cutbssr507dw5LlixBfn4+du/ejdjY2IA4XwBw4sQJTJs2\nDX/5y1/sLsVUJ0+exOrVq1FYWIiysjJkZ2djzpw5dpfltU8++QQbNmzAtm3bsHPnTvTp00f885K/\n8ajjO3ToEOLj4zFixAgAwH333adOheSP1q9fj6ioKFOnx7FTS0sLAODixYsAgKamJnTs2NHOkkxR\nXV2NYcOGITo6GgAwZswYVFVVobm52ebKvPPuu+9i0KBBiIuLAwBMmjQJO3futLkqc7z++utIT0/H\n/fffb3cppgoLC0Nubi6ioqIAAAMHDsQ///lPv2+LAwYMwNtvv40uXbrgypUrqK+vVzMQ/sSjrzpP\nnTqFqKgo5OTk4P/+7/8QERGB//f//p9Vtfnc+fPnUVRUZMn8inbp3LkzXC4XJk6ciIiICFy7dg2v\nvPKK3WV5bdCgQdi8eTPOnj2LXr164a233kJzczO+/fZbdO/e3e7yDDt79ix69uzZ9u+ePXuisbER\njY2Nfv9151NPPQUA+Nvf/mZzJeaKiYlBTExM279XrFiB1NRUhIaanh30uZCQEFRWVmLp0qXo2LGj\n4XmV2xuPzkxzczMOHjyI4uJiJCYmYt++fZg5cyaqqqrQoUMHAHpySEpoaglH7dF63LhxbpcbTWWV\nlJQgNTXV479/aUmkkSNHGqrFLJ9++ikKCgravjbbtGkTlixZclMK0Uj9dj/pJyUlYfbs2Zg9ezaC\ng4ORnp6OiIiItnYI6JP8zps3z+OfOXjwYHGd1BY9/Q25tbXV7fKQkJC2/9aSzlLaT0tba8epPf2G\nL7XFlJQUcRst/Wh2qrOpqQmLFi1CfX09Xn311ZvWaedM+kXb6GTvRj5PO8+jR4/G6NGjUVpaihkz\nZqCysrJt3cqVK8XtpPvK6NGjxW3WrVsnrjOTR191RkdHIyEhAYmJiQCA1NRUtLS04IsvvrCkOF/b\ntWsX0tPT7S7DVIcOHcLQoUMRGxsLAJgyZQo+++wzn8WGrdLY2Ih77rkH27dvx5tvvokxY8YA0N9c\n4A969eqF+vr6tn9/9dVXCA8PR6dOnWysim7lzJkzyMjIQIcOHVBcXIyuXbvaXZLXamtrcfjw4bZ/\np6en48yZM2hoaLCxKnN41PElJyfj9OnTqK6uBgB88MEHCA4Obrup+rMLFy6gtrYWQ4YMsbsUU/Xv\n3x/vv/8+vvnmGwA/JDzj4uLa1W/yRtTX1yMzMxOXLl0CABQUFOCBBx6wuSrvDR8+HMeOHUNtbS0A\nYNu2bUhNTbW5KtI0NDRg6tSpGDNmDF544QWEhYXZXZIp6uvr8cQTT7T9krxjxw7069fP73+5BDz8\nqrN79+7Iz8/HM888g6amJoSFheHll18OiBNdU1OD6Ojom75SCgT33nsvsrKykJmZibCwMERERKCg\noMDusryWkJCAmTNn4sEHH0RrayuGDh2Kp59+2u6yvBYZGYnly5djzpw5aG5uRlxcHFatWmV3WaR4\n4403UFdXh8rKSuzduxcAEBQUhKKiIr/uJJKSkvDwww8jMzMToaGhiI6ORn5+vt1lmcLjv74mJSWp\nI+/9VWJiIvbs2WN3GZaYPHkyJk+ebHcZppsyZQqmTJlidxmmS05ORnJyst1lWGbFihV2l2Cq7Oxs\nZGdn212GJTIyMpCRkWF3GabjzC1EROQo7PiIiMhRglql/DQREVEA4hMfERE5Cjs+IiJylHYxp442\nu4k2q4QV783SSHUamZ1FG0enzfJgBSMzt2jbaFO+WfFOMCOk2YK09qaRZsWwoo0aef+j1qbsnl3o\nRlqd0jFRjyMWAAAgAElEQVTWjoevryWJVqO0X9q1os125ctrTHvfpPReQO3dhL56Tymf+IiIyFHY\n8RERkaOw4yMiIkdhx0dERI7Cjo+IiBzFp6lOKTGnJYN8/RYB7f1dBw4c8Gg5IL+nrT0l6bR36x09\netTtcu3ddP7w5gcpbamdFy2tKqUHff1SYykhqF1jRj7PqnOsXX9SW9TeraglCK1I3ErHa+PGjeI2\n0rWk1a6tk46hFedMe8efdL6k5YB+TrRkrKf4xEdERI7Cjo+IiByFHR8RETkKOz4iInIUdnxEROQo\npqc6tZTP9OnT3S7Py8sTt9ESh1bM66Yln+Lj490u15Jo7SnhKCX7li1b5vFntac5VI2QEmJackzb\nL1+eZ60OKZWqpUu1z5Path2pZCn9qKUEtfuRmSlBb0jnRjsv2vmUrk0r5i3V2n1ERITb5Ub3i6lO\nIiIig9jxERGRo7DjIyIiR2HHR0REjsKOj4iIHIUdHxEROYrpwxm0yOzcuXM93iYoKEhcJ8VivYm9\nakMTJFpkWptM1te+/fZbj7dJSUlxu7w9DVmQhmloQy6k86wdo5qaGnGdL4+HNoznl7/8pdvlWuzc\nyPAIq2jXrjQcSqMdKyuGM2j3AomRtmP0fJpNu79Jx16bVNzoZOqe4hMfERE5Cjs+IiJyFHZ8RETk\nKOz4iIjIUdjxERGRo7DjIyIiRzE8nEGKimszpUtRa6ORfyviyFKNgBx1T0tLE7eRhnBob52wipGo\nsLRNexrCIbVFI2+dMMqKtzNI7U1r99r1JzEyhMcq2r5J67R2nZCQIK6T9lu7B7QX/vDWCWmYmjZ8\nzcibQoycLz7xERGRo7DjIyIiR2HHR0REjsKOj4iIHIUdHxEROUpQa2trq5kfWF5e7vE6LVWmpdRM\nLt0wI6mykydPittYNcmsdJyHDBliyc9zZ8OGDW6Xt5ckmpZI1ZJ0UhvwJu0ppTq19iHVqE3YrU3M\nrW3nD7QEobTf3uyzNDGzljCW7mPaeenWrZu47vz5826XW5E8NpuWdpfattbnSPjER0REjsKOj4iI\nHIUdHxEROQo7PiIichR2fERE5Cjs+IiIyFEMT1It0eLg0jotPjx9+nRvSzKNFKfVYu4SbQiEVcMZ\npM+Nj48Xt6mpqTG1Bulc+3o4gxRzr6ioELfJy8sT11kRFZc+U/tZ0pAV7Rrz9aTiGm1ok5E4u3ad\nSW1bGpIA3PraHDlypNvl2nAGI5ORR0REiOvay7AF6VxqwzS0CafnzZvndrmReymf+IiIyFEMP/FV\nVlZi0aJFOHz4sJn12GblypXYs2dP229LCQkJWLNmjc1VmeP48ePIzc1FQ0MDQkJCsGjRItx99912\nl+WV8vJyFBUVISgoCABw4cIF1NXV4Z133kFkZKTN1Xln7969eOmllxASEoLw8HDk5uYiLi7O7rJM\nsWnTJmzZsgW33XYb+vbtC5fLhfDwcLvL8tr+/fuxZs0aNDQ0ICYmBr///e/RqVMnu8vyWqCeL0Md\n36lTp7Bq1ap2M3OKGY4cOYK8vDy/eBeXJy5fvoysrCysWLECiYmJOHjwIFwuF7Zt22Z3aV4ZP358\n21d0zc3NmDp1KrKzs/2+07ty5QoWLlyIHTt2IC4uDkVFRcjNzcW6devsLs1r7733Hl577TWUlJQg\nOjoaFRUVWLp0Kf785z/bXZpXzp07hyVLlmDbtm04ceIEtm/fju3bt2Py5Ml2l+aVQD1fgIGvOpua\nmrBw4UIsXrzYinps8f3336O6uhqFhYUYN24cHnvsMZw9e9buskxx6NAhxMfHY8SIEQCAESNGYPny\n5TZXZa7169cjKioKEyZMsLsUr7W0tAAALl68CAD47rvv0LFjRztLMk11dTWGDRuG6OhoAMCYMWNQ\nVVWF5uZmmyvzzrvvvotBgwa1PZWnpKTg/ffft7kq7wXq+QIMdHwulwuTJk1Cv379rKjHFvX19Rg2\nbBjmz5+PiooKDB48GI888ojdZZni1KlTiIqKQk5ODh566CHMmTMnIBrudefPn0dRURFycnLsLsUU\nnTt3hsvlwsSJE5GcnIzXX38dCxYssLssUwwaNAh///vf236pfOutt9Dc3KyGHfzB2bNn0bNnz7Z/\nd+vWDZcvX8bly5dtrMp7gXq+AA+/6tyyZQtCQ0ORlpaGL7/80rQitMSZy+Uy7edIYmNjb/oqKSsr\nCwUFBTh9+jRiYmLalksTqGpJtLlz57pdLqW/zNbc3IyDBw+iuLgYiYmJ2LdvH+bPn4+qqip06NAB\ngJ6Kk9KP2j5rqTKzE4QlJSVITU1F7969PdpOqn/w4MHiNr5Inn766acoKCjA7t27ERsbi02bNuHR\nRx+9KW2q1SElErWkoq8StUlJSZg9ezZmz56N4OBgpKenIyIioq0dAnpC2shkxFoKU/qzhqep6hv/\n5DNy5Ei0tLQgKCgII0eObPs737hx48TtpQmnU1JSxG2MJMk99VPOl5aolO5x2vHVOlXt2vSUR098\n5eXl+Pjjj5GWloZZs2bh8uXLSEtLw9dff21aQXY4fvz4v8TYW1tbERpq+mgPn4uOjkZCQgISExMB\nAKmpqWhpacEXX3xhc2Xm2LVrF9LT0+0uwzSHDh3C0KFDERsbCwCYMmUKPvvss4D4LbuxsRH33HMP\ntm/fjjfffBNjxowBoEfz/UGvXr1QX1/f9u+vvvoK4eHhfh9uCdTzBXjY8ZWWlmLnzp0oKyvD+vXr\n0bFjR5SVlaFHjx5W1ecTwcHBWL58OU6fPg3ghyfbu+++G3fccYfNlXkvOTkZp0+fRnV1NQDggw8+\nQHBwcNuN1Z9duHABtbW1Pn2tktX69++P999/H9988w2AHxKecXFx7WZsljfq6+uRmZmJS5cuAQAK\nCgrwwAMP2FyV94YPH45jx46htrYWALBt2zakpqbaXJX3AvV8AV4OYL8eJfd3d955J5YuXYrs7Gxc\nu3YNPXv2DJihDN27d0d+fj6eeeYZNDU1ISwsDC+//DLCwsLsLs1rNTU1iI6ORkhIiN2lmObee+9F\nVlYWMjMzERYWhoiICBQUFNhdlikSEhIwc+ZMPPjgg2htbcXQoUPx9NNP212W1yIjI7F8+fK2v5/H\nxcVh1apVdpfltUA9X4AXHV9MTAw+/PBDM2ux1dixYzF27Fi7y7BEUlISSkpK7C7DdImJidizZ4/d\nZZhu8uTJfh+Fl0yZMgVTpkyxuwzTJScnIzk52e4yTBeo54sztxARkaOw4yMiIkcJag2k6VeIiIhu\ngU98RETkKIbDLdLARW2A8tGjR43+OLekQaFGBrpepw2mlwawa4ODtYHeEmnQuB2RdulYSjUC+uBa\nK165JB1jbZIArX6JVrsvX6uktVGpLWrHwpvX8JhNmytXWiddl0D7eUWPVqNEO8/avbSqqsrtcm8m\nzZDGkWptZ+3atW6XG50kwsg1K+ETHxEROQo7PiIichR2fERE5Cjs+IiIyFHY8RERkaMYTnVKSTot\nbTRt2jS3y7UkqJbKsuJt6dprNqR9S0tLM7UGKUlnVXJQm/lfSm1px97XSUCp/oaGBnGbZcuWefxz\ntDSakVewGGUk3aali7VzKSV0vb32pLSwdv+QzrOWfjQzCegNrUaJVrv2eUZSzrci/TwtQS+lS7Xa\njbwizQg+8RERkaOw4yMiIkdhx0dERI7Cjo+IiByFHR8RETmK4VSnlgSUSEkwLflmRXJTYySFN3fu\nXHGdkX32Jn1lhDa3ppSy82Y+VLMZmY9ROmdacszXaVUpYaylVaXktJak064xaTsjc0/eyMg5k1LN\nWi3tJdWpHWNpv7Rzph0/K9Lf0s/T+gHpHrFx40ZxG2n+ZbPxiY+IiByFHR8RETkKOz4iInIUdnxE\nROQo7PiIiMhR2PEREZGjmD5JtWbevHkeb7NhwwZxnVWTNntq7dq14rqIiAi3y41MWmsVLZIs1a+d\nf1/H/o1E46Vzpp0XbdiHFcNujOyXNuG7kZ9j1dAaqY3Ex8eL2xiZWFw7n768f2jXxKhRo9wul4am\nAL4fTiQdK+0+IA3HycvLE7fxdpjMT8UnPiIichR2fERE5Cjs+IiIyFHY8RERkaOw4yMiIkdhx0dE\nRI4S1Nra2mpkQynGqsVspWi0FmHVIuRG3hDhDakWrQ4pBqzF37V99oZUpxa1lt4EIA1zAPQIvBQv\nNxLdvxWtXUk/z+hbDHwVwwaAoKAgcd1HH33kdrlWu7ZOeruBVUMBtGvJyD1Hu5akdd60RalGbZhJ\nTU2N2+UGb81+TTv20rE1MnyKT3xEROQo7PiIiMhR2PEREZGjsOMjIiJHYcdHRESOYniSaikJpiXE\npMSWr9OZRklpRW2iVikVacWkxrdiJNUpbaPts5Zge+aZZ9wutyIVKSUSAXm/pPoA30++LdWoJWql\niYGNTCoPGJv02htGJszWUsTadSalQb1JrBr5TCNpVV+fF1/RzqWUwjVyvvjER0REjsKOj4iIHIUd\nHxEROQo7PiIichR2fERE5Cjs+IiIyFEMD2eQaJPCSvHyo0ePitts2LDB25I8og2tkCL3WuxYip5b\nNcmvRorja0MJRo0a5Xa5Nplzexmeop0XqS1qtWtDHawgRfulITKAfF604QxahNyKycM12jmT9kEb\nsqDtm3Q+vbk2pZ+nXS/SdWl0yJAVpFq0YyXVqJ0vbZ/NvGfyiY+IiBzF4ye+lStXYs+ePW2/CSYk\nJGDNmjWmF+Zr1/fr9ttvBwD06dMHubm5NldljuPHjyM3NxeXLl1CSEgIli1bhgEDBthdltcCtS3u\n378fa9aswdWrV3HXXXfhueeeQ5cuXewuyxSbNm3Cli1bcNttt6Fv375wuVwIDw+3uyyv7d27Fy+9\n9BK+++47dO7cGb///e/RvXt3u8vy2vXz1draipiYGGRlZQVEW/S44zty5Ajy8vJsmXnEStf3y9ez\nc1jt8uXLyMrKwooVKzBixAj89a9/xYIFC7Br1y67S/NaILbFc+fOYcmSJdi2bRvi4uKwevVqrF69\nGi6Xy+7SvPbee+/htddeQ0lJCaKjo1FRUYGlS5fiz3/+s92leeXKlStYuHAhduzYgRMnTqCyshJb\nt27Fo48+andpXrnxfJ05cwYHDx7EunXr8MQTT9hdmtc8+qrz+++/R3V1NQoLCzFu3Dg89thjOHv2\nrFW1+cyN+zV16lQ8+eSTqKurs7ssUxw6dAjx8fEYMWIEAOC+++7z6UtTrRKobfHdd9/FoEGDEBcX\nBwCYNGkSdu7caXNV5qiursawYcMQHR0NABgzZgyqqqrQ3Nxsc2XeaWlpAQBcvHgRwA8dYYcOHews\nyRQ/Pl///u//jg8//LBtf/2ZRx1ffX09hg0bhvnz56OiogKDBw/GI488YlVtPnPjfm3evBkDBw7E\nggUL7C7LFKdOnUJUVBRycnKQnp6OGTNm+P2NBgjctnj27Fn07Nmz7d89e/ZEY2MjGhsbbazKHIMG\nDcLf//73tl9Q3nrrLTQ3N7ebMJRRnTt3hsvlwsSJE7Fo0SLs378f//Vf/2V3WV778fm6/kvK9Q7e\nn3n0VWdsbCzWrVsH4IcbampqKl5++WX8/e9/xx133AFATgECcsJR+xrHF+nHG/dr//79GDhwIF55\n5RXs27cPUVFRbf/fsmXL3G6vTRospVx99fVcc3MzDh48iOLiYiQmJmLfvn2YOXMmqqqq2n4r1ZJv\nZWVlbpenpaWJ22jHw6zzeeM5+/bbb5Geno78/Hz87//+L3r16nXLnyWlFaVJnrVtzNTa2up2eUhI\nSNt/5+XlidvPmzfP7fJx48aJ2/jqG4CkpCTMnj0bs2fPRnBwMNLT0xEREXHT05GR5KxWv5aAHTx4\nsMc/y51PP/0UBQUF2L17N7p27YqSkhJs3LgRmzdvbvt/tM5948aNbpf7OtH+Y+7O189+9jMMGTKk\n7RrX7h1SktXIROS3Wucpj574jh8/joqKin9ZHhpq+qgIn5L268abjb+Kjo5GQkICEhMTAQCpqalo\naWnBF198YXNl3nF3zlpbW/2+Lfbq1Qv19fVt//7qq68QHh6OTp062ViVORobG3HPPfdg+/btePPN\nNzFmzBgA+i9K/uDQoUMYOnQoYmNjAQC/+93v8Pnnn6udrj8I1PMFeNjxBQcHY/ny5Th9+jQAYOfO\nnUhISLjpqcgf/Xi/9u/fj9jYWJ+PYbJCcnIyTp8+jerqagDABx98gODg4LaL1F/9+Jy9+eabuPPO\nO9GjRw+bK/PO8OHDcezYMdTW1gIAtm3bhtTUVJurMkd9fT0yMzNx6dIlAEBBQQEeeOABm6vyXv/+\n/fH+++/jm2++AfDD/aN3795+30EE6vkCPPyq884778TSpUuRnZ2Ny5cvo0ePHli8eLFVtfnMjft1\n8eJFdOvWDX/4wx/sLssU3bt3R35+Pp555hk0NTUhLCwML7/8MsLCwuwuzSs3nrOrV68iOjoazz77\nrN1leS0yMhLLly/HnDlz0NzcjLi4OKxatcruskyRkJCAmTNn4sEHH0RrayuGDh2Kp59+2u6yvHbv\nvfciKysLmZmZCAkJQXh4OP70pz/ZXZbXAvV8AQaGM4wdOxZjx45V/xbij67vlzbzjL9KSkpCSUmJ\n3WWY7vo58/dwxI8lJycjOTnZ7jIsMWXKFEyZMsXuMkw3efJkTJ48OeDaYqCeL87cQkREjsKOj4iI\nHCWoVcpPExERBSA+8RERkaOYPuhJe12GkUHD2oBWMwc0ekN6xQ0gD+K0e6C0t7Rjrx0PX79ORSLV\nqL0+Rpt0wJehKO34rl271tSfJU1gYNV5NLJv2kB07fOsmBxDCrdocwBLr2JqL/c3o6RjoR137TiZ\nOdECn/iIiMhR2PEREZGjsOMjIiJHYcdHRESOwo6PiIgcxfRxfFoSSUr5aNtoKbXz58+7XW5VKlJK\n7mmvYkpJSfHos9obKX2akJAgbiPtM+Db/dZ+1pEjRzz+PC1VZsUUftL1oqVLpWtJS8tJr9sC5FeG\nGXl90E+hpWql61p7RZbGiiHMRq4XI+Lj48V1UrvX2oAVpOtFenUWoCd0jVyzEj7xERGRo7DjIyIi\nR2HHR0REjsKOj4iIHIUdHxEROYpP5+o0Mm+lxtdzWkr7piWspH3WjpOUmNPSfN7QXp5pZD7D9jLX\nqJYWNjIPopY4lBJn3pwzI3PbSozOc+jruVW19iZdFxEREeI22jmzgpHU8rhx49wuN9p2fPkyXG1/\njbQ5X81Pyic+IiJyFHZ8RETkKOz4iIjIUdjxERGRo7DjIyIiR2HHR0REjmL6cAYtjixNTqrFb6uq\nqrwtySNaPLehocHtcm2fpeh5RUWFuI0UY/c2mi3VotV/4MABj3+Or4czSOesvLxc3MbMoQKANRMA\nS0MktP2StjE6Obg0hECrwSpSvF9rb76emNnMtq8NZ2gvw0w2btwobiMN06ipqRG38dW9g098RETk\nKOz4iIjIUdjxERGRo7DjIyIiR2HHR0REjsKOj4iIHMX04QyPP/64x9toEVZfzdZ9nZGYthaBN3I8\npAi5t6RIu3b8y8rK3C7XhkD4+pxJ1q5dK66TZvSXhqzcitRujLzd4lafuWzZMo8/S3uDgRQ7B6xr\ni0ZIEX5tqIbWFqWhH94MgZBq1I6xVId279D2y4ohAdJQKiNvLNGGcvlq+Amf+IiIyFHY8RERkaOw\n4yMiIkdhx0dERI7Cjo+IiBwlqLW1tdXMD9RSOVJKSUtSapOxGklMekP6eVp6UBIfHy+uMzpRshWk\nCcS7desmbjN37lxx3Ysvvuh1TVbS2q/WTrUJhc2mtY+EhAS3y/Py8sRtfH0d+ZJ2/5DattEJvY2S\n2lVaWpq4jT+cTynVOWTIEHEbl8slrjMzYcwnPiIichR2fERE5Cjs+IiIyFHY8RERkaOw4yMiIkdh\nx0dERI5ieJJqI5FfKfKtxcS1SVB9HduVovjapLDShMLtafJfjRT51rSn4RgSqe1owxl8OWRBo10T\nEm8my/Yl7b4irZNi87f6PF+eT+2cTZ8+3ePPay9tUWPkPuCrewef+IiIyFE8fuLbu3cvXnrpJXz3\n3Xfo3Lkzfv/736N79+5W1GaLyspKLFq0CIcPH7a7FFMtXrwY/fr1M/TbZXtUXl6OoqIiBAUFAQAu\nXLiAuro6vPPOO4iMjLS5OuMCdb+u27x5M7Zu3YqgoCD06dMHzz77bEDs18qVK7Fnz562b38SEhKw\nZs0am6syT6DdPzzq+K5cuYKFCxdix44dOHHiBCorK7F161Y8+uijVtXnU6dOncKqVatg8mQ2tjpx\n4gT++Mc/4tixY+jXr5/d5Zhm/PjxbbNyNDc3Y+rUqcjOzvb7m2ig7hcAfPLJJ9iwYQN27NiBLl26\n4Pnnn8fatWsNvV+wvTly5Ajy8vL84itITwTq/cOjrzpbWloAABcvXgTwQ0fYoUMH86uyQVNTExYu\nXIjFixfbXYqpXn/9daSnp+P++++3uxTLrF+/HlFRUZgwYYLdpZgq0PZrwIABePvtt9GlSxdcuXIF\n9fX1lrw01de+//57VFdXo7CwEOPGjcNjjz2Gs2fP2l2WKQL1/uHRE1/nzp3hcrkwceJEdO7cGdeu\nXcPChQutqs2nXC4XJk2aFFC/1QDAU089BQD429/+ZnMl1jh//jyKiorUgJQ/CtT9CgkJQWVlJZYu\nXYqOHTuq87r6i/r6egwbNgzz589HfHw8XnvtNTzyyCMoKyuzuzSvBer9w6OO79NPP0VBQQF2796N\nrl27oqSkBBs3bsTmzZvb/h/tUV9KlmlJOl9MarxlyxaEhoYiLS0NX375pcfbG0k+jhw50uNt7GBk\n33z5dU9JSQlSU1PRu3dvj7aT0mPapMa+pO2X1hlOmzbN7fL29GQ1evRojB49GqWlpZgxYwYqKyvb\n1mnXu5TeNDIxPmBesjo2Nhbr1q1r+3dWVhYKCgpw+vRpxMTE3PJnSRPWa0lQf7h/SPcBbYJ+X+2X\nR191Hjp0CEOHDkVsbCwA4He/+x0+//xzNDQ0WFKcr5SXl+Pjjz9GWloaZs2ahcuXLyMtLQ1ff/21\n3aXRLezatQvp6el2l2G6QNyv2tram0Jj6enpOHPmjN/fP44fP46KioqblrW2tiI01PBoMbKYR2em\nf//+2LJlC7755huEhIRg//796N27NyIiIqyqzydKS0vb/vv06dN44IEHAuJrikB34cIF1NbWqq85\n8UeBul/19fWYP38+Kioq8LOf/Qw7duxAv379/P7+ERwcjOXLlyMpKQkxMTHYsmUL7r77btxxxx12\nl0YCjzq+e++9F1lZWcjMzERISAjCw8Pxpz/9yarabHM9Sk7tW01NDaKjoxESEmJ3KaYK1P1KSkrC\nww8/jMzMTISGhiI6Ohr5+fl2l+W1O++8E0uXLkV2djauXbuGnj17BtRQhkDk8bP45MmTMXnyZEN/\n+/EHMTEx+PDDD+0uw3QrVqywuwTTJSYmYs+ePXaXYbpA3S8AyMjIQEZGht1lmG7s2LEYO3as3WVY\nJtDuH5y5hYiIHIUdHxEROUpQayBNU0JERHQLfOIjIiJHYcdHRESOYvoIS+19StJIfm3mBW32Al9P\nCCslWbX6pXXae8La0ywb0iwh2owYRs6nto0VpFlAtJkjtFldjLyr0Sjt/XPSeTlw4IChn7Vhwwa3\ny616v5+R9/Fpk1xr43Hbyyw90n3F6P1NumatuF9q93vpWtJGBGj3ezPPF5/4iIjIUdjxERGRo7Dj\nIyIiR2HHR0REjsKOj4iIHMX0VKeRd1xpiT4tZefr+UKlxJH2WhWpRu29Y2a9J+ynMlKLlurUkllS\nCszXqU5pv7Tk2MaNG8V1UsrRiveLaedLSpHm5eWJ28ybN09cJyUErUp1au8aXLt2rdvlLpdL3MZX\nKUFvSNeSlsLU0pS+THVq96qamhqPP09rV9I+G0lO84mPiIgchR0fERE5Cjs+IiJyFHZ8RETkKOz4\niIjIUQynOqX5ArXkm5F5/6xKj0m0lJI0V+DcuXPFbaTElpYok/bZquSjloqSzrOWqNWSeb6eX1Ui\n1a+lALX90lJ2ZtNqlGj1GUmJWsVIilu7Zo0kI32dMJZq1JLTvr6OjNzvp02b5vHP0T7PyPy6Ej7x\nERGRo7DjIyIiR2HHR0REjsKOj4iIHIUdHxEROQo7PiIichTDwxmMTBBtJPKtRXqlGLM3kzxr8W0p\nQqz9POnztP2Shk1YNbRD+1zpPGvDMdpTPF4i1ShFpm/Figi8NHxCG84gtVHtetUmE9baqRW0diVd\nZ9L1Avh2mIlR0jHWriNtv6w4Z0aOo5FhN746l3ziIyIiR2HHR0REjsKOj4iIHIUdHxEROQo7PiIi\nchTDqU4pfRMfHy9uoyW2JEbSo97Q0nlSqshIUlGbZNZIGsob2jGWEp/axLBGJo31NSm9qSXitJSd\nFfssXWMVFRXiNto6I6S2qB0Lq0jHeNSoUeI2LpdLXGdFElc6Z1paUVqnJYy1CdPbS3Jaajtailw7\nJ2b2BXziIyIiR2HHR0REjsKOj4iIHIUdHxEROQo7PiIichR2fERE5CiGhzNIQxO0mLOR+LAWzbUi\ntqsNuZBiuFoEXtpnLY5sdKLkW5Em+V22bJm4zeDBg90u1+r3NSkOrp3LhoYGt8vnzp0rbmPVJOES\n6Xxp+yWdl7Vr14rbbNiwQVzXXvYZkOPx2hAqbdiQFaQhT9o1JtHOi6+HDEk/LyIiQtxG6guMDlkw\n837PJz4iInIUdnxEROQo7PiIiMhR2PEREZGjsOMjIiJHCWptbW018wO1xI6UsNJSalrKS0oNGZkM\n+6eQ0pvapNLS8Th69Ki4jZTm8jZhJyX+tFRqTU2N2+Xjxo0TtzE72WuUluiTjr+WUtOOv7TO16lC\nqe1rSWEpiWiHoKAgcV1ZWZnb5Vr71a5NXyYjtWNs5LrW7ovSNWbFtafda41MmK5df5ykmoiIyCB2\nfHI3z0cAAAzMSURBVERE5CgeD2Dfv38/1qxZg6tXr+Kuu+7Cc889hy5dulhRm08F6n5VVFSgsLAQ\nwcHB+O677zB27FjExsbaXZYpbty3Tp06IScnBwMHDrS7LK+Ul5ejqKio7Su/CxcuoK6uDu+88w4i\nIyNtrs57e/fuxUsvvYSQkBCEh4cjNzcXcXFxdpfltUBsi0Dg3hc9euI7d+4clixZgvz8fOzevRux\nsbFYvXq1VbX5TKDu18mTJ7F69WoUFhairKwMo0aNwqZNm+wuyxQ/3rfs7GzMmTPH7rK8Nn78eJSX\nl6OsrAylpaXo0aMHXC5XQHR6V65cwcKFC5Gfn9/WHnNzc+0uy2uB2hYD9b4IeNjxvfvuuxg0aFDb\nb2iTJk3Czp07LSnMlwJ1v8LCwpCbm4uoqCgAQGxsLC5duoSWlhabK/Pej/dt4MCB+Oc//4nm5mab\nKzPP+vXrERUVhQkTJthdiimut7uLFy8CAL777jt07NjRzpJMEahtMVDvi4CHX3WePXsWPXv2bPt3\nz5490djYiMbGRr9+/A3U/YqJiUFMTEzbv//nf/4H/fv3R0hIiI1VmePH+7ZixQqkpqYiNNTw9LPt\nyvnz51FUVNSu5kT1VufOneFyuTBx4kR069YN165dwxtvvGF3WV4L1LYYqPdFwMOOTxr5cOONVIsP\nSxFcLY6sRePNGrbwU/ZLq0WaJBmQI7gul0vcxuyJgZuamrBo0SIAPwyV6Nq1603rteMonU/tPBv5\nPKOx/+v7Vl9fj1dfffWmdVqEXzpnWkejrZOi4kb3q6SkBKmpqejdu/e/rNPamxQhl4YC+NKnn36K\ngoKCtq/NNm3ahEcfffSmmrWJmdPS0twuT0lJEbfx5XASrS1qQwmkdqUNtxg1apS4TjrXng5n+Cn3\nRe3+LNGGdhj5PCM8+qqzV69eqK+vb/v3V199hfDwcHTq1Mn0wnwpUPcLAM6cOYOMjAx06NABxcXF\n/9Lp+bNA3rddu3YhPT3d7jJMdejQIQwdOrQtXDVlyhR89tlnpo7PsksgtsVAvi961PENHz4cx44d\nQ21tLQBg27ZtSE1NtaQwXwrU/WpoaMDUqVMxZswYvPDCCwgLC7O7JNME8r5duHABtbW1GDJkiN2l\nmKp///54//338c033wD4IeEZFxdnyevFfClQ22Kg3hcBD7/qjIyMxPLlyzFnzhw0NzcjLi4Oq1at\nsqo2nwnU/XrjjTdQV1eHyspK7N27F8APM2MUFRWpMyT4g0Det5qaGkRHRwfE32JvdO+99yIrKwuZ\nmZkICwtDREQECgoK7C7La4HaFgP1vggYGMeXnJyM5ORkK2qxVSDuV3Z2NrKzs+0uwxKBvG+JiYnY\ns2eP3WVYYvLkyZg8ebLdZZgqkNtiIN4XAc7cQkREDmP6JNVERETtGZ/4iIjIUdjxERGRo7SLqQW0\ngZraGB9pIK+v49FajdKgfW0QZ3uarUMaTG9kcDhgzbmRjr82MYKRQcPaoH1ftjltggNpv7T62ss7\n6wC9Fmlws5F3WwLmTxSh0QaPS++8jI+PF7fR3sdnxX5J17uRITfafmnXrLRfRq49PvEREZGjsOMj\nIiJHYcdHRESOwo6PiIgchR0fERE5ik8HsEtJpGXLlonbaHPdSUkjT1+/4S3t1Sda4kxi1SmR0o9a\nCkzaRnv1kJbMsoLUDoykY7W0qpGEsRW0nyWlhbXXvWht9OTJk26Xe3uNGUkJSmlA7bw0NDSI686f\nP+92uRUJXe34S8di48aNhn7WRx995Ha5N69oko6xli6VaMld7XxVVVW5XW4kecwnPiIichR2fERE\n5Cjs+IiIyFHY8RERkaOw4yMiIkcxPdWpJQSNpJRSUlLEdb5M0mm0VJGUftRSXto8nt6QPjchIUHc\nRjr+7eXYGyUlPrVEqpYelI6tr+eNNZKWnDt3rrhOa6dW0JK40rWkJQu1xLhViVVPSfuclpZm6PN8\nmVbVSG1n3rx54jba/d7IPLQSPvEREZGjsOMjIiJHYcdHRESOwo6PiIgchR0fERE5Cjs+IiJylFCj\nG0pxdqMTq0q0CHl7oUX7pWi0rydyBowNk/B1BNpXpIlytfamTWDdXo6TkSi+N5MXm02b/Nxs7eXe\nYuT4u1wucV17aYtG7jfaBNZm7hef+IiIyFHY8RERkaOw4yMiIkdhx0dERI7Cjo+IiByFHR8RETmK\n4bczSBF+LfItxXZHjRolbrNhwwZxnfYmCCtIs4MbmcHejrcbSD9TO/4RERFul2vDMbS3VWjrfEk6\nFlqcXmvbvp7R31PataLFzq1qp9Kx1NpHQ0ODqTVIb6Xw9RspJNqx0IZiSOesvbwpRNsv7U0bZg4B\n4xMfERE5Cjs+IiJyFHZ8RETkKOz4iIjIUdjxERGRoxhOdRohJZG6desmbqNNxqolgIzSEl3z5s3z\n+POkVKqvE6mAsVSnZPDgweK6o0ePiuva0/FwR0ucaclNbXLd9kBLAWrXX1VVldvl3qZzpYS0dk1L\n+1BTUyNuM27cOHGd9LPay6TdWqJWu2bz8vLcLrdjYnx3tDq068jMScX5xEdERI7Cjo+IiByFHR8R\nETkKOz4iInIUdnxEROQo7PiIiMhRQu0uoL3RoszSpLZa7Hj69Olul0txbkCO+3obIZe2l+LPgDyE\nQxt+oEWSpQi5FcMZtEmlpWi0NmRh48aN4jppGIw3EwNLNWqRf+kcG42CG5lo+KeQJgPXJgk3sm9a\nW/TlpM3a9S7dP7RtfE06xkaGSGjXkUZqi0aGn/CJj4iIHMXwE9/ixYvRr18/8YnG31RUVKCwsBCN\njY0ICwvDxIkTER8fb3dZpjh+/Dhyc3Nx6dIlhISEYNmyZRgwYIDdZXlt8+bN2Lp1K4KCgtCnTx88\n++yziIyMtLssr13fr9bWVsTExGDJkiU+f6WMFcrLy1FUVISgoCAAwIULF1BXV4d33nnH788b26J/\n8fiJ78SJE5g2bRr+8pe/WFGPLU6ePInVq1ejsLAQS5cuxX/8x3/gv//7v+0uyxSXL19GVlYWZs6c\nibKyMjzyyCNYsGCB3WV57ZNPPsGGDRuwbds27Ny5E3369MHatWvtLstrN+7Xli1bEBsbi3Xr1tld\nlinGjx+P8vJylJWVobS0FD169IDL5fL7DoJt0f94/MT3+uuvIz09Hb1797aiHluEhYUhNzcXUVFR\nAIA+ffrgwoULaGlpQUhIiM3VeefQoUOIj4/HiBEjAAD33XcfYmNjba7KewMGDMDbb7+NkJAQXLly\nBfX19QG3X3V1dfj6668RExNjd1mmW79+PaKiojBhwgS7S/Ea26L/8fiJ76mnnsJvf/tbK2qxTUxM\nDFJSUtr+XVpaisGDB/t9pwf88HbtqKgo5OTkID09HTNmzEBzc7PdZZkiJCQElZWVSElJwT/+8Q+k\np6fbXZIpru/Xb3/7Wxw5cgQPPPCA3SWZ6vz58ygqKkJOTo7dpZiGbdG/+DTVKX03fGOn82NaYtJs\nTU1N2L59O65evYpXX30VXbt2vWm9kSSblHrS9svM79Cbm5tx8OBBFBcXIzExEfv27cPMmTNRVVWF\nDh06qDVqjE4QbvZkzqNHj8bo0aNRWlqKGTNmoLKysm2dlhSVJtKOiIgQt5k2bZq4zuy/e9y4X48/\n/vhN+6Wl/aTkm5Zw1SZy1lKWRpWUlCA1NdXtt0badXHgwAG3y7VUsi//HqW1Re160SZ1l2ht0eyE\n9OjRo/GLX/wCu3fvxqOPPnrTNay1K2m/tPu9di8yc/Jwpjr/f2fOnEFGRgY6dOiA4uLif+n0/FV0\ndDQSEhKQmJgIAEhNTUVLSwu++OILmyvzTm1tLQ4fPtz27/T0dJw5cwYNDQ02VuW9QN2vG+3atStg\nnoiAwD1nP96v3/zmN6irq8PFixdtrMoc7PgANDQ0YOrUqRgzZgxeeOEFhIWF2V2SaZKTk3H69GlU\nV1cDAD744AMEBwf7/d8g6uvr8cQTT7SNL9qxYwf69eunPrH5g0Ddr+suXLiA2tpaDBkyxO5STBOo\n5+zH+7Vv3z4kJCTg9ttvt7ky73EAO4A33ngDdXV1qKysxN69ewEAQUFBKCoq8vvG2717d+Tn5+OZ\nZ55BU1MTwsLC8PLLL/t9556UlISHH34YmZmZCA0NRXR0NPLz8+0uy2uBul/X1dTUIDo6OiD+fn5d\noJ6zG/fr2rVriIyMVN+P6k8Md3wrVqwwsw5bZWdnIzs72+4yLJOUlISSkhK7yzBdRkYGMjIy7C7D\ndIG6XwCQmJiIPXv22F2G6QL1nF3fr1OnTtldiqn4VScRETkKOz4iInKUoNbW1la7iyAiIvIVPvER\nEZGjsOMjIiJHYcdHRESOwo6PiIgchR0fERE5Cjs+IiJylP8PI7nbgJwELeAAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118c22128>"
+       "<Figure size 432x432 with 64 Axes>"
       ]
      },
      "metadata": {},
@@ -603,18 +620,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can quickly classify the digits using a random forest as follows:"
+    "We can classify the digits using a random forest as follows:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "\n",
     "Xtrain, Xtest, ytrain, ytest = train_test_split(digits.data, digits.target,\n",
     "                                                random_state=0)\n",
@@ -627,34 +647,39 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can take a look at the classification report for this classifier:"
+    "Let's look at the classification report for this classifier:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "             precision    recall  f1-score   support\n",
+      "              precision    recall  f1-score   support\n",
       "\n",
-      "          0       1.00      0.97      0.99        38\n",
-      "          1       1.00      0.98      0.99        44\n",
-      "          2       0.95      1.00      0.98        42\n",
-      "          3       0.98      0.96      0.97        46\n",
-      "          4       0.97      1.00      0.99        37\n",
-      "          5       0.98      0.96      0.97        49\n",
-      "          6       1.00      1.00      1.00        52\n",
-      "          7       1.00      0.96      0.98        50\n",
-      "          8       0.94      0.98      0.96        46\n",
-      "          9       0.96      0.98      0.97        46\n",
+      "           0       1.00      0.97      0.99        38\n",
+      "           1       0.98      0.98      0.98        43\n",
+      "           2       0.95      1.00      0.98        42\n",
+      "           3       0.98      0.96      0.97        46\n",
+      "           4       0.97      1.00      0.99        37\n",
+      "           5       0.98      0.96      0.97        49\n",
+      "           6       1.00      1.00      1.00        52\n",
+      "           7       1.00      0.96      0.98        50\n",
+      "           8       0.94      0.98      0.96        46\n",
+      "           9       0.98      0.98      0.98        47\n",
       "\n",
-      "avg / total       0.98      0.98      0.98       450\n",
+      "    accuracy                           0.98       450\n",
+      "   macro avg       0.98      0.98      0.98       450\n",
+      "weighted avg       0.98      0.98      0.98       450\n",
       "\n"
      ]
     }
@@ -668,21 +693,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And for good measure, plot the confusion matrix:"
+    "And for good measure, plot the confusion matrix (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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IC5iISJBHuidcRbJnXzoWLVuJ4uJiNPD3x2ydFi4uLjaby2zbf68nRoahY9d2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\nCfL/ADUiXKng20C6AAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11c1ccf60>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -691,8 +719,10 @@
    ],
    "source": [
     "from sklearn.metrics import confusion_matrix\n",
+    "import seaborn as sns\n",
     "mat = confusion_matrix(ytest, ypred)\n",
-    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)\n",
+    "sns.heatmap(mat.T, square=True, annot=True, fmt='d',\n",
+    "            cbar=False, cmap='Blues')\n",
     "plt.xlabel('true label')\n",
     "plt.ylabel('predicted label');"
    ]
@@ -701,40 +731,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We find that a simple, untuned random forest results in a very accurate classification of the digits data."
+    "We find that a simple, untuned random forest results in a quite accurate classification of the digits data."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Summary of Random Forests\n",
+    "## Summary\n",
     "\n",
-    "This section contained a brief introduction to the concept of *ensemble estimators*, and in particular the random forest – an ensemble of randomized decision trees.\n",
+    "This chapter provided a brief introduction to the concept of ensemble estimators, and in particular the random forest, an ensemble of randomized decision trees.\n",
     "Random forests are a powerful method with several advantages:\n",
     "\n",
     "- Both training and prediction are very fast, because of the simplicity of the underlying decision trees. In addition, both tasks can be straightforwardly parallelized, because the individual trees are entirely independent entities.\n",
-    "- The multiple trees allow for a probabilistic classification: a majority vote among estimators gives an estimate of the probability (accessed in Scikit-Learn with the ``predict_proba()`` method).\n",
-    "- The nonparametric model is extremely flexible, and can thus perform well on tasks that are under-fit by other estimators.\n",
+    "- The multiple trees allow for a probabilistic classification: a majority vote among estimators gives an estimate of the probability (accessed in Scikit-Learn with the `predict_proba` method).\n",
+    "- The nonparametric model is extremely flexible and can thus perform well on tasks that are underfit by other estimators.\n",
     "\n",
     "A primary disadvantage of random forests is that the results are not easily interpretable: that is, if you would like to draw conclusions about the *meaning* of the classification model, random forests may not be the best choice."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.08-Random-Forests.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -748,9 +771,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.09-Principal-Component-Analysis.ipynb b/notebooks/05.09-Principal-Component-Analysis.ipynb
index 065b1f4a7..303bba21a 100644
--- a/notebooks/05.09-Principal-Component-Analysis.ipynb
+++ b/notebooks/05.09-Principal-Component-Analysis.ipynb
@@ -1,33 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) | [Contents](Index.ipynb) | [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.09-Principal-Component-Analysis.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -45,9 +17,9 @@
     "Up until now, we have been looking in depth at supervised learning estimators: those estimators that predict labels based on labeled training data.\n",
     "Here we begin looking at several unsupervised estimators, which can highlight interesting aspects of the data without reference to any known labels.\n",
     "\n",
-    "In this section, we explore what is perhaps one of the most broadly used of unsupervised algorithms, principal component analysis (PCA).\n",
-    "PCA is fundamentally a dimensionality reduction algorithm, but it can also be useful as a tool for visualization, for noise filtering, for feature extraction and engineering, and much more.\n",
-    "After a brief conceptual discussion of the PCA algorithm, we will see a couple examples of these further applications.\n",
+    "In this chapter we will explore what is perhaps one of the most broadly used unsupervised algorithms, principal component analysis (PCA).\n",
+    "PCA is fundamentally a dimensionality reduction algorithm, but it can also be useful as a tool for visualization, noise filtering, feature extraction and engineering, and much more.\n",
+    "After a brief conceptual discussion of the PCA algorithm, we will explore a couple examples of these further applications.\n",
     "\n",
     "We begin with the standard imports:"
    ]
@@ -58,14 +30,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns; sns.set()"
+    "plt.style.use('seaborn-whitegrid')"
    ]
   },
   {
@@ -79,7 +54,7 @@
     "\n",
     "Principal component analysis is a fast and flexible unsupervised method for dimensionality reduction in data, which we saw briefly in [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb).\n",
     "Its behavior is easiest to visualize by looking at a two-dimensional dataset.\n",
-    "Consider the following 200 points:"
+    "Consider these 200 points (see the following figure):"
    ]
   },
   {
@@ -88,14 +63,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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/jxHYwOAjqDHkRDMKOPT5Z0/PF/JueOGQ1K6DB0eqqCj8M9LQz06blidpjd55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\n002SvK3GzExjIsYo3/3udzV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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x101eb9f28>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -116,11 +94,11 @@
     "editable": true
    },
    "source": [
-    "By eye, it is clear that there is a nearly linear relationship between the x and y variables.\n",
-    "This is reminiscent of the linear regression data we explored in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), but the problem setting here is slightly different: rather than attempting to *predict* the y values from the x values, the unsupervised learning problem attempts to learn about the *relationship* between the x and y values.\n",
+    "By eye, it is clear that there is a nearly linear relationship between the *x* and *y* variables.\n",
+    "This is reminiscent of the linear regression data we explored in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), but the problem setting here is slightly different: rather than attempting to *predict* the *y* values from the *x* values, the unsupervised learning problem attempts to learn about the *relationship* between the *x* and *y* values.\n",
     "\n",
     "In principal component analysis, this relationship is quantified by finding a list of the *principal axes* in the data, and using those axes to describe the dataset.\n",
-    "Using Scikit-Learn's ``PCA`` estimator, we can compute this as follows:"
+    "Using Scikit-Learn's `PCA` estimator, we can compute this as follows:"
    ]
   },
   {
@@ -129,13 +107,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "PCA(copy=True, n_components=2, whiten=False)"
+       "PCA(n_components=2)"
       ]
      },
      "execution_count": 3,
@@ -156,7 +137,7 @@
     "editable": true
    },
    "source": [
-    "The fit learns some quantities from the data, most importantly the \"components\" and \"explained variance\":"
+    "The fit learns some quantities from the data, most importantly the components and explained variance:"
    ]
   },
   {
@@ -165,15 +146,18 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.94446029  0.32862557]\n",
-      " [ 0.32862557 -0.94446029]]\n"
+      "[[-0.94446029 -0.32862557]\n",
+      " [-0.32862557  0.94446029]]\n"
      ]
     }
    ],
@@ -187,14 +171,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.75871884  0.01838551]\n"
+      "[0.7625315 0.0184779]\n"
      ]
     }
    ],
@@ -209,7 +196,7 @@
     "editable": true
    },
    "source": [
-    "To see what these numbers mean, let's visualize them as vectors over the input data, using the \"components\" to define the direction of the vector, and the \"explained variance\" to define the squared-length of the vector:"
+    "To see what these numbers mean, let's visualize them as vectors over the input data, using the components to define the direction of the vector and the explained variance to define the squared length of the vector (see the following figure):"
    ]
   },
   {
@@ -218,14 +205,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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OR+h135AGgIceeghGoxEAsHjxYhw/flxRUGdlmYb9DLGeRoJ1pcxEqacTJ9qg1xdCrw+8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\nY1ATEREJjEFNREQkMAY1ERGRwP4/bV+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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118c75cf8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -235,8 +225,7 @@
    "source": [
     "def draw_vector(v0, v1, ax=None):\n",
     "    ax = ax or plt.gca()\n",
-    "    arrowprops=dict(arrowstyle='->',\n",
-    "                    linewidth=2,\n",
+    "    arrowprops=dict(arrowstyle='->', linewidth=2,\n",
     "                    shrinkA=0, shrinkB=0)\n",
     "    ax.annotate('', v1, v0, arrowprops=arrowprops)\n",
     "\n",
@@ -255,10 +244,10 @@
     "editable": true
    },
    "source": [
-    "These vectors represent the *principal axes* of the data, and the length of the vector is an indication of how \"important\" that axis is in describing the distribution of the data—more precisely, it is a measure of the variance of the data when projected onto that axis.\n",
-    "The projection of each data point onto the principal axes are the \"principal components\" of the data.\n",
+    "These vectors represent the principal axes of the data, and the length of each vector is an indication of how \"important\" that axis is in describing the distribution of the data—more precisely, it is a measure of the variance of the data when projected onto that axis.\n",
+    "The projection of each data point onto the principal axes are the principal components of the data.\n",
     "\n",
-    "If we plot these principal components beside the original data, we see the plots shown here:"
+    "If we plot these principal components beside the original data, we see the plots shown in the following figure:"
    ]
   },
   {
@@ -268,8 +257,8 @@
     "editable": true
    },
    "source": [
-    "![](figures/05.09-PCA-rotation.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Principal-Components-Rotation)"
+    "![](images/05.09-PCA-rotation.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Principal-Components-Rotation)"
    ]
   },
   {
@@ -279,7 +268,7 @@
     "editable": true
    },
    "source": [
-    "This transformation from data axes to principal axes is an *affine transformation*, which basically means it is composed of a translation, rotation, and uniform scaling.\n",
+    "This transformation from data axes to principal axes is an *affine transformation*, which means it is composed of a translation, rotation, and uniform scaling.\n",
     "\n",
     "While this algorithm to find principal components may seem like just a mathematical curiosity, it turns out to have very far-reaching applications in the world of machine learning and data exploration."
    ]
@@ -291,7 +280,7 @@
     "editable": true
    },
    "source": [
-    "### PCA as dimensionality reduction\n",
+    "### PCA as Dimensionality Reduction\n",
     "\n",
     "Using PCA for dimensionality reduction involves zeroing out one or more of the smallest principal components, resulting in a lower-dimensional projection of the data that preserves the maximal data variance.\n",
     "\n",
@@ -304,7 +293,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -332,7 +324,7 @@
    },
    "source": [
     "The transformed data has been reduced to a single dimension.\n",
-    "To understand the effect of this dimensionality reduction, we can perform the inverse transform of this reduced data and plot it along with the original data:"
+    "To understand the effect of this dimensionality reduction, we can perform the inverse transform of this reduced data and plot it along with the original data (see the following figure):"
    ]
   },
   {
@@ -341,14 +333,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
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6XS5oaZVMpDEF9datWzn33HMBOO2003jjjTd6n6utraWiooJgMAjA6aefzpYt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\nme1er6+PUlpajGkmiMdNduy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YglpERMTBFNQiIiIOpqAWERFxMAW1iIiIgymoRUREHExB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\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11920e438>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -371,9 +366,9 @@
    "source": [
     "The light points are the original data, while the dark points are the projected version.\n",
     "This makes clear what a PCA dimensionality reduction means: the information along the least important principal axis or axes is removed, leaving only the component(s) of the data with the highest variance.\n",
-    "The fraction of variance that is cut out (proportional to the spread of points about the line formed in this figure) is roughly a measure of how much \"information\" is discarded in this reduction of dimensionality.\n",
+    "The fraction of variance that is cut out (proportional to the spread of points about the line formed in the preceding figure) is roughly a measure of how much \"information\" is discarded in this reduction of dimensionality.\n",
     "\n",
-    "This reduced-dimension dataset is in some senses \"good enough\" to encode the most important relationships between the points: despite reducing the dimension of the data by 50%, the overall relationship between the data points are mostly preserved."
+    "This reduced-dimension dataset is in some senses \"good enough\" to encode the most important relationships between the points: despite reducing the number of data features by 50%, the overall relationships between the data points are mostly preserved."
    ]
   },
   {
@@ -383,12 +378,12 @@
     "editable": true
    },
    "source": [
-    "### PCA for visualization: Hand-written digits\n",
+    "### PCA for Visualization: Handwritten Digits\n",
     "\n",
-    "The usefulness of the dimensionality reduction may not be entirely apparent in only two dimensions, but becomes much more clear when looking at high-dimensional data.\n",
-    "To see this, let's take a quick look at the application of PCA to the digits data we saw in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb).\n",
+    "The usefulness of dimensionality reduction may not be entirely apparent in only two dimensions, but it becomes clear when looking at high-dimensional data.\n",
+    "To see this, let's take a quick look at the application of PCA to the digits dataset we worked with in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb).\n",
     "\n",
-    "We start by loading the data:"
+    "We'll start by loading the data:"
    ]
   },
   {
@@ -397,7 +392,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -424,8 +422,8 @@
     "editable": true
    },
    "source": [
-    "Recall that the data consists of 8×8 pixel images, meaning that they are 64-dimensional.\n",
-    "To gain some intuition into the relationships between these points, we can use PCA to project them to a more manageable number of dimensions, say two:"
+    "Recall that the digits dataset consists of 8 × 8–pixel images, meaning that they are 64-dimensional.\n",
+    "To gain some intuition into the relationships between these points, we can use PCA to project them into a more manageable number of dimensions, say two:"
    ]
   },
   {
@@ -434,7 +432,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -460,7 +461,7 @@
     "editable": true
    },
    "source": [
-    "We can now plot the first two principal components of each point to learn about the data:"
+    "We can now plot the first two principal components of each point to learn about the data, as seen in the following figure:"
    ]
   },
   {
@@ -469,14 +470,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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62XbkD8PoD6vbdkVXwcw4S3oO0tOxjxEjwtiyNogE8CslgkIQNSz0UDNhexK/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K5QJ0cLtaiwYOUp22UCWgAn6TLePsCtE58CoP3KRUeb0d1pTrw2BcDciQLSha6r61GFyifVu3jB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\n5urqKoi8C1lM/0qhmZ1TaOs6Y2RkBCkpKYi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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1195ba048>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -486,7 +490,7 @@
    "source": [
     "plt.scatter(projected[:, 0], projected[:, 1],\n",
     "            c=digits.target, edgecolor='none', alpha=0.5,\n",
-    "            cmap=plt.cm.get_cmap('spectral', 10))\n",
+    "            cmap=plt.cm.get_cmap('rainbow', 10))\n",
     "plt.xlabel('component 1')\n",
     "plt.ylabel('component 2')\n",
     "plt.colorbar();"
@@ -500,7 +504,7 @@
    },
    "source": [
     "Recall what these components mean: the full data is a 64-dimensional point cloud, and these points are the projection of each data point along the directions with the largest variance.\n",
-    "Essentially, we have found the optimal stretch and rotation in 64-dimensional space that allows us to see the layout of the digits in two dimensions, and have done this in an unsupervised manner—that is, without reference to the labels."
+    "Essentially, we have found the optimal stretch and rotation in 64-dimensional space that allows us to see the layout of the data in two dimensions, and we have done this in an unsupervised manner—that is, without reference to the labels."
    ]
   },
   {
@@ -510,7 +514,7 @@
     "editable": true
    },
    "source": [
-    "### What do the components mean?\n",
+    "### What Do the Components Mean?\n",
     "\n",
     "We can go a bit further here, and begin to ask what the reduced dimensions *mean*.\n",
     "This meaning can be understood in terms of combinations of basis vectors.\n",
@@ -527,20 +531,19 @@
     "{\\rm image}(x) = x_1 \\cdot{\\rm (pixel~1)} + x_2 \\cdot{\\rm (pixel~2)} + x_3 \\cdot{\\rm (pixel~3)} \\cdots x_{64} \\cdot{\\rm (pixel~64)}\n",
     "$$\n",
     "\n",
-    "One way we might imagine reducing the dimension of this data is to zero out all but a few of these basis vectors.\n",
-    "For example, if we use only the first eight pixels, we get an eight-dimensional projection of the data, but it is not very reflective of the whole image: we've thrown out nearly 90% of the pixels!"
+    "One way we might imagine reducing the dimensionality of this data is to zero out all but a few of these basis vectors.\n",
+    "For example, if we use only the first eight pixels, we get an eight-dimensional projection of the data (the following figure). However, it is not very reflective of the whole image: we've thrown out nearly 90% of the pixels!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "source": [
-    "![](figures/05.09-digits-pixel-components.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Digits-Pixel-Components)"
+    "![](images/05.09-digits-pixel-components.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Digits-Pixel-Components)"
    ]
   },
   {
@@ -562,7 +565,7 @@
     "editable": true
    },
    "source": [
-    "But the pixel-wise representation is not the only choice of basis. We can also use other basis functions, which each contain some pre-defined contribution from each pixel, and write something like\n",
+    "But the pixel-wise representation is not the only choice of basis. We can also use other basis functions, which each contain some predefined contribution from each pixel, and write something like:\n",
     "\n",
     "$$\n",
     "image(x) = {\\rm mean} + x_1 \\cdot{\\rm (basis~1)} + x_2 \\cdot{\\rm (basis~2)} + x_3 \\cdot{\\rm (basis~3)} \\cdots\n",
@@ -570,19 +573,18 @@
     "\n",
     "PCA can be thought of as a process of choosing optimal basis functions, such that adding together just the first few of them is enough to suitably reconstruct the bulk of the elements in the dataset.\n",
     "The principal components, which act as the low-dimensional representation of our data, are simply the coefficients that multiply each of the elements in this series.\n",
-    "This figure shows a similar depiction of reconstructing this digit using the mean plus the first eight PCA basis functions:"
+    "the following figure shows a similar depiction of reconstructing the same digit using the mean plus the first eight PCA basis functions."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "source": [
-    "![](figures/05.09-digits-pca-components.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Digits-PCA-Components)"
+    "![](images/05.09-digits-pca-components.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Digits-PCA-Components)"
    ]
   },
   {
@@ -592,9 +594,9 @@
     "editable": true
    },
    "source": [
-    "Unlike the pixel basis, the PCA basis allows us to recover the salient features of the input image with just a mean plus eight components!\n",
+    "Unlike the pixel basis, the PCA basis allows us to recover the salient features of the input image with just a mean, plus eight components!\n",
     "The amount of each pixel in each component is the corollary of the orientation of the vector in our two-dimensional example.\n",
-    "This is the sense in which PCA provides a low-dimensional representation of the data: it discovers a set of basis functions that are more efficient than the native pixel-basis of the input data."
+    "This is the sense in which PCA provides a low-dimensional representation of the data: it discovers a set of basis functions that are more efficient than the native pixel basis of the input data."
    ]
   },
   {
@@ -604,10 +606,10 @@
     "editable": true
    },
    "source": [
-    "### Choosing the number of components\n",
+    "### Choosing the Number of Components\n",
     "\n",
     "A vital part of using PCA in practice is the ability to estimate how many components are needed to describe the data.\n",
-    "This can be determined by looking at the cumulative *explained variance ratio* as a function of the number of components:"
+    "This can be determined by looking at the cumulative *explained variance ratio* as a function of the number of components (see the following figure):"
    ]
   },
   {
@@ -616,14 +618,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
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1PDrYVUoJ4UF+7i6FiIioxfDIYBdC4FJZTd3DXxR8+AsREVE9jwz2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\nmw3PPfccYmJiGhx2/q158+Zh/vz52Lh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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11942e3c8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -645,12 +650,13 @@
    },
    "source": [
     "This curve quantifies how much of the total, 64-dimensional variance is contained within the first $N$ components.\n",
-    "For example, we see that with the digits the first 10 components contain approximately 75% of the variance, while you need around 50 components to describe close to 100% of the variance.\n",
+    "For example, we see that with the digits data the first 10 components contain approximately 75% of the variance, while you need around 50 components to describe close to 100% of the variance.\n",
     "\n",
-    "Here we see that our two-dimensional projection loses a lot of information (as measured by the explained variance) and that we'd need about 20 components to retain 90% of the variance.  Looking at this plot for a high-dimensional dataset can help you understand the level of redundancy present in multiple observations."
+    "This tells us that our 2-dimensional projection loses a lot of information (as measured by the explained variance) and that we'd need about 20 components to retain 90% of the variance.  Looking at this plot for a high-dimensional dataset can help you understand the level of redundancy present in its features."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
@@ -661,10 +667,10 @@
     "\n",
     "PCA can also be used as a filtering approach for noisy data.\n",
     "The idea is this: any components with variance much larger than the effect of the noise should be relatively unaffected by the noise.\n",
-    "So if you reconstruct the data using just the largest subset of principal components, you should be preferentially keeping the signal and throwing out the noise.\n",
+    "So, if you reconstruct the data using just the largest subset of principal components, you should be preferentially keeping the signal and throwing out the noise.\n",
     "\n",
     "Let's see how this looks with the digits data.\n",
-    "First we will plot several of the input noise-free data:"
+    "First we will plot several of the input noise-free input samples (the following figure):"
    ]
   },
   {
@@ -673,14 +679,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11969c908>"
+       "<Figure size 720x288 with 40 Axes>"
       ]
      },
      "metadata": {},
@@ -706,23 +715,47 @@
     "editable": true
    },
    "source": [
-    "Now lets add some random noise to create a noisy dataset, and re-plot it:"
+    "Now let's add some random noise to create a noisy dataset, and replot it (the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.609434159508863"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rng = np.random.default_rng(42)\n",
+    "rng.normal(10, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119676828>"
+       "<Figure size 720x288 with 40 Axes>"
       ]
      },
      "metadata": {},
@@ -730,8 +763,8 @@
     }
    ],
    "source": [
-    "np.random.seed(42)\n",
-    "noisy = np.random.normal(digits.data, 4)\n",
+    "rng = np.random.default_rng(42)\n",
+    "noisy = rng.normal(digits.data, 4)\n",
     "plot_digits(noisy)"
    ]
   },
@@ -742,17 +775,20 @@
     "editable": true
    },
    "source": [
-    "It's clear by eye that the images are noisy, and contain spurious pixels.\n",
-    "Let's train a PCA on the noisy data, requesting that the projection preserve 50% of the variance:"
+    "The visualization makes the presence of this random noise clear.\n",
+    "Let's train a PCA model on the noisy data, requesting that the projection preserve 50% of the variance:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -761,7 +797,7 @@
        "12"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -778,24 +814,27 @@
     "editable": true
    },
    "source": [
-    "Here 50% of the variance amounts to 12 principal components.\n",
-    "Now we compute these components, and then use the inverse of the transform to reconstruct the filtered digits:"
+    "Here 50% of the variance amounts to 12 principal components, out of the 64 original features.\n",
+    "Now we compute these components, and then use the inverse of the transform to reconstruct the filtered digits; the following figure shows the result:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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eAACQPDY8AAAgeWEsvVixYm49iu+qKF4UFz333HPduhr6ZmZWtWpVt75u3TrZ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\nAEgeGx4AAJA8NjwAACB5bHg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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11976a358>"
+       "<Figure size 720x288 with 40 Axes>"
       ]
      },
      "metadata": {},
@@ -815,7 +854,7 @@
     "editable": true
    },
    "source": [
-    "This signal preserving/noise filtering property makes PCA a very useful feature selection routine—for example, rather than training a classifier on very high-dimensional data, you might instead train the classifier on the lower-dimensional representation, which will automatically serve to filter out random noise in the inputs."
+    "This signal preserving/noise filtering property makes PCA a very useful feature selection routine—for example, rather than training a classifier on very high-dimensional data, you might instead train the classifier on the lower-dimensional principal component representation, which will automatically serve to filter out random noise in the inputs."
    ]
   },
   {
@@ -829,16 +868,19 @@
     "\n",
     "Earlier we explored an example of using a PCA projection as a feature selector for facial recognition with a support vector machine (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\n",
     "Here we will take a look back and explore a bit more of what went into that.\n",
-    "Recall that we were using the Labeled Faces in the Wild dataset made available through Scikit-Learn:"
+    "Recall that we were using the Labeled Faces in the Wild (LFW) dataset made available through Scikit-Learn:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -866,34 +908,35 @@
    },
    "source": [
     "Let's take a look at the principal axes that span this dataset.\n",
-    "Because this is a large dataset, we will use ``RandomizedPCA``—it contains a randomized method to approximate the first $N$ principal components much more quickly than the standard ``PCA`` estimator, and thus is very useful for high-dimensional data (here, a dimensionality of nearly 3,000).\n",
+    "Because this is a large dataset, we will use the `\"random\"` eigensolver in the `PCA` estimator: it uses a randomized method to approximate the first $N$ principal components more quickly than the standard approach, at the expense of some accuracy. This trade-off can be useful for high-dimensional data (here, a dimensionality of nearly 3,000).\n",
     "We will take a look at the first 150 components:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "RandomizedPCA(copy=True, iterated_power=3, n_components=150,\n",
-       "       random_state=None, whiten=False)"
+       "PCA(n_components=150, random_state=42, svd_solver='randomized')"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from sklearn.decomposition import RandomizedPCA\n",
-    "pca = RandomizedPCA(150)\n",
+    "pca = PCA(150, svd_solver='randomized', random_state=42)\n",
     "pca.fit(faces.data)"
    ]
   },
@@ -904,25 +947,27 @@
     "editable": true
    },
    "source": [
-    "In this case, it can be interesting to visualize the images associated with the first several principal components (these components are technically known as \"eigenvectors,\"\n",
-    "so these types of images are often called \"eigenfaces\").\n",
-    "As you can see in this figure, they are as creepy as they sound:"
+    "In this case, it can be interesting to visualize the images associated with the first several principal components (these components are technically known as *eigenvectors*,\n",
+    "so these types of images are often called *eigenfaces*; as you can see in the following figure, they are as creepy as they sound):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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2nG544liMyEv71vue3z0380r1rnzpJgUQPC1V4s4GAc8Cc/x3+znwENr42U0B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lAB/bdyHVYWz156czNTnGEl8T0wjjrQYaLkqfk84Xw54XKecRlHGFAD5TztsGspSPuKfV+w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\nAOvXWo1xJNJfUVZ4ODzCDBoCQJ1lUEmCfdPhXHVLoAj4gGcVQxCbx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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b4fb5f8>"
+       "<Figure size 648x288 with 24 Axes>"
       ]
      },
      "metadata": {},
@@ -945,23 +990,26 @@
    },
    "source": [
     "The results are very interesting, and give us insight into how the images vary: for example, the first few eigenfaces (from the top left) seem to be associated with the angle of lighting on the face, and later principal vectors seem to be picking out certain features, such as eyes, noses, and lips.\n",
-    "Let's take a look at the cumulative variance of these components to see how much of the data information the projection is preserving:"
+    "Let's take a look at the cumulative variance of these components to see how much of the data information the projection is preserving (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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G0Wj0ZEnkJqcQyD9ajm92FWNfYQUAwKAPwC1XxyJpeC/ERBp8XCEREUnxaMjn5uYiKSkJ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\nMsWQJyIikimGPBERkUwx5ImIiGTq/wMjrvJM/BfyVgAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b7c3630>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -981,41 +1029,47 @@
     "editable": true
    },
    "source": [
-    "We see that these 150 components account for just over 90% of the variance.\n",
+    "The 150 components we have chosen account for just over 90% of the variance.\n",
     "That would lead us to believe that using these 150 components, we would recover most of the essential characteristics of the data.\n",
-    "To make this more concrete, we can compare the input images with the images reconstructed from these 150 components:"
+    "To make this more concrete, we can compare the input images with the images reconstructed from these 150 components (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "# Compute the components and projected faces\n",
-    "pca = RandomizedPCA(150).fit(faces.data)\n",
+    "pca = pca.fit(faces.data)\n",
     "components = pca.transform(faces.data)\n",
     "projected = pca.inverse_transform(components)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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pLP4/AaxAgjrBavD03zTWJAIxrR1CEWgq/HgtTR1+LrcsHKX4mGdkLZDYtA8xQtSFwvjI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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11985a5c0>"
+       "<Figure size 720x180 with 20 Axes>"
       ]
      },
      "metadata": {},
@@ -1043,8 +1097,7 @@
    },
    "source": [
     "The top row here shows the input images, while the bottom row shows the reconstruction of the images from just 150 of the ~3,000 initial features.\n",
-    "This visualization makes clear why the PCA feature selection used in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) was so successful: although it reduces the dimensionality of the data by nearly a factor of 20, the projected images contain enough information that we might, by eye, recognize the individuals in the image.\n",
-    "What this means is that our classification algorithm needs to be trained on 150-dimensional data rather than 3,000-dimensional data, which depending on the particular algorithm we choose, can lead to a much more efficient classification."
+    "This visualization makes clear why the PCA feature selection used in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) was so successful: although it reduces the dimensionality of the data by nearly a factor of 20, the projected images contain enough information that we might, by eye, recognize the individuals in each image. This means our classification algorithm only needs to be trained on 150-dimensional data rather than 3,000-dimensional data, which, depending on the particular algorithm we choose, can lead to much more efficient classification."
    ]
   },
   {
@@ -1054,39 +1107,28 @@
     "editable": true
    },
    "source": [
-    "## Principal Component Analysis Summary\n",
+    "## Summary\n",
     "\n",
-    "In this section we have discussed the use of principal component analysis for dimensionality reduction, for visualization of high-dimensional data, for noise filtering, and for feature selection within high-dimensional data.\n",
-    "Because of the versatility and interpretability of PCA, it has been shown to be effective in a wide variety of contexts and disciplines.\n",
-    "Given any high-dimensional dataset, I tend to start with PCA in order to visualize the relationship between points (as we did with the digits), to understand the main variance in the data (as we did with the eigenfaces), and to understand the intrinsic dimensionality (by plotting the explained variance ratio).\n",
+    "In this chapter we explored the use of principal component analysis for dimensionality reduction, visualization of high-dimensional data, noise filtering, and feature selection within high-dimensional data.\n",
+    "Because of its versatility and interpretability, PCA has been shown to be effective in a wide variety of contexts and disciplines.\n",
+    "Given any high-dimensional dataset, I tend to start with PCA in order to visualize the relationships between points (as we did with the digits data), to understand the main variance in the data (as we did with the eigenfaces), and to understand the intrinsic dimensionality (by plotting the explained variance ratio).\n",
     "Certainly PCA is not useful for every high-dimensional dataset, but it offers a straightforward and efficient path to gaining insight into high-dimensional data.\n",
     "\n",
     "PCA's main weakness is that it tends to be highly affected by outliers in the data.\n",
-    "For this reason, many robust variants of PCA have been developed, many of which act to iteratively discard data points that are poorly described by the initial components.\n",
-    "Scikit-Learn contains a couple interesting variants on PCA, including ``RandomizedPCA`` and ``SparsePCA``, both also in the ``sklearn.decomposition`` submodule.\n",
-    "``RandomizedPCA``, which we saw earlier, uses a non-deterministic method to quickly approximate the first few principal components in very high-dimensional data, while ``SparsePCA`` introduces a regularization term (see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)) that serves to enforce sparsity of the components.\n",
+    "For this reason, several robust variants of PCA have been developed, many of which act to iteratively discard data points that are poorly described by the initial components.\n",
+    "Scikit-Learn includes a number of interesting variants on PCA in the `sklearn.decomposition` submodule; one example is `SparsePCA`, which introduces a regularization term (see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)) that serves to enforce sparsity of the components.\n",
     "\n",
-    "In the following sections, we will look at other unsupervised learning methods that build on some of the ideas of PCA."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) | [Contents](Index.ipynb) | [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.09-Principal-Component-Analysis.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "In the following chapters, we will look at other unsupervised learning methods that build on some of the ideas of PCA."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.6 64-bit ('3.9.6')",
    "language": "python",
    "name": "python3"
   },
@@ -1100,9 +1142,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc"
+   }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.10-Manifold-Learning.ipynb b/notebooks/05.10-Manifold-Learning.ipynb
index 7ec547ba9..40d083ed2 100644
--- a/notebooks/05.10-Manifold-Learning.ipynb
+++ b/notebooks/05.10-Manifold-Learning.ipynb
@@ -4,47 +4,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+    "# In Depth: Manifold Learning"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) | [Contents](Index.ipynb) | [In Depth: k-Means Clustering](05.11-K-Means.ipynb) >\n",
+    "In the previous chapter we saw how PCA can be used for dimensionality reduction, reducing the number of features of a dataset while maintaining the essential relationships between the points.\n",
+    "While PCA is flexible, fast, and easily interpretable, it does not perform so well when there are *nonlinear* relationships within the data, some examples of which we will see shortly.\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.10-Manifold-Learning.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# In-Depth: Manifold Learning"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have seen how principal component analysis (PCA) can be used in the dimensionality reduction task—reducing the number of features of a dataset while maintaining the essential relationships between the points.\n",
-    "While PCA is flexible, fast, and easily interpretable, it does not perform so well when there are *nonlinear* relationships within the data; we will see some examples of these below.\n",
+    "To address this deficiency, we can turn to *manifold learning algorithms*—a class of unsupervised estimators that seek to describe datasets as low-dimensional manifolds embedded in high-dimensional spaces.\n",
+    "When you think of a manifold, I'd suggest imagining a sheet of paper: this is a two-dimensional object that lives in our familiar three-dimensional world. \n",
     "\n",
-    "To address this deficiency, we can turn to a class of methods known as *manifold learning*—a class of unsupervised estimators that seeks to describe datasets as low-dimensional manifolds embedded in high-dimensional spaces.\n",
-    "When you think of a manifold, I'd suggest imagining a sheet of paper: this is a two-dimensional object that lives in our familiar three-dimensional world, and can be bent or rolled in that two dimensions.\n",
-    "In the parlance of manifold learning, we can think of this sheet as a two-dimensional manifold embedded in three-dimensional space.\n",
-    "\n",
-    "Rotating, re-orienting, or stretching the piece of paper in three-dimensional space doesn't change the flat geometry of the paper: such operations are akin to linear embeddings.\n",
+    "In the parlance of manifold learning, you can think of this sheet as a two-dimensional manifold embedded in three-dimensional space. Rotating, reorienting, or stretching the piece of paper in three-dimensional space doesn't change its flat geometry: such operations are akin to linear embeddings.\n",
     "If you bend, curl, or crumple the paper, it is still a two-dimensional manifold, but the embedding into the three-dimensional space is no longer linear.\n",
-    "Manifold learning algorithms would seek to learn about the fundamental two-dimensional nature of the paper, even as it is contorted to fill the three-dimensional space.\n",
+    "Manifold learning algorithms seek to learn about the fundamental two-dimensional nature of the paper, even as it is contorted to fill the three-dimensional space.\n",
     "\n",
-    "Here we will demonstrate a number of manifold methods, going most deeply into a couple techniques: multidimensional scaling (MDS), locally linear embedding (LLE), and isometric mapping (IsoMap).\n",
+    "Here we will examine a number of manifold methods, going most deeply into a subset of these techniques: multidimensional scaling (MDS), locally linear embedding (LLE), and isometric mapping (Isomap).\n",
     "\n",
     "We begin with the standard imports:"
    ]
@@ -53,13 +30,16 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns; sns.set()\n",
+    "plt.style.use('seaborn-whitegrid')\n",
     "import numpy as np"
    ]
   },
@@ -77,7 +57,10 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -107,21 +90,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's call the function and visualize the resulting data:"
+    "Let's call the function and visualize the resulting data (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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4hvhdWFLSVFJNwpQBALh6uVPUVh3E5O5+9efN/1JBwaGMue1uAPZu+4qE+q3c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IhcFGyGYRjt3YiTtAd2dtpTdZ9q5R7VyX2qlXtUJ/e4e0StAU9EREQsTEEtIiJiYQpqERERC1NQ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\nAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1157d5b00>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -139,7 +125,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The output is two dimensional, and consists of points drawn in the shape of the word, \"HELLO\".\n",
+    "The output is two dimensional, and consists of points drawn in the shape of the word \"HELLO\".\n",
     "This data form will help us to see visually what these algorithms are doing."
    ]
   },
@@ -147,24 +133,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Multidimensional Scaling (MDS)\n",
+    "## Multidimensional Scaling\n",
     "\n",
-    "Looking at data like this, we can see that the particular choice of *x* and *y* values of the dataset are not the most fundamental description of the data: we can scale, shrink, or rotate the data, and the \"HELLO\" will still be apparent.\n",
-    "For example, if we use a rotation matrix to rotate the data, the *x* and *y* values change, but the data is still fundamentally the same:"
+    "Looking at data like this, we can see that the particular choices of *x* and *y* values of the dataset are not the most fundamental description of the data: we can scale, shrink, or rotate the data, and the \"HELLO\" will still be apparent.\n",
+    "For example, if we use a rotation matrix to rotate the data, the *x* and *y* values change, but the data is still fundamentally the same (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ftzlGq46dWDlyDI3WxBAEVLRpS78lMXj5+PyjmDQaDQE+PpwqLeEpYBvKlK+mNjb4d+yE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\nAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1184a4278>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -187,17 +176,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This tells us that the *x* and *y* values are not necessarily fundamental to the relationships in the data.\n",
-    "What *is* fundamental, in this case, is the *distance* between each point and the other points in the dataset.\n",
+    "This confirms that the *x* and *y* values are not necessarily fundamental to the relationships in the data.\n",
+    "What *is* fundamental, in this case, is the *distance* between each point within the dataset.\n",
     "A common way to represent this is to use a distance matrix: for $N$ points, we construct an $N \\times N$ array such that entry $(i, j)$ contains the distance between point $i$ and point $j$.\n",
-    "Let's use Scikit-Learn's efficient ``pairwise_distances`` function to do this for our original data:"
+    "Let's use Scikit-Learn's efficient `pairwise_distances` function to do this for our original data:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -221,21 +213,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As promised, for our *N*=1,000 points, we obtain a 1000×1000 matrix, which can be visualized as shown here:"
+    "As promised, for our *N*=1,000 points, we obtain a 1000 × 1000 matrix, which can be visualized as shown here (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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9Pmx5JYWMUjgfX0/F419jl337a2t87XLEJx6ti9f8peSTOR4Y6UwhFQ/7rzzb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\nmff390mWZSIienp6IpvNJnzu7e1tam9vJyIih8NBra2tQmdeWFggg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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1190e7be0>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -243,7 +238,7 @@
     }
    ],
    "source": [
-    "plt.imshow(D, zorder=2, cmap='Blues', interpolation='nearest')\n",
+    "plt.imshow(D, zorder=2, cmap='viridis', interpolation='nearest')\n",
     "plt.colorbar();"
    ]
   },
@@ -258,7 +253,10 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -281,26 +279,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This distance matrix gives us a representation of our data that is invariant to rotations and translations, but the visualization of the matrix above is not entirely intuitive.\n",
-    "In the representation shown in this figure, we have lost any visible sign of the interesting structure in the data: the \"HELLO\" that we saw before.\n",
+    "This distance matrix gives us a representation of our data that is invariant to rotations and translations, but the visualization of the matrix in the following figure is not entirely intuitive.\n",
+    "In the representation shown there, we have lost any visible sign of the interesting structure in the data: the \"HELLO\" that we saw before.\n",
     "\n",
-    "Further, while computing this distance matrix from the (x, y) coordinates is straightforward, transforming the distances back into *x* and *y* coordinates is rather difficult.\n",
+    "Further, while computing this distance matrix from the (*x*, *y*) coordinates is straightforward, transforming the distances back into *x* and *y* coordinates is rather difficult.\n",
     "This is exactly what the multidimensional scaling algorithm aims to do: given a distance matrix between points, it recovers a $D$-dimensional coordinate representation of the data.\n",
-    "Let's see how it works for our distance matrix, using the ``precomputed`` dissimilarity to specify that we are passing a distance matrix:"
+    "Let's see how it works for our distance matrix, using the `precomputed` dissimilarity to specify that we are passing a distance matrix (the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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LS0MGLAttQ+Sva7B3dkarzcPZ2YXK8nJObtmI3NKSmswM1I6O9Jo0tcksZflP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8aJ4QHejB9w++8BRwVH8lJQhCiR2kOUSD2feR5VsDMAfgMiaa1uJGJSx9jL3YzsyzuO1xwxqE3t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\nCM1B3Cq3EncM7UxJ67UNr4sCN9JtaJtmbJEgCILQHMQVt5Xw8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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119e931d0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -309,7 +310,7 @@
    ],
    "source": [
     "from sklearn.manifold import MDS\n",
-    "model = MDS(n_components=2, dissimilarity='precomputed', random_state=1)\n",
+    "model = MDS(n_components=2, dissimilarity='precomputed', random_state=1701)\n",
     "out = model.fit_transform(D)\n",
     "plt.scatter(out[:, 0], out[:, 1], **colorize)\n",
     "plt.axis('equal');"
@@ -326,7 +327,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## MDS as Manifold Learning\n",
+    "### MDS as Manifold Learning\n",
     "\n",
     "The usefulness of this becomes more apparent when we consider the fact that distance matrices can be computed from data in *any* dimension.\n",
     "So, for example, instead of simply rotating the data in the two-dimensional plane, we can project it into three dimensions using the following function (essentially a three-dimensional generalization of the rotation matrix used earlier):"
@@ -336,7 +337,10 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -366,21 +370,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's visualize these points to see what we're working with:"
+    "Let's visualize these points to see what we're working with (the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ziYIs+yihb7agAJJZwDg/MFC6Vjbf+ehHb+OTn/wkPT3LALfALN9DozLmYKe2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EfM//XCxbCJl8/n2j1bL3U+zzeWXM30ao2bV2Ov5sa7Gvt1JXN1rbHkemDabK6lz0oiSfKSaMHa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\n",
+      "image/png": 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8fj+JRAKfz4fT6SQej7O0tEQikcDpdJY9FMKuW74ajjmrityi71r8sLlfULluufW5xVo0KyG3qDZTFIXh4eFl428LIhLB8HgwXK6MEMOefUoeD9oJJ2AceST6IYfk/0xPPok8OookxBn6m+HNR4Cqg0OGo/vR/a/AaG0t+/NIkpQ3JWXnvG7rc5eJWhSxCE21pqYmM5YjXHi7kfi65S4TdktIy7Xcdlo0y7lhCCQSCcbHx+no6KClpaXg+y7D4iLyrbfCCy8gzc6Cy5UJfgGGy4V+5JGo73gH7CnwyIX88MOZdlCA9gY4cws0umAmAktpjOFu9L89NePul4ncG6lISZU6r+cqt6zGevBCwblCkfhCQyHAnlVeDWkwWAXkLid3XQ4RJSkzsaJUi2a5lrtQtZl1vYJ7evBBmJ1FmpnJaKHpeobgmobh96NeeCFGPpdc03Bedx2ub30r8/8eB/zt4dDqhYQKjW7wOjEGBjB6e2x/Frsodl4Xyi2CBKtVrMFOcK7YUAhd180hkKUCkJqmrYog5X4jt8hdz8zMmBeGnR8gd1JHMczNzeHxeIrqhpfT8plOpwmHw8urzewgFkO6++6Mwsrjj0MigSFuVm43kttdsHXT8Z//ieuaa/YqrnT4YEsXeB2gyLAYgwYP+mGHofv7y9tXBcitFxeBq0gkYhbvVOO8vj8bR0SkXUhRiUh8IBAwvRhrTXzusWw1WO79sgNx3hG912KETynYjZbH43GCwSBer5d169ateNhAKpVi9+7dSJJEf39/RXdl6cUXwenE2LULNRzGkCSkdBo5EoFkkuCmTUwuLREMBrP7vJNJXF//evYwgqN6oMkDSwmIpWGgFQZ7SR3+Fgx3+TrpK9Z82xO46ujowOPx0N3djSzLLC4uMjIywtTU1PLPZWNPq8nFF5H4hoYGOjo66Ovrw+PxEIlEGBsbY3x8nMXFRWKxGKlUqmJyP/XUU7zjHe9Y9vc//OEPnHPOOVxwwQXcdNNNttba55Y7X+7a7jm6lFtujYa3tLRUpZc2HA4zMzPDunXrmJycrHyhZJKkoqBLEl7DQNJ1JFnGaGxEamvD19eH0dREbI83o2kaXq8X/8QEDbHYXknjrT1w8oZMxUtvE0ZKR3J4SW05BcPXteLPu1KIwFUl53UrqknuakavhVUuJEX18MMPc91117Fu3Tpe+9rX8uY3v3lZXKYQvvvd73LrrbcumwGfTqe55ppr+PnPf47X62X79u2ceuqppuZdIewzcgs3PJ1OL8tdl3OOLnQjyI2GBwKBFVkkwzCYnZ0lHo8zPDxsiuJVctEZhsGcx4Pvuefwv/BCZtbXns8hRaPor3gFrFuHO53G3d5Oa2sruq6TnJtDuusu9GQSBaDdC+85DvxO8LjBkNE9CnRvQm/ZXGITGs7dv8ex+FdQXKQGX4/Wdqj5cDUu/nzft93zeq57u9ost0ChG4WQojrllFM49NBDCQQC7N692zQ0djA4OMh1113HJz7xiay/79y5k8HBQTMY/MpXvpJHHnmEMwvUQph7sveRVoZiuetqWO580fBKU1yQuQuPjY3h8/kYGhoy91vJmqqqMv7SS/Recw2+P/4Rcru4EgmksbFMhNsy10vWNJpuvhlpdhZZkkCW4JLjYKA544obOrid4G5AHTgKrSV/+kzAtes2nFN/BiMNuor3mf8i2XsS6Y1vKevzFIPdRp185/Xc/PpKJJoq2Zdd2ElzGYbBwMAAxx13XFlrn3HGGYznEeyIRCJmihLA5/MRKaGzB/uA3KUmaMqybPuHzLXcxVo0y1nXimg0ytTUFN3d3VlfqHj/chCPx5kYG2Pg5pvx3X13pmglD6TJSdRt28ByjJCffhr5iSdg505wSHD24bCtH5DAKYMuY3idJJRmRtIDeOcWTNLkXnxyaBfOyT+Bbm1SMXBNPwAOF/HuU8v6XNWEOK/n5tdTqRSTk5NZjSF2u8DyYV96AdVu92xsbCRqMQrRaHTZtZkPNSN3LXLXVstdrRZN634XFhYIhUJmtVm+vdpdc2lpiYWFBQa9Xrx7ouNYPqd5ecgyuFyZaLn407PP4rjxRpQ//jFTZvrOV8LW3sy5W9PBqYAjo1oS861nYHCIRCJBNBplcXERSZLM4I83shPv8z9C0vNpm0s4AjuQ28uzMIVQDQspzuuRSITOzk4Mwyj7vF5L2K0tr+beNmzYwMjICEtLSzQ0NPDoo49yySWXlHxdTchdbu663JLSYkUpuc+1A8MwGBsbw+l0Lqs2y12zFLnFZw+FQgwPD6M8/TRMTmaInS9avMcdN6vKQiFcV1yB9MILSAsLmSq0rb0QTGRSYA4ZZAVcDnRXMwv+oxnIcXVFcGcpsIB/582gJ7OFIMTnMXQkNQW6RrUSJ9UOXDkcjrLP67WEXctdjb386le/IhaLccEFF/CpT32KSy65BMMwOOecc+ju7i75+qqSWwTNpqenTc1wu2IJdiBJEslkksnJyZLD7O2um0gkSKVSdHZ2lgx8lCJ3Op1mbGwMSZIYGBjI3Nh27ULSdWhogKWl3E2CYaBv2YLR04O0ezeet78d+YUXMjcCQ8+kvToaMjXkaTVTQ+6U0RoHSW54K/rCcossgjvNLhWnlCo43MBAAiOF7vBAssDUkjJQ7fLTfEc4O+f1WtbD19py9/f3m6muN73pTebfTz31VE49tbzjU9XIbXXDI5EIra2tVc1di1puXdfZuHFjVYYNBAIBFhcXcblctuZoF1tTnNV7enqYnp7e+9nn5qCtLfNvj2dvvlr0Gre2or3qVRCN4vrIR5Cffz5j5XU9k/Y69yjwuTMBNcMBGqjNm0ge+V5QXLCQX/l0z46R9MK5ZVV2k5L8REJBNFxVcaurSahSaxU6r+fWw1f7pnPQ1ZaL6rFKctd2dcM7OjpIp9O2vrhiRNR1nampKXRdZ3h4mJGREVt323xrWjvDhoaGlgd9urowWlqQXC4zoCZBhtyKghQOI99+O4rfj/Lkk3uJDXDKBmhwQlLNBNEcCobXRXr9qRlil4Bh6EhGoYo+CUUBR3MfktNHMhJhZGSkIhkj63dRLVRyoymUX0+n04yMjFTlvL4/AmqVoqpuuaIo5l1rJa2ZAvmi4Xa1wwvtIZVKMTY2RktLC21tbWZMwK6EkvV5uq4zMTGBLMt5O8MkScI4/ni46aZMg4g4J1kEEVFVlB07kP/lX5ASCdNVBzLnawPQAV0BpxtJcYLHRteXruN5+Vd7XpwLBcPhBdmFOnwmDZoD3TDo7OxcVnAilE3yReELfUfVwEpvFCK/7nQ6iUQi9Pf377Pz+pqz3FYIkttBIRKWioaXQj7CCvncXG2zSsidTCYZHx83dchzn2eF0d+P9Ne/Ziy3JGVKTw0j07qpKBmyi5p563fx51H4m8GM1XYpIElovnXo3tLBFGX+LyhLL5DxE6yfTUZr2YwhS+jeLgx3E8Ri5r5zC06syibWEsx8YgfVtNxiPyuFtRJyX53XDcOomvLLSlCTHazULbcTDS8Fq0dgGAYzMzMkk0mz2ix3D3YvTMMwzJLUvr6+ZaWC4r3NvP7DD8Nhh2XotWMH0tRU5sIRkfM9pF52KSkKPBdCv38B+dh2jOYWNH8vyc3b8+qq5cKx8DSGlkQi5xghO5G0KFrDMOmBvTJO+S7mXGUTMwq/R+wgnwu/GkQKrCh0RrZ7Xi/3eCLEOlbD91BVcosPVKkcUrGilHIhCJtOp80upcHBwbxfejn560AggKqqeW8SVpjrJRLg9WIceyzS+HiGzB5PRrU033fkcQAGKB4MpxN9wkvibe/FOHQTOH0glfBgDB3H+D0oc08ik33eNgDd6Sd++KXg8tu6SVhhnfaZr2bc6XRiGMaqcUvB/tndTj28neCc+Oyr4fPvd8stvviVuuH51lVVlZGREXp6eswC/0LPLfWjaZpGOBzG6/VmlaSWXO+ww+APf0AaGclY7lQKQ1UzKiy5eGUfHNkDSBhpF/p8A8nvf7+swQLK/NO4R+5AYnkgTQIMbxu4m7L+Xok7nc+FX1paIhwOMzY2VtKF31eoxIoWqocXwbnR0dGi7Z6rwWrDKiA3ZO52pXTDBew0b4hqMyExXMqtKkVuobzi9XppaWmx1Xdu7uWII2B+Hvn734fxcXTDyE/swRY4ah1MZyZ2GoMdqL1dqJEIjqam5c+3wtBR5p7EOfUAcnAEicLpL8NGlL0SyLKM2+1G0zQ6OztNFz4QCKzIzV0pqtERJs7rbrebZDJJb29vwfN6JQUsuq7zhS98gR07duByubjqqqsYGhoyH//v//5vfv3rXyNJEu9///s5/fTTba1bM3LbqesWbng6nWbjxo22hw3YkSwWell2LqRi5BZlpAMDAwTzSQmXWk+WYX4eIx5HdzhQHI78U0JaGyCpZXxnWUaK6xjDPqYCAYw9ZYc+ny9rWJ6AMv807l23gURxYksKhq/4COKVwHrTLeXClxuFr8aeVgpxoyh0Xp+dneVTn/oUHR0dnHXWWZxyyikl2zIB7rzzTlKpFD/72c948skn+fKXv8z1118PZILAP/rRj7j99tuJx+O85S1v2T/kLufMbXXDXS6X7fN1seBXbiDupZdesr3vfEqp09PTpNPpTBmpopR1Nrc+Lzk1hVPTcMoyUqFzeigBbjmjrIIETR6kk17PwNCQGdkNhULMzMzgcrlMskMmeIahZyLwhfaDhN44gNp2RMHvoBooFNNYSRR+JaimKkqhtazn9euvv54nnniC2dlZ/vSnP/GWt7yl5LqPPfYYJ510EgBHH300zzzzjPmYEByJx+PE4/Gyvpv94pbnknDnzp22gzCF1hYD7SsJxOWSVgTh/H4/PT09Zbd8iucbhsHc3BxGUxN9LS2ZEtSxMesTM5Zd12F0CV5egsPXYUgy2rEnoh52MpAd2RVBQmEF9XgAbWk3zlSBUb57oDf0kFp/NnrTUNHnrQR2b3yFovDChXe5XGbv/0pd+H0t+iBJEhs3buSNb3yj7XUjkUhWTEhRlKxBCr29vZx99tlomsb73vc+2+vuU3IXioavRLBB13VTIaXSQJyVtNYy0twgXDnkFjX2TqeTngsuQH/0UeR77oGWloyscTS6V6XU7QZNQ5+QQFJRX3MS6Td+GBz5O9OEame7tIg8eisudanofgxJITX0BrSWTfkfr2J+uhIi5brwyWSSiYmJLBdeVJZVcp7dl73clWQKcls6reOP7r33XmZnZ7nrrrsAuOSSS9i2bRtbt24tuW7N3PJcIcNi0fBSo3+ssN4IRCFJa2ur7Vr2QvvWdZ2FhYWsAQP5nmeHCJqmMT4+ntWMon/xi0if+xzGzAw0NGDs2AGhELS2om/ahPaqV6GddVZGZ62zs3SaKh3FOXo7aPG8rrhh/ltBc7WSbhysuWBeNW4S4ubldDrp7+/PcuHn5+fLduGrrehSC3Jv27aNu+++m7POOosnn3ySzZv3quo0Nzfj8XjMwhq/308oFLK17j6x3KWKUsppzxTPFdVmhQpJyoEkSSwuLuJ2u0u2fJbaZzgcJh6P09vbm91l1t6OfvnlSDfdlKk3P+ooePhh9EMPRd+6Fe2007KUWEpBDo+hBHcjGcm85M7Upcnoribmu88gMBdGkiJZKZxapGyqWVUGpV34UlH4ap657WqWl1tXfvrpp3P//ffztre9DcMwuPrqq/n+97/P4OAgp512Gg888ADnn38+siyzbds2TjjhBFvrSkYVfTLRyyxkioaHh003vL+/v+BZeHx8nPb2dlsknZqaMqeSlFIifemll9iwYUPRHySVSrFr1y58Ph/9/cVlgZeWllBVNW8E1DAyEz4jkQgOh4OOjo781WvPPYf02GMYikJy2zYQI3oNHTk2DWocw9WM4S0SZdU1PM/+N0rg2cyaeZ5ioJDueRXpgdMwvO1A9uwsa4UZZEjT3t5e9POXQjAYRNf1ZeW45UJVVaanp0v+HtYofCwWy+vCLy0tIUlSxZWOVoRCITRNK/r5hHdhTWXtL9TULR8bG7NVlGI3Ly5EEBoaGsx+6VL7KeaWiTLS5ubmvG54ofVyIRpIHA4Hw8PDTE1NFXRRjcMOy/xjGOjJZMZVNnQc0w+hhEcwZDcSkO46Gr1pff59pEKgJgAZaVljiISueEmufyPauv8v65HcwJwghrhok4aKr6EBv8dXsbWrtuUu9V6FovCCZLIs4/F4quKe250TthrqyqFGbnkikSCZTNquDbcTUItEIkxPT5tysnZ7xfOtK6LY0WiU4eFhwuFwWaotVogus9wGEluBN3SUub+gzP0FR2gXusuPpLjQG4dwzD9Nyj+Uv9xUdoBjz2QMsi23gYTu7UDrPb74e1uIMe2LcX/TFAnHFE5NZmixgSMDHTQ2+MwBena+7/0dmMvnws/OzpoFJystpLHjcq+Wdk+ogRKLGN8jcn92UMxyW93doaEhQqHQisb4imCXOF+Llk87a+auJ244+brMSsIwcL34S5wLT4MaR0JFUaMYTh9SYgm9dVNGiSWX3FoKUlG0hi7k0C5kLZb9uOJEbzusdA36HszIYf7UMkZC0lCQSDt0xjsTDPhk/AGJubk5c26WnV7ofWm5S0HUUIi95yukKScKb8cqr6a6+qqTW1VV1q9fz8svv2z7dcXaPislIiwPgIky0lyPotyWT3ETC4fDeQUa7KwnBV7EMf8UkpYEUVWmp8BoQDKSoMYzFtoCeell3C/ejByfA5ZXuRk4MJx+1PYjS34WgQklRErSMDBAkpANiSQqc84YR7asM4fkCXdXCDCKIhprxLpapKxFhNuOC18qCm9X1nhNkltRFFvCbbnIR1gRYe/q6qLJUltdjmSx1S0vNsCvHHLrus74+DiKojA0NFTwhyy6nq5hjNyDpC3XnpbSUQx3G4bL4vUYGsrkn/G8fAuSkc4rdmhIMpKhk2o7Et0/UPKzGBjsVgI845giJekggYZmLvySY4Ej1V46dZ9J5lwBRmvE2ufzVU31s9rkLqedtVgU3m4qbE265ZAdxLL7I1nz4oZhEAgEClabVWK5RYS90AA/u0U0mqYRCoXo6uqircDQPvG+xRCbeBLv4ov5HzRUJDWSCZppSVDcKPNP45q4B4zMTS3v6g4fuiRheArvy4oXHfM85BxhQYnlfTwmpbnds4MLYkfjyMmQ5ys6EeWxkIlD2B3umA/VPLvbLWKxUwsv9PdLvd+aJbdAuYUpQgq5VLVZucIKExMTNDc3Z5WR5sKO5RY/tNfrLUrsYusZRmZEkXd2B04H+TxrwMBQYyjzT6OkE6Q3X4Ar+PKets8ic9Iy74zeNFh0bwLPOmYISYmCKxqSQZAEcSmN3yhczitJ2aNvFUVBURSzY8rhcJgW0m4Qa19Y7mIo5MJHo1FmZ2dxOBymVc914desW25FJeTetWtXXtkiK+xabjFStrOzs2RnTjFyi5LZYDBIb2+v7eqgfIG8iYkJc9g7URcQzftaaY9wmjM8SvDFu0gbCbyai8JVABKSJJMcPB29adjW/pKopOTC36OOgS4ZeAz7l4iIFIuMhrUOXgy0t9MNtq+rykpBuPAul4uuri4kScrrwnu93oosd6mWz3vuuYdvf/vbGIbBEUccwRVXXGHPGyn7k5aAIEo5Pd3xeNwU8S9VyGJ30mcwGKSpqcnWpM9i+evJyUkkSWJ4eNiWWybWs0Kky9rb2zMBqqVDYeZpKNrsoaPoadrdaRLdJ6K8cHPeZxlAauAM0gOnmOmxUghLCZbkeBE/IOP6b0p34MT+hZr73Vjr4MVwQyF6sLCwgKIoZmDOqlu2vy13sbXEhM98x5Kvf/3rPProo5xwwgmcfvrpHH988XSkQLGWz0gkwr/+67/yox/9iLa2Nr773e8SCARKeo+wDyx3MYi2ykQiQWNjo60KtWLr5pJxbm7Odoor93liwEBzc/OKVFJFI4o1XWY0D6O1DOCIFdMcB0ghxWZwB57BkZzLe9ZWJS9BmpFVcCulL+SYlOLn3r8Qk4vPyj4s1clpqfyNJsVQ7P1zRQpzdctEymq1kjvf+d16LPnkJz/Js88+SyKR4Nlnn7VN7mItn0888QSbN2/m2muvZWxsjPPOO88WsWE/kluQR7RV2pUsLuSWC+vY2tpqfni75/Nc0gpC9vb2mtHUfM8rtZ4ovV2WLpNkaBoEG+O+5cgkcmQykx7LgYGCLrswGntNF1Foffl8vrzu4b2ulwlIibzv5TAk3CicHT+Mfr38EtJyA2G5umVCejgUCpkpx0ICFXZR7dncpdZyOp38zd/8DaeccortdYu1fAYCAR566CFuueUWGhoaePvb387RRx/NelG2XAQ1ccuhOLlF8Ycgjxjvawf5CCvKSKshWSwIma8zzO56IuJfbPaY3rweUCCP1lnWe+4hdd76cYeXuebjaOkcxrfnfZPJJNFo1AxMWvPRSUnlBcdcgXA7+Aw3HhxoEjwa1PjcLo2IBm/tlPjIgMN2SWglkCQJr9eL1+vF6XSSTqdxuVwEg0FToELkocupLqum5Rb7LARxDZf7fsVaPltaWnjFK15BZ2cnAMceeyzPPffc/iG3QD5yW8s+rdas0iEGuWWk+SSL7awrnjcxMYFhGAUJaYfcqqqyuLiIx+Ohv7+/8A/d0I3mH0AJj1AsCl4IBpAaej1BdYgWy/6Ei9je3o6maUSjUVOKeLIjidpg5CW3BHhQ0AyD26ecfPx5zaxafzpqMJJI843Nxevvq5XCEoG53Dr4WCxmpqaEe2+nuqwW3W/5UKnyabGWzyOOOIIXXniBxcVFmpqaeOqppzj//PNtrVtTclt7ulVVZWJiIqvazPrcclQ8DMPIW72WC7uRdVVVicfjNDU1mefrQii2T1EB5/f7Swvbywpq17FI8QVkNVxyj7nQGofQWzbCfOGzszXwoxs6TzieKWi1vZqDlCFz0+MbeGDelXW70YEbZ+ArGw1csn1xyEqRLzAnUlMiMCdSU/Pz86ZAYTl18LVApaWnpVo+P/axj3HppZcC8IY3vCGL/MWwT8hdqNpMoNzCFFVVbaml2rG0sVjMFFQs1fJYbD3RX97f3088Hl8mVpEP6e5jccw9CaEo+cf+7HnfnP/XHQ3oLRvQvV3ARNH30NB5UZnnT+5dhOTl53YAT0LibyY6+MLUOh4IufL6EWkDknpxclfTcpfqIrRWl4l02/z8PKlUKivdVi3YPY5V2vBy5ZVXZv1tw4YN5n+fffbZnH322WWvW9MzdyqVMhtJCqmbWF9jB6FQiHQ6zYYNG0qmuUq55WLK5+DgIOPj4yXfOx+5RWOL9WiQSOQPWC2D4iY9eBrGVCPKwtN5tcbzQnaT6j9tWe15LnQM7nK/yF8dM+hFvmKv7OL+pS7uCzkzNeZZkPasBb9Z0Lmgu/YucLkkcTqdtLS0mHXwIt0WCATMazC3Dr4We9I0bdUUsEANLTdg9l5XY8iASJuJQMtK8teGYTA1NYWmaebeKomqW/u4rYMK7AbeALTWLSBJSLEplPjM8vfM+T8DUFs2gau0asusHGFUCRQlNkBCg6/PedBzmket/5aAL+zSOb+r8EVeTctdjcAcwO7du3E6nVkjkERgrpy+a7t15Wue3Mlk0izT6+vrW/F6QtmlsbGRnp4edu7caet1+Ugm1vL7/bS3t5d1EVmfK1J5YlpoqffNB8PYYydbD0XuOApl7PbsdZa/AsPVQmrD39naryppRKVUwcelPf/c+fwQi8vuANlEN4CltM4zu8fp9XsLpqhWU1eYWCdfwYkQ1LC2fZaKhJfa02oqPYUakDsWizE2NkZXVxfhcPmBonzrTU5OlhwJlA+FtNwqWSvfnnLz4FaUqqITA+N0XUfTNHTvOlw5Ezmt3V8GErqrlfgxHwFn6WKfRSnG88osWpHrsdFw4TacPD7ftGwWaDYyi3gdEuv7ejGScTNFZbWE1Uw71WIdazZB1IxbJ4eIOngx6dOKuuUmI6K+fv16NE2zPaEDlksiWYtA8p3X7VxIVgtqPV/bkVQqBE3TmJqaqjiGICL9hmGYg/MMw8CRmMGQXMuKVQTB9YZekpsuWDbnKx/m5Ci/9jzLklz87C8bMkeku3Ea9i5IVYeHIhKntS2XapqamiKZTCLLsln2W8vzrR2UsrayLJt18ICZbssVqGhoaLAtsbSmyS1qb4VVsgsRMRezvYt1h9ltJ5UkySSjaPms9Ms3DMPMsW7atKnoOsXO+qIXXbxefAYluQiKG9Tl5FadzSS2XITh67ElT/ykY4IluXDHl8CJyWG2aF1sbwvz9dnSN7wlDS56VuPBYyWGvcsFEESq02rVRRFNuefb/eHeizr4lpaWrDr4xcVFcy0xNCHfuqup3RP2U4VaPojnC2HFYlrkdgNghmEQjUbp6Ogo2vJZCiKnLqqn7Nwg8nWFibt//py8gqRmizdM+5t4bGgTsdZDaFESbE0lcamKOdoodx8GBs87ZnnJMV+S2G26lyYjE5S8vEvlN0EHO5KlL8yoAWf9ReXZ4/Lnk30+nxm1tlp1wzCKzjvL+hz7KKVWDLl18KFQiFAolFUHn1vmu+bP3ALl5K4h82WGw2EWFxeXlZEWWrvYXTIejzM+Po7L5TJL9ypBMplkbGzMzKnbafnM7e/Vdb0osdFSGYklSTIPvhG3mzsO20rC6SHR4GBCnmO3O8hp8Q20qw3mjdDqIf3Js5snnONFz9kAiiHRo/np0hv37Bd+sD7Fq5+3p/8+mYTZlEG3O3/hkPi31arnm3cmctW5Vn1/We5iEFH49vb2ZWW+hpGRoRobG+OYY44pa91S7Z7iOX//93/Paaedxvbt222vXbPbTDlfqrjLBwIBhoeHSxYflLLcS0tLTE5O0tfXV9adNHfNSCTC2NgYfX19ZeleC7fcFrEho7jicGN42kFykFZc/Oawoxlta2e2yc+SkiQqJQnKcR70jJJ2Z3K7wWAQp9OJpmnM62GedkyWJDbAkekeTktuQrH+/GX8Xjowksh/7CgEUU7a3d3N0NCQWR47NTXF6Ogo8/PzxGIx83vbF2fucmC1yiIw197ezsDAAH19fRiGwZ133slll13Ghz70Iaanp22ta233/NjHPsaXv/zlZc/5xje+YVtHwIqaueV2IVxegJ6eHltns0JegTgXp1IpsyTVrouXa22FAGK+mnU7a1kDZ0WJDeDwYsgutKb1YGj8YbiPF7u70SXJJJ0OpCWdWTnCTnmejokMYQYGBpgnwj3u3SSl4p6SBAxoLZyS2pBNbKDNAS6gcOJsL2TgqhGdW7cu95zsNpfkKp2IqPXs7CyapuFyuVAUZUUa4LVu9xRQFIVDDz2Uyy+/nKamJmKxWPa0mSIo1u4J8Lvf/Q5JksznlIP9ekBIJBLs2rWLlpYWU7nDDvJZblVVGRkZQZZlBgYGyh65K24YojAlmUwyNDRU8cUllDVLEhtAdqD1nwwuH0+uP5JHh9ajKw5y5Yl1dHQMnktO4PV66enpIS1p3OfeRURKFqwbB2jUnLw+tom3RI9YRmzDMPApcJq9NmGgfMtdDCJqLay6y+Uyi5ZGR0eZm5szrXo5qOYZ2G4qTFEUDjnkEFtFVlC43RPghRde4Ne//jWXX355RXuuyZnbSqpCd89gMMjc3JypRppIJMpq+8wnWZxbu14uudPpNJOTk1kCDflQzCJomoaiKLS3t5tRY4/HQ2NjY8EeawDD2058wxt5wP0IRoEacB1IkMblajQLZyJSikUpRkrScBkSKWn555WRODf6CvyaGwOdtKabATnrBbvRWzzbbd3Hek/+z79SSyn21dLSgsPhWJaLdjqdtls/q225S93oRTdbOSjW7nnLLbcwMzPDO9/5TrP/oa+vj9e85jW21q5p+amwsLku7/T0NKlUKkuNtNK2T3GTqGQutxWGYTA2NkZvb2/RApdCaTjr+VrkekVVVCKRIBKJmJrfIream1JJyBqaZBQtKNElaHB4TKlz2cioqyhIeHChGSk0C8F9hpPzEkfT6WxkAYOfTKWZTeoc7zd4bXPGQoia6DPbJL41sby6PB8+MZSnfq4G5afWXHSh1s9CFWa1OnMXQrUnfH7iE58w//u6666jo6PDNrFhH5Db+oFF6afP52NwcHBZ22e5lntmZoZEIlFQstgugsEgiUSCwcHBkpVr+S4Wa6WZEMC3Pl/UOnd2dpodTHNzc6YEcGNjY0ZJU3bQYDhJSSqpQl1iEixJMRKkUSWdW1zPkJREw4mKBLgMmS7Dj9NQ0CWDDnyEVYP3/jXFVMrAKcHtSzCrKZzfqhKNRunq6uI4h06PE6aKKzAB8N7ndW4/2qAvJ2Jeyyh3vtbPXKsuyC4KhPbFmdu672pP+FwJauqWi7ZPh8Nhlmx2d3fj9/uXvaYccguRBr/fv+wmUQ4MIyM1LDTc7Ch85Lr61sBZLrHzwdrBJC7OSCTC7OwsLpeLV7b18EDLBCk5kfcM7UBGQuIZZZoJOcicnK2gagAenDQYmWLWCCnius61uzWeCOtIUibQ4pXhhnGVkxLTDAwM4HK50HWdnxye4rSnjJL9aYE0XL1b49tb9l5C1bTcdpBr1cWNU1h1RVGqRnI7VrmSIpZS7Z4CH/rQh8paF/aB5dY0jcXFRQKBQNGSTbuTRBKJBEtLS2ZapVJYpYYHBwdNFZZSyI0n2I6I50E+l9MbiXDiYie/2TCGkWe5Rt1Fo+FiTo7wslJAd27PCyNSik6tkc+9pPG7eY35dIb8MuCQDBbRGRjoN38TWZY5vNlNuyPBbImfIq7DrsTym/H+yk9L0nKlVeEdjY6OVqSfnrung7623ApJkszusGqM8RWCCCLYUilypYbFXssht7XirBo/qNXlDBtgMJZ/75LKqBJAQkLNEzwDiMpJIrqLPr2ZrtAgD4d0ulywe0+cTsNAN0CWFaY1B9YxBg0yRG04UBrQ49irG1bti3qlNwlZlnG5XHi9Xvx+/zKrXuysng+1cstriZq55alUinA4TFNTE729vbbqwItN+hTpkPXr1xMOh23PC8sNgOWTGrY+zw6EMmUl1roUEukkdzj/WjCtFUdlz8w+PLpMImewgIKEw5A5Qu3maK2fHYaORIp5M4Gd+YzuPb3hz0QNBi2FaS/HDWI2Cws3NmAqzlir5VaL9RKR53xWXRyH7E5FKWW5rcHU1YKakDscDjM5OYnf78fn89kigJ1Jn0IQodzIuiB3Qalh7JXLih94aWmJ5ubmFUXn8yEej/Py3BjqRhknMuk8QTVJyqSiDEAzjKy+UHkPYUFCRmZXXOdLu9K8GDMIa7A3Bi+hStCmgMdi/WdTBhc+nbQt19jtceByZZqEgsGgeQzTNC1vqm1fo5B7n68bLNeq54ovlrLcwjAcFJZ7eHiYYDC4IsniZDLJ+Pg4HR0dy0bulps2m56eRtO0ipVNhSve0dFhjsdJp9P4fD4z2r0SKx4Oh5mfn2egvx+3tEhSUnEaMip61tnbAJPMaSV7v7qRSaO5NZnmRCMf3pFmSdVxIuQW9r5YNcAhwXMxg2OaDFqdEg8GdaI6uCVIlmC4A3h9u4IsyywuLhKJRMzgprDiguiAGWzcl2S3W8SSa9Wt4ovCqtsh92qy2lAjcvv9flRVrSi9JSC0yPv6+pZNIilHLVWSJEZHR/H7/UWPB8XInduDLTrWdF0nGo2azRBut9u0COXcwRcXFwmHwwwODqIoCq9Q1/Fn5wgq2rKcd7FP7UTGYcgcGm7j5bEQczE/S7rCkiY0V7I/+1QKvj6qctu8zo1HunBKEFaLSTVmIAFXbnAw5JGYm5sjmUzS399vXtxWa2eNTwD71KpXEpjLFV8UVl1VVUZHR7OUW6z7X20uOeyDaHk5s7TFxVBMixzsW24xwaKnp6fkCJZCN4xigTNZlrO0tZPJJOFwmLGxMbNYRcgc54NIxamqysDAgLn+sdoAXpw86hgjKCVw6TJxSc0qTskHBZnD9G5O9GxkZlBCDsSZj+p7bhFAjvU2gLkUKJLO7xc0jm6UCKjLxyTI7CV8kwIXRRwc/3MP9zuDDLxeZ3BzX0H3F/a6qtZONmHVRRCqFla9GkUswqqHQiEGBgbyWnVh2cvdf6mOsB/84AfcdtttAJx88sl88IMfLGv9fVLEYgfCnRsbGzMndRT6YexYbhFZtytxmy9/baujy/J6IeEjilUikQgzMzN53XdRw+7xeFi3bl124QsSh2rdPOEYRwJSklaS2AD9WjOnpDeiINOsRjndFeb5qLWbzcj5d6bsZSYFdy6kufLl/PNPdMAjgVuGD/7Ch/N7bu41ADp55MpOLv5TnPYtJbeXZa3zWXXh7VUL1SxigfxWXeixfe5zn2NoaIi3vvWtHHfccbbiMcUGAI6NjXHrrbdy8803I8sy27dv53Wvex2HHnqo7f3W7MwN5ZFbVVUSiURWeqoQiq1rGNlSwzMzMxXlr8shdj4Uc9+dTifJZJK2traCHsWUHCQgxUmjIRXrCNkDGYkhvQ0FmWAwSCAQ4DNH9PHS8yr/a6bDc9cxAIO0YfCTWanAczJ/aXHCGb/04rwh+6JVY3DT2R4ue8mmnLPYbwGrHolEzDp/8bxKrXq1yZ0Lq3LL1772NR544AHuu+8+5ufn+bu/+7uSry/WEdbT08MNN9xgfj+qqpYdwF0Vllucr0X1VikUcsuFNVQUxYysl5u/XmlhSj5Y3XfR5NLQ0EAoFCIcDpvndOuPtyDF0DAwJGswrDCadDdb1E4WFheIRqMMDg4iyzKva4f/nS90NLISerlVN4URZfiHZz0o/+MhX2VqZEpi110S60+rvEJNlmVz9JHowxc3WCCrtNcu0asV5LJzk3C5XGzbto0LL7zQ9rrFBgA6nU7a2towDIOvfOUrHH744bbmg1mxX8ktrGwkEmF4eJjdu3fbXjeXsIWkhu2ez8WxoNrEtkKkW6xNLsJ9z42+G34DAwPFkNCl4vR2GTKnpTYRngmg6zoDAwPm3jd77X6G/FYdoFsx6P5fB7Oqked5GfzyfA/n/m+SwZPtq+9YEQwGTTFMazDSSm5BdhGBVxSlKHn3peiDKHctB8U6wiCTLfrMZz6Dz+fjiiuuKG/T1Lifuxi5dV1nfHwcVVXLFkTIJWwsFmNkZITu7u5lbm45Awei0SjpdNpWjXi5WFpaYm5ujsHBwSwLLdz3gYEBhoeH8fl8hEIhUpNBXKqMbhgFo9cKEl26jzclDsc1lkBRlGUZgfuCeoV38L0R9rc9pTD7qIIWL3y5aAn41TsrU5UNBAIEg0GzD98KcU53Op243W5cLpdZmKJpGul0mnQ6nXV2F6hWV5gdD6ASL2Hbtm3ce++9AMs6wgzD4B/+4R/YsmULV155ZUX58/1y5hbln21tbbS2tla0viBsKcliO/lrXdfxer2k02kzHy5c5ZVI9Ir1RY2zcJULwVpc0WK0M2Gk2S0FsgXM92B9ug3vzBZeiMDtkSXe3OOjs335dzmbypSUhiozqGz+iwP35/1oaunvILEkoaVAKYPjCwsLxGKxrFRaMQiyi15va0ceZKfa9qXlriRaXqwjTNd1Hn74YVKpFPfddx8AH/3oR8vSaNvnbrmYzV1IBNHODyIIK/rCh4eHC97ZSgXfhKunKIoZ4BKjb4XSpahPbmhoKOsH1HWdqakpc/JKOReaV3LxGn0TYzyKliufZMD/jXgZn9CQtTSGw8/Li06ubjVw5AzqO75Z5tczWkXk9mDwpn/1gQ1iA7haNFJaHI9R+oYojmSpVKr4qOMisAblnE5nVqotnU6j6zrpdHrFqbZaWe5SHWFPP/10WevlouaNI9YI9MLCAqFQKG/5J+wlYikXRLhjQlKp2IVRyHIXi4hbR98ahrGsLVNY12JHCTGyuKmpqSLvBKDdaMCLkxTZyixJTeaxmVbkhIqKTJuh8VgAdkRkjmjK3tNbuxT+FND47lQZGvKAohu849N+/AF77qDsgDO+l4nSJxKJouozQuvOMIxlacCVQFh1IbzY3t5u6uBD5QU0ds/clXSb1RI1dcsFxJABWZYLln+CvfOxkBpWFIWuri5be8lds5yIuCRJZm5TtGWGw2HGx8fNQpXcSHcqlTJln1YytkhCokVvIKhkkzuecjAfdeOTncgGjKnQpGuMT0/TGNCy9iRLEm/ocHDHYoqxZP48di56JJ03frqJvmft+df+foOL/pigsdcLeIuqzzidTqanp1EUhe7u7qrHNkQvQmtra5bkVr4CGrCXarPjch805aeQnVravXt33oF5uSgVXRcufV9fHxMTxedSW/dhnZVtGMunftiFtS2zo6MDVVWXRbqdTieBQIC+vj7bInnFsEXrZFIOZtWY75prwoFCKA0qmSKTmK5wzFAvbYqxbE8bXY24JbkksSWgVdY5/So/62wS290qiG1ZR8qvPjM7O0ssFluxlnwhiCKotra2ZXPg7RbQ5LPq5YgjribU1C2PRqPmmbjQwDwrCpHbMDJzw0KhUFZk3c75XJTAVqMwJRcOhyNLVWVubo75+XkURWFxcbGkKKIdbNXWEZaSPKlMouoqO6ZauXfHeoLq3otNAtIGfG1U48ubXMuUXvRwiKMcDnbgs7xiOY7zpPjn5yUe/7O9YgnJASddkc4idj44nU6ampoIh8N0dHTgdrvLPuaUgqqqjI+P097enlfpx4pCBTTi37lW3W4v90FhucX5emlpCbfbvazxoxDykVsEpQCGhobMLzC3T7sQxI9TbWJbIW4+qVSKDRs2IMsyiUSCcDjMwsKCKchvV8opa/9InJBeT/+ki7CU5uvP9xDMqUlRpEx56G8XNL68ae/frdH3V6kpfvGShmbJX4t32PMp+NsWjZFv95buHAGQwdMM3UeVTjMKV7m5udksUrKqz0QiEVMJR+T5y8lSCG2+zs7Oio5BuVbd+o8IzonrqBCBDyrLLdo+R0dHbacJcvPX4kdrampaJjVcTv661JyulcAwDKamppBlOSvqax0ALy7gqakpM83m9/txu922UiyTk5P43G5uiXWxoC53rmXAKYOryFKdTmmPW259UjbRx+c89I2W/n5cjaB44Jj3pVl3XPE7QTFX2XrMERNIotGoraCcgLhGurq6bHmHpZBLdBFj6erqQtO0gu77QWO5JUmivb3dJHUlbZ+lZmnbiayLFs1kMsnIyAg+nw+/37/i3LWA0GHz+XxFdc5dLteyNNvCwgLJZDJL/TT34hAWr6mpCW9TC78fS+Bk+VSQjHAD/MNA/p/zhxNp/uGFfCWoe8tOZWBWV+kuUoUG0DSk8+YbU3jboXmw+M1VEK+jo6OkqwzLsxTxeJxIJGJ6P1ZJaMhU94nAZTWInQtN05icnGTdunV4vd6sCrlc9/2gstwC5ZJbqHrMz88X1SIvVVYqAmeKojA4OLgsdy1IZVcpJhepVIqJiQna29uXWaRiKJVmE+o1hmGYQhV+v5+ElilBHXDBTgu7JWDYDR8ddPCO3uyfcyapc9EzKR4IFSLh3hZQhwSnbvKibzJYeKbA04FUUKb7aIPcrywZhNsvdzF+v0xjj8Gp34iRaqvcokqSlNXRZ+2yE7Ozo9Eovb29NSG2KGfu7e01PbBCBTTJZNJ03VcTVhW5JUliaWkJoGhhili3kFuezw3PRyoxm6pckYV4PM7U1FTWD18JCqXZRkdHSaVStLS0mDc3pwxOCUZyzLZMRol0ky+7ZFY3DE5/PMmugs1aey20BLwr4GLL/3PxQpEguaNBw9lsEItFlynP3HKhm4k/y+gqRGckbj67gYse7MXnW3nGALK77BKJBGNjY3i9XnNiqLgpriQoJ5CP2Lmwuu7T09MrTnvWAjU9c4N9cmuaxtLSEoqiFO3ltq6fu67diHguqYTIwujoqOn++f3+vMGvUCjEwsIC/f39BUUYKoE4f6qqSigUYnBwkFQqZVqqhzQ/o/EG9ByXWQMmU3DOX1Lc90oXG3yZm9PjIY2XE4WUW/YSu1WBH+5u4OmPe3gwDVoRfSVZUTjpmqDZxSduiuO3NzN6rwwGyAoYkoGExNwjPjrW28ms20cqlWJycpL+/n68Xq/5+0UiEXOgpDXPX641Fa5+T09PyRt3Op1mdHSU1tZW+vv7K/5MtcKqsNyi1tzr9S4bsVNs3ZWIKwjkE1kIh8NMTU2h63rWOS8QCJjtlLU4X4k+7MHBQRwOBw0NDWZK69aXY8ymdPSsXp/MZ9SBoAbbHk5xcgtcvcnFI0E9D7GzlVhcwBnf9/L4TVbrWuCGqMDWd6U54s0uoMck1aP/afDQVW4zwq6rgCxh6OCssrecTCaZmJhg3bp1Zg2B9fcTtQfWmIbX6zWPX6UCXoLY3d3dJQU+hOxSc3MzAwMDVfuM1cR+J7dValhVVeLxeNnrVrMHW/TRiuBXJBIxZ0crikJPT0/Vo6IidRiPx/M2l8iyzGGtHsJj1m7q5SktFbhrCU55NEX7sl92L7FlMj/8EY862XKTjWOFDN4Og4HX7H0/SZJwKh4eucqDHs/5vnVIR+Gx/4L1ZxgojpWfRfMROx8cDgfNzc00NzdnBeVE/UEhr6wSYjc1Na1Kiy2wX93yXKnhSCRSlvChqICrtOKsFER+OhQK0draisfjMdVURJqmsbFxRe8r6qx1XS/aQPHaVhkPsPfWVzilFTckxrNUFZZrpzU74cTfekpqvChucDfDEds1Npy518WOL8KPT/ag5hLb8j6jd7n43T8HOOpDoRWdiROJBJOTk/T19ZWlRpIblMtNSYqcutPpLIvYY2NjNDY2ZunerUbsE8udK5IocsO5UsPlRtZVVUVV1ZrkryFzN5+YmKC1tdWUVhZiiNYiFYfDYVqEci5ekcN2u90l66ydssxrWuH3gXyPFiZ6vue0OuDHR7h4ekIhmueZAt1/o/Om/07hbDBo7Ml+7I+fcRIcKUxs8d/zf+qk44su80xsrTO3cwQTwctqxDisKUkhfRUIBAiHw2bLbzHRBZGzb2hoKNm+uxqwT8htre223vlyhQXsklu438FgEEVRapIKEdaip6dn2d3cWjsNmFFuUWUlqtGKWRlrDttu19j1h7l59UNJZorGqHKJnvvfEl4Z1qUl/jxV/OKce0omPCYxePLym8Xcs7KNLhQDX5ex7EwsJn2IKaeinTaX6NUkdi5kWcbr9TI/P09/fz8Oh2OZcq31BiSI7fV6syolVzP2qVsu9MO6urry5obtSCKJwJmoRRbpLI/HY7p+K/3iRd7Zrhvocrlob2+nvb19WTOJuEisM6nE+c5ucYdAt1vmxDaJX8zZObpku+Libw4MTtESPP4BT0lyGjrcdqmLi+9P4LM04BkGuFqTQL7zuojEGyDBaf+WrbqWW48vUpK5Ka1UKsX0dGYCaS1aKfNVtomgau4N6JZbbmFoaIhjjz22aFfjaoNk2D3klgnRKB+LxQgEAvj9fmZnZ+nv7y8YEBFu8PDwcN7HCwXOrG5yJBIxmxH8fn/ZUe1AIEAoFKKvr2/FOVPh+oXDYRKJBF6vF7fbTSAQMKueysW2P8fZUTLmmI/YMOiGf0q4UC9vILRbtlVDLinw+m+l2Hpx5k6Qihrc+HoH808Wt6SyC95xX4KuI+1dXtaUVigUIpVK0dbWRnNzs+0Mil2UU4uuqiq///3vefjhh3nxxRc5++yzee9731u1vdQSNSe3KCNVFIWBgYGihNE0jZGREQ455JBlj9mNiFuLQYRMrlAeLWYBrAMCent7axIRX1xcNEsprYUz5dxETng4zpPFDsoFiP359TKfHHbzP6e4mfmLjJ5c/spCaF5v8N6nE4DBb/4xzV+/11z0xqB44MI7E/QcU/6lJVpDe3p6TLKXct/LgXCvOzo6ShJb6PzJsszGjRvNOE81CmX2BWrqluu6zuzsLJqmsWHDBlvtmeVO/cj3vtae63x5azEFROxHBLZcLldVlUGsEDLGhxxyCIqiLBN9EOf0UmfL17YpPBkt5E8vJ7YCtDuh3SGz4xaZqUfl4jOJciApmdLSVMRgMTLN9IP9JS3+SZ9PVURs4QoLI+D1eku67+UQrVxiT0xMIMuy2ekHHDDEhhqSO51Os3v3bpqammyrUOaqplRrOEBu3lqcpcRZeH5+npaWlorlkIqhUA479wYkhChEiqZQg8vJrQr/Pqbl4Vf+ho9GBVoUifBn3dz6C3dZxAYwNJDdKi889zIPfXADARsiDpFpGXuaL3shMg/5vDtr62puRZqoNsy9YedCELu9vd0WsScnJzEMg40bN666hhC7qBm5hcyu2+0mGAyW/fpaiCsoimIWOOi6ztLSEpOTkyiKYsoCVdpIkg9WrbBiOWxr3XSpBpfXtcm8thXuMlNi+d1w8ReHBK/4iQf15srHDSdDCve/awvhEXuBrZZDylNjtBK7FJHyVaSVir5biV0qgCn0A3RdZ9OmTQcssaGG5BapBsB2YYpALaZ+5CIej5sFNG63e1kjiXCTKz17W3PYHR0dtj+DnQaX/z3Sx1v+kubuYGFiK4BLhmMbJY76iceW/kIhaFGZcNTe99A0aHDUJfatdigUYnFx0Rax86FU9N3n87G0tGQrMyGaQFRVPeCJDfsgFVYJak3spaUllpaWstIs1kaS3AIVQXS75y2hfGpVHqkEhRpcpuYXmY614cVBvMBciTYn/GuvA9cVDbwQq10rouKFjW9Koukq3sEoW96zSCBQfLqpgJgyUimxc5HrvotgrizLZq15Ifdd13VmZmZIpVJs3rz5gDpbF8I+kza2A13XcTqdjI2NmRHuan7JQis7kUgUrDAqVKBira4qduGKPu9yc9ilYHVHNUlBmZZwyhJxPbu+XFyyHU6J6CU+Rh4v4eY6wLA3ZTl7pBggu0FxwobXSxyxXQb8pNOerL5rUeJpzfND5gYrpozUIm8sNO26u7tpamrK676L447IlCQSCbZs2bImiA01TIVB5kI3DIOXXnqJjRs3Fnxe7vlaRLjD4bDtVFYpCJdLluWKJXVVVTX3lU8uSVS1rbTPuxDE9JJ0Os3/al18aZdK2PS39/6MTbLB5pDMmy5qQ1JAT5E/kCaDs8EgHanMsise6DxCZ/vtSRx5Shdy8/yi0EgEEO1OGSkXheSNrfsSIhl33HEHDzzwAMceeyznn38+3d3dVd/P/sI+IffOnTs55JBD8hKqVOBMXAjhcDgrlVVOA4H4sf1+f0l55XLWjEQiRCIRksmkKedUTD1mJRATViRJMi/AUx5L8Gh4+XN7HAbbnpc47iPFP6uvxyAdhVS4cnK/494EnUeUvoSEmzw/P088HjenuFQiGlkMuq4zNjZWkNi5mJqaYseOHYyMjPDwww/zta99rSZZk/2BmpJbjHTZtWtX3h7ociPiglChUMh0+UppotXKTbZiaWmJhYUFPB4PyWSyqqWwsDc45/F4aG9vNz/rh55L8N/Ty3++Ex908vqv+dGLVbJJMHSqRu8rdf78lcrI5WmFs76bZMOZ9sJ1i4uLRKNR+vr6TC9IdAKuRGBBQBC7paXFbPQphtnZWcLhMFu2bKl67fpqwD45XOQTkKskIp6byhITLZLJpEl069kuFosxPT1dUzdZ5LDXr19vFuHE43HC4TBzc3NmwYVdCadcCBFGv9+/zKJs9cswnR2Z7tqtcOp/+sg7SDtr85BKqBz5ngR//kppIuRDOgYt6+3ZBvE9iZSgtR7f2jdfaTWaqCazS+y5ubk1TWzYx+QWqEYPtizLZspInKGCwSDT09N4vV4URSESidSs8aBQDtvaQ5xPwqkcV1QI7eeTBQZ4zzoH1+zWmLEQ+eSfe5CXZHQb40UGT0sS1CZxd3tJzpR/ga97lU77oaXJPT8/TzKZLDgM0XrTNgzDPKdbpZyK3RwFsZuammwRe2FhgWAwyObNm9cssWEfueUTExO0tbXh8XhqOhwA9qY0IpEIsiybLvJKRRVy36OSHLY1UChc0ULxA3GcKKUeOpfSec9fUzwXNRgKyfzth5pITZcuMe09Vudvb11gYXGO+duH+ONHy2ybleHcXyZZ/7riCrTCGldS1mu9OUaj0bxlulZi20k7ivr+LVu2VGXck6ZpfPazn2XXrl1IksQXv/jFrDnbf/jDH/j2t7+Nw+HgnHPO4fzzz1/xe9pFTcmtqqo5cVG4zLUktgg6AfT0ZNQFcrvFVuIiQ/Vy2CI1Ew6Hl8UPkslkRVH3+eckfv53bsJjxb9bb4fBeXfPE5czvcxGysG3hjyoZeTDj3i7ylnfKez7i8i+aMSpxu9tDa5qmkZDQwOxWMx26XAgEGB+fp7NmzdX7Zh25513ctddd3HNNdfw0EMP8YMf/IDrr7/e3O9ZZ53Fz3/+c7xeL9u3b+e//uu/6OjoqMp7l8I+c8tVVa0pscXZtKGhISvoZB1IZ5UOFi5yObn0agbnciurRPwgHo+jaRrd3d1lW5aGHoPIZInmHCe85uuLJJQFBvozxSNzz0toaZuCkg449Rsxtr278PNF3ljX9aoRG7LLdIWOmSzLBAIB4vF40SCmIPamTZuqGn953etexymnnALA5ORk1vFp586dDA4OmkeFV77ylTzyyCOceeaZVXv/Yqg5uQ3DwO12Mzs7i8/ny0zPyCloWCmE+EGxAQG53WJW9RSgZC69ljlsET+QJIlkMklnZyfxeJzFxUXbWm3RObjxtW6MQmdtGRo6DU64doGWYxfo799bPDJ6j0KhxpNcbDo3Ssdp4+zatXdUsDVbIWIRkPGeanX0mpqaMoluFUKcm5vD6XRmyV4tLS0xNzfHpk2bSmqkVQKHw8EnP/lJ7rjjDr75zW+af49EIllGwOfzEYlEqv7+BfdVy8WTyaTZ5XTIIYcQjUazgl5NTU0r7s8VUjz55JCKIVc9JV9bqDgLC2WVWsj9CIhSzKGhITPAZLcUNjIlceOpbkKF5nw54NxfJHANL2B4Yqxbt7d4RE3CX36kYNiw3O2H65z9bRmHp/AEl1AoZM5OrxWxJyYmaGxsNF1xaxCzq6vL7Bp79NFH+eY3v8nWrVs599xza0JsgWuvvZZ/+qd/4vzzz+e2224zv49odG/zfTQarVk6Nh9qSu7p6WlCoZBJFlGKaG2IEEqiTU1NZXdkhcNhUwNrJaRzOBxZXVmCzKIxP51O1yzqDpnobTQaXVaKmVsKK4JL1h5wv9/PQ1/zEipyzvY0G7gOmUXypFnXmx2xfux6B/N/LewROBozwguuRmgZ3jtGKLfBJRqNMjMzY56Fxe9ezeYLwzDM2WzFztjCQ3M6nXz0ox9lcnKSG264gfPPP58TTzyxavsBuOWWW5iZmeF973uf6ZGK33DDhg2MjIywtLREQ0MDjz76KJdccklV378YahpQg4w7GwgECAQCWflo8cNb88LRaBS3220SvZgburi4SDgcpr+/vybdOyLSG4lEcDqdpFKpvLn0lb6HKCctN5psDS798dI+Zu4v3KO85eIgx12xuKzsNvCSxPePd6PllScGJNj6HpUzvlk8aS7UbJ1OJ+3t7VlKOKUmuNiFIHZDQ4OtKkPhiR1yyCFlzXIrF7FYjE9/+tPMz8+jqirvfe97icfjxGIxLrjgAjNabhgG55xzDm9/+9trtpdc1JzcVqRSKXNudyKRoKGhgaampiyi50a3xeOC6LWWQxLvIXLY4txozb9ayycr7f/OLSddyc3initkHv5qHiEGCdqOTHD2L2fo7uncqzyjwc7fyfz6XW7UWOF1HV445xdJBk8unu6ypgVzIdJ/kUgka4JLOZVoq5XYqx37lNxWpFIp06LH43GzKkmcJ605zkgkYmqDC43pcvLL5UCc6YQYQKF6+Hg8TigUIhaLld3/XaictFKoCfjhq9wsvrT3vZUGnU2XTHPYO4P0DHSYI4J1DW692MWLt5T2dhQ3fGQujlzgqYJ0Xq+X9vb2kuuJI084HDY9ITG+uJgmXjnvEYlEmJycZP369bYKWtYy9hu5rUin0ybRY7GYaRWtaapYLMbExIRZuigsejXb8yrJYefzNorl0ivRK7e19yQ88V2FyYdkWg7R6Tt/hNaezHndeuSZ/1MHt7+3GbTSNxSXHy7bGceVp75F3ATFbPJyYRVWiMfjebMCwisQN8FSiEajpnruSmoQBNLpNJ/5zGeYmJgglUpx2WWXcdppp5mP/+AHP+Dmm282P/8Xv/jFvOKe+wurgtxWqKpqEj0ajeLxeJiZmeHPf/4zl1xyialpLSq9ZFmuSu+3yGHbkbstBuFthMPhZbn0UuWk1YC4eeTeoIQndONJDSz+tXTXmiRD99E677hvuUzqSomdC+sNMhqNml5aJBIxvbRSiMVijI+PMzQ0VLWb5i9+8Quef/55/vmf/5mlpSXe8pa38Mc//tF8/J/+6Z9417vexZFHHlmV96s2Vh25rVBVlTvuuIN///d/5wMf+AAbNmzA7/fT1NRkBmesJZ1QOl+dD7XKYVsDS7quo6oqXV1dNXMXS908nr5R4Xd/by+r0HGEzjn/m6KpP/vyEOWe+RpZqgUx9E/X9az0X6FW2ng8ztjYGIODg1Vr6YWMJyDKhAOBAOeeey533XWX+fiZZ57Jpk2bmJub45RTTuF973tf1d67GljVkhMOh4OxsTFuvPFGWlpaWFpaIhAI8PLLL5vnXNGj3dbWZhJdKFeKx4ulyWqZwxa5dJ/PZ7r7wWCQxcVFc2/V6v0WhTyFPI8Hr3Xw4LX2bniDZ0d5849Se/a2130XLZUrLb0tBtFp19TUlCWAKIKoucouiUSCsbEx+vv7q0pswKzpj0QifPjDH+YjH/lI1uNnn302F154IY2NjXzwgx/k7rvv5rWvfW1V97ASrGrLXQiappmzrEOhkDlhpKmpySRLrmpKPjKJNcSsqFpAtJ1aRxNZA0vpdNpWX3oxpFIpc2B8vkKNyYdl/u8iF7F50JOl13/bA/PIHUtmcYr43iYmJmy3VFYCa0qts7Nz2eNWZZfx8XF++ctfsnXrVs4444yajdKdmpriAx/4ABdeeCHnnntu1l6tFWg33ngjS0tLfOADH6jJPirBqrbchaAoSta0RkHSkZER040TAStRmCIURMXdX9d1UqlUTac1iiKb3AKYcvvSi0G4sMWOFGIap2HY0I6Xob3PR0OHz0z/BYNBQqEQXq/XbN+txUSWYsQGsuIrra2tTE1N8dxzz/HBD36Qyy67jDPOOKOqe5qfn+c973kPn//853n1q1+d9VgkEuGNb3wjv/nNb2hoaOChhx7inHPOqer7rxQHpOUuBF3XCYVCpkUXF0NTUxMul8tsYJmcnCSVSplqmSuxmoUgBADLKbIpN5dud2719BMSvzzfTWRGKjkroO8EnQtv3xtEE5rfra2t5gz1anXYCQhiOxwOOjs7S/4OyWSS0dFRuru76enpMUU4q50aveqqq/jtb3+bFQE/77zziMfjXHDBBdxyyy38+Mc/xuVy8epXv5oPf/jDVX3/lWJNkdsKXdcJh8MEAgGCwSCyLONwOPje977H+9//fnp7e03XKhwOV2Q1C2FhYYFYLEZfX1/FFq5ULl24+3ZjBY/9h4N7PutAK+aWKwYfW0wg7/HnRIAuV8w/dx6b9SZZbhWaKOZRFMUWsVOpFCMjI3R1ddHb21vWex1sOCDdcjuQZTnL9R0fH+ejH/0oJ5xwArFYjJmZGTNXLtRcotEoS0tLTE9PVyT1Yy0nLTZhxA5yFV2sDSSSJKGqqm1ip6LgbTPo+/80Ru8u/JMf9xE1i9iFJmEWmscmApl2RSwrIfbo6CidnZ11YtvAmrXcubj33nsBOPHEE4nFYiwuLhIMBtF13TzHiTNlJaWmwrWsZUcUYGqziY6jYn3pL98u8+i3HIz+USncCroHR1ykcuZ/ppGkvZH3Uiow+WAVoRDzyfMde0SJryRJtr6vdDrNyMgI7e3t9PX1lbWnYmsWK1LZnyoq1cBBQ+580HXd7JsOBoNZWuSiXNPawRaLxfIqm4qiDlEiWStii7ZQ6zne6h4D5v4n/+Thd+93EZkGo0g1muKFt/0+wbpXZi4DQezu7u4Vt0jmm08uvtvZ2dmyiD06Okpra2tVo+LFilT2t4pKNbBm3XI7kGXZHNczMDBgWnQRVbe2qorJFFZlU7fbbc6iam5urqnedSAQIBwOL2sLzdeXPj09zZ+v7yGdcmBohYNdigfefkeC7mP2EntsbKzs3vhCsEa3rTdJMXyxo6PDVL8tBKG40tzcXPV01xve8AYzwm4YRlZgcH+rqFQDBzW5cyHOuP39/aZFn5+fZ3Jy0iS6aHQQrvvU1BSSJBGNRs3AUrVbUEWArtSEDmtf+tPNDqZiRYJ5EnQcqtP5igyxRa68VjLQIoYgcsMtLS2Ew2EWFxcLilAIYjc1NdUkj12sSGV/q6hUA3VyF4DX66Wvr4++vj4SiQSLi4ssLi4yNTVlWusHH3yQCy+8kIaGhiwJY3Gx+v3+FRHdqh5aboBu2/t1/vqzws8fODnF3/1MRXZINSc27G3VtbbRFprHpuu6adEbGxtrNk8MsotU3vSmN5l/398qKtVAndw24PF4WLduHevWrSOZTPLII4/wL//yL7z3ve8lEAiYLnxnZyednZ15tcrLbWyx9pRXIgvce6yOp9UgsbD8dc4mg+OumWJiNoJryUUikaCvr6+mxJ6bm0PX9by6atajRTqd5vHHH+f6668nkUhw+umnc9FFF9XkyFOsSGV/q6hUAwd1QK1SfOUrX+G8886jr6+vaE86kNXBZneoYbUi7989ys3SSzkWT4KmAYP3PpMglU4wPj6O1+slmUxWZS55LgSxNU2zJZgoimZEm+f999/PUUcdVbUIuRWlilT2p4pKNVAnd5VgpyfdTgebECeohpDDfx3mITwpZY3olRQ471dJul8Vy6puy9d2aVXJqQSVEHt8fByXy2WOZ6qjctTJXQPk9qR7vd5lFVzWxhaRa/f5fMzNzVWtT/r/vc5NYKdEKiKhJgAJTvhMmmMuz8gQFSuCsargVNozLwp67GiXC2I7HA42bNhQJ3YVUCd3jaGqqtmqGolETMURa0+6qqqEQiHm5uZQFIWWlpaqtIPO/kXi/y50o6sZ3bSBk3Re+80gM7PFiZ2LVCplFqbA3lx6sdeXQ2xRQSjLMhs3bqwTu0rYb+QOh8N8/OMfJxKJkE6n+dSnPsUxxxyT9ZybbrqJn/70pzgcDi677LJV1StbCTRNM4keDofNM66qqjzyyCO85jWvMVMuYkyxtZSzEhc9NgezT8u4m6Dp0Agzs9Mrkmku1ErrcrnM/YkIv11iC/msDRs2VD2N+NRTT/Fv//Zv/PjHP876+2qXSKoG9lu0/Pvf/z7HH38873rXu3j55Zf52Mc+xi9/+Uvz8bm5OX784x/zi1/8gmQyyYUXXsgJJ5xwQE9lVBQla2xtMBhk586dXHHFFWzfvt0MauW2gwqyVNLB1tAJw6fqZj39SvXX82m8z83NmfvTNA1N0wpO9LRCCEUahsHGjRurTuzvfve73HrrrXmzAM888wzXXnvtqpVIqgb2G7nf9a53mUTVNG2ZC/qXv/yFY445BpfLhcvlYnBwkOeff56tW7fuj+1WHaIn/Ytf/CKf+9znOPTQQ/P2pFvHFFsnfJTTwRaNRk21mWoOVsjtS5+amiIejyPLstmYU2h/4vm6rrNp06aaaM8PDg5y3XXX8YlPfGLZY88++yzf+c53Vq1EUjWwT8h9880388Mf/jDrb1dffTVbt25lbm6Oj3/843zmM5/JenwtVAjZwb//+7+b/93a2prVky6ILs7owgUWyqF2OtiEZR0YGKiZ2gxkymMhkx8Glo2OstbrC2KrqsrmzZtrQmyAM844g/Hx8byPrXaJpGpgn5D7vPPO47zzzlv29x07dvDRj36UT3ziExx33HFZj62FCqFKIMty1vRP0ZM+NjZmlreKGWvW0UyhUIiZmZmsDrZIJMLCwkLNib2wsEA8Hs9yxYVMsbUef3Z2lttuu42uri6OOuootm7dWjNiF4NhGLzzne80r6eTTz6Zv/71r3VyVwsvvfQSl19+Od/4xjc49NBDlz2+detWvvGNb5BMJkmlUuzcuTNrqPnBgNye9EgkQiAQMCeTCqJ7vd6sxpZQKMT09DS6rtPd3V3T6LMYO1zojG3tS9c0jW3btnH33Xdz6623cuihh3LNNdfUbG+FcCBIJFUD+43cX/3qV0mlUnzpS18CMhfq9ddfz/e//30GBwc57bTTeMc73sGFF16IYRj84z/+Y9HU0B133MHvfvc7vvrVry577KqrruLxxx83GwX+4z/+44DzAsSYX3H+Fh1s4txq7UkPBAI4HA46OjqIRqMsLCzUpPpscXHRVJyxEzybm5ujt7eXL37xizidTpaWlqqyD7v41a9+Zc7w+sd//EcuvvhiUyLp5JNP3qd72RdYE3nuq666ij/96U8cdthhfP3rX1/2+Pbt2/n2t79ddenb1YDcnvS7776bBx54gK9//esmkcVAglAoVDX9s8XFRaLRqO2GlpmZGaLRKFu2bKnZtNQ6srEmyP2b3/yGtrY2fvazny0jt67rnHjiiWzbto35+XnOPffcLInatYTbb7+dH//4x3zuc58jlUot60kXRE+lUibRK+lgEwU5dok9OztLOBxmy5YtB3Qq80DDAdUVVijqftZZZ/HQQw/lfU0sFuOiiy7i3e9+N5qmcfHFF3PkkUfmPecf6DjssMP4zne+Y+Z1rT3polVVWGzRwSaILgJ2pcpMBbHtuOKQqVeoFbELFagc6PJI1cIBRe5CUfdi8Hq9XHzxxeYFf/zxx/P888+vSXIPDAxk/X+pnnQxB72jo4OOjo5lfdW5jS2isq6UaITAwsICwWCQzZs3V53YhQpU0uk011xzTZY80qmnnnpAySNVC2u+iHf37t1s374dTdPMXuEjjjhif29rn0P0pB9++OEcfvjh+P1+AoEAL730EuPj4wSDQbOCbnh4mHXr1gEwOTnJ7t27zefYJba4kWzevBmPx1P1zyMKVHJhlUdyuVymPNLBiAPKcpcDa9T9zW9+M+effz5Op5M3v/nNbNq0qehri0Xe10K9u9vtpre3l97e3qw56daCmMbGRnOqy8LCAktLSzgcDkZHR80UXCFrHAgEWFhYqBmxoXCBysFS/GQHa4bcr3rVq3jVq15l/v+73/1u878vvfRSLr30UlvrWCPvuViL9e4ul4vu7m66u7uzetJnZmZoaGjg3nvvxeFwcN555yHLsjmaaWZmxgzYCaJLkkQgEGB+fp5NmzbVTNmlGA7W4qd8WPNuebnYtm0bX/jCF/I+Zq139/v9Zr37WoHT6aSrq4stW7Zw5JFH8sgjj3D77bdz6KGHMj4+zuLiIrqu09LSwsDAAIODg7hcLubm5vjpT3/K1VdfzcMPP8yGDRuqop5aCazySKlUikcffXRZt+HBgjVjuctFJZH3g8nlczqdTExM8MMf/hCXy0UgEGBpaYm5uTlTu93v95sVdKeccgrpdJq7776bH/zgB3zta18z68z3BawFKp/61Ke45JJLTHmk7u7ufbaP1YSDltyVRN4PNpfvk5/8pPnfInWmaVoW0d1uN06nk2g0ykUXXURjYyPpdLqmtewC/f393HTTTQBZyqWnnnoqp556as3ff7Wj7paXga1bt/LYY4+ZEkQHY727GCawceNGtm7dSk9PD6qqMjQ0ZM4UczqdNZu6Uod9HLSWuxxUWu++1mGdk17H6sOaKD9djUgkEnz84x9nYWEBn8/Htddeu4wEl112GYFAAKfTidvt5oYbbthPu1090HWdL3zhC+zYsQOXy8VVV13F0NCQ+fhaaALaV6hb7hrhJz/5CZs3b+ZDH/oQt912G//xH//BZz/72aznjIyMcNttt9VdWAvuvPNOUqkUP/vZz3jyySf58pe/zPXXX28+/uyzz3LDDTfUvQUbqJ+5a4THHnuMk046CYDXvOY1PPjgg1mPz8/PEwqFeP/738/27du5++6798c2Vx2s39vRRx/NM888Yz6m6zojIyN8/vOf521vexs///nP99c2DwjULXcVkC+t1t7ebrqLPp/PlAUWSKfTvOc97+Hiiy8mGAyyfft2tm7dSnt7+z7b92pEJBIxA3OQOderqorD4TiomoCqgTq5q4B8abUPfvCDZtosGo3S1NSU9XhHRwdve9vbcDgctLe3c9hhh7Fr166Dnty56UZd18202sHUBFQN1N3yGmHbtm3cc889ANx777288pWvzHr8gQce4PLLLwcy5H/xxRfXnG52Jdi2bRv33nsvAE8++WRWqrHeBFQe6tHyGiEej/PJT36Subk5nE4nX/3qV+ns7OQrX/kKb3jDG9i6dStf+tKXeOqpp5BlmUsvvZTXve51WWuUihyvhSaWXIjP/MILL2AYBldffTX33nuvmYq84YYb+O1vf2s2AW3fvn1/b3n1wqhj1eL3v/+98clPftIwDMN44oknjPe///3mY7Ozs8Yb3/hGI5lMGqFQyPzvOuoQqLvlqxjFIsdrvYmljpWjTu5VjEKRY/HYwdLEUkdlqJN7FaNY5PhAa2LRdZ3Pf/7zXHDBBbzjHe9gZGQk6/GbbrqJt771rZx//vn1nH+VUCf3KkaxyPGB1sRirTz72Mc+xpe//GXzMSGC8dOf/pTvfe97fO1rXyOVSu3H3a4N1PPcqxinn346999/P29729vMyPGB2sRiN36wFoc+7i/Uyb2KIcsyV155ZdbfrAII559/flmyvfuzKaNY5Vk9flAb1Ml9EGF/NmWspfjBgYL6mfsgwv5sylhL8YMDBXXLfRBhfzZlrKX4wYGCOrkPIuzPpoxqxw/qKI26W34Qod6UcXCh3jhyEKHelHFwoU7uOupYo6i75XXUsUZRJ3cddaxR1MldRx1rFHVy11HHGkWd3HXUsUZRJ3cddaxR1MldRx1rFP8/DtpL5saO/jMAAAAASUVORK5CYII=",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1209d3eb8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -391,30 +398,32 @@
     "from mpl_toolkits import mplot3d\n",
     "ax = plt.axes(projection='3d')\n",
     "ax.scatter3D(X3[:, 0], X3[:, 1], X3[:, 2],\n",
-    "             **colorize)\n",
-    "ax.view_init(azim=70, elev=50)"
+    "             **colorize);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can now ask the ``MDS`` estimator to input this three-dimensional data, compute the distance matrix, and then determine the optimal two-dimensional embedding for this distance matrix.\n",
-    "The result recovers a representation of the original data:"
+    "We can now ask the `MDS` estimator to input this three-dimensional data, compute the distance matrix, and then determine the optimal two-dimensional embedding for this distance matrix.\n",
+    "The result recovers a representation of the original data, as shown in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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LS0MGLAttQ+Sva7B3dkarzcPZ2YXK8nJObtmI3NKSmswM1I6O9Jo0tcksZflP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8aJ4QHejB9w++8BRwVH8lJQhCiR2kOUSD2feR5VsDMAfgMiaa1uJGJSx9jL3YzsyzuO1xwxqE3t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\nCM1B3Cq3EncM7UxJ67UNr4sCN9JtaJtmbJEgCILQHMQVt5Xw8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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1228007b8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -422,7 +431,7 @@
     }
    ],
    "source": [
-    "model = MDS(n_components=2, random_state=1)\n",
+    "model = MDS(n_components=2, random_state=1701)\n",
     "out3 = model.fit_transform(X3)\n",
     "plt.scatter(out3[:, 0], out3[:, 1], **colorize)\n",
     "plt.axis('equal');"
@@ -440,7 +449,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Nonlinear Embeddings: Where MDS Fails\n",
+    "### Nonlinear Embeddings: Where MDS Fails\n",
     "\n",
     "Our discussion thus far has considered *linear* embeddings, which essentially consist of rotations, translations, and scalings of data into higher-dimensional spaces.\n",
     "Where MDS breaks down is when the embedding is nonlinear—that is, when it goes beyond this simple set of operations.\n",
@@ -451,7 +460,10 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -469,21 +481,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is again three-dimensional data, but we can see that the embedding is much more complicated:"
+    "This is again three-dimensional data, but as we can see in the following figure the embedding is much more complicated:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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SCOXyMGaVxaaDSIToC/90gUgw5mNdCTQ2t9DQ3MJv3FZa7Kn4bfGYhgF5ZQRa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\nYAKWgRwGBwcYHtCoP9+HLdHkwEObpg3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qCs0Xv1l5fQOcjrdhm5XGoD/IYFstFbkThZPJ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5LNEk2nsThKKK9dR7i0hvPDfbibi2guKr1t130tuDKJwurzqfKP//gdfvCDH1BcXIzZbMFoNFJX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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bc36668>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -501,23 +516,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The fundamental relationships between the data points are still there, but this time the data has been transformed in a nonlinear way: it has been wrapped-up into the shape of an \"S.\"\n",
+    "The fundamental relationships between the data points are still there, but this time the data has been transformed in a nonlinear way: it has been wrapped up into the shape of an \"S.\"\n",
     "\n",
-    "If we try a simple MDS algorithm on this data, it is not able to \"unwrap\" this nonlinear embedding, and we lose track of the fundamental relationships in the embedded manifold:"
+    "If we try a simple MDS algorithm on this data, it is not able to \"unwrap\" this nonlinear embedding, and we lose track of the fundamental relationships in the embedded manifold (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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B7auNR4xsp39M7H7M236Y6aOGmxdSvtVzQBo9B6SZHUOkQXTNV+S//ObKGcyN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\nKl8RERGT/R+73qklHmTvMAAAAABJRU5ErkJggg==\n",
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x122800fd0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -536,7 +554,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The best two-dimensional *linear* embeding does not unwrap the S-curve, but instead throws out the original y-axis."
+    "The best two-dimensional *linear* embedding does not unwrap the S-curve, but instead discards the original y-axis."
    ]
   },
   {
@@ -556,8 +574,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![(LLE vs MDS linkages)](figures/05.10-LLE-vs-MDS.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#LLE-vs-MDS-Linkages)"
+    "![(LLE vs MDS linkages)](images/05.10-LLE-vs-MDS.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#LLE-vs-MDS-Linkages)"
    ]
   },
   {
@@ -566,28 +584,31 @@
    "source": [
     "Here each faint line represents a distance that should be preserved in the embedding.\n",
     "On the left is a representation of the model used by MDS: it tries to preserve the distances between each pair of points in the dataset.\n",
-    "On the right is a representation of the model used by a manifold learning algorithm called locally linear embedding (LLE): rather than preserving *all* distances, it instead tries to preserve only the distances between *neighboring points*: in this case, the nearest 100 neighbors of each point.\n",
+    "On the right is a representation of the model used by a manifold learning algorithm called *locally linear embedding*: rather than preserving *all* distances, it instead tries to preserve only the distances between *neighboring points* (in this case, the nearest 100 neighbors of each point).\n",
     "\n",
-    "Thinking about the left panel, we can see why MDS fails: there is no way to flatten this data while adequately preserving the length of every line drawn between the two points.\n",
+    "Thinking about the left panel, we can see why MDS fails: there is no way to unroll this data while adequately preserving the length of every line drawn between the two points.\n",
     "For the right panel, on the other hand, things look a bit more optimistic. We could imagine unrolling the data in a way that keeps the lengths of the lines approximately the same.\n",
     "This is precisely what LLE does, through a global optimization of a cost function reflecting this logic.\n",
     "\n",
     "LLE comes in a number of flavors; here we will use the *modified LLE* algorithm to recover the embedded two-dimensional manifold.\n",
-    "In general, modified LLE does better than other flavors of the algorithm at recovering well-defined manifolds with very little distortion:"
+    "In general, modified LLE does better than other flavors of the algorithm at recovering well-defined manifolds with very little distortion (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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zJyCkKZw/ji4+jjKtE7ryElpHhlNeXkVMeAjdLLn0tWTzzqCeuFcUsz4gGrQO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\ndXJycgB4/fXXad269S2u5tpZWv/FevXqxapVq7C3t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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11871f128>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -596,8 +617,9 @@
    ],
    "source": [
     "from sklearn.manifold import LocallyLinearEmbedding\n",
-    "model = LocallyLinearEmbedding(n_neighbors=100, n_components=2, method='modified',\n",
-    "                               eigen_solver='dense')\n",
+    "model = LocallyLinearEmbedding(\n",
+    "    n_neighbors=100, n_components=2,\n",
+    "    method='modified', eigen_solver='dense')\n",
     "out = model.fit_transform(XS)\n",
     "\n",
     "fig, ax = plt.subplots()\n",
@@ -623,25 +645,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Though this story and motivation is compelling, in practice manifold learning techniques tend to be finicky enough that they are rarely used for anything more than simple qualitative visualization of high-dimensional data.\n",
+    "Compelling as these examples may be, in practice manifold learning techniques tend to be finicky enough that they are rarely used for anything more than simple qualitative visualization of high-dimensional data.\n",
     "\n",
     "The following are some of the particular challenges of manifold learning, which all contrast poorly with PCA:\n",
     "\n",
-    "- In manifold learning, there is no good framework for handling missing data. In contrast, there are straightforward iterative approaches for missing data in PCA.\n",
+    "- In manifold learning, there is no good framework for handling missing data. In contrast, there are straightforward iterative approaches for dealing with missing data in PCA.\n",
     "- In manifold learning, the presence of noise in the data can \"short-circuit\" the manifold and drastically change the embedding. In contrast, PCA naturally filters noise from the most important components.\n",
     "- The manifold embedding result is generally highly dependent on the number of neighbors chosen, and there is generally no solid quantitative way to choose an optimal number of neighbors. In contrast, PCA does not involve such a choice.\n",
-    "- In manifold learning, the globally optimal number of output dimensions is difficult to determine. In contrast, PCA lets you find the output dimension based on the explained variance.\n",
+    "- In manifold learning, the globally optimal number of output dimensions is difficult to determine. In contrast, PCA lets you find the number of output dimensions based on the explained variance.\n",
     "- In manifold learning, the meaning of the embedded dimensions is not always clear. In PCA, the principal components have a very clear meaning.\n",
-    "- In manifold learning the computational expense of manifold methods scales as O[N^2] or O[N^3]. For PCA, there exist randomized approaches that are generally much faster (though see the [megaman](https://github.com/mmp2/megaman) package for some more scalable implementations of manifold learning).\n",
+    "- In manifold learning, the computational expense of manifold methods scales as $O[N^2]$ or $O[N^3]$. For PCA, there exist randomized approaches that are generally much faster (though see the [*megaman* package](https://github.com/mmp2/megaman) for some more scalable implementations of manifold learning).\n",
     "\n",
-    "With all that on the table, the only clear advantage of manifold learning methods over PCA is their ability to preserve nonlinear relationships in the data; for that reason I tend to explore data with manifold methods only after first exploring them with PCA.\n",
+    "With all that on the table, the only clear advantage of manifold learning methods over PCA is their ability to preserve nonlinear relationships in the data; for that reason I tend to explore data with manifold methods only after first exploring it with PCA.\n",
     "\n",
-    "Scikit-Learn implements several common variants of manifold learning beyond Isomap and LLE: the Scikit-Learn documentation has a [nice discussion and comparison of them](http://scikit-learn.org/stable/modules/manifold.html).\n",
+    "Scikit-Learn implements several common variants of manifold learning beyond LLE and Isomap (which we've used in a few of the previous chapters and will look at in the next section): the Scikit-Learn documentation has a [nice discussion and comparison of them](http://scikit-learn.org/stable/modules/manifold.html).\n",
     "Based on my own experience, I would give the following recommendations:\n",
     "\n",
-    "- For toy problems such as the S-curve we saw before, locally linear embedding (LLE) and its variants (especially *modified LLE*), perform very well. This is implemented in ``sklearn.manifold.LocallyLinearEmbedding``.\n",
-    "- For high-dimensional data from real-world sources, LLE often produces poor results, and isometric mapping (IsoMap) seems to generally lead to more meaningful embeddings. This is implemented in ``sklearn.manifold.Isomap``\n",
-    "- For data that is highly clustered, *t-distributed stochastic neighbor embedding* (t-SNE) seems to work very well, though can be very slow compared to other methods. This is implemented in ``sklearn.manifold.TSNE``.\n",
+    "- For toy problems such as the S-curve we saw before, LLE and its variants (especially modified LLE) perform very well. This is implemented in `sklearn.manifold.LocallyLinearEmbedding`.\n",
+    "- For high-dimensional data from real-world sources, LLE often produces poor results, and Isomap seems to generally lead to more meaningful embeddings. This is implemented in `sklearn.manifold.Isomap`.\n",
+    "- For data that is highly clustered, *t-distributed stochastic neighbor embedding* (t-SNE) seems to work very well, though it can be very slow compared to other methods. This is implemented in `sklearn.manifold.TSNE`.\n",
     "\n",
     "If you're interested in getting a feel for how these work, I'd suggest running each of the methods on the data in this section."
    ]
@@ -653,18 +675,20 @@
     "## Example: Isomap on Faces\n",
     "\n",
     "One place manifold learning is often used is in understanding the relationship between high-dimensional data points.\n",
-    "A common case of high-dimensional data is images: for example, a set of images with 1,000 pixels each can be thought of as a collection of points in 1,000 dimensions – the brightness of each pixel in each image defines the coordinate in that dimension.\n",
+    "A common case of high-dimensional data is images: for example, a set of images with 1,000 pixels each can be thought of as a collection of points in 1,000 dimensions, with the brightness of each pixel in each image defining the coordinate in that dimension.\n",
     "\n",
-    "Here let's apply Isomap on some faces data.\n",
-    "We will use the Labeled Faces in the Wild dataset, which we previously saw in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) and [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb).\n",
-    "Running this command will download the data and cache it in your home directory for later use:"
+    "To illustrate, let's apply Isomap on some data from the Labeled Faces in the Wild dataset, which we previously saw in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) and [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb).\n",
+    "Running this command will download the dataset and cache it in your home directory for later use:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -691,21 +715,24 @@
     "We have 2,370 images, each with 2,914 pixels.\n",
     "In other words, the images can be thought of as data points in a 2,914-dimensional space!\n",
     "\n",
-    "Let's quickly visualize several of these images to see what we're working with:"
+    "Let's display several of these images to remind us what we're working with (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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X15HYx3gjqPV6Pe7krNfrC0dmIUM4hCyo3WlSL8TBwIJYmf/BYBDRMGez+vm4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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11c0ff518>"
+       "<Figure size 432x288 with 32 Axes>"
       ]
      },
      "metadata": {},
@@ -722,22 +749,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We would like to plot a low-dimensional embedding of the 2,914-dimensional data to learn the fundamental relationships between the images.\n",
-    "One useful way to start is to compute a PCA, and examine the explained variance ratio, which will give us an idea of how many linear features are required to describe the data:"
+    "When we encountered this data in [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb), our goal was essentially compression: to use the components to reconstruct the inputs from the lower-dimensional representation.\n",
+    "\n",
+    "PCA is versatile enough that we can also use it in this context, where we would like to plot a low-dimensional embedding of the 2,914-dimensional data to learn the fundamental relationships between the images. Let's again look at the explained variance ratio, which will give us an idea of how many linear features are required to describe the data (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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w+ctQ3WjBusID0IWqcN/kgZDLZP4uiYiI6Bxee/JXQgiBBQsWoKKiAmq1GosX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\nfQQAOHbsGHbt2oWhQ4ee8/3nC1cAKC4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+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11871f860>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -745,8 +776,8 @@
     }
    ],
    "source": [
-    "from sklearn.decomposition import RandomizedPCA\n",
-    "model = RandomizedPCA(100).fit(faces.data)\n",
+    "from sklearn.decomposition import PCA\n",
+    "model = PCA(100, svd_solver='randomized').fit(faces.data)\n",
     "plt.plot(np.cumsum(model.explained_variance_ratio_))\n",
     "plt.xlabel('n components')\n",
     "plt.ylabel('cumulative variance');"
@@ -756,9 +787,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that for this data, nearly 100 components are required to preserve 90% of the variance: this tells us that the data is intrinsically very high dimensional—it can't be described linearly with just a few components.\n",
+    "We see that for this data, nearly 100 components are required to preserve 90% of the variance. This tells us that the data is intrinsically very high-dimensional—it can't be described linearly with just a few components.\n",
     "\n",
-    "When this is the case, nonlinear manifold embeddings like LLE and Isomap can be helpful.\n",
+    "When this is the case, nonlinear manifold embeddings like LLE and Isomap may be helpful.\n",
     "We can compute an Isomap embedding on these faces using the same pattern shown before:"
    ]
   },
@@ -766,7 +797,10 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -799,7 +833,10 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -831,21 +868,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Calling this function now, we see the result:"
+    "Calling this function now, we see the result in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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QdR2PPvoo7rvvPo6soqqbxWLB+vo6FhYWsLS0hKGhIRZSDocD0WgUlUoFFosF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drYtChQVWo7aG/jdrIl6e7uZisLyvOklrfVasWZM2dgMBhw/vx5pNNp7N69Gz09PchkMlCr1ahW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Rj8cxMzODhYUFHDhwYMXf0VhSJpNx1/CJJ55gW5lyuYzR0VFotVqoVCrMzMzwoq/T6ThyhwQe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XV1eXqlUgkLCwvI5/PYunUrOjs7ZfNlJFepVILFYmno0wagjjdmNptRLpfrNm02bcpYMZ5K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fj3Q6jc2bN8Nms+HXv/419Ho9hoeHYTKZUCgU4PP54HK50N3dvA3q8/mwbdumRZ+BCkEq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\nYdG+SqXC2bNncfbsWdx+++3vwjO2Ob70pS9tqWaLdHbDw8O49957t31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hPPDAAxgcHITf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\nR+1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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119104d68>"
+       "<Figure size 720x720 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -863,10 +903,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The result is interesting: the first two Isomap dimensions seem to describe global image features: the overall darkness or lightness of the image from left to right, and the general orientation of the face from bottom to top.\n",
+    "The result is interesting. The first two Isomap dimensions seem to describe global image features: the overall brightness of the image from left to right, and the general orientation of the face from bottom to top.\n",
     "This gives us a nice visual indication of some of the fundamental features in our data.\n",
     "\n",
-    "We could then go on to classify this data (perhaps using manifold features as inputs to the classification algorithm) as we did in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)."
+    "From here, we could then go on to classify this data (perhaps using manifold features as inputs to the classification algorithm) as we did in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)."
    ]
   },
   {
@@ -875,16 +915,19 @@
    "source": [
     "## Example: Visualizing Structure in Digits\n",
     "\n",
-    "As another example of using manifold learning for visualization, let's take a look at the MNIST handwritten digits set.\n",
-    "This data is similar to the digits we saw in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb), but with many more pixels per image.\n",
-    "It can be downloaded from http://mldata.org/ with the Scikit-Learn utility:"
+    "As another example of using manifold learning for visualization, let's take a look at the MNIST handwritten digits dataset.\n",
+    "This is similar to the digits dataset we saw in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb), but with many more pixels per image.\n",
+    "It can be downloaded from http://openml.org/ with the Scikit-Learn utility:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -899,8 +942,8 @@
     }
    ],
    "source": [
-    "from sklearn.datasets import fetch_mldata\n",
-    "mnist = fetch_mldata('MNIST original')\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "mnist = fetch_openml('mnist_784')\n",
     "mnist.data.shape"
    ]
   },
@@ -908,22 +951,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This consists of 70,000 images, each with 784 pixels (i.e. the images are 28×28).\n",
-    "As before, we can take a look at the first few images:"
+    "The dataset consists of 70,000 images, each with 784 pixels (i.e., the images are 28 × 28).\n",
+    "As before, we can take a look at the first few images (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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2JOFEo1EcDgeDg4N8/fXXmM1m0tPTOXDgAGVlZUkp/oB4ful36bU6NTXF/fv3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\nIyMj8xBkIykjIyMjI/MQ/h+NGlY9L5UZIAAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b3d9588>"
+       "<Figure size 432x288 with 48 Axes>"
       ]
      },
      "metadata": {},
@@ -931,9 +977,12 @@
     }
    ],
    "source": [
+    "mnist_data = np.asarray(mnist.data)\n",
+    "mnist_target = np.asarray(mnist.target, dtype=int)\n",
+    "\n",
     "fig, ax = plt.subplots(6, 8, subplot_kw=dict(xticks=[], yticks=[]))\n",
     "for i, axi in enumerate(ax.flat):\n",
-    "    axi.imshow(mnist.data[1250 * i].reshape(28, 28), cmap='gray_r')"
+    "    axi.imshow(mnist_data[1250 * i].reshape(28, 28), cmap='gray_r')"
    ]
   },
   {
@@ -943,22 +992,25 @@
     "This gives us an idea of the variety of handwriting styles in the dataset.\n",
     "\n",
     "Let's compute a manifold learning projection across the data.\n",
-    "For speed here, we'll only use 1/30 of the data, which is about ~2000 points\n",
-    "(because of the relatively poor scaling of manifold learning, I find that a few thousand samples is a good number to start with for relatively quick exploration before moving to a full calculation):"
+    "For speed here, we'll only use 1/30 of the data, which is about ~2,000 points\n",
+    "(because of the relatively poor scaling of manifold learning, I find that a few thousand samples is a good number to start with for relatively quick exploration before moving to a full calculation). the following figure shows the result:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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QPH8Z2z/t/9iLAEH4X+bg4EBqaioRERHk5+ezaNGiJ5YXnwLh/4V9p0+zKKCdtQWWnMjC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\nG6NRi7W1CYMhC9AAZvL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b3d2egD9xWT83cCqrDmwmW8jFzWDNrMH1HhKz2cy+1G9o87jo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Ny5c4HSm4NiYmKw2WyEhITQqVMnAIKDgxk1ahRKKeLj4wGIiooiNjaWDRs24Ofnx5IlS5wV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\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b08efd0>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -966,12 +1018,13 @@
     }
    ],
    "source": [
-    "# use only 1/30 of the data: full dataset takes a long time!\n",
-    "data = mnist.data[::30]\n",
-    "target = mnist.target[::30]\n",
+    "# Use only 1/30 of the data: full dataset takes a long time!\n",
+    "data = mnist_data[::30]\n",
+    "target = mnist_target[::30]\n",
     "\n",
     "model = Isomap(n_components=2)\n",
     "proj = model.fit_transform(data)\n",
+    "\n",
     "plt.scatter(proj[:, 0], proj[:, 1], c=target, cmap=plt.cm.get_cmap('jet', 10))\n",
     "plt.colorbar(ticks=range(10))\n",
     "plt.clim(-0.5, 9.5);"
@@ -982,21 +1035,24 @@
    "metadata": {},
    "source": [
     "The resulting scatter plot shows some of the relationships between the data points, but is a bit crowded.\n",
-    "We can gain more insight by looking at just a single number at a time:"
+    "We can gain more insight by looking at just a single number at a time (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ZWLVqFf773/+iW7du+OijjzjpgIiI6P8xtJHDOH78uLwzw4wZMzjpgIiIqBSGtmowGo1q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\nRERERA6AQRsRERGRA/g/YHDGvgsdgV4AAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11d47fcf8>"
+       "<Figure size 720x720 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1004,10 +1060,8 @@
     }
    ],
    "source": [
-    "from sklearn.manifold import Isomap\n",
-    "\n",
     "# Choose 1/4 of the \"1\" digits to project\n",
-    "data = mnist.data[mnist.target == 1][::4]\n",
+    "data = mnist_data[mnist_target == 1][::4]\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(10, 10))\n",
     "model = Isomap(n_neighbors=5, n_components=2, eigen_solver='dense')\n",
@@ -1019,29 +1073,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The result gives you an idea of the variety of forms that the number \"1\" can take within the dataset.\n",
+    "The result gives you an idea of the variety of forms that the number 1 can take within the dataset.\n",
     "The data lies along a broad curve in the projected space, which appears to trace the orientation of the digit.\n",
-    "As you move up the plot, you find ones that have hats and/or bases, though these are very sparse within the dataset.\n",
+    "As you move up the plot, you find 1s that have hats and/or bases, though these are very sparse within the dataset.\n",
     "The projection lets us identify outliers that have data issues: for example, pieces of the neighboring digits that snuck into the extracted images.\n",
     "\n",
-    "Now, this in itself may not be useful for the task of classifying digits, but it does help us get an understanding of the data, and may give us ideas about how to move forward, such as how we might want to preprocess the data before building a classification pipeline."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) | [Contents](Index.ipynb) | [In Depth: k-Means Clustering](05.11-K-Means.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.10-Manifold-Learning.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "Now, this in itself may not be useful for the task of classifying digits, but it does help us get an understanding of the data, and may give us ideas about how to move forward—such as how we might want to preprocess the data before building a classification pipeline."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1055,9 +1102,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.11-K-Means.ipynb b/notebooks/05.11-K-Means.ipynb
index 8907d80cf..78e5feeb4 100644
--- a/notebooks/05.11-K-Means.ipynb
+++ b/notebooks/05.11-K-Means.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) | [Contents](Index.ipynb) | [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.11-K-Means.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,11 +11,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the previous few sections, we have explored one category of unsupervised machine learning models: dimensionality reduction.\n",
-    "Here we will move on to another class of unsupervised machine learning models: clustering algorithms.\n",
+    "In the previous chapters we explored unsupervised machine learning models for dimensionality reduction.\n",
+    "Now we will move on to another class of unsupervised machine learning models: clustering algorithms.\n",
     "Clustering algorithms seek to learn, from the properties of the data, an optimal division or discrete labeling of groups of points.\n",
     "\n",
-    "Many clustering algorithms are available in Scikit-Learn and elsewhere, but perhaps the simplest to understand is an algorithm known as *k-means clustering*, which is implemented in ``sklearn.cluster.KMeans``.\n",
+    "Many clustering algorithms are available in Scikit-Learn and elsewhere, but perhaps the simplest to understand is an algorithm known as *k-means clustering*, which is implemented in `sklearn.cluster.KMeans`.\n",
     "\n",
     "We begin with the standard imports:"
    ]
@@ -46,13 +24,16 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns; sns.set()  # for plot styling\n",
+    "plt.style.use('seaborn-whitegrid')\n",
     "import numpy as np"
    ]
   },
@@ -67,31 +48,34 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The *k*-means algorithm searches for a pre-determined number of clusters within an unlabeled multidimensional dataset.\n",
+    "The *k*-means algorithm searches for a predetermined number of clusters within an unlabeled multidimensional dataset.\n",
     "It accomplishes this using a simple conception of what the optimal clustering looks like:\n",
     "\n",
-    "- The \"cluster center\" is the arithmetic mean of all the points belonging to the cluster.\n",
+    "- The *cluster center* is the arithmetic mean of all the points belonging to the cluster.\n",
     "- Each point is closer to its own cluster center than to other cluster centers.\n",
     "\n",
     "Those two assumptions are the basis of the *k*-means model.\n",
     "We will soon dive into exactly *how* the algorithm reaches this solution, but for now let's take a look at a simple dataset and see the *k*-means result.\n",
     "\n",
     "First, let's generate a two-dimensional dataset containing four distinct blobs.\n",
-    "To emphasize that this is an unsupervised algorithm, we will leave the labels out of the visualization"
+    "To emphasize that this is an unsupervised algorithm, we will leave the labels out of the visualization (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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AnAS6f/WKqLLXWU9zc3hhDqNWa26uhT//eVf3HGZjsbwL7MHtno7d/pmm1WhG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\ngGOiB2iF4tuTrary7ckW94wz2l6tUhX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ePZH+/ZewatXnQKCrWQl2WrHidzzwgN+te8UV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\nOD09FcSMqVarBcdxAICbmxtUVFRgY2MDYrH4t+t8y3zH2dl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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11581b588>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -99,7 +83,7 @@
     }
    ],
    "source": [
-    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "from sklearn.datasets import make_blobs\n",
     "X, y_true = make_blobs(n_samples=300, centers=4,\n",
     "                       cluster_std=0.60, random_state=0)\n",
     "plt.scatter(X[:, 0], X[:, 1], s=50);"
@@ -117,7 +101,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -131,7 +115,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's visualize the results by plotting the data colored by these labels.\n",
+    "Let's visualize the results by plotting the data colored by these labels (the following figure).\n",
     "We will also plot the cluster centers as determined by the *k*-means estimator:"
    ]
   },
@@ -139,14 +123,17 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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UeKCgRybHuLFt0x907dmNhT8vYNmU1cipWlK5ihd+6CR3LKQ47IjkQwAXOYWE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5BVJ4U6AGxFJcQFlv8cWnhEsIvy/fw7xFCcmkne5iMoJVZuCGnL1IH3EdekeaX9mzVsgnp0MzRy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\n0a9fZmPzQtI7+JDZw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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1189265f8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -157,7 +144,7 @@
     "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')\n",
     "\n",
     "centers = kmeans.cluster_centers_\n",
-    "plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5);"
+    "plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200);"
    ]
   },
   {
@@ -165,7 +152,7 @@
    "metadata": {},
    "source": [
     "The good news is that the *k*-means algorithm (at least in this simple case) assigns the points to clusters very similarly to how we might assign them by eye.\n",
-    "But you might wonder how this algorithm finds these clusters so quickly! After all, the number of possible combinations of cluster assignments is exponential in the number of data points—an exhaustive search would be very, very costly.\n",
+    "But you might wonder how this algorithm finds these clusters so quickly: after all, the number of possible combinations of cluster assignments is exponential in the number of data points—an exhaustive search would be very, very costly.\n",
     "Fortunately for us, such an exhaustive search is not necessary: instead, the typical approach to *k*-means involves an intuitive iterative approach known as *expectation–maximization*."
    ]
   },
@@ -173,7 +160,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## k-Means Algorithm: Expectation–Maximization"
+    "## Expectation–Maximization"
    ]
   },
   {
@@ -184,49 +171,52 @@
     "*k*-means is a particularly simple and easy-to-understand application of the algorithm, and we will walk through it briefly here.\n",
     "In short, the expectation–maximization approach here consists of the following procedure:\n",
     "\n",
-    "1. Guess some cluster centers\n",
-    "2. Repeat until converged\n",
-    "   1. *E-Step*: assign points to the nearest cluster center\n",
-    "   2. *M-Step*: set the cluster centers to the mean \n",
+    "1. Guess some cluster centers.\n",
+    "2. Repeat until converged:\n",
+    "   1. *E-step*: Assign points to the nearest cluster center.\n",
+    "   2. *M-step*: Set the cluster centers to the mean of their assigned points.\n",
     "\n",
-    "Here the \"E-step\" or \"Expectation step\" is so-named because it involves updating our expectation of which cluster each point belongs to.\n",
-    "The \"M-step\" or \"Maximization step\" is so-named because it involves maximizing some fitness function that defines the location of the cluster centers—in this case, that maximization is accomplished by taking a simple mean of the data in each cluster.\n",
+    "Here the *E-step* or *expectation step* is so named because it involves updating our expectation of which cluster each point belongs to.\n",
+    "The *M-step* or *maximization step* is so named because it involves maximizing some fitness function that defines the locations of the cluster centers—in this case, that maximization is accomplished by taking a simple mean of the data in each cluster.\n",
     "\n",
     "The literature about this algorithm is vast, but can be summarized as follows: under typical circumstances, each repetition of the E-step and M-step will always result in a better estimate of the cluster characteristics.\n",
     "\n",
     "We can visualize the algorithm as shown in the following figure.\n",
     "For the particular initialization shown here, the clusters converge in just three iterations.\n",
-    "For an interactive version of this figure, refer to the code in [the Appendix](06.00-Figure-Code.ipynb#Interactive-K-Means)."
+    "(For an interactive version of this figure, refer to the code in the online [appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Interactive-K-Means).)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![(run code in Appendix to generate image)](figures/05.11-expectation-maximization.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Expectation-Maximization)"
+    "![(run code in Appendix to generate image)](images/05.11-expectation-maximization.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Expectation-Maximization)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The *k*-Means algorithm is simple enough that we can write it in a few lines of code.\n",
-    "The following is a very basic implementation:"
+    "The *k*-means algorithm is simple enough that we can write it in a few lines of code.\n",
+    "The following is a very basic implementation (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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pMmPnrSFgTaEsazOmhHR0CVYCW1egKkrIRCBVVXAVZiPJMuW5O7CkNsTvLMO+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YS6T/vSSy+9dKVOVlJirnqjq5Svr1e9vb76em1+/oHsTMkn0Hsvgf8bbVNVlQXLImie+AKBgaEX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\nf+kjNKir5vNWAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118a43588>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -273,9 +263,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Caveats of expectation–maximization\n",
-    "\n",
-    "There are a few issues to be aware of when using the expectation–maximization algorithm."
+    "There are a few caveats to be aware of when using the expectation–maximization algorithm:"
    ]
   },
   {
@@ -284,21 +272,24 @@
    "source": [
     "#### The globally optimal result may not be achieved\n",
     "First, although the E–M procedure is guaranteed to improve the result in each step, there is no assurance that it will lead to the *global* best solution.\n",
-    "For example, if we use a different random seed in our simple procedure, the particular starting guesses lead to poor results:"
+    "For example, if we use a different random seed in our simple procedure, the particular starting guesses lead to poor results (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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UKgWkkojQKzS5pxFvffo2wcHBLsvyL4PBwJD/G+ryXJXwcJ5/rWgK179zGc8e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\n0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GxejyuKgk5jQ6+5gJ9BISfXRWCAZwvqYm4k1rwsLL/bJCI900mgcSvx/faQa3WzL1Hl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\nRK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118926390>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -315,7 +306,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here the E–M approach has converged, but has not converged to a globally optimal configuration. For this reason, it is common for the algorithm to be run for multiple starting guesses, as indeed Scikit-Learn does by default (set by the ``n_init`` parameter, which defaults to 10)."
+    "Here the E–M approach has converged, but has not converged to a globally optimal configuration. For this reason, it is common for the algorithm to be run for multiple starting guesses, as indeed Scikit-Learn does by default (the number is set by the ``n_init`` parameter, which defaults to 10)."
    ]
   },
   {
@@ -324,21 +315,24 @@
    "source": [
     "#### The number of clusters must be selected beforehand\n",
     "Another common challenge with *k*-means is that you must tell it how many clusters you expect: it cannot learn the number of clusters from the data.\n",
-    "For example, if we ask the algorithm to identify six clusters, it will happily proceed and find the best six clusters:"
+    "For example, if we ask the algorithm to identify six clusters, it will happily proceed and find the best six clusters, as shown in Figure 47-6:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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8vZF1W1bQt/tg5i6fyRbHHMzd9Bxdl0PcgAiSzEZSF+SheFSv7RVVRSV7nxVZ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\nAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118bb2278>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -352,12 +346,13 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Whether the result is meaningful is a question that is difficult to answer definitively; one approach that is rather intuitive, but that we won't discuss further here, is called [silhouette analysis](http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html).\n",
     "\n",
-    "Alternatively, you might use a more complicated clustering algorithm which has a better quantitative measure of the fitness per number of clusters (e.g., Gaussian mixture models; see [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb)) or which *can* choose a suitable number of clusters (e.g., DBSCAN, mean-shift, or affinity propagation, all available in the ``sklearn.cluster`` submodule)"
+    "Alternatively, you might use a more complicated clustering algorithm that has a better quantitative measure of the fitness per number of clusters (e.g., Gaussian mixture models; see [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb)) or which *can* choose a suitable number of clusters (e.g., DBSCAN, mean-shift, or affinity propagation, all available in the `sklearn.cluster` submodule)."
    ]
   },
   {
@@ -368,14 +363,14 @@
     "The fundamental model assumptions of *k*-means (points will be closer to their own cluster center than to others) means that the algorithm will often be ineffective if the clusters have complicated geometries.\n",
     "\n",
     "In particular, the boundaries between *k*-means clusters will always be linear, which means that it will fail for more complicated boundaries.\n",
-    "Consider the following data, along with the cluster labels found by the typical *k*-means approach:"
+    "Consider the following data, along with the cluster labels found by the typical *k*-means approach (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -387,14 +382,17 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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cxJ6JQ4frMC5duon1K39n47fb8pI25FZXh1OeWPUiXjjPi512m4ZkNG3RjIc/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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118dd6550>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -415,21 +413,24 @@
     "We might imagine using the same trick to allow *k*-means to discover non-linear boundaries.\n",
     "\n",
     "One version of this kernelized *k*-means is implemented in Scikit-Learn within the ``SpectralClustering`` estimator.\n",
-    "It uses the graph of nearest neighbors to compute a higher-dimensional representation of the data, and then assigns labels using a *k*-means algorithm:"
+    "It uses the graph of nearest neighbors to compute a higher-dimensional representation of the data, and then assigns labels using a *k*-means algorithm (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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9rYiqVg2Hw0HCqUR0uE7j0mWZWLPsd9Z9u7kwaUPB4+pwKpGgnscL57rYmTdo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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118ddb6d8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -459,8 +460,8 @@
     "#### k-means can be slow for large numbers of samples\n",
     "Because each iteration of *k*-means must access every point in the dataset, the algorithm can be relatively slow as the number of samples grows.\n",
     "You might wonder if this requirement to use all data at each iteration can be relaxed; for example, you might just use a subset of the data to update the cluster centers at each step.\n",
-    "This is the idea behind batch-based *k*-means algorithms, one form of which is implemented in ``sklearn.cluster.MiniBatchKMeans``.\n",
-    "The interface for this is the same as for standard ``KMeans``; we will see an example of its use as we continue our discussion."
+    "This is the idea behind batch-based *k*-means algorithms, one form of which is implemented in `sklearn.cluster.MiniBatchKMeans`.\n",
+    "The interface for this is the same as for standard `KMeans`; we will see an example of its use as we continue our discussion."
    ]
   },
   {
@@ -469,28 +470,31 @@
    "source": [
     "## Examples\n",
     "\n",
-    "Being careful about these limitations of the algorithm, we can use *k*-means to our advantage in a wide variety of situations.\n",
-    "We'll now take a look at a couple examples."
+    "Being careful about these limitations of the algorithm, we can use *k*-means to our advantage in a variety of situations.\n",
+    "We'll now take a look at a couple of examples."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Example 1: k-means on digits\n",
+    "### Example 1: k-Means on Digits\n",
     "\n",
     "To start, let's take a look at applying *k*-means on the same simple digits data that we saw in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) and [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb).\n",
     "Here we will attempt to use *k*-means to try to identify similar digits *without using the original label information*; this might be similar to a first step in extracting meaning from a new dataset about which you don't have any *a priori* label information.\n",
     "\n",
-    "We will start by loading the digits and then finding the ``KMeans`` clusters.\n",
-    "Recall that the digits consist of 1,797 samples with 64 features, where each of the 64 features is the brightness of one pixel in an 8×8 image:"
+    "We will start by loading the dataset, then find the clusters.\n",
+    "Recall that the digits dataset consists of 1,797 samples with 64 features, where each of the 64 features is the brightness of one pixel in an 8 × 8 image:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -521,7 +525,10 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -546,22 +553,25 @@
    "metadata": {},
    "source": [
     "The result is 10 clusters in 64 dimensions.\n",
-    "Notice that the cluster centers themselves are 64-dimensional points, and can themselves be interpreted as the \"typical\" digit within the cluster.\n",
-    "Let's see what these cluster centers look like:"
+    "Notice that the cluster centers themselves are 64-dimensional points, and can be interpreted as representing the \"typical\" digit within the cluster.\n",
+    "Let's see what these cluster centers look like (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcsAAAC1CAYAAAAwXdRCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYwkAgMBYAgAg/AskS+OmduoezQAAAABJRU5ErkJggg==\n",
+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118acb780>"
+       "<Figure size 576x216 with 10 Axes>"
       ]
      },
      "metadata": {},
@@ -582,15 +592,18 @@
    "source": [
     "We see that *even without the labels*, ``KMeans`` is able to find clusters whose centers are recognizable digits, with perhaps the exception of 1 and 8.\n",
     "\n",
-    "Because *k*-means knows nothing about the identity of the cluster, the 0–9 labels may be permuted.\n",
-    "We can fix this by matching each learned cluster label with the true labels found in them:"
+    "Because *k*-means knows nothing about the identities of the clusters, the 0–9 labels may be permuted.\n",
+    "We can fix this by matching each learned cluster label with the true labels found in the clusters:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -613,13 +626,16 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.79354479688369506"
+       "0.7935447968836951"
       ]
      },
      "execution_count": 15,
@@ -637,21 +653,24 @@
    "metadata": {},
    "source": [
     "With just a simple *k*-means algorithm, we discovered the correct grouping for 80% of the input digits!\n",
-    "Let's check the confusion matrix for this:"
+    "Let's check the confusion matrix for this, visualized in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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P5TQYDEydOhVXV1f27dv3P1/W9qQDsodNmITfM8/875ehPeaA7Hv3H2D25/Mx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\n/fsR9+/v7+f06dPcu3ePO3fu0NLSEt42MzOD2+3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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11a2d2ef0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -660,8 +679,10 @@
    ],
    "source": [
     "from sklearn.metrics import confusion_matrix\n",
+    "import seaborn as sns\n",
     "mat = confusion_matrix(digits.target, labels)\n",
-    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
+    "sns.heatmap(mat.T, square=True, annot=True, fmt='d',\n",
+    "            cbar=False, cmap='Blues',\n",
     "            xticklabels=digits.target_names,\n",
     "            yticklabels=digits.target_names)\n",
     "plt.xlabel('true label')\n",
@@ -676,7 +697,7 @@
     "But this still shows that using *k*-means, we can essentially build a digit classifier *without reference to any known labels*!\n",
     "\n",
     "Just for fun, let's try to push this even farther.\n",
-    "We can use the t-distributed stochastic neighbor embedding (t-SNE) algorithm (mentioned in [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)) to pre-process the data before performing *k*-means.\n",
+    "We can use the t-distributed stochastic neighbor embedding algorithm (mentioned in [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)) to preprocess the data before performing *k*-means.\n",
     "t-SNE is a nonlinear embedding algorithm that is particularly adept at preserving points within clusters.\n",
     "Let's see how it does:"
    ]
@@ -685,13 +706,16 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.91930996104618812"
+       "0.9415692821368948"
       ]
      },
      "execution_count": 17,
@@ -703,7 +727,8 @@
     "from sklearn.manifold import TSNE\n",
     "\n",
     "# Project the data: this step will take several seconds\n",
-    "tsne = TSNE(n_components=2, init='random', random_state=0)\n",
+    "tsne = TSNE(n_components=2, init='random',\n",
+    "            learning_rate='auto',random_state=0)\n",
     "digits_proj = tsne.fit_transform(digits.data)\n",
     "\n",
     "# Compute the clusters\n",
@@ -724,35 +749,40 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "That's nearly 92% classification accuracy *without using the labels*.\n",
-    "This is the power of unsupervised learning when used carefully: it can extract information from the dataset that it might be difficult to do by hand or by eye."
+    "That's a 94% classification accuracy *without using the labels*.\n",
+    "This is the power of unsupervised learning when used carefully: it can extract information from the dataset that it might be difficult to extract by hand or by eye."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Example 2: *k*-means for color compression\n",
+    "### Example 2: k-Means for Color Compression\n",
     "\n",
-    "One interesting application of clustering is in color compression within images.\n",
+    "One interesting application of clustering is in color compression within images (this example is adapted from Scikit-Learn's [\"Color Quantization Using K-Means\"](https://scikit-learn.org/stable/auto_examples/cluster/plot_color_quantization.html).\n",
     "For example, imagine you have an image with millions of colors.\n",
     "In most images, a large number of the colors will be unused, and many of the pixels in the image will have similar or even identical colors.\n",
     "\n",
-    "For example, consider the image shown in the following figure, which is from the Scikit-Learn ``datasets`` module (for this to work, you'll have to have the ``pillow`` Python package installed)."
+    "For example, consider the image shown in the following figure, which is from the Scikit-Learn `datasets` module (for this to work, you'll have to have the `PIL` Python package installed):\n",
+    "(For a color version of this and following images, see the online version of this book)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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zVhNJ3QgAIA38CE2UM8c7A91jNf7tvU/vUtpUIHwok9F4iy1lNL4WQ2nYVSc5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HbmPPlsCmN2npHLV1CkZPpcH1ohjT5XYvT6Nda0tHLyo4Ns2bAWdbHM9av93HvzzRx98x1uu2MX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VXiEARJRC9JyKsSZyBuKxCCIPDZrkVegGjyZY2hrkpv6xAe8l9VOXVdM51CGIWcjA+eZ2rO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\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YPDRFoVxg78QBAoEAdXODYnkex6rQaBQo1TZJ9iSxHJdKKU+5mCXVE2d1bRHTaiBxsD2T\nQq1BOBih2WqjRYKE+3oZIMJHVy7h2TaBcBDV9vjq9i1KdgvLcXBth1g8QrNZZ31xCcdsIm0by2lT\nb9WJBAyk649Fs20X13HI9PdTLlcxmzbttovjeuiGhq6rOK5DJBrEsmxcIbA8iISjrK+u4uFiujbJ\nVApDNRgdGUV6GqNjgyzNLxAKCtbWm7TMNqqmEEvEaLRbBEIGiVSU3/lvfzlL9m8cIK0EdVQBqqpi\nNm2EGiQYNHB0G88xcUyTWrlMNBwBBULRKK6UGLrRAVQdT4KreriK3+jWpMSzbVzXnyauaVrnpJBo\natBfISoqQc1AIohHIwjA9ixcV+DaDiEjAEg06U+M79IxnUv/GU7G65yELqBIiWLoOKZDxIhy/JUX\nuTz3iFyuxuEDU7Qsh4AjMYtrJNP9OKpDUAMhXRq1KpFEAsezkQ40K03c3hQB16NXKshag5V8jnjQ\noFlvEjBUwCKRSlCvW7imRyah46kuQtVwpEetUqY/mUQXgmzDJOxBs5JDC5uMRRNs5HKkElEK2Swp\nK0692aaUy5MIRXj/Zz8lqhj8hz/+NwSjEeau3SEQiyJMC+lAUlcwELiRICWnzYbdQKqCbH4DXVUJ\nBgKMj4VRVSgULGrNInv3pwgLl1xNUGmZKI5LSFGhaaK1AoSDgoAaY/7qDYY0F+fOHXL5HEZhk7UP\nFxka2k9u9Qnz7/8liahKq1giv7HJnp4gC6trDJQiDNk1tJUKvW6ZRKyX7P1rhPvTqLEotc1ZqEqS\nAUktv4ydGqQn3o/TtjF6e6mtZzkWiLFSblNzPO5TIBDsJSJtQvEeZosObkBFi48SCQVJNaskPBu5\n/wB5NYIa0Mk/uAshmIgIho8dZGnmEaGgzpGRJA6DGE4d1SuhaCo6GZREGkcxGdrjEbGCOLKE6bmE\ntQCZVAY7EaA3WcMRBaqkMGQUoy9EtWSRDMRJ94SJBgNsFTZoiBpffPwRUcPAbtY5eGA/h06e4ONP\nP+PVV9/g4fR9msU60UwcRajbVJvjOL4ABokiJU673anbdsQ0SOnPynRdPMfGdWyMYMe20el9yW5D\nkm61sws4O3fuXxyNtfN1B1yeoTnB31Y6oeGCzrUtcKSHrmi/IB56FqwURcFzJLi+QnTf+F4ajQZL\nqysEwiEcx/EtGkJ0xba+mlYRqFLB0HW/QvU8BN6ufXXZVo/KHVXs7l7l7n3pVpsKohPb161F8ate\n2e3tep3X7ahvcDq+RQUhuvvarVpV386iqP5nJbpzNdkWWcnuMfG8TgrRzu1rR6gE0uuIojx3m9IG\ncBTpx9upKrVWmUdPHhNNhRkdniAVT3B95hqpWAJFkYyN9XPt5jUezzxheO9etlY3KGWLPJ2bJx4L\nc2j/XiZGxrHdfvI5kzffOofZ2mJ9ZQFPkcyvzFPYKnDwwGEcKXBCUSZSA/zJj/+Cb//6r7OVzdOq\nNJk8dJCI4/Gnf/4j7HyFweFh7m/O07JsJg8cJB4L8PDxYwb602iKoFmrYMSSqKYKrgOeS9tsAwqN\npslWtoDneDSbFopqIB1fGd1sNolEgiiqoF5p4SiCtunx6juv8+lH79PXnwZNQZUKI6ODFAtlCsUt\nllZsJkcHaaouQndRTJ/hKNca5IoFpqb2ILRfVmXDfwEwwe8fuKaFomg+W+E6qKqvCutJ9PLS2fOY\nrTYr2XUqjTqa4VOnuqL7iRsKKCKwzb+rQiEY0H2KRXal5Dtz9fxVqbI9DFXrKPn8VbSKFtBACjSh\n4rO6audk5VlBwO4LVPjJIlJKPNufbK4oCkibeCJMuVwnEQ6RSURxWm2UuMGGqaCHBZ7TIojNkeE0\nnpREEKitFvG4SlMoeJoLjSq6qtBDjPJmmWAogghrgEVLChquRyAWp2420YWG47iUqzVi8SjlWo2w\nYVCr1rEcDVUqmK6HbvsXv66AGlUIKyZuQhJyVYJhFTNoIJtt9qUT1Nt1ejJxCq0mWhDisRBhz2ap\n3sLTDdp1k4DQ0AToSghhBNFcj7ATIRUOo4TrtD0X6UiCjsv+aIpsWEfqklbbptVuERZV6eJP0gAA\nIABJREFUltYKJDMpVDWFEQxgthoEK1skFYXM6F7W6k1iY33U2xbCtggHopihOFvNOsFQirrZpseI\nU8q2iA3GyFXKjEaHmC/a1HSDsFNHDw5R3Fhj7569zGPQaAeo19vE+vfRahUYEhoxLYoeNBDKaVSz\nTa28ikxEyYxnsPUoRrOK164Rio3hmYJm0yUz6FKutjjy2st8fPEyEwGP3lCbx3act06/iO2tIgIH\ncKwNokEbXVWplSPEUv1Ydg1BA6kHUM0kuuNiOzaba7NUCzncvgh9sRhbTpsIS+jCYjzcZnP2IR/8\n+6scP3uaw8+/wuOn8zjNKsIOIz2bVruJcCR3bt6ltbTBoeE0IUNH1YO0PRMPAyEkuqpi6FrnJiux\nrTbC85Ce25n4oeHhoSoqjuNgmdY2UAjwKVfh+ZXSMyrXHeuG72P75Ti5Z+wVv/C97Ug36XscFcVX\nbipGEKdzU1M6pn6xi/XpPjzppwpJAcKTZDezzM/Oke7rIxqP4XRAZFv80gVq6fpWD9nJa3UcH1Dx\n/Z+e7IJapzrDz7VVUXfdGXbvx07Cjz/I2QOlA5dC7up9dvuv3SodhOIhPAWvo64VivDD0REI6aII\npUNN+yEGctdh6C7upfRVuV0Q3O6BdsPnO9Vo97NCdkIfBEjpomhQq5coVLc4cuwIi49nWFpdY9Hz\n6Osf5uniPFurS9y4cZtKvsZmqYoRSbGwsMDJY0c4ffoYzWYJ12ny4MkVph9O8+LrL3H7/s8Y7t1P\nJjNOo+nR0zvI0sIWpXyNOzfuMXhsH16hSaY3RXp0kG+//CbttRJ2Jc/+kRG+861voduSSxcvMj44\nRNO2MAIq2VwWqQqeLDzl1ImTHDt1gnKjSraYpZTLEo9EEKqOK0ALBWi2LZDgCoHiSWKxGJ5n42r+\nh1ZrNPCkX6XHkxFK5SIjI31o0QC5XAnZcHEaJuFIgFBEo2m2MWkzPDzEnQcrKIqCZdn0x5PkiiX2\n7tsPnvnLYMh/ATC3g5plZ86a9DoNeZ8KWl/f4sala5w5fYpEb5ym2UZRNFRPoEiBoqj+qkrVtue8\ndSkJPwZrhy4VQqBIud0b6JzReJ4fGO11VLI7F1+HnhACpLd9wu6mObqP7oXtum5H8Sb56KOPuHzt\nU+TcBlEjjlevIMMCq9YimYjw3e/+GuVinqZss3D/FnpbYNoNomhslixiRox+Q4G4TimuUK3WiKgB\nmqpkbCiNqoHtttF0nWQyiUSj0tYQaIQUAyWR9DfU8VAUhaCnIoICtW1iGiFatToNxybsWUSjIRrt\nNp4tKVsmcaGRr5SJB0Js5EvEY2GapkWl2SKRSLFazDOejNLSFFwEiWgS0WgQUFUqrTaReBxNg9JW\nicrGConhOD2ZGD2BQdbvL9CixuD4WZpyk5gZQxYaqMV7pAfHaEUiLGdrWA1J3JAcC6i0jTD3t+o0\n7ApRTSVtJEl7Fk5Io9STxmpE0ISK01oiKXqQhsH4ngTtqkmjpJIc3EvUVZAij12Po6U93GSagNTp\nSanYSRUsj4QbJKGDJEwtEiEhIKnruBwCXEwtQdVMMhJJoqgeDTVFs2kTaW/Sn4YNVxIMBhlL6QQ9\ng2ArxKHeKBHyVM0WdrOAZ0JN08lmszx9/JRa7TO0gEez3SAWNTBrJhEjghYQrGysEwgq3ELlD3/w\nHfbGIuiORq3epqQptD0Ho6Xw1unjXH38hI9++imKZxHSEgSkgm66GFLBk5KlxTWe2zeK47noioqn\nS1RVwxM2UnromobbmUbh2CYCrxMk7qEqAkdCMBzCw0NTVHRVQ0HguK5PbyoKrucLdYQiOvzrswpW\nz/tlcc4vCnZ+MRfWDydXQLpoQkFIm2Q8TjZfQFUNXNd9BvR2/9ttkYvwe5lt18aWHpu5LCnHJh6P\n++EAu8Q521Wi16FKpUBROzm4narbpzd3qkoECD+BfLfWade+dV9f6Yp9dxSp7FTFuys7//U74eWq\n1kk66lK4ovOnK47CFzDtorR36NcuUO8wCqIzasvz/M/So7MA6MQFStf/3FQBjmNSKBUQwsWz6uSK\nmzQ8k2gqwZPHM0ihMDo0QnZri2qxweDIOJV2k7t3rzPUN8Dk5ATv/eRHNBpVarU6oxNDnDxxip5o\nivzmFj3hHlzH5MqnN3j7nW9jtUwmBgdpVgvcXVyitpYllooRDxlEXIndE+RgdJi1uQWWtlYYHxsh\nn9sgEtQp1oqsbzXQFAVXShqWw/SDexzcP0k2n+PdX/kuNy9dYn1tHVsIyvW6f1/3PD+8XgiMgI7n\nediODVKhbVpE4yGisRCW58+/efjwPio2dl1iGEH+q9/+Lcq5TSzZ4vr0dUb3pshuZJmcHCcS7WUr\nnyWVjpPP5ggGde7dm+ZXvvuN/ywm/s0VpqdA58J0O1JmtQNSDoJAIMLI+AQbWznq9QbhRBjP802z\nXSm23/n0BdOu3Ily6uYiIuV2oq3iy8YQiuoL/xQVpTPxQFNVXO//p+zNguzKrjO9bw/nnDvlvTkn\n5hkFVBVQc7FYZJFFUk1KrZDc3Zal6LDc3Wq5wx0OR/jF7w5H+MlPfrAVfpAcskIdGptqcWgNFIcS\nWax5RmEGEkgkch5u3vkMe/DDPjcBiNVSKCMykJnIvPfmybP2Wutf//p/D2pcOYv9BFyGII7/gjhu\nOWRXKjjDF9Zy4sQJ+qMXuXr1OjrNkFnKSnuZuDLB11/9ZQ6cfwKXOVyryrWrd/jxT/4KZ0dUifi/\nfvf/5fb6Kn/y27/F4bhFvVnHKYXbGjCVKKYTRVZkREpRrVRwUmCMJ6nMk0QVsjTHGEdhDHFNooWj\nNTGBizPq3tN1BVPxPBu72yhvGQ4gq9TRqaK6UMP2R8xMHSaWEa7pENKRDjNcrcVARojGNLZeReR9\nZFzBeknkFNNzcxTdHgVJqE7pMtFqMRpWyDZjKpM5j52aZ5OEj/Z6eN9BZTlT1SpHj8yzKzW6ucBc\nZYFKLHHFFt6OyKTmyPRjaJvj44iNjW3qZoVWNCA+rCji42jRQHU1Nj7B5nsfcqG5wCf3bjHsO176\n/OMMFu9w6MgTrLYd09EUbuYgEz2YVLDjHFOHW9RNm5Hb4Iyu0vae2AmqsaDvC6pU6I+qjEZDhq5D\nt8iZmkqpuYyRFyxvKZJEUune5lyrxnuXF6k/NcvzBwS7nQ0yOUUS94glJM1J/uI7f8Fk4wDYNklU\nZdjLmKxMsrG9R5LEHJg5yPbWJs6C0jHvv3OdwqdUqxOce/YUR45fYPfTRS4erzI1MvRWlzjcbLEy\nGFHgUB46K5v8zV9+j612mzwbUa0meCUgTxHGYlQpoO0MSmlAIr3H5kE+TilVkmDG/pWK3IjSrzGI\nEygZdiKlKP0XxficfhRqDZ6J8u+EjX8k0T2yduFDPIVuDLSAfm+PbDSgVquSRBHGOpSUwbhaPkjQ\n+4na2LIbAy8Fc4cOkGYZAkESRfsiBJ+1kuIE+53ZeLdUKYVxlgcLGiF9SRH2OqV7qJwWoXMbfxwY\nqeEaBNawIBiqjOHYsbBDKGTGaTGs6DiENyCKkCCdxBGhlMUaR6SjfRcX3Lgw8fuvQQow3pTdYwkG\nPwSfw/jDUgd4f5oJEsPlS+9x5vRR7t+6QaYUL37xFe5cW+Rf/utf57t/8k0uvfc+fSVYmD/IIMvo\nD/u0O+scnJvme3/9F+R5ytTMNMdPnGZzcx3tEw7PnubgrOL61fc5/fh5nn7+CVZWl1DasLGxiKKL\nH4748ds/5diTp1i/fZs3b67y7Fe/zCESXv/Lv+La2l1u3L3Gzu4m3V6b6ZkWO5s75HkBXpAVOXPT\n03TaXUbDlPnZBS6cu8j9u2tsbG1idZjR1uoVXGEYjEYgoFarsdseUq0mjPIUIRWtZoudXhspJKeO\nHGdpaQnvDV//+jdYWJhHFENee/0NJpoVzj/5FO8MPuTy5WWSSoT30OsOmZyZwHnB889e5MCBmc9M\niX//DFM4CmdJiIKQr5YIYxE4dBSRNFq4RpVYSmIZUZghSkYY5xFa4FChIvIB45c4JHbMMXjwPIgy\njhxQ0tu9CIatgsAUEwIpxgvWY/hlzK5z4cZ75NULxjJdEhA6dK3OO5I44fDhY/RMxiVdYWZqmubc\nDF9++WtMLpxCTlQYDHYQPsIMc1ziiYXDSKggiArBgXNnaBw7TrGzQVNWqDdmuLK9RqtaI3eSTt8R\n1aoMdnImpib55NoNfGGJ4wqRjqnWKkRxhPGKJKqglKKwHiM9lWqValRlfqqFVop+Q7M9bNPNUpz0\niFrE8bl5lEgwjIhsQTbImZqYoLAOYVJiLMdmKoySiMxLJqamGWY5Vivqk9PIuIJWCanqMzlVpzU7\nxcLUJO0bW0h/iKMHFhDJMaRX0NkCkWKER0xOc+LkFM00RdoqlTRjTtbAVbFKISoNKocXmEkrnBR7\nDKMZVmmh4xni+Q7t3kF0dIvcQmNmBhv1iGOo1wU+G1JVlglX0M5SNlZXWcn6FLbK/UhRpH1kMaDb\n69EZZQgrMTEUeRc1tIxGHiMMad4n0poXzh3ll37pK+wVDfJ0hvbgOnNNzcpmh8MzNa6//iPOPnGG\nZDrhUGOaj969ws2b17DKkfYjKs0KPQ8SjRcKnxuk8GidU0kUqZNUlCaJq/TMBJtbnrTYYWllja9/\nZRqTjTgwN8Fou01/dQ8dxUhdI0oqSA/VJOYLX/4yn95f4vab7xIpHQht1qGHGXFjir6VeG9RGkZp\nipNgcxdiqQBKxqb3HhVHeKVIB12UAETYVcMHAfVxNyfGCE4Zffu7iQ9H0EOzDVEe2GPEcJxIx6sZ\nRZ7jtMRYS5qn7Oy2iZMaUkoKk5fjj0c7TAjjmDHU+WD2KfcJMOKh738kaZZJd5/QV/5XmHcCuBK5\nCtegxKXDdeLRx9nfeSxVeMZKrQ6L0hHOFlgKpFdlIi4Tn9AIafHW4oRBMKIo9uj2+0TJFI3GDPmw\nS5pm2EqVJKmjhA7WZTwgFAkR5AqxgSEsylfgvEeWziohd6ryPghEMOU9t29c5dy5E5w/e5K7t69T\nm5/k7MEjtHQEvSHf/IM/ZfneXbZ3Nlhq7/LS517m2Wef4pNLl3jhhRf4+L0PWb5zh/MXzuBI6HWG\nNKdnsNbw9ps/Ik6qHD9ymEEnZzASNCfn6Y92qNcrrN7f5uP33iFqVSmyEVtLS2RTMxzbXmfz3ibH\njx3hk83bVFt1dod7RLGkM+py8akLrN6+z26ny8jlNKp1+t0eT5w7x2vf/yE7K5sUuSfLCiIR4YVh\nNBxiswKdxExPTrGz02Z6ZpbRaIDUmjxz7LY7VCoRTz9zkZX766HoKDxLt+9yduEE3/nuD5iZa6IS\nxQ+//yFaRKT9Eb1ePzDHkRxcOMzCgWk27q3yXtqFnyXJ/gMdprdYLVCZREvoCIOSFbQERIbUWUhK\nPiF1OULFKBkhKfbnJ9JrLGP5qkfnIzCGfcbYvC6D/wFUNBZgxrNPPx//nCgr5v03We5hEdwLQsda\nYv0uxwtJFCWkhaHfz+js7oLQDKynnXpOnblAzyq8DvY8JvNEFYmSOgS38eQibDS71PCb//5/4i+/\n9U3c8jL3by5Syw0TjRqNqqTanGKQFeA0xmZ0Ol28ENh+P3QLO2H2Ei6L2A9w7R1REvPc+dPgMqTy\naCocn54kOjHJ5cV7TCaKlrRQkYxGjkqlhrcCUa2x0JrEFxmRNWBHFFGCETGjQYZMYP5ADS9A1xS7\nlZh23qZq9xCjDqPmIf7m2jb/5ld+kQONRYx/nFzsUs87xHqCmq2QD2eYaTWxYpdK/SDKOQZIdFRH\nkZLphLzTw+QR/XiSfqegcAMqDYXrDaiO3ifN1ukNJ/naqVMs7y5R79yi7WE3r1H1MTvW0l5f5gd/\n8z2czbEuXBOlJd5ahNAYL6kpj5eAyFEyZpAVVKsR0hfY3PLJtdtMzk7z4UdXufDURZ65cJa5qSqH\nD1lUfw+5uUm11uDkE8/zW7/9e9y5d4v+ICURmlgqIu2YmmoBlqmGItYSLUNXF0URlaoidpZJYZh0\nhnbk6DqBzyW//3v/gZnpJunkLFUVU4kjijxHDS0ygTqCUXePb/+fv8Wo6JEWGShJXhTEWnH10kdM\nK8Gxc8eZm5hAugIdxUTFkCEGLx0mUmgrKLwjsh5tPJFQ2DQDZ3BynEACxDcWCnD7nV74zDO+/yyf\n9RbyTujH7DguRehvtNZorYm0QGtFY6KBUprMWKQL2qvOmgcrGo8kvlIAwTm8CHqtlJ2wlBKt5CP7\nkvvQsXiQxB8o+oyhU//gnBGwr0tLuE7js2bMQnXOobxECfXQdSg7QStQCqQfm2hpxNhODDA2J44l\n1gzBD+l0N+l0Oxw6VEXLlF53ncwMEWqaam0C7wVK5ggRI0SEp0BLi3Ie4WI8MnitKElcznwdEoTG\neR9WgKwl9oJsNCQbDPnog49p1GscP/I4lakaa0v36Ky2Kdp9tns9/tmv/Lf8b//7/8oXX3mZn77x\nEypKcvTgEd58/S16nS4vfvVL1Kqae1dvsLG1wfypA+zt7vGFF57hypVP6HSH5Dbly1/5Gv3+Nrdu\nX6X6+EUu3bzHS6++zN/89Kesrqxx995dXjg4x0y1SrdaxyrF3vYGiys3qbSq9DaHNCea9PZ6nDt7\nnsvXrhKlA9o7bdZXN7i9uITXMNWcYDgcoEqUTjqJ9AG1NMKx1d7m7GNn6bb32OvtUqnE5HkOJuPY\nkYNI32Nvdw2pEnxmuX3jFnev3cbkBaPNLrpe5flXX+KZ5z9PtTnF+9/6Ed/+zrepVBLWVtZ47OQx\ntOlz69PrnxkLf3/CLCKU9mypXTp33uX86Qt0mKGwe8R6Dy0zirSgWjtE5mYpbIb3Q5TWSKcRpR6l\nHnPQ/KNuCY+6CYzlr3gkOB4Nip8lDogxslsOwx+QAscqJCosPgsQ3uHSAYdaE0wfmWMw2uLtKOL+\nyn12+l0K41BJjQyDySwaDWa8nxXmR1YA1kHuEfUKPePZXdlgujHJxnaPvFKhOnWYH/zt64yyjFhr\nhNQIEeNtgXCBtBA65kC0kKpU4fIlGy7L2esMWZifxLqCvJcSqwIbRfhBjlUen1RY2txmfm6WzmDI\n3PxB/vrHP0YITRJpqnGM1gqlE4xQKC+oOsG5kyfwyiCIaSiLAerVFpXWDBO1mBNH5xkOO2jdw6oe\nw/6QbLBFNU65t5oz6F7mhhzgbEFeZOSjjP4opd8fojxkJtDs4yLl1/75N5ACalJi9wbEVpEwZK4m\nGd5YZjstwjpRDeKJKY7UptndWmVmpsHHr32MqjhMaqnHCbWJBr1+jySJMXkBJsWKiEQkpIVHVhXW\npigboHsrQRrBj374PtWa5rUf/4S3fvoO33jlOU6cfpzO7oCDjQrXbt/hteu3Wbx7lyRS2MKDdIhE\noKXHFlCPagxMmBtCMOI1oyw42EeaOKlSEQJZZERAo1ZnbyLFOEe9FuTTUA6nHYNBn5nJiUDAijSb\n6+v00x4VJYmkooogynKeO3GCt5Y+ZuON+5zb3GPj7l3iUUbFCCojj0xzIhWTKyhwGAqKqmQoLIU3\nocCUGu/zMdhSLtCPAb0xweRBTMmHVjEeDbKxnN4Dey7EWJ4t8AocDieCCbQuZ3B4KIxFS1XCwKLs\nlB6Cg0uYVJRfK3miYB2mPD8emfs9gk2NXUbKz+SjM9mwtyj2f0tnKePuUfH28TzyYcasjAzeBOsw\nijrIIcbFCKcRKkfIIcKAz2JsrqjEM7i04Oj8GZyx7G33sdmISgNSO0SOUmqVRmD3Cg0uzDSRgemP\nMkil9v8+gdUvwEmkEoBB+JxiNGJ17R5aWLJim2Ge0u4JIqGZNwcY7bbZKwY4pYm14M//4A/JreGj\n9z4mL+C1n77FuVNniYg4eOQwH77/ISePHWX+8HHOP/UMaytL5NKxN8yZnJilmyteePlVVla3uHPz\nLl/6wpdJ+0POPXaKj69e4fSR49yPJAutKra/gRmusLlzj37PUlUJ+Sin2+uQDnOMkiQkLC/fY9Dv\noSPJbnubmdkWRWHIMaS+QFZjBBCVhZHJLbZcKcR5lpbvYApDpZJgTIFEUKvUaNbqXP30Cu2dnMKH\nr+9tt8FDfWKCZ595DqnAZBk7Gxt88cmn+OK/+Xe88dprDIuUKIp55atf5nvf+3N0feJn44B/CJKN\nFaPBHnf2rpFufMwLZ6bpyx5razc4Oq9ob25yeOEAy/evUJk9zuTE0wz6NbxUCCzO5yX0IPAuTNLD\n7PGzn+/BbMX/zNf+rirHwx+L/QAZK3OU84kxsUFqnJdoYahEgsRmyP42p08epZJERMKgVYxNM6yu\nhMNWSrwFk6ZUqgE+LYoMKzxpnqEbVZyFwaDP9ft3mZUx0xMtVjfanDxfpTPIiGJNmmXgc7TUFOX8\nd18FBMpkHILWlcFvPSzeX2Z9e4uiyDhz5BhHZua4dWsJ2c+oz7TQ1RpH5w8x6HepTU2wtHKfbJTh\n/IhBWXgIHWNMQaQ1LhtRx3N4VpEzpBg2OTbRore1zOKwzzO/+Wt8+7e/yXMXHmN3+yNk2mVpw1Kd\nttTNNMVI8eabf05/YJHKorzCSxkqbe+Q3lFDMJAgC0uK480PrjHVqHFtaZN/8XNfYX72AAM1S/Ne\nn/7yGsV0Cz/Tojm3wKV3r/Pex9/Gpind/oCpWow0EdoKhPYM+h0qcYIpLFiBMgJVUxR5zuzsNDvd\nLnEk0CoUXmEdXuGsIVIJWgyxAn78zkd88tF1WhN1TK3GarfD6l6HyDkio0P3rYPJbyw0ldiSZQOc\nLwKDWwgqKiLtDxDOUotiEueREdQnGnS6I4xPOHviNDdvXEfYHNKMWHiqsaQWaeJEop2j1x1w4fmL\n3Hz3dbwNK1ejUZcjE4+xd3uRC5UhJy6epnn4INakWCHJI8mu8kxlnlENttIRqhKTOEPqHUkUoaXE\nuGDo7G1YaxhbeQkxJqeM3x7adXQgHgpOAfszT1EmrX0iZzBfDA4n5U8ppfYTnLUWZwluGjwQFw/3\nesnGdQ/Hsdgn7pQcU4QIuq9jgfXy20oItTwrSohJqnHye4BCPdrNQqQexFqpQr9P7vHeIESYE/ty\nJURFHm9GKNUhyxIMBRMT02SpJ/Y1jNtia/N9pKvgGwdxWUqnbRnlbeYXJvnw0scst+/y8lf+KZOt\nBF96dEoswjqq9QqDdIRUMULmOF8glCLPM3rdDpPNBpW4gfJQZD20trQmKow6Fb773f/EzMIcW502\nx46dolKp093dwrqc9z54iy+89CrHz53C5hZ7OWd2Zo5+bjnz2Fk2Nza4+NgTvPHuWzz1+BOsrWxQ\nb01y89ZNzh87yeLeKoPMsbS0Ta9wnDy3y9tv/5TZiWl6u23e+vHrHD16mKMHDnH59k2Oz85yZGGa\nzbXbfOtPFhkNHYYJpqZnWOt2GMoM7wzTrSk27q8SqZjUZPgs3AALB+a4e/ceWZFiPNRqCdb4cP5m\nOVJFjAYpVR1QB7wjTiLSYRYEJwRYb4niKspXaU5M0BmkgSjkPIXRHJ2ZZ2drm17WY2Vzh5mZeZ48\ncxZ7Y525qUlWtjfY2dvid//o/yPNe/zcNz7bQPrvTZi5H1JpWhqrbc6cOsNa7x5bgy55ewerpkmK\nXdZu3UbVppiqn8fmRWDVWjAetCrJB4xnmGPsvrxJHwrMcUCMqdufta/1yPzjocpRChkc3sfVqA/M\nWmSgFFjhkCrBOhiZnI1Rjz0yLrd3yNKUqlbYPCzVChmUUHJvgrmtUlTqVQSq3CsNs4YiTdna2YXe\nkMJ5tNbk3jMaFbz+xhtILXHWoD1IHMYVKCRj3oHSGmNMCUeBlj4IZBeAlKTGko8ypIcrS/eZOTjD\n/ORB0vYi03Pz1BbmeeetD1lbW0YA1SgBa1AICizGOapClmosBUkckWUFS7s7tCYmqChJlo1w0sFA\n8PFr15iVEQeTmG2lUOoYZ48dZTe9TX9njaW9PmnmwaZEUYU8M2gRsANXZCRJjEs0KjdB+cRnfHL5\nalnNC/7gD/8jjz11lP/m13+N2dYK3ZUVzCDj0MuPs7i2y9/+9G0iaahJyYnDs/T7GcQeXxSoKCbR\n5cqChEI4clOgpEZXI7yQJEmFfjaiIOzSVZxAilAsSC9QhC57ImlwoNlglI2oJnVEMUI6TSEEDaVI\nIg3GIrQg9oqsPwgKKb4I2qBSIiXMTU4S34+xDqJqlUhEKJ8SSUuE4f7yGpVE4GyBt56IiGa9wvT8\nPHiP0RIlBGeef4p2XfDpf/hjvA1z8plGjY/eeJ3tYUbjwCrPftHQjGtUukO6KqXwGfevX+dSp8PR\ns+epTU4itKCmKszOLjDs91HFiMi18LLc5ROUqxfuM2LwgVPJw4zQcXw52DdRxge3DYUsu8WQ5IwP\n4wUlBN6FBX6t1Tiv4j0/A60+/PbofqTcPw/GPpjOljugYyUg7x5KnA8eQwiBGpOKxtyaEgrepwc+\n1JVK4ZEqwM1ClLuYQpcNuCUbpNy98z3q9TlmDp6g02kzNbGAGRn6e21mJqtsb2whXISOgilEVHFs\n7axz6OgsM8cnmJubQWmPN2GOqrAU2S53N7pMHjiGF47EB09K5T1pt0ckHb3uLpVpSXunRy2RXLly\nlSOHTrO72aVZn8fZiPn545w49STSS66/9UOWt1ZYmD/Mx29/yK29Hi994RX+2X/1L/jBD3+IEI7z\nTz7GR6Mel65fJh+OePe9j/jaL/wTplt1lq5e4o7yqMiTKEWG5MnHT3Dz8qc8duw4k5PTXL7yKU88\nc4Gd3U3Wd+8xHLTZ9UN2uvNMVGc4NDtHkQl+5/f/EKcSVKVGOijwHlZXV4m0oDAFtWaLfJRy7PBh\ner0ew+Ew3KeAzTw6irHeIrWiKHKk0JjMYsipVCIy59CRQscJxgXFqxu3Fjk4u8BsXOfdjz+moirg\nFUoLThw/xY2rH1Ctx8xOtPjxD39Evjvi5577Iloqms0maSejvbtBq1Xjjb/9yWdnQiqOAAAgAElE\nQVRkxH8oYUaa+3cvcTRxqFjTZoe9zi1uvLXOUgxHj8W89MITDG2TxFQZ5ANiWSH3EYnUIEoo04MX\nuqxuizI4xEOJsnQZ8O5RhQvn97uuhxeP9+nl5S4WOrDi1HiGsU85N3if47wkI0dojVOK1EQMkcSV\nWjDsdZJ8NGIwyphAIIqCRAl0JaGZVJmdmsGaAi0EqbP88Z/9KdWFBdLM8vGbb+ONp4gcDRGIUufO\nn+Hd994FF6paXxQkAXfBClHKfAUoxgtfLnmPHRLCNXFeIL3EYRHATz+4zGw1ZkoLrt5cZPf6DXr9\nHg6IEOAsGgLMUx4UPs+IlCTPHVZHeCSLdzexYpNqVOGVzz9FfWqW4f0V2ldXaVpN0phC44iTKm+8\n+X0u376GNkEsmSRGSkGiNSYrkFpCYYKOpgor3LEU5DZHRgpfGByeCImTBZ9cusXt/+P/YcY6np6Z\n4u7KPb7zO39A6ix5bknqEcQx1kq8TkhQjGwPJSvEWqO0gsLg9pfZFU5ZdFQjyQsy6Yl0QuFzvMuD\nS4TJiIhRxhMlBdiiJGY7tNVIoUvFGV0azQahqIhQOCkkGkEcSVxRoKQkkTGRVFSaDeRoiHcZFQWR\nijBFwVB4jC1IXJAuk8ZQjTS1SDNTrzLIRihniatVfufbf8rcxDzgyaUksmE21qxVWCJn984moviI\n/vIWOqpiY013mHLoyXPc3Fzl26//mGa1xqlmi7nJGc7OHqIRN/A6CtwB4cMYoeSQS1HOAEuIVIoH\nBLqHwM0HBasPMKl1Fq00FheUtRDlXC98UyDujZGgMjHxkHsGBBKSCxnMGINUgaEY/DN/NrE5L/FF\nYAR77x+w65UM7NQyqfoyXvAebw1yPKN88NuU0G8gEUoJeI8uO859eFaG+Y70Hqk85JbEJ2hmqNfq\n7K3topRlb2UXqSyt5hS3LnfQIiLrbuG9wfqEqbnjKA+r9z8g00MOnnwM4QXDXo9KRYES9NIdas0a\nSQSCApnnxHEDa2F+eprd9jrVRoPrVy7Tarbo90fs7G3z6dXLKCnptHf40rNf5fryMrfuXuULL36e\n3pEjzCzMoaKY9UM95JW7dLf3EFLw4nNPc2djlUqlSqNWYdjb5ld+5Ze4v7HBy7/4Mu0rNzh3+hgL\npw6xsbJEMejy7/79bzDaW+fdd17n1s2PePaZ57m9dJP72xu888G7VCsy7BLXNLfvrNPb3UX4KqeP\nnyCqNUgNdLa7RFUdmOEjh6hVyH1B3TusdNxZukMUxehIB5JnCds768mzUITZcsMhTTMqSYS3HoMj\nswWVOC4RfIvRCbcXN0ELZqan2FnfBi+ZnZvlg/ffYdDfY3K6zigdIqMaP/jB9/mr73wPZ3LS3JDU\nNEU/RTXqbKyt/+MTpreerZ1V2qufMDU1ybrbIRE50CCK4eTxM+SjnKSxy+Kd79Nofo7mxDQ+KhAU\nOKpoFwfGKwYvzD4kOQ6qh9U3pHh4VkEZKI9+z2dBsg9WS9gXMnauII4iatUGQitS5+kOBiFQ4grG\n5DihKFwgAlibcf3aFb58+gSFdXQ2t1na3aGzusm1nbtkGOpIfLXCF7/8CpXGJAfPn+Wjt/6WjbVV\npJIo79EysHuNC1V4YUwJBQVvPIXA+gdVfHkVQie2f+GD9K2ktC2yDmcF1niIoMhzRqZ40I3bUCUL\nIbFeoDwYQGiQkSCyIDCBMSkE1kIuLO9/epWf/7lX2Lm6Br4IWpZxg4OHDvJnf/znbGxt443BGot2\nQQHFO4HCo1VIIgLIUheSthJEPixxCydKWFTskyvqOiJKcypxDe+gVq1TMR6vcnIpMKZAVavIuEJV\nWcRoREXrkgQSbNiUEGgVIUWOkgqhPEVRoJUm0lFAF6Sg8CFRxEogI41WGoUlloIk0tgCvHNEQqDL\nhXshArQsEGjhEd7RatWxuWVyqokqLNKDyQuaE41A/tGaSApwjlF/QFXHKGeZmZ1mb/UeiZYMBx0S\nJRj0OtTiiMIqhJRMNFu88NJTvPWD12koRV1XcEJi8Dzz/LOkiyusuxUGkWHusdN0t7bB5Ux6Rf7p\nHSo1ePXVV3CiwG11ePPNH/LU8Vna7TUmAWMtTgbHerwLAt+lXOr4ZhOMjZkfgmMDc26fQOecC/M1\na4ijGFNYrHMPFa5juHfskTlu+x4Q+sbEm6D7LogivT9u8Q9BqyFxygeBwN9FnDyUhUCI2/KcgP31\nETGGd8cdp5IoJfGlmtC+PKanlLyzKPnAFitIx6Z4lxFFQ86evMDN63cZDLaIklXqtRlE3KTTbXP4\n6Ck67W1WV29Tr0qOnFjA+iGTUy2eOHOCUZRx//4y6uhhYqXxRYpFYYyk2ZomHaRk/V0Ozde5v7pK\nrdZir71Fc6LKnVvLxJUac9Mt3n3jEhtbazRmpzh18gTvv/0Gd5evMz8/R54WLC1+itPgBymDQY93\nL13i+ZPP886nn2IwtCYjDsxPcenddyjSEc9ceIHv/vBH1JsTnL67zHBtnfXdDTLV59jxQ+xu7PCt\nb/8ZJ+amqTbrvPqVr3Hj2iKPP3aGe6srPP3EE7R3V5moNdlaa9OLM3qF5OKZ46xu7dAbGHQtQSiL\nyRxRvcb8VItunqJUzKjTxWDRKKI4CrJ0/SGmCEz0rMio1WoMBkO00sHFJEqIopgokuRFipCBOKl1\njLOGdruN8jWUV6S5I9IVYh2zs7WLx7GwsMDkTI1eb8Dk1AGGvSHf+NWv8Ud/+B/JbJez505x/NQ0\nn356hQtPv/CPT5hr629xaOYQG8Og0Xnx5HN8cPUdDh8/RDHaYXHxBs36iKOPHUPLOqbYwJgJCleh\nUp3COlkmSI+iABxORFAqbvwMicf5fdbreA/pEfmqR3bBHmLNefZp6MKH+aDSGhnFDFJLtaqJlaQe\nafI8xwuFkEGT1gA6jrCDIW+9+wYbww7Lq6ucPHqMobM8/dgTLJw7wPU3fozoGXJjWL6/jLMrXFm7\nR6e3h8eV0lzlzEaooIdrXaiSfQn7uHBAPTzDFTwgSOwz+ES5uzY+YFwwxg2JVCG8xRXFfrXuxtVy\nqadpyvPQCo13AUsLghOm3G/1GF+ws1fwn7/7NxxPqqiKQ6D49l/9gNVBjwmvqGmJsFHJkJQoEQNF\nkMCX5eGkwjEbywTvXVB7caViSjiO0UIQS4EyhgoKKS2oCOkMwqRhRcIGB3Slw2PL0AYy8BYlIEmq\nmCKjEscoLRj0B0RSU6tXSLMRSVIlHRVIDFpIChRWhAVBNZ7hUcK6JUtMKIhsUJ/SKsL7HF3CgMJb\nIhzNeoO9bA8FOBMqXY2gt7eHRmAKiyTYOGkVpP9tYUj3ukxUKlA4lJLEWjIz2cT7iN5wSLNSY5SP\n+MrzF9i+d4+lK1eweRDcFs7RHXY59+Jj9GNDo7nAG+9foZV5OkLQBu6trfLXH/2Eg4eO8fRjZzhz\nYIHzv/jzsDng9ntv4oscF4OwHluSgBzBtuqR8R0P34vl9F+MlytCUpIqLPRb70udWFd2dn7/nh0r\n+cjSHowSYBuHa5CIE/sfj7Ocfygx778i6QK0WybUcRJ1peD4+Dmddw+dDw9+KbGfqMV+bIFAa/EI\ncSlQEAWgA+kHjzcF3uSM0m22tq+QpV2UmmI43KU5IYlUzNb2InFlnunZE3S6exhjOTB3mG5nk3ff\nfJPjp0+zs7HMzvIlOnbE8cdfIBaejbX7SF/QaDWJKy20qLO9sUQ9GnLtk6v0hpbJqQNMtZp0Ntc5\nPDPLnZVtLm1fJapWQcU88/RL3L1zh+bEHN5qFq8ucfbM4+ys9qgLw27e4czJ45zemub6lQ85cf48\nm8vLODOgM8xZvLLCwsIUly7fRKuYz7/6JWaTJvXnLnLs6AwfvfMem8tb1GcOsHFrg62tHo26YtC1\n1OoNpiYXGAxTNtc/RusJDs5O44uIJFGkNuOjqx/R6xQcXjjOnbVF4iQgeELC7t4uaV6g45hqtUqE\nx1mHkEFpqTU5ybCXYqzBGkeWZcRxTFEUJEpR4CiKnHp9gsxkuMIhtMLktiSXSUajIcpEqERS5Cn1\nOOLs6aNYHIPhAG8lzzz1NIt3ljh79gw3rl8nG+Y457m7vISI+yTVGqtb25+ZEz9zz3//P0frVKIp\ntvqCkUtZvXeZCQGfXrrG+k6bwRCmZs6zvGxZvrfKxsZltjffJx9uYTOHFAmFshg9BGHAhUPdOvNg\n8P8QESAsApds2dKQ1pWzj/1dsdJ7zpZWQ4FcEN6NMRTGkBlDdzhkbzDg2uIim9s7DPt9hAsdhJTh\n8IjEGBINqywfX73C+cef4Bd//Vf5wle/zNe/+nPU63UGK5v4wiBjhTOWQbfHdnubREC9Wivntras\neA2bG2vYIsdbi1Khg5E41P7u1UPMv3GoC4VUOnSA3oTZk1QBclYBmtVVjUAQa4nwHiVK54jyAAjJ\nN7Bug0CGBDRSxOBDIkcGiBEXXBXsyGCKHOMtEo+2OTodUReSqtY4keGlwZMjfI6QOVoLlNaoJEHH\nNZAxUmqkrICOsYULULC34XC1JuzxKhc6PuUQKqwQVWPN5FSLOFEoEdGsNhB5Bt6TTDSD6S4CpUt9\nYqkwxpX6+oJsmAOOwlqkitFSECPQQpcqNIIIgVciFEpC4L0tXTYMsXfoOEEIRRxrIsKiRZBRcSgb\nPBDNMA3FAALpDEU+pBilSDTWGKT1FHlGajIqrRrx/CRZJSYXCqzjpRNnmR06tvfaHD1xjJeffY6a\nhBdOHeVzn3uWvoci1ngHsXFcu3WZP//dPyLL1jn22ARPXDyOrUZMVyd57swZRsUOxw+3+OWXP8eL\n88foNiZ5f2mTtUGKFoAYIKMc4cB7ixRBmLtQHlfuZxpvsc7iyjmkcD5I7lmLs6a8p8MhZU3BtWtX\nubO4WEKYllCaWYQMd6HwFu8NjP/uwiCEBWEDxCkcCIfSAiE9QpXvGoQWQZREjhn1wVlFjEk55HiC\nMIAzJjy9FThbkod8gGmFD+sxUhYoVSBluGchwwuHFBHeKbASLRTeBXLicNBB+JxYQ6w8ezsjmvXD\nzEydoZJMMzExx/3lDlevLlNklvb2KoO9bWKZMzddQ2K59OFH1KIEn40QJqM2Pc2xQ6eZrU5i+z36\nvT28UNy/v0mRG/JsQKuRgCm4ef02hw8cJE37DAd9bt28RXuvzdGFKSYqnv7eOr7ocemDt6DoM+ht\nM+ztMDVRZX5mlizP0VFE1h/w7vtvcuHCWX7tX/0KNuvw6pe+xGw0w4WnniNXBc++/ByukFw8eRBt\ntjh7ZBolDTeuvMuZZ05z/PAcFy8+xv/yP/8PPHH8KC88eZEoqrHXLsgyy+rGButbezSaLT73jS/x\n3/3bXyMWis2dHYa54OLFi/zmv/xVWnEVbxRWCmYWpojqEVFF471BRhKpBSoSZCYly1P6gyFplpHl\nhnq9wfz8PFpLXv3SK0zUa9Qij3c5w1EfoUDqQOzLRgZjBCqKiZKEIs+xmWViokUcJWxtb7Czu81w\nlIJI+eCdd5hqztBud3n77bewJgNnSaIqFd2kt9dnr7vzmTnxH4BkR8zU6jzzufNsXvpbbBrR2814\n4fNn6KRbCAe7gyELRydp5DFpKqglEUU2ImnFZDYo/uTCop1C2xi0Q/4X9r18GRre+Qef73eUjy4y\nP0z0G1vmjLO/8yBjTX84wgvHXmeP4UDSak2QxDG5DYzUfJTiS7hXKkkSaaJI88F77zFXb3Lz0xts\nbmzwzMVzKOewPsj2jQYjDh49yt7mNkrp0qE+6ODiocgyGvU6w94QnCutefwjCiRhBhfkyvYRMv9A\nCxconePHv6PD2gJEBe8tQokw0B4/XvmvLEtwAWjlEOMqHHAidBrC+TD/s0VwPZOqPGwc2lsi64mE\nwIiwrB3cGBTeBulCJRTSKyIdoG1R0vRr1QaZKFCdbthXFAQVGBcSjZUCL0FqwBkUQdrLmwjrw96b\nyQq0jjBKk1kXZt9SYIqwb6p8gY7CdU7iCCUjqs06g8wRWUUx6Aeylg/yaAiPJqhLSR88DKPS+qkw\nOTiLEzFSRbiSim5x5X4e9EdDPII8MzSqcegmnafIUnxuEElMIjWx8Mw2JyhMhs9yzhw7xZXNHYxX\n7IwGtMyQZj1hPh/BvVVGuaUd5dxbXWZ3Yx0rIPdhDo01/MI//68xK6u8/pN36P5kkZuf3MKaEYiI\nphWcmj3K7FNP03MRl9fX6G/kHEdzYDhgVUCaGYZZQd2LUKz4sK7gfCkQLoK+8njuEcYaJRv1YSi1\nJNAMBgPiOGZmdpo0HaGU2pe8G3eb4wGD97605nrQtT4cq1KpQO7wpTCJf2g9pGTVBjj3ISIQYn9k\nI8ciA+MVkXJGKcf6r14jifah5fH9L0xG5rapVj0rK2scOHCWXreg1Wqh3AAzamNI2dleYW5ukt2d\nXdbWbtMZpBw79iRzh4+ztbHLMDWcPHkS4wf87WtvMDMzSToa0pyqs723QXu4Hc6AqRoH5qqs3buH\njndpTs/T3tvi5IlT1CYkvfZ9zDBlfWWJUTZit7vNKEtJKoqT504xf+QQi1dvcP3KJWoVzczEBEI6\nOrubTNRj1tc3OHqswZVLnzB/4DBLt29iHdTrk9y4eZt+7xoXLj7LoYUF1mdn6Q+3+Ve/8ctsr2/z\n5PkD/OV3vsexu6e4fuk2M62EmekZNu7dpzU5yfKdu1z/8Br9fITahYOHD3L1+uv88Mdv4KTluRee\n5crlRba++S1+4Z98hTzLaFXr7A5TtIRv/tkfUKvE9IcjHnviMTr9nVKlzVNYQ+4MjXqNosjJ+zla\naXwc1ous93hRML8ww/PPXyRWEZONOjudIXFUpZBByMM6cIUPynDegHQYaTh6fIG99h7VqqY/7DE5\nV+XM2bMs3VnmlS8+S94f4kWFQlYpsnPcunyTYZayvbXHsNvjc59/jrhZ+8wc9fcmzK2tLebmrvDu\nW9+if3dEeyPj888c5vjZGa7caWOHQ0Tkef+9D3FKcezoKazKuH7zUyQHaM7XiXwVIRrgg86fd6a0\nm/u7gBD7BxWUoE4Jt+wzZEtS0CMzy3HCKIMHB6hgd9OY0NQqcdDEJcCH1gcikXeWidY0qAilY3BD\n8nzEx+++Q+PAHN3tberNOs8efJrV5VvUK1WKfoaxFh3H9Np7TM3PstPuhCrdBaNYLaG928YpuT+X\nDfqeweoHXyodjQ8DHyr7ceCPRZVD1R+gKWdBxzFFbiERQRZMhFmpKNfQxXhEFDjWCEBJXbL/ROjM\nH54dEzotJcou3hbouAI2QgiJVrokU4Wko0qoMvyelkhCrVKlMMHOyNkMrRW5D3q92PF6/BgODrq2\nSEnkBT5PqVbrzExNspkVZeecUK3EFHGVSGmibMAeCik1jVYL5QoGnSFJpYkQQRmmVm0iyIMqi7dh\nZ00RHA084Z4RYRle+fId0ASZxCROQqcSbrjykHWEhseG164FzoROM1Ia4QzNVoN6vYbJC2yWU1WB\nIXzhzGlGm5t01jcwuSErCoyukFQiRoM2ctBHRAHWreWCG5eusnnvPrH12DxDCUgdTM4f5F8//zLa\nxpyYOkvn/jKHIsfXvvAVNq4tEh09ym7suHvjDs8dPMRsahksXiPf2cLrGhWvUDpHijoWjzSmZGGW\nJBlh95OJlHK/MHvAkn3wsVKaRqNBFMfkRR7stETwq/Q8sO4KkGn5WJ7S0ipcw7HB8liyL6xuuAdz\n/HFASxBKh583Fvb5C3IfHQhngC3nlbqMqTAHDQp8Yyk+j3flaEOATzOWFm/yyZX3eOHllxhmfeJa\nBa0s7f4eOEeSRKzd77O5sofJ1+gP73Pm8dPcuvMBhw88zZlzzzDopnhtePftd5hoJQzyAZ1uj9nZ\neaSEja01Xnj+OY4fneejDz8miiI27i+SX/W8+LkvUqs7djaXqKsq1URzv8h58eknWd+5T24Mr33v\nL3jhc1+k1+/x4YcfMH9ojkJatndW2by6xrkzT+Cc5cUXX2Jne4/F23dZXV3h6KHDmEKzu7PF3bU1\nnnvyWWwh+b3f/z1OnphGjBTd7SpfeuFVvvefv8/Xvvx5XFMw6ncYdAc8//LnyDa2UPMzFJsDdrtd\nLj71JOtbq2zuBBLSoWOHcXjOPf4UUxMzpMMeWbvgmedf4uq3v4spUu7evUVdS3KfI7XgzvVbxDWJ\nknH4GzvB9tYe1ShBSBU4HyanXq9QqSVIKen3B6xvLrG6codimJPoBKym2x3gYsHsQhOrJKMsJU0L\n6q0qhSuo1gRTsw1sXmCt48SZ4zRnKjz30gucPHmG/s4mq3dWWNnsMHPkOJ98cgNR5FgErrC4WHPp\nk8v8xv/43//jE+bRYxGjbI1D9Tk2Gqs8fvQCPtniztJVdrd2mK4tYArPsYOHGXmJGQlcEjM52aTR\nrOPyDKckDo8VOWiLdhps8KP8mTdRCgzvJ0vJWPBgfNQ/7JE3jrNAJSckHylC11QSZmSkQ7JyASt3\notSetIbcWhIZYBkjPdVajVatTiOpMFSSrc27LC3e4bkLj/PxW28TRZrEO954/TVEXKE7HCGEohZV\niCsxxluEB60VRgqq9RpuOAQcY71L6UH6UEVrJbHCMzYQFAhsMRapD1/2XuGlR2qHllFYD0CELikS\nFCXdHudRUgXVF6EYeYuxBimC5yCMCUeB+em9w5U6o/vwrg2ZN8GjpUQRnk94hSwJS96HpKx1RJpm\nxHGE0ookrmCtCZ6luiR0BCZJSM8iCPILZKnOI4L5rbdM1Bq0ZUxmhwg9S+wsufd4EYWHsGGeZgpD\nc6LFbpqilCiTZk6R9hGqhvYEpxeh8L4IbEnvkc6jPRhl8cIR+QjhCK46WlOr1el1hkg/2F8rGhcd\njSQhij2Tky3am7to5zDCUY8TJup1uqM2hct59tQCW6MRO511xHDEqSOHuH3nFlZaEh8RD3OmnGCN\notwJltRtzNe/9vPMLBzi//7m94hdHsBHX9C5fYu90QavnD9I5/Yi//affpVDxw6zt7rKIB2w8+EV\n3IGIL0zF1G9dZXd7G1GPcY2EeFhw/MAcOwYGhWHoR6CqaCKQHoslekgebxxTzocOc9zVjRnrlCML\nISFOErRW+8bS4+8XUOpMs7/OIUX5OaWzSBgOY11IelKF+bvwvhQfD2Qda7OwO+zHMyNXFtsP9igD\nEzbYZjkfClYpNNLVQYb9vADGG4QwYBMKLIWN+frXfpWJRgu8o6Lgjdf+mueffI7333+X+SMz5EWX\nidokFY6yu9bno/fuENea7Kxtc/LUYVaWFhmOtnjxmYvcvH6d1eV1nHc06yYgLrnk8sfX6Q8G1Got\nTJFx9v/n7M2eJL3S877fOd+a+1JrV1VX73tjbWAADGYnOTRF0rRIWrTDoQiJomiZjrCtC8kXvuEf\n4Ev7To5x2A5ZZkgUl1nImeFgMMAAGOyNbvTete9VmVm5fus5xxcnqxrg0BNBZ0QHOhrZXZVZec77\nvs/7LGfP0W532Vhf4mB/DUHK9vo+J0+cplorsrR6n+Ggy7lL5/nKL3+ZXLuM0j4LMxPMNiZYf7zM\nbLHBVrrCYe+Q3mEfYwJcN2R6dpKNzS2+/zdv8kvf+Dr7rXXmT5xn/7BHlGecXjxBph0uXL7GzuoS\nj+49YnVjieZUQMnzmZqaRCrJR+/fZPPhGuVmk5Nzc2iVc//hfWoFj+2DfS6dOc+5cxcY9BOWl5ap\nlguIOOS1N35GnKSgc65duciD+/cZlYpMNhr4bs7BwR5eGJCkGUobHN+hHpTQMkc6AqUydA6J40Km\nMdLe3dEwxncCHDeg1enasHE3IHAdSDMKnoNbDi3HoehSqZYYdDvoPGFyukY06HLmZJOnn3+JWqXB\n/t199jeG3L69SaFRoHfY5V//qz/iP/zf/5FHK5vIksANPH75V77JWz95D/6nny9Rv3CHubvziB9/\n+y3mSgHnzpSZbGiKRY9+75CpiTov33iGalhldGjQmWJ3p8NwCK5sEo80wjhoLUBZmo/U4wJgPk/m\n4ahASnvBHhkV2/2kJdT83L7zM8SFo2nVAGbc7UqtLREDOd5x2H2HGB/CoyLjCoEnHYSwWYLC82m3\nOmyub7K7vcPW5hbvvvMOge+hlcIRksPDLsPhCM/zKJdr1JtTlEqV40LvuDYM13Fd8vG+VghbmCRj\nlp4UFAtFioUinufhux5qLLQ9en2OsICWKySecPDHripgLcc8x8PzbNq7GRNZpLDZgUeBvEEYHseZ\nHV1s9qKzxg5mLDWQ46/nCWmbFjNOojA2V1NgkK60jFJtC1EpDHAdW+w1gnKxTOD51tbMG0+34yna\nkVbK4TkSoQVCaYRxcZwAkys810M4IWhJrzcgjhOEFEhXokyGMpCrnCzLEDLAKCsPCIMCnmdhxnK1\nahm9jmMnS8aT97ghkOZon2f3tcKAB6gsQkqD6z4x1zjanTarFYquQ7UQUK+XAIM3NjZQJkPlKTO1\nChOew0TN56lnLiBFTLVcsjCRKzAmx5USbRw8JQgzifZdRjWfN95+i2QYWXZ3lBMKn4QMmSecECWC\n7iETRYfHoz4bhSr//qfv4wSSqXLEwqDN4KN7jAZDXCmoCp9yqcRQpDQKkgunpnjmyinOz81DlDBI\nByQ6O0Y4jmznjs6i67qW1DS2uzuKslLK8gXQGqEVeZZhlLKC63GupjYKNUaQhBknqQhj/2vAKI3K\n1TExzSg77TrGkrIcKexnRAg8V47RpDGDVkj78xPjdY621pt5ZgXxO5trbKw9BjUiT1sMhw9xnS6S\nId3uKqN0ByNzlGO49txVRmmHwWCLbnuZrfVPqRRSPr75GmEp5c7tj1CRJhoMaB12KZRnqJcuEOgm\npUKRd9/+PmnSQhjNT370Du3WiMCrsLfVxRMl+ocpoV+l2xry+mvv0O0OaR+02djYYnHxFK50GQ0G\n7G1vcdjZ496DOyyvrbO1t4fWmq3lDSo45K0WstNjcNDioNXCKYWs7+9z4xlvSXcAACAASURBVMWX\nufzUVWbmzrH0eJt+v8fNj28TRSNUprj1yaf4hQr9UUSuDf1+n9npCf7BN3+V/fYeu+0tpucq3Lm7\nxMHQJZw4z/yJczQbEzQbk9x48RW++Wu/xmGvxzPPvkBgJFPT08w1GuR5TMkISkWPQBraG3vcfrDM\n0oNlqrNl/sW//gNOT03ypZde4cUbXyBKMus2haDbaVvehLGWpa4Lo9HQrlZC35rjS4nJwaSSdKRI\nMs0wtg2mcHwKxSLCGOqNKpcvXcKREAQ+xUqF2dk5pPAQwqPT7iFdmJysouKEhYkyP/3ed1loeoyG\nhzQXyyQOTM66zM9P8pWvvIoINDIIMUrz0QcfcvbM4t9ZE3/hhLm20aHeDJmsF8hUnXpoyRFahyRx\nzPryA3bW1gmDEsZpgF8krE/CqIQXOhg5wnU8VO6ADkFmaKGPPV+PDurRLzXemFhIx3yejv6ZwmrG\nRKAnT7ZPeeINOb4QLdUGxgG3YgxjGmnNqwNh34B8DAMrDT95402EI0hVzqgf4wrBaDA8Di3VWuP6\ndirJDPheAQ+F1BniiKE53hdZ/05xbDLtOC4YC40dBQMbrS3JBwsbirE2TAtt9zKOvbSk0aRJggg9\nOyFqjVI2CV4rg+M5SG3hr8wcvX9j5qIjAYc8z8bvlbHSj6NGxXYh+J6Hn9vcUzv0jqNxjS3CztEk\nbAy+Y4O4kVZr6rguw2EEoSTw/fH0aHeJQo+nCJ3bCXucCGFwUCon8IX1f1XgOtColcmFg8LgOhLP\nswkcQbGIGvWRIsBzfWsKoDW5SnH8Av3DPo1mBd2PrCZXa1whGPu3oPMcnUlLOkDhCkOUZ/QHEaHr\n4Si711Na2T2vsQWi6nvUm3X2N9bxEUilSft9RJLhYCDP8bRCmJzmVB3lauI0xXd8PDsGo40gDwsY\nr4zCQ9dCwrkp7q+vIeKI09UiQZZQL/mIWpk00pitAb1ylZvtx9x49gXIhvzB13+Fte//Nc5hmzBL\nEbmDkh49meNO1VkuxEzMnWN1a4v53CWYUJycqLBYrfDpxgbbvRjtBDbR5MmhsuhNPv7MHO0NhZVj\nWMj1532gMWMj8iNawfEO3nqtMl4dWFaqsQzVo88b2OZQaGtuMPaQFmOJiWWIawu3jiF9Y8bwqpHj\nsyERIqc5UcHkRXQW4TgeB7vLVMpFOgc9tBjSP4wpTswhU4Wiz+a6lcmhDGlqcw8/vv0h9UaZ06dP\ns7HyiGZzkZXtbZoTk8zXJon6fe7ff0C32+XRo8dIx+PkySvcuv2Iy5cv4VXqZEjuPnjAF154Hq0U\nF86fZTDoMjNVJY4jtrZWKBaqTDROkDcazM8o9tsdlNTUGnUOtneJ3BQhAw67PaJRxNTMnE3p0A4m\n97h3e43MPODi5Ws898VnuXfnDs/deIFaWONmYYU33nyDSr3My6+8xGSzwpULZ2i3t7l790fUChJv\nrsntT9eIM8Xl65eZbdYoC5ft9W0+eXSPc7PzqJLHzOQi/9v//u94+cWrbG/vMDs9w4P7K7z56B0q\nE01+47d/HTfx+M8nTvFnf/6nmEbE9fMLXHDLfOvf/hnv3fwE6TsI4yOFplQqWXOXOMbzAgrFgDRN\nSNMMz3Pp5SPy/hDfkVbzLe1QISXEWWx9pMmpVEPOLC6wub6C6wjiPCNJUgb9LmHgIqUgTVM67Q6x\nq3FIef1H36ZeqfDOWz9laXuP6okJpqs5p+erRMMea6sbFAoBuZsThhl7rQ0ervz/sMabqFbRgwat\ng30qRUlnp8XmYJvJmZBKuULgQa1ewS80KJ9YYJAU2TrcphwW6QxaVMsn0apI4M9gdPGYJn4E031W\nV2kLoD7CWI8PozyixBhzXBSF4HNRREfHWAhbIiWAwh5GeUQgsAJYbTSbKysMhz26nTZ5FKNzNZ5i\nBQcHBxjAcS3mXm80IRmQCgFKj6OT7MTre8Exo08Zjau19eNUGum5ds+orWG157qo3NLyXSxs67oO\neW7whGflIHlqJ+qjRkFYyEqOkyCK1RpCCzzPQaSGXOVjY/gnMJWR4I7hNqM0eZZbQsbnyFI2R1Ea\na3qtPvO1HIQldJhjzs6xdlZoZfe0rscw1RT9EIwGI8iiiFpjGu0YekrhO2MkwRxdn8JONirDGElu\nNKEU+NJhNJ64TZ7iOYJs/D27nkspDKiWA6QwDPt9Cp6LL46IJJauXvSrIHxSN8d3PAZqNBalawvd\nAq4c79TGnyU5bspcKfEFDHtdwiDHkQKlDUIrjFbMTU7weGUFk8Rcu3KFDz+6Q6IzyqUi07PTtDuH\nuEYglSDTglGmiJF0BiOa9SkwDlLbaT4vlug3DAPXcPWpizz/0gsUSiVaGzv89Y8/YrZU5n/+7/+I\nyrRPd9Tn9NUZhoOMV+V1misbRJ1t4v02rhkizCzKOLgFSVJ3afs1Zq5f5FyxQH1mhvrMFF6Sg0pg\ncMCje/cZKYlXnLaOUp/BTo8KoWMsGnO0l7RB7tqygIV5csaOvV3HSUPHje2TJhiOf+QcGQZwdLTH\nhB2lxj6z8rhGAwKb3yywqwxbuHOd4zjWmk9Kz55tcow2lMIQnWW0Wrs0m3V8T3Owv8HphWvcubdC\nrnIi/5Covc2jpfvUJ6Y42I4Ii0WK5SpSai5eeYX1lVUePujQ3h2Am/LsjVdY21jFKQQs373LF158\nmbv3HzFIJWfPnOZn731MUKvQzyO+9qtfp1oMMU6CK6FJnVZ3m4vnTmM5+YowkPR7+2AmeHB/lTTV\nfOkrL/Nw6R6VSo2CV2J7a4/UOHSHIyqlKtt7B2RGUCzV0H5IsVTj0cMVzMM1tMrZ22nhe0W+99Hf\nMHfiPNevX+Pq9avs7G3Q7Wd8+PEBRT9k0G1x9uxJ6hMV1rYf8aVv3qC1+xBHTbPZ03x86xa//A+/\nyb2fvIs6bHNj+hIXr1zF9QOQktfeep1rl5/hxsQ0fjHkvfd/xje/9A948y+/w1ZrjbnJJu/96CcE\naYFA+gSuT5xaBzDXt4z1KI7IlcIXgjTLMEIQxRGu4+FKF0/6oA2+K0lVSqkUMBwNcT0PISV5LpiZ\nbrK88pBqpUiSZggsehQPY4xyMcpQKBRxBYxG4M0W6fVzSqUhu6NDlIBuK2ZhLuT+J6tkww+4e/8W\nvgdGKCq1KifnFmj1un//gjncDnjxazN8/MHbRHsFJqcDpuYXSOM+ehQzMzdB6M0RZxVmmq8Q7T/C\nCwf02xFq0CfpHXB/5YAXX/5tiuECR6ZVY7Xw8aE7OiiOHsOGx9ZYRzsS/QSOHT9bwzgI94g8Y/8x\nWx5dEMdGWPYycCWZNnQPu+xu7+AajcwtLGTF1AbPkRiVg+vgOy5SugyiEYGyxBEFKG0sROp7dl8I\n5Dq3IlylcIxE5Sm4Ai0k0vPRWYIaazIFVgeos4xE5SiVAxYufOJQdJQgaveBasx0VUqNU+ot5d5z\nASmQjiDXGR5jko20M9VRwdNCoKzzNEdu9XKsR9PCJiKkRtnMUGENoKXWFkZHkEvLDD0qsNpovEKI\nGxQshOZ4FDyBXwiJ8xjf9zEqx5UWkrV7MBdUaqdr7K4zkArHGIxbAq9kBYJOSBorG1WFoVQqEoYe\nOk+ZaNRxHJd+PwUpEE5AoRRSLNbpdftUyz5JlOB4HjrOxtOKLQrSMeM9skuubXNglMInZaZRYDca\nEfqelStJCIQDUtDdP+Dqlat0DtvsrO1QazQpyAwhDIVamUTYzEcpXNJY09poUS02aPcGKOmRIi0r\nVOdMTzVpTU/xxacvcP3SSVQ2Io97iHTEyROTiHqNv7rzKa/WLtNe3mDx4mVKj5dxdvdQ7UNQGge7\n5ki0oTAxyf1Rm9qVRZ6+dJEsNixOnsQPA/L9A+R0md7WPje/811245TSjZdBHJ1BMEfuOOPPV8YT\nnaM24zgw4CgmTCJQY0MH529ZVT5JCrL75qPIvjFt6Ml5Hv+Z0QYh9PE6QBtzHA7vOB4mt8Q8IzQo\niXAkSue40kXlGZ5ncGSOyHy6hyvs7y8j0DQapygHinbrPt+7+TaPV9b4x//kD+l1O6Siy+Xrl8kz\nn+FwB5Uq2geH1Bs1DnbbeGHIg4f3OLlwiuX1+yycq7Fwssjy48eUylN861v/npMXLrB47gKGLp4b\n8Xv/6L8mKAd88tH73NraYn52knQ0JB9FzCzMcOrUOQadLnkC77z9LgsL85QqEzz1/As0Gw0+/Ogd\nXAnvf3STPIZnrj3LoNdn2I7YerxDR2muv/g8TqXM7qPHOBp+6ZsvoZXhrTfeY2Fhkfdv3uXS9Wco\nF0usLi3TarU4v3gGk43Q6ZDd/R1u3V+lEzvMNop84dnnyBJYWXvM9sMVTl+8xky1zu2ffsDS2ib/\n8r/4XXoHe1yar9NTGQ8ePebM5fNooekc9nhqZobpcpFv/ZtvEWcpX/6Nr+HGQzZuPeL21gYPtnZw\nCgUunTzHxQsX+P4PvouQkjhJcB0fISRxlFCoFCwRM88xucYrBARFl1arhTKawAvRQc6wH5HGmjBw\nWF9fJwxDDuJDHBfK5SJSpFy7doX79+9y6syMbcRwmZyY4uG9Zaq1kCQ1bOwNLDdDZhzsQb1a4S++\n9wOMcSlWiizM1zl/cZ48HRGG4d9ZE50//uM//uP/r4K5ufknfLqxxn53SOgFXHn+OQ6TIUkk8HyX\nNBsSFkqovECctMjyHQ4PNwg8QxKl5KOAixdfRQYVXN8aBUjH57Pn7HMdqbEn+Qns+vkAW8wTjdbn\nO1p57Fkq9NHhPfo3LesxNwbpukSjEXF/gKMMcTxid2kJozL6WYbSWDaWI/FcB+n6SNfDk5DGw7Eu\nzYB0cIIQYRxMnpGpFGkUXmp1a6krEH447pS1hezGiQ250WNjBWdMsbYBs3IMsx69Dtdx0EbjeB5Z\npvBCD5Gk1Dwf4bn08owkVyBtRyZUNoYeHbRwGOYZrusgHInneTjSOrVobTgyezHGSixC6TBZrOC7\n9hJsDfrUnBDHd9gcDDFSII3Cdy15qFguMRjFlGoNojjGJ6de8omFj1GKNBoiBERRhETiC2l9hbWg\n4El8T1JyAwqeS1auoMMivf6ArD/ixNwcrufi+o6dKnttGxGVawaDHu32AUZlJEmMyVNUGtFrt1Fp\nRL/XIYljPNdlOLLRcwXXoeSHZNIwijJKfkg98Mi1YipwcVzDQRTjugHlUoHtVg8toIzDZKlMtVxh\nr9NG5dbtp1yrU3cMRaU56MXstA6YbxZ47uxZHu11GbVjkk6MqNZBSqZ9QakSElQCVnWEOztNUeVs\nPrxLb22TKSR1XL5x7Tm2Hy0RnJ2nUihyTU/gDAbkm9tE29skrm0osyCk30soNgpEi3WCi2eZOnOe\niclZZhdOI32JDBWHB2ssf/Ax67c/ZXOwS1opUGouYrwiubRWYlo+OUOIcZEc8wgMetzXWla5Zbzy\nOYa6GTeunyucY2ToqCh/ViLyuYNvOE46EUiEsVaRDlYylOcxymzSat/F9RLCcBJpHISR+J7DMN7n\n/r2bzM1Mcu/eTTzHpxhMgOqzs/2IZDRkemqCWrnGcNSj2xuCFgRhgf39AwSCvb0dhoM+mxsbfPDB\nu1RrZU6eXGSi2eT6tUv0B218XIKwwezULIWi5NqzV2m191mcn6W9vkm700bkmv5hF9d12W/tUWnW\nCColK9tRko2VTTqdDjPT0+zs7VOqNtDG8NO33kKpBIlt4Dw/4L2bn+LVakzMz9MZjggLVUIBC7UG\np+t1SkHAwV6X5eV9Lj9zgcXzE5xaOMGnH96kUatzemGO7c0N7ty5w+PVFQ56fZS0XIvBYMja+gbd\nXoQ2HlFvSCoNGxtrbO8cUAjqhNUKoe8iRML6w/t0en3KzTqhax28akGJNIm488kdHDdEOLD8+DF5\nkvP2ex9TbjRp9QeUqiWGcZfuYZfRaIDjOiR5jkGSJpaQGJRClFbEwwwHh+EgIs8zlLHBAUkajVn/\nVp4kpY3VG0URUlpTftdzabf7zM5McXDQpt5o0m51SBNDv58w6PcZDQcI6eN7RfIstY5z0tCP+ijl\ngvAICwUmp8v0e4eoLGF/u8W/+MP/8edq4i8k/eg84unnzjA5Izl9rkpjYoLF05eoTpTo9DOG+RSH\n/SIH7S69wzVaW5uoUYJCUZ2Y4+S56xTLTQphA0eWLJlHfV6D+Vnywc9nZdrp0j7PFprjneWYlp7n\nGVpZn0yDjS9SWtu4IY6IBwKdWwJRHMd2p6js37U4ufWxNWNiiUaTG0WqNcJ1McKQaTW2EBNkacoo\nioni2Bqoj1+HjbA+suzKx5FKtqvWY/swx5F4nmvdZVyPYrFoiRaeJVocPTJlyUI6V0jp4vshxTAE\nrUnTDNexRCG07dCVsfKQz/KHFQYjBL1+375OrCMNYxajHTjtEj5LUqQjxmkAY6nB2LnHkmaOJD6g\n8wzfFYwGHYwa4cicarWAyka4JsN1xHiiA0ceGVIoQI7T5kEbB0cIttdXWbr1Pqq/Q5Uha3c+ZOfh\nLbYffkJr/THddoudjTXSqMvcZI3TMw2aBXDzIa6xHq4FF7KoD1lKnqYYrazVnRQYbd+XTFkYVmpj\nId08Y7ZR5+TsLFJpsihlZW0TkWtUmmOEg8oFgRsyVZ/A05Ly/i5+ax8/jRnGI6LBCEdLvFTj5ZYM\n1MZgzi0Snl1AlzzCXFHLQ0IFC3PTzJcrOJ7g7OVLvHjxRVqHGY8qDi09ohJFfCn0ON2P2Lp/h+jW\nXWh3iHSGQBKfmiVyXJJmhdaFGfqVIvNPX2fh6lUqhRLisIsgZvftd3j813/FysZjVvUQLQSOVhx6\nOSORkqoYLRS5ysiMwghJrp6EQh8VUXsmP4vuPAnnOzq3Sis7lY+lVeazhXT8PHuGn0T8CWFBpizL\nUcqQZjl5rtHanr8kSUFookjhENLvR+TJEPIhybCF72uqlYCvfOUbxJFLsVjl3Pk50mSXpaUP6bVi\n3nr9Jh/+7DYP7z7kcG+fSiD48d98j3t3buFIuHT5Aj967TXeff9DPvjoJi+89AW+9o1fQhpoHexy\n//YtasXa2G9YACm/+mtfZ235AWcWT/DwwQMmpydZuDDDicUZbn16i/1Wi9X1TYpH+7qhwjEe83Mn\nmJ2dYZTETEyd4IUbXySNI8rFgPX1FbywwNTULEG5xrWnn+H6+css3X7A7/9X/xStFAsn53jnw3e5\ndfceH318H6MUv/7rX+Hux3d5+MEKSbvP9upDNlZ2WVpaZnNrk25/gHYEfrnAIM05d/4CpXKZrYN9\n4iTh0eNH3H70kMlJC+3Pzs/jFQJ8z2Fq9gS10iSCAi+98lUmZy7ywe1Nzly6ShokeI0iZ5++QZ4r\n3vvgYzbW95meWOSf/+F/yyiJOXVylmjQYTRosbu7jhA5URSjgVQp4jhHGIdoEBH4IYEfkOf2cxcl\nCcoo0kRRKJfpDYbkOjteVWUqJ8kVqVLgurR7XZpTNTzXx/dLvP/uPXqHKVtb+6yv7hCNYnq9mIO9\nDnt7u3iFkFNnzxGERbLMoJUlqf3mf/p1mvUm21stRlGOcZ44K3/28QsnzO/96f/KQb9tcy+HLoPe\nFHleYrImKIXTTDRfQJsybuBhlI/vzzExc5m5hVeQfhHhxXhhA2GqaO3bfaJ8Uij/9mNcW8i1Oib0\naKPHQtbxSf3MXkNgo5jAdsyMC4Q5qgbGwn9GjcWtjmB7Z5t00MfkCbv7G/T3dkiTiFRAnCobJ+RY\ntp6ULloIpM7Q8eiJdZ908Esluz8EtFG4GEIFRhhiNFq6CCR5miJ0ZoNohTO+YCzsrI+6cW2rnTGW\ncn+sjQOEYyn8xhhCNBXHRwvJyCiQDkHoW8BLKVwzNkMQgpFSVu4RBONYKjDjpkFi2bTaWBi64gWU\nXY+iH4AxHAwG1NwAN/TYG0akucJ35HjfJAmDkFwpavWapfPrjMCR5NoSYOLhAEdKoijCMQ6ONHb/\nlAsKjiBwHYpOQK1cYOD7hNUa/cGI3ChqtSaTs7MUKzVKpQpZnOAGAUGhRJzlJHECChq1JvVGlcAP\n8F2X0A+o1eqUSmWGwyFploIxhNKhFIQQBgxHI6qeTzPwyJXhqcWTlEs+P3u4RMGvUCj4TFRq+KGL\nHo1YrDVRowHpqE9ZCJSTYoSL5wi047DV6pMMFLO+ywuLc+wFAaPJJjL0MSWPveVVFqU1iPdExukz\n56lMT+PmOXU/pOcExCdnMZ2IYnuA2lynWTAMW5uEJiXzDLrgUCyWUeUyaaFEfukkpacvMPvUVS58\n4QtUK3VkLyaLOrT3l3n41o/Z3FylK2N6WuOpgMBIUiTNqVPgBGNG4hhwHTPIbSRXZuUdYxRHjJuO\no53l0SR6RMM72kUeGSGI8fm2/18c52Yi7PpkDCCNf6+RHiAU0nNwHIk2OY4vx2Qeg+9U2Vprk2U+\nzUaVTmuT0aBDvxPz8P6n7G6v0axVKAQRy48+ZHd3DekK1pdiZmbmSdKIudnruE6dW3dep1ae4PHD\nJcqVBj/84Wucv3iRen2C02fOMDk5werKEicX5smTlM21DRq1CTbXNymVAqR0ePx4ncWFCyRxzInZ\nGYqVAvUTTT6+dYsvf/lrnFk8x5VzF+jt7BMfdhHSY2Njk+FgyDAa0h8OmZye48ev/wytFPFoSLM5\nTZoLNnfaHLQGlEsVquWQ/d0dVpbW2D3osPT4IS9/4VWWHy+TGMnm2g5feuUF8jjisHPAwslTzJ+Z\nIdGG7mBInCScOn2G8+cu0utGoCFNFKVKmS9/5cusbayzubWLEYI0V/SHI1584UWkhHqjQY5gd2uP\n1v4h//ZP/pS7tx/yzOVnGR3u06hCr90m9HyyfsoQg1+t8cMf/Ii3f/Im83MLdEZt/of/7o/4yY/f\nRjoxWgmM49lmWTuo3K4A0jQjTTKyROF5AWmeUa2XqTUqTEw1EFLjeZIszdC5QIvxMDS2BC0UQ0AR\neD79XkS/F3PYGeF5AdV6iWefuUo06jI/f4JcYUMhXI9BNKA/HCCMpFmvIIWk0x7SH/Uplh16vSGl\nUoV//k//5d+vYP7stX+DcKqEssbmyh67rZhyWKH1cJNhuz/uYErs7e2RppraxCxTU8/QH4W4fg2l\nBNXqBHkCQjrWrm2sqTwukp8pnOYzcM6xdd7xiTNH6Cqf9Y402grnhRhHeWlzvLuyB1mPd6KSTGc8\neHAHR+RIk5LnCd3NLYTWJEISpZk1GpCGwJXWfguBazRZEiG0hXYREq9UsoxZITFC4xpDWVh3m4HK\ncQvl4z2ZTqOxmbp9D5S2zIojFxUprQfQ8S52fI8JrEONUoYsTwiNoer4CM+hHY3oD4bEcYxKM7tL\nHNPvcR1GWYpwHMIwJAgCAs8jTxJUrsdzsL3MHKOpuB51P6QShEgBO70eNS/A8z12hkNr1iCw06eU\nFIMS/X6XPLe7rMNWm95hF8/XzDQb9A/bCDRJlGKMNaT3PInOBSVPEvgeZeEyEbgMhUGplNMnplmc\nm2BmokGt4FANJIHjUihU7I4QgdCGZDgijUYM+33iLCOOU0ajiDzNSOIUjKDgBxQKAWmSEEorj2gP\nRyijKPoejTAgMXBpbpbpapk37zwgjzS5HiGz1IZhJDGz5RI+OZ4PBaFx8HAJKRqB1A6HqeZwFFOu\nuLxw/hT3Zcqmk/Lcc9f43d/+bb7z7e8w53tMeQE3rl3AmWzScyUnr15iO+myP+pxPsqpbLdJHq0S\nRR1KRRfXCFYGHWZPneJQwCNPUbl8nlG9yOSpRS7c+AKzjQXAQaqErLvFu9/7M1r379DODzGVMtlg\nRJhITFCmddil2JyhE5TZP+ixu7PD9PT0cYfqCmtZ6XjOEwmUlMfT4nEDylEfa2U34ogFPj6/0nGO\nJ9DPnnH+FhvennWN42YEoQtG2/2kJ+j2DnB9a23o+RFTM2XcwMdxSqyuLJMMY+7ducP0lM9k3ePx\ng0+Zbjb45OMPCINpjCwj/SLV+iQPlzYZjDKq9UlOnlogcMuEhRKtdo/ZE3NEcUqSZUzPnkDlOdtb\nmwSuw9baBo16g62NHUqlEssrj3HcAsvLu3iyQL1eJ0pS7j28j68UrnHx8Hl49xE72zucP3eapeXH\n7O61CYMCea7Z77R57vnnOez2ufHiS3RaQ0qFOounLtLuDtjY3MPBZ6o5Sbu/x0sv3aBSKrO8so7n\nWb2lyDUn5udBCn742vu8d/MDwqqh1qxz+/Yy1alJTp85ydLSMi++8AIP798lcBxae4e8+94HLJ6a\n59333mI4GtFud/md3/1dDtptHt5/QKlY4tPbtzHCol2XL11m0O1Tm2zwe7/325RKBVbW7lGpB6SZ\nZmN7i1Onz7OytMz+fgvpe2xsbfNbv/Ub/M0PXmfQ7+J4kCjItUFIyaA3Ik/VMTSvVY7rOMTDFGUM\nnu8iHE0Q+qRpguMIPNdGHqZJjhDWgtT3XbvjNpbvUSxW6BwM6BwOqNXKTE9PoVTO6soyszNNXrjx\nEjdv3aPaKCM9QZYbauUizz17hUA6tFo96s0qmRry6MEes3MVsjznv/mDf/VzNfEXk37SDoXSSVo7\nEZONE1CGLNlmqjBDve6hqhmdeJOod0Al9JF5glIRvaiNSQpUCjM8frhJNSwxMTNLpC3D5jju528/\njtl1Nrrrc50sdioTUloygDHjvaANNM7zDCEcXOnav6HlMSQEdpxX5AhXc/vOx4gswtHjvV5md4fF\nIMQxklRnluQiJFlufTAtScE6oR8LtaVzTFTQSuO6PkaBTjTSqGMYFmzahs617cDHdHptxjsinfO3\no8u0tu46emzqHgQuoechjBqTgjSlgk+mFVmS4UlpPUIBidUQZklKN+1gAE8+cWSBJ9OClAJ5FPwr\nxgyocYihMdZY/LN6WWmMhWQdScm3TN+R64AWpKOc7fVtHCMplSq0OgPrvyqdYycjpRRSCHKdUiv6\nlDDsHMb0Dzr0Bx3IHRjb6glhv54jBI4XAALf9THSIfAlmVHH2lplOycYVwAAIABJREFUFCpLiPQI\nB213t4ixZEEwikY4gUesFMYNUGlGNBohawWmanV6Q4MWOUIbXMclw5BKQ71cJFUR0vGI8wxXuLRd\nRa9Rou9VQeYkQU5vGPHy5Wv86uk5qrjsvvkhxV5GmkpKzWliV7KSHlI+N0cvHeHudTjRi/EHGxzu\n79ONuhCCjDRZ4HPq2WfZ6PYJzy5ybnEeUS5xaeYEzVoNmWWkYgDJgJ27H7Dy4TuMRl10qYzIAwLj\nMywEdNGEnuH8whnSsIx2yuyO9kG49IcJxUIRIQ1pbgPOlXripJUr9fmIrM/Io6xcxDotHZF4JCDG\nKIlzNImaY370sXTLPqx3bZrEtDbWWV1Z50uvfpU0iTk42AejqVcmaXfXWVq+w/SJcyTKZ/HUVdLR\nAZlq0+qskcYzhN4cn97cp1KeZ2eng5B1/KJitbXK7ImTFEqSOO6x+UmE7ypuvPASf/6X3yYs1NjY\n2GByagrHkbz33k2uXDrH6dOnqRXLuJ7LxtoeSysbXDx3GY3g5S8/z97mLqubbYajmMvXXuDqwhz/\nx7f+T85cCJienqJcK/NoY5nDNCfwXJZXNkjSjHKtAMZOqqdOzrH0YJ1PPvmUw35G67DN/NQ8W3s7\nXLh4gTsfvc/Wox1OnJzjxheeojpRZxjn1CtFfvSDHxPUi0wszhP5EVrOEscwUa0x2ywTBkXOnjpD\nqeAxMzUJRrK/3+L3/9nvc9jf4wsvvsDa2gaTjRl2Nrf48hdfZXNlnbXVNba3d5mZm+ew12F5fRnt\nGk6fmee7f/kXPHX1CpO1JjoOkFpw5/ZN9g5zBr0+FemReD7/2e/9Dg9Xlrl68Rw3P/mYcqNIkiWM\n0hipLZHSGHv3GXIcbddE3lhRIIWgXAxxpGAYx3hOiOMIdJ4Reh5GWkRsOMzs5BlnlMtFsljT6w2Y\nPTFNmo/Y3lkhCF0cx953f/4Xf4o2EoSLQeEJiWsyDrbWkcbFcwU3P7rPiZM1MAKlfdbW/m7z9V9Y\nMEvNCXLZIVMRmzt7TJ9rAhm9XkqjOMvW0hbBRI3JmsOk1yTrKbZG99mNUnJfYlhh+36Lr7/0Vcg1\nRqQIEYwJOZ+HZC0EJI93J0eJCcdJH2MtmNAGxHg/Nx4503hEt9uiWm0QhGXrgC8kSpuxebmFNqO4\nzzAZkGRDdtceI2KXWeEjDQSuR60xidAC5SlEmpFrF2kg9CRKFSHNcYQhzcfMW2lvjlylBNJ6ayIF\nvu+Sa00QhCRpghESpTSucFBjhqHmM5IN+XmJzZPAbJvtJ8YJ9NIq79EmR0qDJ1xcz0WnuW0yjDiW\n5FiVo6AQFtAYkjS27+2Rh56waQ2psjCw44xhWpUfEz1Q4++D8a13tJ/VCScXpsiNIVEpzWYFx3FR\neYJQYHJJv9fDcz2yLEd4AunYDytYVGBmqs7VkyfZWV+n3+7geUVKMiDzLAsTDTq3tVuZHJ0o8vH3\nYVBk2uAfXcf6yIwdPOEc72Udo8FYn18jBcJ1kX5Ikmt0muMZgxdInr5+he/86B2KRYcsM4TlGvWF\nMqWpGUqhz0zF41xzksx3KHpFPMfglEJGfUVBO3hqQK4zdrKEQprywd4S3/3peyTCEGcZI0cQpxn9\nuy1OVc9TTLuwvEu0e0CrIFF+SOgUGcRd8gtVdsojqHbwmotkjTrzp84wXZ3AlWPzeCdh7/5tth7d\nZLi7StkxjFTKcJTBRJM4zvA0THghB619WqWMT5aW2Ak3kQQszJ9AS5dEGxD2Z5+TI7Dwv50gnaNK\n+flpkSf7SzjyLh6jIdh98RFb3Ygnk+bn3IOkxHEcur0evl/k2tWnMQoc6XFiZo4sye3nUc1w/dIi\nxisghUs2GhFWJ0izizTMLCiJzl1ef+tHPPPUJYpFwc7GAL8Ycf7CPIVihVb7gIf3l9lc7dHud7hz\nf41RFJHkDxhFEY1GjXg04LnnbjDod/j2X36HchiQKI9qucH03CJLS+s8/5XnGEQd3HKPYibQuHiV\nGhQbNE6eZXlvj0IpYMGd5ukr1wm1x+ruMo7nc/nCNTa31rjz6QMmp6p89zvfZmHhInMnZxhEA06f\nPs3MzCQzrQl+8sYbXHvqBktLjxG1iEKxxsbjh1y5cIG5hYt88N5HuEGJqNXnqy+9hOMoVCZ45pVX\n6Cwtc/fTm/jlMltb27zx1pt88eVX2Gsd8Nobr4FIOH92gUtXLmJSB9/1+fH3f8j8iRMI4dJq9cjy\njOvnz7K+usq5U+cwKE6fnsHxhqyv7bCzFXD1mbP8o//yP6FYPMGD80sMooTp6VlqpQrL95fYXd9F\nypD9gy650qhMoXWOHOucpSfJsgw/9G0GsiNI0gwpPWs1qSyPIPcyHM8hDAJSpdASPM9Ha0OeG5Q2\nRFFC4BWZbDaxlmA5tUaRs2fPsLt5QLvdo1QqYqJk7IfsUSu75FFE1Ld79CSyCUzbm4d4YUD3MCYs\nOfxdj19YMPs6JxoahigwAWHPx9GGs2dqdPa3MO0R09qDqkehGrImXb73+s+4/vzz3Lm5ysKZRUp1\nH0SOq31cuuRItPTH5dJFGoErDYIY18lReQEokOkEp6DAxES9DiQRgZQkWU6xWidwPRxZJEp75Pku\nItnlcHmZhQtPkXhNcuXjO5A7GbmWZJmm1x1SKRTHDD2fOE3RXgGJgTxlcuEEK6tb/MqrX+few3tE\nWcpCvcGje4+oTk0h4xiTpcSORLohJgMtDQXt46UxWuckeYZUgsCX5CrD9R3ykbW1c7WDRhKTMq7j\nFlK2lfM4IPdIdC9xyDSk0lAyLnGsSX2JND6+Shl4BqVycikQuSaQLtIBJTW5lDhGEUjrl9o3ijjP\nreuRkLiuSxB4eIKxk0/GYjFEFDLe3nPJhKYpfVwhrAdtbpCetbcb9VMeRy20NHi5wfFcVOhSFAGe\ndHEdh2apgl936A4HyE4HN1coJ0GKArkSoA0F5VHIDPFhhHBy8pKLyRW+8XFzF0caMqHJXIGjDEUl\niJwM5SpccowjUAZErvGEj3A9MsCT9hW5eJhckRhNMawSoThIIub8EJQg9X0cGVAu5Vw+f55i0eGL\nT13g0tkzeNKhMMrZ9XMOWh2iB+t0nSqPrtTY39nFedjDvXEJlQk++l/+lH/yj38D98ws337jfQrV\nkMXyWQ7Tj3AnPXTaohDO8nxphuG9uyTDHml6SKAVhdhlL+6RFyr0+ikHJcnH3duUClOcWTjPq1/9\nKiQSOVCIkmG4ucLjt/+K3f19CnMzJGGG2xuAkqigSCSHnPMlm50R7394h1e/8Uv0d1sMCtOUZmY5\nWNkgyZTF/DOb1WqMwvMcLH9aYdNGbAkUCIQRGJPZQmckEg9lbCalcA1ZEiDclCgb4jsehaDBT998\nkxsvXR77HU+glUJ6EWnaBjSeW6JUalByY4phn3j4NlLl7K1u4QWgk0VWt/q88OI3rHuTlODkrD9+\nxKjbwwlcqs0GG8urTJYmKDglRD3k/voSc6U6S0sbHHaGbGzuMjc/xW/+w29y99E6H3zwIZeuXKbb\n61FpNjno9PnZB39FmmT88jd/mVsPt7jxzLM0JgoUggDPdTh98TnW1nfodga8+Mp1VtfXuHj+BIPO\nI1rFAX/wz36Lj27fYtRN2V7e5vu3XydPUh5tLhONEhwhCEpFZKnM9kabwlSFT25+zGgYs9luUyxV\naTQV77zxPl6hyOXnn2Kns0Ovdcj504tULzzPvQf34GwR5Qb0oxHnT5+j19nn8vWzrG61oFhj+3DA\nKI9RqUL0M7746jf46U/fZmuzzaunzqFNm7d+/D7vOrf4xldfIcsdzl58mj/5D/8PFy5dZPHCRfbb\nB2w8OuDkyTlOz00R+hP8u//rT/j6b36NsFFj6kQN30nZ2tul7A7wh2CUwXdymgWXN7c2aVSa7O7u\nkSe2adaeQOQKLY6QN+z0J458sA0mG0+eWjKKIuI8x5MFXMeK5VwX4hwSk6HASvZETrXs8aUvLtLr\nZ6yu7rG1JTh5fp4TJ06goh4PbnfIpcYvWwnh00+d5mDngP3ugDBw6A4ipmdn6Ec7BAUf5UUMEnnM\njfl7FcyknzI5UaPfOuSrX34WP3dZW37A3v4qoVtjbnGGZrVEeKLMYXxI3N/km796ht5oi6+8cpZO\nK6MwodHmHnHcIUkMgVvBK1TQrosSIcL16fU77O2t4KYZU1MnCYt1/MAhGQ7Z2V1jd3sNsnQMURoK\nxTKnzp5jNMzY29tHmCGTFZfQMXR2V3CrEYFfxyDJtSZSglKlysHOJp3WHobsWOxvBOgkxStaQ/XW\n/h6ffHyLrd1dnMCnXGmSKc3i6QtsLj8mM/D8Czd44823uXbhMvudQ1Qe44ywhBpHEPiWLCQcD69Q\nQGhN6HrINEGgyRJrgSfGmZe5sfq2zCiUY6OP3HExFZ6L0A55nqOlxEgBUlhHodjKNhxt8IVjsxxV\njuN4+AZiI8gxkGXj3a61kzNakebG2pgJQ80PIUlp1mrMTJQoP96FVOFkCiMhM5qKccE4RMZQKJVw\nXIfcaMjtTkJEioRDOo4hMIIgByMl0jHWCQkfhSYVLoGE3nCIX/B54ZnrvLXbIhsMMXmMdEMEmtSL\n7IHKMtxcMBCQCZfESNzUwVUSNyxSLIYUKgVAU/R96sUyjWaDUinEdywqERYLlCoV3ChHRSlrnV3u\nv/cJKE2SxZjegPNPP8XS4QE/eutTes9d597PPmT/9Y946Xd+Df/CGVa3W3TiEV86dx7Vj3n2xVcx\nxQJ//pffoTA9gd7tsOCHLNx4iaW0w+1771EVCm8U4ydV9ECx293AMT65SJBG0Esl6tQUKu0gpMv2\nMKIXLVOePc2vfe2PmJm5CEmC0Dmj3mM2fvgD+u1tclz80KBzicln6ettesSIcpEpp8mHD/ZJqg3k\nRJl1PN5PhxhvDn9U4d79JVwB1y6eJ8+s3ElKhyTJcD15vG+0awQ55gyAZTjb5i5XIwzK6opzC+Vr\nlTDsd/GrDZRKuHXnAy5eXaBULoE5JMsPkTolSxOSOGNyUqLbfQaDFjvRY6J0i0EvY2rqCtr1STLB\nqdOz7O4+Is89Akf+v5S9V5BkaXqe9xx/TuZJW5WZ5X1XtZn2Pd6smdlZM8AsRC1BQFoAgoQbhRRB\niVRAFBWQLnWnUASDIiUqQEAAoQWwCyyWu9jF2tnxMz0zPe27q7uqy2RVep/HO11kgxcCAiHkXd3k\nXcWX//e97/PQb7exxkOqzSYnts6w++gQFIFx6PGTt99Az2gY6RS2FZHRpggCl1bH4dkXTvLeB9eY\nn19keWGJ659c5+Tp04RhyMryEjMzFeqNOv1ek7X1OWZmTMIoYHV1np2H9wldBVMMCWOfn33nDVZP\nnKX+cMSw3WTQ6uD0bWq1FqfXz3D4sMpwPEQWZWYX1sAOKE2XJq/2QGLjMy9w5sxJdt6+yt0H2/zm\na79Iq97CtW2eefISg+GYux9d58rJs1x96+f8dH+PvmUxP1fhw+ufMjNf4fi4Trfd4/TWOdr7La6c\nOMWNDz5C1SM2tkpoWpYHOw/Q0wavvf4ZZFFk1B+RTlX41a/9Cv/md3+P7aMD+gOf6ME9Pv/ll5md\nzjI7XaG6e5ex7fFX3/8Rw5eepXn3mPMb55FsEMYubkfGMlTSuSmWZhf58c13UUrTZPQMd2/d4IXP\nXOE7f/xXhKFPsVjEtizESb9ukpaPExRFnqzzSZjoHCed3MnLMSIIQkRFwnZcZFHA9zxCN0CQJYgj\ndE2FRMZMF4kDi+pRHUVL0I2IM+eWmZqu8Nabn6LLAaWZLEPHY255mmazSq3eZqY0Ras1YGi52F5A\n3OuRCBGSojxOxwcTGf3f8vk7Qz9//rv/CwuVeU6sreLaY3JTJpWFAloaclNz7HdazJ2Y49Cr4wYj\nnKDFwa1rXDx1htZ+C3yXnCZhSCJhZBOHYwKrjsSYBIdWt83YHjO22lSr20ihy2jUQpAcbKfD/XvX\nca0evjNCkhMiXAQpIIxshtaQo6MqvuOjqxqBM0RILOLEwR42ERILYpfBYETGzPLx1Q84On4EiYNt\n9wgCn9hPUMMEBQjiENsP0VUDORZQRBlV0Qk8l9D38RyXXrtJrz9AkFS67R6mkWY4HmJmDFRRwB72\n8T2PWBB45vkXafcHOEHAqTNn6I3GpBBQUwZ6OkVuuoiZyxFIoOWyyJKErmuTIHEYIQQxQjwJSUVR\nghRDWlNJyTJhGCMoCkPfnVwc4wlvVxFFJCFGUiTsIIREIAkiYj+acGljJhzYx6lGkQnmLS8rTAsC\nq5VpsobOp+0ukh+j6AojO2AcPf5uRWYQByR+QOJ5EEWT+ksYoQUxaqROkHZhghRPdFohEoEXk5FS\nJLGICqRMmVwscX6xTCRL/ORBlUEMbujijTxGfkicLTDS0qhGFlUzOLNxknMXz/P555/hC88/xRc/\n+xznntnkxcun+crlC3xua50rszOsZlPIbh95/5CDh/fIL5apzMzwv//en/LutWuEyJx84Qo3P73B\n6VyGE2aOvUYTaarEGx+/T0eJuDx/ktvX75DZ3GDUd1hMl9GMLL/1m7/Iaizw+acvEsc2451dNvJ5\nLl04RdmxuLu9TVaWCK0B4sCn3jwiq4osGnkKj+3zQiBgqAIUTUpTCzQ6dYJzC7TLKlLR5KVXvsbz\nL/wSGdkkjFUkXca69Qb3/vz3sB2HoWJgBUNUsYSSTuPFDkEMbqwimWvUkxz33DSCKiGradqSy8LG\nSSwhz1RhioNr7090UpbD7NwChpnB8QLCaNL9naxOHxN/HnOcJ/qtx3QgISEhJIyYsKIjEUmAenUH\nz+swM5Phk+tX+fTGdeJAY2PlBOPxMbX6fQwDuu0hpcIMQgI3dx4SCSGW1cUaDimac4QjCbvXx0gX\nGYwdsmaeXMrkW9/8Y8rFIs1Gm5HvYeanePvt96k1W8zOz1NvtjHSKrKoE9gCB3v73Lt3xPLaCcpz\nM0iKxPLCEvt7e6TSBq+++kW2t7cJPI9yscCo05kkbhWYKWX54J0PWJyZYbqYQTUUjGKK2qiNFYxY\n25jj3u5tSMcsby5iaGkub57lgzff4/7DXQ5qTV559UuMBg7OyOLO/i71WoOv/tqvUllcZDwYYw9t\nevaYWqeBnNZYOrHKa6+/SqPZ4F//q/8LKRE43NsmlcuTnS6wsrJC9fgIyxogyxobmyf58Ttv8eBw\nB9SEV1//LHbfIQpj/uxb3+fcufN8/PENBr0ABQ9Finj7jY+5eeMuq8sz2D689/Y1Lp4/ReImFEyT\n3/83/476UZV8ocT5C0/y3Esv0qg3uHb9Bl/4yis4/R67j47I5AwypRlkU0MYOEiBxAe3bvLaV7+I\n5Nmc3DrJjTt3qTfamGmTOIpwXGcSqIziSZ4jSgiDiZNXEOTJ32GEKE/ORpImI0xq3wReQJwwCd8Z\nGqIgUZouk0QhcSxSr7cw0yl0LUe+MMebb37CuQtruG7IyAoYjMZYtsto5DAeh5MAYiLRalmTupms\nACK2bVMoZFFlCXts88/+6e/8/Qbmd//k/6Dd6nJ80CJfSNFqten0amSLOQRJRErJNMZdxgKMu20E\nx+fZtSdYmMvT7j5CSwcYqk4cRoSMCKUa41GPUHDZrx0ytMZ0uj1ajTpC5BElIWHiMLB79AcDbNd+\nTB2ZRIL9yEfVZEQZgiBCVTTiaELf8VybcqmAoPg4To+QMZIm8+DhAd3OgO//4C9ZXZ0nii1Gwz5h\nEKFKGnIChq4TCQlj10UWRdzhACVJ8C2L8aCLSkg8HqOQYKgacZigPYaGQ4g1HCCGAVIwAVILkoxi\npKi12viBz9zcHAcHB/THI1Y2N1FSBkdHNSQk1lc2CGyPlKhSyOYxzSxZwySTTpFOm+jpDJqqoyQJ\nuihiSsqkryYKDIMAURKJRRFV0RCSBE2VCJOIIBYYEyPKEqGQEIoQS8JjB+FEEUYiICQxOUkhm1Y5\nMV0go6s8CDy6wyGaLjMIIvqegyoIGHFEKCfoSURKBCWKEIgQxIhECAgECTGOEWWZWISxEKKoMrIh\nIcsJsZIQSTGSCDNRwOn8FEXTRC+mmVut8PLlZ3jx3Gk+f+UMnz+7xYtnt7g4V+Hywjyn52dZKBU4\n+vQGzes3+fDTD5guz9Hpjvjf/uX/yY8//JTvXLvBmf/4a7xZbfOjm/cYZQuM0hlmNk9TGw2w+l2O\nmzXW03nsRovZtMRCKkW70UKfq1Cemeb06gqmF7BaKrCSMZhRErLBkDk9pn39Ex7du8Hx4UO8vSq9\nToN7P3+Lwad3cQcDTj//PHIhQ66SZdhzefTgIRVZZiVfpFQxGfsjpLyJW1HwMzJ7cYBxYZPUbIW1\nrad4+pmXyWfTJOMRgucSjHfZ/cEfcnT7QwJJoeXZaGGEIGSwtTSuYBA4Q7LpKYbmKjcdk2bis/nk\nWTYvbnDr3n3yqSxr6WnGQ5vO0Q7joypEsLO7x7XrN1g7sYmZK4AoE/vBY9XbBJIe/zUnD0gSiTgG\nP4hIBBkSEVFQkIQQPzhEllzckcPOzjYzMxUk2UbTXSrlEs3WDq4zxhqG5M1Zet0ut+9cZWpaZn52\nmoO9QyRRpTsYMLQ71JpHqLoJgkSlUqF6cMgzT79Ida/BYOQxcGyOajXWVtZAmID9Z+fmqNfriInM\ndL5C4CcsrJTZr9apHj/CcyLSKZ1ms8HYtnjn3XcpFPMUigUGgwHFqTyiKHLqzClsd0R5OjdJVadT\nqJJKStAxxRTFTIHvfv8HTBcyjDsWnWaH+dkFWtU6qqQzsF0yhSm2H+wguj6enDA9leepV19iZWGe\nXCQxOGxQLpfYvXef0nSRR9v3KRcLBNaYtJbi44+ukysUeOrZp9g6tUUmlWKqUOTa1Y9pt1sIgsxR\n45AwDPjaP/gqpbzK8c5d2u0hvmfxD//R64CMrmuohsjFp89TmlnAyM6QL09x4cJZVFHl4pnzaJLD\n3s4jJCXF9TvbnH36s5RLc/SGXf7iz77L6rmTeAQMnTHdZofm0EHRFbKzFfxqHalU5LjZYb5Y5u23\n38RIaxw82uW113+J40YL17FxPG8SoPRDfD+CRMDzJhCVJBYIowkvOEl4bLERieOIdDoFYYSmasRx\nhKGrgICu6Ti281jEMFntHuz3qdV6bD94hKxG1Ot1ZElkOLTwgwjDMPD9CYs2baYJgglwRtF1VFVl\nPLYed+lBVSZy8X/y3/zzvzET/86VbGcwYm6hjGFGiIZOFFpEfhZdK9Jt1MkW8kxlS3z/52/zxNwM\nuqKiJ1mOa7vkKwmW4yGJDla/hWt1KWamCOQEMRUzl8tz70EDx1IQY50kiAglGT/0EYgYDSyssU8h\nn0M3RGRRg0QmCEMMbaLSUjSJ+YV5Wu02vmvgCAb9ZoOULmBkDG7dv8sP/+o6kmCQMdM41oiR3SFJ\nJuDz0IvwgxBBhVCMUFURhJB01phA2S0XMfJQEh3HtZFFGS+I8SwHSdGIZImQAEWVGA8dUn8d4Iki\nWrU6QhShiSJHO7uE1mT4d7t9BrZFIqr0LY91M4sT11AEibn5BbSUzp0bt8hki5w7d5aPr18nsixm\nKmX6x4fEIgiijIhIMT9FStMnTAA/IqfJ+FYPSVZQxYgAFzGBwPUn1Lm/ZhYIAkEco0oTU4lHSCY1\nhaxKJKMxr2+e4RsHDYqxTCsBQRHxZIFQjHCCiL6foGgyoiKiqiqaIKEkAqWCzFyhRNbMoOsGOS1N\n2UgjqVDU0riqyoPqIXduXUPqDwkMlbE7Yil0mNs8wZ//+c8JRxb6rMmvf/03eefDT3jju3+JIsqU\nn7vAq69/lT/8xjcpoDJKIoJWD0mUsKanUJEgCNm+fQdh2OO55y5jSgqq41Jq9fiNrSdw19YgiKkd\nNpCciMAKSEKHc/Nz3K02yMsiasdh2N9FSEtUKnPkFitYgxHtZg1FldAzaar3Dxn1bbq+y+VLl1AT\nEaWoUdxaIp3LsnfnJrWdXWRdxxYFRFUglhNyq8s87LfQjRz5pTLlVJmp2Q1Kc0tEww6yVyfBQJB1\njt7+NoMH72PJBm4iIUcWkgI9QSFlCCR08dUCQeo89/dbHFhdZq+c5om1EmU/oDUe8srLv8S97h5/\n8J1vsXvrgOXZGfrtDqlsnsDzeOWLX2A8HGHoOrlMlna/T6FQAJi4Lz2HiAA/DolCCUHSULQUYZRM\njC9CxKDfptvdZmlunpWVWT76aJ/9/U/IpH36gyP8YIGDg2tMF2bw3Qi5YNNu3UKVe4yax+wND3HH\nAUpuDidwyU9r2Aj0x4fIjsQnnxwyVVpEEAMKlSne+vAjao1jnnv+WSqVaerHh2SmsvR6fWamlzk8\nPiIJ2xhpiZ2DfaZKOYIgYG11nUa7gxME7FerXLx4kV/4xdf4xp98k1ajgSolKKqKkTGQ5IRUfopB\nZ8TNH31AKZ/HC3z6tkVhKsPprVPc3t5lqlJicXae7/z7H3LxxFn2Dqro2SzN6iO2j48wFYWXPv8i\ny4vzaLrK9XfeIbJ9spkCqZTGS88/i58EvPy5F/ngvbdwTINWo0vK1Li7e4+Lz13irXffRQwDdNnA\nGtiUSnPYoY+hyYS2w2ppFiVxuXtUY31jk16vyw9++ENOnTpNOqNx88P72G6L1155mUJKYLvW47vf\nvcPqyhNs33+E5dY5f+ECR0d1/off+S/5s299h7WFJ5E0+MKXv0R/1OKJs5ukUiLKVAZtf5fTJ9Z5\n8GCbtaV1JAdee+VV/ujbf8rcwjz5TIH6wSHf+pNvcOnCk9y8eY1ElDiu1fG9Ca87DCAMQRASEil8\n3DufeDFdN0bXJRAnsoRsOsVoMERVBGzbJ2UYCImE60yoQH4w4XR7gYQoSeRzJlEyRNdS+P4YTZeQ\nJBUIAAHHDqgfDdGNCTTG9hMGQxdFMbAtm9HQQ1YSwuD/G3X7/zEw24mF1ztgKZ2ntX0LVTaxrZCD\nvUMKmRRFH5xajSfOrhIe9Rm4I9RQ4KBRRS+lKU+ts7PfYDqBhWjJAAAgAElEQVSTxpSL2CMBRdYf\n0yQK7O5VcZlUA+JIQpHVycCMBDwfPHcyYFTdxzSzaLI6MXOIwgTuHQf0+nX80CdMYmqtPpE1KU13\nex6CZDC/uMzeziHl2QqaKuN4MsSgyiqh5KFl0jiui6xI/8HD54YBmighayKhJ5AIEalcjsDzMVSR\nwAsxNJEgcFEUhTgIIAqRVJ0gChEUiXr1iFR28o9m+z5TKQMSCW8wQhFAVnUiIaLTaCCEEyRZu91C\nNHS8OMbq9Mk1m4wtGzFKyJoZXM3A95wJVzOBEye32L6/zdL6JoHrUd/fYX1lid5wQOyGrCo5CCKk\nvEgQRfhB8LhGEOInMZEfkPgumiLTardwF4qIoopj9fGyEl4UUykWUfQSy/kSmqmSkg3SmkhO1ymo\n+uTFnVIYGVDojBjEIZm5GVrdDkc3HjKslMie3OAnP32fer3Hf/Ybv8HNG9dw/RAxnqTgyEwzmlni\nju0ynclSbbfZqrX4q5v3MVZWkJKE/d0jarcesDUzQ6acwVRVCiOf5XKFF5//HGltAlG3HYtLi7Mk\nYYLtOCRCSPuj93DcIZ1uG8+Hfjip3oRyiO+OUPUsuusTIiIXTYTlJVRFBw+qh1X0KMHMmTihSGBF\nyEqGJy+fQ5ZlxlrC3IklUomDNWjw4xtvog594rSETwyCTD+0OfSGCJ5G5exlxpLD3XaNX3zts2SE\nAqHjoBYKRMM+TrPOnZ9+F/q30fQ8ke2i6DKtWEVM5ZDiCFnw8EnT7Mkcj3w8M83ZV14mTkIYD0kE\nCdEz+HSvwVRF4Yn1GeKeTVYzUCrT+IKI5qqsri7x0dWPOPO1X+bHP/gRT1y6wLVrn/DklSvUjo4x\nTZ1Hjx6gKAJLSxtIioE1tlE1nTCJSCR3EhezM4Suys/f/wHprI+kuKhCifT0Kp9efw9V0pjOm9Rr\nTSw3JJcNqNX6eGmZsdelPXaJEp2lxSJJ0qK0XGI0TjBME8f1GfTq/GT7LpYtsXpiicrsFMN+j29+\n+A1U1SQOYxzb4fi4T61T55krF7l24xZXnrnI4VGHB9vbOAOXREwwUsbjFTO89ebbKIKELkk8c+ks\num6QSWexnDFud0zrqMpsJUdeF9lY28TMaNzYvsNRt8mF8/Pcv7OPwiq/8o9+ld/+7d/h177+dW7d\nvEUqb/KPf/m/4t2PPyJtGASeQyqMGFZrJHkTq1WjVj/m5BNbXL16lTu3SyzNzzDsj4j9GBWB05cv\nUW+1SRBZXd3gL/7020yX56get7ACG7MgYg8D/tUf/DGC4pHOaOz+xXU2t5boDVxSKY/yzAyPHv6U\nKxcuYHcsNhazzC9f5s2ff8w7b/8MTTfJlUwCRrzw/BYLMwG/8tWLRJLA+GHAp7dvcGXrFB+8d5XK\nXI5Xn3uW4WiIYMFSdgG35zMYDPj2z37IV77webA83rv6EfmUxtknznLj1q0JFzaOH9fyJit/P4on\nTFdJIiYgimNkRUB6bCr561CQECW4to2hq5TKZXq9IZ4TMRzaRGFIHEcIgoTrxiRSjKIpOP6Q9dV5\nZioZao0GOw87bG6t0Oo2EQWJOAzxohAjpRGEPlEcksobjIcWK5tTxLHLdMXkcL/+t87Ev3Ml+6MP\n/gVOPKZUShP6MY1mD2KZMEwgUelZI2Q1IKCHFA8ozObI53KMXZvEHJGIHo4VYGgKqh7jWEOiYYwU\nhaRMAStwcHwRQdQYjxwC3yZEwB5ZOCNIEgVVClmaLRAEHkN7RJyA6zikdZ1sXiaMx3hOgO/A/v4h\nVs/B6luMhzGdrkdxqoTvBmRNk0ppGlVWCLwJfkvTdOIkmgD1BGECJVAkApgouiSJMEpQUzqRMKHc\n+GFIlMQoChi6jC8EjxmcE0t8kiSEcThJFIoCqiSSBA6RayP5Pr7rEAQ2sgT4IZ41xA2GGJJM6I4J\nHQcjjpFciD0L4XEsuzvqI2oq5ayJ6zmEkkwgSjR7A6aKFcbDAZ1mnbNnnyBB5Kh6xNazV1AyaXaO\nDlk5c4rp1UVa9pjZxSUKU1OEYUDRNBHjAD2OOD0/S1ZRyOcybC4ssHriBIV0nvWNNRY25nA7LVrN\nAadeuEL51AZ/9O3v8MHdu1x56SV6GY3/9V/+W44afT73D77Gm5/e4a1Pb+NoJmc+8xm++eMfst8f\nEiEjeyFGp8vZcomMFOFFAjUpYUozubi6xLmNOebdgGfLszw7W2aznOO0YTI9dpiVJGbChJLlgWfh\n9hs0qruM+m3GnTbtoyPcZoteq8mgO7l/I8kkskJGLWDmi1iyTqKIaJ7F6fI0aiIgVqYors+ytrxA\nKW2wNF1kenoSHhJkkaKZIZ1NkV+uUFwvoacibH+INx7AuE+rcUz1eJcgslFGDomgsl+tIRBSnptG\nW5gmu3KCViIhqgrnzl4mb0wTRwKiIeMf3GH3J9/h4PYtnMgjkGLcJMEWYnwBSFLIsoGhTHHU13kw\nzlGLNNZPVlg5tYSoiihCQkYSORxZ7HV7lA2d+eEuG91Dilqae/U2km6gaAYXnrw0kRBLIr12HUNU\nQZVxPIvNE+vsH+zijIe8895bEAccHR6yvLyAIGkIgoAq+wi49LtV4thCFjV+8lcfoEsGc3MVVEXA\ncwfUq31kKY/vCCSBT71+jJHKI0R5pDikUDRYP7FAo95m/6CJ48jUDj2mi6uM+iGFQon9gxooWebn\n18jmphhZY3YOdxEECVVReLCzz6P9IxzPwfdiUimTR3sHNNt9RFFgpjRFylAZDsdkTJPl5SWqBwdI\nwMPtHS5fOM3sTIZWs02j3uMvv/dTypUyc7MpUmpEMa8ixh7Xrr5NfzikPRhRa/SQRJWHd/e4ceMm\n9njEUa1BbzxmYWGGRr1DKVdkcW2ecqVMrIhsXXyCeqPN8tomg1aD8bDLdHGKTDbFtet3CL0It29j\n9SzqzQZ7R/soksyg3caPYzwSGq0mG2srdJttnr50hW6rRyqdpljMcPnseT69fpPNrTXuP9gj9HzO\nnD1JvV1n91GD2/fus7h2gtDzeebZFZ6+UGJu2sQb+vSaNWq1IddvPqTfH5NTCvhjm/OXnqJnDdh+\nWGXv+IiDvRoH1Rq9wYhPPrmJnlaZX19EjyQC28Ui5u7tbc6cucBh9YhqvTHpyCeT6kiSgCRNDEuI\nj41UivS4FjcJBiVJjCJLaMokISsK4LouY8shDCRkUZvo+pKQKGGC+pdBN2QcK6BSKnCwd4Q1ikjp\nacqzeWq15iScFk9EFKOhxcLiPKmczGA0RtGgOK2SKYKS8skXDP6L//Sf/f0G5vff+Nf0hyOKeQ13\n7EGQkJYz7FQtiFXMrIYvuih6iJkqIWkBmhEiSBHdvoszkKlUcjhuGze0GVkjFpeWSZsag2GHo7rF\nUUPFt0RSUsSJE2c5qDlY4xgNiUwm4uKFEjMpePrJLbIFlUbvkPnZPKYsE0cRB/sNTHOKdrfGL7z+\nWZS0Se3Y4tbtPUQpxf17e4yHDhsrG7iWhz32sLyQEBkh8FAlhSQRUDSdRJSQdAM/iZEeg8tFGSIJ\nAjHETwIiAUIhQjZkEm2CuhNkgSCwEPAAnzgWSRIFUdFImxmCwCdfyBEQEBlpgiQiHSXYAggpma98\n9iRConNUO0aMfBwvoDxVxBdt+nabL3z2NC+9tsHqSpq11QqXn32B7Vv3UZXJr2VZEgmjkF63xfra\nGruP9hmPLBQlTbc9QNUMTDM7sSAMR2ye2ERPpem5NltntmjVj9DjhAVdIyVOygRuNgXPn+df/ME3\nEHWFl7/6Vb7z1ns0+z3WKrNIWoo3379KLQwQRhF7x022uyN6lo9ZrNDtW8iygRbLrJlFJALOri4x\nZTuE9QM812GrMo2pJFiDEUG3z2IQYVUfMa4fY9eaHFWr7NXr9IZtWo0jxCQhtGzEIEQKQjK5DLoQ\nMbMwhacISGaGTNpkpVTCjSLypSKGqeIGNplcBlkU0NSYyMjhWkOWy2VyORUtY1JToa0mjEcOx0dN\ndnf3aFUb1PePEC2HqNkjqjY53n+I7Ay5f/cm7316i8Ohz0/vH9LAJ72axk05KEvzSHMneeoXv8yF\nV19g/smLZE6sk55f4eKFZ1g/dQ4jVEmpKWIVdv7ijzl863sMFYuOIKKEMaowYiDJkBQQkhTpJIWs\nlbhrp9j2FKziDFtPP41RTFNERBiMMUlTa/Y4ijxO5iVyuzfJ9RsUVZVMPOS5yyt8cPsAVXSIBx69\nKKA4P01tv4MoylQPj6h16jQb+wSdPmMnwHNBk3Wu3rjO/PoM8pRKVpTQpAyi4DMY7jH2dvECl1Qq\nQ7s/pD9y6dhNdqt7JIlOIVNGiEUkUaE4VeLgoEo6m6XTqjIejWgcNyhkC8zMzCCIEgf7ezQadTrd\nA9IZA1kRcHoW8+U5NFHj2sfXODps4rkCsirzYHuHr/7C67hewOLKCh99/DG6bqIoGt1Ol1JpGojx\nwpAYgYNqFd0wCaKEKIHhsI/jRPzszQ/JTeW4ePkcnVGL4djh2vW7qGaRVj8gPVUgn5/mww/uMLe4\nzsOdY+bnK8zNLHHv5h1m5hcoz83QrB3T7HSYPbfGyuI6VrPDo4dHVGt9YjckJav8+M03CaOQKI7p\ntmqIssKj6hH//sc/Y+foiNzUNF/80pepFKbwLJtEhKFj88//p/+R7TvbHO1XGQ76LCwtMF+ucOnU\nGbar+/zWf/5rHOzeIdEVnr/wNIIkYw1GFFI55pfLTJfK3Nt5yLe+8wZyAp98cshua0RhfhZFUCnJ\nGoJt8M79O8y+cIF4OGQmn+bs2ZP0ewN6HZuVtQ06wzGGqRCGEefPPkW706fb69Fs1DmxfpLf/3f/\nD7qZ59JTV2g0GxOnsCAQRSGSCKoqE0U+sizjWtFEixgzAdTIIkigGwqiNGGBK4qCNQJJVuj1e5im\nhhcECKJMEATohoGiKERhRLfbA2TGY5t0JkO9XSUMJ8MyThRGQw9VMZBlgVRGZWNrAVH0iMOIbD5P\nlIxJ/Ay/9Wv/9G/MRCH526Cujz//9W9vkOjgdIfgqySBQioo0PVlVCFh9kSanQfXWdmc4f6tHstF\nkZX1adBF7LFLcWqG42qD7rCJmU9NrAdRCl1Is7m1xP3qIXuNGH8ccm51GsGU+fhexN52D81XMYyY\nE5sp1LGNmjUwynPs9oaIo4T9a7ucPP8Eb733Iae3TpMIDplpnRv3dqlXA2wnRpBDiCLEIORX/+Ev\nYyg6kiLhExIQYzsD6rU6Zj5HEIHvB9iOy2g0xPM9hr3+pM4SRPiOM9mFJyBLCTHBJMWFSBKJRE4w\n8WFGgDjhxaqpFGkzgzWyJ1F9y8fGRxQS0qR4+ZXPMIrqNL091laX6A5HRHZCKT0DQcLB/hA3GHHp\nYoWdfguxI6EqMrfvNnG7QzxEZD2N+NdEISFGkWRkUZooCI0cKCJ+EjxOl8F4OGCmPEMUi1Q7NVYW\ny7iHh2RGNq9srrKqaqj5LMmpU/zJ7Xs8vH4fX4Cv/9Zv8MbPfkpeS6FKAi8+/RxvfPoRgiyx4Mrk\nytPE9hGyMzlQpGQZL/IRBxaOZSHqBjUvpNsaEsQu7rDH1y9dYlqBWhhxezAARUFNNBwdkjgka5iT\nXioJw2EfTdNRVAXXdzCy5qT64jpEYgylDJnKFJ2jY4Sei5IyCaOYmaniJBwQTYwbbhRgzWao1Y4J\nj9q8emqdJaPIfruLWKnQ6Y1p9waIqoYnCSS6SrqYQ8+kkbIZMCCdU5kxc0ynCmTTRSIjiy34jAcP\n6fWqPPXcF8jqy5MAGA5CDGIcQSAwchwiL2Bod9CiEfUPP6Q97uALCWYYIisRvioTJDKJL0CikNKn\nOEoy3B37JKrJ1Pos03PzCEoaO4ww5Air3cbzBeRMgdS4ynT3kFT3gGjkkSAgqCJKlKCk07iiijAO\nuBbHvH9vj2a9wcbKFi1BIBAjKnOLtBvDyQmBCfdWsGPKcypXTq+gJTHHrR3soIliDBl0XfK5Mrn8\nHG/87F30dIZT507Q6zVI7Ah/JFCamqVer1IqmfiRjR+GaLk8SZxg9UcogkwqlaPR6TM7UyEIOqQM\nSGVSaGqOvUd1ur0hri9x/fojpmdWsHwPJRIYWyOUlMHh4TGmbtDrdHnqqWfY2dkll00zMzNF4DtY\ntossqcQJSIKMJMvcvn2HU6dX0VSRvb0Drjx5mWq1RigE6EpEPp0nX8xxcHBIxlBJkpAklBg6Pp2h\nxcVz5/jg7av07BGLCyu0212Kc0XyZpFXXnmBxUIJdzhi73iXOzt36Q5t6scDNFllcWGOXCZPIrjM\nzZURRBXfF3jn7avcuXsPwzQoFaYYD5pU5mcQdY2nnnqacr7EW2++iePbWL6LKkmU8nnc0OezX3qR\nTrtGXszx8fVt+kMLQp/yYoXa4QHl4hSnLm/iHDepHrepzBexxj0ETHZ3H/Dk08/x4zff5sypk2wt\nzdE6atEZ9BgEPuWFFR482OelZ59CkyX+4JvfwOpZ/Lf/+J/w/Z/9mF63ga7IVA9qoKhsbGyyurJC\no3GM6wa8f/Uj4ihGUyWSOMT2fJJEIAoFZEmGOHrMGBZRdQVNE0np8qT6JIio8hTZbJoH27fIZdL4\nYczQ9nDdgMXFBQwjzeH+Aa7jTTQTUoRuqIxdGzNrkElpiLJGs9FBFnTi2CWW4YWX13CsBoN2jCir\nRImNrhb44KfVvzET/84bZv9ggK0pzBWL7O7XyaRLuL0xh+0RG+sF2r0mKwsZVpZL1I4GLM9WsMYu\nuVSOYbOJKqloUoySKJTNIpKi4icCUpThuNqjMjVPf3BMfzgim1IZiRGVmQz1o2Omi1O4jksUK8hG\nAT2l0um0aPdG+E24vXfI9MY5FHOZT24doSgOo6CJEJmMhzFiIhCPBxSNNJmUye61a8yVZykWC4hJ\njCaJSJpOeeMssqYhSAoCAo7jIyoygSRg+y6+6zEeDLHsIcPhmCBwcIcdQnfM2HEYOwGyoEBKIUx8\nxDAm8VWiaMznXnkCVQ/wfIH+0OfDn9wjbWqII5u0KBMcVhHMHmYhi5YRObu8iKZM8cl7Nxgd99ha\nmkaTMuw+vE12JU82n6I8d4K33rlLNtZR1BRRIhCHHoos4TouXhwTiZPBShQTCZDKpBnVj5EEAVlR\n6NRqhEhocky3dozmhRALiFGCGkfokc+wc8RrT1/COH2ODgOS27f59cVNnEGL0bhP/M2/YDkZE8Qe\nfcfmYewgiNPgB6QLWfQoISqmSCfgKiEzooeeVwmGAb4kE1oRgediaiaCG1AplmkM+wiZmIypEzki\n6ZTOyLcmYQwxTRz4CKJAKiWjSRExPqKaou8MmS0ZaFmfTKCSKldQUwZeMMYeWbTGbeRApNvvky+U\n0eWQqUUDffEEe06Ev5Chv24yVFVmCyc4M11kOl9ktlAhfAwNzxjGhGsbxwi6iKbI4HlExKh6hlCQ\nEQeLBMMO46MuXnQDRh79doPOwKHZ79MZewwsm2ajztlylkXFw56CQA4wAxFCAUnKEbkBvhajUyLJ\nVXiz02UQjtBLGlc+fwG5G6EYOoOwQ1ZPc31/yMBXubiUQb37DoujLgUphFya49hBVEsEvsOwd0wm\nHTA+dkmLIqftgDNPPwHZZ7h+8xpfO3WZtz7d4Y+/+T0oFTFL0+hDi3EWmofH/NF/8t9RGTXQUzHy\n+kXevXWPj7bvYqQj1MDl6O5dXnnxWUauy1tvvMel8xfxBZ9H1SqPdnssLs1gmtMcHmyTNlPcvf2A\nF174LPdvv03WTLN7eIRupsl7Bg/vHbIwW+bOjet86StfoFd36A5tas0BipAm8RKK2QK14zpzMxWO\nazVSZoogjplbWuCT659AFNPtNGi1axRyGRrHDfL5AkEQs7K8wrDbI4li3vvgGl94+XNk0gWajSpe\nMKDTd/nccy/SazVAkBiPEhI/oNGeVNKQZHL5DDs7x8SiyoXzV7h2/SZPnD+JqATUa3vceFfhT27t\noaUlFmaniOKAK0+eQdR0oqFLp97mo6vvkS+YPLh/Hc0sMHRjcoUyXhSRkQTCxMHMmbS7PcJY5H3/\nKsNWk/PnzhCEDqos8dyzz/G973yPE5trKCSIskq9fkC2nOL8xQ3MjMFxrU8+n8ZzO5TTOsHiPA/3\nW+ztHhCEPitnKqw9s0H+RIX/aOk1NmfW2Pv4DruNFrYz5tyFyzS6PR7u7vDRjY95+ctf4uKlE7Tr\nFr/7+/83Zy+fJRIs0qpGZW6ed69+SBA6/PQnP+SpJ59EV3WIE4LQR1NUvMfDkgQUSURTNRzHRpIn\nOsIomABZYl3B80MCP0ROYtrNKpnM5HVo2S6qIpPEIc1GHdee8GYL+TyNVm+C4hQ8Urk0ohQR+BG6\n7CNKMVlTw7ETIlFlbi5L6ygkW1K5eXMbI6sziv52NN7f+cL8n//7M+wM+6jI2COB2BNo32nxxDNP\nk6gNxmGTUj6H47s8vNdgeWWBbrvH2a0Fev0B6XyKdmOAiIGmOKxvrCOaCeNBCGRww4A48hFjg0J2\nmndv36LaEEhC0KUSui5z8aJJMO6iJmkCRSVIVB7uVinPrfHpRzdodywavTFxmIDnk1YMprImojdG\nVwXCGBQ1heO6dFpNJFlG1w1kVcWUNLL5PGY2h2GkMA0TUZLI5HIYhoGkKoiGhqRrhHKCJ8DAGvDw\n3nXi0CdfLKEKOrblYXljXN8itHx0JeCpJzfY272GmdUYuwkjS8BOTB5evUUSQz6T41Q+zWvPnKEr\nWhTSae7vHzIIbGLT4OF+j/X1HJI8QZCZeoGPf/6AmdzUhDpiC7hxhBBMqjuiJCElAkEQEEcBuq6R\nhMJk1ZHEBGFEGIZomoqARBiDqkLs26iJRD6I+PKJddYlgWwhzfVmk+22x3Qi4+VkhgSEgoaQTG4p\nJ4wCDdlFt0Py6RS6otBMXKZiiYNGlQsL6+zYXQp6DkESCK0+jqwSJjK1dgPBG/P66gorhRT1CK6P\n+2TMHKPAIY59jCRN4ou46RDDVCjk89QbdcypPG4YUC6V2d85otvxGbo9CosSC6tlIk/laLsLmsTi\nyQXcKCZJaejZAnLGRE/nmIoiYlVE04rMVmYoFafxIhtZ0yjqBrqmICcSSRCSBCGB42ANJq/OyHNw\nen2EGPyRhWdbdLoNHNthNEhoN4a0use0gg7BMMQPPTQ9g+P4DCNxEvbyLF5+9gTrSwaeO0Z0wdNl\nkpRCCoUkzBIxRUtW2evLOCmV9fMFzmzpRH2fRJ2jO0pwrIBqa4BZyJO1O2SaD1gU+hjRkND2iFQD\nQctieQGSoBF4I5LQRhM1+raHoWqM/AhLy5KVNIZHe0R6nnphno97I9xmg+X5Apbi4w9dXt9YJdy/\nw8JGFlnLsnMwJJWeprp/m+W1JUTZoNYdUxvYlMt52o0++blpbu/us7NfIzddwBo1KWYNCvkMjYGL\nkVIRE5/SVIFsLo0ki/T7YzzbpX7UYG15Ds8ZsnJyk+t3D6k1HaZyFaqHDW7f32NzdZGt9WWa/QHV\nVp9ep8dsZYZGo4XvOAz6XWRJolAooKoyjVqTxcVF4hhkRWEwGtFqt7lw/hTLC2Xu3vkESUvh+DFn\ntzYY9cbstw6ZLs1zuHPIaOSSzuaJ8dhcW+HwsMF0qUilUuTOnW1iIn7967/Mwf4B1VaXwtw87//8\nTX7jiy9xslKg2e5w2BkwigKq9Tp+INHuNFnbWMfzIrbvH9DvufRGA7aeOEmnUSObTnPcaHDpypOU\nCjm279+j02lz6tRJNk9t8t6HH/D661/lzu0HDJoHeBFcuLTF/Mw8iWXTc4dcv3PAhafOE7QsmvUG\nP/rZm3zuyy+xMTNHVtV5cLxDrVtDdD0W0zO8+cltLr74KpZf49GDXRq1Hqubc2ydPElhqshwbPPx\n+z/E0Kboj/zJ60sS6A56mKkMj44P0SSFtJ4lERVCz6HXH5GIAnHokMQQ+CArMmEQsbK8TG/Qx3Es\nRHESUpydLzIaD0gbBsP+mJSWwjRTDHqdCU9blEjncriuT7czoFjIgxAyHHqMxy6V2RmKMyaHxzVy\nOYNx2+aJM+scVg/pdW2CMEIzc3zll9Y53N5lVPdQFZ1EmSJUD/jw5+O/3wtzbqbIo1Gfglmkkk1x\n8KjB2dMnGA1sju0Om8vTeL6ArmR54dkNHjXa+JHH3TsHCKJEJujTbscInsTmVpbhYES/2cNM5+m1\nbfKFEhnTwA8cBtYBF68sc8EvcnA45NMbj8hlCmiSjEUfdyiSzuq0jw/QdRUFl8rMFJeuPMU3//Db\nTOdnsV0HJfIwRB9Vj0mEBKOQx8wU2Ts8xNdBkcFJXLA9XH9At9/5D/aTMJh0CBMhQREV0qrOdL5C\nNqug6RnMQg41l6IYCxSnZsllCgiiQm6hSISNWVDoOQO0VIRt77N2okR/0GZjuUxARLPlspy5gO0G\nkBqxXlni337v55zZmqeYDZGyCsvFMm3bYXleYqaokk4pWP0+02kZeyNLNpPmhSufod7wObG0yTd+\n9FP2H+4TJAmaLONb/gQpl7gkmkYShfBY0osoEE8kmBNguR8iM7Gt+BL4cUSUCPiyiJAyycwWCd0h\n0yuzVOwYr9cnTIEvCcx5CWldo9vvoi1mJgf62hBFM8kUc/SiEYIOrvH/UvZev7bm933e8/a6+lp7\nrd3L2afOmXJmOIWcoVgl0ZRoBLSaDVtJHMUIEicBjCQ3yW2uAgQxbFhBkAR24EQFsWDLFkmRGoni\nUDMUh9POOXPa7mXtvXp7e8/Fpq5iCOCf8F788Pl9f+/n+zwJQQzV3MAqWSzCkKXtFbx+lwUpITmu\nkZGbEo6WYFo1RFHhySddxgOX5R2TplnhsPuUta1NpqLIyA05m51xFs+RShbG6jLGioHYLPH6vTep\n21sM3Dm1pkUUp6ytbWFYdZBF8p/KuRUhIx6OEMIMIUuJ8oL4ok8aFzx4todiWIwGQ8QoZTIccXLw\nAC8UEMWAPAlIIghjEUGSmfkjSnUbSbCQRAtfTAikCIlNqhoAACAASURBVJoWaaiQiTl51UCNQchC\nZDlFlyO0BLJIZKFVCHUJQ86I45TErNEdWfSEBY1Gi1dfvYNV8plOp7iRTiU1eOKMKRKVjeUVePzn\nrNOlLsqIYoW508XQygiaTux6yLlKpuRopSpBbrJwAqqqQBT7VEwLaTGjXG3yoeexsnmD/YHLxx8/\n5Etf/Rx1Q+HxB3/JzfYWQ9Xm2p03GA177I8PaTVUWlrIGRa53OBHD+7jpeCGAaNgih8EKJ5Pa3WV\np4dndHtndFaatNfX+fAvf8L2xi4kBcf7J2i7JQ6fPKW90qSz3MadeWxsXMf3I9Y6W3zyk2ds7t7l\nySfv8aVXv0jmZrz1H71FWZH5+KOf4E9mhPM+L959hZXOKm8P32ae+Ohli93ta6yutPHcBUHkYJR0\nRFHk+OQMCpGX7t1FlDJu3b2O4y4wLIFKp0L3aZdWawXR1InShC986YuMJw6ffPKIdmedx08PcMYT\nlpsNTo/O+Jvf+BqfPrjP5LJLzZDQtnexlppEn3kRqVRhWgi0drd5OvsQ224RnZ7yymfe5HLcY3/v\nkMlwhqIYuMEY3TQY9XvkWUGvP8S2S1ycn/Dggx47164jKioL1+F0/xA1l+n3J5QUgx/v7bPdajDp\nDXnyzoeYnRov3twiHof8zv/9B9y+foO7S22++atf571nn3K4f4CoaZRsleGlz+Zqm/M0x9rY5GJy\nyi+8+Rk69RoPnx2ydW2DFJ+jJxNef/El7M++SeDD/uE5R6enxFFCIYj0+yNIrwQI0+mULBMp2Ra6\npVEUGbJYwpm6SBIIBVTKZbrdc6ySTVFAnCU0qhVCx0dEIE5TcqEgimOcvouhySBILFyfOL8ymVBc\nNeTTNCZwC3TNRFVlJEFGFUXELOXll3fJi5zVTptGHR49PSPOXCRJYjEJuXF9nUHP4/hyQGfX+Nkn\nzH/yj1/n04MedbnNWXfIfOhQLVSkWoXyusHwcp/bu9uU7Co5IZ/snzEeLSjpJl4osnNLJgks5oOM\ncslhc+U6mWAQ5S6DXoguN/H9EfW2jF6WcBOXqmozmIxIlBr+KKFqxCwSDyXeJApzupMxzaVV9g4v\naTc6PHqyj11qMxkMsfQcwQ9plk1cZ4wsX1m2syQjLHJG8ynFTzFy5AWxkHCVIxppHiGKBZKkUOQp\nRS4jkKLJOnl85ZFUVAnPFxC1lEa5jSYV1Es1VE1h45qNXs2JJZk4E8mKBaKcs7ZeZzw6Q5YLDLGB\nKJQZLyIko0BVFU4PT9jd6NDtHnHt9ia+PyNwI0RBJgwTKvUyw94QO2zROxvw+t1bnJw/Zv32C7z9\n/T3a7U3ee+8n6KpOFCUI+VUgqqpMUKSIgogkycRhRF7kGKqG9NPvF/McWRKQNAlJiPnaSoebskjL\nNDkMHCaCjBQXJDWNklmjf3FJUhFJTIX2ZQS2zdR1qNWbZIuQteurTMd9ZK1ANRTERpXEiVAzld55\nxCRdMPF9Np9b5/333ueby1vUVZjUFfolmflijlm38XWYuwXtxjaKnbHUblCEMTWjxMbyCqphUqpU\nKLfamKaNJsn4/hwZGUXWSMIUOROJfYfM97k8OMWwK0R+TDBzGE96jCcD8CMmsxmIOc5gTBymjGOH\nueORigp+4KHlV4zfZlNHFnIM2yAlRlQVFLPMIs7pzudEcYApSWRujGJVSH0oaQppHqHbGnHooygi\nYZyRxxH3rq1yc3uLi3mAUNKp6yZ5pHCGQT8WiGyF9bVVnr/WQpUlZrlIP1FJIo3zwQClXOG6nGId\nPKAp9FCVAm+RIOcilSWV2I/IowxJtfAWDoqkIdklInLEJMQZ97HCFDcFxawz9xz0xk3+6GhAV7eo\ntloMzk7o9U74tV/72zz7yUfsP/iAX3rhBm/dWOPpxQFhMMUWbJZ3nuPo8pKeM0dv1skQmLtjdF1D\nEDTOzy5xnDmlkgZSgqyYPH12zmgSUC3XqVVN/LkHUsHN2+vUaiUe3P8Ey66zvbVDHEyo1lVmi4iD\nvQlBnHF01EeSFe7eus7x3lPKjSoPH5+yCHzMcpn2coskjhBFmf6gz1J7iZeut/j0yTGvfvZlTs+O\n8Z2MmlVHEiRSScNzpix3VB7tPWNlbZ3VxhLds1N6szmablA2KmztXOc7//bbfObeZwjihB9/8DHN\npTZmSeHzb75BxZQQspj+5TGrN+6ytXGNZ4+eMbg4I8s94iikZNfY27ukkAXaq2vkeYxqG/zZd9+h\nUWlzeHKKZplkeYau6SRhiCxLUGR0lupMZ3PmrsP6yjK/+OUv8O0//i5jJ4Rc5D/5+7/O8OwURZO5\nPJ9Qbdao6BJ7h30OBl3mE5fttQabu5u8+OoLPP3kfZ5//iUef/yIiReRFwKSJNE9P+feS7tMhkO2\ntrbp9y+4ceMGfpTyJ29/zObOGkLsMh4HV0Wzyy6iKhFFIQgignRF9QmcCBBRFBG7aiFIIOTgznwk\nWSXLCgShII4DRFkiSVMkRUYsMgQBZF0mydIrsUUhkEQJrXoVWRIZDefk6ZWFsV6rEgY+cZxw8/pN\nDg4PyYuM1z77Op7r4y4cJEmgXMrIJZl5NCWOwdQzZFFgc7NO/6xPyW7RH4TMnHOefZr9bIH5X/3X\n62SKgeHoLNW2ODg8Z2dpmTP3ElnzERSTsqrgjWYskil6pcHK9gafPPiYzvI6a5sF7793ia40WG4H\naChYlQaC6XJ44HJ5VGAbOpqdUGgphm2gZwJLyx0OuiNyT+bGToOT8RH+2EZWLRTFYr87xE8VDp/0\nyFOZRJYo8oyGKKDlLioZlqxgigaxJBEJKbFQcNy9RJRkhCwHMsRUJCdDkjUoEhCKK9lpkZIJIAoF\nJatEmqTIsoLrRlc6LQokoUCXNQQhomSV+Pov3cMPz5jMI8YTn7sv3uHdH31Es2Vy984KqhiiyAbH\nC5fFOMR0JdqrDRwxI8/LlKsa08kIXfERipSdjVUW4ZRUiRl7CfGRjC0ZrKzWKFUURnOHrcYd/vG/\n/B6TUYjr+lf7S6KAJIGqSsi5BGmBXAhXBJ6fOucKTSQnx5ZVPNehsbLEbDThH7x1j2a/R7vcYn8y\nY6xqlEyDfN1C8AvC/pTqtRKhCvKpR1woXPoeUgp5EFN7cZNnp3u8dHsHwRYZziekbkRFr9DzDOyy\nRKe2hF43wKiwrdeolapMk5i+5FKRC3Y7DbRGCUksIaUqmRxiaDJSIlLEGUKaIHJVFGAyw00cIs8h\ncBLmY5fC9zkYnSJME3rOAil0mc7nzEcReZ5RiDnjLMRuVDElmTjLMWs2Cye4gmMkPoaekxSQiiIK\nOeQZMRl5LFGpG/j+BFMWSPyCQjAZOhGmrFFSZFRZ4mwyprzcoQgDClkiEWQ0JLIsIEpz5r7LV7/8\nJnVLIYtDqkaJytI67/cXjNEoVw2eu7POkt1i6M2JBAPEMuO5z2wRYDZ1OvMjhL19VoWMIsuIDZAF\nAyOKiXCxmk00USYPF6ShS+jGqOUl4kygUrNw5oMrlrBq0JsVnLopvmzxSNAIIgXVKDjde8Tt5+4w\nHk8oSSorpk1dy2mbOct5TpYkOHnMyJuwcEI6m7f44OkRr7zxCj/44Q9RlIx+3yVLQ27ubnJtd5Xu\nxSVRbvDxw0OccE690cGyVC5Pu1zb3iLNXcaTIUvNFrJkc//hQ27cWiEJIkgLHn96gVZdRjdLtBtl\nTg5OUWWNNE9xY4/pwiEMM37ui58HMac/GqJbFheXXd7cWeZiMUCt2CymDpZk8MLu8zzbP+TDJ89I\n4ox6xeL28zf4zGdfZv/RA4aXl1wMHFTdgCIlzmSWmi163UuGgwkIOm998U3KLRPPn6FJOcdPHvH8\n7V1KKx22mus8/XSf9tYa83SBWMDw8AJVrvJH3/ozrj23S2/Qw/V90ghkUeP47ITt3W3c+YI0jbE1\nm8XCIRdSmo0qk9mM1bVVFpMx26trHJ8cIxoqfpzyuXv3kCydZ/cf0e1PKZKMztYy3/y7f4/J4VOe\n7j+hutWmmoUcP9ij0Cyu7awxHY/o7LYYXU6xdI2TyyHlehUzBd20eHpwQbVj0t7YplxZpWSX6H76\nKd/7kx/i+BlO6CMqVy1XVdPxo5DADa6kDUiUqwYbW2s829vHMjTyREJRdFqtJY6O9mkt1Zgv5szn\nHpIsUC5ZCGREaUIhihQIZEVG5OfsbK6iqQXHR5fEsUBeFKjylbii017Ctk0GlxOqtRKf/fwbpFnM\n8dE+iiIQBznH3QswXOyyxa2dFgdPFtTbMWGYIYtlHGeKKiv8+C8mP9uT7Npqg588uaClNpBLIaVW\nztJmiY/uf4wcekx6Brqm4ff6tJslWnKJcJYQ+Rm559IUN1lpXiDrGYZqEi5EnPAYpZrQal2jaZVQ\nZRfJKLgcDfCSOZlY56QbMx/k1GsGx5djmpVNAjlmMpwQ4SBkKhe9IYKt4IxdKCwEUSJRJMrhXyGh\nNBI/pRBy0iRFUFXIr/YoJREkRSTPTUQlwI9CFNkCEnIxQRBkRAHStGC+8LFMnUns8kvf/Cqnp6eM\nx2PGPQcpzSkQkdWcx3vnFAKoSp3AD+ldjHnpudsMJ8cIEvgFTI96ZHKZ9fY13n34IV4mIegQFBcs\nhiZlRUQwS8h1he7lFKkIiOOYPC1Y7VQoiBlHI04GATVXYzo74O//yhe5GEw5Hs3wVZNnp5cInoTf\nd3GkCFlQESkQ84wiidEkFSkTyUhxZi5ba1sUSka9KZFYObFtIZRqBIsFUzPjYHrIrZ1bPBle0Bt5\nWOklq6st1tY7OEmBWVkmCULanTb1psDuF+4yG86IxZSvfO5NUllmrX2D6tIOliQRArZRRpZkiiIB\nSULIc3AdomhBcHGKNImJnVOUVGI+meFEY9wwZjh28XyXwWDIdOzjLzLiYEYceLjTAC+JqJVl0nwO\nkkRulCjbKmMDxE2VmqhhiwZxlOGJMkmaY0cJThiAoRGNXUwSRCkhcRIKo0yQOFRtg6wwKYSMUa/H\ntc02oZuglQ2mi5DQS8nLZYJcw5I05BLEisQ4lLCEMoYkIUoZ4dzHTwuUcp0gzEkEmXLzJn6pzv3e\nnEuvYGe3yq27mxhRihvLhGkJSg2eHpzRaTdZF1Okg/fZmB+jVZq40ylJUWDHJRy/j1VepSwmnDlz\nshDWq8tgVSmrc7L5GLVe5eysz8bmNvnknLk3plRbpbKxxncedpHigN31DmeDU37hm9/g3bf/lOdv\n36K9vI1zMWCY+0jVJt/63tt84+c/y9HlJxjLJQKg0EBSPf6Xf/ZPefnVFzk7cak1G7hen/v7D3Bw\niQIVQzPZ3d0iLxZERYZpNuj3z7GqClFUY7fR4aOfPEAgwC6tkgs63/iVr/Kjtz8h2DCJFJVer086\n8/jal1+nP5/yh996B1m1INXZXGvy4JP7FErO+voaVU0F28IJMrpnc0afnvLaKy/xy1/8RX7w5z9m\n72jAjevrLK9u8vu/921+6z/9u+wfPYIiYW3zGu9/+Ge89NI6WeTy6Mk5WXTliS3ZNlMn4Pd+91/z\nn/3nv4UcB4yG57z1uS/w/Xfe5kamkgx9Yn/OyeMJ65tbVKsVdt/s8PGjPVZur2OUde41nmfv4z0u\nvC7t7TZHJ8cUWcI//C9+k8HpGX/4b/6IzlKNIIlZOFNEsWAyHrHe2cB1Y+r1JrVWCWeW8uzZEW5w\n1QNwfIe7zz3Hp/f3+O3/6Z/wxiu3eOXF68wWY/afPaHeLuMKCWopZn4ZYM8cmpUak2GPSkUjSh22\n125xdnCGoeu0200qpskLW+tIUcRET/nbv/F1TrtTvvMn32fqzKnWq5iGSZjGSLKEKAnYpk2zVSJN\nIurVEpZlc3Z6iWWVmE5HFKTYtsV4MkFRVEShIA0CavUybj/ErOgEUYiIgCQIOK6L3LBRbQNn6KLI\nMlkmgJAymQ256KXI2BglkX/5O3+AaZoUeUaWxRi6gjNLqS+ZCLrKbCJxfNLj9t3n2X9yysSbIFkJ\nWkn892biXzth/oP/ZhlDbSJNAqpLFZwkx+m5nI0nyCWF08OYhR+wYsvcWm0QzH2kSplML7BI2W7X\nGIV9Cs0kCWJWWqscdM+xairtxgbD7oJaVUa3IMkUvLRgMoVy6caVgqV/ybXdJhcXhyRhCVmV6LRb\nnHTPSBWD7hQOHnWRZJVYFjHEgpuKTosCh4ikEBFFkSwtSBA4Hw6Is5g8u2IQNto1ShWb519bZjpx\nGQ1inPmCweWM5bbFSy/eYDw5R5ZMqpWr+8VwsuDsNOfifEQS5nQ6bYaDM5ZWbNrLFS6OpqRZyJd/\n8TlEwUdSBCbzgDiXsMhIJZuTsy7xXEWzBCRFYh54aILBjZUSogxhoXL3bpmFO8ZXZBpak8Xpgpqh\ns6jmWJmBf3zJUqlBtmKwmC9YWd3GT0TMcgvXk6AoM+zPULUyc9+92oP1FwRJxnTm4i0WZHGKkCXU\nqiqrJYHn1lsU0zl3W+vMyAgqZRItx7Z0KtUatlbF1jMqdhldK6GaNlLJQJElijTBUGXySEAoZJLA\nQY49MkmiZLbJZJU0CIgLkD2PfOyxcOfM5gvcOMaNPR5+9GPixYw0Sa+ceFl4tYOWRGSFRRTLyGJC\nToogGZzjsrxURdBAVRT0XCSUchb9IUKQUFYtXFFAQ2bgu5iyjoZEICXEsoCYgpmCWLOYeFOUOME2\ndCgSylqZczdFEVLKIniShV7kCKaAnwbIkkGeSRQUeKFH6PrYuoEoSMQFqJJBLsrkhXglwRV9ICYK\nMkyxwosvvcHy9evM44ijYY/KSoOb19col3SmkymiZZBSxnET5l6MbZVRZhc0nWNWsx6lqkXoB2Sz\nBZJVxvXiK7muLyJJDsvGEr4m4WUhbVlFzgLiwCXOQFR03DTBFDQmksJTq8lebCCqTbzJgDxYkKsa\nYZZQKSI29Qq9xRzbMtje3Wbv/h6yrEMeYRoxB71jpBjELOLR/iluCpZusLWyS5wuMGw4PT+m1myT\nZxbPHh3w2dfukacBoqGQYjAeXSJkKVkikmYO25vXODoesHA9ynWRjbU6/bMptlnl6eEF9aZF4rrY\nep2jsx6ZAqpp0+teUgCartFqVbF1i7JuYMoyR6NLdFXH0BROjy7odNqUGjYZGevLyzx+csD+4RkV\nu8x/99/+Fp/c/5jnXniJ73z7Bxzv7fOVn3uTf/fHb2NaMq1KHdAZeB52q07J1uhUbD754H0+9+W3\nUCoaw5MpUbDg1vVNEs+npJbxfAe1rGDVy+yu3eHD9z7in//+v8KRBBq1MovpglZlhZeev41YBIz6\nQ5483ac/nrBz4yaLxYzFwkHXFTpLbdbXtnn48AG3bm6z//SQRRhQt8q015uMnAVv3HuJOA2ptas8\nu79P5Id4C4eXX71NJrq89fnP8Mm7D5i5MUIEqqkiSBBHAWma4UQqJ+cXvPbaLYaDIapS5sn9U770\nc/fw/CNmTsjuzXt853vv8tH9x1QqV4CP/vjq95csSFQqZVZX2ywmc2bTBa4XEsURqqSSZRm1Wpla\nvcLZ2QVJmpFnKSVLwdQVxjOPrBAQJEjiHE3V0HSJJEuIsoI0zpEEESG7gsmoqkISC6SZwNrmKmcX\n5yCASI6iiNTKZRI/5+4Lt/HjCTNnSOAGfOXLL3P/g0Mu+iMqbZ2X3tjhn//PP/nZJkw5tvDNM9ob\nBouphKLUqVViWu1VHh+eYKlQN6rcXKnQqdcoajlFTeHdx0/oWCaLaMby2jpnkzOqHRNZS5jOJA6P\nx/BCmeHQoWrfwnOmxImCYobsbmxxeOwiGRpr26s8fvYhSxWdWNWQUOisrXLtzjLngy7qccTppylZ\nbpGKoFoCciqRZSGpkKHIOlmSIQsieVGw3mmT5jGGqfPmW29Qr3epdyy+8933STyV3/y1X+b73/8L\nbuyUKFeHrC75lOwKrh+y1K6QCSOuvdhh8a0Ljk9ywiji5XtNNLOG3RQQFImlTpXTgwu6FyfcurOF\ngEI6CKhVLLzQRZZha3eNknaNg8OH5FmCrVeZTRLmnoczmoOkcSAFvPTaMu8+OycKRSoNAakOwXnE\nXzy4zysbN9GbNk/Pn7Gx0iYMBmSixHTUx8rLaEmFr331q1Sba6iWidUw0UyVK9NyTh7nuG5EmkYc\nP/mAKAqoaAZbG9foTgbogkghqLRMhXKpQmEqRKKGJhYEszm6mxAEKdPJJUKSEjoefuThuRGjyQyE\nlMFoSBBcQfOD3hw38jk+CFjIOUkaIc7HSHpOu1IjSTMqhk7NBMdxqJTKWLUSI6VAqJQZeAVOpKDk\nEaUioQhy1rUKUpGhpxlZ7JPpJYTLBVkS0ixZFP4c26xxPrqg3m6hhSlGIRIFOQvPo1UqI1sWhmbj\nLeYUpkKAgue6ZKJDUTVRlBqpMyXXAvzQQHZ9VssiSuEiyAbTQMT3oVmuYptgaAbOVCYLRDJ3hF6S\nSSSZWtkgyRKyW5s8//VvME2rvPekR+Q7fOHF51jqGLhySKNaZjEVQOxw0j+g0CqolYL62RN2ghGJ\ne4Fhlugf99laXUFuypyfTKhWDBw3Ya1cZj5xmCRj5Cyl0V5lgosQhbTsBnngoVZTnPGMublDt3KD\nvXFMkgmUhJTCgMs0oWOX2UnLvPf29xklPpJREJPyrf/397DtGutry1zMp9TVgva1DtVag3fe+wHX\nnr/JxdjBlnXSNOC117Z4vHfCeOCShga+PyDLUvpDlzgaI2g6ZrmE5zlsr65zetTl08dPGQ3HSKpN\nq7nK0eEzlqo2IiKu57PS2SLJHbrTS1bv3UEZD/nKF3+B/+t3fp8XX3keXVCoWSbTwYjJdIpU0/jw\n8SNu3N5GTgr8aUTvso9UUjidXaKrIt2zPiWtConASbfHh4/PkEWLH3z7u5w+3WNpZYVpGvK3/uNf\nJxMTooWHkkooosq7f/kOX/vl/4BHjz5FkBTeffvHRGHE1//W1xiMctwgg1ji7T/9Hm+8/jpnf36f\nl+5c49AVSCoGkSTSXq/xlc9+gdXyCvPhgj975wd8+uwpv/qr3+T7P/qI1tomU9dHEjWEwkeRZKaL\nGaP7D1FVnf39I3LVJB459AqHt3Zf4xe2VhjMh2ipyeXRBY1mhz/6N3/I5tZtslhnMprw+//rtxFi\nmfNhl9EiYH2ng1WukIYZvV6Xv/Obv4FlGRw8OsIPYlJhCprC/f099HxAfanD4f4ex0dn1Ct1gtAn\nyxbEQUwhiCAXzJ0Z/Q8HaJJCkQl4QUxRFKzvdNjc3OC9995jMpnRaNYIo5AoERFlGc3QsVIIogRZ\nlRFJUDUJx/GRJYkszgCBjBRNE1lbW6Xfm+D5Hkgyl73eT9V0ORuba2ystnn40WNKpTJn3RN6/R63\nn1tmc3mdk4NzPH/BdByj2yK5++9nyf61E+Z/+Y92Ma0FrWYHfyGTeBE10+bg/hDZlnnwaESpqrO5\nVEH0ckoNi543ozB0nMmQl5/bobFi8fTwMZIhkOYy3UsHtbAQCSmVmpRLJbJEx/MClpd0wkxk5Mr4\neUoQTEjDgDzKIWrTbnbIshk5Ibmasba7ztODGe++v8D1ApYskUbsY6oRWS5RLgwWXk4mXclJRfmK\n0tNolmivVLm8fMaNO22cRYZqaPhRgmnpUITUyzJh6tPZWiPJEkbjGanj0SxV6cYpi56PPw7QtYxa\nS6e6ZBDJKd5lwcrGCqETMexPUAwV3dIYDoZUzDoffnpIrV7n8sjB0ECQNPSyjChUmF2ekyYF9ZaF\nJbp87qV7LOQAUy/4y+M+eS/kjRev4RsLpm5O9iTk7hu7OP6U2FcYOQPSQiJ1NNY6S/SPQhbjAEsz\nWW/VaDXrNMo1rHqZelWnUjWxyk0EUyJNQupr14gnDnLq4ZPgL0KE2CeYzFk4C8K5xyyJmY8XzIZT\n3MBHUDIIfebTOUF41bRNsghVhihwaVSrpH6ApZpkYkFmqoR5jq6b+ErAeB6wXFEp6QqCIuKHEbmo\n4vs+mqFhxwV+7FBIS0xmKaqSI4tXeqksLNjYaKHaKaHnoMsVjrpDhDzDNhTSJEeIE3xFwxNShHKZ\nOI3IQ52GphILkEVc0Z6MhFQJsL2UIvcpFRKBYjDzctYFC1lMmMomlqlDPEFKHKIIgsLEy03CuEAL\n5yR5zFCyOOmOWa5JvPHWTTxZpWZq+LM5YrVJefU2kt0E4PnndqkpBUnsUMg600Ai1eo8Oj2l3ShT\nlz3yo0/Y8CaIh5eIFZ1pnlKqW5DmqDmYtkbsZRR5iiRpBKaE7ifEkgFEiI0GmqKQnJ5TK8l43gJh\neYd3fZUjcwlfMBHdBWnggxKzubrMeO+Mh2//JdPzcyRNYHljGc+f8vwLd5l5C4o8pD/sYVsaqipQ\nbS1xfHZJkAdU61VSV2DeW9Bqq1xcLnDnKoopY5ZFDp9dsrG2ya2dXQ7P95h5C5JYZGOlQ+B4xIlP\ntWEhoDPoOSzmc5oNi2ZrCbtk8XT/ECSTw6Mu166v4y36vHrvZdZXr5EKBRQpo36f85NLHnz6kOW1\nTTTdYJaGFGHC5WmPWquGl8/5xte+RMfWMQWDwmzwv/2L3yWKQrbX6nz2lRdI04hRGHH3xm2+/61v\nIwkGRllme2sbU7MYOnNWN1exyjLHe4e06+vMo5yLy0seffAR13ZXePnVu5yc9xlMIvzIYXtrFYQA\nRTXQNA1F0tltb/Ld736fs7ML2kurfPpsj+6wS3tpjcXCwbY1wmCOVbb47OdfhyymEGV+/O59RsMF\ntqFhVVSanSWEWIPCQddVrm10AIWP7u8RhCmHJ4e89darCKLA6cMTfM9HM1VSEb785pscnhzw+OCQ\ntZ0dJFVi0D/hxbt32H+yzxd/7ud47933yESVQhYhnFFrNHGcBM20uP/oCZ73U28xBXGU0l5q0Ko3\n2Ns7Jk0ziqIgy65AK6WSRaVSotu9pFKxUBWVX/qlL/HdP/4TKmWbOIkZjRdY9lWzOy8yZEkhywrC\nICFNMooCLNtAUa8mSN9P8P0UWdWQNTBtgzRJN0t6PQAAIABJREFUWV5qstSocf/+U2RZ5MUXbrF/\n+IhyuUzge0RBgW4kXNvdIQznfPlvvMB//4++/f/LxL8Wjff7//p/4Jq9QeBBIoKhRaSzBXe0G2RD\nB6VQKdIcWdPwnQRZazBZDFElFYoQgE6tQiEkGJbFYDTCMCq0WzbeUCNLA1BUirzGfDwmj0G3Sly7\nuYMkRxRCxnQekmcGsSfQrpb4zL0XmI8Dxv0ZsuSwe2uZ0eU51+7ukIYZykJAzSQKSSBMQ5BkZEW+\nMhDYGkkWUW9WaS3Vaa3aNNoVljfKrG1VKNdUam2d/uQM09ARsTjcG+LOC7rnI6IgRFUEdFOhpKsU\nRUzJsMmiBMuqQaGCKOG6c0ajSyQppd6wmU4m2KZNu1xF0UW2d26RZVMmvYJ7L9/l6OiS0XBAEQoY\npTKvf+EF/GGO4kiIbsDeqItV0djabZBnAVapweByRGtJJStJpCHYooYqSFi2hVpTeXDZ44NHJ5wN\nx3R7Q47P+3zy6CM+efKADx48492Pn/LjD5/y7o8+5uTxY9arJvsffMz9995j/8kj3vuTH/Dhn77P\np+/8mB9857t8+PAhR4en7PVHXOwfMXCnRGmIEzikhkIQRlTKGrkQoloaTpIgKjqqppIbIn0pI26W\nmScJiZ8iiyrucEoJhZKeoMkSUZBi6yqXFwMMVaWkW2gZSGrByIlIVRutrAEpQioyF3IknSu1WAFh\nJmBZJg4xnmExTyQiySJSNbRKHSmT0DJI0oIsibAEGYMINQ4pFxKtXMd2cgwU7ETGiXIKrXw1OWdT\n/KTHOPLopxkjUWau2cxNm/FSh2OpiqiWEKwmTqvNieNimwbr60tUq1UyVWOeKSyv3OCd937MjRc2\nuP7cMlngoNUqTMYusrHMg9GCsRCxtmQhnuxTunjA8+GY3Ato7tzksj+lXlNAMlnEBRWtxHCyoGSb\nyGWLi+kcVVGJ0xBZhHqzxOJyQDYJKbfr9NMIT+9wX2tzINgYmYCVReRZQHW5iUrKaP+E8UGfIhKZ\nOT4Df8zOy21iLeP0YohRMkmKjMPjczwnQSx0gggu+mN21rdoV9vEQcJ4OmMxD5HFKn6QEiceyyst\nZBTSJKZ73seqVBEUFV1XGfVHbG5sMF24LLyE7sWASqVMrVLD1Cx6vR61eouzbp+0yMiFkCSOiaOY\nQW8EecL56TGjiyHz6ZwwDhk7c1TLZDCecPv6DQb9AevXVhl5U+7euoORZUz3uzw8eARZQbVex1k4\ndK6vo8k5y7UyJUWjpIjc2Oxw684tnhwc0+1N+e5338EqK/R7fRQF1jod6pUG//Sf/R/0e2Neemmb\nMMnx04wf/vA+zmLK66/fZWNnnYXncGNnh+c3rxNd9nF6M/6fP/gOha3iRz7NpWX2T4/QVYX1zjJi\nnrCy1uT67U2KIqC93OSyO+RrX/1lQs+lVK2xftukrpV5+PSQL33uZaq6xYePnnJ63OfNz3+ewWhI\nmMQEQcqkt6DerHPjxhbjPMGQyzx7+oQszBkOJrhJjNU0KBk1ZsMJYZAynkywyjaXvQE///Nf4cne\nCYNBRJwmKJrG0fEFggJpkSMgIiNTrzb4jV//O7zzg7+goCBNcxRFRjc00iTB8zxUTUAQcl64e4P+\n5TlZ4qBIBYEXsNRqoGsarrNA+ikG76/g+bIik+UZS0tNJFkkihLSNCNNr9yaWZGjWyZxlDLqT+i0\nW+R5SuDmXFz08YOQza3rDIcuURJSq1cw7Zi7L6ygqDFffOu3frbAfO+Hv8104fDR8RnD7pRSoCLH\nFkfv7/H5V5aZ+y61JQ1RKmg0WpwdXvDiizuMhmMsXWO11UIVC8RCwCxXrgDCUULmhHQqTcySQK5Z\nmKUK1bJA6Hs43oKTo30UMSUMI4JQQtVtvMDB9VwOD4/RVAs38K5o81LMCzc3WN1e4ezwlGKWoeWQ\nSwq5pJLlCbKUkhQRd5+/wXMvXMe0FVJi9EJjNphjyWWm5zHBJIcookgywqhA1SukuUSWJ5DlrG2u\nIioF/sJFREdWKzhhwnzhsPAiLk5GpMjEScjKWpOSrZHlBTkSWZYipFMyoeCD+w8Z9D10UybPPXIh\nxjbLFLFEo9PitHdJOIUiBrOjIrU0OmUTS7Y5W4yRE42tzjqxJmKVdAa9HmfnXWKlYJqmiGLB6tI6\new96RF6AoRpkYk4sSCzShGEY4SxizjyXy8mE/sUx8eCURx/8gPPePs+6PSZORN+dEMk5QlWiVNYR\ndZXCEvHzGKtikEsJkiGTSTCeT1HTlDQPGM5nGJUaSZwh5imarBBHOdPhHEXUUCSN1HfRjDKD8Zx6\nU0czFJBAlDTCEFw/QtIMnCIhKFI0s4oXCaSSwNhzSMQyUquEoUugSQiySpIXjP2QLBWpZzlWHmHo\nInqYMx/NaAgSWppcHWjLYNQfoyUBhS1y0J2SFhX6gsJEvyKRDDWTQ6lGvnaNwFbIWw3UxiqKWaG6\n1EKwTfRKHVEqU1KbNJIrNVegXaEjnSjGsyu4mUip0uHkbMjGjRtcRDNub22yXamjaRaT2Ryr1uaD\nR8+otKusSj7Z4/fY8U9ZWiyIHZ9cNTiadel0KiRTF1WUqZdlUsfDKERGwwnu3OPOjV2S0EUzNeaj\nKYLnsF7RmIoSw9EEaut8SJXD1CRVFGwhpPADAgSWyiVm++ewSPnBOz9CLanM/Rnf+JUvMPcv0E2d\nwbCPLCkMJyOSSMJ3BXxf4PC0j27XKKkm0SLlh+/cxw0KAj9DURSqtTKKKmHrdU6Pe5imTpQUnFx0\nGS9mzN0xX/zil+kPhyCozOYhhmVRr1XQFY3eYMRsMeXw5JSsEHnxxTvsbC/huHMcp6Beq9Oot/jR\nex/RG/Q5ODzl9u0dXvnMa4RJRn884XjvgO1r67SWm+zurKIJGf1uj7nnsrGzy+PDA8xyhWa1xs9/\n4U0Glz0ePn7KxHU4617gOAFL7RofffoMrVzh7HzI7Ts3mTkusmJwcXzJu99/l3tvvIpmK3hhQZK5\nlEwLWVQxDJUsTHAGfTr1DvFswf/52/879c0WMyPnF7/yCzx6/yGZkrK61eL1z3yG/kWPcrXO2dkp\nZcsgTWJEWWY+uXrhyvAIoilvvXkHbx5gGBbJzMG2LA5Oz7noTmiuNLCqItPJlPHAp9VcobPU4tP9\nx7z6+gvYlsHp/jkhBc1Ok95wxt/7D79JGnk06k3cRcBzz92hN+ozno7Y2Nhif/8pWaHw6mt3qVbK\nDEd9VlZWWCymCLlI4qfcvfM85AJ//L3vkOUZkiRh2xaappIkMVmeYRo6sppfraD4C6aDCaIoEkU5\nml7i9HzAfOGCKJEUGXkBf6VpvZJRCyiqSJYlCIKArpvEUUzJKhPnEUEcXgHfRQFnMScMffzAoyCn\nWqvS7feIkgR3kbK9tcHR3og4maOrMr/89X/4swXm7/67/xFTKrOyvozre0x6OWqu8mKnhuBPCOUy\noS2yun6NvY9O2V3apFFRCMkIowhbkSjbJsdH5+ilMosgIo9zZoc5rarO8vIKTpiTiT77h59QrclE\n4Zxbu7s0GlUkRUWWNWRZwK7JqJpBUSikqcssHNBu3+bppxNGpzmTachaQ+He822end4nTXOETKO+\nXKK93qAgxY89poshhi0znAyQShIZcNY9ZjybUa7bFEVBu73C0F1wdHLGtD+ibBrcvHmNKAkI8ojQ\n9VEMkxyR7mTK6vYyZ8cTXn35NhejPqAzuJxAZnBy1KVSKyPLBapaYFRKyJbO0vIKruvgOwKqoVMU\nJRaTOc2mhSZJqGrGyB8xYYo/v0LwqapCnueYSsFHHx5RWWoyTV3alQY1q0UwdanZVcJY4uHDczav\nb+M5MUkSk+cxmZAgCBKSJiOKOVmRUEiQFDFWq0KlWkHUVTJNQa7YTEjw0wjN0nC9OYKYE2oKhe9j\nKQVqHlMkGUUYUwJqpkEmFuiVKrNFgCjJWLqOUBSEeUEUJ1c3SyEjyXIWpCh1A7uuIggqbpiSSQJG\nqXZlXRdUYknFjSFOBcRMg1hAKzQsWlRSn5KsI/k50jxCXERYgkhZEFCjDFlSCQNwTYMFMU4RkIky\ns1xnmJf54PEF17bWsZ5b59EwRavfIqy0odSkXl0mVavk1ibV2iod00aVm+TCJrNkDS9r4YU6rqcT\nxhZ+6DGPFvQjF1HKqEcJgqrj1zsETkJQJFy/s43cqvLyy/fIcpl/+6++R7XUpGFYnHXPaC41UU5P\naV08YisYIecpiReTBSGyqVDVZGI3InYlrI6BkIUIskCQZ5hlE6Vk0Ov3qdc7pHmG0pDJUpHxuYNc\nb+DX2/wwV3g6dTFsGymNmDhTKssdqqrG7PA+DVHh4rzPMHBJlZRyRSKNRyROipZXEZHoXp6iayI3\nbz1HkhQ4vs/S8hJTZ87gYoAkZHhBQrO9Tr1R47J3xGQ2Zjp1yVONy/6Y1lKT4/MusiqxtrGMZRno\nmsH+wT7T+RzLrmJbFoPLS5IoYWVjnclizu07z3F0dIIo5KiKSOn/o+zNfi1Lz/u8Z83T3mvPwzln\nn6HOVKeGruq5mxQpUqQmU7KUIJJtBYYQ2AkUxIkVOwkMJYERIBdBgMBB4jsjQS6MALFgCYotULKo\nqJtsdotssqea61SdedrztPaap1wc2jcGDPAv+C5/3/vi9z5PQeP1129xdnbFjz99glEqIJlF3v3Z\n1/jRJ58R+C5v3L0H3oJy2UZVFdQ8YrlgE0znzCIPfa2DEOacHJ5ze3OH8XTCt//4L2i26tx79R5b\nm5usdtqMR10iJLZv38SLFCazCZ3VNcYzlx9/8YjBaEKQREzdKbf2dknmImenl+zcusXB0TmfPdzH\nbtgUS2VcxyHVBdTGNWVsdNHn+eNzPvn8CaVSmWF3QuwHXPX7WIUyizCmUK6hSiYXpyNODnqgKkgZ\nvHr/Bs64yyd/+YzdnW3KdpGTk1Puv3mbTIAwSZnPQo5enNCs2zSqBS6H50iySmf3BnaUcXJ1Ti5B\nrz+laFu8fmubG80mf/xH7xFEMJ37RHFG0S5xcXGFripoesLJwSXbW01Oj8/pXQ3QFZ0kTJBFheFw\nhCBkJGlElmVIkkSSJMRRhCLLIAgUChpRFKJrFmGQkOeQCRJzL8SPczLxJxOrLJBJECcpgiAgCCJR\nmCDL12GJkKNqKvOZc32b+RN2tChdC6kVWSYIPZI0Q5YUyCUWjk8m5JCJ5KnAeDynUBZpNCoMB3N+\n+2/+Nz/lSvYP/hFXgxkTxyEOfexqk4OLAUd+n0NZ5TxJmPoSl0OPi8GEW69ts8jGZHpAmnlIOZia\nhUSOXFA57w1JPYWNpTqttsVsEZEkCnmi4LkpF90zNpc7TPoTLqcTjq4uGQ7GNEo1Ok0LSxIpFwq0\nl2qIUsrNnS1MTaDVadG7OkfPfAxFJMkFxuMZS8tryGWFlbUaw+kV65srTJxLNnaXsMoqj58f4XkR\neSaws71NlnnYlQqHwzMUS0IXJfY2blJrNjl++YJiQUeyFMrFCsNpxHgYcNWfcXY1JpqmbKyVWfg+\nL/enbG8tsXOzhCiIJKFMlkaoWoYka/h+huNMUDBZa1TZWW8zHA+o1iocngwo1YqUKgoxHkbBorNa\np1QsM5y5pKlEkikUihWquk5MhD8fc/vOLtOTS1YL64SCQHOpyiefnJLFGdMgQBEyPFFAzVLkVMAs\nF9E0BUPWMIWcG3aBYDHHtnXS1EcmwRuNKKsVhFxGywVUMaWXxtiijGapaIKMN0sJNJ3IDbEMk67r\nEwsKtlkgcBakusaR55PrBUyrSKNSRchzcjlBMK5xh14wp2LKWNKUPFU4700I4oiaoGInMcUgY9Uy\nEdwEJZbJXY984eO6MceDiP1pyMCJGfkiLzOLR9YSbpTgVTPc8hrDvIBQE7BsDc0uI6kl8to6pwdd\nKp0GVsfish9RWtoj1xREqcDF0GeWaSwEkYXvct4dcDUKcOYLUm9EEo5Q5fx6vaR6lIoSJbOCpkSI\nAjiRz9WsjxznRErOvRu7CFKMULAI5gE3mut89/sfcXZ0zt7WDiutFu6TT7htxCyev8TWC1y6MZqc\noFWLiOgEqY1sGpQMgdPTK+ylJk52ve1QSiX8QKZYLJOrkC/mtKsWgRCitLf5PNF5lKmkVglDVpnN\n56hizkq1ghLnBKeXPH20z/OHJyhylVyE19/cJs5mHL/o02nd4NmzI14e9RBlCc9JGAwc+v0xuzvb\nWJZJrVqm1+9z1e8RJRl+GFOuWUwmU5I4h1whijPG0xHdfo9WrYkoptgFnYJR4uDgELtcxCjoTEYj\nTo6O2bt5C1WROTu/wHFc1je36A16KKpO4KccHp1w1b1ASCXu373P6ekpN24ssbXa4ubGFuPRkPc/\n+JRcE7ErGpmY8f0PPiMIA4I0IfBy2tUy9VqD0dUlZBBKClf9S5aaHS4OT4nnQww5v+aeWhJ2QaVg\nKnz9q+8w6B7y1bffoNWs8tmnj5l7CapdwCpaNJea/PBHn3PZPScMIzorq2yur3N8csbl9JzWUp2b\na+tYooYhZ6xsrRHqC97+6lv4rs+wdwF+zGQyR85EIifii6cvGfkJBbNALgj8e9/4Gt/94AcEuU+j\nVufJ4wueHFw3he/fW6ZUsXjt3l0iL6azUufW3jrLy03OTi8Zz13Gl0PmYcTWxgaSnnLz5jaXVwPe\n/+4PifOMerPO+maHuePSH15bbW7dustoNKJWq/DLv/hX+KM//DOCIGfhh0xnIaKYkmYpmq5Qq9cg\nz1i4LoIgUC6VieMYSZKo1+skSUwY5NdlIDckyhNyIM0y0jwhSiJkWUJAQJZAkWRE8Rqnl5PDT8hl\nqi5zXccRiMMcURQxCypZHqPJCnEcY1gmqSCRpTFJnCBJMmQiRdNm4TiUKhqDnsvW7gqqGfFbv/n3\nf7rA/Of/7H9mMI1QiyZ5KnFxMMafZWiFMoOxSxQmqGqFBw8PWFuvkxGDLCDqKYIoYRo1ltotFv4U\nPwlJc5Fhb07BkhnPfRZRztWwx3B0iWlJVKoViopGpWLTXGsTCTF7u7cZnw9YW6qjCZBGMVdXEwJP\n5cXzAzRFpNpUcJ0Rcq6iKwq1coW9u5s01gosrxSZTwY0G3VEKePu/dtM5jNG8wmuL2GaRYJIwC6X\nkDSZIAzY3NnhxdEzpFzg7GRMRspSe4n+YIpdqvL0SQ/f9Yl9D1mVETDRxBBV9imXKuzuVclTj/OT\nPpapo+syimIhIZFnOVEUYhpFVpdaHD45wXUSUjHl/HJCHMeoakS7bVComORRTOALPPj0ECXxUUyT\nk6MRB/sjGjUDt+9haDqzucvgPENSlzgfz3ny/JiDp13iwMcqWGRBRCKBnIkkgCEp+FGC63uk2XWL\n1y6rxImPmekIiYykFHBzQJVIiXDzgJJVZB4YTAWFue+jly3UWolYjknIiXWJWITQSzBlC01UKVsW\napLhzmbIORQzkSxKKOU5S1lKXdbRhzKmC34cUjBt3NgniHI8o0Qs5XTzES8FmUXzJlM5xTBEqGjM\ndQ29WqXVqFFu1jHKdSr1NWwF7OUCgrhEodjGkCUCJyZNNMZhQGoV8FKoN22alkE0TnCDhNifkPsO\ncP2btXQVMUmRBBNXygiEjFBNEYsG0yAgEyXGYcqJPyOUAaXIWDZJzQqxKNDe3GDrxhqBCIPxCP9s\nSK83oVgv88V779G7uMI0ZWppl7p7gDY9AEkkTkXULMHQrl2sVrXITBxhkZHJOZqlE3hzlExGjwSC\nGBQpR/DmhCOXKFFYHEwQCjbPjSWeLmwGQkzuByiShkhGo1winPaJZ3P+7Dsf8MUnL5k6A46PX9C9\nvODjHzzDNJeZT2PG4yEJAc12G11TGY/nREGCIip4nkO5ZBNFIRkZimITRSn37t7n8ZMvEBAp23UE\nJC6vuty6vUOxYhO5GeWSiZhD72pIqVRnNpshAJ4X0mw0SAKf6WSE4/rU6g2ePH+GrKrMHZfu5ZCy\nVULJNSqlAnaximEVEVORi7N9Op1NqvUqs/mEVrvG5noTCZVarYpuFDg6umR9fZ2j50cUywWWtzps\nvXYXf+4xnsxJ04yLwQW6aeIGKSfHp6ShRBRcrwM9N+HFs5fkBLSbS5TsAjk5t27dwjYMfGVByTb4\nK9/8Jq1qDV2T+eiHH1NbbvFb/+GvE07mqJHCo88fY9s2Vq3AxvoKReDd+69RtFscXQ0ZLeYYnTJv\n/8I7vP7uHr7jIAkJakGiP1oQxjml1EY2TZ6+PGcyd3nnS68xGnbpX0wZD11GoxndfpeXL1+iqBau\nFzCdTpk7LrpVwjB1VlpV5DynfzW6puuoCp2VNg8fPWE4mlGuVFEUmbkzotkskRGTpC4Lx2U4GeBF\nGWkWk+c5mZgRJTHewiUMIsLo+pQv8ANAoFSyIZdI05TJyEEUc1RVQ1JySmUbURAJgghFUkjjFEmU\n0RQFVVJxXRdZlkmSDEmWyQHD1FAUhcBLEQWJNEtQVOknb0bXq1xykiy8NjllAuQZlqkjSiDKGVGc\n4C4Sbt3eZDwZ87d++7/+6QLzX/zh/0SpLBJ5KdudN3n55IDX790lz0SKVZUw1Hn+eEh72WZjq8lS\nq83LgxPMooJllrCKJVx3wcnpCWtrG2S5iG0XUXWTSn2Z/tRHty1kNUWRQqrVCkmYkKc5pVoZWVWI\nw5h6ucL+s0suTocEIZxddLmcjFhaqaMZUKtWEDMRXdVxHA9NF0jJyfKUxWRGq16n3VoGUeDk7BxB\ntDDNKnZmEDseWxurZKlL9/QKyU8pSAbVpQpDZw6CSs0q4fg+/ckcmZzJPMdfDFhqllhqrXL7lSUc\nb0ChUKB7OSAOpmRJhiKWaS218OOA84v+tUNQ1DCLOqKY8ezpPkWrxHA8pz/waHfadFZXqDdM8kxm\nNlown+U4M5iPQr72la+wWDgMB3MESeaqe4YS6oycOaQ6zlzlO+9/yuH+KYPxFCWPkQQgFxBzGUEQ\nyfIMxBRRlfEyCHMBRVepLtfxgilXswm5ojCVVCo3dukOR9glDS9KkNUiSaiS+KCqIqYYogY5oSuR\nzRJaooGdxNRkgXa1gqlJSJHDkuCi+yNUKUEyTcyFwHkaMO6OsItFPk9U/uQwIN2+z3G0ILSqhFaR\ngWLh6FtoBQ2lJBKobfLiPXJRwJAl5PoqSmkFtdig1FnFL5ZIKm2mfk6UOETJgihQGPRdFmGK7wmE\naYU0Vwm9GFFWmfouZ90pabHBWRgzCWNiVWWc5YwVGMo5jpLhyRmxJhBrGWapBJKAWCkyUUW0apl6\nQ6ZVaVGvF7nz+jbv3rrFm/df4/nJPi+eH1HZqON3+1wcH/Ps+Que/fBjZFIm8wll1eS1t99hs1li\n8GKfRqfJeLFg+Xad42djVm+t0z065vZ6jem8jzcLyMsNsjihLgh0jybYlSpK7mJLGoLiE8QKi+1d\n/jyBK0VC01V0zcaSJXzfpVkwSCczbMXiz//kfT794Y+5c3+DSsmkUikTJQJOMMcPFkiyT7UKG+vL\nJLFMlkmsra/iLRZosky/NyCIIo6OjnDdkJXVEvVKmW7vJc48Jk9BFETmzpSbN/fodq+Io5i33n6d\nMPRwZws2b2wyHC84OjyhUqljGkWSNCVNQ3Z3thFlhYvLc5rtFoIsEYcRi/mclZUlbu3d4OmjfXrd\nPgvHYffmHifHJ5yeddnd2eHw6JDOegchEXn06AUg4iwCllc2GA761Nt1ao06416PeDwn9kL6ZwN6\nvQErjRZGqYwsakzcBeVCiUZrlY9+9JBPv3iKrhT41i9+i/OLHpWWzQ8++oxRr0u9mWELKo8/f8Lx\n0YBqs8iTx88ZDzxGZ2fsVNbwJwsevzjAsC3KDYPIX5AEEXNnTKGo8Wff/lP+8//iP2Lr1RvsrCxR\nyuAX7+7xs197lTtvvMkf//6fMOz28LKYsKYwvDwjk0V+5a/+Os58yNryEiIC3//wBzx6eohmGdy6\ntctsNufHnz2n3qxRa1S4f+cOpqJw+OKIZy/PWYwdysUyzw+O8QKXyWROvzems7bKbD5nNp1wY/0G\n5+fHtOoNHj94iKJoZFmK5ydkaU4OSIJAHCckaYaqqsRxTJpcI+/yHHRdw5k7JElEe6mB4yyQRahU\nSozHY7IsI8uuJdOSIiMAfhAhSQL/+ugj5xrvmcY5kqCwcBbXRigRRFkiSTPIJeIoIcszRFlEQiRP\ncsLwWkIdhAGKKpBnImkKV90rsjzj7/5n/+2/lYn/zrOSv/mbdVRT57K3QMoNomDK+nqDYT/l7MIl\nSQUMM+O1e7fRNJGL81NefeUuw9ExWS4iyApp4KMJoKoqqCIXV6esdnaoVDoIms2j549YWSpgSDEj\nx6FerJGEMVbZIBRyHj58SrlQxXNTCnoRyyyQSBKn/T6GJlM2LYY9h+V2h4JhEMQe43H/moQvlRGF\nBNcbU66UCOKA1dVVFouAMAyZT4copsrF8JLtW1ucH5whpTllq4i9lNOfuUipwkZzFS9NicWM7vEx\nTggFw+Xm+hp+JPPy8JRMkzg9WPDzv7RO4gcM+j5JLJKJkAopSaYyPR9TrxUp1UVUK4NcxVAanJ25\nvDzoM53NEcSEpXaRgmkQRwKKZCGKoCsyK8smB6fn+BGIQoHtnSIWAt3FmCWtysHFiKPDGcoiJxMU\ngnCKJMnkKNc7/zQmTTKEHExdxUlTFN1AinzeureHlcyRckikAE+yUCtLDM8vsHIPTZQJk4RarcVk\nMCaJPGQpRUg1TNFCF3085uShS6YpdHOLXO8QzSOWZVjIKfNijbFVZnmccJIN0ZxTvrLbYaqV+OCj\nC37xWz/H9OoHVMp1xoMp1MrIxT3kcIiRDRk7BVLrDj4Toty5FsgGEkKQEeUhPgmOH2HIJioJOWPC\nAPLMIlOuEYgxZbxogSpkZLrAZDGjKOmUikXGRMQp2KqGKuVYBQNRzSmYGo1yiYKooesqhmUSRwmq\nZpBmOXkUEYspwbFPnA44nczJBj71WoH1avWwAAAgAElEQVSsUsHXBDJ/zhcff0I8HKEIMgVZQksi\nbMPAy2PeuXOfr91fYbUQYcULBEPGWySoFkwvRrTXGrgXC1Qlo1UuceInKElMIogYcczU9zClIrIY\nIDTqXGDzvmwxDXTMIEMtiVQ8iYWRUytphP0hwsLn+x/8gIurMV//2tsM5pcQpywvG5wP+sQIPH7Y\ng0Ti3p1lZvMZ5foqJyfnxBGIQsr9V+4xHE55vv+CuTvFNE3ETKbZrOD7EZ4XYJcaPHv+iHqtTqtV\nYTZxyXOB23c2mQymZHnEaDDj7KJHZ3UDQYYgTAnDgEpJZ6ld4/bdV/jBD39EmokcnZ4hCQJv3H+V\nLPCRZYnLiz6WWWT/xT637uxxfnXCfObx5tvv8OzFPkudFq1qjc8fPEbWBGRZg1ykaBnsvzjiG9/8\nCrs7HcbDC+Ig5umLHrJqEhCz/+Qpuqzwd/6Tv8UPP/yIXIBivUSOwP7zlxwdXmKoOt/8+js0llv4\nvo9dUMhEePjgMaKg8q1f/Xn+j//z/2Fvbw9RkMmyFGc6ZWt9i/39h9x9ZYulhkV32KOy3CZ0AoJB\nwDwKuDi5oFCo8snjxxRKZWqVKnGcUqrVWFptMg9H+NMpigiNpTVe7J/z+is38SZ9Qj/lL773I7Zv\n32bjRoc083n+ZB/fSekPRgiyTI5IHIVsbq5yedKlWKowc+YIecZ0NkQzLOZzj9W1NdI0xdBlRsMe\nW7urdNolVDnAMCp8+uAlV12f4WhEkqfIoghZ/m8CM00yBHJEUSBNU2zbBiAKIjRdIghCDENHlARc\nzyOOE9IMZFVE0zXyBNI0Jk0zJEnEtgu4ro9uaEzHHpIoI6sgSxmiJOO4PkkmQvaThJYyNE2DLCEK\nYvJcIOeae63qGZEPEiqKrLDSKfPgs5OfbsL8v//pP6Y3dHCdnIrdJoo99KLOVXfB7tYWNzZqNJsq\nhmHQqLdJ4gXFokyzbqMbZR4/O0LXTchFXNdjNBlTtA3iUODqfILrJciSipRJNO02xWqN8dAj9HPm\nEw9dLVCttKlW27TaZUQxotWq02i1CdMIb+pDCisrdTRd49nzI0RJplCo0u0fo2kaUaDgxwlJltJe\nalEulnGnMxLXR0wiOitt7t67y3g4JHRDtlY72KrC177+JRIZ3vuzz1hqlhlMh+hFjYJpMHc9ZCnG\nm81ZOClRmBNkCkkkkhPQ74WMpzGiKnNxtUBULXrjGZNuxu7eKqurNkHgMxwuiJD59PMDokTCNGyK\nNmiajZyX2H9xgSIX6fYvSbOA6rKJWckp1i3u3LyBSMzx+RHN1SaClxOJGRfnXTqFEiopim5jmddF\nkUxQsWQJUcmwLYWCLpPmkGQZpqRQtVTkNEZwQ7Q0Ix4vWGq08KdTslAgm0eoaUZ/EuLobRaFOiM5\nJ7VlQjllZsQsLBu5WiMqVPBLy2SldVK5jFUuUSkWSRWT5sZNlFggL+l0Lw+oNwrI5SZHlzFbd+9x\ndHJA4BqMHIUAk0nfY+ElvOidM566zPozuqMrBnMX/IDM9YgCDycPCNQcLwsYz0ZkWoFFlBMQEyIQ\nZhGhmKBYFl4eYskq1WYZw5BZabWoFQXWOgU219rcvrHKV9+6y5v3d9le77Bdb1JJc5zAZz6cMTg+\n4XT/kAcPH3HePWU+m+MkLkFeRqlZNFbrbL3+FqVWhULTZMlU0GPY/9EnIGkEmYAZiWSmTkFSiSYe\nr76+zWYZVvSM2XSOobcwRIGCIaAtpsiSRZiLWEab4/kJ1dYy3txDL+jM8hQhK5JlGdMg46O8zKFR\nYSRqKCGsLHVw0zmLsshKsUE4PMYfv2R3o8nwqkehYPP88JwomFE0izz4dJ9i2WDYj9nZXeHLX7qN\nJGZMZz5xIjMeTbi86nHz5jaHRwe8PDhEMzVWOkvMZjNMXebLX/oZ9l88Ionh9KzLSqeNIIgUSybT\n2YR3332Dk+MLdAM6nRpJnJPGCRtrN+gPuui6geu5VKolgtAjiOJrpm2xRA6EgctiMSeOEp48eYkf\nXxdkbmyvUqtbdDqrnJ1fcHHRZziaIYoSB0cnKLpOmqUMh1MiP+K111/h7t1dbu/u8GL/Aa16BW80\nx59NeevdnyUXQdJTfvmbP8cf/MEfoGkGM9enUqsynQ15+93XuXdrl+2dVR4+P+RffecDnjx5gSgo\nOP6M3/prv4EzHjO4GjAZ+lz1TxFEjXrV5sXJERfjCaVmCdMyyGOXulWkVmjhTFM++P5nPHlxxbnj\ns7K1wThYMFt43PvyOxzvvyTNY/bKbb74/Bnvvvk2Z4cDvvfdj+ldTvj8R49wZiE726/y4Mn+tUjZ\nm6DKMi/2T1E0nRs7N3jny2/juAuyXKTV7JDJGZPJgJWVZWYLF6tUJooTcgQWnk+aphRtmyiMEWSF\nleU2Qh4znTvIaoX+oE/gRwiiiIhAHCbkuUAcp8iSSBwnmJaOZelEYQTkSLnIzs4Gtl2g1x+i6zpR\nnJBlGYapkgtcawuTHFUTSaIUVVHRDeXaUrLwEQWZarUAQkSaZMiqShTHpJlAFgsggKaq5Ok1SUgQ\n/vVpikqpYpFl10UkURDxvYA8g//q7/+Dn27C/NKXivTHOQWzQhyMKdk5u3d26V4OEFx45837vDj5\nnEajjT9zuf/KHmQhieATZUWOjkeYukboDSlUdI4vjzENjciBrRu3yRCI0gxn5tOutbmanzIeuoRu\nxN1b28zdGaIiEyQB9VoLXRbRZZG575IICUWjSL9/xfJ6k9lc4f/780dIokypKPP6G6usr6zw8Q+O\n6PYnIAUgpIjohIsF2ze2CeIJYRwRpBkTZ04U+NzYbFA0DILAZ3W9jSy1mC8GDMZDktTBLts8OehR\nMQyCcIQiaRy8cNAKFlquYzclCmYVZ+7h+TOeP++ysbNKZ6PNd/7lZ7RbKmurMq+9vkmUGhyfLegN\nfQY9nyybUTBMqqUKR/tX6IbJ3t4GZAtyAm6+0sCNh/heRlPawpufk1clvFnMYuxSqtcZ9BbIfgFb\nKnDn3TrnJ32yMCGQTR59+ClWw8abTrElmb4T4YkylqZRF0VkQqQkYbEIKJV05EqZwKqSWHVU16Us\nulxGCpG9iq5lVMUuxqKPYDU5ijLqaoUCGoKg4hs2jmDiOAGC72OEHmg6mSKTOlNCPSZeDNheqYNQ\n4OVZgodPLqQIqERKihfMkRUTWSozy0fMFi5SLJMLKbpUQLNtLE1hOpkgqzqmXcDzXWIxpmDZyIJC\nrSIgZhpJklMsChStEoKpoRsmUhRRNhQMQUKKY+Kpw2g4ZdSboaoSUlHnaDKlP3epVSoUNIXaSoNS\nrYEsgm0ZuDOPXE0oejmL2CByZgiLQyaDCGcx50Y94Y//7DGv763Qnfg8vDynVW6RJxmRktE2TOyi\nwf/wO7/N2cfvcadj0ZsOyQSNOirJ/BJzt4U3CihXt3CFEM3UGJwPqK00mc+H6AL4kUEgaQyVMoeZ\nR6E+4enLCWvr32AydanVDFALdB98zM6qyHx6TOBEdI8Fnrzss8gl3nl1G8+55LXXdnjve19glRuc\nX3RZWWogyTGT+ZxcMLk87/LK3TcYDAaIYobrLSgUChwcnlAsllBEiTdfv83jx894/vySol1GN2RU\nXWEyG7CxtoapKRweXrK+WuWVOzd5/y8+RhINckRmzpwg8UkzSJKMcqmIJMvE0fXpgF0pkaYxkecz\nHMxIUhBFid1baximiiRcowk/+fRzut0pum6S5SmN5SZ+4EEe8c2v/hx5FBIshqRpzqfP9vnKu69w\ndX5ILhkUy3WOT8bUGzVeu3eTJ0+fE8Qxy+0Gvf4lP/+Nr+PMZ1iWzfHpERurDbZWX+F/+8f/F2a1\nxsHJAZ1qnfbKEgdHz/nbv/MfIOY6H3zwfd5881Xef+8vsSs2w8EUVdbZ2ewwHp9ycXHJW6++webm\nGuQ53/v+B/SGI2rVVdx5xETw+cpX3iLuORz1R6xYVX588IJ2dYWbG5t89wfvY5cqPHzwEFMzUNQC\ncRZjFQUkKcS2ikxnKReXXTTNwLQN3GBOqVyl1WwjxAn9SR/fCxFSBdf3EWWZSqWC47j/JnDchUOl\nXSfxB9xYbWOXTL54dMJ46pEmXHNkEcjihCi+XsOSgygK1GolfN8njhOKBYNyqcjtW7u89xd/iaZb\nWEWN3qCPokogCMRpSpwmqLmGomWksUwcR5QqBs48JApTTFMlzVKCIEaWRXRDQdJ05o5LnoEqSTTq\nVdyFh+95ZFlOnKaIssTaRp2FO0Uk/wmfGmRFYDqNfroJ81++9z/SXl9HLoS8+nqNu3dvcHU1plIp\n4c0yjo962IUir+xsUbZVpDjDUEzcxOX4pEdBq7CY+dRqFqVmESd08cKYtZVVdFUiCKdMF0NkVcNx\nY3Ixotsb0ag30TWFdrOJoiuIsoSuqiwmM3RFw5kvEHOJ0AkR0VCNJj/85BmqWULVDTY2b/DJh0+o\nFRscvnjKK3e32dlr0+40OL0cIkgyXpThRh6WWcH3oNO5QZrl3L13E1kzSeKIZCpSKlXJVZHPPn5I\nQZHxM49YtOmfOszjBcura8xnCbNJzGqtwHn3uknWbiwxn8yxrSKaqjDt+3ixjKzKqIpM4CbEicjU\nCTm/GBGnAZIiYpcNhEyhe+Gw3GlwcXaMLiikfoIzHyHkEWutDZ5/dMztjQ0WhnhtEpEloiDFqtf5\n/OiSk/MLbr1SRtRjatUCkzikXtB442uvIpJTFCVMu8IkjKgtN5Fsi6EzR7INJoKJpGcsr60i1tbJ\nC6sQ5NQNCzcxMKs3MeIyiVhjzCqLxQoLqcXYVbmaSQwcif7EwfcmhO6cOEwYJAHDMGDoOPg5OF5C\nnFqceRkvPY+ZLJGrMf1AZJwoxJrANPCYiwaSoSOLEhkaWclGtGQUTaNYKaLXDaxagRs3NliuV1ld\nqnHvjS22N9bY6Gyz3CxQMjSKUgEtXJBPXPqXXY5Pz8gup/gTl6fHL3g2meD4EolmQMliZW+Lpa0O\nxXaDvdt73LyxxrKSU5Qkgu4lw/19nn/4A5iP+OLPP0TsX9EuO/z+P/l9/stf36F7+hAOrvjvfmOP\nql3Any3Y6tgML2dIhoary+SLACRIRY0Hz16wvrfJg8NLdNXEKhSwFJ9MVhAXInbbwJcyZDEjyBVy\nEsQ4JfAylHKJCJUDbZn3phJOpKOKIcs1m1ng0axXsLKQZjikVo3RtIhnX5xQ0ld59OyMjdu7rG01\naSzJmIbKs6enZCJoRY1eb0Cl3CLLQ+qtOnapwmg45fSkS783obO6SpIHrK6vU640ECQRQzNIkpg0\nzzg/73Njc53RZEwm+rTaLeYzB2ce0unUiaKUg/1LAj+ks7bLYDgkjEPW1pZRVBXHCfGDFEVWkcTr\nUl4Q+uxsb7GzvU2vN2Fr8xa2XQAh46LbZ+a4mIUqo/GIOE6RZIlCUadUbhCHEUvNGsliwdZKk72N\nJR5+9gW6YVIoWuiWgW5W2b15i1v3dlHVnPPDc7pXQ4oVi+FwTqfTQUhynMmESrlIQVfY297gxf4h\nw3GP7Z01GtUqzdUGrjflP/7bv8nR/j672+uEwYzFdE67YfPOm6/x4tkxZwdnvPPld0EWCUjZuruN\n4885v+iSJJBkCnajTllXaKyuEbkzDFNmpVakaKg8e/aSLBP50WefsLrVYvfOJpWKxc7eLi+PX/Lz\nv/w11lfbtKoVKqUyh0enVKp1dnd3ieOAoqWTJwKj3owwDMiigDxJkSWNwWBAoWCjqjrdbhdDU6lV\nq6iygqApVEsViqbF+fmQKEvxo+vSDwLEYYwkiEiyjCRJ5GmOYRjoukYYBOiqRq1WxPc8hoM+WZbS\n6XSo16tMp2MUVSUIAhB/Mg0iYZd0Aj8hzzMUVbz2/AopWZ5do1CTDFEARVXIpZyUFIGMgqViFbTr\nU5I0vdaKISAKAu+8c5/RYEyahKSxgKHqZEnCP/i9f/jTBeZ33vtHiIJMqyLhZ1OG84z5aYoiCyhy\nRpaI3Fhpc3l5iCwoJFmDwfwSx3NIsRl5AckiYWNVI0i6yIZN0SqiyAG93oB5ENCbX7G9vYqt1Pn8\n+TnlkkUYTQlCMCwLs1Dm6OwAmTZJkqIZZU4vr0gyi+F4Dph88P4XLBYhz56dMR/F2KUigRjw3odf\nMBo4XL3sY6ZQr5X50bMHiFqRzaV1PvjxS44Outy9vc2N9WWScE7keUS+z+bNV3DHDv3hmKJWIDBS\nwnnG135ml/XdTfafviQOr9cOURxjWBKSpZFnAiICRydnpKKAqNk8fTLg7KrHG7fWycKUVmOJKEmQ\nZZ1iMWZzq0ypVCDyLEBj5lyx3N5mNnXY3GviCx5O5CPrOUbBoFgyqW3kBJrE6fERzbrN9maZwaRL\nsVTl1s0l/KDPi/0jmnWN6WJCa9mk73ZRFZmlxiYPnl/SX3ikmYimF3B9n5EfoZXLWCWZgR+RWQ2G\nk5S63easH3MwhIWfEC9mzP0evekp3nxAIoR0F2PELCfJAgQ1Jlcy4jzHFzJSTSdXbdwoYSRITHWV\nXiwwkmK8IMMXiyziHL0c4Sc6uimit1IKhSXKZZvmmkxj1abdWuWVW1vceW2Hd/d2Wd1eZ2Ntia3l\nHYySDppAazFncjXgi08/55NPP+HywTO6R1fsO4d49TYUbOpry6zvvMmtW9ts3Ntmae0mjUaBFUNF\n02OY+oT9LsePH1CeXfHJd77H53/6HZb1If/09/+Eb9RKvHFH40//8AP+97/zFTRhhnBxzt/7668z\n75+jDQb897/32zx578dU8z5v3mrwL75zhlZOuf/NXT764Qve2GxQWS7jpCnj2YKL8yme7RKvJTih\nQ5DJ3CnW8YAiMgsvJnCn5GIKQkRNSpgNQuq7O3wxXTBeWeYiDkhKJbonPZpamWIe0CyVIJ+QuIc8\n+/ELvvej97l5+x4ffvA5r719B90U2d1e5kd/+SG/8NpNPvzwGX/+/nPuv3aL6axPo13EtAxSIeP0\nos/Dh8folk6pYKAbCrPpiO2NNa5OTykVDJaXqsTJmJcvT3EXLpvrW9QbVabOJTf3GqiqwNXllJv3\nWshBhJcK5KZB7Cy4udymVqlydHSG6/rEUYQgCCwtL7F/+IJcBtPUqBeK7Kxv89lnj5BVjZ2tFdLc\no96qU6gUEBSVyWxGtdnCS0MqzRqKqdNuNUgCh3H/kvHc46OPH3F6fs7t129yfj5Gz0VURIJMw5k5\nnBy8RMoT2u06lVabjz95zNrKMs+ePuHdn32DeNzl4PgIRTaplypYNQXHiTg6P+XmuzfZaDf59h99\nwIff+xiynMOT56S+ynff+4xPnx1yc+cOzx8+wazaXA6v2GwusVEsYiYy9XKVbm9IZ3mPslVnPneo\ntRs0zQYPPnnOr/3Gr3H04ilSKCGrKuNwjhMs0FSR0JnijMbEoc/V2ZiHX3xOo13ii4ePCIKQvb07\nHB6foSoy48kUtVwhQUAWM0b9Pppp4fsJ9159nZcvjqk3m5Bf25t8P2DhTHnrzVf5T//6r2AKIl/8\n+AGyrNIfj8nEHFGREbMc2zKJkwhdMxDEmGarTq1aYeHOUBWJOAmxSxUkWWE0cfCClPnC4eXBGVku\nEkUpaQ6aalCrVnjj1g6Lqcti4aBqKiDhuT7kAoIokOQRmQi5CKIk4nkBqqLAT1R0vuvS6TTpdNoM\nhgNyQUDUrr3HYThFUwVkWSfLfYqmwe/+vX+79PPvDMwP3v9fiUMZ29TY3Gly1Y2YDUKaq2X64xlB\nCIPBJaW6xtBxyBWJVEpQFItcyJh7PYRcZTKeUChfN5z651OkJKBolZj4PkPPI8lMPE+gYOqUSyYC\nIiIG/UHAYp4xHSacHBxhGCJ25RogLqkamZRw3OvSnXksHJgPp6wul9le32Tcn9BZa+EGHptrK3ix\ngwfs3rvNaODx8cefsLfZ4Utv3aRZKzFx+hyeHbO8vIogKqSkTCcDWtUakimwubGM6/bQVJUHT08I\n4xRZVq6np9GI5eUOnpcwHI7xvIwki1hbX8H1HGo1m6V2DVl0qFRtfN9jPPI5PenjzD2S2KdUspjN\nJti2ztr6Cqdnl1h2AVnLqVaruG4AaUa9ajEaXVEsVIiTmEqtBMgsXAfVMvBDncU8o9YUaLfKmCWV\nKJ6ReiJnx2PSvM5ffvqYk54DikWxWkGQNA6PLylaNqXKMpq1znxhYGoNosxB0VzQRmiFEK0gsNIp\ngjSis6rSKsdsrBYI8z5TWWSeRHiCyjwTyCwDX8wRTDCLObkt0anorDQtGpUSO5ttvvTmBis3Stze\nW+L2jS02d9e4u9vh7uoua3WDVUOnUt5AcgXyMGR0ckJ20qN3NODw5RGD/WOGvQuG5xf0ZgIFQ0Wz\ndZYby+zd7rB+9x7tzi2+ut6kpMKWXmTYe4n35BEvHz9l+uQjnvzwhxx9/1PeWTH4o3/2bZb8IX/j\nr36Db//zf8Wv7Sl842c2OP3skH/4N75Mfa2G89EzfucXVglkjd4nz/i7v/EW3/ngKbt2kXd+5j5/\n8f4XfOm1dbpTjZMXZ+x++T7PfJdyXeHOlzvsvrWLWVnwxpurOJ7E1dGQX/rWq7z9RpWVRp2rWZdC\nXebHF4cEnsDd+7c5PB+hN6sIYYYeyfiJi1xq8dBJOclFepMhaZbT0hXaKzn1ypCyOaLbOySJBsy7\nEy6f+lSadbyFz+pKC3dxSbtZ4OLqgMDzeHkypNxssLaxzuHhEc1mA03LGPbO0LE5P7yiWFCpVppU\nyjWOj0/Y2tpk68Y6w/6A+TQgnE2pl6vEccitO3cp2gbH++f4jodpxVTrOqooQSRiyQbd/gTNuDaK\n6IrK/vERqCrT+QxF1XBcl8FwiGkWWG63+NVf+RaB6/H5g0ccnpywsbXB88PnREmEKCiUDZv9B0/x\np4trg4xqUCmUIEywqkXCmcONtQ4LL8CqFHj3y2+QegmoJn13Rpgn9LpDxHKJ9kqb7tUZWRyR5yLV\nWo3ZeMzrt29TUAUMASIEXvT73Hz9HgVVxp0E9A7Oqfnw6OEzDLvIl3/p65QbFf7av/+ryDk4i4jZ\nfMTwoke0cFjfWGb/xQGzucPe/V3G3hSzZHF0cEKz3ea9977LZmeV6VWXi4sealHi9PA5mqpgVYtk\nJDRaS2wsLyOJEhs3VvH8AFW1OTk7oVws0K5XqNoleoMZaS6wd+829XaN3d0tRhc95DRnrbNMqVTC\nsgpcXFxyeXnJ5s4WYRSQ5Sm2XcDSVXRVYtjvMgs8vvPeR8yDkCgDQZTI85wkSsjTDIFrVOVi4SGK\nAkmSE8cxOSnVWpXFYsF87uI4c5I0J4pSsixDEASyLCNJEkTxGpsX+D7DXh/HC1ANEz8IkSQZVVWR\nZZEkzcgFAVmWiaKUom0gStcybLtYoFwq0W63cWYLBoMhIKIbOpqucv/+TQZXc9Isw7bLpFmGIAn8\n7u/+3k8XmP/vH/4vyP8/ZW/2I1menuc9Z404se+ZkRG5Z1ZmVta+dXX1PtM9M9RQwxmSpgxZhqzF\nIA2CpiVQhiFZsi5s2IANXRiQLgwKpryQIkVSJHs4azd77+rqrj2rKvc99n05EXHirL7Isa4MA/MP\nnJtz8f6+73vf91E1cqkUkuwSCCQ43mtR7JwgiCr9nk0gqKCFfVg4iIGzgGqp0GF2Pk2rf0A6k+Hw\noEw4HsQTxoz7DtFAhnQ2ihqNkpidYuNZkVq1Tblh0erYhPw59rYPqBcbeE6P0/1D3nnrVbKTcWKZ\nCOV6lcPKFkur55hYiHHt1TukJrK4pk3p5JDCcRW/JJOZkLl4ZZ39o0Nkzc+jp5sktBQ3b61TbXaZ\niErkJ9NMpFIcF4pcunaRg8MTIuE0rXaD3YMdDp7t4Itr7D/ZZWV9ne2tKvML04xMl68+3SMS05Al\nlfHYoXBSod+zUX0iis/GH3BZWszh2F067TrD3pjjwy4OHulMml6/ReFowOJCHlHp4aLj9/kplorc\nvvUG4/GIwnGJcEjAHA0RHJifO0e3OyIzmebouIEa8NFs1XG9IIWCwf5eC9eV6bQGpPNj/N6I/a0e\nybmbfHSvgCC5vHR7gjdez4Dd5PqNazQGA47LdbTEBJ3xCH1cIpORSE1qTOUkPK/I2lqUcMzCdduI\nQp/z53MkYgIMWxjdFrJPwQxHyEwmmZxMk0mFyU7EiIVkJjJhVufmmMlM8ercMkurc6xm8szF4rw0\nN8l0IoJsGDw8KLC9+ZTDjx6x/3CLFw8fcdRo0h10sOUxguxDmbtBMDdFJpdien6Z7FyMyeVFMopC\nUFLxdUb4jCaNkyK7T7bo7j5h++5HfPnTu3xrLc3v/e9/jr9e5de/c5Pf/b1/xz/7G6+xemWB048f\n8l+/HeH667c4+uwRv3ohTH59kYd/9GN+4zvL7BR0hIMDvvu92/zxDx+wmp8ivRDk6f0jbt+5RNn0\nYdY63H51hk8/fEoiJJKcmueH7z9l8sokwcvThHxhStsn/NEfb/BmfhK3biBn/Aw366iqiGN0GPZ1\n2ooPjRFKYpap2RlaVor7Oy+4evNtYqEkw3YPJzfJgRxga6RgazFanRrS2MSuV7BcnWG7idWykYSz\nIvRRe0xIDnHu4jzJZApJHGMaY6KRFJapMZu9hh2Q0K0K//HffY2Bc8LM8iz1VpNe12J96TqL8znW\nVmaR5RhHJ0VmZhfI5SNsP98jO5Eik55mYA5555tv8+nHH1M5KlAplrE9kZVz83RqVVTXotHocvXG\nVT58/y7G2OLmzdtEAgF293aptpsEIlF8Pg3HAX0wZHJyAmvs8Xf+9n/G//F7/wYXEX00xvEE6s06\ntUad5ZUVnm9sks/m2dvZR5IkLNOi0+5QPC0wGgzR9DFDs08gGySYjDAzOcVEIMKXn35JoVziXPYc\nswtZnm3v43dEopEQWjBANBqnXKiwt7nL6soSp7u7XF+/xAcffU7Pkbl07jKP3/+AkdMhNzFJx+zj\nLkcJhOIUTkpYTZPnTx4z9LokUjOEgvkAACAASURBVAnm81lc0WH10hLpmRRbJ3vM52a5dWmdfqVI\n5biETxOwMJidzdLptkFTEIMqiqYSCvoIJIKEFBVXN7B9Ah4iE7N57n9+j+LOIWZ/ROG0zp3XXiUW\niqAJChsbO3RGOleu36TXbDA9OUkiEOL80ir1UoVELI5hWjzf3CIYjvK3/ubf4tGDr9BUH7du3GRx\ncY5g0E8ykeTpkw3a4zFT07PsHh4hywrxRIJhX8cybBzbwxjbiCKEQkEMwwIETNPEcUxMc8zYHCMr\nCpIkMxyayLIKnBmEHNtFkcWzFasoYNs2rusQS8axHIvh2Dor+bcsVNWPbZuIkoRtO6iqhIeFIIEk\nSQyHIxzbod3qMBwYjI2zzlnHcfBEh4CmoPeGuK7CcOwQiMjogwG/8w/+6c8nmLulP+Hh8zKNao1o\n2E+zWmN1YQZTGhANJigc9ZiZSWDZAxTJQ1JM6tUekqvi84vMLMbo90eMbZFYyofqkxiOYDQSuHp7\ngYmZLNG0ytz0HOlQgnDQo11tUTguo2kO3/2VO7hKm3Aygs8XxRiNGPZHRIIKqcQkQcVPQBaxxjXK\nhQqhWIyd/SKSIhHRBC6s5KlWqnTHQ/qGy0Rsklw8iiTIjIUhAX+Kjz/6AiQJSVHY3Snik0SCAR+R\niJ+JXJZ4OAKKR3ZympNyEdOWEfHTqI9pVKr4NRUEEWNoIaKiKgo+n4SsuExmMgx7A1r1FjIq0Uic\n+fk5mq0R+mDIxcszLK0lkGSB4XBMwO/H5wugKUnanRqWozM2x4TCEolkjGgszc5OgZULk/RHHRKp\nGPWGjjWWUFUfuek85UoNxzHITMiIXYd4IkVqOorhDLhydZmxOcC2BCrlDoLsR/LZ+IIO3/uld4iF\nRWSpxfWbSSYmHeKhMQF/H584RpPOohGLMzGy6QCN2gm2OcAdjchPzJKcmmdtcoK5pMp81s9EVCUk\niSg2eC2PQUWndXzK7v4RXz3c4HDjMe3jAkc725R2jul1xgQMCETjRNcvsnhuibnlWa5dvMLa+gqT\nyWV8AZNQq4i/fEq/Wqaz8QC3VuDowQalzz9jZd7iT378HuJRme/emuXH733O//CLr/DaK1doPH/E\nP/z2CteuXmSwe8SvrvW4+eZ17r37Fb/+q9/EbJvUnz3lG+/E2H/eRNDrXL4yww9/uM3VK6tEsinu\nPTnm67fv8Lxep9Ed8kvf+DbvfnCX6XgGbTbN/Y/u8/qlRV4c9Sj0Otx48zoPq31ETFq1Nnc/ecLF\n6QznXptG365zLZbjp3+5STzmR45E2C7XkUWZiJji9HhI2jfJkxd7/O6798hPn2Nn84h75QaJ5Rm2\nux6bih9dDSMbIEYTCOaAjjFCcmIoQghJHJOaDKFpIoLnMhwM2TzaYXunxMFRjXhiml7fQh8ZNNot\ngvjJJ3I0yhV6nTGnx1VUNcrIcDku7LF3UKNSH3Ba3cdFZ2Zmku3nRWIRjXqlx/buC1RZ5u4Xn2IJ\nCtn5GfK5JWZzGfrDLh3dxReeRFFc+p0qkhRnNBrT1/ucHB2j60MGlkm3PzjL5CkKPp+PkTHktdde\nptOuYtsGhjXk6rUrbLx4wYVLV1lfXeNo95BYPMGTzecIfhVUmVgqSb3TxhfUcERIZrO89dbLzKWC\nrGSzCI5EudlACgXpD8aMMZEEmWw2h+b3c3xaZHZ+kU53yNOn26yuXWTr8MzYdLR3iInAaaFMrT3g\n1jffYtivMz89z8r0DFQqfPDhV7S6Jun5WTK5FINmi62nhzzb3WVqdhYtpBINBlHNM9j90ekJjiox\ntAVUv0wyE6NQKhBLZEilslQqdY6OT2jWOzjDAZP5aWqdAY1ik0arRXevwnA4RAunOCyV8CSJaq1F\nudRgNDA4rVUR1ACXLl3kwrnz5MIJSluHPH++zWcPH3BcKVFrd3jna9/gndfeolmqnBFCZIXd3T2O\nj47Ru31m89M0a01GI4OAFmCg60iSxNgYAALjsYmsqAiScNahjfgfhBBcJFlE0zRs28EwbPK5HKrq\nJxKJ4PP5sG0bn0/B79eQZRljbKJpfqLJyBmkOhnD8RwkScAwxtiWd+a+1TRsa4xPVVB9Ch4C5thE\nkmRsy0MSZUYjE0mWcV0PWZEQFYmV5QW2N0/odocMRgMkRUTxufyD3/pvfz7B/Pjzf4WtOuj9Lqrk\no17oIDkmlmSDBT5FYzwaYIxGTE4kUHzgmBoIHrVyn8mpBM1Wl4PDMtP5FLP5BSKJONF4CEkxKVT2\nGI0s+rUeq/lp1l9Pcm59huu3zxOOKZyUThkNBFoNnWK5gidbDEYGtquSjpw1fHjDAa3TU27cXKPa\narB3XCM5GSYS9FE5rpHLTbF7XKRw2mNtaYWd56fEEgHyM3PcfbBFvT4kGMlQr3eJxfzUimWuXlin\n1myhDyzEkIhku3zx1QGxsI9nL0psb5aZzE1SLJXRwkFE8YzR1u8NESSTZDJBdiJP4aRCtdLANARs\ny2NyKsrh0T6W7SJINpYpcHxUp1bu0213ScT8jAcGATXFztY+gbCLzx/Acftkp8JMzWRITcQYGyau\nZ9LqFBDcAD4xQiTiQx/W8Pv9OLbB+sUJOrUmfiXC5v1TkkEfI7OAFvYTiGhIfh+GZdPvjplMxDB7\nVbJRkVzGx2DUBssmIIronQaRgEgiGmA0GIAFw96Aer1FOBqjWiwyNztDzxpSLp0iyC7PN7eoVPuc\n7DcpFbv0HBj7ZTRRIL84T3JlgWtr66ytTZJbmSejTSHFIRCMIlo2o94B5acvON1+RFgv8v4fv8fp\ng0+46LP5/d9/l9cjLtfPJfi3P/gJv/12ngvnZtl5scP//J3XWJhIUri3wd+7rhFKpdj6yX1+5Z2r\nnLRsjNo23/rGGqXnBfzDEbfeWuOv7p5ybTGNHA/z2Wf3efP2GuZY4r17W/z1N77Go/1j6maP119/\nmw9+eJfV1WUWr9zg4V99xtdfv87uYZfewSGvvb7GDz/YYjEXw5udYaNZIhu2cWQJKZbn3ldHHFtt\nkpkwufQEX3z8gF9YzxOZSvD44JiXLkzx5Ok2WwcjIkqY3UKHjWdbVEs6AzFKpVrn6Ysj4heWmFpZ\n50nDpoufzsAkFgtT75XITEyTyUxzsrlJIhqiWGlRLDTZeHyEooZZu7zA0OuQn56l3jyDik/kMuTm\nEzS7J9y7/wTVF2Rv/5grNxZZXIwT9qXZ2iwwss5WmolUlNODOi9dv87O9jE+/1m9mCDazC0uISoS\nf+fv/V2eb+9jCwKyIFKp1Tk6reKKfgaGQX/QJKiliSVzaKpCo10jlUwRiSQQFB+XL19md3efQX+A\nrIi4rgMC6L0umuojGkvw+Wf38Wzo9dtUCqcM+zr9vs7M/Bwvv3qH08Ix165eoVmvEw6HiIUjpPJT\nGJUq0qCDK3gU601Ojkq8dGMdc+DgKgJHJ8eUy1W6xhBPVkgkJ2g2zkDnmakJekaP+NQErYGOFQui\nxaPkctN8+fHHjK0+ju7y5z/4AS1jSKPtkklP8Pj+A0qFIr5gjHAkw5X18+w+PqRwdMytl25zcFyh\ncFrkuFRhIp/l4uV1jk5PqTdGJONx3nvvQwqHZc7NL3B8csjkRJ6Xbt7kx+++z/17j5meW+D55h7G\n2KLe0ilVmoxNh06nTa3eRVAEdHPA1GSeN65eJy0HONzY5emTp2yfHnP3+XPEoB/Lc4lHYrx88yb3\nP/ucbCZNtVJGUmQajSaueyY6t2/dJhIMsbS4xJd37xIKBgiG/AyNIYZtIqs+EEGSzooFQCQYCJKd\nyhIOh1AUBdcFPJFkPE6r1UbXh/T7Orquk87EESSRsXkGmpZ9MpIsMjJHSKrEyBwSDGiMBqOz0vaB\nQUDzY9s2sqIiiQLDgYEiq9iORygYwhib9HWDcDDEeGwgySKBkEYsGUXwJJr1LpZlY45dwhGVgCby\nW/8fxQX/v4L52Yf/G3IoRKXWxrEdTKOLa0KzP0bCYmIizuFumWQ6RyzuO3PFnfaYm8tSPNFRfGcm\nkHpdJx5KsP10B1fs0Wl1UIUI8WSARtUmIAUx9R7bm/tUj+v4JIlOo4tP9BOLpnEli1AsTaVWZ2To\nBPwR9nYrHBdPqZT7iKKEFgxguhZTuSkiEZVOY0D5ZEi5ViKeiDGVTbN/cEinr9PTBxweldjaOyUc\nCbK3d4igwO7OLp4l0ql1abYH7B9WSM1OUNg+JjMTRXVkrtxe49z6AgfHpxjmkM5gQLXeRvGJCAJc\nuLiKaQzp9cZYtouiqiTiCVS/QiIFo6FFr2dhe0MGwz6SoIKtkEqGmMykOdot0mvrzOZT5HIp0rEU\ni7MZJM9D1xsYhotfc3CdMarg42i/iSK75KeDuJ5NJBomFpPYeLyJPZBJhsNcvLZMy+zQ6Oj4RZkA\nCpI5IpuSCfhcHEsnFg+yf7qFK5h0m238YuDMMaaE0TsmtdqI7d0ywVCSTkvHH4pQadSJhVXiMT+6\n1afSqiFqGpnpRdKzC8iRGFOzOcIRP8mAxEQwQ79Rxa6dcrqzR+fgmJPKI/7wj3+K3Kiijpv82R+8\ny//4165hGANaXz7nX/7GL1KzBggnh/zzX76KPxmHwj6//st3WJhZ5Oi9d/n1796g3HZovLjHL739\nCp+/2GFGE7l0c513v3+fN84paFGNp9sl7ly6CJrLgy83ee3qPDsnffr9KudXX+LHnz0gq6rk1s/x\nFz94wssLGTK38rz/o23+2rcu8uykj90/5tb6FH/52UPOpwYEpzS+uLfJNy/G2esZPHt+zJtvXqXv\n84jKSZqjGm3HIJ+L8ub37mC22qxMnaNqe5QqJe68epEHm0VSSwHUtEK3JzM9NUej1+akPsB2VBRH\npmdb+BIxGrUKanwCdWUVb2gSj09QqZeZSeUIhML09veZDAvorRrVZo+llSSZdAifFqBcP8WixPR8\nlMlsGiXgUq022N87IpuZpTawOS026XTGPH+yh97qM2iPCPgn6OkeaxezXFifw9DbpBMTdLp99nYO\nmZhIoWlBPFGmUavx6d1PMUyDsWGjDzucFpqkUlMUyhWu3lik32ugeJNs7e6Sm0qTSkSYzk+ze3CK\n7Qrs7u5imy4TE2mi0RjhSBhFEbCsMeFgkFg8zuHBEbIgEgzKLC8vEI3HqFYa2JbF0eEB5tBAdFzC\nfg3PtNG7fUqVMp1+m1qtzHJugYWZefrjAYXGKdVqm1//m7/Gp3cfk5vJsTY7z/Nn27TaferNFqur\nS5ijIYtTeTY3t3n19k0uX1hjNh5hf2eLy7cuMDkZwzBcTotVVq5d4mS/gqSoDEcDxkNYmJ0lGQvy\n+MFDXNlPNJem3WnzziuvnCUAtADxhIbmszi/vML9LzbJJENM5Sao96pUejXWLyyTi0/wB//m91le\nvYgvEKRSKWAZHoYxpjdw6Ok9DMtlfnGB6dk8pjNm5coq+dlJ9g5OODw9Zvv0gKY9xFYVbt+5ScTn\np99pc35lha/ufQGex4vNZ6wsLzHo9aiWy0iyTKvTZv/4kJ2DPRKRKMcnp1jWCMd1zggieLiCewZ3\ncGxUn4QkK9iWRa/fIxaN0uv3GBtjbMthaWmRVquFYYxxXRefX8EfUJHkn02nsoCHi4eDz+/H9Tz8\nmg/TtFBlFcswiUZDRCKRn+U6RYajMaIoYY4dBFFkbI4ZmzZ+v4qEgOs62I6DP+CnP9IpF6o4FozH\nNggisbhGQIPf/I2f84b51Qf/mkKjxv5+Fw+JWDKC5amYqJxbSpNMx3n2uEQkEWfsNPBJCQL+FJZn\nsrJ8jVjET6FYQZGDRDWVuakwPi2C5saxTQ9/MMLRYYvKaYv85DSVmkLhpIQP6DZ0rLHK8W6ZC5fm\n8PnDKEGNq9fPc3hS4qDYRtJkps/NU2r3efr0BL1qMWx36TZ75KeXSOY1JrMpTEPGclyQZVSfhCBJ\nHJ4WWV/JEdRUpmez9IcjMlNTeMgg+uiOBkzksvQaTQRRZG1qkqmFBH2zx9FhFUX1EGWZ2ZUMly7k\nGBl9QkGNwUBn2D37Qf3+iEQizeRknHK5guoLYhk+HFtFUX3IkowoQa3W59xKitGwzUQ6SS6bZX9n\nl0a9z2g0pHJygqYoBIIO7UYLxfMjCwLBgEwimsEaDvCEESFNwrUVth7tsTw3jaJKiIpHKB5iZ7+L\nYUqEwxEGgxqSAKLk0dMN/EE/lVKZiXyeSqODXw7Q6+r0RkMsxgzHOv6Qj+zcFJY4xBfws3VwxNr5\nJbLJCLVSmWAgRLOkMxENMB5U0eunGMUTGpuH7N57xreWQvzpj96jvvOU3/7mHL//7k952TX57V9b\no1ZrM0uX//5Xl+kTZqq6xXe/c52NnQLJ1h5/45vTfP5Fm5VgnZdfWuGzT7a4MCMxPTnJFx9t8cqs\nj7Yr8vTRNjeupqj0ZQZ7Za6/PMH93SYho8bNt+7wg58+4+rsAtHFSX7y/gZXYhpadoL79x7zzus3\neLxfRqwVuHwnx+5WjeC4zqtv3OaTT3e5nPZh5ybZ/Mld3nopxXbDZPhimwsvX+ZHnx2ST46Jzs3x\n2acv+KVXzvEXf/aAw5pBZH6anacVRoMB59OT7D06QRu4TK2neTHocu9pDTUmIvhkVuZW6ZT6bHd6\nGAEFb+BgKSqyJjDsdpEElWjSz5V3XsX227ijAbIr4gaCSAEN4/lzvnX7FhtfPGFp6Ty9/ggfY/q9\nEcO2g6R5BAWN0UAgEPLT7/d4+dZLFI9bjHSJ1bVzmOMurjNgfmGB7b0Ciew0yck4q6spzP6Yh3cf\nEItoFItVxqZHs9HDL6jMTOVp1usYtsvFqys0qg30joExGDK7OInDiGqxSb10gmeCrpvMzM0SCHoI\ngsvWzh57h0UUXwDHs4mEI6g+H/F4nHarw9K5c8TiMeqNJk+fPCcWT6IFgwxHBn29z3A4JJ2dpDPQ\ncWwXcLGsEWNjhKgoDAyDYDxALhHj0uXL9EyP+08fMTuVZCWfJRmQaY1G7B4Umc2muPXSDT7+4gFT\nU1PU6jVW1pd5+OUjlnNxquUi2WySy9MLlGrHjF2Z/d0tzp9bplyosr1Vpl5pMTaGTE6lqFVaRENB\nJFx2XmyTSSUpnTRxJYvPP3/M2Biy9XwTJaIQiPsIRzTKpQ43XrpELBxh7LRJJAK02m0ET0YFYooP\nYzDm6e42sUSAi+cvUKy3kBWVVrNJOBlhMOgxNZHml7/9BnOCQ8Ads7R6jrEzYGl+mktL84h9l7Ze\n5NzUDEcnJwRE+MY33kb1i2TSEZbncljmmFKphKwqhGJBBqM+/YFOf9gjHAuhajLD0eBszSnKuKaF\nKLiEwzEW5mdpNZtIgksgGKTeaKBpGubYRFUURqMBg4GBZZ91v3qii+1Z9AcDHNdFlERkRcETBWzT\nIpGM42EjSyAhY4zOVr7GyMB1IaAFME0HRVUYDUxwwRNEFFVmPDaRRAFBFHFcD1VTcTj7rqzI2N6Z\n5MuiiyhK/Fe/9XNOmP/in/8j8vNZAlqQdrOOMR4QicUIBALEA0EcQWDsDXEchXAwSq3YJhwKAbCz\nfUyxeIrq+fjrv/Bt+q1T9H6TcqvHQaFKv2uQ9CUJCTEULcj955ssz+YYuwbVZotUdoJoIkxQE5jK\nT/Pg8Sb1Wovd/X2CYoT56QQXLi5Tax4xGPQol0eocQufpNDsDAiEFBJphaFtI3sS/WGbl+68xv7O\nAebAQ1VVuu02sViMZ8+3iMQTSFKAwuEJCS2MpPrY2t8i4UZRBIfhaEwm5kMLiZSGHlsbW7zz6ssM\nWxUWzmXIL6dRBRu/KjMciURCYfTukHa5h+jZuGOZzqBNrzNAUj3GlkkoojDUTXx+hUqpSiYdol7r\n0u13CQST9MY6jqgg+SRkv4jt2SA6JGIBGvU+mewy25s7JDMRnh8ekA7HqJwWmQnncHtD/FkRNapQ\n7xkUiz1ESyamgSgOkNQA/a6B3tDJZ7KIwTSPnm4TCSfp9HS65oDmuI9lWORzc7ieQLlZQAv6ERAw\nLY9uu0uv0UJGAUFAVR1ka8CUP0yrWeV3bs2i+iyazxv843ei+JNQflzhly/Aq7eXef7BM948n+TG\n1SW+/OQp70xHIRbjyfsPefvryzQtm5PNQ958c5qaG8GsFHj57Wn0jkJjf5fzsyG+3GijuR3m11f4\n/kcvuDkxIr5whZ/84DlvzccZ5dLsvSjz9jfXeV5tESodsXBhii+3iyQHLlPX4vz0vW2Wkzqpc3N8\n/skzrr1ynZaQ4OTRC16+scTj0xpmocztt27y/nubvL4oYKRyfP6XG/zC165wOBaonjT42rde4U/e\nu8flGCysrfNXP9zg1ksLFGs63WaLKc+h3JCI5hO0R2U2do9ZnZnn/PwaXz7aJh2Jc+ftmzRNh4m5\nS4yGI6rNDvHZeVKZNL1ym5ULORgNmF/Mk12eJZTxYTd7+Ntd5J1dPvrxjwhFwlxeP0ezVyOi5Tjd\n7+K0e5w/P09+6jLdFmxv7xCJpPjxD/+KpZUZOj2dyskeE4kkc9Oz7O8dEwlHWFmdw7ZGbDwp8vjR\nHsMh2KafR492KZdbOLhUGjUePttCiSWxbAMcmWcv9mkNhniSjGiLjIdjTMMmFkmSzWaQFZNf+d5b\nPHmwSbur49OS1NtdZhYytNs9HA9UzU+1VsMwxiwvLvJX73/wM4EcIqsK+miAJwgkUml6/QGF0zK2\n7RAOBPErCul0Eg+XRquOFlQxRw4YHucmJmgPezjeiN/8ztd4Kxzgva+2+GqvjKhGuLaawR/w44vF\nuHD1Eo/vP8A2TVzP4fWXLxKLeHR1h/uPniEE/GQzExQPisxNL/HVF5u09SGWaBIPqBhDHV0fMTAs\njgolmr0+vb7BaDCgXKqSmZjg3HyO1169hiTJPH2ygTO2uHDhAo+fPuTZ402+/2ePkSSVRCIM4zFO\nx6Q37FOqdDjt9FhZXeTTD+6Tn57EME1Wb1/gF96+SbVU4p03X2Zn4wGfPd1h/kqWXFjhb7/5Og83\ntui2Wvwv/+V/wp//+Es2ikUiuTSlcovt51tUCkVisQSHRxUUf4hqpwdKgHavj6738csyju0yHhvY\njoUoK7iuhzE0EPFIJhK4locqi/R7Opqm4fysG3Y0GiIIHuOxSSQcRh8M8QAb92w6lQRESUIURVwB\nEIUzRJcinFXruTDsG5gjC9t28WkSiiogSC69bg+fTyYSDhKNaIyNEY7tYZk2kZCG6wo/y2qKmKaB\n5ZxNxrIi4lguqixjji1EQeIf/c4/+fkE89//+3+B6lMJaBF63R5ziylESQTDh2BblJoFgjGBqewk\nzx5WCShxpnJBZMXHs6fbhMIWUb+GgoMWsMnP59krlpnNzzCZzGFZApVananZLMlkEsNpMJGeoNnu\nMtRHCK5Ita4TUhLsF4tofoXl+VnK5TrnV5Yp1yvYlsugLnH1yjzzi2s02iOqlT6NVp3yqU6h2MYx\nhvSHNp464NmjXeIx52c/SUbXdS5cvIDjjTnY38dzxywsT9NzRqxfXuHhV1tML02jBBSG4wH9gcN0\nNM1ANHn2YJ9gYJJxtc5UMsJRY8zO0zLpWIjTww6OOSQaSCCLZw0lS2uz+II+XBx8PplGXScQCjE5\nGcccKExm/UiiDy0QZr9QIZT0U232iCYCpJIRXFvAsz3isRSOpVAt1en1h7gOTKXSDIZ9/D4/juji\nqjK1ehctGEQLCcSTKrZjUSkPCYaz7J2coqkqyXga3XSR/TLRoIY7GCHaHlO5LPl8FhkZDO+shCEe\nwq/E6DbbaKrETG6aoBogFNVwPAH8Agg6IOHz+Qh0q9xYXWTjWYFwv8a3v3mF4802IdPg9Vcv8OnD\nbRa1ANmlNHd3e8yaXaaWcnz05QmL05OkFlZ5/5M9bq6cIzqf4/5fPefGfIK+L8KXHzzl9tUJTnvw\n5H6Jb7xxjmcVHa054MbtS3yxc8BizOPC7QU+/vSIlWgPwkkOHx5zc9FPX4ny6P4G79yaojFSGB4X\neOW1VT57csqFmMjUhSW++PKYmysCJJLc/fgZb62obDclalubXHlrjd/75ISFxAyhpRnu/vgLvn57\nBd0MUDsp8rWvvcNPtk85bFQ5MnrEXY9fOL/EUJX54PMvWc7Nsn/cotcfkFk6z9HhCSO5Q6nzjOnp\nOIfFQ6bPX6N9WiAqwFn19BjZ9TifnaBfPkY9btLfOmY81Kl98QzVGpG7eo7Hjx5hGTq/8p1fZPew\nxv/9B3/O+XSc6ysLHJZ6LC4uEYsGcTybqWwG2/YY6CYjx6FYajOZWmSse3SbXcrFCs3WgGKpxXBo\nI0kqoiiSSKTp9nWmslPo/QHBUBB90EfxRNyBjuY/e+hFw0FcxyWRjCDJPur1GoPRiHAkiV9QaDa7\n6LpLtVbG53PBdggFo4hItJpNjMEQyzDZ2nyB67i4LkiywsgYoqoyeFApV7h16watVoPxaIgiCMiy\niD7okUzGmUonCaoqr7z6GgcHB3iuRyY/w7NnL9Akjf2dfRYvXeXRfpVi5YRsMsb9Jxu4okIwGMQw\nxuRyE+gtnYE5ZPHcCh9+8Jju0GD/2TGPHj/l9LTBwf4Bk7kM1XoXf0ghn53l6KiEIIlIig9/UGUy\nl6HbG9A3xliiwPWXLjMeD9GCMu9+/0NOTpq89dbLGGaHTA5ccUyl0SCRynJ4UiMSTdGqnZ1NDE8g\nM5HnxvwMN1fO0ej2aLgGVy6vkdcCaLbF7uERa5dWwfUDKm9fXGRBC3ArP40aCvKv//zH1Ps6izMT\nvPHSVZanJ7hx7SqZ7Bz3H21y+9ZtfJrGi+0jRpaNqmm4ns3IGGKNrf+wirVsG8cDx/UIBYNEwkFG\nQ4NYPEKn0yUWi2KMTEbDEeFwgHA4hCgKBANBer0+giji8v/yKyUkUUBSzm6XlmPheSCIDqFAEM/y\nGA0sDMPG5/chqwqIHqJw1iSkyD6i0Si9XhvPE5AkDcd2UBQJx/bOKDuKSjwRZ6gPECWPZDzFqD8G\n10OSRPyaxu/8w//m5xPMwIaGGwAAIABJREFU//MP/ycS6QRje0Sr1WRpeZpEMsqgZ4F1Fjz1+0c4\nVgjPTSMKIqGwxmmhSDQSQGHIzasX2N/dB9VC9KtMTs9QLjU5PqyRiGVYWl7jwaOHBEMxxj0Tw1JY\nW7lIMBSkWi5zafUCTb3DaanERC4HpodpWSQiAdLJNMsXF1BTST7+9CFP7z3Ddi20RIR8LkerWmFq\nOk0qGeL81WlsV6VRajLqW7SaYIljRNGj1ahyfmUJ0xoyN5MiPxvn/PosEU1m7cpFTov7yFKQjb0i\nVy6uEYo61FsVov4IcSGIqLWIZVNcuXKdQdujVdN59eXXqJRPCfgUJMUjEg9xeFKm3eljWh6OY6L3\nHQKBEKLkYo1EVteyVMpDPFFE9QdRfQ6qrDCR0JhKZTB7JrubFfzBAM+e7FCvdEGQ8EfCDFoNps/N\n06xXScylKTZqZFOz9HsN0skgrmNg44ES5sX2PulkknQ8gyvJ3Hv6jKNCifPLq2TCMbrNDsFwgGLl\nhGQwTqdSRJZEZFVk2LbxSzLxSAxci0BEwfFERFnAdhQkn4ms+lH8EpVimZRg48VCjMcuV2YiuMFJ\nNjf2ef3tizytDXGP21y4EOPESvHs46e8dmuWT0oN5M6IW6/c4f7WKamezpWvLfPTj7e4kpnCn1/m\no7u73JgPkLw4z198/wVvnAuiLS3z2Ucb3FmK0/NHqW7tcePKeZ4XRojNbc7dXOGHH5xwKecjeGGd\nv/zgMZcnVaRIgodfFLizLlN1Q9QfV7jzxg0+elZGHjRYfvUlPvzwCfNyl+jCEn/6o01++fVrFIhT\neL7Bd7/3Nu+9/wkXoyq3X3+Fv3jvHuvXL2C6GntHRf7Td97m0d4Bbq9BeuUi+dQ0m+UTerZIV6ri\nRFxsf4iEJCMmNPYfHrKyvsQwrFEon+BWKhidIYGkj29949uY1gjPsMgvz9G0xnz8p9/HKJ+Qy8S5\nffsVHn71kEa9ycNPnpBIz7Hx5AkpxyUfChGfTnJcPmVpcYEv7z9ke+sQ15GpVy06nSGeJ1Ku1Gi0\n24wdD1nRME2Xi5fW2Xi6yXBoMNINDMPipTs3GZk6y+urdIwh07Oz4HmsXphD0gSCsTD6sI85NlhY\nnEHXh0xkE8QTcTY2djjaqXJ0WKJabzI/N4NtmnjOmWGj3+0jSxIBzYdj2yAJCIJAPJ7AMm0SiRi/\n9h99D1lSCGoBMukkw0GX5cVFZvJ5unqXeDyGT5bJhKNcWV3j//rDd6l32rg/4yM6rkOhOyKysMbW\n0S4bu4dYY4tvfuPrfPjJA9ZWL7H1Ygt9YOL3BRBNgUa7RyIUpFjr8pv/xd+n3G7TbNVxPAlZ8VMo\nnOLYzpkJb2BhWQ5aUGU01rHsHhcvzzMe92lXB3iOS6vZpFxpE01INJodFpdn2Nnfo9MeUC6XEByB\nfC5P8bTGhcuTBKI6MhojqcnXrl3l0cYm6zMZVlIJ6jWTp9vbJKJRBq7N1deuE0hG6VaKbO6UUIMC\njw8PWc6mWQyG+Je/++/IXF1jIiIwPZniD//0R7Q9j6NKmUGrQlSB/UKBo1KFvj5CkGT6wz6GMcQT\nPFzbxfM8BFnANG1cxwVPIJNNIXgeva6O61r0hyMQPGRZRhQFZFFkNByiyAqW7SBJCmPTRhBEZPFs\nsrRsE9MyESQQBQFRFPD7VCRBwHNgNBjj11QUv8LQGKD4BHx+hWBAw3Fser0+juUgihLXr79EuVxi\nOByTSqXo9wcIosB0forlxUUq5Qo+xY8sSQz0IT717EH2T/7xf/fzCeb/+q/+GaNhm1AihOcpGMMm\njjtEVUNY1oCZXBrZFtDNEfmFCcoFnRfPTgmGVLyxzeXzKxztFVCUANHJMBvb++ztFfCLARKxSZAF\nGu0uzVaXzSfblKs6yViSTrNOuXZCYiKBIHmUG3XWLqwwdky0gEa32aFTayP6bO5/+ZBsPEAsnGHz\n8AjPErl95TLPNp/jBPzInoCMzO5+h62tIqbuEApoJKcDyGEVxxhzdf08ru2STEbwEAiEowQkkXx2\nir7bIxGPI6pjtJDK/uYh2JMkpDgd2WJ1aY57W/vUan2OtgbUT6pMRH3c/fwrsvk4x6clZhfSCKIf\nX8BPo9MhmYpiWTY+v0Sp0kZVZbptA1HusbNTYWCYVBttAgGRaCyIorkYY5dSqUluaoFOs89A90hk\nkkiKSq/bYm52Bsse0em2QFbRQj4Oj+v4gyrVaouZbJ5WvYvrSjieiOB5lGptitUO+lBgaX4WjBG2\nriNyhvw6Oj6mfmLyja+/gRaK0Or3UIhSLtQwjBHJZIjdgy0EQaJcqqNFEjRbZfD7iPqCiEqAc8ko\ndsihMPLIBYPslseMRhGy1nPCC/M8uPeC80sR/Pk5fvLxE752eYlacJLKToU7L2dp6x6trU0uz/s4\nGcjopRGrb7zG58UC7t4hl67M8MWRRZ4h8y+/zIdfbDAbUcneuMqjh1tcX4rQUTWef3nCSy/P8dWp\ngNsfc/HWOj89aBBsdfnW27f4bLPBQmDMRD7FRx8ccWc2yTgp8flf3ufrr1zj4GRAtQNvvP01/u1H\njzifMLi4tsYXdx/w8q08FVNid/uYm68t8MFX+yiywtL5Jb746X2+/tbrDJH58KtD1Gs3+bOPP6Mj\nWUwtziKpLqOGidR38fw2uXiCyelJptIJMq6NXxTol3UEW6F4dAz6GH8oSLDX58FPP+P9B4/J5JPk\nZJlIMM4P/vAHhBNhclMThOJpHu08Q3E9JMZMphMEZuNUu006usP23gG379xka6tEJBqmVm9g27C6\neon9gxPa3S6pTBrHhUg4jd7t45fPiC3heIh6vUqpVCYWTSK7LqtzeS4uz/PV51+SisRIRyLUCiUm\n01O0W01SiRj2eMT6+QWuXj5HdibP2DJ49dVXOT4+ptXpMxiY6KMRI+PsVGFYJlokRN8cEYnFkGQV\nUZIYDnS2tp7j92loWogv7t1Dlj0UUcI0jTNX7VCn3WkjOg7uyKLRHzOzuIDp9InHEwS0KEpAptLp\nMOwa1Npt/DIEYiGW52d4sbtFKOTnaPeQYDhAs92m3e5ydHhMLj/B/a/usbm5jQ1kJtPMLS1QbtTI\nZNJIgsbx8TGm7Z4xOkWYmMihqh7ZiTSHh1U8JIKBCPmZDLdvXSKZiNPrm5ieTqtTo9sxcY0wPn/s\nDMSg+ImGXeqnLc4vz8DAI6jGScYDjHSLP3r/M8YKFFtNOsUqj+9+xnw2QUASCfsjnFabYEhcXFnA\n6vVZvriKYbkUujWOSyVkyWNpOku72QFR46TcAkUlPTlBJBpFH+goishw2MdzPSRFwXYcbNvB71OJ\nhmMIAgz0HsZQR0IkEo2gD4YoioSAiGkYiAJoPh96f4gWDDMcjkgmU/Q7PUzDRBAFZFlCkUVkCWTp\nTGQRBBzbRlEUTNNEEEGQBEzTRRTOkgi242GNHRzHOqv88SRKlTK3bl+l12vRbvbOhE8SSacTxKJR\nTg4Pz/imQwMPD08QsC2Xf/ZPf07B3Nh5F2SLXmdMtzFmZiGIPyARi0bQOwbDtkEuu4AUktk92WRn\nq8uNy7cYDnUCWpBy4ZCRqSL7onSGbXz+EEEtgGFY7O6ViYSTWM6IWDSDYbj84ndvcrjznNmVWVKJ\nMPmpSTb3TwkpMSYnkvQGbbK5KTqNCrOzefJTGWS/yEH5CCURJBFJcu3SIiuLE4TjErdvXcazRqQi\nCg4SniSjt7v853//G6xfidE1xkSDAQa9Ht2+jqZEqFUaHB6VaFWaMB6TyCTp1NvkMglazQ7PN4tk\n41GKzSZH1Rq9Ro1rd1aYzgYRQxG2Ng7o1tt4sktuapJWX8fn93FwUkEfGeCdHaBjiQiyqCCLCqbj\nEpRklhfTVBsjPNvFpyjcfuUC6YkweH5K5TrHhz30QZ9mq0c0Hsb2bNqdOvFoFPwyPttjdnGOysEx\nuckcY8nAdRVC/iCHWw00SSMTDyBiIolBGvUui0uLZLJZfKqKJouIkkIgrFFoVkikIwT9AYy+TjgR\nRTcH9Fo6qVQcWXbx+xX8agDLdfEHQ4wHNiPLxPEURFvAk3yonoflFym7EqIvwPP9CgPXh9I/YuHi\nPD991GV64v+h7L1iJbmzNL9fuMxIb6/L6/2tqlu+iqaKRTabw3ac6VlNz0A72FmnkSBBAqQXaTVa\nQLvQ02IhPQ0W0j7sQBpIY3aMdsidZnfTdJNNFlksljfX1HWZ96b3LiLDhx4uoRcZoJ/iLYCMfyBP\nnHO+7/vJrKxO8cnTAm6pS3JqjqNikctrEur4FF/d3eXaXAJnLMLf/PU9Xl+NI+dmePrpc751dRIz\nucjezgu+9YNvc/+wQTqYYPn6G3zyy6+Z0ipkzub46/eOuTQv4c0vcefT+7x1ZYZuJEvh2QG3Ls9R\n0W28WoPF1+b4/EmdFVEjdjnBT356yLXlEEpug8+/3uP6tXksP0rt+JDv/vpL/PJpEzyNuTd/jfd/\n+hmrr16hLUX5i3d/zsS3vs3Xz/b5etAk+PI1PvjJV8jZCFJApVFu0C838bwRGxNL9MwBk+k0jmGi\n2QIPvnyO3ykxNZEjvz3g7R99l3u729y8cYm4FGZqIsHuiwKOY6B1hgxaGpOxOCPfojvs03V8rJBH\nq91hbm0Bx/OZvzjLUbXBTqHEQeGQheV13v/ZpwTUCOVqEwGXmbl5dLuP7RkEAiLZsQzlcpn8YR7H\n8VBVlVQyjSc4xOIqSB61bpt2u83l85sUiycclfJcuHiZerNFMd9EN11E1yaZjLG0OoE17GFZQwon\nbebnkywtZTg+PkYf2cwsLDDQdTzBRkDEcVxM20AQBSzbAh/iahhZFhkMB/R6A+r1GpcubWJbI9KJ\nBINen5devkqv38M0bc6f32Rn95Bqu4c+MonEQpSPCyTDITzLIjc3QziSoKv1+a3feYdOt08+f0Q2\nE2U47OC5EItHaNY7SAGB1994nWrlBEkKMDJsLMuk1+nS63Q5s7FBvV5ncipDsz1gfDzDyNBQFJV0\nKsP28wL9rkG7NQBJQNc1wjIU80V8QWZxZYq52Wl0rY/nCSTTUZSIwHGhTVAVmJ4ZZ3ohhiBHee9v\n77NdqqHZNj++84xREEb6iHe+9x06tSY2Lt99601K+SLL59fZ398nGMuS3z8kMp7kiydb/P2/8x8Q\nNzz6owHv3dkmf1LFHJqc9OtMTk6jmzqyGCCRiLK/f8hQ07Bdk0AwgiiA5zhEwiJBOcrCwiLH+WMk\n0QVXQPBFRFnB8T0CSgDLMgkEAvieSDAUBdFH0wzisSTaUCOVShJQFTzfOb2Kp8lAgi/gmg4CAp7j\nYegGnguu6xIIBvFFD1n2CakBwuHwaVENBBjpNq7rMjs7zdr6CqoYoHhcwnUhmY4xMgbs7u4SCYVw\nndMOWRREREEgGovyT/6bX3Ek+6d//T/R7nTod3vEkwqJrE0wFKXVa+DoIvbAwdIE9o72iOfCyKEE\ne1sVJF+mUu3S0Sz6hojhghK0mJoYJxKN0mwbRGOTTEzOcfv2HdoNjW67w9zUPDv5E4729/BlB93W\nyKXTOJhoXYPtg6fYwwrxZJi1xRnKJwUyk1kiYxnquye4DY1oIssvHt+luFfAbfWYmw+RzcaZWEjh\nIqI1qmQSQerdGpW6RrfWJhpWafdsys0qtmuxMjfL8vIcjmiSiAYJBOHew11WFpYJhyUmkiky09nT\nbs3yiSgyheNjNF0gv98iGg4gBALo5oCRKdMftonFk9i+SDSi4oguoYBMtVYjnk3RaYxQsBECBrIS\nwpccBCREaYiha8xPL9Ao9cmmo4geTE1kCcgeqiIykY0jqSKWZ1FvNKh2eiRjKcJSiE6rRjyYxNYs\nLNNiZW2WqYkxPDuIZbjEg2ESagrPsyhXqyhBCceGaq3CWC5H+bjGQm6S3a0jwqFTA3mp28b1RoRC\nITzHx7V9bMslGolQqpVZnl9EN0ZowwHx8QSH1QoBWaVtOrQ6BmcW50hmJDqFARfOzXCgO4i9IWvZ\nIE07yNZ2hd9+5yUOy20Us87yhSU++7LDhNNmcWOJD35Z4Fy8w+zSHJ/ePmJ92mfu7Do/+eQhr80m\n6SfjPH70nFuX5qk2mpT39rh58zzPmwPips2r37/FBz9/ysWox/VrZ/j0bpENtUlkKslXn+3xyvkJ\nisM0e9sPeeXlDbbzEnqrza0fvsPf/vwhgSmfpSuv8dEnD5n53puUrTB/9ounrN14nYcHJ3yy1+bi\nazf403dv45+9yGwqzZ3CDvOXLlBpthgP2sxNjtNpWbgBmbWZGY63Cly7dpVyvUg4FiIznkYaadR2\n2/TyDS5tniU7L/GP/+Pfwrc6qIZDXFFpmg754wKz8xlu3TiLGZQp9W1kJUwsm2BiJkck5DAcllBE\nKDfr2LJAJJ6h2RnhOKfirdFwhICPY8iMT45TqtRpdVt4vszE+DTHhRK27bJx5gyaoTM1PcFB4YRO\nr8X8xgJKLMj1Sxe4de0Kh8cnlMoNSuU6IwNSiSQn1crpaNU9Df2oV6rsvWgwM5HGHI0Y6jrxeJrV\nlXVqlQaO6xCJRUmlMpy/uM7M1DzZVJJ2o/sNgNhidnYafehgGCNEQeSkWCQSjXB0VEARggiCTKPR\nxnbh5KR8qgaPxnENnWwqhaxEqFZqpLIxqrUatmXz9s1LOCOXtmZhDtqE1NN32wV8T0KWfUTFxbUN\nEES6PZt6swNANBJmdWWObq/G5FQKzz9leWazWRKJJK6r0GxViMcTtJodbNPF59QjeOXKJgcHB2jG\nkGq1wdLSDJJkc+36eSrVEp99sI8YFtlYW2c+4HJ2OkcwOWAlO8Xi7CKy77K8sEjb1ljfXKVYbxAy\nXVLjSaajWfzOkONans31ZVrNDoXeCGVqnnZD48MPf0Z70CeUzLI0O0dvNEBMZhmfzkFIwuz1CAYC\nmNaIbrcLQCwZR43FufXyNd584yZPH22zsjJPo1HBdQUWl2bod4d4rk9/OESWZYZ9jXBYZXZmju+8\n/X0ePHhyCnSWREJBFduyWF1apFIugeDh+O7pTlMUED3wHA/L9pBFmUBAPVXBmhaIAqblElAVBOE0\nnMPzfPqDEbZ1OgZ2PIetZy84OSmeElQ8AcuxsSwDz/OQpNNwBMfzcX04xXcJ/NP/7lcU/fzrP/oD\nIgEZz5aIx1VmFyYQJZvRwKHbMFibX6FeP0GJShgyOLaA1tCIRmQsScANOkiBGCfFE155eRPPthmN\nDEwTJCnM3l6eQCCEpptoQxNFNpidneLNN1+j2++iDSw8V+XJkwKb66uoqkgioRIPixSO9kiNT1Mq\nlREDAeo1my/uvKBSarI5FSebSBCKTlOr6si2jeH0cLCxehZzk1PMTOQgKDIzO4WoQDITRRBdrl1Z\n4JXL50DxEFSRrYePEJQAI9vipNCiVh8xOzVFIb/N6mwS2zO4fmGRjibx9Zd7SIJDKBJCs13mFqfw\nZYlsJoPj+ui6RiQugDtADSg4gkA6k8DSNbKJCOnxCFPZSRxXxnU9xrIhoiGV6nEdGQscm5AsEFYF\nQCAQEJlfmKHX6eAL4EcixKJJbMOm2WqxkJpCQaLb7rJ5YZlgSKTZ6nKwf4xAgOncHMGIz1AfYrgy\ney+OKB81GE/HIOCSSaTwDZdyoczc5BimpeNGFNRgkGQiQ6sxYGJsGiUgUWuUyEzFSYXi9IZd8G2U\nEOi+iEQIU/eZVBXGAxoRVWKvLbA+JpOcS/DVx894aS1JMD1LoVjkzXMWhjDGizsvuLwK1U6c3Sdb\nvHw1R95KMCyVefXmJR6WB8QcjYXLU9x+VGJJdYgvrPHVR4/YnOiTno3z4ad7vDyfRpxO8eB2gVs3\nNik4UXrb+1x97QpPmg6NwhbrN6/z8f026twMkxtX+eNPvmbi6qvkLl3jj//2NtbZS3jAB9t5zn/3\nh/zi3lNud2Hmwnk+/PmniGMZxnKTbOePeOnmFQYtjUapyNVLF5jM6tSKZaZnLiP1G5R7bYxmFcXt\nMrOa4ty1FRyzjhnUaPZ7rI4vUyvW+dnHRyzMLrJwMUZYaeMNtkFSGHYNUmqYYqlIZjZLZiKN22+w\nSJbc+CKaInCwl0dvFhnLhvHpgwOX1i6xV6rz9aMtAoEIqXQGczSi2+og+jA2meGoUOC733ubkaaz\nsb5OIhKj1xkwPj5Gvz9ACShE4xFkGRaXFnnru29xfPwCwbTwdZfR0KJe737DWhXITo1TqVfoDoY4\nlkQklGKo+QwHBkbPQZUk6vkTkrEMjWaHUqXM5asXOTk+YSyV5aXL57l6aYPfeufXebH9gpOTGqPR\nKWj4rbdusbOzh23biJKMpo/wPTBGBq12C0UNEo6F0UYOY5PjNEplVpeXCYdD7B0cE0lmqDXaRMMp\nXuzlaVdbNLt9NEsgkQgjCAFcTyYcTXKwX2J+YZp0KoVtWZw9e4GHD7eYmouyvDKJKIy4sLnBwf4R\nli0QjkTJJhNYpk3h+ATfc+m0OywuzVOtNrFtCwQRSVTQtB6pdIpoQsV2BbrtHq2Gxkm+w85uk9zM\nAqY74Mqlc1hdHVEIojtNkmoERU0yNZ7h/tOHVFpd1KhKVokSiUUpbL/gy+e75MamWJycZC2SZNTK\n46kS20/3mElk+PHdZyQvLPN4q0xI8YiPZSkWT/jea9eZD8gAFAonDHp9FFnF8WBiapphT+Mov0cq\nFScaVjjOFxl0TTzBJJ1OoGsWw+EIWZEJR8KnMGjXZTRyeP58B9uxsR0H3/MYDoYoisKVK5fZ2XqO\n7Z4WS0E6BVAHlCC+62PZPqbh4ns+juNiOx6SKBIKBfE8l0BAQRREPMf/hpmpIHDKjj53Zh1ZFBkO\nDQRJxvNdPM//5l4+nu8jiNI3oQoelmX/6iPZH7/3b1AFke+8dRN9NCSRdTG7Q8wOZFIp1GCE41oJ\nPwbBcBCnoxNyBTLTITLzUdp1mXjc47/6z3/AoF+h0TKxDYlep8vMdJagqiAHolSrLbSRQzQcpVdt\nc+/uV8SCAWLhAJML04iCz9azLZq1MhcuXCKtpJjITZHPV5lfWGQ8kUDDJj4dYXMxSSYdo28ZDHpd\n5ifT4ISJxMeQkbn1navc3dlGjpu0exqubZOMxQmHXWZmAjQrNTxXZ6h36Q2aXLi8Qb11TKujU61I\n3Hr7Vb744DZv/9otXuztkMuOUcxXCURUPALYQ4tgxGdhfZx6RSOXW2F/54iIKrK6kmFjI01szGcq\nGuPM0jqPD/PUyl1s3ebq5Q1G3ToiUGkMOT7SuPnKy7TqQxzbpdMeEpQDGLbFWG6CvjaiWCsxlR4n\njExmPIvsWtiugSU6zMeSKIrK1/deYHkaE1PjHBwWiSeSpCYzHHX61Ow+Pj6ipzIzFmM6HmJxIkA0\nFaRY7OAYPhsXV0kFw8ykojQGPTxbpNnoMtIdGq0OoZiCK+r4jszJcZNYIkUsEsUPh5gii6iqPDmq\nsphMYmU8sullPvvgKet2k/H5IF/dbbEa0JlZP8O//+Uul6UR8YUL/M3nu7wxKxE68xI//uA+b748\nS29skhf3jrhxZQ47HKa6dcLVzUleWB4vPnzG9e/e4Iune2Q6ZdZvnOODpyfkDJlXf+0iH315iB5I\nsnRtk7/5+R2ks5s4iTH+8K8+J/bSLfJ+mD+9u8OZ736fr48HVGoaV966xZ/89HPSoTTL62f4d/e+\nJjs/w0xqkrt3P2Xt2g3azRbNSo3Ll2aYDAV4st8mvbDO7Xff58LNy6xPdlicirO5dJ73/uKn5Bbn\nefudi8SyArIxJBnyiEdlMokxbr35I+qFAfXKAFswuXBzjck5n/yLGsFoCjUZpNwoUemaqIkYpuFS\nKnWxy12+NT/PcirMzLkp+pLDyfGATndAMjvBwtwy9+8+JJGbo9XRyGYmcFyDSqVAIhmn2+vSbFlU\nKx1CqsTSwjyjfodk9NQU7tou2mBAp9NDDYb59huv0KuViUoey9M59ra3WF5Y4uNP77K0Mo9j21y5\neBZ3ZLK0PMnv/0dvoMgjTo5PuHL2Cs12E8IynX6bcCzB460DDos1zl3c5NyFJZpak7e/9xa9xgnr\n09N89MEnFI6LNNpNLMuh3e6y+2IfJRj4xtju4zjOqUhmZgnECOFIGNcbMdJ7/MN/9NtcXz3Dk4ND\nWqMh6ysLeI5Pq9Pn9VevkooITM7n+OXt++TzJTxHZzw+TVgQSQZjhIMSY+kU3Z7OO+98n/zREYf5\nPJsXJwkoMr/+vd/g6cNtypUG8wvzxJNhug2Ldq+HEvBJxQLEgkFCIZV4IkO1Xsf3fV65fh0Pn1q9\nzsgwGZsKMDUxwxe3n9Pvedx4c5lb37lA8eQF7UaNmY0c++UG+f0+oWQay7c4KFa4+7iEbXu0G12O\njkusbK7wzjvvsL//nIPDAw4HdS6eO8t8IMzLV67x4EWeg8oQzRoQGY/htdvs9FtYQ4O/9w9+k+07\nD/jZnYdUWjq+D62uhhMKE52cZn5tg3BYJTsxgee5SKKMKDqEVZV+b4huDBEF8bQYeQ6RSJiRZaME\nVUzbwnPdb4LaPcyRiSzLIMCDR08IRsL4go8UkPAETs9UAMt2sRHxfQ/X8fBdH0USETyQJBlRPkWA\n6doIidP9p+d5ZNMpxjJJqqUSju/S6Q0REAiqQc5vnqdWqxEMBlFVFduyAf80ws+Hf/7P/p+l8f8f\n7/XhH5JNCdhmHyXsY1k+3ZaOMRQwbB9XtLD8HolslkEnwN6jIcsLaQzPoljR+I3feJmwOqRXq2MZ\nIAfCuLZLMhnDcHtUWqVTmrfo0e8PGXY1otE0gqdQq/WQ1QiyKOB6I1zTwR34XNo4T6FeZtAdMp2Z\noDs02N7OI7kisuswnZ4mMJZCUATGxmNIUZf6sEkgIWEPdMov6swupBEHUeLSGIYF+UqfYjWP7MmM\nZ2ZodHTCgQipRJx7T++zsbbCfG6aV1/dZGQaaN0Bj54VSGUzbD3bQVYgGIpS7dSQBJnl9TSJrEKn\nZfH13eesLc1w6eKMyMnbAAAgAElEQVQCQihE8egY2RMRYnHy5RaC6ePJITwDFGzikTABReH4ZMD0\nVJp7Xz1CVgSaLQ1flqi3DDwpQGvQoTvQCYQlBMFnWKmyloqyMTuB6NmMh+L0W1V6A5NoepqeNqRS\nbzA3O0OvNyQQihFLxJjIZjg+OkH22ry6ucig1kWxPEIBle7AZThyqAzqjLoGcTVEz3LQTQ/H8Egk\n00xOTmDaA5ZWZulYDmJURcIjElLo9gfYpQbTk1FUD0pNjZgsILkDQpsr9B8f8drVeU6UKUbVBhcu\nzvGkJZDst9m4lOFeYcC4ZbF6LsbdnRYTdofLN87y1d1DpiSTqYtneP8XW9xcDKFl5/j5xwXePJ9E\nS87xxdcFzl+7TttI8O5xCb5zg3snPf76cZnclQt8vn3C83qf61dv8ZMvHlATFc5eucSzOzusrC9h\nBoK0SgUuX7nCfmNAqXxM8vJ5nj95QavWJDozwZfv/4JcIoMSDvPw0zus3bzK5z+9j284XHv5PM8e\n3sEcDskfnSC4AYaDFoFwn42VJKLdxjSatEs6mi6SjExwLrtKr1RGSdm0vGN++NuXCDkemfQYze6A\nWGSafs9kZT5Ju+3yyccPmE2mMW2B7/3WGZRggIrkEAxbzGZjXLy+jhB1SadDtLp18u0ae4UKkiRh\nGAOMkU2/p3H5yiWmpueYnZ3BdYdo2ghj1Ecb6tQqNdRQiEajgWXZpNIpZFmhUa8RDQWYHBvn2eNd\n9JFLJBHh2pVrRCIRNs5t8pMP3gfFJ5MM0RqN2Fg9hzl02K3nWV2fZSozyZOtAyrtAVdeusQP3r6F\n79jsH1SYio/x1itX2Fye5PDggEAkTOG4QbOtAR6u6+G64LoOogDRSBRZkslNjjM2Mc72zjYjTaPX\nHZKJZ9l9ekgsLJNvdvBkHwWPkBqh3mjT61bJJFJ0Bxbgkc7Gqdf7HBydcHRS5LhWozPoUao2sB2T\nu1/fZ2trBwDLNhBFiVq1gWl7jEYO0XiCZqMFPtguLC/N0q5VWZwf47hQplpvM9QNJEmg1Wqi6yOW\nl6eJxlUajT5bz4uMTaRZWJ6h06sy6DeAEfF4BmtUJptOEgpn0UcakWCUp7sl4uEEfc0gk8kSikp4\ngkC1VCEYCBCOZbAEgdeX17lz5yk/efaQtTMrPNs9wlAkGOgooShnVpbJRlQMd0TP9lGQiafHiUej\niIJPIhslNZXGcyX0gY5p6HiuQbNSxdBHmKaBaTtkx+LEEjEMU0eSwHEdNN0EBKZzU8RiEWLhMPpw\nyPj4BGtra5yUikxMTmLZDrZjnyYG+T6KJOE5Pp7rIckysigiCYAHruvj+z6yIiMHA1i2jWN7KJx2\nkIIkYFo2tm3geC4DfYTr+oiSgG07DAYDbMdGABzbwfM8fN9DkkREAf77/5cOU/z/KpYAougzNzeJ\nj06v7XF4UMYVLWI5idnNGGLQwdJMWsc9Htyp8M7f+Q+ZW12lXOwxnlF58uwjDL1Bq9dHG/jInsLG\nxjpSKEw0nWH97AWQFM5dWCU3k+Clm0ssnZ/lWf6E47rJ3s6A+58eE9dSLK6fwQkm+Kf/8s+I+Fka\njS7h1BjpRJxzt9aIz8b57JdbvPfje8hWjP2tA8y+gWhJ+B0d3xhSazf5+vELBDFJ6bjLQO+htVv8\n4PWXeOPVNygN2zyq7RHNZjE0i3gszfUrNzjZrzNsDijuPmImk+Tlb51h8ozAdv4x02tTLCyd4+7X\n+whulIFu8uVne9z58ghFCaMNTQ4PD9B1nfWzK8jZMabm1khNZIhOyviixtL6JB4SSwurRGJj6AYE\nZJ/RYIQsu1TLHTyCDEcmQ2zEuEytpdNsdhBchb42YmVjlouzY2yEA5yPZFgJjSELITRd56R6hKAG\n6enwyef3iSRkInGRiWCQ2ZDEy5c3mB8fJ2A56LrD5NgyNy++geyqDBwbyYvQGFo8qjawPAXXdgBo\nNZv0un1UKcywrZNwZbKKQkwQ6fY7SMEg4piKpapMZGLMLcyx9+kzbgZVbkQj7BzbuFWNzaVxqr0Q\nWrHOucVJ7h20CNU1Vjbm+PlBG8GG8Qsr3C5oZJM5grOLPNKiRJLXGMTm+aM7bazF8+zEVP6H/RbK\n9bN8aAv8xe6A+I1rfF7V+LOHQ3KvvkW71Ge/2GDz2jW28lW2PIOZb19AGgz40fplFlaWePLx1/z6\nG79GudXif/43f8TCxSXuPLrPsd5hYXmGw61drr36Gi4BfM3kOzdeJexE+Ohf/4QbN95k68EjzOOv\n+dFvneGoVqTvxLh37zmtzgFXzuRwmzvEJRuj7uKKUYYji42zF/gi/4T7Dz6lf1hgIjvPu+9+wqOd\nZ3zxl3cZtH1O9ArZaJAP3n1Evn6MGRhy3B8RTIrUB21a/SoIHn3fYrewS2n/CTEpROWkTEQJIiFz\n/uxZlDBU6zVK5QqG4fDeu59QPKkzPT2NLEUZDjW2d4/o9DVqnQ5Pnj6lUqswOT1Bo1mn02symZvh\nqNDm8dMdfKXNm99ZJp11EAMa5zbP82///K+wTYGFhUn0dpdP/+w2rcMmETnG929dpnxS4icf/5Kl\ns0t8/zff5nf+7m9jezqhiMfFqys8O3rBf/3P/yX/7F/8K2qtDn/yJ3/Ds60tBNHB907/n3zfxXfA\nNh067Q6e51Ot12nWi4xlAkiijYxIpVKm19N5sn9EpVRmcXqaUDBEpVJiLBtn4/wlPnhwj3ytQEez\n0DUX33EJBhxs10WQHDzJR41GKNfaDIYutiehBIJ02jqt5ohSsUYwLCOpPrt727SabbrtGqoqsLC4\nwLfefA3TckkkThXMiVQWX5RwXAdjpKP1dbafnWCZLj/80SsMRx1qzRrdjktADKOKcxzutpFUhZFf\nZPlsFNdzqHZ92lWTxJiKa9nMzIyjymHGohkeP33Oo50CrVaHRrXLH374GT/4h7/P0PR5sHVAsz3A\naLToD0z2XxTpt3vcfXbIeHqOhKgQCHjoA43uoAuSwdSUzNiYgGEPyUxOc+P6FRqlKrGoQkCR0U0N\nz/cIBoM0m6dpaJIMqiqzvDiPJIA+1EgnE1j6EBkIBIKsra3ieS6v3bzByuIyggcBUSEgSsiihCKK\nCIDgOqjyqSNZEAVEWcTxQbdMuv0+hu3gCaAZJoIg4gmnwQd9TcN0XSQ1iKCI+HhEoqf2E0kScTwH\n1z/dKXueDyLI34yjf6UO83//X/8VvU6bwWBEt6vj+i6TcxkIWrhaFMHJEFYXyY7PMWxBvVKnUi2i\nqgq/+7u3ODmskps6QyiYIJvNkUqq7D55yPUrG9TrLYamTkAVEVybRCTK0WGVvcMKI0ckmo3RHvQp\n12ocVJr0ejrNxpCltTl6zTaGLTIaaei6gNP1OXxR5je//xbXXj3HL25/RDIYYfnsWX75y3v4qLiK\nSzqcJKa6DA0ZJSAQiUjIos/nX35JMATrZxaYTWWIB1WC4RQjx+L27cckkmGG/R4zM0v0hgMKrQbb\n2zVWV6YIqAEW1mY5KZbRNOs0ScKXOX9xmZNCh8WFZWbnT4nzriFwWKlTOe6TSySJp5J0NJvXr3+b\n5w8OeOXll3nw6Dn1Vh9bUrCwiCXiuJ6PJ4n0Rjpzy7N4pkYunubmS+vM51JEVYm56TkalRrdfJHV\n1XX2BzViqRwtfUTLcBiMXJSgwNLCBOl0mKnpKJXjNrJiEPZ9GMKjp0WSqTQjY8jTB88xnBG67JNO\nxnBHAgPXw/Y9QoEIjiuBK9NtNhkbSxCUJaSAjBoMIikCiVQcxxoSjoUw7CED1yORDJJdTDCl6yyu\nnuOT/WPmfJ3pjVm2+xZB0SI2N86Hz2t4S6vkrm/y0e3HxKfWmLx+g//tFw/pzJ1hmM7wJ+9+yWB1\nGS/o868/3kW9sUTfFDh4VuHMt85T7Xm8yB+z9MoaVtNB1WWubc5R2NoDz+XKmSW+/OArEtEI04sL\nvPfvfgo5lY3lFf7yvR/z5rff5vHODqSibKyeY/feFslwkMlIksKDXXqFIvOLS9z+5GeImkEwITMy\nDcYSCZIRF0HW0MtD0okwEVFhPJ7l5x8/pdvRCKkTSKEY0axKZiFFwPfoVerYvTa5jWVaeojy8y7n\nZzc5yQ8JzU0yMxVjJhpm67jI7OZlsvNxEhMpXr5wjnapjuw6eL5PRRAIBwPYQxnLEfFNCdcSyZc7\nSKEonuJTrTWQkJHEAAISsWgMVY1y76v7LC7NIIo+V65cZW9vn3giDoKA4zvYjkW5UqHb1Xj67DGt\n1pBKrUkynearO/tsnF3m+KjBh5/eJqLGyGXTHOzuUTdM0skxPGPE2lKGk8M2d54c8Pv/5e/x0qtn\n6B1uc2Z+nsfPnnH90nliksnlcwu8/NJLjE+v8id/+RH1Xh/LO/Vg+8Ip/QLA81w8T8BzBTZW1sim\nUowcG3tgkhtPMDszzdr5s1QqdU4qDWYWF8ilM2w9fEYqk6TV6eAbI5rdAaZxaj8ZDoaMTBPD8wml\nYkiyQjQaozcYYFomlnMq+pEDIRxXwjBtDHOEGg7RqHfwXIFQOI6sBLFsg3y+SqlyzOLcLEogysFR\nmX63iyyeWht+7+//DtVal8Uzk7xy8xKVQo1qqUE4GsHzTNZX17GdAetnphBEiWAgyPNnu9TqNo8f\nFgjHVOYXx0km4wiiQq1ax/NsRFFgfGKCZq/HxtlV6Ax5/+vP6dsu6dQkEVVkbn6Vyxc2adTKTKZi\n6K7FRDLEdn6Pb73+a9R7Tc6tLfCDH/yQX3x8j2qxjT5ok8tliEkeuckM2rBPp9Oj1x+eisccF8s2\nURQZ1wkQiSSwXB0kgZGuITgua2vr7BwcMhgM2NnbRlIE9vZeUK81T/eRQQXXtRH805g61/aRBE7t\nJb6PEpBQwgqicmoRMS0XAQFZEBBE0AwHNRjE8x1cfDwRZFVGEEWCgQDpeIrLly9RrZ0yjnXNxHN9\n/NPXilBI5Q/+218xGu+D9/8XUuMypiUhESCdzSLIAuPpSQo7Lg8fHvP4SQPZc5icCOFYJywujDPo\nG7Qabayhxd5+gy/v7RKPCIQw2dhYQFRc4okoZ88uMjutEgo6xKMKL1508ASTWNZibDLLyBohSEF6\nI4d2t0k6FicSV4lkxrj35WOGjomjhqlrLRbnJ3he2Md3B3z18300XaffkVnZWKTVOaZSt2kUG6TC\nEcpVh8pRDV3XMbwA84vLxJUAo3qfEGFsV6VjdPBVuPPZEWOZIKlommgsioOE4Xc4cyZH7aRGMBDl\n/qMdFhdWGEtPsLCc5eLldSRJRNd0LGtIbjrMxctLmM6ImKTy+msvUatWiUdkLp29RLlYpd012dre\nodnoc/21lyhUDrj28iamOWJpZZmbb75CvpInocbxR316lSFvnD/PdMxG8wQeV1psV9rUOkNavS6z\n6RhPyxWGpks4EUNWwhSOK4xPpsgmw+SP6tR6fWQsvIGDjkt4LE7pqE5N6xBLTCBFPRwxSLvcIxwO\nEoyE6A9GlIsNHNsnGJIJhSUQwTBdis0iCiKGYREIq7j6CFeCuKeQVCMMxDZWLIjdHdHRh0jJBIVO\nk8HSJEfFHo+dLl1FoGab7CRD5HITNPsDSt0oG5fX+fowz65sElrYYOfRIcmJKPZ0huL2CbmZDK+9\n8jZ3PvkFt67eoOMr7HzxGB2TuJrmyZ2v2fjhZZ4+3aPyIs8Pf+O7vPdXPyM5P8Mrr9/k4e1H+A2N\nsBKl2u9SPj5BDQZ58tl9As0Rakzm6YN7rC3M0O52SQgeJ/aQRDxEIhggkkgSkHXqpQL9yoDKoIiS\nDDAo2+wVahTKNZLRKMl0mt2DMgEpxqjb4+jZUxTBpytIHI88insVzs+c44MPH7K9tUdcTrOZm2d8\ndoJSz6DZEXjz1qv0TqqMqUM+e3CfTv103x0zFQw8ogERxxBpt3vMZXM8rxUpaRq1Vu8U5itIOI6P\nOXKxrVMfnTGysS0Dz7dZXl7m4PDw9HcmkvT72mnwdruLrp/ikUJqGtd1yeUmqVdMWu0hd+5sMdJc\netqQRDjCUNeIJlLMry+zeHaeVDTGS5de4s/f+4gb33mbK7kora19cpkcs9MTnF1YxK1VcTSXpw8K\nfPHV1zx6cki702NyaoxAIIKumwjCN4kwooIggue6iKJAs9WkWq8zMgwymRi52RmkoEJ/OETwXXzP\nwbF8+o06l85vcmbzDJ4Lm+urKPEoajBMs9YAPFzvdNxnmw6WaaHIATRNx3EARAS+eYamjeD7BAIq\n3U4f2/KwTAdREJkcS7K6voQouWSzMWqVKmrwtGBO5aZIpZL8o3/8D3j44D6CaGNZIwr5I8xRn1gi\nhChKqGqQfldj2LeoVTsM+hayEmXQcxlpEsWTJtpIo9GuEgopqKEAobDM+toi1UqVYr5KNBZk/7AE\nPuQWFqmWTkjHwnz70nVKjRpDc0ChVsFyXDw8mq068xvLFF8cUWk38L0A5VKdeDTO3s4+N157jWQ6\nRvkgTzCocHB4SH8wRAkETj/uXZdwOHy6WxQVotEIjXodzwVVDZ9GPtYaOJ6Ph8fyyiKGqaEGA4wM\nA0WRMUwTxNPPIgHp/x7Be56PLJ+yThVZwvNPu03pm07U807PDk5pKZ7v4foeiN9UQufU4mJbNi9e\nvGBlZYnFxWUOD/MoioQsi99cBf7gn/yKKtn3/+2/ID2TYr/Yx/d8RvoQx3OweiEkO0AwIuLiMWyD\nYPeZn5+iVbE5f26W/F4Lx7WwXYAwN165TDSokB6b5fnzPAcvKhzs5QnJETKZcU5qh6RzKdSwTzI6\nRuW4yqXNBXLTUTzPZmYmQ72t49Pn0soZzl6cYGZlkvXlOcYSKUatBjdvXSGUCmNofSaz48SmItz/\n6in/6d/7AbolousCI1vg7kEFPyThiQEcy2NxZpXDQp56u4NlBJhIZajWyvQHIyYmEkxms6wurKBp\nOqY7IjuWIBNPMGprTM/NkZtZoNcaEBYEZnNpBCzazRoxNYDse0xkVGTfpdrrcHEuR6VVolhvkQ5m\nqR02iadSDAyHra0dbr7yEsXaHorsEg5GsCyLeDTFZ7e/wnFGjHSdXqvL5HgazTAoD6toropoRzB7\nJrYs8LzcoI/AcXdAraOTzKY4OCySiGcJKBLdQYehAfVmH1EQ0Ecufgh0v4nnhhifnMIUFXZ2j5Ec\nhUa1fSriavc4OeoTVuOkM2PIQY9ERkUQfQJqiKX0NAmCOCOLgaEzlkyghQWskYExtPFNnVklwcgI\nUKq3GYVtDoMKDc3CHfqEs2Fi8UliSZmopCM6HrGxCYaNNj2vT3Q2TvH5FhPpRcoHJcq7VW7+5g94\n9OUTantllm5e5cGX9/jyq8e8/Hs/4suffMLC/DKJhXkef/4L0tk4TmCM/ae7lPJHXL/xbe589Bn9\n/RdM5GY52X3O2FSYYaNP4/iQ7EKOYrnAWiZKIhRlJZTAa3RYWpymdlImJwcZnDRYWD7D5q3f5f2/\n/Clb+8dEUzH+i//k76LIJvFMkGgkytjUJJVRm25riOQ7uH2RrDDOZGKGlmmzcXmDqewEha02B3sn\nDLQuF86vMGgUefvmW6yvXcOUJG5eOktajCFHLGzJQydMr2ZSqvV5LTNBNBahIznsNers7p+QSKjU\nGnUa9SbhcIShriOKCtrQ4uLFK1SqVWRFwnFN4rEwrucxGPRpNpv4Pni+QL+vIQgyCCI+IIqnof4T\nE2N4noEsiyRTAvFQlnq/TjiUpFiqEIyqLGRzvHn5Outn1vjrP3+Xd997j+zCHP/Z73yXulZiLDeP\nIAfQOgZf/OI2f/zjX3I0stipNpBTCQy6bJzJ4dkSvuAwGg0RRRlREFlfXyKbTiE4JvGoiut6iKKC\nMXJodXr0dR1RFui06qytTpE/LjMcWujdDqXyMY+3n6APTHxtQKFaZWZmjkQsRr1ex/O806g1QUIN\nhvB8l0BAxvcdFFkEfGzXwsfF81w0zcBzfdKZNOfOnWV6eoJISKVSreD5HtVKlW/deo14LMmTJ7vE\nkjEQ4Gfv/wTTtLh+9Qq6bnJm7Qr37z1FEhVC4TDtVp/CUYmzG+dotfvU6hrFUpNAME4knKRUrBJU\nVSzHpt3ukR3LEo9G0DUdXFhcXKLT7ZJbGCeWiiAqAtrAIBmL4RhDCuUKgXiEk0IJ3R2huw6xoMd0\nap58uYmkBCiWK1QrZc6sLHDzpWvcvXMHWRY4u7zBl199ieU6DHUTbWQSDofxfRfXd5AVBdd10Uca\nhm6hKEFEQcB1TrO5TdsmkghjmBqe65zitSwHQZQY2Q6CBIIoYTsurucjIhGORNC1EbIsIQgi4jeB\nBrIgg+shCODgo4gCiiwjwmkaEaCGQgiuy2CgE43EyU1P0W43KeSPERBwXOeb+56KjX7lpJ//8//4\nH2mPRpSaLW68fg5VNRAZIVgC6WiS5LiHK3pUqgZRSSGbitHrWLRaOp5rEwmEyExlEEMuhb0TBCPI\nbr6KpAZxXYN4Iobv20TiIUzPRAkFyE1PoHc1JhIJ5saTTI/DxbMZYrEIZ9bnsbsaFy4s4OMQComY\nnslBYZ+wDJapsHuQJ79XIqxKFFsNTF3jxVaDaDzC7tNjZufniI3L1Ep1FE8gGIrQbddYWpwimgqj\nCQ47B0UkOYqqhCgX6sRDKolwEDXkEgrJxCIpus0G6USagTak2+tg9C0a5RKiq3C0lwczgirGEHyb\nC2cukoqliMQlGmaDdmNAQJLZ229Qq7aJx8NU633S8Sjl4zyCbxIJBTGNIeVihWq5jiQIxKJxlIAC\nQRU1FqOhGziKRTSSpNlqoosiVU0nFAijaSKl/oBTwZdDIhan22ojiT6piTjhWAhVkFETUeSkit6D\nXkvHlSLMT0zx00/uI0phZjJjWL7EyuU1jgtlziyf46RUZmSMMM0+586vMBj20A0dxxDB8cnNzjB0\nDIqlE9RElGgkjGaKRIJxSvUqSnwcE5EX1TKeHSUckQgFAoiOwknziKNqmXg0iun0UBB5flwiL5oo\nE4uoh3UypsnUxiSPHj/nW29eISR4vNjJM7uRZFDu4hs+UdfCsS3yL3YRjD5z0yLprkEonMQsFIkD\nS0uL2NUjfnBlHkGJELQaSKMhE5Esgmihaxppx2ZKdrn/+IhevU2h1cEe9Hmyf0Jc0xkaIhsr68yv\nv8Znn77P0GyjdYccPz9C8yySyRSjvR65qTFcQSCaCWOOdBzJIBoP4coB0ukoAVfm68+3SCaXGZgy\ne4dljIbJ/NwkoYyM1+uwlpvCqDRg1CEuJ6jWerRGCslQnL7pspQIMpB8tmsNIsg4pogeEGhVWugj\nGzEYRVRUuu0+g8GIfn+ArIhkx1MgeHiAIAiIsoyu6biuz6CvYZk2luUQjkQJBk+5g57jE4nKDAYm\nN29do9frE4nFyaQnMUdDQtHT7u/+k+c8efycnSfbFLp9Jq+u8M5bl0hSZudRgaAQ4m/+/ae8/8nn\nOLEUq2fOUiwV+d73XyGbDjM3laNVbWPaJrVag5CqsrwyR0ARGQ4G4A5IxoNcOLfEwtwE2UyGerOF\ni8/42DhHRyd4lktIkJldW6dYqhKLqmQmsnQ1jYgaZWNujnvPtmm3anRabSRZwvccgoEAnu8RT8aI\nJyKMjaUYG4+wujp3ep6OgxxQsKxT1abrCuijU7/icaFIOKISiUex3RHasEdYCXK4f0y+WCEai5D8\nvyh7j5hZ0iw97wlvMtLb37vr7626ZW5VdXdVm+npsaAcRUGABIIDSiQkaCezImdmJa20kARBEKCF\noI0gChApiCLZPTM9U9O+q6vLXn9/bzLzT5/hfWiRxdG6t4lERgCBjPOdc973ecsWt2/s4TgLPvn0\nSxAyzs+vvjqg6EymMwzNQChUhsNVYLPnR1TKK1HRxtoGaZoTBAFRnCAWGpqqsbQdBElhPltyenaF\ngMBy4hBObEbzBb4f8t0P3iOKY5aiz9nhCYJaUO62eePOHc7Oxuzd2OGLjz8hFxTaaw2++a3X2Vqr\ncPjiBVWrxi9//ilnF+cousp4MiFJUtIkRxZFKmWTeqPGcuEgigJpmpFlAlESI0gifhiR5jmaoZCR\noakKkiwRhwkCCnGSIkgChcRXI9IVVzZLc8IwRpQBoVilmQgieZKTxRl5liMIAklWIFIgFpDEGaos\nA6uiWQhgWSVEcpIoJkuzld0lDNE0BUVZ4fuyrOAf/6M/+c0K5v/2f/43LOYq83FMHPrIokwRaWiy\nSGPD4OnRExJWeKMXZ30GsymTSUDgBUiWhtarYTsh7VqF8WhMuVZhc6tHe61OFAccPr/m/XffZzJ5\nRqUWERU+eSauomSkDNeZUKtWyZKIs9M+J88m/O0//H2evnhKYSyRHZtIy9DMLiWpwdlgjJmrNFSB\nh/ceISQu6xtbmPU6i6WLpYlYso4mGyj1Eu5iwvu/9QaPP3tOs9Tm7u4OM9vndHSOH+ScnQxxFz6/\n/7uPMHWJ2XxGmoVIoo7vuQyv+1TrLRr1JleDAZOZw3KRYDse4/GC/btVzJLI4HrI6cVLHn95Qklb\nPciLlyO213YZTcbIWsr11Yg7N3dAnGCVdCTRQNNFRDHHcR3eee/RV4+8xP237nA+uKRWs2iX2ihS\nhmgquHG0AqyPx8wDj4P9Nd559BoIIfWaiizFxJFNp9UijxIqFR3D1IjThNDPscQOCDmlmsrlcMTB\nrV0uxhMCBCazBRu9TabjCds7O1xc9iGTScKcL788ot3rUmgykiYRFhHDxRTdKmMIKvVERJ3lpEVM\nqdPATxNmF3P29m7hRyl2ZHPtpqzv73NyfsTtGxukWczxcEKrUsctIrbW62zoIg0loFTVeX52hZK2\n+Jf//J/z3Ycf8OkXv+bzv/gRf/hv/A4/+9HHSEuXbqPBycVz7u40MQBRKTDEkFa3hBUFhP0XrK83\n+fyjLylVBT55eUqVgv7M4Xxyja5ZbJdbuJM5h84CU5IQTJ1G1WQwnLJp6oxyuL2xz/ath/z4J99n\nOp0jJDFq04C/WCwAACAASURBVKRRrxMfj7i6mvLj509oWAapKCMJOZkTI6olgqmNs4zpbt3Gy0Si\nOMcUdQ6fPUeNMpbXMx68+Tq2GjGbXiCpAn/+06d89OoSW1xS6ao8fnXMN995l7iUU6lIxGnCxeUI\ns2zRH0+oNWvcee0epxcXLJcO7z56hzffeJOr/iVpGhGEEfOlQyYmJGlGuVwhiWM8J1i9BGUJRVEZ\nj6cgQBD4mIZOrVahWjLoVtcYnV2x1V0j8BY8vHGL88tr5lOfDIn1mwcsQps//dP/mO+9f4Pzx2ec\nnyy5c+d1nl1c8f9++FMQBNbur+MbNgc32yROHzEKWNpzjs6H3LvzNs9fvEDTBDzXx7Vn/Kf/8O/x\n6Sdf4iwXHOxsEAU2JV1jZ63Nv/nv/C2ePXvGfOIQhynzKGU4GBKFIbWKSZQk5HlOGCR0uk3qGw1U\nReJge4+Tk3NarRayLBPFEe31DrIE/YtLTN3i8uycKAwohIJyxVwRaAqQZBlBKAhDnyhMKVd1wjjh\n+dML7t66AzEcvjxh6ce8//7XmAz6uIs5QeQjqjoXF9fkmUS9WWI8vUZWMlrNDvO5R5pmLO0luqqR\nRCm6rPHgwQO+/PIxuq7hez6WVSMKI2zbYeH4OF5EHIRsNeqsrW1weHFBr7VBxVTRSzLPn75iuBhT\nqloYzRK//bX3GV68QNQL9vbWEbOQtfUmj957ix/8Pz/h449esLt/wMeffYEXhnzv9/6Qctni/Px8\nBXhIVmkkURxgGgYLOyBO4pXKFXnVKRYZkqIgSiJZllKpVjANncVyBUVIi1WyiCCAqikIxaoICgIk\ncY6iy8iqtEo3yQskQURARBQk8nw1qkcUQMgBiTQpEFkh9FZrbxFFknn7zTcolSzOL/q43oofC6uR\nbxIn5FnOH/+mtpJ/9q/+O+YjB0MV0I2cilXDXYirOXz/CM+L2d3usHPQRFLKxGGVW7d28XG4sdel\nXRUJIgcv89lcq1IvJWilnEhYgLhkq1el16lhz2c0q222tup0m1XqVYuNtV0QRcaTCZVSja1uDb1c\n57if8OTwivHC5/xiQre1zSePnyJqBsdHYy6OX3H3/n0ubJvFcEFZszi7nvD5l0cs3QBBNtAlmfMX\np/zuv/u7KKJC6Dq8evESRdRptcvEkc/d+zd5/OSY23fXabZzri7PUJUyrXYDx1lQCNDsVokTl9F4\ngl6WMMo6YeSQ5il+lLKxvcJitTpVdg422draIckkvvzkCF23mCyH1Bs9fK/g8nxEu1MiyVyQRDw/\nJEhi6m2DLFPI8oLL60vq3Qqhv6BkqTiLBZPxCL0EURJDEaKoEd31XTS9IHZD4jBg1B+zt79OlsS0\nmnUatTqO7VFp6swnEwyphOclHL7sE6UBS89mZ2+L0cTnzsMHjKdzalYVTVZx7BmyLlOrVvE9j+l4\nyLe+8w2miwmyCr1ODVnK0U2FRrPBdf8SU1GQJIPEzOk7C5rNGnt7dzg5OSOTchx3yduPvs14ZKPF\nAmQpeS7juwmqppCELvNhn2hyRq/VYBGFZIJCvWViCjn1kkEiFrTkCpoIEbDX1XDdKx49uoWpZZg1\n2FmvYsdj/EDhqj9j4Dqcu1N2bnUo1UyaBx3SUcp6s8kffvM7/OLjzzk+GYCbk6cpuqojSSqmqdMf\nTFk3SswR2N/aYe/1d/nww3/F3J4jAFapQv+sTy8x8dwAsawiyDpp4rKYBaihiFltkIY69WaPk7Mx\nplnD931UTeflqyOMLOX+XotvPbjFzr090kJkkup8/HhAr1vnzq1dismMW/UNfvDxL6jWVMx8lS0Y\nxhL90TXVSp04iTi/uELXDCzDYnR9TcnUSaOYwPe5f+8+y6VDLuaUrQr2fEkUxFSrNVrtFp7rk6Qp\nzXZjlaxh6FiWxGa3ies6xEVCAkznU2wvINU1MlNlMRnTbreYj4b80b//b1MXAsLBgKdPj/jw45f8\n8ouXOIuE0/6Q7/72u3zrQZfdukxD1hDRmbsOGQqz0ZzR1CaMYoo8551H91nr1jh88ZLRtcfu3i7N\nZoMsjwj9HDv2Gc/ntKpl4jwnLlI0RSVJUhr1Op4959atG7zzziNEWWLhOtiejSiKvHx+yI29GywW\nNu1OGz/wkWQZx16ytbmB67rE0Sopw/fjFS0pTiiKDCjY2l3HtGQ2d7qIgsi9B3vomo5nOytoeJSy\nsF1Goyk7O5u4zgxBkpENnTTJse0lzVaDZr2FvQixbWfldZSAovjKCuRQrVT54ovH5GR4noNVMoGV\nxQwRll5AGif8g7/77zEdjyjXLJBSdm5uoUgqz5+9IEgCylWLheOQRgHL+YjF3CX2IsaXQ84uh+hm\nQblU5fPPntFotRiOlgTximc8Wcw5OnrF1uYWy9kSCsi+smUURUEURkBBnrEqYmKBIKxG+lEYI0sS\ngR/g+i66oZEk6SrkQRLJixxZEinSnDwryLOVfSQrMpBWKlahKIjjDLKVD7egQDV0JFkmL1bXFf+1\nikeEggJBllAkEd/1KJerHB6eIcmr+6X41/YSAUGU+OM//g07zKPPv89pv4+oCdQrKoagMJv5PDk6\nRlZL7G+uc+/WJv3BBYv5glpHJ04LnKVLHrrcvttgc6fCnbt3iJcuWi7TbjeRFYONziYlw+SXH/2M\n9sY6r86umA+mzIYhRSQTuiG99TqtHvjimDizqdYl7rxxh7VNje3dKm9+4y6XF+fYXsryeMIb777F\nJ09O8GY+vhvw6eNLPn9xTBLAd7/3TVIKXrtzlyT2GYwGPPvoJXng4fs+O1tdLEsjFVLUahVDgzjy\nydIcq57QanZZLEbUqyZRHKLoBoapEiQecZpgaDprnQ6K4FOuW7z7za8xtpfsbO1wdXJE7npM/VUn\nWS43KVUUxrM+kmJyceqxsVMiF5fY3pJmt0W5ZlFrlikAVTUZTQfopk5WpIyHC6IgY3i5IE48TEum\n1pKxKiUcx6VSNanXLBqNMrP5AFlWEYi4dWsXURAJY5ewWKwAyhRUrRK1Wg/REFnfbuG5NvVai4Xj\nkiQF/9l/8g/4X/6nf0KrZbC5UyUtYjrrbUbXFyRxgB8WXAxHJJFL6C6plnRqpSoKIhJQskqIhkIi\nFeSpQDAO2GtuUhQ5QeLQWa/z4w9/xtXFKYYqoGoFopLQaFdYTOcIacHdmzdxY4fLSR9JkHjnjfs0\nSme0RJHzl6e89eAW/sWE2eWCly+PeOPmPo5/RSIG1Koqy1kfSRCpVOvItsD9+3e5mIw5eOs+rU6V\nZruLIZWI4pi9zRolNWVRJCzCEDU2kLMUVdNIJAFdyBiNbdqmhivIdDsdbr31Df7ywx+wnF4jFxmh\n76OgsiZbJHFCqILnRHQ7TcaTGaKXcPv+65TLdXq9bd54812Ojy5QZJWCglfHR7z19Ye8fm+dsl7m\n50+eYR8POX1xjm+Z/NmvP+PTn32Bkpr87JOP2G9tkQ/mjO2Qnb2bDOdL/DQlDDI8z2dwvfIJ9odD\nBCCOInzPRRZljo+OSKKIPMsJHRdFUrDMEsvlkvliThxHGIZJGLrcurnPw9fvkyURVrXEGw/ucvT4\nFf2rMaeDEbe2t3jvzi2qisDb775DWcvwFwsSP+LXHz/jh3/9Bb96csaN196k0zF4Mbli78YmN1p1\nPn38hNmVTxboTOyMFy9OGc9sxhMX2/WRRIkbB9voWoxtD9nYXClvD48vkTSdhRMycl2WM5febpfT\niz4bnQplq0qt2yFLQ8xyiUqzznQ64fz8AtO0KNfrqIrEcNCnyAS67Q5WqcRyscRxXeIoYK3bQVEk\nlkuX8dhFkGR8P0FTTdrtLpPJNWZZYni55BvffpM4XZAlCcP+mDwR6F8sOD3vk5GS5RJhFFJr1Hjr\n0ZscHh+zf/Mey7lNGmeMRlOm0xkICRQCkiQiyyqLhUeWZhRFwdJ2KEixSiuubpKE3H/zTRzPZTwe\nU6026FRLvH9vj62yzNKxOb8+47w/Zm93g/OTS0RJIiehEJOVlsGJkEQNWVS4uByzf7Cyw7x8fsJw\nOKZSM9GMHKtc5+pqQhDb5HlC4LlEQUhapAiigKarKKqEKouUy2WSJEFWJMSvdr/1ShmxyAnjBF1R\nSJIMTdXIspVdTZBW8PU8zVeRbhkIuUhW5Igi5ORQ5BiaBnmBIkskaUIhFsRJys1b+ywWS8hWo9oi\nh5yCKMkxdJkkyvA9n067wWQyJfub6xSIokRRrADsf/KbFsz//r/+z+nttGiuW9QsCbFIyPICrVHF\ndQJu7HdwZgvGVz6KpdDbbBLFDp1ym63dHpQE7HHMsh8T2C7NVpdB/5wiljl6cUaaBHzw3dfxxWsO\nHvbIMonRYIlnF1h6jSQOMC2NVExXHYXW5PPPn1BVBAQ/49VsgqqaaKZEd3+XX37yJaYmMZpPufQD\nkiRDUiQiUsb9GcnS4dXxGceDARu7Ld56/y5yNWeZJSiGSq1WhTgnXcaIRcT+3i71psxrD7aQRbCU\nOs40pJBkwihBKGRMtUqRCsiCyGyyYOtgg7JlYk+mdGsm9rTPvXt3cOIAw6pgOwvSwkaUBS6vZtTr\nTWxvwZtfqyFqMaKqMpm5BEHGy5eXjPohOQVBGLO9fYOTw3OSIKbekKk1FTY2G5RrOrphYC89RFGk\nXq2QpT6LqY+mGMwmk6/y6RzCMCDNc6p1A0Mp0T+esr2xDYXKy8NDJmOfNEuxlxGyrCIhc/jijGpV\npt2uk2YhQWQzuB5StkxazQbzeYjrp1imRp5kJEEMmUjJrJHnClEqUq6pyJqKJCrIggW6xjRyOJ8O\nuLq+RhNN3nnzHdKi4HJwjmoo+F6A43ooUoHjzEgVuJrMadTanD5+imWZZI5CqdJgEc2QjAqPDwfM\n0pTQDSjXSqiGgGmYyIhUrBWPtJQHKFKGohYYWUDFMPmL7/+K0Pap7YgsvQnX01Mau21aa02kUGC+\ncMjynBRoV8ucDyZsWiUWhUBvbZ17b/82f/mX/4LlcobIamcjSyJbmkGcJAwXCxqWyXJpI2RQFXQa\nvR4zx2O29Dk7u1x1CCLEqcdsMcZyPV5rmvTKbQ6Lgu5rN2i9vsbXv36fN+/usbd/g6nj4ShzvvPG\nLoGU4QQFU3vJYupyfT1F1yvMZzaqZmJ7EbKsYRolihziKKFaq7NcuMRxSqNSQSgEKmZ5dfJ3PVRN\nxypXsRcOQgHNhgVCSqtZo2KWefb8iOuFR6u3jq6qPHrnDvZ4SKlcplkx+fzz50imySJNcISEcrfG\nN7/zDd7/+lt88eQLVFnFX87o7Hfp1Nc4HrrUd++QJQqen9C/GKBaVTzbxVBV1nodptMRN/b3ScKY\n6+GE3uY+F4MhQZwhqTqlksH56QVrzTb2PCSJIixDQ63q+J7D/dt3WcwdTs77mNUGy8UCx3Uo6Sai\nIOAsbbI0YzKZ8O1vfxsoVv+bNEMQVHw/IC1AVQ0o4ODWGs2uTLVaZ7H08HwPXdeYXHk4i4g0haW9\nJM1yKrU2pbKFvbRxbZuryz4P33xIvz9i2L8iS3MUVQJWpBwQybIC3w/I0hXOTdNUzJKOruvEcYSm\naeR5zmQwYrG0qdfrzJcLSrrAdz94G3c2YK1aplprc3l4SqlSYjx2kHQFP/WQRAEhU5jPQ+IIIEEU\nJTY2W7x8+Zyj41MkRWY0WSAqOeVylTQPVipYhNUeMVtZfiRp5S3NsxRNlXEdB1VVCKMIRRYRRYE8\nzVbjWMtCFFeRXoEfkqY5oiiiKCpRGK9+G4k8y8nSAlkChK9iviQRVVZRZRWBAkkV0QwVVZeZTiYo\nokSR5qTxilcrqxqSLCBLElGUUAgpJUuBXMJeekiSAKJImq++32q1+C//i//qNyuY//P/+t+y1zF4\n6+42Lh5X3hgvjmhVKlSrIv4y4/xkQiEZ+HFEJhRUVINBf06vZbC5U0OVJf7sL3+NJJuksQh6znSe\nMBr76EqV1It48/4d5td9nEwhinJarU2u+kNkVcXzY4okp6RqVOUuI8fHmYdUGg121ru4i2vWu3WI\nEvRGk/7IJbRdZtc2JauEpukISUKjU8LolHn+6ozf+e2HJLFLR8957cF9zgdXhH7AZLCg1qzz4ugS\noci5ub/H408Oubw6wplnJA6MB1MarTUcL6PIJWwv5pNfPUfOdJrVDkKeYrsLDKNMtaVgmjn9yxnz\nacjxyQRFSzm408ZxXW7cuIPrTXn/g/uoekaaQZIJlKtlVFVhsXS5daeHrIUkacFyHqDJIrqmsr7R\nRFYgCkOyLGFtbY1h3ycJJaySDrnGeDzG1Cps7+yjKRoXZxMMXUTVwFkURG6EWMgkns56Zw9NlygE\nj+2NbUbDCa1qm+U4gCRnNprQqJdx7SGKajCdLNhcX8dzfNrtNZI8oNkuU22UUI0Vk9H3fCazOWcX\nV2y0GkzOxxR+RtmocHE+WNE3PAdDr6NLJtf9Cc9eHhEVKzaooZfY2N6gu9Hh7GLA1vouumYxvJyz\nt72JO8+ZT2b09kqg+lRaDWZLl6vJknLJp9drU64bzCZjus06SZoilmXEss7RcsBx/xzTUtCbEmrL\nIJNt1jst5o7N/dsP8ROXqpLCUuLFxRhVEomzjG69wsVgQkdXcZCo15u88dYH/Pgnf75CvlGQCTmK\nJLJhSESpiC/kGIpIGCZ0Gl3MTECtVrheOiiajmkYSJIEROzud7l9d5vPvnjFliayt97E2m3xwY1d\nZs++4OJqwEe/fMoH99+gPx8gkhPMxsiTlDzTmXkuSqlCVMjcuXOTRr3BeDwnSUAQFG7eusWTZ89R\nZJU0KwijmCTPKbIMSZK5Hk4Io4g0zWi1O9i2iySq7G5vs7mxThgGJK5PEBZ88uQIq9agUq3wwfvv\n8flnzxhHDr/1B7/LD//iR7Q7VRRToVI2UclY75SoNlSOzg/ZrPfw3Ig7N27xtQf3+ehHP+PO2+8i\nFzqRHRIEHgcH+4wGY8aTGWkcc3F5Baicnw5Zzjym84Dexjq1WgmrLJPHGaIoIuURH7z5NlpVJYkD\nlCxj7rgosoxhmExnC0RJIs0ytne2MY0SmqrT66wx6A8IAo8wiukP+swWc8pli/FkjiTLuJ5HLuRk\nabIalyoJorRSyxa5TqXSZHNzHaHIMHSF/+gf/j3CwmG0mGMYCr21Ctf9KaqkIpATJTE3b+6y1mlT\nr5aYjGc0m00kQSNJYwI/Is8LBEFAUWQMQ2Nvb5fhcICuG4CA50W02m1SMooiRpFEvJnHerXCWs2k\nZXX55ePHpHnE5dghSiJSIQMkNNEkCSHPBRRVIk4jVB16a13W1pos7QA/ykAUKZUlFjOb3d0N+ld9\npK98oUmSICKAKJHnOb1eG01RkSUFWZKIo4RCEIiihDRK0TSNNM3I85wkXXWRaZojyiJ5XlArV0ji\nVZRXmqYIfGX3kGTiZGXlydKMJE4o8hxESLIEURIpshyBgizNEUSBNM1JswxBWgmE4iRFlMF1POIw\nI4lWWZyFAJIikuU5WRLzj//RH/9mBfN//7/+B+7s17AXfWZhiJsnJGmImOaEqc3JyzmzZUS5U4YM\nokBju7fGaJqRLK5Zq+j0tnu09ndYLK/Z7dVor5dxCw+zbhGFGfNLm8nplJ7VpbknEYYLjo9P2L2x\nT6nc4vzc5vLVgpreoigKzFpOuWYRxgtqskan2SSLXQI7JM4kzs9O8eyYSq9FQy/x3a+9TtkU+Q//\n7h9QqkjcvNnj+edXfO/bj8gin+H1hFqnzMHuJs5sxuZ2C0WXuH/vFo+/PFmNk6OQ2czm7GJBZ22H\nLE9Y31inPxgRuAGPHj2iblk0Kw0UQ8BQK5wcD5C+IlS0W3VqtSaymtPpWdTqZexFzHzqsL3ZodlU\nmU5s4hiOXk0JkxjdFKnVVFQFzJLB2lqdIkvY3upQberEiYfrL+l26qgKHL46xdAthoMpIHNyconn\nFDhOxPMnV8xmCzw3oN0rYxgrqbaATKvdJIgz5osBy8WM7a0OSZhjL2zOj/qs9+qE/pJGzUIVNaqW\nhSSqBH5IHIToUpnjo3N66zUqlowTTulttZjNRhRZgmnodNe6iGg0al0kpcQ8iNi78YDLy0ss1WR2\nOWOtXkbKU6yGwmtv36da0zC0AkGQOb48YW2tgYmGHhnMxwv0WpmJGzMczonUGNXSkDWNjfVtBmMP\nRSkYjmcEQUbgOqyvdVguZrieTbXeQdUt3n3tEaYiIKopSZGw3dlgcHKKalj47gSKHMWLudW7wTIr\nuB5NVurLeoWr6ynruoGDyO76NpYo8JMf/ZBF7FFkArmQYSgGPVUgE2QCBW7eOECWNebzOTVJJ5Yk\nogIMy0JSREQpQ1EK0sih22ggJxUsd8rBepllo+DLk+c4GnT2DjjYXmN4/JzLw1d4L494/e23mbsR\nV4MZSq3EZ49f4jkBcRrSbDS5uLjC9wPyJEGg4O//0R+RxDHj6xH3X7tHTo7rhdi2S7lSXkGoKTAN\nnXq9ymK+pLfW4fLikiRMyJBxg4RGrcZvvfc1qoaBphioWcp7+wd4TsBoMiNJEyQhQ8oC3rizR7iY\nIooppbqI7TvomoGZifgzB2fuEc0TXr284vHFK7xozqvhKfO5z8HBPkHoY1o6yCJZEnLzxhZqAY1m\ni6vLPpJUoFoV9tc3Ma0qv/rVp1yeDmmVKrR2txlcD5FFEW9p06o2mc3nFGLO2ek5rm0jFPDs+XNk\nTUVQ4PadO1SrVRqtGp7nISs6oiwTxwEH+3vouopuqGSJSJJkFJlCgQhSwOnJCYUcISHy0a9+jqAX\nrO/WqdQlTE1lPHaQJYUsjXG9iPFkwmQ0xTAVNrc2qDfKWJZFXsBi6aAqK1FKvVFBUWTSlSGUbrfL\naDSiKHLicDXyj8Mlbzx8g1K5zuMnzxETielyzmBqM88yJN1AVEWiOMJUTeIgQxBEsixGEHJ0QyVJ\nU8bjGY4zx1kkhPGqyHU6FW4f3CLyPFRNgiyFvCBJw1VRghUYPYoIvAB7GRAEKQUC2VeEJgQRUZC/\nCqUGSVHIi5wizxEKgTzLMTSDKIqI4wSAQigQhYIsK746PBTk+SqwmkJAWE1poRBIswwEYbX7ZCXk\nWblPCtI4R5CgYDXmzrOCLMtJ85UlJc9Wu9MkyfnTP/kNbSWDVx+ib0BuioiRgiWYtDs9Dq8Wq7FA\nVUdQVuhj0yqjFiKPf9pH8Axu394kzULCKMK+nrKx1mM0GiJ7EvcOtljbquH6DlGy6tJORgMsSaTX\n2qDZ2qDVbjKaDKEoEJWc8XTB0osQlhKyrOM7AudHV2RyQZza6KJKJhlkecre5gaeFxLMpxhKwf5W\ni+nJEF0QcVyfSkXl6PEVpVKVVqsBgC5ltGoloiSmu9vgyfMvaLbX+OTTL9C1EpmsY7W69KdX1Msa\n9WqFar3G0p4zul4QBy556tBqVLm4nJGmFoqmgBizsBeEqc9wcMH42iOKZNbXO+zvN1lfa3BydE6U\nzVjf3ODhWw+J05inXw7QFQlVNLi6HJKEGqaZo+oOhqkxHC8olytoSkyrYXA9WNJstXH8gDhNQIBC\nkEni1Sk6ihPqzTLdtRq27WNWdUqVEsdnVziBjSzJJElEmsSkCYRhxjfff5/ZKCCOM66vx5ydXiLL\n0FtrEToCzjRBljOqVY0gcJn3F9y+d49qo8Z8tKRXXUPKVAy9zJMX5yzcJc9fPCfJMn7y88/Y297G\nLFRwVTRBQBM1dFMhF0Sm4wX+3GOxmDNfOjTrDYplxvWzPjf2djkdXZDrEa/d36JUNSASKRsKzvia\nopA5enWJF4kIko49TYmdnNliSVmvs9Or8+XLF8SZhzN1yWWF4XCB4emMQomXF/3VS0A0OT11KFKd\nWTxHlkyWrstGu87lYMq6rrPMcsKZzeWTX+F6LtPER0QlE3J0UeGgWkfQZJRmi2q1tBKIqTpr5Tpy\nxUIrV5F1g6wogIxy2SD1Iz78/of4dkhpcsmbb97iqloiXcKaVGJ3bZNnj5/y+OIFVgY3Gz3UVOfz\n+YRz16dsVuhubhGELvOFw/Onr4iCmDzP6HZatOpVOq0Gx4evuH37gIurc/7O3/m3ODy6IPB9FEWh\nyAs0VUXXDdIko7fepd2rUyrJfOPrb6NrCoUgYi8dTk5OeePhA3701x+yubvBB7/1HX744S84PxvR\nWd/m1fU5Iyfg2fEV3/2dP2B3+yYXr8YUgsX9Ww8Zjqds9Nb47PApsZHTub3OxtoGhSqTpxHNboOl\n41GvlykkaHTadLotmjWL2XTKcLDgjYcPiNMUvWRwdnGBpki8duc2dhay9GyOz04QEdjf38X3A85O\nrxCElem9Wq2SpwlJEtHpNWh1qty4exPdMKlUyggipFnBYunSaDbQNRXL0CmVdERR4MG9Nzg5vkSU\nZO7cu4FVVkmznPkCquUq5UoFvVrl7HLA5vo2URShlwzSPCMIE3w/RjE1Dg52uLq6otmqc3p2xXAw\npNvrrYRAzRZRHFIqmaRpyng8pVarEkXxyjqhaqRCgqbJSFlMFMeMxnMevvWQp8fntLbX+dWrF8zz\nHCfwiZMUQxbRJAHX8xAlEMUVyCIvJIIoI4klfDehIENXqoRxyEZ3gyTwGPXPmMwckrAgy1Z+VRAJ\nghRVkSmZJcIgIY5zJFECBLI8I8sKFFn9ih6VgQCqoa3YrwWIuYAsSoiiSEFBkq66RkmVUSX5b4qf\nKEsIgkSerzpToZAQCnGVnymt/MJpBiAgUKAbMqWStoIiBBmiJHzFm5VWkApyJEFEEgXytEASxd98\nhzn99PvEYUzNKCMsl+x31iiXFaRyCIqEVTG4/2CD+dwlnjYxxHUevL5Frut4Uxu5kGn3djj/8pLD\nw1O0Wou7O9+gyGNmo0salQ61ZoPNm9sURkBapJw9dbDCLq4QsLNXZzi8wowNjLJJlqSsbbzGbDHi\nanLG7RtrWHWLmSDz8x8fkY4KPNehJEXs3LrJJ8+OUBIJqWJwMR7gxTbtVhNJkgmSHM/32NzsoIgC\ndcuih9XZSwAAIABJREFUiHMaHZPJcomklXn2xRMO1jeJipTTiwWL0YKOrPHejQPamxv82V/8JRud\nHRJDxBcNzk+WnF8tubI9Jt4Mq1zmxtYdBFng9LpPKlShSNnqVtlZbzKfTRBFmTgK2VhvcHn5iiwO\nsEom1VYZ8pBuS6Ve0/HcKx68toFVEqi3SwhFTBT63N3ZQy8rnF2fU61bRNMAsVC4GC9oWR3SXMB2\nfUqWjqrbNGolGg0LTZcZD0LiaMm9W2u0Gm3GE5+55xBlAetrG1xdTPjy8THlmo5YDrjz5jrrmyqR\nnUNaxg0dRF1gPF9SrZSZzZaoio4o5FimydnlEValjKSqTH0fvdpkOLvG8xUG/Qmi5FEpWSS2zek0\n4vJ6wM3be7w4OudqnDC+XtCqV9ne3WYwGFBrNvjVk2OkhoFiVTh6OmTpLslSDwDTKjFbFGR5TKm8\nz2w54PXbb/Ls2QntToMkK0hiH3KfbmONwWiGoKvYXkIWCozmLpkkoFVK1LprnJ0cU64p7N6qcvug\nwl5ni8k0oKyJjIc2LUVnKeaYskCpyBgHIZNEBFFCKkBTBDrtDr4hI2oS6/UWUpoThCGZHyOUK+iF\njiJq6LKIVKQkQYSgakwCB6MHtWuH/Z0d1vdv8MNff4anwtWXr6iqFpKXE8k5n56f82I4JlNVTq/7\nSIVMrVbjiydPyBWJheNQqlUJ4lWivKoaPH7ynGqtzr27D7CnC7yZS7fXwXcddF1FQODunVuEbsJs\nPONr33iESEIeeMRBwOnliA++/QGz5QhFF4mymOGwjx+mTJY+WiFSLZs8unufl6+e8PUP3uadt1+n\nqpt8+tHHnJ4e8c6N15HiDBGJ7//5XzFPBfywoNHuYRYKDcUkMCWuR0O6vTaIOVv1Cn//P/gdosDm\n1ZNj7EXM+998g2dPnzKej3FtlxsbG/zt3/tb/PQXP6ZTtyhrOk8uL9CqdeYzm8urMUbdYhHYWGaV\nq8GAVBJotTs4szn1ksYffvA9nvz6M9579C4/+fHP8IOA/f1tSobGbLZgPJ4xHIxxPRd7PqRhGnzw\njXf5yU9/QdmqIyOwv79Fp9lhOp5jyCYGBv1XA0ZnI6JFSLlc53o8pkBCFkXMUolCFgmjAgmJwHfo\nbbT51rc+oD+4Is8yHM8lz1LefvttLs/PCQIPVZXo9TrYS5c4TIgTkeXcI41TGo0mT1695OhqgFwy\ncG1vpScNshUTWhAIowRFWxU5RZcIvBiKFFNXkeSCIoUsDamWLZYzm9OTK2Z2jLMA3VCRFZkiF7CX\nLpYlAyKu61MUEkm8ouwIK70PsighCSJxGiOJoMgiMgJFWiCJEmmarOg8WYaqqqTpShCUxqu9oyCJ\noKokWQ6yQJpniBLkrA41+Vd4IEX5/7tfZBHNUFANDU2TyNOUNMoRchHyHLICIRcQEFZjXEFAEsXf\nXCX7s3/2PyLVYn757COCXMRPchbuBN/3yYkw9JQ8zMmTHOSAUjkjcmUW9nz1mWLyf/zgQ9b21mnX\nq3Sbdc6HZwz7c5ZLSNGxKp3VIt1JMLQuaibR7DUgi/jRZ59hzzOOjq7Z3+uSqSIhIrPFlLpqYrsh\nZrVKs1Kiu7HHp1+84LUH9+htdfjs0xM67RaqWbCzs8fubgddlrFMBVkXKXSBxchmbb2C484ZXw/x\nvQC1ZKIZNc6ObVw7p0AlSzTWu3W6jQaVRoWLyZDv//VHVIw2kT3jwcNdJt6Qre110tDl/fff4Pbt\nHtPRJXkSIhAymvSZD2I61TqR67GYzKhXakwmU+I8YeksKZlVFFUjF1KevTjizs0Dzk8v6a11uXV7\nn9H1BEWRmcwW6KrJzvYu3mzJ0rbJxRRJsgg8gSDIKdVL3DjY4fxiSL1RYne/Tq9XpWQZxFGAqcm4\n85heu0MSexRZlcU8Ye92hSQCTY1Z25TZvSmytmuwvt1B0Q0WiynVusXz56cIEnQ36oRhQBwV3Ly1\nxeHhFYosUGQJ3rygatbII4HNtQ0sw6Jcltnfv8ut/T2iOOWzT47Z6G7S2m6RiTlCJJJJPt3NLlap\nglkyWDoCUSCgaxKbO1UkVWc0Xh147j64gRfYlKt1nr86IYok4ljGtkNMs8BQyvihS5oKLJcLmu0S\n17M5uSJQKrdxIw9NU0gcqFplkqLA8VP6x2ccdFts7fQoSjrn4ykvH19Q1FRKmcip69Cp1ll4Lhmg\nFAW2UDCJAtqNKrfXN7A0jXavh2QaBFlCEEScnJwSxhFNs4TUsEhLCrEqkIgahaghCKBrEr3eOgQh\nm4qCZcqYW112Dg7wpi5qqcyvP3vF0/4lWZ7gejmBqHA9t9ELjY2NbQZXQ5ZhgICAioyKSLNaJQ1j\nwiiiUqnieT5JEjMZTfjoo48xNRNFUfECD0FRMcsWL44P2drbpGaWECm46F/RbHQZXvW5Gg549Nbb\nzK8mXB6foeoGVrXK2eU5F4MBviTQX0yo7nRxPI/AjSjCgtCPuP3wAUGSUxgqv/7sc9RaGUvVKFVr\n5Dk8PT3iaHhB17RQhIKKZXL/4Q3Wdjf5/IsXnJ+NuXn/FlkW0e9PGI1cvFQliHParQb/5J/+31Ra\nzdVYOs6RFfOrfWDIwwc3yf2IIoFS2UIQBQxVRkIgy3KMaoXPnz/jZDjk6dERG9tbtNstDg9fUa00\nWS4dNjbWiOKAKIko1Zp883e+w7/4lz9EzAomowmiViKNHRqVKmkU0azVePblY3RDw09jEGU0SSHy\nfbI8QTNVvv7eI04Pj4jDBD8KySRwFh62bTO8HhLFMUmSUiqV6fcHhFFEo9EkSTP6/SHkIpVqDc/1\nyAVAFBhPJgRhvEr2EHKSJP9KBZrTajZQVIVCWO0vyQsMU8V1QiyrtPJDAkmcYJomURwTeCFZmqPq\nKpIsousGGxvrTKdTBBFkWfub6+VZhqSscHVFvhql5vnKAiJJ4grCkq/wgmm6mtdmab4SDqUZuq6h\naSqO4wEFoiKCJK3iCMWVklYSBQxdgzQnL1ZdLBSkSbayp2TQ6XQIo5Ao8BEEEd+PECVpte8UWCmP\nv7o/gKIQyPOCP/3T33Ake/jLf4qvTslaCYsi4uxsyahvs7nZY22jh79ICGcmiZvy3jd3qTUa/NVf\nHaLKCoqi8a3v/R4D12H34Da//tknbG7u8Op4wNjOGMwjyi2Dv/rzL5kPRrx2sMunT75ku7HOcLak\nXiojdsv4SUB7Z4tCdJBkmU8+/ow4FFnfrLNz4ybTYUBPt/CimFpNZ62tkTUFDs9P+Pr916j11vjp\nX/2S0fmAtl7Hn865eWsTs6zx2sMdsjSmUq6TJQWypOLEPi+eXbLW3qRUbVFuNDl8dkg49ag1TH75\n2aesr69RapQR9Brnw0vKzQpVxWK/3WMxOSEI51xcnLCztYOu6uRFRpZm9HZbBLnH6fACvdnAKwTW\nt27gewmXV310TcH3bBqdFrWmxai/wDTKSKLEixcnNOttmo0qhlni8MWA589O6NYM2r0er45HzBcF\nV4MlqioThAsuTkcIgsDv/v4j+pcj1jcqXJ7PEQsFKVWYXsU8eXxIr9fi7LxPu9dGKFI6nSqKHIMw\nJwgj6u0Kw9EY24k5PrvCKht0NlMevXePP/vBLyiVTCzTIssj1tfXGA3nq5G1VWFv6yanr06pVyqU\ndY2Pf3rC5q5GvV3l+PySVmOH1JcpmRa//PkTXEfAbBkMrkcsZgmGZfCrX71EkTTiKCIMQ4I4wIts\nDEsmzxPqLQurprBcFPQvC148HbG3t4W78Dk4uMXjL15xdTWgpMm8/sY+sQhaSeH545d8493/j7L3\nirFsQa/zvp3Dyfmcyrk63O7bfXOYwAkakRZFBQ8gmQbIBwG24Qfr0aJMjAG/GIYNGBIM+kWAAAG2\nZIsGRcoYieZwOMOZuTM3d+7q7urKderkuPfZeW8/7B4ahgED83xQhVOoOvXv/19rfesORBb5vIkb\nWiQyrCw1aaxWmQUWWURqoo7XG1NeMll7fZnEtPjwndf57rvv8ejqDMu2KckK3YWNmcuxv7WOO+xR\nK1WJTJMgTvDjCE03sAdTioUcmQTy+QxmLovlLUjkhMD3kAn5rd/8G0wtm51rFS5++imXnSmeppNP\nVO794gmJZvDzgwNkJcPLyza1jS0ePz0iJ2WRBJXHj5/iC7CytMJkZhFIAg4+giJSNAsIiGRzWQaD\nIRtr65ydnTKbWmytb7BzbY9/9I//MfVShUe/+JT3br3GnRv7jMZDlldbPD14wiLwyRVLCGGMrun8\n6KOPseOYIEkIw5gPvvoeL58dosQi/81/8Q9JPIeV/T3e//DrgMTmtT16RyfMBjM8y+Xo/JSlQoXD\n02PUMGZpZYWwmqN2fQu/M8Abjpl2x0yurjg/OWU6npEgMwldquUSH997gh0n+IJEEtosxhMWQkB3\nPCX2BWw3JFcoM+52+fCDN9i+uc3Om7sgRnTPOsznFqoisNJaZTiepOCR4RhJVihXK+iGzmg44Fvf\n+DWKxQzvvPsGZ2dnlEoV+v0RhbyJa/kYpkxltcnIsthcX0eO4eTojEF3QBBEnJ112d+7zsXpJd/8\n+geMnDH5SolqsYoQBFxdXdIfjXCDkIXrkoQJ84VLuVpiaanGu++9zcHTZywcF1lW0gGUkOYXFQVB\nTFg4CxRNpdFsUKlWKRSKDPojFFXD9RyyZpYkDlEMBc8P8IIAXdPRdY1arUqSxIRBQhD4KQJRSvGI\nQRgShBGyIiMI6UCRZJF8Icfz5y/xfR+S1HyUIKRb5SvggCCkeDrtlbtVfNU0EoSvNsAkIUlIY26C\nTPQKcRdFEUEQoChpc0goxAS/pPkEIVEQIYkgxGkkJHmlf0qKhCgJiKQmn7fffgdNUZhNpsSvEHiK\nqoCQnnNVRX5V7cVfxUpyuRy/93u/96sNzH/xz/8pXafPcDHDmidcHFmUSyWUDLQ7XUZtCckpYmoK\nhlygM5DIVWtM+xOa9WX+3fd/RFE3Wa5UMLQQx51jDz2+8xvv4YdnXLudZ31fYGu/QiJHVFdlDg4u\n0JUSC3fKzm6VQk2iWJTZbDQY9SPWV68z6l/SaObQBJXe2Sm93ozT9hmv37rDghnuuM97H7zBcDbE\nyBmcHfWZzAJKxQw7u5tUqgaBP2M+dtO+TTHE0HQqpTKFUhPP87BmPXZvbfH08ABTk/jr33yPJLaR\nMwrFagZdF/HtGa/duIaMhD3xuP/gBUEgkM2X0DMZnj47wbIDPN+lUW+Skzw0SUKVFJIYFpZDt3NG\nYzlHf9Ijk1ERhABJExgMJwR+hCQIFMpZVtca1GoFjl6cMp165LMlctkMUiTgBDHHxx1ExSCRInQ9\nizVMcHyLa9e2KZZkRNFha2uFy9MhhVyJ2Bc5P+7z2s0bJLKHF8Youoes2Wmlz9hjaWmZKIrxgoBc\ntoRmZOh0R1RKWWxryGxi8/qtuynQW4zwvJjpbMDdN25y/PIIwxQYj4cUSxW+/KxNudDgrbd3WdgW\n89GESqmMIiuYOZNu94JQ9NGLCbHhUas2kGURy3ZptMpIskO91KBcqJEtaniBQ6tRI5OJKRY15lMX\nUdCIY4PAE/Aci93dBmcnM3J5g5ypsLuxTKuZpVTTmc0nXNvZZDS8QM+qeIFLGEiM2wsWoxGdYYf1\n7S36R22kWKa2tUOycFm4Y0q6yslBh/1CmdXGNk+evsSMIJvPsbHSwrbHrG9uUl1aoTudE4QhqqEh\nItJrt9FkgbIgsLG1wotpl72bWxSLEq1WBs8e8/L5C+5/+SWPP31BKUq49sFtPhod8kc/+wXmUosv\nnz5hNvPRNBVZSQgdgcnI4vbt1/mPf/d3uPXWG/hRxNHJMY5tkTg+tWyOwHaYTaYsrSzT7fVQFJUw\nComiEN9fcHh6ynA05Ec//CGdizMkEcbTEaOpxelFFyfw8JKYwXjGzs41stkMX9z7Ej+SMHN50v+T\nMv1elzu3btNs1pn0z6iXC4TdIfIiYLlUY6/aQCJCrtU5umrjxTFXkxHXrl0jU8px+PKQRqlCNLU4\nPXzJ6sYqt+6uc3Z8yd03b/PGu9dZODb3751ydNRhc2WT0HHQVYVf/8aH3Nza5bR3RbnV4OKkjWJm\nGI3HIChM5xYXR8fkYpnECXGcgPF4RpzEXLY7bKxu4S1cpDCkkMmhySrti0uy2TRPe+/+PT75+FPs\nhc3O7hbrayspLckes39tB1UV2dioc3V1RTVfxrZtgjDkvQ/e5+XpKVejHo1WncvLLudnV2RME991\nCLyAnb0dhoNRGtNIQDF0EASsmU2+aOI4FlEUUa83iYhBgEKxwHw+Rzd0QjEtay5XKswmU7qdHt1O\nF03TieOYYrGAqqpoqoTrh/hBRBjGuJZDEPhMJmMcx6NWaTGbTchkDKLIx/UCgiRt+1A1LT3j+umW\n7PsLMqaO66Q5ZVVXiHm1VZKiFuMwRgBkSUSU0tdESUSS08gISYIsK68GYApaF8S0hQTSrVQUJWRN\nQVHV9HtHMbqqoEgySZhmLaMYBElCVSUEKUZXZYgh8EOs+QLX9fD8EEVWUn5ymC4ycZSkhddJCtxP\ngCgJf3UN85/+wX/HbDHhte09xucDlhoreIFHbzykUqmTVfPMRyOiKCBj1jm5WjCazug8e4FlTXHj\ngIpZYDjs0Wi0eOvmaxSLOdqDp7z/1dcw1Ab9bsTpyQAnHFJoZMiW8wjxhECxyIkBM6uHkcDFsw5L\n6zmen93j2ubrtJQ9Ts7alBpLnLk2VnfE1965zcKactk5xfVjTq8GzI5nmLqOkc1guTZ37t7k0599\niRwImKaKbbkUyg3Ojnuoosbjx0eIokCzWcOPAnRDw7ZmeL6DRg47guOTK84P2tzd3kERFbKqztQe\nINY05KzBZDKgVMkSiwG98RWRYDMZdzl+OcINErSMQWutRmstR2vZJBFtgsgnChwMXSRbMskVStRr\nBWbTBaqSQZEULk87Kb1CScjnM4zHMwgVmrUmq8tF1rdKjG2bw+c2N65dSx1o8hhJ9HEWYxbzBUut\nKpY1gdgnV1CpNgp4gYNu6oShSzajoKslhoMBUeJTLteYzxdpg3no4vkyhYxBwdTRZYMXB52/ChjP\nbYskjrAXUyq1MpVKEc+zKdVlHh90EBWd0bgHooemuMRhiKyK2PKEUWfI137tbfSiS6I6rC6VqdRN\nzs7a9Hp9llorXF2MEWWbTCntsAscm1xWQ5FlkkhAEjIIicGgM2K5VWF1dZUnTw/Y3V/FXgzoXU2o\nt1SKZQ1Zgdlsghe5oETImoaopB+kcrmE4zq4kzk5SUPVNDwhYRGEOJqK6Bl4C4t5EPOLz07ozeaY\nmoaeCPi2w/Xr1/EzKocXqZ4YiQmxKEAsMun3KCsSN4p5ttYbrL53neP2MWpW5vbt67z55uvcee8u\nCy0mU1KYEfHx4SOWltd4a+82F0/OmE9mrK+ucNy+4Nvf+DataguZhK3dLe5/+hmff/E5C9tC0mR8\nyyaIYkIhNedpooyZyTCdzZjNZli2RbGY587d2wxHFrZlM5la/Gf/+X/CT37+c1Z3V5ENhXKlxrOj\nF4xmE2RZ5fDpCQt/QXOpiSrprC2vMhuPGQ/HNJdXefj0MZEQYgKVZhFP8BElkfOjEwoZE7c/ZGP1\nGpPeCM8LsTyXJ4fPCYSEWBa4OD0jn8iUNZ2M60GccP/ohNFiynK5jiZArVJjpVmlVs0xt2YUywWy\ngkjFzBJJApVWDX/hkM8VGFpTFARcx2d5bZlqs8mnXz4gSmLeePNtnj59wf7uHqPBBEEWGE6mZPMm\nruum1V6TKXPLRlV1BEFid3cXkgjHtbh99yY3bt/i5z/9iHfuXsObOVTrZUDjqtuhXCsiSVCqlRB0\nnaXNDTqXbYQoYjocY3shkZAwGk7SE6XvU282CIE4Dllq1QkCm2q5wGQyo9MZki9m0HSN26+/RrfX\nQxChudTAdT1cxyUOI3RVx/d9FEVFFMHzbIhjZCmNXIhiapSJgtRwI8kixUKR27fvcH5+huv6SLKI\nIKmYWRMAz/NxFy6BHxJGCWGYnleDIAQhSR8OVY0gShtbEAQkBMRXbSGSmFJ8eLVlRn6AKIhIkoTv\np8MrjpNXGLskNf8kCSTpQ0QUhq9sPGDoWgpND5K0dScGhNRBKwoJSRTDK1PQbDYjDAMkIX2vSQKS\npKQbqSARBmk92C+b4wRF4Hv/1a94kv2f/uB7THou9pXHtz74gLOzIxJJIGNmGfY7aJqDroXopslg\nNMHFYzIasVVZolRf4/lxj3fevMFoOsELQpazK3z/z3/BF5+ecHK4wJ6G5LN5Kk2Va7dW8fpZ3GDB\nylKe0nKDh58cYmYamGKJk7MxYhIixgL2JOLz+09wGHDtdpZqdY2SrvPZvYc0KwWMXInDx8+5+841\nNmoN1IKGlJGorhWxgzn2PEiRSn5CvbZCb9ynVCjQbXfZ3btGq7VGrzfi7OyS0WhEFPtcdsdMZw6d\nqxkVs0RGEdm7scf5pAskRMGM+pKJWZAQJY+t7TqKHnHzzh6qGpLVRRr1OoapsphNaVVK6JJMPpNn\nsQioNiuUS1kkKUZSlPTJ0raRZJhO0lC1roEsi7w8PENVZGr1MqqkEzs+3YujFGAc6aztVelc9Uii\nmPXNEuVShkJOZzLpo8gmZkZmNu+SK6gIskSnP+Xp45fU6yWEyAR5hqwJ2JaK7woYpoKmOVSqCp4f\nshjNyOt5FrOYrZ065WoRXVfJF4q0liqcnnQol00kOWU85vJF/FCn0x+wvbvNwfMn+IKDotUJwxxH\nl20qQp0X9w7pTAbcuv4uDz45oVAo0+ldUavs8OLlc/b3m2RLHlMrQNUr6EpMoZghCHxcB6LA4Md/\n8ZDASVhYE3b21ohigcMXB6xuVBn2Hda3CnS7Fyimztz1yBdUckoW0cogWwlv79/m/v2HOLHCwIqQ\njCInZ8cYGZVzZ4jk25hZlWKhihvG3Ht+TLzwyGRMVEln8/otRogctk9JVI28bKKaOoGQkM3kmLav\nMJKQN1abNJoFHk0umVgzduobvLd9k0wAnaPntI/PePHolM7pKe/e3OFulMF+NuJQsDgOFviCQk0z\nsacjHh9fMBp2iOKQD779Vd556y1W1tboXnX58P13mU5nuJ4LSUKUhExnc3zXp1wpE4YhfuCRy5nY\nVsB4NCZnmNy6cYP5eEa9UGBvY5c//nc/IIwTbl67ycbKNtdv3GLQ7zCfT4h0Hc008XyHxsoSRwcv\nefvOGyxtrfKzzz9nY6NFLZ9leW2FSFN4ePySceRzetrl3pMnWJ5LIosYuTyT8QRJkllaXuXs5SG1\ncoFaJceg3+Pu1z7kW3/tO3zxl1/gBiGXox5LqxWK+SyzictVd0S3P6Hf6XLROefa6zc4Pz3DtRx8\nAlwnxA18Li8G3L//hPnMplZf4vLqivXVFZ49eY4fBZSWyuze3aFSqzEcT/Bcn3a7i+f6JELM6uoa\nK0urjEYTjg+PKZbK/PQnP2c6nTGYWOiGTP+8j2QU8AOPKAhRZYlBf8x0NmcyGlMwC7h+SKIpiCIY\nqsFkPk8rtpJUU3MdBzNrcnXZIYkSptMZJCKVSolsxmA8mfD0yXMEMR0kztRBkWRCPyDwI+zFHN1Q\nSZKYXD5DsZDF9Vz297cgTshkUp0y+mV0I0lIiHj06Bm5XAZIT5TE4Ho+tr0giRMUUSYOYxJJJIXu\nxK9aW2LSiZNeWuMkJnRTTVEQU1OPrChESXrOjV+dY2VZQpSkdPD6EYIgoCoyqqYQx6mmKSASBXGq\nN5K8OqeGKfc1fOXUFUUEARQ17cwUEgESEUEUCaMAQYgRXgEhwiAi8EN0PUXy/dIVLioigiKAlPC9\n3/sVB+af/fH/yMbqKttbaxwdvyRJXEqFCpOhjSxJ6HqGhefjRQ4b28t4Scio7xAKKoHgsrXWRPJj\n7r51l88fn3Dv8XMqO01ef/suQiTgWxaaljAfTJGTLJeHJ+SMGudXIe3zCWahzqA3RVULiGKIH8L9\nRxesbqygZ+Hma2v0bYvR8Rmrt9dY3lhlZvuASqtWRXJCbHdBo1kll5OQBBt3NsTQarSnHuOLPmbW\n5Oj5FQjw5b3nFEsVXhyfMh6PUIm49do6q9dqGFkd3RC4trWNWDSIsKhWi8wdH9VXqVZKIAsYBZl6\nRcdQBPqDEcdnV/iWTzT10LQs5+cXNMsVqrk8MhK+L9AbjDByEtbcJokUFo5HfzBB13WGAwszUyLC\nT4d8EpE1dExDw3FtcnkTOUwo5yvkswazRRfZyOHaYTokBn1sy2JlucVwYCEpoKjgOD4ICnN7QTZf\npZjLoclQLuUZDEIGw4DJ0EYQQqIgwJ47JL6GpsWocQbBV5FF6A/bCGJM6CcUKgZXnTaNRpXLdp/J\nxGE4muAHsHCH6IZE72rM/vUlRMOgPxkTRDmsno/buWLeGSIqRZaXGnSvFuSlLJftCRvry1TKeRbO\ngFKpTLdjkS2a6LKIYeb5yV+cgKCyvLqF64tIooipaly7vsrRyRG1pTzZrEyxZDAczlhardMbzTHL\nZfL5EtPBlPNHA6btBcPxiLXrO/zkL++z1lrC8iwcfPIlg82NFbKRgtu2yGdLRHKes6M2OSmLWsgS\nFotceg5XoyGFTB5BhVw+h0RMIsuossjl0UsKKlw3NcysxlG5QK3ZpFlusLZUQhUN9IzMH/3gM3re\ngkylQrNZZtHucDJeEFQLvPf+e2xvbFNoVVEzGQZXfexFSOyG9Do9aqUcnbNTvvLeO3z68ZeMx33s\nhYNhZFBlkUIxw5vvvM14MmI6mbG3u40uS5xfDRGIiQn54sv7VColtq/tY8ceo+GEX/vqh/zpv/8B\ne9u7fPGLzymV8nR7fQLH5yvvvUsuo7HcXKbeanJ+cUrWyJAvVdERyKkaou9jZnLceO0OnhUyCxI+\nf/SQ5voKG9truI7D2fEF+YxOHPuMxj2K1QrljSaCrNFtj3j42X2KeYM4Tuj1J1xeDJhZFrbr4wdj\nltpiAAAgAElEQVQRN+/eJpEVKpUy7V4H341YrS2RKReYjad87TtfZ6lSYzydoOZVNlZarO+sc3p8\niaqouJ7L3/z1b/L1t97G61kcP3rOzLGRFRlFVKhV6hAlvDg55fLiCsfzGI2nZPM5WivLDAd9kkhk\nPBgznowpF022llfpd9p0O21y+RLD/oi1jTWWVxrcuXOLbq/Hzu4umVKG5ZUWWT1Dp9NFVER0Q0FI\nQJU1JEkjiRJ8P2Q+X2BbCwzTRNdNVNVAlFKWbLFYoFwpMxmPWF9bI4pjJDEmjn3yuowYScysOc5i\ngSiI+IGPIAqpzqiqKVkninAcD88JCIMUeYcAiqpgqBqBG6CZOpqqU8jl8TwHRZVBSAiTVIMUBQlV\nk1EUEU3TUBUZUUhwvCCVA0I/LYQWfumQDUmSXxp6XnVbRjFhGJOQ0n4kBTRTTvF5ikIS/T8A9iRK\nkBUBWRHR1bQCzvXSrw/CVLcM/BgSSGKBJE41T0VJz8AIcZr3lEQkReL3/8tfEVzwl3/yz4kiD8eb\no8gyk75AuVxiNL3ADWKiKCGMJSwv4fHRJUHgsL+/Qm15CVMv88mffs4b1/aZ2nMaLYWVlRy5TJH7\nX57yo88+pVLNosxjur0ZO3e3+Jfff8ynP36BHsjUi8u050PuPz0nk1cxTRFFMXjvzbfpT+YIWfjs\nwUs6ZxGrxQxxoDI+P2RohXz0yT026ks4rkWmXEIOZUQnRg5n1BsKZkFDMzOMLZ97j0+5Gjgs7xWQ\n8xaeKHLSGVGo6vwHv/E+ShgyOOlQzZbRE4PTw5dcv7mCqYlcnHQoZDVO2xf0h10yGQklsijVTCQU\nrrf2iCOF8din0+1Sba5hLxbcvnUN257ghB7IMoIItVoBRTP5P//tx+QyJsVcQLVQRdYMEjNiebXA\nuNMhCiFJ0iLcvKHTLNWoVYp88vMvWa6vcH3nNvfvveD1G9d5dnLOdOKiGQt8T6Dbiel1PBJsBEVA\nVk1m1hziiLXVHIYoIIoBL5/3yRkamZJMo1Hlwf0XlCot7LlLqWjSH1iEQkQiS5x0OniJiGzonJ1e\nomdNxpMZjhcTJgGj6RzdLFGsaQiSS6OWZ9YLefzRCTf21uiPewSOxuZ6g0wlxChCz+7y4F6HvFJl\nc6/Gj/7iS4b9OSHgug6mqbO9sYUszGmf2dgzFTNTYTTt4vs+J0d9dD0mihesb1UZTfqcXV5RqNQ4\na3cZDR0CJ8vBgwOuXVvn3oMJgqlQ3y1yNT+nUC1y48Y6UehSLJTJ6Fmyuoo9OSbyRCrlJrGYI0ny\nXJ2+JAgV8o0ssZ6h63VZ29jEdnrsbC7B3Ob93T168zlyBra+ukN1uYSJRLmY5faHX+fOyhrNZoVC\nKPDk6TP6rkWvP6dU1Niv5+ndf8hrux/y2dUZjeUaN9+5w2jS5R/+9u+y11zjaNjh4OUpWx9c59Yb\nNxDjiEvbJZA0nCTm4f1HZI0c1XoNH5exNUGQI3zPZWW5jCpJ3L65jz23+NpX32Nvf5/p3GLhOliz\nKd/62re5s7/H//av/jWKIHDU7VIrFokjn3/wO3+f3/3d75LYc+zhiP/rz/+M2nqDr3/4AV95520e\n3H9AvVQlciNqxTLb9SUefvQZ/sSifTVDEGL8aEqxorKx0cLy5kimhiIGfPPDr7BUrnHvkwfMHI+x\nPWVjf4cHBy8QDQO9aDDpj9i/fosohjAWuGxfUq6WuTw5IQ4jquUGOcPkxfEhraUazsSiOx0TuSEE\nEVsbq5h2yOnVJaVyltc/uEkrl2fenvCv/uiPMat5fCFAVySS0Gdrd4d+v8dvfO3r6IZMq9Wg1qhx\n/cYuk/GQVqNJ96pHkkioukyrVabWKHD//iOyxSyn531cP2A0GROGIbqiMR6M2Nvdpdft8uzpAdPJ\nFEEQcT0Px3HRNIMwipjN5ixsmzBOg/a6qRMTEQYBtj0jAghD5pbFaDZFTGL+07//d/ntb7zFT3/y\nADFTQjNl6ustTk6O03hJAmEYIArpudTzohQnJ0gEYZjmHGNIiDAMDd918fyABBFJTHAWHtZ8jqar\n6ZYqSIiCQIoMSFAVGUEUEAXIZjIsFi6BF6blz1EIqdKJIAhEIQjCL7XF9D0kMQiCSBjGGFkVRAjj\n9AoXhF5q1CF1+RZLaXmBoskEfshikcIdJFFASNJEpigKBEHqyBUEkJVUJw3DVw5ZklcAeYnf/0e/\n4sD83//wv8UXYyw/zbltrjaZzYYY5YirjgWywGwREssxCCrFgoSiybTPxwi+yP7mHgvXwVdjMhmV\nR4+eUy2o1LaKbG82sCZD1rd3ODq6JF+o0B2coOgmT19cctw5Y3upzuZKmValhILCqDPGXczJZHTW\nmhvUa02G7R5LtSX69phmbYneVY+9G6/jzx22X9uns5jzsn1Ju9dmOLawLZGFLdA+7XB5GVAs6biL\nBesrFb764TsM2hNmPRsxgHHX5dOPD/CCmIPDU67f2WfmjZiPLPJmkd39TRaBS6WiYeZFPM8i9BOs\nkcPCifjZvWNOnw3ZWt9jeWOP+aJLrVLAmo1SQoWoIck6uqgw740RVJPqZgvTzNA+6iAmOuP+iEau\njBLHqHIGQzcJwpjpNEBUYoLEoTPpE2gyDx91uGhbPHhyztlluvmVihlyWYXLMwdN11leKeL7IhEe\nmm4SR+npIquVGQ4nxL6GqkYY5grPnnXJFSWcSKBgVtFMg5yp4dkiC9ul3qxh5rLYjk0+r+PF9iug\nc8LS6hKqkqPbH7OyVubFwYCd/Qb5XIwaK3zrW2/y448eMupPcBYO8/MO9UYTsVKgtbJFtzOhks8j\nZ2A2SxjMJxQaOomgY2ZMhqM2MSLlWpXz9oAvPjvk4HGP8dDl+mvbBOECSYpQNYkoSigUK3h+wsnp\ngNHYoduekM8ZrGyaSIbP5l6TWl0il63iRw5XnZfcvv06fuDh+w6i6FKu1FG0DFPAiQKqxTL9SZds\nuYxeFFldKtEqJLRaKiVdJqOKzFyXznhCLGYponLy6RdUFwm9p+d85ytvs7yzw8nVOdZiQs7IYlRy\nlFSDg0ePCcKA3oPnfLW8jFCp8bPTA3aKLd5b2+bu6jrT8y6T4ZxqvUYYeTTVPH/rzXd5be8G76/v\nsOiM+PSjj9F0nSAKUUWR1ZU6iixAKHJ9dxMFCW+x4OnjA9545zr9fhfXttAkkZcHh0yGI9TY4ONf\n/IyxvSDQYXN1k2w+iyCKzH2bT/70BxjFIvcfHfL2W2/y/rvv8u//zR9z9PIZsqaiySrj+YxPH97j\nyclLfvrwIc8HQ4a6iFrNoWUNXjx9gOJ7rJTr9M76LC836I/GzJ0FxycdFvaCcqnA1cUlSagQxaDo\nItXmEj/5ycc41oJep0+lXOaqd4ksihiqTmtpifbVJYVChkKrgSXAtD2kvtyk17+ioJt89Ow5kpLl\nzgdv8Pd+6zd5eXTK0cU57W6bmb8gYxhU63X+7m/9bfbW1/j041/w4MVTrnpd4kRAN3UOnjxl0OsT\nhSFra+u8PD5iaanGdDLl+bNLjGyZs/M+kiiTyZpomoZpmAz7A/rtDk8eP2U8mZDJZHAdPx1mYoIs\nKrieT/jKoZrL5NJTpigQJSFJEqGqEnESoWsaN7d36ff6OEGAomiM+mN+/rOPuPHmDUIhYrVZotft\nYzsLJEnG811URSOKYuI4QkRANwwA4ihEEGQkWUJRRCQJkghARFIUkpQMQBwnGKaMF/pIkvTKtBQj\nSik4niTVzxeLBYqcGh6jKHW4ipL4V67bII5JEEhi0DQNSRSIohQ2LysKjeU6M8sifhX/IRYgSpAE\nARDwfA9ZkVPUXgyyLKNpyiuCT9qXSYqMJUlSQ5IkpcNYEHg14tPXAb73qxZI/x/f/ydctAMQTcbD\nPr3egHzVIFtq4kYKJxcDzJwMEhhaiOc5xHHCZnOVy5cdnh/1ePcrN5h7C548fMLt63c5ffaSZrPM\ncLQgIxR59MnnNPdXeXh4wbfe+zUiBEqbTVZ3N5FNmUyssGjbDOYuk+mcre0d7n/0JWftIxZ2SLNa\nZzYd4ycRjx7fY/fGa/zio4d4Y7DHLiv1KkutHPmyiusr9AYCipllbafOweEho+GCb3/nK/z4hz/n\n/LyDZ8e06qus797gajSmVCuwt7VOuVbCsedpSDeSMDI5rNmQeWxjZkRUoLW1xeMXpwy6c447c7xF\nQrla5/nTQ+bejDuv38SaziFK/yDy2QKj4RRBMbCFhEcHz1Bjn5qaJQoVOvMBZjHD80cnFDSTJBTw\n3YjXb7+FruSBEMmMccWI+naDT++9BFlB1jWyxRKJ55HNhRSLJopYIo5CIqaoqsmw77CyVsGep1mr\nlwdnuEGJl4c9jl5MwAyIgwTdyCIoeQLb5aJ9ydHzS85OB+jZPHpWJYw9/HBBsZijUErbCRRFJA4j\nhCTH1fmY5eUiYqjQaEm4zgTHmnJxdsq3vv1rfHbvhJVWGXvmMRrYWMM+z05P6Q1tspqMYHjMFjbr\nu01Ew8O2YlqtMsWSjGrUOTruYM913vvwDkv1m/SHl2imQxAEeP4CXc2xWHjMpxZnJ11ajW06gz6y\nIqMbCtmywbX1m8yv+kiBS62+RHfYQ1JhMBhhGDqS5FOqZPCdiDBS2WxlmI87uM6ct+9uoksG7+2v\no3pTqgpkdZX+xSV116BYqXC1iMgkORQzw6ef3KepZ2k7M964vUl5b5XhfITuR+wsryMFMf7BKWf3\n7qO0yjzrtFEaRRZZjR9//oBMrsTu+iqhAH/8kx8zlkT29/dptBoYWRnfc5ADj//+D/4Zf/iTv6A/\nH+J4C5w4JF8tsbBntEpN/sHv/A55Lcej+1/Q64zQM1kc20HTs/SGU6Zzi+5wxMILOG5f8HLQxhQU\nCrpJu9fh/PIEazZBWPhcDLo8OzzCcgKiROD+519ybXuX2XyO7Tq8/+67HJ2d8v7Xv0ZvNkUtlQhU\nneWsSSGT5+mzAyRFwnJtnr44xAkjrOGc7mWXIEwoFKsomkCnO8KPQdRg/9o+j798yu7mFvlMnsl4\nhKRKBGJCsVxgPpulVV62RalaYjAcEUwsYickyZlsbq8iSzGT+Yi8IlJqlvnu3/zrvH3jNn/0v/4R\nzsCh79rM5jaVjElVlNjbWeGs84Lbb72GklV54xsfctW5onvZod8dsr9/jeWVFY5Pjtnd3aZWrnB2\ncYXnaUzmNq3WCuPxAE1R8D2PXC6HiIBj20TEqIqCIIo4roeoKChyisOTJAkxEZAEAXeR0tO8hQdS\njKpKICYEoY8iCAw6HcIwIYwEfD8kkXRmToysxdiLkFazwRePHgEpFScFm8fIsvxqKKfnzTAMMAwD\nTdeoVMq4jo0iy5i6ycJxURWRbCaPbbtkMipREiIKaTQkipOUE5ukW5vnBei6juN6KKqanphVhSgK\n0+gJIp6X6ohJFAMCSRwRRCHJqyEWRTGx4FAsZdF1iThKebKiICCKMmEcEpMgyGL6M8XJKzYziKTn\nZlVWSEjjIyCQL2RxFh5RlFZ7CQKIUjp8SQS+9/u/4sD84Y/+CRcnc3RNAtHFKCk4kU8sqrw46ZCg\nkM2r+DYUck12165zcjjAHks06jXkrMLTox6PDl7y5MmQ3mDCyvIuk4mFbc8QNIntaxX2d4qcH5+T\naBIP7z1htbHNwp4xnVhcXVwiqBqFZosImMxmbNy5jprLc//BUwwjg24YrCw3WFldJZ8vks9nyZer\nhLFI9+yUxHcZDz3OLy3cacTe+hKT7pD9W3tkczmKNYPGSo3heIqfhFgLh8jpsbOaZzYcM55bnJ51\nkYwCz0+vuHw5IZMtcnz1nM3WMknkEsgKoqjiBbCzdws5idm9uYvgL2g0DVrVDOeXXQI35vDwjFq1\nzmxi48wdBt0xhaxBMZvBch3MapZMXkMSRfJamYuTKQvHo1pfxlRMhp0BeiLRPjlhY6VFvVHhL392\nQH2pRKNSRZEkJBYsL2epV+rY1oIwEuh3FtTqBabTKY6VOkJLFRVRTihXSjjBDEmTUVWDUilhYsdc\nnI3JmiF6KcLqK0iCQr5YIGcYWLMJmYyMpspEjk/syGQ0lRs7Owy6AzTNY39/ibPnDs26RLVqMhx2\nWVleRtZEXHwuOy6nJxOK+YR8Q8Msa9y4ewMtm+Hw5RGZXBHVcLl+ewl7ElAtmhDPKBRz+L7N1cWU\nhTVjablKu/uYv/Xdd2i3u1i2xfbmDidHZwhIKHKW2cTH8RwyBZM4UPHdAEPVefn8AlXVsKwB/VEv\nrUfLF9nd2cWe2yB6LDwXzcjyxb0zcnUZM5OlP+wjqCI//svH6L7AeNahPU3oTBw64yGeb6BqIn6u\nQhxFhBmZk8PnBHOb9e1V7m62GAsLDo4PUBSDXueK/+Wf/QseHz9HaRUY2z6artEZjulNLL7967+J\nIQYkpkI48/n+z36KWcrz/vXbDC46PP75J/zZD3+ImsnyYnDGrTeusbK6gbWwMDUTIQbHWmBbNocv\nTjk6PMT1Yvw4oVRv4k8CLtvDtCg6CVE0md1rOwiCixAK5OpFEkWhnC/SajZRdZP2aIwTgGOnMZej\n8zOkQg7PC2lVmhw8eUlW0xj3xshIRHGAoan40xm65zEcdJGkmDh28W0HU9UwFZl6dRnZ0BlYNqKu\n8fT5IWYmg0CMZc3pd7tsrm8TeyFx4rN3c59Ko8Cg10cVFaaWhaSobG1ucNm+Yjy12by1Ta83wun0\nsLp9FFFifXmF1c1VajUTzXd5fv9LrqYTvri6xNRUttbWGdoWpWaFrKnRvTynkMkzsWwWYxtTy9Dv\npBrxeDKl3xtRr9VoX17QaY9otpo4/hw3tCkUDCRkSoUC8+kUe27xxptv0un3CaKQMIoI4gjVMBGE\nNJgfBWG6yUUxge+nJ0hBIhETNP2VZhgGyLKMpMts72yzt72L53pcf+0GiimQKUq8eecuZxcvOTg8\nQtVUkljAddL6MOGVUUcU001PVTTiJCEMgjRHiUAUpr/f0WiOLEmki2OEokgEoY+qyURRQuDHKUNb\nlPGDCEEERRGRJTnVNkURIvB9D0WTXxl9QhRZJXqVhRQEAVVTyBQyKLqCpivkshnMbApijxOB0EsI\nvZSHLUgQCwlIaXNKFMXIipRC393gr7RRw9RTh20ivDITpWUI/6+BKKZnYOLkV2fJPnz6PxPbIfbM\n4v1v3qXQ1Jk5c8bWlIXrY6g5TN1EliVm3TmSVOL8fMBXvvIh46lF52pGplhiPLbYXFvD9wWenZwR\nJDHrG3t88fFDimaW/tWI2AMNDT8WsF2H0A5InBFO4PPkaMDnv3jC6LJLxcyjCgLPHh6QUU2yisLV\n5RGFvE6sK7Tb56ysVTg46vPFk8fMZx5S1sRDpTcaM592WarlIbJJvIhqXkeKDFTVT11cskEShyyc\nOUYuw8XJBUv1JkEssFxdolyoUs7kKdXLPH/ynMZKjU6ngxDI9DszYl/H8gS+/PSAhq5z+41bHLx4\nTK1UJpuvQCQhJBKT4RBBTA0qtVoNsgl5Iwt+zO5qk3pDo29N6fU97qzuk8lkWdgDolhkPB9zMbgk\njHwa+QLVeoVWs4hGSDGrMR2dsLaSR82GnJ8v2N5qcNlu8/jBAEOXCcWIaqWA74t4wRBD0fDCkEa9\nQBD5NBoNFtGMOze30fU8o+mIfC4DoYnj21RKVU7OT8nmdV679Rq2lTZhzPsOrdIG7mKOH3RZWa3S\na/tMhhav3W4iig5CopHJVJB0kUh0ubqaUazkkXWJvVtVmntVxtMOh8dXKEaGa7sN2udt7MWM69e2\nWVkpo4ou1UKBxazLcqtJuVhgNjljfS0PicjFWZ9cto7nh4ynNqOxhTVdkAgKne4AWYy5dWONjBGy\nttVAy8oomkg+n2UyGVPIlxhPxoxHU1RNpVorMBn1KGXLtGo1DFXGd0O8hZXqIiigQ2Y9Q5xX0fUE\n3TRJ6i3sqY86F/FjB32rhHU5Zn7eY/edZa70KVE2S++yx+b6OheHF6wXq7wcjbiajuhcjjlvt8kF\nIgtB4cGTZ1y7fY1MpcD3f/Dn2AosNyocPHjMv/w3/5Zv/+a36LoThjObb33wIcLYRZEUXtvfZzAc\nkc0WyOUMdjY3+O3/6O9x78EDrrqXOF7AeDzhP/zu3+Hw+QHvf/gOTuCjZ0wq5Tqz+ZR3336D3/3u\n36GYzbJUKvHXvvNtvvbu23zjm99itpixsr5OqVpgOJkzHI+YTaecXl3SbDQ4PD1nurAZjPuEiYvv\nLUiikC8eP8ETfcbTGXgLyqbBWrWFkig8OzpnajtY9oJYBLOQxV+4bK6sEUUxuVyWKPB4+vSQ6zd3\nUQBdkDg+PcN3I7JGhsFgQOBHLDwfJwjY2N/m5jtvYFk2sRxQqeR4784dTFXja298HaY2nz66R2Fp\nGb2go2VE1oomvh+Ty+o8ffaEeq3IixfnmJkMxUwGz4up18tMh2OcRYQf2EiCSBhGmNkS7aszVNMl\nV5QRiCgX6xw8PSSKQra2tuj2e9iLtMYOQUiD9WGAqqhomkoUhK/qstLAviLLBGGQ8loLBkHsoSka\nURDhJyKGYTKZWHi+Q/viiihZsLRU5OnD5wyGQzK5DPP5gsCPCf10q5NkJR2WUURMqp1K0isjjAie\n52JoBpblEMcx+bwJJCw8H1nUyZk6QZLgRjGaLOCGEbKQulFlRUFMXumgcUToB+lGF7+KjED6YJDE\nxHGCIitks1nMXIYoTptk7MWCMPDTsvmRi6EYLLWaWI5NTJIadeSUPSsAYiKiSkr6sBEnrzZKXuU5\nUzMRCJimgbPwEIQ08iLLrwq4k9QY9F9/7/87Gv9/B+a//pP/gWkvYmNlDwGRzz95jOiblLMlclUJ\n3dQJHJ+M2KTb8ZEEneXWJs9O2jx5ekEhn0MURdqdNnEk0x8NMTIpN/D06BRdVzi+7LJwdR4/uSLR\n4Py8S6tSolbI88a773JwfMmdd+6wd22ZQinP1HKJlIRScwlRjsiVdMrNBvVsgzCQGPaH2AsbKa/h\nenM2t1ZRc0U2t7dIsHj3a3c5H/QIRQPrcpiK2+4cQy7hThMWgzGVcoaNrT3G4ynNcourThfFSAgR\n+cM/+QGGEtNsFXn//Tu8eHrKe+/exXZmXFxdoGZydIcTfGdBTq9weH5MvlRndOFjzedEfsjO9jaq\nptPtdShVSuSKBcx8HntiI7gujj0iUzIZdKZYEwt3MCWTzTFfnFMoF+lPZsgZWGou441dPM/D87zU\ntTefYhgi+YqBUSpx3rli4cxJkgydywVv3N0HIcKynbT1XIyJgoCCmcF2F4ihRBS5JFKVJ58/ZWe/\nwdnFmO7JAklPkNQQEZnpzMIwZOLIolyqMp3Y5I06w9GQUJjg+DaN5g7Pnw/Zf63GqH/xf7d3JjG2\n5fdd/5z5nDvPdW/N45uHnp7dntrtzgBJLOPggCOSIEQEUZBFNoghELJjxzZhhRABgQhJJIQtk3h2\nD253t93d772qV69ezeOdpzOPLE7ZXkSAw7o+q7uoxT3SVf3O//f//T5fFENAzRQYdS0ymoCsgKhk\nSCQfURM5vjggJyXpHUbOwLFDQhNk2ceybXRDwQt6FLJZhEhHkGQm0wFhOCSOY7RMgf19n8HQZjDq\nc3R0iiKrDHo2kiZieTZBBNeu1ZATn5lqg6F1ymTaRxQicgUFQVSYTE02NpbwvQRR0IiiiMnYxg0T\nxgOHUX/M/u4Fklph4rkMzYDOZMrx6YiX7i1y9OgRrblFHD2L6SvsDcc063n+zZf+Mf/zy1+jdL1O\nbb5I56LLemuFbKLy+K2HHB7sUZMqvLfzhNnZ64ymJplQ4d7yTQSjwEdfeZXYHBG6LvlGA8QIUdKQ\nFQXLMnn7g/exxBghiNndO2bz+BBJkpir1jl4skfnvM+95++gGhpn5xf88IOH+L7M7//eP+X4YJd6\nLcdLL91nZ/cZ/cGI85M20/GUdrvPF372NazhkM2nW7Tmm/i2TefilG+/+za9QZeHj7e4/9w9rq9s\nYCCxt3+Ims3QdyboWQPLdRA0gXypgOU6WF4AukZIhKFJbCzPU8oqtNsjfvBkj3yzxfVbdzg9OSLw\n0mxEWZYJkphSpYoQw/nREbdWVtj6cIvT01O2NvfIFIqsLK/wbGcHXTcoFopIkoIoinT7fWqiTlnT\neHDjJkVF5Tuvf4fV5jLBdEI7cjjrDnl38wk7Hz6jdzyk3+uTK2dRpIBCuYI5NPn1X/11VudXcXpd\nWs0Wa+sr/PIXv4DnhXi2zcBKi99wNKFcLvCZz3yK0aBPrdRAUzWmloXnh7TbXWzHxQ98/MuTjpBA\nHMZIooBj2QRB2hpN4nRtIk5iJFlCUiWMjEYYRSRhjCzKSKKMOZ6QzShMRlMC30FIRFzXYjSyEASR\nycRKrUCCAKR3feJlzmRaTMD3o8vika6aCGI6ORqEIQggyTKuFyKKMTFxalmrGng2IEAUAaGArirp\nC+Xldw+8EASRMEoLVxRHJAgoqkwURoRBgqbJuI7H1Eyj2CzbYTqaIosypZkqSSIw6I8xLYtCMUcY\n+YSXWjtFkSFJE0niMCQKf9RqFRDE1BObxD/x2jq2e/nMyY+f9UcxagIC//qvesL8+lv/jsgVKRp5\njvePiF2JglJkrlkkU7WxvBCZIo3iAu2ew9xMk+2dU04uOvR7FsVChkLRwPQmaJpGc24Ga2wROjKq\nJiCqIu2eS2foEUoBq7MtBqbNwLQ4OGyzs7XF6twi7nCK0x9wa2mJ2fI8GhonR8fcXV/BMAQe7+8y\nHNtsPdunUiqjiwLFZplqpoiulTg/2AZ3ykw1x+C0jeSLrMytYcUOxUaL9tDi/Q+fkitWUOSQRr3K\n470dTs9N+j2TgTVlakK/N2VutcXifA5V9tEFFUnwkMQQL0hIpIR6K0ezXKQ1P89xtwexS78zom95\n+BMTRZbwogBBUbl+YwNNSU09k7HP2cEBtVKOYinPyPY4PB4yvzjPcGqxfXTM2q0mAI7jsR8ai3gA\nABxWSURBVLHUwDYD3CBh6k3pDYfomQxBEKIXDJSCjqGJNGoVjo+P6XRFRj2P+YUMe/tdFpZrxIlH\nTsuTNXRM2yOxRZAUQt8njGImpofvimT1EqIMiRBhOxaGoXL39jXq9QLm1OT8/BRFynNy3OHWS01K\nM3kODhPefKNNa6FGfUbAcxwGo4B6s45j9hHlkESUGIxNTMtB12VazTk6pz2yWgVFFBn0pwxGJksr\nSwx6Aa4bsbhUxbYnnB36PPlgSr8Xsbq2SCIK2J6JlhWJEwGSHAgRcZgwP7dItlShVM5SKmdpn9t0\nzidMJkOq9Tz5XJXAgdD3cayERqPJdDrFMiMsy0NTwXVCRElnZ7NLr20hilk6vT4Lq1UiIcT3BWJX\nYjTtMbtxn4PTKZprUCqtYFkB1dCn84PHvP7hI2bXWtgX5zyoNPizb3zIQA7xE5fG7Zv86XdfJ5Ot\nUSjWuXn9OheEHI1HzN+7y5/81z/GHA8pV8rMVZtsvvOYJIzZPjlm9VqTad9ESiT8MESQBH7l85/j\nO996g8P2OVYUMRhNadXq3Ny4zsnpOfu7+7xw7y4EPutzs7zx5nsEscDj7aeUKmU8y6JSyGGZFq/d\nv03sO6ysL5HPawiKTCGf5+jsgp/95Ce5Pr/CaqPJk/1ttvf2CMOAoizRmq+S0VVymQzdThtV0Tg7\nuUBTDTK5EuZkSiGTI/AcFK1ArGYZR+CTcHJ2wsbyCp7t8iuf/5s82nzM4tIS/fMLxt30LvCVj93n\non3KrZu3IYmRZAXXdtjY2CCTybB3cMBgPCYIIkRCrNGUxbkFfvjDD5FUkeJMncrCHPNLC0xDEIwM\n7d0TcoaELAX86t/5BT772qcZtTvsnw0RJJnjk0O2tjaZ9HvoeZlhf8jm48dISDx8tImeyaBlDBYX\n5xEuTT2amsWc2lxcnDG/0LzcHUyYmhZRlC7sc6mHE0UxvbiLYwRJvLwPTB2ssSAgSAKSLBKGPnEY\nIiQCoR+iSAKyDKPROHWj+iKaZuC6PoKUDgYFfkASCeiGjiyKl0MvaTC1LCtEYfiTYR0AEgRBRJRk\nBDF1zcYkKIqK70TEStpy9TyfcrmEYhg4pouuyQSXayPpUE+UJvKIAlpGxw8DZO0yQi6bQVd1HNv9\nSdECipUyjVqdKIxxbZdCtYIsiySCgB8G2I4FYup+1TTt0nsbokgSSZKumQDp1G668kmSxJfShtRQ\nIIrSZQKKePmsl1oE4f+jYG7v/ScSP8AcTZFEAUURCROPTFFEzyfIqs47bx+ztTnAthyMjMAnPvNJ\nXnhhg8AcUpvTkDM654cXzBTn6A9GeKHLoDdGUhQWV+cRtJjTs3NCNyH2fWYaM1jmCE2V6PT6PN1v\nI+WyPHl2zJOdXfbP9jkfDTkb9+kNx0SWR3OpSWLAwlyLk0GbZiFPzmjxvW/8ACkKuLY+w+pSk+PO\nMfMz80ynAb4Zce3WTUr5PDtnhxSLKjlDoGf1icMAR1C4dn+V1fkVBuYZmztHrJRL3NhYYjToYWh1\nth8dIUkCO083SUQBzw0whAyCm+YKZss6Z2cTnm6dky3lONw/Jk5ExrZDbzym325TMjKEksi73/+A\n+XqFiWfSH1u8vHALoZih3znj/u3byHLCQqVGa7ZJr3/KpDMgiHQe7+xi2SYL9Sb1QplMLksghEhC\nTN5XqRoyC8tLtLshni0SBxFJNkBXFeqtOpFtoRs5Tva7dM8jCDXKhSJKpkCnPWEyTk+d+WqO/Wfn\niIKMpkv8+Zef4XsWsuwRRyqaUkeNZMbdiIPdY55tXvD5z30KXbXYe9bj8PScWzeXOXp2ijn1GE1M\nVD3LcDym1Zpj0JtijhUsP0BAQxASul2L4XRKu+NhuSOqLZf1jVUCDzKZPDkhoblcIFtVGA7B6gt8\n/BNLLK/lqNQMHj06pdv2MDJZ8obCgxc3mGlJbG31iURAlOiej1AVjdl6BUWCyTjBnMYMhyYkCv3B\nOfmcyL07N/jeN3c5O5vSmlvEGjt0zybkDYPOcISHQGOpiZnYlBeWMXsxpu1wbE5Zm5nDFRN2e120\nusZMVkJIYj5+/wbPvbzO7duL/OCDD7jemKd32uOjd29Te36DLWvAOw8fU11dIJPNsXt0QLVc5e33\n3uf8osvReZ/6whzX12/wlS9/k2wxj2v69M8HVPJlvv217zKz1CRfLBAEMY7r0x8NeeuN77O/u5UK\nwDMxrcYi/d6AzmCK7QV0BiM2Nm7ybHufJBQIEpExDiPX5Vuvv8GthQ2USESJBaIwoVEt8ZWvf4PZ\ntRXKiY5rhWzvHeDKCQv1BnevX+P0+Bjfc+m026wvrxB5PsPRAHMy4uaNZSaTKf3JBNuDYrlGPpeh\nlM8Q+x4nBye89cYbGJpO7/yCawvzSKJAbWaGWzdv8r++/hZBBIcnbaqNJqNxH1GSUHUNVIFISBAS\nUvlHNcvu0R6HvQ6zK+uMuiOev3eDb/75d3hudo3/8Mf/DUlwcMY+iqHwCx99jhc3Nuh023xj+yGa\nnqU3snlycYJWqlCuGgRTDyWSmV+c4ziykTWNlVyZkTkhn8sjKTrnnUF64i3mIfTI5wxe/shLbD56\niizJRElyGVkl/3hZP3XEikhi2i5E5HLhP04j2MKQXCaPQDp16joRfhAiK2nxsCwP13MJY3BCD1mS\nUGQF1wswjCyO6eA56eK/KEqEQZhGcQk/OnGlrUtiBUQZQYoJEpdKLced+7dZ2Vgi0UzuzC1zfjbA\nCWPKjSbWdEyxlMcMXQQhIpISEgUUQ8PIZcnkMoSxn4Y2S0KqqpNVHMclkzGQZBFFVxmPRgwGfRRF\nwrZsojBhtjXDRaeNqmoIkgBiepcZekFqEUp3YEh3bECRJZIkneSVpHSX9HLmB0WWSNV7aWFVFZUk\nTsUJSZLw+7//l0vj/7Vgfu2r/5bQjRlPHMrlBtV6lSDxGVs2hpHDD0Oac/NsXLvGs2db2BOLvYMz\njg/PeOH2TZQsfOu7H2APA+zRCM3I4ngezWadRrOMH/sMRxaTocmN9VWqtSqBF1GrVQiTiEw+w3Bk\nIWswO1NGk7PkCwpziy2mUwtV08gJMkkc49omrUoNvZShe9rhre89IQhFajkJ25+ghxHN+WWOTgdo\nxTIkPsNxjw/f+5CbL9xgd/uCe3dvMHIcCnqOhw9P6HQOcOyYop7HE6CMgSjKHJ+fMJqa2L6DpIkY\nOQ1RNhiOp3i2jYbE8vXbHHf6OGOHcj5DfqYEKESBjOUGaAUFezrBmU6RihrbmwfISUK51iQKYibn\nQ/Z6HfAiOv0hQRxxujMmwSHWBCaDiHbfopCvUi1UyGWynJyckS3nWVydR1MEhFGe9uGQJ4fH9Kcu\noeuwtFiivqySVzKcnZ8wU9RJJJGtnS6lTJaTsw6FgsF4ZNMeTyiXDMBHlGIKlQKm5VKp5liYq2Po\nKusbc7iOxLvvHiEqCW48odpo8cLH1nACk3b3FNWQmV1qcHbSIQ4SqtUKhmowHFrISgHLsVA0jb29\nU4pFhSAWKdYyWFZAq9ViNLKpNXK8/LFrOLZNXp9nPBlwY73ENBjz9g+2UBWZyDMpFgW6g0MmY4/r\n12+QyeTZ3jzC0FSsSYel5Sbl2gyWYyIkCUkskM0U2Xy0S5LIiEqGo7MTTNen0+uzsj6HpmoM+gOe\ne+kWgeRh5LK4ls2dWzcYTm3iBKpZndXqHCszZfKijD6yWDd0Xv70xzg9OuDm7DVW6i02n+7waHcX\nWZUp9Lr0tvawf7iHutri6KzLR+6/QDZRUCWRO8u3mE5MAt9lrj7D060dPCEGSacx00TyPPYvLlhe\nnuP69dtEcsgv/fxnENWYp083qVYb2HZIjMBo1GdjfZHbdze4cesm+UoeUU0oFOu88fZbmK7LxA7o\ndLt87OMf4fDogG63R75YwAt9quUCpWKBSq0OfszDzaccnZ5iJwHd/jk37t2jMT/H6KjNl7/zNWbm\naiwuNBkP+yRJRLFQ4LzbJV/IkckbTCZDTMcnChPEJMEcWxBq6LrO081tRv0esgCWOcVzHWZnm5SK\nZRbn5un02vRHQ7r9MZosIasCfdPGiyLO+xe4tg2xiChJrN9aTe+lIlCNAtWywZ07a8yUK/RPe+Sz\nef72Zz/PizfWefpsi7PTY0IR8o0CrXoVYTrme6+/RxxK7JxOaBQy2GMTQri7fAdv7PDh1g5HkxG1\nsk7FyBGHNp949aN8+P0PmVomxxfnIMtIgsCo36OYzZHPZlJnrSLR642IktS3KgrpiSwREoyMlhZH\nSUFRVLhc18hkMwhRar0REHDctC2ZiOnvWRQTJDH1ugqChJ7V0TIykiBijT0EUSBIEgIvJIkiQECS\n5UvZeYiiaghC9GNzju9F+J6PpAhkixnmFuY4Oj7GKBp84bOvsf3DXT77+Z/n0QcPmfgWOUVDkgRs\nz6GUz6FndCRJQpZVPM8nIY3u0jQNWVJRJYVhf4iQpKddP/CJ4hBZVcgVcukpGoiCAEEQqdWqdPtd\nEkASJMTLNrYoSnApYEdIiGMui2EqQ0jb0GmepixLxFFCpVLB89J9Ti7tRIIIkiLye3/VKdk/+ve/\ni25kSRIRSdZ5/HiTVmuWp08Pqc5IzM4v071w2d06IZHhtU+tUaxDZ2jhmgGTicPc0jLLzTXKpSz7\nJ10CD6aTEaYzxcip2L6NJAq4pkl7MELTBRx3gmX5NJpztM/6lPM5MrpCrZLj/HwIkko2l+HkeJe1\n1WWmloccyBztHTPbWKXbHzEKBYxCDrNnMhqMaRQreHFIJlQYDcYc7B2kAwiuzGDQI0lCfvDuJhft\nEUmS4+4LN8iqGu9s7xDaLp/8mQf0Ty8wE59as8DS8jLf/u6HrN5YwIs8LC8mSBQMI8dkbDO70uDN\n721zfnjC4lKDMJBx/D4r86scnxyil+C1z7xE5JtkSlkMucRk6nC0f05jrobnO7QWVxA9l1AR2Fi/\nwZvfecTM0ixvvP0hlVyFXKlAuVLC7HUIfYdmq8nK4goHu7scHx9z+GyMYah0rAApU2B1bgHP8VAN\nkXpVIl8QyEsqkeyxsNpk2J2SL9SZ9ofMrbbIFHPYls3gpEtlro5AjO97TK0hkmgxHQrMNJr8+Vcf\nU6iqzCxBrirT7Z+yst5A1mPUjMZgPMKxUs3X1BkhxQKFWoudvX2q1TpeOKRSLFLMlgkim/OjLqsr\nFXJZhe5oQPuiy/3nFtIcPUvg9W+9T7VSQpIhV6pSqgrU60VmZ1Mdoa5lSQRQVZmTw3Ncx77UZam8\n+9YezQWNZr2KgouWkTg/P0fTsgwmE2JNRs8baHqWSq2MokOruUS/P+T47Cmz81VKOYObK8vsPt1m\n6/ExBUPl1Vde5uBgn6ODXYrZDM/X1zjYesL9a7cY7p1QibLs9Z5RWy/RGQ45PmjzWv02RhJyOrZ4\nbzqidWODb3z/bbaOj/lgd4/Dw1M+ce8BkevxS598jf/yp39GLlfECSx6ww7NWp0v/aPfpH1xyMN3\n3iSnV/jwzXdZv7WCbijMVPOEYky1WUWWJRRBY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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ab6eac8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -760,7 +790,7 @@
     }
    ],
    "source": [
-    "# Note: this requires the ``pillow`` package to be installed\n",
+    "# Note: this requires the PIL package to be installed\n",
     "from sklearn.datasets import load_sample_image\n",
     "china = load_sample_image(\"china.jpg\")\n",
     "ax = plt.axes(xticks=[], yticks=[])\n",
@@ -771,14 +801,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The image itself is stored in a three-dimensional array of size ``(height, width, RGB)``, containing red/blue/green contributions as integers from 0 to 255:"
+    "The image itself is stored in a three-dimensional array of size `(height, width, RGB)`, containing red/blue/green contributions as integers from 0 to 255:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -801,14 +834,17 @@
    "metadata": {},
    "source": [
     "One way we can view this set of pixels is as a cloud of points in a three-dimensional color space.\n",
-    "We will reshape the data to ``[n_samples x n_features]``, and rescale the colors so that they lie between 0 and 1:"
+    "We will reshape the data to `[n_samples, n_features]` and rescale the colors so that they lie between 0 and 1:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -823,8 +859,8 @@
     }
    ],
    "source": [
-    "data = china / 255.0 # use 0...1 scale\n",
-    "data = data.reshape(427 * 640, 3)\n",
+    "data = china / 255.0  # use 0...1 scale\n",
+    "data = data.reshape(-1, 3)\n",
     "data.shape"
    ]
   },
@@ -832,14 +868,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can visualize these pixels in this color space, using a subset of 10,000 pixels for efficiency:"
+    "We can visualize these pixels in this color space, using a subset of 10,000 pixels for efficiency (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -848,7 +887,7 @@
     "        colors = data\n",
     "    \n",
     "    # choose a random subset\n",
-    "    rng = np.random.RandomState(0)\n",
+    "    rng = np.random.default_rng(0)\n",
     "    i = rng.permutation(data.shape[0])[:N]\n",
     "    colors = colors[i]\n",
     "    R, G, B = data[i].T\n",
@@ -867,14 +906,17 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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qbBVFURRFURRFUZT1GhW2iqKsN6xIXJ/y/qD3W1kV9G9WURRFWVNojK2iKIqi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cbG5ui22+RyDRrjs2TO6EAceH3aO0tkClUKtWcDQ3FiGqdQwsxmicOA3tY9dDuHlSkTeQMAIt89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kJF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Xkfwe5HAIKeBHlqSTNOn/e1DWL0NxcDXRjAlEcqaduMI3wIAr8n8QZ5WVk5KQyLxZF/a7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\n3YJSG528CQeD7Hn8IXrbDhH2efHZrbjM7Rx88ylCQS9hhQ+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ft9PZqeeBh24AHDgc9YCu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\nQgghhBCiSvt/Nw9T+IjeT4cAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118fcd400>"
+       "<Figure size 1152x432 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -890,21 +932,24 @@
    "metadata": {},
    "source": [
     "Now let's reduce these 16 million colors to just 16 colors, using a *k*-means clustering across the pixel space.\n",
-    "Because we are dealing with a very large dataset, we will use the mini batch *k*-means, which operates on subsets of the data to compute the result much more quickly than the standard *k*-means algorithm:"
+    "Because we are dealing with a very large dataset, we will use the mini-batch *k*-means, which operates on subsets of the data to compute the result (shown in the following figure) much more quickly than the standard *k*-means algorithm:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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PpbDYpnU5wNZCKGYbob/tIjOqh7FN1QqtNCYyDa8tzmGT3Nr2vNgoCDFhnK9s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yz9dw1i6ve75VLAUVNP/u36w5zmh3I0hGDHY3eRsPwsb11XPdCgarg+rH/phk9ztkJi4hmPPu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CWcOHGU41Vd6PUyX/jsY1jt6WhawlA7/3qfOLaDmqoDzJh10zAlgAuprzvM0fffoLh0PuXT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\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vIU9Rn8+LKIoIgoharUWpVOLxuFCrNYSHH3FraGisJXBIOLG5uYnurqAlUHVNJQ/+9W6u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\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117e3d438>"
+       "<Figure size 1152x432 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -912,8 +957,6 @@
     }
    ],
    "source": [
-    "import warnings; warnings.simplefilter('ignore')  # Fix NumPy issues.\n",
-    "\n",
     "from sklearn.cluster import MiniBatchKMeans\n",
     "kmeans = MiniBatchKMeans(16)\n",
     "kmeans.fit(data)\n",
@@ -927,22 +970,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The result is a re-coloring of the original pixels, where each pixel is assigned the color of its closest cluster center.\n",
-    "Plotting these new colors in the image space rather than the pixel space shows us the effect of this:"
+    "The result is a recoloring of the original pixels, where each pixel is assigned the color of its closest cluster center.\n",
+    "Plotting these new colors in the image space rather than the pixel space shows us the effect of this (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Ls+h9awnjQQcdhJtvvhmrVq3CEUccgYcffhirVq0CY8ysawTwnLw4DjroIHz/+9/HLbfc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\n9lPIrjD2w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oImEphMZqQENNTVM3L7FqM3Wyj1xvCaIDkzTnfPDXqn+uge66O9s5v23l76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WLPfv99kuzVsXLw3u0em0BuOiH/wGYGN9hYfOnWNt5S6maZBMJVgMfMTcFAB7uxXozd9nf/Q8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vG3/hzBUGhuqmdxOYzL00idu401XdtpbezCAERR4mY0GckEEBFvULdWbY33HH5uBY64I5AF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\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3rnH+ozcY+jFhp09sU3l0rVbBuoaHTz3G+z99k4vf+1N0Yx+joVIucnJugdmxWd57+ScU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cxLb5FegK0JTMMiXAbUXAfLcbBM/dxJBmbNIs1zMA0sxyaKo9LFZFgUCNIsRROK2WKCrhmovCxS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RwTSqxHUNr2mQiqcwShUy0TiV4hKZRBLPreF5FpoiE1Yk0tEQgmeQjAaJSQLpYICkLLIuHCVi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\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DQt8nlJIwkhiJBKqqx1MSAUKGZFQdTdVpuQPQdaJIEPk+BD6GpiGjmB4ayoMppUCGUZxL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dc4ag5p9VwOD9vMLc7yyisvc+3aJAfp/IcXUeMVvvDqC1xev871my2WFmdYemWBmfwU/9sffo1e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\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ffXTE2mqROPKpVsps793BX5yRL9T47nc+QFSK6HrAw82HvGWZDCWB/PoBaTTDquwgEGFV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\n/gy7N5H+QY8w6QAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11fd2ef98>"
+       "<Figure size 1152x432 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -966,26 +1012,18 @@
    "metadata": {},
    "source": [
     "Some detail is certainly lost in the rightmost panel, but the overall image is still easily recognizable.\n",
-    "This image on the right achieves a compression factor of around 1 million!\n",
-    "While this is an interesting application of *k*-means, there are certainly better way to compress information in images.\n",
-    "But the example shows the power of thinking outside of the box with unsupervised methods like *k*-means."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) | [Contents](Index.ipynb) | [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.11-K-Means.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "In terms of the bytes required to store the raw data, the image on the right achieves a compression factor of around 1 million!\n",
+    "Now, this kind of approach is not going to match the fidelity of purpose-built image compression schemes like JPEG, but the example shows the power of thinking outside of the box with unsupervised methods like *k*-means."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -999,9 +1037,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.12-Gaussian-Mixtures.ipynb b/notebooks/05.12-Gaussian-Mixtures.ipynb
index f5c4d7358..082377b2d 100644
--- a/notebooks/05.12-Gaussian-Mixtures.ipynb
+++ b/notebooks/05.12-Gaussian-Mixtures.ipynb
@@ -1,33 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In Depth: k-Means Clustering](05.11-K-Means.ipynb) | [Contents](Index.ipynb) | [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.12-Gaussian-Mixtures.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -42,9 +14,9 @@
     "editable": true
    },
    "source": [
-    "The *k*-means clustering model explored in the previous section is simple and relatively easy to understand, but its simplicity leads to practical challenges in its application.\n",
-    "In particular, the non-probabilistic nature of *k*-means and its use of simple distance-from-cluster-center to assign cluster membership leads to poor performance for many real-world situations.\n",
-    "In this section we will take a look at Gaussian mixture models (GMMs), which can be viewed as an extension of the ideas behind *k*-means, but can also be a powerful tool for estimation beyond simple clustering.\n",
+    "The *k*-means clustering model explored in the previous chapter is simple and relatively easy to understand, but its simplicity leads to practical challenges in its application.\n",
+    "In particular, the nonprobabilistic nature of *k*-means and its use of simple distance from cluster center to assign cluster membership leads to poor performance for many real-world situations.\n",
+    "In this chapter we will take a look at Gaussian mixture models, which can be viewed as an extension of the ideas behind *k*-means, but can also be a powerful tool for estimation beyond simple clustering.\n",
     "\n",
     "We begin with the standard imports:"
    ]
@@ -53,15 +25,15 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns; sns.set()\n",
+    "plt.style.use('seaborn-whitegrid')\n",
     "import numpy as np"
    ]
   },
@@ -72,26 +44,29 @@
     "editable": true
    },
    "source": [
-    "## Motivating GMM: Weaknesses of k-Means\n",
+    "## Motivating Gaussian Mixtures: Weaknesses of k-Means\n",
     "\n",
     "Let's take a look at some of the weaknesses of *k*-means and think about how we might improve the cluster model.\n",
-    "As we saw in the previous section, given simple, well-separated data, *k*-means finds suitable clustering results.\n",
+    "As we saw in the previous chapter, given simple, well-separated data, *k*-means finds suitable clustering results.\n",
     "\n",
-    "For example, if we have simple blobs of data, the *k*-means algorithm can quickly label those clusters in a way that closely matches what we might do by eye:"
+    "For example, if we have simple blobs of data, the *k*-means algorithm can quickly label those clusters in a way that closely matches what we might do by eye (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "# Generate some data\n",
-    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "from sklearn.datasets import make_blobs\n",
     "X, y_true = make_blobs(n_samples=400, centers=4,\n",
     "                       cluster_std=0.60, random_state=0)\n",
     "X = X[:, ::-1] # flip axes for better plotting"
@@ -99,18 +74,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
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NHX/LXduMjAxeeOEFZsyYUatgDDffuDvZlNH3MWV09Wt1q7tPZ5LP8f3mZRQ7\nyon3CefpCdPR692HuMuxYSsyozbp3VJ/puRdIrWtDqlMXxmMAfRRgeRtSfQIyN4nC/nPb97ireVf\nccqrCIdaJq5Ix6i+D7Ly6H4uBNuRHQpdTc348yPP0K5lG6713D/fZX+bCiRVCH6EwJ5z1b5WSaMi\nL0SioCiTju07VHvM9Vx7r+ZtXIClU6DbXtCSJHHOuwSbrYSIiMY/fP1zaIj/e8HB3rRrX7vn34qi\nsGnDXIpztwASPkEDGZIw7bZvxyg+o2pP3Kv6d0sBOTc3lyeeeILXXnuNPn361Po88Y2qypqdG9h+\n8SgOxUHngFimjJiELMvVfvPcdWQv755YQEVbV+93Z8VpNr31Mv9+8vXKoPz1yrnkFxbiTLdhLzQj\na1X49ojHUWalNL8E2dcPWafGVmJB4121pZ+pbRT5y4/g07U5Tp1MyEU7j3YYxeKtGzFojfQs0TGk\ndS8GTRiEJEmMGziOk6dPotaoaRXvGrK+tr15eXkcIANJddXsbEf1G9YrdgeGAgd6rc9Nvz+qu1e5\nhSXg6/khbtNKXEzNQqO5+z5Err1P6WnJHD/0OXrNJSrsAYTHPEDHTgMbsIWwaumfGd5nKaEdXX+7\nzJxNfPvlYUaPe83tuLzcbPbv+RytKhOrPYTuvZ4gJLR+vmSJXl/tiXtVO7elh/zpp59SXFzMxx9/\nzEcffYQkSXzxxRdNMqNQQ/hkyf9YpjuHLcBJ2ZnLbM1NZN6OlXz1639W+wecfWg1FZ2rhqJlrZq0\n7kbmrFvI42MfYsmWlcxXJWIcUpXb2ppVROGec/SSm+Ef34q9WPDpFEPephMEJnSs7HlojHomth7I\nqFYDMFvMtLu3HS/97w0u9/BB1rhyVu89tACHpJDQbwiSJNG+bfvrvr7cvFzKvWWu7r97RQdScjIN\n73ZRlWXm81lo/Ax0VyLx8/O/lVvpYWT3gazc9wnEuw/dR+dpiI+Lr5drNGVZWWkkHX2eh+6rWmJ0\n8Pghjhx8jS7dq9+Z6eeWmZFKXNhaQoOqvkiFBUs0D1lLVubjhIZF/XTcRU7un8X00VmVcxeWrNtJ\nRcf/EBXdtGaiC0J1bikgv/rqq7z66qv13Za7QmlpCesLjmMNkKjIKsK/X2skScLqVHjiqz+w6Ffv\nc+2SqHR7ARDmViZr1VwoywZgy8XDSB2Mbr/XhfoSctrCP/7vNQ4cP8TelIVI0T749WlJwY7TIMv4\nmmUmtxtbE11vAAAgAElEQVTK41OnVwboz5Z8Q1JgORX7XDteKQ4nam8vXtv0GRZHBRaLhZMFFzFI\nWib1Hkl88ziuFR8XT+hWmaKq2ItXsyCKdyfhWJKI1aTCbrXhI+sZ2rI3v5j0RN1v7E/imsdx/+H2\nLD93AqVFAIrDieF4Pk/2ePC2D382Rof3fcX0Udlw1aB+947lzF0xFxooIB8/toUHh5S7tQmgb3cL\nC7ZsITTMteXnkf2f8tB9VW2XJIkJI3L4bvlnREX//Ta3WhDqn5gefZsdOXWM0ig9lsQ0AvpXPXuV\nZInyAZF8vPg7nhv3pNs5Piov8q6pR1EUfFSuPqgrEYfK41o+gf7Iskyvzj2YlpHC0iN7KYhQ46Vo\nMOQ66NCqDWpJxmazVY5uHE49heLncNtJqvxyAQXJmby37wd0XaLQtDQCFnbu/JiHTvZl8qgJbsFO\nrVYzpdUgvjy3GUdLV0/VkVvKcJ/2/PGFG28JWVezJsxk+IXzrD24FS+NjgemvoTJdPcNVVdHq86q\n9ouJXpPZAK1xiW7WgbPnZdq0cJ9fejpJRXSzqnkFXtrUas836C7+rO0ThNtFBOTbLC46Fs05K5LG\nM4BKkkSqtcCjfEBwexYWnkP2q3r2qz9ZwINDHwYgVhvMJaXY7YNWURRitUGVP88YOZkHyu/ny0Xf\nsDrCgTIggOM4OGo9x/4v/sIHz7yOSqUiqygP737uvV59hD+yRoXXPbGUnkrH19+VWtPeJpD31y/g\nh7Ob8LbKeIcHE6n25fFhUxk3cDRtzrdg+f6NVGCnV3Qfhg4fVKd7dzPimsfxbDW997ud1RaCoige\nQdlqD22gFkGbtt1YNLczLWMPo1K52mW3K+w62pmJ07pUHme1Vf9Yo6KGckFoakRAvs0iwiLoUh7E\nJlt2tb/3V3tuyvDE/TNg+Ry2p5ygRLHSTBvAQ92m0iwyGoCnR07n1Oy/kdXVG5Vei6O8grDDpTw9\n41m3enQ6HQctqShdrnoerdOQ1M7Oss2rmJBwP6FhYVTXD1EZdDjLbchaDfayckoT01D7GlAHmTC3\nCSbnbAb6UAvnA1WcWPB3Ppr2Kq3jWtE6rtWt3yyh3nXr9ThL1+9i/PCqfYgPJ+oIipjSgK2CkeP+\nzZxVb+GlPgJIWOxdGDnuFbdjwmMmcTjxIF3bWyvLjp/REhIp9k8W7gx1Wod8s8SsPBebzcYL/3iF\npDYy+uiq4Kg5V8iHg56hWVjNPTuHw4FK5dm7tlqtzN+4jMtlOUQag5mcMM5jkl1RUSHTlv0ZqZ0r\nQYbicKLYHcg6Df2TfXl5wlO8O/cjdrQ1e+y3nLf9FDicqIyuJVXenZphLzSTv/0U/v1aow32oXDX\nWfz7tUZxOBl5MYRfTHbP2FXfxEzP2rn2Pl1KPUvi4S/w0l3CavMnLPoBOnW5PZn2FEVhx9YfsJbt\nQJacSNqeDBwys9bJTw4fWEVO+g/oNRmU20IJCJ9Mj17j6qVt4v1Ue+Je1c5tW4cs3DqNRsOnr/yD\nRZtXsOboPgoVM2EqHx7oNJ7uHTtX+0ZfuWM9i09vIYtSfJ16hkZ05rEx0yt/r9PpmDF6crXXy8zK\nZP2+zQSY/NEWO7BW2CnYfRaVToOkUWEvtbIr3cGB4mSKHBYsqwvwGdmhci1z0f5kwku0FFpKUSIN\nGFu6EoBog7wJm9CLvC2JBA5qDz8FcUklk1VPebmF+hfdrBUazYscOzQHSWWhosJR7TD2FUcPbyQ7\nfSladSlmWxx9+z+Hr59nApraWLP8rwzvvZiQn2ZUF5XsYdGi04x9oHaTsrr2GA09mlYqUUGoLRGQ\nG9DEwWOYOHjMDY/bc3Q/n2ZtxNHVFzBRAPyYdxL9uoVMG15zOkKLxcLT7/6KSxFOtC2DKdybhKO0\nHPu8JCKm9kVWV/W0s7edwq+1N1pDEHJpMEWrjtE5vBVGh4bh7aeS8MxQXvnvXzjRUuNxHbWvAfPF\nHDR+rpneiqIQqDJ6HHerFEWhrKwMg8HQ5NJI3iqz2czOrV+h4jwVdj/adppOaspRKmxl9L5nQp0m\nqR05tBal5E2mjih1zVtIX86yBcMYN/ltj2P37Z5PtO8/GDLKtYOXohzhfwsPM2zs93h5eXkcfz05\n2ZlEBaytDMYAvt4yXVpt5fz5k8TFtbvO2YJw5xMBuRFyOp3k5ubi6+vLwi0r+PrwCsq8JZS0DHRh\nfhhbhKEKNLLt2HGm4QrIFy5d4ELaRXp07IbJ5I2iKDz29ktkdTbhFe5P/taTBCV0xGmpoPT0Zbdg\nDODftxXFB87j16clapMev/u70OycN69MfaHymNCgEE7gOekMh5OyU5cJGtEJANORfB4a61rKZDab\nmbt+EZfL8wlSm5gx/IEbplHMyMrg0/VzuWDLIz81A4dRjRJsIMCmY2h4Fx67b1pdbm+jV1ZWxrpl\nM3lkfDJa7U/rbdf8SFQYdOmgY+22r5GMs+jT7+af+yqKQs6lz5g6powry4eaRcKAbus5dnQMnTrf\n63Zscc6PdOhVtZ2mJElMG3OeZdu+YeiIWVitVrZt+gyVcgKn4kVAyEi69RxZ7bXPnN7L0C5lgPuX\nqq7t7fywfrcIyMJdTwTkRuajOV/y3e41lEcbcFwsQN0zGu2wVlx5Glx6Kg3LpTxXog2lHIvFwh+/\nf4+TvkU4gr0wLljOyIDOxASEc9FURkBMLEUHkvHv3xZJknCUWVF7e+4qJatVXDuZwKy472s8sstA\nNh7+GuLdZ7UGXHYQG9eBkhNmIlR+zEx4ltCQUAoKC/i/2X8ju5sPslaN4ihh++w3eGfsS0RHRFf7\n+i0WC79e+A8KegdSeqoQdddA9JEBSEABMD83Ed9Ny5k4pG4bcTRmO7Z8xsyJyajVV623HaVnwYoS\nenbVM2ZoMWu2fERBfgL+ATe3X3BGxmVaRKdw7ZrfFs3hwNrtcFVAttlseHtleNSh18vgTMXpdLJs\n/iwen3QUrdZV37mUXWzbnMaAwU96nNcspgOnkrT07Gx3K0+6AOGRIhgLwt0x/tcE2O12nnnvV3xw\nZg3m7sFYbFZKVDY0Ye57D5vaRmFJdc2QbaYO4P2Fn3Gys4wUH4jax4C1YyCLVadYf3g7ks41vKzY\nnaj0rn9rgryxZhZ5XN+aWYg2qGoY1GG20ta/mdsx7Vq2ZYqpO9rjuTjtDhx5pYTvK+KjZ97gg0f/\nwFcz/8pfH/4VLZq7MmJ9sWYuOb39kbWu732SSqaoZyBfbp5f432Yt3EpeV1dr9mWX4Y+0v1ZpRRk\nZMul6je6v1No5POVwfhqWk1V2bD+pRzYV/N9rIm3tzf5RZ4z+e12BST3YXCNRkOpJajaYx1KKPv2\nrGTCsKpgDNAy1oFiXoTNZvM4r1lMPMeSemO1Ot3q2ryvCx06em48Igh3GxGQG4nf/Od1jspZqEw6\nylNzsZeWozJUv/uRJEmYDuby2ICJnChPd9tIAkAO8ya3pACtn5GypEyQJZxWW+W52mBvio+lcmWC\nvT2vFMu2JAyxrtnXjiILrY87mZzgOXv1kdFT+GbiazyW35Y/+I/m81lv1djbTa3Ir3aiUKotv8b7\nkGUpqAzgqKp/e5Ypnh/2dxKbvfo9gO1XdSydTpAkz9n2N+Lt7cPl/F7Y7QpWq5ODR8u5lG5j2YZA\nevd7GEVROHFiP3t2r6WiogKN9/1cTHP/G85fHU6fe2dSVpzolu7yivhmGWRmevasAUaPf4/5Gx9k\n3qo4FqyOYe7qsYwc9++bfh2CcCcSQ9aNQGZWJgc0GQT2cd/uMHPhXo9jFadCXIGeD1/4PQEBgTiU\n6jdtiIpphjY/h0RdPk67g/xtpypzWJvaRFJyMg3flRfp1KYjPaN70/2Vl/lhyzKK7WbaBXfhvlkj\napxA5ePjy4Ojap5MdoVR1gIOz3Kp5pznsb7hbCzLRG3Uo9g8Z/8qikJzzc0N0zY1LdtPY+uerQzs\nU1ZZdjnTjslYdR9Wb/Wld99bWzucMPpN3vnvdOIiT9O/t4bUywppmX5EZKaybd3f6N/9DP7hTrZu\nCkXv/zRbjjxG+uI5mLzKKSiNZOwD7+Pj44taG0VpmROT0f19cinDn87xnj1rAK1Wy6j7f3dL7RaE\nO50IyI3AvO3LMfV23/hA1qjQRweSt/UkAQNcz38Vh5Og/YX8+6W38PV19aJaaUM4pNjdgpaj0EyP\n8N4kjHuGj1d8y+miS+SkpWP5/hDqCD9MkpZHOw/jwYfdEyo8O2Fmvb6uka3u4UTaKpSoqklcSnYp\ng6NrHp6cMGQM6z7ZTXpvLT7dmpO38QQB/dsg6zQ4K+wEHC7iyUmP1Ws7G5u4uHacKPsLc1d8jV6T\nSplZz4XUQh6dLJNf4GDT7jD8wl90mxyXlZXO8aObCY9ojY9vKP7+AZhMpmrrv5yeTMK9mfTu4hq6\njoqAPt2Sees/T/DqLxy4ni+rGD88l69/fIvQYAOPvGBBkiTKzJeYvewNwsK/4J57H2T+4kXMnHSh\n8v1XUKRQWD4Eg8FzWFwQhOsTiUEagXfmf8y2OM/nukVHLsCBNAZ374/TqCFS688jIx50+6DNycvl\nlbnvktZagzrAiJJSQF9zOK8+/H+VH5JOp5MPFnzOjoIzFKgsOHNKCceH6X3v5/6B1c+IrS+Lt6xk\nWdJOciQz/oqOYZHdeWTUg9c9p6ysjC9XzeV8eRYau4SuzIExNIAQrS9TEyZgNLqWVN1NyQkUReHo\n4R1Yykvp3mNoZdIXRVFYs/xNYoJX47Dlk5HtpHULLZm5/uQU92fk2D8THu7vdp/WrniD6aOWelxj\n4YpSxo4wornqWfWC5SU8cL/7s2WLxcnKPS8yOOFxcrIzOLD7X3ipT+NQ9KAZwJDhzzXJjTzupvdT\nXYl7VTsiMUgT1DWiDRvzNqIJdO/RKOfzWfP3H/D2rvmPGhwYxBfPv83G3Zu5eOky/bpOpE18a7dj\n/rvkG9aGXUYVH8yVlaOXT6fzfvIyCszFNwyQdTFh0H1MGHSf63mkRlOrD2qj0cgvJnvO0r2bSZJE\nl279Pcp371zCkB6LKCisoKJCZlC/Kz3TMiyWVSxcbeCRx93XF6tkz8cIABoNOJ0KV8/Avnoi2RVe\nXjLYTwEQHBLOqHHvuP3eYrGQevE84RFR+Pj4epwvCEL1REBuBHyN3hStOYNP/1ZoA71RnApFB5Lo\nG9r+usH4CkmSSOhbc+rD3flnUMVcM1u7TST5O8+wNm0/DzkmVZuOsz5dm8bzfGoKc3YuI8dRSoBs\nZGqf0SLv9S0wF20jIlRi70EbE0a7f6Hz8pLRsdvjnMDQQSRfXEl8jPvg2LkLRvpbnCxdW0JhkQOb\nTUFVzcQ6RVGosFe/lnzz+v+gdS6lXXwmSYf8yCgYzKhxr981CV0EoS7E/5JGYM3JnQSO60ZFdhEF\ne85RuOccxtaRZButNz65FsxUVFsuqWTyvewUF3sOl/+czl1I5pWNH7O7lZmktjL7Wlt4decXnDh3\n8ra2405SU7xTq8o9yrp2H8zuE+M5cMz1fby4xMG3iyJp1el13v+vBbtNoVdXL4b2NwIK2/da3M7f\nvNuH9p0f8qh3357l9Gj5P8Ym5NMiVsuw/mbGD1nBxrUf1vXlCcJdQfSQGwErNtfs57ZRbuUW57W7\nINdMURS+Xf0je7JPYXHaidcFM2vkDIIDg2iuDuL0Ncc7ym1IsoS/RVPvw4qKonDm3Bl0Wi2x1WyB\nOHvHUso6ua8vtrTzZ+6elbzZ8u5JEJGbm4vVWk5EROQtP3M1+A7gctZW9DqJomIHvj7uIx3mitbV\nnjdq7B+4kDKZH9atR+8VwvDxEzh4YD3xzZ1Mn1TV+23TUssXs4s5nxaFQZtHTr6CUwH//A9xOp+l\nWUxV/cV562ne073X7WOSUDl3A/93S69PEO4mIiA3Am18ozlUfgaV3n1YN05b/dKR6ny48AtWB1xC\n1dH1lDhbMZM0920+mPEqIYoXBxbsRtWnGV5RgdhLLBTsOoupfRTaE+X1Opy4//hBPto9n/QQB5Jd\nIW6dnt+MfIK4ZrGVx2Q7SwDPpU9ZjrtjkkhuTiZLFzyHty4RHxOszvSmRYdfM2jIzS9juqffeNYs\nTyTSfxWLVmaTMMBAdKSG8nInC9dG0K77L2o8t3lsa5rHVgXUsyd3MaqfZxa3hyaa+MN7Rn7xWB4x\nURJQAmxn4erTmEzfExDoep/KUvUjOqVFF8nKukxoaMRNvz5BuJuIIetGYPqIB2hzzIk937Xu1Flh\nx3dfLrOGTK3V+eXl5WwvPoPKtyrZvyRJXPAvZ+rXv2VLm1JMD3RFKbZy6YuNZK89isqgw5ZfRmZf\nP75fPa9eXkd5eTn/2DWHnO6+aKMD0MQGcqm7kbdXf87Vk/n9peqXxPir7o6lMkvnPUX/bqd59lET\nD00y8dvnFeSyNzh75uhN1yVJEqPG/oHQlvPwCn6NdQee48d1k1mx+0UGjlrg1oO9Ii83mzUr3mTT\n6udZs/x1LqdfAMA/uCVGg2dPXaeTMOjSiHEfwGHCiGwO7Pm68meH1JHycvd18YqiYPLK51LiNI4f\n3XTTr08Q7iaih9wIqNVq/jnrddbu2EBKVhoGp54HZ46v9W46ubk5FJucXL0Pk6IoVOQUYxrQtrLM\n0C4CvNRIKhmvZlW9770Xz/BwPbyO5VtXUdjBl2unh10Md3Di9Ak6tu0IwKTuwzh5bC4VLasyUqlT\nihjfYXw9tKJxu5SaQoD3Ge7p4T5Zb8JoPe98+jqtWi++pXpDw6IYFvbIDY/Lzkrn6J6nmX5fhmtt\nu6KweM16rNb/Mvq+GSyZ/QFPX/N4ePUmaBWvAtyDrSxLaFRVj1UGJTzDN/OOMaLffppHu9ZML1tX\nxrgRRvz9Svlhxad06DS4SS6JEoTbQQTkRkKSJEb2H1a5vs9ms5GTk0NAQMANZ0CHhoYRUKzi6gFf\nW34p2hDPZ8OG2BAKdp91C8gV2D2OuxVl1nLkAM+3lKJXUWquyjrVrV0XXrHbmX9oHdnOUoJURiZ0\nHEe/rn08zj2fmsKcHcvIdZQSpDIxrd/9xMd4PpduKkpKivCrYbMrP+PPP7nu0L7PKoMxuN53E0eV\n89Z/nuGJF7YT3/4tflz2RyaNtqNSwfptKmy659Do1gFn3eqyWp04qMp3rtFomDT9U77+7CnaNNuO\nySjz8APeqFSua0WGnCc/P5/AwDs705og3CoRkBsZRVH4aNFXbM0/SbHBSZBZw33NejNt+MQaz9Fo\nNIyM6M787OMQ4lr6IqlVSGWes6sVp8LV2zopToUWutB6aft9fYezaOVbODq4P/sOumij55AebmW9\nO/Wgdyf3smudTTnHq1s/x9zRH5A5h5mjmz/hrwOearJLpFq36cjhndWPfGTnlpKbk0lQcNjPdn29\nOrXaHmqbuDy2bvqGIcMep6TVPSzc8iOK00qnbhPw8Qngx+8P8r8fzfiYnPTupic8VMWc5XEk3D/T\nrR5JkoiMas99CYeQZffrFJWYiBUZvAShRiIgNzKfL57NCt8LyDGBqHBtOfj95QOE7PZn6D2Dazxv\n5uiphO8MYvOpQ1icFbQ0xpCkqDj7Uy5oxeFEUsmU7U7GEOWa4ewostDsZAXPPvpCjfXejOCgIKaG\n9WVu4i4c7QLBqaA/nscTncahVt/8W+37nct+CsZVzB0DmL1rOW/EvVwvbb7dVCoV3kGPsm3Plwzo\nUzWBasM2Mw9NULN1828Z++A3bucoisLhg5vJSNuMSu1D914PExwS5nHM9q1zsZn3oShq/EOG073n\ncI/rV9j9Pcpc54NiOwK4NqBIGPEU4NrPevXimbzw0Fl0OsNPezPbScnqx4QH/lJtiswevWewcvMK\n7h9aWHXdCoW80t61fgwjCHcjEZAbmc2pR5HbXPOhFeHNhlP7rxuQAUb0S2BEv4TKn/MK8nnqn78m\nw2hBNumRi6wMD+lCr5CuJF1MJSYggtHPDa/XWdbTR0xiSFY/lu1ei1bWMOlB95zLNyPLUQx4zvp1\nlTddYyf+kq8+zyEzawF6vYzNptC5vY64GC3FpYmkpJwlNtY1AqAoCl9+Mp3JIxMZ1lWD06mwdO1c\nMiLfpVPXoZV1LlvwG8YN3kigv6tXmnxxK5vWnWXIcPcvW83ip7B9z3r696ma5X7qbAURYSouZHvu\nLrZz65fMnHgOjcb1HnHtzaxh7soKfHyr5gAc3L+e/OxNgExo5Aj8It5gzvJPCPVPotRioMDci+H3\n/bne7qEg3IlEQG5kzE4buE3PulJefXKP69l9fD/lvUMJCKuaQLT3cgF9VCqen/h4XZp5XWGhYTw9\n/tE61+MvG0jDczcrP7npD3tGR0UzeZi3x/BxoF8F5/KrtqdcveJrJg1PJC7G9Z6QZYkJo+CT715D\nq/PlUspSiosz0Em7CPCr+vISH+PkVNIiSktnuuXTbdOuN//7vB8ZWZvQ6yVsNoWwEDUBASZCNKM9\n2qkiyS239RV69fnKf69Z8TZ9O8wntpv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/Pz2PffpPjre1u3zW5ISZbx+aVftC3SJZmWmc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\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x116370940>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -118,7 +96,7 @@
     }
    ],
    "source": [
-    "# Plot the data with K Means Labels\n",
+    "# Plot the data with k-means labels\n",
     "from sklearn.cluster import KMeans\n",
     "kmeans = KMeans(4, random_state=0)\n",
     "labels = kmeans.fit(X).predict(X)\n",
@@ -132,22 +110,22 @@
     "editable": true
    },
    "source": [
-    "From an intuitive standpoint, we might expect that the clustering assignment for some points is more certain than others: for example, there appears to be a very slight overlap between the two middle clusters, such that we might not have complete confidence in the cluster assigment of points between them.\n",
+    "From an intuitive standpoint, we might expect that the clustering assignment for some points is more certain than others: for example, there appears to be a very slight overlap between the two middle clusters, such that we might not have complete confidence in the cluster assignment of points between them.\n",
     "Unfortunately, the *k*-means model has no intrinsic measure of probability or uncertainty of cluster assignments (although it may be possible to use a bootstrap approach to estimate this uncertainty).\n",
     "For this, we must think about generalizing the model.\n",
     "\n",
-    "One way to think about the *k*-means model is that it places a circle (or, in higher dimensions, a hyper-sphere) at the center of each cluster, with a radius defined by the most distant point in the cluster.\n",
+    "One way to think about the *k*-means model is that it places a circle (or, in higher dimensions, a hypersphere) at the center of each cluster, with a radius defined by the most distant point in the cluster.\n",
     "This radius acts as a hard cutoff for cluster assignment within the training set: any point outside this circle is not considered a member of the cluster.\n",
-    "We can visualize this cluster model with the following function:"
+    "We can visualize this cluster model with the following function (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -167,23 +145,27 @@
     "    radii = [cdist(X[labels == i], [center]).max()\n",
     "             for i, center in enumerate(centers)]\n",
     "    for c, r in zip(centers, radii):\n",
-    "        ax.add_patch(plt.Circle(c, r, fc='#CCCCCC', lw=3, alpha=0.5, zorder=1))"
+    "        ax.add_patch(plt.Circle(c, r, ec='black', fc='lightgray',\n",
+    "                                lw=3, alpha=0.5, zorder=1))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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WLVrM/v37kGWFEyeOkZIyiQmjVIlLIBAIBsOQ1fKee+4hLy+PzZs38/Wvf50XXnjhC1PZ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\nfSxO4ysiCAIJCfdWNiotLQFAUWSioqIeuSDL3bi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dOokgCGg0GsaMGcuoUf2fW7PZmcal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\nKT9Z+gJrZjzCT77zE468cRqlW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PcFt1FfX4/DYSc+PgFBEPjpl7V88et2GjQxCC4baeoV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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118e9e438>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -203,23 +185,26 @@
    },
    "source": [
     "An important observation for *k*-means is that these cluster models *must be circular*: *k*-means has no built-in way of accounting for oblong or elliptical clusters.\n",
-    "So, for example, if we take the same data and transform it, the cluster assignments end up becoming muddled:"
+    "So, for example, if we take the same data and transform it, the cluster assignments end up becoming muddled, as you can see in the following figure:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Bslln8KIY1FCAvLT4blLh8XhiLc0rqVymqiotLWdpbKynvb2d3t6eWCv6XA3w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\n0M5E8hUEQRCEdiaSryAIgiC0M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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118ee6550>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -241,15 +226,15 @@
     "editable": true
    },
    "source": [
-    "By eye, we recognize that these transformed clusters are non-circular, and thus circular clusters would be a poor fit.\n",
+    "By eye, we recognize that these transformed clusters are noncircular, and thus circular clusters would be a poor fit.\n",
     "Nevertheless, *k*-means is not flexible enough to account for this, and tries to force-fit the data into four circular clusters.\n",
     "This results in a mixing of cluster assignments where the resulting circles overlap: see especially the bottom-right of this plot.\n",
-    "One might imagine addressing this particular situation by preprocessing the data with PCA (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)), but in practice there is no guarantee that such a global operation will circularize the individual data.\n",
+    "One might imagine addressing this particular situation by preprocessing the data with PCA (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)), but in practice there is no guarantee that such a global operation will circularize the individual groups.\n",
     "\n",
     "These two disadvantages of *k*-means—its lack of flexibility in cluster shape and lack of probabilistic cluster assignment—mean that for many datasets (especially low-dimensional datasets) it may not perform as well as you might hope.\n",
     "\n",
     "You might imagine addressing these weaknesses by generalizing the *k*-means model: for example, you could measure uncertainty in cluster assignment by comparing the distances of each point to *all* cluster centers, rather than focusing on just the closest.\n",
-    "You might also imagine allowing the cluster boundaries to be ellipses rather than circles, so as to account for non-circular clusters.\n",
+    "You might also imagine allowing the cluster boundaries to be ellipses rather than circles, so as to account for noncircular clusters.\n",
     "It turns out these are two essential components of a different type of clustering model, Gaussian mixture models."
    ]
   },
@@ -262,24 +247,27 @@
    "source": [
     "## Generalizing E–M: Gaussian Mixture Models\n",
     "\n",
-    "A Gaussian mixture model (GMM) attempts to find a mixture of multi-dimensional Gaussian probability distributions that best model any input dataset.\n",
-    "In the simplest case, GMMs can be used for finding clusters in the same manner as *k*-means:"
+    "A Gaussian mixture model (GMM) attempts to find a mixture of multidimensional Gaussian probability distributions that best model any input dataset.\n",
+    "In the simplest case, GMMs can be used for finding clusters in the same manner as *k*-means (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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KlKKAmq/Kpfdj3fH29vnXumMuXOTTVz9BMSqkkkCMOEOyiCeTVDzxJeucAYNwnkOcRzZW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nMPq3btp8YFsQY1F5w1nLGnndbm129NosXIgRBTtvp2i8ccrmFlbjkYY/C5XVidJdx+y5Ud9tpC\n29TUVPPf7+dQ4q0hAh2Xj51GctfkoPVMJjPP3jCLhoYGamqq+XH7WjZl7UAfFs7o9FHc43Qwb8cK\ncutLkB0ektwmPiteyr8OLETjgv5KLI9ecgcmkz/AlZWVUVtbw+BeAyjvYQt6vdq8Cioqynnj3ud4\n9esPyGgoRAEGkMid197YtN4Nky9n7ydPkzsgDJVOg+L1Ydxp44azb23zOZAkqSkL+n+uPu8Seu9M\nYVnmBjyKl+EJ6Uy9pmO6XAEkd+3OjV2DR4k6ExGQhU4nv6gElS74Lq9e0pGXn8/gQcEBuaV6fj5F\noX/P7izO2YVKF3jHEB+mEBcX365jq3U0P/+yl+YRkZyGJElEdG++iq+sLkCvD+flxx6hsrISl8tJ\nQoJ/+Hr4kCHc4vXS0GDHaDThcrlYt2EDMTEx9Ol14sUKrrt4OjtffIdqg3/+paIoRDSWcM1Vl9Kv\nTx+mZWawevM2LOYoLpp6U6uN7oWOZauycffnz2AbbvF3I1Lq2Lrydf464pqmub5Hy87P4dk1H1I7\nKBJZq+bbn9/nnK09uPrsi/lw2yLkkUnIYRoKGl1Urc0ickBvvAYdu3wKT8x5lb9cfDtPzn2VTEM1\n7nAZY2EDtTYP5uEpAa8TbZPo0iURjUbDg5ff1uJ70Ov1fPLgP3nj84/Js5cRqQrnyov/D6v1xKvJ\njRw8nJGDT7yX9MlKBGSh0xnQuwfz92xE1gdOVbBIDlJ7Bs/BBBgzuB8bvtsesI2iKPTrEsW5Z01g\nydpN7HVqmipmqewVXDRhRLuHrbpGmchrugOVQlYXcisSTqcTo9HY1O2nqsrGZwsWUVnvoHuslctm\nTOPr75bw2YrN2OQIVB4nvUw+/nHPrcREty+b/EhJiYm8+sgdfDR/IaU1dqKMOu68/nZMRv9xDOjX\nnwH9+reyF+HXMnvZl9hGWJueC0uShGOAlU+3fNdiQH5r/VfUD49uysCVky0sL8wj77P/UDnaivzL\nZ1Cl0xJ19kCqNuzDOrYvkiyxRSngwmduQX9pOrIUQxjg7haFauMBnOW1hMX8kvtQXMeUpJHHTJws\nLi3m4xXzqfDVE6vS43P5cGl9uBQvbo+no07RKU0EZKHTOeP00+m/bCUZLn3Tc1mlsY6Jg3tiMATf\nOQNMmjCBPdm5/JBRhMccj+KopWdYAw/cfBeSJPHiXx/m8/kLyMgvJkwjM/WCSYwYeuxSg6EcWTRE\na7LgqCol3BIXsE5YYxWHCgubgvH+Awf4y2sfUmNIQpJVrC0uZ8n6x6jyaiEquanuUo6i8Nxbs3nx\nrw+1+7iOFBcXy0O3/anp36Ifcudx2FMdkKR15PJQqqps5OvqkAl8rCInRbBrz14MUuDzVUmW/A0g\n/iciDEdMGIajLhz1o3qQsKwUa/cIwiQ1Z6eex4RRZzb9ffveHXy/Zz0KCmNT0umakMSspa9Rl27F\na3dSvXkn1nH9kdUqFMXBpm//yZMTbqFPj94Ix08EZKHTkSSJfz36MB98PofMgjI0Kpmxp/djxuTz\njrnd/bfexBXFxaxYt47U5HRGjRjR9De1Ws01l8484WNL7NKFNx69h4/nL6QsIoqcnH1U1crI5hh8\nXg9V+7ej+Dw8+N4C0qK+4/lZ9/P+vG+pNXVrLvCg1mIzp1KdtwfLEaN8kiSRWVpHfX19UEKOcPJz\nu91UFJVA3+DHJC1lR2s0WjTu4HxqRVGo9zhDpO8B3uYpg40FlYQlBJeHlGSJgX0GcO/MW4L+9tGS\nL/nCsR0p1f9oaG3JYvTfldI4PRUJqN2ZT9SEtIC7fPuQKP67bgHP9mi98pXT6eTtbz4iq+EwKiSG\nRqVy/dQrWqxlfSoRAVnolDQaDbde65/4syczk0+/XcaCdTuwGsKYMf50zhx9esjtEhISuOqS4C45\nHSkqKop7/9Sc5LJk2TKeeuVNqhxu4odORB0WTm1hNlsdYbw6+2MOVdTBUc+vJVkOOVzuQ8LrbR7+\nq62t4Zvvl6NRq5kx+Tx0uuAqZkLnV1tbw30fPU1+VwVfRiGm/s3Z9kppPZOSx4Tczmg00s8Xw+6j\nOhjV7cxHYzX4q17FNE+5ayyqQh1pQFEUanfkERYfiau0Bo5qTe47VM34/lOCXs9ut7OwZAvSoOYr\nRTneRGVKDZpfXktWySHv8gs9wYlioTz8wTNkD9XgOFiJq7KebTW5FHxUzGPXhS5okpmdSUlFKael\nj2pXAubJSARkoVPLzjnAY+/NxW7oAioDhxsha94qPD4fl1147Dvmlng8HkpLS7Bao074C/7S2+/z\nfWYxhvQp6FyN1OZnYEzoQUS3flQf3M2uvMMYwsIIVdrA52wIWtYzKrwpi3zBku/5YOkGGk1dUHxe\n5q5+mnuvmMbYUSNP6JiF3947iz+leFQkBlmi4UAptrVZSBoZc53En0ZewAXjg4Pj//x55u08MedV\nsgxVOA0y9v0lhMVHYB2STPWWA9j3l6Ay6XCWVIOiIGnU2HOKiRrXH02kgYYwDVXr9xExMhVZo8KX\nV8U5nh4M6R/cQ3njjs3Up+iDAoM+LZHqzTloY8wo3tAVrkxS6xeLG7dvZl+Si6pV+zGldcXQKwFv\no5vFK7cwZed2hg9ufoxUXlnB3+e+zIE4F0pEGBGfLeLS7mdwycQZrb7OyUoEZKFT++K7Zf5gfASP\nIYYFKze0GpB37trNzsxMThuaTu9evcjIzOSLhd+yr8xOhUeNSfYyuncXHvy/m9uc3LVq/XqWb9yO\nw+1B625gW40W2RyPBKjDwrH2HkZVzs9oTRZMib2wlexhbPoADhbUojqieIjKXs6EtO7sqizGa4rH\n52ok1lPO3bf66wHX1tbwwdL1OCP8Q92SrKLO3J035i7m9OHDjllYXzgxiqIwe9FnrCndQz1OklQW\nrhl1PsP6H39jj1xXOZLsn2+r7xmHvqc/7yBxdyMXn3XswheREZG89Ke/UVpayo7dO3gxeTnaFH/i\nX+SInv6OSbUOvHYnll/qR1eu8s8jBtAnx6CNNqH6OovzRk7g7MEX0i+1L+C/OPX5fGi1WrxeLxUV\n5SieBjAFXqh6axxoGvyBOKyLBfv+Ygy9muvKK6X1TOze+vS9vYf2UV9RSeSoVNS/vIZKpyFq8mBe\n+/ET/ntEQH5+wVvkDdejlvwD8w1D9HyYvY70g2mkpoSu132yEwFZ6NRs9Y2EajdXWd9yC0On08ms\n515ib40MBiufbpyDpyQbhzYStTmGcGtX1IADWJZvR//fj7nzxtYbyr/45lsszrKhjvCXE6rKzcXS\nI/guQ9aG4fN6/GU6aypZeaiB+opSfG4nGo2WnnERXHruWM6fNJGCwkK+X72GaEsi50+6DbXa/5Vc\nuHQZjabEoMlcJYqRzVu3MHqU/8dvT2Ymn3/3A6W1DUQbdcycNI7hQ4a0+l6Elr3/7afMM2Qjp/sv\noPYDT//0Ga9YYkhKSDr2xi3QhfgMA+iktv8Ex8XFcV7cecx/ey1FR8xYklQyvt0lGAc1X7haz+hL\n9Y97STLEYDKZ6RMWxx2PPdyUFFlXV8tz894k01OCBx+WOhW1bgf2dCtVmw4R090aMCzdJcvJFWOu\n5Ztdayn3qpFtLrwlh3EZ1FikcCZ1G8ZFZ02juLSYd5d/Qb7HRrikYXzSEGae3XzBkda9L97sZU3B\n+EjV+uZHNXa7nX1yJZIUWLpL6W3lm60/cL8IyILw24sx6cgMEXtjzS0PNb/+30/Y67IiGX+5izTH\nQZgZ+971xCf7qw01VpXisBUja8L4vCib6rp6Hr7tTyFLSPp8Pu76y2Nszi0lqu+opuXS0ZW4/kfx\nISHhyN9NeOpopHATEYYoFEXB53XTOx7OnzQRgK5JSdx8VXBxDrVajeLzIcmBd8KSz4vul7nDGVlZ\n/PW9r3AYEoBwCuoh4+Pv+JtPYcRQ0Tf5eK0u242cHjjXvTHNwpdrv+WBS1uen3ss47oNJqt8I1JM\ncxqWYmvgjMT2z7l9ZPLNPL/kXfLiPChhMnEFPm4cfS1rsrawvagIu0VFVKnCZYOnc92Uy0Pu4+9f\nvEzWEDWS7A94FUDlqr1YzIlYx/XDtiYT2acQZbbSRxvHvRfdQ1JCIpNGnwUEZ+4XlxbzypfvsGD7\nj6gn9UYT6a/+lluxhdpF9dx4vv8zflr6SCLnhU7e0qma58T7fF58UuimEC01i/gjEAFZ6NSuumAq\nO17+gDpj852J1l7KpdNaruCz51ApkirwylodFt7UQcrrdNBYXYalZ/Od5OoyD3vueZDrL57BpLMm\nNA1hu91urrzzfg451Oisga0fdZY4GsoL0Md0bVqmKD7c9hqs9XkkpSRRFN7c7UaSJFRqLfsOl7T6\nvmecdy5frXmOenO3gOVdtY2kD/Yf9+eLl/8SjJs5DXHM+X6lCMgnoNYX3IBBkiRqvMHL22rG+KlU\nLqph+fYd2IxeLA0qzooZxCXT2/88tGe3FN659Rky9mVQ32Bn2KShqFQqzhp1JtXVVRwuKabnpJ5B\nRV8O5h9k3uallNdXsblsHxEETpmKHJFK3e5DRKSnEDW+P42l1XjXF/PwfX8jNjqmxeOZ++M3fHR4\nNd5+Ueh6DKZuZz6NGhWmtK5I0Xp+2P4z1/sub/pOzTr/Tzz0zSuoo/3fDcXjxTSkO0MNzVW0TCYz\nPb0WDhz1Wr5D1Uwc0PLz9pOdCMhCm+XmHWTNxs306NaNM0af/ptMU0ju1o0X7r6ejxcspqTGjkUf\nxkUXTT3msOyRnaKO5vO4qS8+QET3wOIYskpNfqOW5xdt47WPv2TYoDRirZFU19RQHdUfM1BXtD9g\nG11kLDX5mfiKM/FGJKL2OOiisvPkw7cycsRIHnnhFYoCGz4BoAqRoXo0vV7PPZdO5s15SymTIsDn\nJUnTwEM3XNZ03ivqGoHgkYKKEB19hLZL0ljIO2qZz+UhxRgXavU2u/H8K7nWcymVlZVYrdY2dS87\nlv59mj/DB/MP8tPebQzrl07/vsGFX37YtIpXDyzC3ceKJEnou6dQ+eNuoiYObPo8qQxh+JzNw8aq\nMA31fYx8sPxLZl1xZ8hjqK+v4/NDq/EN+qVvswTm9GSqN+/H53Qjh2moCXM3VaYD+HrnSqKnpjfV\nwla8PjwLM7jv8ccC9n3X2Vfz5JK3KeunQzbqUGXbmKobEDIZ7Y9CBGShVYqi8Oxrb7Em14bPGIuy\nfRPJi5bz/EN3YbUG9zbtaD2Sk/n7vaGbShztoznz2JdzAENqVHOzB8DjdKAxRFCVswNJpQ4aCgZ/\nveyaQ5mQPIBNNXqUqkbsh/ajMscQbk1A8XrwOB2oj+jV3CNazxt/e4A9mZmYDXryDxdjNJmRJIkx\ng/rw88pMJF3zXbLP4yI9pUvQa4dy5ujTGTNqJBs3byYsLIzhQ4cGXATFmvTkhqgnEWMSzSJOxOWD\nz+XFzK9x97UA/paHSdvsXHHzRSe8b7VaTVzciQX2I3m9Xh7/6EW2G8pRkiP5aNtPDF5h4YnrHmrK\nR1AUhc/2LMeTHtWUk6CNNGAe1oP6jCJMA/yjT/acEsK7NVeJq9tTgOX03hRmVR39sk0WrVmGo78l\nqI+vaXB36jIK/XfbrjD0ev9Q/d7sDPbFNiCrmh8JSCoZ1WndWLlhFflVxYRrwrhw/Pn0Su7J7Fue\nZcmaZZQX2Tj3rOvpEt+2787JSgRkoVWLvl/GyoJGZFOcP+M33Ey+YuKlDz7mqQfb38Ksvr6eT+Z9\nTUl1PXERRq6ZeUHT1XN7LPlhBd+u/AnFB6MH96NbYgKfbczC2Os0qg7sQGeJR2eNx1mWj1xzGGP3\ndOQwPbZdP+Kqs6E1BV5M1BfnEtVnBBq9/1gkWcaYPIiqAzsJtyYQkTKQ2kOZKD4vXlcj3SK0/PPx\nPxMZGcnenIN8t20/DeHRSI076GP6mmcevIP8w6Us35WLXRNJmLuOEUlm7rghuBhDS1QqFWNHjw75\nt8unTmTXO3NoOGLYOsxeyqUXHn8zdwHOGHo6MZFRzN/8PfU4SQ7vwjU3Xfq7ze7eRaUAACAASURB\nVAEvLS/j85VfU+1z0C08mivPndl0LB98+ylbejlQhVv9wTbFwg6nm3cXfsxtF98AgM1mozjcgUzg\nXHit1Yg9uxgAd3ENdVtyib1kFD6nm5qtuf4uUCoZdYOLv3/0L9bu24bHpCZMG8ao+N5cP2YmkUYz\nisMVnJVtd6IK1/pLciaPahqu3pm1G7oEf9elBBOPzn+DmItGoHh8LPzsMe4feRmnDx7J+RMmd/AZ\n7bxEQBZatTkjB1kX+CWSJIl9h9tWCOBIpaVl3PfCa1ToEpFUGpSyBtY89gIvPXwHCfFtb/Tw6gcf\n8m1GeVOHp41LdmC1L0SJGYgEWHsPx1lbSf3hHHSKi6njRlNaVkzP5EjO/78Xef3jL9hSVY9KZ0RR\nFOzFuajDwpuC8ZFkjRafx42s1hDRvT8epwP54Ea+fP1DNBoN6zZtYt7PhUjmRFQAmhiyFR8vvP1f\nnnroHq6vrWHXnr2k9uhBfDveY2v69enDkzfP5PPvllNe6yDKGM7Mi89n2ODjH9LbsWs3c5auoLzO\nQYwpnCumTGRQ2om3vzvZ9O3Rm790gjKQGTmZ/H3Ve9gHRyHJEptdB1n33mO8esPfMRgM7KrNQ9U1\nMBFRDtOwqy6/6d8Gg4FwJxydG6l4faRUhpGWY2J0yhmUTzqdf6+cg2QJJ2J4D/+0qbwqCqrr2OzM\nw3xub8IN/guBHXjJWfw671z1dz77bDmVIwIDsmtTPiOT0zgnahSTTvcngmUd2Mfcfaupr1IwpwXm\nRtTtKSTyzL5IkoSkUdEwNJq3N3/NaYNGnFIVvERAFlrV0gzdNjwKDfLul/Oo0Hdt+pJJKhWVhm68\n9+V8/nZP24alKyoq+X5XHpI5sflY9BHkH6on4ojckzBzFGHmKGoOZbC00IMsJ3BgZy7pg0p5ZtYD\nLF7+A9+t3sC2nCL0XVLxOBtQjqqIBOBpqMdVZ/MncVUU0Wgrpl9K36YhwRU//YxkaL7b9jTaaago\nYkuhv/CH2RzR4l3uiUrr14+n+/VrfcU22L5rF//47zc0GuNBMviztmd/zZM3cEoG5c7gv+sX0JAe\n3Vx2VaumZGQEs5d8zp0zb6blhOPmP+h0OoZqurLeVY+sbf7JN+yy8dpdTwe2QVRJLDm4mco9NmJV\nJpLcUawbrkHOdqI2BI4Q1KRbmLvyG2ZNvIGXV3xKXqT/896jWs/91zxO75TA7mVvrZmD66yueNZm\n4a6yo7H4h7FdVXbctjrMA7sGrF8U6yE7J5s+vfq0/YSd5ERAFlp15tA0Ni3aEtBvWPH5GNA19hhb\nhVZQWYckBQ4VS5JEfkVtm/exZuMGnIa4oAsFbWwybtthNNbA50w+l7Opy5Pd3I135y9l1LBhTJ10\nDlMnncN7n33Jop8y8Jis1BXsw9ytb/O2Xg+yWoPP66Embw9hkbFYew/ncNVhSktLiI9PQDniR7Em\nby+yWoMxLhlHbQV3PvY0zz1893ENyf/W5ixZ4Q/GR2g0xPPFkh9FQP6dFHiqAEvAMkklk+/0134b\nGNGd/Y15qHTNd8k+p5s0U2Df30cuv5MX577FNnsejZKbZFU0N465Nqgn8cyzpzOT5nnDb86fDVI9\n6hDTDGWNikpXHf1T+/FO6lPk5+ehUqlISuoatK7H4yHXWwnEYRnbh7qd+dRnFgFQl1FI4nVnBm0j\nu3wYwk+tfAgRkIVWTRw/jn0H81m6MxeHLhrZWUv/SJkHb7m73fsyhGkgRBKwMaztH8XUlGRYkQGm\nwP6rYTo9I6O87CwtwmVKwOuwU1uYjbFLYBGB/FoPFRUVRP/S5vDmKy9j5pQqLrt7FhUOH5VZm9Ga\no/G5nbjt1YRHdUEfnQjRzXfk4bK3qfH7mCH9WfftFuxV5SBJhMckIWu0hEd1IVvx8c+3/8s/Hrir\nze/v91Je1whycFOLijrH73A0AoBRDiPUpapR8l9g3jTtavI//Cc7zDZIjoRDNQyxRXDr9dcGrK/R\naJh15V0oioLX620a3WnNkOT+LCzKxm0Lni7grXXQN6b58Uj37skt7keWZbSKjAP/Bbh5SPO6PpeH\nuu0HiRwVeEedWmMMGdz/yERAFtrkjhuu5coqGxu3bCU1JYXevXq1vlEI5542hIzFW1H0R1z1N1Rz\nzpltnzc7KG0gvU3fsF/xNRXnUBSFHlo7T836G5WVNr5fuZKtOwvZkzoE+aiMaq3kC0rQ2bN3L+UN\nboxxycjacOoP56A1WojuPxp71nqIS25aV/F6Se9qbap6NHHcmcye8zU5Djfhsd1wVBThcdiJ7DkI\nSZLZW1jezrMUWn5BAR/NX8Thqnoi9VouOHsso4YN65B9g7/YSmF98PIY86l1l9KZjIsfyKe23cjW\n5v8H6gPVzBjin4urUql4+sZZ7D+Yw5Y92xg6eAh9e7Y8xCtJUpuDMcDpQ0cxaOty1us0NOSWou/h\nzxD3Od2kZvg4/7a21ZOXZZlBuiQ2ehqQj2gP6cooRtfFArKEbW0m+p5xeOudJJdq+PNl97f5OP8o\nVI8//vjjv9WLNTS4fquXOmkZDGGd9jyFh4fTq2fPpj6/xyM1JYUwZzVFeftprKkkVm7g0jEDuGhK\n+zIpzxw+hLIDO7GVFKF11jAkRsNjd91CeHg4er2eQQP6kz6gP0tWrMQb1lxDWlF8DLbC1LPHB+zv\nmbf/iyuuPxq9CXVYOProRJw1/jve84d0R26sptZWTri7lpFdwvnLnbc2l7lcspRVRW7CY7ujDgsn\nzByF1hhJXWE2ushYtM4aLjtvwnGfM4DikhLuf/Ed9nstVCnhFLu0rNu+i0STmuSux76LaOtnKspk\nYN2WbXi0zXfJOnsJd10yhbjY9j+eONl0xu/eoF4DcGWWUJpzCOfhKjy7i1HK7Wyq2sf2PTvoE5tM\nhMlMlMXKwD5pRFujW9/pEVwulz+R6hiJU2cNGYO2zEFNzmEaMoqILYdLLOncc+Et7Qruo/qks3/l\nFsoqynA4HETnOrkhZSLdPWaqq6pQqdVY8l1c220Cj157L2aTufWddnIGQ1jrKx1BUhSl3XXI6uvr\nefDBB7Hb7bjdbmbNmsWQNtTPFU3SW3cqNZP3eDzt+kIfrS3n6sc16/jwuxUUOmTCcDMgzsjf7761\nabgZoKKigqv+8TpYEgO2VRQf7uwNfP/xO2i1WhwOB5IksXTFCqpqaply1gTi4uJ46Ln/sKshuDNt\n9cHdRCSnMdLcwFMPtX962JFeeONdfigm6Iezt7qKVx976Jjbtucz9fPuPXy55Ecq6h3EmPRcPvns\nU+b5cWf/7v3tgxfY2s8ZkJgVtamS9295tt1FRrbu3c7szd+Q76siXFEzzJDMA5f8X5v3cyLnqqrK\nRlWVje7dUwKapPh8vjY3eTlZxMS0L3fkuH4NZ8+ezejRo7n22ms5ePAgDzzwAPPnzz+eXQmnsBMJ\nxseSl5/PB/O+paCyFrNOy2UTxzCwTyomkykoiQXA7XbhReboUiGSJDNhZHpTfevc/HyeevczyjWx\nyBotX216i4tG9aXB2QghWsUrXg8JDXncf1/oPq/tUVbbgCQFv0ZpbXALxxMxZGAaQwamdeg+hRNn\ns1WyQ1OCrA0sYVk20Mg3q5Zw8TnH7hh1pIrKSp7d+CmN6dFAPA3Aalc13i/f4NGrT+zCsS0sFmvI\n7+EfLRgfj+P6RbzhhhuafqQ8Hk9QzVRB+L1UVlby8MvvU2PqBiodh92w7/vt3NTYyCXTQhfMSEjo\nQrJJouCo5VJtCVdc21yc/5VP5mEzdGsK3J6IROb+tJ9ExYbPYA6oDKYoCh5HPWePOLNDqplFGXUQ\nIvZGG//YDdsFv5LSEhyRao4uTaIyhFFaEarbdsu+XLUAxyBrQCcxWatmu/MQLpcrZIMV4bfR6iXJ\nV199xbRp0wL+y8vLQ6vVUl5ezsMPP8wDD5z4HYAgdIRPvv6WamNgizxFb2Hxhh3H3O7OKy4gov4Q\nXrcTRVFwl+USTzUut/+ZYllZGbm13qDtFHM8MTEx2LI24arzF0pxO+qx7dtCRLc+dEtKDNrmeFwx\nfTLG+qKAZSp7BTPGjeyQ/QudW2rPXkSVBT9d9BXXMrxH+0Y06n3OpjrSR3JofRzMy+WZz17hjo+e\n4M8fv8D6HZuO+5iF9juuZ8gA+/bt48EHH+SRRx5h7NixHX1cgnBc7n3iJdYUByeohNceYv2n/wb8\nHZyWr1iFLiyM8WeObRoqczqdPPHP/zB/xWZUiQPQGi1I9RVckJ7IkH6pPPT2QvQJqQH7VRSFywcY\n2Zl9kG1FDjyOOlTacPTxyURW7WPFF2932FDc3sws3vpsIYW2eiwGLZdOOoPzJo7vkH0Lnd/7Cz/n\nnYr1KIn+/AdvXSOjC428et8T7drPgh8W82T5MlSWwOz5Lj/X4fC6qRrWPKKjPlTLn/tMZ9q4SSf+\nBoRWHVdAzsnJ4a677uLll1+mT5+2V1HpzAkTnUVnTyzpTEKdq9c++JBvcp1IRwXBJG8p7z/9KCvX\nb+Cted9TobYi+bzES7U8dN0lDE4bgKIo3PiXpzisCWxp2Fi8H32YlorKSqy9hgb8TV1TyLuP3IJe\nr+fa+/9MqUuNovhw1VWh0ZuJ1IcxfexQ7rzh2t+tBKD4TLVNZz5PGTmZfL7pO/bXHKa+qoYEg5VJ\nA8Zw8cTp7b7gUxSFR955il293KgiwlEUBW2mjeRyLdlnGJGOKsHXbZeTN294PGBZZz5XnclvktT1\n0ksv4XK5ePrpp1EUBbPZzOuvv348uxKEDnXNxTNY/8RLVB5RnlPVUMmFk0bR0NDAq3OXYjd3a/rg\nl2PmxY/m8uHz/SkuPswhu4I6EhSfl9qCfSg+H87aCnxdUjElpmLL3oY+JgmVzkDdoSwGJ0YQHR3N\n4cOHcZsT0Slq3PYaLD2b51UvzK5B/eGn3Hb91S0ed1WVDVmWiYiIbHEd4Y8jIzuDZbvWo5FUXDx2\nCvFxLdc4zz64n8c2zMYxwALEADGU51STGJtwXKMvkiTx3J8eZdHqpezKySFc0jJzwtW8vXYOkuwO\nWr/cKwLvb+W4AvIbb7zR0cchCB0iIiKSlx++kw/mfk1BRS1GnYap545l3JgxfDF/AXX6hKDEiUKv\ngS3bttKrZ080ihefomDL3kZkz8FNJTcdthIabSVYew+jsaoUZ005skZDfngKj734Kn2TE/Ga4mjM\n201kyqCA/au0etZn5HJbiOPNzsnh5Y/mcqDKiSwp9I7WM+uW69rVaEM4ubwxfzbfKpnIKRYURWHZ\n9//k/3qex+Qx54Rc/4uN3/0SjJu5UyOZt/NHRg8ZdVzHIMsy0ydM4cjc7CjZgKJUBY3kRMnB2f3C\nr0PkmQt/OHFxsfz5zlt54/GHeGHWvYwbMwYAr8/bVNnrSBIybo8Xi8VK/zgD9pKDmJJ6NwVjgHBr\nvD/hy+tFZ4nDGJ8CgKxS83OJnQiDAaWxLuT+Aeoag+88vF4vT7z9MQeIAUsSvsiuZHmiePzV9zri\nNAidUF5BPt859yIn+wOsJEl4BkTzccZyPB5PyG0qvCHKpwEVvublbrebn3f/zKHCQ8d9bFdPuAjj\nz0d1cCuqZUrP0457n0L7iIAsnDJmnDcJvb0oaHmcVMdpI0YA8Nidt2DxVKI1Bg8da81RuOzVKIpC\nXdF+wn5pLeVSG4iPiyFZXYuChM8TXO0pOTYiaNm8hd9QLAdPiTrQoGHn7t0h34PdbmflmtXkHjx4\n7DcrdErLtq5C6RX8/7w8UWZXxq6Q28SoQj+HjPml7vg3a5Zy7YeP8mDBF9yy/hXufedxqmuq231s\ncbFx3DP4YnTf5OBauJfY1RXcHXMOM8ZNafe+hOMjArJwyjAaTfzp/HGE1xTg83rwup1E1B3irsvO\nb6oYFBkZyVXnn4vXfXT3WJAd1diLsqnJ202YOZrwKH9XqQhvHYPSBvL8Q3dz1oAkarM24HX5O2go\nig99bSHXTQ/OUv302yWotMHziBVtOGXlwfWvZ38+h6v++k+e+nYHt/3nM+79x3PU14e+exI6J7PO\niM8ZfCessnuwRlhCbAGXjz4f/d7AAKs5UM3FQ84hryCf9wp+oDbdQlhcBKrUKLKHanl2XvsfKy5a\n+z3PZ32FY1pPtDMGUNpNRV5ZYbv3Ixw/EZCFU8rUiWfz6TMPc/OweO4Yk8wnz/+V00cMD1jnovOn\nEusqCVjmdTuZNCSVc0emYUpMRWv65cezoYqpI/piMBiwWq08+8gDrPtyNjeN6MKYaA+Tu6l588//\nx9BBgc+VbbZK6tSR1B/ODTpGqSKPM47qn7xpyxa+2JKLw5SEOtwI5jgy3Fb++fbsDjgrwm/lgglT\nseypCVimKAqpVQaSu6eE3KZXck+eGnsTw7PCSNrTyKAMFY/2v5TTB49gwabv8fQJbmeaKZXT0ND2\nKm4ul4tPspbj7R/d3Ks8xcK39l0UFQePKgm/DtHtSTjlGAwGLrvogqZ/b/hpC18sXUVxjR2rQcfU\n0UN57r5befOzeeSU2AjXahiemsQdN1yDJEnMW/QdO7LzUMsyEyadzoSj5uFrNBquuPiiYx5DQ0MD\n6Ez4aqtoKC9EH5OEoijYSw6SZtUFdaNavnEbGAMbB0iyzO6CshM8G8JvSafT8ciZ1/HmujnkGe2o\nPRJ9nJH8+aI7Q66/aO33fLN/PZXeeqJVRqb3PoOpY5uTvzz4Qk6n86jA4wnOW2jJtt3bqeyu5ega\nXb7eVpZuXslNF7Q8Q0DoOCIgC51CXV0t730+l/yKWkw6DdPHj2bE0KGtb3iccnJzWbv5J/B6mL89\nH5cxDgwWaoHXf/iZu1Uyzz4cuq7vJdOncckJvn5iYhLdjFBs6U9jdRnVB/3PjPXh4dx1wzVB6/ta\nKBfg9SooivK7zXEW2m9Iv0G83W8QpaWlaLWakHWdAX7cvIq3bKtQBpuBcAqBtw6twLAlnPEj/BeB\n43oP58f8r5ATA3MUUtwRmM3BeQstiYqMQpXnhqMaufka3UTqA59hu91ubDYbiqIWn7sOJgKy8Ltz\nOBzc+cS/KNYlIUkmaIQdny7jjqoaJp99Ym0Lj6YoCk+/8gbr82rwmWKpyt6Kpbd/yFrx+fwFRfRW\nFm/YztRzJnboax9JkiRuvuBcXvz8OxRzF3SRsVBXznn940jrH9xd6bSBfVi35Gfk8CNbSSr0SbCI\nH8WTVFxc3DH/vnjfBpQBgS0Ifd3MLM5c1xSQRwwezrlZ21h+4AD0tOBtdGHZVcsd59zcrmPp3bMX\nPX/QcbB74MWddU8d0270t0b1+Xy8PPcdNtbtxx6uENsYxsze4zn/jHPb9VpCy0RAFn53n85fwGFt\nF+Qjpgy5DdHMW7mxwwPy/3oXq0z+/r6yVkdd0X68zgYklQafx4063EB1lLGVPZ24saNGMqhfHz5f\n+C1V1XVMueR8Bg0cGHLdSRMmsCNjH6tySvGZYvE5G0jExn13hJrdLPwR1PoageCkvzpfYMLhvZfd\nytSDOazYsY5Ig5kLbpx6XA1/HrvoLp5b+BbZ2ip8KkhuMHHH2Tc2NZt4a8GHLEsoRpUajQSUA28f\nXEliRhzp/Vtvvyu0TgRk4XdXUF6NrAruw1pS3dDhPVJ/yshBpWsOto3VFUSkpGFK7NW8rKoUyVHV\nYa95LPOWLGflzzlUuiR25M9j0tAMbrrysqD1JEli1p23MTM3l9UbN5MU34NzJkwQLev+wBLVkRwm\nONu/iypwKFpRFGzVNhIiYjhr1Ljj7r4XFxPLv29+jOrqKtxuDzExga0eN1dlo+p+1B17SgSLdq4W\nAbmDiIAs/O6sBh1KpTuoqIbFoO3wgKM6anhXYzCjiwhMltJZ4gh3hy7S0JEWLv2eL7bmgyEJ2QDV\nwJc7irBGLuXCKeeF3Ca1Rw9Se/T41Y9N+P1dP/5isha/Sm26FUmWUHwKEdsruWH6fU3rFBYX8Y8F\nr3Gou4QUoePDuSuZmTiaKyYdO6nwWCIjQ0+/alBCJ4k5WlgutJ+4vBZ+d1ddOB1zfeB8R6WxjrOH\n9u/w1zpzWBpKQ/OcTpUmdO9XtU4fcnl7KIrCl18vZNYLrzDrhVdYuGRpwN9XbtsD4YF3O1K42b9c\nOOUlJ3XnlYseYWJuFEOytUzKjeLVmX8hKaG5veiLi9/n8Egz6jgTKp2GxkFRfFK5kZyDBzr8eFI0\n/owvr8OFbU0mVRuysa3P4lDuQex2e4e/3qlI3CELv7vo6Cievv1aZs9fxKGKWkzhWiaM7s/lF0xv\nfeN2Omf8eLLzClj68wEcumh8juDC+Yqi0D3aHGLr9nni36+yrhTkMH9w374yk9yCw9x3y40AOFzB\n/ZUBHL/B3blwcoiNjuH+y24N+beammqyNVVAbOAfeltZtPVH7k3p2aHHcvOZM3ls2Vvk2oqInjio\nqSuUzevjL5+8wH9u/UeHvt6pSARkoVPo0yuV5x6595jr5BcU8N9531Bkq8ccruGqaRNIT0s/5jah\n3HH9NVxVXcXGLVsxzRjGa3OXYNMnIskqfF4PsY2HuenSu4/3rQCwPyeHDYfqkM3NmbSSzsSPGQVc\nU1FJdHQUKbERHCwJzGpVFIUec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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1198d1e48>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -287,8 +275,8 @@
     }
    ],
    "source": [
-    "from sklearn.mixture import GMM\n",
-    "gmm = GMM(n_components=4).fit(X)\n",
+    "from sklearn.mixture import GaussianMixture\n",
+    "gmm = GaussianMixture(n_components=4).fit(X)\n",
     "labels = gmm.predict(X)\n",
     "plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis');"
    ]
@@ -300,28 +288,31 @@
     "editable": true
    },
    "source": [
-    "But because GMM contains a probabilistic model under the hood, it is also possible to find probabilistic cluster assignments—in Scikit-Learn this is done using the ``predict_proba`` method.\n",
-    "This returns a matrix of size ``[n_samples, n_clusters]`` which measures the probability that any point belongs to the given cluster:"
+    "But because a GMM contains a probabilistic model under the hood, it is also possible to find probabilistic cluster assignments—in Scikit-Learn this is done using the `predict_proba` method.\n",
+    "This returns a matrix of size `[n_samples, n_clusters]` which measures the probability that any point belongs to the given cluster:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.     0.     0.475  0.525]\n",
-      " [ 0.     1.     0.     0.   ]\n",
-      " [ 0.     1.     0.     0.   ]\n",
-      " [ 0.     0.     0.     1.   ]\n",
-      " [ 0.     1.     0.     0.   ]]\n"
+      "[[0.    0.531 0.469 0.   ]\n",
+      " [0.    0.    0.    1.   ]\n",
+      " [0.    0.    0.    1.   ]\n",
+      " [0.    1.    0.    0.   ]\n",
+      " [0.    0.    0.    1.   ]]\n"
      ]
     }
    ],
@@ -342,18 +333,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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DiIh/nenz36dT68OYDG7+3FEXtekWevTqe7GrV2UqlYro+g+xeNXrDOpVhEql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jP7UHNgMxEZPVHpWiNLq/dtJCw+jbCUrtgAmwTPfbUZh9PFpZMuBCAmPAyl3hfQb0VRiGojmCYY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\nSy+8gKVPvYG7vtqv7jE0B4ZZ0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PsYAAl1fYbj7c9/xJOvvI1Lly+7umQi6kIYyNQlXSw2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\nIBMREckAA5mIiEgG/g99v0ZXnVp78wAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119918748>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -366,20 +360,21 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
-    "Under the hood, a Gaussian mixture model is very similar to *k*-means: it uses an expectation–maximization approach which qualitatively does the following:\n",
+    "Under the hood, a Gaussian mixture model is very similar to *k*-means: it uses an expectation–maximization approach, which qualitatively does the following:\n",
     "\n",
-    "1. Choose starting guesses for the location and shape\n",
+    "1. Choose starting guesses for the location and shape.\n",
     "\n",
     "2. Repeat until converged:\n",
     "\n",
-    "   1. *E-step*: for each point, find weights encoding the probability of membership in each cluster\n",
-    "   2. *M-step*: for each cluster, update its location, normalization, and shape based on *all* data points, making use of the weights\n",
+    "   1. *E-step*: For each point, find weights encoding the probability of membership in each cluster.\n",
+    "   2. *M-step*: For each cluster, update its location, normalization, and shape based on *all* data points, making use of the weights.\n",
     "\n",
     "The result of this is that each cluster is associated not with a hard-edged sphere, but with a smooth Gaussian model.\n",
     "Just as in the *k*-means expectation–maximization approach, this algorithm can sometimes miss the globally optimal solution, and thus in practice multiple random initializations are used.\n",
@@ -389,11 +384,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -412,7 +410,7 @@
     "        angle = 0\n",
     "        width, height = 2 * np.sqrt(covariance)\n",
     "    \n",
-    "    # Draw the Ellipse\n",
+    "    # Draw the ellipse\n",
     "    for nsig in range(1, 4):\n",
     "        ax.add_patch(Ellipse(position, nsig * width, nsig * height,\n",
     "                             angle, **kwargs))\n",
@@ -427,7 +425,7 @@
     "    ax.axis('equal')\n",
     "    \n",
     "    w_factor = 0.2 / gmm.weights_.max()\n",
-    "    for pos, covar, w in zip(gmm.means_, gmm.covars_, gmm.weights_):\n",
+    "    for pos, covar, w in zip(gmm.means_, gmm.covariances_, gmm.weights_):\n",
     "        draw_ellipse(pos, covar, alpha=w * w_factor)"
    ]
   },
@@ -438,23 +436,26 @@
     "editable": true
    },
    "source": [
-    "With this in place, we can take a look at what the four-component GMM gives us for our initial data:"
+    "With this in place, we can take a look at what the four-component GMM gives us for our initial data (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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/y6bNc5k9d/mI2jUtD0k7T1NLB1MnNaBppfUdkEhKwRErvs3bmxFxpcihyHBN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SeeuHplfLrDQ0tbcNQ1rP+0p7ljXFWSqdSfjOEFuP/mB5A3y2w9vZ84GaqEj/tnf4q50+bR2t6J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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1198f5320>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -462,7 +463,7 @@
     }
    ],
    "source": [
-    "gmm = GMM(n_components=4, random_state=42)\n",
+    "gmm = GaussianMixture(n_components=4, random_state=42)\n",
     "plot_gmm(gmm, X)"
    ]
   },
@@ -473,23 +474,26 @@
     "editable": true
    },
    "source": [
-    "Similarly, we can use the GMM approach to fit our stretched dataset; allowing for a full covariance the model will fit even very oblong, stretched-out clusters:"
+    "Similarly, we can use the GMM approach to fit our stretched dataset; allowing for a full covariance the model will fit even very oblong, stretched-out clusters, as we can see in the following figure:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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SYwkMx6F3Bf/mZ3fw8HceRM/VuuR33L2D7e9/iU/+xZ/WHafpOkXLYmAkQm+X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\n3ClIquFiZFyUoBSEN8IhB99bb72Vvr6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6WlvaEsmxinQ7SyRHCNN6OrDKk5egNE2DjrCP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\nsBUHw6MxbLt1EpThcDEwmqJY2v/aS4493rHJOjQ0xBe+8AVuvPFGLr/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sh8OB0xCcd/Ip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rebYr+dTCY3EnG6XNg/krgiWFLIri1vcuMli5gyIbej2OFQ+ewnfaxanzvPOn6sjdfezJ+WXrw0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\nLEgcpiMKv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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119cc9ef0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -497,7 +501,7 @@
     }
    ],
    "source": [
-    "gmm = GMM(n_components=4, covariance_type='full', random_state=42)\n",
+    "gmm = GaussianMixture(n_components=4, covariance_type='full', random_state=42)\n",
     "plot_gmm(gmm, X_stretched)"
    ]
   },
@@ -508,7 +512,7 @@
     "editable": true
    },
    "source": [
-    "This makes clear that GMM addresses the two main practical issues with *k*-means encountered before."
+    "This makes clear that GMMs address the two main practical issues with *k*-means encountered before."
    ]
   },
   {
@@ -518,13 +522,13 @@
     "editable": true
    },
    "source": [
-    "### Choosing the covariance type\n",
+    "## Choosing the Covariance Type\n",
     "\n",
-    "If you look at the details of the preceding fits, you will see that the ``covariance_type`` option was set differently within each.\n",
+    "If you look at the details of the preceding fits, you will see that the `covariance_type` option was set differently within each.\n",
     "This hyperparameter controls the degrees of freedom in the shape of each cluster; it is essential to set this carefully for any given problem.\n",
-    "The default is ``covariance_type=\"diag\"``, which means that the size of the cluster along each dimension can be set independently, with the resulting ellipse constrained to align with the axes.\n",
-    "A slightly simpler and faster model is ``covariance_type=\"spherical\"``, which constrains the shape of the cluster such that all dimensions are equal. The resulting clustering will have similar characteristics to that of *k*-means, though it is not entirely equivalent.\n",
-    "A more complicated and computationally expensive model (especially as the number of dimensions grows) is to use ``covariance_type=\"full\"``, which allows each cluster to be modeled as an ellipse with arbitrary orientation.\n",
+    "The default is `covariance_type=\"diag\"`, which means that the size of the cluster along each dimension can be set independently, with the resulting ellipse constrained to align with the axes.\n",
+    "A slightly simpler and faster model is `covariance_type=\"spherical\"`, which constrains the shape of the cluster such that all dimensions are equal. The resulting clustering will have similar characteristics to that of *k*-means, though it is not entirely equivalent.\n",
+    "A more complicated and computationally expensive model (especially as the number of dimensions grows) is to use `covariance_type=\"full\"`, which allows each cluster to be modeled as an ellipse with arbitrary orientation.\n",
     "\n",
     "We can see a visual representation of these three choices for a single cluster within the following figure:"
    ]
@@ -536,8 +540,8 @@
     "editable": true
    },
    "source": [
-    "![(Covariance Type)](figures/05.12-covariance-type.png)\n",
-    "[figure source in Appendix](06.00-Figure-Code.ipynb#Covariance-Type)"
+    "![(Covariance Type)](images/05.12-covariance-type.png)\n",
+    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Covariance-Type)"
    ]
   },
   {
@@ -547,28 +551,31 @@
     "editable": true
    },
    "source": [
-    "## GMM as *Density Estimation*\n",
+    "## Gaussian Mixture Models as Density Estimation\n",
     "\n",
-    "Though GMM is often categorized as a clustering algorithm, fundamentally it is an algorithm for *density estimation*.\n",
+    "Though the GMM is often categorized as a clustering algorithm, fundamentally it is an algorithm for *density estimation*.\n",
     "That is to say, the result of a GMM fit to some data is technically not a clustering model, but a generative probabilistic model describing the distribution of the data.\n",
     "\n",
-    "As an example, consider some data generated from Scikit-Learn's ``make_moons`` function, which we saw in [In Depth: K-Means Clustering](05.11-K-Means.ipynb):"
+    "As an example, consider some data generated from Scikit-Learn's `make_moons` function, introduced in [In Depth: K-Means Clustering](05.11-K-Means.ipynb) (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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py1/+su66666xx/1Sf/H2T0q//lyfnBbPyZMnVVdXJ8uydPHiRXV0\ndOgv//Iv3S5WVpi+lvucOXPU1tYmSWpra9PnP//5qO2m1l3kfnV2dkadaMyePVs9PT06d+6choeH\n1d7ers997nNuFdWRRPs3ODiompoaDQ0NybIsHT161Ig6i8WyLQbph7qLZN8/P9TdmTNndN999+m7\n3/2uVq5cGbXND/WXaP+c1F9eWtzpiFzvfMWKFaqtrdVVV12llStXavbs2W4XLyN+Wcu9rq5OW7Zs\n0fr161VSUqInnnhCkvl1t2TJEr3++utat26dpPCQx8GDBzU0NKTa2lpt3bpVGzZskGVZqq2t1YwZ\nM1wucXqS7d8DDzww1lMyb968sXkMpikqKpIkX9VdpFj7Z3rdPfPMMzp37px27typp556SkVFRVqz\nZo1v6i/Z/qVbf6xVDgCAQTzbVQ4AACYiuAEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIMQ3AAAGOT/\nAwS7zXKJEUjLAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119c24d68>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -588,23 +595,26 @@
     "editable": true
    },
    "source": [
-    "If we try to fit this with a two-component GMM viewed as a clustering model, the results are not particularly useful:"
+    "If we try to fit this with a two-component GMM viewed as a clustering model, the results are not particularly useful (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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JayeBMArp6kuRKXiYVrzuPcXFLpKLCH3k9g/ffA8rtLGFQ5IWPpTv0Se30Su3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\nAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1197ec048>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -612,7 +622,7 @@
     }
    ],
    "source": [
-    "gmm2 = GMM(n_components=2, covariance_type='full', random_state=0)\n",
+    "gmm2 = GaussianMixture(n_components=2, covariance_type='full', random_state=0)\n",
     "plot_gmm(gmm2, Xmoon)"
    ]
   },
@@ -623,23 +633,26 @@
     "editable": true
    },
    "source": [
-    "But if we instead use many more components and ignore the cluster labels, we find a fit that is much closer to the input data:"
+    "But if we instead use many more components and ignore the cluster labels, we find a fit that is much closer to the input data (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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QHHzuCK6vJKK0DBGN4hjPC1DmHXtg3yH+5St7+fZdNZ7cfZDe/oi+gdmuYVEU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AEeeeAuivkYqXnFa+BVODctL6/o174kymGKJf+QuRIOc2SiyJrlQ2dE3rYTsUiEWAS0ZhPDsLAd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\nFiG4AQCwCMENAIBFCG4AACxCcAMAYBGCG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lQQAdmolowkRQlXPW9Ht4nGlOy8iPHDkSzz77LADguuuusx9fuHAhFi5c6HhtIBDAz372s9P5ugGDYbHTNvCZNEf1LuLU4vnhhQFohuVaHpmJLFFQSlCa5wOD6GBNyfjePW8cAookPidHxlWiBLGEhcKAKj4vWYrYFu809CsvHIlNR1p66Mt3xs5Tn7/qoipcP2uEo06ec47l51UCEIY+ZWDL8nxoiCQc+/3hoSbIlGDVxVXI88lZJan29yYHiHAutuHF7bUikZv8KCt5Z/3osLhxXj9rOF7a3Y4d9anvEy98d18jAOBzl4wGODCswA8GgkjCREw3keeTkeeT+30Yx2Nw4WWJzgCxhNUnBt4nSyjJU+3BFrkacBgTFTWAMPY90UNnDCgKqraB5xxJWQShQLnq4lH46Y3nQc7xWRbjUCWKSMJEJCE8Vt1iUBUKxjkoAUryVCyaWgYlI6ZNCbLi6opEcNn4kqxEckCVMao4iJI8FYVBBeX5fows8uPWS8fgH248H99ZPhWPfnpGloEHAN3ieO9AIzTDwovbTrhGjigBzh9VAJMxtMUN/O/HJ/DRoWbXaiLD4vjwUDOONsWw/aTm+n3v7Gu0G7hOtMZFcxhELqRdM1HfkUDCPPVSWQ+P3uJ1vJ4BFPnUPbW4YSKaEOV8KXwKRdgvozzsR2GA4bKqID6o0ZBIi9moMsVVk8vgVyS70iVXeaT9HongsvHFtuZNLoKqjCsmiI7VdJExVSKYP7EM+QEFlAq5AJqm524FOSKaMPo3JsMl7+5vhJT0umdX+pGfH8bbexpBqbjhXDFRhGJUmUJJCospMoXkUsQe9qsAdAAS/IqEunYt9yqHENR3aHjvQCPc9pRzYMHEUgAiPLblaEtWRVA6hAA1LXHkkuShhKA1ZmBYgYQOzURNSxwjkoJrgKguaozoyPPJyPd7Xr3Hmccz8meAoCrDDIoYdVfEDRMRzUJUN+BmazVTeITFQRVFIRW3zipGSRHDXz85KZqUOMeiKeVYM7cKpiVqvWVCMX9iGd7Z15Cl/phSZ5w3sQyfu7jKluZ1KAoQOOR7v7RgAgKqjDer60GIGA5y5aQy3LNgfM4VQ6qLNJ6UOrjlkircMHsEWuMGCgMKmuprMX70aHx+7mgkDCaEvwIKNMNCh2ZmedFuWjRhvwqfbCGWsLrUyEk9njseD/vYdyTMnJU7KTgHSvN8OVdqjHMUBjtFzKIJE8daYhhVFHTE5CMJEwnTQlFQ9couPc4onpE/Q+T7FQQUCRHNtAdHp0iYopEmnqMBSkxaElOVLM5xrCUmbgg6w22XjsFNF46yRcICiiSMYCz1t4yvXDkePpnijep621O+clIprjt/OErDPle99pxIQhLAYhxr99RDkgje3tsIWaK484qxOQ09IeIY5AdkxA0GxjiGFwYgUwLWLqMgzRCqsgRCCAKqDJ8sIZowEdMtmIzjqfcO4c20/Vg0tRxfuFx8rypLyf9RlId9ON6qObZBocDl40tRHvZ3cRMANh9twfxJpQj75JxNXwAgEWBaZT5CPhnji1UcbDEcXr9CRfVPZpVOXLdwtDmKquKQw9AbFkdjJIGSkM+TR/A4Y3hG/gyiSBRFIRVFEFUwHZphd6iyNNleZsv4wjbumbREO1cFAUXCiMIALMbx5PqDDmO+eGo57rxiLO6aNxa3zK1CS0RHQVCFX6bgEDeQmG51K7CV8vAJAX674TDW7W1w6Na8mRQqu2/RRKHbnja4Q6YkS2ssYTB0JAxX3ZqYbsKnpEI+BOGAgpBPxk9f2Y23dtc7RNRsLZp54+z3/8+HR9EQye46LQv7sfLCUZAowSVjS/Du/kbXfd1woAmLp5SDyyI+v+1YW1bIhgCYPrwACyaWAQBmV/rh9wXwyYk2e31gMI5E2g03Hc1gONocQ1Wx06NnHGiKJlCa5/M8eo8zgndVnQU452iP6zjZrsFkDEFVgipRey6pPZwaHOkh2oRhob5DQ8KwkDA7lQ+t5Hi+X79zEG9U10G3GLSkUuUb1fV44u39aIsbMEyGPL8MiwkJ4Fiqjd+00gaKsGSoJ2mqksM2FEnotFic463dDY48ACA6Wl/bVQdVJgj7ZQR9EnyKGM7hIiYJn0JRmudDUVBFptNKUyGjNDTTwmu76ly/983qensVFDcsvFld71DbTNEY0SFTDkUiWDSlHFJODXrg5R0nIVOCxVPKcf6oAiE/nJwTe8GoAnx94QRcNbncNtA0edIyFzIfH2vDHz866ro9mmHhWEus81infX9TRIfZRX7Ew6O3eJ78GcJMTmSK6gZqmuOIZYZmCKDIFAookDGHwmIcv9twGG/vbXDUiC+tEmEACoIO3cBrLrIFusXwzr4m3HnFOASTwlnCHqWNzEt+v5sxzqQ5lqzWcRM7IwQN7TpGl/b8MvIpFIUBCUVBFTHdRMJkCPqkrARkY0TPWVdOqRj0ESgMoCWqdynGpltAaViFT5FAKWDlKGz5+GgLVl80CpGEhc/MGolPnT8cEc1EfkCEkOI6w8k2DVHdREiVYVgcO2s7kLkwMRnHtmNtONwYxYSKcNZNIK5bON4ax8iigGOfLc7RrplZKwAPj9PFM/J9QGrUnsm4bdw5ONrjBhojerehEcY5LIvD5ELE63/eP4r3bA128d4NB5pgaAHMmi7c4J+9dThn/FiiBBHdRGGod1OM4rqFpqiOkpCKki7EzizOUdzL7/ApFH7Fh4BCwSDKTq2041Sap2Z5vCk4B8aXhUAJQVmeL6e4GGNAUdJoBpJNXuv3uYdsZIlCkSUUSBQJwwIg2bkLi3G8uuskPjjULBqtODAyLOeUbSAEaInpONkWR3nYnyWLEE2YON4Sx4gMQx83LJgW88YMevQpnpHvBamRe/b81AwrwxlQ16EhkshWZ+TJOLwYscdtGd8UmmGlGfhODIvjo5oY4rpwRd/NYawAYZT8ioRoQrw25bQTAnveqZsXbzGOJ9cdxKs76+zBIEunV2DJ9Aq8nhE68ckUy2cMQzjQSyMvS8jzyXbiNt+vIK5biKa8e1XGivMr8dK22qzvXXF+JQqCwniHAwpWzByGlz85mfW6hVPKHUnmO+eNw7v73EspUzcECuB4a8xRPfPidqFrbzFuLxhq2o0sLz4F50Bx0IeoZmFzczNK8nwYWRRw9ABEEiZqWzVUFvodhl73jLxHH+MZ+R7AuUg46haDYTIYSaMu5IJ1lIU756MmDIa6ds2uP7eShtxMJli7qsEGgLa40WX7/ZGmGBjnkCSSsxv1wrFFoERU8WjJRGBRMKVYaYESgnyfDJrhYT657iBe21XnSHS+tqsOi6eWY8m0Cry6q84eJXjtecPwd8smn8JRFMiUIqxKdmdrOgFVQkCVYDEOzbDw4FLx+S9tq7UHh6w4vxJfWzTR8b6vXz0JhBLH65ZOr8Bd88Yl59SK45Tnk7F0xjC8WV3nKC/NvCEUBnxoiYkJXAnTwoeHmrPOm8XFfZISOIy9TAlmjSzAG9Unsfloq30WKQEWTCrD6mTpKgC0awZIG1BZ2DmTwRs45dHXeEY+BylvPTV6j6cZXZMx/PNr+/DCthO2x/up84fj7vnj0BhJQE/eCEyLdVmSl4nFOEKK7AhbOLYpTdY3VwiFArjt0tGwGMfv3z+Ct/c02Nt45eQyfH7uaMgyTd5IOonrFl7dWec+Taq6Hn/44iW4/fIx6NBMVBUHkec/tUvHJ0nwqxQ+WUJdNzK+EiUI+WSEfDL+4bPn41vLpuBYcwz5AQUBl2HkMqX45tWT8aUF412HlrPkYHLT4vjqVeMhEYLXdtXZFUkLp4iyzBRFIQWRhAHDYmiPmyI34HK8ZYlgfGkI+xuidu7kglGF4AA2Z8g5MA6s29sAQpKyB0na4gYIIRhWIObG+rxSSo8+xjPyaTAmhlAkTKvLTtB/fm0fXtx2whEeeGHbCTRGNNx04Sj7x+02n9TxfZzDsoROfEw30BoTib6LRhdh05GWLAXG2ZV++3OunFyGdXsbHVUcikRw5eQyhHxysuzROa5v3d5GKJTi3kUTsralKZo7wSoRoVMzsSLc5RSlTCgh8Csitt0TqQXXz6AEZWE/ysJ+5/kxWdZKJqjKqCrOvqQpJVCpBFUGgj4Z3752Kr5+9UQ0dCRQktTktxi3tWsY56gqCeFESxwV+cSWJsiEc+D6WSMBAB2agbBfrE7+4bXdrtH6lMzzZ2ePdFwPrTEdlADjy/O8UI1HnzPkjXz6XNWuDHuKmG7ihQwDDwiP9939Tbju/OFQJIrnN9dkKSjeOGekGLtncnToOpojBkI+CW/sqsdHh1vsUMOFowsxp6oIm4+Kuaicc1w4phjLx3Qahs/PFd7gujRpgAWTS/H5uaOhGRbe3tPgOgrvjd31uHPe2KyRdV0lWBnnGFUS6JGBJxClhz6lcyZqX0EpsUM6gFjN6EmjLyZY9XzZFFRljC7pvPwzVYbz/Ao4F/Ny500s7Zwdm3o9AS4YVWg3MZXk+QAATcmBJ7lWYwBwojWOcWVO/fqEwaDpFvL9vctxeHjkYsga+dRsVc3ovjEonYY2PSvUkYImhzy/vadBVMNkVMcwBiyfOQwvbD1hG3XDYgBPvipppDYfbcWFo4vwf66dgva48O4L/CriLXX2d0mU4LbLxuDmi0Y5Yu4EBM0RPecMV4kQNEV1jFSds3kDqoSlMyrw2k5ngtUvU1x3/nBH+COTM2nYu0JyMfoJ04JhilCbyU4tXJZJediHaMLEqouqAMAhKzy5RMXSacOy3hP255ZYAIT3z5ioJgr6JPhkCUFVgiJR1LUnIEvUK6P06FOGnJHXTYa4biGRq2A6FxyI6RYkiXTp8fplmmbgOzEsjvcPNYlxccdaHQOqMzEsjk1HWrB0RgXKwsJDVGWKuMtrQ6qMgoAKmRDIEoEkEfgVmrOs0OIcJUkjQiC6UxWJQpUpvr18Cvyy5Mg1XHf+cHxjycSsz5GIEBBTpbNr2LtCokTcjNJspGj2Shr9VJlrDy2/JFFU5PtxvDWOz10yGp+dPdIeEHLoaI1rHb8qU1xQJcJtmRACTB0WBgjQkTBQFFKzEtAnWuOQJeJ59B59xpAx8hYTgmGnbNwhJHg7NAMW5/CrFEumVeC1XScdy/eUVK5mspzVMZxzbD7a2q1KZIojjTGMKQ3Cr0jIUyXEJSLi20QYZ+oiHwAkvfLpFVkdoz6Z4poZw+wW+mz5AYIHl03GVxeOz6oaoiR5M5AoFIkMmNixIlEoEhBAZzwmVcZqssz/ij6F9NMTDijIixuIJEz4FQnDCsTnhFRxQ3WTachVIsM5sKeuA7tf24NLxpbgMxeMgCwRhNJWSZwDR5tiGF+WlxVS8/DoDYPeyHPOEdUttCd64b1DLK3bNaNTUsDiuPb8SnQkDNeYu2GxnMt1kwGU9MzAGxbH/3x4zP7sv7t6MiIK7fEP/54F40Ep8MqOOjvW/6mkVy53E1wPqjLGlCi2tIEwlAPDqPcEklz1uEz7sxEaQsLg5/tk7K3vgGXBrrIKqRLKioM42aZ13ieJWClurWnL+bkpx+CDQ00AgBvnjMTwQj8CitPQH26KYlxZyHUkoYfHqdBrI88Yw/e//33s2bMHqqri0UcfxejRIhlYXV2Nxx57zH7t1q1b8ctf/hIzZ87E0qVLMWnSJADA4sWLcdttt53mLnS1jRytcQMmYz2eTpSJnqYZwyyOk+0aOOe4+aJR+PSs4VnVMxLtenzeqcSIU174xgNNeNJ/EMtGuxta4dlTUAJIVHjakkTwf66dhm9cPSnLK3cj1SSlpP47iIx6b6CUgCbNtypTjCkJ4USayqVfFlo8uskQ0zudh7a4AYmSbkNChsXxwaEmrDivEifaNIwo9MMvd54f0+I40hTDuNLQgFk1efRPem3k33jjDei6jmeeeQZbt27Fj3/8Y/zqV78CIAZxP/300wCAv/3tbygvL8f8+fOxYcMGrFixAt/97nf7Zuu7oS1p4E8Hv0JhMSF/W9uuOUbI+RUpqzQypluYN7EU7ZqBrcdye3TpUCK8N0UiWRrwgPD+Xt1Vh8Ujh8EnS90qPqaTWUUCJG8KqbBL8qbQH2Lq/ZmSPB86NBMdmpnxuIp4SxyabqFNM+CXaY8T+ZQQtGkGfIqE460aRhYGHJ57wmA4nDT03nxYj97SayO/efNmzJs3DwAwa9Ys7NixI+s1sVgMP//5z/H73/8eALBjxw7s3LkTn//851FcXIxHHnkE5eXlvd2ELkmYlt2ZeloQMYKuIaInR+HRLCkCQKwaWmIGNNMCCDB3XAl2nmh39eYpAJKU5+XguHxCCT49azgON8Xwb28fzCrPBEQnpcE5wqfYhERA7Bi6kvTUPYPRO0YWBbCvPuKIw8uU4sVtJ7B+X6eYXGmeD40u4wgzYZyjIJlg5Qw43iqmSKUb+rgulCurioPejdijV/TayEciEeTlddb6SpIE0zQhpy05n3/+eSxbtgzFxcUAgHHjxmHGjBm47LLL8MILL+DRRx91He5dXV3d282yMRlHe6JzGZ3QEti3b98pfw5jHA1RCwmXHyznooEmYTK0ahbMtJdYFs8psEUpcNsFhWAMGF2kwidTaC0NKGIMVo4bk2kxBImZcx8oIZCSKpNScgwfTcoG9zc0TeuTc3wuiOoMJzoM6IkEDh0+hOc/acXGY1GIqY3ifNe1aVBloKu5YBIBZpT70Nhw0vF47XGgLE/Jmot72CehPK9vU2gD+TykGAz7AJzZ/ej1VZOXl4doNGr/zRhzGHgAePHFFx1GfO7cuQgERH321Vdf7WrgARHu6Qviuhg/x8Gxb98+TJyYXQrYFcK7ikEqyr0i6IgbaI2bCLpE/S9uIdh0uDmrc/XCMcWYOGY4FEocuiUAsPAk8PbeBkcna6pWvShPwpTJk0CTYZr0kM1A8vKqq6v77ByfC463xrF5xx4MG16F9/9WCyPj8mAAMqI6IMRZdMM4QBQfyioqoWbE3GUJGFEYzMqLlBT4UB7299l+DPTzAAyOfQD6Zj82b97s+nivMzqzZ8/G+vXrAYjEaiqZmqKjowO6rqOystJ+7JFHHsGrr74KANi4cSOmT5/e26/vEQFVQklIJBzlUzSCnImaZc0ldJKiJaqjJW44dG3SWTGzEheOKRZhEolApsAFVUVYMVMcE7emqtsuG42FU8qgShQBhcIvU6y8cBT+v+tnCB32ZG11yCfDr4gmmoFk4AcDlfl+qJSgOZpb8z6TzDA9B7C9ph3PfHQ0a/VmWuLay8wn1bUlup0b7OGRSa89+auvvhrvvfceVq1aBc45HnvsMTz11FOoqqrCokWLcOjQIYwYMcLxngceeADf+c538Ic//AGBQACPPvroae9Ad1BKkOeTke+XUBryJTshhX4746mxe9m/wNq2OOKme8kl5xzHW+Ko60jYQyXckCnBp86vBOccmw63gFKKj4+2QJUIbpg9EoVBFWG/LAZ5kE6jf//iSbhn/niYjNszRT36D5QSVIRlmF1o3vcEk3Fsr2nD/IliiIiUVtpqWBy1bRpGFAYczsCxlhhU2auh9+g5vbYelFL84Ac/cDw2fvx4+98zZ87EE0884Xh+1KhRdtXNuYBSAj/N/nHwZD20xcSwj7p2DYTCnqyUjmEy/HLtAbyXViN/6bgS3DB7BKRk2CSl3U4IwR8/PIrNR1ocHa4fHGq256PmKlUMqBLGleZ5A577KX6ZoqQ0iCXTKvB6dfaYwp5CCNAWM+BTKMryfA5D3x4z0BTRMbkibBv1VA39hPK8IV/m6tEzPBcRwhhLySRlc1RHTLdcvXPT4vjZ2/uypjZtPNgEQoBVF1c5Xq8ZVk6Jgw0HmnDfVblzBHk+2TPw/ZzysB9fWyzO4WvVnVr7FWEfTrZr3VbXAMJoh3wyDJOjMZJAadgPcOBPW2rw0WHhSHAAS6cNw13zxoka/LQaeq9SyqM7PCOfRodmoL5Dc33OsjgON0bwzv7GnEb7+gtGwK9ItsRwoguJA0oIWjUdeQH3U1CaVDX06N+MKQnhy1eNx62XjkFzVEdxSIVECb72xy04ntY8FVQlJAwra8DIjOH59s1cNznq2zQ8/cFh1LYlkq8Sb3h1l6jCuWeBWC3HdQs1LXFUlQTP/E56DGg8I59EMyzUtuU28LVtcRxv0yCCMe5GuzmqY/3eBlvuwK2ePgXnQEnI3ZCHVNlVf96j/6HKFCMLgzjaHMOIpLLnv687gMaI7nidYTGU5/vR0JGwK21mDM/HlZOcfSJ/23EyzcCnv5/jtV11uPXSMXbopi1uoL5dQ3l+31XceAw+PCMP8QOsaYm7dioyxnG8NY7//uAI3tvfiFyhV8Y51u6uxweHmh2hnNSIuHRjr0gES6ZV5EyepZQn+yMW44jqJjgX3cCqV92DgqCCEl1FU0RHXLc6RyimYVgiHPOda6egpjmOoJIdjtNNhl217V18E0ddRxxjSjr7U+raE/CrkqdaOQhgjNsRAJ9MURhU+uS3NeSDvoxx1LRkl6ulnjvZpuG/PziCDQeachp4RSK4ZGxxmoHvhKNTssAni67T+RPL8OWrxrt+Vn/34pujOqIJEzHdRHNUR2NEh9mDYSuDncoCPwKq1GVZJSViYMzECveEejRhdjmYhXExJCVzIMmx5hg049TF9zz6D4bFUduuoUMzoZsMHZqJpqje/Rt7wJD25DkXXnrCpVSScyFG1qYZrsnTFIpEMKeqCDNHFODDwy1wC+WoMsU3l0yCT6YoCCgYVRTM2Ynan2Pxcd3KuhmajKExoqMgoAzpsj5CCKqKg2iLGTnLKlMyBj5ZQthvoUNzXnchn9zlIO+LxpSAEor6dg2VBZ1NdIwBR5pimFCe1y87nD26xmIcLXELhRk2JpawoPms03b6hrQn3xBJIKqb7s916EiYDG1xI+ckKIUS3HbpaCyeVoE8vwIrh0fLOEd52IeKfD9CqoyQz/2khVS5XxvKXN4iB0drXEcsx7EcKqgyxaRheVgyrSJrILdY7ZXAl/zBFgRVKDLJev+M4flJjSQnlQV+fHa2mCcbTVhoijrj9rrJcLQ5lnMerUf/pSmSyDkuMpo4/d/UkPXkOzQDzTmWQy1Rwzb+BYHc49w4gOKQD4xxvLW7Hm4mPjVMJHU3zg/kjrOV5PXvsW9dzS0FRCKQgPTrG9WZJuxX8O1rpwBwllUumFiKpdM7u78JCIpDKurbEw7vPZWI3XGiHSSZy5lemY9VF49yeOktUQM+WUJeWqNcRDNR157AsAIvETtQaI3p0DJ1MdKI90EYbkgaed1kOJmjkiYSN9Ea7zT+fkXCZeNLskI2MiWYlRzk/MqOk9h6rDVrqU0AXDa+BCsvHAVAiIbl51CRzPPJXeq99wd60t3ZFjdACPp1XuFMM6IwiG8umeQoqwyoEuqSMdcUqiShIKigNdopVUApwcIpFbhiQhmiCROhZL9Ea8yAmi9kplPUdWhQJKdqZUNHAkGfl4gdCBgWy5KuzoQxoajb1fAYznmXCdohF65JVcu4eaWaYaHRxbtfeeEoXDauBDIVA6tTBn7JtAoxCSg1szUDiRJcM2OY7YHlBxTXpBwB6VPhqTNFTzr4OThaY4ZDd38oUlUcRDggY0RRwF7ZlIV98CnO8x/2KQi6hO9UmaIopNoJWtPiaI44QzScASfbNNdErN7LDlyPs0drzOgyB5PCrZs6NcLSSMq0dEX/dh3PAHUdmmuiVTeZWDq7JE4JgIVTy3HJuBJ0aAbCfsX+8XXE9JwNTybjeOTPO3D5hFKsuqgqpxdfFFIGRHdrppJiLjg4WmI6SkO+IduRKUsUVcVBHGqM2seMEoJh+QEca4k5Bq0XBVUYlgbD7PrgagZDW1xHQaAzrGdYHHVtGoYXOhOxR5tjGF8WGvLlrf0VzbAQ13sWirHSIgipsZTpV4rUzTnu/5alD2mLGWiLZ6v4McbR0OGe/GCMoyk5AEKVKUryfA6DHPbnjtkDYq7rhgNN+PPW464GT6YUpTmaovobp2IvrGTN71Am5JNRkdGopEgUwwr8jmNJCUFpnq/L8skU7XETCdO5xI/pFpozErFx3cKJHCFJj3NP6ymoiZpMzKYwLVE+m2ngu3OkhoyR1y2OuhySBU0RPat5BUga+GjCdSRfClWmmDWq0LUiIoVhcazd0+B65y4LDxxvN1eVUS400xryFTdlYR8KAs74eFCRs5LsMqUoyVO7vJHqJkNLVEetS4jmZJuGg40djmusOaKjNdY3tdYefUdct3ocTmOcJ+UwsmMMPTHwwBAJ13DO0RA1MbI021h3xA1EdNPWm0kN5eacoymqQ+9mCQ0AS6ZVQKKkU23SBYkQNEUTGKl2ao0EFCnLAPRnMo18TDfR2KGjtIsh4e1xU+QxhrBi4siiABKm5aiiKAyoSBjOxJtfllEYAloyJBEY43h7b71dccM5MGtUIVZdJATx/rz1ON4/mBIz4w4xs5qWuOssYo9zR7vWvRfPeFpYhnFHcpUAtuJtTxgSRr6+w90bTxgM9R06nt101NabYZzjsvElWDy1wr7big40Zyw+HUoJPnPBcFwxsRT/8vpeuDn+FudZWjWZS/n+Tir2ZzKGf31jH17cVgtKCRjjuO78Snx98UTIGTEHDhG2KenHTV5nGkoJRpeEsL8+AivNCSgLi/kGiTTjn6fKMDMapVIGPt2B2Hqs1Z4OtvFgUzIpLp7/645aMA58+crx4FzE5yeU5Q2YFeNgJmFajvOdCQdPSp4DPBl/T18LE+CUHaZBb+SjCRMtLktWEYfXbAOfrjfz3v5GtMdNXDNjGF7bVYetx1rtG0Cqqib1g2GM483d9dhytMWWhc1Mw/pkiiXTnVo1hQF1wHlXskQAA/jXN/bhpW21jqz/i9tqkTAYHlgyGT4lQ5PFYojpZr8vET2TqDJFVUkQhzMSsZUFftS0xJBeCxBQZNS3J2xt+UwDD4g47QeHml1nkjEO/G1HLdZcOhp5PhkJg+F4axyjij3FynNN+sotpptoiuh26C41wCiVXE2Rqs4T85tP/UY9qH91jPGcypJNUR0dCdNVssBkwMfHWnGsJYaWqJ70zMVrth5rBQAsmzEMAPDarjpsq2l13CRo0sr7FArGgSXTK/ClBZ1aNTIlKO3njU9uyBJBTDeFQc+IKSZMhld31uFzl1ShOM+HcEYlUYdmwi9LQ9qbzPPJqCzw40SaBLFMKSoLAkI/yeL489bj+OBQSsWUY0JZKGecvqtAIgfwxFv78NA1Ym5oa8xAUE0M6RXVucZi3JYG+cVb+/HSJ7V2s9zlowL4esUoNMUSKAn5bIcwFZaRemnggdMw8owxfP/738eePXugqioeffRRjB492n7+0UcfxZYtWxAKhQAATzzxBAzDwN/93d9B0zSUl5fjRz/6kT3Y+0xQ35FwFR6LJQd8d0oWuP9cMuViAeFBbT3WioVTRGeiW408SwqS/fSGmRhVEszqAK3I9w/IGLVCKRo7uhDgokBzTEdAFTmN/LR8A+Mc7ZqBwuDAu7n1JSV5PmgmQ3PateWTJVTk+/HLtfvxwSHnqnJffaRH/QlubDzUhLhu2ddfbZtmzwb2OPtEEkK99Rdv7cfLn9Q6kq/rDkWx7t82wCdLsBjHshkV+OpVEzq999Mohe21pXnjjTeg6zqeeeYZPPDAA/jxj3/seH7nzp34zW9+g6effhpPP/00wuEwnnjiCaxYsQL/8z//g2nTpuGZZ57p9YZ3RzTh7FxNwRhHY4coN+tKsqArKCHo0AxEE2bOu6tECYJ+KcvA5/sVhAdoNyKlBBX5vtwCXAwoThpxzbCyuvnihuWpJQIYXuDP0i+SCEkz8J1YvGuPvStSk85ScC4apTx9m3NDSr31pU+yV8IMwjmMGxZ0S6yK/23dAciUJmdY9J5eG/nNmzdj3rx5AIBZs2Zhx44dnRvMGI4cOYLvfe97WLVqFZ5//vms98yfPx8bNmw4nW3PieUSptFMhpqWmKPbNSVZcKp9SIxzhP0KRhT6c94kmMtQEJmSAZdszaQgoOK68yvhzzhoPpni6mnljptaXDcRSwijHtNNHG2K4WR7/Kxub38kpViZnsRvjuqO+a7pKFLvfuSMAyG/82aiGQwn2736+bNNwrRgWqIfp7vmJfF6hr9+ctIuQT4dM9/rcE0kEkFeXufwAkmSYJomZFlGLBbD5z//eXzhC1+AZVm49dZbMWPGDEQiEYTDYQBAKBRCR0eH62dXV1f3drMAAA1RExFd3CktxvH8jlZsOBoFJbWwOMf5wwJYMiEMk3FMLmBoKfNhe132NB43ZAJMKlERbWuAklAws8KPbSfjDq15hRJcOjKA2pqjjveWh2Tsbep9mEbTtNM+NqdL3GC4djRFa2sQ6w9HQYk4xheP8GPhCIJDhw47Xs8Yx8t7O7D+cBQSBSwGXDU2gK8xPqBlcfviXOgWw7E2A4yLH7Vl5VD55BznVfiwsy7hEMGjAEYVymiJW2hPOJ0NiQAzyn2o3n8YFXmyo9zuEIDhYQUS08/59XS69IffRE9oiwubFDUsmDnOcyYEHFt27cOwsIKOPPmU+1RS9NrI5+XlIRqN2n8zxiDL4uMCgQBuvfVWO94+d+5c7N69236P3+9HNBpFfn6+62dPnTq1t5uFSMIEaYkhNVTtl2v34/2aOETVkvghbK/TUBvlaIgk7ARXaZ6K1pjhiK/LlKAoqKAlZjiqa5ZNH4bKQj8oJfjC8OF4btMxuwSTc2DpDJFoTTdi+X7F0XreG6qrq0/r2PQFhsXQGEngh5OFd17fngABcipPPrn+IN49GoPBOFKVY28fjmNYOcMPPj3j7G14H9NX52KcZuBIU0xcN8eB16vrHEv5lETx9bNG4H8/rsEHh5oBcFhMXM0nOiyxakxep7IkKrxS75EoEZOrMhKuskRgNdec8+vpdOkPv4nusBjH0aYoii0Gi3EsnQG8vqvOVZMmHQ6C2dMmIqjKGFUc6LYufvPmza6P99rIz549G2vXrsXy5cuxdetWTJo0yX7u8OHDuP/++/HnP/8ZjDFs2bIFn/nMZzB79mysW7cON9xwA9avX485c+b09utdsZKTnFLEdQuv7nAfxdbZ8i2MektSLTDToC+ZVgGTcbtO3idLKAl3Co1JlGDVxVW4/oIRMEyGiRXhLIM3GMI0KRSJ2hUBQVXGmFIZDTk0f+K6hdd31Wcdf93ieOajY3h42RSEfIO6wKtbwn7Frri5a944AEKiOLVCSjfWN84ZhWtnVuK/3z+KPXUdMBm3Y/htmokZIwqwdEYFSoI+W7cegLimqVC9JAS2omFDdGh3I59pGOOwOEc0YSJhWnZz093zx8KwmJAn53D0TqTwyRTXnleJoCqDEJyWBlGvf2FXX3013nvvPaxatQqcczz22GN46qmnUFVVhUWLFuHTn/40brrpJiiKgk9/+tOYOHEivvzlL+Phhx/Gs88+i6KiIjz++OO93nA3GiPOapqmaEJ40z1YHVlc/BjuvWoCEqblaHxSKbE9oXBAdpX9DKkyRlYEXCtPhhUEBnRoIhNVpg6da0qJ64XaHNOFHovL8ZcoQW1bHBPKw2dwSwcGJXk+GMl47T0LxtsSxeGAjOZowlFDT0Gwt77DtW5+V207Vpxf6TDwjHHEDQtxw8DwwiAkQmBYHD6ZIqILmYSi0NCueOprOOdJ4y0897aYbucBCQgkCnzukiropoV39zdl/TwoAa49rxL3LpzQJ9vTayNPKcUPfvADx2Pjx3fWgt9111246667HM+XlpbiP/7jP3r7lV2iGVaW6E9JyOdqfHJBCUHCtHLWEgcUKav+GxAnriLf72rgCwKKY7DDYCDTyKsSRTzt5hrXLTTHdAQVCS4VrACE9zLUvfh0hhX4YVgMrTEDAVXCCFWE9vwKxfGWOFKLoTYtd9kvIaLstyxPrBo5FwaecQ5mCS2bsrAPnHNohgXd4jjRFkfQJ3WpV+7Rc1Leu8U4LMZgWiIOT5Ay8AQdmgXD4nh3v/tYUYkS3D1/nN09frq9JYPmV+YmExxQJSycWo43q+tyzmhNJ1U144YqERQG3Z8rDCpZXZ7iPRQVA0An/lQRBqHzhppapViM4z/ePYTXd9WDUlFSWVnox8k2zRF/VCWC686vBAGBabEB2TNwJhhZFIDJOCIZg0WGFwZwvDUOxoCCLlRPOQd8aceyQzPQFNUR9ovVZyRhIj8owycJg54whaRCTUsc40o9WeLeEk2YqGvXUJrng1+hSQMvjH3MEMkTiVK7qsawGNpiBiRKchr5poiOYLEwz6cbBBgURr5dMxAzsuOLjHGsOK8S24+1OmRXg6qUVHbrfG36pKd0dJMhbpgYUxJyvaMGVcnV+BMQDC90D98MdCRKoEjUHgySKvH7j3cP4Y3qZAw+6ejXtsZRWRhAbasmDD8H5o8O4uuLJ4KDI5Iwh3yDVIpUaeXBhohDzMwnSxhRGEBNaxw+RcIlY0uyauoViWDmyAIUBBRYjNtJ2lR+aU5VEZbNGIamSALDCzrlDeK6BZkSNHQkUD5I8kZnC9Ni+OFLu/DHj45BSoYsPztnJB5cMgmUElAinBhFoqBUdK2Kma1i9GOuKANjzlGgva2qSTHgjTxjHPXt2eWPFuNojCTwzKajaMiYqGNYDGVhHxojOgiEB3/eiAIsmVbh+NyUbo1EO4XLVl7YOWtTpgRlOUI75WHfoO4s9CuSbeRliUDTWc4ka22rhifXzEbMsFAcVNFWf9xeisYNC0GTDYihKWcDiRKMKQ3hQEPEMUREGHo/jrdquH7WCACw5Q8YFwnaa6YPQ0Q38actNdh0pDkZtxefseVoCwDg2pmVaE+YyE+Gyhjn0C2G+o4E8pMKrB4944cv7cIzm445Vqn/b0sNOOd45NqpSSPPIUudXnw0GeZUJIrKfD+Otjj7RhSJYPHUcofO0+kusAa8kW+K6o5kq2kxGBaHZTHUtWp4/2Bz1pLIsMSNYdKwPOyrj4JSgk+Ot0GixBYfe21XHbYlJQtSSa4NB5oAAKsurgIlYmSfm6ce9iuDPpnllylSXQ6EEHQkjJxJVkrERPpA0rBohkhIpW6WkYSJYnlwH69TQZEoxpQIQ5+e0/DLMoYnK3E+O3skVpxXiTbNQJ5Pxt92nMT3X9oJQoirVrnBODYfbcHiaeVoiSYQSqsA0wwGVaKoaYljQnle1ns9soloBv740bGsMkjNYPjfLcfxYFKoj1Jid6zGDRMsaYt+t/EwTnZkO6eV+X7cccVYx2ND2pM3LOZQmNRNZnuX7QkTzV1o0zAAu09GAHTapa3HWsE4x+yqInx8tCVLMtiwODYcaML1F4xAVXEwZxy+cggse2WJQqbUvsEOLwjkTLImTIZv/e8OsSJiwCUj/Zg0wURhSEk+b3mx+Qz8ioQxJSHH+EBAKFQOKxB5Dp8ioVyR8KctNVmaN24QCKE4nyw5holwzpEwGQix0BhJoNQTMesSi4kS7K4kTZpjIgGeLkkQSw500QwLb+2pd43Hn2xPZK2GuxpI1BMG9K+qviNhJ6ESpmUbeM44OuImCvzyKWnTmIxjy9FW/NeGQ66a8EDyrsrhWhlCyeCNw7vhT7vJFQQVXD2tHL6MsAuBWG4aFodmMOgWw/vHYvjF2n2OYd/RhKdpk0nIJ2NUUbY8cEiV7RGCCcNy1bxxw2Tcrg7r0EzH8U95/yfbNG8IeA44FyP4GOcoDio5Y+oW56gsCDgq0CzObR15IWHhbiMsJpoN0znd8usBa+TjuoWO5IQV3WQw0y7y9oQJi/Nkkqr4lLU/urrGOQfGlIZcn6vI9w+pmGYgbV8pJfjSgvFYPLUcqkThV6itq5/5WzAY8PquejR2dHqTccPKKXw2lCkIKqgszF4ZhlQZFWE/2hNGj5fz6S/TDAtHWgxbMI4lDRjnwIlWT18oE85F2JZDhISDPhmfnTPS4egAQEChuPnCUfArkuNmmS7M11XS1eLAS9tqHY9lDuI5VQaskU/d7SzGHR4J58KLT/HpWSNwydhiyLT3Qk8pVIliacbwjxSFAXVAjfLrC1IhmxRBVcbd88fhd3dchEvGFqOrw00pcLI93rn6Akd0iM+DzUVpng/l+dkhlDy/jPFleT3uBZEpRVvMwMvba/GTV/bg99ta8JX/2YLfbTwMi3E7TNChmWg7hUHTgx0rLS9nJo8RJQSPLJ+ClXNGwa9QhFQJfoXipgtH4bsrpjm8eMBp5P2KhPkTS3N+39t7GhyzkU/Txg/MmHxctxDVTXDOs5aWMd1yDDmWKMENs0didhmBnFeMn725L+cc1lykbg5Lp1fgS1eOz3o+qMiocPkRDgX8CkUkIc6BT6FIrTQ3HmhGV+NxU7LEcd2CEhBXcUy3kOeTvXptFyryheJp+uoHAMrDfswdV4x39zd1+xmMc7y7vxE7HZOmONbtbQQA3HZZZ8LvRFsceX55UHVq9wbTYo4sR/psVVWS8cPrZ+Bb10xBXbuGiny/HcaNp90kObLt1NXThuHtPY1ZA9lT35FeJz8kPfmUF29YPCvmnqlhnkKVCEYWBTB33KmFbxSJ4FvXTMYf7pqLry6ckHXRKxLFiKLuxYMGK+nhKYkSyBLtlDPIAQFsWeJEstIGEEYo0wPy6KSyIIBil4liX71yYrc/ZJkSTB+ejx0n2mFkODm6xbBubyPiuml7qqbFh3TYJhV/zzTBhIj691TtOyByJ+PK8hx5ukSaFoVuMkfynIMn3+/+3Rbndp08IUMwJq8Zwotn3BmmAUS1TXfKbqnwjSIR+GQKmQJzqgpdY/eKRHDFhFJcMrYUeYHsRY9ExI1jKHs7SkbIxidLKA6qsLpJBK68cCQAccGnqg4ALwHbHSMKA1nNd3l+GcvPq4Tq4rxQIgz8zJEFmDO6KGfNNSVAS0x3rHJbYwYiiaEXQuO8c5h2Oinj3t3vXTeZo9Is03GJ6hYsi2HmiMKsWRaqTHHN9Aq7Tr4vbMuAC9ekvHjTxYj0xECkwjfXnleJNk1U4MgSxZ8/rnF8JiXAZeNLcP/iSWKAdQYEBCOKAp7mB4CQT0JbPBmykQkCqoTLJpTYYYBMFImgvj2BorQpUnk+CYQQtGs6mqIJjCoKeto2ORADuWMOraa75o0D48Dr1SdBCAFjHLNGFuLiMUVQFQmqTLM8ynQY5ygKqlmhzBOtcUwszxsyK1U3Ay8RckoVc+lePCAGFqUwGcPvNhzGxoOpRjbhaUsUIITiqkll+MpVE+3Xn275JDDAjLxmWMk5idx1dmvUxetgEPPTMkeepWqMAdGl9uHhlowTC4R97po0gBCUSu9KG8oEFAkdmlhdyRKFKku4e/44vLOv0XU+KeMcIZ9kN0RxzhFNWPj39Qfw4rZau0V81UUiieXVz2czsigg5uYmiwwkSnDXvLH4zAXD0RzVYTDhTWqGZRtuVaaYObIA22vaHMZcoQRzx5XAr4hzwjm3jXrCYEL0LDw0ck4sOW6RElHh3pty6HRJCt1idgMUADz13iFsPOjsaVAlgmnlPty/9Dz4FclRhtwXnvyA+vXYXjzjWR5JegYcSNalmhZ0g0E3GXRLhHcyo2yiztilK5YJXe+4nr06KAn5hlwlTVcQQhyx+YAiIc8n45oZw7LkChSJYHZlAH5FQjztx/Cvb+7DS9vE7MuYbiFhMjy76Rh++NKus7YfA4mUzk1emiqqxTj8ihA0G10cQr5PRky30BzV7cTfwsnlmDmyABKB/b/ZVUW45rxhADpLBdOp79DsWP1gJ6XLJNFT897TSffk0/+tGRbW72vMsjW6xbGzTmhrEeLscFX6QO5jwBj5hGnZ8UG3UE16iZJhMRguS1OLcRgmc2S02zQzZ52xRAiaos7GhHy/MmS8mlMhmFZW6lMoKCG484qxWDSl3KGiZ1rcXhInTEvI4eoWXttZ51jWAmLU4DObjrmu0DyEoR9dHLSHgqcXIViM44VPTuAXa/fjtxsP4xdr9+ON6rrOXgRCRHw+qX2TMJj9/sySTMaAOpcWfI9sDMsZj0+kOTL1ES3nrFZCiGuTlHK69ZMYQEY+VbfLeHZFDdC5RDIs1mXdMOdwGPquumItzh3DuAOKhMqCwS9Z0BsUiUJNC6sEVFF+l0r8peAAtp7U8MePjokSWIt3WY0jEYI6b/B0TiglGFMSQtAnOUJj//3BEby7r9GeHmUyju01bfjdB0ewvaYtufIVBv3jY6342ycn7Zus2++nJao7HCkPd9KPEUsr8bYYx8vba6HnKEjQLY6XPzmRpUpxur09QC+NPGMM3/ve93DzzTdjzZo1OHLkiOP5//qv/8LKlSuxcuVK/OIXvwAgloHz5s3DmjVrsGbNmlOaCsU5R1tcGPlcBlw3GUzmNPAJ00JjRyIrEQKk6l87u2IzKxN8srPxySdLGFkUHDIJqN6QnigNKDRNmTI7FLZ+bwOONEXRETdRHFRzDxfhfNCMTjxTUEowtiSEQDJ/pBkW3t7bkHXcTcbRGNFdp0ptPtpir5TdnB7O4Rit6eGOCDeaONYcczSU/W7jYVvgMBfv7G/CUxsO2X8Tgj7JR/Uqc/jGG29A13U888wz2Lp1K3784x/jV7/6FQDg2LFjeOGFF/Dcc8+BUorVq1dj8eLFCAQCmD59Ov7t3/7tlL+vXTNtzzuXkY9oBuojCYT9MmRK8ddPavHR4RZQKioNppWpWFnRqXzIuQgdKBLBmrmjEfYreLO6c9jF1dPEMG5AGPhRQ7xUsif45M75r5QSRHUzpzKlyYAf/W0PAGDJtAosmlqOt3bXO0pg/bJoEfeqbLqHUoJRxUEcbYqhti0uxvx1IVaW9X5C0BE3UJ7ny0q+pujQTEQTpnc+cmBaDD/6W7UoHiCieOCqyWW46cJROQXJ0tFNhter63HfQjG8262qrzf06mxt3rwZ8+bNAwDMmjULO3bssJ8bNmwYfvOb30BKTp8xTRM+nw87d+5EXV0d1qxZA7/fj29/+9sYN25cj76vPenFp2K56ViM46n3DuP1XXW2QS/NU9GU8liSr9/VkMBfP6nFdecPt9/LOVAcUhD2K7hr3lh8bm6VPfMyzyfCDapEMaoo4FV49ABCCII+2dYUGlHkz+mhA7Av+jeq67FwShkWTy3HG9X1dnXNivOH45Frp56NTR8UcAAjiwNIZOSdegLjHMPSQpGMw1WWorZN8+SIc/B/X9qFl7bVOrpb1+5tEM5ODjXcTCTS2e2q9JHN6ZWRj0QiyMvrPNGSJME0TciyDEVRUFxcDM45fvrTn2LatGkYO3YsGhsbcffdd+Oaa67Bpk2b8OCDD+JPf/pTt99lWMzWNHFz4p967zDe2l3vMOgnXYaImAzYdKQFS6ZXwCeLuuGSkOqo/ggoEgKFgeR3ia60UcVBz8CfAkFFQkQzwcGRH1CxaEoZ/razrsv3JEyGN6sb8Ls7LsKtl44BAJSGVQRVGRYHvDqm7kmVCBNCMKEiD4umVmSNvVQkgqKgguao4SyhTDb9hVRnpY7byjWuW2iLG151WQbRhIlnXfTldZPhg0PNWSXcuWBp3a59kXQFemnk8/LyEI1GOzeMMchyWktvIoHvfOc7CIVC+Pu//3sAwIwZM2zv/sILL0R9fb3rkhAAqqur7X+3aRaa42K9n0og2d9jMry+62SXqpHpmBbHgSMnMKpQRUilONGS1KCQSVaFjUwBqUjF/sb+Y+A1TXMcm/5KRLcQN8R5umwY8Nou5JRuTkHAsX3PIZTnyQj7JCSaxfnwSQT5/v7XcNbfzoXFODoSnT+ElRNltLUF8NHxmD09Kt9HUN+h2xUelIj/zazw4dIK4Oixo/b71WRHuBsnawhGFvSPIS/95TzUtOkgOTz11I22O19eocDlVQGcOHYYAFDkl7JULntDr4z87NmzsXbtWixfvhxbt27FpEmT7Oc45/jKV76CSy65BHfffbf9+C9+8QsUFhbii1/8Inbv3o3KysqcScypUzuX6IcboyhMJk7FcInOw3SiNQ5ZqndtjHKDA6jRFCydOMbxuCJRx9JIpiJEEw4ofbZk6guqq6sdx6a/YlrMHrk4Qjchvd0Aq5s6aw6CmZPHIqBKCKiyrXtOQFCR7+t3Ce/+di5Mi6E13pnoi+sm/u8YhprmGA41RvH6rpP44JAYAZj6BTEOXDS6CEumD0NhUEV5WmmwKtMum/1Gl4f6RTNgfzkPVQkT/KUT6C4kQ4mwN4xzlOb50NChQZYoLIth0bRheGjpZFsmpLLQf0r2Z/Pmza6P9+osXX311XjvvfewatUqcM7x2GOP4amnnkJVVRUYY/jwww+h6zreeecdAMA3v/lN3H333XjwwQexbt06SJKEH/3oR91+j8U4tLTKmExbXhRST1mDfMOBJnxubpWjecdkzD6YMhV6NIospq4PIXn4PkOWKPyyBM20EFRlrDh/GF7efrJLXSHDYnjqvUO4Z8F4JAzLNvIcYmrRUNLp7wtSP4uRxUFQCjz6cour+fngcAu21rSBcY75E8uwZu7o5Ezjrj+/sUNHVcm5N/L9hZBPxorzK+2GPjc4RDnj166agLKwDz5Fgk8hiCUYOppPYtaUcbaBJ8mbQV/Qq7NEKcUPfvADx2Pjx3dK8H7yySeu73vyySdP6XsyhX0yS7sCioSrppRj7Z4GR7JDIp3tydnbTtASM1BZ0Gk0OBeDu32KhJGFAbvLrKc63R7ZhHyyfYP+xtWTYFocr+yoA6VJAaeMQ8sBvLqrHrvrIvjnm86HYTL7PGiG5Rn5bsi8UtNjwDHd6tK/TBmld/Y1gBLg1kvHdOs8tWsGdG8Au43FOL565QRwBry0vTZrhJ/9Og68tbcen7t4NAAg5FNQGKA43O6MJvTlce3XZyhdOD+Xwf383CosmFgKVSLwKxSqRDB/UmnO8iPGOIqC2UmjVIgmvY2Ycd7jhImHE1WmtnibTCkeWjYFT99xMf7ppvPx/avKoeQoRz3SFMMv1+5Hm2bYYTjNYN55OEXSTUwPo5nQLaEtrxld3xQA4Rhljqkbyugmg0wp7l88Cc/cPTdLXTKd7TVt+N+Pj4NQZ3LVYeT7MEzcr9db6boxuX7klBDcetlo3HTRSLTEDBQFFdvrW7fXKZBFCXDFxJIsrzCoyBhe4F4myTlyyrN6dE3YLyMREedQogTFeSr8KkXNseS0mxzGZ/2+Rqy+uAoE4gbhVykSpuJ5812Q9fNI+3t4gR+UuFenZZKSHPYrUs7CiBQtMR0V+X6vfwRweO75QQWLplTkrI03LI73DzXhhtkjHI+ne+9DwpNnjDvU3NyuT54mceBPSg6kLk7GXeI1HHajR4p8v5ihyXNcp6cyCNzDiSJRxxzYkE8CJQQFftqld0kJQVNyApLJGCKaidq2uDdg+hRIv2x9ioTFUyvQE1vMOGwJ6O5uCowBrTG96xcNEdJnW5gWw62XjsElY4pzvp4SgmiaEysTApKmbDMkjLxmWkhXdXaztbnsbyRhYsOB5ixHkQHYcKDZbt8WFQV+EAi5W7c4pGfiT488n2xfvIQQhHwyfDLFwillOd/DOUdBUHYoH8Z1C61x3RMr6yHpDjjnHLdfPgZXT6uwSyMpkCWWpUoECyaV2iumnoTIck1iG2qkV/2ZFk9KP4/LqT3DOEd5uLMMNX2R2pdJV6Afh2t6kvTMnt0iaIoaOTvMKCFoiRoYVxrOauhgnINmXPqeJ396yBJFQJXs/EpAlSBR4J4F47GrtgPHWpwj5lSJYN7E0qS2OZCaycI4h2mJYd+GxVAQUPpdWeW5pLtDIVGCO64Yi1suqUJzVEdBQMFv3tqFTcfjYsgI57h0fAk+d8lo+z09ufJT8x2G+rlI9+SN5DLVr0hYMKkM6/Y2ZIVtSvN8kAjFidY4ikOqI4eYqz+ht/RbI59dfdEzL5tz3qWyJOMcE8vDyHcZ53cqqwWPnhP2ydAMyz4neaoEAoLvXzcNT79/BO8dEFNyOOeYN7EUt1xcBUAIZ6WLOusmgyxJ0C2G1pjouuyt5vdgo6dHIaU3DwDXTy3A15eeh4ZIAibjUCTplOPrnItxdnlDWM/GtJyy5umhyM9dXIWdJ9pRmyHudrJNw12/2wSfIkq1r6gKYupEkbzt62lz/fbMZC4Ve2qAORcX8hUTSvDe/iaHEp9CCRZPKUdxSHXV5nC/afRi4z0cUCrCNClNG4kSyLIYzHD7ZWOw6qJRaI0ZKAw6k6uZN2rhLYnnDSaafwo9Qw8A2Z40cf5bM8TwkOKQ6hzwokqYNCwflsVQ3+E0RD299js0Y2gb+UxVz7QDFzct1yqk1CtSecf3jsbwi7f24/7Fk3JOo+st/fbM9OQCc/PuU5OfbrpwFADYI+gYFzH5N3c3QJUpvr54IgKKc/ddbxpeVL5PCKkSYolONdGAQiFLFKYlGp2GFTi9F4MxMEMkoGRKIFGSNbjd9Ay9Tebep/62GMe/rzuIV3aetIXfFk4px22XjQHQec1LEsXwQiGlfbItDlG30LNrf6gPX8+siedpRr+li9Cx8zM4Xv6kFvcsGAefHOjT7eu3Rr632BctJVh9cRUYBzYeEB69xTgscLyyUyhWPrR0iut7PfoeQgjy/LJjLkDYL6MtZoBxDs2w0BozkB+UYTHAsjgkidn19LJEEVAoDJNDkTtNmskY2jUDhcH+oaVyrsh05FOG5Vdv78dru4RQWSouvHZPAwBg0UjqTNACKAoqCCgUJ05JO35o/3DSk64Wd44mLQwqPc7rUULQHjf7PL/Rb4180CcBHafzCQSqRLDxQHPW8ISEyfDXT07i3qsmdKu/4Rn+viOoyrbXx7m4EQdVCf+27iDW7Wuwtejnji3G9bNGQEqLp5kWQ4fFoMo6SjPGL+oWQ4dmIOwfusqIJDl4OnW5EoiKpFd21GV5mrrJsHZ3A64YVuY6+tKnSBhTEkRct9CRMHvQTDW0V1HpVWBWxsHyKxLmji3B+4eautWTZ5xjRFHfD8jptyWUPlnqdRlRQJEwIplcypVIooSgMeKs8Y3qBg43RhHzyvTOGHZFU/K0/NfGI3hnvxhurJks2SjSjOe31KCxI5E1ci6asNAeN8AzDE/csIb8eLp0g02T84lzXv8UaE8wxA0LNS0xxHXLGcYnBOX5fkyuCKMs7Ouyeqevq0EGGkZaeMatKvAzF4zA3LElUFLlq0n1z3RUieDa8ypRFOz7+dH91pMHRFNGZjKoK3yyhLI8vz1TtCTky1mKKVTgxBLfZAz/+uY+MdElGbe8YfYIPLR0MtQ+znQPdVSZIqAQ29N8bWe2p2lYHBsPNmPzESGqdeWkMnzukioYFkNdRwKUhmAxjsKACpJmXzo0E4pEh2wHJk2uhADh3HR1/VsWx/rDEfzrxg/ta375ecPwreVTbJEsQITJhhX4UZqnojGiozGScKxuCRHlgEMVzjksK7eRT+WTbpwzEtfNrITFOcrDfjy76RjW7mlIisGJ6pr7Fk04IzfMfm3ki0MqYrppNy9lkuoRCygSwn4FYb8MgzHoyYx1QJVwzYwKvLKzzqEM55Mplp83zA7V/Oub+7LU4/734+MAgEeunXaG9m7oElIoFEqT0+nhOh4QgB1me3tPA3af7MDJdg1Ssqb78gml+PqiiRhe1Jmk4uDoGMLx+XSpiIRpoSmawOKpZXhzd4Pj2lZlimH5Pmw5EYfBYB//v+04CVUWOkOZpIx9Rb4PCZPZq6aQr+8mGA1EMo16Kr+UqmRK18IKBxS7m/gLl4/FZy4Ygda4gckVYbTUHUNAkc9IAUG/NvIAMLIoiLaYgfp2DTHDtL2IgCrBJ0uQJQIpbS0pZawrv3LlBADAXz85CTmp47xsegXuWyQej+kmXnSRB9UMhv+35Ti+sXiSN9OyjyGEoDCkoDikohuZeQBiOZxqmkrNLV27pwHr9jTg07OG48FrOjW4dUsMUu4PWudnG0oITMbwT6/txV+2nhCePePwydRxfZeHVdS2aTAyjn3CZPjL1hO4d2HuXBUhBH5F8nSEkqSXT5qM4Ym3D+D1XXWdlUyTy/GpWWLkaH4yVGkxjt9tPIy1e+ohUQqLc8yrCuInZ8ifHBC/hIKgAr9Ks7RLDIshkbxSY7qJxoiO0jwVJNlYA4hl630LJ2LhSIKCsuEoCfkQUCWoySlVjRE9591ToiJuX57f98mQoU7IJ6MwqGLJ9Aq8vquuS635XDAAL39SC0IIvrW80/uMJiz45FNv7BnoUELwT6/txQtbTziOZ2Y4rK49kVOXRqIEDR0JjPa04ntEuif/i7f2483qemcl015RyfSFy8far/vdxsNYu7dBrFQtsSJ650gU//z6Pvx/nzmvz7dxwJxJ4pLBT3kuqXh6apD38hnD8KUF4x0/cr9MMbIoaP+d8vhL83IPHrEYR0X+0I03nkkUSpHvV3DPfDHM/bVddaAAtFM09rrF8dIntbj9sjFIWAxlydmwkYQ55OaQxg0Tf8kw8G50VeVhMY6yZPWSW+WNh5NUDiSmm3jpk9osR1Q3GdbuqceVk8tQWSBCi27qlLrF8actNfjO8ql9HjkYMEbezSmjxD2e/tcdJ8E4x30LJ7p+lpSWWAqqMq5LTnRJNzB+heIzF4zwQjVnCEoJJImgKKTiSwvG4bZLR+N4axx/2Xoc7+1vzjl0wQ3LYvjsv22AIokW8U+dPxzfWDIRQbX3FVoDkaaI3uPVi5Ss8EgP2fhkik/PGm6Hajwb3z2ppGtTRM8KFafQLY6/f3EnOAcuGVMMiRA77JiORAjq2jWMK8vr023s9S+AMYbvfe97uPnmm7FmzRocOXLE8fyzzz6LG264ATfddBPWrl0LAGhubsYdd9yBW265Bffffz/i8bjbR7vi1iAQ1y28mGGcARFbfGVnnUOPPp3MgpmvL5qIFedXwidTBFUJPlkY+IeWTvYu9DOIKlOoMkVIlRFQJYwqDuK2S8dgwaRSKFQMNu4JFhfeaUy3kDAZXtx2Av/82r4hp1g5vDDQ42lmEiWYOyoInyzkoH0yxYqZlfjmks55zZ4n3z2pmHxJnmp79W7opgjhfHA4twNjcY6KMxAa7rWb+sYbb0DXdTzzzDPYunUrfvzjH+NXv/oVAKChoQFPP/00/vSnPyGRSOCWW27B5ZdfjieeeAIrVqzADTfcgCeffBLPPPMMbr/99h59n5uDUt/RRR1wsk54pBp0Pk6Jo0QMEIMpHrh6Mh5cMhn1HQmUh30I+joHSXucGVSJQjMshPwyDMadnYG2gTn1bjTNZHhh2wl8deF4BFV5yIyoC/lkfOaCEfjzx8e7DHv5ZIql0yuwfLSEv1shVlAAx9RhYcdvY4ilNHpF6qYaVGWsOK/SNWSTjmFxUCArGe6TCG6+cNQZiRz0+urfvHkz5s2bBwCYNWsWduzYYT+3fft2XHDBBVBVFeFwGFVVVdi9e7fjPfPnz8eGDRt6/H2EkCxvvjzcdR18ZsKUEJKzDpUSgqBPxpjSkG3gxeM93kSPU8QnU/smWuBXoEgS/ufDY1i3t9GRvOoNIoGo51zNDVYeXjYZn5o13LEqHVMShCoR22NfOr0CX75yAizG8R/vHsT9f9yKB57djmX/+i5++spumIyBwH317CGwGEfCtBDTTUR1E5GEgdsuG415E0TTU1eOhSITLJhY5lhFLZkYxndXnJnyml7fNiKRCPLyOmNHkiTBNE3IsoxIJIJwOGw/FwqFEIlEHI+HQiF0dLjrFlRXV7s+rhkMmb/7q8YE8dahqEO6QJUI5o0OovHEUaFXwzksI4HjRw7mDDQq1F2oP6CQfrNs1TQt57EZSKTvR0fCsjsGY7qFtbvrskr7eoNpMbTXHcOOZooCX99X2vTXc6GZDJ8ZJ+GaUSPQolkI+Qg4I9BMhjbNQoFfgl+mOHLoEP64vQkf1CSgM27Xyv/54+NobW3FnXNKUO/v/2WSZ+s8pOyIycS/U75lQ9R0rEDnD6eYWVyMmnYD/29ne5a9AsTUu09PkHD9xEq0xi2MzFcQli3s27vnjGx7r418Xl4eotGo/TdjDLIsuz4XjUYRDoftx/1+P6LRKPLz810/e+rUqa6Pa4aVpUT4owkT8Nhfd+OFbSfs2tTrzq/E1xdNdCw99+3fj4kTJuTcH39SFTEdQki/klCtrq7OeWwGEun7oRkW2pMSxEeaYpClkzBYtvetUACE9Mi798kUnzp/OM6bNhmAWEr39Xnsr+dCN5l9PAHRkdkay07zxXUL779Uk3VD1S2OdYdj+L8rL7arQfozZ/I8MMahWwwJk+WMGIRa4rZap8U4XnhrH9bvawYlBIwja7auKhFcPa0CY8aOQ0iVQQlBRYEPB/ftPe392Lx5s+vjvQ7XzJ49G+vXrwcAbN26FZMmdSZsZs6cic2bNyORSKCjowMHDhzApEmTMHv2bKxbtw4AsH79esyZM+eUvtPNG5Mlim9dMwV//doVeOr2i/DXr12BB66enBV37wpCiOsQby9Uc+bxK5K9UioLqzkV+zhyi8VRAijJcIQqUyybUYFvLOmsrNIMq0ej7AYDmePmCCGuI+iaoomcK1SJEjRFhu7s1oQpHI/WuIGYbuU08Jxzhxzzb945hHf2CSGyhJkUPU8aep9MoUgEV04ux13zxsFiHNGEmXzuzK6Yeu3eXH311XjvvfewatUqcM7x2GOP4amnnkJVVRUWLVqENWvW4JZbbgHnHN/4xjfg8/nw5S9/GQ8//DCeffZZFBUV4fHHHz+1jc1hdWVKEFRlVBX3bndyfW5/CdMMdgKKhGiyS/VT5w/HC9uctd6qRDB3XAkA4P2DzkEwqkxx5aQy3LNgHJqiOkpCqhgxmOa/MC5+dEOhS5MQAoVSewQdII6vbjkrjUpCvpw3VItxVBYMrQZAzjk0gyFhWt0OME+R/rq4buH1Xdk6TAyATIAHl0zCsIIARhQFbGfV4tyhtHqm6LWRp5TiBz/4geOx8ePH2/++6aabcNNNNzmeLy0txX/8x3/09itBCEkug5xnIVUxY2bIfKZ3wXZFrlJqz8ifHQKKhJguBrenPPAXtnW25S+cUo7rZw8H58IjWre3EZSKMWsLJpXic3Or4JMoRhYFENct1LTEIRFRg59Ct4aGkQdEYs9Ic8QliUKViOPmGFAlXF4VwsaauOOG6pcpPjVr+JCRbWaMQzMtJAzWozouxrgYaMM4dJPZirUnWuNCO8glzy9RCp8ioSRPzYpG5Kqt70v6T8C5h0iUgLnEZRWZwExe2G5dsPNGB/H341hWGEei7qEaILeH79G3UErgU0Q5pUwpHlw2GXfNG4sjzTHbMwcH2jUTN188CgumlAKcYFiB3zbchsXxn+sP4tWdSd0QzvHpZFOUTLMlMQYzsou1CaoyTM2ZJFw5oxCFhQX4247OqVGfmjUc31wyadBf+ynPvTmaQH1HAmVhn6teT8qoWxaHmUy+ptBNZhcNhANKl5V+RSEVQdXpZKiSyAOe6VDigDPyMiVwkw2X0rx5ty7Y9Yej+Nc39+GBqyc73ucWrwSEFz/UR8qdTUKqnPSmxAVfFFJBCbHDDhbn+P37R/Bq2hi7KyeX4fNzR0OiBL959xDeqE4ul5PXx4vbTgAAHlw2GYwLz2so1MwrknOACCBupHk+CR2aaT8uSwTfXj4F31wyCQ1phm6wX/sJ00KHZuAfXtmLP289bl9P188agQeWToJMKRKmBd1kWfNbu+Ki0UX46EiLozhAlQgun1CKinx/VklqQD07K8sBd8XLEs1Zv+tTCOKGexesbnG8uK0WMb0zNqlI2RU1KYaauNW5RqIE/owW1/TGkCfXHcTryTF2miGGi6zb24jfv38EuslcRc5STVGpc55ZmTVYIYQ4JG5TyBJFQUCBX6bwyxR5Kk1O55IxuiRke7LyWYgTnwssxtGuGYgmLPzDK3vxl63HkTCZ3Sn9l63H8dNXdqMtriOmW1kGPq5bONYcc/ReWIzjdxsO4yv/vQVbjrbCYhwEwrgryVzSnVeMde3PCaryWelHGHCePJDy5rPvsIQQtMe7V5WsKhbeiirnPriDfbnaHwmpMhIms0MKskQQVGQ0RRN4ZcdJxwQeQCyX397dgGXThombco4VXkOHjtEl8il5ZQMdVXIPUVFK7Ga/XDkndRDq/WiGhbhugUPk6v6cNPCO15gML26rxe2XjXV42RbjeGLtfvz1k5N2SeTy84bhrnnj8N/vH8G6fcnmveQaSZUIZo4swI1zRqI8329LDKeTUkk9GzfUAXk2uzLAlQWBLlUlS5PJj4Cae0WQq6TS48xCKUEoIy4aTIYYclWCMM6hKjT3BCTGURZOTgAbIp48kOom7h2DSdSNczFIJpY08ADQ0I0cSmMk4XjsF2/twwvbTkC3GDSTQbfECvFf3tiLtXsaspUnLY7tNe0oyfMhkCPZH/KJx89G+HBAns2uQjZBn4wbZo/IWvqrEsF15w9HQUCFX8n9fsDz4s8lQuvfee7Kwz7XzkFAiJMVhVQsnV6RtST2yRQrzq+EQiVRCaFbiOsmDIvBtNigrp0nhMDXi2oiiZBBE6o0LYa2uJG16i/rRg4lNc5QNy00tmt4aXttVlkl48Ab1fU5V4eUAi0xd1VQSkRPByVnZ9U0II08kDthCgAPLZ2Mz1wwwqHfsXBsCN9ZPhm+bgy8+OwBe1gGBWG/4hCGi+pWzhuvRAliuoW7F4zDwsllyWHJBKokNFrumT9elMglPbCYYSGmW4jqFto1MVrSrZN6MODvhZfo66n0Zz9HN1lyBZj9nE+WsPy8Ya5OwTUzhkEiQEfcQFxnONYa73HdfDqMiRnViktTZsgngxCCgCqdFX2gARmTB8Qd0LC4qzcmSxTfWT4V9y+aaKtKHjt8EIrUvWcj0cHjyQxUJCrkJDoSoj2/LCxCbG5ek0QJCgMK/n3dQby5ux5Scpj1lZNK8dWrJmSdy8zLxWLc9uoIsaBKFKpEB0V1iSwJKedTKR/1D4LB9VryRu5GwrAQNyx8+coJ4FzMtU313iybXoE1l45GPE3rgfTCwKsyxYKJpcj3K1nNTgQEIZ+UdEbOzrEesEY+1a6tm7nPQtAnY8wpapYMxqTTQCSgSkndEMvuhH1x2wlH1ZRPplgyrQK/3XAkWT7JkSocXL+vESGfjPsWOQfHdBWh4Ry2x69KVMS1B3hDXEiVoZs9kyhQ5YF/c+vKwGu6hbgpnpMowX2LJuKueePQGEkgP+ldZ14ewwoDSJuP3i2KRLBgYik+P3e0a0VNKhyZmXs6kwxoi6Z2EZvvDV01RnmcffL9sr3c/caSibjufKeE7pJpFVh18Si8vutkVqVEwmT4246TWVLDHBwx3cThpqijnNbxmqSxjyTMAR/GkWhuee1MciUJBwq5DHxMN7GvrgPNseybXUCVUJ7nA1wMPCBszMiingm1KZTgn246H7deNgZ5PsX1hhn2ywj55LN6Mx2wnjyQnBwvU8TduqN6wWBYqg4mCCHIDyhoiel2J+xXF45HQ4eOsrAKwgl2nGgTPxiXzCwlBCfb4qgs8MNiHAbj+JfX9+KVnSchUwrGxajAh5ZNhupy7hkX8sc+mQ9oSYQ8nwzTMrqcXBRQBvaoRMNiWQbeZAyPvyoanmgyjLd8xjB8JS2Mp5tWlwNW/vO9QzjZrnX7/akQTWFQFcfSpTw7oEgoDCpn/TgPaCMPiLijwvhpe1w+WRrwS9XBiEQJCgJKUi6XJxt3xGUb1UyMLgnlTIxZjCPkE1OnLMbxzWe34UhTDABgWMIgvLDtBBjneGDJZKgSdW0iSknNBs9SoqyvSd0s2+KGaymqEPgbuDcxxjgiWvaq7PFXOxueUvxtx0kAsMN4iS6GF8QNC29W1zs0f1JQImwPJUkNpYml+Nzc0bYSaiYEQobjbMXh0xnwRh4QcrWM85xlUd0hUzok2t0HKopEURh0GinD5GAQy+3lM4bhbzucIRufTLFoarnd1PLv6w7YBj6dhMnw0vZa3L1gHIKqDN1iCCoSSMYN32RihuxANfQSJcj3y4gkTEcCW5Eowsl49EAlqptZoZZcDU+pMN5d88bBJ9MuK2daonpO0TGfLOGfbz4fRlL4LqhKCCiya3MTAVCe7ztnom+DxrL5ZalXqpFu7fQe/Q8l2ZKfOsfpiqNfuWoCrpkxzB6npkrCwN95xVgAoh197e6GnJ8tGmBEvNbiHBHddG2cShn6gYosURQGVREXVmUE1eQxHcAr2FxjIrtqeCIAGiMJEIIuG8aKQipYDkefcY6RRUGMKQ0h368gz+du4CkRcfhU7f25YFB48kCyXVuVoBksS3I4FzKl3TZGefQfFImiMKCgXTPBWOfyPL1SoqY5hoKg4mhLb44lPbIcl0WqEzoFBxAzLFiagTbNFIPdk9UQJuPQ+igHdK5IhQzUQaBRkyue3lXDk2YyPL+5Bl9bNBFBnwTNsJB+T6ckqV3FKeZNLME7+xodIZtUPX0wWSnDuXv1UkoXqNhFYvhsMmiMPAC7wUAzuhejUiQ6oJNpQxVZoigKKogbJrSMeKpfpigIKmiO6SiGahv64qDaZenkkmkVCKZdCyZj+MWb+/Hi9lrISYXCz1wwAg8um5xUKMw9Ds7j7GFYLGe4JajKWDGzEi9tr80K2QDA67vqICedg7yU3C8HkNSmeWLtfvx1x0lIRPRnpLpTOYBrZgzDfYsmIOxXIFGCDpcqLL8swa9ICKgS8s+xNv+gMvIp/MkJ6GIpJ4YBECKW5bJE+rz00uPsImbvKlAkyx7PZjGOX7y1D6/sqLMHiiyeJkI2AYViyfRheG3nyawk2piSIO65chyiCTGZKm5aePzVPXh7TwN0iyPlo/1563EAwLeXizmcbsk4j7NLd47c/YsnImFYeOmTk1nPpcfm7c7TpEl44q19+NuOk44mMp9McfnEUvzdkkkoDvkcFTJBVUJbXLxWIgR+VYJCKRSZdDuw6GzQKyOvaRoefPBBNDU1IRQK4Sc/+QmKi4sdr/nJT36CLVu2wDRN3HzzzbjpppvQ2tqKpUuX2vNgFy9ejNtuu+3098IFklSZTCVUgwp1SNd6DGxE/bcEnywhYVr48V+r8epOp578m9WiA/a+hRNwz4JxIABe3VUHChF7XzylHF9ZKMrpDMbxD6/u6fyMDDSD4X8/Po6vL56IoCrDSib6ve7oc0d3Q939iowbLxyFN3c3uJZZp8TIRhUH7cfiuoW/Zhh4QNwU3t3XiP/7qelZJZB5Phkx3QIlwsEkIJAkgtI8X79wJntl9f7whz9g0qRJuO+++/Dyyy/jiSeewCOPPGI///777+Po0aN45plnoOs6rr32WixduhS7du3CihUr8N3vfrfPdsBjaCIRgpSuoMU4Xv7EvSHqtV11uGf+OEgSwZevGo/bLx/jmAWb4sl1B/Gay4xOx3dSgvqOBMYkSzh1k521wQ8ep45ECUYVB3L2B6SLkaVojCRyjuSTKMHR5hgCiuSYJKXKFGNLQ2iPGzAYR1CR+lVCu1dGfvPmzbjrrrsAAPPnz8cTTzzheP6CCy7A1KlT7b8ty4Isy9ixYwd27tyJz3/+8yguLsYjjzyC8vLy09h8j6GKLBGkCl3qu5KOpQStMR3FYfFjDqgSRqrODsa4buX04NOxGEd5uNMoiAS/Z+T7M0VBHz7tMhw+lTxNv0lLlGBEoT+nrLVmWLj9qY8ceZrvrZhm6/P31xxft0b+ueeew29/+1vHYyUlJQiHwwCAUCiEjo4Ox/M+nw8+nw+GYeBb3/oWbr75ZoRCIYwbNw4zZszAZZddhhdeeAGPPvoofvazn2V9Z3V19enskyuapp2Rzz2bDIZ9APpuP6K6GBcYN1hOrXjLYog0nUC0CUh39DWToU2zUOCX0KZZcG9q70SVCK4aG0TN4QMAgISWwO49e5A3gD35gX49xQ2GRELD3r17u3zd9eMo2tuCWHsoag/9mD86iOvHSag9cggggEQ6JzRdOUa8Nj3vkgrQ6Caz8zT/u6UGra0tuGNOCVJ5W/HfpOBdZoFm8nsoIckKHiGFQAg5o+eiWyO/cuVKrFy50vHYvffei2g0CgCIRqPIz8/Pel9bWxu+9rWv4eKLL8Y999wDAJg7dy4CAeFFXX311a4GHoBjFdBXVFdXn5HPPZsMhn0A+m4/NMOyvbMbjojkaHrFjU+mWDGzEtOniByQbliIGRaeXHcQr+48KQSpOMeiKeXgWVNRO1ElgutnjcDDy6fYg+D37t2DSZMmI3yWdUj6koF+PVmM46NtO+0cX1f8aIpokGpwGdqdStxbyUHdD44YDf9b+8WAc0JgMlFNlVnJk7A41h6K4ZsrLnAdAt4TKBGDck4c2X/a52Lz5s2uj/dqy2bPno1169Zh5syZWL9+PebMmeN4XtM03H777fjCF76AT33qU/bjjzzyCJYsWYLly5dj48aNmD59em++3sMDgFgep8roHlwmBrT/78edg5lXzKzEvYsm2K9XFQk/f2sfXv4kNQRC/Gpf3VWHUcVBnGzTspb0CyaV4YGlk1AW8kFy0QbvB3m1IYtECQJK1yeAcw4zTU66OKQiYTLE9YRt1N2iM1+cNw6fv2Q0WuM6GAPuf2are/I2baRob2CcoyNhnNGS3F5t2erVq/Hwww9j9erVUBQFjz/+OADgpz/9KZYtW4YtW7bg2LFjeO655/Dcc88BAB577DE88MAD+M53voM//OEPCAQCePTRR/tuTzyGJEFVtOrLlOLby6fi64vFDIHSkAqSFKVKEdNNvLDNfcrPsaYYVpxfiVd31oFSApZ2k1AphZRDVKo/VE8MRaIJE3XtGiwGhFTJngBlMgbT6jTsueLrXSERAlkmyFMlDMv3I6qbuZO3GY10pwohYh5CxxlcDfbKyAcCAddQy0MPPQQAmDlzJm6//XbX9z799NO9+UoPD1eENyfZXlZQle3qF3COuGHZA8BrWmI5m2cYgE9fMBxfXTgBjREdpXmqvQTPJcHrjYk8u1iMI66b+P/+uhv/b0uNKH01GT41y8C9CyeAcUA3WE6DbHEGxoRhNpPhl1T8nIKAUvFfQoHMJtaFU8qxdnd91kpvxczKXodq/IpolDrTulle4bjHgEeVKTh4VgcsCEFAlSGZqdh9N0aZAQFZwsgCPwCAMwY1OU3MykjsiuW18BaJ+CrPq+8jGOMwkh65YTG7qZFxjn9+fW9WF+tL22sBLpQlLc5R2xaHT6Jo1wzk+RR70Hvq7FBKQEBAAVBKoZkWGqMJFIWETLDbPeILl40BALxVXW832y2cUo770sKBPSWoysjzyWdNFNEz8h6DAp8smlDc4qaqLDRGJpSF7OqKTCgBCkMqIslBInKy2crgHIbLZyaSwlgmc0rcphon47qFxkgCFfl+MSQiWVFBBtGg7L5AGPFOQ24mQy5uxHQTL26vdW1UenF7Ldo0A2/vrgeDOMcSFcd80dQKfOGyMVnH3WIc//HeIbxV3RmiW5jjtRIluOuKsfjcJVVoieooz/ehMj/Q43OpSBQBVUJQkc76YCLPyHsMGlSZJgd7pw1w5tyO1UqU4tOzhuMvW084DD0lwPLzhkGRKBraE6go8CHQzRLcJxFXz92wGP7hlT2OBHC67k0KiRJIJBkiIKl/D27jbyaNuW4yJCwr54xmAOAM0JNVLYbJYHKGo00x5DpEFud4s7reUR9lMQ4LwFu762FZDLdfPhaEADKhoBR4asNhrN2d1ItPlkuu3V0PALgrqWCaSVFAwYjCQI+8cJkKVdSA6q4xf7bwjLzHoCI1BDxhMrTHDehJ7aIUX1s8EZQQvLjtBAgl4Izj2pmVAIAb/22jPQj82vOG4StXjreHPHMuvPOmaALDCwMwLI5YwunFU0Lwj6/twYsZjTd//tipewOkDBB3aJUTIuaQNkV1DMv3Iz+gDFivn3OOhCm8dD05N7dDM7LyHYAIfegWg2EyEaZhzFXitzCoguVIqnRVnaKbDG/vbcDy8yqFrpVEoCgS3rLnAneSMBneqq7H5y6psnMxcnLodlCRsgZzZ5ISPvQr9JwMCHHDM/Iegw6SNMyyRGElS+hSyJTi/qsn4e4F42yD8+S6g3gpIwzw109OwrQ4vnzVeFiMJ2vr64R3zjnmjQ7ikbGWwzuPJQz8ZduJrHCCZgrdm7vmjUXYp4CQTk8+NZzEZO4rgIeWTYZfkSBRAoX230HbtjFPGfa0HIbJGH6eVPVM3USXTa/AnZePhQWeU7OdM8DgotHNsjgSpoWSPBW1bQnH6yT36Y8OKCFojiZQGFSRl+9PDgTJMTaSAh2agfKwDz5ZctWJT/9cn0zhUyT4ZdovZ0R7Rt5j0MGTIZqUnrfFuB37Tf2kg6qMqmIZsYSBF7fVZkkaJEyGV3fV4fbLx+C/Nhzu1LVJet7vHI7i52/sw9eTY+QAoKFNy6l7QonQPRlRFABS3ZFiY0EJwRNr9+OVnXWOFcD/fnwcCdPC1xeLZh8CEVpSJApVlsR3kc5afZEAJsnvEx2XKfVVmvpvH9wkUsczkWbUc5UqmpZIlr6ys85x8xMyEhx3XTEWccNCc0RHOCByF5bFYHAOnnZKLMbx2F+rsww8IGrfGyLumu4pDIvhx3/bDUmiYIxj/qSynN4/58CE8rycVTOqTG2l2/7irXeFZ+Q9Bh2EECgSsVUKJUogUaHvnepeTP2vri2pe+MyB0QiBE0RXRikDO9ctzhe2l6LJdMroMoSOOdo10wkTPeBIgnTQlQzUNuKrA4qzRDKh5mqigmT4eXtJ3HD7JGuZZyUEshU7OupVPakjH5Nmw5/Y1SsKIj4vFSeQCIEMqV2WaGZNOwmE/mNmG5mhV5iuonDjTGYjKE05IMkEcR0S8j2uuzbm7vqoOkm3jvQZIfFLhtfgpVzRmWFqf740VGcaHMfqN0aN7roV4YdxzcYYCSXDe/sa8TwwoBrA1x6WWTqWvJJIq7uk/vvaioXnpH3GJQEFAmMW1nemkwp0nNgE8pDueuqGcfJ9njOGZmEEJxo01CRL0ou//zx8ZzDSTgHnt9yHDddNCppQMVsAxCC1pgBiRAYLmaKUjFrNFAYyHqOMQ6dcegWoFAKVe6ZsWdc1IgbDDlvSqnQi55cAaV/5/98cBRv722AlKxIuXJSOQzGsG5vg53QJgCunl6Ba6YPyxkWsTjHe/ubkn0M4vkNB5oAAKsuqrJfpxkW3j/YlHN/KCG4dFwx3j/UlFU5RZPWPzMilDAZalvjWDZjmKMB7rrzh+OhZZMRVGUx2P0Ub6D9Ec/IewxKxGARGYlkjXzK+KZix5ppiSoPg2HhlHK8tbve4a2rEsW8SaUoz/d3KVVbEBBTf9piOt7b35TTm+QQBuxTs4bDr0iwLA7Dgp2ky1U2yBiHP1nnLVFRItoS1e2a7tSHCy9bTCTqLjmY63sSpgjBGCYDy7En//3BUazf2+CYrfrG7ros48oBvL6zDpG4mTMswjiywjyGxbHhQBOunzVCqDoSIKIbydm+uc/DPQvGIT+o4K3qehCCZEimFDfNGYX7n93mWlorSxS3XTYG314+Fa0xAyMLAwgHzu0UpzOBZ+Q9BjUypYhZoiomkjBhuniUbo0u8yaV4pZLqiBRgvmTyvDO3kZH3F6hwBUTShGQJTzz0TG8t78RZo4EYgpKCNrihi1JazKO379/BOv3NboKpKkSwRUTS0EIQUtMx//bXIO1exvs5OWVk8rwuUtGg1Jid25GExaCqmQPtiDJ2DwlqYatzrg9YxyRhAndYHYYoyvaYgbWJQ18OrkKWziADw83YfboInx8pNXuPAZEspTneC9NioKVhoIgVAzl6IrFUyswvDCAh5ZOxjcWT0RTtDOMFOtKkoBzTBmWj5BPRmVB9kppsOAZeY9Bh2kxRBImOjQTMd2yDaBfkWAQDoszh7HPbHQpCqnwpSpzLI5bLhGhg3f2Ntix4wsq/fj8paPxPx8cxcYDzd0aeEAYFYUSdMQNAMBftp7ApiMtWUaTJuPjF1QVYf6kMpxs1/DytlpsPipemwrrvL23ATHDwg0XjHC8X6IEvrQYvmZYaIsbKAgoUCUK3bRgMqCmSUPMH3HcBChJS9ZSAglEJK0ZQ317okuP2g1CCK6cWAZFovjwULN9/OaMLsKmwy2uCVvGRfz/eFsMcjIMNX9SmesNZmS+goev6exBUGUxAU6RKGRKUBBQcNOFI/H85hpHR3RAobjpwlFDYlrc4N9DjyGDxTjqOzS0x03bsGeiyAQKJHCZw2LJShHGAC7i+OmxbwoCRQICnOKeeeOw5pLRaIomkO9XUFdbA8NiduiiOxSJYHZVEUDEqMGEaeGjwy2O8k77ewnBN6+eiLBfAYGoUNl0JPu1hsXx4aFmLJ5ahoTBkR+QRbUH6Twez206hg3JxKbFOC4cXYRrzhsGiRKYnGcllNNJefopQ2yY/JTVEjnnKAwpuOGCEbj2vGFoj5v2dlICbDzQ7DhTBMCl44pRkucHJbCTv6kb7fq9DSAQK4CrJpfhuvEyZEqRp8pQFQqZZsfRv3/ddEiE4JlNx+xV0E0XjsJ3V0w7pX0ZqHhG3mNQEEmYONmm5YxtZ0IIgSwlG11A00SrhCFLdzDjJrM9/NElIQBAEyVo7IFnm/KQZ1cJ4wqIZKfo3nR/L6UElgX4JAkSARo0UQHkdkOwGMejL+0WPQGM49JxxbjpolEAYBt4cRMS7918tAUmY7hiYmm3w8gNi0MzLEQSpq21ct6IfHxyvN2xLYTANeFMIPabMfF8yCcj36+KFQIlUCWa9V5CUg1FznS3IlHcecU43HrpaLRrJkpDPuQHZBw+fBiRhIlowoJfSTUiSQgonV2mskTxfz89Aw8tm4K6ds2WmhgqDJ099Ri0JEwLJ1rjvZKVTUEpgZpWGseToZp/T85+FbF6jnmTynDdzOFoiJkoCeXutJQp8ODSSeCMIJz0XC3G8cK2E/jgUDMoQU4jyzlHSZ6CVF9NfkDuMnkJAFbSI3//UDMIIVg2vQLv7W9y9f43HWnFtpo2MMZxUSOxPft0LMbx+q46bDrSYodYzh9ViKunVIASgm01bZ2PjywA5xwf17Q5DPb04WHMm1CKxmgCYb8MvyxDlTkUSawq3juQXQ3DOPDOvibcftlY5PllSMky0c6yRQXl2TOKkhPCLEeCVaYEIZ8s/qeK/44ry3M9joMZz8h7DHiiCeu0DLwbhBA8+c4BvF7tbIJav7cBhslw6TCKkE/BpeOL8f7BZkfIRpEILh5bjNI8v+MzX9h+Ah8ebnb1yDPf65MlWBwwTCGde8GoQnx8rLXL9wKd1SnD8v1dDjRJbe+mIy3QTAuLp5bbdfISpXh1x0lstb9PvHZ7TSsUSvCZ2SOwfOYwdGgmwn7Zbgi69vxKNLQn8O7+JlTXtmNXbQd2nOiwG7EuHF2Ea2dWwmIUTVE950pGogQJy0KZ6st67lQwGUdb3EBb3ACBkKQO+SSEfHK/ncd6JvCMvMeAJ5fe++kQ1y28uiN7uLdhcWw82IRLKkqhyhQ3X1QFSogd90419NxwwUhQIqpeOICEYeGDg005E7SqTME5x8VjirF4agWao4YdeiIQsrYMHNuOCQ/a4rnj4ybj2HKkOWfVS+Zrdxxvx8VjigEAUd2EKlFsOdaa9flGMjdw5eRyBH0UpXlOI+yXZWyracCeug5HWXyqVHLz0Rb4FIpVF1fBp9DcXbKMgYIgrosQTF/UqXNwxAwTMcNEQyQBmVKE/TIKAsqgN/i9MvKapuHBBx9EU1MTQqEQfvKTn6C4uNjxmi9/+ctoaWmBoijw+Xz4zW9+gyNHjuBb3/oWCCGYOHEi/v7v/x7UZaSah8epEFAllOb50BjJbnnvLU3R3J2wlBB06MIAS5Rg1cVVuP6CEXYFi9NoiNDE85trcht4ieDu+WNRVRREJCEauFKx6XSumVGJRVMq0KEJz/SJdQdybv/BxhiKQira4ka33j/A8fbeBhxoEIOuu0quEgKcbI+jJKRClZKaLaqEkCLD5AybjjTnTETbNfAXiBr4y8aXpOUMBIpEcNn4EiQshpPtmqjyAWAwjsoCPwJ9NDjdZAwtMR0tMR0hVUZRSO22VHOg0qu9+sMf/oBJkybhvvvuw8svv4wnnngCjzzyiOM1R44cwcsvv+y4C//oRz/C/fffj0suuQTf+9738Oabb+Lqq68+vT3w8ABQmueDT6aobdP6JHRTEvJ1EQfnCKtOA5xK+Lnx3KZj+PhYa87v4gDGl+VBpRRxwwJLu7HoplBvDCcnCKkyRUmeD02RBCiyOzntbQTQGtNx/qhCbK9pAyHIaXwtBhxsiNrSvF1hWhylIR/8ametf0QzEdUsRBNdNy0Bzl6BlReKBHHmKij1uMU4/rjpqOP5RVMrcN/CCX2qzhnVTXsFUxRUURhUBnyXazq9MvKbN2/GXXfdBQCYP38+nnjiCcfzjY2NaG9vx5e+9CW0t7fj7rvvxlVXXYWdO3fi4osvtt/33nvveUbeo88I+4UX3RLV0RY3cjbB9ISAKmHpjAq8liEa5pMprpxchp42RmqGleWtpiNT4LLxJfYNoizsQ1y30K6ZeHl7LbYea7UN3KxRhVgyrQKUElFe2U3JukQpLhkrwj8dmoH3Dzbjk+NtDs9eIuKG0L23n4SI+vxlM4Y5HubgdoVPV6R3CXe3CnKrDnqzug6mxfDNqyf1uYaMbjHUdWho1wyMKAz0S0XJ3tCtkX/uuefw29/+1vFYSUkJwuEwACAUCqGjo8PxvGEYuOOOO3Drrbeira0Nq1evxsyZM8E5t++Qbu9LUV1d3aud6QpN087I555NBsM+AGdnPxjniCQY2hMWMqcC9pQloyja2wLYcDRmT5SaOzKAFWNlmHoCsaaTiOoWdCu3rW2KmV0OHZxc6sNlwyhqao47Hn9lbzu2nYwnQzzi07cebUEsFsPlo0P2e/c0JnLK7FqMId7eAjMqtmB2OUFCU7GnMWHvz5hCBUfaDJg9tPGcAx8fbcGMYhFayWRSiYq9TQnX0JRMgZkVfjTWnXT97Mb2zn/rJnPtIjYsjrf31OPSCqAyX4WeSODgwUM92/hTYDcBhoWFfs3Z4Ez+Jro18itXrsTKlSsdj917772IRqMAgGg0ivx8Z01TaWkpVq1aBVmWUVJSgqlTp+LQoUOO+Lvb+1JMnTrV9fHTobq6+ox87tlkMOwDcPb3I5Iw0RY3EEvkbnHPxbcndA4LKQn57JjwwYOHMG6cmB7EWLJ8T2dZbfSlhgW+udn1sxWJ4K6rpmSFeTTDwvZ36rMMnMmBfc06brl8AighGFHJ8NdPavHRkZasOnWZEswaVYiRw50e9w3DOkNA8fYWlJeX459e34tT6WLlAHzhIpSFOxOvlBAEVAlTxhK8sO0E3tvfmEy4wla2TIViehJqqWvXINEm174HiVL4C8sxbkyR4zz0NUFFRlVJ8Ix8diZ98ZvYvHmz6+O9CtfMnj0b69atw8yZM7F+/XrMmTPH8fyGDRvw+9//Hr/+9a8RjUaxb98+jBs3DtOmTcMHH3yASy65BOvXr8fcuXN78/UeHqdEnk8MTuZcGONU80wuBcZMAqqEkWruHzu167EBQE0Kn1nQTAaFki4TjG5x/LZ47ti20HXhqMgXBvYLV4zFzReNwh8/OoaPj7ZAoqJq5cIxxVg+fRiQ1IjhSeVJzkX+QJUp6qIEqkwxa1RhWrmkIBXGcbsnMg58fLQVN104EpIkyi6VNGnP9BCMX6bQTHbKVSwFASVnboVxjvFnod5d6+H10d/plZFfvXo1Hn74YaxevRqKouDxxx8HAPz0pz/FsmXLsGDBArz77ru46aabQCnFN7/5TRQXF+Phhx/Gd7/7XfzTP/0Txo0bh6VLl/bpznh4dAUhYohIUJWBsFBujCUsRHSzV15+LlIJ0nDy779bMhn/tu4gXk82VVmM4/IJpbhxzkjX93dn4AoyEgJBn4w7rhgLzajKUeHjDovIqCzw49a5oxFSJWw46EyAmkxIAbvx8bEWrL5kVM7vSU9EF3S7Je7vd7s5qjLF0mkVKAydebXIwuDgUKTslZEPBAL42c9+lvX4Qw89ZP/7//yf/5P1/NixY/H73/++N1/p4dHnKBJFQZCiIPljTpgWNINBMyzE9aRE8SmEMXJ+j0xx36IJuGveWEfYJ6XXblmi5j01kEMip+79A+4VPuniZJnPkaQIGaUEqy6pwvWznQnQE61xbHTpSgWyFTV7Slfbk8lNFwoV0Hf3NYmOYw4snV6BLy0Yf0rfeaoQEBSFFJTlnV4zVn9hcBaGenj0Ap8swSdLtqecGkatGZ3G/3QMf2bYJ+Xxu/HgUuH9v7azzpY/vnJyGVZdLMoLTYt3ufLIFCdLL0/MFRPPvEkUh1QxGMQls+u2ouiK7rZHIiQ51pDCJ1EoslCffGjZFNy3MDsncqYIKBLKw31Xj98f8Iy8h0cOCCFZhi81PzZaL6M87E+boGT1vAyxB8gSxb0LJ+DOK8bmNHCcCyVNk4mxfJxxWMm4+1PvHcJGW25BbNfGpIFddXFVzglW6XTXsOTmiafmyqbfRmRK8N+bj2Ztz/sHmxH2KfjqwvFd1qV3lxM5XfyyhLBftnsRBhuekffwOAUIIVBlgqBCURxSHc9lDrjWre4HXXdHVwauU0nTaWzjuoW39zRkSTLolhAF+9qiiQioEqw2GaOLg+A8NcCjU32Tg4OA4OuLJiHPdwiv76qDRMUUvyXTKnD3vHFifKG9LchpqOO6lTV0BRAj+F6rrsOd88aeVc9ZpgQ+WUJQlQatYU/HM/IeHn1E+sDwTFhSt96wOEwr+V8mhpekBmT3lchaV5IMEiFoiiYwUg3aMflO3I20Wz7hTGzPmUDIFkvwyZ3/zZSLGOx4Rt7D4yxAKYGPSuhKHiW1EjCZuBFYTCRkUxOqGE9q3rOu4/FdSTJYnKMkdOoJxdMJmZyJ7UlBgeTA7dT/CGSJCl0dmfZ5V+xAxDPyHh79hNRKoCdw3lmRk278U39/atZwvLTtBLQMSYZlM4Yhzyf3SKGyr+hKImLJ9AoEVEkIkSUliSVKOscPph5P/ZsSKJJ4jUIpeIs6JDXiTwXPyHt4DEBEPF7E5N348Q3nIaRKjpF3NydH3qU0WaxmBZPKw2DJRqlUuCg9Lp/6O3VP4Jyn/RvOfi2S2jbxT0JI8r/AY9efh3y/gj9trgGlBIxx3HjhKDyyfAp8ijSoBMH6G56R9/AYhPRk5J1dJ9+luk7f8f995jx8Z/nUITmC71ziHWUPj0FMfxt519+2ZygwtNLMHh4eHkMMz8h7eHh4DGI8I+/h4eExiPGMvIeHh8cgxjPyHh4eHoMYz8h7eHh4DGI8I+/h4eExiPGMvIeHh8cgplfNUJqm4cEHH0RTUxNCoRB+8pOfoLi42H5+/fr1+PWvfw1AtEFv3rwZL730EhKJBO655x6MGTMGgBgjuHz58tPfCw8PDw8PV3pl5P/whz9g0qRJuO+++/Dyyy/jiSeewCOPPGI/P3/+fMyfPx8A8Jvf/AazZ8/G+PHj8dxzz+ELX/gC7rjjjr7Zeg8PDw+PLulVuGbz5s2YN28eAGHQN27c6Pq6kydP4i9/+QvuvfdeAMCOHTvw9ttv43Of+xy+853vIBKJ9HKzPTw8PDx6Qree/HPPPYff/va3jsdKSkoQDotZ9KFQCB0dHa7vfeqpp3D77bdDVcUEnZkzZ2LlypWYMWMGfvWrX+GXv/wlHn744az3VVdXn/KOdIemaWfkc88mg2EfgMGxH94+9A8Gwz4AZ3g/eC/46le/yrdt28Y557y9vZ1fe+21Wa+xLIsvWbKEx+Nx+7G2tjb73/v27eO33npr1vs2bdrUm03qll27dp2Rzz2bDIZ94Hxw7Ie3D/2DwbAPnPfNfuSynb0K18yePRvr1q0DIJKsc+bMyXrN3r17MXbsWPj9fvuxO++8E9u3bwcAbNy4EdOnT+/N13t4eHh49JBeJV5Xr16Nhx9+GKtXr4aiKHj88ccBAD/96U+xbNkyzJw5E4cOHcKoUaMc7/v+97+PH/7wh1AUBaWlpfjhD394+nvg4eHh4ZGTXhn5QCCAn/3sZ1mPP/TQQ/a/r7nmGlxzzTWO56dPn44//vGPvflKDw8PD49e4DVDeXh4eAxiPCPv4eHhMYjxjLyHh4fHIMYz8h4eHh6DGM/Ie3h4eAxiCOecn+uNSGfz5s3nehM8PDw8BiRuPUv9zsh7eHh4ePQdXrjGw8PDYxDjGXkPDw+PQcygN/Kvv/46HnjgAdfnnn32Wdxwww246aabsHbt2rO8Zd2jaRruu+8+3HLLLfjiF7+I5ubmrNd8+ctfxqpVq7BmzRrcdddd52Ar3WGM4Xvf+x5uvvlmrFmzBkeOHHE839+PPdD9Pjz66KO44YYbsGbNGqxZsyanGmt/YNu2bVizZk3W42+99RY++9nP4uabb8azzz57Drbs1Mi1H//1X/+Fa6+91j4XBw8ePAdb1zWGYeDBBx/ELbfcghtvvBFvvvmm4/kzdi5OW/qsH/PDH/6QL126lN9///1Zz9XX1/MVK1bwRCLB29vb7X/3J/7zP/+T/+xnP+Occ/7SSy/xH/7wh1mvueaaazhj7GxvWre8+uqr/OGHH+acc/7xxx/zL33pS/ZzA+HYc971PnDO+apVq3hTU9O52LRT4sknn+QrVqzgK1eudDyu6zpfvHgxb21t5YlEgt9www28oaHhHG1l9+TaD845f+CBB/gnn3xyDraq5zz//PP80Ucf5Zxz3tLSwhcsWGA/dybPxaD25GfPno3vf//7rs9t374dF1xwAVRVRTgcRlVVFXbv3n12N7AbuhvO0tjYiPb2dnzpS1/C6tWr+5VHnL7ts2bNwo4dO+znBsKxB7reB8YYjhw5gu9973tYtWoVnn/++XO1md1SVVWFn//851mPHzhwAFVVVSgoKICqqpgzZw4++uijc7CFPSPXfgDAzp078eSTT2L16tX493//97O8ZT1j2bJl+PrXvw5AjEWVJMl+7kyei14JlPU33AabPPbYY1i+fDk++OAD1/dEIhF78Akghp+cy0lVvRnOYhgG7rjjDtx6661oa2vD6tWrMXPmTJSUlJy17c5FJBJBXl6e/bckSTBNE7Is97tjn4uu9iEWi+Hzn/88vvCFL8CyLNx6662YMWMGpkyZcg632J2lS5eipqYm6/GBch5S5NoPALj22mtxyy23IC8vD/feey/Wrl2Lq6666ixvYdeEQiEA4rh/7Wtfw/33328/dybPxaAw8itXrsTKlStP6T15eXmIRqP239Fo1HGQzzZu+3Dvvffa2xiNRpGfn+94vrS0FKtWrYIsyygpKcHUqVNx6NChfmHkM48vYwyyLLs+d66PfS662odAIIBbb70VgUAAADB37lzs3r27Xxr5XAyU89AdnHPcdttt9rYvWLAAu3bt6ndGHgBqa2vx1a9+Fbfccguuu+46+/EzeS4GdbimK2bOnInNmzcjkUigo6MDBw4cwKRJk871ZjnobjjLhg0b7OVfNBrFvn37MG7cuLO+nW7Mnj0b69evBwBs3brVcWwHwrEHut6Hw4cPY/Xq1bAsC4ZhYMuWLQNuCM748eNx5MgRtLa2Qtd1bNq0CRdccMG53qxTJhKJYMWKFYhGo+Cc44MPPsCMGTPO9WZl0djYiDvuuAMPPvggbrzxRsdzZ/JcDApP/lR46qmnUFVVhUWLFmHNmjX4/9u7YxQJYTCK4w9htLfxBB7Cyso6hRDFwsYTaOUJPIJ4D6+gF7LQMltMs8sWM80im/n/TvC+BB4kENI0jZxz6vteURTdHe+HV5+z5HmubdtkrVUQBBqGQXEc35z6qSgK7fuuuq7lnNM0Tf9q7aXXMxhjZK3V4/GQMUZpmt4d+S3ruuo8T1VVpXEc1XWdnHMqy1JJktwd723f5+j7Xm3bKgxDZVmmPM/vjvfLsiw6jkPzPGueZ0nPE/x1XX+6F7x4BQCPfex1DQB8AkoeADxGyQOAxyh5APAYJQ8AHqPkAcBjlDwAeIySBwCPfQEk5vyf9UCjfQAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118e21588>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -647,7 +660,7 @@
     }
    ],
    "source": [
-    "gmm16 = GMM(n_components=16, covariance_type='full', random_state=0)\n",
+    "gmm16 = GaussianMixture(n_components=16, covariance_type='full', random_state=0)\n",
     "plot_gmm(gmm16, Xmoon, label=False)"
    ]
   },
@@ -658,25 +671,28 @@
     "editable": true
    },
    "source": [
-    "Here the mixture of 16 Gaussians serves not to find separated clusters of data, but rather to model the overall *distribution* of the input data.\n",
+    "Here the mixture of 16 Gaussian components serves not to find separated clusters of data, but rather to model the overall *distribution* of the input data.\n",
     "This is a generative model of the distribution, meaning that the GMM gives us the recipe to generate new random data distributed similarly to our input.\n",
-    "For example, here are 400 new points drawn from this 16-component GMM fit to our original data:"
+    "For example, here are 400 new points drawn from this 16-component GMM fit to our original data (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Zj3QbKMrgJvIhHzTY1dWCL3zhRr8SP8We/0Y87lrlm3A6f4CeHvEamUwtkI48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\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119b5ae80>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -684,7 +700,7 @@
     }
    ],
    "source": [
-    "Xnew = gmm16.sample(400, random_state=42)\n",
+    "Xnew, ynew = gmm16.sample(400)\n",
     "plt.scatter(Xnew[:, 0], Xnew[:, 1]);"
    ]
   },
@@ -695,7 +711,7 @@
     "editable": true
    },
    "source": [
-    "GMM is convenient as a flexible means of modeling an arbitrary multi-dimensional distribution of data."
+    "A GMM is convenient as a flexible means of modeling an arbitrary multidimensional distribution of data."
    ]
   },
   {
@@ -705,30 +721,33 @@
     "editable": true
    },
    "source": [
-    "### How many components?\n",
+    "### How Many Components?\n",
     "\n",
-    "The fact that GMM is a generative model gives us a natural means of determining the optimal number of components for a given dataset.\n",
-    "A generative model is inherently a probability distribution for the dataset, and so we can simply evaluate the *likelihood* of the data under the model, using cross-validation to avoid over-fitting.\n",
-    "Another means of correcting for over-fitting is to adjust the model likelihoods using some analytic criterion such as the [Akaike information criterion (AIC)](https://en.wikipedia.org/wiki/Akaike_information_criterion) or the [Bayesian information criterion (BIC)](https://en.wikipedia.org/wiki/Bayesian_information_criterion).\n",
-    "Scikit-Learn's ``GMM`` estimator actually includes built-in methods that compute both of these, and so it is very easy to operate on this approach.\n",
+    "The fact that a GMM is a generative model gives us a natural means of determining the optimal number of components for a given dataset.\n",
+    "A generative model is inherently a probability distribution for the dataset, and so we can simply evaluate the *likelihood* of the data under the model, using cross-validation to avoid overfitting.\n",
+    "Another means of correcting for overfitting is to adjust the model likelihoods using some analytic criterion such as the [Akaike information criterion (AIC)](https://en.wikipedia.org/wiki/Akaike_information_criterion) or the [Bayesian information criterion (BIC)](https://en.wikipedia.org/wiki/Bayesian_information_criterion).\n",
+    "Scikit-Learn's `GaussianMixture` estimator actually includes built-in methods that compute both of these, so it is very easy to operate using this approach.\n",
     "\n",
-    "Let's look at the AIC and BIC as a function as the number of GMM components for our moon dataset:"
+    "Let's look at the AIC and BIC versus the number of GMM components for our moons dataset (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
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JczLnJffOucn9Gz4FJS28/vlJzhl6iA3z5vs3JhMZrONsi6H/g4dgKH9gDSioy8rKiI6O\n7vv8kUce4Ve/+hUmk4mEhAQWLVrUOzp65UpWrFiBzWZj1apVaDSu1/2bHjKVM63FHG4sYGHMgvO2\n6TzVZE4MYf+Jek5VtJIcF+CgKoUQQlxOZ7eZ97YVsbugDjelgtvmxbM4KxaV2+h7a3lQo77twdn+\nKjT0GHls7zNE6SJ4JPOnF2w/U3WO598+TObEEB6+dYoDKhw58le985J759zk/l2ZwvKzvPbPk7S0\ndxMVrOP7NyUTEzoyjxLs1qIW39BpvJjgn8jJs2do6mghWBt43vakKF8igrx6B5UZe/Dxcr1eBSGE\ncEZdPWY+2FHCl4drUCoU3Dw7jpvnxI3KVvS3je7qRqn0kFQADjcevWDb1zOVWawyU5kQQowWpytb\nWf1qDl8eriEiyItf3pPObfPHjfqQBgnqIUkNnoybwo28iwQ1wOwpYahVSnYeqcXq2CcLQggxpnWb\nLLy7tYjfv5NPc1sXi7NiWH1vBvHh/b+VNFpI1/cQaNVakgPGc7zlJA3GRkK9Qs7b7uWhZsbEEPYe\nr+dkRSuTZVCZEEKMuOKaNl7ZeJKGsx2EBmh54MZkEiN9HV3WoEmLeojSQ3u7vy/Vql4w7avlL/Nl\npjIhhBhJJrOFD74s5rm38mg828G1GdE8eV+mU4Y0SIt6yFKCJqFSqshrLOCG+Gsv2J4Q4UNksBf5\nRc20Gbrx1bk7oEohhBhbyuraeWXjSWqbjQT7eXD/Dcl2WSN6JEmLeog8VR5MDpxIvbGBWkP9BdsV\nCgXZaZFYrDb2yKAyIYSwK7PFyrpdpfz2zTxqm41cNT2Sp+6f4fQhDRLUVyQ9ZCpw6e7vWZND0aiU\n7Doqg8qEEMJeKhv0PPPGITbsK8ffW8Mv7kxj5XUT8NC4RqexBPUVmBI0CY1SzeGGo1xs3hith5oZ\nyaE0neuisPysAyoUQgjXZbZY+WxvGc+8cYiqRgPzU8N5+oGZLjeAV4L6Cri7aZgSlExjZzPVhtqL\n7rNgWu/ylzvzL75dCCHE4FU3GXh2TR7rd5fhrVXzs2Wp3Ls4GU9312hFf5vrfUcjLD0klcONBeQ1\nHCXaO/KC7ePCfYgO0ZFf1Mw5Qzd+MqhMCCGGzGyx8vmBCj7dW47FamPW5DBWXJuEl4fa0aXZjbSo\nr9CkwIm4u2k43Hjx7u+vZyqz2mzsLpBBZUIIMVSVDXp+8+ahvlb0T5dO5cGbJ7l0SIME9RXTuKmZ\nGjSZlq5WKvRVF90na1IYGrWSXTJTmRBCDJrZYuXj3aU888YhKhsMzE0J5zffn0laYpCjSxsREtTD\noG/yk4aLj/7WeqiYmRxKS3sXJ8pkUJkQQgxURb2ep18/xKd7y/Hx0vDzO1K5/8ZktC7eiv42Ceph\nMDFgPJ4qDw43FmC1WS+6T/ZXM5XtkJnKhBCiXyazlXW7SnjmjUNUNxmYnxrBMw/MJGVcYP8HuxgZ\nTDYM1EoVqUFTOFB/iLK2ShL84i7YJy7Mm5hQHUeLW2jVd+PvLYPKhBDiYsrq2nl140lqmo0E+rhz\n7+JkJse71itXgyEt6mEyvZ+5v3sHlUV+NahMXtUSQoh/ZTJb+GBHMb958xA1zUaumhbZ+170GA5p\nkKAeNhP9E/FSa8m/TPd31qRQ3NVuvTOVWWVQmRBCfK2kpo0nX8vl8wOVBPp48B/Lp7Hy+gku+V70\nYElQDxM3pRtpwVNo79FTfK7sovt4uquYOSmUs+3dHC9rGeEKhRBi9OkxWVi7vZhn38qjrqWDa9Kj\nePqBGSTHOv8c3cNFgnoYTQ+5fPc3QPZXM5XtkJnKhBBjXFH1OVa/lsumnEqCfT15ZMU07rp2vMvM\n0T1c5P/GMEryG4e3WseRxmPckXQLbkq3C/aJC/MhNsyboyXNnG3vIsDHwwGVCiGE43SbLKzbWcrW\nQ71zT1yXGc1t88fhrr7wd6aQFvWwclO6MS0kBYPJyJlzJZfcLzstApsNmalMCDHmnK5sZfUrOWw5\nVEVIgJbH7k7nzmuSJKQvQ4J6mH3d/X34EpOfAMxIDsVd0zuozGK9+MAzIYRwJV09Zt7efIbfvZNP\nU1sni2bG8NR9mSRG+Tq6tFFPgnqYJfjF4avx4UjTccxW80X38XRXMWtSKK36bo6VykxlQgjXdrL8\nLL9+JYdth6sJD9Ty+Mp07rgqEY20ogdEgnqYKRVKpodMpcPcyenW4kvutyCtd6aynTJTmRDCRXX3\nWHhr82n+8N4RWtq7uCErlifvyyQhQlrRgyGDyexgemgqX1bvIa/hKJMDJ150n9gwb+LDvSkobZFB\nZUIIl3Om6hyvbjxJ47lOIoK8eODGZOLDfRxdllOSFrUdxPvE4O/uR0HzCUyX6P6G3la1zQa7jsqr\nWkII19BjsvD+9iJ+9/Zhmto6WTwzhtX3ZkhIXwEJajtQKBRMD51Kp7mLky2nL7nfjOQQPGRQmRDC\nRZTWtvPU67l8kVNFiL8nj92dzrKrElGr5Fn0lZCgtpP0AUx+4qFRMWtyGOcMPRQUy0xlQgjnZLb0\nrnT17Jre2cUWpkfx5P0zSIyUZ9HDQZ5R20mMdxRBHgEcay6kx2JC43bxtVMXpEXwZX4NO47UMm18\n8AhXKYQQV6ayQc8/NpykuslAkK8H99+QzESZ/nNYSYvaTnq7v1PptvRwouXUJfeLCfUmMdKXY6Ut\nVDUaRrBCIYQYOovVymd7y/rWi16QFsFT98+QkLYDCWo7Gsjc3wA3zY4F4LO9F1/MQwghRpOaZiO/\nfTOP9bvL8PHS8PM7Uvneoomy0pWdyP9VO4rShROiDeJ480m6zN14qNwvul/KuEDiwrw5dLqJ6iYD\nUcG6Ea5UCCH6Z7Xa2JxbxbpdpZgtVmZPCWPFwiS0Hhd/tCeGh7So7UihUJAekorJauJ4y8nL7ved\nOfEAbNhXPkLVCSHEwDW0dvD8O4dZ+2UxWnc3frIkhe/fNElCegRIUNvZQOb+BkhNDCQ21Jvck43U\nNBtHojQhhOiX1WZjW141q1/Nobi6jcyJITzz/Zky+HUESVDbWYQujHCvUE6cPU2nueuS+/W2quOw\nIa1qIcTo0NzWyR/fO8LbW86gdlPy0C2TefjWKXhrNY4ubUyRoB4B6SGpmK1mCppOXHa/tKQgokN0\n5BQ2UNcirWohhGPYbDZ2Ha3l16/kcLKilbTEIH7z/ZnMSA51dGljkgT1CJge+lX3dz+jv6VVLYRw\ntFZ9N3/+oIDXPz+FQqHggRuT+cntKfjqLj4YVtifjPoeAaHaYKJ0EZw8W0SHqQOtWnvJfaeNDyYq\n2IsDhQ3cPCeesIBL7yuEEMPFZrNx4EQDb285Q0e3mcnxAdy3eKIsGDQKSIt6hKSHpGKxWTjST/e3\nUqHg5jnx2GywUVrVQogRUNts5M8fFPDyhkIsVhv3XD+BVXekSkiPEhLUI2R66FSg/+5vgPQJwUQG\nebH/RAONrR32Lk0IMUbpO3p4a/Npfv1KDsdKW0iO9eepB2aQPS0ShULh6PLEV6Tre4QEeQYS6x3N\n6dZi9D0GvDWXntSkt1Udx4ufnGDDvgruvzF5BCsVQrg6s8XKtrxqPt1bTme3mVB/T+64OpG0xCAJ\n6FFIgnoETQ+dSoW+iiNNx5kXmXXZfTMmhBAeWMa+4/XcPCeOYD/PEapSCOGqbDYbh88088GXxTSe\n68TLQ8Xya5K4anokKjfpYB2tBnRnXnrpJe68805uv/12PvroIyorK1mxYgV33303Tz31VN9+a9eu\n5fbbb+fOO+9kx44d9qrZaaUPcPITAKVSwc2z47DabGzcX27fwoQQLq+iXs/v38nnr+uP0dLexcL0\nKJ774SyuzYyWkB7l+m1R5+TkkJ+fz3vvvUdHRwevvvoqzz33HKtWrSIjI4PVq1ezdetW0tLSWLNm\nDevXr6erq4vly5czZ84c1GqZXu5r/h5+jPONpehcKW3denzdvS+7/4zkUD7ZW87eY/XcNCuOIGlV\nCyEGqVXfzbpdJew7Vo8NSEsMYtlVCYQHejm6NDFA/f4ZtWfPHsaPH8+PfvQjHn74YbKzsyksLCQj\nIwOA+fPns2/fPgoKCkhPT0elUqHT6YiLi+P06dN2/waczfSQVGzYyG8q6Hff3lZ1LBarjX8eqBiB\n6oQQrqLbZOHTPWU89tJ+9h6rJzJYxy/uTOOnS6dKSDuZflvUra2t1NbW8ve//52qqioefvhhrFZr\n33YvLy8MBgNGoxFv729aiFqtFr1eb5+qndi0kBQ+KvqMww1HyY6a0+/+MyeF8unecnYX1HHjrDgC\nfeV1CSHEpVltNg6eaODDnSW06rvx8dKwYuE45qaEo1TKQDFn1G9Q+/n5kZCQgEqlIj4+Hnd3dxoa\nGvq2G41GfHx80Ol0GAyGC77en+Dgy3f/uppgvEkOTqSwqQill5lAbf+LrK+4fiJ/fi+f7Udr+dHt\nqSNQ5cCNtfvnSuTeObeL3b8TpS288ulxiqrOoVYpWXZNEkuvlmUonV2/QZ2ens6aNWu49957aWho\noLOzk6ysLHJycpgxYwa7du0iKyuLlJQUXnjhBXp6euju7qa0tJSkpKR+C2hqGnut7hT/KRQ2FbH1\n5D6ujpnf7/6TY3wJ9vNgy8EKrkmLGDWTEAQHe4/J++cK5N45t3+9f03nOvlgRwmHTjUCMCM5hKXZ\nCQT5emLUd2HUX3pBIDGyhvIHcr9BnZ2dzaFDh1i6dCk2m40nn3ySyMhInnjiCUwmEwkJCSxatAiF\nQsHKlStZsWIFNpuNVatWodHICisXMy0khbVnPiavsWBAQe2mVHLTrDhe+/wUnx+o5K7rxo9AlUKI\n0a6jy8zG/eVsOVSF2WJjXIQPd16TRGKkr6NLE8NIYbPZbI4sYKz+Vf+X/Jc51VrE07MeJdAzoN/9\nzRYrj790gHOGHn730Cz8vR0/Qb60ypyX3DvnFhDgxUfbzvDx7lL0HSYCfNxZuiCBGZNCUcqEJaPa\nUFrU8vKcg3wzpWj/o78BVG5KbpwVi9li5fODMgJciLGqsPws//anHaz54jQ9Jiu3zR/Hsw9mkTU5\nTELaRUlQO0hacApKhZK8Acz9/bU5KeEE+riz80gt5wzddqxOCDHamMxW3t1axH+9d4TKBj3zpobz\n3A+zuHl2HBq1m6PLE3YkQe0gXmotEwOSqNLX0NjRPKBjelvVcZjMVjYdrLRzhUKI0aLhbAfPrslj\ny6EqwgK0/OnfFnDfDcn4yRrRY4IEtQP1TSk6yFa1v7c7O/JraDP22Ks0IcQosf94PU++nktFg565\nU8NZfW8midF+ji5LjCAJagdKDZ6MSuFG3gDm/v6aWtX7rLrHbOULaVUL4bK6esz8Y0MhL28oRAH8\n4DuTuP+GZNw10s091khQO5CnypPkwAnUGuupNzb0f8BX5k2NwN/bne351bR3SKtaCFdTUa/nqddy\n2Xe8nrgwb568L5OsSWGOLks4iAS1g33d/T3YVvXimTH0mKx8kSOtaiFchc1mY0tuFb9dc4iG1k6u\nnxHN4yvTCfHXOro04UAS1A6WEpSMWqkir/Eog3mlfUFaBL46DdvzatBLq1oIp6fv6OEvHx3j3W1F\neLqr+NmyVL57dZIsQSkkqB3NQ+VBavAUGjqaKGkrH/BxapUbN8yMpdtkYXNulf0KFELY3enKVp58\nLZcjxc0kx/rz1P0zmJoQ6OiyxCghQT0KzI2YCcDumv2DOm5BWgQ+Xhq25VVj6DTZozQhhB1ZrFY+\n3l3K79/Np83Qw+0LxvGL76bJa1fiPBLUo0Ci3zhCtSEcaTyGocc44OM0ajcWz4yhq0da1UI4m7Pt\nXfzh3SN8urecAG8PHr1rOjfOipOlKMUFJKhHAYVCwdzImZhtFg7UHxrUsdnTIvHRqtmWV4WxS1rV\nQjiD/KImVr+aw5mqc6RPCObJ+zNJjJKFNMTFSVCPEjPD0lErVeytOYjVZh3wce5qN66fGUNnt4Ut\n0qoWYlQzmS28veUMf/noGD1mK/dcP4Ef3ToFL1kvWlyGBPUo4aXWMj0klcbOZs60lgzq2KunRaHz\nVLPlUDUd0qoWYlSqazHy2zfz2JZXTUSQF7/6XgbZ0yJRyEIaoh8S1KPI3MjeQWV7ag8O6jh3jRuL\nZsbQ2W1bbkc9AAAgAElEQVRma161PUoTQgyRzWZj77E6nn79EJWNBhakRfCr72UQFaxzdGnCSUhQ\njyLxPrFEeIVxtOk4bd2DWyv4qmmReHmo2JJbRWe32U4VCiEGo7PbzMsbCnll40mUSnj41il8b9FE\n3GW1KzEIEtSjSO+gsiysNisH6nIHdaynu4rrZ8Rg7JJWtRCjQXl9O0+9nsuBEw2Mi/DhyftmkDkx\nxNFlCSckQT3KzAibhkapZm/t4AaVAVyTHoWXh4rNOZXSqhbCQbp6zHy0s4TfvplHY2snN2TF8uhd\n0wn283R0acJJSVCPMp4qTzJC02jpauXk2aLBHeuu4trMaIxdZrYflla1ECPJZrNxsLCBX758kI37\nK/DVaVj13VSWZifINKDiishPzyg0NzILgL01BwZ97ML0aLTuKr7IqaKrR1rVQoyEqkYDv38nn79/\negJ9h4mbZ8fx2wezmBIv04CKK6dydAHiQrE+0UR7R3Ks5STnutvwcx/4RAhaj95W9Sd7yvgyv4bF\nM2PtWKkQY5uxy8THu8rYnl+NzQbTkoL47jVJhEg3txhG0qIepeZF9A4q21ebM+hjF2ZE4enuxqaD\nlXT3WOxQnRBjm9VqY+eRGh77+wG2Ha4mxF/Lz+9I5Se3T5WQFsNOgnqUSg9Nw8PNnb21OVisgwtb\nLw81C9Oj0XeY+DK/xk4VCjE2ldS08cybh3hj02lMFivLshN45oEZpIyTbm5hHxLUo5SHyp3MsOmc\n626j8OzpQR9/bWY0Hho3Nh2soNskrWohrlSboZtXNhTy2zV5VNTryZocyrMPZrE4K1YGiwm7kp+u\nUeyb5S8HP6hM56lmYUYU7R0mvjhYOdylCTFmmC1Wvsip5PGXD7D3eD0xIToevWs6P7h5Mv7eshyl\nsD8ZTDaKRXlHEOcTQ2HLaVo6Wwn09B/U8YtnxrL7aB3/PFDBnJRwAn097FSpEK6psPwsb285Q11L\nB14eKlZeN54FaZGyFKUYUdKiHuXmRmZhw8a+Qc7/Db3vVS/NTqDHbOWDHcV2qE4I19Tc1slf1x/j\nv947Qn1LB9lpETz7gyyumh4lIS1GnAT1KJceMhVPlQf76nIHPagMYNaUMOLDfcg52cjpylY7VCiE\n6+gxWfh0TxlPvHyQvNNNJEb68ut7M7ln0US8tRpHlyfGKAnqUU7jpmFmWDrtPXoKmgsHfbxSoWDF\ntUkAvLO1CKvVNtwlCuH0bDYbh8808cQ/DvLxnjI83VV8/6ZkHrt7OrFh3o4uT4xxEtRO4OuZyvYM\nYVAZQEKEL7OnhFHVaGBXQe1wliaE06trMfKntUf533XHaNV3s2hGDM/+IIvZU8JlrWgxKshgMicQ\n7hVKgm88p1qLaOpoIVg7+Pc1l2YnkHemiXU7S8mcGIKXh9oOlQrhPLp6zHy2t5zNuVVYrDYmxwew\nYmES4YFeji5NiPNIi9pJzI3sfVVr7xAGlQH46dy5eXYchk4Tn+wpG87ShHAqNpuNnJO9i2d8frAS\nP507P16Swqo7UiWkxagkQe0kpgWn4KXWsr8uF5N1aIttXJsRTYi/J9vzaqhpNg5zhUKMfnUtRv74\n/hFe/KR38YzvzInjtw/OZPr4YOnmFqOWBLWTULupyQrLwGAycrTp+NDOoVJy59VJWG023tt6BptN\nBpaJsaGrx8wHO4r59Ss5FJa3kjIukGe+P4Nb541Do3ZzdHlCXJY8o3YicyJnsq1qF3tqDpARmjak\nc6QmBjIlPoDjZWc5UtTMtPHBw1ylEKOHzWYj73QT724rolXfTaCPBysWJpGWFCQtaOE0pEXtREK1\nwYz3T6ToXCn1xsYhnUOhUHDnNUm4KRW8t70Ik1nmAReuqa7FyJ/eP8LfPj6OvqOHm2bH8ZsHZzJN\nurmFk5GgdjJfz/891EFlABFBXlw9PYqmc11szq0artKEGBW6eyx8tLOEX7+Sw4nyVqbEB/DMAzNZ\nMn8c7tLNLZyQdH07mdTgyXirdRyoO8TN4xahcRvaa1a3zI3jQGE9G/ZVMHtKuCwuIJze15OWvLut\niLPt3QT6uHPnNeOZPl66uYVzkxa1k1EpVcyKyKTD3El+Y8GQz6P1ULNk/ji6TRY+3FEyjBUKMfLq\nz3bwwtqj/HX9cdqNPdw0O5bfPJhF+gTp5hbOT4LaCc2JmIECBXtqhzZT2dfmTY0gJlTH/hP1lNS0\nDVN1Qoycb7q5D3K87CyT4wN4+oGZLJmfIN3cwmVIUDuhIM9AJgYkUdpWQa2hfsjnUSoVrFg4HoB3\ntp7BKq9rCSfx9WjuJ/5xgI37K/Dx0vD/bpvCqjtSCQvQOro8IYaVBLWTmvf1/N9X2KoeH+3HzEmh\nlNXp2XusbjhKE8KuGs528MIHR/nr+mOcM/Rw46xYfvv9LNInhEg3t3BJMpjMSU0JTMZX48PBusPc\nknAD7m5DX4JvWXYC+UVNfLSzlIwJIXi6y4+FGH26TRY27q9g08EKzBYbk+P8WXHteJn2U7g8aVE7\nKTelG7MjZtBl6SKv4egVnSvAx4MbsmJpN/bw2b7y4SlQiGFisVrJOdnAEy8fZMO+cry1Gn506xRW\nfTdNQlqMCQNqOi1ZsgSdTgdAVFQUDz30EI8++ihKpZKkpCRWr14NwNq1a3n//fdRq9U89NBDZGdn\n261w0TuobFP5NvbUHGB2ROYVnWvRjBj2FNSxJbeK+akR8pxPOFyrvptdR2vZdbSWVn03bkoFN2TF\ncvPsONw1MlBMjB39BnVPTw8Ab775Zt/XHn74YVatWkVGRgarV69m69atpKWlsWbNGtavX09XVxfL\nly9nzpw5qNWynKK9+Hv4MTlwIsdbTlKprybGO2rI59Ko3bjjqkT+9vFx3ttWxM+WpQ5jpUIMjNVm\no7D8LF8eruFocQtWmw0PjRtXT49kYUa0/AEpxqR+g/rUqVN0dHTwwAMPYLFY+PnPf05hYSEZGRkA\nzJ8/n71796JUKklPT0elUqHT6YiLi+P06dNMmTLF7t/EWDYvMovjLSfZU3OQFROHHtQA6ROCmRjj\nR0FJCwUlLUxNGPy610IMRXtHD3uP1bEzv5bGc50AxIZ6c9X0SGYkh+ChkXETYuzq96ffw8ODBx54\ngGXLllFeXs6DDz543qpLXl5eGAwGjEYj3t7efV/XarXo9fp+CwgO9u53H3FpCwIz+KD4E/Iaj/CD\nrDvxVHtc0fn+3x3T+Lc/fskHO4qZnxGDWnX5YQxy/5yXo++dzWajsOwsm/aXs+doLWaLFY3ajYWZ\nMSyeHUdStJ+M4r4MR98/MXL6Deq4uDhiY2P7/tvPz4/CwsK+7UajER8fH3Q6HQaD4YKv96epqf8w\nF5eXFZrJhrIv+PzELuZFzrqic3mpFGRPi2T74Rre23SSRTNjLrlvcLC33D8n5ch719ltZt/xenYc\nqaGmqXdd9PBALdlpkcxOCcPLo/dxWXOz4XKnGdPk357zGsofWP2O+v7oo494/vnnAWhoaMBgMDBn\nzhxycnIA2LVrF+np6aSkpJCXl0dPTw96vZ7S0lKSkpIGXZAYvFkRGSgVSnbXHBiWNaZvnTcOLw8V\nn+0ro83YMwwVCgEV9Xpe//wUq/53L29vOUN9SwczkkP4z+XT+M33Z3JtZnRfSAshvtFvi3rp0qU8\n9thjrFixAqVSyfPPP4+fnx9PPPEEJpOJhIQEFi1ahEKhYOXKlaxYsQKbzcaqVavQaIb+bq8YOD93\nX6YGTeJI03HK26uI9710K3ggdJ5qbps/jrc2n2HdzhLuuyF5mCoVY023yULOyQZ25NdSVtcOQKCP\nBzfNjmXu1Ah8veR3hBD9UdiGowl2BaT7ZnicbDnD/x79B1nhGaxMvuOKz2exWnnqtVxqmoz86t4M\n4sIufIwh3W/Oy973rq7FyJf5New7Vk9HtxkFMDUhkKumRzIlPhClUp49Xwn5t+e8htL1LUMpXcSE\ngESCPALIazjK7Yk3o1V7XtH53JRKli8czx/ezeedLUU8dvd0GdgjLstktpBf1MyO/BpOVZ4DwMdL\nw03pscxPjSDI98p+JoUYqySoXYRSoWRO5Ew+KfmcnPrDZEfPueJzJsf6kz4hmLzTTRwobGDW5LBh\nqFS4EovVysmKVg6eaOBwUROd3Rag92cne1ok05KCULnJBIhCXAkJahcyKzyTDaWb2VN7gAVRs4el\nBfzdqxIpKGnhgy+LmZYUJO+zCmw2GyW17Rw80UDuqQbaO0wABPq4k50Wydyp4TK1pxDDSH7ruhBv\njY604CnkNR6lpK2cRL/4Kz5nkJ8ni2bE8Nm+cjbur+D2BQnDUKlwRtVNBg4WNnCwsIHmti6gd+Dh\nVdMjyZoUSkKkL0p5PCLEsJOgdjFzI2eS13iUPTUHhyWoAW7IimXPsTq+yKliXmoEIX7yrHGsaDrX\nSc7JBg4UNvS98+yucWPW5DCyJoeSHOsvXdtC2JkEtYtJ8ksgRBtEflMBS003o1NfeReku6Z3HvC/\nf3qCtduL+fGSlGGoVIxWbcYeDp1q5EBhPSU1va9UqdwUTEsKImtyGFMTAnFXy6IYQowUCWoXo1Ao\nmBuRxbriDRysy+OamPnDct4ZySFsP1zN4TNNFJafZVJcwLCcV4wOnd1mDp/pHTRYWH4Wmw0Uit5B\nYVmTQkmfEIxWJiMRwiEkqF3QzPB0Pi3dxJ7aA1wdPW9YBpUpFApWLBzP06/n8u7WIp68/8qW1RSO\nZzJbKChp4UBhA0eLWzBbrADEh/uQNSmUzOQQ/HTuDq5SCCFB7YJ0ai+mBU8lt+EwRedKGO+fOCzn\njQ3zZl5qBLuO1vLl4RqWL/YdlvOKkVVW187b24rYV1Db9zpVeKCWrEmhzJgUSqi/LCUpxGgiQe2i\n5kVmkdtwmN01B4YtqAGWLBhH7qlGPt5dxg3zZAS4M6ls0PPx7jKOFDcDEPDV61QzJ4USHaKTCW2E\nGKUkqF3UON9Ywr1COdp0gvYePT6a4VkSz0er4Za58by3rYgX1xVw36IJ8gt+lKtuMvDJ7jLyzjQB\nkBjlyz03TCLC30NepxLCCUhQuyiFQsH8yFm8f+ZjdlXv46Zx1w/bua+eHkne6Ub2HK0lIkB72aUw\nhePUtRj5ZE8ZuScbsQHjIny4dV48k+MCCAnxkbmihXASEtQuLCs8gw1lm9lZvY+FMdl4qIZnYJDK\nTcmPbp3CM2/m8cGOYqJDdUyWUeCjRsPZDj7dW8aBwgZsNogN9ebWefFMTQiU3g8hnJDMVODCNG4a\nFkTOpsPcyf663GE9t6/OncfuzcRNqeDFj4/TdK5zWM8vBq/xXCevbjzJL18+yP4TDUQG6fjxkhR+\nfW8GqYlBEtJCOCkJahc3P2o2aqWabZW7sFgtw3ruibEB3H3dBIxdZv667hjdpuE9vxiYlrYuXv/8\nFL986QB7jtURFqjlR7dO4cn7M5k+PlgCWggnJ13fLs5bo2NWeCa7avZxuLGAzLBpw3r++akRlNW1\ns/NILW9sOsWDN02SYBghrfpuNuwvZ9eRWixWG6EBWm6ZG8eMiaGy3rMQLkSCegy4JmYeu2v2s7Vy\nJxmhacMepCsWjqe60cCBEw3EhflwXWb0sJ5fnK/N0M3GAxXsyK/FbLES7OfBd+bEkzU5FDeldJIJ\n4WokqMeAIM9ApoWkcLixgFOtRSQHjB/W86tVSn50WwpPv57L2u3FxITomBjrP6zXENDe0cPnByr4\n8nANPWYrgT4efGdOHLOmhMnCGEK4MPnXPUYsjFkAwNaKnXY5v7+3Oz+6bQoKBfzfJ8dp+WoZRHHl\nDJ0mPtxRwiP/t58vcqrw8lRzz/UTeO6HWcxLjZCQFsLFSYt6jIj1iWa8fyKnWouo1FcT4x017NdI\nivJjxcIk1mw+w/+uP8Zjd01HI6ssDVlHl4kvcqrYcqiKrh4LvjoNS7MTmJ8ajlol/1+FGCskqMeQ\na2MWcKa1mG2Vu7hv8gq7XCN7WiRldXr2HKtjzRenuf/GZBlcNkg9JgvbDlezcV8FHd1mfLRqbp0b\nT/a0SPnDR4gxSIJ6DEkOGE+kLpzDjQXcPG4RQZ7DP0mJQqFg5fXjqW4ysPd4PXHhPlyTPvytd1dk\ntdrYd7yej/eUcra9G627iqXZCVwzPQp3jQS0EGOVPNwaQxQKBQtjFmC1Wdletdtu11Gr3PjxkhS8\ntWre21bEmapzdruWK7DZbBwtbmb1azm8+s+TtBtNLJ4Zw+8ensUNWbES0kKMcRLUY0x6SCr+7n7s\nq83B0GO023UCfDz40a1TsNngbx8fp1XfbbdrObOSmjZ+904+//1hAbXNRuamhPP8D7NYdlUiXh5q\nR5cnhBgFJKjHGDelG1fHzMNkNbGrZp9drzUhxp/vXp1Iu7GHv64/hslstev1nEldi5G/rj/Gb9fk\ncabqHKkJgTx1/wzuvzGZAB8PR5cnhBhF5Bn1GDQ7fAafl23tW6xD42a/ltvCjCjK69vZf6KBt7ec\n4d7FE+12LWdwztDNp3t7ZxOz2mwkRPiwNDuBCTHy3rkQ4uIkqMcgD5U78yNnsaliOwfqDjE/apbd\nrqVQKLhn0URqmo3sOlpLXLg32WmRdrveaNXZbebzg5Vszq2kx2QlNEDL0gXjZC5uIUS/pOt7jFoQ\nPQeVUsW2yp1YbfbtknZXu/Hj21LQeap5e/MZimva7Hq90cRssbLlUBWPvLifDfvK8dSouOf6CTzz\nwAzSJ4RISAsh+iVBPUb5aLyZGZZOc9dZjjQdt/v1gvw8eeiWyVhtNv66/hjnDK49uMxqs3GgsJ7H\nXzrAu1uLMFus3DYvnud/OIvsaZEym5gQYsDkt8UYdk3MfBQo2FKxA5vNZvfrTYoLYFl2Im2GHv72\n8XHMFtccXHai/CzPvH6Ilz4tpFXfzcKMKJ5/aBY3z4mXV62EEIMmz6jHsFBtMKnBkznSdJyic6WM\n90+w+zWvnxFNeX07OScbeXdbESuvm2D3a46Uino9H+4o5kR5KwBZk0K5df44Qvw8HVyZEMKZSVCP\ncQtjFnCk6ThbKneMSFArFAruW5xMbbORLw/XEBfqzbzUCLtf1x5sNhsWq42Wti4+2VPGgcIGACbH\n+bM0O5HYMG8HVyiEcAUS1GNcvG8sCb7xFLacpsZQR6Qu3O7XdNf0zlz2zBuHWLP5NJHBOsZF+Njt\neiazhbI6PbXNRnrMVswWK6ZvfTT9y+fmr75m7ttmO+/zb3/89gODmFAdy7ITmRw//FOzCiHGLglq\nwbWxCygpKGNr5U6+N+nOEblmiL+WH3xnMn9ee5S/rj/Gr+/NxNdLMyznNnSaKK5po6j6HEXVbZTX\ntWO2DP4ZvAJQqZSo3ZR9H7XuKlRaJWqVErWbArVKiUbtRubEEGZMCkUpo7iFEMNMglowOXAiYV6h\nHGo4wnfGLcLfw29ErpsyLpAlC8bx0c5S/u/j4/z7nWmDHg1ts/V2PRdVfxPMNc3fTI2qUEBMqDdJ\nUb7EhXnjrlZ9K2TdUKkU5wXx1x/VKiVuSoW8PiWEcDgJaoFSoWRhzALeOrmW7VW7uT3p5hG79g1Z\nsZTX68k73cTa7cWsuHb8Zfe3Wm1UNRr6Qrm4pu28ecQ1aiXJsf4kRfmSFO3HuHAfPN3lx1wI4bzk\nN5gAIDM0jc9KNrG39iCL465Bq9aOyHUVCgX335BMXUsHW/OqiQv3ZvaUb56Td/dYKK1r7wvmkpo2\nunosfdt9vDSkTwgmKcqPpChfokN08o6yEMKlSFALAFRKFVdFz+Xjkn+yu+YA18ddPWLX9nRX8ZMl\nKTz9xiHe2HSaHpOV+rMdFFW3Udmgx2L95vlyeKCWxEjf3mCO9iXEz1O6p4UQLk2CWvSZGzmTTeXb\n+bJ6D1dHz0Ntx8U6/lVogJYHb57E/3xYwJtfnAbATakgLsy7r7WcEOWLj3Z4BpwJIYSzkKAWfTxV\nnsyLzGJL5Q5y6g8zJ3LmiF4/LTGIh26ZTGNrZ+/gr3Af3NUyk5cQYmyTh3niPNnRc3BTuLG1yv6L\ndVzMjORQbpodx4QYfwlpIYRAglr8Cz93XzLDptHY0cyx5kJHlyOEEGOeBLW4wMKYBQAjtliHEEKI\nSxtQULe0tJCdnU1ZWRmVlZWsWLGCu+++m6eeeqpvn7Vr13L77bdz5513smPHDnvVK0ZAuFcoKUHJ\nlLVXUtJW7uhyhBBiTOs3qM1mM6tXr8bDwwOA5557jlWrVvHWW29htVrZunUrzc3NrFmzhvfff59/\n/OMf/PGPf8RkMtm9eGE/C2OyAdhaucOhdQghxFjXb1D/7ne/Y/ny5YSEhGCz2SgsLCQjIwOA+fPn\ns2/fPgoKCkhPT0elUqHT6YiLi+P06dN2L17YT4JvHPE+sRxrPkm9scHR5QghxJh12dez1q1bR2Bg\nIHPmzOHFF18EwGr9ZiSwl5cXBoMBo9GIt/c3S/pptVr0ev2ACggOlqUAR6vbUxbxX3v/zp7G/Tw8\nY+VF95H757zk3jk3uX9jR79BrVAo2Lt3L6dPn+aRRx6htbW1b7vRaMTHxwedTofBYLjg6wPR1DSw\nQBcjL1YTT4g2iF3lB1kYcRV+7r7nbQ8O9pb756Tk3jk3uX/Oayh/YF226/utt95izZo1rFmzhokT\nJ/L73/+eefPmkZubC8CuXbtIT08nJSWFvLw8enp60Ov1lJaWkpSUNLTvQowaSoWShdELsNgs7Kja\n6+hyhBBiTBr0zGSPPPIIv/rVrzCZTCQkJLBo0SIUCgUrV65kxYoV2Gw2Vq1ahUYjUz26ghlh0/ms\n7Iu++b89VR6OLkkIIcYUhc3BL8pK983ot6l8O5+VbuK2xBv73rEG6X5zZnLvnJvcP+c17F3fQgDM\nj8xC46Zhe+VuzFazo8sRQogxRYJa9Eur1jI3YiZtPe3kNhxxdDlCCDGmSFCLAbkqei5KhZKtlY5Z\nrEMIIcYqCWoxIAEe/mSEplFvbKCwRSazEUKIkSJBLQasb7EOmVZUCCFGjAS1GLBIXTiTAiZQfK6M\nsrYKR5cjhBBjggS1GJRrY3tb1Vsrdzq4EiGEGBskqMWgJPklEOMdxdGmE9TqZbEOIYSwNwlqMSgK\nhYJrY7OxYeOjE/90dDlCCOHyJKjFoKUFTyFaF8HuihxK28odXY4QQrg0CWoxaEqFkmXjbwXggzOf\nyHvVQghhRxLUYkgS/OKYG5NJpb6G/XW5ji5HCCFclgS1GLK7U5egcdPwackmOkydji5HCCFckgS1\nGLIArR+LYq/GYDLyz/Itji5HCCFckgS1uCJXR88jyDOQndX7qDPK61pCCDHcJKjFFVG7qVmadDNW\nm5UPz3yKg5c3F0IIlyNBLa7YlMBkkgPGc6q1iILmE44uRwghXIoEtbhiCoWCpUnfQalQ8lHRZ/RY\nTI4uSQghXIYEtRgWYV4hXBU1l5auVrZV7nJ0OUII4TIkqMWwWRy/EG+Nji8qttPadc7R5QghhEuQ\noBbDxlPlwS3jFmOymlhfvNHR5QghhEuQoBbDamZ4OrHe0eQ1HqWotdTR5QghhNOToBbDqnce8FsA\n+KDoEyxWi4MrEkII5yZBLYZdvG8MWWEZ1Bjq2Fub4+hyhBDCqUlQC7v4TsJiPNzc2VD6BUZTh6PL\nEUIIpyVBLezC192bxfELMZo72FC62dHlCCGE05KgFnaTHTWHEG0Qu2v2U2Ooc3Q5QgjhlCSohd2o\nlCqWJt2CDRsfnPlE5gEXQoghkKAWdjU5cAJTApMpOldKftMxR5cjhBAOYbVZMfQYh3SsaphrEeIC\ntyfdzKmzZ1hXtIEpgRPRuGkcXZIQQlwRm81Gp7kLvcmAvseAwWTs/dhjQG8ynv/xq+02bKz97v8N\n+loS1MLuQrRBXB0zn80VX7K5Ygc3jbvO0SUJIcRF2Ww2GjubaTA29oWv3mTA0HP+fxtMRiy2/ueJ\n8FR54q32IlgbhLfaa0g1SVCLEXF97NUcrMtja+UOZoVnEOgZ4OiShBCCTnMXFe1VlLVVUNZeSXlb\nJUbzpV8p9XBzR6f2IsY7Ep3GC2+1Dp1Gh7fa66uPX32u8UKn9kKlvPKYlaAWI8JD5c6tiTfwRuF7\nrCveyIMpKx1dkhBijLHarDR2NFHWVklZewVlbZXUGRuw8c1A10CPAJIDxxOli8BH4/2tMPZCp9ah\ncVOPeN0S1GLEZIZOY1f1fo40HePU2SImBiQ5uiQhhAvrNHdS3lZFaXsF5W2VlLVX0mnu7NuuUapJ\n9IsnzieGeN9Y4n1j8NF4O7Dii5OgFiNGoVBwx/hb+P2hv/Bh0ac8lvkz3JRuji5LCOECrDYr9cZG\nyr4K5dL2ShqMjee1loM8A5kSmMw43xjifGOI9Ap3it9BEtRiRMX4RDE7IpO9tTnsqtnPVdFzHV2S\nEMIJGXqMVOirerux2yoob6+iy9LVt13jpiHJb1xfSznOJwZvjc6BFQ+dBLUYcTePW8ThxgI2lm0h\nIzTNaf/xCCFGRpe5myp9DRX6Kiraq6hor6al6+x5+4Rog0j1mUy8bwzxPrGEe4U6RWt5ICSoxYjz\n1ui4Mf46Piz6lM9Kv2DFxNsdXZIQYpQwW83UGOqoaK/uC+b6f+nC9lJrmRQwgVifKOJ8eruxdUN8\n9ckZSFALh5gfOYs9tQfZV5vD3MiZxHhHObokIcQI+3oUdkV7NeXtVVToq6jR12L+1vvJGjcNCX5x\nxHpHE+sTRaxPDIEe/igUCgdWPrIkqIVDuCndWJb0Hf5y5GU+OPMpq6Y/PKb+4Qkx1thsNs52naNC\nX0VlezUV7VVU6qvpsnT37eOmcCNSF0aMTzRx3tHE+kQT5hWCUjG2Z7uWoBYOMzEgidTgKRxtOs6h\nhiNkhk1zdElCiGHSZe7unUikvYKytgoq2qvRmwx92xUoCNUGE+sTTYxPFLHe0UTpwlE74D3l0U6C\nWjjUksSbONFyivXFG0kJmoSHyt3RJQkhBslms9HU2dI3u1dpWzm1hvrzniv7u/uRFpxCnE9vF3a0\ndxx0NlwAABQkSURBVBSeKg8HVu08JKiFQwV5BnBtzAI+L9/GFxXbuSVhsaNLEkL0o9vSQ2V776tR\npe3llLVVYjB9szKUWqlinG8s8b6xjPONJc4nFl/30TeRiLPoN6itVitPPPEEZWVlKJVKnnrqKTQa\nDY8++ihKpZKkpCRWr14NwNq1a3n//fdRq9U89NBDZGdn27t+4QKui72KA3V5bK/cxazwTEK0QY4u\nSQjxFZvNRktXK2VtFZS2VVDWXkGNoQ6rzdq3j7+7H+khqX3BHKkLH5Y5rkWvfv9Pbt++HYVCwbvv\nvktOTg5/+tOfsNlsrFq1ioyMDFavXs3WrVtJS0tjzZo1rF+/nq6uLpYvX86cOXNQq+V5g7g8jZuG\n2xJv4NUT77Cu+DMemnqfo0sSYszqsZio1Ff///buPDaqqmED+DP70mmn09LKUqBQ2tLKIpQgwguC\n1igxgiifW3CDRCCiBEWkooJQNnEjBj4X/BLBP0QiBE3URFDgs6Dwmo+ytFRl69sWSjtdprNv5/tj\n2ktLS1vQztyZPr9kMvfOnOmc6cm9z5w7954TOozdeBHnbBfR5L3627JaocLg+IGh65WbgzlRZ45g\njWNfl0Gdn5+Pu+66CwBQVVUFs9mMw4cPY9y4cQCAKVOmoKioCEqlEnl5eVCr1TCZTEhPT0dZWRlG\njBjRs5+AYsLY1NE4VHkEJ2tLcdpahluTsyNdJaKYFJpH2YV6TyPq3Q2o9zSioXn5suMKKuxVbaZv\nTNSZMSZlpBTKafEDoGFvOay69d9WKpVYvnw59u3bh82bN6OoqEh6Li4uDna7HQ6HA/HxV3+DMBqN\naGpq+udrTDFJoVDgvzJnYsOxzfjv4v9Bv7hbQkP/JQzCEPMgpBpTev0lGkRdCYWwOxS8ngbUuxua\nQ7jx6mOeRngD3g5fr1KokBbfH0MTQsNuDjWnw6JPDPOnoGt1+2vRhg0bYLVaMXv2bHg8V697czgc\nSEhIgMlkgt1ub/d4V1JSeIJBNPsn2y8lJRvPK5/G/nNFOFt3AVWOyyiq+g0AYNQYkJmcjszkochK\nHoJhSekw6WJ3JKJw4LYXna44rDh+qQRWZx2srnpYnQ2wOutDN1c93H7PdV8brzOhf3wqko0WJBss\nofvWN0MiL4+SoS6Deu/evaiursZzzz0HnU4HpVKJESNG4OjRoxg/fjwOHTqECRMmYOTIkXj//ffh\n9Xrh8Xhw7tw5ZGZ2PY1hTQ173dEqJSX+H2+/nLhc5IzMRSAYQKXjkjQ13YXGchRfLkXx5VKp7C3G\nlObp6WJvbN+e1hNtRz3D7ffgz4azKK37AyXWMtS4rB2Wi1MbkaxPgkVnRqI+ERZdIiw6Myx6MxJ1\nZiTqEjufS9kFNLjcANzXL0N/2818QVYIIURnBVwuFwoKClBbWwu/34/58+dj6NCheP311+Hz+ZCR\nkYHCwkIoFArs2rULO3fuhBACCxcuRH5+fpcV4M4ieoV7Z2/3OnDBdjW4L9jK24xqpFVpMTg+DUPM\ng6UAl+PcsnLAoJYvIQSqHJdRYi1DSd0fONdwXhpSU6/SIcsyDDl9h0IbMCBRZ4ZFHwpkrUob4ZpT\nd/RIUPc07iyiV6R39tfOP3veVt5u8P5kfZI0xd2wxCFIM/XnUKWIfNtRW3afA2fq/kSp9Q+U1pWh\n0Xu1bQbGD0BOUhZyk7Ix1DwYKqWK7RfFGNQUVnLcWbj8Lly0VYTmqG0OcIffKT0/0NQfd6ZNQt4t\nt3V+GDDGybHtepNAMIALtv+gtC7Uay63VUhfME2aOOQkZSM3OQvDkzI7PCrE9oteDGoKq2jYWYSG\nNqzF+cZyFNeexoma0xAQiNMYMbHfeEwecAeSDZZIVzPsoqHtYk29uwEldWUosf6Bsvo/4fKHfgtW\nKpQYah6M3KRs5CRnIc3Uv8srHNh+0YtBTWEVjTuLOnc9/rfyVxRV/QaHzwkFFBjVJxd3pk1CliWj\n1xwWj8a2izbegA9/NZyTTgK77LwiPZestyAnORu5SVnIsgy74TGv2X7Ri0FNYRXNOwtfwId/XynG\nwYoi/KepEgDQN+4W3DlgIsb3HRvzk4NEc9vJiS/gg9VdhxqXFbWulvuWW500cIhWqUGmJUPqNaca\n+vytL4Vsv+jFoKawioWdhRAC523lOFhRhP+7chIBEYBepccd/cZhStodSDWmRLqKPSIW2i5cnD6n\nFMA1rjopiGtcVjR6bG1OXmwRpzaijzEZwxKHIDcpGxnm9H/0+mS2X/RiUFNYxdrOotFjwy9Vv+GX\nyl9haz7rNjcpG3emTURucnZMjYwWa233dwRFEI0eW4dBXOuywul3tXuNAgok6szoY0hCiiEZfZpv\nLctGjaFH68z2i14MagqrWN1Z+IN+HK85hYMVh3G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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1197c1438>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -737,7 +756,7 @@
    ],
    "source": [
     "n_components = np.arange(1, 21)\n",
-    "models = [GMM(n, covariance_type='full', random_state=0).fit(Xmoon)\n",
+    "models = [GaussianMixture(n, covariance_type='full', random_state=0).fit(Xmoon)\n",
     "          for n in n_components]\n",
     "\n",
     "plt.plot(n_components, [m.bic(Xmoon) for m in models], label='BIC')\n",
@@ -753,11 +772,11 @@
     "editable": true
    },
    "source": [
-    "The optimal number of clusters is the value that minimizes the AIC or BIC, depending on which approximation we wish to use. The AIC tells us that our choice of 16 components above was probably too many: around 8-12 components would have been a better choice.\n",
+    "The optimal number of clusters is the value that minimizes the AIC or BIC, depending on which approximation we wish to use. The AIC tells us that our choice of 16 components earlier was probably too many: around 8–12 components would have been a better choice.\n",
     "As is typical with this sort of problem, the BIC recommends a simpler model.\n",
     "\n",
-    "Notice the important point: this choice of number of components measures how well GMM works *as a density estimator*, not how well it works *as a clustering algorithm*.\n",
-    "I'd encourage you to think of GMM primarily as a density estimator, and use it for clustering only when warranted within simple datasets."
+    "Notice the important point: this choice of number of components measures how well a GMM works *as a density estimator*, not how well it works *as a clustering algorithm*.\n",
+    "I'd encourage you to think of the GMM primarily as a density estimator, and use it for clustering only when warranted within simple datasets."
    ]
   },
   {
@@ -767,9 +786,9 @@
     "editable": true
    },
    "source": [
-    "## Example: GMM for Generating New Data\n",
+    "## Example: GMMs for Generating New Data\n",
     "\n",
-    "We just saw a simple example of using GMM as a generative model of data in order to create new samples from the distribution defined by the input data.\n",
+    "We just saw a simple example of using a GMM as a generative model in order to create new samples from the distribution defined by the input data.\n",
     "Here we will run with this idea and generate *new handwritten digits* from the standard digits corpus that we have used before.\n",
     "\n",
     "To start with, let's load the digits data using Scikit-Learn's data tools:"
@@ -777,11 +796,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -790,7 +812,7 @@
        "(1797, 64)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -808,23 +830,26 @@
     "editable": true
    },
    "source": [
-    "Next let's plot the first 100 of these to recall exactly what we're looking at:"
+    "Next, let's plot the first 50 of these to recall exactly what we're looking at (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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V0GEv8DDVQP1RgEkXmb29PTs6Oooq3+l0Ggzth4eHYBwAmKozSUI5Pj62Wq0WMjlrtZpV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jtRcoUaTN2M8xjM4XBonU4nFG9fXl7OlA5QRE8k4jmEGTsQvtRh2ZAs4XKQJfWCviuSf7+s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\nEJgAsCAwAWBBYALAwgu8y3hsplqZLgAAAABJRU5ErkJggg==\n",
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11a7a9518>"
+       "<Figure size 576x288 with 50 Axes>"
       ]
      },
      "metadata": {},
@@ -833,7 +858,7 @@
    ],
    "source": [
     "def plot_digits(data):\n",
-    "    fig, ax = plt.subplots(10, 10, figsize=(8, 8),\n",
+    "    fig, ax = plt.subplots(5, 10, figsize=(8, 4),\n",
     "                           subplot_kw=dict(xticks=[], yticks=[]))\n",
     "    fig.subplots_adjust(hspace=0.05, wspace=0.05)\n",
     "    for i, axi in enumerate(ax.flat):\n",
@@ -850,17 +875,20 @@
    },
    "source": [
     "We have nearly 1,800 digits in 64 dimensions, and we can build a GMM on top of these to generate more.\n",
-    "GMMs can have difficulty converging in such a high dimensional space, so we will start with an invertible dimensionality reduction algorithm on the data.\n",
+    "GMMs can have difficulty converging in such a high-dimensional space, so we will start with an invertible dimensionality reduction algorithm on the data.\n",
     "Here we will use a straightforward PCA, asking it to preserve 99% of the variance in the projected data:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -869,7 +897,7 @@
        "(1797, 41)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -889,23 +917,26 @@
    },
    "source": [
     "The result is 41 dimensions, a reduction of nearly 1/3 with almost no information loss.\n",
-    "Given this projected data, let's use the AIC to get a gauge for the number of GMM components we should use:"
+    "Given this projected data, let's use the AIC to get a gauge for the number of GMM components we should use (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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WtBDt6TeD1d+P1Kt6UFVTxxd5pWaXIyLSLOeqanl5eTbvbzlCRHgQT81IUuD7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\nIMB7jlbzDbx258+fx2q18vbbbzNp0iQqKio0mw0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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1197ba208>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -914,7 +945,7 @@
    ],
    "source": [
     "n_components = np.arange(50, 210, 10)\n",
-    "models = [GMM(n, covariance_type='full', random_state=0)\n",
+    "models = [GaussianMixture(n, covariance_type='full', random_state=0)\n",
     "          for n in n_components]\n",
     "aics = [model.fit(data).aic(data) for model in models]\n",
     "plt.plot(n_components, aics);"
@@ -927,17 +958,20 @@
     "editable": true
    },
    "source": [
-    "It appears that around 110 components minimizes the AIC; we will use this model.\n",
+    "It appears that around 140 components minimizes the AIC; we will use this model.\n",
     "Let's quickly fit this to the data and confirm that it has converged:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -949,7 +983,7 @@
     }
    ],
    "source": [
-    "gmm = GMM(110, covariance_type='full', random_state=0)\n",
+    "gmm = GaussianMixture(140, covariance_type='full', random_state=0)\n",
     "gmm.fit(data)\n",
     "print(gmm.converged_)"
    ]
@@ -966,11 +1000,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 44,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -979,13 +1016,13 @@
        "(100, 41)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data_new = gmm.sample(100, random_state=0)\n",
+    "data_new, label_new = gmm.sample(100)\n",
     "data_new.shape"
    ]
   },
@@ -996,23 +1033,26 @@
     "editable": true
    },
    "source": [
-    "Finally, we can use the inverse transform of the PCA object to construct the new digits:"
+    "Finally, we can use the inverse transform of the PCA object to construct the new digits (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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xcWKtNxqNXIlN+ixiz2WTJ633HNsP+qxfw1giykhB62EwQV93d3d2eHgY1kGr1QoKGQSu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\nwevBls0zd7H2ztp0eY6IAsmInBRGqSqU9pGh8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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ab089e8>"
+       "<Figure size 576x288 with 50 Axes>"
       ]
      },
      "metadata": {},
@@ -1025,6 +1065,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
@@ -1033,27 +1074,17 @@
    "source": [
     "The results for the most part look like plausible digits from the dataset!\n",
     "\n",
-    "Consider what we've done here: given a sampling of handwritten digits, we have modeled the distribution of that data in such a way that we can generate brand new samples of digits from the data: these are \"handwritten digits\" which do not individually appear in the original dataset, but rather capture the general features of the input data as modeled by the mixture model.\n",
-    "Such a generative model of digits can prove very useful as a component of a Bayesian generative classifier, as we shall see in the next section."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In Depth: k-Means Clustering](05.11-K-Means.ipynb) | [Contents](Index.ipynb) | [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.12-Gaussian-Mixtures.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "Consider what we've done here: given a sampling of handwritten digits, we have modeled the distribution of that data in such a way that we can generate brand new samples of digits from the data: these are \"handwritten digits,\" which do not individually appear in the original dataset, but rather capture the general features of the input data as modeled by the mixture model.\n",
+    "Such a generative model of digits can prove very useful as a component of a Bayesian generative classifier, as we shall see in the next chapter."
    ]
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1067,9 +1098,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.13-Kernel-Density-Estimation.ipynb b/notebooks/05.13-Kernel-Density-Estimation.ipynb
index 5ddf8b59a..9f0ce72c3 100644
--- a/notebooks/05.13-Kernel-Density-Estimation.ipynb
+++ b/notebooks/05.13-Kernel-Density-Estimation.ipynb
@@ -1,53 +1,24 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) | [Contents](Index.ipynb) | [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.13-Kernel-Density-Estimation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# In-Depth: Kernel Density Estimation"
+    "# In Depth: Kernel Density Estimation"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
     "deletable": true,
     "editable": true
    },
    "source": [
-    "In the previous section we covered Gaussian mixture models (GMM), which are a kind of hybrid between a clustering estimator and a density estimator.\n",
-    "Recall that a density estimator is an algorithm which takes a $D$-dimensional dataset and produces an estimate of the $D$-dimensional probability distribution which that data is drawn from.\n",
+    "In the previous chapter we covered Gaussian mixture models, which are a kind of hybrid between a clustering estimator and a density estimator.\n",
+    "Recall that a density estimator is an algorithm that takes a $D$-dimensional dataset and produces an estimate of the $D$-dimensional probability distribution that data is drawn from.\n",
     "The GMM algorithm accomplishes this by representing the density as a weighted sum of Gaussian distributions.\n",
-    "*Kernel density estimation* (KDE) is in some senses an algorithm which takes the mixture-of-Gaussians idea to its logical extreme: it uses a mixture consisting of one Gaussian component *per point*, resulting in an essentially non-parametric estimator of density.\n",
-    "In this section, we will explore the motivation and uses of KDE.\n",
+    "*Kernel density estimation* (KDE) is in some senses an algorithm that takes the mixture-of-Gaussians idea to its logical extreme: it uses a mixture consisting of one Gaussian component *per point*, resulting in an essentially nonparametric estimator of density.\n",
+    "In this chapter, we will explore the motivation and uses of KDE.\n",
     "\n",
     "We begin with the standard imports:"
    ]
@@ -58,13 +29,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns; sns.set()\n",
+    "plt.style.use('seaborn-whitegrid')\n",
     "import numpy as np"
    ]
   },
@@ -75,10 +49,10 @@
     "editable": true
    },
    "source": [
-    "## Motivating KDE: Histograms\n",
+    "## Motivating Kernel Density Estimation: Histograms\n",
     "\n",
-    "As already discussed, a density estimator is an algorithm which seeks to model the probability distribution that generated a dataset.\n",
-    "For one dimensional data, you are probably already familiar with one simple density estimator: the histogram.\n",
+    "As mentioned previously, a density estimator is an algorithm that seeks to model the probability distribution that generated a dataset.\n",
+    "For one-dimensional data, you are probably already familiar with one simple density estimator: the histogram.\n",
     "A histogram divides the data into discrete bins, counts the number of points that fall in each bin, and then visualizes the results in an intuitive manner.\n",
     "\n",
     "For example, let's create some data that is drawn from two normal distributions:"
@@ -90,7 +64,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -110,8 +87,8 @@
     "editable": true
    },
    "source": [
-    "We have previously seen that the standard count-based histogram can be created with the ``plt.hist()`` function.\n",
-    "By specifying the ``normed`` parameter of the histogram, we end up with a normalized histogram where the height of the bins does not reflect counts, but instead reflects probability density:"
+    "We have previously seen that the standard count-based histogram can be created with the `plt.hist` function.\n",
+    "By specifying the `density` parameter of the histogram, we end up with a normalized histogram where the height of the bins does not reflect counts, but instead reflects probability density (see the following figure):"
    ]
   },
   {
@@ -120,14 +97,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
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Gj7UGAADEzzK0XS6XAoFA9Hks4TudGknKz3dbrpPOTB7/yIgr1S3cVV6ea9b/2872/mbK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\nNgAAhiC0AQAwBKENAIAh/h+zvw1TQgOgbwAAAABJRU5ErkJggg==\n",
+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115809b38>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -135,7 +115,7 @@
     }
    ],
    "source": [
-    "hist = plt.hist(x, bins=30, normed=True)"
+    "hist = plt.hist(x, bins=30, density=True)"
    ]
   },
   {
@@ -155,7 +135,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -184,16 +167,16 @@
    "source": [
     "One of the issues with using a histogram as a density estimator is that the choice of bin size and location can lead to representations that have qualitatively different features.\n",
     "For example, if we look at a version of this data with only 20 points, the choice of how to draw the bins can lead to an entirely different interpretation of the data!\n",
-    "Consider this example:"
+    "Consider this example, visualized in the following figure:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -207,14 +190,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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3794ElAcAAABMD75QAwAAAEYiCAMAAMBIto9GIH7znzrtdAkT8uv1dzldAgAARptcZnDuN2G82fygY/tOJO4IAwAAwEgEYQAAABiJIAwAAAAjEYQBAABgJIIwAAAAjEQQBgAAgJEIwgAAADASQRgAAABGIggDAADASARhAAAAGIkgDAAAACMRhAEAAGAkgjAAAACMRBAGAACAkQjCAAAAMBJBGAAAAEYiCAMAAMBIBGEAAAAYiSAMAAAAI/mcLgDusfLwRUkXnS7jE73Z/KDTJQD4BPOfOp3Arbl7Hk1OqvSSKn0AN3FHGAAAAEYiCAMAAMBIBGEAAAAYyfYZ4Wg0qrq6Og0MDCgjI0MNDQ3Kz8+PLT927Jg6Ojrk8/m0efNmrVixQv/5z3/0wAMPKBAISJLuv/9+rV+/fvq6AAAAACbJNgh3dXVpZGREnZ2d6uvrU3Nzs/bt2ydJunz5so4cOaITJ05oeHhYVVVVWrZsmf7617/qoYce0tNPPz3tDQAAAABTYftoRDAYVElJiSSpqKhI/f39sWWvv/667r33XmVkZCgnJ0d5eXk6f/68+vv7de7cOa1bt05PPPGELl26NH0dAAAAAFNge0c4HA7L7/fHXnu9Xo2Ojsrn8ykcDisnJye2LDs7W+FwWHfddZcWL16sr3zlKzp16pQaGhq0d+/eW7YdCoUS1IZzhoaGUqKPZDGRY805ST1uOZ/Jcm0lQ40Aklsi54yTs9U2CPv9fkUikdjraDQqn8837rJIJKKcnBwtWbJEWVlZkqTy8vJxQ7AkFRYWxlW8G4RCoQn0we9dTJSJXDMTOyfTjXOeSM6fz5tm4toKBoNxb8PZ48W1D5ggkXPGydlq+2hEcXGxenp6JEl9fX2x/wFOkpYsWaJgMKjh4WENDg7qwoULCgQC+v73v6/f/va3kqSzZ89q0aJFiegBAAAASBjbO8Ll5eXq7e1VRUWFLMtSY2Oj2tralJeXp7KyMlVXV6uqqkqWZWnbtm3KzMxUTU2Ndu7cqfb2dmVlZamhoWEmegEAAAAmzDYIp6Wlqb6+fsx7BQUFsZ/XrFmjNWvWjFn+uc99TkeOHElQiQAAAEDi8YUaAAAAMBJBGAAAAEYiCAMAAMBIBGEAAAAYiSAMAAAAIxGEAQAAYCSCMAAAAIxEEAYAAICRbL9Qw83mP3Xa6RL+30WnCzDGxM855ySVuOfPuvRJ19abzQ/OYB0AgHhxRxgAAABGIggDAADASARhAAAAGIkgDAAAACMRhAEAAGAkgjAAAACMRBAGAACAkQjCAAAAMBJBGAAAAEYiCAMAAMBIBGEAAAAYiSAMAAAAIxGEAQAAYCSCMAAAAIxEEAYAAICRCMIAAAAwEkEYAAAARvLZfSAajaqurk4DAwPKyMhQQ0OD8vPzY8uPHTumjo4O+Xw+bd68WStWrNC1a9f03e9+V0NDQ7rjjjvU1NSkrKysaW0EAAAAmAzbO8JdXV0aGRlRZ2enampq1NzcHFt2+fJlHTlyRB0dHXr++ef14x//WCMjI2ptbdVDDz2ko0ePauHChers7JzWJgAAAIDJsg3CwWBQJSUlkqSioiL19/fHlr3++uu69957lZGRoZycHOXl5en8+fNj1iktLdUf//jHaSofAAAAmBrbRyPC4bD8fn/stdfr1ejoqHw+n8LhsHJycmLLsrOzFQ6Hx7yfnZ2twcHBcbcdCoXiKv7X6++Ka/1EGBoa0qxZs5wuIyFSpRf6cJdU6UOy7yXemZYoTtaRqLls0nWTLOjDXZzuI5FzZmhoyLG5ZRuE/X6/IpFI7HU0GpXP5xt3WSQSUU5OTuz9WbNmKRKJaM6cOeNuu7CwMN76HRcKhVKiDyl1eqEPd0mVPqSZ6SUYDMa9jVQ43lw37kMf7pIqfUjOzlbbRyOKi4vV09MjSerr61MgEIgtW7JkiYLBoIaHhzU4OKgLFy4oEAiouLhY3d3dkqSenh4tXbo0ET0AAAAACWN7R7i8vFy9vb2qqKiQZVlqbGxUW1ub8vLyVFZWpurqalVVVcmyLG3btk2ZmZnavHmzduzYoWPHjulTn/qUWlpaZqIXAAAAYMJs7winpaWpvr5eHR0d6uzsVEFBgR599FGVlZVJktasWaMTJ07o5MmTeuCBByRJn/nMZ/T888+ro6ND+/bt0+zZs6e3i2lWV1eXFPt+/7NO1vtRiazFDX3NRA3j7ePD7020hrq6uluuCbttJ2qZXV1TXe6GawCJw2ydOmZrYvbBbI1vn6nAY1mW5cSOg8Fg0jwy4fF49HGHabqfa/mkfX/cZyezzodNRy9TrSWebU3nOUlkP3b7+HAfH97vRGvweDySNOaaGG/dT9reVJd92EfPh916idjndJmp59jimY3M1vj3/XGfZbYyW9//nMRsTSQnZyvfLAcAAAAjEYQBAABgJIIwAAAAjEQQBgAAgJFsf30apN27dyfFvt//rJP1flQia3FDXzNRw3j7+PB7E61hvHXstp2oZROta6b2CXditk4dszUx+2C2xrfPVMBvjYgT3+ziPvThLqnSh8RvjZhJXDfuQx/ukip9SPzWCAAAAGDGOXpHGABwq3jvCAMAbjXebHUsCAMAAABO4tEIAAAAGIkgDAAAACMRhAEAAGAkx4PwhQsXtHTpUg0PDztdypQMDg7qscce07p167R27Vq99tprTpc0KdFoVLt27dLatWtVXV2tt956y+mSpuT69euqra1VVVWVHn74Yf3+9793uqS4XL16VcuXL9eFCxecLiUuP/3pT7V27Vp985vf1PHjx50uZ0quX7+umpoaVVRUqKqqKmnOCbPVWcxWd2K2uodbZqujQTgcDmvPnj3KyMhwsoy4tLW16Utf+pJeeOEFNTU1qb6+3umSJqWrq0sjIyPq7OxUTU2NmpubnS5pSk6dOqW5c+fq6NGjOnjwoH7wgx84XdKUXb9+Xbt27dKsWbOcLiUuf/7zn/Xaa6+pvb1dR44c0b///W+nS5qS7u5ujY6OqqOjQ1u2bNGzzz7rdEm2mK3OY7a6D7PVXdwyWx0LwpZl6emnn9b27duVlZXlVBlxe+SRR1RRUSFJunHjhjIzMx2uaHKCwaBKSkokSUVFRerv73e4oqn5+te/rieffFLSzWvL6/U6XNHU7dmzRxUVFbrjjjucLiUuL7/8sgKBgLZs2aLHHntMX/3qV50uaUoWLFigGzduKBqNKhwOy+dz9xdyMlvdgdnqPsxWd3HLbJ2RvR4/flyHDx8e895nP/tZrVq1Svfcc89MlJAQ4/XR2NioJUuW6PLly6qtrdXOnTsdqm5qwuGw/H5/7LXX69Xo6Kjr/2P/UdnZ2ZJu9vPEE0/oO9/5jrMFTdHJkyeVm5urkpISHThwwOly4vLee+/pX//6l/bv369//OMf2rx5s37zm9/I4/E4XdqkzJ49W//85z+1cuVKvffee9q/f7/TJcUwW92L2eouzFb3cctsdez3CJeXl2vevHmSpL6+Pi1ZskQvvviiE6XEbWBgQNu3b9f3vvc9LV++3OlyJqWpqUlf+MIXtGrVKklSaWmpenp6HK5qat555x1t2bIl9ixbMvrWt74lj8cjj8ejUCik+fPna9++fbr99tudLm3SfvSjHyk3N1cbNmyQJH3jG99QW1ubPv3pTztc2eQ0NTUpIyNDNTU1euedd7R+/Xr96le/cu0dSmarOzBb3YXZ6j5uma2O/dX0d7/7Xeznr33ta/r5z3/uVClx+dvf/qYnn3xSzz77bFLdgXlfcXGx/vCHP2jVqlXq6+tTIBBwuqQpuXLlijZs2KBdu3bpy1/+stPlTNmHA0t1dbXq6uqSclBLN7/B5xe/+IUeffRRXbp0Sf/73/80d+5cp8uatDlz5ig9PV2SdNttt2l0dFQ3btxwuKqPx2x1B2aruzBb3cctszW5/o3GhVpaWjQyMqIf/vCHkiS/3699+/Y5XNXElZeXq7e3VxUVFbIsS42NjU6XNCX79+/Xf//7X7W2tqq1tVWS9LOf/Szp/6eIZLZixQr95S9/0cMPPyzLsrRr166kfL7wkUce0c6dO1VVVaXr169r27Ztmj17ttNlpTxmqzswW92H2ZpYfMUyAAAAjOT47xEGAAAAnEAQBgAAgJEIwgAAADASQRgAAABGIggDAADASARhAAAAGIkgDAAAACP9H4FUpE14/F18AAAAAElFTkSuQmCC",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117b17eb8>"
+       "<Figure size 864x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -228,7 +214,7 @@
     "                                   'ylim':(-0.02, 0.3)})\n",
     "fig.subplots_adjust(wspace=0.05)\n",
     "for i, offset in enumerate([0.0, 0.6]):\n",
-    "    ax[i].hist(x, bins=bins + offset, normed=True)\n",
+    "    ax[i].hist(x, bins=bins + offset, density=True)\n",
     "    ax[i].plot(x, np.full_like(x, -0.01), '|k',\n",
     "               markeredgewidth=1)"
    ]
@@ -242,11 +228,11 @@
    "source": [
     "On the left, the histogram makes clear that this is a bimodal distribution.\n",
     "On the right, we see a unimodal distribution with a long tail.\n",
-    "Without seeing the preceding code, you would probably not guess that these two histograms were built from the same data: with that in mind, how can you trust the intuition that histograms confer?\n",
+    "Without seeing the preceding code, you would probably not guess that these two histograms were built from the same data. With that in mind, how can you trust the intuition that histograms confer?\n",
     "And how might we improve on this?\n",
     "\n",
     "Stepping back, we can think of a histogram as a stack of blocks, where we stack one block within each bin on top of each point in the dataset.\n",
-    "Let's view this directly:"
+    "Let's view this directly (see the following figure):"
    ]
   },
   {
@@ -255,13 +241,16 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(-0.2, 8)"
+       "(-0.2, 8.0)"
       ]
      },
      "execution_count": 7,
@@ -270,9 +259,9 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11819d898>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -286,8 +275,8 @@
     "        markeredgewidth=1)\n",
     "for count, edge in zip(*np.histogram(x, bins)):\n",
     "    for i in range(count):\n",
-    "        ax.add_patch(plt.Rectangle((edge, i), 1, 1,\n",
-    "                                   alpha=0.5))\n",
+    "        ax.add_patch(plt.Rectangle(\n",
+    "            (edge, i), 1, 1, ec='black', alpha=0.5))\n",
     "ax.set_xlim(-4, 8)\n",
     "ax.set_ylim(-0.2, 8)"
    ]
@@ -299,11 +288,11 @@
     "editable": true
    },
    "source": [
-    "The problem with our two binnings stems from the fact that the height of the block stack often reflects not on the actual density of points nearby, but on coincidences of how the bins align with the data points.\n",
-    "This mis-alignment between points and their blocks is a potential cause of the poor histogram results seen here.\n",
+    "The problem with our two binnings stems from the fact that the height of the block stack often reflects not the actual density of points nearby, but coincidences of how the bins align with the data points.\n",
+    "This misalignment between points and their blocks is a potential cause of the poor histogram results seen here.\n",
     "But what if, instead of stacking the blocks aligned with the *bins*, we were to stack the blocks aligned with the *points they represent*?\n",
     "If we do this, the blocks won't be aligned, but we can add their contributions at each location along the x-axis to find the result.\n",
-    "Let's try this:"
+    "Let's try this (see the following figure):"
    ]
   },
   {
@@ -312,14 +301,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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G6XQ+bqGeRU1B4NYyNq7vm9SPP1vpM820GVBfBK6x79SjbfU0UrWBy/VIDQ0E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\nAElFTkSuQmCC\n",
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117bcfa20>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -343,10 +335,10 @@
     "editable": true
    },
    "source": [
-    "The result looks a bit messy, but is a much more robust reflection of the actual data characteristics than is the standard histogram.\n",
+    "The result looks a bit messy, but it's a much more robust reflection of the actual data characteristics than is the standard histogram.\n",
     "Still, the rough edges are not aesthetically pleasing, nor are they reflective of any true properties of the data.\n",
     "In order to smooth them out, we might decide to replace the blocks at each location with a smooth function, like a Gaussian.\n",
-    "Let's use a standard normal curve at each point instead of a block:"
+    "Let's use a standard normal curve at each point instead of a block (see the following figure):"
    ]
   },
   {
@@ -355,14 +347,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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mkiST8TfH6Vx7l9qH0grFqrkkeAFRLpXmhXHKD+OUv7IZK29C/3W0QR+97TeL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\n",
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11836efd0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -387,10 +382,10 @@
     "editable": true
    },
    "source": [
-    "This smoothed-out plot, with a Gaussian distribution contributed at the location of each input point, gives a much more accurate idea of the shape of the data distribution, and one which has much less variance (i.e., changes much less in response to differences in sampling).\n",
+    "This smoothed-out plot, with a Gaussian distribution contributed at the location of each input point, gives a much more accurate idea of the shape of the data distribution, and one that has much less variance (i.e., changes much less in response to differences in sampling).\n",
     "\n",
-    "These last two plots are examples of kernel density estimation in one dimension: the first uses a so-called \"tophat\" kernel and the second uses a Gaussian kernel.\n",
-    "We'll now look at kernel density estimation in more detail."
+    "What we've landed on in the last two plots is what's called kernel density estimation in one dimension: we have placed a \"kernel\"—a square or \"tophat\"-shaped kernel in the former, a Gaussian kernel in the latter—at the location of each point, and used their sum as an estimate of density.\n",
+    "With this intuition in mind, we'll now explore kernel density estimation in more detail."
    ]
   },
   {
@@ -403,14 +398,14 @@
     "## Kernel Density Estimation in Practice\n",
     "\n",
     "The free parameters of kernel density estimation are the *kernel*, which specifies the shape of the distribution placed at each point, and the *kernel bandwidth*, which controls the size of the kernel at each point.\n",
-    "In practice, there are many kernels you might use for a kernel density estimation: in particular, the Scikit-Learn KDE implementation supports one of six kernels, which you can read about in Scikit-Learn's [Density Estimation documentation](http://scikit-learn.org/stable/modules/density.html).\n",
+    "In practice, there are many kernels you might use for kernel density estimation: in particular, the Scikit-Learn KDE implementation supports six kernels, which you can read about in the [\"Density Estimation\" section](http://scikit-learn.org/stable/modules/density.html) of the documentation.\n",
     "\n",
-    "While there are several versions of kernel density estimation implemented in Python (notably in the SciPy and StatsModels packages), I prefer to use Scikit-Learn's version because of its efficiency and flexibility.\n",
-    "It is implemented in the ``sklearn.neighbors.KernelDensity`` estimator, which handles KDE in multiple dimensions with one of six kernels and one of a couple dozen distance metrics.\n",
-    "Because KDE can be fairly computationally intensive, the Scikit-Learn estimator uses a tree-based algorithm under the hood and can trade off computation time for accuracy using the ``atol`` (absolute tolerance) and ``rtol`` (relative tolerance) parameters.\n",
-    "The kernel bandwidth, which is a free parameter, can be determined using Scikit-Learn's standard cross validation tools as we will soon see.\n",
+    "While there are several versions of KDE implemented in Python (notably in the SciPy and `statsmodels` packages), I prefer to use Scikit-Learn's version because of its efficiency and flexibility.\n",
+    "It is implemented in the `sklearn.neighbors.KernelDensity` estimator, which handles KDE in multiple dimensions with one of six kernels and one of a couple dozen distance metrics.\n",
+    "Because KDE can be fairly computationally intensive, the Scikit-Learn estimator uses a tree-based algorithm under the hood and can trade off computation time for accuracy using the `atol` (absolute tolerance) and `rtol` (relative tolerance) parameters.\n",
+    "The kernel bandwidth can be determined using Scikit-Learn's standard cross-validation tools, as we will soon see.\n",
     "\n",
-    "Let's first show a simple example of replicating the above plot using the Scikit-Learn ``KernelDensity`` estimator:"
+    "Let's first show a simple example of replicating the previous plot using the Scikit-Learn `KernelDensity` estimator (see the following figure):"
    ]
   },
   {
@@ -419,24 +414,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
+      "image/png": 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H2N2SbH02L+xebd6zYKATyaRtyIXxQFjuMlJSi0anWGCgE8lACIFPesfkLiNltQw6NDkJGgOdSAZdFg9sHm3+2a8GHn8YVyzauznKQCdKMiEEznVz0Re5NWnw/gUDnSjJ+sfGMWj3yV1GyuuxeuEY19ZqRgx0oiQ7y6tzRRACaNbYVToDnSiJBu3j6LF65S6D/kVrN0cZ6ERJdOaKVe4S6BoefxhdGnpylIFOlCQD9nF0W3h1rjQX+7XT7cJAJ0oCIQROXbbIXQZNo9fmxZhGhpAy0ImS4IrFg4EUWahYjbQyBQMDnSjBJEngJK/OFa110ImgBqbVZaATJVjzoANWLmChaBNruqp/zVEGOlEC+YJhnO7kyBY1uNjvgFD5VMYMdKIEOt1p4YyKKjHi9GHYqe4neBnoKU6SBHzBMLyBEHzBsOqvUJRk2OHT1JC4VHChzy53CfOil7sASh5/KIw+mxf9Y+MYdflh9wbg8U++ekxLAwyZeixamIGluZm4KT8LNy/OxsIMflTmIiwJ/MM0Av5+VJdLI26U3R5CTqY6P+/qrJpmLSwJdJndaB1yosfqjfqYsxCAyxeCyxdCn82LTzAR8p/Ly8K/LzOgaFku8hcuSE7xKna22waLyy93GTRHYUmgacCBL992g9ylxISBrlHBsISL/Q6c7x2Dyxea13sJMdF9MOzw4WSHBTcvzsZ/3JyP25cZoNex1+6zRpw+nOniBFxq1dTvwP+5tQC69DS5S5kzBrrGSJJAy6ATDV2WKd0p8TIwNo6BsXEcz9Ch+OZ8fOGWRar9EzXeAiEJ7zcPc+FnFXP7Q+gYdeGO5XlylzJn/F+oIcMOHz5oG8GoMzl/6o8HwjhzxYZzPWO488Y85KX4aA4hBD5qH+VKRBrQ2GtnoJM8QmEJDV1WGHvGZLkJd7Xf0WpxwqobwpdWFeAGQ2byC5FZ04ADrRpdfDjVDDl8GHKM48b8bLlLmRMGuspZ3X78rXlYETfgBATahl1oH3Hh9mW5+NKqAizNTY1g7x/z4p/tZrnLoDg632vHjf/BQKckaRl04KO2UQTDyuqvFQK4NOLCpREXbv+cAV9aVYBluVlyl5UwVrcff7kwpKmFEgjoGHHDeXsQeVnqGdXFQFehsCTwz/ZRVTy00jHiRseIG4XLDPjPVQX4XJ62gt0xHsSfzw/AF0zt+wdaJAmBxl471hUtlbuUWWOgq4w3EMJ7F4dUNxVr56gbnaNu3LpkIb64sgD/tjgbaWnqGxZ2Lcd4EO8a++c9LJSUq2nAgf+8rQCZep3cpcwKA11FrG4//l/joKpXKu+2eNFt8eLG/CysWbkYhUsNSFfheF+r248/nx9gmGtcICShecCBNSsL5C5lVhjoKtE/5sWxC4PwB9U/ZzMwMYrgvYtDyMtegJJb8nHXTfnIWqCOq6BeqxfvNWnnZ0HXd77XjpJbFqviQSMGugp0jLjwfvMwQhq86eYcD6L+kgWnL1tRtDwXd9+cj5vysxTZHSNJAme7bWjosnKOlhTi8oXQNuzEXTfly11KVAx0hWvqd+CDNu1P8hSSBFoHnWgddKIgJwN3LM/FHcvzFDNvjM0TQF3rCAbs6rp3QfFh/NfDc0q80LgWA13BznbbcLIj9ZYus3kCON1pxelOK5bnT0wKVrjUgIKcjKTX4guGcbbbhvO9dg5LTGFWdwBdFg8KlxrkLuW6GOgKJITA6U4rPr7CCZ6unRRs0cIFuPWGHKy4YSFuXpSd0D53hzeIC/12NA04EAixr5yAs1dsuG1JjqKv0qMGuiRJ2L59O9rb25GRkYGdO3di5cqVke1Hjx7FkSNHoNfrsXnzZtx3332w2+144IEHUFRUBAD4xje+gcceeyxxrdAQIQT+ecmMxl673KUojt0bRKPXjsY+O9LSgCWGTNyYn4XP5WVhWV4mChZmxDz7oxACY94geqwe1He4IPVfiXP1pHZDDh/6x8ZxS8FCuUuZUdRAr6urQyAQQG1tLRobG7Fnzx7s378fAGA2m3Hw4EG8++678Pv9qKysxNq1a9Ha2oqHHnoIL7/8csIboCVCCHzYpo4HhuQmBGB2+WF2+QFM/Hulp6UhP1uPxTkZyMtaAEOWHtkLdMhakI4FunSk/+vKKiQJ+ENheANhOMeDGPMGMOr0w/uvycUsniCWqOuJb0qSM1ds6g50o9GIsrIyAEBJSQmam5sj2y5evIh77rkHGRkZyMjIwIoVK9DW1obm5ma0tLTg0UcfRUFBAV566SUsW7Ysca3QAEkSqDONoIWTO8VM+tdV9phXveP0Sdn6bF4M2Mdx8yJl/saPGuhutxsGw6c3AnQ6HUKhEPR6PdxuN3JzcyPbcnJy4Ha7cdttt+Huu+/GV7/6VRw7dgw7d+7E66+/PuW9TSZTTEX7fL6Yj1Uan8+HltZWNPR6cWVM/gm25iMUCsFiUf8EVVppB8C2JMKfT9pxf2Fu9B2vI1EZFjXQDQYDPB5P5HtJkqDX66fd5vF4kJubi+LiYmRnT/wGKy8vnzbMAWD16tUxFW0ymWI+VmlaWlvRG14Ml06PJUvkrmZ+LBYzlixRz7wXM9FKOwC2JRGCAPJvvAU3zeMqfT4ZZjQaZ9wW9Q5SaWkp6uvrAQCNjY2RG50AUFxcDKPRCL/fD5fLhc7OThQVFeGll17C3//+dwBAQ0MD7rrrrpgK1zpJEjjV40HbsEvuUohoDho6rXKXMK2oV+jl5eU4deoUKioqIITArl27UFNTgxUrVuD+++9HVVUVKisrIYTA888/j8zMTFRXV2Pr1q04fPgwsrOzsXPnzmS0RVUkSeD9lmH02AOqvzInSjW9Ni/6bF7F3SCNGujp6enYsWPHpNcKCwsjX2/YsAEbNmyYtP2WW27BwYMH41Si9kiSwH83D+PSCK/MidSqodOquFlDuWR7koUZ5kSaMGAfR7fVK3cZkzDQk2gizIcY5kQacfKyBUJBEy0x0JPkaph3jLjlLoWI4sTi8itqUAMDPQnCksDfmhjmRFp06rIFobAy5vthoCdYKCzhvYuDuDzKMCfSIpcvhPN9drnLAMBAT6iJMB9Cl9kTfWciUq2Pr9jg8cu/HCEDPUGCYQnHLgziioVhTqR1gZCE0wp42IiBngCBkIT/Oj+AHoUNaSKixGkZdGDE6ZO1BgZ6nPmCYfz5fD/6x7hUGVEqEQL4qG1U1mGMDPQ48gZCePeTfgza5f0tTUTyGHL40Dwg3xTYDPQ4cfmC+L/Gfow61T0FLhHNz8nLFngD8twgZaDHgd0bwNFz/bC6A3KXQkQy8wXDON4uz7ztDPR5GnX5UHu2D85xrpJDRBPahl2yjHBjoM9Dn82Ld871R9aiJCK66gPTCHzB5GYDAz1GHSMu/Nf5AQRCynjkl4iUxeUL4fil5Ha9MNBjcL53DH9tGkJIUs4sa0SkPK2DTlweTd7kXQz0OZAkgeOXzPhnuxkKmjGTiBTsH62jcPmSc4+NgT5LgZCE95qG8EnPmNylEJGK+IJhvN88DCkJf9Ez0GfB5QviHWMfOjljIhHFoH9sHP/blfi5XhjoUQw5xnH4414+MERE83Lmig1d5sReFDLQr6N5wIF3zvXD4+ewRCKav/9uHobNk7gHEPUJe2cVC4Ul1HeYcaHPIXcpRKQhgZCEM11W3Jqg5GWgf4ZjPIi/NQ1h2MEJtogo/hJ5a5SBfo1Osxv/05L8p7uIiOKBgY6J1YVOdljQqJB1AYmIYpHygT7q9OH9lmHOlEhEqpeygR6WBM5223CmywaJj30SkQakZKAPO3yoM43A7OLYciLSjpQKdF8wjIYuKy702TkXCxFpTkoEuiQJtA45ceqyhXOXE5FmaTrQhRDotXlxosPC7hUi0jzNBnr/mBcNnVb0j43LXQoRUVJoKtCFEOixenG228YgJ6KUo4lAD4QktA+70Ng3BgvHkxNRilJtoAshMOz0oXXQibZhF9f2JKKUp8pA77D68b+nu2H3JmdZJyIiNVBloPc5AghkMMyJiK4VNdAlScL27dvR3t6OjIwM7Ny5EytXroxsP3r0KI4cOQK9Xo/Nmzfjvvvug81mw49+9CP4fD4sW7YMu3fvRnZ2dkIbQkSU6qKuWFRXV4dAIIDa2lpUV1djz549kW1msxkHDx7EkSNH8NZbb+GXv/wlAoEA9u3bh4ceegiHDh3CnXfeidra2oQ2goiIZhHoRqMRZWVlAICSkhI0NzdHtl28eBH33HMPMjIykJubixUrVqCtrW3SMevWrcPp06cTVD4REV0VtcvF7XbDYDBEvtfpdAiFQtDr9XC73cjNzY1sy8nJgdvtnvR6Tk4OXC7XtO9tMpliKrp4iQ66Bdp48tNvyERmJtuiJFppB8C2KNECKQCfLxhz/l1P1EA3GAzweDyR7yVJgl6vn3abx+NBbm5u5PWsrCx4PB7k5eVN+96rV6+OqWiTyRTzsUrDtiiPVtoBsC1KNZ+2GI3GGbdF7XIpLS1FfX09AKCxsRFFRUWRbcXFxTAajfD7/XC5XOjs7ERRURFKS0tx/PhxAEB9fT3WrFkTU+FERDR7Ua/Qy8vLcerUKVRUVEAIgV27dqGmpgYrVqzA/fffj6qqKlRWVkIIgeeffx6ZmZnYvHkzXnzxRRw9ehSLFy/G3r17k9EWIqKUFjXQ09PTsWPHjkmvFRYWRr7esGEDNmzYMGn7kiVL8NZbb8WpRCIimo2oXS5ERKQODPQE2759u2rOf3VfuWv+rHjWo4S2JaOG6c5x7WuzrWH79u1TPhczHXu994zlmGh1zWcfJXwOEiFNCHkWYzMajTHfLFXT3e60tDRc75840W2Jdv7p9p3LMddKVFtirSfW91LSz2S+57i2Ldeed7Y1pKWlAcCkz8VMx17vPWM55rNmasv1ap9rPcky31EuM2Unr9CJiDSCgU5EpBEMdCIijWCgExFphCrnQ1eTV155RTXnv7qv3DV/VjzrUULbklHDdOe49rXZ1jDdMTMde733jOWY2dYVyz5K+BwkAke5yIxtUR6ttANgW5SKo1yIiOi6GOhERBrBQCci0ggGOhGRRsh6U5SIiOZuppuisgU6ERHFF7tciIg0goFORKQRqg70zs5OrFmzBn6/elcCd7lcePrpp/Hoo49i48aNOH/+vNwlzYkkSdi2bRs2btyIqqoq9PT0yF1SzILBILZs2YLKyko88sgj+OCDD+Quad6sVivuvfdedHZ2yl1KzH79619j48aN+Pa3v4133nlH7nJiFgwGUV1djYqKClRWVibkZ6LaQHe73Xj11VeRkZEhdynzUlNTgy9/+cv4wx/+gN27d09Z7k/p6urqEAgEUFtbi+rqauzZs0fukmJ27NgxLFq0CIcOHcKbb76Jn/70p3KXNC/BYBDbtm1DVlaW3KXE7MyZMzh//jwOHz6MgwcPYnh4WO6SYnb8+HGEQiEcOXIEzzzzDF577bW4n0OVgS6EwMsvv4wXXngB2dnZcpczL48//jgqKioAAOFwGJmZmTJXNDdGoxFlZWUAgJKSEjQ3N8tcUey++c1v4rnnngMw8RnT6XQyVzQ/r776KioqKrBs2TK5S4nZyZMnUVRUhGeeeQZPP/00vva1r8ldUsxWrVqFcDgMSZLgdruh18d/Ki3FT871zjvv4O2335702k033YT169fjjjvukKmq2EzXll27dqG4uBhmsxlbtmzB1q1bZaouNm63GwaDIfK9TqdDKBRKyIc10XJycgBMtOkHP/gBfvjDH8pb0Dz86U9/QkFBAcrKyvDGG2/IXU7MxsbGMDg4iAMHDqC/vx+bN2/G+++/H1lJSU0WLlyIgYEBPPjggxgbG8OBAwfifg5VDlssLy/H8uXLAQCNjY0oLi7GH//4R5mril17ezteeOEF/PjHP8a9994rdzlzsnv3bnzhC1/A+vXrAQDr1q1DfX29zFXFbmhoCM8880ykH12tvvOd7yAtLQ1paWkwmUy49dZbsX//fixdulTu0ubkF7/4BQoKCvDEE08AAL71rW+hpqYGN9xwg8yVzd3u3buRkZGB6upqDA0N4bHHHsNf/vKXuP5Vrr7LKAD/+Mc/Il9//etfx29/+1sZq5mfy5cv47nnnsNrr72mur84AKC0tBQfffQR1q9fj8bGRhQVFcldUswsFgueeOIJbNu2DV/5ylfkLmderr3Aqaqqwvbt21UX5sDEAzS///3v8d3vfhejo6MYHx/HokWL5C4rJnl5eViwYAEAID8/H6FQCOFwOK7nUGWga8nevXsRCATws5/9DABgMBiwf/9+mauavfLycpw6dQoVFRUQQmDXrl1ylxSzAwcOwOl0Yt++fdi3bx8A4De/+Y2qbyqq3X333YezZ8/ikUcegRAC27ZtU+29jccffxxbt25FZWUlgsEgnn/+eSxcuDCu51BllwsREU2lylEuREQ0FQOdiEgjGOhERBrBQCci0ggGOhGRRjDQiYg0goFORKQRDHQiIo34/xFs+pUuy7zIAAAAAElFTkSuQmCC",
       "text/plain": [
-       "(-0.02, 0.22)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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y+FNLRGVvemYW0bRFdAz6iCRJmJxdQjabFR2lJLCoiajs9fSPQHXWiI5Bd5CVWvj6BkXH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\nRERkYCxqIiIiA/v/FdwuAWtQn+YAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10072ca58>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -455,7 +443,7 @@
     "\n",
     "plt.fill_between(x_d, np.exp(logprob), alpha=0.5)\n",
     "plt.plot(x, np.full_like(x, -0.01), '|k', markeredgewidth=1)\n",
-    "plt.ylim(-0.02, 0.22)"
+    "plt.ylim(-0.02, 0.22);"
    ]
   },
   {
@@ -475,15 +463,16 @@
     "editable": true
    },
    "source": [
-    "### Selecting the bandwidth via cross-validation\n",
+    "## Selecting the Bandwidth via Cross-Validation\n",
     "\n",
-    "The choice of bandwidth within KDE is extremely important to finding a suitable density estimate, and is the knob that controls the bias–variance trade-off in the estimate of density: too narrow a bandwidth leads to a high-variance estimate (i.e., over-fitting), where the presence or absence of a single point makes a large difference. Too wide a bandwidth leads to a high-bias estimate (i.e., under-fitting) where the structure in the data is washed out by the wide kernel.\n",
+    "The final estimate produced by a KDE procedure can be quite sensitive to the choice of bandwidth, which is the knob that controls the bias–variance trade-off in the estimate of density.\n",
+    "Too narrow a bandwidth leads to a high-variance estimate (i.e., overfitting), where the presence or absence of a single point makes a large difference. Too wide a bandwidth leads to a high-bias estimate (i.e., underfitting), where the structure in the data is washed out by the wide kernel.\n",
     "\n",
-    "There is a long history in statistics of methods to quickly estimate the best bandwidth based on rather stringent assumptions about the data: if you look up the KDE implementations in the SciPy and StatsModels packages, for example, you will see implementations based on some of these rules.\n",
+    "There is a long history in statistics of methods to quickly estimate the best bandwidth based on rather stringent assumptions about the data: if you look up the KDE implementations in the SciPy and `statsmodels` packages, for example, you will see implementations based on some of these rules.\n",
     "\n",
     "In machine learning contexts, we've seen that such hyperparameter tuning often is done empirically via a cross-validation approach.\n",
-    "With this in mind, the ``KernelDensity`` estimator in Scikit-Learn is designed such that it can be used directly within the Scikit-Learn's standard grid search tools.\n",
-    "Here we will use ``GridSearchCV`` to optimize the bandwidth for the preceding dataset.\n",
+    "With this in mind, Scikit-Learn's `KernelDensity` estimator is designed such that it can be used directly within the package's standard grid search tools.\n",
+    "Here we will use `GridSearchCV` to optimize the bandwidth for the preceding dataset.\n",
     "Because we are looking at such a small dataset, we will use leave-one-out cross-validation, which minimizes the reduction in training set size for each cross-validation trial:"
    ]
   },
@@ -493,17 +482,20 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
-    "from sklearn.grid_search import GridSearchCV\n",
-    "from sklearn.cross_validation import LeaveOneOut\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.model_selection import LeaveOneOut\n",
     "\n",
     "bandwidths = 10 ** np.linspace(-1, 1, 100)\n",
     "grid = GridSearchCV(KernelDensity(kernel='gaussian'),\n",
     "                    {'bandwidth': bandwidths},\n",
-    "                    cv=LeaveOneOut(len(x)))\n",
+    "                    cv=LeaveOneOut())\n",
     "grid.fit(x[:, None]);"
    ]
   },
@@ -514,7 +506,7 @@
     "editable": true
    },
    "source": [
-    "Now we can find the choice of bandwidth which maximizes the score (which in this case defaults to the log-likelihood):"
+    "Now we can find the choice of bandwidth that maximizes the score (which in this case defaults to the log-likelihood):"
    ]
   },
   {
@@ -523,7 +515,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -548,7 +543,7 @@
     "editable": true
    },
    "source": [
-    "The optimal bandwidth happens to be very close to what we used in the example plot earlier, where the bandwidth was 1.0 (i.e., the default width of ``scipy.stats.norm``)."
+    "The optimal bandwidth happens to be very close to what we used in the example plot earlier, where the bandwidth was 1.0 (i.e., the default width of `scipy.stats.norm`)."
    ]
   },
   {
@@ -558,185 +553,11 @@
     "editable": true
    },
    "source": [
-    "## Example: KDE on a Sphere\n",
-    "\n",
-    "Perhaps the most common use of KDE is in graphically representing distributions of points.\n",
-    "For example, in the Seaborn visualization library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)), KDE is built in and automatically used to help visualize points in one and two dimensions.\n",
-    "\n",
-    "Here we will look at a slightly more sophisticated use of KDE for visualization of distributions.\n",
-    "We will make use of some geographic data that can be loaded with Scikit-Learn: the geographic distributions of recorded observations of two South American mammals, *Bradypus variegatus* (the Brown-throated Sloth) and *Microryzomys minutus* (the Forest Small Rice Rat).\n",
-    "\n",
-    "With Scikit-Learn, we can fetch this data as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import fetch_species_distributions\n",
-    "\n",
-    "data = fetch_species_distributions()\n",
-    "\n",
-    "# Get matrices/arrays of species IDs and locations\n",
-    "latlon = np.vstack([data.train['dd lat'],\n",
-    "                    data.train['dd long']]).T\n",
-    "species = np.array([d.decode('ascii').startswith('micro')\n",
-    "                    for d in data.train['species']], dtype='int')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "With this data loaded, we can use the Basemap toolkit (mentioned previously in [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb)) to plot the observed locations of these two species on the map of South America."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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pXMmYcjXOv7HAmh99HaY2JybwdzyIopotHGINvqTSQSl2yHAmAEde4jNKrE/I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\nxC0QzFD+P0Qmdryva36JAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x117f3dbe0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from mpl_toolkits.basemap import Basemap\n",
-    "from sklearn.datasets.species_distributions import construct_grids\n",
-    "\n",
-    "xgrid, ygrid = construct_grids(data)\n",
-    "\n",
-    "# plot coastlines with basemap\n",
-    "m = Basemap(projection='cyl', resolution='c',\n",
-    "            llcrnrlat=ygrid.min(), urcrnrlat=ygrid.max(),\n",
-    "            llcrnrlon=xgrid.min(), urcrnrlon=xgrid.max())\n",
-    "m.drawmapboundary(fill_color='#DDEEFF')\n",
-    "m.fillcontinents(color='#FFEEDD')\n",
-    "m.drawcoastlines(color='gray', zorder=2)\n",
-    "m.drawcountries(color='gray', zorder=2)\n",
-    "\n",
-    "# plot locations\n",
-    "m.scatter(latlon[:, 1], latlon[:, 0], zorder=3,\n",
-    "          c=species, cmap='rainbow', latlon=True);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "Unfortunately, this doesn't give a very good idea of the density of the species, because points in the species range may overlap one another.\n",
-    "You may not realize it by looking at this plot, but there are over 1,600 points shown here!\n",
-    "\n",
-    "Let's use kernel density estimation to show this distribution in a more interpretable way: as a smooth indication of density on the map.\n",
-    "Because the coordinate system here lies on a spherical surface rather than a flat plane, we will use the ``haversine`` distance metric, which will correctly represent distances on a curved surface.\n",
-    "\n",
-    "There is a bit of boilerplate code here (one of the disadvantages of the Basemap toolkit) but the meaning of each code block should be clear:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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HB5FIRPXq1alUsTKBgQHcu38PHW0datRvTJ0GjWnfpSfGpmZFuqaFpiTX/UHJirnuFxD4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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x118332ef0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set up the data grid for the contour plot\n",
-    "X, Y = np.meshgrid(xgrid[::5], ygrid[::5][::-1])\n",
-    "land_reference = data.coverages[6][::5, ::5]\n",
-    "land_mask = (land_reference > -9999).ravel()\n",
-    "xy = np.vstack([Y.ravel(), X.ravel()]).T\n",
-    "xy = np.radians(xy[land_mask])\n",
-    "\n",
-    "# Create two side-by-side plots\n",
-    "fig, ax = plt.subplots(1, 2)\n",
-    "fig.subplots_adjust(left=0.05, right=0.95, wspace=0.05)\n",
-    "species_names = ['Bradypus Variegatus', 'Microryzomys Minutus']\n",
-    "cmaps = ['Purples', 'Reds']\n",
-    "\n",
-    "for i, axi in enumerate(ax):\n",
-    "    axi.set_title(species_names[i])\n",
-    "    \n",
-    "    # plot coastlines with basemap\n",
-    "    m = Basemap(projection='cyl', llcrnrlat=Y.min(),\n",
-    "                urcrnrlat=Y.max(), llcrnrlon=X.min(),\n",
-    "                urcrnrlon=X.max(), resolution='c', ax=axi)\n",
-    "    m.drawmapboundary(fill_color='#DDEEFF')\n",
-    "    m.drawcoastlines()\n",
-    "    m.drawcountries()\n",
-    "    \n",
-    "    # construct a spherical kernel density estimate of the distribution\n",
-    "    kde = KernelDensity(bandwidth=0.03, metric='haversine')\n",
-    "    kde.fit(np.radians(latlon[species == i]))\n",
-    "\n",
-    "    # evaluate only on the land: -9999 indicates ocean\n",
-    "    Z = np.full(land_mask.shape[0], -9999.0)\n",
-    "    Z[land_mask] = np.exp(kde.score_samples(xy))\n",
-    "    Z = Z.reshape(X.shape)\n",
-    "\n",
-    "    # plot contours of the density\n",
-    "    levels = np.linspace(0, Z.max(), 25)\n",
-    "    axi.contourf(X, Y, Z, levels=levels, cmap=cmaps[i])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "Compared to the simple scatter plot we initially used, this visualization paints a much clearer picture of the geographical distribution of observations of these two species."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Example: Not-So-Naive Bayes\n",
+    "## Example: Not-so-Naive Bayes\n",
     "\n",
     "This example looks at Bayesian generative classification with KDE, and demonstrates how to use the Scikit-Learn architecture to create a custom estimator.\n",
     "\n",
-    "In [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb), we took a look at naive Bayesian classification, in which we created a simple generative model for each class, and used these models to build a fast classifier.\n",
+    "In [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) we explored naive Bayesian classification, in which we create a simple generative model for each class, and use these models to build a fast classifier.\n",
     "For Gaussian naive Bayes, the generative model is a simple axis-aligned Gaussian.\n",
     "With a density estimation algorithm like KDE, we can remove the \"naive\" element and perform the same classification with a more sophisticated generative model for each class.\n",
     "It's still Bayesian classification, but it's no longer naive.\n",
@@ -746,12 +567,12 @@
     "1. Split the training data by label.\n",
     "\n",
     "2. For each set, fit a KDE to obtain a generative model of the data.\n",
-    "   This allows you for any observation $x$ and label $y$ to compute a likelihood $P(x~|~y)$.\n",
+    "   This allows you, for any observation $x$ and label $y$, to compute a likelihood $P(x~|~y)$.\n",
     "   \n",
     "3. From the number of examples of each class in the training set, compute the *class prior*, $P(y)$.\n",
     "\n",
     "4. For an unknown point $x$, the posterior probability for each class is $P(y~|~x) \\propto P(x~|~y)P(y)$.\n",
-    "   The class which maximizes this posterior is the label assigned to the point.\n",
+    "   The class that maximizes this posterior is the label assigned to the point.\n",
     "\n",
     "The algorithm is straightforward and intuitive to understand; the more difficult piece is couching it within the Scikit-Learn framework in order to make use of the grid search and cross-validation architecture.\n",
     "\n",
@@ -760,11 +581,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -799,7 +620,7 @@
     "        logprobs = np.array([model.score_samples(X)\n",
     "                             for model in self.models_]).T\n",
     "        result = np.exp(logprobs + self.logpriors_)\n",
-    "        return result / result.sum(1, keepdims=True)\n",
+    "        return result / result.sum(axis=1, keepdims=True)\n",
     "        \n",
     "    def predict(self, X):\n",
     "        return self.classes_[np.argmax(self.predict_proba(X), 1)]"
@@ -812,7 +633,7 @@
     "editable": true
    },
    "source": [
-    "### The anatomy of a custom estimator"
+    "### Anatomy of a Custom Estimator"
    ]
   },
   {
@@ -839,9 +660,9 @@
     "    \"\"\"\n",
     "```\n",
     "\n",
-    "Each estimator in Scikit-Learn is a class, and it is most convenient for this class to inherit from the ``BaseEstimator`` class as well as the appropriate mixin, which provides standard functionality.\n",
-    "For example, among other things, here the ``BaseEstimator`` contains the logic necessary to clone/copy an estimator for use in a cross-validation procedure, and ``ClassifierMixin`` defines a default ``score()`` method used by such routines.\n",
-    "We also provide a doc string, which will be captured by IPython's help functionality (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb))."
+    "Each estimator in Scikit-Learn is a class, and it is most convenient for this class to inherit from the `BaseEstimator` class as well as the appropriate mixin, which provides standard functionality.\n",
+    "For example, here the `BaseEstimator` contains (among other things) the logic necessary to clone/copy an estimator for use in a cross-validation procedure, and `ClassifierMixin` defines a default `score` method used by such routines.\n",
+    "We also provide a docstring, which will be captured by IPython's help functionality (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb))."
    ]
   },
   {
@@ -859,10 +680,10 @@
     "        self.kernel = kernel\n",
     "```\n",
     "\n",
-    "This is the actual code that is executed when the object is instantiated with ``KDEClassifier()``.\n",
-    "In Scikit-Learn, it is important that *initialization contains no operations* other than assigning the passed values by name to ``self``.\n",
-    "This is due to the logic contained in ``BaseEstimator`` required for cloning and modifying estimators for cross-validation, grid search, and other functions.\n",
-    "Similarly, all arguments to ``__init__`` should be explicit: i.e. ``*args`` or ``**kwargs`` should be avoided, as they will not be correctly handled within cross-validation routines."
+    "This is the actual code that is executed when the object is instantiated with `KDEClassifier`.\n",
+    "In Scikit-Learn, it is important that *initialization contains no operations* other than assigning the passed values by name to `self`.\n",
+    "This is due to the logic contained in `BaseEstimator` required for cloning and modifying estimators for cross-validation, grid search, and other functions.\n",
+    "Similarly, all arguments to `__init__` should be explicit: i.e., `*args` or `**kwargs` should be avoided, as they will not be correctly handled within cross-validation routines."
    ]
   },
   {
@@ -872,7 +693,7 @@
     "editable": true
    },
    "source": [
-    "Next comes the ``fit()`` method, where we handle training data:\n",
+    "Next comes the `fit` method, where we handle training data:\n",
     "\n",
     "```python \n",
     "    def fit(self, X, y):\n",
@@ -886,12 +707,12 @@
     "        return self\n",
     "```\n",
     "\n",
-    "Here we find the unique classes in the training data, train a ``KernelDensity`` model for each class, and compute the class priors based on the number of input samples.\n",
-    "Finally, ``fit()`` should always return ``self`` so that we can chain commands. For example:\n",
+    "Here we find the unique classes in the training data, train a `KernelDensity` model for each class, and compute the class priors based on the number of input samples.\n",
+    "Finally, `fit` should always return `self` so that we can chain commands. For example:\n",
     "```python\n",
     "label = model.fit(X, y).predict(X)\n",
     "```\n",
-    "Notice that each persistent result of the fit is stored with a trailing underscore (e.g., ``self.logpriors_``).\n",
+    "Notice that each persistent result of the fit is stored with a trailing underscore (e.g., `self.logpriors_`).\n",
     "This is a convention used in Scikit-Learn so that you can quickly scan the members of an estimator (using IPython's tab completion) and see exactly which members are fit to training data."
    ]
   },
@@ -908,15 +729,15 @@
     "        logprobs = np.vstack([model.score_samples(X)\n",
     "                              for model in self.models_]).T\n",
     "        result = np.exp(logprobs + self.logpriors_)\n",
-    "        return result / result.sum(1, keepdims=True)\n",
+    "        return result / result.sum(axis=1, keepdims=True)\n",
     "        \n",
     "    def predict(self, X):\n",
     "        return self.classes_[np.argmax(self.predict_proba(X), 1)]\n",
     "```\n",
-    "Because this is a probabilistic classifier, we first implement ``predict_proba()`` which returns an array of class probabilities of shape ``[n_samples, n_classes]``.\n",
-    "Entry ``[i, j]`` of this array is the posterior probability that sample ``i`` is a member of class ``j``, computed by multiplying the likelihood by the class prior and normalizing.\n",
+    "Because this is a probabilistic classifier, we first implement `predict_proba`, which returns an array of class probabilities of shape `[n_samples, n_classes]`.\n",
+    "Entry `[i, j]` of this array is the posterior probability that sample `i` is a member of class `j`, computed by multiplying the likelihood by the class prior and normalizing.\n",
     "\n",
-    "Finally, the ``predict()`` method uses these probabilities and simply returns the class with the largest probability."
+    "The `predict` method uses these probabilities and simply returns the class with the largest probability."
    ]
   },
   {
@@ -926,32 +747,33 @@
     "editable": true
    },
    "source": [
-    "### Using our custom estimator\n",
+    "### Using Our Custom Estimator\n",
     "\n",
-    "Let's try this custom estimator on a problem we have seen before: the classification of hand-written digits.\n",
-    "Here we will load the digits, and compute the cross-validation score for a range of candidate bandwidths using the ``GridSearchCV`` meta-estimator (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb)):"
+    "Let's try this custom estimator on a problem we have seen before: the classification of handwritten digits.\n",
+    "Here we will load the digits and compute the cross-validation score for a range of candidate bandwidths using the ``GridSearchCV`` meta-estimator (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb)):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
     "from sklearn.datasets import load_digits\n",
-    "from sklearn.grid_search import GridSearchCV\n",
+    "from sklearn.model_selection import GridSearchCV\n",
     "\n",
     "digits = load_digits()\n",
     "\n",
-    "bandwidths = 10 ** np.linspace(0, 2, 100)\n",
-    "grid = GridSearchCV(KDEClassifier(), {'bandwidth': bandwidths})\n",
-    "grid.fit(digits.data, digits.target)\n",
-    "\n",
-    "scores = [val.mean_validation_score for val in grid.grid_scores_]"
+    "grid = GridSearchCV(KDEClassifier(),\n",
+    "                    {'bandwidth': np.logspace(0, 2, 100)})\n",
+    "grid.fit(digits.data, digits.target);"
    ]
   },
   {
@@ -961,31 +783,34 @@
     "editable": true
    },
    "source": [
-    "Next we can plot the cross-validation score as a function of bandwidth:"
+    "Next we can plot the cross-validation score as a function of bandwidth (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'bandwidth': 7.0548023107186433}\n",
-      "accuracy = 0.966611018364\n"
+      "best param: {'bandwidth': 6.135907273413174}\n",
+      "accuracy = 0.9677298050139276\n"
      ]
     },
     {
      "data": {
-      "image/png": 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/hjz5hE27i7H9wFlcmzkWK65L83Q5NAiebGtrtdnx05km/HiiHkdO1TuvBRASJEVmShSu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\n",
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vZJ6rwod7crDz2DlsPlyATu5qDL7FB30CPdA7wAM9OrvxUk4HxrCXgNf+lwl3Zyc8M7L79VcmhxXZxR1v3ncrlt0dhaTMYuw8dg4Hc8qxPa3Ito63qwphfq6ICvTAgFBv9A/1gr+bNKfMcDQMeweXnKVDyqlSvDghkmO1BKBhIriJ0QGYGB0AACjV1yGjqAqniquRrdPjVLEeGw+exfq9uQCAUB8X9A/1xsBQb4T5N8z5r3FSwN/dGVo1I6Sj4L+UA7NYBazYlYlgbxfMHBJi73KonfLVqjE8wg/DL17HDwAmsxUZRRdwKLcch3Ir8H1mMb5ILWi0nVwG9OzsjgGhXhgS5oO47n5wZfi3W/yXcWA7jhbixPlqvDujL9TKjj81MbUdlVKOvsFe6BvshTm3N8yKeqZUj8LK2ovPADAjt9SIw3nl2Hy4AB/vy4NaKUdcd1+M7d0ZY6M6w92Z3yTbE4a9g7JYBaz64TR6dnbDhD5d7F0OdXByucw2odsf1VusOJxbgW8yzuPbjPNIyizBP7alY3RPf9wV1RndfF0R4KmBjysncbMnhr2D2nX8HM7oDHjvwVj+D0aiclLIMSTMB0PCfPDyn3ohLb8S29OK8PWxIvwv/fxl68kanhmsUsBVrUSojyvC/FwR7v/7YyA9XXg1kFgY9g7IahXw7g+n0d1fi7t6d7Z3OSQhMpnMNvzz4oRInCyuRmFFDYoqa1BcXdcwBGSy4EJNPXLLDNhzurTRw+h9XFUNzwD21yLMzxVdvV0Q6NnwpC8vFyd2XG4Cw94BffvbeZwsrsa/p8dAzptmyE6UCjl6B3hc825ti1VAQYUR2To9sksMOF2iR7ZOj/+ln0Olsb7RuhonBQI8nRHgqYGniwoap4aH2btrnODlooK3qwpdvTWI7OLOqSGugj8RByMIDWP13XxdbZfWEbVXCrkMIT6uCPFxxaiejZeVG0worKhBYWXDn3MX/1tUWYPCihrU1FtgNFlQXVsPq/D7dnIZEOanxZAwH9zfvyvngLqIYe9gfjqpQ0ZRFd64N5q3wlOH5u3a0FvvE3TtsLZaBVTV1qPMYMIZnQHphRdwvPACEg/l45N9eYgKdMeMgSG4p28gNCrpXpXGsHcwn+zLhb+bGnf3DbR3KURtQi6XwdNFBU+XhvH+Mb06AQAuGOux/WghNh7MxwtfNkwGN31gVzw8JBSBntKb+llu7wKo9RRUGPFTlg7TB3SFk4L/tCRtHi5OeHhIKHbNG4bPnxiCoeE++CAlByPf/Amrvj/V6MSwFLBn70ASD+VDBmDawGB7l0LUbtgmgwv1RkGFEa/97wT+9V0WdhwtwrK7ozDoFh97l9gm2P1zEPUWKzYdyseIHv6S/IpK1BxBXi74z4xYfPRIfxhNFkxbux8T3klBwr5cVNXWX38HHRjD3kF8n1kMXXUdZrBXT3Rdo3p2wrfP3o5XJ/eGIAAvbc/AwOVJWLD5KA7llkMQhOvvpIPhMI6D+O+Bs+ji4YwRPfyuvzIRwVWtxMwhoXhocAiOF17ApkP52JFWhC1HCtDdX4v5YyJwV1Rnh7mRiz17B3C2zIiUU6WYNqArlDwxS3RDZDIZooM8seKePjjwwmisvDcaMhnw5H+P4N41+5CaV27vElsFk8EBrEs5A4VchmkDutq7FKIOzVWtxP39u2LXvDjET+mD/HIjpq7eh5kfHsAv2aUdeniHYd/BnSquxmcHz+LBQcHo4sETs0StQamQY/rAYPz0/AgsvKsnMs9VY8a6A5iy+hecLtHbu7wWESXsrVYrlixZgmnTpmHmzJnIy8trtPyjjz7ClClTMHXqVHz33XdilCAZK3ZlwkWlwN/uiLB3KUQOx0WlxJMjwrBn4Ui8encUzpYZce+aX3DkbIW9S7thooR9UlISTCYTEhMTsWDBAsTHx9uWVVVV4ZNPPsGmTZvw0UcfYcWKFWKUIAnJWTr8eFKHeaO680HRRCJydlJg5uAQbH3qNnhonDBj3X78eKLE3mXdEFHCPjU1FXFxcQCAmJgYpKen25ZpNBoEBASgpqYGNTU1DnOmu62ZLVYs2/kbQnxc8PBtfOQgUVsI8XHFF0/chnB/LR775DAS9ud1mHF8US691Ov10Gq1ttcKhQJmsxlKZUNzXbp0wYQJE2CxWDB37twm95OZmdmi9mtra1u8bUfxzakqZBXr8eKITjhzKsve5UiKFI4vurZ/DvdG/M/1eGlbOn4+notnBvtCrWydvrNYx5coYa/VamEwGGyvrVarLeiTk5NRUlKC77//HgAwe/ZsxMbGIjo6+or9REZGtqj9zMzMFm/bUaz45QBu8XPF7LH9+O2ojUnh+KLrS4zqhX9/fwr//v4UztXIsfbh/q1y9/rNHF+pqalNLhNlGCc2NhbJyckAgLS0NERE/H7y0MPDA87OzlCpVFCr1XBzc0NVVZUYZTis2noLDuaUY0SEP4OeyE7kchmeHROBD2f1x9kyIx5Yux/nLtTYu6wmiRL2Y8aMgUqlwvTp0/Haa69h8eLFWL9+Pb7//nv0798fffr0wf33349p06YhNDQUQ4cOFaMMh3Uotxx1ZiviInztXQqR5I2O7ISExwah3GDCjHUHUFxVa++SrkqUYRy5XI6lS5c2ei8sLMz293nz5mHevHliNC0JKadKoVLIMaibt71LISIAMV098fGjA/DwhwcxY91+bJozBH5uanuX1QhvquqAkrN06B/qxedsErUj/UK8sf7PA1FUWYv71vyCs2VGe5fUCMO+gympqsWJ89WI684Jz4jam4HdvPHpY4NQYazHlNW/IKPogr1LsmHYdzB7TpcCAOK6c7yeqD3qF+KFL54YAieFDNPe349fskvtXRIAhn2Hk3KqFD6uKvTq4m7vUoioCd07uWHrU7ehi4czHvnoEHYeO2fvkhj2HYnVKiDlVCmGdfeFXM5LLonasy4eGnz+xBBEB3ngmY1H8Mm+XLvWw7DvQE6cr0apvo7j9UQdhKeLCgmzB2F0T38s2Z6B13efgNVqn+kVGPYdSMopHQCO1xN1JBqVAmse6ocHBnbF6p+y8cSnqTDUmdu8DoZ9B3Igpxxhfq7o5O5s71KI6AYoFXKsuKcPlkzshaTMYkxd/Qvyy9v20kyGfQchCALS8isRG+xl71KIqAVkMhkeHdYN6/88EIWVNZi+dj/K9HVt1j7DvoPIL69BucGEmGBPe5dCRDdheIQfPp09CDp9HZ787xGYzNY2aZdh30H8mt/wZJyYrp72LYSIbtqtXT2xcmo0DuaU459fZbRJm7zfvoNIy6+ExkmBHp3c7F0KEbWCu/sG4sT5aqz5ORthflo8OqybqO0x7DuItPxK9An0gFLBL2NEjuL5sT1wukSPpV//hvTCC/jn5N6itcXk6ADqzBZkFFZxvJ7IwSjkMqx5KBZ/u6M7tqUVYty/U5CpE2eKZIZ9B5B5rhomi5Xj9UQOSKmQ4293RODzJ26DXCbDh4fLxWlHlL1Sq0o723Byti979kQOq1+IF76bfzuOZYjzfGP27DuAtPxKdHJXo4vHzT/fkojaL7VSAa1KIcq+GfYdQFp+JYdwiOimMOzbuQqDCbllRsR05Z2zRNRyDPt2qN5iRW29BQCQVlAJgDdTEdHN4QnadmjOJ4exN7sMg7p5QxAAuQyIDvKwd1lE1IEx7NuZOrMFe7PL0N1fi/MXanGqRI9bgzzgquY/FRG1HBOknUkvvACT2Yp5o7tjbO/OOH+hFmolR9uI6OYw7NuZgzkN19T3D2k4IdvZg3PXE9HNY5exnTmcW45b/Fzho1XbuxQiciAM+3bEahVwOK8CA0K87V0KETkYhn07clqnx4WaegzoxrAnotbFsG9HDuY0TIA0IJQ3UBFR62LYtyOHc8vh56ZGsLeLvUshIgfDsG9HDuVWYECoF2Qymb1LISIHw7BvJ4oqa1BYWYP+PDlLRCJg2LcTh/Marq8fEMqwJ6LWx7BvJw7llMNVpUBkFz5QnIhaH8O+HbBaBew5XYrYEC8+UJyIRMFkaQd2Hj+HnFID7u0XZO9SiMhBMeztzGIV8HZSFiI6aTExOsDe5RCRgxJlIjSr1YpXXnkFJ0+ehEqlwrJlyxASEmJb/vPPP+M///kPBEFA79698fLLL0v2csMdRwuRrTPgvQdjoZBL82dAROITpWeflJQEk8mExMRELFiwAPHx8bZler0eb7zxBtasWYPPP/8cgYGBqKioEKOMds9sseLfSafQs7Mb7urd2d7lEJEDEyXsU1NTERcXBwCIiYlBenq6bdmvv/6KiIgIvP7665gxYwZ8fX3h7S3Nyw2//LUQuWVGPDsmAnL26olIRKIM4+j1emi1WttrhUIBs9kMpVKJiooKHDhwANu2bYOLiwsefPBBxMTEoFu3blfsJzMzs0Xt19bWtnjbtvTOd/kI81ahq6wcmZnS/HbTEXWU44s6JrGOL1HCXqvVwmAw2F5brVYolQ1NeXp6ok+fPvDz8wMA9O/fH5mZmVcN+8jIyBa1n5mZ2eJt24q+zoz8C2fw/Nge6NUr3N7l0A3oCMcXdVw3c3ylpqY2uUyUYZzY2FgkJycDANLS0hAREWFb1rt3b2RlZaG8vBxmsxlHjx5FeLj0wi5H1/DLMMzP1c6VEJEUNKtnbzKZoFKpmr3TMWPGYO/evZg+fToEQcCKFSuwfv16BAcHY/To0ViwYAEee+wxAMBdd93V6JeBVGTr9ACAMD/tddYkIrp5zQr7qVOnYvDgwbjvvvuaFcxyuRxLly5t9F5YWJjt7xMmTMCECRNusFTHkq3TQyGXIdiH0xkTkfiaFfbbt29HSkoK3n33XVRUVGDSpEkYP348XF05BNFS2To9gr1doFYq7F0KEUlAs8bs5XI5br/9dkydOhWenp5ISEjA7Nmz8emnn4pdn8M6ozNwvJ6I2kyzevYrV67E999/j4EDB+Lxxx9HdHQ0rFYrpkyZgoceekjsGh2OxSrgTKkBwyP87F0KEUlEs8I+NDQUX375JVxcXFBfXw+gobf/7rvvilqcoyqsqIHJbOXJWSJqM80axhEEAatWrQIAzJ07F9u2bQMABAVxlsaWuHQlzi0cxiGiNtKssN+0aRMWLFgAAHj//fexceNGUYtydLzskojaWrNP0F66A9bJyUmyM1S2lmydHt6uKni5Nv/eBSKim9GsMfvRo0djxowZiI6ORkZGBkaNGiV2XQ4tu4RX4hBR22pW2D/11FMYOXIkcnJycPfdd6Nnz55i1+XQsnV6jOnVyd5lEJGENGsYJy8vD8nJyThz5gySkpKwZMkSsetyWJVGE8oMJo7XE1GbalbYXzo5e+TIERQUFKCyslLMmhxa9qUJ0Pw5jENEbadZYe/i4oK5c+eiU6dOiI+PR2lpqdh1tbp1yWew9UiBvcvglThEZBfNCnuZTAadTgeDwQCj0Qij0Sh2Xa3uwz052Hw4395lIFunh0ohR5AXJ0AjorbTrLB/5plnkJSUhMmTJ+OOO+7AkCFDxK6rVdXWW3C+qhYl1XX2LgXZJQZ083Xlw8WJqE0162qcY8eOYfbs2QAaLsPsaM6WN3wT0VW1fdgb6sy469/JcFYqMKCbN44VVKJ/qFeb10FE0tasnv3PP/8Mi8Uidi2iyStrCPvqOjNqTG37OQ7nVSC/vAYuKgW+SitCSXUdegd4tGkNRETN6tlXVFQgLi4OQUFBkMlkkMlk2LRpk9i1tZq8st+fh1tSXYsQn7a7Emb/mTIo5TJsnDMYaqUCuWUGBHlp2qx9IiKgmWG/Zs0asesQ1aWePQCUVNe1adgfOFOG6CAPuKgaftS8CoeI7KFZYf/ll19e8d4zzzzT6sWIJa/cCI2TAjX1FpS04bi9oc6MYwUXMOf2W9qsTSKiq2lW2Pv6+gJomOr4t99+g9VqFbWo1pZXZkBsiCf2ni5DSXVtm7WbmlcBs1XA4Ft82qxNIqKraVbYT58+vdHrxx57TJRixGC2WFFYUYPxfbrgYE55m15+uf9MGRRyGfqF8OobIrKvZoV9Tk6O7e86nQ5FRUWiFdTaiiprYbYK6ObjCj+tukXDOGaLFYWVNTc81n8gpxzRQR5wVTfrx0xEJJpmpdCSJUsgk8kgCAKcnZ2xcOFCsetqNbkXr8QJ9nGBn7tzi4Zx1qXk4P++O4nkv49EF4/mXUljNJlxNL8Sj3O8nojagWaF/QcffIDs7Gz06tULSUlJuO2228Suq9XkXbyhKtTHFf5uauSX39hUD4IgYOuRAtRbBHybUYxZt4U2azuO1xNRe9Ksm6qef/55ZGZmAmgY0lm0aJGoRbWmvFID1Eo5/N3U8HdT3/CY/Ynz1ThV0jB52e70802ud7pEjzv+72ck7MuFIAg4cKac4/VE1G40K+yLi4sxdepUAMDjjz+OkpISUYtqTXnlRgR7u0Aul8HfzRnlBhNM5uZfTbQ9rQgKuQwzBgXjQE4ZyvRX/2Xx5jcncbpEj5e2Z+Dpz47gp6wS9An0gJbj9UTUDjR71stLJ2nPnj3bri+9tFqFRq/PlhltJ1b93dUAgNImAvuPBEHAV0eLMCzcFzMGBsMqAEmZxVesd7zgAnZnnMe8UeFYPK4nvskoRnphFYdwiKjdaFa3c/HixXj22WdRWloKf39//POf/xS7rha77/19uNVXhiWRDWGdV27AsO4N9wn4uzWEfUl1HQI8r3+i9cjZChRW1mD+mAj0DnBHV28Ndqefx7QBwY3W+9d3J+GhccJjt98Cd2cn9A/1xttJWZgSG9j6H5CIqAWa1bOPjIzEihUrsGfPHjz11FPt+hm0nd2dselYBapq61FSXYfaeitCfBrmjvd3cwYAlFQ174qcHWlFUCvluLN3J8hkMtzVuzP2ni5DVW29bZ3DueX46aQOTwwPg7uzEwCgX4gXEmYPQkQnt1b+dERELdOssH/uuec6zAnaJ0eEwVgv4L/7z9rmxPnjME5zTtKaLVbsPH4Oo3r6w+1iiN8V1QUmixU/nmg4ZyEIAt745iR8tWrMui1EjI9DRNQqHO4EbVSgB2IDNPhwTw5OFlcDAEK8G3r2Pq4qyGSNwz6vzIC0/Mor9rPvTBlK9SZMujXA9l7frp7wd1Pjf8fPY/+ZMsxNSMWBnHI8MzLMNtEZEVF7dMMnaPPy8tr1CVoAuD/KE6X6Oqz5KRsKuQyBF6cUVirk8HFVQ3fZjVUvbkvHA2v3N7r+3moVsOqH0/B0ccLInv629+VyGcb27ozdGecxfe1+HMwtxzMjw/HgYPbqiah9a1Z39IUXXsD8+fOh0+ng7++PV155ReSybk50Z2fEdPVEWn4lgr1d4KT4/Xeav9vvUybUmS04lFuO2nor/rEtHR//eQBkMhkSD+fjYE45Vk6NhrOTotG+Zw4JQW6ZAeP7dMHdMYHQqBovJyJqj5rVs8/IyIDRaIRKpUJlZSWee+45seu6KTKZDE+OCAMA28nZS/zdf7+x6mj+BdTWWzE8wg/JWTpsTytCSVUtVuzKxOBbvHFf/6Ar9h3RyQ0JswfhgYHBDHoi6jCaFfafffYZEhISMHz4cLz22msIDw8Xu66bNiayEwbf4o1h4b6N3m+4i7ZhGGdfdhlkMuCtaTHoG+yJpV//hoVbjqHObMVrU6Ihk/Gh4ETkGJoV9v7+/vD394fBYMCgQYNQXV19zfWtViuWLFmCadOmYebMmcjLy7vqOo899hg2btzYssqvQy6XYdOcIZg7PKzR+/5uzijVm2CxCth3phS9urjD21WF16b0QVVNPX48qcNfR3dHN9+2e5oVEZHYmhX2bm5uSEpKsj17trKy8prrJyUlwWQyITExEQsWLEB8fPwV67z99tuoqqpqUdE3w99dDYtVwLkLNThythJDLt7l2rOzOxaPj8Sonv54PI4zVRKRY2lW2C9btgwBAQGYP38+cnNz8eKLL15z/dTUVMTFxQEAYmJikJ6e3mj57t27IZPJbOu0pUt30X6TUQyT2YohYb9PaTB7WDd89MgAqJTN+rEQEXUYzboaR6vVolevXgDQrBuq9Ho9tNrfH6ytUChgNpuhVCqRlZWFr7/+Gu+88w7+85//XHM/l27kulG1tbVNbmssaxivT9yXDbkM8DCVIjOzvEXtkDRd6/giulliHV+i3Amk1WphMBhsr61WK5TKhqa2bduG4uJizJo1C4WFhXByckJgYCBuv/32K/YTGRnZovYzMzOb3FbbyQj8rwhZZXW4NcgD/W/t3aI2SLqudXwR3aybOb5SU1ObXCZK2MfGxuLHH3/E+PHjkZaWhoiICNuyv//977a/r1q1Cr6+vlcNerH4XRzGAYDBYZyVkoikQZSwHzNmDPbu3Yvp06dDEASsWLEC69evR3BwMEaPHi1Gk83m7KSAh8YJF2rqbSdniYgcnShhL5fLsXTp0kbvhYWFXbHeX/7yFzGavy5/NzUMdWYMCPW2S/tERG1NkrN3de+kRSd3Z7jyKVJEJBGSTLt/3RcDAcL1VyQichCSDHvOaUNEUsO7h4iIJIBhT0QkAQx7IiIJYNgTEUkAw56ISAIY9kREEsCwJyKSAIY9EZEEMOyJiCSAYU9EJAEMeyIiCWDYExFJAMOeiEgCGPZERBLAsCcikgCGPRGRBDDsiYgkgGFPRCQBDHsiIglg2BMRSQDDnohIAhj2REQSwLAnIpIAhj0RkQQw7ImIJIBhT0QkAQx7IiIJYNgTEUkAw56ISAIY9kREEsCwJyKSAIY9EZEEMOyJiCRAKcZOrVYrXnnlFZw8eRIqlQrLli1DSEiIbfmGDRuwc+dOAMDw4cPxzDPPiFEGERFdJErPPikpCSaTCYmJiViwYAHi4+Nty/Lz87Fjxw5s2rQJmzdvxp49e3DixAkxyiAiootE6dmnpqYiLi4OABATE4P09HTbss6dO+ODDz6AQqEAAJjNZqjVajHKICKii0QJe71eD61Wa3utUChgNpuhVCrh5OQEb29vCIKAlStXolevXujWrdtV95OZmdmi9mtra1u8LdH18PgiMYl1fIkS9lqtFgaDwfbaarVCqfy9qbq6OrzwwgtwdXXFyy+/3OR+IiMjW9R+ZmZmi7cluh4eXySmmzm+UlNTm1wmyph9bGwskpOTAQBpaWmIiIiwLRMEAU899RR69OiBpUuX2oZziIhIPKL07MeMGYO9e/di+vTpEAQBK1aswPr16xEcHAyr1YqDBw/CZDIhJSUFADB//nz07dtXjFKIiAgihb1cLsfSpUsbvRcWFmb7+/Hjx8VoloiImsCbqoiIJIBhT0QkAQx7IiIJYNgTEUkAw56ISAIY9kREEsCwJyKSAIY9EZEEMOyJiCSAYU9EJAEMeyIiCWDYExFJAMOeiEgCGPZERBLAsCcikgCGPRGRBDDsiYgkgGFPRCQBDHsiIglg2BMRSQDDnohIAhj2REQSwLAnIpIAhj0RkQQw7ImIJIBhT0QkAQx7IiIJYNgTEUkAw56ISAIY9kREEsCwJyKSAIY9EZEEMOyJiCSAYU9EJAEMeyIiCRAl7K1WK5YsWYJp06Zh5syZyMvLa7R88+bNmDJlCu6//378+OOPYpRARESXUYqx06SkJJhMJiQmJiItLQ3x8fFYvXo1AECn0yEhIQFbtmxBXV0dZsyYgaFDh0KlUolRChERQaSefWpqKuLi4gAAMTExSE9Pty07duwY+vbtC5VKBTc3NwQHB+PEiRNilEFERBeJ0rPX6/XQarW21wqFAmazGUqlEnq9Hm5ubrZlrq6u0Ov1V91Pampqi2u4mW2JrofHF4lJjONLlLDXarUwGAy211arFUql8qrLDAZDo/C/pF+/fmKURkQkSaIM48TGxiI5ORkAkJaWhoiICNuy6OhopKamoq6uDtXV1cjOzm60nIiIWp9MEAShtXdqtVrxyiuvICsrC4IgYMWKFUhOTkZwcDBGjx6NzZs3IzExEYIgYO7cuRg7dmxrl0BERJcRJeyJiKh9EWXMvj05cuQIEhMTAQD/+Mc/4O7ubueKyBHt27cPX3/9NZYvX27vUsjB7Nu3Dzt37kRNTQ0ef/xx9OzZs0X7cfg7aDdv3oylS5fi3nvvxa5du+xdDjmgvLw8ZGZmoq6uzt6lkAOqqanBq6++itmzZ2PPnj0t3o/Dh73FYoFarYafnx90Op29yyEHFBISgkcffdTeZZCDGjVqFGpqapCQkIB77rmnxftx+GEcjUYDk8kEnU4HX19fe5dDRHRDysvL8cYbb2DevHnw8fFp8X46dM/+6NGjmDlzJoCm5+O5//77sWTJEmzatAmTJk2yZ7nUATXnGCNqqeYcX/Hx8dDpdPjXv/6F3bt3t7itDtuzX7duHXbs2AGNRgOg6fl4oqKiEB8fb+dqqSNq7jF2yZtvvmmvUqkDau7xtXLlylZpr8P27IODg7Fq1Srb62vNx0PUEjzGSExtfXx12LAfO3asbQoGoOn5eIhaiscYiamtj68OG/Z/dK35eIhaA48xEpPYx5fDhP215uMhag08xkhMYh9fDtMtGTNmDPbu3Yvp06fb5uMhak08xkhMYh9fnBuHiEgCHGYYh4iImsawJyKSAIY9EZEEMOyJiCSAYU9EJAEMeyIiCWDYExFJAMOeHNbWrVtbbSbK5ORkLFq06LrrLV++HEVFRY3ey87Otk1je+jQIZw4cQIAMHTo0Fapjag5GPZEregf//gHAgICmly+ZcsWlJSUtGFFRA0cZroEoqtJS0vDrFmzoNfr8Ze//AW1tbX473//C7PZDJlMhnfffRenTp3CunXr4OTkhIKCAowfPx5PPvkksrOz8cILL0Cj0UCj0cDDwwMff/wxzGYzZs+ejSVLlkClUuHFF1/E6tWrERQUhM2bN+OVV16Bm5sbnnvuOQiCAD8/PwBAeno6UlJSkJGRgfDwcJhMJixYsABFRUXw9PTEO++8AycnJzv/xMhRsWdPDk2j0WDDhg1Yu3Ytli5ditzcXKxduxYbN25EeHi47QHORUVFWLVqFRITE/HBBx8AAFauXIl58+Zhw4YN6Nu3L4CG+UtSUlIAADk5OTh69CgAICUlBSNHjrS1u2bNGkycOBEJCQm44447AABRUVGIi4vD888/j4CAABiNRjz77LPYuHEj9Ho9MjMz2+znQtLDsCeH1q9fP8hkMvj4+MDNzQ1KpRILFy7E4sWLcfLkSdt84REREVAqlXBxcYGzszMAIDc3F9HR0QAaZiQEgICAANTW1uLYsWMICwuDt7c3jh07Bjc3t0ZzkV9t2z/y8PBAUFAQAMDX1xc1NTXi/BCIwLAnB3f8+HEAgE6nQ3V1NT7++GO89dZbWLZsGdRqNS7NAyiTya7YNiwsDL/++isANHpq0PDhw/HGG29g2LBhGDp0KJYtW2brvV9t20s1XGrnWm0SiYVhTw6ttrYWDz/8MJ588kksX74csbGxmDZtGh588EE4Oztf82TpokWLsHr1asyaNcs2XAMAd955J44cOYLBgwdj2LBhSE9Px+jRoxtt++STTyIpKQkzZ87EDz/8YHv/1ltvxZtvvons7OzW/7BE18ApjomIJIA9eyIiCWDYExFJAMOeiEgCGPZERBLAsCcikgCGPRGRBDDsiYgkgGFPRCQB/w/hvuUzgQ+qmgAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1206d02e8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -993,12 +818,13 @@
     }
    ],
    "source": [
-    "plt.semilogx(bandwidths, scores)\n",
-    "plt.xlabel('bandwidth')\n",
-    "plt.ylabel('accuracy')\n",
-    "plt.title('KDE Model Performance')\n",
-    "print(grid.best_params_)\n",
-    "print('accuracy =', grid.best_score_)"
+    "fig, ax = plt.subplots()\n",
+    "ax.semilogx(np.array(grid.cv_results_['param_bandwidth']),\n",
+    "            grid.cv_results_['mean_test_score'])\n",
+    "ax.set(title='KDE Model Performance', ylim=(0, 1),\n",
+    "       xlabel='bandwidth', ylabel='accuracy')\n",
+    "print(f'best param: {grid.best_params_}')\n",
+    "print(f'accuracy = {grid.best_score_}')"
    ]
   },
   {
@@ -1008,32 +834,35 @@
     "editable": true
    },
    "source": [
-    "We see that this not-so-naive Bayesian classifier reaches a cross-validation accuracy of just over 96%; this is compared to around 80% for the naive Bayesian classification:"
+    "This indicates that our KDE classifier reaches a cross-validation accuracy of over 96%, compared to around 80% for the naive Bayes classifier:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.81860038035501381"
+       "0.8069281956050759"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "from sklearn.naive_bayes import GaussianNB\n",
-    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn.model_selection import cross_val_score\n",
     "cross_val_score(GaussianNB(), digits.data, digits.target).mean()"
    ]
   },
@@ -1047,32 +876,22 @@
     "One benefit of such a generative classifier is interpretability of results: for each unknown sample, we not only get a probabilistic classification, but a *full model* of the distribution of points we are comparing it to!\n",
     "If desired, this offers an intuitive window into the reasons for a particular classification that algorithms like SVMs and random forests tend to obscure.\n",
     "\n",
-    "If you would like to take this further, there are some improvements that could be made to our KDE classifier model:\n",
+    "If you would like to take this further, here are some ideas for improvements that could be made to our KDE classifier model:\n",
     "\n",
-    "- we could allow the bandwidth in each class to vary independently\n",
-    "- we could optimize these bandwidths not based on their prediction score, but on the likelihood of the training data under the generative model within each class (i.e. use the scores from ``KernelDensity`` itself rather than the global prediction accuracy)\n",
+    "- You could allow the bandwidth in each class to vary independently.\n",
+    "- You could optimize these bandwidths not based on their prediction score, but on the likelihood of the training data under the generative model within each class (i.e. use the scores from `KernelDensity` itself rather than the global prediction accuracy).\n",
     "\n",
-    "Finally, if you want some practice building your own estimator, you might tackle building a similar Bayesian classifier using Gaussian Mixture Models instead of KDE."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) | [Contents](Index.ipynb) | [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.13-Kernel-Density-Estimation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "Finally, if you want some practice building your own estimator, you might tackle building a similar Bayesian classifier using Gaussian mixture models instead of KDE."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1086,9 +905,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.14-Image-Features.ipynb b/notebooks/05.14-Image-Features.ipynb
index 47ddff7da..a1189fad1 100644
--- a/notebooks/05.14-Image-Features.ipynb
+++ b/notebooks/05.14-Image-Features.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) | [Contents](Index.ipynb) | [Further Machine Learning Resources](05.15-Learning-More.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.14-Image-Features.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -30,19 +8,20 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This chapter has explored a number of the central concepts and algorithms of machine learning.\n",
-    "But moving from these concepts to real-world application can be a challenge.\n",
-    "Real-world datasets are noisy and heterogeneous, may have missing features, and data may be in a form that is difficult to map to a clean ``[n_samples, n_features]`` matrix.\n",
+    "This part of the book has explored a number of the central concepts and algorithms of machine learning.\n",
+    "But moving from these concepts to a real-world application can be a challenge.\n",
+    "Real-world datasets are noisy and heterogeneous; they may have missing features, and data may be in a form that is difficult to map to a clean `[n_samples, n_features]` matrix.\n",
     "Before applying any of the methods discussed here, you must first extract these features from your data: there is no formula for how to do this that applies across all domains, and thus this is where you as a data scientist must exercise your own intuition and expertise.\n",
     "\n",
     "One interesting and compelling application of machine learning is to images, and we have already seen a few examples of this where pixel-level features are used for classification.\n",
-    "In the real world, data is rarely so uniform and simple pixels will not be suitable: this has led to a large literature on *feature extraction* methods for image data (see [Feature Engineering](05.04-Feature-Engineering.ipynb)).\n",
+    "Again, the real world data is rarely so uniform, and simple pixels will not be suitable: this has led to a large literature on *feature extraction* methods for image data (see [Feature Engineering](05.04-Feature-Engineering.ipynb)).\n",
     "\n",
-    "In this section, we will take a look at one such feature extraction technique, the [Histogram of Oriented Gradients](https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients) (HOG), which transforms image pixels into a vector representation that is sensitive to broadly informative image features regardless of confounding factors like illumination.\n",
-    "We will use these features to develop a simple face detection pipeline, using machine learning algorithms and concepts we've seen throughout this chapter. \n",
+    "In this chapter we will take a look at one such feature extraction technique: the [histogram of oriented gradients (HOG)](https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients), which transforms image pixels into a vector representation that is sensitive to broadly informative image features regardless of confounding factors like illumination.\n",
+    "We will use these features to develop a simple face detection pipeline, using machine learning algorithms and concepts we've seen throughout this part of the book. \n",
     "\n",
     "We begin with the standard imports:"
    ]
@@ -51,13 +30,13 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns; sns.set()\n",
+    "plt.style.use('seaborn-whitegrid')\n",
     "import numpy as np"
    ]
   },
@@ -67,30 +46,33 @@
    "source": [
     "## HOG Features\n",
     "\n",
-    "The Histogram of Gradients is a straightforward feature extraction procedure that was developed in the context of identifying pedestrians within images.\n",
-    "HOG involves the following steps:\n",
+    "HOG is a straightforward feature extraction procedure that was developed in the context of identifying pedestrians within images.\n",
+    "It involves the following steps:\n",
     "\n",
-    "1. Optionally pre-normalize images. This leads to features that resist dependence on variations in illumination.\n",
+    "1. Optionally prenormalize the images. This leads to features that resist dependence on variations in illumination.\n",
     "2. Convolve the image with two filters that are sensitive to horizontal and vertical brightness gradients. These capture edge, contour, and texture information.\n",
     "3. Subdivide the image into cells of a predetermined size, and compute a histogram of the gradient orientations within each cell.\n",
     "4. Normalize the histograms in each cell by comparing to the block of neighboring cells. This further suppresses the effect of illumination across the image.\n",
     "5. Construct a one-dimensional feature vector from the information in each cell.\n",
     "\n",
-    "A fast HOG extractor is built into the Scikit-Image project, and we can try it out relatively quickly and visualize the oriented gradients within each cell:"
+    "A fast HOG extractor is built into the Scikit-Image project, and we can try it out relatively quickly and visualize the oriented gradients within each cell (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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3aysyZQG/lWskn89jZmYGMzMzKBQKIuj5zXqTxX5o0Mt1rAW/vo/8oP9ubss2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NCTJ08sboJjeI0kmYHe7XZ1cnJi8phgRZQW5urV1ZUVXGFfqtVqlkWG8bq5uVG9Xtf+/r6Vs47H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gCov5P5TUMGPtSqKn1ANQCEjVto2OypNgFzApx1Op0QplzHquOucx+4a/iuZaeaN9/g/x67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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1023a1278>"
+       "<Figure size 864x432 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -102,7 +84,7 @@
     "import skimage.data\n",
     "\n",
     "image = color.rgb2gray(data.chelsea())\n",
-    "hog_vec, hog_vis = feature.hog(image, visualise=True)\n",
+    "hog_vec, hog_vis = feature.hog(image, visualize=True)\n",
     "\n",
     "fig, ax = plt.subplots(1, 2, figsize=(12, 6),\n",
     "                       subplot_kw=dict(xticks=[], yticks=[]))\n",
@@ -129,16 +111,16 @@
     "5. For an \"unknown\" image, pass a sliding window across the image, using the model to evaluate whether that window contains a face or not.\n",
     "6. If detections overlap, combine them into a single window.\n",
     "\n",
-    "Let's go through these steps and try it out:"
+    "Let's go through these steps and try it out."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 1. Obtain a set of positive training samples\n",
+    "### 1. Obtain a Set of Positive Training Samples\n",
     "\n",
-    "Let's start by finding some positive training samples that show a variety of faces.\n",
+    "We'll start by finding some positive training samples that show a variety of faces.\n",
     "We have one easy set of data to work with—the Labeled Faces in the Wild dataset, which can be downloaded by Scikit-Learn:"
    ]
   },
@@ -146,7 +128,10 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -178,18 +163,38 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 2. Obtain a set of negative training samples\n",
+    "### 2. Obtain a Set of Negative Training Samples\n",
     "\n",
-    "Next we need a set of similarly sized thumbnails which *do not* have a face in them.\n",
-    "One way to do this is to take any corpus of input images, and extract thumbnails from them at a variety of scales.\n",
-    "Here we can use some of the images shipped with Scikit-Image, along with Scikit-Learn's ``PatchExtractor``:"
+    "Next we need a set of similarly sized thumbnails that *do not* have a face in them.\n",
+    "One way to obtain this is to take any corpus of input images, and extract thumbnails from them at a variety of scales.\n",
+    "Here we'll use some of the images shipped with Scikit-Image, along with Scikit-Learn's `PatchExtractor`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(512, 512)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.camera().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -198,15 +203,19 @@
     "imgs_to_use = ['camera', 'text', 'coins', 'moon',\n",
     "               'page', 'clock', 'immunohistochemistry',\n",
     "               'chelsea', 'coffee', 'hubble_deep_field']\n",
-    "images = [color.rgb2gray(getattr(data, name)())\n",
-    "          for name in imgs_to_use]"
+    "raw_images = (getattr(data, name)() for name in imgs_to_use)\n",
+    "images = [color.rgb2gray(image) if image.ndim == 3 else image\n",
+    "          for image in raw_images]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -215,7 +224,7 @@
        "(30000, 62, 47)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -242,22 +251,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We now have 30,000 suitable image patches which do not contain faces.\n",
-    "Let's take a look at a few of them to get an idea of what they look like:"
+    "We now have 30,000 suitable image patches that do not contain faces.\n",
+    "Let's visualize a few of them to get an idea of what they look like (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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BYIBgMAi/3y8PtVQqiefHhAYyp/Y6rcLD6tFa4zWHoU6ng93dXYRCIdRqNVSrVdTr9aGN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\naN1aLoVU6c5abDVq1IkqLy+X80sUZbFkx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/3qV1i3bh3q6+slQCIBlb2zNRfk5Zdfxo4d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s8Oe8J/oKXNXYlEIjbn+Ojo6NIsZL7n+Pj4d86XfivO1n2QfABXjo7BzAyX5++ZWuN+H/cB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\nsN7IfelLX/rSl778/1X6xrYvfelLX/rSl+csfWPbl770pS996ctzl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/3y80C5vNhlKphFqthmazCY/H03fTut1uYR7U63WhDpnZxPV6HZlMBqurq7h48SK2t7fh9XoF43O73eJ6nThxAolEAkNDQ/B6vXI7h8NhOBwO5PP5Psv/rxLV389SvVOgi39zQy0tLeHll1/Giy++iF//9V839Nx/+2//rQQJ+UxSk/g1KpRQKIRgMCjYdyqVEkuN1kiv18Pa2hpeeeUVodUYFbqzVBp8Zx5Evi+Vpp4jzeF2u92IRCJotVrY3t42FVzioS0UCgIBcBya60qLjV6M1+sVC4+X0iBsZMYg4VgA9GH+VLDtdhterxeRSEQYGLVaDfV6XYyTSqWCUqkk1h5hG7OGAJU5GTvakwF2LVueWUI2HJdmP/BnCRloFsrdpF6vi3frcDhkr3COdFAxEAig2WwilUphaWkJ8/PzWFhYkPjN1NQUvvzlL2NmZgYejwfb29t3fO6+CpY3abPZxNGjR7GxsYFarYbt7W3R+gD6rDDtnmnYYHBjm1EodF0ASLIAbxhOEnlpXq8XXq9XNhWtpkAgAJ/Ph1arhXq9Lp8xaGXtJ9lsFlevXsXVq1fFEqhUKggEAqhWq8JrbDabuHz5MpLJJIrFIo4ePSqUHItlh/PZbDaRTqfh9XoB7BDtjcperuPgv6lQBl3NQby81WrhwoUL+NM//VOk0+l9N8ug8JLQz6CrRw4l8UcelFKphHw+j0ajgdHRUUkIGR4exvLyMt544w0sLS2ZZpmQWqSZIhybdvkH54uWSywWQ7vdFiqS5okalWKxKEGgra0tdLtd2XfZbBZ2ux2hUEjwaa5Bo9EQL4xniQqV+8Ysi0BzfvnenCON+epAK9+ZWCwZDXSnCV+YUfbvvPOOvAfd7ZGREYTD4T7PrdfriQVPRcrEHip2XqK0LM3ANzabTZIsHA6HUNA0o4F84Js3b+KNN97A9evX0Wq1kEgkcPLkSUxOTuL48eOijywWCzKZjFC69pJ9FWwqlcLGxgY2NzcRDocFxH/nnXdw+/btPjxEK1NaJoPKFugP7hiVSCQCp9MpJPJgMAhg95Ylfw+AHORCoSDWLZWX3W5HOBzuU7hmFunll18WC1YHuejya8uFwcFcLodMJoPR0VG43W6k02mhydBtI8ZtRsxYEoOHk+tVq9Xw5ptv4ld/9VeRy+XE0jAqtMiAXRhHK1oqPAYTgJ31CYfDqFareOutt2C32+H1enH//ffjz/7sz7CwsHBPmONeQUJeaIP8V+36BwIBWYtGo4FCoYC1tTXhWJu5gOv1umDP3BM+n088LuLL3DtMliGfstVqodFoiEVPtxnoD+QZEQZlqFC5TsREqWCYScVAIC8jZkACkLER/jITP/nLv/xL5PN5CYwlk0k8+OCDMu9knTSbTUkKIWRAhUrGCeeu1WoJP9iocP4Zf+GlUSgUkM1mkUqlcPHiRaRSKaRSKUQiETz11FN48MEHkUgkMDIyIgYVYzu8bPajau2rYK9duyYpc8ViEV6vF5OTkwiHw7h48SIuXLiAzc3NPalb+rbkv+mS0o0zMzkAJP2S1imBe5r7Xq8X8XhccC0+T4Ph5LUB6Dv4RuT27dsCkehAFm99UkBoqXi9XlQqFWxtbcHj8fRx/BKJBJrNpnBBeUEYlTtF4IE747LaTS6Xy3jxxRfxq7/6q7h586ZE/81g0gDEDSbUQCWrrVoqEafTKVY+02X5h651r9cTGo3Z+dCcUR3AHByLtsroLm9sbKBYLIoVxfReM/tUR6Zp4bjdbthsNnlHBhL52VqB050mdGW328W1NmNJA+hLwc3n82i1WrDZbJKqSwXLFGLOUz6fF4XG1FKmQycSCXi9XgkuG5Ef+qEfEiOI55V0NFqpPN+0pgGIB2S325HJZPo8P86LGS+YwUYAKJfLWFhYwI0bN3D16lVcvnwZvV4PR44cwdGjR/HFL34R999/v6xfuVxGsViUTLhAICB7Wl9Ee8m+CrZarUp+P906EnA//OEPI5FI4OWXXxarQ0dzGTSg9ao3ttkgF28upvFxwhjpy2azQu8gJkjGwSCWp8dnZqMA6OMyav4qXQxtldMqicfjcDgcWF9fR6vVwsjICEKhkKRMBoNBCRAZlUHloWUQzx2EDnq9He7vt771LfzO7/wObt68KQeN+etmhBcNLa1BJavxRlqFhUIBY2NjSCaTmJiYwPj4OAKBACYmJmSfmbXYgF2oQPNhNQarXUxKuVwWy5FfJ3xkxsvi87nPqRy4VsRn+XPALvtlL34q549YrFmWCfFdBobp/QFAJpMRL44GEJ+VyWQkQaNWq6HRaMDlcsn6eTweU8kXMzMzYoXSwufZq9VqfQkFnD+d8chLwufzoVarCf5Kz8SoNBoNFItF5PN53L59G5cuXcLCwoLUN5mamsKHPvQhScW12+19AT0dBCPTQF9Md5J9Z6rT6cDj8UjOfDabRaVSwcbGBtxuN44cOSJRTx311TjNYFTXLJYEQKJ0/FxtnekAQbVaxebmJoAdWgVBc+YOc+O0221JVDAj3BykunAj6PRGHhZeJvl8XtgLjJ7r53LBzEaMB61Ebs7BW10rY2Lq8/Pz+J3f+R3ZYO12G+l0uo/8bkT4fB5QsjgYCSY80Gq1kM/nxSICIJCE1WpFMplEr9eTfUbOqlEZtEqJ4+l5phJlZF17WDywfJ9B1odRoctPt5F7n9ajph+RTcNMRM6htr651zhWs8ILjVFwPptfz+fzqFQqAgEUi0VJDSZf1Ov1SoCSa3sv3kWlUhHvT38Go/T8GsfG9eAakVXBCxqAKWPg2WefxY0bNyR+NDw8jKeffhqzs7PCy3Y4HPB4PGi1WkJLbbVakhhBb5jpwjqYfyfZV8Hevn0bpVIJIyMjAICtrS2pvkOM5OjRo3J4aA1Q+fCQaRqMtmrNCN1a4mJ+v7/PfaEVqBWOrtzFDa3dFLP0KL4b0/7oNujIOaOttAr5hxHMQYiECtastcR3pAxaPnsxCXq9Hm7cuIFf+ZVfwY0bN8Qq4RgYHDQqXGMeAJ19xAPNdSuXy32BJVoHDJxsbm7i3LlzUjvArGgmALDLjbVYLKI8NFSlFR33B129e1kLAKLISFHjZ2prDIDAIHRBAfTxUoHdwkgauzYjTBohlZHBaqbMdjodyZajgUJmDdOYQ6EQIpGIWI31el2SdIwKM/p09pe+LKjIuVb6kueZpQWfzWaxurqK5eVl4cd+6UtfMjSOV199FYcPH8b3fM/3YG5uDmNjY/D7/cjn8wAg6feEeXRFM64ndYbmNet0/L1kXwW7sbGB7e1trKyswOv1ivtG16NQKCAcDuPQoUNIpVJYX18XvIgTpl3FRqMhNKl7raxFpUrlzSATI8F6sjQEwOhwrVaDz+eTsmtm0/54cAnS0xIj8O52uxGPxwXzIVNBK1YdDNL/Nip7KdZBpcqf49e73S6Wl5fxi7/4i3jnnXdEuTKBY2hoSHjDRoXJFdpC47O4Udvt9p4MiXa7jZmZGUxMTCCTyeD3f//3sb6+bpp7CuwGMLQ7TkuJWKK24PT88HLhv2lRDlK5jIgetw5m6XRP8k2ZPTXIbODvELKgRW4m2AYAN27cALCjwEgTrFariMViGB4elrKjxGqZoEGlUSwWEYvF4HQ6pdIWaVv78T4HhUpLzz0Vkg48kqNKuIBj4Zxls1lcu3YN77//PlKpFNxut+FCL8AOVPGRj3xEGAy8dPgsl8slVjawmzFKSER7zjq4e7dkpX0VLA+Ixn/0S/PFvV4vRkZGJECi3WZOLiv20MrdDxjeS6xWq0T/aBGx1iiws2hMaSN2lMlk5OYhxkO3gpNnZhyhUEgObKPRgMfjkXdmIYxEIiGHR/PueIAHsVqzWBJFK1StXO6kZKvVKv7bf/tveP311wUf1sRvu92OUqlk6iCXSiXhDBJz1CUdAQiequlAXIeVlRVMTU3h2WefxbvvvtsXzDSj3JiJw/1KCh+hgL042JwzvUYsaaktKjOp1NxL+sDxIOdyOalgxcuYio0UpUajIRczaX9UzA6HA/fdd5/hsbz77rsIBoNIJpMYGRmB3++XTCxaqaQY8vOp0DXeCUA8R15SZs4MDSnizZx3YDdACuwGJnnhae+SQbmhoSHxhuPxuCnvM5PJ4Ld+67cQDAYF96dREQwGpZQmn0/Pl0FJshaIbet40j0rWGIee936VHSkeJDuQTOfB57uASlLxCTvhYpDRTAyMiIuFW8Ui8WCbDYrSjYYDEpmFQ8SK3FtbW3B5XIhFosJ5cuIMILZbDbFtSbd69ChQ1KajXnUhAQ0DKAtJd7q96JkNRQy+DX9f26Cl156CV//+teFYjKIW66srKDb7UrdXSPCzc4sNrr7tB4ZWNDpn8Au02R+fh5OpxMvvfRSX2Fpj8djSpkQR9TQ014MisGvBwIBxGKxPYOCFstOAZQvf/nLhsdBip6mhnU6HWSzWdy6dQsrKytirFCpE6fe3t5GtVpFIpFANBoVb49shHa7jU996lOGx7K4uCiBXovFgqGhIaEHsq4AOaHcpx6PR6Aij8cjUfJ6vQ6XyyVRfDNeDvFLffFRtPemIS7uSQbYWFchlUqhUCggkUjg8ccfx6VLlwyP46d/+qexvr6OWq2GVCqFhYUFXLx4EX6/H6dOncLMzIwEzvl+7XYbPp9PLmvNWOJ5ZT2UO8m+CpYBJM0E0BuYuCuLVBCDa7fbUmuyWCyiVCqhUCigXC7fU3SYfFVCE+T4kctJyIBKv9PpSPSUFhRTF7lBmLViRrERcyUMQjwmn8/j3LlzsFgsiEajGB4eRjwel6LAg4dur0CKWXd0PxlUGLlcDl//+tcFh2TwgN9nrdxoNGoKutGEdI/HI1xG0miI13m9XokWc/90u11cv34dt27dEqXDOTEbgKTi2E/2YlPQowA+yKN1u934/u//fjz55JOGx8F1ZuS72+2iWq0inU5jfX0dqVRKXE9NKWs2m9LhgMkY/CyNXZuVfD4vZ5DeGqE00gip9Dwej8Q1Wq2WWLGMp1QqFfHMzKwNqYnkz1J50lPQni4taP5NDPn69eu4fv06bt++jcnJSXzsYx/D93zP9+Dzn/+84XGEQiFJJmGwmgXVWTOC7ABttBFDp3HFokXaSNwv63BfBcsyazSPC4WCBLS4MWiRNptN4Yxls1lkMhmpZk/MdRCbMyokXQMQrmav14PX65UMr0HFrSOojE5zw0WjUYlcmnGJtWJkkZNisShtOej2sQ7nzMwMTpw4IZHKwapA2lW9FwW7l+XFzc8LZ3t7G7//+7+Pl19+WTYWL00qAU0J4nwaFa4pPQoeHOae8/P0uGitaOiJrhjxbDO1CO4lqn2n36OX9MQTT+Dzn/+8qeASk1z0XufBnJiYQDwe71t3nS5LT4jzRK+QHTnMUgrn5uaQTqeRy+WwtLSEbDYrF4rf78ehQ4cwOTkpl5nf75fasRbLTjooMVGbzSZ8d9YoMCr6fejN8MwRtgN2YR6ekZWVFdy4cQPnz59HPp9HIpHAU089hdOnT2Nubk7OtFHhu/j9fknmoPfCC4TwCbC7T6lHgF28lVREXgr3bMGePn1aMApgpyXG8vIy1tfXkclkhPZChccbIJPJ9CliRt/pNg5GTO8mZA70ej2xOnm7Dg0N9SkLAtfcsAAksEB8kNYaa4QaFWaFra+v4/333++LOhKG0MTjtbU1vPrqq9JX6MyZM5icnITf7+9jEdxrkGuQpqUDXq1WC4VCAX/8x3+M//E//gdSqZQcemJuDL6w3Y7b7TYVwGi322IFMAKt+aS6AI9WHnR/Ncam4Yxeb6dWwf9L0a4/hXOlrWqr1Yq5uTn80A/9EAKBgKn2KMBuSiYtII/Hg/HxcYyNjYnlystMR6yHhoYQj8fFsqIVl8/nUSqVTLXQAYCzZ89ifX1dmED0HqvVqpxNWrZk+ExMTGBiYkKKTnu9XgQCAXQ6HcFvafGZESomVhijIuNzNdtjbW0N586dw+3btxEMBjE3N4ezZ88imUxKBTwG5MxgwR6PRxQroQ6dwEH2gt6DvV5P6hbwQqT+YuCxUqnsq+j3nanJyUnJQLJarYjFYkgkEgiHw1haWupTogD60sZ0gIyEYio6sxYH01tZUIXmfDqdFqoLDzAbulHxrq2toVAoSLSUud2M4JpJUWXBXWKs5HXykNIa9Xg8mJubw/b2tuA9y8vLeP311/HAAw/g0UcfxaFDhyTAcK8yaIlp68hut+OVV17Bb/zGb0ixZQ0LALsVhriBstmsKfePa61hJLpR+r14iAYDYMBuhP1ONKX/V8K9ycuHFjPXjXEDv9+Pv/bX/hpGRkaEP21UaK1qywzYpQyynCPrYfDdAYg3Rt5rPp/H8vIyVlZWUCgUsLq6aup977vvPkxNTSEejyOfz2N6ehrr6+vIZrPI5XJIpVLY2tqSPnabm5sYGRnBM888g16vhwsXLiAUCklrpUqlAr/fj1wuZ+oS1gkEhOhoua+urkrQL5fLCQ3U5/PhU5/6FCYnJzE2NibFd+iu0+Azo+h1lqXL5eoLgPK8aAiRZ1nDSIPQGo3H/c7MviMkEVkHskjL8vl8WF5elkkCdknD3JiM9mu36V6ElCqv1yvEdYvFgkgkIuXGCDb7/X5Z0EHcNhgMCjbIDW5G2V+8eFGIx4lEAoVCoS9KCkDSLFlPkzcc3ZDXX38dly5dwoMPPoiPfOQjmJycNB01H5RBqKDX20l5/IM/+AMsLy/3UX6o6BiQpCegCd5GhemPOjpN/FGPh2OklaKpVIzia4aB/j0z779XMJbkcVoZxWJR0mPZc05fDh/+8IfxwAMPfKDoihGp1+vSUVYfQCpvBjo5Lt0hgDGKYrGI9fV1XL16Fd/5zncEm02n0/jP//k/Gx5LPB7H0NAQAoEAVldX0Wg0MDExgc3NTeRyOaysrODmzZtYXV0V3BEAXnvtNSleEgqFsLGxIfTDwXKgRuSFF15Ap9OR39f4p6bJBQIBTE9PI5lMYnh4WPYRf4cXYjgcFkVnZq/yM9hdF4BQwrjvmJ2lU5mpS7gfaD2TcUPL+E5y11RZAKJkCfCy6s/y8rLQL4hd8eF0bbTL+FeRXC6HQCAgRSlYGb9UKmF9fV2i+prMTfyVFC9OHpUrLwOjkslkZIFp/TGS7nA4JBuEkXgGeHio2Pa4UqngpZdewvvvv48nnngCjz76KKLRqOk5obWqhbfv+vo6rl271lcYg8EMBjwIpeTzebn8zAYhuRfK5bIETbRS1fQbjpfdJ5hsoGGSvahmZuaDB5GFfjSnlXgi9xEtSSr3Bx54AF/60pcQCoXkd8xYSbws9HwD6KPt6UIvrBOwuroqhVbW19dx69YtXLt2DTdv3oTP54PH48Hs7KypucjlcmIIuFwupFIpUQjRaBTxeBxutxvvv/8+8vm8YJpUwLVaDXa7HdFoFMlkEuPj43C5XFL/2IwQx2bSAjtbcI3IKedaacVHI4iXE1NYee6MCmMPVJper1eMCnoV3HeEtgiBstYIWVFU0pohdCfZd/doXqPORXY4HBLE4obmZmQ5w0KhcEdKh9moebe7k4HFzcJ22wymxGKxPjCaVgHxLbIIgsGgtKuIxWKmez8dPnwYdrtdEgiobKlEgsGgkLdZFYigutPpRCgUEmyYXMcXXngB8/PzeOKJJwwXut5v7shxfPHFF+Ug0MpmARMC9QT+SeUhXGBW2A6En62VPq1inXevx6+zdfS73QsuTS40x7MXV5gN/Lg/gB1lNzMzg3/4D/8hJicnAezsIbJkhoeHDY0hkUj0JQ9oyIwBUGC3ZQtTp1dWVpBKpaTYeK1Ww+TkJE6dOoVDhw7B7/cjEomYmg8WziEs4Xa7EQqF4Pf7sb6+jqGhIcRiMUSjUWSzWWmztLGxIcEx1j8uFosCh913332mAm6f+tSnhJVATw7YbTuj9YP2GLiXuH+4P9PpNK5du4ZcLodut4tTp04ZGgdjMDotl94FsMvd59ng9wn7UDl3u12pZ8tg136B0LvWg9W0E7oyVqsVW1tbQoQmmE9AOJvNfgBr5UbnQTRjGTC6GQwGxVqi1UyLmgVfvF4vwuGwUFw6nQ62t7exvb0tbR/IK2TU26i0Wi24XC6Uy2XhELI9OHFgWhw6nddms2FkZES4gA6HQ4qIdzodpFIpvPjii/iRH/kRw2OhVCoVLC4uYnt7GxaLRYIS3/jGNyQQwOd2u11xkQFIOqCmOZmBcajAuAf0YeBnUeHqgAR/llbJXmRtM54FsVO/3y/KbTAAyLkgJEIvxOfzIZFI4B/8g3+AU6dOSaCUwVIzip58TW2V8/LQvGfCV8wcYvZfJBLB5OQkgsEg4vE4otEoRkZG+pSzUYlGo+I9dbs7Pbbi8TjS6bRYZJOTkxgeHkY2m5XUWW1902hpt3eq+W9ubt6VWD8ooVBIzg2wa7lz7/Es6swqzY9le2y73S59sK5cuQKXy2X44gN2IaNyuSz0QhojOrNOJ8YAEG4wA1uEtbTsF0fZV8vxRXVAgAEr3kqRSAQej0fc4jfeeOMDeeccBLOniIkaFWZccBOwYAuJ8RaLRbK8SHnhxsrlcuLOkzjNLC+6i0aFFZ9IMdKkcf1e3CRME11eXsbHPvYxsW7ZbI6Wr45kG5Vut4uFhQX8z//5P/H6669LpXqC9bSIeANTcehSgHSDdCENM+PgrX8nj4RrxHeki65pYnyXwcvYjGKLRqN9fZv4GfxcBj8ZkGXAiS7zD/zAD+CjH/2oEO55KZqtfQrsltnj+PeyzHkO+J4M2upgCpkduoKTGdEBPZ1MQ7aL1brTXcPr9Uo8gWcnHA7LeWOtDwZsWZvZqBDrpjLdi3fMC4kXkS6UY7Xu1HNIp9P4jd/4DeTzeTzzzDN47LHHTJX41LUxiKcSx2XWGvc/GQIM7rdaLXi93j5KVr1eF49vfX0dx44d23sd9hsUtbi2Fuk68f+RSEQW4+2338bt27f7NhUnjJuaE25GmMbGlg022057GPYO0pZboVAQorZOlGg2m9jc3JR6jjolzqgcPnxYoobj4+Oo1+uIRCJyuRSLRTmg1WpVIIGFhQVUKhWsrq5idHQU8/PzUg7OYjFfC7bT6eDChQv4d//u3+Hq1atClaPi5kbgptIFSHhLk1LF71mtVoEKjAotw0EMlX/TLefP0qrkRtYlDrWYVSZ+v7+PZwvsUr+I5/H/nANgx5r6+Mc/jr/5N/+m7BVdV8FsXywqZVp9ehw684dwCS9k4olU/kw4GFQGZrLsuOd5NviZfD+Nj+t0YXZR5WfwvPFipgVoVFhwnGeOFD12KaB3pbMy6f0ypuPxeLC8vIyxsTF85CMfwZNPPikV2IyKDi5yb5K/T6VJncB54PM1/svAJS3vbreLa9eu4amnntrzuftqF2p1UpsYfeZtow/yhQsX8PWvfx3nzp2ThSEWydQ7RubMWmy5XE4OQCgUQqFQ6KM46bJ4LN5C0J5KNJPJoNfrIRAI4NChQ2g2m1hdXTXVgphdCljujOXciOVQsfLWZ7puIBBAIBBAMBhEMBgUJU3ci3CBUfnd3/1d/J//83+wtbUlXELWR+DG4ObgjUxFyJ8nsK+DUIC5SlJMLNFWKoA+tgCwe1B1oItf08qIv0vvw6hoBcb/62e4XC6hK2mr+oEHHsCP/uiPCk2IAZdutyucSTPKZLCYDxU2LxS+K2EBKnoqUGYl8owUCgU5bzp4anROSIGKx+NoNBqyXyKRiLBptKUYDAYxOjoqgdB6vS4BZDIu0um0qbVhNiWrUnE+NLVRKzL+zbPLoOjZs2fx6KOPyt7N5/NSftTofOjiO9wDuimpfr6GmqjryD4Bdrm9LCx1J7lrkKvX60nET2On+ga4cOECvvKVr+Av//IvUSqVZIJIVzJLTB4U1hPlJBBYJoZCJVYoFMQdYoS/3W4LuE+Fw+CcLuBtROhOs/zd8PAw/H4/MpmM8Hyz2aykD5PPSHrb1atX8dZbb8HhcODIkSPI5XKC277xxhv4/u//fkPj+Pf//t/LRcJNSsuIh1VXj6Irz5YowK7LBPQrRDMWLCl4TE3WbrGuNjTIg+UzaWHuhdWbpWnt9fO0iNhBgYrBarVibGwMP/VTP4XR0VGxSmw2G4rForjwVqu5tt1UCPodOBc6mUEXUOLP6ypaxPAzmYxYu2YxWNYAqFQqyGQyktxisVgQi8WQz+extrYm780sJzIFWPyHe55wxerqqqmuFysrK30lQulhMWhGRabdc+5bnnV2g+Cc0UM1y9nm+eAzOUc6LkPFri9EnQGp6/PSwDpx4sSdn3u3QREK4IMHLZx8Po93330X7777rkStaQlQueqDxBvCjAQCARSLRTlEvG1dLhfy+TxqtZqA0bzR6H4TlE4mk32sAWZzmJG1tTVRKF6vF9FoFKurq1hYWEA+nxecl5Qxq9UqMAH7y8fjcSkBx/YxxWLRFBasGRMUblxuIv1ueu45f7TO+POA+cpeDDYymEJrZHp6GpcvX+6LxvIZGtfv9Xp949RfNyODP8935aEG0JdE4fV68bf/9t/G0aNHhafMw8yEFO4Ts3t1L9EYM5XHXl7cXuwK/ceMsPA8y/AFAgHZdzQqyLbRAT16rFQy8Xhc0t3J2TaTZXfp0iUcOXIEPp8Pw8PDwiTQGCz3B6EM7TFrqINnmcaF2WxQvU+4rvQ6+Pde/FquBZW/tnJ5+dxJLD2zu/lADuRADuRADMm952keyIEcyIEcyL5yoGAP5EAO5EC+S3KgYA/kQA7kQL5LcqBgD+RADuRAvktyoGAP5EAO5EC+S3KgYA/kQA7kQL5Lsi8P9itf+QpGR0fh9/ulIDJz+cll06mQ5Lsyi2Rrawurq6tYXFxEp9ORCkEkMv/gD/6goUG+9tprfVw15jbrRnkkCm9vb0sZPBYA0RlD5LMxg6vVauFDH/qQoXFEo9G+Ih66bYSu90kOn660pQnVTLtjgQumrKbTaUPjIMGfdSjb7baQwp1OJxKJhKQiMs3RarWiWq0im82iVCr1pbKSyM6EkKmpKUPj+Jmf+Rnk83msrKxIxg/Xn3zXvd5d80A1Z5WpvlzLlZUVQ+P47Gc/K3xrksKB/jbYgxxbXRtAc1Q1x5HJEn/+539uaByHDh2SxAKr1YpoNAq/3498Pi+5/qyLqivVNZtNjI2N4XOf+xysVqt0EiiVSuh0OkJkf9JEf7Af/uEfljTs+++/X3imiURC1og1lBuNhqS///mf/3nfPjx27JjUZDh69KjUlP2+7/s+Q+P47//9v8tZ1RzbweLvOrmAKamDHGCuIde61+vhp37qpwyNgz/HuhTkaFssFoyNjUkyktvtxvr6OjY3N6UIuk4jZhW8tbU1WWO73Y6f/dmf3fO5+yrYUCiElZUVIZOHQiFJOiDJV5N9md3A+qjMdIpEIrh8+TJSqRRGR0dN594Xi0VpfGi1WvvquOoUUKvVimQyKZPPosLs1Kk7dDLX22z1qEFSPhedSmWwRqQuCcjNpAnLZjKnKPl8Xpou6swXoD+zi8kWgUAAbre7LzOF2XC8rO4lCeTLX/6ypChnMhm88847eO2113Dt2jU0Go0PlCdk0kqj0ejL+mLGnSbXm5kXHuDBGqF6DXiZaAXKfaIreg3+jhmaOJ8/NTWFv/k3/yaOHTsml9s777yD3//938f6+nrfuNvtNvx+Pz75yU/2VY1jcRHdSsaMUHnE4/G+pBqmnfMCZAori2IPDw9je3tbElHYvw7YqefKIvxGRSeY6BR7fcEPXnK6joP+DL0WZun7TBdmklC325VegUxt51rwYvV4PEgmk5IMVK1W+7Lher3drtl3kn0V7MTEBJLJJAqFAhYWFrC4uIiRkRGMjY3JpDMLQysRnU7W6/UQj8fx2GOPiSLiDWJUWIeAB4G9vZijzVY0rH1AJToyMoKJiQkUCgUUCgUp9sKCuntlpu0nOk2YN6lOvxysdcqf4bi5yQer9pjNXmJ/JV42Wln1ej1pg8OiFOVyGU6nE61WS7JyWCuCqayVSgW9Xs9Uk78333xTCpzYbDYcO3YMTz75JF555RV89atfRS6XE8XAuha0uvU80bPgBTZYn8Co6Hx2fSgHU3H5HF3DAthdQ45hMI3XyPM//OEP40d/9EelgEm1WoXf78dnPvMZnD17Fr/2a7+G5557DsDufrrvvvtw6NAhySRbWFhAJBJBJBJBqVTC2toaxsbGTM2F3lPc56yrwH5o2uq3WHaq/euml9wjw8PD6HQ64vmYaRkD9JeO1Jls/P/gz/LZdxOz9SqoHHVGI9PE6Z3rvcP1oDFD5c/6utorvpPsq2Bp4VgsFjz44INIp9NYWFhANpvF9PS0lDvjxm42m5Lrzr43vLV0zUXAXD8dwhPVarWvrS4r6HMiBnsucRJisZiUOGw2m7h9+zbm5+elPqwZ14ubEthNOdWuMF0hXb2e49OXBBeaC2bGQuEz97ICuFl02itvWH6t1WqJ+8nap1SwLGZhRFgzlFCJxWLB/Pw8Hn/8cZw8eRL/4T/8BywtLQmkQitZW5LAroLlARgZGcHnPvc5w+PQVi8VM9eIz9UHi9/X9Rt0SUVd4MOMjI+P4+///b8v70YrPhwOw+VyIZlM4gtf+ALefvvtPqvnyJEj8Hg82N7ehs1mw/DwMLxeL0ZGRsSYMGux8X1Zt5i1MZgm63a7+5QlO9hS+eoaCalUCrFYTGoJDw0NGR6HvlDvpjS14tWGD88110mfP6OyuLgo7jzT1Acv8cFiOjRIut2u6DmWPGWRo7sZAvtqOW4CHoRwOIwzZ86gVCpJa+54PI5wOCxuni7moMsWsj1Er9fDzZs3TRXRqNfrssk0FquVHQ8xbxPmPHe7XZTLZaRSKWmASCiDBWOMii5UbbFYpPCLVmbMaaeCpcLT2CuhgU6nIx1IzVT14rvxdwnh8LMHLUHOkd1uFzwJ2IEadDtni8Viyv3r9Xaqk9XrdXi9XnGhstksotEo/tk/+2f4+Z//eWxtbfVZMRpm0Q0IAeBzn/sc/vpf/+umFIre5Brz1paFzkXXip0XFNdPF8zhGI3K2bNnMTk5iYWFBVFGiUQC3W5XGm8eOnQIp06dwltvvSUKjE0iiaETvmKh+Hg8jkwmY3gcfF8ASKVSUjWK9YnpTbGMI9fGYrEgl8vJ/tGXgD4vZrwcrq/G5Ae9vEEPTlu5+nwP/p6ZPVKpVKR2NHvQ6c/mPOi9qeEKGkg0iqiP7jaGfRUsWzHTFabiGB4eRrvdRi6Xw+LiolTMCQaDYlGyTistJtbltNlsOHnypCnznpgte/HQMqXbOVhrkzjKW2+9hffeew+RSASjo6OYmpqStjN74Tp3E8ITVPQacNfl+vTCaCWnIQ4ql06ng3A4jPHxcVPj4ELrqvO0DqlENXRAxaGVPbCzkdiFgRa+UfF6vchmsxgZGcHQ0BDa7TYWFhbQ7XaRyWRw6NAh/ORP/iR++Zd/Way5wUr2fJ/p6Wl8+ctfxuzsLDY3N00dYt3ug/NLb4lzbLFY+joUEGIBPhho4c/QuzAq8XgctVpNygDqts5sVQMA09PTeO211/ounKGhIayvr8PpdGJychLZbBahUAjj4+PSt8uMEGdvNpu4cuUKPv7xj8u7FItF8STZS42lEpeXl+UyajQauHDhgvwuA6VmLh3WSGbcxOPx7InNc1/uh8FrJWvWouf8aw+PY2KlQJvNJu+3V93qWq2GdDotRaV6vV5fN5C9ZN/dw8o2dMd5E/Ewu91uzMzMoFarIZPJYHNzE51OB8lkUuq18nd04zcApiylQCAAi8UiVh4DB3Rx2eai1+thdXUVV69excbGBhwOBx599FHMzc3B4XBIdX8eOH3IjQgPsA5sDYL0+tBq65UWGl1yYPcQJBIJzM3NGR6Hdqm73S5KpZJ4DGwqN8hm4O/QaqP1zbHeS7BtZGQEIyMjotgDgQD8fr/Ux1xeXsaZM2dw4sQJnD9/Xiz6QbjkyJEj+Bf/4l8gEAhgeXkZ1WpVylManY/B4JwOWnHdWJWJ60UrlftzL6jGzEFeWVnp81wYceZFzLWn4ub+e+utt/Cxj31MArS8fAk19Xo9JBIJw+Og8F3eeustuFwufOYzn8Ho6Kh4WTq6XygU8Pzzz0uzQ87f7du34fF48Mgjj4hSIQxoRAghDV5UOtBI0Vj8fvNuVrkC6GtrRKExwi4kvFQA9HW30M+lvtFNEO9ZwfKm4QewUDCtJx4sWq+HDh3CxsYG3n33XbEarVardHblJLPdglGhNUDloV3gTqeDra0tLC0t4cKFC4jH4zh8+DCeeOIJwUQrlYpsNn5OvV43XVNyUKlq7I6io6XaLeXm0YWo3W43xsfHcfz4cczMzBgeh7age72eQChU5LTa9OXBzcSDXa/XUS6XUalUhKWhG/YZER0EoiJzOBxIpVJiGTebTTz11FO4ePGiXMpUsu12G5FIBD/+4z8Ov9+Pra0tFItFjI+Pm7r4dGtsjbVybQkpacuICp7jH6QNcc7MHOZ33nkHlUoFw8PDyOVyQhF0OBzSBLTdbuPSpUt91uvFixexvLwshsTa2ppYVMvLy6Y7TQC7baq5D771rW9hYWEBn/70pzE0NIRqtSrFoi9evIj33nsPtVqtbw/QAr906RLW1tbwwAMPIBKJSBDViLC+ri55ym4junMA96L2rAYVrVl2iRZ207VYLLh9+zbK5bIEx2kM0ejQdWu1frDZbJiYmJB2PoxZ3HOQi9pdWz7ValV4k5wwHm66Oo8//jg2NzexsLAAj8eDkZERAYkpZg7y+fPncfbsWTgcDpRKJQmmXb58GWtra3C5XDh06BC+/OUvC52LFAwukq5Jyoip2SixpmBZLDuV8qkoqFRpLWqqieYMa4pWOBzGyZMnMTMzY8pC0bemdrO4eakYdJFmfSkAu0WxOU/kKJop7Ey6FS/dK1euYGRkBJVKpa8H2qlTp6TIM6ECzs9HPvIRPPTQQ1hfX5ffI6/XqFCJ0CLmPPMZeq50UJC/x1buOpjC3zMT6FpdXcUf/uEf4gd/8Af7OM6JREKahN64cQM3b96U/WC1WpHL5fDrv/7r+OEf/mEMDQ0hkUggFotJw0CbzXZPbd31pdFut/Huu+/i/PnzGBoakq7HxWJRrFZtmQ0GktbW1rC8vCwtYP7RP/pHhsZQrVbFOtQGCnULXXGuBcc6qFx1kOtehOwhnhHNniBkR265Dnxqy5sdFNi9eJCnu+dz9xvUV77yFdx///146KGHxPUjcZ2KQmNcbAvj9Xrh9/sxMjKCTCaDjY0NaX8cj8fh9XpNWSi/+Zu/iQsXLsgBSiaTGBoawpkzZ/DMM8/A6XQKxUI3IuREcjLpDvV6PTnIZm5ErVwHXU2tvPTB1JuKi0Wu49GjR3H06FHTfcq0RU6cjf/n5uB66CZthUIBxWJRXFan04lYLNbXUtnMutCL6XR2OuPSpT169Chu3bolVkI4HEYymRRskvPj9Xrx9NNPi4tP6hBgjmUyNDQk3X2B3ctEk/kHlSewi2VrPrP+HmAeo/+1X/s1JJNJnDlzBqurqwiHw9Jl+aWXXsLv/M7voFKpSFQf2NkjL7zwAtLpNL7v+74PMzMzfcyTarWK69evG27rDnyQn84LuNVqCRdXGwx8HoX4NeeGZ08Xvjci6XRa+LUaLqRyjUQi0lIqFAphe3tbmEJ8jqZNacVv5uw2m03k83kJKtpsO90rdLdjSrlcRqlUkoaufE6lUkE6ne5rsMlGmneSfXfx448/jrfeegsvvvgiPvnJT+LDH/6wmPTU+sSu6HpyMbggTqcTJ0+eFB7q7du3pS2xUfne7/1eHDlyBKOjo9IAkRgkrTAuCuEE4lxUNhpnITZKq8WsaPeOuIyGLfhvjkfzZWkxHT58GE8++SRGR0el26xRaTQa4t5QMfOwVqvVvkQHbqBqtYpSqSRN69gexOFwIBgMwul0ikI0KhrPjcfjYhVFo1HpieZ0OsXrIfeUbIZAIIDh4WHJvCO2nslkTLmhsVgMU1NTKJVK0petXC6LW07lSo7soOLVQVJt9Wqvx6jk83n863/9r/Hwww9jZmYGLpcLmUwG7777LhYXF+Vi03EAKovz58/jxo0bOHHiBOLxOBwOB7LZLBYWFrC8vIyf/umfNjwO7d5qqtog/UkrKQaBeCZ0rzl6HZxLo1IoFORzgP7mg06nUxKY2Jm5Wq1KC3qg3/vQyn7wQrybtFotMRCpw9idRBtMNMbq9bpYqRoXpxcWDAaF+XHPCtbr9eInfuInsL6+jnw+L9YKrTUqOqt1p3dRJpPB+vo65ufnceXKFSwvL+NDH/oQfuiHfkh4fUyhNdN2gpE7HlpgF4ckb09nNGkKxqDLOz8/j+effx6HDx8WRWBUmC3ETcZNq/+tXXagv7spD7DX68Xs7Czuu+8+pNNp08E2kp81hYR4c6/XkzRZKhjOj9PpFFy8Vqv1keyJt+2XlTIoZHJo963dbuPcuXOS/qktUx5SKlpGYxl5Z8ZMoVBAMpk0PI5Dhw7J5UkMMZPJYGlpCfl8Htvb22g0GrIXtHWkL0OtOLjXzXJh2Ybl+eefl4QCzr+2wMiV5XN5KVarVbzxxhsfsKDNBnY0k4V7VCskTc/ieIBdq55f4z7TBoQZWVxcRCAQwOHDh+HxeORip+HBuAoD2Oy3RQiL49dzMMiHNSLsQs2L1Wq1ipHHi8bj8UhmFtO+CUGSLxyJRFCpVPDqq6/C4XBgcnJyX+9zXwX7ta99DQ899BCefPJJMfFbrZYMtF6vI51O4+bNm7h9+zay2SzGx8cxOjqKpaUl/K2/9bfw8Y9/vG+xA4EARkZGTB3kH/mRH0Gz2cT29jauXbsGYIcSw5uPCoy3oyaz08omHBAKhXDkyBEsLy/j2WefRbPZxBe+8AVD49BwCGk+DBzRMtDWAv/oiLbD4UAikcD09DQ6nQ5yuZxpq4BKlMyOWq0m1hv7GulDQdfM7/cjGAyiXq8jlUohnU5LeqCmrhkVzruGSqrVKoLBICwWi2w8n88n3E7+HqPL/N1yuYz19XUEg0GcOHFiT9ftTkKrnRZRIpHA0NAQhoaGUCwWsba2hhs3bkgPMSpOHfji/uB70Msxc/HpNHGgHwPVGX3EVfnsO3GkuY/4NTPCcWtlyXfSypUW/SD/V3tcOmhqFgfd2tqSgKq29ng26M0Q/6f7rgPEe1mz+v9GpFKpIBwOi75gcF57sFT4tK4LhYI0jKQ3xhoR165dg9frlZTZO8m+CvYnf/InZTO2221xI69fv47V1VXB1CYnJ/HZz35WAhyFQgGPP/54H2Cu231rrM2IEGpg6m6n0xHIAYBgOTqvnhYD+bcMsj3//PM4d+4c4vE4Hn/8cYyMjBgehx4PZZBVoKPq+sByU1qtVoyOjmJsbAzZbFbampuZD334er2eWH50aXkjE4fmGMrlslDbdIKEppuYgSo8Ho9EYXVQj8qVFiO77fJw8+9cLoetrS1EIhGEw2HEYjE4HA7JnjEqhw8fxsbGBkqlklis4+PjUjuD+25tbQ1bW1t9De40Xq5d6nuxXgEIM0UXJOHf+rN1sE0fUK14OK9mrTUAsi58J31h6MQKYJemx3ni13jutYI1OycaQuQF02w2hcpHOh73KjOktHU96BneS6Cr1Wphfn4edrsdU1NT8Hq90rWXQsWqx+52uxEIBBCJROQyiMViOH78OOx2uzSTvOP77zcoWhnpdBrr6+vY2NiQ7JTZ2VmMjIzA5/P1UV7sdjuGhoYEJNe4F01/szUAtJIgKTgWi6Hb3WlLvLGxgdu3b8Pr9WJoaEja8XIjMaK7traGW7duIRwO49ChQ/B4PJicnDQ8DipwRtw1bw/YjeTTUu12u0JEppVkt9sxMjKCZDKJzc1NSc4wMx9ut1tcaY21sf98MBiEz+eTwB4pKFR8HAuVD5NBzFqwtOA5hkajIenH7GTqdrvxxhtvyGXIeWq32yiXy1heXsb4+LhYGKurqyiVSqYSL1ZXV5HJZGCz2ZDL5SToGYlEpGAJseFXX31VFA0PLZWZZoEQn3/qqacMj4OwGS9LBiB5+QwGnrg3tRLWnhDX5l6oSTq5hJ/HtNvBrCqdoagNAkIEDA5z3swYA2Qm6DoATHsnh1sH25iyPWjp8t/3Ks1mE+vr6+j1eqIjCoVC3/mMRqN9ypJJCMSJuTahUAizs7OwWCzw+/37nt19Z+r8+fPY2NiQ7K0zZ85gYmICwC7PTicMkEdHK0hz6rhY3MCDxPD9hIoM2FW2xNxcLhcikQi2trZw8+ZN5PN5BAIBDA0NIRgMirtBCtlP/dRPIZ1O9+VkGxUqIs2iAPozgLhRND1LY47RaBTT09MfKNJiZvNQwdZqtb7xWCw79DNuHB5aEtwZ7NNBQo5rEKMzIoVCQaAIXmJerxfNZlOyw3q9Hl544QU5tHrtGVl//PHH0W63Zd3GxsZMkdlLpZJkTI2MjGB8fBwulwvpdBrRaFQgBAA4ffo03nnnHbGmCHtR+DWn04nPfOYz+PCHP2x4HPxM/X5871gshng8LlmG7XYbqVQKuVxO5kQHZfVeAswrF3oqFKZ30iKlMbCXoiXFjcFUWq78GTOwCbFXWnr09DiOcrnch4PruAFl0AWnLjBjlOjkFavVKjEhpg+Tm0+ePbBrsJB1BKAvWYWB2f102b4Ktt1u4+mnn5ZyaZoSRAySf4hdaNeYm55f52EEYIrnqLPANEhNLJTZFUePHhUObDqdRqVSgc/nEyubbkosFkM0GhXL2qhwsxFu0LfrIIldJxpQwVutVkQiEcRiMVSrVcGdBgnNdxMqVzIJeJB5cCqVCsrlsszVoJIn1sSIOwC5FOm6GRG73Y5KpSJWSD6fl8t4dnYWTqcT6XQat27d+gCbwmazoVar4dy5c8hkMgiFQsjlcsIMMcPHnZqakkIeLpdLiP6dTgcLCwvyWbQ2dMlE7YZrt/iRRx7Bgw8+iEgkYngcOsoN7Mz71NQUTp8+jYceekiCPGRblEolLC0t4bnnnsONGzf66GoaJ+W/zQjXnAqflrROSR2kaXHceh6sVivGxsZw4sQJjIyMIJfLYWFhwfA4jh49CqfTKTgmz4g20DTJv1wufwC+ouh/m4UJqtWqUBSt1p3aDLFYTJI5crmcKFhg50IrFouSik6ut1b+Wu/dSfbVLsyG0ouiQXluVmIsFHJRiftRkTBd1azo8oJMbKCroxeBytzv90v9x+3tbayvr8NmsyEUCslBXFtbw9LSEux2O+677z5D46Arr4nIXOhBRauxNX14fT6fpBETnshms8LhNCKZTEaoTWRwcCOw3JzOtdfKm/g0IQ6uFa0vMxefy+XC2NiYWKypVAqtVguJRAKhUAh2ux0vvvgicrmcQAm81HjZbm1t4U/+5E/wpS99SbKfzFLndH1Tl8uFzc1NbG1tIRQKyR5gBpnP50M8Hsfi4qK8s54jBgM/8YlPIJ1Of6DC0t2E3kyn08Ho6Cg+9rGPSfWpQqEg56BarWJubg6xWAyTk5P49re/jRdffLGPCkXlR4zcjGi6mU6qicfjeOCBB+BwOLC0tISbN2/2BQl53oGdgPQnPvEJDA8Pyxk3w8UFIMYVIQoqI31utaHAQJi+YPYKapm9cEgJ7HZ30oJXV1eRSCTkHHDOOFcAhPPf6/VQLBYlwckMnLbvTi6Xy3L7828d8eTNROtIY41UgINKiTxQs665DkTwFtKuBhdEm+tMegB2LOatrS2USiU4nU6Ew2H4/X7DVfMBYHZ2FqlUSm5CYBcj0gEtjk8fEH7d6/XKu9NdJK/YqNDt9Hg8wqmkomRwaxC+4KagRV0ul1Gv1/uyeDQ/1IhUKhWxGkOhkASsWDj6/fffx9e+9jWJrHPO9CFqtVr4vd/7PTz88MMYGxuTnzFTbY0UnGq1ikajIRZ1vV5HKBQSvmM4HEYwGMTQ0BCWl5fl9wcvoPvvvx/AzgEzQxfT7ACv14tHHnkEMzMzgk9TSRWLRczOzvbN/+c+9zm02228/PLLEuTSF0AgEDA8Do6F56bdbsPj8eALX/gCPv/5z8Ni2SkYnUqlcP36dXzzm9/E66+/3gffOBwOfOELX0AymYTNZhOPqNVqCUxoRPge9LA0k4AxAu6DQeaLVrB7KVczXh8pnu12G5lMRuIXwWBQLi/t0XHsHo9H2D7k6Npstr6ynvvBN/sqWFoFpCJpHh/Lqv3BH/wBut0uzp49K26hx+MRLIO/z+gpsVMzOA4L/Xo8HnF3uAjavda0E46Tbhut2mazicXFRVy7dg3RaNRUDYAnn3wS3/zmN1EoFPoUq3Zn+Fxa25oCw5/jgrJUoFlMGtitgaovFU3P0nQfbm4+i8pVFzchzmTGUhoaGhKPhNYeK51duXIF/+k//SexzDUcwzXhGDOZDP7Lf/kv+Ff/6l9JnQgzCpaVkiwWC0qlEpLJJKxWK7a3t1Gv13HixAmUSiWsr6/DYrEgmUzKz3M8nFO73Y65uTk0Gg0EAgG88847ePDBBw2Ng3NstVrxwAMPiLVHXJoXHQAJrrhcLqnD+qEPfQjvvfeewDS8HHu9Hh5++GHD80HhGUskEvin//Sf4sEHH0Qul5OAYCgUwtGjRzE1NYV4PI4//dM/lXV64IEHMDk5KQVrXC4XhoaGEA6HTRlHJOTTkgX2ZlIMJnkMBvi0ktVWrVGJx+N98AQAOYs8A/w6A3udTkc8Dq5doVCA1+tFPB5HLBbDyZMn952PfRVsIpEQRcHDrAs0OBwO3H///VhcXMQ3v/lNNBoNnDx5Ek8++STGx8cliq6jtUB/ypsRoWuhC0drVsKgO0UFrp9BOpPFslPQORaLoV6vY3t72/A4PvrRj+L8+fNYW1uTeeFna2oYNwcteYq+ycmu4GKagU6oEKiIarWaFO8A+sninC9aQ3THiHkxgMFsKzMK9q233hJceWpqChMTEyiVSvjOd76D3/7t38b29jY8Ho+UQuQc8UBpRsl7772HX/7lX8aP/diPYWJiwlT9U5/PB7fbjdXVVVQqFQkmrq6uolqtIhAIiIGg04q5BprUT54y94eZql7a+jt8+LCUARwdHcXt27flWUwoIFTAi9FisWB0dBQLCwuiRLrdLmZmZgxztSnETwHgmWeewSOPPIK1tTVks1lJVKG7brFY8MlPfhKvvvqqvO+xY8cEYyfsxsvQTEcDkvk1ZqrjF/xDS1vP5V44670oVwAYGxsTqhUL9xNqo+LmOczn88jn8wgGg0L/BHYUby6XA7BjEU9MTOCjH/0ogsHgHZ+7r4JldR8+XGdtUcEdOXIEJ0+ehMViwTvvvIOLFy/ia1/7Gs6cOYOTJ08KbYcYCzO53n77bfzMz/yMocnRMAOF8IB2icn905lWmvKhsVPCFGZcr+npaZw4cQLXr1/vS8HU+LSGBjS5mwecqakWyw43mDVuzUAEmn1ABUvLg5b0YKYXrVX+PJWsTtAw61mcOXNGqndls1m88MILOHfuHC5fvoxmsyn4la4gxnFTaL23Wi288cYbmJ+fxxNPPIGPfOQjhseRSqWEleHz+dBqtVAsFjE6Oor33ntPgm8OhwMPPfQQ/vIv/1KUO4V7xO12IxaLSQq20QaQAPDggw/i4sWLQvdJpVLCH9cxC65BOBzG8vKy1EwldKXpWm63G08++aSpWhUUwkhPPvkkstksKpWKBGktFktfgRu/349EIiFeFfH1bDYrSSjb29s4fPiwqVKjPJs68s+LA+gvVqShrb0ggr8KTYuXCY0MBjlZrIkYca/Xk8A4x6rTa7UBVy6XcfPmTXg8HoGVBmVfBXvp0iUB4i0Wi6RXUrkBu3iI3W7H2bNn8fDDDwuVwW63Y3NzUzi0m5ubaDab8Hg8pqKztEz1gRjEC9mjq9PpfCBLjJYZLTsdOTWzcdnR4cUXX8T6+rpYz5pBMLgZBkF9uloMLDD4st8tOCgbGxt9EWKmHjK9j4wLRo65oak02OSNipdULrNZOr/4i78IAMIkKJVKgrFryITKjMEtPU+09HkBbG5u4qtf/Sq+8Y1v4Id/+IcNjcPlcmF1dVWK5mSzWdTrdakDwHTobDYLr9eL1dXVvsuE1j3nkLGHSqWCWCxmeD7Onj2LS5cuSV1cRqKLxSKCwSD8fr90LWi1Wtja2uqj6BWLRdnH3KMejwcnTpww1cpHvxMvcr0XNFVSpxjTQGi32ygWi9K/jannpK+ZSS/n5+tgKqlxGkPVtUIGWRTA3jinmUAXA+ydTgfZbFZqXdPI4fw0Gg1MT09jYmIC6XQaGxsbsNt3OoYwDkWPb319Hd/85jdhsVjuTcEmEglcuXIFnU4HIyMjCIfDSKVS2NzcxOTkJEZGRmQhNRl5e3tbai6Wy2WEQiFMTU3h/vvvlyixGYuNzxgMRvBlWeyFeCM3g86C4WbjJqPVZGaRPB4PZmdnpY2xDiRxc2ruq76piYsyz5ktxa3WnRoCZixpAu20yjSWqgsCU2itsmp9tVrtK+pB+km32+37vbvJ5cuX98TINKas51dH6vU66awlfo4ZVkU6nZaLc2hoCOl0GvV6He+//z7Gxsbg9XpRKBSkU8DCwsIHCp3w+YSNZmdnYbVaTfFxyZygYqLioudC42J7e1vSlhlEY3Ea7bJarVapom+WzcDLI5fL4YUXXsBTTz0luDPPBV1/XkobGxviqp87dw6f/exnEYvFsLm5iVQqJZeOGeNoMEDFr/Ei11aiDurtZ63eqyVLxT06Oip7jNYsce+9oAl6F2Sg+P1+Sen9KxXcjkajCAaD2NzcxOrqKhYXF5FKpTA/P490Oo3JyUl85jOfwcTEBOz2nWZi165dQ7PZxNTUFGZnZ+XmrVar4i7S8jMqmltLxaaVJ60kjX0S36QboHFI/VlmxGLZ6TOv0yAHFchg0Eu7OeSKplIpPPzww4jH432RZKPCDDtgF/8lpspILd+PX6flSkuCtSSA/kQOM5FZraC11cF/0yqk50BXU2PE5MayMtggQd6I0EKz2+2IRqMolUpwu904c+YMwuGw4OxTU1P4sz/7M7lYNYWInggvolKpBL/fb4pFwMh0KpXCrVu3kEwmpdh8tVoVq6jb7WJxcVG40Nq72NraArAbsG21Wjh//rypyD2AvnPyta99Dffdd5/8n0wW3Zj0L/7iLwT37vV6uHDhAn7rt34LX/jCF1CtVpFMJjE7O4tKpSJQwV9FeJFrT5SK8057UBsy9yputxsPPfQQfD4fXn31VYFo8vk8IpGIGDrEaTlG1rg4fvw43G63pOxvb2/vq0fuWnDbbrdjfHwc4+Pjksd99OhR9Ho9PPfcc/hP/+k/4ed+7ucwNjaGYDAorSVojVDZUfFxMGZymrVlCOy2bmDk22KxiJtM2haxWGAXYhi0kJxOp6loNd1q3bKGohUt8Wmdu033fXNzU1x84q9+v9+0u6Pnk4qUVnypVOrbrMxI4Rio8KvVqsApnCuzClb/vGYHaIXLrw9eNpoLqbPJqPSMSrvdRigUQjKZlIpn0WgUDodD6ovOzMxga2sL7777bh9OPwh3NZtN/Mmf/AkeeeQRjI+Pm8Jg33jjDQA7e/vNN9/EM888IxH48fFx1Ot13L59G/F4HCdPnoTX65XiReVyGYuLi6hUKn24Y6fTwauvvmqYyUDRWOatW7fwK7/yK/ilX/olhMNhOZfhcBjlchm///u/j69+9at9kFev18Pv/d7vodls4tOf/rRYcFarFc8//zwee+wxQ+PQTJfBM79XwGow+HWnzzSrZHmJAbsGAc8OMw7ZVoZpvIxV8FLhpeVwOBAKhSRQtp/sq2AXFxfRbrcFHK9UKlhbW8Pi4iJOnz6Nxx57DH6/Xwo1aOI9scBut9tnKTUaDbz66qs4d+4cfv3Xf93Q5BCPYkBFY6l0xehq8zn8PS4quXY6A42Kyah4PB7JTgP2vlG5oTgmAvdU/KVSCdvb29ja2kKtVsPIyEhfUMOIUDnoQiu0XqlQic9yoxAWAHZrARQKBXGNdIDMqHBuCYVoPForUv09nc3D5/H7VK5U0mbGQe4pky1IvwkEAvB6vbBarfjKV74iHXCZ2ktON614q9WK5eVlrKys4G//7b+Nl156ybAyuXDhgkACrAHLmMTa2poozO3tbWnlzX1N6EJDTZyPQqGA3/7t3zYcFOac6D117tw5/Mt/+S8l9Xd7exsulws3btzA66+/LmdMQ1vNZhO/+7u/i5dffhknT56Ey+XC0tIS3nvvPcO1afX543g0tqohAb0H+DX+/OD/zVzAwI5xxOJUrPDFTK2RkRG43W7k83lUKhVsbm5KY0iXy4V8Po/bt2/D7XaLVz85OYlqtYq1tbV9dci+CvbNN9+UzCfWCp2fn0c+n8fIyAg8Hg8efvhh4b1yo9Md5IbSB9ftdmNyclJubqOTo+lPLPiirWHNi6XbxYNLBURskCC72YyhaDQqueMaT9SkcO0C61uTG7hQKODmzZuYn5/H4cOHJQBkhluoF3SvG19j0RpbZACFQQxN/ObmNjsnXFteJNoi5bPIidaFl7UFyYAjcWB+zaiwCy05ivxsh8OBSCSCdDqN//2//zc2NzcFN9PBP70/uccYNzCT2UYrjRfrjRs3UCwWcfbsWbhcLng8HkxPT8vPsZbDuXPn8Pbbb/dF9YF+vujNmzcNjwPYpWlpj+CNN97Ayy+/DKC/gScJ9TqmwPfp9XpYWFjA9evX+6iaZkVboxpzpQyyBPayXrWCNcsqYBCLKdos8M2EI6fTKWeCBg+r8PGcaGOJ+vBunO19T9Pf+Tt/p+9FdPCB/YKKxSLS6TRSqRTi8bgU2uANyCweKsOtrS2cP3/eFN9Suy60lDVBmBtRW0yajqXdFCqie9kkDBJRaWoloK1iYBfIp8KidVmr1bCwsID19XWcOXMGVqsVPp/vnvrec6Ppm1+v1yC3sF6vy2Ypl8tiKZEby6wso0LFpHFMCq1kYNej0BxYrg8vJ+LGWvEaFSYSbG1tYXp6GrFYTDo3rK+v4zd+4zek7bUOyulgKOEcJhuUy2X8l//yX0zxT3WxFtKx1tbWsL6+juHhYRw9ehRDQ0NwOBzI5XJYWlqSPPhBaERbeNqqMyqcQ/15/Hw999pi5v4ZnKdBOqKZS3hQaXK9mWJPZpL+mcH4xl7ekFnJ5/Myl6xTcejQIRw5cgT5fF5gAUI6Pp9PUus5lnq9Lq2Jrl69ilqthvX19Xu3YL1eb18aWbe72xiPi8XC17lcDtlsFtVqFYlEQkrFaUXb7XaxtraGF198EadPnzY8OdoVZ3CG7qY+jNqC1K4OI+5cSB21NhPoqtVq0vlUuzyDY+Rm1u4Rb+xGo4FMJoO1tTXkcjlpe23mNmbR7EKhIIEVzdMjHttsNlGv18VtZYBNF98AdrF2s+yOQf6utnw0L5gcXT1H+vDzsHGudBDMiFitVkxMTGBqakrmZHFxEfPz83j55ZcF19TZiJrpQKWhFRwJ5//rf/0v/MIv/IKhcbhcLlHUOujaaDQwPz+PmzdvfmCdidfz+fxdXWfDrDsMfJDCpC9erSAHYR3d305f3Hovm9mrg1bnYGBYf97g52r6o5F33E8Ym7DZbJIuz35gzBSlN8ouC4OVskgnBHbrJwy+z6Dsq2Cr1WqfG66pLcxGIil5cnISw8PDWFpawpUrV5BMJjEzM9NXF9blcuG+++7DP/kn/6QvF/xusrS0hHA4LOa6jk5rS1X3NOem1NbvoCLW1bmMSKfTkWo/tVoNLpdL5kTTTDREQNdYW7bNZlOs/unp6b6sGyPCpofapae12m63BZxnW2Z6EGyNHAwGMTs7C7vdLsT6UCiEYrGIxcVFw+OgAiGeqdcF2O1rxQOqM++0ta0tFP6emUP8wgsvCL2JpQs3NjbEsmDwgoqr3W5/oNWNnkO+G/ebUaGi1sFNYqy87PU+4JnQJHgqHM0DNWu9Aru0QD6X86DrdgzOMyEczpE+N3ptzCj8QZxdc+Qp2nLmuAat70HFbnaPWCwWLCwsSH0Kq3WnZQzHxPMci8WEH0uPk0L4UbOS3G73vutj6d2LvX0gB3IgB3Igd5V7J5QdyIEcyIEcyL5yoGAP5EAO5EC+S3KgYA/kQA7kQL5LcqBgD+RADuRAvktyoGAP5EAO5EC+S7IvTevs2bNCS2BJL5KpSd1gf6lGowGXyyUkdtJ/1tbWpN1EIBBAr7db+PrSpUuGBhmLxYQnqDOyNO9T58Hzb/JPNf9SU19IPVlbWzM0jt/8zd8UDiUpSuSf6ueTLsYMMnZ6Zf631+tFIBCQUmnMsvrxH/9xQ+P49V//daFhhcNhyS7zer1SWpJ0KM4PsJvhRSJ9rVaTLhG5XE6KWhhNg5yfn5esOF2kJRAIwOVy9TWk5LpwD5DYzfkD+qumtdttTE9PGxrHrVu3JAV1Y2NDygOOjY1JJiLpcVwX1tBlH7GjR48K/7FareK1116Tjgg///M/b2gc3/72t2Uv6MQTzlEmk4Hb7Ybf74fL5ZLUXotlp6QgE3R0jQdmSHY6HVN1EX7u534OAKRbKtM+HQ6HnFlgh+7k9XqlkD7/HqyVy+/xrH3P93yPoXG8/vrrQpv0eDzCtdWNQ5lpyEIrpHGRvkf+PSlv+jwbLTf65ptvSg0Rvg/nG/hgxw3+uX79OpaXl/HII48gEonIOKh/gN2mrHvJXQtuFwoFKRjs8XgQjUaxsbHRR1omZ8ztdiMej8smL5fLcmg1cZx8MjNCha6VJr9Gxa9J/jr9UWc2cQL5dTMstXg8jkgkIgdaj01z+/hsciL5dWaDkJCu6yqYEebY8z3cbjfK5XJfRhs5ndVqFcFgUNJjfT6fJEroFsW8wMwkXrD7Kwvg6Bq05NgywYMKn/VQdXcBnYTAVEwz4yAHlz23uAd4SMkPZn1VVnbzeDyYm5uDzWaT7gNM7X3yySel2pRRKZfLkjbOS4L787d/+7cxOTmJ9fV1PP300xgdHYXH45HKaFTCf/EXf4H7778f09PTopg0T9SokJvMNHNgd1/W63VRpFQ6WjSPl793r/LKK69IOvbhw4dRq9WkbgT53OPj48hms+h2u1KlyuVy4eTJk7Barbhy5Yo0rjx+/Dhu3LiB6elpbG5u4uMf/7ihcRQKBUmmcTgcfS2TNEeaZ5ZJBix0BaCv4y2VPXng0Wh0z+feNeeNtU9ZWs3pdCIUCqHb7SIQCMgAg8GglEGjNQXstuelkmO76eHhYUMTA/RX3dH/5udrIjc3EQ+5tuaogPmzd8sU+cBk2e2YnJwUq5yilT3bgfBz+UzdNJKHjznx91K4gil91WoVnU4HsVhMns8iNiyswsPCBm7dbldyq3mYBtupGB0HLQkS2Enq13NCS0MnFugaDsyuYa+o8fFxU4XQaZVQ0fP9dEJBpVIRI4E1iTUJv9lsStGiQqGAYDAoiRFG5Y/+6I8QCoUkvZZW28bGBm7evImZmRkMDQ0hEAjg+eefh8ViQSQSkWLl4+PjePHFF7G0tISnn34ap0+f7psjM8KsNDam5DwBkALatJoHlbeudQzgntLKKa+//jomJiYwNzeH1157DX6/H7VaTdKbHQ4Hbt++jXw+D4/Hg6WlJeRyOYyNjeHYsWOw2Wy4fv066vU6otEovvOd72B9fR3vvvsuzpw5Y3gcrDlAI0RbovR2eQ50KjGz7HTKMs8yL8b99uq+CrbX6yEQCKBarUqbiGq1Co/HI7njzHShsk2lUlhdXcXHP/5xPPXUU/ilX/olBAIBUbzFYhEATBXRoIvJ4ik8tMxWoULVVqRWLHdKzbtbmtugbG9v49y5c6Jc+VyOkYvFz9WHgkqWm5eLeS/jYBWscDgsSpIpylQufH99gDg+KpNOpyMtQTwezwfSf+8mL7zwAoaGhjA3Nyel7LgpdcqyriavN6NuE722toZvfOMbSCQS+NznPmeqDisPDktYUnnqCmM+n09q4/p8PvkddnegV9Dr7bQMYaNCM3LffffJwdWp2ENDQzhx4gQ+9KEP4atf/SpsNhtu3bolbXEuX76MT3/606L8jx8/jqGhIbn0PB6PqXEAu0XWtRXLbCoqbF5yuq6FzkDUtTsGaxgYlZMnT2J6ehqVSgWnTp0Sr2doaAiJREKsyWAwiFgshkqlgnQ6DZ/PJ+MZGxsDsNN7bXl5GQ6HA8lkEseOHTM8jsG0du4Lnkde/vQYqIj1XO2VPXY3A23fmRoZGcEzzzyDbDaLP/mTP+mrh9hut5HNZuWmp0W2vb2NbDaLYrGIZDKJSCSCra0tnD17Fn/v7/09/PN//s8BwFTFelpDVJh88WKxKPgHJ0JbsoOpm3rjaGvWqBSLRZRKJVEi3AAcE60kAHK4WZ908MDSfTSrXIGdzUIri5uhUCiI0uBGoDLVBVWoTGg56/x7n89nqt/Sn/7pn+L06dOYmJhAMpn8wMHTKajAriWkyyxqV42Hxwy2RiEUoNMrtQXKtSI0w/oU3Fv8XSpFNvszA2WxnuuRI0f69pfFYhGlzUvW6/ViZmZGLLRvf/vbOHbsGHw+H+bm5uD3+6WOhFnPAoB0DualabHsFKDxer1iwQP9+fVcE23E6NrDtNbMKNhnnnmmr6sDsLsvtNLjpagLy3AMZ8+eFcV/6tQpuSTMttHh2eB+29raksauxGU5D1pPDNY72djYQDKZlHKs91yLgC2Qjx07hps3b+K9996TrpTcOM1mU16UlePn5uZQKBTwu7/7u7Db7dje3kapVEIkEsHk5CQ2NzdN5XhXq1Wxvqho2X+JtwvQXwFI30hcEP09/fNGRW84/p9jomhwnspV45zc7Nri1nnXRsRut2NmZkYKdvNPrVaTRacly9uaOBjr0XY6HUxOTgoWxwvLDPa5tLQEr9eLCxcuYGtrC2NjY9JGiG6Y3W6X/mM8PCx0vra2hpWVFTidTmxtbaFQKMDlcn2gyMbdhIEhekm1Wk3gCh4UFgnioaXSstlsYn1zvhwOByqVCnw+n+ExALut2glbce0bjYaUwmNtXu6NbDYrnQ9SqRTsdjvee+89CRQCO5a+mYsPQJ8SZWdfBvF0PQjdA4yig4EsSH0vTRcBCK7NIDcDqzyT+lwCkM4WTqcTt2/fxsTEhOgXGlDaSzAq3/nOd2CxWKQY/cjICGq1GsrlsmD35XIZjz/+OJxOJ27evInr16/D6/Xi2LFjWFtbQyKRkNqwjz32mOxjl8t1x5rB+yrY5eVlXLlyBUeOHBGlFo1G0Wg0sLm5CZ/PB6fTiVKpJIvidDoFElhcXITP50MsFsOVK1fwq7/6q9K10szk6B72vNkYBdYLROWru1Nq5gB/jrcYD5pR0fgix8Lx0WLXLXG0IuVY+VwGWnizm1Gw09PTWFpakvGwzxYPDw8G+7j7/X6k02n5WbrMdIuBHVxuY2ND3DEjsrKyIlbw+Pg4Hn30UUSj0b6eZVTeusRjrVbD+fPn8dxzz2FpaQmtVgu5XE68A/Y7Miq8UHRkl5cXXUGn0ykNJnVBE1rL9ECoHAGYthwPHTqE2dlZGRPH4nK5JOr+2GOPwev14umnn4bb7UY4HJZeT08++SQqlQrm5+flkmAhErMWLAvh03Lne3NdeG44L1qoyHQh/XuV559/Hj6fD0tLSwgEAqKoa7Ua/vpf/+tYXl7G22+/DafTKV0nOp2OBGNHRkbkAmYFuHa7je/93u9FIpEwPI6bN2/i5MmT8Pv9yOVySKVSomDb7TZefPFFjI6O4sEHH4TL5cLa2pqUJ83lcrh16xaCwSDm5uaQSqXw3HPPiS7zeDz3pmCr1SrOnTuHhYUFLC4uCl2k19tp85vP56WsFwFr0k/oethsNkxMTMDpdOLSpUvY2trCoUOHTPUYooLSoDPxRVqLunLQYG1Liq5WpKsaGZV2uy0WIhUmgW9SyDgWWrb8fI5H1+BstVoyZ2YuHLrVCwsLePDBB4UpwOpnpVIJwWAQvV5POvm2222BEJxOJ5LJpLjFpNeZ6aAK7NQEPnToEB599FEkEglMTEzA5/PBbrcLHKGj+ZyHtbU1fOtb38KVK1dw//33Y2JiApcvX8b7778v0AqpVkaECoDzqHucMUqui3k3Gg1hvrC6Ey0j7hHS7cwol7m5OVFIGqqwWCzSMmZqagoul0vwcrfbLc1AASASieDIkSMCbxBSMIvD8uImxglAlAnfna7xoNfCS5GexCD1zMxenZ2dFYs8EAiIYiN7o9FoyJxsbW0hl8uhUqngscceQ6vVwtWrV2GxWEQxPvPMM3j22Wexvb2NeDxueByPPPKIxCzYBYVr73a7MTo6KgyXbreLiYkJDA0NCczXarUwPDyMY8eOyc/yXO13bvZVsC6XC1evXpVbw263I5PJiLtBEL7VaiEQCEgDP5fLhU5np7Wtz+cTN73b7Qq29Lf+1t8yPDnEeLlZddBG46B04bm5iR0RU9HYitmya/w8/q3Lq3Ez8hBrWhiVKq0CYsUM/FBZmrHY0um0BLnW1tbkonM4HMhkMuKSeb1eDA8Po9FowOv1SlO3fD4veGs2mxUc22KxSAsZIxKPx3H48GE8+uijGB4eRqfTEcuLFiItSOJ7lUoF169fx/z8PI4fP47Pfe5zSCQSGB4extraGqzWnTZA8/PzQo+5m3D92fE0FAr1levjvwmH0FUnDss1ZZBLd9AwK4RieGa0a83yliytqfcBrWWPxyMKj8r1bjjfXsIWQbxAAUj1fdZ5pvXKM0yhMiefm9Y4W++YkeHhYSQSCSkqPjc3J2UgHQ4HDh06JAYRaxzb7XaMjY1hdnZWsORGo4FAIIChoSHhpJqZk5MnTwLYPa9cI8251bgrvXZeco8//jh6vZ6sIb1R7TXtJXdtekilQS3NYrV0gcPhMNLptGxYl8uFdDqNQCCAcDgsloCuB5rNZvHss8/imWeeMTQ5ul86lRT/5qTRotTWA7DbdYDWC5UeADmARmXQUtbtPXQ92ME6nPybC6MjtPy+Wcik2+1ibGwMq6urfRYiXexmsylULpfLJY0VuWa8eLxeL7xer/ysGQyWtz/xwkKhILAAXUxaj3y/RqOBbDYLi8WC06dPY2ZmBvV6HR6PB3a7XVpCswOBEbl+/Tqi0SgikQg6nQ7W19el1TUTL9rttgQGnU6ncFYtFguKxaK0EAF215JWuFHhAdTeFtdFK3JS5BjAIuOB/yejQ8+zWQWrZdD6bTab0vCQreP3+/xisdjHmDHT9YLMEUJP5CJzboh/84Jj40qfzycNTTUDxev14qMf/ahp6IINPnWyE9eI6+X1erGysoJwOCzWq8PhkLNBmmOr1eqjPNIT2Uv2VbBW605vIgaT6E7o3kfM0iI0wAydVqsFt9uNUqnUtzC0Kq9fv254coLBIHK5XB93VStbKlx+TUfG9ebRUUoA+948d5oPPoOfo4NpwK5VrS1lupzE+vh9cjbNWtOkfFUqFWlZnU6nEYvF4Pf7+9gCAPrmhcEgciEJubjdbvE+jEoul5NMIW3h8IJhxHkwiEf3ip0paPnabDbhWJsJ6vz2b/82Tpw4gU9+8pN488038eabb4pVf+TIETz66KNot9t47bXXMDExgccee0zoh1evXkUmk8HJkydFCVDRbWxsYHl5GQ888IDhdSmVSggEAnKp0CIluV4HdjRe73Q6MT8/j1u3buEHfuAHZI+wlc/LL7+Mv/E3/obhOdEymDRAQ8Go1xSPxwXKMsuJ3djYwMjIiDR+jEQiaLVamJmZkZYxmUwGzWYTqVQKkUgEa2tr+OhHPypKjGeF3nA6nUar1cLKygo+//nPGxrHH//xH2Nraws/+IM/iFKphDfffBOtVgvJZBJutxvr6+twOBzw+XyoVqsYHR3F6uqq6DkAePrppzE1NYVvfetb4iGEw2Gsr6/fMdvvrhqGmjmbzYplQteGB4luEa1XuvSVSkUOIJVeKBRCJpMxlSHDHkvkd+5FZB9UckA/mwCAKD2Nj5qxYPlZGm7QY+Atr3EtzhUbrennaWjDjILlBcbNR+udcATxLrqeDA4QIy4UCrJ+OuuIYzEqTKrIZrPo9XqCx+sAF90otg6n1dxsNnH58mWcOnUKPp8Pm5ubchm7XC6Ew2HD4xgdHUUgEMB7772HZ599FuFwGPfddx+uXLmCb3/72wiFQhgdHcW5c+dw+fJlHDp0CMePH8e1a9fwx3/8x3C73ZidnZX9QBd+e3sbb731Fr785S8bGsfv/d7vIRQKYWZmBjdu3IDX68WTTz6Jd999F08++SRee+01zM7OYmlpSVr9PPHEE6jX67h8+TIWFxfFu+DFQ0V45MgRw/MB7FKuCNMAu3hwJBJBoVAQN5uQ0p3OAi3Oer2OUqlk6hLmZ+dyObz22mtiLX7xi1/EyMgI3n//fTz//POYmJgQ7H51dRXf/OY30Wq1UK1WYbfb8fnPfx5DQ0PI5XL45je/KWwRowo2nU73JY/MzMwIB/rWrVvo9XqYnp7G8PAwnn/+eUkJLpfLeOqpp/DKK69gdXUVIyMjWFxcRCqVwv333y/xqDvJvgpWWxekuNBqGhoawo0bN8T1IacRQF+QQwc6Wq0WJicnJTJnVGZnZ7G6uioKEuhPGNABJx1c0LzXOxGFzQqfxc/WbTj42aRCJRIJCWb5fL4PKHpN4DYzLnoCpD/1ej0kk0mZG66D5gfrLBV6HOVyWXiyVIJmWBXcbP/3//5fnDhxAqdOncL4+Li8GwMJ+rAzGy4ej+PChQvodncyAq9fv461tTVks1m43W6cOHHC8Di+9KUvoVqt4nd/93exvr6OU6dO4cEHH4TX68Xv/d7v4dy5c9LS6MqVK1hcXMShQ4ewurqK+fl5PPzww0gkEn2ehNfrRSgUMgURnDp1CnNzc/j617+OL3zhC3jllVdw/fp15PN5AMC1a9cwMzODS5cu4YEHHsD58+dx7do1rKysIJFISJqotjJ54ZkJ6AA7eHSxWBRMnWtO2mQoFEKn0xGWia5HMChkd5AhZMaKHRoakmAioSSdNBQKhTA3N4doNCpQyeHDh9Hr9ZBOp5FMJuFyuQSrBXahuv0U26D8tb/21yRbbHR0FENDQwB261L0ej2pEzIxMSFQVjQaxfDwMB544AEJYj7wwAOw2+3SYPP999+/43P3VbDsoqhzdFutFsrlMvL5vOARTqdTMol+6Id+CN/4xjfQaDRQq9UwMjIitQt0EQszPNjHHnsMb775pqSgDipKKjFinMAuWVj/X/+8dpvNCItS9Ho7BUtCoVAfbkmXm9iSJiPzAGuGw+BBMiK82cPhsFxWxJNsNptAAHw+8915e7NhGz0Nju9u/YUG5fHHH0cqlUKpVEI2mxXFTkXOgjQ6Y8bpdGJqagqf+tSn8Oabb0o++sjICJLJJCYnJ2G32w0HuAAgkUjg1q1bmJ+fx+bmJt566y2sr6+jWCxie3sbq6urQli/evUqlpaWkM1mcevWLXg8HjzwwANCV9NY/fj4OB5++GHD4xgeHha8lzEIcriJq9KjOHz4MDY3N1Eul7G+vo5PfOITuH37NpaWlvqUOqlWZoNLOqWTBoCmBGrrlu4uoZ7B+gSDUJoZPJhKM5lM4vOf/7zg9vR0jh49iqNHj8qZ5Fh1UgrnrtfrIRaL4ROf+IQ0KzQqHo8HoVBI9BDPSr1el3Rlv98v3WTb7TZGR0el1srZs2ellsGHPvQhgXzcbjc+9rGP3fG5+ypYv9+PbDYrwQBNoCavktYPo32nT5/GX/7lXwqo7PP5ZJMwuMDFNipPPvkkfu3Xfg2FQgEAPqBEGbEGdjfDIA+Wyo1Wnf59o8IIM5UocVCtIDkeurjEiWnFNRoNhEIhibbzojATXCKfkDzOtbU1xGIxSfrQtQrIVSbGSWyUrjgxdGKFZubje7/3e+X2ZyCDhX14kerP4x7y+/34+Mc/jtnZWWxvb8Pv9yMej6NWqwl9zAzvkxd+LpdDMpnE9PS0WEvf+73fi9nZWSSTSfj9fgwNDeHy5cvw+XyYn5/H9PQ05ubmRMFwjGTAGMVfgR1smcG2Z599Ful0Gk8++SSuX7+OF154AYuLi2Ko9Ho7DRq9Xi+CwSCuX7+OpaUlOBwOZLNZvPfee/jkJz8pAU2z2CcznTSFkvtWp88OGjqkPg4G2bSYWZtKpSKf6XA4hLDP2hk6cs+9Uq1WEY/HBZKkF8QLinvdTHWxV199FadOnUI0GkWtVsP169dht9uxsrKCZDKJa9eu4YknnsDIyEhfhujY2Bh6vR7W19fRbrdx+/ZtPPLII1hdXcXhw4dhsezUYbmTNb2vgp2dnUW73RalaLVapdyadqc0T+2b3/ymYLK9Xg+FQgE+n0/YB8D+5b32Ep/Ph0Ag0AfOa3ebn6n/rylaxJ4GKV4kXhsVTiaFgS6du0zLjVa/xmlJC6pUKqjVavD7/X0JEGbGoYNkH/nIR+Q96FptbGxgcnJSCNuFQkHWT0MU0WhUrAYmkxiVZDIp0VxaJkzP1VaIvtiYYBEIBDA9PY3JyUlxXblWZkn1pNJEo1GEw2F89KMfxfHjx8V6AnaMhUAggIcffhhf//rXce3aNYyOjuLpp59GPB7f89K12WymXPNEIgGLxYLHHnsM2WwWDz30EKanp+FyuZDP5/HFL34RgUAAjzzyCLxeL06fPo1gMIgjR47g2rVrcLvdOHr0qCh7YJf2ZcYd5tipoLRVSNycFizTdnUQS2PyvJC1mIkXXLlyBcViEWfOnEEkEsGLL76Iw4cPw+FwSJIFcfy5uTlcvHgR4+PjeOKJJ7C4uIgbN25gYWEBjz32GM6cOYMXXngBHo8H165dwyc+8Yl9rUct7777LqLRKILBIG7fvo2XX35ZsP6xsTE0Gg28//77ePHFFyV28cUvfhGzs7PI5/N46aWXkMlkYLFYsLq6CrfbjXfffRe3b99GIpHAf/yP/3HP596VBxuPx4WqRRdCZ08x0BKPx9Fut/H2228L385ut/eVRmOwh8EYo3Lp0iVxmwYVls6rp+Lh9/RhGbQktWVrVGixDwaldJk+lmOjwuHtTOWrs8y0mE2V1QkNOpIP7OBedIeYacL6r0wq4Boyst1qtRAMBk0pN64F4QYduOQFw8NKHFxj9nS5aNnT+tXurRGxWq2IxWI4ceIELl++jJdffhk2mw3RaBTz8/OoVCo4c+YMZmZmcPr0aXznO9/BhQsXMDc3h6NHj8Lj8UidYb4P8XEz6bIcM9ugE5+fnJzsgzyOHz+OZrMprdNrtRrOnj0reLTX68Xo6KhcDry8zQgxzVqthtHRUZnjRqMh9WjdbjeCweAHGB3xeFwSMlgYhzhst9s1FT9h5h6936GhIRSLRXg8HqyursoFH41Gcf78eQQCATnXq6uruHHjBvL5POLxuLj20WgUDocDL730kmEFOzk5KXBdPB7HzMxMn4U/OTmJkZGRvkD07Oys1IPQBoOetxMnTuwbsN9Xy62srPRZI9ww5IBy0VutFrxerxRvTqfTkuFABUj8h/QbMwqFgQIq1sHIPTeiVqxULjpSD3yQCWDGciQ+x+fwEBLrrNVqUmuVc6EvI/3zHBvnxswBstvtkrDBQBIvIPIHdTDBbrcjHA6jXq/LRqeCI5WILpyZcTD5Q184VJq0nHgJ0PXWtDbicMzL517Ta2pECFE8+uijKBQKuHHjhhS3LhaLGBsbw/Hjx9Ht7tQbnZmZwc2bN3Hs2DGMjIzIu+ukFLPpy8Aur5pzT4YHXXMqcRoZhLZ0eq/b7Zb5pBdIep8Z4X6gx8A5JXOE66GNJJ1KS0VDSAHYpZWZ8T5PnDghuGan08HRo0eFhjc6OopsNitlCWOxGObn5zEzMyPvHgwGMTExge3tbUxMTGBkZATpdBonTpwQyNCIPPPMMwKDxeNxfPrTnwYAWQNa8qdPnxb4zOv1ypp89rOfBbCrP5577jl88YtfFFjoTrKvgs1ms2g0GnJoaHlyEVjIo9vtSr50OBwWa4VZIpphoKkwRoX4Li2/QXyPn7lfKp/+GhMXdPUlI0LrmJuR70El3Wq1UKlU+ri42h0nnsQDRXeNytiM8HNJkC+Xy0ilUpiZmREM02q1CtYbDof76rdyHnnAuIZmIBMeYp2yvBf1TVP7eLCB3crxujAKrWIzCoXzfPjwYfj9fly/fh3r6+totVo4ceIETp48KVAE5+XUqVP40Ic+hOHh4T4Fz31KLNvMfPCQ1mo1qWnK88F3p5VLpcWavnTPNf1RwyVm9weVIN1dXvoaKmOdA3obOuWbmYGaWM8Lwoz3GYlEEA6HZa8R02RtaRb95nuePHlS+K4PPvggHnroob7L7vjx42i326YL9m9tbfVVfNPxD5vNhs3Nzb5aCfV6va80q81mQz6fx8TEBGw2G37gB34AkUjkrvpj35miq8YDQGXLDaCrQ/EQsc2G3W5HLpdDIBCQG4JVyVnJxqiQsQBALAL+G+jHQmkZDmKvwC5MoOlcZqKz3CT8HV4oVAoMjrA9DC1JTRkDIAea83u3TJpB0S4LS/+53W7Bx+v1ulDDWElLR7J14IDfI85uxjohLKHnRW/cQTycHF26n8AuVqhbE5k9QNwPvV4Pc3NzmJyclPdh6mytVkOr1cKNGzewtLSE6elpaUnD+rjEG7k22nozIrzM6vU6Ll26JN0NNIODlwcVCyt40bPgPtI4+71QC5eXlwHsBJlKpZKkTtNS52XC6lZa4eXzeUSjUVH+xWIRoVBIKvrvZ7ENyuLiosRPNC+aMAGDYCsrK0KD0vpFM3DIhuElZebsvvfee+h0OgLflMtlZLNZoev1ej0cOnQIW1tbePPNN5FOpzExMYFPfepTeP/997GysoKNjQ186EMfwvj4uAQwi8Uifuu3fgv/5t/8mz2fazjRQONttOTYpoSbiHigxWLpK0lGK6Db7UqpNjOb5sKFCwJNULEQRwV2A1Z8nqZyDSYiaOvILP1FQxS0SskLpmtOq/7mzZuw2WwYHR0FsFvaENjNMdcJAmbmg3PKFEbmaTMA53K5sLS0hImJCam7SywxlUoJV7VcLosLCsD0ulBp6rXQylUHQVmbgnNHd5OXNPdVt7tTUpG4m1FxOp1icZI6yPRbBh2z2SwuX76MVquFkydPIhQKyWXIcdGS1kXLjcqf//mfw2az4emnn0alUoHNZsPrr7+Ozc1N9Ho9/MAP/AAuXLiAM2fOYGtrS4K3r7/+OiKRCB544AFcu3ZNuOKMXbCk46lTpwyP5e233xYu78rKCqxWqxhL9XpdFGyr1UIikUAoFILf7xc3WlfHGx0dhdPplAJCZgJu2WwWL774ohRaCYfDeOSRR/DOO+/IGSgWixgeHhbubrlcFn7u8ePH4XA4cOrUKbnIeY7NMiu63S5u3rwpxbzn5ubw3nvvCe+13W5jZWUFV65cgdVqxenTp8XyZzHwq1evSmcG1u3dz7u4a6IB3QIqKZrYPIw8IKzSQ2iANw6zc5hyZrPttAcJhUKGJ2Zzc1MUqMYr94IEqAS1FUtlqNNUzVLF+DwqbyoPuvyskJTJZJBKpfDee+/BYrEgkUgIuM7LR0MEhBvMjoM0HGKc/AxapocPH5ZyaoQrGo0GwuEwstms1CrQ82q2mMetW7cwPT39AWuT76QtkU6nI6mgPOw60EVWxdTUFK5fv46VlRXDCoWBMZ3dpqEGzkmv18Pk5CRisRgeffRRBINBCRDqi45Ker/spr3k+77v+/AHf/AHyGQyyOVyWFlZEY7r1772NRSLRczPz+PMmTNYWlqCx+MRnvmtW7fQbDZx5coV/OiP/ijOnz+PUCiExcVFTE1NmcIbAeCzn/2s5O4zLZ0KlimoLPJCI4kKltYrLVrt8QHmLMfJyUksLi5ibGwMS0tLOHXqlOzHdnun8huDisvLy1JTOhaLodVqYX19HSdPnuwryKIzGY3KiRMnUC6XEQgEkM1mMTQ0BL/fj2g0ivX1dUxNTQkbanJyUi65dnunI8XS0hKOHTsmxkAmkxHP655bxpA9wINHzqTVuts2gtFJWgq06ohp8bZj9NLv9/f1rDIijHBSWfIwUJnqQMug689DNggVULmZwWD1RqMbR+gkl8uh1+shkUhgfHwc+Xwe4XBYLqBmsyn1UycmJuD3+/v4omY2LYMmtEL13DOYwDJrbNwXDAYluJDJZMQdJbwA7EZ8jQqLbNNy1u4crRNeYtVqFZcvX8Zzzz0nuHwqlZJGmd1uF8eOHUOn08Frr72G9957z3A3V7r3DORwLmmV8P8ej0eizrTUSHWjBc2DTCVgBm+kgqZCLxaLknBAt5ZdErgu29vb+NznPod0Oo23335bKGtUbD6fD9evXzdV3hOAtHkiLMfP1FAF14iXHC1m7QmwUp6m75lRbKFQCB/72MfgcDjg9XoxPT0Nu90u5QhLpRLcbrd4E/QiaEHTENPBNxoNZgwk1qX2eDyYmpoSSiErwnF9JiYmZIzcy0NDQ/jMZz4j5TAJXTCF+Id/+Ifv+Nx9dw8VF5UJMaler4dQKIRAICD4DpUxq5bzttStUnQE0wy2xUr7nFBN8xqkYQ0S/jVjYDB6b1Z0kIt/qLxZyNrlcmFqagqhUKivQhM3daFQQCgUEvCcytEM9qnnvN1uI5fLST+nlZUVDA8PS+8jzs/a2pooWW4MTT0BIAVfjIp2wTm3vDA4x5z/cDiM06dPy4V7/vx5bGxsIBqN4nu+53vg8/kwNDSEWCwmF6dR4TqzihgVo6bHadYLcVZeBHSJtdWrMXyj8uyzz8LhcGB0dBTnz5/H8PAw3n//ffz5n/85lpeXJYPulVdewdWrV/Hggw/C7/fj2rVrkpa5tLSE5eVlsZhOnDgBm82Gr3zlK/jZn/1Zw2MhFl8qlcSdbrd36xkz8Mw5oULlPGjlZbYurhaLxSLY76lTpyTATOuPdDaHwyGsI322GTfQ55+8ajMKlgkctN49Hg/m5+dx7NgxKZ9IBd5oNERPUdcwYM95GOwtdyfZV8HSSuJNx5uQGA43NXlyjFhyEtrtNvL5fN/BY9EPM5PDF9P45174KS8BHhptpe3Fe9U0KjPCg8jf57tSaTscDsTjcXkWgzd8b71haCmZGQcxQ6vVKjc8A4vEyYiH12o16ULBoCPzuImTdzodKZRt5uJjVLpUKgkGrQNbGj7q9XoYHx/HsWPHxFq4dOkS7rvvPnz+858XK3hjY0NKLJoRPlcrURa44d5lrQVaa1oB6+Clhr7MrMuHP/xhsVA/+clPIhwO44knnkC328XW1ha8Xi+eeuopzM/P4/HHH0c4HMbhw4fxyiuvIB6P48EHH8StW7fwzjvvIBQKIRqNYmFhAVtbW3jooYdMzQfF5XKhWCxKq6exsTFhBBBr5nxQoVQqlT5OtLYozYpua6SpZxSeZf4bQN/lpi9pYDd1Vv+eEbFYLLh48aK0eMlkMohGoygWi3C73djY2JAg5dGjRxEOh6VtuPaOB/UWL+M7yb4K1u12I5FIIJ1O9/FQ6eZXKhXZxNzQPFCMYrNYLidne3tbXsyM6EWi0mevIN6wvAgGLZa9+Kf8ulnhgmvIYhD30xQbHWxjkXI+V7f/NSODgSS6lXR5WOAjGAyKdRAOhyXCrbN0qtUqAoEAhoeHJUHEqOj0Sx5SDZ3oerC0nui98MLz+Xx9ip57Z2Njw/A4qCw0LEHetsbrGMkeLKOo2QNaAZi1YPWF4vP5UC6XceXKFXS7XZw9exZDQ0MSrSYmbrFY8OlPf1oU3Ec+8hE8+uijUjOC7qyZojPAbn0OXuScn0ajIcwVXRuE8w5A6rHydwYxWDPCc0HmDCEYjo/PG1RkpLTpGEK1Wu1jWpi5/KxWK+LxONbX17G1tSV7N5/PS+B9aGgIVqsVm5ubmJiYEPYU94SGVPQe2k/2VbCdTgfpdFpuPx4WbiJOCLmfVLa9Xg/lcvkDzckqlYrUiDVLrNecSd68wWAQ8Xgca2trfUwFHgoqWr0gGocCzBWu0NawfgY/R+O+OuGBSogcSX4Ob2izwRTWf52bm0OtVhOLy2KxoFwui4IlxadcLkuSB90yHlq677FYDBsbG6ZZFXxXnezQ7XZln9A95P7Rm5WuHpUbf84s11K794OBTRL7dZFrHbwlds3DzAvaLD4P7LYUYqDXbrfjk5/8pMQwKpWKQCQ8H/Q09LhIRSTcwbGbEV5qxJnJRSV7gHuYdSn0pXevyvRO46AnTMog12GQWgnsVqujMaItXxozrHVsBlYjC+PIkSMS2OS708MpFouwWCwIBAJisHAtOFbuUe3F7jdfd62mNYitEheh0iNXz+l0IpfLySbjxkmn06IYeQvp29KIaCXNw0csZXt7u8/15w1DhaWj5EB/FS+zG2kwOMZ3BXaDVDqnXjMvqDjYrkUrA7NCZc0yeLRSO52O9EOj1UDrUkf1CVVQsTALj3QVo9Lp7LaI0fAClZRWWPw310Nj4GzhQnE6naa4llTW2oLVLu3gmmnlo6EajkVnNJkVKhB2+OUZWV1dxejoqBgHvGw0m4U90/h8nRVnlmlSLBZhs9mkShixxXq9jq2tLcEVyaXW63cve/JOwouMDSe1sgX626nTYuTP6YCcxWKR8fJzzcJ7PIuRSEQMLUKK/Dr1GWMLmr3A8WnPVRtbe8m+Ctbr9WJkZAQ3b94UgJgvqF+OG1bnTjudTkk64C3Fn2PmilGhAiVB2efzIZlMAgAymYwoDo218pbhwdLReh30MruZNMY4+Lt0KXmg9aXAzXHu3Dn4fD5Eo9E+FoQZabfbUnWKz9QHhMGq7e1t4YNmMhmpbsYcbFp+vEgDgYDpuqOa2aHxMt72FotFcD4qWW5SKiC2BKESYIk/s+PQPElaOQyo6MCktnL172qvRnsrZsbAgtQMMkUiEdTrdXzzm9+UPnTkVmr3k/uAzI/B8Zqx6IFdRa+TJmjB0gjQBgIvGnp6tVoNwWCwryODNpSMCtsI0UNgiU2OixcZFRYNAJfLJbg8KXNWq1UuDMActKZxXk3t1IEzrgEvYs4L2/hwvbjHefb22yP7rtrm5iasVit8Pp+4kzTzdcoZF4CHifgTy6SxPiknrt1uI5vNGp4cPotuZqvVEv4gv89NQYtAF+zgRGhO5iB4blSoDHWWCbAbXNGTT+ENGY/HMTIy0ncR6KipUYnFYn1Ut15vpzYte1txrU6cOIHbt2+j2+1KUWUeVl52vd5Of6JarYbx8XFTh4cKjFXh3W43pqamJHOHFiI3LuESYJfLyzkjdYnK0Ixw4+uUXe25DDJJbLbdSlODQS59WMwq2Fu3bknt2Ycffljab09OTsJi2anN8Pbbb+Phhx/GjRs3kEql8Mgjj0h7aGCnItfCwgKmpqYwMzMjYzR7CVNxkkGga4iEQiFp9c7LTO8lrinnhd1omX1mxjUPh8My/7Qa3W63KCzuE1qsvV5PgqXacuQ6EMvVGK4R0bqDazEYQ9HsBD6PRW4ITWj9oWMPd5J9dw/bjjgcDok6cyN6vV6xFOr1OvL5vPDnstksisWi1Pc8ffo0gP6MKzN0IG3lMdWSgD0PIxWHpm/wxuZkauV3L3iTVhT682h1EU4ZFFrQbrcbjzzyiETy9Q1oNpji8/kkO4tVkvjuFosFW1tbwh9mxg4tCWJHvCSYZNDr9TA7O2t4HAT68/k8nnvuObz66qvY3t6Wd+LeoOuuXXkqeF5SvCRp9Zo5PJxjelE6oEXFPXixEkvjfGkFw7nRlo4RYbHwXC6H1dVVpNNpzMzM4MiRI2i323jxxRdRrVaxsrKCxcVFHD9+HN/61rdw8eJFvP3226jVanj22WclE4+NGVk03Yzw/ajUODeaQcEuroOURZ6fZrOJUqmESqUif8zGTwhf0WMldEWvhfqA+5NrR3jAZtttkkh2CZW8GVxaQxC8YLn3qBO4T3hhE+fV2DUvKh3r2U8svXsBmg7kQA7kQA7krnJv7OEDOZADOZADuascKNgDOZADOZDvkhwo2AM5kAM5kO+SHCjYAzmQAzmQ75IcKNgDOZADOZDvkuxLwGQbGFaZJ2eNGVlMy2TL5Hw+j1KpJJxMUjF0aiJTKlutFr7xjW8YGuR3vvMdFAoFWCwWpNNpyd5KJBIAgFwuh263i0wmg2KxiEQigVKphPn5eTSbTeni2m63pcPCxsaGUDDm5+cNjeP5558XChKJ9E6nE+FwuI+IrCsy8f1ZQpAZbqRWsb15vV7H3/27f9fQOH7u535OOH2aZkR6E99Lc22ZXccURNKWdAEdckN/+qd/2tA4XnvtNSkaTQoe6XOVSkXaaWcyGWxvbyOfzwttiz9TKpVQq9WkLRB51s1mE+vr64bGwc+tVqtoNBpSiJw8XH5uq9US6horR+l6unw2aWWkiv3SL/2SoXH80R/9kaylx+NBoVBAoVDArVu38J3vfAflchmPPvoojh8/joWFBemscPjwYYRCIQwPD8PtdiOVSsneKJfLmJiYwNjYGL7v+77P0DiAnTND+mS5XIbL5cLIyAiAnYI6b731FtLpNIaHh3H8+HGplcC+cmtra7hx4wbS6TRisZg0bWQCx4/92I8ZGseP/uiPStcEv98vCS0+n0/atFAfAJDqe9QT3KfNZhOJRAKdTkc49p1OB1/72tcMjeOnfuqnpLNwrVZDOByWxpJvvfUW7HY7PvGJT+C+++6T4lWXL1+WTNRgMCgUtzfeeAMvvfRSX/Grv/iLv9jzuXdVsNyYjUZDFC7/UDkw15sTw02ti4oAu7VjySUzKizHR27tgw8+KMrs9u3bSCaTwgtdX19HpVJBKpVCOBxGOBzG6uoqAoEArl69KqlxMzMzQgo3KrpuAJVms9kUDjATBvj9wbx7HmzOwb2UTAR2s1HI7aMS1z2ddAYQEy90nj+zvsg95QY3MybdwoNFq3V6sCZs873JF6YC5mXMFGryls1wLaks+fu6loDulKAvP+5rZrPRkOAFyJ8zWzODnEnuAa/Xi2QyifHxcSwtLaHVakmlfnKUmdAzPDwse6pWq0kxapfLZaq7LQDZB7oOLHmjzDRjanW3u9Mp1mKxyNm5ffs2VlZW0OvtdIymMWGWGxwMBqXmgtvthtfrRTAYlPoY7XZbasJSifEsBYPBvpRmGkdczxMnThgeRzKZRKPRQKlUwsjIiJzbQCCAQCCAer0uHRV0jYpSqYRCoYBIJIJ4PI5EIoFTp04hnU5jdXUV165d2zdRaF8FW61W5WCQNE7iMa0CEpZpPfDndCUpbmRabtzQRiUWiwFA3yI5HA7pqZ7JZBAOh1GtVnHx4kUUCgV4PB64XC7UajUkEgm02234/X7JMd7a2kKtVpO+TEaELciZe8+F1umi3IB3SscDdrOYqIzMlsXTufy6oy7Hw7ln6qkm7w/m6fd6PWl7YbYkHddXJwvw/3wWx6gVFvuWVSoV2TP6IJnNXOJ86KQT/Tz9B4AoMSpDXoC0wGkgMInEqHg8HlGKTFF2uVzweDzIZrOwWCxS9yEWi6Fer2NpaUnKSupiOY1GA+vr66L0zNYi4DvSk2T9EGa8BYNBWK1WuN1uVKtVbGxsyIW3vr6OjY0NdDodJBIJxGKxvjoAZjIPNzc3pZMGLzWv1ytKu9PpiBXLkptU+rVaTf7Pin5+vx9Hjx5FIpHA1NSU4XEwM43Ff9rtne6ywWAQyWRSiiaxiPzS0hJWV1dRLBaxvb2NdDqNarUq+i4QCGB0dFSKN91J9p2pra0ttFotUZ5sM8JGaBw0NzhLGFKJ0mLghqVVq2sDGJHR0VGsra3h8uXLiMVicqNTOfDGrVQqAh1MTU2hVCphZWUFpVIJ29vb2NjYwOjoKCqVipRke//99w2Pg66WLmTNVEZaiHrz0W3WorNEqFTNFq2gctUpe8wq08VudI1PXcmKWUr8P8duNs+crh8rMRFq0BcI/1DZ0kKs1WpygWvFNlgnwIjQ1S+Xy/JvXuoaFuDFT+9BZ9/xgHGfU+mbyTgMh8NS4Lper0vNB7fbjaNHj8rnlUolsYaKxaKMk6UEfT4frFarfE8XLzIqvLiouNkAk4qXn8d1YG851geIxWKifNiaSGcDmpmTbDaL69evw+VyIZlMIhwOA9jtU0elS+Ol9/8tKMUqfswkJbzFGsdmeoOxglg4HJZLnBmlfr8fMzMzUh9kbW0NL7/8svQCTCQS8Pv9WFtbw7Vr1+B2uwW+8ng8iEQid3zuvgp2dXW1DyLgxuPCEdvUL0HlOggL6L5IZtshM395ZmYGxWIRt2/fxsTEhOQHs9Se3W7Hww8/3Ifh6FYVbOAWCoVw8+ZNABBsyYiwhxWtAK1kbTab1PfUB4IbRitAnZoJmK/qpZUQFTUVDCt10QriGHRaITcbsFvSkD9r5iCn02lRhIMFbnQKoq6AVCgUkMvl+hQLXTJdUMSMlaSVMxUEYRhdOYsQBeeAVbxoBLTbbbmkOZZMJmN4HE6nEz6fT4pcM/c/EAhIJ+XFxUVsbW1Jmx6v19vn4dDSjMfj8Pv92NrakjKgZoTwHj0snmFgN5+/2WyiWCyKVZ3NZtHpdJBMJjE2NoZoNIpgMIhWq4VUKoVqtYpoNGoqRdXv94s1Pz8/jwsXLuDKlSs4cuQIDh8+LGUcWdeZ+CjrnzBeoL0C7m0zhgnLdIbDYZTLZbnobDYbhoaGEI1GxRi6ePEiLl26JL0DO50Orl+/jkKhgHA4jEgkgmq1inA4jGg0eu8Klu4FgL6DwzxvHg4C+4O5+Bqn/Ktgj+l0Gu32TvMxYEcJcEOw2+XW1hYymYy0a7l8+TJyuZwURpmfn0cgEBB3OJ/Po91uS0sXI0K3Vys4At9aKejyd7pUn1Y6VLas+2kG16LSYmsLzrm2EnURGW5IFqegFUtXnj/LNtJGZX19HeVyWdp+MM+c+Cr/1sGuXC7X5xFxT+gaAMwZNyO8+HgJUmHy3bXSpTLW+e80DrSFx/kxKrzQfT4f0um0zLXNZkMsFkO73Ua5XMb6+jrW19fFIqOFCEAKqrhcLoGyWJbSjLCIi9vt7osd0IMirMcCJ3TXHQ6HnBOuA+eSCtZMsRdaxaFQCIcOHcLi4iLW1taQyWRQLpcRDAYlwMq9zGaDnU4HkUhELiHWneaamlkb1n/lOeXlwfnZ2NgQiCIej+P06dNIpVLY3t5GoVCQjiAej0eq1LH62X767K4Ft4HdQhpUEFSc+m/tbgK7rjAtYP6hG2lGofCQhkIhzM3N9VkedrtdbqJIJIJut4tsNisTZbfbEQ6HEQgEUKlUpNMqN5eZW5BKigcX6L94OFecA20N8t86QDb4t1HRlYR4eTFgwM/SgTCNB3MNdalDsgzMXnzBYBCBQEDaltOiDgaDwgzI5/NyOHSRHipZvgcPmFnrFegvQUeMTFvFxPe4P2k0cB8Rq6S7yn1hNhjLd9Tzyyh4t9tFPB7H1NQUstmstJBJJpOC3VarVZRKJXg8Hqk45Xa75YCbkXK5jEgkgnA4LFgmoZp2uy2YZjgclmh4LBaDz+dDPB4XxchLgrEPRuKNCiur8fzH43EpZcqv5XI5bGxsSOtwXvYMzpZKJYyPjwtGurm5iWAwiEOHDhkeRzwel+JVNptNGpIWi0WUSiUpZsXqgYcOHRJvMJ1OIx6PiwFSKBQkGBcKhfaNXdzVguWmo0ulMSvNHCBwTAXMTaZdNGC3OLIZC4WLooH5bDaLjY0NeDweKVP40EMPodncaX3scrmkqhFdtFu3biGfz4vbZrfbsbS0ZHgctMJ5oHVNS0bmqbCazWZfcz1da1RfLvwsM645Ly9eDoP/1+vA5zcaDanipS84Xf+SCsGo8GLTEWZaPcS36HbTgiVtiHuL8A8tDL6DGcXGPalxV1pAmorFfxPGouLVVjQVsd1ul31lVHix93q7XSKi0Wgf1h4OhzExMSGWPstOaoiDP+/xeJBMJvu8FKNSLBYRiUTg9/slkKpb2hDCiMViiEQicLlcMl5iwB6PB4FAQNhCgUAAsVisr8Ps3UTvUcIBkUgEFosF2WxWmCit1k47qatXr6Lb3em8PDMzg3a7jc3NTTGmqMza7bapVuYjIyPS1YNWM+eeHSd4sVSrVaytrYlyL5fLcDqdsm95idfr9bvGC/Y9TTTVuQF5KBgJpgIlq0AXrNW/o4sfa7qMUXE6nRgaGpIbnq5ut9vF+vo6ut0uwuEwFhcXkc/nJehCfDKTyUgF93Q6LSD70NCQqcg5WRNUrLS2NP2JgSUd4NLRdQ0TcB74WUaFn0MXhUpRV42ntUalyZJ3LB/HS4/v0mw2BRc0KufOnZO21KTYECao1WrIZrPCWyaEw03JwAUVK9/BbAlJPScavtnr4mBQjYqU0Bb3OK1/KjxSpIzK9va2WPKjo6NiQfZ6PTmoFosFc3NzAICrV68in89LK59oNCpdD1gjl/3UzDI8aOXR2qR7zL1DbufIyAgikYjAGKFQSCxItrmmO88xmfE+tUUO7ASsCV24XC50Oh0UCgU5s8lkErdv30Y2mxUmDAAsLi4ikUiIVZ3JZEztE3aO5XpUq1V4vV7ZJ+zWwjNVr9eRSqWwtbXVx5HmnqUuInRwJ7mrgtUJAlSwVKC6RqKOjgP99TkHXWqz/MKRkRGUSiUsLS2JyzkxMYFTp05hYWFBrI319XVsb28jEolIw79kMolKpSKQAeuUEjDnZjcihBWA/jbhuiAwFTBdVlpSukC5Vr56boyKDrTx/3wWlbVW3Bpz5DrxctCuNQMiRuXWrVt9Fyb3A/F4KjfOk27XoWvXAruW/SD0YkSoUHlZ6CLbGj/kmHRcgL+vrX5ezmbXJZPJ9FmvjIxbLBaBRMhy6Xa72N7exurqKgqFAkZGRjA8PIzZ2VlxW/n8e4lb3Lp1q6+WKo0fJhK43W7EYjFxnW02G4LBoCSpMChEfjCpXoA574LeCWEQ7nt2Mebn5XI5DA0NCURB65QwTq/Xw+3btxGNRmGz2TA+Pi4dlY0I1zqTyQjGTWYF4aBmsymGWiqVEuis19vptMu4hy4WbrPZ9q3Ve9dEA25KviSw2yKGjAIql0F+Jq0Dfo3uiVkWwfb2NprNJiYnJ6Vzgd/vRyQSQavVkjYyhBGYVeb1ekX5stEfrV6r1YpcLmfKJaaC1ZYoxefz9SkzWkL8t7ZcOT/cbGYhAmC3LTULotNa5WcPFpLmz9MqsFqtfe1DNMXKqNAC0fQwWjj6XSl3UqaDHo1ZnrS+9LTS5vvzEqPlSgWrM7a0R8HPI5xiVJj1Q3qWboBIyITQ0fDwsNAPSWQn5afb7aJUKom3ZpaPC0AMj2QyiVgsJsEjehVut1soWKSSEU6g5W61WpHNZpFKpYSiaDZuwfZFvNCazSa8Xq/ojXA4LM/3+XxIJBLwer3ibWxvb8v80Zg7dOgQzpw5Y8rjKpfLkgFKKCoSiSAUCkkQjpfqysoK/H4/QqGQ7FV64oVCQQxEp9OJUCi0r/e5r3bRWTWMsDECSQuWB0xjZ1Si5B8y0MLoLt00o3Lx4kUJYlUqFaTTaXQ6HYyMjMDr9UpqpN/vh9VqRT6fFwDf4XBga2tLWvXWajW43W4sLS3BarVK+qAR4eHUFj3fs1arwev1wu129+GvAOQQ0yUlZDL4WUZFHzpSrKjgaB1r5a15t/rf/B1+HmAuk0tbvrQcB7HmQcWpg4H8e9C7MSu8aDgGHQfQSQ6cJ204cG/T2tZJGprqZkRSqZRQrTguj8eDcDgsrnc+n5duy8PDwwgEAtjc3EShUEA6nZa9TEXHIKLZeclkMvD7/SgUCsJgoDteq9WkfRGtVr/fL0EdRvU7nQ4ymQy2trYErzXrcd24cQOVSgXhcBjDw8OCe25vb0tXaIvFgkgkgkgkgna7LYqz09lJ7WaCAbHas2fPYnZ21pSi39jYkCSCer2OcrksrABa8czcikQimJ2dRS6XQ6PREBZHIBCQrtjUacwTuJPcVcHqLBjNJ+TG46HiptbtIHj4AMhm52eZsQxmZ2exubmJXC4nROdMJiPurtfrRblc7sNleEuWSiX4fD6xLHmD0oItFouGx0FXSmcoMZeeXTw1eVqnb+rUTLrROijT7Xbxj//xPzY0Dq28OBYGZng4ufl40+pLjUqZ1jtZCGYzyvZSjIM0PX6ff+vvU5n/VeABYLc9CnFUYPcS4h7UGVq69Ycet84+4/5mrywjkkqlAEAMgVAoBK/Xi0AgIHuP58DhcGBoaAhTU1MS+Ltx4wbGx8cxPDyMcDgs7bwtFospdxiArPnm5iZCoZB4e6VSCcCux6WDi6Rf0qplZpeON/CMG5V2uy34Z6lUgsVike7TXHfi1sCOEcMEglQqJRlUfr8fc3NzouiZIWdUbt68icnJSfh8PlGcnU4HS0tLcDqdOHr0KIaGhgRKdDgcGB0dxfb2NhwOB0qlEtLptLTwIbWOl/Wd5K6pshpL1XgqNzInRn+dCnevNNB7sVBIBp6fn8fo6KiY8cwbptXC4hi0IjY2NlCr1TA6OopSqSTZZ1QuiUQCq6urhsfByCppLfyjsb/BLKnB255zpKlr9AaMiubPDlpt2stgny1NydLKjP+nwjWbZ64/az+LVStObcHu9Tv3EuBicNHlcglWNkiV43M1nMVDxksQgDBBzCp5ANK3qtFoiIVGl59nhwYKPbK5uTkUCgVcvnwZi4uLWFhYQDQaFUtrY2MDpVLJdLYf8/+3t7dF0fMck2tLXJw4YrfblUSJbreLcrmMTqcj1h2hPTPeFr2LdruNTCYjfNtarYZUKoVsNov77rtP+mGxk63D4UA+n0exWESr1UI+n8fU1BQSiQTi8ThCoZApBVupVFAoFLC+vi6Qp81m68vIopERDAbR7XYxOjqK+fl5zM/PC+d7ZGRExul0OuVyvJPsq2B5OHkYAAi2SMV6J+xwkH6kP2cQv7ybZDIZjI6OYnp6WhRHNBpFKpWSw0SLo1arIZ1OIxqNIh6Po1qtyt9bW1uyyR0Oh+AvRoVZa7w4HA6HKFzeqAT1yePjnGl4ZDAzjhFJM6LZAvQWAIjr4vf7ZRPpuge0oFqtlsAZPIxm6XN7wQF7wQBGoIHBv83sD14s5NJyDiwWi7QnJ1OB+5eGgA5maTiLl6UZjL7b3eFg3759W8ZCOmCr1ZKqYjou4Xa7MT09LZfi2toaRkdHEY1GJXmDismstNttKVZSLpf7kmJowQI760jaFilU5HkSUyaGrLP+jEggEBDe6Pr6OqLRqCQr1Ot1bG9v48qVKwAgFDZS5dxuN4aHh6XlPGEVdqo1I7FYDJubmygWi5IowTkhs6BYLCIcDiOZTPYlvNy8eRMrKyuIx+Oo1WoSJON+3w8LvquCJaitD6mOVGtWAA8cN6cWnRHGDW5UEokELBYLpqence3aNXS7XQwPDwsvjW2F+aJcoGg0KoeMmRv5fB7BYBDhcBjpdNoUp4+BPu1qE9vSlhAtZH3JMLCioQFd1s+MK6qpWTy8jG5q9gKDbkB/oRndUZUKhgEwsx7GnazYO8ECd/qZwYvXjIKlG02Xmu4b55jzTvpRu93uiw7TwuRFpd1Xs9S1dDqNdDotz6MicTqdgrMCu22k2+22MA5YGIWKKBQKIZFI3FOiAc8YK0Qx7ZMWIgshUfkVCgVhl9CapIJl8EvT14zK2NiYKENilZwDAKJgI5EIDh06JCmoPp+vr64EsOPmHz58WC4GM8aALtREj6Db7QovnnugUqlI7YHFxUW89957WFtb60ug0Xg+M/HuJIYSDQAIfqQpMDqPnFgNsGvl8sBq+IAb28wNRDB5a2tLOKWbm5sol8viHvMwMKfYZrOJ+8+NMjExIdFKRnfNKDbS1nQgj/UrdSaSthr1paTxUi5msVgUrMvMOAhT0P3nhUfFxXnWxVY0NYxrSUVMi9iMgt1Pue4FDexlse6lXPXPGBFeJMQJOf8M1OgECP6be1sniAwG+zT9zojY7Xa58El7YjGcWCwmikIHknRSAeuxdrs72UOkEiWTSdNtu6lA2JqbioOuNYn+tVpNYhQ6i05fvGTjMAhkBs4iO4GwHelnPC+jo6MIBALI5XLIZrMAIDn/pLURQ37wwQdx+PBhMSTM7NVsNiupsaTNtdttrK2tYXJyEsPDwwIrNhoNnDhxAouLi7h06RKOHDkiZ4RnWK/hPQe5iMEAu7VQNU6nLTIeHloNeiCDVC2zGUM8fMRca7UaLl26BGCHI+v3+6UwjaZqcaE0D5YWZ61WM03TWl1dhcfjkQwXn88n7AG6UQwU0ArQhV94G/MC4uYdpCndTfL5vNBDNOWK1pbD4ZCxUAkPsgNIXyN1jWtrZhxG8FNtFd9Jof5VlCuAPk4ivSe9V2nx6BoamoPJS4isDM6V5i8bkaGhIfFOyuWyFNwuFApSVIeKnuvC8+L1eqWgNGEuFicyw3ShUAmy6HatVoPT6UQ8HpdIOKlQdrtd2A6BQEDqINBNpkKjMWNGwd6+fRuTk5NYXl4WzHRzc1MyuoaGhiRZZWlpCaVSSbjryWQSwA508JGPfASnTp0SuqHZeAE9GlqtVqsVqVQKVutOHd7V1VXk83m5rCcmJrCysoJjx47hoYceQqFQwNraGtrtttR/ZmB5v2SUuypYYJdPyAPFiLTmtPJG0VavDvZopar5mUbE4/EIlSUYDGJjY0PwrUAgIBXOK5WKEJFtNhuOHj2KSCSCQqGAbDYrTIRCoYBAIIC1tTVTQDkriNE6YoooFR2xGSo9DZ3oIActfmY8MRXxF37hFwyNg3Qgn8+HYDAIAH1JA1QStFAZ0GJQg5eMts54qMxsWn7mfhCAjtLfSbHq/w8GpYwILXU93xre0jir5sDy+5wfWqAkn5vFgmdnZ4WC1Wg0JOGF/7fZbFKL1eVyCfGeBkogEIDD4RCYIZVKwefzIRKJmMr/ByCZhQCkmLS21LU17/F4EAqFRLlqyITuszaUzEAEa2trCIVCUkOVqaqaXUMYgj/H4Prw8DBisRjm5uYwPT0teDC9TzPGAHUT9VixWITFYsHx48dht9uRSqVQKpXgdDoRi8WQz+fh8Xjw0Y9+FIlEQtaPa0ZOMfnLd5J9FSwVJzebph/p1MJBig5dYH2AqQAAiHVnVLa2tkSROxwOIU/b7Xb4/X5kMhkEAgHE43HEYjFxNcLhsDxzY2MDKysr4jaVSiVEo1FTGKyeF97ig5cIRV9ApLZQsXGTapqQGdna2hKMmQELKhJibxpLpCLUdRN0NJ1f1y7QX0V4IAfhgkGIYC9allmlxs/hfuNzdGUsTRnU80HLn1/n72gqkpn1mZmZgdPplFJ4WsE2m00JhLKEH7Czl9LptOwH4qOM4rMwfCgUkmpyRsRqtQqMBOykrGaz2b4aqqSQseALAMGsCZ0NMoZcLpepMxMIBPDuu+9KMoHmkZLDnkgk0Ov1pJ4u17LZbCIYDGJyclI4sHoNSbk0IrRavV4vQqGQFHiJRqNSdpOpsN3uTjLS7OyspAozA5GcacahdMbdXrKvliOlQlsFOtVQu1IUKhDioBqzBXZzos3I6uoq3G43EomEEIMzmYwQs/lcbZXV63WsrKwAgOQgh8NhbG9vI5fLSW44uyUYEd7mvGwG/9Y3PZWnzhaiBaAViE5ZNSrcLHxnBkk8Ho9cQhyPDqbQIuF4qXSpZO6FpjVoqWrrca+/+Xv8mqaOAeijjhmVwfKHLDLDtRhkcNA60wFAzgfrNej1Mirj4+NotVpYWFhAoVAQy5HehsYzbTabWKzValWq5jNBhW5nsVhENpuFz+fD6dOnDY9FF+Cp1+vIZDJYWVkRF5vKnhahtugZ/AJ2z5VOLzUjTCtdW1uTSyYWiwlHnYqNacUMEBNaITOH1ERamWYter/fL8ZFp9MR74/dNY4ePYozZ87AZrNha2sLi4uLGB8fl4twdnYW7XYbwWBQIDhdg+ROsq+CpSvP6Dk3HLlwVKL68NDC1RF0DkCnYppRKFQa0WhU0hp7vR42NjYAQPr56CAYm/GVSiXpzJBMJmGz2cT8Z7EHo6I5vTqnX9/yPKxUIJpMr581yA82Mx86MMcgF7BbJ5b4mV4bBgoBiDIhTYuBHlpORuVOMIB+/70U7GDwi3MzqHSNik4ioTWrLziyLLTC1cpWGw50PXWA1qgMDQ0JjkfOpc/nkyIqTAMF0Mcy6Ha72Nrakq4C/MP3MRvg4pzQCmdVsJWVFYEciB8yu0u735p9oWmZg8rfiGxtbcHn82FsbAxra2sAgFAoJHx1Uq5KpZIUDWo2myiVSlhdXUUsFuur1AbsGn5mvC0dDC+Xy3LJOhwOMdxWV1cl4NxutxGNRhGLxSSZKBqNSh2HYrHYZ6DcSe5ai0A3L2RQSx9CbcFpK5KWkybka1K+GcVmt+/UqqxUKtKcTVvEXq8Xo6OjKBaLSKVSmJycRDgcRiaTwfz8vOQPM0tkampK3AIzeBKwqyh5eeylDAYxaC1UOlSoZhSa/vxqtSqHhBYtrVpyQfXPa04yAAno0BXjz91L8FG/l/6ehgr01wZ/V8+DWesV2C0Iov/memsFz8/XbA6NzRLaIsxiZi4ASMqp3+9Hr9eTakvEOYkzMgimK/mTq8r0WSp6XoRmvT4GYqjANTTGLsxsVNloNKR2AueQZ59/yCcHzO1ZnnXWkmW33VQqheHhYVgsOxW6GO/h5XL48GEcPnxYvALymckr5gVg1JKt1+twu92S2h6Px0VvRaNRqf5G/eL1eqWUotfrxfj4uDAR2MqGxfyZwbeX3DXIRetAB7T0IdDurybnDipRusFUSmY279zcnDQfm5qaEiJ3MBjsy60+cuQIcrmcFO1dWVlBKpVCKBRCu91GKpUSV8fv90vWj1GhdaWtVeCDinTQMhu0cPXfg/82IkzhY380vR7EpjW2yuj5XgGowSDQvUIEWpnuZbkOPns/3NWsBasz2fRnasua+47KVXsQ3NP8fV663PdGJZvNCseWAdhkMompqSkp1sy2MrVaDVtbW5I+6nK5xFJl2Ula4Zo+ZlTYypzKmjQ91uZlYFZfMDzHtA4tFotkpzHJhkk0RsXlcqFQKMDhcCAYDAq/le+7tbUl9QjYrsfpdGJiYkLaQukYAS8dsjKMChV5r9fDzMwMLBaL1CfgHCwtLcHv98s4CSXGYjFJd2YQMhqNYmlpCevr6/feMob8LpKcuRG11acjjjzU2nRnxJ2gsKbRGBVy77gB2ABtZWVFqrWTp0tKCXl/sVhMLFsGDuiWUMneq+igCrB3xhK/vpdi1mI2Wm2x7FR3LxaLsglphegCLnQ1Nd5IXInreC+WvB73ICTAP4N47J3+3MscDI6B3FZdh6HX6/UVHOL+pMKlDAb8eOHoimdGJJ1OC7YPQBgDbJGdz+flsA4NDUn6qMvlks7HJNlzv+hayv+f9r4kNq70uvpUFauKNc9zcaZEsiV1S253Kx2349hx3LYTJzAyAAmQXZBdVtll5UWAJMg+m8AwnIW9SAAHHpIgjpPfbjvtVk/qlpqURIlkFauKNc8jWcO/IM7lV2WKek9A73gBQRKHqlfvfd/97j333HP1GAMiFpf4Ona7HV6vVyAy1cEygKKsJCEj9turcIFWIycYOKOOhcNhjMdjif5KpRL8fr90NS4sLAA4PbCSySRMJpNU6iuVChwOh0TDWi2RSMghZzKZsL+/j8FgIAwkHkjXr18XUah6vY6dnR04HA4Ui0V4PB7RQSgWi0ilUohGoxfO9Xtmo4H6Nx2FWkBQ02WaKg6h0pYoLsGIQ6vxwRqNRhwcHKDRaCCZTGJtbQ1PnjyR0SRcBM1mU9r6uIjn5uYQjUYlLSqVSrojad4DnqKqw+QmZdeW+vNqFVuNnPi59FoymZTNR/Wl+fl5VCoVeX232y2CN4yqVBI5DyU6EXXxaTXVcdIJzDrU2WiW9+S8iJOm957M1gT4+/y8av1gtnOL73teIVdvkYssAHWaAYn9HHvUbreF+8lx1AbDqZqUWijkiBauFb0HIHVoeZ/ZgWQymUQ02m63S8ssAyem416vV0RauF7YMadn0q7RaJSs0mKxoNPp4MGDB/B6vdja2sLW1pYMVLRYLALrnZycYHV1VYqDxGGJe5ZKJTidTs0sAjVDOD4+lo4xsjxarRZWVlZEl4Hzv/L5PAKBALxer+gYhEIheX+OPH+aPVNNS+UlGgynEmrkCjK1UdsvyRIgRUQdqMbIadYhazGT6VSzMZVKwW634+DgAMvLy0KWNhqNMtxwOBziypUrcLlcKJVKEgGz79hsNuPg4ADNZlPX6N+L0vrZjahuXNoslKDeAz2RUiKRkIjs/fffx8nJiVR91TSLDoNNBbzvxLzUg47f19OSeR474CJoYBYeeFoEq3dtNBoN+Sxq4wuLqqQbqsU+tbjFaRx0qE+j3j3LGBlymgcLjmqhkwUk0oz4jPiz5XJZCrq8Xr01CwASnaoNJCpEQIpWKBQS2iSvjcInagAyGo0kOteT9THVT6fTMuZ9PB6jUqmIDsPa2ppEkna7HY8fP0Ymk5GOy1laHeGBSqWieSo0/YTJZEKz2US9Xp/ScHY4HEgmk8jn8yIYz8DojTfewM2bN/Hf//3faDabAvmMx2PR1n2aPTN840JT6VncLKojBSAOVe1i4ukDQJwx0zStRlFpLrxWq4X5+XkUCgX0+334fD65YTdu3BA2gclkkmgim83CbrdL7//R0RHW19d1RSiz9+W8fwPnp7rnFXFUR6PHNjc3JdIsl8t4/PixOEhmC+12W0536onyWarREJ0SF5qejaxinLOR6uzXZp0w/z17D56HpkWMTo2m1bVKR8W0m++lUuZUHJbptPoZtZjFYhHBkr29PeTzeezt7cFsPp3UyoITRzGpgjtk31B7lPgo16deB8shloyA1c6xarUKk8kEh8OBeDwus6hGo9MuMqbIdMhsruF91CPxSYaPy+XC7u4uGo2GRMn1eh17e3visHi9PBzUojolFEnBU6EgLVar1SRLo3gUf59BBQth1WoVlUoFfr8fGxsbuHLliozopl9xOp3CKHhuLQLOySGGx1P/vHZQ/iHmQ6xVhRBUERg9DpaCu+THnZycwO/3C81D7R6rVqs4PDzEwsKCdI4QO9nf35dZPFtbW1Mtt1pMLZbMwgTA+RNmz/t9/vu819BiW1tbgmHxwOAMI2YY1Edg9XWW90qskRuZp7veRoOnFffOK2w9DYvl76rFredlEqjQldo+qzoawjiM2lTMmM+IMJiew4+HGpkBh4eHUnG+cuUKHA4HJpOJdANRhIgBDAd1lstl6aw67z5rMTWrVGEGOiyDwSAZXjAYlO4y3hOOtadcILNQvQpjo9EI1WpVRtQw62UQxPtOSMJkMiEWi4mz73a7knERtlEza63Gw6FYLKJarWJpaUnEv1mXUdknc3Nz2NjYwI0bNxCNRnF0dIROp4N0Oo1arSZC/s8Sy9ekB6u22PGmkE4x2zHCThTgbP4Ui1xquqbHSNEyGo24cuUKMpmMVEgZvQSDQTkItra2BDRvNBpoNpvIZDKYTCYiPNzpdIR7p9VU56j+DUxHoSomy66P837ueVPRaDSKer2OUqmEdrstUIk6UJATTnndxN9sNps4O0IrpNMB+iK285gDqjM9r+ngPCd73r3Rcz844ZhBgIqfnudAuRbJj1XpXOrn0ruJOa1AzSK4zvja7GGfn5+XtadOba1Wq2i1WpJC01HqrRXwcFEjYXVUPfH7vb09hEIhJBIJeL1ewWs58XYymQjWyYxVD/Om0WgIb7Tb7UqDg8FgQKPRQLfbhdl8Ou2VEabf7xeamZpJ8FAiVKnnnvCwUusNZBENh0N4PB40m02RNF1cXEQsFpPrPDo6EnzWaDRKNMss/Wn2TAc7K/CiAs3cmPybESrB8PF4LN8nkK6mqlptcXER7XZbcNPj42M4nU4MBgPhoLG4dXJyArfbLRMOuBgODg4wmUxQr9el2EWKl1bTcs2z0ataob4IStATodjtdiwtLcn1l8tlOJ1OiVR56pPHB0BoWlzQ7Fia7aTSW+R6FjxwXvQ662Rpz1PwA84oRqogPAsZ6prj9/iH6ffc3JxoQ6jQwmzX3bOM3EzCYiTyq9E5X5ODACeTM+YNndfx8bGM3Wa1/3mKsSoUxUhrMpmIziwDDVWkntKEZBCpz4z6H3okHBkIsQhN4W+Px4PBYIBCoQDgrNOKjIvj42MUCgVpk1VFv1nEvcixzZrBYBAdEuL13W5X9mej0UCxWEShUEAsFoPNZhNO7O7urrQ/83eoJ0IZ1Ke+70QvAHhpl3Zpl3Zpmkxfrn5pl3Zpl3Zpmu3SwV7apV3apX1CdulgL+3SLu3SPiG7dLCXdmmXdmmfkF062Eu7tEu7tE/ILuR+vP766+h0OnA6ncLZ48TOa9euodVq4Z133kEgEMDKygp2dnbQaDTw4osvotFo4PDwUPhk7KL65S9/CZvNhj//8z/Hn/7pn2q6yH/7t3/Dhx9+CJvNhhs3bsDj8aDb7eLg4ABra2si/DCZnKrSb29vY3d3VzqU/ud//gexWAx7e3tCwWk0GnjppZcQjUbx7W9/W9N1fOMb35A5QqPRSESEvV6v0NFMJpMIZRQKBSEzn5ycwOfzyaTb4+Nj2Gw2+Hw+BINBWCwWLC0tabqO+/fvS786xUHYOMBrIC2uXC4jm82iXC5L51sgEJDvtdttGI2nc4k4r+mzn/2spuv4zne+g0wmA6PRKPOoqMBfr9cRj8dFAb9WqwlVp9/vS2OEw+FAt9sVAXWr1YpgMIhYLIZ/+qd/0nQdX/7ylwGcUd38fj8ikQg2NzcRj8fhdruF5B6LxVAul0XRCgA++OADfPOb34TVakW5XMZ4PMbLL7+ML37xi4jH4/i1X/s1TdfxrW99C+12W3jgJLWTMF8sFhEIBLCwsAC/349wOCxdS5z4SnFpleoVDoelgUer2Ww2fP3rX8ef/dmfIRKJSBcZcNpwkEql0Gw2Ybfb4ff70el0ZA3F43EkEomprq3j42NkMhmhk2ldI9evX4fNZpOx4JwyQhocqXPtdlumc5D/yz3jcDiEUxwKhbC2tobPfvaz+MpXvqJZ8MXn801xeNU5Y+Ta8nrU1mQ2WpHGplL3KDA1mUxE63bWLnSw8Xgc5XJZyLnD4VB6ibk4+/0+ms0mGo0Grly5grt378oMcXamRKNRbG9vIxwO48qVK7LotNp4PBbea6/XQyQSgdvtRqvVws7ODtbX14VYbTAYsLCwgOFwiHQ6jWAwiN/+7d/GvXv3RMmcprdll46QOgwU6KB6F5WI2NY3NzcnfL1arYbhcCgtrdSZVMU2tJrX65WuJXa4cOGQSM9rIEmdwsqU1OMIDFXxnr+n1RYWFkQQmZJ27OumlKTL5RLFKIpPp9Np+Hw+dLtdeDwenJycoN1uS8smRUa0GpXv2T/PMSTdbhetVgtWqxXNZhPxeFzaITkgc25uDqFQCMlkEpVKRaQvOaFYT1OM+jvVahVerxcejwfZbFZ6/KnQb7PZhLvL56/qRZAETwlQvc05FCph6yv3L3C6Rvx+v0xEpX4F1zEDAlWMiE0GFA7SanSAJOVTX1oV0hmPx/Is2CDDzk0K9HBAp9vtxtzcHDKZDL73ve9pDtLUe6h2T6pcaHWyiMr9p/4shf+BMwEstanoPLvQwVL5/OjoCF6vV/RHfT4fnE4nWq2WEKCLxSKi0SgSiQQqlYpMsGw0GhLNFItFrK6uYnNzU1c3yMrKCsbjMY6OjmTy5AsvvIBQKIR33nlHTvnRaIRcLgeXy4XV1VWYTCYUCgVsbGxgODydFMlrKxQKooyj1fgQTk5OJHJjZM5OFI614GlJgjZlExuNBqLRKPx+v4iDcLNrNco+coO22+2p7ikAcDgc8Hg8ACAZiMPhQCaTweHhoUjXUZykVqvJz2q1UqmEcDgMt9uN8XiMUqkksok2m0028HA4hMvlEkeYTCblsCyVSjLVdH5+XuZP6XEo/X5fZijxULNarfB4PKKYZDQaRYuUTRjs/AIgGgTVahXhcBibm5sSTWo1ar32+30hn9NRsg2UEoYU46ZDPTk5kQgJOIucAoHAlG6rnmshsV5VU2PTAT+X3W5HvV4XUSC2u6sDMLnGGEDo0YOlkI06noWdoSaTSQIth8MhQcp4PEa1WoXNZoPD4YDNZoPf75dxM51OBwcHB8jlcpodrCrNyO5PRq9qExAdK6+PP8fPrDaGqLrBT7MLHSx1Kev1upwynE7JlNxqtaJUKiESiYh+Y7VanRLrrtfr0gXB6EJPpET5QYrdptNpNJtNXL9+HbFYDLlcDs1mU4aSDQYDrK6uYmtrSzbWtWvXUC6X8corr0hEnMlkxAlpsWw2C7PZjGg0islkgkqlIhESO2N4cKiLk22+VPxqNpvw+/1TY7b1dFBRT0Edr8G2Q3YkUfeTAxFtNpuc/kyDmQkwhe90OrodCg8rKvez04dRMw+edruNRCKByWQiY9XL5TIGg4H8PFsRqRav1ajRyZZOj8cjzpXQlupczWaztLVWq1V0u11Eo1HpdKLsY7lcRq1Ww2uvvabpOiKRyFSrbrfbRbvdhsPhwGg0Ep3Y/f39qRFGFHWmdoDagsnJBlSR0mpzc3Not9solUriFFQtimazKdElDwW2WasTOdgFx1bsRCKhS3GtXq9LBKiOMgLOUmy32y16tHReDodDMrxwOIzBYCCHMrU09GQ5jDR5QFCvgxE6I1J1KAB/T/2bnYGquNBFe/dCB5vJZGCz2RAIBGTcis1mk7SCkUC73ZZ5WRwtsbu7i2QyKc5nMBjIuN5Op6PLwb7zzjuIRCLY2trC5uYmdnZ2cHh4CJ/Ph+vXr0tPvcPhQDAYlGjMZrNhZWUFo9EIhUIB7733Hm7fvo0XXnhBNvHOzo7m6+B12+12RCIR6ef3er2wWCwolUoikMP0hloNBoNBFjHHdVMlSF3UWqxarQoUoGplUiG+2WzCbDaLyA3HdBwdHaHRaKBcLqNer8t7ctHPtq4+yw4PD3FwcIDRaCRYMsd4zM/Po1ariV6Ex+NBpVJBqVSawmZ5jwhbGI1G1Ot1bG5uar6OQCAgGhOTyQTRaBSLi4ty7xlZEkZgFAWcRuHj8RgrKyvI5/OimuRyuWA0GtFqtTRfR7vdlhZkYu9zc3Mil+l0OrG9vY1SqYSFhQVpJY9EIjg+PhbpOz67druNXq83hRNqNYrfHB4eysFBoRWbzSajqKkDwCyDUAUxX6byVApzuVy4c+cObt++rek6CoWCZBB8xuPxWBw85TNVbV6ul4WFhak2YUbAFOPR0z5MOJC/pw4wBSBqgePxWAIetlTz3/QzzCgoMXlRdvFMwW06TuBMM/T4+BjZbBahUAhXrlxBuVyeGgMRiURQLBbR7/eRTCYl6rVYLKLkruc0zuVyMBgMSCQSCIVCuH37NlZWVlAul0WFhyK58/PzMm+HD4qFHUbZxWIRyWQS8/PzuHLliubroMoOcU/OXgIgEoiqgAohgMlkMuUIqejE39U7b4nRPHCGD/FkpcANlZD6/b44tydPnuDx48cyvZSOZ35+XnQn9NjVq1dlg2azWeRyOUQiETlQebBwbfh8PjSbTbTbbdEepS7CxsaGTBUlzKLVKpWKRPTE2dVoloUZVZVJ1SJwuVzY39+Hy+XC2toa1tfXJQDw+/2ar4OQDcVbKHJCtShOTKXjoFAzNQCIDdKB9Pt9NBoNEYXWYzwsa7Xa1OQKKuGpM7+oSdFoNOB0OiUKV1XI6GhGoxEODg40XweLZ5RrNBqNIobPgIvRo8vlEqcWDodl8ojX68Xh4aGsGWLYeop+1DcgxMc9qsIGzMi5txilqngsANFCoPbERXahgyU21mw2xSlQ1msymeDu3bsIh8Pw+Xwol8tSFZ2bm8Pi4iIymYwUDTh6mBVLPQ6WKc2jR49gt9vR6/WQTqexvLwskcloNEKtVsPc3BwcDocI4k4mE3g8HmxsbGBxcVEWDud06RlDfHx8jOPjYzQaDdRqNRkHwhPYbrcjlUqJUtHq6qrM6yFeS1EWfo2Rrp7IUQX/x+OxwC+cfqn+TWzNaDSK4AtFqcvlMnw+n6TRTOO02vr6ukjPRSIR9Pt9WCwWSU3n5uZwdHSEZDIp0A3ZCyx4FQoFLCwsoFwui7Qkq8hazWAwoFAowGKxIJFIiFNlEZI6xFTXUivPxOZsNps4eW5sFYfUeh0UOqKADHFWpvzXr1/HcDhEqVSCw+FAq9XC4eEh4vG4HMg8fLneeN/0GB0DISpCWKpsIyEDslA4ZZlRJcfOlEolNJtNCVwoBKPFeD+IaaqwA3FwdRYZx9uoRSjeDx5CDNb0RLCq4BQDFCppqdKUFORRIQJ+XY1eCVc8a/zVhVdYq9UQiUTgdDpRLpdht9tlNk00GkWpVMKTJ08AnJ701WoVFotFTl0KYTOaWF9fl3/rKaYwFM/n88JK6HQ6ItZrMBhkwzKc54NVFYwY4QCnaXa5XMZwONQ8b54Vf8IArPY2m00RWjabzTLCplgsSjWUM+BVrE3VytVz4HChkPLUaDRQKpVQq9VEUYyjiRmNeDweoS6xyEKqGaeb6nVsH3/8MWKxmEQp1AFmRDYajRCLxdDv95HP51GpVGTkyPLysjhaVZZuPD5Vu8/lcpqvg3WCWCwGn883NbZHHRvDyJnFwEqlAqfTiXq9Dr/fL4c1nc7Ozo7gplqMGDIzFoPBgHA4LJX5ubk5NJtNkcVjYZOH46xuMg9urmc9Rmik2WzK4cXxK4QmLBaLrCHi05yOQeZBq9WSAItwn56CGx2QWtAibW1u7nRadLVaBQC43W4R1Scd1GQyyQQIOjneCz1MJO43SlPO0rMYiFAFEDgreKkwAbNxRrrPyiwudLBM1Vwul0QnBHjH4zGWl5dlljtPBmKyrB7y/7FYDJPJBJFIRLigWm1ubg61Wg3BYFDSulAohHa7jcFgAIfDgfF4LNc5q21KKTRiapVKRcD3xcVFzdfBibSESlQcibhVJBKRyIRjNghhcGF4PB45ZHhtemACUm7Uola/35dJs8BZ5bfT6aBSqSASiSAcDmN5eRmRSAT1eh31el20VBm56nH0nU4Hh4eH8Hg8kplUq1VJvRwOh6TIZrMZPp8PxWIRCwsLQu87Pj5GLpeTItX8/Dx2d3fx8OFDzddBahzTz06nA7/fL+LJjDSIgZN+xDEiLpcLtVpNlOqZAZXL5QvFlGdNLXgwy+AAQbvdLkWvTqcDn88ntDZGp4yaiZ/abDZxCHpZBMFgUK6BwxiZgnP/0lmR0sdIl86Mv08HwwBFD1xBlkyj0cBkMpHDmM6KAVAwGITX65Xs7uTkZGp/qUwbXrueoYc8VAj50GkTX2aUrUau6jgfFSoBzvR2WTR8ml3oYLn5GWGRP0fFc0aj6swcdRS21WpFLBZDr9eT+Tkk/Oqx+fl5dDodoQGNRiPcvHkTw+FQRm7Q8asLnPifWlwCTnmk3NB6HH0wGITRaBQR7263i3g8LlqVqgYpZx0BZ1w7pqgul0tOaVbbSdPRYqw0E5hnCsmiEau1hA9KpZLgsmtra1haWhKqVL/fn5qqqadCzKJRv99HOp1Go9EQQWIeIEyxyGKwWCwyPp1zwcgXNZvNaDabODw81BXBRiIR1Gq1qXHXVOanULM65JBpJiN2TiJgOr62tob5+XlsbGxIwVSLtdttcQh8RnQaxLgnkwm8Xq9kIHwGrJKrhT82gfCZ6zG32y3BDQ8y7mXO1JpMJkK15CGvCo0TonA6nbLv9BbbyOPlUENS1vh53W63QCMmk0lofXR6AGRwJNc5swo9fkRlEaiMAOBMA1k9ILlO6HT5PcJNZHZwNM9TP/9FF0XMhtFYo9GQChxTDjoX4GywWL/fRzAYRL1enyqkhEIh8fx6Th/eiGaziVKpJA+FKY7qoNgEoKqP82bwBjWbTTx58gTLy8uah6YBEJ4m0zaOAOFkUD4Uj8cz1V1FbI6fhbPlWRTk6X6RcO/s/bBYLFLAIw3M4XBI0YsLATilyqiRbCgUQjgclgXa7XZRLpeRSqVEAFmLPXjwANFoVKI0l8sljIhyuYy5uTmUSiVJtRhFBoNB9Ho9OJ1OVKtVhEIhOBwOVKtV5HK5X8FJn2VerxcrKysyfoWTCtToB4BMcSWdLJfLYXV1VRTuHzx4gEAggGAwiEKhAK/XKzOytNhwOJSRRqFQSA4VYuVkBJBqxHSUVDs+DwYOwOke5DwpPUbIrtVqoVqtol6vY3FxUWiCrIozOlOnH6jOWD28OV6FsKAWo8A1YTGXyyVMJFIFVZ6v6i+azaa8J/cx/U+9Xtd16HANkoqo4tE8VBi1qsU9Olk1c+e10g88N4vg8PAQoVBIHkCv10Oz2UQ4HJaRFvPz82i1WpJakXvJljibzSZVa7/fj2KxKO2LWo1K9Z1OBw8fPhTmAPE2fp/EYI5J5onF04fD3NgaymvUaqxkEksltprP5+WhEMtUOcMulwvBYFAgAXL4iD3yINBqBPhZkOHD5+sRkyYXlikiscp6vT4VxYdCIYnE9YzQYWaRSCSkMsxON85M63a7U44uGo3iwYMHKBaLcLvdAhd1Oh1pEtjc3NTF7mCay/VA/H92czBaD4fDktWojQYczkenZrFYdOF8LpcLR0dHuHPnDubm5vCpT31KWnS5Fv1+v8BFXANkW/BwoDOlQ65WqxiNRrr2zMLCglS5yUNPp9OCparzsLhmrFbrVKciAxXa8fExDg8PZYqIFmPTBSNUHrQsLrJFlu9Nih+jXhaU1OkMnDOnBx9XC8rqMwfO0n1+z2q1yueedabqRBa10+tpdqGD7fV66PV6CAQC8Hg8QlFivzWjFABCm2CVkNV7Riq3b99GOBzGwcEB7Ha7rvD+Zz/7GSaTCVwuF+7evYtcLofXXnsNGxsbCIfD0hXDqFElNBPAZ7pK/DISieiOClKpFI6PjxGNRhGPxyWlZQ+1GvHzpCVFJRgMCvbKB0TaDrFjrcYGA25+u92OYDCIwWAgTQcAxGGqm7der+Po6EhwRnbLhEIhuN1uXRH9cDiU2Wf8rOr4EepYhMNhPH78GHa7HQ8ePEA2mxUsjJ1bwKmjjMfjCIfDujYPMW/Sn7xer1SjmXHYbDbkcrmp9JT96ExLyX+lzoPT6UQkEtF8HXa7HYFAAC+88AIajYZUnenEyHCZn58XiIAYXr/fR71en6q0k+EyHA5RqVQ0XwcAOWTZVPHw4UNpUGEXISEAOj810+I9c7vdKJVKMhQznU4jHA5rvg7uDabSLITycw0GA7hcLsGHuWcZZbPArVLH6Iz1wHterxfJZBLFYhHZbFYgAxVSI4WLMIiaDbNIyc/B/fos7vgzaVqMLMhjzGaz4nSbzSZMJpMUn7gwGU2yL5tCJkxrucC0WjQalRbT0Wgk9K/9/X3BfjOZDBKJhIT6pGAMBgPs7OxgYWFBBCxYAXyeAYys4DNFYdTFFN1ut6PZbMrwNrPZLGk534udVuxvZpFOq/Hn1YXHnno67kKhIGknI6Veryc8UEYJpKmwCKWns43P0mQyIRKJCJTkdDrlHvPE5/h0Ct4wTeP1RKNRFAoFccrBYFDzdSQSCenOI8bPQ56HHjMaZlGTyekAzHa7jWw2i0AgIFkYxzGPx2NEo1HN1/GjH/0Iq6uriMViuHbt2lRrKrO5Xq+HR48e4eDgQCAu4rBsVqEQDA8AvQUuALL/arUaer0etre3MRgMcPXqVcni1DZV7iM6ex4udrtdIuh0Oo3d3V1dgQlf2+l0ot/vizPnmgXOSP7qVNtKpSJZGuE4OkR1D2u14+Nj1Go14cKyHsOomvCDOj6c781MCDijbdHpch0/zS50sMRBcrkc4vE4bDYbvF6v4FMsgrHVj/gNFXBsNhvW1tYQDoeFw0ZlLT1g+bVr11AoFGA0GlEqlVCv1/GjH/0IX/3qV5FKpZBIJOQ6gsGgYCpMyTY3NwXY5wmonkhajQ7F5XJJNxsXBWEDpnRMz+jASFviA1LFNFSxFi3GtlayCOg8AcjI436/j8PDw6nTmoW/VquFYrEIk8mEUqkEr9eLRqOBxcVFXfS5K1euwGazoVgsIp/PIxqNIhqNIpVK4d69e3C73VLYIv5H/uPS0pJ0UXEAXjKZxOPHj1GpVKZGVj/LnE4nksmkUOJ4P6lK1ev1pjYFcErT8/l8aLVaqNVqMlU0FAoJE6Lb7erqKKvX66hUKjg5OcHh4aFkOqwXsPMxEolMUacIV1AMhmpjLpdLICa9gQCjYPJxqYXASJjQhKr/wHXMLjvS1RwOB/L5PEqlEjqdjq5CaCQSQTAYRDweR6FQkMhYdU6EkUgho0NWxXEcDocIErGIqpfxwucKYMpxqk07ZDWoDpZNPDwsiU8zmHvuCJbhPXBaIeWi54cMBAJS2adzI8ZIYQaeglThIn6iZ8FwE4bDYZF6u3//Pt555x187Wtfk/a5RqMhSkhUuTo5OZHxuyxCcUGrJ5lWM5vN0sFVq9VkoTJa58HBaBEAjo6OpuQAh8OhVK7JPrioEjlrjx49kvSOVLB2uy1dbJFIRJodisWiMDt8Pp+oaJFWx+m7xIsDgYBmARxS0BiVPHr0SFKoxcVFiQaJabJ1ljivKpbBCnw0GsXe3p6u4pLRaJSGCUb1bCPmGgDO+LL5fF5gCqpEPXjwQHQ0GMGVy2VdpPrPfe5zsFgsKBaLuHfvHvb39/HGG29IwZEO4erVq7h9+zZ+/OMfo1qtwmq1TkWt5GuS662nGYZGPJMOmv+mY1HvEzMwVsWpQUJHGw6Hkcvlpgj4Wm1hYUEU0rj2WXBSGT+MTvv9vjhc1kjI3OF+YqahtzlH3WfHx8dTI83VQEvFX+lHgLNakFoUA3ChL7vQwYZCoam2OPZZh0IhgQZYldzf38fLL78Mv9+PDz74AH6/Hzdu3JjqaQZOHQ/xqZWVFU03h3QvUlhcLheWl5dFJGRxcVHSbeCse0TtmOKJqd4wbi6tls1mBZNSFxqJ5OzmIt5LZ14ul6Wtl5gbo186ZD1RweHh4VSqz5HD7KKi+AmVpNSKqcVigc/ng9vtFtyWjQrEUD/1qU9puo7RaITt7W1xYCxwUjWMBTAKzASDQbRarSl5vEAggMXFReGvUqNUT0cZtRAo9kPqGIuaagtus9mEx+ORA4XMhX6/j5deegm1Wg2BQEBwej0H37Vr12A0GiXQIMbKgvBwOBStg2AwiMXFRfzHf/wH6vU6jMZTDQaVQcBoX4+Tp62uruLJkyewWCyCs/MetVotKXKqAYfJZJLDmIwQslu8Xq8Us/Vkn3Nzc6hUKoKpcv3zWbGoWK/XpwqCjKDpGNm2T8aM3o4y7n21K4uvQ4js5OREfAYdL50vnS59CWGNZ0lJPlNNi1U8FlXm5ubg8XiQy+WwtLQk/cTEeV5//XX4/X68/PLLsrlarZZgHN1uF/l8Hul0WrNgxPvvvy94lN/vl8o3CxDVahXLy8swm81TwifqjSKcQaoQe5P1kKbZ2sc+eQo5k0jdarUQCoUQi8XgdrvR6/WkhROASDe63e4pCoxeIjl1XRlpjUYjNJtNSf2Jo5IixRZHm80mUQnTNuAUcqhWq6jVarqKbWoKRdlCu92OdrsNp9MpilhMd1utlqTI1INlkwoVp0KhEGq12nPhjtwoxIGBs+oxoRtWi5eWllCtVkVqcmVlRQRwGBR4vV4RnNZipDGORiNEIhG4XC7B2nmYqw7vi1/8Iubm5vCf//mf2NnZEUe8trYmKTsdjJ4DB4BQ9Xw+n6TZFD0iG4G4ps/nE2fCNJkccUJZvHYWRrUaD1QGHKQpulwuzM3NCTZLkXbis2wxZhZtNBqFEkqoSU/2yYiZhTIycej0GdEDkP8z2lZ1KwgzqQ0bF9mFDrbZbOLKlSvo9Xrwer1yojEVrdfrQuyOxWJSmf3jP/5jhMNhiRCYrgCnJ9ra2pouHVaeZmxDpRrU7u4ubDabkNQ3NjYk1eDp32w2pTuFVUIAEuXs7+9rroqyc8NqtaLRaGA8HosTIbeRKl2klTE6IAbcbrexuroKr9crqQYfuFajTCI/J1v2ut0uMpmMbAoSuaPR6BSWxYiEymBUSSIHVatxc/KwYjXebDYjHA6LshcFwgeDAaLRqDxLo9GIwWCAbrcr+KR6r7QaK88smrCrS22ZZbGVOLTf70e73Za2UOolxONxHB0dwe12y4bXah9//LEEHKqUXSgUmmKVcD16vV68/PLLACBYZLvdxv7+vujREj/WE0kDZ4VUSgLW6/Wpw55BCK+R94bXSLiLFXZW7qnMptUIXdXrdXS7XXldcuHVriyqkbVaLTgcDiwuLsr6Bs6CJZV9odXYKstDRP38hEzobAHIM+L1qvUSRr88jJ4bg93b28OnP/1pqd6T2hMMBrG3t4fhcCjYzI0bN+ByuRCLxeByufDo0SOMx2MsLS0JuZiiJMFgUPN4FAB44YUXJE0AIGNGqD/Q6/Vw//59xGIxeDweWQzszye5nCc3o9jxeIxMJqP5OlgFVZV5WOE0GAzS3dVoNFCpVKR7Z25uDrlcTgp9bJW02WxSaNOTdu3v70tUzm4wCqIXi0UcHBxINBWLxcS5MnqlSA07a1hgAKAL+ywUCrJJBoOBtB9T64DsEXYAUhaPhQWr1YpsNivRzGg0Qj6fl0KpVhsMBsLfVDUoyNF2u92CeZvNZsHCgdPDKpFIyGuRYsa1qn7vWTYenwpFu91ukQlkVhGLxRCPx6cI/i6XCwsLCxiNRqJd0ev1BHNtNptyYOmJGgEIS4AiLnfv3sVweCo6n0qlYDabEYvFcHJyIuLWPPTIk/Z4PBKYUPiJGglajYcn2SYsMLMQq7bTA5ACcLfblbXIVloAkj0+KzWftVlnOIuv8mBhRMpgimtJ1TWZ5dE+dycXTwl6fQoTc1Ol02l4PB7EYjHcvHkTiUQCJycn2NnZwWQywdbWlowUIYe2VqvJZIOFhQVNN6dcLuPk5ERSC0aLdFLs4KpUKqI3yROKD3Vvbw+xWEwOCJvNprvYRsoOKUHUSCC3kWpiAOR0pAOkCHkwGBSdT167Xjzp6OhIIg+Xy4VwOCwpFLuJ+MxYWFELGqzQMuIj34/Rl1YLhUIwm82oVCqycFksoUAJaWiMhJjNmM1mFItFKX6wo41RNgVAtBixPHVTUJiEdCs2YZjNZtRqNdjtdnHK8Xgc3W4XZrMZu7u7kpWMx2O88847+NznPqfpOsiW4X2gFi9rF8xq1JbN8fhUmu/mzZt49913kc/nRQqPBVSTyYRbt25pvh8AZEoB9wtrFtSdJU2MdQE2AKiVfWZZbKUulUoSxWq14+Nj5PN54RWze5H6F1wj7XZbnBwDITYo8RqpEsci7UUE/1njwc5mAwDS4EFnq9YqgOniFZ8Vo3tGvM8KjJ5J0yL5mE0EdLiRSARLS0viaKg3UC6X4XK5hJNKXJJttIVCAU6nE/fv38fNmzc13RymjByDQpUik8mEbDYrrZrlchmJREIqjIzqmA6yikzNS3JHtZrRaBT+KBXw2b7r9XoFk1Z7loltAWdjSYgB93o92O123SkgcUQeJOzlZoRsNBpRLBalgsy2TVZf6WCJq7Hbh0IkWo20FRaR5ufnp0RNstksPB4PVlZW4PV6Ua1WheHA93I6ndKcQPqbWnHXej/IrZ6l2ZAXzANHLaQdHBzIGCGuhU6nI+wYFp20GjOE+fl5GWKoMgiItxNCYMpqNJ62Vy8vL4vIDWeHDYdDvPTSS5qHDNKIabMmsbKygsFggEwmIxkgKXycrkEaIXnTZKmQP3pycoInT57oovJRuIbROH+XDouz6lScU4X0eA/IO6dz04J/qqbqlPAAZqGKBWsezvy6Gu3ya6rYDQ/054YIfv3Xfx39fh8LCwsy2ZL6AoxKK5WKFEw6nQ5WV1eFVM8ijNlsRqFQQLVaxebmJsLhMB48eKD55lDijxQvpgtkFNDhPHjwAPPz83jppZdgMpkQDoelGYJdXrVaTaI/4sFabXt7WwpDbPf1er3I5/MYjUZCyKYzI4+43W7LKcz0ghgpcWs9bZCkI41GI5RKJekUM5vNWF5ehs/nQy6XQ7FYFIobow4W6iiywmo3eYJ62yDZ9shN0e/3sbe3h0wmIxBCJpNBOByWoZEAZPPQofM1eIjrEVnhgqeDIKZIZgZTZc59CofDqFarIo1XKBQQiURkNFGtVsPu7i6uXLmiy5mkUinJZvjZnE6ndCkxNWYarB6UVqsV165dww9/+ENks1mMRqcSk7du3cJnP/tZXS3MAISWxYMqk8nIocE0nfg39zIjejanABBxln6/L7REPZxtFbYBICI7hOmYYQBngcOssZBKWI7Bmh4MlnuGhXoyeHggc09QFoD7iyJXfC/V8bKl9qIo9kIH+/u///uS/pHuA0DSy8FggOXlZYkCj4+PhVRNJ9btdnF0dCTan+RvXr9+XfPN4Q0iX5Kze4xGI2q1mgyLI/Xp+vXrEt3RkTGdoCMj/UIPnrS/vz9VmIpGo5ifn0c0GhWAHjgTySHEooqNsLDDQZAkLOu5Dl73ZDIR4jj7/H0+n3SO8b4Ph0NxYJ1ORyhdjLasVivi8bgwCbQaaXrtdlvWx2QywebmpkAVu7u72NnZwa1bt3Djxg3ZbN1uVw4jHoScTEAIQasRTwemlff5rEjiZzV6Mplgf38f1WpVnhU3ezgchsPhQCwWAwBdnW18D3JY6cSIv3s8HokQecCrvFCbzYZIJIL3338fXq8Xt27dwle/+lVJ1fUYdRlIy+JUZpPJJCNzeKDR4RPWYpux+rlUxoietcpokYwEwns8iKi2R0hIbYohzEQYjbMB/X6/FFS1GnWHScfijDEWQ9XDj5kPs1x+jZ+B10Wq40UF6me2yvr9fmxvbyOTyciJuLi4iFgsJk6LLaPEm2q1mlRDb968iXa7jUwmA4PBIGRuq9WK1dVVTTeHs65IPO52uxIVkObBxcOJA5xzxEiCDg/A1IgVPZFjKBQSzm2/38fR0ZFEn5SHYzcK52apjpZ0JGLGwOkC5PQBrXPK1MidhRTeX+K6LPaFw2GJ2llgIyQwHp9Ogg0Gg4hGo7h+/bquSIlOmxAFHScbPghR5HI5gUeIPZMZMhwOha5F3inVxbQaW5M5JI/pJx0Fu8kIEamVdN7zTqcjayGZTKLdbuP999/H1taW5utgJEiHSIoP35cFGh746rQQZkA+nw/JZBI3btzArVu3ZCSPXi7s0tKSFOp4P0gdpKOgrgifBQCpBzCaVbuumELraXygNGe1WpXCK7FN6i+QFsc9QZiLcBz9DNc8pQv1XAejUdIFeYDQqarCNirjiBQz3gPirhRaev311y/EpJ+pBzsej/Hqq6+i0WjInHt+MFJf6CSKxSLS6TRyuRx6vR6y2Sy2trZw69Yt3Lx5E9VqFe+88w6Ojo50tUIuLi6iXC6jUCjI6aJW41kkId1InY5KCgjbc3l6cRLt6uqq5gmmKysriEQiUvHkQ2FLIu+H3W4Xoj2/zwXNxcwUlnjkcDjULLSiwh50ciSrc2GoXTiJREKEr9WIml1c3W5XGhP0iL1Qs9Vut6NQKAhm32w24Xa7MT9/OvOs1WpN8TEZyTHy5NqZTCaIx+PCndZqxArJ8yXnWuVxMnIJhUIyySAejyORSODOnTuisDUajSTCf/HFF3VFjuPxWJwIBdgZ/TGbY0V+OBxKtkBlqGKxiNFohM3NTVy/fl2KXSo+qdVIhWPqT3hP5VyzDtDtdkWYiYwWCrjzfrEBgk0sWm19fV0OPTp3Pis6WKqdMar3+/0ytSSTyUirbyAQEHaEOgxRi5GHzMxabZnlveXnImavdn6phyVpqtFoFC+++OLzq2lRFJmk5aWlJXlgKo7R7Xbx7rvvYjAYIB6Pi7bm/Pw8stmscAwZVVy/fl1XcWkymYhANZ0D9TQPDw8FR+RD2t7enqroExgn3qJihXoc/Xg8RrFYBACRX5yfn5dhhwAkilKr4nR6FJgGIFELF7geU4tL3EgE4EmVCgaDErnxGgjKMx3M5/PCVWUapydyPDk5QavVwjvvvCMFLArq9Pt9UVHiKB2yOhjNsp2XzA7yZXl/tBpFZpiZEO+b5SoyAmGjRzAYRKVSwcrKimQ5XO+5XA5ra2u6MGmyJZi+jsdjabpgRKuOaGExlJEkNT0CgYC8lnpI6LF0Oo3xeCwHOu8tI0VVSJuHHTslAUhDzWAwkAyI91jPes3n89LkQ4iw0+nA6/VKgxJZBBzR7fF4EI/HcXx8jGKxKANU6eSpj6An22KDEumbLFoxklVnbLHAx4AJwBQHdmFhAcFgUAplrMucZxeu4kePHmEwGMDr9UolmpU8ngBcNLlcDiaTCVevXsWrr74qtCyewg8ePMDW1hZeeeUVwaq0WqVSEdyFzorpJ/vcmWqwK+natWtIJpOCr3Cx8hQjsVnPBuLmY7W+Xq9L9Ey+orqB1FY6FjJY1Z5MJrKo1fRIi33605+WBc+HTiyVRQCmwnxO6qJhddvv96PVaiGXy+Hhw4fo9/vw+XzY2NjQdB1HR0c4OjrCe++9h1QqhUgkgtdff11weOqAUpiDjAEWFVRmA+ETrpdSqaT5fhQKBYFtWKnnZ+d1qCr+LLxw4/L+MSNha/PS0pIucWnCQcxQOp2OpJnAGdmdY7yZAnc6HRnjTllLlV6m9+ADTmsWvCc8PHO5nFwLDxOVVsdWb7fbLUwDYqaqjoGeqL7ZbMpBx33I9m5yjtk1R7y2Xq9je3tbePeEXAh3kJWhJ5JmsTISiQiuzgGqbJiavccqhk/4j0EEC18ffvghPvroI7zxxhvnvu+FDpY3mn/U/ntVpNbpdGJ5eRm5XE7aIl9++WU8fPhQ6CKLi4tTmIqeh2Q0GoXADZziZZylxdZL0sEGgwE++ugj1Go1xONxUVNiy6GqrHR8fKxLwb9UKkmhajg8lUJkq6XL5ZrixqqnPwVUCBfQyfMBejweXRvoN37jNySSIEeRE2QZITNKZXRXr9eRzWZlmmk8Hpd0z2KxIJ1Oo1arIRQK4fXXX9d0HZubm0gmk/D7/XjnnXeQzWbx1ltvYWFhAdeuXUMmkxHnysInuaHE3UhzUVMwRjpaLZ/Pi+4BD99YLCZ4rqrgxEOSUaPL5YLP5xN+drvdRrfbxcrKCrLZrC6ogsVWk8kkrZ4kphMmIpZJKIPNKKoYu5qZ8N96+NoApMGA2ritVgv37t3D7u4u3G63FBkbjQYsFgsymYwIBdEZMeukzjAxfj1NIGrgwLXP7G1ubk58C/FVTl8oFovCUCGswnFVhBv0iKEPBgPEYjHRP6EkpclkQqFQEGoas1w6cgDytYWFBbzyyiuyRr785S8jmUzi3r17T33fCx0seXRqxVNtR1SngbJCyMmm169fx/Lystw0kuDVdEmrqaRwVh65ABkRGI1G5HI5wXvYHcLoiBEl0xur1To1ZVaL0YlZLBYEAgGJRlmtpeNotVpTeA7V9ukQWZVk1PA8nD6r1QqHwyEtyxzkSAI2u5aok1Cr1SSyffLkCT766CMEg0Gsrq7KocGIVKtx4/t8PiwtLckBt7GxAYfDgWKxiO3tbdTrdUSjUSwtLQlnlTQiYsjk0DKt1hOdUPOUDiyRSMhhNhwO5QAjrMLGE5LYq9UqPB6P6OcSEms0GrpofNwr/MNiJiMvpt+MlpiFsHjE3wMgug7kKut1sEajUYj9VG1LJBLo9/vIZDLwer0S3RLbpHoXaVn8vWaziXw+j/F4rHumHpkU/OxqFxfhiePjY4GRCoWCZINM31k8VXUQWE/QahaLBeFwGEtLSwLhpVIpGfHDFnyKKDHgYW3F4XBgbW0NN2/ehMPhwNtvv43d3V3cuXPnQv2OZ2oRhEIhUXnnAuBwO8IHnFjJWVSTyQSHh4e4du0aRqMRHjx4IBETRyfrwT4/+ugjBAIBwdCo+MPKK0H6aDSKhw8fot1u4+OPPxY9Wv4eFxRTk2g0qqt4wNSSGDTTOEIP6mwh4Ez0mNgxcdBOpyMPkpJ5ehz9vXv34Pf7ZUJvPB4XIelWqyVUEgL1PPSsVisSiYQ8k729PRQKBVy9elWKCHosHo/j5ORERq3s7+8DOG1QqVQqomuaTqexs7ODjY0NXL16FZ/97GdlpAyjSyopkUKkZ/OUSiUUCgXReKCU5NzcnDgPMk5sNhsePnyIVquFxcVFYSywOEVKEOlteqAbVWdAbbsk/ABAoi91nZCSxhSZIijkWdIB6zE2bHBNBgIBvPrqq2i32/jBD34gHWPRaFSgHDrD0ehUXJtt4ZSzJKdaz5QHBl6ETxjZs7OLAdfu7q7sUzpwwhSkstHxqVOMtRqhILYn04nv7e0JK6larcq640HAQMzr9cph63Q6sb6+jlgshg8++OBCQaALHez6+rpEjvzw1B9IpVJYWFiA2+1Go9GQSaHXr1+X04dtcKzgE5Mk60CrUQuBi4y/z8g6mUwK+G2xWNDtdnFwcIAbN25MUT+YbnHhcINpNaZVs5N0merQkRKDtdls8Pv9AoZTa5OFJlZs9QiKAMDDhw+FgxsIBOByueB2u0UWUJ3syuYMVmwZxTFNPDw8FCpTPB7H8vKy5usIhULyHMk/Jd5nMpmkCm21WvHRRx+h2WwiEAig2+1KBEnslR00HGmiZ5IARcdJj/P7/RgOh3C73ahUKlLsiUQi2N/fF1qZ3W5HpVKBx+PByckJvF4vSqUS9vb2xCldVMCYNdWx8g8zPq4VQgXAmUOmA2YBmRgu14WWlsxZ44BHZnSU9fyDP/gDRCIR/Mu//Auy2SwKhQLK5bIU1vh+pENy31P3d3l5WTjCWu+Jqs8LnLVtqy2y9C0sSA8GA9GMYCbK+oLNZptiM2mxSqWCo6MjoXuura1JkZX3ib6M3GgW11dXV2Gz2bCysiIiRuxEfBYP1jDRi55f2qVd2qVdmibTB+xc2qVd2qVdmma7dLCXdmmXdmmfkF062Eu7tEu7tE/ILh3spV3apV3aJ2SXDvbSLu3SLu0TsgtpWt/61rekO4g0HLaOAZgS6aUIC3mAlGVTaUjkjHJchVal+L/4i78Q7q3L5YLFYkGtVsPGxgaCwSBKpZJIKobDYZjNZtTrdfh8PphMp1M9R6MROp0Otra20Ov1sLOzI/Skb3zjG5qu46//+q+FHA5AiOQkYnCeVCqVkg4dEqlVkQ0aO22ob/C///u/mq7j1q1biMViCIVC0ipLahhJ/JVKZUqOTx2ix2fJFl11WvB4PMY//uM/arqOL3zhCzg8PESz2ZSx02zPpOYDBcEBCM9QleejyAqnjqpDKz/88ENN16HyTPme5H9SQ5SUQVKjqGXB5hnymtfX13Hz5k0MBgO89dZb0p2oxdSRROrzVuc7ca2wq4lcZfKzSZFS9xn5o3q6ykKh0K+sTVWOj3Q9Gt+Lz029FtLL+LmMRqNmeiMlIClP+tOf/hTAmcarOknB5XIhGo3iyZMncu/UkS3A2aBNUrq06iKwTZ2fR319fl5gWtSFxvXhcrlgtVpRqVSk9Z6f42mc3GcqarCHmF07vBDOFeLN4Rudx/VTX8vtdmNxcVHzoEHgtCWTYtAOhwOlUgnj8RiFQgHtdltkBI+OjlAsFkXkmxqjBwcH8Pl8+J3f+R2Ew2ERJ+GUBj1mMBhEeZ+kafUzskWWh9FFQ9H6/T4KhYJuEjmFQugcuRm4WanEHwqFpKONHFOqGNGJUaaPXVR6iPUvvvgiIpEIHj9+LMpcnIGVSCRQrVZRLBZFQ0KdvsAecM5oY9eV1+vVrbZGPi0FPABMjbABIM6bX+Pn5eFHXmaj0RCSPp2iHmOzgs1mkwMOgLT/8kDkQc3mAl6D2v/OQ0Dv+gAggYz6PNlMQK1a8mM5hYKHMQVPuLZ5Lxhs6eXk5vN5UfIyGAyiSUEnxfvAmXY07h06RYPBgEAggMFgIHKgemxWR4GfiV8jr57dmfw69wVnl5HDDmBqGu15dqGD5ULgh+TNYMuq6sHV8QpcFGqPOYWpE4mEbkFlt9stveF7e3tIJpNoNptIpVJYWVmRGUvNZlO6rPx+P95//33s7e3BaDQiGo2iUChgZ2cHAHD9+nU8evRIokctxnvApgE6OjZSsBWWQibsqFJ/f3ZRqBGGVut2u5hMTkcru91uTCYTtFqtqU4UjmZh1xubHNgGarfbRayGmYgqXK3Ftre34XQ6ZewKiensVuNzIUF8bW0NJpNJhGi4udlqzGuLRCK6lJK4SfiHz4gblwECA4JAICCkcnYWMrorlUr42c9+BrPZLDoOeoxRI50YIzR2b6kOSr3XasTL66fDZUeRHmNjjyqyQtESteOMXWN8H3Zz8b4yUlT1UPXsXXVKBd+T91UNyHiYUcxpdl/Qz3BKB19bq6mHl/q7s36K9wnA1Ofm9VGFjJkzg6mn2YUOlicW34Q3RRUr4dhjtT95Nvz3+XxIJBIi76f3VKYgA1V3+v0+gsGgdEkBp8pflUoF4XAYfr8fpVJJdFu3trbgcrnw7rvvYjQ6nVnP4X8X9RHPGu8DcCbazdep1WrS9cYNTQc7uxBmHa3eXo9qtYpsNiuiLVQ544h0wiFUqqIQDVWsKODBcSDj8elgO7/fr0smMJ1Oiyyix+MR50oNi1gshnq9LjqgbJGs1+vo9/vweDxy/71eL2q1GtLpNEKhkK6hh+wMYhrIaBg4E0+mvOb6+jo+//nPIxqN4qOPPsKHH34oHW5qZ8/ztKfy/SgkxM5FblBqV3BOFx0pTZ0WwN9hhqhXD1Z10Pw/sxxV84DKZeo18lqoSTArAak3cpwN0vi81PSf0TKDNqvVKo5ehVbU56r3+cw6bWYn6v89Hs+UQL36mekPOY1h1lmfZ5oiWGJAPJUp1EF8S4UI+DedIfvkqUv5PDfGZrOJEpTZbJaBchReuX//vmAg6XRaZkGtr6/jpZdegt1uF/k7Rs8cea1n4TYaDQQCAZhMJpH8azQaODo6mhIUmZ1gORuxzKYpeu8HI4hUKoVer4dgMAiHw4Hl5WUcHR3JQqYOKICpBUotBpfLJV/3eDyi0arVKJKdyWREYo/rwuFwIBKJoFariTOl4DiV17iWbDab6LHW63Vp89RqKhSj4v2MiIjBOp1ObG1t4bd+67eQSCQQi8UQDAaRSqWQzWZRq9Uk+lYPI63Gg4WZjZrJ8TopUK9Gq4zY+D31930+n7QB6zXCHtynXJesl/AecQgjr4drh86VP8NoWM+e4evNOkYVflGFb7gX1AmvauTJQ0N1+lpMnVzAz60q2vHzNxqNKcc5+778GV4fn9/T7EIHywehplrskebobjVaJb7i9XoRDodFxZ4PRr0heqK2Xq+HaDQq0fJ4PJYBc9S15KlSLBaRy+UQCATg9XqxvLyMe/fuIR6P4+rVq0in0wBO1fiZ0ms1zjkiDECsJhaLifQbH6RazJhNQ4Cz9ON5jBJ7lGIkNs2NSpFp4mc8GI+Pj6UIyAiBWFupVBIpOa3G0ecul0tUqfh14n2hUEgEgSqVijhVKmsZDAaUy2Xkcjl5virUocWYxTAy4wbi1+gwKCdJ2clkMol4PI7d3V384he/EJjl8PAQrVZLnrNWUyMiFpGYUrPHHjjTbVB1MrjPVJhjMjmdQWc2m3XPsKO4EdccsV86GI6D4Xrl+1IzgEEBsUYGWRQs0mpqij3r5Ph9Xi/v0eyhpMKP/Joa1Wo1tRDP1+P7q3uUNQL1/fizrHsQplPX2nn2TLlCvgGdqqrwzQuidJ7P54Pf75dxIaoTUZ3JeRX1i4wjPjwej8AFRqMR+XxeRoR3Oh28/fbbIhJx5coVdLtdvPnmm1hYWECtVsNoNBKcljeIMIIWY+RO4QmKBVMdq9lsTqnQP+0zqveFC06Ps/V6vQgEAuj1epJiU+2I6lwUtmE0pFbvibcCZ8wH4GzMiFYLhUIYDoeIxWJwOByoVCqw2WwwGo0yMoUFR4p7c1QKI0teBxkIHFyZSCQ0XwcPFjpaYo/A9FrjQfLzn/8cOzs7iMViSCaTUnxLJpOw2+24e/cu7t69K6Ijeo0YPR0K08p6vQ5gOmtR/x6PT+UAOcacGOjCwgL+8i//Utc1zKb1aoGKjlz9bGazGTabDd1uF06nE81mU0SBKpWKiJfrNTW9Vj+7yhghjslDR8Wu6RBV8Ry1zqP3fgQCAZTLZTn4nE6nFOCAs7lqvFbVkU8mE7k+NUt9bger3iCV3kIvT4lCn88nqR5P6/NuAG80H7JWY9pdqVREvJmgOZ15LpeD2WyW+T1WqxWPHj2STUz1nUQiIQrmHHet5zrUUSgU8DaZTFOi2bzpxHj5cFVcR72/fFBajQ6FG4TPxmq1iioUlegpP8dqPWdzud1uUT07L1XSaiywkVXBz0Ipx36/j5OTE1y/fh1Xr15FoVDA/fv3cXBwgN3dXQCQsfAmkwl3797VPQ6EUeZsRKL+PRwO0Ww2cXh4iHK5DIvFAo/HI8+fU365Tljs0bNO+dl5uKrpMGemUSZPpUAxwqRyFP/Pfed2u/H222/ja1/7muZrMZvNkvqrCl+MJGcdmsFwWgBTMzTSoOiY1TRZq7lcLkm7VRgRwNRkW9UJm0wmYRkVi8WpA0I1PWuVxTTCWnzPTqfzK/gw038eAjzouM7486rDfZo9c1fzIdNx2u12eDweGTBIp6raeXCAKndIXEirsdLNqIAn62g0ktOo2+3i6tWr8u+Dg4MpTqXf7xceW6FQECesZ6osdSM5HYF0DeLLPDio4xmPx1Gr1X4lHZpdGOSkarXRaIRSqSQblNEHNy+LNnzfXq8nM7Hm5ubg8XhgMBimuIyz6ZMW+8IXvoCPPvpINFy5Sex2O46Pj7GzswOHw4HV1VXE43G8/vrrIv69t7cnUoeckWS1WuHz+RAIBHStDxoxUxXz40YgtYcTUxlBU5h8NBohn8/j8ePHMr6crAitRmhgFg6io6YoOwspvE46DwYIdLB0KIeHh/jud7+Lv/mbv9F1L9RIns6Ua4aHsgpfqKwDHgb8/FzfemEt7gW+x2wkq643vj4hpGKxOAUtqPf5eQIBYDqi5qGjQifqewQCAbzxxht47733kMvlptgPfK1nFf0udLALCwuiIk51dQoC03nxw6pwgkpv4B86VlZqc7mcZu1Rk8mEXC4nkAB5e51OR9gCsVhM2AwUl2Y0wEiVm5uapHopOOqoDLPZLFCBwWAQuIAb3OVyIRaL4cGDB7+COakHEJ2rHrFraryyiMUZZ6PRSEbiUKjYYDAIk4AFDg6ao0PiWBEAuu7JH/3RH2E4HOKtt94CcDpYjtEzOY1OpxOrq6uIxWIiau3z+aaYIC6XS3ixnNekB7oBpg91Fesj9qhO8aVzJWWMTujo6EgKQ4Qu9GxkOlh1JBIr5nRYahTJIhxwug7IAeX18TXpjPUY33O2GKQ6bhUbpaNllKo6Y74e8Ww9XGke+mqkd55zVbHMarWKO3fuTF37bBaoNxggy0SF7vg6ZLcQZmPUury8jN/7vd/D22+/LQfhrINmcPM0u9DBbm1tTRGNVdoCbXah8I15+lEVno714OAAH3zwAd5880185Stf0XRzstmsYKeLi4twOp3Y3d1FtVrF/v6+MAs6nY5QwRwOB5xOp6Q76XRaom5G3plMRpd4MCN1ikqTlmUymaSpgeZ2u2XmFWlKNHWhsPiih1hPnJVRDw9BDpjjQrFarTK/nQ6Go6mJbwUCAVgsFolwL1Jnn7X79++j2Wyi3W7D6/XKqOXBYACfz4dIJIJut4vt7W10u13cvXsXLpcLR0dHUlCjwnw+n8dwOES3250al6zFSBuc7d4CIJEwN5NKV1K5marTY8CgprNaLBgMSnPBeDyWycmq0Dnfh1Eqo2dGlCS6E2biQapXlJ3XrzpUg8Eg2SCLOPz8hP54v1QYgZG30+mUjkqtFo/Hkc/nZdLGeTCDGkUCmDocuVcYpKiRtB4Ha7VacfXqVTx+/FjuaSAQkC5PwiNM+1nH+Id/+AfkcjnJQtRDi9f83CNjGNXM4lmq0+UN4qmnLmB1TlcqlcJ7772Hn/70p9jd3dU17iGTycBiseD27duIRCLY29sTLmg4HMb6+jomkwkqlYps3EAgMNWGSVij1+vh0aNHsFqtWFlZ0dVo0Gg0ZCw3Wy/NZrO0zjHl5Wb3+/1TpG7V1HupR5kdgEy1JfWIESAXIScsqAccIxL+32QyyaC50WgkwwD1REr//u//jl6vJ11KmUxGhslFIhGMRiOZZNpoNGST1mo1eY1qtSpFFZPJJNMInocKRCOcxNRbTUvpbOhg1efA6EyNVvRcx5e+9CV85zvfkefAMfdvv/321GQDHtAMPFh0VelQ3Mz8HT0TQIAznFAl8vPzM7ujk1D5y3xPsoUcDgc8Hg8WFxcRjUbRbDZlNJAWI9xDrF+lLKpFOPW6WehTGUxq1KolNZ+1breLhw8fiuMk64dZb7fblXtErvDu7q6MFFIPATVDJ5vqafZMmpb6gufhqlyQs9MzORL74cOHuHPnDu7evYt0Ov0rxGUtxqpyIBBAs9nEvXv3kM/nsbCwgHg8jvF4LCnp3NycFEg4ltjpdAovV8W3jEajrpEg8/PzcLvdMJlM0gHEhaMeGIxEfD6fRL1qpDT7QE5OTqTCrMXY9krst1qtolKpyOZUiwKc7cTrJWbLinCv15P2Xka7Wi2fz0v78+HhIer1OpLJJMbj0zZmYp+TyQSFQgEej0eiJL4/D8B6vY5Go4FwOCw4t577weKSyoTgv9V1SmOaq87DUiNWNYLSaj/4wQ/k0LXZbNje3haYhlER4Rs6CGYSfH+1c0qN2PR2ctGRqGO6+TyoFcFAgJkYPzevi854cXERf/iHf4hXX30V9Xod//zP/6z5OqLRqHTuWSwWjMdjcVo0Nd1nQVCNXNVoVS3Y6VkjBoNBWDfqs2aXHbNwNbtQudgqfMJr5mtexLzRFMHSmdKRMv1Xv3Z8fCztkZlMBvfv38edO3cklVcfoN7Th/SdR48eYTQ6nSb5wgsvwOVyodlsSurDggUXTbVaRTAYRLvdxsbGBur1On75y1/C5/PJZFo9EQq5gCcnJ2i1WrIAa7XaFLZHLJRdXuoCUU9usjLUiE6Lca7Q8fEx0um0dAeRF8zrIkTACPv4+FiGAA6HQ5nEOplMhAanh5bEbhan04lIJILJZIJwOIxcLoe9vT3Mzc0hGAzCYDAIV9lkMiEYDArfFThbvMSSPR6PLrqYup7opNgWy9cGzlgszLK4QVXqEnDGyjivcn2RtVotwRtVjrgqsqNCBCrDQcVFGaWp2gB66wV8/oRbZgtvvC/cwxxCWCgUpNNMjWoXFhawtLSE4+NjXYVhlaN8cnKCxcVF7OzsTBWLWIdgq/bsQUdTM2Zem1ajc1ThOX52vj+phio9jNeoHkJcH3z/5240aLVaslBUh0o4gPPl6/U6crkcHj16hPv372N3dxe5XG4qWp1NA/SYyWQSxw0AV65cwUsvvSTgMzGihYUFGT7Ih2G327G+vg6n04lSqSSRpdfrRTqdnsJNn2UskrRaLZkdz5HhXq9XohXSoogN86Hyoaj3ggUVPRvZ7XbLqO6FhQXRYWARa25uThST2CFFrFblQLIhgIuVbaJajXzWXq8nbbksbMXjcXS7Xfh8PoFVOH6dEZ3T6YTf70ez2RTMnFQ4PfeDzpgRIqMe3mduLDoqtfmADpfPRa2q6820ODmYz5swkpo50LGrhTgVf1VTYfX69BphGRWiUpkVXq9Xhj4ygqtWq1OULAYAqVQK3/3ud/Hw4UM8evQI29vbmq+D2dFoNJJmIBVeBE751L/7u7+L73//+yiXy7J2GU2zvkLnqxePBk47FRkZA2dtyXzOvV5vaoDnbJFUhY5UZ/2sQPFCB0sCLqPUfr+PXq+HRqOBcrmMTCaDx48fI5VK4eDgANVqFb1eb+pkUZkGNL0OljzLbreLT33qU1hZWcHh4SEcDodMeVT1EE5OThAIBGA2mxEMBmG1WlEqlVCtVhGLxQQHJB9Uq6lFI14Pp8e2Wi1R+eICGY/HMh1VPTlpfDhMC7Way+WSdmC73S5sCUagLPQAZ1QSXgMAcXKM+Dndk/xgrVYul+U92bEHnC5mTpy12+0CobDY4/f7hbY0NzcnhUi3241isYher6er6EeWhMViEZUmdQ3yfqiRnFqUBc7YE2qKSGep1Rh5qWwGlQ9LHE+l86lVeUISKn2IjlrPc6HxtdQgh+/PLi/Veav/VpW8Go0G3nzzTbz11lsCBWq1dDotBS52dqo1ncnklEf9/e9/X2RH6fT4+dWCnHqY6ml82NzcxFtvvTXFVnA4HNJ9aTKZ5Hnzs/Ow5fPgNat8WAAX7t0LHeyDBw/Q7XbFoRYKBeRyOWSzWeTzedRqNXGoasivgtI0NYpTL06L/fKXv8TNmzfxmc98BlarFQ8fPpR22GKxiEajgVgsJpoDrLIfHBwgk8nIePF+v4+9vT2srq6KapMex8bDgpG92WwW6cJarSZ4Hwna2WxWWkJnuXyqseCk1ShxGI/H0Wg0YLVaBTIg5URdxIya8vm8zHTP5XJyGjcaDXQ6Hd3YJ52aen/m5ubQaDQEJz45ORGIYjAYSGSvOhpufDV11xOlUK5yPB5jb29PPhefrQoLzDpU3iO1cMLf1+vYuPnpyFWpT4PBIAyWfD4/9X6MiMgqYISrsj30PBcAUxqzTM8JnwFn2RiAKTbM7B5m8ZasFb36DNlsFp1OB8fHx8jlctjf3/+VvdDtdqXopkaFvHYGIEajES6XC/Pz8/D7/brYDFTRU7Fd7hfqNKgQAn+WnG7eH9os1Pc0u/Cp/d3f/R1arRbq9TparZa80UXOghemPgQ17VFxF61ms9lw5coVmM1m/Nd//Zeke+ynpsbpo0ePsLS0hEgkgmq1imq1iuXlZeTzeTSbTQyHQ9y6dUs4h2xA0GoGg0Hk//g5eG8Gg4Ho0zocDvT7fdy/fx+vvfaadOjQVLyQaZgeTYRsNotEIoFarSZ4JlkFZFGQFkStATZJ+Hw+lEolScMZ8bnd7qk2QS3GjcnPwSJWuVxGtVqF3W4HcLrZg8EgIpHIFHmds+jNZjOKxaJEuPxMWo0YJ+k3hEhUjJ0RicqqoENj6s6f4/qlIpZWo8Pke6sR7WQywebmJrLZrBTlmG2cJwDDA4hORk8kDUDgidm1pu5dp9M51XPPw0V9b+41HgB6s09GiCp1U8WB1WubvY80fi8YDOJzn/scrFYr0uk03nvvPc3Xwf2lvm6/38fS0hLa7fYUvY7vRwW2paUlPHz4UA7O2Qj8uR3snTt3ph6Qahel/OqbqlEB/282m5FMJi966ynb3NyU6+GGoIhIs9kUWTWmij6fD6lUCvF4HIuLi2g2m7h//z6uXLmCXq8nG9Dn8+nqGGq1WlM6uKqOKIApZwMA+/v7+PznPw+v1zslckzjZmZkp9WWl5dRq9UQi8WwtbWFVCqFk5MTOBwOwYHVIgexJZfLBaPRiFqtJpuPTgaA7iiJ6lhzc3MifE6Bb8ogWiwWJJNJieKpxco1QQWufr8vDRfktWq1RqMhur8Gw2nTB9NvOihqT9DpMWJWMVs6Y7X4o+c6uNbJw1XfAzjlDZOWNbtRCRkwUmPEqUa3eoxROHWJ+ZnoNOfn57GwsID9/f2p1lA1KuNrMIKl89WDCXN/8VmoESTfQ4Wx1GvgddDJLy4u4vXXX8c3v/lNPHnyRFd2Mfu+fD/KdDLL6XQ6Ehn3ej10u11pL5+FXHh9F9mFO+o85zr77/PeYPYhqSeCy+XC+vo6bt++ffEdUWxrawu1Wk34jdy8T548kWo0e9kJZGezWayuruL4+BjNZhPxeBwff/wxIpEIotEovF4vRqPRVHfWs0zddKQfqRXiaDSKYrEokX61WkWr1cLCwoK09KpVepfLJcUlPWkXaS/BYBChUAgWiwWHh4eYTCYyHaDT6Uihg1Vibn46XzpelYKiJ6InW0I9OEgBWlpakgVLvKxer6Pdbk/hrixeEKsGII5Fq/FZsIg2K8DB6InXrKbvs3i1WnjSW1yan5/HysoKdnZ20Ol0pvr9jcZTURMegMw62F0EQDIJOjE6N71YMF+LEbuaDqu8WHYissbCfwOYcsizDAg9B7FalOLBAZz5CFUzQf2dWd7reDxGOp3G3//93ws0qWfPEIrivWeNJBaL4a/+6q/w1ltv4Xvf+55kduPxWHRPGo3Gr7AaZiPup9kzBbf5QufRJrQ6V36oWCyGa9euIRKJ6IocHz9+jBdeeEEEpefm5hCPx6WtksT2K1euADhV31pZWcHW1hby+TwWFxdRq9Wkpe3q1at477334Pf7dV2Hw+GY2hCEKebn55FIJPDxxx9L9EY62e7uLjY2NrCzswOn0ykjVIglEZ/Us1goNfjmm2+iUCjgc5/7HIzGU9nGcDgsFeFKpYJ2uy0RJDcPU+TxeCydPVQH04NJh0IhlMtlUVKjeb1e4TqySMJWXUJNjMgymQySySQ2NjbQaDTEoeh5LnQcxF0ZVZNSRzyVketsiqem4tzYdEJ6nEmn00E+nxf+LR0DcWpCArw2Fm1UzK/f7wtcw+czyzzRYiojYTZrYuPCvXv3JGjg+/E+qHUDNaXX6+h5b2frMefBEvw6KWPFYnHqcFF1RGmNjQAADltJREFUWPXStPi+6nsaDAYcHh7izTfflMNP7e5TO7z4R3X+vCfPDRGcV5CafTEVFFYdMh+kyXSqBnT9+nWsrq7KjdHLYfvpT3+K+fl5mM1mXL16FZPJRKhJN27cEGyRlX3O6HK73Xj33XdhMBiQSCQQjUaRyWSwvLyMZrMpdCstxhOY6Vy32xXaCYUpDIZTzqfH4wFwWij80pe+JNJoPCEtFos0R+iNlPx+P9rtNvr9Pl577TWUSiU4HA7BnZPJpHTCsBV4MplI1wm/p6ZmamFMqzmdTtmMTKs8Ho8wKkwmExYXF7G4uIhSqYRer4fV1VUR4n7y5Akmkwn6/b6oJtXrdYxGI93cYAASlYVCIYmSybCwWq0SzdHxsZJOxbVZrFNNBbW+Pyvhs+kwsVk1MmMDBHDWLupwOPCbv/mbaDQa+OCDD+B0OpHL5XTfC8IKdJx8P+CsGEnyPzUiOp3OVBTLz8TX47/1rtfZYAs4GwzJA4afX2VdqPeK+C0dHw8nraY6dbWBpNlsSoNIq9WaOlDIJmAgdV7w+KwGEE3Hs+o0z3O66odQ/7BSf+3aNZmAytRFj+3u7mJxcREWiwXxeBzBYBD5fF7S0MPDQ7TbbalQ+3w+NJtNtFot3L17F1arFYFAAHa7fUr7lFqXeqzb7YpQscvlwng8Fik6psetVksiSYp6u91uSZetVqsMHNTKp1OtVCqhXC7jjTfekK4gdszU63XBOEOhkMApRuNpBxUr2BzxMktv04NrEd+js7fZbHC73YjH4xgMBggEAggEAlhdXUU0GsXe3h5ee+01vPLKK/jJT36C3d1dmM1m4d6y4MCsRKtxE1itVsRiMSwvL8tgvXK5jFQqJdMKVDETtQ2T6SDTc6aueqAKRt5Mi3mI0blz/ROC4M/w36RCvvvuu9K2TIlHvTStp+HK/DqdPScQq7xTHrwqXAJg6ntabbZwp0bDRqNRaFvq9ARV0FrNIGY76/QEabNwJw8cq9WKZrM5NflZjabpcLluWDdQpUIvOoQ1R7B8kfNCYvVh8OG5XC4kk0lsbm6KzKB6CurpGAoGg+Io2u02fvnLX0pUwnHT4XAYT548QTgcFjWmg4MDmM1maantdDoy0wuAcGG1Gm/ueHzKOc3n8/I9OncuaoqghEIh7OzsIJlM4s6dO+LoqGg1mz5psb29PXzmM58RB7CwsICDgwNZFKlUCsvLy1MiIywkscDH6Mbj8UyxQ/SoWK2urmJ7e1sE0Vm88Xq9cLvdgifu7e2h0WiIlsPNmzeRTqclBXM6nSL2HI1GJRLUasRsWeBKJpN46aWXYDAYcP/+faHRzfIX6WQASCfPeDwWZ6wGFFosGAyiUChMtbcS4+XG5RrhpuUGjkQiODo6wnA4RC6Xk2v9f//v/z1XowEPCDo1Og1+3kAggHq9joODAwBnwZPVahVtC0onJhIJzM/PY29v77nScjWKp4/ggUHJTGLVs85KTcXVr6kRuVabLXKp8JDq3+iMOVKKjpefR+VTq7Kg59kzHaz6UNQPxzdTw3nqa7IVVa0eE98aj8col8s4PDzUfGNeffVVUWUymUxYXl6W36ejqtVqiMfjSKVSmJubQ7lclqh5aWkJHo9HUtP5+Xlks1mYzWbcunVL83XU63XBKTOZjNBcVEUrOuBqtQq/349er4eDgwNsbGzIZlOdK++pHvv6178ur+t0OmXEikqBYecKIyoWBcLhsOCS1FVgqvysvupZ48Kj/sNkMpFJm2wZ7fV6SKVSqFQqiEQi2N7exg9/+ENkMhnBbTudjkgFXrt2DQB0sSrU4sV4PBYna7FYkEqlJBIibU2NOlj0YYSppqR6IYJIJIJ6vS6qbpztpWKoKveXhyuLs8RuGWED2qhA5xnhEDXV5t/D4RBHR0dyHYzYWTdQOakApH0UAGKxmC5KIVkiXCfnQYxq5HheMYmwhQpXqD+j1Xgv1MxFhStVWFOFlNR9pT6L2frUeaYJIlBTWNWpAmd4DvvubTYbgsEgYrGY8PsASGU7k8kgnU7rksVjFc/j8eC1114T7CiXy8HpdOLw8BDBYFCES7rdLkKhEHw+n9wEtts2Gg3BQiORiC6CPwn9R0dH0mDASESN4Fhwou6t0+lEKpWC3+9HNpsVp8frAi5+SLPm8Xhw//59eL1eDAYD9Ho9+dtms2EwGEhDBRtCmA4SC2axsNlsSmrLxaTV3n//fQwGA3i9XrmnjCYpzMNuwGg0CpfLhbt372J/f182WzgclsiNmpzFYlHXc2GUPpmcCqkwMjs5OUEqlZJ0k/eZn5EbmxuOeC2NzkirsWDJKHVzcxM2mw13796VdlEVPqDTYnGFbccGg0GKW9z0z+NM+EeFBoAz/HM2K+UhwOyS+57t1lTG0nNPWNiz2+1S2FMLaLP1G64ftXmJ2YAq5ai3jjN7b8jZZtMQ35/XyqYlNeIFztYMA0aHw3HhgXOhg1Uf6izuwgdvNp9OF6BDc7vdIoMHnDnWdDqNVCqFdrutuypaLBYRCASwvr4uwir1eh0ulwvD4RC3b9/G4eGhjElh6ss5T2zNrNVqcLvdyOfzMJvN+PDDD9Fut/HpT39a87Xs7+8jFArh+PhY+Jp80Kpy1vHxsaTOVqsVR0dHiEQiv1KwOC8FepZ99NFHuHr1qtDQuEhYdCLOTJpWs9lEIpGQlJFsCC5UbiBAn6M/OjoSIXbg7ABiZ9p4PEY2mxWtCG7Mfr8vlCoKb/P9qSmhd/4TnWG73cbe3h46nQ5OTk6QTqeF7M7KuhqxkKKkFjD5erN0r2cZsyoWQKk2poqE8LX5M8BZFM3oSe0aUtNrPUZ8nTCSin2SQcKolU6DeCvXtEojMxpPdYYdDofuaJp4qxq1qxEq/1azCZqKC7OgxGYRPdehvqeKTc/Pz8Plck3N5aIugfres/gs3/tZsquaxnarJx0LChy5Yrfb4Xa7EQ6HpziUTDWy2SyOjo4kClUpEFptPD4dBkfBEBYt1tfXsbW1JZquXDgOh0Nm7XAsDPUK2G8fCoWky0mPRSKR0xs3dzZuhIuPDo73q1ariSYsh95xRPUsfUbvNXzpS19CvV5HOp2WijDFrglZEMagyIvD4ZBnNB6PpbuNDpLyi1qNU22psERohHPByMVtNpsoFArw+XxYWVlBqVRCNpuVyRSsdKvjW/RASKoj7Ha7ePz4sRTzarUa2u02nE4ngsGgyDpSaFmFFoCzg04tDmk1FvqY4pNZYjQaEQ6Hsby8jLt374roOZ3GeDwWShkpUzww2Jmmd52qzkEtVKkQ0KxTnS1wAdPi14xA9UTTqiPie81Gz+rhokaT6vt0u13hbfN1LpokMGsqI8FgMEiBmcXR2Z+lf1LrR+q10w+Sq/w0e2YEywdstVqF6O90OiWMJlmZKQ1nH6XTaZRKpanqnJqm6XEubrcbVqsV8Xgcjx8/lqLXb/zGb8iNYJQGnNKYotGo4JM//vGPsbGxIaOYSemaTCa6IQIyBVhJVPu9XS6XtH4aDKcdWqVSSabxNptNEUV5nplTNKb+6n3lhmL6x5RTXaSULFTl2AAIH7fX68kBosW8Xq9wFdnQ4ff7paNsMjmdmMD7zqgyFApJ91k4HJZuGdK0ms3m1LywZ9l4PJ6KasgoYSswuaXBYBA2m03acuk4SJtTifUqAV+rEaZgtxY3I6d5PHr0aKroSGyczs9qtU4dvHQ6eq8DwFTUymdNbJEiJ1w3fH7qlFeaSm3j9eo1lcGgwh6EZ0wmE2KxGObn55FKpcRnMFiZhSf5mnoGlgK/en/Vwpoa8KnXyp+lADmZSNTWeFYh9EIHyy4hpoF0sCSmq3qbJycnyOVySKVSEiXyw6hO4HlS4vX1dbzwwgv4yU9+gk6nA5/Ph+XlZRSLRZRKJWxvb8PlconoBx+C0+nE//3f/4ms4tbWFlZXVzGZTJDP52Wkh1YjpYTpFTeGSnAPh8NIpVLymavVqoxsYfeUmjY+j8ViMXltMgAmk4lUPY+Pj0WhvVwuY3FxUTArft9sPp0pxk44cnf1RLAc2+3xeETcxW63Ix6PS1ZhsVjkuZRKJeRyOaytrSGZTOLRo0dC8Ka6/OLiIvx+v64DWC2iMCIFpseeVKtVPH78GMCZQLeqB6tWhhnR6a1Us5C2traGZrM5xVKhEh1wFjny+ihzqEaIamGHsnp6jJ+Fzpz35tVXX0UymcS//uu/Cu+T+5TXwXtGeEBlI6h4pBYzGo3iQ4iFE5dVHf+f/MmfwOl04m//9m+nomoAch0qrDAcDkW+VIuRuaAW+/h19RmTncSfVQ+E+fl5fP7zn8fPfvYzebZqNH6eXehgV1ZWpPefN50XxRtAPJQKW2o//nmLk0UxPQ8pFosJ9Ym6q2+//TYSiQSKxaLgWW63Gx6PB9lsFn6/Hz//+c/l67dv38ba2prM8YpEIjCZTLp4sGo0Ttk9LsjhcCjzwBjBAqebuVAowOVyiV4AcJZ6Pg9Qf/XqVVE1YxWYC4hYIzFPjpdhcwJxudFoJPeyUqnIs9QTKZGDOxgMkM/nEQ6HEQ6HMR6PcXR0JIcxYYN+vy/FOD7/YrGI+fl5uYe9Xk+GJ2o1bhZGsWo1muuQ3Eqmh5PJRIqlVISbpe4A+irV5OG+9NJLWFtbw7e//W2ZREoIgBgoYR31+dOp8gDmOppMJrodLO8no3u1NZjPTYU/iHnTwft8PhiNRqGdAacZEOELPTZ7kBkMBtEPzuVyGI/H+MUvfiGyhmo6z73G6JXPRS+9kY5cxV9VPJXPgfdtFiMmvv+zn/1MMHLur4uejWGi925d2qVd2qVdmibTh5xf2qVd2qVdmma7dLCXdmmXdmmfkF062Eu7tEu7tE/ILh3spV3apV3aJ2SXDvbSLu3SLu0TsksHe2mXdmmX9gnZ/wfArnBjwXPHMQAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11a5d7710>"
+       "<Figure size 432x288 with 60 Axes>"
       ]
      },
      "metadata": {},
@@ -275,24 +287,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Our hope is that these would sufficiently cover the space of \"non-faces\" that our algorithm is likely to see."
+    "Our hope is that these will sufficiently cover the space of \"non-faces\" that our algorithm is likely to see."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3. Combine sets and extract HOG features\n",
+    "### 3. Combine Sets and Extract HOG Features\n",
     "\n",
     "Now that we have these positive samples and negative samples, we can combine them and compute HOG features.\n",
-    "This step takes a little while, because the HOG features involve a nontrivial computation for each image:"
+    "This step takes a little while, because it involves a nontrivial computation for each image:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -306,9 +321,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -317,7 +335,7 @@
        "(43233, 1215)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -337,36 +355,39 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 4. Training a support vector machine\n",
+    "### 4. Train a Support Vector Machine\n",
     "\n",
-    "Next we use the tools we have been exploring in this chapter to create a classifier of thumbnail patches.\n",
-    "For such a high-dimensional binary classification task, a Linear support vector machine is a good choice.\n",
-    "We will use Scikit-Learn's ``LinearSVC``, because in comparison to ``SVC`` it often has better scaling for large number of samples.\n",
+    "Next we use the tools we have been exploring here to create a classifier of thumbnail patches.\n",
+    "For such a high-dimensional binary classification task, a linear support vector machine is a good choice.\n",
+    "We will use Scikit-Learn's `LinearSVC`, because in comparison to `SVC` it often has better scaling for a large number of samples.\n",
     "\n",
-    "First, though, let's use a simple Gaussian naive Bayes to get a quick baseline:"
+    "First, though, let's use a simple Gaussian naive Bayes estimator to get a quick baseline:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.9408785 ,  0.8752342 ,  0.93976823])"
+       "array([0.94795883, 0.97143518, 0.97224471, 0.97501735, 0.97374508])"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "from sklearn.naive_bayes import GaussianNB\n",
-    "from sklearn.cross_validation import cross_val_score\n",
+    "from sklearn.model_selection import cross_val_score\n",
     "\n",
     "cross_val_score(GaussianNB(), X_train, y_train)"
    ]
@@ -375,31 +396,34 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that on our training data, even a simple naive Bayes algorithm gets us upwards of 90% accuracy.\n",
-    "Let's try the support vector machine, with a grid search over a few choices of the C parameter:"
+    "We see that on our training data, even a simple naive Bayes algorithm gets us upwards of 95% accuracy.\n",
+    "Let's try the support vector machine, with a grid search over a few choices of the `C` parameter:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.98667684407744083"
+       "0.9885272620319941"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "from sklearn.svm import LinearSVC\n",
-    "from sklearn.grid_search import GridSearchCV\n",
+    "from sklearn.model_selection import GridSearchCV\n",
     "grid = GridSearchCV(LinearSVC(), {'C': [1.0, 2.0, 4.0, 8.0]})\n",
     "grid.fit(X_train, y_train)\n",
     "grid.best_score_"
@@ -407,18 +431,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'C': 4.0}"
+       "{'C': 1.0}"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -431,26 +458,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's take the best estimator and re-train it on the full dataset:"
+    "This pushes us up to near 99% accuracy. Let's take the best estimator and retrain it on the full dataset:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LinearSVC(C=4.0, class_weight=None, dual=True, fit_intercept=True,\n",
-       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
-       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
-       "     verbose=0)"
+       "LinearSVC()"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -464,24 +491,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 5. Find faces in a new image\n",
+    "### 5. Find Faces in a New Image\n",
     "\n",
     "Now that we have this model in place, let's grab a new image and see how the model does.\n",
-    "We will use one portion of the astronaut image for simplicity (see discussion of this in [Caveats and Improvements](#Caveats-and-Improvements)), and run a sliding window over it and evaluate each patch:"
+    "We will use one portion of the astronaut image shown in the following figure for simplicity (see discussion of this in the following section, and run a sliding window over it and evaluate each patch:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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GA2m324/srnAciI1O2aKfQQp+9cHmWq2WDIfDYNoBIENgEw8GKm0Dx2YXYGdo\nEw1mOOQO3eLNzU3Jsp83FThAJ4c6Acx5IWFmJ/LA1MD0cLb19PRU9vf3ZW9vTw4ODpLjJHeRjIUc\nEEvpzlh9w/PNEi894pAbXpyRpQAsFRe/U6Icgie+aSDxmJqXLqfticAp0NV56rIhDttEwR5sNpsF\nAIPOh63jAWBQXLMBKAJAgBXlMNJk5TwmMwe4+4HYCVBh8Q8W72CAGsDY6yueQ0BcBg4+7whreWaS\nbD7CYjTAgl92gnaGyIl4zFCLoggnFiBm6v7UCwCeR1x4iwVYY8MBdUU9Wq1WGA9XV1fy//7f/5PR\naCRv37595JDRGl9ob2s8PgXocvRW+h7vguvAQObNQwuwvbAzjCx2LScN/PbkfQ4emOl0PTCzymjR\nc+t/DshZ4MurHFvpw6IeTGw+nwfWw2IhQAjiX1EUwT8Y8oD9l8iD1X2n0wm2UbDzYr9cSAeK/Eaj\nIev1uvTSETwHcAQbxD3WfyF/Fgu1J1hMULA8tA2OSSG+PtcJkVMDOOrLFvlQtMMmjYEdAA1RUzN7\n3X+sw4RxLiv6oTvkNuFTBNPpVG5ubuSbb76R2WwWzrJaY8tjTDG2FhvHMSDhentAVwWYYnFTTO3F\ngcxDZi++B1QWkxFJKya9e0z5vbS9slmissfKYmDLz+PDxp94G/ZsNpPlchk+/KozkQdvEmAoEB0x\nWGAvBrYAoOn1eiIiYeKySIbfzWYz+NaCpf3FxYX0er0gXsLW7Pb2NuiIUG99OJpFWRiWwnAWmw/Q\ncwEg2Gbu+vq6lBazMN4pZYBnBsYGuzhzijbqdDohbaQFgOJ+A6tFPQCatVot7B4DBNHe8B6LdPf2\n9oLZCt4D8OnTJ/nll1/k22+/lV6v92gsaV0h6qpFuJg0oMdfTMTUY9gSI7kfmKmi7bxQlTW+uGgp\n8pjh8LfFgrxVwxMjc8RCL34MxGLppjoilabF5tiNNMTJ8Xgs8/k8HDXiSc3pcbq4z8DBL83lSSoi\nQRmudUs84QEIMLPgckPpz2wQE4eVwSgLG9wiTZEH1oQy4XmwQd6N5Xqz+2wtkvGOKO4BdFEP1rux\nbRvrs8AYYc7CIibqig0ZpM0mLK1WqyRKc/uhv87Pz+XDhw8yGo3k5OTEBA7+nyOWeVKDN28sEMuR\ncCymZcXNkaissBNAxp2tV4/cDuG0ckKO2OmlawFt1Q7IZaD4hjiCM4SXl5cymUzCMSNWaGNiARSQ\nBuy5MFGZ3rwQAAAgAElEQVSh/4KdGE94AAgU1CIPO2owDeh2u8GkgVddHNyG+DWbzUo6J93X2+3D\nkSg+78igCHEQTIq9u/LuLesOUTYGK2ZyaCssAmzaweVCe7IIDTYFURXtCqAGkCEu2BWbbeClvq1W\nSw4ODoIbJTBitl27v7+X8Xgsv/zyi7x79y5sPvCRKo9FWWPKC96Y9ABLx/fsNp8TcubTi4uW/NsT\nyTw2VgW0rOdyGihXpEyllcsWOT6eAXgAFCaTSTjbh8mKozwsTiAdTCq2EYM5AyvatdgIHReXk/OB\nyIk4bNOFfOHlATZb2swBbQG9EMrAr3pjmzIwNZxIYBEZAQwG9nMsxqIdGFCZyenATAzxWbRkOzQA\nFOsnkR+/JQqAibQAhrA947OxIg8uuFerlZydncnl5aVMp9Owg2vNh9iCrOeZ9awei978eSqD8kJq\nrnnhxRmZnnTWfd34T0H75zybej42KGLp5YAyJivO311cXMhkMilZozPzAhDhWV4hASxsEMouq/EM\nGBYORrOpB8QfZkAiEtgK70Syo0GwK238iYPhYD/8Ig+wO4AirqHt2PyCz0HiObA7Nv9gN0EMcKgT\ngxufsQTY49wj0kD5obhn2zaRB1dIvDiAGfIr7rbbB1s/tCVvLmCsgI2fnp7K3t6e9Pv9MFasRcwT\nOXU8jBEPLDwQqyox6bLwf51XrBw67BQj8+5ZcXWnWCvKU1aI2DM6r1TcWNpWWpp9AiwAYldXV0Gc\nZBsx/QwfjubrCNqGC0DBhrFgGyxa8QFqAB5cAfE5TvaqirqyYSzKq4102RgU5hPWux8B0CzO6bOM\nqDMzI90O3MbcZ8xe0RZ4DuWEuAgAY4BksEYbIA+kURRF6W3qiAsmipMY3F/ID0C2v78f7Mp4XMUA\nKSU95EggsaAByWOFucCZm/dOMTJUTO9mWKhvoXlsZXiOzG6JtimgSq1UVlr6+e12G3RicD8Ni3cM\nBKzWPKF4m1/kwSaL7bQwwQB4mLSs1MdBZn3OEPdwhhObDZvNJnhpQB3g457ZIJgJGAmbQgAgwar4\nrdwMIiwysv2btrFj41u0KYvF2DEEA2XQ5H5HWgi84TKfz4MXWm5PFmGRLm9cFEUhb9++DX2pgQz6\nOIA9xPirqys5PT2VN2/eyGazeSSuIliiprV4WuNSj0cvDsfLATEvWAt5lfDiQIbgiVmp1YM/sTR1\nut7zOq0c+vuUDtCMUgc+pnJxcSHj8ViWy2UQFVnZzeyDj83oe2BZvEPHjAWTj63r9UdEwm4lzALY\nBxifI7y7uyuJUiz6sHseFm1ZxOU6Ig1OB/G1SAVQYGNZ3nFldgaTENa14YP7EI1Z98VeaLmdYRPG\nQCbyAFxYKABMm83mkbda1IFPD4ClFkUh0+k0bPbM5/PgHdcbZ3rMsUitx2DuPLTA0BrPMWaoQ87z\nHjl4cSCzGiTWMLjPIlCKSnvXWZ+Uw+a8lUpPQmtweMEDSpgVXF5eysePH4ODRHZUCOUwAw2LU2zy\ngB1KHGrGwGcdGot0RVGU3i7EYiGsz9n9DU9wLhMmrYiUjhWBvegzl8wutDgMkNQ7hSgfbxTgm+3f\nOC+9YMHlNxTy2AVmf25oD21sjAPpAE/sIlvgCNaFI0yr1SocwC+KItQHejfud4wrvKoPqgb0bWr8\n6jHMgO7NQ2+se/nEpCctUlriZVUmhrATrq5TYl8MjGJp8WDVjcnPe/GssiLEZHgrrs7TCgyE8Dc/\nm82CESUmJ+/oYTLz+UirbVgJD5DQ4gb+Y6LCXMNThiNtPk8IsAJb4x1STEat80Kddbq6/DwRoLfa\nbh92R3lnUzMvESkZt0JJz0bCnU6n5HIITFHb5jFwizwsprwpwIAHBo3d1k6nE94bAJbGhs4Qq5nF\ncbvMZjM5Pz8v7V6ibtxmOSEGJh6QWddi49sjGnox0flXCTvByFKrAIfUimBds9hRCri8fHNWDw/k\ndB29Try7++K7fTqdBkt9ABiU6CzC8Ms6OA8WHThoY01uK7AtPiOpdwRFpMRykBbroaDfQVrw0op4\nOEuojVR1O+j2ZlGZxVSwFpSHRWUGP/aKwTZrRVHI0dGRLBYLuby8DLo9EXkkoiJPPvOJNuAzkgDE\noiiCnzN4zoX7IywA2DTRx7f4vCrafTKZyHb75Qwmv5PAYpqxoBfwGEAhxKzxc4mFdU2PQ6s8sfCi\nQMYTIxVy4nkd6ImmlnzvPe+tWqlyeTTbC5ioy+VSJpNJUGqz9biIlHbGMOi5rCyKYQIiDr7h2YF3\nx8DW2I6MdWYMJgAPmAno3U2RL4fJ5/N5aAtmhdymXEbdZuzzH2WHmQZb5SNNvXPLQMw7o9gAYfbT\nbrdlNBpJo9GQxWIRzFSwcKBseIb9rLE9H3ZvAe5oQ/QRRPP1eh3uQ6xHGzSbTel2u4HZsnhaFEU4\nZ8tvvvJERD32LAal43nz01vU9XyyntF58f/YMylwfnHREsFCZN0oT6GcMYCKiYBWOvp3ipFVATGd\nPl5ogbODAAacNWRFMsAG9xl8WMnNO5awou92u6GcSAN5ATzQTsyEeFOBrcsxQdkIdLlclvLgw9sA\nR4h52KXkNgYosHU/AIoBAtfxnBb/kDeLjgwMEOnwwo/lcim1Wi14kWWWgGd4dxFxAEjazxnaVr93\n4O7uLrgSx1ElsDjY2DErxAJ0e3tbOgmgQcxiRF6wgCUGSh7oWHMoBkgWi/P+x8JO2JF5gGb9t9KA\n+KNDrOPQ4FVYHF+PgRnXK9Wx1rN3d3cByHQ9eOVjoNI7lJiUACI8yxsGeA6rvbb9gjikQZY3FOBB\nFoDB/rqwKwf2hPKKPJxnRPnZDouP/vAr6sBMoGviEwlgRzzhAbBoUywKAHS2osfJA2Z07IsMGyXY\nqGCwZtsxBNQXbcmMiu+jPUUedG8szjcajbCBwvVilqpNRiy2Zf33xp/3XGwsp0CH71sipvVsFTF5\nZ4AsRS0RT//WK0FO58XSTbGpGHBZca1VJpYXBjaAjF0kcxoY1GAolvgJMNL3WJzkTQKIS3gWkwX1\nYBBC2WEiAGCDaMTGsxCdWIwDELA4CaYI0ED+RVEEMWy9Xgfvq2BozH4QmK0BaACEMPTFW9U3m430\nej0ZjUay3W6DmQvYEXYWt9utrFYrub+/DwyM7fe0nopFMz12WZxFwK4v74a2Wq3ADLmN+O1WWhz3\nQCIHFHi8emXnYLGzVNo8lixJh+uQy8peXNmPkGJnuasJB70KII6H9LmrS6phUyKzjsP3MZhZiY8J\nqQ81izyIg2z/hcEFlsHsB2ACdiYiJbc/OA+JnTykzyIqe57AdQTkgZd94D5bsWvzCRbFeHKinhAt\nO51OACMc04LICS+vYChoS4AOv9EI+j3eYYVIzKIrbyKgPigHlPO8cCA99CV/sHHC77HkI03Qv223\nD5sUaEs29UCbQKyE+kHnp8dgLrPJWaj5urXAe/mlAJXnBy/GPKa9sBPKfq+AuYCiVzWLCnsd6zFB\nTz/gXeM8PH2Bp7Pg62wVzh92gMhlthT4mChQVOv/zOKwQwrwhCgHOyZtc8ZW8iIPAMfiEHYlwTo4\nPouFIiKLxSIcx2m1WoFpIU2AHNjSer2Wq6srOT8/D8pwnBAAyFh2crzxweYa2+02HPmazWbBEwg2\nKaBLBMC3223pdrtydXUVNmKYZQCcILbzosH/cU0fwgfAQxznTQutIpjNZnJ1dVXyPmIxGY9ReWwo\nNt5jLClGPCx1izUP+VuXG2PWCjvByHLpo0gcWKwGisn8XjqaXludH9MXeCG2EuF5dujHkxkgwaYT\nLG7AkyszEd65hLtq1A2MgN3dgImAcQHUGGSRH/RjWjekTSOQN8dD/kVRhHOZqBPXuyiK4MaH7cRa\nrZYcHx8/2sGFEh+sk41rYcaAZxhM2HwB39wuUL4jTbwEZTgchpMNIl8WE3YciT5CfBi+oix414J1\nJpZt2NDuELkRFzvbMNNhD79VFmCMQYtd6f9otxQB4eesuDkszyqbN4deHMg8kLBCrPE8dPfy8wCq\nSv4xMIuBpyfmYuBCZGHQ4RVf5GH7H9chsiA+izt6cIOJ8WSFISgs24uiCO+05IPPYF54HqCFcrAu\njkVGTHbUlU08arVaSUwDaCHAWh52WHizEd4dCWYDZsK7suxum32nMSNigGNwZIBjtgfL/MFgEAyH\nUTcwUSwMKMdoNAqHvLfbrYzH4/CCGH6BMdqSgQxthTxxwgDOBHDek8/SWuNYEwZP/LOAi8doCsj0\nWIvNaz0HvfJ56iCEnTC/8JiQ1aBe0BTfyoOBzhI5Oa3Yf522Fc8DUv28jgOmw6KayINSHjtv2FHr\ndDqlnSsNZCJljw2s+4EODqwCJgAAL62T0cwFwMZlRd58nIkNZ1kBjjSYRWpwxDO8c3l3dxfs1lhU\n4wPbYLeoFzYg2Gca6oY20+kAHCFmQt+IcmmDXREJYjU2FNBHLJ5ut9sAhliY+JwqjyWAKECSDZAB\nohcXF3J6ehrGhB6bOeDFbWGBj/XtLf7evI2xxNRcic19kR3SkcXEy1y2ZP22UD0FZJyGVjSmGhzP\n5oCY9RyDAOsjsDLDgBKrPQOPSPkYET7QGWESYwKwUSYmEiag3gljUVJPfpGyrow/zJCsAY2JrM1H\nmJWIPIha7NOMz2haLAtABi+sGugYNHS7oxyr1SoY9PJGBYvQiI9+RXtDpOTFBsarEHfhL40NfHmM\noA8gojPzhg3b5eWlnJ2dydHRUbCB4zFlEQWLNFjg7o1v/T8GUjoPS1rJISmxsBOMLEdHVoXteI3l\n0VPNDPV1/dsLMd2aLp+Oz2lAB4VBD50Ksxw+MB2rJ4CMd78YvPB28uvr6zDh+E1DDGpgXOxZg8FW\n72IywHirMkRoZo0MTqgn0kT+6/W6xNR4kuDDO6LcJ2CivDvKiwTrAPFmJoh+EJXX63UJtPi8Kzzi\nQjcIO7RarVYS4fmcLLcL+gvtgA0b9kbLhsfwGAy2inGggcjbCNPj0gvefUsnpr9zyYjHlFPhxXVk\nImXWk1NoHaxG8phYrAw511Pl88ApFZjZYHKyN1boUcAKmBGw/RKATh+JwURl3Rjeqcj6KVZo87lE\nTFoGM9SNwUR7mLDqiTLiPwMX0mQTCkx4tAW/LYn1hDHxFkAAPSN0ZSgvWCp0hGhz2JoBpPhQOE8y\ntAnKZTlcLIqi9H4FbjtuL7A5XS9eMLgN8D5TlB9pMUvktrbAzAMh/dsLVjoekFljwlrwtHQRCzth\nEJtDVT1a7KWJePpeLph5ZUuxLC+OxRCtNDCIMflYGa3FtXq9/IIOkQfPrmyAikktIqWdOJzVg94H\nYAYxBqIMJlKv1wtAIfLApvAbDBGAx7pK1I/PP/IpAoA2TzY29eDXvLGej9NiMMdCAOC+vr6W4XBY\nOunACnZmOGhvXONNEegR+UUkMJLlo1pghADB9XotIlJS3mPBYCNkgBcYNNsHsg803gXmF6IwM7bG\nph6DHnDFWDQ/rwEstvCn5iaPcb27HCuHyI4Amf4du4bGt8AsRVn5WaSl07Z+e+VI3XtKgJ0VJqbW\nl/FgF5HSy0Qwodm2jBXMYBl80JlfecaTAO5mwE40oxGRkr4HYMDiIdoCoGSZGaAeuMciJBgib4Ag\nHVbGa9DUQMZuuLnP2PDUctWDZwFaq9Uq7B4y0HEZeJMEeUBEZfbMpxxY9GWGijZAn8ArL9g5+oUd\nMLL4GAs5IMYgkgIsTlenySHFzNBGLBbvPJCJ2BTUYmRWZTSYaYDSDMwCsRQLy2Fkqc7hvFMDQNs/\nsb6JgQoDGCDBoh/S45WdmQm/2RqrPe98gi1gt411cXzKYDablYw/wSJZvOEXA4Ml6omIPFkkgg5P\nK79ZXORD5Pzht4MjfaSl/eyzyYf1AdsBGLItHdpbM02YQRRFEU4VIB8GKS1GMdNloMciwe6aOGhQ\nBWv0GJCeXzEgS41rK01vzuh8+DozMt02KbFSZAeADMEruPVbA5LusNSK5DEyHUczv6r6Mo6XI+Zi\ngkDZzjt9mADsqYG9OLBOjPMAm2ExCDt/DGCYeAAkiJdgH+gX7SobopKIlJTyuk7sKog3LxCH+41N\nO/R5TIAY6xIBOigjmAtPlOvr65AvgAQTH89CxIU4CFBkIOOdWwZffc4TrJoBltkpb6iIPPiI06AG\nERVAxSYfiMvGtev1OrA9byxa11JkIjWvcliTnqsWkFuM0ANkDjsBZLrAujH0bosODDr6Y8VNBY/F\neWXOFSVj6XCZMdDb7XZpIjDbwW8cIxKRR+cMMUmXy2UwmlwulyXzAzzDE4qZFKeJ+3Av0+l0wgtI\nWBmtGQuLXBB3teiMtmAdlRYxNJDhGQACOx9EWbl/FotF8NZRFEUQG5n9wDMv0mJfYwCL+XxeAjFm\nP9xHYLWNRqPkErzZbAaTED6SZJmggNXN53NzocJihn6Ahf/e3l503HEaPA41O8LzPJ+qqk68ee1J\nXtzvep7tNJBx4Mp5crwVtE5G/+c4ucGLb60QKZ2aft4DWUxSnOmDOAHRBSswMxKdFtt8FUUR3nS0\nXC5LTvp4N4wnJQAM7Il3IrfbbShbv98PhqKtViuYLwBsAGYMaqgnwAx1Y5FS26rxSs3txPcZ8MB2\n0AbImz19MJDxZIFYCmBB+/O7OReLRTB6ReC6oY+xWGBhQv/hfZTsslyLVgxkYFyatSI+dldh5T8c\nDqPjLzYuc3cKPfVNLGhGhuc9MLXK4ZVpZ+zI9O9UQ3pp8W89SRE8qpoSHTUr1CDmiaBWeh5tB5vp\ndDoyHA5lPp8HpTcf8IaYZ4komHSYXMvlMryyTYMgK9lxAJtNL5gh8s4g9G54YS1MOZjdoMw8KcH0\nAGQQc7FJwNbzaA/kpSc4G7hiV1NEpNPpyHa7DeIl54W0ABAAMtim8cYI7w4ib/gjQ7tg4mNRAHCB\n0bE9HoALLwvBxgn6AW2HMrJpSbPZDKIjm8GISGDdV1dXcnV1JScnJ+6Y1oHnml4w9Nhl0Koq2SAN\n67+VvwdivwtG5jWgDl5lLPZliZr82+ucGGtisI2Vx+s4PBPLo91uS7/fL+2k8eTh3Sxt2Y5BIfJg\n6Mksg8GImQybBGiDWLAztivbbrfB7Q7OPjLjYSADIAAooWsDGAGYAB7cFnheiztsUAp9Fzx4QGxb\nrVZBPIaODGkCJNAObBrBCwIDDYvlqB9PQn1aQO++FcWDHg514NMKDNAaIFFupIO2AAgvFotgTmON\nKWssasDwxL2cBV/H8YhIFQbmPavDiwJZjnIvFnTDWezLeiYWJ2elgVgTA6Nc8ZK/eUCwDopFNj4U\nzHVhsMDE5SNILFZpsQ11gpKaPY9qg1MAGq5DDO10OmHi864lngNjA4PUL/4ACEOvB+Dgb66zyANz\nAaBhMwP6rOl0KovFIsQHe8OGB4u3zAr1YXJuX7QdThagrmzkyoyTFxa9U4tFAQCJgDKx91u4YOLy\nsR5JezGJje3YAouyWsBlSTapxT8GZh5oVpXGRHaEkVkil1cRq+Gsxqsqw+t0YiAVy1+vTpqSx9Lh\nZ5kB8YTB6ozVWuRhQQDQYEXGpGV3zPrQMzYLoAsDiDFY6Q/vImqzEM0UGfxY78YGtywWa50gJqw+\nusTsBADBzBNnLOEdg9nOfD4Pbq7B5gAeLG57fcWAiP7FNbQtlPpoDz6WhP5GO6BeOg8G7Hq9HjwF\ns2sj3vwBkMfKj8CLZw4LwzOpuRSTqmIiqyWtVAk7AWSpkFOxGN3l3zFx8Snl0nlrdsXlyc3HE4GZ\nPbFYhFVfOw7ELhtEF0xarPCIA1EME0ubWFggz+K1Zl8MbFzWVqsVfgMwOS+wMrasZ3YGgEcaDCIM\nFhAzl8tlYLTs7RbHjsAS0QYAMoioAFgsEmwOw5sULA5i5xDeLtCGXA+uP9LlzQttVoI8IJYjPoAU\nCx52Ny3REvXgMZoTtATDAJc7lln09tiifoZ1gDnhxS37NSJrcYsbLSU7a9Di4Mn4MXDjtK37+noO\ni4sFBkG9SmNl52M9vGKzCMQTCxOcz+FhsrOotN1ugxIa+hgLoFgMZPGUGSF/UEZWdrNIyuxP5MHO\nDOXWAMBMUQMZK+Rvbm6CI0IYpfJRH+iUwOL6/b602+3gpHA2m8lkMgnP8lvV+cOiMcbK/f192Fhg\nkNJtaW1G6YmPdtViPjxnsOIf4Mx2cVWCN3f4fm6aFgvzREZPesoVU0V2TEeG8NQOYHaQ+uTkpQeX\nBVzeM/paTC+hQVMzSJ6s7CZa6170ztd2++DJAYp4mAFgh5Hz5ncxctn0REJerAyHDodtz7RJAtfb\nAjwGTtQZDBOgAat1rr/+AAzhthqMjMuP3UcWRV+9eiXT6VQmk4lcXV3J5eVl8MC6WCyC/o11jSJS\nAmEeK9bOJeIzePMcsPTGui0B6nghCjtahMjsjUXOj/V13ljk31VAjNunCvvjwPZ5nK4Vdkq0tFiZ\nvs4ht2E9Kqwpc046OfFiIGWVA/cYJGDkiInGrzoTeXwmkQenyMMhbm0XxSs+gwjEHX6pLMCEdwxZ\n2QxRrCiKwK6wi8k7nGyLpjcPtG6IgUy3oT6MzjuP3DeoE5gdwJDTxGFstOv19bV0u93gwnowGEi/\n35fJZBLc5Ewmk9LOIICWy6TPPCKONg7Wx5v0TjTXnVkq9xuzcwA92iI2JmOLKgePKaXmgAU+Vp5W\nH2uJTJOUnQayXLaTYjU57EqDSZUOQnydBouDsbQ4Ty2OiDyAEtjCbDYLYIZVF/F4O551CQAcgBiD\noS4TWAuYG3xosb4GoApWx5sHWtyDHgw7ktC9tdvtEsDpya/ZDbcTyqIPY3sB/QAwRj56xxDACt0Y\nytntdktABiPTs7OzoNcSkdAneicXTJXHCBsuc3xeVHgxAkABkMGUId7yDjOL+mhnFum9NuLfMabj\nta81B2LPaFCKzXmdJsf36rUTQCaStquyGJqOo4HCYlwxsTIHPGPlj6VjrSZePEwIAIf2YYVVmcUN\nVpbjmy3SmeVg0nEaULxDQY2dL4ihCPoQOP7rwQ09Ecwg0BcADHhOhaiswV0vElr8YhDgeF57s4hu\nxdeAIvLgPQQ+97fbrXS7XRmNRkEfxToq6NIgtrOeD/0B1grA1iYxGtQBaljEwG75YDqs/nu9nvT7\n/Uc6Tm3LZn2ngictWXF0sPq1akhJQzsBZNw4XkVjeiYNXNaH08kFmtj1VH1058UGDt/H4AUQYMLw\n87B50mwA97DjByDjTQKwFHwjT7Az7CyyuIOJ0u/3AwDBoh/MC0CwWq2C+IVjUWCF19fX0uv1ZDAY\nyPHxsRweHoa3C2kmBhaj2UoOm2AQ5Gf5cLnIAwNGe7FSH4p9kS+A1uv1pNFoyMHBQTDhwBlTEQkM\nGnZrbNcnIrJer0tvbgcIsXjO5RV5YI0ASdb9sa4UDKzf78tgMAhp80JniWZVxzWe0XPISlOLiroc\n+l4qv1T8F9+1RIiJerojrLgpwPKArUoZdVmt+7GVymKUufHZ/ohFM1a8MzMDELE+hc0vuA1hmc+K\ndJEHl0IQCQE60KXxcSaUEyAEJgPjVOwO4v719bVMJhO5ubkJB6uZKbJ4pNubAQ5sA+DLu6kwr2AX\nOHz0R+ThSBfs7NBGAGS83RxxuN8g2olIeCs5AIZNINAfcC2EOrJ5hhY9mYmxyMknJUQk1BNvawKQ\nWYFNIHLEPYvRaibshRQ7S0lgKK9ObydFy1ThNBDF4msWxNcsEGOQiAFbTDTNAbEqwcuLJy6us7KY\nWRofRNY7eGBGmCzIB6IeT0zkAVEQH0w+Bh5e/fFMr9d7xOpw4BqiMt6SjTcdMdNAGcEuuG0ZsJjJ\nwigV98fjsYzH45I7b7AhACV0iWyaAiaMssKtD59TRRsgANwB7OzRFkCmF6OiKKTT6YR8PSBj8xMY\nPNdqtZLv/sFgEI61sb0bBwYxvWOZYmqa0cUWeD2OeY7pdKwxj2tYqHPE0p0QLVNgwiHFqFIKYb0a\neCtH1eteWavE5UEORgPrd7AFPoPHAII02MyAJwazEFyD2APgwQSIPcuMCGIsxFeesPigXHx0iX2f\nAUz0JGDdE/e5ZW6BPgHrAqvabrdhFxamCsiffXaxwS1vcMBlz2w2C37xISqjHTkd2HbBEwj3D5vF\nsLkMvHMwQ7SOY+E3zCy4H9BeENsBsJp5WW1mAVhs7OeM6dy5o4mKFc9i5VbYCSAT8XcTq1aKO1+v\nBDqetWLgf26n5bBJrx6x+wAyPvzMyntOw9reZ0U8rmm2hXoCyHh3DfGYPWiRgvVLrNAGi4BJAJt4\niJTPGDK7Ql3Y3ouvawBjo1/s9mKi8/s/4W4HbaTfFGWNETC16XQq4/FYTk9Pg3gOIBGRwFSLoijp\nGdnRJdrRKj9AnH30W+ybbfvYRRPqA9Ef7oEYwDQTS7GxKsTCC7wgWiFGInS8nLgv7sYnNcE1wKQa\nx7sfu4e0rd+plaJq8DqFy4aVHS9bBYCxcz6r7LiOFRkDXETC5INoiMFcFA+eNjDhebdMgw3ENNZ7\nwWOFdnkDkMRpAbbmZ90aJjnS0GIz2kwzPgZNMEMul25fBnmAjrZvY9ABwLRaLRkOhyWmBLDkZ7fb\n7aN3LnDePGbQPrz7y7Z53Me8COAeb0Rg46Xb7QZQ1baFmpXpsWONpxSAWOTAW8A1OFoipVeenLK8\nOJCl/lsV9gwmc2moFVLg8lwQs1iilS4GNgCgKIrSbppeaTkN7mxmP2AX0A9pkYR1KygDi0SYNDi/\neHV1ZVq8W+XDThq/Gb3T6Uiv15Nut/tIxGNdEn9YtOQD5WCoYGL4oL4cWP8EZTszM2a1yBdifq/X\nK/UTxE3d9khPi/Iou+4zZs85EgT3O/RmYNTYRWYgs3Z9LUYWW+A12PB1fc0yyNVgFpv3MUCLzbsX\nFy1jLEnHSTEyxLV+65CiqhbYPJdup/LjwQowgz2ZdtuiB7RuF23btFwuS4wEZgDY7cIbkyDKaCBb\nrwY1QUQAACAASURBVNcymUzk4uJCPn/+LNPptPRC2/l8LuPxuCQa3d3dycHBgRweHsrBwUGJmSBN\nfhM3dHRcN9RFqwKYKWIjAaYq7OmDwU77skebob4MKth4QL4AVwA/6we1Cx7ejUR5kR8Cp8fGu3Bv\nzoCjbQbRdzg/enh4KCcnJyX1ABsAW8zMkkCYCFjgZj2TI4bG0skNqTxeHMhEbPHRisOTNUY1Y89b\nwVr99L3fQm/Aacc6syiKYPZwf192/6zrwUpkFo9EJIAFxC3sAuKDfKDnYat76LCge1oul3J2dia/\n/vqrfPjwIXhJ7Xa7MhgM5Pr6Ws7Pz8NmA79rEfobABiAB+KXPlLEE06zbJ6UEC/X63XwWb9YLEog\nB3c7EAOLoiiZWrAIyTZaqBd0bLwhwO9T2G63JR0fnzXlftZjxwIprQvTYqDWdW42G2k2m3J8fCyv\nXr0qAZne1dXsjINeNPi6Hm9VQCiHAMTURFWuvziQpRrpKY2g7z8VgLwO/y30Y97qBzCC2LPdPrz2\njX13sYily4RBiyNKIg+Kbh7QECkBYvpsJPRP1lGn0Wgkb9++lcPDQxmNRvI///M/cnp6KqPRSI6P\nj+X09FQ+fPggo9FI3r9/L+/fv5dXr16F84qYkNjd4/OJ7AufGQRbyQNgAWKz2axkKgE3PLVaLZwg\nmM/nMp/PZTKZhONft7e3wdEjxGu0DY4pwTQFeiwW39FebLfH7zjQLBmslHdlRaS0iPAOL2/KaPu6\ner0u/X5fvvnmG/n222+l1+s9Ai7rf4yN5QKVRxZYJM9NR6tXnhJeHMhEbHaVKyKm7ldZTXI68Tkg\nhm8LzDgeRD6wg729vTAIwaQwobWtjUhZQY8X00J3wvfxEgycz8MExDeDB7t4brVaAaBev34tBwcH\nMpvNgsL56OhIVquV7O3tyWAwkJOTE3nz5o28fv1abm5uZDqdlgx3weLq9Xrwt29NQK0fg9cKficB\nbMJYGQ5dI3yTzedzuby8lLOzs3BYfH9/P+gnr6+vZT6fy3Q6DS9ZGQwGMhwOQxmw+KC9AEhsY8e2\nerjHAMd142dY18jjgnWXIl9s10ajkbx+/Vpev34dNocY/Fn9YF3XY7QKqHi6rdh8tlg2x/ldApmu\nXBUG9Rxw42B1aIp5PUXMTImSOkBf8vbtW5lMJnJ2diaLxSLYRfFEZr0Um0QgbYgasHGq1WphdxAK\nd4hXrN9h9tBut2V/f18ajUbY4i+KouSEcDQayXK5lL/85S+yXC6D8hn5HBwchI0C1LHVakm32y0d\nj2LQYtGTdyv1ezr5Gg5+43nUcT6fy3A4DCYtV1dXsl6vpdVqyf7+vrx9+1YGg4HU63WZTqfy+fPn\n4M5nNBrJ4eFhONHAwIXyQbxnUGKRFPXCWMepAvQJRH6RB0NYuF7iTRqANBYOduJogRV/e4wsNhar\nhBzi8BQdWSrsBJB5//laTOysoivLbcQcMIvlk8qbQdvqfFjIv3r1ShaLRVDas9gi8mDMae1Ssa7r\n9vY2mBtgMiAPAJkWi1AelKnZbEq32w3Gpjwh2+22vHr1Si4vL0tMZjQalTyb8nlDABmU/dAtaSAT\nebDcR11gagEQYx0i67BqtZoMh8MgWkJXiAUBhqT7+/tydHQkh4eH0ul05PPnzzIej4PuDeUdDocl\nezvoHfmIGB/s5vOWImVdJtuNaTMMxEU/YTHShsU4WsZt5zEuL1hj3WNHVQlCqhw6vafqondCtMwJ\nzxXpuIOtxkvlZYFmlTLxwEjpAjFBWq2WHB4eynK5lM+fP8vl5WXJ0JRXeRYnkQauM6CxslzkQXfG\n7IvZBCZqu90uiXU8kWu1moxGI/n5559LwHJ7eysHBwdycHAgNzc3cnp6Go4jgQHiWI92r8314nZD\nPdkkBeWBnuv+/sv5Q+yWgkliZ/XTp09ydnYWyn14eCj7+/uBQQIgjo+PQ7vAISUzLAAh24AxuDHA\n6YXLAgo8KyKl86HYbGHlP3R2YLP69IU1PvWCySIulylXv8VpxgCLxU1rA+G5LFBkx4HM6ogYLbaC\nJzrmKjc18HEZYquHt8pZ6Vm/sY0/GAwCU7i6ugrsjPVFABc2luVBKvJgjgH9DvJgg1ANZHxESCvd\nMeABLmA2SBv2XAApkS9v+haR4O0CAMmGqRZD1foyPclRRvgTg3ND7Dx2Oh2p1WqPbNqOjo6k2WzK\n4eFhuA4gwdvAAZjatk4zSOwgol1hwoHNDPSJ3llmwEY/MduG6QgvQrzL2e12S+dR9fiydFIxVc5z\n1DoxyagqS8zJj8NOAFlsheLvqrTzKcp9q3O9ULUsMSW/Lg8GfbvdltFoJO/evZPVaiV//etfZT6f\ni0jZcwNPaNzD5GMjT3YHAyCxnB2KPN7u5zp4imtMOOivmAHiWT42xUzMGgcWG9NHoVichIdXdhoJ\nMIHOsdFoyMnJiUwmk+CQEKImb5Iwa+Syijy4BUe7rlarsHuJ8rAjSe5TZp2a5bKIiZ1i9tCLOYD2\nA5ChHJaagtvSAzb9TFURMhXHS5fH03N0ZzsBZFbwVouqla2qcPTA8imyeyx+zmoIq/J+vy9v3rwJ\n5gbYVWOdCRtYct0YIHgiMJjxWUiebBa4cbrWfYAZGB0Ah8Vc1t95RqrWgOfNDK0LYkaJozpIDwAP\npX6z2ZThcCiz2ax04gG7mzjHCPDqdruBQYJhQreHskEPiE0Ufkkwm1nwosL10iIr707rM6VIdzAY\nyGg0CozXYmTIQ4uQsTGZ+q9BSQOhB6Ze+VJp5IQXBzKr0lzh35KFxVYGqzzW8x5tzw2xgWSFRqMh\n3W5XXr9+HRhDrVaTv/3tbyWvEwg8GTSIaUW0VuxrMLPETuSh66N3TVnE5TQRkCZ7l/XUAMzqNCCg\n7KwnZP0fyrzdPpg4gDF1u93SiQkADs60rtfrktNHbE7wUSs2ZYFPMug3me2inxiouezsqofPgXJc\nBrKDgwM5OTmRV69eBUCNjTf0v9XO3P8p/a0XLCDihfM5IYcl7sSuZWoFyRURY7TaeibVwKmVQ8d9\nDjX20sQEwlk/HIeBHojLxMpxi1nxNX22UD/DZxL5P4uRzCr0NbAK3mTQ9eL2tcARQacPoMZH25mJ\nlM9V8vgAGAFsNLuDWAmxr9lslry4MpC12+1woJ/ZLbNdFpu5DGgj/GY9GMBMb3ygXjAVOT4+lsFg\nUDpKxW3I7Wddt8ZbzjW+nivx5CzgTwVRkR1gZM8NGvVjq7v1rBbtqjAmHZ8HkwfSVQI/i2M+7969\nk3q9/uiN0nghiC6XJQJikvM3P6NBzJqEYBmWqAfGA32SN0DBfhiQrUkHUGGWxycQIFYzkGm9G/cP\n282hLsgbfvUBJti0QN6wxYM9mYgEcZLbSy8UWk/GcTSjRZlQXjbwbTabcnJyIn/3d38nh4eHj7z6\n6jaOMTDvGS8d/p8CMisu6qWDTsNicimisHNApgtf9bmnsqIUiFUBNyv+UwaPbgvspN3d3cl3330n\nIg9vQrq6ugpHbkTKoheDGrvTYbGS8/RYGqel9S3Wb6sNGHDYXkqLXSJlvRgfTeL0tPisre6tiYN7\neqxw2mhD6MUApOwtwyoD6/60aM5to4EPddVnJNEW9/f34cUn33zzjfz4449ycHBQEj+1qYrVRwg5\nLC0mtaR0WpaU9JS5kRt2QrTMkYE5pFaYXDDTDcsTIvVM7H8VUM3Jk+M2Gg0ZDAby3XfflXzmYxLC\noSBELmZtUELjw0r2WH20PkpPDkv3YrHVoniwzseBcu1fTYOYyMPurHUQWuSBYUI0ZFbDTga5HVhU\nZdGXd1PxfKvVKh0JQ315pxjl5l1HflGxFnF5k4OBCKya64C6YtPnhx9+kJ9++il49eW2YwDDbw3m\nWnXDbW7NSZTNY05WqApOVhmqhBcHMl1wbkDrHk98q+H1t85P//ZWKeuZVF045Kx4VdLmejabTRmN\nRiUWAIUz3Ngw6wEDgLilHSkyq0AeesXVA5vbjy3wEd8COGYXKJu1KaDZHDMzfYKBd2s1s2Hf9d7O\nK3ZYOU1sqsAyn41duT0sINQM2AIRgAuXV7cJNhzQbyIi79+/l3/5l3+R9+/fB5fW3iIS05dZIMbx\nrLnhzVEOVnwrrlYF6TSseZha6F9ctPTYCwNSbsc8NW+dp+5gL04qndxgMUw94DmPWq0WjvVgZwyH\nwyeTSXh7EL/MFROiKIrg8VQDGcrCk8tjrSgX63V0HUQkgAozJrYD00Cm29gb1FonxmIaGJwW33Rd\n0L5aqc6gwuIp7y7qduLxaoEE4up21KIzO4lkm7Rmsyk//vij/Pu//7ucnJyUNgOsseMt0NZ9q50t\nwpADMgzkKdE0Bm4eIfHSe3EgQ4gxI80OvGupoEEoV7b/3wgxlqYnHX/YvKIoinAYG4aew+FQLi8v\n5fz8PJhqYBJhYuN9ABAvNYhxG/NEZbCxyqWf12ky+OjBrxmKjgPA0d5jWbRj9sL2dVabW+2MfFBu\nXgTwm0HbYopIy2JJDO46LhvG8otmRETevHkjr169kp9++knevHkTTirwYsJpeYATW/Bj7MpqO9Sf\n/1tjNjY3rbmXmndeejsDZBxioOZdq3IfcXKYnlcu67+XTm5IgZhI2cAVOpx+vy+vX7+WVqsl8/lc\nGo1G8EPW7XaDc0GkqYGMB6bFNkQe7NPYnkwPWDba5Dbkie2JYiiHtnJHXtD5cdBgwXZpehfQYhWI\nG+snlE2bqqBNNEtDG6E9WLTV4qhOS7sowvPffPON/Nu//Zv89NNPwdMuLypIm+vBba3Hl243Haw5\nYbElDaLW/IiRhli7Vw07CWSxUAXhq8SxxA5935Ltc/LMWWlig8AqI3/gAwwABeCC8SRso3CUCQpw\nsDOt9GfQ4UmjAQllYbaigZgnEzMtAASLRzHbNAYEBnVsdAA42MU1G5laLILTZ2Dg8gFctQiqz7dy\nu8DVEB9o1yyVQRZlgUty9BeU+z/++KP89NNPcnh4aG7QcBoaaJhF6jHshSr3c5ib97xFJp4KbL8L\nILNEzdRKimDFi1Fvju/J5xbI5IScjtXxdF5aIY4Jx0di+EA1M4HFYiHz+TwAWa1WewRkeiVnmzJm\nD0gX+evVHwHApb/xHPLUrAbBslFD/6He/BZxEQmeKixDXN3eABRLMc911sxVA5nehGBfcSiXFq81\n24YhLu6/efNGfvrpJ/nxxx/l+++/D8eWrPHDoGCJ7dbYqhos4OG0Y+l71/XC4oXU/Z3YtcwJuYBl\nPWcBUO7K8RTwsoA3lo+VX+yavgeRCkdVMIlw8BlANJvNZDKZuO91ZAU3p2+JJEXx8KJdgGKtVotO\nbv6txT7N1riO/I372kMHK/dxT5tAMOvS7Yj6i0ipXPosK4uA+p7FyNA2zFY1U+VywItIv9+Xn3/+\nWf75n/9Z3r59G3XVo9ssZ9NEsyBrwdfj9injVwePHFhpeWPBCi8KZFVoJK/IPBiriHZVwEhP4lw2\npX/H0rfy03lbcfU1TAAwMuza4UA0DjhPJpPwZqb7+/vSWUtMLPiwt9qBA8ATbAymArVareSNQ09+\nZiVa5LR2QLX9GtqW9VYAMi5TvV4Poh2fsbTMIbgMfJ2BTAMw68esnUwGMgYvzeo4P5RtMBjI+/fv\n5e///u/lT3/6k3Q6nZK9GNrFGguavep66nj8PC9WmgBYaVj55zCvWFq6PKl0EXZWtLQmTk5Dxu7z\nhLR0Bfq+Fy+Wb0wcTeVppa87NRW/VqsFn/rwZQWjTkwWTD4wODYLYOBAfbSJBE9yXMMzABUwQnyg\nt+LXvnH6ls6I/yNt3dZsvgBQLYoi7NKyyyDtZSPWrrpMfN1iZB7QcX10PrwA7O3tyeHhobx69Up+\n/vln+cMf/iDv378PZjZVg8XcvDi5afEYtoAwlqZuW06vanmssBNAFkNp3UgxnQeCluFjeVYBqVTw\nOkUDQ86gshihx9j0c7ATAyMBU4FOTB/10ZPOAjLopBiALCU4FNXwR4YXgkDkxGFrZiksemrxiUFW\ng4FmTYgDL7UsPrM/fG4/ZkJ8nY2Jka7OjwFMi9P84bbUmyZYaDqdjhwfH8sPP/wg//RP/yT/+q//\nGmwEnxOesghz+bid+F4srdjc0axUp/3UsBNAZgFYrLGq6MtyZfKctFK0OSePHPZosUYLzLy0oS/T\nExSTAm9l4gPbXA8tZukygQEBuDDxsVu6WCxkuVwGw9zNZhOO7SB9iIWW6MXsEKIvWBaDJh985zdA\noR7r9TrUgVkZs0huT8v4GHXl9tGMindKAdi6LXkBYLZZr9fl8PBQfvzxR/n555/l559/lnfv3pW8\n1T41eKzMAg7d9taYrqLfstLQwK6llNTciM37nQOy54CAFddqzJwVxGvcXPb4nHseg4sxMQ7QCWGi\nIQ1cR4AHV9bxsFiEvLTBLCvFGdgAZHhhx3w+Dy8IGQwGgYlxPblu2nwDTIot65lh6xcKAyz5PCd0\nZ0hLn3G0doG5zpwfT0St+9MsjdP29FV4td6bN2/kH//xH+Uf/uEf5Mcffyy5r+b41ljRaVpxvP8W\nG9JxtYpFs6gY4KXmShUQS4Xfza6lNQH04MgBl5wGy10ZYvqznJADSrngZQ0aTF4AEzOSoiiCSDMe\nj2WxWJR2LTkeszORB5YB8OFdOrCw+Xwus9lMRB4fxOay8q6pZkd4VRx2Q8EgcX+9XpuiGn5D2T6f\nz0vgxMbADJ4Q/dgsRBu8agDTuiIGVdj1cZthJ3lvb09OTk7km2++CaIk3hbuiZNcTi9YY9LTZXnp\nWVKIx6asMnKe3n1OI8YC9X8v3Z1gZKlgoTYGjV4ZrE7ISTt23aLcejWJdZxVJp5YVh01Q/DKF9Nd\nwGCU3+LNdmFwT4PXnWmGYeWjdWkAPyj1wcYgXvLr3rQlPcAVbcHsEXo99reF8jHYsPcM1JE3AKAj\nFJESODPj0m3tmYqwfkxf5z5FufGyYeyg4uA53g36/v17+dOf/iR//OMf5aeffpJutxvaxmJYuQts\n7JqVBm/g6HGJoDdeYmDqsTKLfOQu0LHyi+yA+YVWKuqgJ7NFb3Gdv2NBgwWXh+PEOkt3dgwQrc7w\nrnvxcgDXKiOMX/mQNjMjPK/NCVh3BvHN6iewMexULhYLmU6nwQ03dlDhMlpP/u12G961yUxPRGSz\n2YQ6skkDPrCLA8tBPdiLBJcRL21B31ovXkEduTwoJ7eNZdEPRgd/+vX6l/dn4q3ljUZDXr16JaPR\nSEajkfzwww/yxz/+Ud69excO8HOfWuM7BSAcUtKOJdrpb4sg6Dw1mbDE1dhizGVJ5eeFFwUyvbWt\ngzWRU9Q5Nghi+ei0+LrHCK34qQ7z4lnMzQJxL3g0nw89Y0IjPiYsFPc8UTkv6KZ4BxFpQOTDLiXY\nGCZxt9uVXq8XWKDFcJj9gMGwyIoBzi/mXa/XMplMZDqdhnEEcNavxsOzeJsSi7NgfGyegfLo8llA\npuvDpyX4fZ3I99WrV3J8fCzHx8fy/fffy/fffy+j0ejRyQGLyeSAmKe7ygkpCSAGplqHpsuVGrvW\nszqNWHhxRiaSV1Arbuz5FNuJyfe6I7Qexuoo/I4BqfecVwZrYOtBba2cum71el06nU4Q/1arVQCA\n+/v7oB/TYId02TKeJzmYHpgY3kp0c3Mjh4eHcnx8HHbfWIREWjc3N3J1dSWz2SycDYU//H6/H9xJ\nA3Bh1oGNhOl0KrPZrMQ08Qo4q53BuHCgHuUAcLL4rMVTfpsRA6/eqWR9IEASb4yHzu/w8FDevHkj\nb9++DT730dZPlTKscZQzv1ISkdWOuflXjfOUZxB2VkeWEqdi4qWlF/BYl5W29VxsxeC43v0cezb9\n22NoqaDLql31MKPQzEWLVGBjmPSYvHhus9kEU4vlchmYWL/fl/39/ZIJCL5ZhLu7uwsMi41Y8R8e\nb7ELCSCDDg5Gv1oUBkAx28J/sLPVahVEQT5upYGMD4BrHRmLlWhrdoWN3eNerxeA+eDgQN69eydH\nR0fhnZm8QMSkDy/EFuZYPC+Ot/g+VfSzQi77siQuHV4cyCwRSt/3vqsqC/Wz+r71jEjZuj0nWCDk\npZ26VgW8UFYrz6IogpjTarVkMBgEALi4uCi5cQYQYMJqIGM91Xw+l/F4HNjIcDiU4+Nj6fV6JQNU\nFr1EHt7WfXBwIL1er8RoRB7OfcJBJPRw6/U6sKCieHjJCd4kxG0g8vA2dfje5xeOwM6s3++Hl5gg\nb2ZnLD4DOHkBYHDWL+bFriWY2XA4lIODg1BvBnpt5uKNh+eIj3jOG1Mx1Y2XnwZeDTwe68NvBvDY\nXPDIgMgOmF/ErqWotdfgFlPzaKwHAFUA0sojd8WyANpiZhbY54IxPwfGICIBlObzeQms+FneCQS4\nwYwAlvt3d1/cbPd6PRmNRnJwcGC+3ASgyHXp9/ulCQDGuFqtgjEt74ryQWx+wzYvNEibjWQBeNo+\nCyDFx5uYZfFmBliZfvM356sZIK6hfQ4PD+Xo6Ej6/X44Nsb9mQMiFmDnBv1MLF0LPDlY8S02p8c0\nf4tIiaF7IUZ2RHaEkfHvHCCLAZgOMRT3nqkCQrnAl5uedU1b6MeezSkPDwrWgekPgAcTkhnI3d1d\ncNZ4cHAgg8FAhsOhdLvd0k4g56VtsbzJwUefUH9dZk6HGRriM6CwISw7SER+fLheO2aEDhBugni3\nEoHHn3USoN1uy2g0kpOTEzk+Pn70vgQPCHTaFkurCma54zRXd6af4XxiY1XP89xNDS/sBCOLsZmc\nRuSVIQZMsd2WWJ6pzvwtQMxKS69CqXbKrTunxwaq+h4AjL8BBNiZq9fr0u/3pd/vB4t0BhqLXTLz\n0eADAOXX1rVarSDWAVDY1gtszcrL2qjQdedX6G23D7umrB/TejLNlADe3W436L1Q/n6/LycnJ3J4\neCij0egRE+N0PFbD9/Rvr5+fEryNklg+Xp/q/3jOGqsxUpBTp51gZLmon8MyRPxdPmulsxSJqXxi\ndL9qSAGMNTmt/HQbWgMoVh8tmjEbYqU5x4VeDW5mYAOm2QobLuu6IA8+4A1d097eXnibN4MI/8aG\nw3q9LjE9LfYBmNgwGPZ0uMeODfWOJHvZZSBD+VFWsFOkPxqNZDgchsPybBQc6xcE1ptZfcdpVdHj\nWkGPAS9YrEmXMzZerfnuibPevNVh5xhZjFl4k1JX3nrGSs8DESvtKiGVhjUQRB4zSo+dxVZK/T9V\nfm5TnvyagYFh8ADDwIeJhMgXI1YWDfHeTQZmLSqyQ0TosPCsZf4AnRUYE57Tu7HW0SI2kt1ut6UX\n6UInxno3PhSuTztwG0J8fPXqlRweHkpRFMEwFkBvuRTP7RtrMdaLas5Y9fKMqWc8EE3paFPXrbHu\njf9UW+0EI8N3DMysCsYYUQ4g6cbLHQgxcEyl44FurKwaADiO1wbWIMhZILS4x4prgBrfBxiAwSwW\nC5lMJiFtGMSywp3NQfSGAOfFx6VYRwUXPdBt4TlWxmuGYLG1m5ubYLcGF+HMIAFs+rV1KBu3fbvd\nlv39fXn9+rW8fv06xNU7wRz0wqHvWX1ljT9LqtCM9LcIKXavy5BTJyuevmdJQDq8uGU/K15F7IZI\noT8/a/1OhdzVLJZXFVBMgZgF6rl5pEBW/9dpe6K2VqCzpX5RFMFBIEDt4uJCzs/PZTgcBtGq0+lI\nq9UKSnV+Izd+r9frYHvFLIpBDEwMnjUAXvyfTTbg5BF2av1+P9h19Xq9ksscPsfptTvicR+2Wq2g\nJ8QZS7RnDMB08HRnVfvXetaKlwt2qbH2FOJgXbPmxs7ryLRSGAX2gC2nU2PxdTxLBq8S9ECPxUtd\n04ClQSQGRF66Fvh7g9s6O6pBDZMc5hvsMgf2WavVSvb29uTs7Ez+8z//U0ajkRweHgZAw2RvtVpB\n7GQgY2NSDtvtwxEl1pXx+wlgDsKeadfrtYzHY5nP53J/fy/dble+++47GQwGMhqNZDAYBFACWLOt\nGYO3nmTM+mBeoc07vLGhx32KQT11guPZpzzHz8fGUK5EkwI7b+6mxq/IDjAykcd2JN4AyG2wnAns\nKSW9tHLy1c/F6mDpOzw2FvttpR8b8JYOEeDEYpo+++eBWr1eD6LX/f29dDodOTo6ktevX8v79+8D\nS+v3+3J0dBR29ZAG9G4AHuz+aT1cURQlVz4QNcHE2M4MpwzgSmgymchms5H9/f3wQuOjoyMZDofS\nbrdLxrVF8WB6wSIw192aTHDZw44dcxZUBjENZh5gpKQTjC3L9MjTs6XKynGeMzdjLM6Kl0s0doaR\n6esiTxcVrWdSolwsDWvyP5XF6XQ9cGImFovD3xxSqzf/h2U8myFot9a6r4qiCGJkrVYLLxuBaAVQ\nms/nslqt5N27d/Ltt98Gsw0wK+QDJ4x8cBtAhXayjgxh1xJHmbbbL66u5/O5nJ+fy8XFhSwWC6nV\navLmzRt5/fq1vHv3LrzQGP3QaDRkMBiIyBdWibIhX9YdWgCzt7cn3W73kY+z/41gMZmUGKn1n7jm\nxU3lnWL21rzOkSJi+e6sjuwpCslYpasoTvX9HEYWe9ZbpWJgHGNkGry8Z6uU22sfHFuC+AadlGYk\neJbrzLZXYFDb7Vb29/flu+++k/F4LNPptOSbC/oyMLLtdivdbldub2+DicJ2++Vw9+XlpWy3W+l0\nOiGP5XIp0+k0mG3AKy2bTcxmM9nb25PBYCAnJycyHA7lm2++kffv38tgMCgZ7ULZz2COuvBBca4z\nuxyCrRt0bakxqlmXXiRy9WK5cWIszLqH7xzGxP9TAFVlLlisNlaeFwcyDs9lOxq1q4CktzpxmilK\nz/e8by9vzcAsUOO4sTRz6s3PYiIy0+FdSp0v54FyYwJDfwWzA9hP1et1Wa/XATTA3CDSgg3t7+9L\nu92W7XYrl5eXIa+jo6MAFOPxWD5//iztdluGw6HM53OZTCah/IvFQi4vL6Xb7cre3p58//33B0nE\n9wAAIABJREFU8v79+2DTxWccccgdJiJ8WF6bcTDj4HaCDRnq6bV31UU753mPIerxnBIpRR761BJJ\ncxZTbzFPXfPiVGmvFwUybUBpNWxq0lrxY2geE7Gs+KyjiT2TKlvqmnXO0RK9cxgp4rGOia9xXIhV\nOHTNXh2sw7wQ+cBKLAbJdQLYYOIvFgsRkWAnhoPcnU5HiqIIGwBF8eUc5qtXr0REZDgcltzdQHSF\nweze3p6sViuZzWbSbDbDcaB6vS4HBwfBLRCYWFEUod5wt6PrzmcqddvhA9ZnsTHuD/YwYn2sZ7y+\n1ePAkkAssND55MwpK90q4FR1fsTKHHtmZ4DMA6FUQMNaYOitOLkrm5UGrm235VeXxZ7LoeE6LW4P\nrzNTE4Cf5VWW21vkCythv196knEbs0GptivT5RN5cFw4m82CK2wAT7fbDUDKO4b43ev15OTkRIqi\nCL77UQb4TIPLn0ajIdfX1zKZTIIDQ7A+1J/fOo68AKZFUYTTAZyHfpsU34cPNBwA121s9YUWIa3+\n5f7Xvz3A8ACwinRjjXOrPKn0qwLkU8vH4cVFS175dajSIN5KmCtmaRC1JqZmHjGKnLNC5axaYEA8\n6LWnUrZ6997qw4xA58sTd7vdBrMK77gPlw3XrbhFUQS9Ft5wDvHv6upKNptNMMfgYz7YEIAPMu4b\nsLrNZhPqNJlM5PLyUm5ubgIzAsPCq+j0AXQ+eoW6svdZPoqkGSmfIOh0OrK/vy+tViswVMTTbcXB\n8p5hseWnTH5rjMXSqiLCec96klTO8zlxU3FeHMj0BNBAYQECGyN64BHrnBjIeOyHy2OJUlZ86zmr\nLB7z0sdrGAhgL6Xf6A0TBLZyx0czSXxgItHr9aTf75cORntikHdPLwbMfNijBDYB2K6MjWYBgHB1\nDWBhsEE5J5OJXF1dicjDcSmwy5ubm5KnC647TD/QrjCoRZtqn2OoN7cPzldqz7Qe8Ou28+LyM88F\ns9j/XFHWum7NtxhwWnFioQrA7sQRJT34Y0zMA4qUuMh5VOm82EDSZWAvC9b12LMAK20jpUEK97V/\nLHy0J1M+e6jBkstQr9eDLkvk4d2Y2uUNP8uT3BOB8QyU4tvtNrAtkS/AA4+ysC+DLZgGVNiM6fou\nFouSfguBz4fyMSEY36JevEDwQgAwQx343GVRfNmthA82C8i0eGhdf85Bb28Rf44IpxcjD5yfm4f3\nPxfkdNiJQ+Mi1XfaLDFPNwgGDA8e/tZp6uBNTCtvC4RjDE4DLCYT3DmDdeA9kexkEBNNu1zWYiYr\nrj3XzGgL2HbBSSLe/QhTCM1mWFyN7XKxZ1iAB0RXiJzwZc87p4vFItiGaUBH2dmqH2DNYIa25l1V\nMEQ+AK+9W/AH4iKz4tvb22Cy0u/3S6cDeOx4omNusBZtfc9q86eEmIiYEpN5XsXKV6Uc3n8vvDgj\nE0krKi3giMVHmhqwPDbC17R4i++UaUSMLVpBi4o4XgObKIhQmn1p/RhPXlxnoGM9mtaVcRn5IHS/\n3w/sTL/pGwwFbcKB41mr+Xa7DWwIZcaRJFauA/hgysC2XKysF5EgOkIUZdEZO5JgltqkBO2BxQNt\njna3RHu0d7/fD04SLebP9dYhJj1UBbvfIqTE4NhzFojxvd+iXDll2QkgE/F3SDyGo+9xHCtNL54n\n8nFgscRiYlZ6GsRQFkwOfqMRv0YN/wFe2p6JxRvt6A/XtRiKb0xI3cZgKXjhx/HxcUnPhrOHmt2x\nclsDvQZKBIh7uM/HnAASbM2vmR2DEc54araJ/mI//QyALFLyK+ZY38g6RRb70X6DwUDevn0r/X4/\n1M0y1YmFHLVF6roFIN7zTwWXGFDpe5Y4ncrTY4P4ZoD35vSLAxkX1Kt4DmW2Go9FAp22BzTWJNR5\nW+50vLhIG2CEg8wALJwLtHRhrN+CfohZG4uhrOjXfuUt/ZFeLBqNhoxGI7m7u5Nvv/02lFW73WFQ\n0QOMgVK3F9tY6QnPQK3LLvLY1AP5gHXpMQCGqV1b8xjhjQe9UcLiMAAMH5Ev5yqHw6GcnJw8eju4\nJYrp8ZUKz2UyOp2YeKvL7AHFbyU6Wuni22o/a4xZ4cWBzAop5pNalawVwmJ2+M2NyJ3P4lOuSKnL\noicMmBdeZwZQ8tgWu6Jh5sbPM5hpD6Y5bS0iwcNrt9sNO4VgK/ySWbblwjlLSx/E6Wv9ETM5/GY2\nyQp2xNFACoW7dinEh70tP/0MhFr3BpESbAx5c79gdxVvC7fqrxdDjCer3b3+8H4/hVV5QGWxKeuZ\nVNo6vapMzAOq1H0OL67sZyDha7nob4GY7hAR2xUvx9GAxboWHS/GGPWqj8myXC5lNpsF8GFRhvVY\niD+bzWQ2m8l0OpXpdBqYm/Zdr3Vlnk1eKnA7wZ0Oi6tgIwhsCc9tpsEO8cDINMiywSmDD9oQoMb9\ng77hOrMODXo45Gmxb/Y0C2ardYtapMfRq/fv35fe2anbkEOOekP/f0r/pfL1/qeYWC5oWkCdAl0L\nLD02Zv3nsBOMzGqEGMvheN51DWbeCqMBSD8TY3JeWTBRbm9vw84jQIn1X8y8MJkAWOPxONhHTadT\nWSwWYbI9Z8teB4vOw60PQAaTnttA6w2thQcgxm6aGJx0vtrrBoMcyqo/DN54HroxS7eJxUVEHonm\n+k1J2tyi1WrJ/v6+vHv3LpwNRbl0OWP/q/SLF1LjLycNq2y5ZfXS5n5OsVEPmFIMzQovDmSxFeE5\ncrgWZ7x8vTwtINQgZtFq3o1cr9fhBbZQ5POKz0A2Ho/l/Pxczs7OgusZDXpsoPlbBwaFong4hygi\nQdQCUABgWCTXYpvWaXGbWmKeZnCauWnzCgAiysIiJPvh1zuUzGA1K9YAybvAnU5HBoOBHB8fy/7+\nfmkDBMETq3hie2M6NqGrzIOUCGot8ql5EgPEp4i6OaJiVfB/cct+fOtKMXh4rEiH2Cpl5RsLOn8L\nzDSQYQLAoBNeGSaTSbBEx4Rindd8PpezszM5PT0NQIbJ9X8RrIGF85ciX/xzYVLrBYLZGYvdekME\nA57tuXjSaQcCvCHgjQU2cNX6ML25gDxYZwmdmGZivBhhwel0OvLmzRs5PDwMLomqsBlP/RDrj6rB\nYsX831K55ICYFY/7VKuFdBqpeZhqtxjgIbw4IxORknggIo8mRSrkyOEW9bYCr5weaOp73NAQE2ez\nmZyfnweHgfrIz2q1kouLC/nw4YN8+PBBptOpzGazkoHn/1Ww2qYoiiCesSmGBiq9u4l24c0AxMN/\nfWQI6QFAuFwAc4AgM0fr/KQWIy1DWW2iwmlqkwzc7/f7j0RK3WZeu8bGUU46qeAt4DGWx/8t5pcq\niwViOm9LzPTKE5PIcljfizMyFsksRbUlxln3vXvcCKlG1c96bNC6DrFluVzKZDKR8Xgss9kssBkG\nsMViIaenp/L582f59ddf5ePHj0GJ/38VYiyXgaLZbJaODlm2Utxn7Nsen5gujRkaAx3Hvbu7KzFB\njBP24qr7mEVPLZJCXMeCwf3HBsXQC8JTB44iWW0ZA7OYFKHbz0svNW5z2R7fQ7msORIrSyxYpCGV\nhgdm/H+nGRlPCl45f4sQ65jcZ3OfQR6r1Uomk4mcn58HdqVX+rOzM/nrX/8qHz9+lE+fPgXziZdg\nYDzJWGeFsm632+Bvv16vy9XVVUnc5dWcgQZpadaEZ7gcEAP1DqhmWgxEAK9Go1FiVBYT4/sANW2m\nwkCmT1K0Wq3wxiUWVxkEuD2eGnKf9RaEp6TL/VWlDFaZcvL05iG3o8ciU2LoizMykQc7IV49eWs7\nh5an8vCet+irNdEtZshpw73yZDKR+Xwu6/W6ZBs2m83k8vJSPnz4IH/961/l4uJCxuNxydL+JQIP\nEH0MB20CU4xOp1NycYM3ISEe2BQDi7W6W/e0zozj8UTTzwAEkb6IlOoAsIJeDKxLi/oAOAY5EQne\nbLXjRGY1OSw/h1HljAE9XquCSFV25V3jevO3Va4YOHG5dP9WKfuL68gwiIqiKFl05wyOWOfnyN7c\n4JasbwEZl4vLeX19LdPpVMbjcUknBqXx2dmZ/Nd//Zd8/vxZLi8vS2INGMH/FZjptkF9tFU9l6fZ\nbMrBwYHMZjM5Ozt7JLaJPOg2WTTULzFh5iUiJdZmiYf6w2DILJYZHDMqZphQ7rPICxGVD6DjYHqt\nVgtiJbsHSi2OVYMGw6eOg9RzOezRA5IYYHpt4rWHJULqslVtjxf3EMtAptkAgsWWcF3fzwkx5mXF\n0boETCBYw0+nU7m8vAziJBtTzmYzOT09lb/85S/y66+/ymQykdVq9WLKfH2dAUf76Od+4Ld/93q9\nsAuL/tPMkp0bst0Yi43WUS+tK2MdmAY1XT+U1/LHxmyLF4/7+weHiqz8R5xmsxk8gVgs3mvf3xKM\nPADKGfMWyFgAoRc369t6LsVIn9NGVVjkzgAZ/utVVsRuDCteip5bYIj/2krbe57LCz/zFxcX8vHj\nx/AaMdbPXF5eyn//93/Lhw8fSkwMwaPTv1XwGCWDCWyvICryJGebsFrtyxuHjo+PZTqdytXVVdik\nYLsvMB0AvQYkBinLbAOAybo2BkwGWO5LVtSz4p49WvC5TTzD9mQoN9LFW5/wUuLUhMxhETqNFBtn\nBqlPUuQEa4GOlTEm4ukFWIuXeqzp+yhDCqRy2COHFwUyfhchH3nRHasb32JPHpjFWBYCmwSkBpSI\nBL3QarUKL4EFiKHzVquVnJ+fyy+//CKfPn2S8Xj8v2JWYYnAlvkKi2QcTzMyvevIeWAXE22BegIk\n5vN56B+IYvyeRzbRAGBpFshl1DuViIcjTCwCswjJLIxFRgZbS7mvNxu2221wnsiiJbcnjwvdL9x2\nWlyy+jAmrlUVWzWY6DLFmFcVJuSxPa88OQDsgVjsuRcHMgx2HpDeiquDBWipwcJp6smf08A8ST99\n+iSnp6eyWCweicSLxUL+8pe/yJ///Ge5uLiQ1Wr1LOZlraSaVQEccNZQswMt5ul6MaB79zifdrst\n0+lULi4uglcOEQmMBswKgCUiJcDU1v/MuLjOur/43CZvUPDmCh+2h1jJ4f7+PhxP0icXuG3gArzd\nbpuLA7evJYblsqcYk/MW4JxnU2nF2FcukHlipy6zJQnlljsVXhTIVqtVcIEMH1UMZDHZ2wIx/hZJ\n03tNhfW3fh5lwwSazWZBuY/Jd3NzE/Riv/76a7DSr9pZXK5a7ctr1VqtljSbzZKveeh1tCU8K9T1\nBLNWXWbEvKhwebThqYgETxB4q/jt7a1Mp9Mgkuo+0XXjHUxtXc/P6g0InojYkWT/buwskRX/iM8b\nGyLyCFiRL16IAgeT1tiIsSnuw9h45md1+lZ47n1ewK3NnVhaVh08APZAjJ/R41GXJYchviiQLZfL\nYD3O2+RcmZzOT4UUQxORRxOEr/PggsgCk4rZbFbaCbu+vpbLy0v59OmTfP78Wcbj8ZNAjCdNs9n8\n/9Sd6W6kR3K1o4pr7TuXJrupbkmjGY1hj+Er8J357vzDgAEDxofRjDSaVrO7udfCKu5kfT+IJ3gq\nOt+qYrdkchIgSFa9a2bkiRNLRlqlUrF6vT6R03R5eWm9Xs+Gw+En6wV1kbe+ByCn/azrERVI4moL\nJjqf3d7e+l6RFDhk0buZ+RKn2OdM9LjeUu+vayj1+zg2OiZaGlwXg+s74RPTMkAo0pgnxgJ0FEgW\nW80aw8jMUhMyC0BSgJZlaj7GBNVnUp/bPNeeds3U9bOulwVSWSA2zzM8KZCdnp76pFxeXk5OonlM\nTFoc/MjYpmmHLPDifzU9hsOhLymK9eKHw6Ht7u7ahw8fHm1OpkCUNY/tdts6nY7nM2mUUO8flYH2\nB/eIIKURxth3em70JWqNskKh4OAJoB8fH1uhULBisWhXV1ceTKDEju7KHd+DpvKg7wcowcJ0b4NU\nuaMoW4BoDDjoJI8bsMySm2nyl2pRSc5jgsbz432ygCQLbKJySbGkWW2a6ZsFihG4UtbPrHfV9uRA\nhlbX6g4pIDObnUaQ9V3WMcpSsvwFOvHR2IPBwPb29mw4HE6UkMasevfunX348ME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rxkci6Xc4BkfScRIxbxsqQIIPv48aPlcvfr5nSHHLMHHwoll1mVAD3WDURgerECxuLioleUZSnV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\nVqvZH/7wB69UCqNk0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HcRNUgMJStTwzmPKcLx+BnUFxDD3zSuQ2VOEhI1n4nnYuABRMLk\nGnFS/wLgwQCbmZtJLGO6u7uzk5MTGwwGDjqYWZqUqH0RgUyZhdmD+YTZg2Ar6+CZFhbuN4OFncEW\nabDKbrdrvV7P3r17Z4VCwTqdjr18+dK2trZsa2vLKpWKP7emN2BiIeysVwXINHjD82sUEYDVJVDI\nB2XHGVMA7eDgwH+63e6EeUQO1unpqZ2cnPgqDb4rFArOLBlbgIRn5G8tMMCzRzNN5YzfjEO/37f9\n/X2vV0d5cxQKVUwweRlbjVLiQxuNRs7AY3Q3OtzV76VRyxQby2rKxskaYE7f3NxMFAXQa0XAygKy\nlLM/HqftSYGMMieasa4MS1vsYDQIA6wTnM/VrueaSql14rBECYcpSa+YCWY2QetzuZxrPPKPiFox\nWRB0ZWZm5mxje3vbnxdhheEpMyUBdXl52fb29jwAkMvlHNRTWhMh1aohZpMCzD2Hw6EzEfXzqO8I\nYLy8vLSTkxO7vr624+Nje/v2rdVqNZ+IJIyWSiVPpIUF9ft9Z5Y8iwIvioVUGBiqmkoR+NRnqZHk\npaUlazQaE+YaCgmzDmaGac7f/X7fut2uR5ABN56r0Wh4RRNkLcvRHYF0OBza3t6effjwwTeWYZxJ\nzAbcUhVgATGCTQQZdOWIumqibyyaezrX9FlTDvc4p1SeAPGzszM7ODjwBNq46kLPywKoeP/UMdqe\nFMhIQtWEUx5WExbRYmaTSa5mk5EkJoKZudZUE4fzOY8GI8PUgF2lHNAqCIApYLeysuLCxXUIAiB8\n+A+IDCJwMJ5cLucBB/UxIaC9Xs8rcPDu6peKk4mAAwxCgwD44dgVimdrNptWq9U8l4k+1Pw2zLj9\n/X1bWVnxlIz19XXb2NiwRqPh+5bi0Mf8gGEyDgAL70L/pBiZBoI4l/yp0WjkG9PShwAPwIpJSH8B\nCApko9HIdnd37ccff3RTkslIJRF2Nsrn885I6KfUpES2Ly8vbTAY2MePH73mP6adOvnxmcVlSNGU\nw0yH/bKiJIKEyjDPFN0s6kvU8+JYxRaBTJOCKRYao7fxenrdCJLx+1R7UiDD7qcT1HkKgDBJeWkE\nmUGIy5nMJqMfymr0+JR5CcPBIc05mLUwtGjuYnpiRsHIyITW3Y3IkeJdqCTbaDRsMBi4L0w1OM9g\nZlapVHzy4XvjveNE4h6cz2SDSQIqaoaiAM7OzrxiB/4olljB0Dj2+vrao4Wj0cj29/etXC47iGCC\nk7WvzxhByWwSYDXyhrLjPN4d8CH6enNz48m8RNIIFKiyNDMfH8wg3vXs7MxardaEuQkLh91pjhhN\nASea+2dnZ/bhwwfb3d21jx8/Wq/Xs+vr+411WLcZcyfjb5g/oI0S0jI7rI3VQp3RwlE3iYJZjFDG\neaTvo+/OsyGzCppsdBIzByKQpUjGNAe/ticFMvxQmmYQEVjDxwhzymHPOXGC6ABqWeFoqmoSISxG\nfVJciwz6aNZoKgfn4AfSdBD8WrwPzIxNW9kUhPcBZHQ5Tb1e9+9jtQ7eJ2rSOLHUB6lrVvns/Pzc\nE4Y1oTdGtnhXImdkvAMKrOcETJQVxsmhLEF9aqqM1LRkfEi7wLlPBBmmiGlKH2qeIvKk1727u/OS\n1Wbmznd99zjpU8xFGSPm+O7urr1//95OTk6csbMQnd23NOVHZRkFCxPDlQH44jfVBfgob4hBZFsR\nyFSGogkf31PPUaBFEWmtOWXWqXvPA1bT2pPvohQ7SQUkxZoYZM22N7MJIDF7mGDqTzJ7KE0cfUZc\nQ+tvIYBquqkwkCYCOKH5tJIGAQRN3uTZT09PHSAXFxetWq3aeHwfJNDoG3XaqWlVrVY9b0lXFiBM\n+l7avxxDgAATOvoXOZf+o0Z9t9udqKtWLBZ9HDV3S1NHCBTAWklRwNxjEsYlL5h9mPssPtfINs+I\nXwuWyrvwfrBo9RkiOzERG78ZBQBKpdIEe9E+SjGxmPZze3u/xOjg4MD9YsPh0PL5vDUajYl9K1nV\nEK+pzJMsf+rH5XI5r057eXlpx8fHE4GwXC7ncsn1FIgVxFJgkiVLqfO0LwEx/OBsyFKtVjPxQFlZ\n7AO9Z6o9KZBpB+gkjPSWH/xpSl8RHo3ORH/C3d3dBHipwOlAaFIjYIWJqcKr5mwWa4NNjcdjjxCq\nfw/GxgRHY9VqNWeOgCj+P0wmauHznEtLSxM7MsWoFPdWFqPPjEJJhcpxorP6AT8M96cPYct6Xc6F\nAd3d3fmkw4em25HBuNSPB0iq4ol5UYCPbsCCaUr6haYuxMXVKB0mIOsdiR6q0kxZADrB1BUCkFJO\n/PDw0IbDoY3H99uw1Wo131syFb1V4CVJmR8W/aPg8M/ir1OlGq0d5HRaEHrbwgAAIABJREFUakTK\n1xqVf3xW7ScFNJ1X+BW5h14rBiIiiD1bIFOtqhpNBVfZGQKMiaaaGQHS5FEFSi2HAzNSM3U8Hrsp\npMKo/gkdfD0PZ776+iikhyAyqZVZwlwopsd9K5WKa1LMI3ZL6vV6NhwOvXJnvV73xen4TQAe1e70\nNyAaQUxBTxNaNUmZdwcw8AEyJpif9DlKRE0OHPGj0cgODw/dDASc8WvpGr6YE8gkMXsAJmQAEMTk\nBEhicqgqMt41+rzwPfFO6uPRe2sgin4gzeTo6Mi63a6byGxUozsVpQCFPiPfDOatO2Yp6Kt5r4pY\n/azRXFSFF5laBC31w/J8Oje4PxFpndtUNC4Wi9ZqtWaarZ9jZj75WssIZJrtH5mZRvGIPCmbUB9a\nHAgihsrMoo9GWR2TQYVaNYZSdZ08CgZoQ9gBAIt5q740JhjghakBA1teXva8J4Ae3wOMkf6hb1LF\n/lTwom9Nv1dhiv0ZTVECCDF6yndqEgLo5G5xDmYcKReYnqkMdZ5DgZn/NdSPCcnkBmj5rTlvqizN\nzNlvBOfYR4CNMgmSc9U1gGKCzcbSPtr/6hYhGZrcO03HQAnA/hhvBXd91ghM0TSO465zgzmAPKSU\nIK6AGJQhvaRardpoNPIxnbelwC62JwUyBkEpPoNg9mnCnplNaNSlpSUP9UathpCbTVYtjZMgRWWV\nHcK24rpQoqcxEsPvXO4hzQKh4jdCxCJ1JpsKE4EA8qCKxaLV63Vf2N3v920wGEzsVsQP1xsMBp7F\nD3NQLZpqjIeZuUByjrKzGC1mAmn/8o667pBNbXXM+RumdnJy4r4z2C3vCEvT5WD6TPFd9Fl41ihf\nupwmxU60D/Q3Ywe71v9xB/CsXAfw5Fmi+aamJEGMeF1yBwF/xjtWxYhAkzLp9N5q5ai1w1wAwKKs\nK6Cracl1mXssKWs2m8lKGamxmwfEzJ4YyHT/Rs2ZMZus1aTCHk0Bom2qTVI5VUp/OVfZlR7P/XR5\njU5w7gGYKbNQkEBwC4WC+20AFBUWBUo145SNqTkCQxgMBp7nhGnMj7IIBQ/tpygYKVNCx0DNFYRb\n+14BIGXCmdkEY4oTDe1NcIRz9f3JBYtJroCRnsezRgd8XEkCY1QgiwvTY4AgBS4EfZBJnp17pUw2\n7QuNOGrGvgIcYKOgqOOrchx9iSlrJYKJ9kGUkQhe8Rw1h5EFmPHNzf2WhoeHh15aXeUi3kN91/He\nqfakQEY9c9iKmkEInJbj0QmlQo4AaBoExzG4CphoPKX3OvhqqhBgUId1NHP5LB6rzwhT0j0mlVma\nTaZC6GTDtGIyVyoVq9VqdnR05DuRExlUZlYqlTxfbDAYTFQW1XWOtCjckX3k83l/vriuUs1H+lQn\nHyCK7xCwRfgxjTSKimzoXp2kdygwAEbK4tSE1BULyEeMgEbGxrNo9FUDBDFgYPbAxHVZTvQ10lKT\nHx8YYMgYEU3lfsgT46F5gLwLZif+YFXEKr80HcOULNCi+ZlljiL/zCf8owcHB77bvQKq9ol+poD2\nbBkZyYmqydUcUDBT8Ipah0RLs8m8MjObAEVld2hNQCQOHIOu98NHAjim/G1mD9VEYV5k98c8IQAL\n1qeRH/XdRDaBkGJmHR8f+5o80k3w8QBu+Xx+IroHYKjfJwqV/la/CM+ozwSIx6gj/ce96A+9Lmkv\ngBFjpkoO/4/mu9F4Bi2DreAVcxGjXyyyJAWqaBFEP1aU39Q1o+vC7NPcsFhFQpPF1deZyz1sSQeD\nZSxVPlJzJrpqFLT02WaxteiWSLG4aD0xp2C6+Moo1vCl7VlsB4cgMAGio1jD/KpJEHiNAioYRnqq\npqMWLDRL+1E0ZK1a7fr62s9TXxG/lZnpBNPzWRKD6ahAjnBr8qIyH6pjkETbarXs4ODA6+LjHNal\nPo1Gw6+rS3LUXzWPzyI6+hVoVekom9T8MJiNbmbCcTBJ2BpmCbv4aCqHAq8yR4I6UV5U0Wjys46d\nmmEqLypvkUWk/k/1XTTVeTeUii7/4pjIUBl//IWj0cjdCspM6fNoTiqI6vxB0cSlVvo+00xO/V6J\nANdAFghyUYIpl8tNANk003VWe/J6ZGaTD56lIel0BiH6V+iomBVN56pzEQ2by+U8AKCmCpML0NPv\nVZPGyaJsUrU276mpBrE0jDKVFGBiNivQs9MODnGEdzAY+DIVFqoDhgqElAjSfQI02hWbCpr2DebP\nwsKC/9aEUDVjlWUpo+MZMPk1uTmfz3vSqMpIasIra85qKbYUmZSOrU5q/W4aC4v30fdW81TfG5CP\nn9HfbAaMBcLxqjhUjqOPLDWu/A+I8n7KWjkuBhF4RwUxDQpEWWAsr6+vbTAY+IoJHYMs0JoFZk++\nRImmIIAZyARRcOFYXbqjviYtp6PmpzrjdXUAzn9lg5g6aCq0nTr99d7K9HhWHXgqaaAtWaDO+7Eq\ngMxujd4yuNFRrO9cq9UmmN3KyopXmcD06Pf7XrKaZVGYNDiVyYwHzKa1LNNTAxkoCnWq0xSYzWxi\nkpiZszQSR7XMtm5SwsoDKqyy9jCaUbTI2OM7pVh/Kro4Dbz4rWOG3ChAKehqmo4GhejPXC5ntVrN\nOp2OZ/anHP8oW1VcvEc0g/W9ddmdmXmkOfaF+gT13bguY6ryq6lFABnrfGGaEfTj+GQpV9qz2KCX\nFoUvakj1S6jZp6YNSzOYjOrUNXvwy+n90G5RE6PtERJ9NmVaZjaR06YDGpM3AUmibvhGdB1jnOAK\nqNofCuKsCsA8UF+JlqdBaAEYziWXS0tHRz9RqkWWpiyZ58YpzaSKSkbZlTIDmAf3oOaWmq86yWu1\n2oTAx3QTfbaUqaRyl3JJxGePTCKyFjUVGUtlXbpqQKOVyizNzFk8QQSujYKN7g3SVXQnJuRHTewU\nkPNsShwgAPp+EWSy+kqbziv11xKcyWrP3rRMTY7og0FgNS8lmqFxQqvmiJpRO0xNP03L0OfDga1O\nbc7VrG6Oj5NTqX101haLRa8nVSgUPjlXJwCCHaNfZubCTMFGGCGMhppX+GJOT08nzDWNgo3HY89f\ngtGlHOypFn0wKsho4ijo6leL17q5ufFMdvYaUKGmdFC73bZOp+PrFukfddLrWMcopAYw+D9G41Im\nY2Rpeg3ugwshpTwBLl1epcfSCoWCb5ysidXMDVUEZg/FO2FAWggy9rOOD9dRFh3BL/rCUkwpZQ2p\nz5t7UCGmWCwmgSzLHE61Z5HZH3/U8az+jkj9tYOUzcBEGFxYQaTAZg8CquF0M3MWoQxBWRKTgfdQ\nsFTw4l4RWJeWljyZEXOIBc7sJh3NANXAWeYPoMRiZ/ohn897rS4mHNpRS0hjAlarVfflxVyqVL6f\n9qWyM/5PmWiYo/g4UwDO+RoJ5vo4vkej0UTSKecCxpq7lzIF1UyH+Stz4xm0r5V1paKbmuqj4Bjz\n0DAzNfVIx1rTSoj6aVBLlScrQqj4kcvlJqwNdZ/QBypH9JuCbYwwRwDTMUuxXJX5CLykZGSBmI6R\ngmeqPXn6hbINs083Q4jakchHdG6aTWp4/E0Ika5JM3sYQI7RfByuazZpnqjJonk+eo8oGNwrRgZx\numNW6qCqP44Wc8rMHgSFd+A4UjPUT1KpVLxqJ74Jopb0Gwu4mQw8B1FOFiuzDIWmkyNqzwhmcVKk\n/Go6yThXJ4weQ7+picbf1MMHJDSJFnAg2Ta6IBREo1nF+BN9puIGCkOfVYFL00gUuFIMEBnRncox\nP5kfnAeAFwoFq9VqVq/XPdUnRsBTQK6mqbJXfmeNM+8b+ygCmfqodc4iW9S5m9ZSsjXRVzOv8Bs2\nZVcaYo6mgWod9U3F6BHXVOcyi6eVzWjH673U9xD9AhFsNXvZbDK6ynNoxyNsCLPZQ7WNarU6EalC\nE6pzPE5eBdfIFjGxzR4A8OLiws1ZQuA6gdD2vBugqr5F6vLji1PHdfRFTRtzmvYn50amyRipaaLL\nlnRHJ4IabNNHwEP7CgaseWWaRBtNdx3/LECKkV49J7I0deJHsFRzbHFx0aOUZjaxw5U63PFtkiTN\nzu+qmBVMIoOOrhcdQ55XFbTKWfT/pVwgupuX+oHNHnaRisElHf8oK1ntyYFMAYgoDGitg6aMSiOJ\nep1oJqi/AhBTH5cCFcKLyaXXVvAEdNUE1YGJbIKmQEYOFflAd3d3vu8kEwSmoA3hYgKZ2YSW076h\nkCD9dnt7O1EKGd+JVgrhfxY5wwBZ6IwvI5fLTZSV4bk1yjprvOPE1wmhbI3x5B3x/1CkkXdkvC8v\n7/e3pMqG+jAVdJh49KFWftUdoVTBKXjzfyqqpyCiEzwy+1Q/Ibuw6nK57HmBMCc1lQE8mBgpKjyr\nMiq1QhSYlJEp2Cn4atY/4xSVu/r4UKa6QF43U8ZdwLxRII8EYRaImT0DIKMhrCmtrlpANRaTKks4\n8vm8p1EoECkt53g1JdUPg1kI+KgG436qUc3MAVnzvqL5Z/YAmux2DrOhhpaaW9G8UT9GZAL6/rA+\nnmVhYcH6/f7E9Wg6uRFkAIv3V+aytLTkxfLoNzK31ake02yi6a0m/7TjtLQ2ZbB1bwOADTNf2VzW\nuKnsaQQwmlgxt0uVWcr0VD9aNKujXCtYwxDxk1JCXF0iCmDlctk3ftElXyg8M/vE5aHzKfVMKd81\n36kFpbmU+v4KTCgDlhLqmKhrRN1JcV7x/7T2bIDM7CE1IgUmdJRqYdUSKWEByMbjsSeIahRUj+cz\ntI9qaz4vFAoTYBbNqTg4cWC4Jj4xliLhVL+5ufFdgOL6wBjNUqCBadCnCvKwmLgEiIx+ZRUqgGqK\naY4Ri+ApkEg2vtn9xBgOhxPmne6YFMc8+mv03vpZ9E3RiP5yDKwRkAXIU/5UbernoemkBKDjXgU6\n/iqzWcCVaoyRrg/FFGPhuD6T2cN2eRSnJDcQgNFVAuqfSvm4VHGpvKpSi8+rVkpMFYqmr96LysnI\ntEaNldnp2EdZyOrTZwFkqn1V4yhz0nOyJkP8zd/qd1OANPs0BSTF/qDpCJoOJscqIKaEIWodhOv2\n9qE+PgUVYTSj0ch3oTG7Z3rUalf/TFy2o4xVF0+bmadoFAoF6/V61u/3P1nqAmOMkxLwBAQvLi6c\nPXCvXC7nDMHMPOKpayVxWsckzKjY1OzXSajHKXjg41PTGXOX94rXUsamz6F+oru7u4lnTtV4m5dB\npJ4lBjXu7h7KIrFaw8w8WAFgl0olB+4I+Mo2VW5ji32MDOv36tqJ7gFkmuNQ6Ko44g/nEhHXPU5T\nsqfPktWeDZDxv76sRvvUfEv9xGvG+2igQH1l0TRVEFJ/HdGwLK0WNdp4PFncTgWGYwAyti4rlUoT\npgkRTYTo5ubGer2e77WoC4t1kHk/rVOmNbyoLMt2d2bm5Z0RxGh+6zswqcfjsYMkE4zqrviaMC/J\nuqe2vpojqaaAoOZIZFRq3sGaYAnKnsbj8SfgwYTTa0eGSB9oqoSmn6SeeZocqqLh3goI9AsArKkJ\nWniyXC5PBK+UDamFoFn/+ozKfKOyiP2v8pUFZgqgWAVqqkcFApAR9dWE9NiX87DbZ1Gzn8Gk6QDH\njtWBUfTWDtJzoK/aeXSu+lTUKcx5caOSLI0W/49Apc/OhFH6TcY2IAugqbkawVOBJQquRon0HF1h\ncHd3ZysrK156uVqtWr/f9+hY9CmqIonaEtAFsHq93gTAsXsOqw/U54QgA3hcRydP1PA65iovmuQK\nEDMG2iKIpZSi9q/6+mJKwrSmrC+atVkMWgEnFpXURfXRYolgAhPTvRDi+0YHPs9MH8XxTllGCpy4\nKnTc9L5m5mOjAHx+fm79ft/9fNHtM4vlmj2DzH4VIDXnUpRSGUAUjJRZqVqKzwEyNbnoOKXJav9P\nY4DaokaO2lY1P343tG7MYUMwFeQjk+A4GixMq5KaPVToQNgQPpJnYVJMIkoTq69vmhmlvqT4nOrH\ngQEygW5vb20wGHjUk7LOscpFZE6RwTCekb3r+EVGkcXwdCyRH01jyPIbxabMD6DSyazpRoC8LtUZ\nj8cT9eeY5Po94xnBjHfXJGfkRftM/4/vo+xI76eWB0DGuGv/I0sqrxyj4zgej72aMb5WZaR6f/0d\n27NgZMo4YtNOiEBmNll+RQU2DrRGtjTSkgoqpNgO11Sw5TMGUAdINZsCk7Ij1U5M1FSYnmfN5/NW\nq9VsYWHBl+4QOUJo8VdphjuaURlGTA3I5XK+GkAXm2tGPdEz7a84TvEd2QlbFQcliFiWpcuKYrQT\nFhTHlIicKjACAcoeUJARfPV6NHV8RxCLsqDyqhM7/tY+1+uPx2NXJCQg64J4xoyfyK5joEmfQ4GM\noEdkoFw/zkNtWe4TBWYNGKnyjT5aNd25HoEuM/Nio+Q8sgZTI6jT2rNgZGaTRd5oTF4FKO2YFEPK\nst9VE2lnRv9SZB+aL8Y1U9chIqrvpKaP2UO+TDwnXj8KJoNJQABzQ3fTwfelbFVNZrRnNC/VFCFv\nTaPCmvKgkyf2tY6XsgT8YpwLkLHIG4e1Pjt9hV8qRrdiJDU65lVJqLtB+1MZSVQYyrrUr6lO8OhP\n438FCzP75JnoI0CM/C8qmFCaSZOh1W+qf6cACDlSU1XnSmT08X11HmhT8FFQVL+zPpMq55RbhH7V\nzVOYHyQcR+aWei7ak9cjy6L5UQMoKGRRYrNJvxvHqpnCtYm4cU8GU4VQ2UoUerNPl9noRI/O02ji\npNgBz6ACoO+QMqWUHcVIEe8QfYA8j7LIXC7nwgQY44vBDF1aWprIpYIVRfOM59DifmqCUI+KDHs0\nt6ZKxCqnel1NNdA8OjY4jhM/JSvKUiNQ6rjEaCXfR9NK+zkm3erY5vN532S5Xq9bs9n0tZGwMVWi\nMZ8t9YxRxlAWalLS72Y2oexSLCyC+6ymzxqfXWUPWQOouQcBL5bB4VpQRT+rPXkZH0Vqs8lIJs5a\nBbrooNTjaTq5ETgFxru7OzdDYCFM6iztrKYk3yvgxaUc2lK0OE4uBU193xj4QFOjybi+Mi8F3lxu\nMhlYr43ZFIEihuoVHNg9+uzsbGIC63tFLaxjyDhQ1pnGMYCYbjCiLECXKAEoZg8bfWipm5TJz7vQ\nl8oGUuYjUVD2KdBr6jsqYMXoIdeCOVerVWu32+4/xIGv46WFLqMfLMoR78+46wY0Ko+RLTEXojzG\nsY9/x7kV2b02VSbRj8cPfT8ajWwwGHziStB3zTIxn02paxUwmnbUwsLDEodIU7Oamp4KSPh70KgK\ndPEZok+M/5X10PG6xCo+ZxRqfR/6IEbbOJa+UHai19MlMUxuDX/rs3PtxcVFX0wM22M3aPxS0aS6\nubnfLFZBR2toqQkXzX6YQjQveHd+M3FZUZDyPQFmCm4k+xLk0PtGVhZZkjJ/3hPwSO1zoCxQJ3A0\n8TCz+b9YLFq73bZ2u22tVssTWXUZmZrU0RXA+6QAhX7BZKUWmZrWWYGySCJSLFbHTfud6yt4qyWh\nJi1jxP3V3L67u7PBYGBm5mlCcW5Pm+/Pwkemkzlliqm5OG9TZqJMK5oEOvl4Bs5Psa94HT5TX4E6\nUVMgxvkaaVNHqQIvLQqGCqjZQ2KoApAyEe6tzCROBJ5J9z9Aw3MedctWVlZ8FcLp6elEaoL6p1SQ\n1Q/Ku/Ic2k+pH51UCl5qlkbndlQqUQno+Clb1zQOLXio4KJymZJb/mfcCoWCNRoN29jYsHa7bdVq\n1VkYY59aiK+MKcV09SeVeqPnZvkGY/9O+yxeT+U1Jed6vJ6jcxnZHQ6Hdnd3Z7VazRl3ihGm2rPw\nkekg0XhpFTY9XiclTSdn7OwY5VEhn+b4jCzDLC1UKYYYmQfPq6wODazH4JeKtD8FDvqe+m7R15Iy\n/7gfgIqDNZfLeYSS8/BZsSnsaDSy09NT6/f7VigUvOAfia86SSKgp0AgjmcEmZRSwUVAMmUU9Kx+\nmzZB4nMq45o2JlFmlAWVy2Xb3Ny0TqdjrVbLmS9zQIMakV1HwFW5UzCHHUXGFefRPP2h7xB9vXHM\n9F5RsaLQYuBCWTJmuxaWXF1d/YTh6TOl2rMAMoBGO4TOVy2qvokIJLFFtkHnQfW1ugH3S014bTq4\nel2+S/kw4jOpcOqE1Imd8jXoPVSAVGC5NtpZJ6H2pTbt8yi0OIeVLepGuaRQrKyseFVZWAHPEheN\na/9MYzSxv7Q/tN90XKb1f5yIqd/TmoJHSrFFHxGug0qlYs1m0168eGHNZtOrkpC2oOs3NWCg1ooq\nK13rGwtiMkYpH6v2V5bSyOqLFFjzuUZY4xhlnafPoP3HEjPtC+2HZ8vIFG1TVPf/t3d2TWktSxhu\nUKOmEFBMatc2+yr3+///pJ3EoGDERBTPReoZn9WZRXJ1lKrpKkqBtRbz0fP2293zkS2l09eIgcSS\nlcAL0jebTXFBmF7w5s2bolhc7w7PsbOaVcvBzL4y5vfEUnBPKZ/jQFkZMyhltgnYuWz+rVpGKBuI\n/f39zqJpmCuui2ecf/nyJdbrdQE2BiQAl9mh3em+fu9jTrUBSN0M8DXAyowrS99A5rnZfaUeJI8w\nzkyt+PDhQ9mGG+bL9uG4rD6IxOX0fC0MBHHGwWDQ2Rhyb2+vGAy+53PX2X1cM6i/Iwc2LDBAz1FE\n/1iilg2+25PfAoy97tLj6U9CSi8+IdaNUgtAZvfM93jQ9gGZLRnuE/c9PT2VoD+Bd7M3Zyozm4ro\nBnTthuQYTC5fjSqbmQJo/o0MfrVBnp9FG+TAfWY/ZqVPT88LlRm8HqTs2GEmkE+z9v/sZwZ4oqw5\ns1cDeTNof+brGTQMWurw9PRUgvTuQ4Od563BkGqsIseXcgYbfSKeeHR0FNPpNGazWbx//z4mk0np\nW+rv/fqpR/YiclKDFRvur1o8lOttrHP8qo/51qTGrAAjGJmfkV3zzJQzCaCsXqrGsYC1xENNXjxr\nmdmOOzRfg4DSxIVMUfmbQYwgthnP09NTmTbhgKmX2/i5fk8HYAlzpzlwTx22uYsoLvfQmb7HA8hK\n2cfGuNYgZjfAbqXdatcNRQV4AP6I7pKog4ODGI1G8fXr1/j69WsZaOfn53F8fFzqM5/PY7FYdDJW\nuT2oT97Zg3IScKcfxuNxvH//vjAWAOLz589xe3tb7qef9/efNyNka5kfP36Udaa1NgA0nFnMZxjs\n7+/HbDaL8/Pz+Ouvv2I6nRYdZaWED4GBlTHD3fu8OZFBeWhr6uNsqY2s42Zc5/ayTltH3f9Z13M/\nWZ+8OBzjY901M7TOmXlSd/Tk7u4uDg8PO1OStsmLA1nEs2VzEDx/l8HMjRDRn1m0Fc0DN7NBrgfg\nssvjZ9dcS+qQ5xD5en9Wo9wwBWeyspiFOltJ+YbDYScAXqunX5m+O/aWFR8Wy2fMV/LusVwDe6NO\nw+EwptNpSa3blc6AT91ZRO5MIm3CVIOLi4v4+PFjYYgkItx3ZjTD4bCz8+3h4WF8//49Li8vy4TM\nbADc3gYydHAymcTJyUnMZrOYzWZlBQbsy0zM97JKg90sbIABLtqD9sUIUxaYGCAGoEc8ny/h+2uk\nwbHUmtT03+OrFi/2tCRPacmegI0GDJItrPpYYpZXAWSImUaNweR4SgYFxJ0Fm6CRCIrmjKlBjEbm\nWQZAS6bolNGLv93B7ugc+0Ix+TwiyjwtJo6i+Nn6GdDM0Hydwd2M0vFDgtB5ljYDZDh8zrKaGbE8\nir2x2Ovs6uqqMB1YwsnJSZyfn5dBu1qtSrY06wGzvdn6h4mrbCE0Ho/j7OwsPn78GP/++28p42Kx\niKurqwJYuHvj8bhkxGgT9mcjvvfp06e4vLzsBJw9WdZrQR8fH0us8OLiIi4uLmI0GsXh4WFny202\n9fT+cY+Pj2XNKcfyEQbgZbfca15rBnez2ZS4FLP6MSgcWO15i33hkazf+b11Io+fWjyMMWEGa7Fe\nwu5ou9PT0/hTeVEgI7uVB9rvJNPpnNI1mzAF9hbCxCZQCD8XcWIgx0XciZkNAjB5PpXLzzNyXCcD\nh5XAZcurCDz9ggHnCbYGPL/6srU8L8dPXB+C3HZNB4OfE2sZ7LAF3CTiWQgnN+GeuB8eHh7KSgKW\nNHFy0f7+fpydnRUA+fDhQ2mT6XQa0+m0MEbah4XZTPqdz+eFTdno+JQot531FHdpOp3G6elpvHv3\nLk5OTuLp6akAb+3k9uGweyAye7fZhTQz5xg4mIoZmGNPAAHPGgye92PDsJpp81vWx6z/7vN8vetj\nZpZjmnm8ZhZuIPT6XnYWrjHBmrwokHmbYgZWxK/BxZqP7s/tdmZXiUFppXh4ePhFWa0YfE6ZsOy1\nDqy5lwaKHMS15eNzMqZ2B83MhsNhcbEMdrUJiZmhOTNIfMvBYmcu+W0DdK4TAwxhOQ3Bax+3Nh6P\nOzs48HvEpB4fH8tOp95jC6HsuBns+PHt27c4ODiId+/excXFRZmjRVzo7u4uptNpAQ22CWLLoslk\nEvf397FYLGKz+bmOdDKZxNu3b+Px8TE+ffpUmEPfYGK//H/++Sf+/vvv0kbX19cFdB0ncvCeuXje\nJty7YxDH9HI8gMljxH2NsQZgMYDcYyDLRjUH4/k8h0b8nT93HLHGxnJoxkbUoSH05Pb2tmOI/0Re\n/FxLGgwXkMFohDYDiPgV6DLiA2Z5YBJ7wj0BOCKiuGI5dkT2pNapfWzLZcn35Y6xu+spCQYA6oFr\nwoG+Dw/dzR95XgZ3l82B81oZs2Jn1pknDxswATW3pw0A/Ww3dTAYdADci8Xpc37/6OiosyUQB5+M\nRqMChj4mDgYX8XyqUI7fMEWA+5hVDrPxuke7dbA+XD/Wn+IqkwhxiMFg5gB4bhvrAtOBDED+jmVm\nAEHOwjr7nUMdWZ/NksymrKuZneegP8Dr0AT1zEkH9Ck/y3XMicAaoYl4BSeNRzw3Fszk+/fvRbkc\nQzIQ9IFHH3uDlUU8dzIxF99jmmzwsFXL8QGXI9PvWibI16PcDBKp8gtpAAAOaUlEQVQvD7KS8B53\nBYCtbd+DmP3lRAmf+ZUZHAPIU1NoS0DeyshnDB6uJWZEhthxHw79vb+/L0egeVA4o3t0dBSr1aqw\n6+l0WpgNW23THvv7+2VfNZgXbG4+n5c+OTo6Ku0MO/Seabi3tAnxNtZM3tzcxPX1dcnGAt7j8ThO\nTk5KRrfW/7jf6M3Bwc/T58naPT4+xmq1KgwXneM+9ODt27cdvbWOo0s54ZXHiUM0/s6G1Z/nGJsz\nu9SN6234AN4aKFFG7rOhzFnPLK8GyKgUsYOM8AYr3ue/di8Rgw4dSmAalw3AosEAL1yTiCj76jsW\nVfsNl9Xsx9flBIAzq7ZEdk0ZDExOxVVEORi8Vii7n24/x8RQDoOYv+f5VnS3r6cloKRmGr6eMgNG\nsJOIKG4RA9+xQepBxtFTEzioxUyaZ7HHPbPpPZXD5cKoETuDrfEcNoAcDAaFrQ2Hw7i6uirTKd68\neROz2axzYhXtAmCiO7wwErzH2PKd4470hccMGcnxeNzJGjuhgE7nhFUGkZrh7SMNiMMmBkrrFuW0\nbjiuZrbpvt1sfp71enBwUPat2+ZmvjiQuQHdmTnr50bO7k+NkeVOoKFwX/gc9gAooMxsiZ1ZoqdG\nZIAykGUQQzKQGXAMZihADoRSN2IwLmc+0Yj7chvZrUNsARlEfrl/7MoCMBz9Rv1qbRQRpdzM4cI4\nEDuygWGrn729vRiNRp1TqznYhDM33f48jy28+R1A0S/Alf51LJL5XTYSvC4vL+PLly+lDzl0BUYH\n06T8GGdnQx1WIHPLPEb6CP0E0DLr4nQvNqc0YwOQ7bJ5nLjvsx5kg1UTG2aHA9wX6BRABlDlMA5j\n02NuPp+XdsEV75MXP0XJysdnOZCeY16+NwcTDSYR9fVlACjMzJYfRSVN7+UjedJofmUxeJgFZKuI\nghoka6wMC8493lqGAfLw8FBcTbNatyf353JYqVwvp/5pA9eD7/mtXFdeDuA6uJ+nk7ieDEZcUU9Y\nhRltNpuYz+fx48eP4poyaHxgBzEl6x8Md7lcxrdv30qiAANhJhgRxe0bDAYloUA70R+8Z+KtARoh\no8tE7Rpzz+EF2unu7q54El4qZmZMkN8hmew21nQ1ezU2RjUW5/Fq9l8jGa6f62WywjggafP4+Bjn\n5+clHvwqY2QRv87CzwPO11DZ3Dk5NuW/+X4amsGyt7dX5vrgSsJoOD/SndynHLWyUI5cFgaf6+I4\nE26VwYzn5+2LAWFAjDibt3MxQ8vtUEsI+BoAJuI5c2n2QJ0MZ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i2u1MKEYjm7Ch0+kYhKOmoVZjY3/7/T4uLy8t1YbOBbWpqPloDwE3+XuEa4PBAIlEwmyshYUFNJtNu1etVjNHRWjOvUSftW56jX4W+o2/1jPkrO/8Ov9goz90czbFz6FBhO4TxQxRv5s1qXrfOE3oJ9arZJVC3mM2HA4NrlxeXqLRaODk5ASnp6emWRjXIMOQQKvVKgqFAhYWFsydS5hEhuC+FjIHPWT8TmFDq9XC+fk5+v2+/Y7QjsRNOEYmJiOTKRKJhEX2yYzMRFBMP5lM7H50WzNm1Gw2La0HAEqlEvL5fCTTxNFBXIta05C2Cv1uXqZRuBbV5maYRCIxpf69ZyaE86nmZg14FgPFSaW49yFjU/um99eJ4ljJIGQCQiJGyOklW1xcNMLUXCfmjp2cnJg9MhwOp2BPt9s12JPNZlEoFOyZ1AK9Xm/K7phMJpZrtrCwYNe+fPkSf/rTn9DpdDAcDg0qUZAsLS0hk8nY8wj9UqkUrq6uMJlMUKlUkE6n8dVXX9n3lUoFlUoFg8EAwDX06/V6OD09xeXlJVKpFN6+fYsPPvgADx8+RLVafWcN2bwd4WnCtyj3NYB3tIHSY9S6++f4z2Yx8VwME3qQ96dHcWaI2HmdbmhS74xnEi9J5sXEocGH4i/e8KOUZsIiA3vtdtsM4mKxaAyjEAuAuWtzuRzK5TL6/f4UtEqn0wZrFhcXzfi+vLxEt9u1OUgmkwbldINYOp02qMTkyvPzc5ycnJjXixqB90mlUlhZWbEMZabI6JwuLS3h/PwcZ2dnOD4+xng8RqlUwtbWFjKZDNLptM3f1dWVjWU8HlvAlg4CzX/z9gaf5+2K0LrFEfIsaDbr9W20HNtcuWShB/H1PA+MG7R2fBZ+ZJtnx2YcJNS+KPPqxi1CGuZ9EZIBNzXKGEikHcD+MwWFEn15edngECEQ7Y5MJjNVZ1ohEZmJ7zX1hfPE3zDWk8/nsbi4aPEdaj3gOmZDbxi/I+Gq4U97pdPpYDweY2lpCaurq4YWmLbDteOz2u02Tk5O0Ol03tFuUesSslHmXcsomyZ076jv5tUsbDONfq8+9bXf8RbSHNqijHrtuGea0CRq8xF3lVz+fUhy8bVm+g4GA1xeXuLs7GwKuxPOaCPh5/N5s30Yr6D98fjxY+zv7xuMI+ETpjFBkmPRtHq+JmN424r343Wrq6sm3fv9PprNptkgZH5qck3qnEwmaLVaNq5SqWQ22mAwQC6XM5c4jX/O/8XFBZLJJLrdLgDg5ORkKg8tiig5X7P2RvHaWVomBLND97mtwNcWyzCKx3lzn04fJe19yj4/U+L1xMzFCO1tiWohG4jv/WL5++geC6aV9Ho9dDodHB0dod1uI5lMmqbQtBTaNKPRyCSqEvzm5ibK5TIqlQoePnyIra0tNBoNnJ2dodfrodfr4ezsDPv7++YIWFhYMOcAYY160Qj9VOvR8E8kEvY8Mlev10OhULC0fmoL9rFSqZiAuLq6ss1sV1dXSCQS2N7eNpvt+fPnuHv3LkqlEgAgnU5b/tnFxYXZfNSM3NnJuY+jsZBxrkTv1y7uflFaSoVPSJCH4Fyoza1hPGO8D3eG7k1G8Z+xzUrb5+9CfQvlLek1lMxXV1fmJmZmMQOL9FxRAzEiPplMTGoTqjEtfzgcolAoYGNjA+vr6/aebmRN7yfcUnczjfB2u412u43JZIJMJmNbAfh7jlNtIrZUKoV8Pm/zpek2k8nEjHlCPdpCHm5xdyezEyaTCYrFos0v5zGdTput9+rVK9y9exepVArb29u2XnFQOiTYooifTWlHmzKhP07Fe3Wj7KCodutCfr7T82LAEOd6KRKlRkOagc2rdJ3AEAzT51CaDgYDdDodNJtNNJtN9Pt928mntg0hBP/IcIRlzNW6uroymFYqldDtdm0PfjqdNoLns2nXUIPRrlH7hATPZEzV4Go36fxpKozWhtYAKmNFmUzGGEYlMW0xZhVwpyjvRw3N6wnt9vb2UCwWsbGx8U4N5xAj6GezbJ1Q8544v97av1DW87ztVoFL/14J1dsNs+6nsCnOTpnFQF5qhaBgyLCkxuj3++h0Ojg7O8Pp6emUZ4yahW5bps4D1x4w2gNkBhIEtROZp16vI5/P2/584n9KdhKmMgwZikw6mUwsXZ+GfCKRMM/deDw2pmUKzNLS0hRKyGQyVh8gm83i5OQEk8nE6gTwPpxD2lqEcHRv08inoDk/PwdwzaDMzn7x4gWWlpbwwQcfTNl+6lYOuYFnaaFZRO5hvodbnmb9NbPaTA2j+yn4UD+AEPzxsEgZI+Qg0O/jDPTQez8BvG9UY1Jjt9s1W4V/lP4AbHOX1gPLZDLI5/Oo1WpTae5kJmobMs/R0RGWlpZweHhoaTLLy8vmYcvlckEcT8ZZXV1Fu902w537+q+urgw66pxSYzCpkJvUNJ1mNBoZs/BzFRDML2PkPpFIIJvN2j13d3fR6XQMJrbbbbOx6PI+Pj4279rf/M3fTAUzvRZUmOQFqM4NP1dHk6cvpRvdnqGZEvPSU6jNrWE8XIoykuIerqrRfx7SAr6FnhnFWN4zxu/oNu12u9jb20O9Xsf5+bkZvgqzKEXpgqXUpgeIjgBqGnW7ZrNZK0ahdgKNY907r86VZDKJYrFoxE3GUluFHjVWf6E2YqCT9hCJiWNmkJEaljDr8vIS/X7ftBxdzJq1fHFxYRqIjMPt1MvLy5aNTa/i1dUVGo0GXr58iS+++AKFQmEKIgLvooB5CTdq/f13US0E/+LgoLa5bZgQ/Arh0NBr/X0owW2eyYpionmYR2EJNUuj0cDp6Sk6nY5lHZNASBT0RJFQ1FNGmyORSCCfzxvsIHRaWlqy39HY5v0uLi7MRauGeCKRsPtTWjJWQmJeWlqaksiVSsXsHCZUch4Y0+FzvdeQzE4CJ0MuLi5af9lU87AvwHWwtlKpTGU6UCv0+30cHByg1WpNZTV72lDbxm9ViIJgft1D4QhPP1HtNkJ/brey/9wP6H2aLt4sW8VPUEjbzDIkR6MRms0mTk5OcHR0hPPz86n6YGxq+JIAtB4YF4gZwaurq2b7kDDJhJPJBI1Gw+qKMdWF25Cp0ei6BmBajbaIzlMymZyS9NRU/D01Ad29mo2gBH15eTll7+TzeQvU+gIctI/IuEz15zNpHzGORefGZDLByckJXrx4gUKhYO5zXS8VFt44j6KNKFqJS89X88Ejj1kmgLZbecl0IH6Q7HScATVPFN9PYEiyzGP48Vr+Hw6HqNfrePnyJdrttu0mpI3C+l5MQKQ2opeIqfT0bAE3Gb2dTmcq5yyRSKDVaqHdbqPT6aDX66FWqxmcy2azxiiZTAaFQmFqMxYl/vn5OXK5nEGsZDJppZOo4fhc5r2R8cfjmx2W1D50B1N4cDyM0dCOa7VaaDQaU+VqCeE4l4wRlctlm8fxeGx2lWrJ7777Dpubm8jlclP1AULrp17PKFgdRYPKNN6p4Onyfe2YWyVfxn0+L7PMc7038rTFDSzKrmKa+/7+PprNpsEcSkW6jhW+UDLTbiHxcRF6vZ4ZlfobLhjjGpT2vV7Pfk9pTeLodrumrYDp+gb0wtGopoHOcRJacX71VGleozaOMqSPvdDOYdMMAbrZed+lpSW7Zz6fn4LcWtkzmUzi5OQEZ2dnqNVqU1sQotZbbbsoKKaMoN9F0dP70pRvcxXBCL2fR7N4z1jo+nngmD7PQzCvbUI2DaX1/v6+JUIymVChlFZgoZeIQTvF1wCMwGnoAje1fxOJhN2XfdJoOAth8DfdbtecBoQ6GlvRtH8SKm0jxkM0zqK/oRE/mUxwcHCAyWRiGoOallUyfS4cHQLMLVP7JZvNYjK5Dt4WCoWpAh70NLI/TLFZW1vDyspK0Nb1EIlaNOTtDDFSFP14DaSfKa2EwhGhNrMYOTvnCTuKcPW3/F0oRSXERNpULYeyDOKere9JGMfHx2i321OwiHtYiPf5WxKYSlR6mHgNCYN/jIRzbNQEvB+hT6PRwNXVldX9Um1Dotc5YHEKdUDwGXQYaHCVz1tcXES1WkW1WrUsgK2tLRwdHRk8YzoMtSc1CTMXCoWCzX+32zU7hFupqS1arZYJBI5D7ayXL1/i0aNHWFtbw71796YOsvLNfx6Kp3F9lOg9wUfZvOp08kJ4HpNh7jMuo9RjSDvEBQ1DWDJKm/C5IRWq91G7xzO2ZtFSuhK2aKFu2gO0FagFSNAkFBrm9FYlk0mUSqWpBEpKf6bJTCYTnJ+fm5ep3W4jkbgx1Lllmb/xxjEzkJm9zPgItQ2Zhw4HMgEDi6PRCPl8HolEwva7HBwcmOubz1pcXDSN0+127X6lUsk8g/TsaRCzUCiYV/Di4gKVSsXczIlEArlczryTapj7dQwJ0ii4pPCYwo3zr3SogtcL6JBNNKvNXZcsSj36QXgVO28LGfreyaD98X301/A93aHcFagJl7xW4yfAzfEQnGQSHOeAkpn2A4lRn0nDmDYBhQhtB+0Hs321brLupNQMAtoztFtUi2kqD+M3ZNLxeIxsNmtEpnCTwU8SPA19rgUdH+r61ecwL45jyGQy5gVkHtu89BBCCyF7xb8PEbunV0+bUZkqcW2uwKVnmpCdENXpqHvytTf09Jo4CTDrWSR2JjE2m03kcrl3rmHMQZlTkxVJJLo3hYY754QpKJTAvIYEqNqZ32mAkTYQXcZXV1dWHIMeNdoS9DJRKzIXjeNlUUDgGlLRhd3v97G6umoBWjozCoWCQTBmOtCdDNx4AjVqzr4CN/uI6DzgzlFqXPaT8Z3QGs2C51EtRI+zrvfr7Gl51r1uVTUmRLChgXq1SGIKufhCHYx6TmhyeY2He3wmPT2DwcAkJRs9P2QO3QSWzWbRbDbtNzTcea/z83Mzfnu9nkGmRCJhmoUSV9NgNE2fvy+VSuj3+5a0yYr97OPq6qrBuv39fdubwsJ6uVzOCJh7Wjh2MiEr3HAsnU7H9uZfXV2hVCrh4ODA8tVoh6iWYQVO9k1PCaBbmfYbsyY4t0dHR1heXp4SLEozIajuaSFE0CGhSoHiNYh/rYJSY4txTPNe2cra+aibK/NERWBndU5tEw/V9H5REmkyuXb/UoJS4hF+UHKyHBH7S0ystgTdrvxjDCWRSEylkRBe6ZktfDaJhfv2dTzZbHbKJmFf+/0+Tk5OjCHX19enagwMBgODbswLoxMjkbhxIVNzqE1FTx+ZuVgs2nfJZNI08mRyHZhkKky1WsX5+bk5TaiJ2ehxW1paQqVSMTvy7OwMjUYDGxsbU2vk1zSOFrwwVYInPXAd9Rqll9Cz59Vqcx2o5D0RfiChz0IRV68FQnAsrvkBxqlx3pNQge9JxCQCraKvbmXF6gqvyDCUuozf8FnqVuZ8qUFKo10Xmy5sErjGMEjghEjUgrSlNIeMUFG3I9ABoTs9J5OJQTMtiqHFOeiU4PWFQmGq4CCAqfsqAqAWIhPTE9lqtXB6emrbnaNoKMq5FMUwoftoX0J2cNSzZ7Vbn0Cm/0Mcqp+HpL//LOQGnKV1ovoY+kztEABWp+vy8hK1Wm2qQDgJTPPJyCi0A/r9vkEw4Ob8SWotLRzOpjWNJ5OJETcZigzJuVAmJxHTtUvNQ81IwqcLularmc3DMrN8NmESma1er5uNogUuOG4a8Ol0GsvLyxbA5f4cZlqrd5GNnjWFs+12Gy9fvsTDhw/t+pAG0PeqKdRemSW8/XXz0E2IoXybKw4TN6AQrAp5Nnz6dZzHI66p5AgxtHcekDiJzfWgVNUCvJdGzdPpNNrttpVXYprLxcWFVeMnEWlKv+4P4WeaF6b9ox3FeSZz0SFAaAVcw7pGo2FBTMXqqmmoNdvtttlNk8nEigtSw5ydnRkTp9Np25pMSHl4eGjVOQeDgaXWUOj4eSeDaeHBROJ6zw7n5M2bN+j3+8HKnSEB67VLnKfWp8REQX7Sjl6nr+ParQv5hW4YZcBHGfchKBXnPvT9uI1HxbtlCW0opclExPMaf+EC0NVKzK6wgzWPifkJo2g38ff0FpGIqBXUfZtITNdM0PgQ54Z79YGb6jXUIBQG9K7xesIsQkkdE+0N7v3nn+6SvLq6sqxu3hOA1R3guGhnMX7DzGw+a3Fx0WqmUcOGhJ9f7yhmCf1OoaG/hxewfL5nnLg2155+4N3z5LWDcVgy1EJaJs5rNq+NE5osLmgikTDYo2koJNjxeDxVLJzeHWUoprtQOtGDxlwqMpoyFeEZCUQrWpJhNLGR96G7llqGjRDI31chF3ADdTU3TI/1Y+0y9jWXy71zD2VwXgvcBFiBG0hK2MuAMMemJzlrvTQ/5lCU3WtiTwNewKrgibqX/0w3SEbBN21zF8HwA/D/vYaYxQCzBhPnzdD3xPQhlzNtAEbGdTciIcj5+bl9Tncpr6VnjNXxKUWJ96mRaDeQcBcWFrC3t2cOAUpbzotuQGNEnv2l8U9NQi8WG4tb0LDnzksyFW0pBg5JsOwfs7E5dnr4SOzUxpeXl7a7M5PJWLkmdaCw8dl0OFAr0UnCRobkFgAf2/FCV208bxuHWhwK8p+roImDb7794MqX2rwxr14x5f4oVaqv30dj8VqPq7PZLIrFokk34MYZwGRMRvKZ2Xx5eWnFxlknWcshkTEYD6EWG4/HVny81Wqh0+lMBTkptcmcTHvRxE3aW7ovh00zmpkZwGg+MxBarRb6/T5yuZy51TnnjUbDtIoyGg9t0uRL9iedTqNarZqGnUwmWF1dNUamo4PaK5lM2vYAag4SJj1lChtJT+oAUne8QqeQpzXKuRSlVfinnkt95nsb/SHodBto5JkgqiNR2oHPi5sM/W3oHgxCFgqFKWzN2IQSMSUrF4bZvNyNyXvTfUvtRQhHeELPWz6fN2cBCUKj8iR6xl/IHOfn51NSlUQK3NgUCp9YRJDeO8Ihesu0aIXWbeYmt0QigW63i2KxOOUK91VwNPhMYlc3OPtMQuQ8q4HNkrY8KoNrFGok4iiDfBZNRUEzb7dEQb5Qe68zLj00iuuo947d1mgPGWtxGNWrbcKmcrk8tSeFkIZnqhAGkUAogQlDSIgkfOZiKdzT4ywKhYJViWm328aEdNlqRgEr0BC2MeeL86epKgCM0LVEE4mbSZC6P4cMxC3M1KIaQzo/P5+qXabrRYbxWcZkZgoi1TDsD2sa8J4sYMhqOSGh7Nc1ykaeRTu8JmRCeIaZF9HM3KLscV7opiH1GBpMnPqMg2pRz50Hs5IAS6US6vW65VHxe60mqUTJ4yT4Obf4AjCtEIIx1Dz0mlUqFdRqNfOa8bp2u20JmuVy2X6jdoYa6PwtcHOKMrdcc6foycmJrdlwOMTZ2ZmlvuiWaMahgBtHwGQymdqjQzcw7TtmL/PedE4w9YfMRG1LD+JoNJraUAbATpcO0Y+3XbhOKkBUCHvmjiN8zyBRNvp7QzL/MH/TeVSYb3EwLXTP0PfelgoxodpMOsm6K5JQgYuXzWbNSKYblS5RGuckeFa51P0sXmDwuXQUUBNpoJCMRUjH55ApNHtAjXIKsvX1dTOuWbWTz+c2aeZ80dAHbsobsT+MH1EbkOE6nY652sm81GRaBJDOAF6je/5TqZTBzHQ6jUajYduifcSf8+Zt5ChUEQp86280eyN0HZ/H/s9qc7uVozod9Tt//bw2jUqL0OdeqsThWP97nTgNUGp0XyWoFpHQKvgAbK+9BhDZD3X3KoSjS5m2DMdIicw+KbzxWb/KMGR2aoxsNmv1oBOJ61rLevgScH08Os/bpNGve/eV8Qlb2Uc1ipWB1Q5RI52fs9oNtQyLbagQ1nWfZU9EwXJ/TQiKKT140+EH2zChzntbJKT29DrtnHYyBL1Cz49jKuDdgfrfeKxKO4HER2jDP0puAIbzk8mkRdxJGLR9fJ9I6JSgiUTCzrAkgVFLsenxfPye0p+Ey1wvMhMZRqU8nQ2cg9FohFKpZLlctVrNPH/lchn7+/vmlPDQj/2bTG4SVcn0hF3U0MrEnB+FYLTpOC9aaEQ1hCKIEDOFaCP0fRzcIk1wzUKQ/71tmFnwiATB9wqDQr/xMImd81oldM+oa0PX83tqCboyO50OOp2OeZo04XIyucm3uri4MNtAkx/JJHQiaFoKPWqM25DI6QRg7TLuQSEM0/KzKpk5R9wqoDlruqgaDCUDqTua8abLy0uUSiWsr6+j2+1iZ2cHBwcH2Nvbw9HRkZ30TIbNZDI4OzubcoQwJkQGpTZWO4/MT88dXd2Ebclk0nZo6pgATK2FooF56TPOZiG98hqFgmT0kAPAt1tVjdHBhbhTP/e4Puqe/n0cDPQwjBJKf+OZR8uftlot0zB0eVLyM6JOu0HTQ3g/MgADeolEwoxmxlto6LN0Ep9Ply5tDeL8fD6Pzc3Nd0otqY0FvBvI8/EZMjWZ3m9d1ioxtIsymQxWVlaQzWaxtraGRqNhz6DdQ6jGvrO8Em0w71JOJBLmqSMDcdsBISYTOVWbqqCbRbRxZkKc4c7PfMCUKVLzwPuZXjL/8KjX+lnICNbvfmiL02ChfpHYFHbozkeNj2gaPGESvVmJxM12XfW0ATfua27KKpVKZhhz7wqDpBqj4Pbp8Xhsx1loHTI2jUfoPht1AHCPCxmGDKdCYDAYmCakfcJNayTekM3ktwjopjUPixTe0h2v9c0qlYoJHP5OBUMIUvn19cjDXxNHM6GmNmWcZnvvDWShjoQ0RpT9cpuB6QDiJIpfOH2eN0oBmMTj54QZk8nE3MNMTqQUymQyVvSC9+UBraz4qOe16F+xWDSmJZbvdDpotVqWMqLbDWi36DxogBOAEWK/37fC6tQuWlyDEX0yDA9KIuzU/DrvBeQcaooNi7LrXn9qavaR0NdvXqtWqxb49PAyFB+Jgl5xnrQ4u9u/Vk0OxFePuRXDeIkX9b0yiGLIOJvIDzBO7Ub93rsoAZjNUSqVkM1mLS+MEpj9pqZhThljLJonBcByx/h8hXR8LiHY+fm5EQw9bmQ+9pU7LZnkqftggGnjWaP7JD4yAHO/WPeLbl1CH4U8w+HQtjvv7u7i8PAQb968wWRyvalObS0yNddcYR61JDOqmSLD53N+NLu6VCphY2PDDntiFoLe29ODRzpRjqUo+vJCNUQ7USaGb7fWMIoDQx6KEPyKIvhQ57zXK8pDwu9Cr/V3GmSkQa8ZubxWPTzUCGQWRq2Jz7njkLBTmYSnLXPvDO0GEgUAO6OFG6yYx8bn07ZST5raKNQqJHz1OnE8hJLUopp5TKO+VCphbW3NbDGWf+J11DBcE10bjoU2nRI+5waYTp2h5uLpBAoxCZs1e1jHHiVcQzQRogf9Lu5+P8iGYVPNEfrvHxayVfQzr3FC94vreJRkCTENiYOLqgmEurgKS7jwDCCSgBhP8G5iYnwayru7u6jX62YztFqtKWixsbGBUqmEQqGAQqGA8XhsDEQ7yvdPXd+aC9bpdKaYkv1l/3jKmJ42xvGkUilzUpyfn5tXi1pOC3twLtWDRaamhiXxe5phIufCwoLZL9497jVMHB1qCxF7nMEfRU8h4R9qM3dcqqryhDovUYeaQgS9fpZK9NdEqVLtHyU54w0kKuDdQoH8nIROYuOpyIVCwQxkEh+1y9u3b/H27Vt8/fXXGI1GttuR7ymFHz9+jA8++MAIsNfroVqtGhN5Sa2eJNoj1GRM9yHD0m1OW0LjHfRw5fN5I1zOBe2rbrdr2QD+3Ez2lwKCzABgqmCGMk0ymbRYULlcxv37923nJudDmdB7zLz9GaKrOEbygpqf+TavsJ6ZrRzHMLMeHqVxooy4qD7EfRal5dhI8Iw0E0IwN4xGLBtzn1g6SBeyUqlMeb8YQ7m8vDQYRnf0f/gP/wGrq6sYjUZ4+/atnUh8dHSEDz/8EI8fP0YqlcLe3h7S6bTt1x8Oh3aEhGoZxjvIGIwtsUwrjfxqtYpWq4XDw0O8evUKJycnAK5tpTt37lg8pVQqmTtby9qqs4OvNduAmolODwA2n2rTkF5o+xWLRezs7OAv/uIvpmrBqc2igkKbj/dp83ZznKdUP59Xo/g2dzFyTf2Iuy6q3YZJ5rlf1L1CGJaBtLt37+Lg4MBympi3Ra0zmUze2d1IyDEcDpHNZq3ypB5nR+IpFApWEYWByclkgkqlYvAul8uhUCiYcX52dmZ9JHEq8bBfZCbaSVqgj9ApmUyiWq1aXKhSqWB3dxfANcGurKxYvInHny8vL6NYLJqTgZpzMrk5d1PtOZ1zajbmyinc1e3ThJylUgmVSmXKGcR7RXnEZtGEz0cL0cQ8ntSo3/o2d9UY36nbeLzm6Qi/n8fIn8VMXjMSSj158sS0AGMsoRwyAJZsSMZiTIGMwv/ATWnZSqViNgHtEeDaZmFtsdXVVYNFAKyyJR0T3PqrcSPaWLqtmJ4xMj8j59z9yTwxQqZyuYytrS3k83nTTkdHR6YVVlZWbCzUCmQY3fFJBkgkboK//A2hGIOyfE2t6TeN+TUOeVTnhUpKJ1FeWd7jhwjrmRpGCVRfxxF2iItD30Uxgv6WixMHvXwAzT87nU6jVCrhww8/xIsXL3BycmKnIvskRv6ezEKXL4CpoCIdALxWYRTxPYn+l7/85dSmtUqlglarZRpncXFxKl1GU0b4bGobahYGHTc3N+31vXv3sL29bdsH/v3f/x2ZTAaVSgU7OzvmZFhYWMD9+/dt7hjo1Fw17h3iHHh4rTYg54bamkX+aI9QoLCiJ20yPl+1hF9D9R76FgXHPC1G/c7T0jzCeC5IFrJFojrorwup2dD7qKhxqD96nZdGIUaidlhbW8OzZ8/w7bff4rvvvkOv17OINyu7EMvTo0RjnRqAREWiVjyvi6Ru3I2NDXMMMKGT9hR/r3tr/PjJLPSO0ZAnM+jZm+12G5lMxpwKR0dHmEwmFgzVsrmFQsGCrIlEwjKwuc/m8vL6INxCoYCzszMjbC18oVqG2phxLjKM1nFW17baaFy/0Pij7BLvwgbwTsqQfqe042NTofuH2g+K9Mept5Am8Z9HtdsYZFHwTxsnLZfL4d69e1Zg4vXr1+9El70E5XvCE5/rpXtc9Pm6O5EEz+/JnGojaU0A/pbjo6ZR41f7xkAj4Rc3pVGrMNs4l8tNaZNSqWRudy0XRYjIk5V5b+4b0lwsalTaProzlfZMpVLB8vKybUn2/fcax9NVHKoJ0YG2EFPp9bc1/m+1gWyWDeEhln+v12gL4dRZ+HOWV8Q3xgru3r1r0uX09HSqYgvhg4cBmqVLYiGz6Heaju/Hy/8au1C3qbqP/VyoncVnkrnU5mIqDAB7XSwWp7KXOd5er2cGeCqVssIddHzwwCkyEx0f7JcPtIb6QA2yurqKra0tc3SEaIljn4c5Qi3kXfO0Gmcjz2sf38qtzM/iHs5rPBSL0jIe5uk9/O/8NbNsKv2cfcrlcrh//z5qtRq63S6++eYbnJyc2MGumupCJiDc0D0rZBTCECUiwhrd0ktNQKPXL7CeJenHr9Fy5qrpYbDUgKrlCNno/WIu3GAwsIr/7Xbb4Am9dwyAcrx6VAUFgc5NInGTsQ3cnEbNfLZcLoe/+Iu/MG0XJ+S8PTNPi0M5vL9qbM6NNs5B3L3YblWMnK9DRlbU9aH3IQMySgV7hphH8szKTKAT4IsvvkA2m8Xr16/x4sULO2uSWgK4Sf1gTpZKxBAEI9NQco9GI5O6KoA4L8xkVsjlmYXuWnUz8/4amdcjytknhZscO1s6nZ7an8/XSmgat9J10t2hwE1tMg0KF4tF3Lt3D1988QVWV1en5sELVJ3XWS0KrehvQ4wXoqsop1ZUm/t8mFCH4z6bZVDNoypD/dH/s7Rc6DOOKZ1OY3t72+wJFvRTraCZxoza+8kOQShKfBJfaA40Bcdv6/X2izKKPlMNZzK4/l4zBHid14TUVMBNWr7CR00U1X4Rmnp7hXlw6XQaq6ur+Oijj7CxsWH20SwJ7tfrttez+b1Ms56hsCyuzV1mKXSzOG3D7/21UdfEfafSfNag1Num7/lbLloikUCtVjNmuLy8xO7uLprNphXCYHo9I+gKvZRw+VqfqR4632d6lSaTydQ+EeAG0vh58MxDrUBtyPtoH3XuyDzqodPcMPVqaY6Xn0NlPP0tx8BCIeVyGY8fP8avfvUrFIvFdxwFfr3jUAj/c6whWlHbkv+99ojSJl7jxTHYXEZ/SLLrA3xnQi1kA2lnPRyJutes56gq9vfzf8nk9XbZzc1NjMdjrK2t4fvvv0e9Xkcul0O32wVwfQBsuVye8kRpgiQJZjweG97XZ/ucKX6nHjr2mVqOhKsb3GgX8I8MnUjc7FXhfTmvWhWG86AaRu0tPpOv2Sfd668Mpd9rrbTFxUX8p//0n/D555/j4cOHU2k0ISL2uX1xNOSZV+/jIW+ccPWQ0H8X1d7brRzCkbOMr6jPo7SLn4Cofnio4+/p70OpS9xfKpWMkC4uLqx8q27nBW4Km/O5JHhCMNU8hDIkSJ0bzSrgXnj2TRmREtVrS923z2AoP9OovBI4ML2rkJBRoZUyvkItPU1NiZxzy7JUXItnz57hk08+wd27d6eYRfvlGdivX9x6h1rIPolDLfw+ZEe9N8PMg+m8pojqKD8P2TKznuO/n3Vt3Hc6mWQcjTP0er2ptBZWv6RXTAlMpZSPnSgU0n3v/B0hjLdHyAhRuXtqV5Ch6a0jc6lAYH+oEXSN9Boyh24v5me614ZNGWYyud7Ixp2nH3/8Me7cuWNu69D6hITcvHaEXuubp7EoJojqV+g7bbeqGjPre31QCC+GGMYbuvOo1dBzZ3lE9Bq/aDRS19fXMRwOrTo+C4qz1KsyDIN/oedTc1Gip1IpcxurZlBtwv8kWo/JOW7GfCaTidlahFhMrdFoPHBjR2m1SWoUn9ypBTR4Dcu96nz5zOJsNovV1VU8ffoUv/rVr1CpVCIdBqF19BpY51JhqLdRPeP41/q7ENyLY5xQu3XgUvF31MNDdgr/cxChlIVZ70OGmRJFnDvZG8GhiWFFEzoBuG+dpV51k5SPedA7xPceBg0GA3S73alqLCRy9WaRATgm1U66FjT0Cet4YJEebqTz4R0Rel+FdrpvX7ci63qqAyGZvM6Q/sUvfoHPP/8cn3zyCWq12pQmVkdL1DrrXIYyHaLsZK/p4jRMlEAPvY9qtyqzxBv7wXqPg/9N1L3iPp8Frebtb8iWUebRNh6Pp8r/0C5hiSDdP6M2jKa+kJjpriazdDoddLtdq42mMEqj/CqV2QcVUL5gBW2W4XCIZrNpWct+d6VCUD9+ldbKJJq9rV5Avk+lrgv0ffLJJ/j5z3+O+/fvo1qtTnnEQpr/NpDaQ0ttZC4PM/01oXvfVrOw/eBcsnnez+MMiCLwkHPhtn0MPSek4VQykwH4GXBtz6iXiPAqkUjYwoWkNxmm0+lYreNKpfKOsUt7RwOfTPfnc+juJsPQeGfCpB45GAVLPbThn7qd1b4i02ogNJG43v+zvb2NL774As+ePbOi6qG18rQRgkj8bJbhrw6DKO0zTx/ep81l9EepU1WDugA+JyiKCfxAQtLBOxVCg/bEH5I0KmFDz/KMxBoAWsY1n8+j0WjY9gCNmah2oXuXNgF3cLbbbTuolSku1B7UBmyMsK+urtqefTJVs9m0+VHm7fV6OD4+NoJfW1t7Zw08jFTjXrXnZDKx3DDCUmZKn5+fo1wu44svvsDPfvYz/O3f/u07c+HXUZs6OkJMo32Nglm8p7rQvYDVsXptGurXD4ZkUT5xZZAQA3DQcerQ+9NDXqHQRHl/vY/ohjSHZ6IoB4HegwzDgKCmppNQmcelHi5eQyK8uLjA+fk5zs7OrD4zN5GRqdQeIAMw65dHWOjxgaenp2i32wCmSy9NJhMrjUtNVSqVjOEJudTGUmPfe8gWFxft9+l0GvV6HYlEAmtra7h37x4+//xzPH78eColJ8prqq9nxVyU2ENOg5BXzd8rSnDqM3x/52lzeclm2RZR3/uJ0nvG3Wse+8WXeYqCd7MmLqoPTLik3cLKL71ez4hMN5ypHUPPE8+a5PEOjPkwzYZtMpmg3W7bXhU9nZmGPTVVo9FAvV63+s0sPqiSlk4AFvHQgnxeo/CPzKLpMdR6ZAhuPltfX8eTJ09w9+5dVKvVWA/U+0LpKEKOWkfm7UUhiFDf3qfNZJh5GIQEE9eZOHzp76f2QAjXhp4fel7oT7+LU88ApnZU8hAg7irkAmlqO+9JiU+NwO3JlUoFGxsbU8RLxul0Ojg7O0Oz2US1WrXMYW5hvri4QLfbxcnJiRW+oOZTm0Y1davVmqowCWAKwml9sxDD+BMFuCu0UCjg8ePHWFtbQy6XC8KiOITBpozmtQ619izvmrYQ3fjnsYVspVAqkG9zp/eH7AR97R8S5QL2+NR/Dty4CtWrE3WP0D3jrpllUGo/ksmkpcIvLy/j8vIS9Xodx8fHUzXJGNjk8+lCbjQa6HQ6KJfLePLkyVSxCU2VTyQS2NzcxPr6ui04SykdHBxYLWQe5krm1P3/ZBjWBeBzeNIZg4jq4WNBDe7/13nXuM7i4iJqtRoSievT3DY2NnDnzp2pw6R0zf266mfsr85x6HO/Ft4m8ehC32utBX+/EB1pX2YhnFt5yfwDQ4MLMcG8MC4Kt0bFV27TR2ouZaDQwkX1i5OpUpefq6YgLOCZkVoaVVPi2R9Kez9vjOCrU8V74tSGoovZ1x0gMXU6HasHRluGzgStmqP2AKEn+7yxsYG1tTVsbW3ZdurbwHZeGyXZ/VrMq6n4eQjlRPUrxEzz2DNza5hZDBDqlP/MQ6C4SQ69j3uu3tf/PqRtSGghjRTVdE8If68xGzIBADOUCWNI7L5/GqTT2I9uTGPtYn/upe4W1f5wHsjI4/HYfq+ZChqs1L3w1F6lUsk0DJNUl5eXUS6X35H+obULeThDTBBlA/kW+m3oufo66rMoqDfL5pqbYeI6FdUhJcaQSgXelTYhyOR/O0ua8f5K0JpqEhrXrACb9lnjAGQiJfCrqyvUajVjGqaaaLkmfY7aIHQ5s/AGyzbRi8V0GNYl0JiJer7odGAgFoA5FABM7djUg6M4F7VaDXfu3LGzXXZ2dkxLhmCWXxNP3PqZt3mAaftD7Qv/jBCC8XTm15nXhNbY0+QsoTzT6FcIEcdAoQd6ggzZNPzOG2FRHrBZLuF5HApR2ipO3fM/X3PR/VZl2hHKoIPBALu7u1YOiYcYMRbDQCPvS2hFg1+Ju9fr2Wa3xcVFK7lE9zS1CXDjci4UCqhUKlPFNxhQpTtbM6AXFhawsbGBjz76yDK5da3YT13zOKmv33u3sGeIKMN8HlQThxRCNBdqvk++3fpQWM+V/ru45n+nEifuN3GMGjcxfgK9UadM7O8VpdUoQEJQjoSne1AYlEyn09jb28OLFy+wtrZmFSdZ3I5bBzKZjB2dx2drrhdLNWkxciZ29no91Ot1S/N/+PAharWaaa1EImG12Bh4VThHIZBIJCxqH9rQpmkyXD//fpbwidIc2qKIeh7GCH3nP4/6/r0ZJpQSHtW5qE7FtXmumbeF+qbE4D9jC40v5G5Wu0W3H4cWyNc0Y87VwsKCBR65k1OL5zEzuNfrTWkyAFMHEzHiTm3D082azSYODw9th2gul5tKVyE8Y+KnzocXCvl8/h3D2LeQNI4jNg+ZQt/NI3TjGGEWcghpo3lgPlsswzDNnGrUE4m6e9VoDBlxcVCJTX/joVNoMkKSIkqjhIg79HmU+5O/YSCw1+tZLhevVWLQxMrBYGAVKbPZLM7OzvDkyRODZr1ez5iGp5FRs/A5k8nEIBmZiwcV9Xo9nJ6e4vXr1+h0Onjw4AHu3LmDhw8folgs2m9XVlasoqW36bxdxpplHiqH0EAUjIkjyCghpfGXqPuF7sP3IdtHfxtHM1Hvtd3qjEvPLNpC70P7GmZ1iC00oVEMFCVV5rG/PLPELVYikbDC5qzumEwmLR7CcbOvJEq6cZkSs7e3Z2WLlpaWsLm5aVCp2+1a/a5+v48//elPWFtbs+/fvHljJxQ3m01LCO33+9je3saHH36I9fV1rK6u2vZlVsjU7dWhozC63a5pQx5LqEIx5ATR+QrNW8hu9fapN9R1rUOaPLQuUQjD9+GHaDBgDobxad3aAb72nY4jujjvSqjNUtNRGihKe/j76WLzz6ea+GfpMRjqkQOmD2lVSKjaqVqtYjAYWN5XsVi0MrRM8qTzYGdnx4p10EuWyWTMnjk7O0Mul8PW1hZ2dnawvr5uR3KQwVkmSjeG0Sum4+f2BpaQ9XPkU/3n0QRRwswzTUigqsYI3SOkNeIg2TyCetZ1t2IYL+XjHuSJSFuIaKPU96xn6nP9/1D2geYbKYPonnVN0dfrksnk1L4YTVRUbeJdo9oowXmfVquFcrlsLmXd35FKpbC8vGxH+l1eXiKfzyORuCl60e/37dg/HoVHY532FoOZdEkz6KlzzbEw2KobwPwcemERxThRaxcF0TwjxQnL0L3jmCX0uX/WrN8Ac9gwyjRRHQp1ehYWjRuIh1FRaTl8zjyMons9CEe4X//i4sLOWmHkmwa2MosGKavVqrl/lYD0eWwaIyFE4oGr3W4X3377rZ0+ls/nUS6XLStA8736/b7ltHU6HRweHhrsAjCVoqPxoUQiYd41Vv/nFgRCZ6b65/N5rK2tBVGEH5+HYlFEF0eYIW3jrws9w2v/qOdHwb/Qb+Zpc58PE2cD+O+IPfUav5V0HqgV+vM77Mgcnqlo6PLset31qF4magqvOXyqu/Y3lUphc3PTnqVHc3NsPhtZ+zsajYyQGcXnceMrKysYjUbm+SKDk6lprwwGg6m0fjIHy9zStmKkn7lpPLFgMBjYOvH+S0tLqNVqWFlZCTKD/nmCDK1nHFz26xyyWUlDUdqH4/Z9CWk0/a/X+mtmtbnOh4m6WRTD6Hf8rToBoiaW16mGiTLa9bUyi6Z8nJ+fo91uG4Gdn5+j3+9PZRNTsnJbMX+v1VK8B4wEyQOUhsOh7V+hZ0yNVUbxffo5oZIeVwHcaCQyLDWf3jtkYynD8E8zkxm7oWAgRGVmwPLysqXD+HW/jSS+LXyOE54hraBtllaZp3mhENfmSr70N/EwKMTVUSrZ3yM0YM8oPuDon6nJgoQ55+fnaDabaDQaFtjze0CoeWizUPISBvE7n6TJqi1bW1sol8uWgsJr1QZSAeAZKZG4qYespZM0t4saCbipdcwaA5p6T8ZjtgGhmabRkPGYCUCGIfNsbGxYtZdZuV1KXFFEHQfj520hOOifOcvuidIk/rVqtKg2t4YJMQ07E6Vu9b0axv46lTLKIP5/iDEVbrTbbavET9tESxsNh9dH2XW7XTSbTZycnFglF553r8a/nwc+f2FhAR988AGePHli+1R4bDjnRu+heVrATd7UZHJTJYbE22q10Ov1pqLlmh3NbdO6BiGG4ZzqEX+Ep3RIqOYqlUq4d+8eyuUygOmi4p5ooyBPiA5CzBFl9M9jp/hnzvOM0FpGfTZLy9xqT7/P64nDrNpR/jZku4S0RpxW0QES65+dnaHT6Rjh66liZKRms4mDgwPb/ajEovDHS85QozRiOgttAACW/q8E5/tPjaKp/Xr2ilbX1PJNuibUYtwZ6rcOADAtSa1LCEohwoTOTCaDDz/8cMpLFycEaTuE1j5Kq8yyL/z7kN0RhUj4WoXMLC3G6/mMEMOG2q2rxkQlw4U6Og+W9Z9F/WkjIQ0GA9TrdWMCSlMayM1mE0dHR2g2m6ZReA6MlhCKarOkGxMvWTWTxJpKpcwLxUapr/NHZvHJiJq5y6bMrDYMEzj1dDReQ4FBG06FAhmGh9lubm5OMar+j4POIYgzCxbNavMQrn9OFOOFaMf32TPLD4ZkXDhKMLYQQUdJEl1I3yGvRXSPiH7ORu9Xq9XC/v6+ebtohxByvXjxAt98843tl59nz8WsudA5YTHwbDZrZ8uwaLhnFmoD3TejkJP3Ze6Z2jHqEiexA3hnw5gWGtSiGYSmGlSlY2N5eRl37tzB6urq1BgVJusa6esQYUcxlrZZWkafPQ/zzGPkhxjiNpqFba7UmJCxGsUUoRbCq34wCsGU6TyUGY1GVuro6OjItu0yrf27777D119/jf39fcvcnWciZrWQ7TSZXO9xqVQq5igYj8dWzRKAZQ7TfiNT0Y6g3UMhwcIbvI41i9XmYexH54zuYdV0hGOcAzIRP08mk9jZ2cHDhw+nnAscnx+/X/MQQYe0Tgh2Rz1HW5TWiPouKo0ndN8oJgV+wJ5+9T5pHGUeZvFaRz8PdWgeODaZXOdP7e/vTxWCuLy8RKPRwKtXr/DVV1+hXq+b1vmhTZ/vNYKe2FWpVNBut62Uki6Aahbud/HwRV3BCusUgk0mN7WVVVPztZZ4HY/Hlu/G6zTTeTQaoVwuW+o/xxclbaOYJQqW+fmbZVuEDO6QcA09Q+/rodg8TPdns2Eo0ROJxJ+F+IB3J95/ToIMMdxwOESj0UCz2bRdgldXVzg+Psbe3h6+/fZb7O7uzmWfzNvXqEbCpCDJ5XLmEmbxbtVCCm2V0HSBySiaoqNZxcog1Ai0hbRcksaR9F5kFrqV8/m85a/peOeBOL5P/nVoDqOg2G0QQIgBQibBrN9FPX9WX+ZiGKpzLVTHpoQe9WA/iJC00s9UA/G3w+EQvV4Pe3t7dtY93bC/+c1v8Pr1a5ycnJhq9hDjfZqfPPXCqE0AXGuRSqWCxcVFvHr1CsPh0Cq4EJKpncE/LeAXst+0kTnYuBbKvNQihKr02LGgIIOxLNLHPTIh5g3NQVS/oq4LEXQU8UYxUNxvop4ZQjF6vfeQxd1PWyzDaHV4ppd7KKFSL4p5PCPo+1n2zWh0fezEy5cv8fLlS2MWZvr+j//xP/D27Vucn59PQcj3baG+cQE0oZGVK8m4iUTC4iAPHjzA/v6+ubrH47FF0LlfX+9FyMb0e2oknWvVIpo1wfww3YFJW4YubwYue73eVMp+qVSyFB2v8fyahARcyJDW9wpfozSQt3EAvENnIbgWpWk0IM7P/PhCv50XkcQyDPONmPbhU0S0RQUytXktwgFqZ/1nuVwOp6enaDQa6Pf7xsBnZ2d4/vw59vf37fN5WpRm04i5n3S/GLyOzdshxWIRq6urFhRlBUqOm/OojEIjH7g5spv3VO1D+0VTXsgUmjzK+WBMijBMYzWFQmHqeEEvaeexJ5TY/fVR0Nu/D/3OM0qUltH5Dz0rChK+DxwEZjBMv9+3/CQNsoVanHHnOd3bKqEEOv5fXFzEycmJFaSbTCZWdPv58+dWwnWelkgkpvKtCGeUOCnx2RfvBtex8L0yFP9qtZpBrU6ng/PzcyQSCYvZKEQjs3DcGi/xklmTTzWrmtF8JmgSRtP5QWcDcMM0hULBtkiHNEaUVtD59NdFNSXqWQQeQil6n7jnh2BllABXdBT1DN9iGYabm8gws1L9+bCQFPGcHspH8/chsR0cHKDb7RrDvHnzBt9++y2eP38+c6E8Y25ubqJSqdiej6OjI3Q6nSm7SA8QYr9IxNQufj+Mwiv2vVqtIpvN2unMzAbQ8QHTBxwp9AJuagYrzJxMJmbTtdtty2ymV4z3OD8/RzKZnNojw3lgCSc1+jU5NiQkQhooCmbpf7+uoWv1taKMKC3HOffP83FCvTf/1BGizBKnxdhiGebk5MSkfCh9xEvaqCwAfhaCQ37AACyWcHV1hZcvX9qJXSSS3/3ud9jb24u1VTyeBa7h3dOnT22/Okuxan1hH1FnUxhF28VrGYVqWjFybW0NJycn6Pf72N3dRa1WQ6FQwNLSkpV11Xtpmgy1IOef0It7Y7hNmZkOuk6Eexok5bxoZoDCZG0+u9wzTYjwowQpr1HXtb8nn6k5croeXiPw2hC6CfVD76Hrext4Fssw9Xod+Xze6mP5XCttcZBsll3jO93tdq14Q71eN4K+urrC6empJShG3S9kgywsLGBlZcXypWgkqztWt+2GJlclrZZX9c9mQJKEn8/nzR7sdrtWfZ9bh4vF4jvnwwDTxe00k4HOBDKMz0IGbhIy9b4K+S4uLvD27Vusra2hVCohm82+s0baH888UTQwL0QLtZCgCrWQFoli3ve1VaJaLMM0m02Uy2Xk8/mpQnFemoQgGP/rd1EwjP95/1arhaWlJXMbq2v76OgI5+fnwbgQMb4GFqkVcrkcHjx4gHQ6bRnGzGrWs1E8o/C/X0R6okJMQycJx7W0tIRyuWwS8+joCO122xInq9XqVCFATdsnc/KPKUEsr8TcOK9V9LxLzguAqXF+//33uLy8tJrJemqZIga+9kwTWnt+5h05bFEaSlsUkUfZxVF9iRJ+UcwTJwzYYhnm4ODAIEOpVLLUD97UZ4dGqXdfDC5qokajkR0+xKIPuj9kMBhgb2/PmEAhGdPbk8mbPSXULFtbW3j06BH+/u//Hv/3//5f20jGHDOvPf3khhbu4uICjUYD7XZ7Kj2fTK/Ex3SZVCpl2wAODw/RbDbR6XTw8uVLczl7QufzCccYtNWsYyaB8rg+9fTpax/QbbVa+NOf/oRXr17h/v37+PDDDy1u5A10XTNCoThoFQfN2Pzc6nx5+0i/A6KLMIZsIoVyIWb0v43reyzDED4sLS3ZcQ+UUKGSq/M8MEorTSYTtFotvHjxAplMxtL0ucgXFxdot9sWc1Gcz/SOVCqFTqdjjMKawH/1V3+FTz755J29L7pfXmFXaLJ9/4fDIV68eIFyuYwPP/xwKkGSmkPzxpTwlpeXkUxeF8s7OzszxlWNp4apBhZp32k8iNpJ98IA0/toAFi+Hbc6PH78GNVqFQDw3XffodvtYmtrC5ubmyiVSjZetVmjmCKkMUKSOk5rxNELfxcX1/Fr5iHZLMg3D5PHMgwhAPfCE4LM0ziBXGwPwfxkX1xcoNls4vXr1/j4449tizHhA4NvnU7nnX3s2WwWyWTSAq2U5MvLy/j888/x8OFDVCoVfPnll1Np7grFdFK1X3HjOzo6wqtXr/D9999PSXcyD+EVtaVqHt0vk0qljImB6WP0Li4upghAIVcmk0Eul3snuVO1vScmHg714sULjEYjbG9vY3V1FcViEUdHRzYvd+/etTHNMx+hFif19Xv/XejzOI2n94ti0hCzzCPgfZuZ3s9UeSYWKmbXzoce6iP5OjA/mE6ng6OjI7x48QI///nPDaPTG8W0faZ20FYoFotYWloyBqPGqdVq+PDDD/G3f/u3SKVSaDabhtnZ4qBYqPl+n56e4uuvv8bS0hJWV1enmEPtEEb3qXEYA2EiZTabtZSW8/NzC9Iy6MnALEs0Eb4R3nlvYMg25HfMkHjz5g329/fx6NEjPHjwAB9//LHZRsfHx0gkErh79+5U5jXv5dssaHYbQRRn18Q5lvy1cbBaIRrvo2N872xlAObKrdfr6Ha7qFQqQXej2i6cJMIJ3wFqB13E/f19vHnzBoeHh8hms6ZVODAyDH9LzH737l28efMG3W7X4M6TJ0/w5MkT/PVf/zU2Nzfxr//6r/jXf/1XK4jBNJF2u21R9VnMAuAdyHN5eYmTkxP88z//M968eYO//uu/xs9+9jN89NFHKBQKZoQvLi5azha1CwuQ00umnkBugru6ukK73cbZ2ZlVkMnn88Ykekwg7RwdA41vPnM0GqHdbls52ouLC/zxj3/Ey5cv8fr1a/z1X/81yuUyxuMx/uEf/gGPHz/GnTt3cPfu3akjOnTtPVTz2iAEwUJz7BFIiOC59nqd1xjzMFyob5raFddmMgxxL125tGNCXK6vfQasv45nQ45G1+fL7+3tmbEf5VXh5GQyGStcR62TTCZRLpfx2Wef4aOPPsLDhw9x9+5dHB0d2anD3IGo6SRRhqA29b7pYtLde3l5iePjY/zTP/0TXr58id/97nfY2NhAuVxGuVy2ErE0wOm00BgJnQUszsGs42KxiFKpZDadQi6dFzKLro3OIe3Ak5MTnJ2d2bjpaHnx4gUSiYTVZa5UKrY5r91u4+nTp+ZU8fELP1f62l8TglQ6JrWX9H0IPvlr9P6h/kWtr793nFt7LoahLcOzSELuRd/iOJ2BN5YWYr7TeDy26ivecOPiJpPXR4KTkI6PjzGZTJDP57G1tYWnT5/i0aNH2N7eRi6XM81Iu8efuhW3+Hw+VbaHmCREpvS/ffsWJycnODk5werqKiqVCsrlMtbX162Kvp4Ho/tkQi5l2inpdBq5XM6cHUpEZCwfFWf/qEl45ia3bCuBDYdDc7gQcrOYOuerVCpNHZcemqd5CVRhfBSsD732WkJpgxoiBMGi+uf7OY9NM9eefq22Qi+OH3ich0QnZjgc4uDgAGdnZ1hdXcXOzg6SyesaX5T+NHTVy8RG7VIqlVCtVvHdd98hm81ic3MTP/3pT/HJJ59gc3MT+Xwe5+fnODw8RL/ftwxgpr6rAyMOhrEPPlqu0WbaJyTQdruN7777zjQTbRUKCNpd2WwWhUIB1WrVmKtaraJWq1kWAD1h7Ac1PJlFD3P1+X6EYPV6HXt7e3j58iVOT0+DQd/JZGK1EQ4ODpBKpfDgwQMAMO322WefYWtrC4VC4Z111ffeXlDm8HSjcJ3fh2zfkLNANbO310ICJNTmta3Y5q5LNhgMLO7AvCvv4vODDDHS5eUlvv76awBArVZDuVy2vfmHh4dYWlpCt9s1I1dztfL5PO7cuWNuW8Y3fvrTn+LDDz/EZ599hjt37phN8M0331gpJbqlWchvHmZRyKS2GImVi3J5eWkM6QlDbQu209PTdyCJBhipcZaWluzwpVKphGKxaNkKhHda+GMwGKDVaqHRaKBer+P09BSnp6dWp021U5TEvbi4wOnpKf77f//v+OKLL3D//n1sbW3h5OQEv/71r7GxsYHPPvvMThjw9so8Bj0ZxWsYbWQGn5jr78VrVcvw+xAaiGohSBdqczPMeDy2OAZrW4UCWGwk+HceuLCAtbU121POc+wZ/V5bW0O73Tacz3gLYdjKyoolP/b7fTx8+BAffvghHj58OHUUHiePiYkkJsXzvt/eJauxppBjQGGRbuH28+AX3BubPtWDv0kmk2g2m+YNo3bK5/OmgRjQpNu90Wjg/Pzc3NS+pGyoj77Rrvnuu+8Mht+5c8c0djKZxKeffmob0NRgjoJDUe+9Q0gZZJYdTBrhmvpshKj3/n6eWWKFaOzMYXpXny7CaDQyH32c8ecx6sLCAlZXV3F4eIjhcIh2u42joyNzpY5GI9TrdSvITU3Gk3y5q5FJhx9++CHu3LmD9fX1qWMa6KFiySVWUMlkMgaj2M8Qgyhc8NLTOwpUi3iJGJKenom8UUthNBqNzDPI76l5mG3MKjVM8afnzxcP1DZLigLXTMM1Aq7RQCaTsYwLHhpLqOklfKh56a/QKnRN6DMPAX0Kjv99lMbxz5jl+GGLZRjP/Z1OB+12G51Ox86R9wQWMt40+3RhYQHFYhHfffed5WMdHBwYMx4fHwMAPv30UxQKBSMWHuF9fn5uyYzJZBL37983g5qGdKfTQafTMYO62+3i5OTEfkM4pZ4qrUUcUu/ePtC8OjJMFCQN4XEfgQ85ObzUVoeJfnZbHM7r4qQp140wfGFhAc+ePUO1WkW/38c///M/o9fr4fHjx9ja2poiZJ/J7Ynaxzx0nnRMIVsmiiFUe+jY/BhV68w7F9rmqnzJRk9Lo9FAp9OZii34P28kK1PlcrmpAWSzWfzVX/0VWq0Wdnd38fbtW3z44Ye2CYuTR9y+uLiIlZUV5PN5bG5u2oFEDGAyR4v9oOSlhEwmkygWi1MHI3nPn2YoADdlkDyjKAMpQ4ViT/o6xBD+d0psXsrqffyahZ4ZarO+570uLy/xu9/9DpeXl7h//z4++OADXF1d4auvvsLp6Sn+43/8j1hbW3vHjlOoFGpRWQlReWK+71Gf6/chuyXEXD4FKarNhGTaKZ6pwpRyEkhI7YWYiH+ZTAblchmDwcAkv1ZdYelXwi9uvEokEuZpSqfTdqZKrVZDpVJBPp9HvV7H2dkZ6vU6crmcxWkSiYQVDlcbIcoQVC2jRBrSGMC7CYmzjMgQBFAG4WvPHCEcH7d282qcUP80tsMCJIlEAvl8HisrKxgMBlPZDv7kMuBdia6CIcQsUf99327zX+fDfx6ChXFt7vNhgBv3cqPRmIpthLRIaCL4R+OVJwpfXl4im83aM1jWlVqAZYvooiXDEMfTLZtOp1Gv19FqtXB0dIRCoYButwsAKJVK5lBg4FK9V+ybd0vOO7nq3vSwihqSLY6IPcPo+xAB/P+SaXQLxXg8xsnJCUajEXK5HCqVCoBrl/NXX32FjY0NKzfr6SZua4Cfyyi7QtfHf8f+6RxFrV2Uvf1ngWS+jcdjcwEfHx9jY2Nj6uhshS9qQKv7mYPe29uzqveZTMYM1Ww2i36/j++//x4A8OTJE7TbbZso2h1kHJ7alc1mkUqlUKvVUCwWkUwmsb+/j3w+j88//9yOvaP7+vDwcIo5eP+o/DK1w3QB2FQ7eGgSBaO8FPUQL6Rh/LPjJPCfs00m1zGms7Mz/Pu//ztqtZrB4V6vh3/4h3/ARx99hE8//RTlcnkKkgE3m+G8IPVaM6TJ44g9ZMP5OfM2jD7Dw97QfGqbTw9Jo3v26OgIrVbLPGbKHHFcPBwOcXx8jF//+tf4+uuvcX5+jrW1Nfsd92O8ffsWr169Qj6ftzwmxl00YzeZTFrKi56qxbgLf88+ra6umscHuNlQxQ1autA++KUxGT+5ujieEWnf+Pv5a0K/C/0mJD1Vs4eyEuZpum05qjFV6re//a3l8C0uLuL8/BzPnz/Hv/7rv1pNNPYlbt7inuPH7gWLn++o+eHnoRbScj/IhvGNuU5nZ2cWEAwFjKImhe7Kk5MTLC0t4eLiwozx8XhsRRk6nQ7Ozs4wGAzsACGN7XBQV1dX6Pf7SCQSFp9hSgkXjgzHai08Y/L8/Nwm3Kf4e5vBC4QoyT7LaPTS0jPDrHuFtMyfqxHyAjBPnH82bZtGo2GJskw0bbfb2N3dxe7uLra3t61WQdR8+THFfe8hk2pi/9k8GjYONr53LlmUUXx1dWW2wvLy8jsxCA/BtHMXFxd48+aNESsL8HGzGI/L5rkvR0dHVpCOVfF1Z2O/3zd7iv1dWlpCpVKxHLhcLofJ5CYin0gkUK1WLQ8tZLeEFjpkm2nzRK7XqtqPghb8H4IooWs9vPDQ77aNHkueuxkqq0WGuby8xP7+PsbjMXZ2dsw5c3p6ii+//BKFQgErKytTwesoaOW1SIjBQuPxlXRC6TBRc+kdOTpv780wUW04HOLo6AjHx8fmLcnn81MuxJDRT21wdnaGVqtlrulPP/3UNBXjKZPJBGdnZ/g//+f/4D//5/+MZDI5VciPSZjcG08tQ4KtVqv47LPPLDjabDZRr9eN6Jlyr3vcvbTSyVaoEudZi9IwGgT1n4e8cVHQ1j8vzgV7W6bp9/t2j/X1dXPIeMnOpMxOpwMA+Ld/+zf8xV/8hWVmfPXVVyiVSnjw4AHu3r37Th/9ax23D36GBMi8cIstTph4RpkVfJ0r0u8JhBqh2WyaHcPF840d6vf7OD09tSBlMplEp9PBN998g8XFRduHcXh4aMHEfr+P7777Dr1ez7xrdDFr3ENriSWT1zW3AFh2LSXmP/3TP2Fvb8+IoFQq2djION6gTiTeTe2fJYX4Wx1/3OchTxGJzHvyvAYLzfUPaZybhYUFlEqlqX1I2rSKzcHBAY6OjlCr1ZDL5TAYDPD9999jPB5bkmyoeQIOEbLXBKHfevgc1fw9Q4xIh1VUm0vDaCKcepM0g9lDA9/RTqeD09NTHB4eotPpIJPJ2Mlgf/zjH/H5559jcXHRDPVMJmNwa39/H5VKBYVCYSqPTU851lwuptBzP0mxWEQikbCAK4vqMWOYqTI+STLKgI5S/7oooc+jYFyUXTOPN2xeIpm3cQ4uLi6s9BIZQ+/FOb+6ukK328XR0ZF5LZkhcHh4iL29PSsGGdXXuDkLfRb6fB6bLnSNesq43j6WpC3WS+bxu/4HrquOcFurPtDDsdFohKOjIxwcHJg0KpVKyOfz6Pf7ePXqFQaDAYBr6LOysmKF7tLpNH7961+j2WxahixwY+yrB4qvmXfGverZbBblchl/93d/N1X4gQtcKBSmCmmoLaYucmA+AiQhRwkP/R6IdyH79Qh550Kv5+1rqLG8Fe3B5eXlYNETbp5jwcXj42NcXl4aNDs6OsJvf/tbO/0tDpbN8xdlo4QcM/oXQgr+j4qAO3mj2q3dysAN07RaLdvvH2UkcvL1INKDgwMAMKKeTK5rZFEajcdj5PN5S8Dc3d3FmzdvcHR0hEqlgmQyaVt5T05ObJPYZDKxE7cWFxdRqVRMgvA4ir//+7/HL3/5S9tqzWRGGqnLy8v2XM88WlUyrkUtrk/B8djaM6V+z89CklqJJs6OmrclEglks1nLGSyVSrZHR589Ht8c186qnl9//bVto2ZZrN/+9rdoNBoGMZUBQnOp/dfr+ecrlOrchQSb19RkJF/PmqW6COlDbe7UGG+cAbD4ByvK+M6xrCmlldbOGo/H2N7expMnT/CHP/wBk8nEdl7u7+8DgC1aIpHA4eEh8vk8PvvsM0s5Z4o/4zG8L491IFMwNYZV9T/++GMMBgP8t//232zvDQOwXKROp2PbATTHaBZB6nehxVTjdtZ8h+7954ZgUY3aZTKZoN1uY3V11QhcT16bTCa2vaDT6eDk5MSKkVDTfP/997b1gnM8ax58X4Cwrfc+4/WanMynWimqvZeGYSMs0oN6+McqLizRRGnOJEm6dp88eYKdnR2kUikrtnF0dITJZGLXTSbXHrO3b9+ah4tbC7RyPYtm8AxH2l10g15dXWFpaQl37tzBz3/+c4sVkNm4+3F5eRnLy8tTBbznZZjQ4nhpNwt6+WfMg89/aPMSmh5LeicZb8nn8+/UXeD8cv9/vV4HcFPA8eDgACcnJyaA3pepoxgnNJYQHPZjBabXQDVPVJubYfyEkhB5vDermnC/xMnJCQ4ODnB8fGzbaGlbVKtVc0M3m03Dwqenp/j973+PRqOBZPK6qMXKygoAWIr+27dvze6gdNBDhRi8ZM4bF5iHDV1cXKBYLOLZs2f4L//lv+AnP/mJwbxsNotisYhqtYr19XUUi0UjGiXyeaSix88+yq1wy8Op2zJI1O9uQ5ghKE14kk6ncXJygpWVFdy/f988j3wePZrc/ckdtRR4V1dXeP78OZ4/f/5OFsM8Wd3a4rQN/4f+Qtd79KQwLardKr3fd248Hltp13a7bcmO3BKrOF5jJysrK7YBrNvt4rvvvkO9Xkc6ncaDBw+wurqKjY0NLC0tmWuT1er/6Z/+CX/zN3+DXC6HbDZrx2oDMC1BI44HGTHo2Wg0rLhDoVDAL37xC/R6PWxsbODrr7+emigmd5LZ6VAIpbiEmjfs4+wUnVc90s8zpreNQgbwn6PxPoRi9+7dwx/+8Afb7Xnnzh3s7e1NHbxLb9r5+blljNPuGY/HODg4QCKRwP379y0VCpgNyUIODv2Nd0zFrUuUs0S/m6UB57JhQgYUP2PKP4tkU4XrcQ86oHQ6jVqtZp4TxcqEV48ePTLDK5VKoVqtWiyF6eQ7OztYX1+fqjPMfrB4BO+n9b6y2SwuLi7sZIKtrS0kEtfF/3i4LJNCdeei99aEJl0Xw0t8fR8y9OPuEcUg/r5x183bVCqzGg0rbA4GA6RSKSwvL9ta0+5kyIH2zNHRkdW1Jj202228fv0atVrtnXrbf67+32acup6ahziZTFAsFoO/u1WkP+SdYaE/aoBkMmmuRZ/BnEhc53vVajU0m00LFNZqNStW1+l0sLW1ZbGRpaUlrK+vI51O23bmr776CgCwsrIydXYNNRafy4ngvRgwJWzc3NxEtVpFLpdDsVjEt99+a3EiAFNFzVW6z4PDleBDQmfW7/3v5mGaP4d3TF/rUSC1Ws1skzt37qBcLluAWavmEF4fHR2hWCzigw8+sMLxDCE8e/Ys0riOg6TzaFCFuXHoSJ+jZcTS6TR6vR42NzeD95+LYUId9e5FjUxTTVOKKDRhCv7x8bF5p+7du2cFHMbjMU5PT/Ho0SNkMhm8fPkS9+7ds6zkVCplMK7f7+NXv/rVVC7a/v4+hsOhHXjK34zHY+zu7uIPf/iDuaF/8Ytf4PPPP7d9N/fu3bN4Aj11AKag2Dz2izci6YHRxQrtj/E2De+hGi2KEPxnP1RK8zmdTgd7e3v4yU9+YuV79/f3sb29jUajYVsZdM8Sy9EyE4QweDAY2GnXzMJQoRKisxC6CX0/LzOplud6Xl1dWUIwbe1PP/00eI/3Sr5ky+VyuHv3rp3qxcAfJ4J/elQdcHM0BSVTNpvF+vq67YVhnlkikTDnQKVSwfn5OY6OjgBcb1x68+YNvvvuO6yvryOXy5lRzyznWq2G4XBoOwFXV1dxcHBgC8moPyUM4QRd4KyyT0j3PkTojU51x/rmNRmv1/vEGfe31WKh57MxM5zHehCm8Iyee/fuWTGO1dVVHB8fW5YANVOr1cLbt2/x6NEj++zy8hKvXr0yG1E1Y8ilHvJohRwnoRy0ee0Z2o5ER1p/27f3Sr6kLbK1tYU7d+5ga2vLYiHqlqO2UdxP4qExzs7RZmCa+OnpKQBYxcd8Pm+5Sqzq3+/38fr1aywtLVmwjKpV3djcP5PNZi2iz3gNj8e4vLw0r1A+nwcAw7F0nc+zEFFNvYtxzMLXcff5/wfGZ6P2pseRWrzb7SKVSlm53mKxaK599pH5hv5k6uPjY2xublrJLDYdf5Q2mTV3/l7aQvckLfIYR63sGmq3ZhjumeC5KNvb26jValP1oWirsOPEw3ytFVqY5s/7plIp29U5mUzw9OlTJJNJ20XJvf981qtXr1AqlayWcSKRsKwCALY/hn8rKytTOXDczbmwsID19XUsLy+jXq8jk8lgfX0dk8n1uTWnp6e3ZpYoN7J3HKixGdr4FecNmwcizttC8GYyud5pube3h4cPHyKdTmN3dxcHBwfY2trC8vIy2u02qtUqUqmUhQQIwdrt9tT4E4kEDg4OcP/+fdufFBICUXZZ3OuQlvLzr40CPp1OY319HZubmzM33s1VZomLkslksLm5iZ2dHXzyySd21JxCN93ByIdnMhkzCIkbi8Wi7Yrka0KfTqdjBF6tVvHxxx/j9PQUr169snKphAzj8RgvXrxAr9fDkydPsLS0ZPYMC5BTk21sbKBWqxnj8CCjQqGAtbU1PHjwAM1mE1dXV3j16pVpnVqthtevX8/FLN4T5hfewx4PPzQ2QZtQv/eayHvc/hyeJm+DJRIJ0xS5XM6ECnP1BoOBBYGpkbjerOus4zo7O8Pp6Snq9Xqkcc358fMa1VffQsa9/5wCitvd6V31JxVom8utvLCwgHw+j2fPnuHu3btGeIRX/E9mYdBL8ThLGhHLMoeLW45LpRImk4nV/aV0arVaSCQSWFlZQTKZxIsXL3BwcGAnfW1vb2N9fR1LS0toNptYXV1FOp1GIpGwg1/JBDx8iUa+ntrFklEsucoNUe1226TkPHOl/3XBdGE1584TuTKJl4rKdF6ihvryQ6EbIWomkzGiX1pawt27d23bBcvZAjA79vnz51MeqG63a/YtcM2I9XodBwcH2NjYmNmPkJCY10kQWhNteg3TfuI0TGykn4zCCO+jR49w584drKysYGlpySaATEHG8YmKfpB0J7Mq/Wg0sgRIMhBTa5hFkE6nsby8jJ2dHYs0Ly4uWmX8xcVFdLvdqbw19oNR/nq9bi7vXC5nrlEy9vHxMer1Onq9ntUzY9nVWcQXUv1ReNsnDnrtw+9DGcxRf74f79sImfP5PAqFgmVbUOqy1C+zJ+hcITMxBEC7ZTwem9NHU064tqEx6Fz58fvv437j58TPjdKk95pFtVgNk8vlsLa2hs3NTXz88cdYXl6e8kpQY9DbxRyvdDptMRk6AdSG4cBYn+z58+dYXV21nZatVssyoDOZjO3/Z0rL73//eyvovbW1ZUTNw4ceP35sGpDbl7nFgNosn8+jUqmg0+kYjPjqq6/w4sULNBoNFIvFqUqfIXwfmvyoxVTC8HaHvveQKuq7ECz7c2gVbsDb2dmx7Q/lctmIO5lMYmNjA7lcznIJeahVrVbD+vo6KpWKVRkFYFJbayUz0B3SklHQMmSjhL7z94prFPLU7H5PlG+xDPPLX/7SDghlgIpN6ytT+nBiWA5WK+XT1qEnQlNlmP6STqexvb2NwWCAFy9eoNVq4eDgAC9fvkSn07Fj7j799FOcnJyg2WzaEXOETgsLC/j++++xvLyMjz76CADsHHu6oiuVCtbW1qx6JgDU63XU63W8efNmKu2DhQDT6fSUDeYXxMOEqMxXb9jzfRQzxElYfT6vCX03LxMlk0k8evQIn3zyCX7605/iq6++mqqXQMFDhwiLxuspB+l0Gg8fPsTr168t8EwEsbCwYIzH3EB62DhP3gZUZ4kfW8gm9O73KJgcesY8KU+xDLO1tWUFr0NSgLaCdkhVGiUKkyK5H4aLw/+FQgGtVsuCRhsbG6bGDw4O8ObNG9TrdZRKJXz66aeo1Wpot9tW4IIOAwC2W67f72N/f99c3tRCtI0mk8nUdoPJ5Do7mrlOCwsL+M1vfmMalM4E+unjYJCHZd6VrAwT2uXp2221xm0ZhW1tbQ2PHj3CT37yExOGhMfFYtH2voxGI6yurlrqEG0yOle2trbsFAF6SzXDnDTSbrfx4sUL3L9/38ICcXMxS4PP20IOgVkIgi2WYUql0hQX8sY04kloWpdMy8dqpJsxFk25p6TO5/Not9vmEFhaWkKtVsPFxQUODg6snE+v18OjR48AwLxgDIACsNO6KA2Pj4+nmJ75ZNxkls1mbZ8GA6hbW1tIpVIol8uo1+vWp0KhgIWFBfutJh3yP/9Cmcm6IKp1KNn8gnpIEkcQfw67ZXFxEffu3cP29rYd18c1pleTmeGj0cjcyQz8JhIJm98HDx7gzZs3RhcsvkibhvTA8rMs08QDb70QnsUMIeEV5RSYNQ+z2tzZyvxPiUjt4j07wE2wi6qaEp5SJpFITAWI+Dm1AhdoZ2cHvV4P33zzjVXo/93vfodEIoG9vT2cnp6i1WpZXWUmVhIeDIdDvH37Fh988AEePHiAZDJptZx7vZ5tja7VasY4uVzO3Od/93d/h6WlJQyHQ+RyOXM28PwUP8nKLH47LL9XyOBxt3cIRMGwuEX2/ZmH4XgEyS9/+UtkMhk7nJcu32aziWq1iqOjI/R6PUwmE6yurhqkoq3JGsz0otLh4tOA+MdDdYHrgiVra2tmO+kceXstdC9tIXjns1binCWxcxX5DYBer2ebqOgVoZYgROJ3vg6vJi0yAq+f8aht2jM8Bq7ZbJo0W1xcxLNnz5BIJOzQpVwuh52dHdvht7+/b/YMs4856KurKzSbTXz77bc4OzvDz372M+TzeTuZ6/LyEru7u6jX62i321hfX7fJPjk5wfb2Nv7yL/8SpVIJ//t//2/z5FFDUmuqZqGTQxctZMf4xYryiMU1r/3fR9MkEteZ2o8ePTJh0ul0kMvl8Kc//QmTycQ8hlrscHFx0TxpicR1ChN34BaLRaytrVmd7EwmY2vvt7FTK/GMoKOjI9y9exelUumdI8+j0I5+z+aZJjRubRpIfu9Iv96UmJYD1kNdFZ6xs6p1qGVoD7AwH4mNKeSMDqs0oCdsNBoZY6yurmJ1ddW8N5rCz4RAZXJO7IsXL6woeSaTwdu3b21bAPtSKBTs2PN8Po/l5WU8ffoUz58/t2g/PXa9Xs+cGsooPnrtF9W75Ml8PMB1nnZb6BHlQeP8M8lSt0LQVVwsFlGv1+2oRBIj41remcNMENqMNPz18FoSJreaezjPc0xZ1tefBhc3H/rZrN2ZQNj7GNVmRvq1g3q6F9UvJRCxLr9XvMqJptuOeWTqCqUBTkOSCzkcDrG8vGwGNxMnl5eX8fjxY/z2t7+1LNPRaGQb0eh940SMRiN8+eWX+OKLL4wpiMF5bANwvWWASYLZbBaVSgV37tzB48ePLQBKRwUAC45yvnx1FZ9Dx98yUErNrLbVvImeccziP4+739XVFY6Pj81TubCwgG63a6e65XI5nJ2dIZPJGPEynYjeL+0Lc8zozKGG4Twpw3C9Sdj9fh8HBwdWzjeRSNghuupk8kZ6nNCIYjK9z7xafSbDsDyrHpNNm4S7EUejkVVoUeOOdsrl5aVtCGPhinK5bO/pdeLmMpZwBWDVR9bX11GtVvEv//Iv+PLLL+2Zn332Gf7xH//RMmoJFxOJBJaXl22LM+HB//yf/xP379/Hs2fP8Ktf/Qr//u//jv39fTQaDezu7uLk5ASFQgF37twBcE3wpVIJf/M3f4PT01O8efMGjUbDIuAkcD6f86aLQK3CfTfLy8vmih+NRuZW73Q62N3dxeHh4VQ+XBwh6H+P8f11IWLg+pyentpYS6USMpkMHjx4gEQigbOzMxSLRYxGI4NJ3ExGxqeWpZAsFAooFotW51rr1yn6YD0AdcGTJrhFoFwuo1ar2fZoHZeOaV5PV1Sbh2liGYa5QZQEegxePp83ictr6eEgg1FD8D8zhjudjh0/QbVPjMu6ZLu7u7bJjLsiAeAnP/kJ/u3f/s1yy5h1MBwO0el0po5VoLahIc/F2NvbQ6fTwbNnz/Do0SNsb2/j66+/xuvXr207dKvVMqfD2toayuUyfvrTnyKdTuPf/u3fzJtXLpdRKpUsi7fRaODq6moKFuZyOYshqeMDgBVeZ9Rc4zd+AamlvJtftZlP6vQeutBrlfSESF988YVphlwuh8vLS+TzeVs3Lxx4ZDw1FE9hoBDT0xGAm5zDbrdrXkw6ifTeRA2Msz19+nTqRIc4YaGCy9sl3rEyL7SdqWFI1GpvqJFLFyu9U9QwmpFMCMfP6SGhtiK2BWCZAsT5DHTxeQw6Liws4Pz8HJubm7Y4hDQcOH397CMXhKn9u7u7AK63FnCXJ4/x6HQ6ODw8tJ2aOzs7ljf14MEDNBoNk7Dc11EqlaaexfrNzMkis9A+ILFqgmjU/hfOHW1G2ksKKUiUmvnMZ5AZfKNwq1QqePDggR1Otba2Zk4erlG5XDYNw8wOjpfbkFkt09eiZryG75UpdGcnx8LxK7OxPjZhmi90HjLk4/Z0RWmUOMaJZRhmBKuxx4FptjBTJFhJRF2rZBYyDP/6/b5JCkIydpRSOZ/P28FH7A8LZdBrQ9vHBzA5cJ4qfHFxgUqlYoyYTN4crrSxsYGf/vSnRgzj8djqQDcaDbOR1tbWUKvV8Omnn+L777+3vR/sN6UviZD90nEtLCxgb2/P7JTJZDLFMPq5joNrkM/nbU8PISEhDo11dbYAsPFzHfT+zKu7f/8+fv7zn2N9fR2lUgmXl5c4OzubSm3i3n4KRk2wZf4dJb8yOgmea6PMwiIZuklPBTIFAADb+kxnEx0OOk9R3i9tofn13reoFsswesQEJ4N2DTEsF4DeJt2ApZ4j3iuVSmF9fR1v374FAJNenHy6mlkeiZKTeV2DwQBPnz5FIpGwwgXcD7OysoJyuYy9vT00Go0pONjtdqcOiqUnJ5G43p/x+9//Hr/4xS/w8OFDPH36FL/5zW/w7bffotvtot1u4/DwEDs7O9jY2MDW1hY+//zzKffx119/bQ6NXC6Hjz/+GM+ePcMHH3yAhYUFdDodvHnzBn/605/s0Fpq1n6/j3a7balE1DSqbTifJFpqNi4whRgdJnQX09lyfn6OFy9eWNFFMlU2m0WtVsPW1pYVpyAsvbi4MGF29+5dEwiEXBSmTG5ljh4ZiHCOsRr2j4KNDM0TG3hv1ZpkIp7r02638fbtW7Tbbdy9e3dK08xru4RiVVHu6VsxjO6CpHrkoiUSCUvFB2A7IHkNYYq6lDkJmnLRbretcj8XkUFPVh1hEbj9/X1cXFxgdXUVpVIJS0tLeP78udUkazabWF9fx71797C8vIyjoyOcn58bgXBSFLrwPJSFhQX88Y9/xMHBAWq1Gj744APkcjnTQt1uF7u7uzg7O8Pe3h7u379vNaBzuRz+8i//Eqenp7i6usLq6ip+/vOfY3l52QiIxvTm5iY++OADK41Lu4m2IN2pSrTUJqwcqVCEDKtuajYtPMJkUyIEzjWzwpmTVy6XsbS0ZIXbiSRWV1enbAKFW9yRSm8WA5JM66/X67YGDB0Qtqpdw3sTaqrA0BrXhNpv3rzBnTt3TDDMir2Emg+F6GehFssw6snhf1WX+l/tHI3yszN0O3Oy6f06Pz9HtVq1ySEk0j0z3ITU7XbNq0LGJFTQCDyx9uLiIvb29oxpSJRcjIuLC5MwdKVSWtP/X61WbQJ54hrhx8XFBWq1mtXuyufzSCSuz1bhNgRqV46NgeBMJoNKpWIxH2pEfsaTC1j2ln8Apl6rkc/mbQd1PtDGZCOU6/f7tltyMrlOjmRhC2ozjkP3PfEIR0b0uV6tVssO5FVm4W8AGGTXprabErOPCZJpzs7OcHV1ZYJrnrjLrPaDGIawi6rUSzAuAFUsiVaNU+JvDVzR88I0fuB6sjKZjAUE6Z9XCKN2xmg0wsbGhtlOzHJ+9OiR7Qzl/hefacBFobTTe3W7XfzLv/wLarUa1tbW8OTJE+RyObx8+dKI+JtvvsHe3h5WV1fxwQcfoFqt2pbdSqVicIJODtVqxWIRn376KT788EN88cUX+F//63/h7du36Pf72NraQqFQMA1DJiZRM4BIwmD/yRzAjYDiHwWCxkw43wCMiOv1uq05s5T1uBB18miZ4JOTEwyHQ7OxmLrPLR4UUOwjD9Ki/aKEqtrTQyUKSX43HA7x5s0b27OztbU1FauZ1ZQx/V9Ui2UYSmCmbavHjIxAW4ZVQ2jUd7tdO+eFjMVoLgALHHICq9WqOQP4TNo0evRCsVi0nX10gVYqFdMy//iP/4hvv/0Wx8fHePDggZ3oC1zDD42kJ5NJC272+308f/7cAmTcu85TBDY3N/GTn/zE0tKPj49NCh8cHOD777/Hs2fP8OTJE3z22WdTTDkajQxOEIure3Vzc9Nyr8bj64qdqVQKpVIJT548wZ07d8xB0ul0cHx8bB6j58+fW+a2bnSjllDGoS3BmBfbaDSyFBZqkVKpZK5wdfWqM6fX6+Hs7Axv3ryxGFM2m7USWtTiHBc1T7/fR7FYNCeM7sYkfVDAqKOIa6bI5uLiwiB7u93G9va2lfiNikspU2jmgTLSezFMv983xuAfpYymc9D1qJiTsMxjbDICCSeTyVjuEu/B7cx8zsbGBvL5vKly3m9xcRGtVgsArJDBnTt3cHJyYh6e1dVVg1bpdBqnp6dmzHLCyciUtHRqMEBHqZpOp81VvL29bakfxOx/+MMfsL+/jxcvXuDx48embbiFwGvbXq9nsSwSIheZXjbGeZjJWyqVsLy8jIuLC/T7fTx58gTHx8c4OTnB4eHhFHHRYeG3P9CuoSt4MrnZGq4MTi2ua8e/i4sLHB0dYXd3F71eD1tbW8hmsxgOh2g0Gmb0k1lYrLzRaJhDIp/Pm/PAx5I0K4T98gStWo+Oh8PDQwwGA9uqHsUsKlhCn70Xw/R6PQu2pVIp87Gr14sTTonPB9PWUEbjIGnYMd7CrAGNN6ihXqvVUCgUzEClVGBgjAGzQqGAe/fu4fLyEvV63bJsGTCl84HpMHTpahBPXa+FQmEKgjBgWywWzdDkmC4uLnB4eGinrDWbTWxtbZkrmgYxI/6M7LdaLUu3AW424bH2ANN4KImZWkPCqtVqKJVKVvdYtwf3ej07foLMSbg3HA5xcnJiNgMZWVEAJby6iElQzWbTGHU4vC6cyAwQPY6eMIz2arfbNc1CVKDQiIyt9q53F3v7hmvEsk7MTazVau/YNFEM8mdhmFarZQulx0JwgtWY5THeg8HADECtD8br2CHaK4VCwcrEMgOW0I5N90nU63U0Gg2DgsVi0aT04uIinj59iqWlJTvprN/v28JkMhk7WoOGKm0vlXJcuH6/b3BGDVwyK2MW3BtC58LZ2Rl+/etfW3bzzs4Onjx5gtXVVaysrFg5UtYgIManrZJOp+2+9MQR2tLNqlKVWw3IoHTv0qXMzAWmmuzv7+N3v/udVedhjTnu3WcREPUccb0mk+uTr7/++mu8evUKzWYTi4uLFrvZ39+fOnGMUKzRaKDZbNqeJu4vUijm7UsyCgVAFIGrfURv6cXFhRVw1OBlyE6h0+oHMwwJgDCEnEsp5ieVREcipGQm0fN6DeQRNw8GA4uIX15emlbjoUzAtZtbj784PT01P/xwOLTSP3fv3sXW1pZtOVZJR025vb2N/f39qQxbLh4Xi4y/sLBgGo4CgTCG7l7WNGPGAY3mRqOBVquF169fI5fLoVQqWW1iCiEuumbrkhmazaZ5n2jAqwOAp7lls1lsbm7auTbUphQG9MqVy2WrU82j6fi9Bp45DyRGavJGo4E//vGP+Prrr42xHjx4gMnkOoOdv1EodnJygkajYTmF1ETcycm6diRe/pZCln8a2uCcsf8UIup2BoB79+6hXC7bNSGjXpl0VotlGFWPJHASO5mG16m2oUTgwLl5jJJZtQdTLHgfJVIuFqHP5eWlbeTSDAPdCt3tdqdKw9IT12q1cHh4aITH/C/mcRECqpYhcTJPiv8Jmzg/xPksO8QotBINHQQsqkGYlc/nUa1Wp/YdkTi4n4cCRD2SlKTtdvsdKUqpTkmvXk4SdaVSmQpg+oxjdeDQJj0+Psbh4SFevXqFROI6uZXn6WjeoK7l+fk5Go2GrVOpVEKn0zG4P5lMTBsSuZAufFNNQMZRDUg4zTnf3983oUDPJdfW33ce7QLMYBhCE95Eg0OMKZCzNSJLAufvuSPPHrpwc6oX4RyZj/vnNehJbxwXhLWbKfHy+bzZSSxhmkpdlwra2NgwPN/r9axwH12R3HaskXcuSCKRsMwDdQvTGOV1tIvIMLS5NJeOQoWETuFRLpenNkzRzuGckrho21GQMB6kx4R3u11zMAA3pw94hhmNrjfsMTDK33M8ZDoyCzMlXr16hYODA7RaLZTLZUtKZcYB506ZhUxNm7BcLqPdbhvEoybk+FSDkIYUnuln3sNFm5Rr1el0LGOa3lC9rzLLn8WGodQiIVGtcVAk0nQ6bbXDiIkZS2FH9Kx3Vfmp1HUxPk44iZL3JoEwI5n7VBgk5KITttC9yj7yOIvV1VVUKhV89913tg9mbW0Nq6urAGC7Nxmc1L7SyaFQgQFBjl8zbev1uh3mxMXS/tMTR+m7t7dnruxKpWLxnFqtZtsmOD+Ep2RESulut4sXL17YXnxN/FTvnOZokbk0KZLCgYKNxd2//PJL8xT+5Cc/QblcfiegSGHC2AzP6xkMBhY0HY1GaLfbFrPj/iPOn8JSErQ6jZQxeT3n4urqCvV6fcrLd3R0hGTyujSUCvIQ48xilpkMwyJ7lFAMMGqwiRpgaWnJcLvmPhFuEdpwoNpUotOGYeO1mkhIZuCOSBarUMbq9XrWD9Y/e/ToEUqlEo6Pj3FwcIDd3V2T6IVCAQ8ePLBg4cHBgWm1KOzL70ajkREpPVyEC8TTisVrtRoATME8bkTjEYgkdj1bkl4zLWRIJlACUwOYmoxah8xB17V6p9gH5rNxX87l5aWV2NUsAzYyGu1NHj9Oe4/B2lwuZ9sfvIbwRjzph3tlSAM+0ElmYR+4ZpoxQk/r/fv3re/eZtH7xu1DmpthSMRqGGsQk8EnqkN+RruA92EMRAN5Cs+YvcxOk8g4oZQqlJq5XM6gFiUV70dCpFHLHXyM/xBqsD4AXc+FQgHLy8tmP2imrRKmQjJOuhqi6kpXIaFjo01GxiN0I9Mz6ZTuaNo+6vnTGA9hG7dJ8LV6o+ieJwwlgzBbmtqUQUcAU7Ek1XhkNm4Q41mXXOfhcGh2IzUkIS0RBOmJdBMVbPRNPWsK0zTgSYF1dHSE7e3tqThVqP1gDaMuTBI1F4WNhjkZhDYLiZsGrEox3ddORqMLUT0l6vIl/GEAkzCMB8TqBixOKGuikThZYYaxgBcvXuD09NRc1YwSr6+vW0pNv9+3hQ6pcA3kkvBUGrNPCh3I3IVCwfA1Xdmq2QhhVEsBN/lk7C9d/2QkMpPOHXCTgc6Aq9o4CjvVpauxGhVeahuxpC+ZTG1OZq/zWq4503WoFXmNCht1NHHNle5Cdgf7puGA4+NjCxOos4BN8yHj2sxi5Lyxl2R0AHAyCUvU5mFHyGgkGmbBcnsrPWpqH+lmKhqVOolkBgb4aJzS1azbh5XBOIZyuYxnz55hc3MT9Xodr169wtdff22HlxYKBSu2wdgKA3LUKDo/OuEMonFshE60aRgAHg6HODs7s4J3ZKDNzU2b74uLCxwfH08d+ETCAmA2DZlCCUpdtRy/Er+mmCjsiQoWqheK8R0GJAnZ6Q4nCuCuVM4dACuJRW9niBH5WgUe6UfhnMZX9PVoNDJNTNjKTIhKpTK1dmyzmAWYI5csdFNKI1WnjFYTwzebTfsNUyFUTfvIPvG/ZzzGgciomqqhhEnGOTs7MynGICjxLQN/ShC0B5jWcXp6ap6pvb09g0Esek4bh3lwKuWUuNQuoyQmYZAISJAKRZrNJnZ3dw1SMeeKKT7cFsEKnppUqhpQvUtsCoPpNtYYBAUXMJ0hzHUio9BlTahKbx7tH46HFXqYJkOEoAmSugmNz1K4y5Sd0Wg0tWGMkNs39expjbpkMol2u41KpWJeWbVjdIxxLZZhohaAN9d0eRI6pahmAlB6ap6QMp1KGG4m0+tJ5LxGJ01dktwvQmik7mreWyeVfSXG3t7eNoJihjTHRk3B3zEXS2ur6Rz5eeN8koFUaqrE1/GSOZTpGOwkpOUxIfxenRQesnAcPpKumkYZjFpMJb8a1xRohKyaKqVFESk8FV5TSBLGe3cyn0sXOp0UzDNkf3yfdXx6z2QyaRqR2+Oj2nu7lZVhAEwRru5v0Y5yIrT8q04SB0wDk54x4KYYg9Y7pm2ihp0ypUpyahkeawFgaoJ1khV6MKZ0584dcwownZ8apdFoWPHySqWClZUV2/PRarXsGMHQ5Hu7R41SfkYmpOdKGaBer9s9FhcXsba2hrW1Ndy5cwebm5umcZV5SdhqlzBGEcLqqo2IIijUdOsz+0aPGCGZ0gphj24bJ7NwPrLZrLnbz8/PjW50ngCYNlTbMKp5d7R6JglvGXbQ6jMcs2q5qBbLMMr9XEB2hFzPgdLIJSEysZL1tphaQ4Kn+udEaLReg2eU4lwI1T50mfI3icT1Eeb0AjWbTWxsbExpGWpBTTRURwXLP9GIPjo6wtnZGXq9nvn0mSlQLpexvr6O7e1ty9VigqEaz6oR+V8JlhBCY1sKTfgbatR+v29Z13TV93o9271Jrcn6Z1pUhHNHTaB2lwZ3vZ3mmYdZBKQNjiOVSplDZTy+zvJQJ87CwnVZ2o2NDVQqFWM6rTrEtdZUKwoA2r1e+HCulHGZl6e5kNxisLKy8k4MaZ42VyE/H0yiZ4XGN9Wlqnh1b9KoJXP5AKBm63Kg+lqNOY9b+SzCMHqOANj+dTI0xwTcZCXzHvwsk8mYE2EymRjDalCVblR1WdMdTZc03awabfeYmf89hFLNR1c6mZxxGDpdmHlNRiV2HwwGtuOROXvMiFAXM2EUmUA1kApK/oWgH+eVhMksAv09v69Wq1hZWZkq7kfBQg3kPXTqLtbQgTKiCj8KUgoMMhnpjVpYQxrztpmlYtV96iOrHByxonIsB6wdpdRQtyVtIVW16lDwMEe1nBIdr9U8LkpLSlxleMI/HSchojoBSPCEIFwQ4njVOGQc2kTEy3SlRz3Xax7VSEqQ/M/5p5HN+AeZkoxGKJtIJAxSaXqTpuuwr9oXzyhcew8zOQdMnk0mr1ORuM4AjJF4EnYikZg6HpH3Ct2b/SUDeobhtd5Dq259f03IHc323jYMOR6A5fz4DnKytLyOaqSFhQWzZ/R3XNyFhQXD2bxXv9+fyqRVg5oMppiX3/NeTI9n4iXvoxCTE6fBOs1dY1Yt7RnGFwhVNFrNXX8cT6FQwMrKiu0EHY2ui9ERP3MHaWhxfCSbzyIUa7fbUxV1eOwgYxpkCG/UKmwh45Jg+ac2inoAQxBSiZmQj3CXVUB5TS6Xs8qllUrFwgacd1872dtY/E7pgGOiplP7i/TEdCwKaXrkNNE25Gh4b4ZRPKlEt7BwU+1DDSvF3srVhBQaSNTOcZJIcNxbThhHVzQHzYmhROTgaeBz8VdWVkyVN5tN1Go1TCbXuV6Hh4fodDp2b/aDnhjCK2Y9l0olKyXL6jBkWIUwV1fXZWOZRUxbolKpYHl52YxP1gbodDpoNBpTniKVtMPh0DK8+Z16owhnOW4N9vo/70EiFKO2DDGxJx51kuj9qPkosOiEYZImsyiAm9Pr2Ff9TqGpD1wDN8fIe2ip68Bn+wyIyWQyFYhmQFWfO6vNzFbmJKmxrkY5tYuHZGxcGLUzmIinkkS9V7o47If+kXi8q5SSSxmWsRgdDxlXoRKAdwKp3tHBul25XA6Hh4emddgnMj4nn1iZBEnYwsISxWIRKysr2NjYsIVkfplKeO9659xQmPAzau6QZ46SVjU8HS6aihRiEGDaJcx7ADf5grotgWk8zH/T8rKqveiR1Dw8Ci2lg5DTxDM1BYjOm2YMkL54XbvdtkwWpefQHGib6VZWW4IdUKLQLcx+ktkJEh0nyy+eqmEPJVTahvCtvuYzSEzMY1LsrQYltR6ZhNuBuXgqwYbDoe18ZH0B7pdXqe37SxuD2w7o9KAnSZ0EzOzlDlJCYN1DpC7pVGr63BYPX9UG1fVgXzVvTOfb26J8ljItBVMqlbLIPd8XCgVUq1WLi3EOyahK0IS+RCFkGIVKapd55whjVRR66t3TPmvAezS6TjdaW1szJtL7vzfDqHbQvDDFyJSaSrQq1bgwJFA10IFpI5YeEPXX+7iEajNKN02OpC3C4J4WzuCkkFhZ04p2h5Z6Zf9oGKvUT6VSVg5J3cgsKEgi13kg8V9eXuL58+c2dzwmkLEd2id0OHDXK8fILGzeU3c5sm+e8LUfJFg15tVLpQJFCU73BdGbWCqVUK1Wbau2VghSL5vajcoAHL8+y9vDHkUo01BY0x6jkKTtqZqP9Ext1ul0jF7Oz8+Rz+fn8pbNLBXruZMdJgOoJ8JLM+9L58R1u12LdxC3K1xSG4eTS4hDo9bnPIW8HmQelVxkEHp09DkaR6J2UChCwk0mk5YMSQk5HA5N83B3pRIu+8Exk/n6/T5ev35t6TCMR2i1SwqGbDaLarVqvwWAnZ0dI0x1z/N7EhT7rjhd01m4DpqFQQ+aagXaL3fv3sXq6ipqtZo5HsjEWsdZQwhq33L+meXO37J2s9ojKiCjnA+cU46dz6ZHku5/tl6vZzXetI+z2kwN4ztHglSPkseYSoSED+pK7fV6Js19NoF3q/qgFIlYIZPCNPXd+6g+ACNaD7lIULwHmdgvGF8zpqFwiGkiynDqUFANrPBWF1dPQfAZx/SE8XmU0NpnhTKEuEp46qDg3KmrW7WPeskUQtVqNezs7FjmN72PdL8zbqXaQNeM46B2UeeSvg8Rsac1NQc0OOohnUIzjrFeryObzZqzaJ42N8PwwarqVbP4a71xyiovVJ9qZHqVqwTOgXqjThdfn5VOp6cCjCECUgYAYC5OvZ8GOqlFFaJUKpWprAQ6F7jbM5VKWUUXuqCV6VQj61xonEDtIv5poUFqIPUaabyGAkPXUt259BYxKda7c9lHEiQ13MOHD3Hnzh3T9rpdQO0JL2iUWbgdWz2wnGP2V50Zuo5qZygq0PiK2rcM+lLAkblPT09RqVSsJoFC16gWyzDqcfIDI87mIimsombQCLXGYThA2iOaxOkNf2UeSmNvI6kHhp4j3lNtIE6kSifGEZhbxAWjlOWzSJQ8ZKrb7U7FE1Kp60TI1dVVq0hzdnZmhwGx/phqFJXeKiBUC3LcbLSVWOXSa2jVYt6rpITmiULXzBvAtPuePn2Kzc1Nq9xDj54WfFevIQDTlD5FheEJCjmFVxy7Rx68n3dK8BmqNT38JJ1wfmiTJhLXGeua8/jeDKPeA/VMqL3hJ1qbeljokZpMJlPbkLn/XyGFShSFMl7D+In1RELC5oSqDaGGuRKGwkwlRrVn+KxUKmWuaWpQ1hu4d+8eKpUKKpUKXrx4YduSubdGJZ2PoKtA8ASu3i9vqPvrPaT2kFnXRpNZGZ+hl7FcLuOjjz6yEk4MdFI7aRCXa0qDnBCSthnXnutKY5u/JQrRMXkG8YKE8UHvffPMpQxButOYUMi08G0mJOMiKNEq0UW54VRiENZQalMye1vEPxeYdhyodPaE4omCz9a8J2UQTownSO2Pl9b8r/EnLi4n/urqysoncSGZCk/Iw0r49IKFtgdwDtSQ5R8lNAOrdIhwYx6Jg04JCj161DhODzXVSKfnkGfv8NQ3euoYeOVa0KblM9WxolBY/3R+FLKqUPBwLERnfLZfU/XOqZDh/DHXkPPvbcBQm7njUnPAlGMJB/ggXWxew84RlhHn0qtEtcwz3v2keYYhsWqU2E+SEje9IlqNU49E4D04Fl3UEERJJG5iSXwWPVqqbRKJhJWHZYnY58+fW+7Z9va2uTNZylWZlotN4uSYKfkXFhZQqVTw8OFDFAoFq1n8zTffYDKZGLFms1lsbGwYjDw4OLC+azgAgNVOI7RaWlrCgwcPsLm5ic3NTdskRgjG4h2JRMKKEupzAZhnbzKZWIBVbQ+6fgnXNXjJdfCpL6oxSB/UZmQw2lRcO45TBTEryDDrglkYs9rMK7ya0kGrFKQUUcONUkx94iQsNVSZLqI5aKoJVNJTmnoDWiGVFq7QXYrtdhvj8dgKSZBQfMyAjOM1mXpfyGTUHFwwEvlkcr3nI5vNYnV1FUtLS6jX6zg+PsbR0ZF5iZ49ezYlTHScV1fXhfxOT08NBqVSKezs7ODp06f45S9/iUwmYxUwWVCjUqmgVCpZFgPPrPztb3+LV69eWQF33TVJg501iX/2s5+ZC5t9oLOAY+ZzarXaVACUBRQ5p5lMZmqDWblctkxiHpnBNCKuN4WVCic2dZaQpkhP3pnEpFlG9dVhRU1/enqKu3fvvnP/WzMMT8/y3hPeNGQ4ei8LcOMDZxYxD+pRT5ASoKpX71FSzaZqVFWvMiEnnhF/erbG47H5/NW9S4bkNbyHz3rwHiT2jUzLGAuZlxFxFsjW+BW1RigVvVqtWoCUx0vcu3cPH3zwAe7du4dsNovl5WXz9Jyfn5sdlc1m8fr1aywuLmJ5eRnPnj2zLQAkJjpRFhcX7TyctbU15PN51Ot12yPDbd+0Oxjo1aKLHmqRCSlI1IblGrN+nWZRs6kNqQgiBMVJYxrpV43C8bKfRD1kWKXFOFg288g+Eo7aH2pwhxjGG6DsIAcUch3yN3QGeFvE+9GViXmNxkSUEZRpuAeepUs1iVAdHKE4BL0qlFTqiVHGJsMofCNz0iND45SpMlx0epN4L845M50TiYQVIOSRgEzp6Xa7dnYocONR4th3dnbw9ddf25Zh72TRA6F6vR5arZYJD84TPV7lcnmqDgO9kdSOanfSk8goPCEsv1Ma8U1jSFH2NMehKTV+7ZT5dJ3ouFAPbCj+MxfD6AAmk4nVwyUmVBevQhVlGn6v1VKI9TkwhXGcXI9nea0+Q710KonYb2JZekUYLGVNLiaC0oOjxj6fpROv3iBqOODGS0MXKbUG+0OiIxExZSeZTBo80W3OPKKD3qa1tTXbKp3JZLC8vIxyuWwFMghx2+029vf30W63Ua/Xsbe3ZzYWAKysrBhsy2azUxVCS6US7t27h8FgYIck8QAr2mOq6UnsZOhyuTy1cU3TjOjcmEwmU/2hTUYBF3LrKsTic/11hMu0iYkMgJtdw7SdFRGoZ1ALk3gn1BRPRLPLTQyDHFoqld6R3iEOVtWmRE8tU6vV0Gg0AMAq4HOAxWLRMgEo6cl4xLQKn/z9+ZoShHibaSPMlF5cXDQJqsE0Sj/uneBYNFbExdZ8LCU+daUmEgk73Us1xmRynY5/dHSE1dVVgzfMZiak63Q6Bq8I2YbD6x2f6sYtFotYX1+381+0Os5wOLSCHnQi5HI5O22AEJanGQDA06dPUSwW7TftdtuqX3JOOSamLFE4qNYg9CI04z20SiW3T3tIz/nWDAxFNap9KLTUfmaaDmmD3jv13BKW/eEPf8CTJ09QrVZt/W7NMCrdNT5BgoxyheqgPLQisdIzxsnWiQ3ZKCGXn4d9qrJJ9JqRSzuEKfrD4dCCboR4hEScfJU4KuW8MND0Fu23BiKB6cqh1IA8soP5WJSiS0tLU14y9p+u5LOzsylvILWHumoBGINRQ5VKJfsdoRHrLtAmmUwmli9Hrxwrh1JAEgWQQdS+0JgVEx0pDACYO1chlqcb/WPzsSVPWwrb2U+FzaQ/fZ1KpXBycoKdnR0TXFFtLoYJSe8Q0WrznylBkxhU4pG4OIEKebyrWp/hJ1DtGo2V0J6g3cA0fcZDSOiaR0XJx/FrjEMFAccH3BiXehQdGcH3X/OvVMoySErG5bUXFxdTrvI3b95gMBhYpjCzvemRpDZThgFgDKV1287Pz+0MHwCWQMp5WllZsXt51y+FDddOtdZkMrGsCJZe4hz5ou9KN/rfZ2kobXk7mShEnUk+NqUMSnpgDQY+K6rN3ECmcRBNU1HC4YQpIal7mFKJA1YCSqVSaLfbRgh61omHXSrdKBl1AvUaxbW8TlP/qeUAWCCOE6Y5UZTCfluBMrMuHIlbryXx6jX0mvE79c7wdDRCNM5HLpezo7Zfv36NL7/8EtVqFaurqygWi1hYWJgiTrW5Op0Odnd30Ww2jXmOjo4spyuTydipxzTqNzc3jciTyZucsXa7/U65o/Pzc6MVRu8Z4zg8PMTm5iaq1aql1tPzRvsyxAxsKhQ9DPdOHdosFEakNZ+nxueps0CD3FFt5gYyAFMYUqGR2jGakh9SperpUIhFghsMBu9Mgve9h7xZGsRUqaeEybQc/SM0Y7Q8nU7bxi1CNOJnfy4Ox6uQSzUg0+PJaJr4pxrMQ0/gJh2f31Or6HNpYHc6HTx//hzHx8colUpYWVkxRwaP8eCaXFxc4Msvv8RgMEA6ncb29rYVpKD9RoGnMFsFIO0OHm2iSZY6VuCa6bkVnLGXcrmM8Xg8VddZc8pUAHovF/ug66xoQp0RnGfdLh2yf5SuGKvTHay3ZhivMjVA5POqOCBt3r3MRVBm4DWdTsfcrhos9M174zxT+ueHPDCcVGobrVHGCDY1DZsfu0o51Zp8HaXaSRgKOelUoGHqM4f9Bjw6Y9gfBkvb7bb1Ww/PpUeLBxlRa2jmc6FQAHCz1YF9SCaTRkR0v1Lrci0UNjKr4vT01OpRM4DL2IzuwdFsYzbPNH7+QvBcaYnzq7tW+Z7zrbRL+Ev0EqI7tliG4U5CcjYlCFMRfHEGPzivbRRa+cEzPYRMoxPif6OD8rEcPlevpxZQqEd7hgtGguf1ms2q49FMYpV2ClGYoOjngE01C4lGIYTXnPQg8tnqcSyXy0YUtFHa7bYxPT1Ro9EInU7HGEMhCoCpbRGcV8If3WmZTE6fGKe/pYTm4VSdTgeDwQCbm5smCOl6JlxU1KICVo12XVtter3CcQog9od2qp62xnmk9tE0obg289hxDo75RXp0tlb0V7uEUjdK83hVyuO9u90uOp0O7t69i0KhMHUfajg1ANm8baBMROICbqSn2jP8DaX74uLiVFxDA6WU6iRoQi2vcUql0pTHSyEkiYAMkkgkzM4aDAY4PDw0piGkoKajMc/5o8bRe1UqFfOQVatVvHjxAq1WC/1+fyozmqeCcY7ofOGfFgShNuGcejjJOAurnJ6enlqJo6WlJdy5c+edWnGcS47BQ20+Q5sKZWUQ7Rf/k0mYlsPSVuoF5fpx7b3jIdRmpvcrwfp4BKUjiYgcq+5lD62U+PU3tVoNiUQCjUbDpJVmF2vQUu/pJbgyo38eiU3tE/X0kHnogEgkEoa3GdcgwRGGcPzqaGC/KLFD0EIhF4B39oSobUfiuby8nDJOqUEIIweDgSURLiwsYHNzE5eXlyiVSnayGQOe1WrVzn/k9drUk5RIJKbWnQFHSnBuRGNwlZKc9ciWl5cN2mmSrWq5kHDRNdf/Skv+v3rr2G9WBuU6KcrQg6m4Ln8WhmFnvKHnpXsIcpEI4gbNYnmMeC8sLFjWawirztPUgUBYqYatSlTPYJoZkEgkLJHRSzg/TkI1TRvRFH39z/ng8zXTwDMyCRW4SVvX+BHtFHWR83sKAe6Y5C5DNbh1bqPsBPaB9gzjaFqyinYDiymura0hnU7btbrHXm2XkCbmM/1rb7d6Y55zphnM6mhRdMJ51iB9XJu541I9U6om+aCQUc8B6ITEuQ2TyaRl7zL7VoNc3jaJ0jAKA32/qWE00q4GOKUSf6fZ1YRXnmn0+R6Lq60D3GyTUJihkEIhpGpF/pbX+4wDXstx8oTm0WiEo6Mj09i8LzOXmeqiGlDRhPadjdfSyaDbnTVfbDKZWPmozc1NTCY3xycSOpJhNAPc20+hdZ3VdD25tUCZUtdNbVkNkcQxTWIyi6V+bD+2H5u1+O1lP7Yf249tqv3IMD+2H9st2o8M82P7sd2i/cgwP7Yf2y3ajwzzY/ux3aL9yDA/th/bLdr/AwQdYMHQyMHgAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11d140fd0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -507,9 +537,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -518,7 +551,7 @@
        "(1911, 1215)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -548,18 +581,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "33.0"
+       "48.0"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -573,22 +609,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that out of nearly 2,000 patches, we have found 30 detections.\n",
-    "Let's use the information we have about these patches to show where they lie on our test image, drawing them as rectangles:"
+    "We see that out of nearly 2,000 patches, we have found 48 detections.\n",
+    "Let's use the information we have about these patches to show where they lie on our test image, drawing them as rectangles (see the following figure):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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i4gInJyc4Pj7GxcUFLi8v0Ww2HaPhRE6SxJlMKAtSRT2fK3ix/GRH3W4XjUajZA7BvqO+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\nZkHCE++2AAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11d19f198>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -622,58 +661,53 @@
    "source": [
     "## Caveats and Improvements\n",
     "\n",
-    "If you dig a bit deeper into the preceding code and examples, you'll see that we still have a bit of work before we can claim a production-ready face detector.\n",
+    "If you dig a bit deeper into the preceding code and examples, you'll see that we still have a bit of work to do before we can claim a production-ready face detector.\n",
     "There are several issues with what we've done, and several improvements that could be made. In particular:\n",
     "\n",
-    "### Our training set, especially for negative features, is not very complete\n",
+    "**Our training set, especially for negative features, is not very complete**\n",
     "\n",
     "The central issue is that there are many face-like textures that are not in the training set, and so our current model is very prone to false positives.\n",
-    "You can see this if you try out the above algorithm on the *full* astronaut image: the current model leads to many false detections in other regions of the image.\n",
+    "You can see this if you try out the algorithm on the *full* astronaut image: the current model leads to many false detections in other regions of the image.\n",
     "\n",
     "We might imagine addressing this by adding a wider variety of images to the negative training set, and this would probably yield some improvement.\n",
-    "Another way to address this is to use a more directed approach, such as *hard negative mining*.\n",
-    "In hard negative mining, we take a new set of images that our classifier has not seen, find all the patches representing false positives, and explicitly add them as negative instances in the training set before re-training the classifier.\n",
+    "Another option would be to use a more directed approach, such as *hard negative mining*, where we take a new set of images that our classifier has not seen, find all the patches representing false positives, and explicitly add them as negative instances in the training set before retraining the classifier.\n",
     "\n",
-    "### Our current pipeline searches only at one scale\n",
+    "**Our current pipeline searches only at one scale**\n",
     "\n",
-    "As currently written, our algorithm will miss faces that are not approximately 62×47 pixels.\n",
-    "This can be straightforwardly addressed by using sliding windows of a variety of sizes, and re-sizing each patch using ``skimage.transform.resize`` before feeding it into the model.\n",
-    "In fact, the ``sliding_window()`` utility used here is already built with this in mind.\n",
+    "As currently written, our algorithm will miss faces that are not approximately 62 × 47 pixels.\n",
+    "This can be straightforwardly addressed by using sliding windows of a variety of sizes, and resizing each patch using `skimage.transform.resize` before feeding it into the model.\n",
+    "In fact, the `sliding_window` utility used here is already built with this in mind.\n",
     "\n",
-    "### We should combine overlapped detection patches\n",
+    "**We should combine overlapped detection patches**\n",
     "\n",
     "For a production-ready pipeline, we would prefer not to have 30 detections of the same face, but to somehow reduce overlapping groups of detections down to a single detection.\n",
-    "This could be done via an unsupervised clustering approach (MeanShift Clustering is one good candidate for this), or via a procedural approach such as *non-maximum suppression*, an algorithm common in machine vision.\n",
+    "This could be done via an unsupervised clustering approach (mean shift clustering is one good candidate for this), or via a procedural approach such as *non-maximum suppression*, an algorithm common in machine vision.\n",
     "\n",
-    "### The pipeline should be streamlined\n",
+    "**The pipeline should be streamlined**\n",
     "\n",
-    "Once we address these issues, it would also be nice to create a more streamlined pipeline for ingesting training images and predicting sliding-window outputs.\n",
-    "This is where Python as a data science tool really shines: with a bit of work, we could take our prototype code and package it with a well-designed object-oriented API that give the user the ability to use this easily.\n",
-    "I will leave this as a proverbial \"exercise for the reader\".\n",
+    "Once we address the preceding issues, it would also be nice to create a more streamlined pipeline for ingesting training images and predicting sliding-window outputs.\n",
+    "This is where Python as a data science tool really shines: with a bit of work, we could take our prototype code and package it with a well-designed object-oriented API that gives the user the ability to use it easily.\n",
+    "I will leave this as a proverbial \"exercise for the reader.\"\n",
     "\n",
-    "### More recent advances: Deep Learning\n",
+    "**More recent advances: deep learning**\n",
     "\n",
-    "Finally, I should add that HOG and other procedural feature extraction methods for images are no longer state-of-the-art techniques.\n",
-    "Instead, many modern object detection pipelines use variants of deep neural networks: one way to think of neural networks is that they are an estimator which determines optimal feature extraction strategies from the data, rather than relying on the intuition of the user.\n",
-    "An intro to these deep neural net methods is conceptually (and computationally!) beyond the scope of this section, although open tools like Google's [TensorFlow](https://www.tensorflow.org/) have recently made deep learning approaches much more accessible than they once were.\n",
-    "As of the writing of this book, deep learning in Python is still relatively young, and so I can't yet point to any definitive resource.\n",
-    "That said, the list of references in the following section should provide a useful place to start!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) | [Contents](Index.ipynb) | [Further Machine Learning Resources](05.15-Learning-More.ipynb) >\n",
+    "Finally, I should add that in machine learning contexts, HOG and other procedural feature extraction methods are not always used.\n",
+    "Instead, many modern object detection pipelines use variants of deep neural networks (often referred to as *deep learning*): one way to think of neural networks is as estimators that determine optimal feature extraction strategies from the data, rather than relying on the intuition of the user.\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.14-Image-Features.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "Though the field has produced fantastic results in recent years, deep learning is not all that conceptually different from the machine learning models explored in the previous chapters.\n",
+    "The main advance is the ability to utilize modern computing hardware (often large clusters of powerful machines) to train much more flexible models on much larger corpuses of training data.\n",
+    "But though the scale differs, the end goal is very much the same the same: building models from data.\n",
+    "\n",
+    "If you're interested in going further, the list of references in the following section should provide a useful place to start!"
    ]
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -687,9 +721,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05.15-Learning-More.ipynb b/notebooks/05.15-Learning-More.ipynb
index 17d8cc77c..7eac4c547 100644
--- a/notebooks/05.15-Learning-More.ipynb
+++ b/notebooks/05.15-Learning-More.ipynb
@@ -1,33 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) | [Contents](Index.ipynb) | [Appendix: Figure Code](06.00-Figure-Code.ipynb) >\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.15-Learning-More.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -36,76 +8,34 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
-    "This chapter has been a quick tour of machine learning in Python, primarily using the tools within the Scikit-Learn library.\n",
-    "As long as the chapter is, it is still too short to cover many interesting and important algorithms, approaches, and discussions.\n",
-    "Here I want to suggest some resources to learn more about machine learning for those who are interested."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Machine Learning in Python\n",
-    "\n",
-    "To learn more about machine learning in Python, I'd suggest some of the following resources:\n",
-    "\n",
-    "- [The Scikit-Learn website](http://scikit-learn.org): The Scikit-Learn website has an impressive breadth of documentation and examples covering some of the models discussed here, and much, much more. If you want a brief survey of the most important and often-used machine learning algorithms, this website is a good place to start.\n",
-    "\n",
-    "- *SciPy, PyCon, and PyData tutorial videos*: Scikit-Learn and other machine learning topics are perennial favorites in the tutorial tracks of many Python-focused conference series, in particular the PyCon, SciPy, and PyData conferences. You can find the most recent ones via a simple web search.\n",
-    "\n",
-    "- [*Introduction to Machine Learning with Python*](http://shop.oreilly.com/product/0636920030515.do): Written by Andreas C. Mueller and Sarah Guido, this book includes a fuller treatment of the topics in this chapter. If you're interested in reviewing the fundamentals of Machine Learning and pushing the Scikit-Learn toolkit to its limits, this is a great resource, written by one of the most prolific developers on the Scikit-Learn team.\n",
-    "\n",
-    "- [*Python Machine Learning*](https://www.packtpub.com/big-data-and-business-intelligence/python-machine-learning): Sebastian Raschka's book focuses less on Scikit-learn itself, and more on the breadth of machine learning tools available in Python. In particular, there is some very useful discussion on how to scale Python-based machine learning approaches to large and complex datasets."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## General Machine Learning\n",
-    "\n",
-    "Of course, machine learning is much broader than just the Python world. There are many good resources to take your knowledge further, and here I will highlight a few that I have found useful:\n",
-    "\n",
-    "- [*Machine Learning*](https://www.coursera.org/learn/machine-learning): Taught by Andrew Ng (Coursera), this is a very clearly-taught free online course which covers the basics of machine learning from an algorithmic perspective. It assumes undergraduate-level understanding of mathematics and programming, and steps through detailed considerations of some of the most important machine learning algorithms. Homework assignments, which are algorithmically graded, have you actually implement some of these models yourself.\n",
+    "This part of the book has been a quick tour of machine learning in Python, primarily using the tools within the Scikit-Learn library.\n",
+    "As long as these chapters are, they are still too short to cover many interesting and important algorithms, approaches, and discussions.\n",
+    "Here I want to suggest some resources to learn more about machine learning in Python, for those who are interested:\n",
     "\n",
-    "- [*Pattern Recognition and Machine Learning*](http://www.springer.com/us/book/9780387310732): Written by Christopher Bishop, this classic technical text covers the concepts of machine learning discussed in this chapter in detail. If you plan to go further in this subject, you should have this book on your shelf.\n",
+    "- [The Scikit-Learn website](http://scikit-learn.org): The Scikit-Learn website has an impressive breadth of documentation and examples covering some of the models discussed here, and much, much more. If you want a brief survey of the most important and often-used machine learning algorithms, this is a good place to start.\n",
     "\n",
-    "- [*Machine Learning: a Probabilistic Perspective*](https://mitpress.mit.edu/books/machine-learning-0): Written by Kevin Murphy, this is an excellent graduate-level text that explores nearly all important machine learning algorithms from a ground-up, unified probabilistic perspective.\n",
+    "- *SciPy, PyCon, and PyData tutorial videos*: Scikit-Learn and other machine learning topics are perennial favorites in the tutorial tracks of many Python-focused conference series, in particular the PyCon, SciPy, and PyData conferences. Most of these conferences publish videos of their keynotes, talks, and tutorials for free online, and you should be able to find these easily via a suitable web search (for example, \"PyCon 2022 videos\").\n",
     "\n",
-    "These resources are more technical than the material presented in this book, but to really understand the fundamentals of these methods requires a deep dive into the mathematics behind them.\n",
-    "If you're up for the challenge and ready to bring your data science to the next level, don't hesitate to dive-in!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) | [Contents](Index.ipynb) | [Appendix: Figure Code](06.00-Figure-Code.ipynb) >\n",
+    "- [*Introduction to Machine Learning with Python*](http://shop.oreilly.com/product/0636920030515.do), by Andreas C. Müller and Sarah Guido (O'Reilly). This book covers many of the machine learning fundamentals discussed in these chapters, but is particularly relevant for its coverage of more advanced features of Scikit-Learn, including additional estimators, model validation approaches, and pipelining.\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.15-Learning-More.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "- [*Machine Learning with PyTorch and Scikit-Learn*](https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312), by Sebastian Raschka (Packt). Sebastian Raschka's most recent book starts with some of the fundamental topics covered in these chapters, but goes deeper and shows how those concepts apply to more sophisticated and computationally intensive deep learning and reinforcement learning models using the well-known [PyTorch library](https://pytorch.org/)."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -119,9 +49,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/06.00-Figure-Code.ipynb b/notebooks/06.00-Figure-Code.ipynb
index 73940a1c1..31ad5c89f 100644
--- a/notebooks/06.00-Figure-Code.ipynb
+++ b/notebooks/06.00-Figure-Code.ipynb
@@ -1,33 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--BOOK_INFORMATION-->\n",
-    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
-    "\n",
-    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
-    "\n",
-    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Further Machine Learning Resources](05.15-Learning-More.ipynb) | [Contents](Index.ipynb) |\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -50,9 +22,9 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -66,9 +38,9 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -95,14 +67,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Ojg529uxZ1tHRwQoLCynrCbJyc3OZm5sby83NFTSLV9Z+L/OaZY05inZ1cqz/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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115e7f908>"
+       "<Figure size 432x324 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -110,7 +85,7 @@
     }
    ],
    "source": [
-    "# Adapted from astroML: see http://www.astroml.org/book_figures/appendix/fig_broadcast_visual.html\n",
+    "# Adapted from astroML: see http://www.astroml.org/book_images/appendix/fig_broadcast_visual.html\n",
     "import numpy as np\n",
     "from matplotlib import pyplot as plt\n",
     "\n",
@@ -296,7 +271,7 @@
     "ax.set_xlim(0, 16)\n",
     "ax.set_ylim(0.5, 12.5)\n",
     "\n",
-    "fig.savefig('figures/02.05-broadcasting.png')"
+    "fig.savefig('images/02.05-broadcasting.png')"
    ]
   },
   {
@@ -327,14 +302,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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JKisrOXLkCGFhYXTr1o20tDR69+7NwIEDnUcOVFdXc+jQIecypb+/P/n5+bzxxhssWrTo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\nSUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b1c5f28>"
+       "<Figure size 576x432 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -447,7 +425,7 @@
     "plt.axis('equal')\n",
     "plt.ylim(-1.5, 5);\n",
     "\n",
-    "fig.savefig('figures/03.08-split-apply-combine.png')"
+    "fig.savefig('images/03.08-split-apply-combine.png')"
    ]
   },
   {
@@ -464,9 +442,9 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -497,13 +475,13 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
-    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "from sklearn.datasets import make_blobs\n",
     "from sklearn.svm import SVC\n",
     "\n",
     "# create 50 separable points\n",
@@ -539,17 +517,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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IirQNaHve+eRTxIyfCa3u5h50c30trMV5WDRv7t39Yk7EWd/L/QnHWBmcNU1OIT4uHvFx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\nYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118eae860>"
+       "<Figure size 576x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -563,7 +546,7 @@
     "format_plot(ax, 'Input Data')\n",
     "ax.axis([-1, 4, -2, 7])\n",
     "\n",
-    "fig.savefig('figures/05.01-classification-1.png')"
+    "fig.savefig('images/05.01-classification-1.png')"
    ]
   },
   {
@@ -582,17 +565,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Yza24du2qO6m2J9aIiEiMGzdRcnxNzU3s379HkljVavlkmJGRjZUrf9Lt3qj2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\nSUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b2131d0>"
+       "<Figure size 576x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -616,7 +604,7 @@
     "format_plot(ax, 'Model Learned from Input Data')\n",
     "ax.axis([-1, 4, -2, 7])\n",
     "\n",
-    "fig.savefig('figures/05.01-classification-2.png')"
+    "fig.savefig('images/05.01-classification-2.png')"
    ]
   },
   {
@@ -635,17 +623,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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zFkXyipRbVKbOT/aoItvQ3bfdmrT+w2DFxSV65Nt3a8tbb6k7HJWiUS2YUaXL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\ny9GmRucLAuAagghgCFJh6cJgNTXtk2UlnrJsWdk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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bbbacc0>"
+       "<Figure size 1152x432 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -664,7 +657,7 @@
     "format_plot(ax[0], 'Unknown Data')\n",
     "format_plot(ax[1], 'Predicted Labels')\n",
     "\n",
-    "fig.savefig('figures/05.01-classification-3.png')"
+    "fig.savefig('images/05.01-classification-3.png')"
    ]
   },
   {
@@ -685,9 +678,9 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -726,17 +719,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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MF8/QXG3jlccfop+LEcTHT5xi3fZfCPD1ZvrE8Wi12np7FkGoS25LxFlZtdse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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bbc6cc0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -750,7 +748,7 @@
     "format_plot(ax, 'Input Data')\n",
     "ax.axis([-4, 4, -3, 3])\n",
     "\n",
-    "fig.savefig('figures/05.01-regression-1.png')"
+    "fig.savefig('images/05.01-regression-1.png')"
    ]
   },
   {
@@ -769,17 +767,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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8fMrTq7wwcurewueejOM4WOUqnuuSMHWctTL6gEahUNgwqriT1B3Bw4mwqu8h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86Pi+tdU1HJYk0nEAjQ2tp6ugY4m0VRAEVHsapEU0lsFsh2xc+41rn3Ze78Obz1yj6I2bErVhIp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\nXMqXUzX/HwaHgccrBGvOFY7HOUDpzdNLV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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b926198>"
+       "<Figure size 576x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -791,7 +794,7 @@
     "segments[:, 0, 2] = -8\n",
     "\n",
     "# plot points in 3D\n",
-    "fig = plt.figure()\n",
+    "fig = plt.figure(figsize=(8, 6))\n",
     "ax = fig.add_subplot(111, projection='3d')\n",
     "ax.scatter(X[:, 0], X[:, 1], y, c=y, s=35,\n",
     "           cmap='viridis')\n",
@@ -818,8 +821,9 @@
     "    tick.set_visible(False)\n",
     "for tick in ax.w_zaxis.get_ticklines():\n",
     "    tick.set_visible(False)\n",
+    "ax.grid(False)\n",
     "\n",
-    "fig.savefig('figures/05.01-regression-2.png')"
+    "fig.savefig('images/05.01-regression-2.png')"
    ]
   },
   {
@@ -838,17 +842,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Z4utvv2ZkSxlacQ6q1YJaVUDPqjy+/frLiPaGrvPJnz7kQnYSA/VFDNQXcbU4lY8+/pCA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1w/qU1deiSEEnPMrOVLz7zsaEZtko8V3fBBMXEsc9pMt6ToXncfbKXxa5H9uaVou3kNdavXwa7a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\nAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bbd3400>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -873,7 +882,7 @@
     "format_plot(ax, 'Input Data with Linear Fit')\n",
     "ax.axis([-4, 4, -3, 3])\n",
     "\n",
-    "fig.savefig('figures/05.01-regression-3.png')"
+    "fig.savefig('images/05.01-regression-3.png')"
    ]
   },
   {
@@ -892,17 +901,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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IMkxEEBEREREREZFkmIggIiIiIiIiIskwEUFEREREREREkmEigoiIiIiIiIgkw0QEERER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\nqlgLAP3798e7776LgQMHNuvC56ZbBYuLp5gFC96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pPkkQEbU09c55ys/Px3XXXQdRFKHT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\nRK5CYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115e829b0>"
+       "<Figure size 1152x432 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -922,7 +936,7 @@
     "format_plot(ax[0], 'Unknown Data')\n",
     "format_plot(ax[1], 'Predicted Labels')\n",
     "\n",
-    "fig.savefig('figures/05.01-regression-4.png')"
+    "fig.savefig('images/05.01-regression-4.png')"
    ]
   },
   {
@@ -943,13 +957,13 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
-    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "from sklearn.datasets import make_blobs\n",
     "from sklearn.cluster import KMeans\n",
     "\n",
     "# create 50 separable points\n",
@@ -977,17 +991,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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EUI3PCACqQxDDV6ZPn6FYLFbwvlgspnh8uu0xmOQFwEsYI4avRCIRtbV1TBgj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\nAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11a705208>"
+       "<Figure size 576x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -999,7 +1018,7 @@
     "# format the plot\n",
     "format_plot(ax, 'Input Data')\n",
     "\n",
-    "fig.savefig('figures/05.01-clustering-1.png')"
+    "fig.savefig('images/05.01-clustering-1.png')"
    ]
   },
   {
@@ -1018,17 +1037,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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KTWvFv4sXj//pqTsac9syqhWv/fu1ig8UQlQ76axVx7WK6kLs5J/YeuyPvPOR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\nv9AAAAAASUVORK5CYII=\n",
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4/pIj6dKvepnJ8/52OtPe/oHyYj+VZ5o8Pjfd9unM0tmxV/qGAmE69GyXnDclhGiSZDo6xbS9C73tJIhsBoIVjxqgPKi8p1DukWmMTmituaj/Vaz7ZWPM64bDwI7YOFwOHE4Hv//n+Zxw2YRqz1v98zoeveYZZn82DzR0H9iFS/5xPg6nwV9P+DuBskCV5zucDgYd2J97v7CS8r6EEOkl09FNhC59eq8EDGCDLkfv/AuqcEq6Qmu1IpEI2zfswJvpoWxXOVvWbY/73N1TzJFQhEgowmPXP8eII4bQpW/HKs/rNqAzd390M6FgCDti4/F59lw75+ZTeOH217FtTTgYxpftpW3HPG555ZrkvEEhRJMlSTjVyt+kagKuJLIBHV6DcnZNaUitldaaDx7/hGf+9ir+Ej+RiE2vId2oVrapBpFwhA+f+IRL7jk/5nWX21XtsbNvOoXDJx3Ml69Oo6SolH0PGsCoo4c1uX3CZcXlfP7SVJbMWkb7bgUcdeE4Cru0TXdYQrQokoRTLk4CBlAGEIh/XSTUG/e/z7N/exV/panhpbPqV5kpEoqwcWXdt91tWbuNlQvWkNe+DWf86cQme97uL3NW8KfxFuFQGH9pAJfHyUt3v8VV//4dE+pRjlIIUTNJwqnmPgj87wGx9pG6wNEjxQG1TkF/kOdu/V+VBAzR0bHDYaCVwo6z17cyt89Nv5G1F6koLynn/857iBmT5+L2uoiEI+R3yMN843p6Dene4PeRDJFIhL8ceycllU5BCgWie5ofuvy/7HvQADr36Rjv5UKIepCNoimmsi4DFetQdx9k/RGl5HtRKiyft2pPUYi9RSI2OW2zade9AJfHicNp4Pa6MGI83+EwOPo3tY8Mbzv9XmZMmUsoEKJ0Zxn+0gDrl23k2kP/xs6tuxr9fhJp9ifzqn052S0SjvDefz5OcURCtFyShFNMOXuh8p8HZ3/AE61epXIh+waMTKlglSpOtzNu0QyA7LwsXlj+CC+u+g9vbHmKp39+gI69O+DL8uL2ucnI9pGVm8ldH/6F3MKaS02uWbyOeV8tJOQPVbsWCob58IlPG/1+Emnz6q3Ykdi/m3AowrolG1IckRAtlwy70kC5BqMK3kNHNoL2g6MrSqV/UY6ObILQXFA+cI9BKXe6Q0qaXkO64830UB7j5B6318WECw9DKbWnlnNmm0ye/vkB5n29kNUL19K2Uz77HTMs5sKrvS2esQyH0wFUT8LB8iBzPv+Js286pdHvKVG69OuE4Yg9S+DyuOg9tGlNnwvRnEkSTiPl6JDuEADQOoTeeQv4P4A9iVejc+7C8B2T1tiSxTAMrn3iMu448z4C5b8ulnN5nBR0acvEGHt/lVIMPXQQQw8dVK++snIz4y7AUgpym9ihDUMOHUhuYRv8pYFqswWGw+C43x+VpsiEaHlkOlqgi+8G/0dAEHRJxX+lsPMGdPDHdIeXNGOOH8k9n/6N4eMH483ykluYw0lXHsu/f/g/MrJ9tTdQRyOOHBL3WGdPhodjfzc+YX0lglKK/5tyC2075ePL9mI4DHxZXryZHv76v2tlm5IQCSQVs1o5bZegNx9A7K1RCjzjMPL+k+qwWpzvP5jF7WfeTygQ2lPww5vp4bAzD+TaJy5tkluVIpEIP3w4h1UL1pDXIZeDTx2T0C8nlfsp3VlGZk5GxbS9EC1PvIpZkoQbQetysHeCkd9s75/q0AL09vOio99YjHYY7aamNqgWatWitbx+73v8/MNS2nbM48QrjmHM8SObZAJOhXAozHO3/o93Hp5MKBDCcDo4+jfj+N3/nYs3w1N7A0I0I1K2MoG0vRO9ywL/x0TrPjvQvkmo7D+iVO0LdercT2QT+KeALgP3SHCNSvwHtpELuvqCoSrXRaPs2l7M5Ke+4McvfyKvXRuueez3DDygf73aKCsuZ9pbP1C0aQc9h3Rn5JFDMIzmfTfpjrPuZ+bkub/ekw+G+ei/n7F09gr+9c3trfbLiWhdWl0S1qGf0eWvR+s3u0ehfCejjOy6v16H0NvOgshq9qx21UDZ8+jIOlTevxISp13yBJQ8QPRmYii6t9jRC/KfrVe8tVGOzmhnXwgvIPpGKvOB77yE9dUarZi/imsPNQkFQgTKgyil+Oq16Uy4aByXP/CbOiWa796fxZ1n3w9KEQ6EcHlctCnM4d4vbqVdt+Z57OGK+auqJuAKQX+I5fNWMefznxgxfnCaohMidZr3V+l6skseQm87A8pegMBkKLkXvWUcOrS47o0EPgV7A9W3m/gh8Bk6vHLPI1r70XZZvePUgWlQ8jDREpcBogc8lEF4CXrnDfVurzYq915QbYDKRUQyol9SMk5NeH+thdaaW0/5ByU7SvckG601/tIAU57+glmfzKu1jU2rtnDHWffhLw3gL/ETDkUoL/GzefVWbjrmLupyO2nVwjU8dOWT/OXYO3nq5pfYsnZbo99bY82YPJdIOPa5yv4SP9++MyPFEQmRHq0mCevgbCh5AvCzp2SkLge9C110aZ0+zAC0/9NoQoxJQfBbdGgB9raz0ZuGozePxN56Ijr4Q91jLX0cKI9xJQiBr9GRxH6IKmfP6OlNWX8A10hwH4bKvQeV97hU8GqEX+asYPumnTGv+UsDvP3Qh7W28e4jU2ImKztis3n1FhZ9v7Tm1z86hcv3u5EPHvuYGZPn8vp973HRgKuZ+XF6V70bDiNuxTKlFA5nq/loEq1ci/uXrsNrsXfeir15HPaWo7FLHouuAC57jriHI+giCNXxQ6mmBVhKoSNb0dvPgdAsIBL9L7wIvf136MD3desjXMMhAsoNkbV1a6celJGHkXUpRtuXMfIfR3mPahIFRJqz7RuKcDji/19s1sc/cnzmOfx20DV8/vLUmF8El81dQTgYe8SIUqxdvD5u++uXbeSx654lUB4kUlEHOxQIEygLcNtp/4xbmjIVxkyMv77Bk+Hm0NMPSHFEQqRHi0rCOrQYvW0ilP8P7HUQWQ4lD6O3nQrh1VS/57mbAfamOvWhvBOBONs0dASCM6Mj7Gr86OI769QHjs7xr+kQNJEiH6JmPfbtRigQf9FbOBQhUB5k9aK13H/Jf3jypherPadTnw4YcRK5UlDYNf6e3clPfR63/CTAt2/XfXYm0br07ciRFxyGZ69V0J4MN8MOH8w+Y/qlKTIhUqtlJeGdf4kWmSBc6dEARNZV/DnO1KoOg7P2k3AAcB8A7v2oev8UwAeZF1eMgON88IWXou04W4EqUZm/JW6ix4Euf79O7bQm5aV+Xr77Tc7vcwWnd/gdt595Hyvmr0prTO27FzJ03L64PLVP6ftLA7z14IfV7tee8Iejcbljvz4jO4Ohh8Wv3rV59VbCoXDMa6FgmKI4U+WpcvUjF/P7f55H++6FGA6D/A65nPe307n1jetlZbRoNVpMEtaRLRCOt8AqWJGIY02vusA1EOXsU6d+lFKovEch62owOgJecPZFtbkLI/tq4pZGir6aOv3KPeMh4yzAEyPmMih5AL3tBLRdVKeYWzp/WYCrx97MC7e/wYblm9ixeSffvPEdVx5wMz9+uSCtsd388h8ZOKYfHp8bb6YHp6uGKX6lqi1I6jGoK5f883zcXhdOd/S13kwPWbmZ3PnBTTVuU+o/uk+1keZuLreTnoO71f8NJZBSiomXTuCFFY8wJfQqr65/gjP/fJIU7BCtSosp1qHDq9FbJxJ7QdNuDqKLsjxER6sGOHui8p9CGfkJicMuuiK6gjrWecGuoRhtX6tzWzq0FF10Bdix7hE7wXcaRpvbGhxruujAl+iShyH8S/QEqYzzUJnnNbjgyev3vcczf32l2nYXgHbdC3hh+SNpH1mtmL+KxTOW8cUr05j9aexV0S6Pk9/edQ6nXnN8tWsbVmxiytNfsHXddvrv14fx5xxca/Wq0p2lnNPjD5TurLqQ0HAYdOjZjqd/fqDZ7zUWorlo+cU6HJ2ii5Zi3o/dLfLr/2ZMQnmPBdewhH5Aq+zr0MFvK6bFK3/B8aGyb6lTG1oHARc4e4AdbxFWGPzvQgOTsLZL0eXvQmgGGAUo36koV/0KSDSEXfocFN/Lni9LumJkH/gK8p9u0GKwj578PGYCBti1tZiVC9bQc99uRMIRvnt/FsvnrSK/Qy6HnH4A2XlZjXg3dddzcHd6Du5OTkE2i75bEvP0JsMwGHnU0Jiv79izPRfedla9+sxsk8k/PjO5+bi7CJQFsW0bpRQFXdryf1NukQQsRBPQYpKwUk501pVQcm8tiRggBKE5qJybEx+Hsye0fQNd/E8IfAnY0WMBs/+Ecg2suJdro4ycKq/TWqPLXoHSR6OLxJQXvCfw6xeHGHQ5Wut6f4nQ4eXobWeDDgBlgANd9go68yKM7Gvq94br069dDMX/oPoqdT+E5kV/X976H2YQKI+/ytdwGATKAqxftpHrDjMp3VVGebEfb4aHR695hhueu5KDTx1T7z4bav/jRtCxV3vWLF5HKPDr/VqPz82II4fQY1DXhPbXd0QvXl77GHM++4mta7fRdUBnBh7QL+0zA0KIqBaThAFUxnloHYLSh6MP6NL4Tw79hNahhJaZ3BOHsycq799VHtOhn7C3nQGhn6I/O7qjcm5BeQ6M/lz8z2gRkT0jxHIof4voPeQYU9sAzvp/mGqt0UWXgd7BryP1iq1UZc+gPQei3KPr1WadBaeCclYk/72VocvfQjUgCY86ahiTn/o89n5aW9Nj365cMvR6tm0o2nM03+7tOX8//yH6DO9Jx17t691vQzgcDu77yuLhq57i69emowwDpeC4S47gt3efk7Q+R8UZYQsh0qtFzUcppTCyfotq9z3kPUPNb6+Oi6QSILp16hwIzSW6cjsMkWXoosvQganR4htlz1L9fnawIsZYXxS8qKxr6x9MeBHYG4m5glv70aXP1r/NutLB2P1W6r8hzrzhRNy+6r8jb4aHs244iaWzVrBj085qZ+MCRMI27z46pUH9NlRmm0xuePZK3tj6NE///ABvbH2aS++9EJc78V8IhRBNW4tKwrsp5cZwDwX3wcRerazAfWDKilHo4nuJVuramx+9604ITiP+pEQQHD1BZYDK+vW/HBPlHVf/YCIbib1KHEBX2s6VBO79o9vBYvKBp2GHxXfs2Z57v7DosW833D43vmwvGTk+zvnrqUz6yyms+2Vj3Ipo4VCYlT+tblC/jeXN8FDYpS1ujyRfIVqrFjUdvTeVcxN626yKMpO7p3QNUBmonBsB0Docva6yUCpJ30mC04k7AoysRsctg1nB2Q2V+3r0vikKXEMbfnSis3cNidABrn0a1m4dKEcHtG8ilH9A1S8lTjByUb7qq4Lrqu+IXjwx7142rdpCWXE5nft23JPcOvQojFsi0eF00LV/DcVRhBAiiVp2Enb2grZvoUsegMDn0Qc9h6Gy/ghGIfbOv0H520AElA+deREq89LEj5CVUcMsrAb3WKoWGKksA+U9DqW8kIB7tcrZHe0aDKE5Mfp0oTIuanQfNfafcwfaKISy5wAdrTLmORCVcwfKyGh0++27Vz9VaMihA8lqk4m/xM/eA2KHy8HEyxo2AhdCiMZqMfuE60PrCHrbaRBeSvS+625e8E7AyP1HQvuzd1wL/g+JucDKORij4A3sXf+oujALAHd0H3PbNxo+8o1B20Xo7RdBZEU0CeKMxtbmbgzfcQnrp8YYdDB6nKTRJqFHM8azcsEarhtnEvSH8Jf4cXlcKAVXPXoxEy5owLR+mpUVl7N01nLcPjf9RvXC4ZACF0I0ZfH2CbfOJOz/HL3z2jinIXlQBR+gnImrJqTDq9HbTq5YrV05EftQ+c+h3EMrtii9CqX/BntzxRal01DZ16CMxO9l1VpHF4qF5oPRBjzjk9JPUxIoD/DV/6azdPZyCjrnM/7cQyjolJgiLamitebpW17mzX99gNPtxLZtPD4Pf3r6ckYfMzzd4Qkh4pAkXIm984aK7T+xeFDZN6Ayz01onzq8Gl1yP/g/AyIVe4evQ7kGVn+uDgFO2cspqnn+9td49e/vENjrBCSPz819X99Gv5F1rIEuhEipeEm4Ra6Orl1Nt8IVJGHVtHJ2w8i9H6PDPIwOCzDyn4yZgAGUctWagLUOokM/oUM/o3WcfcSiRQkGQrz2j3erJWCAoD/EC7e/noaohBCN0aIXZsWjvMei/R/EmY62wdO07xHapS9CyX1Ep7Y1qEzIuaNhW5ZEs7Fh2ca454NorVk4fUnS+l44fTGv3fsea35eR5f+nTjt2onse+CApPUnRGvRKpMw7gPANRSCc6i6VcYHGWeimvB5vXbZm1D8d6rErcvQO66O1l52j0xbbCK5MnIyCIfilzGt7UCHhnrzgfd56uaXCZYH0RpWL1rLzClzucA6k9OvOyEpfQrRWrTK6WilDFTeE9Hzf1U+YICjM+TcjMq+Kd3hxaW1rhgBxyn8UXxfqkMSKVTYpS09BnYl1p0Kt8/NcZcckfA+t6zdxpM3vUSgLLhne5fWECgL8sxfX2Hzmq0J71OI1qRVJmGoqKqVfSVG++8wOvyMUfgFRsYZTXsxlL0N7BoOYg/FPiJPtBx/fvYKfNk+XO5fJ7E8GR6679OFk648JuH9ffnqt3GrjWlb88Ur0xLepxCtSeucjm6ulJcaT1VSsQ9wF4mntWbOZ/P56KnPKd1RyqgJw5hw4WFktslMar89BnXlyQX38+YDH/D9h3Pw+NwMG7cv7boVMOuTeex39LCE1qAuLiquctpTZaFgmOJtxQnrS4jWSJJwM6KMLLR7JAR/oHoJLhd4T0xHWK2ObdvcNekBvv9gFv7S6Erl+V8v4qU73+TB6XfSqXdy1xQUdG7LJfeczyl/PJ4bJ9zOe49+jNY2DocDw2Fw61t/YuihgxLS1z7798OX5Y15/rEv28vAA5J/BrUQLVmrnY5urlTObaCyqfr9yQNGO1T2FekKq1X54uVpVRIwRI9G3LW9mDvP/ldKYtBac9PRd7B28Xr8pX4CZUHKissp2VHKLcf/H9s3FiWkn9HHDievfRsczqofFQ6nQZuCHPY/bkRC+hGitZIk3MwoZ09UwfuQcS4YncHRHbIuRRW8gzLy0h1eq/DWgx9UScC7aVuzcsEaNq7cnPQYFn2/lI0rtxAJV98jbkcifPjEpwnpx+FwcN/Xt9Nvvz54fG4y22Tg8bnpO7I39399Gw6nlMsUojFkOroZUo4OqJy/QM5f0h1Kq7R9446411xuJ0WbdtKhR7ukxrBy/mqIcT4yRAt3/DxjWcL6atsxjwen3cnapRvYsHwTHXu2o0u/TglrX4jWTJKwEPXUd0Qvtq7dVu1EJoBQIETnvsnfZ57fMQ/DEXsiy3AYMU+TaqwufTvSpW/HhLcrRGsm09FC1NPZN52M21f9VCu3z81hZx5ITn7yT4UaNWEoDlfsqWCX28nxvz8y6TEIIRpPkrAQ9TRgdF+uefxSPBkeMrJ9eDO9uL0uRh01lKsfvTglMThdTm575wZ8WdG+IbpYyu1zc8FtZ9Jz36qngGmt2bRqC2uXbiASqWGbmxAipVrlKUpCJIK/LMDMKXMpL/YzcGw/OvdJ/VRt0eadfPTkZ/z8/VLadyvkuN8fSY9BXas858evFnD/JY+xZe02DEPhyfBw8T3nNstzlIVoruQoQyFaoaWzl3PNIX8lUBas8rgnw801j1/K+EkHpykyIVoXOcowhXRoIfbOW7GLLscueQpdU6lJIZLomb+9QrA8WO3xQFmQ/97wQtySlEKI1GjVq6O1DkNoNtgl4NoX5Wj8thK7+D4ofQYIAjYEpqJLH4H8F1Cu5nP0m45sBHsLOLrI/uNm6Kv/fcsLt7/OygVr4j5n17Zitm0ooqBTfgojE0JU1mqTsA5MRe+4FggBCnQQ7T0W1eYOlKq+8rVObQZnQOmzVD3lqBx0ObroMij8PKkHRGi7CPxfRPt3j0Y5+9S/jcgm9I7rIDQXlLvi93IUKucOlJGR8JhFYtm2zc3H383MKXOrVzat9ly9Z1GXECI9WmUS1uFf0EWXA+VVL/gno5UL1ebOhrVb+jyxjxkEdFH0lCP30Aa1XRu79GkovhdwANEqStozBpX7MKqOBztoHUBvOwPszUAEdMU0pv8TtL0Vlf9cUmIXiWOd9k9mTp5bp+f2Gd4zJduphBDxNdt7wjq0FHvnX7G3nYO900SHf6n7a0seJzpdvDc/lL+Dtnc0LKjIeuIPPwywNzWs3VrowFQovp/oeyoHAtH/At+hd91e94b8k0HvpPpJTQEIzkWHFiYqZJEES2Yt44cP59T6PMNh4Mvypmw7lRAivmaZhO2y19HbToXy1yE0A8r/h956CnbZ23VrIDSbuEcCKg+ElzYsMNdg4k4u6BA4+zas3VrokkeIPQIPVHypKKlbO4FvQJfFuWpDUFa1N2Vfv/4d4VDsYwd3c3mcHD7pIB6ZdQ99hvVMUWRCiHiaXRLWkc2wyyKadHYn0kj0511/Rdvba2+kpoVGOgwqt0GxqcwLiJ2EneAagnIm6UMvXEOdYOWEyIa6taOyiftPQjlByT3hpiwcCtd4H9jpdnLDs1dyw7NXSvlJIZqI5peEy9+nxk+a8vdrbUNlnAP4Yl90tIcGLGgCUM4eqNwHoslKZQJeUD5wDkTlPdygNuvEURD/mg6Bo22dmlG+k4A49491GLzj6x2aSJ0xx43El+WNe71tpzzGnrRfCiMSQtSm2SVh7C3Evp8LEKjbSNg7ETxjgMojOzeoLFTuvxq1gll5x6HafYdqczcq5yZU/gsYBa/H3eajI+vR/s/RwdloXf1Yujr1mfEbYn+pcEZXSRt124Ki3EPBd1yMtryQfYNsVWrihh42iJ6Du+GKseI5v0Muj866B5dbVkML0ZQ0u9XRyjUIrTJBl8a4mIly7VN7G8oBuY9C4BN02atg7wTPgaiMcxOyV1gpL3iPrvE5Wpejd1wPga9BuQAdHT3nPoxyD6tfh76TIfANBCq2J6Gjo3EjD9Xm7/WLPedOcB+ILnsaIpvA2QeVeSnKs3/9YhIpp5Ti75/8jf9c9yyfPPcVaI0yFBMuGsfv/3kBbo8kYCGammZXtlLrIHrLOLC3sXsrTpQBRiGq8HOUavofNnbRZRCYSnQlcyUqA1UwObpATNtg5NdpZK61htAsdPk7oEtQnkPBe0ydtyeJliUYCFFSVEJ2fpaMfoVoAuKVrWx+I2HlhvyX0EW/q5iaVoANRkdU3hPNIgHr8NrYCRiixTG2ngS6GFDg6AQ5t6A8h9TYplIK3KNQ7mp/x6IVcntc5HeQ2wdCNHXNLgkDKGd3KPgYQj9CZC04ukZXHyexGlVChRdGp6B1jCRMGHSl+9qRleiiKyDvEZTnoJSFKIQQIvma38KsCkoplHsYync8yj20+SRgACOXWmsKVuFH77o7ScEIIYRIl2abhJs110hQ8beSxBRZXueiG0IIIZoHScJpoJQDlftgdA8x9biHrRxJi0kIIUTqNct7wommw8shtChaScu9f3QLU5Ip937Q9n102bMQnAVGAdilEJ5FzKlq1wiUilNgRAghRLPUqpOwtovRO66A4GzAGV1ojQfyHoomySRTzq6onFt+jSe8Br3tZNAl/Lr9ygDlrfI8IYQQLUOrno7WO66sOJQgAJRGC4Do7eiii9F1rbecQMrZFdX2bfCeEC3coXzgORLV9vU6FSERQgjRvLTakbAOr4hOAxOKdRFd9gIq+08pj0s5u6By70l5v0IIIVKv9Y6Ewz9HTwaKKVgxRS2EEEIkT0qSsFLqEqXUTKXUzC1btqSiy9qpmqoJKTAaX0NaCCGEqElKkrDW+nGt9Sit9ajCwsJUdFk793417NX1ojImpTQcIYQQrU+rnY6O7tV9uOKgevfuRwEfZJwO7tFpjE4IIURr0GoXZgEo90gomIwuezG6SMvRLjoCdu3XvMpgCiGEaJZadRIGUI4OqOzr0h2GEEKIVqjVTkcLIYQQ6dbqR8JCCCHqz9Y2b6+ZwYsrp7ItUEKXjHx+03sch3fYt9bXloYDzNy2DFvbjMjvRRt3RgoibpokCQshhKgXrTW3/PgqUzf/jN+OFjxaUryBW+e/xtLiDfy+75FxX/viiqn8Z+knOFV0IjakI5zd/UD+0O+oVrkWR6ajhRBC1Mv8HWuYuuXXBLybPxLi+RXfsMW/K+brPts4n8d++YSAHaI0EqA0EiBoh3l11bf8b9X0VITe5EgSFkIIUS9TNswlEAnHvGag+GrzwpjXHl/6Gf5I9VLBfjvEU8u/QOsYJ8i1cDIdLYQQol78kRA61pGrQASboB07Qa8qjV8xsTjkpyTsJ9tV85GtYTvC1C2LWVe2jU4Z+RxcOACn0XzPWpckLIQQol4OajeATzfOpzwSrHbNwGB02z4xX5fp9FAc9se8pgCvw1Vjv0t2reeKGU8TtMME7TBuw4nLcPDQqIsY0KZzvd9HUyDT0UIIIerl4MIBdPDm4lJVR6Aew8nI/J70ye4Q83UndBmFO8ao1akMDu+wLy4j/rjQHwnxhxlPsiNUSlkkQFhHKIsE2Bkq4/IZT+GP8YWgOZAkLIQQol6choMnxvyeQ9rtg8tw4HO48RguJnYZxT0jzo37uov7jKd7ZiE+h3vPY17DRTtvG67b5/ga+/xi00+E7EjMa2Ed4dONPzXszaSZTEcLIYSotxyXj7uHT6Ik7GdnsIy2niy8lZJrLBlOD08f8Ac+2zifD9bNIaJtjuowhKM7DcPnrPm1K0o2x5z+BiiPBFlZsrnB7yWdJAkLIYRosCynlyxnvBPpqnMbTo7pNJxjOg2vVz/tvbl4DVe1bVEQHU138OXWq72mQqajhRBCNHlHdRwSXb0VgwaO6jg0pfEkiiRhIYQQTV62y8f/DZuE13DhqVjA5TGceAwXdw07m5xatjY1VTIdLYQQol5Kwn5eWPEN762dhT8SZFheDy7uM54BbTqjtWZD+Q6Ugg7e3GqlKP2RID8WrUIDQ3O713ovuLKxhf1569DreW/tLFaUbqZHZiETu4yiwJOd4HeYOirVFUpGjRqlZ86cmdI+hRBCJEZpOMAF3/6bjf4ighWrlRXgMVxc0OtQ3l47gx3BMgDyPVncMPAExhb2B+DVld/y76VTcFTUjY7YNpf0PYJzex6clveSSkqpWVrrUXs/LiNhIYQQdfb6qu/Y5N+xJwFD9J6s3w7x2C+fVnnuhvIibpjzEv8aeQFFoVL+vWRKtYVVjy/9lLbuLI7pXL+FWi2FJGEhRItmaxuFapUn9CTDe+tmEYhTljKWgB3ioSWTKQ6Vx1zZ7LdD/OeXTyUJCyFESzJr23IeWjKZRTvX4VCKg9vtw1X9j6FzRn66Q2vW4tWFrsminevi1poG2FS+g0AkhKeWspUtkayOFkK0ON9uWcw1s55l4c61aDRhbfPVpoVcMP3fbCrfke7wmrWxhf333NOtK4dSuFX8QxYchoGrGR/C0BiShIUQLYrWmnsWvltt6tNGUxoO8PTyL9MTWAtxfs9D8BjVR6xGnE28BopD2u3DMZ1H4IyRvJ3K4KgOQzDqmdhbitb5roUQLdZG/w62BUpiXotom883LkhxRC1Lp4w8Ht//EgbkdMJdUTc6x+Xjgl6Hkunw4KiUVhzKIMvl5cr+x3BFvwl7ql7t5jVcFHpyuGrAMel4K02C3BMWQrQote+6bH0Hxydav5yOPDf2CrYGiikPB+noy8VpODip6348+csXfL15IUopxrUfxEW9x9He2waAFw+8kvfXzWLy+h/RaCZ0HMrELqPIdHrS/I7SR5KwEKJF6ejLJc+dwUb/zmrXHCgObT8wDVG1TAWebKiUPzv68rhl8CnAKTGfn+H0cEb3sZzRfWxqAmwGZDpaCNGiKKX408ATqt23VCh8Tg+/6T0uTZE1Lba2+WHrL7y5+nu+37oUW9vpDqlVkpGwEKLFObjdPvxjxLk8+PNHLC/ZhFKKMQV9uWbAcXT05aU7vLRbXrKJq2Y8TUnYT0RrHEqR5fTy4H4X0SurfbrDa1WkbKUQokUL2mEMFM5WugVmb0E7zMQv/86OYGmVu+MKaOPK5P1xN+A24o/PApEQYW236vu4DVHvspWWZTmA3wFdgMmmaU6rdO0W0zTvSEqkQgiRQDUllNboi40LCERC1ZanaaLVrT7f+BNHdxpW7XWLd67nX4s/YG7RKgA6+/K5esCxHNxuQNJjbslquif8GHAosA140LKs+ypdi33XXQghRJO2tHgDZZFgzGvlkSC/FG+s8tiqki1c/N1jnDf9YWZtX0FE20S0zeqyrfxl7st8vvGnVIQd17qy7fxv1XReXfktq0q3pjWWhqjpK+Jo0zSHAFiW9TDwiGVZbwJnE/doZSGEEE1ZO28bPIaLQIw6zh7DRaE3Z8/P68q2c9F3j1ASDsRsK2CHuHfR+4xrP6jOtbl33wJtbC1vrTX/WPQe766diSI6kn9oyWTGt9+Xvw05rd5VvdKlpij3HPJommbYNM1LgLnA50BWkuMSQgiRBEd1HFLD1eje3d2eXPYF5eHYo+bdikPlrC8vqrXf6VuWcM60hxgz5WYO/sTEnPc/tvp31TXsat5Y8z3vr51F0A4TsMMEK/77YtMCnl3+VYPbTbWakvBMy7KOrvyAaZq3AU8DPZIZlBBCiOTIdWdy6+DT8BguXBX1nF3Kgcdwcevg08l1Z+557tTNi4jUUtxEozFqGdV+smEef57zIkuLN6CJLg77eP08zvv2YXYESxv0Pp5d/lXcU5leWjm12Wy5ijsdbZrmuXEe/y/w36RFJIQQIqnGdxzMwNwuvLn6B5aXbKJnVjtO6bo/nTKqbt+qy5RxgSeHDt7cuNcj2uYfC9+tNv0dwWZXqJxXVk7j0n5H1St+rTWbYhRj2a0sHKA8EmoWK7hl2aAQQrRCHX15XN5/Qo3PGd9+X95eO4NwnFGl13Bx46ATa0zWy4o3xj3+MKQjfLxxXr2TsFKKNq4MdobKYl53Gg68zeRYxOZx51oIIUTKXdR7HJlOb8wTknplteOh/X7D/gV9a2yjYhlW3Ot2A2tVnN59DJ4Y28/chpMTuoxqNguzZCQshBAipkJvDi+MvYJ/L5nC55sWELYj7JvblSv6H82wvB51aqN3VnuchgGR6tdcysERHQY3KLaLeh3Gj0Wr+GnHGsortlz5HG56Z7Xnin41j/CbklorZlmWpYBzgF6mad5mWVY3oINpmj80pEOpmCWEEK3L++tmcc+Cqmc8GyhyXD5ePuhq2nqyG9Su1pqZ25fz2Yb5RNAc3n4Q+xf0aZJnE9e7YlYljwA2cDhwG1AMvAHsl9AIhRBCtEjHdx5JptPLw4sns65sO4ZSHFQ4gGv3Ob7BCRii94b3a9ub/dr2TmC0qVWXJLy/aZojLMuaA2CaZpFlWe7aXiSEEELsNq79IMa1H4Q/EsKpDKnlXaEuSThUUUdaA1iWVUh0ZCyEEKKZKw6V89WmhZSE/eyb241Bbbo0uppVTZrLquVUqUsSfhB4C2hnWdadwGnALUmNSgghRNK9t3YW9yx8B0MZhHUEhzLondWeB0ddRLbLV+d2VpduZW7RSnwONwcU9iPL6U1i1C1LjUnYsiwDWAH8GRhPdJ35SaZpLkpBbEIIIZJk4c613LPwXQKV9vCGiLCkeAO3/PgqD4y6sNY2gnaYm+e+wvStS3CgUMogom2uGXAcJ3fdL6kj6paiLquj55imOTxRHcrqaCGESI6t/l28vOpbvtm8CJfh5PjOIzix8yiWlGxgRclmCj05jCnoi9NwcNOcl/h80wJ0jLKUbsPJG4dcR3tvmxr7u/Ont5i8fk6VRL6bx3ByStfR/L7vkWQ0g8pVydaY1dGfWZZ1KvCmaZoN21UthBAiqVaVbuU30x/FbwcJ2dFNuf9eMoWHFk/GWbFlx1AGLsPB/SMv4JfijTETMIDbcLCmdGuNSbgk7Oej9XPiVsMK2GHeWPM9s7av4JkD/iALseKoSxL+PXAtELYsy090SlqbpplT88uEEELUZleonGlbfiYQCTM8rwfdswob1M5dP71JSdhfJbHuTpBhXalSRgSumPEUA3I6saos9vm7IdumsJZR8Pqy7biUgyCxk3C0/whryrbxxaYFHFnj6U2tV61J2DTNhm/iEkIIEdf/Vk3nwcUf4VAGWms0mv0L+nLXsLNxxyjJGM/OYBk/7VgTd2S7N1vbdMnIZ+GutfgjVQ9WMFD0yCqke2ZBjW3kubMI6RhlsPZSHgnyyYZ5koTjqPVv2bKsQ2I9bprm14kPRwghWofvty7l4cWTq03nfr91KfcufJ+b9j2pzm2VRQI4lFGnpAjR4/5KwwFO6TqaN1b/QMgOY6PxOdz4HG7+b9ikWtso9OYwsE1n5hWtxq7DcYfNga1tfixaxc5QOX2zO9A5Iz/pfdblq9afKv3ZC4wGZhGtoCWEEKIBnlr2RczzcAN2mA/Xz+aqAcfU+Si+Qk8OboczZnuxOJVBp4w8rux/DMd2Gs7762azM1jGqLa9ObLjYLyOutVjum3Imfzmu0cpCZXjj3Nv2OdwN4tR8Lyi1dww90XKwwEUipCOMCq/N3cNOyupC8vqMh09sfLPlmV1Bf6VrICEEKI1WFGyOe41h3KwsXwHvbPb16ktp+Hgt70P58HFHxKpw6lEDmVwYpdo5eF+OZ24NqdT3YLeSwdfLq8ffC2T18/l+RVfs6F8R5VRsUs56OzLY1z7QQ1qP1U2+Xdy5cyn9hwEsdvM7cv4y9yX+Vcdtms1VEOqXK8F9kl0IEII0Zrk11AzOawj5Hsy69XeqV1HU1v+dWDgMVxc1f8YutVyz7euMpweTum2P28ecj3XDZxIgScbA4XHcHFCl1E8MeZSXJXub2utmbFtGXf/9Ba3z3+DrzcvIhLnvOJUeX3VdMJ29an8oB1m5vblrC3blrS+63JP+CHY89XGAIYBs5MWkRBCtAJn9ziQexe9V21hlAODYXndyXNn1au9skgIh2Fgx0gmu53WfQyndB1Nz6x2DYq5JkopTu82htO67k/QDuMyHNVOMwrbEa6Z9SzzdqzeM+r8dON8umUW8J/RF9d5+j3R5hStjHs/3aUcLN61ni4ZbZPSd11GwjOJ3gOeBUwHbjBN89ykRCOEEK3ExM4jGFvQD5/DvefIe5/DTYE3G3Pw6fVuL8flJcMRP4l1zyzkun2OT0oCrkwphcfhinmc4PMrvmZu0aoq077lkSArSjbzwM8fJjWumuTX8IVHA21cGUnruy4Ls3JN03yg8gOWZV2992NCCCHqzlAGdw+bxOztK/hg/WzKwgEOLBzAkR2HNOiQA0MZnN/rEJ745bNqo2uv4eKSPuPr3FYgEmLxrg04DYP+OZ1wJOh83ldXTScQY/FY0A7z0fo5/HngCWkp6nFqt/35ftsv1e4JA3gcTobn90xa33VJwhcAeyfcC2M8JoQQoh6UUoxs24uRbXslpL1zehzEpvKdvL12RkWVLEVYR7io92F1WqGstealldN44pdPUUqhtcbjcPGXQSdzaPuBjY5vZ6gs7jVba0rDAdq4kzfqjGd02z4c1XEIH2+YtycRu5QDp+Hg7mGTEvYlJJa4SdiyrLOBSUBPy7LerXQpG9ietIiEEEI0iKEMrh84kQt7H8bMbctwKIP9C/qSU8cTkd5c8wOP/fJJlZF0WSTILT++ykP7XcSwvB6Niq+Dtw3ryotiXvM4XGS50nP6klKKvww6mfEdBvP6qu/YFixmSG53zuoxlo6+vKT2XdNI+FtgA1AA3Fvp8WJgXjKDEkII0XAFnmyO7jSsXq+xtc3jv3xabSobIGCH+M+ST/jP/hc3Kq6Leo/jnzEWo3kNF2d2H5vUEWdtlFKMKejLmIK+Ke03bhI2TXMVsAo4IHXhCCGESIdtgRLKwoG41xfuXNvoPiZ2Hsmykk28sfp7FGpPJa1D2u/Db3uPa3T7zVFdtiiNAR4iujfYDTiAUjnAQQghWg6fw11joY+GLBbbm1KKawYcx6TuB/LNlp+JaJsDCvolbM9yc1SXhVkPA2cBrwGjgPOBfskMSgghRGplubwMye3GnKKV1Wo9u5SD4zuPSFhf7X25nNZtTMLaa87qNAFvmuYvgMM0zYhpmk8DRyc3LCGEEKn2l31PJsvpxaV+3SbkMZx08OVyUSudLk62uoyEyyzLcgNzLcu6h+hirfTdPRdCCJEU3TILePWgq3l51TS+3LQQl+Hg+M4jObnr6LRVs2rp6pKEzyOadK8ArgG6AqcmMyghhBBVrSzZzHMrvmbu9pXkuHyc1m0MR3calvDiFgXeHK7sfwxX9j8moe2K2JSuw4kblmX5gG6maS5ubIejRo3SM2fObGwzQgjRaszctoxrZz9HyI7sOezA63AxNLc794+8IC1VpkT9KKVmaa1H7f14rdPKlmVNBOYCkyt+HrZX8Q4hhBBJYmubv/74Kv5IqMppQ/5IiHk7VvPpxvlpjE40Vl3u7d4KjAZ2AJimORdIXiFNIYQQeyzYuZbyGAU0IHr4wZtrfkhxRCKR6pKEQ6Zp7tzrsdrnsIUQQjRaaTiAoeJfLwmVpy4YkXB1WZi1wLKsSYDDsqy+wFVES1oKIYRIsgE5nQjGOSPYqRyMbtsnof35I0H8kTBtXD6UqiH7i4SoSxK+ErgZCAAvAVOAO5IZlBBCiKhcdybHdhrOR+vnVjsG0G04OLvHgQnpZ13Zdu5Z+C4zti1DAbnuDC7texQTu4xMSPsitppOUXreNM3zgItN07yZaCJuEKXUJcAlAN26dWtoM0II0Sr9eeAJOJTBe+tm4TachHWEAk8Odww9k/a+3Ea3vy1QzIXTH6E4VI5dcbdxS6CYfyx8l9Kwn7MSlOhFdXG3KFmWtRA4AvgIOAyoMi9hmmaDjjOULUpCCNEwxaFyfineSI7LR6+s9gmbLn5kyRReXDmVUIxp7wyHh4/H34zbqMvEqYgn3halmn6r/wE+A3oBs6iahHXF40IIIVIk2+VjeH7iN6d8sWlBzAQM0Q/+n3euZ0iezGImQ01HGT4IPGhZ1qOmaV6WwpiEEEKkUE3n+Go0TkMqFSdLrb9ZScBCCNGyHdNxOJ44080uw0m/7I4pjqj1kK83QgjRyp3afX/aerJxqqrlLz2GixsGniBlMZNIkrAQQrRyWU4vzx5wOSd33Y8spxencrBvm67cO/I8jug4JN3htWh1OsAhkWR1tBBCiNamwQc4CCGEECI5JAkLIYQQaSK7r4UQQiTEsuKNPLnsC2ZvX4HP4ebELqM4s/tYfE53ukNrsiQJCyGEaLTZ25fzx5nPErDD6IrSl08u+5yPN/zIUwdchtchiTgWmY4WQgjRKFprbp33On47tCcBAwTsMGvKtsuZxzWQJCyEEKJRlpdsZkeoNOa1gB3inTUzUhxR8yFJWAghRKP4I8EaS1/6I6G411o7ScJCCCEapXd2B2xtx7zmUAZjCvqmOKLmQ5KwEEKIRvE6XJzf81C8hqvaNbfh5Pxeh6YhquZBVkcLIYRotN/0HoehDJ5d/hUAER2hc0Y+5uDT6ZyRn+bomi5JwkIIIRpNKcVFvQ/jnJ4Hsbp0KxkOD50y8tIdVpMnSVgIIUTCuA0nfbI7pDuMZkPuCQshhBBpIklYCCGESBNJwkIIIUSaSBIWQggh0kSSsBBCCJEmkoSFEEKINJEkLIQQQqSJJGEhhBAiTSQJCyGEEGkiSVgIIYRIE0nCQgghRJpIEhZCCCHSRJKwEEIIkSaShIUQQog0kSQshBBCpIkkYSGEECJNJAkLIYQQaSJJWAghhEgTScJCCCFEmkgSFkIIIdJEkrAQQgiRJs50ByCal9lL1/LcJzNZuamIroW5nHfESEYP6JbusIQQolmSJCzq7MXPZvPvd6cRCIbRwOrNO5i9dC0XThjNxcfun+7whBCi2ZHpaFEnW3aU8NA7U/FXJODdyoNhnpr8PWu37EhXaEII0WxJEhZ18snsJVTJvpVEbM1HM35ObUBCCNECSBIWdVJcFiAUjsS8Fo7Y7CoNpDgiIYRo/iQJizoZ2rsTPo8r5rUMj4vhfTunOCIhhGj+ZGGWqJPR/bvRMT+HVZuLCEfsPY87DEVelo9DBveqc1taa97/biFPTv6BDdt2kZedwdnjhnPO+BE4HfK9UAjResgnnqgTw1A8fu3pDO/TGY/LQZbPjcflZHDPjjz1pzPrlTz/9eY33P3K56zevINQxGbzjhIe+2A61z/2LlrHufEshBAtkIyERZ3lZfl47I+nsX7bLtZt3Umntjl0LmhTrzY2bi/m1S/nEtzr/rI/GGbG4rXM+WUdI/p2SWTYQgjRZEkSFvXWqW0OndrmNOi1X89fjlKxr/mDIabMXCxJWAjRash0tEipcCRCvBlnDYTCduyLQgjRAkkSFik1Zp/uqDhD4QyPi8OG9U5xREIIkT6ShEVK9erYloP27YHHVfVOiNvpoGthLgcO6pGewIQQIg0kCYuUu+u3xzLp8OFkel14XA48LgdH7zeA/153Bg5D/kkKIVoPleotIaNGjdIzZ85MaZ+iaQpFIuwq9ZPl81QbGQshREuilJqltR619+PyySfSxuVw0DYnM91hCCFE2sjcnxBCCJEmkoSFEEKINJEkLIQQQqSJ3BNuRoKhMIvWbMZpGPTv2k4OOxBCiGZOknAz8eqXc3n4namAQmuNy+ngxrMOZ8Ko/ukOTQghRANJEm4G3v9uIQ+89Q3+YPjXBwMhrOc+JjfTy/77dE9fcEIIIRpM5jObOK01D78zrWoCruAPhXn43W/TEJUQQohEkCTcxBWXBSgqLot7/efVm1MYjRBCiESSJNzEuV1Oaqpp5nU3nTsK4YhNeTBEqquwCSFEc9V0PsFFTF63kzH7dOPbBauw90puTofBsaMHpCmyX23dWcq9r3/F53OWEtGa9rnZXH7iWI4dvU+6QxNCiCZNRsLNwI1njScnw4Pb6djzmMfloLBNFpdNHJvGyKC4zM+5//cSn85eQihiY9uaDdt3cceLn/LS57PTGpsQQjR1koSbgU5tc3j9bxdw3hEj6d4+j14d87n42DG8csu55Gb50hrbG9/MZ2dpORG76ijdHwzz73e/jbmgTAghRJRMRzcT+TkZXH7igVx+4oHpDqWKKbMWEwhFYl4zlGL+ig3s179riqMSQojmQUbColEUqubrNV8WQohWLSVJWCl1iVJqplJq5pYtW1LRpUiRY0YPqOEsYM3gnh1TGo8QQjQnKUnCWuvHtdajtNajCgsLU9GlSJFTDhpM25wMXHvVsfa6nVx9yiE1JGghhBAyHS0aJdPr5oUbJ3H8mIF4KxJurw753HnRMZx28JA0RyeEEE2bSnVhhVGjRumZM2emtE+ROratMQy5ESyEEJUppWZprUft/biMhEVCSQIWQoi6kyQshBBCpImsmhEJobXmix+X8dLns9myo5R9urXjwgn7MaBru5T0Xx4MsXF7MXlZvrQXMBFCiLqSJCwaTWvNbc9/wsezllAeDAGwbutOvp6/nFvPP4qjRvZPWt+hcIT73/iat6b9hMNQhCM2I/t14dbzj6KwTVbS+hVCiESQ6WjRaLN/WVclAQPYWuMPhrGe+6TK44l2y9Mf8da0nwiEwpQFQgTDEX74eTXn//3lpPYrhBCJIElYNNo7037CHyfhGYbi259WJqXfNVt28NW85QRCVetTR2zNrrIAU2YuTkq/QgiRKJKERaPtLPPHPfM4YtsU+wNJ6XfWkrVxV2OXB0J8PW95UvoVQohEkSQsGm3//t3wumMvL9AahiSpdKXH5cSooTh1hseVlH6FECJRJAk3UbatWbZ+K0vWbiEcsdMdTo0mjh2Ex+WsdliD2+lgeJ9O9OrYNin9HrRvDyJ27N+Nz+Pi+DEDk9KvEEIkiqyOboK+nr+cO1/8lBJ/EAW4nA6uOfUQTjhgULpDiynb5+GZP53Fn594nzVbduB0OAiGwhwypBfW+ROS12+Gl6tPOZgH35pa5dxir9vJfv26sP+AbknrWwghEkHKVjYxs5as5YqH36q22MjrcmKefxQTRiVvu08irNy4nW27yujRIY+2OZkp6XP6wlU88eF3LN+wjbzsDCaNG84pBw/GYchEjxCiaYhXtlJGwk3MQ+9MrZaAAfyhMA++PZWjRvZDNdFDeldv3sHUn1YQsTVtMr0pS8IHDOzOAQO7p6QvIYRIJEnCTcyClZviXtuyo4TisgA5md6k9K21ZvbSdfzw82o8bidHjOhLt3Z5dXrdnS99xgffL8TWGq3hP+9P56BBPbjrd8ficjj2PDcQCjN1/gq27iqlX5dChvXu1GS/VAghRLJJEm5iPC4HZYE4C7E0uGs5nzdi29i2xuV01Pi8vZX6g1z2wBss27CN8kAIp8PgiQ+/47SDh3DtaYfWmChf+3oeH/6wiEAosuexcMRm2oKVPP7+d1x+4oEAfL9oFdc//j5aayIVpy11ys/hkatPkepWQohWSW6aJVg0wTR8NfOEUf1xOKr/tRhKMap/l7hbgdZt3ck1j77LmCsfYsxVD3H67c8xbcHKOvd710ufsWTtFsoD0aIb4YhNIBThzanz+WT2khpf+8yUGVUWRu3mD4V55cu5RGybjduLueY/71LqD1IWCBEIhSkPhFi5aTtXPfx2neMUQoiWREbCCVIeCPHIu9/y1rT5lAVCtM/L4uJjx3DygfvWa7r1DyeMZepPK9hZ6icYjo4snQ4Dn9vFjWcdHvM1m3eUcM7dL1FSHsCuWGi3bP02/vTYe9x+4dGMH9G3xj5LygN8Nmfpnv6qvK9gmKenzKix/vPmHSVxrwVDYUrLg7z29Y9EItUXAUZszerNRSxctZGB3TvUGGc6lQdCvDV1Pu9MX0AoHOHQob2ZdPhwGcELIRpFknAChCIRfnff/1i2ftueRLapqIR/vvYl67bs4MqTD65zW21zMnnllvN46bPZfDTjZyK2Ztyw3lxw5Cja52XHfM1Tk3+gLBDck4B384fC3PO/Lxg3rE+N5/xu2VmK02HETMIA67ftqjHmvGwf23aVxbzmcBhkeN0sXLWJUCR2+0oplm/Y3mSTcKk/yPl/f5kN23bhr1g0t+6z2bw5dT7P/flsurev/b65EELEItPRCfDVj8tYuamoWhLzB8M8/+lsNhcV16u9vCwfl594IO/f8Vs+uut3/PmMcXETMMDnc3+JW9CjpDzA6s1FNfZX0CazxoIgHfNzanz92YcPxxPjXrXb5eDEsYNwOgw6tc2J/0VAQWGb1KykboinJ//Auq079yRggFDEpqQ8wG0vfJLGyIQQzZ0k4QSYMnPxnnupewvbNqff8Tzrtu5MWv+1TXbXNh2e7fMwblhv3DEWc/ncTi48qtrWtirOP2IU+w/ohs/t2lM1y+dxsU/Xdlx98iEAnHHoUNyO2IvFvC4Xo/p3reVdpM870xfEnCXQGn5auYGdpf40RCWEaAlkOroWEdtm+sJVLFq9idxMH0eM7EfeXofG27XUOykpC3DVv9/m9b+dn5TtOONH9OX1r+fFHM1mZ3jp1i631jZunnQEqzfvYOWmIsoDIRyGwulwcOKB+9ZaIMTpMLj/shOYt2IDn85aSsS2OWxob/br33XP++3ftR2XHn8Aj74/nXAkQsTWeN1OHIbBg5ef2KQLa8T7ggXgMAzKAkHaJGnbmBCiZZMkXINNRcX87r7XKCouozwQwuN2ct8bX3HzpCOq1CWeMKof3y1aFffDWgMbtxezaPWmpNz3vGjCfkz+4Wd2lQWq3Bf2uJzceNbhdUr8WT4PL9w4iR8Wr+H7Ravxup0cObIfPTvk1ykGpRQ92+fTq2N+3FOVzj9qFAcN7smb38xn044SBvfswIlj923yCWxQjw7MWLwm5jWPy0m7XFmcJYRoGEnCNfjjI++wcfsuIhVD3d3bcO586TMGdG1Hn84FAIwb1oenJv/AkrVb47ZlGLB2686kJOHCNlm8cNM5PPDm13zx4zIitk2/LoX88eSD2X+fuleSUkqx/4BuDaq5/OH3i7j9xU8xlCIYDuN2OemQl81jfzyNgkr3e3t1bMv1ZxxW7/bT6bKJBzB/+YYq94QhWqP64mP3b9KjeCFE0ya1o+NYsnYLF/7jlZj7Xx2G4vgxAzHPO2rPY7tX0K7YuD1mez63iyeuPZ2B3dsnLWaI7lO2tU5pYli6bisX/P3laknKYSgGdm/Ps38+O2WxJMtXPy7jthc+IRAKo5TCtjW/PWY0F03YTyp+CSFqJbWj62nN5h1xE1nE1ixbv63KY5leN3//3XGc9/eXq9V+NpSiY9ts9unWLmnx7qaUwpHipPDiZ7Nibj+K2Jola7eyfMO2pB1nmCqHDu3Nx4N78vPqzQTDEQZ0a4fPLecVCyEaR+bR4ujYNrvavtvdDKXoHqOmcp/OBdx41jjcLgceV3QlcIbHRUGbTB68/KQWO2Jaum7rnin7vTkdBqs370htQEniMAwG9ejA8D6dJQELIRJCRsJx7NOtPe3zsli9aUe1ZOx2OZg0fnjM1504dl8O2rcnU2YuZntxGft0a88hQ3pVOcQgkcr8QTYWFZOXnVFt1XaqdCnM5ec1m4n1ncW2NR3y4+9xFkKI1kyScBxKKR74w0n89p+vUhYIURYI4XIYGIbiqpMOYp9u8e/tts3JZNLhI5IaXzAU5p+vfcV73y3EYShCEZuRfTtjXTAh5aUUJx0+nG/mL692/1wpaJ+fRf8uhSmNRwghmgtZmFWLYCjMZ3N+Yf6KDeTnZHDc/vvUWkEqFa577F2+XbCqyv1nh6EobJPFm7deGPegh2R5esoPPP7Bd0RsTThik+Fx4XW7eOr6M+p0HKIQQrRksjCrgdwuJ8eMHsAxowekO5Q9Vm0qYtqClQRDVRdDRWzNzjI/H89azAkHDEppTBdNGM344X15b/oCtu0qZ3ifThw5sn/KvwwIIURzIp+QzdCspWsx4izyKg+E+Gb+iqQn4eLyAAtWbsTtdDCkVyecDoNu7fK4/MSDktqvEEK0JJKEmyG3yxE3CUN0RXayaK15+J1pvPT5HFwOA010Gvyv5x7J+OE1H5kohBCiKknCTZjWmne+XcDTU2aweUcxhW2yuOCoUYwf3jfuqUc+t6tKSc1Ee2ryDF7+Yg6BUJjKVTr/+vRkCtpkMrRXp6T1LYQQLY3sE27C7nr5M+753xes2bKDQCjC2q07uff1r/jXm19z1ckHVbvf6nU7OWBgd0b165KUeELhCM98PCNmFTF/KMxj73+XlH6FEKKlkpFwE7V8wzbe/25Rtepb/mCYKTOXcM74kdz7+7b898PvWL5xO21zMph0+AhOHDsoaUVBNmzfFbeACcCClRuT0q8QQrRUkoSbqC/mLiNsx55yDoUjfDZ7CZdOHMsBA+t+QENjZXk9ROJMg0O0dOePy9fzwqezWLmxiO7t8zjviJEM7S1T1EIIEYsk4SYqHIlgx0l4ttYxD5lPtvycDPbp3p55y9dXq47lcTnp06ktlz3wBoFQGK1h+cZtfLtwJX+YOJZzjxiZ8niFEKKpk3vCTdSYfbrjjVOf2OdxceC+PVMcUZR1/lFk+7y4nb+W4fS6nXQtzOX7n1fjD4b3JGito9PnD78zjY3bi9MSrxBCNGWShJuoIb06MqhH+z0HQezmcTro17mAEX06pyWubu3yeNO8gAuOHEW/zgUM7tmRP58xjmNGD4h7L1qjmTJzcYojFUKIpk+mo5sopRQPXn4y97/xFe9NXwgqOrI8dv99uP70Q9N6IlN+TgaXnTCWy04Yu+exh9+ZGneKPBS22VlaXuf2d5b6eXf6Apau20rXwjaccMAg2ufJIRBCiJZHknAT5nU7uens8Vx72qHsKCknN8uHx9U0/8oG9+xIhsdFWeXNwxUyPK467x+e88s6rnj4LbTW+INh3E4HT02egXnekRy9X9MpHSqEEIkg09HNgMflpH1edpNNwAAH7duT/JwMHEbVEbrDUORl+ThocO33sAOhMFf/+23KA6E9e5GD4QiBUBjr+U/YvKMkKbELIUS6SBIWCeEwDJ689gwGdm+Px+Uky+fG43KyT7f2PHX9mTiM2v+pffnjsrj7kKPVw35KdNhCCJFWTXdoJZqdwtwsnv3z2azaVMS6bTvp3LYN3dvX/RjDTUXFce8rB8MR1mzZmahQhRCiSZAkLOokYtuEwjYel6PWRWHd2+fVK/nu1qNDPm6nI2ZdbK/bSd/OBfVuUwghmjJJwqJG23eVce/rX/HpnKVEIjbt87L4w8SxHJeEQyLGDuxBptdNeSDE3pPShlIpPyNZCCGSTe4Ji7hKygOc+38v8fHsJYTCEWyt2bC9mDtf/oznP5mZ8P6cDoPH/ngabdtk4nE5UUSTr8tp8LfzjqJNpjfhfQohRDpJEhZxvTXtJ3aUllerF+0Phnn0/emUx9iO1Fg9OuRz1Ih+2NpGEy3RqW2wnpvCzCVrEt6fEEKkkyThNCr1B3n+05mcecfznGY9yyPvTqOouCzdYe3x8czFMY8tBHAaBj8uX5/wPn9ctp43p80nFP418Ydtm/JgmOsfe59QJPU1s4UQIlnknnCaFJf5Off/XmbzjpI9xxWu/WQWr38znxduPJtObdukOUJqXIClgWTU7Hrt6x+rHd+4W8S2+eHnNRw4qEeD2rZtjT8Ywut2YRjpqzgmhBC7SRJOk8c++I6N23cRqjTVGwxHCJf6uevlz3n4ipPTGF3UMfsN4Jd1W/HHSIq21gztnfj61Vt2llY7oWk3rTU7Supe/nK3cMTmiQ+/55Uv5lAWCOJxOTn14MH84YQDm3QBFCFEyyfT0Wny/ncLqyTg3Wyt+eHn1Um531pfJ44dREGbTJyOqv9MvG4nV510EF534hPY8D6dq5zQVFnE1gzoWljvNm968gOe/2QmxeUBIramLBDif1/9yBUPRctjCiFEukgSTpOakqyhFP5g+pNwhtfN8zdO4oQDBu1JuD3a53H7BUdz5mHDktLnaQcPqZb0AVwOg0E92tO7U/32Ci9dt5VpP62sNpoPhCIsXL2J2UvXNSpeIYRoDJmLS5MB3doxf8XGmNdyMrzkZvlSHFFsbTK93HLOEdxyzhHYtk76vdSCNpk8evWpXPufdykPhlAowhGbwT07cO/vJ9a7vWk/rSBkV59xAPAHQnw1bxkj+3VpbNhCCNEgkoTT5A8nHMg1j7xTbYTmdTu5bOIBaT2qMJ5ULWYa3LMjU+6+hDm/rGN7cRl9OxfQo0N+wxpTNS8ga4K/ZiFEKyLT0Wmy/4BumOcfRU6Ghwyvm0yvG5/HxWXHH8DJBw1Od3hpZxiKkf26cOTIfg1PwMChQ3rHPTzC63Zx+PC+DW5bCCEaS0bCaTRhVH/GD+/LwlWbCEciDOzeISmLnVqznh3yOXJEXz6ds7TKnmev28nIfl0Y0rNjGqMTQrR28omfZk6HwZBekgiS6dbzJ9C/azue/WQmW3eWkpvlY9K44VwwYVSTnPYXQrQekoRFi2cYinPGj+Cc8SPQWkviFUI0GXJPWLQqkoCFEE2JJGEhhBAiTSQJCyGEEGkiSVgIIYRIE0nCQgghRJpIEhZCCCHSRJKwEEIIkSaShIUQQog0kSQshBBCpElKkrBS6hKl1Eyl1MwtW7akokshhBCiyVNa69R2qNQWYFVKO62/AmBruoNIMnmPLUdreJ/yHluG1vweu2utC/d+MOVJuDlQSs3UWo9KdxzJJO+x5WgN71PeY8sg77E6uScshBBCpIkkYSGEECJNJAnH9ni6A0gBeY8tR2t4n/IeWwZ5j3uRe8JCCCFEmshIWAghhEgTScJCCCFEmkgSFkIIIdJEkrAQQgiRJpKEhRBCiDRxpjsAIUR1lmVdBVwGzDZN85x6vrYHMNY0zZeSFNsVwB+B3kChaZotvQyhEEkjI2EhmqY/AEfWNwFX6AFMqu+LLMty1PGp04AjaPo14IVo8mSfsBBNjGVZ/wF+AywGniK6+f8hYF/ABdxqmuY7FSPe54HMipdeYZrmt5ZlfQfsA6wAngWKgFGmaV5R0f77wD9N0/zSsqwS4DGiSfVyogn8KsANfA/8wTTNSJw4V1a0KyNhIRpIRsJCNDGmaV4KrAfGmaZ5P3Az8LlpmqOBccA/LMvKBDYTHS2PAM4EHqxo4kbgG9M0h1W8viaZwPemaQ4FtlW0c6BpmsOACNCQkbgQoo7knrAQTd9RwAmWZV1f8bMX6EY0UT9sWdYwogmzXwPajgBvVPx5PDASmGFZFoCPaKIXQiSJJGEhmj4FnGqa5uLKD1qWdSuwCRhKdFbLH+f1YarOenkr/dlfabpZAc+apnlTIoIWQtROpqOFaPqmAFdalqUALMsaXvF4G2CDaZo2cB6we2FVMZBd6fUrgWGWZRmWZXUFRsfp5zPgNMuy2lX0k29ZVveEvhMhRBWShIVo+m4nuiBrnmVZCyp+BngEuMCyrB+BAUBpxePzgIhlWT9alnUN0dXMK4CFRO8bz47ViWmaC4FbgI8ty5oHfAJ03Pt5lmVdZVnWWqBLRUz/TczbFKL1kdXRQgghRJrISFgIIYRIE0nCQgghRJpIEhZCCCHSRJKwEEIIkSaShIUQQog0kSQshBBCpIkkYSGEECJNJAkLIYQQafL/if7QnS2iOhsAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b365198>"
+       "<Figure size 576x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1040,7 +1064,7 @@
     "# format the plot\n",
     "format_plot(ax, 'Learned Cluster Labels')\n",
     "\n",
-    "fig.savefig('figures/05.01-clustering-2.png')"
+    "fig.savefig('images/05.01-clustering-2.png')"
    ]
   },
   {
@@ -1073,17 +1097,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Xi8V8c1IBtSTfTTy1h7T01RKkPT33uL6vsObHpQ9T0UZcBTJ7+EnS66//X8Xj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\nA532xVzn6tbEAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119c48c88>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1101,7 +1130,7 @@
     "# format the plot\n",
     "format_plot(ax, 'Input Data')\n",
     "\n",
-    "fig.savefig('figures/05.01-dimesionality-1.png')"
+    "fig.savefig('images/05.01-dimesionality-1.png')"
    ]
   },
   {
@@ -1120,17 +1149,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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JO26muCKb2KBr0SjRRPhGU1GbhyG4jF/97tkmu364O5nOIEQrpOzcgH2g49Je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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b8d2908>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1150,7 +1184,7 @@
     "cb.set_ticks([])\n",
     "cb.set_label('Latent Variable', color='gray')\n",
     "\n",
-    "fig.savefig('figures/05.01-dimesionality-2.png')"
+    "fig.savefig('images/05.01-dimesionality-2.png')"
    ]
   },
   {
@@ -1181,17 +1215,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118e76748>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1202,8 +1241,8 @@
     "ax.axis('equal')\n",
     "\n",
     "# Draw features matrix\n",
-    "ax.vlines(range(6), ymin=0, ymax=9, lw=1)\n",
-    "ax.hlines(range(10), xmin=0, xmax=5, lw=1)\n",
+    "ax.vlines(range(6), ymin=0, ymax=9, lw=1, color='black')\n",
+    "ax.hlines(range(10), xmin=0, xmax=5, lw=1, color='black')\n",
     "font_prop = dict(size=12, family='monospace')\n",
     "ax.text(-1, -1, \"Feature Matrix ($X$)\", size=14)\n",
     "ax.text(0.1, -0.3, r'n_features $\\longrightarrow$', **font_prop)\n",
@@ -1211,15 +1250,15 @@
     "        va='top', ha='right', **font_prop)\n",
     "\n",
     "# Draw labels vector\n",
-    "ax.vlines(range(8, 10), ymin=0, ymax=9, lw=1)\n",
-    "ax.hlines(range(10), xmin=8, xmax=9, lw=1)\n",
+    "ax.vlines(range(8, 10), ymin=0, ymax=9, lw=1, color='black')\n",
+    "ax.hlines(range(10), xmin=8, xmax=9, lw=1, color='black')\n",
     "ax.text(7, -1, \"Target Vector ($y$)\", size=14)\n",
     "ax.text(7.9, 0.1, r'$\\longleftarrow$ n_samples', rotation=90,\n",
     "        va='top', ha='right', **font_prop)\n",
     "\n",
     "ax.set_ylim(10, -2)\n",
     "\n",
-    "fig.savefig('figures/05.02-samples-features.png')"
+    "fig.savefig('images/05.02-samples-features.png')"
    ]
   },
   {
@@ -1246,15 +1285,15 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
     "def draw_rects(N, ax, textprop={}):\n",
     "    for i in range(N):\n",
-    "        ax.add_patch(plt.Rectangle((0, i), 5, 0.7, fc='white'))\n",
+    "        ax.add_patch(plt.Rectangle((0, i), 5, 0.7, fc='white', ec='lightgray'))\n",
     "        ax.add_patch(plt.Rectangle((5. * i / N, i), 5. / N, 0.7, fc='lightgray'))\n",
     "        ax.text(5. * (i + 0.5) / N, i + 0.35,\n",
     "                \"validation\\nset\", ha='center', va='center', **textprop)\n",
@@ -1280,17 +1319,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Xr17p1atXtUeBNmPnzp1J4vvmIKpp8dxKEU455ZRs27btX+5BWrt2bevlxx9/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\nnOjGf5sKAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bdaa6a0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1300,7 +1344,7 @@
     "ax.axis('off')\n",
     "draw_rects(2, ax, textprop=dict(size=14))\n",
     "\n",
-    "fig.savefig('figures/05.03-2-fold-CV.png')"
+    "fig.savefig('images/05.03-2-fold-CV.png')"
    ]
   },
   {
@@ -1319,27 +1363,32 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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UeXm5Vq5cqWXLltmeBGCaefjwob766iu53W45jqPdu3frypUrun//vuLxuNatW6fCwkJ1\ndHQoMTFReXl5Wrhwoe3ZwKRFdFmyZs2akbcrKyuVnp5ucQ2A6ejOnTtatGiRNm/erHv37ikYDKqv\nr0/79+9XNBpVTU2N6uvrVVFRoczMTIIL+F8iugyrr69XXV2dqqqqxjxf19mzZy2tAjAdffTRR7p4\n8aIaGhqUlpYmr9erUCikffv2yXEcxWIx9fX12Z4JTBlEl2E7d+6UJAUCASUnJ1teA2A6u3HjhhYv\nXqxPPvlEnZ2dOnPmjIqLi7Vjxw45jqPz589rzpw5crlco14CCMCfhugyLDs7W5JUW1ur1tZWy2sA\nTGf5+flqbGzUhQsXFI/HtXfvXl29elW1tbV69eqVSktLlZycrLy8PJ06dUo5OTlasmSJ7dnApEV0\nWZKamqpAICCfz6eEhO8fRFpVVWV5FYDpZM6cOdq/f/+o2/Ly8sZ83LJly3iwD/AOEF2WvPfee5Kk\np0+fWl4CAABMILosSUhIGLm+S/rxxa4BAMDURHQZ1tbWpvPnzysUCunq1auSpHg8rmg0qi+++MLy\nOgAAMF6ILsPWr1+vsrIyff311/L7/ZK+P+uVlZVleRkAABhPRJdhSUlJmj9/vhoaGmxPAQAABvHa\niwAAAAYQXQAAAAYQXQAAAAYQXQAAAAYQXQAAAAYQXQAAAAYQXQAAAAYQXQAAAAYQXQAAAAYQXQAA\nAAbwMkATVDgctj0BwDsWDofV29tre8ak19vbqxkzZtieMSWEw2H5fD7bM6YNl+M4ju0RGC0WiykU\nCtmeAeAdi8ViikQitmdMCbm5uXK73bZnTAn5+fl8LQ0hugAAAAzgmi4AAAADiC4AAAADiC4AAAAD\niC4AAAADiC4AAAADiC4AAAADiC4AAAADiC4AAAADiC4AAAADiC4AAAADiC4AAAADiC4AAAADiC4A\nAAADiC4AAAADiC4AAAADiC4AAAADiC4AAAADiC4AAAADiC4AAAADEm0PwFixWEyhUMj2DGBELBZT\nJBKxPWNKyM3Nldvttj0DGJGfn8+fSUOIrgkoFAopHA7L5/PZngJIkiKRiHp6ejRv3jzbUya13t5e\nSeJ7GxNGOByWJBUUFFheMj0QXROUz+fjmwATSjQaldfrtT1j0uN7G5i+uKYLAADAAKILAADAAKIL\nAADAAKILAADAAKILAADAAKILAADAAKILAADAAKILAADAAKILAADAAKILAADAAKILAADAAKILAADA\nAKILAADAAKLLsu+++069vb22ZwAAgHGWaHvAdHPjxg39/d//vTIyMrRhwwb98z//s2bMmKG//Mu/\n1C9/+Uvb8wAAwDjhTJdhv/3tb/W73/1Ov/71r9XQ0KBz587p3LlzunDhgu1pwIRUV1enhw8fqqOj\nQ99+++2Y92/duvVnjw8Gg+rv79fAwICOHz8+XjMB4PfiTJdh8Xhc8+bN07x58/Tpp58qNTVVkuRy\nuSwvAya2ioqKt97++753Ll26pJycHM2dO1efffbZeEwDgD8I0WVYWVmZ/vqv/1rNzc36zW9+I0mq\nr69XYWGh5WWAWQcPHtTatWtVVFSkUCiklpYWZWRk6MWLF+rv71dlZaVWr1498vHnzp1TZmamVq1a\npaamJj148ECzZ89WNBqVJEUiEZ08eVKO42hoaEjbt2/X0NCQenp6dOTIEe3atUtHjx7VgQMHdPv2\nbbW2tiopKUkzZ87U559/rnA4rIsXLyoxMVF9fX0qLy/Xhg0bbH15AExBRJdhv/nNb3T//n0lJPz4\nP7urV6/WihUrLK4CzPv444/V0dGhoqIitbe3a+nSpcrJyVFpaan6+/tVV1c3Krp+EAwGFY1GFQgE\n9OTJE12/fl3S9w9K2bJli3Jzc3Xt2jW1t7fL7/fL6/XK7/crMTFx5KxYU1OTAoGAZs2apcuXL6ut\nrU0lJSV68uSJDh8+rNevX2vbtm1EF4B3iuiyYPHixaN+/Wd/9meWlgD2FBcXq6WlRc+fP1dXV5dq\na2t1+vRpBYNBpaSk6M2bN2897tGjR1q0aJEkKTs7W1lZWZKkrKwstbW1yePx6OXLl0pLS3vr8YOD\ng0pNTdWsWbMkff/9ePPmTZWUlCg3N1cul0sej0cej2ccftcApjMupAdghcvl0vvvv69jx45pxYoV\n+uabb1RYWKhdu3aprKzsJ4/LyclRV1eXJOnZs2d69uyZJKm5uVkbN25UdXW1FixYIMdxJEkJCQmK\nx+Mjx2dkZGh4eFgDAwOSpLt372ru3Llj7ueH4wHgXeFMFwBrPvzwQ+3cuVONjY16/Pixmpub1dnZ\nqdTUVLndbkWj0TEXyi9fvly3bt1STU2NsrOzlZGRIUn64IMPdOjQIaWnpysrK0uDg4OSpMLCQh09\nelQ7duwY+Rx+v18HDx5UQkKC0tLSVF1drUgkMuq+eHALgHfN5fDPOaP27Nnzkz/Mv/zyS0lSd3e3\nJKmgoMDYLuDndHd3q7u7W16v1/aUSa2np0cFBQV8b2PC4O8bszjTZdjGjRttTwAAABYQXYb98CjF\ngYEBdXZ26s2bN3IcR319fTyCEQCAKYzosqS6ulp5eXnq7u6Wx+NRSkqK7UnAlBKJRPT8+XMVFRXZ\nngIAknj0ojWO46i+vl4+n08nTpwYeSQVgHfj+vXrevDgge0ZADCCM12WuN1uvXr1SsPDw3K5XIrF\nYrYnAZPCw4cP9dVXX8ntdstxHO3evVtXrlzR/fv3FY/HtW7dOhUWFqqjo0OJiYnKy8vTwoULbc8G\nAKLLlk2bNunkyZMqLy/XypUrtWzZMtuTgEnhzp07WrRokTZv3qx79+4pGAyqr69P+/fvVzQaVU1N\njerr61VRUaHMzEyCC8CEQXRZsmbNmpG3KysrlZ6ebnENMHl89NFHunjxohoaGpSWliav16tQKKR9\n+/bJcRzFYjH19fXZngkAYxBdhtXX16uurk5VVVVjnq/r7NmzllYBk8eNGze0ePFiffLJJ+rs7NSZ\nM2dUXFysHTt2yHEcnT9/XnPmzJHL5Rr1TPQAYBvRZdjOnTslSYFAQMnJyZbXAJNPfn6+GhsbdeHC\nBcXjce3du1dXr15VbW2tXr16pdLSUiUnJysvL0+nTp1STk6OlixZYns2ABBdpmVnZ0uSamtr1dra\nankNMPnMmTNH+/fvH3VbXl7emI9btmwZ10oCmFCILktSU1MVCATk8/mUkPD9M3dUVVVZXgUAAMYL\n0WXJe++9J0l6+vSp5SUAAMAEosuShISEkeu7pB9f7BoAAExNRJdhbW1tOn/+vEKhkK5evSpJisfj\nikaj+uKLLyyvAwAA44XoMmz9+vUqKyvT119/Lb/fL+n7s15ZWVmWlwEAgPFEdBmWlJSk+fPnq6Gh\nwfYUAABgEC94DQAAYADRBQAAYADRBQAAYADRBQAAYADRBQAAYADRBQAAYADRBQAAYADRBQAAYADR\nBQAAYADPSD9BhcNh2xOAEeFwWL29vbZnTHq9vb2aMWOG7RnAiHA4LJ/PZ3vGtOFyHMexPQKjxWIx\nhUIh2zOAEbFYTJFIxPaMKSE3N1dut9v2DGBEfn4+fyYNIboAAAAM4JouAAAAA4guAAAAA4guAAAA\nA4guAAAAA4guAAAAA4guAAAAA4guAAAAA4guAAAAA4guAAAAA4guAAAAA4guAAAAA4guAAAAA4gu\nAAAAA4guAAAAA4guAAAAA4guAAAAA4guAAAAA4guAAAAA4guAAAAAxJtD8BYsVhMoVDI9oxJLxaL\nKRKJ2J4xZeTm5srtdtueAeAdy8/P53vbEKJrAgqFQgqHw/L5fLanTGqRSEQ9PT2aN2+e7SmTXm9v\nryTxZxKYYsLhsCSpoKDA8pLpgeiaoHw+H98E70A0GpXX67U9Y0rgzyQA/O9wTRcAAIABRBcAAIAB\nRBcAAIABRBcAAIABRBcAAIABRBcAAIABRBcAAIABRBcAAIABRBcAAIABRBcAAIABRBcAAIABRBcA\nAIABRBcAAIABRJdlXV1dticAAAADEm0PmG46OztH/fof//EftXfvXknSn//5n9uYBAAADOBMl2GH\nDh3Sb3/7W126dEmXLl3S06dPR97G1FFXV6eHDx+qo6ND33777Zj3b9269WePDwaD6u/v18DAgI4f\nPz5eMwEABnGmy7DW1lbV19frF7/4hX75y19q8+bNOnDggO1ZGCcVFRVvvd3lcv3scZcuXVJOTo7m\nzp2rzz77bDymAQAMI7oMS0lJ0YEDB/Qv//Iv2rdvn2KxmO1J+CMcPHhQa9euVVFRkUKhkFpaWpSR\nkaEXL16ov79flZWVWr169cjHnzt3TpmZmVq1apWampr04MEDzZ49W9FoVJIUiUR08uRJOY6joaEh\nbd++XUNDQ+rp6dGRI0e0a9cuHT16VAcOHNDt27fV2tqqpKQkzZw5U59//rnC4bAuXryoxMRE9fX1\nqby8XBs2bLD15QEA/Ayiy5K/+Zu/0X/+53/q+fPntqfgj/Dxxx+ro6NDRUVFam9v19KlS5WTk6PS\n0lL19/errq5uVHT9IBgMKhqNKhAI6MmTJ7p+/bok6bvvvtOWLVuUm5ura9euqb29XX6/X16vV36/\nX4mJiSNnxZqamhQIBDRr1ixdvnxZbW1tKikp0ZMnT3T48GG9fv1a27ZtI7oAYIIiuiwqKytTWVmZ\n7Rn4IxQXF6ulpUXPnz9XV1eXamtrdfr0aQWDQaWkpOjNmzdvPe7Ro0datGiRJCk7O1tZWVmSpKys\nLLW1tcnj8ejly5dKS0t76/GDg4NKTU3VrFmzJEmLFy/WzZs3VVJSotzcXLlcLnk8Hnk8nnH4XQMA\n3gUupAf+CC6XS++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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bbe4908>"
+       "<Figure size 576x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "fig = plt.figure()\n",
+    "fig = plt.figure(figsize=(8, 5))\n",
     "ax = fig.add_axes([0, 0, 1, 1])\n",
     "ax.axis('off')\n",
     "draw_rects(5, ax, textprop=dict(size=10))\n",
     "\n",
-    "fig.savefig('figures/05.03-5-fold-CV.png')"
+    "fig.savefig('images/05.03-5-fold-CV.png')"
    ]
   },
   {
@@ -1356,9 +1405,9 @@
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -1378,9 +1427,9 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -1409,17 +1458,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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goaEB0dHRmDlzJh5//HFs2bIFzzzzDGpqalo974mcoRDMsaFOaNeuXRg/fjxC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\nAGkUEwAAAECaL44a1wDqBaGbAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b1b8d68>"
+       "<Figure size 1152x432 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1442,7 +1496,7 @@
     "ax[1].axis([-0.1, 1.0, -2, 14])\n",
     "ax[1].set_title('High-variance model: Overfits the data', size=14)\n",
     "\n",
-    "fig.savefig('figures/05.03-bias-variance.png')"
+    "fig.savefig('images/05.03-bias-variance.png')"
    ]
   },
   {
@@ -1461,17 +1515,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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NyMhI7r7JcvWYkEgk+Q6vlclkBQ691a9LTU2Fn58fLl26BIVCwb2GPj4+cHR0xO7du9G6\ndWsMHz4cNWvWRM+ePTFy5Ej89ttvXFn6GK1JkyawtbWFlZUVLC0tufuxnlgs5tbZ29vjxYsX0Gg0\nqFKlCqpXr47AwEBs3boVTk5ORvW1s7MDn8+HjY0NF9fweDwsWrQI9erVQ5s2bdCuXTs8ePAAAIr0\nGisUCgQHB+Prr7+Gm5sb2rVrZ/B/KDMzE59//jkmTpwIZ2dneHt7o3v37ty1qD9PBwcHWFhYmHTd\nE1IYmmOCVAhffPEFevbsabDsxo0beQ4niI6OhlqtRpMmTbhlXl5eRuPwatasyf0ulUq5rm6LFi3C\n0aNHAeje4PVDNtzc3CAU5vyXcHd3N+imqNelSxeEhIRg5cqViI6Oxt27d8Hj8aDRaGBnZ4fhw4cj\nKCgIP/30Ezp27IiAgIACx4/mnvDKysoKzs7OBn/r6x0VFWU0LtLb2xv79+9HcnIykpOTDRIgudsn\nKioKjDFuaIMen8/H48ePweebnqMUCoVgjOU5KzRjzCC5ABi+DhKJBGq1mqvTwIEDuXUODg7c2MyM\njAw8f/4cM2fONChLpVIZDOfQb19UQ4YMwd9//40OHTqgWbNm6NKlC/r372/y/m+eE6DrKhwXFweV\nSoWhQ4caXI9KpRIxMTFo3ry5QTlRUVFo0KCBwbImTZpwr7mPjw/CwsLw/fffIyoqChEREUhKSoJG\no8n3vI4dO4Zbt25x1yagGwtalNeYEEIqsvKOGTp37ox58+bh5s2baNq0KU6fPo1169YBMO19O797\nl1gsxpAhQ/DHH38gPDwc0dHRuHfvHhwcHPKta0H3VR6PhxkzZnDrHj16ZDB8QaPRwNPTM8+6AIbx\niaWlpUG9c8cn0dHR8PDwMNjX29sbu3fv5o79+eefc+vq168PS0tLACj0vlmtWrV86/cmOzs7JCcn\n57tePwzB3t4eTk5OqFatGs6ePQt/f38EBwfjk08+4ep77tw5eHt7c/tqtVqDWK648UejRo3QuXNn\njBs3Dh988AE6deqEwMBASKVSk8t481rVDwEtymv8+PFjaLVaNGzYkFuW+/+Ik5MT+vbti+3btyMi\nIgKRkZF48OBBvtdLUeMVQvJCiQlSITg6Ohq80QJAfHx8ntvqkwdvBhVvevODmH77KVOmYOzYsdzy\nypUr57m9Vqs1+pAN6J78cODAAfTv3x8BAQEICgpCp06duPXz58/H8OHDcfr0aZw9exYjR47E0qVL\nERgYmGc9BQKBwd/5TSZkZWVltEyj0RgkCHK3Se4ki1qtho2NTZ6P4qxcuTLCwsLyPGZe9DdPuVxu\ntE4mkxndXN9sw9x1fPM11NdZfyNbu3Yt6tatm+fxgeJPblm3bl2cOXMG58+fx7lz57Bx40YcOHAA\nhw4dMqnMN18zQHcu+nrv2rXLqNfGm4Fl7v1yE4lEXLB34MABLF++HIMGDUL37t0xZ86cfB/HyhjD\nmDFjkJaWBj8/P3Tu3BkqlQqTJ08u9HwIIeRdUp4xQ5UqVcDn89G5c2ecOHECcrkclpaWaNWqFQDT\n3rf1H8rfPFZGRgb69+8PBwcHdO3aFb1790Z0dDQ2b95ssH1+99W8YhY9jUaDTz75xCgJX9A9723i\nE61WW+CHUn3Zhd03ZTKZyZMsent7Q6VS4f79+3n2VA0LC0OjRo24a6JXr144ceIEGjVqhNjYWPTo\n0YOrk7+/PyZOnGiwf+5r5M3XsCg2bNiAiIgInD59GmfOnMGePXvwyy+/oG3btibt/+bron/9i/Ma\n5xc3vnjxAv3794e7uzvat2+PQYMG4d9//8XNmzfzLKco8Qoh+aGv0Mg754MPPoBQKDSYpOfOnTsm\n37j0AY3+R/8Gn7urJKC7gb35oRgA9u3bh/nz52P69Ono1auXwQSSr169wjfffANnZ2d8+umn2Llz\nJwIDA/H3338X51QNuLq6GiUQbt26BVdXVzg6OsLJyYnrzgkA9+7dM9g3IyMDGo2GO2+tVovly5dz\n3RhNZW1tjZo1axpMqKUXGhqKxo0bm1RO/fr1DeqrUCi4wFIqlaJSpUpITEzk6uvi4oI1a9YUOiTH\nFH/88QdOnTqFrl27YsmSJTh8+DAiIyPzLLsos07rr6fk5GSu3g4ODli+fLnBJGt69evXR0REhEFy\nKffrtnfvXkyYMAFz585FQEAA7Ozs8OrVqzwD7MjISFy/fh3btm3D+PHj4evryw3TKSwgJ4QQc1XS\nMYP+w2nv3r1x9uxZnDp1Ch999BFXXlHet/X0+169ehUvXrzAzp078cknn6BNmzZISEgw+T28Vq1a\niIiIMFjm5+eHixcvwtXVFXFxcQbncvjwYZw8edKksgtSp04do/jk5s2bcHV1BWB8v3/y5An3mMmi\n3jcL4ubmBk9PT26y8tzi4+Nx6NAhDB48mFumb5vjx4+jXbt2XGLE1dUVMTExBm11/vx5o8kyiyM6\nOhorV65Eo0aNMGnSJBw6dAjNmzfP93UoSgxSlNfY1dUVAoEg37jx1KlTkEql2LhxI0aMGIHmzZsj\nNjaWuxb1TyvTK851T8ibKDFB3jnW1tbo168fli9fjtDQUNy+fRvLly8H8HbPd37+/DmWLFmCqKgo\nLps9dOhQo+3s7e1x9uxZxMXF4fr165g1axZ4PB6USiXs7Oxw8uRJLF26FLGxsQgLC8P169cNuscV\n15gxY3DixAn89ttvePLkCbZv347Tp09j2LBhAIChQ4di3bp1CAkJQVhYGFauXMntW7duXbRv3x4z\nZ85EWFgY7t+/j9mzZyMlJSXPcY0ymazAhMWoUaOwbt06HD16FAkJCQgPD8eCBQvw7NkzDBgwwKTz\nGTZsGE6cOIF9+/YhOjoa8+fPN5hZevTo0fjhhx9w6tQpxMbGIigoCJcuXcozWWQKa2trREZGQqFQ\nQKFQYNmyZfjvv/+QkJCAgwcPwsbGhgui3twPAO7evWtQv7zY2Nhg4MCBWLx4MS5fvoyoqCjMmjUL\nDx8+RO3atY229/PzQ3Z2NpYsWYLHjx9j06ZNBgkfe3t7XL58GY8fP0Z4eDi++uoraDQarh42NjZ4\n+fIl4uPjYWtrC4FAgL/++gtPnz5FcHAw1q9fDwCF1psQQsxVacUM7du3R2pqKvdkLb3C3rfzov/w\nZm9vj6ysLAQHByMhIQEHDhzA7t27TX4PHzlyJI4fP44DBw4gNjYWq1atgkwmg5eXF0aPHo1//vkH\n27dvR2xsLPbs2YNNmzbhgw8+KHYb6A0dOhQPHz7E2rVrERMTgz/++AN79uzB8OHDAeju9zt37kRw\ncDAePnyIBQsWcF8KmXLfzP3hVqFQQCaT5VuXZcuW4c6dO/jqq68QFhaGZ8+e4e+//8aoUaPQvn17\ng8REgwYN4OLigu3bt8PPz8/gfCIiIvD999/jyZMnCA4OxurVq7lHkReVjY0Nnj59ihcvXsDW1hZ7\n9+7F+vXrER8fj8uXL+PBgwf5xonW1taIjo4u8Jz1ivIaSyQSBAQEYNmyZQgNDcXly5fx888/c+v1\n82GEhIQgLi4OmzZtwsmTJ7lrUR8b3b9/HxkZGcW67gl5EyUmSLkzNTDIvd3s2bPRqFEjjBkzBlOm\nTIG/vz+AnG6MxQk2fH19IZfL0a9fPxw7dgw///wzqlSpYlTe8uXL8fDhQ/j7++Prr79Gz5494enp\niXv37kEkEmHjxo2IiopC37598fnnn6N9+/b5Tsr1Zj0LqneTJk2wZs0a7Nu3D/7+/jh8+DB++OEH\ntGnTBgAwYcIE9OvXD1999RUmTpxocPMFgFWrVqF27dr49NNPMXLkSFSvXh0bNmzI81iTJk3CsmXL\n8q3L8OHDMW3aNPz666/o3bs3xo4di6SkJOzatcto4qf8tGjRAitWrMDmzZsxYMAAVK9e3WC+hU8/\n/RQff/wxlixZgoCAAERGRmLLli3c0Ju82qqg9hs9ejTWrFmD9evXY/jw4ejfvz++/vpr9OrVC2fP\nnsXGjRvzHOOpn5B02LBhOH/+fJ5l5z7unDlz0L59e0ybNg2DBg2CSqXC1q1b8+xKaWtriy1btuDu\n3bsIDAzEtWvXEBAQwK2fN28eMjMz0a9fP3z55Zdo2LAhunfvzn0j1r17d/B4PPj7+0MkEiEoKIgL\nsDZt2oQFCxZAKBQafAtCCCHvsooSM4hEInTr1g12dnZo1qwZt7yw9+2C7l1eXl744osvsGzZMgQE\nBODw4cMICgpCamoqnj9/XmidmjVrhsWLF2PTpk3o06cPbt26hc2bN0MikcDT0xOrVq3CgQMH0Lt3\nb+zYsQPffvstOnTokGdZRWmTqlWrYuPGjbhw4QL69OmDn3/+GV9//TU3pCAgIABffvklli1bhhEj\nRqBTp06wsbHh9i/svpm7LsuWLStwiGLdunVx4MABSCQSTJ48Gb169cIvv/yCMWPG5Bnz+Pn5gTHG\nPcoTAJydnfHLL7/g0qVL8Pf3x3fffYcpU6YYxVUFyV3ngIAAPHnyBH379oWTkxPWr1+PM2fOoHfv\n3pg9ezaGDh2a7zxXw4cPx759+7BgwYJCj1nU13jRokVo3rw5Pv30U8ybN89g6EXPnj0REBCAr776\nCgMGDMCVK1fw9ddfIzo6GkqlEvb29ggMDMT06dNx8OBBzJ8/HxkZGfle94SYgseojw15B506dQrt\n2rWDWCwGoBt2MWzYMNy+fTvP8f+EEEIIeT9RzEAIIRWfyT0mQkNDjSYx+fPPPzFkyJASrxQhhdmw\nYQM3XOLevXtYtWoVunbtSgEGIYS8Qyi2IGWBYgZCCKn4THoqx6+//oojR44YdLu6d+8e/ve//5Va\nxQgpyOrVq7knXYhEInTt2hVz5swp72oRQggxEcUWpKxQzEAIIRWfST0matWqZTAuKyUlBT/88APm\nzZtXahUjpCB169bFtm3bcOPGDVy+fBlLly41eswUIYSQiotiC1JWKGYghJCKz6TERLdu3bjublqt\nFvPnz8ecOXMgFovpMTCEEEIIKTKKLQghhBCiJwgKCgoyZUO5XI4TJ07Azc0Nhw4dwpUrV3D8+HFE\nRUUhKSkJH374YYH7q9Ua7tnPhBBCCCEUWxBSvn744Qf8999/aNu2bbH2VyqV3Pwdnp6eJVw7Uly3\nb9/Gxo0bYWdnV+zHnFZUaWlpWLFiBZKTkyGTybBlyxZUq1YNTk5Ohe4bHx+PtWvXQqVSFfvx8++a\nK1euYMuWLahatSr3ZLuKyqQ5JvQYY2jatCn+/PNPAEBCQgKmT5+OuXPnFrpvSkpG8WpYTipXluLl\nS3l5V8NsUfuWPmrj0kdtXLqofUtf5crGj8gta+9TbAHQdV3aqH1Nl52dDZlMhho1ahWpzd5sY6nU\nFi9eJFK7l5CSuIbl8iwAgEKRbXavi0KhAABkZam480xLyzTpPFNTc+4Z5tYu+VEodG0kk5nWRiWh\nuLFFkb5mKM5zngkhhBBC8kOxBSHlIzn5FQCgUqXCv2kuiINDJWRkpCMrK7MkqkVKwPsxHI69/gEA\nuo+YA5MTEy4uLti7d2+hywghhBBCTEGxBSHlJympZBITjo6OAICUlOS3rhMhhdEnsxnT/eiWlWOF\nSImhgZmEEEIIIYS8Z0qqx4S9vQMAIDU15a3rREhhchIT1GPC3FBighBCCCGEkPdMUtIr8Hg8ODg4\nvlU5dna6xIRMlloS1SIl6v34wE49JswDJSYIIYQQQgh5jzDGkJT0CnZ29hAKRW9Vlp2dPQBAJqMe\nE6T05cxLxHLNpUGZCXNAiQlCCCGEEELeI+npCiiV2XB0fLthHABgYyOBUChEair1mKgozHvyy5yh\nHDTHhHkp0uNCyfuNMWDrVhEyX0+6PGmSqnwrRAghhJB3HsUXZU8/H8TbDuMAdN9g29k5QCZLAWOM\nnrRTgZjjS6E/J11SgnpMmBPqMUFMdvKkAL16qTFpkgo3bwpw5w5dPoQQQgh5OxRflD39EzRKIjEB\nAPb29lCr1UhPV5RIeYTkL/dQjnKtCClhFfKdPzycj8uXBcXaNy6Oh6pVJYiJKTxzVpRtCRATw8eh\nQ7pONrVra5GQUHLtplQC06dbokEDCZo2tcH69fmPd9y7V4iqVSWoVk1i9O/Tp7wil0cIIeT9QPFF\nxVRR4gtrH6+gAAAgAElEQVQASE4Gxo2zQsOGErRoYYONGw23l8mACRN06728bLB0qcU7+eEoNVWX\nmLC3L5nEBE2AScpK7seF6ntMUC8d81Ahh3KMHi3GtGnZaN266PvWqMEQHp4OJ6fC7xJF2ZYAY8ao\noFTqfo+IEGD8+JLrahkUZIkbNwT43/8y8PQpDxMnilGzJkNAgNpo28BANbp0Sef+1mqBYcPEcHXV\nwtmZFbk8Qggh7weKLyqmihJfAMCoUWJkZ/Nw8GAG5HIeJk2ygkAAjB2rq9OsWVZ4+ZKHP//MwKtX\nPIwfbwVHR4aJE9+t4ScpKfqhHA4lUp7+kaEyWQpcXGqWSJnkbZjve0/OUI6cyS8pMWEeKmRi4m3w\neEDlyqb9ZyzKtuYsJQXYvNkC69ZZoEcPNZo00SIrS/cNRvfuavTrp7t5i0S6n6tX+WjbVoOqVUum\n7TIygF27RNi1KxNNm2rRtCkwaZISW7aI8gwcLC0NX7ctW0R4+pSHQ4eyilUeIYQQUhiKL4ruXYsv\nwsL4uHZNgJCQdNSpo6vDwoXZWLjQkktMnD4txPr1mWjQQIsGDYB+/dS4eFH4ziUmUlOTYWMjgUhk\nUSLl6Z/MoZ+7glQM5viBPfdTOYh5qXBDOQIDxYiL42HaNCt8+aUVqlaV4PvvLdCggQRffWUJALh2\njY8+fcSoXVuC2rUlGDJEjBcvdBdp7u6T+t//+kuIVq1s8MEHEgwdKsbrJHGB24rFMNgWAJ484aF/\nf91xO3a0xk8/idCihU2e57F1qwgtW+qO2amTNU6ezOk6GhvLw7BhYtSpI4G3tw1+/DHnpvDsGQ9j\nx+q6CDZqZIO5cy25bxH0dXyzPZ4942HkSCvUri1B8+a6boWqXPfHwEAxpkyxyrfNHRyAkSNVUKuB\nb7/NxtSpSsyZo8Ty5VmYMMHKoNurQgGEhAgxebIy3/JCQgQGQyz0P9WqSbBvn3Eu7O5dPpRKwMdH\nwy1r1UqD27cFhXaPVCiANWssMGeOEra2b18eIYQQ80TxBcUXQMHxwJMnfNjbMy4pAQDu7lokJvIQ\nH897fU4M//ufbqLO5895OHtWAE9PjXFhFZhKpYRCIS+x+SUAGspBylLup3Loe0yUZ31ISalwPSa2\nbctEp042mDBBiXbtNNi3T4jLlwU4eTIdGo3uxjV8uDXGj1fip5+y8OwZD1OmWGHtWgusWJENwPji\nXLfOAhs3ZoIxYPhwMTZssMD8+coCt7W3t4GfH5/bVqPRDRdo0ECLkyczEB7Ox/Tpuu57b7pzh4+F\nCy2xdWsm3N21OHBAhM8+E+POHQUsLYGBA63h7q5BcLCuW+Fnn4lRs6YW/v5qBAZao25dLY4cyUBy\nMg9Tp+pu+N9+m82Vn7s9AF3X1MaNNThzJh0vX/Ixa5Yl1GoegoJ0+2zfnglBIUNqz50ToGFDrcE3\nPM+e8cHjweDmfeiQCJMmKaFSAZcuCdChg/HN2MdHg/DwdKPlAGBra9xeL17w4eDAYJEraV+5MoNS\nCbx8yUOVKvlnE377TQRLS2DYsJxI6W3KI4QQYp4ovqD4Aig4HqhcmUEu5yE9HbB5nReKi9O9kMnJ\nPNSowbByZRYmTbJCnToSaLXAhx9qMHNm/smUiqgkn8ihJxaLIRSKkJYmK7EySfG9f1/EUWbCHFS4\nxIS9PSAQABIJ424y48crUauW7vfERB6mTs3GhAm6D6I1ajD4+alx/Xr+d8aZM7Ph5aUFAPTvr8at\nW4VvW7my4bbnzwuQkMBHcHAGJBKgfn0t7t1T4vBh40mU4uL44PMBFxcGFxeGKVOU8PbWQCTS3aAT\nE3k4fToLEgnQoAGwcmUWrK0ZzpwR4PlzHk6cyOS+/V+xIgsjRogxb15O4JC7PS5cECA2lofg4Gzw\neECdOhqsWJGNQYPEWLgwG3w+YGdXeLufOydEx445QUBaGrB0qSWmTlWiTRvd8iNHhFi82BLffmsB\nrZaHo0cz8ixLKCxaF9bMTBgEDQBgYaHbX1nIvf733y0wdqzSIDB6m/IIIYSYJ4ovKL4ACo4HmjXT\noHp1hpkzrfDdd1mQy3lYtcrSYPvHj/lo2lSLmTOzkZbGw9y5Vli0yBJLlmQbF1hB6Z/IUVITXwK6\n7vW2trZIS0ujR4aSUpUz+SUDTX5pXipcYiIvNWrk3ISqVGEYPFiFX34RITxcgIcP+bh7l4/mzfPv\nRqe/yQKAVMqgLmCagfy2jYjgw9VVC4kkZ9sWLTR5Bg6dOunGUXbubA03Ny0++kiNoUNVsLICHj40\nLkc/xnLdOgu4umq5oAHQfTugVgPR0bqs/5vt8fAhH6mpPNSpk1MgY4Barcvy5z6fgly4IMCQISoc\nOSLEnTt8REXxsXp1FlxccvYPCFAjIKDwx0BdvizAxx+LjZbzeMDq1Vnc+epZWRkHCEql7g1GbFwM\nJzSUjydPeBgwoGTKI4QQ8n6h+ILii9wsLHQ9a8aPt0L9+hLY2THMn6/ErVuWkEqBmBgeFiywxK1b\n6dw8GN9/n4WBA8WYMkX5zkx2qn8iR0n2mAAAW1s7JCcnITs7C1ZWFHCR0pEz+eX72DPEvL0TiQlL\ny5yr7vlzHrp1s4aHhxadOqkxYoQKJ08KcPVq/t9SiESGV21BF3F+2wqFxvvlV45YDBw/noErVwQ4\neVKAv/4SYutWCxw9mmGUuTfcz7hAfXdKTa64KHd7aDRA3bpa7NqVaVSf3Df9gkRE6IKPr75Swtoa\nCAgABg0SIzRUABeXok8W6e2twdmzeXe1zOubjmrVtEhN5UGt1rUzoPvmytISXLCUlzNnhGjWTGs0\nSVZxyyOEEPJ+ofiC4os3NW2qRUiI7okb9vYM0dH6XipanDkjhL09M4g7PD010GiA+HjeO5OY0D+R\nQ/8kjZIileq60KSlySgxUUGYZ0+CnMkv6akc5qXCTX5ZmGPHhLC1Zdi1KxNjx6rQqpUGMTH8fG/i\nRblOC9q2YUMtYmL4UORK6N++nXewcv06H99/b4FWrTSYP1+Jixcz4OTEcPq0AHXqGJezcqUFpkyx\nQr16Wjx+zIcs1/C8a9cEEAoBV1dtnnWsV0+LhATdtx21a+t+nj/nY+lSS2i1pp33+fMCNGumgbV1\nzjKZjIfIyOJdHpaW4Ory5o9NHnN5NWmihYWF7lz1Ll8WwMNDA34BVbhxQ4C2bY0Dm+KWRwgh5P1F\n8YXhsd7H+EImA/z9xUhK0iUZhEIgOFgIDw9dT5SqVRlSU3lITMxprAcPdPNl1KplYqNUACkpyRCJ\nRLCxkRS+cRHY2uoTE2klWi4pOmbGXQkMh3IQc1IhP6bZ2DA8eiRAaqrxndzRkeHZMz7OnRPgyRMe\n/u//LHDsmJDrmgcYftNQ2DVr6rYdOmhQs6YWU6da4dEjPv78U4hff7XIM9iwstI9KWLHDhHi4nj4\n+28hnj3jwctLi06dNHB21mL6dF05p08LsGWLBbp2VcPXV4M6dbSYOFGMe/f4+O8/AebNs0JgoBr2\n9nnXsWNHDT74QIvPPxfj7l0+rl3jY/p0SwiFOeMqU1MBuTz/czt/XmgwyZRarRsmUaWK7iYbGVm6\nWUixGBg4UIXZsy1x6xYfwcEC/PyzBcaNy5nQMq9zuH+fj4YNjQMBU8ojhBDy/qH4guKLguILOzsg\nM5OHRYssERPDw9GjQnz/vQWmT9fNH9GihQaNGmnxxRdWuHePj+vX+ZgxwwqDBqnhULKdD0qNVquF\nTJYCe3vHEv+WWZ+YkMtpAkxSenJftznJiaJdy5TUqJgqZGLi009V2LFDhLVrjW/MAQFqDByowrhx\nYnTvboP//hNg6dJsPHrER/breYdy71PYe66p2/J4unGHL1/y0KWLNdautcDQoSpuEqXcmjTRYt26\nLGzaJEL79jYICrLEN99ko317XYZ+x45MpKTouozOnm2FmTOz4e+vBo+nW8fnA716WWPcOCt89JEa\n33+flW8d+Xxg585MCIUMvXtbY9QoMdq00WDNmpx9xowRY/5848d53bzJx7JlFrhwQYC7d/m4cEH3\njYJQCIwapcK1awLs2CECY6XfPWrxYt2kYP37G7ZJQefw6hUv366YhZVHCCHk/UPxBcUXhcUXmzdn\n4tkzHjp1ssHKlRZYuzYL3brpkisCAbB7dyYcHBgGDBDj00/FaN9eg1WrsoyOW1Glpyug0Whgr89I\nlSDb15OYyGSUmCClL3dywfQcGw35qMh4rIxSRi9fFpBSr4AqV5Ya1PnVKx7u3OGjU6eczP+GDSKc\nPi3EoUOZ5VHFd9qb7UtKHrVx6aM2Ll3UvqWvcmVpeVfhrbyL1wfFF6WL3jcKFh8fi6NHD6JFi1bw\n8WlXrDLya2OVSonNm9ejZs1a8Pfv/7ZVfW+VxDUcHh6K8+dPo1u3Xqhf362EalZx/PTT93B2dkGV\nKtVx+/Z19O//MapWrV7ofi9ePMf//rcbbdq0gbd3mzKoafkLC7uJixf/RY8e/qhbt36ZHLO4sUWF\n7DFRUY0cKca2bSLEx/Nw7pwAmzZZoE8f+haeEEIIIcVH8QUpKzJZKgDA1rbkx56IRBawshIjLY16\nTFQc5ttDQPfVOg3JMCeUmDCRkxPDr79m4rffRGjXzgbTp1th7FgVRo+meQsIIYQQUjwUX5CypE9M\n2NmV/FAOQDecQy6X0xj+cmfe7c/j8cAYyzU3jvkmYN4n78TjQiuKHj006NEjo7yrQQghhBAzQvEF\nKSs5iQm7UilfKrVDYuILpKcrIJG820PFzIG5PkVTn5jI+bscK0NKDPWYIIQQQggh5D2QlpYKkUgE\nsdi68I2LIeeRoTScozyZf4cVfSZCd6Il/YQZUj4oMUEIIYQQQoiZY4xBJkuFnZ19qX2Qy0lMpJVK\n+aSozPMDO4+H10M5ive4UFIxUWKCEEIIIYQQM5eRkQ61Wl1q80sAgFSqe2SoXE49Jkhpy5ljgjpM\nmAdKTBBCCCGEEGLmcp7IUXqJCf3cFTSUo7yZ91gO3RwTQM55UmbCHFBighBCCCGEEDOnTxaUZo8J\n/YSXCoW81I5BTGe+PQl4KE7yxXzbwzxQYoIQQgghhBAzV9qPCgUAgUAIa2sbyOU0x0R5MvfJL/U9\nJvRzTNDkl+aBHhdKSh9jsNq6CbzMLABA5qQp5VwhQgghhLzzKL4okrJITAC6XhOvXiWCMUYfGEmp\nyJn8Mudv8u6jHhOk1FmcDIaylz8yJ02B6OZ1CO+ElneVCCGEEPKOo/iiaGSyVAgEAtjYSEr1OFKp\nFFqtFhkZ6aV6HPL+0iW8GGiOCfNiNokJflwsnKragR/z2OD3/FivWAK7QD+TyhaE34Hw8iWj4xDT\nCGIew/LQQQCAprYr+AkJJVOwUgnJ9C9RqcEHcGzaAOL1P+a7qeXeXXCqagenavZG//KfGtdHMm0y\n7Pr1Lpl6EkIIeWdRfFFxlVp8UZDsbDj4tobowrkCN+PJUiGdMBaVGtaCo1cj2CwNKrn+9YXEP7zk\nJEjHjdYdu0VTiDdu4B4Vamtbeo8K1ZNIdE/moHkmypOZj+WAYY8JYh7MayjH6zdabY2aSAqPBHNy\nMmn7wtiNHoaMaTOhbt0GWpcappVNOJljPgOUSgCAIOIuMsZ/USLl2gTNg+jGdaT+7y8IniZAOvEz\naGvWRHZAP6NtswMHQNmle84CrRZ2wwZC41oHWmcXg21F5/+F1a4dULX7sETqSQgh5B1H8UWFVFrx\nRb6ys2E7/hMIHtwvdFPJrK/Af/kSqX+eAP/VS0jHfwKtYyVkTpz81tUoLP6xGzUUyM6C7OBR8ORy\nSCeNh0qrhZKp4Oxc462PXxipVDcBplwuR9Wq1Uv9eKQg5tmTICe5RnNMmBPzSkzo8XhglSuXYIG5\n0nF8fgmX/e7ipSRDvPkXWK9bi+wevaBp0hTIyoIgJhrK7j2R3W+gbkORCBCJILx6Baq2H4JVrfr2\nB8/IgHjXDsh2HYCmqQc0TT2QOWkKrLZsyjMxAUtLg9fNastGCJ4mQHboT6NypTOmQNWqzdvXkRBC\niHmh+KJMlGt8kQ/BwweQfv6pydtbnD4F+fqN0DRoCE2DhsjuNxCii+eKlJiwXvUtBHGxkP/fzzkL\nC4l/hKG3ILx2BckhN6CtUxcAkL5wMSTzZwMTJ5b6/BJA7h4TNAFmeTH/ngQ8buJLYj4q3FAO6fgx\nkE4Ya7BMMvMrSMeOAgAIr12BXZ+P4FS7GpxqV4fdkH7gv3iu2/D1BZpXd0jBwwew9+8Bp9rVYDcw\nAPzkZG5dXmXi2TMAgF2gH/hxsZBMmwzJlIlGZfOfPYV07ChUalgLlRq5QjJ3Bpe9129r8ddROLTy\ngtMHVWA7dAB4KTnHfpPV1s1wbOkBpw+qwKFTO1icDObW8WOfwHbYQFSq4wJH78YQ/7gmZ10+9dDX\nwfr771CpwQeQfDWJ29525Mdwql0djs2b6LoYqlRceXaBfpBMmVjga8UcHJE1cgygVkPx7WpkTJ2B\njDnzoVi+CtIJYyG6HMJty1PIYRFyAZmTp+ZZlijkouEQC/1PNXtY7ttttL3w7h1AqYTKpzW3TNWq\nDUS3bxb+bqxQwGbNSqTPmQ9ma2ewymb5N1C27wBV23YFl0EIIeSdQvEFxRemxBf5EYVchOpDX6Qe\nP2XSpz7m4ADL/+0HMjPBf/4MFmdPQe3pza3Ps53UauNy3vgmuLD4hx/7BMzenktKAIDavSmEr17B\nNjUVdnaGcU9pyOkxQYmJ8mauHQnoqRzmqcIlJrIDB8Li9ImcN2etFpbH/0R24ABAoYDd8EFQdeyM\n5IvXkHrgD/Bjn8B67SrjgnJfoEol7IYOhKa2K1JOX0B2L39Y7fxNty6fMrFsGQAgbdtOaJ1dkL54\nOdKXrTQsW6WCXaAfeJkZSD0SjLQtv8Pi9ElIFn1tUBXrdd9DvnErUo/8DVHobVhv+L88z11wJwyS\nhXOhWLYSyZduIjsgELafjQFPnqY7h4EBgKUVUoPPQL52PazX/wjLQwdMqofocghSTp5HxuSvAAC2\no4dCW6kSUs5cQNpPv8Li5D+wWfYNt33a9l0551sA0bmz0DRsZPAtD//ZM/10udwyy0MHkTFpKqBS\nQXT+X6NyVD6tkRQeiaQ7j3T/6n/uPEJ23/5G2/NfvABzcAAsLLhl2spVAKUSvJcvC6yz+LetYJZW\nyBo20mC58NoVWP51FOlBSws9b0IIIe8Wii8ovjAlvshP1uhPkf7NMsDKyqTt5Su/h8V/5+FUxxmO\nnm7QVqmGjJlzufV5ttPSoELLLSz+YZWrgCeXA+k5E0/y454AAKwzMmBrW5Y9JmiOCVKaWK7ERDlX\nhZQIk4dyhIaGYvXq1fj9998RERGBpUuXQiAQwMLCAt999x0cHR1LpELKLt0ABojOn4WqczeIQi4C\n2dlQdu0OnkyGjKkzkTlBl5XX1qgJpV8fCK9fNS4o103L4twZ8JKTIP9uLSAWQ1O3PiwungcvJRm8\nzMy8ywy9oSvG3gEQCMAkUjCJFLyUlJxyT5+E4PlzpJ74F8zWDhoA8hVrYDdiMNLnLeK2y5g5F2qv\nZgCArP6DILx1M89zF8TFAnw+NC41oXWpgYwp06Hybg4msoDFuTPgJyYi5fTPgEQCTYOGUKxcA2Zt\nA4szp/KtR+bw0QCAzPEToa1VGwAgunAOgtgnSA0+q/ufXKceFCtWw25QX6QvXKzrTmpiVz+Lc2eh\n7NiZ+5uXJoPN0kXImDoDqja6XgeWRw7BZvFC2Hy7GNBqkXr0H+OChMIidWHlZWaAWVgaLGOvb9I8\nZXaBU/5Y/b4NmWM/BwSCnIVKJaTTJkOxdKVRLwpCCCGlo6xiC4DiC4ovypbgcRTUTT2RPnMueGlp\nkMydAZtFXyN9yQqIzv+bbztlf+QHu491CROeStdDxvLoHwCPB8XqHwCNpsD4R9WsBbTVXSCdORXy\n79aCL0+DzaoVYIxBoNGUyVAOKysrCIVCyOWUmCh/5vmJXddjIne0X7TzpGEgFZNJiYlff/0VR44c\ngY2NDQBg+fLlWLhwIRo2bIh9+/Zh06ZNmDNnTsnUSCRCtp8/LI/9CVXnbrD88w8oe/TUzRFQpQqy\nBn8M8S/rIQy/A8HD+xDeDYeqecsCixQ8fAhNbVdALOaWqTy9YfHvabDKlfMsE61b51+gvtxHD6Fx\nrWPwQVbt0wpQqyGIjoLWQRdQaWq5cuuZVAqoVUZlAYCyUxeom3jAoXM7aNwaI/ujnsgaOhKwstKd\ng2sdQJLziCf9GEvxuh/yrodGA97rY2lqfJCrPR6Al5qKSnVyJn3kMQao1eDHxXIBhilEF84he8gw\nWB45BOGdMAiiIqFY/SO0LjmTK2UH9Mt73odchJcvcTdiA69vxNx40teYlRV4ymzDTV93cWVi6/yP\nE3oLgicxyBow2GC59eoV0NSpB2XvPgXWkxBCSMko09gCoPiC4gtD+cQX4h/XwPqHNdw2sj3/g7pV\n4a9ZbvyYx5AsmIvkW/egrVoNAKD4fh3sBvVFxpQZEDx6mG87MUdHpJz9T1eXzT9D8Pw5FAsXA4xB\nW7kKLM6cLDj+sbCAbNtO2I4fA6f6NcHs7JA+/xvY3LwOlVgMqdS2SOdSHDweDxKJLc0xQUpN8XtI\nmGeixlyYlJioVasWNmzYgFmzZgEA1q5dC6fXs0ar1WpYWloWtHuRZfftD9uJn0GxYg0sjv0JxY8b\nAAD8589g380Xag9PKDt1QeaIMbA8GQzh1cuFlsl7MzMmEhVYpvWta4WWycR5dOfTaAz/BcBeHytn\nQT5ZOrEYqcdPQXjlMixPBsPyr6MQb/0VqUeDAQtR3vuYWA+W6zXiadTQ1K0H2a4DRnXJfcMvjCDi\nHvipKUj/aiZgbY3sgH6wG9QXwtDbUBahHABQezfjbsRv0lauYrysWnXwUlN1XXKFusuYn/hCF2A6\nOOR7HIszp6Bu1sJogiyrQwfBf/kClVydAbz+lkKjQaU6LkiKLoPHjxFCyHumrGMLgOILii8M5RVf\nZI3+1CDZoa3uXKTjAYAw7LZunofXSQkAUHt66RJL8bEFt1ONmlxcw+wdoFUoDBI6psQ/mqYeSAm5\nAd6rV2D29hBER4HxeNDWqAk+v2xGcUulUqSmJkOlUkH05nVKyoC59wjgvX5cKM0xYU5MSkx069YN\nCbmeDa0PHG7evIndu3dj586dJVopVYeOYHw+xL9sAE+tgrJjFwCAxbE/wWxtkbbrALetePPPxkEB\nYJBKUzdqBMHjaPDSZFzWX3gntMAyYUL3IE29BrpyZalc10TRtSuAUAiNax3dGL8i/EcRXr8Ki/P/\nImPaLKhbtUb6vEVwaNscFqdPQtOoEQQxjwGFgvtWw3rlMvCfJiA7cAAEj6OM6yEQcDet3NT16oOf\nkADm4JDTHpcvQfzrL5D/tNnk+lqcPwtVsxaAdU4PBZ4sFYLIRyaXwbG0hLa2a+HbvaZu4gFYWEB0\n7QrXpVN0OQRqDy+ggJuu8MY1qNq2N1qeeuS4weRc1r+shzD0NtJ+2VKEkyCEEGKqso4tAIovKL4o\nHLOzN3m4SX60VXXJA15iIlgVXfJD8OA+wONBU6s2eMlJxW6nwuIfniwVdsMHQ7Z9N/foWcFfR/Cs\nenVYVym9p5a8SSLJmQDT0bFSmR2XGDLXz+tvJiIoMWEmmIni4+PZ4MGDub+PHTvG+vTpw+Lj403a\nX6VSm3oonS++YEwqZWzcuJxle/bolp08yVh0NGMrVjAmEDDWsiVjMTGM8XiMRUUZ/q47OGONGzPW\nrx9j9+4xtmULY1ZWjHXqxNjevfmXqde0KWMzZzKWnGxYtlbLmJcXY35+jIWFMXb2LGP16jE2cqRu\nvzfrwRhjQUGMffhh3ud8+zZjIhFjGzfq9j18mDFra8bOnGFMo2GsUSPGhgxhLCKCsePHGXNwYOzA\ngYLrkVcdNBrdOfXqxVhoKGMhIYy5uTH28cc52yQnMyaTFfwa+fkxtnhxzt8qFWN8PmPbtun+vn+/\n4P3f1uefM+buztjVq4wdOcKYnR1jBw/mrM/rHGrXZmznzsLLnj9fd30QQggpNWUeWzBG8QXFF2+P\nx2Ps9GnDZbnPS61mzNubsW7ddO126RJjnp6MjR6tW29KOxWksPinWTPdaxQVxdj+/Uxjbc12DR3K\njh079vbnbqJ///2XBQUFsUePHpXZMUmOkJAQFhQUxCIiIsq7KqVi7dq1bO3atezQoUMsKCiIpaam\nmrRfQkICCwoKYsHBwaVcw4rj8uXLLCgoiN27d6+8q1Iokye/zO3IkSPYv38/fv/9d9jamjZWLSUl\no0jHEH4UAPuff4bsoz5QvXw9eU6nnpAMGAzLQYMAAGqvZsheugI2yxYjNeEVHHg8JCcpAIEAjq9/\n10p1+/J/3w/p1EkQtWgBdeMmUI35DMI7oZB16glJ/0FGZUqXL8bL+FeApSWsRo2FzTcLoIp4CMU3\ny3KVrQB/6y5I5s6ARes2YDY2yBrwemKql3LwkxRG9bBOz4ZIpYHsZR4TAjnXgeW6X2C95jsIpk6F\ntmo1ZHyzHFlNWgBJ6eBv2w3pnOkQNW8OrVNlZM6Yg0zfHsCr/OvBf/HcqA4AwN++B5J5syBq2w4Q\nWyG7Vx8ovlkGvK6XXWAANB/UguLHn4xfm5vXYfn3MYhPn4aSJ0Tmob+g+tAXACAZ9Qlw5jzUSWlQ\ntWkHTV7nCaByZSle5rPOZHO/gTR1Giw6dwGTSnXt0aF7gefglJiINIEVlIUc2zqjgNfpHVEibUwK\nRG1cuqh9S1/lytLyrgKnLGILgOILii/enhOPB1lqRs71A+Pz4u3YD8mC2bDo3BlMZIHsPn2RPv8b\nrh0Ka6cCFRL/8H/eCumMKRB6eELr7Izo2fPwiKnQzsKmRNrGlDbm83VDfBISXsDOrux6arxJrVYh\nLOwWRCILNGni+U58s14S17BCoZuHJC0tyyzvo1qtbhhHZqZufpWkpHQolYUPU8p9zzDHdsmLQpEF\nAGxAg3cAACAASURBVJDJMsvsnIsbW/AYM21a0oSEBEyfPh27d+9GmzZt4OzsDIlEAh6PBx8fH0ya\nNKnA/d+1F58C4tJF7Vv6qI1LH7Vx6aL2LX3lnZh432ILgK7r0kbta+zmzau4fPkievXqi9q167x1\neaa0cUJCHI4cOYDmzVuhVat2b33M4jp37hTu3g0DAPj6doW7u0e51cVUJXEN3759HSEh59GzZwBc\nXeuWUM0qjp07t0Cj0cDZuQYePbqPkSM/44YPFSQx8QUOHtyF1q1bo1mztmVQ0/IXFnYTFy/+ix49\n/FG3bv0yOWZxYwuTe0y4uLhg7969AIArV64U62CEEEIIIXoUWxBS+mSyVAAok0eF6uk/JCoU5Zck\nyshIx717dyAQCKDRaBAWdguNGzd9J3pNlBTzP1Vzn+Tz/VI2U/MSQgghhBBCypw+MWHqEKmSIHk9\nkapcXn6PDI2MfAjGGNq0+RB16tRDSkoSUlNTyq0+pOTwePqncuT8Td59lJgghBBCCCHETMlkqZBK\nbSEQFGtquWIRCISwtrYp1x4T8fGxAABX13qoUaMWAODZs/fjEfCmDdR/d+kTEYweF2pWKDFBCCGE\nEEKIGVKrVUhPV8D29WNJy5JUKoVCIYdWqy3zYzPG8OxZPGxt7SCV2qJaNWcAwPPnT8u8LuXLfD+w\nMxMevfwmyl9UbJSYIIQQQgghxAylpckAlO38EnoSiS20Wi0yMtLL/NhJSa+QnZ0NZ+caAABHx0rg\n8wVISnpV5nUpH+bdZULXQ4JRjwkzQ4kJQgghhBBCzFB5THypV54TYD59Gg8AqF7dBQDA5/Ph4OCI\nlJQkmPhAQlKh6eeY0Ccmyrk6pERQYoIQQgghhBAzVJ6JCalUn5hQlPmxX71KBABUrVqdW+boWAlq\ntbpcJ+QkJYPH08+joU8yUWbCHFBighBCCHmPpKQk48KFs9i9e1t5V4UQUspkMt1QDlvb8hnKAQAK\nRdknApKSXkIgEMDe3oFbpp9nQy6XlXl9yov59iTQD+V4/Zf5nuh7peym5yWEEEJIudBoNIiJiUJ4\neCgSEuIAANbWNuVcK0JIaasIQznk8rIdyqHVapGcnARHRyfw+Tnfwep7cJR1fUjJ0/eYoDkmzAsl\nJgghhBAzpVDIce/eHdy7d4ebgM7FpSaaNPFE7dp1y7l2hJDSlpaWCmtrG4hEojI/ds5QjrJNBKSm\npkCj0aBSJSeD5foeHO/DUA5zn0eDJr80T5SYIIQQQswIYwzx8bG4ezcUjx9HgTEGCwsLNG3qDXd3\nDzg6VirvKhJCyoBGo4FcnsY9KrOsWVmJIRAIyjwxkZT0EgBQqVJlg+VS6fuTmMhhrh/Yea+TEjT5\npTmhxAQhhBBiBrKysvDgwT3cvRuK1NQUAICTU2U0aeKF+vXdyuUbU0JI+ZHL08AYK5dhHIDuW2yJ\nRFrmc0wkJycBQB49JsrvKSGkZPF4PBrKYYYoMUEIIYS8wxITXyA8/DYiIx9ArVZDIBCgYcPGcHf3\nQNWq1SlgI+Q9pZ9fojwmvtSTSm0RHx8LtVoFobBskqP6xGzuiS8BQCQSQSwWv1c9Jsz77Z/lGrJS\ntBM196Eu7ypKTBBCCCHvGLVahcjIhwgPv43ExBcAdDPOu7t7wM2tCcRicTnXkBBS3spz4ku9nF4K\nCqNEQWmRyVIgFAphYyPJoz62SE5+BcaYWSdtzf1zt67HRHHmmDDf19wcUGKCEEIIeUekpqbg7t0w\n3L8fjuzsbPB4PNSuXRdNmnigZs3aZh1oE0KKpmIlJuRlkphgjCE1NRV2dvZ5vh9KpVK8fPkCmZkZ\n78mTiczznqB/KkfO3+Z5nu8bSkwQQgghFZhWq0VMTDTCw0MRH/8EACAWW6NZMx+4u3twE7oRQkhu\naWn6xIRdudWhrOd1yMhIh1qtyjcJok9GvD+JCXNl+FQOYh4oMUEIIYRUQOnpCkREhOPu3TCkpysA\nAM7OLnB390SdOvUhEAjKuYbk/9m70+DGzutO+H9sBLjvO9jc2U0CTXQL3ZIVbS1r9xYnlhMvEzke\nTlTlvJ+yVJxxVUZTcWpSqXFVUp7KJO4ESUayZjoq2XIsy1osqyW5RS0tSkL3BReQbDabILgTJMEF\nIJb7foDAxSKbAAjwLvj/vpi+BC4OrtDEwcHznEMkZ8vLSzCZTDAaTZLFcNSTMOL9JYqL9y5MmEyx\nbW4bGxtHEo901P2BPfWtHCRnLEwQERHJhCiK8Ho9EAQnxsZGEI1GYTDkwGq1wWKxfaLLPBHRXqLR\nKFZWllFZWSVpHEe9YmJ5ee/Gl3F5eXkAYismSPnU3isk27AwQUREJLFgMLg16tPnWwQAlJVVwGq1\noaOjEzk5ORJHSERKsrrqRzQalXQiB3D0hYmDV0zECxPqXjGh9h0OO1dMsDChHixMEBERSWR+fhaC\n4ITbPYBwOAytVov29hOwWm2oqaljwkVEKVlZWQYgbeNLIDai02Qywe8/qsJErK9GScnezzs+sShb\nVkyo9T1k+2mJUGuDz2zEwgQREdERCofDGB11QxCcmJmZAhDbhx0f9RlfakxElCo5TOSIKygoxNKS\n70i+3V5e9sFoNG71kvh1ubnZsWJC/WKvo9hrSuJQKG1YmCAiIjoCy8tLW6M+A4EAAODYsWZYrTYc\nO9YErVYrcYREpBbbWxrkUJgowvz8HILBwL4Fg3QQRRErK8soK6vYtwCSPSsm1L2XI/7fNxrlVg41\nYWGCiIgoQ6LRKG7cGIMgOHHjxnUAsa7wp0+fhcXSjaIi6cb4EZF6bY8K3bvXwlEqLNzuM5HJwsT6\n+hoikchN/64ajSZoNBoEAlwxoWTxYoQoRpMqTLCGIW8sTBAREaXZ+voaBgYE9Pdf3RqTV1NTB6vV\nhtbWduh0fPslosxZWlpCTo4RJpN0o0Lj4g0w/X4/KioyNyUk3lcjPqJ0L1qtFiaTKQtWTGQHrphQ\nF2ZGREREaSCKIqamJuFyOTE6OoxoNAq93oCurm5YrTZUVFRKHSIRZYHYloalm25pOEpHNZkjXgQ+\naCWayZSH9fXVjMYiF3L4758Jqa6YIHljYYKIiOgQNjc34XYPQBA+wuLiAgCgtLR8a9Sn0WiUOEIi\nyiarq35EIhFZ9JcAYj0mAGB1dSWjj7OyEjv/zVZMALE+Ez7fAiKRCHQ6XUZjkoqo9nmhH4tGo+BU\nDvVgYYKIiCgFCwtzEIQrcLv7EQqFoNVq0dZ2HBZLN+rqzPwWh4gkIaeJHMDurRyZ5PfHtnIctGIi\n3gAzGAwgLy8/ozFRZmyvmOBUDjVhYYKIiChBkUgY166NQBCcmJqaBBBLuk+fPouurpNMcolIctuF\nCekbXwJAfn4+NBrNkW3liDfb3I/RGOu7EQwG+TdbsXYWJliZUAsWJoiIiA7g96/A5bqCgYGr2NiI\ndXNvaGiE1WpDY2MLR30SkWwsL8tnVCgQaziZn1+Q8cLEysoycnPzoNcbbnq7nJzY9rrNzWBG46HM\n2dljgls51IOFCSIioj2IoogbN65DEJwYH78GIPZNm81mh9XaLZtvI4mIdpLbVg4gtrJsZmYK0Wg0\nI4XcaDSK1VU/KiurD7xtvO9PMKj+woRaFxPEnxencqgLCxNEREQ7bGxsYHBQgMt1ZWv8XHV1DSwW\nG9raOg78No6ISErLy0vIycnZ6qUgB4WFhZie9mJtbfXA5pSpWFtbQzQaTejc2VCYUH/zy+0VE1ot\nP86qBf9LEhFR1hNFETMzUxAEJ0ZH3YhEItDr9ejstMJisaGq6uBv4YiIpCaKIpaXl1BWVi6rb5K3\nJ3P4M1KYiDe+TOTc2bWVQz6vgXSKv7RT7TGh/sKNMrEwQUREWSsU2oTbPQhBcGJhYQ4AUFJSCovF\nhuPHu2AymSSOkIgocaurqx+PCpXXVrP4ZI5M9ZmIjwo9aCIHsHPFRCAjsdBRiBUjYuNCk78fyRML\nE0RElHUWFxfgcjkxNNSPzc1NaDQatLS0w2q1ob6+QVbfNBIRJWplRV6NL+PikzLikzPSLbkVE9tT\nOUiZdo8L5fu1WiRcmHA6nfje976Hp556Cjdu3MCf//mfQ6vVor29HU888UQmYyQiIjq0SCSCsbHY\nqE+v1wMgNsbOZrOjs9O69Y0eHR3mFkTptbQkv8aXwO6tHJkQL3gks2IiO7ZyqBObX6pTQoWJf/7n\nf8Z//Md/ID8/Nuv3r//6r/HHf/zHOHPmDJ544gm8+uqruP/++zMaKBERUSr8fj/6+69gYEDA+voa\nAMBsPgaLxYamphbodDqJI8xOzC2I0k+OEzkAoKCgAEAmt3LEV0wcXGDOhuaXgNp7KGw3v2RhQj0S\nmtfT2NiIv//7v9/6/y6XC2fOnAEA3H333Xj77bczEx0REVEK4qM+X3zxP/DDH/4z+vreRTgcRnf3\nLfjqV38fX/jCo2htbWdRQkLMLYjST66FCaPRBL3eAL8/cysm8vLyodMd/J1rNjW/VOuHdm7lUKeE\nVkw88MADmJyc3Pr/OzuZ5ufnZ+yPDBERUTICgQ0MDrrgcl3ZStArK6tgtZ5CW9txGAwc9SkXzC2I\n0m952QeDwYDc3DypQ9lFo9GgsLAwIysmotEoVlf9qK6uTej2er0eWq1W1Ssm1D50YnctgoUJtUip\n+aVWu73QYm1tDUVF6R/7Q0RElKiZmWm4XE4MDw8iEolAp9PhxAkLLJZuVFXV8BsVBWBuQXQ4oihi\nZWUZJSVlsvybV1BQCJ9vEaHQJgyGnLSdd21tFaIoJrSNA4gVSYxGo6oLE+q3/fqW42udUpNSYaKr\nqwuXL1/G2bNn8eabb+JTn/rUgfcpLc2DXq+sJbOVlWyElkm8vpnHa5x5vMaZdbPrGwqFIAgCLl++\njKmpKQBAWVkZzpw5g1OnTiE3N/eowqQ0yJbcAuDfjUzL1uu7srKCcDiMqqqKjF+DVM5fUVGGiYlx\nGAzRtMa3vr4IAKiurkz4vLm5udjc3JTta+WwceXlxQo/JSV5sn2Oh5Gbu13Y0uu1CT/HSGRt62c1\nXpe9FBTEptAUF+fK/jmnVJj49re/jb/4i79AKBRCa2srHn744QPv4/Otp/JQkqmsLMTcHJeRZgqv\nb+bxGmcer3Fm7Xd9fb5FuFxXMDTkQjAYhEajQXNzKywWGxoaGqHRaLC6Gs5YkzU1kVOSkg25BcC/\nG5mWzdd3cnICAGAyFWT0GqR6jQ2GWLH4xo1pAKa0xRM7H6DTmRKOS683YHl5WZavlXS8htfXNwEA\ny8sbsnyOhxUIhLZ+jkTEhJ/jzvcMNV6XvayuBgAc7Wsh1dwi4cJEfX09Lly4AABoamrCU089ldID\nEhERJSsajWJsbBQulxMezw0AQG5uHuz229DV1Z3wEl6SF+YWROnj8/kAACUlpRJHsrf4SOZ0F41X\nV2OjQgsLE9/+ZTSaEIlEEA6Hoden9D0tSWjn9g1u5VAP/kskIiLZWltbRX//VfT3X8HaWmwJZl2d\nGVarDc3NbZyqQUT0saWlWGGitLRM4kj2tl2YWEnrebdHhSZemMjJiW0FCIU2VVmYEFXe/ZKFCXVS\n379EIiJSNFEUMTk5gYsXXRgcHIQoisjJycHJk6dgsdhQVlYudYhERLKztBTrtSDXFRPxlW2rq6tp\nPW98BUYyK+fizTc3NzdlN8GEksPChHqwMEFERLIQDAYwONgPl8u59c1feXklrFYbOjpOpLWLOxGR\n2vh8i8jNzYPRmL7+DemUnx8rHPj96V8xkZubB70+8XHQ8dHRoVDogFuSHO1eMSFhIJRWLEwQEZGk\n5uZmIAixUZ/hcBharQ4dHZ24887bYTQW89sQIqIDhMMh+P0rqKszSx3KvvR6PXJz89LaY0IURayu\n+lFRUZXU/eKF7lBoM22xyJM63z+5lUOdWJggIqIjFw6HMDLihsvlxMxMrKN6UVExLJZunDhhQW5u\nXlZ31yciSsbS0hIAoKREnv0l4goKCrG4OA9RFNPygXJtbRXRaDSp/hLAzsIEV0wo0e7XDgsTasHC\nBBERHZnlZR8E4QoGB10IBmMjrBobW2C12nDsWJOiv/kY6buMa47zMHomEDQ3oKXncbTZz0odFhFl\ngXh/idJSefaXiCsoKMTc3Aw2NjaQl3f43g5+f7y/RLKFifhWDnWumGDzS3XJlvyChQkiIsqoaDSK\n8fFrEAQnJibGAQC5ubm45ZZb0dV1EkVFxRJHeHgjfZcR6HkMX/dObh272HsJI44nVZk8EJG8xPvy\nyH3FxHYDzJU0FSZiEzmKipIrTMSncmxuqrMwEafWz+zZVJjIpvyChQkiIsqI9fW1j0d9Xt3aU1xb\nWw+r1YaWljbodOp5C7rmOL8raQCAe72TeNpxXnWJAxHJj88XXzEh78JEQUGsgLC66kdVVc2hzxdf\nMRE/b6LY/FLZNBrtjp8lDOQIZFN+oZ6skIiIJCeKIrxeD1yuK7h2bRjRaBQGgwEWiw1WazfKyyul\nDjEjjJ6JfY57jjgSIspGS0s+6HQ6FBQkPjJTCvH44gWFw0p1xUT2NL9Up8P2mFDSVpdsyi9YmCAi\nokMLBoNwu/shCFfg8y0AAMrKylFUVIuf/zyI55+Pwmx+Dz097bDbOySONv2C5oZ9jsu3Qz4RqYMo\nivD5FlFcXAqtVnvwHSS0vZUjXYWJlY/PyxUTuynng3cqdhYmrl714ZVXXkgov1Di6opsyi9YmCAi\nopTNz89BEJxwuwcQDoeg1WrR3n4cFosNXu8a/st/WYXXe27r9r29r8PhcKuuONHS8zgu9l7CvTv3\ngNbVo6Xn8YTu39fnhsMxDI8nB2bzpmoLOESUfmtrqwiHQ7LfxgFsr5hYXV1Jy/n8/hWYTKatFRCJ\nypYVE2rtvzAz49v6eWmpGs8++xXmF3vo63Pjuef6UVcH/NM/9eHLXxZlfX1YmCAioqREImGMjg5D\nEJyYnvYCiCWbFstt6Oy0IC8vHwDwV3/1Arzer+y6r9d7Dg7HBVm/MaaizX4WI44n8bTjPIweD4Jm\nc8Jds/v63Ojp8e+6Vr296kywiCj94o0v5T6RAwDy8vKh1WrTsmJCFEX4/SsoKytP+r7qXzGhbk6n\nD1VVu48xv9gtnls0NNyCurqX8Pbbd+Kll2ZknVuwMEFERAlZWVmGy3UFAwMCAoENAMCxY00fj/ps\n/sQSYo9n72+w9juudG32syk1onI4hrOmgENE6RdvfCn3iRxA7Bv8goLCtPSY2NhYRyQSSXobB6D+\nqRwKaqGQkpUV/VZhQhS3V4Uwv9gWzy0aGt7dOib33IKFCSIi2lc0GsWNG9chCE7cuDEGADCZTDh1\n6gwslm4UF5fse1+zee+Eb7/j2SrbCjhElF5LS8opTACxfhCTkxMIh0PQ6w0pn2e7v0TyI6e3V0zw\n/UiJCgsjWz/vLEwwv9imxNyChQkiIvqE9fV1DA4KcLmubCV/1dW1sFptaG3tgF5/8NtHT087entf\n39Vjoq7udfT0tGcmaIViAYeIDmN7xYT8t3IAQHFxCSYnJ7Cykto2jLjtwkTyk0h0Oj00Go2Kt3Ko\ne8mE3V4OjyfWaDtemGB+sZsScwsWJoiICEBsv+70tBeC4MTo6DCi0Qj0ej26uk7CYrGhsrLq4JPs\nYLd3wOFww+G4wKaON8ECDhEdxsLCPAoLi7a2J8hdUVFshcPKylKaChPJr5jQaDQwGHKyYMWEOptf\n1tdXwuNxAwBKSqbx6KMXmF/8mnhuAeRuHZN7bsHCBBFRltvc3ITbPQBBcGJxcR5AbEmw1WrD8eNd\nMBqNKZ/bbu9gonAAFnCIKFUbG+vY2FhHY2OL1KEkrKgotgVweXn5UOdJdVRonMFgUO2KCbX3mNg5\nbeS226rwyCOflTAaeYrnFs899x4A4PbbL+HLX75F1rkFCxNElDEcgShvCwvzcLmcGBoaQCi0Ca1W\ni9bWDlitNtTVmVU7ZkyOWMAholQsLsaWs5eXV0gcSeJ2rphIVV+fG729IygqAv77f38b3/zmiaT/\nhhoMOQgGN1KOQQnU+jbO/CQxdnsHDIZVXLo0iz/4AztaW+W7WgJgYYKIMoQjEOUpEong2rVhuFxO\neD+eiZ2fX4DTp8+gs9OK/PwCiSMkIqJExVe5HWZLxFErLo4XJlJbMRHPL774xTzk5ITxzDNfx6VL\nyecXOTkG+P2HW7VB0tBotDt+ZpFCLViYIKKM4AhEefH7V7ZGfW5srAMAzOZGWK02NDW1fGLUJxER\nyd/CQrwwoZwVE0ajCUajMeWtHA7HMKamfhelpW9hbq4SQGr5hcGQg0gkgmg0yvdAhdlZjEiuMMEi\nhpyxMEFEGaHEMUVqI4oiJiauQxCuYHz8GkRRhNFohM1mh8XSrZgO7kREtLfFxQVoNBqUlirr73lR\nUQkWF+chimLS33h7PDkoKPDDYAjD5yvddTwZ8VGl4XAIOTmp91KSN3V+EN/9mlHnc8xGLEwQUUYo\ncUyRWmxsbGyN+owvla2qqobVegptbR2HmhufDPYYISLKHFEUsbg4j5KSUuh0ykrpi4qKMTc3g7W1\nVRQUJDfu02zexNSUDwCwuFi663gy4mOvw+GwCgsT6u1+2dfnxnPPuVBXF/v/S0ur0gZEaaOsv2JE\npBgcgXi0RFHEzMwUXK4rGBkZQiQSgU6nw4kTFlitNlRV1RxpPOwxQkSUWaurq9jc3ERDg3K2ccQV\nF8cmc6ysLCddmOjpacf8/NsAAJ+vDEBq+YXBECvSq3UyB6C+5pfx3KK29izq6l4AALzzThhtbcwt\n1ICFCSLKiGRGII70XcY1x3kYPRMImhvQ0vM42uxnJYhaeUKhEIaHByEITszPzwKIJXxW6ykcP94F\nk8kkSVzsMUJElFnxxpdKmsgRF5/Msby8hLo6c1L3tds78LWvDcHrBWpqBDz6qHvP/OKg3GLnigm1\nUeu40HhuUVPTt3Vsfb0GDscwcwsVYGGCiDImkRGII32XEeh5DF//eEIEAFzsvYQRx5MsTtyEz7cI\nQXBiaKgfm5tBaDQatLS0wWKxwWw+JnmXavYYISLKLCVO5Ig77GSO/PxYs8r/9b/uQ2Fh0Sd+n0hu\nsbPHhHqpa8lEPIcQRe2ex0nZWJggIkldc5zflTgAwL3eSTztOM/CxK+JRCK4fn0UguDE5OQEACAv\nLx/d3afR1XUy6eWwmcQeI0REmaXEiRxxRUXbWzlSsby8DK1Wu++I60Ryi+0VE2ouTKhLPIeIRrcL\nLqKogdkclCokSiMWJohIUkbPxD7HPUcciXytrvrR338V/f1Xsb6+BgCor2/4eNRnK3Q6ncQRfhJ7\njBARZdb8/CwMBsNWvwYlyc8vgFarxcrKUkr3X1lZQlFR8b5jPhPJLbZ7TKhvK4dam1/GcwtR3G56\nmps7g8ceOyNZTJQ+LEwQkaSC5oZ9jie351RtRFGEx3MDguDE9eujEEUROTlGnDx5GhZLt+yX7ibT\nY4SIiJITCoXg8y2ipqZO8q17qdBqtSgqKsbSki/pkaHBYBCBQOCmTZ0TyS3U3GMiToEvjZuK5xbP\nPvvu1rE77zQknVuIam3CoXAsTBCRpFp6HsfF3ku4d+c+0Lp6tPQ8LmFU0gkEAhgackEQnFhejn2T\nVFFRBavVhvb2E1vf8MjBQY3F4olCfGSowzG86zgREaVmYWEOoiiisrJK6lBSVlpahqUlHwKBDeTm\n5iV8v/gqi5utFEkkt1Bzjwmlf+6+WX5ht3egqEjEL34Rm8rxwQeruHjxhYS+/FBboUZtWJggIkm1\n2c9ixPEknnach9HjQdBszsqpHLOz0xAEJ0ZGhhAOh6HT6XD8eNfWqE+5fSOWSGMxjgwlIsqMubnY\nFKbKymqJI0ldSUkZgFH4fItJFSaWlnwAtvtU7CWR3GK7MKHeFRNKbH6ZSH4xNja19bvp6Va88MJn\nmV+oAAsTRCS5NvvZrCtEALGluCMjQxAEJ+bmZgDERqhZLDZ0dlpgMuVKHOH+EmksxpGhRESZEX/P\nUPqKCSA2ZSqZkaE+3+Ku++/noNzCYFBz80vlLplIJL947bVpHDsW+10kEuszwvxC+ViYICI6YktL\nPrhcTgwOuhAMxkZ9NjW1wmq1oaGhUXarI/aSSGMxjgwlIsqMublZ6PX6j1cdKFNJSayB4dLSYlL3\ni6+YKC09XK+l7FgxoTyJ5Bfz8/qtwkQ0ut0AlfmFsrEwQUR0BKLR6MejPq/A4xkHAOTm5sFuvw1d\nXSf3nMMuZ4k0Fkt1ZGhfnxsOxzBmZvJQXb3OpplERDuEw2H4fAuorKzedyqFEsSLKvEVEIny+Rag\n1xtQULD3qNBExZtfhkJqXDGhXInkF+Xlka2fdxYmDsovXK5Y/vWTn1zHv/2bn/mFzLAwQUSUQWtr\nq1ujPtfWVgEAdXX1sFhOoaWlTZajPhORSGOxVEaGsi8FEdHNLS7OIxqNKnobBwCYTCbk5uZtrYBI\nRDQaxdKSD2Vl5YdeXcgVE/KUSH7x4IN1GBqK/T5emEgkv/hv/20NX/4y4PU24ec/Z18KuUmpMBEO\nh/Htb38bk5OT0Ov1+O53v4vm5uZ0x0ZEdKCDJkNIQRRFeL0TEIQrGBsbQTQahcGQA6vVBovFhvLy\nCknjS4dEGoulMjKUfSmyG/MLooOpofFlXGlpGbxeD8Lh0Fah4Gb8/hVEIpG0bGHZHhe694oJOeYX\nyVLAztBPSCS/aG83Y2joMgCgrs6NRx+dSyi/mJ39NID3t44xv5CXlAoTb7zxBqLRKC5cuIDe3l78\n7d/+Lb7//e+nOzYioptKpHPzUQoGAxga6ofLdWVraWp5eQWs1lNobz+BnBx17X1MpGmp3d6R1Bs+\n+1JkN+YXRAebnZ0GoOzGl3HxwsTS0hIqKioPvH28H8VBjS8TcbPChNzyi2SJCp8XelB+odFsb9/4\n2tfacObMpw48J/ML+UupMNHU1IRIJAJRFOH3+2EwHFzhJCJKt0Q6Nx+FublZuFxOuN0DCIfDCCQn\nSQAAIABJREFU0Gp1aG8/AavVhpqaOkU0s5SLVPtSkDowvyA62MzMFPR6A8rKlL/6Lr7yYXFxPqHC\nhM8Xb3x5+MJE/O/LXls55JJfHJ4684+dedXOIsXNmM2bGB3d+zjJQ0qFifz8fHg8Hjz88MNYWlrC\nD37wg3THRUR0oEQ6N2dKOByG0+nE22+/i5mZ2DztwsIiWCzd6Oy0JjWTnbal0peC1IP5BdHNBYMB\nLC4uoL6+QdGNL+PixYiFhTkAnQfe3udbAIA0beWIFSZCoU8WJqTML+hgOwsTif476Olpx8DA5Y/v\nHzvG/EJeUipM/Nu//Rvuuusu/NEf/RFmZmbw2GOP4fnnn1fdMmUi2haflJBor4CjkEjn5nRbXl6C\ny3UFg4MCAoEAAKCxsRkWiw3HjjWpIlGU0s6+FLOzeaiq4lSObML8gujmZmZi2zhqauokjiQ9PJ4V\nAMAvfjGIp546eEqCz7cIjUaDkpKSQz+2VquFRqPZcyuHFPkFJS6VwoTd3oG//Es/rl4Famuv49FH\nLzC/kJmUChPFxcVb+7IKCwsRDocRjUZvep/S0jzo9crqPl9ZWSh1CKrG65t56brG7747iD/4gzV4\nPNtNCd955008++wkbrvtRFoeIxX2P/0jvPnOW7h7xzcYb5rNsP/pH6X19RWNRjE8PIz3338fIyMj\nAIC8vDzccccdsNvtKC0tTdtjEfDww3Y8/LBd6jBIAsnmF0rMLQC+/2Wamq+vIMwDAI4fb5X0eabj\nsd99dxDf+tYmvvSlEuTmbuLZZ38X77zzq31zC1EUsbAwh8rKStTUpOd9N7adI/qJ53NU+cV+DvsY\nJlNsNUhZWb4q/z0EAtujYouKchN+jnfdZcHVq2/gi19swmc/+9lMhScrBQUmAEBxceLXSSopFSa+\n8Y1v4Dvf+Q6+/vWvIxwO40/+5E9gMplueh+fbz2lAKVSWVmIuTm/1GGoFq9v5qXzGn/vex/tKkoA\ngMdzN773vQv43/+7Pi2PkYryli74/un/fKJzc3lLV1qe+/r6GgYGBLhcV7C6GjtfTU0drFYbWlvb\nUVNTirk5P1/LGcK/E5kntyQl2fxCabkFwNd1pqn9+l67dh0AYDIVS/Y803WN47nF9PQUOjsHUVjo\nv2lusbTkQygUQklJedqeu06nRyAQ/MT5Mp1f3Ew6ru/GRmwViM+3Bo1Gff8elpa2//avrW0mfL0W\nF9e2flbz34mdVldjq3uXlzeO7DmnmlukVJjIy8vD3/3d36X0gESkPHLuZJzIZIhkiKKIqalJCIIT\n164NIxqNQq83oKurG1arLaHmXImS4/YYIikxvyDaXzQaxczMFEpKymAy5UodzqHFc4ipqRp0dg6i\ntnYafn/RvrnF/PwcAKT1fdhgMOzZ/BJIf35xVPr63HjzzXGUlwPf/e6b+E//yaq63GJnw0tuoVWP\nlAoTRJRdpJ6UcBQf4Dc3g3C7ByAITiwuxpprlZWVw2Kx4fjxTuTkGNP6eH19bvT0+OH1bq9E6e19\nHQ6HW3UJBBERHd7c3CxCoRBqa9XRXyKeQ0xP1wIAamqm4HZ37JtbzM/PAgAqKtIzJrWvz43Z2Q1o\ntRH84R++oIovB+K5xenTrSgv78Mrr3wGFy+6VJdbaLXJ95gg+WNhgogOJOWkhEx/gJ+fn9sa9RkK\nhaDVatHWdhxWqw21tfUZG/XpcAzvek4A4PWeg8NxQVXJAxERpcfk5A0AgNl8TOJI0iOeW0xN3QIA\nqK2dvmluES9MlJcffsVEPLf4zGcqUV09g2ef/YoqvhyI5xanT/8MACCK6swtdo8LVedI1GzEwgQR\nHWjnpISj3naQiQ/wkUgYo6PDEAQnpqe9AICCgkKcPn0rurqsyMvLP3TcB5Hz9hgiIpIfjydWmKiv\nV0dhYju3eAHBoB5tbSP4xjda93xvF0URMzNTKCoqRm7u4bexxHOLcPg6DIYwNBpRFR/gsyW32D2V\nQ3kNkGlvLEwQUULs9g5J3qzT+Sa7srK8NepzY2MDANDQ0Air9RQaG5uPdDmg1NtjiIhIOcLhMKam\nJlFWVoG8vDypw0mbeG7x0kvP49q1YXR01Ox5O59vEcFgEI2NLWl53HgOEQrFplfodGGEwwbFf4CP\n5xC/vohAbbnF7h4TXDGhFixMEFHaZKIXxGE/wEejUUxMXIcgXMH4+DUAgNFowqlTdlgs3SgulmbU\np5TbY4iISFlmZryIRCIwmxukDiUjamvrce3aMLxeD4qKinf9rq/PjR//+DLq64GLFxdRWnr47Rbx\nHCIcjn0UMhhihQmlf4CP5xbbNKrMLXavmEj+SyVRFNMZDqUJCxNEMjHSdxnXHOdh9EwgaG5AS8/j\niuoGnaleEKl+gN/YWMfAgID+/qtYWVkGAFRX18BiOYW2tnbo9YaUY0oHKbfHEBGRskxMpNZfQim5\nRV2dGQAwNTWJEycsW8fjucXZs7Wor5/BT3/6W3jhhYG05RbxwoReH1bFB/h4bvHiix8BAB588AX8\n3u+pcSrHzh4TbH6pFixMEMnASN9lBHoew9e9k1vHLvZewojjSVkmEHvJVDPHZD7Ai6KI6ekpuFxO\njIy4EY1GoNfr0dlphdVqQ2VldcpxZIJU22OIiEhZrl8fhU6nS6q/hJJyi/LyChiNRkxMjEMUxa0P\nnrHc4nfR2vq3WFvLw+xsJUSxOm25xUsvfQgA+NznnsNjj1lU8Z5st3fA7x9Hf/9VPPHEOZSWlkkd\nUtoddsUEyRMLE0QycM1xflfiAAD3eifxtOO87JKH/WSy4dJBH+BDoU243YMQBCcWFmJzzktKSmG1\n2nD8eBeMRtOhYyAiIpLC8vISFhcX0NjYAoMh8dV+SsottFotjh1rwvDwEBYW5lFREZu84fHkoKZm\nBkVFfjid3RBF7dbxw7LbO7C2NgFBcOKJJ+5CWVnFoc9JR4OFCXViYYJIBoyeiX2Oe444ktRJ0cxx\ncXEBLpcTg4P9CIU2odFo0NraDovFhvr6Bo6QIiIixRsbGwUANDe3JnU/peUWTU2tGB4ewvXro1uF\nCbN5EwbDMABgeLht67bpyi10uthHoXA4kpbzyY1a86CdBTq1PsdsxMIEkQwE92lmFTSbjziS1B1V\nM8dIJIKxsREIghNebyy5ys/Px6lTdnR2WlFQUJjWxyMiIpLS9esjAICmpuQmUigttzh2rAlarRZu\n9yDs9tug0Wjwn/9zG15++SIiES1GR2OFiXTmFjpdbNRkJBJOy/noaOTkGLd+5rhQ9WBhgkgGWnoe\nx8XeS7h35z7Qunq09DwuYVTJyXQzR7/fj/7+K+jvv4qNjXUAsSZgFosNTU0tW8kFERGRWqyu+jE1\n5UVNTR3y8vKTuq/Scguj0YS2tuNwuwcwMTGOY8eaUFubh9LSdSwtFcJmezHtuYVeH18xoa7ChNqn\nTuzM+TguVD1YmCCSgTb7WYw4nsTTjvMwejwIms2y7Zx9M+lu5iiKIiYmxuFyOXH9+jWIogij0Yju\n7ltgsXSrsqETERFR3NDQAERRxPHjXUnfV4m5RXf3abjdA+jrexdm8zH09b0LAPjmNz+D73ynPu2P\nF9/KEYmocytHNmCPCfVgYYJIJtrsZ2WdLBylQGADg4MuCIJza9RnZWU1rFYb2tqOJ9X8i4iISIlE\nUcTgoACdToe2ttSK/krLLaqqatDc3IaxsRH88IcOrK76cexYE2pq6jLyeHp97Jt3ta2YyCYcF6oe\nLEwQkSyIoojZ2WkIghMjI0OIRCLQ6XQ4ccICi8WG6uoaqUMkIiJCILCBgQEBN25cx/LyEqLRKIqL\nS1BbW4/jx7vStppvetqL5eUltLcfz6rpUvfe+yA2N4OYnJxAbW097rvvkYw1ONxeMaHOwkQ29IXk\nign1YGGCSEZG+i7jmuM8jJ4JBM0Nsl9ymQ6hUAjDw4NwuZyYm5sFABQXl8BiseHEiS6YTLkSR0hE\nRBQroDudH+D999/G5mZsKkRBQSH0ej2mp72YmprEBx+8h9bWDnzqU3eiuLjkUI939epHAIDOzpOH\nOo/ScguTyYTf/M0vIxQKZXyFJHtMKF82PVe1Y2GCSCZG+i4j0PPYrpnjF3svYcTxpKwTiFT5fItw\nuZwYGupHMBiERqNBc3MrrNZTMJuPcfwTERHJRjAYxC9+8QJu3LgOkykXt99+N44f70JeXh6AWJH9\n+vVrcDrfx+ioG+Pj1/Abv3EPLJbulN7PlpeXMDrqRkVFJerr956ukQgl5xZHsW1T7SsmAPXnUskU\nlZhbyhsLE0Qycc1xflfiAAD3eifxtOO87JOHREUiEVy/PgpBcGJyMjZfPS8vH2fOnEJnZzcKCznq\nk4iI5CUQ2MDzz/8Yc3MzaGhoxH33PbJVkIgzGAxobz+OtrYODA8P4le/eg1vvvlLjI9fw333PZz0\n6r/3338Hoiji9Omzh/owlQ25xWHEe0yw+aXyWCw2uFxOlJQcbmUSyQcLE0Q79PW54XAMHzjuMtHb\nJcPomdjnuOdQ55WD1VU/+vuvYmDgKtbW1gAAdXVmWK2n0NzcylGfREQkS+FwCC+88Bzm5mZw4oQF\n5849cNM97RqNBh0dnairM+O1117G+PgY/v3fn0JjYzeeeWYhofzi6aevorV1HIGAEcvLh/uGV825\nRTrEV0yobStHNrjnnvtw1133sseEirAwQQnJxAdxuenrc6Onxw+v9ytbx3p7X4fD4d71XBO9XbKC\n5r2XagbN5pTPKSVRFDE5OQFBcGJsbASiKCInJwcnT56CxWJDWVm51CESEZGE5J5biKKI1157BTMz\n0+jo6MS99z6Y8OqFgoJCfP7zX0Jf33t47723IAi9mJq6H++++xt45x3NvvnF448v4/OfX4dGA/z7\nv/8unnpq/FD5hdpyi3RTa4+JbMGihLqwMEEHytQHcblxOIZ3PUcA8HrPweG4sOt5Jnq7ZLX0PI6L\nvZdw7859oHX1aOl5POVzSiEQCGBoqB8ulxNLSz4AQEVFJSwWGzo6TsBgyJE4QiIikpoScou+vvcw\nMjKEmpo63HvvA0lvqdBoNDhz5jZcuHADlZU+PPDAq2hsHMdPfvLFffILN269NQdVVXN4770zuH69\nGUDzofILteQWmaL+HhNEysHCBB0oUx/E5cbj2fsD868fT/R2yWqzn8WI40k87TgPo8eDoNks+87Z\nO83OzsDlcmJ4eBDhcBharQ4dHZ2wWm2orq5lwyEiItoi99xietqLy5d7kZ9fgEce+cLWB9hUuN0l\n+NGPfhe/9VvPoaNjGN/61j/g4sVzmJzcPuf6+joqKqZRU7OCGzca8MorD2797jD5hdJzi0yLbyUN\nh9XZY4K5FykJCxN0oEx9EJcbs3kzoeOJ3i4VbfazikoWwuEQRkbcEAQnZmenAQBFRcWwWLpx4oQF\nubl5B5yBiIiykZxzi9gEjp9DFEXcf/8jh34vM5s38c47+Xj66a/jjjvewj33vIEvfOFniES0+OlP\nVyGKUczMTKGmJoyxsSY888zvIBw27Lr/YSgttzhK8a0calsxwRGapEQsTNCBMvlBXE56etrR2/s6\nvN5zW8fq6l5HT097SrdTs+VlHwThCgYHBQSDQQBAU1MLLBYbjh1rYoWeiIhuSs65RW/vG/D7V2C3\n33aoUZ1xO/OGS5fuxEcf2fDAA8/g7NkFeDzjAIDS0jIUFdXhn/6pFBsb2xM8si2/OGrsMUEkHyxM\n0IGy5YO43d4Bh8MNh+PCTRtxJXo7tYlGoxgfvwZBcGJiIpZI5ebm4pZbbkVX10kUFRVLHCEREcnR\n/PwsJic9WF9fg8GQg6qqanzzm62yzC0mJycwMCCgvLwSZ858Ki3n3DtvOAW7vQOh0CYADQyG2AqJ\nmprsyy+ktN1jQp1bOYiUhIUJOlA2fRC32zsSel6J3k4N1tfX0N9/FS7XFaytrQIAamvrYbXa0NLS\ndqh9t0REpF4TE+N4551LmJub+cTvjEYTvvvdJrz00v/FxIRJFrlFJBLGG2+8CgA4d+7+tI6y3i9v\n+PWG0NmUX8iBXh/vMcEVE9mEW13kiZ8oKCF8o8wuoijC6/VsjfqMRqMwGAywWGywWrtRXl4pdYhE\nRCRT4XAYb775SwwOuqDRaNDU1Iq2tg4UFhYjGAzA4xnH0FA/JiYGceedZXjwwc+hvLxC6rDR1/ce\nlpZ8OHnyFKqra6UOh46AVhsrTKitx0Qct9aSkrAwQURbgsEg3O5+CMIV+HwLAICysnJYrafQ0dGJ\nnBzpm5IREZF8bWxs4Oc/fw4zM9OorKzCuXMPorKyatdtmppacPbs7XjvvV5cvfoRfvSj/4uHHvoc\nGhtbJIoaWFxcwAcfvIf8/ALcdtsdksVBR0uj0UCv16uuMMEVAaRELEwQEebnZyEITrjdgwiHQ9Bq\ntWhvPw6r9RRqaupYcSciogMFgwE8//yPMD8/i46OTpw798BWc8FfZzSacNddn0ZdXQN++csX8eKL\nP8X99z+CtrbjRxx17EPcG2+8img0irvv/jRycoxHHgNJR6fTcSsHkQywMEGUpcLhMEZHY6M+Z2am\nAAAFBYWwWG5DZ6cFeXn5EkdIRERKEQqF8LOfPYf5+Vl0dZ3EPffcn1BRu7W1HXl5eXjhhefw6qsv\nwmDIQWNj8xFEvG1gQMDU1CSam9vQ3Nx2pI9N0tPp9Gx+SSQDLEwoWF+fGw7HsOobUlJ6LS8vob//\nCgYGXAgENgAAx441wWo9hWPHmqDVaiWOkIiIpJRsfhFfcTAzM4WOjs6EixJxtbX1+Mxnvojnn/8R\nXn75eXzhC4+ipqYuHU/lQOvr63j77TdhMOTgrrvuPZLHJHnR6/VcMUEkAyxMKFRfnxs9PX54vV/Z\nOtbb+zocDrckxQkWSeQtGo3ixo3rEAQnbtwYAwCYTCacPn0GXV3dKC4ukThCIiKSg1Tyi4EBAW73\nAKqqanDvvQ+mtP2vrs6Mhx76PF588T/w4os/xaOPfg1u93TGc4ve3jcQDAZx5533oqCgMK3nJmXQ\n6fTY3AxKHUaGcCsuKQcLEwrlcAzvShoAwOs9B4fjwpEXBJ5++lV85zsmbGzIo0hC29bX1zEwIKC/\n/wr8/hUAQHV1LaxWG1pbO/bd+0tERNkp2fxifn4Wv/rVazAajXjooc8dasRmU1ML7rjjHC5duoin\nn/4h/uf/tMDvz1xuMTExDrd7AJWV1bBabWk5JylPbMWEurZysPklKRE/lSiUx7P3dIT9jmdKX58b\n//W/ehEI/H+7jktVJKHYm9H0tBe/+lU/XC4XotEo9Ho9urpOwmq1oaKi6uCTEBFRVkomv9jcDOLl\nl3+GSCSChx76PAoLiw79+CdPnsLQ0Ajm5ibw0EMBPPusiPi3vunMLeIjTTUaDc6du5/bGLOYTqdT\n3VSOOPYuJyVhYUKhzObNpI5nisMxjECgYc/fHXWRJNttbm7C7R6AIDixuDgPACgtLYPFYsPx410w\nGtllnIiIbi7R/EIURVy8+AssLy/h9OkzaGpKz6hPjUaD1183obAwH1arC9PT1bh06a6t36crt/jg\ng3exvLyE7u5bUFlZnZZzkjLpdDqIoohoNMoCFZGEUi5MnD9/Hq+99hpCoRC+9rWv4Utf+lI646ID\n9PS0o7f3dXi957aO1dW9jp6e9iONI5YghPb83VEXSbLVwsI8XC4nhob6EQrFRn22tnbgzjtvR15e\nGUd9EpGiML+QVqL5hSA4MTrqRm1tPW699Y60xjAxYcLVqw14/HEv7rvvNczNVWFoKDZGNB25xdzc\nDD744DLy8wtw662/cejzkbLFtx9FIhEWJlSOObG8pVSYeO+99/Dhhx/iwoULWF9fx7/8y7+kOy46\ngN3eAYfDDYfjgqQNJ2MJwikAbwK4e+t4bu6LR14kySaRSATXrg1DEJyYmpoEAOTnF+D06bPo7LQi\nP78AlZWFmJvzSxwpEVHimF9IL5H8YmZmGm+99TpMplw88MBnDtVXYi9m8ybeeecU/t//K0BPz0f4\n7d/+MRyOHvj9fYfOLcLhMF599SVEo1F8+tMPISeHqzuz3XZhIgyDwSBxNETZK6XCxKVLl9DR0YE/\n/MM/xNraGv7sz/4s3XFRAuz2Dsl7OMS+WZmG11sD4EcADDCZJvA//ked5LGpkd+/ApfrCgYGBGxs\nrAMAGhoaYbHY0NTUwko/ESka8wt5uFl+EQgE8MorP0M0GsX99z+SkUkW27lFK557bga/8zsT+NrX\nfoCGhhOw2+881Lnfffct+HwLsFptaGhoTFPEpGQ6XezjUCSipgaYbH5JypNSYcLn88Hr9eIHP/gB\nJiYm8K1vfQsvvfRSumMjBdj+ZuWjj79ZWUdPz6dYlEgjURQxMREb9Tk+PgZRFGE0GmGz2WGxdKOk\npFTqEImI0oL5hbyJoojXXnsZfv8Kzpz5FI4da8rI4+zOLUowO7uOqqoFFBauIBKJpLxCY3TUDaez\nDyUlpbj99rsPvgNlhZ1bOdSGWxdISVIqTJSUlKC1tRV6vR7Nzc0wGo1YXFxEWVnZvvcpLc2DXp/e\npX6ZVlnJedaJePhhOx5+2J70/Xh9b259fR0ffvgh+vr64PP5AAB1dXU4e/YsLBZLQssNeY0zj9c4\ns3h9s0uy+YUScwtAua/r3t5eXL8+iubmZjzyyAMZXaW3M7cQRRHPPPMMBgcH0dv7Gr74xS/e9LH3\nur6zs7N47bWXYTAY8NWvfgVVVfvnrHQwpb6G95KfbwIAFBebUFEhj+d12OtrNMZyxPLyAhQWyuM5\nyYFWu92jRk2v4ZspKIi/vnNl/5xTKkzY7XY89dRT+P3f/33MzMwgEAigtPTm39r6fOspBSgVNe3P\n7+tzw+EYlrQXxa9T0/VNJ1EUMTMztdVULBKJQK/X48QJC6xWG6qqagAAS0sBAIGbnovXOPN4jTOL\n1zfz5JakJJtfKC23AJT7up6cnMCrr76KvLx83HPPg3jllQ+PNLe46677sbS0gqtXryIcFnHu3AN7\nfhu81/X1+/34yU/+HaFQCA8++DloNLmK/G8gF0p9De8nFIoCAObmViCK0k8wS8f1DQRijekXFlYR\nuHm6mFWWlta2flbTa/hmVldjL4Dl5Y0je86p5hYpFSbOnTuH999/H48++ihEUcQTTzzBpUIy1dfn\nRk+PH17vV7aO9fa+DofDnbYEQo6FD6UJhUIYHo6N+pyfnwMAlJSUbo36NJlMEkdIRJR5zC/kye/3\n45VXfgaNRoMHH/wsBgYmM55bAJ/ML77xjZOIRCIYGBAQCoXw6U8/BL3+5qms37+C55//Efz+Fdx6\n62+grY35Ce2m1ap3KweRkqQ8LvRP//RP0xkHZYjDMbwrcQAAr/ccHI4LaUkejqLwoWaLiwtboz43\nNzeh0WjQ0tIGq/UU6usbmJATUdZhfiEv4XAYL7/8U2xsbOCuu+5FXZ0Zf/VXL2Q0twD2zy/+8R9t\n0OkEjIwMYXl5Cfff/whKS/feluHx3MAvfvECNjY2cPr0Wdjtt6UlNlKXeI+JaFR9hQnmkaQkKRcm\nSBk8nr3HYO13PFmZLnyoUSQSwdjYKAThI3i9HgBAXl4+urtvQVfXyYx0OCciIkpWvNnl7OwMjh/v\ngtV6CkDmcwtg//ziyScv4PvffxRvvPEqhob68cwzT+HECQs6O0+ivLwC4XAYXq8HguDEyMgQNBoN\n7r7701uxE/06NTe/JFISFiZUzmzeTOp4so4iOVGL1VU/+vuvoL9fwPp6bI9bfX0DrFYbmppa0z4H\nnoiIKFWiKOKtt17HyMgQamrqcM899219+5rp3AK4eX6h1+tx330Po7m5Fb29b8LlugKX68onbltZ\nWY277/40qqtr0xYXqc92YSIscSTpI4ocF3ozvD7yxMKEysVmgb8Or/fc1rG6utfR09OelvMfRXKi\nZKIowuO5AUFw4vr1UYiiiJwcI7q7T8Nise27/JSIiEgqoijivfd6ceXKhygtLcdnPvOb0Ou3J0Fl\nOrcAEssvWlra0dTUirGxUdy4MYbl5SUYjQbk5xehqakVDQ2NXMpOB9LpYh+HuGKCSFosTKjc9izw\nCxlpTnkUyYkSBQIbGBzsh8vlxPLyEgCgoqIKVqsN7e0nEhr1SUREdNREUURv75twOvtQVFSMz33u\nt2Ey5e66TaZzCyDx/EKr1aK1tR2trbHjapsYQZnHrRxE8sDCRBaw2zsy1u/hKJITJZmZmYbL5cTw\n8CAikQh0Ot3H+3Jjoz75zQ0REclVIBDAxYuvYGxsBCUlZfjCF760b9+jTOYW8fMzv6CjoO7CBPNO\nUg4WJggAMNJ3Gdcc52H0TCBobkBLz+Nos59N6L6ZTk7kLhQKYWRkCILgxNzcDACgqKgYFosNnZ2W\nT3zTREREJDczM1N45ZUX4PevoK7OjAcf/Bzy8vIOfV7mFyR36ixMsIcCKQ8LE4SRvssI9DyGr3sn\nt45d7L2EEceTCScP2WhpyQeXy4nBQReCwSA0Gg2am1thsdi4r5WIiBRBFEVcufIB3n77V4hGozhz\n5lM4c+ZT0Gq1hz438wtSAjU2v4xjKkpKwsIE4Zrj/K6kAQDu9U7iacd5Jg6/JhqNYmxsFC6XEx7P\nDQBAbm4e7Pbb0NV1EoWFRRJHSERElJhAYAOvvfYKrl8fRW5uHh544DMwm4+l7fzML0gJ2PySSB5Y\nmCAYPRP7HPcccSTytba2iv7+q+jvv4q1tVUAQF2dGRaLDS0tbRz1SUREijIzM4WXX/4ZVlf9qK9v\nwAMPfAZ5eflpfQzmF6QEatzKwWmYpEQsTChMX58bDsdwWhtBBc0N+xw3H+q8SieKIrzeCQiCE2Nj\no4hGozAYcmC12mC12lBWViF1iEREREkbGBDwxhu/hChGcfbs7bDbb8OHH44wv6CspMbCxDbu5SDl\nYGFCQfr63Ojp8cPr/crWsd7e1+FwuA+VPLT0PI6LvZdw7849oHX1aOl5/BDRKlcwGMATOPBIAAAg\nAElEQVTQUD8E4QqWlhYBAOXlFbBaT6Gj4wQMhhyJIyQiIkqeKIp4551L+PDDyzAajXjwwc+hoaGR\n+QVlNXUWJrhkgpSHhQkFcTiGdyUNAOD1noPDceFQiUOb/SxGHE/iacd5GD0eBM3mpLpmq8Xc3AwE\nITbqMxwOQ6vVob39BKzWU6ipqWUzSyIiUixRFPH227/CRx+9j5KSUnz2s7+F4uISAMwvKLupszAR\nw9R1N14PeWNhQkE8nr2/qd/veDLa7GezMlEIh8MYGXHD5foIMzPTAIDCwiJYLN3o7LQiN/fwo9KI\niIik5nI5Py5KlOE3f/NR5OcXbP2O+QVlMza/JJIHFiYUxGzeTOo47W95eWlr1GcgEAAANDY2w2q1\noaGhKS1j0oiIiORgamoSly69jtzcXHz+87+9qygBML+g7BZfMRGNqqcwweaXpEQsTChIT087entf\nh9d7butYXd3r6OlplyokRYlGoxgfH4MgfISJiXEAgMmUi9Onz8Ji6UZRUbHEERIREaVXKBTCL3/5\nEkRRxIMPfm7PsdbMLyibbW/lCEscCVF2Y2FCQez2DjgcbjgcF9LaNVvt1tfX0N8voL//ClZX/QCA\nmpo6WK02tLa2by3hIyIiUpvLl9/GysoyTp06g/r6vadkML+gbKbOHhNcMkHKw09kCmO3dzBRSIAo\nipiamoQgOHHt2jCi0Sj0egMslm5YLDZUVFRKHSIREVFGLS/74HT2oaioGGfP3n7T2zK/oGylzsJE\nHLs9knKwMEGqsrkZxNDQAFwuJxYXFwAAZWXlsFhsOH68Ezk5RokjJCIiOhqXL78DURRx++13wWAw\nSB0OkSxptWouTBApBwsTpArz83NwuZwYGhpAOByCVqtFW9txWK021NbWc9QnERFllcXFBbjdAygv\nr0RLC3tFEO0n3vBcjYUJpr+kJCxMkGJFImGMjg5DEJyYnvYCAAoKCtHVdSu6uqzIy8uXOEIiIiJp\nfPTR+wCAW2+9ncV5opvQaDTQ6XRsfkkkMRYmSHFWVpbhcl3B4KCAjY0NAMCxY02wWGxobGzmqE8i\nIspqgcAGhocHUVxcgqamVqnDIZI9nU6vqhUTHBd6cyIvkCyxMEGKEI1GcePGdbhcToyPjwEAjEYT\nTp2yw2LpRnFxqcQREhERycPAgIBIJAKLxcbVEkQJiK2YUE9hYhv//e/G6yFnLEyQrG1srGNgQIDL\ndQV+/woAoLq6FhaLDW1t7dDr2cyLiIgoThRFuFxXoNfrceKERepwiBRBfYUJrggg5WFhgmRHFEVM\nT09BED7C6OgwotEI9Ho9OjutsFptqKysljpEIiIiWZqensLKyjI6OjphMpmkDodIEXQ6HUKhkNRh\npB0XTJGSsDBBshEKbcLtHoQgfISFhXkAQElJKaxWG44f74LRyASLiIjoZkZGBgEAHR0nJI6ESDl0\nOh0CgYDUYaQNWyiQErEwQZJbXJyHIMRGfYZCm9BqtWhtbYfVakNdXQP3xxIRESUgGo1iZMQNkykX\n9fXHpA6HSDHU1vxyG3NoUg4WJkgSkUgEw8NDcLk+gtc7CQDIz8/HqVN2dHWdRH5+gcQREhERKcvk\n5AQ2NtZhsdig0+mkDodIMbRaLaJRNRYmiJSDhQk6Un7/Cvr7r2JwUMDa2hoAwGw+BqvVhqamVo76\nJCIiStHY2AgAoK2tQ+JIiJRFp9MhGo1CFEWVrNTlXg5SHhYmKONEUcTExDgEwYnx8WsQRREmkwk2\n2y2wWGwoKeGoTyIiosMQRRHj42MwGo2ora2XOhwiRdFqYyuMotGoqlYbqaLGQlmDhQnKmEBgAwMD\nLrhcTqysLAMAKiurYbXacPvtZ7C0pJ4mQ0RERFJaXFyA37+CtrbjXH1IlCSdLvZvJhqNqKIwweaX\npEQsTFBaiaKI2dlpCIITIyNDiERif+BPnLDAYrGhuroGAGAwGACwMEFERJQO4+PXAACNjc0SR0Kk\nPPFiRCQSgcEgcTBpxSUTpBwsTFBahEIhDA8PwuVyYm5uFgBQXFwCi8WGEye6YDLlShwhERGReo2P\njwEAjh1jYYIoWTu3cqgDl0yQ8rAwQYfi8y3C5XJicLAfm5tBaDQaNDe3wWq1wWw+ppIGQkRERPK1\nubmJ6WkvqqtrkJvLLwKIkrVzxQQRSYOFCUpaJBLB9eujEAQnJicnAAB5efno7j6Fzs5uFBYWShwh\nERFR9pia8kAURZjNjVKHQqRI8b4sHBlKJJ1DFSYWFhbwpS99Cf/6r/+K5mYuHVS71VU/+vuvor//\nKtbXY6M+6+sbYLHY0NzcqopmQUREJD3mF8nxeGJfEtTXN0gcCZEyba+YUMtWDiLlSbkwEQ6H8cQT\nT8BkMqUzHpIZURTh8dyAy+XE2NgoRFFETk4OTp48BYvFhrKycqlDJCIiFWF+kbzJyRvQ6XSoqamV\nOhQiRdruMaGuFRPcUU1KknJh4m/+5m/w1a9+FT/4wQ/SGQ/JRCAQwNBQP1wuJ5aWfACAiopKWK02\ntLd3fjxVg4iIKL2YXyQnENjA/Pwc6usboNfzvZkoFfFxoWrpMSFyXigpUEqFiR//+McoLy/HHXfc\ngX/8x39Md0wkodnZGQjCRxgZGUI4HIZOp0NHRyesVhuqq2vZzJKIiDKG+UXyJic9ALiNg+gw4ism\n1FKY2Ma8nZQj5cKERqPBW2+9hcHBQXz729/GP/zDP6C8fP9l/aWledDrldWDoLIyO5o4hkIhuFwu\nXL58GV6vFwBQUlKCM2fO4PTp08jLy8vI42bL9ZUSr3Hm8RpnFq9vdkk2v1BibgGk93X9wQdzAICu\nrg7+e/kYr0Pmqe0aFxbmfvy/Rlk8t8PGkJOj3zqPXs9ZB3F6fXjrZzn8dz4KBQWxbZHFxbmyf84p\nvVJ/+MMfbv38e7/3e/jLv/zLmxYlAMDnW0/loSRTWVmIuTm/1GFk1NKSDy7XFQwOCggGY6M+m5pa\nYLXa0NDQBI1Gg7W1CNbW0n8dsuH6So3XOPN4jTOL1zfz5JakJJtfKC23ANL/uh4buw6tVguDgf9e\nAP7dOApqvMaBQOwD6+LiKgoLpX1u6bi+m5ux5zM/74dOx8JE3PLy6tbPansN72d1NQAAWF7eOLLn\nnGpucehXKpf2K0s0GsX169cgCE54POMAgNzcPNxyy62wWLpRWFgkcYRERETMLxIRDocwNzeLiopK\n9n4iOoT4VA61Nb+k3fi+Im+HLkw8+eST6YiDMmxtbRUDAwJcritYW4tVC2tr62G12tDS0s5Rn0RE\nJCvMLw42NzeLaDSKmpp6qUMhUrTtHhPqGBfK3pekRFzbo2KiKMLr9UAQnBgbG0E0GoXBYIDVaoPF\nYkN5eYXUIRIREVGKpqdjfaFqauokjoRI2eJTOdS3YoIrBEg5WJhQoWAwuDXq0+dbBACUlVXAarWh\no6MTOTk5EkdIREREhzU1FS9M1EocCZGyqW8qB5dMkPKwMKEi8/OzEAQn3O4BhMNhaLVatLefgNVq\nQ01NHfdVERERqYQoipie9qKgoBAFBfJqYkqkNNs9JtSxlYNIiViYULhwOIzRUTcEwYmZmSkAQGFh\nEbq6utHZac3YqE8iIiKSzvLyEgKBDbS3H5c6FCLFU9+KCSLlYWFCoZaXl9DffwUDAwICgdgYmGPH\nmmG12nDsWBO0Wq3EERIREVGmxL+MqK5mfwmiw+JUDiLpsTChINFoFDdujEEQnLhx4zoAwGTKxenT\nZ9DV1Y3i4hJpAyQiIqIjMTs7AwCoqqqWOBIi5Yt/oae2FRPcxk1KwsKEAqyvr2NgQEB//xX4/SsA\nYh24LZZutLZ2QK/nf0YiIqJsMjc3A41Gg4qKSqlDIVK8+IoJtRQmRM4LJQXiJ1qZEkURU1OTcLmc\nGB0dRjQahV5vQFfXSVitNlRUVEkdIhEREUkgGo1ifn4WZWUV0OsNUodDpHjxHhNsfpkdWLiRJxYm\n0qivzw2HYxgeTw7M5k309LTDbu9I6hybm5twuwcgCB9hcXEBAFBaWvbxqM8uGI3GTIROREREMvXr\n+cVXv1qNcDjMbRxEaaLTqXMrB5GSsDCRJn19bvT0+OH1fmXrWG/v63A43AkVJxYW5iAIV+B29yMU\nCkGr1aK1tQNWqw11dWbuESMiIspCe+UX8/MXcO4cUFnJwgRROmyvmFBXYYKfH0hJWJhIE4djeFfS\nAABe7zk4HBf2LUxEImFcuzYCQXBiamoSAJCfX4DTp8+iq+sk8vLyMx43ERERydde+UVeXiEANr4k\nSpftHhPcykEkFRYm0sTjyUn4uN+/ApcrNupzY2MdANDQ0Air1YbGxhaO+iQiIiIAe+cRdXVTiEY1\nKC+vkCAiIvX5/9u7++Aoy3v/45/dTTYEEiDBBAkbQEgCkkjQ9aGV2tJOmaH92dZWqEwpzLQ7ZVqP\nHX9Vp7V1au0fjtOe057+gZ6Wdg9T+3NkRnE6HHuO9VSIVrAWtxJMYvMAiCyLEBDyRJJ9un9/xIQk\n5IEse++99533a8aRXPeSfPciyX7v717X9xrMvZ2yYoIeCrAjChNp4vNFJxw3DEPvv/+eGhrqdfz4\nUUlSXl6eamv9qq5epblzizIWKwAAsIfR+YXHk9C1136g/n6vPB7SOCAdnHYqB2BHvKKlSSBQqQMH\n6hSJrB0aKyur09ati/X22wfV2HhYnZ0dkqTS0mtVU1OriooqumkDAIBxjc4vSkrOKCcnobKyhVaG\nBTgKp3IA1qMwkSZ+f5WCwRYFg7sUDueqsrJTt96aUH39B0okEsrJydH119eourqWPaEAAOCKjMwv\nvLrhhjOSpJUrl1scGeAcg6dyOGUrxyCaX8JOKEyk0apVS/Qv/9KnxsZ6nT3brnPnpLlzi1RdXavl\ny1dqxowZVocIAABsxu+vGmqkXVf3v2pqOs2bHEAaDa6YcM5WDnpMwH4oTKTBhx+eU2NjvZqbmxSN\nRuVyubR0aaVqamq1cGE51UoAAJAW7e2n5fF4VFQ0z+pQAMcY7DHBVg7AOhQmUpRIJHTs2MBRn5FI\nWJI0a9YsrVp1k1auvEEFBYUWRwgAAJwkHo/r3LmzKikpHbqRAnD1Bt9EdM6KCYyFN4uzG4WJKeru\n7lJT02E1NTXo4sUeSZLPt0jV1au0ZMkyeTwehUItCgZfUzjslc8XVSBQObQEEwAAIBX79/9DyWRS\nf/97VK+88ifyCyBNXC6XPB6PYwoTnBYKO6IwcQUMw1A4/L4aGg7pvfeOyjAMeb15WrXqRlVX16qo\nqHjosaFQiwKBLkUim4bGDhyoUzDYQvIAAABSEgq16Ne/Pq077pDefvsTOnToRvILII08Ho/jml8C\ndkJhYgJ9fb365z+b1NhYr46OC5KkkpJSVVfXqrJyhXJzLz/qMxhsHVGUkKRIZK3+9V93aNcuEgcA\nADB1wWCrZs2aJUmKRMo++j/5BZAubrdHiQQ9JgCrUJgYw+nTH+jAgb165513lEgk5PF4tHz5StXU\n1Kq09NoJ9yeFw94xx19/fYZCId7VAAAAUxcOe7V69SnFYjk6e7ZkaJz8AkgPj8ftqBUT9FOA3VCY\n+EgsFlNbW7MaGurV3n5akjR79hzV1NRqxYpqzZiRf0Wfx+eLjjkejc5SMNhK4gAAAKasvLxPpaVn\nFA77lEy6h8bJL4D0GFgx4YzChEGTCdjQtC9MXLhwXg0N9WpublR/f79cLpeuu26Z1qz5uAoLS6Zc\nbQwEKrVnz0uKRtcPG31NUrXC4aa0xg4AAKaHjRtL1dTUqkhkwbBR8gsgXTwej6LRsd9gBGC+aVmY\nSCaTOnbsiBob6xUOvy9Jys+fKb//Nq1cuUqFhYUqKSlUe3vXlD+331+lNWv2a9++Hkm5kmKSqiWt\nkM93KJ1PAwAATBPFxQNbRSORiKQ9Ir8A0svtdiuRiFsdBjBtTavCRE9Pt5qa3lFT0zvq6emWJJWV\n+VRTU6vrrqtI25ng3//+GjU3dykSWTs0VlZWp0CgMi2fHwAATC9nznwgSYrFFkj6/NA4+QWQHm63\nR8kkzS8Bqzi+MGEYhk6ePKHGxnodPdomwzCUm+vVDTesVnX1KhUXX5P2r+n3VykYbFEwuEvhsFc+\nX5SzxgEAQMrOnDmt3FyvfvGLUv3nf5JfAOnmdrsdVZig+eX46MGRnRxbmOjv71Nzc5MaGg7rwoUP\nJUnz5pWopqZWVVUrlJs79ukZ6eL3V5EoAACAqxaN9uvChQ9VVubTzTcv1803L7c6JMBxnFSY4MYb\nduS4wkR7+2k1NNSrtfWfisfjcrs9qqq6XjU1tZo/fwHVQwAAYCvt7WckSaWl11ocCeBcbrdbhmHI\nMAzuFwALOKIwEY/H1dbWosbGQzp9emAP5uzZc1RdvUorVlQrP3+mqV8/FGpRMNjKskoAAJAWw3OL\n2toPtGCBVFo63+qwAMdyuwd6zSWTCXk8jrhFAmzF1j91HR0X1NhYr3ffbVR/f58kafHipaqpWaXy\n8iVyu92TfIarFwq1KBDoUiSyaWjswIE6BYMtFCcAAMCUjc4tfL7ntWBBu06f7lVFhcXBAQ41eN+Q\nTCaVpn74FmPVB+zFdoWJZDKp48ePqaHhkE6cOC5Jys/P14033qLq6lWaPXtORuMJBltHFCUkKRJZ\nq2BwF4UJAAAwZaNzi7KyiC5ezNczz4S1Zs1qCyMDnMvjGShMJBJJ5eZaHAwwDdmmMHHxYo+amhrU\n1HRY3d1dkqQFCxaqunqVli2rtGTJVVvooObU/Yce0FNq02Lt033q0m2SpHDY3OaaAADAeUbnFm/k\nb1Nx8Xm1tS1TOJxndXiAYw1fMWF/NL+E/WR1YcIwDJ06dVINDfU6erRVyWRSubm5qq5eperqWl1z\nTYllsbWFDqovsFW/O3vyo5G/6v+pTvfqeXXpNvl8UctiAwAA9jNWbrGjrFWn9DlFImXy+c5YGh/g\nZJcKEwmLI0kP+nfCblIqTMTjcf3oRz/SyZMnFYvF9O1vf1uf+cxn0hZUNNqv5uZ31dhYrw8/PCdJ\nKi6e99FRn9fL67X+HYOjwR3aHDk5YuzrCus5bddbZb0KBCotigwAAHsyO7/IdmPlFivKvDolqbf3\nQ3ILwESXml/af8UEp4XCjlIqTOzZs0dFRUX6+c9/ro6ODt11111pSRzOnm1XQ0O9WlreVTwek9vt\nVkXFctXU1GrBgoVZdXRPXvjEmOMfu+YtfSf4f+kvAQDAFJmVX9jFWLnFSZ9PkvTwwwvILQATOWsr\nB2A/KRUmPve5z2n9+vWSBn54c3JS3xGSSMR15EirGhrq9cEHEUlSQUGhqqtv1fXX12jmzFkpf24z\n9fvKxxxftLaWxAEAgBSkM7+wo9G5hSHpRHm5cqJRffzjq6wJCpgmhh8XCqfKnje5cbmUXvHz8/Ml\nSd3d3br//vv1ve99b8qfo7OzQ42Nh/Xuuw3q6+uVJC1atEQ1NbVatOi6jBz1eTWWBrZp34HX9elh\nSy73lS3U0sA2C6MCAMC+0pFf2Nno3OLcvHnqnTlT186zrqcWMF04a8UEezlgPym/FXHq1Cndd999\n+vrXv67Pf/7zkz6+qGim3G6X2tra9NZbb6m1tVXSQBJy++23y+/3q7i4ONVwTFFSUjj+tfWf0T9f\n2K3d27cr9/33FVu0SNX33acVt92WwQjtbaL5RXowx+Zjjs3F/E4/U8kviopmKifHk6HI0me87+vR\nucWZ6mpJ0o233szPwhQwV+Zz4hwXFMyQJM2ePcPy53e1Xz8nxyOXy2X588g2Xu+lotN0mZvB7+s5\nc/Kz/jmnVJg4e/asAoGAHn30UX3sYx+7or/zyit1amw8rK6uTknS/PkLVFNTq2XLqpSTk6NEQmpv\n70olHFOUlBROGs+8pSv1yV8+NWIsm55DNruS+cXVYY7Nxxybi/k1X7YlKVPNL86fv5iBqNJrsu/r\n4bnFvn0v6+S7DSosnMfPwhXi94b5nDrHfX1xSdK5c93yeq17fumY31hsYDuKE/+drkZXV8/Qn6fL\n3HR390mSOjp6M/acU80tUipM/OY3v1FnZ6eeeuopPfnkk3K5XPrd734nr9c77t/5299eV05Ojlau\nvEHV1bUqKSlNKWAAAOBMqeQXTvbBBxHl5uaquPgaq0MBHM/jcdZxoYDdpFSYeOSRR/TII49M6e98\n4hNrtXz5SuXlzUjlS1omFGpRMNiqcNgrny+qQKCS5pYAAJgglfzCjq4kt+jr69X58x/K51uU9X23\nACdw0nGhgB1lrN31qlU3ZepLpU0o1KJAoEuRyKahsQMH6hQMtlCcAAAAU/bmm/+8otzi5MmBo0PL\nysY+BQxAejmr+aXECRSwG0rwEwgGWxWJrB0xFomsVTDYak1AAADA1rZvb7yi3OLEifclSeXlizIV\nGjCtcVwoYC0KExMIh8fe0zreOAAAwESOH88dc3x0bhEOH5fXm6eSkvmZCAuY9py1YoLjQmE/GdvK\nYSehUIueeeY9tbWdl7RbUrWkFUPXfb6oVaEBAAAbGuwr0dzco8lyi87ODnV2dui665bRXwLIEGcV\nJiQXOznGZRgUbrIRhYlRLvWVuHvY6Gsf/X+FysrqFAhUWhEaAACwobF6Vk2UW4TDA9s4fL7FmQsS\nmOYGCxOJhP0LE9x3j41iTXajDD/KWH0lpE/qmmv+Wxs27FIwWEjjSwAAcMWmmlscP35UklReTmEC\nyBTn9ZjgLhz2woqJUcbrH1FRsURPPbUuw9EAAAC7m0puEYvFdOLEcc2dW6y5c4syER4ASR6Pk7Zy\nsGQC9sOKiVHG6x9BXwkAAJCKqeQWJ068p3g8rqVLK8wOC8Aw9JgArEVhYpRAoFJlZXUjxugrAQAA\nUjWV3KK1tVmSKEwAGea0wgRgN2zlGMXvr1Iw2KJnntmttjaXfL6oAoFK+koAAICUDOYWweAunTkz\nU6WlF8fMLfr6enXs2BEVFc3jmFAgwy4VJuzfY4Lml7AjChNj8PurtH69X+3tXVaHAgAAHMDvr5Lf\nX6WSksJx84uWlneVTCa0YkW1XKzDBjLqUvNLp6yY4HcI7IWtHAAAABZLJBKqr/+HPB6Pli9faXU4\nwLTjpBUTNL+EHVGYSEEo1KJ77/2TvvjF/9W99/5JoVCL1SEBAAAbe/nlferq6tTRo3P10EP7yC2A\nDBtcMZFIOGPFBIuuYDds5ZiiUKhFgUCXIpFNQ2MHDtQpGGyhDwUAAJiyN99sUFPTu/J4crV79xZ1\nds4htwAyzEnNL+kxATtixcQUBYOtikTWjhiLRNYqGGy1JiAAAGBbyWRS+/btV35+TK+++il1ds6R\nRG4BZJrH45zCBGBHFCamKBz2TmkcAABgPG+++bpmz+7RkSNL9cYbHx9xjdwCyBwnrZgA7IjCxBT5\nfNEpjQMAAIylqekdvf32W+rvz9Vzz21QMjkyLZs586xFkQHTz6VTOWh+CViBwsQUrVmTJ7f75RFj\nbvfLWrMmz6KIAACA3Zw6FdFrr72ivLwZuvXWNSoo2D/qEa/p8OFZNMEEMsR5Kybofgl7oTAxRfv3\n9yuZXCRpt6Q9knYrmVyk/fv7LY4MAADYQTKZ1F//ulfJZFLr139Ba9bcpBtuaNLw3EIq1dmzm+kz\nAWSI8woTgL1wKscUDez3XPHRf8PHmyyJBwAA2MuRI606e/aMqqqu18KF5ZKk3l6fpK9c9lj6TACZ\n4bTCBMeFwm5YMTFF9JgAAABXo7l54M0Mv/+2oTHyC8BazuoxAdgPhYkpCgQqVVZWN2KsrKxOgUCl\nNQEBAADb6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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bd77dd8>"
+       "<Figure size 1152x432 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1501,7 +1560,7 @@
     "ax[1].text(0.02, 0.91, \"validation score: $R^2$ = {0:.2g}\".format(model20.score(X2, y2)),\n",
     "           ha='left', va='top', transform=ax[1].transAxes, size=14, color='red')\n",
     "\n",
-    "fig.savefig('figures/05.03-bias-variance-2.png')"
+    "fig.savefig('images/05.03-bias-variance-2.png')"
    ]
   },
   {
@@ -1516,21 +1575,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 51,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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pXbo08+b9QGJiEkuXLgYgJKQ633+/mH/+cyA//LCAJk2ecUr5+T6cw8LuUKNG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QQW7ly4sQ6z6w/9BfvaIoipdxuKhNE/bulbKXgUKRIlCp0vVCHBbm71UpzqgwK4qieIHUVHFN79kjghwogVuRkVC5cvotIkL3hAMdFWZFURQ3SU2V6Ondu0WM/b1fHBQk+8IOEa5USYKzlNyFCrOiKIoLpKZKBLVDjP1Z9tJiESGuWlVulStLrWcld6PCrCiKcgvS0iSSetcuEePkZP+tpUyZdCGOjpYSlUreQoVZURQlE2w2qba1Y4dYxwkJ/llH0aJQowZUrw5VqqhrOj+gwqwoiuLE6dMixjt3Qlxczs8fHCwu6Ro15FamjAZr5TdUmBVFyffExcEff4ggnzmT8/OXKJEuxFWraiGP/I4Ks6Io+ZKUFElt2rZN0pxyEosFKlYEw5BbqVJqFSvpqDAripJvsNmkXeK2bWIh52REdUiI7BMbBtx2m+4VK1mjwqwoSp4nPl7c1Fu3wtmzOTdvkSIiwoYhoqy9hpXsoMKsKEqexGqVFKfff5d7qzVn5g0Phzp15Fa5shT9UBRXUGFWFCVPcemSWMZbtsjPOUHRouliXKmSirHiGSrMiqLkemw2KY25ebO0U8wJ67hYsevFWIO3FG+hwqwoSq7lypV06/jCBd/PV6iQCHGDBuKmVjFWfIEKs6IouQqbTVoq/vqrpDulpfl2vpAQCd5q0EDyjIODfTufoqgwK4qSK0hNlVrVv/4KJ074di6LBapVg/r1oXZtLfih5CwqzIqiBDSXL8ve8ebN4rr2JWXKQKNGIshFi/p2LkXJChVmRVECkuPHxTretcu37uqwMBHiRo2gfHndN1b8jwqzoigBg9UqnZw2bpR9ZF/hcFU3bgy1ask+sqIECvpxVBTF7yQnS3T1hg1w8aLv5ilZUsS4YUMoXtx38yiKJ6gwK4riN+Lj4bffYNMm3/U7DgqSqOqmTcVKVle1EuioMCuKkuOcOwe//CL1q1NTfTNH8eLQpIlYyBrIpeQmVJgVRckxjhyB9evBNH0zvsUCNWuKdVyjhpbGVHInKsyKovgUm036Ha9dC4cP+2aOIkXEOr79dihRwjdzKEpOocKsKIpPsNnEMl63TlKffEFUFNx5J9Stq5HVim94//33Wb16NR999BGVKlXKkTn1o6woildxpDytWwenT3t//KAgqcbVvLk2j1B8y7vvvsvkyZMBGDp0KNOmTcsRcVZhVhTFK6SlSTDXzz/D+fPeH79wYXFXN2smnZ0UxZeMHz+ezz//nFKlSnH+/HmSk5NzTJxVmBVF8Yi0NMlBXrcO4uK8P36ZMtCiBdSrBwUKeH98RcnI4sWL+fzzzxk6dChFixblo48+Yvr06QwZMoSnnnqK7777zqfzqzAriuIWaWmwfbsEdfmiKEjVqtCypURXq7tayUm6du2KzWajR48eTJo0CYBq1aoxffp04uPjfT6/CrOiKC5htcIff8BPP0FsrHfHtlgkkKtlS6lbrSj+ICgoiB49etxwvFq1ajkyvwqzoijZwmqVhhI//uj9PeQCBSTVqUULTXdSFBVmRVFuis0mUdY//ghnz3p37CJFJLq6WTMoVMi7YytKbkWFWVGUTLHZYN8+WLXK+2lPxYpB69ZSLlMDuhTlelSYFUW5gaNHYeVK71fqiogQQW7YEIKDvTu2ouQVVJgVRbnG2bNiIe/d691xy5SBu+6SwC6tX60oN0eFWVEU4uJkD3nbNnFhe4vy5UWQa9XSlCdFyS4qzIqSj7l6VSp1/fabd9svVqgAbdtqDrKiuIMKs6LkQ1JTYeNGqdaVlOS9caOioF07ab2ogqwo7qHCrCj5CJtNcpFXrvRuta6yZUWQDUMFWVE8RYVZUfIJR4/CsmVw7Jj3xixdWgS5dm0VZEXxFirMipLHuXhRLOSdO703ZqlSsodcp45GWSuKt1FhVpQ8SmKiBHZt3Oi9wK7ixUWQGzZUQVYUX6HCrCh5DKsVfv8dVq+WqGtvUKgQ3H23lM4M0W8NRfEp+i+mKHmIw4dh6VI4dco74xUoII0lWraEsDDvjKkoys1RYVaUPMClS7BihbRj9AZBQdCkiVjJRYt6Z0xFUbKHCrOi5GIc+chr10JysnfGrFcP2reXutaKouQ8KsyKkkvZtw9++MF7vZGjo+Gee6Rql6Io/kOFWVFyGbGxIsh//umd8SIioFMnrWetKJlhs9mwebOAfDZQYVaUXEJKiqQ//fwzpKV5Pl5YGLRpo5HWinIzRowYwYgRI3J0Tv13VJRcwP79sGSJWMueEhQkYtymDRQu7Pl4iqJ4FxVmRQlgLl8Wt/WuXd4ZzzDEbV2qlHfGUxTF+6gwK0oAYrXCpk1SJMQb3Z9Kl4Z774Vq1TwfS1EU36LCrCgBxvHjsHgxnDzp+VhhYVJCs1kzCA72fDxFUXyPCrOiBAiJiWIhb9ok7Rk9wWKBxo2hQwcoUsQ761MUJWdQYVaUAGDPHgnuunzZ87EqVICuXTUfWVFyKyrMiuJHLl8WQd6zx/OxihSRwK6GDTUfWVFyMyrMiuIHbDbYuhWWLxcXticEBUHz5pL+pI0mFCX3o8KsKDlMbCx89x0cPOj5WJUqQffuULas52MpihIYqDArSg5htcKGDbBmjTSf8IRChcRt3bixuq0VxZecOXOGkydPUq1aNUJDQwkJCSEoKMinc/p2dD+QkpLCjz/+yNq1a0n19NtPUbzEyZPw2WfSmtHTj2WjRvDMM3D77SrKiuIrtmzZQkxMDG3atKF///7s3LmT3377jbZt27JkyRKfzp2rLebk5GTGjBnDsWPHmDJlCsnJyfTr14+9e/cCUL16daZNm0ZkZKSfV6rkV1JTpSXjzz+LxewJpUuL2zo62jtrUxQlc3bs2MHDDz9MVFQUQ4cOZdq0aQAUL16ckJAQRo4cSZEiRWjTpo1P5s/VFvOkSZOYM2cOUVFRACxatIg9e/YwePBgxo0bx9mzZ5k4caKfV6nkVxxW8tq1nolygQLQsSM88YSKsqLkBBMnTqRixYp88803DB8+/Fp3qfr16/Ptt99SvXp1Pv30U5/Nn6st5qVLl/LAAw8wZswYAJYtW0bRokV58cUXCQkJ4ejRo8ydO9fPq1TyG2lpIsbr1nluJdeoIVZyiRJeWZqiKNlg69atPPXUU4SFhZGQkHDdc+Hh4fTt25cPPvjAZ/PnamE+deoUjRo1AiAhIYFNmzbRtm1bQuw97KKiorh06ZIfV6i4i9UqaUDO2GyBv6d66hQsWiT3nlC4MHTpAvXrB/57VpS8SMGCBbN8LikpCaunV903IVe7skuVKsW5c+cAWLduHcnJybRt2/ba86ZpUqZMGT+tTnGX1FQR5eRk+Osv2LIFrlwJbIFKS4OffoLJkz0X5QYN4Omn5T6Q37Oi5FUaNmzI4sWLM33u6tWrzJ07l/r16/tsfrcsZsMw7gcWm6bphb437tO8eXOmTZtGaGgoM2fOpFChQnTs2JFLly4xf/585syZQ//+/f25RMVFrFYICZGKWD16SK5vbCyULAkjR4pbN9A6JJ0+LVayp00niheX91ezpleWpSiKmzz77LMMHjyYQYMG0aFDBywWCzt27GDfvn3MmDGDEydOMHr0aJ/Nb7G5WC3fMIxPgMeA5UBvb4hzTEyMbcGCBS6fd+nSJZ577jk2bNhA4cKFefPNN+nWrRtbtmxh4MCBNGvWjI8++ohixYp5ukQlB0lMhLvugmLF0gOe5s2D8ePh4YfhvffkOX9jtcIvv0heclqa++NYLHDHHdC+PYSGem99iqK4z/r163nttdc4duzYdcdLly7NK6+8QufOnT2ewzCMLaZpNs143CVhtotyV6AicAzYhRfE2V1hdhAbG0t4ePi1PYGrV6+yb98+GjZs6MmyFD+xYQMMGABTpkjLwqAgWLZM+gkvWAC33QZVqsg+rL+4cAEWLoQjRzwbp3Rp6NlTKngpihJY2Gw2du/ezZEjR7BarVSoUIF69epdi2PylKyEOdt7zIZh1AHa2W82oB9QCmjvlRV6QERExHUb9YULF74myrGxsf5alpJNHAU3HLEUx4+L4DVqJKI8fbqI8ptvSpTysGFw6JB/1uqocf3f/3omykFBUtv68cdVlBUlEDlx4gQTJkygYsWK3HvvvXTr1o3ffvuNCRMmcP78eZ/OnW3ZN01zt2EYdUzTTDMMwwKcB+40TdMDJ57nzJo1i3Xr1nH16tXrouTS0tK4cuUK+/fvZ+fOnX5coXIzbLb0PeUXXpBbrVoQGSldl4KD4aGHYOxYePll2c/dtAk2b4Y6dXJ2rVevSo1rTztBlSkD990H9vR7RVECjD///JPBgwcTHx9P9+7dKV68OCDbpzNnzmTx4sV89dVXVPLRVbVL9nhGEfa3KH/22WdMmDCBggULEh4ezoULFyhXrhwXL14kISGBsLAwBg8e7M8lKjchNVVEOTUVYmLg7FnpjlSihLir//53uHgRxowRUbZaYedOKFcu5wPA9u2Db76B+Hj3xwgKgtat4e675X0rihKYTJgwgSJFivD1119TpUqVa8dHjhxJv379GDp0KOPHj/dZAatcnS61YMECateuzS+//MLXX3+NzWZj+vTpbN68mVdffZWkpCTdZw5gQkIkDWr0aGnK8O67ULWqRGBPmybPlygBFSrApUtStOP118Wd3bJlzqwxJQW+/x5mzvRMlEuXhkcflQAvFWVFCWy2bdvGQw89dJ0oO6hUqRKDBg1i06ZNPps/Vwvz8ePH6dWrF+Hh4VSqVInixYuzefNmgoODefDBB+nateu1GqdKYHLggLipFy+GHTvSj9eoAT/+KEFer70GpUrB8OHi+l6+XKxPTyKhs8Px4/DJJ+I6dxeLRazkxx+XCwxFUQIfq9VK4k0apdtstps+7ym5WphDQkIoUqTItcfR0dGYpnntcfPmzTnkryghJVMyFstp0ED2i4sUkYCqVavSn6tZU/aZFy6E//1PrOi1a6V2dGqq7D/7AptNmk588QV4EuNRqpRYyR07qpWsKLmJRo0a8fXXX2daOfLKlSvMnTvXp97YXP11Ub16dbZu3UqfPn0AqFq16nWBXnFxcSQnJ/treUoGnPeUT5yAhARxVd9+u+QC3323RF6Hh0Pz5nJOZKTcGjdOHyctzXdCd/myXAj89Zf7Y1gs0KIFtGsnFxGKouQunnnmGQYNGkT37t3p0aMH0dHRWCwWjhw5wvfff8/Zs2d56623fDZ/rhbmmJgYRo8eTXJyMm+88Qbt27fnueeeY9KkSVSrVo1p06ZRq1Ytfy9TIV1ML1+Gfv1g/344cwaKFpU95kceESu1dWv4xz/g7bfhzjvl3Iw1sn1lKe/bJ6J89ar7YxQvLhHXmWxNKYqSS2jYsCFTp07lnXfe4YsvvrjuuVq1avHWW2/R2Nla8DK5WpgHDBjAqVOnmDlzJiEhIdxzzz20bduWSZMmAdIFZOTIkX5epQIipgkJYhUXLQovvijHli2Dxx4TC3XMGLGc27WDV16BV1+V1/u6XnRqKqxcCRs3ejZOw4aSbx0W5p11KYriP5o2bcrcuXOJjY3l+PHjWK1WoqKicqT/Qq4WZoDnn3+eESNGXKvE8sknn7Bp0ybi4uJo3LgxkZGRfl6h4mDJErF+J02SvWWQEpu1aoko164NAwfKPnOrVvL47rt9u6Zz56TcpyeNJwoVkhrXdet6b12KogQGERERRERE5OicuV6YgRvKozVr1sxPK1Fuxr590ujBkZPvaO34+uuSn/zyy9Cpk+zP7twpucy+wmaDbdvkYiElxf1xqleHXr0Co3a3oijeY+3atXz33XecO3eOtExSQCwWi8+yfnKVMHfo0IFRo0bRoUOHa49vhcViYeXKlb5empKBtLQb94LDwqSV44kTkqtssaTvH3fqBCtWSEGRMmXSq3o5Asa8SWKipGd5UhAuJETWfMcd2ppRUfIaM2fOZMyYMQBERkbetDezL8hVwly+fHkKO3UuKF++vB9Xo2SFQ0wTE2H1ahHbevVg6FD4979l73juXLGWnalUSSKynfG2KJ88CXPmSBMKd4mKkkplpUt7b12KogQO06dPp1atWnz22WeUKlUqx+fPVcI8Y8aMmz5W/I9zP+W77hI38Z49Uqnriy/gnXfgySfh/vslGjsqCo4eledq1fJd/WibDbZsgaVLPStM0qqVVO/yVWS4oij+5+TJk4waNcovogzuC7NrTZxzkMuXL2OxWAjPaHopOUJQkIhx376So/zhh9JjOCEBDEOqXwUHy35y69byXMmSkmY0a5a4hR17z94iKUmaT3jiug4PlzSo6tW9ty5FUQKTypUrc+7cOb/N764w+21XzWazsXbtWvbt20flypVp3749ISEhbNiwgTFjxvCXvTJE7dq1eeGFF2jdurW/lppvOXYMDh6EUaOgfv3rn4uPF+t1xw6YOlXc3eXLSzR2cLD395RPnxbXtScVvGrUEFF2KjKnKEoeZvjw4YwdO5bOnTtTs2bNHJ/f3a/AZsAhL64jW1y6dInhw4ezfft2bDYx2uvVq8err77K8OHDKVSoEB07dsRqtbJx40Yef/xxpk6dyh133JHTS83XOAK8MkY7p6ZK3emRI6FJE/jb365/3psVvRxR199/n97v2VWCg6Wc5p13aoCXouQntmzZQpEiRejVqxdVq1YlIiICS4YvgYCLyjZNc4u3F5IdPvjgA/bu3curr75K8+bNOXnyJGPHjmXo0KFUqVKFGTNmUKJECQDOnTtH3759mTJligqzD8ks+jo8XMpoLl0qlqYjBTAkRKzP06fFos5YlM1b+7bJySLI27e7P0ZEBDzwgFjziqLkL9atWwdAuXLlSEhI4Pjx4zk6f64K/lq9ejX9+/dnwIABAFSrVo1XXnmFRx55hIEDB14TZYBSpUrRt29fDRDzIQ63c0KCpB/t3y/lNqtVkyCvBx+UfeUXXhChBmnzWL9+ulh7m7NnxXV99qz7YzRqJBW8QkO9tixFUXIRq1ev9uv8uUqYz549S/UM0Tc1atQAMk+dioqKIi4uLkfWlt/IGH0dFyc/nzkDb7whwV/790tq1O7d0KWL5DF//LHs1fqiBswff8C337pfMCQ0FLp1S69KpiiKkhWxsbE+qwiWq4Q5JSWFsAyFiAvY2/cUyKSNj8ViybRii+I5QUHiMu7eHcqVg5kzoXBhqFpVArwuXYJnnpEylX//u+QzV6gg1vTChXK+t6Kv09KkR/Ovv7o/Rrly0KdPumWvKEr+ZtasWaxbt46rV69idepXm5aWxpUrV9i/f/913Qy9Sa4SZiWwOHYMYmPFbV23ruwdf/45jB8vYtm1q/zcqpVY1CEhULmyBFJ5K/r68mUpVnLkiPtjNGkiFr22aFQUBeCzzz5jwoQJFCxYkPDwcC5cuEC5cuW4ePEiCQkJhIWFMXjwYJ/Nn+uE+eLFi5w4ceLaY4erOjY29rrjABc8Ke+k3JJLl+DPPyWQ69134ZtvpHVjx45QtqzkMLdsKfvOznn6Dje4pxw5IqJ8+bJ75xcsKBa/uq4VRXFmwYIF1K5dmxkzZnDhwgU6derE9OnTKV++PF9//TVvvvkmDRs29Nn8Ln89GoYRBEQDR4Eg0zSTvb6qmzBu3DjGjRt3w3Ft75jzNGokfZRHjJAOS9WqyR5vp04ier//LvnK/fpdf56n7mubDX77TVpGOnmYXKJMGdkH91NhH0VRApjjx4/zwgsvEB4eTnh4OMWLF2fz5s3cd999PPjgg2zZsoVp06bRpUsXn8yfbWE2DCMEeAsYYT/vNuBtwzBSgcdM07zikxU6cd999/l6CiWbOPaH//tfKa8ZFSW1o8uUETf23r1ScSs62rvzpqRIFa8dO9wfo3FjcbOr61pRlMwICQmhiFNFoejoaEzTvPa4efPmvPfee76b34XXvgl0Bu4BltiPfQB8BkwAnvDu0m7krbfe8vUUSgYy5ik7ukEFBaX/3LGjPDd3rohxXJykLBUrJha1t4iNha+/lr1sdyhQQAS5cWPvrUlRlLxH9erV2bp1K3369AGgatWq1wV6xcXFkZzsO2exK07FAcCTpmmuxV4r2zTNn4FHgBgfrE0JAIKDJU/5669lL9fRqhGur4aVkCCW7JAh8MEHkqe8caPsJXsjMH7fPpg82X1RLlUKHntMRVlRlFsTExPDggULGDlyJFevXqV9+/Zs3ryZSZMmsWTJEqZNm0atjBWSvIgrFnMp4Ewmx68AhbyzHCVQcERN22zwyisizLGxIrxFiqRbyw4KFYLp06U+dvHikn7kjehrmw3WrYM1a9IvCFylXj3o2VP2vRVFUW7FgAEDOHXqFDNnziQkJIR77rmHtm3bMmnSJADCw8N9GtdksWXz284wjEXAOeAx4BLQAIgFvgJSTdPs5e4iYmJibAsWLHD3dMXLONzXly/Da69J28ZlyyQP+Z//FHEuXPh6cc4o1OB5nnJyskR679rl3vlBQRKIprWuFUVxh9TUVEKcLItNmzYRFxdH48aNifRC0QPDMLaYptk043FXbJmngYWI1VwI+B6oBBwEeni8QiVgcLiv77pLrN9evSTA69NPRaitVnjooevFOTPh80SUL16E2bPh1Cn3zi9SRAqGVKni/hoURcnfhGRw9zXzRcnCzObN7gtN0zwO3GEYRnugtv1cE1humqabSStKoLJ6tdSbnjwZHD1Ahg0TkR49WkR38ODM3dqecviwBI9dcTPOv2JFSYUqVsx7a1IUJe/SoUMHRo0aRYcOHa49vhUWi4WVK1f6ZD2upEt9DrxtmuZqwL8VvhWf46jq1aiRPE5IkH3kb74RoR4zRgR56FCxnL3F5s2wZIn7+cnNmkkVL291qlIUJe9Tvnx5Cjt9kWXWeyEnccWVHQOM9dVC3GXVqlWsWLGCs2fPkpJJ9wJf9szMK2S2F9y8uVjB06eLpVyoULo4P/64RDi/+67Uln7gAc+LhqSlwQ8/SL9mdwgJgR49wIfFeBRFyaNk7EL44YcfXtetMKdxRZjfBT4xDON94DCQ6PykaZp/eXFd2WL27NmMHj0agIiICEK1T5/LOKKmU1KkvGbZspJaFB0NrVvDlCniGu7SRUQZJCjsgQfgwAFxa/fokf6cO1y5Iq7rw4fdO79ECakuFhXl/hoURVEc9O7dm759+/LUU0/5ZX5XhPkN+32nTJ6zATnuPJw6dSo1a9bkk08+8bvrITdis6W3buzaFf76S6ziF16Q25tvwoABcn/4sERjb90qnaR69pTmFTVrwqpVUnPaHU6dkiCvixfdO79GDYiJ8a47XVGU/M2FCxco5cd6va44IKvab42AZsCdQFP7sWpeX1k2OHHiBP3791dRdhGrVVzHFotYyh06yM8vvSSCO3q09FG+/XaxZCMj4fnnoWRJeT4oCP7xD9mDrlRJOka5w9698MUX7oty69bw4IMqyoqieJfu3bszd+5czp0755f5XbGYTyKlNx8n3TpOA2Yhuc05TnR0NLGxsf6YOldy5ozUsnbsByckSCWt6tWlMEj9+nK8QgXZP7Za4Y03YNYs6eS0aZPUw773Xnndxx9D0aLi/nYFmw1++QVWrnSvaEhIiESHO9arKIriTYKCgti/fz9t2rShcuXKREZGEpQhkMaX8UuuCPME4F4kZ/kXRJxbAhOBcUCOt3d6/PHHGTt2LB07dvRpebS8wPbt8PDD0nSieXM59vDDYhGXL399atHYsWJBT5ggIj5iBNSuLTnBv/wCTzwhIr92rbixXRHmtDRYvFhc4u5QrBj07y9rVhRF8QXr16+nZMmSACQlJd3QUtjXuCLM/YEHTNP8yenYEsMwrgKzyQFhHjJkyA3HkpKSiImJoUqVKkRGRmLJkFCrUdlCcrLkHTdvnl7Z6/XX4epVWL4cdu6UgC9HTvKYMXI/caI0pRg7FsLCJOBr+3ZJo1q3TgQ7uzhqbh865N57qFRJgrzCw907X1EUJTusXu3fjGBXhDkIKcmZkfNAjnxVHjt27IZjjquaxMREjh8/nhPLyJU0aya3xEQJ4urYEYYPF5f14MHw5JMwb57kKDvE+c034dIlEe1SpcR6fvhhOc/VGtjnz8NXX8m9O9x+uwSoeVJ3W1EUxVvExsYSERHhk7Fd+ZpbBbxjGMZA0zTjAAzDKIH0aM6Rywt/X8XkBfbvhz/+kG5NhQvDoEHw5ZcSfd2nj7i2mzdPF+eJE9N/tlrThdEVgTx4UMZNSHB9vUFBkqrVrJnWu1YUJeeYNWsW69at4+rVq1idKh6lpaVx5coV9u/ff10rSG/iijA/D6wBjhuGsd9+rCbwJ+B2AwvFt2Tsp1yvnuQm/+Mfku5ks4nFPGuWiHO/fiKizpazo9WjO0VEtm6VdpDuVPIqVEhKa1at6vq5iqIo7vLZZ58xYcIEChYsSHh4OBcuXKBcuXJcvHiRhIQEwsLCGDx4sM/md6lWtmEYdYEuSK3sBGAvsNI0TTcb8nlG+/btb9hTdsZisVCwYEEiIyNp0KABDz/8sF9z03Iah7s5IUH2kZOSpNtSixbw73/D//2f3EO6OA8aJM0rtm6FOnXSx3LVWrXZJOp6/Xr31l6mjFwo2HcqFEVRcowFCxZQu3ZtZsyYwYULF+jUqRPTp0+nfPnyfP3117z55ps09GGZQVdtoAcAi2ma/zZN80NgAHC/95eVPVq0aEF8fDzHjx8nNDSU2rVr06hRI0qUKMGJEyc4d+4cJUuW5OLFi0yZMoXevXvneHSdv3AuHtK8udS0fvBBaNxYgrfuvBPGj5eqWf/+t7izq1eH//1PRNow3J87JUWCvNwV5dtug0cfVVFWFMU/HD9+nF69ehEeHk6lSpUoXrw4mzdvJjg4mAcffJCuXbv6NKg428JsGMbLwMdAEafDR4HJhmE86+2FZYc6deqQkJDAxx9/zJIlS5g0aRITJkxg/vz5zJo1i6CgIHr37s13333HokWLAJg4caI/lpqjOIqHWK3iso6KEneyIzWqSxfYskUEe/x4EcDx46WTlGHA55+L+zstzfW5r1wRcd+7172133mnpENpdVVFUfxFSEgIRYqkS110dDSmaV573Lx5cw65m16SDVyxmJ8E+pqmOctxwDTN14CByP5zjjN16lSGDBlC+/btb3iuUaNGDB48mMmTJwNgGAYDBgxgvbtmXIBz4ACcPCk/BwdL9PUHH0iZzYcfFvd0TAx88omU0eza9XpxTkqCn3++fkxXOzSdOyei7k5wfFCQlPXs0sXzhhiKoiieUL16dbY6FVuoWrXqdYFecXFxJCcn+2x+V74CSwA35ivBQaCMV1bjIufPn6fsTapbREZGcvr06WuPy5QpQ3x8fE4sLUc5eVLyiZ2FdfJk+PBDOebINbbZoEED+OgjsYy7d4fff5dAr/nzYepU99dw5IiU17xwwfVzQ0Nh4EBo2tT9+RVFUbxFTEwMCxYsYOTIkVy9epX27duzefNmJk2axJIlS5g2bZpPi1q5IsxrgTcNw7iWs2z/+TXg5yzP8iE1atRg4cKFmV65JCcns2jRIqpVSy/jvWvXLqLyYAuiqCipwNWnjxQSSUiARx6RfOXUVHFnp6SkB3DVr58uzk2bgmlKoJe77uvdu6U9pDvpUCVLSlvJ6tVdP1dRFMUXDBgwgMcff5wff/yRkJAQ7rnnHtq2bcukSZN44YUXSExMZORI39XUstiyWazYMIwqwAqgHOBIl6qOWNE9TdPcn8WptyQmJsa2YMECl8/76aefeOqpp6hcuTL9+/cnOjqaggULcvDgQebPn8+ePXt4//336dy5M6+//jrz5s3j6aef5sknn3R3qQFNWppYv4YBn34KBQpIStRnn4kre/p0OeZg61ZxPX/wgetuaxALfONGifh2p+Z15cqSnlWkyK1fqyiKktOkpqYS4lS0YfPmzVy8eJHGjRsTGRnp8fiGYWwxTfMGX2G2hdk+SEGk7WNtIBnYBywzTdONLNV03BVmgDVr1jBu3DiOHj16LXXKZrMRFRXFSy+9ROfOnYmNjeXuu++ma9eujBkzhoIFC3qy3IBm/HhpSDFsmIhySAi8/bYEZLVseaM4O8iY73wrrFZYtgx+/dW9dTZoIJ2qtJKXoij+5tlnn6Vnz560adOGApl9QfqIrITZ1a/FMGCVaZrfG4ZRD2lqkYgUHvEL7dq1o127duzdu5cjR46QmppKxYoVqV+//jWhLlGiBFu3bs3RX3hOkJmYjhwphTlGjBAr9t//lnaOANOmSSDYlCmQ8drEFVFOSZE9aXcjr9u1g7vv1kpeiqIEBqtXr2bFihUUK1aMLl260LNnT5o0aeK39WRbmA3D6AZ8DfQ2DOMAsA44A7xuGMYLpml+6qM1ZotatWpluRkfFBR0Q8uu3I6jeMiVK2IZx8dDw4Zw//3w9NMiys/ak9j+8x8R56AgGDcOatSQBhbucOWKFCLJpGz5LQkOht69tV2joiiBxS+//MKyZctYsmQJ8+bNY86cOZQvX54ePXrQo0cPqudwEIwrFvNYpL3jKuBN4BRQBynHOR7wuTB36NCBUaNG0aFDh2uPb4XFYmHlypW+XlqO4qhZffmy7ClfvZreuWnzZhHfZ56R1z77rFimjkpfFSvCQw+5N29srBQicacFdliYVPKKjnZvbkVRFF9RrFgx+vTpQ58+fYiNjWXp0qUsXbqUTz/9lE8//ZTatWvTq1cvunXrliPVI10RZgOYYZqmzTCMnsAi+89bgRzpjlu+fHkKFy583eP8SFCQ5B136yYBVO+9J/Wke/WSDlFWq+wrP/OMiPLzz8PFi5LO9OijMoare8onT8LMmWKZu0qJEpIOVbq06+cqiqLkJBEREQwcOJCBAwdy+vRpli5dypIlS3jrrbf4z3/+Q/PmzenVqxedOnWiUKFCPlmDK8J8AmhoGEZJoB5ScASgM3DIy+vKlBkzZtz0cV7H0VQCYNMmKejxxReS6nT6NERGSo3p778X0R03Ttza8fFS+cu5mpYronzwIMyeLRcDrhIVJaKsPZQVRcltlC1bloceeoiHHnqIo0ePsmLFCtasWcOoUaN4/fXX+f33330yrysbrxOA+cCvwEbTNNcbhvEKMAlxbSs+JDVVRNmRsn3unFTYctRXee89qf7liL6eNEn2lU+ckDzmdevE0na1y9Pu3eK+dkeUa9aUYDMVZUVRcjvFixcnIiKCyMhIQkNDSUxM9NlcrnSX+tgwjF+AKsAy++GVwHemaW73wdpu4OWXX3b5HIvFwrhx43ywmpzDeU+5RQsR2s6d4fbboXhxmDtX9pCXLJEmFU8+KRbul1/CmTNSI9ud1o2bNsmY7uQoN2kirvY8FnOnKEo+Ii4ujhUrVvDDDz+wceNG0tLSuO2223jqqafo3r27z+Z1KV3KNM1twDanxxu9vJ6bsnDhwkyPWywWssrHzs3C7LBug4IkRendd8U13LChuKznzhX39YwZEtDVpYvUyN6yRZpBjB4NzmXEs5ueZLPBjz/CTz+5t+4OHaB1a02HUhQl9xEbG8vKlSv54Ycf+O2330hNTaV8+fI8/PDD9OzZk5o1a/p8DbmqvMOqVatuOBYXF0dMTAzjx4+ncePGfliV9zlzRoTXYW0mJ0vBkPXrxTXcoIEcL1VKBPvs2fT940OHYNEisVg7dZJjrgR6Wa1iJW/e7Pq6g4MlAM2xPkVRlNzA+fPnWb58OcuWLWPz5s2kpqZSvHhxYmJi6NmzJ01zuJB/rhLmChUq3HDMEaUdGRmZ6fO5jaQkyUsuVgxee02OHTsme8ynTsHhw+mvTU2VKl69e8PLL0t+8OXLEBEhe84g1m92RTk1VQqH7Nnj+rrDwqS8ZtWqrp+rKIriT+666y5sNhsFChSgffv2fqkC5kyuEub8QkKCRFGDFPP4+msps1mokERhG4ZU+HKUsxw4UKzn9euhQgUR9JCQ9CIk2SExUeZyFv7sUrQoDBqUHoimKIqSm2jatCk9e/akS5cuhAdAtOpNv7YNw6h2s+edMU3zL8+Xo4SGwtix4pJ+5x0J7qpQQYT3hRfELe0Q6aeflnMqVpT8ZEeOMrgmyvHxEih26pTr642MhMGDJVdZcQPnHDhFUfzC9OnT/b2E67jVV/d+ILOoKovTccfPbvQnUjKjZElxSYeHS+elzz+X1Ke6dcVSDgqSoiGQLs4Z95GzK8oXL0rw2Pnzrq+zfHmx1rU7lBukpYkga9i6oigZuNXXt+4Y+okvv5Rc5dGjYepUMaxefhnq1RPLGaTEZny8pE+507bx3DkR5bg418+tXl32lPNwoy7f4viDHTokm/p3361XOIqiALcQZtM0b9hxNAwjCIgGjgJBpmkm+2htN7Bp06Ybjl2+fBkA0zSv65vpTLNmzXy6Ll8QHS23SZOktOb//ifHHeL8f/8n1u7SpfDii657Q0+eFPG/csX1tdWvLwFn7lwM5DtsNgl1z/jL2rYNnntO+maWKSNXOE8+CX//u1+WqShK4OBKd6kCSBOLEfbzbgPeNgwjFXjMNE03vuJdY/DgwddaOWbknXfeyfK8Pe6EGQcIlSvDhx9KG8cZM+Q7fuBA2Q8eO1YCwRzFQ7IrzkeOwFdfScCXq9x5pxQ30W3RbGKxpIvyL79AlSqyB/Dvf0se3Lp1cnW0fDn8859QuLAItKIo+RZXorLfQOpi3wMssR/7APgMKdf5hHeXdiNPP/10lsKcl4mOFnF+7jm5/9e/oGlTMbYsFhHr7G5V7t8vUd4pKa6vo2NHaNVKRTlLnP8QjiulAwfEVT1ihAQNTJ8uieezZ8OCBeDw5rRtCzt2SBWZli2lioyiKPkSV4R5ADDQXiPbBmCa5s+GYTwCfEMOCPOIESN8PUXA4nBrL1sm3+sjR7peZnP3bslTTktzbe6gIOjRQ8p9KhlwdlU7/yEcolyvHtSuLY2yH34YbrtNRLlkSdlXBli8WNwhS5ZIqbbkHNsdUhQlC1atWsWKFSs4e/YsKZlYMhaLhWnTpvlkbleEuRRwJpPjVwDf9L5SrsORFuXAlZSorVvh229dr3sdEgJ9+ojLXMkEh6s6Pl5+wWFhUnKtaFGJkOvQQQR33DgJq3ecExIC990HO3dKVZlOnWDhQsmNS0jw73tSlHzO7NmzGT16NCBtIEOdW/PlAK4I80rgH4ZhPGZ/bDMMozjwFrDa6ytTbkl2RXnjRvjhB9fHDw2FAQNkWzTfk1UQV1KSbPZPnChJ3Vevisv6X/+CoUPFUl62DPbtk2LmIGIdFCQR2WPGSCRdVJQ8FxMjlVpattToOkXxE1OnTqVmzZp88sknlC9fPsfndyWJ8mmgAWI1FwK+B44BlYBnvb80xVNsNmlE4Y4oFy4suqKiTPp+cWZCuXSplEx7+20J5Jo+XQIAnnkGfv9dhLlMGdlHSE2Vc6KioGtX+SU3bCiP09LEsl69WvYtVJQVxW+cOHGC/v37+0WUwbW2j8eBOwzD6ADUsp9rAstN03Sxy6/ia2w2WLUKfv7Z9XOLFoUhQ6B0ae+vK1disUgo+2efyQZ/+/YSmh4eLsdKlEiPpK5QQVprVaokQQFTpkhg1+bNIs6ODh/PPQexsWI99+kjOczffy/7zhqVrSh+JTo6mtjYWL/N73LZIdM0V5mm+ZFpmhNN0/xBRTnwsNnEe+qOKJcsCY88kg9F2WrNOipuwQLJE1u+XKqyDBok4hkcDNu3y3OOzfvkZLGEBwyANWukpNrAgXDwIGzYkD5mgwYS8PX227KnfOAAvPEGfPON1GFVFMVvPP7443z55Zfs3bvXL/Pfqlb2GjIvyXkDpmm2v/WrFF9js4lHNJNaLLekTBmpe120qPfXFbA43NRZhbafPQuvvCKW7fjxcsXi6PaRkAC1aslecVKSBH45csl69oSPP5YqMPfcI7/cDRvgwQflF2y1isX93HPw1FPSJkxRFL8wZMiQG44lJSURExNDlSpViIyMvCFV159R2c42VyTwGLAI2AykAI2BB4CPfLE4xTWsVulKtXWr6+dWqCCGnb2LZv7B8c+2aRN89JGURGvVSjbYo6Nln/jECZgwIb191sCB6ef36CHCvWeP5JM5BPbcOYmei4uTKL0mTWQ/es8euOOO6y8EVJQVxa8cO3bshmMlS5YEIDExkePHj+foem5VkvNfjp8Nw1gOPGua5n+dX2O3qof5ZnlKdrFaYdEiqVHhKlWqiOc1hzMCAof335dWXnfcITlp774rgjphgljCiYnpAu6wsNPSxE3du7ec+9prEpkdHS1C/t//isBXszdoe+01cX/fcYe/3qWiKFmwenVgJRa5ki7VCngmk+MbgA+9sxzFHdLSpHDI7t2un2sY8MADedRos1pFRG9WquzwYalPOmgQ/Oc/8sscPTq93dbdd0uZtP37r08cv3BBWnzdfz9MngzDhslec8eOcnWUmiqubEc/zBo15KYoinILXBHm34FRhmE8aZpmAoA9j3kMIs5+Ze7cucyfP5/Zs2f7eyk5SmoqzJ0Lpun6uXm2GYWjNGbGfeOsCopv2yYug23b5BdatKjUr75wQaLh2rYV8e7SJV1cjx+XPLR77hG399KlUsFryxZpu/XEE9JEW1GUXEf79u1vWv7ZYrFQsGBBIiMjadCgAQ8//DClvPj/7oowD0dyl08bhnEA6cNcAzgMdPXaitzk9OnTbN++3d/LyFFSUqTu9f79rp/buLFsj+apdsAZA7l++01yi+vUgXvvvVGUbTZxPd9zj7TrKlZMhDk+XsZo2VLc3G+8IcFfr70Gr78uQV6LFkn+8T33yFiNG0OjRlpIXFHyAC1atGDlypXExcVRrVo1qlWrRmhoKIcOHWL37t2EhoZSt25dLl68yJQpU/jmm2+YM2eO1/KeXclj3mMYRi2gE1DbfngnsNI0zVSvrEbJNsnJEhx88KDr5zZrJvUtcr2GZLSALRY4fVpaKD7+uFi0ERHSiuv116XoR3h4+nmOSl5ffAF//CFR0+XLi1gfPiyNrqdOlc4h770Ho0ZJPvLly/IHePfd9IpdjvkVRcn11KlTh++++46PP/6Y9u2vTzjatm0bjzzyCL1796ZPnz6Ypsmjjz7KxIkTb9rl0BVcsZgxTTPZMIy1wEkgGNivopzzJCfDzJmiHa6Sa9s2OtzTzqUxM76JS5dEKLt0kYIda9ZAuXIiqB99JLWqe/S4sZJX2bJy69jx+vHeflvmAgnc6txZxDs4WDbms1sTVVGUXMXUqVMZMmTIDaIM0KhRIwYPHszkyZPp06cPhmEwYMAAZs2a5bX5s+3INAyjoGEYHwLngU3Ar4hbe5phGAW9tiLlpngiyq1b5yJRTkoSF3KbNvLY4Z52FtTVq+HHHyWCGsQV/dhjYinfeaekKFWoINZygQJSn9R5LGeGDxd398KF4oZ46y05p2fP9NdUqyapUv37qygrSh7m/PnzlHWkR2ZCZGQkp0+fvva4TJkyxMfHe21+V75dJgD3Aj2AXxCLuSUwERgHjPTaqpRM8USU27YVjQtoUU5KgvXrpeRlwYLSIrFmzetfk5oKn38uYpuSIoFaxYrBP/8pQVf33SdlMsuVk9fbbFC1qrRf/O03qbBVvXq61ey4v+8+ePFFKfiRkiLzvPSS/OICGZtN1pqYmJ7alZQk7yEtTZ5zvnfcHDg+EM4fjKAguSgJCUm/d/45NFT22QsV0gsUJU9So0YNFi5cSL9+/ShY8Hq7Mzk5mUWLFlHNkQoJ7Nq1iyjnbS0PceW/qj/wgGmaPzkdW2IYxlVgNirMPsUTUe7YUazlgGf1anEfly0rbuf+/eV4fLzsDYMUAnn/fXj2WQkpP3JE8o0HD5ZzOnQQK3njRhFbR8WUXr2kk9PPP4swO3AI0r33wl13wcqV0iXqrrty6l3fSFKSuOXj4yU6PLP7hIR0Ibb6sSpuSIgItEOow8LkbxUeLhdNGX9WIVdyAc888wxPPfUUvXr1on///kRHR1OwYEEOHjzI/Pnz2bNnD++//z4Ar7/+OvPmzePpp5/22vyu/JcEAecyOX4eCPfOcpTM8ESUu3QRr25A47BaT56U8pZ794rIJiRIH+OpU8FRmeett8SafvRREfA6dcQijokRUf/yS+jbV+pbP/dcenpTz54SrPXLLyLimbmzw8NF7H1NYqLkSV+8KG54x73j58RE36/BW6SmSjDc5cvZe32hQpLbXbKk3Gf8OU8m1Cu5jTZt2jBp0iTGjRvHW2+9dS11ymazERUVxfvvv0/nzp2JjY1l3rx5dO3alUcffdRr87sizKuAdwzDGGiaZhyAYRgl0H7MPsUTUe7WTSKwAxLnIC7HfffuIrxPPinieuCAWK9nz4ob+o474OhRSU0qWza94IdhiNhOmCC5xwMGSBT1li3pwly6tKQzLVsmx339i0lLk7WcPy+3c+fS769c8e3cgUxCgtxOnsz8+aJF5W9VqpTcHD+Hhwf4PoyS12jXrh3t2rVj7969HDlyhNTUVCpWrEj9+vWvCXWJEiXYunUrBbx8QemKMD8PrAGOG4bhyJytCfwJ9PLqqhTAfVG2WMRAbNzYN+tyiStXJELaEVXtcLsGBaUHcTnuR42ScpaFCkmZyypVJJc4Olqs5jvukGO//54+BojIN2kiwnfokPRDrl9fCoffe6/sQYMUAqlT53pXtje4elVSsk6fltupU3IxkVW3KiVrHNb3X39dfzw0VES6TBmJH4iKkouzghp3qviWWrVqUatWrUyfCwoKIsgHxSBc6sdsGEZdoAuSx5wA7EXymLPVgUrJPp6Icu/e0LChT5aVfeLjJQ94506JhnZ8eB33R4+K2O7fL+LZp4/kC7dsCWPHyv4piDXctq1ES//3v1Ii85tvxN3t+GexWMQFXLSoWKQg+8tvvCFrqF9fjt1zT3pBEHe5ckXc6sePi9V36lT23biK+yQlye/dudmAxSIelXLl0sW6fHm5sFMUF+jQoQOjRo2iQ4cO1x7fCovFwsqVK32ynmwLs2EYhYBHgFqAo91BfaCPYRiYpjncB+vLl3giyvfdJ61+/Y7D9XjokBTxrlNHLNu4OLGMZ86EypXF4pk3T9zL48eLQE+dKtHZV65Ib+K774Zp02DVKnFxf/ihVOpyRF+fOyclM6OjxXIGKYlpGBKN7S4pKSK+x4+ni/HFi9747SjewGaTv/25c3IB6KB0aWlGUrEiVKokrvA8VeJO8Tbly5ensFNrPW9V8HIXV1zZs4E2wI+Itaz4gFwnyikp1wfspKXJF2ZIiBTz+OEHmDNH0pssFnEvL18Os2dLqHhwsOQQz58vlbmqVJFote++kwjqzp1lb7huXSkSsmCBBHk9+qi87r77pMb1wYMi2BERso5y5WSv2RUSE8WSP3RI/gAnTvg34llxj7Nn5ebofxoaKpH6lSrJ56tSJY0OV65jxowZN32c07jy6ewAdDFN8+dbvlJxi5QUKbOZK0T5xAlJjH78cRjplCnn2C/et08EulEjafDw+utyfMoU2QNu105Saw4dkj3ac+fEXf388xK19s03sGKFCHONGuKC/vRTcRv37SuRvEuXypfvbbeJKLtqHSckyC/78GFZx6lTsmYlb5GUJHvWf/0l2yohIWJNV6kit4oVVaiVgMKVT+NeF1+vuEBqqhiRrta+zlFR/vlnEcA33pAAqn//G5o3v/41mzfDU0+J+/r222UvOD5ezm3dGh58UO7DwuCTT6QLR4ECkvL07bcizG3bSmGRTZvEfVyhguw9//vf0mLx73+HTp0kQduVSN20NHFJHzgge9snT6oQ50dSU+VC7NAheRwSIlZ01aryuStXTiPA8xkvv/yyy+dYLBbGjRvng9W4JrQPAXMNw5gFHAGu8/GZpjndi+vKV6SlSevGAwdcOy/HRNm56cPcuZLWZBgyeVKS7BsXLy6vnThR9vPWrxcXwPz5EmE9b54I8mOPidV7xx1iVT/3nIjxa69JDvKWLbJP3K6dpD/df7903OjZUyxzZ6s4O1+esbHyiz1wQK56HEFleYmgILnQcVTkCg2VvfuQEPFgZLx37vPpuDBxvrdaRbwcFdCc71NSxOXvuOWFC5vUVPlsHDwoRW7Cw+XCs2ZNuddgsjzPwoULMz1usViwZfEZDyRhNoBnuXGP2QaoMLuB1Sra5Wo/5Ry1lB0CePfdErC1fLkIZXKyCGzbthKw9fvvsqf84ovpYeG33y5W78qV4rIuXFgs30uXxLp2pC6FhIiIfvONCPPQoSIwn38uwlO3rpx3K6xW2Sc2TbmdP++TX4nPcZQaLVJEhML53nFzVNoKCfGPhWezpVcfS0iQe0dlssuXb7xPyCWhKfHxsH273CwW8djUrClbJmpN50lWrVp1w7G4uDhiYmIYP348jXM499QVYX4cGGSa5le+Wkx+w2qVbdXdu107L0dEOS3t+t7GjiCvAQOkTeLBg2K9duwoRTtAomHPn09fWFpaeuGQjRtFdAcMkGCtyEgR5eRk2fdbtEjGGzNGgsEqVpRgsBEjbr3W5GSxiE0T/vxTLgACnfBw+R2ULCnehhIl0u+LFcsde54Wi1wYhIXJum9FSkp6dbMLF268D0ThttnS07TWrJH3WauW3CpX1mjvPEKFChVuOOaI0o6MjMz0eV/iyn//OWCHrxaS37DZJPD4jz9cOy/HLGWHu/PcOfnSdDSTGD5cXNPr1klgV69eItQrV4qlW768tEbs3DndzVm3rgjN0qUizJ07S63r/v1FnH78Udzc//iHWM0VK6a/2axISJD96z17JKgnNQC7jwYHS6pOZOSN92Fh/l5dzlOgQHpFr8xwBAGeOydR1Y6fL14MHJf5xYtykblxo3h/DENEulo1LSeqeA1XhPlZ4BPDMN4EDgLXfROapvlXpmf5mEOHDlGlSpXrjsXHx5OcnEyEI3UmwLDZYMmS9GyO7OIzUXZYto695LQ0ydmaOFECZEqWhGHDJKiralUR2sWLpQVikybyeNo0sZ7btpXQ8pEj0xtP1K0r4r5unVTGeuYZcXkuXy5u5+efh4ceurWVmJgoVvGuXWIhB1JlrSJFxM1Ztmz6falS1+/nKjencGGxQitXvv54SooItKOgi+OWnOyfdTq4elX+ibdulT19R9589eq5w+OhBCyufHq+sd8vtd87LmEt9p9z/Bto2bJl/P3vf+edd965diw+Pp5HH32UlJQUFixYkNNLuiU2m3h+N21y/dxevXxkKTvEw2Ghjhkj0dJdu8oe8ldfwQcfiNAMGyY5xKNGSVGHu++W/eYPP5S9uSeekNe/9RY8/bS4tydPFmvi1CnZi37pJSkQ8txzt7Yck5LEPb1rl0RSB4JlXKiQ7DtWrCj3UVHpFyGK9ylQQH7Hzm31bDbxrpw6lV4E5vhx/4l1crK4v/74Qz7TdeqISFepou5uxWVcEeaqPluFm7Rp04YmTZrwj3/841ot02HDhrFnzx4+/PBDP6/uRmw2KV61caPr5/boIZ5jj7BaRXwzuojnzJEgq0mTRAg//VQE9tVX5fmqVaXX8bffpgvzCy/Inttdd0kLq//8R6LYhg6Ff/0LPv5Y9o1r1BBRHTZMvqwclbkckcRZrfOvvyT4Zu9esZj8RVCQCIJDhCtWFA+CBgD5F0c5zshI8ciAfG7OnJH94KNH5d4fwX+JiRII+fvvcsFWp46Uha1YUT83SrZwpVa2G/2NfEtYWBiTJ0/m8ccfZ+PGjVgslmui3KZNG38v7wbWrpV0Xle59950PfMIx5W7w3WdnCwuuAMHxPq97TbZH46KEmsXJH3pyy/FNbd1a3p5zXbtxJ09bJh86bRuLe7soUPFIu7eXSLbjh4Vl3i3brde35kzIsY7dviv/nRwsAhwlSpS4rNSJW2UkFsICkqvm920qRy7elU+gwcPyrbM6dM5u18dHy+d0X77TTxOjRqJ28vRWEUJCDZl4sK8bP8OMk2TkCy2Jpr5qEudJascrZwkJibG5onbOSkpiSeeeIItW7YErCivXy+FrFylUydo1cpLi4iPl+CtQoUkYMuxp/z113I8NlaE6cQJSdd54QWxpjt0gNq1RaAfewxeeUXymQcMkEjrbt0klemll+DIkfTgrexw5YpcFGzblnUrQF8SFJReXCI6WtauQTx5l4SE9OIiDqHOaSwW2Ydu1EgCx3Q/2u/UqlXrWitHZ2w2W6bHHezZs8ejeQ3D2GKaZtOMx/PEJyI0NJTJkydz+fLlgAz42rLFPVFu186LogwSoGQYUrnLUbSjQAER4ipVpOBHrVoSuPToo3I1MW+eXB1YrbJXvGKFCHOfPlLF6+uvpVxmz56Sd5yd37/NJhbMli3iqs7pIK6SJcXFXr26CHJo6K3PUfIGhQrJRWbt2vL4yhXZNtm3TzxHOdEr22aTeIn9+2U7p149yff3c+OE/MzTTz99UwHOafKEMAMUKFAgIEX5jz/E4+sqd90lcVVexWKRClu7dqU3lYiJkS+HS5dkvw7Egp0+XVzRDlFeuVIs6p9/lqjrAQPEeo6Kki8aR27nzbhyRSzjLVtkrJyiQAFJZ3GIcQB+ThQ/UaSIbMXUry+f45MnRaT375c9al97FBMTpdDO5s2yhdK0qQi1em1ylBHZqZeQg+QZYQ5E/vxTtM3V/+0WLaB9ex/GiYwdC//8p6Q0deoke8tnzsiXFMiCCxWS/eQGDcTd95//wODBUiBi61YR5o8/vvVc/rKOixSR96U5pkp2sVjEai1fXhq0JCSIQO/dK2Lt64hvR2T5smVSOa9pU8lqUPIdKsw+4tAh2Z51tWtgs2biGfapV6VmTXj3XfnH/+c/5YuoUSMR4qZNRdCGDYNx4yRNKi5OwsJfflmey87ikpPFOv7tN8lBzQlKlUov+FChgqapKJ5RqFC6NZ2aKi7vvXsll96XLu/ERPj1V7lFR8uXQu3amhOfj1Bh9gHHj0sqr6spt7ffLlu/Pt/qsFolyOmdd6Sl1XffyRdA2bLyvKOeda9esu92773ZD+i6cEHE+Pffc6ZhRKlSki5Tty6UKeP7+ZT8SUiIXJTedpv8/xw7JlXndu2SbSBf4WhLWrSo1BRo0kT+P5U8TZ6Iyg4kzpyRGhqulv1t0AB6985hIy8xUZpOxMTI4/PnJTDKVWw2cRFs3Cj+e19/piIiRIjr1RMxDqCgjdzA+++/z+rVq/noo4+oVKmSv5eTu7HZJBPhjz/E4+TrOu0hIeLmvvNOdXPnAfJ0VHagEBsrMVOuinLt2n4QZZCgr969JWf54kXX/e5pafKF9MsvckXiS4oWlauXevW0w48HvPvuu0yePBmAoUOHMm3aNBVnT7BYxNsUHS2epYMHJXhyzx7feIxSUyVeY8sWCWa8804JaNT/hzyFWsxe4tIlmDJF9M0VqleXOCq/pDJarXI14Mhnzi5JSfLFsHGjb914BQrIfnGjRpLWpHvGHjF+/Hg+//xzSpUqxfnz54mMjKRgwYIqzr4gNVX2ordtkwAyX37Pli4teZX16+s+dC5DLWYfcuUKzJjhuihXqiSVLv1WX8AhdNkV5fh4EePNm8UN7iuqVBF3XZ06mmPsJRYvXsznn3/O0KFDKVq0KB999BHTp09nyJAhPPXUU3z33Xf+XmLeIiQkPfbh8mWpaLdtm28CIc+elfK3a9ZISsftt2u1ulyOCrOHJCVJQayzZ107r1w5ac6UK/5/zp8Xd/W2bb5LdypaFBo3lps7+9zKTenatSs2m40ePXowadIkAKpVq8b06dOJj4/38+ryOEWLSsnaVq0kMnTbNnF3e/viNi5OYkbWroXmzSVYrFAh786h5AgqzB6Qmiq1NlytJBkZKSnBAd+S9+xZ+SffudM3rjiLRfbJmjSRaFd1VfuMoKAgevToccPxatWq+WE1+RSLRbIbKlaUnuQ7d4r36fhx785z9apYz+vXy/9WixZamzuXocLsJlarNFM6dMi184oXhyFD0mt5BCSnT4sg797tG0F2WMe33w4lSnh/fEUJdAoUSPcQnTghAv3HH97tpJacLE1pNm0SgW7VSgXaDebOncv8+fOZPXt2js2pwuwGNht8/70EXrpCeLiIcvHivlmXx5w6BT/95Pobyy5Vqoh7zTA0SEVRHJQvL7Xm77lHOqtt3uzdLIfUVClWsmWLCrQbnD59mu3bt+fonCrMbvDjj/IZd4WwMHFfO8pRBxQnT4og793r/bFDQiRatHlz2VhXFCVzwsLkwrVZM9/UBcgo0K1bi/dKCThUmF3kt99Ew1yhYEEYNCi9sFbAcO6c7EXt2uX9sYsVky8YrVSkKK5hsUh6YNWqUhzh11+lPr23anVnFOi77hJ3nhIwqDC7wM6dsHSpa+cEB0P//q61KPY5cXFydbFtm+tFRW5F+fLQsqXW9lUUbxARIYVL2rUTcf71V9fzMrPCIdC//y4BYi1b5oKI1PyBCnM2OXDA9U5RFou0LQ6YwNcrV2DdOgkG8XbaU40asndVpYpWIVIUbxMWJuLZvLnEgPz8s+vpIFmRkiLBnps2ifV8xx1+LK6ggApztjh+HL7+2nUt69Hj1i2Kc4TERInO3LDBu63rgoKkgEKrVrp/rCg5geN/rk4d6Xb1889SBtQbJCTA8uWyt92unRT50RRGv6DCfAvOnYOZM13Xs44dJRvIr6SlyT7Sjz96t7h+gQLy5lq00HQnRfEHFovU861eXSyHn3+W4E1vBIpdugTffCNFhdq3F+tCvWA5igrzTbh0Sap6uappd94pRqTfsNkkmnPFCu+WACxYUNxcLVoEeCK2ouQjKlSQ2r5nz0pRkR07vBM7cvasuAorVYIuXWQeJUdQYc6ChAQRZVfjLBo0kKI+frvAPHkSli1zvfLJzQgNlb2tO+/UCGtFCVRKl5ZucW3aSCyJt4I7jx6Fzz6TL7cOHQK4EEPeQYU5ExylNl3N8a9RA3r18pMoX7oEq1bJ1bK38h7DwtIFWWvuKkruoGRJKVhy113eFegdOyTwrGVLcQnmikL/uRMV5gzYbLBggfQ+d4WKFaFvXz9kCCUni/vql1+8V84vNFT++Zo31/QJRcmtOAv02rXS4cpTgU5JkVTL338X67lhQ91/9gEqzBlYtkxKRLtC6dLw4IM5fAFps8lCly+XvGRvUKCAiHGrVmohK0peoWRJceXdfbeI6vbtnnvVLl+WVpO//ir7z9HRXlmqIqgwO7Fhg2QKuEKxYlLVK0e3Xs+ckUon3kqTCAqSCkB3360l+pRbMmTIEJfPsVgsTJs2zQerUbJNyZKyB92yJaxe7Z0SvCdPwtSpsv/cqZN+f3gJFWY7O3eKtewKhQpJ/esci4VITJTUp99+886ekcUidazbtpUKQ4qSDY4dO+bvJSieUKaMlCM8ehRWroTDhz0fc8cOME3Jf77jDs1/9hAVZiSAeeFC184pUEDc16VL+2RJ12OziftpxQqp3uUNDENyFAOugLcS6KxevdrfS1C8QaVK8NBDsH+/BI6eOuXZeElJ8MMPsv/crZu6tz0g3wvzmTMwe7ZrVb2CgiTQq1Il363rGqdOweLF4C0rpXx5aS9XpYp3xlMUJ86cOcPJkyepVq0aoaGhhISEEKTWU+BisUDNmpJSsnOnuLgvXPBszDNn1L3tIflamC9dkqpeiYmundezp3yWfUpysritN270jtu6eHGJoqxfX6Mo8zk2mw2bt1Lq7GzZsoWxY8eyx97Le8qUKaSlpTFq1Cheeuklunbt6tX5FC/j2NaqXVsCutauFQvYE9S97Tb59jeVlCSi7GpAc7t20KiRT5aUzp9/wkcfSQqUp6JcsKAI8jPPyBWsinK+Z8SIEez1Yu/tHTt28PDDD3PlyhWGDh167Xjx4sUJCQlh5MiR/ORqr1TFP4SESFbGs89C06aef1843Nuff+69phv5gHwpzGlpUmnu9GnXznMELvuMS5dgzhz46ivPU6AsFvnHevZZyWMsUMA7a1SUDEycOJGKFSvyzTffMHz48GvWeP369fn222+pXr06n376qZ9XqbhEkSLQvTs88YTU4/aUEyeketjy5d5tpONDDmVSPTE+Pp7Y2Fifz53vhNlmk/rsf/3l2nm33SbxDD4xOK1WcR999JHrSdSZUbUqPPmk/GNpA3TFx2zdupWYmBjCwsKwZPgHCQ8Pp2/fvuzbt89Pq1M8omxZyQcdOBBKlfJsLKtVvIAffywBZwHMsmXL6N69O99///21Y/Hx8Tz66KMMGzbM5/Pnuz3m1atl68MVypeHBx7w0RbJ6dPw7bfSIcZTiheXQt21a6vLWslRCt6kuk5SUhJWb8RJKP7BESBWrZr0bF6zxrP954sXpRFB/fryfRWAxkObNm1o0qQJ//jHP6hl7907bNgw9uzZw4cffujz+fOVxfz771I61hVKlpSLRa9X9UpLk+CuTz/1XJRDQsTH/vTT0qdVRVnJwJAhQ9iwYUOWz69evZpu3bq5NXbDhg1ZvHhxps9dvXqVuXPnUr9+fbfGVgKI4GCpmz9ihJTi9JQ//hAv4dat3qvv7yXCwsKYPHkyTZs2ZefOnQDXRLlNmzY+nz/fWMwHDkjWkSsULixeHK93ODxxQvzprm5yZ4ZhyFWnFghRnEhISOCCU9rLb7/9RqdOnYjOJLfUarWydu1atwuHPPvsswwePJhBgwbRoUMHLBYLO3bsYN++fcyYMYMTJ04wevRot9+LEmCEh8N990nQzfffe/Y9lpAg34U7d0q6SwB1rgoNDeXTTz/liSeeYMuWLXzwwQc5IsoAFm+nTbhDTEyMbcGCBT4b/8wZ+OIL17wvBQrA0KHSnMJrpKaKleyNaOuICLj33hzI21JyI7GxsXTp0oXLly9n6/U2m41WrVrxxRdfuDXf+vXree21124Q99KlS/PKK6/QuXNnt8ZVAhyrNd297WreaUZCQ8XIaNw4oLx+KSkpXL58mQgfGD+GYWwxTbNpxuN5Xpjj4yVS35W+yhaLVKwzDC8u5OhRuTI8d86zcYKDJcq6dWtxYStKFvz000/88ccf2Gw2PvroIzp16oSRyYc6KCiIiIgIunXrRlEPikHYbDZ27drF0aNHsVqtVKhQgXr16hGin9O8T3y8lPfcts3zsapXDzjr2VdkJcx5+j8mJUX6KrsiyiDR114T5ZQUiTjbuNHzfZQqVSTS2tPoSCVf0KZNm2uutxMnTtC/f38aemNvMAssFgv16tWjXr16PptDCVDCw6VBRuPG8N13nhkgBw5I5PY998DttweU9ZxT5FlhdvRVdjWu6q67JP3XKxw/Los4f96zcQoXlg+p9j5V3OStt9664VhKSgrr168nKCiIli1bZtuy1e5SSpZER0vu888/S6StK7WOnUlKEoHftUus5xIlvLrMQCfPCvOKFWCvDphtGjSQvg4ek5YmJe3WrfN8L7lRIxHlHO0rqeQ1kpOTGTNmDMeOHWPKlCkkJyfTr1+/axXAqlevzrRp04iMjLzlWJkFiZ0/f56kpCSKFy9OdHQ0VquV48ePc+HCBUqUKEF1bxSpUHIHISHSsa5ePRFXT7pX/fUX/Pe/Ek+TjwyTPCnMmzdLfJUrREfLhZnHf/dz58RKPnHCs3EiI6FHD202oXiFSZMmMWfOHO6//34AFi1axJ49exgyZAi1a9fm7bffZuLEibzxxhu3HCtjd6nVq1fzt7/9jbfffpuePXte17Ri8eLFvPLKKwwcONC7b0gJfEqVku5VW7dKxS93g8OSkmDRIqm73aNHvjBS8pww798PS5a4dk5kJPTr52Eslc0m1btWrpToa3cJCpJG5m3banCX4jWWLl3KAw88wJgxYwCpbFS0aFFefPFFQkJCOHr0KHPnznVr7Pfee49+/frRu3fvG57r3r07u3fvZuLEidrIIj9iscg+8W23Sc1se06wW+zZI0G0vXrl+WyUPFVg5PRpmDvXNe9x4cJSQMSji7C4OJgxQz54nohy2bIwbBh07KiirHiVU6dO0cjefSUhIYFNmzbRokWLa/vKUVFRXLp0ya2xjxw5QpWbeHbKlSvHmTNn3BpbySOEh0v5xAEDPGsDGR8v3Ye+/z7X1Nx2hzwjzJcvS+8HV3KVg4MlLcqj9LQ//pA9EFeLbzsTFCQW8vDhUv9TUbxMqVKlOGePlF23bh3Jycm0bdv22vOmaVKmTBm3xq5atSrff/89aZkE+iQlJTF//vxM07SUfIhhwFNPeV45bNMm71RNDFDyhFmWnCxpUa42ZOrdGypXdnPSpCTxmW/f7uYAdqKiZCFly3o2jqLchObNmzNt2jRCQ0OZOXMmhQoVomPHjly6dIn58+czZ84c+vfv79bYw4cP54UXXuDBBx8kJiaGSpUqkZiYyOHDh5k1axYnTpzQ7lJKOoUKSeWwunUlOCybRXBu4Px5qRx1991yy0P9nnO9MNtssHCh67FW7dpJDXW3OHEC5s0DT9p/BQfLIlq2zFMfKCUwGTVqFKdPn+add96hcOHCvPnmmxQrVowtW7bwzjvv0KxZM5555hm3xu7atSuJiYlMmDCB1157DYvFcq31Y4UKFZg0aRKtWrXy5ttR8gK33SbW87Jl7hcmsVqlmuJff8H99+eZoiS5vvLX6tWSmeQKDRuKkepyBLbNJuHeq1Z5lgYVFQUxMVC6tPtjKIobxMbGEh4efq0b1NWrV9m3b59XCo9YrVZ27drF8ePHsVgsVKpUiTp16ng8rpIP2LdPuuy5az2DWOI9e0p3vVxCVpW/crWp9scfrotylSpupkVdviytylascF+Ug4KgTRsJ8FJRVvxAREQEFy9eZPv27Vy+fJmQkBCvdX6y2WxYrVasVivBwcHa6lHJPjVrSnc8Ty4QExLg668lMCwlxXtr8wO51pV97JiUnnaFUqUkLSo42MXJ/vxT8uiuXnXxRCciI2VfxatdMRQl+2zZsoWxY8eyx155Z8qUKaSlpTFq1Cheeuklj9KZ1qxZw+jRozmdodNQmTJleO2112jvlco9Sp4mLEy+I2+7TVoBJiS4N86mTXDkiESB51IDKFdazHFxMHu2a5lJhQvDgw+KtyPbpKXJ/sdXX3kmynfcAY8/rqKs+I0dO3bw8MMPc+XKFYYOHXrtePHixQkJCWHkyJH89NNPbo29efNmRowYgc1m4/nnn2fSpEl8+OGHPP/881gsFp599ll+//13b70VJa9Tty48+aQ0s3CX06dh8mTYsiXgej1nh1y3x5ycDFOmwKlT2R8/JERaOFaq5MKi4uIkKdrNHrWA5Ov17u3ZB0xRvMCjjz7KyZMnWbBgAVevXqVly5ZMnTqVFi1aEB8fz4ABAyhatChfffWVy2MPHTqUU6dOMW/evBu6U8XHx3P//fdTuXJlPvvsM2+9HSU/YLPBb7/J9qEn9SHq1pWKYWFh3lubl8gTe8yOxhSuiDJIoRiXRPnPP+GTTzwT5Xr1JOJQRVkJALZu3UpMTAxhYWFYMgRYhIeH07dvX/bt2+fW2Dt27KBPnz6ZtowMDw/ngQceYLunaYVK/sNigebNxdsYFeX+OLt2ifXsqnD4kVwlzKtXg73mfrZp08aFtCirVUpqfvWV+/sbBQuKlXz//S76zRXFtzgisTMjKSnJZ8FaFouFlFwejKP4kdKlJWD2rrvcb2YQGwuffy51u3MBuUaYd+yQZk2uUKeOFNTKFpcuwbRp0q7MXaKi5OquUaN80wVFyR00bNiQxYsXZ/rc1atXmTt3rtvR2Q0bNmTevHlczSQOIz4+3qOxFQWQiN0OHWRPslgx98ZITZWI4UWLAj5qO1dEZR896noEdvnyEuCXLX08cEB85FeuuLU+QAqFdOjgRsi3ovieZ599lsGDBzNo0CA6dOiAxWJhx44d7Nu3jxkzZnDixAlGjx7t1tjPPPMMQ4YMoXv37gwaNOha3ey//vqLr776itOnT7s9tqJcR5Uq0u/5m2+k25Q7bNsGJ09C376SLROABHzw18WL8Nlnrmlm0aLw2GPZuLCyWuGnnyQZ2t3fQ3i4uK5r1HDvfEXJIdavX89rr712Qz/l0qVL88orr9C5c2e3x161ahVvvPHGDelS3hhbUW7AZpO0qOXL3Q8MCw2VohZ163p3bS6QVfBXQAtzcrKUQs3wv35TQkLg4YehQoVbvDAhAebPlz6R7lKjhohyeLj7YyhKDmKz2di9ezdHjhzBarVSoUIF6tWrd63LlCekpaWxa9eua8JfoUIF6tat65WxFSVTTp2S8sj2Bi1u0bw53HOPX7ydWQlzwP7HOCKwXRFlEPf1LUX51CmpEHPhgnuLCwqS1owtWuhespKrsFgs1K1bl7p163L58mUsFovLwvnyyy+7Ne+4ceNcPk9Rbkq5ctKVb+lS9wO7fv013bUdIEZWwArzTz+5HoHdtm02vBJ//CE1Wd3d/C9WDPr0cTH/SlFyHpvNxtq1a9m3bx+VK1emffv2hISEsGHDBsaMGcNf9laltWvX5oUXXqB169bZGnfhwoXXUq6y63FTYVZ8RsGCkhNbrZpUDHOl96+DI0ekjWS/fgFRCCoghXnPHmkY4gp160pqVJakpUmi+saN7i+sZk0xyQsXdn8MRckBLl26xPDhw9m+ffs18axXrx6vvvoqw4cPv9b20Wq1snHjRh5//HGmTp3KHXfcccuxDcPANE0iIiLo0KEDnTp1okWLFhQoUMDXb0tRsqZ+fYn6nTPHdVcrSD+EqVOha1do0sT763OBgBPm06eljaMrlC9/i25RV65IFa9Dh9xblMUC7dtD69bqulZyBR988AF79+7l1VdfpXnz5pw8eZKxY8cydOhQqlSpwowZMyhRogQA586do2/fvkyZMiVbwvzNN99w7NgxVq5cyYoVK3jiiScoXLgwbdu2pVOnTrRp04awAKyypOQDIiMl53nJEvdc22lp0iP6xAm4914JWvIDASXMCQlSAzs5OfvnFC0KAwZAlhfrx4/LfvKlS+4tqmhRKRZiTwFRlNzA6tWr6d+/PwMGDACgWrVqvPLKKzzyyCMMHDjwmigDlCpVir59+zJjxoxsj1+xYkUeeughHnroIWJjY1m5ciUrV65k5MiRBAcH07JlSzp16kT79u0pnkd65Cq5hAIFxLVdubJ0mnInanvLFrES+/UTDchhAqbAiNUqRq0r8VgFCogoZ/l727pVCmu7K8rVqknBEBVlJZdx9uxZqmcoB1vDntJXvnz5G14fFRVFXFycW3NFRETQt29fJk+ezIYNGxg7diyhoaGMGTOGVq1a8dBDD7lVg1tRPKJxY7GeIyLcO//YMdl3PnLEu+vKBgEjzCtWgD0WJdv07i1u7BuwWuGHHyQJPS3N9cVYLLJhPWhQwETpKYorpKSk3OBOduwBZ7YXbLFYSHPnfyUD4eHhdOvWjffee4/Zs2dz5513snHjRt58802Px1YUl3FEbdep49758fHwv/9JznQOEhCu7IQE2LDBtXPuvjuLCOzERDG9DxxwbzFhYeK6rlnTvfMVJR+zbds2Vq9ezapVq/jrr78ICgqiWbNmdOzY0d9LU/IrYWGSSfPrr1KQxNWa8FaruMTPnIEuXXIk3zkghPniRddebxjQrl0mT5w/Lw0ozp93byFlykD//u67PhQlgLh48SInTpy49tjhqo6Njb3uOMAFN3P6k5OT+eWXX1i1ahVr1qzh/PnzhIaG0qpVK4YNG0a7du2u289WFL9gscCdd0o/gzlz3Cu/vGmTaEufPj5vUBQQlb9atIixde6cvX7MjkYjoaEZnjhwQCzlxET3FlGvnpRnu0kHHkXJLdSqVeuG9o4geceZHXewZ8+eW4594cIFfvzxR1atWsX69etJSEigZMmStG3blo4dO9KqVSuNylYCl0uXJCD4+HH3zo+IgAcfhFKlPF5Krqv8lRlhYWLQXifKjmbaP/zgXr1riwU6ddIqXkqe4r777vPZ2K1atcJms1GxYkX69etHx44dadKkyU0FX1EChmLFpG7z99+7l1LlaCHZpw9kCLD0FrnGYrZYYODADL0i0tIkX23LFvcmLlxYfrlVq7p3vqLkQ2rVqnXt5+yKscViYffu3b5akqK4js0m2rF0qXtBwkFB0Lkz3HGH20ZdrreYO3bMIMpXr4o74vBh9wYsX15y1DTHUlFcwpfWuKLkGBYLNG0qsUVz5kgEtitYrSLqZ89KMRIvBoXlCmGuX1/aHV/j/HmYOVNcCu7QsCH06OG3qi6Kkpt56623/L0ERfEelStLvYo5c+DoUdfP37xZNKlvX68FhQVMHnNWREVJTNY1T8Hhw+Lfd0eULRZp79W7t4qyoiiKIhQtCkOHwu23u3f+wYPu61ImBLQwFykiwV7X6iHs2AHTp0vis6uEhkqZsJYtNchLURRFuZ6QEPGkduninkacPy/ibO9H7gkBK8zBwU5bwDab9IFcsMC9TfqSJSXH6rbbvL5ORVEUJY/gyHceODCTnNxscPWqVArzMNAxYIW5a1dx/ZOWBosWwZo17g1UpQo89pgkQCuKoijKrahRw/0626mpUlPjl1/cS+ElQIW5WTN7O8yEBJgxA7Zvd3+gwYO1f7KiKIriGo5qVu40MbLZpPznkiWulwAlAIX59tvFxc+FC/DFF+71UA4Kgm7d5JYDdU0VRVGUPEjhwmLcNb0h1Th7bNrkei9jAiRdKihItn/r15fKmJaTJyQdyp16pmFhErZerZr3F6ooiqLkL4KDxcgrXdq9CpN//glTp0oZz2z2dg4Ii7lsWVlz/fpgObBfNs/dEeUSJeDRR1WUFUVRFO9hsUDz5tIK2J2gsJMnJWL77NlsvTwghPka27dLdygXzX4AKlSQ/QAN8lIURVF8QfXq8Mgj7lWMjIuDKVPgyJFbvjRwhPnnn2HhQrc2yqldGx56CMLDvb4sRVEURblG2bJiBEZFuX5uQoLU4ti796YvCwxhvnQJVq5079yWLWVP+VoVEkVRFEXxIUWLSoeqmjVdPzc1Vfo8bN6c5UsCQ5jd2U8OCoLu3aXEplbyUhRFUXKSggWlmmSzZq6fa7PB4sVZPh0QUdkuExoq7RqvazelKIqiKDlIUJBUwypZUvKWvUTuE+ZixaRcWtmy/l6JoiiKkt+xWGRLtUQJKRudmurxkLlLmEuXlnB17aGsKIqiBBJ16sje86xZUjPbAwJjjzk7VK7sfpi6oiiKoviaSpWklkaJEh4NkzuEuXZtKYvmpSbUiqIoiuITIiMlnapcObeHCHxhbtZMAr00HUpRFEXJDYSHSzqVm1UoA1uY27eXiLegwF6moiiKolxHaKgEKtev7/KpgRn8FRQEPXpA48b+XomiKIqiuEdwMMTEiAW9YUO2Tws8YS5QQFzXt93m75UoiqIoimdYLNC5s0RsZzPXObCEuUoVcV2XKePvlSiKoiiK92jZUupvLFkC58/f9KWBIcwlS8KTT4oga3lNRVEUJS9SvTo89RScOgWxsZLznAmBIcxhYVrJS1EURcn7BAdLm+IKFbJ8iYY7K4qiKEoAocKsKIqiKAGECrOiKIqiBBAqzIqiKIoSQKgwK4qiKEoAocKsKIqiKAGECrOiKIqiBBAqzIqiKIoSQKgwK4qiKEoAocKsKIqiKAGECrOiKIqiBBAqzIqiKIoSQKgwK4qiKEoAocKsKIqiKAFEQLR93LVr1znDMA77ex2KoiiKkoNEZ3bQYrPZcnohiqIoiqJkgbqyFUVRFCWAUGFWFEVRlABChVlRFEVRAggVZkVRFEUJIFSYFUVRFCWAUGFWFEVRlABChVlRFEVRAggVZkVRFEUJIFSYFUVRFCWAUGFWFEVRlABChVlRFEVRAoiAaGKhKHkJwzDGAK1N02ybjddWAQ4CNU3T3O/jpbmFt9aYcRzDMBoBRU3TXOeNdSpKXkEtZkVRcoqjQBQizgALAcN/y1GUwEQtZkVRcgTTNNOAU06HLP5ai6IEMirMSr7DyaXaC/gAKAV8CvzPfqsFrAIGmKZ5xTCMisC7QEfACswGRpqmmWgfrw4wGbgdWA9c5+61nz8J6AScB74CXjVNMzkba60KfAi0AeKAj03THOc07g3rAsq5+P4cv49BwDtAOPAl8LxpmimZrCnL92MYxiPAJ0AD0zT32te/E3gK+Mk+T03gc6QX7WeGYbQGEoBo0zS7Os3zFlDPNM0et/o9uYNhGHWB3aZpau9bJaBQV7aSn3kJ6Ak8AbwAzANeBO5FhPARwzAKAqsRsWoL9LE/PwHAMIxQ4HtEcG5H3LOPOSYwDMNiP3YBaAIMBLoDb91qcfaxlwMpQAvgUeBFwzAG3mpd2X1/GaZ8FRgA9LbfxmSyppu+H9M0pwA/IxcEIBcsP5qmOS3DUDHAMeDvwHPALKCjYRglnV7TFxF9r2MYRoj9fXxqf0+KEjCoMCv5mTGmae4wTfNLIBaYbZrmKtM01wI/IpZlF6AiMMj+2jXA08DjhmEUR6zV0sCTpmnuNU3zY2CR0xztgWrAMPvz6+znP2MXh5vRESgPPGSa5k7TNJfZz71ys3UBxV14f868ZJrmOtM0fwT+BQwzDCPjd0R23s9w4C7DMGYCTe2Pr8M0zVggDbhkmmYcsA5xc/cGMAyjGWL5f3uL35FbmKaZCnRFLlJUnJWAQl3ZSn7mL6efE4DDGR6HArWB/XYhcfALEIy4ZOsAB0zTjHd6fjMinNjPLwHEGca1OCcLUBBx5abdZH117HPHOQ6YpjkTwDCMf9xkXQVceH/O/JLhPUQAZTO85lbv54A94nos8CbwtGmax2/yHh3vy2YYxmzESp4K9AO+NU3zSlbnGIbRFNh0q7GzwWPACmCuF8ZSFI9RYVbyM6kZHlszeU1CJseCM9xntLac92VDgH2IuzcjRxGLOCtutgednXVl5/054/x6xxgZz7nV+3HQELnoaAd8fIt5HXwF/GZ3Z/cBRtzi9TuRCwV3iQC+BvYAiz0YR1G8igqzotycvUANwzAinKzTFojo7Ee+3GsYhlHSNM0L9ucbO51vApWA847n7cFOzwGDbzH3PqC6YRjFTNO8ZD93NGKZfnWTdWUU5OzSCAkKA3FBnwbO2OfL9vsxDKMHItzdgG8Nw+hpmmZmLunrgq5M09xmGMYBZN+5GPDDzRZrD77b68L7u4bd7b4REeVepmlmdqGjKH5B95gV5easBP4EZhiG0cAwjLZIYNNs0zTP258/DEwxDKOOPSr5AafzlyOBYTMNw2hoGEYLJCLZ6ojqvgnLECv0M8MwahuGcS/wLLD0ZutCIqXd4T3DMJoahtEBGA18lEnE8k3fj2EYRREL+W37nvh/gI8NwyiWyXzxQC3DMCKcjn2FCPP87EStu4t9j/k1VJSVAESFWVFugmmaViQgyYZYWHOA74Bh9udTkCCiYsAWJNDpY6fz04AeiCX7i/3cdY7zbzF3GpLyFAH8jqQhvWGa5te3WpebzEZcurOBKcDYLNZ0s/czDnHBv2N/PBZx7b+dyXyTkGC1zzOsIQyJ0vYppml+r6KsBCIWm01T+BQlPxNIZUENw7gbEeeK9osPRcl36B6zoih+xzCMssBdSJ71FBVlJT+jrmxFUQKBYkia1FXS3eCKki9RV7aiKIqiBBBqMSuKoihKAKHCrCiKoigBhAqzoiiKogQQKsyKoiiKEkCoMCuKoihKAKHCrCiKoigBhAqzoiiKogQQKsyKoiiKEkD8P9VQa7q6yqmCAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118d1d2b0>"
+       "<Figure size 576x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1539,11 +1603,11 @@
     "y1 = -(x - 0.5) ** 2\n",
     "y2 = y1 - 0.33 + np.exp(x - 1)\n",
     "\n",
-    "fig, ax = plt.subplots()\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
     "ax.plot(x, y2, lw=10, alpha=0.5, color='blue')\n",
     "ax.plot(x, y1, lw=10, alpha=0.5, color='red')\n",
     "\n",
-    "ax.text(0.15, 0.2, \"training score\", rotation=45, size=16, color='blue')\n",
+    "ax.text(0.15, 0.05, \"training score\", rotation=45, size=16, color='blue')\n",
     "ax.text(0.2, -0.05, \"validation score\", rotation=20, size=16, color='red')\n",
     "\n",
     "ax.text(0.02, 0.1, r'$\\longleftarrow$ High Bias', size=18, rotation=90, va='center')\n",
@@ -1561,7 +1625,7 @@
     "\n",
     "ax.set_title(\"Validation Curve Schematic\", size=16)\n",
     "\n",
-    "fig.savefig('figures/05.03-validation-curve.png')"
+    "fig.savefig('images/05.03-validation-curve.png')"
    ]
   },
   {
@@ -1576,21 +1640,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 53,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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JZyGEEMLHSDgLIYQQPkbCWQghhPAxEs5CCCGEj5FwFkIIIXyMzwxC8tZbEBRkIiVFIzpa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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11a78a240>"
+       "<Figure size 576x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1599,11 +1668,11 @@
     "y1 = 0.75 + 0.2 * np.exp(-4 * N)\n",
     "y2 = 0.7 - 0.6 * np.exp(-4 * N)\n",
     "\n",
-    "fig, ax = plt.subplots()\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
     "ax.plot(x, y1, lw=10, alpha=0.5, color='blue')\n",
     "ax.plot(x, y2, lw=10, alpha=0.5, color='red')\n",
     "\n",
-    "ax.text(0.2, 0.88, \"training score\", rotation=-10, size=16, color='blue')\n",
+    "ax.text(0.2, 0.83, \"training score\", rotation=-10, size=16, color='blue')\n",
     "ax.text(0.2, 0.5, \"validation score\", rotation=30, size=16, color='red')\n",
     "\n",
     "ax.text(0.98, 0.45, r'Good Fit $\\longrightarrow$', size=18, rotation=90, ha='right', va='center')\n",
@@ -1620,7 +1689,7 @@
     "\n",
     "ax.set_title(\"Learning Curve Schematic\", size=16)\n",
     "\n",
-    "fig.savefig('figures/05.03-learning-curve.png')"
+    "fig.savefig('images/05.03-learning-curve.png')"
    ]
   },
   {
@@ -1643,17 +1712,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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0Tq3fv+PCwHMCFdFdMRUKvLB+o0VnB2zfriLBy/qJdCZLHzJb7PZhfkOZjh2P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\n9HPGrhZOPcuH06lE4MmHhjg0mqn4GAeHozz20O8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Rd/zgZyTNxYmA00UPX7/zR3z8HzdG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nwpV/imzXt4a1E1N195ad+LLAtXRztISaSkJGW1W5y4eDGlxSW8u/U9SmfOYPt77yHCMar/tpEP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\nAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bd53978>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1687,7 +1761,7 @@
     "    \n",
     "ax.set(xlim=xlim, ylim=ylim)\n",
     "\n",
-    "fig.savefig('figures/05.05-gaussian-NB.png')"
+    "fig.savefig('images/05.05-gaussian-NB.png')"
    ]
   },
   {
@@ -1710,17 +1784,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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KvLzxGA6XT+uQYsLpCxa+OHKRfrkp3DEir1s/2+fz0thYj9ncRCAQ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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bb9fef0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1779,13 +1858,12 @@
     "ax.set_xlim(0, 10)\n",
     "ax.set_ylim(yfit.min(), 1.5)\n",
     "\n",
-    "fig.savefig('figures/05.06-gaussian-basis.png')"
+    "fig.savefig('images/05.06-gaussian-basis.png')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
     "deletable": true,
     "editable": true
    },
@@ -1811,7 +1889,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
@@ -1856,8 +1937,7 @@
     "    Z = Z.reshape(xx.shape)\n",
     "    contours = ax.contourf(xx, yy, Z, alpha=0.3,\n",
     "                           levels=np.arange(n_classes + 1) - 0.5,\n",
-    "                           cmap='viridis', clim=(y.min(), y.max()),\n",
-    "                           zorder=1)\n",
+    "                           cmap='viridis', zorder=1)\n",
     "\n",
     "    ax.set(xlim=xlim, ylim=ylim)\n",
     "    \n",
@@ -1889,7 +1969,7 @@
     "        clf = DecisionTreeClassifier(max_depth=depth, random_state=0)\n",
     "        visualize_tree(clf, X, y)\n",
     "\n",
-    "    return interact(interactive_tree, depth=[1, 5])\n",
+    "    return interact(interactive_tree, depth=(1, 5))\n",
     "\n",
     "\n",
     "def randomized_tree_interactive(X, y):\n",
@@ -1906,7 +1986,7 @@
     "        visualize_tree(clf, X[i[:N]], y[i[:N]], boundaries=False,\n",
     "                       xlim=xlim, ylim=ylim)\n",
     "    \n",
-    "    interact(fit_randomized_tree, random_state=[0, 100]);"
+    "    interact(fit_randomized_tree, random_state=(0, 100));"
    ]
   },
   {
@@ -1925,14 +2005,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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oUWG4RCJh27dvZzzPMwsLC3b9+nW5ZYhEItanTx+Za11MTAzr1asX43merVq1Si6G+qRB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\n",
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Eec8/ZVxLxZWXuAVC0WvQX/Fza1Z/IkpmjO2E0Cs2EMJcEk0UVR7l1saAiHILo5auorfiINR7X5IhTFRW5q8yD81R8Jdn5npeJCt3GWNfQ+i5VX5xySC05WmK39YQjIGs7flECB8rTQFMUlwpjLEzEOYChWT+YCKiu0zYSuVXCHNrWijSj4JgwKzKwbguTJT5856I3usaSQ95ryRzu6ZLW6ipHQQMpy1UQ99DdQVlcKb/PZBzA74EgtH0AEIXoh0RWRCRIwkbAHrnELekoczX94ohjdwuv6JSRDFsWFnxM7PBp9Txmo46BheVjkSUSkRdIUxInw/BCKdMvyMZY3XzKNY09yAGT047hyvzb7eO+TddQ9wvdYwbVoj31AZAecX/ezQssyYIBhMA9FN8rZd0pkJ4cUZD6HlwIiJppravfKawBd7qQtF7o+x5z2u9yRXGWE0IHzsMgkFTE8K8Vjsiclbck3Iagtr9kLDtQXMIQ/+/QDCMxRAmWi8HcJMxViFLnHUQevrHQxgqj4EwF2kEgEuMsSmFfY+FSLHmvRZKQ1uoRok1nBhjgRB2ms2AMFmuDITVUZrCivFhb5ZAItpBRO+yBNPlq7koyWmIMLPfGx1kKbvsrRhj1jmGLHra4kPP5olM7kod8zNMqIzrkl+lNEFEZ4loEhH5QOgl+wxCr5YDNMxX0oIyfyrlECZzw6xLfhoiyjzI6T6LIm5ByW3uUmYcIWySWdJRDod+QUQbieh1Fv+iaPuUwzNtWaY97gqJHhDeXQeJ6AsiitAwLK71nkjgCBGNI6IGEEYahkPoKasMYWVd1jiviGgpEXWD0B40gdAbzQDMYozVKYwbywFlnbHOY5uuj7wH1Ns1XdrCEtUOlkjDiTFWER+Ou5gJYWJ4MoAOjLFRGqLYQxhSALR3vbcpTB3zQSNF74wmfBV/YyEszcyNixAMSgagQy5hiwzFZMPJip/PIAyVKlEea2HHGPPKo2jlJFg/VkQHCRNRIhH9AWHlBwA0ZLlsxqngsuKvVw75qdxsNRGGcy5VXlHmXx0mnK+Vn7jFWjYZY1b4MME2AIJxrO1apgiXF0PLUFG+nIqz7Vul+GsHYeg7V3RZ+KIgx/tR1FOdRxCI6B0Jpx0oe458cwlPRHQBglHyFMJ7tLmu6eWTzG16Xoz5guS9cigvPz1RDyC8rwChNy8bimFzP8XPy5rCGColznBSVK4QCBOlz0JYjn4bwlg1ACxgjFXNEk25VwUA1NYgsyyEPUH0iTk0nykmgTARHhBWGGibQ6VCseLib8XPmUw4M0ojjDFjxTh4oaIY4liODw3YTBJW8il1vI0PBtB8lsMO04qdYzPvOr4dgqFsC+CHQtA160TJzCjnOzBkn1CpiR0QGpwy+GB0ZU5LCmGeBQDsyGECuaFzFMJeKiJoX3kHAGCZdrxWsBFCfazOGBuex7gFoReEBRExAA4QUay2C8A2RZwAVoQ77BcTyjkxmto+CwDfFXaCRHQRwvJ5APiJMZbjyl3GWBkA+3QUr/V+FHwHDXPnmLADeE5Dr8q6rmprcmobFHVXOam8SI/pUSxi2Kn4OSOnNj0LBcl75YpCGx3TUqF4T+1Q/Byn5SNyKIShQoIwv6zEUOIMJwgbRLaG8LU+INOL51cIS3alADZlriAKQ0L5kl7HGKsHqCqSP4RVa0V6jIUOvIfQ5TtO2YvCGKsMYUy9OoSJcHPzIG8yhK7nqgBOM8Y6KI0TJuDBGPsKwh5RjQrrJhhjlRljn0P4wlEaDmsVX3RZGQthyXlLAEcZY82VS36ZcERObcbYDxC+XlTLh4koGsAM5X0yxn5ljKm6gxljZRljXyni6sJNxthsxlhjZUOpeEZNIGxCCQAXNAzvZoOErSeU9zqXCccESRQyq0J4OVSBsBT+Rx31MzgU81jGQGj0PmOM7VLWKwBgjJkwxhoxxuYjSy8pEUXgw3DIciYcj1MhU1xLxlg7xthmaGhQ2YfjGMLyqLay92gPEWXkEvYUhOERMUr+wbOHFX8XMcZ8lT07jLHGEAzgMkWU7ucQVgmaAviXMbaMMdYgUx1nTDiuZwaA+9C9J0V5P52ZcESKVCHPgTG2AMJB3jEa4lkBuMcY+07RtogU8ZTvgZ8U4TJvrzGbMbadMdYtswHNGHNijP0CYe4TZdKpKJkCoROgKoDjjLFWmZ6lGWOsM2Ps3yxxCpL3/yn+Nmf5O95kNoT3dDkA+5jiCCzGmETxjvhFEW4tCVuXlByomDeOUnSYhED3DTCXZopXCx92G9W0uWB5CMYCIfsmh15Q3649IdPvGGQ6GkSD3DDksOEjMh1RkcM9a5QBzUeuZD0GIANAXw0yXbXprPBvDGGIjDLJjYb6cQEEDRvu5ZJ/ynjvM+VTND7soK683gEYnYusjlA/NiJFISstiyyXLPEYPuw+rbxioX6cQIiWcpe1bGROP0NRHjKn/wZAHS35FqLhnqT4sOO7pvxMQS5HruTwvIIVYcIKWAeVcjSV91z1yBR2UJbylIQsR0BoSUMEoUcyc/69V+RF5qNuQnOoSzo/AwhzV5RyO+sYR7kD/Lm8PH9tZQO6bYA5XYtMrXF1KN+VoX7URzI+7OicBOE0BaWfq67lXMdnaIUPu6lnrWOZ2wu5huelNW0IPeqZ42Y+ImmNpmcBoecksx5pGsrqfWQ6qgXCoqKsZTQui9uUPORzjvmog38rZG9LoqH9yJWC5L0JPmzWq9ywNkpxZX5GGuMr/AKgfuTKO6i3rUeQ85ErwTmUrShFGL/8lM2CXPrucdJlA0xrQNVluhlCl+g+IlqZVRgRPYOwjTwAfMcyHY5LROcgrJLaBSHzTCAUhJUA6kH7fjXFBUEYSvgKwC0IX7rvAPwDoCkJ823yJlAYh68GYRjzNIQKYwOhwlyEYPH7kuYN93TBCh/yyRxC43UNwHoIO9KWI6Jl2qMDRLQfwhfUjxDGuVMVOsYpdJ4LoCEJPTmZ4xERfQmht2obBAPRTBH/MoSvnZ+gG10hbCp4CsJO5RYQKvd1Rfo1iei6jrJAwmZvHSF0RZ+A8LylAB5BaNRrE9Fu7RJKDkS0HsLy7iUQvlBlEMpFDITGb5rCP2s8GRGNgjA3ZDOEZyOB0DvxGMJ+SmOgfWO8vKLcdDMe6ptJ5oRyuLsJEzbNLJGQsCdYEwjP+TUEozUWwmafjSmXA3gLmHYcEfVSpP8/CD1QcRDKSByEkYC5AKpT3lbO9oHQq34LggHGINTfICIaqiVOHISz05ZAOJblDYQhvUQI+wV9B6AeEWXeLmQxhJ7x3RCOEGIQyukTCO1OSyKanQe9CwQRhUKoT/Mg7OeVAaHO3AfwO4QtVTKHz3fek9Cr7A9gE4T21RbCghwX6LiVEQmbVdcGsBqCoSOF0B6ehDAi0Z6074dmsDCF5cbRE0w4FXsagA15bDg4HA6Hw+EUM/ruceJwOBwOh8MpMXDDicPhcDgcDkdHuOHE4XA4HA6HoyPccOJwOBwOh8PRET45nMPhcDgcDkdHeI8Th8PhcDgcjo6UhpO/OZxSjWJ3YG8Im9lpO/+OU7LJgLCR4XESjnzhcDgGCh+q43AMFMaYxNzcfD4RDXRychLVrVsXlpaWIiMj3lFc2khJSaGnT5/KLl68aGpmZnY2NjZ2HBFd1bdeHA4nO9xw4nAMEMaYxNLScl+zZs2aLlmyxMzTM9vm25xSSFxcHDZv3kwTJ05MSEpK8iOiEnVqPIfzMcANJw7HADE1NZ3esmXLb/bt22dmYmKib3U4xcxff/1FwcHBMUlJSU5EJNe3PhwO5wPccOJwDAzGGLOysnp08ODBit7e3vpWh6Mn3N3d4x88eNCZiE7oWxcOh/MBPlmCwzE8PIyNjct4eXnpWw+OHhkwYIBUKpUW1iHHHA6nkOCGE4djeFRyd3dPZ4zpWw+OHqlWrZrI1NTUQ996cDgcdbjhxOEYHmbm5ub61oGjZ6RSKRhjFvrWg8PhqMMNJw7HAOG9TQKMMfj5+ekcPiwsDIwxTJ8+vch0Cg4OBmMMUVFRRZYGINw7LwccjuHBDScOpxSiy0vX1dW1WAwADofDKU3wncM5HE6poUmTJrh16xbs7e2LLI05c+Zg8uTJKF++fJGlweFwDBduOHE4nFKDVCpFtWrVijSNsmXLomzZskWaBofDMVz4UB2Hw8nG0aNH0aFDB9jZ2UEikaBq1aqYPHky3r9/rxbus88+A2MMd+/eVXMPCgoCYwz+/v5q7vHx8TAxMUHLli3zpM/z588xYMAAODo6wszMDA0bNsTWrVuzhctpjtOFCxfQrl07WFpawsrKCm3atMGZM2cwffp0MMYQFhamky7a5jjt2bMH/v7+KFu2LCQSCcqVKwdfX18sX748T/fK4XAMG244cTgcNVauXIm2bdvi1KlT6NatG7788kvY2dlh3rx5aNq0KWJjY1VhlYbR0aNH1WQof58+fRopKSkq9/DwcGRkZGQzqHLi3bt3aNq0KW7cuIFBgwZh4MCBePDgAQIDA7FgwQKdZBw/fhwtWrRAaGgoOnXqhDFjxsDMzAytWrXC+fPnddZFG6tWrULXrl0RERGBgIAATJgwAZ06dUJycjLWr19fYPkcDsdw4EN1HE4pJqfVZZkNICWPHj3C2LFjYWFhgfPnz6sNe40aNQq//fYbvvnmG6xatQoA0Lp1awCCoTRixAgAwJ07d/Ds2TO0bdsWhw8fxqlTp7IZWMp4unD9+nX06tULf/zxB5QHHE+ePBkNGzbEd999hx49eqBy5cpa48vlcgwZMgSpqan4999/0bFjR5XfihUrMHLkSJ110cbKlSshFotx7do1ODo6qvlFR0cXWD6HwzEgiIhf/OKXAV0AAlq1ahVLBQCAztfDhw9V8X788UcCQN9++202mW/fviVLS0syNTWllJQUlburqyuVKVOG5HI5EREtW7aMANCZM2dIJBKpyapduzaZm5tTWlqazvchEonowYMH2fymTZtGAGj69Okqt9DQUAJA06ZNU7mdOHGCAFCrVq2yyZDJZFS1alUCQKGhoTrpFBQUlO25NWjQgKRSKb19+1YnGbqwZ88esre3P04GUCb5xS9+fbj4UB2HU4rJqfK7uLhkC3/58mUAmnuEbG1tUb9+faSkpOD27dsq99atWyMmJgZXr14FABw7dgxly5aFt7c3GjZsqOplevPmDW7evInmzZsjLwcXV6pUCW5ubtnclfs7XblyJcf4Sv/mzZtn8zMyMkLTpk111kUbgYGBSEpKQo0aNfDll19i165dePPmTYHlcjgcw4MbThwOR4Vy8re2VWNKd23znORyOUJDQ1Vu/v7+uHTpEt6/f49jx46BiPI0vwkAnJycNLo7Ozur6awNpb82Odrc88JXX32FDRs2wMXFBb/88gu6d+8OJycntGrVChcvXiywfA6HYzhww4nD4aiwtrYGALx8+VKj/4sXL9TCAR96p44cOYKrV6/i7du3KuOodevWkMlkCA0Nzdf8JgB49eqVRneljpl10YSVlVWOcrS555WBAwfi7NmziImJwb59+zBkyBAcP34c7du3571PHE4pghtOHA5HRf369QFA49L82NhYXL16FaampqhevbrK3dnZGTVq1MCJEydw4MABAB96oZo1awaJRIKjR4/i2LFjquG+vPD48WONu5srdcxNntL/5MmT2fzkcjlOnz6dJ31yw8bGBp06dcLq1asRHByMt2/f4vjx44WaBofD0R/ccOJwOCr69+8PExMT/O9//8O9e/fU/KZOnYq4uDj0798fEolEza9169ZISkrC0qVL4eHhgYoVKwIAzMzM4OPjgz///BP379+Hn5+famWcrshkMkyaNAlyuVzl9vDhQ/zyyy8wNjZG//79c4zfrFkzuLu7IzQ0FPv371fzW7VqFSIjI/OkjyZCQ0NBRNncX79+DUDYmJPD4ZQO+HYEHA5HhaurK5YsWYLRo0ejQYMG6N27NxwcHBAeHo4zZ86gWrVqmDdvXrZ4/v7++PXXX/H69Wt8+umn2fyUvUN5nd8EAHXq1MG5c+fQsGFDtGvXDrGxsfjzzz8RGxuL+fPnw93dPcf4RkZGWLNmDTp06IAuXbqgR48ecHd3x/Xr13H48GF07NgR+/fvz7NBl5nu3bvDwsIC3t7ecHV1BRHhxIkTuHDhAho2bIg2bdrkWzaHwzEseI8Th8NRY9SoUTh48CC8vb3x999/Y9GiRXj9+jUmTpyIM2fOwM7OLluczD1JWecwZTaW8jq/CRBW850+fRo1a9bE+vXrsWHDBri5uWHLli2YOHGiTjL8/PwQHh4OPz8/7Nu3D7/88guSk5MRGhqq2gNKORcqP8ydOxeNGzfG5cuXsXz5cqxfvx7p6emYN28eQkND87SKkMPhGDZMU/cyh8PRH4yxgFatWm06duxYzrOeOYVCs2bNcO7cObx//x7m5ub6VkfF3r17MXjw4BNv3rzJ2/k0HA6nSOE9ThwOp9STlJSkcaf0kJAQnD59Gu3atTMoo4nD4RgufI4Th8Mp9Tx+/Bj169dH27ZtUaVKFWRkZODKlSs4efIkbGxs8PPPP+tbRQ6HU0LghhOHwyn1ODk5ITAwEOHh4QgNDUVqaiqcnZ0xaNAgfPfdd7lOMOdwOBwl3HDicDilHltbW6xZs0bfanA4nFIAn+PE4XA4HA6HoyPccOJwOBwOh8PREW44cTiljLCwMDDGMH36dH2rUmQEBweDMabxKBZ9ERUVBcYYgoOD9a0Kh8MpQrjhxOGUMPgLumRx5MgR9OvXDxUrVoRYLEalSpUwcOBAPHz4UN+qcTicfMANJw6HU+KYM2cObt26hfLly+tblRx59OgR2rZti/DwcLRv3x7jx49HxYoVsWnTJjRq1AhPnjzRt4ocDieP8FV1HA6nxFG2bFmULVtW32rkiqmpKX777TcMGTJE7diVXr16Yfv27VizZg1mzJihRw05HE5e4T1OHE4JYvr06XBzcwMAbNiwAYwx1RUSEpIt/NWrV9G5c2fY2NhAKpXC19cXp0+f1ig7IyMDy5cvh7e3N6ysrCCVSlG/fn38+uuvkMvlOut46dIljBs3DnXr1oWdnR1MTU3h4eGBCRMm4N27d9nCh4SEqPQPDQ2Fn58fLC0tYWVlhc6dO+PWrVvZ4mia45R5CPP+/fvo2bMnypQpA0tLS7Rr1w43b94EALx58wbDhg1D2bJlYWpqisaNGyM0NDRbGs+fP8fMmTPRrFkzODs7QywWo1y5cujXrx8iIiJ0ehZOTk4YMWJEtrPq/Pz8VLpwOJySBe9x4nBKEH5+foiNjcXSpUtRt25ddOvWTeVXr149tbAXL17E/Pnz4ePjg6FDh+Lx48f4+++/4e/vj6tXr8LT01MVNj09HQEBATh48CA8PT3Rr18/mJqaIjQ0FF988QXOnTuHTZs26aTj6tWrsXPnTvj6+qJNmzaQy+W4dOkSFi1ahP379+PcuXOwtLTMFu+ff/7B7t270bFjR4wYMQIRERH4999/ceHCBURERMDe3l6n9KOiouDl5YXq1asjODgYUVFR2LlzJ/z8/HDmzBl06NABVlZW6NOnD96+fYs//vgDHTt2RGRkJCpVqqSSc/z4ccydOxetWrVCjx49YGFhgbt372L79u3Ys2cPTp06hbp16+qkU2bS0tJUz7J9+/Z5js/hcPQMEfGLX/wyoAtAQKtWrWJJCw8fPiQAFBQUpNE/NDSUABAAWr9+vZrfihUrCACNHDlSzX3atGkEgMaMGUMZGRkq94yMDBo8eDABoF27dmlTSY2oqCg1GUrWrFlDAGju3Llq7uvXrycAJBKJ6MiRI2p+kydPJgA0b948NfegoCACQA8fPlS5KZ8LAPrxxx/Vws+cOZMAkK2tLQ0fPpxkMpnKb+PGjQSAxo8frxbn1atXFBcXl+0+rl69Subm5tShQwc199zyhYgoOTmZOnbsSABoyJAhWsMREe3Zs4fs7e2PkwGUSX7xi18fLr0rwC9+8Uv9KizDqVmzZtn80tLSyNjYmBo2bKhyk8lkZGdnR87OzpSenp4tzrt374gxRr169dKmkk7I5XKysrKiVq1aqbkrDafAwMBscR48eEAAqEePHmruORlOrq6u2Qy3R48eEQCSSqXZjKGMjAwyNjYmPz8/ne8lICCAJBIJpaWlZUtfW76kpaVR+/btCQANHTpUzXjTBDec+MUvw7z4UB2HU0pp1KhRNjcTExM4OTmpzTWKjIzE27dv4eHhgR9//FGjLDMzM41zjTSRnp6OlStX4o8//kBERATev3+vNkfq2bNnOutbsWJFANA4N0ob9erVg0gkUnMrV64cAKBq1arZhglFIhGcnJzw9OnTbLL27duHFStW4OLFi4iOjkZGRoaaf3R0tM6T1JcuXYqDBw8iMDAQq1atAmNM53vicDiGAzecOJxSio2NjUZ3Y2NjyGQy1e+YmBgAwN27d3Nc4ZWQkKBTun369MHOnTtRuXJldO3aFc7OzpBIJACAJUuWIDU1VWd9jY2FJiqzvrlhbW2tVY4mP6V/enq6mtvSpUsxfvx42Nraom3btqhUqRKkUikYY9i1axeuXbum9V40oZyA/tVXX3GjicMpwXDDicP5yFEaE927d8eOHTsKJOvixYvYuXMn2rRpg/3796sMFgCQy+WYP39+geQXFxkZGZg+fTqcnZ1x+fLlbL1KZ86cybPMxMREAICVlVWh6MjhcPQDN5w4nBKGchgqL70wOVGtWjXY2Njg7NmzSE9Pz7Z0Pi/cu3cPANClSxc1owkAzp8/j+Tk5ALpWlxER0cjNjYWn376aTajKSEhAZcvX86zzGHDhqFDhw46rw7kcDiGCd/HicMpYdja2oIxhsePHxeKPGNjY3zxxRd48eIFxo4dq9G4efHihU57F7m6ugIQzsvLzOvXrzF69OjCULdYcHR0hFQqxaVLl9SGKNPT0zFu3DhER0fnWWbz5s3RrVs3iMXiwlSVw+EUM7zHicMpYVhYWMDLywsnTpxAYGAgqlatCpFIhC5duqBOnTr5kjl16lRcu3YNK1aswN69e9G6dWuUL18er1+/xt27d3Hq1Cn89NNPqFGjRo5yGjdujGbNmmHHjh1o2rQpmjdvjlevXmH//v3w9PRUTdI2dIyMjDB27FjMnTsXtWvXRteuXZGWlobQ0FC8ffsWrVq10rhpZk4MHDgQ4eHhqk0+ORxOyYT3OHE4JZBNmzahc+fOOHDgAGbMmIGpU6fma/hIiYmJCXbt2oWNGzfC09MT//zzD37++WccOHAAcrkcs2bNQmBgYK5yRCIR9uzZg5EjR+L58+f45ZdfcPLkSQwdOhQHDx4s0DBgcTNr1iz8/PPPMDMzw8qVK7Fjxw40atQI58+fV9sok8PhfFwwItK3DhwOJxOMsYBWrVptOnbsmOYlYJyPgr1792Lw4MEn3rx501LfunA4nA/wHicOh8PhcDgcHeGGE4fD4XA4HI6OcMOJwzE80lJSUvStA0fPpKSkgIh4QeBwDAxuOHE4hsfrJ0+e8Lr5kfPkyRNKS0vTfD4Nh8PRG7xx5nAMj2tv376V3blzR996cPTI1q1b4+Pj43fqWw8Oh6MON5w4HAODiOSMsW1Lly7NyD00pzRy9epV3Lx50wTAIX3rwuFw1OGGE4djgCQmJv6wadOmp99//316WlqavtXhFCPnz5+Hn59fclpaWjCf48ThGB58HycOx0BhjDlZW1vvzcjIqN21a1dq0KCBmYWFBRhj+laNU8ikpqbi+fPn8j///DPx5cuXGSkpKcNkMtl2fevF4XCyww0nDsfAYYxVANDNzMzMUywW27BCtpzkcrlFRkZGcyIqIxKJbotEoiuMMXlhplGSkclkZWUyWTMAIpFIdF4kEj0sgjRSkpKSnstkskMAThMRf/4cjoHCDScO5yOGMfYpgHWKn4OIiE9G1gBjrByA3wG0hPC8viCiJP1qxeFw9AGf48ThfIQwxiSMsaUA/gYQCaA+N5q0Q0TPAfgD+AnAIADnGGPV9KsVh8PRB9xw4nA+MhhjlQGcBDAWwFIAzYmo0IefShtElEFE3wPoAMAJwEXG2AA9q8XhcIoZbjhxOB8RiqG5ywA8AHxKROOJiC/bywNEdAhAPQCXAGxkjK1ljEn1qxWHwykuuOHE4XwE8KG5woUP3XE4Hy98cjiHUwwwxuqYm5uPkEgkvkQkKebkjeVyuTMACWPsPWMsBkC6TCa7FhcXt5qIjhSzPiUaxlh5ExOT4ZaWlgFEZElEZkTkBMCIMfaGMRZfzCrJAUS9e/duI4CtfEUeh1O0cMOJwyliGGN+5ubm/0yYMMHU399fZG5urm+VkJaWhgsXLtCPP/6Y/P79+29SU1OX6VunkgBjzFUqlV7o37+/Vc+ePcW2trZ631dLJpPh9u3bmD9/fuKjR492JiQkDCTesHM4RQY3nDicIsbW1va/NWvW1OjRo4e+VcnG/fv3UbNmzZTU1FRbvkt17lhaWq4ZNWpU8Lx580T61iUrCQkJcHNzS4qOjvYmohv61ofDKa3wOU4cThHCGLNJTk726NKli75V0Yi7uzuqVq2aBqCpvnUpCTDGPhkwYIDBGU0AYGFhgZ49expDWPXH4XCKCG44cThFi42lpWWaiYmJvvXQSrly5QDAVt96lATS09MtnZ2d9a2GVipUqCA2NjYuo289OJzSDDecOJyPHH3P0SlpGPLzYowZtH4cTmnAWN8KcDgc3dm9ezeuXr2Kly9folOnTggICNC3Spx8wvOSwymZ8B4nDkcPxMXF5Sueo6MjevTogdq1axeyRpyiIKd85nnJ4ZRMeI8Th6MHQkJCkJiYiGbNmqFRo0aQSnXbeNrHxwcAcP78+Wx+p0+fxsmTJ+Hq6orTp0/D3NwcgwcPxqtXr7Bnzx5kZGSgR48eKhmcoiEpKQkXLlxQ5cHYsWM1huN5yeGUTLjhxOHogdGjR+PGjRs4c+YM/v77b9SuXRvNmjVDtWrVCjRH5eHDh2jevDkWLVqEvXv3YvXq1ahbty5+/PFHREZGYsWKFWjQoAEkkuLeg7N0Q0S4desWTp8+jRs3bqBatWro1KkTatWqlW+ZPC85HMOEG04cjh4QiUSoV68e6tWrh4SEBJw/fx5///03EhIS0L59e7Rq1Spfcu3t7dG0qbCzQKNGjfDvv/+ic+fOMDY2Ro0aNWBsbIzXr1+jYsWKhXk7HzWhoaE4ePAgLCws0LRpU/Tt2xcWFhYFlsvzksMxTPgcJw5Hz5ibm6NChQqoWLEikpKSEB0dnW9ZlpaWqv+VWyBYWVmpuaWmpuZfWU42oqOjkZSUhIoVK6JChQoorJ3heV5yOIYJ73HicPTE69evcebMGZw7dw5mZmbw8fHBp59+qvbC5Bg+vXr1QocOHXDu3Dn88ccfSElJgZeXF3x8fODo6Khv9TgcTiHDDScORw9s2LAB165dQ5MmTTBixAhUqlRJp3gymQxyuVx1paenQyQSwciIdx7rE0tLS7Rp0wZt2rTBo0ePcObMGcydOxd169ZFUFCQxjg8Lzmckgk3nDgcPeDr64vAwEAYG+etCm7atAlnzpxR/f73338RFBSkmgvD0T8uLi5wcXFBz5498fTpU63heF5yOCUTfsgvh1OEMMZc7e3tb75586ZwJr4UAR07dow7cODAYCL6W9+6GDpmZmaJT58+lZYpY5inmsyePRvTp0+fl5aWNlnfunA4pRXeJ8zhcDgcDoejI9xw4nCKFplMJjPow8PS0tIIQIa+9SgJMMZk6enp+lZDK2lpaZSRkZGmbz04nNIMN5w4nKLlVVJSktGrV6/0rYdGiAjXr183BnBP37qUBCQSyeNr167pWw2tnD17NpGIeF5yOEUIN5w4nCKEiNJMTU33fvPNN6lyuVzf6mRj+fLl8pSUlNcAIvStS0kgLi5u1ZQpU5ISEhL0rUo2wsPDER4ebgRgr7514XBKM3xyOIdTxDDGrKysrA4ZGxvX9vPzk1taWhoREeRyuZiIjAHIRSJRKmOs2CpjSkoKnT17Vh4dHR2XmJjoS0T3iyvtkgxjzMjS0nKtTCb7rGXLlmkODg6i4to+gIhARCZyudwEACnKjDw9PZ1u3rwpi4yMNEpJSelGREeLRSEO5yOFG04cTjHBGKsOoAmA8gCGAXABcBTA3wBkxaxOBoBbAM4RkeF1hRk4jLFyAHwB6GO30hoABgMQA9gC4AyAJwBCiYjPb+JwihhuOHE4xQhj7FMA6xQ/BxHRTn3qwymZKAy33wG0hFCeviCiJP1qxeF8HPA5ThxOMcAYkzDGlkLoXYoEUJ8bTZz8QkTPAfgD+BHAIADnGGPV9KsVh/NxwA0nDqeIYYxVBnASwFgASwE0J6KH+tWKU9IhogwimgqgAwAnABcZYwP0rBaHU+rhhhOHU4QohuYuA/AA8CkRjefzUDiFCREdAlAPwCUAGxljaxljUv1qxeGUXrjhxOEUAXxojlOc8KE7Dqf44IYTh1PI8KE5jj7gQ3ccTvHAV9UZOIwxFwjDPGb61oWjEz4QDCZAMJrO6lGXnEgGcJeIHulbkZKIYiisAQAbAIZ4pI4dgK8B1AJwGMBKAHyI2PDJAPAGwFUi4scgGSjccDJQxGJxf3Nz8+9lMlmlatWqpZmbm4MxQ2yfOUpkMpkZEYkZYzIjI6MkxphB7o9EREhMTMTt27fFIpHocWJi4o9paWmb9a1XSYAxVtnGxmZpcnJym8qVK6fa29vD2NhY32ppRS6Xm8rlcgmETVYTDbVMcgTS0tLw7Nkz9urVK2ZsbPxnfHz8l0QUr2+9OOpww8kAkUgkX9jY2MzdsmWL1M/Pz6AbZk7JJSMjA2FhYQgMDEyKjY2dnJqa+j9962TIMMYqS6XSc99++63diBEjjOzt7fWtEqeU8uDBA0ydOjV17969EfHx8b7ceDIsuOFkYDDGKkql0sj//vvP1NXVVd/qcD4CoqKiULNmzZSkpKSqRPRE3/oYKra2tuGTJ09uPmnSJD43lFPkyOVy9O7dO/Wff/5ZmJKS8r2+9eF8gDcABoaRkVGvTz/9lLjRxCkuXF1d0b17dzIyMuqpb10MFcaYbXJysteoUaN4m8kpFoyMjDBx4kSJRCIJYnyehkHBGwEDw9bWtmu3bt34RHBOsdKtWzczGxubrvrWw4Bp1qhRoxRLS30cTcf5WGnSpAlkMpk9AGd968L5ADecDAwisnNwcNC3GpyPDEdHRwAoo289DBg7Jycnkb6V4HxcMMZga2ubDmGVJMdA4IaT4WH0sU8GDwsLA2MMISEh+lalwISEhIAxhrCwML3qERwcnOOqTEWZ4+2BdoxMTEw++uESXjcLHx3qJoHXTYOCZ0YJQtloKS+RSARbW1vUqlULQUFBOHDgAErKZP+oqChMnz4dV69e1bcqHE6B4XWTw/l4+Li7Nkoon332GTp16gQiQnx8PO7cuYNdu3Zh48aNaNOmDf766y/Y2NjoW80ciYqKwowZM+Dq6op69erpW50iY8CAAejbty/EYrG+VeEUA7xulhx43eTkF244lUAaNGiA/v37q7ktWrQI33zzDRYtWoTPPvsM+/fv15N2nMyIRCKIRHxqzMcCr5slB143OfmFD9WVEkQiEX7++Wc0b94cBw4cwMmTJ9X8o6KiMGDAADg5OUEikcDd3R1TpkxBUlJSNlnv37/HpEmTUKVKFUgkEjg4OOCzzz7DgwcP1MKlpKRg+vTp8PT0hFQqhY2NDWrXro2JEyfmqGtISAhatWoFABg0aJBqeMPPzy9b2PXr16NmzZqQSCRwcXHB/PnzNcq8ePEiunfvDnt7e0gkEnh6euKnn35CRoZupxZs27YNXbp0QaVKlSCRSGBvb49u3brh+vXr2cK6urrCz88Pt2/fRufOnWFpaQlra2v07NkTL1++zHavWedRKN2OHj2KmTNnwsXFBWZmZvDy8sLZs8IJLeHh4WjevDnMzc1RtmxZzJo1K5sehw4dQp8+fVC5cmWYmZnBxsYG7dq1Q3h4uE73zCkeeN3kdZPXzdIF73EqZQwZMgQnT57Evn370Lx5cwDAo0eP0KRJE7x//x6jRo2Ch4cHwsLCMGfOHJw6dQpHjx5V7U7+/v17NG3aFI8fP8bgwYNRs2ZNvHjxAsuXL4eXlxcuXrwIFxcXAMDo0aOxbt06DBw4EF999RUyMjJw9+5dHDt2LEcdW7ZsiSlTpmD27NkYNmwYWrRoAQBwcnJSC7dixQq8evUKQ4YMgY2NDTZv3oxJkyahQoUK6Nevnyrcvn378Omnn6JKlSqYMGEC7OzscObMGfzwww+4evUq/vrrr1yf26+//ooyZcpg2LBhcHZ2xv3797Fq1So0a9YMly9fhoeHh1r4Z8+ewc/PD927d8eCBQtw7do1rFy5EnFxcTh06FCu6QHA5MmTIZPJMG7cOKSlpeHnn39Gu3btsHHjRgwZMgTDhg1DYGAg/vzzT/zwww9wc3NT680ICQnB27dvMXDgQFSoUAHPnj3DmjVr4O/vj9DQUNVz5RgGvG7yusnrZimBiPhlQJednd1/p06dIk2EhoYSAFqwYIFGfyKiS5cuEQD69NNPVW79+vUjALRv3z61sF9//TUBoDVr1qjcxo4dS6ampnT16lW1sFFRUWRpaUlBQUEqN1tbW+rYsaNWXXJCeS/r16/X6le2bFmKjY1VuScmJpK9vT15e3ur3JKTk8nJyYlatGhB6enpanIWLVpEACg0NDRXfRISErK5RUREkFgsppEjR6q5u7i4EADatm2bmvuoUaMIAN2+fVvltn79+mw6KN3q169PqampKvfdu3cTADI2NqYLFy6o3FNTU8nZ2VntvrXp/PLlSypTpky2fAkKCiKhumvm1KlTZGdn9x8ZQB0wxAtAcJ8+fbI/cAW8bvK6mZvO+a2brq6u7wHUJgOoB/wSLj5UV8qwsrICAMTFxQEQtu3fs2cP6tevj06dOqmF/fbbb2FkZISdO3cCEIzoLVu2oGXLlihfvjyio6NVl7m5Oby9vdW+2KytrfHff//h5s2bRXIvgwYNgrW1teq3VCqFt7c37t69q3I7fPgwXr16hUGDBiE2NlZNZ+X96vKVaW5uDkB4BnFxcYiOjoaDgwM8PT1x7ty5bOHLlSuH3r17q7m1bt0aANT0y4mRI0eqTUxVfoV6eXmhUaNGKnexWIwmTZpkk6vUGQASEhIQExMDkUgELy8vjTpz9Auvm7xu8rpZOuBDdaUMZaOsbKTfvHmDhIQE1KxZM1tYOzs7lC1bVjU/4s2bN4iJicGhQ4egbRNOI6MPtvaSJUswYMAA1K5dG5UrV0arVq0QEBCAgIAAtXD5pXLlytncypQpg5iYGNXvW7duAQAGDx6sVc6rV69yTevKlSuYOnUqwsLCkJiYqObn5uams24A1PTLiawybG1ttaZna2ubTe79+/fx3Xff4eDBg4iNjVXz4yc0GB68bmaH101OSYQbTqUM5YRJT0/PPMclYUgCbdq0waRJk3IN37VrV0RFReHff/9FeHg4jhw5grVr16JFixY4cuRIgZf56rLiRanzggULtC6dLleuXI4yHj9+jJYtW8LKygpTp06Fp6cnzM3NwRjD+PHjkZCQkCfdlDrlhjYZutx3QkICWrZsicTERIwfPx61a9eGpaUljIyMMGfOnFznsnCKH143s8PrJqckwg2nUsbatWsBAJ07dwYAODg4wNLSEv/991+2sO/evcOLFy9UjZqDgwNsbGwQFxeHNm3a6JSenZ0d+vfvj/79+4OIMHnyZMyfPx+7d+9Gr169tMYrrK8u5cRQc3NznXXOys6dO5GQkIA9e/aoVhQpiYmJgUQiKbCehc3Ro0fx/PlzrFu3DoMGDVLz+/57fpC6IcLrZt7hdZNjiPA5TqUEmUyGr7/+GidPnkSnTp3QrFkzAEL3fUBAAK5cuYIDBw6oxZk7dy7kcjm6d++uChsYGIjz589j+/btGtN5/fq1Kj1NXdD169cHALx9+zZHfS0sLHQKlxvt27eHo6Mj5s6dq1FWcnIy4uPjc5Sh/IrM+jW6evXqbEuYDQVtOh86dIjPoTAweN3kdRPgdbM0wXucSiCXL1/G5s2bAUBtd+JHjx6hXbt22Lp1q1r42bNn4/Dhw+jWrRtGjRqFKlWq4Pjx49i2bRtatmyJoKAgVdiffvoJp06dQu/evdG7d294e3tDLBbj0aNH+Pfff9GwYUOEhIQgPj4eZcuWRZcuXVC/fn04Ojri4cOH+O2332Bra4uAgIAc76FGjRqwtLTE8uXLVfvMODo6qiZx6oq5uTk2btyIbt26wdPTE4MHD0aVKlUQGxuL27dvY8eOHdi5c6fGfWiUdOzYEVKpFAMGDMCYMWNga2uLU6dO4d9//4W7u7vO+80UJ82bN4ezszMmTJiAqKgoVKhQAVevXsWmTZtQu3Zt3LhxQ98qfpTwuvkBXjd53Sy16HtZH7/UL122I1BeRkZGZGVlRTVq1KCBAwfS/v37NcYjInrw4AH179+fHBwcyMTEhNzc3Ojbb7+lxMTEbGETExNp5syZVKtWLTI1NSULCwuqVq0aDR06lM6ePUtEwjLcyZMnU+PGjcnOzo7EYjG5uLjQoEGDKDIyUqsemdm3bx/Vr1+fJBIJASBfX1+1+9S0HFrb0t0bN25QYGAglStXjkxMTMjR0ZF8fHxo5syZFBMTk6su4eHh1KxZM7KwsCBra2vq1KkT3bhxg3x9fcnFxUUtrIuLi0rXzGjSO6clz5qWYgNQW1ae031fu3aN2rdvTzY2NmRhYUG+vr50/PhxjWH5dgQFu6DjdgS8bvK6SVS4dZNvR2B4FyMqGQdPfiyUKVPmv71799Zo2rSpvlXhfEScPn0aAQEBETExMdmXeHHAGAvu06fPr3/88Yd57qE5nMLDzc0tLioqqjkR8a4qA4HPceJwOBwOh8PREW44cTgcDofD4egIN5w4HA6Hw+FwdIQbThwOh8PhcDg6wg0nDofD4XA4HB3hhhOHw+FwOByOjnDD6SMlJCQEjDGEhYXpW5UCUVrug1M64eWz6PHz84Orq6u+1eB8RHDDicMxAN68eYOffvoJDRs2hLW1NRwdHdGlSxfcvHlT36pxOLh69SqmT5+OqKgofati8KSmpiIkJAStWrWCs7MzrKys4O3tjb179+pbNU4hwQ0nDscAWLZsGZYuXQpfX18sWrQIgwcPxrFjx9C6dWu8ePFC3+pxPnKuXr2KGTNmcMNJB86cOYMRI0agQoUKmDFjBqZOnYqXL1+ia9euOHz4sL7V4xQC/Kw6DicPxMfHw9LSstDlduvWDRMnToS5+YeNqStVqoTRo0dj3759GDp0aKGnyeFwCh93d3dERkaiUqVKKrfOnTujZs2a2LBhA9q2batH7TiFAe9x+siRy+VYuHAh3N3dIZFIULVqVWzYsCFbuG3btqFLly6oVKkSJBIJ7O3t0a1bN1y/fl0tnJeXF5ycnDQevnnw4EEwxrBkyRKVGxHht99+Q8OGDSGVSmFhYYFWrVohNDS0SO4DANasWYMGDRrAzMwM1tbWaNeuHU6ePJktHGMMwcHBOHr0KJo3bw4LCwvVAamurq7w8/PD7du30blzZ1haWsLa2ho9e/bMdmr727dv8eWXX8Ld3R2mpqYoU6YMGjZsiAULFqjC1KtXT81oAqD6nZycnKdnwTE8SnI9mz59OgYNGgQAaNWqFRhjqrrx6NEjMMYwbdo0tTjt27cHYwyLFy/Opnf16tXV3K5fv47u3bujTJkyMDU1RY0aNTB//nzIZLJcdcuJu3fvYsCAAShbtizEYjFcXV0xceJEJCYmZgsbHh4OHx8fmJmZwdnZGePGjcN///0HxhimT5+uCieXy7FkyRLUqVMHlpaWsLKygqenJ4YMGYL09HQAQMWKFdWMJoDX5VKHvg/L45f6ldMhv4WJ8jBLLy8vqlOnDs2bN4+WLFlCnp6eBIBOnjypFr558+bUtWtXmjVrFq1evZomT55MdnZ2ZGFhoXZw6LJlywgA7d27N1ua/fr1I2NjY3r16pXKLTAwkIyMjKh37970v//9jxYuXEj169cnkUhEu3fvLvT7+OabbwgANWnShBYtWkQzZsyg8uXLk7GxMe3bt08tLACqWbMmWVhY0JdffkmrVq2iVatWEZFwmGiVKlXIycmJRowYQb/99huNGDGCGGPUtm1bNTmtW7cmY2NjGjNmDK1atYqWLl1KI0aMoE6dOmm9r2fPnpGbmxuVKVNG7XkVFfyQ35wv5HLIrzZKQz27du0aDRs2jADQlClTaNOmTbRp0yY6ffo0ERFVrlyZmjdvrgqfmppKUqmUjIyM6JNPPlG5v3//nkQiEY0aNUrlduHCBZJKpWRtbU1TpkyhX375hdq2bUsAqF+/fjo9Y02H/V68eJGsrKyoUqVKNG3aNFq1ahWNHj2axGIx+fj4UFpamirsiRMnSCwWk5OTE02fPp2WLFlCTZs2pYYNGxIAmjZtmirszJkzCQAFBATQsmXLaMWKFTRp0iSqXr06xcfHa9QvLS2NPvnkE2KM0eHDh3W6p8zwQ34N79K7AvxSv4rbcKpXrx6lpqaq3J8+fUpisZj69u2rFj4hIfs7IyIigsRiMY0cOVLlFhMTQ2KxmHr16qUWNi4ujqRSKQUEBKjcduzYQQBo5cqVamHT09OpYcOG5OrqSnK5vNDu4/bt28QYo2bNmqmFffbsGVlbW5OLiwtlZGSo3KE46V5TY+fi4kIAaNu2bWruo0aNIgB0+/ZtIiKKjY0lAGrPKDcePXpElStXJisrK9WJ90UNN5xyvgpqOJWWehYaGprNb+jQoWRiYkKJiYlERBQeHk4AqH///mRpaUnp6elERLRnzx4CQNu3b1fFbdq0KYlEIrp27ZrKTS6XU69evQgAHTlyJEe9iDQbTnXq1CFPT0+Ki4tTc1c+i/Xr16vcGjduTBKJhO7fv69yS0tLo6ZNm2YznOrXr0/Vq1fPVSclKSkp1KlTJ2KM0bJly3SOlxluOBnexYfqPnJGjRoFsVis+l2+fHlUrVoVd+/eVQun7GomIsTFxSE6OhoODg7w9PTEuXPnVOHs7OwQEBCAvXv3IjY2VuW+fft2JCUlISgoSOW2efNmWFpaolu3boiOjlZdsbGxCAgIQFRUVDY9CnIfu3fvBhHhm2++UQtbrlw5DBo0CI8ePcKVK1fU5NatWxdt2rTRmGa5cuXQu3dvNbfWrVsDgCpdMzMzSCQSnDt3TqeJtenp6ejYsSNiYmJw9OhReHl55RqHY/iUlnqmidatWyM9PR0nTpwAABw7dgyOjo4YN24c4uPjceHCBQBAaGgoGGNo1aoVAOD169c4ffo0unTpgjp16qjkMcbw3XffAQB27tyZZ31u3LiB69evo1+/fkhNTVW75+bNm8Pc3ByHDh0CALx69QoXLlxA165dUblyZZUMExMTjBs3Lptsa2trPHv2TOPQvia++OIL/Pvvv1ixYgVGjRqV53vhGCbccPrIydxYKClTpgxiYmLU3K5cuYJPPvlENZfHwcEBDg4OuHHjBt69e6cWNigoCCkpKfjzzz9Vbhs3boStra1qjhAA3Lp1C/Hx8XByclLJU17KeQWvXr0qtPt4+PAhAKBmzZrZwirdHjx4oOZetWrVPKcJQJWuWCzGkiVLcPPmTbi5uaFmzZr44osvcPToUY0yz5w5g4iICHz99ddo1KiR1rQ5JYvSUs80ofxYOHbsmOpvq1at0KBBA9ja2qq5161bF3Z2dgByro/Vq1eHkZFRtvqoC7du3QIATJs2Ldv9Ojo6IjExUXW/Sh08PT2zydHkNnv2bJiamqJFixYoX748AgMDsXXrVqSlpWULm56ejg0bNsDf3x/Dhg3L831wDBe+qu4jRyQSaXQnYXgCAPD48WO0bNkSVlZWmDp1Kjw9PWFubg7GGMaPH4+EhAS1uB07doSDgwM2btyIYcOG4fHjxwgPD8eIESPUvrqJCA4ODti6datW/WrVqlVo95EfpFJpntPMmu6IESPQtWtX7Nu3D+Hh4di+fTt+/fVX9OnTB3/88YdavDdv3gAAKlSoUCC9OYZFaalnmnByckKNGjVw7NgxJCUl4dy5c/jf//4HIyMj+Pr64ujRoxgxYgSuX7+OL7/8Mt/p6IrymU6YMAEdOnTQGMbW1jZfsn18fHD//n0cPHgQoaGhCA0NxdatW/Hjjz/i5MmTKqMQAOLi4pCWlsbrcimEG06cXNm5cycSEhKwZ88eVTe7kpiYGEgkEjU3Y2Nj9OvXD0uXLsWDBw/w+++/g4jUhg8AwMPDA5GRkfD29oaFhUWR34fyq/+///6Du7u7ml9ERIRamMKmbNmyGDp0KIYOHQqZTIYBAwbg999/x4QJE9C4cWNVOHd3d4wePTrbyiNO6ceQ6xljLEf/1q1bY/ny5di7dy/S0tLg7+8PAPD398fXX3+N/fv3g4hUvVMA4ObmBkCoj1m5ffs25HJ5vuqjh4cHAMFY1TbMrkS54/idO3ey+WlyAwALCwv06NEDPXr0AAAsX74co0ePxtq1azFx4kRVOFNTU4wePRre3t55vgeOYcOH6ji5ovxaztp7s3r16mxL75UoG++NGzdi06ZN8PT0zDZfZ+DAgZDL5fj22281yijI8IEmunTpAsYYFixYoFo6DAAvXrzA+vXr4eLigvr16xdqmklJSUhKSlJzE4lEqjkdb9++VfNzc3PDmDFjUK1atULVg2P4GHI9UxpcWcurktatW0Mul2PGjBmoVKmS6sOkdevWSE1NxZw5c2BsbIyWLVuq4jg6OqJp06bYu3ev2g75RIQ5c+YAALp3756rblmpX78+atWqhRUrVmgc6svIyFDdh7OzMxo1aoTdu3erhU1PT8fSpUuzxY2Ojs7m1qBBAwDZn41YLMaYMWNURiSn9MB7nDi50rFjR0ilUgwYMABjxoyBra0tTp06hX///Rfu7u4a95KpX78+ateujcWLFyMuLg6zZ8/OFqZnz54YNGgQfv31V1y+fBmffPIJ7O3t8fTpU5w5cwb37t3L1xwHbXh6emLixImYP38+WrZsiT59+iA+Ph6rVq1CQkICtmzZkuPwW36IjIyEr68vunfvjlq1asHW1ha3bt3Cb7/9Bjc3N7Ro0UIt/M6dOzFo0CCsX78ewcHBhaoLx7Ax5HrWuHFjGBkZ4aeffsK7d+9gbm4ONzc3lZHm5+cHIyMj3Lp1S63c1qhRA87OzoiIiIC3t3e2zWOVu+W3aNECo0ePhrOzM/755x8cPHgQ/fr1y5fRwRjDpk2b0Lp1a9SpUweDBw9GzZo1kZSUhHv37mHHjh2YM2eOSs+FCxeibdu2aNq0KUaNGgVra2v8+eefqnlLmXvbqlevDm9vb3h5eaFcuXJ48eIFVq1aBbFYjL59+6rp8ezZM1SvXh1BQUEICQnJ831wDBh9L+vjl/pV3NsRaFperGl5b3h4ODVr1owsLCzI2tqaOnXqRDdu3NAYVsnChQsJABkZGdHjx4+16rJx40Zq3rw5WVpakkQiIRcXF+revTv98ccfhX4fRESrVq2ievXqkUQiIUtLS2rTpg0dP348WzgAFBQUpDFdFxcX8vX1zeYeGhqqttw5Ojqaxo8fT3Xr1iVra2syNTUld3d3GjduHD1//lzr/WReLl0c8O0Icr5QwO0ISno9IyIKCQmh6tWrk4mJica60aBBAwJAGzduVHPv16+fag8oTVy9epW6du1Ktra2JBaLqVq1ajRv3jy1rUFyQtuziYqKouHDh5OLiwuZmJiQnZ0dNWjQgCZPnpztOR09epS8vLxIIpGQk5MTjR07ls6ePUsAaN68eapwc+bMoRYtWpCDgwOJxWKqUKEC9ezZky5dupQt/YcPH+bYhugK347A8C5GVLDJs5zCpUyZMv/t3bu3RtOmTfWtCucj4vTp0wgICIiIiYnJvsSJA8ZYcJ8+fX79448/zHMPzSkN/P333+jZsyd+//33bL1JxYmbm1tcVFRUcyK6oTclOGrwOU4cDofD+WghIqSkpKi5paenY9GiRTA2Noafn59+FOMYLHyOE4fD4XA+WlJTU+Hi4oLAwEB4enoiJiYG27Ztw/Xr1zFp0iQ4OzvrW0WOgcENJ8NDrmkSKIdTlCjKnFzfehgw8vT0dD6voRRiYmKCzp07Y/fu3Xjx4gWICJ6enli2bJlB7PadkZHBwOumQcENJwODMfZWuQkih1NcvH79GgBicgv3EfP21atXMn0rwSl8RCIR1q1bp281NEJEePfunQkAzftAcPQCn+NkYLx79273rl27kvWtB+fjYteuXcmxsbG79a2HAXPq4sWLpvHx8frWg/MRcf78eYhEomgAmjfy4ugFbjgZGHK5/K8dO3YwXQ6E5XAKg6ioKOzcuZPJ5fK/9K2LoUJE78zMzM4tX76cD5lwigW5XI4FCxakpqambiC+/N2g4IaTgUFETzIyMr7x8vJKOnz4sMZN7zicwiAjIwOHDx+Gl5dXUkZGxjdE9FTfOhkysbGxg2bOnPl21qxZcj6czilKHjx4gAEDBqQeOnQoIjU1dZ6+9eGow/dxMlDEYnGgubn59zKZzMXT0zPNwsIi1/OiDAWZTGZGRGLGWIaRkVESY8xgC5lMJrMgIiNjY+M4fetSXBAREhIScOfOHbFIJHoUHx//Y0ZGxhZ961USYIxVtra2XpqSktKmcuXKqfb29jA2/nimispkMlMikohEogTGmMHO+ZLL5WK5XG4GQC4SiRIZYyWipzAtLQ3Pnj1jr169grGx8V/x8fFfEhEfHzYwuOFk4DDGXABUASDVty46IAXwDYAGAHYDWA/DXw1SG8BPANYB2KVfVYqVJAD3iOiRvhUpiTDGpADqA7DBx9NzbwtgNYCzABbqWRddqAVAeUDfHAA3cwhrKGQAeAPgKhHx4QYDhRtOnEKBMeYOYC8ADwCjiGi1nlXSGcbYAQCNAVQmovf61ofDMUQYY8sBfA6gOhHd07c+usAYqwKhXaqCEtYucQyXj+VLiVOEMMb8AJwH4ASgbQlsnL4FYAdgor4V4XAMEYUB8jmAVSXFaAIAha7eAI4CWMUYW8wY+3jGVjlFAu9x4hQIxtjnAJYDuAsggIju61mlfMEY+x1AFwDuRMSX/nI4mSjp9UNhLC0EMA7AAQB9ee8yJ7/wHidOvmCMGTPGlgBYBeAIAJ+SajQpmApArPjL4XAUMMYaAOgLYHFJNJoAgIgyiGg8gGEA2gA4o5hewOHkGd7jxMkzjDFrANsAtAewBMDE0jCRMdMcjmol3AjkcAqN0jYHUDG14G/Fz0+JKFx/2nBKIrzHiZMnFF9pZwD4AxhGRF+WBqNJwSwAaYq/HM5HD2OsFYQPpNmlwWgCACIKA+AF4DWAI4rpBhyOzvBJcqUIxpgIQFMAVQGIClk8QZj8/aXid1tFA1TiYYyJAfgCcAEQDuAzxtg9AHnZEJIAPANwjIhSCl9LDkd3mLDpW20IW4OICyBqMoB3AJIZY8MyuccDCC3BQ3f3GGPeEHrOVzHGakBYHCID0ARATRTs/fgWwFEieldgZTkGBx+qKyUwxmpKpdLQcuXKmTZu3NhILBYX6m6Z6enpxg8ePBBfuXKFZDLZD+np6T8Wpnx9wRhraWpq+o+HhwfVrl1bZGxsbCSTycwAyIyNjVN1lZORkUG3b9+W3bx50zg1NbUPEf1ThGpzOFphjNlZWlqGmpqauvv6+pK5uXm+RhbkcrlILpdLjIyM0oyMjDIyuePNmzey8PBwsbGxcUh8fPwoIjL0/do0kmXSeLi5ubl7mTJlbHx8fJipqWm+2lC5XI6nT59mnD59WsIYm5WcnPxToSrN0T9ExK8SfgEwkkqlLzds2CCnIubu3btkY2OTCKChvu+7oBcAS4lEknD48OFCez7nzp0jMzOzJADl9X1//Po4L2tr6wNjxoxJlcuLtjl4//491apVK5ExNkzf91zQC8Dn5ubmNH/+/EJ7Ps+ePSNnZ+cEAO31fX/8KtyLz3EqHXg5OjpKBw4cWORnslSpUgWjR48Wm5mZDSjqtIqBzj4+PvI2bdoUmsAmTZqgS5cuxBjrXmhCORwdYYxZpqSk+P3444/ioj6iycrKCj/++KPUxsZmWO6hDZ4wExOT1AkTJhSawHLlymHy5MlSa2vrwYUmlGMQcMOpdFClQYMGxXaQXYMGDYylUmnt4kqvCKni7e1tXthCvby8pFKptEZhy+VwdKCivb19mrW1dbEkVq9ePaSnp7sVS2JFS5UaNWqkGBkV7iuxfv36zNjYuHqhCuXoHW44lQ6MJRJJsRlOJiYmYIyZFFd6RYhILBYXeh0wMTGBkZFRQSbkcjj5RWRiYlJsE1dNTEwgl8sLeyGKPhCJxYVfZU1MTEBEfBFWKYMbThwOh8PhcDg6wg0nDofD4XA4HB3hXYillEOHDuHBgwcYMWKEyu2PP/4AYwxdunTBX3/9hRs3bsDIyAhNmzZFQEAAjIyM8Pr1a2zcuBFPnjyBSCRC9erV8fnnH9f+cFOmTIGfnx/Onj2LmJgY1KpVC8HBwTAxMcGJEydw8OBBJCYmokqVKggMDISNjY2+VeZwtMLbgvzD2wKOJrjhVErx8vLC3r17kZSUBKlUCrlcjosXL+KLL75ASEgILC0t8eOPPyItLQ2//vorbG1t0bJlS+zZswc1atTAhAkTkJGRgUePHun7VvTCpUuXMHbsWJiYmGD+/Pk4c+YMHB0dsXPnTowfPx7lypXD9u3bsWbNGnz99df6VpfD0QpvCwoGbws4WeFDdaUUa2treHh44NKlSwCAmzdvwsLCAra2trh58yb69OkDiUQCS0tLtGnTBhcuXAAAiEQivH37FrGxsTAxMUGVKlX0eRt6o3Xr1rCxsYG5uTnq1KmDJ0+e4Pz582jWrBkqVaoEY2NjdO/eHffv30dMTIy+1eVwtMLbgoLB2wJOVniPUynGx8cH4eHhaNGiBc6dOwcvLy+8ffsWMpkMEydOVIUjItja2gIAevTogd27d2POnDmQSqVo27YtmjVrpq9b0BtWVlaq/8ViMd6/f4+EhARUqlRJ5S6RSGBhYYHY2FiUKVNGH2pyODrB24L8w9sCTla44VSKqVevHrZu3Yrnz5/jxo0b6NGjB0QiEYyNjbFo0SJo2rPEysoKAwYIe1veu3cPixcvhoeHBxwdHYtbfYPDxsZG7YsyNTUVCQkJfF4Dx+DhbUHhwtuCjxs+VFeKMTExQYMGDbBmzRq4urrCzs4O1tbWqFGjBv766y+kpKSAiPDmzRtERkYCEMbz370TzqWUSqVgjGlsVD9GGjdujNOnT+PJkyfIyMjArl274Obmxr8wOQYPbwsKF94WfNzwHqdSjo+PD06ePImgoCCV26BBg7Bjxw5MmzYNKSkpcHBwQPv27QEAUVFR+PPPP5GcnAxLS0v06dMH9vb2+lLfoKhevTq6du2KFStWICkpCe7u7h/dKiNOyYW3BYUHbws+brjhVMqxs7NTfW0qMTMzQ2BgIAIDA7OF79GjB3r06FGcKhocs2fPVvsdEBCg+r9ly5Zo2bJlcavE4RQY3hbkHd4WcDTB+11LMUSEI0eOoHHjxjA1NdW3OhwOR0/wtoDDKTy44VRKkMvlar9TU1Mxbtw4REREoEuXLoWaFhGBqNiOwypSiuI+Ssuz4ZRMspa/om4LSgu8LeDoCh+qKx28e/bsmSyzg0QiwS+//FIkib18+RIZGRmvikR48fLuyZMnKQAK9RP8+fPnsuTk5JeFKZPD0ZF3b9++FcvlctVE7qJuC0xMTN4XifDi5d2LFy8K/aD0ly9fgjHGN3cqZfAep9JB2IULFyTPnj0r8oTkcjnWrVuX+P79+x1FnljRc3DXrl0UFxdXaAJTUlKwZcuW5IyMjP2FJpTD0REiesoYe33kyJFiSW/Tpk0Z6enpu4slsaLlwuPHj41v375daAKJCGvWrEFMTMzTQhPKMQgY70osHZibm0+xtrae+v3335s2aNAAJiYmhSpfLpfjyZMnWLZsWfKFCxduxsfHtySilEJNRA9YWlquKV++fN/JkyebV69eHcbG+euElclkuHfvHhYuXJh47969o/Hx8d2JSJ57TA6ncGGMdbSwsNj+zTffSFq1aiUyMzMrVPlEhJiYGPz5559p27Zti01MTGxIRCXeOJBIJJ9bWFgs+f777828vb2ZWCzOlxy5XI6XL19i7dq1KUeOHJElJiaaA1gCYCIRZRSmzhz9wA2nUgJj7HMAv0ml0iSJRPIGQGFnLDHGXr97924rEa0noqRClq8XGGNGAHrb2toGMcYqE5FI17hEZE5Ezoyx14yx94yxJ2/fvt0EYDNvIDn6hDHW0NzcfIREImlKRJJMXkZyudwFQIqRkdGLAsh/Hx8fvzs9PX01EeVbjqHBGPOzsrIabmxsXI+I8v31yRiLjo2N/Usul4cAmApgHIADAPoSUWkY2vyo4YZTCYcxZgxgIXjFLHYYYwzAWQDlAHiUhh44TumGMfYTgCkAGhDRFX3r87Gg+LBdDuAugAAiuq9nlTgFgM9xKsEwxqwB/APBaFoCoUJyo6mYIOGrYzKACgBG61kdDidHGGNlAXwJ4HduNBUvRLQaQFsATgDOM8b89KoQp0DwHqcSCmPMHcBeAB4ARhLRGj2r9NHCGDsAoDGAytxw5RgqjLHlAD4HUJ2I7ulbn48RxlgVCO12FQCjFAYVp4TBe5xKIIqvlfMQvl7acqNJ73wLwA7AxNwCcjj6QPHC/hzAKm406Q/Fs/cGcBTAKsbYYsV0C04Jgvc4lTD4WLlhwhj7HUAXAO5ExPdw4hgUvHwaFnxuasmG9ziVEBhjxoyxJQBWATgCwIcbTQbFVABixV8Ox2BgjDUA0BfAYm40GQZElEFE4wEMA9AGwBnF9AtOCYD3OJUAFJPAtwFoD74fiMGSaQ5JNW7UcgwFPgfPsFFMvfhb8bMHEYXpTRmOTvAeJwNH8RVyBoA/gM+J6EtuNBksswCkKf5yOHqHMdYKwgfXbG40GSYKQ8kLwGsAhxXTMTgGDO9xMmD4l0jJg++TwzEU+D5jJQs+slBy4D1OBoriq+MwgFcAmnCjqcSwAMA7ALP1rQjno6c7gCYApnGjyfBR9Ah+AmApgPEA9iqMKY6BwXucDAy+2qLkwxj7GoIB1YobvBx9oGhHbgKQA6jDey5KFnz1tGHDDScDgnfVlg4YY2YAIgE8g7D6kVcyTrHCGBsCYA2AT4lop7714eQdPlXDcMnVcGKMOQDwA2ADgBW9Sh8tDhCO7XAEsBXASf2qkysZAB4BOE5E6YUllDHmAWGipLSwZOqJZgAGAvgNwFX9qmKQpAK4AeBKQQxLxpgJgBYAXAHwjQQFTAD8CGHIeK6edckvSQAuENEdfSTOGLMC0ApCe6zP954DgDGKvyXhvWDoxAIII6LXBRGi1XBijJlYWVltTUtL69KsWbOUsmXLGotEOh8cz8kDcrlcJJfLJQBgZGSUamRkJNO3TrmRlpZG169fl92/f1+Unp7+eUZGxu8FkccYK2tlZXWQMVbFz88vw8rKSmRkVHKn4BERZDKZGQCIRKJkYZ4uR0liYqL81KlTLC4uLjYxMbETEV3PqwwjI6NeEolknZubm7xevXoisVjMHzIAmUxmQkQmJaUtyYpcLkdcXJw8PDxcJJPJouLj49sS0bPiSJsxxiwsLBamp6ePbtSoUaqrq6vI2NhYr+WKiJhMJhMDEDHGMoyMjNJ4e5J3ZDIZXr16lXHixAmJWCzeHxcX15eIUvMljIg0XlZWVpvatm2bmJCQQByONq5cuULW1tZJAPxIS1nK7QIgsrCweDBt2rS0jIwMfdwGRw/I5XLatGmT3MzMLBaAA+WtzLS0srJKunTpkp605xQ1MpmMfvrpp3Rzc/PHAEwon+1LXi6pVPp9zZo1E16/fl1s98kpXhITE6lTp06J1tbWf1I+y4nGHifGmIVEIol+/vy5xM7OLr8GHucj4bfffsO33367OzY2tlt+4jPG/Dw8PPZERkZaFrJqnBJA9+7dE3ft2vU1Ea3QNY61tfXfs2bN6j527Fj+6V3KqVWrVtx///3Xk4gOF2U6it6m1ydPnrSvW7duUSbF0TNxcXFwdHRMS01NdaR8LL7SNhbiVb169RRuNHF0oVOnTkhPT/fLb3zGWKsePXqYF6JKnBLEp59+am5nZxeQlzgymcyvc+fO3Gj6COjZs6eFiYmJfzEk5WJiYiKtU6dOMSTF0SdWVlaoW7duMoCm+YmvzXCysre3540SRyfKlCmD9PR0s/zGNzMzs3dwcCi5E5o4BcLe3h6MsTx9paWnp0vLlClTVCpxDAh7e3sjU1NTh2JIytrGxiaDzx/6OHB0dDQCkK99srS+rD6WwsMYw/bt20uMXENEUVbyXWAYYwZf3sLCwsAYQ3R0tL5V0UhwcDA++eSTIk8nJCQEFhYWhSozv/lv6GVGV4rimRYmfn5+GDNmTJGnM336dNSqVSube0loHwwRXq4EtJWrgiw+0vtX/uXLlyESidCsWbMiTUfbw+OUPgpqRBRXhS5Mli5dis2bN+tbDb0THBysetGamJjA0dERrVq1wrJly5CeXmi7ZuQbV1dXLFy4UN9q5IkdO3Zgzpw5+lZDr/ByVfiU5HKld8NpzZo1GDVqFG7evIlbt27lGt4QCqm+SEtL07cKHAPF2toaNjY2+lbDIGjTpg1evHiBqKgoHDp0CAEBAZg2bRpatGiBxMREfatX4rCzs4OlJV+3wctV4VKSy5VeDafk5GRs3boVw4YNQ8+ePbF27Vo1/6ioKDDG8Pvvv6N169YwMzPDypUrAQDr169HjRo1YGpqiqpVq2Lx4sWQy+Ua0wkJCcGMGTPw33//qb4aQkJCVP5v375Fr169YG5ujsqVK2f7cn/27Bn69u0LW1tb2NraonPnzrh7926u95eb3Bs3bqBNmzYwMzODnZ0dgoOD8f79hwn+yp6TefPmoUKFCqhQoYLqmfz9999o27YtpFIpatSogcOHPyw4SU9Px9ixY1GuXDlIJBJUrFgRkydPzlXf0sqNGzfg7+8PKysrWFhYoG7duggNDdUYNjg4GOHh4Vi2bJmqrERFRan8r127Bi8vL0ilUjRq1AiXL19Wi3/69Gn4+vpCKpWifPnyGDlyJOLi4rTqJpPJMGTIELi5ucHMzAweHh6YP3++WllWloOlS5eifPnysLW1xaBBg5CUlJQtjBI/Pz+MHDkSEyZMgJ2dHRwcHLB06VKkpqZi9OjRsLGxQaVKlbBp0yY1fSZPngxPT0+YmZnB1dUV33zzDVJSStYxZxKJBM7Ozihfvjzq1auHr776CmFhYbh8+TLmz5+vCvfu3TsEBQXB1tYWZmZmaNOmDf777z81Wbnl5/Hjx+Ht7Q0LCwtYW1ujSZMmuHnzpka9/Pz88OjRI0ycOFHj8NPRo0dRq1YtmJubo1WrVnj48KGa/969e9GwYUOYmprCzc0N3333XY4fUzExMfjss89QoUIFmJmZoWbNmli/fn02nUaNGoUpU6bA3t4ejo6O+Prrr9XKX9YeWFdXV8ycORPBwcGwtLRExYoVsW3bNsTGxqJv376wsLCAh4cHDh06pIqjSzk3dHi5EuDlCpr3cQLQvW3btu+Lej+FjRs3Up06dYiIKDQ0lBwcHCgtLU3l//DhQwJALi4u9Ndff9GDBw/oyZMntGrVKnJ2dla57dmzh5ycnOh///ufxnSSkpJowoQJ5OnpSS9evKAXL15QUlISkXCzVL58edq0aRPdvXuXJk+eTCYmJvTo0SMiEvZ88PDwoKCgILp27RrdunWLhgwZQpUqVaLExESt95ab3ISEBCpbtix17dqVrl+/TmFhYeTh4UGffvqpSkZQUBBZWFhQv3796MaNG3T9+nXVM/H09KQ9e/ZQZGQkDRw4kOzs7Cg+Pp6IiBYuXEgVKlSg8PBwevToEZ06dYrWrVtXgJzKmYSEBDIxMUmjfO6JYW5uvmzRokWFpk9QUBB17txZ9btWrVoUGBhIt27dort379KOHTvo9OnTGuPGxsaSj48PDRo0SFVWMjIyKDQ0lABQ48aN6dixY3Tr1i1q164dVatWjeRyORERXb9+nczNzWnhwoUUGRlJZ8+eJW9vb+rRo4dWXdPS0mjq1Kl0/vx5evjwIW3bto2sra1pzZo1avdjZWVFQ4cOpYiICDp48CBZW1vT7Nmztd6zr68vWVpa0rRp0ygyMpIWLlxIAKhDhw60ZMkSunv3Ln3//fckFovp+fPnqngzZ86kkydP0sOHD2nfvn1UsWJF+v7771X+69evJ3Nz8zzkRu7s37+f7O3tz1AeyoxYLE6OjY3NJivrc8hMQEAA1axZU/W7S5cu5OnpSeHh4XT9+nUKCAigChUqqNqG3PIzPT2dbGxsaMKECXTv3j26desWbdmyhSIiIjSmHxMTQxUqVKAffvhBVbaIhGdqbGxM/v7+dO7cObp27RrVq1eP2rVrp4p74MABsrS0pHXr1tG9e/fo2LFjVLVqVZowYYLW5/r06VOaP38+Xblyhe7fv08rV64kExMTOnLkiCqMr68vWVlZ0dSpU+nOnTu0bds2EolEtHXrVrUwo0ePVv12cXEhW1tbWrZsGUVGRtJXX31FEomEOnbsSBs2bKC7d+/S4MGDycHBgZKTk4lIt3I+bdo0tfxR8uuvv5KVldVaKuI9nADUdXNz0/je4+XqA6WlXHXp0iUOwlmweS8rGh2LyXDy9fWlBQsWEJGwGZ7SQFKiNBIWLlyoFq9ixYq0ceNGNbfFixdT9erVtaal7eEBoMmTJ6t+p6enk5mZGW3atImIiNauXUtVqlRRvRyJiDIyMsjOzo62bdumNb3c5K5atYqsrKwoLi5OFUb5cr579y4RCZXV3t6eUlJSsj2TFStWqNyePn1KAOjEiRNERPTFF19Q69at1XQuSgzdcLK0tKSQkBCd42et0EQf8ubAgQMqt5MnTxIAevLkCRERDRgwgAYPHqwW78qVKwSAXr16pXP6kyZNIn9/f7X7qVChAmXeHHTo0KHZwmQ1nLy9vVW/5XI52dvbU0BAgMotLS2NTExM1OpcVn777Tdyd3dX/S7JhtOkSZPIzMyMiIgiIyMJAIWHh6v8Y2NjycrKilavXk1EuednTEwMAaCwsDCd79XFxUXV5ilZv349AaDbt2+r3DZv3kxisVhVh1u0aEEzZ85Ui7dz504yNzfPUz3v06cPDRkyRPU7azkhImrTpk22MFlfcH379lX9jo+PJwD0xRdfqNyU7dSFCxe06pK1nJdUw4mXq5JZrgpiOOntbKd79+7h5MmT2Lp1KwBh5URgYCDWrl2Lnj17qoVt1KiR6v83b97gyZMnGD58OEaOHKlyz8jIUBb+PJN53w5jY2M4ODjg9WvhKJtLly7h4cOH2cZik5KScP9+zgdW5yT31q1bqFOnjprcpk2bwsjICBEREahSpQoAoFatWpBIJDnKLleuHACoZAcHB6Nt27aoWrUq2rVrh06dOqFjx44FWkVQkvnqq68wdOhQbNiwAf7+/ujRoweqVauWL1nannuFChVw6dIl3Lt3D9u2bVOFUZbJ+/fvw9HRUaPMFStWYM2aNXj06BGSk5ORnp4OFxcXtTA1atRA5iOPypUrh3PnzumsK2MMjo6OqF27tsrNxMQEtra2qnIDANu3b8eSJUtw7949JCQkQCaTQSYrcad2aISIVMMYt27dgpGREXx8fFT+1tbWqF27NiIiIgAg1/z08fFBcHAw2rdvD39/f/j7+6Nnz56oVKlSnnWTSCTw9PRU/S5XrhzS0tLw7t072NnZ4dKlSzh//jzmzZunCiOXy5GcnIyXL1+ibNmy2WTKZDLMnTsX27Ztw7Nnz5Camoq0tDT4+fmphcu6b1G5cuXUyoQmMsexsLCAVCpVK1tOTk4AoCZHl3JeEuHl6uMrV3oznNasWQOZTKZWGJSF58mTJ6hYsaLK3dz8w96IyrHLFStWoGnTfO1dlQ0TExO134wxVTpyuRz16tXDH3/8kS1ebhuE5iQ3JzKPUWe+d22yleGVshs0aICoqCgcPHgQR48eRVBQEOrWrYvDhw9/lMbT9OnTERgYiP379+PgwYOYMWMGVqxYgcGDB+dZVk7PXS6XY+jQofjyyy+zxStfvrxGedu2bcP48eOxcOFCNG3aFFZWVli2bBl27lQ/0D4/ZUlTnJzknD17Fn379sW0adOwePFi2NjYYM+ePfj6669zTKekEBERgcqVK+caLnO+5paf69evx/jx43HgwAHs2bMH3333HXbt2oX27dvnSTdjY/WmWFPZmjZtGnr16pUtroOD5i2OFi5ciJ9//hlLly5F7dq1YWFhgSlTpmR7eRVF2cqqv67lvCTCy9XHV670YjhlZGRgw4YNmDNnTrZl4wMGDMD69evxww8/aIzr5OSEcuXK4f79+xg4cKDOaYrF4nx9OTdo0AC///477O3tC3XVUvXq1bFu3TrEx8erep1Onz4NuVyO6tWrF1i+paUlevbsiZ49eyI4OBje3t64d+8eqlatWmDZJREPDw94eHhg7NixGDlyJNasWaPVcCpIWfnvv/9UvYW6cPLkSXh5ealNksytJ7OoOHXqFMqXL4+pU6eq3B49eqQXXQqbmzdv4sCBA/j+++8BCPVPLpfjzJkzaNmyJQDhGIYbN25g0KBBAHTPz7p166Ju3bqYNGkSOnbsiA0bNmh9wRWkbN2+fTvPZSsgIAADBgwAIHyYRkZG6mX1pSGV88KEl6uPs1zppfth3759iI6Oxueff45atWqpXX379sX69etzHHabMWMG5s+fj8WLF+POnTu4efMmNm7cmOOeEK6urnj06BEuX76M6OhopKbqdihyYGAgnJyc0LVrV4SHh+Phw4c4fvw4JkyYoNPKupzkSqVSDBw4EDdu3MDx48cxfPhwfPrpp3kqxJpYtGgRfv/9d9y6dQv37t3D1q1bYWVlhQoVKhRIbkkkOTkZo0ePRlhYGKKionDu3DmcPHkSNWrU0BrH1dUV58+fR1RUFKKjo3VeoTFp0iScP38eI0aMwJUrV3Dv3j38888/GD58uNY4VatWxeXLl7F//37cvXsXs2bNQnh4eJ7vszCoWrUqnj17hi1btuDBgwf47bff8Pvvv+tFl4KQmpqKly9f4vnz57h27RoWLVoEPz8/NGzYUNV75uHhga5du2L48OE4ceIEbty4gf79+8PKygr9+vUDkHt+Pnz4EJMnT8bp06fx6NEjhIaG4vr167mWrRMnTuDZs2d52kz1hx9+wNatW/HDDz/g5s2buH37NrZv345vvvlGa5yqVavi6NGjOHnyJG7fvo0xY8ZkW1FVXBhSOc8vvFwJ8HKlJ8Np7dq1aNWqFTQdmdCrVy9ERUWpLa/PytChQ7Fu3Tps2rQJdevWRYsWLbBq1Sq4ublpjdOjRw906tQJ/v7+cHBw0PmFIJVKcfz4cVSuXBm9evVCtWrVEBQUhHfv3sHW1lYnGdrkHjx4EHFxcWjSpAm6du0KHx8frFu3Lt8ylVhaWmLBggVo0qQJGjRogKtXr2L//v2QSqUFll3SEIlEePfuHYKDg+Hp6Ynu3bvDx8cHixYt0hrn66+/hlgsRo0aNeDg4IDHjx/rlFadOnVw/PhxREVFwdfXF3Xr1sW3336rGpfXxPDhw9G7d2/069cPjRs3RlRUFCZMmJDn+ywMAgICMHHiRIwfPx516tTB4cOHMXPmTL3oUhCOHDmCsmXLolKlSvD398eePXswffp0HD9+XG3oe/369WjSpAm6dOmCJk2aICkpCQcOHICZmXB6UG75KZVKERkZiV69eqFq1aoICgpCYGAgJk2apFW3mTNn4smTJ3B3d9c6FKKJ9u3bY9++fQgNDUWTJk3QpEkTzJ07N8d5L99//z2aNGmCjh07omXLljA3N0dgYKDOaRYmhlTO8wsvVwK8XAFMU88OY6x727ZtQw4dOmRV5BpwSjyJiYmwtbVNT0tLE+cnvoWFxbJZs2aN0jTmzyn9HDhwAAMGDDj75s0bn9xDC0gkkuTXr1+bWlvn66gpTgli2bJlmDJlyrr3798PKcp0GGN13dzcjj948IC/9z4CunbtGr9nz55hRJR9AnMufHwzhTkcDofD4XDyCTecOBwOh8PhcHSEG04cDofD4XA4OsINJw6Hw+FwOBwdyZPhlPUg0dKC8uDcixcv6lsVjgFS2stH1npdWut5TmQ9cLSkEhYWBsZYnpakc4qe0lK+Csr06dNRq1Ytrb9LCh9dj5MhvhTGjRuHRo0awdTUFK6urhrD3LhxA76+vjAzM0P58uUxc+bMfB8xoysXLlxAQEAAHB0dYW1tjW7duuHFixdFmqYhUrFiRbx48QL16tXTtyolCl5+ip+mTZvixYsXGrd6KSoiIyPx2WefoVy5crCwsIC/vz9u375dbOlzip/8GoJff/11jvss/fHHH/Dx8YGVlRWcnZ3x5ZdfIiMjoyCqFgml1nBKS0vTtwo6I5fLERQUpHUn9Li4OLRt2xZOTk64cOECli5digULFuS4F1FhEBoaCn9/f4SGhuLIkSO4f/8+Ro8eXaRpGhppaWkQiURwdnbOdnwBRzvp6em8/OgBsVgMZ2dntWObiprTp0+jevXq2LdvH86cOQO5XK7a7JHDyYyFhUWORv2hQ4cwfvx4XL58GatWrcKaNWuwfPnyYtRQNwpkOKWmpmL8+PFwcnKCqakpvL29cfLkSZW/stv46NGj8PLyglQqRaNGjXD58mU1OevWrUOlSpUglUoREBCA5cuXZ6v4e/fuRcOGDWFqago3Nzd89913asaRq6srpk+fjsGDB8PGxkbjhlzTp0/Hhg0bsG/fPjDGwBhDWFiYyv/Ro0do27YtpFIpatSoobYJp0wmw5AhQ+Dm5gYzMzN4eHhg/vz5artKK3uzli5divLly8PW1haDBg1CUlJSjs/xf//7H7744gutx6Fs2bIFSUlJ2LBhA2rVqoWePXti0qRJWLRokVqv04YNG1C7dm1IJBI4OTkhKChI5ccYw2+//YauXbtCKpWiatWqCA0NxdOnT9G+fXuYm5ujXr16annzzTffYPz48ahZsyYaN26MgIAA3Lt3L8d7KSwOHDgAS0tL1dfGvXv3wBjDiBEjVGG+//57tGnTRvU7IiICnTt3hqWlJRwdHfHZZ5/h5cuXKv8LFy6gXbt2sLe3h5WVFZo3b44zZ86opcsYw7Jly/Dpp5/C3NwcU6ZMyTZUV5jlOiuLFi1CnTp1YG5ujvLly2Po0KGIjY3NMU5aWhqmTJkCFxcXSCQSVK5cGb/88ovK//jx4/Dy8oKpqSmcnJzw5Zdf5unD4sCBA2jRogVsbW1hZ2eH9u3b49atWyp/5fP5/fff0bp1a5iZmWHlypV6LT/5QS6XY8qUKbC3t4ejoyO+/vprtfq9efNmNG7cWFW+evXqhWfPnqniVqxYEf/73//UZEZGRoIxpiob79+/x7Bhw+Do6AhLS0v4+vrmOAS8YsUKtQOpjxw5AsYY5s6dq3Lr378/hg4dCiD7UF1ISAgsLCxw9OhR1KpVC+bm5mjVqlW2nZ7nzJkDJycnWFhYYODAgZgxY4Za7/eNGzfg7+8PKysrWFhYoG7duggNDQUgtHs//PAD6tevj9q1a+Ozzz4z6HzWF4ZYvnJLVxPBwcEIDw/HsmXLVO/RqKgond6RuQ3NrVu3Dn369EGVKlXQpUsX1K5d2zDLEhFluwB0b9u27XvKQlBQEHXu3Fn1e+zYseTs7Ez//PMPRURE0NChQ8nc3JyeP39OREShoaEEgBo3bkzHjh2jW7duUbt27ahatWokl8uJiOj06dPEGKO5c+fSnTt3aNWqVWRvb0+CagIHDhwgS0tLWrduHd27d4+OHTtGVatWpQkTJqjCuLi4kKWlJc2bN4/u3r1LkZGRWdWn+Ph46t27N7Vp04ZevHhBL168oNTUVHr48CEBIE9PT9qzZw9FRkbSwIEDyc7OjuLj44mIKC0tjaZOnUrnz5+nhw8f0rZt28ja2prWrFmj9nysrKxo6NChFBERQQcPHiRra2uaPXt2Nl00sWDBAnJxccnmPmDAAOrUqZOa2/nz5wkAPXjwgIiIVqxYQRKJhH7++We6ffs2Xbx4kebPn68KD4DKlStHW7dupcjISPrss8/I0dGR2rVrR7t27aI7d+5Qx44dqXbt2hp1u3HjBtnZ2dHKlSuz+SUkJJCJiUkaaShLulzm5ubLFi1apCYzPj6ejI2N6cyZM0REtHr1arK3tydPT09VmGbNmtGsWbOIiOj58+dUpkwZ+uabbygiIoKuXbtGn3zyCTVp0oRkMhkRER09epQ2btxIERERdOvWLRo9ejTZ2NhQdHS02nNycHCg1atX0/379+nBgweq8nHhwgUiKrxyrYnFixfT0aNH6eHDhxQWFka1a9em/v375xinb9++VL58edq+fTvdv3+fjh07Rhs2bCAioqdPn5JUKqXhw4dTREQE7d27l5ycnOirr75Sxc9ar7P+3r59O23fvp0iIyPp2rVr1KtXL3J3d6fU1FQiItXzcXFxob/++osePHhAT548UdMxp/Kzf/9+sre3P0N5KDNisTg5NjY2x+eSF3x9fcnKyoqmTp1Kd+7coW3btpFIJKKtW7eqwqxdu5b27dtH9+/fp3PnzpGfnx+1aNFC5T9x4kTy8vJSk/vDDz9Q9erViYhILpdTs2bNqFOnTnTu3Dm6e/cuff/992RpaalqM7Ny69YtAkAvXrwgIqLvvvuO7O3tqX379qowFSpUoE2bNhHRh7L55s0bIiJav349GRsbk7+/P507d46uXbtG9erVo3bt2qni//777ySRSGj16tV0584dmj17NllZWam1RbVq1aLAwEC6desW3b17l3bs2EGnT5/Opu/Tp0/Jzc2Nvv32W52euy78+uuvZGVltZby2b7oegGo6+bmlu29VxgYavnSJd2sxMbGko+PDw0aNEj1Hs3IyNDpHTlt2jSqWbOm1t+Z2bRpE0mlUrp69apWXQpCly5d4gD0pfyUFY2OOhhOipelqoEmIsrIyKDKlSvTd999R0QfKvGBAwdUYU6ePEkAVA1r37591RoBIqLPP/9c7QXTokULmjlzplqYnTt3krm5uepF5eLiQp988kmuDyvrS4HoQ8O/YsUKldvTp08JAJ04cUKrrEmTJpG/v7+a7AoVKlBGRobKbejQoWphckKb4dS2bVsaNGiQmtujR48IgKrxKl++PE2aNEmrbAA0efJk1e8bN24QAPr5559VblkbXSXXrl0jOzs7+umnnzTKLgrDiYjIy8tLZXQGBgbS9OnTydTUlJ4/f06JiYkkFotV+TN16lRq3bq1Wvy3b98SADp37pxGveVyOTk7O6teOsrnNGbMGLVw2gyngpZrXdi/fz+JxWKV8ZeVyMhIAkD79+/X6D9lyhSqUqWKWvz169eTWCymxMREIsrdcMpKQkICGRkZqZ698vksXLhQY/jcyo+hGE7e3t5qbm3atKEhQ4ZojaM0apR5fu3aNQJA9+7dU4WpUqWK6r6PHj1K5ubmlJSUpCanbt26NG/ePK3pODs7q16wzZo1o7lz55K5uTmlp6fT3bt31XTQZDgBoNu3b6vkbd68mcRisart9Pb2puHDh6ul2bZtW7W2yNLSkkJCQrTqSET05MkTcnV1peHDh6tkFwalxXAy1PKVW7ra7mf06NG5ysr6jtTVcNqwYQNZWFjQ4cOHddY7rxTEcMr3UN39+/eRnp6OZs2aqdxEIhF8fHwQERGhFrZOnTqq/8uVKwcAeP36NQDg9u3baNKkiVp4Ly8vtd+XLl3CTz/9BAsLC9XVr18/JCYmqg3FNGrUKL+3k6uegNBt3qhRIzg4OMDCwgKLFy/Odo5ZjRo1IBKJ1ORkllEUvH79Gs+ePYO/v3+O4TLfn/JcpNq1a2dzy6rvxIkT0aZNG0yZMqWwVNYJPz8/1VBqeHg4OnbsCC8vL4SFheH06dMwNjZWlZ1Lly7h+PHjamWkYsWKAD6clv369WsMHz4cVatWhbW1NSwtLfH69etseahrOSpoudbEsWPH0LZtW1SoUAGWlpb49NNPkZaWplbOM3PlyhUYGRmhVatWGv1v3boFb29vGBl9qOrNmzdHWlqazl3g9+/fR79+/eDu7g4rKys4OTlBLpfr/Nz0VX7ySub8BLLX3cuXL6Nr165wcXGBpaWl6n6Vz6FOnTqoXbs2tmzZAgA4d+4c7t+/r5o2cOnSJSQlJanaD+V18+bNHE909/X1RVhYGJKSknDhwgUEBwfD3t4eFy5cQFhYGNzd3XM8wFsikcDT01PtvtLS0vDu3TsAupXVr776CkOHDkXr1q3x008/aZz8PWvWLDg7O+O3334r1jlWJQVDLV+5pZsXdHlH5kZGRgZGjRqF+fPnq03FMCSKZLZr1kpjYmKSzU/XE+eVYadNm4ZevXpl88t8oGHmgxbzQ056btu2DePHj8fChQvRtGlTWFlZYdmyZdi5c6dWGUo5eblXTTg7O+PVq1dqbsrfzs7OOsvRdH+65M3z58/h46PzMWKFhp+fH3799VfcunULcXFxaNiwIfz8/BAaGgpHR0f4+PhALBardO7cuTMWLlyYTY7SIAwKCsKrV6+wePFiuLq6QiKRwN/fP9t8H13LUUHLdVYePXqEzp074/PPP8fMmTNRpkwZXL58GZ999lmRLHbQ9eX2ySefoEKFCli5ciXKly8PY2Nj1KhRQ+fnpq/yk1dyqruJiYlo37492rRpg02bNsHR0RHR0dFo0aKF2nPo378/1q5dix9++AFbtmxB8+bN4eLiAkAoG05OTjhx4kS2tK2stB+P5ufnh0WLFuH06dOoUqUKnJycVPUgIiICfn5+Od5X1kUN+Smr06dPR2BgIPbv34+DBw9ixowZWLFiBQYPHqwK8/z5c3h6enKjSQuGWL50TVcXdH1H5sb79++RmJiI6tWr5ylecZJvw8nd3R1isRinTp2Cu7s7AGEC9ZkzZ/K0oqJatWq4cOGCmtv58+fVfjdo0AC3b99GlSpV8quuCrFYDJlMlud4J0+ehJeXl9oSzJys+MLEx8cHkyZNQkpKCkxNTQEAhw8fRrly5eDq6grGGMqXL4+jR4+ibdu2hZ7+H3/8AVtb20KXmxvNmzdHamoq5s+fj+bNm0MkEsHPzw+ff/45nJyc0KFDB1XYBg0a4M8//4SLi0u2BkrJyZMn8csvv6Bz584ABOOzqJbH61Kus3Lx4kWkpaVh8eLFql7Lf/75J8c49erVg1wuR2hoqNrzUFK9enX8+eefkMvlql6nkydPQiwWq+ptTsTExOD27dtYvny5qlfr8uXLeVoirK/yU5jcvn0b0dHRmD17Ntzc3AAAO3bsyBauX79++Pbbb3H27Fls27YNs2bNUvk1aNAAr169gpGRESpXrqxz2n5+fhg5ciS2bNmiMpL8/PywZcsW3L59G3PmzCnQvSnLamYjSFNZ9fDwgIeHB8aOHYuRI0dizZo1anEWL17MV57mE32VL13TzYqm92hhvSOtra1x4cIFtV5SQyPfQ3Xm5uYYOXIkJk2ahH///Re3bt3CyJEj8erVK4waNUpnOWPHjsWhQ4ewYMEC3L17F2vXrs1mof7www/YunUrfvjhB9y8eRO3b9/G9u3b8c033+RZb1dXV9y8eRN37txBdHQ00tPTdYpXtWpVXL58Gfv378fdu3cxa9asHPejyAv37t3D1atX8fz5c6SlpeHq1au4evWqyuLv168fpFIpgoODcfPmTezYsQNz587FV199pfq6++6777BkyRIsXrwYkZGRuHr1Kn7++edC0W/s2LE6VabCxsLCAg0bNsTmzZtVL21vb288ffoUZ8+eVfvSHj16NN6/f48+ffrg3LlzePDgAY4cOYJhw4YhPj4egJCHmzdvRkREBC5cuIC+ffuqeqwKG13KdVY8PDwgl8uxZMkSPHz4EL///juWLFmSY5yqVauid+/eGDp0KP7++288fPgQJ06cwKZNmwAAo0aNwvPnzzFq1CjcunUL+/btw+TJkzFmzBhIpdJc78PW1hb29vZYvXo17t27h/DwcIwYMSJPL0h9lZ/CpFKlSpBIJPj111/x4MED7Nu3D1OnTs0WrkKFCvD19cWIESPw/v17tV7yNm3aoFmzZujatSv279+Phw8f4syZM5g2bZrGXgIl1apVg7Ozs1o9UA5jP336NNcep9wYN24cQkJCsG7dOty9exfz58/HuXPnVG1LcnIyRo8ejbCwMERFReHcuXM4efIkatSooSZn5syZWLlyZYF0+VjRV/nSNd2suLq64vz584iKikJ0dDTkcnmhvSNfvXqF/v37q63cNTQKtB3BvHnz0KdPHwwaNAj16tXD9evXceDAAZQtW1ZnGT4+Pli9ejV++eUX1KlTB7t27cKkSZNUPSsA0L59e+zbtw+hoaFo0qQJmjRpgrlz56JSpUp51vnzzz9H9erVVeOwp06d0ine8OHD0bt3b/Tr1w+NGzdGVFQUJkyYkOf0NTF06FDUr18fixcvxosXL1C/fn3Ur18fz58/ByBY4IcPH8bz58/RqFEjjB49GhMmTMBXX32lkjFy5EgsW7YMq1evRq1atdChQwf8999/haLf/fv39bYTsZ+fHzIyMlQvB1NTU3h5eUEikajNyyhXrhxOnToFIyMjdOjQATVr1sTo0aMhkUggkUgACEtdExIS0LBhQ/Tt2xeDBw/WuuFoQdGlXGelTp06WLp0KRYtWoQaNWpgzZo1Goces7Jx40b069cPY8eORbVq1RAcHIz3798DAMqXL4/9+/fjypUrqFevHgYPHozPPvsMs2fP1uk+jIyMsG3bNly/fh21atXC6NGjMWvWLNUz1QV9lp/CwsHBARs2bMCuXbtQo0YNzJgxQ+s+av3798e1a9fQqVMntZ42xhj+/fdftG7dGp9//jk8PT3Ru3dv3LlzRzVHThu+vr6QyWTw9fUFILy4ypcvn+v8Jl3o27cvpk6dismTJ6N+/fq4efMmRowYoSqrIpEI7969Q3BwMDw9PdG9e3f4+Phku//Hjx/zDU7zib7KV17SzczXX38NsViMGjVqwMHBAY8fPy60d2R6ejru3LmT6zY+ekXTjHFoWVVXXIwfP55q1aqlr+Q5eaSoVtWVNni51owhrKrjqNOtWzedVikXB6VhVR3H8CjIqjqDGJBesGAB2rZtCwsLCxw5cgQrVqzQ+YuYwzFUeLnmlASSkpLw22+/oUOHDjA2Nsbff/+N3bt34++//9a3ahyOQaLNcKL8TKDOLxcvXsTChQvx/v17uLm5Yc6cORg3blyxpc8pGIqyku/lZEQkK87yVlzwcq0bMpkMRJSn8sMYK9Y2qjTDGMP+/fsxe/ZsJCcnw8PDA5s3b0b37t31rRoAoXzI5fLiyGx5QVdAc0oOGRkZhHy+t7QZTm+ePn2af43yyLZt24otLU7h8/TpU5iamr7Pb/zk5OQXjx49SgegeTlcCYWXa9148uQJMjIynucljlgsfv/s2TMzOzu7olLro8HMzAxHjhzRtxpaefz4cUZycrL2M0AKj+jo6GiJTCZT24uPUzp59OgRALzJT1xtk8PPP378WFRcy+05JZs///xTRkR78hufiP7dtm1buq4rHDmlByLCpk2b4t+/f789L/HkcvnuP//8k3c5lXIyMjLw+++/p8pksn1FnRYRvTA2Nn6qPIOPU3p58uQJ7t+/bwzgdH7iazSciChdJBLNbtOmTdKlS5eUE+c4HDWSk5Oxbt06LFiwICkhIWFxAURdT01NPdOjR4/k/OxWyymZREdHY/z48enXr19/DWBvXuImJiYu+fnnn5NWrVpFycnJRaQhR588efIEvXv3TomPj78M4FJxpJmQkDCld+/eyceOHSvwxsUcw4OIcPXqVbRu3TpRJBItIKLU/MhhORlFUqn0S5FINMnExMTSzs4uPfOxDRztEJEJEUmNjIwSAJTKr+KMjAz24sULiamp6Y3Y2NgRRFSgho0xZmppafm/9PT0z2xtbeXm5ubykrgDMRGZEZGYMZbIGNN9l8j8pWXCGCuR3XQpKSns9evXYlNT00Pv378fQkR53q+AMVbfxsZmZUpKSh1nZ+dUExOTEvOFp2wjGGOpjLGUok5LURZLxPMhIiQlJRm9ffvWSCwW/xUXFzeKiIrNOjY2Nu5jYWExRy6XOzs4OKSLRKIS8dyywORyuSUAmZGRUWJRJZKpvUsy9LZILpfj3bt3JmlpaQkymWxhcnLyQspnr1COhhMAMOHt5QrAFkDJe5N9wAjAIgCXAWws4rQYACsA+Z73UwLIAPCUiGIKUyhjzARAFQC5785omEyGMOFwfhGn0wHAjwBmII+9NQZCCoCHRFTgzVoYY3YAKqKIjpAqIroCCAAwAkJdKirKA9gB4F8IZaWkkATgPhEV/llDOsIYqwDAAQXc71BPDIVQtoIB3CzCdEwArILQZg8E8LAI0yoM3kFodwpkDOdqOJUWGGOzAXwLYDgRrSqmNBkANyJ6UBzpcQwDxphRXleJ5ScNAMcA1AdQk4iKbzUHp1AojnKiSOcnAFMAfEJERT5XiKNfGGMOAB4AOEREPYohvXIQjPIJRBRX1OkZAh+F4cQY6wngLwCriGh4Mab7PwC9AbgTUUJxpcspfhhj4wGcKOiQZR7TdAdwHcBxAJ0K+hXFKXoYY58AsACwrbjyizEmgTBHyBaCkR1bHOly9ANjbDGAsQBqEVGxnlvCGDMDkEZEpXKKipKS2AWZJxhjtQCEADgDoTAVJ5sBOAL4spjT5RQjjLHmEIaBBxVnukR0H8LQYIfiTpuTdxhj9gDWAvgGQLGtd1dMgA0G4ASgIIs4OCUDOYCVejCaLCGsUptZnOnqg1Lf48QYGwhhLog3EeVpr5hCSn8HgDYQep3ytWcEx3BhjEkBXIMwv6Z2cfcsKobsVgJYQ0TnijNtTt5gjG0F0BNAIyK6rof0JwF4RkSbizttTvHCGGP66IFmjK0C8DmAnkRUareeL/WGEyC83ApjEmo+064OYXLeUiL6KrfwnJKFolt8PIDWRMQ3gOFohDHWHcIk7R+IaJa+9eGUPhhjngAcieiEHnWQAAgDUBuAFxEVzknzBkapNZwYY98DuF6QjRkLUZe1AJpB6JEw6CWbHN1hjDUBcBbAb0Q0Ws+6mEIYLrxAROv1qQtHHcUQxj0A/2/v7KPtnK88/vlWQk2a8f5OpGG80yCod6pFEuNlbmawxrIitFpN0OmMd2ZIgqhlEMIksyaMJbSK6AwdZohYlYl6naEdLFHqfcZUVVVHseeP3+9yGjf3nnvPec5+nufsz3/W4p7vus49Zz977+93v0zqfLt+Bkg6ibTr5PqeDdqLpDuAfYFNzOwdRx0bknbqfg3samZveWkpilruOOVl8OnARG8tmW8DX/D+wAzazuOk/7enewsB3ge2AS7PNuqgJOQvsanAcSX5DNgIOElSWT4fgxaRtCdwKHCJZ9EEkFdieoBfkWJ5akftOk55GXwpyW20/1CTQYsgdwVWN7PXvbUErSFplTK9twAkjQGeJFx2paGk75NVgEeANUnOq9p1BLqJHHvzAClLaXMzKyzwcjB0Km7Dg1p1nCStASwE3iEtp5XmAysv8T4MXOutJWiN7KJbJmmst5ZGcl7Y6SSX3WRfNUF20T0j6RhvLY3kz8XjSC67y5zlBK0zAdgLOL8sRROAmX0k6Q8k3Syp8DypTlKrwgn4c2AU0OPhoOuPXHnfDBwmaQ9vPcHQyC66+aTR2HPOcvpiDrAYmJE7C4Efs4ENSd3vUmFmjwCzgMmStvXWE7TE6sBDpKiLsvEh6fLI9XkaVAtqNarLLcutzeyn3lr6QtIIYBnwLLBvjFKqh6TLSLlcpXXRSRoNfCYS6/2Q9CfArZTYRZcL63Fm9qC3lqA1vOIHmkHSRqTR8LvALnUYDdeicJI0AXixCtbH7Gi5GphoZnd56wmaJ4/oHqAELrpmiJM/PuQR3U8oiYuuGSRtlgNVg4qQC9/Dge+XPak7L68vAu4lnf4ptd6BqPyoLrf/vkd1EnHnkbpOtZr5dglHAS9QDhddM/wN8Hi47DrOgSQ30eSKFE0HAc+Gy65ynEha/9jbW8hA5K7mycAuwGbOclqm0h2nvAz+MDAC2Llse00rQtIGwOtlba0GfZM7OOtVxRXZcMtuManDGe+3DiFpAzN7zVtHM8Qtu+qRs8GWkcKVD6jC33b+/FzLzN701tIqle04SVoJuImSLoP3h5m9ZmYmaT1JK3vrCfpH0o6SRluiEkUT/N4tu/GEy65wJK0taR9If+PeepolbtlVkm8B6wBnVqFoAsifn28qcUaVl8Ur23GS9A2Sg+hEM5vrrWew5Myd/wTOMLOrvPUEfdNwi+43wNiqfEj1kmMw7gN2JHUTXnaWVFsk3QQcAYyuUoHdi6SZwFmkHZQ7vfUEfSNpHeB54B4zq9zKR94BfJIKL4tXtuNEsl4eU8WiKfMz0pjxXEmf8xYTrJAZpGC5U6pWNMHHMRjHA/8DbOosp7ZkF91RwMwqFk2ZC0gju028hQT9sjHwEnCOt5ChkEd1PaRp0Y15elQpKtdxkrQF8KaZ/cJbS6tI2o2Ucl5ay3I30+Cim2NmU731tIKklaruZCkrDS66V0iHTUu/EL4i4n1SDcocP9Askk4kBUJfaGZne+sZDJUqnBqWwV8H9q76GwdA0m3Al4ExdViaqwsNI7qVgB3M7NfOklpG0jDSbb0bY2TXPvKIroeUiVS6sMuhIOkI4F0zu8dbS/AJkg4D7q3J55GAuaT9ui2rFJtSmVHdcsvgp9WhaMqcDaxKWuANykPvbtDxdfiQymwMnAfMyx9aQYvk3+NDwDk1KpqGkcZ28/PDalAC8jL17cBp3lraQf4OnwrsVaWiCSrUcZJ0IXAmFV0G7w9JG0cHIOgEkqaSToFMMbP53nqCciJpHGmN4AYzO85bTwCS7gD2JU0nKr+qsjySxgNLq7AsXomOk6TDSUXTvLoVTQC9RZOkDb21dDv5KOVCSTt5aymI3lt2l0cwZmtIulrSkd46imC5W3YRjOlMTt4+FLikpkXTRsBCYEEVlsUrUTgB/w5cCUzzFlIUko4GXqxytkVNmAEcBqzmLaQIsstuCjAMuMZZTmXJLrqTgC28tRTIBaSAxbk5cDFwII+DLybt9l7hLKcQzOwVUrL4waT3Xakp9agu/7G+Z2YfeGspGklrkbI57jezw7z1dCNVu0XXCpKOAp43sx97a6kadXLRDYSknYGtgAU12iutFJJWI+023WJmtX3YaVgWPwGYZGa3OktaIaUtnHK77p/zP07ohj9aSWeTOh57mtkSbz3dRB1ddM0iaXidv/zbTR1ddM0Q7xM/clGh3DGuLfn8z/3A9sBWZd39LfOobjqpbXd7NxRNmcuBN4CLw/XUcb5JCrqsk4tuQCRdCiyM91tzSPoiKehyepcVTUcDPwmXXWeRtHs2D1ndiyb4+PxPD3AqqaNbSkpZOEmaRFoGn1vHZfAVYWbvkua740hf4kHnuJzU2VzkLaTDvABMAMI51QRmtpT0+7rYW0uHeRYYQ9yy6xi5+7IA+J63lk5iZq+a2d/ne66blXFZvHSjurwcvZR0x23/XIF2Dfno79pVOlpcZfKI7rN1dKo0Q9yyax5JG+Ul1q5E0gxS7lzcsusAkqaRTFEHm9nd3no6jaTRJHPCFWVLFi9jx2kY6QBgT7cVTQBm9r6ZvZovSK/vracLmAk8mRcwu47lXHYRjLkCsotumaQ9vLU4Mp1PXHYxsiuQbIw6F1gEdGV6u5m9QOq4nSWpVMeMS1M49X5gm9kTwB5m9pqvInfmAEtyByoogOyiO4W0R/e2tx4vcmrv6cDewJbOckpHdtFdA/yUdPKpK8kPspOBdUjjyqA4vkX6PZ/ZRTu+fTGNNIG6vkxRPaUZ1eVk8JGkK/S1X4IbCEkHAz8EppnZVd566kY3u+j6Io/sNjKzl7y1lI1uddGtCEmjzOzn3jrqjKQrgA3M7M+8tXiTg6EfBd4FdilDsngpOk4Ny+CrRNH0MXeTbJnnSvqcs5Y6MpO0gD+l24smSCM7M3spj4gPjJFdIo/ous5F1x+9RZOk3WJkVwxmdgrpfdf15H3fHuBfSMWTO+6FU26/XUdKB69tMvhgye3ZM4F1SW3boE1kl8ZmwNVmdr+znLJxKKlon+ysoyxsDTxC97no+kXSBqSw2Mu8tdQJSZvkO4FEE+ETzGyJmU01s/clfdZbj+uoLj+tPAyMAHYOJ9mnkXQbsA3J8fSht566kDsqw83sfW8tZSJcdp9G0srxPvk0kmYCZxEuu7Yh6TrgSNLYvCudvv0h6fPAvwGneSaLe3ecxgJrkRx0UTT1zUnATlE0tQdJX5U0OgfKxZfhcoTLLiHpIEn7QHK6euspKY237GJk1yJ5+nIscFUUTSvkVeC/cV4Wd18Ol/SHZvYrVxEVQNJwYISZ/dJbS1VpuEV3pZmd6iyn1EiaCswm7YDN99bTSRpu0b0E7BojkxWTx0pLgRvMLEJUW0DSHcC+wJgonFZMGZbFXTpOknoknQAQRdPASBoGPEak9g6Z7KKbT0rKPsdXTSWYA3yX9HTXbcwG1iAVjVE09YOZPULqPD3drd3JdiBpT9J+4awomvqnYVl8FHCjR7J4xwun3F67HpiSC4JgAMzsA9LC7rGStvXWU1Fm0IW36IZKdtkd1W27K+GiGzxmdoGZzeryvKFW+TzprM2V3kKqgJktIZnJRgAdd513dFQXy+BDR9JawPPAIjM73FlOpchpzz8CrjGzb3rrqRL54eZ04GUzu95bT5FIWhP4L9Jx0d3M7HfOkiqFpCNI4cV/5a2likhaKXZZB4fX76xjHafcTltAaq/FMvggMbP/BS4BDuvysw9D4SmSnfx0byEV5CPgQOBKSRt7iymYt4GLgMlRNA2JnYG/lDTRW0hVkLSSpPGSFEXT4DGzDyWtI+kHnVwW71jHSdJXSDd3vm5mf9eRF60ZkkYAzwH/ZGZf89ZTBSR9JvZUWkPSZqSj2w8AE+o4kon3SetIWoWUebUmKcril76Kyo+kY4AbgIlmdpe3nirisSzesY6Tmf0r6WRBFE1DxMzeBfYCvu6tpQpkF90Tkjb31lJlzGwZcAZwMFA751R20T0laby3lirTcMtuPcLIMiC50JwOPE5KxQ6GQJ5eTSJNsxZ0Ylm88MJJ0nb5Cwwze7To16s7ZrbMzD6StEYOKwz6oMFFNxJ43VlOHbgaWAx8p4YngGaTjAOveAupOvkz/mJgsqRdvfWUnK8Bo0mHfKPb2QJm9iBwMunhbnrRr1foqK5hGXw4sEV+IglaRNJWpBM1U83sRm89ZUTS3wKnAl8ys0XOcmqBpDHAWmb2sLeWdpFddLcC55lZ4R+43UDupIwH7qjjWLcdSBoJLCPtXx4Qv6fWyXEYc4EvA18oMuqosMIpt8vuBL4E7Jftg0EbyJ2mR4DVga0i2fj3aQi6DBddQUha18wqnfHUEHQZLrqCqMP7pAgkjQUWAkea2UO+aupDLtpHmtmbRb5OkaOeGcBBwLQomtpLbuueScr+iCXxTzONFHQZLroCkHQy8GwNXHZHkYIuw0VXANn9+4KkCd5ayoaZPQFsHkVTezGz/zOzNyUNl/TXRZ0CKqTjlG88LQbmhfurGHJb8j7SAeDNItTxE/J5mk3M7HlvLXWkTi47SVua2TPeOupIuOz6RtJ+wFIz+62zlNoiaUfgIdJB4D9ud9RDUR2nHwEnkp78gwLIX1ZnAOsChzjLKQWStpW0ppn9Loqm4ljOZTfZV83gkbS2pG0AomgqjrzTehzJZXeZs5xSIGlT0hWImd5a6oyZPU6qP8aTTgK1lbZ2nHJbbKSZ/bxtPzToF0lbmdnT3jq8yS66/yDdVturyl2QKpD37O4DdiR1E152ltQ0km4CJgKbehwI7TYkzQTOIrKKkHQdaUT8R2b2krOcWtOwLH4CMMnMbm3Xz25bx6khGXyJpFXb9XOD/uktmoqa5VaImSRL+TlRNBVP3rM7HvgtMNZXTfM03KL7ThRNHeMC4ElgnLcQT3Ky9bHA7Ciaiid/D0wljezm5IfrttC2jpOkC0kLyyea2dy2/NCgKXL67LWkJ/8XvfV0mnDR+SFpVTN7z1tHM4SLzo8qvU+KQtIdwH7AmHxCK+gAkjYixai07Wh3WzpOkiaRiqa5UTS5sBgYBpzvLaTTNARdvkC46DqOmb2nxLEVcNnNJlx0LvQWTZJ2l7S/t55Okz+n1gVmRdHUWczsld6iSdIh7UgWb7njlJcsf0xy2ewfIZc+SLoU+AtgBzN7yltPp8hdhPnAZRF06UN+onuWErvs8oflLOAtM4vFXAfyXtxjwDp0ocsu79wMi6Ldhwa3/0VmdlZLP6sNhdNI4FLg/HwzJnBA0lrA88AiMzvcWU7QZUiaBlwJHG9m/+CtJygnknYm7ZzcYGa1u3vYF5J2AF6PIFB/JM0Fvgr8qZl9f8g/Z6iFU36CW8XMfjPUFw/ai6SzgfNIjo1aOxtz6/tq4AIz+5m3nm4ndxMWkRbFS+WykzQLuMvMFntrCUDSDOBsusBll78nHwc+BHYqYze2m8jZYvcD2wNfHOp0ppUdp+kkB93IFn5G0F4uJ51gqXXRlJlJyhAa7SsjgI9ddlNIu3bz8ljCHUlHAKcB+3hrCT5mOulG2zxJqztrKZqjSV/Ss6Jo8ievEvUA7wC350Jq0Ayp45SXwW8hksFLi6TVzOxtbx1FEC668iJpMvALM/tBCbSEi66k5JHdAaTdxA+89RRB/lJ+GngLGJcfLoISkM8BrW9mtw3pvx9s4ZSzKJYSy+ClRdK1wO7AjnX7Y20IuhwGbB+nZsqLJHk+Zeegyx7Sl1bbrMhBe/F+nxRFw97fwWZ2t7eeoG8kjRrslGZQo7ocsriQ1OaaFEVTaVkM7EBqE9eN00hBl1OiaCovkr4NLPQa2Uk6gBR0OT2KpvIi6UDg0ZqO7LYE7gXu8RYS9E2OxnhOUs+g/rvBFPqSRgG3AqeY2ZLBSQw6RV7UfRRYjbTz9L6zpLaRd+ommtnN3lqCFePtsst/A8cAN8WIrrzU3WUnaeU6ff7WjTxOXQxsxyCWxZsunHrbqXVtq9YNSQcDPwSmmdlV3npaJZ/x+Si6nNVguVt223XyxISk1bstI6jKNLjsDjGzO731tErerVu/m/L0qkzOoXsEeBfYpZlTTE2N6vIy+HcljYiiqTLcTbJdfqMsDqcWuZDU0m/bvaGgOBpu2Q0D5nbqPZhddMskbd+J1wvaQq/Lbm5NRnZnA49JWs9bSDAwZvYKMAkYBSxoJll8wMIpL4NfB2wM1NL9UEdygTsF2KPqxa6kvYFTgMWRG1YdzGwZcAbwFWCnol8vP+lfSzq/83TRrxe0h9xFngysRxqvVhZJmwInAf9oZm946wmaw8weBKaRjG8Dfl/2O6rLy+APAyOAnSMZvJpUOaw0XHTVJo/stunE2ELSAtKTY7joKkjuEj5V5Qc9SdeRTAmblykENhgckob3txs5UMdpAal91RNFUzXJy2+PkgIjq8hMwkVXWczso96iSdK4okZ2eUR3NOGiqyxm9mTeo928iiO7PJ05FpgdRVN1kbQr8Ex//85AhdMuwNRw0FWX3AZ/FNg6P/1XBkkrA3sCc+KAb7XJtt9bgA0Keon9SactLi7o5wcdIE85FtOB0W4BjAXeAC5y1hG0xsvAqv39Cy0f+Q2CIAiCIOgWKtWBCIIgCIIg8CQKpyAIgiAIgiaJwikIgiAIgqBJonAKgiAIgiBokiicgiAIgiAImiQKpyAIgiAIgib5f/HIB/FnSsqUAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bbbfc18>"
+       "<Figure size 720x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1957,14 +2040,14 @@
     "text(ax, 0.62, 0.3, \"Does the animal\\nhave wings?\", 14)\n",
     "text(ax, 0.88, 0.3, \"Does the animal\\nhave a tail?\", 14)\n",
     "\n",
-    "text(ax, 0.4, 0.75, \"> 1m\", 12, alpha=0.4)\n",
-    "text(ax, 0.6, 0.75, \"< 1m\", 12, alpha=0.4)\n",
+    "text(ax, 0.4, 0.75, \"> 1m\", 12, alpha=0.6)\n",
+    "text(ax, 0.6, 0.75, \"< 1m\", 12, alpha=0.6)\n",
     "\n",
-    "text(ax, 0.21, 0.45, \"yes\", 12, alpha=0.4)\n",
-    "text(ax, 0.34, 0.45, \"no\", 12, alpha=0.4)\n",
+    "text(ax, 0.21, 0.45, \"yes\", 12, alpha=0.6)\n",
+    "text(ax, 0.34, 0.45, \"no\", 12, alpha=0.6)\n",
     "\n",
-    "text(ax, 0.66, 0.45, \"yes\", 12, alpha=0.4)\n",
-    "text(ax, 0.79, 0.45, \"no\", 12, alpha=0.4)\n",
+    "text(ax, 0.66, 0.45, \"yes\", 12, alpha=0.6)\n",
+    "text(ax, 0.79, 0.45, \"no\", 12, alpha=0.6)\n",
     "\n",
     "ax.plot([0.3, 0.5, 0.7], [0.6, 0.9, 0.6], '-k')\n",
     "ax.plot([0.12, 0.3, 0.38], [0.3, 0.6, 0.3], '-k')\n",
@@ -1975,7 +2058,7 @@
     "ax.plot([0.8, 0.88, 1.0], [0.0, 0.3, 0.0], '--k')\n",
     "ax.axis([0, 1, 0, 1])\n",
     "\n",
-    "fig.savefig('figures/05.08-decision-tree.png')"
+    "fig.savefig('images/05.08-decision-tree.png')"
    ]
   },
   {
@@ -1994,17 +2077,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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AsUahy7F4lrOxUWAnXev3eYShZ9gkclKOfspra252ve19ZlEbHx/YRUTUhEHN\nKmeeO42x5lPWxmgpvQV7PghhyYbv3rfjIUlS/x6sgKdi5nBiXzkOuem4OWm4UeWDe9AyDn38cwK9\ny9B2WFHVNpZFz3zlrp2G0PHTOXfpBDMmdT/YHE+1YmJ05xxPjdEdKLc4Rmv06PVc/QmgX2SWe/4/\n6jBIqB3795kIwkC7mLyDV9dWIUmdsWb9siY+PvQpgaGDO6Ij9cQh7DsO8OzMDvKKJPZ+NI7lG755\n3+TGg8Sa2EXL2buzBnfryzjZdXCj2hfP4AUc2fJjAkfcornNhibTVBJWv3TXeOvhN4GC62cIC+r+\n/cl0RyYtmYKNjRW1LW5Ad2FVWZbRmNx7PdfDjYDq+fkYjJJ4sBKeOLnpn7BxZQO3/82+vLqOjw7v\nwOfZbw9qO04e+IRRdkmsm20kM2cPhy5MY8lzr9/3uAd5sRO37Dm27mzExzEXGysTN2oC8A6ezbFt\nPyBwRDWFzfZorWYyd+mzdz2HtVM41XUZeHt0X/vSNW/invXHYDByMcuFseHdD2AGg4xB7j0Z/jCx\nRu4l1siyWGNEePKUZu9kw9JmQIGjA7y+tpJ/Jm5n0TNvDGo7End+QMSINNbNNnHu0n6OZ8xh/sr1\n9z3uQf5+xy7bxIe72/F3L0SSoLQuBO/AKE5+8p8EeNeSX++MwiWemfOW3vUcRlUg7Zpc7O26v9c3\n60YS6epIcFg4mRm2JMzqrtnT0mpCZdv7ynf97ZfdSZZ63r+INV8OwyaRYyNV9iiU5eVci1arH7T5\nyEajkbbyvTyzSAdIhIwG3xGF7D66n3nL1gzINeetWI9e/zRtLRpC4104vO3XvLLqdg0JPe2aDPYe\ndGfeCstOj6ZdS1FBMYHBgVwuXMr+pBOE+TeRe8MNpdti3L0637qNDF/B3uN/Z2mclo4OmV3H3BgX\n9+jvJXziPJLOZRA/ozPgybJMet5oVmwMeOTXEoSHYauq7pGssFdVDWob2lo1WGkOsyDOAEiMC4cR\nntmcPZ3MjLi5j/x6kiSx6JnX0Gr1aDU6wt2dSdz6YzauuPX5Hjpq609z+rg3sxMWWRzb0txGyfUS\nQiIiSDsxn+LyswSObCO7yANn/zXY2naO5HH2W8aR09tYMFtPa5uZT4+OYPaK/k3R7AufkBgycq4w\ndVxnHQ6jUaakPoxxvRR8FoTHyV5d08u26l72HDgV5ZX42id9XrtCYmqUjItTOlcuRTN+0oRHfj21\nWsXS598a6NZjAAAgAElEQVRE067FYDAS4WRP0o7v8+Lyhs/3aOd62TEupY1m0nTLqU4NdU2U3yxn\n8oxZJB4owcchg1Ge7WQV+jByzPMoFAqsra2QXBaSnL6XOdFG6hpkdp3wY+HzKx/5vTj5TCe/uJCI\n4M4XZVqtmWbTWJE0Fp44X4wrnSNie8afgZR35SpTg1IZEwogMWuKCdXlU9wsicFvtN8jv56dnQ3L\nXvgurS2dSd0QtYqLB7/H80tu1x5tISN7L0XXwgkJtxxxXVNVR011LbMTlrJjdyVBHldwd9FzqXAk\n4dEvAeDm4UKWKY4L2ceZFmXmVpWZ/WdCWf7SvEd+L2qnKdysKMVvZGfftLHZjEEd9civIzx5hk0i\nRy+7AcUW2xrbnLseEgZDeVk1kYEN3Pmx2tgoUJkrBvS61tZWWHtaYTKZcLEuRas1YzKDg70CezsF\nKqPl55J64hDq9iNMDG8h56wDJuIYv2Azt8qqmLLUx6IgYeSESTT7/4JPUk6gUlsz5+n4h6491NFh\n4Pzpk+i1rURFx+Lp7YF/oD/5rV9hW2JiV7HjuJUvPNR1BGEg6E2ugOV3WmdyHdQ25Odc5anx7dw5\nZNfDTYHu8nXg0SdybrO1tcbW1pq6mkaCfcpp13QWabe3U+DprsBwKR/oTuScPPgprlIyY4PbuHTM\nCVvHJYRN20xVRS2zVo+yGMU3ecZs6moi2XY6GRs7Jxa9EPfQtYe0Wj3pyccxGTuYMnMeLm5ORI4f\nT9aFTWxLTEKt0KIxBzBvjYg1wpNHZ3QBGr+wbXBjTe7li6yf01lP5raQ0RLnT16FAUjk3HZ7mlNe\nTgHTx1fT1q5EqQBbWwVB/pB+4jLQmciRZZmjuz/Ez/E8Ef5azh9wxXPUWgIin6GuppG4Z30tRgbN\niF9MRflEtiWnYu/sxcqXZz3QNM07tbVquHDmOADRc+Zj72DHlBmzuXDGwOWjKSilDvRSKAvWPv9Q\n1xGEgaA1ugJVX9g2uFONK25c5YvvoaInmNmafHFAEjm33S4VcfbESRbFtNLSKmNtJWFtrWBqlMyW\npLSuRI7ZbObwjr8QNiKLEG89qXs8CYx8Hm+/F2hubGXB86MsXvTNXfoMJcXT2HoyAxdPX1a9/NRD\nzxJprG8i89xJVGobnortfCabNX8JKcchJScDBWbM1mOZt/Lph7qOMDQMm0RO1IyVfLyvgGcXNWFl\nJZGaqcRuRMKgTqvy9vHgyhUnxoV3F8symWQM5oHpeOl0ehQKRddwPLNZJjevFRulCbVaorrWyOJ4\newzm7hFJtdV1OBv3Ez/XCKjxHaknIzuRipuTCAkP7vU6zq6OzFt27xUjDAYjpw/vRm0uQ292Ymz0\nMkb6juyxX0NdA6kHNvPcolrs7SSOpZzgpst6Js+YTUTUBCKiBq5jKAiPQuik5ew8UsLqhDYUCjiR\nao1H0OJBbUNgcDA5WdbEuXev7qLRmFFYew3I9TQaHWq1qiuxIkmQeqGd9rbO+FrXYGLVIgeMcneN\niaL8QsLdjzFprExnrNFyLGUfem00IeG9j7Tz8HJn/vJ7j/jTaHSkJO7EWqpCZ3JhSsxq3L16TsEq\nLysn/+zvWZvQiFotcfBUEo6jX2bMxMlMmPYUTHvqAT8NQRgco8KXcij5PRbFdC5Fe/CUHX7jlg1q\nG0aHRpCdLzHhjnUbqmrNOLkPzGhZTbsWK2t1V5JXqVRwKFHLmDAVBoNMU4uZNUvsMd0Ray6lpRM7\nJoUAXwlQ4zeqjc+OforVxGkEh/n3ep2Rvj6M9L33iL/mxhbST+7CRlGPzuzJjHlrcXR26LFfUX4+\nlVf+wtp5bcgy7Dl0Er+JXyMoLIxpMXMZyOS6IDwKrv4LOZW2ldinOjCZYNdRRyKeWjG4bfAMoLzS\njK9Pd1I1r0jCPzhiQK7X3qbBxta6e4ScpGLbZ20Ej1ah0cpodWaWzbdDqe7+zp89lsjqmIu4OisA\nNQF+TWw7uIOIqF/i5t57PaHRwaMZHTz6nm2pra7lcsperBVN6GUfYhY/3etL85zMC2jL/8Fzc3R0\ndMh8+mkSUfHfwWeUD7PmLwGWPOCnIQxVwyaR4+3jzcyV/8Wu5ETMBg1hE2Kwaagnae+HoHJm+tzF\nDzzFqqWplXMndmKrqEVrcmNa3FrcPHomZ2xtrdHbzCUz5xCTx8m0a8xsOzSCuDWPdshuW0s7p/b9\nH96O1zGZFdTrx7Lw6ddJPriDf33dClvb7iKBf/xAQ1T8/K5jsy+k8vyczukYt02Ngj/vOkFh5k6c\n1OV0mJ2w84ojOjahz206vP0PvLgot6tA4adH8rCO+yHunpafU0bybl5ZU9dVX2TBbAPbDx9Enj5L\nrE4lDAkBwcG4uP2UHacTkc0djJ0Wz63SGyTt/QCVjRfT5y544LnO9TX1XDyzGxtFE1qzF7MXPIN9\nLyvEuHu5cal9FnlFyUSGdA6j3ZHoz9IXF/Vy1gdXX1NP+rF3GeFUit5gRYt5EgvWbuL8yR386+v2\nqFSd31mjUeb372uZs3Zh17GlBRdZP1e2ON+8GR28s2sP7vaVOKir0BpdcfVfwKTps/vUHlmWObp9\nM6+tKUOplJBlmX/sLSDu6Z/2GH2Zd/4zXljeOecfYMU8HduOHGDMxMkP8YkIwuCJiJpA3YifsO3U\nMUBiwvT5FOVfIakgHSsHX6bHzn3g2nS3bt4iN30/VooWOiQ/5ixZ2+vy4CHhIRzcNhkXx4sE+EpU\n15n47FQ4q1/u3/K491NeWsbVlH/i7VxOm9YWg8105i57juLL+3nzFceu/oFWa+Y3fzWy6rXuuhXN\nNbkEjLPsP8RMaWX33j04KPKxU9WhNXkyInRZn7//RqOR5M9+xWtP1yBJEmZzPu/tKmLFpp/0GL1T\ncmUvGxbdno4Bzy5uY8uRfQSF/dtDfCKCMHgmTZ/NrZtBbE06hUJlzcT587l6MY3CrCTsXIOJnj37\ngUetlRRdpyjrCGpFOyZ1ELGLV/U6vXDS9Gj2/OMMq+bkMcJTwc0KM6l5k1i+4dEmcory87l+aRsj\nXKpoanNA4RLH7ITltFae5rUN3QWWG5uM/PL/JDZ9t7tfY9YVf57E6TY+pJoTBw+h1J7HVtVIu8Gb\nwInPEhwe1qf2tLVquHx8My+saALAYMjn79tKWfXyf/bYt7rwAOuXdK7uaWsr8dKqJj5O3I3P0994\ngE9CGA6GTSIHwN7BjvilqwE4sW8r00YnkRAvodeb2fJpOnNWvd3r25R7MZvNnPxsM6+tqUSh6Hxw\n+PvuIhas/2mvD2sxC1dRmD+WrScvoLZ2ZcH6eb12jh7G6YPv8/LyfBSK2x2bDHbtd8Ravt6VxIHO\nOa6eXs5EjB/ftc1rVADXy8wEB3QH0do6I4a2K2xcq/t8i46svJ3kXfEhcvy4+7anvLSCiYH5XUkc\ngLULWtmadIiE1Rss9rVR1PVI2Lg7NqDV6CxWjBCEJ5mzqyPzVnQOWz20410SJqXjO15Bu8bMRx+l\ns/TFH/Q7maPV6jmf+Gs2rmz4/MHhGu99eoOVL/+o1yTn/FUvkJs9kUsns7Fx8GL5xriHKjjem/PH\n32PT8uLPr6+lsfksxxLdcFSVdCVxAFQqCZ9R3gQEje7aZuvoRUOjCTfX7liTV2TCQc7khSX6z7dU\nkpy+hfKyQHz9ey8AeKfszCwWzSjrqocmSRLrFtWz99RR4hYvt9jXRlnX43hbZX2PbYLwJPPwcmf+\nynUA7P3nb1k95yqekxU0t5jY9s8MVr38//r9EqShrpHitN/wwqI2ADo6Cvhw+01WbvyPXvdfsu6r\nZF24SOqpa9i7+LFq0+xH/uIl5+zf2Lji9pTVNsorj5F+2htnqxKLa9naKvAP9Ouq4wdgVjij15st\nVsXLzJFxVZzmmcW3C43eZO/xD2gJDMOpD/3A9NPJPLuwCunzIqIKhcTaeRWknE1h+pwYi31tVT3j\nSm/bBOFJNspvJKP81mM2m9nz4S/ZsKgYJ0cl1XVn2Lf1Eite+Jd+n/NmSSlNBb9nw4LOpb7bNfns\n+KSKpc/3TDxIksSqjd/lQkoKbVdLcfUMZdn6aQ99X3cymUyUZH5wR82tFnKu7efCuRGMdL1psa+r\ni4qQsECLZxOdwR5Zli1i0sVsiVGjDjI/7vYI6VK2HngXv8Bf9qkfeD75MM8tbuR2IlitloibdJ1r\nV/MJH2uZxLJT9xZrGnpsE748hmVJ6/LSSmz1xwgN7PxSWFsr2LiyjvST+/p9rgspqayeW9GVNJEk\niWcX1pKenHTXY0IjQpm3Yj1zFi7uSuI0N7aStH8nx/dup7ry4QqIOapLu9oDnR0ba7mINo2alPNa\nqmq6p1tISsvl+2xsbNmf2E5rW3fxvf/5SwsJM9os9psQaabyelqf2tPQ0IDXHVM8oLPTo1Roe+yr\nNXsiy5Zv6etaPLAdpILUgvAoZWfmEOKe2jUU2N5OwYvLbpF28mi/z3U++RjrFtV3dRAUCollMTe5\nlJ5x12PGRI1j3or1zIqf35XEqa9p4PjeHZzY9wkNdY13PfZ+9PoOPOzLLDosrs4KZM01mlqVnE3X\nUt9g6vqdyspyWLFabcP2PW3odJ2xprXNzF8+bGHNAo3FfnOiDeRfSu5TmxrrahjhaRk/bGwUGDta\ne+yrM/VchUZj7Nsy7YLwpEk+mkRs1BU83TtjjbOTkpWxRWSkpvb7XJlnD7N2Qfd3xspKYmpYIaXX\ny3rdX5IkJkZPJX75Bp6KmdP1Zr7yVhXH924j6cCuroKhD6KyopYIf8vVMX19JNrrr1DXpOBsupaW\n1rvHGhk1W3e3YjB0xoa6ehP7EttYNV9vsd/SOC0ZZ0/0qU269iacHC27yK7OEu0tPR+aNL2s5Nnb\nNkEYCvbv+ISn4zuTOADeHkpmRl6hIDe/3+e6dukYC2O6nwXs7RT4u16huann32zojDXRs2cTv3wD\nk6ZHd/U/SopLOL53K6cO70Or1fd6bF/kZucye5LlM9i4cJmGW5cpr5RIOa9Fo+lexVdpZfkMpdPJ\n7NjTisnUGWvKKwxcztEyb4bBYr/lcY1cONu32CybNKjVlonxEZ5m6mtre+z7xT6MLMtoDCLWfJkN\nqxE50DnnMXHHz3l6gdliu1IpoZSa+n2+tpZGXJ0tv2AO9hJ6bUufz1FafJ2yzP/lmfltKJVwLOU0\nNRWbiIiaTNLef2BLHu2t7dyqVjE6NIKAMfMIjYxAlmWSj+xF0uZhkq3wHD2H8VOmYZKtAcsgeP1G\nEyH+WiJCrMgr7CDlvJboyY5IjtMt9isrzOTNVxw5cUaDvkNGpZJ4ZZ09ja2WD0eyLGOW+/bPY2xU\nJKc+8STAt/uhMa8IPHyn9Nh3evwzvL/rBqvjK3Fxkjh82hb3oBViWpUw5NTV1HHh6O/4xouWnX17\nOwVGXc8/wPdj0LdhbW35PfBwhZbivr9tuXYlm9aS93g+rrPjtD/pLO4RX2NUQCDJ+/6Gg+o6TU3t\n1Dba4B8UQeikRQQEjcZoNHLq4E7UpusYzLb4RSQQEhmJ3mAFWHZQigoriQprITLUiuw8PRqtTHCg\nI7aesyz2a6u/xqvrHTl+RovJ1Fk8cPkCa1rbZVzvqKFoNoMk9S3WRMfM4dDhg6y+Ixl07pKS0PE9\np2ZFzXqaDz67ydr5tVhbSew76YjvmHvX+hKEJ1Fp8XVuXPqQZ75mObrXx0tBy9Vbdznq7hSSzuJl\nEICXu5HcxiYC6L2mzBddTk/BqnkL6+d2YDTCrsMphE7/Ng6OzqQd/RB7VQkNDVqa2uzxCQgnasZy\nRoz0QafTc/rQDmykcgyyI0HjF+PuNYK6divA8oVQ8bUbRI9vJjLUinMZOlQqCWdXJ9wDYi32U5nL\nWbnCkcNJnckkB3sFU6NA3yFbPCDpO2TUVn17aRQVHcvx1BMkzOqOf4lnbZg8I67HvqGT1rL1wJ9Z\nPa8JWYbPTrgSPn1gVioVhIGUfyUbfc0BPN0tR8iPCYWtp4sIG9O/aU5KSddjm4tDB21tWpxd+rZS\nZPqpRLwUu9kQb0anM7N951miF34Pg9FA1pmPsVeWU1OrQ2twwmNUBNNiV+Pm4Uprcxupx3dgq6im\nw+xCZPQKnF1cqKtUEeDbfX6TyUxJQR6xU7WEB1tx4qwGd1clRlzwj7RcYcrNoYH5U+w5cKwdSQJ3\nVyV+oyRMJrhzQHRrO9ja2/fp/gLCp3Mx5yxTxnU/tyamuDB9Zc86fqMiVrE78e8sm9uOVmdm5zEv\nnlosihp/mSl//OMf/3gwLtRmKL3/To9AyrH9vLQ4l3MZOiLDujs9La0mrjfMICCkb3MWb3P3HEna\n6WTCA7vfBh1PtSJ4yivYO/SsXdGbCyf+wbpFt1AoJCRJItjfTPqFaoryr/Nc3DkmRHQwcYyZqHA9\n1beuY2PKoV4XyKWzh1k08ShTxzYxPriOlprL3Kj0Qm92xklZgLNjZwclJUPG1trAmoUydrYK/H3V\nWFsp2Zs2i8kz4zh3bCvlBckUXSvHxtELb7tsosZYEx5iRWiQFQ3NSo6nexM9vr2rc3c81Ra/8S/h\n5OJ0r1sDQKFQYFL5kpx8g4aGdjLzHMktn4iktMbd09tiapmNrTUhUbGkXXEnq3g042e/SkBwUH/+\nlwhDlKPV6EG5zqDFmqOf8sKSEs5l6AgJ7P43Xl5hplmxkJF+vvc4uicrWxeKr54lYFR3UnXfCTum\nznsNdR+naV1O/htrEzqnL0qSRESQkbOpdRTmXmLj4mzGhXYweZxMiJ8WbcsNWmuzMVuPJyVxC8/E\npDAxopmokFpuFl2iUR9CXYOZkS6l2Nl2xoW9RyEiSE9CjAI7WwWB/mrqGyWyKpYzOjSCjJPbKLt2\nhpKSWmTJhvCRBYwJ74w1IYFWVDdYcyrDk2njNV3J291HHZgY9zo2fVhhUK1W0azzIC2tjIZGLedz\nXChpmITBYMbLZ6TFKlcOjg6MHjOX0xeduXozjOj5r+Hje//pW8LQN9xiTcbJ7SyfU0FuQQd+I7tj\nQVYe2Pqsxd2zZ7Hve9HolbTXXMDrjhe5+0+589T85/tcB+NqynusiG9BkiSUSolxoR0knW7iRm4K\nm5bnMzbEwJTxMt5urdhKJRTnZuHs8xQn9/yZFxZkMj60ifHB1WRfzMTG/SnyCxoJHXULK6vOuPD+\nNjMLYvRMn6zEzlZBSKAVuQUyjVYbcXJx4dLpHZTmn+XWrRZ0OgNTwsqICO2MNYH+aspqHLmQ48yk\nMd0PktsPujFryauoVPdf/tvewY7yGgcyM8upb9CRlu1OtWYSWq0O71EjLWp8uLi5MTI0jqQ0ewqr\nxzFz8au4e4q35F8GgxFrBivOAGSd2UL02Goam8x4uHf/Gz+VpiJo8kt9fu65raZWi505q+t5BeBY\n+igmzVrapxe4JpOJ6xf/ysLPC76rVBITwrUcTmrj1rXjbFpRwpgQA9OiZKyVTfi5l5KWkoVfeAzH\nP93MpqW5jAtuIiq4iuSkTIKjlpKacp3xwXVdU7T/+y8yrz2nZ2y4Cjs7BeHBViSfB7vR38TYoSMn\nbSeleeeoq++grbWFp8ZWERFiRXiIFf6j1BSUOlNYZs240M6kryzL7EgcSezSDX26RzcPd64WqsjJ\nqaCmTk9Klict5km0t2nxGTXSIiZ7eI/A3T+W4+dsKGuaStzyl3F06ltCTBja7hZrhlUip7T4OldS\nd9DS3ERpuYGqGiPenipyCzrYfsSP5etf73exLhtba2pbXMnIKKe6RsuFq+6oPFcRGjmmz+eoKDjM\nmCDLETRF100oaWZCRPcQQRsbBXkFHSyMVXI2rQ0bUx5TxnW/DRrhKXM+U0Pc8le4cNWeyzlGsou9\nuHYrmOcSblq8eXJzUXD1+gg0lft5fmEJY4PqCPMpIv2SiavF9kQGNmKllmhqNnEgNZJlL/wHBxIb\nKSyBy4WjcA99nsCQ3lex6o27pydB4+aidJlD7pVCYsZlMSPiCulnkqlqsGek3+iufRUKBb4BAQSG\nhffp4U0YHobTw1VB7lUKL+2hpamV4hsGGppMeHkoyczWcywzioSVz/R7lJmTixPXy225fLmCqmod\n56964Rb8HKMC+r46TMW1/YwJtnwDll8MdspqxoZ2J6MdHRRk5+pZPl/i0PFWRjnnEBnSnUDyH2ni\nTJqO+atfJTldydVrMlnFIymvH8WzCyst7s1vpJKUyy7Y6faxOv4m44Lq8HPNJ6vAiaw8M+OCW1Gp\nJKpqzZy7NpW4FV/j0PFGikoVZBb6EzxlI94jR/T5Hr18RhE4Nh6z/Uyu5eSQMOUKk4KyOX3sNK16\nD7x8ulfLUyoV+AcFEhga2udkmDD0DadYk5N5kZKcA2g1GnLy9LRrzXi4KTl7Xk9+TSzT4+bf/yRf\n4DXCm0tXIfdqFRXVBlKzfRg96UU8vPq+6l3Vtd1EBpsstl28IhHkU07gHSsFu7kquXBZz5pFMtt2\nNzE1JBv/UXcsZR5g4MTZDhLWvMLR00b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9nCr3oaHZg/VLmwnPFLHOkXnpVSNPrlITGKCi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8LK7JXIn5XTb9CvN4unM5DzP+IS7V5lBHIZOJ1XRrXf17f1FrtRUanU1v6V9774DrnUglw1hP79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fSBfyO8/uFWnPY8pD8HVewZbDl6gon5Rbxx8AiaxKyY/SWtCVGti0nxBVCY4ujt7SYlOY0+\ntxOVJbb1pCBJKEM+rpu3mOkTenn81fU0h5QIskyWOswDa+7D5/Wy95VXCduHrmlxtzNr2u0ApBo1\nNJx3TjkSpjAtldUrYoVWRVHEHp9MQJOIpNHjbasZ/MzdXoslJ1bTp8+SybOvvsij94yVFIxx5SiU\nSv7+kW+wZ/cuOuq7mFRQOqqmy98qm7Zsx2fNHXQkwtYstuzYj9Vq5r19JzGkF8fsLxnjEaSRwVVB\nbcDv86HRaunv70eVNew4lQZVaIBpJSUkJCXx7Itv0BFQI8lBcm1K7rprLU2NjZx45UNkU7T8SZZl\nkpQDZJ/TxUjUS3Sdd85IKMDEcfmsuuP2mGuJkkRcUgqSJhVRpcbTcgbhXKcYd1stluxJQ+MWBDqw\nsfH111h5x1+X2P4Yny06g4F/eORb7NixHUdnHyWTbycrN+ejD/wbYfOHewjZsgeXhYK2HN7euosF\nTif7yhux5MVqQIjWNCJ15SPOI0tD5QSeAS9adWzprEpvgbCL7Jwc1qy8nhdf30JXWIMqEmBcsoGb\nbr6To2VlVL5/AsEQXSmX5QiZZgUmswWD0USc0o/rvHOG/R5mTJ/CdTdcF3MthUKJLTkNtT4DQZJw\nN55EUkdtjaezcbCjDUQD2nW9Mgf27GHm3L/t7PMxPh72uDh+uPabfLj9Azw+D7PnLiPlI9pm/y3x\n7o5DRKyZCICgEPFac3nrnQ/IzUyhvLUfW/4wW5OQS7i5bsR55PN8Ha/fj3pYWajGHIfe10JcfAJ3\n3riQ197ZQW9Eh0b2Mz3LxrwF8wmFwzQebkE8Jy0hh8PkJpmRJImJUyajf3cfAe1Q1kxkoI+lSxZQ\nMmNYW3G1GltKOjpTNFPQVXcUtSpq+3x9HTF+jahQcbKxh5qqSvIKRnas+2tiLJBzFREEgdkTZ3Mt\nqlScrj/N7qbDAMzPLKUoq+gzvb7DF0RQxq4O9wVl9hzeixifTaC/M+YzWZaxaVREQkFExXk1k452\n/veNDYQQUITCOBsrsBcNGQd/fxdJcfEIgoDdauefH/kS7WdbkSQFCedKuQx6A7dNLuaFD7fjF5UI\nAS82o4YN729m5qRpPLjiZp5483VawyokOUKOVua+VQ8ynDc/eJdOUxaKc4ZQl5pPf0M5kXCISHjY\nuAFRUrKtphn5lRf54uqPX0c5xt8ukiSxYOGiqz2MUTl+9Chl1SeRBJHFpfNIz8r6TK/v8ARgWFym\nzx3gaNlxVHGZBD39KDTnlcCGw1h0EvIwbQjB18+//Px3IAgIkRD9jRWYsycMfu7paCQrO1o7npaW\nxj9+96u0NTeh1ekHu7Tk5uWzsLiCt7cfICgoIeAhMdXGWxvfYs7s2axZdSPPvfIWXUENCoIUxGtY\nufKhEX/Tmxs302/KHQyEG9LH0Vd3AlmOroIxLOAtqrVs2HECX0jg7ruvjsjxGH8dKJRKli677qN3\nvAoc2L+P8qYqVIKC6+YuITHl4wtNXg4Otx+GZfn3uf2cqa5HZYon5HWj1A9pQ0QCPswaxQjhYDHo\n5R/+7ZdIogB+F+6z9RiShrqzuFqrmTQuOqnJLyzgR9/Pp6WxAZPFgtkS1e+bXlJCRfkZdpYdJIyE\nEHAj56SwaeMmFiyYx+pblrJu4wd0R7RoZD+TUs0su36kKPGGt9/Da8kbDIQbs6fQV3f8nPaXOCLL\nT1BqeebNbXT3ubhplE5fY4xxqag0apYvv/a6b8qyzK6dO6jubEQnqli+6AasdttHH/gJ4vD4QTts\nm9tPQ2sHCo2e8DAdvbDXiVE3MiQQ8br53r/+ErVSJOLuw9fXOaghCtFF6KnnsvOmTJ3CxEkTaWls\nwB4Xj8EUtWWLlyzmzJknKausIoyIFPQQKkxny9ubWbh4Iauum83GD/fhiOjRyl5K85NHBHEANr23\nk8B5i27m/FL66o6jscQzsmoCQoKKXz73Fncu62DhogWXeQf/chgL5Iwxgr0V+3k5tB9mRg3Pycp3\nuPd0P7OKP7rz1CeFXauiIRjrvNhUAuPyinm7fhehARfhgC+qSQNEWir40aNf5revr6dNaUdhsBJo\nPUNIbcBtj64IytYw4cNbcNRGH/ygp59IOMTcwtgVw+RRdF/2nalCWzATjRyht+oIbcYMehwSW9/c\nzNKcZH7y6GO0tbegUqmIsyeMOB6gyz2AIMWKGqtMNoJuB/qkHPqbzmDJGups5WypxJg5nqN9Luqb\n6snOyB5+yjHG+Ivm/Q+3smWgAqnQAoSp2PcaD3pu+EzHYDeq6fGP3JaWloKy1kVfcwNqczyiQhmd\nUHVU8JMf/YD/efxp+jXJSBoDvuYKIvp4fOf0ayL6FHxHtyIDarOdgMsBskzxlKGAuCAIpA7rCiHL\nMkcrG9HnzyQSDuGoLqNZSqWjMcy24y+zYlYR//y9r9Ha1IjBaMRiG708rtftRxBiBR9FlYZI0I8u\nLhV3e100BfkczqYzmHOncLCqnRscvVisn63TOcYYnzZvbnqDXapWFIVGZDlIxfvP86WFd3+mY4gz\namgats1m0hBnM6O2RqKdpwpKEEQJWY6g66/lhz/5AT//9dMMmDMRJRX+ppMI1kz8lqjwsLIwHuep\nXYT9A6iMNvz9PYiSyLjCIX9BEATSs2L9h1AwyOnmboz5MwgHfPTVnaBBTqKlIcT2Y89xx7JSfvz9\nr9Lc2IDVZsNkHtmQAaDXE4DheqPnSi9URisDPW3o7EO6X57ORqx509h1rJrrli1Gpb56wupjjPFp\n8ML6Fzme4EQq0CNH/JS/8Tv+/vZHPtMxxBk1Mdm7sixjN6qx6LXoE7PpqzuBrWB6VOQ7HCY+0s1D\n3/97fvnk8wTtuSCDr/E4ypQigkYbQcA4Tk9PzVF0cWko9Wb8/V2ICiWTxw9lvEiSRGZObsxY3C4X\ndd0+jPmlBL0unE1nqAomUlPj48Oyp7j/1kX85LtfobmxgfjExAvqZ/W6/TBcx/qcrVFo9PidvahN\nQ75LwN2LIX86H+4/wYKF86+Z0vFPmrFiwjFGsK3jMBSc58gX2tl29vBnOobV19+IvbeWsM+NHA6j\n7K7j1pml5GbnMt4AxpRc3GfrcdQeJ3B6Fz+8925SktP4l698k28snM5tKRoyrUaM6UMGRhAl7Inp\nZJt1hL1utBotkxIt3HvjyouOZcDrocUbBqJlCeaMYjSWaBaPYEtjW20rjj4HKclpFwziAMQbdMjh\ncMw21UAviRE3RvdZstQRgrWH6W8op6/uOEqtAYVah2BK4FTV6Y9xN8cY49pkX+MJpPShCYI8Lo5t\nx/d+pmO4/ZYbMPTXEvZ7iYSCqBx13HL9fEpmlJKhdGLOKMbVUoWj9jiR2r388FuPYo9P4F9/9H0e\nvn4SKydYSIy3xKyIiwoVyanpJFvUhLwu9HodU3PiWbny4hpT7S3NdAajwWlXSyXWvKmoDZZoTbo1\ngw8OVBAMBEjLzLpgEAfAZlCf0/YaQo8PS7ATc8RJmur/t3fn8VXVd8LHP+fcPcvNzb6QhUDYwxbC\nHiCAyKYsClVR61K1au2M7dj69Jlp7XSmM8902pmn0z61tWrV1qV1QRAUEVkCCKJsArKEJRshZL3Z\nc5dzz/NHNOFyA2FJcpPwff+Xk/M793t5wY9zvuf3+36baDmzl9qCI9ScOoA1Mh7VYMRtcXDmVODy\naiH6Ms3r5bPKfIzxrQ8IiqKgjY3no91bejSOJQvnYHOexudxo3lchDhPs2zRHObMmU2C9xz2tJHU\nFh6l5tQBDEWf8Y8/eIK4+AT+/Wc/4pszMrh9fCwR0ZHYotu7RxmtoaSkDCA21ITW0ojdHsbU4UnM\nvWnuZWM5sG8f9ZbWYsf1JSeIGpaNKSS8tUB61EA2bGu950tLH3TJJA5AVGjgNlO70UtYQyExFi8J\neiVNp/dSW3CYmpP7CY0fiKIo1GsWqisrOriiEH1XQ109hzylGCJbV/EqqoI7O4ENWz7s0ThumZeD\nqeYMPs2L5mrCXneK25YuYvHi+Tiai7GnDv/q3+Q+QisO8vT3nyA1LY1f/Ox/ccfEAaycmEhodByW\n8PZnQZM9hvQB8ThsKl5XM46ICOaOH0xWdvZlY8nblof3q4YwDaWniRqajdEagmow4o1MZ92mnRiM\nRgYOzrhsEfSosMCkr93gJqS+kIRwI7HuEhpP7Wuba8IHDAGgtqW1xmB/JStyRIAG1R14TAk81p3s\n4XZ+9uh32LV3N9X1dcxZdj+hX3VUeeKu+9j26U5OlxmIs9uZPyPXryBw5rBRZA4bxYHiswHXDQsL\n5+ePPcaZwlPYbCEkxicFnHMxk9GERfHhAXweF0abf0rYGx7HoeNHmDk557LXWTZ3Psf+9DznrPGo\n1lCU6mKWT8xiwcw5bed8fnAvz312DOMFk6dSU8qkObd0GqcQfU2jHjivNPp6dq6JT0jgmae/y87t\nO3C5XMzMfbStu8OTf/coWzZvofS8keSEaGbNzm1rHawoCmOzWuuafXLwZMB17VHR/NP3v83p/Hwc\nkQ5i4jrv3BMaHo7p6z8TXQ/YbllHKGdLikgfPOSy11m2ZBFn/t+L1FgHoJosGJzFrLx1LlOnt2/k\n/fijTaw5WI7R2j6fWV3VDB85oqNLCtFntTS30GLWA254m3RPj8YxaPAgnvnhY2zfmoeqquTMWtp2\n7/KD7z/Opo0fU5FoYlBqAtNntL9BVlWViVOmAPDR7sNcHHV0TBzf/84DnD6ZT0xsLJHRMXQmJiYG\n1f0F2FqTN18XMP2a06PSUF932SQOwLJbbua3L75BfWgKqmrEVFvEPauWMWbs2LZz3nl7NduKNQzG\n9vu0SKOL2PiEji4pRJ9VU1GJy27kwvLuiqJ0eK/TnUaPGc1PMwaTty2P0JAYpuasbLt3+dH3H+PD\njZuoTRzCyKHpftuYDEYj02fORPN6eS/vIN6Lrps4IJlHHryLMydPkjQg+Yq6ScbEROM7dgqDIRzV\nYAhYGVPVcGV/NksWzOL519fTHJ4CgK2+iIcfuZdBGe1Ngl555TX2Oa2oavtsHx1q7Ncr/ySRIwIk\nee2c8OvepJPgtXcyquupqsr0idMCjiuKQu6UHHI7GT916BAKD59BCWt9c+1zt5CZ2LqSZtDAK+8O\nZjKZGT8gjt11TSgGE15XM8YLigsaGysZlTGj0+tYLVZ+8sjjfLLvU85XVZIzd5lfS3WACWOyyDp2\nlAPO8ygRcSg1pcxKiyM+rvfc8LjdbvL27MRsMjE9e2rbfw5CXK0Eg92ve5Ou+Ug0Xf7BoTsYjEZm\nzs4NPG4wcNO8wLoQFxs3PI2NR6owhLTOk76WBsZkDEBVVTKGXXmhvQhHJMMTQjna2Pr2yKd52zpf\nAYTTRGJS5wUdIxwOfvz037Fj+w5qa+uYee/dAdulZs+dw5fHn+dkQwtqaBSKs4SbJg7rNS3IAVqa\nm9m+LQ+HI4LsyZP77dJo0b1Cw8OIabHgvOCY1uIhOaTnO/eZzRbm3hxYQ8hkMrNw8cJOx49MT2B3\naROGr4qX+5qcTMgajMFoZMjwK0/CDhw8mLSwjRRpXnSfFlCHJ9yoXVGL8ISkJJ55+gnytm6jpcXF\n7DnfIiTU/2XXLbcu4uRvnuOsy45qs2NwFrNgdhYGY+95BGmor2dH3nYSExMYM368zDXimgxITyNy\nq0Zzavsxb10z6ZGDLz2om9hCQpi/MLAOldVm63R1sMFoZGiSg8N1btSvks16fQWT547BbLYwbOSo\ny46/0MTJk/lo224qfaHoPi3g9x2t6uvI4IwhPPPUt9m6ZSsAs+c85tdSHeD2226l6LcvUGGIQTGH\nYKkr4pZFs3rVv+ea6ip2fbKL9PR0Roy68j/HS1H0i9dfd5PdW4/1xMeILuCsrea3n7xCSYqGAiQX\nG3ki534iwjvPvPY2W3bvYPfxfDw+H8MSYlg5/9Zrak+o6zrvb/uIoyXnOH7qBGrKGAyhdny158lJ\ntHPPrbd1adynC05z5OQxJo4eT0J8zxZkvJwTp/N57oMN1EekgE8jqqGUJ1d+IyAh1ZEpuT1TMHvL\nueM98jni+pWWnOWlDX+lIk5Hceuk1lp5bNVD3D19PibVyBsbtwU7xCv24YaNfHGiEJ9PJzMjhUWL\nF1zTzYOmaax7bz2nisvIzz+FKXUcBksIem0p88YNZPEtnT/sXY1jX37JmdNnmDx1MlFX8Ca/pxzY\nf4DX123DZU9Bd7cQo5Xz5OMPEm7v/KVC7qieuWmWuabvOHkyn9e2rqE6QcXQqJHRYueRu7/FHZPm\n9qm5Rtd11q5Zx7GCc6iqwoTMDObMmd35wA643S7WrFlHQXE5pwuLMaeNQzVZUJwlLJ0xuksLhOq6\nzhcHDlB27hzTZ8wgLLzzJFFP2bljJ+9u2Ys3IgWtuYFkg5Mnv/vIFb3F74m5RuaZvuXQFwd5e89G\nnElGTLVeRhHLfXfex8oJueCF9W9uvarrLb9zLm5N4+2tO7sl3kvRNI233nyHM2U1mA0qUyeMYuq0\na2vP09TYwOp311N09jzFZRWYU8eiqEYMziLuXDiN7ImX3551NXRdZ++ePdTUOMmZOQNbSEiXXft6\nffjhR3y45zi6Ixm9sYb0UBdPPP7wFb0Qv9RcI4kccUlnz5WgAEmJ0tLvax6PmxdWv8nB4jI8mpfh\nMXa+d98jN8yqlH976UVKbP6JpRG+Sr57172djpVEjuiIruuUnCnEYrUSl9S68mxF1qw+9XDVHZqb\nmvjTK69zrKgCXfMwNmMA33rogV71Zqm76LrOz3/5LNW25AuO+Rgb0cT931zV6XhJ5IiO6LpO0cnT\nhDsiiIptTVrKXAPOmhpefvVNTp6tRPFpTB07hLtW3RnssHqE5vXyzC+epcneXnjep3nJSYIVKzt/\nQSeJHNERTdMoOnmKqNhYIqJaO8WtyJrVpxI53aGstJS//G0tBWXVGNCYO20cS5ZcfnVQf9Hc1MhP\nfvUiWmT7XKO5mrlldBTzOlilebFLzTVS7Fhc0oDEZEniXOSlNW9zwGtHSR6JOW0M+eZEXl+/Jthh\n9ZiKppYOjvXfIsp6Jb4AABqNSURBVGKi+ymKQsqggW1JHNHqxZdfJ98djTFpJKaUsRyutfH++g+C\nHVaP8Ho9VDX6VwJRFJWquuYgRST6A0VRSBsyuC2JI1o9//IbFOjxmAZkYkwZy6clHrbnbQ92WD2i\nqqIcp+a/tUM1GCl3NgYpItEfGAwG0ocNbUviiNZE+h///BalxiTMyZkYksey+Ug5h774Itih9Yj8\nEydoMfv/fTBYbJSUVV7XdXvPBlUhLqG5uYk3NqynrL4Ju8XI0hmzSL6COhEXO3HqBGUV55k0fiJW\ni7XDc3RdZ9+h/Zw5d5aJI0eTljLQ7/cnq2pRHe01PAwmCycqAosq91eRVjNlFx1zWC/uPSpE3+Ss\nqeGdNeupbnARGWJm6S0LiImLvapr6LrO0SNHqHXWMnHyJIymjv996LrOnl27OHe+nKmTJxGflOT3\nu4KKepSo9s9WraEcLTjH4mv7an2K0WjCbjVQf9HxiJAr20svRG93vqyM997/CGeTm5hwK7cvv/WK\ntg1eSNd1vti/H4/bw/iJ2ZdcGaxpGp/s2EFNTS0zZk4n8oKOdw11dZTUaahR7Sv91JAIvjh6mhkz\nO6/919dFxsQQpri5sNyqrvtwhPbf4qjixlJYUMCGTXnUN7tJig5nxe3Lrrr4r6Zp7P98L0ajgbFZ\nWZdcGezxuMnbuo3mFhe5ubP8tlAWFxRw3mPjwv/F1fBYPtt3mNFjxlzLV+tTBg5Mx+zZiU77PO/z\nuolxXN82U0nkiF7vl39+idLwNBRLawG9U6vf5affvBf7FdTs0TSNfYf28d7OHZw3R4M1nNV7X+DO\nnClMHjsh4NxfvfI8p3Q7hlAHm9Z/zOzUWO5YtKTtHGMH9XUMav/f6vC1RZOyeWnbLrToVPDpWKsL\nWHrrjfBoKfo7TdP4v7/7E7X2wSiKnbONOgXP/ZmfPP2EX1e8S/F43Oz9dA8btnxCtSkOjBbWbf2M\ne5bfzIhRI/3Odbtc/OrXv6dMiUW1hrLt8LvMn5jBggU3t51jVBUuLgtovEHmGkVRuGnaON7ZdhAc\nyeial9D6QpasvDvYoQlx3Vqam/n1H1+jJbK16cLZOh/Fv3uBf3r6ySvaOulqaWHnju1s/mQ/ddZE\nUFTWfryLh+5ZRmraQL9z6+rq+O/fPk+1JQnVbGXbb19j+ezx5Mxo7bJpMBoxoHNxjQWj4caYa0wm\nM7OyhrBxfwFKRBKax0VkUzG33vdQsEMT4rpVVVTyuz+vxRM5EBQ4W6FR+rvneep737mi8U2NDWzb\nspUd+45Rb00E3UfMxu08/tDdxMT6v+QqP3+e3/zxVepCU1ANRvIOvMiqW2Yxbvw4AMxWC6oeWOz4\nRnmGsjscTBmWxI78cgz2ODRXE/HeMuYveOy6ritbq0Sv9uWJLylR7SgXJFCao9J5f/vWgHNdbhe7\n9+6msLgAgGMnT/CjPzzLr9Z+QHlkBgZ7LAazFVfMIFbv2oPP5/Mbv2X3dk6p0RhCW1fcqJFJ5BWU\nUVVT1XbOuJQktOaGtp99TbVkp6dyo8jOHMc/37WSXLuXm6J1/vWB+xmclh7ssIS4brt27KTGktj2\nIKUoCnWhKWzdvDXg3OamRnbt2ElZaSkA+/bu45n/eJZn//ohtY6hGMOiMFpDaXYMYvWGwPHvr9/A\neUsKBlsYiqKgRCaz9fPjuFpa2j57RFosPnf7Vka9oYpJY3umzlRvMGNmDk9/azlT473MG2zlx089\nRlx850XVhejtNn30MU32gW0/K4pKpSGWvXv2BJxb53Sya8d2KisqANiet4Of/OdzvLwmj8bIYRhD\nIjDawmmIGMTb720KGL927fs4wwdhsISgKCp6VBobdxxou/+xhYSQER+Kz3vBVsb680ybNK5rv3Qv\ntnDRAp5cNZ8pcR4Wj7Dzjz984qpXRwnRG238eAtuR3tNFsVgoKjRTFFBQcC5VRWV7NqxnVpnDQAb\nNmzkJ//9J/668TOao4ZiDAnHGBpBTVg6b65eHzD+3XUf0ujIwGCyoKgGvJHpvL95V9vvExKTSA3z\noV/w7KXWljB71rUVUO6LVq68jceXT2dyrJtl4+N4+qnvYjZf3+o/WZEjejVnXS26yX8blKKquDxe\nv2O793/OXz/5lMaweNSWowy16dS6PDRED8ZQ3+zXwhegymeisqqCuAu6LRVXVGKw+rfe9YbHcuTE\nUWZObn17tWL+YiybN/JFcSmKAhMGp7Fg5pxr+m7lled544N1nKusZM6ECcyb2Xmb494gOiqGOxYt\nDXYYQnSpuro61IvmGtVooqGxye/Yls1beH/nIVwhcRi2HWJkgpWi83W0ONIxOhtRFP/3IxX1LjSv\n16/VbrmzAdXgv5y2jhBKzxaTPngIAPfcfSehq9eQX1yGyaAyefpwpk2fdk3f7WxxEW+vXkd1dQ2L\nFsxm0pRru05PS0hK4hvfuD3YYQjRpZqaW1BU/y2XismGs7bW79h7761n24EzuENiMW49xPhUB4cL\nK/BEpmOsbQhYvVPu9J+rAKobXCiKf9eWWo9CQ30d9ojWl1YPP3gvf3tzNUXlVVhNRmbMG3/NWx1O\nnjjB2nUfUl9Xy8oVSxiZ2Te2TKSlp5OWLi+lRP/i9mgB84RuMlPndPode+ONt9hzshzNFo1x60Em\npEfz+akqiErD4PTf5KwoChW1gfXqqhtccNHuzqoG/xqaTzz6AG/87R3O1TQSajEyd+msgFWEV+rI\noUOs3/AxLU2N3HvvHaQPyrim6/S0YSNGMGzEiC67niRyRK82aVw273y6lyZre7Vu3XmO6fPb22Jq\nmsY7u/fgihnU+hfaGsrR5ga0uhKsdtB9PnRd95vMwvDgiPAvOpUcE83uU1V+yRxvVTEDB2S1/awo\nCkvmzmcJ1yf/zEn+/a3VWFJGoSQl8ZeDp9i6by8/f/Lp67yyEOJazJo9i837X8QX1X4zrzpLmHlX\ne/eW5qYmPth5CC1yYOtcYwlhX2EhitGMFfzeNH3NbjOhXlS7ItYRxpf1Xr8Es9JQQXT0BTVxVJXb\nb19+3d/rwP4D/OFvm7AmDUGJSeaPa3ezc+dnfO8f/v66ry2EuHo5OVPZ/eIaiGyv9WepKyZnxrfb\nfq4sL2fzgTMokalfzTXJ5B08iDV+ECYuMdd0UEMqMszCmRr/+x+9qQar1db2s9FkYtWqb1z398rb\nmsfrH+/HFp+OHq3zXy+vZ+rQz/nWww9e97WFEFdv4vhM9r/3KWp4+72Fw1vDiNGj237OP3acXaed\nGBzJGADdksqmnTuIGDENhSufa6LCLJRfnN9xNeDz+VC/2lVhtdm4/77r3yK9bu163t9fhDV6ELrV\nx7/85lUW52Ry++033osf2VolejWj0cQD8+YSW1+MXn6K8JoClo0aRMbA9szrubKzVCk2/3HWEHRP\n67aEsKTB1Jzch09rXcXjqy1n5pCBmM3+E9GcqTMZpFXhrq8GoKmimOaWFn63Zg0trsBuTdfjjU0b\nsaZmtt1chcalcrrew5HjR7r0c4QQVyY0LJy7Fk3H0VSMXnmGiMYiVsyZQHRMe4ebI18cosXq3/HG\nEpmI3tT6diskNgXn6YPovtZ94HrtOXInZga8EVu8eAFxrmI8TXXouk7DudO4fCr/84eXArZ8Xq+3\n3vsI24ChbTGEJ2XwRUEF57/aFiaE6FlJAwawbOZowhuL0CvPENVczKpbc7Ha2u9jPv10DzhS/MZZ\n49PR6lu3WFkj46kt/BJd/+pFlbOEm3L86/4BLF2yiIi6k3hbGtF1H3UlJ2hWbPz22Re6/Hut2bgd\nW3xrIlxRFOxpo9h5MJ+mxoZORgohusPIzEwWjk8lpK4AX+VpYl0lfHPlIr/C6AcPf4nBHuc3zpIw\nGE9t61xjDo+i/mw+uq6j6z4MNQXMzw3cDrV08c1Yq46jeVzoPo3awi+pJ5Tnnn+5S7+Tz+djw859\nWKMHAK1bUx2Dx7Nhyx40r7eT0f2PrMgRvd6ooSP456Ej8HjcGI2mgIei6KgYQn0uLv7nmxxipKKu\nHKM9DnvaSJqO7WLisKHMzZ3EyKEjuZjBYOC23Dn8y1/fornqHNbIeBzpKTi9Hj7M28LSeQu77DuV\n1zXARVuwVbOFI6dPMmrYqC77HCHElZuQnU3WhAl4vZ4O55q09IGom/aBLaz9oKIwIBQqGmswh0ei\nGE00Hd/B5Aljmb1oHumDBgV8jtliYfb0bF54Nw9d17HFJGMOc1DR0sAn23eQM2tmwJhrVdvoxhTl\nf0wx29i//wALLuiUJYToOTNnzWDGzBy8Xk+HxdQzMgaz8cguDGHtiWNVVUmwuDjfXIc1Mh7FZKHl\n2HamT81mzm1LSRowIOA6drudSWOG8XbeYdB1QuLTMNnCKGyo4ujhw4zIzOyy79Tk0bFddEw3hZB/\n/DhjswKTTEKI7jd/wTxunn/TJeeapIQ4tDOFGC64rzGbLcRq5VS0hBESm0yzsxxP/g5ypk1m3p2r\niI6NCbhOfEICY4alsmnfGXRdIzRhEEaLjWPlZykvKyMuIaFLvo/X48GDmYu/iUe1UFlRTnzijXVf\nI4kc0WdcqnOMzRbCtIGJbC2tQg2Pxqd5sdcU8A/3P0xJ6Vk+PXoES4iRRd9/iihHVIfX+FpJWSm2\nlBEYTO3Fp1SjCWdz4N7z65HsCKfA5/Mr4uxtaWToRe3OhRA9S1GUS841sfHxjEsOZ395LYaQCHxe\nN1HNxfzwh9/j8KFDHDp6ktB4Kwsf+ymhYZdvKVlRXkl46ki/ZJFqCaW8srpLv09MmBHnRVtLfS0N\nZI6WhLEQwXS5uWbYiBEM2bKd/KYGDNYwNHcLSVTy1I+e4tNdu8k/U4wj2cH8v/9Xv5U8HalrbCEi\nzf/llWpzUFxc0qWJnHCTz++Fmq77UFz1pA8afMkxQojud7m5Zsq0qez4dB9n3UYMZitaSyODw1w8\n8fhTbNuylcLScuLSk5h38zc77eDZ7PZhT/VvyuA1hnZpIsdssWD1+e/h0jUNk9ZEVExggqm/k0SO\n6BfuXLSUoYcPcODkSexWC4uWP0iILRSH3UHm8Ct/YJkyfiLv7v8Tnpj2t+i+ugqyxmRdZtTV+7tV\n9/HdX/wcb+wgjGYb9aX5ZMZGMC5zbJd+jhCia913392M2L2b46cKibKHcfP8xzFbLGRlZ5OVnX3F\n15k2fSpb//AmRF6wfcJ5lqm3L+vSeJ947EH+90//EzV+KKqqUn82nymj0khOTet8sBAiaB5/9CF2\n5G2noKSMhJhI5t50GwaDgWk505mWc+XXmTBuNLvfyUO1tzd3MNYVM33GA10b78P38PNf/QFT0nDQ\nvDSU5jN/ehZ2h6NLP0cI0XVUVeUf/v4xNm/ewrnyalKT4pmZeweqqjJ77tU1cxmZMZADO05iCIlo\nO2b31jB8VNe+OHpw1VL+58U3sSYNQ3M10XTuFHfcMqfTRFN/JIkc0W9kZY4jK/P6WmbabCGsmDyB\nNXv2UW0IIUxrZnp6MqNHdN1bq68/5/lnfs77mz7gRNEZpi+9hewuTBbpus7GHVv4suQsZoOBOVkT\nGJExrMuuL8SNSlEUJk+dyuSp19cyMyYujoVThvPxp19Sr1sJV1rInTiMxC7e7hQdE8uz//N/WPvO\nO5w9d57cJXczanTXzWc+n48P1n/A6bMVWM1Gbp6TQ9rAgV12fSFuVKqqMjN3Fte70XLI8GHMHpXP\nzkNnaNStRKjNLJg5vtNVg1drYPogfv/rf+PN11+jts7FzXc9QnpG13WS8Xo8rF27jpLKOsItRhbO\nn03CDbaNQojuYDAamXfzvOu+zuRpUzldWMLek0U0YSbK0MzyBTkYTabOB1+FsePH8+wvR/LGX17F\n5TGy+KEnSUpO7nzgFWppbmbNmnWcdzbhCDVz6+L5REZdfkdHsCi6rus98UG7tx7riY8Rokt4NS+l\npcXExsRjs4V0PqCXeXXdavIqvW17Xg3OUh7NncqoYYG1gXrKlNzhnZ/UBbacO94jnyO6z4qsWZhU\nI29s3BbsULqd2+3ifGkp8YlJmC2Wzgf0Mi+8+AqHaiyoltbtHcbaYr57762kpKYGLabcUT2zlUPm\nmr7vRpprWpqbqThfRuKA5C5/sOoJv/7NHzijxaAaW2O31Jzm6e/eh8MR2cnI7tMTc43MM/3DiqxZ\n4IX1b269qnHL75yLW9N4e+vO7gmsGzQ1NlBdWUlicopfYeW+QNd1/uOXv+G8JQVFNaDrOmG1p/jx\nD74T1Hu0S801siJH3FDKys/zzpZN1Lg8RNvM3HHTQiIjA28CjAYjqSnpHVyh99M0jc8Lz2GIaY9f\ncySxad/eoCZyhLiRFBYWsOGjPOqa3cRGhPCN25cQEhoWcJ7ZbCFlYN+caxob6jlSUosa1b5NyxuR\nwqbN23ng/utvMSqE6NzxY8fYtG03TS4vA2LCWbFiGWZz4AOH1Wbrs3NNaXExp2vB4GhPQLU4BvLh\nhx9zxx0rghiZEDeOLw4cIG/3AVo8GumJUSxbvqTDRE1IaFiH9zt9wRf791Pqi8Sotn4vRVGoD0tl\n06aPWbR4UZCjCyTtx8UNw+V28V9/+yuHieKsJZ6DmoNfvPZnNE0LdmhdStM0XL7AhXYt3q5tayyE\n6Fits4ZnX1nD8RYH55Q4DtaG8OvfvRjssLpcU0MDHiXwzb7LI3ONED2huLCQF97+mFOeKM6pceyp\nMPH7514KdlhdrrqqCp/Rv6izoqi0uG+8dsNCBMORQ0d45YM9nPZGUarEklfs5U8v/SXYYXW58vIK\nVGuo3zHVaKahsfkSI4JLEjnihrFp5zbqHO1vjhVFoTp8ANv39J3lilfCbDaTHOZf8MvnbiEjLjpI\nEQlxY9n40RZcfnONyjlvOMe+PBLEqLpeTHwCcRb/BylfSz3DBgW2QRZCdL3N2z7BG9FeMF01GDlV\nrVFdWRHEqLreiMxMIrz+Hf20hirGjhoapIiEuLFs370XX3h75ymDycKx0lrcLlcQo+p6OTNzMNcV\n+x3zOUuZMmlCkCK6PEnkiBtGU0sLitF/N6FiNFPf1BikiLrPg4uXkFhfhK+yCEPlGcZbm1k+r/ct\nCRSiP3J7vCiK/3+vutFCfV19kCLqHoqicM+KxUQ3F6FVFWF0FjB5gJncObODHZoQNwSPL3D1m6aY\naGzsX/c1BqORu5behKOxEK26GLOzgNnDYxiX1bUdRYUQHetorvH6FDwedxCi6T62kFBW3DyV8PpC\nvNUl2GoLWTQpg9Re2sRBauSIG8acKdPZ9vqb+KLb35RbaoqYu/z+4AXVTRLi4vnxQ9+mrr4Ws9mC\n1WINdkhC3DCmTcpi7982g7397VWEu5ys7G8EMarukT4onX/8wRPUOmuwhYR0WJtDCNE9sjKHcnjT\nYdSw9hW38eZmklPTLjOqbxo1OpORmaOoc9YQGhbeJws2C9FXjcpI4+S+Ugy21m53uq4zwG7s8u53\nvcGkyZPInphNnbOGcHsEBmPvTZfIihxxw4iOjGbVlCyi64qg/BQx9cXcNyuHEFto54P7KHt4hCRx\nhOhh6RkZLJkyFHtjEVSeIs5dyn0rF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lDbG1aFNsb+nRoAaDoddk5yfVUk0JmfatDJ1nDrBoO9x1FlhMcMIzi4UevF+LDStCZ0lXTpFKqS/P3VRK3xf2ECu6eH1s/7Sre6yLnHX1VvxMEfhcAlwGIgBqbWTf1dPnc8DWhYn2r7iECUBaa1xgzAgr/rwN/rZLKBOAuyW2XtyurJ09HddgeNU7YM5w/HPrLPVRZOrh9HODd/RX8V0LA+/Jrq4hMC5IpG4qJr6QAMlANCCRes+iwP1Eg7J25Z8vwDYK1NspFBqXz0uh9+weHW0casUXCEBui6153Xl886m3osFVQr+dqOPMRhCcMsFXlor4YgKcKqGjOite+lgq8bXu0qIMBsORMcv1r6hNpwZJALqfG7xzY6zuWBa6L3V1jQDTaxKpT3vE0wiwWOhokBW3PO97Hy+0oBKAnf+uZzhsbYUXAu8/w63olTornqMBtTm2ntwS29t7+nxj7WhCicBrC2iN20/qaxnwCbCrhFZDrPipJ1KJu4zD12A49szxUteUC30lATYDcbVU16yK7C+siZzVXV3jgK2Lku1fsYERmfc0HGzFu55KJW5pZZHKP5+osKbkO4AL8bSf+P0YO1zQX8azFah9fWQ/sUNZu3v6fFOd8JwEYQblyUfGAfRhBgICrH5O+D9DrPjvT/uJP/Z0bIPB0DMIjAsTqZuL6KCDI6xNpP53Seh8cnNsdxk5W0w6MTeR+qYEarLrj8EyXvdYKvmFQptLXayVqCsNyuXxVOIH4+3oyWoZz4hAzWtC5/E9Wu7r6TNOd/1LHcKYAlrj1FvqlkzqtqiR6po6Ff/qhcD7b0/HNry6eVUX4fVIO4MtdTMRHCJYmT/OQKk+mCTtdnXdaDsam6ng7xLBEoSEAEomOeGNhc5vUNbTDHSIjmFGFDG2TXOCcw5XUGprbDc84yf+8JSf+F1vnC8AMMiKpwNdr3IEIUEEKQieRzx5shPO6s34BoPh8FQIVVIr1XUZrZFEsAXBHWrFH5PgLnV4shPO9IinCYKX1RoJVE5xg2sKnb9bycc1w889phlhzNg5xQnOsrq5FwBsiO1NT/mJ3z3jJ/7QG+cLANTKeGaBelVZKKM1liC4xcRzxtjRuN6MbzAYDs9gGQ+oEPoNmfmJEARHEBKj7KjbjmbT3OA1NjAyozVSEBIWMHCaG1xV6PwmJR/VjCD3mGaECmie6ARTBbhb7+rqyFn9lJ/89Tw/8ZfeOF8AoEqqc0TXiy/K0UunQuir66xoUG/GNxgMh2e0HY3NNArxiECC4BKQHGeHN3V33RlucLUEBuauPxzCqDPc4KJC5+/X4gnNeWsoIGZGPM4OJxC6ziZiEF6JnKVP+slfPusn/tYb5wsAlEs9R1DhzfKMvrpEsIng1Ep1vWlaYOgpr2oHTJ0V1wMoFO2h6qx4WFfX1Ug1NX+XhwjCI55U6PzVkbO6SYu/MiPSjHZmKADSJYyvs+KbL0m2/7xGxpW9tb9KqPIZrv+a6a4/p1C3k3KhigXgMVAwhaCAR9erkmpmb+0wGAzdM8SKxxZ6Dwlw+ktV09V1VVKdlR/mTwSriPjMQucvCZ0XW7R4JEdrNAFWgnBmvRV//JJk+11lQvWoC1su/WXc7yzXv3iaE5zjFeh2UiVUWTrjCKrQ9flaQ4BbI9UZvbXDYDB0z0ArnsjovCKxgAFFpL1C1wBAudCzO4X5E5wSoc8udP7CwH24nekFZoQZrWECZBHxnOFW/JlLku3fKqQVh2OwjAfMdP1LpjjBTLvA5lSNjCuZWTMXnLt10hoGMEiqKb21w2AwdE+1VJMLrIXIJXS7uVIi9Kx8B2qmE1pBrZkfuPcETK9oRpCjNVQq+LWj7OiLr02kvnC4zaVCDLOi+pmuf+kkJzij0EbYABlXa0bI3FlPuYDPh4F4iBVP6K0dhlcnr+oUpBYt9nVRIdtq0aJLL2nItJ+BMJ0KeAgNdJn3OM9P3FMl1CNjnfCiKqHfIdIFeQUBFjOcKU54wyMp65s9tT2bOsVpBxIPSqQ4E2K8RoLF3ETqphLiCzNdlDzmQxOTrHB0mqgw4pCpsac2AOmw5emu/7oKqedqRtt2Zd2/LHQX9WYMg+F0p41pHwqnIcpWppaurouYmpg5prwdGA0cKHQ+g/CEn/h5rYz/MdYOLy8TfEVmUSUIsIhRO80J3/6kn/hlT22f5fqX10r17qzWDLbieGno3LI5trc5YGtuIvWxJPHMTCeUDnZ2qTVAGDI199QGIN3udpobvKFU6FkxU9OW2PrbqshZ0ZsxDIbTHT+tNZ2cEwzEAVPBOnUAEAONzNBEHXVKpcfrhALpR1PJbw+Scf8xTvimYuILMvMaEGDZjPoznOB1zwWJ+3pq+xwv9T8VQr8x40DSQ6y4bX7gfmqXsvYWk06c4/mf9ognZrSog51daQ0AFfRSa8qFKp7shFcXC31GxLRzY2Tfsy621/dmDIPhdCdgasqshTo4djXQ5ZwGAGLGPptQn3uMGToGmgrfR0QPpZJfrLeiwaPt8F1JwlnZOREBlgueOMUNzlsUeE/1xO5MGYkPFRPPzZo8xIobn/W9TzVr2VolVNlZnv85Bxh28JKOtnY5dKqXWlMt48qJdvimhOCJIdPmtZH91+7StwynDydFBEwx6cQQKxp0JLslR8NOZe31mZZxTmibZkQB04pCtVyyrIrsp5EOf+Oc64K9Stzb3f0atWy2gJL8MH0iyARxj3eDi0knBme6KgmCJwgJQUiOtqNbCIyZrn9FMfH5mTSHRGZCwszQzFAhY90eJX6ZHz7MQLwush/qqR0ZEftCtdTXOIRRnsDUYVb86Zmuf2lPxzAYTiSlpIvqrGhQoZ3V48n6yF4XA9uZD0XBaEbYxjRvv5ZdOm7XRvbD+ZEzmhHsiK2/dne/Xcra6xBqC+xo28VC9zjNsFbGVbVSXd9Ba4DiCU74MQA42/OvSRCfldEajwhgPqg1ccBY3qToj7lak9FNtSqyezRZAoCMU/kbVUK/xSGMSBCfOdqOvjzV8U3EnuGkpEKokjorGthdiuHxYEXovKSBlkykLYC0ZuzX4t9dFdQFgI2R/c9MkUrkXrc1trqd12xX1k4XPERQ5+LfZVKf01O7h1vRsHKhr8pojZvRmsopTjqdYZbnf9AjnphN38zTmijFWHBA07/ztEYzEKyI7AU9tSNJ2j3X879XIfQbHMLwJPHZ453w62PtcHxPxzAYTiRVQpUPlvGA7lJxjgcrQ+dZBsLcaDTNCPYoeU93122L7XsLrD+iDZH9z+6u2xzb21zCsPwNKUHwqrqIninEBCecWkw8J6Mzbibdsv8ZTnAtAMxw/U85wMhMyriTqzXpeRuebmfMy02LYobSwL41kb2yp3ZUClU6yw2+Xyr4MocwrIh4zhQn/M5QKxrS0zEMpy59GgFDYJzn+ddWCH1lZveUdin56xcD74ETZcPTvnfHLNd/d4ngCwBQSmPhs4H7g+6u2a9l27LQ+fRYJ/yQwxjNQOteJe59IfD+dbj7RUBzpmhTh8mKBnrc1Wi4HU1EgVB/AZQOkKq2QurX5S+8AHCLpoee9hM/zU7CZrh+a3+prhFARQRsXh/Zd3fneMpnpB2NconH5t5LENxaqf5Xgh82xXwNJwt5UWEKTqi3xfKul0Jv3om4P4PwrO99boYbvDcJng1AtWqa94zvdVnBHwB2KGv36kh/foQdfdAG6jXQvEvJ378Uuoe1OxM9o4g6dmBTXUTPFKLeis9gQOU6jYlANmN4krRbJvTF+QsvAKpRiz8/73v3KpAmMGa5fqqf1G8RQEkIrF4VOj/qTSe3CU443QL6U3aHndLNCAZb6obFIV7s6TgGw/HGJW3P8fz/y0aFTXXCcGNs3dlda+ZjSQzSLwbup6Y64fsS4OkMhAc0PfqM7/2uu+s2xPbGpNBfrbfiGy1ggAL2bo/lL1dEzrIe3LPJYebcAtzM4K6iZwox0IrPzI9IJoJIgKdKsCgiPidfyxgIdin5qwWB+5AGsQSL2Z7fWpEuQOwFjGVLQ/eugEWEHjLRCecIoDRb4DOrNcPs6N2rIueTPR3HYDjelJBOzvb8W7JRYdOA1tWRfceayFlzIu7fyiL1cuB+coITvt8DT9RA+z4l/vVi4P67u+teiZylDunvDrLUeyTQLwYaNsfWTzfG9qbD3VMx9luEAbnHmKEiRo+1pkaos/MzGIhgFQmeVSHU/3MJYwtoTWpLbH1vSei8wCA4YGu257+7VOjXEmClmBa+FLg/6k176/FO+DpCujZexgYBhjvajv5nU2zf0dNxDKcmfeqAme4GF5QL/XoiONnf2rVSXT/ODresjJxXToQNKRbh8si5Z4YbjLWBuoTAjAsT/o9XhM5XNsT2xq6u2xjbmzfGdq9/Ga8OnSf7ef47co9pRuAz1r4+2fZ7ARSHwJqVoXNXV+0TA6ZWFCisS4BIMbVTXsvrgx8TRO4O2ILAewzAY719hizlQhf00hLglAhd1Kxlt2GIBsOJYpbrX5WNCss6E+os9dFGHW3eHNtbT4QN+7VsWxU6f5niBmMk0K9Y8PmvTaYmLgnc27Ypa0dX162OnNWrI+fm3t5vfWw9MM0JL8nVA80IIqadVybb/pReoNCypaHzo64cr2FaawrWyQqZYhSOoiQgnaIApJ1PzweJ+wHc39tnyFIi9JBC3VUk0GX9HIOhL5jt+u9OZqLCMlrjDbfiW3fF1vt6WwDySNmjrKYNEf91nBOOFkBxueDLL022T17ge7d1Z8Py0F28PHQLNhPoji2xdd8YOzozb1ETakZ8ZbLtrwTYKaYFiwL37kYtmwuNETG1ZlKm8xc+fmayU7CoLwM6u+hRIP1MuuPREXc9KiI9PL/uFgDYwOAjHdNgOB7M8vybs1FhmZfDHWtHt22PrevaWPjdX31s2KasHaWxvneEHY0kwK2S+m2XJNqnPOcn7uiuHfXLoffcyyGe6+39GpR1Tz3Fn8rd+GUgtogr35BsuxcAtTE9+6Lv/aSFRXuhMWLQAYA7pUxroE10kxnCgM52bgxB8ZN+4ucAft7bZ8iSJB6Zv4FFBHLAXdYgNZw+9GkKUo1UV3QKkQecQVZ8QlNYznSDz9jA0GyYvUXoN8EJv2KBCzkyjopGLZtXRPatIWMDp9/mlhTjpSThTEkoJYJwgDGTnfAb5UIV519fLePKJiV2aqA5L+wvSjG9vE/Llv1aPFagYni4I7aePJbP0qTkRhSYFDHgt+iuhddgONFUSH15gagwOdSKLzxRNhAYU93gy1a6+r8rCK4FDJzqBl86HqHDW2N7+7rIvi1ibGMGFGNfwFiaJD5LEIqIIF3iydPd4Fv5ndgIjFoZ99sRyzUMRPnpli1Mj8cg1cr0DDPyd5fV1tjq9cSqOw5osTE/PQIAFNCl48pg6AtKBL+GOkeF0Ug76nE6ztFig60JTvgVi9Avkxro2MDQMz3/08fjfqsjZ/Xm2PpWzNgNADFjT8BYkxR8VrarUoJ4xizPvyNf6wiMATKu3hbLl5Hn7NWMYJ8W/4pBOsW0KDeFM30txPrInn8sn6WFxdr8TnIAEDE2Hcv7GAxHg0vaThLPLNCKmcbYUcEi/ceDUtJFI+3oC5JQmil54LiE8TM9/8PH435LQndBg5I/VpmIl4ixPWZsTRDOzKQL2UXE55ybSH0+/1oJFgNlXLs1tuZxXhZBJnXq741a7o+AzQWKfNOayF58LJ+ljWlVp7UaQwdMa4/lfQwnJ31dhLdTG0EikODOO53Hi2qhKhxgeH64GQHWGCec+EroLjnW91wXOevWRc5HBZg0iF+fbPtN7uKQCMQMa6wdzXkhkP8FgAEyrpmWdhQNAYAQ2KoYqx1gDADdzvT8C773IwB40ffuPT+RGpkAT83sKNlNWtxztFFFA2RcPdCKx7VpsXd1ZK9YH9sbRtjRYg88VRBcZjADYYOSPzfpR4aTiULFtgEIAT5hWjPMiocKoDw3TD+jdxVDrbh+Y2xvPtb3XBE5y1ZEzgeyLWGvSLb/NU9rBDHcCU541suh+xwADLWiIROc8DMWUA2AQsY6Iuy1GcMAqFamx+elEj8HgPm+96vzEqnBNmMk0qlK1k4lf360ReTqrGhgjVSjDmjRsC6y174SOi8PtuJNNmNYrtZsjq2fHc19DIZjDRUutk1E3FXb5GPOGCeciLxIEiJIhzGySqjyrqJQjoYloTt/SejOF2BKEnsXJVK/z10cEkFKRuUYOxq3KnJWAsAYOxwzyo5ukUApAPIZKyygwgIGMxDt1+Lfz/neXwFgUeD+YJbnf0Uy+iPtqJFbY/mdo32WYVY0tFKq+iYlN2+M7U3LQueZ2kTqzcSoIYLDDMVAvC62f3U09zEYjiWii4gwABAnUGtGO+FZ+ceIYCXBMy2wjEEFOyMeDQsD73EAjwsw1UrV7yw3+HGe1tgOY2SdFQ3cGtsNADDJCc4YasX/R4BHgPAZS2xgsARqGAgatfjrgsB9BAAWB+43znCDrwhGOTJO4XWR/bUUiy6LmB8OAmOkHY0qFXrgbiXXbo3thuWh88Acz7+CGSVEsDNaE66K7COO4DOcOvSpA2afEo9WS31N7oJAM/xdSj5+omwg4q5FrHAqz1EhwOQQ2z6LMBs2K9KTj452AY5NXJ7+O+MMN7gtUwNBAIDDGBoRNj+YSrxFMelckQtB8cOp5O2DZTygXKrahtja0KRlwboPI61oRL0dXS6B4r1KPrM4dOYVymGc46XeVSH0VQwoSIURdrRrnu995vFU4o5pbnBhlVBzFah1a2z9y3QmMZxsHNDiyXKhL8/dmWYg3K6sp0+UDZmFWcFQl+OhNRIsLGIZsIg0iB2wVSiNB4BwM1pjg61JTvjVTA0EAgAHGOkzLXvc994aM8W5aYytLFIPtBd9pt6K6oqFrtwS2Wu7CvsdZ4cTBlnxJQTYu5V8fGnoLOC8OWS2O0EJ8fkMqEFS0Qg7Wv9kKvHFx1OJW89wgkvKhD47BjVuiuz7TWcSw8lGG9PzReBz8gpF8pbIfuFE2dBFGjIAQNCxLwpsgYUARAiKNYiLRFyEwhHWnCCuANL1K8bY0W2CkMh+6AHjWpmefs73PhYyxbkbOY1a7v9ve/LmYVY8LEFcsjG2VrezCPJvQGBMdMLp/aW6kAFuiK2HV0TO0kI2X5hIfcYjngpAD5JKjLKjxY+nEnc8lUp8fIobvK6E9PQItGNdZP/DdCYxnEykWIQBY6ULjMvdQCZArovshSfKjkIO55zPulxfHSkWWBJAUUZrioUqR+FIfFVEXAagob+M+w2z4s/krjVdYFKzFvctDNx7faYwd93ToKxdO9rl+0ba0SgbcNdF9qoQFOffg8CY6oSzq6Waq4D2rbH1QKH6OwnSzvmJ1G02MBwAD5ZKjrKjJ59IJe56xvc+PNkJX58kPSlg2rQ6cu7rTS1Ow6lLnzpgFgTefy5IpMYnwWdmIzXaND1bLPSgc7zU1Rsj+9kj/UG0wGKaG1xQKdRcfcgx0Kk69W5lNUUIttuMIfntF9dE9mGLz/UUAmO257+pSui3EuDGwM71kf0DpBdknQSMgWiPki8DwHArHi6Bylz7iCAsxsBzXP89xYLnEOC0My18OXB/sjezI7RNWTu6qy0xzfFn11nq/wiwiSAGUzy1UqqZj6aSd+aeN9oOx5QL/YbcWj3EGHymG7z7sVTyrgWB9yiAR4/6SzIYjhPzA/cPcz1/pAMejUxR2QOaHqqRanKV8Meui61n9iirYAvEw+GArTNc/5LytGOgaWNk37c+tjfkn7cxtjaOd8J2ZnhZ50YmkqN1Y2x1WW+qt0iwONfzrykT+goC7AjYvCp0vlcqdG2h8wkQ25VcDABjnXByRg9yo3RsDzx5tuffkGnbKNqYnp3vez/LOlsydXS6rKUz0/Uvq5XqegIcIlA9xdMrhH7gST/x69zzJjvhzGLiObla4zJGzXCDNz3jJ/70fJD4N4BuC/wZDH3JwsD96WzPr7MYAwFoAqx9WvxjqB3NGWzFrasj++l9R1gfrZh0YoobXFZCenoIalgX2fdtie3t+eetjexlw6yO6wVm6AjYeqQ6VwgHbJ2bSL2vhPgiADIEVi8L3O8NsuKpKOAEIsDaHFvLAWCME85C3sKJCE4x+PzZnk9FxOcC0C2aHn8+8H7tswgZhO5q8wHAeZ7/P+VCv0EQPGbwSDuaWS7UH/LbYU93g9d6lI7ezR7zwFOnu8HFLwbeQ8/6ib8B+NsRfjUGw3Hn5dD99gw3+FomUoMByCZFf53khJeHTPtWhM4zXW2IHI5yoUomOeEVRaQnBkybVkXOfTuUtSf/vHWRPX+gVDflHmOG8pkWRwWcFkdKMenELM//YBHxbADCZ1r6UuB+d4DUs1AgmwKA2BRb6wFghB3NyXdKC4JbJvTrzvH8uiTxmQxEzVr89znf+0MM0gzC2sjpNhXowkTqw0XE52W1ZqwdnVsi9F357bDPdIM3Z7sqHXwe8JzJTrhgSei++HS6bpXhVUafOmBikHoklfz6YBkPqJRqUJJ0ba3U7y5Ot23kfm7wzq1x/L0j6VRyYSL16cwvV4+ZebQdzSgq8GIAwOLA/eoZbnC7YJQiLWJ6TWR/pTeV8w/HWW5waZXQb8/+sreBgaPt6EsB0yso0GNeA/tXZ9qZWelwwkJFd63cfPMkeOYszx/2QHvypsOlAAkwDbbUTXkdjLwkeOYwK+qQDjFAqrPzd86JYBWlu7nc1esvw2A4waRYhA+mkrcOtaIhJUJXl5EeUSX1WwGWDOhqqd61Pla39zblUIDpomT7V21geDo1hvUEJzw7IfTXlofuy7nnahC/EjpfnuCEtxGn3zsG/OWhc1tvKucfjtme//YyoQ/W17IZQyc64R0x0JDrWAHSWhMxNmfDdGV6IlNo91yWEF+UDfMtBp93biI16IH2osMWIvdIO7VSvTtfa8qEvqJaxvfnLgirpZqTXwCTCE6Z0HMB/KkXX4PB0Cc0a9n6QHvyo8OteHhS6IoqoaaVC30VATYDcY1U166I7FvXRc663ozrkrbnJlJ3WkAtERyXefxUJ5zrEt+av1DwWYRrIvu20Xb0eaTnDqSB/YsD95h21jgvkbqxKFPcHAAcxphpbvANBkLKm7EwAynG4mw0rgScLiJ1rIwT1gaAUsEXz/H8qodTya8dzp5KoUorhL46v4NRtdTvKib9UCuLVM655+fXBRMEt1KoCwA81LtvwmA48exSVuN/2uX7R9nRGId0sr/UF1ZKfguBXQbC2kTq2pdD55PZ3+89pUyoovM8/wcElAqC7TKPm+EGF70c8sfyx9qnZcum2PpmvRV/Aul0HREDOxcG7veP5bOe4/mfcoknZyMLPfCkszz/q5nN6Q4wA62aHvczKUMivX7ppDUCKM7U0ZEEOBVCXzknkSp6PJX8yeHsqbOigcXEc/M7Mw6S6v1LwM/kRgqXCT0nvy6YIHjVUs0BTBfHVyt9XQMGQDpSo42pdY7nfza/v/tgS928ItLz/V7k3g23omEe8bTsL9fsizFQqvcuBj+T75zIhJu9d4QdjbQAZ11kr45BygFbk9xgdpJ48AEt1i0PnYW51xIY+SH0XVEt1dUFCg5Lm7guf1FEBChGU3bsrbHcNNaOCqUOyA6hhwQpGOVj7WjiKwVCbnMpFbpYAEUFPuJ+Uo3MdcAoIJX+T8f/Nwx0Cv81GE5mNsX2lsEyioa78eezEWXZRcBwK/74ytD53162EZxqA0NztEYQ4NZb8fuWh+5N+edviO1Nm2PrupF2NAoA1kX2WgXSSdLuBCc81yOuadJixYrQWZKrLb3RmgqhX1+gppSQQL/8c4mAiGl39t+7lNhQb3XUKebMqXk51jZjaL6zthC1UtWiQOoVA2F/qYblOmA00MYMnR+NqNG5KKbBcLLCIKyP7Q1jrHBsmcWXZ39PZzcyxtjRR9dFzod6M+YkJzxXAtU5E35JgBxhRdevjZzP5J+/KnJWrIvsd420ozExEK6P7HUMQinponFOeJ5DXL5bySVrInvlkWiNA7aKiS/I0wVBDIfQuYsQAA6ZDkbkNiuxub9UHXauM1qDvDGdBHh6P6HK9x6m3kt/qeoZCClvR5yBuL9UA9fF4mDKoga1F8oIVSDTQMBwysAgrImc1dMcf3YR8bk5WuMyw5lgRzduje0v9GbMienuiSXikCPTAkOMtaN3bo3tO/PPXxK681dH9ruGW/HoFFPrptjaxCBUCVU22o7Ok8RFO2JrQW5kcLYYd0+0pkqoci/tfMmvKdUvv510htgHHcyeaNe0o1J03nzKjpMzplsCfo1H+leHW3NWCz2C09HU+diVUpXv7jiv6TR/YYbWgNGaVzEnhQPGJW2f4/nfQWF7dJ0V1x8uFCyXKqmGo8BvVgEkSoUuKhT+yyDk7kiVC1V8rud/RwBlBCQqhPYHWvGmx9oTn53uhpf1k+ptAihloD0G9u7X4vFFgftvn0U4yQnOGCzjt0pCRasW8xeHzl8ISBYw1ZJAJXN6IXTwgRlBsxLPZP89wo4nMhB0ManphCd0xeHOadWiLTNR6fSdH9CiQ0jz+sh+vNIN3px7rmYEjUr8syf2GAwnC2VCFU1zw2+hQNqfABK1UvUrFGbb9Xh6eKG6KhYwsKtrFEivjpzV2X/XyrjfWW7wnUxxOLdC6GCQVEsfS3lfO9sL3lIp9BsJSGigXQG7m5R8cGHgPhyD1DQnmD3Ait9EQFGLFvMWBu49hXSCkG6JW0Br/EYln83+e7ClJmd04eAzER2arOQ/SlLocgDdOmAaldxbaKebAHufkh120zZH9oMTnPCC3EmVZgS7lPxHd/cwGE42amRcOcaJbkOBn30LGOyRdnqzsVRCenSh9sgOYWhX18QglVuTrd6K6qY44TcBWJkd36sHWfHT81LeT2Z7wbWlQl9GgKOBtpjRsEfL/74UuE8wwGe6wUXVUl1JgN2sxaMrAucRFK7/YBXSGgaC3co62LGon6XGI72xk7sAKqg1DMQlQpcdzgHTqMWOUYWaOwD2Xi125x7bruQ/h1M8Ka8GYdCg5L+6u4fBcLJRb0WDh1jq4+hceJtc8ITejldEPL5Ae2ThplO4C+KzCFdEzvLsv0fb4Zixdlr/CLAqnfDNg6z4HwsD929ne/57i4kvACA10BYxtu5S1j8Xh85zEhAzXP+KSqlfC4AblXxguxJLOJ3OmY8A0pqRFwWjt8fWwTo4pUKPRXpNmJta3dW8hoqIE35ed6J89muxfXAXda6aVceam7uVvG+gVB/Ma50dbY7sB7u7h+H05qRwwEx2wjmZ7iCdIMBq1aK5N+M1a7FlgOxceJuBsKftkac64VskUHUwlBXwbMbQ8xKpDxURzjm44w0kHWBIldDvuCCROntXLO8fbKmPZD8vE/rycz3/rHbGgmJgbn6ED9JRLIdsTKcEbLCISy9Ptv0gZuxrZbEU6NQSrSAEWNti67DdjmKQ3qPkH6qlujZrKzNCBTS5pEtyq5fvUNaeDbH66jAr/kimYDDv1+K/LwTefT2xyWA4WZjohJcRkCikNQBEixatvRmvVYstVUKHhEOFJAFAAT124kxywusyRW+zETmeB558QcL/vyTxrOz7KYEiCQyrker6CxOpM1q0WJr7/lYIfdX5idQ0n7HUA6bkRZF00hkAHDCWlwo18vJk21sjpp0ppo3ohdZsiezDOsZbWaSatfhXbloUM8KIsaVcqtodSu7KRhauj+0NZUJ9b5ClbhKAx4Bu1OKelwK3U+qowXAyM8EO35Kpp9QJBqKQqVcpzm0sNpSw8vOdMBHQ4+Kw4+zwAwQks1G3BHjF4LnnJ/wSj/iMHK0ploTRA0kNKU2kxiqmttz3t0rod8zwggkxsNVi1HeoGZWu83ToWdNao9uZnu8vo3OH2dH7QqYtEaMJXRQlL4DeGluHfc5dytrbxuFzSfDZuVoTMFYOkKp+vxbLszvuy0N3cRHxL2uluo4Ai4F4l5K/Xh66i3tok8FwUjDGjq4FYBXSGg0UbMDRHe1M6zzmqblOGGZwmJ4fHBYCY5Qd/V9uge3M5tLVc7zUOIcwPru2kkCJJIyvo3h4sdB1LvHAJPHs7PtbI9W7k0I/yelokfwsgoJac0DTw6Ps6M0TnXCEz7QGzBJ5DphDQ3REAU1NWuw/3DOuj+0No+xolQMem6s1KcaioXY0fG1kr8lqzaLAfdLz/OpKod9CgNCAvy2WdxeqFWh49dDnDpjBMh5QK9Xb89NzgPTL5DMt782ONJAO6x9hxatzX4zMLurv4h62Ry4S+qzccDcgnbNXhEPOl7zPXJtRP8hS78sL/7cEo7xFyxUJqcZnimWJ9EcdU4iA9C6RJFRUEr+RCLZNGOqyHo+8iQozVARstxg1mbEsAGqvFn/Zpay9PXnG5wPvX9OcoLG/FV9lAf0FUCKBiqGW+ni91e4vCtxbskWQl4fu4ldC591VQpe3MLX1pj6OBAuX2C7UscBgOFEMt6KhlenijJ12SJnBrUxP5dYI6AmvhM6iQVa802YMyrQsZQbCbbH8RU/HSBJPy0+5SWtNp04q2c9cDzzVk2pKntY4FqNup5I/GGSpemJ4SO8+ZcfODd8FM2KbMMwjnkoEywIPSRBPy18RMSMOGeucdAV/yvzRDUr+pKcF/p72vd9Od4OtNVJdIYEaARRZhMHDrfgzQ6143/O+d0ujlvsB4KXQe3ZJyM9XCl3WrEVLbwr5WWBhE1tH0y7SYDhaxtjhuBLBF+X/fgfSoefNWtzf27pPy0Pn6ZpE6m3EkJmWpcxAuCmyf9PTMVzC2E4pz4CVID6rkK2C4JWALwJxh7QgQXAT4MkbY+ubQ634/5gh04dhIe3AzdcaJIinC0IREaQFrgMhRud5TeQzVnjAOBzqGsebY+s7PW1n+0Qq8b0Zrr+6SupLBFAlgIRDGDPKjr4wzI62PZ1K3JrV+RcD70EL/EiF0KX7tDjQm5a5FlhKYnEsawUaDL1lohNMTRCfmf9eA2mt2aPkX3s75orQ+e85nn85MwQRLGZoBsK1PWyPXEyctIBORf8Z0C5hYldaUyn0W5CX7izSaUEXboitO4ZZ8aeQcaTkpBnmR9BxieCLCHCJICzwYCaEDES5kbXMCH3Gcg+YCEAy0nq0LrK/09N078dTiS/P8PyryoW+QAKVAJwEYfpYO5o+zIpXPZ5K3BaBYgZhnp+4xwb/o1zokiYt9h+uTmcudrqDJQp1YjKcuvSpA6ZaxpVnpEPvC6XnQAP7nvG9XheNYxAeTyW+fKbrX1Eu9VzNaG1Q1v1LQ7dHrdn6CVVeKDWHGYoKh5wBSNeSIKCswHHXIa54sD150yQnmFkq9LgU0/b+Ut+QH5pPAGWigToIkGa0a0Z7znclFGPPS6HzpaGWmimJE9tja/6m2N7Sk2fM8nLoPjdIyQ1nusFdmZzyROZZvclOcHNDyrr14PODcLgQ4FwEmM7x/LdXpFMonBjYsS6yv5ebftEbJjrB1IFSXQbA2q3ko0tC5/meCqXh1U29FQ2e6ITfLJSew+nfvA1PpxJ393bcGKSfSiVumeYGV5UKPUsxNW2Jrb+vzAnF7Y4BMq4utArT3DENqNApXeQ+k0VIPNievGGyE5xTLPSIgGlXjdTX5tdFACAEUJR18mQmcI5iNIFRlDO+jICdiwP3W0OteLYglpsj+/nuOqzlwyAsDLzHR9vh9rF2dHu2fgUAEMOZ5gY3PJpKfjt7vgLpPVru6+n4Flie5/nXlwp9CQArAjatCJ3v9FYPgfTu3RQnPLtGqtcAiBuUfMDsiht6yhg7HJtpr1xwUylkrJnne3/o7bjtLIJ5vvd/U5zw6iKhz4iYdm6MrXt6Wsy3zooGZbpN5m/6RN1pTebzzvOhTP2DR1KJ6ybZ4dyk0IMjppYqqd9SQGtIpCMP0+88QTDDUYwGCdTg0PjSZ9r0SmjfXW/HZzOg1kf2sz3dVALS2vFCkPjPFCfYW2/FHyeClbXfYtRPd4O3PuUnfps9Pwap3mhNgrQz2/NvynaECxmrloTud4+kY6dMd+ucWyX0XA20bY2tfxfq1mkwFGKyE8wYasWfKrRRwwy0M+a/GLgP9HbcJi0PvBC4H5loh29OCp4YMG1eG9l/zXQ7PCy1Mu4yBRt5aYcdbE47V1SBGk6Rz7T/iVTiuglOcIFLXM2gOFPcvFB6s3MwopggwPAixiYbqMu5t2xlWr46sn81yIrPUkz+2sh+JrsR1BNCUPysn7h3puu31Ep1Q2ZzzwYAFzzuTNe/7PkgcTClMQLFvdGaMqGKZrrBzQniswDAZ1qyMHC/19iLdVgWG2yd4QavKRd6tgL2bYzs+00ETt/Spw6YcXZ0eeZF6VQciYFgXWR/80ijJkJQnGk7eF9vrhsg45oZbvC9ggs1IA4YyzxgWsGdLUBpoNEC+ucdD/drWn+ul7quTPAVDITlYIsBnVtwMrujlZ/OAKRTnRiIc8LtyAMmjbOjNz/hH75id3fUWfH0/Ni8dL4nJkqw6I2nNpdZrv+GCqEPFh+2gUFj7OgrTUq+vzciBACzvdQb+wn9zuxYQyieVC70hCf9RI8jDQyvXkba0Zvyw1WBQ1qzInS+fqQtE9tY+PP8xJ8B/Lk31w23oqETnfAb+YufTBitCoHVDmN0oZ0tSudOt8gCDt9mJTbN8VIfLBZ8frp2FFsAiBmc0/5aaaBFEsrzr5dAJQNRrtYkgbOH29HWZ/3EPb15xnz6SzUj/3mJIJPgGUcz7rme/78lQr82W6DUZgyb7IRf36Pk9W0selXAd67n31Aq9GuzqR7DKT6jTOg/Pesn/n40NhpeHQy1ond24XxhBlIvh+43j7TrWbOWLRnnwW8Pe3IO4+xw4ig7+mIBrWEGQg3ss4ABha6ldPemTsUm07VgaMd5nv/JBPH09Nylc8dGZsQZranocH165TSQgUgcukKUCb50gKU2He37Vi3VOfnpWoJglwh9Lnr5/eVyjud/pCjdOcUCAAcYO90N7tjdLm/oaYR1lgsTqU8kiM/MtrEdbUdnFQt998LAe+JI7TO8eqiz4mu70BqtgZYFgfeDI92k3KOspieU9bPeXjfV8Wdl6tF02KzO2LSfgITswuGbsbTTupQAq13TgXO81K0uYVxmXuMUuEeogZSkjvOiTLhMPQOhOLR2k1WC33ZA6I3P+ol7e/ucuVQINbdQR7VyqecCOOKaUme7/q2ZqEUJAB54yizPv+2/7cmbe/P/VYDpokT7bQ5hVLpbJ/QEJ5ydFPqOZaH70pHaZzg6uozmOBF46Q5AndMBgGBVZN+6MnIOW8vkWDPBCf8nkyN9UASY0xMVAGpLbP1NA83MCDPHAaRTnCJg/T5FDzGDOxxnbBlqqY+XCb6KCJYgJDMLBZnp+hEyI4yBbQ1K/pIL1GAgSotQ3jGnRPCFPX02CywnOsHUSU4wPUHaGWmHIy9OtH+qn1RvQIFcSACR7mE9iEJUSf2GAr8c5CgnnNubcYpIe9VC/0+BNraXVcu48kjtM7x6cImH5Kf5AIAGWpaFzicO18nneDDaDm8gwMt15mZrszDgb4qs3zDQyowot1icZvjtTAtaND2ZrzUBY+VYJ/pqcSb9Ias1DEADrZoRMCMMgfV7lPgrMzo5nTJak59+6VYIfWlPn80lbU9ygukTnWCqDbbG2eHE1ybaby0V+nwU0BQ+ii5HBEap0Jfld34CIMc54azejFUt48qy9FgHF26C4PYT+p1FpHtUBN3w6samwgW4FbB3UeB+dGcvojmOFcPt6CZBcHOduVmt0UDbxsj6kWakmBHna80BTQ+2M+bnz3famRZNcaLvJzLOiIzW2AxozQe1JgqYXmnW9ABzl/OaTlpTLdXlPX22YtKJyU4wY7wdTpJgMckJpl+SaP9ignhyZt7WAQ30KG2yq3sVEZ+d21I20/kuOdYJJ/dmrGFWVJ8gnpHVGiKQoHS3Tgnu07m54dQgEz3WiYix+cXA/Uizlr2qaXe0CDDVWeoDXWmNAlo2x9Z3M9qg8rQm2KvpLz5jRVZrmNPH2zReONMNf+wSJmW0piijNSqTHeAzI2pnWtiqMa/Qew+k368O9hLcAemo+h5RJlTRFCeYOdYOxwloOsMJzr000Xa7TagrqDVMnRq+9JT+Mu7nEkbnrkWJICXQf6gVD+3NWOPsaJJDGJnbrVMQ3Horfv+R2mc4evo0AqZF09LcdtFAeme2jWnemshZ0xc2ecTj8hdqmZ1gAiMxzI6vf6g9+d6JTjirTKjxDnEdMaz9WjzbqsWOYXb86byCdEIQSjKh/h0QBFsz4vmBexMBokFZuwSY+ifb30mM6i4KhXYgmxKVJO1WCl25S8k9hXbyh6V327+SnewMs+JsbQgr06a2QyVxzQgOaPHw0aT4FEotI8CygJLejFMjVf9M6HTH3TsgqpVqSG4bW4OhEK1aLKkQemiuw5cZ0X4tHugL5wsAOIQRneoxZLRGMMrqLPXGR1OJ68c74exS0mMd4kEAqFHLJxiIB0r1wZyIluyYwwp1ShEEO2bseylwPx4D8S5l7XVJ25ckUu8koLgn9mYdwCWkk6VCl+5ScnehHd+xdjhutB19Mfvv4Vac2eyGndWaXDQjaDqKjmoiXXui0I6atHr4bFlqpRqSSbno1Ma2Rqr+G2Ox6UjtNLw68JlWFIH75c4jNCPYq8TftitrZ1/YZAGD849ltEZYjOpaqc55MpW4fqwTzi4ReqzNPICBaI+yHkmQrq4S/Nrs3CCz0IgTxFML1dMSBCdgbFoeOt/xtUjt0XJfKemiCxKpt6DApl9XzReAdDfKJHHRTiV3F4oayqZgIJ3WQCPtiCk9p3EytnZAM4LdR9FRzSV20UXhYKeXWlMl1TAU2mwDnHKhS48kzcDw6iJkrHWBSbnzCM3wdyjrL7v7YF5cJnSxAIryj2fecekwhpQKPeJZ37thpB2dXSz0GJu5P4NSO5T8b6XQU13isblaw0BbkcCsLurhOe0aL62KnN+2atHSpOWBGhlXnu0GlwEF51adyETtoVKoUpfY26nk7kLrnhmuf+FAqT6QqRVDo+xIEWDnFODtgGYE25W8/3DfWVckiIsLRR4C0B5xr7SmTKrhhdJIJTCAwD1qBW449vSpA2ZJ6D5yQSL1OjCqM3VOQqTTAXpU6Ol4EDP2SqB/wUkBgWzGCEEsXgrdeQDm5X5+WaLtjgIRH8ICagulEWTg3CLDGsTrI/s7o+3oK0CHKJzsL2qRcyxuYzw/10tdWy70lZnIGb1DyZ8vCLzHDtoNxgQnvDXTaaWgHdniVdlIn1amx5/1vV93YXOPSDEtTIJnd9jhB8JdSi7ozTh7ldxdSDwIsBqV3F7oGoMhl2Wh849zPf8CYhRntCZgoGVp6BzxL8ijRQHNhSYrQHqHIgGe1s4iyISjdwhJvzzZ9pO8iA8gXQizYD2tDJy7AAxYRNtj+ZPBlvpoXsRfthBlbmROeEDTExcm2j9YQnwhpxc80ebY+sGS0H0xe54FlqPt6POCurYjqzUANAPxfi3+9XzgHfGiSIF0wFjhAh2c5wRgW2wt6s1YjUpuH2HFBbVmr5K7C11jMOTySuj8foYbTGeGKwiOZgQK2L0kdB/tK5sy6YqlhT4jglUkeNYBFj+eH3iPAHgk9/Mrk21/6BRdxnC4+w5GvDW2D7aYP8CibY8W/69a6GvztCbO2nDQVkawT9HjFyfaP5UknpneBEfbusi+M7e2Vgnp5FAr/lTeBl6HhVa2ADCAmIForxZ/XhS4T3Zjd7c0adEcA7ttYFDucQKsjbG1pDdjNSu5daDsvL5iQO3X4oh3zg2vHlZHzi8mOeE3wLAovaHrR8DGZaHzQl/Yk+ky26UuEMEpE/q8Ri3/2hjIBwEcbMPskrYvTaQ+nRddRoJRwgB3uYAC6S2xfXAdsFtZTft1+O8ywVfkzgeYETMA0VFr/CYln74k0f4lj3gS0lE6+1aGzh259VFqZdxvoFQfIIKT3ew5jNb4O4+yo9qW2Noy0QkjdC5JQRtiq1d1NFu02NpP6E61vDSw1zhf+o4+dcC0swgeTyU+OtkJLywVemI706YVofPQPi27/OVzvL11FqH6cOeozAKFwBhpR6MSpMuKiAc4hDH55xJBdtFrHsyIWzQ9nn98VWS/MsCKHy8Gz6V0e8SIgbBJib/3k/qdGQ+sjIEtrVqsrJH6uowwAAAGSHXjKDvcsjZy1gLAIKkGZAr7HvaL26fF317wvT8di2rbLwfuz2d5/mjBKEV6l5oOaPHg6shZ1ZtxWli0N2vxz0wbTA84GAL9XG87ZBlenezTsuXJVOKDk5zg4iLBo1o1rVkeuo921cWHMnOI46U1EixEgfotuaQnHWm9E2AaZUdjHdJFZcRDC9VrIILV1dSHGeE+LR7MP744dOdVydQcDzyZAIeBQANt+zU9Vin4agYiADJgWqlBQRnpy4lgZyJEvHor/kSjkjdnC/KOsKPR6EFqKwPhHiV/vyBw/92bziNdsSx0v3+GG3yTGC4yWrNXiz/1plAwAOxQ1p42Dp9N4lAbTM3w92vxn552fDK8utmhrN3zfLppvBNekiCuP6DFsqWh80RXnbmOt9YkSDuF6srlcfCdtcBitB2Nl6TdCqHHUuGIVbsrazUj2KPkf/OPL/S9f1+UbJ9tM4ZmtUYBe9s0LS4TfGmm/p2dYlrgCQxMpjszZe/jjrKjL+xU8j3Z+eFIO5oKoIN2FNo4Y6C9QcmfLA7cp3tbo6XzWISVofPNiU74NeR0s2xQ8u7u5q2FWBfb60fY0SoXPC63W+ceJX9/LDTRcPqzMbY3tTHdNNaOLnOIBzRrsWhJ4D7T1c/58V4/lQldii4K7B6ED31ug63RTjhBMpwqGU9H4UjWbrVmh5IP5R9fGHh/mJtITZOMfgS4DIQRsClk7CgGzslEuTotTI9XSH22RzwpGx1tAbUTnPD27Upe52c0e5gVn5XvBOpCa9o2xNY3V4bOkiOtnZlFgfT6yP76SDv6Ag51fhKbYuvO3nZeWxE6L9dZ8Q6bMTjr4NKMYHssf3k0NhqOjj5vQ51iEb4YeB08ofm4pO3Zrn99ieDXEGClmBa9FLh39aYjDwBUC1VRb0dTQ6aW9ZG1fLQdT3eJy3couXRzbG+tlXGVBCoOk/rDE51o5sbYWjbb82+3gAE54fUFyXpGc8LqMs9OL78QeL/pdAMQnkgl7hplRw9XSzU5ZNq3InSebWPhF5N+YKgdjWnTonljbG96XbLt+/kpBwQ4Q6z40qwDJkpPbHqyKPIPaLHuWLU626tl84PtyfePd8IzEsT9dii5LNdT3Rue9r3fneEGm2uEupwIslHJhxcG7iOHv9JgSNPCov25INFtxEsx6cQsz7+piPhcAGhjena+7/3kAIu23txrgIxrBlnxRJ+paXNkrRntRDNsoGhLbC1qUNauYVY8jA4zUSFAjLGj8fu02D3DDb4mgLKM48Pqrda0MT37ot85ykSB9COpxO3j7GhipVTjUky7lofO8wGLqEKofw6y4pEHtNi9Jba3vz7Z9pv8CD8C5DA7On+bsv4EAIopRM9qi3GTFmuO1UJju7J27mmX1493whkOccm22FpyJF1JAOCJVOL7Z7rBiiqpXssMtVvL/7wUuE8dCzsNrw6atDww7zAFq6uEKpvuBh9MEM8AoFqYHn/O937h97KF+hArGlQr1dhWLXY2KLFptB3PEoC9Mbbm71ZW00g7moS0I7VTulAWARTVWdEgAdBkJ/wqAV5mp1d2EQ0MoKDW8AEtHlwUuJ2KyIag+OH25KfHO+G0MqFHtGqxdXnozI9Bqlqoe2uteGiTkg07lNx1ZbL93tyd8OxtR9vROS+md84RAz1q0ECAtTO2Vh+t8yXLhtjeuFPJ68ba0QyLOLExshf2tqlAlidSidtyunWmdijrviWhO/9Y2Gl4dbBbWU27ldVtV7UBMq6e4oQfdoknMxDs1+KBZ33vd719J4Zb0bAqqYY3a7GlSYndI+x4JtKtm19s0vLAcDuannFudDm3sQj9q4Qqq5KqdqwdfQnpDRMPgOil1ugmLf66PHRfzr+mlUXqofbkzROd8Kxioev2a7F+Rei8rEB6gIx/XyXV4D1KbmnVou01idQfC6Q3ybF2NG1xJrpXpbXmsIXTCXC2x9a6o3W+ZFkROcu3xda7RzvhDABiXWQvaO6loxdIz/OeSiU+nenWOVsx9m2N7b+tiJylx8JOw5HR5w6YnnCe5384STxbZH4hJ8Azzvb8H74YOB/Zrewe5TnOdP3L+kv1nkzoPA2zYpfTL5WolQpDrfhfy0PnPhQuRnsQIsh+Up3TT6pzbKCuUF5iF9cd7G4SAzvWh/YPV8VdtxtkENZEzpo1ETrUwmllkcoNaytU+4AIlNkJBgDsUlZjiGC9wxiVTQfKpAAQkO6MwpxuazvAiq8KmFp62kb3cMQg3dP2393BICwKvKcAmIWQ4bhxruff6qbrQFkAUAQ+b04iNeY53/tEk5YHejLGeV7qHZVCvymjNWKEFTuZIrOyVqrr96jo/zUo62Uc3lEha6U6a6gVjZFATaEiwkC3obAqAjatCu3vb4idTV3dhEFYETnLEaHDO79Py5Z9oTw4ucmvi5JBiBwNWh9b68c64T5i1OZ0d9PI6GqO1ljDrOh/FPCbnrbRPRwhKF4cus8f7TgKpF8MvIcAdNpZMxiOBQTGLM//qgUMzPxOliXgiy/0UqOe8RO39iTaisC4IJG6qYT4IgY0pKLRdjqyBAD1l+qGbSr+YUqLfTjMvAYAD5bx9EqpX9ddtGx3YfchY+3S0PnudmV3WedGgfSy0F0EoENq4B4t9+0J006MTERQoQUciRwNWhU6S4ZacZzX3U1n7DqoPQzQeCe8QUb8682xve0w30OPaGcRZNLQj4oj7dZpMPQUG2yd6QbfyrzXgoBEudBvuCjRPvwZP/HVnnSatcDiwkTqFo/4DAA8UCoJG5KBEAD6S/W+jbF1h047ert1VDAQDbOiqQMs9V5BPa+blKs1PmPposD93l5tNXd1fgxSmflAhznBDmXtyUbOl5IuQhfamDuvWR06LwxIpDoUrM2maueuqRhQZ7rBjctD5zfHKjr/AIu2hYH35NGOc6TdOg3Hj5O+0noJ6WQR8TkiPy8QKJvlhj8bb4eTDjfGABlX95fqPURwBCGRiRghQfAEwcnkJV5RLnRVwFhVqDNIFmbomHEgSVywKFTOeYWPA3q3kn/rzvnSG/Zr8Ximds5BNMPfreSTucfm+97XQmBVpqNKFAObN8fW11JMC7ILJCIIlzBulB19cawdjj8W9hkMpwoDZFyTKQB3cJJPBCGB/ud6/i9GWNHww40xwoqGVwh9dY7WuEhrTSKrNdVSX+sz2hWwu1BnkBxiDfiZNoS91moGgobY+nN3zpfe0KLpGWZ0CH1lINqu5LOH/k14KXC/EAGbsloTAms2x/KOgLEs6/glguUJTB5vR3cMtaIhx8I+g+FUYZQdjc44VTt0uLAJIy5IpH4xSMb9DzfGRCecXkx8QUZrvGznkZy/O4Ol+vD2WG5gIOiiMwiAdN0RSter69ddqnJXkXcaaNsS23/ozvnSUxiEFNPC/HkYAdgUWwfrTUWgeHno3BoDOzKdJCOfacnWWH4r0FjNDJ3p9mEniM+c4oTfrpVx1dHaZzCcSox3wjMISHSoj0aQHmHqRYnUj6uEKj/cGNPc4IJswxRB8IhgZ94tL6s3w6z4k+tCexEOHynCNnFZFxs6h0UBTRtj+4/dOV96ygEWbSGwPn8eRoBYH1sv5Z63OrK/EDP2ZrWmnfHCdiW/HzI2ZbSGBMFLEp9zpht8N+PcMRi65KR3wBQJnUSBF5ooXQF7uB39Hx3mfR9ixdPzC8blTyQIsPpb8RkvBt7XA8bKzOJB5ztSGIg2xvYDnJd73BMyNtsDpLrxLNe/uLfXF2K+7/0jYFqmGYFmtDMjOqDFQ8tCp0PUSaOWzQ+0F31mnu9d/3zgvvc/7UU3Lw7dF3ymTQBU7qRLENyhVvTOY2GfwXCqUCx0Waa+Ugcy7603xgk/fLgxBlrxWfkTi3ytYUDVWWrqosD9YgRsyGgNF9AatSGynkQPQl+7sDk5xIo/McUJZvT2+kIsCLzfhsAGzfCzWtOoxZ/zI1galLXrv+1FH30ucN/7rO+954H2ok8tDr0XItAe4NBuNZCOqhlpR28+FvYZDKcKHnEZCnXAoXT3wAlOeMPhxqiR6pxCHc9yYSCut+OxS0PnszGwPbN46KA12cL7W5Q1H4eJlCm0sUQESELZCDv6/Bg77FQH70hYFLh3xcCOHK0Jdyj50x3K6lAIe2Nsb/5ve/LGFwL3xqd977qHUskvvhR6z2qgPW/BSQRYY+2ox+2tDYbTATetNZ3ea0rXJSif4gSHnetXCjX3cFoDgKotNWBV2lGxh9OtppGnNZoBf4+SrxSyKZeutMYi1Iy1o68OsaJBnc/oPUsC91sKaNSMVKaldbA5tu7cr2WHtPPVkbPqP+3J6+cH7k1P+t61j6SKvrEw8J6kzOZ1jo2CAHe8E5x/LOwznL6c9ClIu5Tcq4ADFtCv0OcSKKsUurxRy+aRVjRimB290SKuOaDFCy8H7n/aWQQx0I4Ck51cGIhippZ9WrY8mCq69ZJE25e8dM/5bCgrGAg3xdY3NsX2lmFW/Eip0Jfk5ilnw3M1w4+BXTajvtCOkSC4tVK9C3ndBjo/G4sz3ODCKqEuZFDQoOS/loXuS7nnhKD4oVTyy0OtaEiZ0AN2Kbl+p7L2djVmo5b7c//tEg+mAq0kHcLYK5JtP21nWro8dP7UFy3tDIYTyebI3jjO7rq2mQ2MEGDSIB5nhxPqrPhKCS7bp8UziwLvoQgUx0wpLtAyPQ8OmVp2KmvvA+3yY5cl279rA0Oy72FGa9rWRPZt25W9czxH85PgGdSxhXb2P6EC9ljp4mqdEAR3kBVfuyR0u+085oCt6a5/abnQ5ylQ85bYum9V1DFKr5VF6sH25CeHW/HwYqGrt8XW6u5apea3h3fSWtPB6U8EkQTPvDzZ9pM2LRYsDp2/HkmOs8FwKrE5tlYMK9BtC0i/Ex74YGTvJCeYPlCqKwjsNmr52EuB+4QCacXUyswqt8tgAYTP1LI5trdtj+WHLk2mfiaBqpyweShg34rQ+dK22G6Y5IT5qcpZNAORZuzPRO50thtw6q3onasj54vdPXsRaW+qG1xRKvTMmGnXhsj+e27HESA9T/lve/KDo+xoTIK4dFNsrchfEGVhEHblzXlsKlik3C4T+tLLk22zWrR4blHg3tvGwu/OVoPhVGdrbC0ZIFVBjSCClRR8BpBO/ZvqhOfUyvgSANilrIcWh86zDIIGtTJzh82TTmMBVrsWLQ3K2tUQyw9ekPB/IdK1MQ+uoWJg5+LA/VKDkjvHOlF+qnI2zUhxppOQYJR1oTX2KDt885bY/n53z14uVPFkJ3xDkdBTQ6atayP7b/l1KBuUtWtnu7xhjB2Nt4kT6yN7eVe6wCDkpxbJAmtTQXCrpX775cm21zVr8fh837s/Oka1NQ2nDyd9BAyDsC6yv5XZuSl4ShtT+wQnmDLeCb+eJD7PJYytEvqd5ydS37TAYlXozAc6R7McHCBT0GlVZM8D0ilLHmFiXioCCMAuJTcCwHOB+5s2pheZEWlGoBitAWNZxNgWMlY3xPIPGjjQVdivACpmu6k3dPfsFyZSHx8g1fs8gYkJwdOHWfFnZrn+FYXO3RTbW5aE7ovdOV8KcUCLxZo7FrNjTk+mLMKAEuLXzHSD75cJZcLpDKc1ISjeHFvfZkZcSCsYaNUgPsPxzxtlR19KEM9yBcbXSH3dRYnUFwmMdZH9NLpx9mbyhIMVobMIAEbY0UgLGFRAa5ytsbURAF7wvR/4TEuzWhMz9gWM5THQ4DMt2xZbv2Ug1ZW+2UD9ma5/QVc2ERgXJtq/XC31/7oC4xLEs0bb0W1nOP65nb8DwvrY3rAkdF/szvlSiFYtXtb5KUwMCELCJgwsE/rycz3/2y7pIwpNNhhOFfZr2dag5E+YoQq9txpoBoCzXf/1w6z40wnB0z2BiQOkev8FidRHAWBjbD1cKGIvCzO0AhrXRvZqABjvRGcIoCTXYZPZBU9siq3NwMFU5TWZqLxQAXtCxoqYsSPFtGibsn4BoHA3JwIlCFMnOcH0rmyywPL8ROpb/YR+u0sYkyQ+b4ITfmO8HU7uZH+6Dt7qJaG7oCvnS1ekmJZlazTkfB8QhGKbMLhC6KvmJlJ3iK672xoMpwU7lLV7rxZ/7EprFKMRAOZ4/v/WWfFHPIGpnsDUOiv+yBzPvw4AtsbWvwB0uTvFDBUAa7OF78c50bkEOHmRISDA3q6snTmpyluyKT0K2BForIqBHe1MzzfE8mfoWmtEMeGC7iLuikknzvX875cLfbVLGFNMfOFUJ/xuoVRyDeKVkfPK0tBd2FunbMC0Kn+dx5yOCrQJdVVCv+PCROrW3oxpeHVw0jtgAGBV5Kx8xnev18C+3Fw9zQgOaPGQzyIcasXvzeZAA+n0JAvoP9EJZ7ax8JeHzmdQQECYAQ0cWB46n87+ki8Vul+hiQ0DcZnQVQAQsIgeTSW/9ZTv/e9LgXvz9lje7RBGW8BAT2DKEEt9rJ1pYcTY0FUoXbXU1wyQcU2hZx5iRYOSxDNzw/4Ewa2R6hoHfMwil5aEzuMx0KAZPpCetBEdSpsggiTAm2CHxyRlymA4mVkSugvmB877GWjL/aWqGUGjFvcSGIPSxeNytcZ1iMeMtqNxe7Tctyayv1iotktmt3nPS4H7qexuSDFxNQo4bBjQpemWjmhh0f5QKvnleb53/cLA/VCjEvc4hFEWMDBBPL3eij9xQNOjMdDQldYMkuqDXeUkj7WjCQ5hVLbDUSaX2R1kqfceLr2zNywNnfs1sC/r8M0Uz8y10xJA2UQnPPuY3dRgOElZEHiPLgvt/2Ok04KyxzUj2KnkH13SdrVU1+R2HhMEr4h49mAZD9gc29s2xdY3CzqL0xOYrfN977PZtrOJtNZ0mvMR4DrENnAwVfmW53zvhvmBe1OLpidtwnCLMDBJPGuIFX98n6b7FKOxC62hYVb8SbuLOcokJ5yViaBxsucLgjvMjg6bctUblofOHzXQmqM1+cWDHQsYOC7dIcpgOK151k/8bW1kfZHz1kCaEWxV1p8qhCopF/rKvPWGVy706yuEKlkVOSt3KPGzrrQmZKx+LpW4PXvMI64mdOyYCAASKM/+PZOqfPMLgXvjC4H7vhTTCpsw1CYMLiKeM8hSNzdp8TfNaO1Ca8QoO/pUV3OUSU5wkQBKRWZzK5saNNKO/vfw31jPWRXZv2Qgla3Fma81guC4xBNNrTtDPqeEAwYAmrR1YJ7vfaiN6WnNSClGc5MW9z7te78EAAsYmH8NAYkSoesBYENsb9ql5K86RXsAwarI/vKG2N6UPbY9tjYVaqNGgDPajj51ZbLtzxcn2m+plXFVs5atO5TcNdhSN2UWZQJIi1cx8blLQufr7YznutidFnVWPLXQB1VC13exuyWqpKro6nvqLT6L8LFU4hM7lPxpi6bHuEBrR0FwE4Lrj9U9DYaTmR3K3vNi4H44xfSiZviK0bhHyd8853t/TxC7AigtcBmVZ7RmVeSsbNb093ytARC+HLif266sg4Uqtyu5uqtidGe6wTevTLb96aJE+0fKhSpp1HL/fi32V0v9v1kHUHYBUyb4kud971Nhuoh4JxjAMDuaUOg+ZWm7O/0uEEB5dmF2LNivZdsTqcSHdiv5qxZNj6FQbS8gkSQ+JrndBsPJzobY2bQ4dD7iMy3JRLftalDy7oWB91SV0JWFrmEg7idVPQAsDd0FbYwnOkWWAf6zvvfZ3PbIu5Vc0cV4bRcnUr+8Mtn2hwu89vcXkfb2aLkvZorLBV+VKbpJmaKbbrng1z+V8j7YlcMX6db0BQuWlwhdLwiJ/OMWcEzf+V3Kanza927cq8QfWjSeQOE6WrJYaKM1hlcFKyJ36YrQ+VTAWKkZQcTYtjm27lweuotrpBqY75wB0qUZaqQaBADzg8TDAWNxfnFsBlofSyU/d4DFwSi1Ji1W5K8lmMEaOHBlsu0Pr0+2/b+5XuoaG2ztUtbeBHFRMfF5WQfQIa3Rlzztu+/XQHMhrZFAeX+pCm5iFwkeleu8zoxLDvHQHn9pPWBzbG971vdubNLiL20a81A4UkiVG60x5HHS14DJZZ+WLY+mkt8p9FkM7LCButxjDKRatdiS/ff8wP3v3ERqcCn4tRnnBu1U8udrI2dt7nWtLFI7lfxFrVQ35CyONCNdAAoAkuCzz3KDcQ+nxHvLhS5DgZoPDMS1Uo96IpX45uXJ1D/QueiUJcGdvMQA0Khp85DCdSR0ozo0qToWBCyiBYH3GIDHXptoL02Az8zN9dSMoEWLgpM3g+F0ZJey9j6csr6WfzzFCDRwIHcnJwPv12Jz9h/P+Yk/zk2kqovAszNao7fG8ge5zhcgXSelSUf3VAj9pkzdGIW0TghJqASAYvDcczx/9APtyQ8NsOLBKFAAnIGoVqohLwbe7ed5/u/zPyfA6SqWZb+mrTWyc263BppDpq6L4hwBbSz8FwLvAQAPvC7ZVu8AI3M/Z8Dfr8UxaUttMJwKbInt7Vti+wv5xxu1KFh3jQCrMWde84Kf+Mk5nl/qgSczoBgI1kf2t/JrKW2M7c3D7OiJYvD5md3pGOmNJk9mdolLBF98npeqfzBV9NkBVjyCgaiQg7hc6orlgf3VqW70owL2uVFn5zMAoE3TtnKBSOTVnYuB7YXOPxr2a9n2XJC4j8C4PNk+qUAdQd1ktMbwKmJdbK9fF9u35B/frWTDWDvq9J4TYO9RsiH77/mBd+dMz/+swxjJAGugZVXofDXMq2+yInSWDJJqqQeeLAieZoSEdIRrtvNbmdBXnp9I1T6SSt5ZLdVoBnT+JEQApQzSGyPrOyPs+LYCj2QHTAVThto1NhYRzsvtNMcMDtONR44pjVo2z/MT99hg67Jk++87NWIArN1KbujqesOrk1PKAdMdm2Pr58Ot+NZM3iFl8pd3Lwudg20LNYifSCV/WibU7yuF7tegZEPAouAC48XAe2i4Fa2us+LzAegyoS8ThIMh/EQQxEhMsKOZKyJ7fhdmiQNa7IghNNJpBp2iaiRxp90gAKi31LnI25Vmht6j5O/zxe5YsjqyfzPFCSeCYRPB0oxAAbuXhs5Tx+ueBsOpAoPQoOQvB0n1oezuimYEIWPt6sg+WLQ2AsWPppLfrhTq56VCl2+Lre0xqGDntGf8xJ9H2+HLA2V8rgaFFUJfJTrWhLEko99YO5qwS8mt1MVEaZ8WO9vTkxFGZ2cvu6RLCt1/gKXOy434y+w0qe2x/AV39sscM9ZH9s/G2NFXCLCIIDXDj4CNr2Tq4xgMr2YCFtFeJf7QT+p35WpNiunFrbF9cFHUyiL1UCr55WqhKpJCF2+Lre0KVLAO1ROpxN3j7GherYxnaiAoF/yGXK0RBMcBRtVb0eBmLXZ0EQksm5RsKha6CIW1RhcL7gdgc/61VVLPJXRYEAGA2hTZv+jVl9MLGIQtsXX3UCu+hTJFQTXD95lezt98MxhejezTsmW/Fv8pFfrSbBSKZvgHtHioScsD2fOatDzwQHvRp2tl3M8ldrfG1vZCcwQG4dFU4qsTnfDMKqGmaSAsF3xFbtMSQXCT4LOrhCpr1WInpOq0R8RA1KZFKpBoR2GtUZVCV+faCKTr2pVLPg95WsOAWhfZvz3Cr+mwRKB4h5I/HyDVjdm1qGb4LUyPZ+vjGAxZThsHzPLQXRwwfXaoFV9tgatbtHjx5cD9V6FFz34t23pS1G1DbG/aENu/ITCuTLa/Kf9zAmxP6H4+i7BZi3+UC/3G7ESJGWEIrF8X2+uB9M4uAR1qMDAQKRbt+eO6pO0Kod+c3zEEgNqdbt923Ngc21sDpg+OsaMrXOLBLZoWLwndR3wWBYthGQyvNhYF3lMpO2wcbMVXSeKyZiWeXhR4DxSaiDRpeSB/clCINZGzek3krK4UqvQ8z7+qwCmUFLpfY+Qsb2V6ogg8N0drgnam+TuVtTdTe0EhT9sZCBSLTrvSFUKVlBBflF8sjxm8XVkr888/lqyOnFXtTB8ZaUWX24SaZi3mL850eDme9zUYThWeCxL3T3aChgEyvkIAbqOWjy0K3McKnbtHy33Q3UfHMggrImfpishZOtSKhpQ54eUFIlxUidD9lofu4nF2tMxN72I7wKG6e60sUi6zg3QUjd3xHggUUyetqbOigQniKR3a0Ke1Rm+OrePqCFkaugtbtfjkUDt6ncUob9Ri3uLAnXc872kwnEo85Xu/muaEa2tkfCkA7FbWgy+HzjOFzs3vOlYIDeKl6e6LCybY4eQyEV2S3x2SgbhU6IpVkb18mB1ttxj1WYewZvh7tfhzDNIRC5+BML+uDANxVKBswhg7Gm8BdXl1n8CMeHNsbTr8t3HkLAi8x8bY4bbBVnypYHi7lXxyaU4ggMGQ5bRxwADA2shZuzZyvnGsx2UQImB9gXB5lelnj2d8748z3GB3tVRvIMA9oMVTCwL3nuy5+7V4qEzoy/NyEvXaTOelXMozxTc724GoTOiBAI5rKNtOZe3dqazfHM97GAynMisiZ/mKyFl+rMfdp8UBBezPD5cnQGyPrRUA8GQqcfdZrr+lUurLAFCzFg+/6Hv3A+kdmDameUnwbEEdJjtqVWR3akVdJXRNoTQDBoIqofvvUuhVV7XesjW2G7bG9s+P5z0MhlOZpaG7YCm6byN/JGyPrYbJTlioDpO1NeMQeSKV+NpZnv+mcqEvYiDaq+S/FwTug0C6VXTAWOkC47Nh/pkub6nVkd0pZblC6IEMxIUWYVVSVbXGYtuxfsZcMhtqdx/PexgMpyoMwkuh+wzgFnS6HA1bY2v9yAKRuwB4e2xtYxCeTiVune4Gby0R+lwNtO9W8h+LAvdJANgcW1smOOEuYgzKdnFjho6BHVtiq5NulKTXSZ0gwCoWOtmsZeuxfL58VkfO6tWRs/p43sNw6nNaOWCyjLTDkf2EHt3KtGNF6CzWoKNu5bE6dO6a4IR3AJAE2AwEbUzzsiGsDML8wHsUwKOFrn/O934/J5EqKQafj0zu5PrI/s7eAq1cMzVeIuR5ewmwdinZIWd585PJqob5iUH1c9u2DJzpdxqrOzY8VFSjIhIjLmvdKToFGhsMhu4gMMbY0fhyoYfu12LLqshefrQpOwzChsj+3kg7+nwm/F8yEDZrcf8OZe0GAAXSzweJfwL4Z6ExnvO9H53j+U4SPAMAK6BxVeh8s71ABMxOJRsmFPg9QIDToGSHFIJ1/y2u3bPcrR15Rcv66vFhj9vCagWsf6C4v+WyGnZx256eXmcwGNIIMI13winFxAMbtVi7LrLXHq3WRKB4ayx/MMRSH0Naa4iBaLeSv8lGCIegeJ6f+AuAvxQa44XA+8ZMN/ikB54IgGNg+9LQ+XqhKLYdSm4YZsUFi3rvVLJDeP7Ke0sG7d/oVI5764G1ZfVRj9vCqhC09t8lA5P9VGrInPaCNXQMBkPXWGAxwQmnJ4irdyn5ysbY7pRK2FsOsGjbqeQv+0v1HqTnGxqA3hbLu7IlFVpZpJ7yE78F0ClFiEFYGLhfPMMNPu0wRgBACKxbFLhfL6SDe5VcO0CqQnXtWvZrcdD5ohXwyh/LhrbvkcUT37V/dVGN6nHNuzhFYu2/iweWDYlae7v2MhgAgLiLMvYA8Pf1015/Am05agSYLkqkPpkgnoF0rqBSwO6nU4lbWgqk+vSWKqHKxtjhBTahfI+Si1ZG9rLeToKKSSeKhE7uVaKp3lL1SaHLN0X26lYWqdzzpjv+nEGW+nBuznIr0xOPp5I/BtIv/1/fUPfhA5vt8yAQsYZTPT7459X3bvvt4Zwpmx5P9nvsk7Wfi1rFYABsJXjvuV/Yc/vYq1sOFsLb8nSy8vlvVL21dYc13qtQm6e9r/mv4992YGt342oFxD4Jp4hP+RQCrYAXvlU1ZctTyXMsj9smXrP/0dzv59XC1SNe/teJuM+ppjU22LookfqyQzwK6VpNOgLWP55KfKGrulK9oVbG/Uba0fkWkNyh5AtrImdNb8coIZ1MCO3tVaJphB2PcoiTGyJ7ZSovnfBsN3VltdTXZHemGQj3aXHvM+mFF1KN0rr3jYNvadtlTQMhBsMaODP1+yt/13Df4WxYeW/JoGe/Uv352KcqAOQU660XfXvX7fXntzdmz1n77+L+C35Q+dbUXjkyWaPWnPWRxntGXNbWbb60VoAOSViJU19rVAiad3v1jIb53lluiW6a9r7mR16NjiqjNYUpJp2Yk0jdYQEDkHaUsM/08mOpREFHR28ZLOMBw+xobibyZd7GnI6QPaVcqGILsPZpcWCkHY2RgLU2sldGefXq5nqp68qEfp0geJlomXCXkj9/MfAeBoB96+zE/e8a9AV/nxyZ1Zrhl7T++LU/2PX44Wx46SfloxbeVfkZHVEJAOFVqNWv+/mOr9VMCg4uuJb/obR+yS8r3uo3i7qSQfGycz67955BZ6eauxtXhSBWRKeD1gQHhJx3W7/Zu5d5UxNVaueMDzc9crjnPx0xWlOYKqHKzvb8bwmgDBmtaWV64olU4u5jUQ9umBXV11nxuQyojZH91DZl7ejtGJVClQLAAS3aRtnROAb0usheFedp4WsS7R9LEs/KaI1mINoSW3cuDt0XAaBhgVf2wPsH3Ba1iwFIO4Ro/NsP3DnnS3sOG2343Nerpiz7XfknWcNhDVncP375DX/Y/q3SuvjgJteiH1eMXvGn0reEbaKmfGi0cM5Xdv/9cBtXKkx/ydLporf2KUTbbmnP+0r1nKbVzoTiAfGWmZ9ofDRXi18tdKc1p5UDZpoTzK6z4o/m9rJnRrRfi/886Sd+1Ze25VIuVPFsz7/NSndtUgRYDUrevSDwOkwyRtrhyHorvlQAid1KPr00dF7MiuADN/a/ZPMTRTewpoNRMiTZH/fWA3fOvW1PV0WBAQC/PGPY98MWUQ9Qpu4Ds/S48T2LNrxHOuCGBV7Zv/530I90jCSYLIA1SYTnfWHPJya888CW/PHiFIn73zXof/a84r6eFVyvXK856+ONP5zw9s7n5rLl6WTlzpe8/kMvattysr2Yf3193fua1jqvYQUXgCaBeML/7P/6eV/Y+6oqEGomKoWZ5fqvq5Hq3bkphZoR7FXi988Fifv70rZcamRcOcMNviqBKqQnGWJTbH0rk5t9kHF2OGGQFV8MQOxQ8rFXQndJ9rO/v3XQO3Yv8d7Emg6mDpDgYMZHm26dftO+Lh1DcYrEr2YM+6UKqDKdgQ0ArNxSven6RRv/D0hH1Tz6sdrvs4KX1iNWZMG/5Ic7PzzsNW2d0p9SjdL657UD37NvnXMxa9iJfmrZ3Nv2fP9wDosNDxXV7F3p9ht1RcvGipFRqrtzTyRaAX98Tf0trTus6azIA3FEAmrGzU2fn/6Bfa+qEGajNYWZ46WuqRD6KsoplqsZ/nYl71oUeE/3pW251FnRoKlOeDsBSaQLZvKayL5tVeQcrCVFYEx0wum1Ul3AQLgtth7KDdX/0yVDPty80T4fTIeKkAsOL/rWrg+PurK1y8Vay3bL+eNr6n+rYzpUZ484LqqJl1w7b/OXAWDJr8qHP/+Nqm+whg2QAHEsbW696s/bP1Bo/tG8wU78930Dbtq/xT4XgCjuHy98zXd3/WDAdL/bml6r/1Ey8MAWu2zc2/avL+6vTpraeWEbiT9eVH+73yxHsiIPgkMSiOfetvsT497SclzTv042jNYU5jWJ9o8WEc/NpvkAaa1ZF9m3r4icpX1pWy4j7XDkODv6Mh2K3AuWh84Xcp3HAkxTnPDsflKdpxgtm2L7gQ2xvTH7+e/OHfrFtl1yKkAHn5UEh2/867Z3104JOnSQy6VhgVf2z3cN+kXu2gvEUfnw6Il3PLjlLgB4/htVk5f8qvzzrOGkm19zZHnc+M5HN3+wUJTNzpfdkkc+0v/m1p3WDAC6tC569nU/3XF3d3MVrYCV95QOSe2RyYnv2r/Oq9DHrTlLb2nZbjl/fX3dnVGb6M+aPBIckIR/6d07Ppq7+fZq4IgdMOXV9rLjYtFx4g8/qKm77IKisvzjDbviaOJrtp40k9nf/6Cm7jXnJksd+1CJKD/QPOfNDWvWbYoiABhWZ9mfu7mi5szJXtHm7VH49R8173pukX/wZTzDnzti/Cg30bjLwt4dh1Kq99u7D6wpfa5Lx4cXFzsT9l8wSkB2cGcrxHpt6fMbW+zG1OC2CTW1/vDq3HMYGvvtPQfWlj7faey6tok1Nf6wg+czGIpitbT8odVKxB13jRgoC2uLBqbG1CRVWZKhmSBol7dh97aiV06KXd9EXOKM339+p+8ooiBeXPHAquPYGOako3lPNOlE3KdySPGmE3GfY8WDv+rX/8yJTqcW8ktXhcGF1+7dWeiavuC/v6iqPWOC41k5P8qpgHnyFTu37duf3tEdO9yyPnNjScXkMbazcn0cfu3HB5qXr40PThLO2j9n8MgRJHdts9G8N70uYjD2Jre1bKlc3iHMv60pqASAokq3qdivdEc2Tq+VbHV4YzQUr+g/ryGw2uOhjZOrKlMDinPrjWtoNBZtO7Cl4pVOBUWHNk6uqkj1L8q+mxoasQjVsgFPbuuUacqE8lSNN+DAyAovLrKZmImJtpetadpdsumkcPh29R2lrJZoRf95DV1ddzrStKV16Im4z6k2r3n5wcGj6wfZTv7xx55tP/CWG3d1u8lxoiAAi/47eNSQQZYrxKEf5ZZWrUfP3bIyCNMTzRlTXO+zH6qoHVFvuy+/ErTf/oN9u9ZujA5qzVx16fi6YUo0bHLRsi+dGamheWdi7a7tyZUdHLLtLWoAACRL5I6qYHBpfevUwRJWh8YFGpoXV/x3pRKxHrP/nKElcb/inBrA0FC801u7a3vRqk7O3jH7z6kvjiuLc7SGA9EWLC9/bF3+HIBYUGUwuGRganStoxNOesOdsLlo6bZGb8thi7CfCKqCutL61ikdviMGo8VqbF1dNm9TH5p2wjlR85pTTWs2PjdkXFmJ7BBDrzXjD/9oafzIlxp7Ha1yPJACWP3UkHGV5Yfs1Jqxu1FFEy7cujo7Czj/bC/5qRsragb1t5znF6Vav/KDfbu371QxkH5fL6JLJ/Svi7F1nYv21vRQimLeVrG2pbFke4e0x7bG9mIAKKpKtlYfqEsMbB5ZLFnmzWs0Fg95fDcIGNcwszIRlXRI7Y4RY727LGjr37i/wwMxMHbHzEovKrJEZh6kodHmNodr+7/UnP/8QktUtNV6/fcPK7a1Q5wJltlYvWz/gUTjSeHwLfQdMTSakrtSm6tf6dK5dTrSuHFfbVefdVsDJtXcaX1xUrNrJ+I4Zlgd57Jobub4ZHqW15zT0fkCAESEK84vLbvje617a6uFfPSPNSNLiklaFmFQf+nc85P+RVdd27jh6efD1HuvSZZ/49atCa0IlsNY+EQJ7vhAPaJAQEXd/38T7BEXaOfGAKJWj1LkwlOViXznA0HAi0q9QmNXq6H9cs8nEIiJSpqHle4QW5qzxx125RQ9a4QL1yaITDOE9HU1/vDqpvBAqpF29/nCqFzXJtPR0R2/I8m2VM2lMqSgYDthw5Fz4ABOKVHe26QrtOYOiw2tGY37ODxZnqW0mMS08R2dLwCgFfPFZyfwi7+2t4weKu0Hf9VvVMIlISVhYK20zpnez5vz9r3rl62Og1s/UFx1y3vXSxURbIfxxD/K8b1P1kFpIIrQ6Vl1gP4A0Op7sEMXhfz7DCDle2i3NNywzMlv9iYg4AblbqvvdTzOgipSA4pFzvkCAkJbwj0wKNHo7j3ooE6ohDWleVp/m21JaUVKJ5IDGLh/dOUetIUtdkufT1Yq26tdYtHJpevFxXZryuvcdNNw1JxMc4GesG8fx/WDOhaujWPmXTtx0sxrRg2Xdv9qyxF5P8pEwDlTSpL/eSRoO2ua7d33834jXBckBGFAjXTmzkyUnnnxnjXbGlR855dKa9/zzjUiDtNac/+v+uEXtw8AwIh8gVTY8VnjOOUBQEq5iLTT5Zvi7/cQUwRPlbqU90IJSHL9ykQq6ji2y55VrKuKBQ49kIAgRycdq7nWa6Hmgwu0Ei7zJuoZwySkIFCHec3Qtil1e1MH1viUOuq01KMloWqK8h1UBEIyLk+eLD9Hhr6ltY1VWUnH1vNxDG5sPnmiK86e7iVcp+P6SQhCabGQUyY47uJXwuC1cxJFv/52zdCEl35/+9cUV1x4TrJ0xhXb1rS2af3jr1UNvPKiVQhDgm0zfndnf9z745p0/nUrhW2B7BB5Erbr9MY+y1R5KK2uqoq27ZUpEOBEyU6FICxY8IJia/fe5g5jF6sS242SMn9eUxSU2/HuojAQ/sH1RrmqcMf7E2uz66dcPRu2e3LZ/ORzW2OK+zx9aajfr5ODiiBQlKqw2/bKkyYCua/pNgLm0smfO6XC51576YbBH/7Iou9KyQd/myhFweOP1n/vu98+69nssbe8beWomWc3zEi1W61/+dO4p5cvq2k+kXb+64F77sm1MW0nwpcW9f/NFz8359/fuPPxt4yfsPdtUnacdDU1ekt+++tJP7n5/xZ8P/ezIEX456/74Re3D/SpquJrYkDN4q7uzczQK9b+HMw1yJ3eE1rE2JHXkpRKbdjyZrSn3o6O3Qo0bPsZOWb4tzuN98qa+9F5qRChKPk7OazuYDqGWr3hFkTRTBR2/HFm/Du7sv1EoXftHc97Gr8IIJH3kS/GjnwnWfKk+WV0vHlw6e0nJFR33D++fEppzQcGLx134+Dlt0nK0Rqm4Jfbx3/5+1unLgcAAY0P1y2dOLNs59R9kdf0w62Tn1rVXtnj4rVHS63T7jx6xj/+LKjj+xZrSv1779Af3rp+9rx/TP73jSOT+y8RHUOO9Ra/5Kn/7h364PvzntFvJ/z66wPwj19VB/uv7X9L6+v7dejItv79P/whtwUtY2+87bOUiqyany38HWkuzn7OALMtdu7+wMz3QxCqfrf4/VZj+yWUowkMRHFN0b8a/2fKb3LHtne2lFT+adlvKb/FNiHlj+539/7XjX4qe6z6J/O/IVLxGEKedyc9vo5riu7LH78vKH143ezEit0fIe6oNSxp/66bz76mr+zqC5Z9+/9OiNacavOaD39k4czXXrrxE/nzmp/cPe0T//nXyM0AYFmKbnjfkjNGj2ka39iY2PmrX0x+ZkdDSY+L1x4tZ87YUfWlrzzzUyHyHUWUuvevY7/yu99MWv6b//evW6trUmfltqHWGtHaNZX3bdpUtv41F2/6qJR80OuaaiN89+N1eOqfFaEYMvAmKi3pECH7xMrvfQ0ALhj30c+yHyT1uk2/RcfGBRpSrpHjRn4KANTq9Z9FFM9Ex7lKgOLk/5ND6zoUNNdNzXXcsOvbADp6gYF2qqq4QwyoWQJk5j8r1/4UmvujsLs0RHHyd/nj9wVq07bXobXt3chr7gAhtsjxoz7UN1b1DSdqXnOqpSDNdP3LaqW6Pi+12n8xcG/apaxGIF2kd6ITziwVekQ707ZlofPssah711NGWNHwCU749dxSExk7g8Wh89Etsb39dcm2bzuEUXmf+7uV+A1AyE8fT7URvnDtcCx9vtgXI+vfTZ7XYZ7WQWsOtPbTW7b/BB3XRzFsa74cM+LrAKBWrr0TSo/OHUMh1uto5fbxE978wQ527dwzifc23Yp06mYuKRpQ8ylRVbEZAFhpoVet/R0YBbvkAkhReeldYvCAY97Fqreo9ZuvQcq/CnndNWHJxXLsyC/0iVF9RHda02lyeirz8IPDt93/j1FfTaXkTmZwFFHrooX9f53rfPneXY9cd+11y782blzj26ZO23XtHd986mdve8eKMSfSzq1bSh7TCnm7r8R/u2fMPADo1y81NN/5AgBFxdHgSy7bMCc3PxMA3ATjtW9rApKJv3TnfAHSkTY0oOYrIGoGkALQDqCVqvvdRlIqABADah4AUQvSnZgAIAbgU23VHwuNBylfQbq+RC5MZSUvdzgSRTPQXdQVs6ubmgd35xQ8EVBN1QoI0YBDzw8AETz3P105X5gZevfecWr95neozdsu45RfVOg8w+nB3dsmr/znnqHf9bVoYgYHWuz77976H+Y6X/499d8fu37Qii9MLm58y3kV29/950kP/uLqmnWDT5SNu8JkuDNMLtKM/J9Zvnvb5AUAUOX4Q0WengiCqLCD+kv7bbpYgDvokJdkXH5tI6dmlPws3/mSDyfsuHXm4C+zoFYWaGdCCpKaW+YOuz27sdxyXv29EJTizLvGQAxBqZY5Q+/LHy/qX9LCtiyU3mWlJtYczE8XbaHdlfMlC8Uq6a3cMwC6b7Wm5bz6+SxFCwMHd7kYiILBpQW7zgAANKPkqU1TK/+87J3l/1p1sWxO5S8SDacRP/z+mS/Of2HAz6NItDCDfV/u+fe/Rnw963xx3Fj8+nf/+dLlr19/y9hxTW+ZdXbDe3/8s4d+duaMHVUnysaFCwY0trY4m1jn/BwzWGvy//yncSsAoLgkqqO8EBQhYFdWpYadOWPHJbnOFwBIFDGuuLZRoaLsW/nOl3zIc9upsvzrODSn8UG0Swzq/82D51RX/T8APg69ayGI9ov+NY90Gq+8dDuIOnWNAyCpsvxQOntLWz9orkJ3sWpKl/L+lpru7D8RiP79ngLBR8e5WoSS4k7zuiystdDbd56l1m9+p97aMJejwl2sDKcH8wP3gUYt/qwZ7czQEWPb6sj+Ytb54pF2Xptsv3OIFX+0Uuq3DpTqposTqR+VC1V8uLGPFRtia4MG9jMfKlTLDK2AXVtjazsAZAqWd0AQvCRxfZVUl+U6X4D0GuqydzVG1K/ytnznSz5UWrwXZSU/BBAgqzVCbBV1A3908Jyqil9kPs++a0EAP9xNDZ3SETN60nmuQoipouxg4xNuah4ORrfvH0dxFbe0njDd7wpRU/VfpOd0uROsCBVlXc5rOFaW3rbjXLV+8zv1tp2zWanTvjfvadeG+hc/m7r4Fz+b+r6KypS9v9mNtD70c/2aizcOGjFi3xVZ50b6v4y3vHXVR/7yp/EfOFE2fv7WOb+687uPV1RXt5/JTEpriv7z7xHfXrqkthkAtm4tfaWmtu0sKXO80Bp63z53Vf4EJkuihFvk8CF/68n9RWX5Fi4vfTfvaRoDZknVlSuzzhcAoITXJobV3ax37L4CYTQRtrVJ1FTd19UkiAbU/JC37/gmGA6QWcwlvH+IyvL8/PQQ6OxYOkgcT+eGXVN5x+4D1L/6K6KqYlNPnudYQ0QQw4d8Vm/e/kFE0XkHP/CDK9T2nSvloP4dihwzM/S6TR9CEM5Bencp1C1t19LA2lsKfAeG04TPrZ/93BfWz3qu0g7spsiNdM7v0A/ULRs/yGudZVF6USEBV4Cdj9Qtuenvu0feeqJs/PCqud/7ybgnPlNp++MAUhGL9p9um3jH9qA4AIDtfvEr5VYwSnYs8BnvCJKvuELlR4ABACrr1Y6mT9V3WrQUom1W3er2Kf2vLVrUMBaSVOuZg1bDlgcXAOGwisZ9V437cMm8zW8QreFIVeKubplT/89ocFlzofFaz667s2Te5q+BmZDRmqC+/NfhkPKD9WJYCo1MweEuzCK5z7+w7KG1F5Q+vmHPgdcMv80fU90ndXs4Ycf7rh7/8fL/rPmoaAunI50oxe6W/dcUP7dlQ+vsISs6XKA0Vf/6pVtFazgZDA8E392475rmy8d8LBhR2amOheH04CtfPvdhIfTDZeVBp3nNBz700qzyimBc1oEhJXuC2H7/B16+buG7B3y7y0GPMV//2tlf/fStz3+uqCisB4jDUOz7xU+n3h4GlgaApkZvZSLR2p9ycg6VQtiwrXj54CEtUwuNOXhsvEoO6v9iT+4vBtYu4urKa3jvvnGwZIr6Va6hnEwFUVm+VUtxM+9uvAqxGgLXWSIG1PyHPLdTSDwJoamq4pu8t+nz2UMACOWl3yPXORRZZMkA3ScKOkj5b9BbG65Cg9gs6gZ9hYqTzT15nmMNeV6bqBv4Mb1t5yeg9ThkWgFj/4EP6qTXkD/f4ji29NpNX4NSQwF4nPJTfKD1XWJY3ccomTgp0mwNxxYG4Vk/8TcB/rtNbOVHtkx1gostoC7rwBCEBDGsKU74pkwL6RNi4+LA/cIUN/i8xagGQBHQ8HLgfiXbpCRgWueBp3SItmP4B1isTEBNyB+TCDxmRvS86F+9vCc2yLqBT3FNOJ+bmsfAtg5QVcWGDlpT02+VtqyP8d59V0Gr/nDdBS+nHp7J1HnHhxw7RHnp99B84GPIFC8HQFRZ8U0S4pCzVAof3QdNJNDW/k7d1n4NLLlSDK37GnnuUXf/PRKopLiRBtZ+gnfs/hSYhyKrNXv3fVJ77i2irLRDl0v2g4TesOVOaN0PQIJTvs8HWnaIkfWfIscp5Ag/LTjtHDBZ9jUlOoXEnT17+8R0weyO70AiGQ+s7d/q7tpZfEL+RzfuTYbvvuaKOyZM3FM2uK6l9Okn67anUvbBF+3HP5r22I9+suf1rhv3kxKu1oi0puj3v5v4/2xby9FjGq8GclIGFMItm0sP26YxFxJCU22/lV1+nky0yBH1f+rJWKK8dAcXJd7Du/bO5FiVUVnJy6KirHPL5oT3AFL+lcgPgT30P8QCYIG5H+/Y/VW1b//DULqGEt4iGlj79AlN/XGdFOJ4DA5NrtIL1H37P8nVle/KFQXe2zQKQTgXh57LBeDwrr0fRGX5LSfMZsMJR0Ngb9RZa2aU7pok0THNkAiUdoScOFa1V7adv+hNn5tdtqOy1mlP/Hvv0IaI5UEBvGPT9Pt+PeGxiwBVLAmOYoSKRepbm8+496zSXYNuGLTivNznUIxgaVu/B3tjAyfsuPXc+i4nNmF9eVNjffmvezJW+/SBG4JhFe8ufn7LLIp0IjWhZmEwqmp3h/t5llIV3lNyn39eTmvtgx8DAHHGERyqgaUPr7+9aP625yni8nBQyQsHLhz+fK6T6HgTVyVbRSoamxEayjiyUbRg+2dbZwy6NteW0ic3nilaw8nEmfBrhseKnZInN74nGFH5jRNls+HEo7UoOK8ZO7bxjPzoERKQ1dXtU/8/e9cdJkdxfF91T9h4Od8pnTISkhCIKHIw2QGDA8aADRgHnHDCgYwTNk44xx/GGGOMMWCiyTmDEEJC+XS60+W9zZO6fn/s7t2mCwIRbO59Hx+6me6entmd2urqqvfetMkBeOH5xqEPvv895++z7/a6QMDVH3xgWnd+oOhPf1jy1699/fF9SLApJXTPg+XYMvKrXyy/86ST1/UeevjWBfn34XmUfvH5xjt2Zg6k6zZly4PKQVRW9KCy4teTGUs01b/EFaGPq/7BfcEsRU31UxQOFpCOU8AfgyZXw/V2R6E/nc8hl7GfnmpXHdsvgq6tA7OfQsGHqbnhGaLx4je7GIaRgFLt2b8ERnyVvq9yTdUn8+eiunoOzQVfsof8YNZVV88H5JyZv3vzJj2FNxsKxBZTia2pFLx3cfYIEfSQUCsAvCkBGADo9LTu7Un5qRbpNTHAXZ5WsKB/1dH/sLthfw8MnQiaYlgu0L3KMh/Z00yH66X6aEGZlSLr8cfa7t6ZOZBppGicigNRU7UNNVU/y/3tveLtPVZb2db8GFdVrlEDQ/uC4Im6mieKg5xUXdnJO/q6oNR0oCBrOd/WZN5V11uoNm+7AJrsAbOgivB9kw0u7SqQpqWZuSX7Z8bWMOvc1ft5VFZckN9WdfW8G0o1YnSD3gelWtX2nmPlrGn/fDPn/Wbif6oEaSIMDPgHmUtKZaAU2UNDvjedJO3l1fXDd93Rvi0/+AIAPTtC1nmfOvILzz/XeE1fr/+p9a/W3HzJhSs/dd9/ZnbddUf7ticeb/2155HlupT0PNiDg/6XLrlo5Z/f7Pnng3TdpqaGx8nQu5FIzuB0aQmOmDntLzCNe5FJTXMA9CPg/yMyJU75XggBCCNtvQeOcyBHY+eq9Zsv53xvbheBmcHxZBVbdiEHQzTWCOaqMl087h9aXNA2nliC0mAmwfPe1NK2Kbx9MOT4Bj2UprDbLN4SRYzHhpsH/9k3e3t+8AUAVsXrY6etPvIzz0Ubru+2Ak+9EKu/8cw1R3z6ieHmoZ9uW7b62Wj99R7DdhQlPIbTmQ49/rl1B72lfAZejT8VO3DmoypkDJhbIrPkcLqEQXLwpEW/9Cp9jzPgMuCwLrrs1vA1IFiUZ2sIEOSqBq0/dbw2nD7Y/0rf5+queeGrb0hpkmIYWyM1ImoVZAEGXuiek83oKQSzHljVMzP/kL49tnwk+JJ3DzJuL9n1E57CfwOiUbNXKZT4L7YlB8u1f6Px5BOt/fffN6O7+Of6sUfber/59YM+9eq6mpv6ev1PrXqx4S9f+OwRn9u6tTL546v2emTzpso7lYLjupRQCs7WLRX3/PDKvd9SmW0K+GOioe4RklqUY/HZ7Lolm5ZiZtv3oWnPI+PHuCDaCr/vb8iUJ+RDQqnZsOyjYTuH8mDkK2rDlk+8EfNmpQTHEjXFJUM8MLQ78sodsyAobkA8WVNwNJneB6UcOBpse69dPuEp/FfAAXqK11DMYJfpTVcwZRC2e9qO4uALAGx29S1PW+Z5ESVuTSp6steTf7g/5f+yDXKfsnz/TjA9ygw7FlcqbTHWvFx74x9+t/QtldmmUCAi6mseJSFSPBybw16hASUiiOktF0PKV5CzNUKsh8/8JzKllfnQ4XlLYdlHwnYO5/7Bi7xNHSe/EfNmz5McS9Sw6xXYRjU0vBeKsx0AAc9bWNwWlrU3SqsjDKStMYNW/wv4n82AKYc//n7Js0cctSUmhGcIkYkgeh6sV1+tviWXJvt2QXdXOH3hNw6+FUAJgc8Vlx5wT3NL7OHDDt86a+uWysFHHp5WYoBeL9h1NXhKK0i3HQdqMDKdu3uuyNUociQqUV35g/z0YZJCybmzfsW28wc4TgABfwSxRJ3q2H7qGMPmorw+eF47d/euoEmmI09qzn2Dc7iv/8tQXAcAMPRHxaxpV5Ou26RpFpcPUBKkLHCsSNMGOVNeVViykeHRmcI7ED/uWPbIwdXbT1dgQ2RT7j0m64lI89jcHm8R1iRq42esOfLGcufOXHPkPxYEBu88onbbjBdjdT0PR1oHdvX1RcLWyVXCq/RNKgMx+MS2+aEntl2S+9v/cg8lVrReFj9gNMtGhU27/2PLfygjqZ+LhGM6rRXDwWe2zzG6YiVOSDbyIQGAGD4Zs/YIPr19fmKftnXFbV8rQg9vWRJ8YccX4KkwAHJrAncPfnD337IhlQroKRTuaOUgsudGoPxaHxPskQyeLFgTkV011yn8d+G6axfdc+kVD51ExFou5d7zyHrk4bZJZbC+mVj1YmPk/M83/qX4uFICn/30UX9YtLjvH3ss72l94fmGrjdCHIFtxwBlNowm01519ezJg5GvIRO0IB6MuNTU8E1RV70514Z8voRcMPsyTlt+KKVRwB9TXT17cWpM1ynnV/hg2UeqSPRfoqpil5VAep3d+2I4+hlkArUMv+9m0T79L1m+vhTKl0xRtpxqFFL0wIWHYtskxFSp4zsUGxz9X8sM+1DKy2JnwN7qan9/K+dVDt2e1tvtaSWZtR5I3ZsK/LhButf84Kvbv/v0alPubn7ghl19fbZtE0J4pGmTytz3tnUdguHYZ5CxNcyDkTS1Nn0t3zZQKDgkF875eo5jkvy+hNex/Qikx3SdRrPwkqkPcDJ1564sH/S2bj8SsfjHkbERCqHAtXLmtFsBgIRIcik3KAB4EFR4XIg+eGo2Cm2TghRvemDvzcQ7KgMmldLVpRet/HJvT+AJz6O0bYuhl1Y1/PWr5x9a4hC83dHdFU7/5c+LX9kVwRe2bJ/q7lmuevvnK9sxvFc3f1at3XiDWr/5r94r669SQ8OtE46xo++rWXbuQPY/E0PDX+K0VcIjQYZuUzAQIaIMoZUQ3SiNlBbDx2lrl2WUsGX7uafvcihuRqa8SIft7K+2dJ4DABQMRKDJ1UABgakCUYzqa9cW3E9j3WMg2Cg0NhYC/rKL2in872NLuiL19Q37fanX9j/vMVkpT/Y/ONTyu/PWHbRTKfVvB6xN1iSu3rZ0za4Ivmh9iWD4/k17Bp7ZPlvrTQTqfv/s1+p/88wNdX947vr6Xz39HXPTYN24AzieCD3V+U1iBPL+8wef6fo6pd2SIIZX5U87rRXDAJBY2rQJggpWRWWNDkMaXdG55U69FhhbhmqCz3Z9i1xVSwyDGLo2mDyi+qY1HwCA1OLGDtZldxERr8um1pFeWN+dP1Z8v2n3IZPVM0pASLCs9pq33WJ7Cm8OXni+ceg3v1r21UjEXON5sFMp2X3/fdN/8tMfr3jirZ7bzuLl1fXD116zeM2uCL5wIlmpunr2UoOR6ZxIVnlrN1yqXt30N7Vu09+8tRsv5GQqPG5/2zF4MPJVZBabAWQ2WMLc0/f1ckIB5DNTucUN1dWswuRE5D3EE3N2+ubGgBqMTEcken7WFzMAmEil36M6uo4CAGqoXQ2iJIqJeHXtOfIXko9SXc2/gRICd4uqKnf5YnUK/x3ocPXt6xz9WzZjPTNsh9Gx2dW+u9Yx1kzc++2FXk8bfOhp2xqMvH7VD47Fa1VXzwo1NNzCw7FG75UN31evbr5erd14g7du05fYssclyudEsiobfDGQsTMBMFdxd8/55dqT35fIva+itvppTG4t73A0PmMnb21MqJ6+xYjFz0FuvQf4EU9+VHX1rAAAaqx9Ehk7k/98bZjGfQXcNgCopuofQLEwDRyqqf6fLT8C3mEZMEDGWfnY6ce/Y2vleTjWyKl0A1WGN89cYKl62fGep27T38eKMmVAvQNAJpqZ+W54ajZ37fguh4NnjhXJ5XiiGkqVk2H0eGBoyURZK9TSeAVv33E5mHMOka/MWGnoWtfO3e3Y4N7+YjlKADBg2YeyUr8gIZSY2fZ9tXX75+G4ewIApNgoWpquJEEFBpsMw6K25i9xd9+5cN3dQRRHMPB3MaP137tqvlP478MdAzN33DEw85KJW/5vwvdyb6s2lKpO7dawcbdFw1r9oxs+sOZWcQwzHAAC2MpgaJTdYRUpZ0Hl7a9ekZOoLofASz0zoLiUyJtZBF7onpPYd9rYWSu6VLGVMy4JP7L1Yig2keFbyfw/HwTXrfTtsh3p4LNdB4ELHSRimHpP/FgAf4UgRE5ccGHl7a9+WSSdhQCggvrqyHHzSwhUnbbKSPTQWV8NPd55rkg781iKIau9+trhY+Y+sqvmO4X/Ptx2y9wtt90y94KJW/7vgZnBg5EZcN0Q1VStX77PQCj6Sv9pr76sHwyGDUBmA5Y6ctkcrrtMbd3+rZxEddlxB4YWodwOLnMVR2ONVEQkmQ8ydJvqay7jvsFvIGNfJEq57wCAYJq7zNbwYOQoFEu/AiYSyXcDuJuEUNTadAF393wZnpoFANC1p8WMtp8UjyWqK7uU43yTB4bOgadmQlAvVVb8UTTVv6WlGlN4a7HOMdauc4yygYH/dTAzeGCoHZ4yqbZ6/f4HdtV0PBo9q6ND2ytra7Ts4kBDLijiOPuqLdvGVy8aiCxHqa0R8NQctmzfeJUIFAwMo7rySgwNn58dQ0N5sROdAr5dVi3BkejxZa5jcjT2HrQ0Pk2GYVFzwwXc0/9lKNUMgGEYD4mZ035TPJaor31Vud4VPDR8FpRqhhCdVFP1u/xMw/9FvOMCMG8WWGehKmCIQaSpTHn/m43Fu+2o2/xA5JPRXloKwMXAgNk8L0JP3FUpWBEwtjoRgWFwT/+eYwZSpLQx1m6PEBOWFYiqih1cET5bbek4Bcn0Kcjww+TPxwVRUjTVj+jbMzO4q2c/jicOB+BQZfgO0dQwaceAlfKhfNRY5vgYyOdLyPmzr2DL9oFZjMUozmnLz8OxJTD1l6m2+i+ivmbDZObgdXQdglj8NDBXQcoN1FD7y7dK+WkK/81gQQSduaxs6psKAjCj2TBmXvf4d1K9PAeAV/Fcp7nwuCE8enuVhAIou0DIZ47L9pVwVVXwqc75YwVSlF8fSwlAsE+b8P6Ty1s2pRY3nlHzt5c+rvUnjwHgMGDk5sGAy7ociB0447mRTo4nKu/ZeJCxPXowSxFPLWq4bWfKk8hRfpQrMeJRcmN7WmWk7xMrviEHkn4QwavxlyizAIDeHQubWyK7uXX+Z9Lzpv0ytaRpYpU1xai++ZXjjc7hk6E4pAL66thBM3+VXlCYXTOFKUwE5WPJPkgZoUmV8LyREAJob/GbZse6H6di1ArAE5F+IzBvmF54oVJm913H8nE1eF67Go42Fity5F0gjfJ+jSA5sa0RjfWrubbmNLVp62dhOyuR8WvyF2I2pNxEddUj/gK7nqa2dx+BVHo/CDFAtdW37JRPoFQA5ewjj5Ibi6qKHaiqOJ/T6SCEcMdSGeFYooaT6bnw+R6h6sorJ1MmxUoJtaXzZKRSJ4Dhg649K1qafl1MXjyFKUyEhGtoCiTCmvWW2xpNA2Y3hfz65nW/spJUA0DpsT5DzRoWXRsqxQS2xoDj7qUJfb1LTnmqi4ytKZeJw5BiwhIm2dr0pKqr+Qhv3vY1uO5SoKR00IKuP5OvZMu2bartPUfDsvaElF1UX/MvUVU5eZ+AOYBy9jHf1tRWb0Ft9ac5lQ5Bk/ZYpZ88HGvktDUTAf9dorbqMQqHJsy2ZtfV1JbO05C2jgKgwdAfE9NaflOcyfd2xlQAZheDiRH7BJ/izsD7IWDARo/vEf5Z4C7xpjJQ53DMsRunf+zsF79iGl6bUhDPPRTC9z4zw0jGJB69o2qywxA8FRrzpN+XgKa9kH3xcw4GA7AgZWJcJyeHVKoSyfTJKN29cWAa94vmxmvzHQW1cevZSFtHIpMtw9w/tJeXtq7J1R9OBFFd9YyKJc4qOqygydX5ktwAMF70WQ1G2rir5/vIvEs6J1If8Iajd8s5M3873vVVZ/dKRGOfxohCgreAu3u/x6b5ibdKpnIK/21gETCdMwTxsch8/7ZZjvyR42mb3orZnNO6ev777qpsbaiWGtGLePi2SvzoS9PgWAIP3VJd0n6MsDSLtFtC4J1DemF9d8X9mzpheTNzmTMMKNZEH9mebm4arLPax5dj9r3a36wNJN9Feb9/GWkkWG5d4K7hd839a77yUN01L3xZxqw9ieFjgEOPd+yrDSZ/OnzMvIfLjV8y53m1T+g7Yu/LZtvkrud6lb6SEhGvNlA28AIAwSc754ce77gUgABDGp3RD/nW9V8/dPLicUsdq29ec6LRMXwaZa8vEs7Syrs2XOk0hc7yqvyT4viawjsbKsx69Bz+hKrHYSAISvL6wC10lfk8vSVBvLM/8cKy/zukcUE4IDWil3DXX2vwy2+1wnMEHrq1arLDeHDGtjVUX7uW+wfj2UBpLqjhQop1nErVA0wUCg6NdwEeGJoH29kPpQpJKfh9t4lpLTfklIdYMan1my+D580BYAKe4u7eA5XtXC7GUXYqmHM49BAPDK1EIXmuA9MoITMm39gLFdXds5wHIhcgY6YFJ5KnetHYr+T01v+Md321qeOjSFvHIefXOO7eqmP7bLFgzjkkxduKZ3EKb0/0WaHAjV17njfs+vcBQEFpvXx846qrZgUH3pIg3he//OTK6/ZpWujTpQS/hH/8uh7XXNkEOy3w2B1VmFylISAhhYvyARhqrH2GI8MeCvelHOjakzw03I5goHtC7pa+gb3guotRuIZiADEE/f8U01tvHjnouprasPWHWeUhE663O3fuOEy53gWirmbjZO6HgoH7eTi2AIW2xoLff39JW78vPtY43raugzEcOw+Z+2YVT5xONVXfEy2NT493fbWp47Ownf2R26y3nQPVlm3TxII5X3xTleVeB95RHDBvBuJn8LHuTJwMCR8IAiaa04fiIns3Hp/b4A1AOGxp5376+SsCAXeaprMwTMbyA+P4yk9zm6aESRoPSdWVz4/XQMxs+wH0AiWAHgAm9/Zfztu6f+69suG7OeKocuDByB4oZecHAEGtTX+C3xwxPhyL1yJtvQujLz4BMBFPfpRtZ6xMngJQRagf4dCvkak7TAFIgWhAtDT9eDL9R+bS0/8ZAMHsXDJpxmnrKNU/2D5uv2j8gyhMSSYAUvX2H7Ez15/COxcB0zlVEB9DBIMIgggzTN37tiA15nv2RmFeYCj46WmrLp3WLDXTBxgmY+Wxwzj3kpwa/eRtTXJZ07j15JETFlyi/PorWYUjlzXqJlc1hB7ruKzqlrW/rv/tMxeKmDWmHfCt7d+7pCQIABhy6N0Lr3GrfCNBkMDz3TNlzNorpzxEABHD9L068Al4alI3lFzestluqbiBCQ4TkkxIsSG7h4+eO26QtgCKEXy684vE8BPDJEAjhmFsj35wIt4cozN6MuUFfwgQYDZCj3QcMOnrT+Edjegn+FOqHodAQANBcADzEu/n76kAv+mbePvt31l/4nvWf6O+Vmo+P2D6GEd9YBAf/Gxuj2eStobgUnXVljFPC2Jqa/o6pNiEUb+mC56axz39l6ktnb/zXt30xWK1knxwNL4SpdnFBCJHTGu5AZoc2d3mnr6l8LzZGPULBACThyLnTnwz2YGb6l+Az7wHo35NGlJuFNNbrp/sGOwpwYOR87PzMJAraYjGz+W0FRi7nyeRto5HoV8jwRzinr49Jnv9Kbyz8bftK7467Pr3BkgDSCY8c9E/u5df8UYIE06E97z31fZDD+v4XFWFkD4/wxdgvO8TfTjqg7lY0CQX+oL6LaTHzGQhXbepueHrEKITmUw5F0TdcNx9uKf/UrWp40/ehi1nleOeyoETyUNQqlpGECIiWptvBY0GQLm790Ao1YDRd1UC8HHf4Mcnd0MAtTY9CF1/CoCFjK2xoGmrxLTmSfMcsmX78rhv9Oz/DR6MfJE9r5wwQaZfKh2C7RyAQtuqwVNtPDA07trr7YSpDJhdDKcd74UoqvVl1qxZyXO0x9QfRVXFmDtGTAx7KZrJhmOsodfNNH/qaS8vJWI9p4wAAIaPsdchMQRCHpLxst/vnJHIyVpKhEO/zaWQsutqavuOI5Cy9oEU/VRbfauoqeogn5mU82dfzmnLz8l0NXft+Anyf4g9b57q2H6enD/7u2UnS2OWKgne1HEtAwqG/pCYOe0XHEvMRGmZEgAojieaqKZq4rR8AHJG6z2cSD3JQ5ElkDJGjXUvFZNDTQjPW4hSKyw5lliKupqxMxFYVZU5asDz3vRA3RT+OyGIjyMqtDWkWA8/EznLYu0v6b0qxrEhTJpULcyU9JQYdxd3Mji3bfW+KHoPTD/jqFOG8LOvtRWfyswg8w4TaNTWpHZr+GEuK0NGUr6KezcdrQ0k91B+vTOxd+ut6fn1O+xplZG+c1d8XeuNB42O4cbwI1u/n68KJOL2kuqbXzl94LRlZQMcrAkLmWBv8e+fqP/dszeA4Lq1gbsHT178e317dDbKpQYr9ut9iZDTFJ6UosDQKYv/bmwZute/tn+RV2EOxPedtmYsnpty0PoSIXJUfZlTyvdK3+5We03JrtMIPC7NXmToIuWUpiVNYQpFUGHWVR0OgsjbWSUQmAPWrOjpvqeMGykYGB6rPxss7KVoEX2I6VtozHaTxbvfu/5gIi4IevgCjBNOH8B1P24aq1vG1oz6NUS1Nd/JZWVwKh1S3b3HwrYXQdM2icb6WykcHBSVFT2orPgiJ1NhjsZmcv/QhcguEgAAtrOf6ujcKmdN/0fZqwpKopytYQ6pVzfdAMCG33ezmDX9r5y2ZqM0AxhQ3DKJx5K9KYKcM/O3ajh6C6KJ+fAZ3VRXs35ndoR5ONqSU7MsgstDw/OpuaH8ZpzjGhhDzY1dd8rWTGFCbE3WVCU8czFAed8/ko4rGm68ev4HDzty8611C+wxM7dSnq6tizc2N5rRoWZfdMysi8niqKM3H0XEBe+CP8B439n9uPv62rG6FdsaRQ1130MfzgIAjierVE/fCXCcudD1V0Rzw20U8MdGynUSyUo1NLwYkejnkBMIAYC09S7u7N5I01rK/9ZnyLWLK7sBpZqztiaFYOCvYmbbLZy25qI0WAN43qxJPJbM5YRQcn77D9RgZBoSqVkI+Dp2lkKBh4bnYIyNd45Ep9EY47FlhbP9iu2Ugu3UAHhLssB3FlMBmF0NidIdAoKkYXsv7uxb7g0M/UvOnvHn4ibpfXl68jj+JnRUAyBK8ZbQtXS5vpEir3Uqfr9bjsw2w0ZnFK8pGEKAldBeoraWqzAcXQhPBamm8jkKBYeYGayU4PVbroDntQMw4UJxV88hynYuEU31q4GMEoDq7d8fpYsWDY67D3tKlEtFZVYCxTLOoxAABGxnpdq8zaCmuv/DYKScgyAp4N8p2TIK+qMU9L8eAssEgOIFjkOajIzbS9NfgOOsRGEWWpoC/nEzjaYwhTyUkjoq6Pr61MGhG4cOtJaG/q//WzNLSvIMzV1gaN7XkGGvlwy8nLL07yoWZTmOJgOfcH0ELtkJljqDCCjcuGGQALsh31PDx8z9ZeClniXwWE8ub37GaQrHoBgiZhm1f1n1Q3K8RmIYIuEsqbxj/ZEi6X4luUfzFgBwG0KJ8INb9iiTzWJog6nDAJTPMPFUCSFmdnoimwljaAPJI2tuXK3Sc+sexoYy5ciCHLcmsFO1xvbM6kF7ZvWkypaKocJmGgRVJhTEKmSMG0BTfn2tSDm7Uf5vAcG1p1e99FrmMoV3FlQAOqhMtrSrDAwkj1Wbe45BZfinclpLSZlL4kS1p7UvvgiCDoIUEX664hd0lYi+dg4Zw/B8RKULfcNXbu+EQQKKdfM+am28BgOR5QAx1dc8TT4zxczgVDrIm7f9LCsCYMD1FqutnUeLaS2fp8pwDwBQwB9T23fsj3IEt8n0uwCUD8AwjwoaFCI3fx9S6feorZ0pMvROTsBGsR9EO78Zlw0cvSbCTTKNGJcPpEgY+ti2xjRSELQDiotVMwVVhN+SEvwp/Hch6voCAKviZQunWe95wjjpxqunnbT76cNXHPD1/heK+96yY8lBa2PNn+JMoqpWoyfu/+j0x35hCu81l77puucXotT2+fxj2hqPTf+/qLHuXxgaXg4iixrqniFDt9EL+ODX1dZtP89KxOtwvUVq87ajxcxp51HQHwWypLqd3YeiNEBicjx5DICSAEw2M0agfEpOzmYFkUiexp3dwzD0rbDsdMk1pNhpoRNRU7UNNVXbdrYfAMDQIyhvazQyjOhY3agi3MO0w84+x3zoVF25tmyntyGmSpB2McQgnoTKZJFMN2M4omobZssI9MfimTTOVPpE1TdYIDvIBovkCXw5DDRCwISAwQHMjp/KX5/sdVkxcdoKsBpl/L35pnkvUJHjpDxg2wYTw4OFJcmGyV5oQc1n5YI5F4lQYFi0Nj0hprfcC9NIeOs3f1q9/OqN/MqGm+B581GcIjsY+UTRdMbZbillJFbD0UYMxz5XrnHRWAYcZx8yzRh0/QlkUt9ySMPvu5l85pgcCm8IAv6bAOTzKCgQHGqse2y8bqKl4Y8gimb7MoA0NG0VNTc+9QbOdgr/Q2DgReYMc/4cXwRHVG3DNCMK/cmERgzDfDF+uv+J4Yb8PkTKb2jeJUSoIYKPCDoBi/yGU+79KwuylNA60gF4o9GAm3pnl9Trug7w/EOhHKf1yKx9QeUkTp778f6P7/k9p6UiOvyuuY8MHzvvfpZC1f3h2S81/uTxm+r/8NwNZHstucwWAiQYZvDpzjOLHkJ5W8Plmc+Dz2yfY24b/mjhjAAABfVExDC1/uTRieXNryq/vp5p1NYwwbKmV/6ZDfmmcRqogO66tYG7C+YBeCxFNLb/9HHJx+Mrp/8cgpJMSHPmyVhetf/BxIrWSRMJT+GdC62HkpTGNjAYYOwWGMThVdvQ5E9BezaZSRsfjn2W04Ulxs5crrH2x9egIQwJHwR0VYW9Yh/jMyZ7bfaU4LTlz0+9f+ThaY8rRU5+O8cCHrmjsqAvCUaoykuI9umnyrmzfi4CgZiY1vKgmNb8EFzX563d+C318qv/5E0d14K5CqMZtToAv+rp+0DRdMZa4JS1NaqrZ88sH0rJqaK/fUim3kNNDU9DiF6M7pwDgIXK8O/Ljf9GgYKBYeja0yiUhXUhxTaqGadkiwhUV/tj5MoRMvdpwe+7aTIEvlOYwsJwd7ckTgAZ9Y+lwT4cVtmJGp8FuSptsiLzpT9XfjU9JAqCms9Hpk1/JdZynoIIMISfQfqgEzz4H117vney12bPk5y2CoKfzz7T9JDnUQFPmpUi3PfPqoK+UlOoaXIHaHb7h+XsGdeIUHBYTGu5X7Q1P8bJVI23dsMVB6qjF++lDpoHRhCjQREdzCG1o/fEwsmMSZFX3tZs6zo8yzVVcqrob5NjiZNEU8ODIIpjtOKBAVhUXfXH8td9YyBqqjoh5SYU2jwbmvbCeMTdJISimqqrkLEzDjLZMDZCwd+PxzfzdsNUBswuRugG+mPsbLXbxXOeajy4arv0ICBbFV44P4hLPz4Lnks6x2J7IU8pJ3U4L4KAASrYoZQcwGxnLtfo62lc8ilvW/dKRGOfAHMQmUDE7XDcmRvWqKafXzj9xU9f2rFMKSg7TT4rJfDtcwul4KUhOpzquqs8UbM1d4w9JTger+fu3o/D9ZZjbJUkQKm2/D9FXc2TKhovDsq40ORzvKNvL2XZ0+F6jWBVAdN8AZ5XgfLfxfJGyHF9Ys7Mq9S2rqORTB0JQELX1lE4+DQz480kYBKzpt2otmxTSKbeB0YQmlxNTfW/GEtZIAcKhwbE3Jln847+ley69eT3vUJN9S/+t5BHTeGth2VrPw+b1pU/aH+sammwX3hM0FsUHvxCJa764jQwEQfujyxP7Vt5Z66PqXl7FY9DBB3A3gTWGYULm2LUfnvrMb4XYqdBwQeN4na77w6t11mwxlI113yjddWHPty5p+uCBUsR6ddw1fnTC68V0l7dsbL9h+mW+pFdXUo5mtkxXBu+b+P5Iu3NoTF+lwggkXJn5x9L7d74qNEV/WB+ZggTHLfa/1DFXesP0CLpaZR0GsjjoNMUekobSC4oTq8nAFy+Rkonj8XAR5ZeXHn7qyfovfFDGFCqwveyPaNqXJ6aNwKDH9j9d9X/eDml9yaOhWJThY1nh4+c88t8wuBySC1u7HQaQ2eFHus4UKTdKmt61YuJfdpe2ZkSqCm8s+G/nX6A99rf+dm8h4KzfFHBTNBaPPzrU3X4/RUtAODyQGR3am0aIZZOH8QHoPi9EjC8RhwBoESKNB/MDLV52ylIpt4PQAfRAHzmPbCdZX+/VASbK+rWHPHe3mWOA0hI6tpq4LeXFFbqCF2+lKyc9kPyjWaqseMYHE/VcFf3xWA0ovzua2amrrsw/wBVVtzHvf2HoTB7zoLPvF9t6zqYbacFrtcIwKSg/1GOJw9DuTT/8ipFfpJCifbpX1Hbd7wXaWs/EDkw9FUUDGwtM8YbCtE+/Sq1pfNMWPbhAAQM/XExreVXE/knoqF2HQcDZ6u+gYOgVIAqQs+I8UqxpzCFPGjEvG/1pu++HG275JezH/TVG2liBvRWD3/6aBNu+k0DwKDV11bO2eu8oZFMhxejbYdxkd/AILMrXXUsxspOG20Ib8OWM7PBUglB3fCZD8ByVvz8vJBWF6rasPzAwUWeQxCCaP2LAVz/s8bCETT96Uhw1o/INEY2gNmyfZxI1nFXz7cBVBAIBCpHUKXDdnbLP0Dh4N08NLw7Cu1HGn7f/aqj63B2nOasrSEKBx9EPFnMvZRDOVsTItNIi5nTPq+6e94Py14OohRM43ny+3rHfVZvAMSsaZeoju2fyHK6MEzjfjGjdcKgs2hufI5DwXNV/9BKgHWqqnxcVFdun6jf2wlTAZhdDK2DYlckn77+wKrO83TJElCABJYdEMcJp/fj5t/XuxCFqf6sjfk5MOvjZymp3v4FGI5+DqMvXwip9CnIZo/8+49VLQ/cFB4+84rEH9c8IvZ54O/BvZVHBjKRT4Wayu+gpek5AcBrYH/yXbxSPjm0xPjXwAryuCRVvyyICl5aCvhjVFP1PR6MfCl7SEJQJzw1izNa9aNGxXH3GGkzOaRVMlkvQoFBMa3lTrVh8zI47h6w7CbuHTiYByPrxZyZF5GmTSjdtitARJCzpt8E4Kad7msYFk1vufcNmNYU3gFwlez7/szHb9kz1HuqLkbLfw48PoKn76vAg/+uUipQUlY0ts0nUFkhxCyqfr19b9/zsY+RyilccJWxLvWhnDfx12/WNd/0Y8NtOOL57lZ7Rf+T//TvxopMEBwwvNh+076V3HfaqwBAlU5Yn5Vc6f/HjmW+fw/uAcUCPCoHPRbYlAU/sOmF9d3mpsGrfesHPgWQAlhTPm2DFknvpw2lDs0pDxEAGbeWZ3eWytnUgkszwKyJPv+a3unJ5S2bh4+ee1vtX17cW6TddjGQbAk/tOWowPPdz/R/dNmVEwVAdhXYkGrwQ0v+DKCkhHUiuPXBROTdC++cuOUUplAK35PU8eMzHnyw3Td8jCZHjcQJZwzgqXsr8NITIYKUBSV5LKChXGCzXDlTEVRH17uQTJ2MnP/B3IBU+tTc+Z9+saXl11caqcaDVvfX7Ng7+cKD/pkA+ZDbEW1p+ApqqjsJmUyc9Eq1v/6P3j21x6O7U2b6E5H1K0htS/4B0VC7zkskr0UieRoy/pMGKdcglT6BU+kcgWRmuhFnT4xta4szexlC7FBDw62iunK7aGn8l9q8bT8o1Yq01cbbdxzv9Q/+R8yZOWEAZFeBdN2Wc2f9GsCvd7pv0B+Vwbbb3oBpTeEdgJW1G9Z+q3X1CyHh7SPzNqXP+OoOPHN/BbZtNGWgwSuwNYqFjvJ0CxOuK2YkZgSRto5F7v1V3IZk+iO585d8dLrlnynjDftsjAQ2reBXnvY3ILOGSQNI0vSWL6Ei3E8A7N25Mb1C7Wf+qXsvuSGxMGtrytEl5MOFXqhcSS2NT3Laug2p9LuRyUQzoGkvIp48I3tPI2PyYGRvjG3PiuWoPQjRw9FYPVWE+0Rj/c2qY/v+UKoeqfQMta3rfRjw/122T//bRM9tV4F8ZlLOa/8RgB/tdN9waECGQ/96A6b1pmAqADMGWGcR/xAf7s7EkWA4+iv4d/Af9BiVzwArwJLdew/RZSFxky/AOOLkIdz8+3ol6msLaqX9D9Bqa/+ilQ+DYWPHRGS8PBQ9AeVfvtxEtcSwDF59QXNAzmi7kmv75yMa3xdCJEVdzf1UEeoHAGsFtyXey98XHZbu//eASd44K7FCWKiq+EPxQdHS+Aw31H6EByNzoOsx7h34YJbzpNggjh3gEWItlGpHxmHJtQugb/ByNRx7GD7zZTjusoIxXG++2tZ9vJw17ebJ3sAUpvBWgsCaz3COFYIPBhB3PXmz5WiT4gJaHBo4RBdFJHFBxmEnDeLB26u86IcbC6SObVc+o0lV8A4yw2PgZebxeRn8T0TfOxJ8GZl73r8ZRqovwHffWIWaTx1+cWj51iVmx/BeytQGE/u0PWBPq4wAgDYrMV9rT16qrUpK312Dxni2JrdS4UxSrp1c0vR/xW2Gj5v/QHw4/bh/TW+7WxMYDD+w+XNQHKaiQAsxzFKGusw1VEBbJVLuguwBAwCRq2rDD235vm9d/79YCkuk3Tn5ZL8yZu1Zec/GA4ePnffgeM9tClN4u8CrZV/iZD7Ra8K+ZGHQfJr+4f8PvTKZvjPbovtrkgtsh+lXOPjECL/0ZDhODbUvF5x7mp5wZ/OpBYMouGIQ45bnAgASifdi/M0fI9ldrd36d7+7x4JlX6Wa3r04mdodmuwVjfUP5NLQEyeq5db+uEB/OC61Z2MaTc6tUQBsqq/5a/EJOWvavzht3cPDsRnwmX28fcdlyKggFqNc5ksGmnwRrrcbMqYos3BUqo237/iJNxz7ExxnGpRqRb5/btmH8Y6+x2mSctRTmMJbje50Reju3kXvG3ICe5jC7d6rauuNK6q3TCobqkJ4exbTJ0iNsd/Rw6rn/+o6d/tAtIBzZEF4x0N9A+GjuMBmsF1vxMYmp8+iOd0cwPhyRmZiS5NxY8fG7n3m7/5Vqurdl9PWAuh6p2iqf5BMIw0A8Q+pg+2lOM/4V0TKdUm5M7ZGNNTdnH+QiCBnz7iGk6l/ciwxDX5/N2/b/lOUtytj2xop18Dz5iHjC+kAREYQpeuXqKz4MZLJg8Fci/x1WTJ1shoYenJnCXWnsPOYCsCMgeHP8+dVLfbLKRrZe2KuqudpFb+kCSODriNTzOB89SEAsG2yqKH2WxQMRPKPixg5vgf4svQh+CZyGooeksGb6dsTTpRVGBNroflgOzMBQDTUrUNDXUntf/IY/gwkgvq9UYIzoeXwQNQFKQaoqvLvoqm+gMyRY/FajsbnwGfuEA11awHA275jL0w+yyUDXVsnmhu+ozq6LoVS05HdyAYgYTsr4XozUGp8DKTSBwO4eaeuNYUpvCVgBHz2RQQsyCka6dLbjUj9Jm0b90zU22WRLj6mFOCyiEc+3vI1r94oCKooFjHHE1fqUp2PzI+/ADCQtvWrJroWuWXUdIogICjIIROCED9w5qo4UMRPwtBmJr9IAn7jjmHAmtDWuEoX3Wxq3cmlTX9L7N22Pv+kb11/o9ExPNNuCXfG95v+ChSj8vZXF9EYNnEsQ+nWBZ9K7t74s8o713+HPK6lnK1hSL0n/h7WRH9+8AUAiOEztkcPAjAVgJnC2x5ssIiex99jH1ohYLAfnDqCl3ph/n7on6KEw6kYnioN0CoPUKBB0dZ8QbGCoPkCddu786+cRTgXDBeAoDS2ha6libMqeExC/gIEOWySIKaWxqcBFNwDGyysfXE+BEzj1ghoYluTBlEfNG0r1ddcL4rUFNVgpA2pdCuFgptFY90rnEyFWanGsQYbA4xg4G4KBn7DXT1XYnQnO+OLxxNngpBEqW9ucjyxH4CpAMwU3vaIOH7zL537/NhlWQ2QnlZ6+339C1ZYSrtwZe2GCQO+DDhUtLHsuQQzrLYf/avui4vb71+zcd2WZO3121I1HyawwyAZkNYrJ7U8VxJELYZgOeGuugJzEGGTpPSorflRAI8WzK2WffZSfAYChvHv4YlsDQOIQ4gINPkqNdZdn9sIH7le/9AsWFYDVYZfFY11a9RgZDqYJ65GKJ52VcVfSUqLe/tz6rO5oC8wHP0scuupQkiOxvbCVADmDcdUAKYMrCXcqGqxP0SeAZDwuTNwslfL/5IDVLLoyccD90//98kfWLuXlKMvjOeRta238YeiTPADAAJ3iZd9j/Bp6YN4N3LI9t2PdeRNIoYa8D+EaHwBxouCAmnymeMSLnIQC0EgslQ50dUcMg5YRegXcnrrfSVjMENt6jgdqfSJyBAjSa+3/1UxZ+YlIEpmOWomD+YQhYJDUKoZpWsnE2AT5WXX2r0NW04Xs6ZdS1KWkzibwhTeFjA0bx4B8/PlpIlgaoLPBPjeTFnN2HhwqPVfJ9ZvniVp1NYokLVqt+mXJSpqysqxW47+pO3yaYb0FjKQsF25fuIYLuDM8D1srE02Fwci8qHAHBPDqbE8BQq7IUjUAwClVdmr5rJdAFBqSdP3ooe1ly4OPUV1f37xMzKSOhgM17+6Rws93vH0wEeWXQmCVYYdf1yQ44WctsoB8rimTPCmXOIMGICI28urb1h90tBJu90EKSadNjiFKbzZSB7He7GJphG/JkNKYNrLcRb+iQkDMKtfqr9l+Z47Tpcyz1YJsjf1z/omVYbLcgeE/yz+4zbyY9bePF8O0pDvUdoyqcka+hOw7CMxvo9KMYqMSbpvL0ULKLvYGHtBpJD1a6ih9iLRUFeyOGTPk2r9lgvgusuQkWLW0KPfSzPa/oDxvKXyYChVQZqW5vKlCU62lLL4OMGyj/S2dm4S01vvnuKKm8LbGff2LTjYY1ExKidNxID5bGTGGStrN3x1ov7DStxZKdTxIs8v0n1szfh49Bv9SpaVsv9w21P/6ExV3bM23jyn3oj1Lq3s7JzMXCN6xKpxajSMs0EsQBTH8JjrPmsFzwFn5JTJLm8SGAoKSkloKWppvKA4wAsAbNum2thxCTxvNgCPh4Z1+MwbRWP9XbzzojmKlApD09LIrMdKJZuzorhFxyUSqVO8zu5umQk2TeENwpQKUhm4s3hadsemEAzPbUfdRP2v+dPuq598ovk3rktJzyPL8yi96sX6v/z0xyueGK+fSJAbuEOs8v+H1k4q+AJAtDbfB01bhUztcxqj7PO5wIMNIXqpqWF8qWUPCQBwDg6jDBODC00+j4D/ehj6Q4gnT/bWbrhE9fQXEEdxT98SpNLHIfNCBwCY8NTuasPWixH034RC1aKJ4FAwkHleRLEy520YxrMoZOrPgZC23qc2bp20sssUpvBWQAiePsYpnyCecBf4wo37PPpMtOF6jyntMVmOosS9Q22/+kXnknF3mZjJslztBdvVJhV8AYDBL0y7WYXlRhZIs0CaM7lyirNM+yxgJbS43S96x2Sh57RMI9veObwC7CuSmwQcL2w85rSEb3ArzMd8a3o/Vv+7Z74ReLarPb9d5d0bDpSR1IGU4Y0JEGDImH1A7V9e/KLTELotXy1oEnCsWTXPKlN6IJQjIVZujf/R4jGzbHrS2B49reZvL310J643hSm86fAaMD2X0VsAA5PK4rjs4gP+vWlj9b89j2zPI9txRPSuO2ddec/ds8YlPtR6KBm8VTw/6eALANHWfA2E6MKooo6LjE+T84vSwxiIxWjsRZHoQwxZuWrniDDYLLFzNgz9fgT8N0LXnuX+wc956zZ9SQ0NF8goq46uE+C6S5HZkQ8AMGA7x/C2rlNh6A+jvA8yFjyqrHgOhp7AWIpKpnE/yvtKOmKJT6mtnSfsxPWmMIU3HUNOcDZDlGyCWEqbNpn+j6Z918aZHmSGwwzbYwxscPRL+pWMjNevzR+JHlH/ynOTDb4AwIbw+hiIBpGxNWlk7IyLPFvTi+7BNKXGFCiQAxTJ2Rr7kBBYL7U1Pega7KKtfdDkat7Re4H36ubzOBqrz2+kOrpOhefNRaaUKgBAR9r6kOofPBCaXAOU9U/GAlN15Woy9WGUD2RrMI37UN7W+BCJfsnr7D5gJ643hZ3EVAZMGegbaKu1H5c+G4LQ1mNSLNGXX7LynmDQvm/R4v7qdWtrIsPDvjeEFJakUHLB7MtV3+AcTqVmkc/cxro+jP7Bd8PzWmCaz4qWxrtI18Z9cbWtuMmdiVO83QM+691VMP8ZyXw7UgwCBOprfo8d/ReBuRqABoVW7hvYTSl1hWhueAEAOJo4GOXqtj1vEYjuRcB/Q1bVIEeWZ6K8E+JB0x4fkWQOBv6OeOLMvLEZgCtam25Qvf2diCXOK/doYDsrOZH8PQUDZSPmU5jCWw1P0WZNlH0HkoqpmEC3BAoCH1tzxD9qtPS/dg/1Vz0XaxiKecYbkvXl1ep29+8WfLXi770L9Q6rzVoQ2MgGeaHbB06kpKpzZvsff2Hzg8dhvFk7wuW4vBsh70jnwJCpPZeE/kAss/eUYgAQsf2nX1N1z8YroThIgGTHbgk/vGUZMV+Q2Kt1AwAY24YPp6IsFwIgo9aB1syqy8Es9L7EcWAYuYyYMtLTAOC59YE7Evu0rQMApzF0i94TP5Gy5L0MKAhKRd6z23VVt7xi693xDxd/WAQIvTdxgkjY16mgsTMO0hSm8KZB7sBmdyZsyKLsMAvdk+nvupI/95kj/1Rbl7xu1qzh8KpV9UO2pb0hBNTk9yXEgjnncU/fEnacBgoF18Lz/Dw0fDwUhynof3B17M6jxhtD30LDIsJPqyrsZR9XZWirUtCeTWa2HdMMAhTqav6Orp4fIeNbCCinibfvWKGIPi+qKjLPJZUuVj7KIG2diPqaCxCNO7Dsw5DxmCyMnY3sIuC/bkSS2dAfgu2szBvbhRD91D79T7xhSxC2864yYwjEkx8CcOsEj3AKU3jLUGvEXx2wg4cWB2F80pmUopcL8u5LBa4OkvpdUKhAnycHx1Rmfp2wpa3EgjnncE/fcnbdKqoMv4SU1cjD0WMA6BQK3vvq8B3jBj3Np6kzeQxv4gDmWB+u1bW1acj1VkZeMZMRk+qmbYNLeO/ZcL0GAATbblIdXfuKWdM+PUJLYdkHo1xmXCJ5Bprqv4CByPvhOAcgY8XGW0O5qAj9ggL+GIAYNPkyXG8RRsu6HEi5TrTP+K1av7kerrt3mTEkorGPAFNZMG8UpgIwZWC8TH2ij+9XdTh4xFlRSGsbcZ2MjE9UmY9EwvCeerJlXBLdXQWRkbUekbZGdeWvdqZ/+Dd0Y+xsVl6Dd7r1kVryZpnw/aYPMuUBgIf+oQ+BuVgu2uSh4TPR3JDJNCEaa9eZkEi9R+429zxm/js8TyNNc1V371IeGPoGMushBiBhmndRVfhBUV87wvUgZrTerrZ2CiRSp4A5DCnXUmPdL8jvS4iqylUqliiXXgcAiodj7ap3YCaYNaqufCxfpowTqQpOpeqpItQ5kWz0FKbwRsBxtY2G9F4EsIQIPmYwANv1xO+ASdK4ARh0fe6DkbY33tZIQvSDja8AGMmwSRxd+7Pcv/kT6riJhrCervqduXw4RZp1curTDXDnm/D9cQACDCY44cc6TodiP2VTggkgZhiB57o+ktir9WIAYFHe1hBAvo2DJ/Sds+JCeOr/yPYk+3W34s71K/2v9H0eubRbAuz64G3pxY0PppY2jaQCD75/0V9qblrj6DviJ0KxXwWNF6KHzfqFCuiuNavmeb07fhLKLcYYwr9qx2yjM7oYRF5yadOj1tzakWC9sTVSo/Unq9IL6rZNBWmm8FYgcDs9by/jDvZjBgRMcIbq33x6fEnoYgz0B+yB/sDAGzXPHEgQl5DO1teOclW9wuMGYACg4hd0VewMdZZXy0cnv9IE49YIfNcN5lYsjN7+s5BZlOSywQUAg3sHTkJVxdWZiYyZ4SIwHDtKzmv/CSv1SzATSel5WzqPRTzxcYxm7Ljw+26h2uoHR4IvAET7jJ+rLdtiSFvvAqBB154Sbc2/IiGggoFn2B4+FOUFFkJe/+AsROMrIMgSdTUPUSg4lDvJ0Xgd206Yqis6pkqwp/BW4Ij6Vx7enKg/2WaqA8gAWBHY2btqc4lgx3hIsEgnvFKeu10NkkJRS+MzIwcq0YOm+lH+uigmzDoL/5ouiX/QO0/V4YDEJa0wbhiE7x8jr6U5mxc0C4j8YIkE4FM7+o6Rs2fkuGrG8g0Iw7ED5Pz2H7BSV43Ymo1bP4xU+n3I2BoCUQp+85+ivvZhCodGbLSYNf07qmP7WdkAD8HQHxbTW39DUij4fc8gFt8T5UqwFNeqvoF5HIsvh5BR0VD7cDaokzkdiTbB83xUXbWVxOT91SlkMBWAGQOVP6ZfJE7ml5w5OII82PrLuD14i3jurZ5XOXgVnt91+84W6+LLKMVJMv23iWmtd+3MC0FMqPgN3eStXn8ySln9NbjeLJRbeCjVNDJGdeU9vKP3GJR7kTlTTkFEgKa5zAxOphYj4/AIAA4qw1fLaS0lhJZEBDlz2m0AbgMAth1Dbev6gNfde0i2ZGCsAAx4MPL13DU4mfqAF439VkxruUdt2Pop2PahABze0ScRCv5Rzmy7faSf62lqa+cHkLaOBEAwzXvFjNa/TpRJNIUp7CwSlvFtn+EeLoU6GIyY48lbbVdb81bPqxxkzK6svmbHJ/TnE7vBQ8RaGrpp8Ly2hzAxj90oPKGsJ6v+3PiTx99fwrfCkCLpzKWi95kAEpY3kr5szav7t3y2a+9ipSMAII8DmckKZr9wyfaE3ptYlJXZ1iAonVzS+IPYoe2l9lyXavADu18P4HoA0PoSwco7159adfv6/VhSEmPVawnYoSc6L0dmPmx0Dp+a2q3hB/EDpj9bc/1L58uYtQKAE354C6Xn1109fMy8h3NdZSTlq7pt3Ue0wdTBAFy7KXx75D0L/8HGmyNvPYV3BsghVfkjfD1+Mr/La8F+ZGHAfIJu9j9EG9/quZWDU283qIG+c+Sm1Byy0Ueh8A0iQ7Y7aYgo2RXfsa9VmzuOQDl/1/XmoQwJJTx3pOSRQsFbeTj2RZR797O8djkCYnZcHba9ADmyS6Io1dd8JydGkA/SpCvnzPwDgD8AAMeTVWr7jtPhuMuzZdflOSmIhrCj78rseU/Fkx+huppLqKpio9qy7RtwvQUAPN7R61Ft9fdzGcoAwMlUWG3fcTpse1+AUggGbhYzWv89xSkzhV2JsGbZH53+2Bfu6ll0/IATWu4TTtc+1Zv/uTOlQW8m7KbUDN7ef5bcak0nB51UWXl9sbjIRNB6KFlxUfyv3NWzJ0qz4FQQYT+VmhAd7qitQcD/bySSZ5YZnqCKbE3aCsBxZyPjc0gQ9VNzwyWipqrkGZNppOXcWVcDuBoAeDjWqLZu/wxcdzGIhjAWlxVRjHv6r0DGH3NUPPFRamq4gPzmkOrY/i14agYAj3f0pamh9gpRX/tqrivHEjWqq+fjcJxlEBRFKHSDnN4yoSrVOwlTAZgxQB5x6Hp6CMBDEzYug+TRarG9Ow4Hg42XcG/gLvHyxL12HumV3M5re36gPxPXcuRP7CXOVhu3zJRzZ+1UFgwAwNCfyabF5v/429nSo1IIMfKyk98cYkEboHh+USsHpvFw/gHV0XVkVuM+t8OjYTj2GRUY2jqR/JnatPXCrJNhZM2Gg1FFlxzc7LF8QygRS5yttnSGYY+k+mUWevHEmaq3f2OOJFlt3PoFOM4+I/NLp09UG7fOkAtmXzbe3KYwhZ0HqbSt3wNgQtWjUjBM3d1LE+pgBqUcT9zluNobsqAyNHdx9Y+3Xa6tSYusranxPz78ufp+p6nv8vYbdmowQVABfbVIOosKgigEJo+ripszAM83el92S7jLv0p0wVFtBaVFBMtpDBUEcav/ueZkbTB5BHH2XVccCry44wJ7RtUnrfaasbOGPEU1N6z+HtleCwEaeahlwOFSUl4XCnpB0IgB/yt9n9d74/fImLVX9to6APjW9X/Omlm9Ib2wvhsAaq5ffZFIOfNy/Y2u6Cm1f13V0H/6Hj+f6DFOYQo7AxElu+L3dCteQwkLS6bE+3h/tx0HkI2I+Sjd4XuKtk3cc+cRf5+3r3HN9gvkdocoo8pYoyKRr7Hj/krOaN0pO0lBfxRC7MhKO+e/txrGkrrWRgPgHAxswHBsEEBtUas0BQMF/qHasu1s2M7+yPnWzNXcO3AJV4TPJJ85ZnEmO46htnZelfWzJJhrMOrD5Ps1NpjDGPXdJQDwwNAXeTi2Bq63EHl2iAeGvsEV4Y9T0B9lxaS2dH4vu2mmAVyBeOIMtamjIm8HfgpT2CWoMxKpU6c99XcAf9/ZvmlPk3f2Lj64O125wied3n2rN92+MLyj5w2YJmoPWeijB7t+KgZdogxRRLUaGFwApb67swFfqqro5O5eC8zFARgpIMtzrur66NowFHwRiWQCpZvgFlUEH8s/oLZ2fjFLDJ6zNQ3c3fsdrgyfMV7mGydTYbWt66rsNUTW5uSy9fLtowXmSoyu0UwAzL39n2WiJDzVjtw6kdnPPf2XclXFR0nXbXYcQ3VsvyrbX0JxGNHYp7yt2w05o/Wuseb2TsMUCe8bgOgn1PvTh+AiVYfDVD0OTx+Ki6NnqXfv6uswMVJ72F/THx0NvgAAedDZso/kRKpiZ8cULU2/B9EAMoRUNgALhv4QxgrWEeLK8aSKxurUls6fQnF7UQsLUmwW01oK5bsTyXejNEqsc+/AJ73N295dTE6VgxoYmgnXm4/C1FwdgAfTvBlSroZp3Inqyu+ifFTXRSp9LEodL4OHY0cAAMcT1XCcfYuuYcB1l/JwbGdlJ6cwhTcMAdM5R5fqK0LgYEF8lKl53/XpzsG7/kos/FsSX9deGQm+AADIhW6sS54shpyyGWjjIXbIzJ9BUpQJKSZYTLDd2sDdoDIE6ABYEwlKOdK/uqet6t+v/owc1UQYqV9kJlgqoK+JHDP3jvx+ek/8uByny+hgkBX/2fjp6n+uOd7YNlxV7nrhRzqWkuPVU57tI0AHIe3W+m9Tfu0ltz5wS2p+3U8gqDQzjpm0gdQRJapRDBFYteMgAPC/2D1DpJ05+cEbYphyKHWY1pfYOdW4KUzhDcTwl/kr9nJ8TtVipdeEY5Pv46sSJ6g9dvV1vGo2lUh+WfSMBF8AAKSgIx4/nXnnM92poe5KAEmMkvraMM3bMZYwAIkku56megfmZjli8jegGBkC3yeopWmEH4EVEyz7cJSWDQm1tfM8b2vn0ZxKh8pdjnf07Z/Npsnf+NIARGAYd0HK1fCZNyIc/D3KlSkwV2a5IYrtMKv+wX2BrEiCUrUoKiNHKv1e9tTUWmAKbws4SojfbD3o8nXxxnOH3cABPVbFCbfsWPqzxwfb5+7qa8mqoJzesrCCol4u+AIAIIbOkegZOzseCaGotvp7yJD55kh9bZjGnQqqfEarFDF2Xc3r7l2Gnr7vAcgXX8iotPnMO6mhbnXuYDb7ZTkK33cCs09t3Hqe17H9CLbssiIOqqf/CBSWXQKZDJoOGPp9kPIlBHx/QcB/A0qz/ghKtcPzFqJMhh73DiwDRuyZv6iNiXjiQ2WfwTsUUxkwYyD2UXWkMx8fgYYqSmOL70H6hf8+GlfKGQCcFhV0Z+FDEHkvBsF05+A0r4Hvlr00pmzizsKdhSox6NRBp8y+bD40UpxMNlLQHwUA1dWzgqOxd4M5AJ/vATGt5XbSZMlCh0KBiFgw+xPc07+cHaeOwqGXqSLUpdZuLL+o89RSXrfhRoy+zPkvrIKmvSjmt19ekuJaXtNewPPmI5GcoxLJ01BVcZVsay6I+iJtNWJU4algJlRfc3uuxpoTqQo1NFzu+y0xVrodZ+6BU1Ytypc1uZxO11Fl+A2JxE/hnQiG33DeKwW/H0CIgXW2o13teLKshHQ+NOE2CuJ3UVZqlQgCgKlJdS4cfgSgXVb/r0s1XWx3jLJJ8QRhbEhVplfo/WCgyW2trP/VU98lhrRbK+6OHDfvP+UkmtPz63fY06o+Fnpk694i5VSkF9S94FX4EjXXv1RCPkkAtEj60IZfPX1o9s8Ri5INwii32v/AQLmskfKy2ZpIOHsYycjuxtbI6Yk9Wy6LHzhzVX4DOZxuRLlNCoYROXHBNV6VPw0A/tU9rf5X+8s9GQ1jqbRlx9UGUvUYw55pvYkqtz6YKHNuCq8DD2z+1c8mbvU/CEFo/sjeVTVHLKwQhkaJdT3pzl8+OGDviE4oEBBY2GS0h45tElLLvHYEAYIZWxj/1tM/vW5cFaSdRbh6um/utr20cmFYZq54aPNvfsbEcDyrqQ0zHe+V9VcC5FE4+G9qbXq4XDmNqKvezBXBM7hnYB9Wyi9qKp9h1zN5+45jy04inT5Frd1wCkbEzgrgwu/7R2nWCBPKlw2ZcNz94Lh7qVjiTGqqv0DU1Wwq6Om4jShP3lsh580asWmqp28xx8Y0CeVVlbK2hh2nfow2GhzbD+mbsjW7GGcduv4daWtIl1T3wYOrw/vMD5GUlFzTkey95t4BbzgxYVltcMVcf+Ppsl4YOb4U0hik3fV89Xd+f/n6SRGGTxbawvZGsc0CpUuXBEq5bQ9nfytcz26ahtmu98r6q0CUoIrwLWNlx4im+tVcFT6T+wb3ZmZN1FY/xfFkK/WlTiw7iVjiPLV243konxDhIhz8rZzRVpg14nk6yq9jTNjOIbCd/VUsfia1Nn9phEw8B8dtQ/nMvyo5r/3HI/ff2b2SkykXpeugFMpzUwFZ+8KuW1e2DZdmN7+T8T8TgGFiWPtjFuvQfI9iIzk05oueXsnt9iJeTinE/PfQI1o3FfzwxE9RK53dcE5OspH9mJU6ki+TPfi08TL1jTWutYLbEu/BFaAyfCQM116Eaf5evFqm62uCiMJSM8zS4AsAKJYUDnUCgLel8zjEE2cg99IlktPUhi3L5YLZFwOA085ViZP4dFWNveAipq+lv4f+0nA/+gfnIpVugaHHoMmX4Hp7lpvGeFOEUo1l64t95kNFJUj542XGjEQ/x431T+fzrlBFeD0PDZfbbfcoHBz5bCjoj0LXnygoI8owf2+BaTyJZOoDKDRCNlWG7gMAqgx3cE9fufvSqLJiU5njU3hHgUnXvNkAlOPKzWOT5TIMzVsohdqdmQYsV3uEuZA81m84J0jBHybKfhcZ803d/Z6n6CzFYkyH2NDceYbmXYLy3EeaJlStq+SkFNsmA8WIe3NMlM1NEeRai4ODANCemF3V4rSGpeMKADA3Dc6s+/MLC/vPWP5TABANVoM+L34mmWp3eDTo9Zh/iZrtj4ee6lxgdERnpHb3rWFDboPttZdTGxprfgRIYblls9O8SvNxOZQ+uCiTBQBELjgTfL77i/H9p5+ZHyiyp1euMzcNlozHmhjwKnwjxICpxY3bQ490bMgvI2KCrULGc8rUBrT+5FEF5UkEL7Wo4WEASM+vWx9YtaP0MyRyrdk1O0qOT+F1Q00o7v42giAE5zaaKu2o1NaBcfnHKpZO8wXmNJp2b9QdemJjgp3CuF7raftV1x68ICyMTBAlOL/RN/vydzet+eJ1ncVtC8ZdNt038zNHNJBeSvSk1wQ0rtSIbXeXETC6rqW8OWYmlFF0x7Zme14gc6mFyeV6I7eY8NR8AOBItJ0te4acPeNaAEjvzdNSR/LHOIi5ZGGH+bR2jf+2xkfQO7CYY4l2qqlazURRMBeXFgHj+zU6PK8kQ5eEUNDkKrjekjL9BbL+BvcOfB51NZ8t6Ov3reVkqlRBSYoCf4Pq617mgcjAaBkRAMCCadwLparhuCtQ6NeTqK1+AgAoHHqFo/HS+yIahGlOBV/eAESjiE3c6u0BMjQKzWvyu9G0l+roH1uUQhCq95kT9s+s96W2DVhDj6+PwitcbrWff3RbeOmMUM7WBHabHmj50vvly5/7v/Vlx8yi9tDdKhvPPLReaKW2xmiu1Xf186xIOil3D0NnH5UEYRJaws79VixN7KtXodaEp+YAAA9GFni28yc5s+3fAJA6hOemD+Qz2I+ZlEKH7xHzj757Gh9G38BiHo7NQV31S9zXU1zik8MEtkaV0D9QMDAMIbZDqRllxhQAfGAYvKP3XFRVXFTQ12+uYts+EIW2hqHJVwraNdY/ycOxs7Ob5bk5Wgj4b4Jlrcg+i/yAM1F9zQsAQIHAGk6kHBQHpKXYgCmM4H8iAGPvzo3xU/hSaKgGwKmjYPvvxqX+B6jkZR8+T53uteJ4EDQwHGchnxG4FRf4HqMtI+Mtwim54MsICDJ1CB9lvEx/KTcHlkyJd/Ol0FA1xj6Erm2dnIT1ZCH7KUWefMw6qfoA8+YhSemMhAoMApm+a8lnptjzJOKJ01AYbDDhuotU/2A7pld3xD7GP4CGWghIaKh0dudPJRf2f8R341AYgOKhYR1SrEZpjeBEUNBkgQOhItEmJFOtVFN1D+/o2w2eNzs7ZrmILHNkeCbV165nxQRWgsLBQfh9NyOVPiHbh5Eh8P1lcd2jmD39R2pL50ezRLoShv6ImN76O+iapTZsnQ3HWYGMi2cg4L9BNNa/DABk6DYqQr9GNP5JZAwIAXAQCv6BfOYuy2Cawn8fdOnNNHX3YmTSRMnUvFja0S50PVm0C8wImPYXBGE/AAbAtibtM9K29iVXyZEMKin4/SPBFwBEIGZopu4dlLLFHSgDAuuG5l1CVFInPAKPReR13WjxeEr2e63Gq/Yh4d2Mh2KC0pzZ79WJU3uFf8V+qbSOdKAh3VaRz/RPDFNG0gebGwevs5dWpIzdo1eBECKCgOAK2ZL+cuj6rTHjyWgAIPa/3KMpU67b2Xx4BhwvZBbYe//qnjatP9kQ37vthor7N8+G4zUCkGAYJUZMcdDcPFRnzantg+MJYiC5rHlL4IXuh2UkvZIYPgY8ENzUkqarC8UMgMFTFl9adevaj2lDqYNAxG6N/96h9yz8P1JMNX97qVUknd2QCV9Jq73mN6nFjZ0A4LRWDNutFdcZ26MfzAaDFAhuakHd1ezTphRM3gDM/vV5573Vc5gMDM1daGjeN5Hx0whAX8rWLvSULFIgYhE07QuJsBsymw3WtLMOUUlb/6JSIpprE/LZf6M8FkiSAlqF317467Ovsxz98XJzEKTCAdP5A5Vhj8wMIuLtPzv3MzvnFkwEhjKtnzu7R9v0VSkii8ECgEbKPrLlstkfO+8539PRuprvbvmDKFy7+JBKv4fT1k3OMsOffC9fCQE/CMQaKtIH8oXyrq6U1p80ADAPRiSIOlHK7TIRLBjGCB8VM4MHIrPg2NXUUPtb7u67MKsYKVFuF1ip6ew4Bum6zZ4SIIAa61fxcOxluO4iZBZGDgCP6moLePxIEIsZbV9Tnd3nwHH2AcGGz3e7mDntr7CsoNraeTE8NR2ZrDqBqoqrKJSRuBXVldu9gaG7sv6QiSzHDNVW/3SKhPeNwdw/fvnrb/UcJgNTd1boUn0JWTVSBramLP0SxaIg4EFgI+izvwOgEZnvUHrmp9/FCUv/CrNIAYAgFQiYzp+LbY3RUKkW/fkLf7ZdrWChn4MmvSaf7l49lq0hTXbv+ufJmqdZv/f+Fa+WWyyQxWAJQJCnTp7zjdkn7feq//b4iVW/3/hxWRhLMBFPnMaeusPan9pSR/O3Rzbsw1iUOkp9W/67w5EDduZeBiPkwHYltLEyR8ZCmgx9JBuamcH9g3PhekE01P4IPf2XgNlAPp/lKERWejrT1/MkiJiaGx/leOJEeGoaMrbGBuBQY90f8zuTrjliWsv5qrvnXDjuMhAlEfDfJGa23YxY4j7V2X0plKpDVlmS6mquyKnJUkPtGo4MPwPH3Quj9syl+tpf7OT9/0/jfyIAk3g/fxMGGkEjv8b+1FF8sfkUThfJ0cq+9N48zWvFCRDZH0WCBINTR/FnfY/RF0cG1FBZchEBnf2ZH2omhqqEIYZhE2fer/QBaIdEEOWMh4It+vGQvokiu+ymswj/mn4aO6vW8XbzHaLfExWU5qi0/b/VvNoMOVzayidsK5xVKj09eSw3QSIMkWddJJn2SVX1vhuHwATYx1TCXeJbFvhhb0FN9gRQACxqqL0eANhTQm3Y8iU4zt7IBT1M8w7UVf8OieRCxJNnoNRZ0aBpCW/95k/Cso8AICHlGtHS+BNOpV/gWPwgEFlUXfmfYuJeTqVDPBhZROHQE9Q+44/FilByfvv3OBqr52S6kSrDW8jvixecn956rxqMrOeh4UPALKiq8kFRV715sjc/hf9FsDB19xIAVTkngRmmT3cvjnvi7PxFiKF5uwvCfkQjuww+Zhim7p7tWvLyvEHLBVEMgLM2iEEEgxl2bnxDd5dgjBUPMyxP0b+YaSzp1NeMlKVfgfMaPuuuCO6j3x8lAIOphaGrY8fWPwsA5kuJBgUFUbyhQ7CNzmgbnyRaQDBp1E6DBAz7A9W15n1RsGRY76mCatYX+3/dz+TwuKuCXDSYAReC0vEDZ9wKACJmGbV/XfUtkXQWILMQ0e2W8PX29KqXjK2RJUZX7P0oTfcXLIVX94fnzpfD6QMACBUyno+8a85PfBsGHzM7IvsrXUaTezTfnd6toSu/o7FtuMq3tm9hemH9PYm9Wn9eHJzpO2fFhb5X+pq1wWRtakH9Rq82UBDEHTp58T+CT29f5Xu1fyULcpJLmu5PL2rYpWUdU/jvAhEbhuZdRIRA7hgzWnyG+7VEWn45v61Pdw8gwm55tsbPDN2nux9JWsYvgEyGGMpnywlBOX+HiQCdMWo7DN3bG2OU7DLDchX9edcGXzKzTTnGN/mbLV8wHo4tMR6OExvUk9w9/IP44RmVDXN1ok2RYsGi+OIuJ5INqSP0A0HQC/wxSUb6g9VG6MUk2CRYJ1eDNWr3XTsAmrAIa3R8EMVEU/19AMCpdFBt2XZZdjHjAdAQDv2GTKOb44nlSFvHopDTAQActuyA2tTxlewChaHrj9H0lh9iMLKUk6m9IGW/qK+9iypCBSThHI3XcTQ+j2qr/yHqqn9QMGrAH5ML556vBiPT4LhhqqncQLpe8DsgZs/4Lff2P86xxD4QlBB1NfdSRXjMrO4p/O9DClWpS/VVojz/m9HuN5zzEpb57fy2PsM5GsD0vE0jPzOafbp7Yso2/gYAgjiIjP9fDCYa8WsEAVq+rdGldwDGyAZhhuV44k+v+SbHBLlp1r/G3239onlvdJ7+ZIJVQG5LLK+8MrmyqgMAjI5U3RgmUEM6XZE+yPe+MlUPhvW+aiPwgx5wUCD94RpQvwP+1zDGrs0ogQ0h+qi54Ukgqyq0bfsVUFyLzPOVqKr4EUmZ5Hhif1j2oSjeyCaKczRep7p2fB6utxiAB9O4n2ZO+xb6B/fhVHopNK1LNNbdTcFMoDYHFRluRjw1i+pr/1SirFQR6hcL53yKByKzoDwf1VSvz6e0ICKIue1X8o7e5ZxI7QlNDIj6untzweApZPC6AzBeLfviH+EPew04CAqWvhm3hP6PbifvzdEEtxdxPZtozgu+ZECQ6YN5t8AdNFLX7yzmpSUDEIj9mM0GC7Izr4YYxDOqHodC5D0fD2l9M56Mn6z2t5fhHGiogoeYsZp/H7pOPDDmBBksOnFL5a/p2td9s2UgI2RX/YB+onyhq+09g9Os5TgIEvON1dzr/w+thc8XRSbCWRzckAgGNqkq7FGuZIorJJiA9Nl1sN9VCSgmZ88A9KcSgJrQ5WIASTTUXSyqKrsBQG3rOjabcWKMzMWyjiLb95KYOe02b+2GfYsY/B1o8mXuG3xfnmIR4Hm7qc7u74kFsz8+lkyc17H9cETjn0Im0EM8MBgR01ovoHCwoJ6AKsJ94zkfoqaqAzVV14x/q1N4syBIhX2Ge5og3gdAwvXE39OOdv+uXwCUh5EpO/IV7OxkMlYqdamm5/O2aEItQ9GPYTbwsCT/GAMvgbEHFdovy1PieZ9hH6kJPgOZIM2g44lfWo4+Jis/M+AqXJO29Z1WOJkMFItEwvZ9J7Hc1IwVle2aplaCsUT33EHH0zanl4d2VPyhzGfBMKyZVR1kquX52T4j49ZkYr/JLzfB3TsISnvkPhCDtiZd4MaV+5QZYEiKDR/W/nV7WmUEAKpuXfdBkXAWUp7NM7pip9jTq1YNnbz4hoZfPnUQsspGQEY5yav0PVr5n43nibi9JHdcxO1l1beuu6z3k3ufFxP0TLlnUv331ScZ26MfRsbWiOAz27sG37/oG25DqCClP6t4NGb9emJF6/rEitZx07On8OZBCq/Wp7tnEGEZMu/edZajP/lmXd/Q3BJfhTIbRrOFUBWjmS2AFGrvvOBLrq0mwHvl/maQA2ArM2YW7TCT49GLfsN+nxR8CjKByR22K39mu9qYyo3MgOvRT9KO8cjruc+x4CkxlFC+C5P7mZp+YPVCTap9wdhH87yE68nt6T1CHcHb+srymVAouINDaCng4ctCNelgAIlLW+DN84GGPNIfjEFusQGehF9D1EfTW76Wy4RV27o+AU/NRL4vHYufg8qm80RdzV/Uuo2HlKTw+8xbuaPrYig1feS44+zLHdsr5II53wLwKIrAzFAbt34Maes4ZGyN9PoHXhWzZ1xSEmSpqRpTmYqIQI31q9FYv3qsNlN4c6FJr8XU3DOIsJCBHteV11iutmrinrsGuubtjaKACRE0AHsDLJAXMhCC9y3+DSeCIQXvA+BvAOAq0Q8gjtKsds1xxZqAaX9UEJ+AjE+/1XLkTxxP24SxN5WU48lvW472/Ou81bJwPdntwv/l5KGmYRxZs1hKXsGMlZrr/cdVstduD/YEyk/Nht8XZT9aStafkuC1GmABxH/YBtWkg7od6I8nIHrcydkaITpp1rSv5zL71fbuL0JxE/LLeiLRL1L79DOpumK92rB1JTI+T25oCwH/jWpb1/eyZZYZegfLPoS3dRlyXvtVAEpkoVkxqQ2bv5BVc3MRGda8voFnxJxZ3ycpRr4LRAQaZ1OaBDG1ND4L4Nnxb/Wdi9fFfM7EiH6GL/OacRx01MBEszMPZ0Q/zafvqglOPIkxCFVzZ/NAcUTAZQgPFdJwRg1Q8B90DVwMwkMKDA8e0iKKF2gYw/ae+AJ01IAgoKHSXoJPJ49VS3yPYhMUkgXzYTAc9Fb+nK4hh5S9mBuiZ6v3Rs9W77UXc8MuuPsRJE/gfZIn4EqvDe/1mnF86gi+bPiT6gMkhUJF6HfIsP3n5paGrj8taqo69E20FmVIIMUWGwgI2MdUQn84hopTN0NblcqIF4YFq7D+Any+60GIIkPKlA8C4Mfg0IdHJ5jKpb3mw8fxxBEAIGZNvwKGcX92nhZ07Ul4yoRtH1XUT2Qlz5aVew4cjddlS4d0AAEAfihuUNu7P1uu/RT+W8AiYDpXCuIjiFBNhDZNqk/5Dac8sdkbMYOxyJtHEjHy29IQyqlVoLCG2XK0XwGIMSPFDJcZlmK6n4grNMHnECFMBEGEOl2qr+jSnWU7pc4ZMxQDr6Zt81aAoEmv1W/YJ/kN+wQpSmuIXw98hnOUoXtXCOITheB3m7r3fZ9hH+U1m+lt/o5hL68gnAlptzZwpz2jalBF9ZdZIV0wmGLItWmoBg3uPkEYt0YQPn0r5EYLkICqluzWmE/aTaG/MVGSgZFK7Sw7JsHjUOiZ7SflhtT7E4dRccCZYfjWDxwMQRh6324XeJXm40ywmZD0KnxPiKQ9U8TtPQvUiABJjtcYeK5rdrnnEHiua5axPfpBYujECBDDR5Y3ver2Vz/++p/yFN4qELHPb7g/IsKBRKgkwixdqi/5dGflmziNsf2aIp+HmfqZy7IzDeX/YTnajwAkmZFmhsMM21N0oybV7lLwh4gQyNqaFkPzLtaEV2878ikU+YlZO/VkLviiS3eW37BP9hvOsYJU+LXecDn4DOcUQ/MuFMTHC8Hv8+nuj03d2cdaFh7s8fUkvULyCQsB/1/JNNKyGy/AK1I4chnai0l4c014s02Yfx5A+BNbIXbYGVtTLxmm9hB85o3I+SGFIDDXcu/AKHFvvuT0KAQPRfcjTbpiWsuXoWnPAXBBiMHQH0ba2h9KtRf10+F6Czkaryv3HHhH3x5IW0dj1K8x4XrzVUfXyZN/mlN4u0EKVeXT3R8SYR8iVArCPF3zvmVo7pKJe7/hKLVBjH7mwmANM5gZeWWRxLYrf5C1M1bO1riKfmfq3nGC+AQazYSdaeretwWpoOPJR4GSsR3FdGcm+MIwNHe+37BP8RnOkYJ2LZuX33TP0TX1NUF8rBT8fp/h/tzQ3EXp3St7I3IolWdrGICFitDvSAgl+vE8VBHZvq2gPZeEu1cQqkaD/2e9CH9uG2jYBQSgmjQPuv4fmMZNyGyQF2csE5Rq5d7+gwCAHcfIlhMVk3wr7h9aQT5fglqbvgwpVyOTpTcEQ38AieRJYK5HoQ03YDsHsGWXI/0Gb+8+BPaICmwAgAHH3VN1dh218091CuPhdWXApA/GXPZhRsFOg4DpteB4r4qvk5FdkwafOoznpw/iT7IfM+AgonXg1vAf6WZySBlrqJ/S3Mk+zCzIfWe4vvtpTf44gTvoSXspZ8h1cxFLBUtuw78oL9td30LDVd/BucljeR9VjUZtC9b676E1w1/hL4OKHHsB09oD7w3cTqsCt+Ki5PF8CSQCAAgeYsF/0SXEhPgp6gB7Ob6A7IsQn82nGs/zj0N/E697B4kNFvZyfKaAt4ZgejNwijOb79LRep/q6evhSPR4MAcpEHiA2poeAADf/Vhn7YUnVDX2gYQPCg5cJv/PepWq1QzqtuH/RR/IZozIz3qKuF43ZN2M69hx/662dJ4Jy3oXClOcJVxvKTuuTrrmgMouRhnZFEQyjZScN+tqAFdzKh1SG7f+FhhNvy6B55WV2FZDkRUo/eGQcL1lrJQgISafADiFtw1M3d0TQHV2ZwYAQARTCv4gwLfsqiwYU3OX6Zp3NoAWAH2uopvStn4nQHBcudHUvCTzaBYMZxZDA44nCnYdLUc+GDDVqczQ89parqIb8tu5nuxJKPFxU3P3J+JqV4mXHFfbEPRZ3y2TLaLrmjrO8bSrbVdenOWH0JG5+f60rX0XAHyGfawm+GPI2BrlN5yPOq683HK1F1/v8yFSfk3wx6jQDpqa4LOJ+MGtwS3DcS8SWWju0wuGtKZX/id65JxHAcBZF3pa1lvrWOf5JOBjhk02C99v+1k167p8JQXfdYMgJ7tfD0DEFXnTtfTQiUv+IiOpf1TftObTctg6EHkOBQG6jKRXAvgpADCRS6W+I+ekop3mcKz/Y3teCQDmhoH6qtvW/Zy4rAIJACiZsMsuKn2v9u8HLtxlJ0CTw9b+ublM4b8Ppu4eiEym2+h3jGBqUp0GB7ss48PUnf11qc4EUAeg2/HE9ZajPQQQbEd7UZeF7hMzPAbWF/My2K6802e4xyHPn8um7P8tv53jyS2eoo8burc/gcOuJ591PNkR8lm/LmNrhK55h6ds43rHE9/RpcqVPWX4IWz9pwDgN+xTpeD3ZK7NbsBUp1uu/Ibjaq+bbFEKVSMFn0SjGboSgNSl+qzl8GkbQxviUauvf75c3geCR+HQHSKz44rgTXTf8Pl8NJtozfo1FlJK+q4dZHeRT9eeScL893ChrRlWxDWBbq2u7S+cSv9Dbe38KlxvKQp/XAykrcMB5LgAy/E0MSgTEKOKUL+sCF0KAGpgaCZ3916J8nx3AOCx44QI6C8+wbF4MWlmbi6H5M1lCv9lMDT3XcCojwBkbI2ueafauzALJptNeyqASga2uq641nL1ZwDAduQTmqnOyW+fC7KiqGDG9uTNpubthyIBC8eT/yho52ovu574uKl7BwBsOp58wvVkT8hnXVeG804Yurcybet3uUJdrUn1aWTL+RhYk7L1P2Y49ZzPCOJsNjzbmqnOSDval11PFpQEvxbo0pspiA/Om5sGQDM07zwH+P0a86W+plSDnCuX9IMoQVUVt+U4I4P/pFuin+JDoaMKAj4opCmqNPOfQ+QcXiH1h2LQH4ln1lDZwcWQJxEKvSRbW+7nRPJmtXX7RVCqeKPHRCJ1FIA7QaRQPijPyApoi+rK7aiu/AYAqB19S7h/8FsY29YAruuDaaSLD3MieRhKbY2JZOpwAHeO9xynsHN4XQEYr4EbMcZOjdeMChkp/SHZWVhLuDF1FF8xwttioNadjTOGLuVTfA/y5YE7xergDXR5/IN8CQw0gMFQSAbuoEtFulCGVcTICV2PLydO5E9yGEugkBIDeFx24BVVwYaIjgaMRILc0N+pIBWUda4sy/GiZ2qofU9Qh/k0PpY+AO1gsO8xbCaPWFWwYe+Bz43cQxb2MnxW3cFP5V+3HOzF3JA8ik/iMGaLYazz30k3gUDJ4/jDqhKLYWOwJDAEAAzHXsLz9I30lGisfxlZg5EPYkLllfhh8kTe052NvZHCkP9euk9uViexco7Qn07q4MKPmDwAO6z5qMsQNZHPeJUt63CUqzGnTCkahYK38XDs0yhRHgqXvNCqp/8gjBIPloNG1ZUvlDtBJOwxMhW8rBHLEFkNDbeBSFFVRdcUAd3bH4KQIVAtRRCZ78rkq/jHgK657brmfSvP4W/SBH8q5LNPtRx5keNpmyxHu9DU3YuYkQsADqUd7ZLir6piEbMc7auG7n4KjPkAYkrhEaWoG2CJPIloZrLTjv5Afn8CSgKMRBCUlfGzXW2t7cqPGpo3m5lsxxNbAYIgFS4TIIGueedbrjx9bMWmDDTptRqa+34iTGNFq9KO9k8hOGxq7qlEmMeMfpSv7/Z06c0AgH7ZE+87a6/LS1ooUulHay7S5yf2FZXOMrbFDm+V/lBwmM6kiLWv9lRCh11kaxyGviW5BwB4Vf60V+nbmA1wFGdvjnR0mkK3G9ujHyIuCEg7ycWN9xZPKfj09qPB4/4OyuTSprXlTrAUFnJklwWTzgs2e4r8q3umK7+etubVTcnX/xdAgBuLS3qy2Fmy1jFh6s4yXarz82xNmy7Vl3Rpn5Yl9e62XXlRNshqIPMd60rb+veKx3KV7LFd/paueecSMBPAoGI8xEyJ4hICxSKZtsV/ioYox0OlUZYbxnL0Z22HP6Jr3mzFFM8RjmvSa5aC35tnayQAmJr3ecfVPjPRM9A1t92Q3kkgNChFT1uOdqsUqsnQvFOJMD27q+6h1K8wNKFqQUCP2B7dbcHJlxSPLWLkVF6JLyffzQe6zdhNRNARuNF7giL4lFxvLYXLGllFtsZmoDe1P+rwF/L7EtC0rVlFo2IHYdT++cz/IG29C4UZd0rU1pQE6nhg6L1l7iVvAnCpqrKj/DmystctsjWjviN7SnBkeDrperyYO2YKb08I4rbi32oAIGCXZcj7dPsQTfAnRzavGO26pi7UNWtrytYv8pQYdDz5bV16X0Hm+yUZWJ+y9auLx3JcbZMArsjbpOr1FD2AkQTh0VdFsYilbFHs35fLWtEJHAKAtKM/QC4/rktvlmIacr2MYIGhuQuKAiQ+Zpim7n7S9eS3JnoGhuYu1KX3XhCqlaJH0rZ+u655c3TN+xABTcwYQvn1RjMESxDQLToiCxa878LiBlo3JSqvwnnJE/hgrx5zZT82BP7ivoAUfUG+mp4n16VLbY3FgJc8HMD9FAxEIMWOMgEYgDK2hjTNha49CcfdG0U2hBpqS8rSOTJ8MsYLvgjqR8AfKXuOqCQoU3ycXVfjSHQ6+XyDU7wurx2vKwBjrKa19nIuHUMhrW9AqX7na0DyOP54SXCBAEgE0ofgQuNFPtNYQ/3VF+HT9l5oYwO6+QS2jMVBY7xEPcZLdHFGMpqvUHU40K7DSvsAJt9DfHngDlHCK8LESLyf92cDdWBwQRBGwZLb8fDI1Dxi/0PYmN/f2hMzUX7RoqwVmOW/F+uKTyTeo1bYy/BhNlAPgWA23156frTHT+dDwWBIBECQMNCIcsZDwJQ9NKHyEjEh+C8qrNWbO+uXbNl/0tYNfgFE+xbHNChvwUv1NU/zcOxTRcO60LVncsRM1Nb8ANvOdKTSJyLjVBFCwT+X5XHxvFqMbTwYQf+1FAoOlTtJDTVPcDR2TtFhG6bxABFBDUbauLv3QjBXAwB39/aI1qZLqTK8SxWqprBr4Sp6RYqygbUu7AyF4jgwpPcZFP+4Zd6qSlP3LnM8ebrjyW2OJ87SpZoBgB1PdIwVJ3Q82eF48muG5s41NO8SIXCYIdRhhmZ7liu/5bhaGUlzJp/uHoaMg8H5O2PMSHsqP2OOlO1qBbwhuuYtRObdLHbq/JpUDTmHJh8+3TlIk+oUAFXILMaICIIFzwqYzuHIvIu5jIAGlL9h08vUfo8PTyhnTfgxAI/lDvV/bM8rtb5EMLx588UQmF9mT3kkoJFc1vyIsW34I/nfBAZstzZwX+7vofcsvLn2ulXN2lDqMGTScTk9t/aXqaVNJYsbkXbrqczvYHZ4lZ5be7VX5S/rkCSXtzyYVS8a7UewnIbQ7QAQfKpzXuiJbd+AYj8Awfdu2jJ04oLLnNaK4bEf0BTeanhMrwjmdH4QhhnMwC7j6NGlOo+o1NYwo8Gnu5fEPXGO7WrrbFeerktvJoOsUqW1UWTaal8wdXeZLr0LBOEYQ/OONTQvmXa0r7ueLMM/xNKfIdXkYlsDwPI8McJ5wyDXdrUCP0WTaneU3+xoI2KTmYpKeBg+wzlay2TMhJHJcBVEIMrYmiMAVAMwsnNpQPkyec0rygIqB5EgN3Qd3Y8RngMfsGD2JSKZCnO0/4dMaCrjJY76NVWV9/GO3qNR6ItY8PtGFpViRtuf1KaOajjOPsiopDlUXXVVMd8cAMBTxaUA+VCoqb4yn2MhH1RddTfv6D2iZC7BwC0AoLp7l/HA0FeQWTxr0OTLYua070ypNr694bF4gVjtU2RrFDPG5F/aWWiSzy3KHM5dZ7rPcC5IpM0vW472vOXIj+iaN4uZYuX8hBwsV3vBcrVP+3TnQE2qz0rB75bCe4+heYMpW/+Gp0TJd5/Ahs9wjgdgMcNfZGtc15Mjaw9msmxXK9j0kEItRalvRmDsVn6WLPyG8x4p+Bhk7Ey+rZkZ8NlHENCUF9Apv4YCbKiyWW4FkENkha+huwHcnTkSABbO+Rolk5UO9f4CGXtXBBrxa6gifBcPDO2FovebgsHbc3+IGW0/U1u2fXWETJcoSfU13yXTKH3HFZctZczCpfraK8faeKaK8G08MLSsaC5pCoduBQCvs3slItHzAIABDbr+lJg9/YekabvED38n4fUFYNZQv9zOt3itOA6ZL7ICwzWfxc/I2Qmu53HAFVg2Zh4EA+lDee/QdXQ/McF8Gp1jtCzsRozECfz1rGT0yOjpg/BN36N8WnFGSuxc/oA7AydBZI1k5kdbQcEmC52UQix5tFrsv4dezg/8JI5Xy+zl+BCbaBzpmw+CJnsRKZibwSL2ET7WnYfTR0qK8gPLAhoYAWTSXGV2nLGekCCrbOnPpGAdpjc5s2o3q4Day3g4nh/FtWEa97FSQm3e9kGk0sdjNAvBBSAg5atieutPRm6VCHL2jGs4bf2dk6k6CgV7yNDLZv5QMPAiZ2Smy+1CElLWSgD/LNvX50tQQ+2F3Df4FTBXAiDo+pNiRttvWDFxd+9lYK5B7okq1aa277hQVIQ+M5UJ8/aF42obDek9BmSUhbKcB57typJdmtcGJiK0j/MVkKbuLbYc7QWA4Hhy6yTHFYbmXUiEUP5RU/MudFx5ZnFGSsB0zhXEh+YcsmzymQfAYcarmfHc+bYr1+X7C9lyhvcDqEH54KVQigqUvgis+Qz7FCnw3pwjwjzqoBFBz2b6UK4cY0w52sxkXqNsMgP7YFpqRcPLpKk5+qMJSW7mxplgOY2hO0TS0apvevkMrT95JBiCM5wKDgBNhYznh96zcFRCUZdq4PQ9fq71xv+k9yaq07NrdrBfL+scOE2hZ+Rwet/iEqQsqY/Qu2MHAHiwXF9rdk1/clnzdwKrdnw+F2Rxq/0PDL1vt7+JmGWEHt92CSkezS5Iu+1Vt637ct8nVnzztT2nKbwZsBztOU3YrwBYmLU1DgDXdrRf74rxidgHoL78ORAzqnSpZjqe3AKQyhJUTmZcU5fe14sWcz6f7l4Q92QJB1rQtL9GhGX57z4y77DLjOeJVLUu3VmOp+URLTJ8hnN4NohSifI+pJN9ZvlzM/y6fZYQOLQcEXdW6a0e2UVS9thYwQpG+Q2tCcHESL3fnOX56p7TwgNH6U8ktDxPNY1Q4FZOW37Vsf1s2E6u1NFDhqNBg6E/Ima03TQyb11z5Pz273MiVcG2HaaKcPdYQRT4zCeRSM7FGPYZ8cQKAC+U6yrqqjd7qdTPMBw7F7myU7/vVjG95S5OpCp4YOgbBeO63iLVsf2TWaLNKbxNYdnag5rPPo4ZbVlbYwGwLUfbJeIPUqgajFHKnw1gtEuhqj0lhgDyJls6KIWq0qT6XH72DjOafIbz+UTaLMoSYQr47CsImJVrn7U1DgBWjIelULMYcAqDzCz8hnNcNogSRvlgb7z4gCDl9xnO5wWhLOk/EUwwphWVmI6zynxt9e2ss0h9yDdXefXP8H8GD9SeTWp53l6aKkL/5kSqQnV2fzIrUAJkbI0FQIfPvJOmNY9sLJHPTMoFcy7ieKKaHTdAlRVdxequIzD0p5C2jkf5jDvJscRi1NeW/axFc8OLnmX/CfHER5Gxf4xg4K+itekpFRluRiT6eeRv8DnOCrWl81Q5Z+b/7czzmcIuUEGq/Jn4v+TR6llnIVbCRdr3ON1jPkOTltCMnaaOdBbgw9BQRUls8N9Lv/Q9QqMOhyhTWpMHLtE8nRjObqiHgYYygQtOHcyLgrfSCNu2V82mOwMnF8yDkAm/JLCew1jkLManHcBL78fdFb/G17QuSkQ/pk5w5+NjI0ESRuGrrOBQHGuMl2gk0pw8Vi1Jr8RXIREqmFvxLMd2TAqhYDkLeL757OQ/jxyGP6/O9ppwFCCls7BRWWfVc/CL22zZ5Qjo2jNiZtvv1aaOM7PEcDkj5wFIUlvzV0RVRdmMEvKZKfKZY7L0AwA11q3iaOzJPAeo6L5UO0dj9WMpGImGunVcX/txxBJ1MLQU+XwJAFB9A/PAnOHnyWsOpVp4ONpMWcWmKbw9kbCMH5m695AUal8wIrYr73GVnGTmEiO7I3ISgCADL1uO9ovczrDILIomIqYvVwI1LrLKSeVsWECX3sz8xY0UqjpLMjxKBJvZFQczthJhsSZ5EeB5uuZtSFrGhcxkBwzroyITRJHASPv8nS6bGY8qFiPKPD7d2V+T6rMAAvlBp+IAVG7MSSCtyTIptBOCyTxg8EvkUytApKfOb/LSH3NF8AvbLDHkSa/K/9DQe3e7sfbaF86XUWsf4qwDB7isif7IcfO/Zc+qLptt6TaEEsVqRMWIHj77EWPb8BEi4Sylos+fAMiYvUIOpvxejb/sTnLskFnPxQ6ccbq5eajOrQnEc+3CD21ZBsXF42ki6SzSeuPBieY1hbcSxAnLuMTU3X2lUHsyU4/tavdkFimTAQu/4XxICj4OgMmM59OO/ovcznCWFNvF2P4Xg16LrXGXoFTNhJjROrrIykCX3vT84Eu2bY7MewcR9tYk761JTxma91zCMr4DkAqY9ucF4eBxbI2lmG7LL3vyGfa7NMFnATDH2+PYCVtja8Jrm2TbEXg+ltEv8SUcxDyQqTnLm9naYXPo/M40JZSEz7xdTGv5j1q78dvwvPnIV2SUcrOY3vIdCgbKZq9R0B+loD9a7lwOorXpDrVhyyFlSw0AwLKPYebfjrURJKe1PMQt6hHEE3Xw+yK5DSyvf3DfMs112M5BrPhHYy7SpvCWg0FuIm18xdTdA4VQuzPTNsvR/lPM8zQWCKz7TecMQXwEAMmMx1O2/mvFIg4AUqhGZPzysd4tzpQp7hx0zVuBUlsjwVgCsJaflWxo3u6Uka428toiG6SNCsKhQqpDNKlYad79SStT+hQ07QuJsHQ8W+Mp+nv+HPyGfYoU/AFkeHXGw2TvWQhDlSvRHBduLfti5/F32UQzhN9w9m1msT4N/9c7lOZIB0H/9dTc8Ix6ZcPPoVQLRj8fC5q2Ssxo/Sn5fSXBJQCgUHCIigjWSybd2vR3tWnrvmA0lxsCydTJAG4eq7+c2fZvdt27kEjVIOAfIl1zAIAHhw9C6bMzsuvAqQDMTuJ1B2AAIHCnWI07sdOydvEPqYOcRTgnl+nBAcxLHsffkd34hL6RIgCANDrgx8yyAxCE/yF6amevSxZclF9sETmFOzduO+rKKicBjDB2z0lxAJDwYVr8Q3yG1smr3Pk4a8wgCgM0hCcqfkc/G7nONA6nD8K3Coh0xwLDA4MwieCT6B1b+rRgSGYoeMHIsprTvFY+QjYMG5QbXRBYkyrx3embqy52LqOAP8aeJ5G2jkHh4lIC8COZmoYxAjCTQUZDftYP1ZpXl4JRVXa+thsiYEwJaSICiuugmQXKR4Q1TqRm4DUGYNTQcCv39J0D11sAQUOoCF8r25rfEHnOdzYIlqO9Jlk7v+G8Vwr+8MiCg7HEp7tXJpQ4i5nSiikFIIJMCnxZ2I5WVvZ8PDCTgzFsTX4KKgBIoVqR2Wkt/o4yEebnORQSjPk+3XkfwAkh8P6xgijMgGL8J2UZv8sd06TXpEn1xXK156Xzz9YgTxz01TxFOyYaDwDgKRJJNyw04yxqsA8kQ8kRW6ORVHW6F7tq1gvuPYGfuA2hhL4jFpZRa1/iAoUiDa6q1waS1WMFYCYDNqTq+/ieFzX+7IkbwGVtL8m45RsrAAMAkIKtObWFtohZovxOtzS2R+tfawAm+Mz2OYFntp8l0m4767Intajhj7FDZj33WsaawnggZTl6QancZBEwnTMF8dF5wY09/Ybz/XjaOAcg5XmiD3r5AEyW1DvluHJSWS9Fcx4r25WKVZKkUNNRPmNNEjCjwNYAe/oM5whB3CIIh41nazyFm1O2/tfcMUNz52qCzyq3E12MrK2hcXajc9A9Jcb87S8YUzE5QaqNLqr6BM+xV4hAXFBuqWMQVJvhJC+Z8WD4V/xH8pkpNTTcAs+bh0IbrMPz5sBTY/O3TAJk6LaYP/t89cr6f6L8b4KEYgE5dvY4SaFQUi7NEuWD/ILTVoACvtdka9SOvsU8GPk4lGqFFNuopvp3orHuldcy1hTGBoPctKPnlcpNHgHT/nxWQSn3+R8QMJ22eNr4AkBwPbHN0DyFMgGY7PvW5Sk5UHxuEpN2UT4jRaEoMCMFT0f5tWYARRtAAnyoqbtPaUIdQpkMlhEU2Rr2FP6csvXbcsdM3VkhBb+/uLRzDCjmSdkapRwxqTI+VkrYlbIxtqDqk7wouVSYKRq1NQLuHIO3vjvWP3fV8k+Roduqp38hlKpD4WdjwnX3hBCvMZs4A/L74jRz2pd487ZrUd7WjCU6MDqGprkltkYpifKfZZA9T+Yks3cWqqtnBUeiZ0Cpeki5geprfyvGkbj+X8EuCcC8VtgL8cEi5R4CQUsdwUfoG+lGAPA9il+lD8MlIOgg5FggFBRs8yn8QOugSUWK86FvoCFK8noOYn5ehgpDIe2/l9Z41WyqGvi1TYjo69FXNuMkM5/iY0LVYaVdhwPGKQsCGLbWj7VyYJTUKHUI7zuupHYueyZjNGUuR37Mqyh4ZKHT/x+a8AfT69h+OGLxM5lREd6yDclTWuGJoq8GQXCtmEsBf+Z5O66B8lF1YtetmuiaE4GIANN8EGnr3WVOO9xQ1RWbGVzBBBHsTL0gLVUsGVk6Zm3Nq9w7UH4nIG3NBfDEzs6TE6kK3r7jB0BW+UqxH5Ho5xSgRFtzifOuhqON3Dd4NJSqo4D/SWppfGxKmemNR1ZNo5h93zA1d/+0o9+XKSsSv9Cl+hKyHAR5Kflp25WXM3Ze1c3xxBZTxyAzmnJBjKzj0+N4olOQ8hPB8JQY9pTYBnjlHGmtXGaKFHwYgJoJdnpSSomXOW9HSpde+cyyLHK7TNl5ivxjY7R3mfHihGoEilH9zzXvNjqjHwRzUIV9SJ7ZDKUV2hoiSK6j3XJBCjmYCqM8GaeSMatq3GtOBlKwFzKelTF7/5KUSEnDdktlSlrYhwme0vFCTuFkPCT2bHnev7q3nK1hY0tkUXKPli07O03fur6m0CNbv50rlyLbmxF4ofsC1sUl8QNmlGyA+FftmB5YteMoclTYnl75SPTQ9qchpsos31iwJoiPKdrtlcwIm7q3zHK05xjkuop+pwk+G4W2xgUQtxzt4uLSxMnAcuRLuvScfD4XZrgMrFEsYoJUEIBQLGKuEps1qcr5f6KMrdGk4KOyJQTjIekpuSo/+0WT6giMQzybZ2ty3gxNYGscxXTfRBkCrJjU5o4PI5V+twB84Y0BJM9rBBfZGgjS3XZtMfmyiyzbqUJ5Di2XbbuSyigU7QxICgUp1sNT80pOCrHVrdD9qWbfUnI5GexIrhJq4lIrCodWcSxR7ol5GBxahkDzo2XOjQvVPzib+wcvQi6I7Km53DdwqdLkl0RtdUn5reobnMOR4SPBrFNF+AHRVL/LFHymUB6CVJgI+xZlzGrMaNWl1+542ibFIu4pukEKfj8KNwRcAANpR/v2a7m27conNak+mX8sKzf9KEAqK0evFIuEp2iTFGV/v8tl3OqaUEcTYckEtsZyPe35/AVQtt+YgYXXYGssT9E/wTRuUIFdT1ObOz4Gy36XJOihjSEkFzcAsvDnX5ga+fdvC9LaLPWC41SjfBCLYdsBlON22QmIYCDmEfVnZagLoclVdoVWlW40FwtbRYIdqZdpvPVnDgHfeqTLLrVc7h9cQGWEXiaC6u5dxoORr2DE1niLeEfvd9nQP12OTFz19C3i4fhhIHhUWXGvaKgt4VD9b8FbGoCBlmHZL4CAzv5RtYHA3WKNHOTPpw/k4z0f6mQEG7UOvOJ/mF6ZSD1oPISupe/GP8IXcABzADBs9PnvxPeGP8efUXU4GADgYND/AP1A24Br3Tn4yEiwSMHKEgOXvroMhpjguTI8ShXVLmYWZ+VNgYIFD3G4iMOHtnFjtlnqBNGN28J/oeuIx7diqqtnT0Tj5wIwCYBM2AhduwXx3dugFhTZMg8jOynkM1MQtAOKW4uGFKKyYqezocpBtDZdrzZ1HJjlbBmdRsDYEJvju8arNRkA0o0mBbanLw9tTY6boUBSKBDi4BJCLC9fTWBnoHr7c+pP+Q/a5Gj8wyjaPVW9/Qu4d+AyZAJXGtvOPhxPHibmt186xT/zhqNcGqlOxCMZL5ajP6mU+xVdU8cSc4ViWu8pWpshhHutRL+EtK1d5DPcbzCjJXtwW9rRfpjlYNgbmbe223K07yumWwX42DwOmAz3QPmAicT/s3fe8ZIUVfv/nqrunpmbNydgA2nJGQSUHr7HhQAAmTBJREFUZEAwIGYMmPA1YhZzQH++BlR8zREVcwRFERAFFSXnnGFhYXfZ3Xv3ppkOVef3R/fcnTt3btgI6j6fz34u9FRXV89Mn6l66pznab1YGAVlDEk9bqwpatD7vJIWInUTEjWAc55f1ZLoV+O1q6P7/LueGj207hV1dyLbX6PjGw8wsOdCdEHT3MzLSDp/becZK7r/fG+CavPkKqjuMbulQ9GGov9pO3572u9u30f9qO+JZtPbHoxW1X7keypOAJPhfciHfciEGQqFcO/YLAchxZqN0uVqv3r5s8fYXiulyi2rXtJMwHT99b6DKjetOA0lELB2Xe3Q6KH+v69+9X6bSTNpG1rBiJZo/ayKsD7W1JLooijIlgXWPVOUkle5y3lze5LZu2Bj9fPExWnw4VKYfVCVGeQszp1xEnytvRx/QmBPAIX74yT4rCr/Ap5UaE8o+ffVNo+/eM5L5JoNE+0uG9UxscbSItYUfSbAY16xAjMbhULHaZ86zw8bd73Hg3/goRdSrT2PYl4TrB2m87MP0n/WIugZvTCSdH06v0zrulcfa7lJI9LTNUXdr4khc2efqctX/B9Nzknp9LbewR3LP/TtpQyQ6rxytevuwQ+UVycTZuZKW2WttnJIggQZNytqQuia3hcy9ncl1DW9z2fGtDMbD7ply59G/+AbKOZBunrtU9zQ8Dl2x4U/3Zhrb8PUYIx2Mc7GhDFMq+e3VZPoF6UguzOw7mkAzsudXs0dSWbv3kh5E7ya4SSzH40C9z6gAzCqXB+n9hft5fgMgR0BFG6rJcEZCneiLBWhVMSaelnUqAEUz3mucTkxrFdpzuxqubnaoDfziCoVYMZE5Y5F+2rm5bu1JPyzwEHjtQXw9y87hTh5GhCKQrBqkM6PVxn40SKI1j+S6j1Z7/B64d1p3bdpX38r59gBOto3PCupBWT2jM/oytWfZfQ8JEvmdNrBJeXvaSXKAKrzKn3dtw+8P1qXTlza1N6+zK9d1yp7M8Vs3LxG+/pPYjQ5KEDgH1tznO3q+FFjW3ffshcxXH0xRWzSVauPdsPVs+yi7f60Mdd+vLHBtX+b9eLruKHI6FgPRy24f3SJQekaWd59pvnW9E+bT3V/w/y8/Y/mxk0hXwDC+6Rv2ifM+zq/K6d0/FjePO1j8qbkIJ7mZ/IUDCGGkBJzqk/TT7T9Sf5SuoxPmV6ulHVcH93M19EW4rYKMsBtTPS+5lowrnKhXN54uPxPuXJM1oziSXjMPsS5DHEflgrj13LmyDNjXLCSu+1qmZRB1f6B59OcKp8p0Xl9o92nHbXgAX7b2ExmzTgTqJKLRuXiUZXyr6W7c7PYrUqlPGR2XXIKnR1fRWQFhUOBGU727P79LZXo/jVtGGlDpDI8v/xBH8jkhGKl/IdivI3IZHpPS6HNSZFls2m1APZ+evMhXd37ZvL3uj7OMlm2p65cvedGXXsbpgyFO3Qsw59l3tzYeCB1wf3DcfS1obj06WoS/TrJwls2nnwpLuLtysFa6W3VJHhjNQneMFgrvbMUZC8T4SARgmIHa/tSmH26Ggc/Tp0503uu9Z6rM2e+QgvBSVXwnhuYeHdZgaE4taN2IzNn/0VT+YEq3isPOS/nKjwieWrwhM9TwRl67+1tOoX3qPRg3wukucwnU6KLRssqqCd2j4W/HDkQWj+8z9wvqBCrkChkKiTx4mnf2VxaKsmiaWsfe80Br0p26P6OWlkLZAISrh7ad9pvbiyFy9e1CbQJdJiUD006PTSCm175q+aLzEb4oYMXXN7ynMm6rGZzpUX8l9SNtiz1SuWWlW8RpVRvL0rZ9lWPqtyycoO1M7Zh6igWBa3Kb23mRjssJllwx3Bc+tJQXPpsNYnOKYjeTcqGTJ19cLAWvaGWBG+uJuHrhmqlD5ai7G0CexaxJhDYsRxl/28oDs/MnPmGV25Q5YrMma8xjpC291zPBHOPItY8mjrzQOPxzJlLaHoGVHEKdzkvv1NYuwGxRjNnb57Se1StnUDTvEYzCP/exA954uh6RshjiaKY7s6vFGPOyBduCT1dX9pcLh9mWvdys9Oik2mr/BgYKK5jgrVD+3X/6obIrh1uw0gFYdrAkvb3TtaflEtVguA6GJOZ52T2zOtbnTMpvG/lCmNwfk7jAc2ygP7B15O/13Viv0S19gIdHO7ZqGtvw5SQOfMoY39fAII0M6M2JuIsuGEoLn1+KC59vpZG5+XuiZu26ZdkwR2Dtei1tTQ4dTgJXzMUR/+vHLkPC+zcEGt2L0fZh4dq0emZl+955SZV/pE5+Q60Ngjxyi1MILJdxI9bmt2WnDd/VqU2pq1yo1PO1Vywt3Myraki1pgsszdM9h6p84Y4eToNawABiJXgitFTE828rjrnupGMDmlv66O97Yfkn2Fd6DuWGdO+sLk2ZM2sGXebJTu8ikrpN+RrtQwIwhXr9u351Q2hGYwrGKmoYXb/zu1vnaw/6e5ciTEPMvrzUUQGZcb0jXMJ9L6VIH1I5kbHmmqtg+HqS8hjTV38o8Tg0Gs1SSaX7ngC4nHNgGn7nZw1+ErdE0MZQwklMeu4vu0PssEaD5Nh+Di/V7InT0Xx0U1c3HaRuQ3ycqR6GzdPj22hwSLVp+phnWebi/hDrlA/9Gy/L0qKEo08n0VSm3axNzEryZ2PWu1Ma+lyzmgsPwII75W+6Br9fHIg70LxCFZqLA8e4KJ0Ka8Z5Yg0GQxBuhsvAP4+aVuvXc2HREHuN+jaANoTwpuqNfuwXlK5suPcUZeZNeMubW87xT+25il43yZdndeYGdMeGHMJkye+yxTSaceMJQgy6e68SQcG/4fi+yoAmaf9svtIFs+gSKuX4fnlXTuWVSdMgTMLt/ulv3fZLJLkSAp3GZnW/WXT0zU1/Yrm8bVVbtA4eSqjayo9YQu9EO8Xtugi1Gp1V9hwDaVtmDriNPhqOczOUCUg/7FMvcpfpqr6P3UopTA7KDD+CEWqqTMXpllwL4DzdjWAiEZF6nCjNaSoYqPQ7R+n4eUxXA5QDtMnA051fRlSnRg1hqcAK4ryppZETJzajzcvWFJnH7LWfacogcjIrUvv9p7rrdEXNWTfjJue2zDuMAjc8+IsuHHiliBurJideOAui/ZZCFOCW6pD2mf/VGPmpY009sBRi6+PF097Q/u1jxyO17C6+6wrarvPHlvypFjAI1OKlKPgu0pJsl33XdFD69qkHmuUPNb8417WvXT/etMucczXgAlLrtaeuPu3p//ylk47GB8IeIwMD+0//4x0bucGl80CpLPbrzFDyT6NJJZC5rrL1zS2C1cNduLGxnXAlx7o27W655wpuQVuw8ZASDL7xShwnyRfkAaAc15+NXXB8KlCTTnMDrPGH6ZIX5rZP6XOPgRC/VrWuBn1BdHICAWjSncU+J1raXgJaa49UY6SY2lKy6/HGms4DliryqzxyJJqEny0ecGSZMGt1vhfWqMvpdC3UuU6hces0edsSKwBgjDwz0odk2dxqVaaD4nzcHuIHm0RTQhuqq4LHpJz2q7vvKGxnd1+/t+1a+BOv7bvMMCbGdP+2TIV3ooVp25jlkpSLg1LKXpQh6sh9Vjjc8Gu9n/dT/9z9wQR0YBFWZttD4bdhESzWbjgC/7Bh99P5nYHPCLrZM7M/62LZ24wougaqrUdGL25lFCKRmkuav9gK5FPgFT7B3aUjrbNPpffhjrEp85+IbTug+QPngF85uXbjYL7mwcalMPsSGv8gYqsSlJ7fubtShDNnF0BEAbZjuTZJY3uQgHK4sD46bUkugC4gNwQ4QVA0CLWqDWcAAyqUhonA9dX4/AzzQdrafDPNpPubtBnkseayKtcImjFCs+VFk6PE8CGgXtqltifT9jKuYBWxLQHbo3QAwxSSwhura19+KLLq8O3P1pjt4aLLN7+935t33W6rv8QxMRm5vTLpKOtb0x3gQSSabZRsaatMkAQrIBYqMcap+AdbVc9yODTdgUR6yOz/yRdAWC2n/9J//AjH8L5RYBizAqZP/tTGy32HYbXkyTHMPp9rElbeVTs0HUDi8lJu+Z1tdP+wQUyc/pG6KY9vnhcCZjoDlnTfQavrx6vh/suZgUPcFvlQrllsrKZDUX/G/yLssW8uCgbonYMT8l28D/q+q75fb2NikKriYVgNWTkx7x2iO4QP5kPjSFq6kO2lCkxj4xeQma20IIRLbWuU+z4tbnCXayviJ+ku5h+GYyu0gf7PsGPmnRypgS1o21vx0W5dBnD1Xk0fKk1MCQLZ2b2p2jnhfchXj1wlIoc6BfM/VAjWSFtlQG7cLvzW3Ud94TTB3Zqf7uPzD6At1X/t57b+r85Fb2WURgc2pkWO3OSekw1wbeXQDAm01qLs0efY62zuyz+slZr39Na3C1dnSvGtY2cAmTe7Kt1YOg2smx3chImRojN3NlnjW0svc3lVEAiYbjNfWkLI3N2+bCX10Whe7Kg05w31+e7QJsXbaX0DUb0qXlav/pS4I62ol+ppeEIGSroeFkrIg1EXhRkexRWj6XmRgXKqiwAelWZ3kJQbuQHtxm1JLrQiP9HYP1OXmWNc+ax9nLyk2ZXlKlAoNVifwyynvKVwZrq06RhTBoY4vmzUvO9TDovuQ9UBXh2e/jIIX3PXfqhZPvuvnrbZGHP2mRhz3ktx5Axzya8A1gKpCpc6MqchWyYNXa0vH+XgsQZBbuuBl7rZK9BxmTRjYHvKiWrTzng0+GjA522r9ZR22XGCqzZaEeS/qft+JeZZ9/wdOJse1HKKtSwZrD/mCW/bGyX9VSGEVJ0zGev2fTKtlizhZFkwV2ZM6eUQvcU0LbM2aunbls/VSjtpeR9IuxXxBpXCrOnG6OfitPghnqrguBoqTPQGGtKYXpYIZY76jvTEANKqswGBlTpaREbpEWGIQDVJPqVEX9BaP1ip7JSVWqVKD2rSbtiUohgRHVs2XorBMENZNkBNGYjG0MyfUYSfGlA2q94QApR75OctYeaxdt/TMql4ZFrdXeutN2d57TqenheefHQDpW3q5XFQC0YyM6ddkv/z6ekodAArdWW0iSGKUDwWFN1utNJSRSplIfs0p0+ooPDPeqyknR1rtyUXXSzYO65/r4Hj8Dr9GKMNYxZZRbMHTXXk7byWm2dGRVQKW/UptY2TB1xGlyfOfOGKMgOB4LU2Svqzo6bD2ray8n/E1hSxJqsHGXHJZl+qHEOVcSTVnNp36jNUonS463RlzSTK8XXVchjjQGGVOls8TUOtKUuizAcR9+xxv86MH77zMtyEcrlMPu/ZgemKSAQmXxeI1GYYM19OL8jDaszUSXpmBmXPrXSVG5YLgKlndl+2hy6ujXNwkZi1EzveZjpPS03RQZ3qOxenVc+Va3MRxkK16U/77lt4Pcb/GTXkr1pjjUKwYpRe0FTyvCTzva1dred360DQ9NRb8dzo50qzLzZP/HLlh9QONTmscba+2XenH+Mum6lvFJbZ3yHUqlskjbX44XHVwMGsH2SdPxUWqp/x/vo3NoReqyWmR7ezxVt58gV4jaMZcsWaEe2mJc02UiXsp042c3UP9fLdEQFs06v893sz+gUNQ3v4hbfpZHpl6R2mB7PZCrbQkDErPF+jtMlHE6xw90M2ydJ2wVyC4CbTQWrG2yBhoJZw9V97/Zv8TM4HCUOlvGHzrPkHElH74ab7eae6+998GCc3458J1x9e2mFdrVf0vmHm04qdlvz+1Ut6SMr309P1zsmHYIg/Us7/1cNcxAxgHEVc0Tvnl1tM6/t+/RUbiPtCDoHFrUdy47R4XbVQMkOxJTufgwTF3FCwJdCUPXiWVt5pHbvVN8iqZSHpLJx7gCj+jHGm12WnK4rVh2gw9U9CYOVZs6svzVO5kbQ2X42/YNvYn1qdIbIOpk7e4OdvLZhw+HVVGuJ+XOr10LrdggD9wzQNuftZXFqr9vQFN3AujlG9On1H/tiglEKrH8jqV5Wz0TxaoYUHkDZsYk0sZkzdwsaKJKF1j2PSfRdyEUzZ2jrWKNh4I9MXWu9kryO29xUjH0eU8uvG32B3I3gmvZS/G4RDtz9Sy8rPfaHG1a1Ugjvf8ZOP5r261v3IvPTKSbt6ZzO+11X6appv7z9pcWCKP9NStz87gvufvtjrz/w9MkHQWATPgd0Sn7RCOUZtoa4Ct+eyn2IY7pJOL66z4JD0gU9JuitEt2zGpPm8zwtB3mGYz5JuVstU67RTud1DqTzNi7rpRG+PUofe83+p3X+/YFDwseGdnFdpYcHjlj0D9ddHkUGaTlwyYKuX0XL+19Sz5ZRIdXILh980va3beo4tmFyeDUD1cS03JgIg2yn0PqnAkHmzCVJFmzwZxIFbpfCJaQE1C2cbWjdm+M0+J96u8yZ5YQMMNaRSzInD4NaEBdaP0qkfBxYEXrGiTW+FLojq4ltSZB6NQNxEWuiINuN1m5vE0IV773c3F6KPyzCnrt87oWlVb+8OuahsW3N/Dnf8A89ckYxqTcKJpvffVu0Jr6tcvkDLxAaYo1zS/xDj7zW7rx40syatN12DC5q+zRCJXcKoJJ1BM/v26MrmXZL/2+mch+1GdGcoe0qx9n5iw+VgZoP1gyZ0r1r8gwdwHWU6jecmthfZWM/5XJ76Wjr2xzbl1IuDZudl5yqK1Y9WeNkoZSi+2Te7H81l2FJuTxEKbqoKMOof38SguAmM617+WYYyjZMAufN2moStXzuik2co4AsdebiejbuhqAUZgfWyRcoslogCAP3+iQLTqu3SzN7ZxS4VtFh2Hn6QA2ILwwRJnPhCUUIx4k1rhRmh9TS8B8tX/Sm13nTm489PYwp6OS1gPdeHmgvx5+URfHSXc58kVnxkyseafWrL3NmnamPrvxMseFhFEy6ZNa1lbvXLi/dsPzZUsgOGCyddLf7ZctfaHdc+LOxPY1GbXo0e3i7yumI5M+V0JH2hK/s36VjoPuuwSk5ZlXnlLYfmls+Ppi1aKnUEh+uGjTR/WvzbDvAdxcfg2oSDLmLpvbWFPfd2b7R7pPN/ZidFr3Rr3jsCNJ0vlTKd8jc2Vc1b4xLd+cqVgRXk2YHsj7WxJSiv0l7pX9sz098iI7zDQd45t4ffs5WHMsoDB/r96gdzccBiyHAUTPruK77s/KZDcmQGT7W71E7io9gaRv1gmO4cqF8pHKpjDC46c46feBk/cyIOLAQ4hggoBNFzWouJaTDT+PQTbk3GeSOaZ8wp03WTq1K7yf1RwQTMLGtnJA8GTEPU2K7EUFgT2xXcGH3l8x3x3Shiq5cvbfW4u2krXyvzJpxp1++4hD6+t8FNKfypmbx9q+T9rFpco0Y2r6y29D2lY8jMvp81XTajeteFQ65lh73dcTTwpnrlnZ+CckFvgDIPOKVrnNvxg7UfHXv+VQP2sFLpvd13TP4mdLa9AnPgvrlKw7R/oGX4LWHKLzKLJj7kxFnqScgLrjp/7X8Yd/c2O2c0x+3WFMK00MK96OwSM+veZWLh+NoSov3Osphenhg/dtERj8zqsTVJHhTvQQJILBufjnMPs36etYAGCYXC/Ze5Y8iursRdt2Ue/Oey4bi0ucmaydo1JwB04xWqbuqVIEhYFq9ttrHmdehytnJtT2/HdNJ6kznZQ/uZ9fFc5Idum8f3n/+/T2/u/2Zpft6T5GmSZKCX/WWQ56vkZ0wQ83GHCqOd0hTrFJIswovQiYufZSM7W3CGUBFVAURSB2SObp/cxOmlungoYt8vMdcBW51Jc5QyxP7B98r3effdXTpwb4T8dqWTa/8Y91xu/xyQjvtxxk3f+Gd//GxphwlzyhK/+rC7Ynz8svqFESsG1GJ0mdZ418jTVbyquhQLXrBKNezINuxFLj/x/q6eUuug1YmL4/6tTX6NBHmbux9qaJe5dzhOPr+ZG2t8d3NGTBT6B+gj3zs7fXdc1dLeeT7/1rd+9c7x2ymiAoz3ay2SCPba3urw2YoW1hb3L2d26HHNFU2ZGR6efs/lk02julLD+6cve9R00wQjoqEWVz1d/3qiy2ooNFom7NDaYejXzpHbFFRWsQaU03p/u1NkDkGjt6JZNF0hlbcP7z8snNX+zTe6Ky5rQKFBen2nfOy+Z2CkVV2xeCy6MF1umlyRlsUtYHVi7bGdR7PWFOJkpcU7kcR+UohzZz5Vi0NL96Qftqi5BXW6oubj6uSDNZKL2w8FgXZ3lHgPkxd7TJ/XutCwUnm5ezA6MkiLU0RpgRVUufl7GoS/W6ytoF1C5ozYKbQPwrLBaYD5fommaulLPvChQ/0X3HfmLWLUSuzdF5XQGDXyqqBqgynS92+82cxd3pzYnJNatnVbZdPSk7O3v+pPdN3PajbNLkpxf1r03t//42JHSeBzu2XVhYc/txZo2JN4rADNbrPvRlU6TtuqWZzO2XgoTsHH/nXeWvUb5L79RaHqLB9urBrTja3U4EVwSP9D4fLBjZRzmiLYqJY87hnwIyH2uG8eVTpjaXsu9m/dgy7Vv7ClG2ngmWyEmmR8i+EwQOjxfLCu2XttI/zhuozdC83nRnpnryBgJ7CHhs/kyNlkNtw1LANDG4rEmQ8eBK7giun0lScaHSLfjfZm7cgRAiCJ0FJESIy1hHQPSojx5OaR/mjn8szR7kxGUpuLs90PXq27VsvYKyhGoDCNvAmAL9qza709b+NseQLgCAT27IB+NB0oGiL98W7sm2fjIAZXNR2EkJHkT2TI7SoV4aO3LHWfckD3y6HXVeUb+qXcDB7whIYzTAL5l7JgrlT+vw3B9IO2ymZpkHNT1qe9d8JldD6tzaV3pQN+ozAuvM2JJ3XeXk0sK0FuL03oxbsmbOPDDrz2lLo9hb8rMDq60XWu8IZ9HhVblGIG8c2xfrletua8+aayVuCIonz8lNr9KQmB6aEnCRaSz4hGYk1qiTe8ydjOK5R2M6UAqNh+iLQ344KjNYbxOjA0UtGans7L71/v9L9va+l9Q6VnyLX3kHrCGxRAqSlUOH6RgmvAyqSx7b8YGhRaxh68pLB4KE1Xxvef+7NKjgMm7m2fgvBCOueveslwJR2yjYZCuLpUqGGmfj9/m+FoFFR5tNIcpas0ZcY8RdMZq3cCK/yqG0tmDugTankaRbcm2X25CjM9hXROYHRV4uMOAGG1uiLNBe+nNFIikxi0dr8WuyaBM3Hg/NmnVc536DHNsSaFKiRk0Kri7GM0h/xXi41Ro9rLF2w5ZA5L9q/a/XFN40ud7EGvGeFeWjkPZ0bL+jezu3QPbZSExT1zseTvv+mVCqLsWM6MEEoUzl/7kHPnNNM3hBavBHW7TcrXX7d+cvXXf/Pmr869S6pPnEZjCYsC+4ZWNYop1Zffm8hBJVO65KqV5c9scmpxwnG+C5r9MUNz7OQZ+O+XlL9mzJ1dyyv8ohRrbXIWhlTepJkwU2psydHQbavEZ1fzCfqm99hYPS1qtwN7NY4Z9jAWOObBc3HQ+bscg2yK4BDGjRgGmPNKmBWc6xR5SYRntaYoWzLIXNeevDsMQSMNXjndKU8POImsJ1fPH0Wc8YYcQAofkqxxpYrnc3kC4CNSlOKNfMOPnbBmFgTWVx3mb7du5Jld/z54cEr/pW4pOZ9Gv/bxJr7gzsH7g8aKIAtGWtECCodgasNZ1uCnHpCEjBqVSix/ZgXBJsu1qUVZMoETHS7rDZr9V9+Ok9qsJHOzFr+GT4g65rbixNt+5PcNHiSPxLq1EsBQ6Qd7GHWcZ3vYh8Ei5LhGMJRpcRchHDU73v+5ciKLJ4qCavafyN/nOr4O35qLh1e41cn+3OChnQGD/H39l/IRWY43+EafobfvfYU3kbEXDyJXcZv7BqWJ3M5tlV/bjYdto+12QLtGHylnuqncTCAWauXd5wtX7MPppmuWn06NGUM5cgI7K1TydiorKjdVp1bGvv98gyU1iSTuiT5kt1zFPlShxGyOZ3W7rDgYjvsmUAsfYsi7bCdg4vaj3dls7tJ/D0dDw6fF63L+h6XwbRAdU5p+8FFbaeplQUAJvaXT7ul/8sbrL/zHw5rtJvWRKMLjN9lQwiY1AX3RaGr2y3mNnlK5r38QWnl2iYuToPrK1FyAoyphy4BOynci7KE9TtJvYXWwuxmvYa6lWyRultTuLeWBpMLcReoJtE5pTB9KLD+WUDJOfOXWhr8tZCipRSmB4XWvwmYRm7T+JOcsfCttLPaCNSSiTMz4xnRboNvp+T3BpwO2Uvi67q/Hd0z2NZ2w6MfHOOMRJ694nrK/yScOPsFwFtusK7JOjePvPdPkQzYRVoROEZIFk3Phnef/s8p9LHFII5pJuE5AktUuM1F/PGJRASZlF1MyruBmQAIf83KfHND9Xf+0xFYP5/WU8UstH5JnE2NwACIU3tDaN0qVebXF1mqZJmXn7f6KiuSxml4dVspeR1Nmh1FrJkNPKrKLPJ5oVdYSf5szmolhlksZAIgLixop+y4MxyH3yuH2Z3W+mMBdc5cUEuDf9XHXg7TowLrX0dOrg5mznzHiC5slaEXzujUpW/8xAcB7Lza/HCXwbcT6lIg0QF7fnxNzw8rNz42v+vie7/U6jlXIdbZPX9Y+rJP/HCycZuUHUn5DA0lXQpebHDd0jd+4hOTnR8M03rX3hrcPosfmXnoW942c7JOtiAkY45JeY7Adipc7yIuxPCE2bwxCfuajLeT6455Ff7gypy9MaLr/8kIrd+RcWzjA+vnb4gmVZwFlwU2eYUqYZ00USVLnflRq/aqUovT8Ir2UnwaTRsrIpQ0fwbXqtIBI/09TP6ZzmihZVffDAqBxKv8JXXBlAVXh+Loi+UoO8YafwxKnHl7XpwG19Vfr0TJc6zRl5ETMr2pM18PjD+qVYZe2w6zq0fv9o4PAlSfoktqT9W3a4VFeGrBg/y281vyC12xZnd9bE3L0mkVYlk4/4dLT/zEHyYbt0k4QDPe16jVpZCZSvtflr7xE1+b6FxxdNiYs1u+GFjcobvfNuepu390TssGWweSsZ1Jea7ALBWucCX+gkxNh2ZrwMYcLo43ka8PMjX8wpX47ebMtnlCEjDiRHG6bqQUqA4ls2vZYGGv7i/Jmb2n6SfpZE+KH2A/jUOqR+sulUvkrlbnuB7mtdR6EWznl/hMcqjslC3Snc1qHvElSul+vJU6G1DPiFEUTzW4nR9oJ9PtKh5oO0+uNLXJM0ga0XahuaXtwtYuOW0XmdvaLuKNboaWTT+JpOKT3XRmsr+O/WwdQ+G99AIMnKIf1wpL6no3fhqHDJyiPd1vWPMHWs3gAIy5y2y/4IypjDkYdkOl1clX45nRW9GChhK0fXn1c/XOq7NLC6pzS0cDQWlNcln78trIFopk/mE1Zi6tlOQ8Y4izrYm0I+js3avrK0WGTuQjs2ff7l3P7L5j4O2l3se/DMqHEgwsaf80Qmf9/fMlc0jfHl2nzriu7/OP9/ieSPBeBmm9KBKvssGxZrgWnt5eTj+vyiKKKGCMPjOw7uLM2ZZpp4IuoHUsLg3VovdHgdvLGl3ovDxgxM8LrJ4C+CYHAQUGMic/NsJ0p3J3nAbXsoG2tnEaXhOntMyaidPw6jjVq0W0oio1EA1tttiaFixoJg+RGYeoRHv1fxrL7GIRZ2h3R5UO7KtUfr6ypfaFAr4juqH3hN2+MZUxa8BjmvEjPK8sDjkg8yFn1tuYlMXiOBJwavmbD2ksN1gF44qWT1nrZUtAMmbZhP8DSgIhyp5BjeNciVPVMmEW4VYZn6PDpHyysfxLlaNsjWFXYdJylP8mOC9raP2cB05l0k2J0RA/HAcfaCtlZ6oyu1i0+MDoi61xlztvW3xvFdD549iwVgZr0Sml0O1nROc5L3dZ4/a0hpPIXdhMQ6zxwOrMy68NTM+8uSXJ7E0bppkl1NLwMlIua/VqLQ0vJdVLi1hTBaEUZvsZdc9p3IlX7yEJbgeg5KJw94HPIXQW70eJTnd86cA+U/nRyqSVuLYCrrt8ee+Ju/9kKqP2IfdKxh9Rnl0cckDVRYzEKpOyVDIORxj2AX/VgMbPdi0wo9U4kJb25VsNJmWhSTkDCAQClD2CGs/IyrzziZDVJhlzTMaHpFHPSHm2jel1ZX4/wan/dXBeVoR2nFiTx6EpQ1WSahKcVomyM1XpKp4tF1r/qjTzN7Z2XFIQ5rYiUwRKg7Xof0phdqARZmZObg0Df5Sgzwaygugpro0DHnJeLgC6nTfXJVkw5Q344oq+loQXAy1Lr3L9HP2DiJbrsUbCtFPEHzwq1jiP7TM3AKQLtat6vH4aW/zuWSrZQl4w8DqN20/rX0yLjF4FzWa0/bnvuUuntAHvQ641jstVOYx8gy4Den1UECsKJmUfcRyM0OdD/qKWtQBqGIYRgrx5HF5lw9fRmxMmYXeTcTp5rLEoewZVjskqfGCykvGtMr6UHYuy9nqsCfG81CY85kq01B7aGDwhCRiA8E5+nO7GKY1ZK2T0Vs6XKaXUNyLZm3l0sOtINkshIlU9Wt9euUTe0tx+4GT/dLeQF41xRVK8DHKbrRpX+St3gtwZ76/zh16sXx4l8quNZ5CVr5CrortkXMGiZA+dVTtCj9GIjvAOrqxctOFOUI221tHtstou01+7HXg+QkQexFzpSv5PnGh8kG6nFRY2lSiF2s4ubpaZYfpaXsKZHReeLqVoyhoC3XcNXhqvCm+qzi0fLKpJ+0PVK4JhNwywbqf2w+PZpXeQfwcl6wielfSEP51268A5AJVHaz8fWti2N83igapJ1JdMKmAF+ccwuLjtgHh6dIwoWemx+IKOh6q3T3X842FgcdtzCnIjJ+hEQlAzuLDtRaXedVNaNG5JDO7QdgAQjCKvRCJXNoe5yEQ2mbqo3386FMmcl99aoyc2psMrLEsyu4E/9BAGfhdgXsPEI1QlKIXZmzJnP9zcvhIlLzaGp9K0elElU+UqEJIsuBm4OQqypYHV/xmnJMkDtSQL/jGRBWVg3YLQuqMFgsybf264E5SQT1JypC64Pwz8pQY9EihplqFO1T/U/WWAYMnwHhi6m6wpIyr+SRJyH9qyZCt77PUHfnJDRuXK/E4yrjAZByAMupArMblTka3xLPG8mnwHTcXzXPF83ZXy8hwf8EOT8UFpijUKiQ/46ZQGoIhNOEw8TwEGfcD5PmwtfrwhsCkvJS+PsgACkUKnSTneWX45yelbHCbjcJq+uwIllONgGwHTCK9mwKv81aBHNaTDJ6pcX7dy3RBEgd8H6K7HGhEiVWw5zF41FNsvNrfPHdrYtzmlX5XUq/wDxOekba6LZQ0nTRBrhuMk/GvrzL4coXWLQuuOADR19m+ps5NqrIzG6FgTp/aGwLibgL1FKPskU828d/d3fQsg3GnoYHIdr/VuJEKJTnecoH+kdZZb/+rX7D/mvZoIrsIPJOMvJmNvhF4XcVV959bWeIV4TiAXAnc24fne8xkfcS2AWn6I463NelfksWZqz7NibcKR4nmSwhof8kcN2GRreZPyGnKiN/8+QUlhlk05wpVaL163JkzG0TRnb0GJ/P3eRsA0IHP2UQ2yG4F9GrJxY6/yV69mg4n7KHBPokEPRYSSKjPLUfbC4ThqyhxT2kvJ+wUWtog1sfPytzz7N7wSoBwlxxrRZzWWATWcpwoD1SS8YKLNpCjIdg2sP1yVWursXzc8noo2xppaGvyj3SbHoSwSoezjVH2cuc7z2n4MUHuqHkFzVqClnC3mRPJyzrFZPKFZvuaV+05dV1Agq3CmSfm9OHZTw0ofcm2doAhqnIryFPLf28zGvMiHfMSH3Ing1fBLPCe1iDWpD8fJxGuGJ7IJx4hygMKjRazZwM2CsTAZb2icbxWxZrFJ2d9HrTcAtyZMxnE0ZY8JlHE8D/4LCJjOH5oLB1/sh9I9eJFauswaruz4tfxkQ7NHAJI9dC9a7XKXWOB6NGrURHGztZLuzhswTdkvCmT0tZ8r/9d4uHaoHkPzjpKM/DUEtA8/R18WfUFaKuwPH+f3qh3JRwGLYN08npku1QtL1/LX2pP0WYR0Bvfxj/Zfy2Ub4gDV/XXzs+pRek2ylx4mKbXyZXJJdIusAvDddBakzGgoLju45/7o7r7mRZHH2js2hHypo9SXri31pReM6syKjWeVTh1R9y6apt3hy9OO4OJwMBtoX167G5EPDc8rv1ZD2QmweHpLvclPuu8c/MtUrt27T/ers3Z7PCJlVHV4+8rhWUfwvZ7bB/60offRCFe2e4yQL3WIWF8yu493jgrSt0fX89PO4HkIFZP46zvvHfpmqTfd7DvsaqVM6+1I0UACksd/N+uJhGoS/qwcpasDo88Dyl7l79Uk/MWG7ejmsMbvRdNCXgRB2W1sWzezsGMc9V0qsllW1dLwO43HA+ueydiU3vpfq8q0cpg9dziJWhKUpTA9LLT+neRx30TGPdsa/1Pnze2hdccBYebNJXEaXLUh9z4ch1+LAve3wPqDVp13w1FrLrx55Y4vPO0uACn5TlpnGOnwk2fdHN3TN8q8VSFz3aV/TfnijR0GrHQBo51nPG3ieU3TJMTieBOef2JIfMT1wCdNxmuB7cl3m1ap5fs+4uqpXDuo8U6UJwmUFdSkHCWeL7oSG3Uv62+K3WTsoiNC2WOCc6yt8XJRngmECFe4iG+rZfNrZSllWtvQbpDDzX8LhuPwG5UofdgaPQ6wzsvF1SSckntOM4zR/Zt1GYrsln2b24Y2W2xEn9as8UJOpiyrJsHPG9sH1j+nudynIdYEqswrRenRtSS6sNXYylFyXGC0ru2kJZOdYI3/uiK9gXFPIydlLkqy4Kap37HoUBx9qhRm+1uj+64859qnrLnw5pU7nfSB5QASaie0JHSD6h4z/9m5auh4dFTpUJLNbPvr1K+/HhrwkAtG+y9JxkzxnCjrv/sBEJiMt/mQVyOoK3GpjaniOBmYRx5oH/YB3/XhFLQNFYIqHwOWFqUJ3iY83Xs+7qPWWdIbgJ2aS7QEynh2Y5zsATyRjXmtKEcDqPA3V+KsLVK2pLTROtZM5t71X4nhOPp0OUpfaI0+lVxs+/xqEk5a+tIKRvTgFvEgNOhBwCgCphS6/QqHtjGxRuGOahKOygAJjE4Ya1B2LoXZQXXCphmVKHmZNXoiECF4a7ITU+M/CwSB8UcpVDNnL9iwzBnJhmrR+0th9iSTyNGrfvmvnddecPOKI3Z40xoAX6aLVsK+ljbpbP+rru07iMbSISFO53W2jJWTwYfcS8go9yqTsgTliFEZGhCalFN9yFsBXInf2IS1OE6iXh4M9/mQb2vA5A5lShDU+CywXXEdZxOe6ZX3+ZD7N+ZeGrBDi2Ml8ewE4xAwnvYg5n9QDgWcChe6Mj/eQmVL7dL6t6SVXMFGY6sSMNk8bR98hZ7ip3M44OyjXNT5PfmRGZKWb2DHL81l0DpFdUNg+lmDtkhr8iRmaPSHFx+oO6MtbMuUpHIBn4hubkoVDkZcTFpDsL6HvVu9pKLUDucdo8SGhZKbz7OH5+kzEQIEk+zLvtkiPbTnszKpm0kjKpfK3Y0uT3WUruSe6lNbjtmV7oxuZ3rPZ3Rt32nkCyeDkTVmu7mbrXQlnhHVJx6joWS1WdEu4WB2LUD7w9W72h+uvn+jrtETTs/a7XMaslQEiJJp4RvSsrk0rPmNdgOxib8vC2U3RNY/P6repDpuUOrds+ukrDM4sU46+cgctG7Xzh1nXtP7PybTzRpAKitqN8Qzo9ETFVWVTO+vZyD9p8Ma31MO0zeIcCAQOy/nVZPwV613UYRaEv0ZaGlRvSFQZA1o3WGkEWNcc0LrdydPK21erPpqErzfeTOq1E6g1Cqld+R1IRSj+wMtCBi1Ye7Q1DjRiazRk61xKRCJIKH4A6xJLxqOozFuaeNDSLLgliTjlpXnXre/DsUj3+fs4fItdn6tlRZUb9wz867azmu/Wr57zVtAHKjRUvBw37N2/ebUrz0xTMYS8lTc5smSN47tvMmzVHzETT7iHRt1jZQdUA6tT4aKRUyE4114rqBVidZUITygyvzGyYBCqoyfXRPUeHPj5EyVw23MwqzC2zZn/TKAD7jaJiOlX/XxOeC6cU75j0Ng3ZxSmL1RYC9g0Hn5TTUJzxuHA/eFe8fUdiAngCqPqZK20CoYk20bWL83Y3WmUMUM1sJ35e6pozCha4gIJSu6HzBmUWHEVwqx4UYB0Ciw/m1AIkJZFY0Cd4g1/mfVJBrrljb+lTVOw2uBa1f98cZ9dSge2Uhyj5auNzOT5kxCJZV7hvdZcFfpnr4fRw+teyV5zLW+Lbyj9zlLfzz1a08M41hKa92NdvFMq5cHuBJXwtSMGMZcI2Vv1pMvFHEhMhnv89Ho53AjsBxGO24qxCrjL7aCGh8A9hohuJWnBjXmZW18ZBPHMgZquVzynelGEi1T2USS+98IoXULozzW7AL0Zs78pJaGLYXWFcmqSfRz4OetXt8QKLJSVV2TcK4qY8vmrPH7MHYTClXSoVr0kRZxcbJYU7ZG96DFM1NsYr2wQQ/PAja0/gOAa4g1Tzai36il4QYQruLjNPyXrAjT1X+8Za7W1gvVRnfItdUd9ERkVDmcN+u4XubNvlartT9QrZ2QkVmDiO9su6bvOUs3m8ufOHanNUGwPYpB8AgUWb4bJcRvEw4DFjTMa6yCNSmn+ZA3bfzogfx7M6/pWA0ZJ5NPIajx/4CFUuctlGcFNaZnFTYog3EqUMM/cBzQpL+TqGHKmopTQUvHji0BFWXgf/R0P4MjsZSxtLv5HN//Fn37lr525U9yHY6BUVkfntg+zLmSjl6Q2cdk7ZiMFgAhrD6DD6x7uz8l2147AVyPRmYdjxaEzbiQuHW9nZtLO+EIM9kIgyEaSXGzlP00Dqodrosnu9epwAxIWrqKz+NJcNTw1PDEpX9xhqmJM/PnXGt2XfJKmTn9f2XOrA+a3XZ+k3R2bHKmhotMlFVsJRjK+saUdwEINhh2Y5TVNwbJtHARrV0iTP+una/YlL7blw3/DiVGtdD8UQ8kbQ9Xf9GqvQqSdQbPG5XxI2IQ2gcXth2wKWNphag/W1danXwV1QSvVbxWUdZ1PDD8hc19rScm1FSi9HMiPEmEkghd1ugL20rpq7b0lZPUXgak2kD4qhJnXsZ8N7xK7zjdmEqUfaYtSl4homXIFzVe5QFVxhVRVsWjrXc2AuvnMpYUyruW9cSOCGUjepw1boxOwcZA+8MBtyr6piqJemL11NRTze5rOwOEdc/a9dLVJ+938vD+8z49cNTi01a96eB3ZXM6Nl3bxFPCU1bDalpnY4Rqxi5UNwbFrk2r1XZkE47blL59wM+BRAt9seJv7CNaT+Y8FZSjmtJ7A2CuyTbN0rwVNOARNfyoIIWqClVgtSsxoUjgfwpEtFQOs88L7CdCJMJ0a/SVlSg9YUtfO8mCi8g1E0bIE1Xi1JkxBKyqrIWW8xTfUU6/WomSF0KuG2fEd3rlrkL4siVUSVVlvFhTFw1vhqln7IggIpSs0ZcZ8ZtlV9GtLK/wveHPirKuRD1VlMH07vYvAfS+YI/frTlp71cP7zf/M/1P2/Edj/3PQR/xXaVNzwb1lPFERaxpOadW2Tx6TeLZmdYL1m6TsElzCR/wA4W4EDCncNMadhEtF62SMZNG8gUosn92k2zjLc3HHV/E7Sr8QfN4WI81D7vS6AyM/1RY43tKYfY5gd1FCEWYHVj/5nKYPnlLXzvN7O8YGz+SNLO/am6ryhpyIrIZtqOcfKUSJc+C3H3VGt+jyu2TxJpYtfUaKrR+V8bJwGyONYH1rwdt1XaDUfmr3GlXcBGepFhHVcnoaztXvi0i2B0Xnm0Wbf+6O0u3PXZN6V93rX7dAZ/VaHJDgUnhqeCJEFbTOp4Ps7mcSfx6oreOYpKzwKTstEldW85S1s9lNf++9LqIK1q1Nxk7kZNBI+vGotT5cPzGW5qPBxdxOcI/G2JNDbjHRWzAZsHk2GoZMPFhLG6hO1Ly0zks216/HTwkW8xK2NTEdf6Q04ZeoK/3PRyAoxo8wLmdZ8m5zW1LV8vDw8fpPdrGLiNlSLmorlBijpvH8f3/owdHN+hZyYG8C/Dkbkg5ZKR9Dk9curLVjjSYXmq0nriPhUK6RHcq/1Pu10hN/5v01W4ez0IIUVL7ML/r+pb8uJlQGg/t55iro+v1dbUj9BAUrfxNrgiWrf8MJAwTya2pNxmuZEp9e3Sd6srmMEBwuoLU30VodhohJVRTSfWuysp4A+vEWyMcyB6tzmsxURHBVeyTgO+MPWtqKPWmq7vuHnzn0A5tL3Gh7AZ4RMqDi9rekXYGv+66d2gUU+8DCWg1aRKMj0xLq7pNRfddg5emjwTXVueV95XMD7cvq95gnP5XuJKUQlfXRhj5sRWhZNBngf4IWmfcbQ54NdVaGry7FGZvQNkTGMi8/KKWhGN2ipPM3hIFbq0qcxvcBSjIkPnG6PPaS8n+mZM/BFbfRE4ohkU6byvbxjTJgpY/EN7Tx9QJ9zSwfonzdo2gYaWUvMnISP197Lz8tJqE50w1dKU3dV/sZsY3BNvVDlIncXZf2xU6FIxkYrnplerAEYtunOLYJkSh/v8uYL/i0EPAw5qn0ea18JAgXKGWvs1xTTU8Im7sb6kAeI4Bpux614xCLPg9JuMkzd2wPFCyMe/xAT/3EaNitHg6aD0B8+LZIrHGlfmdOP5hMvZUYZ0PufmJIKS3NVAKssPIs8caNY7K1ugL2QxZLhPBebM2ToP3RXms2RlYkznzo1ap+nEaXBHY5BRVSvWxFrHGANtboy9pLyW7e5WbrNFXUGRytIo1dbe1OLPnN1+neH2YqceaLLB+uyQzdxvxlXKUvsMIhxTnD2dOvlNLoymVHAMk1/b8xs6t/cvOjffTTAaze9qv0JodWdxlczoGB+Z03DDV/iaCOKbbmPcCS4tD9wJrFWbVy5CKDJI/bTYRW2lNsAtgHMd6cq2ZjYGPuB3hgybjpapsRxFrghrv8yE/8SGjDCtEmQYtssUhE2WasvmFPl2FH0rGBcaxVIXVPuTWzZ3V90RFFGTHALZZ4yiw/qTxxKw3F1JnHxbRj4aBO0VgIbAidfasJAvGaCrGaXBpWyl9WSGmKzASawJgoTX66rZSsgRkwIg+h4ljjQfiOA0ubTWu+ilTvI3AGj/NebvaGt9dDtP3irAneazpT535cpyGV02xL7q/ZL5TO1QvSnfTvaSftW3ny1V1d1oA6WjrWxusqTkfT9nyezxIxnyb8B5gMflt30FODET1EmWFWA0/32zPg7BMFW0uSyzGcwwh97Q6bSrwJa4k4RMm40XAHPJ7CoMap/mAHzeZJEAea8ab13Tq5naFFDSr8H+S8Rvj2EkNy33A3Zs71mw1AsbN1Onj6Y642XQFD22B+vQGhHfL2p7PyGen0rbr6/KJdW/TL1JiAcLor58hIKAnOYj3jtGJqaNOwiQ8Uv4XX2v7s2kp/Gpq4vBaHVHSnhhqV+Y/vuveqm/yczi2QWsmdNvzgv43qHZ/VVpaw7VC+ICsCx+Qi6bafmPRu2fX233JHDJSsmNZgJX5KCmq/QBm2F067Y6BKY99MpRXJ4/276QrMcxrtVKdKhTQQILmMqHy6mSFid33+/boPhPDNEQCtTKzNqf0Hg3km406NTbVVJwu00AWNXUvpTXJZiG5WiEczAbCuwc3m2DUvwtEdDqtf5StES153XIEDEDm7IrM2dMnbyk6nATva4uyL6syXWT05KMQ1dw+sPqWVpaIdWi+ZXlfmtmvjSd0WQjzeqa2MAqcN48AtJWSD4hwYMO4StboqypRGleTqOUCrOX1V5dWJ6tLm6S9NBXYmA8BuzZMShblf4g1LwOr1w5vNgFbH3CHSRkCOjepI83XUTTZN/uQZSr8wCZ8gbw22QIzTcZHYL3AJ4BaVpPvgjXrIoQ+YJMFyMcdumWts5s3PfffAUWsaZWRsGnfhSkidfaB1NkPTNZOkbSaBO+vRNkXVWlvEWtKwD5WdO9CDLNlWUARa25L0uDrrZ2WIPP20RKucRtqIoSZNytBqZTSTwssaRhXW2D1rSXSapyGUy4zcSvKj7oV5Uen2n6joGBjPgXMq5cHal4W4oFasbsbq+F3rrT5iDgXcXVQbUl6wIYId2kxs20iSn3I3Qo/toXVtuS1aTNMyu7AhxpJGB/wgElb/856O1qzYnOi0PraZCHQfzeIMEta6Y5Az9a4fpIFtydZ8O7J2nk1A3FqP1IK3acLwndMrDFwNGhWzGtazm1UQZVr4jT4tlfTsnTeObkrtEw11uC9WVdkSJ8JzGwYV1do/QdUsw8nWXDrVPoCKF8uD5Yvlwen2n6joAQ24TNAd50MUdiTwoGtIKGG1PArV2KzredcxN+CGm9k7HvrWxwbH3WjheZYE3EzoCbj40AoIKrMNCn7qvDORp0atdxB1pKvqKndcu5xGvCw2wwC5+Nhq5Ugla6RO1uWnShpdDNb9sdyA5HuzGwiZo77FTMtU/lHQ8Cu4dq2P5mbJ2pml/FbfFMqmZLhG3ZMPInUeLDyZ7nNzdaKn81YBRdB3HacoHYD7ZM2A5LucNrQgvLOrmTGiKFlZVP2JfOkUaK1eUQWjEQIXQgVX7FPHp5f3nNzjqvt4eoXaU7TU43DwWzcIJW12bbheeUlaZtt692j8/mPHTr9Z6sPnvabxw6Z9tWh7SojKfy9e3W9pG+v7h9gmDVKC0akFE+PTh7VZ9lUTOzvQFVR9cXfOBjM/lBZFU8uhrUNG4QsM7fQOrat9CqblynfRIRGFwNtE3CElsnjdOa9/G1iRyPBK3/WsSR41ljWVKT7Xp85u9waN1OEfZvHJoKxRl8yyZi2CCRjpknZCW2RcZKnxO/clKZa12Qpk+sbdIpylMnYefMNCrzlK9oUa4odqQvGPc3RaVKW4KjYKicHVX4ZVPltMMwXTFqI1CnYKv9jE74JdElDurVAyWS8elSfng6FW3Vk/oovapfP3lwZP9uwHs6bm2j+3HNthA12UNvSCK3fhXwHfTwIk2/K1ZwzF6XOPjReA1WpqXKNjtXdGxNrvMrF3pv+MHA7CuzQItbY0PqXTjKmLQLJmFuIXY6Jv0VK/IxGbaYi1hhyocZ2oFs8TzUZCzffoHAqnNUi1tS8HX/xJY4ek7IYT9lWeWNQ5VdFrPmUZMwpOjFBlXfblC+SO6+Z4r4EiEzKy0f16elSuKMh1jiFRC1ffSLYVv+nwXm5TnW0uLEqXpUttom3sQis35O68mJrSLOAeAsMZ97+MfN23AV25oPHFO5rKsNUyMsQG47VnJdfK5KWwmw/oKfF2Gxo3QsnGdPmh4JkLDApiwpidBRMyr7kmS7rM5/yf5Y8zrQDM8XzDHHFs7w5YKiqcI6OlXJINWhdlgggjhkmZRGOtqDKu4Mqvy5izUfF0Q2AJwqG+ZjJ+H+N91b8DW3KC0Z1qnQA9zfEmlQh9gFn/jtn2261DJhguQyGd+j30qW8rijZ8YCWruRMSaZWNrO5UT1Sd0p31j1NP6vb/ihXmCHJ0p10WvXp+lZkQmV1ZYIdaQAcNbOWSZnRru/Jr9adqjP9LI5BcShp6Rq+jMEke3CiWjqCR/l7+8/kt6KCm6Nd43YmhFrGytAWUYUeA2/F9u7T/U5XNoeipEML22y0NvlOzx2DIxMBDczE71MeBUMs06vzyu8Phtw7Nhcp0fFw7c6sM/xeMi18DUqGEJjYX919x8BvBpa0H1SbFb1cjcw0ib+l/YHh71fnl5+WdQYnFm0jQBDJd9ID2WFoh8ong8HsjUlPuH3aGbxwFPEy6p6YpoKIot5gevfp/pxamVf8HAmqWdiX/mTabQPnbo773IbRyLxd6bz7baGMH5JPVn2S2S9tCHG/+aBEgdvDGr+zV3k0ToOrQXxg3dwwcK+ntTZLHVOJ0akfR4+hEdU4+k5bKekADqawkk0yc4bJa8mfDUSFK8t5ANboDFg/sWlCxxTGtfngKRWCj3sCmUkRtZzpSutrhiV3yWj5W9Lwqed6KCmnq+H1alm3WYYXcYXE/BrP86mL/gp/dSUutjFHiOMlQBfCdS7khzblJShPA1JT7GbXiSOFnUzKZ73ltTblIFGeJq3r3AFmrx8EZRvzf0CPgBRaDt4bvurLXLo57nMbRiPJ7N2B9X8rbNgjIAGyJA0eJw0clVLo9jHiF3s1D8SpvQFEQ+sWBta/apJFz5Tmg1OKNUn4xUopPa0ow/RAX5yazwRWl1qjxwKaeTm/loQXARjRVlp4dfRMZVybC+LotDEfAZYAzqRkPuAzxY5tjoljTT3cBAo7mJT/9ZZXby5SwpX5U1BjoebxIyHXszrXB1xtazxTPCcCFRUudxE/CRLegPIk8rgZFmOsx5o9bMJnMsspNuY5hZPbmFhTxJPtGt6jniLWtDXEGvWW//Wl/x4B7q2JOA2uCUxyE7A3eYZjDNRqafC9x2dEakphdqARne+8uTvJ7K0gREG2qzX64nGydeqwzRbVLRA4L49MNopaEn6iHKUfQNmR/Hu4opbaz4bWH2yNHg3EmTO/q6XB3wFEmEmLTa1iLJtF+26qEMdMG/MxYC6gJmXYh3zSN7geFfOalu9UQ6yxCjvZhE9nllM2Fynhyvw4qDFblUPI59GBGn7oA+61NV4gnuOAQIVLXMS5QV7+vQfginmNNsSafW3M/8sqnGpjXkmuHzX2c8i/G4tG/j9jnk34EnlGnhQ6eOpCPqghE2w6PvGxVV2QOn9g/hQfqDfFB+hheLLyZfKP6HZZvTXHALkg8Lp369v8TJ4CWJQ02Udf1/5L/cDQi/k0AdPGXafly5GxXiSjdV8yPENtf5RJU7IlEd/zBflaNke/7+bRHd3Gyjoh1f5rLm9u78sa4klGaenUr19j2XiOUlsC63bvfJErF9ktBSGVTI9ePzyvfHfbo7X7IS+DEaePqGX7CfnwHEF1bvlplVXxD6dy/cGFbXvHM6KnorjSmvjijmXV25rb9Nw+8MesYv8az4wWBoPZY6XedM3AkvaDqnNLp404EpXMkwZ27dgfAJGI8X847PB2lWN8ZHZgAuV2cbpcCkZ+cHH7QWplNiLr24sEaVd4HHDuVO5zGzYc1ST6aWizKwLrDwKGkiz4R7Or0NaBmvZS8uGi1jiwaBraZE01CT5RDrMvAB3jPRXjTVAaj6uSAqvjNGht3dd4HpIMxaXPGvEdxmhH5szK/PcMSJt3NLQuwtjy+qqbbHm6QQhiTiH/wQ6pP3uO94jjjUXZDT7gIZNSZWJCqw5jUp7iLJPbciqYlIOM40iFqgZc4JtsIRFwZX4qjt+LZ3s1PKqWPlvjGeJ5vax3JDrCJhwMBMW9jCGoi0mGtSmHieOpMvH9jIzDJhwFdNQnPMXkLDCe4zzbCJgtA2E4Dr8aBe4vufuHrItT+/ei5G8rj0SjtnLyKck1GgKLywLrlsWJ/VIpdJ9jgu/RFGNNonBvktm7xrYcDa9maKhW+pg1vlvQcl5mJKSOexmjiaSoSks9vCKFa0pW8JsLNuYd5JbMI3Msk/ERH/BqDMNQlB1mk7P5Dc/ywa40BZ0OxdiEw8RzmEKfD/mTNtldF/oE3xDHz8QzTw0PqWXQ1nipeF5QjzUoTw9iDgPKE8Qao1AxKfuK5xkyjqWz5lqHI/Mrk3I8eZbMSKxRsMbzdP9f5IC2dSE6FEf/rxS4fY3xuynyWJwGlxXPzlaFEd/eVkrPICcsQmtcFlp3a5KZH0eB/3/NttJA3YoamFKsiVW5OnN20lIz503vUK10mjV+Gqh13q4GyBwPAb9puopBGaTFxpIq6r2MWXNtSdiYDwLbNxARZZPyCR9wcr0U2QfcYN24GzAjKJ7ldpOy+5Qs6fPSpiPFc5DCSh9yvjaX9gkuq3CGOGaIZ5a3PIihGlR5yyixf+U5Qcwx5POPlpvukhPSc0zGElGOkfHKXHNns5GsLpvyQopyyIb7NCbjSLeNgNkwlK6R5aVrZIxy9tZE9Zm6t5/JkxvsnwMM0fDzeA+GNqRFyr/iSFhDSA9mjEU1eIaIWYWhZHu5ru0c+YVdMzow+nYNkn3Z3q6gN7xX+hpfC1bKcLCScS2CsznaNvAG/Yi2FerTzeFDSdsuks1uxzUR0vbgGaOIhRxhbXbp6DoBA9Dx4PDnBha3fxq0HTAT0N5G7cQ761mbbe/fueOlWZt9GkJb/fjwdpUj457w9zNu6h9D3gRVVw0eqt7hAwkGlrQfVJ1detMYRyLVCJHJSj0CtdI97qv5L0xcWRF/o37IVewOtJrUmM2XKph0BT3D21WO8FY6SmuTq9uX1/6tg9LmQuqC+1I3vl3v1kA5zJ4swp4NO8+BKnPKYfZ2xrGVVsWp8phIPrlpeg2gX5W1QOi9XF5Lg1/TZK8tolFg3HbOm8e8mlH6Wl7NoHfju3JY43sqUXo6MB9QHTNVoRpnwdencv+bDcqRLX7YxaQc7myhsSCoD/m0STmdfLE5EeNrYX38aAVx9JiEl4tyJHmarAG8pBwjjrNdeay2g1oG1XI7nsjGHCae18hoRyKrxa7xJHdsUToZJwOp2HGOfch3Gw4uGYes2X6Sa00ZkjHTZBwBRGq53IeTZ3n+50MoxCi3mMbOVFCO0uMEFjUsfgKUhaXIvQXWC2I2oigTegyYSVPmQ/Hc93qlX8A4L3+rJeG5zV9dI75ijZ/nvFnZTDwVpPe4xHdg3ZxymH2SPMvFq45ZaKyL03CzacNNCiUE9m+RBaI25UBXKnSODIkP+JzJ+AB5bJgI9bKkcSEZs23KK1EOJSdnDeBswrHe8RVfGkugqqVPLX14KrbGEeJ5UWOMLBY83VPI+RSZONZ4oOpCfjJygrJTczyWXMNh8eSXmxokY57JeDKAD7hMgyeWVMHjAyHOghuAGx7PUZSj7CXAnAZtugDYIwr8mxh/c9IDa8hJm1Fz7SLWrC4EvNV5+XM1CcdsjhjxHdb42Zm3jzQTT86b3onGHFq3qBRm9bkBLeY1q6ppcO5EfWxOiGM6ua1y87rDmpQ96gL7aulXy9dwvJVcK2UiKJPHmu1tyskoBwCmuH5mE57lXesMNrWsUcsacXSYGs+gKStXINLJ4yCAR+linOzBotxpwEcNm9PKjs3vkeS/bZvkxNQIk7JIHE8CEh/wdw3YKokhW52AeSIg3ZkDxpQYCVYrLKaVBbVCeCNflpQ42Z+3j3ldAEs7ZeYHD/Crrm+aMQKPgy/yhyX78zby7JnA9OnVXV+TL5gBmZJC9uDJ+oZRzkz5uBxD3G3XcGP7b+Xc4NGtrm8x3vM26nh5Rbws6k1eO7C4/YikO3wxlmnUtS1GZ8XEUW/6z/EupgbTu1fXZ9XK/BblP6HrCF7w2MHT9pp2S/9Hg2E3isyqzYzm9u/c8Vmg1EjcrB/xpORLPr6+9GpU/fB2lUNpJFZUvaT+1rbl1e/X5pSPG543/R3itdcOZf9CiZGmoKhU+3dsP6TzvqGrRFtPfKaCoe0quw7tUPkE+fsZZp3BifGM6I/Tb+r/wcb2uQ2bD9b4Q5vT/otJy5JWKbqquNSZzxhhhhX/2ubXRUDzyXIpc+bbtTT8c3ObcpQcGxh9HeDBhV7lkuE4/HozSTMeylH6LnI9hpFYWJBCt3uV62ppeJ6qjGuHvYUwpVjjA+7whtfYlKeK5wWal0oF5BONRmRqGd/xICdQvghMa5poGMDgeV0wzH5ZhU8hoy03Tcpik/Ip8ratSJ6xC+GxB70PuNY4VuPYuZFYKRZEV/mAn0vGC4JcIHOlCjcq1FqQMKlJ2NeH3LApFXgmYf9iwWkAI54XiuNHrYiobdj6MEYPb955FqGEskha6O+pkqSZ/YQ1fi9j9EXNrxexplugLXXmc3EajslEqUTJi63RFwMZuMCrnDcchz+caqlnOcw+DMxudJHKLa65zatcWUvCC3QLutaNQQsNhgaMmiP4kGu95dU25Xg8zyWfD0SMJX7FB+NnhYij0yacSb57PHJqEXescbzLVNk/K/Ol5tICk7CnyfgoeQiZuNx75BbHfDrWB9xglE48JzcSxkWs+asLON8mnELCjsBDCg8CezfuYheksJiU3Xy4aWSkjTlK8gWnBdQmvEQdX3El/rYp/W7D5oERfVKzMUAxz1kwDtE7mGT29NC6443h6ObXi1gzHehMMvvRse5KSlspPcWIHgekJVzgvPyomkRT/O1RU5AvPQ3OTHWtmFudN5fV0uCvU50jbSYI45d4j4o1LuJScVxvUp6L5mU/FM9pi2d53OwXyZhjEz5PnhXXGGsCAOP4mFQ5z5X57piOYw4Rx3vJY8LG2nlbH3C7OC5GeU5T/PAqnOct/7Qx7yYvebxH4QFgh8aMxCIulU3KEh9u2iarrXGCeF5Jfk/eJpzkPf/rI67flH6ngq0mwvtEglTpQ1v41DsGR4nf1tsPc3fnT80l5cvkxpbZMXUYStkiXpzN0VGT7ngfnZscwLuwtGGpYAh9DwcOvEZfMdUx+xkcPsZ1SfHBcv7Z/TXzky1Bvii5gKw3re85GMouRrX5/UrLj8WX1s/v3bPrRY8dOv3naw6Y9su0Kzy+Y1n10923D7yh897BU8XpfahWi3+JHXZ/6Xhg+IbxxjO4sG0fNTKrBfmSQwQNZMm6pZ2vb35pYHH7OxC6MdI2Tv6jRzVtOqaoJsW/2Fbd39ofHL6pY1n1lnBd+hNUE7wOo5raqvtzz60Dnxpa2P4pV7FPx8pMDWTnrDt8FV77UI2LPvN/Rtprc0rvXrtfzzvGu9+pYHhB+e2IVPLSKRFESllH8Ozq7NKCTel3GzYPFFnbQvgWoG8cQb1r4jS8MsnMuOSACFLYT74OdNQPYRRkOwdGTxGhLEKbCKERPbISpc+eynhFtCSwl4wlohPnzQXVJPr1FiFf8izCSivBy3xg/EMZE7PVB/yrON/YKq8OqvwqiPmZeA53IR9wEW92Ie8EHtXcMaBWiNL+2oc8MN5wbMqTWe841GI4AOxla4wWCM3Lld5XnNs2zmrOt7iXXLQXkmJ852jAQy7iMhX+WBwfro/dhXzdZHxelKdIvqO4uygnAYOa60Lkogx51z0m44O2xqvGu99JoRiT8S7JU4FDASsQiefkEWG9bXh8oazVJjK/EAR+rFH8tjieeZU/x1lwU+rN5bT4PgKIYEQohda/sfm1UpgeZI2+UISoiDWREX1WOcyeMpXhBsbNAubJ2DlVmjn7q2oS/WGLkC+KFLFm7OOZ67Tc2KL80riwKIXyREGVNwdVfhPE/Fg8e7iIt7mIU33Ae4He4lmtKqRq+I5aWjpGAZiUZ1DoG7R6PV8tcqiNeWbTfRiT8X7JF1OVcWKNThBr4iKenKWWPlfijwh/LwQu67HmLBfyW5vxeeDgItbsK/Bcivsr+qsPda5J+YSt8Zzx7ndSeCJxvEUKi13JM4IicbwFP6GuyDZsJShjBd1VcQorVMeIkifOyzlJFtyZefv35nlPHSOxJnCnNL9WjtKnG9FjRQjrscYafUUUZHtPZbxR4HYEyk0W3gJokgVn1dLw4i1CvohgorJpRbMUMWG5js0GUR8WJIqnLajynqDKb23C9wXmuYj/cRFv8wEfJP+9rxb/ErV8AUN1vOGYlBNoEvQdNdw8G+4ZNuHQUS94yuJ4T/FMlieINc3i4KNjjeVMDLEr8xOEaxtiTU0tX1LLFTbjc8A+Raw5ROAo8vOzep/FRtgik/JZG3PEePc7GcTRIznpXI81oeTmBu+chIzfLPivzICp/EUuGViioyfOnji8i59m8zlIu9gbSxlPipKVL5GvAwSPylB0lX4+OZj3oICh1EILJkuXMj9Yud4jPX6SPplmsssQubk8Hfj+JtyKyjipXJuKwR0qu1fnV96uhtlAGgxk50y7uf9njbfbc8fAL9bu3b3Ql8z+KClCGPWmP2x7pHYvQN/unSekXcGL6+U+GrBkcFHbp6fd3H9KqTddU16ZvHNoh8rurmznRL3JnZXHkgkFt1zZzmuZodQIkcCVzZOB/6sf8oEEGsrSMVku9aLTPBfRFMccSg0hMsPuz9FAdr2PzKyoL72l7dHaA/VTp906cG5WsRfG08PtwoFsVdSfrVuzd9drECojBE/9r2VG0JuelU0LX9fkBlV2ZXPY8NzyOW0r1vc9VbiSKamV+WNeUDSeHu25zWHp8UeS2fPLYfYMGJVNEqfOfD+w/vnF7nS5UOxP4yw4C8B5uzpz/ltBvvDx5OVKzbDW+Gn1mmeAwPqn0ZwaLpSs0eOB309hyBNlY210ptZEMAkHmoy3kJch1NTwU1fmvMY2WYnvBDXmaG716sjLFb+qQW5BaGu8SpTj67u3CrvZlM9lZV6HYVUW8CaTspco073l5klTTJW5TJbKCyHK0cBIiYR4pgMzmyc49V3nhoWKLRYvCRCp8BsMy1C6fMCNIxaMAq7CD8XxG/HM85ZHMAzZYd5PvjAZ6bC4TkmFn6O8oiFtV4AyynPFcZ5a1k54763u1TGL1voQqTiWquXKDe1zGzYvUmfPiQJ3AKM/pyTN7HejwL1RlZlFrImB4TgNfgmQZsF9gfG/tEZfSv4VjVrEmpmgAQ2ESGD8M1tk95Wt9ceT8o/JxqvrH4vxXtvsKDIrTiF3DhlSy3ddU3mPK3Gmjfm45juwdR2Gz2IYAghi3laI1YbFWPe2CZ/JKrxBA1b4kNealP1E6fABN0zmPibKDjKBphyAQAnP04Hz68dMxiJaZL3o+nPq/1uPNSm5YOYPMfQDbd5yjQY8VpzgswpfEcfZ4pntLQ9jqAbDnEFDBmF9dqMwrMLFKM9viHdCPtZX4bl4osXgeDCOxYx1XgHwxrHYmyeew9h/GzJnfhVaf1pTxl2WZPabpcC9X5UOEUoF2dIbp8EfAeLUXmuNv8igx8FINvAoCGNdwwKjx7fQlYlC656RZMGkLlATxJOJslA2CX27dz5n14Pevb0JAkOV7/uAr/totJ6Vi/hfm3C6wvRiHKkP+SSSkw1BjQ8BS+sbQaocYhNmZm28V+FRH/Iqm7A/UHIh19Zj1HgQZeF4m0ojbaCM52mwXoPUZLmwbnPbpljjyfVZEnKyxKrhG4ViauADrhyJhUKWVfi0OKaLZ7q3LMOQBMN8pykzB83nMfWsu2fUXy/mNyUcb0S5bGOEh03GruRxsfl7WBHH7DGaOJsZ/5UETHif9FX+xAdrx+hbtcKOOAaC+/hlx9lyIYaLhp+nB6eLOFCGWVP5i1wU3SUjE9aO35or3WV68vAz9LB0T95Cc2qvEIb3NX1oBttCtpeWx8aBeYxL/SyObs6CKf1T/jXVPqaKeFo4Y3i7yscRqU+ubNYZnNi3e+fgtNsGRhZGJtVs5rV9/1ubGc4dnlc5GiNlybTfhxKYVLO0O3x+k9aKoGqHtq8cGQ5m92RtdruwP7u3Y9ngJVMZV9if3pVMC6cSLEur9+95b9c9g9+J+rM+8eopFm0tW6+fbRpUa5VHa5+L+tIHS33pWgBvMOuWdj53aIfK+wCCgeyCnjsGzguqrhosdyN6K75s9xtHdSJqeyy+tn9aOCYzB0XTnmBnVoy/Gz8eTOrrC7jmkgNvE7/Bi6xt2PzInF2eiJ4eBe7N5BP63syZH8ZpeEWc6tXlMDvcGN1blUfiLLjYe9NfP7eWhn82mb+iFGZHW6MnM3aSrt6bvqZjltaLmillO6pKosoNwD5NZQsSZ3ZSod8NhUlZaDLe15D23o7nZBvTO0q00lDN2viQ5O2fQv7bFRcZMyrKsxoXMZIvECKbcrAK/aLM8pY7fMCNUxmXGu4ST43JBX1nBFVOzUp8D8OwCjET1F/I+j+i0O8tn9eA+9WS6/QooY15gSQcQ25j/XtX4s+FtszdDf0sHecinT7gXzblxS3Gnopjx40hYNQwQOvvkEE2vL9t2PxIsuBOET0jtP71IsxRZVXqzHeSLLgpzew7SlF2pBFdqsoDcRr8xasZKdOtJtGvrPEXR0H2TGv0+Ywl2/pozgoZfzNkSunpOXGcLVNlcVMWTJo7qmxemIQ9i8yK+r114XiLSVjT6HCklnVZG++UlJ2N47CRVYYiKGWUQ2W01opV6DYpewKRKD3ecrMvyOHJoIabcRwqk8eaxbbK61yJH2FIVKgycYZefZGCwiM+4Bs+4N66kDCeio15qSQcBgyq5TeuxGVqWdfkDrfDOLFmtlquloxnkhNajciMYztvNlwkU4VeWq9NAt0Wa54QiNPwaiH9RmD9q4BpwMNJZr+RZsGdzpk3l8LsGBGWeOWuOA0vXZ81KwzH0XcD434fBe65xuixLYiVVlo/rVyLpIVmVEukmb23FLgh1fVZMEW24JrUmYcmOX2D0b9T++FJT3iyXb/pO8NknIbwnkbdNA1YmVneaFL2FMfBQIpiURDHbGBXGavrtEhSFooyV5R2H3C9WibUvxm5nuFGPLtORvii7GtrvMyV+DmCRxhiogw9RuKgAvf4gB/7gLvqzm/i6DYJL5Ncd2atD/iFj7hWLWub5iOzWvWvsAOWc3E8hbFl3aE4ZowQyRsANawV13peozlJvUXxX0nAAFT+LvdW/i7vVFFEG75XDm3/jVwJ4+/o2VVS7fyx/KXv3X43P4sjR8R8PbF9lAuCh2SU6GXpGvlntlBfNGrC4knNqqnXs3b+QL7b/0adqZ3sSV7SoKWrOTO6Q8ZNbd1YDC+oHE3zD7tIOe0KT4SmnemyqQzs2PFhNTIbI+WszdaS6eFLpt247j2ttVYIk+7wxGRa2IlCPCOS4fnla2Zc33eG+IkZzPbltXuqc8vX+JI5EJFSIQ8qYyuuBV82h63bvWunmVetfZN4vB12f3Zt9qkjhFD93LHQoOrW1MkXgN59e97hyubQ+rlpT/iKtfv2LJ5xXd+XRl020/s10EWtSJik3c4ttGCa3xO1Vb9iovseD+LxwUD2u6wzeF7DfTmUgfYHhq4ddf2uoDvtDudHvcnD4aAbaNnhNmwRJFlwS5IFbx674SuuloZ/B8Z1S/NqBqpJ9Pv2UrwveWlQ3U0ndl5+1pyinzlzSRS4I2ms48/bXjzV8RYWsh9BcxtW8gXRZ1TNBu9mTgbJRuqZ1x/LdzWeD6NdQ8TRbVM+Rq6VUFZHLajyQFbmo7TWP7DiOEXyTBaxDqMpF7sy35yM+vYh15mMexR2FijVd+ubTxMQVY4KauyQtfFeDEMI16pyQMMOedNG0Qg6RpEvQFDlo8BuIxMkz//YmHmuzNlN5z6qML3VbRSCmq0mFVYNGxVrMAwj/E3zkqd6llEGLPfB6EVWsas1u9jVGldYfhs2P+I0vCpOuao51iiS1JLwz8AYzag6nDe91ST8eUc5OVCVhXWNKlXizJnvN399nTN/Fut3b8yCyWONuXCq460mwacqUfYxzTPOFBiKU/uJLVEOYFyeft90ODKOEzwNFtMUWgl5rCkBJcl4lnHc5EK+yTg75ibjtKL/PNZk/MqV+cVk43IRfwuqnKAwd5JYY1COC2LmZBX+VwMeJeF+hR1lvdWrp/Wzv6DJRluCGp8FFhRxag6Ot9sa3a7c7FLFWqBVSbMpYkCrxVyoZsMXRAAasIqEmzV3vYuK+0qAm5sXWZIxu8hqvB/D1tYl+69GLQ3/mrsnjo41Xk21mkTN36FRyLxd5RLzw/ZyclCRmRfWNVlSZ89qbu+8/NkafVUjWaNKLfNmivMa0TgNPloKs4+pjpTMrq2lwekT7JdsNOLp0ejN5xyhZBxPyDcaD5qMRSbjw+TPcCgZzzGOv/mACxgnE8ymfJpCi806rBq+7cpMGnddxB+DGsdqLs4dTRBrQjwnBjW6sgrf9AF3mpR12uBGpOClKdYUG18LR7kwjdbTC4DZJuP9opzpSjQnEAwCXY0HimBbUmHdONk7omZ8ofeJ4APuNimPaIPGjEKM8Pfm7D3JmCd5hvK99QylTcV/pQZMI0aRLxuI7i/J18Lb+LYMcbcMcWd0E1/v+rJ8r7ld6Wp5OLyNb+NJcAzjiWWI2zvPlh9M9Vp2jdSmfcp8vONseWPlT/LBnk/KK9p/a7ZI2rdaqQtXjkazkCwwsFPHs9TKXEyRLWOkrFbmDOzU8RyT+BtQbZ5IOQw9iJQxUkak5EvmgP6dOiasG1dB1u7d/SpfMgcBFlUHaKNv3eixilWhZ3CHtr0Bpt3a/11bdZeimqLqcPoIreveo+F55eMHlrQfpILEPeF0VzaHN2XylFzZPCXpDqc1nthx/9DZgLYYC/Gc8svC/vRnI1ow+ZhT8fpI+4PDN7ORmHZL/0/DdenP8NqLas3E/oqe2wfeawoyS4G1+3S/vm/PrrOGtqt8rHfv7h+s3bvrVVsk53IbJsHGx5qhOPpf5+VHqtzrlVtTZ75YTaJzmtslWXCr8/LLQshySJVEleuqSfjbqV7LqxkYqpVOq6XBW+LUfmCwFp2cZMEWsZ0W6BznR7V5RxWTcBLQU98pLv4uLvRa7mmRZhyQ91+RvG45EuUYk7LPhINSbFDjTcCu5DtOjrzcs3h5zD2EwEKTsgggK3EmwhUKWXHuQzBWW6y4pxebhH0K7ZhF5JktjZk8JfE8Fz96l9AHfKvVWAA1jucVujEjtfbFAubWMZa2G4CszNdU+J1Cv0IV4dKszIcb6p9MUOVdNuY7JuXjQY2zbY0TNvZ627Ap2NhYIzoURx8sYsgDXrkhdfZTtTS8tLllLQ3+6VUuaog1qVf5Wy0J/tqi45Zw3q4erEWn1tLgHXEanDZYi16buuD+jRz8xFC6mksDBYScsBwFm3IKOdFbEopYo+wtjkW00L8gj0Udo2KN54X1mDAuPKWgxjvJCY6pxJoI5YDCQQVX4hPkmjWuOHcZtNTYiG2VV5uUpQBFDJzTtLteEs/Lm7UPvOVb48wXvHEcgfAvZT35UV/ATFZ6NRGyMp9W4SKFIYUhFS7Kynx6/ZWJgmE+ZhO+XsSaH9vcBncbtjo2LtYokg7H4bu8l9+p8qAq1ySZ/UicBtc3t60m4fmqXF7MZ+qx5rw4Da5t1XcrpM4+PFiLXh+nwXviNHj3YC16Y+bsFnHWUiNj3FwlL+MbE2tMylvJ40YkORlSRjkKJaC1xESJ9RpzZYFQPK+vx4TxII6OoMZ7yTOWgoKsnSjWlFCejqeEoC7ig8AdRazJgAcbn/sGpLbGySbNHdFsymHk87CgsW9xYzXpvOWsFuMA8OLZBbhLG+ZSmmcJn1vPtNlgCGRlPoTwN811dPpVODcrs97l09MeDPNZm/Blk3J6UOXHJuGgjbpeE/5rM2A2BdWn6dJkqR4kMYOVS+TS8GwZd1epjs6zzUVutv4jPkB3sqtkbela2Sh9jug2WQ1b1iIrWptcmbVXnsVol5/Mxn4M4ZO12UPGWFGLRFmbPbjr7sHPrFvauTOqlaJUy6MkGOlsal9Ou4IjYfyMoL7dOp+dddhnt7C9rvfR4hjiS2YG5OVSM65f9zUfyrd8aKJg2A2vPrDndB+ZPUb6zIkTce3B8dWKPSaeHt4a9qZ/ptVzoqRpZzA7WpeOpP6Ve9M1A7G/Usv2SWOaW5k/7daB363buaM3mR6+QEXagqq7rOvOgV9uCv8uik67deC3QMsFdv8uHUdn7fbpiIQU9bZZR/CsgR3b7+y6d+iKTbj0NmxxKKXA7WOt31eVNXEaXOLVTKrjUk2iXxnxFwTWL/ZeVmbeblQda+Y27rwNgRr+ieMgGe3yk6qMzn4BEGV/aZUt4znIRXzJJnxG89frhI6TsTouJXEcBuOXItkaJ6McJeO4iozzvHqUGcADGKpZhTMKwUiLUA2qfElh+4asGAAR5bmScaxx/EvhXlrrrKh4etSsL231EQ+YjLsFdm4am6iyg6vwWRvzKJ5nk+9GX+JK/Hq8e54SBOcq/Bj4cauXbczzGsozwuJdeYVJuWtTXVG2YUtDTSnMDrBG9/AqK+I0OM/rWDfH0cjLCazxv7bG7+C8edh5sxElIkLm7BbXK1PD3/EsEUa5/MRqWmQgKnu22N0ti2cfH3KGSflEfmikjW1BJAfiOJAJxL6DmLeiHNKw+zo6R7L1aZl4eor0/YGsjdNHCFrBBVW+qYVQdr1PcvL5REl5lmScV2Rjt9K4akMJkfULGl/iBjPMYzSVBwhYlEVZhY/amOfiORZADX9yJcZYCG8QDImr8G3g261etjGvYn2GTD5/c7xZMu4c0c/ahico1JbD7FBjdCfvZVk1CX+uSHOGZxPED8WlL1rjzrZG5ztvHiws7jcQQursso0b99QRDGeXZR3B8xo1HzXXtxs9r8kfzp2biWHAimd3H/B5k/F+1ocGIdd+a27vTcb+zjJuRpCNeT+w+wbGGhWlXSHWgNVZwPvxlMnJmzCo8a0i1tSzYgC6xPN88TxXHD9E2YXW5ZUzmw/4En81w7yepg04yfX2FmZlTrcJL8RzFA0l2uPd85RgGMoq/B8NuqGNCGLeSv4Zjcw9Tcb71PK6plLNDcY2AmYDse7t/nVuHs9EiFCygSV6UuViPlL5i0wqDGZXSbXtT7LR2Q5bC+0PVW9PpkcXZu32meRpgV6U3s67B8ekBppMV7pAdxllJ62q4nRlqTddPfOa3lMGF7Yd4iMzPepNbx7arvJGjWT3UZ2oqngmLItJu8LntkjpmwymtDq5JW237QgmHHQDJtXMpC4DmHZT/6fWLe14SdYWHI2hE4hGhHqNlH1o9ohnRnNpFZuEUmlNMiaQR4PZlXHJ7NugnwOqalJ/J0D33YMTlpxsbiTd4bGjxgIgUk6mhc8EthEwT1go7aXkPSIcDJQRkrZS+vI4DU5LnZ00i8GrGUgyM6k43eMNF3F5UONQVeqkpQceGYcsWA3MbTxQZJms1ICHMsNrbcqTUDp9wHU24aOMXWQ4ZOJYI8qx0poImQiBWu4UR6cKHsNQ465MVuaDNublKE8m3ykPWb9gK6tyuMC+43WudizproYr8CxszJhRcCrcioArcwFwwQbex0ZDPM9o8b5FJuOp2wiYJzLUtJeT0wV2FaFsVGtByb+8mgTvbBT4Hg/Omz43VovqCQdX4sKgyiEKSxmRFuAOV2qZut9H0yKgyOxY7UPu9HYk1pR8wFU24cvklveNyGCCEjwlIH/uG3eGpwQfsEwcPSrEGKqN5TeuxHtswsmqHMj6DMM6UVRCeS5KNs61+hvJl5GhClejPKNxrJq7JN2E4F2Zc4Fzpzj8TYYoR8vY0idjMp7sgsnLvrbh8YGIRu2l5HPAfBHKRrQW2OSk4Th8l1czaWm883a181t2A3pzoOuuwV/17t29nyPdEREkCBOEq120XtgWqD/wg4zNjEkR1hQ6Ka8zGYcC4i1X24TvMLZ6RZkg1oijh7y0eUNjzYAKveKYpsIwhhgzkmGXuIh32ZRXqbI3eawxDWOL8Lx6gmu1LocWblHl4EaSqSCvbsGQuDI/BX46teFvInLdrye1INe9yTjEWS7alO63ETAbgPhAXeDmcRymCPx5RkFYPVLfXvmLvPnxHd3mgwDTb1z33ers0p/i6eEeNvarOx4cvqGVRkvl0dpvBxe3HUKT80Lbo7XfQJ550nXP0D/rL2Rt9pzanNKOTWRKYhL30Nq9u14RVP2DHfcPXW4ybRb8G1OSMOJi1Hys6NMOZn8aWNL+Ng1lNwDJ9J6uuwc/V+pNVwMk08I5PjI7FLccjCKRgLxEivnjCOvKmFIjoOO+ob8nPeEJaplX6NSkKGn7suoPWnXyOGJbFdITGFHg9hTh4AZ9hUiVMAqzN6XOfvBxHdzmhKBZhS+YlMXi2UWFR3zIza1+sX3AL0yWa7I0HM58WGgWGGJXWp9Fpxnn4Dmlqb0DBm2NV6jhbh9yNWPV86dEvjQ8QKkKv7Ixn4CRkoNbXIkz6vouxrFAlO0odo5adBeQ12W3gqHF8+oizm+o5y4Vqbk1Hz2xFiAtSsO24QmEcpgdVidfAAqnpKgcZq8aiu0XHu/xbTbkzhsfNRm7imeRGh7wAXe2ijVq+RmOU2W93pGSx5q8vMow5Er8ZaR9xh/wnNgUazwQFLHmFh9yw6hr6ShiZEI0PECJGn4WVPk/cjJaEK7KSnypvjASx3Yo8xlfD0YZJ7uPXFR7DHzEL23Mk7UorypKD/pdNEYv5vHEpiQTb8NWQDlMnwEsqGu5FLHGlqPsBcNx9IPHd3SbD0HN12Ze1fuua2oXfSfq6slmH3bc53xIy9JKNfwSzysaYo0HYhfmG6Rq6Xe2gSROuUSVI2W9PpICDmWmrfFyNVznQ25vijUlNjzWxGr4TVDjO+RlSxRlx9+oa6CIZyHr9bta/c6PF4Py3lrAhfzAJuxVZNaERaxZ6cKtt3G9tbCNgNkAJHvoXrT6kpWY76ZpyfbKf5QIWGVVvHwyK+O2R2v3+UhOr84tn6JWFojTRyor4u+2PVK7r1X7jvuHrk47g7+4in0qgkUZRMnSzvAkjIRZh/rajHD19BvWnRrUfBWgNj2c2Up/BmCMEG9hKy2Jv9OX7e4ayBJE8lTcgJ37d+n4xMwre98cz4jmDOzY/nmgXLgzje3ba1yQbGMDhZJW55aXVlbFdwXDbsgbjFqxNtV0+vXr3jOwpP2YrN3uZ1L/cPtD1fNLvelmF0ueCqJ16QXxzGhJU0ZOLepNpyyUuA1bH9b4PWkiAkQQlF0fpyFtURSTkwm1H3zEjcDnTMargdnA/T7k2+NZBbqIv9iYvdB894h8VzsUz8kCgXqcyViWVXj3yIQiZXtaTBh0/Z8ROrbYQvcK/xLl6eT20/VU3D1tzAeyNj5oUhablP+lqPEeh41IGd+ZQE3G4sKCulos3ATDUFbmVJtwLMpuKtznQ/6kdsur97ceJBfieVnTIjTRYP1CdRueeDBG92thJ21gEq2kf0cI+JA7YWIrY1fi7zbG4HgZ+eLjDhfxrUbB7FHtQ86xMTsC++VXYTXQIfkOcCCeF5iMW7MKH0HycGJSdmSCWNO4A1zEGqdwvnheRJ5FJwCqHBjEnJpVOMMk7G0yPtKwmBsP4y2KOiVjlhr6McSFDgVqWetKvMmkHIeyowq3u4iLHi+RbRX+ivLMpiwY54PJ7c+34fGDET2o2fVIhNCgBwI/eHxGtWUgwNCj99X6l8cDM488bty5jSvxOxuTkj/X7Qg3upBvj6drkkWcHcTMU9itOPQoMFM8ryEvXXqhyfhn1sbnR8aSsSMttPbq8aG5FKkoA/9FMVdqFFk/0tYYchXOsjFPFsfbJ4k1hvGJ0dnimKHCAIakiDWqAcud8GaT8CxgOxVucBF/2Widl02BoAhXaEOZaAHjg/GNeqaKbQTMBsCsYw3aQhTJk5jBloKu/xXoeLB6S8eD1XdA3RWp/fjHDpr2cuN0ZeWR2jltK2oPAAwuqOwyvEPldPIAnAtApf5BjcxeI6U/IgbD7L7du06deV3f53wgQf+unV+hlVCnCHgdbKEpg1qZhZXpdfKlOG7VMrM6r7y4Orv0NCAcyXopiJsGUV8FUkn8zRqZA8c6LRFV55c/XJ1fFrz2YqQHsJLpPR0PDJ/Zc8fAVk3/Hw9ddw1e2lvp3ilrt8ehJAhhMJid17lN/+UJDUXWgMaMrZ19XBbXTxT4iKt9xNUA4ug0KScEKa9HWOZCfqtBbmFpEvY3GR8gjxuO3LFjhcAe9YWL5LaJi22Nk12Fs/C025Qv0XrC4IGaNFkgFoTL9qxPv60fDxR2Fcd0k/ESCvKleG1U/XUhnjkMLNfc0aQZoUn5nEkRckeS6eTXusWVOHNrp/+PB1fid0GNxaocTk4oBWr4kQ+54/Ee2zaMD1VWqpKKjMmK+K+2/HUlLgUuBRDHdJPwPEnYVYV7fMQ59bJAG3OEON5O/ki74m8/uchtHVZhbxvzbFfmPMmYaRyfGmdlkpKHiFGEbJEGvzO53kwjOROpciieyGS8spEAbRFrMmAl0KY5YTwGNuEbxSm9wIyio2tdxP+5MpPoAm0duBJnBzXmaU4SZoBRy9c14JHHe2zbMD4UWamqThocYTW3rZiSZft/JPJy4fOB8wEkY45JeaEkLFLhVh9xbn1TxdZ4tnheR/5Yp6zXoSo3xZojTMxlvsQVJmWh8Zw2TqxJyHmAZr2rEGUPmuZCkouAHwucJY5XTRJrUuAOYEfNdWuaEdiYbxWn9ZPHGo9wWVbi667CjyZ977YCshJfDWpMU9iJPNbgA87YVP0X2EbAbBDazpdr4wO1H0NEPYB4YvsQ50i6+W0T/93gQwl69+k+Q63MRSRyqksHl7Qd5iP5mBqJqvPLHwdMI5mhkdl7TOlPbiN9qAKDC9sORGhvWQakmprYXePL9vBR4ryqzmT+Xm9t99hz8BpIWSOzAyKjv/85CZOipCb1t3Y8OPzdYCDrXbtvz9cxOrPJcUlGBLYMs+qvacDOAzu2fybqTV5rU33cSbl6OVnSGfwy7Q7nRb3J8nDIDT7e49qGiRGn9rKg5E9WJSp2o3M7WC8/f7zH9oSAp83G/B95+U2oyi424UivvFc8O4jjPaynU+vP+Z7NUSTfQs4nFDbhKPK011bIgOuV0TshxU7RA6KjtWlGRqmUULaTsROZujNRinC9i/gukNqYb2m+u13vn+Lcunjv7IbX9rQx/5tVeGN9V/1xheCzCl8Uxw/EM6uwod7s1uXbsHmRZMGfK1H6fFUCkZGsijh1ZuvU2T/BIY7pNuYr5E4lAcrONuYYF3GqSTlUlFManu+6wOXOLWON50TgPJPyHFq7v9Vxq8I+jaSuQqzCI6IsaTXMor85rV4o0vhThMtdxPdR2mzC1zUnhuv912NNvbRhfaxR9rcxH83aeO8EY956MCRZG5+UjJmizChsqLf+Dvk2bBCSzP6uHGZHM/q7n6SZfUIQe483JGOBTfgi+XNpUXayMU91Jd5qYp4v8IKGuFKPNfNbzVmM4yUerjApJzFxGdB95OTCCBSqKvSKtjyvvtZq5bqkRaxJVPibK3G2OObblC9qThzX+69vXtVjzaziNaPKYUFMKas0uJ49njAMZW28v7Ch7vAB97PNhnrrQxLxnd+X95q1/BNHlZQ1wT38uOsb8rPHe2xPBAwsaj9MrcweIUNEDCKl6tzy66pzy28v/r/5tNblP2D6d25/ctpmd2r1IqqgZF13Dn4HTy+qSXE8QRnuuG/4LLSlHaNWHq3dbavuxpFz1veZ2WF34ezL17505jV9nyw/ljwa1HzNxu4GaHrgGu9j9H8LQlfvnl1v9oE8YQjOaCDrb3+4euc28uXfA6qmWkuC96hyjSpVVVZlXr5VS8JtpWOATXg6edZJCCO7wyWTcbI43ipj1aFg/Iz8so05BN9yYVM/adBFfBsYLIiTOoGyzoecTeusmXVqeVSFm7QpfhQClj/P2jgpq/A5taxVwwBwF0XbVoNtShU2wFxb41Voy+s/LlDLWh9y5zby5d8DzpveOA1OU7hZlZoqy1NnzozT8KrHe2xPBJiEE6iTL+TZbUDZpJwkyqubydVJ0GljDhFlUauTimf+YRfxLXJb1LQ4HgMrfcjPaCJuCg2I+zBUEe5o1lxSqKnlG1kbL8sqfEUtg0X2zkPkGTtTiTUBsIutccITgOodgQa5KPI28uXfA5mzy5PMftgrdxax5v7U2U8lWbAtSxKwOVlSKuYzFPObdpPwIoHnTjXQFO1m2JhDgAXjxRqFG33I1xVqun7eEQP3+4DfMjaWOIS69XersqrYB3wma+MVrsJ3MMRqWU5ekunr1x1nvPX/jlCeZGKOmOLtbhVowKM+5O7NRb7AtgyYDUZ4r/T1fFY+P3nLrY+hE/yB8QG8log5UuOh0r/4dttF5ratdX1XsaO1RgpoIDswPtk3nkiTxDNLb0XG2SUSgcyviIbc4PQb171tcHHb07OK3cPG/r72B4f/FA1k/eny6uer21U+2dz/mv16Phb1p5fgWYvRaYiU8JoAw533D/+qsW1tRjTHVewRo7JlWovyNo5NfMUeuXbf7p6Z1/SdPnHjbdiG1si8XZnF9v893uNohXKYHhFYfzIwTeGeJLXfSl3QUvdpS0CUXaVZIwdEdbQtcxN84y7MqHMd72QC22mFezSg1wlvMSnPRNmp0EH4M4YhdXwFx3sZPZcoB1U+ppZLcTxZoU3WC1j2uihPOa7DZOxCg03kVCZbklcpPyeo0ZlV+MoUTtmGbRiD1NllqbMffrzHMRZKOUqPC4y+GOhUuC1Og29kzj66tUYgylJpmisXu9N7khMkreKGa441xcIjKmJN1Jiu39AvHm7UgEeKWHMcyg6NOgia8UM8r286bUZQ5QPe8A/j2KvIbAkVasAjLhotYGlSDiRfmNUXepO/DyB4XmFjOlyZn0zhlG3YhjFIsuDOJAueGJlUo6BSidIXWKMnABVVrqul4Te3ammDsrOMLQeKyF2GHK1jzRg7kiLWdBaxptQq1hSd3+BD7lHhVJNyPDBbDde4iL8hZJpxHsoLGs6wKIuDKu9Ryx9wvJm8vCgoYs2dPuT6xksUmcXd9fuqlypNBAExjlOlRrsr86dJmv/bYhsB8x+C4Wf43eNDeR+mEERqY0ntGE43ffqu8lUyqW3t5oCtufuzDltrJmHE6SNqZcE4p6Vk+ggBi0aJ4YqAFHoLqoqqjAoxqj4cdv8ECKqu2nPbwO+B34/quCfcB3Aj+jIAQrtGsk88I9rVDrkLbeJXZBW7m43dfR0PDF/UnCGS9ISLUDJkXIHMFuEPEAl8ZPYcnlta2LYifnDcc7dhG/7NUArTQwPrTx1xMoDdSqH7tFfzpq01BhXuJrcqbLRgVuABYI8x7fM/NfIMltnSIIZb7GC31ftoMZlxakccCQacZRRJCyCeAwAvo3enu4ADcOypwi8QPMqSgrj5S4OdY72PxWzYbnp9/KEqR4rjJ2r/u3U7tuE/CwX58toR4U5ln3KYfX7YyylbawxFrNm5qfTQIdyLctCY9vmfteQLn67GBUdTrKF5YaSQasBNUAjf2rFEh3gOIo81jQu16ao8yTj2V8P3gB6UBWq4sb6YaupjMVN0ext1HpTwnIjnN83xaxu24d8ZlSh9uTV6QoNI8EGVKN1pSKJvbbVBCPepMqep9DBR4T5R5jU3L+LK/cB8LXRgGmKNpSHWtECqAbcBaMBKF/D9McPJrexHCYIDs1SZKY5DvOUr4lkIzCqIm8uay6FF2UWatAynSPiW8LwS5ULGOlX+R2AbAfMfgvggXlQnXxoQ1J6szy1fJV/bGmPouH/oX8m06KVqdTYiYSFkm1RWxN9NesIDsnb7rBH76ZxoSdsfHP5gZWV8f//OHc9IO4Ij1NCDlbk0Ti5yYkZRVUQMqimeNZ33Df1hovG4ij1sRKelGSJl126P77p38NXhoBvXSjHqT5fV5pTGPid5CdQwIAjlMQQRgOKyjmA72EbAbMN/DkLrX9rsZAAEUZA9fWuNwUVcFNQ4oVjgBJpn0iU+5Psm40RVDpXR6vxVF/FeNTxmE56F5xByEcpZjf0WxIxqPukwRZnRQy5ab2/dEspBMo6mg0AJ5cVZmZcj44u1q2GZuLFzpeLAMHn/Ea1z8FJxzNtGwGzDfxICoy9pjDUiiCphFLonb60x+IhzbMwxmpchWc13omMX8kOb0KWwm6zXaAJY5yLep4aqTTgBz77AXIGexn6LxZLWtVeKzLjbfTiS4j8e9mreJS/6E/JFy0lZhVdNuMoRlpET0qPcJQsSu0pR0lkfZ/NbIp4ZapjQIXMbtuHfB2qayBdEsKq0hV3pjltrFC7kpzbhAM216GxRFlT1eazZpdB7qWu/AKxwJT4KGJNwIsruwHYC7Y39FrHGU2xtK8QIV/iQe8YdjKdS9NVCNyKPNcbxoqyNUye6J82J6lozCdMQa0LWl5I3o4xSRh4ft7UtjW0EzH8KotELCQAMVivM3lpDsKmm025e9+6BHdufm1XsQeJ0Zdujtd+0PVK7V5dxS9/unX1pZ/hchJKpuau67xr8bjjkhgB6bh84Hzh/7V5dJ2WdwYtonmAofVFf+ktXNjvaqr+r8/6hS2zsJ7b91taWkQ2vJ0lPND8crI5rSVl+LHl0cHt/hS+bQxrIozgYyH4bDLv7xWtcWhXf3bd39/dpXpQKQdSXbrWyjG3Yhq2Eac0HRAhFWsSgLQXDkCvxdpPwPJR9EB5yIb/WgId8wJdtzAo8x5KTKP9wZX5Q1wlwZX4L/DaocqoqT2sxwVimhr+ibK+Gm13EP6ZQ9ztEnvEyLsQxfTzLbAAfcLtJuVthlwbyKFbDDxB6VRj0huVBwncY+9sdqWXZJGPchm34d0Nni2MlQVsJQG4RqGWNi3ibSXkBWrgghfxGA1Zmhk/amFeiHAU4FS52ZX6K5NoqrsxPgZ8Gw3wc2L9F9zer4RaUWWq4zkVcPgVB7SrQMcHr3SgRMr4uiou4KqiySmFeURapQKKWrwGZGtaoMmwzvkQr+1r7X+xasw3/cRAhoHV5j5VAW8WgLQINeMjBO2zKC1VHXJB+q5Z1Tni/TXiNKocCNRX+6Mr/v707j5OrKhM+/jvn3FtLL0lnhRACosnI5gIMLoDMy7yiMILjh3GDAXTUccN5XV9XFJ1XfUcY8Z3RwXX0VWEEBUSRAAOCgo4sLiyySdghJCQk6b3q3nvOM39Udaequro73V3d1Wme7z+QW1XnPgXp0/c+95zn4dKR+cIXKytYoiH+lYYETDX58muxPClCjzhuCjG/nTCYysOiwMTFwvea7Dv5HL+ISry2uiUzHplrguNcA04smwh0Ws9ZjL2u6V+oyRfQBMyCYbdwi9+bVdiaSSRQjjbOvFf5VERDfmjJnX0XAnXdWowgS+7qv4xJ2qV2PFm6vq+76yRq/26KlOP+7Iqee/rHXanSTGFrcvHwnvkPjyZOGhlyuR3ppG0Ll92249zeP+s+Nl0UvdwISW5bcvmihjbO+W3J18rLcu8EctUVOyU37G8obE3mbK/6RNIu193/nK7X+YI9zHjZUniq/MOuR4fnrD6QWjhE+ANwdEMryVII5vfAc+csDkevL/LdMS8YspEbn4k+HyKusilHU7MUv5rwuMwX+PmUYrFcTODtjXVpGuKdeHWKgazIWa7MqwgcDfSHiMtCjtvqxsn4AYHXjTxREiiJ5TJxkySc50i1Ze8bjXAwhsdDxIUhRhPRasoE7kF43kh3pqqyD/ZOYM5WwUjEVh8xdiuCpeyLfAv41kSfDxE/sxkH1j4FFiiFiB+H3KQrXupjsVxO4KQJ5pq+iZIvABh8VuDDLuHVBF6M4ekQcUljy3jxXEklSV0YTdJYvjPRSr65ZDJWuZQ3IqzD8KCP+YFEujJHTY2ISYAngDUNLxk/6B4BDp6zWCI2ZhH/Oua4Y6Ba523CWm9i+SmBdzTMD2mIuSzE3L/LgRgyDNeJ8D8mmGsmv7+xDPs877MJJyG8EMPGEHFxiOuL+NphbhHh0OpcE4BUHF+bL+0FbMq+NuNkhH0w3ONjLpSILTMZUxMwC0TnpeaSvnfJEUQswVIkMGxKPN55sbmm3bFNRWFrsilZVD6ntEf+vdW6K9YN+esX39138VTH6n5w8FYfm68nS3NvwrAYkNGW1yKlaNBfGQ9kk960mEDoubf/amDcDjSL7xv4+eCa4sbSivzxYijmetNfdG8Y/PVUY54NPmdz2w9efC6WZRgTScSaob2LB4kzn+9+aOgPk4+g1E6lNP5uMZe+QIQOKjcUJRHuKqXRTcDJbQ5vl4WY+03gq3jeTmXFnRXL5T4/teQLgM9zjSvTSeANVJ4+je6briZ1Ltilm5ZK8ugnwE/GPVeBH9mE+63nFYANjmumehM3W4ynu9oevMuAE2G1TTkE+PiULv6UAspp9NVCnJ0jQkTlJiAJwn8lmdutHh6EHL81gQsI/O3IMbFcMJ2fW5/nQlfGEXg1lflXRrZOUlnF8q1dummxDPsCFwEXjXuuAt9wCXcQ+EsqT62vDDnummrMs8FkrHAJX6JS+8KKsJdLONzD+yTahRtDpWokmft/uch/lsqKjwhIg5if+VL0dJtDmxKf51pXYi+EV1NJZARxfGM6v3+zAl93JTKEY6lsf66ba0I0ceJ5hDj6qw/Lxj4w23mus13Cy6oPn/pCxBUTbpGaQzblWTblHCrFzY0Iq1zCS73h3eLYMd1xNQGzQEQbzWDPP/GeoRPkpWEZa9wmHuj4mbnVJGa3K1606MHBW7oeHjwtWZpbGQ363mjYT7udac99A9d6y3XbD+05K+TswYhEgBAY6tow8MMWhk3nY8P3dD42fE8rx2yFgf06jsKwuKGTU760Mv+W7oeGJty/qVQjH+y2wXLuHfkoO8oYWRnE3lNO3e01dW13Gz7PdQg3GM9ysWzHMvG2xvEY8AUuw7M+KvNPwL5SWdIcgKd9rrWV/EOO2wL1K2PmA5vySqq1MmC0tkXOppwaYs5qc3hqN5N598RQMG/Lxf5og/T4YO+oJF/myWPRKfAFfoKw3niWiePpaa8iMYgvcL7xXObKfBFYXp1rBHjE5/hVy4I24PPcBNw06XvnmE05iUrL3pHuKlYg51Je12wFgVITSbLo/izYv89H/miQrizYW9MsesAwttD2vGbAF/kegYtMYIk4tk67dbIh80W+bjIucQlfpNKJzlGZa+4McQuvQQzB5/klTFJvrw1sxslUky9QKXAskLcpJ3jH+dMdVxMwC4gdNFnXRebGdscxQoCh1YW1EtmO4pOle10SJl4WW8MGQmFrsqkVcQys7To6xPaAmoK8BivdA2u73rD09t5vt+Ic85nPuzXYJu3BK8WOlZoyEVMupfGUV4rMGsHYjOciRCHm3ildcBgyiWjJXONSXgWsMTv3k1uBZS7hr3xh4u2XC4ER9qvtTAWjSZh92hWT2r0FsUOlxF7V7jhGCc6m7I8hhIj7ptShw5C2aq6xCW8EVtR0Z7IC+7iEo3y+vu30QlSda+rqU1RX3T27XTGp3VsItm84sRM295hTQlyda8oh4k9TyjtbymJbdl3zVmpaSVP5nX6wTXl+yFW6ti1owrMaawUaiJnhXKMJGDUryj3x0r7ndn1OnFmGEIZWF2xxU/ns7gcHJy78VCPrcB1967remBXdESZIf357csmi+wfHPN3xeZvfcUD33/mi+wtAoiH/88V3933PpZICpN3RS8YkIIyJs6J7CbDgEzDRULYh63Slxv8GJpOH2xSSUi1jMla7hM9SabkoNiOEmP8TYnZ5NZrx9NiEU4xwGPB0iLio2fYA4+l2CW+lUggvEct6n6+2mAZM4IjG/dLVdopHMEn9q4VADPdU24PX1tURDH9qZ1xKtYJNWWfT0WKRxqaUQsynQswudzo0GStdyqkIBwMbQ8R/hBxjtlQZz1KX8A6EQ4AhsVzq8/x05DbACC82DdfwBgoEXgoLPwEjhrur7cFH6x4KZGLmxxYppWbCJrzAZnxs5I82ZYfPcaZEu14A22Ts7VJOQ1gHPBJivt+sHpvJ2MslvB04COgVxw/qtmILhzTpvJY3nj+HZ0ACxnDvOO3B/ziTYTUBo2ZF37quD4kzqzDGjlwwDO+Z/0hhc+nNI52PxjO4urBuaFXhFMnZFwAWY6w4s7K0Iv/eENlF1Y5Jo7Y/f/GZITYHjqxwyTrd8dufv3iP5b/b8XkA4+VpRDzG1D8tCbKjdd94/up6aOim8tLca8WwN8bkEfFA1rGxtEv7N5WatwRcwpnA0tonFDblUyHitMlWwtiE51WXlx5ItUUjsMJmfNQI5/o8v6k7V5nPA6urNz5FAidFJRZnRb4GgGGbyM76L9WPCYbdah/5dPkc10YlXi2V/x+5ahvNxMd8r92xKTUjgrUpnzI13c4ECtW55q2TPZ22CYfbjDcCa2G0letym/Fc4NN1tVWEyJX5Zyo/R5ZKYuU0V6bgC4xsnd4B7FEfIl54Zsw1IcdPXJmXC3RWu6ukwHDIcUm7Y1NqRgIFm/GJhqLdK13CR7KID0742co10dHG8zqqK0+rq1CX2ZSDgQ/VJYwDnS7hHCp12wywEs87XRlqkjD9VB5w1UoxkzQWWCCq7cFfJJVtSLFAAvT53Ph1QXeFJmBUy2UFW5DYHIAxja2kw9BexcMW3z8w+nSm/9mdh5eW504RZ5a6cvhDvCO5sbRn4aOMdBOqZUw+6YlPFVg/8kJpRW5VqJwrrnlfLuTtYeWeeGl+R7qtY2Npff/azldQu1xVpFzYmkyrBoxYbHlZbg83HAZ2pYhvu1kvfultvR/pf07ny7NOd7jJZHPHxtLlxafK2i1A7daMZw9geZNW0samHFi7PNaVRy9KusVwM5bbjef9zSr8G8jjeRPsTMDYjAOAlbVPnQ3kRTiWwHewlEPEpTblUOrHTEI0zdUvgjOePcTSi2XCxPW8YCllBd7rEo5HeL7AoyHmpxKxtd2hKTUTNmMdzbfXdduMfUJcbQNfSdQebypFcvNiuBHDEybw9+PNNTbjtJDjoyPHXMLhVBILtvZ9BE5C+CEGQsQPbcaHG8bMQkzdA6pdJkTVuWYblmnX3Zsr4uj1ec6wKScg7C+G+0PMz2ZSFFOp+cBmvADqtzZWaxztZzyLxNEHgGBdmdeYwHFUmghcC3gTeH2TlbhGILYZrw8x54wcd5VOkLnaa6jq9c/JUEnAiOVHjJ2/fIj5xbS+YCBnAivEsgU7Sce2eUAiNnvDGTbhRODZYrjT57hyptdkmoBRLWeEQKUETJPXxI/8e9/aziNKK/PvH2kT7Qv2L/yehZcB0Zjky+gAdISczY3Uk8mKbhlCVu2YtJOQ+Q7Xw450W/Gp8hO+YD8zvKrwLnFmbwLb8tuS705lO9SI/n2Lzx9eXTyTkcx0YHv3hoGPFudJu+nxuCQk1ZVD07s4U2o+MnjGLo2tvAKjc40r8SoTePPoBYRwLJ6X1y5fb2JF3YCBZeO8T4zQKVAOMfcRONtWOiutBDaHiG+GmPum8rUAbJkjrecD7Ixxs8/xIYnonepYc6rSXeVS4NJ2h6JUC3maV/81YmrmmjKnm8CJNXPNCQimcbtQg1V1fxKW0VDfpKpYPe5DjluNcF41UbwEeCxEfFUiNk7pWzE6P76t5pwPZ3k+gpvfiRhx9HvHD9odh1It5sd7oXauiUr8A8JRo3NN4LVA1GS7EFBN4khDPTZhOc3bTC8eDSbPf7oy+WqXx25gQ4j5t+kkO90wpxjh9eyM8e6syJnTLhQ8R8SxrdrJqWU0AaNazpVDYpPw+5Czh9Z13gHpfGx4NOlRWp4/fST5AoAxDhE7bvIFIPCUrSnmW3iq/MDQ3sWxFyqGYtrh9oTKfseuR4f/2PXo8BnC9PsnJN3RouHVxc/UbWWysqR/Xde/5LdvP9l6GXfSVEq1nji2Ao8IOwsyVlsklkZrwAiYwCm1T28MRLvQsqlur3SIuNv5pr8zY5OxWlxlOW7Ic2uAW5nBZGMyVlnP/27Yc7yHS/hy5jh9N2wCo9RuLUQ8YFP6pdJ5Z6TFfAA2i6OymrTyZPfEhrlmoiRvZQxTX69KHHea0PQhVqiutnkIwOe5Hrh+JnONTXludXVO7VzzrKjMOVkH75neqEqp6Qoxt9usfgVMdTvv3SOrLoxnMcLRtfNLddvvuKo1kupqtojlDhM4kfrtTgDGZKySiCerXR4vBy6f0VxT5kVGeEPDFu0Do2E+nXVw5vRG3X01zZIpNVOL7+n/kknlHkQSREoE2db52PBZrhx2tnm1TZ4oG2MQGTuHiAgi5eKm0nm1P/tRKQznn06+gUig9mPGUF6Z/0CyKOqpG34G32lo7+KRND6VqlSNKPTv23HYDIZWSk2Tz/NZ4GGBRKAMbA4xn6zpTuKAriYfbXqtUq2jUAoxX6877tgmlguFupkGA8Z6ziQ0rMKbwWRjU15Bw+/n6nCLbcafTX9kpdS0GMTn+CSwUaBcrQPwqM/xmZrCuN00+cmX8eeaDBj2cf2T1RDziBiuaTLXOJvyaaTh2n1mc81f03yuWWOy+hozSqk5YEhDzCcFtgqUqnPNBp/n7NG3BJbDrreyr9ZIGgg5Lq49HmJux/D7hnkGIHYJn2gS27RZz+ubdBMCeB5hTI2ZBU9XwKhZEQ/6wRW3bv9EeUm8LMS2WNhSfsJI/UWISWWDxBxYt+IlSBmDIOIwJkYkRcjcsL9+vLoli+4f+PmWZUvPoHHhjCBDexVfnOvrn1GhpNHhbNNlepWwc6anFedQSk2NOLZlHbzfZKwEInFsrPsVX1myuxFY3fDRtHpj5KorYlKgJIZrQsz6Zt0GQsxVrszJTbYTiM04JOS4uUVfa7yLEYPQ06JzKKWmQCI2Zo53Gc8qIEjE5rrXLduBEg21YoCsZq5xIzdDYrgq5LhKHNsbzxUirnQpr2hs6w4UbMa66WxrHEfnePdURlgs1H9HpdTsCzEPhIi3GM9qDOXqat+drzset2nTbYpZdWVeVK0bkwLbxHJ1iLlKHAN17zYQLFdYz6HUrIKp1ozZ02SsnErnpUkUxjkuJtAllqEWnWe3oAkYNavy29NxK/J3Pj789YFndXwBJKomW0o2kz91399/7uCajuNDzu4bDfs7uh4cvDYqhdK4J6kkdsY+YTKIQcLYD0xPx5OlX/cujv+OsTngUNyaLPxWbErNYxNdJISIf7MZZ7Ez2VLCcIuP+Z5NeRWwpxh+6/P8ArPrT5UaTzPNz40hjmtMxvHNziFu19trK6VazIBENK/5Zgji+AqeD1K5vrZAWQxXhZirbcpfAcvE8huf48aaVXpNx2KclTO0cK4JjvXWc0iTJEwWospWJ6VUG1TmmubNMixlsXyLSu2muPJuErFcII47TMZxQKdYbvA5bp5w5YoZ5x6qonXXNZYrCbyjSSjD4tjSqvPsLjQBo9qm48nSw27Yv2twTfGVEtuVcX/6u64HBv/LBkJ+R98FuzqOEcSWwq9Dwb60rhsSmI7HSze1Kt78tnRL1Jf9IFsUnVJzWNxAdllha7KpFecQYHCf4oFZV/RsN+Qf73pk6PbGlUNKqakJOf4oln+wGa9A6AmOm0LMrRjER3xnV8cRRz9wn8D+pn47ooSY21oY7wbjuQrhuNpziOHfqzHMnGBsygtMYG+xPBRi7tLaMkrNjM9zk035oMk4FiiK48bqMn98xDd3dZxqXZmtAqtG6rNUV9EMhogNrYo35LjZDnOzwItrD4vlSzNIRtertPA+zAT2EMd9Ieb+loyr1DOYL3C1TXnAZPxPwErEdaMr42LO29VxQszdNiMRKDTUt3q8lR0MfZ710TDHCHXbqH1w/N9qEmjmhMglvAhhqTjuGO1ONw9pAka1VX5Hui2/I51xFfuee/vP237Qoh6J2R/BA774ZOnsVreJXvrHvgtLS+PrBtd0vAagsLl0deem8iOtGDs447YdsviTIWcPBBw9sS+tzD+x5I7ej024AkgpNSmJ2OQjvjfTcXyeL7gynxJYQ+UipRRiPteym5WR8xQ5z6RcbjNOoNJh6cpxn7xPVSAflfg8sDcQmYC3GfdlRT4z37sRKDXfhZhHifn3GQ1iwOf4tEv4tMByKsmX3mrNmdY9lDGQdfA5k7DWeo4D+kKO9Y1bHqY9vKfblTmHSqemyASCzbg5K/DFln4PpZ6BQswG4hkmZA0hxHzSpnxKKvXyDLDJ5/hsS4LceR7JOviQTXie8RwDPFXdgrmjJcN7lrsy/0y1W5wJYDKu8kW+1YrxW00TMGq3MLwyv3p4z/wxgCtsTX7VsbH0QO3r0ZAfWnHr9jPLS+MVPu+6C5tLD9vQuqVztQrb0qcK23q/0epx+9d2HhNy9qCazlCxRKzpX9f1miV39l3Y6vMppcayKc82npcBPkT8QiIer31dHDuyDj5gMlYZIR8iHpmtGwmJeczHfLXV47qEvwb2rakvEQvs78r8pS/wn60+n1JqLJtygMl4KYahEHH9mJoyEZurNWf2NoINEY/O1io1ybHBw1daPa5NOBVYWVs3S4QX2ZTDQ45bWn0+pVQDAZvyQuP5cww7Qsx1I10bR4SYh0PEW2zGvmJIp9POfleFHHcCd7Z6XJfwTqCnrg238EqbckOI+VOrzzdTmoBR817vus6XlVfk/xeVv69moCs6odwTn7/k7v6fNL43vy3dAuluuZcw7Y6OrGvLDWBMLutwRwKagFFqlrkSJ5rA6VT2VItLeI14vuzz/LLxvRLx5O76+NYEjmws7mkgT+Ao0ASMUrPNDXO6EU4EcgjeJfxNCHw+5PhD3RsrdSAe323nGuFFjUXLDRSs5yUBTcAoNduiEu9FOBLII2SuzOtDzCfGbAU0lQ5s7YmyBYQXmrHdnXPGcyjzMAGjbajVvBYiE5WX58/AmDzGOIyxGJNPe+LT0k7XrLXsbst4tiNNigYH+toQjlLPLIFOE3iTgbwBayoFe3PG825kwT2s6G08ICCY1iwFVkqNz2SsNMKrq3ONMZWOJXmb8V5kwVViGmg8IJCJzjVKzTqbshbhKFOt72IgNlCwKe9pd2yzoFkXpRRaVDevxTQBo+a18vLcXpgmFyRCVlqRX9uGkGZNcVPpZ9BQR0KkXNhavqQ9ESn1zGEznkPjz9/O1/ae43BmVYi4RKDccDgJET9tS0BKPYNYz/6Ab/JStwksnut4ZpM4ftRkrvEh0pV2Ss024zmI5vf6+y20ZK9YfizQWC/Th5gb2hLQJDQBo+a1aMBvp9lWOYOLhnzLqnPPBx1Plh4sbip/AS9PISIE2ZHfmpzX/dDQ79sdm1ILnVi2Utl61CgSy/a5jmc2hRy3i+MrAtsFRGBLiDg7zLSYn1JqUmLGLXAbxDI4p8HMMp/nBrH8f4H+6lzzRIj4jES0pHOkUmoChi3QtLB+/0Irgu3zXCaWSwSGq3PNQyHmYy3rHNliC21ZtVpg4oGs35XCjb5gd9ZHEUltKvcVnyo/PsnHdzvdDw7+tvvBwbcFZ5zx4hdUelqpeUwiNpJwl8BBI/VRBMoYfiNu7Jad3V21rs0vERym6dN4pdQsqLZ93SSwZqQ+ikBZDFe0upvafOALXIFwBehco9Rc8jluiYYZEMgZcFCdaywXtTu2ljOIL3ARwkWAxcxOI5ZW0RUwat5bcnvvl6P+7BKCbCNIbzTo1y+5o/cf2x3XbLKafFFqzmUFPieG9QK9AtvEcmlW4F/aHdes0hsipeaWgazAx8VwbXVlyFaxXOALfLfdoc0ag841Ss01Q+bzfAjDrwQGBDaJ5Rs+v4C3G1fmmnmdfAFdAaN2A9aLX1ppwzxnnYCGV+T2Gtyn47QQ2/2Nlyc6nix9v/Px4fvm6vxKqTawJL7It4Fvz9kpU/azKacDz8LwYIj4foh5eK7Or5RqA8ugL3IecN6cnTLlAJtyKrAXhnt8zPcl4sm5Or9Sau6JY3tW5ItzeU6bcIjNOAVYjuEPPsf5ja2vn+k0AaNUg3JPvLR/bde5GAoYY8WZZYP7FD+LyMc7nyjdP/kISik1OZOxxqZ8gWo3FBGW2pTnieF9ErGx3fEppRYGm7K/TflHA3kAEY5wCYd4w7vEaUcipVRr2ITDbcaHa+aaY1yZw7IC78Qy3O745gvdgqRUg8F9iidgiDGm9ucjN7yqcErbglJKLTgu5bVU9mYbgOo/cy7lpPZGppRaSGzKqSM3RACmcv2fsynHtTEspdQCYzPe3DDXOKDoUl7WxrDmHU3AKNUg5OwajKnvhmKMCZFd3aaQlFILkbDGNPweNmAR9mlXSEqpBWlV4wEDsRH2bUcwSqkFa3njAQMFAnoPVUMTMEo1iIb8HYiU6w6KeFf2f2xTSEqpBUgMt0tDi0iBVCy3tysmpdQCZLhLqC9MWe28pHONUqqVNgj1La4FhrHc066A5iNNwCjVoOvBwWuMly2jSRiRBGGo65Gh/2hzaEqpBSTk+DHQJ1CGattr6A3xAu5QoJSacz7mfGBQIAEQKAGbfY7r2huZUmohCTHfBEoCKYzONQ/5HLe0N7L5RYvwKtUgKoXS0tt639+/X+cxWYc72CXh4a6Hh66OB7L+dsemlFo4xNGXFTjDJRyLsFYMf/I5rsUy1O7YlFILh0Q85Q3vtimvRNhHDHf6HNdjKwkZpZRqhRDzsFjOsCnHIewplt/5HDfuDq2h55ImYJRqwpVDuefe/quAq9odi1JqAbMM+gKXtTsMpdTCJo5e7/hhu+NQSi1s4tjqHee3O475TLcgKaWUUkoppZRSSs0yTcAopZRSSimllFJKzTJNwCillFJKKaWUUkrNMiMik79LKaWUUkoppZRSSk2broBRSimllFJKKaWUmmWagFFKKaWUUkoppZSaZZqAUUoppZRSSimllJplmoBRSimllFJKKaWUmmWagFFKKaWUUkoppZSaZZqAUUoppZRSSimllJpl/w0f89sAYk6engAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115d78e10>"
+       "<Figure size 1152x216 with 4 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2025,7 +2113,7 @@
     "    visualize_tree(model, X, y, ax=axi)\n",
     "    axi.set_title('depth = {0}'.format(depth))\n",
     "\n",
-    "fig.savefig('figures/05.08-decision-tree-levels.png')"
+    "fig.savefig('images/05.08-decision-tree-levels.png')"
    ]
   },
   {
@@ -2044,17 +2132,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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j1VdgMZslvH0kCau2fUfmZERET+9x9xYc+TAOlBUdw45Mff9rX28RAS5F0HXrx+20ACIi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\n4A+byZI8PwAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118fc00b8>"
+       "<Figure size 1152x432 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2066,7 +2159,7 @@
     "visualize_tree(model, X[::2], y[::2], boundaries=False, ax=ax[0])\n",
     "visualize_tree(model, X[1::2], y[1::2], boundaries=False, ax=ax[1])\n",
     "\n",
-    "fig.savefig('figures/05.08-decision-tree-overfitting.png')"
+    "fig.savefig('images/05.08-decision-tree-overfitting.png')"
    ]
   },
   {
@@ -2095,7 +2188,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -2106,9 +2202,9 @@
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -2126,17 +2222,22 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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xEQVCuBWMZJ2IwHL9XsxmFQQhHpcu1SI9PYn9TNQGAxtEREQBcv78OWzYsB46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\nNmAZ8c5GJyrD3NeuHwcOtMRc+dC9yiGv23Nd701tbZMikXx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/3zl3l3Nuv3Nu/8aNG+vfQBGpiZ6QXzugiLQ5BTpERERk3TEzP4Ugx+ecc/c1uj0iUj8dfi9X7Owj4PMwEU8T8HlqXohUROpLu66IiIjIumJmBvwv4KfOuf/a6PaI1Fq9t1Ktplq1XTugiLQ3ZXSIiIjIevN24BeBG8zs6eJ/72t0o0RqIRpP89SRCdLZPH3hAOlsnqeOTBCNpxvdtCW1cttFpLGU0SEiIiLrinPuUcAa3Q6RehgZi5W2TwVK/46MxZo+o2GtbZ+fDZJIZ2vaXhFpHgp0iIiIiIi0qalEhr55Sz1Cfi8TLZAVsZa2z2aDhAM++sIBEpkcx6KJWjVVKmjlJVPS+rR0RURERESkTXWH/C27lepa2l6eDWJmhAM+Al5d+tSLlh1JoymjQ0RERESkTQ0ORHjqyARQyIZIZHLE01n2bulb0XEaMTu/lrZXygbxK9BRN628ZKqcslJal/7fLiIiIiLSpgq7i6xtK9VGzc6vpe2VskEyuXytmirzTCUyhPzeObeF/F6mEpkGtWjllJXS2pTRISIiIiLSxta6lWojZ+dX2/ZK2SBpBTrqZjbQNPu3Aq2zZGpWu2SlrFfK6BARERERkQW14ux8pWyQ7b2hRjdr3RgciBBPZ4mnszjnSj8PDkQa3bRla8W/e3mDMjpERERERGRBrTY7P7+uwmXn9NIbDvDtQOtc+rR6bYjZQNPIWIyJeJrukJ+9W1a2ZKrRWu3vXuZqaEaHmX3azE6b2XML3G9m9pdm9rKZHTCzN9e7jSIiIiIi61krzc63Q12FdngN8Eaw47q9m1ZcF2Yps+/RI4dO1+y9aaW/ezlbo5eu/C3wnkXufy9wfvG/jwP/bx3aJCIiIiIiRdUoaFprsxe+9z55lGPRBLm8K20rGw74GBmLNbqJy1Zpa9xWew21VK9AUCv83cvCGpq/5Zz7vpntXuQhtwCfcc454DEz6zWzrc65E/VpoYiIiIjI6rX6EoRZay1oWkuzF77hgA8P4ME4dHKavVu66Ar6Cfm9TLRQNkSlrXFb7TXUUj2LhDbz370srtEZHUvZDrxe9vvR4m1nMbOPm9njZvb46OhoXRonIiIiIrKQdlmC0OzKL3y7gn7MIOj3cjyaAFqvrkKlrXFb7TXUkoqEynI0e6Bj2Zxzdznn9jvn9m/cuLHRzRERERGRdU5LEOqj/MJ3W2+IZCaHc3lmktmWrKug2hCLUyBIlqPZAx3HgHPKft9RvE1EREREpKlp5rk+yi98u4J+9m7pJu8gT74l6yqoNsTiFAiS5Wj2PZYeAH7LzO4GrgImVZ9DRERERFpBs21P2S71QuYbHIjw1JEJoBBI8nqM7X2hlg4OqDbEwtph61qpvYYGOsxsGLgeGDCzo8AfAX4A59xfAV8D3ge8DMSBX2lMS0VEREREVmb+BXgikyOezrJ3S1/d21JesLMvHCCRyfHUkYmWDgbM0oXv+qNAkCyl0buuDC1xvwP+RZ2aIyIisi616yyvSKM10wV4PXeqaARd+IpIuWZfuiIiIiI11M6zvCLNoFkuwNtpy1IFZ0VkKc1ejFRERERqSLtCiKwP7bJThbbsFZHlUKBDRERkHdOuECLrQ7vsVKHgrIgshwIdIiIi61i7zPKKyOLaZctSBWdFZDlUo0NERGQda6ZdIUSktpqlXshaNNuWvSLSnBToEBERWceaaVcIEZH55hce7Y8EODw6Ayg4KyILU6BDRERknWuHWV4RaT+VdoU6PDrDno2djMfSCs6KyIIU6BARERERkaZTXngUKP07HktzxU5lcIjIwlSMVEREREREmo4Kj4rIainQISIiIiIiTUe7QonIainQISIiIiIiTWdwIEI8nSWezuKcK/08OBBpdNNEpMkp0CEiIiIiIk1ndleogM/DRDxNwOfhip0qPCoiS1MxUhERERERaUraFUpEVkOBDhERERGRJhKNpxkZizGVyNAd8jM4EFEWg4jICmjpioiIiIhIk4jG0zx1ZIJ0Nk9fOEA6m+epIxNE4+lGN01EpGUoo0NERNqGZkFFpNWNjMUIB3yEA4Vh+uy/I2MxLeGQmlDfKe1IGR0iItIWNAsqIu1gKpEh5PfOuS3k9zKVyDSoRdLO1HdKu1KgQ0RE2kL5LKiZlX4eGYs1umkiIsvWHfKTyOTm3JbI5OgO+RvUIqm32eDDI4dO1zzooL5T2pUCHSIi0hY0Cyoi7WBwIEI8nSWezuKcK/08OBBpdNOkDuqdYaG+U9qVAh0iItIWNAsqIu2gsJ1qHwGfh4l4moDPwxU7+1QzYZ2od4aF+k5pVypGKiIibWFwIMJTRyaAwmxUIpMjns6yd0tfg1smIrIyhWBH7QMbKkLZfKYSGfrmfQYhv5eJGmV0qO+UdqWMDhERaQuaBRWRdlSreg0qQtmc6p1hob5T2pUyOkREpG3UaxZURKQeZoMR4YCPvnCARCbHU0cmqnIhWsttbJUpsnqNyLBQ3yntSBkdIiIiIiJNqJb1GmpVhFKZImvn9RgHj0f5yWvjpLI5ZViIrIIyOkRERBpkNbOemikVWT9qWa9hdonEbCYHVGeJRC0zRdpdeQbP/l0bStkcIrJyyugQERFpgNXMemqmVGR9qWW9hlptY6vtSlev3juuiLQzZXSIiEjTaPVshZW0fzWznpopFVkfZr9LjkcTnJhMMrghwsaujqrWa5gtQjkyFmMinqY75GfvlrUvkahVpsh6UO8dV0TamQIdIiLSFGpZdK8eVtr+1QxoNQgWaX/l3yXn9IUJ+ryMnJkhmc2xrTdUlWDErFoUoaxHMc1WD4ovREEikerR0hUREWkKrZ6yu9L2ryYlvd7bDrYzM/u0mZ02s+ca3RaRcvO/SzZ1B7l0ey/bekMtEfit9Xal7byEr1bLiUTWI2V0iIjIHPWaKZt/nuPRBL0hP4dOTjGTytLZ4WNrT5DkvAv7ZrXSbIvVzHo2YtvBNva3wKeAzzS4HSJztEPmVi23Kz1wNMqxaIJsDjo7vGzrDZWCyq2+hK8Wy4mq1ae3axaNtC9ldIiISEm9ZsoqnWdkdIYfvzpOJpenO+gnk8vz7LEoZlU9dc2sNNtiNbOetZ4pXU+cc98HxhvdDpH5lLm1sGg8zdNHongwuoM+MjnHoZPTZHP5lil2Otv/PXLodMX+dfZ7/rq9m9b8/V6tPr2ds2ikfSnQISIiJfVaPlLpPAGfh/F4GpiNbBiOFolysLqU49UMaKs5CBaR5qPlCwsbGYvRHwlgBmZG0O8l6PcycibWEoGgegcMqtWnt/rSUlmfFOgQEZGSem0LWOk8fq+HrV0h/F5jKpklk8sR9ns48Hq0JWaOlG3Rfszs42b2uJk9Pjo62ujmyDqh75KFTSUyDA5ESGZyJDOFQJBzecZnUi0RCKp3wKBafbq2DJZWpBodIiICFGaaTk4meeHkNBsiAbb1hugK+muSMl2psrzf68Ef9rB3SzfTyQyHTk5jQGfQx8Fjk/zgxVEu39nLvh29TTvgr+W6dKk/59xdwF0A+/fvdw1ujqwj+i6prDvkJ53Ns3dLN8ejCaaSGXwe4/IWCQTVu/5KtXZx0W4w0ooU6BARkVI67UBnB7FUhplkhhdOZtjVH8HjoerFLisV1ewN+3FAPJ3l2EQcwxHL5DCgL+xloLODI+NxcnnHFTsL7VFhNBGR9WO27wgHfFywuatUkHnfjt6an7saxTjrHTCoVgFrFcKWVqSlKyIiUkqn3dQd5MKtPXQWi4GOzaRqkjJdKTX7Hedv5JrzNxLweTg1lSTS4aOzw0dfOEDQ7yPo95LNQTjg48DRqAqjyZqY2TDwQ2CvmR01s19tdJtEZHG1WtazVIHQatXWWKj+Sn8ksOj5V6ta75eWU0krUkaHiIjMSaftCvrZu8WPc46JeLpmA5mFUrNnb0tn8/z0xBQdvsK64FQ2T2eHl5Dfy8HjUS7e1luaFZv9tx22F5T6cM4NNboNIrJy1V7WMxvECAcKgfVEJsdTRybmXMiX19aAN/qcA0ejdAX9y87yqLR97JaeTg6Pzix6/rWo1vul5VTSahqa0WFm7zGzQ2b2spn9XoX7P2Zmo2b2dPG/X2tEO0VE2l2zbWc4O+vl8xjJTLZYeC7Htt4QiUwOh6kwmkgbWmpmXaTallMgtFIxzmwuz9OryPKYv3PWeCytHU1EaqBhGR1m5gX+B3AjcBT4iZk94Jx7ft5Dv+Cc+626N1BEpElVY53wfPVef7vUa5gdCHo9xtNHJujv7OCCzZ14PUY8neX8TZ0qjCbSZpYzsy7NpRb9Ub0tp0BopdoaI2Mx+js71pxZuND5X5+Il+5v1fdWpJEamdFxJfCyc+6wcy4N3A3c0sD2iIg0vWqtE56vnutvl/saesMBrr1gE7/0tkEu3tZDNu9K7dq3o7fiOudW2F5QRCqr99absja16o/qbTkZjZVqa4zH0gxumNvnrCazsNL5R6dTnJhMrPq9rWdmlLKwpFk1skbHduD1st+PAldVeNyHzexa4EXg3zjnXq/wGMzs48DHAXbu3FnlpoqINIeF1gmvpjZFpZm42d1Mammlr2HhWh5z1znv3fJGYKYdZhlF1pt6b70pa1OtuhWNtpyMxvLaGq9PxJlJZvF4YORMjD0DRlewEBRZTWZhpfOPnIkxuKFzVX19PTOjlIUlzazZd115ENjtnNsHfBP4u4Ue6Jy7yzm33zm3f+PGjXVroIhIPVVaJ7yaGaRGzsRV6zXMX+dcHuRoh1lGkfWm2WoFyeIWrlsRbanv3+VmNPaGA/RHApyYTBBNZOjq8DE6leSZo1GmEulVZxZWOv/WniAbuzrmPG65/WQ9M6OUhSXNrJEZHceAc8p+31G8rcQ5d6bs1/8J/Mc6tEtEpGlVWie8mguBamaGrFS1XsNCGvnaRGT16l0rSNamYt2KMzH6I4GW+/5dzo4i0Xiabz5/Cp8Z/Z0BUtk8Hf4cXoNDp6a4Ymf/nMzCtZ5/tf1kPTOjlIUlzayRGR0/Ac43s0EzCwB3Ag+UP8DMtpb9ejPw0zq2T0Sk6VRaJ7yaGaRqZVWsxmpfw3LXATfytYnI6tWzVpCsXcW6FTOps77L2+X7d2QsRi6fpzsUwMwI+r30hTvoCwc4d2MXgwMRRsZiValVsdJ+srx/PDmZZHQ6Nef+WmVGKQtLmlnDMjqcc1kz+y3gYcALfNo5d9DMPgk87px7APhXZnYzkAXGgY81qr0iIs2gfJ1wpdoUy7WarIpq1b1YzmsoP5cZzKSyvHxqhv5IgMGBSCkdutJFUK0zRkSkdpYzs97uWqXGUKXv8st39uHzzp1HbZfv36lEhv5wgFQ2R9Bf6F86fB5GZ1Js7glWtVbFSvr6+XUysjnHc8eiXEIvG7s6apoZpSwsaWaNXLqCc+5rwNfm3fZvy37+feD3690uEZFmVo0LgZUOTqpdcGyx11B+Lp/HOHBskuMTcXZviOD1GC+emmbvlu7SOuD5x9HAS0RaVasVd5z/XT7bfmi/79/ukJ9sznFkvFB/osPnZSqRZjauU+0lk8vt6+cv19zUHeQSehmbSeHz2qonRJbfxrVPvojUQkMDHSIi0hgrHZzUs+5F+bkOnZyiNxTg1FSSqWSG/s4gAMejCS7Y3FVxHbAGXiLSqlq9xtBavn+bPZNlcCBCNJ5mZ3+E8ViK0ZkkXo+HGy/awqtjsYpLJutRq6JSnYyNXR34vMZ1ezfV/PzKwpJmpUCHiMg6tZLBSaOKm82kcnQHffQE/UwW13h3+LxMJTOLpkNr4CUiraiRxR2ruzxx5dudrzSTpd6BkfIgjs9rnLe5q3TO8Vi6tGRyOpnheDTBmVia3pCfaDxd03ZpuaZIZc2+vayIiFTZcot6lqtnwbHyc3V2eEll83SHfHg9RjKTI5nJ4vPYqoqwiog0MzM4cDTKE6+Nc+jkFNNLBHWrpdHbcq90m9JGtXehbc1ni4eenkrywskpZpIZ/B4Y6OyoebuqVaRcpN0o0CEiso6sdnBY7YHUYsGW8nNt7QkSTaTJ5PJcOdhPzuUZm0mxc0O4adesi4isRjSeZjKRYTqVJeD1kM7mOHB0gtHpZM0vWucHGnJ5x7GJBPc9+XpdAggr3S1rpYGRWpsNgIzNpMjk8nQG/Vy4tYdN3cGat0u7FYlUpqUrIiJtrjy99+RkkoHOjmWv/y5/rtdjpLI5ksXZxdXWvVgqRbk8PTiZyXHxtm4AnIOLt/U03bptEZFqGBmLsakryIZIB8ejCWZSOTqDfrpD/pp/55UvmZlOZjh0cooOnwcPnkV3uaqW+csvppMZDo/OkM7lKi5Lmb/EZzqZ4dhEnFNTSYCG9BO94QBbeoK8aWs3Zla6vR5LjxYqCtus9U5E6kGBDhGRNjY/qPDCyWliqQyhgJeuYCEVeqFBWKWARDydXdVgd6XBFtXYEJH1Zvbi3czYu6Xw/eycq0t9jvJAw/FogqDfCxidQatLQdTy3bKyuTzPHoviMPZt76kYaClvbyEwM43h2NwdXFZg5vRUkkcOna56EKAZ6mW02s49IrWipSsi0lTGx8cZGBjghhtuIJ/P1+28q6lbUc9zrrZ989N7N0QCeMw4Hk2UHrPQIKxaqcHzl8tEE2leG48xnXwjJXmxFGVZf8zMa2a/bmb/3szePu++P2xUu0RqqVa1kJbTf5QvGZxOZnAOkpkc23pDQO2/o8uXX7xwaprOoJ/LdvTSHQpU7HvK23tsIo7hcMD2vvCifVUinQUgl3c1qe3RDPUymm1ZTyM0YkwnzUeBDhFpKo888ghnzpzhu9/9Lr/8y79MLpdb+klr1IiiZis551raN3/d87beEHngTCy15CBspWumF3J2sKUDDywr2CLr1l8D1wFngL80s/9adt+tjWmSSG3V4iJ5uf1HeaAhD+Rx7N3SVcr8q8V39PyLUYArdvZx3sZO9m3vLZ0bzu57ytt7aipJpMPH3i3dczIVK/VV47HC6/Z5PTUJAjRDvYxKfXc2l+fJdXLh3+jCutI8FOgQkaYyMTFR+vmzn/0sv/RLv0Q2m63pORsx+7GSc66lffNnCLuCfnb1R+gNBZYchFVrdrFisMU5zsTSqhAvC7nSOfdR59xfAFcBnWZ2n5l1ALb4U0VaUy0uklfSf8ye/8Nv3sH23hBej9XsO3qxi9Hyvme2XsgPD5/h5GRyzsXqbHvffv5Gdm6I0BX0L/p4gGTm7EzRamerLLQzS73M77unkxmePRalw+dZFxf+ymiRWarRISJNZWRkpPSz1+vl85//PB0dHXz605+u2TnnFzWD2hcPq3TObC7PwePRs4qHraV95eueQ34viUwOjwduumTLkoOvSs+Np7Ps3dK3kpd61prlrqCfXRs6GZtJMVEc1K62sKm0rdIfg3MuC3zczP4t8B2gs2GtEqmxpeoTldc7Wk59idX0H+UFoWv1HV1+MQpv1Go6cDQKwNNHogT9HjI5RyTgnbNV6/zgwWxfNZPM8tp4DA8s+Pig/+w53lbIKCz/3GfrnDpHxb+B+X334dEZHMaegc7ShT/UtuZKIzViTCfNSRkdItJUZgMdsxXLu7u7+fa3v41zrmbnrNW66JWcczqZ4cCxSQJe76KzWytt31pmCKs1uzibjn16KskLJyb5p1dGGTkT463nbmjYjJc0vcfN7D3lNzjnPgn8DbC7IS0SabDVpOSvtv+odVbCQssrnj4yQYfPy1t29XEmlua1MzOYxxbdqnUlW7v2RwKlc7VKRmH55+7zGM8fn+Tg8Sl8Hqv4NzC/707ncuzb3rPoUqB20ogxnTQnBTpEZEVqXeApGo0C8Ja3vIVcLscnP/lJnnzyyTlbtVVb+broqUSaZ45O8KPDY0wnMzVL7Zy/Fvvw2AyGY8/GzrNSLde6bnsykeHFk9M8ezTKiyenmVzB4KYag93ecIA9GzsZOTPDeDxDfzjA4IYIh0dn2jZ1VtbGOfcLzrmvV7j9fzrnNFqVdWk1KfnNUByzkkoXoyNjMfqLO3J1hwJs7Qlz+Tl9RAK+JWtvzG7tevWegUVrdYSK2QxejzWshsZKlX/uJyaT9IQC9IYCnJhMLvg3MLfv7sfnnXvJ184X/s36Ny/1p0CHiCxbPQo8/cmf/Amf+tSn+I3f+A0AHnjgATZs2FC141cyOyBIZXM88doEOHjLrn46fN6arWOdP+OSyua5dIHia2vJrHjtTIwvP3mURDrL1p4QiXSWLz95lNfOLG+tarUCW+OxNJdu7+Vt5w4sOjMnIiKVraZAdDMUx6yk0sXoeCzN4IY3LkY7Owpb3M6k3qjTlcjkMGNOv/TamRhPHZngldFpDhyNztnRa6EL+k3dwZbJKCz/3GdSWTp8Xjp8HmZShUDRUn8D6+3Cv1n/5qX+VKNDRJZtoTW11Vznefnll3P55ZcTjUb5zd/8Tb773e9y4sQJtm7duqznL7V+eaH7e8MBuoJ+rtqzofS6yl93Ldaxlq/F7g75SWfnFkkrH6AttW57IY+9coaekJ+ecAdA6d/HXjnDrg2LD3JmgxzhgI++cIBEJldxffRytNqa2ZWugxcRqbX59Y5gJctQmuv7q1IdkPM2dzJyJkY2F6Ozw0t30M8rYzN0dfhwzpHI5BidTuKADl9hmefodIrvHzrNJdt72bu5mwPHJjlwdIJLt/fi83pWVVeqkaLxNAeORnnpdCHL87xNXZhR+tw7O3yksjnAioGgpf8G6lFzpdk049+81J8yOkRk2aq13ehy9Pb28t73vhfnHPfcc0/p9koZBrO3ffXAcb74xFHGZ9IVM06Wykip5+ubr1YzLqPTyTlZIlAoBjo6nVwyW6Oalctbac2stqYTkWbUbjPz5csrBgci5POOmWSGgBfS2TyvjM3QF/azc0O4NDPfHfKzqStY6pcm4ml6Qv7iBXyAy3b00hn088Kp6ZabyY/G0zz60ijPH58k5PMQ9Pk4eHyK49EEo9NJ4uksW3uCTCbSRBNptvYEl/030OidYEQaQYEOEVm2Wl+szr/wvvnW2wD4/Oc/P+f+8gvQH7w0yqMvjZLO5omnsvgMjozHmEllz7owX+rCvdEX416PcfB4lJ+8Nk4qm1v1YKT8fYxn8pyaTs65fzqZIdzhXfJivpqBn3oO0Ne63EZb0zUPM/v2cm4TWQ/aOSV/ZCzGxq4g+3b0EfB5SefydHX42NYb4toLNpUu0J1jTr80k8rSFfSXlnF0Bf3s297LeRs7W+69GRmLEY1n6AkFCAX8hAI+ekMBsnlHd8hPwOchm3dctK2Hi7d1k827Zf8N1Lq+mkgz0tIVEVm2am03WkmlZRJbL30bkUiEH//4x7zyyitM+fvPWjozmciAg10bOoml83SHAqSyOY5HE+zd4p+zPGKp5RO1fH3Lfe37d21gdDrFi6dmmE5m2dYbWtGyifnv497NXXz3hVMAbO4KMp3MMJnIcP6WriWXIa02TbqSeqXOVmO5Tasts2lHZhYEwsCAmfUBs9WIu4HtDWuYSJWtdJlcO6Xkl7/2l0dnuHBzF92hAHu3FPoY59xZ37vz+6XODh/TyQydZZmL9c4WrNZSx6lEhkwuTzjwxnM7fB6mEjmcgyt2rm4sUs1lqCKtRBkdIrJstZxNqjSL3t/TzbXvfi8Ad999d+Xt6LKOTK5Q26Kzw0sqm6fD5y0VLysf8CyVsbHS17fYDMlKZk/KX/tMKsuR8Rg+g3gqu+JlE/Pfxz0bO3nnhZuZSeU4MZkgFPDxoTfvIBLwLZmtUe0sjHqkzlYjG6PRmT0CwK8DTwAXFv+d/e9+4FMNbJdI1aznZXLzX3uHz8Ozx5YuJDq/X+oLB5gsBqcbsZynmp9hd8iP3+sp1uAoSGXz+Hy2pv5HWYqyXinQISIrUquL1YWWSVzznlsAGB4erngB6vMZ/uK2adt6QyQzOaYSaSIB71kDnoUu3PsjgVJQYnY716Ve32KDm5UOfMpf+/FogqDfS3coQCydX/GApNL7ODgQ4arBfn7rXRdwx5U72bUhsqyL+VZMk67Gcpt2Wwffipxz/805Nwj8H865Pc65weJ/lznnFOiQttDuF6CLBfzPCsoPdOIwDo/OLPq9O79f6u8M8KE376C/M9CQfqqan2EhE8TPZCJNIp0hkc4STRRqkKyl/2lk/TGRRtLSFRFpCgstk3jXu2+kv7+fgwcPEj85Qrq7kLU+u7SkJ+THgHg6S2eHj539YUbOzBDu8BHweeYsjyhfPvH6RJyZZBYzeGU0xuCGCBu7Opad0rnYDjQA+Ty8Ph5nJlVoV184sODuLeWvfSaVpTvoJ5XNlyqqr2TZxHKXmyx3mU6rpUlXY7nNeqxQ36ycc//dzN4G7KZszOKc+0zDGiVSJe28TG6p5RLzX3uhtkYPh05NLfm9W6lfWmoXsVqp5mfYGw7wjvM3ztl15eJt3ezb0bum/qeay1BFWokCHSKyKtXefnOhC+8rdvbxkY98hLvuuouvfvmL/O4f/vGcC9Brzt8IULqtvzPAW3afs2BbesMBBgcK7R/o7ODImVipgGko4C3tUFIpKDF/PfGO3hCvj8c5PZ0ikc4SCngJ+j10dviZSWYIBXzFoEWO187MkMyGuGJn31nvXX8kwOHRGQAiAS9TiTQOY9eGLmBlA5KVBTDa72K+WnVWWi3A067M7O+Bc4GngdkUJAco0CEtr50vQJfajr7Sa/d5PVyxs3/VtSgWUt7nHpuIV/XY1f4Me8MBrr1gE9desKlaTWxY/TGRRlOgQ0RWrBaFrRa78B4aGuKuu+7i7rvv5k//9E8rDoJWclFaPgBbqoDp7Os9cDTK00cm6O/sYHBDhFzO8a3nT7JrQyfRRAYPMJVIs70/zE+PT7Gxq4O+SOErNuj3kcrkmElmK753h0dn2LOxk/FYmnCHj8lkhsENETo7fKX03fkDkoUCTZWyVjqDvuKSHOZ8Ps1+Mb+aYFq7BnDWsf3ARc451+iGiFRbO1+ANkvx7/l9bi5f+CpJzVu6uVrVfB3VnkCapX5R1isFOkRkxZaaqVmthS68r7nmGrZt28bIyAg/+tGPeOtb37rqc8DcAVh5AdOpYhG08tmY2UHSsYkEA50dmHl48dQMeecIeD0cHptha08Iw0jlCsfpiQQYi6Xoi3TQ4fOQyubJQyngUOm9G4+luWJn35yMj4UGJEsFmuZnrcwOvlqpyvpagmnNHsCRFXkO2AKcaHRDRKqtnS9Al8p0qNdrn9/n+r2FJaGTVapPsdDrAHjqyMSygxa13hlF/aKsRwp0iMiK1Xtdsdfr5Y477uDP//zPGR4eXnOgY3YAlssXCp4dOjlNyO/hnP7wWRkUs4OkbN7RHfRjVtjl8lg0zoVbu/nHV86QzuaJdHg5f1Mn2bxje08QnMPvNaaSWTo7vGzqitDfefa6ZDj7vVtqQLJYoGlwoPDvk0cm6PB52DPQyUwqy/FogjOxFKenUtx0yZamH0jXKpgmLWcAeN7MfgykZm90zt3cuCaJVE+7XoAuJ9OhHq+9Up8Lhd1MqmX+61hN0EJ93vpSq+wdmUu7rojIijVi+82hoSEA7rnnHnK5taWcDg5EOD2d5JmjUXweD4MDEVI5x8mpFKlsbs5gZLZaeWeHr7TlW4ev8NWZyzvO39TJno2d7NrQiddjmMFEIs2pqQTJbI7zN0U4pz+Mx1M4bzXeu4UqqB+PJkq7vaTSOY6NJ7jvqaN87cBxJhNpBiIdRBOZlti+UFXipeiPgQ8B/zfwX8r+E5Em1iy7dlXqc+GNfrwWVrMTi/q89WM9bytdbwp0iMiKNWL7zf3793Puuedy8uRJvve9763qGLOdyzOvRxmdTuI1SOfy9IYDXHPeRrb3hXjp9AwjY7FShzM7SJrdujaZyZLM5NjUFWQykeG8TV0kMlkm4ynOxFIcn0jy+niCgc4gr59J8OjLY3OCJ9V47xYKlswks4QDPnJ5x5lYmlze4fKO6VSWk1MpookMGyKBlti+sBHBNGk+zrlHgFcBf/HnnwBPNrRRIrKo8r4W4LJzehu2bHJ+n5spTpT01LAvmQ1aTCczHDo5xROvTXDkTIzj0cSCz1Gft360+7bSzUSBDhFZsUbM1JhZKatjeHh4xc+fH0HP5Bw+r4cLNnexrTfEsWgCD4YH5kTXZwdJXo9xweYucnnH2EyKC7d28aE372DnhjCbu4OEAj6SGUc8nWH3hjCDA53sHgjTEyqkn87f4nYt791CwZLOoK+U2bGtN4TDkcrmMQNzVrq9FWaJGhFMk+ZjZv8b8EXgr4s3bQe+3LAGibS42b7wkUOnazKL3Gyz1fP7XK+nsPy0Y172RDV1h/yMTqc4dHKaTM7RHfQRS2U5MZlc8H1Qn7d+KHunfhToEJFVmR08XLd3U91mamYDHffeey+pVGqJRxfMDrruffIox6IJcnnHTCrLTDLHK6Mz/PCVMV4+PU3Q78UMuoL+OdH18kFSNu+4eHsPv/S23Vx7wSZ2bYhwxc4+3r9vG3dcuZO+iJ83be2mPxLEzAj6C9vLvnR6Zk6b1vreLRQs2dYbKmR2pHL0hQPsGugk4PWSyTpCAWOgq4OuoL8lZomaJe1ZGu5fAG8HpgCccy8B1dt3UWQdqUcQohlnq8v73O194Zqfb3AgwsiZGQxXLEiewwGDGyILvg/q89YPZe/Uj4qRiqwD7VL06KKLLmLfvn0cOHCAhx9+mJtvXrweYXlBMA/gwXj69Qlw0Bv2E09nmIilODOTYs/GQo2NXRsKsyflBUKXWzDNcICddWvh9rPbtpbPpFKbBgcKVd59XkhmcngNztscwQERv5fIItvVNqN2LdInK5JyzqVniwCbmQ8q/B9KRJZUj4KX9S5WXg8r7a97wwF6Qn5eODHFsZEz+H0eLt7aQ9DvWXTWXn3e+tDO20o3G2V0iLS5ZksjXauVLF8pH9R1Bf2YwUwyy0wqS3+kg229YfoiHfi8Hkan0+zd0k1XsBBRXyy6vlDq73mbuphMZkhmcjjnSGZyTCYLdTwqPX+ln0ml85bfNjIWY8/GTnb2hxmbSZHLOy7b0cuFm7vJOgh3+Go2S1TrdGhZtx4xsz8AQmZ2I/APwIMNbpNIS6pHyvxis9Wt2E+spr+OxtOcmkySzOQ4d2MnO/sinJhM8uNXx7H5cyGy7ih7p34U6BBpc82YRlrJcgdAd955JwD3338/MzMzFR8zq3xQN1tMNJbOksnlSWZyeMy4+twBbrp4C5EOL16PLbk2drFBz74dvezoC5FzeaYSGXIuz46+EPt29M45xmo+k0rn/cFLozz60uic2w6PzrBvRy+/9LbdXLy9h2giw9hMiq09Qbb1hmqSzdNuwTRpKr8HjALPAr8OfA34w4a2SKRF1SNlfqFaE/2RQEv2E6vpr0fGYgR8HnzeQuWvgNeDc45DJ6d59mh0WYGSxSY1WuF9k8U1Yvn3eqRAh0iba4WiR8u5UJ59zGupMPve/DMkEgkeeOCBRY9bPqjrCvrZu6WboM9HKpvH7zW294Y4Hk3w3PEpuoKF7WOXiq4vNujpDQe45vyNXLyth8GNES7e1sM152886zir+UwqnXcykSEazyzYlsGBCOGAl90DEc7pC9dsYNkqwTRpPc65vHPu/3PO3eac+0jxZy1dEVmFehS8XGi2ejyWbsl+YjX99VQig9/r4bxNXXg9xng8TTSWpi/kIxLwL9oXL3dSQ8EOkaWpRodIm5u92J9diwvNV/RoqXXD5bU2+sIBbvzghznw5E/4zGc/x0c/+tEFjzt/HaTXY1y6oxsHhP0+XhuP4QH8HtjZHyGXd6UAx+w556/JXWr98XLW2K7mM6l03mzW4eaVKyhvSz3WYy/UtlZfky3NwczeDvwxsIvCmMUA55zb08h2ibSi2SDEyFiMiXia7pCfvVuqP5tcqR+cSkRbop+YX4/DjBX3190hP36vB6/H2D0Q4bUzjg5vIcOjM+hbtC+u1G9PJjLgYNeGztJtCz1fRN6gQIdIm2uFokdLXSjP7/g/cMut/Pm//wO+/a1vMj4+jifYWbFQWKVB3TvO3wjAw8+dJJPLsyHSwbbeEF1BP/F0lpGxWKmoZzjgw+cxDh6b5AcvjnLe5k5OTibI5Nyc5600cLSaz6RScMTnM3BzF/yWt6VeAYhWCKZJy/pfwL8BngBySzxWRJbQqIKXteonqllsff6kSiKTYzKRwciwsSu47P56cCDCsYk4RyfiOOeYiKXBCu/9tt4QsHBfvJpJDRGpTEtXRNpco4oerWQ96VLrhuenjm7YuIkr334d2UyGvx/+wqLLXmZf/2Xn9ALwzOtRRsZidAZ9XL1nYE4B0tl01NnAymQiw3cOnebl0RnGYym+d+g00XiWbM4xk8zwwolJTk8lV5z6u5rPpFLKcU/IX9w9pnIacr22MKtHOrSsW5POuYecc6edc2dm/2t0o0RkZZbqJ1ZTg6La9aEqLcPc1BWkO+RfUX/dGw7wjvM3ctG2HhLZPB6v0R/uoDPo5cVT0xw6OcXodKpiXzy/355OZjgxlWBkLMahk1NMJwtLZmo5maB6INIulgx0mNm/NLPmmfoVkRWrd9GjlQ4+lhoAVbpgf/cHfg6Az35ueNF1v9F4mu+/eJrP/NMIB49P4vMY6WyeE5MJRqdTc445O3CYSmTI5vL8aOQMXqAvHGA6mWV8Jk1PyE9X0Edn0E8mD2PFrWlHxmLLHhSsZgaqUnDkmvM38o7zNy44AKtXAEIVxKWGvmtm/8nMrjazN8/+1+hGiawX1broXayfWG3Aotr1oRaqx+EcKx5D9YYDXHvBJn71HXu49YodnJiM8+LJKcZnUpyeSvLcsSj9kbOPU95vTyXSPHM0StDnYXN3cE0TLMul4uLSTpazdGUz8BMzexL4NPCwCoGJyGIOHI1yLJogm4PODi/bekOlwUellNml1g1XWupx5fU3EQgEeOKxR5k+c5rw1m2l482mdM522McmEgx0dpDI5PnOC6cZ6OrAa8bzJ6boDPrOSkcdGYtx8NgkuTx0h/0YRiqbpyfsZyqRpi8SZO+WbpxzvD4R5/DozJxU16eOTCw4GKqUGrvY489+n85+zEJpyPVaj71Y20TW6Kriv/vLbnPADWs9sJm9B/hvgBf4n865P1vrMaW9VHNZRCt67UyMbz5/klwe+sN+sjlHNJ5edSB7oX5itfWkqr08sxbLa6LxND8aGWfnhgipTI7JRI5YOse+Hb2Mx9Ls2jA3WFHebx88HqWrw8dlxZ3bjkcTnImlGZtJcdMlW2ryt1iv2l7tLp1O85//83/mggsu4NZbb8Xj0SKKRljyXXfO/SFwPoV1sh8DXjKz/9vMzl3ryc3sPWZ2yMxeNrPfq3B/h5l9oXj/j8xs91rPKSLVNX+259mjUR569gQvn5xhIpZkMpHh0Mlpsrn8olXKF8s6qZjNcMlu3ve+9+Gc46sP3Md0MsOhk1M88do4B45GMXujw87mHTkHJ6eSeM1DMp2ls8NHPJ2tuNPK4ECE8ViakM9DOpMnnc2TyzvSmTwHj09zZibJdDJDIpNjJpld0YxSvXco0RZm0sqcc++s8F81ghxe4H8A7wUuAobM7KK1Hlfax3qf2Y7G03zz+VP4zNjY2UE2D0fG4+TzVL2/Wu3ucNVenlmLLMiRsRgzyQypTJ5ExtET8rOtN0Q6m1vw9c322+du7GLfjl66gv7SznFX79nAlp5gzfryVtiprxW88MILfOITn+C2225j3759/MM//AP5fL7RzVp3llWM1DnnzOwkcBLIAn3AF83sm865313NicsGGTcCRylkjTzgnHu+7GG/Ckw4584zszuB/wDcsZrzibSDZptdmp+dMDqd4pvPn8Dn9RAJesnljZOTSbb0BBk5E+PibT2rPlelmaChoSG+/OUv87Uvf5G977qd7qCfgNfDZDLDZCLDdDLLOX1hOjt8HB6docPrxe8zYqkcZrC9WEz0ip19Z53r8p29vHBimtfOxMBBKpfjxGSaYMBLh9fDM0ej7OgLlTJCyi02o6QdSkSWz8x6gD8Cri3e9AjwSefc5BoPfSXwsnPucPE8dwO3AM8v+ixZN9b7zPbIWIxcPk9/ZxAzI1js58ZjKXxeW+LZK9Md8jM6nWIinmYmVZiI6AsH6O9c/H1eSWHv2fHTc6+eBuChrz/M5OTZXyMzySynp5Mk0jlCAS+buoJ0Ble/d8OR8Thj0yk8Bj6vh7yDXD6P3+tha0+Qz3Z2LPjcMzMpcnmHz/vGvHQ2l8frsUWftxaNOGe7uuaaa3jyySc5ePAgt99+O319ffzlX/4lv/ALv9Dopq0bS/4/18z+NfBLwBjwP4H/0zmXMTMP8BKwqkAHyxtk3EJhWzmALwKfMjPT0hlZj9ay5KFW5g8EJ+JpfB4jHPCSzubp8BkBn4fRmSRhv7fq60mvu+46OjqCvPjc0zz1zAHOOfdCdvaHuWxHL16PcfB4lIl4mulklldGp9nRF8bhw+uFZCbHBZu7Fpyl2Lejl1zesbk7yDd/ehKPedjSE2RDpIMTU0l2bQgX63WsLNVVO5SIrMingeeA24u//yLwN8CtazzuduD1st+P8sYymRIz+zjwcYCdO3eu8ZTSStZ7UHoqkaE/HCCVzRH0F/qrDp+H0ZkU523uquq5+iMBvn/odKlPnU5mODYR50Nv3rHo8yotz9zSM7sLW7Q0IQRv7KQWi44C8PzB53j+4HNVfR0ii5mYmOCTn/ykAh11tJwQZT9wq3PutfIbnXN5M/vAGs69nEFG6THOuayZTQIbKARd5tBgRNpdM8wuzc8oOR5NcE5fuHT/TCpHf6SDqWSW8zZ3MTadJJbKksrmedu5A1UJyCSTSR566CGGh4d58MEHSaWShbY9/49c/9b9JItprNlcnrGZNN1BH36Pkc7kefjgKXxe48ItXVywqQuf10O4o/IKvtkB1MPPnSQS8HLuQCcbu4NEAj6SmRw+Dzi38q1iW2G7X5Emcq5z7sNlv/87M3u6Xid3zt0F3AWwf/9+TbKsI+s9KN0dKtTkODJeWKbS4fMylUjj9VD1SYvxWJpLtvcWJiZSOTqDfs7pj1SsYTFfebZn+YSQz2McPD7JD148TWfQx67+TsIBH//7H3ySnt4+egc2Ewp1sLP/jeMfGY+RzTkCvjeyNNPZHD6vMdDZwdhMimQ6RzDgZaCzY87fRjydrXj/gaNRphJpwEikcySyOQzY2hNi/+7+Jd+bhY5bS404Z7vJZrPcfffdPProo+TzeTweD1dffTV/9Vd/1eimrStL/tU65/5okft+Wt3mrJ4GI9LuGj27VCmj5MRkgqDPy6buIFAoPBpPeYmlc3jN2NkfYSqRJusKGRJLHX+hZTnZbJbvfOc7DA8Pc9999zE1NVV63qVv3s8551/KzR/7V6VZp+PRBMlMjm29ITZ1BvnmT08wOpOmq8NLT8iPy8N3D53m7edu4KZLti7Ypt5wgC09Qfbv6iebdxVntVZa8LOeBUJF2kDCzN7hnHsUwMzeDiSqcNxjwDllv+8o3iYCKCg9OBAhGk+zsz/CeCzF6EwSr8fDjRdVvwjmVCLDxq6O0lgCwDm34vHN7IRQLu948dQMQX/hIv3QyWlwEAp46eru5l//3h+Vjn/d3k2l5z9y6DR94QBmbyzNmS06Hg54CQfmFi+fv2tMpfv3j8UYn0lXXJYzf9mstI9nnnmG3/zN38Tr9fKxj32MT3ziE5x33nmNbta608jw3HIGGbOPOWpmPqAHOFOf5ok0l0bNLs0GIJ48MkGHz8Oegc5SEc3BDZ2MnImV6lT0hQMcm4izb0cv6WyO0ZkUXg+lgdFCwYxKszCPvHAS/5lXePqRr/LAl+7j9OnTpTZdeMllfHToTi58203sPXeQmVSWQyen8WRyBLwexmIpUpk8b9nVxyujM4xOp+kK+enwF4qL+v1enHPE0tlFB2zReJqTk0lOTBWKj27rCdEXDjCVSJPJ5phOZnjk0OkV10vRDiUiy/bPgb8r1uowYBz45Soc9yfA+WY2SGGscSfw0SocV9rEeg9Kl79+n9c4b3NXzeqCLTS+MSssOVluXbLZCaEXT00T9HsJ+r0458Hv8+Ax43g0wbbe2d1LUvSGCuOP2WMu1I6ZZHZOVsP8jNrFMm5nA0bn9IfnBEFqsTVsLTVbjbhmd9lll/Gtb32LwcFB9uzZ0+jmrFuNDHQsZ5DxAIUBzQ+BjwDfUX0OWa+qMbu00o6qPADhATwYh05Os3dLF11BPxu7OjgTS/HqWIzR6SQbu4Lc8KbNZPOOqURmzsBosRojpd1Rcnm+9eiPeeI7X+GfvvUAZ04eL7Vlx+5zec8tH+aDH/oIm3fuIZ7O4vUYiUyuWI28i5dPT/PCiRm8XjinL8LYTJpDJ2eIpbP0hfykcw6/18OOvhC5bJ54Krfkax/o7CCWzhLwGseicaZTWbwGG7s66PB5S59Fo+uliLQj59zTwGVm1l38fWrxZyz7uFkz+y3gYQrby37aOXewGseW9rHeg9LzX/9sv1jti91K45vT00mMwpKZ5dYlmw1UzKRydBcLiKayOXb2h8nmHUejCaYSaTxm+L0eBjo75hxzoXHWUkXHF8u4bYeAWTPWiGsF73rXuxrdhHWvYYGOhQYZZvZJ4HHn3AMUtrT9ezN7mcIszp2Naq9Ioy236NZCnc5qOqryWYquoJ9MLk/Q7+V4NMHeLYUq6VPJLJdu7+FNW7tJZHKMzaQqHnOxGY/nX3iRf/rGA9x/7z0ce/Xl0nO6BzZz0wdv5c3Xv5/r334VkY652SupbGEwNJnIMJXIcCKaoC/SwVWD/SQz+eIOMF46Az7imRw+j4feSICx6TQ9YR8bu4IspLy9oUDhNXs9HnpDfrb2BunweddtNX6RejGzDRR2XXkH4MzsUQq7rqw5u9M59zXga2s9jsh6UMuL3Urjm56Qf8X97GygwlcsOG5W+Hfvlm4S6RxPHJkg4/eyIVLY4rUr6CeezpaOuVBQYmQstmhGbbvXc2mGGnEiq9HQyjKVBhnOuX9b9nMSuK3e7RJpVpWKbuXzhe3eXjg5xROvTXDjRZsrFu9aTUdVPkuxrTfEoZNTdPg8zCQL+8uPnJlhcEPnso45e6zpZIbj0QSvHz3K09/7Gj/81oO8dPCZ0uPC3X1cds1NXH79+znnoivY3BXkTCxzViGskN/L2EwKA3CFLdECfi9BvwczY1N3kC09IWaSGbqChcKpm7r9+AzGppMMdHbx1nM3LPhel7/2QsaIv7Sm1zlWtKWsiKza3cD3gdmCpD8PfAF4d8NaJFJj5dmXs+UinKOhSwZqfbE7P3vkkUOnV9zPzgYqvB7j6SNR+iMBLtjchddjeDxw8bZuzukLz6nBMf+YlbJ4BgdYNKN2sYzbdsiGaHSNOJHVUgldkRY1MhYjny9UCQ/6vWzsDDKVSPPN50/ykbecU3rMQjukwNIdVfksReFiv5vDozPkyRPwedjaE2Jj19x91Rc6ZnfIz8uvn+D+L93LT779FV54+sfMrkTr7Ozibe96D9ve/C4G911FOBgilc1xTn+EbDaHgwXXze4eiLBrQyfZvKM76CeVzZcyTrb3hhiPe7nhTVt45miUnx6fIprIsGdTmKGrdi1azX2pGZpEJkcu7zgeTTCTyuLzGDs3hBc6nIiszlbn3L8v+/1PzOyOhrVG2koz1h2YX7Pq2WNRHMa+7T2ks/mGXSTX+2J3tVkSveEA116wiX07ekufbbjDs6zMjMWOudjyk8Xun/0sWzkbot0zVqR9KdAh0qKmEhnGY6liwa3C/5W7QwFGZ1IcOBoll3fzdkhJztkhBZbuqAYHIjz60ijReIZMLo/f66E37Ocd528rdfBLdX7T09Pcf//9fOazn+M73/oWuVwWAH+gg31XX8/7P3Qb77jh3XQEQzzw1DGOTWbY7vExuLETrxlpYPeGMPF04XkLrZvt7PCRyuYKW+AlC4/tj3QwmSzU8nj7uQO8eWffnErpi1mqJsqjL41ydCJOd9BPwOthMplhMpGZU9hMRNbsG2Z2J3BP8fePUFjyKrImzTrTXp45cejkFD2hAGCcmEyyd0t36TH1vkiu98XuQn3wlp7OReuEzA9eXXZOb+n+SpkZp6eT9IT8SxYWX6pey0L3t0M2xHrfgUhalwIdIi2qO+TnhZNTbOx8I3CRyubpD/t56fQMl2zrmTODMLghwsiZmVJwYDkd1WQiw8hYjNHpFHmXx+vxEA546Q752bejd8HOb1dviC996UsMDw/zla98hUSisBukx+vlsrdex/53fZBr3v0ezt+xGeccT7w2zlV7OrlqzwZGp1OciaWYSWXZ2NnBpq4I/Z2FImGLrZudXVqTyuSIdPiIp7N4PHDjRZsZj6VXXARsqRmc7pCfzoSfdA46OzxctqMXr8daapZGpAX8b8BvA58t/u4BYmb264BzznU3qmHS2pq17kD5hfFMKkt3sBBImA3gN+oiud4XuwvVJTs8OrNgcGqp4NX8Y5pRKng6+5oefWmU7pC/akuF2iEboh0Kqsr6pECHSIvqjwQ4GU1yZDzOQCRAdyiAx4yd/WGmU5mz1rZu7Oogmc0R8HmW1VFF42m++fwpvB4j5PfwymiSDp9x4ZZujpyJk8s79mzsxOsxnjs+ST6bYWrkAE9996t85YEvMzk5WTrWO97xDoaGhrjw6huJ9PTP6fCfeX2C/uK2bdv7wsykcpwT8BLp8HFOf7i0DdtC62bLM04y+TypTJ6N3UECPk/p9S22RGWx1OXFZnCcg33be+es9Z2t4bFczZg2LdJMnHNdjW6DtKdGzrQv9t1ffmFsBi+NTpNI5ekKeplOZvB6rCEXyY242J3fBy+1DGQ5wavyYz51ZGJOwdNc3nF0Ik5nws++7b1VyfJpl2yI9b4DkbQmBTpEWlA0nubw6AyX7+zjmaMTnJlJMZ3McNk5/Xg8cN6mroozCNt6Q1yxc3md68hYjJlUlplkhmg8S18kQDaX56XTM7xpWzf5PHzj4Ek49SLf+dqXePjBLzNxZrT0/CuuuIKhoSHuuOMOdu7cWWr3/A5/PJbmLbsKbZrdJvbYRJxTU0nO29y15EBqJpXl9Yk4sWSWSNDH4ECEt583sKxByVpSl5eapVkqiNGsadMizcbM9gG7KRuzOOfua1iDpC00aqZ9qe/+2QvjmWSh/52Kp/F5PfSEgjxzNMqOvhDXnL+xpm1cSKMvdpcKTq00eDX/8cejCbqDhUxNM6tKls9qAkSaBBGpDgU6RFrQ7KzFQGeQjV0dHI8mOBNLk8zkuG5vYQC02hmE2Q72H18a5fXxOJGAl1zeEfJ78Hu8jMeSvPzTgzz8Nw/zg4cfYPzUsdJzdw6ey3tu+TD/5td/hQsvvPCsY1fq8C/f2YvP6yk9pivoZ+eGCOdt7loyKHPgaJSJeIbdGzrp8HlIZfNMxNMcOBrl2gs2Lft9XE3q8lqrrDdr2rRIMzGzTwP7gINAvnizAxTokDVp1Ez7Ut/9s/3kw8+dxOf1lupy5B0E/UZPyF/qR9bbBfFSwamVBq/mP34mlSXg9dDZ8caYpBpZPisJEGkSRKR6FOgQaUGLbX062xGuJsX0tTMxvvn8KWaSGQ6PzvDyaIxcLk9v2E8wMcZLP3yYF/7x60wcHyk9Z/PW7bzn5lt57y0f5sKL9xFNZLhw78JBhvkdfqUsj+UONl8+PU1P0E+wuEwn6PfS4/y8fHp6WYGOtaQur7XKejsUKBOpg7c65y5qdCOk/TSq7sByvvt7wwG29AR509buBZdHrscL4qWCUysNXs1/vM9jTCYzXLajt/SYetfT0CSISPUo0CHSghaatTCD7794mpdPT+MwtvWEiHR4mSoWFR0cYImaHCdJZ/KMzaSJJbMkJk4zduB7PPb4t5h6/VDpsZ09fbzl+vdyy623ccP11+HxFGY/4unsigcEaxlsOozC5O7cWwu3L22tqctrqbLeDgXKROrgh2Z2kXPu+UY3RNpPI5ZiLPe7f6nHrccL4qXGCysdT8x//M4NYSYThToozjlGp1OMnImxtadQ9L0eGTOaBBGpHgU6RFpMNJ5mOpnh6SOFIp6DGyL4vB5Gp5PMpLJMxDP0BP3E01m+89OTbOsLc+XuftLZ/KKzPSNjMXJ5GB8/w4+/8VWe/8evc+ynTxSqbgL+YJg3vfUGrr7xZm6/5X2cu6WXw6MzJLN5Qn5bU9pvpcHmclJyz9/UyfPHJzEzOnxeUtkcU8kMF23rWdZ5a5W6vJyBbLsUKBOpsc9QCHacBFIUNklwzrl9jW2WyOos97t/qcetxwvi5YwLVhq8qpRlOjIW4/WJOCcmEwxu6GRjV0fdMmZqMQmy3pY4icxSoEOkwVbSAZWnqr5lVz8jYzGeeG2Cy3f20h3yE01k6A0FCPq9nJ5O0hcOkMnmOTGZLK3zrTTbMzMzw733DPPQl+/l6ce+Tz5X2MbO6wuw9ZKrOfetN7F939VcvXcbv/DW3aX29YT8NUn7XW5K7r4dvUwlMkTjGSYTafxeDzv6wuwrSztdTK1Sl5czkNV2bSLL8r+AXwSe5Y0aHSIta7nf/Us9rvyCeDqZKdXq6g35iZYtY12pZr0oXs1SndW8lvLAx0BxRzioX8ZMtSdB1uMSJ5FZCnSI1EClzhWoeNtiHdD840wnM3NSVS87J0A8nSXg8zCVyJDNOpw3z2tnEhw6NUNP0Iff62EmlQPmzvakUikeeughhoeHefDBB0kkEgB4PF62XnQVe676WXa++Tr84U7O6Q8xGcswHkstuQSmGpabktsbDvCO8zeuaVBWi9TllQ1kNdAQWcSoc+6BRjdCpJqW+92/2OPKd2d5bTyGB/B7Chfnq72QbeaL4tlxQS7vePHUNDOpLD6P4fVYxZpca30t1cqYWWmwpdqTIOtxiZPILAU6RBaw2lmNSp3roy+N4oBNXcE5Ha7XYwt2QIMDZwdBnj4ywVt29c8532zH2x3yk87lOTaWoCvopyfoYyaZJY/jfOsEYCaZ5qdP/CN/9x8e5L777mNycrJ0nKuufhtXvesDvPn69/G915JMJdIQ8LGps4OTEwk2dHUwEAmVlsDs2djJ4dGZmgyIZgcYs7NUM6kckYCHcIfvrJ1YVhosqNdslYIYIlXxlJl9HniQwtIVQNvLipTvzpLJ5dkQ6WBbb4iu4tLV1VzIrvaiuB796lQig89jvHhqmqDfS3fQTzKT5ekjE+zb0XvW+dZ6gV+NJSSrDbZUc/ywHpc4icxSoEOkgrXMBFTqXKPxDBjs3tBZug3gueOT/MwCgYtKx+nv7GBkLMZl57zRhtmOd3AgwqMvjZLN5XD4iHR4GZtO0h3289KzT/CVv/4G3/ral5kYGy099/LLL2doaIg77riDXbt2lQYrMzbK6akUeQeTiTTnbAizoy9CT8hfas9jr5xh90CkJrME3SE/o9MpjozHiwMaH1OJNJPJ7JpTcpt1tkpEKgpRCHD8bNlt2l5WhIV3Z1nthexqLorr1a92h/wcPDZJ0O8l6C+MN8w8pXHR/HHHWi/wq7GEpBmyKVT4XNYzBTpEKlhL51Spc83k8iQyOQ6dnGImlaOzw8vWniCGW7ADqnScwQ0RnnhtgtNTScZjKcbjGTLZHHs2dTKVyGBm7OgPMzaT4tSrL3L0sW/wxHe/ytiJo6VjnH/++QwNDTE0NMSFF1445/izswizHXw44OP545PFQp95tvWGgEKnPzqd5E1bu+c8v1qzBIMDEZ54bRyfGR0+D6lsDld8/WsZIDTDoENEls859yuNboNIM6vmhexqjlWPfnW2CPuTr42zuTvEtr4QPo+HZCbHBZsL459qvJZy1VhCUo9siqWyaVT4XNYzBTpEKlhL51Spc83k85yIJukLd9Ad9JHK5jlwbJJdG8LE09nS8cs7oJGx2FnH8Xk9nLe5k5EzMXL5PB0+D/G047UzcfZt7yF15jiPfP3LPPv9h3j15Te2g920ZSu/8NEhPvrRj/LmN795zszPQrwe4+DxKEfHk2zpCXLJ9h66goUBQiKTY2NXsGazBL3hAFt7QsRTWaaSGTo7fOzaEKGzw7emAcJCn+vrE/HS/c1UfE1kvTOzHcB/B95evOkHwL92zh1d+Fki60c1L2RXc6xaX8yXZ4xcdk4vr4/HeeHEJHu3dLN3S1dhCXCHpyqvZb61LiGpdTbFcrJpVPhc1jMFOqQpNbrq91o6p0qdayqTpz/ip5BxDeAwHJ0dPvbt6K3YAc3W6Cg/TjydpbPDx6XbewgHfBw6OUV8Yox/+upX+C/ffIBXnn+m1I6unj4uv/Y9XHXjB/jNOz/I4MauOe1cqGDqgaPR0ta1ezd3s6u/k8dfPcNzxyYJeD34fEZPyM9bz93A4dGZs9pXrVmCbb2FeiDln0E8nV3TAKHS5zo6neLEZIKBzg4tZxFpPn8DfB64rfj7LxRvu7FhLZKW0eixRD2s9kJ2ofdmpceq9cV8ecbI+Zu7yTvDcIT8XrweW3Tc4fUYzx2fxHCct6mr7v16rbMpVlK4XVmrsh4p0CFNpxnqKKy1c5rNhnAY52/qZM/GTnpDfk5MJksZCpdu7yWbdwt2QAsNOJ55PUo6NsXXvvgg99w9zKGnf4RzhQBKONLJW995E/37ruedN7yLjd0R+iMdvHomTl+ko/T+VXqPf/DSKAZE4xkGOjsw8/DiqRm294YI+L2cmUmzpacDnGEUtpat5SxBLQYIlY45cibG4IZOLWcRaU4bnXN/U/b735rZbzeqMdI6mmEsUS+rKcq92HuzkmPV+mK+PGOkK+hn75Yujk3EOTWV5LzNXRXHHa+difHN50+Ry+fpDwfoj3SQy7vS/fUtSl67cZIKjYosToEOaTrNUEdhLTMks4OH/bs2lDr8rqAPn9fD3i1v1LSIp7MV0y3PbkfhnDMzMzzw5S/yV5/+DD/8/nfIZgprUn3+AFe87Z1c855buOPDH+LFsRQYXLbjjUHG6akkDz93ki09wYrb1IYDPiYTGXCQzTu6g/7S8pZnj0XZ2hMincuXdnx5o6J7X80+k1oMECodc2tPkI1dHXMep4GCSNM4Y2a/AAwXfx8CzjSwPdIimmEs0ayq+d7U+mJ+fsZIV9DPzg0RztvcddYubFAYhz3w9DEm42k85mEynmF0OsWO/jAPP3eSzqCPE5NJBjdE2NjVUfMAWC2zKVRoVGRxCnRI02mWCPVqOqez93nP4fNCfyRQmk1YyYxHKpXi61//OsPDwzzwwAMkEgkAPB4PV13zTt71/p9j4OK3E+zsZt/2HpzXw3hsmrfseuO408kMr43HyOTyvGlrd3Gb2uicxwBksw6HoyvoJ5XNEfT76PB5mIhn2NoTpLPjja+Len0etRggVDqmBgoiTeufUajR8ecU1v79E6ACpbKkZhlLNKNqvze1vJhfacbIgaNRXh9PsLGrg4DXQybnODIe51g0zs4NnXgMfAZHxmOEAt5S7bFGBcDKs0tmy6c5x7IyTVRoVGRxCnRI06llhLrW6Ypv7PM+U9oWNZnJ8fKpGW65YjvjsfSSMx65XI7vfve7DA8Pc9999xGNRkv3ve1tb2NoaIifff8txLydczrGbN4R7vBw+c5efN43MkWORxN4gA2RDsyssE1tJMDImRiX7Xjj/Ol8jjPTaZLZPGPTSbb1hgj5C9vUTiUz7NvRWXpsOwUCNFAQaV7OudeAmxvdDmk9mu1eWCPfm5WOw1aaMfLy6Wk2RAIYhpkR8BmZXJ6JeI4rdgaYSeXoDgVIZXMcjybYu8XfsABYeRawz2McOFaoJ3Lp9l7S2fySmSYqNCqyOAU6pOnU6sKzHut1u0N+Dh6f3efdC4BZIaNjPJaumGYJ4JzjscceY3h4mHvuuYdTp06V7rvssssYGhrizjvvZNeuXct+nVB4/87E0vg9lLaGhcJ7/I+vjIErbH2byeU5EU3QHQ7QE/QR8IR49Uyczo5ClfN83uH1GM65tgsEaKAg0rzM7O8o7LISLf7eB/wX59w/a2jDpOkpiL2wRr03qx2HrSRjxGFs6AxwaioFgN9nzKSzOOfY1hvieDRBKpunw1eYxIHGBcDKlxAdOjlFbygAOE5MJktLnZfKNFGhUZGFKdAhTadWF571WK87OBDhBy+eZqCzA+c8pLK54j7vXRX3eX/22Wf5/Oc/z913382rr75auv28885jaGiIoaEh3vSmN62oDfPfv96Qn4HOjlJ6JkAykyfk94KBYZyJpekOB7h4Ww9TiQzpnGPv5i52bghz7QWbSjMw7RoI0EBBpGntmw1yADjnJszsiga2R1qEgtgLa9R7U49x2PmbOnn++CRbeoKcnkowMpZkbDrF+ZsKO89t6w1x6OQ0qUyWSIePeDrbsABY+RKimVSO7mDh/ZgNwGiplcjaKNAhTakWF571WK/bGw5w+c4+jpyJl3ZX2bUhMmef91deeYW7776b4eFhDh48WHru9u3bueOOOxgaGuItb3kLZlaa/aiU4rlY+mf5+zd7jHg6O2enkYu29rCpOwjAE69NEPAW3qPZWQTnXOm9USBARBrEY2Z9zrkJADPrR2MXWSb1XQtrxHtTj3HYvh29TCUyHJ9MMJ3MsqUnyJ6NETwGzxyNsm97Dzv7w4ycmSHc4SPg81Q1yLPY2Gz+fWZv1Ajr7PCSyuYBV6qJpqVWImujwYKsG/Vak7pvRy+5vCMc8JUCC0eOHuXlx77Jb9z7D/z4xz8uPba/v5/bbruNoaEhrrnmGjyeN2prLJbiCSw7/XM5O410dnhJZ/PMpLI1fW9ERFbovwA/NLN/KP5+G/CnDWyPyLq11jpn9RiH9YYDvOP8jTz83El2DRgbIoHS0t3DozMcOjXFFTv7ecvuc6qewbLccZvPYxw8PsnxaIKQ38tFW3vY2hOcU6OjUqZJvbbFFWkXCnRIW1msE6jXmtTZwMLTLx/li/d/ie89dD9PPPYozhV2XYlEInzoQx9iaGiIG2+8kUCgcie1WIrn7O+rTf/sCvrmDDa29YZ45miUrg5fW9bhEJHW5Jz7jJk9DtxQvOlW59zzjWyTyHpUjTpnax2HLfdCvzccYEtPkDdt7cZmK7ZTmIiaiC9cL22tljNuK+zKVyhYv6M3xEw6y8iZGFt7gly8rZBRO1tcvjzTpB515kTajQId0jaW6gTqsSZ1ZmaGBx54gOHhYR5++GEymcI6y0AgwI03vYdr3nMLl7/tBjb19zA4EFkwyAFLp3guN/2z0vsylcgQSyXI5ByZXB6/10N/2M/W3lDpvdnS01kcUEQ1cyAiDVMMbCi4IdJA1aivsZZx2Eov9Buxs8xyxm0vnpouFax3zkM653jT1m4CPs+iAZh61DcRaTcKdEjbWE4nUIs1qalUiq9//esMDw/z4IMPEo/HAfB4PNx4440MDQ1xw03v5/DU3OUsS0Xil+qkl9uBV3pfQn4fI2Mx+opbsGEQ7vCxb0cvveGAZg5ERESkpFr1NVY7DlvphX4jdpZZzritvOhoKpujs8O3rPexHvVNRNqNAh3SNqrdCSyWIpnL5fje977H8PAw9957L9FotPS8q6++mqGhIW6//XY2b94MzK7LzK8oEr9UJ/2Dl0aZTGTIZh0+n9ET8nPN+RuX9b6Mx1IEfF4u2/FGhx9PZ0vt0cyBiIiIzKp3hsT8MdjxaIJz+sJzHrPYGK8RO8ssNW576sgEPi8kMznMCv/u2hBZ1vvYiAwVkVanQIdU1IoFj6rZCVTKaHjytXFyp17iK1/6Ivfccw8nT54sPX7fvn0MDQ1x5513snv37rOOt5ogzGKddDSexgAcOBw4w8qeW/75nZxMks250g4rAOPxNP2LtEczByIiIjKr/CI+m8szcibG+EyKy3f2EY2nqzpGrDQGOzGZJOjzzhnLLDXGq/fOMksFV67Y2YfXYzx9JEp/JMAFm7vwemxZmSaNyFCpl1a85pDWoECHnKVVly1UsxMoz2h48acHeej+e3nogXs5/vprpcece+65DA0NMTQ0xEUXXbTo8VYbhFmokx4Zi7GxK8iuDZ2l22YzMgYH5u7Iks05njsW5RJ62djVQSKTw+vx0B/pmHPM8vZo5kBERERmzV7EHzgaLV2ov2VXPz6vp+pjxEpZpYMbIoycmQEKWanj8QxeD9x40ZaqnLNaFguu9IYDXHvBJvbt6C1d2M8vOrr4ceuboVIPrXrNIa1BgQ45S6suW6hmJ/DCiy/xT994gIfuv49XXvxp6faBTVv4xZ8vBDf2798/p5r3YqodiV8s42L+57epO8gl9DI2k8LnNbpDfm68aDOHR2eIp7MV27PS9ioaLyIi0t56wwG6gn6u2rNhzkQIVHeMWGmMs7Grg/F4ipEzMXL5PP3hAP2RDg6PztAT8rfUmGO1mSb1zlCph1a95pDWoECHnKWVly2sthOIxtP8+OArPPClL/LIQ1/muaefLN3X09vHje+/hXe+70NcefXb2T84sMp2VS8Sv1jGxUIDhGT2jfsB9mzsZDyWrtielbRX0XgREZH1oR5jxIXGOPk8XLq9Z87t5fXFpPW08jWHND8FOuQs62nZwsTEBH8//AX+9u8/x9M/+keccwAEQ2Guuv5nufnDt3Pd9e8ia17i6Sznbe5e9bmqGYlfLONiZCx21uc3Op3i8OgME7F0aTvZ3rCfd5y/ccFgxHLbq2i8iIjI+lCPMeJCY5zOYGGHknK6KG5t6+maQ+pPgQ45SzsXPAKIxWI88MADDA8P8/Wvf51MppDh4A8EeMf17+a9t3yEn7n2XXgCQbqC/uJyjOWtoayXxTIuZmt0wBsFw554bZyA11PKukhlcxydiHPgaJRrL9i0prYoGi8iItJY9VpCWo8x4kJjnEoTOa1wUazlvQtr92sOaSwFOuQs7VjwKJ1O8/Wvf53h4WEeeOAB4vE4AB6Ph7e87Vo++HO38e73fpDunl4AnHNMxNNcsbN6X7TV7ugWyrioVDAsEvAS8nk5OZmgw+8lEvDhnOOl0zNrDnQoGi8iItI49VxCWq8xYqUxTvlETqtcFGt57+La8ZpDmocCHVJROxQ8yuVyfO9732N4eJh7772XaDRauu/qq69maGiI2267jRPpDtLZfE0v1Ovd0c0vGPbqmTgBnwcDxqaTRDZ0Aobh1nwuReNFREQap55LSBuZndCKF8XrbXnvav4+2uGaQ5qTAh3SVpxz/PjHP2Z4eJgvfOELnDx5snTfpZdeykc/+lHuvPNOdu/eXbo9WAxCQO0u1BvR0ZUvKdnZH+KV0RidHT5iqSzJTI7JZIaLt62+5sisVhx4iIiItIt6LSFthuyEVrsoXs5n0y5LW5rh70OkXEMCHWbWD3wB2A28CtzunJuo8Lgc8Gzx1yPOuZvr1UZpLc899xzDw8PcfffdHD58uHT7nj17GBoqbAd78cUXV3xuPS7UG1HHonxJyXmbuphJ5hiPJQEj5/Ls6Auxb0dv6fFr6WhbbeAhIiLSLuqxhDQaT/PwcyeJJtJsiHSwrTdEV7Bw/HbNTqiGpT6bdgoOrLfsFWl+jcro+D3g2865PzOz3yv+/n9VeFzCOXd5XVsmLePw4cPcfffdDA8P89xzz5Vu37p1K3fccQdDQ0P8zM/8DGa25LFqfaHeiDoW5UtKOjt87N3SxcgZY2tPiK5goR3PvB6lO+SnPxLg8OhMW3S0IiIi60mtl5DOXoxHExkGIh2kc3kOnZxi75ZuOjt8Kj6+iKU+m3YKDqg4vTSbRgU6bgGuL/78d8D3qBzoEJnjxIkT3HPPPQwPD/OjH/2odHtfXx8f+chHGBoa4tprr8Xr9S5ylPprRB2L+Zkq/Z0B3rL7HIDS7MFsW775/CkGN0TaoqMVERFZT2qdmTp7Mb4hEiCdcwT9hTHC8WiCc/rDKj6+iKU+m3YKDqg4vTSbRgU6NjvnThR/PglsXuBxQTN7HMgCf+ac+/JCBzSzjwMfB9i5c2cVm1p77bI2r1YmJia47777+PznP8/3vvc98vk8AOFwmA996EMMDQ3xsz/7swQCjX3PFvscG1XHolKmymyQozyokcvnGY+l2NQdLD2uVTtaERGR9aaWmamzF+PbekMcOjkNQMDrYSyWYkNn4KxJm1YY19azjZU+m9nzvzw6Q4fPw56BztJSoFYNDqg4vTSbmgU6zOxbwJYKd32i/BfnnDOzhbZ+2OWcO2Zme4DvmNmzzrlXKj3QOXcXcBfA/v37176VRJ2009q8aorFYjz44IMMDw/z0EMPkclkAPD7/XzgAx9gaGiID37wg0QikQa3tGA5n2Oz1LGoNHvQHw4wHs/Mua1VO1oRERGpntmZ+q6gn71bujgeTTAWS9MbCpw1Xm2FcW2j21h+/gs3d/HssSjPHI2yb3sPPq+nZsGBWgd3VJxemk3NAh3OuXcvdJ+ZnTKzrc65E2a2FTi9wDGOFf89bGbfA64AKgY6WlU7rc1bq3Q6zcMPP8zw8DD3338/8XgcAI/Hw7ve9S6Ghoa49dZb6etrvshwK32OlVIL+yMdTCYzxNNZReFFRESkZH7Nr3P6w2zoPDvIAa0xHhoZi5HPw+vjcWZSOTo7vPSFA3Vr4/z3aN+OPg6PznDo1BRX7OyvSXCgXsGdZpnUE4HGLV15APhl4M+K/94//wFm1gfEnXMpMxsA3g78x7q2sg7aaW3eauRyOR555BGGh4e59957mZh4Y/Odt771rQwNDXH77bezZUul5KDaW270u5U+x0qphR4P3HjRFsZjaUXhRaStmdltwB8DbwKudM493tgWiTS3lczUt8J46Hg0wampJCG/j+6gj1Q2z2vjMZLZHFfsrP0Ez/z3qCvoZ9+OXibi6WWdfzWZGa0QgBKptkYFOv4MuMfMfhV4DbgdwMz2A7/hnPs1CgOQvzazPOChUKPj+Qa1t2bWY+Ee5xw//vGPGR4e5p577uHEiROl+y699FKGhoa48847GRwcbGArVxb9bqXPcbEBy64NzbEUSESkhp4DbgX+utENEWkVy52pX+54aK3LKNby/JlkFg8Q9BcK1wf9XlKZLDPJbFXatpTlbDm70PlXm5nRCgEokWprSKDDOXcGeFeF2x8Hfq348z8Bl9a5aXXXDIV76lWQ6eDBgwwPDzM8PMzhw4dLt+/Zs4c777yToaEhLrnkkqqcqxqvaSXR72b4HFdCqYUisl45534KLGvrcRFZmeWMh9a6jGKtz+8M+oilMiQzWTp8XlLZHHnn6Az66rLEY7H3aKnzrzYzo5Um5ESqpVEZHVLU6MI9tf5CHxkZ4e6772Z4eJhnn322dPuWLVu44447GBoa4sorr6zqgLNar2kl0e9Gf44iIiIijbac8dBal1Gs9fnbekMEfYXx3FQyQ2eHj00bOunvXH0gYSUWe48q7YxXfv7VZma02oScSDUo0NEEGjm7Xosv9JMnT3LPPfcwPDzMY489Vrq9r6+PD3/4wwwNDXHdddfh9XrX/gIqqNZrWmn0W1kSIiLNYbGd35xzZ9UFW+Q4Lbt1vUijLDUeWusyirU+f3AgQjSe5pz+8JyL/sGBCM+8Hq3LEo+F3qOlXttqMzM0ISfrkQId61y11uxNTExw3333MTw8zHe/+13y+TwA4XCYW265haGhIW666SYCgdp/oVZ6TdlcnueOT65oKYui3yIirWmxnd9WeJyW3LpepJ5Wulx4rcso1vr8xS76G73EY6nzr2Vsqgk5WW8U6Fjn1vKFHovFePDBBxkeHuahhx4ik8kA4Pf7+cAHPsDQ0BAf/OAHiUTqW+By/muaTmZ49liUzqB/RUtZFP0WERERWdhqlguvdSKpGhNRC130N3qSa6nza2y6dvWqTSiNp0DHOrfSL/R0Os03vvENhoeHuf/++4nFYkChqNsNN9zARz/6UW699Vb6+lbeIVTri2f+azo8OoPD2DPQiZmtaCmLot8iIu3FzH4O+O/ARuCrZva0c+6mBjdLpCWtZrnwWi/Wa3mx3+hAwnLOr7Hp6tWj2Kw0DwU61rnlfKHmcjm+//3vMzw8zBe/+EUmJiZK91111VUMDQ1x++23s3Xr1lW3o5pfPPNfUzqXY9/2HrqCb2SpaEstEZH1yTn3JeBLjW6HSDtY7RLotV6s1/Jiv9GBhEafv53Vo9isNA8FOqTiF6pzjp/85CcMDw/zhS98gRMnTpTuu+SSSxgaGuLOO+9kz549VWlDtb94yl9Td8hPOpufc7+21BIRERFZm0bXtJDm0CrLQapVm1BagwIdMsfBgwcZHh7m7rvv5pVXXindPjg4yNDQEENDQ1xyySVVP28tv3gavd5SREREpB3VY4zVKhfR7Wg5730rLQdRYG59UaBDGBkZ4e6772Z4eJhnn322dPuWLVu44447GBoa4sorr8TMataGWn7xNHq9pYiIiEg7qvUYq5UuotvNct/7VloOosnP9UWBjnXq1KlT3HPPPXz+85/nscceK93e29vLRz7yEYaGhrjuuuvwer11aU+tv3i03lFERESk+mo5xmqli+hWtVDWxnLf+1ZaDqLJz/VFgY51JBqNct999zE8PMx3vvMd8vlC3YpwOMzNN9/M0NAQN910Ex0dHXVvm754RERERKRcK11Er0WjlucslrWx3Pe+1ZaDaPJz/VCgo83F43EefPBBhoeHeeihh0inC19Ofr+f97///QwNDXHzzTcTiUQa3FJ98YiIiIjIG1rtIno1Fgs2ADUNgCyWtbHc917LQaRZKdDRhtLpNN/4xjcYHh7m/vvvJxaLAWBm3HDDDQwNDXHrrbfS39/f4JaKiIiIiFS2Hi6iFwo2HDgaJZd3Na1PsljWxmXn9C7rvVdWtjQrBTraRC6X4/vf/z7Dw8Pce++9jI+Pl+676qqrGBoa4vbbb2fr1q0NbKWIiIiIyPI04iK63stIFgo2PHd8kku29dS0PsliWRsree+VlS3NSIGOFuac4/HHH+fzn/8899xzD8ePHy/dd8kllzA0NMSdd97Jnj17GthKEREREZHVqedFdCN2eVko2GA4Qv65mwJUuz7JUhkzCmBIK1OgowU9//zzDA8PMzw8zCuvvFK6fffu3QwNDTE0NMSll17awBaunfZMFxEREWku7T4+a8QuLwsFG87b1FXz+iRadiLtTIGOFvHqq69y9913Mzw8zIEDB0q3b968mTvuuIOhoSGuuuoqzKyBrawO7ZkuIiIi0lzWw/isEbu8LBRsAOpSn0RZG9KuFOhoYqdOneKee+5heHiYH/7wh6Xbe3t7+fCHP8zQ0BDXX389Xq93kaO0Hu2ZLiIiItJcWmF8ttaMk0bt8rJQsEHZFiKrp0BHk4lGo9x3330MDw/zne98h3w+D0A4HObmm29maGiIm266iY6Ojga3tHbWy57pIiIiIq2i2cdn1cg4abZdXpRtIbJ6CnQ0gXg8zoMPPsjw8DAPPfQQ6XShw/D7/bzvfe/jox/9KB/84Afp7OxscEvrYz3smS4iIiLSSpp9fFaNjBPVrBBpHwp0NEgmk+Eb3/gGw8PDfPnLXyYWiwFgZrzzne9kaGiID3/4w/T39ze4pfXXbNF0ERERkfWu2cdn1co4URaFSHtQoKOO8vk83//+9xkeHuaLX/wi4+PjpfuuvPJKhoaGuP3229m2bVsDW9l4iqaLiIiINJdmH581e8aJiNSXAh1VdPToUR599FFuu+22UoFQ5xyPP/44w8PDfOELX+D48eOlx1988cUMDQ1x5513cu6559atna2wNZii6SIiIiLNpZnHZ82ecSIi9aVAxzIsJzDw0ksvcd1113HixAkGBwfp6upieHiYu+++m5dffrn0uN27dzM0NMTQ0BCXXnppvV/KutgaTERERETWl2bPOJnVChOOIu1AgY4lLCcwMDIywg033MCJEyfYs2cPv/Zrv8Zzzz1XOsbmzZu5/fbb+ehHP8pVV12FmTXq5bTE1mAiIiIiIrOWGxxo5owT0ISjSD0p0LGEpQIDBw8e5Nprry3V2zh8+DAAPT09fPjDH2ZoaIjrr78en6853upm3xpMRERERGRWOwUHNOEoUj/NcfXdxJYKDNx0001ziop2dXVx4YUX8qlPfYorr7yyrm1dDhVqEhEREZFW0U7BAU04itSPp9ENaHazgYFy5YGBj3zkI/T399PZ2QnA9PQ0P/nJT3jwwQfr3tblGByIEE9niaezOOdKPw8ORBrdNBERERGROaYSGUJ+75zbQn4vU4lMg1q0ektdV4hI9SijYwlLVXD+i7/4C/7iL/4CgKmpKUZGRhgdHeXqq69uVJMX1SqFmkRERERE2ikbebk7w6hgqcjaKdCxhJUEBrq7u7nssssa0MqVafZCTSIiIiIi0F7bxi7nuqKdapKINJICHcugwICIiIiISP21WzbyUtcV7VSTZLmUwSK1oECHiIiIiIg0rfU06bjeCpYqg0VqRcVIRUREREREmsB6K1hansFiZqWfR8ZijW6atDhldIiIiIiIiDSBwYEIj740SjSeIZPL4/d66A37ecf5GxvdtJpYbxksUj/K6BAREREREWkSDsDACv9T+L1NrbcMFqkfBTpERERERESawMhYjE1dQS7b0ccFW7oI+rwcjyZ4+LmTRNswy2FwIEI8nSWezuKcK/08OBBpdNOkxTUk0GFmt5nZQTPLm9n+RR73HjM7ZGYvm9nv1bONIiIiIiIi9TSVyBDye5lOZjh0copMLs9ApINoIsNTRybaLtgxu6tOwOdhIp4m4POoEKlURaNqdDwH3Ar89UIPMDMv8D+AG4GjwE/M7AHn3PP1aaKIiIiIiEj9zC7lOB5NEPR7Cfp9JDM5NkQCpSKd7bYDzXraVUfqpyEZHc65nzrnDi3xsCuBl51zh51zaeBu4Jbat05ERERERKT+ZpdynImlCHg9JDM5kpkc23pDhPxephKZRjdRpCU0c42O7cDrZb8fLd4mIiIiIiLSdmaXcvSGAozF0vi9xt4tXXQF/SrSKbICNVu6YmbfArZUuOsTzrn7a3C+jwMfB9i5c2e1Dy8iIiIiIi0kGk8zMhZjKpGhO+RncCDSErUfesMBbrpkC08dmSAc8BHye0tFOvdu6Wt080RaQs0CHc65d6/xEMeAc8p+31G8baHz3QXcBbB///523oVJRERERNpIq16QN7NoPF0KFPSFAyQyOZ46MtEyhS5nMztGxmJMxNN0h/zs3dIabRdpBo0qRrocPwHON7NBCgGOO4GPNrZJIiIiIiLV0+oX5M1qZCxGOOAjHChc7sz+20rFPFWkU2T1GrW97M+Z2VHgauCrZvZw8fZtZvY1AOdcFvgt4GHgp8A9zrmDjWiviIiIiEgtlF+Qm1np55GxWKOb1tJmt2ktp2KeIutHQzI6nHNfAr5U4fbjwPvKfv8a8LU6Nk1EREREpG6mEhn65mVuhPxeJuLpBrWoPcxu0zqbyQGomKfIOtLMS1dERERERNraSi/IVc9jeQYHIjx1ZAIoBI4SmZyKeYqsI828vayIiIiISFsbHIiUdtRwzpV+HhyInPXY2Xoe6WyevnCAdDbPU0cmiCr74yyzxTwDPg8T8TQBn0d1T0TWEWV0iIiIiIg0yEp212iHApv1pGKeIuuXAh0iIiIiIg203Aty1fMQEVkeLV0REREREWkBs/U8yqnApojI2ZTRISIiIiLSAtZLgU0VXBWRtVJGh4iIiKwbZvafzOwFMztgZl8ys95Gt0lkudZSYHO2kOkjh043dQFTFVwVkWpQoENERETWk28Clzjn9gEvAr/f4PaIrMhssOO6vZtWHORoheBBecFVMyv9PDIWa3TTRKSFKNAhIiIi64Zz7hvOuWzx18eAHY1sj0g9tFLwYCqRIeT3zrkt5Pcylcg0qEUi0ooU6BAREZH16p8BDy10p5l93MweN7PHR0dH69gskepqpeCBCq6KSDUo0CEiIiJtxcy+ZWbPVfjvlrLHfALIAp9b6DjOubucc/udc/s3btxYj6aL1EQrBQ8GByLE01ni6SzOudLPgwORRjdNRFqIdl0RERGRtuKce/di95vZx4APAO9yzrm6NEqkgVppt5bZGiQjYzEm4mm6Q372blleLRIRkVkKdIiIiMi6YWbvAX4XuM45F290e0TqodWCB4X2Nmfblktb5Io0lgIdIiIisp58CugAvmlmAI85536jsU2SdtVMF7vtEDxoFbO73IQDPvrCARKZHE8dmVj2LjkisnYKdIiIiMi64Zw7r9FtkPVBF7vrV/kuN0Dp35GxmIJNInWiQIeIiIiISJXpYndptch4aYYsmqlEhr555wz5vUzE00s+txnaL9IOtOuKiIiIiEiVtdKWro0wm/GSzubpCwdIZ/M8dWSC6DKCAfU85mqsdpebZmm/SDtQoENEREREpMpaaUvXRijPeDGz0s8jY7GmOuZqrHaL3GZpv0g7UKBDRERERKTKVnuxu17UIuOlWbJoZne5Cfg8TMTTBHyeZdVmaZb2i7QD1egQEREREamyVtvStd5mM15ma5fA2jNeanHM1VrJLjezdTleGZ0m4PWyZ2MnXcFCm5UFJLI6yugQEREREamB2WDHdXs3abeVeWqR8dKKWTTldTn2bu5mOpXlwNEJphLplmi/SLNSoENEREREROpqtcs76n3MWiuvy9EdCnDZjl46g35eODXdEu0XaVZauiIiIiIiInW3kuUdjTxmLc3firYr6Gff9l4m4mmu2NnXwJaJtDZldIiIiIiIiDSAducRqQ0FOkRERERERBqgFeuKiLQCBTpEREREREQaoBXrioi0AtXoEBERERERaZBWqysi0gqU0SEiIiIiIiIibUOBDhERERERERFpGwp0iIiIiIiIiEjbUKBDRERERERERNqGAh0iIiIiIiIi0jYU6BARERERERGRtqFAh4iIiIiIiIi0DQU6RERERERERKRtKNAhIiIiIiIiIm1DgQ4RERERERERaRsNCXSY2W1mdtDM8ma2f5HHvWpmz5rZ02b2eD3bKCIiIiIiIiKtx9eg8z4H3Ar89TIe+07n3FiN2yMiIiIiIiIibaAhgQ7n3E8BzKwRpxcRERERERGRNtXsNToc8A0ze8LMPt7oxoiIiIiIiIhIc6tZRoeZfQvYUuGuTzjn7l/mYd7hnDtmZpuAb5rZC8657y9wvo8DHwfYuXPnqtosIiIiIiIiIq2tZoEO59y7q3CMY8V/T5vZl4ArgYqBDufcXcBdAPv373drPbeIiIiIiIiItJ6mXbpiZhEz65r9GfhZCkVMRUREREREREQqatT2sj9nZkeBq4GvmtnDxdu3mdnXig/bDDxqZs8APwa+6pz7eiPaKyIiIiIiIiKtoVG7rnwJ+FKF248D7yv+fBi4rM5NExEREREREZEW1rRLV0REREREREREVkqBDhERERERERFpGwp0iIiIiIiIiEjbUKBDRERERERERNqGAh0iIiIiIiIi0jYU6BARERERERGRtqFAh4iIiIiIiIi0DQU6RERERERERKRtKNAhIiIiIiIiIm1DgQ4RERERERERaRsKdIiIiIiIiIhI21CgQ0RERERERETahgIdIiIiIiIiItI2FOgQERGRdcXM/r2ZHTCzp83sG2a2rdFtEhERkepRoENERETWm//knNvnnLsc+ArwbxvcHhEREakiBTpERERkXXHOTZX9GgFco9oiIiIi1edrdANERERE6s3M/hT4JWASeGeDmyMiIiJVZM613ySGmY0CrzW6HU1iABhrdCPWCb3X9aX3u770ftdXu73fu5xzG+t5QjP7FrClwl2fcM7dX/a43weCzrk/qnCMjwMfL/66FzhUi7Yuod3+FtqZPqvWoc+qNehzah2N+qwWHF+0ZaBD3mBmjzvn9je6HeuB3uv60vtdX3q/60vvd/2Y2U7ga865Sxrdlkr0t9A69Fm1Dn1WrUGfU+toxs9KNTpERERkXTGz88t+vQV4oVFtERERkepTjQ4RERFZb/7MzPYCeQpLXX+jwe0RERGRKlKgo/3d1egGrCN6r+tL73d96f2uL73fNeSc+3Cj27AC+ltoHfqsWoc+q9agz6l1NN1npRodIiIiIiIiItI2VKNDRERERERERNqGAh1tzsz+k5m9YGYHzOxLZtbb6Da1MzO7zcwOmlnezJqq8nA7MbP3mNkhM3vZzH6v0e1pZ2b2aTM7bWbPNbot64GZnWNm3zWz54vfJf+60W2S5mBmv2NmzswGGt0WqUxjruan8UNrUF/YeszMa2ZPmdlXGt2WWQp0tL9vApc45/YBLwK/3+D2tLvngFuB7ze6Ie3KzLzA/wDeC1wEDJnZRY1tVVv7W+A9jW7EOpIFfsc5dxHwVuBf6O9bzOwc4GeBI41uiyxKY64mpvFDS1Ff2Hr+NfDTRjeinAIdbc459w3nXLb462PAjka2p905537qnDvU6Ha0uSuBl51zh51zaeBuCttDSg04574PjDe6HeuFc+6Ec+7J4s/TFAYN2xvbKmkCfw78LqDCak1MY66mp/FDi1Bf2FrMbAfwfuB/Nrot5RToWF/+GfBQoxshskbbgdfLfj+KOj9pQ2a2G7gC+FGDmyINZGa3AMecc880ui2yIhpzNR+NH1qQ+sKW8BcUgvH5BrdjDm0v2wbM7FvAlgp3fcI5d3/xMZ+gkAb2uXq2rR0t5/0WEVkLM+sE7gV+2zk31ej2SG0t1q8Af0Bh2Yo0AY25ROpHfWHzM7MPAKedc0+Y2fUNbs4cCnS0Aefcuxe738w+BnwAeJfTfsJrttT7LTV3DDin7PcdxdtE2oKZ+SkM7D7nnLuv0e2R2luoXzGzS4FB4Bkzg8L33ZNmdqVz7mQdmyhFGnO1NI0fWoj6wpbxduBmM3sfEAS6zeyzzrlfaHC7tHSl3ZnZeyikEt3snIs3uj0iVfAT4HwzGzSzAHAn8ECD2yRSFVa4mv1fwE+dc/+10e2RxnLOPeuc2+Sc2+2c200h1f7NCnI0J425mp7GDy1CfWHrcM79vnNuR7GPuhP4TjMEOUCBjvXgU0AX8E0ze9rM/qrRDWpnZvZzZnYUuBr4qpk93Og2tZtiobffAh6mUJzqHufcwca2qn2Z2TDwQ2CvmR01s19tdJva3NuBXwRuKH5nP12cJRGR5qcxVxPT+KGlqC+UNTNl1YmIiIiIiIhIu1BGh4iIiIiIiIi0DQU6RERERERERKRtKNAhIiIiIiIiIm1DgQ4RERERERERaRsKdIiIiIiIiIhI21CgQ0REREREZJXM7LfNLLzAfb9lZi+bmTOzgXq3TWS9UqBDRERERERk9X4bqBjoAP4ReDfwWt1aIyIKdIhIczGznzGzA2YWNLOImR00s0sa3S4RERGpDjP7pWJf/4yZ/X3xtt1m9p3i7d82s53F2//WzP5fM3vMzA6b2fVm9mkz+6mZ/W3ZMWfM7M+L44Zvm9nG4u2XF597wMy+ZGZ9xdu/Z2b/wcx+bGYvmtk1xdu9ZvafzOwnxef8evH264vP+aKZvWBmn7OCfwVsA75rZt+d/1qdc085516t7TsqIvMp0CEiTcU59xPgAeBPgP8IfNY591xjWyUiIiLVYGYXA38I3OCcuwz418W7/jvwd865fcDngL8se1ofcDXwbyiMEf4cuBi41MwuLz4mAjzunLsYeAT4o+LtnwH+r+Jxny27HcDnnLuSQkbG7O2/Ckw6534G+BngfzOzweJ9VxQfexGwB3i7c+4vgePAO51z71zl2yIiVaZAh4g0o08CNwL7KQQ7REREpD3cAPyDc24MwDk3Xrz9auDzxZ//HnhH2XMedM45CoGKU865Z51zeeAgsLv4mDzwheLPnwXeYWY9QK9z7pHi7X8HXFt23PuK/z5RdpyfBX7JzJ4GfgRsAM4v3vdj59zR4rmfLnuOiDQZX6MbICJSwQagE/ADQSDW2OaIiIhIA6WK/+bLfp79faHrGbeC4+bKjmPAv3TOPVz+QDO7ft65y58jIk1GGR0i0oz+Gvj/UUhd/Q8NbouIiIhUz3eA28xsA4CZ9Rdv/yfgzuLPPw/8YIXH9QAfKf78UeBR59wkMDFbfwP4RQrLWhbzMPDPzcxfbN8FZhZZ4jnTQNcK2ysiNaQopIg0FTP7JSDjnPu8mXmBfzKzG5xz32l020RERGRtnHMHzexPgUfMLAc8BXwM+JfA35jZ/wmMAr+ywkPHgCvN7A+B08Adxdt/Gfir4vavh5dx3P9JYUnKk2ZmxbZ8aInn3AV83cyOz6/TUSxW+rvAFuCAmX3NOfdry35VIrIqVljuJiIiIiIi0prMbMY519nodohIc9DSFRERERERERFpG8roEBEREREREZG2oYwOEREREREREWkbCnSIiIiIiIiISNtQoENERERERERE2oYCHSIiIiIiIiLSNhToEBEREREREZG2oUCHiIiIiIiIiLSN/z/WviH/eZ5B6AAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ba330f0>"
+       "<Figure size 1152x432 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2167,7 +2268,7 @@
     "          title='principal components',\n",
     "          xlim=(-5, 5), ylim=(-3, 3.1))\n",
     "\n",
-    "fig.savefig('figures/05.09-PCA-rotation.png')"
+    "fig.savefig('images/05.09-PCA-rotation.png')"
    ]
   },
   {
@@ -2184,9 +2285,9 @@
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -2207,7 +2308,7 @@
     "\n",
     "    def show(i, j, x, title=None):\n",
     "        ax = fig.add_subplot(g[i, j], xticks=[], yticks=[])\n",
-    "        ax.imshow(x.reshape(imshape), interpolation='nearest')\n",
+    "        ax.imshow(x.reshape(imshape), interpolation='nearest', cmap='binary')\n",
     "        if title:\n",
     "            ax.set_title(title, fontsize=fontsize)\n",
     "\n",
@@ -2240,14 +2341,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ixAk1atRIbdq00bhx4+q8bF3GcCIy8l9+fr7+9a9/ad68ebUuF645rV+/XsXFxYqKilLn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\nAAAHopkDAAAAAAf6P1QpRwQf0IzUAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11d01edd8>"
+       "<Figure size 1123.2x172.8 with 18 Axes>"
       ]
      },
      "metadata": {},
@@ -2263,7 +2367,7 @@
     "fig = plot_pca_components(digits.data[10],\n",
     "                          show_mean=False)\n",
     "\n",
-    "fig.savefig('figures/05.09-digits-pixel-components.png')"
+    "fig.savefig('images/05.09-digits-pixel-components.png')"
    ]
   },
   {
@@ -2282,14 +2386,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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89thjAIDXX38d/fr1g7+/v+b7nd3n3MVV+cjNzcXzzz+P+Ph4VKtWDb169UJU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\n4oCOiIiIiIjIoP4/QFJA2Z7Z0loAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ce84a58>"
+       "<Figure size 1123.2x172.8 with 20 Axes>"
       ]
      },
      "metadata": {},
@@ -2303,7 +2410,7 @@
     "fig = plot_pca_components(digits.data[10], Xproj[10],\n",
     "                          pca.mean_, pca.components_)\n",
     "\n",
-    "fig.savefig('figures/05.09-digits-pca-components.png')"
+    "fig.savefig('images/05.09-digits-pca-components.png')"
    ]
   },
   {
@@ -2330,9 +2437,9 @@
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
-    "collapsed": true,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -2364,7 +2471,10 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -2386,14 +2496,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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p527nPX1+6V/2VnjM9Caw2WztvgOampqyvxCKyL5P+act5Z/2lH9kT1P+ka5S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iFr88UXsZaW6Lsul5eX6R88iHT2LHsyGdbPnGFPGNKs10nU69TOniX8hV9AH2fgvKtwy6Fj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trp0iFAKICYr5kkngikajRmXU6XQayyKnG+byxTw3gZW7mYuu/yI0JapQWYVY\nYQdAzFLUVhSJRIzXJD6Q33XXXZg/fz5DE1EamH8y357OLjP/JL4NYP4pNMw/hYn5p3ix4agHyfRg\nmmpYMweEpqYmIwD5/f6YVQ7MlTBJapvx3zxxYi5Wd+iuE7g4qamq2mGX5s6CV6LrMr0t/j7pViLN\nFYVM5LKy2NFt5qpOSUmJ5UNTNBo1vlxYNTSZV3QpKSmxbNd/oK1qGIlEIElSu+rnSy+9hE8//RT/\n/d//ncctJOpZmH/ab1v85Wxj/uka80/6mH8KE/NPcbPuO7OHKtRui4mIk7GoyHg8npiDSEerhogu\np+KgY9WTWrLj+QupCtVR8FJV1QgaTqcTDofDCLmdPS6T27pLKBQy9i2VMJarLvTJvgd0vW2eBRGa\nzMMerEZ87jVNM1bXsCoRAIH2Xc0/+eQTLF26FGvWrLHsa0WUL8w/1sH8w/yT6m3xlzvD/FOYmH+K\nHxuOeqB0Tkzmk2QyRGgQSktLYwKQruvQNA2SJBkHEEVRjBZ3K0+cCMTuSy4qhrmS6AQejUZjqlPd\ndVLLdmURaOsKLOaLsNvtCcOfuQKZzyERXQUuTdOM7v42m82oUGc76OWauSrtdDotu6ILcDoA6nrb\n6jTmqqGiKLj++utRX1+PsrKyPG4lUc/F/JN7zD+ZY/5h/rEa5p+egQ1HFpXPA3pXotEoAoGAsY0O\nh8MITSIwxVfZzF2z3W63sTSt1cTvi9UnHDTvS3ePg892JTISiRiroKQ6frw7utB3dFsyXemj0WjS\n+5KqXHehj0ajiEajsNlscLlc7YZuxN+/kEUiEaiqmnBc/89//nNcddVVmDBhQp62jqg4MP8UJuaf\n7GH+Yf5h/qFCxIajHiTTg09XB2/Rci5OtB6PB+FwuNOu2WK5RtE128rLmor9j0ajRbEv5uVmrTxR\nn663zUUgxlynsy+FcgI37wsAY0WXVKqOmdyWTIhLV3yVPpFMg1o2Q2C8aDRqvMfi51p49dVXsXfv\nXvziF7/odP+IKDeYf3KL+acwMf8ga7cx/zD/EBuOLCffB+6OqKqKQCAAVVVhs9mMCSBFiEq0qoa5\nO7NYQtOqXbOTHc9vBaK7aTEsN2sOgFbfFyD25JzvMNtZ8Ep0XfxtmqYZqww5nc6YCnwyj89liEtG\nfKgS3eYlScLu3bvxyCOPQNd12Gw2bNiwAbNmzcLSpUvhcrngcrnwpS99CaNGjcrb9hNZDfNPYWL+\nKUzMP7nD/MP801Ox4agHyvbBJhKJxIxnF5PUieeJRqNoaWkBEHuQEQcau90Om81mHEStMjZZsOp4\n/kTMAdDqS5sWW2iSZRmhUKggQhOQWVd6VVWN6lo2hgDkowu9+Tbzz5qmYe/evVi/fn3MktMrV66M\n2eadO3eivr4+tR0looww/2QX809hYv7JLeaf07cx//QsbDiyqO5uaU50YBSVGXO3UZfLFROO7HZ7\nzIR8iXR1e6bbnKvunbquQ1EUI/B5PJ6YuQysFjjEyUzXdTidznbLaFqJudt8MYQm8wSdhRCaMiGO\nG7quZ23VoGzPB5GKcDiMSCRifGYAYMaMGZgyZQoeeOABeL1ezJ07F7IsG/NMyLLMahtRmph/kt9m\n5p/kMP8ULuafzjH/UHdiw1EPks0DiqIoCAQCRjgSXbOB2G6Ufr/feEw0GjWWabTb7TGVqVRauTO9\nT66Ew2Gja7pZtsYeZ1J57Oq1VxTFqIBYeXJOILarudWXaQVOV3SB4ghNoqIruixbmeg6b7PZ2n3R\n2LJlC7Zt24ZVq1ZZ+v1HVAyYf5h/OsL8U7iYfwoX80/PxIYjC8rnh1CMyxUHcrfbHdOdN9EEkLoe\nO6Gdx+OJqcx1t0yDl3lssiRJMRNAdvX4fI5L7qxyaF7WVEzYme1wF385F4oxNIlA6/P5LDvZKHC6\nCipW3fB4PPnepIyIzwmAdkMajh49iiVLluDFF1+09PuPqNAw/2SG+Yf5xyqYfwoX80/PZd1PIaUt\nkxN3MBg0Vs0QXbPNvzc+NGmahtbWVqiqCkkqjJU2MjmRK4piBMBMuzNnOr44k/t0FODMcy/kUi4q\njyKgiyqwx+OBpml5CXDZED8OPt+fm0zJsoxoNGpMnmqF16Aj8d3NzVVQTdNw44034r777kPfvn1z\n8vzbtm3DfffdhyeeeCLm+nXr1qG+vh4OhwOzZ89GTU1NTp6fyKqYf5h/mH8K/9zL/FO4mH96Nmt/\nEnuw7q7aiOeLRqNwOBzw+/3GmGlxEhYnKXFAFOOSdV0viokGZVmOWWo306phPk/k5jHwwOkuwLkK\ncB3dlqsKpDl0dCZXXeZT7Vbf2fZnaxx8PkWjUWNpaisfB4RwOAxVVeF0Otu9Nvfffz8uvPBCXHTR\nRTl57kceeQSrVq2Cz+eLuV5RFNxzzz147rnn4Ha7MW/ePFx88cXo3bt3TraDKF+Yf7oX8w/zD/NP\n+ph/sof5J//YcGQx5mDSHczdK4G2wGCuMlmha3amzCGjUKqGmYjvzmxeOjefr1FnISvRdfFd53W9\nbenPVLrOi//z2YUeaB+qzF3no9Go8d5LdN9E16Ub7nIhfo4CK0/SCbSFQLFSTXzF/Y033sDGjRvx\nv//7vzl7/urqajz00EO4/fbbY67fu3cvqqurjXlVJkyYgM2bN2P69Ok52xai7sT80/2Yf7pvu+Iv\nM/8w/xQa5h+y7tGfUpbqwVG0+iuKYlwXP6FjZ12zbTYbvF6vpSezM+9PfMiwokJebjadE7nYH13P\n3lLA6Qa4bHS9jw9w3dV1Xsh2VVHXdaNK7Xa7oeu68SXMil3oxfEAaD+u/8SJE7jzzjvxwgsv5PQY\nMW3aNBw8eLDd9YFAAKWlpcbPPp/PWAacqKdj/kkd80/3Yf5h/imU92JHmH8IYMORZWVSHUjmsbIs\nxyxNquu6EaDiD/CJumZbfTlT4HSloFj2x7zcbLZCRj6Z9yebK6Hk60RuDoFutxsejyfrwS2VABd/\n/0yZq/BdyWW3+FTuYyYq1QDajevXdR033XQT6urqMHDgwKT2Mdv8fj8CgYDxczAYRFlZWV62hSiX\nmH9yj/mnsDH/MP8ke590H2/G/EMCG44ohjg4iAOc1+uF2+02PpBiHH981+xwOGystCHGJFv1pKzr\n2R/Pn2/mpYCLYX/iQ1MxrFAhKqHm/ensRN6d0glesiwbE3WK7vOZhrvuZv67m78oyrKMxx9/HNu2\nbYPL5cKJEycgyzLeeOMNvP3223C73SgtLcWcOXPQq1evnGxb/N9k+PDhOHDgAJqbm+HxeLB582Zc\nd911OXluomLE/MP8YwXMP92L+Yf5h05jw5EFieCSbaqqIhAIGN2SzctfigNIMBhsdxBXVdXYHqfT\nGbNcq/mxmbZ4dwddL67x/EBbpUOEQK/Xa/mJBs2hyePxwO1253uTMiK+rGiaBpfLVZD7k8rnUnyR\nysVwgO6uPHb0vKqq4rXXXsP27dtjtm/fvn0xP/fr1w8zZ85MbSeTJP6mq1evRigUQk1NDe68804s\nXrwYuq6jpqYG/fv3z8lzE+UL80/uMP8UPuaf7sf8w/xDp0l6vpoyKS2KouDkyZPQNA0VFRUpP76h\noQE2my2mFVjX2yZzFBUZt9sdc7ATYUKcfLtTtrtsdnZfsZ+aphXF/ATmSmixhEBFUWJW2zAvh2xF\nuq4jGAwaK1RYfTgAcDqo22w2+P1+y++P+EIJtHWHFqvvaJqGw4cP44YbbsDSpUtRUVEBWZaNf5Ik\nYdy4cZb/zBEVCuYf5p9kMf8UPuafwsf8Q/H4ilpUJu195seKyc7EB93n88WcjMSqIaISILphhsNh\nYylTt9ttVHG6ar3OVmt4rmmaFjNeNldhLVfVR3PlsBhCIFCcoUlMPFosocm87KzP57P8/nQ0rl+S\nJNhsNvzgBz/A97//fUyYMCGfm0nUozD/5BbzT+Fh/il8zD/UE7DhqAeLRqMxK0z4fL6Yk6sITcDp\nk7cIWvmuSqUSwLq6r6qqxkoHNpvNWBGgo7HL+R5v3FUAE/uk623zMDgcDkSjUWNyz3QCXfzl7mae\no6AYupuLE7KiKHA4HEURmlRVLaplZwEYXc5dLle7oP7oo49i+PDhuOyyy/K0dUSULuYf5p9kfl/8\nbfnA/FP4mH+op2DDkQVleoAV1ZhQKASgbbJA84FbhAMxEaQgy7LxmHyvSpGNE7r4O6iqmlZX5mQr\ng9moPnZ236620TzfQjZ0d/VRVHEAxMw7YVXifSdCUyEtCZwuMbklgKKo7gIwulzbbLZ2k49u27YN\nf/3rX/Hyyy9n/Xl1Xcddd92F3bt3w+Vyoba2FpWVlcbtL7zwApYtWwa73Y6rr74a8+bNy/o2EBUq\n5h/mn/jrOttG5p/CwvxjDcw/1BFrH4EoLeLALUkS/H5/TPVCVNlEtcZ8f9E1uxi6yYrKoZgI0+v1\nplwhKJRqlAhQYvlcoG2STvNrlItAl4/qozg556OrfDbDejQaNd53Vg9Nuq7HTNZp9Woo0FY9FF8S\n41+jlpYW3HLLLVixYkVO9nXt2rWQZRnLly/Htm3bUFdXh/r6euP2pUuX4qWXXoLH48Hll1+OK664\nAqWlpVnfDqJixPzD/JPoOuYf5p90MP9kF/NP4WPDkcWYP8DmcJMMWZaNE53T6YzpThlfZRO/V3S/\nFMtKmse5WpUIGLquF8XYakmS2i03290rU+Si+mjuQm9+z6VbfcyFVMIa0LZP4jPmcDiMuTXS+X2F\n8J4VXc476s5sRWKfgPbVQ13X8b3vfQ933nknqqqqcvL8W7ZswaRJkwAA48aNw44dO2JuHzlyJJqa\nmjp8jxEVK+afzDH/ZB/zD/MP8092MP8UPjYc9QDiQBCJRIzrzLP9d1Rli0ajBdM1OxtEt2XR7dfj\n8cDlchXVPuVr/Hu2T+jhcNjoQh8/90Rn8tFVPp3qo67rMZ/HTOSqspjMfc1dzq1+fBDEykIul6vd\nZ+mJJ57AgAEDMGvWrJw9fyAQiKmgORwOY04VADjrrLMwe/ZseL1eTJs2DX6/P2fbQmR1zD9tmH9y\nh/mH+cfqxweB+Ye6woYjC0rl4CSWUlRVFTabzTi4mwNSV12zi2UyPrFPklQcS7Pqeuxys6kEjEIl\nAkUkEklrnwqhCpEoZIlVeCRJgsfjifksZiPYmS9rmpb9neqCoihoaWkB0P1zQMRfzoQsy0Y3+vhx\n/Tt37sTTTz+NNWvWZOW5OuL3+40hCQBiQtPu3bvxf//3f1i3bh28Xi++//3v4+WXX8b06dNzuk1E\nhYL5J3XMP9bA/MP8k+p94y9ngvmHkmHtMwd1SFRixAfQ7XbD6/Wiubk5psLWVdfsdMa+F5psjOcv\nNKKKqigKbDZbUaziYA6CVt6n+BN6JBIxQpPf7++2fcpl9VHTNCOgmfcnnepjNmUSwMQxE2gbynLs\n2DFs374dDocDkiThRz/6EWpra7F//3643W643W7069cv619Wxo8fj/Xr1+PSSy/F1q1bMWLECOO2\n0tJSY44VSZLQu3dvNDc3Z/X5iayO+ec05h9rYP7JLuYf5h/KDUnP1zuc0qKqKhoaGhCNRlFeXp7w\nIKzrbZO1iUqM1+s1xnw3NTVBVVV4vV6jyiZJbUuYKooSc+Awd720ahfMYhvPD8QGwWJZlcJcEbVy\naIonVuIplooocLqKD3S9yksygayz27IV/tKxdOlSvPjii53e55JLLsFDDz2U9nMkouunVxUBgLq6\nOuzcuROhUAg1NTVYvnw5nn32WbhcLlRVVeFnP/uZ5XsPECWD+Sc1zD/WwPxjHcw/pzH/9ExsOLIY\nVVXR2NgIWZYTBidFURAIBIyKmd/vjzlYNzc3Q1GUtJ+/u7thptsdU7SeF9N4fqDt9RcrOBRLEDSH\npmKpiALFGZo0TUMgEICu65YawtFV1/ZIJGJ8ERH7dPDgQbz22mvYv38/Dh8+jHHjxkGWZWMoQSQS\nwcUXX4wZM2Z0/w4R9UDMP8lh/rEO5h/rYP5h/iEOVSsaopurmMzR4/HEnFRF90qPxwNVVY2Dhqqq\nxlh+SZJiWm47a9nO53jiZEKWeR+dTqcxdrxQxhKnQ1GUmK73bre7KEKT6HJut9vh8/ksv08AYiZW\nLZbQJCr5um69ZWc76zkgVq+JXx546NChiEajePLJJ7F27dp2Y/6JqDAw/zD/WBHzj3Uw/zD/UBs2\nHFmYObwEg8GYccTmg5p5TL/dbofdbjcqUiI0ZXIizkd3zFTGEot9zKZkK4XpVBcTPSYajRrVQzHG\n1+rEibiYupwDp4cHAMUVmsTcH06nsyjef0BbaBJV0fj3Xzgcxo033og//vGPDE1EBYb5h/nHyph/\nrIP5h/mHTmPDkcVFo1Gj66TT6Ww3NlpM4ibG8ovrxDKS4oCRyRjRQqhIiTkKxAnL4XAYQTCTENfV\nY5INb9kUCoWMEAV0f4jLxmtczKFJVEW7Gv9uJeFw2KiKFsPwAOB0GATavozEDw/4z//8T9xwww04\n++yz87F5RNQF5p82zD/MP4WA+cc6mH8oXcXxqe5hxEErFAoZkzmWlJTETOYoTugiNAnmyRIdDkfC\nA4YVmbvG5ms8fy6qi4qiGD+bKzf5Dm9A+sFM13VEo1Houg6bzQaHw2FUi7t6bPzvLyRi/gUARbHc\nsRCJRGJWeinEv32qzBVEt9vdrtv5s88+C0VR8PWvfz1nzy8mgHS5XKitrUVlZaVx+/bt2/GLX/wC\nANC3b1/ce++9RVPlJMoE8097zD/MP/nG/GMdzD+UieL4ZPcg5gqSOJj5/f6Yg7S5a7Y5SIkJzYDi\nGiMuljDNRvUwE9k8sYsDuwgXya6y0R1d5Dt6TDrzPWiaFlM9TFU2q4vp3heIDU0lJSWWGv/eGTFE\nQJKkoglNQNuxU1QQxYpLwr59+/Db3/4Wa9euzdnzr127FrIsY/ny5di2bRvq6upQX19v3L5kyRI8\n+OCDqKysxMqVK3Ho0CEMGTIkZ9tDZAXMP7GYf9o/zvx/ouuYf5h/ksX8kxvMP9bGhiOLESEBaJv0\n0O/3x4Qj88krV12zC4V5WdZiWsJUzNmgaVrK3Ziz2Z06XR2FLPE+FNVec7jINMRlEt4yZf4yI0kS\nZFk2gry4znzfZP5P9T65oKqq0ZW5WFZ6Adp6HYgwGP/ZkmUZ3/nOd/CHP/wBXq83Z9uwZcsWTJo0\nCQAwbtw47Nixw7jtww8/RHl5Of70pz9hz549mDJlCkMTEZh/zJh/2mP+Yf7JFuYf5h9KzPpnzx7M\nPNY2UZUNKN6u2eb9KpZlWYHY5WZdLldM93urSHSCF5Pw6bqe82pvMuGrs9tSva85rOm6DlVVs7g3\nyUk2kKXSnV4MfRCvlaqqluk23xFRyQYSj+v/4Q9/iEWLFmH06NE53Y5AIIDS0lLjZ4fDAU3TYLPZ\n0NDQgK1bt+LHP/4xKisr8e1vfxtjxozB5z73uZxuE5GVMP8w/xQi5h/mn0LF/EPZwIYji/F4PAiH\nw0aXayB2nHdHXbPzNe49F2RZzvt4/lyIRqPGQb2Y9sscBj0eT7uusdnWXSd1URkF2sKFeeWJVAJa\nMvdJ9jHm6mO2mI8jHUmnqpjtx3TFPPwh0bj+1atXo7GxEYsXL07q92XC7/cb7x0ARmgCgPLyclRV\nVWHo0KEAgEmTJmHHjh0MTtTjMf8w/1gN8w/zT2f/Z+sxXWH+oWxhw5EFxXfNTrRqiOjCXExds3W9\ncMbzZ5s5DHq93qIZI26ujHZHaOou5u708aEJKIyKVLrBTJZl40Ru/nwlG+JyEd6S0VW4Mh8rNU3D\nW2+9hY0bN8LlckFVVSxfvhy33HIL/vrXv8LtdsPlcuGcc87BoEGDsr6t48ePx/r163HppZdi69at\nGDFihHFbZWUlWltb8fHHH6OyshJbtmzBnDlzsr4NRFbE/MP8YxXMP8w/3YX5h7qLpOfjHU5p0zQN\nTU1NCIfDcDqdsNlsMQcHVVWNOQBsNpvR1Tdf44SzpVjH85sro8UWBs1Ls5aUlBTNqgi6fnopXat2\np++IqObb7fasTAaZyy7yyT6mI0uWLMHf//73Tu8zYsQI/M///E+n90mHrp9eVQQA6urqsHPnToRC\nIdTU1GDTpk247777AACf+cxn8IMf/CDr20BkNcw/zD9WwfxjPcw/sZh/KBE2HFk71kdYAAATAklE\nQVSMpmloaWkxuvRmSybdJjPpehl/OZFiHc8vxlFHo1HYbDZ4vd6YJWetzNztvJgqiObQVEzvReB0\n1beYvpgAbUMFAoEAgLYALz5j4XAY77//PlatWoXS0lJceOGFxpcYsQTvmDFjcP755+dz84noFOaf\n4jnnMP9YD/OP9TD/ULYVR9N+D7JixQrU19ejqqrKWPbSbrcb3bLPPvts9O7dG7IsG11IxWR8LpcL\nLpcLTqcTTqcTDocDDocDdrvd+CcqeKmOn82GROFKdEcHYBzIxfKl2R4r3J3MFUS73V5UqzYUc2gS\nr1mxhSZFURAKhYyqb7G8F8XwDgDGMVDw+Xw4ceIEPv74YyxfvrxoXkuiYsX8w/xT6Jh/rIf5h/mH\nksceR0Xgpptuwpo1a/DlL38Zt9xyC+x2O8LhMMLhMEKhkHE50c/m6yKRCEKhECKRiHFbJBKBqqpw\nOBwxoUtcFtd7vV6UlJQY/5eUlMSEtkTBTTzeHNrEP9HtvDu7LXdHhVGMLxYVxGx1iS0U5rkKfD5f\n0XQ7F6FJUZSUlwgudOaKVDG9ZsDprueJXrNDhw7hmmuuwcsvv4zy8vI8biURpYv5JzuYfzLH/GM9\nzD/MP5QaNhwVgZ07d+LIkSOYOnVqXg7mYkI5cxgT4Sud4BYKhbBjxw4Eg0EMHjwY/fv3R2NjY7vQ\nZr4sQpvX64XH4zHCW2fBTVQcRXiLD25A/iuOnf2fzmM6e2ymxGsOFNcJ2NylvthCk6ZpCAQC0HW9\nqOZhAE7PMSFJEvx+f0wVUVEUzJ49G7W1tZg4cWJOnt88jt/lcqG2thaVlZXt7rdkyRKUl5fj1ltv\nzcl2EBUz5h/mH+af3GH+sSbmH8qV4jiy9XCjR4/G6NGj8/b8kiQZ4aRXr14Z/76PPvoIM2bMwIwZ\nM1BbWwuv19vp/XVdRzQabRfI4oNZU1NTWhVHRVFgt9sThjaXy2WcTBNVHMWysm63G7Is48SJEygr\nK8Po0aONE3F8V/lCD26JrlNVFYqiAGjrEitek1TCXiEyhybRpb6QtzcV8cuzFlNoElVtAAm7ntfV\n1WHGjBk5C00AsHbtWsiyjOXLl2Pbtm2oq6tDfX19zH2WL1+ODz74IKfbQVTMmH+Yf7KB+ac95h9r\nYv6hXGLDERWcqqoqbN68GSUlJUndX5IkI8SUlZXleOva03UdiqJ0Gtx27dqF+++/Hw6HA4sWLcKx\nY8eSCm7hcBiKosBmsyWsNIp/5opjfHATJ0Xxv7niKOaIyGZwi0Qiaf0du7vC2NU+ivHhIjQVU5d6\nEQjFfAXFskwwcHrfxBLI8ZXf9evX4/3338fdd9+d0+3YsmULJk2aBAAYN24cduzYEXP7O++8g3ff\nfRdz587Fvn37crotRGQNzD/MP4n+T+Y+zD/JYf5h/qH0seGIClKyoakQSJJkhJHS0tKE96mqqsKu\nXbvwrW99C2PGjMnq8ycT3MLhMFpaWlKuOJqDg9hHt9ttBC9FUaBpGiorK9G3b18oimIEt466ypvn\nh4ifoFSENlEhyWfFUUxKKrS2tuYssHV3IItEIsbrWkyTXAIwquRi/hGzTz/9FD/5yU/w0ksv5Xyf\nA4FAzPHA4XBA0zTYbDYcO3YMv/nNb1BfX48XX3wxp9tBRNbC/JM85p/sYP4pDsw/lGtsOCLqBkOG\nDMEDDzyQk9+dTHDLhb1792LGjBkYNmwY7rnnHlRUVHQa3ILBII4fP55WcBP72NEEpeaKo3meB3Nw\nE4Gvs5V1RHCLRqNGxUaS2iYqzbXuqjBGo1FEIhFIkmSpLyjJUBQlZt/Mfw9N03DDDTfgV7/6Ffr0\n6ZPzbfH7/QgGgzHPL74Q/O1vf0NjYyO++c1v4tixY4hEIhg2bBiuuuqqnG8XEVF3Yv5h/ukK80/m\nmH+oO3BybCJKi6qqWLt2LT73uc/lfVWGZCqOXf0sAtv+/fuxd+9elJeXY+jQoThx4oQxHMDcPd78\nswhqPp8vYVf5RMGts67y+aw4mi/nosKYq4qjeaLLRJOT3nvvvfB4PLjjjjsyfq5kvPLKK1i/fj3q\n6uqwdetW1NfX4+GHH253v+effx4ffvghJ4ckIrII5h/mH+afjjH/FC/2OCKitNjtdkyfPj3fmwGg\nrRus3++H3+/P+HfdfvvtaG1txWOPPYbq6uqkHiMqPZ0Ft9bW1qSCW/w/WZYBoNNVddxud8KKozm4\nSZKEI0eOwOPxYMyYMdA0LWHFUVQdxb/ulElgk2UZuq4bXaK3bduGpqYmuN1u7N27F6+//jp++9vf\n4pNPPoHb7YbH48lphXratGnYuHEj5s6dC6BtQsrVq1cjFAqhpqYmZ89LRES5xfxzGvNPdjD/kBWw\nxxERkYmmaVAUxVKrbCQT3O6++24cPHgQX/3qV1FVVdVpcIu/DCDhpKSJglv8HA/mCUo7WhI628Ht\n5MmTuPrqq7u8380334ybbroprecgIiIqJsw/zD9EnWGPIyIiE7GCi5WIAOLz+Tq8z1tvvYVBgwbh\n2muvzfrzq6raZXALh9svCR1ffexoSWhd1zsMbUDbhJCDBw9GeXk5HA4HPB4PZs6ciWAwiIaGBgwc\nOBB9+/ZFJBKBLMvG5Jjjx4/P+t+CiIjIiph/Usf8Qz0JexwREZFlfe1rX8POnTvxpz/9CcOHD28X\n3ILBoLEsLBEREVExYP6h7saGIyIisqx33nkHqqri/PPPz/emEBEREXUL5h/qbmw4IiIiSpGu67jr\nrruwe/duuFwu1NbWorKy0rh99erVePzxx+FwODBixAjcdddd+dtYIiIioixg/um5bPneACIiIqtZ\nu3YtZFnG8uXLcdttt6Gurs64LRKJ4IEHHsCTTz6Jp556Ci0tLVi/fn0et5aIiIgoc8w/PRcbjoiI\niFK0ZcsWY+6AcePGYceOHcZtLpcLy5cvNyYZVRQFbrc7L9tJRERElC3MPz0XG46IiIhSFAgEUFpa\navzscDigaRoAQJIk9O7dGwDwxBNPIBQK4cILL8zLdhIRERFlC/NPz+XI9wYQERFZjd/vRzAYNH7W\nNA022+lajK7rWLp0KQ4cOIDf/OY3+dhEIiIioqxi/um52OPIgtasWYPbbrst4W21tbWYPXs2Fi5c\niIULFyIQCHTz1hERFb/x48djw4YNAICtW7dixIgRMbf/6Ec/QjQaRX19vdFlm4gyw/xDRJRfzD89\nF1dVs5ja2lps3LgRo0aNwi9/+ct2t8+fPx/19fUoLy/Pw9YREfUM5lVFAKCurg47d+5EKBTC6NGj\nMWfOHEyYMAFAW9fthQsX4pJLLsnnJhNZGvMPEVH+Mf/0XGw4spiXXnoJffr0wTPPPNMuOOm6ji9+\n8YuYMGECjh07hjlz5mD27Nl52tLkrVmzBn/7298SBsEVK1bgmWeegdPpxPXXX48pU6Z0/wYSERFR\nXjH/TOn+DSQiIjqFcxwVqJUrV+Kxxx6Lua6urg6XXXYZ3nrrrYSPaW1txYIFC/CNb3wDiqJg4cKF\nOPfcc9t1ISwk5gpivOPHj+OJJ57A888/j3A4jHnz5uELX/gCnE5nHraUiIiIco35h/mHiIgKDxuO\nCtScOXMwZ86clB5TUlKCBQsWwO12w+124/Of/zzef//9gg5O48ePx7Rp0/DMM8+0u2379u2YMGEC\nHA4H/H4/hgwZgt27d2PMmDF52NLkRCIR/Md//AdOnDgBv9+Pe+65BxUVFTH3qa2txT//+U/4fD4A\nQH19Pfx+fz42l4iIqKAw/zD/EBFR4eHk2EXkww8/xLx586DrOqLRKLZs2YLRo0fne7MAtFUQr7zy\nyph/O3bswGWXXdbhY+KXe/R6vWhpaemOzU3b008/jREjRuDPf/4zZs2ahfr6+nb32blzJx599FE8\n/vjjePzxxxmaiIiIMsD8k3/MP0RExY09jorAsmXLUF1djalTp+Kqq65CTU0NnE4nvvKVr2D48OH5\n3jwA6VUQ/X5/zKoowWAQZWVl2d60rNqyZQu++c1vAgAuuuiidsFJ13UcOHAAS5YsKfh5GMyT37lc\nLtTW1qKystK4fd26daivr4fD4cDs2bNRU1OTx60lIqKehvmncDD/EBEVNzYcWdDEiRMxceJE4+dr\nr73WuLx48WIsXrw4D1uVfWPHjsX9998PWZYRiUSwb98+nHXWWfneLEOieRj69u1rVNB8Pl+75YCt\nNA/D2rVrIcsyli9fjm3btqGurs4Igoqi4J577sFzzz0Ht9uNefPm4eKLL0bv3r3zvNVd6yoQLlu2\nDCtXrjT25ac//SmGDBmSp62lfOEXB6LCw/xTGJh/mH+oeDH/UEfYcEQFx1xBXLBgAebPnw9d13Hr\nrbfC5XLle/MMiaqIN998M4LBIIC2CqG5qzlgrXkYtmzZgkmTJgEAxo0bhx07dhi37d27F9XV1UZI\nnDBhAjZv3ozp06fnZVtT0VkgBNq60i9duhTnnHNOHreS8q1YvzgQUeFi/ikMzD/MPz0Z8w91hHMc\nUd5NnDgxZinaa6+9FlOnTgUA1NTUYOXKlXj22WdxySWX5GsTkzZ+/Hhs2LABALBhwwacf/75MbcX\n8jwM8eLnWHA4HNA0LeFtPp+v4OdfEDoLhEBbcPr973+P+fPn4+GHH87HJmZk27ZtWLBgQbvr161b\nhzlz5mDu3Ln4y1/+kocts5Zkvzg4nU7jiwMRUSqYf5h/uhPzD/NPMph/qCPscUSURfPmzcMdd9yB\n+fPnw+VyGYHQCvMwxPP7/Ub1EAA0TYPNZjNus9r8C0JHgVDs2+WXX45rrrkGfr8fN954IzZs2IDJ\nkyfna3NT8sgjj2DVqlXGijUCK0Sp6+x9YuUvDkREucD8U/iYf5h/ksH8Qx1hjyOiLPJ4PPj1r3+N\np556CsuWLUOfPn0AxFYRFy9ejJUrV+Lpp5/G1772tXxubqfM1cOtW7fGdCcfPnw4Dhw4gObmZsiy\njM2bN+O8887L16ampLNACACLFi1CeXk5HA4HJk+ejF27duVjM9NSXV2Nhx56qN31xVAh6qiSuGzZ\nMlxxxRVYuHAhFi5ciP3792fl+Yr1iwMRUS4w/xQ+5h/mn2Qw/1BH2OOIiBKaNm0aNm7ciLlz5wIA\n6urqsHr1aoRCIdTU1ODOO+/E4sWLoes6ampq0L9//zxvcXLGjx+P9evX49JLL20XCAOBAK644gq8\n9NJL8Hg8ePPNN1NeDSefpk2bhoMHD7a73uoVoo4qiUDu5mTo7H1i/uLg8XiwefNmXHfddVl9fiIi\nyg/mH+afQsH8Q4WEDUdElJAkSfjJT34Sc93QoUONy1OmTMGUKVO6easy11UgvPXWW40JPC+44AJc\ndNFFed7izFm9QiQqibfffnu728ScDMeOHcOUKVPwrW99KyvPWaxfHIiIqHPMP8w/hYL5hwqJpOu6\nnu+NICKi7Dh48CBuvfVWPPPMM8Z1iqLg8ssvx1/+8hd4PB7MnTsXv/vd7yx1sj948CBuu+02LF++\nPOb6hx56KGZOhvnz51tmTgYiIiLKDuYf5h/KLfY4IiIqMpIkAUCPqBAtWrTIWBZZzMnA4ERERNTz\nMP8w/1DusOGIiKiInHnmmUZV6oorrjCut2rXerP4DrJWn5OBiIiIsoP5h/mHcosNR0REZAmJKonF\nOCcDERERkcD8Q4WAcxwREREREREREVFCtnxvABERERERERERFSY2HBERERERERERUUJsOCIiIiIi\nIiIiooTYcERERERERERERAmx4YiIiIiIiIiIiBJiwxERERERERERESXEhiMiIiIiIiIiIkqIDUdE\nRERERERERJQQG46IiIiIiIiIiCghNhwREREREREREVFCbDgiIiIiIiIiIqKE2HBEREREREREREQJ\nseGIiIiIiIiIiIgSYsMRERERERERERElxIYjIiIiIiIiIiJKiA1HRERERERERESUEBuOiIiIiIiI\niIgoITYcERERERERERFRQmw4IiIiIiIiIiKihNhwRERERERERERECbHhiIiIiIiIiIiIEmLDERER\nERERERERJcSGIyIiIiIiIiIiSogNR0RERERERERElBAbjoiIiIiIiIiIKCE2HBERERERERERUUJs\nOCIiIiIiIiIiooTYcERERERERERERAmx4YiIiIiIiIiIiBJiwxERERERERERESXEhiMiIiIiIiIi\nIkqIDUdERERERERERJQQG46IiIiIiIiIiCghNhwREREREREREVFCbDgiIiIiIiIiIqKE2HBERERE\nREREREQJseGIiIiIiIiIiIgSYsMRERERERERERElxIYjIiIiIiIiIiJKiA1HRERERERERESUEBuO\niIiIiIiIiIgoITYcERERERERERFRQmw4IiIiIiIiIiKihNhwRERERERERERECbHhiIiIiIiIiIiI\nEmLDERERERERERERJcSGIyIiIiIiIiIiSogNR0R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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11d01b518>"
+       "<Figure size 1152x432 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -2416,7 +2529,7 @@
     "\n",
     "# plot the results\n",
     "fig, ax = plt.subplots(1, 2, figsize=(16, 6),\n",
-    "                       subplot_kw=dict(projection='3d', axisbg='none'))\n",
+    "                       subplot_kw=dict(projection='3d'))\n",
     "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0, wspace=0)\n",
     "\n",
     "for axi, title, lines in zip(ax, titles, [lines_MDS, lines_LLE]):\n",
@@ -2426,13 +2539,12 @@
     "    axi.view_init(elev=10, azim=-80)\n",
     "    axi.set_title(title, size=18)\n",
     "\n",
-    "fig.savefig('figures/05.10-LLE-vs-MDS.png')"
+    "fig.savefig('images/05.10-LLE-vs-MDS.png')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
     "deletable": true,
     "editable": true
    },
@@ -2443,7 +2555,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
     "deletable": true,
     "editable": true
    },
@@ -2461,14 +2572,17 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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9l1dffZWysjJWrFjBkiVLAA4qn8FNN93EVVddxd13382FF17IihUrWL58eW6/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X28d1NtxBLMvqM+63jfPV1VVSqRSnTt1+btDtcCd0AnamY4RCIfL5/EAqRafTIZlM4vf7EUWx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BIKhfY4XRzEg2pK5PN5NjY2Hjmo8Sjn3BUqHTHiXmanJtguXcQWAsOFuuu6aE6P6cnD7chE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qmqYRRQfX//7y84R4EhPDo7Tmuqy0q9iZJL7nozQcLgwdO7RfLW+sYmQOTpK047UPzVPy\nNHQ6HW58dIuVuVXufjRPppBmcHSQrcoWq0trFLNFdloVUmGvUouqqJzkItORR5sWcRK7SST9yCOw\nHMJuRNtpkrazLMwtQRCBrVBtVgmdiLiWxOk44GlUl2psxbfJ5fLUwwrxeJyP3/0EZ9snFUuRip3B\ncbpsLTeJjAKvvfXwqZLPm7MTx3n/7jVqmoudiuO0u1jdiAvjh69jXaluYSf2X9wpikLFbR5lc6lW\nqtz+5B63Pr5DY71JMpdkaHSQra0Nqott4mYcp+NCo4mma+TiBZygwx/Wf8PK1hIjTLHEXSL2H4dt\nJU60GWOueY/CdJHNnXVwVGwjhtv2CF2FtfubZJN5nLaPVghQFIUPf3MF3TPJ5/LkL+VptVq02y3K\nJ4aYPrk/x8Tz7OLoCf54/zpuTMWK23TqLXKRxempw/vLttNANQ9YXmIZbDQrRxpsWF5cYeHmEjc+\nvInbCMgW0+T6ciwvL9PZ8khaKdrdNp7nEUvGwAqhG3Hz5nVYtshFxUOr3qWiLP6Gx+3mdS4f+y7L\na0uonoZl2LgdH8cNWWaVfLxAs9lmsFKh0+nwye+uE9PjDAwMUCqVqNfrOG6H02+cpK/Yd2T7QogX\nTb1eB1eBR6xq7Da6BEGAYRgPf+IRyuVzBKqPwcPbEE/Hnqnk05+TYIN4qT1PswWKxSLz8/N7lnN8\n2eMmrBTiMKcnZph2HFa3NrAsk4Hh/odGyRVFPTQIpvB4lQ6eRBAEvPfPH2AEFiYWthpjZ7HG9T/e\noFlr0ml0MMMYRrJBo1ndXU4BvSUVaR7834tc1vVFitEg7bBFJpnHMkzqtSqeH+DYG4Qt0BSdiBBN\n0XH8LpqnsryyxPD0ELliiU+vXKe6WidmPgi+6JpOu+6y/vub5PpzTE1PPpMXA09C0zRemzlDo9lg\nq1Yhkx0gn314Cd6H9YajvBtTr9X56NdXsVQbW4vhKiHLN9f44Dcf0qm5NOtNPNfDi1xMTEI/ou22\nKGWGGVs9TkDAbT4hIsTDxYtcOrSIk9wNWCmKQn+nzCdzH1HSe8kCNV1FQ6cVNEh4cZZWF7n05qtk\nYmmuvH8FOuqeKzFN0+i2XP7wyz+SSMUZHB585u5SPal4LM7bJy+yXdmh3m5SHCyTTCQf+prHuZt3\nFJYXlrnz/n1M3cJSYoSBy6337rHT2MZpujTrbYKOj27oGEaMbrOLbip03Q7OSkAm2huA9SOPNs09\n/UVXDHLtQa7Of0xJH8YNA0xdQUOh7dfIOFmWthe5cOE8fj3k9tU7xPQH76soCqqi0ql7/PrvfsP3\nf/a2zGwQ4jE9zs07AJ6B46+u6+QGM3Q2Ds7VAr1rosGx4jfYqscnwQbxUnueZgvkcjmWlpYOnSL1\nVRNWCnEYy7IYHy4/1nOH+we4eWsJLb9/2nzeSBzZQOn+nXk0zwAVEskkba9Fbb0BbR0Nk+xAhu1r\n26S0HMpgyGZtHaWtkQryD9Y4R20aRgXTMBjWx0jFMgSGQ2QE1KpVul2Xtt/EazlovknSSqErGugq\nVtKkmO2n5TXQDY1MX4qNhQ2ML5Skq1VrLNxYwlBMHN/n/odLrN5Z49UfXCAef3TJrOdFKpk6dNnE\nlw3n+1nZvoOd2Pv9oygibxxdhY57N+5jqb2/Tb6Y4+aHd/BqAdQ1ErEEnWyHSqWCqVioNnS9Fnqo\ncWflNlmvD0OxKEZD3OYTlrjLb/l7fNzeUpyotxRHVVRURcVpOtSMKqlYhsiJUIyATCZDNp6l2amR\nTCToH+xj+d4KudiDi8P1lXXW729h6hbdsM2d9+aZv7XE5e+/+sIEqAAKuTyF3OMNivusFAtBY985\n2Om6HEsd3ayG+RtLmHrvlmcyl+TulavgqDjbPv39A7Rq92jstIkpMQJ8IhPCIOTa9Wv0B6O7UbUw\nCrnJh3uXbn2hv9hKjM36MraVJGEncV0XtJBifoCYGcNvO+iWTrlcZml+mZz1YPbC3O37tDY76LpB\npxXw8f+9RnEix+kLpw76SkKIL0in00R6+MjnWUnzmcizNHv+BO/+8j10f/9UjCAIMPMaTamftQAA\nHJ1JREFUkzPP5rK7F+fsJcQTKBaLuyUvD9PpdOjv/2qJYD5XqVS4desW169f59atW1QqlSd6n8/N\nzs7ied6+Nnc6HTzP+0oJK4V4GlRVZSY3QrfxoNRkFEW4201ODI4f2ec2Ks3dAZiiKGhGbzAShSGB\nF5CwkpTHRzFLCmoKjh07Rm4qzWZ2kWXjLuv6Io7VZcgcIxnkcFyHVrNJo9XAqwe0Ox0iB1JGlqSS\nISIijDzqVNAsKGZ7xwQFhU7QZGRshGQqSagGu/tg8fYyhmqBohBpETE7hhHaXPvg5pHtl2ddNp1h\nkBRO+0FVkCAIYKfNifLRJbdrVR/0T9u2UdQIoghFUfE8n3yqj9JoCXI+Qcwl1ZegY7SoNXYwlN7F\n3eczWwD8z6oBuHRZZo6bfLj7/n3REPWwght1qVMhkUqSjfeCwJEHWkoj35cnlUvh+b33cRxnN9AA\nvSoUhm4QNuD2tTtHtl+edeNDZcyaj+95u9tcxyPv6AweURnMKIpo1x+cYzPZNH7gEUURhmLiui7F\nQoncaAYv0cazu2AErO+sE7b3ztC5yYcsM7dbBeeg/pKOcrTDNt2wRYsmuXyBuBVDUXr9ojReRDcM\n4snY7s2R7c1t2ltddL03S8K0DWzTZvt+lfXV9SPZL0K8SDRNozCcf+gNxyAIKJW/+myBKIq4d+se\nv/uHd/mnv/kVv/ybX/Her99nZWn1idtrmiaX37lEcsCmG7TpdDt0uh08zaXvWI5Lb118ZmfBffuh\nGiG+RUc1WyAIAq5du0YQBMRivTVUURQxPz/P0tISs7OzTzRb4rASnl8nYaUQX9doaYhMPMn85goO\nASndZurY9JGucTQsfU/5t2Q8iTZqsL66wcrcIi2nRbvTxncDus0uoR6hopP2CjSDOkYQI6mlaAQ1\nTMXCNmO0whbJMIHjd3prNE0VTVeIJ2J0qk00xUQLQwxLBz1C13RShQQzJ44TRiFD5SKNWovGcptG\nvQG+AhoEYUC2P737m6+t1440l8Wz7vTEDMXtTVZqWwSE5M0U48dnj3R/mLaB7/YuKqMoIpfL4Voe\nTadObadC2I3oOG1cJ8CpN3F1j9CPerNnPuNHHpscfLG4ySrTkYeuGOiKgY+HHpp4iodp66gGKKrG\nwGiR4kCBrtvl/HdP8+kfr0MXttYeBBq8wKU01LvAVVWV7ZUKnD6yXfNMU1WVN46/wsLaMlvNOgow\nmexnuHx0sxoURUE3H5yfO50Ow+URWvUma9urtDsurXobz3VpN12CVgNN1Wm2OsTC+O6shsftLymy\nrIT36Qv7aOtNTNtANXrXDGPHR+kr5nGjLm98/zL/8n/+gKXEqW7V0T6rWuIEHcrDvUCdaVisLWxQ\nGnwxqpgIcZRmz5/g99vvornmvjFAGIYYWZVjJ6e+0nuGYcgffvVH/Fpv/PB5ImK/FnH7D/eobleZ\nfeXwPDUPY5omZy6dJrgQ0G63UVWVeDz+zAYZPifBBvHSm52d3RMY+NyTlLf83LVr1w4tpxlFEdeu\nXePMmSfP0n9QCU8hvk2ZVJqz31CZS4Dy1Ah/vPcxMbP3m9VtHcvt5V8YLY+xfGeVcEfFb4KqGiT0\nDFs7W+BHFNR+tqINfCzUUMdXPVAi/KiDGqRIpOMQKMSsOJ12hygIMZU4ruOgKzqNehNChdRAklOn\nZ7DiBlZBY2J6ovf7Vq+x+sEKrucQKD6Z/jRjE18oGxrxUgcbAEqFIqXCN7e+tH+0yMLHy+ia0RtM\n2joKKoqhUB4e5eZHt1GaJmG9iWnHiOkJ6jvtPTkD2jR371B/mUuXNq3dXCAqOr7noysm1Z0abtIl\nN5Jh+tRxrJjJ4PF+srksr759gSt/+JTWrRZd10GzVPrG8vT3P9g3YfDoqb4vMkVRGBscYewb/My+\n4Ry1pQcX85Eaolk6yWwcW+3jk7lPURwNpaljJmKoiopbc1B4EKR43P6ioBKGAb7vo4Y629s7xFIx\nBqb6OHZiCjdyOX5xCtu2Off2WW58cItWt0ngRVhxg9HpMokvlBINXvL+IsTj0nWdN/7kMjeu3GRr\naYfQ6QWj9YRGaaLIzOyxrzyQv/bxDYL6wcuvTcNi8+4OS7llRkaHn7jdmqaRSj3e0sVngQQbxEvv\nac8WeFQ5TUVRCIKAarUqAQMhnlA6nWbqwhh3P5rDUmMUB/v4l1v/QjaVIVfIsXh7hYiAKIyIG0na\nfhPFV9Eji1CJSKsZXLrYagIv8ImrccyY3St96Gg4vouK0gsIRBq6GmCYBo2gSipZgGREfMBm4FQ/\nr/30AgNDvTutiqJw+uJppk9P88//+9ek7My+oEIil3gm1oC+TMYmRmnVW6zd2SRmxsn2p/noX64w\nMjKC63kYmkHDb2KoJrqi0uo0MQOT8AuVJ+IkMbEPHECa2MTpDfiiKEJVNXTNoKHUyCbSKKmQbDnJ\n6LlBLv34PNlc79hvWRaX3rrA2MwIH/7TVVKJ/WUgM33Pz0Xli2L2/Czvtz+kudHBNm3sjMn84jwT\nk5MszS8RN+NUmlUSRpwg8vC6EckwS4XN3fd43P7i0CWuJUBV8XUHI5HDyCj0H+tj6nKZ17/36m6O\nl2wuy+vvvEaqL8n6rW1i9t6E0UEQkCtJVQohHpeu65y+cIrwXLi7RPlJZwuEYcjW4jaWengid9Ow\nWJ1b/VrBhueNXO0I8ZmnNVvgcctpbmxsSLBBiK9hbGKU4fIQi/NLRGEfnt5l/eY264u936A1bBJE\nIXqo0XUCVFVDCzU8zcP0TTzVIQhdNEVDQ6OQK+DQIWmmaXXn0S2TwA0I3F7WeVM3iatJ8v19xBIW\nhXyWt//Vm7uBhi+yLIvpV6ZYuraKjkGj3kA3dHRb59Tpb7eM7stq9pWTTMyMs7q4ytDpIm7k0Fzt\nsHZjjUwmTaiEhHXwPJ9Wt4KmmRBFRGFvmZ2uGBSjQZaZ2/feRQZ3qwxUlS2KdgnTMkjrSUoDJYyU\nQTqb4kc//5MDKwr1l/opjudprXeJwohWs4Uds1FtmDoluXi+aaqqcumti9SqNbbWtxmYfR0/cPGb\n0Ky1yBWzuKGD6pg43Q51v4FpmLies/sej99fNhlLThFqAXY8R6m/n0QhRqYvyQ9+8r0DBz0nTh9n\nZ+1dIifCcRy6nS7xRBw9o+6dRSWEeCyqqu6ZIfQktre2UXyNR1SopL7VPHT59otIgg1CPGXPUzlN\nIZ53uq4zMTUOQHWzQV+yn0Q2RqfewVJjOC0Pt+ET02N0PYfA97FNi7baJGPkaTp1bNWif7SfVrOJ\nnbBpelWMuEHba6DqKqqmk7DiRHrAeN84mWKKbDFNabxIdbPO6PjBbZs6McnG+gZXf/spYRf0mM7w\nzACG+e3V637ZxWIxJmd669sXJ1dQR3QwQhaiNQqlPHM3FoiaEEvG8CPIKgWq7iY5eglBj3MeYG91\nAQZ3twP4pksmlUUxYahvgOJgH8m+OENj/awsrDJ1/OBEmOcun+Xv//YfWPx0BSVQMJI6k2fGn4lq\nSC+rTDZDJpvB8zzGZ8aw9Bi1RpX2mosR19leqOIFLjHFgraGHho4fgdL6QWUHtVfoihCjSkYCQMz\nZpPry1MaLpEZTJFJZWg2mwdOl9Y0jYtvnePv/vrv2bq/g6YZ2BmTU0MnXpoBjBDPGt8PUJWXd3nk\nYSTYIMRT9jyV0xTiRVIcLnB/fYmh8hA37NvUNmrYSYtmrY0ds4mciEa3i6Zp2HGThGHTqFXpGE0c\nr0s2lcVzPWJGEtIquf40qq2wvVIlqSRJJlOgROiGRjwdJ11IoqqHX9gvLSzj7oSc/lJ+lo9/8wnf\n/bM35RjwLcsOZKgvthgaHebelQWCboQeU2nWOuQzeTY721iqSV3Zxuu6GIqJqqic5CLTkUebFnES\nu3eoAerWNqX+Irl8hkQ8QUBASEg8HSPTl9mtmnKQG5/cJK1lOXu+8GCjBx/+7gqv/+DSUe4K8QiG\nYZDqSxA0FMqTw/xx7gq6auCpLqEa0JfvZ62zSikxwGJzjpGgt9b7Uf1lK7bM2OA4fdlCLzeEFhBp\nAWZcp69UeOhyq0/+8CnDxVGGiw9mMrTXXa59fINT554sAZ0Q4snlCzm86Bb6I4bXVtJ6qYKCEn4R\n4ik76nKaQoiDlcdGSAzY3Lt9H8uycX0HVVGJkh7tsEnN2Mazu7hmG0VXaastBidLTI/NkEzFIRky\nONXPwHCJ/mKRzECGn/z8x8y+chwr2RtohrpPohgnX87QN9DH0Pjgoe1ZubeKqZv7tuuhxcLcwlHu\nCvEYTpyZwde7rMyvYlomnW4H3dSJUgE73jbtWBXX6tKfHWAnsYYTPSifqSsGaSW7Z+DYTlX5yX9+\nh9nzpzDjBhGgxSFdilMczZPIxiiPjRzYliiK2JjfOjAA1dlxqNVqT/37i6/mxPnjVJrbVLZq6JZK\np9vBTloEtsuWs0Yn1cQ3HUaKoyybdwiiB7MXv9xfoiiikd/iL//rv2NkYgjT0vEDDyOpkx5IMjI9\nTKaYPnDJDUCj0aC9vT8XhKZpbC5sEYaSJFKIb5plWaT7H55jJwzDJyqn+TyTmQ1CPGVHVU5TCPFo\n2UKafH8WwzDIl9Ps7FSxFjWufnKNydI08Xgc1VDYrm6RSCbIj6TZuldDDXRMxcQLfdLpNEPHSqj5\nkMgK0BIK9aiC661z8tQMl75/HtMyKR0rkC/kD22L03HR2R9sUFWVdvPgLPXim6PrOslciuJwnljC\npn6sxvbGDup8xNzNBU6UT2OYBpEe0r89QCOssL21Q7ilkQx7lVfCKKSVqFKcyPHWj37A+OgY11q3\nWFpaRDVVzr9yjlcvXwQ1ZPr85KGzWXzfx3dDLGv/Y6ZuUqvUyWQyR7k7xCOkM2kS2QTFoQLJTIJK\ndYeNlS2ieZetpR3ODLyCZqh4uPTvDLDuLLG9VifRzGArvQSPXuTSilcpjGf5xX/4C7RAY21xg8Wd\nZZK5OKdfu8ypM6cIVY/ZV48f2pZ6tY6h7T+2APhuiO/7+6phCSGO3snzM7z3yw92l1J9URRFKImI\nqRMHL6V7UUmwQYgjcBTlNIUQj7a5tE1/sZ/+Iriuy433b6P0aXRLPmqkoOoqjVaNXCZPRERttUUm\nl6G6U6Naa2GpOmMnRxiaGCS0fFZurjGcL1N+Z5xWq029UuP6rWv8p//27x8aaACwEzZ+bf8dRt/3\nSWW+XiIq8fVFUUR1rcbAwCAMQGWngunH8JsQ9CloloqiKtTrNYYGB2i3M4z0jdL1O9y9f5fQD0nk\nY/zln/2cyRMT1J0qtYUWx0aOMT16nHqtTn2jxsr2Av/2P/7rhyYf03UdI6bDATekHd+hUHx4XxNH\nr1arEbYiRkZ7s1OW5peJk6ZbcdDSNkZMJwoj2q02wyND5Ds5gqGAlZ0l1jfWIIT8YJa/+jf/hfGZ\nMeaW7xBWNM6cOMuZk+eo7VRZubvB5LkxfvjT72MYh+d2yRVy3AzuEdP2D2gMW3voa4UQRyeRSHDp\nnQtc/+Am9Y0mpmYREeHjUhjOc+rCyZeu7LUEG4Q4Ak+7nKYQ4vH4XoD62altfWUDU7PothyUSCGe\nTJDNZtFqCq1KB9uMYVgGAQFjE6O9qc1BjYtvn0fTNP7fr35NMTa0+97JZJJkMkmz1aDdbj8y2DB+\nvMzV397ENvZWp4ksn/J4+el/efGVRFFEGES7V0Jbqztoqk6r3sDWbZLpJMlEAvSQerVOzEzg4TDQ\nN8TE8BRhFBIkXC6/fYkojLjyjx8zkhvfff9sLks2l6W6VH/kXWZFURieHGD1xuaedfphGJIuJb52\nlnTx9XU7XTT1wd+mulGj6zh43ZCYHSNbyKKpGs6yQ7PWRFMNNDvk1dnXYLaXFDpRNjl76TTVaoWd\nhRqD+QfLavLFAnkKrN7ZeGSwIB6Pkx1M0dl09wxcfN9n6PjAS7UeXIhnTSKR4NW3LtDtdtna2EJV\nVUqDpZc2T5MEG4Q4Qk+rnKYQ4vGk80maa70lCr7jA6AAitZbTwlg2RYbrS1iZgwUyPanCbsBqqIR\ns2PsbFdIF5Moh5wik3aKTz+4wUh5hHa7za0rd6iuVwnDiEx/hpmzU6RSKYqlIjOvuty7toDb8ECN\nSBeTvPLqBRkMPANUVSWRjcNnKXZ6/UXFNCzqUYt4rA8ARVVw2j62AbFEDCtlEDlRLx9IqOK5Lo2g\nQdJIH/g5iqMzd3eOmRMz7GzvcPfTOepbTTRdJT+YZfb8SXRd59jJKaIoYuXuGn43QNUVCiM5Tl88\n9Q3tEfEwfcU+rmm3MDCIwojADQAVyzBoKR00tTeQCEIP1TXQbMj1ZfFVDz00QIHQjwiCgLXqGv2Z\ng/O91NbruK6LaZosLyyzcGuZdr2DGTPoL/cxc2oaRVE49/pZrr5/je2lHUI/QrNUBqdLTM8e+wb3\nihDiMLZt786EeplJsEEIIcQLY3J2gvdWe+slTdvAqXqYCZN4PoZh6DTqdToth3qriupr5CcyjI6N\nUq/V2KlUyJZS6GmFV394jhsf3QRv/2cEYYAVM/F9n/f+6X3MKIalxkEFd8fn/X/+iNd/dAnbthke\nHWZ4dJjuZ1UwZHrzs2Xy1Bg33r2NqdkYMQPfD7ESJulSkgio7lRp1drUmlXCMGRsZIjRsTLbOzvU\n6hUKAzniQxZnT85y9725Az8jJMC0LGrVGh//+lNsPUbc6M1UaK52ebf2R9585zKKojA9e4xjJ6dw\nHAfDMF7aO2HPIk3TGJkeZPXmBoZuots6pq5jJAziWogf+NQrdZr1Jjgqlm9TzgxS6MuzvbVDo9si\n3ZdjaLZIfMjkvb+9gsb+v6+qq6iqyuL9Je59uICpm73+4sPGnR26nau8cukMqqpy9tJpggsBnudh\nmuZLNz1bCPHsk6OSEEKIF0YikeDVH54n1m9SKGfpaE3GT41w4sI0XaVDvdHA9Rxy/VmUvgDLtlhe\nWSRRiHHhe2c5/8ZZvvvjN0gmkwzPDBCGwb7PaAdNLr5xnrlb99GD/Rn9LCXG3Rt7B562bUug4RlU\nGixx6jsn0TMq+ZEUrt5m+sIkx05PUmlv0eq00XSVRNHCKql4rs9mZYP8cJbv/ORNzn3nLBdfv0Au\nlyM7cvDMBjOnMTY+yr3r97H1vWvsFUUhbMDK0uqebbZtS6DhGTQ9e4zx82WUeERmOEFouZx6Y4bR\n6RE2q+t0ul2SmTRqBpJDMSrbFVpuk4GpIu/8+Q+5+L1zTM5McvbCGTzD2ff+YRRSmuxD13Xmby7u\nq2ajazrbCxW63QcJZjVNw7ZtCTQIIZ5JMrNBCCHECyWZTHLu8lkALnzvHNc/uEVlvcbK4jLJdInc\nQIZsX47mRhtDNXHDLrPnThJGIYkBm3S6N2j8yc9/xP9Y/p84Gy6WGSOMQppenTf//FVSqRR3anOH\nXuC3a+0Dt4tnT1+xQF+xAPQG/fc+vc/a/AaryxmMEYNSuQ/LtnF2fDRFQ7UjTpw9jus5jBwf3O0D\nP/mrP+V//fe/xXDjGLqB73t09TY/+8sfoSgKrWobjf0BJ13XqW5VGS4P7XtMPHtGx8uMjpd5/Z1L\n3L1xj6U7K3SdLv3lIoatMzI1jO960NKJiIjnTSZmJnD8DhMnx4De3/wHP/8u//jXvyapplBVDddz\nIO3zk7/4GUEQ4DQc4tb+/mIbMdZW1hmfHPumv7oQQnxlEmwQQgjxwspkM7z+w0tsbGygoZFKPqiB\nXS3UWFtcw695NIM6YzNlZk5NA9Dtdpm7dZ+Lb57n/twckRcRzya4/PZPyeVyABimxmEFLHVTTq/P\no6GRQYZGBrn60acMFEtoX0jWuLG2ztZahUanCbGQiTOjjH6W6LNer7O5vMXrf/oad+7cxTIsssUs\nr7/dW04DYNo6QXP/Z0ZRhGlJmcLn0dSJSSaPTxBP25w48SDLfBRFLC0sUduq0+y46BmF6ZOzu0Gt\n9bUNAjfk8o8vMHd3jridYGjiGBcu95LTRlGEqh8cyPR8l2RKEoYKIZ4PcjUkhBDihZfJZNCMvdPS\ns9kM2WyGttvirZ+9sVsxYGd7h4//31UsNYaiKAykhukGHc68ObsbaAAoT43wwfwVbGPv1HjXd5mS\nu47PtWw+S2WusWdb/0CJ/oESge7xxp+8trt9/t489z5a2O0HI7lRAt3l0lsXdwMNAP1j/Sx+vLqn\n2gSAE3QYnz53hN9GHCVFUUhkkvi15p5t5bEy5TGwCgYX3nxl97GrH3zK9v0qpmEBKiP5McycxsXX\nL+wGKxRFoTCcp7na2ZdMVkuq9BX7vpHvJoQQX5cs8BJCCPHCsyyLdCm5b3sURWRKqT2lCa+/fwtb\ni++5yLe1GDc+uLXntZlsholXynT9NmEYEkURHb/N8IkS/aXi0X0ZceSGy0NEpr9vux/4FMuFB//3\nfe59PL8n4KSqKkZoc+Ojvf1lfHKM3FiKjttbYhMEAU7U4eTl45LP4zk3dqxMx9u/dMr1XYYnBnb/\nv721zdZuoKFH13X8WsTdm/f2vPbUhZMYWQXH6+V28H0fX3d45fXTR/QthBDi6ZOZDUIIIV4Kr1w+\nzQe/+YhOxcU2bRzPwc6ZnH3twcV7u92mW3OI2/tPj27do9lskkw+CFqMT40zMjbC4vwSURRRHhuR\ngeMLQFEUXvnOGa78/ipRV8XQDTpum8JobnepDcDi/BKmah/4HrWN2r5tpy+con2izdrSGrppMDI6\nLIn9XgCJRILjl45x+8O76KGJoii4kcPo7DClwdLu81bn17CM/UllVVWlsl6Fkw+2aZrGpbdfpVqp\nsr2xQyIVpzRYkrK5QojnigQbhBBCvBRM0+T1H75GtVKlslMll8+SzWX3PCeKIhQOvphXFJUoivZt\n13Wdianxo2iy+BZlshne+ul3WF9bp9PuUBosEYvtXTITRdGhg78w3N9XAOLxOJMzk0+9veLbNVwe\nYnB4gJWlFYIgZLg8tG/JDAccP3YfOqS/ZHP7j1NCCPG8kGCDEEKIl8rDLt4TiQRGygBv/2N6Qt0z\nq0G8HEoDpUMfGy4PMXdlkbgR3/dYpj9zlM0SzyBVVRkZHTn08b6hPrYX7mEaexOCRlFEVvqLEOIF\nJHP3hBBCiC+YPjuJ4++tM+H4HabOTMgUZrGHZVmMnhjaXVf/OSfqMHN26ltqlXhWDQwNkCjZ+P6D\nfCBhGBJYHsdOSn8RQrx4ZGaDEEII8QUDQyXiP4xx/9YC3VYXK24xOzNNJit3HsV+07PHSOdSrNxf\nw3d9YukY50+e3lOJQojPXXzzPAv3F9lc2iYKQ7LFPJPHJ9A07dEvFkKI54wEG4QQQogvSWfSnL0k\nWd/F4ykNlvYkAhTiMIqiMDYxytjE6LfdFCGEOHKyjEIIIYQQQgghhBBPlQQbhBBCCCGEEEII8VRJ\nsEEIIYQQQgghhBBPlQQbhBBCCCGEEEII8VRJsEEIIYQQQgghhBBPlQQbhBBCCCGEEEII8VRJsEEI\nIYQQQgghhBBPlX7YA0EQALC2tvaNNUYI8Xz6/Djx+XFDPBk57r7cvsrvSPqKkP4iHtdXPUdLfxFC\nPK5HHV8ODTZsbm4C8Itf/OIImiWEeBFtbm4yNjb2bTfjuSXHXQGP9zuSviI+J/1FPK7HPUdLfxFC\nfFWHHV+UKIqig17Q7Xa5evUqxWIRTdOOvIFCiOdXEARsbm5y+vRpbNv+tpvz3JLj7svtq/yOpK8I\n6S/icX3Vc7T0FyHE43rU8eXQYIMQQgghhBBCCCHEk5AEkUIIIYQQQgghhHiqJNgghBBCCCGEEEKI\np0qCDUIIIYQQQgghhHiqJNgghBBCCCGEEEKIp+r/A6qjJ8cpeQANAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bc67dd8>"
+       "<Figure size 1080x288 with 8 Axes>"
       ]
      },
      "metadata": {},
@@ -2476,7 +2590,7 @@
     }
    ],
    "source": [
-    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "from sklearn.datasets import make_blobs\n",
     "from sklearn.metrics import pairwise_distances_argmin\n",
     "\n",
     "X, y_true = make_blobs(n_samples=300, centers=4,\n",
@@ -2544,7 +2658,7 @@
     "         ha='right', va='top', size=16)\n",
     "\n",
     "\n",
-    "fig.savefig('figures/05.11-expectation-maximization.png')"
+    "fig.savefig('images/05.11-expectation-maximization.png')"
    ]
   },
   {
@@ -2566,14 +2680,21 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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SRxRCr9DwkfqM+HwkgYGBTuvyL4PBwMtvvuL0XJmyZRn0bs4Urn/nMl4+Fub0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\nAAAAAElFTkSuQmCC\n",
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed1359fd1a134c999ea35ab6a9fc5796",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11df6f898>"
+       "interactive(children=(Dropdown(description='frame', options=(0, 50), value=0), Dropdown(description='n_cluster…"
       ]
      },
      "metadata": {},
@@ -2583,12 +2704,11 @@
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn; seaborn.set()  # for plot styling\n",
     "import numpy as np\n",
     "\n",
     "from ipywidgets import interact\n",
     "from sklearn.metrics import pairwise_distances_argmin\n",
-    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "from sklearn.datasets import make_blobs\n",
     "\n",
     "def plot_kmeans_interactive(min_clusters=1, max_clusters=6):\n",
     "    X, y = make_blobs(n_samples=300, centers=4,\n",
@@ -2645,7 +2765,7 @@
     "            plt.text(3.8, 9.5, \"2. Update centroids to cluster means\",\n",
     "                     ha='right', va='top', size=14)\n",
     "    \n",
-    "    return interact(_kmeans_step, frame=[0, 50],\n",
+    "    return interact(_kmeans_step, frame=(0, 50),\n",
     "                    n_clusters=[min_clusters, max_clusters])\n",
     "\n",
     "plot_kmeans_interactive();"
@@ -2679,22 +2799,27 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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BQ/5fqhwo49urqoqqgK6rhEwTwzDQdb38WmKyI1kzu5HW224Uw6NxAq2+h5n4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\nk4yv0gAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bec04a8>"
+       "<Figure size 1008x288 with 3 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "from sklearn.mixture import GMM\n",
+    "from sklearn.mixture import GaussianMixture\n",
     "\n",
     "from matplotlib.patches import Ellipse\n",
     "\n",
@@ -2707,52 +2832,46 @@
     "        U, s, Vt = np.linalg.svd(covariance)\n",
     "        angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\n",
     "        width, height = 2 * np.sqrt(s)\n",
-    "    else:\n",
+    "    elif covariance.shape == (2,):\n",
     "        angle = 0\n",
     "        width, height = 2 * np.sqrt(covariance)\n",
+    "    else:\n",
+    "        angle = 0\n",
+    "        width = height = 2 * np.sqrt(covariance)\n",
     "    \n",
     "    # Draw the Ellipse\n",
     "    for nsig in range(1, 4):\n",
     "        ax.add_patch(Ellipse(position, nsig * width, nsig * height,\n",
     "                             angle, **kwargs))\n",
     "\n",
-    "fig, ax = plt.subplots(1, 3, figsize=(14, 4), sharex=True, sharey=True)\n",
+    "fig, ax = plt.subplots(1, 3, figsize=(14, 4))\n",
     "fig.subplots_adjust(wspace=0.05)\n",
     "\n",
     "rng = np.random.RandomState(5)\n",
     "X = np.dot(rng.randn(500, 2), rng.randn(2, 2))\n",
     "\n",
     "for i, cov_type in enumerate(['diag', 'spherical', 'full']):\n",
-    "    model = GMM(1, covariance_type=cov_type).fit(X)\n",
+    "    model = GaussianMixture(1, covariance_type=cov_type).fit(X)\n",
     "    ax[i].axis('equal')\n",
     "    ax[i].scatter(X[:, 0], X[:, 1], alpha=0.5)\n",
     "    ax[i].set_xlim(-3, 3)\n",
     "    ax[i].set_title('covariance_type=\"{0}\"'.format(cov_type),\n",
     "                    size=14, family='monospace')\n",
-    "    draw_ellipse(model.means_[0], model.covars_[0], ax[i], alpha=0.2)\n",
+    "    \n",
+    "    draw_ellipse(model.means_[0], model.covariances_[0], ax[i], alpha=0.2)\n",
     "    ax[i].xaxis.set_major_formatter(plt.NullFormatter())\n",
     "    ax[i].yaxis.set_major_formatter(plt.NullFormatter())\n",
     "\n",
-    "fig.savefig('figures/05.12-covariance-type.png')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "<!--NAVIGATION-->\n",
-    "< [Further Machine Learning Resources](05.15-Learning-More.ipynb) | [Contents](Index.ipynb) |\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+    "fig.savefig('images/05.12-covariance-type.png')"
    ]
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2766,7 +2885,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.9.2"
   },
   "widgets": {
    "state": {
@@ -2782,5 +2901,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/notebooks/Untitled.ipynb b/notebooks/Untitled.ipynb
new file mode 100644
index 000000000..363fcab7e
--- /dev/null
+++ b/notebooks/Untitled.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/data/president_heights.csv b/notebooks/data/president_heights.csv
index ade149d72..e4c5eb6be 100644
--- a/notebooks/data/president_heights.csv
+++ b/notebooks/data/president_heights.csv
@@ -41,3 +41,5 @@ order,name,height(cm)
 42,Bill Clinton,188
 43,George W. Bush,182
 44,Barack Obama,185
+45,Donald Trump,191
+46,Joseph Biden,182
diff --git a/notebooks/helpers_05_08.py b/notebooks/helpers_05_08.py
index 0f3b15aa9..09d900083 100644
--- a/notebooks/helpers_05_08.py
+++ b/notebooks/helpers_05_08.py
@@ -1,6 +1,6 @@
 
 import numpy as np
-import matplotlib.pyplot as plt
+import matplotlib.pyplot as plt; plt.rcParams['figure.dpi'] = 600
 from sklearn.tree import DecisionTreeClassifier
 from ipywidgets import interact
 
@@ -30,8 +30,7 @@ def visualize_tree(estimator, X, y, boundaries=True,
     Z = Z.reshape(xx.shape)
     contours = ax.contourf(xx, yy, Z, alpha=0.3,
                            levels=np.arange(n_classes + 1) - 0.5,
-                           cmap='viridis', clim=(y.min(), y.max()),
-                           zorder=1)
+                           cmap='viridis', zorder=1)
 
     ax.set(xlim=xlim, ylim=ylim)
     
@@ -63,7 +62,7 @@ def interactive_tree(depth=5):
         clf = DecisionTreeClassifier(max_depth=depth, random_state=0)
         visualize_tree(clf, X, y)
 
-    return interact(interactive_tree, depth=[1, 5])
+    return interact(interactive_tree, depth=(1, 5))
 
 
 def randomized_tree_interactive(X, y):
@@ -80,4 +79,4 @@ def fit_randomized_tree(random_state=0):
         visualize_tree(clf, X[i[:N]], y[i[:N]], boundaries=False,
                        xlim=xlim, ylim=ylim)
     
-    interact(fit_randomized_tree, random_state=[0, 100]);
\ No newline at end of file
+    interact(fit_randomized_tree, random_state=(0, 100));
diff --git a/notebooks_v1/00.00-Preface.ipynb b/notebooks_v1/00.00-Preface.ipynb
new file mode 100644
index 000000000..7d635a808
--- /dev/null
+++ b/notebooks_v1/00.00-Preface.ipynb
@@ -0,0 +1,203 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "| [Contents](Index.ipynb) | [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/00.00-Preface.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preface"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What Is Data Science?\n",
+    "\n",
+    "This is a book about doing data science with Python, which immediately begs the question: what is *data science*?\n",
+    "It's a surprisingly hard definition to nail down, especially given how ubiquitous the term has become.\n",
+    "Vocal critics have variously dismissed the term as a superfluous label (after all, what science doesn't involve data?) or a simple buzzword that only exists to salt resumes and catch the eye of overzealous tech recruiters.\n",
+    "\n",
+    "In my mind, these critiques miss something important.\n",
+    "Data science, despite its hype-laden veneer, is perhaps the best label we have for the cross-disciplinary set of skills that are becoming increasingly important in many applications across industry and academia.\n",
+    "This cross-disciplinary piece is key: in my mind, the best extisting definition of data science is illustrated by Drew Conway's Data Science Venn Diagram, first published on his blog in September 2010:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Data Science Venn Diagram](figures/Data_Science_VD.png)\n",
+    "\n",
+    "<small>(Source: [Drew Conway](http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram). Used by permission.)</small>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While some of the intersection labels are a bit tongue-in-cheek, this diagram captures the essence of what I think people mean when they say \"data science\": it is fundamentally an *interdisciplinary* subject.\n",
+    "Data science comprises three distinct and overlapping areas: the skills of a *statistician* who knows how to model and summarize datasets (which are growing ever larger); the skills of a *computer scientist* who can design and use algorithms to efficiently store, process, and visualize this data; and the *domain expertise*—what we might think of as \"classical\" training in a subject—necessary both to formulate the right questions and to put their answers in context.\n",
+    "\n",
+    "With this in mind, I would encourage you to think of data science not as a new domain of knowledge to learn, but a new set of skills that you can apply within your current area of expertise.\n",
+    "Whether you are reporting election results, forecasting stock returns, optimizing online ad clicks, identifying microorganisms in microscope photos, seeking new classes of astronomical objects, or working with data in any other field, the goal of this book is to give you the ability to ask and answer new questions about your chosen subject area."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Who Is This Book For?\n",
+    "\n",
+    "In my teaching both at the University of Washington and at various tech-focused conferences and meetups, one of the most common questions I have heard is this: \"how should I learn Python?\"\n",
+    "The people asking are generally technically minded students, developers, or researchers, often with an already strong background in writing code and using computational and numerical tools.\n",
+    "Most of these folks don't want to learn Python *per se*, but want to learn the language with the aim of using it as a tool for data-intensive and computational science.\n",
+    "While a large patchwork of videos, blog posts, and tutorials for this audience is available online, I've long been frustrated by the lack of a single good answer to this question; that is what inspired this book.\n",
+    "\n",
+    "The book is not meant to be an introduction to Python or to programming in general; I assume the reader has familiarity with the Python language, including defining functions, assigning variables, calling methods of objects, controlling the flow of a program, and other basic tasks.\n",
+    "Instead it is meant to help Python users learn to use Python's data science stack–libraries such as IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools–to effectively store, manipulate, and gain insight from data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Why Python?\n",
+    "\n",
+    "Python has emerged over the last couple decades as a first-class tool for scientific computing tasks, including the analysis and visualization of large datasets.\n",
+    "This may have come as a surprise to early proponents of the Python language: the language itself was not specifically designed with data analysis or scientific computing in mind.\n",
+    "The usefulness of Python for data science stems primarily from the large and active ecosystem of third-party packages: *NumPy* for manipulation of homogeneous array-based data, *Pandas* for manipulation of heterogeneous and labeled data, *SciPy* for common scientific computing tasks, *Matplotlib* for publication-quality visualizations, *IPython* for interactive execution and sharing of code, *Scikit-Learn* for machine learning, and many more tools that will be mentioned in the following pages.\n",
+    "\n",
+    "If you are looking for a guide to the Python language itself, I would suggest the sister project to this book, \"[A Whirlwind Tour of the Python Language](https://github.com/jakevdp/WhirlwindTourOfPython)\".\n",
+    "This short report provides a tour of the essential features of the Python language, aimed at data scientists who already are familiar with one or more other programming languages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Python 2 vs Python 3\n",
+    "\n",
+    "This book uses the syntax of Python 3, which contains language enhancements that are not compatible with the 2.x series of Python.\n",
+    "Though Python 3.0 was first released in 2008, adoption has been relatively slow, particularly in the scientific and web development communities.\n",
+    "This is primarily because it took some time for many of the essential third-party packages and toolkits to be made compatible with the new language internals.\n",
+    "Since early 2014, however, stable releases of the most important tools in the data science ecosystem have been fully compatible with both Python 2 and 3, and so this book will use the newer Python 3 syntax.\n",
+    "However, the vast majority of code snippets in this book will also work without modification in Python 2: in cases where a Py2-incompatible syntax is used, I will make every effort to note it explicitly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline of the Book\n",
+    "\n",
+    "Each chapter of this book focuses on a particular package or tool that contributes a fundamental piece of the Python Data Sciece story.\n",
+    "\n",
+    "1. IPython and Jupyter: these packages provide the computational environment in which many Python-using data scientists work.\n",
+    "2. NumPy: this library provides the ``ndarray`` for efficient storage and manipulation of dense data arrays in Python.\n",
+    "3. Pandas: this library provides the ``DataFrame`` for efficient storage and manipulation of labeled/columnar data in Python.\n",
+    "4. Matplotlib: this library provides capabilities for a flexible range of data visualizations in Python.\n",
+    "5. Scikit-Learn: this library provides efficient & clean Python implementations of the most important and established machine learning algorithms.\n",
+    "\n",
+    "The PyData world is certainly much larger than these five packages, and is growing every day.\n",
+    "With this in mind, I make every attempt through these pages to provide references to other interesting efforts, projects, and packages that are pushing the boundaries of what can be done in Python.\n",
+    "Nevertheless, these five are currently fundamental to much of the work being done in the Python data science space, and I expect they will remain important even as the ecosystem continues growing around them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using Code Examples\n",
+    "\n",
+    "Supplemental material (code examples, figures, etc.) is available for download at http://github.com/jakevdp/PythonDataScienceHandbook/. This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission.\n",
+    "\n",
+    "We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example:\n",
+    "\n",
+    "> *The Python Data Science Handbook* by Jake VanderPlas (O’Reilly). Copyright 2016 Jake VanderPlas, 978-1-491-91205-8.\n",
+    "\n",
+    "If you feel your use of code examples falls outside fair use or the per‐ mission given above, feel free to contact us at permissions@oreilly.com."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation Considerations\n",
+    "\n",
+    "Installing Python and the suite of libraries that enable scientific computing is straightforward . This section will outline some of the considerations when setting up your computer.\n",
+    "\n",
+    "Though there are various ways to install Python, the one I would suggest for use in data science is the Anaconda distribution, which works similarly whether you use Windows, Linux, or Mac OS X.\n",
+    "The Anaconda distribution comes in two flavors:\n",
+    "\n",
+    "- [Miniconda](http://conda.pydata.org/miniconda.html) gives you the Python interpreter itself, along with a command-line tool called ``conda`` which operates as a cross-platform package manager geared toward Python packages, similar in spirit to the apt or yum tools that Linux users might be familiar with.\n",
+    "\n",
+    "- [Anaconda](https://www.continuum.io/downloads) includes both Python and conda, and additionally bundles a suite of other pre-installed packages geared toward scientific computing. Because of the size of this bundle, expect the installation to consume several gigabytes of disk space.\n",
+    "\n",
+    "Any of the packages included with Anaconda can also be installed manually on top of Miniconda; for this reason I suggest starting with Miniconda.\n",
+    "\n",
+    "To get started, download and install the Miniconda package–make sure to choose a version with Python 3–and then install the core packages used in this book:\n",
+    "\n",
+    "```\n",
+    "[~]$ conda install numpy pandas scikit-learn matplotlib seaborn jupyter\n",
+    "```\n",
+    "\n",
+    "Throughout the text, we will also make use of other more specialized tools in Python's scientific ecosystem; installation is usually as easy as typing **``conda install packagename``**.\n",
+    "For more information on conda, including information about creating and using conda environments (which I would *highly* recommend), refer to [conda's online documentation](http://conda.pydata.org/docs/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "| [Contents](Index.ipynb) | [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/00.00-Preface.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/01.00-IPython-Beyond-Normal-Python.ipynb b/notebooks_v1/01.00-IPython-Beyond-Normal-Python.ipynb
new file mode 100644
index 000000000..5d01277e6
--- /dev/null
+++ b/notebooks_v1/01.00-IPython-Beyond-Normal-Python.ipynb
@@ -0,0 +1,152 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Preface](00.00-Preface.ipynb) | [Contents](Index.ipynb) | [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.00-IPython-Beyond-Normal-Python.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IPython: Beyond Normal Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are many options for development environments for Python, and I'm often asked which one I use in my own work.\n",
+    "My answer sometimes surprises people: my preferred environment is [IPython](http://ipython.org/) plus a text editor (in my case, Emacs or Atom depending on my mood).\n",
+    "IPython (short for *Interactive Python*) was started in 2001 by Fernando Perez as an enhanced Python interpreter, and has since grown into a project aiming to provide, in Perez's words, \"Tools for the entire life cycle of research computing.\"\n",
+    "If Python is the engine of our data science task, you might think of IPython as the interactive control panel.\n",
+    "\n",
+    "As well as being a useful interactive interface to Python, IPython also provides a number of useful syntactic additions to the language; we'll cover the most useful of these additions here.\n",
+    "In addition, IPython is closely tied with the [Jupyter project](http://jupyter.org), which provides a browser-based notebook that is useful for development, collaboration, sharing, and even publication of data science results.\n",
+    "The IPython notebook is actually a special case of the broader Jupyter notebook structure, which encompasses notebooks for Julia, R, and other programming languages.\n",
+    "As an example of the usefulness of the notebook format, look no further than the page you are reading: the entire manuscript for this book was composed as a set of IPython notebooks.\n",
+    "\n",
+    "IPython is about using Python effectively for interactive scientific and data-intensive computing.\n",
+    "This chapter will start by stepping through some of the IPython features that are useful to the practice of data science, focusing especially on the syntax it offers beyond the standard features of Python.\n",
+    "Next, we will go into a bit more depth on some of the more useful \"magic commands\" that can speed-up common tasks in creating and using data science code.\n",
+    "Finally, we will touch on some of the features of the notebook that make it useful in understanding data and sharing results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shell or Notebook?\n",
+    "\n",
+    "There are two primary means of using IPython that we'll discuss in this chapter: the IPython shell and the IPython notebook.\n",
+    "The bulk of the material in this chapter is relevant to both, and the examples will switch between them depending on what is most convenient.\n",
+    "In the few sections that are relevant to just one or the other, we will explicitly state that fact.\n",
+    "Before we start, some words on how to launch the IPython shell and IPython notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Launching the IPython Shell\n",
+    "\n",
+    "This chapter, like most of this book, is not designed to be absorbed passively.\n",
+    "I recommend that as you read through it, you follow along and experiment with the tools and syntax we cover: the muscle-memory you build through doing this will be far more useful than the simple act of reading about it.\n",
+    "Start by launching the IPython interpreter by typing **``ipython``** on the command-line; alternatively, if you've installed a distribution like Anaconda or EPD, there may be a launcher specific to your system (we'll discuss this more fully in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)).\n",
+    "\n",
+    "Once you do this, you should see a prompt like the following:\n",
+    "```\n",
+    "IPython 4.0.1 -- An enhanced Interactive Python.\n",
+    "?         -> Introduction and overview of IPython's features.\n",
+    "%quickref -> Quick reference.\n",
+    "help      -> Python's own help system.\n",
+    "object?   -> Details about 'object', use 'object??' for extra details.\n",
+    "In [1]:\n",
+    "```\n",
+    "With that, you're ready to follow along."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Launching the Jupyter Notebook\n",
+    "\n",
+    "The Jupyter notebook is a browser-based graphical interface to the IPython shell, and builds on it a rich set of dynamic display capabilities.\n",
+    "As well as executing Python/IPython statements, the notebook allows the user to include formatted text, static and dynamic visualizations, mathematical equations, JavaScript widgets, and much more.\n",
+    "Furthermore, these documents can be saved in a way that lets other people open them and execute the code on their own systems.\n",
+    "\n",
+    "Though the IPython notebook is viewed and edited through your web browser window, it must connect to a running Python process in order to execute code.\n",
+    "This process (known as a \"kernel\") can be started by running the following command in your system shell:\n",
+    "\n",
+    "```\n",
+    "$ jupyter notebook\n",
+    "```\n",
+    "\n",
+    "This command will launch a local web server that will be visible to your browser.\n",
+    "It immediately spits out a log showing what it is doing; that log will look something like this:\n",
+    "\n",
+    "```\n",
+    "$ jupyter notebook\n",
+    "[NotebookApp] Serving notebooks from local directory: /Users/jakevdp/PythonDataScienceHandbook\n",
+    "[NotebookApp] 0 active kernels \n",
+    "[NotebookApp] The IPython Notebook is running at: http://localhost:8888/\n",
+    "[NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n",
+    "```\n",
+    "\n",
+    "Upon issuing the command, your default browser should automatically open and navigate to the listed local URL;\n",
+    "the exact address will depend on your system.\n",
+    "If the browser does not open automatically, you can open a window and manually open this address (*http://localhost:8888/* in this example)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Preface](00.00-Preface.ipynb) | [Contents](Index.ipynb) | [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.00-IPython-Beyond-Normal-Python.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/01.01-Help-And-Documentation.ipynb b/notebooks_v1/01.01-Help-And-Documentation.ipynb
new file mode 100644
index 000000000..39879ee90
--- /dev/null
+++ b/notebooks_v1/01.01-Help-And-Documentation.ipynb
@@ -0,0 +1,355 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.01-Help-And-Documentation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Help and Documentation in IPython"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you read no other section in this chapter, read this one: I find the tools discussed here to be the most transformative contributions of IPython to my daily workflow.\n",
+    "\n",
+    "When a technologically-minded person is asked to help a friend, family member, or colleague with a computer problem, most of the time it's less a matter of knowing the answer as much as knowing how to quickly find an unknown answer.\n",
+    "In data science it's the same: searchable web resources such as online documentation, mailing-list threads, and StackOverflow answers contain a wealth of information, even (especially?) if it is a topic you've found yourself searching before.\n",
+    "Being an effective practitioner of data science is less about memorizing the tool or command you should use for every possible situation, and more about learning to effectively find the information you don't know, whether through a web search engine or another means.\n",
+    "\n",
+    "One of the most useful functions of IPython/Jupyter is to shorten the gap between the user and the type of documentation and search that will help them do their work effectively.\n",
+    "While web searches still play a role in answering complicated questions, an amazing amount of information can be found through IPython alone.\n",
+    "Some examples of the questions IPython can help answer in a few keystrokes:\n",
+    "\n",
+    "- How do I call this function? What arguments and options does it have?\n",
+    "- What does the source code of this Python object look like?\n",
+    "- What is in this package I imported? What attributes or methods does this object have?\n",
+    "\n",
+    "Here we'll discuss IPython's tools to quickly access this information, namely the ``?`` character to explore documentation, the ``??`` characters to explore source code, and the Tab key for auto-completion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accessing Documentation with ``?``\n",
+    "\n",
+    "The Python language and its data science ecosystem is built with the user in mind, and one big part of that is access to documentation.\n",
+    "Every Python object contains the reference to a string, known as a *doc string*, which in most cases will contain a concise summary of the object and how to use it.\n",
+    "Python has a built-in ``help()`` function that can access this information and prints the results.\n",
+    "For example, to see the documentation of the built-in ``len`` function, you can do the following:\n",
+    "\n",
+    "```ipython\n",
+    "In [1]: help(len)\n",
+    "Help on built-in function len in module builtins:\n",
+    "\n",
+    "len(...)\n",
+    "    len(object) -> integer\n",
+    "    \n",
+    "    Return the number of items of a sequence or mapping.\n",
+    "```\n",
+    "\n",
+    "Depending on your interpreter, this information may be displayed as inline text, or in some separate pop-up window."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because finding help on an object is so common and useful, IPython introduces the ``?`` character as a shorthand for accessing this documentation and other relevant information:\n",
+    "\n",
+    "```ipython\n",
+    "In [2]: len?\n",
+    "Type:        builtin_function_or_method\n",
+    "String form: <built-in function len>\n",
+    "Namespace:   Python builtin\n",
+    "Docstring:\n",
+    "len(object) -> integer\n",
+    "\n",
+    "Return the number of items of a sequence or mapping.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notation works for just about anything, including object methods:\n",
+    "\n",
+    "```ipython\n",
+    "In [3]: L = [1, 2, 3]\n",
+    "In [4]: L.insert?\n",
+    "Type:        builtin_function_or_method\n",
+    "String form: <built-in method insert of list object at 0x1024b8ea8>\n",
+    "Docstring:   L.insert(index, object) -- insert object before index\n",
+    "```\n",
+    "\n",
+    "or even objects themselves, with the documentation from their type:\n",
+    "\n",
+    "```ipython\n",
+    "In [5]: L?\n",
+    "Type:        list\n",
+    "String form: [1, 2, 3]\n",
+    "Length:      3\n",
+    "Docstring:\n",
+    "list() -> new empty list\n",
+    "list(iterable) -> new list initialized from iterable's items\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Importantly, this will even work for functions or other objects you create yourself!\n",
+    "Here we'll define a small function with a docstring:\n",
+    "\n",
+    "```ipython\n",
+    "In [6]: def square(a):\n",
+    "  ....:     \"\"\"Return the square of a.\"\"\"\n",
+    "  ....:     return a ** 2\n",
+    "  ....:\n",
+    "```\n",
+    "\n",
+    "Note that to create a docstring for our function, we simply placed a string literal in the first line.\n",
+    "Because doc strings are usually multiple lines, by convention we used Python's triple-quote notation for multi-line strings."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll use the ``?`` mark to find this doc string:\n",
+    "\n",
+    "```ipython\n",
+    "In [7]: square?\n",
+    "Type:        function\n",
+    "String form: <function square at 0x103713cb0>\n",
+    "Definition:  square(a)\n",
+    "Docstring:   Return the square of a.\n",
+    "```\n",
+    "\n",
+    "This quick access to documentation via docstrings is one reason you should get in the habit of always adding such inline documentation to the code you write!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accessing Source Code with ``??``\n",
+    "Because the Python language is so easily readable, another level of insight can usually be gained by reading the source code of the object you're curious about.\n",
+    "IPython provides a shortcut to the source code with the double question mark (``??``):\n",
+    "\n",
+    "```ipython\n",
+    "In [8]: square??\n",
+    "Type:        function\n",
+    "String form: <function square at 0x103713cb0>\n",
+    "Definition:  square(a)\n",
+    "Source:\n",
+    "def square(a):\n",
+    "    \"Return the square of a\"\n",
+    "    return a ** 2\n",
+    "```\n",
+    "\n",
+    "For simple functions like this, the double question-mark can give quick insight into the under-the-hood details."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you play with this much, you'll notice that sometimes the ``??`` suffix doesn't display any source code: this is generally because the object in question is not implemented in Python, but in C or some other compiled extension language.\n",
+    "If this is the case, the ``??`` suffix gives the same output as the ``?`` suffix.\n",
+    "You'll find this particularly with many of Python's built-in objects and types, for example ``len`` from above:\n",
+    "\n",
+    "```ipython\n",
+    "In [9]: len??\n",
+    "Type:        builtin_function_or_method\n",
+    "String form: <built-in function len>\n",
+    "Namespace:   Python builtin\n",
+    "Docstring:\n",
+    "len(object) -> integer\n",
+    "\n",
+    "Return the number of items of a sequence or mapping.\n",
+    "```\n",
+    "\n",
+    "Using ``?`` and/or ``??`` gives a powerful and quick interface for finding information about what any Python function or module does."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring Modules with Tab-Completion\n",
+    "\n",
+    "IPython's other useful interface is the use of the tab key for auto-completion and exploration of the contents of objects, modules, and name-spaces.\n",
+    "In the examples that follow, we'll use ``<TAB>`` to indicate when the Tab key should be pressed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tab-completion of object contents\n",
+    "\n",
+    "Every Python object has various attributes and methods associated with it.\n",
+    "Like with the ``help`` function discussed before, Python has a built-in ``dir`` function that returns a list of these, but the tab-completion interface is much easier to use in practice.\n",
+    "To see a list of all available attributes of an object, you can type the name of the object followed by a period (\"``.``\") character and the Tab key:\n",
+    "\n",
+    "```ipython\n",
+    "In [10]: L.<TAB>\n",
+    "L.append   L.copy     L.extend   L.insert   L.remove   L.sort     \n",
+    "L.clear    L.count    L.index    L.pop      L.reverse  \n",
+    "```\n",
+    "\n",
+    "To narrow-down the list, you can type the first character or several characters of the name, and the Tab key will find the matching attributes and methods:\n",
+    "\n",
+    "```ipython\n",
+    "In [10]: L.c<TAB>\n",
+    "L.clear  L.copy   L.count  \n",
+    "\n",
+    "In [10]: L.co<TAB>\n",
+    "L.copy   L.count \n",
+    "```\n",
+    "\n",
+    "If there is only a single option, pressing the Tab key will complete the line for you.\n",
+    "For example, the following will instantly be replaced with ``L.count``:\n",
+    "\n",
+    "```ipython\n",
+    "In [10]: L.cou<TAB>\n",
+    "\n",
+    "```\n",
+    "\n",
+    "Though Python has no strictly-enforced distinction between public/external attributes and private/internal attributes, by convention a preceding underscore is used to denote such methods.\n",
+    "For clarity, these private methods and special methods are omitted from the list by default, but it's possible to list them by explicitly typing the underscore:\n",
+    "\n",
+    "```ipython\n",
+    "In [10]: L._<TAB>\n",
+    "L.__add__           L.__gt__            L.__reduce__\n",
+    "L.__class__         L.__hash__          L.__reduce_ex__\n",
+    "```\n",
+    "\n",
+    "For brevity, we've only shown the first couple lines of the output.\n",
+    "Most of these are Python's special double-underscore methods (often nicknamed \"dunder\" methods)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tab completion when importing\n",
+    "\n",
+    "Tab completion is also useful when importing objects from packages.\n",
+    "Here we'll use it to find all possible imports in the ``itertools`` package that start with ``co``:\n",
+    "```\n",
+    "In [10]: from itertools import co<TAB>\n",
+    "combinations                   compress\n",
+    "combinations_with_replacement  count\n",
+    "```\n",
+    "Similarly, you can use tab-completion to see which imports are available on your system (this will change depending on which third-party scripts and modules are visible to your Python session):\n",
+    "```\n",
+    "In [10]: import <TAB>\n",
+    "Display all 399 possibilities? (y or n)\n",
+    "Crypto              dis                 py_compile\n",
+    "Cython              distutils           pyclbr\n",
+    "...                 ...                 ...\n",
+    "difflib             pwd                 zmq\n",
+    "\n",
+    "In [10]: import h<TAB>\n",
+    "hashlib             hmac                http         \n",
+    "heapq               html                husl         \n",
+    "```\n",
+    "(Note that for brevity, I did not print here all 399 importable packages and modules on my system.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Beyond tab completion: wildcard matching\n",
+    "\n",
+    "Tab completion is useful if you know the first few characters of the object or attribute you're looking for, but is little help if you'd like to match characters at the middle or end of the word.\n",
+    "For this use-case, IPython provides a means of wildcard matching for names using the ``*`` character.\n",
+    "\n",
+    "For example, we can use this to list every object in the namespace that ends with ``Warning``:\n",
+    "\n",
+    "```ipython\n",
+    "In [10]: *Warning?\n",
+    "BytesWarning                  RuntimeWarning\n",
+    "DeprecationWarning            SyntaxWarning\n",
+    "FutureWarning                 UnicodeWarning\n",
+    "ImportWarning                 UserWarning\n",
+    "PendingDeprecationWarning     Warning\n",
+    "ResourceWarning\n",
+    "```\n",
+    "\n",
+    "Notice that the ``*`` character matches any string, including the empty string.\n",
+    "\n",
+    "Similarly, suppose we are looking for a string method that contains the word ``find`` somewhere in its name.\n",
+    "We can search for it this way:\n",
+    "\n",
+    "```ipython\n",
+    "In [10]: str.*find*?\n",
+    "str.find\n",
+    "str.rfind\n",
+    "```\n",
+    "\n",
+    "I find this type of flexible wildcard search can be very useful for finding a particular command when getting to know a new package or reacquainting myself with a familiar one."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.01-Help-And-Documentation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/01.02-Shell-Keyboard-Shortcuts.ipynb b/notebooks_v1/01.02-Shell-Keyboard-Shortcuts.ipynb
new file mode 100644
index 000000000..f50e9fb1c
--- /dev/null
+++ b/notebooks_v1/01.02-Shell-Keyboard-Shortcuts.ipynb
@@ -0,0 +1,207 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) | [Contents](Index.ipynb) | [IPython Magic Commands](01.03-Magic-Commands.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Keyboard Shortcuts in the IPython Shell"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you spend any amount of time on the computer, you've probably found a use for keyboard shortcuts in your workflow.\n",
+    "Most familiar perhaps are the Cmd-C and Cmd-V (or Ctrl-C and Ctrl-V) for copying and pasting in a wide variety of programs and systems.\n",
+    "Power-users tend to go even further: popular text editors like Emacs, Vim, and others provide users an incredible range of operations through intricate combinations of keystrokes.\n",
+    "\n",
+    "The IPython shell doesn't go this far, but does provide a number of keyboard shortcuts for fast navigation while typing commands.\n",
+    "These shortcuts are not in fact provided by IPython itself, but through its dependency on the GNU Readline library: as such, some of the following shortcuts may differ depending on your system configuration.\n",
+    "Also, while some of these shortcuts do work in the browser-based notebook, this section is primarily about shortcuts in the IPython shell.\n",
+    "\n",
+    "Once you get accustomed to these, they can be very useful for quickly performing certain commands without moving your hands from the \"home\" keyboard position.\n",
+    "If you're an Emacs user or if you have experience with Linux-style shells, the following will be very familiar.\n",
+    "We'll group these shortcuts into a few categories: *navigation shortcuts*, *text entry shortcuts*, *command history shortcuts*, and *miscellaneous shortcuts*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Navigation shortcuts\n",
+    "\n",
+    "While the use of the left and right arrow keys to move backward and forward in the line is quite obvious, there are other options that don't require moving your hands from the \"home\" keyboard position:\n",
+    "\n",
+    "| Keystroke                         | Action                                     |\n",
+    "|-----------------------------------|--------------------------------------------|\n",
+    "| ``Ctrl-a``                        | Move cursor to the beginning of the line   |\n",
+    "| ``Ctrl-e``                        | Move cursor to the end of the line         |\n",
+    "| ``Ctrl-b`` or the left arrow key  | Move cursor back one character             |\n",
+    "| ``Ctrl-f`` or the right arrow key | Move cursor forward one character          |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Text Entry Shortcuts\n",
+    "\n",
+    "While everyone is familiar with using the Backspace key to delete the previous character, reaching for the key often requires some minor finger gymnastics, and it only deletes a single character at a time.\n",
+    "In IPython there are several shortcuts for removing some portion of the text you're typing.\n",
+    "The most immediately useful of these are the commands to delete entire lines of text.\n",
+    "You'll know these have become second-nature if you find yourself using a combination of Ctrl-b and Ctrl-d instead of reaching for Backspace to delete the previous character!\n",
+    "\n",
+    "| Keystroke                     | Action                                           |\n",
+    "|-------------------------------|--------------------------------------------------|\n",
+    "| Backspace key                 | Delete previous character in line                |\n",
+    "| ``Ctrl-d``                    | Delete next character in line                    |\n",
+    "| ``Ctrl-k``                    | Cut text from cursor to end of line              |\n",
+    "| ``Ctrl-u``                    | Cut text from beginning of line to cursor        |\n",
+    "| ``Ctrl-y``                    | Yank (i.e. paste) text that was previously cut   |\n",
+    "| ``Ctrl-t``                    | Transpose (i.e., switch) previous two characters |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Command History Shortcuts\n",
+    "\n",
+    "Perhaps the most impactful shortcuts discussed here are the ones IPython provides for navigating the command history.\n",
+    "This command history goes beyond your current IPython session: your entire command history is stored in a SQLite database in your IPython profile directory.\n",
+    "The most straightforward way to access these is with the up and down arrow keys to step through the history, but other options exist as well:\n",
+    "\n",
+    "| Keystroke                           | Action                                     |\n",
+    "|-------------------------------------|--------------------------------------------|\n",
+    "| ``Ctrl-p`` (or the up arrow key)    | Access previous command in history         |\n",
+    "| ``Ctrl-n`` (or the down arrow key)  | Access next command in history             |\n",
+    "| ``Ctrl-r``                          | Reverse-search through command history     |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The reverse-search can be particularly useful.\n",
+    "Recall that in the previous section we defined a function called ``square``.\n",
+    "Let's reverse-search our Python history from a new IPython shell and find this definition again.\n",
+    "When you press Ctrl-r in the IPython terminal, you'll see the following prompt:\n",
+    "\n",
+    "```ipython\n",
+    "In [1]:\n",
+    "(reverse-i-search)`': \n",
+    "```\n",
+    "\n",
+    "If you start typing characters at this prompt, IPython will auto-fill the most recent command, if any, that matches those characters:\n",
+    "\n",
+    "```ipython\n",
+    "In [1]: \n",
+    "(reverse-i-search)`sqa': square??\n",
+    "```\n",
+    "\n",
+    "At any point, you can add more characters to refine the search, or press Ctrl-r again to search further for another command that matches the query. If you followed along in the previous section, pressing Ctrl-r twice more gives:\n",
+    "\n",
+    "```ipython\n",
+    "In [1]: \n",
+    "(reverse-i-search)`sqa': def square(a):\n",
+    "    \"\"\"Return the square of a\"\"\"\n",
+    "    return a ** 2\n",
+    "```\n",
+    "\n",
+    "Once you have found the command you're looking for, press Return and the search will end.\n",
+    "We can then use the retrieved command, and carry-on with our session:\n",
+    "\n",
+    "```ipython\n",
+    "In [1]: def square(a):\n",
+    "    \"\"\"Return the square of a\"\"\"\n",
+    "    return a ** 2\n",
+    "\n",
+    "In [2]: square(2)\n",
+    "Out[2]: 4\n",
+    "```\n",
+    "\n",
+    "Note that Ctrl-p/Ctrl-n or the up/down arrow keys can also be used to search through history, but only by matching characters at the beginning of the line.\n",
+    "That is, if you type **``def``** and then press Ctrl-p, it would find the most recent command (if any) in your history that begins with the characters ``def``."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Miscellaneous Shortcuts\n",
+    "\n",
+    "Finally, there are a few miscellaneous shortcuts that don't fit into any of the preceding categories, but are nevertheless useful to know:\n",
+    "\n",
+    "| Keystroke                     | Action                                     |\n",
+    "|-------------------------------|--------------------------------------------|\n",
+    "| ``Ctrl-l``                    | Clear terminal screen                      |\n",
+    "| ``Ctrl-c``                    | Interrupt current Python command           |\n",
+    "| ``Ctrl-d``                    | Exit IPython session                       |\n",
+    "\n",
+    "The Ctrl-c in particular can be useful when you inadvertently start a very long-running job."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While some of the shortcuts discussed here may seem a bit tedious at first, they quickly become automatic with practice.\n",
+    "Once you develop that muscle memory, I suspect you will even find yourself wishing they were available in other contexts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) | [Contents](Index.ipynb) | [IPython Magic Commands](01.03-Magic-Commands.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/01.03-Magic-Commands.ipynb b/notebooks_v1/01.03-Magic-Commands.ipynb
new file mode 100644
index 000000000..e5ee9d164
--- /dev/null
+++ b/notebooks_v1/01.03-Magic-Commands.ipynb
@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) | [Contents](Index.ipynb) | [Input and Output History](01.04-Input-Output-History.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.03-Magic-Commands.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IPython Magic Commands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The previous two sections showed how IPython lets you use and explore Python efficiently and interactively.\n",
+    "Here we'll begin discussing some of the enhancements that IPython adds on top of the normal Python syntax.\n",
+    "These are known in IPython as *magic commands*, and are prefixed by the ``%`` character.\n",
+    "These magic commands are designed to succinctly solve various common problems in standard data analysis.\n",
+    "Magic commands come in two flavors: *line magics*, which are denoted by a single ``%`` prefix and operate on a single line of input, and *cell magics*, which are denoted by a double ``%%`` prefix and operate on multiple lines of input.\n",
+    "We'll demonstrate and discuss a few brief examples here, and come back to more focused discussion of several useful magic commands later in the chapter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pasting Code Blocks: ``%paste`` and ``%cpaste``\n",
+    "\n",
+    "When working in the IPython interpreter, one common gotcha is that pasting multi-line code blocks can lead to unexpected errors, especially when indentation and interpreter markers are involved.\n",
+    "A common case is that you find some example code on a website and want to paste it into your interpreter.\n",
+    "Consider the following simple function:\n",
+    "\n",
+    "``` python\n",
+    ">>> def donothing(x):\n",
+    "...     return x\n",
+    "\n",
+    "```\n",
+    "The code is formatted as it would appear in the Python interpreter, and if you copy and paste this directly into IPython you get an error:\n",
+    "\n",
+    "```ipython\n",
+    "In [2]: >>> def donothing(x):\n",
+    "   ...:     ...     return x\n",
+    "   ...:     \n",
+    "  File \"<ipython-input-20-5a66c8964687>\", line 2\n",
+    "    ...     return x\n",
+    "                 ^\n",
+    "SyntaxError: invalid syntax\n",
+    "```\n",
+    "\n",
+    "In the direct paste, the interpreter is confused by the additional prompt characters.\n",
+    "But never fear–IPython's ``%paste`` magic function is designed to handle this exact type of multi-line, marked-up input:\n",
+    "\n",
+    "```ipython\n",
+    "In [3]: %paste\n",
+    ">>> def donothing(x):\n",
+    "...     return x\n",
+    "\n",
+    "## -- End pasted text --\n",
+    "```\n",
+    "\n",
+    "The ``%paste`` command both enters and executes the code, so now the function is ready to be used:\n",
+    "\n",
+    "```ipython\n",
+    "In [4]: donothing(10)\n",
+    "Out[4]: 10\n",
+    "```\n",
+    "\n",
+    "A command with a similar intent is ``%cpaste``, which opens up an interactive multiline prompt in which you can paste one or more chunks of code to be executed in a batch:\n",
+    "\n",
+    "```ipython\n",
+    "In [5]: %cpaste\n",
+    "Pasting code; enter '--' alone on the line to stop or use Ctrl-D.\n",
+    ":>>> def donothing(x):\n",
+    ":...     return x\n",
+    ":--\n",
+    "```\n",
+    "\n",
+    "These magic commands, like others we'll see, make available functionality that would be difficult or impossible in a standard Python interpreter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running External Code: ``%run``\n",
+    "As you begin developing more extensive code, you will likely find yourself working in both IPython for interactive exploration, as well as a text editor to store code that you want to reuse.\n",
+    "Rather than running this code in a new window, it can be convenient to run it within your IPython session.\n",
+    "This can be done with the ``%run`` magic.\n",
+    "\n",
+    "For example, imagine you've created a ``myscript.py`` file with the following contents:\n",
+    "\n",
+    "```python\n",
+    "#-------------------------------------\n",
+    "# file: myscript.py\n",
+    "\n",
+    "def square(x):\n",
+    "    \"\"\"square a number\"\"\"\n",
+    "    return x ** 2\n",
+    "\n",
+    "for N in range(1, 4):\n",
+    "    print(N, \"squared is\", square(N))\n",
+    "```\n",
+    "\n",
+    "You can execute this from your IPython session as follows:\n",
+    "\n",
+    "```ipython\n",
+    "In [6]: %run myscript.py\n",
+    "1 squared is 1\n",
+    "2 squared is 4\n",
+    "3 squared is 9\n",
+    "```\n",
+    "\n",
+    "Note also that after you've run this script, any functions defined within it are available for use in your IPython session:\n",
+    "\n",
+    "```ipython\n",
+    "In [7]: square(5)\n",
+    "Out[7]: 25\n",
+    "```\n",
+    "\n",
+    "There are several options to fine-tune how your code is run; you can see the documentation in the normal way, by typing **``%run?``** in the IPython interpreter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Timing Code Execution: ``%timeit``\n",
+    "Another example of a useful magic function is ``%timeit``, which will automatically determine the execution time of the single-line Python statement that follows it.\n",
+    "For example, we may want to check the performance of a list comprehension:\n",
+    "\n",
+    "```ipython\n",
+    "In [8]: %timeit L = [n ** 2 for n in range(1000)]\n",
+    "1000 loops, best of 3: 325 µs per loop\n",
+    "```\n",
+    "\n",
+    "The benefit of ``%timeit`` is that for short commands it will automatically perform multiple runs in order to attain more robust results.\n",
+    "For multi line statements, adding a second ``%`` sign will turn this into a cell magic that can handle multiple lines of input.\n",
+    "For example, here's the equivalent construction with a ``for``-loop:\n",
+    "\n",
+    "```ipython\n",
+    "In [9]: %%timeit\n",
+    "   ...: L = []\n",
+    "   ...: for n in range(1000):\n",
+    "   ...:     L.append(n ** 2)\n",
+    "   ...: \n",
+    "1000 loops, best of 3: 373 µs per loop\n",
+    "```\n",
+    "\n",
+    "We can immediately see that list comprehensions are about 10% faster than the equivalent ``for``-loop construction in this case.\n",
+    "We'll explore ``%timeit`` and other approaches to timing and profiling code in [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Help on Magic Functions: ``?``, ``%magic``, and ``%lsmagic``\n",
+    "\n",
+    "Like normal Python functions, IPython magic functions have docstrings, and this useful\n",
+    "documentation can be accessed in the standard manner.\n",
+    "So, for example, to read the documentation of the ``%timeit`` magic simply type this:\n",
+    "\n",
+    "```ipython\n",
+    "In [10]: %timeit?\n",
+    "```\n",
+    "\n",
+    "Documentation for other functions can be accessed similarly.\n",
+    "To access a general description of available magic functions, including some examples, you can type this:\n",
+    "\n",
+    "```ipython\n",
+    "In [11]: %magic\n",
+    "```\n",
+    "\n",
+    "For a quick and simple list of all available magic functions, type this:\n",
+    "\n",
+    "```ipython\n",
+    "In [12]: %lsmagic\n",
+    "```\n",
+    "\n",
+    "Finally, I'll mention that it is quite straightforward to define your own magic functions if you wish.\n",
+    "We won't discuss it here, but if you are interested, see the references listed in [More IPython Resources](01.08-More-IPython-Resources.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) | [Contents](Index.ipynb) | [Input and Output History](01.04-Input-Output-History.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.03-Magic-Commands.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/01.04-Input-Output-History.ipynb b/notebooks_v1/01.04-Input-Output-History.ipynb
new file mode 100644
index 000000000..c8e5463fe
--- /dev/null
+++ b/notebooks_v1/01.04-Input-Output-History.ipynb
@@ -0,0 +1,222 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [IPython Magic Commands](01.03-Magic-Commands.ipynb) | [Contents](Index.ipynb) | [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.04-Input-Output-History.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Input and Output History"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Previously we saw that the IPython shell allows you to access previous commands with the up and down arrow keys, or equivalently the Ctrl-p/Ctrl-n shortcuts.\n",
+    "Additionally, in both the shell and the notebook, IPython exposes several ways to obtain the output of previous commands, as well as string versions of the commands themselves.\n",
+    "We'll explore those here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## IPython's ``In`` and ``Out`` Objects\n",
+    "\n",
+    "By now I imagine you're quite familiar with the ``In [1]:``/``Out[1]:`` style prompts used by IPython.\n",
+    "But it turns out that these are not just pretty decoration: they give a clue as to how you can access previous inputs and outputs in your current session.\n",
+    "Imagine you start a session that looks like this:\n",
+    "\n",
+    "```ipython\n",
+    "In [1]: import math\n",
+    "\n",
+    "In [2]: math.sin(2)\n",
+    "Out[2]: 0.9092974268256817\n",
+    "\n",
+    "In [3]: math.cos(2)\n",
+    "Out[3]: -0.4161468365471424\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We've imported the built-in ``math`` package, then computed the sine and the cosine of the number 2.\n",
+    "These inputs and outputs are displayed in the shell with ``In``/``Out`` labels, but there's more–IPython actually creates some Python variables called ``In`` and ``Out`` that are automatically updated to reflect this history:\n",
+    "\n",
+    "```ipython\n",
+    "In [4]: print(In)\n",
+    "['', 'import math', 'math.sin(2)', 'math.cos(2)', 'print(In)']\n",
+    "\n",
+    "In [5]: Out\n",
+    "Out[5]: {2: 0.9092974268256817, 3: -0.4161468365471424}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``In`` object is a list, which keeps track of the commands in order (the first item in the list is a place-holder so that ``In[1]`` can refer to the first command):\n",
+    "\n",
+    "```ipython\n",
+    "In [6]: print(In[1])\n",
+    "import math\n",
+    "```\n",
+    "\n",
+    "The ``Out`` object is not a list but a dictionary mapping input numbers to their outputs (if any):\n",
+    "\n",
+    "```ipython\n",
+    "In [7]: print(Out[2])\n",
+    "0.9092974268256817\n",
+    "```\n",
+    "\n",
+    "Note that not all operations have outputs: for example, ``import`` statements and ``print`` statements don't affect the output.\n",
+    "The latter may be surprising, but makes sense if you consider that ``print`` is a function that returns ``None``; for brevity, any command that returns ``None`` is not added to ``Out``.\n",
+    "\n",
+    "Where this can be useful is if you want to interact with past results.\n",
+    "For example, let's check the sum of ``sin(2) ** 2`` and ``cos(2) ** 2`` using the previously-computed results:\n",
+    "\n",
+    "```ipython\n",
+    "In [8]: Out[2] ** 2 + Out[3] ** 2\n",
+    "Out[8]: 1.0\n",
+    "```\n",
+    "\n",
+    "The result is ``1.0`` as we'd expect from the well-known trigonometric identity.\n",
+    "In this case, using these previous results probably is not necessary, but it can become very handy if you execute a very expensive computation and want to reuse the result!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Underscore Shortcuts and Previous Outputs\n",
+    "\n",
+    "The standard Python shell contains just one simple shortcut for accessing previous output; the variable ``_`` (i.e., a single underscore) is kept updated with the previous output; this works in IPython as well:\n",
+    "\n",
+    "```ipython\n",
+    "In [9]: print(_)\n",
+    "1.0\n",
+    "```\n",
+    "\n",
+    "But IPython takes this a bit further—you can use a double underscore to access the second-to-last output, and a triple underscore to access the third-to-last output (skipping any commands with no output):\n",
+    "\n",
+    "```ipython\n",
+    "In [10]: print(__)\n",
+    "-0.4161468365471424\n",
+    "\n",
+    "In [11]: print(___)\n",
+    "0.9092974268256817\n",
+    "```\n",
+    "\n",
+    "IPython stops there: more than three underscores starts to get a bit hard to count, and at that point it's easier to refer to the output by line number.\n",
+    "\n",
+    "There is one more shortcut we should mention, however–a shorthand for ``Out[X]`` is ``_X`` (i.e., a single underscore followed by the line number):\n",
+    "\n",
+    "```ipython\n",
+    "In [12]: Out[2]\n",
+    "Out[12]: 0.9092974268256817\n",
+    "\n",
+    "In [13]: _2\n",
+    "Out[13]: 0.9092974268256817\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Suppressing Output\n",
+    "Sometimes you might wish to suppress the output of a statement (this is perhaps most common with the plotting commands that we'll explore in [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb)).\n",
+    "Or maybe the command you're executing produces a result that you'd prefer not like to store in your output history, perhaps so that it can be deallocated when other references are removed.\n",
+    "The easiest way to suppress the output of a command is to add a semicolon to the end of the line:\n",
+    "\n",
+    "```ipython\n",
+    "In [14]: math.sin(2) + math.cos(2);\n",
+    "```\n",
+    "\n",
+    "Note that the result is computed silently, and the output is neither displayed on the screen or stored in the ``Out`` dictionary:\n",
+    "\n",
+    "```ipython\n",
+    "In [15]: 14 in Out\n",
+    "Out[15]: False\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Related Magic Commands\n",
+    "For accessing a batch of previous inputs at once, the ``%history`` magic command is very helpful.\n",
+    "Here is how you can print the first four inputs:\n",
+    "\n",
+    "```ipython\n",
+    "In [16]: %history -n 1-4\n",
+    "   1: import math\n",
+    "   2: math.sin(2)\n",
+    "   3: math.cos(2)\n",
+    "   4: print(In)\n",
+    "```\n",
+    "\n",
+    "As usual, you can type ``%history?`` for more information and a description of options available.\n",
+    "Other similar magic commands are ``%rerun`` (which will re-execute some portion of the command history) and ``%save`` (which saves some set of the command history to a file).\n",
+    "For more information, I suggest exploring these using the ``?`` help functionality discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [IPython Magic Commands](01.03-Magic-Commands.ipynb) | [Contents](Index.ipynb) | [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.04-Input-Output-History.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/01.05-IPython-And-Shell-Commands.ipynb b/notebooks_v1/01.05-IPython-And-Shell-Commands.ipynb
new file mode 100644
index 000000000..6fe0dd875
--- /dev/null
+++ b/notebooks_v1/01.05-IPython-And-Shell-Commands.ipynb
@@ -0,0 +1,255 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Input and Output History](01.04-Input-Output-History.ipynb) | [Contents](Index.ipynb) | [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.05-IPython-And-Shell-Commands.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IPython and Shell Commands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When working interactively with the standard Python interpreter, one of the frustrations is the need to switch between multiple windows to access Python tools and system command-line tools.\n",
+    "IPython bridges this gap, and gives you a syntax for executing shell commands directly from within the IPython terminal.\n",
+    "The magic happens with the exclamation point: anything appearing after ``!`` on a line will be executed not by the Python kernel, but by the system command-line.\n",
+    "\n",
+    "The following assumes you're on a Unix-like system, such as Linux or Mac OSX.\n",
+    "Some of the examples that follow will fail on Windows, which uses a different type of shell by default (though with the 2016 announcement of native Bash shells on Windows, soon this may no longer be an issue!).\n",
+    "If you're unfamiliar with shell commands, I'd suggest reviewing the [Shell Tutorial](http://swcarpentry.github.io/shell-novice/) put together by the always excellent Software Carpentry Foundation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quick Introduction to the Shell\n",
+    "\n",
+    "A full intro to using the shell/terminal/command-line is well beyond the scope of this chapter, but for the uninitiated we will offer a quick introduction here.\n",
+    "The shell is a way to interact textually with your computer.\n",
+    "Ever since the mid 1980s, when Microsoft and Apple introduced the first versions of their now ubiquitous graphical operating systems, most computer users have interacted with their operating system through familiar clicking of menus and drag-and-drop movements.\n",
+    "But operating systems existed long before these graphical user interfaces, and were primarily controlled through sequences of text input: at the prompt, the user would type a command, and the computer would do what the user told it to.\n",
+    "Those early prompt systems are the precursors of the shells and terminals that most active data scientists still use today.\n",
+    "\n",
+    "Someone unfamiliar with the shell might ask why you would bother with this, when many results can be accomplished by simply clicking on icons and menus.\n",
+    "A shell user might reply with another question: why hunt icons and click menus when you can accomplish things much more easily by typing?\n",
+    "While it might sound like a typical tech preference impasse, when moving beyond basic tasks it quickly becomes clear that the shell offers much more control of advanced tasks, though admittedly the learning curve can intimidate the average computer user.\n",
+    "\n",
+    "As an example, here is a sample of a Linux/OSX shell session where a user explores, creates, and modifies directories and files on their system (``osx:~ $`` is the prompt, and everything after the ``$`` sign is the typed command; text that is preceded by a ``#`` is meant just as description, rather than something you would actually type in):\n",
+    "\n",
+    "```bash\n",
+    "osx:~ $ echo \"hello world\"             # echo is like Python's print function\n",
+    "hello world\n",
+    "\n",
+    "osx:~ $ pwd                            # pwd = print working directory\n",
+    "/home/jake                             # this is the \"path\" that we're sitting in\n",
+    "\n",
+    "osx:~ $ ls                             # ls = list working directory contents\n",
+    "notebooks  projects \n",
+    "\n",
+    "osx:~ $ cd projects/                   # cd = change directory\n",
+    "\n",
+    "osx:projects $ pwd\n",
+    "/home/jake/projects\n",
+    "\n",
+    "osx:projects $ ls\n",
+    "datasci_book   mpld3   myproject.txt\n",
+    "\n",
+    "osx:projects $ mkdir myproject          # mkdir = make new directory\n",
+    "\n",
+    "osx:projects $ cd myproject/\n",
+    "\n",
+    "osx:myproject $ mv ../myproject.txt ./  # mv = move file. Here we're moving the\n",
+    "                                        # file myproject.txt from one directory\n",
+    "                                        # up (../) to the current directory (./)\n",
+    "osx:myproject $ ls\n",
+    "myproject.txt\n",
+    "```\n",
+    "\n",
+    "Notice that all of this is just a compact way to do familiar operations (navigating a directory structure, creating a directory, moving a file, etc.) by typing commands rather than clicking icons and menus.\n",
+    "Note that with just a few commands (``pwd``, ``ls``, ``cd``, ``mkdir``, and ``cp``) you can do many of the most common file operations.\n",
+    "It's when you go beyond these basics that the shell approach becomes really powerful."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shell Commands in IPython\n",
+    "\n",
+    "Any command that works at the command-line can be used in IPython by prefixing it with the ``!`` character.\n",
+    "For example, the ``ls``, ``pwd``, and ``echo`` commands can be run as follows:\n",
+    "\n",
+    "```ipython\n",
+    "In [1]: !ls\n",
+    "myproject.txt\n",
+    "\n",
+    "In [2]: !pwd\n",
+    "/home/jake/projects/myproject\n",
+    "\n",
+    "In [3]: !echo \"printing from the shell\"\n",
+    "printing from the shell\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Passing Values to and from the Shell\n",
+    "\n",
+    "Shell commands can not only be called from IPython, but can also be made to interact with the IPython namespace.\n",
+    "For example, you can save the output of any shell command to a Python list using the assignment operator:\n",
+    "\n",
+    "```ipython\n",
+    "In [4]: contents = !ls\n",
+    "\n",
+    "In [5]: print(contents)\n",
+    "['myproject.txt']\n",
+    "\n",
+    "In [6]: directory = !pwd\n",
+    "\n",
+    "In [7]: print(directory)\n",
+    "['/Users/jakevdp/notebooks/tmp/myproject']\n",
+    "```\n",
+    "\n",
+    "Note that these results are not returned as lists, but as a special shell return type defined in IPython:\n",
+    "\n",
+    "```ipython\n",
+    "In [8]: type(directory)\n",
+    "IPython.utils.text.SList\n",
+    "```\n",
+    "\n",
+    "This looks and acts a lot like a Python list, but has additional functionality, such as\n",
+    "the ``grep`` and ``fields`` methods and the ``s``, ``n``, and ``p`` properties that allow you to search, filter, and display the results in convenient ways.\n",
+    "For more information on these, you can use IPython's built-in help features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Communication in the other direction–passing Python variables into the shell–is possible using the ``{varname}`` syntax:\n",
+    "\n",
+    "```ipython\n",
+    "In [9]: message = \"hello from Python\"\n",
+    "\n",
+    "In [10]: !echo {message}\n",
+    "hello from Python\n",
+    "```\n",
+    "\n",
+    "The curly braces contain the variable name, which is replaced by the variable's contents in the shell command."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Shell-Related Magic Commands\n",
+    "\n",
+    "If you play with IPython's shell commands for a while, you might notice that you cannot use ``!cd`` to navigate the filesystem:\n",
+    "\n",
+    "```ipython\n",
+    "In [11]: !pwd\n",
+    "/home/jake/projects/myproject\n",
+    "\n",
+    "In [12]: !cd ..\n",
+    "\n",
+    "In [13]: !pwd\n",
+    "/home/jake/projects/myproject\n",
+    "```\n",
+    "\n",
+    "The reason is that shell commands in the notebook are executed in a temporary subshell.\n",
+    "If you'd like to change the working directory in a more enduring way, you can use the ``%cd`` magic command:\n",
+    "\n",
+    "```ipython\n",
+    "In [14]: %cd ..\n",
+    "/home/jake/projects\n",
+    "```\n",
+    "\n",
+    "In fact, by default you can even use this without the ``%`` sign:\n",
+    "\n",
+    "```ipython\n",
+    "In [15]: cd myproject\n",
+    "/home/jake/projects/myproject\n",
+    "```\n",
+    "\n",
+    "This is known as an ``automagic`` function, and this behavior can be toggled with the ``%automagic`` magic function.\n",
+    "\n",
+    "Besides ``%cd``, other available shell-like magic functions are ``%cat``, ``%cp``, ``%env``, ``%ls``, ``%man``, ``%mkdir``, ``%more``, ``%mv``, ``%pwd``, ``%rm``, and ``%rmdir``, any of which can be used without the ``%`` sign if ``automagic`` is on.\n",
+    "This makes it so that you can almost treat the IPython prompt as if it's a normal shell:\n",
+    "\n",
+    "```ipython\n",
+    "In [16]: mkdir tmp\n",
+    "\n",
+    "In [17]: ls\n",
+    "myproject.txt  tmp/\n",
+    "\n",
+    "In [18]: cp myproject.txt tmp/\n",
+    "\n",
+    "In [19]: ls tmp\n",
+    "myproject.txt\n",
+    "\n",
+    "In [20]: rm -r tmp\n",
+    "```\n",
+    "\n",
+    "This access to the shell from within the same terminal window as your Python session means that there is a lot less switching back and forth between interpreter and shell as you write your Python code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Input and Output History](01.04-Input-Output-History.ipynb) | [Contents](Index.ipynb) | [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.05-IPython-And-Shell-Commands.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/01.06-Errors-and-Debugging.ipynb b/notebooks_v1/01.06-Errors-and-Debugging.ipynb
new file mode 100644
index 000000000..a7625d5ef
--- /dev/null
+++ b/notebooks_v1/01.06-Errors-and-Debugging.ipynb
@@ -0,0 +1,426 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) | [Contents](Index.ipynb) | [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.06-Errors-and-Debugging.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Errors and Debugging"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Code development and data analysis always require a bit of trial and error, and IPython contains tools to streamline this process.\n",
+    "This section will briefly cover some options for controlling Python's exception reporting, followed by exploring tools for debugging errors in code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Controlling Exceptions: ``%xmode``\n",
+    "\n",
+    "Most of the time when a Python script fails, it will raise an Exception.\n",
+    "When the interpreter hits one of these exceptions, information about the cause of the error can be found in the *traceback*, which can be accessed from within Python.\n",
+    "With the ``%xmode`` magic function, IPython allows you to control the amount of information printed when the exception is raised.\n",
+    "Consider the following code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def func1(a, b):\n",
+    "    return a / b\n",
+    "\n",
+    "def func2(x):\n",
+    "    a = x\n",
+    "    b = x - 1\n",
+    "    return func1(a, b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-b2e110f6fc8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-1-d849e34d61fb>\u001b[0m in \u001b[0;36mfunc2\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-1-d849e34d61fb>\u001b[0m in \u001b[0;36mfunc1\u001b[0;34m(a, b)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "func2(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calling ``func2`` results in an error, and reading the printed trace lets us see exactly what happened.\n",
+    "By default, this trace includes several lines showing the context of each step that led to the error.\n",
+    "Using the ``%xmode`` magic function (short for *Exception mode*), we can change what information is printed.\n",
+    "\n",
+    "``%xmode`` takes a single argument, the mode, and there are three possibilities: ``Plain``, ``Context``, and ``Verbose``.\n",
+    "The default is ``Context``, and gives output like that just shown before.\n",
+    "``Plain`` is more compact and gives less information:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exception reporting mode: Plain\n"
+     ]
+    }
+   ],
+   "source": [
+    "%xmode Plain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n",
+      "  File \u001b[1;32m\"<ipython-input-4-b2e110f6fc8f>\"\u001b[0m, line \u001b[1;32m1\u001b[0m, in \u001b[1;35m<module>\u001b[0m\n    func2(1)\n",
+      "  File \u001b[1;32m\"<ipython-input-1-d849e34d61fb>\"\u001b[0m, line \u001b[1;32m7\u001b[0m, in \u001b[1;35mfunc2\u001b[0m\n    return func1(a, b)\n",
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1-d849e34d61fb>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0;36m, in \u001b[0;35mfunc1\u001b[0;36m\u001b[0m\n\u001b[0;31m    return a / b\u001b[0m\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m\u001b[0;31m:\u001b[0m division by zero\n"
+     ]
+    }
+   ],
+   "source": [
+    "func2(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``Verbose`` mode adds some extra information, including the arguments to any functions that are called:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exception reporting mode: Verbose\n"
+     ]
+    }
+   ],
+   "source": [
+    "%xmode Verbose"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-b2e110f6fc8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mglobal\u001b[0m \u001b[0;36mfunc2\u001b[0m \u001b[0;34m= <function func2 at 0x103729320>\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-1-d849e34d61fb>\u001b[0m in \u001b[0;36mfunc2\u001b[0;34m(x=1)\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mglobal\u001b[0m \u001b[0;36mfunc1\u001b[0m \u001b[0;34m= <function func1 at 0x1037294d0>\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36ma\u001b[0m \u001b[0;34m= 1\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mb\u001b[0m \u001b[0;34m= 0\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-1-d849e34d61fb>\u001b[0m in \u001b[0;36mfunc1\u001b[0;34m(a=1, b=0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36ma\u001b[0m \u001b[0;34m= 1\u001b[0m\u001b[0;34m\n        \u001b[0m\u001b[0;36mb\u001b[0m \u001b[0;34m= 0\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "func2(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This extra information can help narrow-in on why the exception is being raised.\n",
+    "So why not use the ``Verbose`` mode all the time?\n",
+    "As code gets complicated, this kind of traceback can get extremely long.\n",
+    "Depending on the context, sometimes the brevity of ``Default`` mode is easier to work with."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Debugging: When Reading Tracebacks Is Not Enough\n",
+    "\n",
+    "The standard Python tool for interactive debugging is ``pdb``, the Python debugger.\n",
+    "This debugger lets the user step through the code line by line in order to see what might be causing a more difficult error.\n",
+    "The IPython-enhanced version of this is ``ipdb``, the IPython debugger.\n",
+    "\n",
+    "There are many ways to launch and use both these debuggers; we won't cover them fully here.\n",
+    "Refer to the online documentation of these two utilities to learn more.\n",
+    "\n",
+    "In IPython, perhaps the most convenient interface to debugging is the ``%debug`` magic command.\n",
+    "If you call it after hitting an exception, it will automatically open an interactive debugging prompt at the point of the exception.\n",
+    "The ``ipdb`` prompt lets you explore the current state of the stack, explore the available variables, and even run Python commands!\n",
+    "\n",
+    "Let's look at the most recent exception, then do some basic tasks–print the values of ``a`` and ``b``, and type ``quit`` to quit the debugging session:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> \u001b[0;32m<ipython-input-1-d849e34d61fb>\u001b[0m(2)\u001b[0;36mfunc1\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32m      1 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m----> 2 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m      3 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "ipdb> print(a)\n",
+      "1\n",
+      "ipdb> print(b)\n",
+      "0\n",
+      "ipdb> quit\n"
+     ]
+    }
+   ],
+   "source": [
+    "%debug"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The interactive debugger allows much more than this, though–we can even step up and down through the stack and explore the values of variables there:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> \u001b[0;32m<ipython-input-1-d849e34d61fb>\u001b[0m(2)\u001b[0;36mfunc1\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32m      1 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m----> 2 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m      3 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "ipdb> up\n",
+      "> \u001b[0;32m<ipython-input-1-d849e34d61fb>\u001b[0m(7)\u001b[0;36mfunc2\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32m      5 \u001b[0;31m    \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m      6 \u001b[0;31m    \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m----> 7 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "ipdb> print(x)\n",
+      "1\n",
+      "ipdb> up\n",
+      "> \u001b[0;32m<ipython-input-6-b2e110f6fc8f>\u001b[0m(1)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32m----> 1 \u001b[0;31m\u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "ipdb> down\n",
+      "> \u001b[0;32m<ipython-input-1-d849e34d61fb>\u001b[0m(7)\u001b[0;36mfunc2\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32m      5 \u001b[0;31m    \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m      6 \u001b[0;31m    \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m----> 7 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "ipdb> quit\n"
+     ]
+    }
+   ],
+   "source": [
+    "%debug"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This allows you to quickly find out not only what caused the error, but what function calls led up to the error.\n",
+    "\n",
+    "If you'd like the debugger to launch automatically whenever an exception is raised, you can use the ``%pdb`` magic function to turn on this automatic behavior:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exception reporting mode: Plain\n",
+      "Automatic pdb calling has been turned ON\n"
+     ]
+    },
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n",
+      "  File \u001b[1;32m\"<ipython-input-9-569a67d2d312>\"\u001b[0m, line \u001b[1;32m3\u001b[0m, in \u001b[1;35m<module>\u001b[0m\n    func2(1)\n",
+      "  File \u001b[1;32m\"<ipython-input-1-d849e34d61fb>\"\u001b[0m, line \u001b[1;32m7\u001b[0m, in \u001b[1;35mfunc2\u001b[0m\n    return func1(a, b)\n",
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1-d849e34d61fb>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0;36m, in \u001b[0;35mfunc1\u001b[0;36m\u001b[0m\n\u001b[0;31m    return a / b\u001b[0m\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m\u001b[0;31m:\u001b[0m division by zero\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> \u001b[0;32m<ipython-input-1-d849e34d61fb>\u001b[0m(2)\u001b[0;36mfunc1\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32m      1 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m----> 2 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m      3 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "ipdb> print(b)\n",
+      "0\n",
+      "ipdb> quit\n"
+     ]
+    }
+   ],
+   "source": [
+    "%xmode Plain\n",
+    "%pdb on\n",
+    "func2(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, if you have a script that you'd like to run from the beginning in interactive mode, you can run it with the command ``%run -d``, and use the ``next`` command to step through the lines of code interactively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Partial list of debugging commands\n",
+    "\n",
+    "There are many more available commands for interactive debugging than we've listed here; the following table contains a description of some of the more common and useful ones:\n",
+    "\n",
+    "| Command         |  Description                                                |\n",
+    "|-----------------|-------------------------------------------------------------|\n",
+    "| ``list``        | Show the current location in the file                       |\n",
+    "| ``h(elp)``      | Show a list of commands, or find help on a specific command |\n",
+    "| ``q(uit)``      | Quit the debugger and the program                           |\n",
+    "| ``c(ontinue)``  | Quit the debugger, continue in the program                  |\n",
+    "| ``n(ext)``      | Go to the next step of the program                          |\n",
+    "| ``<enter>``     | Repeat the previous command                                 |\n",
+    "| ``p(rint)``     | Print variables                                             |\n",
+    "| ``s(tep)``      | Step into a subroutine                                      |\n",
+    "| ``r(eturn)``    | Return out of a subroutine                                  |\n",
+    "\n",
+    "For more information, use the ``help`` command in the debugger, or take a look at ``ipdb``'s [online documentation](https://github.com/gotcha/ipdb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) | [Contents](Index.ipynb) | [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.06-Errors-and-Debugging.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/01.07-Timing-and-Profiling.ipynb b/notebooks_v1/01.07-Timing-and-Profiling.ipynb
new file mode 100644
index 000000000..76f0db5cb
--- /dev/null
+++ b/notebooks_v1/01.07-Timing-and-Profiling.ipynb
@@ -0,0 +1,548 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) | [Contents](Index.ipynb) | [More IPython Resources](01.08-More-IPython-Resources.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.07-Timing-and-Profiling.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Profiling and Timing Code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the process of developing code and creating data processing pipelines, there are often trade-offs you can make between various implementations.\n",
+    "Early in developing your algorithm, it can be counterproductive to worry about such things. As Donald Knuth famously quipped, \"We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.\"\n",
+    "\n",
+    "But once you have your code working, it can be useful to dig into its efficiency a bit.\n",
+    "Sometimes it's useful to check the execution time of a given command or set of commands; other times it's useful to dig into a multiline process and determine where the bottleneck lies in some complicated series of operations.\n",
+    "IPython provides access to a wide array of functionality for this kind of timing and profiling of code.\n",
+    "Here we'll discuss the following IPython magic commands:\n",
+    "\n",
+    "- ``%time``: Time the execution of a single statement\n",
+    "- ``%timeit``: Time repeated execution of a single statement for more accuracy\n",
+    "- ``%prun``: Run code with the profiler\n",
+    "- ``%lprun``: Run code with the line-by-line profiler\n",
+    "- ``%memit``: Measure the memory use of a single statement\n",
+    "- ``%mprun``: Run code with the line-by-line memory profiler\n",
+    "\n",
+    "The last four commands are not bundled with IPython–you'll need to get the ``line_profiler`` and ``memory_profiler`` extensions, which we will discuss in the following sections."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Timing Code Snippets: ``%timeit`` and ``%time``\n",
+    "\n",
+    "We saw the ``%timeit`` line-magic and ``%%timeit`` cell-magic in the introduction to magic functions in [IPython Magic Commands](01.03-Magic-Commands.ipynb); it can be used to time the repeated execution of snippets of code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100000 loops, best of 3: 1.54 µs per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit sum(range(100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that because this operation is so fast, ``%timeit`` automatically does a large number of repetitions.\n",
+    "For slower commands, ``%timeit`` will automatically adjust and perform fewer repetitions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 407 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "total = 0\n",
+    "for i in range(1000):\n",
+    "    for j in range(1000):\n",
+    "        total += i * (-1) ** j"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes repeating an operation is not the best option.\n",
+    "For example, if we have a list that we'd like to sort, we might be misled by a repeated operation.\n",
+    "Sorting a pre-sorted list is much faster than sorting an unsorted list, so the repetition will skew the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 loops, best of 3: 1.9 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "L = [random.random() for i in range(100000)]\n",
+    "%timeit L.sort()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this, the ``%time`` magic function may be a better choice. It also is a good choice for longer-running commands, when short, system-related delays are unlikely to affect the result.\n",
+    "Let's time the sorting of an unsorted and a presorted list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sorting an unsorted list:\n",
+      "CPU times: user 40.6 ms, sys: 896 µs, total: 41.5 ms\n",
+      "Wall time: 41.5 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "L = [random.random() for i in range(100000)]\n",
+    "print(\"sorting an unsorted list:\")\n",
+    "%time L.sort()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sorting an already sorted list:\n",
+      "CPU times: user 8.18 ms, sys: 10 µs, total: 8.19 ms\n",
+      "Wall time: 8.24 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"sorting an already sorted list:\")\n",
+    "%time L.sort()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how much faster the presorted list is to sort, but notice also how much longer the timing takes with ``%time`` versus ``%timeit``, even for the presorted list!\n",
+    "This is a result of the fact that ``%timeit`` does some clever things under the hood to prevent system calls from interfering with the timing.\n",
+    "For example, it prevents cleanup of unused Python objects (known as *garbage collection*) which might otherwise affect the timing.\n",
+    "For this reason, ``%timeit`` results are usually noticeably faster than ``%time`` results.\n",
+    "\n",
+    "For ``%time`` as with ``%timeit``, using the double-percent-sign cell magic syntax allows timing of multiline scripts:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 504 ms, sys: 979 µs, total: 505 ms\n",
+      "Wall time: 505 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "total = 0\n",
+    "for i in range(1000):\n",
+    "    for j in range(1000):\n",
+    "        total += i * (-1) ** j"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more information on ``%time`` and ``%timeit``, as well as their available options, use the IPython help functionality (i.e., type ``%time?`` at the IPython prompt)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Profiling Full Scripts: ``%prun``\n",
+    "\n",
+    "A program is made of many single statements, and sometimes timing these statements in context is more important than timing them on their own.\n",
+    "Python contains a built-in code profiler (which you can read about in the Python documentation), but IPython offers a much more convenient way to use this profiler, in the form of the magic function ``%prun``.\n",
+    "\n",
+    "By way of example, we'll define a simple function that does some calculations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sum_of_lists(N):\n",
+    "    total = 0\n",
+    "    for i in range(5):\n",
+    "        L = [j ^ (j >> i) for j in range(N)]\n",
+    "        total += sum(L)\n",
+    "    return total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can call ``%prun`` with a function call to see the profiled results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " "
+     ]
+    }
+   ],
+   "source": [
+    "%prun sum_of_lists(1000000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the notebook, the output is printed to the pager, and looks something like this:\n",
+    "\n",
+    "```\n",
+    "14 function calls in 0.714 seconds\n",
+    "\n",
+    "   Ordered by: internal time\n",
+    "\n",
+    "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
+    "        5    0.599    0.120    0.599    0.120 <ipython-input-19>:4(<listcomp>)\n",
+    "        5    0.064    0.013    0.064    0.013 {built-in method sum}\n",
+    "        1    0.036    0.036    0.699    0.699 <ipython-input-19>:1(sum_of_lists)\n",
+    "        1    0.014    0.014    0.714    0.714 <string>:1(<module>)\n",
+    "        1    0.000    0.000    0.714    0.714 {built-in method exec}\n",
+    "```\n",
+    "\n",
+    "The result is a table that indicates, in order of total time on each function call, where the execution is spending the most time. In this case, the bulk of execution time is in the list comprehension inside ``sum_of_lists``.\n",
+    "From here, we could start thinking about what changes we might make to improve the performance in the algorithm.\n",
+    "\n",
+    "For more information on ``%prun``, as well as its available options, use the IPython help functionality (i.e., type ``%prun?`` at the IPython prompt)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Line-By-Line Profiling with ``%lprun``\n",
+    "\n",
+    "The function-by-function profiling of ``%prun`` is useful, but sometimes it's more convenient to have a line-by-line profile report.\n",
+    "This is not built into Python or IPython, but there is a ``line_profiler`` package available for installation that can do this.\n",
+    "Start by using Python's packaging tool, ``pip``, to install the ``line_profiler`` package:\n",
+    "\n",
+    "```\n",
+    "$ pip install line_profiler\n",
+    "```\n",
+    "\n",
+    "Next, you can use IPython to load the ``line_profiler`` IPython extension, offered as part of this package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext line_profiler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now the ``%lprun`` command will do a line-by-line profiling of any function–in this case, we need to tell it explicitly which functions we're interested in profiling:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%lprun -f sum_of_lists sum_of_lists(5000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, the notebook sends the result to the pager, but it looks something like this:\n",
+    "\n",
+    "```\n",
+    "Timer unit: 1e-06 s\n",
+    "\n",
+    "Total time: 0.009382 s\n",
+    "File: <ipython-input-19-fa2be176cc3e>\n",
+    "Function: sum_of_lists at line 1\n",
+    "\n",
+    "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
+    "==============================================================\n",
+    "     1                                           def sum_of_lists(N):\n",
+    "     2         1            2      2.0      0.0      total = 0\n",
+    "     3         6            8      1.3      0.1      for i in range(5):\n",
+    "     4         5         9001   1800.2     95.9          L = [j ^ (j >> i) for j in range(N)]\n",
+    "     5         5          371     74.2      4.0          total += sum(L)\n",
+    "     6         1            0      0.0      0.0      return total\n",
+    "```\n",
+    "\n",
+    "The information at the top gives us the key to reading the results: the time is reported in microseconds and we can see where the program is spending the most time.\n",
+    "At this point, we may be able to use this information to modify aspects of the script and make it perform better for our desired use case.\n",
+    "\n",
+    "For more information on ``%lprun``, as well as its available options, use the IPython help functionality (i.e., type ``%lprun?`` at the IPython prompt)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Profiling Memory Use: ``%memit`` and ``%mprun``\n",
+    "\n",
+    "Another aspect of profiling is the amount of memory an operation uses.\n",
+    "This can be evaluated with another IPython extension, the ``memory_profiler``.\n",
+    "As with the ``line_profiler``, we start by ``pip``-installing the extension:\n",
+    "\n",
+    "```\n",
+    "$ pip install memory_profiler\n",
+    "```\n",
+    "\n",
+    "Then we can use IPython to load the extension:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext memory_profiler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The memory profiler extension contains two useful magic functions: the ``%memit`` magic (which offers a memory-measuring equivalent of ``%timeit``) and the ``%mprun`` function (which offers a memory-measuring equivalent of ``%lprun``).\n",
+    "The ``%memit`` function can be used rather simply:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "peak memory: 100.08 MiB, increment: 61.36 MiB\n"
+     ]
+    }
+   ],
+   "source": [
+    "%memit sum_of_lists(1000000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that this function uses about 100 MB of memory.\n",
+    "\n",
+    "For a line-by-line description of memory use, we can use the ``%mprun`` magic.\n",
+    "Unfortunately, this magic works only for functions defined in separate modules rather than the notebook itself, so we'll start by using the ``%%file`` magic to create a simple module called ``mprun_demo.py``, which contains our ``sum_of_lists`` function, with one addition that will make our memory profiling results more clear:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting mprun_demo.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file mprun_demo.py\n",
+    "def sum_of_lists(N):\n",
+    "    total = 0\n",
+    "    for i in range(5):\n",
+    "        L = [j ^ (j >> i) for j in range(N)]\n",
+    "        total += sum(L)\n",
+    "        del L # remove reference to L\n",
+    "    return total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now import the new version of this function and run the memory line profiler:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from mprun_demo import sum_of_lists\n",
+    "%mprun -f sum_of_lists sum_of_lists(1000000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result, printed to the pager, gives us a summary of the memory use of the function, and looks something like this:\n",
+    "```\n",
+    "Filename: ./mprun_demo.py\n",
+    "\n",
+    "Line #    Mem usage    Increment   Line Contents\n",
+    "================================================\n",
+    "     4     71.9 MiB      0.0 MiB           L = [j ^ (j >> i) for j in range(N)]\n",
+    "\n",
+    "\n",
+    "Filename: ./mprun_demo.py\n",
+    "\n",
+    "Line #    Mem usage    Increment   Line Contents\n",
+    "================================================\n",
+    "     1     39.0 MiB      0.0 MiB   def sum_of_lists(N):\n",
+    "     2     39.0 MiB      0.0 MiB       total = 0\n",
+    "     3     46.5 MiB      7.5 MiB       for i in range(5):\n",
+    "     4     71.9 MiB     25.4 MiB           L = [j ^ (j >> i) for j in range(N)]\n",
+    "     5     71.9 MiB      0.0 MiB           total += sum(L)\n",
+    "     6     46.5 MiB    -25.4 MiB           del L # remove reference to L\n",
+    "     7     39.1 MiB     -7.4 MiB       return total\n",
+    "```\n",
+    "Here the ``Increment`` column tells us how much each line affects the total memory budget: observe that when we create and delete the list ``L``, we are adding about 25 MB of memory usage.\n",
+    "This is on top of the background memory usage from the Python interpreter itself.\n",
+    "\n",
+    "For more information on ``%memit`` and ``%mprun``, as well as their available options, use the IPython help functionality (i.e., type ``%memit?`` at the IPython prompt)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) | [Contents](Index.ipynb) | [More IPython Resources](01.08-More-IPython-Resources.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.07-Timing-and-Profiling.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/notebooks_v1/01.08-More-IPython-Resources.ipynb b/notebooks_v1/01.08-More-IPython-Resources.ipynb
new file mode 100644
index 000000000..ad87f002d
--- /dev/null
+++ b/notebooks_v1/01.08-More-IPython-Resources.ipynb
@@ -0,0 +1,99 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) | [Contents](Index.ipynb) | [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.08-More-IPython-Resources.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# More IPython Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this chapter, we've just scratched the surface of using IPython to enable data science tasks.\n",
+    "Much more information is available both in print and on the Web, and here we'll list some other resources that you may find helpful."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Web Resources\n",
+    "\n",
+    "- [The IPython website](http://ipython.org): The IPython website links to documentation, examples, tutorials, and a variety of other resources.\n",
+    "- [The nbviewer website](http://nbviewer.jupyter.org/): This site shows static renderings of any IPython notebook available on the internet. The front page features some example notebooks that you can browse to see what other folks are using IPython for!\n",
+    "- [A gallery of interesting Jupyter Notebooks](https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks/): This ever-growing list of notebooks, powered by nbviewer, shows the depth and breadth of numerical analysis you can do with IPython. It includes everything from short examples and tutorials to full-blown courses and books composed in the notebook format!\n",
+    "- Video Tutorials: searching the Internet, you will find many video-recorded tutorials on IPython. I'd especially recommend seeking tutorials from the PyCon, SciPy, and PyData conferenes by Fernando Perez and Brian Granger, two of the primary creators and maintainers of IPython and Jupyter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Books\n",
+    "\n",
+    "- [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do): Wes McKinney's book includes a chapter that covers using IPython as a data scientist. Although much of the material overlaps what we've discussed here, another perspective is always helpful.\n",
+    "- [*Learning IPython for Interactive Computing and Data Visualization*](https://www.packtpub.com/big-data-and-business-intelligence/learning-ipython-interactive-computing-and-data-visualization): This short book by Cyrille Rossant offers a good introduction to using IPython for data analysis.\n",
+    "- [*IPython Interactive Computing and Visualization Cookbook*](https://www.packtpub.com/big-data-and-business-intelligence/ipython-interactive-computing-and-visualization-cookbook): Also by Cyrille Rossant, this book is a longer and more advanced treatment of using IPython for data science. Despite its name, it's not just about IPython–it also goes into some depth on a broad range of data science topics.\n",
+    "\n",
+    "Finally, a reminder that you can find help on your own: IPython's ``?``-based help functionality (discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be very useful if you use it well and use it often.\n",
+    "As you go through the examples here and elsewhere, this can be used to familiarize yourself with all the tools that IPython has to offer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) | [Contents](Index.ipynb) | [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.08-More-IPython-Resources.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/02.00-Introduction-to-NumPy.ipynb b/notebooks_v1/02.00-Introduction-to-NumPy.ipynb
new file mode 100644
index 000000000..e527c4355
--- /dev/null
+++ b/notebooks_v1/02.00-Introduction-to-NumPy.ipynb
@@ -0,0 +1,191 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.00-Introduction-to-NumPy.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Introduction to NumPy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This chapter, along with chapter 3, outlines techniques for effectively loading, storing, and manipulating in-memory data in Python.\n",
+    "The topic is very broad: datasets can come from a wide range of sources and a wide range of formats, including be collections of documents, collections of images, collections of sound clips, collections of numerical measurements, or nearly anything else.\n",
+    "Despite this apparent heterogeneity, it will help us to think of all data fundamentally as arrays of numbers.\n",
+    "\n",
+    "For example, images–particularly digital images–can be thought of as simply two-dimensional arrays of numbers representing pixel brightness across the area.\n",
+    "Sound clips can be thought of as one-dimensional arrays of intensity versus time.\n",
+    "Text can be converted in various ways into numerical representations, perhaps binary digits representing the frequency of certain words or pairs of words.\n",
+    "No matter what the data are, the first step in making it analyzable will be to transform them into arrays of numbers.\n",
+    "(We will discuss some specific examples of this process later in [Feature Engineering](05.04-Feature-Engineering.ipynb))\n",
+    "\n",
+    "For this reason, efficient storage and manipulation of numerical arrays is absolutely fundamental to the process of doing data science.\n",
+    "We'll now take a look at the specialized tools that Python has for handling such numerical arrays: the NumPy package, and the Pandas package (discussed in Chapter 3).\n",
+    "\n",
+    "This chapter will cover NumPy in detail. NumPy (short for *Numerical Python*) provides an efficient interface to store and operate on dense data buffers.\n",
+    "In some ways, NumPy arrays are like Python's built-in ``list`` type, but NumPy arrays provide much more efficient storage and data operations as the arrays grow larger in size.\n",
+    "NumPy arrays form the core of nearly the entire ecosystem of data science tools in Python, so time spent learning to use NumPy effectively will be valuable no matter what aspect of data science interests you.\n",
+    "\n",
+    "If you followed the advice outlined in the Preface and installed the Anaconda stack, you already have NumPy installed and ready to go.\n",
+    "If you're more the do-it-yourself type, you can go to http://www.numpy.org/ and follow the installation instructions found there.\n",
+    "Once you do, you can import NumPy and double-check the version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.11.1'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy\n",
+    "numpy.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "For the pieces of the package discussed here, I'd recommend NumPy version 1.8 or later.\n",
+    "By convention, you'll find that most people in the SciPy/PyData world will import NumPy using ``np`` as an alias:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Throughout this chapter, and indeed the rest of the book, you'll find that this is the way we will import and use NumPy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Reminder about Built In Documentation\n",
+    "\n",
+    "As you read through this chapter, don't forget that IPython gives you the ability to quickly explore the contents of a package (by using the tab-completion feature), as well as the documentation of various functions (using the ``?`` character – Refer back to [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)).\n",
+    "\n",
+    "For example, to display all the contents of the numpy namespace, you can type this:\n",
+    "\n",
+    "```ipython\n",
+    "In [3]: np.<TAB>\n",
+    "```\n",
+    "\n",
+    "And to display NumPy's built-in documentation, you can use this:\n",
+    "\n",
+    "```ipython\n",
+    "In [4]: np?\n",
+    "```\n",
+    "\n",
+    "More detailed documentation, along with tutorials and other resources, can be found at http://www.numpy.org."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.00-Introduction-to-NumPy.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/02.01-Understanding-Data-Types.ipynb b/notebooks_v1/02.01-Understanding-Data-Types.ipynb
new file mode 100644
index 000000000..82b128e48
--- /dev/null
+++ b/notebooks_v1/02.01-Understanding-Data-Types.ipynb
@@ -0,0 +1,830 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) | [Contents](Index.ipynb) | [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.01-Understanding-Data-Types.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Data Types in Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Effective data-driven science and computation requires understanding how data is stored and manipulated.\n",
+    "This section outlines and contrasts how arrays of data are handled in the Python language itself, and how NumPy improves on this.\n",
+    "Understanding this difference is fundamental to understanding much of the material throughout the rest of the book.\n",
+    "\n",
+    "Users of Python are often drawn-in by its ease of use, one piece of which is dynamic typing.\n",
+    "While a statically-typed language like C or Java requires each variable to be explicitly declared, a dynamically-typed language like Python skips this specification. For example, in C you might specify a particular operation as follows:\n",
+    "\n",
+    "```C\n",
+    "/* C code */\n",
+    "int result = 0;\n",
+    "for(int i=0; i<100; i++){\n",
+    "    result += i;\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "While in Python the equivalent operation could be written this way:\n",
+    "\n",
+    "```python\n",
+    "# Python code\n",
+    "result = 0\n",
+    "for i in range(100):\n",
+    "    result += i\n",
+    "```\n",
+    "\n",
+    "Notice the main difference: in C, the data types of each variable are explicitly declared, while in Python the types are dynamically inferred. This means, for example, that we can assign any kind of data to any variable:\n",
+    "\n",
+    "```python\n",
+    "# Python code\n",
+    "x = 4\n",
+    "x = \"four\"\n",
+    "```\n",
+    "\n",
+    "Here we've switched the contents of ``x`` from an integer to a string. The same thing in C would lead (depending on compiler settings) to a compilation error or other unintented consequences:\n",
+    "\n",
+    "```C\n",
+    "/* C code */\n",
+    "int x = 4;\n",
+    "x = \"four\";  // FAILS\n",
+    "```\n",
+    "\n",
+    "This sort of flexibility is one piece that makes Python and other dynamically-typed languages convenient and easy to use.\n",
+    "Understanding *how* this works is an important piece of learning to analyze data efficiently and effectively with Python.\n",
+    "But what this type-flexibility also points to is the fact that Python variables are more than just their value; they also contain extra information about the type of the value. We'll explore this more in the sections that follow."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A Python Integer Is More Than Just an Integer\n",
+    "\n",
+    "The standard Python implementation is written in C.\n",
+    "This means that every Python object is simply a cleverly-disguised C structure, which contains not only its value, but other information as well. For example, when we define an integer in Python, such as ``x = 10000``, ``x`` is not just a \"raw\" integer. It's actually a pointer to a compound C structure, which contains several values.\n",
+    "Looking through the Python 3.4 source code, we find that the integer (long) type definition effectively looks like this (once the C macros are expanded):\n",
+    "\n",
+    "```C\n",
+    "struct _longobject {\n",
+    "    long ob_refcnt;\n",
+    "    PyTypeObject *ob_type;\n",
+    "    size_t ob_size;\n",
+    "    long ob_digit[1];\n",
+    "};\n",
+    "```\n",
+    "\n",
+    "A single integer in Python 3.4 actually contains four pieces:\n",
+    "\n",
+    "- ``ob_refcnt``, a reference count that helps Python silently handle memory allocation and deallocation\n",
+    "- ``ob_type``, which encodes the type of the variable\n",
+    "- ``ob_size``, which specifies the size of the following data members\n",
+    "- ``ob_digit``, which contains the actual integer value that we expect the Python variable to represent.\n",
+    "\n",
+    "This means that there is some overhead in storing an integer in Python as compared to an integer in a compiled language like C, as illustrated in the following figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Integer Memory Layout](figures/cint_vs_pyint.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here ``PyObject_HEAD`` is the part of the structure containing the reference count, type code, and other pieces mentioned before.\n",
+    "\n",
+    "Notice the difference here: a C integer is essentially a label for a position in memory whose bytes encode an integer value.\n",
+    "A Python integer is a pointer to a position in memory containing all the Python object information, including the bytes that contain the integer value.\n",
+    "This extra information in the Python integer structure is what allows Python to be coded so freely and dynamically.\n",
+    "All this additional information in Python types comes at a cost, however, which becomes especially apparent in structures that combine many of these objects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A Python List Is More Than Just a List\n",
+    "\n",
+    "Let's consider now what happens when we use a Python data structure that holds many Python objects.\n",
+    "The standard mutable multi-element container in Python is the list.\n",
+    "We can create a list of integers as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L = list(range(10))\n",
+    "L"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "int"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(L[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or, similarly, a list of strings:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L2 = [str(c) for c in L]\n",
+    "L2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(L2[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because of Python's dynamic typing, we can even create heterogeneous lists:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[bool, str, float, int]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L3 = [True, \"2\", 3.0, 4]\n",
+    "[type(item) for item in L3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But this flexibility comes at a cost: to allow these flexible types, each item in the list must contain its own type info, reference count, and other information–that is, each item is a complete Python object.\n",
+    "In the special case that all variables are of the same type, much of this information is redundant: it can be much more efficient to store data in a fixed-type array.\n",
+    "The difference between a dynamic-type list and a fixed-type (NumPy-style) array is illustrated in the following figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Array Memory Layout](figures/array_vs_list.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At the implementation level, the array essentially contains a single pointer to one contiguous block of data.\n",
+    "The Python list, on the other hand, contains a pointer to a block of pointers, each of which in turn points to a full Python object like the Python integer we saw earlier.\n",
+    "Again, the advantage of the list is flexibility: because each list element is a full structure containing both data and type information, the list can be filled with data of any desired type.\n",
+    "Fixed-type NumPy-style arrays lack this flexibility, but are much more efficient for storing and manipulating data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fixed-Type Arrays in Python\n",
+    "\n",
+    "Python offers several different options for storing data in efficient, fixed-type data buffers.\n",
+    "The built-in ``array`` module (available since Python 3.3) can be used to create dense arrays of a uniform type:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array('i', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import array\n",
+    "L = list(range(10))\n",
+    "A = array.array('i', L)\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here ``'i'`` is a type code indicating the contents are integers.\n",
+    "\n",
+    "Much more useful, however, is the ``ndarray`` object of the NumPy package.\n",
+    "While Python's ``array`` object provides efficient storage of array-based data, NumPy adds to this efficient *operations* on that data.\n",
+    "We will explore these operations in later sections; here we'll demonstrate several ways of creating a NumPy array.\n",
+    "\n",
+    "We'll start with the standard NumPy import, under the alias ``np``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Arrays from Python Lists\n",
+    "\n",
+    "First, we can use ``np.array`` to create arrays from Python lists:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 4, 2, 5, 3])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# integer array:\n",
+    "np.array([1, 4, 2, 5, 3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember that unlike Python lists, NumPy is constrained to arrays that all contain the same type.\n",
+    "If types do not match, NumPy will upcast if possible (here, integers are up-cast to floating point):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3.14,  4.  ,  2.  ,  3.  ])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array([3.14, 4, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we want to explicitly set the data type of the resulting array, we can use the ``dtype`` keyword:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.,  2.,  3.,  4.], dtype=float32)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array([1, 2, 3, 4], dtype='float32')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, unlike Python lists, NumPy arrays can explicitly be multi-dimensional; here's one way of initializing a multidimensional array using a list of lists:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 3, 4],\n",
+       "       [4, 5, 6],\n",
+       "       [6, 7, 8]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# nested lists result in multi-dimensional arrays\n",
+    "np.array([range(i, i + 3) for i in [2, 4, 6]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The inner lists are treated as rows of the resulting two-dimensional array."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Arrays from Scratch\n",
+    "\n",
+    "Especially for larger arrays, it is more efficient to create arrays from scratch using routines built into NumPy.\n",
+    "Here are several examples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a length-10 integer array filled with zeros\n",
+    "np.zeros(10, dtype=int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.,  1.,  1.,  1.,  1.],\n",
+       "       [ 1.,  1.,  1.,  1.,  1.],\n",
+       "       [ 1.,  1.,  1.,  1.,  1.]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a 3x5 floating-point array filled with ones\n",
+    "np.ones((3, 5), dtype=float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 3.14,  3.14,  3.14,  3.14,  3.14],\n",
+       "       [ 3.14,  3.14,  3.14,  3.14,  3.14],\n",
+       "       [ 3.14,  3.14,  3.14,  3.14,  3.14]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a 3x5 array filled with 3.14\n",
+    "np.full((3, 5), 3.14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create an array filled with a linear sequence\n",
+    "# Starting at 0, ending at 20, stepping by 2\n",
+    "# (this is similar to the built-in range() function)\n",
+    "np.arange(0, 20, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create an array of five values evenly spaced between 0 and 1\n",
+    "np.linspace(0, 1, 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.99844933,  0.52183819,  0.22421193],\n",
+       "       [ 0.08007488,  0.45429293,  0.20941444],\n",
+       "       [ 0.14360941,  0.96910973,  0.946117  ]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a 3x3 array of uniformly distributed\n",
+    "# random values between 0 and 1\n",
+    "np.random.random((3, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.51772646,  0.39614948, -0.10634696],\n",
+       "       [ 0.25671348,  0.00732722,  0.37783601],\n",
+       "       [ 0.68446945,  0.15926039, -0.70744073]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a 3x3 array of normally distributed random values\n",
+    "# with mean 0 and standard deviation 1\n",
+    "np.random.normal(0, 1, (3, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 3, 4],\n",
+       "       [5, 7, 8],\n",
+       "       [0, 5, 0]])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a 3x3 array of random integers in the interval [0, 10)\n",
+    "np.random.randint(0, 10, (3, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.,  0.,  0.],\n",
+       "       [ 0.,  1.,  0.],\n",
+       "       [ 0.,  0.,  1.]])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a 3x3 identity matrix\n",
+    "np.eye(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.,  1.,  1.])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create an uninitialized array of three integers\n",
+    "# The values will be whatever happens to already exist at that memory location\n",
+    "np.empty(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NumPy Standard Data Types\n",
+    "\n",
+    "NumPy arrays contain values of a single type, so it is important to have detailed knowledge of those types and their limitations.\n",
+    "Because NumPy is built in C, the types will be familiar to users of C, Fortran, and other related languages.\n",
+    "\n",
+    "The standard NumPy data types are listed in the following table.\n",
+    "Note that when constructing an array, they can be specified using a string:\n",
+    "\n",
+    "```python\n",
+    "np.zeros(10, dtype='int16')\n",
+    "```\n",
+    "\n",
+    "Or using the associated NumPy object:\n",
+    "\n",
+    "```python\n",
+    "np.zeros(10, dtype=np.int16)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "| Data type\t    | Description |\n",
+    "|---------------|-------------|\n",
+    "| ``bool_``     | Boolean (True or False) stored as a byte |\n",
+    "| ``int_``      | Default integer type (same as C ``long``; normally either ``int64`` or ``int32``)| \n",
+    "| ``intc``      | Identical to C ``int`` (normally ``int32`` or ``int64``)| \n",
+    "| ``intp``      | Integer used for indexing (same as C ``ssize_t``; normally either ``int32`` or ``int64``)| \n",
+    "| ``int8``      | Byte (-128 to 127)| \n",
+    "| ``int16``     | Integer (-32768 to 32767)|\n",
+    "| ``int32``     | Integer (-2147483648 to 2147483647)|\n",
+    "| ``int64``     | Integer (-9223372036854775808 to 9223372036854775807)| \n",
+    "| ``uint8``     | Unsigned integer (0 to 255)| \n",
+    "| ``uint16``    | Unsigned integer (0 to 65535)| \n",
+    "| ``uint32``    | Unsigned integer (0 to 4294967295)| \n",
+    "| ``uint64``    | Unsigned integer (0 to 18446744073709551615)| \n",
+    "| ``float_``    | Shorthand for ``float64``.| \n",
+    "| ``float16``   | Half precision float: sign bit, 5 bits exponent, 10 bits mantissa| \n",
+    "| ``float32``   | Single precision float: sign bit, 8 bits exponent, 23 bits mantissa| \n",
+    "| ``float64``   | Double precision float: sign bit, 11 bits exponent, 52 bits mantissa| \n",
+    "| ``complex_``  | Shorthand for ``complex128``.| \n",
+    "| ``complex64`` | Complex number, represented by two 32-bit floats| \n",
+    "| ``complex128``| Complex number, represented by two 64-bit floats| "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More advanced type specification is possible, such as specifying big or little endian numbers; for more information, refer to the [NumPy documentation](http://numpy.org/).\n",
+    "NumPy also supports compound data types, which will be covered in [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) | [Contents](Index.ipynb) | [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.01-Understanding-Data-Types.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/02.02-The-Basics-Of-NumPy-Arrays.ipynb b/notebooks_v1/02.02-The-Basics-Of-NumPy-Arrays.ipynb
new file mode 100644
index 000000000..f9dad509a
--- /dev/null
+++ b/notebooks_v1/02.02-The-Basics-Of-NumPy-Arrays.ipynb
@@ -0,0 +1,1572 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) | [Contents](Index.ipynb) | [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Basics of NumPy Arrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data manipulation in Python is nearly synonymous with NumPy array manipulation: even newer tools like Pandas ([Chapter 3](03.00-Introduction-to-Pandas.ipynb)) are built around the NumPy array.\n",
+    "This section will present several examples of using NumPy array manipulation to access data and subarrays, and to split, reshape, and join the arrays.\n",
+    "While the types of operations shown here may seem a bit dry and pedantic, they comprise the building blocks of many other examples used throughout the book.\n",
+    "Get to know them well!\n",
+    "\n",
+    "We'll cover a few categories of basic array manipulations here:\n",
+    "\n",
+    "- *Attributes of arrays*: Determining the size, shape, memory consumption, and data types of arrays\n",
+    "- *Indexing of arrays*: Getting and setting the value of individual array elements\n",
+    "- *Slicing of arrays*: Getting and setting smaller subarrays within a larger array\n",
+    "- *Reshaping of arrays*: Changing the shape of a given array\n",
+    "- *Joining and splitting of arrays*: Combining multiple arrays into one, and splitting one array into many"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NumPy Array Attributes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First let's discuss some useful array attributes.\n",
+    "We'll start by defining three random arrays, a one-dimensional, two-dimensional, and three-dimensional array.\n",
+    "We'll use NumPy's random number generator, which we will *seed* with a set value in order to ensure that the same random arrays are generated each time this code is run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "np.random.seed(0)  # seed for reproducibility\n",
+    "\n",
+    "x1 = np.random.randint(10, size=6)  # One-dimensional array\n",
+    "x2 = np.random.randint(10, size=(3, 4))  # Two-dimensional array\n",
+    "x3 = np.random.randint(10, size=(3, 4, 5))  # Three-dimensional array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each array has attributes ``ndim`` (the number of dimensions), ``shape`` (the size of each dimension), and ``size`` (the total size of the array):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x3 ndim:  3\n",
+      "x3 shape: (3, 4, 5)\n",
+      "x3 size:  60\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"x3 ndim: \", x3.ndim)\n",
+    "print(\"x3 shape:\", x3.shape)\n",
+    "print(\"x3 size: \", x3.size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another useful attribute is the ``dtype``, the data type of the array (which we discussed previously in [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"dtype:\", x3.dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Other attributes include ``itemsize``, which lists the size (in bytes) of each array element, and ``nbytes``, which lists the total size (in bytes) of the array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "itemsize: 8 bytes\n",
+      "nbytes: 480 bytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"itemsize:\", x3.itemsize, \"bytes\")\n",
+    "print(\"nbytes:\", x3.nbytes, \"bytes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, we expect that ``nbytes`` is equal to ``itemsize`` times ``size``."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Array Indexing: Accessing Single Elements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are familiar with Python's standard list indexing, indexing in NumPy will feel quite familiar.\n",
+    "In a one-dimensional array, the $i^{th}$ value (counting from zero) can be accessed by specifying the desired index in square brackets, just as with Python lists:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5, 0, 3, 3, 7, 9])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x1[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x1[4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To index from the end of the array, you can use negative indices:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x1[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x1[-2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In a multi-dimensional array, items can be accessed using a comma-separated tuple of indices:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3, 5, 2, 4],\n",
+       "       [7, 6, 8, 8],\n",
+       "       [1, 6, 7, 7]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x2[0, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x2[2, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x2[2, -1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Values can also be modified using any of the above index notation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[12,  5,  2,  4],\n",
+       "       [ 7,  6,  8,  8],\n",
+       "       [ 1,  6,  7,  7]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x2[0, 0] = 12\n",
+    "x2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Keep in mind that, unlike Python lists, NumPy arrays have a fixed type.\n",
+    "This means, for example, that if you attempt to insert a floating-point value to an integer array, the value will be silently truncated. Don't be caught unaware by this behavior!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 0, 3, 3, 7, 9])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x1[0] = 3.14159  # this will be truncated!\n",
+    "x1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Array Slicing: Accessing Subarrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as we can use square brackets to access individual array elements, we can also use them to access subarrays with the *slice* notation, marked by the colon (``:``) character.\n",
+    "The NumPy slicing syntax follows that of the standard Python list; to access a slice of an array ``x``, use this:\n",
+    "``` python\n",
+    "x[start:stop:step]\n",
+    "```\n",
+    "If any of these are unspecified, they default to the values ``start=0``, ``stop=``*``size of dimension``*, ``step=1``.\n",
+    "We'll take a look at accessing sub-arrays in one dimension and in multiple dimensions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### One-dimensional subarrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.arange(10)\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3, 4])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[:5]  # first five elements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5, 6, 7, 8, 9])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[5:]  # elements after index 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4, 5, 6])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[4:7]  # middle sub-array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 2, 4, 6, 8])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[::2]  # every other element"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 3, 5, 7, 9])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[1::2]  # every other element, starting at index 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A potentially confusing case is when the ``step`` value is negative.\n",
+    "In this case, the defaults for ``start`` and ``stop`` are swapped.\n",
+    "This becomes a convenient way to reverse an array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[::-1]  # all elements, reversed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5, 3, 1])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[5::-2]  # reversed every other from index 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multi-dimensional subarrays\n",
+    "\n",
+    "Multi-dimensional slices work in the same way, with multiple slices separated by commas.\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[12,  5,  2,  4],\n",
+       "       [ 7,  6,  8,  8],\n",
+       "       [ 1,  6,  7,  7]])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[12,  5,  2],\n",
+       "       [ 7,  6,  8]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x2[:2, :3]  # two rows, three columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[12,  2],\n",
+       "       [ 7,  8],\n",
+       "       [ 1,  7]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x2[:3, ::2]  # all rows, every other column"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, subarray dimensions can even be reversed together:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 7,  7,  6,  1],\n",
+       "       [ 8,  8,  6,  7],\n",
+       "       [ 4,  2,  5, 12]])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x2[::-1, ::-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Accessing array rows and columns\n",
+    "\n",
+    "One commonly needed routine is accessing of single rows or columns of an array.\n",
+    "This can be done by combining indexing and slicing, using an empty slice marked by a single colon (``:``):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[12  7  1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x2[:, 0])  # first column of x2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[12  5  2  4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x2[0, :])  # first row of x2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the case of row access, the empty slice can be omitted for a more compact syntax:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[12  5  2  4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x2[0])  # equivalent to x2[0, :]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Subarrays as no-copy views\n",
+    "\n",
+    "One important–and extremely useful–thing to know about array slices is that they return *views* rather than *copies* of the array data.\n",
+    "This is one area in which NumPy array slicing differs from Python list slicing: in lists, slices will be copies.\n",
+    "Consider our two-dimensional array from before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[12  5  2  4]\n",
+      " [ 7  6  8  8]\n",
+      " [ 1  6  7  7]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's extract a $2 \\times 2$ subarray from this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[12  5]\n",
+      " [ 7  6]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x2_sub = x2[:2, :2]\n",
+    "print(x2_sub)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now if we modify this subarray, we'll see that the original array is changed! Observe:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[99  5]\n",
+      " [ 7  6]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x2_sub[0, 0] = 99\n",
+    "print(x2_sub)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[99  5  2  4]\n",
+      " [ 7  6  8  8]\n",
+      " [ 1  6  7  7]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This default behavior is actually quite useful: it means that when we work with large datasets, we can access and process pieces of these datasets without the need to copy the underlying data buffer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating copies of arrays\n",
+    "\n",
+    "Despite the nice features of array views, it is sometimes useful to instead explicitly copy the data within an array or a subarray. This can be most easily done with the ``copy()`` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[99  5]\n",
+      " [ 7  6]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x2_sub_copy = x2[:2, :2].copy()\n",
+    "print(x2_sub_copy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we now modify this subarray, the original array is not touched:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[42  5]\n",
+      " [ 7  6]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x2_sub_copy[0, 0] = 42\n",
+    "print(x2_sub_copy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[99  5  2  4]\n",
+      " [ 7  6  8  8]\n",
+      " [ 1  6  7  7]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reshaping of Arrays\n",
+    "\n",
+    "Another useful type of operation is reshaping of arrays.\n",
+    "The most flexible way of doing this is with the ``reshape`` method.\n",
+    "For example, if you want to put the numbers 1 through 9 in a $3 \\times 3$ grid, you can do the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3]\n",
+      " [4 5 6]\n",
+      " [7 8 9]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "grid = np.arange(1, 10).reshape((3, 3))\n",
+    "print(grid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that for this to work, the size of the initial array must match the size of the reshaped array. \n",
+    "Where possible, the ``reshape`` method will use a no-copy view of the initial array, but with non-contiguous memory buffers this is not always the case.\n",
+    "\n",
+    "Another common reshaping pattern is the conversion of a one-dimensional array into a two-dimensional row or column matrix.\n",
+    "This can be done with the ``reshape`` method, or more easily done by making use of the ``newaxis`` keyword within a slice operation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3]])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([1, 2, 3])\n",
+    "\n",
+    "# row vector via reshape\n",
+    "x.reshape((1, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3]])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# row vector via newaxis\n",
+    "x[np.newaxis, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1],\n",
+       "       [2],\n",
+       "       [3]])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# column vector via reshape\n",
+    "x.reshape((3, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1],\n",
+       "       [2],\n",
+       "       [3]])"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# column vector via newaxis\n",
+    "x[:, np.newaxis]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will see this type of transformation often throughout the remainder of the book."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Array Concatenation and Splitting\n",
+    "\n",
+    "All of the preceding routines worked on single arrays. It's also possible to combine multiple arrays into one, and to conversely split a single array into multiple arrays. We'll take a look at those operations here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Concatenation of arrays\n",
+    "\n",
+    "Concatenation, or joining of two arrays in NumPy, is primarily accomplished using the routines ``np.concatenate``, ``np.vstack``, and ``np.hstack``.\n",
+    "``np.concatenate`` takes a tuple or list of arrays as its first argument, as we can see here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 3, 2, 1])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([1, 2, 3])\n",
+    "y = np.array([3, 2, 1])\n",
+    "np.concatenate([x, y])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also concatenate more than two arrays at once:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1  2  3  3  2  1 99 99 99]\n"
+     ]
+    }
+   ],
+   "source": [
+    "z = [99, 99, 99]\n",
+    "print(np.concatenate([x, y, z]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It can also be used for two-dimensional arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "grid = np.array([[1, 2, 3],\n",
+    "                 [4, 5, 6]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6],\n",
+       "       [1, 2, 3],\n",
+       "       [4, 5, 6]])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# concatenate along the first axis\n",
+    "np.concatenate([grid, grid])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3, 1, 2, 3],\n",
+       "       [4, 5, 6, 4, 5, 6]])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# concatenate along the second axis (zero-indexed)\n",
+    "np.concatenate([grid, grid], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For working with arrays of mixed dimensions, it can be clearer to use the ``np.vstack`` (vertical stack) and ``np.hstack`` (horizontal stack) functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [9, 8, 7],\n",
+       "       [6, 5, 4]])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([1, 2, 3])\n",
+    "grid = np.array([[9, 8, 7],\n",
+    "                 [6, 5, 4]])\n",
+    "\n",
+    "# vertically stack the arrays\n",
+    "np.vstack([x, grid])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 9,  8,  7, 99],\n",
+       "       [ 6,  5,  4, 99]])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# horizontally stack the arrays\n",
+    "y = np.array([[99],\n",
+    "              [99]])\n",
+    "np.hstack([grid, y])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similary, ``np.dstack`` will stack arrays along the third axis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Splitting of arrays\n",
+    "\n",
+    "The opposite of concatenation is splitting, which is implemented by the functions ``np.split``, ``np.hsplit``, and ``np.vsplit``.  For each of these, we can pass a list of indices giving the split points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3] [99 99] [3 2 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = [1, 2, 3, 99, 99, 3, 2, 1]\n",
+    "x1, x2, x3 = np.split(x, [3, 5])\n",
+    "print(x1, x2, x3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that *N* split-points, leads to *N + 1* subarrays.\n",
+    "The related functions ``np.hsplit`` and ``np.vsplit`` are similar:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3],\n",
+       "       [ 4,  5,  6,  7],\n",
+       "       [ 8,  9, 10, 11],\n",
+       "       [12, 13, 14, 15]])"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid = np.arange(16).reshape((4, 4))\n",
+    "grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 1 2 3]\n",
+      " [4 5 6 7]]\n",
+      "[[ 8  9 10 11]\n",
+      " [12 13 14 15]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "upper, lower = np.vsplit(grid, [2])\n",
+    "print(upper)\n",
+    "print(lower)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0  1]\n",
+      " [ 4  5]\n",
+      " [ 8  9]\n",
+      " [12 13]]\n",
+      "[[ 2  3]\n",
+      " [ 6  7]\n",
+      " [10 11]\n",
+      " [14 15]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "left, right = np.hsplit(grid, [2])\n",
+    "print(left)\n",
+    "print(right)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, ``np.dsplit`` will split arrays along the third axis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) | [Contents](Index.ipynb) | [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/02.03-Computation-on-arrays-ufuncs.ipynb b/notebooks_v1/02.03-Computation-on-arrays-ufuncs.ipynb
new file mode 100644
index 000000000..5296859e5
--- /dev/null
+++ b/notebooks_v1/02.03-Computation-on-arrays-ufuncs.ipynb
@@ -0,0 +1,1109 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) | [Contents](Index.ipynb) | [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.03-Computation-on-arrays-ufuncs.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Computation on NumPy Arrays: Universal Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Up until now, we have been discussing some of the basic nuts and bolts of NumPy; in the next few sections, we will dive into the reasons that NumPy is so important in the Python data science world.\n",
+    "Namely, it provides an easy and flexible interface to optimized computation with arrays of data.\n",
+    "\n",
+    "Computation on NumPy arrays can be very fast, or it can be very slow.\n",
+    "The key to making it fast is to use *vectorized* operations, generally implemented through NumPy's *universal functions* (ufuncs).\n",
+    "This section motivates the need for NumPy's ufuncs, which can be used to make repeated calculations on array elements much more efficient.\n",
+    "It then introduces many of the most common and useful arithmetic ufuncs available in the NumPy package."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Slowness of Loops\n",
+    "\n",
+    "Python's default implementation (known as CPython) does some operations very slowly.\n",
+    "This is in part due to the dynamic, interpreted nature of the language: the fact that types are flexible, so that sequences of operations cannot be compiled down to efficient machine code as in languages like C and Fortran.\n",
+    "Recently there have been various attempts to address this weakness: well-known examples are the [PyPy](http://pypy.org/) project, a just-in-time compiled implementation of Python; the [Cython](http://cython.org) project, which converts Python code to compilable C code; and the [Numba](http://numba.pydata.org/) project, which converts snippets of Python code to fast LLVM bytecode.\n",
+    "Each of these has its strengths and weaknesses, but it is safe to say that none of the three approaches has yet surpassed the reach and popularity of the standard CPython engine.\n",
+    "\n",
+    "The relative sluggishness of Python generally manifests itself in situations where many small operations are being repeated – for instance looping over arrays to operate on each element.\n",
+    "For example, imagine we have an array of values and we'd like to compute the reciprocal of each.\n",
+    "A straightforward approach might look like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.16666667,  1.        ,  0.25      ,  0.25      ,  0.125     ])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.random.seed(0)\n",
+    "\n",
+    "def compute_reciprocals(values):\n",
+    "    output = np.empty(len(values))\n",
+    "    for i in range(len(values)):\n",
+    "        output[i] = 1.0 / values[i]\n",
+    "    return output\n",
+    "        \n",
+    "values = np.random.randint(1, 10, size=5)\n",
+    "compute_reciprocals(values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This implementation probably feels fairly natural to someone from, say, a C or Java background.\n",
+    "But if we measure the execution time of this code for a large input, we see that this operation is very slow, perhaps surprisingly so!\n",
+    "We'll benchmark this with IPython's ``%timeit`` magic (discussed in [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loop, best of 3: 2.91 s per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "big_array = np.random.randint(1, 100, size=1000000)\n",
+    "%timeit compute_reciprocals(big_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It takes several seconds to compute these million operations and to store the result!\n",
+    "When even cell phones have processing speeds measured in Giga-FLOPS (i.e., billions of numerical operations per second), this seems almost absurdly slow.\n",
+    "It turns out that the bottleneck here is not the operations themselves, but the type-checking and function dispatches that CPython must do at each cycle of the loop.\n",
+    "Each time the reciprocal is computed, Python first examines the object's type and does a dynamic lookup of the correct function to use for that type.\n",
+    "If we were working in compiled code instead, this type specification would be known before the code executes and the result could be computed much more efficiently."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introducing UFuncs\n",
+    "\n",
+    "For many types of operations, NumPy provides a convenient interface into just this kind of statically typed, compiled routine. This is known as a *vectorized* operation.\n",
+    "This can be accomplished by simply performing an operation on the array, which will then be applied to each element.\n",
+    "This vectorized approach is designed to push the loop into the compiled layer that underlies NumPy, leading to much faster execution.\n",
+    "\n",
+    "Compare the results of the following two:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.16666667  1.          0.25        0.25        0.125     ]\n",
+      "[ 0.16666667  1.          0.25        0.25        0.125     ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(compute_reciprocals(values))\n",
+    "print(1.0 / values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the execution time for our big array, we see that it completes orders of magnitude faster than the Python loop:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 loops, best of 3: 4.6 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit (1.0 / big_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vectorized operations in NumPy are implemented via *ufuncs*, whose main purpose is to quickly execute repeated operations on values in NumPy arrays.\n",
+    "Ufuncs are extremely flexible – before we saw an operation between a scalar and an array, but we can also operate between two arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.        ,  0.5       ,  0.66666667,  0.75      ,  0.8       ])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(5) / np.arange(1, 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And ufunc operations are not limited to one-dimensional arrays–they can also act on multi-dimensional arrays as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  1,   2,   4],\n",
+       "       [  8,  16,  32],\n",
+       "       [ 64, 128, 256]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.arange(9).reshape((3, 3))\n",
+    "2 ** x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Computations using vectorization through ufuncs are nearly always more efficient than their counterpart implemented using Python loops, especially as the arrays grow in size.\n",
+    "Any time you see such a loop in a Python script, you should consider whether it can be replaced with a vectorized expression."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring NumPy's UFuncs\n",
+    "\n",
+    "Ufuncs exist in two flavors: *unary ufuncs*, which operate on a single input, and *binary ufuncs*, which operate on two inputs.\n",
+    "We'll see examples of both these types of functions here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Array arithmetic\n",
+    "\n",
+    "NumPy's ufuncs feel very natural to use because they make use of Python's native arithmetic operators.\n",
+    "The standard addition, subtraction, multiplication, and division can all be used:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x     = [0 1 2 3]\n",
+      "x + 5 = [5 6 7 8]\n",
+      "x - 5 = [-5 -4 -3 -2]\n",
+      "x * 2 = [0 2 4 6]\n",
+      "x / 2 = [ 0.   0.5  1.   1.5]\n",
+      "x // 2 = [0 0 1 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.arange(4)\n",
+    "print(\"x     =\", x)\n",
+    "print(\"x + 5 =\", x + 5)\n",
+    "print(\"x - 5 =\", x - 5)\n",
+    "print(\"x * 2 =\", x * 2)\n",
+    "print(\"x / 2 =\", x / 2)\n",
+    "print(\"x // 2 =\", x // 2)  # floor division"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is also a unary ufunc for negation, and a ``**`` operator for exponentiation, and a ``%`` operator for modulus:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-x     =  [ 0 -1 -2 -3]\n",
+      "x ** 2 =  [0 1 4 9]\n",
+      "x % 2  =  [0 1 0 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"-x     = \", -x)\n",
+    "print(\"x ** 2 = \", x ** 2)\n",
+    "print(\"x % 2  = \", x % 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition, these can be strung together however you wish, and the standard order of operations is respected:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.  , -2.25, -4.  , -6.25])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-(0.5*x + 1) ** 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each of these arithmetic operations are simply convenient wrappers around specific functions built into NumPy; for example, the ``+`` operator is a wrapper for the ``add`` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 3, 4, 5])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.add(x, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following table lists the arithmetic operators implemented in NumPy:\n",
+    "\n",
+    "| Operator\t    | Equivalent ufunc    | Description                           |\n",
+    "|---------------|---------------------|---------------------------------------|\n",
+    "|``+``          |``np.add``           |Addition (e.g., ``1 + 1 = 2``)         |\n",
+    "|``-``          |``np.subtract``      |Subtraction (e.g., ``3 - 2 = 1``)      |\n",
+    "|``-``          |``np.negative``      |Unary negation (e.g., ``-2``)          |\n",
+    "|``*``          |``np.multiply``      |Multiplication (e.g., ``2 * 3 = 6``)   |\n",
+    "|``/``          |``np.divide``        |Division (e.g., ``3 / 2 = 1.5``)       |\n",
+    "|``//``         |``np.floor_divide``  |Floor division (e.g., ``3 // 2 = 1``)  |\n",
+    "|``**``         |``np.power``         |Exponentiation (e.g., ``2 ** 3 = 8``)  |\n",
+    "|``%``          |``np.mod``           |Modulus/remainder (e.g., ``9 % 4 = 1``)|\n",
+    "\n",
+    "Additionally there are Boolean/bitwise operators; we will explore these in [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Absolute value\n",
+    "\n",
+    "Just as NumPy understands Python's built-in arithmetic operators, it also understands Python's built-in absolute value function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 1, 0, 1, 2])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([-2, -1, 0, 1, 2])\n",
+    "abs(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The corresponding NumPy ufunc is ``np.absolute``, which is also available under the alias ``np.abs``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 1, 0, 1, 2])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.absolute(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 1, 0, 1, 2])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.abs(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This ufunc can also handle complex data, in which the absolute value returns the magnitude:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 5.,  5.,  2.,  1.])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([3 - 4j, 4 - 3j, 2 + 0j, 0 + 1j])\n",
+    "np.abs(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Trigonometric functions\n",
+    "\n",
+    "NumPy provides a large number of useful ufuncs, and some of the most useful for the data scientist are the trigonometric functions.\n",
+    "We'll start by defining an array of angles:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "theta = np.linspace(0, np.pi, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can compute some trigonometric functions on these values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta      =  [ 0.          1.57079633  3.14159265]\n",
+      "sin(theta) =  [  0.00000000e+00   1.00000000e+00   1.22464680e-16]\n",
+      "cos(theta) =  [  1.00000000e+00   6.12323400e-17  -1.00000000e+00]\n",
+      "tan(theta) =  [  0.00000000e+00   1.63312394e+16  -1.22464680e-16]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"theta      = \", theta)\n",
+    "print(\"sin(theta) = \", np.sin(theta))\n",
+    "print(\"cos(theta) = \", np.cos(theta))\n",
+    "print(\"tan(theta) = \", np.tan(theta))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The values are computed to within machine precision, which is why values that should be zero do not always hit exactly zero.\n",
+    "Inverse trigonometric functions are also available:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x         =  [-1, 0, 1]\n",
+      "arcsin(x) =  [-1.57079633  0.          1.57079633]\n",
+      "arccos(x) =  [ 3.14159265  1.57079633  0.        ]\n",
+      "arctan(x) =  [-0.78539816  0.          0.78539816]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = [-1, 0, 1]\n",
+    "print(\"x         = \", x)\n",
+    "print(\"arcsin(x) = \", np.arcsin(x))\n",
+    "print(\"arccos(x) = \", np.arccos(x))\n",
+    "print(\"arctan(x) = \", np.arctan(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exponents and logarithms\n",
+    "\n",
+    "Another common type of operation available in a NumPy ufunc are the exponentials:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x     = [1, 2, 3]\n",
+      "e^x   = [  2.71828183   7.3890561   20.08553692]\n",
+      "2^x   = [ 2.  4.  8.]\n",
+      "3^x   = [ 3  9 27]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = [1, 2, 3]\n",
+    "print(\"x     =\", x)\n",
+    "print(\"e^x   =\", np.exp(x))\n",
+    "print(\"2^x   =\", np.exp2(x))\n",
+    "print(\"3^x   =\", np.power(3, x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The inverse of the exponentials, the logarithms, are also available.\n",
+    "The basic ``np.log`` gives the natural logarithm; if you prefer to compute the base-2 logarithm or the base-10 logarithm, these are available as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x        = [1, 2, 4, 10]\n",
+      "ln(x)    = [ 0.          0.69314718  1.38629436  2.30258509]\n",
+      "log2(x)  = [ 0.          1.          2.          3.32192809]\n",
+      "log10(x) = [ 0.          0.30103     0.60205999  1.        ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = [1, 2, 4, 10]\n",
+    "print(\"x        =\", x)\n",
+    "print(\"ln(x)    =\", np.log(x))\n",
+    "print(\"log2(x)  =\", np.log2(x))\n",
+    "print(\"log10(x) =\", np.log10(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are also some specialized versions that are useful for maintaining precision with very small input:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "exp(x) - 1 = [ 0.          0.0010005   0.01005017  0.10517092]\n",
+      "log(1 + x) = [ 0.          0.0009995   0.00995033  0.09531018]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = [0, 0.001, 0.01, 0.1]\n",
+    "print(\"exp(x) - 1 =\", np.expm1(x))\n",
+    "print(\"log(1 + x) =\", np.log1p(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When ``x`` is very small, these functions give more precise values than if the raw ``np.log`` or ``np.exp`` were to be used."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Specialized ufuncs\n",
+    "\n",
+    "NumPy has many more ufuncs available, including hyperbolic trig functions, bitwise arithmetic, comparison operators, conversions from radians to degrees, rounding and remainders, and much more.\n",
+    "A look through the NumPy documentation reveals a lot of interesting functionality.\n",
+    "\n",
+    "Another excellent source for more specialized and obscure ufuncs is the submodule ``scipy.special``.\n",
+    "If you want to compute some obscure mathematical function on your data, chances are it is implemented in ``scipy.special``.\n",
+    "There are far too many functions to list them all, but the following snippet shows a couple that might come up in a statistics context:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from scipy import special"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamma(x)     = [  1.00000000e+00   2.40000000e+01   3.62880000e+05]\n",
+      "ln|gamma(x)| = [  0.           3.17805383  12.80182748]\n",
+      "beta(x, 2)   = [ 0.5         0.03333333  0.00909091]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Gamma functions (generalized factorials) and related functions\n",
+    "x = [1, 5, 10]\n",
+    "print(\"gamma(x)     =\", special.gamma(x))\n",
+    "print(\"ln|gamma(x)| =\", special.gammaln(x))\n",
+    "print(\"beta(x, 2)   =\", special.beta(x, 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "erf(x)  = [ 0.          0.32862676  0.67780119  0.84270079]\n",
+      "erfc(x) = [ 1.          0.67137324  0.32219881  0.15729921]\n",
+      "erfinv(x) = [ 0.          0.27246271  0.73286908         inf]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Error function (integral of Gaussian)\n",
+    "# its complement, and its inverse\n",
+    "x = np.array([0, 0.3, 0.7, 1.0])\n",
+    "print(\"erf(x)  =\", special.erf(x))\n",
+    "print(\"erfc(x) =\", special.erfc(x))\n",
+    "print(\"erfinv(x) =\", special.erfinv(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are many, many more ufuncs available in both NumPy and ``scipy.special``.\n",
+    "Because the documentation of these packages is available online, a web search along the lines of \"gamma function python\" will generally find the relevant information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced Ufunc Features\n",
+    "\n",
+    "Many NumPy users make use of ufuncs without ever learning their full set of features.\n",
+    "We'll outline a few specialized features of ufuncs here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Specifying output\n",
+    "\n",
+    "For large calculations, it is sometimes useful to be able to specify the array where the result of the calculation will be stored.\n",
+    "Rather than creating a temporary array, this can be used to write computation results directly to the memory location where you'd like them to be.\n",
+    "For all ufuncs, this can be done using the ``out`` argument of the function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  0.  10.  20.  30.  40.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.arange(5)\n",
+    "y = np.empty(5)\n",
+    "np.multiply(x, 10, out=y)\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This can even be used with array views. For example, we can write the results of a computation to every other element of a specified array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  1.   0.   2.   0.   4.   0.   8.   0.  16.   0.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "y = np.zeros(10)\n",
+    "np.power(2, x, out=y[::2])\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we had instead written ``y[::2] = 2 ** x``, this would have resulted in the creation of a temporary array to hold the results of ``2 ** x``, followed by a second operation copying those values into the ``y`` array.\n",
+    "This doesn't make much of a difference for such a small computation, but for very large arrays the memory savings from careful use of the ``out`` argument can be significant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregates\n",
+    "\n",
+    "For binary ufuncs, there are some interesting aggregates that can be computed directly from the object.\n",
+    "For example, if we'd like to *reduce* an array with a particular operation, we can use the ``reduce`` method of any ufunc.\n",
+    "A reduce repeatedly applies a given operation to the elements of an array until only a single result remains.\n",
+    "\n",
+    "For example, calling ``reduce`` on the ``add`` ufunc returns the sum of all elements in the array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.arange(1, 6)\n",
+    "np.add.reduce(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, calling ``reduce`` on the ``multiply`` ufunc results in the product of all array elements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "120"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.multiply.reduce(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we'd like to store all the intermediate results of the computation, we can instead use ``accumulate``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  3,  6, 10, 15])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.add.accumulate(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1,   2,   6,  24, 120])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.multiply.accumulate(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that for these particular cases, there are dedicated NumPy functions to compute the results (``np.sum``, ``np.prod``, ``np.cumsum``, ``np.cumprod``), which we'll explore in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outer products\n",
+    "\n",
+    "Finally, any ufunc can compute the output of all pairs of two different inputs using the ``outer`` method.\n",
+    "This allows you, in one line, to do things like create a multiplication table:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  2,  3,  4,  5],\n",
+       "       [ 2,  4,  6,  8, 10],\n",
+       "       [ 3,  6,  9, 12, 15],\n",
+       "       [ 4,  8, 12, 16, 20],\n",
+       "       [ 5, 10, 15, 20, 25]])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.arange(1, 6)\n",
+    "np.multiply.outer(x, x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``ufunc.at`` and ``ufunc.reduceat`` methods, which we'll explore in [Fancy Indexing](02.07-Fancy-Indexing.ipynb), are very helpful as well.\n",
+    "\n",
+    "Another extremely useful feature of ufuncs is the ability to operate between arrays of different sizes and shapes, a set of operations known as *broadcasting*.\n",
+    "This subject is important enough that we will devote a whole section to it (see [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ufuncs: Learning More"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More information on universal functions (including the full list of available functions) can be found on the [NumPy](http://www.numpy.org) and [SciPy](http://www.scipy.org) documentation websites.\n",
+    "\n",
+    "Recall that you can also access information directly from within IPython by importing the packages and using IPython's tab-completion and help (``?``) functionality, as described in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) | [Contents](Index.ipynb) | [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.03-Computation-on-arrays-ufuncs.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/02.04-Computation-on-arrays-aggregates.ipynb b/notebooks_v1/02.04-Computation-on-arrays-aggregates.ipynb
new file mode 100644
index 000000000..53e6462fd
--- /dev/null
+++ b/notebooks_v1/02.04-Computation-on-arrays-aggregates.ipynb
@@ -0,0 +1,646 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) | [Contents](Index.ipynb) | [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.04-Computation-on-arrays-aggregates.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Aggregations: Min, Max, and Everything In Between"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Often when faced with a large amount of data, a first step is to compute summary statistics for the data in question.\n",
+    "Perhaps the most common summary statistics are the mean and standard deviation, which allow you to summarize the \"typical\" values in a dataset, but other aggregates are useful as well (the sum, product, median, minimum and maximum, quantiles, etc.).\n",
+    "\n",
+    "NumPy has fast built-in aggregation functions for working on arrays; we'll discuss and demonstrate some of them here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summing the Values in an Array\n",
+    "\n",
+    "As a quick example, consider computing the sum of all values in an array.\n",
+    "Python itself can do this using the built-in ``sum`` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "55.61209116604941"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L = np.random.random(100)\n",
+    "sum(L)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The syntax is quite similar to that of NumPy's ``sum`` function, and the result is the same in the simplest case:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "55.612091166049424"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(L)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, because it executes the operation in compiled code, NumPy's version of the operation is computed much more quickly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 104 ms per loop\n",
+      "1000 loops, best of 3: 442 µs per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "big_array = np.random.rand(1000000)\n",
+    "%timeit sum(big_array)\n",
+    "%timeit np.sum(big_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Be careful, though: the ``sum`` function and the ``np.sum`` function are not identical, which can sometimes lead to confusion!\n",
+    "In particular, their optional arguments have different meanings, and ``np.sum`` is aware of multiple array dimensions, as we will see in the following section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Minimum and Maximum\n",
+    "\n",
+    "Similarly, Python has built-in ``min`` and ``max`` functions, used to find the minimum value and maximum value of any given array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1.1717128136634614e-06, 0.9999976784968716)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(big_array), max(big_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NumPy's corresponding functions have similar syntax, and again operate much more quickly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1.1717128136634614e-06, 0.9999976784968716)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.min(big_array), np.max(big_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 82.3 ms per loop\n",
+      "1000 loops, best of 3: 497 µs per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit min(big_array)\n",
+    "%timeit np.min(big_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For ``min``, ``max``, ``sum``, and several other NumPy aggregates, a shorter syntax is to use methods of the array object itself:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.17171281366e-06 0.999997678497 499911.628197\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(big_array.min(), big_array.max(), big_array.sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whenever possible, make sure that you are using the NumPy version of these aggregates when operating on NumPy arrays!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multi dimensional aggregates\n",
+    "\n",
+    "One common type of aggregation operation is an aggregate along a row or column.\n",
+    "Say you have some data stored in a two-dimensional array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.8967576   0.03783739  0.75952519  0.06682827]\n",
+      " [ 0.8354065   0.99196818  0.19544769  0.43447084]\n",
+      " [ 0.66859307  0.15038721  0.37911423  0.6687194 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "M = np.random.random((3, 4))\n",
+    "print(M)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, each NumPy aggregation function will return the aggregate over the entire array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0850555667307118"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Aggregation functions take an additional argument specifying the *axis* along which the aggregate is computed. For example, we can find the minimum value within each column by specifying ``axis=0``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.66859307,  0.03783739,  0.19544769,  0.06682827])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M.min(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The function returns four values, corresponding to the four columns of numbers.\n",
+    "\n",
+    "Similarly, we can find the maximum value within each row:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.8967576 ,  0.99196818,  0.6687194 ])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M.max(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The way the axis is specified here can be confusing to users coming from other languages.\n",
+    "The ``axis`` keyword specifies the *dimension of the array that will be collapsed*, rather than the dimension that will be returned.\n",
+    "So specifying ``axis=0`` means that the first axis will be collapsed: for two-dimensional arrays, this means that values within each column will be aggregated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Other aggregation functions\n",
+    "\n",
+    "NumPy provides many other aggregation functions, but we won't discuss them in detail here.\n",
+    "Additionally, most aggregates have a ``NaN``-safe counterpart that computes the result while ignoring missing values, which are marked by the special IEEE floating-point ``NaN`` value (for a fuller discussion of missing data, see [Handling Missing Data](03.04-Missing-Values.ipynb)).\n",
+    "Some of these ``NaN``-safe functions were not added until NumPy 1.8, so they will not be available in older NumPy versions.\n",
+    "\n",
+    "The following table provides a list of useful aggregation functions available in NumPy:\n",
+    "\n",
+    "|Function Name      |   NaN-safe Version  | Description                                   |\n",
+    "|-------------------|---------------------|-----------------------------------------------|\n",
+    "| ``np.sum``        | ``np.nansum``       | Compute sum of elements                       |\n",
+    "| ``np.prod``       | ``np.nanprod``      | Compute product of elements                   |\n",
+    "| ``np.mean``       | ``np.nanmean``      | Compute mean of elements                      |\n",
+    "| ``np.std``        | ``np.nanstd``       | Compute standard deviation                    |\n",
+    "| ``np.var``        | ``np.nanvar``       | Compute variance                              |\n",
+    "| ``np.min``        | ``np.nanmin``       | Find minimum value                            |\n",
+    "| ``np.max``        | ``np.nanmax``       | Find maximum value                            |\n",
+    "| ``np.argmin``     | ``np.nanargmin``    | Find index of minimum value                   |\n",
+    "| ``np.argmax``     | ``np.nanargmax``    | Find index of maximum value                   |\n",
+    "| ``np.median``     | ``np.nanmedian``    | Compute median of elements                    |\n",
+    "| ``np.percentile`` | ``np.nanpercentile``| Compute rank-based statistics of elements     |\n",
+    "| ``np.any``        | N/A                 | Evaluate whether any elements are true        |\n",
+    "| ``np.all``        | N/A                 | Evaluate whether all elements are true        |\n",
+    "\n",
+    "We will see these aggregates often throughout the rest of the book."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: What is the Average Height of US Presidents?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Aggregates available in NumPy can be extremely useful for summarizing a set of values.\n",
+    "As a simple example, let's consider the heights of all US presidents.\n",
+    "This data is available in the file *president_heights.csv*, which is a simple comma-separated list of labels and values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "order,name,height(cm)\r\n",
+      "1,George Washington,189\r\n",
+      "2,John Adams,170\r\n",
+      "3,Thomas Jefferson,189\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!head -4 data/president_heights.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll use the Pandas package, which we'll explore more fully in [Chapter 3](03.00-Introduction-to-Pandas.ipynb), to read the file and extract this information (note that the heights are measured in centimeters)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[189 170 189 163 183 171 185 168 173 183 173 173 175 178 183 193 178 173\n",
+      " 174 183 183 168 170 178 182 180 183 178 182 188 175 179 183 193 182 183\n",
+      " 177 185 188 188 182 185]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "data = pd.read_csv('data/president_heights.csv')\n",
+    "heights = np.array(data['height(cm)'])\n",
+    "print(heights)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have this data array, we can compute a variety of summary statistics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean height:        179.738095238\n",
+      "Standard deviation: 6.93184344275\n",
+      "Minimum height:     163\n",
+      "Maximum height:     193\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Mean height:       \", heights.mean())\n",
+    "print(\"Standard deviation:\", heights.std())\n",
+    "print(\"Minimum height:    \", heights.min())\n",
+    "print(\"Maximum height:    \", heights.max())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that in each case, the aggregation operation reduced the entire array to a single summarizing value, which gives us information about the distribution of values.\n",
+    "We may also wish to compute quantiles:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "25th percentile:    174.25\n",
+      "Median:             182.0\n",
+      "75th percentile:    183.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"25th percentile:   \", np.percentile(heights, 25))\n",
+    "print(\"Median:            \", np.median(heights))\n",
+    "print(\"75th percentile:   \", np.percentile(heights, 75))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that the median height of US presidents is 182 cm, or just shy of six feet.\n",
+    "\n",
+    "Of course, sometimes it's more useful to see a visual representation of this data, which we can accomplish using tools in Matplotlib (we'll discuss Matplotlib more fully in [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)). For example, this code generates the following chart:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn; seaborn.set()  # set plot style"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wAwBgEIIdAACDEOwAABiEYAcAwCAEOwAABiHYAQAwCMEOAIBBCHYAAAxCsAMAYBCCHQAA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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116102f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(heights)\n",
+    "plt.title('Height Distribution of US Presidents')\n",
+    "plt.xlabel('height (cm)')\n",
+    "plt.ylabel('number');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These aggregates are some of the fundamental pieces of exploratory data analysis that we'll explore in more depth in later chapters of the book."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) | [Contents](Index.ipynb) | [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.04-Computation-on-arrays-aggregates.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/02.05-Computation-on-arrays-broadcasting.ipynb b/notebooks_v1/02.05-Computation-on-arrays-broadcasting.ipynb
new file mode 100644
index 000000000..c1cae6ddf
--- /dev/null
+++ b/notebooks_v1/02.05-Computation-on-arrays-broadcasting.ipynb
@@ -0,0 +1,804 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) | [Contents](Index.ipynb) | [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.05-Computation-on-arrays-broadcasting.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Computation on Arrays: Broadcasting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We saw in the previous section how NumPy's universal functions can be used to *vectorize* operations and thereby remove slow Python loops.\n",
+    "Another means of vectorizing operations is to use NumPy's *broadcasting* functionality.\n",
+    "Broadcasting is simply a set of rules for applying binary ufuncs (e.g., addition, subtraction, multiplication, etc.) on arrays of different sizes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introducing Broadcasting\n",
+    "\n",
+    "Recall that for arrays of the same size, binary operations are performed on an element-by-element basis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5, 6, 7])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([0, 1, 2])\n",
+    "b = np.array([5, 5, 5])\n",
+    "a + b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Broadcasting allows these types of binary operations to be performed on arrays of different sizes–for example, we can just as easily add a scalar (think of it as a zero-dimensional array) to an array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5, 6, 7])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a + 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can think of this as an operation that stretches or duplicates the value ``5`` into the array ``[5, 5, 5]``, and adds the results.\n",
+    "The advantage of NumPy's broadcasting is that this duplication of values does not actually take place, but it is a useful mental model as we think about broadcasting.\n",
+    "\n",
+    "We can similarly extend this to arrays of higher dimension. Observe the result when we add a one-dimensional array to a two-dimensional array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.,  1.,  1.],\n",
+       "       [ 1.,  1.,  1.],\n",
+       "       [ 1.,  1.,  1.]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M = np.ones((3, 3))\n",
+    "M"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.,  2.,  3.],\n",
+       "       [ 1.,  2.,  3.],\n",
+       "       [ 1.,  2.,  3.]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M + a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here the one-dimensional array ``a`` is stretched, or broadcast across the second dimension in order to match the shape of ``M``.\n",
+    "\n",
+    "While these examples are relatively easy to understand, more complicated cases can involve broadcasting of both arrays. Consider the following example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2]\n",
+      "[[0]\n",
+      " [1]\n",
+      " [2]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.arange(3)\n",
+    "b = np.arange(3)[:, np.newaxis]\n",
+    "\n",
+    "print(a)\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [1, 2, 3],\n",
+       "       [2, 3, 4]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a + b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as before we stretched or broadcasted one value to match the shape of the other, here we've stretched *both* ``a`` and ``b`` to match a common shape, and the result is a two-dimensional array!\n",
+    "The geometry of these examples is visualized in the following figure (Code to produce this plot can be found in the [appendix](06.00-Figure-Code.ipynb#Broadcasting), and is adapted from source published in the [astroML](http://astroml.org) documentation. Used by permission)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Broadcasting Visual](figures/02.05-broadcasting.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The light boxes represent the broadcasted values: again, this extra memory is not actually allocated in the course of the operation, but it can be useful conceptually to imagine that it is."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Rules of Broadcasting\n",
+    "\n",
+    "Broadcasting in NumPy follows a strict set of rules to determine the interaction between the two arrays:\n",
+    "\n",
+    "- Rule 1: If the two arrays differ in their number of dimensions, the shape of the one with fewer dimensions is *padded* with ones on its leading (left) side.\n",
+    "- Rule 2: If the shape of the two arrays does not match in any dimension, the array with shape equal to 1 in that dimension is stretched to match the other shape.\n",
+    "- Rule 3: If in any dimension the sizes disagree and neither is equal to 1, an error is raised.\n",
+    "\n",
+    "To make these rules clear, let's consider a few examples in detail."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Broadcasting example 1\n",
+    "\n",
+    "Let's look at adding a two-dimensional array to a one-dimensional array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "M = np.ones((2, 3))\n",
+    "a = np.arange(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's consider an operation on these two arrays. The shape of the arrays are\n",
+    "\n",
+    "- ``M.shape = (2, 3)``\n",
+    "- ``a.shape = (3,)``\n",
+    "\n",
+    "We see by rule 1 that the array ``a`` has fewer dimensions, so we pad it on the left with ones:\n",
+    "\n",
+    "- ``M.shape -> (2, 3)``\n",
+    "- ``a.shape -> (1, 3)``\n",
+    "\n",
+    "By rule 2, we now see that the first dimension disagrees, so we stretch this dimension to match:\n",
+    "\n",
+    "- ``M.shape -> (2, 3)``\n",
+    "- ``a.shape -> (2, 3)``\n",
+    "\n",
+    "The shapes match, and we see that the final shape will be ``(2, 3)``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.,  2.,  3.],\n",
+       "       [ 1.,  2.,  3.]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M + a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Broadcasting example 2\n",
+    "\n",
+    "Let's take a look at an example where both arrays need to be broadcast:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a = np.arange(3).reshape((3, 1))\n",
+    "b = np.arange(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, we'll start by writing out the shape of the arrays:\n",
+    "\n",
+    "- ``a.shape = (3, 1)``\n",
+    "- ``b.shape = (3,)``\n",
+    "\n",
+    "Rule 1 says we must pad the shape of ``b`` with ones:\n",
+    "\n",
+    "- ``a.shape -> (3, 1)``\n",
+    "- ``b.shape -> (1, 3)``\n",
+    "\n",
+    "And rule 2 tells us that we upgrade each of these ones to match the corresponding size of the other array:\n",
+    "\n",
+    "- ``a.shape -> (3, 3)``\n",
+    "- ``b.shape -> (3, 3)``\n",
+    "\n",
+    "Because the result matches, these shapes are compatible. We can see this here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [1, 2, 3],\n",
+       "       [2, 3, 4]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a + b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Broadcasting example 3\n",
+    "\n",
+    "Now let's take a look at an example in which the two arrays are not compatible:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "M = np.ones((3, 2))\n",
+    "a = np.arange(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is just a slightly different situation than in the first example: the matrix ``M`` is transposed.\n",
+    "How does this affect the calculation? The shape of the arrays are\n",
+    "\n",
+    "- ``M.shape = (3, 2)``\n",
+    "- ``a.shape = (3,)``\n",
+    "\n",
+    "Again, rule 1 tells us that we must pad the shape of ``a`` with ones:\n",
+    "\n",
+    "- ``M.shape -> (3, 2)``\n",
+    "- ``a.shape -> (1, 3)``\n",
+    "\n",
+    "By rule 2, the first dimension of ``a`` is stretched to match that of ``M``:\n",
+    "\n",
+    "- ``M.shape -> (3, 2)``\n",
+    "- ``a.shape -> (3, 3)``\n",
+    "\n",
+    "Now we hit rule 3–the final shapes do not match, so these two arrays are incompatible, as we can observe by attempting this operation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (3,2) (3,) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-9e16e9f98da6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,2) (3,) "
+     ]
+    }
+   ],
+   "source": [
+    "M + a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note the potential confusion here: you could imagine making ``a`` and ``M`` compatible by, say, padding ``a``'s shape with ones on the right rather than the left.\n",
+    "But this is not how the broadcasting rules work!\n",
+    "That sort of flexibility might be useful in some cases, but it would lead to potential areas of ambiguity.\n",
+    "If right-side padding is what you'd like, you can do this explicitly by reshaping the array (we'll use the ``np.newaxis`` keyword introduced in [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 1)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[:, np.newaxis].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.,  1.],\n",
+       "       [ 2.,  2.],\n",
+       "       [ 3.,  3.]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M + a[:, np.newaxis]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Also note that while we've been focusing on the ``+`` operator here, these broadcasting rules apply to *any* binary ``ufunc``.\n",
+    "For example, here is the ``logaddexp(a, b)`` function, which computes ``log(exp(a) + exp(b))`` with more precision than the naive approach:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.31326169,  1.31326169],\n",
+       "       [ 1.69314718,  1.69314718],\n",
+       "       [ 2.31326169,  2.31326169]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.logaddexp(M, a[:, np.newaxis])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more information on the many available universal functions, refer to [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Broadcasting in Practice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Broadcasting operations form the core of many examples we'll see throughout this book.\n",
+    "We'll now take a look at a couple simple examples of where they can be useful."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Centering an array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the previous section, we saw that ufuncs allow a NumPy user to remove the need to explicitly write slow Python loops. Broadcasting extends this ability.\n",
+    "One commonly seen example is when centering an array of data.\n",
+    "Imagine you have an array of 10 observations, each of which consists of 3 values.\n",
+    "Using the standard convention (see [Data Representation in Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb#Data-Representation-in-Scikit-Learn)), we'll store this in a $10 \\times 3$ array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "X = np.random.random((10, 3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can compute the mean of each feature using the ``mean`` aggregate across the first dimension:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.53514715,  0.66567217,  0.44385899])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Xmean = X.mean(0)\n",
+    "Xmean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And now we can center the ``X`` array by subtracting the mean (this is a broadcasting operation):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "X_centered = X - Xmean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To double-check that we've done this correctly, we can check that the centered array has near zero mean:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  2.22044605e-17,  -7.77156117e-17,  -1.66533454e-17])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_centered.mean(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To within machine precision, the mean is now zero."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting a two-dimensional function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One place that broadcasting is very useful is in displaying images based on two-dimensional functions.\n",
+    "If we want to define a function $z = f(x, y)$, broadcasting can be used to compute the function across the grid:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# x and y have 50 steps from 0 to 5\n",
+    "x = np.linspace(0, 5, 50)\n",
+    "y = np.linspace(0, 5, 50)[:, np.newaxis]\n",
+    "\n",
+    "z = np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll use Matplotlib to plot this two-dimensional array (these tools will be discussed in full in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LLB6BTSeAm0BtVGgQ2+y9R0HtcXia/X4buD1hWHSqNm/a3V/7CWlmMzTo2HwY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\nHIwtxPGVX5uCmie15H4fjF7PZkv34g0GhNbr2EYra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vqG276mezUiOieinyain9dr/rbb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\nrQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10becee48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(z, origin='lower', extent=[0, 5, 0, 5],\n",
+    "           cmap='viridis')\n",
+    "plt.colorbar();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a compelling visualization of the two-dimensional function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) | [Contents](Index.ipynb) | [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.05-Computation-on-arrays-broadcasting.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/02.06-Boolean-Arrays-and-Masks.ipynb b/notebooks_v1/02.06-Boolean-Arrays-and-Masks.ipynb
new file mode 100644
index 000000000..e17269f9d
--- /dev/null
+++ b/notebooks_v1/02.06-Boolean-Arrays-and-Masks.ipynb
@@ -0,0 +1,1283 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) | [Contents](Index.ipynb) | [Fancy Indexing](02.07-Fancy-Indexing.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.06-Boolean-Arrays-and-Masks.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparisons, Masks, and Boolean Logic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This section covers the use of Boolean masks to examine and manipulate values within NumPy arrays.\n",
+    "Masking comes up when you want to extract, modify, count, or otherwise manipulate values in an array based on some criterion: for example, you might wish to count all values greater than a certain value, or perhaps remove all outliers that are above some threshold.\n",
+    "In NumPy, Boolean masking is often the most efficient way to accomplish these types of tasks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Counting Rainy Days\n",
+    "\n",
+    "Imagine you have a series of data that represents the amount of precipitation each day for a year in a given city.\n",
+    "For example, here we'll load the daily rainfall statistics for the city of Seattle in 2014, using Pandas (which is covered in more detail in [Chapter 3](03.00-Introduction-to-Pandas.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(365,)"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# use pandas to extract rainfall inches as a NumPy array\n",
+    "rainfall = pd.read_csv('data/Seattle2014.csv')['PRCP'].values\n",
+    "inches = rainfall / 254.0  # 1/10mm -> inches\n",
+    "inches.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The array contains 365 values, giving daily rainfall in inches from January 1 to December 31, 2014.\n",
+    "\n",
+    "As a first quick visualization, let's look at the histogram of rainy days, which was generated using Matplotlib (we will explore this tool more fully in [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn; seaborn.set()  # set plot styles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cWANA4cQaAAon1gBQOLEGgMKJNQAUrnGqJzAV3nzzzTz33IFRje3o+JfMnDlzgmcEAO9u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\nngAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10b617a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(inches, 40);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This histogram gives us a general idea of what the data looks like: despite its reputation, the vast majority of days in Seattle saw near zero measured rainfall in 2014.\n",
+    "But this doesn't do a good job of conveying some information we'd like to see: for example, how many rainy days were there in the year? What is the average precipitation on those rainy days? How many days were there with more than half an inch of rain?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Digging into the data\n",
+    "\n",
+    "One approach to this would be to answer these questions by hand: loop through the data, incrementing a counter each time we see values in some desired range.\n",
+    "For reasons discussed throughout this chapter, such an approach is very inefficient, both from the standpoint of time writing code and time computing the result.\n",
+    "We saw in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) that NumPy's ufuncs can be used in place of loops to do fast element-wise arithmetic operations on arrays; in the same way, we can use other ufuncs to do element-wise *comparisons* over arrays, and we can then manipulate the results to answer the questions we have.\n",
+    "We'll leave the data aside for right now, and discuss some general tools in NumPy to use *masking* to quickly answer these types of questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparison Operators as ufuncs\n",
+    "\n",
+    "In [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) we introduced ufuncs, and focused in particular on arithmetic operators. We saw that using ``+``, ``-``, ``*``, ``/``, and others on arrays leads to element-wise operations.\n",
+    "NumPy also implements comparison operators such as ``<`` (less than) and ``>`` (greater than) as element-wise ufuncs.\n",
+    "The result of these comparison operators is always an array with a Boolean data type.\n",
+    "All six of the standard comparison operations are available:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x = np.array([1, 2, 3, 4, 5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True,  True, False, False, False], dtype=bool)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x < 3  # less than"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False,  True,  True], dtype=bool)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x > 3  # greater than"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True,  True,  True, False, False], dtype=bool)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x <= 3  # less than or equal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False,  True,  True,  True], dtype=bool)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x >= 3  # greater than or equal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True,  True, False,  True,  True], dtype=bool)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x != 3  # not equal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False,  True, False, False], dtype=bool)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x == 3  # equal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible to do an element-wise comparison of two arrays, and to include compound expressions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False,  True, False, False, False], dtype=bool)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(2 * x) == (x ** 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As in the case of arithmetic operators, the comparison operators are implemented as ufuncs in NumPy; for example, when you write ``x < 3``, internally NumPy uses ``np.less(x, 3)``.\n",
+    "    A summary of the comparison operators and their equivalent ufunc is shown here:\n",
+    "\n",
+    "| Operator\t    | Equivalent ufunc    || Operator\t   | Equivalent ufunc    |\n",
+    "|---------------|---------------------||---------------|---------------------|\n",
+    "|``==``         |``np.equal``         ||``!=``         |``np.not_equal``     |\n",
+    "|``<``          |``np.less``          ||``<=``         |``np.less_equal``    |\n",
+    "|``>``          |``np.greater``       ||``>=``         |``np.greater_equal`` |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as in the case of arithmetic ufuncs, these will work on arrays of any size and shape.\n",
+    "Here is a two-dimensional example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[5, 0, 3, 3],\n",
+       "       [7, 9, 3, 5],\n",
+       "       [2, 4, 7, 6]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rng = np.random.RandomState(0)\n",
+    "x = rng.randint(10, size=(3, 4))\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True,  True,  True,  True],\n",
+       "       [False, False,  True,  True],\n",
+       "       [ True,  True, False, False]], dtype=bool)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x < 6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In each case, the result is a Boolean array, and NumPy provides a number of straightforward patterns for working with these Boolean results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with Boolean Arrays\n",
+    "\n",
+    "Given a Boolean array, there are a host of useful operations you can do.\n",
+    "We'll work with ``x``, the two-dimensional array we created earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[5 0 3 3]\n",
+      " [7 9 3 5]\n",
+      " [2 4 7 6]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Counting entries\n",
+    "\n",
+    "To count the number of ``True`` entries in a Boolean array, ``np.count_nonzero`` is useful:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# how many values less than 6?\n",
+    "np.count_nonzero(x < 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that there are eight array entries that are less than 6.\n",
+    "Another way to get at this information is to use ``np.sum``; in this case, ``False`` is interpreted as ``0``, and ``True`` is interpreted as ``1``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(x < 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The benefit of ``sum()`` is that like with other NumPy aggregation functions, this summation can be done along rows or columns as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4, 2, 2])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# how many values less than 6 in each row?\n",
+    "np.sum(x < 6, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This counts the number of values less than 6 in each row of the matrix.\n",
+    "\n",
+    "If we're interested in quickly checking whether any or all the values are true, we can use (you guessed it) ``np.any`` or ``np.all``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# are there any values greater than 8?\n",
+    "np.any(x > 8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# are there any values less than zero?\n",
+    "np.any(x < 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# are all values less than 10?\n",
+    "np.all(x < 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# are all values equal to 6?\n",
+    "np.all(x == 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "``np.all`` and ``np.any`` can be used along particular axes as well. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True, False,  True], dtype=bool)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# are all values in each row less than 8?\n",
+    "np.all(x < 8, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here all the elements in the first and third rows are less than 8, while this is not the case for the second row.\n",
+    "\n",
+    "Finally, a quick warning: as mentioned in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb), Python has built-in ``sum()``, ``any()``, and ``all()`` functions. These have a different syntax than the NumPy versions, and in particular will fail or produce unintended results when used on multidimensional arrays. Be sure that you are using ``np.sum()``, ``np.any()``, and ``np.all()`` for these examples!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Boolean operators\n",
+    "\n",
+    "We've already seen how we might count, say, all days with rain less than four inches, or all days with rain greater than two inches.\n",
+    "But what if we want to know about all days with rain less than four inches and greater than one inch?\n",
+    "This is accomplished through Python's *bitwise logic operators*, ``&``, ``|``, ``^``, and ``~``.\n",
+    "Like with the standard arithmetic operators, NumPy overloads these as ufuncs which work element-wise on (usually Boolean) arrays.\n",
+    "\n",
+    "For example, we can address this sort of compound question as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "29"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum((inches > 0.5) & (inches < 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we see that there are 29 days with rainfall between 0.5 and 1.0 inches.\n",
+    "\n",
+    "Note that the parentheses here are important–because of operator precedence rules, with parentheses removed this expression would be evaluated as follows, which results in an error:\n",
+    "\n",
+    "``` python\n",
+    "inches > (0.5 & inches) < 1\n",
+    "```\n",
+    "\n",
+    "Using the equivalence of *A AND B* and *NOT (NOT A OR NOT B)* (which you may remember if you've taken an introductory logic course), we can compute the same result in a different manner:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "29"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(~( (inches <= 0.5) | (inches >= 1) ))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Combining comparison operators and Boolean operators on arrays can lead to a wide range of efficient logical operations.\n",
+    "\n",
+    "The following table summarizes the bitwise Boolean operators and their equivalent ufuncs:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "| Operator\t    | Equivalent ufunc    || Operator\t    | Equivalent ufunc    |\n",
+    "|---------------|---------------------||---------------|---------------------|\n",
+    "|``&``          |``np.bitwise_and``   ||&#124;         |``np.bitwise_or``    |\n",
+    "|``^``          |``np.bitwise_xor``   ||``~``          |``np.bitwise_not``   |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using these tools, we might start to answer the types of questions we have about our weather data.\n",
+    "Here are some examples of results we can compute when combining masking with aggregations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number days without rain:       215\n",
+      "Number days with rain:          150\n",
+      "Days with more than 0.5 inches: 37\n",
+      "Rainy days with < 0.2 inches  : 75\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number days without rain:      \", np.sum(inches == 0))\n",
+    "print(\"Number days with rain:         \", np.sum(inches != 0))\n",
+    "print(\"Days with more than 0.5 inches:\", np.sum(inches > 0.5))\n",
+    "print(\"Rainy days with < 0.2 inches  :\", np.sum((inches > 0) &\n",
+    "                                                (inches < 0.2)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Boolean Arrays as Masks\n",
+    "\n",
+    "In the preceding section we looked at aggregates computed directly on Boolean arrays.\n",
+    "A more powerful pattern is to use Boolean arrays as masks, to select particular subsets of the data themselves.\n",
+    "Returning to our ``x`` array from before, suppose we want an array of all values in the array that are less than, say, 5:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[5, 0, 3, 3],\n",
+       "       [7, 9, 3, 5],\n",
+       "       [2, 4, 7, 6]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can obtain a Boolean array for this condition easily, as we've already seen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False,  True,  True,  True],\n",
+       "       [False, False,  True, False],\n",
+       "       [ True,  True, False, False]], dtype=bool)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x < 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now to *select* these values from the array, we can simply index on this Boolean array; this is known as a *masking* operation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 3, 3, 3, 2, 4])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[x < 5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is returned is a one-dimensional array filled with all the values that meet this condition; in other words, all the values in positions at which the mask array is ``True``.\n",
+    "\n",
+    "We are then free to operate on these values as we wish.\n",
+    "For example, we can compute some relevant statistics on our Seattle rain data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Median precip on rainy days in 2014 (inches):    0.194881889764\n",
+      "Median precip on summer days in 2014 (inches):   0.0\n",
+      "Maximum precip on summer days in 2014 (inches):  0.850393700787\n",
+      "Median precip on non-summer rainy days (inches): 0.200787401575\n"
+     ]
+    }
+   ],
+   "source": [
+    "# construct a mask of all rainy days\n",
+    "rainy = (inches > 0)\n",
+    "\n",
+    "# construct a mask of all summer days (June 21st is the 172nd day)\n",
+    "days = np.arange(365)\n",
+    "summer = (days > 172) & (days < 262)\n",
+    "\n",
+    "print(\"Median precip on rainy days in 2014 (inches):   \",\n",
+    "      np.median(inches[rainy]))\n",
+    "print(\"Median precip on summer days in 2014 (inches):  \",\n",
+    "      np.median(inches[summer]))\n",
+    "print(\"Maximum precip on summer days in 2014 (inches): \",\n",
+    "      np.max(inches[summer]))\n",
+    "print(\"Median precip on non-summer rainy days (inches):\",\n",
+    "      np.median(inches[rainy & ~summer]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By combining Boolean operations, masking operations, and aggregates, we can very quickly answer these sorts of questions for our dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aside: Using the Keywords and/or Versus the Operators &/|\n",
+    "\n",
+    "One common point of confusion is the difference between the keywords ``and`` and ``or`` on one hand, and the operators ``&`` and ``|`` on the other hand.\n",
+    "When would you use one versus the other?\n",
+    "\n",
+    "The difference is this: ``and`` and ``or`` gauge the truth or falsehood of *entire object*, while ``&`` and ``|`` refer to *bits within each object*.\n",
+    "\n",
+    "When you use ``and`` or ``or``, it's equivalent to asking Python to treat the object as a single Boolean entity.\n",
+    "In Python, all nonzero integers will evaluate as True. Thus:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(True, False)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(42), bool(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(42 and 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(42 or 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you use ``&`` and ``|`` on integers, the expression operates on the bits of the element, applying the *and* or the *or* to the individual bits making up the number:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b101010'"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b111011'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(59)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b101010'"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(42 & 59)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b111011'"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(42 | 59)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the corresponding bits of the binary representation are compared in order to yield the result.\n",
+    "\n",
+    "When you have an array of Boolean values in NumPy, this can be thought of as a string of bits where ``1 = True`` and ``0 = False``, and the result of ``&`` and ``|`` operates similarly to above:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True,  True,  True, False,  True,  True], dtype=bool)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = np.array([1, 0, 1, 0, 1, 0], dtype=bool)\n",
+    "B = np.array([1, 1, 1, 0, 1, 1], dtype=bool)\n",
+    "A | B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using ``or`` on these arrays will try to evaluate the truth or falsehood of the entire array object, which is not a well-defined value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-38-5d8e4f2e21c0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
+     ]
+    }
+   ],
+   "source": [
+    "A or B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, when doing a Boolean expression on a given array, you should use ``|`` or ``&`` rather than ``or`` or ``and``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, False, False,  True,  True,  True, False, False], dtype=bool)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.arange(10)\n",
+    "(x > 4) & (x < 8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Trying to evaluate the truth or falsehood of the entire array will give the same ``ValueError`` we saw previously:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-40-3d24f1ffd63d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
+     ]
+    }
+   ],
+   "source": [
+    "(x > 4) and (x < 8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So remember this: ``and`` and ``or`` perform a single Boolean evaluation on an entire object, while ``&`` and ``|`` perform multiple Boolean evaluations on the content (the individual bits or bytes) of an object.\n",
+    "For Boolean NumPy arrays, the latter is nearly always the desired operation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) | [Contents](Index.ipynb) | [Fancy Indexing](02.07-Fancy-Indexing.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.06-Boolean-Arrays-and-Masks.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/02.07-Fancy-Indexing.ipynb b/notebooks_v1/02.07-Fancy-Indexing.ipynb
new file mode 100644
index 000000000..00cc188a5
--- /dev/null
+++ b/notebooks_v1/02.07-Fancy-Indexing.ipynb
@@ -0,0 +1,932 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) | [Contents](Index.ipynb) | [Sorting Arrays](02.08-Sorting.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.07-Fancy-Indexing.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fancy Indexing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the previous sections, we saw how to access and modify portions of arrays using simple indices (e.g., ``arr[0]``), slices (e.g., ``arr[:5]``), and Boolean masks (e.g., ``arr[arr > 0]``).\n",
+    "In this section, we'll look at another style of array indexing, known as *fancy indexing*.\n",
+    "Fancy indexing is like the simple indexing we've already seen, but we pass arrays of indices in place of single scalars.\n",
+    "This allows us to very quickly access and modify complicated subsets of an array's values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring Fancy Indexing\n",
+    "\n",
+    "Fancy indexing is conceptually simple: it means passing an array of indices to access multiple array elements at once.\n",
+    "For example, consider the following array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[51 92 14 71 60 20 82 86 74 74]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "rand = np.random.RandomState(42)\n",
+    "\n",
+    "x = rand.randint(100, size=10)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Suppose we want to access three different elements. We could do it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[71, 86, 14]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[x[3], x[7], x[2]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, we can pass a single list or array of indices to obtain the same result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([71, 86, 60])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ind = [3, 7, 4]\n",
+    "x[ind]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When using fancy indexing, the shape of the result reflects the shape of the *index arrays* rather than the shape of the *array being indexed*:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[71, 86],\n",
+       "       [60, 20]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ind = np.array([[3, 7],\n",
+    "                [4, 5]])\n",
+    "x[ind]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fancy indexing also works in multiple dimensions. Consider the following array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3],\n",
+       "       [ 4,  5,  6,  7],\n",
+       "       [ 8,  9, 10, 11]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = np.arange(12).reshape((3, 4))\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Like with standard indexing, the first index refers to the row, and the second to the column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2,  5, 11])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "row = np.array([0, 1, 2])\n",
+    "col = np.array([2, 1, 3])\n",
+    "X[row, col]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the first value in the result is ``X[0, 2]``, the second is ``X[1, 1]``, and the third is ``X[2, 3]``.\n",
+    "The pairing of indices in fancy indexing follows all the broadcasting rules that were mentioned in [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb).\n",
+    "So, for example, if we combine a column vector and a row vector within the indices, we get a two-dimensional result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  1,  3],\n",
+       "       [ 6,  5,  7],\n",
+       "       [10,  9, 11]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X[row[:, np.newaxis], col]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, each row value is matched with each column vector, exactly as we saw in broadcasting of arithmetic operations.\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0],\n",
+       "       [2, 1, 3],\n",
+       "       [4, 2, 6]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "row[:, np.newaxis] * col"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is always important to remember with fancy indexing that the return value reflects the *broadcasted shape of the indices*, rather than the shape of the array being indexed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Combined Indexing\n",
+    "\n",
+    "For even more powerful operations, fancy indexing can be combined with the other indexing schemes we've seen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0  1  2  3]\n",
+      " [ 4  5  6  7]\n",
+      " [ 8  9 10 11]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can combine fancy and simple indices:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10,  8,  9])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X[2, [2, 0, 1]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also combine fancy indexing with slicing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6,  4,  5],\n",
+       "       [10,  8,  9]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X[1:, [2, 0, 1]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can combine fancy indexing with masking:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  2],\n",
+       "       [ 4,  6],\n",
+       "       [ 8, 10]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask = np.array([1, 0, 1, 0], dtype=bool)\n",
+    "X[row[:, np.newaxis], mask]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All of these indexing options combined lead to a very flexible set of operations for accessing and modifying array values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Selecting Random Points\n",
+    "\n",
+    "One common use of fancy indexing is the selection of subsets of rows from a matrix.\n",
+    "For example, we might have an $N$ by $D$ matrix representing $N$ points in $D$ dimensions, such as the following points drawn from a two-dimensional normal distribution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100, 2)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean = [0, 0]\n",
+    "cov = [[1, 2],\n",
+    "       [2, 5]]\n",
+    "X = rand.multivariate_normal(mean, cov, 100)\n",
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the plotting tools we will discuss in [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb), we can visualize these points as a scatter-plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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vvffexa81Nzdr+PDhF18PGzZMx44dM1hVAAC8od9wLikpUUlJSb8FDRs2TE1N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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e332048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn; seaborn.set()  # for plot styling\n",
+    "\n",
+    "plt.scatter(X[:, 0], X[:, 1]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's use fancy indexing to select 20 random points. We'll do this by first choosing 20 random indices with no repeats, and use these indices to select a portion of the original array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([93, 45, 73, 81, 50, 10, 98, 94,  4, 64, 65, 89, 47, 84, 82, 80, 25,\n",
+       "       90, 63, 20])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indices = np.random.choice(X.shape[0], 20, replace=False)\n",
+    "indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20, 2)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selection = X[indices]  # fancy indexing here\n",
+    "selection.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now to see which points were selected, let's over-plot large circles at the locations of the selected points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YsACHDx/G448/DgDYuHGjlMUTUS/jKzl3rjXu338UqqrOYcyYBJSX17hnW2s0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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118687240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:, 0], X[:, 1], alpha=0.3)\n",
+    "plt.scatter(selection[:, 0], selection[:, 1],\n",
+    "            facecolor='none', s=200);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This sort of strategy is often used to quickly partition datasets, as is often needed in train/test splitting for validation of statistical models (see [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb)), and in sampling approaches to answering statistical questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modifying Values with Fancy Indexing\n",
+    "\n",
+    "Just as fancy indexing can be used to access parts of an array, it can also be used to modify parts of an array.\n",
+    "For example, imagine we have an array of indices and we'd like to set the corresponding items in an array to some value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0 99 99  3 99  5  6  7 99  9]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.arange(10)\n",
+    "i = np.array([2, 1, 8, 4])\n",
+    "x[i] = 99\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use any assignment-type operator for this. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0 89 89  3 89  5  6  7 89  9]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x[i] -= 10\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice, though, that repeated indices with these operations can cause some potentially unexpected results. Consider the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 6.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.zeros(10)\n",
+    "x[[0, 0]] = [4, 6]\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Where did the 4 go? The result of this operation is to first assign ``x[0] = 4``, followed by ``x[0] = 6``.\n",
+    "The result, of course, is that ``x[0]`` contains the value 6.\n",
+    "\n",
+    "Fair enough, but consider this operation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 6.,  0.,  1.,  1.,  1.,  0.,  0.,  0.,  0.,  0.])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "i = [2, 3, 3, 4, 4, 4]\n",
+    "x[i] += 1\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You might expect that ``x[3]`` would contain the value 2, and ``x[4]`` would contain the value 3, as this is how many times each index is repeated. Why is this not the case?\n",
+    "Conceptually, this is because ``x[i] += 1`` is meant as a shorthand of ``x[i] = x[i] + 1``. ``x[i] + 1`` is evaluated, and then the result is assigned to the indices in x.\n",
+    "With this in mind, it is not the augmentation that happens multiple times, but the assignment, which leads to the rather nonintuitive results.\n",
+    "\n",
+    "So what if you want the other behavior where the operation is repeated? For this, you can use the ``at()`` method of ufuncs (available since NumPy 1.8), and do the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.  0.  1.  2.  3.  0.  0.  0.  0.  0.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.zeros(10)\n",
+    "np.add.at(x, i, 1)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``at()`` method does an in-place application of the given operator at the specified indices (here, ``i``) with the specified value (here, 1).\n",
+    "Another method that is similar in spirit is the ``reduceat()`` method of ufuncs, which you can read about in the NumPy documentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Binning Data\n",
+    "\n",
+    "You can use these ideas to efficiently bin data to create a histogram by hand.\n",
+    "For example, imagine we have 1,000 values and would like to quickly find where they fall within an array of bins.\n",
+    "We could compute it using ``ufunc.at`` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "np.random.seed(42)\n",
+    "x = np.random.randn(100)\n",
+    "\n",
+    "# compute a histogram by hand\n",
+    "bins = np.linspace(-5, 5, 20)\n",
+    "counts = np.zeros_like(bins)\n",
+    "\n",
+    "# find the appropriate bin for each x\n",
+    "i = np.searchsorted(bins, x)\n",
+    "\n",
+    "# add 1 to each of these bins\n",
+    "np.add.at(counts, i, 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The counts now reflect the number of points within each bin–in other words, a histogram:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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s2RKHDx+OK664IrZt2xY33nhjqWcDgEGtVyfh/fv3x9mzZ2Pv3r3x8MMPx44dO0o9FwAM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\nwgCQRIQBIIkIA0ASEQaAJP8P7quNPHF17C4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1186d90b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the results\n",
+    "plt.plot(bins, counts, linestyle='steps');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, it would be silly to have to do this each time you want to plot a histogram.\n",
+    "This is why Matplotlib provides the ``plt.hist()`` routine, which does the same in a single line:\n",
+    "\n",
+    "```python\n",
+    "plt.hist(x, bins, histtype='step');\n",
+    "```\n",
+    "\n",
+    "This function will create a nearly identical plot to the one seen here.\n",
+    "To compute the binning, ``matplotlib`` uses the ``np.histogram`` function, which does a very similar computation to what we did before. Let's compare the two here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NumPy routine:\n",
+      "10000 loops, best of 3: 97.6 µs per loop\n",
+      "Custom routine:\n",
+      "10000 loops, best of 3: 19.5 µs per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"NumPy routine:\")\n",
+    "%timeit counts, edges = np.histogram(x, bins)\n",
+    "\n",
+    "print(\"Custom routine:\")\n",
+    "%timeit np.add.at(counts, np.searchsorted(bins, x), 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our own one-line algorithm is several times faster than the optimized algorithm in NumPy! How can this be?\n",
+    "If you dig into the ``np.histogram`` source code (you can do this in IPython by typing ``np.histogram??``), you'll see that it's quite a bit more involved than the simple search-and-count that we've done; this is because NumPy's algorithm is more flexible, and particularly is designed for better performance when the number of data points becomes large:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NumPy routine:\n",
+      "10 loops, best of 3: 68.7 ms per loop\n",
+      "Custom routine:\n",
+      "10 loops, best of 3: 135 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.random.randn(1000000)\n",
+    "print(\"NumPy routine:\")\n",
+    "%timeit counts, edges = np.histogram(x, bins)\n",
+    "\n",
+    "print(\"Custom routine:\")\n",
+    "%timeit np.add.at(counts, np.searchsorted(bins, x), 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What this comparison shows is that algorithmic efficiency is almost never a simple question. An algorithm efficient for large datasets will not always be the best choice for small datasets, and vice versa (see [Big-O Notation](02.08-Sorting.ipynb#Aside:-Big-O-Notation)).\n",
+    "But the advantage of coding this algorithm yourself is that with an understanding of these basic methods, you could use these building blocks to extend this to do some very interesting custom behaviors.\n",
+    "The key to efficiently using Python in data-intensive applications is knowing about general convenience routines like ``np.histogram`` and when they're appropriate, but also knowing how to make use of lower-level functionality when you need more pointed behavior."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) | [Contents](Index.ipynb) | [Sorting Arrays](02.08-Sorting.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.07-Fancy-Indexing.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/02.08-Sorting.ipynb b/notebooks_v1/02.08-Sorting.ipynb
new file mode 100644
index 000000000..8be3373c0
--- /dev/null
+++ b/notebooks_v1/02.08-Sorting.ipynb
@@ -0,0 +1,786 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Fancy Indexing](02.07-Fancy-Indexing.ipynb) | [Contents](Index.ipynb) | [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.08-Sorting.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sorting Arrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Up to this point we have been concerned mainly with tools to access and operate on array data with NumPy.\n",
+    "This section covers algorithms related to sorting values in NumPy arrays.\n",
+    "These algorithms are a favorite topic in introductory computer science courses: if you've ever taken one, you probably have had dreams (or, depending on your temperament, nightmares) about *insertion sorts*, *selection sorts*, *merge sorts*, *quick sorts*, *bubble sorts*, and many, many more.\n",
+    "All are means of accomplishing a similar task: sorting the values in a list or array.\n",
+    "\n",
+    "For example, a simple *selection sort* repeatedly finds the minimum value from a list, and makes swaps until the list is sorted. We can code this in just a few lines of Python:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def selection_sort(x):\n",
+    "    for i in range(len(x)):\n",
+    "        swap = i + np.argmin(x[i:])\n",
+    "        (x[i], x[swap]) = (x[swap], x[i])\n",
+    "    return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([2, 1, 4, 3, 5])\n",
+    "selection_sort(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As any first-year computer science major will tell you, the selection sort is useful for its simplicity, but is much too slow to be useful for larger arrays.\n",
+    "For a list of $N$ values, it requires $N$ loops, each of which does on order $\\sim N$ comparisons to find the swap value.\n",
+    "In terms of the \"big-O\" notation often used to characterize these algorithms (see [Big-O Notation](#Aside:-Big-O-Notation)), selection sort averages $\\mathcal{O}[N^2]$: if you double the number of items in the list, the execution time will go up by about a factor of four.\n",
+    "\n",
+    "Even selection sort, though, is much better than my all-time favorite sorting algorithms, the *bogosort*:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def bogosort(x):\n",
+    "    while np.any(x[:-1] > x[1:]):\n",
+    "        np.random.shuffle(x)\n",
+    "    return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([2, 1, 4, 3, 5])\n",
+    "bogosort(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This silly sorting method relies on pure chance: it repeatedly applies a random shuffling of the array until the result happens to be sorted.\n",
+    "With an average scaling of $\\mathcal{O}[N \\times N!]$, (that's *N* times *N* factorial) this should–quite obviously–never be used for any real computation.\n",
+    "\n",
+    "Fortunately, Python contains built-in sorting algorithms that are *much* more efficient than either of the simplistic algorithms just shown. We'll start by looking at the Python built-ins, and then take a look at the routines included in NumPy and optimized for NumPy arrays."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fast Sorting in NumPy: ``np.sort`` and ``np.argsort``\n",
+    "\n",
+    "Although Python has built-in ``sort`` and ``sorted`` functions to work with lists, we won't discuss them here because NumPy's ``np.sort`` function turns out to be much more efficient and useful for our purposes.\n",
+    "By default ``np.sort`` uses an $\\mathcal{O}[N\\log N]$, *quicksort* algorithm, though *mergesort* and *heapsort* are also available. For most applications, the default quicksort is more than sufficient.\n",
+    "\n",
+    "To return a sorted version of the array without modifying the input, you can use ``np.sort``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([2, 1, 4, 3, 5])\n",
+    "np.sort(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you prefer to sort the array in-place, you can instead use the ``sort`` method of arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x.sort()\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A related function is ``argsort``, which instead returns the *indices* of the sorted elements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 0 3 2 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.array([2, 1, 4, 3, 5])\n",
+    "i = np.argsort(x)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first element of this result gives the index of the smallest element, the second value gives the index of the second smallest, and so on.\n",
+    "These indices can then be used (via fancy indexing) to construct the sorted array if desired:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[i]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sorting along rows or columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A useful feature of NumPy's sorting algorithms is the ability to sort along specific rows or columns of a multidimensional array using the ``axis`` argument. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[6 3 7 4 6 9]\n",
+      " [2 6 7 4 3 7]\n",
+      " [7 2 5 4 1 7]\n",
+      " [5 1 4 0 9 5]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "rand = np.random.RandomState(42)\n",
+    "X = rand.randint(0, 10, (4, 6))\n",
+    "print(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 1, 4, 0, 1, 5],\n",
+       "       [5, 2, 5, 4, 3, 7],\n",
+       "       [6, 3, 7, 4, 6, 7],\n",
+       "       [7, 6, 7, 4, 9, 9]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sort each column of X\n",
+    "np.sort(X, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3, 4, 6, 6, 7, 9],\n",
+       "       [2, 3, 4, 6, 7, 7],\n",
+       "       [1, 2, 4, 5, 7, 7],\n",
+       "       [0, 1, 4, 5, 5, 9]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sort each row of X\n",
+    "np.sort(X, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Keep in mind that this treats each row or column as an independent array, and any relationships between the row or column values will be lost!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Partial Sorts: Partitioning\n",
+    "\n",
+    "Sometimes we're not interested in sorting the entire array, but simply want to find the *k* smallest values in the array. NumPy provides this in the ``np.partition`` function. ``np.partition`` takes an array and a number *K*; the result is a new array with the smallest *K* values to the left of the partition, and the remaining values to the right, in arbitrary order:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 1, 3, 4, 6, 5, 7])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([7, 2, 3, 1, 6, 5, 4])\n",
+    "np.partition(x, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the first three values in the resulting array are the three smallest in the array, and the remaining array positions contain the remaining values.\n",
+    "Within the two partitions, the elements have arbitrary order.\n",
+    "\n",
+    "Similarly to sorting, we can partition along an arbitrary axis of a multidimensional array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3, 4, 6, 7, 6, 9],\n",
+       "       [2, 3, 4, 7, 6, 7],\n",
+       "       [1, 2, 4, 5, 7, 7],\n",
+       "       [0, 1, 4, 5, 9, 5]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.partition(X, 2, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an array where the first two slots in each row contain the smallest values from that row, with the remaining values filling the remaining slots.\n",
+    "\n",
+    "Finally, just as there is a ``np.argsort`` that computes indices of the sort, there is a ``np.argpartition`` that computes indices of the partition.\n",
+    "We'll see this in action in the following section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: k-Nearest Neighbors\n",
+    "\n",
+    "Let's quickly see how we might use this ``argsort`` function along multiple axes to find the nearest neighbors of each point in a set.\n",
+    "We'll start by creating a random set of 10 points on a two-dimensional plane.\n",
+    "Using the standard convention, we'll arrange these in a $10\\times 2$ array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "X = rand.rand(10, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get an idea of how these points look, let's quickly scatter plot them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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n5Pg1PDysysomHTt2tyKRS2vrjY09euih/9b+/cGMXVufLZ9/uji9/xtlK7h9Pp+GhoZi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\nIbgBADAIwQ0AgEEIbgAADEJwAwBgkP8PJjFcqzTtbqoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10be92fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn; seaborn.set() # Plot styling\n",
+    "plt.scatter(X[:, 0], X[:, 1], s=100);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll compute the distance between each pair of points.\n",
+    "Recall that the squared-distance between two points is the sum of the squared differences in each dimension;\n",
+    "using the efficient broadcasting ([Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb)) and aggregation ([Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb))  routines provided by NumPy we can compute the matrix of square distances in a single line of code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dist_sq = np.sum((X[:, np.newaxis, :] - X[np.newaxis, :, :]) ** 2, axis=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This operation has a lot packed into it, and it might be a bit confusing if you're unfamiliar with NumPy's broadcasting rules. When you come across code like this, it can be useful to break it down into its component steps:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 10, 2)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# for each pair of points, compute differences in their coordinates\n",
+    "differences = X[:, np.newaxis, :] - X[np.newaxis, :, :]\n",
+    "differences.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 10, 2)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# square the coordinate differences\n",
+    "sq_differences = differences ** 2\n",
+    "sq_differences.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 10)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sum the coordinate differences to get the squared distance\n",
+    "dist_sq = sq_differences.sum(-1)\n",
+    "dist_sq.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just to double-check what we are doing, we should see that the diagonal of this matrix (i.e., the set of distances between each point and itself) is all zero:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dist_sq.diagonal()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It checks out!\n",
+    "With the pairwise square-distances converted, we can now use ``np.argsort`` to sort along each row. The leftmost columns will then give the indices of the nearest neighbors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 3 9 7 1 4 2 5 6 8]\n",
+      " [1 4 7 9 3 6 8 5 0 2]\n",
+      " [2 1 4 6 3 0 8 9 7 5]\n",
+      " [3 9 7 0 1 4 5 8 6 2]\n",
+      " [4 1 8 5 6 7 9 3 0 2]\n",
+      " [5 8 6 4 1 7 9 3 2 0]\n",
+      " [6 8 5 4 1 7 9 3 2 0]\n",
+      " [7 9 3 1 4 0 5 8 6 2]\n",
+      " [8 5 6 4 1 7 9 3 2 0]\n",
+      " [9 7 3 0 1 4 5 8 6 2]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "nearest = np.argsort(dist_sq, axis=1)\n",
+    "print(nearest)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the first column gives the numbers 0 through 9 in order: this is due to the fact that each point's closest neighbor is itself, as we would expect.\n",
+    "\n",
+    "By using a full sort here, we've actually done more work than we need to in this case. If we're simply interested in the nearest $k$ neighbors, all we need is to partition each row so that the smallest $k + 1$ squared distances come first, with larger distances filling the remaining positions of the array. We can do this with the ``np.argpartition`` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "K = 2\n",
+    "nearest_partition = np.argpartition(dist_sq, K + 1, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to visualize this network of neighbors, let's quickly plot the points along with lines representing the connections from each point to its two nearest neighbors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RaLCxqUVMzFoSEp4dvjE3P4eDw358fJywsLDI1mfqU//5TR7rKYQeUalUfPbZ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\nYBGCGwAAixDcAABYhOAGAMAi/weoi5+ciElD0QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115ae2898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:, 0], X[:, 1], s=100)\n",
+    "\n",
+    "# draw lines from each point to its two nearest neighbors\n",
+    "K = 2\n",
+    "\n",
+    "for i in range(X.shape[0]):\n",
+    "    for j in nearest_partition[i, :K+1]:\n",
+    "        # plot a line from X[i] to X[j]\n",
+    "        # use some zip magic to make it happen:\n",
+    "        plt.plot(*zip(X[j], X[i]), color='black')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each point in the plot has lines drawn to its two nearest neighbors.\n",
+    "At first glance, it might seem strange that some of the points have more than two lines coming out of them: this is due to the fact that if point A is one of the two nearest neighbors of point B, this does not necessarily imply that point B is one of the two nearest neighbors of point A.\n",
+    "\n",
+    "Although the broadcasting and row-wise sorting of this approach might seem less straightforward than writing a loop, it turns out to be a very efficient way of operating on this data in Python.\n",
+    "You might be tempted to do the same type of operation by manually looping through the data and sorting each set of neighbors individually, but this would almost certainly lead to a slower algorithm than the vectorized version we used. The beauty of this approach is that it's written in a way that's agnostic to the size of the input data: we could just as easily compute the neighbors among 100 or 1,000,000 points in any number of dimensions, and the code would look the same.\n",
+    "\n",
+    "Finally, I'll note that when doing very large nearest neighbor searches, there are tree-based and/or approximate algorithms that can scale as $\\mathcal{O}[N\\log N]$ or better rather than the $\\mathcal{O}[N^2]$ of the brute-force algorithm. One example of this is the KD-Tree, [implemented in Scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aside: Big-O Notation\n",
+    "\n",
+    "Big-O notation is a means of describing how the number of operations required for an algorithm scales as the input grows in size.\n",
+    "To use it correctly is to dive deeply into the realm of computer science theory, and to carefully distinguish it from the related small-o notation, big-$\\theta$ notation, big-$\\Omega$ notation, and probably many mutant hybrids thereof.\n",
+    "While these distinctions add precision to statements about algorithmic scaling, outside computer science theory exams and the remarks of pedantic blog commenters, you'll rarely see such distinctions made in practice.\n",
+    "Far more common in the data science world is a less rigid use of big-O notation: as a general (if imprecise) description of the scaling of an algorithm.\n",
+    "With apologies to theorists and pedants, this is the interpretation we'll use throughout this book.\n",
+    "\n",
+    "Big-O notation, in this loose sense, tells you how much time your algorithm will take as you increase the amount of data.\n",
+    "If you have an $\\mathcal{O}[N]$ (read \"order $N$\") algorithm that takes 1 second to operate on a list of length *N*=1,000, then you should expect it to take roughly 5 seconds for a list of length *N*=5,000.\n",
+    "If you have an $\\mathcal{O}[N^2]$ (read \"order *N* squared\") algorithm that takes 1 second for *N*=1000, then you should expect it to take about 25 seconds for *N*=5000.\n",
+    "\n",
+    "For our purposes, the *N* will usually indicate some aspect of the size of the dataset (the number of points, the number of dimensions, etc.). When trying to analyze billions or trillions of samples, the difference between $\\mathcal{O}[N]$ and $\\mathcal{O}[N^2]$ can be far from trivial!\n",
+    "\n",
+    "Notice that the big-O notation by itself tells you nothing about the actual wall-clock time of a computation, but only about its scaling as you change *N*.\n",
+    "Generally, for example, an $\\mathcal{O}[N]$ algorithm is considered to have better scaling than an $\\mathcal{O}[N^2]$ algorithm, and for good reason. But for small datasets in particular, the algorithm with better scaling might not be faster.\n",
+    "For example, in a given problem an $\\mathcal{O}[N^2]$ algorithm might take 0.01 seconds, while a \"better\" $\\mathcal{O}[N]$ algorithm might take 1 second.\n",
+    "Scale up *N* by a factor of 1,000, though, and the $\\mathcal{O}[N]$ algorithm will win out.\n",
+    "\n",
+    "Even this loose version of Big-O notation can be very useful when comparing the performance of algorithms, and we'll use this notation throughout the book when talking about how algorithms scale."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Fancy Indexing](02.07-Fancy-Indexing.ipynb) | [Contents](Index.ipynb) | [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.08-Sorting.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/02.09-Structured-Data-NumPy.ipynb b/notebooks_v1/02.09-Structured-Data-NumPy.ipynb
new file mode 100644
index 000000000..ea4ee0bec
--- /dev/null
+++ b/notebooks_v1/02.09-Structured-Data-NumPy.ipynb
@@ -0,0 +1,603 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Sorting Arrays](02.08-Sorting.ipynb) | [Contents](Index.ipynb) | [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.09-Structured-Data-NumPy.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Structured Data: NumPy's Structured Arrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While often our data can be well represented by a homogeneous array of values, sometimes this is not the case. This section demonstrates the use of NumPy's *structured arrays* and *record arrays*, which provide efficient storage for compound, heterogeneous data.  While the patterns shown here are useful for simple operations, scenarios like this often lend themselves to the use of Pandas ``Dataframe``s, which we'll explore in [Chapter 3](03.00-Introduction-to-Pandas.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Imagine that we have several categories of data on a number of people (say, name, age, and weight), and we'd like to store these values for use in a Python program.\n",
+    "It would be possible to store these in three separate arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "name = ['Alice', 'Bob', 'Cathy', 'Doug']\n",
+    "age = [25, 45, 37, 19]\n",
+    "weight = [55.0, 85.5, 68.0, 61.5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But this is a bit clumsy. There's nothing here that tells us that the three arrays are related; it would be more natural if we could use a single structure to store all of this data.\n",
+    "NumPy can handle this through structured arrays, which are arrays with compound data types.\n",
+    "\n",
+    "Recall that previously we created a simple array using an expression like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x = np.zeros(4, dtype=int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can similarly create a structured array using a compound data type specification:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('name', '<U10'), ('age', '<i4'), ('weight', '<f8')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Use a compound data type for structured arrays\n",
+    "data = np.zeros(4, dtype={'names':('name', 'age', 'weight'),\n",
+    "                          'formats':('U10', 'i4', 'f8')})\n",
+    "print(data.dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here ``'U10'`` translates to \"Unicode string of maximum length 10,\" ``'i4'`` translates to \"4-byte (i.e., 32 bit) integer,\" and ``'f8'`` translates to \"8-byte (i.e., 64 bit) float.\"\n",
+    "We'll discuss other options for these type codes in the following section.\n",
+    "\n",
+    "Now that we've created an empty container array, we can fill the array with our lists of values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('Alice', 25, 55.0) ('Bob', 45, 85.5) ('Cathy', 37, 68.0)\n",
+      " ('Doug', 19, 61.5)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data['name'] = name\n",
+    "data['age'] = age\n",
+    "data['weight'] = weight\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we had hoped, the data is now arranged together in one convenient block of memory.\n",
+    "\n",
+    "The handy thing with structured arrays is that you can now refer to values either by index or by name:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Alice', 'Bob', 'Cathy', 'Doug'], \n",
+       "      dtype='<U10')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get all names\n",
+    "data['name']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('Alice', 25, 55.0)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get first row of data\n",
+    "data[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Doug'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the name from the last row\n",
+    "data[-1]['name']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using Boolean masking, this even allows you to do some more sophisticated operations such as filtering on age:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Alice', 'Doug'], \n",
+       "      dtype='<U10')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get names where age is under 30\n",
+    "data[data['age'] < 30]['name']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that if you'd like to do any operations that are any more complicated than these, you should probably consider the Pandas package, covered in the next chapter.\n",
+    "As we'll see, Pandas provides a ``Dataframe`` object, which is a structure built on NumPy arrays that offers a variety of useful data manipulation functionality similar to what we've shown here, as well as much, much more."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Structured Arrays\n",
+    "\n",
+    "Structured array data types can be specified in a number of ways.\n",
+    "Earlier, we saw the dictionary method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype([('name', '<U10'), ('age', '<i4'), ('weight', '<f8')])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.dtype({'names':('name', 'age', 'weight'),\n",
+    "          'formats':('U10', 'i4', 'f8')})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For clarity, numerical types can be specified using Python types or NumPy ``dtype``s instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype([('name', '<U10'), ('age', '<i8'), ('weight', '<f4')])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.dtype({'names':('name', 'age', 'weight'),\n",
+    "          'formats':((np.str_, 10), int, np.float32)})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A compound type can also be specified as a list of tuples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype([('name', 'S10'), ('age', '<i4'), ('weight', '<f8')])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.dtype([('name', 'S10'), ('age', 'i4'), ('weight', 'f8')])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the names of the types do not matter to you, you can specify the types alone in a comma-separated string:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype([('f0', 'S10'), ('f1', '<i4'), ('f2', '<f8')])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.dtype('S10,i4,f8')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The shortened string format codes may seem confusing, but they are built on simple principles.\n",
+    "The first (optional) character is ``<`` or ``>``, which means \"little endian\" or \"big endian,\" respectively, and specifies the ordering convention for significant bits.\n",
+    "The next character specifies the type of data: characters, bytes, ints, floating points, and so on (see the table below).\n",
+    "The last character or characters represents the size of the object in bytes.\n",
+    "\n",
+    "| Character        | Description           | Example                             |\n",
+    "| ---------        | -----------           | -------                             | \n",
+    "| ``'b'``          | Byte                  | ``np.dtype('b')``                   |\n",
+    "| ``'i'``          | Signed integer        | ``np.dtype('i4') == np.int32``      |\n",
+    "| ``'u'``          | Unsigned integer      | ``np.dtype('u1') == np.uint8``      |\n",
+    "| ``'f'``          | Floating point        | ``np.dtype('f8') == np.int64``      |\n",
+    "| ``'c'``          | Complex floating point| ``np.dtype('c16') == np.complex128``|\n",
+    "| ``'S'``, ``'a'`` | String                | ``np.dtype('S5')``                  |\n",
+    "| ``'U'``          | Unicode string        | ``np.dtype('U') == np.str_``        |\n",
+    "| ``'V'``          | Raw data (void)       | ``np.dtype('V') == np.void``        |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More Advanced Compound Types\n",
+    "\n",
+    "It is possible to define even more advanced compound types.\n",
+    "For example, you can create a type where each element contains an array or matrix of values.\n",
+    "Here, we'll create a data type with a ``mat`` component consisting of a $3\\times 3$ floating-point matrix:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n",
+      "[[ 0.  0.  0.]\n",
+      " [ 0.  0.  0.]\n",
+      " [ 0.  0.  0.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "tp = np.dtype([('id', 'i8'), ('mat', 'f8', (3, 3))])\n",
+    "X = np.zeros(1, dtype=tp)\n",
+    "print(X[0])\n",
+    "print(X['mat'][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now each element in the ``X`` array consists of an ``id`` and a $3\\times 3$ matrix.\n",
+    "Why would you use this rather than a simple multidimensional array, or perhaps a Python dictionary?\n",
+    "The reason is that this NumPy ``dtype`` directly maps onto a C structure definition, so the buffer containing the array content can be accessed directly within an appropriately written C program.\n",
+    "If you find yourself writing a Python interface to a legacy C or Fortran library that manipulates structured data, you'll probably find structured arrays quite useful!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## RecordArrays: Structured Arrays with a Twist\n",
+    "\n",
+    "NumPy also provides the ``np.recarray`` class, which is almost identical to the structured arrays just described, but with one additional feature: fields can be accessed as attributes rather than as dictionary keys.\n",
+    "Recall that we previously accessed the ages by writing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([25, 45, 37, 19], dtype=int32)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['age']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we view our data as a record array instead, we can access this with slightly fewer keystrokes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([25, 45, 37, 19], dtype=int32)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_rec = data.view(np.recarray)\n",
+    "data_rec.age"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The downside is that for record arrays, there is some extra overhead involved in accessing the fields, even when using the same syntax. We can see this here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1000000 loops, best of 3: 241 ns per loop\n",
+      "100000 loops, best of 3: 4.61 µs per loop\n",
+      "100000 loops, best of 3: 7.27 µs per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit data['age']\n",
+    "%timeit data_rec['age']\n",
+    "%timeit data_rec.age"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whether the more convenient notation is worth the additional overhead will depend on your own application."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## On to Pandas\n",
+    "\n",
+    "This section on structured and record arrays is purposely at the end of this chapter, because it leads so well into the next package we will cover: Pandas.\n",
+    "Structured arrays like the ones discussed here are good to know about for certain situations, especially in case you're using NumPy arrays to map onto binary data formats in C, Fortran, or another language.\n",
+    "For day-to-day use of structured data, the Pandas package is a much better choice, and we'll dive into a full discussion of it in the chapter that follows."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Sorting Arrays](02.08-Sorting.ipynb) | [Contents](Index.ipynb) | [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.09-Structured-Data-NumPy.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/03.00-Introduction-to-Pandas.ipynb b/notebooks_v1/03.00-Introduction-to-Pandas.ipynb
new file mode 100644
index 000000000..9a5487ae9
--- /dev/null
+++ b/notebooks_v1/03.00-Introduction-to-Pandas.ipynb
@@ -0,0 +1,167 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) | [Contents](Index.ipynb) | [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.00-Introduction-to-Pandas.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Manipulation with Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the previous chapter, we dove into detail on NumPy and its ``ndarray`` object, which provides efficient storage and manipulation of dense typed arrays in Python.\n",
+    "Here we'll build on this knowledge by looking in detail at the data structures provided by the Pandas library.\n",
+    "Pandas is a newer package built on top of NumPy, and provides an efficient implementation of a ``DataFrame``.\n",
+    "``DataFrame``s are essentially multidimensional arrays with attached row and column labels, and often with heterogeneous types and/or missing data.\n",
+    "As well as offering a convenient storage interface for labeled data, Pandas implements a number of powerful data operations familiar to users of both database frameworks and spreadsheet programs.\n",
+    "\n",
+    "As we saw, NumPy's ``ndarray`` data structure provides essential features for the type of clean, well-organized data typically seen in numerical computing tasks.\n",
+    "While it serves this purpose very well, its limitations become clear when we need more flexibility (e.g., attaching labels to data, working with missing data, etc.) and when attempting operations that do not map well to element-wise broadcasting (e.g., groupings, pivots, etc.), each of which is an important piece of analyzing the less structured data available in many forms in the world around us.\n",
+    "Pandas, and in particular its ``Series`` and ``DataFrame`` objects, builds on the NumPy array structure and provides efficient access to these sorts of \"data munging\" tasks that occupy much of a data scientist's time.\n",
+    "\n",
+    "In this chapter, we will focus on the mechanics of using ``Series``, ``DataFrame``, and related structures effectively.\n",
+    "We will use examples drawn from real datasets where appropriate, but these examples are not necessarily the focus."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing and Using Pandas\n",
+    "\n",
+    "Installation of Pandas on your system requires NumPy to be installed, and if building the library from source, requires the appropriate tools to compile the C and Cython sources on which Pandas is built.\n",
+    "Details on this installation can be found in the [Pandas documentation](http://pandas.pydata.org/).\n",
+    "If you followed the advice outlined in the [Preface](00.00-Preface.ipynb) and used the Anaconda stack, you already have Pandas installed.\n",
+    "\n",
+    "Once Pandas is installed, you can import it and check the version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.18.1'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "pandas.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as we generally import NumPy under the alias ``np``, we will import Pandas under the alias ``pd``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This import convention will be used throughout the remainder of this book."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reminder about Built-In Documentation\n",
+    "\n",
+    "As you read through this chapter, don't forget that IPython gives you the ability to quickly explore the contents of a package (by using the tab-completion feature) as well as the documentation of various functions (using the ``?`` character). (Refer back to [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) if you need a refresher on this.)\n",
+    "\n",
+    "For example, to display all the contents of the pandas namespace, you can type\n",
+    "\n",
+    "```ipython\n",
+    "In [3]: pd.<TAB>\n",
+    "```\n",
+    "\n",
+    "And to display Pandas's built-in documentation, you can use this:\n",
+    "\n",
+    "```ipython\n",
+    "In [4]: pd?\n",
+    "```\n",
+    "\n",
+    "More detailed documentation, along with tutorials and other resources, can be found at http://pandas.pydata.org/."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) | [Contents](Index.ipynb) | [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.00-Introduction-to-Pandas.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/03.01-Introducing-Pandas-Objects.ipynb b/notebooks_v1/03.01-Introducing-Pandas-Objects.ipynb
new file mode 100644
index 000000000..2e5f8f7b3
--- /dev/null
+++ b/notebooks_v1/03.01-Introducing-Pandas-Objects.ipynb
@@ -0,0 +1,1563 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) | [Contents](Index.ipynb) | [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.01-Introducing-Pandas-Objects.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introducing Pandas Objects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At the very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices.\n",
+    "As we will see during the course of this chapter, Pandas provides a host of useful tools, methods, and functionality on top of the basic data structures, but nearly everything that follows will require an understanding of what these structures are.\n",
+    "Thus, before we go any further, let's introduce these three fundamental Pandas data structures: the ``Series``, ``DataFrame``, and ``Index``.\n",
+    "\n",
+    "We will start our code sessions with the standard NumPy and Pandas imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Pandas Series Object\n",
+    "\n",
+    "A Pandas ``Series`` is a one-dimensional array of indexed data.\n",
+    "It can be created from a list or array as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.25\n",
+       "1    0.50\n",
+       "2    0.75\n",
+       "3    1.00\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.Series([0.25, 0.5, 0.75, 1.0])\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we see in the output, the ``Series`` wraps both a sequence of values and a sequence of indices, which we can access with the ``values`` and ``index`` attributes.\n",
+    "The ``values`` are simply a familiar NumPy array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.25,  0.5 ,  0.75,  1.  ])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``index`` is an array-like object of type ``pd.Index``, which we'll discuss in more detail momentarily."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=4, step=1)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Like with a NumPy array, data can be accessed by the associated index via the familiar Python square-bracket notation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    0.50\n",
+       "2    0.75\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we will see, though, the Pandas ``Series`` is much more general and flexible than the one-dimensional NumPy array that it emulates."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ``Series`` as generalized NumPy array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From what we've seen so far, it may look like the ``Series`` object is basically interchangeable with a one-dimensional NumPy array.\n",
+    "The essential difference is the presence of the index: while the Numpy Array has an *implicitly defined* integer index used to access the values, the Pandas ``Series`` has an *explicitly defined* index associated with the values.\n",
+    "\n",
+    "This explicit index definition gives the ``Series`` object additional capabilities. For example, the index need not be an integer, but can consist of values of any desired type.\n",
+    "For example, if we wish, we can use strings as an index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    0.25\n",
+       "b    0.50\n",
+       "c    0.75\n",
+       "d    1.00\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n",
+    "                 index=['a', 'b', 'c', 'd'])\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the item access works as expected:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['b']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can even use non-contiguous or non-sequential indices:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2    0.25\n",
+       "5    0.50\n",
+       "3    0.75\n",
+       "7    1.00\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n",
+    "                 index=[2, 5, 3, 7])\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Series as specialized dictionary\n",
+    "\n",
+    "In this way, you can think of a Pandas ``Series`` a bit like a specialization of a Python dictionary.\n",
+    "A dictionary is a structure that maps arbitrary keys to a set of arbitrary values, and a ``Series`` is a structure which maps typed keys to a set of typed values.\n",
+    "This typing is important: just as the type-specific compiled code behind a NumPy array makes it more efficient than a Python list for certain operations, the type information of a Pandas ``Series`` makes it much more efficient than Python dictionaries for certain operations.\n",
+    "\n",
+    "The ``Series``-as-dictionary analogy can be made even more clear by constructing a ``Series`` object directly from a Python dictionary:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "California    38332521\n",
+       "Florida       19552860\n",
+       "Illinois      12882135\n",
+       "New York      19651127\n",
+       "Texas         26448193\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "population_dict = {'California': 38332521,\n",
+    "                   'Texas': 26448193,\n",
+    "                   'New York': 19651127,\n",
+    "                   'Florida': 19552860,\n",
+    "                   'Illinois': 12882135}\n",
+    "population = pd.Series(population_dict)\n",
+    "population"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, a ``Series`` will be created where the index is drawn from the sorted keys.\n",
+    "From here, typical dictionary-style item access can be performed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "38332521"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "population['California']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unlike a dictionary, though, the ``Series`` also supports array-style operations such as slicing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "California    38332521\n",
+       "Florida       19552860\n",
+       "Illinois      12882135\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "population['California':'Illinois']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll discuss some of the quirks of Pandas indexing and slicing in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Constructing Series objects\n",
+    "\n",
+    "We've already seen a few ways of constructing a Pandas ``Series`` from scratch; all of them are some version of the following:\n",
+    "\n",
+    "```python\n",
+    ">>> pd.Series(data, index=index)\n",
+    "```\n",
+    "\n",
+    "where ``index`` is an optional argument, and ``data`` can be one of many entities.\n",
+    "\n",
+    "For example, ``data`` can be a list or NumPy array, in which case ``index`` defaults to an integer sequence:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    2\n",
+       "1    4\n",
+       "2    6\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series([2, 4, 6])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "``data`` can be a scalar, which is repeated to fill the specified index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100    5\n",
+       "200    5\n",
+       "300    5\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(5, index=[100, 200, 300])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "``data`` can be a dictionary, in which ``index`` defaults to the sorted dictionary keys:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    b\n",
+       "2    a\n",
+       "3    c\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series({2:'a', 1:'b', 3:'c'})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In each case, the index can be explicitly set if a different result is preferred:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    c\n",
+       "2    a\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series({2:'a', 1:'b', 3:'c'}, index=[3, 2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that in this case, the ``Series`` is populated only with the explicitly identified keys."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Pandas DataFrame Object\n",
+    "\n",
+    "The next fundamental structure in Pandas is the ``DataFrame``.\n",
+    "Like the ``Series`` object discussed in the previous section, the ``DataFrame`` can be thought of either as a generalization of a NumPy array, or as a specialization of a Python dictionary.\n",
+    "We'll now take a look at each of these perspectives."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### DataFrame as a generalized NumPy array\n",
+    "If a ``Series`` is an analog of a one-dimensional array with flexible indices, a ``DataFrame`` is an analog of a two-dimensional array with both flexible row indices and flexible column names.\n",
+    "Just as you might think of a two-dimensional array as an ordered sequence of aligned one-dimensional columns, you can think of a ``DataFrame`` as a sequence of aligned ``Series`` objects.\n",
+    "Here, by \"aligned\" we mean that they share the same index.\n",
+    "\n",
+    "To demonstrate this, let's first construct a new ``Series`` listing the area of each of the five states discussed in the previous section:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "California    423967\n",
+       "Florida       170312\n",
+       "Illinois      149995\n",
+       "New York      141297\n",
+       "Texas         695662\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "area_dict = {'California': 423967, 'Texas': 695662, 'New York': 141297,\n",
+    "             'Florida': 170312, 'Illinois': 149995}\n",
+    "area = pd.Series(area_dict)\n",
+    "area"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have this along with the ``population`` Series from before, we can use a dictionary to construct a single two-dimensional object containing this information:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>423967</td>\n",
+       "      <td>38332521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>19552860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Illinois</th>\n",
+       "      <td>149995</td>\n",
+       "      <td>12882135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New York</th>\n",
+       "      <td>141297</td>\n",
+       "      <td>19651127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Texas</th>\n",
+       "      <td>695662</td>\n",
+       "      <td>26448193</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              area  population\n",
+       "California  423967    38332521\n",
+       "Florida     170312    19552860\n",
+       "Illinois    149995    12882135\n",
+       "New York    141297    19651127\n",
+       "Texas       695662    26448193"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "states = pd.DataFrame({'population': population,\n",
+    "                       'area': area})\n",
+    "states"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Like the ``Series`` object, the ``DataFrame`` has an ``index`` attribute that gives access to the index labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['California', 'Florida', 'Illinois', 'New York', 'Texas'], dtype='object')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "states.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Additionally, the ``DataFrame`` has a ``columns`` attribute, which is an ``Index`` object holding the column labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['area', 'population'], dtype='object')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "states.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Thus the ``DataFrame`` can be thought of as a generalization of a two-dimensional NumPy array, where both the rows and columns have a generalized index for accessing the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### DataFrame as specialized dictionary\n",
+    "\n",
+    "Similarly, we can also think of a ``DataFrame`` as a specialization of a dictionary.\n",
+    "Where a dictionary maps a key to a value, a ``DataFrame`` maps a column name to a ``Series`` of column data.\n",
+    "For example, asking for the ``'area'`` attribute returns the ``Series`` object containing the areas we saw earlier:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "California    423967\n",
+       "Florida       170312\n",
+       "Illinois      149995\n",
+       "New York      141297\n",
+       "Texas         695662\n",
+       "Name: area, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "states['area']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice the potential point of confusion here: in a two-dimesnional NumPy array, ``data[0]`` will return the first *row*. For a ``DataFrame``, ``data['col0']`` will return the first *column*.\n",
+    "Because of this, it is probably better to think about ``DataFrame``s as generalized dictionaries rather than generalized arrays, though both ways of looking at the situation can be useful.\n",
+    "We'll explore more flexible means of indexing ``DataFrame``s in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Constructing DataFrame objects\n",
+    "\n",
+    "A Pandas ``DataFrame`` can be constructed in a variety of ways.\n",
+    "Here we'll give several examples."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### From a single Series object\n",
+    "\n",
+    "A ``DataFrame`` is a collection of ``Series`` objects, and a single-column ``DataFrame`` can be constructed from a single ``Series``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>38332521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Florida</th>\n",
+       "      <td>19552860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Illinois</th>\n",
+       "      <td>12882135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New York</th>\n",
+       "      <td>19651127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Texas</th>\n",
+       "      <td>26448193</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            population\n",
+       "California    38332521\n",
+       "Florida       19552860\n",
+       "Illinois      12882135\n",
+       "New York      19651127\n",
+       "Texas         26448193"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(population, columns=['population'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### From a list of dicts\n",
+    "\n",
+    "Any list of dictionaries can be made into a ``DataFrame``.\n",
+    "We'll use a simple list comprehension to create some data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b\n",
+       "0  0  0\n",
+       "1  1  2\n",
+       "2  2  4"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = [{'a': i, 'b': 2 * i}\n",
+    "        for i in range(3)]\n",
+    "pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Even if some keys in the dictionary are missing, Pandas will fill them in with ``NaN`` (i.e., \"not a number\") values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a  b    c\n",
+       "0  1.0  2  NaN\n",
+       "1  NaN  3  4.0"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame([{'a': 1, 'b': 2}, {'b': 3, 'c': 4}])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### From a dictionary of Series objects\n",
+    "\n",
+    "As we saw before, a ``DataFrame`` can be constructed from a dictionary of ``Series`` objects as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>423967</td>\n",
+       "      <td>38332521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>19552860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Illinois</th>\n",
+       "      <td>149995</td>\n",
+       "      <td>12882135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New York</th>\n",
+       "      <td>141297</td>\n",
+       "      <td>19651127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Texas</th>\n",
+       "      <td>695662</td>\n",
+       "      <td>26448193</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              area  population\n",
+       "California  423967    38332521\n",
+       "Florida     170312    19552860\n",
+       "Illinois    149995    12882135\n",
+       "New York    141297    19651127\n",
+       "Texas       695662    26448193"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame({'population': population,\n",
+    "              'area': area})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### From a two-dimensional NumPy array\n",
+    "\n",
+    "Given a two-dimensional array of data, we can create a ``DataFrame`` with any specified column and index names.\n",
+    "If omitted, an integer index will be used for each:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>foo</th>\n",
+       "      <th>bar</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>0.865257</td>\n",
+       "      <td>0.213169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>0.442759</td>\n",
+       "      <td>0.108267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0.047110</td>\n",
+       "      <td>0.905718</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        foo       bar\n",
+       "a  0.865257  0.213169\n",
+       "b  0.442759  0.108267\n",
+       "c  0.047110  0.905718"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(np.random.rand(3, 2),\n",
+    "             columns=['foo', 'bar'],\n",
+    "             index=['a', 'b', 'c'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### From a NumPy structured array\n",
+    "\n",
+    "We covered structured arrays in [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb).\n",
+    "A Pandas ``DataFrame`` operates much like a structured array, and can be created directly from one:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([(0, 0.0), (0, 0.0), (0, 0.0)], \n",
+       "      dtype=[('A', '<i8'), ('B', '<f8')])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = np.zeros(3, dtype=[('A', 'i8'), ('B', 'f8')])\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A    B\n",
+       "0  0  0.0\n",
+       "1  0  0.0\n",
+       "2  0  0.0"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(A)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Pandas Index Object\n",
+    "\n",
+    "We have seen here that both the ``Series`` and ``DataFrame`` objects contain an explicit *index* that lets you reference and modify data.\n",
+    "This ``Index`` object is an interesting structure in itself, and it can be thought of either as an *immutable array* or as an *ordered set* (technically a multi-set, as ``Index`` objects may contain repeated values).\n",
+    "Those views have some interesting consequences in the operations available on ``Index`` objects.\n",
+    "As a simple example, let's construct an ``Index`` from a list of integers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Int64Index([2, 3, 5, 7, 11], dtype='int64')"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ind = pd.Index([2, 3, 5, 7, 11])\n",
+    "ind"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Index as immutable array\n",
+    "\n",
+    "The ``Index`` in many ways operates like an array.\n",
+    "For example, we can use standard Python indexing notation to retrieve values or slices:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ind[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Int64Index([2, 5, 11], dtype='int64')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ind[::2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "``Index`` objects also have many of the attributes familiar from NumPy arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5 (5,) 1 int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ind.size, ind.shape, ind.ndim, ind.dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One difference between ``Index`` objects and NumPy arrays is that indices are immutable–that is, they cannot be modified via the normal means:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Index does not support mutable operations",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-34-40e631c82e8a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mind\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/Users/jakevdp/anaconda/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1244\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1245\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Index does not support mutable operations\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1247\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: Index does not support mutable operations"
+     ]
+    }
+   ],
+   "source": [
+    "ind[1] = 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This immutability makes it safer to share indices between multiple ``DataFrame``s and arrays, without the potential for side effects from inadvertent index modification."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Index as ordered set\n",
+    "\n",
+    "Pandas objects are designed to facilitate operations such as joins across datasets, which depend on many aspects of set arithmetic.\n",
+    "The ``Index`` object follows many of the conventions used by Python's built-in ``set`` data structure, so that unions, intersections, differences, and other combinations can be computed in a familiar way:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "indA = pd.Index([1, 3, 5, 7, 9])\n",
+    "indB = pd.Index([2, 3, 5, 7, 11])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Int64Index([3, 5, 7], dtype='int64')"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indA & indB  # intersection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Int64Index([1, 2, 3, 5, 7, 9, 11], dtype='int64')"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indA | indB  # union"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Int64Index([1, 2, 9, 11], dtype='int64')"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indA ^ indB  # symmetric difference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These operations may also be accessed via object methods, for example ``indA.intersection(indB)``."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) | [Contents](Index.ipynb) | [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.01-Introducing-Pandas-Objects.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/03.02-Data-Indexing-and-Selection.ipynb b/notebooks_v1/03.02-Data-Indexing-and-Selection.ipynb
new file mode 100644
index 000000000..9cce1353f
--- /dev/null
+++ b/notebooks_v1/03.02-Data-Indexing-and-Selection.ipynb
@@ -0,0 +1,1604 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) | [Contents](Index.ipynb) | [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.02-Data-Indexing-and-Selection.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Indexing and Selection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In [Chapter 2](02.00-Introduction-to-NumPy.ipynb), we looked in detail at methods and tools to access, set, and modify values in NumPy arrays.\n",
+    "These included indexing (e.g., ``arr[2, 1]``), slicing (e.g., ``arr[:, 1:5]``), masking (e.g., ``arr[arr > 0]``), fancy indexing (e.g., ``arr[0, [1, 5]]``), and combinations thereof (e.g., ``arr[:, [1, 5]]``).\n",
+    "Here we'll look at similar means of accessing and modifying values in Pandas ``Series`` and ``DataFrame`` objects.\n",
+    "If you have used the NumPy patterns, the corresponding patterns in Pandas will feel very familiar, though there are a few quirks to be aware of.\n",
+    "\n",
+    "We'll start with the simple case of the one-dimensional ``Series`` object, and then move on to the more complicated two-dimesnional ``DataFrame`` object."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Selection in Series\n",
+    "\n",
+    "As we saw in the previous section, a ``Series`` object acts in many ways like a one-dimensional NumPy array, and in many ways like a standard Python dictionary.\n",
+    "If we keep these two overlapping analogies in mind, it will help us to understand the patterns of data indexing and selection in these arrays."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Series as dictionary\n",
+    "\n",
+    "Like a dictionary, the ``Series`` object provides a mapping from a collection of keys to a collection of values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    0.25\n",
+       "b    0.50\n",
+       "c    0.75\n",
+       "d    1.00\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n",
+    "                 index=['a', 'b', 'c', 'd'])\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['b']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also use dictionary-like Python expressions and methods to examine the keys/indices and values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'a' in data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['a', 'b', 'c', 'd'], dtype='object')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('a', 0.25), ('b', 0.5), ('c', 0.75), ('d', 1.0)]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(data.items())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "``Series`` objects can even be modified with a dictionary-like syntax.\n",
+    "Just as you can extend a dictionary by assigning to a new key, you can extend a ``Series`` by assigning to a new index value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    0.25\n",
+       "b    0.50\n",
+       "c    0.75\n",
+       "d    1.00\n",
+       "e    1.25\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['e'] = 1.25\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This easy mutability of the objects is a convenient feature: under the hood, Pandas is making decisions about memory layout and data copying that might need to take place; the user generally does not need to worry about these issues."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Series as one-dimensional array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A ``Series`` builds on this dictionary-like interface and provides array-style item selection via the same basic mechanisms as NumPy arrays – that is, *slices*, *masking*, and *fancy indexing*.\n",
+    "Examples of these are as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    0.25\n",
+       "b    0.50\n",
+       "c    0.75\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# slicing by explicit index\n",
+    "data['a':'c']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    0.25\n",
+       "b    0.50\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# slicing by implicit integer index\n",
+    "data[0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b    0.50\n",
+       "c    0.75\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# masking\n",
+    "data[(data > 0.3) & (data < 0.8)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    0.25\n",
+       "e    1.25\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# fancy indexing\n",
+    "data[['a', 'e']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Among these, slicing may be the source of the most confusion.\n",
+    "Notice that when slicing with an explicit index (i.e., ``data['a':'c']``), the final index is *included* in the slice, while when slicing with an implicit index (i.e., ``data[0:2]``), the final index is *excluded* from the slice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Indexers: loc, iloc, and ix\n",
+    "\n",
+    "These slicing and indexing conventions can be a source of confusion.\n",
+    "For example, if your ``Series`` has an explicit integer index, an indexing operation such as ``data[1]`` will use the explicit indices, while a slicing operation like ``data[1:3]`` will use the implicit Python-style index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    a\n",
+       "3    b\n",
+       "5    c\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.Series(['a', 'b', 'c'], index=[1, 3, 5])\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'a'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# explicit index when indexing\n",
+    "data[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    b\n",
+       "5    c\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# implicit index when slicing\n",
+    "data[1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because of this potential confusion in the case of integer indexes, Pandas provides some special *indexer* attributes that explicitly expose certain indexing schemes.\n",
+    "These are not functional methods, but attributes that expose a particular slicing interface to the data in the ``Series``.\n",
+    "\n",
+    "First, the ``loc`` attribute allows indexing and slicing that always references the explicit index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'a'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.loc[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    a\n",
+       "3    b\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.loc[1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``iloc`` attribute allows indexing and slicing that always references the implicit Python-style index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'b'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.iloc[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    b\n",
+       "5    c\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.iloc[1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A third indexing attribute, ``ix``, is a hybrid of the two, and for ``Series`` objects is equivalent to standard ``[]``-based indexing.\n",
+    "The purpose of the ``ix`` indexer will become more apparent in the context of ``DataFrame`` objects, which we will discuss in a moment.\n",
+    "\n",
+    "One guiding principle of Python code is that \"explicit is better than implicit.\"\n",
+    "The explicit nature of ``loc`` and ``iloc`` make them very useful in maintaining clean and readable code; especially in the case of integer indexes, I recommend using these both to make code easier to read and understand, and to prevent subtle bugs due to the mixed indexing/slicing convention."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Selection in DataFrame\n",
+    "\n",
+    "Recall that a ``DataFrame`` acts in many ways like a two-dimensional or structured array, and in other ways like a dictionary of ``Series`` structures sharing the same index.\n",
+    "These analogies can be helpful to keep in mind as we explore data selection within this structure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### DataFrame as a dictionary\n",
+    "\n",
+    "The first analogy we will consider is the ``DataFrame`` as a dictionary of related ``Series`` objects.\n",
+    "Let's return to our example of areas and populations of states:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>pop</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>423967</td>\n",
+       "      <td>38332521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>19552860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Illinois</th>\n",
+       "      <td>149995</td>\n",
+       "      <td>12882135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New York</th>\n",
+       "      <td>141297</td>\n",
+       "      <td>19651127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Texas</th>\n",
+       "      <td>695662</td>\n",
+       "      <td>26448193</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              area       pop\n",
+       "California  423967  38332521\n",
+       "Florida     170312  19552860\n",
+       "Illinois    149995  12882135\n",
+       "New York    141297  19651127\n",
+       "Texas       695662  26448193"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "area = pd.Series({'California': 423967, 'Texas': 695662,\n",
+    "                  'New York': 141297, 'Florida': 170312,\n",
+    "                  'Illinois': 149995})\n",
+    "pop = pd.Series({'California': 38332521, 'Texas': 26448193,\n",
+    "                 'New York': 19651127, 'Florida': 19552860,\n",
+    "                 'Illinois': 12882135})\n",
+    "data = pd.DataFrame({'area':area, 'pop':pop})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The individual ``Series`` that make up the columns of the ``DataFrame`` can be accessed via dictionary-style indexing of the column name:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "California    423967\n",
+       "Florida       170312\n",
+       "Illinois      149995\n",
+       "New York      141297\n",
+       "Texas         695662\n",
+       "Name: area, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['area']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Equivalently, we can use attribute-style access with column names that are strings:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "California    423967\n",
+       "Florida       170312\n",
+       "Illinois      149995\n",
+       "New York      141297\n",
+       "Texas         695662\n",
+       "Name: area, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.area"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This attribute-style column access actually accesses the exact same object as the dictionary-style access:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.area is data['area']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Though this is a useful shorthand, keep in mind that it does not work for all cases!\n",
+    "For example, if the column names are not strings, or if the column names conflict with methods of the ``DataFrame``, this attribute-style access is not possible.\n",
+    "For example, the ``DataFrame`` has a ``pop()`` method, so ``data.pop`` will point to this rather than the ``\"pop\"`` column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.pop is data['pop']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In particular, you should avoid the temptation to try column assignment via attribute (i.e., use ``data['pop'] = z`` rather than ``data.pop = z``).\n",
+    "\n",
+    "Like with the ``Series`` objects discussed earlier, this dictionary-style syntax can also be used to modify the object, in this case adding a new column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>density</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>423967</td>\n",
+       "      <td>38332521</td>\n",
+       "      <td>90.413926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>19552860</td>\n",
+       "      <td>114.806121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Illinois</th>\n",
+       "      <td>149995</td>\n",
+       "      <td>12882135</td>\n",
+       "      <td>85.883763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New York</th>\n",
+       "      <td>141297</td>\n",
+       "      <td>19651127</td>\n",
+       "      <td>139.076746</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Texas</th>\n",
+       "      <td>695662</td>\n",
+       "      <td>26448193</td>\n",
+       "      <td>38.018740</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              area       pop     density\n",
+       "California  423967  38332521   90.413926\n",
+       "Florida     170312  19552860  114.806121\n",
+       "Illinois    149995  12882135   85.883763\n",
+       "New York    141297  19651127  139.076746\n",
+       "Texas       695662  26448193   38.018740"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['density'] = data['pop'] / data['area']\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This shows a preview of the straightforward syntax of element-by-element arithmetic between ``Series`` objects; we'll dig into this further in [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### DataFrame as two-dimensional array\n",
+    "\n",
+    "As mentioned previously, we can also view the ``DataFrame`` as an enhanced two-dimensional array.\n",
+    "We can examine the raw underlying data array using the ``values`` attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  4.23967000e+05,   3.83325210e+07,   9.04139261e+01],\n",
+       "       [  1.70312000e+05,   1.95528600e+07,   1.14806121e+02],\n",
+       "       [  1.49995000e+05,   1.28821350e+07,   8.58837628e+01],\n",
+       "       [  1.41297000e+05,   1.96511270e+07,   1.39076746e+02],\n",
+       "       [  6.95662000e+05,   2.64481930e+07,   3.80187404e+01]])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With this picture in mind, many familiar array-like observations can be done on the ``DataFrame`` itself.\n",
+    "For example, we can transpose the full ``DataFrame`` to swap rows and columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>California</th>\n",
+       "      <th>Florida</th>\n",
+       "      <th>Illinois</th>\n",
+       "      <th>New York</th>\n",
+       "      <th>Texas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>area</th>\n",
+       "      <td>4.239670e+05</td>\n",
+       "      <td>1.703120e+05</td>\n",
+       "      <td>1.499950e+05</td>\n",
+       "      <td>1.412970e+05</td>\n",
+       "      <td>6.956620e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pop</th>\n",
+       "      <td>3.833252e+07</td>\n",
+       "      <td>1.955286e+07</td>\n",
+       "      <td>1.288214e+07</td>\n",
+       "      <td>1.965113e+07</td>\n",
+       "      <td>2.644819e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>density</th>\n",
+       "      <td>9.041393e+01</td>\n",
+       "      <td>1.148061e+02</td>\n",
+       "      <td>8.588376e+01</td>\n",
+       "      <td>1.390767e+02</td>\n",
+       "      <td>3.801874e+01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           California       Florida      Illinois      New York         Texas\n",
+       "area     4.239670e+05  1.703120e+05  1.499950e+05  1.412970e+05  6.956620e+05\n",
+       "pop      3.833252e+07  1.955286e+07  1.288214e+07  1.965113e+07  2.644819e+07\n",
+       "density  9.041393e+01  1.148061e+02  8.588376e+01  1.390767e+02  3.801874e+01"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When it comes to indexing of ``DataFrame`` objects, however, it is clear that the dictionary-style indexing of columns precludes our ability to simply treat it as a NumPy array.\n",
+    "In particular, passing a single index to an array accesses a row:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  4.23967000e+05,   3.83325210e+07,   9.04139261e+01])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.values[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and passing a single \"index\" to a ``DataFrame`` accesses a column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "California    423967\n",
+       "Florida       170312\n",
+       "Illinois      149995\n",
+       "New York      141297\n",
+       "Texas         695662\n",
+       "Name: area, dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['area']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Thus for array-style indexing, we need another convention.\n",
+    "Here Pandas again uses the ``loc``, ``iloc``, and ``ix`` indexers mentioned earlier.\n",
+    "Using the ``iloc`` indexer, we can index the underlying array as if it is a simple NumPy array (using the implicit Python-style index), but the ``DataFrame`` index and column labels are maintained in the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>pop</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>423967</td>\n",
+       "      <td>38332521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>19552860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Illinois</th>\n",
+       "      <td>149995</td>\n",
+       "      <td>12882135</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              area       pop\n",
+       "California  423967  38332521\n",
+       "Florida     170312  19552860\n",
+       "Illinois    149995  12882135"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.iloc[:3, :2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, using the ``loc`` indexer we can index the underlying data in an array-like style but using the explicit index and column names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>pop</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>423967</td>\n",
+       "      <td>38332521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>19552860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Illinois</th>\n",
+       "      <td>149995</td>\n",
+       "      <td>12882135</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              area       pop\n",
+       "California  423967  38332521\n",
+       "Florida     170312  19552860\n",
+       "Illinois    149995  12882135"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.loc[:'Illinois', :'pop']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "The ``ix`` indexer allows a hybrid of these two approaches:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>pop</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>423967</td>\n",
+       "      <td>38332521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>19552860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Illinois</th>\n",
+       "      <td>149995</td>\n",
+       "      <td>12882135</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              area       pop\n",
+       "California  423967  38332521\n",
+       "Florida     170312  19552860\n",
+       "Illinois    149995  12882135"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.ix[:3, :'pop']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Keep in mind that for integer indices, the ``ix`` indexer is subject to the same potential sources of confusion as discussed for integer-indexed ``Series`` objects.\n",
+    "\n",
+    "Any of the familiar NumPy-style data access patterns can be used within these indexers.\n",
+    "For example, in the ``loc`` indexer we can combine masking and fancy indexing as in the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pop</th>\n",
+       "      <th>density</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Florida</th>\n",
+       "      <td>19552860</td>\n",
+       "      <td>114.806121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New York</th>\n",
+       "      <td>19651127</td>\n",
+       "      <td>139.076746</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               pop     density\n",
+       "Florida   19552860  114.806121\n",
+       "New York  19651127  139.076746"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.loc[data.density > 100, ['pop', 'density']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Any of these indexing conventions may also be used to set or modify values; this is done in the standard way that you might be accustomed to from working with NumPy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>density</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>423967</td>\n",
+       "      <td>38332521</td>\n",
+       "      <td>90.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>19552860</td>\n",
+       "      <td>114.806121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Illinois</th>\n",
+       "      <td>149995</td>\n",
+       "      <td>12882135</td>\n",
+       "      <td>85.883763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New York</th>\n",
+       "      <td>141297</td>\n",
+       "      <td>19651127</td>\n",
+       "      <td>139.076746</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Texas</th>\n",
+       "      <td>695662</td>\n",
+       "      <td>26448193</td>\n",
+       "      <td>38.018740</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              area       pop     density\n",
+       "California  423967  38332521   90.000000\n",
+       "Florida     170312  19552860  114.806121\n",
+       "Illinois    149995  12882135   85.883763\n",
+       "New York    141297  19651127  139.076746\n",
+       "Texas       695662  26448193   38.018740"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.iloc[0, 2] = 90\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To build up your fluency in Pandas data manipulation, I suggest spending some time with a simple ``DataFrame`` and exploring the types of indexing, slicing, masking, and fancy indexing that are allowed by these various indexing approaches."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Additional indexing conventions\n",
+    "\n",
+    "There are a couple extra indexing conventions that might seem at odds with the preceding discussion, but nevertheless can be very useful in practice.\n",
+    "First, while *indexing* refers to columns, *slicing* refers to rows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>density</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>19552860</td>\n",
+       "      <td>114.806121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Illinois</th>\n",
+       "      <td>149995</td>\n",
+       "      <td>12882135</td>\n",
+       "      <td>85.883763</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            area       pop     density\n",
+       "Florida   170312  19552860  114.806121\n",
+       "Illinois  149995  12882135   85.883763"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['Florida':'Illinois']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Such slices can also refer to rows by number rather than by index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>density</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>19552860</td>\n",
+       "      <td>114.806121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Illinois</th>\n",
+       "      <td>149995</td>\n",
+       "      <td>12882135</td>\n",
+       "      <td>85.883763</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            area       pop     density\n",
+       "Florida   170312  19552860  114.806121\n",
+       "Illinois  149995  12882135   85.883763"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, direct masking operations are also interpreted row-wise rather than column-wise:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>density</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Florida</th>\n",
+       "      <td>170312</td>\n",
+       "      <td>19552860</td>\n",
+       "      <td>114.806121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New York</th>\n",
+       "      <td>141297</td>\n",
+       "      <td>19651127</td>\n",
+       "      <td>139.076746</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            area       pop     density\n",
+       "Florida   170312  19552860  114.806121\n",
+       "New York  141297  19651127  139.076746"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[data.density > 100]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These two conventions are syntactically similar to those on a NumPy array, and while these may not precisely fit the mold of the Pandas conventions, they are nevertheless quite useful in practice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) | [Contents](Index.ipynb) | [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.02-Data-Indexing-and-Selection.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/03.03-Operations-in-Pandas.ipynb b/notebooks_v1/03.03-Operations-in-Pandas.ipynb
new file mode 100644
index 000000000..6206ac790
--- /dev/null
+++ b/notebooks_v1/03.03-Operations-in-Pandas.ipynb
@@ -0,0 +1,1038 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) | [Contents](Index.ipynb) | [Handling Missing Data](03.04-Missing-Values.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.03-Operations-in-Pandas.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Operating on Data in Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the essential pieces of NumPy is the ability to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with more sophisticated operations (trigonometric functions, exponential and logarithmic functions, etc.).\n",
+    "Pandas inherits much of this functionality from NumPy, and the ufuncs that we introduced in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) are key to this.\n",
+    "\n",
+    "Pandas includes a couple useful twists, however: for unary operations like negation and trigonometric functions, these ufuncs will *preserve index and column labels* in the output, and for binary operations such as addition and multiplication, Pandas will automatically *align indices* when passing the objects to the ufunc.\n",
+    "This means that keeping the context of data and combining data from different sources–both potentially error-prone tasks with raw NumPy arrays–become essentially foolproof ones with Pandas.\n",
+    "We will additionally see that there are well-defined operations between one-dimensional ``Series`` structures and two-dimensional ``DataFrame`` structures."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ufuncs: Index Preservation\n",
+    "\n",
+    "Because Pandas is designed to work with NumPy, any NumPy ufunc will work on Pandas ``Series`` and ``DataFrame`` objects.\n",
+    "Let's start by defining a simple ``Series`` and ``DataFrame`` on which to demonstrate this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    6\n",
+       "1    3\n",
+       "2    7\n",
+       "3    4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rng = np.random.RandomState(42)\n",
+    "ser = pd.Series(rng.randint(0, 10, 4))\n",
+    "ser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A  B  C  D\n",
+       "0  6  9  2  6\n",
+       "1  7  4  3  7\n",
+       "2  7  2  5  4"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(rng.randint(0, 10, (3, 4)),\n",
+    "                  columns=['A', 'B', 'C', 'D'])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we apply a NumPy ufunc on either of these objects, the result will be another Pandas object *with the indices preserved:*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     403.428793\n",
+       "1      20.085537\n",
+       "2    1096.633158\n",
+       "3      54.598150\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.exp(ser)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or, for a slightly more complex calculation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>7.071068e-01</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-1.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.707107</td>\n",
+       "      <td>1.224647e-16</td>\n",
+       "      <td>0.707107</td>\n",
+       "      <td>-7.071068e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.707107</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>-0.707107</td>\n",
+       "      <td>1.224647e-16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A             B         C             D\n",
+       "0 -1.000000  7.071068e-01  1.000000 -1.000000e+00\n",
+       "1 -0.707107  1.224647e-16  0.707107 -7.071068e-01\n",
+       "2 -0.707107  1.000000e+00 -0.707107  1.224647e-16"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sin(df * np.pi / 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Any of the ufuncs discussed in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) can be used in a similar manner."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## UFuncs: Index Alignment\n",
+    "\n",
+    "For binary operations on two ``Series`` or ``DataFrame`` objects, Pandas will align indices in the process of performing the operation.\n",
+    "This is very convenient when working with incomplete data, as we'll see in some of the examples that follow."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Index alignment in Series\n",
+    "\n",
+    "As an example, suppose we are combining two different data sources, and find only the top three US states by *area* and the top three US states by *population*:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "area = pd.Series({'Alaska': 1723337, 'Texas': 695662,\n",
+    "                  'California': 423967}, name='area')\n",
+    "population = pd.Series({'California': 38332521, 'Texas': 26448193,\n",
+    "                        'New York': 19651127}, name='population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see what happens when we divide these to compute the population density:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Alaska              NaN\n",
+       "California    90.413926\n",
+       "New York            NaN\n",
+       "Texas         38.018740\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "population / area"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The resulting array contains the *union* of indices of the two input arrays, which could be determined using standard Python set arithmetic on these indices:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Alaska', 'California', 'New York', 'Texas'], dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "area.index | population.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Any item for which one or the other does not have an entry is marked with ``NaN``, or \"Not a Number,\" which is how Pandas marks missing data (see further discussion of missing data in [Handling Missing Data](03.04-Missing-Values.ipynb)).\n",
+    "This index matching is implemented this way for any of Python's built-in arithmetic expressions; any missing values are filled in with NaN by default:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    NaN\n",
+       "1    5.0\n",
+       "2    9.0\n",
+       "3    NaN\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = pd.Series([2, 4, 6], index=[0, 1, 2])\n",
+    "B = pd.Series([1, 3, 5], index=[1, 2, 3])\n",
+    "A + B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If using NaN values is not the desired behavior, the fill value can be modified using appropriate object methods in place of the operators.\n",
+    "For example, calling ``A.add(B)`` is equivalent to calling ``A + B``, but allows optional explicit specification of the fill value for any elements in ``A`` or ``B`` that might be missing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    2.0\n",
+       "1    5.0\n",
+       "2    9.0\n",
+       "3    5.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.add(B, fill_value=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Index alignment in DataFrame\n",
+    "\n",
+    "A similar type of alignment takes place for *both* columns and indices when performing operations on ``DataFrame``s:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A   B\n",
+       "0  1  11\n",
+       "1  5   1"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = pd.DataFrame(rng.randint(0, 20, (2, 2)),\n",
+    "                 columns=list('AB'))\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>B</th>\n",
+       "      <th>A</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   B  A  C\n",
+       "0  4  0  9\n",
+       "1  5  8  0\n",
+       "2  9  2  6"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B = pd.DataFrame(rng.randint(0, 10, (3, 3)),\n",
+    "                 columns=list('BAC'))\n",
+    "B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      A     B   C\n",
+       "0   1.0  15.0 NaN\n",
+       "1  13.0   6.0 NaN\n",
+       "2   NaN   NaN NaN"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A + B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that indices are aligned correctly irrespective of their order in the two objects, and indices in the result are sorted.\n",
+    "As was the case with ``Series``, we can use the associated object's arithmetic method and pass any desired ``fill_value`` to be used in place of missing entries.\n",
+    "Here we'll fill with the mean of all values in ``A`` (computed by first stacking the rows of ``A``):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>13.5</td>\n",
+       "      <td>10.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      A     B     C\n",
+       "0   1.0  15.0  13.5\n",
+       "1  13.0   6.0   4.5\n",
+       "2   6.5  13.5  10.5"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fill = A.stack().mean()\n",
+    "A.add(B, fill_value=fill)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following table lists Python operators and their equivalent Pandas object methods:\n",
+    "\n",
+    "| Python Operator | Pandas Method(s)                      |\n",
+    "|-----------------|---------------------------------------|\n",
+    "| ``+``           | ``add()``                             |\n",
+    "| ``-``           | ``sub()``, ``subtract()``             |\n",
+    "| ``*``           | ``mul()``, ``multiply()``             |\n",
+    "| ``/``           | ``truediv()``, ``div()``, ``divide()``|\n",
+    "| ``//``          | ``floordiv()``                        |\n",
+    "| ``%``           | ``mod()``                             |\n",
+    "| ``**``          | ``pow()``                             |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ufuncs: Operations Between DataFrame and Series\n",
+    "\n",
+    "When performing operations between a ``DataFrame`` and a ``Series``, the index and column alignment is similarly maintained.\n",
+    "Operations between a ``DataFrame`` and a ``Series`` are similar to operations between a two-dimensional and one-dimensional NumPy array.\n",
+    "Consider one common operation, where we find the difference of a two-dimensional array and one of its rows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3, 8, 2, 4],\n",
+       "       [2, 6, 4, 8],\n",
+       "       [6, 1, 3, 8]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = rng.randint(10, size=(3, 4))\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  0,  0,  0],\n",
+       "       [-1, -2,  2,  4],\n",
+       "       [ 3, -7,  1,  4]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A - A[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "According to NumPy's broadcasting rules (see [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb)), subtraction between a two-dimensional array and one of its rows is applied row-wise.\n",
+    "\n",
+    "In Pandas, the convention similarly operates row-wise by default:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Q</th>\n",
+       "      <th>R</th>\n",
+       "      <th>S</th>\n",
+       "      <th>T</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>-7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Q  R  S  T\n",
+       "0  0  0  0  0\n",
+       "1 -1 -2  2  4\n",
+       "2  3 -7  1  4"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(A, columns=list('QRST'))\n",
+    "df - df.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you would instead like to operate column-wise, you can use the object methods mentioned earlier, while specifying the ``axis`` keyword:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Q</th>\n",
+       "      <th>R</th>\n",
+       "      <th>S</th>\n",
+       "      <th>T</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-6</td>\n",
+       "      <td>-4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Q  R  S  T\n",
+       "0 -5  0 -6 -4\n",
+       "1 -4  0 -2  2\n",
+       "2  5  0  2  7"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.subtract(df['R'], axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that these ``DataFrame``/``Series`` operations, like the operations discussed above, will automatically align  indices between the two elements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Q    3\n",
+       "S    2\n",
+       "Name: 0, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "halfrow = df.iloc[0, ::2]\n",
+    "halfrow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Q</th>\n",
+       "      <th>R</th>\n",
+       "      <th>S</th>\n",
+       "      <th>T</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Q   R    S   T\n",
+       "0  0.0 NaN  0.0 NaN\n",
+       "1 -1.0 NaN  2.0 NaN\n",
+       "2  3.0 NaN  1.0 NaN"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df - halfrow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This preservation and alignment of indices and columns means that operations on data in Pandas will always maintain the data context, which prevents the types of silly errors that might come up when working with heterogeneous and/or misaligned data in raw NumPy arrays."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) | [Contents](Index.ipynb) | [Handling Missing Data](03.04-Missing-Values.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.03-Operations-in-Pandas.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/03.04-Missing-Values.ipynb b/notebooks_v1/03.04-Missing-Values.ipynb
new file mode 100644
index 000000000..180ca09e7
--- /dev/null
+++ b/notebooks_v1/03.04-Missing-Values.ipynb
@@ -0,0 +1,1299 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) | [Contents](Index.ipynb) | [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.04-Missing-Values.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Handling Missing Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The difference between data found in many tutorials and data in the real world is that real-world data is rarely clean and homogeneous.\n",
+    "In particular, many interesting datasets will have some amount of data missing.\n",
+    "To make matters even more complicated, different data sources may indicate missing data in different ways.\n",
+    "\n",
+    "In this section, we will discuss some general considerations for missing data, discuss how Pandas chooses to represent it, and demonstrate some built-in Pandas tools for handling missing data in Python.\n",
+    "Here and throughout the book, we'll refer to missing data in general as *null*, *NaN*, or *NA* values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trade-Offs in Missing Data Conventions\n",
+    "\n",
+    "There are a number of schemes that have been developed to indicate the presence of missing data in a table or DataFrame.\n",
+    "Generally, they revolve around one of two strategies: using a *mask* that globally indicates missing values, or choosing a *sentinel value* that indicates a missing entry.\n",
+    "\n",
+    "In the masking approach, the mask might be an entirely separate Boolean array, or it may involve appropriation of one bit in the data representation to locally indicate the null status of a value.\n",
+    "\n",
+    "In the sentinel approach, the sentinel value could be some data-specific convention, such as indicating a missing integer value with -9999 or some rare bit pattern, or it could be a more global convention, such as indicating a missing floating-point value with NaN (Not a Number), a special value which is part of the IEEE floating-point specification.\n",
+    "\n",
+    "None of these approaches is without trade-offs: use of a separate mask array requires allocation of an additional Boolean array, which adds overhead in both storage and computation. A sentinel value reduces the range of valid values that can be represented, and may require extra (often non-optimized) logic in CPU and GPU arithmetic. Common special values like NaN are not available for all data types.\n",
+    "\n",
+    "As in most cases where no universally optimal choice exists, different languages and systems use different conventions.\n",
+    "For example, the R language uses reserved bit patterns within each data type as sentinel values indicating missing data, while the SciDB system uses an extra byte attached to every cell which indicates a NA state."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Missing Data in Pandas\n",
+    "\n",
+    "The way in which Pandas handles missing values is constrained by its reliance on the NumPy package, which does not have a built-in notion of NA values for non-floating-point data types.\n",
+    "\n",
+    "Pandas could have followed R's lead in specifying bit patterns for each individual data type to indicate nullness, but this approach turns out to be rather unwieldy.\n",
+    "While R contains four basic data types, NumPy supports *far* more than this: for example, while R has a single integer type, NumPy supports *fourteen* basic integer types once you account for available precisions, signedness, and endianness of the encoding.\n",
+    "Reserving a specific bit pattern in all available NumPy types would lead to an unwieldy amount of overhead in special-casing various operations for various types, likely even requiring a new fork of the NumPy package. Further, for the smaller data types (such as 8-bit integers), sacrificing a bit to use as a mask will significantly reduce the range of values it can represent.\n",
+    "\n",
+    "NumPy does have support for masked arrays – that is, arrays that have a separate Boolean mask array attached for marking data as \"good\" or \"bad.\"\n",
+    "Pandas could have derived from this, but the overhead in both storage, computation, and code maintenance makes that an unattractive choice.\n",
+    "\n",
+    "With these constraints in mind, Pandas chose to use sentinels for missing data, and further chose to use two already-existing Python null values: the special floating-point ``NaN`` value, and the Python ``None`` object.\n",
+    "This choice has some side effects, as we will see, but in practice ends up being a good compromise in most cases of interest."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ``None``: Pythonic missing data\n",
+    "\n",
+    "The first sentinel value used by Pandas is ``None``, a Python singleton object that is often used for missing data in Python code.\n",
+    "Because it is a Python object, ``None`` cannot be used in any arbitrary NumPy/Pandas array, but only in arrays with data type ``'object'`` (i.e., arrays of Python objects):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, None, 3, 4], dtype=object)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vals1 = np.array([1, None, 3, 4])\n",
+    "vals1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This ``dtype=object`` means that the best common type representation NumPy could infer for the contents of the array is that they are Python objects.\n",
+    "While this kind of object array is useful for some purposes, any operations on the data will be done at the Python level, with much more overhead than the typically fast operations seen for arrays with native types:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dtype = object\n",
+      "10 loops, best of 3: 78.2 ms per loop\n",
+      "\n",
+      "dtype = int\n",
+      "100 loops, best of 3: 3.06 ms per loop\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for dtype in ['object', 'int']:\n",
+    "    print(\"dtype =\", dtype)\n",
+    "    %timeit np.arange(1E6, dtype=dtype).sum()\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The use of Python objects in an array also means that if you perform aggregations like ``sum()`` or ``min()`` across an array with a ``None`` value, you will generally get an error:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for +: 'int' and 'NoneType'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-4-749fd8ae6030>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvals1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/Users/jakevdp/anaconda/lib/python3.5/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'"
+     ]
+    }
+   ],
+   "source": [
+    "vals1.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This reflects the fact that addition between an integer and ``None`` is undefined."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ``NaN``: Missing numerical data\n",
+    "\n",
+    "The other missing data representation, ``NaN`` (acronym for *Not a Number*), is different; it is a special floating-point value recognized by all systems that use the standard IEEE floating-point representation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vals2 = np.array([1, np.nan, 3, 4]) \n",
+    "vals2.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that NumPy chose a native floating-point type for this array: this means that unlike the object array from before, this array supports fast operations pushed into compiled code.\n",
+    "You should be aware that ``NaN`` is a bit like a data virus–it infects any other object it touches.\n",
+    "Regardless of the operation, the result of arithmetic with ``NaN`` will be another ``NaN``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 + np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 *  np.nan"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that this means that aggregates over the values are well defined (i.e., they don't result in an error) but not always useful:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(nan, nan, nan)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vals2.sum(), vals2.min(), vals2.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NumPy does provide some special aggregations that will ignore these missing values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8.0, 1.0, 4.0)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.nansum(vals2), np.nanmin(vals2), np.nanmax(vals2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Keep in mind that ``NaN`` is specifically a floating-point value; there is no equivalent NaN value for integers, strings, or other types."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### NaN and None in Pandas\n",
+    "\n",
+    "``NaN`` and ``None`` both have their place, and Pandas is built to handle the two of them nearly interchangeably, converting between them where appropriate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1.0\n",
+       "1    NaN\n",
+       "2    2.0\n",
+       "3    NaN\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series([1, np.nan, 2, None])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For types that don't have an available sentinel value, Pandas automatically type-casts when NA values are present.\n",
+    "For example, if we set a value in an integer array to ``np.nan``, it will automatically be upcast to a floating-point type to accommodate the NA:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = pd.Series(range(2), dtype=int)\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    NaN\n",
+       "1    1.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[0] = None\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that in addition to casting the integer array to floating point, Pandas automatically converts the ``None`` to a ``NaN`` value.\n",
+    "(Be aware that there is a proposal to add a native integer NA to Pandas in the future; as of this writing, it has not been included).\n",
+    "\n",
+    "While this type of magic may feel a bit hackish compared to the more unified approach to NA values in domain-specific languages like R, the Pandas sentinel/casting approach works quite well in practice and in my experience only rarely causes issues.\n",
+    "\n",
+    "The following table lists the upcasting conventions in Pandas when NA values are introduced:\n",
+    "\n",
+    "|Typeclass     | Conversion When Storing NAs | NA Sentinel Value      |\n",
+    "|--------------|-----------------------------|------------------------|\n",
+    "| ``floating`` | No change                   | ``np.nan``             |\n",
+    "| ``object``   | No change                   | ``None`` or ``np.nan`` |\n",
+    "| ``integer``  | Cast to ``float64``         | ``np.nan``             |\n",
+    "| ``boolean``  | Cast to ``object``          | ``None`` or ``np.nan`` |\n",
+    "\n",
+    "Keep in mind that in Pandas, string data is always stored with an ``object`` dtype."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Operating on Null Values\n",
+    "\n",
+    "As we have seen, Pandas treats ``None`` and ``NaN`` as essentially interchangeable for indicating missing or null values.\n",
+    "To facilitate this convention, there are several useful methods for detecting, removing, and replacing null values in Pandas data structures.\n",
+    "They are:\n",
+    "\n",
+    "- ``isnull()``: Generate a boolean mask indicating missing values\n",
+    "- ``notnull()``: Opposite of ``isnull()``\n",
+    "- ``dropna()``: Return a filtered version of the data\n",
+    "- ``fillna()``: Return a copy of the data with missing values filled or imputed\n",
+    "\n",
+    "We will conclude this section with a brief exploration and demonstration of these routines."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Detecting null values\n",
+    "Pandas data structures have two useful methods for detecting null data: ``isnull()`` and ``notnull()``.\n",
+    "Either one will return a Boolean mask over the data. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = pd.Series([1, np.nan, 'hello', None])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    False\n",
+       "1     True\n",
+       "2    False\n",
+       "3     True\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb), Boolean masks can be used directly as a ``Series`` or ``DataFrame`` index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        1\n",
+       "2    hello\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[data.notnull()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``isnull()`` and ``notnull()`` methods produce similar Boolean results for ``DataFrame``s."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dropping null values\n",
+    "\n",
+    "In addition to the masking used before, there are the convenience methods, ``dropna()``\n",
+    "(which removes NA values) and ``fillna()`` (which fills in NA values). For a ``Series``,\n",
+    "the result is straightforward:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        1\n",
+       "2    hello\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dropna()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For a ``DataFrame``, there are more options.\n",
+    "Consider the following ``DataFrame``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0    1  2\n",
+       "0  1.0  NaN  2\n",
+       "1  2.0  3.0  5\n",
+       "2  NaN  4.0  6"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame([[1,      np.nan, 2],\n",
+    "                   [2,      3,      5],\n",
+    "                   [np.nan, 4,      6]])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We cannot drop single values from a ``DataFrame``; we can only drop full rows or full columns.\n",
+    "Depending on the application, you might want one or the other, so ``dropna()`` gives a number of options for a ``DataFrame``.\n",
+    "\n",
+    "By default, ``dropna()`` will drop all rows in which *any* null value is present:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0    1  2\n",
+       "1  2.0  3.0  5"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can drop NA values along a different axis; ``axis=1`` drops all columns containing a null value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   2\n",
+       "0  2\n",
+       "1  5\n",
+       "2  6"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(axis='columns')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But this drops some good data as well; you might rather be interested in dropping rows or columns with *all* NA values, or a majority of NA values.\n",
+    "This can be specified through the ``how`` or ``thresh`` parameters, which allow fine control of the number of nulls to allow through.\n",
+    "\n",
+    "The default is ``how='any'``, such that any row or column (depending on the ``axis`` keyword) containing a null value will be dropped.\n",
+    "You can also specify ``how='all'``, which will only drop rows/columns that are *all* null values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0    1  2   3\n",
+       "0  1.0  NaN  2 NaN\n",
+       "1  2.0  3.0  5 NaN\n",
+       "2  NaN  4.0  6 NaN"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[3] = np.nan\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0    1  2\n",
+       "0  1.0  NaN  2\n",
+       "1  2.0  3.0  5\n",
+       "2  NaN  4.0  6"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(axis='columns', how='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For finer-grained control, the ``thresh`` parameter lets you specify a minimum number of non-null values for the row/column to be kept:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0    1  2   3\n",
+       "1  2.0  3.0  5 NaN"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(axis='rows', thresh=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here the first and last row have been dropped, because they contain only two non-null values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Filling null values\n",
+    "\n",
+    "Sometimes rather than dropping NA values, you'd rather replace them with a valid value.\n",
+    "This value might be a single number like zero, or it might be some sort of imputation or interpolation from the good values.\n",
+    "You could do this in-place using the ``isnull()`` method as a mask, but because it is such a common operation Pandas provides the ``fillna()`` method, which returns a copy of the array with the null values replaced.\n",
+    "\n",
+    "Consider the following ``Series``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    1.0\n",
+       "b    NaN\n",
+       "c    2.0\n",
+       "d    NaN\n",
+       "e    3.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.Series([1, np.nan, 2, None, 3], index=list('abcde'))\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can fill NA entries with a single value, such as zero:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    1.0\n",
+       "b    0.0\n",
+       "c    2.0\n",
+       "d    0.0\n",
+       "e    3.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can specify a forward-fill to propagate the previous value forward:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    1.0\n",
+       "b    1.0\n",
+       "c    2.0\n",
+       "d    2.0\n",
+       "e    3.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# forward-fill\n",
+    "data.fillna(method='ffill')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or we can specify a back-fill to propagate the next values backward:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    1.0\n",
+       "b    2.0\n",
+       "c    2.0\n",
+       "d    3.0\n",
+       "e    3.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# back-fill\n",
+    "data.fillna(method='bfill')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "For ``DataFrame``s, the options are similar, but we can also specify an ``axis`` along which the fills take place:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0    1  2   3\n",
+       "0  1.0  NaN  2 NaN\n",
+       "1  2.0  3.0  5 NaN\n",
+       "2  NaN  4.0  6 NaN"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0    1    2    3\n",
+       "0  1.0  1.0  2.0  2.0\n",
+       "1  2.0  3.0  5.0  5.0\n",
+       "2  NaN  4.0  6.0  6.0"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.fillna(method='ffill', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that if a previous value is not available during a forward fill, the NA value remains."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) | [Contents](Index.ipynb) | [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.04-Missing-Values.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/03.05-Hierarchical-Indexing.ipynb b/notebooks_v1/03.05-Hierarchical-Indexing.ipynb
new file mode 100644
index 000000000..1122989bb
--- /dev/null
+++ b/notebooks_v1/03.05-Hierarchical-Indexing.ipynb
@@ -0,0 +1,2804 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Handling Missing Data](03.04-Missing-Values.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.05-Hierarchical-Indexing.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hierarchical Indexing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Up to this point we've been focused primarily on one-dimensional and two-dimensional data, stored in Pandas ``Series`` and ``DataFrame`` objects, respectively.\n",
+    "Often it is useful to go beyond this and store higher-dimensional data–that is, data indexed by more than one or two keys.\n",
+    "While Pandas does provide ``Panel`` and ``Panel4D`` objects that natively handle three-dimensional and four-dimensional data (see [Aside: Panel Data](#Aside:-Panel-Data)), a far more common pattern in practice is to make use of *hierarchical indexing* (also known as *multi-indexing*) to incorporate multiple index *levels* within a single index.\n",
+    "In this way, higher-dimensional data can be compactly represented within the familiar one-dimensional ``Series`` and two-dimensional ``DataFrame`` objects.\n",
+    "\n",
+    "In this section, we'll explore the direct creation of ``MultiIndex`` objects, considerations when indexing, slicing, and computing statistics across multiply indexed data, and useful routines for converting between simple and hierarchically indexed representations of your data.\n",
+    "\n",
+    "We begin with the standard imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## A Multiply Indexed Series\n",
+    "\n",
+    "Let's start by considering how we might represent two-dimensional data within a one-dimensional ``Series``.\n",
+    "For concreteness, we will consider a series of data where each point has a character and numerical key."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### The bad way\n",
+    "\n",
+    "Suppose you would like to track data about states from two different years.\n",
+    "Using the Pandas tools we've already covered, you might be tempted to simply use Python tuples as keys:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(California, 2000)    33871648\n",
+       "(California, 2010)    37253956\n",
+       "(New York, 2000)      18976457\n",
+       "(New York, 2010)      19378102\n",
+       "(Texas, 2000)         20851820\n",
+       "(Texas, 2010)         25145561\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "index = [('California', 2000), ('California', 2010),\n",
+    "         ('New York', 2000), ('New York', 2010),\n",
+    "         ('Texas', 2000), ('Texas', 2010)]\n",
+    "populations = [33871648, 37253956,\n",
+    "               18976457, 19378102,\n",
+    "               20851820, 25145561]\n",
+    "pop = pd.Series(populations, index=index)\n",
+    "pop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With this indexing scheme, you can straightforwardly index or slice the series based on this multiple index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(California, 2010)    37253956\n",
+       "(New York, 2000)      18976457\n",
+       "(New York, 2010)      19378102\n",
+       "(Texas, 2000)         20851820\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop[('California', 2010):('Texas', 2000)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "But the convenience ends there. For example, if you need to select all values from 2010, you'll need to do some messy (and potentially slow) munging to make it happen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(California, 2010)    37253956\n",
+       "(New York, 2010)      19378102\n",
+       "(Texas, 2010)         25145561\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop[[i for i in pop.index if i[1] == 2010]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This produces the desired result, but is not as clean (or as efficient for large datasets) as the slicing syntax we've grown to love in Pandas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### The Better Way: Pandas MultiIndex\n",
+    "Fortunately, Pandas provides a better way.\n",
+    "Our tuple-based indexing is essentially a rudimentary multi-index, and the Pandas ``MultiIndex`` type gives us the type of operations we wish to have.\n",
+    "We can create a multi-index from the tuples as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiIndex(levels=[['California', 'New York', 'Texas'], [2000, 2010]],\n",
+       "           labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "index = pd.MultiIndex.from_tuples(index)\n",
+    "index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Notice that the ``MultiIndex`` contains multiple *levels* of indexing–in this case, the state names and the years, as well as multiple *labels* for each data point which encode these levels.\n",
+    "\n",
+    "If we re-index our series with this ``MultiIndex``, we see the hierarchical representation of the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "California  2000    33871648\n",
+       "            2010    37253956\n",
+       "New York    2000    18976457\n",
+       "            2010    19378102\n",
+       "Texas       2000    20851820\n",
+       "            2010    25145561\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop = pop.reindex(index)\n",
+    "pop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Here the first two columns of the ``Series`` representation show the multiple index values, while the third column shows the data.\n",
+    "Notice that some entries are missing in the first column: in this multi-index representation, any blank entry indicates the same value as the line above it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now to access all data for which the second index is 2010, we can simply use the Pandas slicing notation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "California    37253956\n",
+       "New York      19378102\n",
+       "Texas         25145561\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop[:, 2010]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The result is a singly indexed array with just the keys we're interested in.\n",
+    "This syntax is much more convenient (and the operation is much more efficient!) than the home-spun tuple-based multi-indexing solution that we started with.\n",
+    "We'll now further discuss this sort of indexing operation on hieararchically indexed data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### MultiIndex as extra dimension\n",
+    "\n",
+    "You might notice something else here: we could easily have stored the same data using a simple ``DataFrame`` with index and column labels.\n",
+    "In fact, Pandas is built with this equivalence in mind. The ``unstack()`` method will quickly convert a multiply indexed ``Series`` into a conventionally indexed ``DataFrame``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2000</th>\n",
+       "      <th>2010</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>33871648</td>\n",
+       "      <td>37253956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New York</th>\n",
+       "      <td>18976457</td>\n",
+       "      <td>19378102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Texas</th>\n",
+       "      <td>20851820</td>\n",
+       "      <td>25145561</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                2000      2010\n",
+       "California  33871648  37253956\n",
+       "New York    18976457  19378102\n",
+       "Texas       20851820  25145561"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop_df = pop.unstack()\n",
+    "pop_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Naturally, the ``stack()`` method provides the opposite operation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "California  2000    33871648\n",
+       "            2010    37253956\n",
+       "New York    2000    18976457\n",
+       "            2010    19378102\n",
+       "Texas       2000    20851820\n",
+       "            2010    25145561\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop_df.stack()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Seeing this, you might wonder why would we would bother with hierarchical indexing at all.\n",
+    "The reason is simple: just as we were able to use multi-indexing to represent two-dimensional data within a one-dimensional ``Series``, we can also use it to represent data of three or more dimensions in a ``Series`` or ``DataFrame``.\n",
+    "Each extra level in a multi-index represents an extra dimension of data; taking advantage of this property gives us much more flexibility in the types of data we can represent. Concretely, we might want to add another column of demographic data for each state at each year (say, population under 18) ; with a ``MultiIndex`` this is as easy as adding another column to the ``DataFrame``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>total</th>\n",
+       "      <th>under18</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">California</th>\n",
+       "      <th>2000</th>\n",
+       "      <td>33871648</td>\n",
+       "      <td>9267089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010</th>\n",
+       "      <td>37253956</td>\n",
+       "      <td>9284094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">New York</th>\n",
+       "      <th>2000</th>\n",
+       "      <td>18976457</td>\n",
+       "      <td>4687374</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010</th>\n",
+       "      <td>19378102</td>\n",
+       "      <td>4318033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Texas</th>\n",
+       "      <th>2000</th>\n",
+       "      <td>20851820</td>\n",
+       "      <td>5906301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010</th>\n",
+       "      <td>25145561</td>\n",
+       "      <td>6879014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    total  under18\n",
+       "California 2000  33871648  9267089\n",
+       "           2010  37253956  9284094\n",
+       "New York   2000  18976457  4687374\n",
+       "           2010  19378102  4318033\n",
+       "Texas      2000  20851820  5906301\n",
+       "           2010  25145561  6879014"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop_df = pd.DataFrame({'total': pop,\n",
+    "                       'under18': [9267089, 9284094,\n",
+    "                                   4687374, 4318033,\n",
+    "                                   5906301, 6879014]})\n",
+    "pop_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "In addition, all the ufuncs and other functionality discussed in [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) work with hierarchical indices as well.\n",
+    "Here we compute the fraction of people under 18 by year, given the above data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2000</th>\n",
+       "      <th>2010</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>0.273594</td>\n",
+       "      <td>0.249211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New York</th>\n",
+       "      <td>0.247010</td>\n",
+       "      <td>0.222831</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Texas</th>\n",
+       "      <td>0.283251</td>\n",
+       "      <td>0.273568</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                2000      2010\n",
+       "California  0.273594  0.249211\n",
+       "New York    0.247010  0.222831\n",
+       "Texas       0.283251  0.273568"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f_u18 = pop_df['under18'] / pop_df['total']\n",
+    "f_u18.unstack()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This allows us to easily and quickly manipulate and explore even high-dimensional data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Methods of MultiIndex Creation\n",
+    "\n",
+    "The most straightforward way to construct a multiply indexed ``Series`` or ``DataFrame`` is to simply pass a list of two or more index arrays to the constructor. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
+       "      <th>1</th>\n",
+       "      <td>0.554233</td>\n",
+       "      <td>0.356072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.925244</td>\n",
+       "      <td>0.219474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
+       "      <th>1</th>\n",
+       "      <td>0.441759</td>\n",
+       "      <td>0.610054</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.171495</td>\n",
+       "      <td>0.886688</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        data1     data2\n",
+       "a 1  0.554233  0.356072\n",
+       "  2  0.925244  0.219474\n",
+       "b 1  0.441759  0.610054\n",
+       "  2  0.171495  0.886688"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(np.random.rand(4, 2),\n",
+    "                  index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],\n",
+    "                  columns=['data1', 'data2'])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The work of creating the ``MultiIndex`` is done in the background.\n",
+    "\n",
+    "Similarly, if you pass a dictionary with appropriate tuples as keys, Pandas will automatically recognize this and use a ``MultiIndex`` by default:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "California  2000    33871648\n",
+       "            2010    37253956\n",
+       "New York    2000    18976457\n",
+       "            2010    19378102\n",
+       "Texas       2000    20851820\n",
+       "            2010    25145561\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = {('California', 2000): 33871648,\n",
+    "        ('California', 2010): 37253956,\n",
+    "        ('Texas', 2000): 20851820,\n",
+    "        ('Texas', 2010): 25145561,\n",
+    "        ('New York', 2000): 18976457,\n",
+    "        ('New York', 2010): 19378102}\n",
+    "pd.Series(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Nevertheless, it is sometimes useful to explicitly create a ``MultiIndex``; we'll see a couple of these methods here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Explicit MultiIndex constructors\n",
+    "\n",
+    "For more flexibility in how the index is constructed, you can instead use the class method constructors available in the ``pd.MultiIndex``.\n",
+    "For example, as we did before, you can construct the ``MultiIndex`` from a simple list of arrays giving the index values within each level:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
+       "           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'], [1, 2, 1, 2]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "You can construct it from a list of tuples giving the multiple index values of each point:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
+       "           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "You can even construct it from a Cartesian product of single indices:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
+       "           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.MultiIndex.from_product([['a', 'b'], [1, 2]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Similarly, you can construct the ``MultiIndex`` directly using its internal encoding by passing ``levels`` (a list of lists containing available index values for each level) and ``labels`` (a list of lists that reference these labels):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
+       "           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
+    "              labels=[[0, 0, 1, 1], [0, 1, 0, 1]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Any of these objects can be passed as the ``index`` argument when creating a ``Series`` or ``Dataframe``, or be passed to the ``reindex`` method of an existing ``Series`` or ``DataFrame``."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### MultiIndex level names\n",
+    "\n",
+    "Sometimes it is convenient to name the levels of the ``MultiIndex``.\n",
+    "This can be accomplished by passing the ``names`` argument to any of the above ``MultiIndex`` constructors, or by setting the ``names`` attribute of the index after the fact:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state       year\n",
+       "California  2000    33871648\n",
+       "            2010    37253956\n",
+       "New York    2000    18976457\n",
+       "            2010    19378102\n",
+       "Texas       2000    20851820\n",
+       "            2010    25145561\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop.index.names = ['state', 'year']\n",
+    "pop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With more involved datasets, this can be a useful way to keep track of the meaning of various index values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### MultiIndex for columns\n",
+    "\n",
+    "In a ``DataFrame``, the rows and columns are completely symmetric, and just as the rows can have multiple levels of indices, the columns can have multiple levels as well.\n",
+    "Consider the following, which is a mock-up of some (somewhat realistic) medical data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>type</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th>visit</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
+       "      <th>1</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>38.7</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>36.7</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>37.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>37.7</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>36.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
+       "      <th>1</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>37.4</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>37.8</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>36.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>37.8</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>37.3</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>36.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "subject      Bob       Guido         Sue      \n",
+       "type          HR  Temp    HR  Temp    HR  Temp\n",
+       "year visit                                    \n",
+       "2013 1      31.0  38.7  32.0  36.7  35.0  37.2\n",
+       "     2      44.0  37.7  50.0  35.0  29.0  36.7\n",
+       "2014 1      30.0  37.4  39.0  37.8  61.0  36.9\n",
+       "     2      47.0  37.8  48.0  37.3  51.0  36.5"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# hierarchical indices and columns\n",
+    "index = pd.MultiIndex.from_product([[2013, 2014], [1, 2]],\n",
+    "                                   names=['year', 'visit'])\n",
+    "columns = pd.MultiIndex.from_product([['Bob', 'Guido', 'Sue'], ['HR', 'Temp']],\n",
+    "                                     names=['subject', 'type'])\n",
+    "\n",
+    "# mock some data\n",
+    "data = np.round(np.random.randn(4, 6), 1)\n",
+    "data[:, ::2] *= 10\n",
+    "data += 37\n",
+    "\n",
+    "# create the DataFrame\n",
+    "health_data = pd.DataFrame(data, index=index, columns=columns)\n",
+    "health_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Here we see where the multi-indexing for both rows and columns can come in *very* handy.\n",
+    "This is fundamentally four-dimensional data, where the dimensions are the subject, the measurement type, the year, and the visit number.\n",
+    "With this in place we can, for example, index the top-level column by the person's name and get a full ``DataFrame`` containing just that person's information:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>type</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th>visit</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
+       "      <th>1</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>36.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
+       "      <th>1</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>37.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>48.0</td>\n",
+       "      <td>37.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "type          HR  Temp\n",
+       "year visit            \n",
+       "2013 1      32.0  36.7\n",
+       "     2      50.0  35.0\n",
+       "2014 1      39.0  37.8\n",
+       "     2      48.0  37.3"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "health_data['Guido']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "For complicated records containing multiple labeled measurements across multiple times for many subjects (people, countries, cities, etc.) use of hierarchical rows and columns can be extremely convenient!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Indexing and Slicing a MultiIndex\n",
+    "\n",
+    "Indexing and slicing on a ``MultiIndex`` is designed to be intuitive, and it helps if you think about the indices as added dimensions.\n",
+    "We'll first look at indexing multiply indexed ``Series``, and then multiply-indexed ``DataFrame``s."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Multiply indexed Series\n",
+    "\n",
+    "Consider the multiply indexed ``Series`` of state populations we saw earlier:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state       year\n",
+       "California  2000    33871648\n",
+       "            2010    37253956\n",
+       "New York    2000    18976457\n",
+       "            2010    19378102\n",
+       "Texas       2000    20851820\n",
+       "            2010    25145561\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can access single elements by indexing with multiple terms:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33871648"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop['California', 2000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The ``MultiIndex`` also supports *partial indexing*, or indexing just one of the levels in the index.\n",
+    "The result is another ``Series``, with the lower-level indices maintained:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "year\n",
+       "2000    33871648\n",
+       "2010    37253956\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop['California']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Partial slicing is available as well, as long as the ``MultiIndex`` is sorted (see discussion in [Sorted and Unsorted Indices](#Sorted-and-unsorted-indices)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state       year\n",
+       "California  2000    33871648\n",
+       "            2010    37253956\n",
+       "New York    2000    18976457\n",
+       "            2010    19378102\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop.loc['California':'New York']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With sorted indices, partial indexing can be performed on lower levels by passing an empty slice in the first index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "California    33871648\n",
+       "New York      18976457\n",
+       "Texas         20851820\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop[:, 2000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Other types of indexing and selection (discussed in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb)) work as well; for example, selection based on Boolean masks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state       year\n",
+       "California  2000    33871648\n",
+       "            2010    37253956\n",
+       "Texas       2010    25145561\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop[pop > 22000000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Selection based on fancy indexing also works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state       year\n",
+       "California  2000    33871648\n",
+       "            2010    37253956\n",
+       "Texas       2000    20851820\n",
+       "            2010    25145561\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop[['California', 'Texas']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Multiply indexed DataFrames\n",
+    "\n",
+    "A multiply indexed ``DataFrame`` behaves in a similar manner.\n",
+    "Consider our toy medical ``DataFrame`` from before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>type</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th>visit</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
+       "      <th>1</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>38.7</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>36.7</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>37.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>37.7</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>36.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
+       "      <th>1</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>37.4</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>37.8</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>36.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>37.8</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>37.3</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>36.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "subject      Bob       Guido         Sue      \n",
+       "type          HR  Temp    HR  Temp    HR  Temp\n",
+       "year visit                                    \n",
+       "2013 1      31.0  38.7  32.0  36.7  35.0  37.2\n",
+       "     2      44.0  37.7  50.0  35.0  29.0  36.7\n",
+       "2014 1      30.0  37.4  39.0  37.8  61.0  36.9\n",
+       "     2      47.0  37.8  48.0  37.3  51.0  36.5"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "health_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Remember that columns are primary in a ``DataFrame``, and the syntax used for multiply indexed ``Series`` applies to the columns.\n",
+    "For example, we can recover Guido's heart rate data with a simple operation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "year  visit\n",
+       "2013  1        32.0\n",
+       "      2        50.0\n",
+       "2014  1        39.0\n",
+       "      2        48.0\n",
+       "Name: (Guido, HR), dtype: float64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "health_data['Guido', 'HR']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Also, as with the single-index case, we can use the ``loc``, ``iloc``, and ``ix`` indexers introduced in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb). For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>type</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th>visit</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
+       "      <th>1</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>38.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>37.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "subject      Bob      \n",
+       "type          HR  Temp\n",
+       "year visit            \n",
+       "2013 1      31.0  38.7\n",
+       "     2      44.0  37.7"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "health_data.iloc[:2, :2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "These indexers provide an array-like view of the underlying two-dimensional data, but each individual index in ``loc`` or ``iloc`` can be passed a tuple of multiple indices. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "year  visit\n",
+       "2013  1        31.0\n",
+       "      2        44.0\n",
+       "2014  1        30.0\n",
+       "      2        47.0\n",
+       "Name: (Bob, HR), dtype: float64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "health_data.loc[:, ('Bob', 'HR')]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Working with slices within these index tuples is not especially convenient; trying to create a slice within a tuple will lead to a syntax error:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-32-8e3cc151e316>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-32-8e3cc151e316>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    health_data.loc[(:, 1), (:, 'HR')]\u001b[0m\n\u001b[0m                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "health_data.loc[(:, 1), (:, 'HR')]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "You could get around this by building the desired slice explicitly using Python's built-in ``slice()`` function, but a better way in this context is to use an ``IndexSlice`` object, which Pandas provides for precisely this situation.\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th>Bob</th>\n",
+       "      <th>Guido</th>\n",
+       "      <th>Sue</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>type</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>HR</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th>visit</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2013</th>\n",
+       "      <th>1</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014</th>\n",
+       "      <th>1</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>61.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "subject      Bob Guido   Sue\n",
+       "type          HR    HR    HR\n",
+       "year visit                  \n",
+       "2013 1      31.0  32.0  35.0\n",
+       "2014 1      30.0  39.0  61.0"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "idx = pd.IndexSlice\n",
+    "health_data.loc[idx[:, 1], idx[:, 'HR']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "There are so many ways to interact with data in multiply indexed ``Series`` and ``DataFrame``s, and as with many tools in this book the best way to become familiar with them is to try them out!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Rearranging Multi-Indices\n",
+    "\n",
+    "One of the keys to working with multiply indexed data is knowing how to effectively transform the data.\n",
+    "There are a number of operations that will preserve all the information in the dataset, but rearrange it for the purposes of various computations.\n",
+    "We saw a brief example of this in the ``stack()`` and ``unstack()`` methods, but there are many more ways to finely control the rearrangement of data between hierarchical indices and columns, and we'll explore them here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Sorted and unsorted indices\n",
+    "\n",
+    "Earlier, we briefly mentioned a caveat, but we should emphasize it more here.\n",
+    "*Many of the ``MultiIndex`` slicing operations will fail if the index is not sorted.*\n",
+    "Let's take a look at this here.\n",
+    "\n",
+    "We'll start by creating some simple multiply indexed data where the indices are *not lexographically sorted*:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "char  int\n",
+       "a     1      0.003001\n",
+       "      2      0.164974\n",
+       "c     1      0.741650\n",
+       "      2      0.569264\n",
+       "b     1      0.001693\n",
+       "      2      0.526226\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "index = pd.MultiIndex.from_product([['a', 'c', 'b'], [1, 2]])\n",
+    "data = pd.Series(np.random.rand(6), index=index)\n",
+    "data.index.names = ['char', 'int']\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "If we try to take a partial slice of this index, it will result in an error:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'KeyError'>\n",
+      "'Key length (1) was greater than MultiIndex lexsort depth (0)'\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    data['a':'b']\n",
+    "except KeyError as e:\n",
+    "    print(type(e))\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Although it is not entirely clear from the error message, this is the result of the MultiIndex not being sorted.\n",
+    "For various reasons, partial slices and other similar operations require the levels in the ``MultiIndex`` to be in sorted (i.e., lexographical) order.\n",
+    "Pandas provides a number of convenience routines to perform this type of sorting; examples are the ``sort_index()`` and ``sortlevel()`` methods of the ``DataFrame``.\n",
+    "We'll use the simplest, ``sort_index()``, here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "char  int\n",
+       "a     1      0.003001\n",
+       "      2      0.164974\n",
+       "b     1      0.001693\n",
+       "      2      0.526226\n",
+       "c     1      0.741650\n",
+       "      2      0.569264\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = data.sort_index()\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With the index sorted in this way, partial slicing will work as expected:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "char  int\n",
+       "a     1      0.003001\n",
+       "      2      0.164974\n",
+       "b     1      0.001693\n",
+       "      2      0.526226\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['a':'b']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Stacking and unstacking indices\n",
+    "\n",
+    "As we saw briefly before, it is possible to convert a dataset from a stacked multi-index to a simple two-dimensional representation, optionally specifying the level to use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>state</th>\n",
+       "      <th>California</th>\n",
+       "      <th>New York</th>\n",
+       "      <th>Texas</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000</th>\n",
+       "      <td>33871648</td>\n",
+       "      <td>18976457</td>\n",
+       "      <td>20851820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010</th>\n",
+       "      <td>37253956</td>\n",
+       "      <td>19378102</td>\n",
+       "      <td>25145561</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "state  California  New York     Texas\n",
+       "year                                 \n",
+       "2000     33871648  18976457  20851820\n",
+       "2010     37253956  19378102  25145561"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop.unstack(level=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>year</th>\n",
+       "      <th>2000</th>\n",
+       "      <th>2010</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>33871648</td>\n",
+       "      <td>37253956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New York</th>\n",
+       "      <td>18976457</td>\n",
+       "      <td>19378102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Texas</th>\n",
+       "      <td>20851820</td>\n",
+       "      <td>25145561</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "year            2000      2010\n",
+       "state                         \n",
+       "California  33871648  37253956\n",
+       "New York    18976457  19378102\n",
+       "Texas       20851820  25145561"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop.unstack(level=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The opposite of ``unstack()`` is ``stack()``, which here can be used to recover the original series:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state       year\n",
+       "California  2000    33871648\n",
+       "            2010    37253956\n",
+       "New York    2000    18976457\n",
+       "            2010    19378102\n",
+       "Texas       2000    20851820\n",
+       "            2010    25145561\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop.unstack().stack()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Index setting and resetting\n",
+    "\n",
+    "Another way to rearrange hierarchical data is to turn the index labels into columns; this can be accomplished with the ``reset_index`` method.\n",
+    "Calling this on the population dictionary will result in a ``DataFrame`` with a *state* and *year* column holding the information that was formerly in the index.\n",
+    "For clarity, we can optionally specify the name of the data for the column representation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th>population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>California</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>33871648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>California</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>37253956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>New York</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>18976457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>New York</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>19378102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Texas</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>20851820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Texas</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>25145561</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        state  year  population\n",
+       "0  California  2000    33871648\n",
+       "1  California  2010    37253956\n",
+       "2    New York  2000    18976457\n",
+       "3    New York  2010    19378102\n",
+       "4       Texas  2000    20851820\n",
+       "5       Texas  2010    25145561"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop_flat = pop.reset_index(name='population')\n",
+    "pop_flat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Often when working with data in the real world, the raw input data looks like this and it's useful to build a ``MultiIndex`` from the column values.\n",
+    "This can be done with the ``set_index`` method of the ``DataFrame``, which returns a multiply indexed ``DataFrame``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>population</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th>year</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">California</th>\n",
+       "      <th>2000</th>\n",
+       "      <td>33871648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010</th>\n",
+       "      <td>37253956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">New York</th>\n",
+       "      <th>2000</th>\n",
+       "      <td>18976457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010</th>\n",
+       "      <td>19378102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Texas</th>\n",
+       "      <th>2000</th>\n",
+       "      <td>20851820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010</th>\n",
+       "      <td>25145561</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 population\n",
+       "state      year            \n",
+       "California 2000    33871648\n",
+       "           2010    37253956\n",
+       "New York   2000    18976457\n",
+       "           2010    19378102\n",
+       "Texas      2000    20851820\n",
+       "           2010    25145561"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop_flat.set_index(['state', 'year'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "In practice, I find this type of reindexing to be one of the more useful patterns when encountering real-world datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Data Aggregations on Multi-Indices\n",
+    "\n",
+    "We've previously seen that Pandas has built-in data aggregation methods, such as ``mean()``, ``sum()``, and ``max()``.\n",
+    "For hierarchically indexed data, these can be passed a ``level`` parameter that controls which subset of the data the aggregate is computed on.\n",
+    "\n",
+    "For example, let's return to our health data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>type</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th>visit</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
+       "      <th>1</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>38.7</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>36.7</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>37.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>37.7</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>36.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
+       "      <th>1</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>37.4</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>37.8</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>36.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>37.8</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>37.3</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>36.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "subject      Bob       Guido         Sue      \n",
+       "type          HR  Temp    HR  Temp    HR  Temp\n",
+       "year visit                                    \n",
+       "2013 1      31.0  38.7  32.0  36.7  35.0  37.2\n",
+       "     2      44.0  37.7  50.0  35.0  29.0  36.7\n",
+       "2014 1      30.0  37.4  39.0  37.8  61.0  36.9\n",
+       "     2      47.0  37.8  48.0  37.3  51.0  36.5"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "health_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Perhaps we'd like to average-out the measurements in the two visits each year. We can do this by naming the index level we'd like to explore, in this case the year:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>subject</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>type</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2013</th>\n",
+       "      <td>37.5</td>\n",
+       "      <td>38.2</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>35.85</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>36.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014</th>\n",
+       "      <td>38.5</td>\n",
+       "      <td>37.6</td>\n",
+       "      <td>43.5</td>\n",
+       "      <td>37.55</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>36.70</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "subject   Bob       Guido          Sue       \n",
+       "type       HR  Temp    HR   Temp    HR   Temp\n",
+       "year                                         \n",
+       "2013     37.5  38.2  41.0  35.85  32.0  36.95\n",
+       "2014     38.5  37.6  43.5  37.55  56.0  36.70"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_mean = health_data.mean(level='year')\n",
+    "data_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "By further making use of the ``axis`` keyword, we can take the mean among levels on the columns as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>type</th>\n",
+       "      <th>HR</th>\n",
+       "      <th>Temp</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2013</th>\n",
+       "      <td>36.833333</td>\n",
+       "      <td>37.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014</th>\n",
+       "      <td>46.000000</td>\n",
+       "      <td>37.283333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "type         HR       Temp\n",
+       "year                      \n",
+       "2013  36.833333  37.000000\n",
+       "2014  46.000000  37.283333"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_mean.mean(axis=1, level='type')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Thus in two lines, we've been able to find the average heart rate and temperature measured among all subjects in all visits each year.\n",
+    "This syntax is actually a short cut to the ``GroupBy`` functionality, which we will discuss in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb).\n",
+    "While this is a toy example, many real-world datasets have similar hierarchical structure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Aside: Panel Data\n",
+    "\n",
+    "Pandas has a few other fundamental data structures that we have not yet discussed, namely the ``pd.Panel`` and ``pd.Panel4D`` objects.\n",
+    "These can be thought of, respectively, as three-dimensional and four-dimensional generalizations of the (one-dimensional) ``Series`` and (two-dimensional) ``DataFrame`` structures.\n",
+    "Once you are familiar with indexing and manipulation of data in a ``Series`` and ``DataFrame``, ``Panel`` and ``Panel4D`` are relatively straightforward to use.\n",
+    "In particular, the ``ix``, ``loc``, and ``iloc`` indexers discussed in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) extend readily to these higher-dimensional structures.\n",
+    "\n",
+    "We won't cover these panel structures further in this text, as I've found in the majority of cases that multi-indexing is a more useful and conceptually simpler representation for higher-dimensional data.\n",
+    "Additionally, panel data is fundamentally a dense data representation, while multi-indexing is fundamentally a sparse data representation.\n",
+    "As the number of dimensions increases, the dense representation can become very inefficient for the majority of real-world datasets.\n",
+    "For the occasional specialized application, however, these structures can be useful.\n",
+    "If you'd like to read more about the ``Panel`` and ``Panel4D`` structures, see the references listed in [Further Resources](03.13-Further-Resources.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Handling Missing Data](03.04-Missing-Values.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.05-Hierarchical-Indexing.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/03.06-Concat-And-Append.ipynb b/notebooks_v1/03.06-Concat-And-Append.ipynb
new file mode 100644
index 000000000..7566c851c
--- /dev/null
+++ b/notebooks_v1/03.06-Concat-And-Append.ipynb
@@ -0,0 +1,1640 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.06-Concat-And-Append.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Combining Datasets: Concat and Append"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some of the most interesting studies of data come from combining different data sources.\n",
+    "These operations can involve anything from very straightforward concatenation of two different datasets, to more complicated database-style joins and merges that correctly handle any overlaps between the datasets.\n",
+    "``Series`` and ``DataFrame``s are built with this type of operation in mind, and Pandas includes functions and methods that make this sort of data wrangling fast and straightforward.\n",
+    "\n",
+    "Here we'll take a look at simple concatenation of ``Series`` and ``DataFrame``s with the ``pd.concat`` function; later we'll dive into more sophisticated in-memory merges and joins implemented in Pandas.\n",
+    "\n",
+    "We begin with the standard imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For convenience, we'll define this function which creates a ``DataFrame`` of a particular form that will be useful below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "      <td>C0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "      <td>C1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>C2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    A   B   C\n",
+       "0  A0  B0  C0\n",
+       "1  A1  B1  C1\n",
+       "2  A2  B2  C2"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def make_df(cols, ind):\n",
+    "    \"\"\"Quickly make a DataFrame\"\"\"\n",
+    "    data = {c: [str(c) + str(i) for i in ind]\n",
+    "            for c in cols}\n",
+    "    return pd.DataFrame(data, ind)\n",
+    "\n",
+    "# example DataFrame\n",
+    "make_df('ABC', range(3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition, we'll create a quick class that allows us to display multiple ``DataFrame``s side by side. The code makes use of the special ``_repr_html_`` method, which IPython uses to implement its rich object display:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class display(object):\n",
+    "    \"\"\"Display HTML representation of multiple objects\"\"\"\n",
+    "    template = \"\"\"<div style=\"float: left; padding: 10px;\">\n",
+    "    <p style='font-family:\"Courier New\", Courier, monospace'>{0}</p>{1}\n",
+    "    </div>\"\"\"\n",
+    "    def __init__(self, *args):\n",
+    "        self.args = args\n",
+    "        \n",
+    "    def _repr_html_(self):\n",
+    "        return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n",
+    "                         for a in self.args)\n",
+    "    \n",
+    "    def __repr__(self):\n",
+    "        return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n",
+    "                           for a in self.args)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The use of this will become clearer as we continue our discussion in the following section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Recall: Concatenation of NumPy Arrays\n",
+    "\n",
+    "Concatenation of ``Series`` and ``DataFrame`` objects is very similar to concatenation of Numpy arrays, which can be done via the ``np.concatenate`` function as discussed in [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb).\n",
+    "Recall that with it, you can combine the contents of two or more arrays into a single array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = [1, 2, 3]\n",
+    "y = [4, 5, 6]\n",
+    "z = [7, 8, 9]\n",
+    "np.concatenate([x, y, z])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first argument is a list or tuple of arrays to concatenate.\n",
+    "Additionally, it takes an ``axis`` keyword that allows you to specify the axis along which the result will be concatenated:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 1, 2],\n",
+       "       [3, 4, 3, 4]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = [[1, 2],\n",
+    "     [3, 4]]\n",
+    "np.concatenate([x, x], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simple Concatenation with ``pd.concat``"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas has a function, ``pd.concat()``, which has a similar syntax to ``np.concatenate`` but contains a number of options that we'll discuss momentarily:\n",
+    "\n",
+    "```python\n",
+    "# Signature in Pandas v0.18\n",
+    "pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,\n",
+    "          keys=None, levels=None, names=None, verify_integrity=False,\n",
+    "          copy=True)\n",
+    "```\n",
+    "\n",
+    "``pd.concat()`` can be used for a simple concatenation of ``Series`` or ``DataFrame`` objects, just as ``np.concatenate()`` can be used for simple concatenations of arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    A\n",
+       "2    B\n",
+       "3    C\n",
+       "4    D\n",
+       "5    E\n",
+       "6    F\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ser1 = pd.Series(['A', 'B', 'C'], index=[1, 2, 3])\n",
+    "ser2 = pd.Series(['D', 'E', 'F'], index=[4, 5, 6])\n",
+    "pd.concat([ser1, ser2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It also works to concatenate higher-dimensional objects, such as ``DataFrame``s:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df1</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>B3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>A4</td>\n",
+       "      <td>B4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([df1, df2])</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>B3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>A4</td>\n",
+       "      <td>B4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df1\n",
+       "    A   B\n",
+       "1  A1  B1\n",
+       "2  A2  B2\n",
+       "\n",
+       "df2\n",
+       "    A   B\n",
+       "3  A3  B3\n",
+       "4  A4  B4\n",
+       "\n",
+       "pd.concat([df1, df2])\n",
+       "    A   B\n",
+       "1  A1  B1\n",
+       "2  A2  B2\n",
+       "3  A3  B3\n",
+       "4  A4  B4"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = make_df('AB', [1, 2])\n",
+    "df2 = make_df('AB', [3, 4])\n",
+    "display('df1', 'df2', 'pd.concat([df1, df2])')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, the concatenation takes place row-wise within the ``DataFrame`` (i.e., ``axis=0``).\n",
+    "Like ``np.concatenate``, ``pd.concat`` allows specification of an axis along which concatenation will take place.\n",
+    "Consider the following example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df3</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df4</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>C0</td>\n",
+       "      <td>D0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>C1</td>\n",
+       "      <td>D1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([df3, df4], axis='col')</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "      <td>C0</td>\n",
+       "      <td>D0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>D1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df3\n",
+       "    A   B\n",
+       "0  A0  B0\n",
+       "1  A1  B1\n",
+       "\n",
+       "df4\n",
+       "    C   D\n",
+       "0  C0  D0\n",
+       "1  C1  D1\n",
+       "\n",
+       "pd.concat([df3, df4], axis='col')\n",
+       "    A   B   C   D\n",
+       "0  A0  B0  C0  D0\n",
+       "1  A1  B1  C1  D1"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3 = make_df('AB', [0, 1])\n",
+    "df4 = make_df('CD', [0, 1])\n",
+    "display('df3', 'df4', \"pd.concat([df3, df4], axis='col')\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could have equivalently specified ``axis=1``; here we've used the more intuitive ``axis='col'``. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Duplicate indices\n",
+    "\n",
+    "One important difference between ``np.concatenate`` and ``pd.concat`` is that Pandas concatenation *preserves indices*, even if the result will have duplicate indices!\n",
+    "Consider this simple example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>x</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>y</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>B3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([x, y])</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>B3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "x\n",
+       "    A   B\n",
+       "0  A0  B0\n",
+       "1  A1  B1\n",
+       "\n",
+       "y\n",
+       "    A   B\n",
+       "0  A2  B2\n",
+       "1  A3  B3\n",
+       "\n",
+       "pd.concat([x, y])\n",
+       "    A   B\n",
+       "0  A0  B0\n",
+       "1  A1  B1\n",
+       "0  A2  B2\n",
+       "1  A3  B3"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = make_df('AB', [0, 1])\n",
+    "y = make_df('AB', [2, 3])\n",
+    "y.index = x.index  # make duplicate indices!\n",
+    "display('x', 'y', 'pd.concat([x, y])')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice the repeated indices in the result.\n",
+    "While this is valid within ``DataFrame``s, the outcome is often undesirable.\n",
+    "``pd.concat()`` gives us a few ways to handle it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Catching the repeats as an error\n",
+    "\n",
+    "If you'd like to simply verify that the indices in the result of ``pd.concat()`` do not overlap, you can specify the ``verify_integrity`` flag.\n",
+    "With this set to True, the concatenation will raise an exception if there are duplicate indices.\n",
+    "Here is an example, where for clarity we'll catch and print the error message:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ValueError: Indexes have overlapping values: [0, 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    pd.concat([x, y], verify_integrity=True)\n",
+    "except ValueError as e:\n",
+    "    print(\"ValueError:\", e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Ignoring the index\n",
+    "\n",
+    "Sometimes the index itself does not matter, and you would prefer it to simply be ignored.\n",
+    "This option can be specified using the ``ignore_index`` flag.\n",
+    "With this set to true, the concatenation will create a new integer index for the resulting ``Series``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>x</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>y</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>B3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([x, y], ignore_index=True)</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>B3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "x\n",
+       "    A   B\n",
+       "0  A0  B0\n",
+       "1  A1  B1\n",
+       "\n",
+       "y\n",
+       "    A   B\n",
+       "0  A2  B2\n",
+       "1  A3  B3\n",
+       "\n",
+       "pd.concat([x, y], ignore_index=True)\n",
+       "    A   B\n",
+       "0  A0  B0\n",
+       "1  A1  B1\n",
+       "2  A2  B2\n",
+       "3  A3  B3"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display('x', 'y', 'pd.concat([x, y], ignore_index=True)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Adding MultiIndex keys\n",
+    "\n",
+    "Another option is to use the ``keys`` option to specify a label for the data sources; the result will be a hierarchically indexed series containing the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>x</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>y</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>B3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([x, y], keys=['x', 'y'])</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">x</th>\n",
+       "      <th>0</th>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">y</th>\n",
+       "      <th>0</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>B3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "x\n",
+       "    A   B\n",
+       "0  A0  B0\n",
+       "1  A1  B1\n",
+       "\n",
+       "y\n",
+       "    A   B\n",
+       "0  A2  B2\n",
+       "1  A3  B3\n",
+       "\n",
+       "pd.concat([x, y], keys=['x', 'y'])\n",
+       "      A   B\n",
+       "x 0  A0  B0\n",
+       "  1  A1  B1\n",
+       "y 0  A2  B2\n",
+       "  1  A3  B3"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display('x', 'y', \"pd.concat([x, y], keys=['x', 'y'])\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a multiply indexed ``DataFrame``, and we can use the tools discussed in [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) to transform this data into the representation we're interested in."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Concatenation with joins\n",
+    "\n",
+    "In the simple examples we just looked at, we were mainly concatenating ``DataFrame``s with shared column names.\n",
+    "In practice, data from different sources might have different sets of column names, and ``pd.concat`` offers several options in this case.\n",
+    "Consider the concatenation of the following two ``DataFrame``s, which have some (but not all!) columns in common:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df5</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "      <td>C1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>C2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df6</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B3</td>\n",
+       "      <td>C3</td>\n",
+       "      <td>D3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B4</td>\n",
+       "      <td>C4</td>\n",
+       "      <td>D4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([df5, df6])</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>C2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>B3</td>\n",
+       "      <td>C3</td>\n",
+       "      <td>D3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>B4</td>\n",
+       "      <td>C4</td>\n",
+       "      <td>D4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df5\n",
+       "    A   B   C\n",
+       "1  A1  B1  C1\n",
+       "2  A2  B2  C2\n",
+       "\n",
+       "df6\n",
+       "    B   C   D\n",
+       "3  B3  C3  D3\n",
+       "4  B4  C4  D4\n",
+       "\n",
+       "pd.concat([df5, df6])\n",
+       "     A   B   C    D\n",
+       "1   A1  B1  C1  NaN\n",
+       "2   A2  B2  C2  NaN\n",
+       "3  NaN  B3  C3   D3\n",
+       "4  NaN  B4  C4   D4"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df5 = make_df('ABC', [1, 2])\n",
+    "df6 = make_df('BCD', [3, 4])\n",
+    "display('df5', 'df6', 'pd.concat([df5, df6])')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, the entries for which no data is available are filled with NA values.\n",
+    "To change this, we can specify one of several options for the ``join`` and ``join_axes`` parameters of the concatenate function.\n",
+    "By default, the join is a union of the input columns (``join='outer'``), but we can change this to an intersection of the columns using ``join='inner'``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df5</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "      <td>C1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>C2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df6</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B3</td>\n",
+       "      <td>C3</td>\n",
+       "      <td>D3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B4</td>\n",
+       "      <td>C4</td>\n",
+       "      <td>D4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([df5, df6], join='inner')</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B1</td>\n",
+       "      <td>C1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>B2</td>\n",
+       "      <td>C2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B3</td>\n",
+       "      <td>C3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B4</td>\n",
+       "      <td>C4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df5\n",
+       "    A   B   C\n",
+       "1  A1  B1  C1\n",
+       "2  A2  B2  C2\n",
+       "\n",
+       "df6\n",
+       "    B   C   D\n",
+       "3  B3  C3  D3\n",
+       "4  B4  C4  D4\n",
+       "\n",
+       "pd.concat([df5, df6], join='inner')\n",
+       "    B   C\n",
+       "1  B1  C1\n",
+       "2  B2  C2\n",
+       "3  B3  C3\n",
+       "4  B4  C4"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display('df5', 'df6',\n",
+    "        \"pd.concat([df5, df6], join='inner')\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another option is to directly specify the index of the remaininig colums using the ``join_axes`` argument, which takes a list of index objects.\n",
+    "Here we'll specify that the returned columns should be the same as those of the first input:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df5</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "      <td>C1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>C2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df6</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B3</td>\n",
+       "      <td>C3</td>\n",
+       "      <td>D3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B4</td>\n",
+       "      <td>C4</td>\n",
+       "      <td>D4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.concat([df5, df6], join_axes=[df5.columns])</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "      <td>C1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>C2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>B3</td>\n",
+       "      <td>C3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>B4</td>\n",
+       "      <td>C4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df5\n",
+       "    A   B   C\n",
+       "1  A1  B1  C1\n",
+       "2  A2  B2  C2\n",
+       "\n",
+       "df6\n",
+       "    B   C   D\n",
+       "3  B3  C3  D3\n",
+       "4  B4  C4  D4\n",
+       "\n",
+       "pd.concat([df5, df6], join_axes=[df5.columns])\n",
+       "     A   B   C\n",
+       "1   A1  B1  C1\n",
+       "2   A2  B2  C2\n",
+       "3  NaN  B3  C3\n",
+       "4  NaN  B4  C4"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display('df5', 'df6',\n",
+    "        \"pd.concat([df5, df6], join_axes=[df5.columns])\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The combination of options of the ``pd.concat`` function allows a wide range of possible behaviors when joining two datasets; keep these in mind as you use these tools for your own data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The ``append()`` method\n",
+    "\n",
+    "Because direct array concatenation is so common, ``Series`` and ``DataFrame`` objects have an ``append`` method that can accomplish the same thing in fewer keystrokes.\n",
+    "For example, rather than calling ``pd.concat([df1, df2])``, you can simply call ``df1.append(df2)``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df1</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>B3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>A4</td>\n",
+       "      <td>B4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df1.append(df2)</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>B3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>A4</td>\n",
+       "      <td>B4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df1\n",
+       "    A   B\n",
+       "1  A1  B1\n",
+       "2  A2  B2\n",
+       "\n",
+       "df2\n",
+       "    A   B\n",
+       "3  A3  B3\n",
+       "4  A4  B4\n",
+       "\n",
+       "df1.append(df2)\n",
+       "    A   B\n",
+       "1  A1  B1\n",
+       "2  A2  B2\n",
+       "3  A3  B3\n",
+       "4  A4  B4"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display('df1', 'df2', 'df1.append(df2)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Keep in mind that unlike the ``append()`` and ``extend()`` methods of Python lists, the ``append()`` method in Pandas does not modify the original object–instead it creates a new object with the combined data.\n",
+    "It also is not a very efficient method, because it involves creation of a new index *and* data buffer.\n",
+    "Thus, if you plan to do multiple ``append`` operations, it is generally better to build a list of ``DataFrame``s and pass them all at once to the ``concat()`` function.\n",
+    "\n",
+    "In the next section, we'll look at another more powerful approach to combining data from multiple sources, the database-style merges/joins implemented in ``pd.merge``.\n",
+    "For more information on ``concat()``, ``append()``, and related functionality, see the [\"Merge, Join, and Concatenate\" section](http://pandas.pydata.org/pandas-docs/stable/merging.html) of the Pandas documentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.06-Concat-And-Append.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/03.07-Merge-and-Join.ipynb b/notebooks_v1/03.07-Merge-and-Join.ipynb
new file mode 100644
index 000000000..c46383e57
--- /dev/null
+++ b/notebooks_v1/03.07-Merge-and-Join.ipynb
@@ -0,0 +1,3573 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) | [Contents](Index.ipynb) | [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.07-Merge-and-Join.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Combining Datasets: Merge and Join"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One essential feature offered by Pandas is its high-performance, in-memory join and merge operations.\n",
+    "If you have ever worked with databases, you should be familiar with this type of data interaction.\n",
+    "The main interface for this is the ``pd.merge`` function, and we'll see few examples of how this can work in practice.\n",
+    "\n",
+    "For convenience, we will start by redefining the ``display()`` functionality from the previous section:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "class display(object):\n",
+    "    \"\"\"Display HTML representation of multiple objects\"\"\"\n",
+    "    template = \"\"\"<div style=\"float: left; padding: 10px;\">\n",
+    "    <p style='font-family:\"Courier New\", Courier, monospace'>{0}</p>{1}\n",
+    "    </div>\"\"\"\n",
+    "    def __init__(self, *args):\n",
+    "        self.args = args\n",
+    "        \n",
+    "    def _repr_html_(self):\n",
+    "        return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n",
+    "                         for a in self.args)\n",
+    "    \n",
+    "    def __repr__(self):\n",
+    "        return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n",
+    "                           for a in self.args)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Relational Algebra\n",
+    "\n",
+    "The behavior implemented in ``pd.merge()`` is a subset of what is known as *relational algebra*, which is a formal set of rules for manipulating relational data, and forms the conceptual foundation of operations available in most databases.\n",
+    "The strength of the relational algebra approach is that it proposes several primitive operations, which become the building blocks of more complicated operations on any dataset.\n",
+    "With this lexicon of fundamental operations implemented efficiently in a database or other program, a wide range of fairly complicated composite operations can be performed.\n",
+    "\n",
+    "Pandas implements several of these fundamental building-blocks in the ``pd.merge()`` function and the related ``join()`` method of ``Series`` and ``Dataframe``s.\n",
+    "As we will see, these let you efficiently link data from different sources."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Categories of Joins\n",
+    "\n",
+    "The ``pd.merge()`` function implements a number of types of joins: the *one-to-one*, *many-to-one*, and *many-to-many* joins.\n",
+    "All three types of joins are accessed via an identical call to the ``pd.merge()`` interface; the type of join performed depends on the form of the input data.\n",
+    "Here we will show simple examples of the three types of merges, and discuss detailed options further below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### One-to-one joins\n",
+    "\n",
+    "Perhaps the simplest type of merge expresion is the one-to-one join, which is in many ways very similar to the column-wise concatenation seen in [Combining Datasets: Concat & Append](03.06-Concat-And-Append.ipynb).\n",
+    "As a concrete example, consider the following two ``DataFrames`` which contain information on several employees in a company:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df1</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee</th>\n",
+       "      <th>group</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>Accounting</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>HR</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee</th>\n",
+       "      <th>hire_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>2004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df1\n",
+       "  employee        group\n",
+       "0      Bob   Accounting\n",
+       "1     Jake  Engineering\n",
+       "2     Lisa  Engineering\n",
+       "3      Sue           HR\n",
+       "\n",
+       "df2\n",
+       "  employee  hire_date\n",
+       "0     Lisa       2004\n",
+       "1      Bob       2008\n",
+       "2     Jake       2012\n",
+       "3      Sue       2014"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
+    "                    'group': ['Accounting', 'Engineering', 'Engineering', 'HR']})\n",
+    "df2 = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue'],\n",
+    "                    'hire_date': [2004, 2008, 2012, 2014]})\n",
+    "display('df1', 'df2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To combine this information into a single ``DataFrame``, we can use the ``pd.merge()`` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee</th>\n",
+       "      <th>group</th>\n",
+       "      <th>hire_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>Accounting</td>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>2004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  employee        group  hire_date\n",
+       "0      Bob   Accounting       2008\n",
+       "1     Jake  Engineering       2012\n",
+       "2     Lisa  Engineering       2004\n",
+       "3      Sue           HR       2014"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3 = pd.merge(df1, df2)\n",
+    "df3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``pd.merge()`` function recognizes that each ``DataFrame`` has an \"employee\" column, and automatically joins using this column as a key.\n",
+    "The result of the merge is a new ``DataFrame`` that combines the information from the two inputs.\n",
+    "Notice that the order of entries in each column is not necessarily maintained: in this case, the order of the \"employee\" column differs between ``df1`` and ``df2``, and the ``pd.merge()`` function correctly accounts for this.\n",
+    "Additionally, keep in mind that the merge in general discards the index, except in the special case of merges by index (see the ``left_index`` and ``right_index`` keywords, discussed momentarily)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Many-to-one joins"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Many-to-one joins are joins in which one of the two key columns contains duplicate entries.\n",
+    "For the many-to-one case, the resulting ``DataFrame`` will preserve those duplicate entries as appropriate.\n",
+    "Consider the following example of a many-to-one join:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df3</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee</th>\n",
+       "      <th>group</th>\n",
+       "      <th>hire_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>Accounting</td>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>2004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df4</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>group</th>\n",
+       "      <th>supervisor</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Accounting</td>\n",
+       "      <td>Carly</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>Guido</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HR</td>\n",
+       "      <td>Steve</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df3, df4)</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee</th>\n",
+       "      <th>group</th>\n",
+       "      <th>hire_date</th>\n",
+       "      <th>supervisor</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>Accounting</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>Carly</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>Guido</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>Guido</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>Steve</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df3\n",
+       "  employee        group  hire_date\n",
+       "0      Bob   Accounting       2008\n",
+       "1     Jake  Engineering       2012\n",
+       "2     Lisa  Engineering       2004\n",
+       "3      Sue           HR       2014\n",
+       "\n",
+       "df4\n",
+       "         group supervisor\n",
+       "0   Accounting      Carly\n",
+       "1  Engineering      Guido\n",
+       "2           HR      Steve\n",
+       "\n",
+       "pd.merge(df3, df4)\n",
+       "  employee        group  hire_date supervisor\n",
+       "0      Bob   Accounting       2008      Carly\n",
+       "1     Jake  Engineering       2012      Guido\n",
+       "2     Lisa  Engineering       2004      Guido\n",
+       "3      Sue           HR       2014      Steve"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df4 = pd.DataFrame({'group': ['Accounting', 'Engineering', 'HR'],\n",
+    "                    'supervisor': ['Carly', 'Guido', 'Steve']})\n",
+    "display('df3', 'df4', 'pd.merge(df3, df4)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The resulting ``DataFrame`` has an aditional column with the \"supervisor\" information, where the information is repeated in one or more locations as required by the inputs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Many-to-many joins"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Many-to-many joins are a bit confusing conceptually, but are nevertheless well defined.\n",
+    "If the key column in both the left and right array contains duplicates, then the result is a many-to-many merge.\n",
+    "This will be perhaps most clear with a concrete example.\n",
+    "Consider the following, where we have a ``DataFrame`` showing one or more skills associated with a particular group.\n",
+    "By performing a many-to-many join, we can recover the skills associated with any individual person:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df1</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee</th>\n",
+       "      <th>group</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>Accounting</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>HR</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df5</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>group</th>\n",
+       "      <th>skills</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Accounting</td>\n",
+       "      <td>math</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Accounting</td>\n",
+       "      <td>spreadsheets</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>coding</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>linux</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>HR</td>\n",
+       "      <td>spreadsheets</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>HR</td>\n",
+       "      <td>organization</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df1, df5)</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee</th>\n",
+       "      <th>group</th>\n",
+       "      <th>skills</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>Accounting</td>\n",
+       "      <td>math</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>Accounting</td>\n",
+       "      <td>spreadsheets</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>coding</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>linux</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>coding</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>linux</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>spreadsheets</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>organization</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df1\n",
+       "  employee        group\n",
+       "0      Bob   Accounting\n",
+       "1     Jake  Engineering\n",
+       "2     Lisa  Engineering\n",
+       "3      Sue           HR\n",
+       "\n",
+       "df5\n",
+       "         group        skills\n",
+       "0   Accounting          math\n",
+       "1   Accounting  spreadsheets\n",
+       "2  Engineering        coding\n",
+       "3  Engineering         linux\n",
+       "4           HR  spreadsheets\n",
+       "5           HR  organization\n",
+       "\n",
+       "pd.merge(df1, df5)\n",
+       "  employee        group        skills\n",
+       "0      Bob   Accounting          math\n",
+       "1      Bob   Accounting  spreadsheets\n",
+       "2     Jake  Engineering        coding\n",
+       "3     Jake  Engineering         linux\n",
+       "4     Lisa  Engineering        coding\n",
+       "5     Lisa  Engineering         linux\n",
+       "6      Sue           HR  spreadsheets\n",
+       "7      Sue           HR  organization"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df5 = pd.DataFrame({'group': ['Accounting', 'Accounting',\n",
+    "                              'Engineering', 'Engineering', 'HR', 'HR'],\n",
+    "                    'skills': ['math', 'spreadsheets', 'coding', 'linux',\n",
+    "                               'spreadsheets', 'organization']})\n",
+    "display('df1', 'df5', \"pd.merge(df1, df5)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These three types of joins can be used with other Pandas tools to implement a wide array of functionality.\n",
+    "But in practice, datasets are rarely as clean as the one we're working with here.\n",
+    "In the following section we'll consider some of the options provided by ``pd.merge()`` that enable you to tune how the join operations work."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Specification of the Merge Key"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We've already seen the default behavior of ``pd.merge()``: it looks for one or more matching column names between the two inputs, and uses this as the key.\n",
+    "However, often the column names will not match so nicely, and ``pd.merge()`` provides a variety of options for handling this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The ``on`` keyword\n",
+    "\n",
+    "Most simply, you can explicitly specify the name of the key column using the ``on`` keyword, which takes a column name or a list of column names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df1</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee</th>\n",
+       "      <th>group</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>Accounting</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>HR</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee</th>\n",
+       "      <th>hire_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>2004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df1, df2, on='employee')</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee</th>\n",
+       "      <th>group</th>\n",
+       "      <th>hire_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>Accounting</td>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>2004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df1\n",
+       "  employee        group\n",
+       "0      Bob   Accounting\n",
+       "1     Jake  Engineering\n",
+       "2     Lisa  Engineering\n",
+       "3      Sue           HR\n",
+       "\n",
+       "df2\n",
+       "  employee  hire_date\n",
+       "0     Lisa       2004\n",
+       "1      Bob       2008\n",
+       "2     Jake       2012\n",
+       "3      Sue       2014\n",
+       "\n",
+       "pd.merge(df1, df2, on='employee')\n",
+       "  employee        group  hire_date\n",
+       "0      Bob   Accounting       2008\n",
+       "1     Jake  Engineering       2012\n",
+       "2     Lisa  Engineering       2004\n",
+       "3      Sue           HR       2014"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display('df1', 'df2', \"pd.merge(df1, df2, on='employee')\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This option works only if both the left and right ``DataFrame``s have the specified column name."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The ``left_on`` and ``right_on`` keywords\n",
+    "\n",
+    "At times you may wish to merge two datasets with different column names; for example, we may have a dataset in which the employee name is labeled as \"name\" rather than \"employee\".\n",
+    "In this case, we can use the ``left_on`` and ``right_on`` keywords to specify the two column names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df1</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee</th>\n",
+       "      <th>group</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>Accounting</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>HR</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df3</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>70000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>80000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>120000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>90000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df1, df3, left_on=\"employee\", right_on=\"name\")</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee</th>\n",
+       "      <th>group</th>\n",
+       "      <th>name</th>\n",
+       "      <th>salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>Accounting</td>\n",
+       "      <td>Bob</td>\n",
+       "      <td>70000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>Jake</td>\n",
+       "      <td>80000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>120000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>Sue</td>\n",
+       "      <td>90000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df1\n",
+       "  employee        group\n",
+       "0      Bob   Accounting\n",
+       "1     Jake  Engineering\n",
+       "2     Lisa  Engineering\n",
+       "3      Sue           HR\n",
+       "\n",
+       "df3\n",
+       "   name  salary\n",
+       "0   Bob   70000\n",
+       "1  Jake   80000\n",
+       "2  Lisa  120000\n",
+       "3   Sue   90000\n",
+       "\n",
+       "pd.merge(df1, df3, left_on=\"employee\", right_on=\"name\")\n",
+       "  employee        group  name  salary\n",
+       "0      Bob   Accounting   Bob   70000\n",
+       "1     Jake  Engineering  Jake   80000\n",
+       "2     Lisa  Engineering  Lisa  120000\n",
+       "3      Sue           HR   Sue   90000"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
+    "                    'salary': [70000, 80000, 120000, 90000]})\n",
+    "display('df1', 'df3', 'pd.merge(df1, df3, left_on=\"employee\", right_on=\"name\")')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result has a redundant column that we can drop if desired–for example, by using the ``drop()`` method of ``DataFrame``s:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee</th>\n",
+       "      <th>group</th>\n",
+       "      <th>salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>Accounting</td>\n",
+       "      <td>70000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>80000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>120000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>90000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  employee        group  salary\n",
+       "0      Bob   Accounting   70000\n",
+       "1     Jake  Engineering   80000\n",
+       "2     Lisa  Engineering  120000\n",
+       "3      Sue           HR   90000"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(df1, df3, left_on=\"employee\", right_on=\"name\").drop('name', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The ``left_index`` and ``right_index`` keywords\n",
+    "\n",
+    "Sometimes, rather than merging on a column, you would instead like to merge on an index.\n",
+    "For example, your data might look like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df1a</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>group</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>employee</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bob</th>\n",
+       "      <td>Accounting</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Jake</th>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lisa</th>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sue</th>\n",
+       "      <td>HR</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2a</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hire_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>employee</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Lisa</th>\n",
+       "      <td>2004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bob</th>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Jake</th>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sue</th>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df1a\n",
+       "                group\n",
+       "employee             \n",
+       "Bob        Accounting\n",
+       "Jake      Engineering\n",
+       "Lisa      Engineering\n",
+       "Sue                HR\n",
+       "\n",
+       "df2a\n",
+       "          hire_date\n",
+       "employee           \n",
+       "Lisa           2004\n",
+       "Bob            2008\n",
+       "Jake           2012\n",
+       "Sue            2014"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1a = df1.set_index('employee')\n",
+    "df2a = df2.set_index('employee')\n",
+    "display('df1a', 'df2a')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use the index as the key for merging by specifying the ``left_index`` and/or ``right_index`` flags in ``pd.merge()``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df1a</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>group</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>employee</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bob</th>\n",
+       "      <td>Accounting</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Jake</th>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lisa</th>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sue</th>\n",
+       "      <td>HR</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2a</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hire_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>employee</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Lisa</th>\n",
+       "      <td>2004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bob</th>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Jake</th>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sue</th>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df1a, df2a, left_index=True, right_index=True)</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>group</th>\n",
+       "      <th>hire_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>employee</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Lisa</th>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>2004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bob</th>\n",
+       "      <td>Accounting</td>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Jake</th>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sue</th>\n",
+       "      <td>HR</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df1a\n",
+       "                group\n",
+       "employee             \n",
+       "Bob        Accounting\n",
+       "Jake      Engineering\n",
+       "Lisa      Engineering\n",
+       "Sue                HR\n",
+       "\n",
+       "df2a\n",
+       "          hire_date\n",
+       "employee           \n",
+       "Lisa           2004\n",
+       "Bob            2008\n",
+       "Jake           2012\n",
+       "Sue            2014\n",
+       "\n",
+       "pd.merge(df1a, df2a, left_index=True, right_index=True)\n",
+       "                group  hire_date\n",
+       "employee                        \n",
+       "Lisa      Engineering       2004\n",
+       "Bob        Accounting       2008\n",
+       "Jake      Engineering       2012\n",
+       "Sue                HR       2014"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display('df1a', 'df2a',\n",
+    "        \"pd.merge(df1a, df2a, left_index=True, right_index=True)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For convenience, ``DataFrame``s implement the ``join()`` method, which performs a merge that defaults to joining on indices:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df1a</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>group</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>employee</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bob</th>\n",
+       "      <td>Accounting</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Jake</th>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lisa</th>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sue</th>\n",
+       "      <td>HR</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2a</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hire_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>employee</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Lisa</th>\n",
+       "      <td>2004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bob</th>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Jake</th>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sue</th>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df1a.join(df2a)</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>group</th>\n",
+       "      <th>hire_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>employee</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bob</th>\n",
+       "      <td>Accounting</td>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Jake</th>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lisa</th>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>2004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sue</th>\n",
+       "      <td>HR</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df1a\n",
+       "                group\n",
+       "employee             \n",
+       "Bob        Accounting\n",
+       "Jake      Engineering\n",
+       "Lisa      Engineering\n",
+       "Sue                HR\n",
+       "\n",
+       "df2a\n",
+       "          hire_date\n",
+       "employee           \n",
+       "Lisa           2004\n",
+       "Bob            2008\n",
+       "Jake           2012\n",
+       "Sue            2014\n",
+       "\n",
+       "df1a.join(df2a)\n",
+       "                group  hire_date\n",
+       "employee                        \n",
+       "Bob        Accounting       2008\n",
+       "Jake      Engineering       2012\n",
+       "Lisa      Engineering       2004\n",
+       "Sue                HR       2014"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display('df1a', 'df2a', 'df1a.join(df2a)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you'd like to mix indices and columns, you can combine ``left_index`` with ``right_on`` or ``left_on`` with ``right_index`` to get the desired behavior:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df1a</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>group</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>employee</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bob</th>\n",
+       "      <td>Accounting</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Jake</th>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lisa</th>\n",
+       "      <td>Engineering</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sue</th>\n",
+       "      <td>HR</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df3</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>70000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>80000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>120000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>90000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df1a, df3, left_index=True, right_on='name')</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>group</th>\n",
+       "      <th>name</th>\n",
+       "      <th>salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Accounting</td>\n",
+       "      <td>Bob</td>\n",
+       "      <td>70000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>Jake</td>\n",
+       "      <td>80000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>120000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>HR</td>\n",
+       "      <td>Sue</td>\n",
+       "      <td>90000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df1a\n",
+       "                group\n",
+       "employee             \n",
+       "Bob        Accounting\n",
+       "Jake      Engineering\n",
+       "Lisa      Engineering\n",
+       "Sue                HR\n",
+       "\n",
+       "df3\n",
+       "   name  salary\n",
+       "0   Bob   70000\n",
+       "1  Jake   80000\n",
+       "2  Lisa  120000\n",
+       "3   Sue   90000\n",
+       "\n",
+       "pd.merge(df1a, df3, left_index=True, right_on='name')\n",
+       "         group  name  salary\n",
+       "0   Accounting   Bob   70000\n",
+       "1  Engineering  Jake   80000\n",
+       "2  Engineering  Lisa  120000\n",
+       "3           HR   Sue   90000"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display('df1a', 'df3', \"pd.merge(df1a, df3, left_index=True, right_on='name')\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All of these options also work with multiple indices and/or multiple columns; the interface for this behavior is very intuitive.\n",
+    "For more information on this, see the [\"Merge, Join, and Concatenate\" section](http://pandas.pydata.org/pandas-docs/stable/merging.html) of the Pandas documentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Specifying Set Arithmetic for Joins"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In all the preceding examples we have glossed over one important consideration in performing a join: the type of set arithmetic used in the join.\n",
+    "This comes up when a value appears in one key column but not the other. Consider this example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df6</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>food</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>fish</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Paul</td>\n",
+       "      <td>beans</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>bread</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df7</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>drink</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>wine</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Joseph</td>\n",
+       "      <td>beer</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df6, df7)</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>food</th>\n",
+       "      <th>drink</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>bread</td>\n",
+       "      <td>wine</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df6\n",
+       "    name   food\n",
+       "0  Peter   fish\n",
+       "1   Paul  beans\n",
+       "2   Mary  bread\n",
+       "\n",
+       "df7\n",
+       "     name drink\n",
+       "0    Mary  wine\n",
+       "1  Joseph  beer\n",
+       "\n",
+       "pd.merge(df6, df7)\n",
+       "   name   food drink\n",
+       "0  Mary  bread  wine"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df6 = pd.DataFrame({'name': ['Peter', 'Paul', 'Mary'],\n",
+    "                    'food': ['fish', 'beans', 'bread']},\n",
+    "                   columns=['name', 'food'])\n",
+    "df7 = pd.DataFrame({'name': ['Mary', 'Joseph'],\n",
+    "                    'drink': ['wine', 'beer']},\n",
+    "                   columns=['name', 'drink'])\n",
+    "display('df6', 'df7', 'pd.merge(df6, df7)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we have merged two datasets that have only a single \"name\" entry in common: Mary.\n",
+    "By default, the result contains the *intersection* of the two sets of inputs; this is what is known as an *inner join*.\n",
+    "We can specify this explicitly using the ``how`` keyword, which defaults to ``\"inner\"``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>food</th>\n",
+       "      <th>drink</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>bread</td>\n",
+       "      <td>wine</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   name   food drink\n",
+       "0  Mary  bread  wine"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(df6, df7, how='inner')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Other options for the ``how`` keyword are ``'outer'``, ``'left'``, and ``'right'``.\n",
+    "An *outer join* returns a join over the union of the input columns, and fills in all missing values with NAs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df6</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>food</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>fish</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Paul</td>\n",
+       "      <td>beans</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>bread</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df7</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>drink</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>wine</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Joseph</td>\n",
+       "      <td>beer</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df6, df7, how='outer')</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>food</th>\n",
+       "      <th>drink</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>fish</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Paul</td>\n",
+       "      <td>beans</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>bread</td>\n",
+       "      <td>wine</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Joseph</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>beer</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df6\n",
+       "    name   food\n",
+       "0  Peter   fish\n",
+       "1   Paul  beans\n",
+       "2   Mary  bread\n",
+       "\n",
+       "df7\n",
+       "     name drink\n",
+       "0    Mary  wine\n",
+       "1  Joseph  beer\n",
+       "\n",
+       "pd.merge(df6, df7, how='outer')\n",
+       "     name   food drink\n",
+       "0   Peter   fish   NaN\n",
+       "1    Paul  beans   NaN\n",
+       "2    Mary  bread  wine\n",
+       "3  Joseph    NaN  beer"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display('df6', 'df7', \"pd.merge(df6, df7, how='outer')\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The *left join* and *right join* return joins over the left entries and right entries, respectively.\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df6</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>food</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>fish</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Paul</td>\n",
+       "      <td>beans</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>bread</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df7</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>drink</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>wine</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Joseph</td>\n",
+       "      <td>beer</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df6, df7, how='left')</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>food</th>\n",
+       "      <th>drink</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Peter</td>\n",
+       "      <td>fish</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Paul</td>\n",
+       "      <td>beans</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>bread</td>\n",
+       "      <td>wine</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df6\n",
+       "    name   food\n",
+       "0  Peter   fish\n",
+       "1   Paul  beans\n",
+       "2   Mary  bread\n",
+       "\n",
+       "df7\n",
+       "     name drink\n",
+       "0    Mary  wine\n",
+       "1  Joseph  beer\n",
+       "\n",
+       "pd.merge(df6, df7, how='left')\n",
+       "    name   food drink\n",
+       "0  Peter   fish   NaN\n",
+       "1   Paul  beans   NaN\n",
+       "2   Mary  bread  wine"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display('df6', 'df7', \"pd.merge(df6, df7, how='left')\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output rows now correspond to the entries in the left input. Using\n",
+    "``how='right'`` works in a similar manner.\n",
+    "\n",
+    "All of these options can be applied straightforwardly to any of the preceding join types."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overlapping Column Names: The ``suffixes`` Keyword"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, you may end up in a case where your two input ``DataFrame``s have conflicting column names.\n",
+    "Consider this example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df8</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df9</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df8, df9, on=\"name\")</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>rank_x</th>\n",
+       "      <th>rank_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df8\n",
+       "   name  rank\n",
+       "0   Bob     1\n",
+       "1  Jake     2\n",
+       "2  Lisa     3\n",
+       "3   Sue     4\n",
+       "\n",
+       "df9\n",
+       "   name  rank\n",
+       "0   Bob     3\n",
+       "1  Jake     1\n",
+       "2  Lisa     4\n",
+       "3   Sue     2\n",
+       "\n",
+       "pd.merge(df8, df9, on=\"name\")\n",
+       "   name  rank_x  rank_y\n",
+       "0   Bob       1       3\n",
+       "1  Jake       2       1\n",
+       "2  Lisa       3       4\n",
+       "3   Sue       4       2"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df8 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
+    "                    'rank': [1, 2, 3, 4]})\n",
+    "df9 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
+    "                    'rank': [3, 1, 4, 2]})\n",
+    "display('df8', 'df9', 'pd.merge(df8, df9, on=\"name\")')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because the output would have two conflicting column names, the merge function automatically appends a suffix ``_x`` or ``_y`` to make the output columns unique.\n",
+    "If these defaults are inappropriate, it is possible to specify a custom suffix using the ``suffixes`` keyword:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df8</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df9</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pd.merge(df8, df9, on=\"name\", suffixes=[\"_L\", \"_R\"])</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>rank_L</th>\n",
+       "      <th>rank_R</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jake</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lisa</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sue</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df8\n",
+       "   name  rank\n",
+       "0   Bob     1\n",
+       "1  Jake     2\n",
+       "2  Lisa     3\n",
+       "3   Sue     4\n",
+       "\n",
+       "df9\n",
+       "   name  rank\n",
+       "0   Bob     3\n",
+       "1  Jake     1\n",
+       "2  Lisa     4\n",
+       "3   Sue     2\n",
+       "\n",
+       "pd.merge(df8, df9, on=\"name\", suffixes=[\"_L\", \"_R\"])\n",
+       "   name  rank_L  rank_R\n",
+       "0   Bob       1       3\n",
+       "1  Jake       2       1\n",
+       "2  Lisa       3       4\n",
+       "3   Sue       4       2"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display('df8', 'df9', 'pd.merge(df8, df9, on=\"name\", suffixes=[\"_L\", \"_R\"])')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These suffixes work in any of the possible join patterns, and work also if there are multiple overlapping columns."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more information on these patterns, see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) where we dive a bit deeper into relational algebra.\n",
+    "Also see the [Pandas \"Merge, Join and Concatenate\" documentation](http://pandas.pydata.org/pandas-docs/stable/merging.html) for further discussion of these topics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: US States Data\n",
+    "\n",
+    "Merge and join operations come up most often when combining data from different sources.\n",
+    "Here we will consider an example of some data about US states and their populations.\n",
+    "The data files can be found at http://github.com/jakevdp/data-USstates/:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Following are shell commands to download the data\n",
+    "# !curl -O https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-population.csv\n",
+    "# !curl -O https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-areas.csv\n",
+    "# !curl -O https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-abbrevs.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the three datasets, using the Pandas ``read_csv()`` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>pop.head()</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state/region</th>\n",
+       "      <th>ages</th>\n",
+       "      <th>year</th>\n",
+       "      <th>population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>under18</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>1117489.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>total</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>4817528.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>under18</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>1130966.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>total</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>4785570.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>under18</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>1125763.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>areas.head()</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>area (sq. mi)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>52423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>656425</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>114006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>53182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>163707</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>abbrevs.head()</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>abbreviation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>AL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AK</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>AZ</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>AR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>CA</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "pop.head()\n",
+       "  state/region     ages  year  population\n",
+       "0           AL  under18  2012   1117489.0\n",
+       "1           AL    total  2012   4817528.0\n",
+       "2           AL  under18  2010   1130966.0\n",
+       "3           AL    total  2010   4785570.0\n",
+       "4           AL  under18  2011   1125763.0\n",
+       "\n",
+       "areas.head()\n",
+       "        state  area (sq. mi)\n",
+       "0     Alabama          52423\n",
+       "1      Alaska         656425\n",
+       "2     Arizona         114006\n",
+       "3    Arkansas          53182\n",
+       "4  California         163707\n",
+       "\n",
+       "abbrevs.head()\n",
+       "        state abbreviation\n",
+       "0     Alabama           AL\n",
+       "1      Alaska           AK\n",
+       "2     Arizona           AZ\n",
+       "3    Arkansas           AR\n",
+       "4  California           CA"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pop = pd.read_csv('data/state-population.csv')\n",
+    "areas = pd.read_csv('data/state-areas.csv')\n",
+    "abbrevs = pd.read_csv('data/state-abbrevs.csv')\n",
+    "\n",
+    "display('pop.head()', 'areas.head()', 'abbrevs.head()')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given this information, say we want to compute a relatively straightforward result: rank US states and territories by their 2010 population density.\n",
+    "We clearly have the data here to find this result, but we'll have to combine the datasets to find the result.\n",
+    "\n",
+    "We'll start with a many-to-one merge that will give us the full state name within the population ``DataFrame``.\n",
+    "We want to merge based on the ``state/region``  column of ``pop``, and the ``abbreviation`` column of ``abbrevs``.\n",
+    "We'll use ``how='outer'`` to make sure no data is thrown away due to mismatched labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state/region</th>\n",
+       "      <th>ages</th>\n",
+       "      <th>year</th>\n",
+       "      <th>population</th>\n",
+       "      <th>state</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>under18</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>1117489.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>total</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>4817528.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>under18</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>1130966.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>total</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>4785570.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>under18</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>1125763.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  state/region     ages  year  population    state\n",
+       "0           AL  under18  2012   1117489.0  Alabama\n",
+       "1           AL    total  2012   4817528.0  Alabama\n",
+       "2           AL  under18  2010   1130966.0  Alabama\n",
+       "3           AL    total  2010   4785570.0  Alabama\n",
+       "4           AL  under18  2011   1125763.0  Alabama"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged = pd.merge(pop, abbrevs, how='outer',\n",
+    "                  left_on='state/region', right_on='abbreviation')\n",
+    "merged = merged.drop('abbreviation', 1) # drop duplicate info\n",
+    "merged.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's double-check whether there were any mismatches here, which we can do by looking for rows with nulls:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state/region    False\n",
+       "ages            False\n",
+       "year            False\n",
+       "population       True\n",
+       "state            True\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged.isnull().any()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some of the ``population`` info is null; let's figure out which these are!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state/region</th>\n",
+       "      <th>ages</th>\n",
+       "      <th>year</th>\n",
+       "      <th>population</th>\n",
+       "      <th>state</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2448</th>\n",
+       "      <td>PR</td>\n",
+       "      <td>under18</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2449</th>\n",
+       "      <td>PR</td>\n",
+       "      <td>total</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2450</th>\n",
+       "      <td>PR</td>\n",
+       "      <td>total</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2451</th>\n",
+       "      <td>PR</td>\n",
+       "      <td>under18</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2452</th>\n",
+       "      <td>PR</td>\n",
+       "      <td>total</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     state/region     ages  year  population state\n",
+       "2448           PR  under18  1990         NaN   NaN\n",
+       "2449           PR    total  1990         NaN   NaN\n",
+       "2450           PR    total  1991         NaN   NaN\n",
+       "2451           PR  under18  1991         NaN   NaN\n",
+       "2452           PR    total  1993         NaN   NaN"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged[merged['population'].isnull()].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It appears that all the null population values are from Puerto Rico prior to the year 2000; this is likely due to this data not being available from the original source.\n",
+    "\n",
+    "More importantly, we see also that some of the new ``state`` entries are also null, which means that there was no corresponding entry in the ``abbrevs`` key!\n",
+    "Let's figure out which regions lack this match:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['PR', 'USA'], dtype=object)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged.loc[merged['state'].isnull(), 'state/region'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can quickly infer the issue: our population data includes entries for Puerto Rico (PR) and the United States as a whole (USA), while these entries do not appear in the state abbreviation key.\n",
+    "We can fix these quickly by filling in appropriate entries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state/region    False\n",
+       "ages            False\n",
+       "year            False\n",
+       "population       True\n",
+       "state           False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged.loc[merged['state/region'] == 'PR', 'state'] = 'Puerto Rico'\n",
+    "merged.loc[merged['state/region'] == 'USA', 'state'] = 'United States'\n",
+    "merged.isnull().any()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No more nulls in the ``state`` column: we're all set!\n",
+    "\n",
+    "Now we can merge the result with the area data using a similar procedure.\n",
+    "Examining our results, we will want to join on the ``state`` column in both:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state/region</th>\n",
+       "      <th>ages</th>\n",
+       "      <th>year</th>\n",
+       "      <th>population</th>\n",
+       "      <th>state</th>\n",
+       "      <th>area (sq. mi)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>under18</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>1117489.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>52423.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>total</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>4817528.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>52423.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>under18</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>1130966.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>52423.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>total</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>4785570.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>52423.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>under18</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>1125763.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>52423.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  state/region     ages  year  population    state  area (sq. mi)\n",
+       "0           AL  under18  2012   1117489.0  Alabama        52423.0\n",
+       "1           AL    total  2012   4817528.0  Alabama        52423.0\n",
+       "2           AL  under18  2010   1130966.0  Alabama        52423.0\n",
+       "3           AL    total  2010   4785570.0  Alabama        52423.0\n",
+       "4           AL  under18  2011   1125763.0  Alabama        52423.0"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final = pd.merge(merged, areas, on='state', how='left')\n",
+    "final.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, let's check for nulls to see if there were any mismatches:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state/region     False\n",
+       "ages             False\n",
+       "year             False\n",
+       "population        True\n",
+       "state            False\n",
+       "area (sq. mi)     True\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.isnull().any()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are nulls in the ``area`` column; we can take a look to see which regions were ignored here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['United States'], dtype=object)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final['state'][final['area (sq. mi)'].isnull()].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that our ``areas`` ``DataFrame`` does not contain the area of the United States as a whole.\n",
+    "We could insert the appropriate value (using the sum of all state areas, for instance), but in this case we'll just drop the null values because the population density of the entire United States is not relevant to our current discussion:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state/region</th>\n",
+       "      <th>ages</th>\n",
+       "      <th>year</th>\n",
+       "      <th>population</th>\n",
+       "      <th>state</th>\n",
+       "      <th>area (sq. mi)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>under18</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>1117489.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>52423.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>total</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>4817528.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>52423.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>under18</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>1130966.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>52423.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>total</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>4785570.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>52423.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>under18</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>1125763.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>52423.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  state/region     ages  year  population    state  area (sq. mi)\n",
+       "0           AL  under18  2012   1117489.0  Alabama        52423.0\n",
+       "1           AL    total  2012   4817528.0  Alabama        52423.0\n",
+       "2           AL  under18  2010   1130966.0  Alabama        52423.0\n",
+       "3           AL    total  2010   4785570.0  Alabama        52423.0\n",
+       "4           AL  under18  2011   1125763.0  Alabama        52423.0"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.dropna(inplace=True)\n",
+    "final.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we have all the data we need. To answer the question of interest, let's first select the portion of the data corresponding with the year 2000, and the total population.\n",
+    "We'll use the ``query()`` function to do this quickly (this requires the ``numexpr`` package to be installed; see [High-Performance Pandas: ``eval()`` and ``query()``](03.12-Performance-Eval-and-Query.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state/region</th>\n",
+       "      <th>ages</th>\n",
+       "      <th>year</th>\n",
+       "      <th>population</th>\n",
+       "      <th>state</th>\n",
+       "      <th>area (sq. mi)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>total</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>4785570.0</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>52423.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>AK</td>\n",
+       "      <td>total</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>713868.0</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>656425.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>AZ</td>\n",
+       "      <td>total</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>6408790.0</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>114006.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>AR</td>\n",
+       "      <td>total</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>2922280.0</td>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>53182.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>CA</td>\n",
+       "      <td>total</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>37333601.0</td>\n",
+       "      <td>California</td>\n",
+       "      <td>163707.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    state/region   ages  year  population       state  area (sq. mi)\n",
+       "3             AL  total  2010   4785570.0     Alabama        52423.0\n",
+       "91            AK  total  2010    713868.0      Alaska       656425.0\n",
+       "101           AZ  total  2010   6408790.0     Arizona       114006.0\n",
+       "189           AR  total  2010   2922280.0    Arkansas        53182.0\n",
+       "197           CA  total  2010  37333601.0  California       163707.0"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data2010 = final.query(\"year == 2010 & ages == 'total'\")\n",
+    "data2010.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's compute the population density and display it in order.\n",
+    "We'll start by re-indexing our data on the state, and then compute the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data2010.set_index('state', inplace=True)\n",
+    "density = data2010['population'] / data2010['area (sq. mi)']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "District of Columbia    8898.897059\n",
+       "Puerto Rico             1058.665149\n",
+       "New Jersey              1009.253268\n",
+       "Rhode Island             681.339159\n",
+       "Connecticut              645.600649\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "density.sort_values(ascending=False, inplace=True)\n",
+    "density.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a ranking of US states plus Washington, DC, and Puerto Rico in order of their 2010 population density, in residents per square mile.\n",
+    "We can see that by far the densest region in this dataset is Washington, DC (i.e., the District of Columbia); among states, the densest is New Jersey.\n",
+    "\n",
+    "We can also check the end of the list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "South Dakota    10.583512\n",
+       "North Dakota     9.537565\n",
+       "Montana          6.736171\n",
+       "Wyoming          5.768079\n",
+       "Alaska           1.087509\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "density.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that the least dense state, by far, is Alaska, averaging slightly over one resident per square mile.\n",
+    "\n",
+    "This type of messy data merging is a common task when trying to answer questions using real-world data sources.\n",
+    "I hope that this example has given you an idea of the ways you can combine tools we've covered in order to gain insight from your data!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) | [Contents](Index.ipynb) | [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.07-Merge-and-Join.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/03.08-Aggregation-and-Grouping.ipynb b/notebooks_v1/03.08-Aggregation-and-Grouping.ipynb
new file mode 100644
index 000000000..be00723d1
--- /dev/null
+++ b/notebooks_v1/03.08-Aggregation-and-Grouping.ipynb
@@ -0,0 +1,2660 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) | [Contents](Index.ipynb) | [Pivot Tables](03.09-Pivot-Tables.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.08-Aggregation-and-Grouping.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Aggregation and Grouping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "An essential piece of analysis of large data is efficient summarization: computing aggregations like ``sum()``, ``mean()``, ``median()``, ``min()``, and ``max()``, in which a single number gives insight into the nature of a potentially large dataset.\n",
+    "In this section, we'll explore aggregations in Pandas, from simple operations akin to what we've seen on NumPy arrays, to more sophisticated operations based on the concept of a ``groupby``."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For convenience, we'll use the same ``display`` magic function that we've seen in previous sections:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "class display(object):\n",
+    "    \"\"\"Display HTML representation of multiple objects\"\"\"\n",
+    "    template = \"\"\"<div style=\"float: left; padding: 10px;\">\n",
+    "    <p style='font-family:\"Courier New\", Courier, monospace'>{0}</p>{1}\n",
+    "    </div>\"\"\"\n",
+    "    def __init__(self, *args):\n",
+    "        self.args = args\n",
+    "        \n",
+    "    def _repr_html_(self):\n",
+    "        return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n",
+    "                         for a in self.args)\n",
+    "    \n",
+    "    def __repr__(self):\n",
+    "        return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n",
+    "                           for a in self.args)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Planets Data\n",
+    "\n",
+    "Here we will use the Planets dataset, available via the [Seaborn package](http://seaborn.pydata.org/) (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)).\n",
+    "It gives information on planets that astronomers have discovered around other stars (known as *extrasolar planets* or *exoplanets* for short). It can be downloaded with a simple Seaborn command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1035, 6)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "planets = sns.load_dataset('planets')\n",
+    "planets.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>method</th>\n",
+       "      <th>number</th>\n",
+       "      <th>orbital_period</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Radial Velocity</td>\n",
+       "      <td>1</td>\n",
+       "      <td>269.300</td>\n",
+       "      <td>7.10</td>\n",
+       "      <td>77.40</td>\n",
+       "      <td>2006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Radial Velocity</td>\n",
+       "      <td>1</td>\n",
+       "      <td>874.774</td>\n",
+       "      <td>2.21</td>\n",
+       "      <td>56.95</td>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Radial Velocity</td>\n",
+       "      <td>1</td>\n",
+       "      <td>763.000</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>19.84</td>\n",
+       "      <td>2011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Radial Velocity</td>\n",
+       "      <td>1</td>\n",
+       "      <td>326.030</td>\n",
+       "      <td>19.40</td>\n",
+       "      <td>110.62</td>\n",
+       "      <td>2007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Radial Velocity</td>\n",
+       "      <td>1</td>\n",
+       "      <td>516.220</td>\n",
+       "      <td>10.50</td>\n",
+       "      <td>119.47</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            method  number  orbital_period   mass  distance  year\n",
+       "0  Radial Velocity       1         269.300   7.10     77.40  2006\n",
+       "1  Radial Velocity       1         874.774   2.21     56.95  2008\n",
+       "2  Radial Velocity       1         763.000   2.60     19.84  2011\n",
+       "3  Radial Velocity       1         326.030  19.40    110.62  2007\n",
+       "4  Radial Velocity       1         516.220  10.50    119.47  2009"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "planets.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This has some details on the 1,000+ extrasolar planets discovered up to 2014."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simple Aggregation in Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Earlier, we explored some of the data aggregations available for NumPy arrays ([\"Aggregations: Min, Max, and Everything In Between\"](02.04-Computation-on-arrays-aggregates.ipynb)).\n",
+    "As with a one-dimensional NumPy array, for a Pandas ``Series`` the aggregates return a single value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.374540\n",
+       "1    0.950714\n",
+       "2    0.731994\n",
+       "3    0.598658\n",
+       "4    0.156019\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rng = np.random.RandomState(42)\n",
+    "ser = pd.Series(rng.rand(5))\n",
+    "ser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.8119254917081569"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ser.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.56238509834163142"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ser.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For a ``DataFrame``, by default the aggregates return results within each column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.155995</td>\n",
+       "      <td>0.020584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.058084</td>\n",
+       "      <td>0.969910</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.866176</td>\n",
+       "      <td>0.832443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.601115</td>\n",
+       "      <td>0.212339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.708073</td>\n",
+       "      <td>0.181825</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B\n",
+       "0  0.155995  0.020584\n",
+       "1  0.058084  0.969910\n",
+       "2  0.866176  0.832443\n",
+       "3  0.601115  0.212339\n",
+       "4  0.708073  0.181825"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({'A': rng.rand(5),\n",
+    "                   'B': rng.rand(5)})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "A    0.477888\n",
+       "B    0.443420\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By specifying the ``axis`` argument, you can instead aggregate within each row:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.088290\n",
+       "1    0.513997\n",
+       "2    0.849309\n",
+       "3    0.406727\n",
+       "4    0.444949\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.mean(axis='columns')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas ``Series`` and ``DataFrame``s include all of the common aggregates mentioned in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb); in addition, there is a convenience method ``describe()`` that computes several common aggregates for each column and returns the result.\n",
+    "Let's use this on the Planets data, for now dropping rows with missing values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>number</th>\n",
+       "      <th>orbital_period</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>498.00000</td>\n",
+       "      <td>498.000000</td>\n",
+       "      <td>498.000000</td>\n",
+       "      <td>498.000000</td>\n",
+       "      <td>498.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.73494</td>\n",
+       "      <td>835.778671</td>\n",
+       "      <td>2.509320</td>\n",
+       "      <td>52.068213</td>\n",
+       "      <td>2007.377510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.17572</td>\n",
+       "      <td>1469.128259</td>\n",
+       "      <td>3.636274</td>\n",
+       "      <td>46.596041</td>\n",
+       "      <td>4.167284</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.328300</td>\n",
+       "      <td>0.003600</td>\n",
+       "      <td>1.350000</td>\n",
+       "      <td>1989.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>38.272250</td>\n",
+       "      <td>0.212500</td>\n",
+       "      <td>24.497500</td>\n",
+       "      <td>2005.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>357.000000</td>\n",
+       "      <td>1.245000</td>\n",
+       "      <td>39.940000</td>\n",
+       "      <td>2009.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>999.600000</td>\n",
+       "      <td>2.867500</td>\n",
+       "      <td>59.332500</td>\n",
+       "      <td>2011.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>6.00000</td>\n",
+       "      <td>17337.500000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>354.000000</td>\n",
+       "      <td>2014.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          number  orbital_period        mass    distance         year\n",
+       "count  498.00000      498.000000  498.000000  498.000000   498.000000\n",
+       "mean     1.73494      835.778671    2.509320   52.068213  2007.377510\n",
+       "std      1.17572     1469.128259    3.636274   46.596041     4.167284\n",
+       "min      1.00000        1.328300    0.003600    1.350000  1989.000000\n",
+       "25%      1.00000       38.272250    0.212500   24.497500  2005.000000\n",
+       "50%      1.00000      357.000000    1.245000   39.940000  2009.000000\n",
+       "75%      2.00000      999.600000    2.867500   59.332500  2011.000000\n",
+       "max      6.00000    17337.500000   25.000000  354.000000  2014.000000"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "planets.dropna().describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This can be a useful way to begin understanding the overall properties of a dataset.\n",
+    "For example, we see in the ``year`` column that although exoplanets were discovered as far back as 1989, half of all known expolanets were not discovered until 2010 or after.\n",
+    "This is largely thanks to the *Kepler* mission, which is a space-based telescope specifically designed for finding eclipsing planets around other stars."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following table summarizes some other built-in Pandas aggregations:\n",
+    "\n",
+    "| Aggregation              | Description                     |\n",
+    "|--------------------------|---------------------------------|\n",
+    "| ``count()``              | Total number of items           |\n",
+    "| ``first()``, ``last()``  | First and last item             |\n",
+    "| ``mean()``, ``median()`` | Mean and median                 |\n",
+    "| ``min()``, ``max()``     | Minimum and maximum             |\n",
+    "| ``std()``, ``var()``     | Standard deviation and variance |\n",
+    "| ``mad()``                | Mean absolute deviation         |\n",
+    "| ``prod()``               | Product of all items            |\n",
+    "| ``sum()``                | Sum of all items                |\n",
+    "\n",
+    "These are all methods of ``DataFrame`` and ``Series`` objects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To go deeper into the data, however, simple aggregates are often not enough.\n",
+    "The next level of data summarization is the ``groupby`` operation, which allows you to quickly and efficiently compute aggregates on subsets of data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GroupBy: Split, Apply, Combine\n",
+    "\n",
+    "Simple aggregations can give you a flavor of your dataset, but often we would prefer to aggregate conditionally on some label or index: this is implemented in the so-called ``groupby`` operation.\n",
+    "The name \"group by\" comes from a command in the SQL database language, but it is perhaps more illuminative to think of it in the terms first coined by Hadley Wickham of Rstats fame: *split, apply, combine*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Split, apply, combine\n",
+    "\n",
+    "A canonical example of this split-apply-combine operation, where the \"apply\" is a summation aggregation, is illustrated in this figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](figures/03.08-split-apply-combine.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Split-Apply-Combine)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This makes clear what the ``groupby`` accomplishes:\n",
+    "\n",
+    "- The *split* step involves breaking up and grouping a ``DataFrame`` depending on the value of the specified key.\n",
+    "- The *apply* step involves computing some function, usually an aggregate, transformation, or filtering, within the individual groups.\n",
+    "- The *combine* step merges the results of these operations into an output array.\n",
+    "\n",
+    "While this could certainly be done manually using some combination of the masking, aggregation, and merging commands covered earlier, an important realization is that *the intermediate splits do not need to be explicitly instantiated*. Rather, the ``GroupBy`` can (often) do this in a single pass over the data, updating the sum, mean, count, min, or other aggregate for each group along the way.\n",
+    "The power of the ``GroupBy`` is that it abstracts away these steps: the user need not think about *how* the computation is done under the hood, but rather thinks about the *operation as a whole*.\n",
+    "\n",
+    "As a concrete example, let's take a look at using Pandas for the computation shown in this diagram.\n",
+    "We'll start by creating the input ``DataFrame``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>data</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>C</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  key  data\n",
+       "0   A     0\n",
+       "1   B     1\n",
+       "2   C     2\n",
+       "3   A     3\n",
+       "4   B     4\n",
+       "5   C     5"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({'key': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
+    "                   'data': range(6)}, columns=['key', 'data'])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The most basic split-apply-combine operation can be computed with the ``groupby()`` method of ``DataFrame``s, passing the name of the desired key column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.core.groupby.DataFrameGroupBy object at 0x117272160>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('key')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that what is returned is not a set of ``DataFrame``s, but a ``DataFrameGroupBy`` object.\n",
+    "This object is where the magic is: you can think of it as a special view of the ``DataFrame``, which is poised to dig into the groups but does no actual computation until the aggregation is applied.\n",
+    "This \"lazy evaluation\" approach means that common aggregates can be implemented very efficiently in a way that is almost transparent to the user.\n",
+    "\n",
+    "To produce a result, we can apply an aggregate to this ``DataFrameGroupBy`` object, which will perform the appropriate apply/combine steps to produce the desired result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>data</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>key</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     data\n",
+       "key      \n",
+       "A       3\n",
+       "B       5\n",
+       "C       7"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('key').sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``sum()`` method is just one possibility here; you can apply virtually any common Pandas or NumPy aggregation function, as well as virtually any valid ``DataFrame`` operation, as we will see in the following discussion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The GroupBy object\n",
+    "\n",
+    "The ``GroupBy`` object is a very flexible abstraction.\n",
+    "In many ways, you can simply treat it as if it's a collection of ``DataFrame``s, and it does the difficult things under the hood. Let's see some examples using the Planets data.\n",
+    "\n",
+    "Perhaps the most important operations made available by a ``GroupBy`` are *aggregate*, *filter*, *transform*, and *apply*.\n",
+    "We'll discuss each of these more fully in [\"Aggregate, Filter, Transform, Apply\"](#Aggregate,-Filter,-Transform,-Apply), but before that let's introduce some of the other functionality that can be used with the basic ``GroupBy`` operation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Column indexing\n",
+    "\n",
+    "The ``GroupBy`` object supports column indexing in the same way as the ``DataFrame``, and returns a modified ``GroupBy`` object.\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.core.groupby.DataFrameGroupBy object at 0x1172727b8>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "planets.groupby('method')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.core.groupby.SeriesGroupBy object at 0x117272da0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "planets.groupby('method')['orbital_period']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we've selected a particular ``Series`` group from the original ``DataFrame`` group by reference to its column name.\n",
+    "As with the ``GroupBy`` object, no computation is done until we call some aggregate on the object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "method\n",
+       "Astrometry                         631.180000\n",
+       "Eclipse Timing Variations         4343.500000\n",
+       "Imaging                          27500.000000\n",
+       "Microlensing                      3300.000000\n",
+       "Orbital Brightness Modulation        0.342887\n",
+       "Pulsar Timing                       66.541900\n",
+       "Pulsation Timing Variations       1170.000000\n",
+       "Radial Velocity                    360.200000\n",
+       "Transit                              5.714932\n",
+       "Transit Timing Variations           57.011000\n",
+       "Name: orbital_period, dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "planets.groupby('method')['orbital_period'].median()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This gives an idea of the general scale of orbital periods (in days) that each method is sensitive to."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Iteration over groups\n",
+    "\n",
+    "The ``GroupBy`` object supports direct iteration over the groups, returning each group as a ``Series`` or ``DataFrame``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Astrometry                     shape=(2, 6)\n",
+      "Eclipse Timing Variations      shape=(9, 6)\n",
+      "Imaging                        shape=(38, 6)\n",
+      "Microlensing                   shape=(23, 6)\n",
+      "Orbital Brightness Modulation  shape=(3, 6)\n",
+      "Pulsar Timing                  shape=(5, 6)\n",
+      "Pulsation Timing Variations    shape=(1, 6)\n",
+      "Radial Velocity                shape=(553, 6)\n",
+      "Transit                        shape=(397, 6)\n",
+      "Transit Timing Variations      shape=(4, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for (method, group) in planets.groupby('method'):\n",
+    "    print(\"{0:30s} shape={1}\".format(method, group.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This can be useful for doing certain things manually, though it is often much faster to use the built-in ``apply`` functionality, which we will discuss momentarily."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Dispatch methods\n",
+    "\n",
+    "Through some Python class magic, any method not explicitly implemented by the ``GroupBy`` object will be passed through and called on the groups, whether they are ``DataFrame`` or ``Series`` objects.\n",
+    "For example, you can use the ``describe()`` method of ``DataFrame``s to perform a set of aggregations that describe each group in the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>method</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Astrometry</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2011.500000</td>\n",
+       "      <td>2.121320</td>\n",
+       "      <td>2010.0</td>\n",
+       "      <td>2010.75</td>\n",
+       "      <td>2011.5</td>\n",
+       "      <td>2012.25</td>\n",
+       "      <td>2013.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Eclipse Timing Variations</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2010.000000</td>\n",
+       "      <td>1.414214</td>\n",
+       "      <td>2008.0</td>\n",
+       "      <td>2009.00</td>\n",
+       "      <td>2010.0</td>\n",
+       "      <td>2011.00</td>\n",
+       "      <td>2012.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Imaging</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>2009.131579</td>\n",
+       "      <td>2.781901</td>\n",
+       "      <td>2004.0</td>\n",
+       "      <td>2008.00</td>\n",
+       "      <td>2009.0</td>\n",
+       "      <td>2011.00</td>\n",
+       "      <td>2013.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Microlensing</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>2009.782609</td>\n",
+       "      <td>2.859697</td>\n",
+       "      <td>2004.0</td>\n",
+       "      <td>2008.00</td>\n",
+       "      <td>2010.0</td>\n",
+       "      <td>2012.00</td>\n",
+       "      <td>2013.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Orbital Brightness Modulation</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2011.666667</td>\n",
+       "      <td>1.154701</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>2011.00</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>2012.00</td>\n",
+       "      <td>2013.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pulsar Timing</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1998.400000</td>\n",
+       "      <td>8.384510</td>\n",
+       "      <td>1992.0</td>\n",
+       "      <td>1992.00</td>\n",
+       "      <td>1994.0</td>\n",
+       "      <td>2003.00</td>\n",
+       "      <td>2011.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pulsation Timing Variations</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2007.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.0</td>\n",
+       "      <td>2007.00</td>\n",
+       "      <td>2007.0</td>\n",
+       "      <td>2007.00</td>\n",
+       "      <td>2007.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Radial Velocity</th>\n",
+       "      <td>553.0</td>\n",
+       "      <td>2007.518987</td>\n",
+       "      <td>4.249052</td>\n",
+       "      <td>1989.0</td>\n",
+       "      <td>2005.00</td>\n",
+       "      <td>2009.0</td>\n",
+       "      <td>2011.00</td>\n",
+       "      <td>2014.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Transit</th>\n",
+       "      <td>397.0</td>\n",
+       "      <td>2011.236776</td>\n",
+       "      <td>2.077867</td>\n",
+       "      <td>2002.0</td>\n",
+       "      <td>2010.00</td>\n",
+       "      <td>2012.0</td>\n",
+       "      <td>2013.00</td>\n",
+       "      <td>2014.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Transit Timing Variations</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2012.500000</td>\n",
+       "      <td>1.290994</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>2011.75</td>\n",
+       "      <td>2012.5</td>\n",
+       "      <td>2013.25</td>\n",
+       "      <td>2014.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               count         mean       std     min      25%  \\\n",
+       "method                                                                         \n",
+       "Astrometry                       2.0  2011.500000  2.121320  2010.0  2010.75   \n",
+       "Eclipse Timing Variations        9.0  2010.000000  1.414214  2008.0  2009.00   \n",
+       "Imaging                         38.0  2009.131579  2.781901  2004.0  2008.00   \n",
+       "Microlensing                    23.0  2009.782609  2.859697  2004.0  2008.00   \n",
+       "Orbital Brightness Modulation    3.0  2011.666667  1.154701  2011.0  2011.00   \n",
+       "Pulsar Timing                    5.0  1998.400000  8.384510  1992.0  1992.00   \n",
+       "Pulsation Timing Variations      1.0  2007.000000       NaN  2007.0  2007.00   \n",
+       "Radial Velocity                553.0  2007.518987  4.249052  1989.0  2005.00   \n",
+       "Transit                        397.0  2011.236776  2.077867  2002.0  2010.00   \n",
+       "Transit Timing Variations        4.0  2012.500000  1.290994  2011.0  2011.75   \n",
+       "\n",
+       "                                  50%      75%     max  \n",
+       "method                                                  \n",
+       "Astrometry                     2011.5  2012.25  2013.0  \n",
+       "Eclipse Timing Variations      2010.0  2011.00  2012.0  \n",
+       "Imaging                        2009.0  2011.00  2013.0  \n",
+       "Microlensing                   2010.0  2012.00  2013.0  \n",
+       "Orbital Brightness Modulation  2011.0  2012.00  2013.0  \n",
+       "Pulsar Timing                  1994.0  2003.00  2011.0  \n",
+       "Pulsation Timing Variations    2007.0  2007.00  2007.0  \n",
+       "Radial Velocity                2009.0  2011.00  2014.0  \n",
+       "Transit                        2012.0  2013.00  2014.0  \n",
+       "Transit Timing Variations      2012.5  2013.25  2014.0  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "planets.groupby('method')['year'].describe().unstack()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at this table helps us to better understand the data: for example, the vast majority of planets have been discovered by the Radial Velocity and Transit methods, though the latter only became common (due to new, more accurate telescopes) in the last decade.\n",
+    "The newest methods seem to be Transit Timing Variation and Orbital Brightness Modulation, which were not used to discover a new planet until 2011.\n",
+    "\n",
+    "This is just one example of the utility of dispatch methods.\n",
+    "Notice that they are applied *to each individual group*, and the results are then combined within ``GroupBy`` and returned.\n",
+    "Again, any valid ``DataFrame``/``Series`` method can be used on the corresponding ``GroupBy`` object, which allows for some very flexible and powerful operations!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregate, filter, transform, apply\n",
+    "\n",
+    "The preceding discussion focused on aggregation for the combine operation, but there are more options available.\n",
+    "In particular, ``GroupBy`` objects have ``aggregate()``, ``filter()``, ``transform()``, and ``apply()`` methods that efficiently implement a variety of useful operations before combining the grouped data.\n",
+    "\n",
+    "For the purpose of the following subsections, we'll use this ``DataFrame``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>C</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  key  data1  data2\n",
+       "0   A      0      5\n",
+       "1   B      1      0\n",
+       "2   C      2      3\n",
+       "3   A      3      3\n",
+       "4   B      4      7\n",
+       "5   C      5      9"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rng = np.random.RandomState(0)\n",
+    "df = pd.DataFrame({'key': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
+    "                   'data1': range(6),\n",
+    "                   'data2': rng.randint(0, 10, 6)},\n",
+    "                   columns = ['key', 'data1', 'data2'])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Aggregation\n",
+    "\n",
+    "We're now familiar with ``GroupBy`` aggregations with ``sum()``, ``median()``, and the like, but the ``aggregate()`` method allows for even more flexibility.\n",
+    "It can take a string, a function, or a list thereof, and compute all the aggregates at once.\n",
+    "Here is a quick example combining all these:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">data1</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">data2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>min</th>\n",
+       "      <th>median</th>\n",
+       "      <th>max</th>\n",
+       "      <th>min</th>\n",
+       "      <th>median</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>key</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    data1            data2           \n",
+       "      min median max   min median max\n",
+       "key                                  \n",
+       "A       0    1.5   3     3    4.0   5\n",
+       "B       1    2.5   4     0    3.5   7\n",
+       "C       2    3.5   5     3    6.0   9"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('key').aggregate(['min', np.median, max])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another useful pattern is to pass a dictionary mapping column names to operations to be applied on that column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>key</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     data1  data2\n",
+       "key              \n",
+       "A        0      5\n",
+       "B        1      7\n",
+       "C        2      9"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('key').aggregate({'data1': 'min',\n",
+    "                             'data2': 'max'})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Filtering\n",
+    "\n",
+    "A filtering operation allows you to drop data based on the group properties.\n",
+    "For example, we might want to keep all groups in which the standard deviation is larger than some critical value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>C</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby('key').std()</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>key</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>2.12132</td>\n",
+       "      <td>1.414214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>2.12132</td>\n",
+       "      <td>4.949747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>2.12132</td>\n",
+       "      <td>4.242641</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby('key').filter(filter_func)</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>C</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df\n",
+       "  key  data1  data2\n",
+       "0   A      0      5\n",
+       "1   B      1      0\n",
+       "2   C      2      3\n",
+       "3   A      3      3\n",
+       "4   B      4      7\n",
+       "5   C      5      9\n",
+       "\n",
+       "df.groupby('key').std()\n",
+       "       data1     data2\n",
+       "key                   \n",
+       "A    2.12132  1.414214\n",
+       "B    2.12132  4.949747\n",
+       "C    2.12132  4.242641\n",
+       "\n",
+       "df.groupby('key').filter(filter_func)\n",
+       "  key  data1  data2\n",
+       "1   B      1      0\n",
+       "2   C      2      3\n",
+       "4   B      4      7\n",
+       "5   C      5      9"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def filter_func(x):\n",
+    "    return x['data2'].std() > 4\n",
+    "\n",
+    "display('df', \"df.groupby('key').std()\", \"df.groupby('key').filter(filter_func)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The filter function should return a Boolean value specifying whether the group passes the filtering. Here because group A does not have a standard deviation greater than 4, it is dropped from the result."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Transformation\n",
+    "\n",
+    "While aggregation must return a reduced version of the data, transformation can return some transformed version of the full data to recombine.\n",
+    "For such a transformation, the output is the same shape as the input.\n",
+    "A common example is to center the data by subtracting the group-wise mean:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.5</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.5</td>\n",
+       "      <td>-3.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.5</td>\n",
+       "      <td>-3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>3.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   data1  data2\n",
+       "0   -1.5    1.0\n",
+       "1   -1.5   -3.5\n",
+       "2   -1.5   -3.0\n",
+       "3    1.5   -1.0\n",
+       "4    1.5    3.5\n",
+       "5    1.5    3.0"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('key').transform(lambda x: x - x.mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### The apply() method\n",
+    "\n",
+    "The ``apply()`` method lets you apply an arbitrary function to the group results.\n",
+    "The function should take a ``DataFrame``, and return either a Pandas object (e.g., ``DataFrame``, ``Series``) or a scalar; the combine operation will be tailored to the type of output returned.\n",
+    "\n",
+    "For example, here is an ``apply()`` that normalizes the first column by the sum of the second:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>C</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby('key').apply(norm_by_data2)</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0.375000</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>C</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df\n",
+       "  key  data1  data2\n",
+       "0   A      0      5\n",
+       "1   B      1      0\n",
+       "2   C      2      3\n",
+       "3   A      3      3\n",
+       "4   B      4      7\n",
+       "5   C      5      9\n",
+       "\n",
+       "df.groupby('key').apply(norm_by_data2)\n",
+       "  key     data1  data2\n",
+       "0   A  0.000000      5\n",
+       "1   B  0.142857      0\n",
+       "2   C  0.166667      3\n",
+       "3   A  0.375000      3\n",
+       "4   B  0.571429      7\n",
+       "5   C  0.416667      9"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def norm_by_data2(x):\n",
+    "    # x is a DataFrame of group values\n",
+    "    x['data1'] /= x['data2'].sum()\n",
+    "    return x\n",
+    "\n",
+    "display('df', \"df.groupby('key').apply(norm_by_data2)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "``apply()`` within a ``GroupBy`` is quite flexible: the only criterion is that the function takes a ``DataFrame`` and returns a Pandas object or scalar; what you do in the middle is up to you!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Specifying the split key\n",
+    "\n",
+    "In the simple examples presented before, we split the ``DataFrame`` on a single column name.\n",
+    "This is just one of many options by which the groups can be defined, and we'll go through some other options for group specification here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### A list, array, series, or index providing the grouping keys\n",
+    "\n",
+    "The key can be any series or list with a length matching that of the ``DataFrame``. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>C</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby(L).sum()</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df\n",
+       "  key  data1  data2\n",
+       "0   A      0      5\n",
+       "1   B      1      0\n",
+       "2   C      2      3\n",
+       "3   A      3      3\n",
+       "4   B      4      7\n",
+       "5   C      5      9\n",
+       "\n",
+       "df.groupby(L).sum()\n",
+       "   data1  data2\n",
+       "0      7     17\n",
+       "1      4      3\n",
+       "2      4      7"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L = [0, 1, 0, 1, 2, 0]\n",
+    "display('df', 'df.groupby(L).sum()')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, this means there's another, more verbose way of accomplishing the ``df.groupby('key')`` from before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>C</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby(df['key']).sum()</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>key</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>7</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df\n",
+       "  key  data1  data2\n",
+       "0   A      0      5\n",
+       "1   B      1      0\n",
+       "2   C      2      3\n",
+       "3   A      3      3\n",
+       "4   B      4      7\n",
+       "5   C      5      9\n",
+       "\n",
+       "df.groupby(df['key']).sum()\n",
+       "     data1  data2\n",
+       "key              \n",
+       "A        3      8\n",
+       "B        5      7\n",
+       "C        7     12"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display('df', \"df.groupby(df['key']).sum()\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### A dictionary or series mapping index to group\n",
+    "\n",
+    "Another method is to provide a dictionary that maps index values to the group keys:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>key</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2.groupby(mapping).sum()</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>consonant</th>\n",
+       "      <td>12</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vowel</th>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df2\n",
+       "     data1  data2\n",
+       "key              \n",
+       "A        0      5\n",
+       "B        1      0\n",
+       "C        2      3\n",
+       "A        3      3\n",
+       "B        4      7\n",
+       "C        5      9\n",
+       "\n",
+       "df2.groupby(mapping).sum()\n",
+       "           data1  data2\n",
+       "consonant     12     19\n",
+       "vowel          3      8"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = df.set_index('key')\n",
+    "mapping = {'A': 'vowel', 'B': 'consonant', 'C': 'consonant'}\n",
+    "display('df2', 'df2.groupby(mapping).sum()')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Any Python function\n",
+    "\n",
+    "Similar to mapping, you can pass any Python function that will input the index value and output the group:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>key</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>\n",
+       "<div style=\"float: left; padding: 10px;\">\n",
+       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2.groupby(str.lower).mean()</p><div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    </div>"
+      ],
+      "text/plain": [
+       "df2\n",
+       "     data1  data2\n",
+       "key              \n",
+       "A        0      5\n",
+       "B        1      0\n",
+       "C        2      3\n",
+       "A        3      3\n",
+       "B        4      7\n",
+       "C        5      9\n",
+       "\n",
+       "df2.groupby(str.lower).mean()\n",
+       "   data1  data2\n",
+       "a    1.5    4.0\n",
+       "b    2.5    3.5\n",
+       "c    3.5    6.0"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display('df2', 'df2.groupby(str.lower).mean()')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### A list of valid keys\n",
+    "\n",
+    "Further, any of the preceding key choices can be combined to group on a multi-index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <th>vowel</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <th>consonant</th>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <th>consonant</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             data1  data2\n",
+       "a vowel        1.5    4.0\n",
+       "b consonant    2.5    3.5\n",
+       "c consonant    3.5    6.0"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.groupby([str.lower, mapping]).mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Grouping example\n",
+    "\n",
+    "As an example of this, in a couple lines of Python code we can put all these together and count discovered planets by method and by decade:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>decade</th>\n",
+       "      <th>1980s</th>\n",
+       "      <th>1990s</th>\n",
+       "      <th>2000s</th>\n",
+       "      <th>2010s</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>method</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Astrometry</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Eclipse Timing Variations</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Imaging</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Microlensing</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Orbital Brightness Modulation</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pulsar Timing</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pulsation Timing Variations</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Radial Velocity</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>475.0</td>\n",
+       "      <td>424.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Transit</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>712.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Transit Timing Variations</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "decade                         1980s  1990s  2000s  2010s\n",
+       "method                                                   \n",
+       "Astrometry                       0.0    0.0    0.0    2.0\n",
+       "Eclipse Timing Variations        0.0    0.0    5.0   10.0\n",
+       "Imaging                          0.0    0.0   29.0   21.0\n",
+       "Microlensing                     0.0    0.0   12.0   15.0\n",
+       "Orbital Brightness Modulation    0.0    0.0    0.0    5.0\n",
+       "Pulsar Timing                    0.0    9.0    1.0    1.0\n",
+       "Pulsation Timing Variations      0.0    0.0    1.0    0.0\n",
+       "Radial Velocity                  1.0   52.0  475.0  424.0\n",
+       "Transit                          0.0    0.0   64.0  712.0\n",
+       "Transit Timing Variations        0.0    0.0    0.0    9.0"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "decade = 10 * (planets['year'] // 10)\n",
+    "decade = decade.astype(str) + 's'\n",
+    "decade.name = 'decade'\n",
+    "planets.groupby(['method', decade])['number'].sum().unstack().fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This shows the power of combining many of the operations we've discussed up to this point when looking at realistic datasets.\n",
+    "We immediately gain a coarse understanding of when and how planets have been discovered over the past several decades!\n",
+    "\n",
+    "Here I would suggest digging into these few lines of code, and evaluating the individual steps to make sure you understand exactly what they are doing to the result.\n",
+    "It's certainly a somewhat complicated example, but understanding these pieces will give you the means to similarly explore your own data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) | [Contents](Index.ipynb) | [Pivot Tables](03.09-Pivot-Tables.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.08-Aggregation-and-Grouping.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/03.09-Pivot-Tables.ipynb b/notebooks_v1/03.09-Pivot-Tables.ipynb
new file mode 100644
index 000000000..717549875
--- /dev/null
+++ b/notebooks_v1/03.09-Pivot-Tables.ipynb
@@ -0,0 +1,1379 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) | [Contents](Index.ipynb) | [Vectorized String Operations](03.10-Working-With-Strings.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.09-Pivot-Tables.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pivot Tables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have seen how the ``GroupBy`` abstraction lets us explore relationships within a dataset.\n",
+    "A *pivot table* is a similar operation that is commonly seen in spreadsheets and other programs that operate on tabular data.\n",
+    "The pivot table takes simple column-wise data as input, and groups the entries into a two-dimensional table that provides a multidimensional summarization of the data.\n",
+    "The difference between pivot tables and ``GroupBy`` can sometimes cause confusion; it helps me to think of pivot tables as essentially a *multidimensional* version of ``GroupBy`` aggregation.\n",
+    "That is, you split-apply-combine, but both the split and the combine happen across not a one-dimensional index, but across a two-dimensional grid."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Motivating Pivot Tables\n",
+    "\n",
+    "For the examples in this section, we'll use the database of passengers on the *Titanic*, available through the Seaborn library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "titanic = sns.load_dataset('titanic')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>survived</th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>class</th>\n",
+       "      <th>who</th>\n",
+       "      <th>adult_male</th>\n",
+       "      <th>deck</th>\n",
+       "      <th>embark_town</th>\n",
+       "      <th>alive</th>\n",
+       "      <th>alone</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>man</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>no</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C</td>\n",
+       "      <td>First</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Cherbourg</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>S</td>\n",
+       "      <td>First</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>man</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>no</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
+       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
+       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
+       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
+       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
+       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
+       "\n",
+       "     who adult_male deck  embark_town alive  alone  \n",
+       "0    man       True  NaN  Southampton    no  False  \n",
+       "1  woman      False    C    Cherbourg   yes  False  \n",
+       "2  woman      False  NaN  Southampton   yes   True  \n",
+       "3  woman      False    C  Southampton   yes  False  \n",
+       "4    man       True  NaN  Southampton    no   True  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This contains a wealth of information on each passenger of that ill-fated voyage, including gender, age, class, fare paid, and much more."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pivot Tables by Hand\n",
+    "\n",
+    "To start learning more about this data, we might begin by grouping according to gender, survival status, or some combination thereof.\n",
+    "If you have read the previous section, you might be tempted to apply a ``GroupBy`` operation–for example, let's look at survival rate by gender:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>survived</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>0.742038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>male</th>\n",
+       "      <td>0.188908</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        survived\n",
+       "sex             \n",
+       "female  0.742038\n",
+       "male    0.188908"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic.groupby('sex')[['survived']].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This immediately gives us some insight: overall, three of every four females on board survived, while only one in five males survived!\n",
+    "\n",
+    "This is useful, but we might like to go one step deeper and look at survival by both sex and, say, class.\n",
+    "Using the vocabulary of ``GroupBy``, we might proceed using something like this:\n",
+    "we *group by* class and gender, *select* survival, *apply* a mean aggregate, *combine* the resulting groups, and then *unstack* the hierarchical index to reveal the hidden multidimensionality. In code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>class</th>\n",
+       "      <th>First</th>\n",
+       "      <th>Second</th>\n",
+       "      <th>Third</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>0.968085</td>\n",
+       "      <td>0.921053</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>male</th>\n",
+       "      <td>0.368852</td>\n",
+       "      <td>0.157407</td>\n",
+       "      <td>0.135447</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "class      First    Second     Third\n",
+       "sex                                 \n",
+       "female  0.968085  0.921053  0.500000\n",
+       "male    0.368852  0.157407  0.135447"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This gives us a better idea of how both gender and class affected survival, but the code is starting to look a bit garbled.\n",
+    "While each step of this pipeline makes sense in light of the tools we've previously discussed, the long string of code is not particularly easy to read or use.\n",
+    "This two-dimensional ``GroupBy`` is common enough that Pandas includes a convenience routine, ``pivot_table``, which succinctly handles this type of multi-dimensional aggregation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pivot Table Syntax\n",
+    "\n",
+    "Here is the equivalent to the preceding operation using the ``pivot_table`` method of ``DataFrame``s:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>class</th>\n",
+       "      <th>First</th>\n",
+       "      <th>Second</th>\n",
+       "      <th>Third</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>0.968085</td>\n",
+       "      <td>0.921053</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>male</th>\n",
+       "      <td>0.368852</td>\n",
+       "      <td>0.157407</td>\n",
+       "      <td>0.135447</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "class      First    Second     Third\n",
+       "sex                                 \n",
+       "female  0.968085  0.921053  0.500000\n",
+       "male    0.368852  0.157407  0.135447"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic.pivot_table('survived', index='sex', columns='class')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is eminently more readable than the ``groupby`` approach, and produces the same result.\n",
+    "As you might expect of an early 20th-century transatlantic cruise, the survival gradient favors both women and higher classes.\n",
+    "First-class women survived with near certainty (hi, Rose!), while only one in ten third-class men survived (sorry, Jack!)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multi-level pivot tables\n",
+    "\n",
+    "Just as in the ``GroupBy``, the grouping in pivot tables can be specified with multiple levels, and via a number of options.\n",
+    "For example, we might be interested in looking at age as a third dimension.\n",
+    "We'll bin the age using the ``pd.cut`` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>class</th>\n",
+       "      <th>First</th>\n",
+       "      <th>Second</th>\n",
+       "      <th>Third</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
+       "      <th>(0, 18]</th>\n",
+       "      <td>0.909091</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.511628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(18, 80]</th>\n",
+       "      <td>0.972973</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>0.423729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
+       "      <th>(0, 18]</th>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.215686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(18, 80]</th>\n",
+       "      <td>0.375000</td>\n",
+       "      <td>0.071429</td>\n",
+       "      <td>0.133663</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "class               First    Second     Third\n",
+       "sex    age                                   \n",
+       "female (0, 18]   0.909091  1.000000  0.511628\n",
+       "       (18, 80]  0.972973  0.900000  0.423729\n",
+       "male   (0, 18]   0.800000  0.600000  0.215686\n",
+       "       (18, 80]  0.375000  0.071429  0.133663"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age = pd.cut(titanic['age'], [0, 18, 80])\n",
+    "titanic.pivot_table('survived', ['sex', age], 'class')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can apply the same strategy when working with the columns as well; let's add info on the fare paid using ``pd.qcut`` to automatically compute quantiles:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>fare</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">[0, 14.454]</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">(14.454, 512.329]</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>class</th>\n",
+       "      <th>First</th>\n",
+       "      <th>Second</th>\n",
+       "      <th>Third</th>\n",
+       "      <th>First</th>\n",
+       "      <th>Second</th>\n",
+       "      <th>Third</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
+       "      <th>(0, 18]</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>0.909091</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.318182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(18, 80]</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.880000</td>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>0.972973</td>\n",
+       "      <td>0.914286</td>\n",
+       "      <td>0.391304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
+       "      <th>(0, 18]</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.260870</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.818182</td>\n",
+       "      <td>0.178571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(18, 80]</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.098039</td>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>0.391304</td>\n",
+       "      <td>0.030303</td>\n",
+       "      <td>0.192308</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "fare            [0, 14.454]                     (14.454, 512.329]            \\\n",
+       "class                 First    Second     Third             First    Second   \n",
+       "sex    age                                                                    \n",
+       "female (0, 18]          NaN  1.000000  0.714286          0.909091  1.000000   \n",
+       "       (18, 80]         NaN  0.880000  0.444444          0.972973  0.914286   \n",
+       "male   (0, 18]          NaN  0.000000  0.260870          0.800000  0.818182   \n",
+       "       (18, 80]         0.0  0.098039  0.125000          0.391304  0.030303   \n",
+       "\n",
+       "fare                       \n",
+       "class               Third  \n",
+       "sex    age                 \n",
+       "female (0, 18]   0.318182  \n",
+       "       (18, 80]  0.391304  \n",
+       "male   (0, 18]   0.178571  \n",
+       "       (18, 80]  0.192308  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fare = pd.qcut(titanic['fare'], 2)\n",
+    "titanic.pivot_table('survived', ['sex', age], [fare, 'class'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a four-dimensional aggregation with hierarchical indices (see [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb)), shown in a grid demonstrating the relationship between the values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Additional pivot table options\n",
+    "\n",
+    "The full call signature of the ``pivot_table`` method of ``DataFrame``s is as follows:\n",
+    "\n",
+    "```python\n",
+    "# call signature as of Pandas 0.18\n",
+    "DataFrame.pivot_table(data, values=None, index=None, columns=None,\n",
+    "                      aggfunc='mean', fill_value=None, margins=False,\n",
+    "                      dropna=True, margins_name='All')\n",
+    "```\n",
+    "\n",
+    "We've already seen examples of the first three arguments; here we'll take a quick look at the remaining ones.\n",
+    "Two of the options, ``fill_value`` and ``dropna``, have to do with missing data and are fairly straightforward; we will not show examples of them here.\n",
+    "\n",
+    "The ``aggfunc`` keyword controls what type of aggregation is applied, which is a mean by default.\n",
+    "As in the GroupBy, the aggregation specification can be a string representing one of several common choices (e.g., ``'sum'``, ``'mean'``, ``'count'``, ``'min'``, ``'max'``, etc.) or a function that implements an aggregation (e.g., ``np.sum()``, ``min()``, ``sum()``, etc.).\n",
+    "Additionally, it can be specified as a dictionary mapping a column to any of the above desired options:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">fare</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">survived</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>class</th>\n",
+       "      <th>First</th>\n",
+       "      <th>Second</th>\n",
+       "      <th>Third</th>\n",
+       "      <th>First</th>\n",
+       "      <th>Second</th>\n",
+       "      <th>Third</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>106.125798</td>\n",
+       "      <td>21.970121</td>\n",
+       "      <td>16.118810</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>male</th>\n",
+       "      <td>67.226127</td>\n",
+       "      <td>19.741782</td>\n",
+       "      <td>12.661633</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              fare                       survived             \n",
+       "class        First     Second      Third    First Second Third\n",
+       "sex                                                           \n",
+       "female  106.125798  21.970121  16.118810     91.0   70.0  72.0\n",
+       "male     67.226127  19.741782  12.661633     45.0   17.0  47.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic.pivot_table(index='sex', columns='class',\n",
+    "                    aggfunc={'survived':sum, 'fare':'mean'})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice also here that we've omitted the ``values`` keyword; when specifying a mapping for ``aggfunc``, this is determined automatically."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "At times it's useful to compute totals along each grouping.\n",
+    "This can be done via the ``margins`` keyword:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>class</th>\n",
+       "      <th>First</th>\n",
+       "      <th>Second</th>\n",
+       "      <th>Third</th>\n",
+       "      <th>All</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>female</th>\n",
+       "      <td>0.968085</td>\n",
+       "      <td>0.921053</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.742038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>male</th>\n",
+       "      <td>0.368852</td>\n",
+       "      <td>0.157407</td>\n",
+       "      <td>0.135447</td>\n",
+       "      <td>0.188908</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>All</th>\n",
+       "      <td>0.629630</td>\n",
+       "      <td>0.472826</td>\n",
+       "      <td>0.242363</td>\n",
+       "      <td>0.383838</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "class      First    Second     Third       All\n",
+       "sex                                           \n",
+       "female  0.968085  0.921053  0.500000  0.742038\n",
+       "male    0.368852  0.157407  0.135447  0.188908\n",
+       "All     0.629630  0.472826  0.242363  0.383838"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic.pivot_table('survived', index='sex', columns='class', margins=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here this automatically gives us information about the class-agnostic survival rate by gender, the gender-agnostic survival rate by class, and the overall survival rate of 38%.\n",
+    "The margin label can be specified with the ``margins_name`` keyword, which defaults to ``\"All\"``."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Birthrate Data\n",
+    "\n",
+    "As a more interesting example, let's take a look at the freely available data on births in the United States, provided by the Centers for Disease Control (CDC).\n",
+    "This data can be found at https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv\n",
+    "(this dataset has been analyzed rather extensively by Andrew Gelman and his group; see, for example, [this blog post](http://andrewgelman.com/2012/06/14/cool-ass-signal-processing-using-gaussian-processes/)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# shell command to download the data:\n",
+    "# !curl -O https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "births = pd.read_csv('data/births.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Taking a look at the data, we see that it's relatively simple–it contains the number of births grouped by date and gender:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>births</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1969</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>F</td>\n",
+       "      <td>4046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1969</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>M</td>\n",
+       "      <td>4440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1969</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>F</td>\n",
+       "      <td>4454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1969</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>M</td>\n",
+       "      <td>4548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1969</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>F</td>\n",
+       "      <td>4548</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year  month day gender  births\n",
+       "0  1969      1   1      F    4046\n",
+       "1  1969      1   1      M    4440\n",
+       "2  1969      1   2      F    4454\n",
+       "3  1969      1   2      M    4548\n",
+       "4  1969      1   3      F    4548"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "births.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can start to understand this data a bit more by using a pivot table.\n",
+    "Let's add a decade column, and take a look at male and female births as a function of decade:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>gender</th>\n",
+       "      <th>F</th>\n",
+       "      <th>M</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>decade</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1960</th>\n",
+       "      <td>1753634</td>\n",
+       "      <td>1846572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970</th>\n",
+       "      <td>16263075</td>\n",
+       "      <td>17121550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980</th>\n",
+       "      <td>18310351</td>\n",
+       "      <td>19243452</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1990</th>\n",
+       "      <td>19479454</td>\n",
+       "      <td>20420553</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000</th>\n",
+       "      <td>18229309</td>\n",
+       "      <td>19106428</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "gender         F         M\n",
+       "decade                    \n",
+       "1960     1753634   1846572\n",
+       "1970    16263075  17121550\n",
+       "1980    18310351  19243452\n",
+       "1990    19479454  20420553\n",
+       "2000    18229309  19106428"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "births['decade'] = 10 * (births['year'] // 10)\n",
+    "births.pivot_table('births', index='decade', columns='gender', aggfunc='sum')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We immediately see that male births outnumber female births in every decade.\n",
+    "To see this trend a bit more clearly, we can use the built-in plotting tools in Pandas to visualize the total number of births by year (see [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) for a discussion of plotting with Matplotlib):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GayRRaIZ8ff2oqChn06b13HXXSEuHI4QQohFtT4sH4K52Q38zCbEu/LW+PNztQUxmEytT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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1173ed940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "sns.set()  # use Seaborn styles\n",
+    "births.pivot_table('births', index='year', columns='gender', aggfunc='sum').plot()\n",
+    "plt.ylabel('total births per year');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With a simple pivot table and ``plot()`` method, we can immediately see the annual trend in births by gender. By eye, it appears that over the past 50 years male births have outnumbered female births by around 5%."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Further data exploration\n",
+    "\n",
+    "Though this doesn't necessarily relate to the pivot table, there are a few more interesting features we can pull out of this dataset using the Pandas tools covered up to this point.\n",
+    "We must start by cleaning the data a bit, removing outliers caused by mistyped dates (e.g., June 31st) or missing values (e.g., June 99th).\n",
+    "One easy way to remove these all at once is to cut outliers; we'll do this via a robust sigma-clipping operation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "quartiles = np.percentile(births['births'], [25, 50, 75])\n",
+    "mu = quartiles[1]\n",
+    "sig = 0.74 * (quartiles[2] - quartiles[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This final line is a robust estimate of the sample mean, where the 0.74 comes from the interquartile range of a Gaussian distribution (You can learn more about sigma-clipping operations in a book I coauthored with Željko Ivezić, Andrew J. Connolly, and Alexander Gray: [\"Statistics, Data Mining, and Machine Learning in Astronomy\"](http://press.princeton.edu/titles/10159.html) (Princeton University Press, 2014)).\n",
+    "\n",
+    "With this we can use the ``query()`` method (discussed further in [High-Performance Pandas: ``eval()`` and ``query()``](03.12-Performance-Eval-and-Query.ipynb)) to filter-out rows with births outside these values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we set the ``day`` column to integers; previously it had been a string because some columns in the dataset contained the value ``'null'``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# set 'day' column to integer; it originally was a string due to nulls\n",
+    "births['day'] = births['day'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can combine the day, month, and year to create a Date index (see [Working with Time Series](03.11-Working-with-Time-Series.ipynb)).\n",
+    "This allows us to quickly compute the weekday corresponding to each row:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# create a datetime index from the year, month, day\n",
+    "births.index = pd.to_datetime(10000 * births.year +\n",
+    "                              100 * births.month +\n",
+    "                              births.day, format='%Y%m%d')\n",
+    "\n",
+    "births['dayofweek'] = births.index.dayofweek"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using this we can plot births by weekday for several decades:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/DBo0iNTUVN5//30WLlzImjVr2LhxIy0tLSxYsIDk5GTWrl3LwIEDWbp0Kdu2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\nz7DXppEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1179ace80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "\n",
+    "births.pivot_table('births', index='dayofweek',\n",
+    "                    columns='decade', aggfunc='mean').plot()\n",
+    "plt.gca().set_xticklabels(['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun'])\n",
+    "plt.ylabel('mean births by day');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apparently births are slightly less common on weekends than on weekdays! Note that the 1990s and 2000s are missing because the CDC data contains only the month of birth starting in 1989.\n",
+    "\n",
+    "Another intersting view is to plot the mean number of births by the day of the *year*.\n",
+    "Let's first group the data by month and day separately:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1  1    4009.225\n",
+       "   2    4247.400\n",
+       "   3    4500.900\n",
+       "   4    4571.350\n",
+       "   5    4603.625\n",
+       "Name: births, dtype: float64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "births_by_date = births.pivot_table('births', \n",
+    "                                    [births.index.month, births.index.day])\n",
+    "births_by_date.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a multi-index over months and days.\n",
+    "To make this easily plottable, let's turn these months and days into a date by associating them with a dummy year variable (making sure to choose a leap year so February 29th is correctly handled!)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2012-01-01    4009.225\n",
+       "2012-01-02    4247.400\n",
+       "2012-01-03    4500.900\n",
+       "2012-01-04    4571.350\n",
+       "2012-01-05    4603.625\n",
+       "Name: births, dtype: float64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "births_by_date.index = [pd.datetime(2012, month, day)\n",
+    "                        for (month, day) in births_by_date.index]\n",
+    "births_by_date.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Focusing on the month and day only, we now have a time series reflecting the average number of births by date of the year.\n",
+    "From this, we can use the ``plot`` method to plot the data. It reveals some interesting trends:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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yYhU6hxyIRGOCH7nMkF3wihgGW1eVYHOzGSNWL3pHXSgvVkEqyX+zKjEohYSb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\nARgkExEREREF+P/ODMa/AE3zGwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117326a20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the results\n",
+    "fig, ax = plt.subplots(figsize=(12, 4))\n",
+    "births_by_date.plot(ax=ax);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "In particular, the striking feature of this graph is the dip in birthrate on US holidays (e.g., Independence Day, Labor Day, Thanksgiving, Christmas, New Year's Day) although this likely reflects trends in scheduled/induced births rather than some deep psychosomatic effect on natural births.\n",
+    "For more discussion on this trend, see the analysis and links in [Andrew Gelman's blog post](http://andrewgelman.com/2012/06/14/cool-ass-signal-processing-using-gaussian-processes/) on the subject.\n",
+    "We'll return to this figure in [Example:-Effect-of-Holidays-on-US-Births](04.09-Text-and-Annotation.ipynb#Example:-Effect-of-Holidays-on-US-Births), where we will use Matplotlib's tools to annotate this plot.\n",
+    "\n",
+    "Looking at this short example, you can see that many of the Python and Pandas tools we've seen to this point can be combined and used to gain insight from a variety of datasets.\n",
+    "We will see some more sophisticated applications of these data manipulations in future sections!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) | [Contents](Index.ipynb) | [Vectorized String Operations](03.10-Working-With-Strings.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.09-Pivot-Tables.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/03.10-Working-With-Strings.ipynb b/notebooks_v1/03.10-Working-With-Strings.ipynb
new file mode 100644
index 000000000..75c004b84
--- /dev/null
+++ b/notebooks_v1/03.10-Working-With-Strings.ipynb
@@ -0,0 +1,1407 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Pivot Tables](03.09-Pivot-Tables.ipynb) | [Contents](Index.ipynb) | [Working with Time Series](03.11-Working-with-Time-Series.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.10-Working-With-Strings.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Vectorized String Operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One strength of Python is its relative ease in handling and manipulating string data.\n",
+    "Pandas builds on this and provides a comprehensive set of *vectorized string operations* that become an essential piece of the type of munging required when working with (read: cleaning up) real-world data.\n",
+    "In this section, we'll walk through some of the Pandas string operations, and then take a look at using them to partially clean up a very messy dataset of recipes collected from the Internet."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introducing Pandas String Operations\n",
+    "\n",
+    "We saw in previous sections how tools like NumPy and Pandas generalize arithmetic operations so that we can easily and quickly perform the same operation on many array elements. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 4,  6, 10, 14, 22, 26])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "x = np.array([2, 3, 5, 7, 11, 13])\n",
+    "x * 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This *vectorization* of operations simplifies the syntax of operating on arrays of data: we no longer have to worry about the size or shape of the array, but just about what operation we want done.\n",
+    "For arrays of strings, NumPy does not provide such simple access, and thus you're stuck using a more verbose loop syntax:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Peter', 'Paul', 'Mary', 'Guido']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = ['peter', 'Paul', 'MARY', 'gUIDO']\n",
+    "[s.capitalize() for s in data]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is perhaps sufficient to work with some data, but it will break if there are any missing values.\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'NoneType' object has no attribute 'capitalize'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-fc1d891ab539>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'peter'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Paul'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MARY'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gUIDO'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapitalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-3-fc1d891ab539>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'peter'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Paul'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MARY'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gUIDO'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapitalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'capitalize'"
+     ]
+    }
+   ],
+   "source": [
+    "data = ['peter', 'Paul', None, 'MARY', 'gUIDO']\n",
+    "[s.capitalize() for s in data]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas includes features to address both this need for vectorized string operations and for correctly handling missing data via the ``str`` attribute of Pandas Series and Index objects containing strings.\n",
+    "So, for example, suppose we create a Pandas Series with this data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    peter\n",
+       "1     Paul\n",
+       "2     None\n",
+       "3     MARY\n",
+       "4    gUIDO\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "names = pd.Series(data)\n",
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now call a single method that will capitalize all the entries, while skipping over any missing values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    Peter\n",
+       "1     Paul\n",
+       "2     None\n",
+       "3     Mary\n",
+       "4    Guido\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.str.capitalize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using tab completion on this ``str`` attribute will list all the vectorized string methods available to Pandas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tables of Pandas String Methods\n",
+    "\n",
+    "If you have a good understanding of string manipulation in Python, most of Pandas string syntax is intuitive enough that it's probably sufficient to just list a table of available methods; we will start with that here, before diving deeper into a few of the subtleties.\n",
+    "The examples in this section use the following series of names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "monte = pd.Series(['Graham Chapman', 'John Cleese', 'Terry Gilliam',\n",
+    "                   'Eric Idle', 'Terry Jones', 'Michael Palin'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Methods similar to Python string methods\n",
+    "Nearly all Python's built-in string methods are mirrored by a Pandas vectorized string method. Here is a list of Pandas ``str`` methods that mirror Python string methods:\n",
+    "\n",
+    "|             |                  |                  |                  |\n",
+    "|-------------|------------------|------------------|------------------|\n",
+    "|``len()``    | ``lower()``      | ``translate()``  | ``islower()``    | \n",
+    "|``ljust()``  | ``upper()``      | ``startswith()`` | ``isupper()``    | \n",
+    "|``rjust()``  | ``find()``       | ``endswith()``   | ``isnumeric()``  | \n",
+    "|``center()`` | ``rfind()``      | ``isalnum()``    | ``isdecimal()``  | \n",
+    "|``zfill()``  | ``index()``      | ``isalpha()``    | ``split()``      | \n",
+    "|``strip()``  | ``rindex()``     | ``isdigit()``    | ``rsplit()``     | \n",
+    "|``rstrip()`` | ``capitalize()`` | ``isspace()``    | ``partition()``  | \n",
+    "|``lstrip()`` |  ``swapcase()``  |  ``istitle()``   | ``rpartition()`` |\n",
+    "\n",
+    "Notice that these have various return values. Some, like ``lower()``, return a series of strings:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    graham chapman\n",
+       "1       john cleese\n",
+       "2     terry gilliam\n",
+       "3         eric idle\n",
+       "4       terry jones\n",
+       "5     michael palin\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monte.str.lower()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But some others return numbers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    14\n",
+       "1    11\n",
+       "2    13\n",
+       "3     9\n",
+       "4    11\n",
+       "5    13\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monte.str.len()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or Boolean values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    False\n",
+       "1    False\n",
+       "2     True\n",
+       "3    False\n",
+       "4     True\n",
+       "5    False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monte.str.startswith('T')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Still others return lists or other compound values for each element:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    [Graham, Chapman]\n",
+       "1       [John, Cleese]\n",
+       "2     [Terry, Gilliam]\n",
+       "3         [Eric, Idle]\n",
+       "4       [Terry, Jones]\n",
+       "5     [Michael, Palin]\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monte.str.split()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll see further manipulations of this kind of series-of-lists object as we continue our discussion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Methods using regular expressions\n",
+    "\n",
+    "In addition, there are several methods that accept regular expressions to examine the content of each string element, and follow some of the API conventions of Python's built-in ``re`` module:\n",
+    "\n",
+    "| Method | Description |\n",
+    "|--------|-------------|\n",
+    "| ``match()`` | Call ``re.match()`` on each element, returning a boolean. |\n",
+    "| ``extract()`` | Call ``re.match()`` on each element, returning matched groups as strings.|\n",
+    "| ``findall()`` | Call ``re.findall()`` on each element |\n",
+    "| ``replace()`` | Replace occurrences of pattern with some other string|\n",
+    "| ``contains()`` | Call ``re.search()`` on each element, returning a boolean |\n",
+    "| ``count()`` | Count occurrences of pattern|\n",
+    "| ``split()``   | Equivalent to ``str.split()``, but accepts regexps |\n",
+    "| ``rsplit()`` | Equivalent to ``str.rsplit()``, but accepts regexps |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With these, you can do a wide range of interesting operations.\n",
+    "For example, we can extract the first name from each by asking for a contiguous group of characters at the beginning of each element:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     Graham\n",
+       "1       John\n",
+       "2      Terry\n",
+       "3       Eric\n",
+       "4      Terry\n",
+       "5    Michael\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monte.str.extract('([A-Za-z]+)', expand=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or we can do something more complicated, like finding all names that start and end with a consonant, making use of the start-of-string (``^``) and end-of-string (``$``) regular expression characters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    [Graham Chapman]\n",
+       "1                  []\n",
+       "2     [Terry Gilliam]\n",
+       "3                  []\n",
+       "4       [Terry Jones]\n",
+       "5     [Michael Palin]\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monte.str.findall(r'^[^AEIOU].*[^aeiou]$')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ability to concisely apply regular expressions across ``Series`` or ``Dataframe`` entries opens up many possibilities for analysis and cleaning of data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Miscellaneous methods\n",
+    "Finally, there are some miscellaneous methods that enable other convenient operations:\n",
+    "\n",
+    "| Method | Description |\n",
+    "|--------|-------------|\n",
+    "| ``get()`` | Index each element |\n",
+    "| ``slice()`` | Slice each element|\n",
+    "| ``slice_replace()`` | Replace slice in each element with passed value|\n",
+    "| ``cat()``      | Concatenate strings|\n",
+    "| ``repeat()`` | Repeat values |\n",
+    "| ``normalize()`` | Return Unicode form of string |\n",
+    "| ``pad()`` | Add whitespace to left, right, or both sides of strings|\n",
+    "| ``wrap()`` | Split long strings into lines with length less than a given width|\n",
+    "| ``join()`` | Join strings in each element of the Series with passed separator|\n",
+    "| ``get_dummies()`` | extract dummy variables as a dataframe |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Vectorized item access and slicing\n",
+    "\n",
+    "The ``get()`` and ``slice()`` operations, in particular, enable vectorized element access from each array.\n",
+    "For example, we can get a slice of the first three characters of each array using ``str.slice(0, 3)``.\n",
+    "Note that this behavior is also available through Python's normal indexing syntax–for example, ``df.str.slice(0, 3)`` is equivalent to ``df.str[0:3]``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    Gra\n",
+       "1    Joh\n",
+       "2    Ter\n",
+       "3    Eri\n",
+       "4    Ter\n",
+       "5    Mic\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monte.str[0:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Indexing via ``df.str.get(i)`` and ``df.str[i]`` is likewise similar.\n",
+    "\n",
+    "These ``get()`` and ``slice()`` methods also let you access elements of arrays returned by ``split()``.\n",
+    "For example, to extract the last name of each entry, we can combine ``split()`` and ``get()``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    Chapman\n",
+       "1     Cleese\n",
+       "2    Gilliam\n",
+       "3       Idle\n",
+       "4      Jones\n",
+       "5      Palin\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monte.str.split().str.get(-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Indicator variables\n",
+    "\n",
+    "Another method that requires a bit of extra explanation is the ``get_dummies()`` method.\n",
+    "This is useful when your data has a column containing some sort of coded indicator.\n",
+    "For example, we might have a dataset that contains information in the form of codes, such as A=\"born in America,\" B=\"born in the United Kingdom,\" C=\"likes cheese,\" D=\"likes spam\":"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>info</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>B|C|D</td>\n",
+       "      <td>Graham Chapman</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B|D</td>\n",
+       "      <td>John Cleese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A|C</td>\n",
+       "      <td>Terry Gilliam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B|D</td>\n",
+       "      <td>Eric Idle</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B|C</td>\n",
+       "      <td>Terry Jones</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>B|C|D</td>\n",
+       "      <td>Michael Palin</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    info            name\n",
+       "0  B|C|D  Graham Chapman\n",
+       "1    B|D     John Cleese\n",
+       "2    A|C   Terry Gilliam\n",
+       "3    B|D       Eric Idle\n",
+       "4    B|C     Terry Jones\n",
+       "5  B|C|D   Michael Palin"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_monte = pd.DataFrame({'name': monte,\n",
+    "                           'info': ['B|C|D', 'B|D', 'A|C',\n",
+    "                                    'B|D', 'B|C', 'B|C|D']})\n",
+    "full_monte"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``get_dummies()`` routine lets you quickly split-out these indicator variables into a ``DataFrame``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A  B  C  D\n",
+       "0  0  1  1  1\n",
+       "1  0  1  0  1\n",
+       "2  1  0  1  0\n",
+       "3  0  1  0  1\n",
+       "4  0  1  1  0\n",
+       "5  0  1  1  1"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_monte['info'].str.get_dummies('|')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With these operations as building blocks, you can construct an endless range of string processing procedures when cleaning your data.\n",
+    "\n",
+    "We won't dive further into these methods here, but I encourage you to read through [\"Working with Text Data\"](http://pandas.pydata.org/pandas-docs/stable/text.html) in the Pandas online documentation, or to refer to the resources listed in [Further Resources](03.13-Further-Resources.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Recipe Database\n",
+    "\n",
+    "These vectorized string operations become most useful in the process of cleaning up messy, real-world data.\n",
+    "Here I'll walk through an example of that, using an open recipe database compiled from various sources on the Web.\n",
+    "Our goal will be to parse the recipe data into ingredient lists, so we can quickly find a recipe based on some ingredients we have on hand.\n",
+    "\n",
+    "The scripts used to compile this can be found at https://github.com/fictivekin/openrecipes, and the link to the current version of the database is found there as well.\n",
+    "\n",
+    "As of Spring 2016, this database is about 30 MB, and can be downloaded and unzipped with these commands:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# !curl -O http://openrecipes.s3.amazonaws.com/recipeitems-latest.json.gz\n",
+    "# !gunzip recipeitems-latest.json.gz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The database is in JSON format, so we will try ``pd.read_json`` to read it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ValueError: Trailing data\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    recipes = pd.read_json('recipeitems-latest.json')\n",
+    "except ValueError as e:\n",
+    "    print(\"ValueError:\", e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Oops! We get a ``ValueError`` mentioning that there is \"trailing data.\"\n",
+    "Searching for the text of this error on the Internet, it seems that it's due to using a file in which *each line* is itself a valid JSON, but the full file is not.\n",
+    "Let's check if this interpretation is true:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 12)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with open('recipeitems-latest.json') as f:\n",
+    "    line = f.readline()\n",
+    "pd.read_json(line).shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Yes, apparently each line is a valid JSON, so we'll need to string them together.\n",
+    "One way we can do this is to actually construct a string representation containing all these JSON entries, and then load the whole thing with ``pd.read_json``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# read the entire file into a Python array\n",
+    "with open('recipeitems-latest.json', 'r') as f:\n",
+    "    # Extract each line\n",
+    "    data = (line.strip() for line in f)\n",
+    "    # Reformat so each line is the element of a list\n",
+    "    data_json = \"[{0}]\".format(','.join(data))\n",
+    "# read the result as a JSON\n",
+    "recipes = pd.read_json(data_json)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(173278, 17)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recipes.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see there are nearly 200,000 recipes, and 17 columns.\n",
+    "Let's take a look at one row to see what we have:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "_id                                {'$oid': '5160756b96cc62079cc2db15'}\n",
+       "cookTime                                                          PT30M\n",
+       "creator                                                             NaN\n",
+       "dateModified                                                        NaN\n",
+       "datePublished                                                2013-03-11\n",
+       "description           Late Saturday afternoon, after Marlboro Man ha...\n",
+       "image                 http://static.thepioneerwoman.com/cooking/file...\n",
+       "ingredients           Biscuits\\n3 cups All-purpose Flour\\n2 Tablespo...\n",
+       "name                                    Drop Biscuits and Sausage Gravy\n",
+       "prepTime                                                          PT10M\n",
+       "recipeCategory                                                      NaN\n",
+       "recipeInstructions                                                  NaN\n",
+       "recipeYield                                                          12\n",
+       "source                                                  thepioneerwoman\n",
+       "totalTime                                                           NaN\n",
+       "ts                                             {'$date': 1365276011104}\n",
+       "url                   http://thepioneerwoman.com/cooking/2013/03/dro...\n",
+       "Name: 0, dtype: object"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recipes.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is a lot of information there, but much of it is in a very messy form, as is typical of data scraped from the Web.\n",
+    "In particular, the ingredient list is in string format; we're going to have to carefully extract the information we're interested in.\n",
+    "Let's start by taking a closer look at the ingredients:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    173278.000000\n",
+       "mean        244.617926\n",
+       "std         146.705285\n",
+       "min           0.000000\n",
+       "25%         147.000000\n",
+       "50%         221.000000\n",
+       "75%         314.000000\n",
+       "max        9067.000000\n",
+       "Name: ingredients, dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recipes.ingredients.str.len().describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ingredient lists average 250 characters long, with a minimum of 0 and a maximum of nearly 10,000 characters!\n",
+    "\n",
+    "Just out of curiousity, let's see which recipe has the longest ingredient list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Carrot Pineapple Spice &amp; Brownie Layer Cake with Whipped Cream &amp; Cream Cheese Frosting and Marzipan Carrots'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recipes.name[np.argmax(recipes.ingredients.str.len())]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That certainly looks like an involved recipe.\n",
+    "\n",
+    "We can do other aggregate explorations; for example, let's see how many of the recipes are for breakfast food:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3524"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recipes.description.str.contains('[Bb]reakfast').sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or how many of the recipes list cinnamon as an ingredient:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10526"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recipes.ingredients.str.contains('[Cc]innamon').sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could even look to see whether any recipes misspell the ingredient as \"cinamon\":"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recipes.ingredients.str.contains('[Cc]inamon').sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the type of essential data exploration that is possible with Pandas string tools.\n",
+    "It is data munging like this that Python really excels at."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A simple recipe recommender\n",
+    "\n",
+    "Let's go a bit further, and start working on a simple recipe recommendation system: given a list of ingredients, find a recipe that uses all those ingredients.\n",
+    "While conceptually straightforward, the task is complicated by the heterogeneity of the data: there is no easy operation, for example, to extract a clean list of ingredients from each row.\n",
+    "So we will cheat a bit: we'll start with a list of common ingredients, and simply search to see whether they are in each recipe's ingredient list.\n",
+    "For simplicity, let's just stick with herbs and spices for the time being:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "spice_list = ['salt', 'pepper', 'oregano', 'sage', 'parsley',\n",
+    "              'rosemary', 'tarragon', 'thyme', 'paprika', 'cumin']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then build a Boolean ``DataFrame`` consisting of True and False values, indicating whether this ingredient appears in the list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cumin</th>\n",
+       "      <th>oregano</th>\n",
+       "      <th>paprika</th>\n",
+       "      <th>parsley</th>\n",
+       "      <th>pepper</th>\n",
+       "      <th>rosemary</th>\n",
+       "      <th>sage</th>\n",
+       "      <th>salt</th>\n",
+       "      <th>tarragon</th>\n",
+       "      <th>thyme</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   cumin oregano paprika parsley pepper rosemary   sage   salt tarragon  thyme\n",
+       "0  False   False   False   False  False    False   True  False    False  False\n",
+       "1  False   False   False   False  False    False  False  False    False  False\n",
+       "2   True   False   False   False   True    False  False   True    False  False\n",
+       "3  False   False   False   False  False    False  False  False    False  False\n",
+       "4  False   False   False   False  False    False  False  False    False  False"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "spice_df = pd.DataFrame(dict((spice, recipes.ingredients.str.contains(spice, re.IGNORECASE))\n",
+    "                             for spice in spice_list))\n",
+    "spice_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, as an example, let's say we'd like to find a recipe that uses parsley, paprika, and tarragon.\n",
+    "We can compute this very quickly using the ``query()`` method of ``DataFrame``s, discussed in [High-Performance Pandas: ``eval()`` and ``query()``](03.12-Performance-Eval-and-Query.ipynb):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selection = spice_df.query('parsley & paprika & tarragon')\n",
+    "len(selection)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We find only 10 recipes with this combination; let's use the index returned by this selection to discover the names of the recipes that have this combination:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2069      All cremat with a Little Gem, dandelion and wa...\n",
+       "74964                         Lobster with Thermidor butter\n",
+       "93768      Burton's Southern Fried Chicken with White Gravy\n",
+       "113926                     Mijo's Slow Cooker Shredded Beef\n",
+       "137686                     Asparagus Soup with Poached Eggs\n",
+       "140530                                 Fried Oyster Po’boys\n",
+       "158475                Lamb shank tagine with herb tabbouleh\n",
+       "158486                 Southern fried chicken in buttermilk\n",
+       "163175            Fried Chicken Sliders with Pickles + Slaw\n",
+       "165243                        Bar Tartine Cauliflower Salad\n",
+       "Name: name, dtype: object"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recipes.name[selection.index]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have narrowed down our recipe selection by a factor of almost 20,000, we are in a position to make a more informed decision about what we'd like to cook for dinner."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Going further with recipes\n",
+    "\n",
+    "Hopefully this example has given you a bit of a flavor (ba-dum!) for the types of data cleaning operations that are efficiently enabled by Pandas string methods.\n",
+    "Of course, building a very robust recipe recommendation system would require a *lot* more work!\n",
+    "Extracting full ingredient lists from each recipe would be an important piece of the task; unfortunately, the wide variety of formats used makes this a relatively time-consuming process.\n",
+    "This points to the truism that in data science, cleaning and munging of real-world data often comprises the majority of the work, and Pandas provides the tools that can help you do this efficiently."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Pivot Tables](03.09-Pivot-Tables.ipynb) | [Contents](Index.ipynb) | [Working with Time Series](03.11-Working-with-Time-Series.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.10-Working-With-Strings.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/03.11-Working-with-Time-Series.ipynb b/notebooks_v1/03.11-Working-with-Time-Series.ipynb
new file mode 100644
index 000000000..c9b4d828b
--- /dev/null
+++ b/notebooks_v1/03.11-Working-with-Time-Series.ipynb
@@ -0,0 +1,1960 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Vectorized String Operations](03.10-Working-With-Strings.ipynb) | [Contents](Index.ipynb) | [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.11-Working-with-Time-Series.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Working with Time Series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas was developed in the context of financial modeling, so as you might expect, it contains a fairly extensive set of tools for working with dates, times, and time-indexed data.\n",
+    "Date and time data comes in a few flavors, which we will discuss here:\n",
+    "\n",
+    "- *Time stamps* reference particular moments in time (e.g., July 4th, 2015 at 7:00am).\n",
+    "- *Time intervals* and *periods* reference a length of time between a particular beginning and end point; for example, the year 2015. Periods usually reference a special case of time intervals in which each interval is of uniform length and does not overlap (e.g., 24 hour-long periods comprising days).\n",
+    "- *Time deltas* or *durations* reference an exact length of time (e.g., a duration of 22.56 seconds).\n",
+    "\n",
+    "In this section, we will introduce how to work with each of these types of date/time data in Pandas.\n",
+    "This short section is by no means a complete guide to the time series tools available in Python or Pandas, but instead is intended as a broad overview of how you as a user should approach working with time series.\n",
+    "We will start with a brief discussion of tools for dealing with dates and times in Python, before moving more specifically to a discussion of the tools provided by Pandas.\n",
+    "After listing some resources that go into more depth, we will review some short examples of working with time series data in Pandas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dates and Times in Python\n",
+    "\n",
+    "The Python world has a number of available representations of dates, times, deltas, and timespans.\n",
+    "While the time series tools provided by Pandas tend to be the most useful for data science applications, it is helpful to see their relationship to other packages used in Python."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Native Python dates and times: ``datetime`` and ``dateutil``\n",
+    "\n",
+    "Python's basic objects for working with dates and times reside in the built-in ``datetime`` module.\n",
+    "Along with the third-party ``dateutil`` module, you can use it to quickly perform a host of useful functionalities on dates and times.\n",
+    "For example, you can manually build a date using the ``datetime`` type:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "datetime.datetime(2015, 7, 4, 0, 0)"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from datetime import datetime\n",
+    "datetime(year=2015, month=7, day=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or, using the ``dateutil`` module, you can parse dates from a variety of string formats:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "datetime.datetime(2015, 7, 4, 0, 0)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dateutil import parser\n",
+    "date = parser.parse(\"4th of July, 2015\")\n",
+    "date"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Once you have a ``datetime`` object, you can do things like printing the day of the week:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Saturday'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "date.strftime('%A')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the final line, we've used one of the standard string format codes for printing dates (``\"%A\"``), which you can read about in the [strftime section](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) of Python's [datetime documentation](https://docs.python.org/3/library/datetime.html).\n",
+    "Documentation of other useful date utilities can be found in [dateutil's online documentation](http://labix.org/python-dateutil).\n",
+    "A related package to be aware of is [``pytz``](http://pytz.sourceforge.net/), which contains tools for working with the most migrane-inducing piece of time series data: time zones.\n",
+    "\n",
+    "The power of ``datetime`` and ``dateutil`` lie in their flexibility and easy syntax: you can use these objects and their built-in methods to easily perform nearly any operation you might be interested in.\n",
+    "Where they break down is when you wish to work with large arrays of dates and times:\n",
+    "just as lists of Python numerical variables are suboptimal compared to NumPy-style typed numerical arrays, lists of Python datetime objects are suboptimal compared to typed arrays of encoded dates."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Typed arrays of times: NumPy's ``datetime64``\n",
+    "\n",
+    "The weaknesses of Python's datetime format inspired the NumPy team to add a set of native time series data type to NumPy.\n",
+    "The ``datetime64`` dtype encodes dates as 64-bit integers, and thus allows arrays of dates to be represented very compactly.\n",
+    "The ``datetime64`` requires a very specific input format:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(datetime.date(2015, 7, 4), dtype='datetime64[D]')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "date = np.array('2015-07-04', dtype=np.datetime64)\n",
+    "date"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have this date formatted, however, we can quickly do vectorized operations on it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['2015-07-04', '2015-07-05', '2015-07-06', '2015-07-07',\n",
+       "       '2015-07-08', '2015-07-09', '2015-07-10', '2015-07-11',\n",
+       "       '2015-07-12', '2015-07-13', '2015-07-14', '2015-07-15'], dtype='datetime64[D]')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "date + np.arange(12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because of the uniform type in NumPy ``datetime64`` arrays, this type of operation can be accomplished much more quickly than if we were working directly with Python's ``datetime`` objects, especially as arrays get large\n",
+    "(we introduced this type of vectorization in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb)).\n",
+    "\n",
+    "One detail of the ``datetime64`` and ``timedelta64`` objects is that they are built on a *fundamental time unit*.\n",
+    "Because the ``datetime64`` object is limited to 64-bit precision, the range of encodable times is $2^{64}$ times this fundamental unit.\n",
+    "In other words, ``datetime64`` imposes a trade-off between *time resolution* and *maximum time span*.\n",
+    "\n",
+    "For example, if you want a time resolution of one nanosecond, you only have enough information to encode a range of $2^{64}$ nanoseconds, or just under 600 years.\n",
+    "NumPy will infer the desired unit from the input; for example, here is a day-based datetime:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.datetime64('2015-07-04')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.datetime64('2015-07-04')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is a minute-based datetime:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.datetime64('2015-07-04T12:00')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.datetime64('2015-07-04 12:00')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the time zone is automatically set to the local time on the computer executing the code.\n",
+    "You can force any desired fundamental unit using one of many format codes; for example, here we'll force a nanosecond-based time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.datetime64('2015-07-04T12:59:59.500000000')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.datetime64('2015-07-04 12:59:59.50', 'ns')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following table, drawn from the [NumPy datetime64 documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html), lists the available format codes along with the relative and absolute timespans that they can encode:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "|Code    | Meaning     | Time span (relative) | Time span (absolute)   |\n",
+    "|--------|-------------|----------------------|------------------------|\n",
+    "| ``Y``  | Year\t       | ± 9.2e18 years       | [9.2e18 BC, 9.2e18 AD] |\n",
+    "| ``M``  | Month       | ± 7.6e17 years       | [7.6e17 BC, 7.6e17 AD] |\n",
+    "| ``W``  | Week\t       | ± 1.7e17 years       | [1.7e17 BC, 1.7e17 AD] |\n",
+    "| ``D``  | Day         | ± 2.5e16 years       | [2.5e16 BC, 2.5e16 AD] |\n",
+    "| ``h``  | Hour        | ± 1.0e15 years       | [1.0e15 BC, 1.0e15 AD] |\n",
+    "| ``m``  | Minute      | ± 1.7e13 years       | [1.7e13 BC, 1.7e13 AD] |\n",
+    "| ``s``  | Second      | ± 2.9e12 years       | [ 2.9e9 BC, 2.9e9 AD]  |\n",
+    "| ``ms`` | Millisecond | ± 2.9e9 years        | [ 2.9e6 BC, 2.9e6 AD]  |\n",
+    "| ``us`` | Microsecond | ± 2.9e6 years        | [290301 BC, 294241 AD] |\n",
+    "| ``ns`` | Nanosecond  | ± 292 years          | [ 1678 AD, 2262 AD]    |\n",
+    "| ``ps`` | Picosecond  | ± 106 days           | [ 1969 AD, 1970 AD]    |\n",
+    "| ``fs`` | Femtosecond | ± 2.6 hours          | [ 1969 AD, 1970 AD]    |\n",
+    "| ``as`` | Attosecond  | ± 9.2 seconds        | [ 1969 AD, 1970 AD]    |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the types of data we see in the real world, a useful default is ``datetime64[ns]``, as it can encode a useful range of modern dates with a suitably fine precision.\n",
+    "\n",
+    "Finally, we will note that while the ``datetime64`` data type addresses some of the deficiencies of the built-in Python ``datetime`` type, it lacks many of the convenient methods and functions provided by ``datetime`` and especially ``dateutil``.\n",
+    "More information can be found in [NumPy's datetime64 documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dates and times in pandas: best of both worlds\n",
+    "\n",
+    "Pandas builds upon all the tools just discussed to provide a ``Timestamp`` object, which combines the ease-of-use of ``datetime`` and ``dateutil`` with the efficient storage and vectorized interface of ``numpy.datetime64``.\n",
+    "From a group of these ``Timestamp`` objects, Pandas can construct a ``DatetimeIndex`` that can be used to index data in a ``Series`` or ``DataFrame``; we'll see many examples of this below.\n",
+    "\n",
+    "For example, we can use Pandas tools to repeat the demonstration from above.\n",
+    "We can parse a flexibly formatted string date, and use format codes to output the day of the week:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timestamp('2015-07-04 00:00:00')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "date = pd.to_datetime(\"4th of July, 2015\")\n",
+    "date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Saturday'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "date.strftime('%A')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Additionally, we can do NumPy-style vectorized operations directly on this same object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2015-07-04', '2015-07-05', '2015-07-06', '2015-07-07',\n",
+       "               '2015-07-08', '2015-07-09', '2015-07-10', '2015-07-11',\n",
+       "               '2015-07-12', '2015-07-13', '2015-07-14', '2015-07-15'],\n",
+       "              dtype='datetime64[ns]', freq=None)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "date + pd.to_timedelta(np.arange(12), 'D')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the next section, we will take a closer look at manipulating time series data with the tools provided by Pandas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pandas Time Series: Indexing by Time\n",
+    "\n",
+    "Where the Pandas time series tools really become useful is when you begin to *index data by timestamps*.\n",
+    "For example, we can construct a ``Series`` object that has time indexed data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2014-07-04    0\n",
+       "2014-08-04    1\n",
+       "2015-07-04    2\n",
+       "2015-08-04    3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "index = pd.DatetimeIndex(['2014-07-04', '2014-08-04',\n",
+    "                          '2015-07-04', '2015-08-04'])\n",
+    "data = pd.Series([0, 1, 2, 3], index=index)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have this data in a ``Series``, we can make use of any of the ``Series`` indexing patterns we discussed in previous sections, passing values that can be coerced into dates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2014-07-04    0\n",
+       "2014-08-04    1\n",
+       "2015-07-04    2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['2014-07-04':'2015-07-04']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are additional special date-only indexing operations, such as passing a year to obtain a slice of all data from that year:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2015-07-04    2\n",
+       "2015-08-04    3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['2015']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Later, we will see additional examples of the convenience of dates-as-indices.\n",
+    "But first, a closer look at the available time series data structures."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pandas Time Series Data Structures\n",
+    "\n",
+    "This section will introduce the fundamental Pandas data structures for working with time series data:\n",
+    "\n",
+    "- For *time stamps*, Pandas provides the ``Timestamp`` type. As mentioned before, it is essentially a replacement for Python's native ``datetime``, but is based on the more efficient ``numpy.datetime64`` data type. The associated Index structure is ``DatetimeIndex``.\n",
+    "- For *time Periods*, Pandas provides the ``Period`` type. This encodes a fixed-frequency interval based on ``numpy.datetime64``. The associated index structure is ``PeriodIndex``.\n",
+    "- For *time deltas* or *durations*, Pandas provides the ``Timedelta`` type. ``Timedelta`` is a more efficient replacement for Python's native ``datetime.timedelta`` type, and is based on ``numpy.timedelta64``. The associated index structure is ``TimedeltaIndex``."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The most fundamental of these date/time objects are the ``Timestamp`` and ``DatetimeIndex`` objects.\n",
+    "While these class objects can be invoked directly, it is more common to use the ``pd.to_datetime()`` function, which can parse a wide variety of formats.\n",
+    "Passing a single date to ``pd.to_datetime()`` yields a ``Timestamp``; passing a series of dates by default yields a ``DatetimeIndex``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-06', '2015-07-07',\n",
+       "               '2015-07-08'],\n",
+       "              dtype='datetime64[ns]', freq=None)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dates = pd.to_datetime([datetime(2015, 7, 3), '4th of July, 2015',\n",
+    "                       '2015-Jul-6', '07-07-2015', '20150708'])\n",
+    "dates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Any ``DatetimeIndex`` can be converted to a ``PeriodIndex`` with the ``to_period()`` function with the addition of a frequency code; here we'll use ``'D'`` to indicate daily frequency:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PeriodIndex(['2015-07-03', '2015-07-04', '2015-07-06', '2015-07-07',\n",
+       "             '2015-07-08'],\n",
+       "            dtype='int64', freq='D')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dates.to_period('D')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A ``TimedeltaIndex`` is created, for example, when a date is subtracted from another:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TimedeltaIndex(['0 days', '1 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq=None)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dates - dates[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regular sequences: ``pd.date_range()``\n",
+    "\n",
+    "To make the creation of regular date sequences more convenient, Pandas offers a few functions for this purpose: ``pd.date_range()`` for timestamps, ``pd.period_range()`` for periods, and ``pd.timedelta_range()`` for time deltas.\n",
+    "We've seen that Python's ``range()`` and NumPy's ``np.arange()`` turn a startpoint, endpoint, and optional stepsize into a sequence.\n",
+    "Similarly, ``pd.date_range()`` accepts a start date, an end date, and an optional frequency code to create a regular sequence of dates.\n",
+    "By default, the frequency is one day:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\n",
+       "               '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\n",
+       "              dtype='datetime64[ns]', freq='D')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.date_range('2015-07-03', '2015-07-10')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, the date range can be specified not with a start and endpoint, but with a startpoint and a number of periods:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\n",
+       "               '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\n",
+       "              dtype='datetime64[ns]', freq='D')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.date_range('2015-07-03', periods=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The spacing can be modified by altering the ``freq`` argument, which defaults to ``D``.\n",
+    "For example, here we will construct a range of hourly timestamps:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2015-07-03 00:00:00', '2015-07-03 01:00:00',\n",
+       "               '2015-07-03 02:00:00', '2015-07-03 03:00:00',\n",
+       "               '2015-07-03 04:00:00', '2015-07-03 05:00:00',\n",
+       "               '2015-07-03 06:00:00', '2015-07-03 07:00:00'],\n",
+       "              dtype='datetime64[ns]', freq='H')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.date_range('2015-07-03', periods=8, freq='H')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create regular sequences of ``Period`` or ``Timedelta`` values, the very similar ``pd.period_range()`` and ``pd.timedelta_range()`` functions are useful.\n",
+    "Here are some monthly periods:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PeriodIndex(['2015-07', '2015-08', '2015-09', '2015-10', '2015-11', '2015-12',\n",
+       "             '2016-01', '2016-02'],\n",
+       "            dtype='int64', freq='M')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.period_range('2015-07', periods=8, freq='M')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And a sequence of durations increasing by an hour:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TimedeltaIndex(['00:00:00', '01:00:00', '02:00:00', '03:00:00', '04:00:00',\n",
+       "                '05:00:00', '06:00:00', '07:00:00', '08:00:00', '09:00:00'],\n",
+       "               dtype='timedelta64[ns]', freq='H')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.timedelta_range(0, periods=10, freq='H')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All of these require an understanding of Pandas frequency codes, which we'll summarize in the next section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Frequencies and Offsets\n",
+    "\n",
+    "Fundamental to these Pandas time series tools is the concept of a frequency or date offset.\n",
+    "Just as we saw the ``D`` (day) and ``H`` (hour) codes above, we can use such codes to specify any desired frequency spacing.\n",
+    "The following table summarizes the main codes available:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "| Code   | Description         | Code   | Description          |\n",
+    "|--------|---------------------|--------|----------------------|\n",
+    "| ``D``  | Calendar day        | ``B``  | Business day         |\n",
+    "| ``W``  | Weekly              |        |                      |\n",
+    "| ``M``  | Month end           | ``BM`` | Business month end   |\n",
+    "| ``Q``  | Quarter end         | ``BQ`` | Business quarter end |\n",
+    "| ``A``  | Year end            | ``BA`` | Business year end    |\n",
+    "| ``H``  | Hours               | ``BH`` | Business hours       |\n",
+    "| ``T``  | Minutes             |        |                      |\n",
+    "| ``S``  | Seconds             |        |                      |\n",
+    "| ``L``  | Milliseonds         |        |                      |\n",
+    "| ``U``  | Microseconds        |        |                      |\n",
+    "| ``N``  | nanoseconds         |        |                      |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The monthly, quarterly, and annual frequencies are all marked at the end of the specified period.\n",
+    "By adding an ``S`` suffix to any of these, they instead will be marked at the beginning:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "| Code    | Description            || Code    | Description            |\n",
+    "|---------|------------------------||---------|------------------------|\n",
+    "| ``MS``  | Month start            ||``BMS``  | Business month start   |\n",
+    "| ``QS``  | Quarter start          ||``BQS``  | Business quarter start |\n",
+    "| ``AS``  | Year start             ||``BAS``  | Business year start    |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Additionally, you can change the month used to mark any quarterly or annual code by adding a three-letter month code as a suffix:\n",
+    "\n",
+    "- ``Q-JAN``, ``BQ-FEB``, ``QS-MAR``, ``BQS-APR``, etc.\n",
+    "- ``A-JAN``, ``BA-FEB``, ``AS-MAR``, ``BAS-APR``, etc.\n",
+    "\n",
+    "In the same way, the split-point of the weekly frequency can be modified by adding a three-letter weekday code:\n",
+    "\n",
+    "- ``W-SUN``, ``W-MON``, ``W-TUE``, ``W-WED``, etc.\n",
+    "\n",
+    "On top of this, codes can be combined with numbers to specify other frequencies.\n",
+    "For example, for a frequency of 2 hours 30 minutes, we can combine the hour (``H``) and minute (``T``) codes as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TimedeltaIndex(['00:00:00', '02:30:00', '05:00:00', '07:30:00', '10:00:00',\n",
+       "                '12:30:00', '15:00:00', '17:30:00', '20:00:00'],\n",
+       "               dtype='timedelta64[ns]', freq='150T')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.timedelta_range(0, periods=9, freq=\"2H30T\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All of these short codes refer to specific instances of Pandas time series offsets, which can be found in the ``pd.tseries.offsets`` module.\n",
+    "For example, we can create a business day offset directly as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2015-07-01', '2015-07-02', '2015-07-03', '2015-07-06',\n",
+       "               '2015-07-07'],\n",
+       "              dtype='datetime64[ns]', freq='B')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pandas.tseries.offsets import BDay\n",
+    "pd.date_range('2015-07-01', periods=5, freq=BDay())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more discussion of the use of frequencies and offsets, see the [\"DateOffset\" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#dateoffset-objects) of the Pandas documentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Resampling, Shifting, and Windowing\n",
+    "\n",
+    "The ability to use dates and times as indices to intuitively organize and access data is an important piece of the Pandas time series tools.\n",
+    "The benefits of indexed data in general (automatic alignment during operations, intuitive data slicing and access, etc.) still apply, and Pandas provides several additional time series-specific operations.\n",
+    "\n",
+    "We will take a look at a few of those here, using some stock price data as an example.\n",
+    "Because Pandas was developed largely in a finance context, it includes some very specific tools for financial data.\n",
+    "For example, the accompanying ``pandas-datareader`` package (installable via ``conda install pandas-datareader``), knows how to import financial data from a number of available sources, including Yahoo finance, Google Finance, and others.\n",
+    "Here we will load Google's closing price history:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2004-08-19</th>\n",
+       "      <td>49.96</td>\n",
+       "      <td>51.98</td>\n",
+       "      <td>47.93</td>\n",
+       "      <td>50.12</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2004-08-20</th>\n",
+       "      <td>50.69</td>\n",
+       "      <td>54.49</td>\n",
+       "      <td>50.20</td>\n",
+       "      <td>54.10</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2004-08-23</th>\n",
+       "      <td>55.32</td>\n",
+       "      <td>56.68</td>\n",
+       "      <td>54.47</td>\n",
+       "      <td>54.65</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2004-08-24</th>\n",
+       "      <td>55.56</td>\n",
+       "      <td>55.74</td>\n",
+       "      <td>51.73</td>\n",
+       "      <td>52.38</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2004-08-25</th>\n",
+       "      <td>52.43</td>\n",
+       "      <td>53.95</td>\n",
+       "      <td>51.89</td>\n",
+       "      <td>52.95</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Open   High    Low  Close  Volume\n",
+       "Date                                          \n",
+       "2004-08-19  49.96  51.98  47.93  50.12     NaN\n",
+       "2004-08-20  50.69  54.49  50.20  54.10     NaN\n",
+       "2004-08-23  55.32  56.68  54.47  54.65     NaN\n",
+       "2004-08-24  55.56  55.74  51.73  52.38     NaN\n",
+       "2004-08-25  52.43  53.95  51.89  52.95     NaN"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pandas_datareader import data\n",
+    "\n",
+    "goog = data.DataReader('GOOG', start='2004', end='2016',\n",
+    "                       data_source='google')\n",
+    "goog.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For simplicity, we'll use just the closing price:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "goog = goog['Close']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can visualize this using the ``plot()`` method, after the normal Matplotlib setup boilerplate (see [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn; seaborn.set()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eLa1T7JqZiwG5d2NAJiKKoM9/OIl7/v6tZD9xsHLHDIBcJi0c8cjP88XHDMi9\nGwMyEVEEfbDhGAxtHdh5uCbk13rrWaclxeG62bbqTBMzUjzOU+/Bak9ERBGy7dA58fF7a49gSGo8\nJo3yH0SrXQpITB8/0Os1VxeOwhUz06GQs4/Vm/GnR0QUIR99c0zy/LlVuwO+5sutFeLjWdmDfV7H\nYNz78SdIEdXQYsLGPVVi5jei/kQXYhatnYfOoarO1kO+9fLxktrH1PdwyJoi6tmVu3CmzghdnApT\nx6b1dHOIIqqh2YRErRpNeltFpiSt7wD9303Hsebb4+LzCRnJ3d4+6lnsIVNEnbF/269pbA1wJVHf\nYrUKaGgxIS0xDo/eOh0AkJvl+0upazAGABXTYfZ5/AlTz+DQG/UzTYZ2WAUBKQkxUKtsOfytVu9T\nN6Z2CzQx0gFM5qfu+zhkTT0iVm37g3SsqglKuRzpg3tfIniiUDS0mAAAyboYyOW2L6TeAvLKr4+I\ntY1dKRX8EtvX8SsX9QiVUo4zdQb8+e0deOytbWg3W0LOXkTUmzgCcpI2Bgr7CJHFS/Jpb8EY8EyT\nSX0Pf8IUMa4rqy0WARXVzpKdi5/7Bv/ecMzby4h6PZPZgtc/OwDAHpAVjoDs+SV0YFKc1/dQyNlD\n7usYkCli9h2vFx9bBQEms0Vy/ostFe4vIeoTPvqmHG3ttt/3CRnJPoesrYKAcy4LHlVK559obnnq\n+ziHTBEjd/mGb7FY8f7XR3uwNUSRs/+E88togkYNQ5sZgLSHvOdoLf7+71LJ626ZNx5b952JTCOp\nxzEgU8RYXBLf1za1eU2E32rqQFwMfy2p77AKAmLsq6odHMPPHRYBLcZ26DRqbNxTJZ7/0bThyBye\ngKvmjMFsP9m5qG/hkDVFjGPIDgCMpg6v17gOaxP1Bf/ecAzHzzQDgFjH2BGQ95bX4Z7nv8Ohkw2S\nL6LD0uJRMHEwh6n7GQZkihiTS0CubzZ5vSacknRE0apRb5Ksjbjz2mwA0ukbAPjv5hOSNRVZI5Ii\n00CKKhwbpIh58/ND4uO95XVer2E9V+pLlr78vfh41JAEMSGIeyEIhVwGY5tt1OjZO2chJSE2co2k\nqMEeMkWEr2ISAxKlf3gsFu5FpsiwCgLe+V8Z9hyt7bbPaO9wfsG8t2iy5NxzdxWKj/cdr8fBkw0A\nwGDcjzEgU0SYO7z3fC+bMRKF2YNx1awMAECHl0QJRN3h5NkWrNtZ6bGyuau0tTvXSeSNS4M2TiU5\nn6yLwRvHYA6vAAAgAElEQVRLL+qWz6beiQGZIqLNPj/mnvTgomnDsOiqiZhiX+xSUa332J9M1B0c\nFZcC2binSlyUFYo6+zqJC3KH4q7rcoJaoDUo2XtSEOofGJApIhwrrJN0MeKx0UMTxD9SSVrb8dJj\ndXj6vV2RbyD1O8FUHGvSm/DW54fwp/+3Hc//uxRNeu+LEb2pa7ItUEwNYQj60dtmBH0t9T0MyBQR\np6pbAADD0+LFY679hVSXueRweiNEoTrXEDggL3tti/h499FafPr9yaDff+vBagCBA/K880YCANRK\nucd+ZepfGJApIhy1XaeNddZ/5fKt/uOLLRX4+Nvynm6GSBAEfL3ztPj8Hx+WotXL3nhDm/SYr8WJ\n7iprDdi87ywAYGCAYeiiuZm44YLRuOfGyX6vo76PAZm63Z6jtaiqNQCQ9oT1RnNPNYkibPX6o/hk\n0wkAtqBmbPOeGCZSjlVKR2F2HanFOpcADXgPvsFUXLJaBTzi0rMePTTB7/UymQxXzszAhIyUgO9N\nfRsDMnU717k612xE53zM4Y0cpO32NvUlVbUGfLGlIujeW3c4W2/E51tOel1N7zrv2mGxYtFT6/Hr\nv23EybMtkWwiANtWp2fe34W/vLPD45wmVroK2tvvpyOhxwcbjuLZld7XOriurp4zZSizbVHQAgZk\nq9WKhx9+GPPnz8fNN9+Mo0ePoqKiAgsWLMAtt9yCxx57TLx29erVuOGGG3DTTTdhw4YN3dlu6kW+\n3OrMVBSnVuCmH2UBAPJchq8B4G+/OR8AYDJz61OwjpxuxLLXtmD1+qM4WtnUI20wtJnx8Ks/4IP1\nx/D9/rMe50+fc5bZdD2/uxP7fw+eqMezK3eF3NP+rvSMuN/XXWK8Wnz8xmcH8cJH+wAAP71ojHg8\nPtb2hfLzHypw4ESD1x0BZpe99NfOHhVS+6h/C5ipa926dZDJZHj//fexdetW/PWvf4UgCCguLkZ+\nfj6WL1+OtWvXIjc3FyUlJVizZg3a2towf/58FBYWQqVSBfoI6uMGJmvELSBKhRyXTh+B7FEpSEmI\nkVyXoFFjYHIcTO09O5zZmzz5zk7xsft8ZyS8/cUhbNjtLIpwukYvOS8IApa+8J34/M3/c2Zrq6o1\nQBCEsHqQz6zcDQDYXnYOc6YMDXh9RXULPvym3GOe+Dc3Tsbz9n3IjgGGAyfq8d1eZ4Wl1IRY3H9T\nLp5buRtrt58WdwQAtt7/wGSN5D3P1tmmZ/LHD5RcSxRIwIB88cUX46KLbJvXq6qqkJiYiM2bNyM/\nPx8AMGfOHGzatAlyuRx5eXlQKpXQarXIyMhAWVkZsrOzu/dfQFFPp7F9KRuUHCf+8R06IN7rtbEq\nBVqMwe0PJWBwqgZn6owAgPYe2L/tGowBePRYW1p9rxPYdugcRg1JEFcZB8t1aN6xtcifs/VGPPrm\nNo/jf/11IZK0MVhwcRbeW3sEJ84244U1ez2uSx+sE1O6Nhna8fpnB8VzD7+6Ba89eKHk+qfs2/ZK\nuzEDGPVNQc0hy+VyLF26FI8//jiuuuoqyX8Q8fHx0Ov1MBgM0Ol04nGNRoOWlsjPEVH0adS3Qwbg\n8TvOC3htjFqBtnZLj86H9iauvWJfBTsiIXu0bUGSawGRz7ecxL3P23rHaqX3PzWr14deE9t1nvq/\nm08EvN6x2tnVv343V+y9OuaFP/OxpSk1MdZnT9fq5/c0f/zAgG0jchX0oq4nn3wSX375JZYtWwaT\nyfkfvsFgQEJCArRaLfR6vcdxoka9Cbp4tUdCfW9i1AoIgjQHMHl34EQ9mg3O0YRggpvFau2yAh7m\nDmfwvfv6yZAB2HG4BlarLUh9sP6YeD43a4D4+I6rJ6JobmbYn/ve2sOS58Y2373wHWU1+NQtaKuV\ncsnvojzAkLlcJguqRrcgCKiuNyJJa5uLvvmSsQFfQ+Qq4G/Zf/7zH1RXV+OXv/wlYmJiIJfLkZ2d\nja1bt2LGjBnYuHEjCgoKkJOTgxUrVqC9vR0mkwnl5eXIysry+97JyRooleFvhE9L0wW+iDxE+r41\nG9oxdIA2qM8dMkCLfeX1EBSKgNfvPVqLh1/ahEvPS8fdP8ntqub6FS2/c+cajHjWPo/qKjVV61Ha\nz9Vv/74RZRUN+M8z1/i9Lhh6+3D0eZMGY+iQRHFf+e1Pr8fkMQMk1971k6m4vLIJ3+6uxEXnZSAu\nRokPNtgCdlKyBtsPnkNyQgzGpwfe+rNxzxnJc7la5fXnsqvsnNch6LhYpeT6hATnPuFErRrvPHY5\nBEHAa5/sw7RxA8VrL5+Vgc+99MibTBaMGZ6Ej9YfxZuf7gcADBkQj5HDkwP+W4IRLb9zvU1vvG8B\nA/Kll16Khx56CLfccgs6OjqwbNkyjB49GsuWLYPZbEZmZibmzZsHmUyGhQsXYsGCBeKiL7Va7fe9\nGxqMYTc8LU2HmhoOiYcq0vetw2JFW7sFSoUsqM9NirfNN+8/UoPYAB3qh1/aBAD435aTuLYwHSVf\nlmHM8CRcOHVYp9vtTbT8zm3YXYm3vygTnz+zZBYeeGkzAOAvb27Br66Z5PO1ZRW2FcZnq5ug6sSX\nYcAZkM1mi8d9cZ0/vXr2aFhMZqQP0CD94iy06tvQqgdyRqdib3kd/vz6Fuw4XAMAePWBuQH3+o4c\nqEWFy8rtxkaj19+VP7z6vedB2HJYu7bXYHDOQ2tilOK5a+0FTxzPL546DMNTNcgYrENifAx+/beN\nAICte6uQGKPAJxudIxQKWXC/74FEy+9cbxPN983fF4WAATkuLg5/+9vfPI6XlJR4HCsqKkJRUVGI\nzaO+bO8xW93jw6cag7p+cIptxeq5EL+s3flX2x/H7/dX44IpQzvd+4tWVkGQBOOHbpkmSbay5UC1\nJCCfqTNg3Y5KXH/BaMmwa1cU1XLMnzpGfAckxqLWbZHVS8UXYPiwJK9/HKdmDcDe8joxGANA2alG\nTAqQICNBqwbOAYXZg7Fp31l0dHJ6w3W7kybW95/EZF0MZk4aLD6/89psvPjxPry/9giOnGoUdxIA\nQIyaKR4odPytoW6173h9SNfHqm1/EI+cDryn1tdK7bP14Y+8RLvH/9928fHd1+cga3gSAOD2qyaI\nx10XxL2/9gi+3nka/918QnLcYu38ojnB/h6OOdilN0+TnP/19TmIUfvuhXur+/vcyt2oDVD0wfHP\n2Ftu+7LnukXJH0fPO94t6E7OdA6vJ4ewTck1Jeb2shrJOXUnRx+of2JApm4jCALW76oEANx/U3Bz\nvI5VusEkjUj3kdFr2WtbQu5h9wZ7y+twwp7dKn2wDlNdEqsUTBqMRPtiItdg68iSdrpGj2fed2aW\n8rc6OFiOj3GMRqQkxOKPLtWKxo5I8vv6EQO9//yaDP63vTm+WEyfMAgA8L9tp7xep1TIMXKgFktv\nnoY/LZoh9n5dF5i5u7wg3e9nu9JpfE/JDUnV+DxH5AsDMnWbqjpnUMwalhjUawSXkhPuSSbctdsz\neuWMTvU4d+qc/9f2Rp98d1x8/MurJ0rOyWUyMcA5ArLFakW1vaLRvvJ6HKpwThtYu6KH7DZkDQAK\nhfOJNs5/UqBkl1KcGpfhdJWPLVLOz7X9/6ghOpdjnv8ei9UKtVqBsSOSMCxNK36Gt+xejr3yvr4k\neJOkVSMuxrMnnD0qRcxGRxQKBmTqFKtVgMHHtpMDJ2zD1QUTB0EdbFk5l7+rL328z/dlgiD2pPLG\npXmc95eQojcSBAEDkmxDpE/8sgBDUj2H6xX2yOgItmdqfY8SdMWQtaOX7bptKNy5+1uvGI8fTRse\nVNsc/z7XYeHyKmmxCKsgQBCc9wQArpxp6/0W5gzxeM+//LIATy+eGVTxCAeZTIYX7rtAcmzs8EQU\n/zQ3pPchcuBvDXXKsyt34e6/fYv6Zs+MSY5jl0wfEfT7uf4trq73PZf4xdYKMXezt57Yf749jtue\nXBdU4oho95eSHVj01HpsOVANtUqONB/l/BT2IOAIaN6+lIwdbhupeOvzQx7nQuX4Wbn2kFX2NigV\nwQVmx7D2xIwUqFRy+/v6D8iCIEAGSL7kuY+mOHrBrgvZCnOG4O+/OV9SAtQhPlYlfuEJ1R1XTcTY\n4Ym4fs5oPLBgaljvQQQEscqayJdWU4c4DHq0sgkz3BbpOLbFuC+i8Uetcn5H9PeH+f9csip565U5\nes9rNpbjavv2ld6ousEoKRoxKFnjM5GFY6jX1G6BJkYpfiEaNyIJZacakaBRQWuf93QsiOoM90Vd\ngG0e+bc35XrtwXtz/0+nwGS2Ii5GCYX951jb2IaBSXFe52iPn2nGYfuCP9fsX47pi89/OIkTZ1tw\nqf1LoGNe3cHfvG+4ZmYPxszswYEvJAqAAZnC9ofXnTVf29o98ygbWm29lEBzia4mjUpBgkaFZqMZ\nF+cP93mda8rIAfZtP6kJsajz0lPvzVoM0l5uqpeVyQ4x9i8zjj3JDhfkDsWUMQOQNy4NaqUcOw/X\n+F39HKyth84BsKWmvPUK5yrviSHU9VUpFeJ+aEee81c+sSXXGDlQi0ddFokBwJ9cVpkPd5nv3Xqo\nGjVNrVi73VbTuNq+qM91SxNRtOOQNYXFYrVK9l16m/cztJkhkwGxQaQddJDLZFhyra0gyd7yeuwo\nO+f3+nuLJmN4mhbLfpaPx26bjmfvnCXZjgLY9uL2Vu5buLQa319uXLfvuNLFqzHvvJFIS4pDojYG\n2aNTYGq3+Jz7D0ZDiwlrNpYD8L9qORTuAx0V5/SS1KDutHEqFP9kCgDgWGWzGIwBoKLaNoTNakvU\nmzAgU1j+8aE0JWGHxQqrVYAg2P73/b6zOHK6CYIQOFewO0dPqbreiBfW7PO7Itixwnr00ARoYlVI\nSYjFRdOkPeu/28vr9UZv/N9ByfMpPoIuYEu04U2C2zCtDLb7e/ffvg2rTR0WK+5/YZP43PEFqrMU\nXqYe7v3Hd1jy3Dfi9Ie7+ACjL+5D1kTRjAGZQlZdb0SpPQPXGPt2psoaPW5/ej0+31KBbYfO4V+f\nHgj7/d3nhL0NQ8fHKjEsLd5rLd0UnbRXpDf23hXXjsxQP7lwDJ69cxamjfUdkH3VFR7mlkBlUoYz\nx3JDS+gVog6caJA8D/ULly/pg72nFDSZLfjz29u9FsUItHqfPWTqTTiHTEHrsFix7F9b0Kh3/hGf\nPn4gjlY2iQn//73hGCakO//gzwpjsYt7T+lcYyvS3FbACoKzp+du2rg0XDkzXSynZzR57jvtDayC\ngO/320oHzp4yBPGxgefiX3/wQuwoq8GkUSk4VtmEuFilxxecS6aPwMp1trzL97+wCW8svSikdtW4\nZNLqyopGjqxjgO0LiGv1qiZDu7iSeuRALe77qS3RjK+yjg6cQ6behD3kPuzE2WZJibzOqmtqw7nG\nVrE04pUz073uAT540tmDun7O6JA/xyMgN3hufxIgeMw5OshlMtxwQaa4xWegPZgfPtUYMAtUNDnm\nsro6mGAM2HrJ+eMHIi5GiezRqcgc6pmQRSaTSZJyhOrdr2zlD/PGpXVpIY+4GCUeu20GVtx9Pi6a\nNgwpCc42trVbxJ75tLFpYqB17SH/+PxR+Oe9czDSZbFXAgMy9SIMyH3U2Xoj/vjWdvzlnZ2S49X1\nRny17RSajaEHptZ2aU9z9NAEr/mIHX4+b5zf8764b00p+bLM4xqr4HuI1mH+xbbe2+TMVFQ3GPHk\nuzsluaCjnePfN2Z4cFnOQuE6VP31jtP4+Ntyr0PCb31+CLc9uU6SitQxBH7j3MwuL+IxYqAWifFq\nqFUKPHtnId5YepH4pe7f9nKNcS7b6Fx7yONHJkETq8SVLtvcmKCDehP+tvZRJ+05jx3/71Dyfwfx\n/tdHcO/z3+GR17agNYTh3O2HpAn0p2bZescP3TLN49ob52bigtzwek86PyuJHQTbmLVfjp62VRDw\nxZYKAN7no6PRibPNeH+trSeaPSr4bUThePerw/hk0wn88pkN+PyHk5JzG/dUAQCeW+WsvayQyxCj\nVmBQcmTyNU/OlKZGdd3X7ppm01GYhMPU1FsxIPdRjr2cgDRv8VmXLUCVtQZsD7CtyNUme1WdrOGJ\nkiICWcOT8PDCPMm1V4SQpN+dt16NR65iwXObjDuZ/YKqWoM4F9sbCIKAP761HcfP2L5MxQSbdjQE\n40d6L/zwwYZjsAoCfjhwFj+43DO53PkzaTK0RzTojRykk+SM1rgM37v+rjiu0YSwzY4omjAg9zEV\n1S0w2vf/Ojy7chcsVis6LFaxQL1DKH/sHXPHD92SJ0nKANhWW184zdYjHjUkIczWO101Kx1XzkwX\ntzWZzNK58GCGrB0B+1BFo5jJCQD2nwitJGSklVVIa0d3R0CeYa+U5E1NQyte/eQAXv2vc6V8db0R\nFdUtsAoCWozmiM/NtpqcP39fAdeR+nLogHhMHz8Qv7xmotfriKIVv0r2IcY2Mx59cxu0cSrEqhXi\nH7FDFY244+kNkpq5Dv4S+X+5tQItRjOuv2A0mvTtaDdbMMhHHmUA+OmFYzAwKQ5zu2Chz/VzMgEA\nr9u3T7UYzeKQJGDPZxygh+xrfvMfH5bi5fvndrqN3aXarXRkZxZg+XJB7lCkD9ZJMl85HHL70ubw\n380ncNXMDFgFAfE92At1T8W66MoJiFUrxO1Xcrmsy/ZGE0USA3If4shU5CuJwsmzniUJW4xmbDt0\nDmOHJyLRZc9ms7Edq+xbY+LjlFDI5bBYBb85e9UqBS6bMbIz/wQPKnvv0FEsQLDPB1usQsAessrH\ngp4RacGX2OsJjuxUc3OHQqmQIyfTs7xkZ8lkMmT42Pd7+FST5PnMSYPx/f6zSB+kw9Pv2xYJBiqR\n2NXuuHoi/mXvsSe67S32Vr2JqDfikHU3+HTzCXy/L/JzlvvchmIvyB2KB+ZPFdc+nThrK1F36xXj\nxa0hK78+gpc+3ofXXBJ5vPrf/bj3+e/E5x+sP4aVXx8BAMSpI/sdbrs9X/Kb9oxVB0404AP7attA\n63vdk0I45j2PVTXDYvVcUdzTth06hy+2VGDfcdvP8bLzRmLBJWO7LPGGO19faFzn2y+bMULc2lRV\nZxBHXX42b3y3tMmXgonOIfZQcqMT9SbsIXexU+f0+Mie4zctKa5btqz4MjE9GUdPO3s3VxSkIy0p\nDhfnj8BX20/hiP3csAFa3H9TLu5xCbpl9l7R/uP1+GF/tc/PmD0lsr0RR2+/4pytd79upzNfccA5\nZLch63Ejk7D1oC3AHzrZiEndvHo5FMY2s6T+c+awhIisYr44fzj2HqvDzZeORZvJghfdalDnjE6F\nxj5E7Pp7EemgKJPJsOxn+ZLFXUR9DXvIXczRowOAI5WNfq7seo7qShmDdXj2zllidiv3ObdhafEe\nf1A7LFZUVLdItrcAwMxJzp7J5MxUyTxuJMye7PwCsOdoLXYdqRWfB9PLffTW6UgfZBuaTdLGYFia\nbQ9tNNVJbja249dueaXdM5N1lwUXj8UTv5qJ7FGpyB8/0GOLUWpCrEe+6PzxAyPSNnejhyYEXdaR\nqDdiQO5irquBOzoiOyxqNNl6k3dely1JyHGVS6KEaeMGIkalgEzmufDl0Te3iY9HDtLipeILcMfV\nk8SA7poSM1IWXjYOgC0wuBeJOFbZHPD1IwfpsOznebjtigm4pnAU7iuyVQeCn1rLkSQIgmR6wCFr\nWORGVly5ruiOUSkwMDnOY4vTois8FwcSUedxyLqLudYFNlsi+0ffsWLafR+vXC5D0dxM1DebcPdN\nU1Ffb9uLPH38QAxdNANvfn4I5VXO4DZpVAp+dc0ksWbu8/fMxuFTjZJcw5Hi+Ld0JqGHQi7H+fae\ntiZWibgYBarqjHj6vZ34+bzxGJQSmQQX7gRB8NlTn5M7NLKNsXMNyP+8b7bXaYGuqKVMRJ7YQ+5i\nrvVbP918AuYAveTqBqPHNpdwWexfALyVsbu8IB03XzoWCrdgPSxN65HecuRArWRIWyaTYdzI5C5P\nk9hZ4QYGTYwS+lYzDlU04un3d3Vxq7zbdaQG9/3zO7G+sSAIWLF6Dz7+9rjkuh+fPwov3DcHCnnP\n/Kfpek9d2/DordMBAAsuzop4m4j6CwbkLtagl5az23rQ9wKpVlMHHnrlB/zxra7Jr+zoIXsLyP5M\nHSMt6ec6xB0NBiR65sN+/Pbz8PxvZof1fnExzi8bDS2mLi3A4cu7Xx1Gk75dXPBXVWsQV1MDwOUF\nIzEoOQ4XThuGuB7c4+ta0MHVyEE6vLH0IlycPyLCLSLqPzhk3UnnGozQxKpw+pzea2/r9c8O+twn\nWWVPYxlKPmlfahpbUXHOlmox1N5VTmYq5kwZgukTBmFgUlyPBgRvxgxLRG2Tc8j6rutyMHRA+It7\n3BeDtRjNSEno3mHYgUlxqG82oarW9jM3maVtuPb8USiaO6Zb2xAMx7SEa81kIoqM6PrL28u0my1Y\n+soPSIxXh1XWz3XRV1t7h8cKZqtVwKp1R9FhtWLBxVl+A+0f39oGgz15hkIRWg9ZG6fCLy6P3oU6\n7qt8vZV8DEWs21B3V3whCqTZaFtwV1VrwKuf7Jdk37rtiglQKaNjXnbMsEQ8f89sj3tERN3Pb0Du\n6OjAww8/jMrKSpjNZixevBhjxozB0qVLIZfLkZWVheXLlwMAVq9ejVWrVkGlUmHx4sWYO3duJNrf\no07X2Ho7gYKxLc2jZ5Bs1LdLHg9Okf44vtp+Cl9tPwUAGJoajx/lDff6/sa2DjEYA6EPWUe7SRkp\n+HqHbf/x2C7Y1+2eG7q9m1fDG9s6cKbWWdTjhwPOaYwEjQozJvTMNiJfmHiDqGf4DciffPIJkpOT\n8fTTT6O5uRk//vGPMX78eBQXFyM/Px/Lly/H2rVrkZubi5KSEqxZswZtbW2YP38+CgsLoVL17f+w\nXWvK+mPusEoKqQO2YPv+2iPi87qmNgx2W+1beqxOfNzW7tmLM3dY8cKavWJaScC2QjpQwozexnWh\n0YJLxnb6/dxHImoaW7ukIIYvp861QACgVMjQ4bby/rfzp3r8bhBR/+R3svHyyy/HPffcAwCwWCxQ\nKBQ4cOAA8vPzAQBz5szB5s2bUVpairy8PCiVSmi1WmRkZKCszLOofF/TbJAG5JmTBiF3zAA8MH+q\n5Li3HphrMAZs9WaPVTmzbAmCgEqXXtWGXVUe77HzcA1Kj9XhaKXtdXKZDPf/NDf0f0iUyxisg1ol\nxzWFGRg5yHv+5VDc9CPpXO3L/9nv48rOqa434l//3Y+dh23JTK6ameFxTWfmwomob/EbkOPi4qDR\naKDX63HPPffgvvvuk9SljY+Ph16vh8FggE7n/EOp0WjQ0tLSfa2OEiX/Oyx5ftWsDPzmxsnIchtW\nDbT1yeGJkp3i4/fXHpFsoaprbvOoCey+N/f2q6N3Hrgz4mKUePn+ubh29ugueb+ByRq8sfQiXDq9\ne1cMP/TqD/h+f7U47TBuZBJGD5X2xLsrTzUR9T4BF3WdOXMGv/71r3HLLbfgyiuvxDPPPCOeMxgM\nSEhIgFarhV6v9zgeSHKyBspOLGZJS+t8bylcW/adER+rVQr89OKxmDzeVgnJPXDqEuKQ5tITqqzx\nrLoEAFZBQFqaDsY2M9ba50wHpWhQbd+7qoxVS/YMt7uVTpyRMwxpQSS56Mn7Fk3i450Lq4K9J8Fe\n1+AlkUl+zlDkThiMrQeqscJeNam//Cz6y7+zO/Dehac33je/Abm2thaLFi3CH/7wBxQUFAAAJkyY\ngG3btmH69OnYuHEjCgoKkJOTgxUrVqC9vR0mkwnl5eXIygqcQKChEwkx0tJ0qKnpmV54s6Edj7+5\nVXz+8v0XAICkPffcOBmf/XASR0834Wx1M7aUVkKllGPGhEFY/OQ6ALbcvA8umIZfPbsBgK23dLqy\nEUv++o34Pn/4eT7e/PwQth86hxc/2I3brpgAY5sZidoYnLUH9twxA3D7VRMgt1gC3pOevG/Rxmh0\nTjkEc0+CvXer1h3Bl1tPeRxvaWoFAEwckYDRQxMwOTO1X/ws+DsXPt678ETzffP3RcFvQH7llVfQ\n3NyMF198ES+88AJkMhl+//vf4/HHH4fZbEZmZibmzZsHmUyGhQsXYsGCBRAEAcXFxVCr1f7eulc7\n4lJR6aaLvO8dnTJmAErL63D0dBNKy+vwb3vJwBkTnMUaBiTGQqWU4+4bcvCPD/di9NAE/Llkh+R9\n1Co56ux/yLccqMaxyibUNrXh/p/mipWL7ro+u8cyO/VmrgMZVqvQZZnIvAXjZT/LFx8r5HLJcyIi\nIEBA/v3vf4/f//73HsdLSko8jhUVFaGoqKjrWhbFahpbxcezp/jOOVxnT2bhCMaALSnF8DQtTtfo\nccfVEwEAUzJtmbIci7McRg3RQSGXY/GPs/Hgy98DgJggw7UqE4NxeFyrYN3+9Hos/vEkyRcmf77e\ncRqpibHIdcty5p505ParJiAhXu0xd0xE5I5/ycPgGpD95VO+2ksKys9/qIBCIYNKKRcDqa+emaMX\nlZYUh19c7r0gvK+9yRSYexrIYFdbNxvb8e5Xh/G8W/UpAPjPdyfEx7+bPxWzsocge1Sqx3VERO4Y\nkMPgCMgv3DfH7ypZb1WEyqua0dDcFjD5wpOLZ0r2Ew9KltbHjVUr8NhtM/CTC3s+3WJvFRejxLzz\nRkqO3fP8tzC0mf2+7sQZ73NTlTV6fOpSvWl8D5SrJKLei6kzw1DT2AqdRhUw57PGy/mDFQ0wtVtQ\nMEk6NPpi8Ry89ulBFOYMxphhidBppHPwIwZqxcc3XDAaV3rZ00qhc08R2WI048MNx/Czed5HJABp\nqk1zhxUqpe177SmX1fOLfzypi1tKRH0dA3KIzB1WVDe0IkETOAuZt6Fok71e8oBE9x6vEr++Psfn\ne2liVXhj6UUhtpYCUXvZdhcoA5vJ7KwOdbSyCRPsPeHjVc6e8/Tx0ZUOk4iiH4esQ/RtqS1jlqNY\nQM1kW7kAABUFSURBVCCO0oGpbjWHVSEWgKDuEaPy/E/A4KfYhCAI2LTXuQf9GXuFr6pag5gA5JrC\njD6XvpSIuh8Dcog27T0LAB7ZuHzJzbKtwm0xSgtQ6Fu7v8IQBeatylKbn4C87dA5ybY3AKisNeCt\nLw6Jz2dlD+66BhJRv8GAHCJHj8rXqmd340bY6steOG2Y5LgAwdvlFGHedozlZvku7+haocvhkde2\nSLKzJWpjPK4hIgqEc8ghMpmtUCrkGJIaXFGAaWPTsPTmaRiepsXxMy04fKoRAHBlQXp3NpOCJIPn\n0LJj/7irTXvPYOuhUuSPHeBxDgCOVTYDAB5cMNWjvCMRUTAYkENktQpQhDD/K5PJMNbeS76vaAqq\n6gxIH6xjUYEoERfr+Z9Ao965qKuyRg+ZTIbXPzsIAHD86BddOQGb953FwZMNktdm2X/WREShYkAO\nUqupAy3GdlisVijCDKYxakW31t2l0OWMTsG8GSNRMGkQ9pbX4cNvymFodS7Ye+T1rZLrdx+1lVJM\njFfj19fn4K4VGyXn+UWLiMLFgGxX39yGqjqD16xKPxw4i1c/OQAASNbFdFnOY+p5CrkcP7HnIx85\nSIfv91fjjL26lr7V90r6CRnJUMjluLxgJD7/oQIAcEGu7zSqRESBMCDb/fbFzQCA5++ZLcmi9fR7\nO3GoolF8HmiPKvVuVbUGALYvYaeqvZfJTEuKFdOeuiZ/WXBx4ApnRES+9OuA3GrqwDv/O4zLZjhz\nGhvazGJANndYJMGY+o93/3cYhjbv259cF23tP14vPva2hYqIKFj9etvT1ztO4/v9Z/Hom9vEYw+9\n8gO2HKgGYNtfSv2Tr2AMAC0uQ9lXeikgQkQUjn4ZkM/WG/HM+7uwx75Ax90rn+yHVRBQYR+yjItR\n4KX7LxDTIf7mhskRaytFD6XC9p9Lk8te5IzBvouNExGFot8NWVutAh5+9YeA193+1Hrx8b1FUxCj\nUuDWK8bjomnDMG4kq/j0VXnj0rCjrEZ8PiwtHvcVTUGsWolDlU345wd7JNc75pC595iIOqvfBWS9\nW2m9kQO1WHxtNlJ0MdiwqxIr1x31eM3IgbZeUKxayWDcx/3qmkn45TMbxOexKgVS7HnIB6d4JoOR\nyWR47q5CseITEVG4+l1ANrrNDY4bmYzB9rrFl84YiQunDcevnt0gnmeFpf5FqZBDp1GhxV485FhV\ns3huctYAXFOYgZxM6da4ZB1TZRJR5/W7r/UV1bYSeUqFDDFqBX6UP1xyXqWU4+GFeQBsdYep/xF8\npBmXyWS4dvZoZA4NrrAIEVEo+l0P2bFN5bc3TUXW8ESvZfLGDEvEirvPhy4ucM1j6ntcE4LcfMnY\nHmwJEfUn/S4gf1tqq2WblhTnt2ZtYrw6Uk2iKHbh1GGBLyIi6gL9asjatURekpYBl7wrujATALDk\n2mymSSWiiOlXPeT6Zlvay+njB/rtHVP/dvl56bhsxkgWiiCiiOpXPeSqOlvmrSGpmh5uCUU7BmMi\nirR+FZAPnrDVrk1ndiUiIooy/WLIWhAE3PH0Bljtc8ijuW2FiIiiTJ/vIQuCgKfe3SkG4yGpGq6g\nJiKiqBNUQN6zZw8WLlwIAKioqMCCBQtwyy234LHHHhOvWb16NW644QbcdNNN2LBhQ7c0Nhxn6404\nfLoJABAXo8Sfbj+vh1tERETkKWBAfu2117Bs2TKYzbZkCU888QSKi4vxzjvvwGq1Yu3ataitrUVJ\nSQlWrVqF1157Dc8995x4fXc7eroJm/ae8Xn+pD0zFwD8457ZXKxDRERRKWBATk9PxwsvvCA+379/\nP/Lz8wEAc+bMwebNm1FaWoq8vDwolUpotVpkZGSgrKys2xqtbzVD32rGrsM1+Ms7O/D6ZwfR1u69\nfm2ryQLAVjSAe0qJiChaBVzUdckll6CyslJ87ppcIz4+Hnq9HgaDATqdc+WyRqNBS0sLOksQBI/9\nws3Gdtz7/Hce17771WHccEEmkrTSRP9Ge3WnuJh+sX6NiIh6qZCjlFzu7FQbDAYkJCRAq9VCr9d7\nHA8kOVkDpdKzjqzFYsW1v/svhqTG45WHfiQJyo88udbre23aexab9p7Fx89cA4W9J/z2/x3Ah9+U\nAwBGj0xGWhq3OwHgfegE3rvw8L6Fj/cuPL3xvoUckCdOnIht27Zh+vTp2LhxIwoKCpCTk4MVK1ag\nvb0dJpMJ5eXlyMrKCvheDQ1Gr8c/WG+rSXymzoD9R85hULItkUerqQOVNQa/73ntA5/g0VunY0hq\nPD74+ojzRIcFNTWd77X3dmlpOt6HMPHehYf3LXy8d+GJ5vvm74tCyAH5wQcfxCOPPAKz2YzMzEzM\nmzcPMpkMCxcuxIIFCyAIAoqLi6FWh7+16PMtFeLjh175ARfnDUeToR1n6pzBeGByHM41tHp9/T8/\n2ouWVumisvhYDlkTEVH0kgmCr+qv3c/XN5jbnlzn93XPLJmF8WPScPX9/wEAPPmrAsSqlXj+w1KU\nuxSUB4C8sWm4ZPoIjB2R1DWN7uWi+ZtjtOO9Cw/vW/h478ITzffNXw85KhODpCbE+j+faDt/7exR\nGDciCQMS45AQr8ayn+VLrrt+zmjcdX0OgzEREUW9qBzHNZq8b2ECgHuLpoiPrykchWsKR0nOv3z/\nBdhedg4zJw1mRSciIuo1ojIgd1isGDlIi7yxaVjz7XEAwBO/KkB8rAraOJXf16pVCszKHhKJZhIR\nEXWZqAvIgiCgo8OKGJUCVxeOwlWzMgCAvV0iIurToi4gV1TrIQA4cdY2Ic9ATERE/UFULeqyCgJe\n/s8+AIC5w9rDrSEiIoqcqOohP/jSZtQ1mwAAo4b0viwrRERE4YqaHvLhU41iME4fpMNvb5rawy0i\nIiKKnKjpIW87dA4AkDU8Eb+9KRcqLzmuiYiI+qqo6CGfONuMr3ecBgDcODeTwZiIiPqdqAjIJV8e\nFh8PStH0YEuIiIh6Ro8OWd/25DoUZg9GVa0BapUcz95ZGDDxBxERUV/U43PIm/adBQBcUZDOYExE\nRP1WVAxZA8BlM0b0dBOIiIh6TI/2kJ+9cxYOnmzA1KwB0MSyd0xERP1XjwbklIRYFOawEAQREVHU\nDFkTERH1ZwzIREREUYABmYiIKAowIBMREUUBBmQiIqIowIBMREQUBRiQiYiIogADMhERURRgQCYi\nIooCDMhERERRoEtTZwqCgEcffRRlZWVQq9X485//jBEjWDSCiIgokC7tIa9duxbt7e1YuXIl7r//\nfjzxxBNd+fZERER9VpcG5B07dmD27NkAgClTpmDfvn1d+fZERER9VpcGZL1eD51OJz5XKpWwWq1d\n+RFERER9UpcGZK1WC4PBID63Wq2Qy7lujIiIKJAuXdQ1bdo0rF+/HvPmzcPu3bsxduxYv9enpen8\nng+ks6/vr3jfwsd7Fx7et/Dx3oWnN943mSAIQle9mesqawB44oknMGrUqK56eyIioj6rSwMyERER\nhYcTvERERFGAAZmIiCgKMCATERFFAQbkPopLA4iIepeoDchGo1Gyp5mC19jYiNra2p5uBhFRt+mL\nMSIqA/I777yD4uJicfsUBW/NmjW47LLLsHLlyp5uSq/z7rvv4r333sPBgwd7uim9ypYtW/Dhhx8C\n4MhMqEpKSvDGG29g//79Pd2UXqWvxoioCciCIKC+vh6XX3456urq8Oyzz2LatGmS8+Tbrl27sGjR\nIuzevRvZ2dk4//zzAfC+BUOv12PJkiU4ePAgkpKS8Pe//x3ffPMNADD1axC+/PJLfPXVV6itrYVM\nJuPvXBCMRiN+85vf4ODBg4iJicEbb7yBY8eO9XSzol5fjxFdmqkrXBaLBQqFAikpKcjMzER6ejpe\nfPFFNDc3IzExEQ888ABkMllPNzMqOdKTVlVV4fbbb8fMmTPx1ltv4ciRI5g6dSrvmx+O3zuLxQKd\nTocHHngAiYmJ+P/t3WtMU/cbwPFv13KwzIgWsBBLkYWmKzjXBHVR2FyM8VJFbMxCsgvbyIKJiZuJ\nJu6FJiSbsmzeJhEyExdxEkti5xZk88JcdGNGmXNBSYbEaBhEBBUGVPDSdi82kf9/KuzMcmp5Pm+B\n9He+OT1Pz6E9vXv3Lp9++imzZ8+WW78O4ccff+TChQvY7Xb27t3LqlWrZJ8bhjt37jBmzBjWr1+P\noiicP3+esWPHar2ssGcymbDZbBE7I/RFRUVFWj14f38/xcXFnD17lo6ODux2O729vVRUVJCVlcXr\nr79OeXk5bW1tTJ8+nUAgEBHRH4d77c6cOUN3dzcul4vk5GTu3r2L1+tl+vTpJCcnS7MHGLzfdXd3\nEx8fT01NDU6nkwkTJtDb28sPP/yAoig4HA6CwaA0/JvH46GhoYEpU6YAEBMTQ2JiIi+++CI1NTUk\nJSVhNpul2QN4PB7Onz/PlClTuHr1KlarlcmTJ7Nz504qKyvp7u6mqamJzMxMed4OMnif8/v9+Hy+\niJ0Rmr387+/vZ/v27RiNRhYsWMCuXbuora0lJSWF/Px8cnNzMZlMFBUVDXzPspyt/GVwO5fLRVlZ\nGcePH8fn82EwGEhJSeHQoUMA0uz/DG43f/58SktLaW1tJSkpifLycjZs2IDH42Hp0qU0Njbi9/uf\n6Cf441ZXV8dnn31GX18fAPHx8cydO5dJkybhdDr5+uuvAaTZA9TV1bFz5076+vpISUnhhRdeACA7\nO5va2lreeOMNPB4P/f398rwdZPA+p9frsdlsvPrqq7jd7oibESO++o6ODgCioqI4d+4cbrcbh8NB\nQUEB33//PePGjWPJkiX09PQA0NLSwpw5c1AUZaSXGnYe1u6dd97h2LFjtLa2AjBz5kxiY2Npb2/X\ncrlh5UHt0tPTefvttzl69CiLFy+msLAQs9nM+++/T0JCAjabDb1er/HKtXWvG0BTUxNjx44lNTWV\nrVu3An9d9gcwGo1kZWXR2dlJVVWVJmsNN0O1u/f+BIvFQkxMDF1dXcybN4/o6GhN1hsuHtZt8+bN\nAGRkZOB2u+nq6gIia0aM2CXrtrY2iouLqa6uxufzYTKZ0Ol0XLhwgWnTpvHss89y7NgxFEXB7/dT\nWlrKvn37qK+vJycnB4vFMhLLDEtDtbPb7Rw/fhydTofD4eDKlSucOnWKtLQ0Jk6cqPXyNTWc/a6m\npgZFUcjIyKCtrY2Kigp+/vlnFixYQFJSktaboInB3W7evMn48eOJi4vDZrPxyiuvsHHjRrKzs4mL\ni8Pv9/PUU0/x9NNPYzQaSU5OHtX73b9pd+bMGbxeL7t376auro7c3FxSUlK03gRNDNWtuLiY7Oxs\nEhISOHXqFLt376aioiKiZsSIDeQ9e/ZgNBpZvnw5Z8+epba2FqvVSnt7O9HR0QMHPo/HQ2FhIS+/\n/DJms5mVK1dGROj/YjjtdDodX3zxBcuWLSMxMZHY2FicTqfWS9fccNvt27ePvLw8JkyYgMFgYO3a\ntaN2GMP/dvvll184efIks2bNwmw2oygKPT09VFdX43K5Bi4TGgwGUlNTR/UwhuG1O3jwIC6XC7PZ\njNPpJD4+nvfeew+r1ar18jUznG5VVVUsWrSIpKQkZs+eHXEzIqQD2ev1Ul5eTmNjIy0tLeTn5w+8\ner58+TLt7e2kpaVx4MABFi5cSH19PYqikJmZiaIoo3rnVNPOaDSSmZmJXq9n0qRJWm+CZtS0i46O\nZtq0aYwbNw673a71JmjiYd3MZjO//fYbzc3NAy/yZsyYQXFxMVarlWeeeUbjlWtPbbu0tDQURWHy\n5MnaboBG/m23jz76aKCbwWCIuBkRsoG8adMmzp07R0FBAYcPH6a6uhpFUcjKysJoNBIMBmlubiYn\nJ4eLFy+yf/9+Tp8+TWFh4ah/hS3t1Psv7RISErRevmaG6qbX62loaOC5555jzJgxADgcDiwWCyaT\nSePVa0vaqSPd/ilkn0Pu6ekhLy+PjIwMXnvtNSZOnMjBgwdZvHgxDocDk8mEz+fDbDazZs0aOjs7\nR/UBcTBpp560U2eobnFxcdy6dYuYmJiBjzTNnDlT62WHBWmnjnT7p5C8yzoQCDBv3jymTp0KwDff\nfMNLL73EihUr2LBhA5cuXeLkyZN0d3fT19eHwWCQg+LfpJ160k6d4XT76aef6OrqeuI/5/m4STt1\npNuD6YIhvt9Yb28vb731FmVlZSQkJFBWVsYff/zBtWvXWLt2rRwQH0HaqSft1JFu6kk7daTbfSG/\ndebVq1eZNWsWPT09fPjhh9hsNlavXk1UVFSoH/qJJ+3Uk3bqSDf1pJ060u2+kA/ke3enaWhoIDc3\nlyVLloT6ISOGtFNP2qkj3dSTdupIt/tCfsna6/XS0dFBQUFBRNxJZSRJO/WknTrSTT1pp450uy/k\nA1luMq+etFNP2qkj3dSTdupIt/tCPpCFEEIIMbQn+6sxhBBCiAghA1kIIYQIAzKQhRBCiDAgA1kI\nIYQIAzKQhRBCiDAQ8huDCCFGRmtrK/Pnz8dmsxEMBrl16xZ2u53169cTFxf30L/Lz89nz549I7hS\nIcSDyBmyEBHEbDZz4MABvvrqK7799lusVivvvvvuI//m9OnTI7Q6IcSjyBmyEBFs5cqVZGdn09jY\nyN69e2lqauL69eukpqZSUlLCJ598AkBeXh6VlZWcOHGCkpIS/H4/FouFDz74gNjYWI23QojRQc6Q\nhYhgUVFRWK1WvvvuOxRFwePxcOTIEfr6+jhx4gTr1q0DoLKykhs3brBlyxY+//xzvvzyS7KysgYG\nthAi9OQMWYgIp9PpSE9Px2KxUFFRwaVLl2hubsbn8w38HKC+vp4rV66Qn59PMBgkEAgwfvx4LZcu\nxKgiA1mICHbnzp2BAbxt2zbefPNNli1bRmdn5z9+1+/3k5mZSWlpKQC3b98eGNpCiNCTS9ZCRJDB\nt6YPBoOUlJTgdDr5/fffcblcuN1uTCYTdXV1+P1+APR6PYFAgOeff55ff/2Vy5cvA7Bjxw4+/vhj\nLTZDiFFJzpCFiCAdHR243e6BS87p6els3ryZtrY2Vq9ezaFDh1AUBafTSUtLCwBz5swhNzcXr9fL\nxo0bWbVqFYFAgMTERPkfshAjSL7tSQghhAgDcslaCCGECAMykIUQQogwIANZCCGECAMykIUQQogw\nIANZCCGECAMykIUQQogwIANZCCGECAMykIUQQogw8CepwhihftgpswAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1162f5e80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "goog.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Resampling and converting frequencies\n",
+    "\n",
+    "One common need for time series data is resampling at a higher or lower frequency.\n",
+    "This can be done using the ``resample()`` method, or the much simpler ``asfreq()`` method.\n",
+    "The primary difference between the two is that ``resample()`` is fundamentally a *data aggregation*, while ``asfreq()`` is fundamentally a *data selection*.\n",
+    "\n",
+    "Taking a look at the Google closing price, let's compare what the two return when we down-sample the data.\n",
+    "Here we will resample the data at the end of business year:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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I/tXpWsrUde30BQghxCjkraqi5l9/QfXPfhoZh021mZg+KXVQ97EnGQf92vtr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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1186afcf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "goog.plot(alpha=0.5, style='-')\n",
+    "goog.resample('BA').mean().plot(style=':')\n",
+    "goog.asfreq('BA').plot(style='--');\n",
+    "plt.legend(['input', 'resample', 'asfreq'],\n",
+    "           loc='upper left');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice the difference: at each point, ``resample`` reports the *average of the previous year*, while ``asfreq`` reports the *value at the end of the year*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For up-sampling, ``resample()`` and ``asfreq()`` are largely equivalent, though resample has many more options available.\n",
+    "In this case, the default for both methods is to leave the up-sampled points empty, that is, filled with NA values.\n",
+    "Just as with the ``pd.fillna()`` function discussed previously, ``asfreq()`` accepts a ``method`` argument to specify how values are imputed.\n",
+    "Here, we will resample the business day data at a daily frequency (i.e., including weekends):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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51bR+3HpVH6rrjLzw/kEOnSxXJIuDC7UelQqGSqEWQgjh5FQqFTOSYnjg5sHY\nbDb+b91hNh3Id3gOhxXqmnojJwur6RMVgL+3h6NuK4QQQlyWkf3DeOT2Efh5e7Bm0wnWbMrGanXc\n9C2HFer0k+XYbLLIiRBCCNcTF+HP43eMJDLUh00HCnjt0yM0GS0OubfDCrU8nxZCCOHKQgO9ePRX\nI0iIDeLQyXJeWHOQs3VNnX5fhxTqRqOZzJxKIkK8CQ+WTTiEEEK4Jm+tht/PG8rEIRHkltTy3OoD\nFJTVdeo9HVKoD2XrMZqt0u0thBDC5bm7qbnr2gHMmdKbypomVr6XytEzFZ12P4cU6pXv7APAQ6PI\nHiBCCCGEXalUKmaOi+W+GxMxW2y8+vFhfjxU2Cn3ckjlPD827vPtZ9ibWeqIWwohhBCdLmlgOH+4\nbRjeWnf+891x1m49idXOG3qobA7YIuSGZV80fx0d5sszyUmdfUshhBDCYUqrDLy69jCllQZ6R/jR\nZLJSXGngi5dmXfa13dtz0uzZs/H19QUgOjqa++67j+XLl6NWq+nbty8pKSntvmFxRX3HkgohhBBO\nKjzIm8fuGMnK91I5XWzf5UbbLNRGoxGA1atXNx+7//77eeihhxg1ahQpKSls2rSJqVOntuuGESE+\nHYwqhBBCOC9fLw1uKvtv39zmM+qsrCwMBgPJycksWrSI9PR0MjMzGTVqFACTJ09m9+7d7b7hzHG6\njqcVQgghnFhxhcHu12yzRa3VaklOTmbevHnk5OSwePFi/vexto+PD7W1F2/mu6lVRIT4MHOcjjEJ\n4ZefWgghhHBCkaHeFOjt+4i3zUIdGxuLTqdr/jowMJDMzMzm1+vr6/H397/oNT63w8N0IYQQwtnd\nNmMAL72Xatdrtlmo161bR3Z2NikpKZSWllJXV8eECRPYt28fSUlJbNu2jbFjx9o1lBBCCOGKJg+P\nZvLwaLtes83pWSaTiRUrVlBUVIRareYPf/gDgYGBPP7445hMJuLj43nuuedQdcIDdCGEEKK7c8g8\naiGEEEJ0jKzpKYQQQjgxKdRCCCGEE5NCLYQQQjgxKdRCCCGEE5NCLYQQQjgxKdRCCCGEE5NCLYQQ\nQjgxKdRCCCGEE5NCLYQQQjgxKdRCCCGEE5NCLYQQQjgxKdRCCCGEE5NCLYQQQjgxKdRCCCGEE5NC\nLYQQQjgxKdRCCCGEE5NCLYQQQjgxKdRCCCGEE5NCLYQQQjgxKdRCCCGEE3Nvz0mzZ8/G19cXgOjo\naO644w5iRh/pAAAgAElEQVSWLFlCbGwsALfddhvXXnttp4UUQgghuiuVzWazXewEo9HI/Pnz+fTT\nT5uPrV27lvr6ehYtWtTZ+YQQQohurc0WdVZWFgaDgeTkZCwWC0uXLiUjI4OcnBw2bdqETqfjscce\nw9vb2xF5hRBCiG6lzRZ1dnY26enpzJs3j5ycHBYvXsy9995LYmIiCQkJ/OMf/6C6uppHHnnEUZmF\nEEKIbqPNFnVsbCw6na7568DAQCZPnkx4eDgA06ZN47nnnrvoNcxmC1VVBjvEFUIIIVxDWJifXa7T\n5qjvdevW8cILLwBQWlpKXV0dDzzwAIcPHwZg9+7dJCYmXvQa7u5udogqhBBCdD9tdn2bTCZWrFhB\nUVERarWahx9+GE9PT5555hk0Gg1hYWE888wz+Pj4XPRGen2tXYMLIYQQzsxeLeo2C7W9SKEWQgjR\nnTis61sIIYQQypFCLYQQQjixdq1MJuxnb2Yp63fnUFRuIDLUm5njYhmTEN5tcwB8mPoju/TbMWtq\ncTf5MT5sEvNHTlEkixBCOBt5Ru1AezNLeePLjBbHl8xKdGiRdJYccK5Ib69e3+L4pICZUqyFEC7N\nXs+opUXtQOt357R6/L2Nx8nMqXRYjtRsvVPkADhg2wZeLY/vKtvBfKRQCyGEFGoHqaptolBf3+pr\n9Y1mth8udnAi5XOofavwGFiLqpXXzJoah+UQQrRPZzwy+/bbr8nLy2XJkl9f8s+mpaXy+efrePrp\nlRc8Z9u2rbz++t+YN+9W0tJSee65F3nwwSX84Q+PsmnTBkJCQrnxxtmX80fodFKoO1leaS0b9uWz\n71gpF3rG0DPYm9/NG+KwTH9de5iSypYrxTkih9VmJevsMXaX7abQUHDB89xN/p2aQwhxaX75yKxA\nX9/8vVLjWwBUqtY+6v/Xzp3b+O1vH2L8+InMmXNru37G2Uih7gRWm42jpyvYsC+fY7lVAESEeNM3\nOoBt6S1brDdOjCM8yHGbmtw4Ma7VZ9SOyHFIf5RPctYCMDh0IDQEcKR+T4vzxveY2Kk5hBA/9/Hm\nk+zPKrvg62frmlo9/tbXmXyy9VSrr40e0INbrurT5r2PHEnnd797AIOhnrvvXkxTUxOffroWi8WC\nSqVi5cqX8PcP4JVXXiQzMwOLxczddy9pXmirqamRxx77IzNmXMe0adc0X3fHjm3s2bOT48ez8PcP\n4LHHHuaLLzbgoKFZdiOF2o6MJgu7M0rYuD+f4opzLdaBuiBmJPViUO8Q1CoVA3XBrN+dS3FFPREh\nPswcp3P4p9Hz91Mix+CQgUyLuYJxEaMI9+kBwIepIewq24FZU4Ot0ZcI8xDmXyXPp4VwJhZr68Xt\nQscvhbe3Ny+++CpVVVXce+8iZs26mZde+iuenp689NJK9u7djaenlurqav75z/9QV1fHRx+9z4gR\nozAYDPzxjw9xyy23MWHCpJ9dd+LEyWzbtoVp02YwaNBgaPVBm/OTQm0HNfVGNh8sYEtaIbUGE25q\nFeMH9WT66F7EhP981N+YhHBFu4kclSOvpoAe3qFo3bU/O+6mduOmPtf97Nj8kVOYzxRMZiuPvrmH\ngoZ6cvR6YsPCOi2fEOLnbrmqz0Vbv0++vZeCVsbZRIf58kxy0mXde/DgYQAEBQXh6+uDm5sbzz//\nFFqtlvz8XAYNGkJpac5PxRZ8fX1JTl5CWloqhw4dJD6+DyaTEYB16z5m69YfUKlUPPnkswC4WAO6\nBSnUl6GwvJ6N+/LYnVGK2WLFR+vOzHE6rhoRTZCfp9LxHM5qs3K0/Bib87dz4uxp5vadxZW92t+F\nrXFXM3V8EF+UfsNbaad4bvp9nZhWCHEpZo6LbfWR2cxxusu+9rFj565bUVFOXV09n3zyIevWfY3N\nZmPp0nODzGJj49iyZRMAdXV1PPnkCu64YxHjx0/kd797mAceSGbw4KHMmXMLc+bccoE7uWbFlkJ9\niWw2G5k5VWzYn8fR0+emMvUI9GLa6F5MHByBp0f32ymsyWJkb/EBtuTvoKyhHICBwf3o5Rd1yde6\nekg/vtngTaXHafbnnGB0bF97xxVCdEBnPjIzGpv43e/up6GhgRUrnuCLL9Zx772LcHd3w88vgPJy\nPddeez0HDuzjgQfuwWq1ctddi5t/PigoiOTkJaxc+Qx//vPfLnKnc13f5weTucqgMlnwpJ1MZit7\nM0vZuD+vufunX3QA05NiGNYnFLXaNf7BO8OJqlO8mvYG7io3RvccwVW9JhHp27PD1/s28wBfl3yM\nl7EnL1/zkB2TCiGE48juWQ5S12BiS1ohm1MLqK43olapGDUgjBlJMcRFyBQiONfL8GPBLob3GEKA\np33+x1z23Z9p9Cjlhp7zuSZhhF2uKYQQjiSFupOVVBr4fn8+O48UYzRb8fJ0Y/LQSKaO7EVIgLbt\nC3QxNpuNzMpson0jCPDs/A8oe89ks/rMW7g3BfKXGctxU8v+MUII1yJLiHYCm81Gdv5ZNuzLJ/1k\nOTYgxF/LtNG9mDQkAi/P7vfXZbKY2Fd6kM35OyipL2W67kpujL+20+87Jq4fG7KGk3dKw/6sMsYm\ndLwrXQghXFn3qzytMFusHMgqY8P+fHJLzrX8e0f6MyMphhH9Qrtla67OVM+PBbvYVrCLOlM9apWa\n0eEjGNFjqMMy3D/uJh49sodPfzzNyH490Lh3v38HIYTo1oXa0Gjix/QiNh0ooKq2CZUKRvYPY8bo\nGPpEBygdT1G1xjq+OfM9Xu5eTIu5git6TSDQ07F/J2GBXlw5IopNBwrYmlbItNG9HHp/IYRwBt3m\nGfX/7nnsZvQjtGkQJaeDaDJa8NS4MWlIBFNH96JHYCtbOdnRgdJDbMjZTImhjJ7ePZgRexWjwod1\n6j07miNdf5T+QX3Ruis3J7zWYGT5G7txU6t5Yck4vLXd+rOlEMKFyGCyS3ChPY8pTGRM73iGxIeg\n9fhvAQjSBhLu3XJVrIqGKvQ/zRP+X+09P7vqFBtyN7c4767EBYwKH3bZ12/v+W3lcDZf78rh022n\nmTlOx5wp8UrHEaLbsndDw2Kx8PvfP4DZbOall/6Kr6+vHdNe3I03zuCLLza0OJ6S8ihFRYVcf/2N\nqNVqRo8eQ0rKo7zxxjvMmzeLNWvWodFo2nUPhw4mmz17dvNfYHR0NCtXnttS7KuvvuL999/nww8/\ntEuYzrJLvx08Wh5XhZ9ib1MGezN/fvzKXhOZ23dWi/PT9UdYd/LrFscv9fxf2pi7hVHhw+x2/Y7m\nOZ/D2Uwb1YtN6afYVLSBURXz0IXI0qJCONqB0kO8k7Gm+fui+pLm7zv6vqHX62loaOCtt1bbJeOl\naX3ti9TU/Xz99ffN35eUFP/PwijKrJfRZqE2Gs+tn7p69c//IjMzM1m3bl3npLIzs6b1PY+tbk3c\nEDejxfFY/9afhcYFxHL9ZZy//sxGbK0sYVdcX2qX67f3/LZyOBtPDzeGDjdzwJDDOwe/4Klp9ygd\nSYgu6Yldq1o9/uz4FWzIadkLB7A68yO+OPVti/Pb489/XkVBQR4vvbQSvV6PwVCPxWJh8eL7GTFi\nFAsX3kpMjA6VSs2JE8dZs2YdlZWVzJkzk6+++h4vLy+WLLmLt99+lxdffJ6ysjIqKsqZOHEy99xz\nHytXPk119Vlqamr405/+wuuv/42cnDNERkZhMplayfMnDIZ6Vqx4mMmTryA3N4ebbprzP2coswRp\nm4U6KysLg8FAcnIyFouFpUuXotPpePXVV3nsscd44oknHJHzsrib/LB41LQ8bvTn2rir232duIAY\n4gJiOnz+wbJ0iupLWpwX4RNul+u39/y2cjij20ddxcFN+yjTZHO4IIch0bFKRxKiWykxtL4FpsVm\n6fA1ly1bTkrKo/j4+BAX15u5c+dTXq7n/vvvYe3aL2hoaGDRosX06dOXF154lqNHD1NQkE/v3vGk\npu5Dq/VizJhxlJaWkpg4mEceuRGj0cjs2ddxzz3n9goYOTKJW265ja1bf8BkMvKPf/yL0tIStm5t\n+cFj2bJH2LZtC6tWvcy3337tNEuMtlmotVotycnJzJs3j5ycHJKTk+nbty/Lly/Hw8Oj3ft62quv\nviMmRE5kW/k3LY5frbvSobnmDbmOv+7+V4vjcwdf2y1zXKrr46/jy/yP+CDzK64e/qjScbqFt37c\nwOa8LZg0NWhM/lwVcyX3TGnZiyO6hn/cuPKCr0X7R5BXXdjiuC4gipeuebxD9zMaa9Bo3CguLuDW\nW+cSFuZHWJgfgYH+qNVG1GoVI0Yk4unpyaxZM0lN3U9hYSF/+MPDbNq0CbVazbx584iNjWDt2mxe\nfPEZfHx8MJvNhIX5odVqGDx4AGFhflRWljJ69Mjme0RGRhAW5sd9992HwWCgX79+PP7446jVKsLC\n/PDz0+Lt7UFwsA8ajRthYX6o1SpCQ33x8GjlWWonarNQx8bGotPpmr8uKirCzc2Np556iqamJk6d\nOsWqVatYseLiXR1KDiZTWX6af2vywOZmwt3kz/geE7kxYbxDc/XzGsBdiQvYmLuF4vpSInzCma67\nkn5eA7pljks1LX443538gRrPfD7ZtYspfQcrHalLax6E6XHuyZzZo5qNJZ/T8J2J+SNlv/Du5uro\nKbxTvabF8auip3T4faOysh6TyUJERDRbtmwnJCQKvb6MqqqzmExuWK02Kirq0WiM9O07mP/7v9fQ\nar1ISBjByy//BQ8PD37zGx3vvvsBGo0XDz74BwoK8vn444/R62tpbDRRW9uEXl9LWFgkP/zwPddc\ncxPl5XqKi4vR62t59tmXmvPo9bVYrVb0+lpqaxsxGIzNGc+9ZqO8vM75BpOtW7eO7OxsUlJSKC0t\nJS4ujvXr16NSqSgsLGTZsmVtFmmlpZdlgAbu7LeQpLh+imYZFT7MKQZsOUuOS6FWq7m5z0w+zPsP\n3x1NZ3KfQU7TNdUVXWgQ5q6yHcxHCnV3c/794pcf8C/3fUSlUnHHHXezcuXTbN26maamJh555DHc\n3Nz438FbGo2GHj16EhERCYBOF0twcDBwrnv76acf5+jRw2g0Gnr10lFe/vMZMZMmXcH+/XtZsuQu\nwsN7EhQUfKFEF0t7GX/SjmtzepbJZGLFihUUFRWhVqt5+OGHGTbs3D/M+ULdnlHfSrXUmkwmHtry\nFCqbO3+bloK6G64y1tX8+bNdZBxv5DezBzOin4wA7ywP/PBHWvscZLOqeH3qnxwfSAgX47AWtUaj\n4eWXX271taioKKefmrX15BFwNxFu7StFuotYMHkoT2TvY92PpxjaJ6RbLvHa2T5K3X7B19xMzjuO\nQYiuqMu/wxUUWDEV9mZ89Eilowg7iQjxYdLQCIorDGw/XKx0nC7FZLby7objbNxVDha3Vs9pzI/j\n480nMVusDk4nRPfUpQu1zWYj60QTmvIEpvRNVDqOsKMbJ8bhoVHzxfYzNBk7Pj1E/Fd5dQMvvJ/K\nlrRCorwjeXTEI0wKmIlbUwA2qwq3pgCGaqYRYuvNd/vyeG71AYor6pWOLUSX16WXEM0rreWpd/Yz\nNiGce2dJoe5qPt12mq935XD9pEhmTxigdByXduR0BW9+mUF9o5nxg3pyx4z+eGou0KI2mlmz6QQ7\nDhfj4VdL0kgti5KmyaMlIX7BXs+ou/Rv1sFsPQDD+oYqnER0hmvHxODdO5tN9aspqq5SOo5LMlss\nvPXjVl79OJ0mk4WF1/QneebACxZpAK2HO3dfN5D7bkzELSaTVMMPPLrx75TWVDswuRDdR5cu1IdO\nlOOmVjG4d4jSUUQn8PJ0Z1BUNCp3M//a/6XScVxOSXUVy7//G2mWb/CPrGTFr0ZyxbCodk95SxoY\nzsPj7sazKYxaj3ye2f1nNhw72Mmpheh+umyhLjtbT15ZHQN1QXh5ytaIXdWipOmojN4Ukcnx0par\nJonW7TqVxXN7XqXBoxhvYyQrbppGXIT/JV8nLjScF6c/RD/3Mdjcmvii6ENe3PyRDDQTwo66bKH+\nLHMLnoN2oIszKx1FdCKtxoOJYVeiUtv4z6EvlI7j9KxWK//c/Q3vnXkHq3sDfdySWDX9QXr4B3T4\nmu5ubvxu8hxuj1uE2uTDiVMmnn83VQaaCWEnXbZQZ1cfR+1dx6h4ndJRRCebO3wi7k1BVGty2HPm\nuNJxnFaT0cIb6w9zsGovKqs7syJuZemUubirL/w8+lJMiB/Iyil/ZGzkCHJLann63/vZll7U7v0A\nhBCt65KFuryuhgZNGe7GINm7uBtwV7sxM3YG5tIYtu6tlMLQiuKKep5bfYD9GZX0ODuJZcN/yzUJ\n9l9bwN/Li+SZCecGmqnV/PvbLF7/7Ch1DS23FBRCtE+XfHi7MSsVldpGnHdfpaMIB5k+cARHD6s5\neqaSjDOVDJIBhM0OZJXxr2+O0Wi0cPWIaG69ug/ubp37GT1pYDjxkQH886sMUrP1nKjPYOboAUwb\n4FrrywvhDLpki/pIRSYAU+JGKJxEONLcK+JRAWu3nsIqrWqaTCbW/JDF658fxWqzce+sBG6f3q/T\ni/R5IQFa/rhgBNdPisLYM53PCtfwwub3aTQZHXJ/IbqKLleojSYL1cZqVEZvhkbFKh1HOFBMuB9j\nE3uSX1bHnowSpeMoKrdCz/JNr/Bj2SZ6BnvzxMJRjE3o6fAcarWK2RP6s6D3HahNPuSTziOb/kxG\nUZ7DswjhqrpcoT6ef5bGjLGM0cyRlZK6oZsnx+HupuKzbacxmbvn0qKbsg7xYupfMXqWExys4tGF\nw4kK81U008T4BJ6d9DDB5njMnlX8PeP/8cH+7TKeQIh26HKVLO3EuT1Ik/pGK5xEKCE0wIurR0ZT\nZS3hvX0/Kh3HoaxWK/+3/TM+LVyDTW1isOcknp92P75aT6WjARDk48Oz05cwOXAmKouGTTvP8vrn\nMtBMiLZ0qcFkVpuNQyf0+Hpp6BPd8XmhwrVNTYpgm/nfHKiDWXWjCPG99IU8XI2h0cSLmz5B752G\nyqzllt63MKXvIKVjterWEVOYUjWCf5dnk3pcz+miGhZfn8AAXZDS0YRwSl2qRZ1TXMvZOiND42WP\n4u4sxNeXAV6jwN3EW/u+VjpOp8srreWZfx8g71gwvg1xPDbm905bpM/rGeTHHxeM4OZJcVTXGXnp\ngzQ+2XpKVjQTohVdqpqlnTi/CYfMne7u7hp9LZi05FoPc6a8VOk4nWbH4WKefzeVsrMNzEzqw6pr\n7yMyMFjpWO2iVqu4YUIcK341gtBALd/syeHRL//DseJ8paMJ4VS6VKHeU7oHjV8dg+Jc441KdB5f\nrZakoEmo1FbeOdj1NuwwmS38+9tj/OubY7i7qfntnCHMmRKPWt2+DTWcSXxUAE/dlcTgISrqA47x\nf0df5z97v8dqlda1ENCFCnVWSQGG0HQC4s/g6WGfJRGFa7t95FW4Gf0pt+VyuqRC6Th2c7y0iOXf\nvMG2wwXEhPuSctdol9/K1cvTnaXXXcWkgJmoULGv/nse+/7/oa+rUTqaEIpT2doxP2L27Nn4+p6b\n3hEdHU1ycjJPPPEEADqdjueff77NqVB6fa0d4l7Y6zu+IMO4k9E+U1k0Znqn3ku4ji3HjvPuV7kM\njQvnd/OGKh3nsn1xZA8bi78CdxNxxkn89urr8LjI3tGu6GRZMX9PfRejZzkqk5bb4xcyrk8fpWMJ\nccnCwvzscp02R30bjedWEVq9enXzsV//+tcsW7aMkSNHsmLFCjZv3szUqVPtEqijTtZmY/OAqf1H\nKZpDOJcrBvRj78F60k9VcDyviv4xrjmy2Gyx8JdtH5NrS8OmVjPGZxp3XjVN6Vidok+PCP40bSl/\n3/k52fXHeWvdGTYPzKRUcxizRy3uJj/Gh01i/sgpDs92oPQQG3I2U2Ioo6d3D2bEXsWocGWWRXWm\nLKJztVmos7KyMBgMJCcnY7FYWLp0Ka+99hoqlQqj0Yher8fPzz6fGjqqpLqKRg89nsYQol1kII1w\nDJVKxbwr43l+dSprt57isTtGolK51nPc8po6Vu18g0bPUlRGb+7sv4CkuH5Kx+pUHu4alk6Zx/GC\nSl6r2EiR70EAVIDFo4bt1etp3F/P9YPG/OznAj0DcFe3fFurajyLxdZyAZxLOf9IeSafnPiq+fui\n+hLeyVgDQHxA7GVf/1LOv1gWKdZdT5uFWqvVkpyczLx588jJyWHx4sVs2LCB4uJi7rrrLvz8/Bgw\nYIAjsl7QxuOpqFTQ27drv3mJjomPDGBU/zAOHNeTelzPqAE9lI50UR+m/sgu/XbMmlrcjH5YS+Kx\n+IG/KopHJt7VLeaFn9c/OhhV+MlWX9tfu5X9u7f+7NjjY5YR4RPe4tzXDr1FiaGsxfFLPb81G3O3\nYLFa7HL9y82zMXeLFOouqM1CHRsbi06na/46MDAQvV5PZGQkGzZsYO3ataxatYoXXnjhotexV199\na6r1fpjK+zJn3uROvY9wXYtvHsLBFzfz2Y7TXDU2Bk+NRulIrXrrxw1sr14PHudaj1bPGtCl0cc8\nmefn34K7W9d6Ht0eZo9aWusDsdngyt7jfnYsOjyUYK+W7wHjdCOoaqxucfxSzt96Zner+UrqS5k1\nYPplX/9Szr9YFnkP7HraLNTr1q0jOzublJQUSktLqaur48knn+TRRx9Fp9Ph4+PTrjW1O2swmdFk\nISOrgVC/wUR6h3T6oDXhmjTAqKFa0k0b+cu3ldwz7jqlI7Xqh9zN0MqKn3mWdKoqr3d8ICfgbvLD\n4tFy9Le7MYB5cTf/7JilDvR1Ld8DpkZc1eq1L+X87LIzFNW33Oylp0+4Xa5/KedfLIu8BzoPe31o\narPCzp07l9raWhYsWMCyZctYtWoV999/P8uXL+fOO+/kyy+/5KGHHrJLmI7IzKnCaLIyvJ9rT08R\nne/6Mf1ReTaQVrOb6oZ6peP8TE1DA//Y+TXmVgoSgFnTfacpjQ+b1OrxcT0mODTHjNjWi+t03ZUO\nzQHOlUV0vjZb1BqNhpdffrnF8Q8++KBTAl2q86uRDZfVyEQbogODidcM47Q1lbf3reehKbcoHYni\n6irWpG3ktPEwuF94cwp3U/d5Lv1L80dOgVTYVbYDs6YGVZMvxsLe9Bzl2Clb55/9bszdQnF9KRE+\n4UzXXanIM+ELZRkeNtjhWUTna9c8anvojO4Yq9XG0td2oFKp+MtvJqB2sdG8wvGq6ut5fOcqbCoL\nj47+g2KzBPRnG/ho/y4yVd+jUlvBrKG3xxCCtAGkGja3OH9SwExFpiM5o+q6Jh795x7UKhUr7x2L\nn7eH0pEU12QxsibrE0xWM/cOXqh0HPETh3V9O7PjBRXUGowM6xMiRVq0S5CPD8P8xqNys/CvA184\n/P65JbX844ujLH9jNwcPmVCbfBiincyqSY+z7IpbuXvsNUwKmIlbUwA2qwq3pgAp0r8Q4OvJjRN7\nU99o5tNtp5WO4xQ81BqqGs+Srj9KZsVxpeMIO3PpFvWqze+R13iCW+IWcOXA/na/vuiamkwm/rDh\nVZpKInhm9mzCg7079X5Wq5VjuVV8tzePjJwqAKLDfLlubAwj+4ehce9+I7kvl9li5el39lNUXs/j\nd44iLqL7Pho4r6C2iBf2/5Uw7xAeS3qo1fnYwrGkRQ0UGU+j0hhJ6q1TOopwIZ4aDXf0uRNTeSTr\nOrFFZrZY+PjgNpZu+BOvfPc9GTlVDIgJZOktQ3n67tGMTewpRbqD3N3ULJjWDxvw/vfZWB3T3nBq\n0X6RTI4eR5mhnM3525WOI+zIZT9yHSnMxepRh78xBh9PrdJxhIsZ1T+MuAh/DmSVcbqoht6R9muR\n1TU28mHaZg5V78PmYcDmAdG6Xtw5XFp+9jRQF0TSwB7sO1bGzsPFTBoaqXQkxV0fN53U0nS+zfmB\n0eHDCdIGKh1J2IHLtqi3nE4FIDFkoMJJhCtSqVTccmU8AJ9sPYk9ngDVNZh4f/t+Htn2HGkNW7G6\nN9LD0p/fJDzIU9f9Sop0J7jlyj54atz45MdT1DdeeNR8d+Gt8ebmPjOZGDkGrbs0YLoKl21Rn647\ngc1DxXTZhEN0UP+YIIbEh3D4VAVHTlcwJL5jc/ErqhvZuD+fbelFNJmNeA32QKdJYMGI6UQHy/z+\nzhTsr+WGCbF8svUUn28/w+3TZBnhsRHyntjVuGShrqipp8lkxoswwv0DlI4jXNjcK+I5clrPewd+\nYGXs3EtaojO/rJbv9uaz71gpFquNID9PbhwVx6ShU/DRypQhR5k2qhfbDxez+WABk4dG0quHr9KR\nhLArlyzUR0+fpSlzHLOujFM6inBx0WG+xAwrpEyTwZrUEBYmXXy7VqvVytYTR/j2zBbOFgRjKY8m\nMtSHa5JiGJsYjrubyz5NclkadzW3T+3LXz5O572Nx1l++wiX2yFNiItxyUKddqIcgJH9eyqcRHQF\nC0dew0uHjrG3ahuzGyfiq235bM9stfDF4T1sL96BybMCPCCwpwe3TxnCEJnHr7hBvUMY3jeUtBPl\n7MksZVyivDeIrsPlCnWj0UxmThXRYT70CPRSOo7oAuJCw4lRDyZfnc6/93/Lbyb9d6MHk9nKD4dP\nsL7sI6wedeAJvsZorou/iil9BymYWvzSbVf35eiZSj7efJJhfULx8nS5t7dOcbzyJCerzzAzbprS\nUUQHudz/yRlnKjFbrAyTtb2FHSUn3UDK7qNk2nbzwA97cDf6EWUdSsnpIKrrm/AcpCJM3Ze5CdMY\nEh2rdFzRitBAL2aO1fH5jjN8tTOHW65y7Frgzshqs/LZya/JrytiYHBfegfEKh1JdIDLPVA7mH2u\n23t4XxlNK+znh+NpqNwsqFSgUtmweNaQ57WdBq9crknS8ezkZTwzfbEUaSd3zZgYQgO0fH8gn6Jy\n59ohTQlqlZp5/W4C4OPjn2O1WRVOJDrCpQq10WziUN0OAsIMxPaUzdGF/ezSt76Sk0fUuZZZqH/n\nLr85Q0QAAB/hSURBVDMq7MND48ZtU/tisdp4//tsu8yPd3XxgbGM6TmS/LoidhbtVTqO6ACXKtTb\nTmZA+EmCYspkVKewK7Om9bXou/M+0K5qWJ9QBvcO4VhuFanH9UrHcQo3xl+H1k3Ll6e+o84oPQ2u\nxqUK9d7CwwCMipQ9V4V9uZta76HpzvtAuyqVSsWCqX1xd1Px4eYTNBktSkdSXICnHzN7T0Ojdkff\nUK50HHGJXKZQW61Wik2nweLOVf2GKh1HdDHjwya1frzHRAcnEfYQHuzNjKQYKmuaWL8nR+k4TmFK\n1HieHPsH4gJkEyNX4zKF+lDBGWweBgKs0Wg1suqTsK/5I6fIPtBdzPXjYgny8+S7vXmUVhmUjqM4\nN7WbrP/tolxmetaPZw4CMDg0QeEkoquaP3IK85HC3FV4ergx/+q+/L/Pj/LBphP8bu4QGdsiXJLL\ntKir8npgzhvI9P4jlI4ihHARo/qHMVAXxOFTFaSfrFA6jhAd0q4W9ezZs/H1PbfQfXR0NAsXLuTZ\nZ5/Fzc0NDw8PXnzxRYKDgzstZGVNIwWFFhJihxHiK4N7hBDto1KpWDCtH0/9ax9rNmWTGBeExr39\nG690ZWarmZNnzzAguK/SUUQb2izURqMRgNWrVzcfu+OOO3jyySfp378/H330EW+++SbLly/vtJCH\nTp5f5ERWIxNCXJqoUB+mjopmw758vt2bx6wJspkPwFtH3+No+TFWJP2eKN8IpeOIi2iz6zsrKwuD\nwUBycjKLFi0iPT2dV155hf79+wNgNpvx9PTs1JDnN+GQ1ciEEB0xa0IcAT4erN+dS/nZBqXjOIXJ\nUeOwYeOj45/JwjBOrs1CrdVqSU5O5u233+app57i4Ycfbu7mPnjwIGvWrGHRokWdFtDQaCYrtwpd\nuB/B/jJiUQhx6bw83bnlyj6YzFY+3HxS6ThOISGkP0NDEzlVncP/b+/e46Is8/+Pv2YYhqOIKHEU\nSMFTphhmpqnoauWmlZap5WnDNt00T48Kw2JNxc392vrNdLN26/v1sAuVZlZbqZlSah4wzSREBE+g\nyEHOIMPM9f3Dn/PLRCFl7nvUz/Px6BHMOPf1Bob7w31d131de/J/0DuOuIoGu74jIiIIDw+3f+zr\n60tBQQFpaWmsWLGCd955hxYtWjTYkL//tS35uWlvFlabjd7RIdd8DCGEGBrrzfZDZ9iXWcDJomru\n6nCb3pF090zP0cz4Yi6fZP+H/h164OkqOxI6owYL9dq1a8nMzCQxMZH8/HwqKyvZtWsXKSkprFq1\nCh+fxk3uKiiof4nGhnyQsRb36NME+Xe85mMIIQTAE7FtmXusmOVrDzAvrgcmlxvmxheHMGDm/rBY\nPs/ZxBc/pdI3tJfekW4qTXVxaVANDE5YLBZmz55NXl4eRqORWbNmMWnSJIKDg/H29sZgMNCjRw+m\nTJly1YaupcjWWGqZtfXPGGyuvDkoEaPx1v6lEkJcvzUbM/l63ylGxLZlcE9ZpavWaiG9+DBdW90h\n95k3Mc0KdVO5lkL9ZXoan55JIch2B3MGjndAKiHEraayxsLsFd9jqbOR9MeetGjm2Mmw4tbVVIXa\nqS9Rd+dd2ITjnpAuOicRQtwsvNxdeTy2LectVlK2HNE7jhANctpCbbPZyLcegzpX+kV11juOEOIm\ncl+XIG4P8mH3z2fJOH5O7zhCXJXTFurDeWexVrvTQoVhNrnqHUcIcRMxGgyMub8dBmDN5kzqrDa9\nIzmVWmut3hHELzhtoc7IrqI24x4eCXtU7yhCiJvQ7UE+9OkaTG5BJd/sy9U7jtPYnruLOduTOFsl\n+1Y7C6ct1D8cKcTkYqBzm5Z6RxFC3KQe69cGL3cT67/LprRSriIBPFw9qKyrYu2RDXpHEf+PUxbq\ngpJqThVU0DHcDw+3G2YnTiHEDaaZp5lhfdtQfd7KR1tlxTKAbv530q5FJD8VZXCwMF3vOAInLdT7\nL67t3U7W9hZCOFZsdAhht3mz/eAZsnJL9Y6jO4PBwBPtHsFoMPJh5gYsVovekW55TlmofzhSAEB0\npBRqIYRjGY0Gnrq/HQCrNx7GZpMNKoK8Augfeh9FNcVsOrFV7zi3PKcr1GfLSsk27iA0woKvtyxE\nIIRwvKhQX3p1DuREfgXbDuTpHccpDL59IDG3deWu22QdC705XaHelJmGy20naBlcoXcUIcQtZERs\nW9zNLqzbdpSKaunu9TC583Tnpwj0CtA7yi3P6Qr1waILkxf6tYnROYkQ4lbS3NuNR++7ncqaOtZt\nO6p3HCHsnKpQV9XWUGbMxVDrRZfgML3jCCFuMQNiQglp5cW2/XnknC7TO44QgJMV6i2ZBzC4WAkx\nt5WdsoQQmjO5GHlyUDsUsGZTJjZt9iwS4qqcqhruPf0TAD1Du+qcRAhxq+oY3oIeHW8jO6+M7QdP\n6x3HaRTXnON/Dv2bczUleke55ThNobYpRcmRcIynounTtpPecYQQt7An+kdidjXy0dajVNXIxDKA\njOIs9uT/wMdZn+sd5ZbjNIU6J6+MsjIj3Vp2w+TionccIcQtzM/HnaG9IiivsrD+2xy94ziFnkEx\nhPu0Ju3sATLPyWQ7LTlNof7h4mpkUbLIiRBCf/ffHUZACw++3neKk2fldlGjwcjIdo9iwMCHmZ9g\ntVn1jnTLcKJCXYDZZKTT7X56RxFCCFxNRp4a1A6lYM3GwyiZWEa4T2t6Bd9NXuUZUnN36h3nMslp\n23j+y/n86euXeP7L+SSnbdM7UpNwikKdX1zF6aIq7rjdDzdX6fYWQjiHzm1a0i2qFZmnStmVnq93\nHKfwcJvBNDN7c956Xu8ol0hO28a3pZ9jNZdhMCis5jK+Lf38pijWTlGodxw+BgYb0dLtLYRwMqN+\nF4WryUjKN1lUn6/TO47uvM1evHZvPA9G/E7vKHY2pdh+9tt6n9tx9juN0zS9Ru0hOXz4cLy9vQEI\nDQ0lKSkJgIULF9KmTRtGjhx5XSFSSz7DPbqUjrf3vK7jCCFEU/P39eD3PcP55LscPt1xjCf6R+od\nSXdmF7PmbdpsNk6VFJNVkEtuaRHulWGcKa7i7Llq8s9VY7qrHEM9r6tzvfEXrmmwUNfWXthMfeXK\nlfbHiouLeemllzh+/Dht2rS5rgB5JcWcNxfiVtuKVs28r+tYQgjhCIPvCWP7wdNs2nOS++4MIriV\nl96RbloV1Rbyi6s4U1zFmeJK0mo2UmkrwWIqx+ByYQKbUlCzdxAoF9zNLoT4e3G2xhs8yi87nsni\no/WX0OQaLNQZGRlUVVURFxeH1WplxowZtGrViqlTp5KamnrdATYdTsNggMhm7a77WEII4QhmVxdG\nD4xi6dqDzPvfvVjqbAS38uSheyO4p5M+m1bsSs/n853HyCus0jVLcto2dhR8S51rOSZLM3r592FU\nTL+rvuZcZSWZZ3M5du40eeVnKTpfhDm/KwVFFiprLh1ecI8+Da4WTHXeeFp9aWH2I9Dbn+6joglt\n1RwfT1cMBgPJaVV8W3r5Pd6h7td3MekMGizU7u7uxMXFMWLECI4dO8YzzzzDV199RUhISJMU6vRz\nP4MZ+reVTTiEEM7rfO2Fq7nzlgv/P1VQyYoNhwA0L5C70vPtbeuZ5eIELsxgAPsELtJgeJf7OFtS\nTX5xNfnnqsgvvvDfKd+vUJ6/Wt3MBJaSQFp5BhIZ0pwAP08C/DwJbOGBd7OuBLXwxWS8+kTjUTH9\nIO3CmHSdaxkGqzvKpZpj1kNs2X+cAdHhjvtGOFiDhToiIoLw8HD7x76+vhQUFBAQ8NveDP7+zS57\nrLSqinJTHqZaH/p1kdXIhBDOa+PevfU+vnbbUdw9XDXN8tEVdvfSOst3Z78Ft8sfTz33Hza/XYyt\nsvkljxsN4O3tg9niQQu3lgR630ZEyyA6BLamw/BgzK7Xl33qg0OYyhD7518cSGPlf9JZ/cNRamqN\njB3cEaOxvpFs59ZgoV67di2ZmZkkJiaSn59PZWUl/v7+v7mhgoLLxw5S07OwlfkR1iK83ueFEMJZ\nnDhT/zmqsLSG/07Zr3Ga+mmdxf3u+idwYVC0DnIjwjuYgBaeBPh5ENDCE39fD1xNA+o9VmlJDVDT\npPm6B7ej9fBQ/vbhAT7acoTjeaXEPdQRs0a3Add3gXotGizUjz/+OLNnz+bJJ5/EaDSSlJTUZDtb\nHcmppTazO8PGSbe3EMK5Bbfy5FRB5WWP+zVzY1hfbcdB16Vmc6788vuYtc7y75M7UO6Xz6o21TYn\n8fHfa5bjagL8PEkYG8Nb6w6yJ+MsxeU1TH2sCz6e2s9cv1YGpdFyO7++YrbZFNOXfoeLi4HFz/XG\naLjxuiOEELeOX48LX/Tsw3foPkatVxb7GPWv9Gn+UIMTyrRmqbPy3n8y2JWej2/YGZ7u15vOIY4d\nt26qK2qXP//5z39ukiM1oKqq9pLPj5wqZcu+XHp2CqBb1G/vShdCCC2F+nsT6OdJfnE1lTUWQlp5\nM3pglC4zrZ0lS+fgCMqLzeSWncVmrMVU25z7Wg50uiIN4GI0EtPOn3LbOY55fs3e/P2417WiTatA\nh7Xp5VXPAP410O2KOmXLEb7afZLpI7rQpa2sSCaEEEIbK3dv5vuyTQD09n2Ap7rXP25+vZrqilqX\nJUSVUvxwpBA3swsdw1voEUEIIcQtalyPgQwLGYXBZmJH2Zcs3voBNptN71hXpEuh/jH3BOda7KJt\npBVXk2zCIYQQQluDOnbj2U7PYKj15GjNQd7+/Acsdc5ZrHUp1Fuz92JqlUdQkB6tCyGEENAlNILZ\nPZ+nVXE/9h4q5Y2U/VRUW/SOdRldCnVO5RGUMjCovdyWJYQQQj8hvn4kjOhPTHt/Dp8sYcGqNM6e\nq9I71iU0L9THiwqwuBXjXuvPbT7NG36BEEII4UBuri5MfrQzD/YII7+4ivkr08g6Vap3LDvNC/Xm\nI2kAtPNpr3XTQgghRL2MBgNPDIhk7APtqaqp47+++ZAP9l3/fhZNQfNCnVGSAcDvIqXbWwghhHPp\n3y2EZ4a1wRiYzbaSz3gzdZ3uM8I1LdTV5+soPdQJn/xeRAUEa9m0EEII0Sj3tAsnrn0cWNw5XPc9\nczf/k/MW/SaZaVqof8opps5i4p7QLlo2K4QQQvwmMWFtebH7VEznfSk0HeHlr9+kqKJClyyaFuof\njhQAyJKhQgghnF54S39ei52GV20IVaqUNz5Mo7C0WvMcmhXqOquNH7OK8PNxIyzAW6tmhRBCiGvW\n3MOLpEFTuNs0jNP5VuavTCPn9OU7hjmSZoX6yMkSqs7XER3ZCoPslCWEEOIGYXJx4elB3Rg9MIry\nylpeX7OPfZkFmrWvWaH+7kgmGK10ayfd3kIIIW48g7q3Zspjd4IBlq07yFe7jmsyI1yTQm2z2Thg\n+wKPrqlEhfpo0aQQQgjR5LpF+RP/1F34eJlZd/Qz5m/5X2rrHDsjXJNCvePIYZRrNb4EYzaZtGhS\nCCGEcIiIQB9eeOpO3PzOkW/8mYTNyyipqnRYe5oU6o0ZuwDo4t9Ji+aEEEIIhwr2a87cPtPxqA2k\nypxH4rb/5niRY8atNSnUP1fvQimosZzXojkhhBDC4Vp4ebPgd1Pxt7anzq2ERXuXsu94dpO3Y1BK\nqSY/6q88kTLZ/nGf5g8xKqafo5sUQgghNGGz2Vi+/RMOlf0IWb24404rmbV7qXMt58NRy6/7+I0a\nMB4+fDje3hfufQ4NDWXSpEnEx8djNBqJiooiMTGx0Q3uOPsdo5BCLYQQ4uZgNBqZ0mcY3/98D+/n\nfcPPHAAzNNWNyA0W6traWgBWrlxpf2zy5MnMnDmT7t27k5iYyObNmxk4cGCjGqxz1fZGcSGEEEIL\nPTsGs+ZYDk19w1aDY9QZGRlUVVURFxfHhAkTOHDgAOnp6XTv3h2Avn37snPnzkY3aLLI7VlCCCFu\nTlZzeZMfs8Erand3d+Li4hgxYgTHjh3jmWee4ZfD2l5eXpSXNz5Yr9vuu7akQgghhJMzWZphNTdt\nz3GDhToiIoLw8HD7x76+vqSnp9ufr6ysxMfn6lfJymbAtc6HAWH9mdjvgeuMLIQQQjin34UNYOOZ\n9U16zAYL9dq1a8nMzCQxMZH8/HwqKiro3bs3u3fvpkePHqSmptKzZ8+rHuPD0dc/600IIYRwdhP7\nPcBEmvaCtMHbsywWC7NnzyYvLw+j0cgLL7yAr68vc+bMwWKx0LZtW+bPny8bbQghhBAOoMl91EII\nIYS4NprtniWEEEKI3+6m3SGjrq6Ol19+mdzcXCwWC5MmTSIyMvKaF2pp6iwDBgwAYOHChbRp04aR\nI0fqkiM4OJh58+bh4uKC2Wxm0aJF+Pn5aZ4jPDycV155BYDw8HAWLFiA0ej4vyOv9rP59NNPWbNm\nDcnJybrkCAoK4tlnnyUiIgKA0aNHM3jwYM1zREdHM2fOHMrLy7Farbz++uu0bt3aoTmulOWzzz6j\nsLAQpRS5ubl069aNxYsXa54jODiYxMRETCYTERERLFiwwKEZrpYlMDCQxMRE3Nzc6NChA3PmzHF4\nDpvNxpw5c8jJycFoNDJ37lzMZrPm59f6ckRGRgLanlsdTt2k1q5dq5KSkpRSSpWWlqrY2Fg1adIk\ntWfPHqWUUq+++qratGmT5llKSkpUbGysKi4uVhMnTlSDBg1SycnJmue4+D0ZM2aMysjIUEoplZyc\nrBYuXKhLjueee07t3btXKaVUfHy8rj8bpZQ6dOiQGj9+vBo5cqRuOT788EP1/vvva9L+1XLEx8er\nL774Qiml1Pfff6+2bt2qW5aLSktL1aOPPqoKCws1zXHx/TplyhS1bds2pZRSs2bNUt98843Dc1wp\ny2OPPab279+vlFJqyZIlasOGDQ7PsWnTJvXyyy8rpZTatWuXmjx5si7n1/pyFBUVaX5udbSb9op6\n8ODBPPjggwBYrVZcXFwuW6hlx44djV5Rramy2Gw2TCYTVVVVTJ06ldTUVIe3X18Oq9WKyWRiyZIl\ntGzZErjw17qbm5suOd566y3gwkp4BQUFNGvWzOE5fp3l4s+mpKSEJUuWkJCQYL/K1yPHoUOHyM7O\nZvPmzYSHh5OQkICnp6emOVxcXNi3bx/t27fnD3/4A6GhoSQkJDg0w5WymH6xRe6bb77JmDFj7O9d\nrXJcfL927NiRkpISlFJUVlZekk3LLC4uLpw5c4auXbsC0K1bN7Zs2cLQoUMdmmPgwIH2nqe8vDya\nN2/Ojh07ND+//jJHbm4uzZs3p7q6WvNzq6M5rG/xwIEDjB0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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11861cba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, sharex=True)\n",
+    "data = goog.iloc[:10]\n",
+    "\n",
+    "data.asfreq('D').plot(ax=ax[0], marker='o')\n",
+    "\n",
+    "data.asfreq('D', method='bfill').plot(ax=ax[1], style='-o')\n",
+    "data.asfreq('D', method='ffill').plot(ax=ax[1], style='--o')\n",
+    "ax[1].legend([\"back-fill\", \"forward-fill\"]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The top panel is the default: non-business days are left as NA values and do not appear on the plot.\n",
+    "The bottom panel shows the differences between two strategies for filling the gaps: forward-filling and backward-filling."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Time-shifts\n",
+    "\n",
+    "Another common time series-specific operation is shifting of data in time.\n",
+    "Pandas has two closely related methods for computing this: ``shift()`` and ``tshift()``\n",
+    "In short, the difference between them is that ``shift()`` *shifts the data*, while ``tshift()`` *shifts the index*.\n",
+    "In both cases, the shift is specified in multiples of the frequency.\n",
+    "\n",
+    "Here we will both ``shift()`` and ``tshift()`` by 900 days; "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PZ8zRFHy9nGnT1Mvq869NmgI9U94zn/921sO9ad/cu8S+/v4eJCRdZsrCkvPl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\niIgJKJBFRERM4A/3oO5fXZCK2gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11864e3c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(3, sharey=True)\n",
+    "\n",
+    "# apply a frequency to the data\n",
+    "goog = goog.asfreq('D', method='pad')\n",
+    "\n",
+    "goog.plot(ax=ax[0])\n",
+    "goog.shift(900).plot(ax=ax[1])\n",
+    "goog.tshift(900).plot(ax=ax[2])\n",
+    "\n",
+    "# legends and annotations\n",
+    "local_max = pd.to_datetime('2007-11-05')\n",
+    "offset = pd.Timedelta(900, 'D')\n",
+    "\n",
+    "ax[0].legend(['input'], loc=2)\n",
+    "ax[0].get_xticklabels()[2].set(weight='heavy', color='red')\n",
+    "ax[0].axvline(local_max, alpha=0.3, color='red')\n",
+    "\n",
+    "ax[1].legend(['shift(900)'], loc=2)\n",
+    "ax[1].get_xticklabels()[2].set(weight='heavy', color='red')\n",
+    "ax[1].axvline(local_max + offset, alpha=0.3, color='red')\n",
+    "\n",
+    "ax[2].legend(['tshift(900)'], loc=2)\n",
+    "ax[2].get_xticklabels()[1].set(weight='heavy', color='red')\n",
+    "ax[2].axvline(local_max + offset, alpha=0.3, color='red');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see here that ``shift(900)`` shifts the *data* by 900 days, pushing some of it off the end of the graph (and leaving NA values at the other end), while ``tshift(900)`` shifts the *index values* by 900 days.\n",
+    "\n",
+    "A common context for this type of shift is in computing differences over time. For example, we use shifted values to compute the one-year return on investment for Google stock over the course of the dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TN8ADznlo7vBi8YV6WdsUOrwfYu3cMRHDejrLm2a0c14UPXSDcw8KKnYSeMfO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\n3AkrUPtYNzSZQnoN8GpH8aR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YZW6ttfXABazdVYovfj0hfTDbNqtkgBJaohgMzF4SiX4O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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118a41160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ROI = 100 * (goog.tshift(-365) / goog - 1)\n",
+    "ROI.plot()\n",
+    "plt.ylabel('% Return on Investment');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This helps us to see the overall trend in Google stock: thus far, the most profitable times to invest in Google have been (unsurprisingly, in retrospect) shortly after its IPO, and in the middle of the 2009 recession."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rolling windows\n",
+    "\n",
+    "Rolling statistics are a third type of time series-specific operation implemented by Pandas.\n",
+    "These can be accomplished via the ``rolling()`` attribute of ``Series`` and ``DataFrame`` objects, which returns a view similar to what we saw with the ``groupby`` operation (see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb)).\n",
+    "This rolling view makes available a number of aggregation operations by default.\n",
+    "\n",
+    "For example, here is the one-year centered rolling mean and standard deviation of the Google stock prices:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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oI9alHq/MJKtypznncf+w9cmNdc6sOW2AeZXZu0sBnLukErs1N9/ZYprcOtYg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\nX4vfgTqYSFNT4+Jwoier9nFKOB4hkoj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xs3ZDctpMWdnjUHx7vXfpu+kLe9jTvY9/ffE7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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11924aa58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rolling = goog.rolling(365, center=True)\n",
+    "\n",
+    "data = pd.DataFrame({'input': goog,\n",
+    "                     'one-year rolling_mean': rolling.mean(),\n",
+    "                     'one-year rolling_std': rolling.std()})\n",
+    "ax = data.plot(style=['-', '--', ':'])\n",
+    "ax.lines[0].set_alpha(0.3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As with group-by operations, the ``aggregate()`` and ``apply()`` methods can be used for custom rolling computations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Where to Learn More\n",
+    "\n",
+    "This section has provided only a brief summary of some of the most essential features of time series tools provided by Pandas; for a more complete discussion, you can refer to the [\"Time Series/Date\" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html) of the Pandas online documentation.\n",
+    "\n",
+    "Another excellent resource is the textbook [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) by Wes McKinney (OReilly, 2012).\n",
+    "Although it is now a few years old, it is an invaluable resource on the use of Pandas.\n",
+    "In particular, this book emphasizes time series tools in the context of business and finance, and focuses much more on particular details of business calendars, time zones, and related topics.\n",
+    "\n",
+    "As always, you can also use the IPython help functionality to explore and try further options available to the functions and methods discussed here. I find this often is the best way to learn a new Python tool."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Visualizing Seattle Bicycle Counts\n",
+    "\n",
+    "As a more involved example of working with some time series data, let's take a look at bicycle counts on Seattle's [Fremont Bridge](http://www.openstreetmap.org/#map=17/47.64813/-122.34965).\n",
+    "This data comes from an automated bicycle counter, installed in late 2012, which has inductive sensors on the east and west sidewalks of the bridge.\n",
+    "The hourly bicycle counts can be downloaded from http://data.seattle.gov/; here is the [direct link to the dataset](https://data.seattle.gov/Transportation/Fremont-Bridge-Hourly-Bicycle-Counts-by-Month-Octo/65db-xm6k).\n",
+    "\n",
+    "As of summer 2016, the CSV can be downloaded as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# !curl -o FremontBridge.csv https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once this dataset is downloaded, we can use Pandas to read the CSV output into a ``DataFrame``.\n",
+    "We will specify that we want the Date as an index, and we want these dates to be automatically parsed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fremont Bridge West Sidewalk</th>\n",
+       "      <th>Fremont Bridge East Sidewalk</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-10-03 00:00:00</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-10-03 01:00:00</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-10-03 02:00:00</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-10-03 03:00:00</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-10-03 04:00:00</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Fremont Bridge West Sidewalk  \\\n",
+       "Date                                                \n",
+       "2012-10-03 00:00:00                           4.0   \n",
+       "2012-10-03 01:00:00                           4.0   \n",
+       "2012-10-03 02:00:00                           1.0   \n",
+       "2012-10-03 03:00:00                           2.0   \n",
+       "2012-10-03 04:00:00                           6.0   \n",
+       "\n",
+       "                     Fremont Bridge East Sidewalk  \n",
+       "Date                                               \n",
+       "2012-10-03 00:00:00                           9.0  \n",
+       "2012-10-03 01:00:00                           6.0  \n",
+       "2012-10-03 02:00:00                           1.0  \n",
+       "2012-10-03 03:00:00                           3.0  \n",
+       "2012-10-03 04:00:00                           1.0  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('FremontBridge.csv', index_col='Date', parse_dates=True)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For convenience, we'll further process this dataset by shortening the column names and adding a \"Total\" column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data.columns = ['West', 'East']\n",
+    "data['Total'] = data.eval('West + East')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's take a look at the summary statistics for this data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>West</th>\n",
+       "      <th>East</th>\n",
+       "      <th>Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>35752.000000</td>\n",
+       "      <td>35752.000000</td>\n",
+       "      <td>35752.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>61.470267</td>\n",
+       "      <td>54.410774</td>\n",
+       "      <td>115.881042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>82.588484</td>\n",
+       "      <td>77.659796</td>\n",
+       "      <td>145.392385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>16.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>65.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>67.000000</td>\n",
+       "      <td>151.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>825.000000</td>\n",
+       "      <td>717.000000</td>\n",
+       "      <td>1186.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               West          East         Total\n",
+       "count  35752.000000  35752.000000  35752.000000\n",
+       "mean      61.470267     54.410774    115.881042\n",
+       "std       82.588484     77.659796    145.392385\n",
+       "min        0.000000      0.000000      0.000000\n",
+       "25%        8.000000      7.000000     16.000000\n",
+       "50%       33.000000     28.000000     65.000000\n",
+       "75%       79.000000     67.000000    151.000000\n",
+       "max      825.000000    717.000000   1186.000000"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dropna().describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualizing the data\n",
+    "\n",
+    "We can gain some insight into the dataset by visualizing it.\n",
+    "Let's start by plotting the raw data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import seaborn; seaborn.set()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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tj8t7zgtGo2U41IqszWJH7s3W28BIHoyFBRDKO+bx0otbqe9dTrXe8N6kSRM8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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1192479e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.plot()\n",
+    "plt.ylabel('Hourly Bicycle Count');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ~25,000 hourly samples are far too dense for us to make much sense of.\n",
+    "We can gain more insight by resampling the data to a coarser grid.\n",
+    "Let's resample by week:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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W5D7tRhaNGNdRt/jTlfNJwSDkYBD20jLYywYBiMf5FVmGHImAdbri8U0S/l7B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NKz6nnpwC4AphcNvlj73Zv70etFZtUXD1j1tnb72Hush5pyu9afutHdzPMNL3LU1cS4ns9z95cv\nIBSO8qOnt1BebOXfVszO7scYRqhenqrPngs0hbd+QlX6khLYv0+pAClLDimqUQIV4Y52dKorkQe+\nu/ojguEoZ82qpM8bYvHsSqaOjyuJhaJhjDrl99eJyQurv208xt82N/Ld6xciGEN0B3qYXTJd267W\nOY4PWhVWuWCMEMSDjDIp3vjFeIezQDjKoSY/bk8p/opG3j62jovrz8/7mgaDGsWS8mT1W2fNBgFs\ns+YkfZ/o8Ytpws8nEr5QkB+++iqTrDP4+nLl9xUQtCYtTquRf1o+nQ93tzGrfCEb+95lypTkeXZh\nxTwO9h1ma8dOLraWsbtrLw6DnXrneP586A3eOPoOdy78JjWO/HpTZIMfP/sxwXCUH990lvZZod2k\nLWYgXrKayeMn5vFfuLCOyn7ytUfb3FhNes6ek+wIqfMfpI6ivbu1GafVyOlTB4b6p9UWMS1Djt4d\nUt53p0F530PRMHu692LWmZlWPJlp4wuJSEP3wFWUOM3IBcchANOKk+fjQrMyZ/SFXEmG/8mdz7Kn\nex8/Oee+rI2/oNPn7/E/+eSTWZ1kLMMcM/ze7dsApZmK2t846nYhFg9vjkeFLMsEG48hBwO4PthA\n0bKLACW/L0ciGMfFX2Lb3NMouuQLGCsqsM09DX1BbkblotrzqHOOZ1JhPZ19fjY1tDNpXEGSMdl4\n6CCbetexdOkCTitPbawvObOWi86o1oxIOBrmVzuepivQDQKceYbyKBkNOv7xC9OprRiZlqaqSMuQ\nPH77QI8flFB+f8M/gEsgyzkL9STi7usX8Mr6I3y8v4OpNYXoYqI7sizz+31/4kDvIZZPWEaFtYxq\nexXvfNzEoRYXZ0yv4FCLi3++dCZOm5FXdnwMQK0jXoJYYVVCmg2tjdSXlEMblFqV5+Bw31H+fmwt\ni6vOYPa4GUwaV0BrXw0PfvITPu7YfkINv0pY7R/qj/T14j9wAMeC1KTDaGx70WxO2ZExqVHSSWD1\n93lDOK0GbdH8fssH9BZtRFcZARTDL4pCkv77OfPGcc68cezptrF1xwa84eTnqdY4DVEQ2daxizml\nM3GF3JxZuQBRELEarEiyRJuv44Qa/ge+/rlBt8nG8Kse//SJpdjLku/HjLoi/uvflgwIdSca/lQ6\nDB09fnpcwZSGfzBs2tcMMgT8yty141A7TzY+RaV+At9dOpn5eRwzEyZVO2k7cgir3sL0omROzj9M\n/DxXTLoUgy7Z2dzSrtimNl8HVbaK7E6k04Esaxo1qTA6CltHCOaJilymypjXFxSgc8QMv8uN4QQZ\n/qirTztn33vvUHjhMgRBINSitAY2jUsgKBmNlMVU+XLB+ztaaO/x8/nPjddygeGIlz5PaMDL9ca2\nBloLdjOlOHO52UNbHsNpcnDjnOs50HuYInMBy2qXsmbfyzR7FWbtM3/bx1mzKk56MxgVotkMopgX\nuU/SQv3J3qGadgl3DUwBqVwCXUGBpv6YL7EPwGzUc9UFk7mKyUSlqLbQUoyJzHFvK7/e+Qwzi6fx\njdNuwGkzMrWmELNRR6HdRLHThCAISCETdv8EJhfEJWGrHVVM4iz27bRgXqCktwpMyvPuDQXY1rGT\nYkMZH7VuodJWwaUTLqLKWk6rt21Y64z7I07ui4f6ZUni+GOPEDh4AMM99yVJbqtQc/zp0l/J5L4T\nK94jyzJ3/nIDobDEFxbV8eXzJnGgQ+HlbGz9mOtmXJXx95tWNJmHln4PXyDK4RYXR1pczJpQzGMv\n7KVy7niOuY9i1Vu4b9EdGru+3KIsSNt9J7+yprXbx5N/2cOCaWV8/nO1Wj+KSKZQfywydqDFw9sN\nW/nS0knUVcYdBH8wgj8YodBh0uYPQ1GC4mqKUP/K8weSWlV09Pr5eF8Hk2sKmDRuoMP0rfO+hMt3\nGVazYgZn1VZgOm5CZ8pPDCgTfGEffzv6Ln0hF4urzhgQrbPoUzsLDoMdd9jDwd7DWRt+QevQl76W\n/9SL3+cAU3VNnCwnCOgcTq1W9EQS/LQWpIJA6PhxAgf2K58fV1Sj8m0pm4gCuxFBIEmmtarExtUX\nThkQ/rpokfJAWfQW9hztYc+RZAU3XyDMq1t2ctTdiCzLiILIjJKpfOv0f+Xs6kXYDTZkWcYXiDCl\npgBfMEJUktjXOEws1RwgCAI6my0vcp/WxS2Nx5+K+6FGFsy18UXTUAx/Iu7/8D954MP/1P6/fNIX\nKDEr967UonhCC6aVc868cUyqdvDFpTXs8nzMR8e3smLBfH5y6b8yrSSeGrIbbHzr/BV858vnszvG\naFYlf8M+5T1oaD/Kx+3b2d6iPJMV1grCUoSXPtoxLNeUCqnIfa4PNhA4qDRbUkv1+kNl9acjvJ5M\ncp8gCPzi1qV844rZLF9USzgi0XIsPq41m9/nrS1NvP1xEz98ajNHW5NFwkRB5I2PGvn24xtobPew\nt7GX9l4/X1s+nXPq5gOwrXMX5dYyKqyKJ/qntxTCaZuvg7AU4fm9L7KvJ/8GVengDYQJ95OALbQb\n+fJ5k1g4TYki6ZxOEIQk6fP+UMPP63e1M7HKSWlhvMzucIuLx17awQ+f3kIwFD9XUqg/x/cqFJHo\ndgUJhVN7vjpRpMhuwaRXvGyzSU+JuYiuQA+yLPPkX/bw8rpDythlOWM59GBwhzz8/di76GQTsxzp\ntQ7645vzbwTQCJ67uvbyPzueIhxNL5qklUtmCPd/qj1+QafDXD8B/94GdM4CBFHUQv2REyjbG2pT\nmNLOs5bg2rCe3rXvYJkylWDM408M9eeL2RNKkkh6u7v28sL+V7mk/kLOqJyftK0nFl606m28+M5B\nFs2sZEaC2E8wLLGtazvoGVA+JAoi//f0O7n7fz5CN+so112s5C+feGUX7T1+br/6NIwG8cTLVCaO\nyWbLy+NP18UtMdQ/YJ9Y3b6prg7vju3K/nka/s4+P/f+eiNnz62itMBCt9dNuS1+D816E9fOWMnj\n2/6XSYWKByzJEr6In1cOvs5HLZuJyFFkdzGTzp1GaeFAL0IQBKWZkSkEkbjHP3t8DcJBgc6YZr8h\nonhIs4tn0twSYVZdcthz6/5ODh7v44tLJmgloLIsE4pImHLsX96/jj/q89H5x99rcrC+hj0UL790\nwH6ax59GzTKZ3HfiQ/0GvciCmCEE+N6VV9LkXsR/bX6EI92tzJg0A72jl1kmL2bbwEn5wgU1fP7M\nWkRBYOm8+BzQG5xFo7uZOkcNnb1+ip1mej1BppRX0SqLtPs7+KhlM+ubP2RT68f87NwfDOt13fXL\nD6itcHDHNfF5w2zUJ6UL1bkzG4//c7OrmDinSuMKAfzxXYXl/9A3liTtoy8sBEEAWdZy/NsPdrG5\noZ1dR7qZXlvEsoU1KdvnVpfauGZZem5Tqm6QJZYijntb8Uf8zJpQjMmgQ5Il/t/Hv8Qd8nDPmf8x\nwFvPBhW2cq4YfxWit5T6wuw7wFXayv8/e+cdIFddrv/POWd629neN1uyaZtsekJ6QkLvcFHBS7Fh\nQ382hItyRbkiesGGoCIqXhApIoKCoSSQ3sOm191s732nl3N+f5yZ2ZmdmW0pBOT5b2fOnD0zc+b7\nft/3fd7nIUVnQw5VaR/f93sA2t2d5FsSnyc8LjmcXv+/deAHtdzvPnY00juPLvWfLfha1cCfsmIl\n7uqTOHbvIvDxm/A1NyNoNGgzs0Y4w9ihETW0utrZVn2C/qZMVs9VhU4cbj/1XWqGn2q08p1bZsS9\nNtWqx5DWgzggUplREfd8iknPQ59fhNUUkl5VFFYuMZBqzMagk9hzrIOGdgfXLR8dMfF0IZktKl9i\njDavyVj9mvSQWUhnfOAPM/q1mdmqr7zXM2b2cRjpNgOPfHkJsqKo1ZL+ABZdbPCelDqRh5f/ILL4\n/GT7r2lw16HxphHUy2QZMumkm73VrSytKIyUMaPh8gRYlX4lZcV6TKESo1bUkKK3RbKaC8rUFsH8\nvJnMz1M3e3uOdeD2BlhamYvD7UenlQjKMlpEHG4/j/3tAIXZljGzoCOl/lDg71n7OsGBftKvvZ6B\nHduTTlREevxJSv3nMuPv7HNjNmgjLm7he6/AmsePlt6HSat+zi8c38yGtq3MLZoIxNq/hlnza2vX\noxU1rCxYgiRKdHVBcWApJSm5PPTnvbg8AX7wmQV8bNUkjm3PwOV3sbP1PQC8wbFNRowGj35t+aiO\n06TY8bW2JP3dhTP+ykk5GIZsSqM3FdEQJAmN3U6gpyfKmU+htdvFxPwUdFqR7n5PwsA/Eu7+zTZs\nZh3fvXWQQ1JTGwALdHl6WDA1DwW1wplvyWNj01a2NO9kecGiYc6aHGvKxy6QJQoi/7PkXkRBjIzW\nTk+fmjToA2qPn+FL/f/2gd9YWkYPg57pkcB/Fkv9/tbBWX37qtV0PPdn+t5Zj6+lGV1u7rjsZKPh\n8QV4fv1JJhfauaBCNXjJMqlZa4+vGyHqN9fW4+JIYyuY4cXjr1KeWsr1E6+MO2ePt5dUvR2DJrE7\noNcf5NFn9rBmbgG27H4e3fc7luYtZHr6NE42S6SYz53Mr2Q2QzCI4vUgjMEYJzjEmS8MUatTs5kE\nPf6IAYzFgjYjA19T47hn+AVBiASOycVmaACzLn4TEZ1xWHRmcENA341dm8aMzKmsa9jI4Y5qFiuJ\nZbbX7qynd8DLnIlTYxboFG1KJPDnJOgnCgLsPtbO0spcllbGLjxmg4Y18wqYVT52Madwxh5m9Xvq\nagFIXXMRgb5efO+sx1N7CuPE2OxN9nhVG+okErCC3jCYLZ5lVv8bOxvYvL+Fz145jefWnWBiQQoX\nzy9kQo41EvQBukJGSOmGxPLZr28/xVrnOmwGMxcWLgNg/Z5GUkIM+ns+OSdGpe7u+V/FE/Ty3S0P\nAmAW7DHckLMFWVH48Z/3kptu5vbLpgDqGuqtr0N2uxNXvcKBSBJ5/OUDGHQaPn3FoKSN1xfE6fFj\nNWljZMM1qWkEenoiFZzKsgwqy0a+zxxuP1sPtJCXYU4oxvPgHRfEKf5dO3seNQN2dKKWLk83D+78\nGWuKVnBJ8So2Nm3lYNeRuMDvC/oBBZ00elXVZPAGfeiHnCfMDdnZqhJ2F+YO3yoY7PF/lPEnhbF8\nEpLFiqFEzUY1ISOVwNns8be2IlltSGYzKUuX0fXqy3S/8S8Unw9dbv7IJxgBAgJ5mQaea/8VJ8WZ\n/OfUG0nR2dBLOjQGFxfOGQwIZXkpfMV6FS2ONp499hLugIcs11yMeg1zQyM3h0514vS5h2UOp5j1\nXDBPT1PgKFMtc0nV29nWspvNzTtYWbCEyyZdc9rva7SIlu0diyOenITVD2q539tQjyLLMRuzSOA3\nm9Gmp+NrajwtF7iDnUfo9HQTZnCZkpB+wsi02DkS6kpNsOcxOW0i6xo2UjzRl1DUBOCTFyXOyLUd\n01B0TQhSELsmfqGsLEuP0xII4+k3j5Nq0cWUukcLcUjGH3Q6ETQaRIMR0+Sp9L2zHtfRI3GBP+j1\nDCthLQgCosmE7HSe9Yz/kxdN4hMXTqSpv5Pbry3gyHE/f3n7BP/1n3Mix6zdUc/+jka0Rl3MZiAM\nRVE40n+IoMbP9IzBTdnnroqtsoU3h/VtA7xb1cziihx+uOQ79HkHSNNmntGgLysKHm8ArUaKUfUU\nBYEbVpRhNQ3eY1Koahrs6yXY10vP+nVkffymyMYsXHr+66ZaystLmDpB5au0dDnx+WX2Hu9g0/5m\nvnJDZUwGr83OxnOqBsk6tkkhWVbo6vcmlOyVFRmf7MVijH1uceFsFjMbfyDIT998Ha/Bh17SY9en\nYNVaaHPGC3k9uPOn+IJ+Hlz63WGv53BtN9sPtbFqTn7CCsXP9/6GU/31/HzFD+MqJrIis6vtPYwa\nAzPSE+n/DWI0gf/fmtwH6iJf+vDPSLvyavXvSKn/7AR+2e/H39mBLkfNxEWDgZQVqyKL3pno7+t1\nEjOmGPErPsK3jyAIZJky6XB3IiuxZJciawELc+dSYiui093F/lOtRFu3O9xBphOrdLkAACAASURB\nVDk+we2Tbk36P7UakU7xJG+3v0abq4MleQsIKkG0ooZl+eMrjY0X45XtDTodagapj18oNOkZKIFA\nnGWzHB34QzLL4yX3bT3Ywq/3/pkXj7/CiydeAcCoGd6zPXrmN9ecTVmK2vvf1LQt4fEOv5M/HnqW\ntbXr4p77+hUXcmPB7UwKrsaSoNKgkcTI4t8z4OWVzaeoOqG2PyqKU8lIMaIoCgdquiK2raNB2HhK\nDrH6ZZcrsnkzTlY5I+5jR+NeJ3uS21SHIZlMoe/07Fec+v39/Pi9h9nWvZH/WFnGvbfMjVnA7RYd\nWpOHdGPi2fKAHKBGsxGA6elT4p4/3tAbY50tKwo5aSZSrXqsOgsF1tyErZ3TQZ/Dx12/3sbTIX+O\naEwqtJObPnj/DY709dH9xlr63lmHO0RchsEe/8QJ6SysyKYg5BR6rKGX3792mPlTs/jpnUvjgmLG\n9TdS8I27IsTr9XsbeWtXw4jXbjPruGlNOfOmxG9GO93d3LXpezxz5MW452RZQZbBlKb+tifY1JHY\nHHMWXZ6eUIYf+ny8/XS4u+jz9UdMtYa7nokFKViTbMhNGiMBORDhXB3pPs67DVtw+Jx0uLvwBX3M\nzqyMG/kbCkHzEblvVIguFYp6PYLecNYCv7+9HRQFbSjwA6SuXkPPm2shGDwjjH6AjpAZS6ZxsCSW\nZcxQRXc27ufKedNIMetobHfg9QcpyrZSYMnjRG8Nl61MoyRlkMy1cFo2C6cNP0rS6+1jQ+NWtKKW\nElsRWaYM3us4wKqCpejlFLYcaKE0zxazUJwtjFe2N+hwIlksCfuTYWtdf2en6p8Qfk2oPSCaLRFu\nhmQZPjMJBGX6HD7sVl0M6XHBtCyebfdHxrW+s+AbkRZNMuij+ja55mwMGj3XlF2WdHRML+nZ276f\nElsRlxavjnt+1dTJrJqaWOFxaO9WlpVIoAln+gdquvjHllquW1ZC/ijHoAVRRNBqo8h9TjShDbjG\nakOXX4D75Ik4Ua2g1zuikqVpWgWB3t6zKsPa3e+hvr8Vv0YltjU7WjjWVU2rq41ZWTNI0av3Q0W5\nhWBbIKKdMBTRC/qkIXPeOw638dtXD1GWb+M7t6i94uIcG8U5Njp73Zxs7KPf5ePdqib+Y0XZsDoa\n7a4OTvXVMz9n9ogjmqlWPY99fZQ9/shIXw+eGnW6QPYMBsNwBjp3ag5a02ClZuWsfFbOSl7p1Kam\nxhhcCaiTBqeDo82qlHlrR2yp/70THfzmlUPceslkvJouREGkyKpe2wRbIQE5iNPvRCep7/Vwt+q0\nqBW1yIqMXw6wtnYdE1NKmGgv4eE9j5Frzub2ipsoyLRQkJmcZGo3qOfs8fZi1VnY3VbF9pbdTEuf\nTLYpkweX3od3NMZMH5X6xwfJZIrM2Z9p+KL6+2Fo7KkRhr9hwtisWxNhx+E2NjYfBREyTIOl2esm\nXsEUaQkdnYMZ//6aLvYca+f//cdM8kOl/EZHMyUpY7uO/zv8PAApOiuSKGHTWbl3wdcB2FPdyLb6\no1htU89J4B+vQ1/Q6YhkLUOhjZrljy45R0r9JhMpy5aDApZZiYlKYXQPeLnnN2pG/od7Low87gv6\nUKI80Gr7G8iz5MS9PhppRisSWhalrWJmiHh58YTkYjtaUR1ZanN1xD3n9Qdp7nRiMWrJjCJftTrb\n2dK8g+reWgqsedxQfhWpVn1CsuaM0uTtgOEg6HQoPi+KLCM7nYjZg+/bNHkKvU2NeOvrMJYNBkTZ\n60WToC0Tjexbbh/ztYwVJ5v6+MvB9fjS1apEm6uTn7/9GmJmIyUpRZHALwgCmcZ0im2FSc9138Jv\n4g36IuqMYVQ39XHLJZNZOSs+MWjvdfPcuhPMnZzFqtn5ZKQMXyV6pXotVR0HsOmtTE0bvxztH147\nQnuPi3v+U+05h387vpYWfM3qhFK0GmOYbCZIGqpOdPLPbbVcu6yEacVpiIKAPyDjcPsx6KRIOyMR\nVs0ZnUV8UJZZt6cJu0XHgqlq4tLZ6ybFoiMlRf2dTS+M5apMK07jF19dikYDL2xsIt+cE+ndXzcx\nfrLkSJdaCbl7/lcxa020ONtYW7uOfEsuTQ51rbdo49e8QFDmpQ3VXL2kJPJeU/Vqq6TX00eRtYB+\nr0owD4/cakUNWp2GVmc7vqCPIlviz+GjHv84IRoNMQ597uqT+FpaSFm67LTP7Q+N8g3V6M/65C2k\nXXp5xDXwdJCVakTocoEfMo2Di3Cqwc7iqbGB7fILJkTc+gpkdVHpcvbzyuZTZKcaWTgtmzd2NlCQ\nZU6o4R9GkbWAYz0nmZEZ713t1DdwyvgmXn06cPochpEQ7rGPJfArwSCyy4WUn/jHpAnP8g8Z6ZOd\nTgS9AUGjQdBoSL34khH/V5bdyBeuqcDjC8Zq6wfU0cDwolHbX8fivPnDnYrZWZXMGaNDW7gatLV5\nJ4vzBlXZalv6+fGz73HDitIY5z6H38n6hk0ABJQA2iG6/Ydqu9m8v4U1cwsoyx+fXLGo1yN7vWqG\nqCgxPfmwgFJ0FU5RFIIeD9pRelecTSyYms17fon9nTArczpVHQfRZ3TjB9KNgyQ+i9bMl2d+NiLP\nmgiJSJUANyfhZeyv7uTt3Y189sppFGYlrlYNRcNAIwDB3nRkuxKj9TEUgaCMzx9Ep5XQSLHVgTXz\nCojWApNCgd/x3t7IY0qUNkO41P/02ydZvXQSH79wIruPdlDbMsCVi4vZd7KTP799nI+vmhghJZ8O\n9rTv43hvF9O1gz3xR/92AI0ksnK1WjFIM8a2FcKjqCc7GpGDoPcPv4nNNmUyKXUiOSFVzLCY0syM\nikjgL7MXA+rmbX91FxUlaUwqtOPxBXljZz3XLiulq8+DVlHv+Z4QwbbfN4Be0sURqh+t+h2SIPGD\nxfckvKZI4P9onG9sEA1G5La2yKLc+fJLuI8eAQFSlpxe8B/M+GNvbFGrjXtsvCjJtWHp8EMnEQew\n0SDXnMW1ZZezIGMRbzY3kJlqJBBUIo5ZwwX+K0ouIsuUwYKcOXHPhXuaXZ6eMb6T8WGw1D/6PnPA\n6VLZ30lIYNok6n3BcRLHwhlINJ7fcBh0UGiaQIerk1N99SOeZzwl7CJrPvUDTUhCLAlsUqGdz105\njYqSWMZ5XlQwuqr0kkh5eMuBFmpbBrhkYSEzStMwh3qXLV1OalsGWDgGBUdRpyfocCCH/NxjZvBD\nY1zRVTglEFCtWs9B7340aBhowqqzMCVtElUdB/ELLgySIY6cmWk6s2qgVpOOlbPzybQbEQQBd8BN\ni7ONAkt+XNUA1A3TgN9JviWX3792hP++fT6p1uSfYV3bAD99fh8XzSvg2mWxFZ6h7YRwqT8sRAYg\nRxsvhTLQyaUZ5KSZ0Gkl0m0GXny3GllRmDclK2E/Phr+gMw/ttZSlGUZ9lhf0M9LJ17FITkpsclA\nPoqiUDD/GDhT+dvWDsgYzKajoSgKBuxcm/pFctKHZ+pfUXpxzN8dbjXw51vzmGQv43hvNeV29XML\nygrtvW4uD6kV3rxmEoKgTh/85C97KZkYRJREPCGuQJ+vnxRdPAnQprPQ4mxPOjYZsT3+KOMfG0Sj\nUR0HC/gRtLpIAGn/89MYikvR548/a/W1toAknZHMfjjcMeM2+n0DceSw7n4PG/c1U5pno7Isgy3H\nTrKz/x2WFM5lQc4cLpqwEoAbVw6WVK9clpdwjC8aWkkbkz1GI6w2V1VXx8UTlEgw+K/fbqMg08KX\nr4/XDjgdRJf6vc3NuI8dwTp/YUK2fhiBAbWslkzoRRMR8YkN/LLLOWbdhe5+D5Ikxui6A1yzZCJi\ndQWT0iZg0mvRSboxaxGMBl+a+Rn2tu+PE3ISBIFF0+M3nyatiQm2QoySgekhRvHb9RvY1nuABWlX\nkGrVs3j6YAVr68FWOnrdzJyYMWqymaDXI3d3RQSRou11xZAda9A9aLwUlvcVhmH1nwsEZZn3aprp\n8fYyLW0yhda8yMYqwxhvdnWmUZJroySqePjTDS/QLBziW3PvpCSlKO54h9+JL+gjw5jO0uWlaKTh\nr68sL2X0Pf6Qel90GUCJLvWHMtBFM/IjBLQ0m4HPXx2vDZIMiqKgEdXx4eGgk7T8v9mf56d7f83O\n1r1cUXox/b4BqjoOUGqahNGgxSuLuJwiDNmLvb27kde21/Hl66ZTXpC49ZcM4Yw/y5jB5ytv41R/\nPeWpqibGpEI7RdmWSFUhTJQ1GwR+8OmFaLUCsARREJEVGYfPSVZKfJyw6azUDzThCXoTk38/muMf\nH0SD+mHKHg+iVofs9oAkofh8tPzmMSZ87wdJZ4eHgyLLeJua0eXkDsoqngU8t+4EBp0Ut0OPXIdC\nSJFK4d3D1TSnnGCSuzjp+X574Cnq+hv5+cofjkuvPS00szwQ7Mcfpez2X/85F5c3cMYtfKNZ/W1P\n/R5PTTUdf30B+6rVZFx3Q8LP3lGtSnPqshIHccloRDSZY0r9SiCg3iNjzPjX7WnkXzvqqSxL5/rl\npZHMqcCayxdn3QbAQkYv6zlWWHUWVhQsHtNr7pp7JwqDm5BWZzut/npmzbXEqTLesKIs0SmGhajX\no/h8ERGl6M9UTJDxh4mA73fG7/IEeK1qP6RBgTWPYlsRX5r5Ge7Z/IOYMv+5wuSsIpo7DtHibE0Y\n+MNtHouYwvIZp0ckfm1bLbuOtvOV6ytJT1HbXZLFQnBgUPwsWoY5EoiSrH1BWabf6UerEZOOouq0\nElcvjfdtSIQ8Sw6pmgyaPY30OF00Dqht1pL0XL6x8HI8vkDMiGIYq+bks2Zewbg2bR2hwJ9hTEcn\naeM4FAZdfNwQBCFu/QvKQS4tXh1R1oxGuEqRKLGDj8b5xo2wAIvsVksuQbcLXXYOKctX4GtpxrF3\nz7jO62tuQvF6MJSM7sYdL0rzbDHkrGik2Qxcs6yYyUWpiILAJYvVMq51CAHF5Qnw+9cO89Aze2jp\n78aisYzbpEUnabHqLGiNnhg5V6tJy1P/Osr/PvfeuM6bDOGs3VNzEk9NNdrsHCSTiZ61r9O3eWPC\n1/TtV7XoTVPjOQph6HJz8bU00/m3v6IEg4PZ6RgD/42rJnLtNRpMBfUxC9xQ86TzCUM93+2hBanP\n288fXz/Cs28dP73zh3r1YT93KWHgj87439/AHx6rs5p0fPbS2SzPX8TUkNWqgMClxauZnXlmK1mj\nwZwidW1pcbYlfD5Fm4K9dx7ttSn0DHh59KX9vL69Lun5/AEZl8dPIBivdz9vSha3XzYFmzl6ll/N\nkHUhrowcbbwUCKCIEn94/UjC/9Xa7eYHT+3i7d0jj+qNFmbBDih0uLvY36ie1zugD4lladEk2IRo\nJJGWLhdPvHqIPcdi5/Z7PL3s6zgYGbkL40j3cV44/goVGVO4vOSihG2W4aAoCj0D3ohPgVbScmXp\nxSzLvyDu2Ejg9yZWl/0o8I8Tgxm/G0VRkN1uRKOR1IsvBaB347vjOq+nRs0qw2JBZwsLpmazZEZi\nScdf7/sj3950fyTIOHzqDWzRxZa4DXqJ0lwbS2fm4JEdWDSnZ7NbkT6FYltsBiIIAjdcWMBXPpZ4\nfGy8CPeHw8zi9Guupei730PQ6eh69e9x9q8Avfv3I5pM6CcUJz1v9i23oc3MpPv1f9L06M8JOoZv\nDwyH/f27OOHfHSMucu/vdvDfv98x5nO9HwhnIv/acxyzURuj2y4rClUnOuMWzeEQVu/z96jKdtGl\nfskU3ojHZ/zC+0Due/zvB/nO77ZHgmG+JZePT74uMoJn0Zm5qvSSuFbKuUDYwS1Z4E832fnh9R/j\na1esxGTQ4M3dxXHNW0nPt+9kJ3f9eivbDrbGPZedaqI4xxarshfq85umqeX7GHJfMIggSUwrTlwJ\nyc8w87OvLE1aqQRo7nTy90011DSPbtx6er665niUflLS1IrD7OKRJ5Z0GhGdVsI+hP+wvWU3Txz4\nP071xW6Wavrq2NC4hTxzDleUXDSqa4vG2h31fP+PO6lvH1kqPtucRYltQtJE7KMe/zgRVnuT3W61\nlyjLiEYTupxcjJOn4D56BF9r64hkPNnvp+FH/4Nl9hzSr7oGT+25CfzDQRRE3AEPL205SuWEXGra\n1bEuizZWsKXD1UG9YTP5llyUDoUs6+mVLT9RfgMb97Ww5UALS2bk8s57TbxStQP/hO1cVXpJwpny\n8UIQRVWxzeVCNBqxzJqDqNORevGldP/zVXreXEv6VYNKgv6ODrxt7ZhnzxlWLllfUEjRfffT/Ktf\n4jp4AOckdcMyllJ/ICjT0uXCrDHT4mzDLw+y5B/4zIIRe5fnC8KkI43BywXl2TFELwGV+DcWhn84\ncw/0qATQGEvdUAUuXGGBqFL/+9Djv/WSyZgMGkRB4GSjysAuybOeUyOqZAj4JMSggequ5shjrc42\ngoocp++u10oENAM0OjqS2i6PhnAXjTDfxVwxnd633ogt9QcCSDoti06DsR+eQEhUgQhjU9N2Xjv1\nJrdM/ThL8hayNG8hBo0h4m2fOQrC8/q9TTR2OEgxx95f2Wb1/f1m/1N8sfJTTM9QOS9hx8Q2VwfT\n0seeyFyysIjLLhjdCPWCnDkJSdQRaD7K+MeFwVK/O1JeDGcdKStWAtA3iqzf21CPt76OnrffRAkE\ncNfUIOh06JOMjJ0J9Dm8PPHPKrYdbEn4fGpolEhj8DLg8nG0Sc0MLNr4rHVn6162Nu9UX6cf35hW\nGJIo0tTpjBiRzJ+WyvWr1N14XX8j9W1n1hQpXCq2zl8QCQ5pl16GZLXSvfZfMX3IsO3rcGX+yHlN\n5ojKY++778T8r9Gg3+njt68eorNTrbisq6qOPKeRxBjHsvMZ9tD9kJ0lxrG7BUHgy9fP4NKF8T3m\nZAgr6wV61cCfuNQfxeoPk/vOcam/rceFyxuIKGIere/hL+tOIMvnR5vGbNAy2V5OWVphRKHzgR2P\n8KOdPwegqdNJTXN/xGY3w5SBX/bT7xv772/PsXa+/8ddHKgZJLymX30NBXfdg6E45B45ZI5fHIYb\npSjqBFF4iigRctJMXLusNKbCNBQOn5MBnwNRENCgY92uVt7Z28hM60JuLLuBVMPIhL2PXTiR7946\nj4yU2JZpeGwPiCnnhwN/ewJ9jNEgevrF6XdxOKQP8NKGarYfjq+2DAdhFOS+jwJ/AkQyfo+boEtd\nbMKbAcvsuUgWK31bNyP7h3fC8tap5SDZ6cTx3l58TY0YJhSfVWKfViPRad/Bs+2/jOtDAaTq1Zu+\nrFjVVf/66mv53Ixb44hImaYMjBoDba4OhKD+tAO/KArceslk5oeyhzpHA8/XvADAkfZTPP7yQfyB\n5Lv4Mf+/UPndtmjp4GMGI/ZVq1G8HtzVg77lriOHADBNGTnwq8dNRUqxE+hWFzxpDAYwaTYD//PZ\nhVROUIlV1hT1PSuKwuGuY+zrOBQnqXw+IsuUyedn3EahOJ2fv7iPQ6e6T+t84c1ZoFs9T7SpjqDV\ngiTF9PjfL3LfgeoufvznvTS0O2jpcnLl4mLuu21eTLn7/YQoCtw5/xbunP2pUHVPXb8mh9oQJxp6\nefrNY3T2qfwlR48aiOu6EwcXrz+IyxNIuLEpy0/h1ksnU5Y3SEDTWG2YJk9BCLVuokv9BIM4/TLP\nrz8x9FTqscD9f9zFc+sSPz9ahN+zUWNAkgRc3gA2s563N/exY7N23FwlUNdFURBJN6RSbh8ksWaF\nAn/rOAM/qLyq5k4nvz/4DI/t+z193gHMBi0N7Q6ON/QiKwpBeeS14byY49+3bx8PP/wwTz/9NPX1\n9dxzzz2Iokh5eTnf+973AHjhhRd4/vnn0Wq1fOELX2DlypV4vV7uuusuurq6sFgsPPTQQ6SmplJV\nVcWDDz6IRqNh8eLF3HnnnWf8msWQcYPs9kQWm3DWIWq1WOYvoO+ddXjr62OUxIYi7DIG0PnyS6Ao\nZ73MbzJo0BkC4Fcwa+L11sMBvMejkqhyzdmRvmA0VKlKVZTnmvRPsarwzBIS+7yDmvd+0cUDn56R\nkGE7XtgWL8Gbl4dhYuz3o0lTNzhhcR9FlnEdPYI2NRVd7uh8sgVRxLpgIb1vvQGMrdQP6shPOFCE\nfE3o7vfy6JaXkMx9PHrhQ2M63/sBg0ZPZWYFLx48SUO7I04trmfAy97jHRTnWinLG3nTGO7Vh0v9\nknnw3hUEAcloQnZFZ/zDW/KeLayZV8iaeYX89+93YDFq+fbNw5RczwM0O9SKXl2dQGepm5Wz81k5\ne3AcuSwjjxMte+kP9iZ8/Yb3mnhlyym+fN2MuN683aLHbkn8+QsaLQjCkFJ/EJ1el3REThQEfv6V\npQmfC2N/dSenWgZYPjMvqf5AWDPfqDEiCkJkNDlsOnY60IoaHlj8XxgkQwzrXy/psOtTxp3xA/z4\n2b3YTFpmXFDOsZ6TPLbvSb45707uf3I3/oCMJAn88P/2UFGSxscvnJhU/jeSWL5fpf4nn3yS7373\nu/j9KgP2Rz/6Ed/4xjd45plnkGWZt99+m87OTp5++mmef/55nnzySR555BH8fj9/+ctfmDRpEn/+\n85+55pprePzxxwG4//77+elPf8qzzz7L/v37OXo03rzjdBGd8YfLi9FWq7pM9QYaatgyFN66WgSt\nFk1aOv529QdoKD37/X1XwIVZY0o4jhLWg37mnQO8+O7JYXtlYXOKwuLgGZlHbuly8uI7JzlS18ML\nmw4CUGJT+1r1ITWxM4XUC9eQ8+nPxV13uIQc1vH3NTcRHBjAPrNyTO/RtnCQbTuWUn9bt4sn9j3D\nuvqNXFa8moxQpSU9xUBOphazLvH3dr6iINPCjSvLyE6L3WT2Ob00dToJBkdXAg9n7mE77KE2uqLR\nSDARuU///szx//ft8/nsldN4uWo7Lx55jS73uRGnGg3W7qjnrse30tThiKjHLS2fREBy8MT+P7G3\nfX/k2Ck56iag25u4YnPxgiIe+/qKpIS8ZBAEITSiGdvjN5r0zJk0/gCskcQR2yrRGf/ZgF2fklDX\n5MrSS7iu7PJxT+d8/9ML+OYnZjMlNB3S5GhBK0r88I4LuHnNJMryUrj1kskEg/LwXKD3m9U/YcIE\nHnvsscjfhw4dYt481WBi+fLlbN26lf379zN37lw0Gg0Wi4Xi4mKOHj3Knj17WL58eeTY7du343A4\n8Pv9FBSoPfKlS5eydevWM37dUkyP3x3zGIAUcokKDGPkI/t9eJub0BcWYl2wMPK4oWTsM85jwaFT\n3XQ5B9AKiXfDxbZC7ij+OqXifFo6XUjDyHWGA39t/5kZrznZd5JeqRabScuCSjULrMycRpYxk+4B\nV4zz2NlCuAUQDCnE+drUDZm5dGwVDf2EYrQhPfmxsPp3HGmjxdFBpiGDYPMk3tg0GDBcARfmBLre\n5zMWTc9JKK9anGPj1ksmD9uLjUZ0yV7QaOJIe6LROGScT22zncuMv7PXzZG6Hjy+ABpJZMDlZ3fL\nAd5t2RAJNucD5k3J5K6bZpGTbuJwm9pu1AfttDjb2Nd5iNquQf5PobWAu+bdyUVFK8f8f5o6HPzg\nqV28uTOxwqSg18co9ynB4Ij6J/0uH519yT/LacVpXLe8dFi1wUjGL6mB/3hDL4++vJdNB+vPKnl2\nUe485uXMPu2Ne7T9+d83nWLANbgurpydz7dvnoPR5uZId+IR2vd9nO+iiy5CiupnR++EzGYzDocD\np9OJNcpn2WQyRR63hJTWzGYzAwMDMY9FP36mEdPjH1LqB5BsId/pYQK/r7ERgkH0E4ojgV+y2SKl\n5vFCURR+88pB/rQ2caUjzaYnKHgxaxNbw2pEDTNLc7nnk3P46n8Mn+WW20v5YuWnWJq/MOkxY8Gb\nra9zStxGfqaFAb8q1LIkbyFXZ9zO319zc6C6a4QznD6G6viH3fW0KfFCGcNBEATSr7kW09QKtDnD\nOxdGY82CbJD8ZJszyE03URHKpGRZxhVwY07g0/7vgOixvEStE9FkQvH5Inrvgxn/uQv87b1uXt5U\nw5G6HhRFwWLUYk31ISCM6KJ4LpGRYiQr1YQkikhaPyIiMwuKaXGo4jId7YPLvtcDz/+jk39tbabN\n2c5Th/5Cs2Ow3+/xBdQef4IsNj3FwC2XTGZBEudOUaePE/Bp6/Py2rbapNf+65cP8tjfDo7xHcfi\njsrb+J/F90bcDk84DnM05Tme2/suR2rPn8rMUASCMk2dTnr6fdw9/6t8ovBTiIKAKMCuo+28svlU\nJIb+5dhLPL7vDwn5QOFxvvNGuU+MGndxOp3YbDYsFgsOhyPh487Q4hzeHIQ3C0OPHQ0yM0c/h+6R\nM6gDtEoQk6h+sKk5aaSFzmGakEsToPW5kp5X06X+eDIrJpM1pwLP6gsxFuSTlTW2ADMUtS39NHU6\nuWppacL/bbVr0R/SkZliH9N7ToRMrBTnjT6ojYQiey5VrYfRWwQK03JwyU4m5GZRnCdw8eJzM+Lo\nFbPV7zboJTPTildRd9Naq5XUMX5emVdcBFeMbWa3JkReK0jL4Yo5g/yDtTtPICsyQb/2tL+38wFB\nWeHNHXWYDRqWzx55ikXItBOePNdZrXGfQafdhhtINWvQ2qw4JXUBTMtOxXaOPq8VmVZWzJ/Axtod\nVPU38dKLXpylHWSY08jPObMa/KeLmu566nob+a8Lv4g/6Of1LXW8Vv8u6ODmC+eSmaZ+ZvZUE7dc\nPo2sDB0P7/wlDX3N1A7U8aOL7sFmsPLkKwd5c0cdP/v6CvIT9JQL81PjHguj0WzE63REvssTwSAW\ni5GZk7OT3uMPf23FsO/rn5trcHkCfGzNSK6Cg9/HEnESr7cABQeZP+cW7Ibz8/d1qKaL37xykBtX\nT2L1/KnMjVoS1+5uZO/Rdm69soINextRAgZkRcZgE0gZ8n40aRaaAfMwctnnNPBPmzaNXbt2MX/+\nfDZu3MgFF1zAjBkz+NnPfobP58Pr9VJTU0N5eTmzZ89mw4YNzJgxgw0b8DoVWQAAIABJREFUNjBv\n3jwsFgs6nY6GhgYKCgrYvHnzqMl9HR2jrwwEXWqJxNXbT9Cs7hAH/ALB0DkCQfVjc7R1JjxvZqaV\nzkNqRu5Ly6Gz04H9plvHfB2J4Bhws6gih3SLLum5Hln+ALIij/i/Ot3dPHPkBeZmz0qoEHWmkaZV\nf4y3PPRXZEcqX7n+Rjo7RzbScXsD/OzFfVy/rJQpE5IvNKOBHKo8urr76OgYoL9NrTJobLbT/m5G\nQiAos/GYOqZjIfb/Tc63Mtc5iwJL/lm/jjOF99oPsL5hI1eXXkZ5auzGTVEUDp/soDDLMqr34/QO\nZi6KwRj3moCoZm/tje3oMsHZqz7f7wrgPY3P63hDLy++c5I7r59BShKiWjQUReFXO54C4N5PfJ0H\nd7rI1BeeV9+ZxxfgvrV/wm9ppkhfjE1npSzbAq2h0WRP7Oebl2pg/alNNPQ1k2XMoN3VyY/e/TVf\nnf05rlk8gWsWTwCUMb9HWdIS9Hhob1cro0ogQEqKiZy0+O93tPB7AzgcnjG9XucfrCC5egP4pfPn\nu4pGllXHA59Rq6tD398NS0u4etEE+npdHDrZCUa1FXaqpZV8S2zV1ulQFzlHv4tkOKeB/+677+a+\n++7D7/dTVlbGpZdeiiAI3HLLLdx8880oisI3vvENdDodN910E3fffTc333wzOp2ORx55BIDvf//7\nfOtb30KWZZYsWUJlZeUZv86wcp/i8USYxDE9fosFBGHYHr+3rg5Bo0Gfd2ZtaLNTTREb3UR4Y2c9\nzZ1OPrG6HKN++E7OsZ4TnOitoSyl+IxeYzLkhMQvli+wcdXUJTEGLn0OL25fkJy0+BZFW48LURDo\n6vec9jWIOh2CRhMh94XV97RWC6d/9uHh8gTYfqgNU7qdLFMGe493sOtoO1cvKSY33cynp998lq/g\nzMIT8FDTV0eHu4tuTw//PPUmX5v9edJD5jS3Xjpl1OeKLtlLpvh7QByi3hfuHZ+ucl992wAOT4CR\nxvAdbj/vneggO6oA9madquOQbT67hltjhV4rMa+khG0dzfz075tJE/P56n9UsirzUjo9HQn5PzPt\n83iju4kcw1T62UCL2MmR7uPMyEg+4hoIyvzomT0UZlm4/bKpcc+LOj3IMkogEGkpjjTK7PT4cXoC\npNv0CQWRllaObvImGqao9pl2jFK67xe6+z28vKmGuZOymFWegSgK6EX1s/vkxZNYW9vAqZoD9Hh6\n44SZRtPjP+uBPz8/n+eeew6A4uJinn766bhjbrzxRm688caYxwwGA7/4xS/ijq2srOT5558/Oxcb\ngqDRIGi1BN2JWf2CKCJZrUl7/LLfj7epEX1h0bjMfEbCtkOtbKhq5uY15XHiKSW5NvQ6acTRuOre\nWv5y9G8YJD0Lcs+eIUw0ckzqqmlJ9caRcx5+roqsVCNfuWFwI+f1B9FrJYpzbNzzyTM3NiWaLYM9\n/lDrSGOzgfPsqubZzDoeuPFa4FoAXmnbQCCrDbOxHFlRRm1he74gLOLT5+3nYNcRuj09vF2/gY9P\nvm7M54om8yXs8YdFfELqfWdKq3/NvEJWzMobcQ7f7Q1wpK6HupDt6szM6Vw/8UrKUkriFt73G4Ig\nUJaRz7YOWDLPxiSTSly1e8s5dTKVQKUcsz68tq2WLQda+fbHr0WjETlUm8kF03IRBQGXJ4AggEEn\nxU/IiAI3XzQJmynxZEXMLL9WDbin2pwcqWpi5azECdFL71Zz8FQ33711HjbzmZvYmOq5CmEYIvP5\ngqAss+9kF4GgTHmBHa028To+qBvQznRiN13nReD/oEI0GFVyn8cd+Tsaki2FQGfimU1HdQ0EgxH1\nqjOJLQeaeb3hdRZUVCY04plUaB+RSf1u4xZePP4KAJ+quDmiOnW2kWvOYn72bAossT96d8DNf16f\njj2kKtjY7uBYQy//3FrLqtn5FGVbqZyYfsYCo2Q2E+hT55aDDgdIklrRcY7cdjiTqPUe47jrJEbD\n9fzf2qNsP9TGA59dmNRg6XxDWK+/fqCR2n6V2b29ZTfXlF2GQWNgf3UXDe0DXDy/aMSNaHTmnkgQ\nKXrSBs4cuU9WFDy+IL6APKxqYqbdyB1XVfD6qbfgFCzLv4AUvY3lBYtO6/+fLYR/0/3BbiaE/N/D\nGgRDMWdSJrMmZpBi0aGRRBZXDLLKn/rXEQ6e6uYXX12GVhP7+xMEYViNhvDEhezzRqR2U+1mpCTz\n58CwVaKgLPPSuzUUZlkS2kcPhzsvXzam498v9PR7WbuznqsWF7NgamJ+1cmmPk7WypRaSyKGPdEY\nzRz/R4E/CdTxIU/CjB/UDNHX2IDs88WNHvVWqZrQpqnx5a/TRUDbR6/hOG92H+ca/YJxnaPQko+A\nwBUlF0e0ps8FTFoTt1fcxKcfWs9vWM8f7rkQgIaBJn5Z9QSXFa/mytJLeGlDNfuqu3jgswtp6XSy\ncV8zZfk26toGyM+wDDvKMxpIZjO+lmYUWSbocCBZLOdkdr6tRx1ZLMiwoNdJ2ELGSA6fg9suncJN\nayahlT44YprhwL+/U1U+nJs1kytKLsIQmp/u7vckVX0biujMXUxU6h+i1y9HxvnGnxX+a0cdZoOW\nF985ycULirhqcXHkObc3QHVzH7lpZtKjxIkuK17Dwpx5WHVjN2Y6lzh4RN0YHe+sg/Lhj81Nj99o\nhV35vnTd+B0GIxm/z4eiUTdVGekW0sfg4RANRVEdPRWS309trg5+sutRluYv5LqJV4zr/7yfyLAb\nufc/h6/A9g54wWfhUzM+FWPyFUa4yvxRxj8OiAYDgb5eZJcLQa+P602FZ/mD/X2IGbEZc+97VSAI\no9J+HyvmlBbyQnPy53/32n7SbUauX5b8115mL+aRFQ+gl94f8ZPPXF+ES+7H5Xdh0pqwh2SEG3s7\n8fgC/L8bZ0aOzc8wM29KFttD7Y3/WFl22oFfNJtBUdSKjsMRcRQ72zje0Ms7e5u47dIpTMixolHU\nYLbp8CmunjM7xrL4gwCTxohW1JBjyuLGSdeSZcqICYjRCnEjITrwJxJEGqrXr3i9aktumJ7xcO0T\nWVbweIN09Hp49GvL457vd/lYu6OeyrIMLppXwD+31jKp0M7kolTSjadHMD0XWFlZzIbtZpo7XRyr\n72Fy0eivOSjLfPUXm5hRls6Xrp0+7LG/eeUg3f1e7r0lPliFv1PZ641UdEZqfbq9ARxuPzazLu73\noJHEEY1s3AE3nqDnvLa4Hg1eWK+Kq920pjwuKUlmnBSUg0iiNCoBn48CfxKIRiOK10vQ5YzL9gE0\noVn+QH8/2qjAH3Q5GTh+AkNp2Zg03EcLq87CBGsRdf0N/GX9MW66MNYJyp9xlHXO3czt/0pEgCcR\n3q+gD9CrqeH12rcpzPocU9LKI73iI83NtOe743gLABdUJBaKGQ/C30twYADZ5UQqOHumSdFYVpnH\nssrBMmqqQd08Khovu+qPIOg8VGRMPWuKY2cagiBw56zPYdNZIj3HcZ8rSoEv0e9m0DhrMOOXDIk/\nJ68vyP+9cZQ9xzv41deWo0lQRRFFgeuWD04ivNu4hamp5RH3texUE9/6hGqr6/OrrYCTTX1jCqDv\nJ2wmHT9a/h26+zzYzGO7nyRR5LGvL0cUBZweP5IoYNAlDhVXLi5OWi2LlPq9XkSTOlN+qK4X07F2\n5k5O7Pi3aX8Lb+1q4I6rpyWV9h0O0XK9H2RUlqXT1uMadSWyzzvAI3seY2HuXC4yqVWa82aO/4OE\n8EIT6OlBlxXfaxnM+GMJfq6jR0GWMVcMv1MeL55bdwJZawJBYda0+JKo1QY4wZRAp/98QZ9P/czs\noVKxTtJi0ZoxZ4BBr6G+bYDcdHOkL/yPmjdIM9hZkndmhITC5DF/h+oXL1nOTdl2wOegzdVBjikL\ni85Mulnd8LgCTv60aydKaiPfX3TPBybwA0y0J+extHQ5qTrZSUVxWsLNXDTC2u4oSpJSf3zGn4jY\n5/L4ufeJ7SyZkcsvvrosYdBv6nSSlz4ojVzT3cSLx1+hxFbEt+YNjgcHgjJbD7biD8jcsOLsKm6e\nDeg0GrJSzZH++ljglb20Ozr406v1uBwiD30hMZchmV48DPIvFJ830m/OTLdgTk2+Nl08v5CL5ydO\nWFq6nGze38LMiRlJeUyDgf+D8xtKhCkTUpOOLrs8fjbuayEr1RiRP7bpLExOncjrp97ClOElH2AY\nk54PTkPxHCM80kcwOELGH6vX7zp0AADTWQr8OekmcgwFzMiYRqo1Pmt3+tWM6HxWgOvzqoE/3CMG\n1Tyox9PL8foenvjHYerb1TG7rc27WFu7jrfrNtDQ2cdbuxr47auHaO91I8vKuEp64VKyr1WVjJEs\n50bQY/3xKn6299cRX/ASWyHXll3O8knTmFamXsP5/L2NFR5fkH6nL071zR3wEJRjF6WwtjskLvWH\nbbHDSppyksBvMmj53qcWMG9KVsLWiSwr/OG1Izz71qAD3M83/A2AiyasijzW0O7gaH0PB091xxkQ\nfRAQCMp8/dHN/PzFfeN6/faW3fxk96NctNKYNOiPhOhSf1hxMS8nhcKs8W20NZIYMwKcCGdbp/98\ngIJqghVdDBAEgUuKL8SuT+Ff9eqY6Uel/nEgOtgnCvyJMn5FUXAeOohkNp0VRj8QGoNJ3Dtt73FR\n3doJGiIEq/MNQTnIwS5V3MggDV5jeepEzFIKJflmrr1Sx6NHf4KhWo8zZDj0xZmfYu+BPuraBkgx\n6/ju73ag1Yg88JkFCQkuwyEcWPztqrri2cj4FUWhvs0RYVQDVNU1gnHQITHHnM26rX00yB3UGGux\naM3opXPrNnc2ICsyDQNNmFOMfPzCWK5Jn3eAB3Y8zOTUiXxuxi0xzwl6PXg8cQY9EF3qD7H6fV4k\nQ2K1vFSrnlSrHkVRCMpKTNYvigLfvnk2bd3qBqLT3Y1ibyLXlM2MKKJrTXMfOw63cftlU9h4qI7W\n/l4uqMjCqrOclq3ruYJGEvnep+aPeySuoVEVVKrubGXRMJ2w6qY+fv/aEVbOzo/L1MOlfsXri1jE\niiP0+P2BIH0OH0aDJm7KItNu5IpFxcO+/sOS8Q8Hs0HLTWvK8csB9rRVoRG1zMysIMOYxm3TPsET\n234NDF/qP//v4DOEPqePV7ec4odP706oOz0U0eN70Tr9YWhSwnr9Ufay7e0EOjuxV1aOKFRxunht\nWy3//fud9DkHTTBMBi1aQxCDZDxvF6fo64ruX63OvYjmPVPZc6Qbo9aIXW9DQSHLlMkdlbeRZcrk\n0oVFfP7qCj6xupwHPruAB++4YMxBHwad38IGPWcj8Pv8Mr/46z6qmwbvj6nl6j0VHlsEmFxoR8mo\nxh1ws6ZoxXn7vY0FJ3tr+MnuR9nYGG+g9Xb9u7gDbqo6DnCoK9ZvIhwooi15I8+ZBkv9iqKgDOnx\nK4rCW7sa6BlQ2exNHQ7u/PlGXt5YA8Cf3zrOq5tPAarATbj1sKetClmRWR367ANygD1t+7Dn9/Lt\nm+dgt2nY4n+WV3uf4N4t/8N/bz3/LZPDsFv04x6BvXC6Kicta1x4fckzx9x0M1+6bjqrZufFPSdE\nZ/yhILTzaCfH6pPr5R+t7+XHz+7lveOd47ruVQVL+cmy+5maNpKk7wcfAvDU4ed4q+7dyGMpehty\naAn5KOMHHvvbAU429fHAZxeO6scQo9RnSp7xR6v3+VpUur1l0gjzM+NES5eTp/b+g5x0IxdPXM30\nkvQYPWaLUYtZr8WonJ9a1KAG+2/NvRONGLsxslv0/ODT8zlc20OeLo/7F9097Hmy7MZxj+ANZvxq\n4BfH4K43Wuh1Ep+9choW42DW0utVNwGp+sH+ZMVEK89u2YNVZ2FFweIzfh3vB0pTijFIBvZ3HsbS\nMxO7Rc8FFTn0+wbY1LQdi9ZMZUYFRdbYVFIIjeYlzPi1IcVFtwvF71e5AHo9gaDM8+tPYjPr8PqC\nPPPmMb5yQyXZaSZ+8sXFkaxxRmk6XQlc3xoGmgDIkgqQFQUBgacO/4UJ1gJmZlbQ6mrHp3ix61MI\nKkEqM8/8pM75iLBd9K6aWlr37+PbNycW0DIZNJgMiX8/kVK/zxsJQnk5tmGncmaUpvO/X1qS8Ll9\nJzs50djHqtn5MSOW0ZBECbN4/vKbzhR2HW2nrnWALGMGLc42FEVBEAQMkp50cwbQ+dEcP8C9t8zF\n7Q1g1I/uLQtR2UTCUr/FCoJAsG8wo4vIv47R6W20MOg0dGmO0+cQ+VTmNQmP+c7Cb5z3oywlKUUJ\nH3e4A7y9p5GyPBvXLktu2lPdW8vfq1/j8zNuh6AuJriOBpHA36lmFZL17JD7phWn8c7eRp558xi3\nXDKZ5r5ONIIGS5T1rlFj5M5Zn2PA50D3Pk5anEloRA1T0yfxXvt+GuVWUszqCNb6+k34ZT/XT7yC\n5Qk2Odr0dIJOR9LZfNFoRHa5VSU4wBGAPzxXRZ/Dy323zcNk0EaqeRpJjCnxV5YlbgtcVXYp3Q2p\n/PqvJ/n+p9MxG7RkGtNpdrRzqqWfDkElgF48YdWHZmM2Ghg0BkwaE8Z0mW9fPbJqZjjwRCMc+JWo\nHn9xQRqGYch9w6Hef4R2cQBZOb+UEt8PuDx+DDqJDGM6ra52Tvaeojy1lBS9jf+64Juc/NPnPsr4\nwwgHfbc3gEYShpXplEYo9QuShGSxxGT8wYFw4E/hTIi/BmUZjy8YyVrsFh1+3GSZ4stqALuPtrPn\neAdXXDCBgnESaN5PKIrCVYuLKS8YXuDjQOdhavrq+MnGp+k6MJUff2HxmIJ/JMMPBQnpDGf8G6qa\naOt2UT7NT5V/B7MqF9DV58HdZyIvNX78qTRl+NnkDyIqM6bxXvt+jPkNLJqqTmOsLFyCIAgsykss\nPJX7+S/F2LgOhWg0qTLaIbles83MpQuKyMswYQr9RoZW8zy+AHptvNxsGNmmTL59+ZUxj2WZMmhz\ndbCuqpobVpVy69SPU/Ih/I5GQqAvFY9WwOV38/Cex1iQM4dLiy+MO+7Zt4+zeX8LD39pceR7gMEK\njuwdZPWPNMcflGV6BrxoJTHONKmqbwf9DJBpv/x039oHHitCkseP71PXsBdPvMK9C74OjE6y94Pf\nUBwF2ntcOD2q/eqeY+1887EtHB7Bl1k0Dp/xgyrbG93jjwT+UVoFD4eOXjdf/tlGXt9WF3nMHfAQ\nUILYdBYONddzz4vP8+KWQe/q/EwzlWXpmMeYAZ8veHXLKf76bjX+QLzHdDSuKbuMfEsuPWIt939+\n+tgz/iHjYmea1Z+aouGg+DpPHn6Kk84jNAuHmFqcxo+v+AJ3L77jjP6v8xVzs2aSZ85ha8tO6vob\nAFXb/5qyy9CKiRd/Ua9XPROSQFXTdEU2BwaLmVnlGWQlySD/+PoRvvbLzTzz5nGe+MchHG7/qK49\ny6iOSF24OI1Ug52FuXPJMmWM6rUfJjxy5Ve5b/kXWF+/iTZXO/+oWZvwuEvmF/G/Q4I+DCn1hzL+\nd6taaOpILo3d0+/loT/v5e09jTGPK4pCt6eHNEP8iJvD5+QXe39LTV/tWN7ehwJXll5MhjGdW6d+\nPPKYIIogCB8F/je31/Gtx7fSM+Bl6oQ0/veLi5g5cfgfcjS5T0qQ8YMq2yu73ch+lWA3mPGfXuCX\nFYUn/3mY1XMKuHHVoGf7xsMhcpJgptFbw0D6HvKLBhez3HQziypyTlvZ7v3C/AUCV15qQDeCgp0g\nCFxYuAwZmU1N21AUZcTNQjREk4noWZih5L5XN59i075h5BFHQE62xPzCyczImIZO1FLf3zjyiz5k\nkESJm6Zcz3zbSna95x0VoXYkiEYjis8XYfZL+uFbIzetKefxb67govmFTC1KxaBLfF/5A0G1IuNV\ng1M4yLe7Entx/LvA4fJz75Nbeat2c+QxvxzPFE9PMST0ORCiSv3hNmhRcdawG/UMu5GHv7QkTjfB\n6Xfhk/14PSLHuk/iDQ6Smqs6DnC8t5ravvqxvcEPMAZcPv6+qYamOg3fX3Q3BdbYKrAgScP2+P8t\nAv+qJamULT9AjesI2zu28eiBxxPewBv3NfPWbjU7GWmcD6JH+tSbOnAGM/6PrZoYpxJmMKrXbNNZ\nyLOqylcDwR78geB539cfDX6z/yl+e+BP7Os4OOKx87JnkaKzsqV5J+uravn9a4dH/X8EURxs34hi\nzPcrywq5GWZaul20dDl5dcsp2nqS+1onQpYpg6vKLuULlbdTaM2n2dHG1371LkfqekalW/9hQWlK\nMcWamWglKen7VhSF2v56XP6RP+PwBjzQqxosVXe4+eVf99OZgLQHKidGFARy0kwsm5mXUMwHYPuh\nNh58Zg8nGtXzpkrZTDHPQiefvyTZcwGjXuL6y60EBA9lKcV8a+6dSMNMnQTl2M23GFXq93d1AVA5\nf3JcCX806PJ0A9ARaOSXVU9QH6oiATj8qtNmtjmL72//CQ/u/NmYz/9BgygKyIpChj3JVJOk+ajH\nf7j9BKf667kg4KHX00eDo5kNtbu4IHdezO6ztdsVcUaLHedLXuoHdaRPm55O0DGAoNWq4j+O8Tu9\niYJAWX4KvqCfngEveq2EyaBhZlERkvk6Cq35GEMz8O2uTh5/+SArZ+dzvKmLjn4Hn7t8Jlrpg/fV\nyoq6cDQMNDMzc3gBJI2oYXnBEva0VdHY18lVi8dmNiSZTapcr9mslsZCEEWB+VOymDc5k30nu3B5\nAmMaiXK4/Ty37gQzStNZOC2b1UUrmKDppqXAyj+2nGLKTbPHdJ0fdAy1X/3XjjqKsq1UFKfxjy2n\naJSPcND/Lh+bdO2I5Lnw79Dfpuov5OSksqw8d1hXvUBQxusPDnvMspl5LJs5mDEZ5XRonI5iTxvx\n/X2Ycbi2h3XvBrjmgltZNKk4qTGRw+3nvid3MLnIzheuGfzdDgr4+AiEAr8+M5ORGi7d/R4UBVJt\nOgQEBEGgx6NuyibYCqnrb6B+oInyVLUq0OFSz51pTMfhc8YIg31YYTZouX75YFXkZFMfggAlOTa6\nvd0giR8F/l31qnvYRHsp5allrGvYyN+rdpClTIph+34sqqw+mh5/RL0vxOwPDvQjWa1nxOntYOcR\nfr3/j4jVi7lj9TJmlKaTarCzLF9V0QrIAXSSjoNdRynWl1NRkka15wCHgv9ibzsszB3e4el8Rq45\nsR3lUKwpWs4lE1bFfN4vbajmRGMfd988e9jvQTRboKMjaX+/xdnGKWEPFTNLRrTJbelysqGqmRtW\nlCKJApOL7BGFsZmZFczMBGYOe4p/C/Q6vGw50MqikOeCQa9hAhM5HNjIzta9Iwf+0Fht979eA0Gg\neOkCslOSewT0DHj55mNbALjvtnmU5A4GBG/Qx/3bfsyCnDlxLm6leTa+ckPluN7jhwkzJ2aM2BIF\nMBs03HfbPOxDWoxhYx7F58Xf5UVB4LE367h2edmwWf9Df3sXTU4tQXMbJq2R+xZ+iwxjOhcVrSTP\nksOfDj8XGcME6HB3IgoiaYZU3AEPOaNcPz5MaOxwsH5PI3dcXcEvDv6GT8peUv7dtfr3txzHoDNF\nPKrt+hT8mb1MLx0spT/5z0McE9ZzccVMLipePqKAD4AmTX19oEclCgYHBtDlnP6oyUPP7KEl51UQ\nYdK8DmaUqpuT59adwKTXcPXSEjSihouLVvHPU2+QV9GKRpqNxQq0g1n7wZxj/ez0W9jespsZGaOb\nldYkIIlNL0ljZlkGCqrARTKER/qG9vff3l3P290vM6BRF5Y9bfuYnj512E3EjsNtvLmrgcqydKYV\np8UY8YRR01eLJ+Cl3F6KVvpgki/Hg/q2AZ596zgNHU7uumkW3//0fKRQhWXFzDy0GpG9OzPpcI0s\n2BKt15+yYiWWiWW4OwaSHm+36PjFV5fS0O6IE3pqcrTQ7xsgqASRZYVehxdJFGIC0qambVS1H+SG\n8qvIs5wZg6gPIwRBSGwPK4oIWi2yz6dWQ6025s/IH5bDE5ADiGU76fM7EPwCDr+TFmcb3e06BqrL\nyJuTj0EyDAn8XaTp7QTkAArKh1q1LxqnWvpZv7eRJdNzWTkrn5Wz8nni1UMIFkkV8fl3z/hljZup\n6TMii/eUtHK2t+ymydFCoVUtRa5enMZ7VQ38vaaBXKWCiuI0EEWQ5Rgxn2ho09SA7O/uUtWpfD4k\n6+n3Be+4Zirf3/0SKOAM9a9AzUSiSWxripYTUAKsLlwGgMuv9jo/qIF/dtYMZmeN3/8bGLV7WpjZ\nLw4J/FOL05G0V+Ox1nKo+xCn+ut5fO02LplRwcQkY4bXLisdVnegudPJI5tegpQ2frLs/n+rwO8P\nypQX2plUZEcjipGgD0QCgE4w4Ay0ISvysMqF4d+hZLWScf2N/OrFKnr7PXz+6oqExwuCgNWkY1rx\nYMn+RE81f///7d15fJT1ncDxzzP3lckxmcl9EcKRcCccglxqrbdFS6l0lW7Vql2tFet6Vuz2gNrV\nV10V13bXV0WtF96tJ66CIIpQkUPCEQgJkDuTezL3/jFhSEgyBBISwnzf/0ieZyb5TR7zfJ/f9f2W\nvBf+OsOSRm1TOyue38K5E1K4ak4uu8ucuDx+StwHKXbu7ZZsSvTM5w/dmzqvpVD0egIuFz6nE0N2\nDoVT0qmJ8LC217mfZm8LE2Kn4G+JxRNzEG/Aiy3WSmqiGZ1WTUZMKvsaDuD2e9CqNEx2TMCo1ofT\n9QaJjnU0Wo2K3LRYHPHH4tMNl+XzyD8/wa8inCa5J1ER+CE0zH/U6PiRfFGxmU2lezBl2rDFGmj0\nHetx6PUdRUMMRgK9lOUF0CSEbii++rrwqtWBCPztSlMoC5PKwFUjv0fJ4UZyUq1MG9t1CEur1nL5\niO+Gvz5aoMc0TAP/QPAH/NS115+wTOzRvfzH7+FPSzSTlpgD5JBgtHKgqQzFWo0ttm9TJ+9u3sNm\n5+dclF/ItLTQQ4wj3ojR4sWraDEN83KhJys3NZbc1Mh5GRoagqBIncmfAAAgAElEQVSBFncb1l6y\nwAHoUtNAUbD/8EeozWYumJrJ4crGXl/fk7WHN1LaFFr9rVfryIsfQYLByKO3nht+TWllM7sOOmnP\nqkajqLEZonuu/3iv7/s7Jo2R8zPnhrdmvv9lGa+vK+GXP5zcpXKeSqfHW1sDfj9aW89JlDobaxvF\nvVN/QVmFmy9KGlk69+LwubTE0Chdvm00Fp0Fl8+FXh/LD0aFkpmVNYd2z6iiY8066XYL6XYLW3bX\nsPdQI5PzEtFp1WhVGgKKlOXl0YsfxNPpIXNCYgGLkm7m06/qKLC6iLfqOdRSAcA425hw9SiVwUDQ\n5+016YQmNg4UBV99fXgrnzqmfwtL2j0+Ui3JPDz7Idx+N6+uKae8qoaliyYSY4q8fanN11GZ7wwu\nyXs6BYIB/rjpKSobG7jMtoQLinou7wm9D/V3VmAbg82QwJgMW8Qtkn//vBSNWoXFqMVobaeq6VvK\nW+xMIxT4NWoVaoObGG3sgKz/ONvMGZXPvgYDQSVyT808bjy5/7Uy3PMfk52Azdx99KTN24ZOretx\nKuj6gh/RmHc5akWNQWPoMafAd6dlUjjewrKNoUCilh4/ADvrivnvbX8lEAyQZHJwYadqhvMmp3JB\nUXq3nRMqvT485FzapuGD5zZz7YV5XUZ+jpcek0qqJcg5vWQ+7/xzO8uMSee2STeSbuk5wdnZKhAM\nsmlXFRNH2nB7/TibfNLjB0i3plDjPhb5DRo9cwpGMKcgNArwzoYDfLrbx9xp53PJ6HMxaEI3eUNW\ndrgn3xNFo0ETF4+3vu5Y4D+Jgi/1Te20uX1dalo/9uo2fP4A915biE6t5SeXhFart7Z7eeC9v5Cd\n6OCGqT2n61UraowaQ9TMcR1PpajQalR4tU1MGh25l3a06Evn6+VsdvM/f/+WaWMdzJ2UhkVn5j9m\n3hPx+wSCQXz+AB9sKqMgJ4ExE0PTLQ7Lsd5NaVMZLd7W8LYj0VUoG1z3jHA96W3arbN3S9ew4cgm\n7pzyM9JjUmlwN7L+8BeMjs8jL34EcfruIxCNLW7cvgCOjoWcNa5TKxJzNjNqDOGdN9/JmtdlWsag\n6zmUKJ1KJydkpjAl387r+97h3LQZERfxqhQFry/AFzsrQIF9hxqxxxm5bGZ2xDaOSTg9dVLOVBu2\nV7BhewU/vaIAg06Dx+vHEIxBpdLKqv4TufScbIrGOIiP0bNzfz3f7Ctj0fkjSfnZreG0rr3RJCTQ\nfmB/eGV/X4f6W1xefvPsZr43O6dL4L/rmslU1LV220Jm0mto0JVQESF4/GTcj/r0s89mKZYk9jeV\n4lGage7FXo7S2kKrlbWJx1YtmwwaLpmRhUHftYe3raSWD78qZ8GcEd2GrVWKwvdmj+DCGcn4gwHW\nH/4CAFunDGNHyw+Pih+J6K6t3cuR2jZssYY+J59ye/3c/9QGclNiuHxW1xLYu+r3EgwGSTKHcl1U\ntlbzXunHKCjkxfe8FuOx1dsw6jUsXTSRjTuqSLLZOS9jNpMdsrr/qLROPenc2Gy2VG0ly5oZLugT\nCAZxuX1dtk52rruQNjKDKkcdn36+gbWHPueJ8/4Q8eepVQp7yhvIydLism3Hp8sGsgfyIw179jgj\nl8zIwtSRjl6nVXPv/CUc3FCKp6X3xGFRH/h3ldbz4sf7+Pn3x4cTfuSmWVEpof2jnGBoVpuQQHvJ\nPtyHQ79kTR8Cf3l1C8FgkF8tKeq2GlalUkizdx81aPO5CCoB7Ja4bufEMeGsa67aiCuxLVMKSbvj\nl5jGHttBoNOoGJUZ023xXXyMgQunZpBq6/lBYq9zP499/TTz0+awZf8h0HcN/MlmB/dPW0q8IfJc\nd7Tac6iRv39eyhWzssOBv7qthg8OfkJVaw3zMmZR6JjYZZpErVJYeH4e7S5Pl+/V4G6ksrUKs9bE\n6j1vMTV5Co3uUD2Nnnr6Rz3446lAaKqtuMyJs8XE1TMvH+iPOqzp1TpunXgDMToLJQ0HeL74Vf5l\nzEISjQkEg0GWPr6e1ERzl0p+qk49fm1iIrGGjjzyfViAp1IpXH9ZPjtqd/Hats2MSIq+tMkn0nk9\nRWeKWi09/kgCQbh67ghsHQF48qjIi8KOp+lY2e8+WAqA2nLiOf7Syib+sfEgP++0V7ipzYOzyU26\nw9xt/qvOVc8re94EwKqL7mxiJ+Iwhm4Oqz7ZQv6Csb1uHVJUKswFXZMENXtbuG/9b5mZOo3FY64O\nH89wWMLrPo63u8zJt+V+VIqK7TXFuDpGiOKPyyku28F6N2lkIpM67Rf/tHwDq/e+HQ4OdXvrGWcb\nG56Cg9C6iUmjHN1WiO+q3wtAsimJ9Ue+xGGy4+vI0tmXxC4GnYYbLouO0runYqwtVOe+0dPU8d/Q\n719RFB6+ZWa3v7fOQ/2vb3Wi2WdiQmIB22p3dsm9v9dZglFjJM2S0uUBLxAMsKZsLQAJhu5B7qOD\nn2I32pjUz91AZ5OPvionqc2HIdB7GvOoDvyBYABtnJN4rfmUF11pOlaqustCxXT6MtQ/e0Iqsyek\nhtPsvvnZfr78tgqt0Ys2to67Lr0YQ8c8fa2rnie2/oUaVx0GtYEC2+hTame0cJgSUStqivITek3R\n2psX1v2ToCqIXtX3NRIajQq/V0WGOZPSlgOMNs5hTE4RuijasjfQvqnZQZAgCzK/z4H9CiPStF2C\nfiTF9XsAmJk6lZLGAzjbG/B3zEvHRujxt7Z7aWr1YI8znvT/N9HIqgs9RDV5jlUn7ekhW9WRxEdl\nsTBuTApGk56Dnhy21e5kX8MBpiWHAv/qve9Q2VrFH2YvC9/7ANrdAfY27AfA3dp1cfOnhzbwZsm7\njIrLlcDfidcfAHXkBalRHfgPNJbx2NdPMz25kOvyQ9WNShpKWbXlfdJUY/np/Lkn/B7a+I75rfbQ\nHtKT2c539GFjdEYcFxRlsLF6A2+WbGRzVQrnps0AINGYwC0Tf4JBrceqG5isgGczh8nOn+b9LuJ+\n8N5kZSrsOASpFkeX4y6fi//d+D4Hyt089L2ruuyuOLpdbU1ZPqX7DjB9TMqwzpo4FDx+Lxv270Sn\n6Jk1cizegB+rLoaRljF4rU4mp3RfBLbvcCOPvbaNc8clUzj62PXSKBqSTHbyOx6Qne6G8AN2XIQe\n/zsbSvlmXy2piWbmT05j3IgTbz2LZkdHHpvcXUdcWtu9qFUKOo2aKmdbeKhfm2Bj5AgbdnsMltLR\nuHzt4RwqdS4nh1qOkJ8wukvQB9BqFLQBM15VKymWrkP9f9//AUCvqYSj1SUzsjj0hZm2CLXBojrw\n58RmYjPE82XlFvxBP/9asBi3302tsp/s2L5l4NN03puqVodXi/dm695a9rXuwq2v5qLs+diMCYzt\nSDCypWorKkXV7ek16QR70sUxpxLwj3IpoQWayeaugV9BYbf3S+zZjl5zvucnjOYN/sHOumIJ/CfJ\n4/ewuvxvJASzmDVyLI7a87B6faQlWshO7jlYJyeYuHp+HrrjtgBem/+DcKDXqDTUtzfwnax5JJkd\nWLS9L/b84fl5LJyfy2fbKig50iSB/wRidGYUFBo9zbT72nlky0pswRy2fZ7ADZfloygBnv9wH/8W\nE+qoHF1MC5BsTuKyEReGv95eFyqw1VPGTq1GzW/n/JIaVx05sV2ny1LNyZQ0loZ3GoiQNm8b7hNU\nRIjqMS2VoqIwaRIAm6u2AsdWrno0Dd1e7/MHulXBO5q9D0Jbw07UI2/3+vii8is+r/iyy/F3D3xE\necsR8hNGRbxBiROrdrax7JlNvLa2JHxs485KnM3uXt/jcvuoaKkGuj9oGTQGRsXnUtVeSZO3qcu5\nRz9dzf1r/0iQIFnWjKgoEDLQTFojCgoJ8aHb0cUzspic5yDCVm8sRi2TRztwxHd/0FY6FubG62Nx\ntjcwxTGBK3MvPuHfplqlYt6kNK48Nyfi60To3jkteQqj43OpcdVxpLWS2Lggty5JpsW0j5cr/oe8\nsR7UHT1+jc3Gf7+1g+ff29Xte31TE6qlMj6x50JbFp2ZnNjMbsevy1/EqLhcrsi9aAA/2fD36tZ1\nFDfuj/iaqO7xA0xLnsKHBz+h0BGqohKrj8GsNVHZVhV+TWVrNRWtVWz/WkOKzcJ3OiWGUZnNKDpd\nKF1vLwVfOisYaeGFqmqyYjKwdWyDKWko5R8HPgIIP4iIUxdn0fOTS8aGi+v4AwH+9tEe/uP66b2+\n57Oth9lzpAZzjLnHzIcFiWModu5lW/Uu5mTMCB93qepo8NVg1pr496LbBv7DRAGVosKoMdDSkXnS\nHmcMX7vK+jZe+ngvBTkJXf7u+uLyERf1eWrM7fHjbHFjMWoj1osXxxydHt1S9Q0ATZ5mnvzmf8Pn\nLyrKJWZjO25Aa7MxNdNBm6GGQ82qcP34Q81H2OPcR2ZMGvE9LN6LJNFo4/YpNw3MhzmLKEFNKFd/\nBFHd44dQJbhlM/6dH41dGD6mx0JNq5OKulaCwSArPl3F/+x4jh2qf3BOwXHDwIoSTt3bl/n9b2p2\nEAgGmNJpf3BuXDbXjv0BM5KLmGSXRSr9pdOqsZp1vLhmD63toSGvf1swPuIe8QunZ3HDmBt5aEbP\nCXvG2cYA8MHur7oc9+uaMWoMxOqkp98fRrWJRlcL7Z6uaUYtRi0zxyXTbN3Oewc+Dh//9OvD/Orp\nz6mo6z2vRWHSxC5/Z5Fs3l3NfX/+gq17JXHPyapxhcrinpNSRIw2NN+eos4lw5KOPj0DFAXjyDwK\nRzt48+BLPLfrlfB70ywpzEufxeIx3x+Stp+NCrIS8asiP/BGfY8fju39PipOH0u9txq1LrQP0mR1\n0+RVmD9yEhZjKHi43D4MOjWKoqBNsOGtrOy2h/9ARRMHK5uZnp+EQafmqbd2UmMLBY7jb0gzUoqY\nkVJ0uj5iVAkGg/jVbagM7fxzTw2zJ6QyJqujkqI/wHMf7ObSmdnhLG1HdS7RfDyHyU6sJoG2QDUe\nvwedWkdNYys1rlqyYjJk0WU/BbwaXP421mwuZ/PuGhbNH8nY7AQsRi3Txibx5votaNU6Ls45H4CC\nnARyMxOINQ/MLWxGQRJ6rZpJebJX/GQdrayYZHbgr00naNlLsm8yH28+hCM+mYlP/QVFo8Hj99Du\nc3dZjKcoCgtH9ZyJVJwarUp7wh7/kAT+q666CktHqtT09HRuvvlm7rnnHlQqFXl5eSxbtgyAV155\nhZdffhmtVsvNN9/MvHnzcLvd3HXXXdTV1WGxWFixYgXx8X2ryNZXC0ZfiNs/l3iTkSBBmn3N5MRm\nclF26KZT0+DiT69+w42X55OdbA3v5T++x69SFL4s34kl1seU7EwmjzXz4uEK8uJGhPevioFX66rn\noS/+QHZcJlnxEwkEk8OL/nYddKLXqvF3VBIrr25h76EG5k3NOuHw1y2Tr8PZ7kSn1rHq/WLW7t6N\nYXwAq1qKuPTX1Ix8qtvsTM2JZUSGieS4rtMtVl0MVZ3S6NrjjNjtMZRX1PBB6Wd4Al6STHbGJ47F\neAqFkNQqFUVjHCd+oeim2lWLgoLNEM+KK5fgC/ppdXl59MtnSCeZSXkLaWv38vR7WyBWVuGfbkdq\n3ARO0BEZ9MDv8YQyba1atSp87JZbbmHp0qUUFRWxbNky1qxZw6RJk3juued44403aG9v55prrmHW\nrFm8+OKLjBo1iltvvZV3332XlStXcv/99w9oG0fEZoX/3eBuJBAMEK+PC/fqXG4fC+ePDPcYteGh\n/q7DvfoYF2WWNbx9ZCtvfzKXm64cx39k3017R/lIcXokGOJQKSpKm8oobSojKyaD3LhsAMaPsDG+\n04ptnz9AeXULdY0u7JbIRZAyYlLJ6JibnD0xFUdOA+8cBrtBdl3015W5oSpsL+5+nfWHvwhlOiS0\nirvkcCMVVX585lCPsfOe/rp2J2/vfz/89bIZd3UJ/Lvq9rCrfg+zUqeFU/iKgXXDuH+hvt0ZLoqk\nRo3WrKFRdZj4jpwIGrWKMbkm9tUSng4Qp4cWPWq1HnD1+ppBD/zFxcW0tbVx/fXX4/f7ueOOO/j2\n228pKgoNc8+ZM4cNGzagUqkoLCxEo9FgsVjIzs6muLiYLVu2cOONN4Zfu3LlygFvY4vLywsf7SHT\nYUGtd2NoHkGcIy18PjMphsykY717rT10Q9HEdV2csuFIaOX+5LhpZJ6bQ6rNFHp4iJBIRPSfWqXG\nbrRR1VbDnLSZ4aAPUN1Wy9bq7YxKyCXbmklOipWcFCt2e0zEOuHHy0mxkp08nelZY7ql+BUnr93j\no+RIE5VNofniznvuU2xm8tNT2OasosnThEFjZ9UHu2lp93HLFflMso9na812YnVW7MauQ/V7Gkr4\nuHwdE+wF9F4SRpyKJk8zO2qLSbUkkdOpswShBZsxWjPNnhYgtO4mPVULtdLjP93Ozy+gZvt0nLs/\n6PU1gx74DQYD119/PQsXLqS0tJQbb7yxyxY5s9lMS0sLra2txHQaOjeZTOHjR6cJjr62L+z2vifW\nifX6mTkxjewUK8k2E9NrcnEkmLCae+4R2i4+D7NewT5vLmq9nmAwyH+98Q0H4r7CqrfwkzkXo1HL\ncorBdF7uTIprS/jpjEXoNMeu26GKg7y1/z0mVM3igcsK8Pq9rNr6GhdozyXLnn7SP8eBLOobCEdq\nW3jlkxKqE6vQmjVkpji6rJvIdSazzQkqkx+7PYarzsvj87It1FLJv0y5ku0f7qQwbRwOx7Hr4Q/4\n+fD/PgFgREoK9j7suhF956yt4YXiV7lizHeYmlvQ7bwGI872hvC9NzkYz/ikMYxKyTyp+7E4ea0x\nRpwRzg96NMrOziYrKyv877i4OL799tvw+dbWVqxWKxaLpUtQ73y8tbU1fCymj5nyTqY3BzA+K9R7\nb21uJ9agxt3mpqYttA+80d3Ew5/9lWCbld9f8WMA1FPOob7JA3iw22MYVdDG7kPtzE2ah7O+9yEX\ncXqca5/FufZZNDrdwLH9+1p3aBi40eOkpqaZZ9Z9yhbfWrQqDRenf3eIWiu0wEM/LuLuz95Fr7ZS\nW9v1gT7XNJIfjlqAut1ATU0zZo3C+4feYnOdjfum3cED0+/EqrP2+nfub1FT4zq5e4CILOAKpYWt\nbKjr8feu+PX4gh6OVNZT3+Rl9Se1zCu6iixd3Enfj0XflVU1U763jkgTkIO+ne+1115jxYoVAFRV\nVdHS0sKsWbPYtGkTAOvWraOwsJDx48ezZcsWPB4Pzc3N7N+/n7y8PCZPnszataGiDWvXrg1PEQwm\nk9ZEk+oIJntjr68pbgntbZ2ZOnWwmiX6IMEQj4KCztxOIBikJRh6Lh5tzx3ilgl/0E+rrxWbsft+\n7i83u/nbKx7UgdCivyZPM26/Jzy07zDZe8znr1WFpmFkOmbg9Za296jsxNBammZvC2aDlun5SWT2\nkolRDJxgkDMvV//3v/997r33XhYvXoxKpWLFihXExcXxwAMP4PV6yc3N5aKLQok3rr32WhYvXhwq\n+bh0KTqdjmuuuYa7776bxYsXo9PpeOSRR05LO5ev+1/qW1rRHZ5Gis3MrVcd21+vVWlItSRT1VaF\nP+BHrTr2S65ytmG0GLhm9FXsdu7DIel2zyhatZZYvZW6dicqRcHm8EMFpMUkg+fE7xenh8vn4r3i\nLwCwG7tvq7xwagaXz8rGbNASCAZ59K3PIbHn13b2+1kPSErX00Sr1mLUGMKV+o53YdZ8ZqedQ4zW\nglatpWiM46TX0oiTl5UcQ8yIRGq39v6aQQ/8Wq2W//zP/+x2/Lnnnut2bOHChSxcuLDLMYPBwGOP\nPXba2ndUu8qJ19jAXQsn4vF0r2ucEZPGoZYjVLXVdCm5umV3Dcuf/ye/uX4ac9JnnvZ2ipNn1cRS\n1lrOztJaKtuqUSkqkiyJNNTLbouhUt/ewMfV7+GrymTx/O7JXBKsXYu3TMo3sqYa7KbIgd+kPfmt\nfaLvXL52XL52AsFAtzoZnmYzO0vaMY/1kGKTEZfB1ORri3g+6jP39SY5JgFv0MOX9euoVw52Ox+v\nCfXk39j8dZfjl8zI4vFfzu9SwU2cWSYmTCFPNZ3isjrKGyuJ18fLUPAQM3ekSZ481hoxGZLPH0Cl\nKKiMoRubwygJd4bSTwoWc3Xe5T0Wx3J7/AQCQVSKwq6DTp58Yztb91QPQSuji7PZzUeHPov4Gllq\n3ou4ji1375d+zNiEUUywd121OjIhE8oh3tG98EuC1UBNTeTqSGLoXJQ3k4vy4NvSeg7tn0pBsiTg\nGWrmjsJU3mDPhZTcHj/3PL2REalWbrt6AumWFGZmFOIwyd78oRSptsiYrPhwxkyDTk1SdiMudS1w\ncjUXxMkLnmlz/MOFyn9siNDfbuh2fkRcBv9edBup5mPD/AcqQnNd8QlSXW84yM9OID/78qFuhiC0\nbkan1tHi7Tn3vl6nZvaFrbgCFRyszOGDNX4un30hsXrZFjYcxJi1rG34O0cOZPPziTcPdXPOavEx\nejTayCOYMtTfC4f5WC8wy9Z9gZ5WrSXLmtFliHh9yQ6eXvsxjW2R51fE0NuwvYJn3y/uVmZZDB2z\nxkSrt/e/nQMtJXxRuQVbrJ6r54xgbHbk+X0xtNravbyz4QCbdlXR5nMRCAaINciq/sGgOkHeGAn8\nvZiSUsD05EIAki0932A+3XqYX67cQMnh0LY+d9x+mh0bCaplefiZrsXlZf22CqnGdgaZYC8g3zaq\n1/NWnYVAMIAn2M7ozHhSEmVk7UzmDwT4wv0Wnzd8xB9eDu3YsBpkhOZ08/kDuLuvR+9Chvp7EauP\nIaGjPnRCL3Wi87MTKMhOIDE2NBVQ0VqFQW3AZoyntrVvGQXF0Jg1PgWVSgEpqnfG+MEJqrS1NIXm\nLUtra0jIlJ7jmS7GpKdNXYtLrXD+9Am8Wo5MzQwCRQGdKgao6PU10uOP4K0PGvBVZvW6F98RZ8Qe\nZ0RRFGoaW6lqrcFusEuJ1mHg6/ot1Fg2MTlP8iwMFyMcoWvlDFQOcUtEX8VoLbR4W/DrGwCwmaQq\n6emmVqlYODFyFlLp8Ufwy0svxOMLhFf496a+tYmDzhqCBNAHpADPcLC1ejvFzr3YjTa+kzVvqJsj\n+iDdGiqzU+nqvScjzixBn5ZGXwMOUyJ5cSOYlj4Jd5OsqzntZFX/qRudeeKn019/8t9UBQ6wIDs0\nTDkxLfs0t0oMhAChm8+W6m8k8A8Tkx3juc98BylmqbM3XOgVE0ECvP9xM3f+4Casegs1SOa+0+3r\nfc6I1ShlqL+fRiWloChBgmo3s1KnM6JTCVhx5jqaMCZe3/P6DXHmUSkq0iwpPSaLEWem7MRQgqXZ\nhTZUMgU6aILqyH8j0uPvp7GJI1hfuYHSyibmpZ1PtlUCyXCwMO8K1IqKq0ZeNtRNEeKs9Z2secxN\nn0myJFoaVFPzUyh/p/fz8ujcTzmxoRLDe52h/apieIjVW/nXgsXE6mV1uBCnS7NTx8bNLqrqe87I\nKE6PVn/k37cE/n6K1VvRBSy0qWv53uycoW6OEEKcMWobXLz/ZRlen1RIHEzv7fsi4nkZ6h8A45NG\ncrj1SEfiHinOI4QQANPzkxg3wobFKEWwBpNKEzkOSeAfAD8e90NZcCSEEMdRFEWC/hBItUdeaybR\nagBI0BdCCHGm0Ggj9/glYgkhhBBnkbrmyMn6JfALIYQQZxF9nJ39Wb3vWJI5fiGEEOIscnHBdCiY\n3ut56fELIYQQUUQCvxBCCHEWKa9uYfWnJb2el8AvhBBCnEUUBQy63iv0yRy/EEIIcRZJt1tIt1t6\nPS89fiGEEOIs4g/4KW8+3Ot5CfxCCCHEWaShpZ3/2yKBXwghhIgKWo2WBF3vpZAl8AshhBBnEatJ\nx+Uzs3s9L4FfCCGEiCIS+IUQQogoIoFfCCGEiCLDch9/MBjkoYceYvfu3eh0On73u9+RkZEx1M0S\nQgghznjDsse/Zs0aPB4PL730EnfeeSfLly8f6iYJIYQQw8KwDPxbtmxh9uzZAEycOJEdO3YMcYuE\nEEKI4WFYBv6WlhZiYmLCX2s0GgKBwBC2SAghhBgehuUcv8ViobW1Nfx1IBBApYr8DGO3x0Q8P9AG\n++eJ/pNrNvzINRt+5JoNvWHZ458yZQpr164FYOvWrYwaNWqIWySEEEIMD0owGAwOdSNOVudV/QDL\nly8nJydniFslhBBCnPmGZeAXQgghxKkZlkP9QgghhDg1EviFEEKIKCKBXwghhIgiEviFEEKIKDIs\n9/EPBZ/Px3333cfhw4fxer3cfPPNjBw5knvuuQeVSkVeXh7Lli0Lv76+vp5rrrmGd955B51Oh8vl\n4s4776SpqQmdTseKFStwOBxD+InOfv29ZkeVlJSwaNEiPv/88y7HxcAbiGs2Z84csrOzAZg8eTJ3\n3HHHUHyUqNHfaxYIBFi+fDk7d+7E4/Fw2223MXfu3CH8RGc/Cfx99PbbbxMfH8/DDz9MU1MTV155\nJWPGjGHp0qUUFRWxbNky1qxZwwUXXMD69et55JFHqKurC7//lVdeYdy4cfzsZz/jjTfe4C9/+Qv3\n33//EH6is19/rxmEskQ+/PDD6PX6IfoU0aW/16ysrIyCggKeeuqpIfwU0aW/1+ytt97C7/fzt7/9\njaqqKj744IMh/DTRQYb6++jiiy/m9ttvB8Dv96NWq/n2228pKioCQr2MjRs3AqBWq/nrX/9KbGxs\n+P1LlizhlltuAeDIkSNdzonTo7/XDODBBx9k6dKlGAyGwW18lOrvNduxYwdVVVVcd9113HTTTRw4\ncGDwP0SU6e81W79+PQ6Hg5tuuokHH3yQ+fPnD/6HiDIS+PvIaDRiMploaWnh9ttv54477qBzCgSz\n2UxzczMA55xzDrGxsRyfIkFRFJYsWcILL7zABRdcMKjtj0b9vWZPPPEE8+bNY/To0d2upTg9+nvN\njgaQVatW8dOf/pS77rpr0D9DtOnvNXM6nZSVlfH0009zw9PZstIAAAPPSURBVA03cO+99w76Z4g2\nEvhPQkVFBUuWLGHBggVceumlXeoDtLa2YrVau7xeUZRu3+PZZ5/l+eef57bbbjvt7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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118a35240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "weekly = data.resample('W').sum()\n",
+    "weekly.plot(style=[':', '--', '-'])\n",
+    "plt.ylabel('Weekly bicycle count');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This shows us some interesting seasonal trends: as you might expect, people bicycle more in the summer than in the winter, and even within a particular season the bicycle use varies from week to week (likely dependent on weather; see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) where we explore this further).\n",
+    "\n",
+    "Another way that comes in handy for aggregating the data is to use a rolling mean, utilizing the ``pd.rolling_mean()`` function.\n",
+    "Here we'll do a 30 day rolling mean of our data, making sure to center the window:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YWIjhw4ejsLAQOTk5kMvlEIvFqKioQFpaGvbu3Yv8/HwIhUKsW7cOjz76KKqqqsBxnI3F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\n4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9NLSBzs3yJ5Tui49oLl82Lb4YoVK3j00Ucxm80MGjSIWbNmIUkSN910E4sXL8Zut7Ns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\n/wfxJKUnYTfWmQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11ad10908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "daily = data.resample('D').sum()\n",
+    "daily.rolling(30, center=True).sum().plot(style=[':', '--', '-'])\n",
+    "plt.ylabel('mean hourly count');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The jaggedness of the result is due to the hard cutoff of the window.\n",
+    "We can get a smoother version of a rolling mean using a window function–for example, a Gaussian window.\n",
+    "The following code specifies both the width of the window (we chose 50 days) and the width of the Gaussian within the window (we chose 10 days):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eXYN18pR+zeeKoEBbKwQCGLJz4tYGY1YW9uklOLdspnPfXqwTJ8WtLamg/b1N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\nGbuLW6hrOVa7+lntV+w+ut/nlZ5GJSMqRINEIiAIAumhCwhWTr6+NBC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2N+1BQGB17BkB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IlpWu37ceX3b7Mz+2vui/0vBrfu7+pyPVpdfepXNaVt48aNNG3alOeee47i4mKGDx/OjTfeyNSp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\n1EVERDyEQl1ERMRDKNRFREQ8xP8D2G7R4lwJBEEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118a9fb38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "daily.rolling(50, center=True,\n",
+    "              win_type='gaussian').sum(std=10).plot(style=[':', '--', '-']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Digging into the data\n",
+    "\n",
+    "While these smoothed data views are useful to get an idea of the general trend in the data, they hide much of the interesting structure.\n",
+    "For example, we might want to look at the average traffic as a function of the time of day.\n",
+    "We can do this using the GroupBy functionality discussed in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0n1RC80xPyCJy3iTd0BUkG7uSz7AJ+e677+buu+8+7fH169ef9tjy5ctZvny5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\ngg/4f1unlzCpFZdIAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10192d5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "by_time = data.groupby(data.index.time).mean()\n",
+    "hourly_ticks = 4 * 60 * 60 * np.arange(6)\n",
+    "by_time.plot(xticks=hourly_ticks, style=[':', '--', '-']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The hourly traffic is a strongly bimodal distribution, with peaks around 8:00 in the morning and 5:00 in the evening.\n",
+    "This is likely evidence of a strong component of commuter traffic crossing the bridge.\n",
+    "This is further evidenced by the differences between the western sidewalk (generally used going toward downtown Seattle), which peaks more strongly in the morning, and the eastern sidewalk (generally used going away from downtown Seattle), which peaks more strongly in the evening.\n",
+    "\n",
+    "We also might be curious about how things change based on the day of the week. Again, we can do this with a simple groupby:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kZWVBoVBAr9cjOTkZ+fn53Vc1EVEvY5w8BZq0dFj37YXtUF6wy6EQpWhrg3nz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\n8BD/A/9r7TmuhSCKAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119484d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "by_weekday = data.groupby(data.index.dayofweek).mean()\n",
+    "by_weekday.index = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']\n",
+    "by_weekday.plot(style=[':', '--', '-']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This shows a strong distinction between weekday and weekend totals, with around twice as many average riders crossing the bridge on Monday through Friday than on Saturday and Sunday.\n",
+    "\n",
+    "With this in mind, let's do a compound GroupBy and look at the hourly trend on weekdays versus weekends.\n",
+    "We'll start by grouping by both a flag marking the weekend, and the time of day:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "weekend = np.where(data.index.weekday < 5, 'Weekday', 'Weekend')\n",
+    "by_time = data.groupby([weekend, data.index.time]).mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll use some of the Matplotlib tools described in [Multiple Subplots](04.08-Multiple-Subplots.ipynb) to plot two panels side by side:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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L0xFCCBFjIvU6paiqtvfwKisbtTy8JrKybHLecSIezxk6ft6uygr23/1zbOdMpMdNtwBQ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\nCAt7cXHruiB5eYH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Ht2eWL5+xzOew42tpCVvxv58pJxcAV7kkx0KI6Ne05XOcB0qxjZtA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fUE102J7+midaqcohqWr81i8vBwxg85Mz5+YvhYthTsZE/xXvaWHGCYve3BsSRJhN5+FzmFhVSu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\nRhAEQRAEQRCEPun/A1rsNddykhZ+AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b2305c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "by_time.ix['Weekday'].plot(ax=ax[0], title='Weekdays',\n",
+    "                           xticks=hourly_ticks, style=[':', '--', '-'])\n",
+    "by_time.ix['Weekend'].plot(ax=ax[1], title='Weekends',\n",
+    "                           xticks=hourly_ticks, style=[':', '--', '-']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is very interesting: we see a bimodal commute pattern during the work week, and a unimodal recreational pattern during the weekends.\n",
+    "It would be interesting to dig through this data in more detail, and examine the effect of weather, temperature, time of year, and other factors on people's commuting patterns; for further discussion, see my blog post [\"Is Seattle Really Seeing an Uptick In Cycling?\"](https://jakevdp.github.io/blog/2014/06/10/is-seattle-really-seeing-an-uptick-in-cycling/), which uses a subset of this data.\n",
+    "We will also revisit this dataset in the context of modeling in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Vectorized String Operations](03.10-Working-With-Strings.ipynb) | [Contents](Index.ipynb) | [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.11-Working-with-Time-Series.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/03.12-Performance-Eval-and-Query.ipynb b/notebooks_v1/03.12-Performance-Eval-and-Query.ipynb
new file mode 100644
index 000000000..b6e2a142b
--- /dev/null
+++ b/notebooks_v1/03.12-Performance-Eval-and-Query.ipynb
@@ -0,0 +1,1150 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Working with Time Series](03.11-Working-with-Time-Series.ipynb) | [Contents](Index.ipynb) | [Further Resources](03.13-Further-Resources.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.12-Performance-Eval-and-Query.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# High-Performance Pandas: eval() and query()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we've already seen in previous sections, the power of the PyData stack is built upon the ability of NumPy and Pandas to push basic operations into C via an intuitive syntax: examples are vectorized/broadcasted operations in NumPy, and grouping-type operations in Pandas.\n",
+    "While these abstractions are efficient and effective for many common use cases, they often rely on the creation of temporary intermediate objects, which can cause undue overhead in computational time and memory use.\n",
+    "\n",
+    "As of version 0.13 (released January 2014), Pandas includes some experimental tools that allow you to directly access C-speed operations without costly allocation of intermediate arrays.\n",
+    "These are the ``eval()`` and ``query()`` functions, which rely on the [Numexpr](https://github.com/pydata/numexpr) package.\n",
+    "In this notebook we will walk through their use and give some rules-of-thumb about when you might think about using them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Motivating ``query()`` and ``eval()``: Compound Expressions\n",
+    "\n",
+    "We've seen previously that NumPy and Pandas support fast vectorized operations; for example, when adding the elements of two arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 loops, best of 3: 3.39 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "rng = np.random.RandomState(42)\n",
+    "x = rng.rand(1000000)\n",
+    "y = rng.rand(1000000)\n",
+    "%timeit x + y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As discussed in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb), this is much faster than doing the addition via a Python loop or comprehension:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loop, best of 3: 266 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit np.fromiter((xi + yi for xi, yi in zip(x, y)), dtype=x.dtype, count=len(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But this abstraction can become less efficient when computing compound expressions.\n",
+    "For example, consider the following expression:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "mask = (x > 0.5) & (y < 0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because NumPy evaluates each subexpression, this is roughly equivalent to the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "tmp1 = (x > 0.5)\n",
+    "tmp2 = (y < 0.5)\n",
+    "mask = tmp1 & tmp2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In other words, *every intermediate step is explicitly allocated in memory*. If the ``x`` and ``y`` arrays are very large, this can lead to significant memory and computational overhead.\n",
+    "The Numexpr library gives you the ability to compute this type of compound expression element by element, without the need to allocate full intermediate arrays.\n",
+    "The [Numexpr documentation](https://github.com/pydata/numexpr) has more details, but for the time being it is sufficient to say that the library accepts a *string* giving the NumPy-style expression you'd like to compute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numexpr\n",
+    "mask_numexpr = numexpr.evaluate('(x > 0.5) & (y < 0.5)')\n",
+    "np.allclose(mask, mask_numexpr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The benefit here is that Numexpr evaluates the expression in a way that does not use full-sized temporary arrays, and thus can be much more efficient than NumPy, especially for large arrays.\n",
+    "The Pandas ``eval()`` and ``query()`` tools that we will discuss here are conceptually similar, and depend on the Numexpr package."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ``pandas.eval()`` for Efficient Operations\n",
+    "\n",
+    "The ``eval()`` function in Pandas uses string expressions to efficiently compute operations using ``DataFrame``s.\n",
+    "For example, consider the following ``DataFrame``s:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "nrows, ncols = 100000, 100\n",
+    "rng = np.random.RandomState(42)\n",
+    "df1, df2, df3, df4 = (pd.DataFrame(rng.rand(nrows, ncols))\n",
+    "                      for i in range(4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To compute the sum of all four ``DataFrame``s using the typical Pandas approach, we can just write the sum:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 87.1 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit df1 + df2 + df3 + df4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The same result can be computed via ``pd.eval`` by constructing the expression as a string:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 42.2 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit pd.eval('df1 + df2 + df3 + df4')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``eval()`` version of this expression is about 50% faster (and uses much less memory), while giving the same result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.allclose(df1 + df2 + df3 + df4,\n",
+    "            pd.eval('df1 + df2 + df3 + df4'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Operations supported by ``pd.eval()``\n",
+    "\n",
+    "As of Pandas v0.16, ``pd.eval()`` supports a wide range of operations.\n",
+    "To demonstrate these, we'll use the following integer ``DataFrame``s:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df1, df2, df3, df4, df5 = (pd.DataFrame(rng.randint(0, 1000, (100, 3)))\n",
+    "                           for i in range(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Arithmetic operators\n",
+    "``pd.eval()`` supports all arithmetic operators. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result1 = -df1 * df2 / (df3 + df4) - df5\n",
+    "result2 = pd.eval('-df1 * df2 / (df3 + df4) - df5')\n",
+    "np.allclose(result1, result2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Comparison operators\n",
+    "``pd.eval()`` supports all comparison operators, including chained expressions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result1 = (df1 < df2) & (df2 <= df3) & (df3 != df4)\n",
+    "result2 = pd.eval('df1 < df2 <= df3 != df4')\n",
+    "np.allclose(result1, result2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Bitwise operators\n",
+    "``pd.eval()`` supports the ``&`` and ``|`` bitwise operators:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result1 = (df1 < 0.5) & (df2 < 0.5) | (df3 < df4)\n",
+    "result2 = pd.eval('(df1 < 0.5) & (df2 < 0.5) | (df3 < df4)')\n",
+    "np.allclose(result1, result2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition, it supports the use of the literal ``and`` and ``or`` in Boolean expressions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result3 = pd.eval('(df1 < 0.5) and (df2 < 0.5) or (df3 < df4)')\n",
+    "np.allclose(result1, result3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Object attributes and indices\n",
+    "\n",
+    "``pd.eval()`` supports access to object attributes via the ``obj.attr`` syntax, and indexes via the ``obj[index]`` syntax:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result1 = df2.T[0] + df3.iloc[1]\n",
+    "result2 = pd.eval('df2.T[0] + df3.iloc[1]')\n",
+    "np.allclose(result1, result2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Other operations\n",
+    "Other operations such as function calls, conditional statements, loops, and other more involved constructs are currently *not* implemented in ``pd.eval()``.\n",
+    "If you'd like to execute these more complicated types of expressions, you can use the Numexpr library itself."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ``DataFrame.eval()`` for Column-Wise Operations\n",
+    "\n",
+    "Just as Pandas has a top-level ``pd.eval()`` function, ``DataFrame``s have an ``eval()`` method that works in similar ways.\n",
+    "The benefit of the ``eval()`` method is that columns can be referred to *by name*.\n",
+    "We'll use this labeled array as an example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.375506</td>\n",
+       "      <td>0.406939</td>\n",
+       "      <td>0.069938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.069087</td>\n",
+       "      <td>0.235615</td>\n",
+       "      <td>0.154374</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.677945</td>\n",
+       "      <td>0.433839</td>\n",
+       "      <td>0.652324</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.264038</td>\n",
+       "      <td>0.808055</td>\n",
+       "      <td>0.347197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.589161</td>\n",
+       "      <td>0.252418</td>\n",
+       "      <td>0.557789</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B         C\n",
+       "0  0.375506  0.406939  0.069938\n",
+       "1  0.069087  0.235615  0.154374\n",
+       "2  0.677945  0.433839  0.652324\n",
+       "3  0.264038  0.808055  0.347197\n",
+       "4  0.589161  0.252418  0.557789"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(rng.rand(1000, 3), columns=['A', 'B', 'C'])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using ``pd.eval()`` as above, we can compute expressions with the three columns like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result1 = (df['A'] + df['B']) / (df['C'] - 1)\n",
+    "result2 = pd.eval(\"(df.A + df.B) / (df.C - 1)\")\n",
+    "np.allclose(result1, result2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``DataFrame.eval()`` method allows much more succinct evaluation of expressions with the columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result3 = df.eval('(A + B) / (C - 1)')\n",
+    "np.allclose(result1, result3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice here that we treat *column names as variables* within the evaluated expression, and the result is what we would wish."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Assignment in DataFrame.eval()\n",
+    "\n",
+    "In addition to the options just discussed, ``DataFrame.eval()``  also allows assignment to any column.\n",
+    "Let's use the ``DataFrame`` from before, which has columns ``'A'``, ``'B'``, and ``'C'``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.375506</td>\n",
+       "      <td>0.406939</td>\n",
+       "      <td>0.069938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.069087</td>\n",
+       "      <td>0.235615</td>\n",
+       "      <td>0.154374</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.677945</td>\n",
+       "      <td>0.433839</td>\n",
+       "      <td>0.652324</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.264038</td>\n",
+       "      <td>0.808055</td>\n",
+       "      <td>0.347197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.589161</td>\n",
+       "      <td>0.252418</td>\n",
+       "      <td>0.557789</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B         C\n",
+       "0  0.375506  0.406939  0.069938\n",
+       "1  0.069087  0.235615  0.154374\n",
+       "2  0.677945  0.433839  0.652324\n",
+       "3  0.264038  0.808055  0.347197\n",
+       "4  0.589161  0.252418  0.557789"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use ``df.eval()`` to create a new column ``'D'`` and assign to it a value computed from the other columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.375506</td>\n",
+       "      <td>0.406939</td>\n",
+       "      <td>0.069938</td>\n",
+       "      <td>11.187620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.069087</td>\n",
+       "      <td>0.235615</td>\n",
+       "      <td>0.154374</td>\n",
+       "      <td>1.973796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.677945</td>\n",
+       "      <td>0.433839</td>\n",
+       "      <td>0.652324</td>\n",
+       "      <td>1.704344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.264038</td>\n",
+       "      <td>0.808055</td>\n",
+       "      <td>0.347197</td>\n",
+       "      <td>3.087857</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.589161</td>\n",
+       "      <td>0.252418</td>\n",
+       "      <td>0.557789</td>\n",
+       "      <td>1.508776</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B         C          D\n",
+       "0  0.375506  0.406939  0.069938  11.187620\n",
+       "1  0.069087  0.235615  0.154374   1.973796\n",
+       "2  0.677945  0.433839  0.652324   1.704344\n",
+       "3  0.264038  0.808055  0.347197   3.087857\n",
+       "4  0.589161  0.252418  0.557789   1.508776"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.eval('D = (A + B) / C', inplace=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the same way, any existing column can be modified:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.375506</td>\n",
+       "      <td>0.406939</td>\n",
+       "      <td>0.069938</td>\n",
+       "      <td>-0.449425</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.069087</td>\n",
+       "      <td>0.235615</td>\n",
+       "      <td>0.154374</td>\n",
+       "      <td>-1.078728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.677945</td>\n",
+       "      <td>0.433839</td>\n",
+       "      <td>0.652324</td>\n",
+       "      <td>0.374209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.264038</td>\n",
+       "      <td>0.808055</td>\n",
+       "      <td>0.347197</td>\n",
+       "      <td>-1.566886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.589161</td>\n",
+       "      <td>0.252418</td>\n",
+       "      <td>0.557789</td>\n",
+       "      <td>0.603708</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B         C         D\n",
+       "0  0.375506  0.406939  0.069938 -0.449425\n",
+       "1  0.069087  0.235615  0.154374 -1.078728\n",
+       "2  0.677945  0.433839  0.652324  0.374209\n",
+       "3  0.264038  0.808055  0.347197 -1.566886\n",
+       "4  0.589161  0.252418  0.557789  0.603708"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.eval('D = (A - B) / C', inplace=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Local variables in DataFrame.eval()\n",
+    "\n",
+    "The ``DataFrame.eval()`` method supports an additional syntax that lets it work with local Python variables.\n",
+    "Consider the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "column_mean = df.mean(1)\n",
+    "result1 = df['A'] + column_mean\n",
+    "result2 = df.eval('A + @column_mean')\n",
+    "np.allclose(result1, result2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``@`` character here marks a *variable name* rather than a *column name*, and lets you efficiently evaluate expressions involving the two \"namespaces\": the namespace of columns, and the namespace of Python objects.\n",
+    "Notice that this ``@`` character is only supported by the ``DataFrame.eval()`` *method*, not by the ``pandas.eval()`` *function*, because the ``pandas.eval()`` function only has access to the one (Python) namespace."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DataFrame.query() Method\n",
+    "\n",
+    "The ``DataFrame`` has another method based on evaluated strings, called the ``query()`` method.\n",
+    "Consider the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result1 = df[(df.A < 0.5) & (df.B < 0.5)]\n",
+    "result2 = pd.eval('df[(df.A < 0.5) & (df.B < 0.5)]')\n",
+    "np.allclose(result1, result2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As with the example used in our discussion of ``DataFrame.eval()``, this is an expression involving columns of the ``DataFrame``.\n",
+    "It cannot be expressed using the ``DataFrame.eval()`` syntax, however!\n",
+    "Instead, for this type of filtering operation, you can use the ``query()`` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result2 = df.query('A < 0.5 and B < 0.5')\n",
+    "np.allclose(result1, result2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition to being a more efficient computation, compared to the masking expression this is much easier to read and understand.\n",
+    "Note that the ``query()`` method also accepts the ``@`` flag to mark local variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Cmean = df['C'].mean()\n",
+    "result1 = df[(df.A < Cmean) & (df.B < Cmean)]\n",
+    "result2 = df.query('A < @Cmean and B < @Cmean')\n",
+    "np.allclose(result1, result2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Performance: When to Use These Functions\n",
+    "\n",
+    "When considering whether to use these functions, there are two considerations: *computation time* and *memory use*.\n",
+    "Memory use is the most predictable aspect. As already mentioned, every compound expression involving NumPy arrays or Pandas ``DataFrame``s will result in implicit creation of temporary arrays:\n",
+    "For example, this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x = df[(df.A < 0.5) & (df.B < 0.5)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Is roughly equivalent to this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "tmp1 = df.A < 0.5\n",
+    "tmp2 = df.B < 0.5\n",
+    "tmp3 = tmp1 & tmp2\n",
+    "x = df[tmp3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the size of the temporary ``DataFrame``s is significant compared to your available system memory (typically several gigabytes) then it's a good idea to use an ``eval()`` or ``query()`` expression.\n",
+    "You can check the approximate size of your array in bytes using this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "32000"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.values.nbytes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On the performance side, ``eval()`` can be faster even when you are not maxing-out your system memory.\n",
+    "The issue is how your temporary ``DataFrame``s compare to the size of the L1 or L2 CPU cache on your system (typically a few megabytes in 2016); if they are much bigger, then ``eval()`` can avoid some potentially slow movement of values between the different memory caches.\n",
+    "In practice, I find that the difference in computation time between the traditional methods and the ``eval``/``query`` method is usually not significant–if anything, the traditional method is faster for smaller arrays!\n",
+    "The benefit of ``eval``/``query`` is mainly in the saved memory, and the sometimes cleaner syntax they offer.\n",
+    "\n",
+    "We've covered most of the details of ``eval()`` and ``query()`` here; for more information on these, you can refer to the Pandas documentation.\n",
+    "In particular, different parsers and engines can be specified for running these queries; for details on this, see the discussion within the [\"Enhancing Performance\" section](http://pandas.pydata.org/pandas-docs/dev/enhancingperf.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Working with Time Series](03.11-Working-with-Time-Series.ipynb) | [Contents](Index.ipynb) | [Further Resources](03.13-Further-Resources.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.12-Performance-Eval-and-Query.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/03.13-Further-Resources.ipynb b/notebooks_v1/03.13-Further-Resources.ipynb
new file mode 100644
index 000000000..16c8a8ebd
--- /dev/null
+++ b/notebooks_v1/03.13-Further-Resources.ipynb
@@ -0,0 +1,96 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) | [Contents](Index.ipynb) | [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.13-Further-Resources.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "In this chapter, we've covered many of the basics of using Pandas effectively for data analysis.\n",
+    "Still, much has been omitted from our discussion.\n",
+    "To learn more about Pandas, I recommend the following resources:\n",
+    "\n",
+    "- [Pandas online documentation](http://pandas.pydata.org/): This is the go-to source for complete documentation of the package. While the examples in the documentation tend to be small generated datasets, the description of the options is complete and generally very useful for understanding the use of various functions.\n",
+    "\n",
+    "- [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do) Written by Wes McKinney (the original creator of Pandas), this book contains much more detail on the Pandas package than we had room for in this chapter. In particular, he takes a deep dive into tools for time series, which were his bread and butter as a financial consultant. The book also has many entertaining examples of applying Pandas to gain insight from real-world datasets. Keep in mind, though, that the book is now several years old, and the Pandas package has quite a few new features that this book does not cover (but be on the lookout for a new edition in 2017).\n",
+    "\n",
+    "- [Stack Overflow](http://stackoverflow.com/questions/tagged/pandas): Pandas has so many users that any question you have has likely been asked and answered on Stack Overflow. Using Pandas is a case where some Google-Fu is your best friend. Simply go to your favorite search engine and type in the question, problem, or error you're coming across–more than likely you'll find your answer on a Stack Overflow page.\n",
+    "\n",
+    "- [Pandas on PyVideo](http://pyvideo.org/search?q=pandas): From PyCon to SciPy to PyData, many conferences have featured tutorials from Pandas developers and power users. The PyCon tutorials in particular tend to be given by very well-vetted presenters.\n",
+    "\n",
+    "Using these resources, combined with the walk-through given in this chapter, my hope is that you'll be poised to use Pandas to tackle any data analysis problem you come across!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) | [Contents](Index.ipynb) | [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.13-Further-Resources.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/04.00-Introduction-To-Matplotlib.ipynb b/notebooks_v1/04.00-Introduction-To-Matplotlib.ipynb
new file mode 100644
index 000000000..ebf07e3bd
--- /dev/null
+++ b/notebooks_v1/04.00-Introduction-To-Matplotlib.ipynb
@@ -0,0 +1,532 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.00-Introduction-To-Matplotlib.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualization with Matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll now take an in-depth look at the Matplotlib package for visualization in Python.\n",
+    "Matplotlib is a multi-platform data visualization library built on NumPy arrays, and designed to work with the broader SciPy stack.\n",
+    "It was conceived by John Hunter in 2002, originally as a patch to IPython for enabling interactive MATLAB-style plotting via gnuplot from the IPython command line.\n",
+    "IPython's creator, Fernando Perez, was at the time scrambling to finish his PhD, and let John know he wouldn’t have time to review the patch for several months.\n",
+    "John took this as a cue to set out on his own, and the Matplotlib package was born, with version 0.1 released in 2003.\n",
+    "It received an early boost when it was adopted as the plotting package of choice of the Space Telescope Science Institute (the folks behind the Hubble Telescope), which financially supported Matplotlib’s development and greatly expanded its capabilities.\n",
+    "\n",
+    "One of Matplotlib’s most important features is its ability to play well with many operating systems and graphics backends.\n",
+    "Matplotlib supports dozens of backends and output types, which means you can count on it to work regardless of which operating system you are using or which output format you wish.\n",
+    "This cross-platform, everything-to-everyone approach has been one of the great strengths of Matplotlib.\n",
+    "It has led to a large user base, which in turn has led to an active developer base and Matplotlib’s powerful tools and ubiquity within the scientific Python world.\n",
+    "\n",
+    "In recent years, however, the interface and style of Matplotlib have begun to show their age.\n",
+    "Newer tools like ggplot and ggvis in the R language, along with web visualization toolkits based on D3js and HTML5 canvas, often make Matplotlib feel clunky and old-fashioned.\n",
+    "Still, I'm of the opinion that we cannot ignore Matplotlib's strength as a well-tested, cross-platform graphics engine.\n",
+    "Recent Matplotlib versions make it relatively easy to set new global plotting styles (see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb)), and people have been developing new packages that build on its powerful internals to drive Matplotlib via cleaner, more modern APIs—for example, Seaborn (discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)), [ggpy](http://yhat.github.io/ggpy/), [HoloViews](http://holoviews.org/), [Altair](http://altair-viz.github.io/), and even Pandas itself can be used as wrappers around Matplotlib's API.\n",
+    "Even with wrappers like these, it is still often useful to dive into Matplotlib's syntax to adjust the final plot output.\n",
+    "For this reason, I believe that Matplotlib itself will remain a vital piece of the data visualization stack, even if new tools mean the community gradually moves away from using the Matplotlib API directly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## General Matplotlib Tips\n",
+    "\n",
+    "Before we dive into the details of creating visualizations with Matplotlib, there are a few useful things you should know about using the package."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing Matplotlib\n",
+    "\n",
+    "Just as we use the ``np`` shorthand for NumPy and the ``pd`` shorthand for Pandas, we will use some standard shorthands for Matplotlib imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``plt`` interface is what we will use most often, as we shall see throughout this chapter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting Styles\n",
+    "\n",
+    "We will use the ``plt.style`` directive to choose appropriate aesthetic styles for our figures.\n",
+    "Here we will set the ``classic`` style, which ensures that the plots we create use the classic Matplotlib style:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.style.use('classic')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Throughout this section, we will adjust this style as needed.\n",
+    "Note that the stylesheets used here are supported as of Matplotlib version 1.5; if you are using an earlier version of Matplotlib, only the default style is available.\n",
+    "For more information on stylesheets, see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ``show()`` or No ``show()``? How to Display Your Plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A visualization you can't see won't be of much use, but just how you view your Matplotlib plots depends on the context.\n",
+    "The best use of Matplotlib differs depending on how you are using it; roughly, the three applicable contexts are using Matplotlib in a script, in an IPython terminal, or in an IPython notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Plotting from a script\n",
+    "\n",
+    "If you are using Matplotlib from within a script, the function ``plt.show()`` is your friend.\n",
+    "``plt.show()`` starts an event loop, looks for all currently active figure objects, and opens one or more interactive windows that display your figure or figures.\n",
+    "\n",
+    "So, for example, you may have a file called *myplot.py* containing the following:\n",
+    "\n",
+    "```python\n",
+    "# ------- file: myplot.py ------\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "\n",
+    "plt.plot(x, np.sin(x))\n",
+    "plt.plot(x, np.cos(x))\n",
+    "\n",
+    "plt.show()\n",
+    "```\n",
+    "\n",
+    "You can then run this script from the command-line prompt, which will result in a window opening with your figure displayed:\n",
+    "\n",
+    "```\n",
+    "$ python myplot.py\n",
+    "```\n",
+    "\n",
+    "The ``plt.show()`` command does a lot under the hood, as it must interact with your system's interactive graphical backend.\n",
+    "The details of this operation can vary greatly from system to system and even installation to installation, but matplotlib does its best to hide all these details from you.\n",
+    "\n",
+    "One thing to be aware of: the ``plt.show()`` command should be used *only once* per Python session, and is most often seen at the very end of the script.\n",
+    "Multiple ``show()`` commands can lead to unpredictable backend-dependent behavior, and should mostly be avoided."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Plotting from an IPython shell\n",
+    "\n",
+    "It can be very convenient to use Matplotlib interactively within an IPython shell (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\n",
+    "IPython is built to work well with Matplotlib if you specify Matplotlib mode.\n",
+    "To enable this mode, you can use the ``%matplotlib`` magic command after starting ``ipython``:\n",
+    "\n",
+    "```ipython\n",
+    "In [1]: %matplotlib\n",
+    "Using matplotlib backend: TkAgg\n",
+    "\n",
+    "In [2]: import matplotlib.pyplot as plt\n",
+    "```\n",
+    "\n",
+    "At this point, any ``plt`` plot command will cause a figure window to open, and further commands can be run to update the plot.\n",
+    "Some changes (such as modifying properties of lines that are already drawn) will not draw automatically: to force an update, use ``plt.draw()``.\n",
+    "Using ``plt.show()`` in Matplotlib mode is not required."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Plotting from an IPython notebook\n",
+    "\n",
+    "The IPython notebook is a browser-based interactive data analysis tool that can combine narrative, code, graphics, HTML elements, and much more into a single executable document (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\n",
+    "\n",
+    "Plotting interactively within an IPython notebook can be done with the ``%matplotlib`` command, and works in a similar way to the IPython shell.\n",
+    "In the IPython notebook, you also have the option of embedding graphics directly in the notebook, with two possible options:\n",
+    "\n",
+    "- ``%matplotlib notebook`` will lead to *interactive* plots embedded within the notebook\n",
+    "- ``%matplotlib inline`` will lead to *static* images of your plot embedded in the notebook\n",
+    "\n",
+    "For this book, we will generally opt for ``%matplotlib inline``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After running this command (it needs to be done only once per kernel/session), any cell within the notebook that creates a plot will embed a PNG image of the resulting graphic:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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W/n4vc2LxF9Jhy84JIaSUkvfeU0sL/+9/uhMFtoUL4fXXYYV3erMCyvHjUKcO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\nbxiGEYBM8TcMwwhApvgbhmEEoP8Hf41iEpQega0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c21e668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "plt.plot(x, np.sin(x), '-')\n",
+    "plt.plot(x, np.cos(x), '--');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Saving Figures to File\n",
+    "\n",
+    "One nice feature of Matplotlib is the ability to save figures in a wide variety of formats.\n",
+    "Saving a figure can be done using the ``savefig()`` command.\n",
+    "For example, to save the previous figure as a PNG file, you can run this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "fig.savefig('my_figure.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now have a file called ``my_figure.png`` in the current working directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-r--r--  1 jakevdp  staff    16K Aug 11 10:59 my_figure.png\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh my_figure.png"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To confirm that it contains what we think it contains, let's use the IPython ``Image`` object to display the contents of this file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TQGYm7yR8PMp6hIjECDjZiHPbHxnJ3kdqXm4V5BokVFspS0vWrmvbNt5J1MVo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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image('my_figure.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In ``savefig()``, the file format is inferred from the extension of the given filename.\n",
+    "Depending on what backends you have installed, many different file formats are available.\n",
+    "The list of supported file types can be found for your system by using the following method of the figure canvas object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'eps': 'Encapsulated Postscript',\n",
+       " 'jpeg': 'Joint Photographic Experts Group',\n",
+       " 'jpg': 'Joint Photographic Experts Group',\n",
+       " 'pdf': 'Portable Document Format',\n",
+       " 'pgf': 'PGF code for LaTeX',\n",
+       " 'png': 'Portable Network Graphics',\n",
+       " 'ps': 'Postscript',\n",
+       " 'raw': 'Raw RGBA bitmap',\n",
+       " 'rgba': 'Raw RGBA bitmap',\n",
+       " 'svg': 'Scalable Vector Graphics',\n",
+       " 'svgz': 'Scalable Vector Graphics',\n",
+       " 'tif': 'Tagged Image File Format',\n",
+       " 'tiff': 'Tagged Image File Format'}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig.canvas.get_supported_filetypes()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that when saving your figure, it's not necessary to use ``plt.show()`` or related commands discussed earlier."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Two Interfaces for the Price of One\n",
+    "\n",
+    "A potentially confusing feature of Matplotlib is its dual interfaces: a convenient MATLAB-style state-based interface, and a more powerful object-oriented interface. We'll quickly highlight the differences between the two here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### MATLAB-style Interface\n",
+    "\n",
+    "Matplotlib was originally written as a Python alternative for MATLAB users, and much of its syntax reflects that fact.\n",
+    "The MATLAB-style tools are contained in the pyplot (``plt``) interface.\n",
+    "For example, the following code will probably look quite familiar to MATLAB users:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DOr4O69ertmmjWrGiaq9eqps22fPxeC02bFB95BHVffdV7d5ddfPmmF8iLtLx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\nwpx7rjX+114L48fbHUEyy8z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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d658438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()  # create a plot figure\n",
+    "\n",
+    "# create the first of two panels and set current axis\n",
+    "plt.subplot(2, 1, 1) # (rows, columns, panel number)\n",
+    "plt.plot(x, np.sin(x))\n",
+    "\n",
+    "# create the second panel and set current axis\n",
+    "plt.subplot(2, 1, 2)\n",
+    "plt.plot(x, np.cos(x));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is important to note that this interface is *stateful*: it keeps track of the \"current\" figure and axes, which are where all ``plt`` commands are applied.\n",
+    "You can get a reference to these using the ``plt.gcf()`` (get current figure) and ``plt.gca()`` (get current axes) routines.\n",
+    "\n",
+    "While this stateful interface is fast and convenient for simple plots, it is easy to run into problems.\n",
+    "For example, once the second panel is created, how can we go back and add something to the first?\n",
+    "This is possible within the MATLAB-style interface, but a bit clunky.\n",
+    "Fortunately, there is a better way."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Object-oriented interface\n",
+    "\n",
+    "The object-oriented interface is available for these more complicated situations, and for when you want more control over your figure.\n",
+    "Rather than depending on some notion of an \"active\" figure or axes, in the object-oriented interface the plotting functions are *methods* of explicit ``Figure`` and ``Axes`` objects.\n",
+    "To re-create the previous plot using this style of plotting, you might do the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DOr4O69ertmmjWrGiaq9eqps22fPxeC02bFB95BHVffdV7d5ddfPmmF8iLtLx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\nUTSOiswAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d64f240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# First create a grid of plots\n",
+    "# ax will be an array of two Axes objects\n",
+    "fig, ax = plt.subplots(2)\n",
+    "\n",
+    "# Call plot() method on the appropriate object\n",
+    "ax[0].plot(x, np.sin(x))\n",
+    "ax[1].plot(x, np.cos(x));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more simple plots, the choice of which style to use is largely a matter of preference, but the object-oriented approach can become a necessity as plots become more complicated.\n",
+    "Throughout this chapter, we will switch between the MATLAB-style and object-oriented interfaces, depending on what is most convenient.\n",
+    "In most cases, the difference is as small as switching ``plt.plot()`` to ``ax.plot()``, but there are a few gotchas that we will highlight as they come up in the following sections."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.00-Introduction-To-Matplotlib.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/04.01-Simple-Line-Plots.ipynb b/notebooks_v1/04.01-Simple-Line-Plots.ipynb
new file mode 100644
index 000000000..03acda4e3
--- /dev/null
+++ b/notebooks_v1/04.01-Simple-Line-Plots.ipynb
@@ -0,0 +1,647 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.01-Simple-Line-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Line Plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perhaps the simplest of all plots is the visualization of a single function $y = f(x)$.\n",
+    "Here we will take a first look at creating a simple plot of this type.\n",
+    "As with all the following sections, we'll start by setting up the notebook for plotting and  importing the packages we will use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('seaborn-whitegrid')\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For all Matplotlib plots, we start by creating a figure and an axes.\n",
+    "In their simplest form, a figure and axes can be created as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wYf3888/q6OgoxIhzItsezp49qzt37mjv3r365JNPFIvF9N133xVq1LzLtotly5apvLxc\nlZWV8nq9qqure+xTrCXZdjE0NKTz58+rp6dHPT09unXrls6dO1eoUQtqtt3Madhramr0008/SVLW\nm5mSyaT6+/v1yiuv5PLt/1Wy7UKSDhw4oPv37+v48ePpr2QsyraHcDisb775RidOnNA777yjxsZG\nbdy4sVCj5l22XZSVlenevXvpHyLG43G9/PLLBZlzLmTbRXFxsRYtWiSfz5f+2+3o6GihRp1T//s3\n19l20/XO06cRCATU19eX/r40EokoFoulb2bav3+/du/eLcdxFAwGtXz58ly+/b9Ktl2sWbNGp0+f\nVm1trcLhsDwej5qamrRhw4YCT517br8n/kvcdtHe3q59+/ZJktauXav169cXcty8ctvF3/9izOfz\nqby8XJs2bSrwxHPj758lPGs3uUEJAIzhBiUAMIawA4AxhB0AjCHsAGAMYQcAYwg7ABhD2AHAGMIO\nAMb8H/Ams7WyTe/nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x102e12390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = plt.axes()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In Matplotlib, the *figure* (an instance of the class ``plt.Figure``) can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels.\n",
+    "The *axes* (an instance of the class ``plt.Axes``) is what we see above: a bounding box with ticks and labels, which will eventually contain the plot elements that make up our visualization.\n",
+    "Throughout this book, we'll commonly use the variable name ``fig`` to refer to a figure instance, and ``ax`` to refer to an axes instance or group of axes instances.\n",
+    "\n",
+    "Once we have created an axes, we can use the ``ax.plot`` function to plot some data. Let's start with a simple sinusoid:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kkB09Ktvi0e2WLJHRNt7eqpOQXnl6AjExcq+MH+/gMRz50OLFixEUFIRFixah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\n5P4f+jwGTxP/IVYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107527f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = plt.axes()\n",
+    "\n",
+    "x = np.linspace(0, 10, 1000)\n",
+    "ax.plot(x, np.sin(x));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, we can use the pylab interface and let the figure and axes be created for us in the background\n",
+    "(see [Two Interfaces for the Price of One](04.00-Introduction-To-Matplotlib.ipynb#Two-Interfaces-for-the-Price-of-One) for a discussion of these two interfaces):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kkB09Ktvi0e2WLJHRNt7eqpOQXnl6AjExcq+MH+/gMRz50OLFixEUFIRFixah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\n5P4f+jwGTxP/IVYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107066518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, np.sin(x));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we want to create a single figure with multiple lines, we can simply call the ``plot`` function multiple times:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3b7o6fO18EZPGzkxERwfw8pLHA0qDo7eOolW9VmhWpxltKaIhhQmTvbk9DHUM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\nPYfD4TAON3oOh8NhnP8DK1eUocOflXQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1076d5cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, np.sin(x))\n",
+    "plt.plot(x, np.cos(x));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's all there is to plotting simple functions in Matplotlib!\n",
+    "We'll now dive into some more details about how to control the appearance of the axes and lines."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adjusting the Plot: Line Colors and Styles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first adjustment you might wish to make to a plot is to control the line colors and styles.\n",
+    "The ``plt.plot()`` function takes additional arguments that can be used to specify these.\n",
+    "To adjust the color, you can use the ``color`` keyword, which accepts a string argument representing virtually any imaginable color.\n",
+    "The color can be specified in a variety of ways:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xR5W7Gc6ekZFBmzZt3I4QsEfz4ObM6jnL/QVadVJRiCHXeywDQNu2bcnKynI/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rQk4I5eV+KXgeUEWM/jcDjIz88nMzNT+6NmS5ISYPli+FPzRJBpO0g2LEF36WcDoq/+Amsoo3kR\nwn+/Dc99V5PFInI5zjtEkEQPa2vd+5dfhg8/hIsXA2QY0CepD48NfYxf7fgVIITLMjJgnm97LPym\nZ8+eFBUVYbVaqbHW8Py25/nznD/r52BBNDhETxQpHODGjRvExMSoNwUrlx89Dh+vg1sVYK+Byg8g\n6WldHWw6I+jJVPbzO/GDd1fCqCEBnYJ1xbfL0RsjIfEJqHiT4qIbhIaGqj7UIYuHFkB+ARw6DJX/\ngORndDlqtiSSRCbzn2zkh0gffwmZ3WH8CF1taImFanbxErP4n3aaNp06iVz4c88FyLhmfjXpV2y4\ntIEtZ47z6qtiuYjeQVt4eDgZGRnk5+fz692/5p4+9zC6y2h9jQCRqzftxGbK4/r162Rmaqtu6pKM\nzrBopoigK9+F6CkQ1kt3M6bzOkf5K+U394qF5s+s0N0Gb3y7HD1A9DQcElhurdP3qNmS0BD4yePw\n+h8heASE99PfBsRikobGKk7d+iP89ImA2OBkG8+TzULSca3W+eyzQqt+jTqb/hQRFx7HK3e/wkOf\nPM1jT9hlL/1Wm65du1JVW8W5gnO8Nu21wBgRFAcJD9N48w+kp3eWv/RbbR5bCodz4Ng+8eUTAOLo\nyiTp3/m67lGk5feKdaIdjG+fozcYuVK7gF7xO4kOD2Df3rhkyLDAmriAmWCUjMz/v3FsfeY85i46\nFPLcUMA+LrGe6bzu9prQULHQ45lnAluY7Vn9OHWVkSTO8XMJhR8YjAbeKXiH57KfIzE8MWB2lNsm\nIDka6JF4PmA2EBkKTwTDWxHQqL9ch5PRW4dSH1rDmUeUL4vRkm+do7916xYVdXEY4hZA+Z/8Xjmo\nCKkRbv0efv4ofLUXLl7R3waATbtIPxLMoLBH2UJg8iJNNLCBp5jNn4jAc4534kRYuBB+/nOdjGtD\nXR189wkjf5n5Nn848ipXqgLzd/vz4T9zxXqFLslduHr1akBsaGpqIi//Mvakn2Cs/gCabgXEDqo/\nhwmp0Hcg/P3TwNhQVUPQH95jvv2vbAn+JWYC9F544Fvl6G02G3l5eWRnZ2NMfBgaS9wOf2hK1cei\nC6jbQnjmO/Dyn6HRR41rf7lVAf/7T/ivHzM16FUK2c95VutrA7CNF0imL/2Rt0nr9ddhyxbxn978\n4hcwdSo8eV9vnr/reb771XdxyNB+UZNLFZd4Zc8r/HPhP8nKyqKsrIyqABxx8vLySExMJC55MMQt\nhFt/1D9osl0Vg5ApP4Wffw82bBdzIHoiSfDGWzBnChmZSxnCCr7iSe86ODrzrXH0kiSRm5tLamqq\n6A4whECnn0PF34TGjF5Yz4qF38k/FpW8+dMgNZnYD+TLKfuN3Q7/9SexAatvJmFEcx8f8zU/ULaJ\nSiH5bOE8K5nP32UvFYmNhXffFboyPqj3+s2GDfD112KxCIj5jyZHE7/b9zvdbGhoauCRNY/w0uSX\n6J3Ym5CQELKzs8nNzaWx0bvwmlqUlpZSW1tL7969xQ/iHwRHNdTqeA87GqDsdUj8rgiakhLg50/B\nf/weTD5IgvvLuq1w5bqQOgCm8jLVXOMEriepA0XAHb0vWtv+cOPGDRobG+nZs+edH4ZlQcIyKP0N\nSArWlPmKvQpKfwspP7uzv9JggJd+RPiBE7DniPY2ALzzhWgEf+KB2z/KYAyj+SFfstz3DToKqKOE\ndTzGvbxPJL4Vr6ZNgyeegOXLvWtbqcHVq+L3ffopODsIg43BfHLfJ/zp8J/YV+iDRrof/GzLz+gS\n26XVwu+kpCSSk5PJzc31KnqmBhaLhfz8fPr3739HztsQAp1+JZZ8NOjUA1vxF9HmGdNCYXXGXUIS\n/NW/6HO6yC+A/3sfXvv57SHDYMK4j4/ZxvOUckZ7G2QScEefn5+P2SxPL14pVVVVFBYW0r9///Y9\n87GLRERQ/ldtbw7JLiKQmOkQOab1Y3ExVD/3OLzyf3cGqbTi0An48hv47c/b9cxP5N8JJlzzfH0T\nDXzOIkbwPXqhTHjqpZeEBs6LL6psXBusVrj/ftHeOaGN4m1GbAbvLHiHZauXUVSrfJG3HD4/+zmb\n8jfx7sJ323WKZWZm0tjYSEFBgaY2NDU1cebMGXr06EFMTEzrB/+/9s49qqrrzuNfDAooPiiIwdUu\nNLpYBmPoIE07k2A0jiI+kviookJITJ0KY2vFKNbRoNVZFGPquFJSX4lk0IqWGEOT+DailyDgFTM8\ngpQKglyQyxu83Nc53/njVCMI3gcXDpXzWYu1BPe+fO/mnO/de5/f/v0G+khhwnd3AEJjj+pA82lA\nXwh4rXk0vjXmbaDsDnDMQmbJbmtoBTYmAGvekvJYPYQ3JiIEu5GC+dChvmd1WInsRj9u3Djk5eXB\nYLCtJqW16HQ6FBYWwt/fv/MQMCcnaQtHnw809dAeNQnU/qNmaxchYEb/8UBUOLBmG9BoZ6V5S5SU\nAVv+APz3O4DXo9EaA/AUFuIoSnAaanuLIFuAIL7AKgzDDzEFW+x+HWdnIDVVKib+yScOFPgQoiit\nGsaN67oO7By/OYgOisa8o/PQanw0h5EjyCjPwK9O/Qqpi1M7rdkwYMAATJw4ERqNBlptzzwIJInv\nvvsOw4cPx+jRoztv5D4FcJ8GVL9rc41Zq2m7LiUtG/Vu5zUbXF2AXZukqk49tUI2mYDY3wP/+i/A\nq52fJA9AOCbgNaQiDAJ6b1utSygj165dI0mWlpYyJyeHJpPJoa+v1+t59epVajQay41Nd8mypWTL\nJYdqIEnWJ5MVq0jhXpdNKisrpX/sOUSu2EDq2hyroaqGnP0WeTrdYlMti7iTo1jATx2rgeR5buJe\nBtLA1i7bPBgLKygsJL29ybNnHaHue0SR/PWvyZdfJtss/ClEUeSKkys458gcGswGh+q4lH+J3u95\n89TfTlls29TURJVKxfr6eodqEEWRRUVFzM3NpSAIFhoLZPUOsnobKZodquNuRSZZuojU3bDcOK+I\nnL6czLvpUA0UBDJuN7l2O2l+/Psz08QjnMfjXEKBjh2L+95pLX3C6EVR5M2bN6lWqx1m9vdNvqys\nzIZOJWTZIseafUMKeTuCNNU9ttkDcxMEcuv/kL+IJVu7/mCwCc1d8vX/IA+ftLpLJdXcSW/e5BcO\nkSBS5CX+jn+kP1upffzvtsHoSfLKFXLkSPKUZS+0ClEkN2wgJ00iGxqs62M0G/nq0Vc578/zqDfp\nHaKjoKaAPu/5MCk3yeo+DQ0NVKlUbGxsdIgGURRZXFxs270pGEjNRrL6d6TooMmb/m80/32hbffm\n5Wzy38PJG4WO0SAI5LY90r1p5UTMyDYm8RWe5AqHmX0jK3rX6M+ePcuYmJhO/+/YsWNcsGABlyxZ\nwq+//rrTNg+LvW/2OTk51Ou7d6O0trYyMzPTNpO/j76ELFtMNn3ZLQ0UBbLuIFn+Fml6vLGRHcxN\nEMjtH5BvvkPWWek0XVFym5yzgjyaZnPXCl5lAkcyl9YbTWcINPNLrmYin2MzLa+ubDV6kszIkMz+\n+HF7FH6P0Uj+8pdkUBBZW2tbX4PZwIXHFnJm8kw2tnXPaLPuZPHpXU9zz6U9Nvetq6ujSqWiVmv5\nunscgiCwoKCAarWaRqPRxs4Gsmqz9CXouqWDum/JskWsK7d+ovKAb9SS2Wde756GNj25cSe58rfk\nPdvej54tPMRpTOECGtm9sSjhOSZwZO8Z/Y4dOxgaGtqp0Wu1Ws6dO5cmk4ktLS2cO3dupxdKR7Gi\nKLK0tJQZGRlssHYq1YGamhqqVCrrtmu6wlAhGbR2j3TB2oq5maz6L7JyLWm27n08Ym6CQH6YLJm0\nvcvPM5el5euXF+3rT7KGhdzNsTzNGJpo+wdwM6v4v5zJQ5zGNlpnfvYYPUlev076+pIbN0qGbStV\nVWRwMBkaSjY12SWBJsHE1V+upt8Hfsy7m2dzf1EUuf/afo7cOZKfF31u91g0NTUxIyODt27dsrzd\n0gk6nY5qtZp5eXk0W9ii6BLRSNbsIst/Id1TNvcXycYT0ir7Xo7dY8Hr+eTMN8hDf5HuK1upqCIj\n1pKbd0mGbwcm6pnKZdzLyaxlsc39BQq8wt9zJ0exlOm9Z/RfffUVs7KyOjX6CxcuMC4u7sH3q1ev\nZl7eoxd9V2Jra2upUqlYXFxs9UxCr9ezsLCQmZmZjlm2Cq3SPmP5m6Qu17o+oki2XJBWBLV/smnZ\n2uVFfPEbckY4ufsjssXKrZxqLbkhXtqu+a7Eag1d0Uotj3I+EzmJpbRu6SxQ4HUe4nv04QVuoZkO\nGAsrqKkhQ0LIgADy6lXr+pjN5P790opg61b7vKAjSblJ9Nrpxa1fb6XOaN0srqSuhCHJIXz+T8+z\nSFtEsntjodfrmZubS7VazSYrP7kEQWBFRQVVKhVv375NURTt/v0kpXui6QuydAFZ/2dStHLiZCgn\nK98hK6JIozRp685YsForPft6az1ZfMtKDUbyyEnylWXStmc3x0KkyCz+kQn0YgZ3WT1xquINfsyX\n+RFfYiPLSdq+R28xwUlqaio+6RDWEB8fj9DQUGRnd/5Uu7W1tV341eDBg9HS0nkZv87w9PTECy+8\ngFu3biErKws+Pj7w9vaGu7t7u9Aykmhubsbdu3dRU1MDHx8fBAUFOaaIyIAh0pP9exmA9g+As5dU\nGGTwTx6tAmVuAHSZ0ik9J2dgVBzg6t99DYBUWDjgWSmt8WsrgQUhwMxgYPyY9qFlZgHIKwK+uCjV\n01w8B9i21iFFRIbAC0vwKfKRgpN4Ez/AeARiJfwwB4PQfixacReFSMU17IULhiEMn+GH+GkXr+x4\nRo4ETp0CDh8GFi4EnntOKvM3cyYwpMOf7c4d4LPPpCyUo0YB584BAQGO0RH540hMf2Y6fnP6Nxi7\nZyyifxKNxRMXY4JX+/S1RsEIVbkKH+d+jFMlpxD7YizW/mwtBj7V/bwtLi4uCAgIQHV1NfLz8zF0\n6FD4+PjAw8Pj+xj4f6DX66HValFZWQk3NzcEBATA3d0BtSKcnKQ6EG6TgbpEoPxzYNhrUprjgR3q\nQNAItP0f0HIaaLshpRUf/rpjSgKO8gIOxAMnzgDR70r31PwQIGgS4NKhDkRVDXBOJYVojvMFPkoA\nxnS/ZoUTnPAC/hNjMR3nsB5Z+ABBWIVJWIoR8G3X1oQ2lCEd13EA5VBhKrYiECvxFOzzNifS/uDx\n7OxsHDt2DO+//367n1+8eBFXrlxBXFwcAGD16tWIiorCxIkT27VTq9WYPPnxqXHb2tqg0WhQW1sL\ns9kMV1dXODs7w2w2Q6fTwcXFBd7e3vDx8YFLT9VzowDcSwdazkthmM5e0hcJCFopbtgtEBg6W7qg\n7ciIqdFoug5bu0+FBjj+FZB+FWgzAD/yAdxcpZOAtyuB0d7AK/8mnXgdMczON/t4zDCgAMdxA5+g\nAt/AA8/AHU+DENCEcuhQh/EIQSBW4hlMt/rE68NYNRZWYDAAR45IIZhXrwK+vsDo0dLB4Nu3gaYm\nICQEWLUKeOmlnks5XFBTgMScRKTdTINAAeN/MB5uzm6ob6tHcV0xnh35LH7u/3OsDFyJ4a7tk9w5\naiwEQUBNTQ2qq6vR0tICNzc3DBw4ECSh1+shCAI8PT0xevRoDBs2rOeyuhr+DjT/VZoYwUmKv3ca\nJNVzNlUCg8YA7i8DQ2dJacUfwlFjAb0B+OsFqRRhcSng4w14eUhpSCo0gNEkTbBenwE813MpSu8g\nG2rsx02kwRkuGA5fDMRg6FCLOtzEKDyPAETieSyHC9qfW7DGOx+mR4y+trYWK1asQGpqKgwGA5Ys\nWYKTJ09i0KD2n5xqtdreX62goKDQr7HF6B2amzYpKQm+vr6YNm0aIiIisGzZMpBETEzMIyZvq1AF\nBQUFBfvo1oxeQUFBQaHvI3sKBAUFBQWFnkUWoyeJuLg4hIWF4Y033mhX1b6/YTabsWHDBixfvhyL\nFy/GxYsy5MfvQ9TV1WHq1KmyFdToS+zfvx9hYWFYuHAhPv2092sF9AXMZjPWrXfWKFAAAAM7SURB\nVFuHsLAwhIeH99vr4ttvv0VERAQAoLy8HMuWLUN4eDi2bdtmVX9ZjP78+fMwGo1ISUnBunXrEB8v\nU93LPkBaWho8PDxw5MgRHDhwANu3b5dbkmyYzWbExcXB1dVVbimyk52djdzcXKSkpCA5ORlVVb1Y\nM6EPkZ6eDlEUkZKSgujoaOzevVtuSb3OwYMHsXnz5gc1B+Lj4xETE4PDhw9DFEWcP3/e4mvIYvRq\ntRrBwcEAgICAAOTn58sho08QGhqKNWvWAABEUXTMGYB/UhISErB06VJ4e3vLLUV2VCoV/Pz8EB0d\njaioKEybNk1uSbIwZswYCIIAkmhpacHAgfLVhZULX19fJCYmPvi+oKAAQUFBAIApU6YgMzPT4mvI\n4iodD1Q5OztDFMVHc8X3A+6nTm5tbcWaNWuwdu1amRXJw4kTJ+Dp6YkXX3wRe/fulVuO7DQ0NECj\n0WDfvn2oqKhAVFQUTp8+LbesXmfIkCG4c+cOZs2ahcbGRuzbt09uSb3OjBkzUFn5fb2Dh+NnhgwZ\nYtVhVFmc1d3dvV2xkf5q8vepqqpCZGQk5s+fj9mzZ8stRxZOnDiBjIwMREREoKioCLGxsairq5Nb\nlmyMGDECwcHBcHZ2xtixY+Hi4oL6+r5RxKI3SUpKQnBwMM6cOYO0tDTExsbCaOyFanB9mIe98t69\nexg2zPLhSFncNTAwEOnp6QCAGzduwM/PTw4ZfYLa2lq8/fbbWL9+PebPny+3HNk4fPgwkpOTkZyc\njAkTJiAhIQGenraVGHySmDx5Mq5cuQJAKrep1+vh4eEhs6reZ/jw4Q9SMQwdOhRmsxlib9SP7MP4\n+/sjJycHAHD58mWrziPJsnUzY8YMZGRkICwsDAD69cPYffv2obm5GR9++CESExPh5OSEgwcPdnrA\nrL/QY8fv/4mYOnUqrl27hkWLFj2IUuuP4xIZGYlNmzZh+fLlDyJw+vvD+tjYWGzZsgUmkwnjxo3D\nrFmzLPZRDkwpKCgoPOH0341xBQUFhX6CYvQKCgoKTziK0SsoKCg84ShGr6CgoPCEoxi9goKCwhOO\nYvQKCgoKTziK0SsoKCg84ShGr6CgoPCE8/92qsOw20QysQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1077b3a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, np.sin(x - 0), color='blue')        # specify color by name\n",
+    "plt.plot(x, np.sin(x - 1), color='g')           # short color code (rgbcmyk)\n",
+    "plt.plot(x, np.sin(x - 2), color='0.75')        # Grayscale between 0 and 1\n",
+    "plt.plot(x, np.sin(x - 3), color='#FFDD44')     # Hex code (RRGGBB from 00 to FF)\n",
+    "plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 to 1\n",
+    "plt.plot(x, np.sin(x - 5), color='chartreuse'); # all HTML color names supported"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If no color is specified, Matplotlib will automatically cycle through a set of default colors for multiple lines.\n",
+    "\n",
+    "Similarly, the line style can be adjusted using the ``linestyle`` keyword:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JeEHBIdLSptV6zd09RIiIWwosGBcYSbojid1dd1N8spgbZt5Az+M9af3P1sJE/EhBAQcL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\nHE6LXCmlHE6LXCmlHO4PeozvnTG5Su8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1079d9160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, x + 0, linestyle='solid')\n",
+    "plt.plot(x, x + 1, linestyle='dashed')\n",
+    "plt.plot(x, x + 2, linestyle='dashdot')\n",
+    "plt.plot(x, x + 3, linestyle='dotted');\n",
+    "\n",
+    "# For short, you can use the following codes:\n",
+    "plt.plot(x, x + 4, linestyle='-')  # solid\n",
+    "plt.plot(x, x + 5, linestyle='--') # dashed\n",
+    "plt.plot(x, x + 6, linestyle='-.') # dashdot\n",
+    "plt.plot(x, x + 7, linestyle=':');  # dotted"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you would like to be extremely terse, these ``linestyle`` and ``color`` codes can be combined into a single non-keyword argument to the ``plt.plot()`` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0gABsEbwWOaB2YU7+cTI6L+8MrUGLpFeS8NVTXzHEyWrxilwWrVZtoW/RQj1e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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1077be9e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, x + 0, '-g')  # solid green\n",
+    "plt.plot(x, x + 1, '--c') # dashed cyan\n",
+    "plt.plot(x, x + 2, '-.k') # dashdot black\n",
+    "plt.plot(x, x + 3, ':r');  # dotted red"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These single-character color codes reflect the standard abbreviations in the RGB (Red/Green/Blue) and CMYK (Cyan/Magenta/Yellow/blacK) color systems, commonly used for digital color graphics.\n",
+    "\n",
+    "There are many other keyword arguments that can be used to fine-tune the appearance of the plot; for more details, I'd suggest viewing the docstring of the ``plt.plot()`` function using IPython's help tools (See [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adjusting the Plot: Axes Limits\n",
+    "\n",
+    "Matplotlib does a decent job of choosing default axes limits for your plot, but sometimes it's nice to have finer control.\n",
+    "The most basic way to adjust axis limits is to use the ``plt.xlim()`` and ``plt.ylim()`` methods:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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F09smGPZq0QJ48klgyRLZScge8+eL7jRvb9lJnGvcOHGF4spY1GuxbRtwyy3An/8sO4nz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107ab9f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, np.sin(x))\n",
+    "\n",
+    "plt.xlim(-1, 11)\n",
+    "plt.ylim(-1.5, 1.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If for some reason you'd like either axis to be displayed in reverse, you can simply reverse the order of the arguments:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xuFfAa6/J2FUjPb0fOAB8/LEMVSNyh9BQoEoVYN061Umcx2YDJk4EpkzRzoAE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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1079fa160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, np.sin(x))\n",
+    "\n",
+    "plt.xlim(10, 0)\n",
+    "plt.ylim(1.2, -1.2);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A useful related method is ``plt.axis()`` (note here the potential confusion between *axes* with an *e*, and *axis* with an *i*).\n",
+    "The ``plt.axis()`` method allows you to set the ``x`` and ``y`` limits with a single call, by passing a list which specifies ``[xmin, xmax, ymin, ymax]``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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F09smGPZq0QJ48klgyRLZScge8+eL7jRvb9lJnGvcOHGF4spY1GuxbRtwyy3An/8sO4nz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107508b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, np.sin(x))\n",
+    "plt.axis([-1, 11, -1.5, 1.5]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``plt.axis()`` method goes even beyond this, allowing you to do things like automatically tighten the bounds around the current plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Wqpw40aWn0a1GjaQlsnGjuWc5UsWWLpWGAZUtPl4GN+i50Ot2INWmTfLiqHFj1Um0a/Bg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\nBk/pWBptAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107d1c9b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, np.sin(x))\n",
+    "plt.axis('tight');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It allows even higher-level specifications, such as ensuring an equal aspect ratio so that on your screen, one unit in ``x`` is equal to one unit in ``y``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BTkSkOQY5EZHmGORERJpjkBMRaY5BTkSkuf8HwX56wZ2U5kIAAAAASUVORK5C\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1076ded30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, np.sin(x))\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more information on axis limits and the other capabilities of the ``plt.axis`` method, refer to the ``plt.axis`` docstring."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Labeling Plots\n",
+    "\n",
+    "As the last piece of this section, we'll briefly look at the labeling of plots: titles, axis labels, and simple legends.\n",
+    "\n",
+    "Titles and axis labels are the simplest such labels—there are methods that can be used to quickly set them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+/hw/fpxLly7h5eXFnj17GDFixE2P5efnV+TzW1FSUpLhrkXNmuDtDb//7keL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\nIgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107d57438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, np.sin(x))\n",
+    "plt.title(\"A Sine Curve\")\n",
+    "plt.xlabel(\"x\")\n",
+    "plt.ylabel(\"sin(x)\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The position, size, and style of these labels can be adjusted using optional arguments to the function.\n",
+    "For more information, see the Matplotlib documentation and the docstrings of each of these functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When multiple lines are being shown within a single axes, it can be useful to create a plot legend that labels each line type.\n",
+    "Again, Matplotlib has a built-in way of quickly creating such a legend.\n",
+    "It is done via the (you guessed it) ``plt.legend()`` method.\n",
+    "Though there are several valid ways of using this, I find it easiest to specify the label of each line using the ``label`` keyword of the plot function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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u3r2Kd6g37775LqObjla7HLNzcIBatQyP9XrDKUv7F6+nnqfVLlWbn/r8ROuw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\nXAghNO7/ADRl1uEb30mBAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107d7ca90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, np.sin(x), '-g', label='sin(x)')\n",
+    "plt.plot(x, np.cos(x), ':b', label='cos(x)')\n",
+    "plt.axis('equal')\n",
+    "\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, the ``plt.legend()`` function keeps track of the line style and color, and matches these with the correct label.\n",
+    "More information on specifying and formatting plot legends can be found in the ``plt.legend`` docstring; additionally, we will cover some more advanced legend options in [Customizing Plot Legends](04.06-Customizing-Legends.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aside: Matplotlib Gotchas\n",
+    "\n",
+    "While most ``plt`` functions translate directly to ``ax`` methods (such as ``plt.plot()`` → ``ax.plot()``, ``plt.legend()`` → ``ax.legend()``, etc.), this is not the case for all commands.\n",
+    "In particular, functions to set limits, labels, and titles are slightly modified.\n",
+    "For transitioning between MATLAB-style functions and object-oriented methods, make the following changes:\n",
+    "\n",
+    "- ``plt.xlabel()``  → ``ax.set_xlabel()``\n",
+    "- ``plt.ylabel()`` → ``ax.set_ylabel()``\n",
+    "- ``plt.xlim()``  → ``ax.set_xlim()``\n",
+    "- ``plt.ylim()`` → ``ax.set_ylim()``\n",
+    "- ``plt.title()`` → ``ax.set_title()``\n",
+    "\n",
+    "In the object-oriented interface to plotting, rather than calling these functions individually, it is often more convenient to use the ``ax.set()`` method to set all these properties at once:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WZvBgMXapWzfRGcResUCUwcyZot/0d98Bjz0mO406vR748ENxyt6tmzijILIkBw8CAQFA\nQgIQEiI7zb316iXuLQYHiwNCe8QCUUrz5on7Dl9/DVj6+D2dDnj3XaBRI6BHD15uIstx+LA4cPng\nAzHg09J17Hh7JoMff5SdRnssEKWwdCmwfLkoDo88IjtN6VSqJC6FPfEE0Ls3b1yTfCdOiN5K774r\nvnCthb8/kJgI9OwpCpw9YYH4B+vXA3PnihvAHh6y05RNpUpimg83N+DllzmlAMnz55/izGHmTOCl\nl2SnKbvu3UXnlB49gN9/l51GOywQ97FnDzBsmJgLxstLdprycXAQI0RPnADeekt2GrJHubninsOA\nAWJWYmsVEgK88YYodPbSAYQF4h5OnAD69AGSksTEe9bM1VUUubVrxVQgRFq5cUN8sT71FDBtmuw0\nD27sWNH9NTDQPi7bskCouHJF3ECbMUMcLdiCmjWBf/9bzDRrz932SFsxMcDff4t7eDIHk1YUnQ5Y\nvFjM1Dx6tOw05scCcYfCQnEq/PzzYmptW/LEE2Lkd0iImEOKyJxSUkS38M8+AxwdZaepOA4OwMcf\ni4GyCQmy05gXC8QdZswQ60MvWiQ7iXm88IKYGiQwUMxCS2QO6eliANyGDXKnzzCXKlWAjRvFRH97\n98pOYz4sEMXcWnFq7VrbXpIwOhqoV0+cIbFnE1W0ixfFAcjSpcDTT8tOYz716onvi6AgoIwreVoN\nFoibMjPFEoTr1lnPWIfy0unEqfHhw6IbLFFFKSwUE0b27WvZo6Qryosviu+N0FBxQ97WsEBA9EYI\nCQEmTxazONoDV1cxjcCkSfY3+IfM5513xCXat9+WnUQ7b70l7kvMnCk7ScVjgYDoaVGnjphV0p7U\nry/+oPv1A4xG2WnI2v34o5iS5tNPbeum9D+5ddN62TKxRowt0bRAKIqCqVOnIiQkBBERETh79myJ\n1xMTExEQEICIiAhERETg1KlTZs/0xRfinsOHH9pGN7yyCg8X60nYW3GkinX5srjM8sEHgKen7DTa\ne/RRMR3HgAHA+fOy01QcvZY72759O/Lz85GSkoL09HTExsYiPj6+6PWMjAzMmzcPDRs21CTPuXNi\nZOfatbbZ06K03nsPaNpUHAUNGCA7DVkbRREdHrp1E4NL7VWXLuLvZ+BAYMsWMdWNtdP0V0hLS0Pb\ntm0BAE8//TSOHDlS4vWMjAwsW7YMYWFhWL58uVmzFBaK/5GRkcDNSHbLzU1MxxEVxfERVHaJiWL1\ntYULZSeRb+ZM4NIlIC5OdpKKoWmBMBqNcHd3L3qs1+tRWGwR5R49emD69OlYtWoV0tLSsNOMQ37j\n4sQcMRMnmm0XVuWZZ4AxY4BBg7iuNZXeqVPAuHFiAKaLi+w08jk6iraYPt02lizV9BKTwWBAbrHF\nCQoLC1Gp2HnYwIEDYTAYAADt27fH0aNH0b59e9VtZWVllTtHZqYDpk6tgU2bLuDPP627b1pOTs4D\ntUVxAwYAqak1MHPmNbzyivUtIlGRbWHttGiLwkIgLKw6Xn01DzVqGGGpTa/158LNDYiKckVoqCs+\n//wC9Jp+y1YsTaM3adIE33zzDbp27YpDhw7Bx8en6DWj0YiAgABs3boVLi4u2Lt3L4KCgu65rTp1\n6pQrg8kk+mjPmAG0aVO7XNuwJFlZWeVuCzUpKUCLFk4IDn4IGt0KqjAV3RbWTIu2WLJEdOyYMcMZ\nDg5VzLqvByHjczFhgujRtGpVHYuaRTm7jCP6NC0Q/v7+2L17N0JujqCJjY3Fli1bcO3aNQQHByMq\nKgrh4eFwdnZGy5Yt0a5duwrPMH++qPCRkRW+aZvg7Q3Mni16N/3wg22PKKfyO3ZMXG/fu1d086SS\nKlW6vfRv9+7i39ZIpyjWN9lCWloa/Pz8yvxz6eliEr60NMtdU7qszHF0pChi/v5mzYCpUyt002bF\nM4jbzNkWJhPQqhUweLBYL8XSyfxcfPwxEBsrvnMs4R5NWb87baAjVunk54teS/Pn205xMBedTkzP\n/N57wB0dzYiwYAFQtSrw+uuyk1i+/v3FnE3WOsrabgrE/PliydCBA2UnsQ4eHmK6hCFDbHOOGSqf\nEydEgbCV9R3MTacTPSZXrBBXMKyNXRSIY8fEIh/vv88PdVm8/LJYGGXxYtlJyBIUFoqJ6SZPtt4l\neGV49FFxmWnoUHF5zprYfIEoLBSjPKdO5aWlstLpxJFPbCzw66+y05BsK1YAeXmclqU8hgwBHnrI\n+taZsfkCsWKFqNrWcDPNEnl7i8GEL7/MAXT27Nw5MWtpQgJ7LZXHrYOtuXOt62DLpgvErQ/1ihX8\nUD+IUaPE6nNcO8I+KQowfLjoGu7rKzuN9Xr8cTFztDUt1GWzBeLWh3r4cOCpp2SnsW4ODmLlrLfe\nst2Vs+je1q0TN6c5Lc2DGzVKTK2/cqXsJKVjswUiNVV8qGNiZCexDb6+4jJTVJTsJKSlS5fEl9rK\nlYCzs+w01k+vF20ZEwP873+y0/wzmywQly8Db7whrpfyQ11xJk8WI2f/8x/ZSUgrMTFifemWLWUn\nsR2NGomb1mPHyk7yz2yyQEyeLEYCt2olO4ltcXUVfbojI8U9CbJte/cCmzeLqVeoYk2ZAnz/PbBj\nh+wk92dzBSItTSwAFBsrO4lt6t5dTA3O9rVtJpM4EJg/X4yaporl5gYsXSp6V+blyU5zbzZVIAoL\nxYc6NhaoVk12Gtu1ZAkQHy8GIJJtev990W8/LEx2EtvVs6dYF37+fNlJ7s2mCkRCgliwg9NpmJeH\nh7iMN2yY9XTXo9LLzhbT4cfHc+YBc3v3XTFTQWam7CTqbKZAnD8vumHGx9vGWrCWbvhw4OpVMVsl\n2ZboaDGlRoMGspPYPk9PsSLfiBGWebBVqq/SK1eu4JtvvsGGDRuwc+fOEqvCWYrx48WKaI0by05i\nH/R64IMPxIf70iXZaaiifP01sGcPLGqRG1s3Zgxw9qwYb2Jp7rtg0KVLl7BgwQL89ttvqFu3LmrV\nqoX09HTEx8fDx8cHo0aNQo0aNbTKek+7d4uul0ePyk5iX5o2BYKCxOpZy5fLTkMPKi9P3MN7913R\nY4204egoDrZCQoAuXYAqFrQ4330LxHvvvYdXXnkFdevWveu1zMxMxMXFYarkFWVuzbO0cKFlNay9\nmDVLXIrYuxdo0UJ2GnoQCxaIm6Y9e8pOYn/atAFeeEF0f7Wk2ZPvWyCmTJkCACgsLESlYhf2jUYj\nvL29pRcHQHQVq10beOkl2Uns00MPiS+WYcOA/fth1Qu027OTJ8VMowcOyE5iv+bNE9MCDRokupJb\nglLdg4iIiMCff/4JAEhPTy9aU1q2c+fEIJ733mNvC5lCQ0W34rg42UmoPBQFGDlS3JzmOg/y1Kgh\nvs+GDbOcmZNLdbw3fPhwvPrqq2jatCmOHDmCJUuWmDtXqURHi2UP69WTncS+3Vo1q00bIDgY4LLQ\n1mXjRtHNMjVVdhIaMkTM1fThh2LuM9lKdQbx5JNPonr16tizZw8aN26Mx8q58o6iKJg6dSpCQkIQ\nERGBs2fPlnh9x44dCAoKQkhICNauXXvfbW3fDvz4I2eYtBT16wOvvcbJ/KxNbq6YjC8+HnBykp2G\nKlUSgxQnTQIuXJCdppQFon///ggNDcUXX3yB2rVro1+/fuXa2fbt25Gfn4+UlBRER0cjtth8DSaT\nCXPmzEFiYiKSk5OxZs0aXLpP/8nhw9nbwtJMmiSK9rZtspNQac2cKc78OnaUnYRueeYZcdl2wgTZ\nSUp5iSkpKQmPPPIIAGDIkCFo1qxZuXaWlpaGtm3bAgCefvppHDlypOi1zMxMeHp6wmAwAAD8/Pyw\nf/9+dOnSRXVb9eoBL75YrhhkJq6uotPA8OHA4cOAi4vsRHQ/v/wiZh/4+WfZSehOM2aI3oF79sid\ndPS+ZxBTpkzBiRMniorDLb6+vvjll1+KejmVltFohLu7e9FjvV6Pwpt3Y+58zc3NDTk5OffcloXc\nBqE7BASInhiWPL8MiRvTI0aIKVMefVR2GrpTlSqi6/6wYaIrvyz3PYOIiorC4sWLceTIEdStWxc1\natTAlStXcOzYMTRu3BijR48u084MBkOJUdjFu88aDAYYjcai13Jzc1HlPgMbnJ2zkJVVpt3bpJyc\nHGRZWENMnOiALl1qoHPnC/DyuqHZfi2xLWT5p7bYuNEF2dnuCAw8b/N/R9b6uWjbFoiLq47Zs6/j\nlVckzV6hlEJOTo6ya9cuZfPmzcqePXuU3Nzc0vzYXb766itlwoQJiqIoysGDB5VXXnml6LWCggLl\nhRdeUK5cuaLk5eUpgYGByh9//KG6nQMHDpRr/7bo3LlzsiOomjtXUbp2VZTCQu32aaltIcP92uLq\nVUXx8FCU77/XMJBE1vy5OHZMUapXV5Tff6+Y7ZX1u7NUN6nd3Nzg7u6O2rVrQ6/XIyMjo1zFyN/f\nH05OTggJCcGcOXMQExODLVu2YO3atdDr9YiJicGQIUMQGhqK4OBg1KpVq1z7IflGjwbOnGHXSUs0\nfTrw/PPi5jRZtnr1RFf+6Gg5+y/VTeoRI0bg0qVLePTmxUqdToemTZuWeWc6nQ7Tp08v8VzxaTw6\ndOiADh06lHm7ZHmcnER3vf79xRQCxW4vkURHjgBJSUA5j/FIgokTxX29bdsAf39t912qAnHx4kWk\npKSYOwvZmHbtgE6dxBHrggWy05CiiB5m06YBPDm3HsV7B/78M+DsrN2+S3WJqW7duvjjjz/MnYVs\n0Pz5wKpV7EppCVavBnJyxCULsi4BAUDDhtr3DizVGURaWho6duyIasXW8dy1a5fZQpHtqFVL9Oke\nNgz47jsu5iTLlSti7Y7UVMDBQXYaKo8lSwA/P7EM7OOPa7PPUhWI//znP+bOQTbslVfE3DJJScDg\nwbLT2KcpU4AePTgluzXz9ATGjgXeeAPYvFmbCUrvWyDi4+MRGRmJqKgo6O5Is3DhQrMGI9vh4CBu\nWHfvLtYaqF5ddiL7kp4OfPopF9SyBVFR4kBr0yagVy/z7+++J/ydOnUCALRv3x5NmjRB06ZNcejQ\nITRq1Mj8ycim+PmJNTtiYmQnsS+FhWKVuFmzxHTSZN2cnMTEim+8ISZaNLf7Foj69esDANauXQtv\nb2/s2bMHUVFR+Prrr82fjGzOrFnAli3ADz/ITmI/kpKAggJg6FDZSaiidOwoRlnPmmX+fZXqluGt\ncQ9Xr15Fjx49SqwuR1RaxVefkzm/jL24dEmcscXH88a0rVmwQEy0aO7LhqX6pjeZTJg/fz6ee+45\n7N27FwUFBeZNRTYrNFTcg+Dqc+Y3bpxYwOm552QnoYr2yCPA1KlibISimG8/pSoQsbGx+Ne//oVX\nX30Vly5dwty5c82XiGzardXnZs2CzU8SJ9PevU748kttLkOQHMOGie7Ln3xivn2Uqpurl5cXvG4u\nVtu9e3fzpSG7UHz1OQ7Qr3h5ecD48Q9hyRJxWY9s063egYGBogtz1aoVvw/eTCApJk4E9u3j6nPm\nMG8eULfuDfTpIzsJmVvz5qLr+OTJ5tk+CwRJ4eoqloyNjASuX5edxnacOCFG3M6efUWTgVQk39tv\nA2vXAmlpFb9tFgiSJiAA8PUVR7z04BRFXJeeNAnw8NBuoSaSq1o1YM4c8f/+RgX/b2eBIKmWLBFn\nEpmZspNYv+Rk4PJlYORI2UlIaxERYhDdihUVu10WCJLqscdEd8wRI8zbXc/WXbwo2nH5ckBfqq4n\nZEsqVQI++EDcizh3rgK3W3GbIiqfW6vPrV8vO4n1Gj1ajDHx85OdhGTx9RUHWq+/XnEHWywQJJ2T\nkzjyfeMNcSRMZbN5s5i+hGMeKCYGOH264sZGsECQRWjdGujXTxQJKr2//hI3JxMSADc32WlINicn\n4KOPxBijiljjjQWCLMbs2WJsxOefy05iPaKigN69AS7lTrf4+QFDhojLTQ9K09tZeXl5ePPNN3Hx\n4kUYDAbMmTMHDz/8cIn3zJ49Gz/99BPcbh4OxcfHw2AwaBmTJHF1FQsL9esnZqvkuhH3t3Ur8O23\nXM6V7jZ1KvDMM8C6dUBQUPm3o+kZxKeffgofHx+sXr0avXr1Qnx8/F3vycjIwMqVK7Fq1SqsWrWK\nxcHOtG0VYShWAAAPLUlEQVQr1o0YNUp2Est25YqYriQhAeCfCN3JxUUcbI0cCVy4UP7taFog0tLS\n0K5dOwBAu3bt8MMdCwMoioLTp09jypQpCA0NxXp2a7FLb78N/PgjsHGj7CSWKzparNDXubPsJGSp\nWrUSPdsiI8vfq8lsl5jWrVuHpKSkEs/VqFGj6IzAzc0NRqOxxOt///03wsPDMXjwYJhMJkRERKBR\no0bw8fExV0yyQMUvNbVqBdSsKTuRZfn8c+Cbb4CDB2UnIUs3e7aY7v2TT4D+/cv+82YrEEFBQQi6\n4+LXyJEjkXtznbzc3Fy4u7uXeL1y5coIDw+Hs7MznJ2d0aJFCxw7dky1QGRxrmgAQE5Ojk22hbc3\n0KePO8LCHJGYeKlU8wrZalsU98cflfDqqzWxYsUlGI0FuOMYq4g9tEVp2XtbLFqkR1hYdfj4XEBZ\n13rT9CZ1kyZNsHPnTjRq1Ag7d+7Ec3esZHLy5EmMGTMGGzduhMlkQlpaGvrcY0rKOnXqaBHZ4mVl\nZdlsW7zzjjiD2LixDiIj//n9ttwWgLhMMHSoGAjVq9f9T6tsvS3Kwt7bok4dcUly/PjamD//9zL9\nrKYFIjQ0FOPHj0dYWBicnJywcOFCAEBiYiI8PT3RsWNH9O7dG8HBwXB0dERgYCC8vb21jEgWxMkJ\nWL0aaNMGaN8eeOop2Ynkeu89sYyouaZ2Jts1bhzw1Vdl/zmdoljfDDhpaWnw45wCAOzj6CghAVi6\nVNy4dnG59/tsuS0yMsRYhz17gCef/Of323JblBXbQigsBA4eLNt3JwfKkcUbOlTck4iJkZ1Ejtxc\nsbb03LmlKw5Easp6/wFggSAroNOJaYxTU+1vlPWtNR6aNQMGD5adhuwNJwYmq1C9OvDZZ8CLL4pZ\nK594QnYibaxcCfz0k7i8xhXiSGs8gyCr0bw5MG0a0Lcv8PffstOY36FD4rLaunWciI/kYIEgqzJs\nGNCokfi39XWvKL2LF8UcOkuWAPXry05D9ooFgqyKTgcsWyYuu7z3nuw05lFQIOajCgwEwsJkpyF7\nxnsQZHXc3MQ8Ta1bi149XbvKTlSxoqLEGJA5c2QnIXvHMwiySo8/Lq7NR0QAR47ITlNxli8Htm0D\nPv0UcHCQnYbsHQsEWa3WrYFFi0TPpopYPUu2L74ApkwBNm0CqlaVnYaIBYKsXP/+4iwiIADIybHe\nfqB79wKDBolxHpy8mCwFCwRZvWnTxDKLgwdXw7VrstOU3bFjYtnQxESgRQvZaYhuY4Egq6fTAXFx\nQO3aNxAcLHoBWYvMTKBLF3FDukcP2WmISmKBIJvg4AAsXnwZOp3oGpqfLzvRP8vMBDp1EoPhBg2S\nnYbobiwQZDMcHYG1a4G8PDHa+vp12YnurXhxeP112WmI1LFAkE1xcQHWrxdjJXr0wD1XXJMpLQ1o\n2xaYNInFgSwbCwTZHEdHsdCQt7f4Ij57Vnai2778Ugzsi48HXn1Vdhqi+2OBIJvk4CCm5AgLEz2D\n9u2Tm0dRgHffvd2VtXdvuXmISoNTbZDN0umAN98E6tUTl5umTQMiI7WfNjs3V5wtHD0qVoR7/HFt\n909UXjyDIJvXsyewezfw4YdAr17AhQva7XvPHuDZZ8XcSrt3sziQdWGBILvg4wP88IM4m/D1BZKS\nzDtdeE6OOHvp2xeIjQU++ghwdTXf/ojMQUqB2LZtG6Kjo1Vf++yzz9C3b1+EhITg22+/1TYY2TQn\nJ2D+fGDzZmDpUqBdO+D77yt2HyYTkJAgCtGffwKHD4siQWSNNL8HMXv2bOzevRsNGjS467ULFy4g\nOTkZGzZswPXr1xEaGorWrVvD0dFR65hkw5o2FUt4JiUBAwcCdesC0dHACy8A+nL+RRiN4hLWokWA\np6eYcO+55yo2N5HWND+DaNKkCaZNm6b62uHDh+Hn5we9Xg+DwQAvLy8cP35c24BkFxwcgCFDgOPH\ngQEDgOnTAS8vcVlo+/bSDbI7f15MOd6vH+DhAXz3HZCSAnz7LYsD2QaznUGsW7cOSUlJJZ6LjY1F\nt27dsO8efQ6NRiPc3d2LHru6uiInJ8dcEYng6AgMHiz+OXJEjMSeMkVcGnriCbHcZ82aQJUqYo4n\noxE4fRo4cUIUiJYtxcpvcXFAjRqyfxuiimW2AhEUFISgoKAy/YzBYICx2NDX3NxcVKlSRfW9WVlZ\nD5TPVuTk5LAtbnrQtqhWDXjtNfFPbq4Ov/6qR2amHn/9VQk5OTo4OQEeHoVo1uwGvLxuwNvbVLSo\nT34+YEn/G/i5uI1tUX4WNQ6icePGWLx4MfLz85GXl4fffvsNTz75pOp769Spo3E6y5SVlcW2uKmi\n2+IeHz2rwM/FbWyL27Kzs8v0fosoEImJifD09ETHjh0RHh6OsLAwKIqCqKgoODk5yY5HRGSXpBSI\nZs2aoVmzZkWPBxWb6zg4OBjBwcESUhERUXEcKEdERKpYIIiISBULBBERqWKBICIiVSwQRESkigWC\niIhUsUAQEZEqFggiIlLFAkFERKpYIIiISBULBBERqWKBICIiVSwQRESkigWCiIhUsUAQEZEqFggi\nIlLFAkFERKpYIIiISBULBBERqWKBICIiVXoZO922bRu+/PJLLFy48K7XZs+ejZ9++glubm4AgPj4\neBgMBq0jEhHZPc0LxOzZs7F79240aNBA9fWMjAysXLkSVatW1TgZEREVp/klpiZNmmDatGmqrymK\ngtOnT2PKlCkIDQ3F+vXrtQ1HRERFzHYGsW7dOiQlJZV4LjY2Ft26dcO+fftUf+bvv/9GeHg4Bg8e\nDJPJhIiICDRq1Ag+Pj7miklERPdgtgIRFBSEoKCgMv1M5cqVER4eDmdnZzg7O6NFixY4duyYaoHI\nysqqqKhWLScnh21xE9viNrbFbWyL8pNyk/peTp48iTFjxmDjxo0wmUxIS0tDnz59VN9bp04djdNZ\npqysLLbFTWyL29gWt7EtbsvOzi7T+y2iQCQmJsLT0xMdO3ZE7969ERwcDEdHRwQGBsLb21t2PCIi\nuySlQDRr1gzNmjUrejxo0KCi/x4yZAiGDBkiIRURERXHgXJERKSKBYKIiFSxQBARkSoWCCIiUsUC\nQUREqlggiIhIFQsEERGpYoEgIiJVLBBERKSKBYKIiFSxQBARkSoWCCIiUsUCQUREqlggiIhIFQsE\nERGpYoEgIiJVLBBERKSKBYKIiFSxQBARkSoWCCIiUqXXcmdGoxFjx45Fbm4uCgoKMGHCBDzzzDMl\n3vPZZ59hzZo1cHR0xOuvv44OHTpoGZGIiG7StEB89NFHaNWqFSIiInDy5ElER0cjNTW16PULFy4g\nOTkZGzZswPXr1xEaGorWrVvD0dFRy5hERASNC8TgwYPh5OQEADCZTHB2di7x+uHDh+Hn5we9Xg+D\nwQAvLy8cP34cvr6+WsYkIiKYsUCsW7cOSUlJJZ6LjY2Fr68vzp8/j3HjxmHSpEklXjcajXB3dy96\n7OrqipycHHNFJCKi+zBbgQgKCkJQUNBdzx8/fhxjx47F+PHj8dxzz5V4zWAwwGg0Fj3Ozc1FlSpV\nVLeflpZWsYGtWHZ2tuwIFoNtcRvb4ja2Rfloeonp119/xejRo7F48WLUq1fvrtcbN26MxYsXIz8/\nH3l5efjtt9/w5JNP3vU+Pz8/LeISEdk1naIoilY7i4yMxPHjx+Hh4QFFUVClShXExcUhMTERnp6e\n6NixI9auXYs1a9ZAURQMGzYMzz//vFbxiIioGE0LBBERWQ+rGiinKAqmTp2KkJAQRERE4OzZs7Ij\nSWMymTBu3Dj0798fL730Enbs2CE7klQXL15Ehw4dcPLkSdlRpFu+fDlCQkLQt29frF+/XnYcKUwm\nE6KjoxESEoIBAwbY7eciPT0d4eHhAIAzZ84gLCwMAwYMwPTp00v181ZVILZv3478/HykpKQgOjoa\nsbGxsiNJs2nTJjz88MNYvXo1VqxYgZkzZ8qOJI3JZMLUqVPh4uIiO4p0+/btw8GDB5GSkoLk5GS7\nvTm7c+dOFBYWIiUlBZGRkVi0aJHsSJpLSEjAW2+9hYKCAgCiF2lUVBQ+/vhjFBYWYvv27f+4Dasq\nEGlpaWjbti0A4Omnn8aRI0ckJ5KnW7duGDVqFACgsLAQer2m/Q0syty5cxEaGopatWrJjiLdrl27\n4OPjg8jISAwbNgwdO3aUHUkKLy8v3LhxA4qiICcnxy4H23p6eiIuLq7ocUZGRlHP0Xbt2uGHH374\nx21Y1bfKneMk9Ho9CgsLUamSVdW5ClG5cmUAok1GjRqFMWPGSE4kR2pqKqpXr47WrVvjgw8+kB1H\nur/++gtZWVlYtmwZzp49i2HDhuHLL7+UHUtzbm5u+P3339G1a1dcvnwZy5Ytkx1Jc/7+/jh37lzR\n4+K3m93c3Eo1xsyqvlkNBgNyc3OLHttrcbglOzsbAwcORGBgILp37y47jhSpqanYvXs3wsPDcezY\nMYwfPx4XL16UHUuaqlWrom3bttDr9ahbty6cnZ1x6dIl2bE0l5iYiLZt2+Krr77Cpk2bMH78eOTn\n58uOJVXx78r7jTEr8TPmDFTRmjRpgp07dwIADh06BB8fH8mJ5Llw4QKGDh2KN998E4GBgbLjSPPx\nxx8jOTkZycnJqF+/PubOnYvq1avLjiWNn58fvv/+ewDAH3/8gevXr+Phhx+WnEp7Dz30EAwGAwDA\n3d0dJpMJhYWFklPJ1bBhQ+zfvx8A8N1335VqPJlVXWLy9/fH7t27ERISAgB2fZN62bJluHr1KuLj\n4xEXFwedToeEhISiua7skU6nkx1Bug4dOuDAgQMICgoq6vVnj+0ycOBATJw4Ef379y/q0WTvnRjG\njx+PyZMno6CgAN7e3ujates//gzHQRARkSqrusRERETaYYEgIiJVLBBERKSKBYKIiFSxQBARkSoW\nCCIiUsUCQUREqlggiIhIFQsEUQVYvXo1oqOjAQATJkzAp59+KjkR0YPjSGqiCjJixAi4u7sjPz8f\nCxculB2H6IGxQBBVkPT0dISEhCA1NRUNGjSQHYfogbFAEFWA/Px8hIeHIygoCOvWrcPq1avtehEn\nsg28B0FUARYuXIhOnTohODgYbdu25SUmsgk8gyAiIlU8gyAiIlUsEEREpIoFgoiIVLFAEBGRKhYI\nIiJSxQJBRESqWCCIiEgVCwQREan6f8M6gCXBCtQMAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107f67cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plt.axes()\n",
+    "ax.plot(x, np.sin(x))\n",
+    "ax.set(xlim=(0, 10), ylim=(-2, 2),\n",
+    "       xlabel='x', ylabel='sin(x)',\n",
+    "       title='A Simple Plot');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.01-Simple-Line-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/04.02-Simple-Scatter-Plots.ipynb b/notebooks_v1/04.02-Simple-Scatter-Plots.ipynb
new file mode 100644
index 000000000..eaf6c4249
--- /dev/null
+++ b/notebooks_v1/04.02-Simple-Scatter-Plots.ipynb
@@ -0,0 +1,358 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) | [Contents](Index.ipynb) | [Visualizing Errors](04.03-Errorbars.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.02-Simple-Scatter-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Scatter Plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another commonly used plot type is the simple scatter plot, a close cousin of the line plot.\n",
+    "Instead of points being joined by line segments, here the points are represented individually with a dot, circle, or other shape.\n",
+    "We’ll start by setting up the notebook for plotting and importing the functions we will use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('seaborn-whitegrid')\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scatter Plots with ``plt.plot``\n",
+    "\n",
+    "In the previous section we looked at ``plt.plot``/``ax.plot`` to produce line plots.\n",
+    "It turns out that this same function can produce scatter plots as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ewhg9kGesAIbX0KMH8owVwPAagh7IM1YAw2sIeiDPWAEMryHogTxjBTC8hslYIM9YAQyv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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bdaae48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0, 10, 30)\n",
+    "y = np.sin(x)\n",
+    "\n",
+    "plt.plot(x, y, 'o', color='black');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The third argument in the function call is a character that represents the type of symbol used for the plotting. Just as you can specify options such as ``'-'``, ``'--'`` to control the line style, the marker style has its own set of short string codes. The full list of available symbols can be seen in the documentation of ``plt.plot``, or in Matplotlib's online documentation. Most of the possibilities are fairly intuitive, and we'll show a number of the more common ones here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ijo5GSUkJLC0tER8fjyFDhrT7vsjISNjb2yM4OFgvQTvEcQAtzWVUHB2H4dCh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\nYmKosBNCiIn5/5UUNVEacdwFAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bf7e470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rng = np.random.RandomState(0)\n",
+    "for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']:\n",
+    "    plt.plot(rng.rand(5), rng.rand(5), marker,\n",
+    "             label=\"marker='{0}'\".format(marker))\n",
+    "plt.legend(numpoints=1)\n",
+    "plt.xlim(0, 1.8);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For even more possibilities, these character codes can be used together with line and color codes to plot points along with a line connecting them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YAwcOhJOTE3JycuDj48M6DhEYmUyGHj16oE+fPnBwcEDnzp2h0WhsYuTas54dUy/0x49W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\nTwghIkeFnhBCRI4KPSGEiNz/Aak6QmZ56l4lAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1023b1940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, y, '-ok');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Additional keyword arguments to ``plt.plot`` specify a wide range of properties of the lines and markers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YTCam1C0wtHLZ4XAwJXKBoaczMV83Id4XCQkJOHv2LFOd12QywWazYdasWRN6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\nLjyprHyiCgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e7a6208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, y, '-p', color='gray',\n",
+    "         markersize=15, linewidth=4,\n",
+    "         markerfacecolor='white',\n",
+    "         markeredgecolor='gray',\n",
+    "         markeredgewidth=2)\n",
+    "plt.ylim(-1.2, 1.2);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This type of flexibility in the ``plt.plot`` function allows for a wide variety of possible visualization options.\n",
+    "For a full description of the options available, refer to the ``plt.plot`` documentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scatter Plots with ``plt.scatter``\n",
+    "\n",
+    "A second, more powerful method of creating scatter plots is the ``plt.scatter`` function, which can be used very similarly to the ``plt.plot`` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e8a18d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x, y, marker='o');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The primary difference of ``plt.scatter`` from ``plt.plot`` is that it can be used to create scatter plots where the properties of each individual point (size, face color, edge color, etc.) can be individually controlled or mapped to data.\n",
+    "\n",
+    "Let's show this by creating a random scatter plot with points of many colors and sizes.\n",
+    "In order to better see the overlapping results, we'll also use the ``alpha`` keyword to adjust the transparency level:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BbCa7IqzNRpMroxfBVGbwSBij4aZYlvNlzs/PMTm3xAOnDuNyOxg42EVXX4vJ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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e8fce10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rng = np.random.RandomState(0)\n",
+    "x = rng.randn(100)\n",
+    "y = rng.randn(100)\n",
+    "colors = rng.rand(100)\n",
+    "sizes = 1000 * rng.rand(100)\n",
+    "\n",
+    "plt.scatter(x, y, c=colors, s=sizes, alpha=0.3,\n",
+    "            cmap='viridis')\n",
+    "plt.colorbar();  # show color scale"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the color argument is automatically mapped to a color scale (shown here by the ``colorbar()`` command), and that the size argument is given in pixels.\n",
+    "In this way, the color and size of points can be used to convey information in the visualization, in order to visualize multidimensional data.\n",
+    "\n",
+    "For example, we might use the Iris data from Scikit-Learn, where each sample is one of three types of flowers that has had the size of its petals and sepals carefully measured:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xWSboCmOzWimUimQX80xcnOTQA/dt4Ce3eRaTSZwbmPrp9PmYWVjARgWn8+qT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\n4XkXT3EAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1107c79b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "iris = load_iris()\n",
+    "features = iris.data.T\n",
+    "\n",
+    "plt.scatter(features[0], features[1], alpha=0.2,\n",
+    "            s=100*features[3], c=iris.target, cmap='viridis')\n",
+    "plt.xlabel(iris.feature_names[0])\n",
+    "plt.ylabel(iris.feature_names[1]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that this scatter plot has given us the ability to simultaneously explore four different dimensions of the data:\n",
+    "the (x, y) location of each point corresponds to the sepal length and width, the size of the point is related to the petal width, and the color is related to the particular species of flower.\n",
+    "Multicolor and multifeature scatter plots like this can be useful for both exploration and presentation of data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ``plot`` Versus ``scatter``: A Note on Efficiency\n",
+    "\n",
+    "Aside from the different features available in ``plt.plot`` and ``plt.scatter``, why might you choose to use one over the other? While it doesn't matter as much for small amounts of data, as datasets get larger than a few thousand points, ``plt.plot`` can be noticeably more efficient than ``plt.scatter``.\n",
+    "The reason is that ``plt.scatter`` has the capability to render a different size and/or color for each point, so the renderer must do the extra work of constructing each point individually.\n",
+    "In ``plt.plot``, on the other hand, the points are always essentially clones of each other, so the work of determining the appearance of the points is done only once for the entire set of data.\n",
+    "For large datasets, the difference between these two can lead to vastly different performance, and for this reason, ``plt.plot`` should be preferred over ``plt.scatter`` for large datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) | [Contents](Index.ipynb) | [Visualizing Errors](04.03-Errorbars.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.02-Simple-Scatter-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/04.03-Errorbars.ipynb b/notebooks_v1/04.03-Errorbars.ipynb
new file mode 100644
index 000000000..094ae9c89
--- /dev/null
+++ b/notebooks_v1/04.03-Errorbars.ipynb
@@ -0,0 +1,259 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) | [Contents](Index.ipynb) | [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.03-Errorbars.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualizing Errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For any scientific measurement, accurate accounting for errors is nearly as important, if not more important, than accurate reporting of the number itself.\n",
+    "For example, imagine that I am using some astrophysical observations to estimate the Hubble Constant, the local measurement of the expansion rate of the Universe.\n",
+    "I know that the current literature suggests a value of around 71 (km/s)/Mpc, and I measure a value of 74 (km/s)/Mpc with my method. Are the values consistent? The only correct answer, given this information, is this: there is no way to know.\n",
+    "\n",
+    "Suppose I augment this information with reported uncertainties: the current literature suggests a value of around 71 $\\pm$ 2.5 (km/s)/Mpc, and my method has measured a value of 74 $\\pm$ 5 (km/s)/Mpc. Now are the values consistent? That is a question that can be quantitatively answered.\n",
+    "\n",
+    "In visualization of data and results, showing these errors effectively can make a plot convey much more complete information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic Errorbars\n",
+    "\n",
+    "A basic errorbar can be created with a single Matplotlib function call:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('seaborn-whitegrid')\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hFMsg56sbisF0Okhm7CETyyAnsAFIhRt1puwhE/ogp/XsLhNaF4BfCuVI2Ae9J4Q+yAls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\nw/0Pa4w4rypgtw8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bf54080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0, 10, 50)\n",
+    "dy = 0.8\n",
+    "y = np.sin(x) + dy * np.random.randn(50)\n",
+    "\n",
+    "plt.errorbar(x, y, yerr=dy, fmt='.k');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here the ``fmt`` is a format code controlling the appearance of lines and points, and has the same syntax as the shorthand used in ``plt.plot``, outlined in [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) and [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb).\n",
+    "\n",
+    "In addition to these basic options, the ``errorbar`` function has many options to fine-tune the outputs.\n",
+    "Using these additional options you can easily customize the aesthetics of your errorbar plot.\n",
+    "I often find it helpful, especially in crowded plots, to make the errorbars lighter than the points themselves:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bec4d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.errorbar(x, y, yerr=dy, fmt='o', color='black',\n",
+    "             ecolor='lightgray', elinewidth=3, capsize=0);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition to these options, you can also specify horizontal errorbars (``xerr``), one-sided errorbars, and many other variants.\n",
+    "For more information on the options available, refer to the docstring of ``plt.errorbar``."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Continuous Errors\n",
+    "\n",
+    "In some situations it is desirable to show errorbars on continuous quantities.\n",
+    "Though Matplotlib does not have a built-in convenience routine for this type of application, it's relatively easy to combine primitives like ``plt.plot`` and ``plt.fill_between`` for a useful result.\n",
+    "\n",
+    "Here we'll perform a simple *Gaussian process regression*, using the Scikit-Learn API (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) for details).\n",
+    "This is a method of fitting a very flexible non-parametric function to data with a continuous measure of the uncertainty.\n",
+    "We won't delve into the details of Gaussian process regression at this point, but will focus instead on how you might visualize such a continuous error measurement:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.gaussian_process import GaussianProcess\n",
+    "\n",
+    "# define the model and draw some data\n",
+    "model = lambda x: x * np.sin(x)\n",
+    "xdata = np.array([1, 3, 5, 6, 8])\n",
+    "ydata = model(xdata)\n",
+    "\n",
+    "# Compute the Gaussian process fit\n",
+    "gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1E-1,\n",
+    "                     random_start=100)\n",
+    "gp.fit(xdata[:, np.newaxis], ydata)\n",
+    "\n",
+    "xfit = np.linspace(0, 10, 1000)\n",
+    "yfit, MSE = gp.predict(xfit[:, np.newaxis], eval_MSE=True)\n",
+    "dyfit = 2 * np.sqrt(MSE)  # 2*sigma ~ 95% confidence region"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now have ``xfit``, ``yfit``, and ``dyfit``, which sample the continuous fit to our data.\n",
+    "We could pass these to the ``plt.errorbar`` function as above, but we don't really want to plot 1,000 points with 1,000 errorbars.\n",
+    "Instead, we can use the ``plt.fill_between`` function with a light color to visualize this continuous error:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QJ7qnC2klWzgcRjAYlPfySNc3JTIyuvex9xbsLMvi8OHDaG9vx9KlS3vdubW/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\niMKdEEKyEIU7IYRkof8PKWVUtmMej+IAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e55bba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize the result\n",
+    "plt.plot(xdata, ydata, 'or')\n",
+    "plt.plot(xfit, yfit, '-', color='gray')\n",
+    "\n",
+    "plt.fill_between(xfit, yfit - dyfit, yfit + dyfit,\n",
+    "                 color='gray', alpha=0.2)\n",
+    "plt.xlim(0, 10);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note what we've done here with the ``fill_between`` function: we pass an x value, then the lower y-bound, then the upper y-bound, and the result is that the area between these regions is filled.\n",
+    "\n",
+    "The resulting figure gives a very intuitive view into what the Gaussian process regression algorithm is doing: in regions near a measured data point, the model is strongly constrained and this is reflected in the small model errors.\n",
+    "In regions far from a measured data point, the model is not strongly constrained, and the model errors increase.\n",
+    "\n",
+    "For more information on the options available in ``plt.fill_between()`` (and the closely related ``plt.fill()`` function), see the function docstring or the Matplotlib documentation.\n",
+    "\n",
+    "Finally, if this seems a bit too low level for your taste, refer to [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), where we discuss the Seaborn package, which has a more streamlined API for visualizing this type of continuous errorbar."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) | [Contents](Index.ipynb) | [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.03-Errorbars.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/04.04-Density-and-Contour-Plots.ipynb b/notebooks_v1/04.04-Density-and-Contour-Plots.ipynb
new file mode 100644
index 000000000..3fea071b4
--- /dev/null
+++ b/notebooks_v1/04.04-Density-and-Contour-Plots.ipynb
@@ -0,0 +1,331 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Visualizing Errors](04.03-Errorbars.ipynb) | [Contents](Index.ipynb) | [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.04-Density-and-Contour-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Density and Contour Plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes it is useful to display three-dimensional data in two dimensions using contours or color-coded regions.\n",
+    "There are three Matplotlib functions that can be helpful for this task: ``plt.contour`` for contour plots, ``plt.contourf`` for filled contour plots, and ``plt.imshow`` for showing images.\n",
+    "This section looks at several examples of using these. We'll start by setting up the notebook for plotting and importing the functions we will use: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('seaborn-white')\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualizing a Three-Dimensional Function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll start by demonstrating a contour plot using a function $z = f(x, y)$, using the following particular choice for $f$ (we've seen this before in [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb), when we used it as a motivating example for array broadcasting):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def f(x, y):\n",
+    "    return np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A contour plot can be created with the ``plt.contour`` function.\n",
+    "It takes three arguments: a grid of *x* values, a grid of *y* values, and a grid of *z* values.\n",
+    "The *x* and *y* values represent positions on the plot, and the *z* values will be represented by the contour levels.\n",
+    "Perhaps the most straightforward way to prepare such data is to use the ``np.meshgrid`` function, which builds two-dimensional grids from one-dimensional arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x = np.linspace(0, 5, 50)\n",
+    "y = np.linspace(0, 5, 40)\n",
+    "\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "Z = f(X, Y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's look at this with a standard line-only contour plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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du8jPz4/279/Pqe/evTvFxsbSlStXqGXLlvTs2bNi96kEAF26dImcnZ156xUKhVolnqur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N69ezdjxbOgoCDaUUgGVZYmS5cu1Yhokozap6nZxu7duxEUFKSRczGBl5cXfv31V8b7jxo1CuvW\nrVPrmlKpFI8ePUJGRgbmzp2LkJAQpKSk4M6dO2pT627fvg0LCwsEBQWp3ZyRkZEBgUCgsvVURUUF\nfvnlF7i7u6Nz585Yv3690svn4uJiLFmyBLq6uhgyZAitJVx+fj7i4+PB5/Px3Xff4fjx46S/5/v3\n75Geng5nZ2fo6elh4cKFtOyempoaZGVlYfTo0dDR0UGPHj2wbNkyXLlypUWYPWKxGN9//z3YbLZC\nquuRI0co04OXL19G9+7dSbdlZ2fD29ub9tyvX79Gx44dmd00Dfbu3QsTExPSFbdG2R3v379HaGgo\njh07pvKFACAhIQEJCQlMLomUlBRGvGqgkeWhjMB/VVUV+Hy+RpbHEydO1Njy/+jRoxg0aJBGzsUE\n3bp1U1jplqG2thadOnVSusBEEAT27duHqKgo9O3bFx07doShoSH8/f2RmJiIrVu3Yvz48TAzMwOL\nxYKvry/WrFmDq1evqhQEampqMGPGDOjp6aktgZqVlQUul6v27DwvLw/Dhg0Dn8/HihUrFDqxfIqa\nmhqkpqbC0NAQrq6uyMjIoPxtamtr8fPPP6Nr166wsLBASkoKJU311q1biIqKAo/Hg4uLCzZu3Eg7\n4RCLxcjJyUFUVBTs7e3Rvn17uLu7Y9GiRRrh9ufm5sLa2hre3t6MOnsjIiIohaTo3qXff/+dsrAo\nA0EQ+Oqrr1R2d/oYs2fPxoABA+RYNxoL0sXFxRg+fDhl3leZIP3TTz9hzJgxCvcDGm1pzMzMGO27\nc+dOpXO5ycnJGsn/Pn36FGw2W+kXjwwXLlxAr1691D4PUwiFQsZpqmPHjil8sD+FVCrFxIkT0bVr\nV6xatQo5OTm0Ofzi4mLs27cPU6dOha2tLYRCIdasWaNSeuX06dMQiUSYNWuWWi/avXv3oK+vT9sE\nocy5ZLKjU6dOVVrtr6GhAZmZmejXrx9EIhESExMpC5YyNbxhw4ZBR0cHEydOxNWrV0mvJ5FIcOzY\nMQQFBaFjx44YOnQoMjMzFSruVVRU4NixY1i4cCHprL2hoQHXrl1j/G6cO3cOhw4dYvSblJSU0EpD\npKenIywsjHTbjRs3YG9vr/AaIpFIoas7E9TX18PT01MufaORIF1WVgZvb2/aZZYyQfrs2bPo27ev\nwv2AxheciSIe0DjT79Spk1KUn7q6OhgYGNAqVzFFSEgIEhMT1T7P7du3GXVbagJSqRRfffUVY/ug\niRMnYu3atYzPX19fj9DQUPTt21flHPaNGzcwYsQIcDgcLFy4UGkGxZs3bxAYGAhbW1u1Vk1Pnz6F\ngYFBM3EcdZCfn49ly5bB3NwcpqamWLx4sdI1kps3b2LcuHHQ1tbG+PHjcfv2bcp9CwsLkZiYCBMT\nE3Tp0gU//vgj5Yy5srIS27ZtQ79+/aCjo4OxY8fi+PHjKq1qSktLYW9vj3bt2oHH48HOzg6dO3eG\njY2N0uf6FLNmzcL06dMpt69YsYJSCuLx48eMJJEdHBxUdmr/FOXl5TAyMsLu3bubPtNIkF6+fDl6\n9eqF0NBQhISEIDQ0VK4IokyQfvbsmVIddUOGDGHcrDJ69GilX6Kff/4ZvXr1Urt99v79++DxeIys\njujw/PnzVus4LC8vh7a2NqN9GxoawOPxGAuji8ViBAQEYODAgRqRd338+HGTctzMmTMZeVzKQBAE\nduzYAQ6Hg9WrV6uc937+/DmMjIzUzsl/em95eXmYMWMGeDweXF1dkZqaqlSNo7S0FMuWLYNIJEKP\nHj2QlpZGyd+XSqU4e/YsRo8ejU6dOiEoKAinTp2iLCAWFhbihx9+gIuLC9hsNiZMmICcnByluwgJ\ngkBRURFu3LiBFy9eqP2eFBYWKmxMi4qKQlJSEum2V69egcfjKbyOsjUbRbh9+zY4HE5TIbFNdhxK\nJBJ89dVXjEflXbt24eDBg4z2PXfuHKytrZVePtra2jIeCOgQEBCg9gv89u1bdOrUSe17YYL79+8z\nbmS5dOkSqSU9GRoaGuDn5wdfX1+1TT4/RVFREebMmQMdHR1MnTpVKS2W58+fo3fv3vjuu+9U5sm/\nePECJiYmWLhwoUZylR9DIpHg+PHjGDVqFDp16gQ/Pz+lVnkNDQ04duwYhg0bBm1tbYwbN45WmfDt\n27dISUmBk5MThEIh5s2bRzsI5+fnY/Xq1XB0dASPx8O4ceOQlZWldsBVFnV1dRg1apRCiYegoCDK\nWsL79+/xv//9T+G1xo4di/T0dJXukwoHDhxocotpk0EaaNS+1fTLCzSO1jY2NkorpV26dAlCoVDt\nnPKff/4JPT09tbSPpVIpvvnmm1YxF7h48SJ69uzJaN/ExETMnj2b0b5JSUktrulRXl6O6dOng8Vi\nIS4ujjGTo76+HkuWLAGLxcLixYtVKnYVFhbCz88PBgYG2LVrV4uIPb17965Jn9rPz09pne6SkhKs\nWLECfD4ffn5+yM3NpZ283L9/H3PnzgWHw4GXl5dCet7Tp0+RnJwMDw8PdOjQAYMHD8aWLVvUllxQ\nhJycHJibm8PPz4/2P79//z44HA4lF/ratWuwtLRUeL3Q0FA5yytNYNasWQgICEBBQUHbDNItibS0\nNPj4+Ch93KRJk0j1CJSFp6en2n+qubk5KddV0zh27Bi8vLwY7evn58dIjvTBgwdgs9ktrrktw4sX\nLzB+/HhwOBwsX76csdhWfn4+AgMDYWBggAMHDqiU7vr999/Ro0cP9OjRA3fu3FH6eCaora3F6tWr\nweFwEBkZyZizLUN1dTXWr18PY2NjODo6Yvv27bQDU01NDX755Rc4OztDX18fCQkJCsW3KioqkJGR\ngVGjRkFbWxvdunVDVFQU9u3bp1Raig5FRUUICgqCoaEhjhw5QrtvdXU1bGxsaGfAixcvZiS25uXl\npbJbCx1qa2thZWWF1NTU//eCdE1NDbhcrtKqbm/fvoVAIFBo76UIp0+fhrW1tVqzq4EDB7bIg/Ep\nMjIyGOueCIVCPHv2jHaf+vp6dO/eXSMsCGXx+PFjjBo1Cnw+H+vWrWOcijh37hy6dOkCd3d3lQKt\nVCpFWloaOBwOEhISWmSFCDQWQGVpnjFjxuD3339XamCRSZx6e3uDy+UiJiZGYcfdn3/+iZkzZ4LH\n48HZ2RkpKSkKC7eyhp6VK1fC19cXHA4HIpEIAQEBSEpKwvnz5/HkyRPawVQqleLFixfIyclBSkoK\npk6dCg6Hg/j4eEYrzPDwcISEhND+Pt27d8fZs2cVnsvBwYExRVVZXLt2DT179vx/L0gDjW3fqogo\nZWRkwNbWVq1lOkEQ6Natm8p6tEAj9zM1NVXl45kiLS2NUXt8YWEh2Gy2wqCwfPlyDBgw4LMayN65\ncwe+vr4wNDRkPEOur69HSkoKuFwuZs2apRITRZYCsbKyUnugp0NpaSnWrl0LKysrmJmZYeXKlUqn\nGGSGtmw2G76+vjh27BhtIfDTXPmQIUOwb98+RrlogiDw5MkT7NixA5MnT4arqyuMjIzwzTffoH37\n9jA1NUWfPn0QGBiIwMBA2Nvb49tvv4VAIICbmxsiIiKQlJTEWIZ1586dsLCwoB0EiouLoaOjw+g9\n19PT09hqgAz/T6Y7gP+r/DJpKf8YBEHAy8sLK1euVOv6u3fvhoeHh8rHr1y5ktQCSNNYu3Ytozxz\nVlaWwu6syspKaGtra0S4ShM4e/Ys7O3t0adPH4X+gDK8fv0aYWFhEIlE2L9/v9KDDUEQ2L9/PwQC\nAaZPn96iTicEQeDKlSuYMGECtLW14ePjg6ysLKXqIR8+fEBaWhqcnZ0hEAgwd+5chRZoVVVV+Pnn\nnzFgwAB06NABQ4cOxY4dO5Tu7iQIApWVlXj48CHOnz+PjIwM7NmzB9evX1eZrvnXX3+Bw+HQUhGB\nRsNkJgp3BEH8f+x9dVhU6fv+gyDS3R0q0iiihFIKKHa3ayKriO7auQYGgqJrsOpis3Y3roWJCqII\nJoiKioAK0jHn/v3hNfyMmTlnhsHY7+e+rnM58j7ve96Jc5/nPAl5eXmpO4g/xQ/rOJQEsbGxYtUu\nHjBgAKKiosQ+T1ZWFrS1tTmHmglCRUUFtLW1Wc0DwrBr165v0i187ty5+OOPP1jlZs2axZpRuXbt\nWrFKxn4JhmFQUVGBgoICZGdn4969e7h+/ToyMjIk1sxramqwYcMGGBgYYODAgZx/P4mJibC3t4ef\nnx8uXLgg9nnfvn2LYcOGwdTUFEuXLkVOTo7Ya4iD4uJibN68GW3btoWenh42bNgg9meWkZGBadOm\nwcjICK1bt8axY8dY13j79i22bt2Kbt26QVVVFcHBwdi3b993aRp8584dNG3aFOvXrxcpV15eDmdn\nZ4GNbL9Ebm5uvUda/bAkzTAMunTpIpZnfdKkSZzTyYGPSRCGhoYS3QUjIyPr/Ng+YcIETv0cBeHO\nnTuws7OT+NxcMWXKFCxdupRVrm/fvtixY4dImaCgIM6hknwwDIM///wT2trakJOTg7y8PLS0tGBm\nZgY7Ozu0atUKlpaWMDAwwKBBg7Bp0yaJsr+KioqwePFi6OnpoWfPnkhJSWGdU1VVhbi4OFhbW6Nt\n27Y4ffq02L+HpKQkjBo1CpqamggKCsI///xT723RUlNT0aJFCwQFBUmkNNXU1GDv3r2wt7dHq1at\nWBsI8FFcXIxt27bBx8cHOjo6GDx4MHbu3Fkv5WM/RXV1NZYsWQIdHR1s2rRJ5HfE4/HQr18/9O3b\nl5PPaPXq1RgwYIA0t/sVfliSBj42W+VysfCRmJjIKY3zUwQHB4tsZikMVVVVcHJyYiUmUbh37x6M\njIwkah1UUVEBBQWFenNC8REeHo6YmBhWuVatWuHKlStCx0tKSqCioiJWCGNpaSkGDx4MJycnpKen\ni9S+srKysHHjRvTr1w+6urpo3LgxQkNDceLECbGK/ZeUlCAmJgZGRkbo3LkzkpKSWOdUV1cjPj4e\ntra2cHNzw6FDh8R2CpeWliI+Ph6BgYHQ1NTEqFGjcPny5Xqz3VdVVWHBggXQ0dHB5s2bJToPj8fD\n7t274eHhATMzMyxdupRzgk12djZiY2PRuXNnqKqqwsvLC4sXL0ZqaqpU3nNNTQ0uX76MGTNmwMbG\nBv7+/pzMbNOmTYOXlxdnxa158+b13vj4hyZpLtrZp6ipqYGurq5YJoTLly/D0tJSIqJMSkqCgYFB\nnarbeXp64tChQxLNtbW1rbewLj7GjBnDKRJDV1dXpHPq6NGjAkvYCkNmZiacnZ0xcOBAsePBeTwe\nUlNTER0dDTc3NxgZGWHGjBmcHUvAx0feNWvWwMTEBEFBQSJvQJ+ed//+/WjRogUcHR2xc+dOibrB\n5OTkYOnSpbCxsYGLiwt27dpVb11lbt++DWdnZ3Tu3LlOJpebN2/W9lIcOXKkWOn15eXlOHXqFMLD\nw2FtbQ1dXV34+/sjLCwMsbGxSExM5HSNvXv3Djt37qyte+Lk5ISZM2fiypUrnG6af/31F5o0acL5\nek5JSYGZmVm9Nzz+oUl63rx5YtdhHj58OFauXCnWHB8fH4krmI0fPx7Dhw+XaC7wMeVc0op2ffr0\nwT///CPxublg2LBhrNlU/MwsURrQr7/+KrQS2Zc4efIk9PT0sGrVKqloVffu3cOkSZOgr68PLy8v\nxMXFcXY8VVRUYP369TA3N4e/vz8n+zPDMDhx4gS8vLzQpEkTbNq0SaJoIB6Ph6NHj8LT0xPW1tZY\nv359vTw5VVZWYs6cOdDQ0MCQIUMkLrsKfHSsLlq0CCYmJmjbti3i4uLEcs4zDIPnz5/j1KlTWL58\nOUaOHAl3d3eoqalBX18fLVq0gIuLCxwdHWFnZ4dmzZqhSZMmsLKyqk2YiY2NFdvkdfz4cRgYGIjl\nZxo/fjwnf01d8UOT9N69e8UuqH3o0CH4+fmJNef06dMSxy1/+PABpqamnOIpBaG0tBSampoS2VHn\nz5+PmTNnSnRerujfv/9nxV4EIS0tTWRmFsMwMDc3Z229xOPxEBERAUNDQ1y8eLZpuTYAACAASURB\nVFGi/YpCVVUVDh8+jG7dukFDQwOhoaGcteuqqips2rQJ1tbW8Pb2xtmzZ1lvIPzC/O3bt4e5uTnW\nrVsncRRAYmIigoODYWhoiGXLlkm9bybw0cm3bNkyWFhYoFWrVti+fbvEDr7q6mrs378fPXv2rK2Y\nt3v3bont7QzDICcnBzdv3kRKSgpSU1ORlpaGjIwMPHjwAI8fP5Y4Azc5ORm6uroiC8R9ifLyck6d\nW6SBH5qk09PTOVWh+hRlZWVid6dmGAYtW7bE3r17xZrHx+HDh9GkSROJL8Bx48ZJdEfev3+/RJmT\n4qBnz56sn8uJEycQGBgodPzJkycwMjJiJbV58+ahZcuW9R7pAHwsnjNnzpza7tl79+7lpO1WV1dj\n69ataNq0KZo3b464uDhOxHPt2jUEBwdDR0cH4eHhrCFgwpCamor+/ftDRUUFXl5emDZtGo4ePSpV\n51tNTQ1iY2NBRKw3aC4oLCzE5s2bERAQAGNjY2zevLneTQRcceLECejp6WH//v1izZs/fz5ryKk0\ncO7cOUydOvXHJenq6mqxHId1wfHjx2FnZyex7a9Xr16svdaEISUlBebm5mL/cLOzs2FoaCjRObmC\nXzNYFLZv3y7Sw82lw8Xz58+/S+Pa8vJyxMfHw8fHB/r6+pg+fTqndHUej4cTJ07UEi+X7Dzgo4Nz\nzpw5MDExgaurK9atWyd2rD7w0cT077//Yt68eWjfvj1UVVVhZ2eHkJAQbN26Fffv35eYCFNSUmBq\naorFixdL3XF5/fp1eHp6wsnJCevXr6+XJwIuSExMRLt27WBhYSH2U9uBAwdgbGxcr7/ViooKTJky\nBUZGRtixY8ePS9LfEgzDwMPDQ+KaGi9fvoSOjo7E3ZRdXFzE7hDCMAy0tbXrtWBN165dWR2bf/75\nJ8aOHSt0fNGiRax96ObPny9yjW+B+/fv47fffoOOjk6tds3lcZ+fnaelpYVOnTrh6NGjrDf7mpoa\nnDp1Cn369IG6ujoGDBiAhIQEiZWE6upqJCcnY9WqVejbt2+tjdbHxweTJk3Czp078fjxY1bS5XeZ\nETdUUhzwbfY9e/aEhoYGfvnlFyQmJn6TLNQLFy7Az88PlpaW+Pvvv8X2FSQlJUFHR6fe0sCBjxYE\nFxcXdOvWDXl5eT+2ueNb4+rVqzAyMpI4C2zdunXw9PSUSIP5888/JYq3DAwMrFN6ORs6d+7MWqxm\n/vz5IuO9Bw4cKLIPJY/Hg7m5Oeesv/rGp9q1np4epk6dyqnOS2lpKTZt2oRWrVrB1NQUCxcu5HQD\nLSgowOrVq9GiRQuYmppi1qxZUmmAXFBQgNOnT2PRokXo0aMHTE1NoaqqihYtWqBv376YOXMmNm3a\nhEuXLuH169eIjIyEsbFxvRLQlxCUwi5tjmAYBmfPnoW3tzesra2xefNmiRy5WVlZMDQ0ZL0eJAU/\nJ0BHRwcbN26svWn9j6S/wODBgyV2xvF4PHh6eiI2NlbsuQUFBVBTUxM7dXbGjBn16mEODg5mLWg+\nceJEkd1YXFxcRMYbJyQkoHnz5hLvsT7x4MEDTJ48Gbq6uvDz88POnTs5RVgkJycjJCQEGhoa6NWr\nF86cOcPp5n3nzh389ttv0NPTq42OkGbX9bdv3yIpKQnx8fGYP38+hgwZAg8PD+jq6sLNze27XZ8M\nw+Dq1asYOXIkNDU1YWJigu7duyMiIgKnTp0Sq9vOhw8fcOnSJaxevRojRoyAo6MjmjRpgq1bt0oU\nagt8DO+ztbXFqlWrJJrPhlevXqFDhw5o1arVVwrBf5akGYZBWlqa2I9QOTk50NLSkriMZlpaGnR0\ndCSyV/Xt2xdr1qwRa86+ffvq1XnIpQyjqKLnNTU1UFRUFPl00rdvX4mKRZWXlwu1Jx49ehTGxsZw\ndXXFuHHjsG3btjrZaSsqKrBr1y74+/tDT08PU6ZM4VS/uaioCGvXroWTkxMaN26MyMhITo7RyspK\nHDx4EF27doW6ujp++eUXJCQkfJd06m8NhmGQmZmJ3bt3Y8qUKfDz86ttTBwQEIDOnTujV69eGDhw\nIIYPH44xY8YgPDwcffr0QZMmTaCkpAQ3NzeEhIQgNjYW165dk5icgY/BCP7+/ggPD5fiu/z/OHfu\nHAwMDDBnzhyBGv5/mqQbN24sVlgNH4sXL0anTp0ktpHNmjULvXv3FnveyZMn0bJlS7HmZGdnQ19f\nv97seUFBQawk3bt3b+zevVvg2MuXL0W2IOLxeFBVVRWr7yTwMTqjdevWGDlypMDxkpISPHv2DJcv\nX0Z0dDT69OkDCwsLTJw4UazzCMKjR48wZcoUGBoaolWrVpzKczIMg+vXr9dqin5+fti4cSOnJ6fc\n3FxER0ejdevW0NDQQL9+/RAfH1+nJKqfDTweDw8fPsSpU6dw5MgR7N27Fzt27EBcXBzWrVuHmJgY\nbN++Hffu3asTIX+JnJwcuLm5YeDAgfWSUHT79m3o6OiI9Ef9FCTdvHlzicKyoqKi8Msvv4g9r7Ky\nEs2aNWONahCGsrIyWFtb4/jx42LNq6mpgZGREWuVsU/BMAz09fXrrbJccHAwq81bVARIWlqayBoj\n9+/fh6WlpVh7un37NszMzDB//nyxb07CLrSUlBSxP8Pq6mqcPHkSAwYMgJqaWm3TA7Z43fLychw4\ncAC9e/eGmpoaOnfujK1bt3KK8nj9+jX+/vtvdO3aFWpqamjVqhXmzp2Lq1evSpWc/of/76OqjygX\n4OPN19zcXKiCw8dPQdJBQUGcKlJ9ifz8fGhoaEgUQ3ru3DmYmppK7ERMSEiAhYWF2L3dpk+fzqkb\nxKfo2rUr6xctKbp06cIa3SHKuXjhwgW0adNG6Nzt27ejT58+nPdz8OBB6OjoSP39Ll68GDo6OrC3\nt8eUKVNw8eJFsUivqKgImzdvRmBgINTV1TFw4EAcOXKE1TxRVFSE7du3o1u3blBTU0OnTp2wZcsW\nToRdWVmJc+fOYdq0aXB2doampib69OmDuLi4bxJr/l8FwzBYt24ddHV1pdpg9lNUVFTAw8ODkz+J\nK3c2oO8IV1dXSk5OFnuejo4OBQcH07Zt28Se6+fnRz4+PjR//nyx5xIRBQQEkKenJy1YsECsecOG\nDaMdO3ZQdXU15zmtW7emGzduiLtFTpCVlSWGYUTK1NTUkKysrMCxd+/ekba2ttC5ycnJ1LJlS057\nKSgooOnTp9OJEyeob9++nOZwxYwZMyg3N5fi4uJIQUGBJk6cSPr6+pSdnc1pvpqaGg0bNoxOnz5N\njx49Ik9PT4qMjCRDQ0MaPXo0nT17lmpqagTOGzx4MB06dIhevHhBAwcOpEOHDpG5uTkFBwfTpk2b\n6O3btwLPKS8vT35+frR06VJKTU2le/fuUXBwMCUkJJCzszM5ODjQpEmTKCEhgSoqKury8fyfQUlJ\nCQ0ePJj++usvunLlCnXq1Enq5wBAISEhZGxsTHPnzpXqwnVCXTTp/fv3o1OnThKdNzExETY2NhI9\nruTm5kJXV1fiIka5ubkS9fLz8PAQK8znzJkzIrXVuoBLxmFAQABOnz4tcGzjxo0YMWKE0Llt2rQR\nKz78Wz7Sv3jxos6ZcdnZ2YiMjESLFi2gp6eHMWPG4OzZs6zv48OHD/jnn3/Qq1cvqKmpoV27dli3\nbh3nmPiamhokJSVhwYIF8PT0hKqqKjp06ICYmBiJHOr/F5Ceng5bW1uMHDmyXsvFLlu2DC1atOCc\nxv5TmDuys7NhYGAg0Q+LYRisWbNG4sI0sbGxEsc+Ax9jiPv37y/WnHXr1ok1p7CwEMrKyvXSebtP\nnz6szWX9/PyEEm1kZKTQRBYejwdlZWWxww6/NzIzM2Fvb4+ZM2fixo0bnH8bT548wdKlS+Hq6lpL\n2KdOnWI1iZSWlmL//v0YOHAgNDQ04OXlhcjISNy5c4fzNfH+/Xvs27cPo0ePhpWVFfT19TFgwACs\nXLkSV65cqfca1j86zp07V1tvur7PY2xszJkHc3NzsWjRoh+fpBmGgbGx8Xexs/F4PHh4eEjcT7C4\nuBgGBgZiJWrk5eVBXV1dLHu2nZ1dvSSDDBo0CNu2bRMpI0qTXrhwodC48/fv30NNTa3Oe/zW4PF4\nuHr1KqZOnQobGxsYGRkhNDQUly9f5rxGZmYmli1bBnd3d2hoaKBv377Yvn07q/+koqICx48fx9ix\nY9G4cWMYGBhg8ODB2LZtm1iZp1lZWdi0aRNCQ0Ph6uoKRUVFODo6YuDAgViyZAmOHTuGZ8+e/Z/Q\nuBmGgZubm8S1e8TBsGHDOHMJj8dDYGAgxo8f/+OTNIDvGh+anp4ObW1tiSMo1qxZI7IAkSAEBASI\n5RwbMWJEvTSmHTlyJDZu3ChSpkuXLkIduxEREULLzWZnZ8PU1LTOe/zeePDgASIjI1nbMgnDpxEb\n/FTu6OhoTlmOWVlZWL9+PXr16gVNTU04Ojri999/x7Fjx8Sqi1FeXo6bN29i8+bN+P333xEQEAAD\nAwOoq6vDy8sLISEhiI6OxpEjR/Dw4cN6eWr7Xjh//jyaNm1a70WfGIaBkZER52zSZcuWwdPTE9nZ\n2Zy4U0561m3JIC8v/93ObWdnR5MmTaKRI0fSmTNnSEZGRqz5o0ePphUrVtC5c+fI39+f05z+/fvT\n7t27OTvIPDw8KDExkcaOHSvW3tjQqFEjVqeTgoKCUBkZGRkCIHCsqKiI1NXVRa6dm5tLb9++JXt7\ne24b/g6wsbGhqVOnCh0/d+4cKSsrU8uWLQU6WA0MDGjkyJE0cuRIKi8vp7Nnz9LRo0fJx8eHlJWV\nqWPHjtShQwfy9fUlJSWlz+ZaWlpSSEgIhYSEEI/Ho1u3btGZM2doxYoV1L9/f7KzsyM/Pz/y9/cn\nLy8vUlZWFrhHBQUFatmy5VdO3IKCAkpLS6MHDx7Qo0eP6OzZs/To0SPKyckhMzMzatq0KVlbW5OF\nhcVnh4aGhtjXyffCsmXLaMqUKdSgQf3GR2RkZFCjRo3I2tqaVTYpKYmioqLo5s2bQp3yX+K7k/T3\nxpQpU+jgwYO0fv16Cg0NFWuuvLw8RURE0PTp0ykpKYnTj7dHjx7022+/0YcPH0hNTY1V3t3dnSIj\nI8XaFxcoKChQZWUlq4wwkm7QoEGdSHr37t2UkZFB69ev57ZhDmAYhmpqar7ZjZ+//1evXlG7du0o\nMDCQAgMDyczM7CtZRUVF6ty5M3Xu3JliY2Pp7t27dOrUKYqMjKR+/fqRh4dHLWk3a9bss9+SrKws\ntW7dmlq3bk2zZ8+miooKun79Op0/f54iIiIoJSWFXFxcyNfXl3x9fcnDw0MoafOho6NDfn5+5Ofn\n99nfq6qqKCsrix4+fEhPnz6l7OxsunDhAmVnZ9PTp09JRkaGLCwsyMzMjExNTb86TExMvqvixcfd\nu3cpNTWVDhw4UO/nSkhIoMDAQNbrv6ioiAYMGEB//fUXmZubU05ODqf1/xMkDYDu379PdnZ2Ys+V\nk5OjrVu3kre3NwUFBZGlpaVY8/v160dRUVG0f/9+6t27N6u8pqYm+fj40OHDh2nIkCGs8ra2tpSX\nl0f5+fmkq6sr1t5EQRqatLAQPi4knZiYSD179uS2WQGoqqqi9PR0un37Nt2+fZtSU1Ppzp07tHbt\nWoGf6/Tp0ykxMZEsLS2pWbNmZGNjU/tvo0aNJNpDWFgYhYWF0atXryghIYESEhJoxowZdO3aNWrc\nuLHQeQ0aNCAXFxdycXGh6dOnU1FREZ07d45OnTpFMTExREQUGBhIAQEB1L59+69CHRUUFGoJef78\n+VRaWkpXr16lixcv0rx58yg1NZWcnZ1rZTw9PVlJmw95eXlq1qwZNWvW7KsxAFRYWEhPnz6lFy9e\n1B5paWn0/PlzevHiBeXm5pKZmRnZ29uTnZ0d2dnZkb29PdnY2Hz1tFCfiI6OpvDwcFJQUKj3cx0+\nfJgmTZokUgYAjRkzhjp06CD+776O5pgfosBSbm4uNDU1kZubK/EaUVFR8Pb2lihF9NSpU7CxseEc\nRrZjxw6xWmt169aN1cknLubPn89aJ3v8+PFCm9VGRUXh999/Fzi2c+dO9O3bV+TaRkZGdep+MXv2\nbNjZ2WHQoEGIjo7Gv//+KzKd+tmzZ7h06RK2bNmCGTNmoEePHrCzs8OpU6ck3oMg8Hg8gU45hmGQ\nnp7OqevLgwcP8Oeff9Y2c3V1dcX06dNx9uxZTtEapaWl+PfffzF79my0adMGSkpKcHZ2xqhRo7B+\n/XokJyfXm+25qqoKGRkZ2Lt3L+bPn4++ffvCwcEBCgoKsLCwQHBwMKZMmYKtW7fi1q1bEnddEQWG\nYSAnJ4cHDx5Ife0v8eHDBygrK7M2BklISICtre1n399PEYLHR3l5ucT1mvkIDw+vU6GUmpoatG3b\nFlFRUWLPZRgG3t7eIst2foqioiKoqqpy7rC9efNm9OrVS+x9icLy5ctZ61388ccfmDt3rsAxUXHS\nhw4dQpcuXUSura6uXqcQvfqOThgxYgQmTZqEAwcO4M2bN3Ve7/Xr1zAzM4OpqSlGjx6NvXv3cqrR\nUVlZiYsXL2L27Nlwd3eHsrIyvL29MXfuXJw7d44TaZeXlyMpKQlr1qzBL7/8Ant7eygpKaF169b4\n9ddfsX79eiQlJdULYfJRVVWFR48e4dChQ1i0aBEGDhwIJycnKCgowMrKCkOGDJFq7enffvsNo0eP\nlspaovD06VOYm5uzyi1YsOArR/tPRdL37t2DhYVFnb6gN2/eQFtbW6yGk1/i6dOn0NHRkagFUmJi\nIiwsLDhHq3Tp0oVzx/T8/Hyoq6tL3MZLENavX49Ro0aJlFm9erXQgv0HDhxA165dBY6dP38e3t7e\nItdWUVH5bt07uODixYuIiIhAx44doaGhASsrKwwaNEjscgCfgmEYZGRkYPny5ejYsSNUVVXFbnZc\nXFyMU6dOYfr06Z+R9pw5c3D69GnOn2lxcTEuXryIlStXYvjw4WjevDkUFRXRrFkz9O/fH0uXLsWJ\nEyeQk5NTrzfE6upq3L9/HzExMWjWrBlsbW0RExNT57Zh79+/h76+fr13gEpNTYWjoyOrXO/evb9q\nLv1TkTTDMDAxManz40lERIRY9SIEYfPmzXB0dJQoSSYoKAjr1q3jJLtlyxZ0796d89re3t5SrTfw\nzz//oF+/fqwywswWiYmJ8PLyEjiWkpICZ2dnkWsPHjxYqjed+gSPx0N6ejq2bNkikLB4PB5ev34t\n9rqVlZV4/vy5wLGCggJOmi2ftGfMmAFvb28oKyvDxcUFYWFh2Llzp1jXZWVlJe7cuYOtW7fit99+\nQ7t27aCrqwtNTU34+PggLCwMGzZswLVr1+p0sxIGhmGQmJiIQYMGQV1dHYMHD66Tdr1hwwa0adOm\nXm8yiYmJnLKCmzZt+pW14KciaQAYNWoUVq5cWac1SktLYWJiIrIQPRsYhkGPHj0wZcoUsefeuHED\nxsbGnB5B3717B1VVVc6FnpYvX86q+YqDI0eOsNrFExIS0K5dO4Fj6enpQjuJP3nyROwKeD8znj17\nBk1NTZibm6Nv375Yvnw5Ll++XKdsv1WrVkFZWRmenp6YPn06Tp48yUlLrqysxLVr1xAVFYXu3btD\nR0cHZmZm6NevH1atWoUbN26IbY/Ozc3FmTNnsHz5cgwbNqxW67axsanVuk+dOiUVsxAfBQUFiImJ\ngY2NDQICAiRau6amBi4uLqyZtXXBsWPHWK+j0tJSKCoqfvW5/3QkvX//fnTo0KGu20F6enqdE2Ty\n8vJgaGiICxcuiD23e/fuWL58OSfZoKAgzoktT548gZ6entRq33IxSYjSiN+8eQMdHR2BY/n5+dDS\n0qrzHj9FZGQka/3r7wmGYfDw4UNs27YN48aNg6urKwICAuq0ZmlpKc6ePYu5c+fCx8cHysrKYne/\n5u9ry5YtGDNmDBwdHaGsrIy2bdti6tSpOHDggES9NKuqqnD37t1ardvX1xcaGhowMjJCz549ER0d\nLZW09OrqasycORPGxsYSXY8XL16EmZlZvdnb4+PjWUs93Lx5U+B19NORdGFhIdTU1H4YO+WxY8dg\nbm4udouj5ORkmJqacor02LBhA2sUxKewt7eXqNmBICQnJ7OaJHJycqCvry9wrLq6GvLy8gLNQtXV\n1WjUqJHEdVUEwdLSEqmpqVJb71tAWKbb7du3ER8fLzY5VlRUCCW9mzdvcu5OXlRUhISEBMybNw8d\nO3aElpYWTE1N0adPH0RHR0v8FMDvvBIfH49x48ahRYsWUFRUhIuLC0aPHo24uDiJeeLkyZMwNDTE\nqFGjxGq5BXwsgTB06NB6MXvs3LkTPXv2FClz7NgxgTfsn46kgY/dp+sSliVtDBs2DOPHjxd7npeX\nF6fGArm5uVBXV+dMZr///jvmz58v9n4E4enTp6yp2zweD40aNRJ6wTZu3FhoqylbW1uJqwwKQqtW\nrXD16lWprfc9cfHiRXTv3h2ampqwsbFBaGgodu/eXaeOLH369IGKigqcnZ0RHh6Offv2cTYRMAyD\nx48fY/v27QgLC0PLli2hpKSE5s2bY/To0diwYQNu374tUdheeXk5rl+/jtWrV6Nfv37Q1taGg4MD\nJk+ejH///VesG3lhYSEmTJgAXV1dxMbGcn6qLCkpQfPmzREZGSn2/tnw8OFD1uiOrKwsGBsbf/X3\nn5KkfzQUFBTAwMBAbO11586d8PPz4yTr6enJOVb39OnTQp114uLDhw9QUlJilRNFxMHBwUJLr/bq\n1UuqtsBu3bqJ/aj/o6OmpgYpKSlYvnw5OnfuXOfPi2+PjoyMRKdOnaCnpyexc/ZTch06dCjs7Oyg\npKQEd3d3jB8/Htu3b8fjx48l6qBz/fp1zJs3D+7u7lBVVUXnzp2xefNmzoR9584dtGnTBq6ursjI\nyOA058WLFzA2NsbBgwfF2i8beDweNDQ0ROZoMAwDNTW1r27CUiXp1NRUDB48WODYf5mkgY8RDg4O\nDmLZuSsrK2FoaMgp9nvZsmUIDQ3ltG5ZWRlUVFQ4x1eLAsMwaNiwIetF3L59e5w8eVLg2IQJE4R2\nE587dy7mzJkjdN2srCycOXOG835DQ0PFbub7X8GSJUuwYsUKXL9+XazfoTACffv2LRYvXozz58+L\nFaXx4cMHXLhwAVFRUejduzdMTU2hra2N4OBgzJ8/H6dOnRI79r2goAA7d+5EUFAQjI2NERUVxcnk\nyTAM1q9fDz09Pc626hs3bkBHRwe3b98Wa49saN++PWsrOi8vL5w7d+6zv0mtM8vff/9Ns2fPFquj\nyI+CnJwc6tChQ5323r9/fzI1NaWoqCjOc+Tl5SkkJITWrl3LKtutWzc6fPgwa5cUoo/1Hzw9Penc\nuXOc9yIMMjIypK2tLbQ7CB+WlpZCu5g0adKEHj9+LHDMzs6OMjIyhK6bmZlJCxcu5LxfIyMjevXq\nFWf5/xJsbGzo8ePHFBISQpqamuTh4UETJ06kd+/eiZwnrJZEVVVVbTccPT09at68OY0dO5aOHDki\ncj1VVVXy8fGhyZMn0969e+n58+d09+5dGjVqFJWVldGSJUvIzMyMXF1daerUqXT69GkqKysTuaa2\ntjb179+fTp06RceOHaOUlBSysrKiGTNm0OvXr0W+t5CQEPrnn3+oT58+FB8fL/I8RERubm60du1a\n6tq1Kz1//pxVnitcXV3p9u3bImWcnZ3p7t27kp2A7S6RkJCAZ8+eCY2p/ZE1aYZhEBgYWGdbVHZ2\nNrS1tcWK43758iU0NDQ4ab22tra4fv06p3Wjo6MxZswYzvsQBXt7e9bEnUWLFmHq1KkCxxISEoSa\nde7evSs0RA/4mIGnqanJ+XH59evXEkUh1BfKy8ulGnLGFcXFxTh//jyWLFki9ClIHBNEeXk5rl27\nhpiYGKk8qVRWViIxMRF//PEH2rRpA2VlZfj4+GDhwoWck8SysrIQFhYGTU1NjB49WmgsOR9paWkw\nMzPj3Fh2+fLlaNq0qdgOSGHYuHEja2Psv/76C8OGDfvsb1I1d+Tk5PyUJA18LMIuLsEKQkxMDHx8\nfMS6APr06cOpFvS0adMwa9YsTmvevn0bNjY2nPcgCv7+/khISBAps3fvXqGZhXyiFRTFUFVVBXV1\ndeTl5QmcyzAMmjRpUqeY9m+FzMxMhISEIDg4GM7OztDW1oa8vLzQxKmcnBwcO3YMmZmZ9V7L+Evk\n5+fDwMAAQ4YMQXx8vFSIaN26dejQoQOWLVuGW7duiRUGWlxcjBMnTmDixIkwMzODs7MzYmJiON3g\n8vPzMXPmTOjq6mLnzp0iZV++fAkHBwdMnTqV0zU6efJkBAQESCWkNS0tDaampiK/a76i9/Lly9q/\n/dQkHRcXxznWmAtWr16NVq1a1amPXk1NDVq2bIm///6b85x///0XTk5OrD+ay5cvw8nJifM+1NXV\n61RMio+hQ4eythV69OiRSO+1hYWFUMdit27dEB8fL3TuH3/8gQkTJnDa6/fE69evsW7dOhw5cgQp\nKSnIy8sT+Z1eu3YNgYGBMDExgaqqKjw9PREaGvrN4rwzMzOxbt06dO3aFWpqamjZsmWdrqd3797h\nwIEDGDduHGxtbaGlpYVevXrhxo0bYq3D4/Fw7tw5DBkyBOrq6ujWrRsOHjzIame/desWmjZtiiFD\nhoi0VxcUFMDV1RVhYWGsN8fq6mr4+/tzVo7Y4ODggMTERJEy06dP/0ybljpJC4vnrQ+STkpKgrW1\ntdTiGvntaubNm1endVJTU6Grq8uZIHk8HqytrVm1xZqaGmhra+PZs2ec1g0ODsa+ffs4yYrCjBkz\nsHDhQpEyPB4PKioqQmNwBwwYIJTo161bh6FDhwpd+8mTJ9DV1a3Xwj7fG2/fvsX58+exatUqod/Z\n8+fPkZ2dXS9xvPwCTcI67EiCly9fYtu2bZwjKwShqKgIcXFxaNu2LXR180FSqgAAIABJREFUdTF9\n+nSRTseSkhKMGTMGFhYWItuZFRYWwtPTE8OHD2fVkt+8eQNTU1OpfDZLlixhDQAoKiqCgYEBbt26\nBeAn16QZhoG9vb1EGUbCkJOTg4sXL9Z5nWnTprHWvPgUS5cuxciRI1nlBg8ezLlN1pIlS6Siga5d\nu5ZTZIm7u7vQz2716tVC09UzMzOhr68vUqvZs2dPvdSB+Jnw119/wdDQEBoaGvD29sb48eMRFxfH\n+aZdF6xcuRJeXl6YMWMG59RzNowYMQIxMTGc29I9fvwYo0aNgo6ODqKjo0VGHB06dAj6+vqYO3eu\n0Cfj4uJieHt7Y9y4caw3vuvXr0NXV5dTSzNR4BdnY3sq2LhxY209kZ8+Tnr58uUitbDvhdLSUlhb\nW3MudpSbm8vJgbh792507NiR05pXrlxB8+bNOcmKwqFDh9C5c2dWuZCQEPz5558Cx27dugV7e3uh\ncxs3biz1kKc9e/bUe3Wz74E3b94gISEBUVFRGDx4MI4fPy5QTpqZnCUlJfj3338xd+5c+Pr6QllZ\nGS1atBArPPJTMAyDo0ePYsSIEdDR0UGLFi2wcOFC3Lt3j5Uw09PT0bVrV5ibm2Pbtm1Cb+6vXr1C\nUFAQWrduLfRGVlhYCCcnJ0RERLDuOTY2Fg4ODnVWFry8vFh5oaamBs7Ozti7d+/PT9L8ztp1qTlc\nXzhz5gwsLS05x6z26dOHtTpeYWEhVFRUOP1QKisroaysXGet59atW3BxcWGVW7t2rdDa0VVVVVBR\nURFaWjIsLAyLFi2q0z6/RFxcHBwdHeu1y/z169exd+/eH7KrdteuXWFqaoqRI0dKNasT+HgDuHz5\nssTNmT9FdXU1zp8/j/DwcLRv357zvMTERLi7u6N58+ZCTSo8Hg+RkZEwMzMT+jt4+fIlLCwsWJOE\nGIbBL7/8Uuf602vXrkXHjh1Z7eHnzp2Dubk57t69+3OTNPBRg/tRi+oEBASwdtvm4+TJk2jVqhWr\nnI+Pj1Dt6Ut4enp+FRwvLt6/fw9VVVVWIkpNTUWTJk2Ejnfu3PmrWrl8XL58WeodmxmGwaJFiyQu\ngsUF/KcVV1dX1giYbw0ej4dHjx4hIiIChoaGaN++PY4cOfJNumKHhobiwIED9d5VnGEYbNy4ETo6\nOiKzBCMiIuDh4SFUYbpx4wb09fVZ61O/f/8eWlparOF+olBWVgZ3d3fWjkfARyeir6/vz0/S9a3F\n1OWx8cqVKzA3N+ekTdfU1MDIyAjp6eki5RYvXsy5Vkh4eDiWLVvGSVYU9PT0PgsLEgQejwctLS2h\nGsuGDRuEVgJjGAYtWrTAoUOH6rzXL3H69Gno6+tjxYoV9fJb4fF42L17Nxo3boyOHTvWyVFWX6is\nrMT27dvRs2fPeifpmpoabNmyBW3btoWBgQGmTZtWZ1sum3Pv1q1b0NfXF+p05fF46Nq1q9DmFAAw\nbtw4TmV+J02ahN9++41VThTevHkDKysr1i5NDMPg2rVrPz9J1yeuXr0KV1fXOhWeDwwMxIYNGzjJ\nTps2jbVGdXJyMpo2bcppve3bt9e5wQEAtG3bFmfPnmWV69GjB7Zv3y5w7PXr19DQ0BB6w9q3bx/c\n3NxYifTixYucn074ePr0KQICAiQqus8VlZWVWLFiBRwcHOpdg5Q26kvRefDgASZPngw9PT2JzQSV\nlZVo1qwZVqxYIVLZuX37tkiiLiwsRJMmTbBlyxah40ZGRrh06ZLI/eTk5EBbW7vON56MjAzo6emx\nXlc/vU26vsEwDHr16oVx48ZJvIY42vT9+/dhYGAgMlabx+NBT08PWVlZrOs9ePAAFhYWYu1XEEaN\nGsWpm8yff/4p1C4NfIwAEVbjg8fjwdbWltVskJmZCR0dHanbWaUFadXy/pZYvnw5vL29ER0djYcP\nH0p9/crKyjq1rMvIyEBQUBCcnZ3x+PFjoXJ8ot67d6/A8Xv37kFHR0eoQ3nPnj2wt7dnvVaXL1+O\n9u3b1/nmdu7cOejq6op8+vofSXNAYWEhrKysOJUVFQZxtGl3d3dW7+/gwYMRGxvLuhaPx4Oamlqd\nM8qioqI4hfOlpaXByspK6PiKFStE9uvbtm0bfHx8WM+zZcsWODg4/DSttX50lJeX4+jRowgJCYGR\nkRGaNm2K33//vU7EyhXilEpdu3YtdHV1RV6LbES9e/duWFhYCCz5yjAMOnbsiCVLlojcS3V1NZyd\nnTn3HxWFLVu2wNLSUujn8J8k6fp4dLt69Sr09fVZ7bKi5pubm3N6DF6zZo3QaoJ8bN++nXPvQz8/\nP85lToXh2LFjnDzvDMNAT08PmZmZAsdfvHgBLS0toe3AqqurYW1tzeoYZRgG/fr1Q79+/eqcIbpj\nx446dwbhAk9PT/Tu3RsxMTEStaf6VmAYBsnJyZg3bx7S0tLq/XytWrUSK9MxKSkJjRs3FvobAz46\nsXV0dIQ+FYSHhwu1P2dlZUFdXZ21Zd3ly5dhbm4uFb6ZNWsW3NzcBIbg/udIes6cOZybvIqLefPm\nYdCgQRLP9/Pzw9atW1nl+EQm6iLOycmBlpYWJyfQxIkT6+w8fPPmDdTV1Tmdb8SIEVi1apXQ8R49\neohMyDl37hyMjY1ZwyrLy8sRGBj4VUEacfDmzRt07NgRurq6mDZtmlRCyoQhMzMT27dvR2hoKBwd\nHaGiogJfX19MnTr1pzSRSAvZ2dnQ19fH+fPnOc/h8nnNnTtXqB2cX9hMWChr+/btWeuS8xUSaTQg\nYRgGY8eOhYeHx1ddnv5zJJ2SkgIDAwOx21lxQXV1dZ26Ypw5cwa2traciM7V1ZU1dK5JkyacKobF\nxcWxauZcYG1tzan29f79+xEYGCh0/OLFi6zhdmFhYRgyZAjrucrLy2vTZ+uCR48eYeLEidDS0kL3\n7t1x5cqVOq/Jhvfv3+PUqVNCa21XVlYiPT39h9W4pYmEhAQYGBhIlR/y8/OhqakptCpicHCwUKWJ\n38SADb169RLqKBcXPB4Po0ePRtu2bT+7efznSBr4WBBIWgVRpAmGYdCyZUtOtu0FCxZg4sSJImVG\njx4tUmPl4+bNm5wLM4nCoEGDOBWO+vDhA1RUVIQ+LjIMg+bNm4s0aZSUlKBx48b1EpInCsXFxVi/\nfr1Q5+a3RFZWFpo0aQIFBQXY29ujb9++WLBgQZ1NV8LA4/GwfPny71JaFfgYWtq6dWupZkqGhYVh\n2rRpAsf27t0rtITus2fPoK2tzWpKW7lyJUJCQuq8Tz54PB6GDx8OPz+/2lo1/0mSfvHiBbS1tTlF\nP3xrHDhwgFOYWWpqKiwtLUXKxcfHsza3BD4GzysrK9f56WLNmjWc6osAQLt27UQmF2zdupW1S/al\nS5dgaGgotXq+0kBOTs43zy4sKytDSkoKtm/fjmnTpmHu3LkC5V68eIFdu3bh+vXryMzMRGFhIete\nS0pKkJWVhaSkJHTr1g2enp71GqYoCgzDYMCAAVJ9inn69Cm0tLQE2norKiqgo6Mj1Fzh5uaG06dP\ni1w/OTkZtra20thqLWpqajBkyBAEBASgvLz8v0nSwMcMox49enyTc4kDfpgZW80DhmFqU0KFgR+v\nycV8EhQUJNTbzRXJycmws7PjJBsTEyMyMaCiogIGBgas5pPff/8dffv2FZsY165diwULFkhVK2MY\nBs7OzmjcuDGmTp2KixcvfhOHI1fcvn0bvXv3hqurK8zNzaGiogI5OTmhpq49e/ZAUVERZmZmcHV1\nxaRJk8Rqu/WjgGEYkWUS+vfvjxUrVggcGzt2rNAKj9HR0axKSXV1tVSip75ETU0N+vfvjw4dOiAr\nK+u/SdLl5eWIjo6u1+yqqqoq1uxAQdiyZQurFgl8fFRjCwWysrLitIfVq1eLjF/mgurqamhpaXH6\nDrOyslirfS1dupQ1QqWsrAxOTk5id8158eIFunbtCh0dHYSFhSE5OVkqGjA/8mHmzJlo1aoVlJSU\nxG7y8C1RUVEhtGjXt240UF8IDQ0VmqACfLzeBgwYIHBs3759QptVJCcnw9HRkfX8rVu3FlkWVVJU\nV1ejQ4cOmDZtmnR6HP5oUFBQoEmTJlGDBvW39bt375K/v7/YPfUGDBhA6enprL3MgoKC6PTp0yJl\nPDw86Pr166zn9PHxoYsXL4q1zy8hJydHXbp0of3797PKWlpakp2dHR07dkyozIQJEygtLY0SEhKE\nyigqKtKxY8do8+bNFBISQhUVFZz2amJiQocPH6YbN26QtrY29erVi5ydnamwsJDTfGGQkZGhFi1a\n0KJFiygpKYny8/Np5cqVAvsEFhQU0K5du+jx48ecelPWBxo1akTq6uoCx+rz2vhWqK6upt27d1NA\nQIBQmXfv3pGenp7AMYZhSF5eXuCYgoIC1dTUsO5BQUGBKisruW1YDMjJyVFsbCylpqZykv/5v816\ngKurK/366680bNgwAsB5nry8PIWFhdHKlStFyvn6+tKtW7eopKREqIy7uzsnkra3t6fCwkJ6+fIl\n530KQp8+fWjv3r2cZIcPH05btmwROq6goEAxMTEUHh5OVVVVQuVMTU3pxo0bVFRURF5eXvT06VPO\n+7W0tKR58+ZRZmYmbdiwgTQ0NDjP5QIlJSVycXEROPbu3Tvas2cPBQQEkLq6Orm7u9OoUaNo9+7d\nUt3Dzw4AlJCQINY1xMeVK1fIysqKjIyMhMrk5eUJJemqqiqhJC0nJ8eJpBs1aiTy91sXWFhY0N9/\n/81J9n8kLQSzZs2it2/f0q5du8SaN2zYMDp48CCVl5cLlVFRUSE3Nze6cOGCUBmuJN2gQQPy9vau\nszYdEBBAGRkZnMi+d+/edOnSJcrNzRUq07lzZ7K0tKTVq1eLXEtVVZV27dpFQ4cOJXd3dzp+/LhY\n+27QoAG5u7sLHEtLS6Px48fT4cOHqaioSKx1RaFp06Z04MABys7OpufPn1N0dDS1bNlSaFf6J0+e\n0NGjR+nhw4d16lz/M6GgoIB69+5NkydPZu1ILwjHjh2jLl26iJR58+aNSJJu2LChwDE5OTlO30Oj\nRo3qRZMWF/8jaSGQk5OjVatW0dSpU6m0tJTzPENDQ2rZsqVIcwARu8nDycmJsrKyqLi4mPWc0jB5\nyMvLczZ5qKioUI8ePWjHjh1CZWRkZGjlypW0ZMkSev36tcj1ZGRkaMKECXTw4EEKDQ2l2bNnE4/H\nE/s9fAltbW0yNTWltWvXkomJCbm7u9OkSZPo8uXLdV6bD01NTWrTpg2FhobS4MGDBco8f/6cYmNj\nKTg4mFRVVcnGxoY6depEO3fulNo+fiScOHGCnJ2dycrKim7cuEE6Ojpir3Hs2DHq3LmzSBk2kham\nSTds2JCTJi0vL/8/kpYG7t69S1OmTKmXtdu0aUNt27YV+2IaOHAgxcfHi5QJDAwUSdLy8vLk4uJC\nN27cYD2fj4+PSK2cK3r37k179uzhJDts2DDatGmTyEdZGxsbGjFiBE2dOpXTmp6enpScnEzXrl2j\noKAgysrK4jRPGIyMjGjq1KmUkJBA+fn5tGTJEtLW1hbqaygqKqoXTdff359OnDhBmZmZVFRURAcO\nHKDQ0FBq3LixQPktW7ZQr169KDw8nCIiImj9+vV08OBBscxB3wMlJSU0cOBAGjt2LMXHx1NUVBQp\nKCiIvU5hYSHp6elR8+bNhcoAoOzsbKHmkIqKCqGatKysLGdzx49A0nLfewN1hZWVFR07doycnJxo\nyJAhUl8/Li5O7B9ajx49aMKECVRcXEyqqqoCZZydnendu3f08uVLMjY2FijTsmVLSklJoXbt2ok8\nn6OjI+Xk5Ig8HxcEBQVRSEgIZWRkkJ2dnUjZtm3bkry8PB0/flykxjN37lxq3bo1RURE0OzZs1n3\noKenRwkJCbR06VJq1aoVubq60siRI6lbt27UqFEjsd8THwoKCuTn50d+fn5CZaKjoyk6OposLS2p\nWbNm1KxZM7K1tSV/f3+h35G4aNSoEdnb25O9vb1QmbZt25KSkhK9evWK8vPz6datW5Sfn08VFRVk\naWlZK8cwDJmYmJCqqippaGiQiooKKSsrk7KyMsXHx3/lQARA69atI3l5eWrYsCHJy8vXOkYHDBjw\n1T54PB5FR0dTSUkJFRcXU0lJCZWUlFBFRQUdPHjwK6dqo0aNyM3NjWJjY4U6NblAQ0ODEhMTRcpE\nRkaSsrIyOTk5CRw/fvy4UD64desWNWvWjHUfubm5pKury77hesZPT9IqKiq0Z88e8vf3Jzc3N04f\nvjhQVFQUe46GhgZ5enrSyZMnqW/fvgJlGjRoQG3btqWLFy/SwIEDBco0b96cNQqE6KNm0KxZM8rI\nyKDWrVuLvV8+5OXlacSIEbR161aKjIwUKSsjI0OzZ8+miIgI6tSpk8AoCKKP38/Zs2fJ19eX5OTk\naPr06az7kJWVpVmzZtHvv/9Ohw4dog0bNtC4ceNowIABNHLkSHJ2dpbo/bFh4cKFNGvWLHr8+DE9\nePCA7t+/TydPniRDQ0OBJH3p0iWqrKwkc3NzMjU1lUhrFARra2uytrZmlZORkaHbt2/T+/fvqbCw\nkEpLS6m0tJTKysoERngAoPT0dKqurqaqqiqqqqoiANSgQQOBJN2gQQN69+4dqaiokLm5OamoqJCq\nqiqpqKgQgK++84YNG9Jvv/0m+RvniOPHj9Pq1avpxo0bAk0aycnJlJGRQX369BE4/8CBA9SzZ0/W\n8zx48IBsbW3rvN86o64xfz9KqdKNGzfCwcGhNuXye2PDhg2sXcVjYmIwZswYoeOpqalo1qwZp/MN\nHTpU7IL5gnD16lXOqeZc60QDHwvfNGnSBFFRURLt6+nTp5g7dy5MTU3h6uqKiIgInDlzBu/fv5do\nPWlg2bJl8PX1hZWVFeTl5aGvr49WrVrh5s2bAuX/K/HL0sTbt2/FShh58OABdHV1RWYvduvWTWhZ\nhaqqKmhra7O2ySosLISysnK9fmf/2YxDYWAYBoMHD+ac3lzf4FeXE1UXOTk5WSQJV1VVQVFRkVNz\nWq51odlQXV0tsnjNl9ixYwfatm3LSfbFixewsrJCTEyMxPurqalBQkICJk+eDG9vb6ioqKBZs2YY\nOnQo1qxZg5s3b9a567Ok+8rJycHVq1eF9tNr27Yt9PT00Lx5cwQHB2PEiBGYOXNmvTbU/RFRUVGB\n/fv3o3v37lBTUxPaH/NLFBYWwsbGRqQycvv2bRgYGAjNGD179ixatmzJeq7r16+jRYsWnPYlKTIy\nMjhx509v7uBDRkaGYmNjKTk5uV7PwzAMp2QBPT09cnZ2pjNnzggNJXJ2dqbXr1/TmzdvSF9f/6vx\nhg0bkq2tLaWlpQkNM+PDwcGBTp48ye1NiICcnBz5+/tTQkIC/fLLL6zy/fr1o3nz5tHFixfJx8dH\npKyJiQmdP3+efH19qWHDhjRu3Dix9ycrK0sBAQG1SQ41NTWUkZFBSUlJdOPGDdqwYQM9evSINDQ0\nqHHjxrWmA/5hZmZG+vr6Uk/4kJWVJWNjY5G26/Pnz1NeXh69fPmScnNzaw9hpqKAgADKy8sjXV1d\n0tPTI11dXdLW1qZff/1VoK0UAkwQPxKePn1KkZGRtG/fPnJwcKAhQ4bQli1bONmveTweDRo0iNq3\nb0+jRo0SKhcREUFTpkwRaqY8dOgQ9ejRg/V89+/fr1dTx4kTJ2jmzJmcZP8zJE300f7JRhR1waNH\nj2jYsGF0+fJlThd5z5496eDBg0JJWlZWltq0aUOXLl2i3r17C5RxcXGh1NRUVpJ2dHSke/fusb8J\nDujQoQOdOHGCE0nLycnRjBkzaP78+XT27FlWkjAzM6Nz586Rr68vlZWV0eTJk+tELHJycuTk5ERO\nTk40evRoIvp4I3316hU9efKEMjMzKTMzkw4fPkyZmZn0/PlzKioqIiMjIzI2NiYjI6Paw9DQkPT1\n9cnAwIAMDAxIW1ubZGVlJd7bl5CVlSVDQ0MyNDTkJL9t2zbKzc2l/Pz82kNUzHGzZs0oPz+fNDQ0\nSE1NjVRVVUlNTY1UVFQ+cyqamJgIvEGWlZXRrVu3qFGjRqSgoEBycnLUoEEDUlRUJAsLi6/ky8vL\nKT09nSorK6miooIqKiqorKyMGIahfv36fSUPgMzMzCg5OZnMzc05fQZERO/fv6dx48ZRSUkJxcTE\nCJU7fvw4Xb58mbZu3Spw/MOHD7Rv3z76999/Wc9548YNkc7duiAtLY1GjRpFq1at4uRM/0+RdH2j\nSZMmxOPxaM+ePdS/f39W+c6dO9PSpUtFat8eHh6UlJQklKQdHBwoIyOD9VxGRkb04cMHKi0tJWVl\nZVZ5UejatStNnjyZ81pDhw6lqKgo2r9/v9D38SksLCzo0qVL1KVLF7pz5w4tXLjws6iFuqJBgwZk\nYmJCJiYm5Ovr+9V4RUUF5eTk0KtXrz477t69S7m5ufTmzRvKzc2lwsJC0tHRIX19fdLT06s9+Jot\n/7WOjg7p6OiQurq6VDVZcQidiCg9PZ0+fPhAhYWF9OHDh9qjuLi41qlYWloqNPwsPz+fZs2aVUu6\nPB6PeDweWVpaCnxKe/36NY0ZM4YUFBRqD0VFRXJwcBC4vpWVFWftkejjTWPv3r00a9Ys6tGjB23Y\nsEFoWN2hQ4dozJgxdOTIEaG/2fHjx1PXrl1ZI5eeP39Ou3fvlprS8ynOnj1LAwYMoFWrVpGHhwen\nOf8jaTEgIyNDixYtonHjxlHv3r1JTk70x2dtbU0qKip09+5doSnGrVq1ooiICKFr2NracsrCk5GR\nIXNzc3r27Bnrj5ANenp61Lp1azp+/LjQ6JRPIScnR3FxcdSrVy/y9fXllLxgampKly5dosWLF5Ob\nmxsFBgbSlClTRMbGSgsKCgrUuHFjoXHKfFRXV1NeXh69efOG8vPzKS8vj/Ly8ig/P58eP35c+7qg\noIAKCgqorKyMtLW1SVtbm3R0dGpfa2trk5aW1lf/ampqkqamJikqKkqF3OXk5EhLS4u0tLQkmm9u\nbk6XLl3iLG9lZSV18yIAunnzJm3atIn27NlD7u7uFB8fL/IJeffu3TRhwgQ6ceIEubq6CpW5fv06\npaSksO5h/vz5NGbMGLFukFywdetWmjp1Ku3bt4+8vb0pJyeH07z/PEmnpaWRg4OD1DScdu3akaGh\nIe3YsYOGDRvGKt+xY0c6efKkUJLmx0LX1NQIJH1bW1u6f/8+p71ZWFhQdnZ2nUmaiKh///60a9cu\nTiRN9DERZdCgQRQWFsY5lV5VVZWWLFlCM2bMoA0bNlCXLl3I3t6epk6dSv7+/t/dvtqwYUNWO/On\nqKqqordv31JBQQHl5+fTu3fv6O3bt/Tu3TvKy8uj+/fv1/7t/fv39O7dO3r//j0B+Iy0NTU1SUND\ngzQ0ND57zT/U1dVJXV299rWwzLqfCfn5+bRjxw6Ki4ujiooKGjFiBN29e5dMTExEztu+fTtNmzaN\nEhIShMZMP3/+nMaPH08nT55kfTLMyMigo0eP0qNHjyR+L18CAC1YsIC2bt1KFy5cENvWLQNIUP3k\nE+Tk5FC7du3o7NmzrB/otwaPxyM3NzcaOnQoTZw4UWrrXrp0iUaMGEEPHz5ktU2fOHGCIiMjRaZt\n29jY0L59+8jR0fGrMYZhSE1NjV6+fMnqYAkNDSUnJycaO3YstzciAoWFhWRubk4vXrwgNTU1TnPK\ny8vJxcWFFi9eTL169RL7nFVVVbWZakpKSjRq1ChycXEhBwcHUlFREXu9nwXl5eW1hM2PeS4sLPzs\nNf//RUVFVFRURIWFhbWv5eTkau3QampqpK6uLvA130bN/5f/+lObdX3fGBmGoWfPnlFGRsZnx6NH\nj6hr1640cuRIatu2Les+ANDatWtpyZIldObMGaGKCY/Ho3bt2lFQUBDNmDGDdX89evQgLy8vmjx5\nskTv70tUVVXRmDFj6N69e3Ts2LHPAgS4cud/WpOWlZWlAwcOkIeHBzk4OFD79u2lsm6bNm2oe/fu\nVFxczEqcvr6+1K9fPyoqKhIq27p1a0pKShJI0g0aNCA7Ozu6d+8eeXl5iTwXX5OWBjQ0NMjX15cO\nHz7MOZNTUVGRNm/eTL169SIfHx+xazbIy8vT8OHD6ZdffqFjx47RgQMHaOPGjXT//n0yNDQkJycn\ncnR0JCcnJ7KwsPhMsxRmq/wZoKioKJbG/ikAUHl5eS1h8+3Q/Nf8v79584aePHlSa6P+1F7Nzyis\nqKggZWXlWuJWVlYmJSUlUlJS+uy1oqIiNWzYkOTk5EhOTo5kZWVrX8vIyFB5eTmVlJR8ZgcvLS2l\nt2/f0sOHD0lbW5vs7OzIzs6OvLy8aPTo0eTk5MT5Rvzo0SMKDQ2lDx8+0IULF6hJkyZCZaOjowkA\np9IEt27dolu3btE///zD+fMXhcLCQurTpw8pKirShQsXJPYV/adJmugjccXHx9PgwYMpJSWFDAwM\n6rymjIwMRUVFcZJVUlIiDw8POn/+PHXv3l2gjJubG926dUtoaJGjoyOlpaWxkrS5uTndvn2b0764\nYODAgbRhwwax0u09PT1p8ODBNHz4cDp8+LBEoW4NGjSgrl27UteuXYnoY5jdkydP6O7du5SWlkbb\nt2+nly9f1mqWhYWFtfWV1dTUSElJiRo2bFhLJJ++/vT4lFw+lZOXl/8sdVpeXv4z5xg/+oHvKFNU\nVPyMwJSUlKhRo0bfxFwjIyNTe+662lB5PB6VlpZScXExFRcXU1lZmcCD73zkHzwej2pqaqiyspIY\nhiElJSUyMTGpjSbhE76WlhbZ2NhIXLrg6dOntGjRIjp48CDNnTuXwsLCREbf7Ny5k1auXElJSUms\nUTpVVVUUHh5O06dPlyjLWNB6Xbp0IScnJ1q1ahWr/0oU/vMkTfSxwE1ISAgNGjSIEhISpBpWxQW+\nvr6UmJgolKSdnZ1FFnGysbHhZCPT19enN2/eSLzPL9GjRw+aOHEUkBXCAAANrklEQVSi2DGjixYt\nIn9/f1q8eDGnECM2yMnJ1dbSEGQjB0ClpaW1JoCKigqqrq6mmpoaqq6u/uzgE8qX//Ll+enS1dXV\nVF5eTh8+fKDKysrPwsw+PcrLy6m8vJzKyspq/y0rK6Pq6upaDfTLg29eEHWoqqrWarT816qqqnW6\n2NkgKytbawb5kcAn50OHDtHYsWPp8ePHrM7RuLg4mjt3Lp05c4bMzMxYzzFhwgTS19enX3/9VSp7\nnjRpEmloaNDq1avrHJP/f4KkiYjmzJlDgwYNouzsbE51EaQJb29vkTZxfoyzsFA9Gxsb1oIzRNIn\naXl5eQoJCaE1a9bQ2rVrxZq3Z88ecnNzIzc3NwoKCpLangRBRkamltx+FL8Ij8er1TqFHZ8WLsrL\ny6stTcv/+5dHSUkJNWrU6Csb86cH36n4qT2abxLim4dUVFS+u1NWFBiGobS0NLpw4QKdP3+eLl++\nTGPHjqVHjx6xknNJSQmFh4fTpUuX6Pz589S0aVPW88XFxdGFCxcoKSlJKklO27Zto9OnT9ONGzek\nst7/GZKWlZUVu4C/tODm5kYPHjygDx8+CNRS+B78Z8+eCYwXbtq06XfRpImIxowZQw4ODrR48WKx\nKpsZGRnRzp07qW/fvnT9+nWByRD/ZcjKytZqv9ICACorK/vMnvylDfrDhw/09u1bysrKqv37p07G\nwsJCKi8v/4y0P40iEfbvp6+lYQ74FBUVFfTgwQO6cOECXbhwgS5dukQ6Ojrk4+NDffv2pc2bN5Om\npibrOjdv3qSBAweSt7c33b59m5ON+8aNGzR9+nS6dOmSVJ4gUlJSaNKkSXThwgWpdQv6P0PS3xP8\nEo5Xr16lDh06CJRxdHSkO3fuCCRpKysrev78uchC5kREWlpaVFxczConDoyMjCgwMJC2bt1K4eHh\nYs319vam6dOnU48ePejkyZNS8Qf8X4aMjEytyaQu9ufq6urapJdPo0g+/ffly5dfRZbw/5WRkam9\nAfE1+k/NM3x7v6ysLDVo0KD2NY/Hq40p58eX5+fnU1VVFVlZWZGvry/179+f1q1bJ7Jt1pfg8Xi0\nbNkyWrlyJa1Zs0Zo9bsvkZeXR71796aNGzdKpXpmQUEB9ezZk9atW8earVhcXExnz57ltO7/SLqO\nmDVrFrm7u7O2+uG3uBJG0g4ODpSeni7Qbt2oUSMyMTGhp0+fko2NjdBzNGjQgHR1dSkvL0+qj/1h\nYWE0YsQICgsLE/vxbcKECVRYWEgtWrSgbdu2SS3C5n+QHA0bNqxNshEXwP9r71xDmnzfOP7dsp9i\nVhu1snJL08qOVBOSLKjMaiMqpJY6s0B9F1pgqQU5FJuLCIuy7OCLLCpIwyDpfHjRm9J5qDxhOZ2H\nnC7TbGXt8H8hz/NX29nV1nZ/4OYhdj/zbmzf5z5c1/cy4MePH/RM3tiWDLXXr9fr6axFnU4HJpOJ\n5cuX01ma1HXy5Ml2b7+0t7fTFXEqKirA5XKtum9oaAgikQj79u0zeVZkC3q9HrGxsRCJRBYfEhqN\nBgKBwGxUykiISI+T2bNno6SkxKJIR0REQCqVmnw9NDQUz549M/l6cHAwPn78aFakAYDD4aCnp8eh\nIh0REQE/Pz88fPgQAoHApnsZDAYkEgnWrVuHvXv3Ijk5GcePH//rh7e28v3791F+GWON76lGHRIa\na5ThkbHm5eVFR46MbSOjRca2sfvQfn5+f7U6OIPBoCNajJmC/S30ej3u3LmDlJQUpKSkID093erv\nlEajQXR0NDgcDiQSiUPGU1lZidbWVqtMzmQyGTgcDrKzs62atFgUaYPBAIlEgsbGRvz333/Izc21\n+mnl6pw8eRJRUVHjSkUWCoXIzs626I7H5/Mhl8tN9gsODsbly5dN3s/lcqFUKi2Oh8ViObToKjD8\nwzx48CDy8/NtFmmKyMhIyOVyxMXFISoqCjdu3HB42q01GAwGqNVqKBSKUa21tZVO86aW4COd50Ya\nFVGNx+PR4X4jQ/ioxmAwYBi2A/6tURElVDQJFVFCRZF8/vwZ7e3tv4W+jdyHHhgYgEajwaRJkzBl\nyhQ6JXxkY7PZmD59+m+eI+OZvTqToaEhOuFp0qRJKCkpsRiaOpKvX79i27ZtmDt3LoqKihw2WSgv\nL8f27dstRt+0trbi3LlzqKqqsvrztyjST548wc+fP3Hr1i3U1NRAKpWioKDAupG7OHPmzEFsbCzk\ncjl8fX3teo+goCBMmzYNcrkcYWFhJvtRfg3Nzc1GT5xDQkLw4cMHk/fzeDy0tbVZHA+LxUJfX591\ng7eBPXv2ID09He/fv7fbHczf3x+PHz9GTk4O+Hw+iouLLZYGs5ehoSE0NTWhvr4edXV1qK+vR319\nPVpaWjBx4kQEBgbSbcGCBdi0aRNtpMThcP4ZEdPpdBgcHER/fz+daj62NTc3j/IeUalU0Gq1mDFj\nBvz9/WkXwLGugAEBAWCxWC7xOfT396OwsBBnzpzBsmXLcP78eWzYsMGmsQ0MDEAgEGDJkiW4ePGi\nQ1cg9+/fR15ensV+aWlpSElJAY/Hc5x3R2VlJdatWwdgOJ73TzhDOQuxWIzS0lLk5+fb5M41FqFQ\niPLycrMiDQzPpisqKoyK9KxZs+h9PWMRAVwu16pisywWC1++fLF67Nbi7e2N5ORkFBYW4uzZs3a/\nz4QJE0Ztf2zZsgUikQhhYWF21ZPTaDRoaGj4Lc24ra0NQUFBWLx4MRYtWoQdO3YgIyMD8+bNG1f9\nPVdjwoQJdNidNfHAFBqNBiqVCp8+fRrlBPjy5Ut0dnaio6MDSqUSBoMBXC4XXC4XPB7vt2tAQIDD\noz0ofv36hcbGRly7dg1Xr16FQCCgK5HbypcvX7B161bw+XyHxC6PpLu7G01NTVi7dq3Zfi9evMCb\nN29MWqmawqJIDw4OjhINLy8vq43v/wVkMhnCw8ORlJRksjy8JYRCIXJzcy32W7VqFaqqqozWNGQw\nGJg3bx4+fvxo9Eto7XYHm83+IyINAElJSVi5ciXy8vLsXnlQUNsfRUVFOHXqFCorK8FisejY6rCw\nMHC5XPT19dEmRSOvCoUCdXV16OzsxPz58+k0Y7FYjCVLliAkJMQtjIf+FL6+vvRKwhz9/f1QKpVo\na2ujr0+fPoVSqYRSqUR7ezv9gKDEfKylK3WlrFwNBgP0ev2o7Z/e3l68ffuWziqtra1FU1MTAgIC\nIBAIIJfLbfKgHklfXx82b96MNWvWID8/3+ErgwcPHiAyMtLs902r1SI1NZX2pbEFiyLt5+eHb9++\n0f8eK9A6nQ7AcGXdfxEfHx/s3r0bt2/ftqpigzFCQkJQVFRkcfkSGBiIzs5Ok/1Wr14NhUJh9NTd\n19cXfn5+Fv+Gv78/dDqd1UspW2AymdiwYQOePn3qMEvRhIQEJCQkwGAwQKFQoLa2FjU1Nbh79y5U\nKhXYbDamTp1Kx+hOnToVM2fOxIoVK3DkyBHweDyj+4oqlcoh4yOAjpM25jJHCWxXVxc6OzvR1dWF\n3t5eNDY2Qq1Wj3qwfv36lb5v7EHqlClTEBoaioULF2LlypWIiYnBggULRhX3tfc7nZ2dDT6fj7S0\nNHR0dNj1Huaora1FZGSk2fHV1taCw+EgPDyc7kdpJqWhprDogvfo0SM8f/4cUqkU1dXVKCgowKVL\nl+jXKyoqIBaLrf4PEQgEAuH/3Lhxw+xWqUWRHhndAQBSqXRUwsWPHz/w7t07cDgclw+rIhAIBFdB\np9Ohp6cHS5cuHbViGMu4/aQJBAKB8Odwj9M/AoFAcFPsFmmDwYCsrCzExMQgISHBqsgDd6empsYm\n72V3RKvV4siRIxCLxRCJRGazKN0dvV6Po0ePIjY2FmKxGM3Nzc4ektNRq9VYv349WlpanD0UpxId\nHU0fmlsK/7U7Ldydk1zs4cqVKygrKxt3pe5/nXv37oHNZuPkyZPo7+/Hzp07sXHjRmcPyyk8e/YM\nDAYDN2/exOvXr3H69GmP/o1otVpkZWWZ3X/1BH7+/Alg2NLUGuyeSbtzkos9zJ071ybPZXdFIBAg\nNTUVwPBM8k+a1Ls6mzZtQk5ODgBYVaPS3ZHJZIiNjbU7H8FdaGhogEajQWJiIvbv34+amhqz/e0W\naVNJLp5KVFQUiW4BaHOgwcFBpKam4tChQ84eklNhMpnIyMhAbm6uRRMud6a0tBTTpk1DREQEPD1W\nwcfHB4mJibh69SokEgnS0tLMaqfd0xxLSS4Ez6WrqwsHDhxAfHw8hEKhs4fjdPLy8qBWq7F7926U\nl5d75HK/tLQUDAYDr169QkNDA9LT03HhwgW77FL/dQIDA+nsSaqgck9Pj0lXQbtVddWqVXj58iUA\noLq62qoyNZ6Ap88Sent7kZiYiMOHD9udwekulJWV0Ylf3t7eYDKZHjuRuX79OoqLi1FcXIzQ0FDI\nZDKPFGgAKCkpoc2Yuru78e3bN7O+NXbPpKOiovDq1SvExMQAgFmvZE/CFRzDnElhYSEGBgZQUFCA\n8+fPg8Fg4MqVKx7po7F582ZkZmYiPj4eWq0Wx44d88jPYSye/hvZtWsXMjMzERcXByaTiRMnTph9\neJNkFgKBQHBhPHPtRSAQCP8IRKQJBALBhSEiTSAQCC4MEWkCgUBwYYhIEwgEggtDRJpAIBBcGCLS\nBAKB4MIQkSYQCAQX5n820q0uNZ2xtgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bed1320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.contour(X, Y, Z, colors='black');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that by default when a single color is used, negative values are represented by dashed lines, and positive values by solid lines.\n",
+    "Alternatively, the lines can be color-coded by specifying a colormap with the ``cmap`` argument.\n",
+    "Here, we'll also specify that we want more lines to be drawn—20 equally spaced intervals within the data range:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GL7/8kssuu4z4+Hj27NlDYWEhW7duJSEhAb/fT1NTE1arlZqaGkwmE36/n7a2NgDC4QgR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NP/SMvXHyCknIu39BCSBEhepKYaZcHdWosgSxi6MxPViToyMhKn06nRwbKysiguLubcc8/V7svz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S1fvpw33niD559/XlSy+nFfu3Ytzz77LHfdddeM8+LMM89k165dghL478SxxUkgEMDj8YiK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\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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e2dd048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.contour(X, Y, Z, 20, cmap='RdGy');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we chose the ``RdGy`` (short for *Red-Gray*) colormap, which is a good choice for centered data.\n",
+    "Matplotlib has a wide range of colormaps available, which you can easily browse in IPython by doing a tab completion on the ``plt.cm`` module:\n",
+    "```\n",
+    "plt.cm.<TAB>\n",
+    "```\n",
+    "\n",
+    "Our plot is looking nicer, but the spaces between the lines may be a bit distracting.\n",
+    "We can change this by switching to a filled contour plot using the ``plt.contourf()`` function (notice the ``f`` at the end), which uses largely the same syntax as ``plt.contour()``.\n",
+    "\n",
+    "Additionally, we'll add a ``plt.colorbar()`` command, which automatically creates an additional axis with labeled color information for the plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JBrLobG0HG3efh48dqrqVH7NEBC7xADdD1wlslCkKATeBFSJLudmoAgtcXtFAnlELMAts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\nuXA8TEFtIykAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e63ffd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.contourf(X, Y, Z, 20, cmap='RdGy')\n",
+    "plt.colorbar();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The colorbar makes it clear that the black regions are \"peaks,\" while the red regions are \"valleys.\"\n",
+    "\n",
+    "One potential issue with this plot is that it is a bit \"splotchy.\" That is, the color steps are discrete rather than continuous, which is not always what is desired.\n",
+    "This could be remedied by setting the number of contours to a very high number, but this results in a rather inefficient plot: Matplotlib must render a new polygon for each step in the level.\n",
+    "A better way to handle this is to use the ``plt.imshow()`` function, which interprets a two-dimensional grid of data as an image.\n",
+    "\n",
+    "The following code shows this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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thwGGvjLbR40sprGYxuiVlISVhDWCHwsV1oJ+27bsdrsDj1cpM3PMRNpsNgdh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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10eec17f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower',\n",
+    "           cmap='RdGy')\n",
+    "plt.colorbar()\n",
+    "plt.axis(aspect='image');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are a few potential gotchas with ``imshow()``, however:\n",
+    "\n",
+    "- ``plt.imshow()`` doesn't accept an *x* and *y* grid, so you must manually specify the *extent* [*xmin*, *xmax*, *ymin*, *ymax*] of the image on the plot.\n",
+    "- ``plt.imshow()`` by default follows the standard image array definition where the origin is in the upper left, not in the lower left as in most contour plots. This must be changed when showing gridded data.\n",
+    "- ``plt.imshow()`` will automatically adjust the axis aspect ratio to match the input data; this can be changed by setting, for example, ``plt.axis(aspect='image')`` to make *x* and *y* units match."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, it can sometimes be useful to combine contour plots and image plots.\n",
+    "For example, here we'll use a partially transparent background image (with transparency set via the ``alpha`` parameter) and overplot contours with labels on the contours themselves (using the ``plt.clabel()`` function):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/ZL+DKhd3o1opdDLS0igtKXVGIt0wYetgtlXmPtqdQZZlGjRokJSFxY8K3XHH\nHSxesoTyYBBDEKz5bCYL27V7D82aNXO5lonN77dTA1W0qugE8XW006PEa2Pu5ty/bz/59W0AM6JA\npusYugvEdI2HHxrN7l27WPvWmwgYjqjvTpAoyzJ33XUXK1asIBwOV4jkd9/ciYDsiiuuoKCggJkz\nZxKJRIg48WE6w4cOYdeeX1n2xlqQPWZ8WCBAQdNGDLvpRsa++AKiLOLxiKY7KQvm6KQgOOPDhkE0\nI66qk5OWjixIzFz2KjNeeImiohPceHUXtJAp6odLS0jzeXjpuWeYNm0aX2/caOYkkyW83mhm1gYN\nGjBnzhw++OADli1bFnPegiDQqFEj3nnnHTZv3kxubi6apvH222/TqVMnunbtyuWXX87333/PW2+9\nhc/no2nTpixatAhFUTh8+DA//fQTmqZRXFzMggULmDVrFg0aNEgarZ+sX7v7SSI2/HswsBoRP96c\nG82EtIz0dEpKS3A4kWABlSAkTFFTVTaWn5/vzAtLBV6GYdC8eXPat2/Pildfwx1CUVR8gheWvMiF\nF1xQ6WmlpaU5+ctOVuBM1HkzMzMdAEv2m33795sMzAmwM1mYCV66C8BUvLLErJnTGTFiJEo4hCiQ\nkI2ddtppdOrUiTVr1sSMSKYKrbBZmKZpDB8+nLVr1/LPf/7TGY1UVR1Jlnly3hxGPTKFQ8XF5jzJ\nQBpSIMBtN/yR2llZ/HLkAB6fhMcrIksuTcwR9Q1XeIWBYIgM696DNo0ac1HbM5l6z11owRB7du7m\ngw8/4aFHpvLee+/TslkTnpg3h9sGDODggf3WfMnY1Mw2iG3YsIGlS5fGnPO5557L8OHDueGGGygo\nKOD48eM0adKEvLw8iouL2bZtGz///DO6rnPJJZfQsmVLzjjjDNavX8/TTz/Nc889x9SpU1FVlaVL\nl9K8efMKbmCya5ysnyRjxPZgQnVZjYgfZ4b7nWGQkZ5GSYk1euf6hWCFUsRH4ydzyeKtQYMGFeZE\nJozKtzrF3XfdyZNPP2N5kCKabvDXfrdx3XXX0LXrHx2X0nmNq4Pf73dc1mSWbHjcfu/eZgNY/Hdu\n239gv5lMzsXADN12JTXQtBgQu/bKLrRpfTrzFywwGZgUBTA3C7vzzjtZvnw5kUgkKYglu3nS0tIY\nPXo0Dz74IMePF6GoqpkXXjc459xzuOWmGxk+aRqCz4eUFkAKBPCkBXhh3BjatWxhjkjaYRWSSxOz\nRX3DMFM03B/aAAAgAElEQVRSW5qYoItc1LotBXn1efPDf3L08FHWffAxy1at4VBhIc8tWcqLS1+m\n69VdGHTvQPr2708kHHLSSse7k/PmzWPDhg28+OKLFVinz+fjD3/4A+np6bRr1w6AGTNmcPbZZ9Oz\nZ0927dpFo0aN2Lx5M7Vq1eLtt9+mbdu2jBkzhnr16vH1118jSVIFbawyBpaofyRjYdXNwGpEfMuM\n+A/WBoeBGe5IfPOdO5SiquEU9sWtX78++/fvr1zIt8r1113P3r2/suXbrRw+coSevW8mGAoxc8YM\nYnWxRCeEM0lYUZSTGjG16xz/Gu9CJurYhYWF1LUXbojXwWzg0txApvLo1Mk8Pnsuu3ftjIkFc4NY\ny5Yt6dixI2+88UalI5LuOZK2oN+xY0fOOeccZj72GIqiWfqVmZLmweFD2brtR157dwNiIA054EcO\n+PAGvHjsKH2PiCybKaglEUQrLbNF3M1sFY6ob6af9okyWf4ASjDEL7v3MvTW3tzV6y+0aVFAp3Zn\nIRoag++5i/bnnsOgwUOQXZH6NoB5PB7q1avH/Pnz+fDDD3nppZdirpEbsH0+Hz169GD06NGcd955\neL1eDhw4wIIFC/j3v/9NnTp1KCoq4qKLLkJVVTZv3kxWVlZS8Ep2jZP1l1QCfg0Dq2YA23OkkE9/\n+N5xHaNqvkFmejolpaW4UU2w3MhkoRS2pWq4+vXrOwwsWWdxF0mW6HvrrQwfMYpzO3amVatWvP3W\nm3g83jj9K/kx7Qh6t51M+IT9ahjmFJajR4+mfDKbcT+yBf7RGLeoiO9acckCspbNmzJq+FDuuHsg\nGEZCBibLMgMGDGDFihUoipI0BU0iBmaD2KBBg9iwYQMbPvjAYmBm9lZfejpPz5/NsPGTKTxRihQI\nmHFhfgvAfFJUB7MYWHxgq8nAdFTVQFV0lLCGGlZpX9CC737awcHCw6QLAl9/+x1dzu9I4aFDLFr8\nIrPmzmPOjKkcPXqUufPnOaDlBjJZlsnLy2Pu3Lm8//77LF26NOU52+ft8/kYOXIkF110EVdddRUt\nW7akffv2bNmyhe3bt1NeXk7Lli0Tjk6mArFE+pf7fTyIVbcLWaOBAdv27WX5Jx9Zn6xYfOtCRBkY\nLlYjJAxmrSrqG0bytDrJ3Uno378/ZeXlLH1xCdOnTcXn88e5jK4bOO6YgiBUSIOTqI6V1TsRgCVy\nH+zPTm3iGZjhFvJVpwi6xuC/DcTn9TJr1uNWdH6sFibLMq1bt+bss8/m7bffTulCJooLUxQFv9/P\nuHHjGDtuHIWFh00WpuvoCLTv0IEB/W7hrhFjzEVA7ClGPg8enznNyO1GSqKAaCn5htV1dAfEdBPE\nIipqWOG8guZcelZbRs/7B43r5PKvLzcx9+lFdGp3Jn6PhyefWcSLzz3L4sWLee+9d60l1OQYFmaD\n2OzZs1m3bp0zNSeR+xYPZs2bN6dVq1bOYihr167liy++4I477nAAJhmIxfeDyvqIO7I/fsSzuuzY\nsWMcOXIkZTl27Fi1Ha+6rHoDWaUEcyGtN1mZ6ZYGFr1gAoBQ0YVMpH8lEpcBJzOrPRReacEcCv/n\nJx9x6aWXmrVIgDUVYm5dZq8zmMh9rEoMmLvT5uTkOPE1ib63iay5X1et3FqYNRppaBpoKmgqhhUj\n9ew/5jN3/jy2b/seETMprT0qaQPZHXfcwSuvvIKu6zF5tFKl3HGzsbPPPptrr72WMWPGEA6HzZxe\nqulSjhw2hMNHjvHU0uXmakbWqKQc8CP7/azd9CXHg6V4veZUIydS35lqZLanw8h0A80Cs64dOzL6\n5j5cdV57jh0/zqTBf+PM5k25/g+XcOjAQRrm5bL42acZMuQBio8dRRZFPLIUA2L26OSsWbNYsmQJ\n//73vyvME02kQ7kBrWnTpjzwwAN069aNgoIC7DU4kzGweD0skSXTUePrUl1Wt25dGjRokLLUrVu3\n2o5XXVbtAKbEPRXsGzAzLZ1S1yikCV4mdsQvV2a/VsXvtrNSHDlypIo6mE0Kbb3LfO+OGYvvVvEd\nzePxpKTvVR2RtDWw0tJSJytCPAtzXEYnl1qUhRluHUwzGZihqRiqYgKZrtE4vwEjHhjC2HEPI2Ag\niVFR33Yj27ZtS+vWrR0WFi/ox9fZ7cbYruTtt9/O3r17Wbp0qRVaYS7HJsoeFj25gEdmzmH7nl8R\nvD5znmRaADngY8+RI4x+fhGSLOLxulxKMTZ/mIG1xqRmZnBVFQ0lrJGblkGoLIQaVpANnYN79zP8\n4b/Trk0r/vXpv9iwYQOrlr9M3Tq5ZmofWU4YJ9a0aVOmT5/OzJkz+f7771O60slCGuL1KXega6IJ\n3ydjiR4g1QlgNS4kcXMhHa/HvPkyMtI5UVIagw6pGNjJxFQ1aNAgYShFQpesQsex1oy0bxW3dpfE\nquJCptruro8oimRnZycEYHd9ogyMKIhZwayGG8QsBmYXQde4964BbN26lU8//TRmsrdbC7v33nt5\n6aWXYrSwZHFh8QzMbovx48fz+OOP8+OPP1qjkjqaDqe1bMXYkcO4feiDqIKIGAggWwA27Jab0DFY\n9P47eB13MsrARCF6ypphhlWY7qSGGjaLoWhcfOaZPL5oCaOnzeKai8+ndmYGb77zDme0PI1/PPU0\nx44csWLD5AoMzHYnzzrrLMaNG8f48ePNSP8kgxmJxPRU8yNT6WCJgCz+uIn0sOpmYDUABngkCUV1\nu5DRi5OVkc6JEpOBuUcg3Qws0WheZcGsYLqRyQCsoguJlaUMVzCra7/OfynO0wVgVR1ssOscr2sZ\nhrlEXHxeM3d9JFlGiSiuLS4WZoGX6UKaDAzNZmAmCwt4PUyeOJ5hI0aiqSqiKCLHifpt2rShQ4cO\nrFq1KmVMWCItLBKJEIlEaNSoEbfffjsjR40iGAqZTMkQ0BAZcPtt1K9fj78v+AdSIGAxMD/+9AAL\nhg7mpQ8+YOPP2511JZMtCKLplh4WsfUwFTWkcEnbtjz415uYOfhuLj67LRs++SeD+t9Ct2uvpHF+\nfbIz001mJ4l89tlnPPvss2zfvr0CiF188cUMHDjQCg+J9snKAkpTAZmbMVU1qDVZ34kH0eqyUx2F\nNAyD8ePH07t3b/r27cuvv/7qfHfkyBFuvfVW+vbty6233krHjh155ZVXAOjevTt9+/alb9++jBkz\n5pTrXa0AVjc7h8vPOjshAJijkPZUoqgLiRANZDUMowJ42ZZI/7Ivqp0XzL0tVcHtQrpCJ1xSeQKm\nFjVJkmI6XbI6x9c3Uf0hFoDdv7EZY1ZmJsUnTligZdbNMPSooB8TTqFGgczaJhg6N/boRoMG9Zm/\ncKHpQsoVwyoGDhzIq6++Snl5edLMDfEg5gawSCTCDTfcQHp6OgsWLoyuZiSIIHn4x9zZvLz6DT74\nYhNyIOCsKdkkvy4L7r+H4c8+w6Hi4yYDE2wGZrMRnLgwzcXAlJCZgloNhcny+PBqOm9t+IgzmjUl\nP7cWb6x9m9o52ehqBEkUWPTc8+zYsYMWLVqwcuVK9u7d65y/7Tp37dqVa6+9lgkTJsRoq8nCGeKB\nLH7qTzx4ucX8kwWv34uBnWoc2HvvvUckEmHZsmUMGzaMqVOnOt/VqVOHJUuWsHjxYoYNG0bbtm25\n8cYbnUyyixcvZvHixUyZMuWU612tANY0ry4DrromoYZkivglMcBgw4fX6yU9PT3hgrFVGYls0KBB\n0qwUCWk7yYetnW0k7lw2yGqadtKuo/337mMaRuKRVOc34OThj6lNdJgOw2JitpBvqFEhH4uRibrG\n/MdmMHvOPHbv3IUkiKaYL0nIkoRHlmnevDmXXnopK1eurJAvLJUrGR+l/+CDD7Js2St88eWXRBSF\niKKi6Dq16+Ty9LzHuWPogxSeKDH1MJ8P2e/j8g7tGX3zTeiCgZwoCaKd+cg+bStzhabp5sIbERUt\noqCHFXp3uZwvNn/DqMkz2LdvP5d2Oo/stAA7fvyJYFkZd90xgBu6/hHD0CksLEzoTg4YMIC0tDRe\neOGFlGw0HtQSfY4vVXEf3e8Txeadio6WyoqLizl27FjK4l67wrZNmzZxySWXANCuXTu2bt2acP+T\nJk1i4sSJCILAtm3bKC8vZ8CAAfTv358tW7accr3lU/7LqprhDqOIpqFxq/iCEE0tnZaWZn5dRQ0M\nzMysVZ1OlKwDnMz7eAZWlfomcx8NwwzG/emnnxLXzTDIzs5m965d8XuMZWDWb51vNQFBEKPrFwIF\njRsxdPB9DBk6lNWrV0VDK2QJWZPx6Dp33XUXN998M926dcPv9zv10HW9wvnZ22wAkySJSCRCdnY2\nI0aMYMSIEaxZs4a8vDwMEQzB4JKLL+aWm3px+wMPsurJ+WYCxICGpOvcfN01hIMRIsEIhm6YfUPT\nEUzfEcOgYg4x3RT2NUVHlVREAWqnZzBuQH80WaJW3TqkZedghMMUHTtCqLwMj2Awe+ECZEnisksv\nRlV1Zzk2d9s//PDD3HbbbbRr145zzz23wnV0f07W5xKxpsoAKJ7NpxqFry6rU6cOmZmZKX/jXg/V\nttLS0pi/k2UZXY9daXvDhg20atWKpk2bAuZ84gEDBtCrVy927drFnXfeybp1605JY/sdVyWyXTXT\nstItEd+VTtodN5ooGj8VE3ODgC3iVwZYqb6391XZezAFT1t/ONkI5UQgljKWDcjKyqTIfvoJcfty\ngZih6yYD01QMVcXQFFBVUBVQFQRdZcg9d3GosJAVK1ciiubKPbLLjWzcuDHXXXcdy5Ytc9hIMhYW\nz0Lco5IXXXQR5557LpMmTTJDKxTVcSnHjBpBWTDEzGeeR/D5EP0+R9T3+L1mjJhPtgJdJUvUF02X\n0hX2omO44sQ0tIiGGlLRQgrpkkzttDQ8uo4eCmJEwpzRvBlHDh/m0Zkz2bxlC1MnTUQWJZ599hme\nffZZHn/8cQDnvOvWrcv48eOZPn06x44dqwAk8X0rUfqbRPpXKhcylfZ0sn3tZOxUNTB3inWgAngB\nrFmzhhtvvNH53KxZM2644QbnfU5OzimvePQ7TuZ2vzfIysiwRHy7/9n/mw0TH0qR7KIla0SPxxMT\nEJrsSfhb2JhtoihWSX9I9pSNP24qAMMwXEu5JRgttd1IV0YKOx7MUM2QCrugqXgkkQWPz2Ts2IcJ\nBYMJRyTvvPNOunfvHqMLxYdVuNvVrQHZWlg4HOa+++7jiy++4M033ySiKCiWqC96fDz/9D9Y+PwS\n/rn5GyS/Jeqn+a1IfQ9en2TNlxTxyII14TtBFlfbjVR0a1TSFPW1UAQtGEYPhcwSDpHukZkzeTwX\nn9+RgqZN8Hs9vPrqq3z+xZcMGjSIhg0bsnr1amekUpZlOnToQPfu3Zk6dSqGYcTcnIkekPHgdTIu\nZCLGdTLa6m+xU9XA2rdvz0cffQTA5s2badWqVYXfbN261WGwAK+++irTpk0D4NChQ5SVlZGXl1fh\n76piv1s6Hed/q40zMywX0h1G4bpIubm5FZ5yqS4exAJElZMbVgHM7H2fiiXraKmOkZeXx9GjRyvk\nNTNb0KBObh0OHz4Sv1eXmB/PwLQY/csEL8UR9C/s1JHLL7uU6TNmIEkisiTH3LR5eXmcdtppMeJ2\noqdwMh3MDGY1lxKbMGECEyZM4JdfdkZFfVGiYeOmPD1vNrcNGcmhkhLkdHNU0hPw4fHbi4GIlIXL\nkSXRjF8Toil3wA5uBU010JQoA1MtYV8LhdCCIYeBGUoEQVO4tHMHev7pepRIiI8+/pg5j88iMzOT\n1q1bc+LECTweDyUlJc759+vXD0mSeOWVVxIykVSMKxH7qgzEEj3Af28X8lTDKK666iq8Xi+9e/dm\n2rRpjB49mjfffJMVK1YAZoR/vGvas2dPSkpKuPnmmxk2bBhTpkw55RCNatXAgpEIa7/5kt5XXg7E\nQll2RgbFJ0qcz84AuaWB5ebmcvTo0ZT0OdlFMwyDJk2asGvXLjp27JjwqVgZ64rfX6JX9/fxnfhk\nqb173+68ZtnZ2XGuK+TnN2D/gQMVB3djRiSjIIauYxgSDss1rLeijCB5ENCZOXUyZ3XoRO+bbqTV\n6adjIKEbhhOkawOaW99KFpnvPif7s/37li1b0q9fPx4YOpRXXnkF2etDQ0QSJa68+mpuv/Vm+g0e\nwfqXn0UK+B3WiCKz8YedDJr/D5aNeJBMbxq6ADoCOuYUIwNLzAdEw0DQDQQVRElElEAUQRQMDh47\nSr38BgRkGVQPgqZwTts2nAhGqJeXh2EY7NmzhyeeeII//OEPrF69mg8//JA+ffrQpk0bvF4v48aN\n4/bbb6dDhw40a9bMdX0Sa12VPSzj+0BlA0KVPcx/q1UlzivR94IgMHHixJhtBQUFzvvatWuzatWq\nmO89Hg8zZ878DbV11ala9mJZWIkw/bUVUZblvBqkpwUIWal4AScI3gayBg0acPDgwQruYlX9fpuB\nmYernIVV5kKmYmTxroS7voneuy3ZsRs1asTu3bsr6CSGYcaJHS8qIhwOxwbdOju19qtbRXMxMVVF\nV1V0xXYlIxiKQt3cHB4e8yBDhg5DMAwkMepGJgv0dDOyVJpYvEvZvXt3cnJymDFjBuFw2GFoiqox\nYuhgfD4fYx+dBx6vqYn5/EiBAOefcxbdLrmIe/6xAFXUnSwWshXsai/RJjptay8ZYLuVJiub8OTz\nTFjwNFrI5VJGQmT4PFzT5XKmTpnKC88t4qILL6R+vbpkZKQzfPhwli1bxoEDB5wUPAMHDmTevHkx\n7lYiXayqfTC+X7n7TiKXLn7tz+oMLD1VDew/bdUcyCqj2ADlXCDrSwGyMjMsLcfeFh2FLCgoYPfu\n3QkbK1kDujtDkyZN2L1790m5iok6UzLAcr8mEiqrYslAUtd1GjRokDAtEBZY1q2bx4GDha6RD3cA\nruEKq7AYgKajaxq6qlnApWIoCoaigBoBJcJd/W4hFAzy8rJlpisZl6nCBq9wOMzhw4ed7K3JAl3j\nXUp3jNiYMWNYt24d69ats7aZ8yUNQeKZhfNY8cZaXlu/Abx+RL811SgtwIP9bqF5w3xGvbAIySsg\ne60UPJKdBFHA3S3sxUF0x61UmdDvVl597yPWf/ypA2JGOIwRCXP5BZ2ZNW0Skyc+zGUXX8TxY8f4\n8w03EAwGOeuss2jcuDHhcBiPx0PXrl3x+/2sXbu2QphJvHt3sg9N2xKBRjIgq05AKSkpobi4OGVJ\nNAr5n7bqBTA5bi6kYY9GmjdYdmamKeQb7hBS8yI0a9aM3bt3A8n9/lTWuHHjKmtgqcCrMkCLd5tS\nWTLNItExU+Y1Axrm57P/wIEoeFVgYW4GZqBrGoaqWS6Zm4EpGEoEQ40gYTDvsRk8POERiouLEjKw\nffv2mWsvfvQRixYtcpIfxrscdl2Tpd3x+/1MmDCBsWPHsnv3bnNdSU1DM6BWnTq8/NwzDB77CN/t\n3O0AmJQWwJsWYPbg+ygJBpm5+lVH2LezV0hCdKliw8rkqruF/YhGji+NeUPu494pMzmw/4CZijoc\nwlAioEZI83oQDY3PPvs3ZWWlbN+2jW+//ZaCggLGjRvHCy+8wOzZsxFFkZEjR/Liiy9y/PjxShP9\nJRPuqwJeVdGjJEmqUj+sitWqVYs6deqkLLVq1aq241WXVftcSEVVMQx7cVv7P/NNZmYGxcUnEGKU\nfPPC1atXj1AoRGlp6UlTWMMwqFu3LsXFxRXWWPwtxd53fGdL5kJWxZIdq169ekkBDMNaQm7ffrvB\nonF0VhvbbM1wxPyoG6lbIGYolgupWixMU+hwzll0u6Er4x4eX4F9lZWVsWbNGv7yl79w22230aZN\nG7799lvn5ol3neKn2tgupO0ytmnThptuuokhQ4ZQWlZuZqzQDTQkzml/HjMmTaD3wPs5HgwhBfxm\naEWaj7SsdBaNHs7BouNEdMWZLymJsaK+YZjamG5lrNDs0IqwwgWnt6ZPlz9w398fRQsGTQamRMzw\nEk1B0HWGDroXv9fHyy8v4+yzz2bt2rU0a9aMYcOG0ahRIzZu3EiLFi3485//zFNPPRUDXpUxsHgw\nS9a37H3FA5l7mbzfw4X8n54LefToUS6//HJ27tyZemeiGa+jqlpMSmnDcnFysrLMKTG2CdEwClEU\nadq0qTOJtqoMzO4AoijSpEkTfvnll9/MvpI9Je3P9ghbMqsKa4w/jntCeqIndvNmzdjxy85o4FwM\nA4uOSEY1MB1DtcBLscFLiYKYEkHQVERdZ+qEcXz44Ue8//77MeyrqKiI+vXr06pVK37++We+/PJL\nGjduXKkGFp8zzAaxcDhM7969SU9PZ+rUqSaAGeZUI0PycFOfPtzwx+voO2QEhtdjxYb58QS85NWp\nxaJRD5CTmY7sMSP0zelGVh8RrNAKLBfSYmBaREULq2hhhaHd/8LeQ4f4btt29HAYImEnPk40NEQM\nBt93D5MnTaSgoIC8vDxGjhyJx+MhGAxy4sQJZ1Ry27ZtfPfdd0lZWHW4j/Z+Tyas4VTtfxbAVFVl\n/Pjx+P3+KuzOoPcll+Pwrxgx3yA7MyNmOoIQl4urcePGMVkAnN8lGFaOOap9kzdvzs8///ybWZd7\nn4m+UxQlJYAlbZ0Ux61Xrx6FhYVEIpGK3wOntTyNH3/+mVgGFm1Jc/+2mB87tUi3WJiuqFEQUyJO\nbFhWRgYL581h0KD7CJaXOVOMMjMz2bJlC++88w7PP/88PXv2pFWrViiKwsGDBx0mat9M7jonymYa\niURQVZWHHnqIDz/8kFWrV6OoGoqmo+gGGgKTJoxD9nh4cOosRJ/PXNXI50Pye/H4vch+D7LPg+yV\nTSCzphuJoohgJ9U3DAfIdVVHVzX0iIpkwLoZUzg9vwF6JIIeMYEcm4npKqKhkxbwIwgQDJazf98+\nVq5cwY4dO7jhhhuQZZn09HTuuecennzySYBTApVUIGa/xoNX/MIsslx9QQRVcVv/K0X86dOn06dP\nnyonM3voxt54ZdkBL/c1ynYxsCiPiD5xbAYGVQ/mc1vz5s3ZsWNHtbqMiUAtnoGd6oV1H1uWZerU\nqRPDwnRdN8MFDIPTWpzGTz/vsMArdlFex520hXwHxCw2pupRPcxiY7qiYDg3cJirL7+UC88/nymT\nJyNiIIkiBc2a8cgjjyDLMl26dOGiiy6iqKiIefPm8dZbb7FmzRoMw0gqZtvnEA9ifr+fyZMnM3ny\nZL755pvoqKSioCOw6Il5vL3hI55b+Tp4vQg+v7nad1qaOQnc78MT8CIHPHj8MrKli0m2Wyla/QbM\n56hrdFZCQIto6BHFLOEIuiXoGy4ga1S/Hj3+0o1ly17mwP4DjB8/nkAggCiKeDwerrrqKtLT03nv\nvfcqzBmtCiuLt/hBq8qAyx4Nri77nxyFfO2118jNzeWiiy5K2OiVm4uCGQY52VkUFZ9wtjt6NDgA\n9uuvvyYdiUx4BFenKCgoOGUGFr+vVKBWmQuZqI6J6hz/2Q4FSeRCnnZaC376+ScrkYY9ChkV9F1S\nYzQ+TItqYfaIpK6oDnjpilkMNQJqmJlTHmHpy8v45pvNSKKALMvk5+fTqVMnsrKyKC8vZ9y4cbRo\n0YIWLVrw008/8fnnnyfUZNwuZSJNrGnTpgwePJhBgwY5I5z2dKOsnFosX/Ic46bN4qMvvzazuFrR\n+lKaHznNzGIh+WQ27vwZ2ScjWdlcbQCz84jZbEyPYWPm6KQJYmH0cBg9Eo4yMc10KbtcfhkPjhzB\ng6NG0LBhPqIo4vV6HY3w/vvv56WXXqqwqlOiEdpUfcPuC6lGIBMBWHUysP9JF/K1117j008/5dZb\nb2Xbtm2MGjXq5JYXt1V86+7KzszgRPGJmJ/YDAygSZMmMQAGiS+qs/s4YGjevDk7d+48qfCJqgJZ\nvAvp8/kSnnKqTpvKTdV13VkJOpELmZubiyAIHDl6zMW+RBcDiza6YbtQusnEdIeFRWPCdMXlSlog\nVrdWNpMnjOX+IUMxDB3ZyiHfvHlzunbtyr59+7j66qsZMGAAHTt2pFu3bnTu3BlZljGM2FRIbvCK\nH5UMh8OEQiGuuOIKOnfuzLBhwwgGgzHTjVq2Op3nn1pAv/uH8ePe/WYSxPQ0RxeTA15KIiEeXPw8\nz76/3nIno4vk2s1i2O1sA5hixofpERUtHDEZWMTFwDQFwZqxIGLglSUzRk6SYxYE8Xg8tG3blnbt\n2sWs6hQPXpX1h0T9J1X4hDsFUnUysLKyMkpKSlIW95zH/18sJYC9+OKLLFmyhCVLltC6dWumT59O\nbm7uyR3BdY1ysrI4Xlwck9AwGQOzt1VVyDcMc43FtLS0lMusneycyESf7bggt1V1sMG9n/jiDmaN\nH35HEGjVshXfb9seFwsWG1JhOPFghjMaqUZUk3m4YsEc9qW4daAIfW/qSU52FvPmzY8JqfB6vTRs\n2JDvvvuOr7/+mrVr1/L999+zbds2XnrpJVauXBmTsSIRA3PHhdmi/j333ENxcTFz5841F8e1tDBd\nlLjs8it4ZNxDdB9wD0fLypHS0pxU1HLAS716uax+5GFW/vMTZr2+ygyrsFLviM4IbRTMTQZqp94x\nGdiRw0dMF9I1KokWFfUl0UyC6J5q5X696667WLVqFcFgMEZ0T9Zvq+JGpmJfvxcDy87Opnbt2ilL\ndnZ2tR2vuqzKnPC3+b+mC5ntyqpgPyHdDKx+/foUWRHnVXUhnSNYnaJly5Zs3749IXBVdQ5kKiCL\nRCLoup6UgVXaEilYXuPGjdm5c2fsdl13vMJ27c7m681bXNpXHIgZ9mhklIWVlgdp2/cuJi56kXAo\nHBXzI7YGZodWmOxDNHSenjeb+QufYOu338aMShYUFDBo0CDC4TBnnHEG7dq1IxgMcvXVV5Ofn8/a\ntWsTamDxLMwNYLqu88gjj7BixQrefmedw8B0QcaQPPTteys39vgLPe/8GxFBRA74kQI+M3NFwEPj\n/DlsfawAACAASURBVDxWTxrPpz98z7iXXwQMM5Or/Ww0sNb/NZmopmomA1NUtv+ym0vuHESwpNRk\nYUo0a4dgA5gAkmRlxXCBl12aNWvGBRdcwFtvvVWBgZ3KPVOZC+kOc6nRwE4CwBYvXhwzxymZrftq\nE0dOnIiJ/7Lf17I1MMPa5tx/ZuPYmsuBAwcSjjpWpSFbtmzJtm3bTtmFrIyRlZeXk5aW5oy6JbOq\nug5ugLXnc9oTgA3DcER8w4D27c9j46ZNVqNZ7qNovhdE1zZXfNgHm7ZwRrMmfL9rNzeMnMCe/Ydc\nWpjlTkYUjEjYuokjNGlQj1nTp9D/ttsIl5chCVEWcuaZZ3LNNdfQpUsXfvzxR8477zzy8/NRFIXT\nTz895YIg8cn+bJcyKyuLKVOmMG7cOL799lsT4CIRIlZe/bEPjqRp0yb0HzwSTZTNHGI+nyXsB6hX\nL4+VEx9m77GjzH77DSSfjOSVED0SoiwiSgKC6Oo3lrB/WoMGtGnahBfXro+yUyvQF2vit6BrCLrG\nO++8ja6pFiOLZUH9+vVjzZo1KIoSk8EjXtA/GQBINQppH+Pdd9+t8v4qs/9JDexUbNF76/i10Mzt\nE3N7GwY57rxWcZO67YvbqFEj9u3bV+UnQDzYJGNg1QVkpaWlpKenVwCvVGCW6DeJ9p+dnY0gCE5a\nIIcx6ub3HTqcx5dfbnQxLxFBEBHE2FHJaDMZXH9+R5Y8OIwlo4ZzfeeOXDVkND/u/hXNBrGIYoYU\nhCOOG2WoCjd2+xNntW3LpL//HUkwRyXtJcm8Xi8lJSUcOXKE4uJiPv74Y+rWrUthYSFvvvmmc2Ml\nixNLJOqfdtppDB06lIEDB7J//37XfEkVzYCFsx/jQOERRkx+FEM2RyYla2RSCvjJqZ3DkodGc88N\nXU1R3wEx0QIx0TUyabepztBe3Xl86UpC5cHoAIcSwVDMGDE7g8dTTz3DM8886ySBlOUoiDVv3pz2\n7duzfv16B2CSgVdlQBbPvpKB2FdffcWaNWsq7XNVtf95BlZV88gyEVXBdhuj4fgG2ZmZFBUVEwUv\nHAYG5qsdC2Z/jm+8ysTRVq1a8eOPP/4mIT8R87JfS0pKSE9PP+X2SeSuuidwN23alJ9//rnipG5M\ndll4+LAl5NtupIg7rEJwXEpbDwOf7EUA7vnT9ayc+BDN6tQ1tSBnRFKJi4tSEFSFuY9OYfnKlXz+\n+WdmPi6XJtasWTNuueUWPvzwQ/Lz8zl69CibN2+mY8eO+Hw+3nnnnaRhFclcyssuu4xrr72Wv/3t\nb5SUlBAOR1AUc31Jjy/AyqWL+ehfnzHjqUVWaEUaUsDUxaSAj/SsdOrm1Ub2e5C85sikJJssTBAF\nBJFoILBhYGgGHVqeRouGDVj2zvuxMXJKBEOzRiQxeHTaZB577DGOHz9uJYGMdSf79u3LqlWr0DQt\nBmiSMbBE/Tf+ezeQuTWwkpISpkyZwuDBg0+5H8ZbDQOzzCt7iLiWHAMcLzInO4uiGPcyloHZABbP\nwJzfVgJehmFQq1YtvF6vE2j5W8Ar0fuysjKHgSUCu6paMhBr1qyZM5sgHoRFUeK889rzxcavouAl\nmsV0kVyJ4+3j6FE9zNB1WuU3BN1AV1xupAVetgtp62F5OdnMmzmde+67n1AwaObOd2libdu2ZciQ\nIVx44YUUFhYyYMAAWrRoQefOnR19xr6GyRhYvCbWv39/srOzGTt2rMnCVNUJcs2qVZvVy19m8fJX\neXbFKkQ7tCIQMKceBbzIAYt9+aQoA5MERHuNNidcznBGaIf26s5jLy0nEgw7gxyOoG8xsDNan07P\nnt15dOZMa3m2WDeydevWtGnThvfeey/p4sCpWEwq8IoHsUcffZRrr72W9u3bn3S/S2Y1DMwyrywT\niVn01bCmEhnUys7k2PEioghWUdtK5ULav01kbkD4vdxIgBMnTpCRkfGb2ijZ/nVdp1mzZhUYpKmD\nma3WqVNnPvvi82iue7cLaUeiu7UeVyCnrhkxWSp0xRXQGYmYon4kEpOC+s/XXc1F55/PqDFj8FgM\nzL0wbGZmJpqm4fP5yMzMpLS0lOeee47c3FxnvqT7vBOloLZDK8LhMIqiMGbMGL777jsWLVrkzJe0\nhf36DRuxZsUypsyez5oPPraCW/3IATNaX/Z7kX0ysldG9EggCXzyw/cgWjqYhWBuUO/UqiUj+9xI\nMFjuuJAoYSukwozOFzF4aNQoXnllOUVFRRWSQMqyzC233MKaNWsQBCGlBpaqL8cDWbz7WFxczFdf\nfcWgQYOqdRSyvLycsrKylKW8vLzajldd9n8AYFHLzsyktKzcXNGHqAvptksuuYTJkydXSbx36yvu\n11atWlUZwBK5mva+4vdrGAbnnnsuw4cPP2nWlcw1ja+LG3wTjZxefPHFfPLPf+EI+aIIgpW9z9bA\nzNZxZZ02olNrrJgwB8AUDc1mYGFXaIULxGZP+zuff/ElK1auxCPLyLKExyPj9Xjw+XxkZ2dz8cUX\ns3z5cpYvX86FF17IZZddViUx352G2mZisiwzbdo0tnzzDaFw2AVioCPS/LSWvLbsJf428iH++dUW\nBL8f0e9DCviQAl4kvxfJZ7qRR8tLmbxiOb0fncF3v+5BkKJAZhAFsV6XXUy6x+OsbG6ev2ouFmzo\nCBjUr1uHrn+8npeWLk2Yfqhdu3akp6ezefPmmODeUxHyE41EiqLIjh07OP3000lLS6vWUcisrCxy\ncnJSlqysrGo7XnVZtQPYJW3PpGFurqO/GK7pLaIgkJmRHp3QLUTDKOySlpbmiNmJ2FdlnSARA0um\nh1VVJ7P3C5CZmemsrlJV9zHRb5Idq3Hjxhw4cIDy8vKEudQ7d+7Mpq++IhQOR91ISUQQJQTRYmOW\nS/nD7l/55eAB62aNHle32Jiu6YSCYa4fPpbvf9ppamJh1Zleo4dDGJEIGT4PLz79D0Y9OJpffv4R\nScBZ5dpmYhdeeCH33Xcfd/8/9t47Poqq7f9/z8y2JLSEUEV6NSAKNpCqNCmidBAiTUTxVgEFBBW8\nBVGKAmLBGwTBgiIIilhBpCiCCEoHqQEEpJdky5TvH1N2djO72WB8fvfze7xer5Mtmdk9e86Zz3yu\nz7nOdR58kJYtW+ZKhBgrBU90BgvTpSxevDjPPvtsBDvTo/VDhGSZOnVq885bb9B9wENs/G0HuLwI\nHh+iLxkxKQnRYGXlypZi5ZSX6NS4Ef2mTePJuW9z6vLFsLAv2YJvwRY/p1gFY5s6VIUHB/Znzttz\n0TTNMS6rW7duVl59pyVGV+OO2c/Zt28fNWrUKPC1if+4kIZ1adiI6ytWwlrTYvo+BoilFSvG2bPn\nsIR84eoaL/q1HUxMAHPa2CNejianXOV5sTPzdX4sHgtzuVxUqFDBqn90PvVChQpRo3p1Nv2yRXcj\nRR28dC1MMsRqnYktXrOOz37caHyRcTMx8oWZkekuQeT+li3o8vTzHDn6B0owqDMyC8ACaHKQ62tV\n5+mRT9BvwECUUBCXERvlsbmURYsWpVixYtZre7ySUxJEJwCzz0w6lXPnzxOUFUKKRpMmjXlz+lQ6\n93uQLbv3IXiTEJNSdGHf0MWkJC+eFB9927dm/auvUCItlTueeoqss6etmUl9EBLBVlUrHZGxv6Yc\nAlXmlno3kJ5enJUrVzoGmLZq1YqsrCwOHTrkuCFKoppY9Dh3ArCCFNX/EfGjzYy+tF7oj6lFi3Du\n/HnAUsD0v3kAV6Kdrmmatezm5MmTf1n3ShS4EgWxWMBoB6tq1aqxc+fOmJtB3H57Q9at/9Em5Eez\nL/0iKZVajFNGWxtfGqmHySpqSOHehg3o36YV/SdMwX8lGzUQXuSsrxHU3cnB/fpQrmwZnp/wAi5R\n1DUxmx5mL7EYWDSAaVrsJIh24Dp27Bjz5s1j2PAn+OKrr5FVFUUTaN26DTMmv8S9/Qax/cAhxCSd\ngUk+U9j34kpyI3ndFEstwrN9e7Nu2hRSixRGkKLCK6xdnsJrSLGDmCIjaBoPDhzA7DlzHKPjk5KS\n6Ny5M8uWLYsbD/ZXGFitWrX+FkC5GvalaRpjx46lR48eZGZmkpWVFfH/efPm0b59ezIzM8nMzOTQ\noUN5npMf+xv3hTQjKMIMTNM0UosV5ey58+FsFELuu0x+3UYnq169ekRAa17uYqJLjPJyG6P/5wRy\neX1PtWrV2LVrVy5gM1/f3rAha9YbOpjFwCQLvBD0GbeSaamcPHsuHNdqifqGDmawMCWk8FDbtpQo\nWpSRM99CMYDLYmBm7jBN5a1XX2bJJ8tYuWqlroN53LnA69SpU+zcufOqXMjonY0CgQBXrlzhzTff\npHz58jz55JOs/+EHtu/cbeUSa9+hPVMnTqBD7wHsPnLMCK/whQHMSMNjxoaVLZVOWmqRcICrLSui\nzsDUiIXw5u5O5oxkt04d2br1V44cOZJrfaLL5eKee+5h48aNXLhwIa4W5jSmY40tQRAIhUIcO3aM\nqlWr/tcwsG+//ZZgMMjChQsZPnw4EydOjPj/jh07mDRpEvPnz2f+/PlUrFgxz3PyVe+rPjMR08LM\nSzOALK1YMWMmkggVPxH0j/U8/HVhcKlZsyY7d+6MC1h5pfyNBVzRz/PXJPEBTVVVatasybZt23LV\n0axn40a389OmTWT7/QhCNHiZIRUC11WqwLaDh7AjWDjltBbOzhDSZyVfHjSQn3ftYe3mrREupJm9\nVVBlSqQWY8HsN3n4X49z9OhRPG6PNTPp9Xrxer1cuXKF8ePHc/LkyVyR6Wa/Rf/mWOslg8EgO3bs\noGrVqlx//fUoqsqevftIKVwERQNZE9BEF506d2bi8+No1zOTQ3+cNFxIu7Cv5xCTPBKSGaHvCreV\ntRTLSr2j2FxI/VFQZNBUkpOSaNv2LitwNTrFTWpqKrfddhsbN25MWMh3GkfR418URWRZxuPx/Ndo\nYJs3b6Zx48YA1K1bl+3bt0f8f8eOHcyaNYtevXrx1ltvJXROfuxvATArdhUM5hX+T/HUYpw9dxYw\nBfzYy4XMxcFOQGZarMGQkZHB9u3bC9R1jOdCOr2O9b94n61pupCfnZ3NH3/84ehGFilajOtr1+H7\n9T+iiSJIkiHiSwiSiCDpjzXKl+NidjZ/XjyPvZk0kxHbxHxVVink9vLpv8fSoEYNlGAINRCyMjZY\nIRahII1urseIoY/SO/N+gv4cSw8zwyxuuukmBg8ezL///W9CoVAul9IOaNFt4sTGsrOz2bt3L4FA\ngGnTptGyZUvS0tI4dvwPvv7mGzb9sgVZ0+jevTtjRj7Jnfd2Z+eBw8aSIx+iL8l41GcrRa8HyeNG\n9LgQPS4EA9AEyZYU0WowDXPLOnM2UgBa3NGcVatWhUMcooJNGzduzIYNG3KlgHbSwaLHcazx5na7\nSUlJsZKCFiSA+f1+cnJy4ha/35/rvMuXL0fs++hyuSL2jGjXrh3PPfcc8+fP55dffmH16tV5npMf\nK3AA23nkCJv37iMiWtUS8zXSUotx9twF2/9yg9fmzZuZPn06b731FitXriQUCuXLJ9c0jZo1a7J3\n715CodBfBrFosFEUJS6QJWrxvq9OnTps2bIlJgtr2fJOvvpmlaWBIelFsBXJ7eLhe+8m2x+0xP3I\nazPMyMwQC6/osuWS11MxK4FgeEsyg5UN6X8/1atWYeiwYYiaakTqh0X9bt26Ua9ePV5++WWLocUC\nsVjCvglgNWvWpFixYsydO5fk5GTatm1LIBBg2LBhLPlkKRMmTmThh4uQFYX7MzN57umnaN31Pn7d\nsx88SYjeZH120mdoYyaQebxIHjeS243odiG6XAguSdfFzMkQ7ECmFwGNO5o1Y+3atSiyjCjmXnTd\noEEDdu3aRU5OjiMLszOoWJ6EHdTN52lpaZw9e/aqxls8K1SoEEWKFIlbnOIfCxUqFJFmJ3rHrvvv\nv59ixYrhcrlo0qQJO3fupHDhwnHPyY8VOIBt3LeHLzZvtl5Hs7G01KKcPXsuvCuR0XfmIN60aRO7\nd++mbdu2ZGZmsnXrVnbt2pUQ+7IDSXJyMmXKlGHfvn2OAyH6vLzKuXPn2LBhAzk5OblcoIKw6Do6\nAZh1jKbRqkULvvzmW9vsoyHkSyYL013Kod07UfmaMuGZtnBXGMVwKWXNypelBo2kfwaA2cMqzN18\nBCXEG1NeYsvWrbzzzjv6WkkpclZyxIgRXLp0iQ8//DBC3HcS9u3t4LTou2PHjjRs2NAS9Z9++mnS\n0tIY0L8/xdOK43K7yc7xk+3306NbN6a8MJ52PfuxafseBG8ygjdJn6X0GjFjXi+S14NoMjG3y1r8\nbbZdeFWWZmm45mAuWSKdKlWqsGnTJkQht1ZUqFAh6taty5YtWxy1JKcbcSJjMzU1NX85+RK0q3Uh\n69Wrx/fffw/A1q1bqV69uvW/y5cv0759e3JyctA0jQ0bNlC7dm1uvPHGmOfk1wp0Z2593Z1tKZHV\nGZr1PK1oUTad22E7KbJxNm3aRMmSJalRowbBYJCKFSuSnZ2dZ2M6uWQZGRls27aNjIyMCIAQRTEu\nAEUPGnMGTNM05s6dS+XKla3t5hNqFlvd7BYNhPY6ZmRksGLFighGYgGZKFC7dm2yc3LYt/8gNatW\nRrMxL1GSUCOATDCWStrpF9YEi2ZOumgCgqBECNqWtmZ9ht6dgiBQONnHh/Pfpnmb9tTOyODGevVA\nFNFsv2vy5MlkZmZSsWJFbrvttggwjuU2ARGupHlc8eLFyczMtDKCPPHEE3g9HipXqUyFihX54aeN\nLFnyCV06deTee+8hOSWZezIH8OHbb9HophsRRQnNYFZ6wKoMiqg/aqoeBGxMgFhjzMR9A7wEWx+2\nuPNOvvvuO2655Za4bmSjRo0iwCtaGknkhmq6WGlpaX8LgCUyKeD0/5YtW7J+/Xp69OgBwMSJE1m+\nfDk5OTl07dqVYcOG0adPH7xeLw0aNKBJkyZompbrnKu1ggUwwON2EbCvhYwCsfS0VM6eM+LAoq4n\nQRBo3bo1y5cvZ9SoUciyTNGiRbn11ltj6gXxLCMjg59++onu3bvHZVfRgyj6/2vWrKFKlSrcfffd\nBAIBFi5cyIEDB6hatWpMcErEnEDXLJUrV+bUqVOcO3eOkiVLRgKYKoIk0LpVS5Z/+TU1Hx1iuZGC\n8ShKIqrNFcoNXtY1Ga67qllrU0338vCpU7z22XJeHvYIoqCFNUtJQnC7qVG5Am9Mf5n7BzzAqm++\nokSp0giiZF10ZcuWZerUqTzyyCOkp6dTuXLlCBfRfnHa2yK6Tc33vV4vsixTunRpRo4cScOGDfnx\nxw2ULl2aC+cv0K17N5YtXUoopNC+XXsWFCpCt779eePll7in9Z1oog5QmhxCk0Q0WUAT0TUuyx0w\n9Flz6VFEg+nPBaBp0yZMnjwF0Whj0aZ3mQA2c+ZMgFzucjz9K9aY0DSN9PR0Tpw4ke+xlpclMtPv\n9H9BEHjuueci3qtkS7t19913c/fdd+d5ztVagbuQXrebgBzKlUrHeELx1GKcPnMOMBdyR3ZolSpV\nuHLlCn379uXpp59m2LBhlC1b1rHTYwn4Zrnhhhv45ZdfLM3qakvlypU5evQoBw8eRBRFLl68yLFj\nxyK+82ot+oK1g2rNmjX57bffcutgms6ZOt59N0uWLrO5kJEamOkK5da/TJDCFhdmhFQY+pdeQpQq\nXITjf55h6MuvIvv9+q7WwaA1MymqCh3vasXAfpn06pOJHApZbqTX68Xj8XD99dfz7LPP8txzz1kz\nk04Lns02iM5aYY8NM0vr1q1p164dNWrU4IknnuDy5SvcfMst3HTTzRRPL0HVGjUJqgL1brmNL1Ys\n57FRzzBz7ruGkG+K+V5Ej+FGut2ILkkvRtthuJHWGDaKoOlNWCglhZAsIwqC5UbaRfv09HSKFSvG\nH3/84ehCmuM43hiOLnXq1GHTpk1XPd5i2T+BrIZ5Xe5IBmaa4a6kpxXjjE2EjO4/SZK47bbbyMjI\noFChQuzbt499+/ZFZGlNBMgASpQoQdGiRXOti4wljDuVQCBARkYGiqIwZcoUxo8fjyiKNG7c2JH6\nx3ud17H2+mmaRu3atfn1119j1FmjebNm/L5/PwcOHUazi/kuF4JkFJekF9OdtAvUNo3aWiyh2na2\nljVETeSNRx9h+/5DTHz7fV3U9xuifo4f1Z+DFvAz6tGHqVKxIg8PGYKgqbgEcEkiHre+3OiOO+7g\nwQcf5JlnnuHKlSsxg16dEiJGg5o5Q5mRkUG5cuW4dOkSGzdupHLlyixZsoQqVarw0UcfMXLUKIYO\nHcqB/QdY/c2XzJq3gOHPjkcRJXB5wO1F8OhLkASvT390exHcHgSXB8Hl1ovkAsllW/GgMzNVM8Rn\na0zmXsNYrVo1Dhw4kJCm5OQ22ouiKNSvX5+ff/7ZygxcUGbuUxCvBAKBAvu+grICB7AKJUvS6Lrr\nsBKxRlyoGumpqZw2RHzdcndqs2bNWLhwIffccw8vvvgin3zyCRs3boxLvaPNHAQ33XQTmzZtyhfj\nsgPa7NmzmTlzJtWrV6dbt2707NmTfv364fF4rO9xeoxnebkJZh0yMjJiMDCdhbncLrp07sy7H3wI\ngqCHVIhS+IKTXBabmLpoCd/9ti0KvHRKphm6lr7SyFgraYGYik9yM3/EEyxa9T3zln1hAZji96Pk\n5KAG/AhyiDdffolDhw4xefJkJBEjvCLMxrp27Uq7du0YN26clcEikYh9s61ixYuVL1+eWrVqMXLk\nSCvJ4k8//cTDDz/MlClT2PLrr6SmpbHqi+Vs2baDHoMeIzukgdsLbh+CJ0kHL48PweM13jfAy+XS\nbwiWi27mXsPK/WU1JWHgMkuNGjXYv39/3BAKp3FrLyYTVVWV1NRUypQpY93YCsqSk5MpVKhQ3JKc\nnFxg31dQVuAAVr3sNdzXvDlhBCOCfhdKSUJWZGM2L/Jcs1NPnDjBkSNH+PTTT3nzzTcJBAJs3bo1\n17FOj/rXhTu2fv36CQNYNBs7c+YM586dY8CAAZQqVYp9+/bxyy+/EDIYZizQyu/AilWPGjVqsHfv\nXvx+fy72pWkaqgY9e/bgvfc/QEXQs1KY4OUy2ZcL0SVxbamSvP3F1xEMTIhiYJHgpaHIGkpIdyeL\nJaUwf8STvPDO+xw6nIWSY4RVGAxMCwZIcrv4eP7bzH/3PZYuXYZbEnG7XXgNpuX1ehk8eDC1atVi\n4sSJCILgyMBiuZVO6ahNEOvQoQO9evWiU6dO/Pnnn/Ts2ZO0tDSOZGXx66+/4Q+GKFIslU8//giv\nz0eTu7uw5/DRMHB5kww2ZjAwt87AiGZgtsy3qqov6LYa0oFh1axZk3379sWcfbT/Rvt4cPISzHLr\nrbeybt26AmVg/7iQjqaFtyk0igCkp6XpWUWJXMxtWqlSpThw4ACyLHPkyBGuv/56ypQpY4Uw5IeB\n1a1bl127duH3+/PtRp4+fZorV67w+++/U6NGDe644w6OHj2KJEmO4JUf9mWvo/25vR5JSUmUK1cu\nYl2kLuKHN7ytX/8mNGDj5i3hkArJ5kIaelin5o349cABDp06FbHgW+8lwXIhVU13IVUlzMBMEKtY\nPJ2VkyZStlgqSkBnYKpfZ2BmDvkyJdL45IN3GPHUaH755RfcRsYKM0rf5/MxZswYkpOTeeWVV3Ll\n1DJdyOjYsGgGFs3CgsEg5cqVIxAIUKJECXbt2sWlixcZN24cnTt3JiWlELIG7qRk5s2ZzQP9+9G8\nQxcWLPnMAC+7C+m1AEyQ3AabtS2YN9YemQzMHIvR7qMoimRkZLB3715rNjXe+I13U7WD980338yP\nP/5YoAB2tWEU/1/b3wNgUTGsdgYGUDwtlT9Pm1PBkVqWIOjJ4O6//35mzZrFzJkzqVatGvfeey8p\nKSmOjRmvYZOTk6latSqbN292BIl4bKxSpUp07tyZrKwsPvvsM+bMmUPt2rX1nxV1vPVzY7iU8Vha\nvIFbp04dNm/enNuFVHUGhiDSp3dv3p6/wFhSJIHk1t0eV1jQT05OolfLO3j7y6/Ds5K2FMthANNQ\nbO6jIqvIxlZkckCmqNuH4g+i5hgaWE62zsBCAQRZF/XrZtTiP69Np3ff/mRlHcHjDetdXq+XlJQU\nJk6cyLlz53jttdci1hPmtct3rGyu9sXfzZs3Jycnh/kLFtCgQQPuatsWWdXQEFFFF7g9PPDAIL76\n/FMmTZ/JQ0+OIagJCB4feLwIHp2B4fboTFZyWW1rZr1VNY3Fi5dQtkwZrHALBxArWrQoxYsX58SJ\nEwlpuHndZFVVpW7duuzdu7dAZyP/ATAni4hiNZ5rGiWLF+f0mTM2GSYSjARBoHHjxvTu3ZsePXpw\n9OhRFi9ezDfffJOr4xNhYc2aNePbb7/Nlw62f/9+Fi1aROHChdm+fTvlypXjkUceoVGjRlfFvmLV\n04mF2QdwvXr1+OmnnxxdSLP07duXJUuXcebceQPAXGG3x3AhRZfEAx3bsnjtOi7k5CBI+g7W2BYy\n27rI+H7TlVSRjQ1h5ZCCHJSRgyHkQBDZbwa5hlNSC6Eg7e5szjMjn6RTl26cPXPaSL3jsmYoixQp\nwrRp0zhy5AizZs2KSFVtsrFotyuaiTllsTBBbcCAAfTq1YsePXro74VChBRFB2dNQBUEMmrXYf33\nqzh99hxN7urI+p+36OBvFMF6rrclooQmisiqxsDBD7Fnzx5enjrFsa/tpXDhwhHeQzwR33x0EvDN\n4vF46NixI4sXL4475vJj/wBYlGk2CSzMwvSXJdKL8+efp7FfOdF3pcuXLzNv3jxOnDjBtddeS716\n9di7d2/CDWkHlDvvvJP169dz+fLlPHUv873Vq1fzxRdfsHXrVsuNS01NzRUEGw1A0ZZopzvVS9P0\nafO9e/dy6dKlXIPZfF2iRAnatG7NvPcWWgCmg5fb0MJcCG4X5UqX4u7bG/DroYNWxLkOEg6ajdRn\nAQAAIABJREFUmF3UN1iZrGjIBiOTzaVG/hByTgglJ2CUHBTDrRzQqzs9Ot9Lly5dybl0EUnAyiHm\n9XpJTU1lxowZ7N+/nzlz5sSdmXRaDB3tYjkBmVmi3wuFZGRFISm5EO8vmMeggf25b8CDdOx5P1t3\n7gHJjSbpe1NiA7KQotKn/wMcOZzF58s/o3CRInq/57pZh/s/OTnZMRg70XHgBGLdu3cv0G3VzLr+\nbwIv+DsALBdwmU/Mf2qUTE/j1OnT4YBBBzpduHBhsrOz6dChA9WqVUMURc6fP8+ZM2fybOBoRlO0\naFHq1q3L6tWr47qMZgkGg8iyTO3atS1xOCsri8OHD0d8bl5uZJ5NFXVe9GerqorH4+G6665j06ZN\n1iC2D2jz+eAHHmDW7LeRNc1wG91GGIXJwnSX8uXHH+bOm2609ks0U8pYa5htXaeia2KKShSAqchB\nBSWgsOdAFoMmTMZ/+QpKjh81Jwc1Rxf2CQV4evij1K2dQZ++/VCCASO0IjwzWbx4cWbOnMnu3bt5\n++23851PLBaAmaBlB7EIMJNlZFlBVlVUTaBP795s27yRO+5oTvuuvej1wBBWrdvA8VN/ohlueSCk\n0L1PPy5eusSypUtItjZ30dVek7lGW3Jyci791hzvsfo++rfZAUyWZdLT02nSpElC4ywRi9VW0e32\n32YFDmAWXhn9aN2dbHepksWLc+rP0/oBZvCzg2tYtmxZXn31Vb766iuysrLo3bs36enpV3VXaN26\nNV9++aUjy4kuoihSt25dfvvtN4YMGcLy5ctRVZVrr73WEbCs3x7j/TzbLOozzXqZA/imm25iw4YN\nEYAVrYnddPNNpKam8tW3q0E0pv5N8DIWKutr/SQEI5WMBWKioO9mHRl3bmlidl1MB7EwAytbpBjn\nL17ikRdfIXQlWw+rMAEsGEBUZGZOGk+hlGQe/tejiIDbE5l+Jz09nddee43t27czZ86cXIu/o7Wx\n6HaL50rm5OTw5JNPsmvXrghQC5kupaKiaBqKBh5fEg8/NJgdWzZTp04dnps4mfq3Nyf1mkrc0uQO\nGjZviShJfPzRR/iSknOBFzGYuAlgpjkJ+U4yghP7MvU/WZbp379/vsdaLEtKSiI5OTluSUpKKrDv\nKyj722ch9b92BgYl0tP48/RpW1LD3OAlCAK9e/dm0KBBeL1e9uzZw7p166xdu2PN5OSqgTEYGjRo\nwL59+zh+/Hie+pcgCNSvX5/Zs2fTqlUrQqEQ999/f57n/aWWisMM69evz08//RRxJ86liSHw0KAH\neP2t/1gaWJiBuRFMEHNJVj54M+tCpAsZpmEqmsHANBRV1TfWMBlYSAcwQYbXH3qYQ8eP88xrs5Cz\ns1H82caibz2XmFuA+W/O4MyZs4waPQa3y2VF6ZsgVrJkSd544w22bdvGnDlzIpiXEwNzYilOAGbO\n2g0YMIDffvvN5k4aQKCqKKqms01ENEEipUhRRo4cyepV33L86BH2793N9Gmv8PSYMXzw/nt4vF7D\nvTb7DdA0BwdSH58pKSm5XMh4Y9d+c43HwhRF+UtjLrqe/2hghukXs9mxYK23M94omV6cU8YspJP7\naD5PTk5m9+7dbNy4kbS0NGrWrMkHH3xgfU+8gRCtTbndblq3bs3HH38cF4ROnz7NZ599xrp16wiF\nQrRv357bb7+dcuXK5cncYlkiIBtdbztAlS9fHlVV2bdvX8yZKQ3o3KUz27bvYOu2nboO5nIhSG4D\nuIxUMVYxXUjRciF1FmbUgygx3xD0ZcWclTTEfH8ItyYy5/HHWf3LVibPe89gYAEIBo2ZSZkUr4fF\nC+byy5YtTHjhhQjwMkuJEiV4/fXX2bFjB6+++iqiKMaMDbO3VTQziXaH7rzzToYOHcqQIUM4cuSI\nteO3LOub5ioaurCPgCpIaKLL0L90LTGtREka3N6Iezt1RnJ70QRRDz0xQCvi0daXZl0vXbqUKwg0\n3phNxIU0S0HZPwBms1eWfuKkZ+qm6QB28tQpTE1MILLT7WX16tU8++yz3H///TRo0ABJkqx9I83j\n7Y+5vy4MYt26dePzzz/n4sWLMQFs2bJlnDlzhtWrVzNmzBhee+01Pvvss7ihF9HfFcvizTyZz2OV\nxo0b89133+ViYXY25vF6Gfr4ozw3/gV9aZFgW1rk1tf7Ce5w6hjBJaGKWK6lKAnhbccEg5GFu81w\nJ3XmIRuaWMhwKQt7k3hv1AgWrVzDjr0HkP2BXNH6hX0ePvvoPT7//HOmvPQiEnoeMTNWzOPxUKJE\nCd544w1OnTrFlClTEAQhYXHfbEM7sJsXetOmTenevTsPPfQQ586dC89M2osJDIqig5tRFEU13Oew\niG4v8YJONU2f0bYvcM7LnPo/etb1Hwam298CYLuPHWXP0aPsyTrKn+fPWzFg5sVaKr04J0/9aTEy\nwMp0aTeTha1Zs4b169fz+uuvU6JECUqUKOFIxfMCsZIlS9KwYUOWLFniOEiuXLnCb7/9Ro8ePRg1\nahTDhw/H6/XSrl27hMHral3J6POiv6dRo0asXr067sWiqhoD+g/gly1b+OnnX4ylRS4jLkxf3yca\nQCZ6XPxnxZe89NHHSBGamGgk6DMYmR3EMOLENGNW0ighWUMOKaQmFearieOpUqI0ck4QOSeAku23\ndDE1kEPxlGQ+/+gDPvjwQ6ZPnx6embQxsrS0NKZPn46mafz73/9GVVXL5TR3O4oVamGaU6hFt27d\nqFOnDsOGDSM7OzuX4G8v0YzOBIx4xSkoOhQKcfToUcqXLx+3v53GUTQYO7GwgrT/beAFfwOArd25\nnYMnTjB58RKmLVnGvBVfk22mojWArFR6OqdOn0FVFOs906IbbeDAgciyzNq1a6latSodO3YkKSkp\noYZ1AphevXqxaNEiK8mafaD4fD7atGnD4sWLOXjwICdOnGDfvn2kpqbmAq3ogRb9Xqx65GXRn2cO\n3Fq1anHhwgUOHjzoOK2uB7ZqeH0+nho1imefe15f9mIyMJcbwe02mJgLye2mS4umLPzuew6d/lMP\nq8gl6mPJ+qbmY2limgFeiqqXkIoclBEVAdkfQskJImf7kXP8KNmGsO/XlxyVKV6ULxcvZMG77/Pa\na69ZAGYPdi1SpAiTJk2iTJkyPPXUU/j9/lxrJ/MS96MDXxVF4bHHHkPTNIYPH87FixcdwcsJxJxK\nLPZllxkOHTpEqVKl8Hg8eY6DWGMrnpBfUPYPAzNs9fZtLBk9hv889ihvPDqEk+fOcdpkYeiCvtfj\npnChFD01ro2FAbmE/dTUVNq3b8+YMWO46667KFasWK6p6HiNGw0IFStWpFatWnz++eeOmlaTJk0o\nX748S5cuZd++fXTo0CGua5eXBpZf8LLXOfqzGzduzMqVK+O4Lbru2CezDwcPH2bVmvXG0iK3sb7P\nHWZgbhdlSpXgX13v4d/vvZ9rVlIQhAhNzEx8aLqRso2BmbFhIWNmUvbr2pjiD6Dk+I3YMDMdtR5e\ncU2JNL78ZCFz5y/gzTdnRYCXueQoJSWFcePGUa9ePQtw7FldE91v0i7wA0yYMAGPx0Pfvn05fvx4\nTOCKBWKJMjBVVdm5cyfVqlX7S2MgFgMrSBcyGhhj/e7/NitwAEvyeFi/axeHTp7kl32/UyQlBZdk\n5E202JZGmVIlOXb8OPYYMcFaExsb+ePdEaIFXrvZB8R9993H22+/zQ8//JALKCRJ4uabb+Zf//oX\nHTp04JZbbskXaDkxs7wslisaPYCbNm3KypUrHWciFVW1Qh5cLjfPPv00Y557HlUQ9QXJtmJuZiG5\nJQZ36sC+Y8f5bvs2RJdN1Bcjsu2EwyrACDswGJjJwszgVr+MnBMyXEidhZ3787QttMKPEAogKCHK\nly7J18sW85+33+bNN9/IJer7fD58Ph/Dhw+nXbt2PP744xw6dCiXBhY9FqJ1o2h3UBAERo8eTaNG\njejVqxeHDh3K0420X8ROIObUJ5qmsW7dOurUqZOvG11039vZ5N8FYPb2jlW8Xm+BfV9BWYED2OMd\n7uHEuXNM+XgJH36/hq7NmlA2vXjYTTSuhLKlS3P8jxMWqFnMyyGwVRCEXFpHIvQ21oCpU6cOo0eP\nZsqUKYwePZpTp06haRrZ2dmcPn06YcaVKAtLxGINYHvJyMjg4sWL7N2718GFNGPCdBDr2rULLsnF\nnPnvGXFhBnhZbqReklKSmDJkEKPmzCU7GLTpYEaEvrnUS7DHhhmupAFism12MhRSCAVk5EAI2R/E\nf+UKLR4dwaIvvzVYWEDfqi0YQJBlKpQtzcrPP2XWrFm88vLU8JIjj8fKYuHxeBg4cCCPPPIII0eO\nZMWKFRGLwGNF6kPsNZSKotCnTx+6d+/Oo48+yvnz5y3Qsj86AVk8ELOXFStWcODAAVq0aJGQ1OA0\ndp0YmL0OBWX/W13IAk8pfebiRXo1bcYDbdvg8Um4knzhjjLdRQ3Klo5mYFjbRCbamNENm5cbaS+3\n3XYb7777LvPnz6dPnz5kZmby/fffU79+fQYNGuR4F3TSweKxsPxYLAA2H1VVzzzQokULvvjiC2rW\nrOnoRpoZEiRRYPq0qbS/+146tm9L6fQ02xIjD7gVBEVDlDWa31qfCYP6kVQoCVExdhDTND1bq4Cx\ncxF63jB7VJ/xREVfXyhgUDRBQ5P1zXNdksSbj/+L+yZOQkWge7tWgICkCQiqgKDBNelpfPfFctp0\n7MT5s2d55ukxuEQBXBKa5rK+qH379tSsWZPRo0ezZcsWhg4ditfrzQUiebWpvb26devG4cOHGTFi\nBJMnT6Zw4cJWvzrtIhTdt9HMywS5/fv388orrzB16lR8Ph/BYPAv3+ycxmRBWSIA9d8IYAXOwH7e\n/zu7jupbhV/O8WMBlAVe+mPZ0qU4fvwPoteQmapLNDBdLXhFm30QeL1eBg0axOuvv85PP/1EzZo1\nGThwYK47X/Tz6AGYiNvoVL+82GN0fTVNo0WLFnz99dfIspyLhUWzgNq169CjezeeGvtvYzmMzsRw\nua2UMfquPG46NLmdpCQfkseFyy0huSQkl4gk6WAoOriVes9pllspaxDSTLdSM9iYQtWSZXhv9Cie\nmTWHhZ9/jZzjR87OCUft+3Mok1qEb5Z9zOrvv+eJJ59EQtP3mjR2//Ya+li1atWYN28epUqV4uGH\nH+bgwYN57nhk18ai3UtFURg6dChpaWl07dqVDRs2OLqSsWYnnRjalStXeOqppxg4cCCVKlXK5VYm\nak433ugbVkHa1bAvTdMYO3YsPXr0IDMzk6ysrIj/L1++nG7dutGrVy/GjRtnvd+pUycyMzPJzMxk\n9OjRV13nAmdg7erfxKGzJ3l31SqSfB4yqlbmuppVKJbsC4OVpnFN6dL8unsvoOcY14PAYzeeeXeL\nBV55AZg5AMy7qB1sKlSowCuvvGIdGy/m62rcR6c7dyKzp06Dt0KFChQrVoyff/7ZioszWVdEznV0\nLevpMaOpf/OtfLf2B5o1vBVcCoJOsRAUFVFRERUFFAVNVSzmpaoqmiKioqEhGG6jHvBq5uQ3ZyYx\nMr1pmoYqCPo5iooWMhUCjSoly/D+mNHcN2EimqbRo20rJONcAQ1NUEkvnMwXH79P5z4DeGjII7w2\n81U8LhcChoRg5AkTRZGnnnqKr776ijFjxtC3b1/atm1rjQOnfokW+AVBsDQkSZIYOXIkGzZsYMyY\nMdxxxx089thjpKSk5JnMz4kBT506lfLly9OuXbuI2cq/KjlEg9jfEQeW1zHR9u233xIMBlm4cCG/\n/vorEydO5PXXXwf0NNUzZsxg+fLleDwehg8fznfffcftt98OwPz58/9yvfNkYKqqMnr0aHr27Ml9\n993H77//Hvf4r7ZsZt63KwHYeSSLVxYtYfOuPWCF5ANoXFO6FMeO/5FrFtLuQkL4+fPPP8+KFSuu\nyjd3YkhO4FCQoBXLEtUSoutpv0hMN9JJOLbPSKoaFCpchGmvvMxD/3qcnGBITxNjZBwVPfrMpGRs\nbCEZwr7OvsIMzIwNkwSw5UHU1wCCEcmuMzBL2Jf10IpQUCHkVwj5Q1ROL8X7Y0bhUomMD/NnQyAb\nQgGKJSfx2YcLuHDxApn9+uubhNjWTppistfrpU2bNsyZM4fly5czefJkZFnOU+CPF1N16623Mn/+\nfC5cuEDXrl35+uuvI+LF8pqlk2WZzz//nI0bN/LEE0/EDLFwYu+JjIf/CRcyvyxs8+bNNG7cGIC6\ndeuyfft2638ej4eFCxda6ddlWcbr9bJ7926ys7MZMGAAffv25ddff73qeucJYKtWrUIQBD744AMe\ne+wxXn755bjHuySJelWq0Lt5c5rVrYMkiVzOzgkrwAaIXVPGBLDwuWbckVPD1ahRg507d+a7ge1A\nYD7+T5Voi65foqwxeuA2a9aMNWvWcOHCBUc3UlEUY1YSVATad+hAvXr1GD32+XCaZBuIiW6XDl5m\ncUtGdmoRSRKQRL2IgmCkDwuHV4QFfZA1zXIhg8bMZCioi/ohf4iQP0il4iW568Ybkc3MFf4ctEA2\nWiAbIeRHVEMUSvKx5P35FCtalHs6d+XSpUuWCxm9BKlq1arMmzcPt9vNkCFD+P333+NG6TuxGDsA\nJScnM2bMGB599FEWLFhAhw4drLjBWEAWDAb5/vvvGTJkCNOnT2fcuHH4fL5cbv3V3Pz+p1zImOMo\nhr5o2uXLlylcuLD12uVyWccJgkBaWhoACxYsICcnh4YNG+Lz+RgwYABz5sxh3LhxFthfjeXpQrZo\n0YI77rgDgGPHjlG0aNGYx2pAsZRCbN15kNlffY3X66Jh7QxjAGm2TT40ypUpzVFTxNf0SHxzn0gn\n7SsjI4Nly5bF1L5M18HJXbPqZ/t/XsfFKlc7EKMtP9pd9HcXL16cm2++maVLl9K3b19HEd/cQFUP\nsRCZMWM6t956G3c0bczdbVqCS0NQFAS3gqDICKqCqKqICsj+IHeNeZbXhzxE+eIlUVEMoNJnODXd\nA0VUQRQiRX29dzVdoBdsN4+QAKJipFDS9AkBYxNcEw1FVUDUJAQkPL5k5r4xg9HjxtOqdRuWfPwR\n5a6tgOaSQFMx73yCIFCkSBGef/55Pv/8c55++mnatm1Lr169cLvduS686P4yX5tupWkNGzakUaNG\n7Nixg1mzZvHOO+8wYMAArrnmmojxt2vXLj766CN8Ph9du3Zl/PjxSJKUa5byal3IWDfngmRfgHVj\nyOuYaCtUqBBXrlyxXquqmiuoeNKkSRw+fNjaI7NixYpUqFDBel6sWDH+/PNPSpUqle96J6SBiaLI\nqFGj+Pbbb5kxY0bcY+tXqUr18tfww55dXFsqnRtqVqN4iVQbA9Mf09NSuXwlm5zsbDxJyQaIAeQG\nL0EQqFWrFgcPHiQYDEbkTI8Gs0TAyf4Y/T+7hmIX8OMNvvyCWaJMzMnVMOvTtWtXxo0bR69evXLp\nXyaI2dunSJGizHt7Dt179uKG71dSvmxpdF9OQXB7EVUVVA1RFUgqBH3btubBGTNZNm4sLo+kg5P5\nGxV9tlHT9JlJUxNDsKJi9JlJBARV0+9KqgayBoK+gawmKCCGdAAzYjREVUBURURjrAhuDxPGjKRM\nyRK0atOORQvf5brrMkASAZelh5m/tX379tSvX59p06YxcOBA/vWvf3Hrrbc69p+Tfmrvd/N1rVq1\nmD59Ops3b2bhwoVcunQpoi9KlSrFiBEjrFiv6DCHeMuMnMZMLG031o27oOxqNbB69erx3Xff0aZN\nG7Zu3Ur16tUj/v/MM8/g8/ksXQxg8eLF7N27l7Fjx3Ly5EmuXLlCiRIlrqreCYv4L774ImfOnKFr\n166sWLECn8/neFxQltmwezfrd+1E3aGyZudO7m7akFvr1dEPMBiYKAhcU6Y0R48do3LVqtb5JgOL\nLsnJyVSoUIHff/+dmjVrOnZuNMMy/+c0SOKBTTzQyusuGutzo+tlvuf03P459u+wf2eNGjUoXbo0\n3377LW3bts3FvhRFsdpFFEUUQeDWW2/hX0Meps+AQaxc8SkuyY3gMuMmVARNQ1RBU1T6dWzLj9t3\n8MyCBUwaMMDYAFc06qKhoVrpZEAIgxhYyRAFTUPGEPXNzIiCDnoaoInGClhjNkBS9fAK82YnKAq4\nXAzpn0mZ0iXp2Lkbb70+k+bNmoFguohSxO+85pprmDhxIj/++COTJk1ixYoVPPLII5QsWTKiX+P1\nu/ncPv5uvPFG6tWrl6uPzc+LzhgRLz4sFnCZ7zkBmP11QYOX/fPzay1btmT9+vX06NEDgIkTJ7J8\n+XJycnLIyMhgyZIl1K9fnz59+iAIApmZmXTt2pWRI0daN98XXnjhqnc8yhPAli1bxsmTJ628XHlt\nr3Tw5Al+3LObKQMH4PFK7Dt1itlffMWtN9YBs+MMIb9cmdJkZR2lcpUqxtlC3NnIjIwMdu3aRa1a\ntWLekRJhQXkd4wRe9vfy4w7kBZbxQCy6ztH16datGwsWLKB169Yx2ZcFZgCiwNChj7N6zRqemziZ\n558eZQCX7pKJmmYAlYqkqbwy9GFaPPIki9evo1OD261dvDVUPfWMBpqqywPWLCQGvGm6a6hq+iyz\npujsyy6FYgMvFNVib/ofFVGV9c1n3R46t2tDmTKl6dlvEE8OfZyBDwxEQkRU1AgmZu6K3ahRI+rV\nq8c777zD4MGDyczM5J577sHlclmzd043CbP/YzEf+3nmsdHR8jGDjB0YfayxEouJxRo7f9WuloEJ\ngsBzzz0X8V4lW+aNnTt3On7W1KlTr6KWuS1P2GvVqhU7d+6kd+/eDBw4kDFjxjj6wqal+HwkeTxc\nzM7mwImT/HHmDJXKlMaahTRjwTSNcteUJevYMcAMo8AxkNW8IGvXrs22bdscwc20eB2RCJvKL/OK\nfh7PEmVf8epsvxBuu+02/H4/a9eujXnxRIj6qoYgSrw9Zw7z33uPr777Xt+B2u1FdHsRvV5EjxfJ\n60HyuClarAhznx7BhPcWcj77Mi6XqBdJxCUKuETCM5PYlh1p4awVsuE5mlkrQiGVYEghGFAIBmRC\nOSFCOUHk7ABydoA1P21i8YqvOXbksD47GfSDHERUZBrfchOrv/iUOfPeYcTIpxAFwRLzo8V9j8dD\noUKFePDBB5k9ezZr1qzhscce4/Dhw3Fz7McCorxmH2NF7Ns/I78upP11vDFfEBbve/5u5vdXLE8A\nS0pKYtq0abz77rssXLiQ5s2bxz2+dLFUbq5WncfenMX49xfyzc9b6HZnU/2f1p3XALCyZck6mmW+\nCeQOZLWXpk2bsm7dOoLBYMxjErFEASoe8zI/x/6ZiVqiOkb050fXC2DQoEFMnz6dnJwcxzt/xGtN\nQ0WgZOkyLFjwLv0eGMyeA4f07cM8HgSPF9Grz0xKXg+S101G9UqseW0qpdPTcLlNABOsAFdrdjIi\nvIKo8ArbmkkjvCJom508ceo0oewgx4+f4JUPFhHMyWHYS9PY//t+K7e+oIQQNYVqFcuz9usVHDp0\niG49epCTkxOx7MgOZmZwa5UqVXjrrbe46667GDp0KC+//DLnz5/PlSDRbGf7hEh0G9r1rXgglh83\nMhaI5QUifwcD+/8dgOXXXJJEk+syeKZnTx5sexf3tWiGgEmNbQwMjfLlynIk66gl7gvGHyfqLAgC\npUuXpmrVqmzcuDHfDR4LdAqKlUV/pt2cBp3TY34GqPk9DRs2pGLFiixYsMAxqDJWadCgAf8eN46O\nXbpx+uw5NHveMLcOZoJXZ2VlypQ2AM2Dy+vSi0fC5ZZwuUTcksnKRFwiuAxAi9yxTbO0sejF4Bv3\n/s6Id+Zx36TJdG3cmLtvu412t93KkawTRjoeI3I/Oxs1J4ciXjefvPcOFcpdwx3Nm3Fg725EVP27\nJQm3y6UnSTRy63s8Hnw+Hz179uTjjz+mSJEi9OvXj9mzZ3PlypVc0fvxUvQ4AVx+Sl7SQ7Tn4VTs\nm/8WlCVa//82K3AAkxWFSUsW89Hatew8fITlP25i+kdLbLhlghiUL1eWw0eybBmnIiPyIfedoVWr\nVqxcuTLmwu5ELdoF/CvupP1zYpmTXheLiTkdG6vOqqry6KOP8uGHH3Lw4ME8Y3nsDCEzsw8dO3Sg\ny3334w8pIOlJDwWP12BjPkSvD8nnRfLpjEzy6gGvbo8Lt1vCbYCYSxIMt1IPeJWEyHQ8ultpupY2\nt1LRaFq7Lq1uqE9a4SK0qX8zwYDMFz/8BCEF+UoAOdvP2ZN/cubESZScbFR/DpIS4tUXx/PQwH60\nbHMXX3/1JZIg4HaJ1hIktw3AzJKens7w4cN57733yMnJITMzk3fffdea3Y63MDwWiMViWfkR8aPH\nerSOGQvICsrsSSLjlf82K3AAk0QJDY1RXbrQt2ULhnfrhD8QJKx9Yc0yVbjmGrKOHtNnnMwPiAFc\n5nutWrVizZo1BIPBmMfEArNoFhY9iP4u7ctuf5WBOdWhRIkS9OnTh0mTJuUJXtG62HPjxlK8eHEG\nPzZczwHvcoPbg2gCmM9wK70eXD43Lp8Ll8etMzCPZOlibhPEhDCIOW7VZriVVkZXRSUoK1x3bSUy\nrq3A5I8W8eay5ZQumkq9ilXIuZTNbzt289KsOTw/bSbLln+B6s+BQA5CKMCg3j35aP7bDH1iJC9N\nmmSAmMHAbBH8pjtpPpYrV44xY8Ywd+5cjh8/Tu/evdm2bVtCABaPoSTiNibKwpxYl71c7cydk/3j\nQtpMEkXmrVzJJz/8yLwvv6FC6ZKoijk/ZXchy3Dk6FE0zYjcJczAwPniLlGiBDVq1OCnn376Sw3t\nxGbs/0uEjcU6Ni+L9fucjolXf3vp1KkT58+fj1hiFA+8ZFlGUVU0QWT2f95i5+49THx5uuFChhmY\n5PUhecMMzOV1s+Xgfpb88AMut4TbLeJ2CbkYmL4aU38E855lLvwmrIupGiEFJNHFg62XX6s/AAAg\nAElEQVQ7ULFEKZpnXM+DrdogZwc4eOQoH3+9En92DpcuXmLRii85+PvvhjbmR5ADNKxXl/XffM6a\ntevo3vM+Ll++hMfI3GqCmH3Bt/15xYoVef755xk7dixjx47ls88+i4gzjG7vaE00GrziuY75ZWHx\n2Fde0QD5tUS+6/8MgI3u0p3KpUvjDwapXLo0j3frpP94g3lpmg5ihVNS8Hq8nD5zBsxI1hhxYNFu\n5DfffBMXvGKBWaIaWPSgS/TuGf0dpsViV3nV2an+TheQKIoMHz6cGTNmcPbs2VwzaE4upKrosVzJ\nhQqz+ONFzH57HguXLLNpYD5Er1d3Ib0eC8RKphfjhfc/YO2O7TZh3xD3RZ2FRa+bBFPY1yxh33Qh\nQ4pKUNZnJ1vWqU/pQsXYceAwC1et5vzZC6CoPH9/b26qVoWhPTqhBQO8v3gpoydM4vixLERV5pqS\n6Xzz6RKqV69Ko6bN+W3bbxEamNPGIPb01A0bNuStt95i0aJFTJ06lUAgEHMXpLwAK56AH2/8JAJe\n0Szs/7r9bftCNr4ugx5Nm9D8xrrWJKNmupD6C9A0KpYvx6FDRwATv/J2se68805++OGHiGDNWLqR\n0+urdR2d2Jf9/Hjf4VSXWHWOBcZOn2+/qGrVqsWdd97Js88+i9/vj8vCzN13FFXfF7FUmbIsXbqU\n4aOe4rMvvwlncTXXTBpupOTzUL1yRd55eiSPvfEmG/ftxW2k4HG5JMOV1IskikiCYOhhMUItVNOd\nVPWkiMa+kzeWr0LbuvX589x5zl28yIGso5y/cIk//jjFE5NnUKJIIW6qVZ3/vPMB2RcvgBzCI8Ir\nEycwacK/6dS5K3PnzkUCXJKI2xT3ozbNtYNY5cqVmTt3LoFAgP79+7Nr1y7HRJqx2j8WSCUSAxY9\nRux5yOzgFQ1iBWX/uJBOZoZMoEWBmGbhWMXy5Tl0+DCCrT+dQCvajSxfvjxbt26NeaHnh8nEei8/\nYGY/PtqcADUW88qLQUYzguh6Dh48GI/Hw9NPP21t7BpLyI8uNWvW4OOPPmTQkEf5dvU6Y39EXRMT\n3Kaon4Tk89GgXl3eHj2Ch2a+xpbDB3H7XLi9LtweCbdHxO0W8bgE3JKA22RlogFmRN6sVKIWhBuu\npYbIbdVrUbdiFSYt/Jia5crx07Zd3FC1Co3r1Kb1LTezdfsugpeusHTZcn7+aSOa/wr3tmnJyuVL\nefPNWQwePBj/lUtIgoZLEgyB350LyMznqampjB8/nscff5zRo0ezd+9exw1EnDSyRMaM0zFO48IJ\nvOyb/JqPBWX5Hev/LfY378xNePYx/MRiX2galSuW58Chw9bh8S5m+/MmTZqwdu3aPPWkRPWw/LCu\neK/za4m4kLGYpVPdJUli7NixXLlyhfHjx1vpk6Pjl5yBTKHejfV495259O7/AOs3bjaEfY8eEe/x\nIfr0IiX5aHpLPd4cMZSHZrxKthKywivcbp2JuSURtyTgEcEtCLgMtzLXdm2aLauFGo4bCxppee6q\ndzPP9LiPW6pUp16VqtQoew2hnCDPzfwPNSuUZ8PPv/DVqu9Z/OlyJkyaiurPpkbFa1n7xafIcog7\nW7bmwP79ephHHBZmLyaTfeqppzh48GCeM5RmXyQ6npzOix4DsdxGezrtgrJ/ZiHzMBOzwuxLZ2aV\nyl/LocOHQV8Vl2tXolgsrGnTpqxduzbuMU7glrtezjOTV3NHivVZpsWj5HlR9kTAy3RVXC4XEyZM\n4OjRo1aeLCfwysXMFF3Yv/322/nPrDfo0iuTLdt26u6koYkJ3iQdwHxeXEleWt5+C6tfe5niqUV1\nBmayMLc+M+kWRR28LG3MjBGzifsQmdVVDafkCcp6Wh6v6CYUUChZuCgfrvyOSfPe58jxP+jU+HY2\nbfmN0QMy6dexLR4RCOq7H6V4Xbzz2jQe7H8/Le9qx+IlS3BLUi7wip6hNAGiWbNmPPnkk4wYMYJj\nx47F3AUp1hhI5EYYbXbwcmJhfxeg/ONCOppme9SLCWKmoF+xwrUcPKxrYOZ6oliMxP66Zs2ayLLM\noUOHEnbJYtYyH4Mu3nH2YxK1qx00sVxfE8Q8Hg8vvfQS27dvZ+bMmY5CvhMDU1QVRYNWrVrz6oxp\ndOx+Hzv2HTBmJn2IvqSI2DBXkofSZUrg8kWBl8HCPIYL6RYIi/tmV5v1R7Piw0wQC5oCv6wStBIj\nylxbtDivPTSENvXqMf2hwezZf5CiPh9pSV527NyFS1PJPn/OiN4PImoKD/btw+dLPmL8Cy/y+LBh\nKIqSazbSSRNzuVy0bt2ahx9+mOHDh3PixIkIYLH3X17jJ6/x4jR+/ycZ2D8AZjfNVnK9DoOXpkGl\n8tdy8NDhcAiFcUpeoCSKIo0bN2bdunV5NnIsJhNLv4qnbzmxrkTAK1a98luiLRq87CCWlJTE5MmT\nWbduHS+//DLBYDDPff8UVQ91UBHoeE8nXnpxIu06dWPn/oPGrKQPyWBgUpIXl8+MD3Pj8kqGBhbp\nQrojAlyjZyf1cWBnYLKqJ0YMqprFwEwACxnbtlVKKwFBmUsXL5F1/A/eXryMPfsPcF2FsiRJmhFi\nEURQZUQUbqpbm5/Xr+bChQs0b96cP/74w5GFOYFYx44duf/++xk+fHjEEqS8bi72Pkn0ZufkQgqC\nEAFe/80u3f+0FTyAaeEnWsRrLOCy4sHQqFS+HFnHjlubjkaHUUB8N3L9+vV/6a4RC5ASdSXjfU60\n5dd9zOv32893qr+maRQpUoSZM2eyZ88eRo4cyeXLlx2ZWOQmFgqyopcuXbow/t/P0brDvWz+dTuq\nKKFJLjQzXsyXZAn7ks+Hy+fF5fOSo8m4knRgc/uMyH2PpIvokqizMiN2zBL3BSFyI13jJmdpY0bg\nq7WRbkjhntsa0PT6Oly+nE3Xpo1pVDsDOSeIkhNAyQnoGV+NTXULez188PZ/6NW9G40bNWLNd6sQ\nUXVgFQUkUcQl6bOpJkCYQNa9e3dat27NhAkTEAQh1/KjWNpYfphLrD53EvJdLhebNm1K6HPz893/\nMDAANIbNmc2lnBwdqszBaLAvzRTENA2vx0OZUiU5YuxmIpDYxSsIAjfffDN79uzh4sWLCTV2vDtm\nPJ0iP1pYLG3DqS6J/M5YS6YSYWUmE0tJSWHq1KkkJSUxePBgTp48mWdmBXv65K5duzB10ot06Nyd\nHzf9oq+bdHnAY2piepGSkpCSfcgugTajn2bFLz/j9rmNIuHxSHjcIh6XXkxmZgr8EezM+Fn6DKVm\nzVBGhFyEFOSQSr1KVRnQujXFfMmEssPgpe8KbuTe9+uFoJ9hDw9i7qzXub9ff16f+RoiGpIgWOEf\nJnDZ9TC3281DDz2E2+1m3rx5ESwo0RnK6D5KZGzEigH7888/eeONN/IcZ4la9Hf9nw5kBdiwZw+X\nsrPDb5jRFDbwMkvVypX4/cBBazG3afEuWlEUSUpK4vbbb3fc7MPpfLvlBTJ5uZHxXMl4lig4xwKx\nvATkaHfSfC6K+k4+DRs2pH///uzevdtiYXE3rFAUFEWlQ4cOvPn6TEaPex5FkNBcHnDrACaYAObz\nISUlkVK0CO+OG8ML7y/kP998pbuWhj7mcUs6gEmCpY+5DAYmCSARuQhcw7b8yHAvZcVIzSPr2pgc\nlJEDMrJfRvGbu4IHUPz2jUNy0AI5qEb+/RaNGrDm6+W898EHPPDggwT82ToLs8WLRYOYz+djwoQJ\nrFy5kh9//DECUOLdbMy+TmTcOfW7UwzYq6++Svv27fMcb4lafm/U/y32twFYitfLFX/A5jbaHiH8\nvqZRtXJFft+/HwgvJUr0gs7MzOT999+3glpNS+RukR+h1anzEu3YRNhWrPfjsa54wGxnYPaSmZnJ\nAw88wCOPPMLy5ctjMq9Id1JF0TRatmjBF59/pmetkNxGaEUyojdZZ2FJSUhJPlxJXq6/rgZfTH+J\nxWvX8twH7yO6RWMBuMHAJGOG0sgr5hJAImoBuPk7CMeIRe4EbriSQUUHsEAI2R9i9cZfmPvJZyg5\nftQcP4o/zMC0QA5aMAfkAJWuKcPqLz5FAFq1acepEyd0F1JyRbiQdj0sPT2diRMnMnXqVE6ePJlr\nbaJT0GuiN8/o8RuLgW3ZsoWdO3daWVALwpzWWjqV/zb7GwBMV7eSvT6y/X7b21ExYIYOpmkqVStV\nZP+Bg8aB+WMjderUoVy5cqxcuTLfrpZj7fNgXYm4m/EselBfDQtL5AKxA1h0frA777yTqVOnMnv2\nbCZMmMCVK1fisjBFVVBUUBBAdKFJOgO7HAjx2ber+XTlasOF9OFK8uFK9uFK9lLh2rJ8Me1F9h47\nxojZsw0XUmdgbskMchX1BeCCffmREMXANGP3b3P5ka6DhQyBXzZyi8kG+ypZqDBvfPIpw6bOIOfi\nRZ2B5WQbux/pC8EJBRDkEIV8Xt5563W6dLqHO1q3Ye++vfqKAgcX0iw33HADAwcOZNy4cciyHNfl\nSnTcxRsPdgDTNI0pU6YwYsQIkpOTE/rsRL8/0Rvnf5P9bQws2evlsl/fmVvTwnfTsDgbBrKqlSqy\n9/f94aysNkukUTMzM/nggw/ybPB4nXC1wGUeG++z7N8f/RgPoJzu6Hn9xlh1jU7QV6lSJWbNmsXF\nixfp168f+/btc2RgIVkmJIeXHKkaKIiENHjuxUmcPn+BTb9uZ9a7CwmoAqLPGzFLmZaexuIXx9K/\nQxtctjgxjxFmEZHJQgq7krmZWDjYVVU1FEVDkVUU2dTCTBYmUyEtnc/GjuXIiVMMemEqwStXUAMB\nVEPMVwM5aEamV0GRkTSNUcMeY/zYZ2jX/m62bdtm6GG5Z/3M0r17d6pUqcKsWbMct3H7Kxe/0xgw\ny+bNm0lJSaFVq1b/LCXibwSwQj4fl/3+sNdoMhWimJgGNapVZe8+Y8NcLRxOkShLadSoEWfPnmXf\nvn0JNXxeHZGovx/NwOJZoszL6Q4eHdToxERj/V4nILOHWTzzzDPce++9DB48mE8++cRiYo5gZhP2\nV333HeWuKUdm797c17M7G3/ZijclBc3lRZHcRsiFro0lFS3KbTfW1ZlZkhdXshdXkgd3khuPET/m\nMSP4rQBYwSou+yylLQTD1MdUTdNBzQA2WVZJ9nh467FHOX3+Ik/OeItQTgAlEELxB1H8QR3QAgHU\ngF93KUN+enW+h+mTX+Keezvx69YthrivZ1eJBjC3282oUaPYsGEDO3fujBDzYwGak3uZiNnPOXTo\nELVr1y7whIb/AJjdNBjSrh11K1YMv2E+RGtimh5KceyPE/j9OeFjhcRBzOVycc8997B06dKYjR6v\n8WOxqfy4j/bj87J44BWLgSUCXvbfGQvEnNzKtm3bMn36dN5//33GjBnDuXPncoFWMBiMeH327Dlu\nuOEGZEVh9tz5NGncCE108cPm3/jPh0sYN2MWf1y4ZMxMJiMlJxvupRd3kgd3skd/9LnweCXcXmOW\n0tDHPJKAR7RH8BMRBBv+bTZWZoRZKMaCcDcSc4Y+zr6sY2zfewDZH0IJBFECARS/AV4BvwFifrRQ\ngE7tWzN98ovcc29ntm37Tc+uIQlWaIXdlUxNTWXYsGG88sor1goIJ00sFnAlAgjR4z8rK4sKFSpY\nfV9QdrUApmkaY8eOpUePHmRmZpJlRBOYtmrVKrp06UKPHj1YtGhRQufkx/42Bla3UmVKpabqL2yx\nYfZAVsOfxO12UalCeX7/fb91nDkjmYioLwgCHTt25Ouvv8bv9+e6oJ0u6lidEcv+qvYVbfF+ixNw\nRYNXft0V+8ykU9qXa6+9ljfeeAOfz0efPn347bffHEEsaLiVHo+X6TNeZe47Czh67Bg9e/ZAlVy8\nMG0mdeveQN26dflg+VeIvmSkpGRcyUlIyUk2EHPz497d+NWQxcA8btEIsxAMkd8WYiEK1jpKE8Is\nJdVkYEqka6mEFJIlNwufGkWNMtfo4OUPovgDKIEAqj+gu5OmSxkKgByiU/u7mDb5RTp17UbW4cNG\nfJgrQsw3waxFixZUqFCBRYsWWe/bNatYfZXoGIkeK1lZWVSsWPFvYURXw76+/fZbgsEgCxcuZPjw\n4UycONH6nyzLvPjii8ybN48FCxbw4Ycfcvbs2bjn5NcKFsAigla1yPfCklf4he15jWpV2b13X8SH\nCHkI+vZSpkwZrr/+elatWhW3A/Lq9ESmj2O5jXm5k051SYRdxXpMFLjiMTB7QKvL5WLo0KEMGDCA\nxx9/nAULFhAIBBzcSJmmzZoycuQIypQtQ3qJkqiIzH7nXapUrULDRo25956ObNm1mz/OX2DGuwtZ\nvOp7PXI/2WuxsJVbt9Dx2XFknTmFx2Rg5kJwJxfS3HbPNsQ0Td/WUrGBmKxoKIa4rwQV1KCCEgxF\nupAGAzPZF8Hw5iGCKtOlYztGDn+ce7t24/z587mCW00w83g8jBgxgqVLl3Ls2DHHANf8AEK8MSMI\nAocPH6ZixYoFHpclAIKmxS8O523evJnGjRsDULduXbZv3279b//+/VSoUIFChQrhdru56aab2Lhx\nY9xz8mv/A+l0zKea9VYYuzQL6GpUrcLuPXsilxQZT2Jd+NGlc+fOfPLJJ/keJHpV8hcXFouB/RUX\nMh6gxWJg8WYnnX5DrOR79lCKJk3+H3vvHSBFlbX/f6qqw0QYcgaZgRGGnINEA4LuqiwgAhIEBUUU\nUYKIiqwJVzGQFgkqBnhVlBUVQVQEFURAgoCgyJAkSmZCd1f4/VGhq2uqw7DDvv72/R49VHVNdVXX\nrVtPPee5597biVmzZrF8+XImTJgQEVLKstlaqVA7M4sOHTrSr18/VEEkrXQZbvjLX8GXxIv/nEdq\nWhqbd+5B9Po4nZfHI6/MRkzWwcuT4uP5B+5mZK+/8rcnnuLLbVst/StmCGnTwMCugWFoYOEQUgkp\nyAEFJSAbboSQBgNTAgE9dAwUoAUDIAd1YV+VETWVUcOH8ZcbetC3/wAURXHVwTweD9WqVWPYsGFM\nnz49ImfrUlskzTri/BwIBDh9+jTVqlUreQamKom5wy5evEh6err12ePxWJN/OP+WkpLChQsXyMvL\ni/qd4tplAzCbkmS1PoaFe901i4mp5NTLZseun+00zRozKh5wmQ90hw4dOHr0aJEO3tFYWCIVIFrr\npLme6PdMc/sN8ULHWE300cAskYTXaKOIyrJMxYoVmT59Ounp6QwePJiff/7ZCCMj9bBgKES9+vUJ\nyQoNGzZk3rz5PDZ5Crt/+ZW//uVGzuXlMXzoEFo2b0797GxO5xXw5eZtfLdzN1JyEkN73sQ7Ux5h\nytuLGTNvAeeDheHsfaPF0hyixz6tm0cSEEUBUYxssTQuUK9fxiS9qqKiyiqqrOgeUti2ey9KIIga\nDBkeRAsG0UIhCAWtFspnJz9K+bJlefLJJxENQV8X9SO79fTp04eCggI2bNjgOnZ9rBdOInVH0zRC\noZB1zhI3TUnMHZaWlkZeXp712UyaNv928eJF6295eXmULl065neKa5exL6Tjc4SAbwMxY9m0YQ7b\nf9phY2VFxfxowGWue71ebrjhBj799FP9q3GYTpGf7sKu3P4ebZ/ihI7Opds1xQOxeAAXS3eJF1Yq\nij7D9+jRoxk4cCAjR45k6dKlug4WxWvVqsXChQvp1asXs2fOwOdPIqQoJKWlsX33L1SoVIk7J07h\n6JnzvLdqDe98/jViSgptWzTj23mzqFKhPHmqbISZOkvzJht9Kf32VkpzOjcBjxien1ISiGTwZlUz\nZhRXFQ1V1pCDMqNf+SdvLl+FElJQQzJqKIQaCqGFgqihIFooiCbro1nMmf4iixYt5rtvvkEUNERR\nQBIjUyz8fj933303b7zxBoIguAr6xXm5uHlKSgqapkWAQomZg1xEdYc1b96cNWvWALB161ays7Ot\nv2VlZXHgwAHOnz9PMBhk06ZNNG3alGbNmkX9TnHtMrVCamzbl8vLH31k69BtJq7iKBQVNI16dbI4\nePh38vPyrBgzHEq6P/xu3rNnTz799FMCgUBc4HLbFg2YYoFXrLwwt1Ag1u93Y1Wmq6pKvtE9KxZo\nRRON3UAsXkgpyzJdu3blpZde4s033+TRRx/l9OnTrgBmCv2ZmZnIikJBQYCNP27l4clPkZKaxpZd\nu6lZswaDB/Rj5rN/Z9f+g4RECSk5mdLlyvD3+0aQXad2uKUy2c7EzKF67HNRCo4Zwm2Z/GadMwFA\n0V1VVNBgxn338NQb7/BL7iGUoIwajAQxzRD0UUJULJPBP2e8zJ0jRpB34YKeqyZF9k30eDx07NiR\n9PR01q5dW4R9RRsMMREgMxtfACpUqMCJEyeK7P/vmn4uNY4XBbDrrrsOn8/HbbfdxtSpU5k4cSKf\nfPKJ1agxceJEhg4dSr9+/ejduzcVK1Z0/c6lWolzUfMS8wKFfL9nj41pYTGroiCm4fFKXFknix07\nd9KqbVsEi7IJCEL88NGkobVr1yYnJ4eVK1dy0003xfxeLKCxricGC0vks5tFC2tjsamDBw8yadIk\nTp48SWFhIUlJSaSmplKhQgXGjx9vvcXMCi+KIpqmuV5vNC3P/B3m9+39KatXr86sWbOYP38+/fr1\n47HHHqNdu3auY71rqooqCnS7vhutWrXgu3Xr6PmXG5n20ivcdccQhKQUZs5fiOj1kZpRBjUYQPRI\nCB4JVRIRPSKKJCAGRYSQgiALiCEFUUQPB0UdiFRFRBNMeQJLqohgYBjvSFVDU1Q0Qe8gXrdyFcbe\n2pvhz73EypeeIVkUQBQRRBFNNMQ2BBBEECVu7HYtH3bswNPPPsszTz+NhIjmESIYq9fr5e677+aZ\nZ56hQ4cOFojZ70m0F6b9vrjpq2b5VqhQgePHj1OvXr2E6lrCFoVhFdnHYYIgMGXKlIhttWvXtta7\ndOlCly5d4n7nUu2yaWDpyUlcyC8AzHoUqYNpaEVArHHDBmz76aeIghKEcKtTPBAz1wcNGsQ777wT\n8QA7Ldp2KB4Lc/uem0UL5RJhX2vXrmXUqFHcfvvtrF69mnXr1vHJJ5/w2muv0bdvXx544AFrst9Y\nIWS0MDJWy6Q9hcLj8XDvvffy4IMPMmXKFJ577jkrPLDSLKxUCwVZ1ShTrgK33NITweuneYsWTJ/7\nGstWrebL79bz0P2jEFNSEJOTkFKT8aQkW92QvCk+PClePEkS0z/6iCNnT1sMzD7WmH04Hr0vpS1X\nzAI1vYVStcJIFTWkMujqrlQsXZpnFi7WWVjI0MIMBqbJIZD1VklBU3juqSm89/4HbN+2Per4XG3a\ntKFy5cqsXr06IQ3Mre7ECiPLly/PyZMno9azSzZN1ZtzY7n2f2BmbtBxKj0pWR9Ox9bsqDlBzBFG\nNmlYn23bf7L+7gwhEw2/WrRoQWpqKuvWrYv6HdMSYV7x0iWiCfbRLBEgNhnUq6++yowZM3jppZe4\n+eabEUU9K7xUqVJUqVKFG2+8kZkzZzJ79mymT59uTQfmBmLO63UCmBPM3Dp5B4NBmjZtyty5c/nj\njz8YOHAgP//8cwSIhUIhYzwxFVnVUBBRRYlrru/OoCFDEDw+Xn7pRarWqgV+P56UVKRkE7ySIlIt\nPEke0tNS+Oujk1nw+UpUQdVnQPKGZz8ydTBRNLP1jWs1KqNZz/QwUjVATEFTVKYNH8aaLds5d+6C\nET6aIaQu5KPIoCgIqkKFsmV4+onHGDN2HKIQ1rmcIDZixAjefvttoGiob78nbvXPWaecAyJWrFiR\no0ePFrvOxTVVBjUUx+WSO18J2eXrSpScwvkCl+F0wnzfFlLqy2aNG7Fl67bwzobFAyw3EOjTp0+R\nlAo3N4+fqCUSMsZqgYzHuMzPwWCQRx55hF27drFw4UIaNGgQtYk+JyeHt956i7NnzzJgwAB27NgR\nszk/XuqFm8CvqmrEePopKSk88sgj3Hbbbdxzzz0sXbqUQCBAMBgkEAhYbn4OBoOEgkHatmlDjxt6\nkFk7E1nV0ATJGCTRqw/R40tC8CUjJqUgJqfgSU3j/tv7svylqXy/5xc6jp3A29+sQZYES+S3ZgxP\n8uDxS7r7JCSfiOTVQ1JBEhAlAUHUJQmM6y1fujQrn3uS9OQkMMR+TVFtrqApsg5kqsLAfn05ffo0\nmzZtKlK2Znk3bdqUMmXKsH379oSz8u11ycm67P1YGzVqxPfffx+hi5WIaQn6n8wuW1eiUinJnM/P\nRzPyO8Iho4Vb2MELTaNZwwZs37kL2RidNdEsfDfv1q0b27dv58SJEzHDKDc2FovaF2c9lsUDs48/\n/hhVVZkxYwblypUr8qA4W7jKlSvH008/zbhx43j44YfZv3+/q4CcaMuk2wPkxsquvvpqpk2bxhtv\nvMETTzzBuXPnXMErGNBDy6CRCBuSZU6cOs3fBgzhl9yDBoD59SF6/MkISSmIKalIKalIySnUy67L\n/zw7hTcee5ivt//EO2vW6Awt2WuN/Orxe/D4PUg+CcknIXolRAPAdBDTgUwQBQQRW0KZYAQCun6n\nqSZw6Y65VBUkAe4cOpj5r70WUbbOe3L99ddbYWQ0ET+RsN7ZCb9Vq1bs2LGD06dPoyhF0xou2Rxy\nTlT/k9llY2BeycOC++4PP9DmtcfQwEqlp1GtSmV2795jE/ET7xNp95SUFLp168Ynn3ziyrjihZLR\n3o729VgaWaxjuf0OO8AUFhbyzjvvcN999+Hz+Yo8INHe/JIk0aVLFx588EHGjh3LmTNnXPdPFMjc\nUivcwspq1aoxc+ZM8vLyGDJkCHv27HEHMTN/TJYJKQpp6aXo3KkjXW64iQWL3kfzeMGXBP5kxOQU\nxGQdvKSUFF0jS02mdbPGvDv179zbtyeeZH1SEa+NgUkGAzPZl+SVdACzwEtEcJkyXFPNBggzzFTR\njORNfakzMDSNwQP68cmny63x8d3GzerWrRvr16+3crdihfVuZe8GZOZkLS1atM/HIfsAACAASURB\nVODrr7++5ORPV9PUxPxPZpc1E79zw4aWlgP2hzwaums0b9qETT/+aKG9ne4Xl4X17NmTZcuWWWK+\n/RimxQMyp0VrvUvUYgGxWcmXLFlCq1atqFu3rivwuDEw+/Ivf/kLN998M2PHjo3QxGJpMfbri5Ve\n4QQxE5Q8Hg8TJkzgpptuYsSIEbz22mvk5+eHQSwY1JNgrWF6FFRNYPjw4Sxf9hFzXltIz9uH8vsf\nZ/Tp24wQUkw2O4I7RP7UJCvVwpOsg5foFVFEzWJgJnhJHhHRI+ggZrQwCpbAilUfIxiYGg4hUWQ9\njFQVBE2lQrly9Oh+Pe+8807Ue1OxYkVycnL44YcfYg43HS10d2NhJhPr3Lkzq1at+n8AxuUEMM25\n1CK32UBLX+oxffMmjfhxy1YgsdbHWKFYTk4OpUuXZuPGjVFZmN2igVc8sCpO6oTb9dgr9vnz51my\nZAnDhw+PCl6xGJi5HD58OHXr1mXy5MkAUcErXigZLTfM2UJpAlm3bt2YMWMG69atY+jQofz8889h\nBhYKEQqGCIb0rki6wC9wZU4D1ny9mpatWtG263W8+f6HCP5kpORUayQLKTVF7wyeqgv9XjPZ1aaB\nbd73Gx0fHMeCz1dxMVRohJGig4EZ1ysIVpdwzRT6bQmvYf0rHEaiKKCpCGjcM/wu5s2bhyAUzQmz\nh5FfffVVXOYbL5R3AtlVV13Fhg0bKCgoiFvvEjarMS2W/x8KIYEIwNKslXDXIg0HiGkaLZo2ZvOW\nLdaXhWJ06LYDgp2FffTRR677mRaNzke9rBghZLzWoVjhrCiKLF26lC5dulCzZs2ooZ9bSOlcejwe\nHnvsMQKBAE899RSapkUNJ6M9UNEeJLeZvu1AVq5cOZ599lm6devG3XffbXUKDwQCBhMLj2oRkhVC\nioogeZjw8MMsW/YvZs6Zy1/79OPQsRPG2Ps+BJ8f0ZjWTUzyW7OD271Dyya8Pmks23JzaT1qDMOn\nz2T55s0ENBnR59EZWURIKXLojz9Y8NlKXl+xqggjCz/UtofbGCnlqnZtEUWRrVu2IAii3gLqKNeu\nXbuybds2iwW71U9nvUqEAaemppKTk2NN7Fwyloj+9X8BwLQoHyPKIVohqbRo0pjtO3YRCASAcB7Y\npYCXIAj06NGDDRs2cO7cuSIPaHGBC/79ENJuzt+uaRorV66kZ8+eEcwsHgtzAzFJkkhKSuLll1/m\n1KlTzJo1q8iAe87vuZWfHcyAqA+VE8xkWaZHjx7MnDmTFStWcO+993L48GELyAoLC4u0VgaDQepf\nWY+vvlhFy5YtaHlVZ6bPmUdQ0VBFfSo3zetH8CUbQn9qWCczvHWzxsx//GF+XPgq17VpyeI1a/ni\npx14knxIfh8hNP7n2295/oMPuWbCI9w4aQq7Dh6idtUqCB5JF/pFEUTRqHyO+mHUVQF9JIU9v+zB\nTNpw1sO0tDSuuOIKcnNzo5brpYLY7bffzqJFiy657hUxOQhyII4HS+58JWSXeWZuiqK3+eA7EN/U\nxtLTUqiTWZvt238KvxAvIYQ0vVSpUlx11VV88cUXURlYYpdRFKiKC2LxfuvOnTtJSkqiXr16MdmX\nG7g5wctcT0tLY9q0aXz//fd89NFHcZlbrPKJxcii6WPly5dn2rRp1K9fn9tvv53PPvssArgiUi8M\nZoYgMPahh1jx6cf869PlXNXtRjZu36mL/F6/bTo3XegPa2XJxthjSVSoVJHBt9zIh/94kj7XdUEy\nJt8t1EL8sGcPChrTRo1g55uvMv3Be7m2TQtESbKYmd5SKYbfoEVLg6zM2uzbt8+4t5G9Kcz1K6+8\n0hopOF7jiVs5Ryvj5s2bU6FChYTqXYKVE00QY3oRMP8T2OWbmRtYsu47Ply3LtwfUrOBmAVcqoOF\nabRp1ZwNP2zEpOtmuV1qKNm9e3dWrVoVNXSLFV66Xl4MMHOzaCGr01etWkX37t1jthxG08HcwMv0\nMmXKMGPGDObPn8/mzZujgli8ByzWgxVLG1NVldtuu40nn3yS2bNnM2nSJE6dOuXOwqw0C4WsrCw+\nXvohI+8eQa/bh3D/xMmcyS+0WioFo6VSTLGxsBR9fkqPMTuS5Uk+pCQvlSqVZ86EB5gyfDBtG+fg\nTfIZ6RZSONVCDLdUChEsLLL+ZmZmkrtvHwJaEanDLMd69erFHOrczr5j6V7O5GJZlpkwYULMelos\n+2/UwGRZZvz48QwYMIBbb72Vr776KvEja/DH+fP8fPCQTe/CpoFFB7E2LVvw/cZN4Sz8Ygyp47be\nvn179u3bF5ETdimWSCpFLIsFoLIs8/XXX9OjR4+Ia4jnbukVboJy7dq1mTp1KpMnT+bQoUMJDX3s\nFkLGCm3cQMwOULVr12bWrFn4fD769u3LZ599ZoFYuFO4LvLrqRYqiiZwa9++/LD+O0KKSpN2nXn9\nf5ageZOMENJorUxJ0YevTtZHfpVSkoxZkvRUC0+SD4/fi8fvRfJ7kXxeJJ8HyedB9Hj0vpiS7qIT\nxCIrAUZFJrP2Fezff0DvMilQpO6JokiDBg3YvXt3QszLLN9EQaxELZ7+ZUVSfy6LCWDLli2jTJky\nvPPOO8ybN48nn3wy4QNrQEZqKmcuXsRU8XX8coSNTrEUjbYtm7Nh4ybg0roSOd3v99OlSxfXMLK4\ngGYX6RMJIZ3HjnYd69evJzs7m0qVKiUEXPHAzM3btGnD6NGjGTt2LBcuXIjbVy/atUcLbRRFcZ0E\nxJ6dLwgC99xzD4888gjz5s3j/vvvJzc31wCxkCXu662UKoqmz4KUUa4CM2bO4sMPP+CNdxbTodsN\nbNy+E9EAMckEsRTb9G7J+sxIksG+pCSfDl5+A7i8ngj2ZYn7VqqFoJMvZxhpvIhr16pFbm5uxLh1\nThDLysrizJkzlgbrdu+LU872xpOSTWT9L0yj6NGjB6NHjwawJi1IzHQgKpOWxmn72EX2B90p5ts0\nsSvrZHH27DmOHj2KHkaaLZLFT2g1vVu3bgnNHWk//qXYpSazfvvtt1x33XVR2aSb/hUtpSJaKOnx\nePjb3/5Gt27deOCBBygoKCjSl8/8HCtUdT549oct3kMnyzLBYJDs7GxmzZpFTk4OgwcP5qOPPooI\nKQsDAQKBYLjV0gC3hg0b8fnKzxgx/C569e3HsJGjOHz0OAoimtElCY+vqFaWZEy+a7iQlISYZLRq\n+v2IPj+Cz4/g8yF4fYgeH3i94PGC5AHRA6Kki/uirhclJSVRUFgIVg0tes8lSSIzM5ODBw8Wuedu\ndSeeiG926SpxFvbfCGDJycmkpKRw8eJFRo8ezZgxYxI4ZPhWlktP59SFCw79S1/XInIsIlmYKAi0\nb9uab9ettw4nGP8k0iLpFgq1bt2aQ4cORe1a5AZexWVmsT67Hde+3LVrF02aNHEFrnjhZCJhpB2c\nRo8eTfPmzXnooYesUSacIOaWLHup+li0DH5N0+jTpw9Tp07l9ddf58EHH+T333+3gKywsNDysE4W\nQJYV+vTpzcYN31O6dAbN2nZk8jPPcfZCgd5aKfmMbknJCP4Um+utl6LlSZYL/iQbiPkRvD4Ej9dw\nD4IBYoIogSFo/7h1G00aN4qZXGBGAMFgMGo9c6tL8RJaS5yBhQohWBDbQ4Xxj/Mftrgi/tGjRxk8\neDA9e/bkhhtuKNbBy6amc/rChXCnIOMfLQb7MnNtOrZrwzfffhf+G1pEOKmvJsa+RFHE5/PRsWNH\n1q5dW6LM699JozDPd+7cOS5cuBAxXZYbICeaUhGtf57pXq+XCRMmkJWVxcMPP+w63rsz1SIRkR+I\nysKi5YwFg0Fq1qzJzJkzqVGjBv379+eDDz6goKCgiMhvtlQGgrrQn5KawhOTH2ft6i/Yf/AwDdt2\n4J+vv01ABc1gYPj0fpWmi0kpCCYT8ycb81fqLEzw+xG9OgOz3ONFkHQWJkiSzsIECRBZ/tlndLaP\ndRWlLpgAZi+vWOBlLqOB12XRwURPYv4ns5gA9scffzBs2DDGjRtHz549Ez6oCVZVy5Zl2rBhBjDp\nQr4WRjIXgTC83rF9W4OB2YbVMWelERIHL7tfc801MWct+nfDR+v6E2iRtPsvv/zClVdeiSRJrqAV\nj4ElClx2UPL5fEyePJly5crx6KOPomlaTPYVr6UyEcbgNmGuKeBrmsaAAQN49tlnee+99xg5cqSl\njRVtqQy3VsqKQtVq1ZkzexYfvLuYjz9bSeN2nXnrvaXIogfBnwIG+xLNZZLOvoSIEDKpSBhpMjAd\nvMwQUg8jFVVlyQcf0qd3b6vOa7ZysJvP53NlYNHqTSIMzPQSs//GENKcen727NkMHDiQQYMGWTci\nphn3z+/10qpuXcf2cGtkJPOyC/sqzZs04rfc/Zw9c8Y6YFhPTRy07A9bu3bt2L17N2fPno0bRhbH\n/p2kVkEQ2L17Nzk5OUV+rxO8EmFhiYCYfRz3Z555Bo/Hw9///ncAV/aVSAhpXr9bF6RYaRbOlsoa\nNWrw8ssv07RpU+644w5mzpwZMcJFIBBuqQzKMiFZH3NMRqBR46Z89K+lzPnnLN54ZzFN2nZk8dJl\nqB6/wcAM/cufrK9HhJG6Dib6/Ihev8XAsIGYroWJIIh8/c23VK9WjTp16uI+4VjYTABLpG65gVes\nLl0lZRF9QGP4n81iAtikSZP49ttvefPNN3nrrbd488038fl8Mb7h8gBrjhULxLSwFmaAmb1V0uf1\nclXb1nz19Zow2KFhtlknons5tyUnJ9O+ffuoYSS4d/W5HGY/x549e6hfv37E704UvNyArDgglpyc\nzAsvvMCFCxd48cUXEUXRNZR0nsNevkVueRQGEW2kV3tIGQgEkGWZnj17MmvWLPbv30+fPn344osv\nHOkWQWtYnpBsDJ5otFi279CJlZ9/zvTprzBrzlwat2zNvIVvUyir+nA9Pj+CCVL2da/ebcl0e/iI\nycAECRV4Y+FC+tx6awTrcmuhBj2EDPcscW/McdNQ42Xkl2xnblOD/i/KAysZ07Dxa4tlRUaNdvAK\n54R1v/ZqPlu50vpshpJmCAnFDyW7du3KmjVrYoaQprl9vlSW5vZ9c33//v3UqVMnJgBHa4ksAmJF\nwKxoS6QTyFJTU3n55Zc5dOgQkydPtsZ3d3o8sb84Qn+sVAwT4DIyMnj44YcZP34806ZN4+mnn+bs\n2bNFBk60g581eGIoRIcOHfnyyy946cUXWfqvZWTVb8TT/5jGH6fPoiCgCCKqMaCiKnlQTaAyXBM9\nesumKOnZ6IjIqsqD4yaw/acdDBo0qAi4OFmTpmn88ccfZGRkuIaXxWHtbmVXYlaCQ0oHAgHuv/9+\nBgwYwIgRIzhz5kyRfd544w1uvfVW+vbty8yZM63tnTp1YtCgQQwaNIiXXnop7rkuc2fucEujXbd3\nsjBnK6SJ+D26XctnK1ehqWp4fLBihpBO79ChA1u2bKGgoCDuvqb9O6AVzczjhUIhTp8+TZUqVRJi\nke7gFS20FBHF2OBlekZGBnPnzsXr9fLAAw+Qn58fF8SigZedmTkZRryWSrMfpZ2h5eTkMGfOHC5e\nvMhtt93G119/7SrwO8ceM1naVR06sOT991j2rw/45de91G/WmuH3jWHlV2sIyIoBVN5Il0z3oAke\nNEEiEAoxeNhwftyyhS8+X0lGRobFhNzcvNZffvmFOnXqFGFp8cwJ+HZQNNdLykoyhFy8eDHZ2dm8\n88473HzzzcyePTvi74cOHeKTTz7hvffe49133+W7777jl19+4eDBgzRo0IA333yTN998M6Gsh8sK\nYBZ82TUvc4OTdrt43czapKWlsnXbVgsMrXSKKKDj9sDbH/qMjAwaNmzIhg0b4gLT5QAu5/FPnDhB\nxYoV8Xq9Ma/JTUAXzXHg7X+XRGvyVTt4uYWQ9nWv10tKSgr/+Mc/aN68OSNGjODEiROuzMv+3eIy\nMHMZq3XNDmImEHk8Hh566CFGjx7N888/z8SJEzly5EhcEAvPKC6TfWU9Zs+ayfffruXKK6/k78/+\ngyvqN2HE6If47MuvOXT0OIog6rlkkhdEDwoi23ftZu7rC7n+r7dw4eJFPl62jFKlS8cFLk3TOHXq\nFAUFBVSuXNm1LBI1N/b177aAR1ggHwovxvZAfvzjAJs3b6ZTp06AzqjWr18f8feqVasyf/5867Ms\ny/j9fnbs2MHx48cZNGgQI0aMIDc3N+65Sr5dVItcvvvNt6iCxqDrr7HCSM0IGyPEe1WLYF+CjYUt\nX7GSpk2aIAiYvc7Q0KI+8OZDr2nu+3Tt2pVvvvmGLkYTeCLhZEma/RxHjhyJmCo+EXeCmICAJpiF\nLlhhuWCr9PbvK4pSJIy1+9ixY6latSp33303L7zwAllZWTHLxq01zM4Q7A+a/feoqmodz9xu3jcn\nKEiShKIoNGrUiDlz5rBo0SL69+/PiBEjuOWWW/D7/RYImlPVS5JkLUUT8AWBSlWqcO+993LfqFEc\nPHiIfy1bxgsvz+DXX3/l3PnzZNauTZ06WRTkF7Bh40YqVKhAu7ZtGDBgALcPGIAoSaiqhuryO52+\ne/du6tSpE3Htl9ro48bESsqsOQni7OO0JUuWsHDhwoht5cuXJy0tDYDU1NQiE/FKkkRGRgYAzz33\nHDk5OdSqVYuTJ08yYsQIrr/+ejZv3sy4ceNYsmRJzN90GRM79IepIBhg77FjNtZl/M3WCClEYWBo\nGjd2u5Ynnn2eRyaMw0yjKG4Y6QSzLl268Oqrr7oCnN3cHvKSNjcAiyfWuzJNq8zN4g2/KEwws+9v\nBw/n9QmCwIABAyhXrhxjxozhqaeeonHjxpF3N0o4ZN9mApMdoOz7OMtXVcPzUYqiiKIoRRieCVBD\nhgyhS5cuzJ49myVLlnD//ffTvn37CPDweDzWwy5JIqImIYmi1QikIVLjitqMfuABHhjzIIIAFy9e\nJDc3l72//YbX62PBawuoULGi9VJQNRVVdRfXnSGepmns2LGD+vXrFyt0dJan87N57y6LBhZvH4f1\n7t2b3kYqiWn33XcfeXl5AOTl5ZGenl7ke8FgkIkTJ5Kens4TTzwBQMOGDZEkCYAWLVokNH3cZc9M\nq1i6NOt27wbMx8sWPhIGNXcQU+nYvi07f97NyRMnKV+xog28Ll3Ir1q1Kmlpaezbt4/atWsXeXid\nn+12KWBm39d5nqNHj1KtWjXX40cFZAOwBKMBX5Vlzp07x5kzZ3Q9rXJlqlWvZkiGhv5o6IdmK4gg\nitYdcTsv6F3J0tPTGT9+PCNHjozoaO7G6KIBpPOhdjNneOn2d7Nym59r1qzJ888/z/r16/nHP/5B\nnTp1ePTRRylXrlwR9qa7iipJqJqEKGq6axqiJurZEYJASmoaDRs1pmGjMGArihrx22KFv87PGzdu\n5NZbby1SBomC2eV8edrNnH073j6JWPPmzVmzZg2NGjVizZo1tGzZssg+99xzD+3atePOO++0ts2c\nOZOMjAzuvPNOdu/eTZUqVeKe67LNzG0SrUqly3DszJmwBBbhYXZgLk3g0jQVQdPw+3xc17UzHy9f\nztAhQ0DQ0IrBwOzsy3y7C4JA27Zt2bhxI5mZmRH7ugFYtMpzqSBmt9OnT9OkSZOo5w07lpsTHu7c\nsYu/3nwTv/9+hPT0dMqWLUOZjAxy9x+gTetW3Dn0Dm7o0R2PR6f+gqYhGkCmgd4lRtQPp7icWxRF\nOnbsyOuvv85LL73E9ddfb00yYuoWJiuSZTmCJdr77rlpQ/Fa4+xA5lZ2dsBr164dbdq0YcGCBQwa\nNIgpU6bQrFmziJAyWotpNO3OPKcz/HWyLTMtxA3I1q5dy/nz52nRokXC5WC/3lj1IVaduiRTtfgM\nTE0MdPv168eECRPo378/Pp+PadOmAXrLY61atVAUhU2bNhEKhayMgIceeogRI0YwduxY1qxZg8fj\n4dlnn417rsvOwKqWLcNRqxnV4gKEw8hw06QOYs7xwVR63vQXFi/5kKFDBgPmjSt+dyJTY1FVlTZt\n2rB06VJuu+021/3D5yHiPE67lEpkP8/Zs2cpW7Zs1POHwUsg/B+cPHGcm2+5hScef4zbb+uLxyNZ\n5Zifl897Hy7lxZdeZtT9DzB44O2MHnUv5cqXA2P2as383YLx4IoCqipGnNd8qOvXr8/s2bORZZlA\nIMC3337L8uXLqV+/Pl27dqVUqVIWqJlgYLIyE8gEQXANsYCIUD4akMUKVzVNsyaUbdy4MRMnTqRz\n587ce++9ZGRkOFhYJIiZ4Wo0AHP7LU4As+e3mSBWUFDAjBkzGDVqlFUOiTBRswxisfbLwsrUBELI\nBEPWpKQkXnnllSLbhwwZYq1v27bN9buvvvpqQucw7bIOaAhQoXRpzly4QFDW53rULNAyhfxIQV9z\ngJepg639dh0XLlwokgsWm7VE99atW7N161ZkWS4CdJerorgd6+zZs5QpU8b6e9TrsDHOQGEhvXr3\nYUC/2xg8oB8eEWPiVX0W6ZQkH4P792XtquV8/vFSzpw+RaPmLZk+cxZyMIAISIJgtVZKkohHCrdG\nRnOfz8euXbs4fvw4U6ZMwe/3s2XLFnw+Hz6fr0iqhdfrjQoc0RivXkdiJ3HGyupv06YN8+fPp7Cw\nkH79+vHll1+6pFaEiizt7py8xN4y6gZYznVFUVi8eDE1a9akefPmUZlXLBBLtD6XmJVgHth/0i7r\nrEQa+mw4yx5/HEkQsd8vJ3hpNsAy181l6VLpXNW2NctXrNS/bCj5xQEsp5cpU4aaNWvy888/J1Q5\nLgttB86cOUOZMmUSBi+Au4aPoHr16jzx2CSjcunzF6IY8xeaM0krCjlX1mH2S8+zesUyPl/1Bc1a\nt2X5Z58hAJIoGOAlIXmK5oaZIGQClM/nY926dXTo0IHSpUtz/PhxMjIy8Hq9/Pbbb3zyySesWrWK\ns2fPWuDlls2faLekeHqTs2+lCVB+v5/Ro0czfvx4XnnlFcaPH8/evXujgpgTvNzGNHOCWCzwOnbs\nGIsWLWLEiBERWfPFAbJYUUGJgxegFVxAyz8X2wsulOg5S8Iu/6xEGjSoVQPRFI2tG2ZnWxQJG4kA\nNJVb/nIjHy372DF4nKEJ4X7DnW9657Jt27b88MMPCb/pSpqJqarKxYsXKV26dPRzOq7t6aef5rff\nfuON+XORBBA0BU2Vw67IaErImIhVtsAtJ7suK5a+x0vPPcPDkx7lpp5/4+jRIzYGFpkjFo2FZWVl\noSgK+fn5nDp1ig4dOuDz+Xj99dc5efIka9as4YcffgDg3LlzgHv/Sjcgs6qN4+GO1Tk82jDWwWCQ\nBg0aMGfOHDIzM7nrrrt48sknOXz4cJEcsWiAlag7f8+cOXPo0aMHlStXjvjd9tAzVr34X2FgXn3k\njpjuTSq585WQXSYAcySDabZVW/pERNiouoeP5vLmG69nxaovKMjPJ9w3Sz/mpbIwE8CihY3RwMtZ\neS61Il24cIG0tLSIgSKdlddCaDQ2b9rIq3Pn8q8P3ifZ7wPNmDVaMd3GvuSQzYMgh9DkIN2v7sLW\ndWto07IFzVu35ZWXX0YOFIIiI6gKgqagd5rRdIZmApzHg9fjoVq1arz22mu899571K5dm1KlSvHV\nV1+hqiqjR4+mW7dudO7cmRMnTvD+++/z9ttv89VXX1kjYNjdmeFvDz/jJccmAnimgN+vXz8WLlxI\neno6gwYN4ptvvnFlcG49AaKxM+e+5vG+/PJLNm/eTL9+/SJYmZt+5kw5sdcBt25kbn1cS8zihY+J\naGT/C/Yf6AtJpGhvbnBoXiaImeCkOUCsYoUKtGjalM9WrIgQ/sOaWOIsyvRmzZqxZ88eCgsLEwKv\nkgIu0woKCkhOTi5yvIjzgdlewatz5zJm9P1UrlhBLyN7+GiFjiE02e7B8DIUBDmIV9B4bOwDrPns\nY5avWEHrdlfx3TdrEVQZUVMRUZEEXSfzGOzM65HweD1ce+21zJ07l/79+5OSksK5c+coLCykV69e\npKamIor63Ja5ubl07dqV/v37c/ToUZKSkjh+/Dg7d+6MADMniBV3MMVYepldM0tNTWXYsGE89dRT\nPPXUU8yePZsLFy5EBSs3NhYN3MzGjTfeeINp06YxefJkfD5fRPhoDyOLEzrGAq8SBbD/xuF0LtUi\nbosVIjr1LnvU6BDxVTcmptG3V08Wv/s+4QlGzbdX7CbnaJ6SkkL9+vXZtm1bseh6NOAqLqAVFhZa\nAOYGXubFCQCqyldfrebGHt1tgqtiuabobExTwkCGYmNgBniFgSzAlZm1WLFkMZPGjWHg0DsZPHQY\nx47+rjMwwdDIRBPEPHg9YbCpXr06Y8aMoVq1arRr144NGzYwbdo0zp8/T9u2bREEgaZNm3L+/Hka\nNmzI0qVLWbNmDb/88guLFi1CluW4HcWLo5npVc19KB97iGe2qB44cIBbb72V5cuXR2Vf0cYwc+57\n4sQJxowZw3fffcfMmTPJzMwskhcWK9nVXn/cwMst5cNcLynTNC1+X8gojQ7/m3ZZRXz7UDpmXpjm\nADM7WGnGg6mDmF3Q14Hs1p4388Xq1Zw8ccICNlMpKm74aD4QrVu3ZtOmTcX6rt3+HRYWCARITk6O\nCl4WAwP27fsNWZa5MruOwb5M8DLXw+GjpoTQZNmVidmBDDmIoMr0/usN7Ph+LdWrVqF5mw68MmMm\nihyyhZASHkPkd7ZK+nw+cnJyePHFF+nduzejR4+mWbNm/Pzzz3g8Hnbt2kWpUqXYvXs33bt3Z9iw\nYaSkpHD48OEiLZaX0kkcorOwaOORlS1blkcffZRJkybx1ltvcdddd/HTTz+5hojxwOyHH37gjjvu\nIDs7mxdeeIGyZctGaGNODSxaSoi9PsUDMXvZlJj9PwZmM836B02D344e445pxtAYjrAx7Pb8L9V1\nWapUGjfd0IO3Fy3Wk16Nk5lSUaKho71ymADmFJPjve1LAsjsIWQ8BrZ6323lGwAAIABJREFUzRq6\ndO6MoBFu0lZtQKbo7EuTZZDlCP1LC4XBSzPBywAy5BCCEiI92c/UyRP55vOP+XzVl7Rq15E1a7+x\nBH6PEUK6AZjpzZs3x+fzUaZMGZo0aWKNblGpUiUyMjKoV68eHo+HvXv3kpmZaR0j3lA9ibZaxkq7\ncOsonpOTw+zZs+nWrRvjxo2jb9++TJ8+nS1btlBYWBhV+zp06BBLlixh3LhxTJkyhYceeojBgwej\naZprYqvbiBVuIGa/907gdgOwktXAlMT8T2b/kUGuSyUns3nvb1boCJEifmTIGBvE7hjYn/vGPswD\n99+HEDFbsIAguHfejtWxu2nTpuTm5pKXl4ff749408cCP3O7uSwuvRYEgcLCQpKSkiK2OZfm1X29\neg3XX3dt+E2oqqDZQkhVsVInNFWx6YQG20UAUTCy7/VRRVElBElFUyV92GRN5craV7Dig8UsXb6S\nYcNH0L5tW5575mmqVquGYCbAigKCJqISmZMnmnllGRkMHDgQWZbJz9dHMFi3bh2rV68mPz+fGjVq\nUKFCBWtiD+eDriiKlWTqZFexytO+DF83Mb8riiI9evSgR48e7Nmzh/Xr1zN16lTOnDlDdnY2SUlJ\nlguCwNatWzlz5gytWrWiU6dOPPjgg6SmprqClhuAxUuhiMa+og1OWVKm5Z1D9cff589ml200Cvtt\nKZdeioJgkLzCQtLTUm0hY6QuZjIxMxtfU1Wje1EYxDpd1Z5gMMj69etp1749lsKtP13FDiX9fj+N\nGjVi69atlnZTHB3s3wkhg8FgnBFujetC48etW5j86ETC4mG4ZUhTTAamhAFM1VtpNWxAJujAJRjT\ngqEoCJICooQgyWiiZI393rP7tVzfuSPPvjKTZq3bMnzYEMaOeYD0UqURNBBNLcB8aUgiogCqKCIa\nI0eYD54sy/Tu3Ztly5ZRr149unbtit/vt0aLsANXNNHbPvqCc+l2T9zkAvs9dIKIKIrUr1+fnJwc\n7rzzTo4ePcrBgwcjZkgKhUJ0797dGnzSzvAS/f1u7Ms8f7SXrxPE7Ey1xMyfgpCUGmefUMmdr4Ts\nsjIw8x4JgkD18uU5ePwkDdJSLR3MLtw7O3ObfSHNFknB0rw0hg8dzKvzF9CufTv0EDI8wkA83cus\nRPbtrVq1YvPmzbRv3z4ueJUUA9PLR4vQdexm36bIMocP/07tWjVtIbjR+TZCB9OXmiw79EOjw7wg\ngCjqLEoQEEQJTVH0qcIkUR8yWZL12WckmRSfxJPjx3DXwH5MnvoCDZq1ZPyY0dx5x2B8ScmIZnmI\nAoImoIp6srJo655jjiiRmZnJqFGjIjSppUuXUqlSJVq3bq1fZxQAiMdeYrGYRMJOtzpRuXJlKlWq\n5Lq/+fvs7NGpubm1iDrBK1ZLZCz2dVkALIHO3P93NDAgUrWHmhUqcPDEyTAbsO0SL2x0jtQ6pH8/\nln36Gaf++KNYaRTR3sgmgLn9LVofOecDEe9hcTOzH57zOJHHhMOHD1OpYkX8fr9RpkZ5WFqYrSXS\ndFm2XLUt1VAINRhCC4ZQg0G0YBA1GEANBtCsZWHYQ4XUqFiO117+B5+++zZfff01DVu04Y2FC1Hl\nEJKRL+aRRLyShNfrKaKNme73+y1PSkqifPnyLFiwgNtuu43333+fQCBAUlJSxH52d6ZdxNLO4vVv\ndIaXbuJ/vBEnoq0nmn0fL43CTcB3NnqUmKlaYv4ns/9IHhiaRs0KFThw4oQBWpFpE9bNtDEu6+9W\na2RY5C9bNoObbujOG2++rR8/jogfC5hEUaRRo0YcOHCAixcvJgRabmzsUswOYG7hqXlxubn7yaxd\n20UjNHPBVFAM9qVEuiorEeClGa6GgqgGeGnBAFoggGoDLsxlKKC7HKRJvTr86635vDNvNu8tWUrT\n1u15d8kSBE3RUy08kqvAHw2QbrnlFj788ENeeOEF9u3bR+/evZk6dSr79u2LAK1oCbCxwMsNxIpW\ny+gZ/26pGPE0Ljfgi8fC7BYrhHQbBrwkAUxTE0ij+L8IYOYlP3jLTQzo2tXapkUwrzCYFWVjRR9a\nNI2777yDuQteQ1OVYjMwJ4j5/X4aN24cNR8Mx/HNz6ZdKog5GVhR9qWL+Pv276N27VqEGa0NuAwB\n356Rr9laJDVZRg0paCEZLWQwsFBQZ2EhvYVS/2wAWYRHgpggBxHkEO1bNOGLpYuZ/eJzzJm7gOZt\nOrDkgw8RBQGvx2OBTCwGZrIwv99Pq1atePHFF1mxYgVZWVlMnDiRu+66i88//xxN06ImvRZ3ghEn\nA3OClzOL302fc8sviyfcF4eFxWJf9hCypBmYngcWxy9BJrncdnlHozDiRA3ISE0jxe9zaF/mrg4Q\nU8M6WESoZAOytq1akJqSwhdffBXebiS32gHN2SUjGpA1b96cLVu2xOxWBMRcL1YRGQ9LIrk8+3P3\nU/uKK8KFag/P7czV9rZUFRVNUSOYmCorqCHTZdSgjBKUUYN6WKkEQiiBIEowiBIIogb0pRIIoAYC\nxrLQ8qvbtmLtJx8w7eknmD5zFk2ateDtNxciF+QhKEEEJYSoKYiaooeaaHjMcNOjZ/f7bEBXuXJl\nRo4cyapVq7j77rtZvXo1N998My+++CJbt25FFMWY4WW0bkpuk5FEY2zF1c6cn6MBY6Lho309HpB9\n9913xapzsez/r/NC/mfmCjefMbCtEAlkJrCpZkKrZmuB1D9rgooghhNYRw6/k5n//Cfdul0Hmopg\nHloAQSseK2vatCmzZ8+OC3YQPXQUhOKJ+WaqQDw7c+Ysta8wGJhVoPaCNei9WdFM8DLDAsUMyyPv\nCQIIomA1gCDqwr4gqQiigiCJCKICkoQgKQiSjCBJ1kzV5vp17dtw7ccfsGrttzz3yiwen/J37r/n\nboYM7E9Kapr+ltRMrVLTxx4TRDRRQFVB0vTx5U3W4vF46N69O9dddx2HDh1i+fLlzJs3j9zcXFq2\nbEnLli1p1aoV1atXjwkUehUrPnOI1jjgdhy380RrZLAvzboSS0u1yxxOhnnhwgVeffVVKlSoUKxr\ni2bK+TMoYuyJcpXzf77RKC4DgBlPhludsREGzMrgZGSarUXSRH3bJB+oGoj6+oBbezFpypPs3fsr\ndepeaR1P7z8oFCu0bNKkCbt37yYUChWLfZluVs7igJiZYhDP8gvySUlOjigrt5Bb0xw6hgliitkV\nxHaLTBP0hgIEA8gkVW9VFEXdJQlBEkGUjXXTdQATPB4043O3q9rQrdNVbNiynZf+OZdnX5jG0IED\nuOeuYfrIDMZwjKKo82RVA00S9FsNrqkImZmZjBw5khEjRnD8+HHWr1/P+vXreeutt1BVlRYtWtCo\nUSMaNWpkja4bq6Uy2r1xAyLny8UtDLV/J1FmZv4tGmu317FoDOzdd9+lffv2/Prrr3HrT0KWlA7J\npWLvU1gypypJu7wMTLN7BHqFV+2optofTINpmdtFZ4ukRkpyEncNGczL02cxc7o+AqRgTm8RBcBM\n1uPcnpaWRq1atdizZw/16tWLC3hOKw6ImX/zer3IshyXJeTnGQBWJDa3PRiWCKtFsDDVBC9FI5wX\nphehDvTGP4KZZiHYAEyIADGdnYlhEPNICLLBxDweg5lJtGlUj/+Z8zJ7Dx5mxtzXada+Mz2uu4Z7\n7hpGq1Yt9VQOBDRBn1hDXwqu2pNdk6pevTq9evWiZ8+eKIrC/v372bBhA1u3buW9997j5MmTNGjQ\ngHr16pGdnU3dunWpXr26lT4TC8zcgMbOkBN9KcUKJ+3HMetQtOM666wdvPLz83nvvfeYNWsWU6ZM\nSeh3JfDDw6Qi1j5/Mru8eWC2NQ39TauoKqImWaCm2ZiX6UKRztyRzCvsIvcOH0rD1lfx98mPUaZc\nOeN8BqsoZh/JZs2asX37durXr1+s79nDAWcFjSgPRwWQJIlQKH5yYH5BPskp5qgVWtEXgqUVGqzV\nDl52N8oZZ1215QJHgJggGMAlRixFC8B0MBM9HpA94DHCS48HJIk61SrzylOP8cSEMbzxP0sYPPwe\nypUry8jhd9G7199ISk5BEyUQRDRBLAJYbq2C9s/Z2dnUqVOHfv36oaoqZ86cYevWrezcuZPVq1cz\nZ84cTp06RVZWFllZWdSuXZvMzEwyMzMpZ9QVO7C4Zf5Hkw0SAapon6PVE7d65QQvURR5//336dSp\nEzVq1IhbdxI1s47E2+fPZpcNwMIyl4amCQgaTF+2DEEUGNO7J+EHEeMhDC9NIV9Qw6zLnqFvT26t\nWrkyN994A6/On8/D48fbKoRg/p8QAImiSNOmTfn888+jjpMfi4HFLQ+Xt7/H40FRlCL7OC0/v8Bg\nYGbBmgVnrNtCSs3I17E3iyuywu4Dh/jyx634PB6Gde/myEnUWLFxM19t3U7r+tm0vjKbWpUrIpqg\nZWdkkqiHjB4RUdYZmeaREA3gEjwSgqJrZHi8CKpC2bQUHhwxlNEjhrFi9TfMmv86Ex6dzODb+zP8\nrjvJzKoDRu8AVQBV0IfyUVXVmMbMcCNzv4gbYFOxYkWuvfZarrnmGguMzp07x549e9i7dy979+7l\n22+/5ddff8Xv99OsWTNatGhBs2bNLKZmb410u3eJ1AM3Zud2nGjmVjfNZWFhIYsXL2bRokUl3BdS\ni58mkWAaRSAQYNy4cZw6dYq0tDSmTp1qDZtu2tNPP82PP/5Iaqqe/T979my8Xm/c7zntsgCYhsvr\nXYMqZcqydtdO/aPFBhyhkBbWwHQgU3VGJhpCvmh8FlQ0QUTQVMbcN5Iet/RhzP33409KtvQvDeJm\n6NvF+hYtWvD8888nBFpu+pjz7RrPJEkiGAwWLT97eQCFgUIjiRVb3Od8AMLxumZrNVFklR4PP87J\ns+e4pmkTbmjdCk0JM1/zqzk1anL45Cm+2LyVZxa/h6ZpdGrUkLtu6E7jrNoIomYAmYYqqYiyhCqp\nBiNTUT2qLvR7DMHfo1ifkUKGZuahR8f23NClE3sPHGTeW4to36krTRs3YvgwfQYlr9eL3kNJrwei\n8WIT0DAbAERBRBUFNE1En2BWD51NILOzqXLlylmzFtmZ1cGDB9m4cSObNm1iwYIFyLJM8+bNLa9R\no4a1r8n+orFqJ2tz9r0194tVNxLRwwA+//xzWrZsSVZWVkLzJiZqGglMq0ZiDGzx4sVkZ2czatQo\nli9fzuzZs5k0aVLEPjt37mTBggXWBLegz1oU73tOuzx9Ie1ubkMjp2YN5q5YEWZbhKMge1ciE9jC\nQKYaU6/ZxHxr4EORxg3q07hhA95etJhhQ+/Q50I0WyKx9OmoOpgJYlWrVkUQBI4dO0b58uWLDOMS\nDdAS1bycn5OTkyksLHQNQewF6vf5XYHOKtywtBiWx4yy/XrrdmRZYcP0aaAJ+oOumGUcDimrZpRl\nSNerGXL1NYDGgT9O8tW27RTkB1CCCoKgGqGlMW2aAV6CKCJ6FJsuJhqtlQYbMwV/j2SBmCB5yKxS\niakTx/LE2AdYunwlM2b9k3sfeJC+vXoysF9fGjdqiGiM+CYYS9FgZ7ocIaIBqgkcFoBEtkq69UHU\nNI2srCwyMzOtORsPHTrEpk2b+OGHH3jjjTdQFIXu3bszZMgQa8gjO1u238tY4FXcF5szZHX6gQMH\naNSo0SVFAbFMUzRdJ42zTyK2efNm7rrrLgA6derE7NmzI4+jaRw4cIDHH3+ckydP0rt3b3r16hX3\ne2522eaFjNygh5F1qlTlwMmT5AcCpErJETqOpYWpTuCyjb5gZ2HmNkEHsfFj7mfkmLHcMXgQokcM\na9OCgKAJRvuXFgFadp3D1BoaN27Mzp076dq1a9wQsrhAFlEsmkZycrI1g3GsAk1JSSa/oMA8S4wC\nt7Mv3d9a+SUDr7vaAi+n24V9/U2iH79GRnmGdLkaBAEloOjgJQgIoooqCvx8+DD1atXA4/HooaZk\npF2YGpnHbLUUw+Dlsbde6uDmlzz0++v19LvlBn47cJC3l/yLW28fQunSpRnQtze9brmJalWrogm6\n2C8i8M7idylfoQKtW7ehVOnSBoDhALLoAOa2Xrt2ba644gr+9re/oSgKBw4cYP78+fTv359Ro0Zx\nzTXXFAENU+g361K0BqJ42mg8sx/n2LFjNGvW7JKljGimyiqqHHu4HFUuysCWLFnCwoULI7aVL1+e\ntLQ0AFJTU7l48WLE3/Pz8xk4cCB33HEHsiwzePBgGjZsyMWLF2N+z81KFsD012X4o/FwaJoe0vk8\nHjIrV+KXw4dpll0nDFo2toWmoano+pcq6oBmCx118LKHmjqQdel4Fe3btuHYsWNUrV7d+DFiERoW\nL5xs0qQJO3bs4Oqrr47KvsA9pSIeeDkZVnJyMgUFBUUYmJONJSUlU1BQaCvcKOGjjfWaetjwv/Sg\nfvXqYdAyUitUUyuzNMdw6G+D5HDrpFGGgiCgaCoPzpnL8bNnuaFVS25s05q2Derj8XgMABMs8BI9\n4VZL0SNZICZ6JJAkQ/jX/55VtSKTH7iHxx4YyZrvN7How2VMffEVGjfI4e0Fr1KxUiVmvjqP/IJC\nzp07z9Zt2xk/fhyi6OH0mTPk5u7nCmOcfpMtxQIwt6Wq6nNIZmVl8dRTT/Hjjz/y7LPP8tFHHzFm\nzBhq1aoVcS/t33Oye7slWkfcvmP3o0ePWpFCibIwkxDE28dhvXv3pnfv3hHb7rvvPuvFnJeXR3p6\nesTfk5OTGThwoJWE3KZNG3bv3k16enrM77nZZetKpJlisz2M1DQaXXEFuUeP2cIdk23ZHnDb0uol\nb3ezG41N1BfQWDB7BtWqVo6cOxKKTEvmBC07SDVt2pSffvopZuhYEpXHZGDmeFlR90NnYAUF5n5C\nUfyyF7VVdvq2NvWuJDUpKYJ1qUa4oCqa8eY1PKS7YnpQQQkqyEEZJaCgBGSUgIwga3w0YRKLxoyl\nXEoakxe+TbMR9/LUwreRCwLIBUHkgkKUgkLk/EKU/HyUAt3VgnzUQtuyMB+tMB8tkA+BAggVIqky\nV7dryYIXn+HQ1u954J47qVSuDIGCfHJz9zNx3IMMHTKQ7T/9RHJSEnkXLzJ9+gz+9dG/ePzxx9m/\nfz9+v59vvvmGQ4cO4fF4IjL0oy3dvGXLlixevJhrr72WUaNG8cMPPxTpshSr61K0uhKv/kQDwGPH\njl0WAIvbjSgRkd+w5s2bs2bNGgDWrFlDy5YtI/6em5tLv3790DSNUCjE5s2badiwYdzvudnlaYU0\nmZhDAwOB54YMweP3GMwsHDrahXuTXUUwM7uAL6oWsAmqqjfDW+GmGA6h0MJApsVujTQrX4MGDfjl\nl1/iTngL0TPyXYvE5c2blJREfn5+nO4mRqiZb4SQ4djY+GzAtUXOnGGO/T4Ywr1mq5QG443o62Z7\n71gm2EIgUc/gr1muAndffwP39LiRQ6f+4MAfJ5CDsi60SyKCZCxFAdGjIkqKzsIkRWdmHglRklA9\nBjuTPODxGOK/rpX5JIm/dOkIcog/jh2neuVKEAqw8fsN5GTXRZCDbNzwPaf+OMmc2TPZtOlH/jl7\nFv373cYHS5ZQqXIlREHg0UmT+PXXvfy2L5eGDRtQqVIlXS8TRUv8d7r9fg8YMIB69eoxZswYXnzx\nRerUqWPdVyd7M7dFCykTCSfddNNAIMD58+dLLPs+4vgJdBVKtCtRv379mDBhAv3798fn8zFt2jRA\nF+lr1apF165dueWWW+jTpw9er5eePXuSlZVFtWrVXL8Xyy5jHpiBYqYGZuSBiZIYITZb0Y9atAVS\nMER6waZ/IeoFLYhhDczMCQsDmgpauPUTTDYWe3RWURRJT0+nVq1a/Prrr2RnZxcBL/1Y7mwu4uqj\nhAp2cDL76OXn55OUlFQkxNT3h1LppTh37pwJxw4QCy/tv8k0t2fEBLVw1G7TIAm3Tmp2ScAANcF4\nMekvBA1UAUEUqFKmHNXKlScUMjL5FQ1RFBAllW25uZRKTSW7ZnVESQcyPaQ0l0aY6THFfzlCMzND\nzMql07l4/ixz587n8LHjXFGzJmphHj98v56WjRtCoICftm4h/8IFDubuY9SIOylXrhzLPlnOiuWf\nsu77DaSkpPDNN2u5e8QIqlatagUBbiDmzIJv2bIljz32GGPHjuWZZ54hJyfHNeR3ivr2e+92j6LV\nG+ex8/PzSUlJsUCwOOFoPDNfYvH2ScSSkpJ45ZVXimwfMmSItT506FCGDh2a0Pdi2X9mOB2wvdIj\nReaIcFG1VQSTsqqqbR9b6Kg6wktbzlhEx2+KP/FHw4YN2bVrV7E0sISKwKXCZWRkWBPA2vezM7Aq\nVatw5OhR46+RoKUjs5ORhXeLPG74PmhmyoUVvhsPsV70KBoomoaiasiqhqxpKJqxrmrIikpI0Zey\nrHsopOgeDHswoLBtby59/v40f5n4OAuXf87pU+eQC4KE8kPI+QFC+UHk/AByXgA5vxA5rwA5P19f\n5uXrIWh+PgQC3HJ1F5BlOjRvwvZt2/l9fy7Ncq5ECRZy7PBBdu7cQeOcehTmXaRWtcocOXSICuXK\n8Onyz6h/ZV0mTRhHrZo1WLHiMzySPgS2Oeel3d2GrvF4PHTr1o0nn3ySiRMn8v3337t2FE8ktLTX\nIbeXn5uXKlWKgoIC8vPzSxzAnIMBuHbk/hMOaHj5GJj19g6L+PqTIjhyV03WpW8TTHFZdLRGGixM\n0zRbXpiosy0hkomZzEwALubn409KRpQ85g9KCMB+/PFHevXqFVPTcAMxe6WK9ZY1vXTp0pw+fZor\nrrgiytscqlerxqaNGx1hoglcYLEvO3gR7S0d1sfCTMzWemfdF6ysn7CcaXzBumbNEPY1BFUwfpag\nD70v6H0eBQH6tO1ArzZX8e3PO/lg3Xc89c5irm7ShKfvuIMypdMRRFkPNT1mC6bZAGDre2mEm/Vr\nVCHniuoIksTVbZojiBKlWzZh4fu5PP7Uswzo3ZMO7VozfsqzVK9ckY1bttGmVUvOnD5FmxbNkASN\nXTt30btXTyRJRELvBaBhMjCDObkwMPN+d+7cmRkzZjB69GhGjhzJNddcExFGmqGkPa3C2Z0p7uPj\nYHMme6tUqRJHjhyJyJ8qCQueOkkwELvVL3ixIObf/zcsIQDbtm0bL7zwAm+99Vbxjq7ZcCy8yZBT\ntDCgWSGjyQZUBEPLiggrVc0YjcIIIwVT8wqzME1QETSB3w8fZvo/51KmTAb7cg8wdeqzlMooEzOE\nNL1Ro0a89dZbCQGX+dm5HiuEtFtGRgZnzpwpAlzhfTWqVavG4d+P2Eoy3CoYEVJaYaT+wrhy4J1s\nmv0KyR6f7Zi2G+Mgwyb70jDyq7C9aDDJm2b9BPOOmuF5+OeYIAaiAWiiINAxO4fO9Rpw9mIeX/60\nlSRNJJQXCOtlHjMNQ2+9VO0g5jF7AEjWNtHoupQsSdzT72/G6BgSqDLdO1/Fjp07kUNBurRvzZGj\nR1m3fj15eXnkXbxIsyZN8IjGMNqC3jcznNmvA1ks9tS0aVPmz5/P0KFDqVGjBnXr1i3SqmnOC2AH\nIHtdcasjzlDUvk3TNKpUqcKRI0eoX79+iTIwT3oGnlJpsfcR4qc1/KctLoDNnz+fjz76yEr5L46F\nwQodgBAQNI1AMMiBIyepf0VNA6CwmEARMd98qkRbqCioEf0iNU2MSHAtLAjy1qLF3Ni9Gx07deS1\nhW+zbNnHDBw8GIjPwLKzszly5Aj5+fl4vd6E6L9dnDU/Ry0XBwM7e/Zske12Bla1ahV+P3LEPLAt\nZBQQjIk6wmwMK3xM8Sdx9mI+yWV8jvMX/R12HUzFADNzH7CBmS0ODV+tkWMXPr1o/BzRADbJADJJ\nECjlTaJXi7aoAQVZVhEkwUiIFTh18QI7Dx+kU+OG+P0+RIuViWjGumaAGLa0DIz+l6b4361DG67v\n3B4kD6Dy1+u6snjpx6xYuYqnJj9CpfJlOXj4ML6kZCpXq2YAmICqGn0yxciXnHPuAkEQqFu3LpMm\nTWLy5MnMmzeP5OTkCPYlGZObmKAWT8Qv+vIqeo8qV67M77//XrLho3meeCJ+CZ+zJCyuBlarVi1m\nzZqV+BGtSu7chvUaP3XhAn2enmroV7a3ju0hsjomGzqX1cfPCCmtoXYsD2tkX639htOnz9Dpqvac\nOX2a3/bupTBQgGrrDhILwPx+P9nZ2ezZsyduKkVxtDC3liU3BhYBYppG5UqVOXHiBLKiGEBlzi5k\nTJEmilaWvN0z0lM5V5Bn688YdivJ1+7RUjQoysQih0rX9TG7y6quo8marpmFDA+qGkFFJahqhBRV\nX5c1QrJKMKRy+I/TvLJ0GU3uvo97p89m9ebtBAtDhAplQgUyocIQcmFI19AKgsj5QT11Iz9gLI3U\njYICK5VDLSigVJKfu2/vy+MP3kftKpVQAwV89eUX5DRpSs+ef+OLlSvQQgEEJYSgygiagoiGJKDr\nZFGGdO7WrRtXX301zzzzDKKhp7nNa2kfMDHRYa7dOrNXqlSJw4cPW8MOlZSpRm5gPP+zWVwAu+66\n6/7tTqOaLdNb06BKmTKk+v38+vsRx9/CsUwEqDmAS3+anF2KwgJ/5/ZtyM/PZ9or07l71GhKlUpn\n+J13IklSZH5YHB3s559/LlLholW8eCAW7Y1atmxZTp48GRXANDQ8Xi/VqlXlt9wDFtsKMy8TzCQd\n0KQwgDWoXYsNP+/Rf7OR+lDEBXt5hLV/O6GzSDRhwV/DYGo2VzS7h8V/WdMImW4HMlUjKGsEZZWg\nrBKSVbKrVGfRuAl8MvkJGta8gskL36LzmPF88+NPyIUhHcTyQ4T+v/auO76KKm0/Z2buTe+FFCCB\nQICQANJEaYKAgCBFLBQVV751109wUar6W1mVBdZPd0Wxl10UwbIq6oq6LoIa21JMREFaaAmENBJC\nyi1zvj9mzpkzc+cWMLTdvL/fyZw7M/fOZObMM8/7vO85p9ENT4O/qo0LAAAgAElEQVQdiDVppaEJ\n3oZGeBu1ouqFNjVAbW4EbW7ETddOQMn27zB6+FDMvut3GDvuGvxQ/D2I6tFGkoWqsUZZghxA2J87\ndy5qa2vx3nvvmQDMOoqE3Rj9dpKDHYixkTjy8vLw9ddftziAwUQGApQLzM7ekNI+AgoLz2vrLs/r\nhsIdPwkMTN+kWoCLDa1jYVlUXyeyMBaRjAwPx9w7fo3SsjI8t+pxLJx/DwiAo2Vl2oCFugAeCMDy\n8vI4gJ0uAwvkGojrKKVITU3FsWPH/AOYfgkL8gtQ/MMPurAk+S3GQIQEN1w5FG9s+lwALIZ/7Lwh\nLLmK5i9Pli+5i+mPffECuFXATcFZmIsyFqYzMc7CNAbmcnvhcnmRGBWLqYOvwPr7l2DJtBlIjoqB\nu9Gjsa8mDcAMEDPAy9vQDG+Dxry0wkCsAWpTI9SmRtCmRj15thHRYTL+Z9p1KPr8nxh31QiMuWYS\n7ph9F44fPaozMKIPg23PwBRFQUREBJYuXYqXXnoJpaWltkNW+wsI2LUJEbysIHbJJZegqqoKO3fu\nNI2Y8UutJRNZz6WFDGBn6v/aupMUuLxrVxT+uFMHK5gZmNWlFBgYBziBiRmpFJSzsI4dstC+bSbi\nYmOwdes2zLhlJv64fBkee+wxQUYKjYH5Y2HBWJm/6yhey5SUFJSXl/vRv4x6jx4FKCr+wcS6mBtJ\nJAlErOvu5NDePeFVVdScqhcYlwZk+qSOAqAJmRd+PEnhVrEkDHgFV9JrKRzIVMa+wNmXW1V1ENPd\nR48Kl8erg5gKt0uF2+WFx+VFn+wcZMYlwS0CV6MLnkYXqipr9LqZgYngpTY2cvBSmxu1HgDNjdqk\nJc3axCUKVPzvrdOx46vPEBsdiUsGDMJzz78AQqieaiH7pEuIn3NycnDHHXfg4YcfBoDTTqewayMM\nvMSJQgghGDNmDN55550WZWD8OQtULkYNjFmoDygzKlZ0XOIsjFJc1q0bvt65Sxt0jwMWDPBShaVq\naGC+biM16C2fkk2r/+6OX2PnTzuxbMUjmDB+HFY9sRIVlRX46OOPedTM2qBYI8vJyUF5eTkaGhpC\nBq1gepidm+APwMy6INCjoAeKf9hhdh8lAcw4AyP6KBEEikPBZ0/8CUnxscY5ayFBA8gIEbpbmV1H\nu3vK3UdKTTqY18LGPNTiPurgxV1Ir8DCPAYDc7s14HLpOWTuZi9cTR5dA2MA5oK70Y2j5ZUYcNdc\n3LR0Bd7+7HOcrD0JT0Oz0YWp0XAj1cZGeJs0FkabheLSCtxNgNuFxJgoPPKH+/Dlx+/h+Zf+ijvu\nnAOPxw1FNrMulv8l5oHdcMMNiI+Px9tvvx00JywQCwNg2zOAgdnYsWOxYcMGNDc3n9YzGchcFRVo\nPnYsYHG14PA9LWUhAVhmZibWrVt3mj8tci/GKMAfyLSEeIy/tD9qTzXyXahKhX0sWhjrbsPEfUHk\n9+0vSTmYfV/8A26ZMQ3XT5kMUIqcjh2RnJioP6j+gcnpdCI3Nxe7d+8OaWaj0wF4kWHFxsbC4/Hg\n5MmT/kEMFAUF+SguLtaGXyYMhBhwyQL7kk3DQEuKLh7LRpEkoqUtSHpdZGeE6KkPRj6XSRcTMzjM\nd1fIavctvi4mjKRYlcKjqvB4NWFfTJB1syRZjxcetwqPW2NlHpcH8WFR+OLh5Rjbuw9e27gJfe6Y\ng989+Qy+3bET3mYXPE1sViV9lqUmF9SmZq3wGZa0OTGpy5j/Eh43cjtk4cuP30dNTQ1GjBqN8vKj\nPB1ElowEWOtUZ4sWLcKaNWtQVVUV0I0MJuDbaWCspKeno3379vj2229DbnPBTI5LgpyYErjEJQX/\noXNs5yQTn/I/5pVLb7kZ8VGRJrbBtC/DfVQNIGMsSxUZmL+iAVxe11y89vobeP/9D3Dj1OnYvHkz\nsrKyUFNT02I6mNUCsS87V7FNmzY4evSofQPWl+3atUdDYyPKy49D8/EE8JLZiA5Gtxs++gMbPVWR\neX6VpMj6kuhLoS4TrUgEskQgSzCWhPB0CAksv4ulTBCDxUFwR32ug+F+csADS9ugHOy0AIBQV2EK\nArh1sHM6nBjXbwBenjMXG5b8AR3T0vDDvgPw6B3RxU7oWnHD08ymkNNmJ/e6tCnk+ES/bheouxnR\nYQ688fJzGD3ySgy4bCD279kDog8cIEnm2YJYvUOHDpgyZQqef/75gAJ+oPZjF4UU56H0eDwYM2YM\nNmzYEPIzGNTsNABruZg1sDMyQfmlNiyMrxXcTL5dtQKZIOwLwCa6jXZsrGd+HhbPm4vq6mpc2r8f\nbrzhery2di3mzr0b1dVVAPxrYd26dcPu3bv9si9/3/V7OWxcSEop2rZtiwMHDtgKuDzAQYDLBgzA\n5198KSSwmkHMyFpXTCAmmcbl0jPeFZb5zoBLq8s6M5MlwgHLKNALMS0lWICMGAxNvBqUNQl2r0FN\nAQHefUmoc7ZGqYWxsS5NlHdlSoqKxa+uHIWpg4fC6/ZqxSUWjzayhsujg5cOYhy8XKAul87GXKAe\nF4jqxf3zfodF8+Zi3IQJqKut0f9fySfSyMpNN92Eb775BjU1NX5BjLUda/vwB16qqvJJdD0eD4YN\nG4by8vLTeRoD2n+8iB+6UdPCxLxsWJh9CgUM/YcDGZsazMy0qJV5WYfeoRQ9uufhukkTEBsTDa/H\ng2k33oBrxo/HurXrAjIrK4DZARlwevqgVcSnlCIrKwslJSV+XUgGYiNHjcIH//gQ2ow+EihzGWVZ\nyyiXLUAmdsNRJJxyNeN47QkdrCSDfZlATIIsa+yLszDmOhECGeCftQdZcDchsjGzm2l321X9PqsC\nkHkBs6upCrlkFD4MjIEXK16XqruYKjzNXh2stOjl0fIqHzamgZdbY2EubZZyxsCo28Vdyt/cejOG\nDhqIRffeB0J8GZhY4uPjMWLECGzYsCHgTOF2bScQiIkMTJIk/P73vw+53QWz/9g8sF9qophP9RQK\nEwvjS+HhFpmXCGQM4IQEVx/AElxO6NoZqIpPPv0Xdvz4I66/djIaGhqwZ+8eJCYmBgSwzp074+DB\ng37nirRjYj7/v62mZV6flZXlw8B8CijGjx+PDz/8EC6320hoFRmYZAUt2XAdZRnvFX6NaQ+tQIPb\nZQCWD4gRfRQJwW2UzOxLIlYQE7oPgbEum2thU7RsfxYU0Ce4pdDdRwiBAHNU09DMDAbGNTKBeXn0\nsmP/AVxxzwKsfPNdNNQ3cBfS63LzWchFBka5HuYC9boArxuPPLQE/9r4GT799FMN0C3uo1iuv/56\nvP/++6CU8u3s5Wcn4PtrG1b9iwGY2+1GZGTkL308zcf/T45CnpkJbqP+kbuS+gYDxJg4Qk0X0wAs\no24oxYbWZehiVAAyA9jGjxkFr9eLB/7wIDZu3IhLevVEXvc81NbW+gWmyMhItGvXDgcOHDit8fFD\ncSXFxtq+fXsTA/MZH0y/hhkZGcjNzcWmzZ9rKRREAojOvixupGRhYESRcfPVo9ArtxN+89hKUAJI\nCuGjphr6l+5KSobbyNkXAy/4BzFrQqxdi7ADMRWMfRnRTJ4Iq9q4kF5WVI2JcfAyWJiogeWmpuOd\nxffi2527MOyehdi09XudgblN7qOhgenF4wY8bsDrRmx0BJ594i/47Z134eTJOr/gJcsyunbtioyM\nDHz77bcBdTB/bcPqPtqBWMsmsobgPv43MDD2sHG9wyYPTOxOR1UVdz/3IhoazZNbMEFMZGVUdwmp\nXte2qXy9mAdm1sK0XJ5Zt8zA5InXICkxEa+ueQ3Ll6/Ao48+ik2bNvkFsS5duti6kcFcAX/rAF9W\nlpGRgfLycjQ3N5vAS3Qh2OcJEybgnXfXa5eQEIGBaQI+ZDalGZvmTIGkKJAcCiSnA4/N/Q2a3G7c\n//JqSIoM2SFDYsWpQHZq62SHrNWdMhSHBMUhQVYkXldkrThkAocswSERvU60uqUoRCsOfelPX5Os\nRQwMsCQ1U2MyNzFb9qo/gFkpqXhpzhw8MHUqfvf0c1j8/Ms41dAkzKPp1eteoXhAvV7A6wVUL0YM\nHYTRo0Zg/sJFtr05RKCaMmUK3n333ZCG1Al0/oFcyZay5vLjaCo7GrA0lx9vseO1lJ2z8cCoCFqC\nK0kIwdGqKvxre5GPW2kAGQQQo2awUtk+gohvE42kqooe+XnoWZCPL74sxLixY/HmG69j1qxZePfd\ndwMC2J49e4KyLnHp/xr46l+UavNDZmRkYP/+/aYGayfqT5w4Ee+uX69N8qEzMCIxFiaAl6yAKA4N\nxBwODmThkZFY/eAiFO3djxlLH0Gj6oHsUCAz8HLKlroAZE4ZikOGokhwOCQoCisCiEkSnFYAIwKQ\nSYBCAAfRlqzIxAA5g+2xiKfZTdXT1wzGZ73O+h+jDYkuEjC8Rw/8848PIy4yEhKINsS2ajchsAZo\nUL2gegFV8aeHl+Cf//wURcVFIBxsfdn58OHDsXfvXlRWVvpl79b2IbYLfxP7MhBryUx8JSEZjqTU\ngEVJSG6x47WUnX0NjLUma8QRlAPVpMsvw5tffGmAFxNGBL2Mg5vgUjIGFljENwv6h48chsfjwbRp\n2uS127dvR15eHgD7iGJubi727dsXMgMLRdC3e8t27twZu3btCsjAmF7Wp3dvrFn7OigMBsbBS1ZA\nZIcBXvpScjggORQQh4LEhHhsWLkc144YipjoSEhOWQMxVqzg5TBYGC+KBAcDLs7ECJw6A+NLDmQa\naDEwUySDjRnABZtidl9N4MUutc0lZxKF8fKDEMGmiIuIxPzrroVDlnTw0gHOAmJQvSYGBlVFTHQ0\nrpsyGe+tX+/DwkQGFh4ejry8PL8MPtS2YcfAWrovZGsUEj7OorGesy/K2ZcRpaQY07cvvvt5NypP\n1HKG5ZNGYeNS+hP7zdvMya3tMjMBUKxa9TRunDoV77//PoYPH+5X2+rUqRP2798fkP6fDojZCaGU\nUnTq1Am7d+8OyMDYurvumoO/rFwJL6WgRAIki/vIi8NwHx0aE2OfwyMjMHX0cMhhDsgORQMxp+yz\n1JiXJACZzAHMADEByGzAy2lhYQ7mSkpEZ18GC7MWw5Vk4EVMzEsrggvG/jAWr1oAgT+MImhRDliq\nKrAvVldVjX2pRluaMH4c3nvvfQ1MGXjZAFleXp7tqCZi+7FrD1bm3Qpg9nYWx8RnLqJeB4xxv0BB\nqDZGPgEQFRaGkb174e3Cr3D7uDEcjIje4AihgETN61ldVcF5vKWbER8fX6K84RFKseyhP+DAkVJ0\nzeuOK0eM4HqCHTBlZmbi5MmTqK+vR1hYWEg6hv21oD6fxZKTk4NNmzb5NFg7MBsyZAgiIyKx4aNP\nMP7qsQChIFQBlVQNxMRXCZtWDpqbQwlAJaIVD9HneVRB9IeWeAmqauoRHxUF6iWQvBSqzJaq/rAT\nUEnVZjXyUlCiFwpt4lnBs1d1EFGJMRKvSvT/H5qOpxMlvZ2YAz/ssjKQEoME7LPPtdZamLakRLv1\nRBuvTDtXCCAGfUxMwtkXlVSTFkY4+9JAjFAVAy/tj9KyMhw8eBBtM9vqPRp8hfq8vDysXbs2IHu3\ntglWZyO6MleRARb73tkAsGD7hGLNzc2YP38+qqqqEB0djeXLlyMhIYFv37VrF5YuXQpCtLHRioqK\n8NRTT2HQoEEYMmQIsrOzAQCXXHIJ5s6dG/BYZ29atSAbRReSUuD6wYOx/qtv9ZsIk/vI3pyMWZly\nwzgjC54Tpk2dThETHYWC7t0xcsSV/JT8NTBZltGxY0ccOHAgJPE+EAuzC5Oz0qFDB5SUlMDtdptA\nzNadBDBnzhz8ZeUTOnjrbqTAvAwNTC8Oh+5GOnRG5oDk1IrsVCA5NZHfSyhGzF2M2SufxpHqal0P\nU3Qmpmg6GNPCHDIcYlGM4lQ0RuaUJTgUfSlLUGQLY+OfdUYmSfroD1qRJd/Cc8/0LlCSfglENmRy\nM4N49bbtjbE1y1jxrMHKsoxxV4/FP/7xoX6v7dtPQUEBlwbsdFO7NhKIgVlZWEuZmZ36KTbeg52t\nXbsWubm5WLNmDSZMmOAzw3bXrl3xyiuvYPXq1Zg+fTquuuoqDBo0CIcOHUL37t2xevVqrF69Oih4\nAec6D8zkOgo6BSgGdOmCtYvmC/Rf0L14Xez4rQrhXXPfSJOYT6mQemHXZ5KahODTcSND1TVM18Py\nlmUlIiICKSkpPB/M2nBNdZVi8uRJ2LtvH7Zs26Z75CwvTNZdSgHMFAeI4gQUB4jDCeIIM5ZOJ4gz\nDJLTCcnpRHh0NApfXoWszHRcOXcR5jz5DHaVlkIKc2juZpgTSrgDcrgTSoQTSoTDKOEOOMIVKOEK\nlHC2TuHrHGEynOEKHGFa3REmw+E0F6dT0pY6KDodrC75FkUrhksr82iprEiQHVp6iKxIkBxGuojY\nL5T1Df1m5y58v3+/ZdQOAm2UD3Z/OUoChGDcuKvxIevO4+fWx8fHIyEhAYcOHTrdR8fUZqztxRoQ\n+sXWgmkUW7duxZAhQwAAQ4YMwddff227X2NjI5544gncf//9AIAdO3agvLwcN998M26//XaUlJQE\nPdZZcSEpLMFu7kpSwZUkJlCTJIJofWoxUGKaF5Kq+pcIBYj2JqP6Z0JULZ2AqNpoDKq2DyXC1Gum\nJRUmDFG1RqmjqvkNbpScnByUlJSEDFh2roE/d0Esubm52LFjB7p06eLXjVRVFSohUBQFv7trDn6/\n5A/4YP16SEQDMCLJ/Opr14nwPp8gEqjkBZG8mmbm9Wquo+w1tB6vF/GJ8bj/9lvx2xsm4/m31mP6\ngytw/ZVDcf/M6cbM3vqSsgieqC2peh9OfZ3KmbZlCZv1egMyXnzmh4bY1Lkmxl1lg33xDul6gq4x\nfLU+g7g+esfKd97DhCGXo19+V9OwRNoPmIGLHaggPx979+7z9xhwy87OxpEjR5Cenh50X3/mD8Ra\nyhqPlqNBcQTex+P2WffWW2/hb3/7m2ldcnIyoqO18fWjoqJQX28/lv5bb72FMWPGIC4uDgCQmpqK\n22+/HVdddRW2bt2K+fPn46233gp4TmdXA4MOVsTQvThogdoCGeEMjICoVAcqcP0KKgUlqv5QGuBF\nJKIxLbZNBy1tMlx9SZkWJjAwqCYx2A6ksrOzsXXr1pAY2OlEl6zuYY8ePbB9+3ZMnjzZB7xMIEa1\nuQJmzZqFtWvXYfmfHsHiBfMEhqDrgl7t0aYc3HQdR18SWQMsqPo10oVr6PWUlCQsvn0mFt12E06e\nOgU53MmFbUmP2KleITfPK4AY1bqeGK6YWCCsMwAMJgAzCWNGexI+g30WNTHCdDJi1CXD3SQ6aEky\n4ROIvPjhJ9h/9BimDB9iGn5bSxYmNuCl3ef6+lOIjY01kNOPsTHyAz4rAhixF56dbmptSy1lzuQU\nhIWFB96nuQkoNbOiKVOmYMqUKaZ1s2fPxqlTpwAAp06dQkxMjO3vvf/++3jiiSf45/z8fD76c58+\nfVARwvA9ZxXAANboNFZFCYRJPqxApq/WQY0IjZpQ6MClIxxzB7lwr4mwRGBiZuZlAS6VGuugz+TN\n26c9gB06dMgvWIXiOvLrEQDIevTogdWrV/McHzv9S5wcwuFw4q233sQVVwxDdnYWpt5wvXa99AeP\nspFnVQkgHlBVBlG9gKSCqBpYEdnLo2yERdu8hp5IVS/gUBEf5uCfRdZ1w+IH0dTcjME9CzCoRz56\n5nSAQhwmNnbkeCU+2/49So6VY3S/PrikU472f1CChqYmeFQvnLIDit54rWAGGzATjQgVDdAMRsaB\nzAJgRNLA67WNm/DU+g/w/iMPIjIinDM0g4FJHMy03zVArLauDnGxsUHvuSjEn047CYW1t5SF0tcx\n1L6QvXv3xubNm1FQUIDNmzejb9++PvvU19fD7XajTZs2fN2TTz6J+Ph4zJo1C7t27QqJsZ41ADPc\nRnEltGiQzrr4OgZgnIVRM5AxF1JnYEQHMqoyJsZYmSSAFtGYhcQiktQEZnMXLMKi+fORmp4O6DEr\n+BnkMCsrC6WlpSbwOBMQs75lrSU1NRWSJOHQoUPo1KmTrf4lzjWoUoo2ael4+523MWbMWLRr2xaD\nBw3SrjuV+MNG2eit1Auosh7g8PJgBwMzFmUz1al1nSykIKh48Q8L8MW2Ymze8j3ufuIZHC6vQP+8\nLnhq3mwkREeBgGDd5s1IS0zAiH6X4Lvdu5GRnIi2KcmgKsWGwi34dufPONnQiCmDB2J4z55Yt3kz\niksOYHjPnhjZqxdKq6pQWlmFcIcDOenpiAwLMwOZxa8kOpIRnZ7x+QCYrqW7je8WfoMVa9/E+uVL\nkJ2RxoGNdQkgbOQIqwup/3hd3UnE6u5PoEjBmQCYXZsR2w1rFy1mbAiQYPuEYFOnTsXChQsxbdo0\nOJ1OPProowCAv/71r8jKysKwYcNQUlKCzMxM0/d+/etfY/78+di8eTMURcGyZcuCHuusMzDArIFp\nE9saPgFnXwzUKPDdz7tR29iAq/r21sELurbFgMuoG26kPmekykCMmKdi45PiavXde/ah8KuvMWny\nJJ52QLh2Yi4RERFISkrCsWPHkJKS4gNc1jor/twCOyBjDbxXr17Ytm0bOnbs6NeNtAJpXl53vPTS\nS7hx2nRs/PRTdOmSqz/EkgbY/GCMoVJAlTVw18GMiJFbXmesTAM5FhghXPNSkeAMw/gRV2D88CGg\nKsXxymp8XbQDyanJGguiwClXM7rn5uDSbl3wwbf/RlldLbLbZ2LvkVL8XHYUMdHRiImOwoAe+Xi9\nsBCQZdw5aSJe/uhjZGemo3DHj6g+WY/jNTUY3a8vruzVi7cfrRFZI8Ew+ZbMjTQJ9DJBdmYa3lr2\ne+R2yNLGTlNkEIei14XuWGysNX0OSRCtxdTWnkBMTIzgBtsXSZJ8Ioahgo+dNBFqwOh0jEf6g+wT\nioWHh+Pxxx/3WT9z5kxeLygowJNPPmnaHhsbi2effTakYzA7B12JBAFDdAP0QnkxIo8ulxtLX3td\nn1MPWhY1NS6yuC81foCHvcUkJD4lujBrESjFoMv644vCQu6CWpuDHQuzcyPF/f1eAUG7YEt/WlhB\nQQG2bdsWELzsypXDh+OB3/8e10yciMOHD2sjO0DnlkQClWS9KEKUUotQQnECShjg0IoWnQwDcYQD\nznAQZzhIWASIM0JbhkXqywhI4Voh4ZGQIiLQJjMDE0ePgDM6Bhv+vR3zn3oBz7zzAdIz0iFHRuKU\n243UtFTIkRGobmqCBxQP/e8sFOR2wo4jR/D1rp/RJ78bOnXMwklXM0oqK+FSVfxm8gTcOWUSSioq\nUO1qhBIZZpQIp1EinVAiwqCE6+vDjW1yuFDCnLi0Vz4KunSGFObUitMJyeHUI7ROPXKrRW9ZVJdK\nMs9dO15RgYSEBD0y7D9vjxDC54e0RqHt2k8g0BKLJLXc40vV4EPp/NclsopmEmBNhZoYGKXAZd26\nITIsDBu+24pxl/XnmghUnWkxcCJUdx91Nibp6yUVhDMwyl0g7k5SFZf164t7H/wjE+IAHsmybzjt\n2rVDWVmZX8bFPge9DkFArHfv3nj++efR3NwMRVFCYmBMaL7llltQXVONfpcOwIL583HHb34Np8MB\nAtUQpLlArl9HUH1SYPYiEPOdtGslAr/B0rTvUZVFis0pK1RVMX7Ulbhy8EB0ys7Ca59sRENjI64Y\n0A/pGRn46UgZMjIzEBkdheqmJshhYVDCnQiPCEdCUiKUqHDUNjQgNSUJ+8qPITYxDlWNp+ABRUR0\nJJRI0Y00HqzvftyJ7bv3orquDidO1qO67iTKq2vwPxPHYeIVA/UAD9O2jAlQ+KgdsmIsHXoKCgN6\nSQGVtFm8PR4PXnrpZTz88MP6MEBm4BKB7OjRoxg6dKgP8w7FAjH8lmRhLZnIei7tnAGYAGEAcyJN\nrqMh5hNCMHfiBKx48y2M6d8HMlEM7YuCu5CmjHxGgSVxvZ6NzwR71Sh9evZA8Y4f4WpuhhIWBgCa\n+A37BtK2bVuU6bNj24FWMBATRVk7EGMNPjExEe3bt8eWLVswePDgkAGMEi3SNvd3c3H12LFYsGAh\nnn/hBTyyYjnGjr7KOBFCTXeBsAeJCZFUjNZSDlYsf44I67TrzlxzHfyE7RKliIuKxh23zsBPe/aj\nvKICV/Tvi2aXCz9++2/MuGYskpOT8PTf38PJU/VYNvdOZLbNwFNvvI34mBj06JqLS/v2xPtffY2t\ne/fh+10/IzcnGwnJiXAoMrjQL7SsQ1WVOFhxHAkxMcjtmIWE2BikJsTjkq65kMPDdPlKBDBhOjph\nJFsIicAaA9NZGJFBiYTVr76K5ORkjBw5El6Ph0dcrSzM5XJhz5496Ny5c0jiO5MZzjTafaZ2qqwc\n4VJgODilttzoFy1l5w7AKHtPUl2cZ+vZBmIS84f37InH3nkXH363FeMv66+zM5GFqaCUCA+RUFf1\nt6yuh/FuRZLxUEZHRSCnQzaKfihGnz59tYYD+7ccIVqXoh07dgQU8EMBMWvdzo0cOHAgNm3ahIED\nB9qCF5teSzwHzZ3Q/t/c3C5499138fHHH2P+wkVY9fTT+J9Zt2HUyFGIiozUpDAtN8V0gwh7q3Ag\n0gusoGYAF6ubGJz4XUohR4Tjkt69+PZwSnHL1OsASrHwzl+D9aoApUhITsQ9cbGoPlGLDu3SERsb\nh2tGDsPuAwfhUlVce/VIozHxBmMsbpo0Djf5C1USFkWEBlrEAC/z3AJseCIdwPQRPhh4NTQ24sGH\nlmLNmjUA9IEYbVxIVVVRUlKC5ORkREVFobm5OSiAWduPndsoLlvKHGmpcDoDp1E4XE3AvrIWO2ZL\n2LkBMJF8mdZRHXigPyDghRCCuydNxOp/bcS4Af00psaYlwpDuDe5kAy8qCHcqwYL4wK0/tD179Mb\n3323BX379NHBC3pU0x7AmAsJhEbnrUI+4N91FN/el19+OfIjmIMAABfKSURBVObMmQO3223rRjJN\nxd404Z4QgqtGj8bwK6/Eq6++iqefeQ6z/ud2DBs2DJMmTcKYMWOQlJRk3BpOy3RQEsPDInjB0B6J\n3jULJsBSje9S8TcYazOzNPa7VADBzl06mY41esRQjIZZ7zSOyxqTpW3Zghgxlgy8WITRMjQ3JE3A\nZ8yLyA4tmkuBVU8/i379+qFfv34+Xb+s3cB++ukndO3a1Qe47IBMbC/BXMcW18CoNpBksH0uNDtn\niazCGrB0ClDC25TIvkQWNqRHAU+jMJJcYbiN7IFTDfCiqp4jpuq6mK7JEFM+GEX/vpfgi6+/xR1U\na8PW+yM2FjsNzM7ERhfsTeuPhbVp0wZpaWnYvn07BgwYENR9FM9HFI4J0fpy3nrrrfjVr36Fqqoq\nbNiwAW+++SZmz56NlJQU9OnTB3379EHfvn2Rn5+PhIR4C7MxAINpZprrJrxw2H0GoMWFxDcR+z7R\nXXnJYGpsmx0wccrOPsP/ev7P8z/+bo6+BPjcmjqYMVeSRxr1oAebBZhSQAVFZVU1/vyXx/HJJ5/w\nUVHZAIPWkSK8Xi9+/PFHdO7c2a/AbwdibMlAihW7kV9bylQE7yl0ZokgZ9fOSSIrM9bIeS4Y82Is\n7EtkYU5Z5tupzsI0MdpIp+AsTI86EkJ0PUzlAxoSiXXoZt2IKPpf0guPrnyKH5DlTdm9/ZKTk9HY\n2IjGxkY+vvnp6hJ2DdcfCxs0aBA2btyI/v37B3QfuQZGjZA9W4rbCCGIj4/HtGnTMG3aNKiqit27\nd2Pbtq3Ytm073vr73/HTTz8hNjYWXbt0QW5uZ3Tu3Bk5HTsgp2NHZGW1h9PhhAgcPCjD3lCmB0AI\nuFijN4Tdc5FJsS9bWBWlAnm3YVxWAPB38UWayfLDTPldjIlJet4c4WDGzsDjUXH3vAWYMGECcnJy\nbAFLBDOPx4Pi4mJNJ9OTk0UQY/ffdJpCu7POJWkHYi1lXr0E2+dCsxYHMEp90nKgUy5QvSGbwEtw\nI/0XKmhf+k1XAUIYu6ImF5Lq0UjCQEzjx+Y8J6oir2tnHCk7ivr6ekTFxhnt2wbEJElCmzZtUFFR\ngfT09KCuo+91CS2zmoHY0KFDMXv2bMyePRuxsbEcvFhOkb0GBhOIBdPrcnNzkZubi6k3TgUIoKoq\nDh86jN27d2PPnj3YtetnbNjwEUpKSnCktBTpaWnIyspCdlZ7tM9qj+z2Wchq3w7t2rZFenoaFEV7\noAjlN1dEOc21ZJ+pZQl2+SnHJyPYYN3Hcr0DrSM2axmYaZqB+TNfpwVGKCRQQlBXexJ33X0PjpSW\n4o116zSwUtWALKywsBAOhwM5OTlwu92295rdM3ZP7O6bCF5WEGspo5SG4EIGoWjnwc5JZ26+gv3/\net3qRtqxMe0lTbhGxvpIgjAg08GLuZB6oqsGZpIBXJIYRdMaj6LIyOuSi+IfduDygQP1c/OfIpGa\nmorjx48jIyPDFrz8AVooOpj17ZyamoqCggJ8+OGHuP7660PSwAKBl12AwVTX/7Rt2xZt27bF8OHD\nhRsGeNxuHDp8GAcPHsTBgwdx6NBhfPTJP3Hw0EGUlpaioqISbVJTte9nZiIjIwOZmRnIzEhHRno6\nMtIzkNYmBQ6HQzig2E6o3kzYeVrYu9BEiMCmrEBFxJZHYANuFhBjB4PBw4064PWqWP3Kq1jy4EMY\nOWIE3lj3OsIjIuBVvfB6fQcYFMuaNWtw3XXX2d7fYC6kdRIQKwNjs363lNWVHtOi/YH2of9FUUj+\nYrXqX7zq60Zq3xHZGOsHqa/2AjKhHNR4aoWqarRfH2kCOphBUi0RMoGF6Z979chHUfEPHMACiadp\naWk4fvy4331Cui7UnJEfCMwmT56MP//5z5g0aRLvjhLIhRTdR7vzZ+avbmdsK5FlZGVnIzu7A98g\nwp/H7UbZsaM4cvgIysrKUFZWhv0lB/BF4VcoKyvDsWPHUFFRgbi4OKSnpyGtTRrS0tLQpk0q2rRJ\nQ1qbVKSmpqJNahukpqYgPj5eOzeigZIJuESwIrzmC2I+oGaGRF8+QXm7A4AvvyzEvHnzEBYejjde\nX4eevXrp4KSawMuOhW3fvh3V1dW4/PLLTe6jKPDbMZpQNTBFUaAoLff4hqWnIdwRFngfdzNwsOUm\n020JO0sA5sPBOGjxHt0CeFEITIwzLeggBUACXG4PJj34MF5ZeA9SE+MNFqbqPqsu2GtivaaBEWG8\nMLGrDI+gUYpeBd2xvfgHAOKD4gtQANCmTRsOYNZtoQCYCF5s/0BAlpeXh6ioKBQWFuKKK67wK+QD\nMDEv8fdDYV/BzI5p+tQlCRkZmcjMbOt3P6/Xi6qqKhw9ehTl5eU4fvw4ysvLsXvPHnz+xReoqKjA\n8ePHUVlZiaamJqSkpCAlJQXJyck+S7EkJSUhISGBa5OB/ke2ys4bqqurw1dffYXPP/8cn3/+OUpL\nS/Hggw/i2muvBQABvMw6l3WqM6/Xi1dffdX0PTsGpp2HcSL+wMuqezH21epCntNEVsOoyaVkojx0\nFqbVifAmBAWcsoz+XXLx2N/fxfJZMyHmhGlMDLrrSI2lINiLdTFJs1dBPl5es047CPFtRMwYA9u1\na5fPdn/fsf/fQ2dgsixj8uTJeOONNzBkyBCTkO/vd0Ug8/d/BL8/9g3V33cDAZz1c3x8POLj4/lE\nKuJ2cdnY2IiqqipUVlbyJavv3r0bVVVVvFRXV6Ourg4xMTFITExEXFwcYmNjERcXh7i4OMTExMDp\ndMLhcMDhcMDpdKKpqQm1tbU4ceIEampqcOzYMezZswe9evXCoEGDcN999+HSSy+F0+k0AZBV52KT\nzDIA83g82LVrF/bt24fFixf7BS8riNkxeiv7YktFUeBwOFqUgYUyXuEFmIh/jjpzixWiR/uYj6mD\nF2dhsDAw9j0KzLlmPIbMX4T/veZqtGuTormYOohR6yCIVGdgHLh8o5CgFAXduuKnXbuheryQZXAG\nxkxsUBkZGaY5JK3bxe8EvB4CiLHP/oBs8ODBePHFF1FcXIxevXrZdyPSj2nVv8RzOV3gOp23rT3T\n8b8ukDvLPsuyrLmU+nAr/lgxq3u9XtTV1XEwq6urw8mTJ1FbW8uHbnG73XC5XGhoaEB4eDiysrLQ\no0cPxMfHIyUlBfn5+QjXB9VkxePxcOCxizaKM2V7PB64XC6sWrUK1157LQgh/PtWN9JOA7OClx2I\nMfbV0hoYmxU92D4Xmp31PDCGUaZ1MEtjlLMwKwPjOwAqkBAdjanDhmLVe//AsttmgkBPn6DQwEqi\nfMlHXlX1ZFYqmVxHxsKioyORmpKMkgMH0KlLt4CuV2pqKioqKk6L3di9Za3r/YEXpdrY6zNmzMAL\nL7yAlStXmgBM/C078V48JxEwbe+VjUtjt//psjO7bXbXLNB1DwZerB4dHY3o6Gi/4B3Ki4VFCwH4\n6FYMiETX0cq+3nzzTTQ3N2PMmDGmeRztwCvQNbFzIUXwamkNTEXwNIn/qjwwyvKq+GfNeC6YEJrU\nGJnxDUqFSCRjazqI/Xr0aFyxcDFmTxyPzJQknYXByANjKRSS1r2IUklgZmbwYstuXXKx8+fd6NSl\nm3aOfh4QBmB2287ENbNzJe06BI8YMQKvv/46/v3vf+PSSy/1C5T+mFmw8wm2tDt3f+uCXYdg4MWW\noYBYqMBmt/Rn4v9uFd39uZCsfP7551i7di1WrFgBACags8vW93f9/LmQVvBqdSHPugupgRQVWJiP\nvK+voJTBmTDgIcc4A9CSY2Jwz+SJqDpRh4ykJJ2FaT9gHkefdTBm7qQKIwvcXLp16YydO3fhmgkT\n+InaPSzJycmoq6vjkUBxO7NQgIz/6zbgxaKNYlEUBbfccguee+4529EtrecpdjGxno/1obFqMf7q\ngb4fioXKwkKt+wOwUAHNei524G19qfhLWnW73di6dSseeeQRPPDAA0hNTYXb7TbNoO3PfTxdF/Js\nMbATpUehBoGDOvyXpFHYARbVKwykwJIdGfhoUMRdSUMHA0wsjAK3jhypddamLPWCJbhCY2PCiBRs\nuBcDsIS+eDrYdcvNxZff/pvvw5o4X+r/jKIoSEhIQHV1NeLi4mxpv/jZ38MdDBDsypAhQ7Bu3Tr8\n61//wsiRI6GqquncxMbPfjMQePlzYa3rrPsGO/9QLRQAs1sXCoCFCmz+THQhrQzMLtv+xx9/xJIl\nS7Bw4ULk5OT46F6BopD+ro0deFkjkjt37jzt6+7PojLSEK0ETqPwepqBssoWO2ZL2NnvC2mzzpyk\nreteurhPGdKZdDCYWJiR5Mr2o0Y/SGroYGIEkjcaKgCa/kPdcjvj+b+9KpycXogBxKzRp6SkoLKy\nEvHx8Xz96bIuO2AJpIUxFnb33XfjvvvuQ3p6OvLz803nZffw2h3bugxUxH3s6uLS37H8WTBX0t+6\nUAAtFKCznoO/6+RPA2Nl3759uO+++zBnzhzk5+ebXEx/uV/B3Efr/2ZlYJIkoaKiAkuXLkVSUlLA\n6xyqqaBQbTLjrPtcaHbOopCMhYltRmRlTKsnYDtDBx+2s5mFMeCCPjIFdxt5+gT46K3MleQjtVoY\nWZdOHbB7715ogx1qhyP8r7lRJSUlobq6mn8WzR+AiA8E2xYKiFhdyi5dumD+/PlYvHgxVq1ahfbt\n23PGFSqAhQpeYmH5W8eOHUNNTY0pyldfXw+XywWXy8WjfOy8xfNxOp1wOp0ICwuD0+lEREQEIiMj\nERUVhaioKERHRyM2NpanWYSHh58WC7Mr1nOwuz7BrpU/Bub1elFaWooFCxZg5syZ6N+/v48+ZjcU\nkt1LwI7JWxmYlY2tXbsWI0eOxLZt22zP/3TtP1YDo5RiyZIl+Pnnn+F0OrF06VK0a9cu6A8zsOLg\nBZhcSdMOvBOJto5SJogR0yxGVhamkShimsGIckZGtYEEbBiYyMIopUhM0NhUTXU1Etukg6Emga8b\nmZCQgNra2qBv8WDXVPxeKCDCHoABAwZg5syZmDdvHp5++mkkJSXZPqjsd63HFR8ga8DA5XLhwIED\n2LNnD/bu3YsDBw7wDPqYmBikpqYiISGBg010dDQSExNN+VWKovDjiw8+A7jm5ma43W40NjaioqIC\nBw8eRENDA06dOoW6ujrU1taitrYWsiwjISEBSUlJSEpK4gmrbKSO9PR0xMbG2v7v4rpgIBbo/tkF\nVJjb+NFHH+GZZ57B9OnTMWzYMJ9EVrvoI6vbtQfR7P4fkYGpqooPPvgAjz/+eIsBmJdSeIOw5mDb\nz4cFBbBPP/0ULpcL69atQ1FREZYtW+YzVbho/txGYrfNQsm4nsXzxCCAGXxYGKFAfUMDJEVCdGSE\nwbokEkADM3QwFpUkADpmZ2F/SQkSU9PMJ29p7PHx8Thx4gT/zJah6ioMvEJ151jaBGNikiTh6quv\nRkVFBRYsWICVK1ciKirK70MZ6Dg1NTUoKipCUVERiouLcfDgQaSlpaFjx47o2LEjevXqhbS0NCQn\nJ8PpdAY8V7tjWet2ZseGKKVobm5GTU0NampqUF1dzZNNf/jhB5SXl6O8XOvSkp6ejszMTLRr146X\nrKwsRERE+Az+JwKC9f75u2dWEKuursb//d//4ciRI3j44YeRnZ3tA17+GFgg15EtA4EXq3/55Zfo\n2LEjsrOzA17b0zHmoATb50KzoAC2detWDB48GADQs2dP7NixI+QfF/FJBDETkhmUzNyHjcI8BRvf\nH5yJUUpw/99exSWdOuLW0SN1UGLbzONXMbbFB06EwdZAgQ5ZWdi3fz/6XjrA9n9hjYwBmF3jPxM2\npl0C/0zJH6DNnDkTlZWVuPfee/GrX/0K3bt35+zHDkAopaisrMT27duxfft2FBcXo6KiAt27d0dB\nQQF++9vfIicnBw6Hw68bGei8rMey+7+CmXhNFUVBaqrWP9K6jdUbGhpQXl6OsrIylJaW4ssvv8SR\nI1pfzKSkJHTo0IGXjh07on379nA4HD59RcXf93cPXC4XCgsLsXLlSgwbNgzz5s2DLMsB3UV/0Ufr\n/2t3HewYJQOwd955B9ddd12LDmioInie10WZB1ZfX2+aWZeNENqSF++X2PgB/fGXd9bj1tEjf9Hv\ndMjKwoGDh4Lul5CQgCNHjvyiY7WEEUJwzz334LXXXsOjjz6K6upqDBw4EIMHD0ZGRgYqKipQWVmJ\n8vJyHDt2DMXFxThx4gR69OiBnj17YuzYsejUqRMHPNHNuRiMEMJd2dzcXNMDT6k2kQYbOWPTpk34\n61//iuPHjyMzMxM5OTlo164d0tPTkZ6ejoyMDKSkpECWZZOrd+jQIWzZsgVbtmxBUVER2rZti3vv\nvRf5+fkcuM61HT9+HEVFRVi1ahXq6+tb7HerDh9FEwJn9p+CF3C22CFbxIICWHR0NJ8mHIAPeLGb\nWHXKuJjie8XEUIQlIYLGRIj2ma3T5+6DJLwlxTn92Dh0soTctm2hAthdWobYmGhIEtHm9ZMIiCJB\nUmRIigJJkQBFgeRwgChOEKe+VJwgjjC0bZuJ2pMncaS0FF6vCo+qwuvxwiPk+rhcLkRHR8PpdKKi\nogLNzc1wuVxc1xG7lLCMbvEtbL0mhBDeAVmMMFk770qSBEVRTNvYG3ncuHG45pprcOzYMWzZsgVr\n165FTU0NkpOTkZiYiMTERGRnZ2PEiBEm0Z+5kHZCdShCv3U7+2y3DNWsbMiO3VpZkz+NKyoqirNL\nts7lcuHo0aM4fPgwjh07hm+++YZ3Hq+urja56wCQnJyMgoICDBw4ELNmzUJ0dDQopaiqqjLpXFa3\nUUyjEJlrsHYgFqYrMm2RBUC2b9+O0aNHo66uDlVVVQDQIkCaPXYI4iKiAu5T23gK+HT9Lz5WSxqh\nQVrZJ598gs8++wzLli3D999/j6eeegrPPfcc375lyxZMnz79rJ9oq7Vaq9nbmjVr/CY4B7MTJ05g\n1KhRqK2tDWn/uLg4fPLJJzyN6HxbUAATo5AAsGzZMnTo0IFvb2pqwo4dOzgFb7VWa7VzY16vFxUV\nFbwT+pnaiRMnQnZHo6OjLxjwAkIAsFZrtVZrtQvVLgwlvtVardVa7QzsjAGMUooHHngAN954I26+\n+WYcPny4Jc/rrFpRURFuuumm830aIZnH48GCBQswffp0XH/99di4ceP5PqWApqoq7r33XkydOhXT\np0/H3r17z/cphWxVVVW44oorUFJScr5PJSSbPHkybr75Ztx888249957z/fpnBc7465Ep5vgeqHY\nCy+8gPXr1yMqKnDE5UKx9957DwkJCfjTn/6E2tpaTJw4UZ9w48K0jRs3ghCCtWvX4rvvvsNjjz12\nUbQLj8eDBx544BdpSefSXC4XAGD16tXn+UzOr50xA/slCa7n07KysrBq1arzfRoh25gxY3DXXXcB\nAO/UfSHbiBEj8NBDDwEASktLERcXd57PKDRbsWIFpk6dyhNnL3TbtWsXGhoacNttt2HmzJkoKio6\n36d0XuyMAcxfguuFbiNHjryooqWs03N9fT3uuusuzJ0793yfUlCTJAmLFi3C0qVLMX78+PN9OkHt\n7bffRlJSEgYOHHjauWvny8LDw3HbbbfhxRdfxJIlSzBv3ryL4vlraTvj13mwBNdWazk7evQo7rzz\nTsyYMQNjx44936cTki1fvhxVVVW47rrr8OGHH17Qrtnbb78NQggKCwuxa9cuLFy4kHeUv1AtOzsb\nWVlZvB4fH4+Kigo+h8B/i50x4vTu3RubN28GAHz//ffIzc1tsZM6F3axvGkrKytx2223Yf78+Zg0\nadL5Pp2gtn79ep7oHBYWxvvvXcj26quv4pVXXsErr7yCrl27YsWKFRc0eAHA3//+dyxfvhwAUF5e\njlOnTiElJeU8n9W5tzNmYCNHjkRhYSFuvPFGAFqC68VkZ9rx+lzbs88+i7q6Ojz11FNYtWoVCCF4\n4YUX4HReYJ3SdBs1ahQWL16MGTNmwOPx4L777rtgz9XOLpZ2MWXKFCxevBjTpk2DJEn44x//eMG/\nKM6GtSaytlqrtdpFa/99kN1qrdZq/zHWCmCt1mqtdtFaK4C1Wqu12kVrrQDWaq3WahettQJYq7Va\nq1201gpgrdZqrXbRWiuAtVqrtdpFa60A1mqt1moXrf0/XDiHq3iohfYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10eeec5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "contours = plt.contour(X, Y, Z, 3, colors='black')\n",
+    "plt.clabel(contours, inline=True, fontsize=8)\n",
+    "\n",
+    "plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower',\n",
+    "           cmap='RdGy', alpha=0.5)\n",
+    "plt.colorbar();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The combination of these three functions—``plt.contour``, ``plt.contourf``, and ``plt.imshow``—gives nearly limitless possibilities for displaying this sort of three-dimensional data within a two-dimensional plot.\n",
+    "For more information on the options available in these functions, refer to their docstrings.\n",
+    "If you are interested in three-dimensional visualizations of this type of data, see [Three-dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Visualizing Errors](04.03-Errorbars.ipynb) | [Contents](Index.ipynb) | [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.04-Density-and-Contour-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/04.05-Histograms-and-Binnings.ipynb b/notebooks_v1/04.05-Histograms-and-Binnings.ipynb
new file mode 100644
index 000000000..1873ff7c5
--- /dev/null
+++ b/notebooks_v1/04.05-Histograms-and-Binnings.ipynb
@@ -0,0 +1,396 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) | [Contents](Index.ipynb) | [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.05-Histograms-and-Binnings.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Histograms, Binnings, and Density"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A simple histogram can be a great first step in understanding a dataset.\n",
+    "Earlier, we saw a preview of Matplotlib's histogram function (see [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb)), which creates a basic histogram in one line, once the normal boiler-plate imports are done:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('seaborn-white')\n",
+    "\n",
+    "data = np.random.randn(1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KycnRyJEje9wurkEHADiHI0UBwBCOBf27776Tz+ez9e2HA3Hs2DHdc8898vv9uv322/Xr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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1023e5a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``hist()`` function has many options to tune both the calculation and the display; \n",
+    "here's an example of a more customized histogram:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bfc0588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data, bins=30, normed=True, alpha=0.5,\n",
+    "         histtype='stepfilled', color='steelblue',\n",
+    "         edgecolor='none');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``plt.hist`` docstring has more information on other customization options available.\n",
+    "I find this combination of ``histtype='stepfilled'`` along with some transparency ``alpha`` to be very useful when comparing histograms of several distributions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TIr1goKc5r9eL/9d+DooMeLxR5OdN3g7M330aZa4rMKmcFXXLMkaqV0EUJ37N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\nOhGRTjDQiYh04v8DF2mFWN13xe4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e666358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x1 = np.random.normal(0, 0.8, 1000)\n",
+    "x2 = np.random.normal(-2, 1, 1000)\n",
+    "x3 = np.random.normal(3, 2, 1000)\n",
+    "\n",
+    "kwargs = dict(histtype='stepfilled', alpha=0.3, normed=True, bins=40)\n",
+    "\n",
+    "plt.hist(x1, **kwargs)\n",
+    "plt.hist(x2, **kwargs)\n",
+    "plt.hist(x3, **kwargs);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you would like to simply compute the histogram (that is, count the number of points in a given bin) and not display it, the ``np.histogram()`` function is available:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 12 190 468 301  29]\n"
+     ]
+    }
+   ],
+   "source": [
+    "counts, bin_edges = np.histogram(data, bins=5)\n",
+    "print(counts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Two-Dimensional Histograms and Binnings\n",
+    "\n",
+    "Just as we create histograms in one dimension by dividing the number-line into bins, we can also create histograms in two-dimensions by dividing points among two-dimensional bins.\n",
+    "We'll take a brief look at several ways to do this here.\n",
+    "We'll start by defining some data—an ``x`` and ``y`` array drawn from a multivariate Gaussian distribution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "mean = [0, 0]\n",
+    "cov = [[1, 1], [1, 2]]\n",
+    "x, y = np.random.multivariate_normal(mean, cov, 10000).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ``plt.hist2d``: Two-dimensional histogram\n",
+    "\n",
+    "One straightforward way to plot a two-dimensional histogram is to use Matplotlib's ``plt.hist2d`` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DjRs3+uqtAGCCJiIzUSQfZ6GqKoqKivDwww/j+uuvP6VvjMViQUtLS8/FfgZc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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1106647f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist2d(x, y, bins=30, cmap='Blues')\n",
+    "cb = plt.colorbar()\n",
+    "cb.set_label('counts in bin')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as with ``plt.hist``, ``plt.hist2d`` has a number of extra options to fine-tune the plot and the binning, which are nicely outlined in the function docstring.\n",
+    "Further, just as ``plt.hist`` has a counterpart in ``np.histogram``, ``plt.hist2d`` has a counterpart in ``np.histogram2d``, which can be used as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "counts, xedges, yedges = np.histogram2d(x, y, bins=30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the generalization of this histogram binning in dimensions higher than two, see the ``np.histogramdd`` function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ``plt.hexbin``: Hexagonal binnings\n",
+    "\n",
+    "The two-dimensional histogram creates a tesselation of squares across the axes.\n",
+    "Another natural shape for such a tesselation is the regular hexagon.\n",
+    "For this purpose, Matplotlib provides the ``plt.hexbin`` routine, which will represents a two-dimensional dataset binned within a grid of hexagons:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IoOWyrphAUUB8CZ3HZaWPaGWTYEk1JTKm7jD88mgdMy0nJIRfHmvgV7MNnDpW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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e666550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hexbin(x, y, gridsize=30, cmap='Blues')\n",
+    "cb = plt.colorbar(label='count in bin')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "``plt.hexbin`` has a number of interesting options, including the ability to specify weights for each point, and to change the output in each bin to any NumPy aggregate (mean of weights, standard deviation of weights, etc.)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kernel density estimation\n",
+    "\n",
+    "Another common method of evaluating densities in multiple dimensions is *kernel density estimation* (KDE).\n",
+    "This will be discussed more fully in [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb), but for now we'll simply mention that KDE can be thought of as a way to \"smear out\" the points in space and add up the result to obtain a smooth function.\n",
+    "One extremely quick and simple KDE implementation exists in the ``scipy.stats`` package.\n",
+    "Here is a quick example of using the KDE on this data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nEZKeR6tsfaiyLBRazz2brV/6AWBXYsecTjEZD/yxxx7DeeedBwBYuHAhhoeH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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e36a198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import gaussian_kde\n",
+    "\n",
+    "# fit an array of size [Ndim, Nsamples]\n",
+    "data = np.vstack([x, y])\n",
+    "kde = gaussian_kde(data)\n",
+    "\n",
+    "# evaluate on a regular grid\n",
+    "xgrid = np.linspace(-3.5, 3.5, 40)\n",
+    "ygrid = np.linspace(-6, 6, 40)\n",
+    "Xgrid, Ygrid = np.meshgrid(xgrid, ygrid)\n",
+    "Z = kde.evaluate(np.vstack([Xgrid.ravel(), Ygrid.ravel()]))\n",
+    "\n",
+    "# Plot the result as an image\n",
+    "plt.imshow(Z.reshape(Xgrid.shape),\n",
+    "           origin='lower', aspect='auto',\n",
+    "           extent=[-3.5, 3.5, -6, 6],\n",
+    "           cmap='Blues')\n",
+    "cb = plt.colorbar()\n",
+    "cb.set_label(\"density\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "KDE has a smoothing length that effectively slides the knob between detail and smoothness (one example of the ubiquitous bias–variance trade-off).\n",
+    "The literature on choosing an appropriate smoothing length is vast: ``gaussian_kde`` uses a rule-of-thumb to attempt to find a nearly optimal smoothing length for the input data.\n",
+    "\n",
+    "Other KDE implementations are available within the SciPy ecosystem, each with its own strengths and weaknesses; see, for example, ``sklearn.neighbors.KernelDensity`` and ``statsmodels.nonparametric.kernel_density.KDEMultivariate``.\n",
+    "For visualizations based on KDE, using Matplotlib tends to be overly verbose.\n",
+    "The Seaborn library, discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), provides a much more terse API for creating KDE-based visualizations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) | [Contents](Index.ipynb) | [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.05-Histograms-and-Binnings.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/04.06-Customizing-Legends.ipynb b/notebooks_v1/04.06-Customizing-Legends.ipynb
new file mode 100644
index 000000000..ada12a045
--- /dev/null
+++ b/notebooks_v1/04.06-Customizing-Legends.ipynb
@@ -0,0 +1,438 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.06-Customizing-Legends.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Customizing Plot Legends"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot legends give meaning to a visualization, assigning meaning to the various plot elements.\n",
+    "We previously saw how to create a simple legend; here we'll take a look at customizing the placement and aesthetics of the legend in Matplotlib.\n",
+    "\n",
+    "The simplest legend can be created with the ``plt.legend()`` command, which automatically creates a legend for any labeled plot elements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('classic')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XAGXK+BKjlw4etBb6o4/aarOsfPHJRRfZk+3I+hfbtvkdUdQxsceigwet7ql9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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c21e9e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0, 10, 1000)\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x, np.sin(x), '-b', label='Sine')\n",
+    "ax.plot(x, np.cos(x), '--r', label='Cosine')\n",
+    "ax.axis('equal')\n",
+    "leg = ax.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But there are many ways we might want to customize such a legend.\n",
+    "For example, we can specify the location and turn off the frame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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330N3yVA6fFi1Xz97QU2b5nQ0VCAzZqiWLas6Zowlymi0bZuVb6l1v3fsaH3X33/vbFj+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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c21e9e8>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.legend(loc='upper left', frameon=False)\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use the ``ncol`` command to specify the number of columns in the legend:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UinD2sf8DwLQwno+IiIJQ9HQHiMgMAGef+CMACmCgqk7JPWYggCxVTcnvXMnJ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\niBzDxE5E5Jj/B+vIEdfZxz8dAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c21e9e8>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.legend(frameon=False, loc='lower center', ncol=2)\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use a rounded box (``fancybox``) or add a shadow, change the transparency (alpha value) of the frame, or change the padding around the text:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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EKmZOmQKcf77bEXmIqucGKL7+GrjrLlt8e8stnO4YfdassXluDzxgb9dRThV4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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c21e9e8>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.legend(fancybox=True, framealpha=1, shadow=True, borderpad=1)\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more information on available legend options, see the ``plt.legend`` docstring."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Choosing Elements for the Legend\n",
+    "\n",
+    "As we have already seen, the legend includes all labeled elements by default.\n",
+    "If this is not what is desired, we can fine-tune which elements and labels appear in the legend by using the objects returned by plot commands.\n",
+    "The ``plt.plot()`` command is able to create multiple lines at once, and returns a list of created line instances.\n",
+    "Passing any of these to ``plt.legend()`` will tell it which to identify, along with the labels we'd like to specify:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QDnhxTyv/rCzA09O6DoCPDXoM6y+tR5NO2fHkX1th9QMsR+rxx1Ww8avVAnv3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\ncePGTS3ErfzduHHjphby/7xsYKsXV7EBAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d779518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y = np.sin(x[:, np.newaxis] + np.pi * np.arange(0, 2, 0.5))\n",
+    "lines = plt.plot(x, y)\n",
+    "\n",
+    "# lines is a list of plt.Line2D instances\n",
+    "plt.legend(lines[:2], ['first', 'second']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I generally find in practice that it is clearer to use the first method, applying labels to the plot elements you'd like to show on the legend:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QDnhxTyv/rCzA09O6DoCPDXoM6y+tR5NO2fHkX1th9QMsR+rxx1Ww8avVAnv3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\ncePGTS3ErfzduHHjphby/7xsYKsXV7EBAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11025d908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, y[:, 0], label='first')\n",
+    "plt.plot(x, y[:, 1], label='second')\n",
+    "plt.plot(x, y[:, 2:])\n",
+    "plt.legend(framealpha=1, frameon=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that by default, the legend ignores all elements without a ``label`` attribute set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Legend for Size of Points\n",
+    "\n",
+    "Sometimes the legend defaults are not sufficient for the given visualization.\n",
+    "For example, perhaps you're be using the size of points to mark certain features of the data, and want to create a legend reflecting this.\n",
+    "Here is an example where we'll use the size of points to indicate populations of California cities.\n",
+    "We'd like a legend that specifies the scale of the sizes of the points, and we'll accomplish this by plotting some labeled data with no entries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zz1vZ8YP58pe/3GIuAm2SGLRJ39TUVACeffbZUTXB2+grDGkbiRgK38BgmLJp0ybeeecd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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117d74cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cities = pd.read_csv('data/california_cities.csv')\n",
+    "\n",
+    "# Extract the data we're interested in\n",
+    "lat, lon = cities['latd'], cities['longd']\n",
+    "population, area = cities['population_total'], cities['area_total_km2']\n",
+    "\n",
+    "# Scatter the points, using size and color but no label\n",
+    "plt.scatter(lon, lat, label=None,\n",
+    "            c=np.log10(population), cmap='viridis',\n",
+    "            s=area, linewidth=0, alpha=0.5)\n",
+    "plt.axis(aspect='equal')\n",
+    "plt.xlabel('longitude')\n",
+    "plt.ylabel('latitude')\n",
+    "plt.colorbar(label='log$_{10}$(population)')\n",
+    "plt.clim(3, 7)\n",
+    "\n",
+    "# Here we create a legend:\n",
+    "# we'll plot empty lists with the desired size and label\n",
+    "for area in [100, 300, 500]:\n",
+    "    plt.scatter([], [], c='k', alpha=0.3, s=area,\n",
+    "                label=str(area) + ' km$^2$')\n",
+    "plt.legend(scatterpoints=1, frameon=False, labelspacing=1, title='City Area')\n",
+    "\n",
+    "plt.title('California Cities: Area and Population');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The legend will always reference some object that is on the plot, so if we'd like to display a particular shape we need to plot it.\n",
+    "In this case, the objects we want (gray circles) are not on the plot, so we fake them by plotting empty lists.\n",
+    "Notice too that the legend only lists plot elements that have a label specified.\n",
+    "\n",
+    "By plotting empty lists, we create labeled plot objects which are picked up by the legend, and now our legend tells us some useful information.\n",
+    "This strategy can be useful for creating more sophisticated visualizations.\n",
+    "\n",
+    "Finally, note that for geographic data like this, it would be clearer if we could show state boundaries or other map-specific elements.\n",
+    "For this, an excellent choice of tool is Matplotlib's Basemap addon toolkit, which we'll explore in [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiple Legends\n",
+    "\n",
+    "Sometimes when designing a plot you'd like to add multiple legends to the same axes.\n",
+    "Unfortunately, Matplotlib does not make this easy: via the standard ``legend`` interface, it is only possible to create a single legend for the entire plot.\n",
+    "If you try to create a second legend using ``plt.legend()`` or ``ax.legend()``, it will simply override the first one.\n",
+    "We can work around this by creating a new legend artist from scratch, and then using the lower-level ``ax.add_artist()`` method to manually add the second artist to the plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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vvEG3b98u9s0wGfz9/WnKlCl0/PhxsrGxKbZt0RkyJo/TarX06aefUocOHYpF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\n2BljzMpwYmeMMSvz/8NbTOcDCpp6AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117f1e3c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "lines = []\n",
+    "styles = ['-', '--', '-.', ':']\n",
+    "x = np.linspace(0, 10, 1000)\n",
+    "\n",
+    "for i in range(4):\n",
+    "    lines += ax.plot(x, np.sin(x - i * np.pi / 2),\n",
+    "                     styles[i], color='black')\n",
+    "ax.axis('equal')\n",
+    "\n",
+    "# specify the lines and labels of the first legend\n",
+    "ax.legend(lines[:2], ['line A', 'line B'],\n",
+    "          loc='upper right', frameon=False)\n",
+    "\n",
+    "# Create the second legend and add the artist manually.\n",
+    "from matplotlib.legend import Legend\n",
+    "leg = Legend(ax, lines[2:], ['line C', 'line D'],\n",
+    "             loc='lower right', frameon=False)\n",
+    "ax.add_artist(leg);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a peek into the low-level artist objects that comprise any Matplotlib plot.\n",
+    "If you examine the source code of ``ax.legend()`` (recall that you can do this with within the IPython notebook using ``ax.legend??``) you'll see that the function simply consists of some logic to create a suitable ``Legend`` artist, which is then saved in the ``legend_`` attribute and added to the figure when the plot is drawn."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.06-Customizing-Legends.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/04.07-Customizing-Colorbars.ipynb b/notebooks_v1/04.07-Customizing-Colorbars.ipynb
new file mode 100644
index 000000000..6620f4a49
--- /dev/null
+++ b/notebooks_v1/04.07-Customizing-Colorbars.ipynb
@@ -0,0 +1,572 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) | [Contents](Index.ipynb) | [Multiple Subplots](04.08-Multiple-Subplots.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.07-Customizing-Colorbars.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Customizing Colorbars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot legends identify discrete labels of discrete points.\n",
+    "For continuous labels based on the color of points, lines, or regions, a labeled colorbar can be a great tool.\n",
+    "In Matplotlib, a colorbar is a separate axes that can provide a key for the meaning of colors in a plot.\n",
+    "Because the book is printed in black-and-white, this section has an accompanying online supplement where you can view the figures in full color (https://github.com/jakevdp/PythonDataScienceHandbook).\n",
+    "We'll start by setting up the notebook for plotting and importing the functions we will use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('classic')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we have seen several times throughout this section, the simplest colorbar can be created with the ``plt.colorbar`` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BsHGB0C+kAtk7H+JWerTAcgM96mvWc5NFy490DI1j7CcO2irUFo8O4mxrMLYxtVw+cdKu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nx4w76IT9F7Kq15yXS87v2/CvzVlxz7gwVDSDsvSgYZm3XZyMaULCEbsEvAhgPbl89oy0dASiwf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\nNNNMMwHwf3ceQw5DTt3BAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c21c630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0, 10, 1000)\n",
+    "I = np.sin(x) * np.cos(x[:, np.newaxis])\n",
+    "\n",
+    "plt.imshow(I)\n",
+    "plt.colorbar();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll now discuss a few ideas for customizing these colorbars and using them effectively in various situations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customizing Colorbars\n",
+    "\n",
+    "The colormap can be specified using the ``cmap`` argument to the plotting function that is creating the visualization:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P9UhnZNsRi5Irr9cmlbBNkwtwscKqyug18Jwhzxo20mBXoRD4Xu6gS/MauX5u3c4l9jkd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\nRfwfAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d902588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(I, cmap='gray');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All the available colormaps are in the ``plt.cm`` namespace; using IPython's tab-completion will give you a full list of built-in possibilities:\n",
+    "```\n",
+    "plt.cm.<TAB>\n",
+    "```\n",
+    "But being *able* to choose a colormap is just the first step: more important is how to *decide* among the possibilities!\n",
+    "The choice turns out to be much more subtle than you might initially expect."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choosing the Colormap\n",
+    "\n",
+    "A full treatment of color choice within visualization is beyond the scope of this book, but for entertaining reading on this subject and others, see the article [\"Ten Simple Rules for Better Figures\"](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833).\n",
+    "Matplotlib's online documentation also has an [interesting discussion](http://Matplotlib.org/1.4.1/users/colormaps.html) of colormap choice.\n",
+    "\n",
+    "Broadly, you should be aware of three different categories of colormaps:\n",
+    "\n",
+    "- *Sequential colormaps*: These are made up of one continuous sequence of colors (e.g., ``binary`` or ``viridis``).\n",
+    "- *Divergent colormaps*: These usually contain two distinct colors, which show positive and negative deviations from a mean (e.g., ``RdBu`` or ``PuOr``).\n",
+    "- *Qualitative colormaps*: these mix colors with no particular sequence (e.g., ``rainbow`` or ``jet``).\n",
+    "\n",
+    "The ``jet`` colormap, which was the default in Matplotlib prior to version 2.0, is an example of a qualitative colormap.\n",
+    "Its status as the default was quite unfortunate, because qualitative maps are often a poor choice for representing quantitative data.\n",
+    "Among the problems is the fact that qualitative maps usually do not display any uniform progression in brightness as the scale increases.\n",
+    "\n",
+    "We can see this by converting the ``jet`` colorbar into black and white:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from matplotlib.colors import LinearSegmentedColormap\n",
+    "\n",
+    "def grayscale_cmap(cmap):\n",
+    "    \"\"\"Return a grayscale version of the given colormap\"\"\"\n",
+    "    cmap = plt.cm.get_cmap(cmap)\n",
+    "    colors = cmap(np.arange(cmap.N))\n",
+    "    \n",
+    "    # convert RGBA to perceived grayscale luminance\n",
+    "    # cf. http://alienryderflex.com/hsp.html\n",
+    "    RGB_weight = [0.299, 0.587, 0.114]\n",
+    "    luminance = np.sqrt(np.dot(colors[:, :3] ** 2, RGB_weight))\n",
+    "    colors[:, :3] = luminance[:, np.newaxis]\n",
+    "        \n",
+    "    return LinearSegmentedColormap.from_list(cmap.name + \"_gray\", colors, cmap.N)\n",
+    "    \n",
+    "\n",
+    "def view_colormap(cmap):\n",
+    "    \"\"\"Plot a colormap with its grayscale equivalent\"\"\"\n",
+    "    cmap = plt.cm.get_cmap(cmap)\n",
+    "    colors = cmap(np.arange(cmap.N))\n",
+    "    \n",
+    "    cmap = grayscale_cmap(cmap)\n",
+    "    grayscale = cmap(np.arange(cmap.N))\n",
+    "    \n",
+    "    fig, ax = plt.subplots(2, figsize=(6, 2),\n",
+    "                           subplot_kw=dict(xticks=[], yticks=[]))\n",
+    "    ax[0].imshow([colors], extent=[0, 10, 0, 1])\n",
+    "    ax[1].imshow([grayscale], extent=[0, 10, 0, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11656ceb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "view_colormap('jet')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice the bright stripes in the grayscale image.\n",
+    "Even in full color, this uneven brightness means that the eye will be drawn to certain portions of the color range, which will potentially emphasize unimportant parts of the dataset.\n",
+    "It's better to use a colormap such as ``viridis`` (the default as of Matplotlib 2.0), which is specifically constructed to have an even brightness variation across the range.\n",
+    "Thus it not only plays well with our color perception, but also will translate well to grayscale printing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1165767f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "view_colormap('viridis')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you favor rainbow schemes, another good option for continuous data is the ``cubehelix`` colormap:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noQtQSOgCFPoDAVPczXaO6jAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1165caf60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "view_colormap('cubehelix')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For other situations, such as showing positive and negative deviations from some mean, dual-color colorbars such as ``RdBu`` (*Red-Blue*) can be useful. However, as you can see in the following figure, it's important to note that the positive-negative information will be lost upon translation to grayscale!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c20e908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "view_colormap('RdBu')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll see examples of using some of these color maps as we continue.\n",
+    "\n",
+    "There are a large number of colormaps available in Matplotlib; to see a list of them, you can use IPython to explore the ``plt.cm`` submodule. For a more principled approach to colors in Python, you can refer to the tools and documentation within the Seaborn library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Color limits and extensions\n",
+    "\n",
+    "Matplotlib allows for a large range of colorbar customization.\n",
+    "The colorbar itself is simply an instance of ``plt.Axes``, so all of the axes and tick formatting tricks we've learned are applicable.\n",
+    "The colorbar has some interesting flexibility: for example, we can narrow the color limits and indicate the out-of-bounds values with a triangular arrow at the top and bottom by setting the ``extend`` property.\n",
+    "This might come in handy, for example, if displaying an image that is subject to noise:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OiYpYGd9flNO4iMqlJxNVxj7JREN2dOnCC6yULAFUAtKJAwIypDjwrEsj3Uu4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OAAAg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\nczUA4OGDB3DjjTfe3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tWFRj5Q6A0KLGfl+ZczjZXMGvZerlGs9CzQC6CPy0WbABbefu5T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SUHqXTKFrALCUQLtCCtJ96wbQGZfVGKixrjIdqDL3YAuYoqH1lHK8Yvl3WJix68KzATtCWRPI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\naNFiBvx/yiMZc7t4oy0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d93be48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# make noise in 1% of the image pixels\n",
+    "speckles = (np.random.random(I.shape) < 0.01)\n",
+    "I[speckles] = np.random.normal(0, 3, np.count_nonzero(speckles))\n",
+    "\n",
+    "plt.figure(figsize=(10, 3.5))\n",
+    "\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.imshow(I, cmap='RdBu')\n",
+    "plt.colorbar()\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.imshow(I, cmap='RdBu')\n",
+    "plt.colorbar(extend='both')\n",
+    "plt.clim(-1, 1);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that in the left panel, the default color limits respond to the noisy pixels, and the range of the noise completely washes-out the pattern we are interested in.\n",
+    "In the right panel, we manually set the color limits, and add extensions to indicate values which are above or below those limits.\n",
+    "The result is a much more useful visualization of our data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Discrete Color Bars\n",
+    "\n",
+    "Colormaps are by default continuous, but sometimes you'd like to represent discrete values.\n",
+    "The easiest way to do this is to use the ``plt.cm.get_cmap()`` function, and pass the name of a suitable colormap along with the number of desired bins:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ja4kShBC65/tvMrdtrMjF6YHE+EOdO8dXKjI7245/vG8THn+D7W5BKQ3beSqcGRiYYk8D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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116d71940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(I, cmap=plt.cm.get_cmap('Blues', 6))\n",
+    "plt.colorbar()\n",
+    "plt.clim(-1, 1);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The discrete version of a colormap can be used just like any other colormap."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Handwritten Digits\n",
+    "\n",
+    "For an example of where this might be useful, let's look at an interesting visualization of some hand written digits data.\n",
+    "This data is included in Scikit-Learn, and consists of nearly 2,000 $8 \\times 8$ thumbnails showing various hand-written digits.\n",
+    "\n",
+    "For now, let's start by downloading the digits data and visualizing several of the example images with ``plt.imshow()``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UYtDKKnq9uq7Ipa5bbs11qmlDBwcHyGQyHjpBiwtYmhyLxQxw+fvJXWYyGctW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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bb38d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# load images of the digits 0 through 5 and visualize several of them\n",
+    "from sklearn.datasets import load_digits\n",
+    "digits = load_digits(n_class=6)\n",
+    "\n",
+    "fig, ax = plt.subplots(8, 8, figsize=(6, 6))\n",
+    "for i, axi in enumerate(ax.flat):\n",
+    "    axi.imshow(digits.images[i], cmap='binary')\n",
+    "    axi.set(xticks=[], yticks=[])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because each digit is defined by the hue of its 64 pixels, we can consider each digit to be a point lying in 64-dimensional space: each dimension represents the brightness of one pixel.\n",
+    "But visualizing relationships in such high-dimensional spaces can be extremely difficult.\n",
+    "One way to approach this is to use a *dimensionality reduction* technique such as manifold learning to reduce the dimensionality of the data while maintaining the relationships of interest.\n",
+    "Dimensionality reduction is an example of unsupervised machine learning, and we will discuss it in more detail in [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb).\n",
+    "\n",
+    "Deferring the discussion of these details, let's take a look at a two-dimensional manifold learning projection of this digits data (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) for details):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# project the digits into 2 dimensions using IsoMap\n",
+    "from sklearn.manifold import Isomap\n",
+    "iso = Isomap(n_components=2)\n",
+    "projection = iso.fit_transform(digits.data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll use our discrete colormap to view the results, setting the ``ticks`` and ``clim`` to improve the aesthetics of the resulting colorbar:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YbNmU7EoUavuj0QjMRg2utOQvNcUcDOu+2MIDj/+bV9/9sM/9p8+ZzgiXAyUc\nQrsv34qEmvoGTr/iOi5c8jPK+4iYFAKkVIgGfESDfoIdbfzg5t+yZuNmIgEfsqsq0K0/uJqtrz/D\n4ksWAWA0Ggf1+lQOH1V4DxG7du1ixowZzJgxgwkTJrBy5YHTSQJUVFSwbt06Qvv53n4Z5eXl/P3v\nf+fFF19kIMsHUkp27NhBZWXlQZ0H4IUXXuCcc87htttu4+KLL+aPf/zjQR8jJ9NKS3M9Usp4GbGm\nxloys0aQ7somL38kjY01IAQuVy4OpwshBAWFY6ko343N5oxrfpmZI9DqNAnXHQ4FSLIkvlQ2NLbS\nUF9NY0NNV1+B0RgTeiaznZr6mG9zdL8CxYrs/yeycfNe9tQYcHtT2bDdS1XNgQX9QPnwkzVc9NPb\n+P0/n2XxHfdx3yNP9OqT6XLx4gO/5/pLzkMnYteuREJoTRY6wgqfbt3Nrx/8v17jTp01k6J0O1qT\nBSE0aLQ6WhUtwpyEzmTBEA1yzw3f57orL1WF9TDnhBTeNTU1/PrXv+bmm29mz56+q4QfiFAoxJ49\ne7jmmmuYNm0a5513Hnv37k3os3z5cnbs2AFAXV0dp512GllZWaxZ08sjEoDHH3+csWPHMnv2bE47\n7bQBa+t79uzh9NNP5wc/+AEXXXQRN9xwwwH7K4rCVVddxbhx4ygqKuIPf/jDgM6zjxdeeCEhQOlQ\ntO9Uh4UkC5SVfk7YX0tVxS4Cfm+CsEhKsiMVBY028Sua4khFKomBL3q9kfq6Spqb6mioryIajdLk\n7n4Abt9RiV/JJiMzl7T0LEp3bSYc2r9asEI4HMbnbSESy2KFp72JcLCdPXv7rgpf36TEF1XNlhSq\nawenTuV/31vRw3tEw3/eeK/PfiNysvnNT5fy55t+SFFGKmZjog26tqG51xghRLygs5QK2v0Ci0KK\n5KqLzlMF9zHACWfz7uzs5Nxzz2Xz5lgOrWeeeYZPP/2UzMzMLxkZq7N46aWX8tprryW0b9q0ic7O\nTj744IN4m7+PKLP6+nq++tWv4na7E9qllCxbtoxAIJbrec2aNZx//vl89NFHff6I3nzzTV577TWy\ns7OJRCIJGvQjjzzCPffck1COqycvvfQSTz31FBDL3XHrrbdy6aWXUlBQ8KXXD5CWlnbA7S9jb1kt\nFfUGUlKLSUmFmqpdmDR+wkpSvKQZxMqYaXV6PJ42nKmZaLVaWprrMZktyHA9gUAnRqOVutoK7HYn\neoORlJQwz5e0AAAgAElEQVQ03M0NpKZlokSjrP18Nx6vBo/HS1pGzKat0Wiwp6Th9XrwtLuxJTsI\n+hqZODmNdRvLScucQHNTHX6/F6NBS7J9JBUNERqad3LK7G4XQK/Xi7ulAaG1YrHGbOLi0OLQepGS\nFMs8qETCyGiEveVt/Orev3DDdy8nzens1X/ROV9h0Tlf4an/vsqN9/8DYv7OnDE7FpYfCAR47YOP\nQcaCcjJSbHyxvSNWxm2/74lBp9qyjxVOOOG9YcOGuOAGKCsrY/Xq1QkugP3xwAMP9BLc+/jss8/w\neDwkJ8e0mu9///s899xzRKOJ4cWtra18/vnn1NTUcPLJJ5Oenh4L3+4KSd7HypUreffdd1m4MDHp\n4jvvvMP5558fC10GZsyYkbDfYrEcMDBk06ZNCdvRaLTfbHN9ceutt1JSUsKqVasYN24cd99994DH\nArS0BdFqLTQ21CCEQGgMJDuyyUntoGT3NjQGF1Iq2FNSqaspx2ZLoWzPdsLhEMGgj7TUVPJyU0m2\ntrO7dAtp6dNpa2umw9OGp82N0GhIS3UQDHmJavIxmDUont6ac5LFSJbTR2qqhqzMXIxGI53+ZkwW\nSEvPoqmxlnRXLJhFp9PR1mGhs7OTpKQk6hta2Lyjg7zCKbS1NhMKBzEZokyaPDi5un/0ncv4v2f+\ni9QZ0BrNRDVa/vb8a6zcuJWXHvpjn0mfwuEw8+fM4Lc/8PH3Z18mGAoRCAZ5+4OP+OGd99HeVcDn\nN3/+Pxo7/WgtVqS3I/aAUBRAoCXKv+797aBcg8rQc8IJ75ycHEwmU1zL1Wq15Obm9tm3tbWVn//8\n52zfvp2TTjrpgPlM/H4/U6ZM4X//+x9Tp05lwYIFXHTRRTz77LO9+u47VlFREe+88w7FxcVceuml\n/OMf/0jo11NDr62t5YYbbmDFihVxwQ0xe3dhYSFlZWUA2O12PB4PKSnd6UcVReGpp57iww8/5Omn\nE1N2XnLJJUyYMKHf69qfzMxM3n//fUKhUL/a/YEw6iSl9ZXk5MYKIdTXVSI0GqQwsHDBFD5dX481\nOZemxjrsjjScThfprmw2rf+AjKwCdHoT/rCNtnofaKzs3rWJ8RNnI4TA3VKP3exh8qQUdu9VkF32\naltyCpUVu7Bak4lEwlisNuxWHTOmJ1ZyN+m7Xf2klAQDfjyeVqSUmEzG+KLo7rI2TJYMAFIcaXS0\nVnDanOK4P/jhkp7qxGyx4O+av1ZvIBr0U1JZxxmXfZ///vVeCvPz4v1Xf76Ra2+/m5r6BjRKhCgC\nhIa/PPUCf378Pxgd3W9Hjb4wIW8HemsyOmsy0aAfGQ6jsybxxG9/yZmnnToo13CsYn/t8aM9hQFz\n3Ni8pZS89957vPLKK7202J4UFxfzt7/9jcLCQnJzc7n33nuZNWtWn31vvPFGHn30UVavXs39999P\nRUXFAedQXl7OHXfcgdfr5YEHHmDkyJGMHj06oY/ZbI4/BPbu3cvll1+Ooig89NBDCVr0hAkTOPPM\nM+PbP/zhD3nhhRdobk60Y+bm5sYF975j7ntgKIrCjh07WLx4MVdffTWPP/54guAvLCzk6aefPiT7\n5qEIbgC73UhWdkF8OzNrBK3uetJSrdjtNuafkku2o5WArx6nM+by5ml3UzxmOtm5xbgycmh1N5Fs\nT6WqppXs3OL4/J2pmdgdqaQ67VgtIv7WYzZbMRsloaAvlmLWXc2E0b215KkTs1CCtfg66jHpPLS2\nNpDuyibdlY2vsx6zOeYhs39UtMlkOmjBrSgKL73xFq+8/S7tnt4JxMcW5fdqk1JS1dLOOYt/RFt7\n95jfPPRPahuaEBodwmxDb7Wjt9jQ6vWgSVzMlVKiNZhRuopfCKFBYzCQk57KtIlqzMKxxHGheUsp\nWbJkCX/7298AmDt3Lm+++SZJSX1XLbn66qu5+urElJVtbW289tprTJ48mcmTJwOwbdu2hD6ffPLJ\nl87F5/PxzW9+k7fffrvP/UlJSQn28LVr12K1WsnLy2PGjBl8/etfJyUlhUsvvZT09G4Bs2vXrl7H\nGjVqFGeddRZffPFFQrtGoyEQCHDRRRfx+uuv9zvXoqKiI+6vazGbCIX8mEyxmHIpJXrRTlZm7J6b\nzWZGjsyjwxeho2tNMRQKxk0YAM7UDBrqKklOcRIOJdaE1AiFT9fupNWjIRCoRCtCJCWZ0BsdpDti\nD4NwOEhHpx+HI9H8kJycxPy5sYft1u2VuL0x+7IQglTXaBoamsjMdJGTYWJvTQdGk41g0EtOxsEF\nqzS73Zx55XXUeXzIcJgRLidvPfYX0lO77dm/vn4x3/vVclq9fqLhUCy3SNCHxmihpcPHh6vXcuE5\nsRi4Dp8/5gOIRPSoASp0BjSRMErAhzCYEELEUrqaLCihANGAH7NBx2kzp/DrpdeQkX5w6xcqR5fj\nQvMuLS2NC26AVatWccstt/Dhhx/S3NzcS1vdn02bNpGdnc2VV17JlClTWLZsGQBz5sxJ6Pdlgk6n\n01FdXd2v4Aaw2XqHLgcCAXbv3s0zzzzDb37zG5544gk+/fTThD5z587tNa66upp33303oS0zM5PL\nL7+cxx9//ICCe/To0Yfk5ne4pKen4m4sx+frJBIJU1tTRoqj92LxuNFZBDqrCIdD+H3tCWsH7W3N\nCI0GT5ubDk8bbW3NhMMhyvduQ4mGCcksbPYs0jMK0RlsFOQmk+LoDlzR6414fft7m+xP4uJjNBrG\nYIgJ6eKibCaO0pOa5GZcoWD8mBF9HaBfHnriWeo7Al1ar5GKhmZeeKP7/9jW3o7VZODlB3/Hgsmj\nCXd4EDo92vgDT8Fq6db0L144PzZfIbrs111XEAkhhAat2UrU244SDqEzWzHptPz424vY+N9/UvHx\nazz9p7sYM7K7nqfKscFxIbx1Ol2vV/8HH3yQBQsWkJ6eTnp6OhMnTqSxsW8/3Ouuuy5BG16+fDnn\nn38+119/PcuWLeP888/nrrvu4oorruhzfEpKCosWLSISifTS1vcnEAhw8sknH7DPxo0b+eY3v8mq\nVavibffffz8uV2LknN/v7/VAOe+887BarXi9vd3WhBAsWrSI2tpaSkpKmD598NOVDgSXy0k0GqGz\no43snEK02t5fQ5PJxFfmj2VCUYQLvjoOk6YeT1sdzQ1l6IWXjvZqxk2YQfGoiXR62mluqiPLZQSN\nMSFgR6OzYE82EfB1/+8DPjeZGbE1gfb2DnaXVtHYlOgBNHpk7OGxL3jIYe3A6ewO/MnKTGPCuBHk\n5vQfzdgf3kDvB8c+l8g1Gzdz2hVLWLjkZq6++fd869wzycjNRQn4iPh9RANevnfBV/nKvG7b9MnT\nJzN1VCEmnRbF107Y10nY60GJRpg1rhiiEXQ2B1IqpJm0vPqX33Prj35AXm6ums74GOa4MJsUFhZy\n8803c+edd/bbZ9u2bVx11VW89dZbvfY1NDT0anvllVfwer28/fbbcQGpKApFRUWUlpYycuRItmzZ\nQigU4tprr+XRRx8d0Fxra2u54oorOO+883jiiScoKSnpt+9jjz0W17hNJhPjxo1LeAClpaVxzz33\ncPnll9PQ0MDEiRPjbw0XXXQRDz/8cNz/fMmSJSxfvrxfU9KRpDg/mZ3lYWy2dAI+N+OK+06kJIQg\nsyvU++TZiZn6XnqzBIsldi25I4ppbKghMz2JlBQzLWWdGE2xfTo6SU/PZdZkI7v2NCKEhtFjbThS\nkqmrb2bLTi8mSyrltR3ktVUzdlRs8dpgMHDmaaOprKrDZDSQlTV4oe8Xf/Usnnl7BcForGZnliOZ\nS752NgDL//EkDe0x75+yhhbeX7OJ1x9ezuqNm7EnWZg5aQI52d1paXfv2cuipb+M+YVLicZoQUbC\n6Cwxk1BZXROKEkVGIgghaPaHsdusg3YtKkeP40J4A/zud7/j4osv5qmnnurXfa2vgByv19tvWtf3\n33+fefPm8dJLL+FyudBoNFx//fV99l23bl3CdlFRUa/AnX3U1tayfPlyfvzjH/Pd736XZ555ps9+\nq1evTti++eab2bhxIx6PB7PZzGOPPcaCBQvi0ZKjR4+OL5zl5+fz8ccf89Zbb+F0OrnggguGTeBF\n/ogMkm3ttLhbSR+dgv0g6x3u2FVFZL9ISL+3hXAkmWhEUJyro6G5Ca1GYcbsmEnD4UhmzszE8+yt\n9GCyxB4ORrONypp6xvYoNanVaiks6NsTCWJvUWs+r6TTLzAaJNMmZpDqtPfbfx8zp0zk7f+7l+df\nfxur2cSSqy6LL4YGQuGEvv5AkFFFBYwqKiAUCvX6H/709/cR1hjQxmKFiAZ8SGKFFzR6A65UB23h\n7rcKKRXCYbUK1PHAcfXONHnyZG677TbOOOOMPvefdVbvJIcPPfTQAf2cV69ezX333RffDgQC/Pvf\n/2bSpEkYjUYKCgrYsGEDS5Ys4fbbb2fevHlcddVVfPrpp/16ZLz55pssXboUvV7P1772tX7PbbXG\nNKRgMMjHH3+Mx+Nh5cqVvPTSS2zdupWvf/3rQMxsM3ny5F4eDzk5OSxevJgLL7xwWAjuQCDAF1vK\n2LS1AoRgZHHeQQtugN0VfhRFIdwVCdnYWIXZ4qAzXMDGkjCbt1Uzc2ous2cUY7VaBn7gg4yx2bSl\nGmHIxmbPwmDO5ottAw+PHzeqmF//eAk3Xvu9uOAG+M4F56DvMqEkmQxceV4sz/Y9//c4oxZ+i1Fn\nX8xfHv8PAH996llWb96ReAlSotUbyUlN5qtzpvKPO29h5piC+L4rzj6dkUVHp1iCyuAyaJV0hoJD\nraQTCoVYv349Wq2W++67j9LSUpKTkxkxYgTz5s1j8eLFCCG44447uO222770eEuWLOGhhx7C7/cz\nf/581q5NrB2RlZVFbW1tr3G33npr3JSj0+lISkqira0tvv+cc86hpqaGLVu29BprMpl49NFHefvt\nt3niicTcFsdi9Z5oNMoHK0vjyaH83kZOnu4iOfngzDhudztvfFCK2Wyjo6Mdm82O39/OiPxuN7em\nxlpG5RuYPLHggMeqqW1iW2kAk9lBINBJXkaYJIuR8qpYNZWCPBt5uf3btFeu2YvUdO/3ddRzzoLD\nN6+s/2IrO8vKmTpuDBPGjGLdps1840e/ij9btALeevgPfPPHt9La7kFr6n5Ahb0e0tNdvPX3uyns\nKmfm9fpYsXotZrOJM06ZMywe5EPBYFTS2fTrPw+o79Q7fnhcVdIZNhgMBk455RQAnn32WW6++Wbu\nuiuWPvyJJ57A7/fzwx/+kIceeihhnF6vZ+rUqQkmECEEV1xxBW63mwsvvLCX4IZY7hKIPTSefvpp\nfD4fixYt4ne/+x2XXHIJpaWlzJ07t1cwTF/2d4i5+v3kJz/B7/f3EtwAP/jBD7jsssuOqXzKlVV1\nGCzd7n5mq4vKmhYmHqTwXrW2ihH5Mfu3LdlBm7uO5D5suNEBpNbOyU7HavHQ0OjGPsKCwWBm/ZYO\nTOZYAE7J3jas1nacjr5NIXaroLkjEo9oTbIOTj7vmVMmMnPKxPh2c2tbwktBVEJLWzsQKyocDfhA\naEgx6zll/lyuvfTCuOAGsFotfO2s+YMyN5Xhw3EpvPenZ84RgIcffpisrCwslsRX6ltvvZW5c+fy\njW98Ix7oc+211/Lkk0/yt7/9rd+MfTqdDkVRuOSSS3jppZcA+POf/8yKFSuYNGkSkyZNAmDhwoW9\nIhz7QlEU7r33Xq69tu+k+n6/H5/Ph93+5fbV4YLFEvPvNhpj91xRFAz6g7Pabdu+B42u28xiMpnJ\nTNcjiNLqbiQQjLnfhYJekpN6a8z7CgX3JCUlmZSU2DF3lVYhNOZ46L7JbKGpuX/hPXliIVu2l9Ph\nlRj0CtMmFffZ73A5adpkXBYDda0ehEbD9LHFzJ46mR9+exF3PfYswmRhbF4Gz91/JxmuwQnRVxn+\nnBDCu7i4OCGb344dO/jWt74V16g9Hg+nnnoqS5cuxel08sEHH7BixQqKi4vRarUsWrTogMefMWMG\nO3fujAtugJ07d/L666+zePHieNsvfvELampqBlTxOhQKYTQa+9w3bty4Y0pwA2S40nA1lFHV4EWj\n1WMzdTKq+OBqPbZ1gtfriVeSD4dDuJINVDcJPB1tjMjvjmatrqujuMt12e/388naCnxBA1pNhElj\nUxjRhzkk2WameUttPHS/rbWZL3svnjS+4KCu4VD47V8eocEbRGs0YdDALxZ/G6vVwo++823mzZxK\nY7ObOdOm9JnzROX45YQQ3nfffTc+n4933303wf/5+eef59FHH2XSpEmMH99d6XvOnDmkp6fz3nvv\nHdCVbx+BQIC6ujp0Ol1C/pMf//jHjBkzhrlz5/Lggw9y4403HjA/Sk/OO+88vvGNb3DPPfcktNts\nNv7zn/8M6BjDjSmTChk3JkQ4HMZq7d+Loz9aWtxotTYaG6qJRiK0tdbTnp5EWsZE/P7EBGDhHvUk\nt5bUYrDkYuh60dq+q7ZP4R0Jh8nM6g5LT3GkEQy5e/U7kkgpefXjz+J26pACn27cwplzY7EC0yYO\nPC+NyvBDCJEPjJJSvieEMAM6KeWAStgfV94m/ZGdnc3//ve/Xot8wWCQn/zkJ4wdOxadTsfDDz9M\nfn4+OTk5TJ8+nWuvvZY//elPCUme+qK5uZlvfvObvQRzZ2cnCxcuJBAIcMsttwxIcF9xxRU8+uij\nPPfcc5x22mnceeedOJ1OHA4HixcvZvPmzUyZMuXgb8IwwWAwxL1oDhahS8aVkYsrI5esnALsDhe2\nlJG4WxpRlGj8/iqKQijYvTC8v1thVOn7a5+cbCUY7P7dxOo0HtJUB0xldTXrv9gST5S2P0IIUu37\n+cErEbbv3DWgwhsqwxchxDXAC8Dfu5pygZf6H7Hf+OH8BThUb5P+2FcUobS0NKG9traW+vp6Zs6c\nGa+m3ROn08nVV1/NqlWraG1t7TU+JSUlwYtkfxYuXMhHH31EMNh3SPasWbPQaDScf/75LFu2rJc3\ngKIoXUmEjk8vgYHy2ru7SU7pDlBpbqojLT0rVng44KfV3YjFkoTRZKYg18jMqTGXuL1ltZRWazCZ\nkpBSoonWMHNqPnX1zTgdyaSkdJugSnZVUVrhB7Q4bGFOnfPlleYPlX+//DrLHnyUYDjK9JH5PH3v\nHTgdMUWhrLKKn9z1J/ZU1VKQkUZ1k5vm9g5GpDsorW9BCg2LTp/Db264ln++8DKBYJhLzj2L8aOP\nTB3N4cjR9jYRQhiBjwEDMavGC1LK3xzgfJuA2cAaKeW0rrYtUspJA5rviSS8AbZv385pp51GS0sL\nAOeeey6vvfYaL774It/61rf6HFNYWBgPuNm+fTsLFizoMyrzQFx//fVx7xaDwRAvdTZnzhzefffd\nPnOeqCTy+aY9tPkc6PUGvB3NBIJhUtP2VY5vQavVY01KJhQKUJQdoqiw27ulvLKBZrcfg06SlZHC\nhm2tmK0uAv52RuZpKCrsfihIKWNCfghDx6WUTPr6ZTR1dKdluOnqi/jx4isBuPwnt/D+51vj+36w\n6Bwu/eoCzrjmZwQ7PAghUKJhigryqWmNmQJdyVZe++sfyc87eJPU8cDRFt5dx7BIKX1CCC3wCfAj\nKWVvF7VY3zVSyjlCiI1SymlCCB2wQUo5eSBzOCHMJj0ZP34877//PjfffDPLly/nueeeQwjBG2+8\n0e+YOXPm8L3vfY/77ruPsWPHsn79ep599lnGjRtYCk2dTscdd9zB+++/z/PPP095eTlPPvkkV155\nJXl5edx///39auUq3cyYWkxRdpA0m5uTpjnJTovQ5q7A31lDXkYYm8WPDDeS5fQmCG6AghEZzJxa\nwOSJhewuc2O2xmzeJrOd0orEIC0hxBHJ+RHcL9Ix0GO7prElYV9ds5twOEqww4POZEafZEdrtFDZ\n0BLLya1EafR4+XTDZvrD5/Oxau16dpQefOk/lYEhpdyXj9pITPs+kPb5kRDiZsAshPgK8Dzw6kDP\nNeQLlkKIcqAdUICwlHK2EMIBPAvkA+XAxVLK9qGeyz6mTJmSYDf+6KOPeOyxx/rt3zN8vbW1laVL\nl3L22Wfz4osv9rmguXjxYp5//nk8Hg8ajYZ77rkHp9PJggUL4n06Ozt58skngVhdyKamJv7854E9\n9U9kCgtiGvKevbV4/MmkOO34vU1kZdqZkt67RNg+qmubqKvvRKuVBEJhdAmOPEdehxFCcO1FX+fu\nJ/8LQpCX5uDSc7sjgM+YNYWSytqYyUZKFsyexoRxYzBoQBGx+UolikZnQKPTEQ0GEJoI0WiY+x55\ngk83bSMU9FNeUw9aHd89byFvfrKeL/ZWodcIbr/2Cr5/2UVH/LqPd4QQGuBzoBh4SEq57gDdlwGL\ngS3AtcAbwCMDPtdQm02EEHuBGVLK1h5ty4EWKeUfhRC/BBxSymV9jB10swnEBLDNZuOVV17hlltu\nobS0dMBeIDabjY6ODgwGA9dddx2lpaWUlJRQWFjISSedxPTp01m0aBGKorB9+3bsdjt5ebGqJ5FI\nhD/+8Y88//zz7Nmzh46O7sWxwsJC9uzZc8LbtQfKOx+VYrJ0p5LVygZOnd23n3VtXTNbdgcwm2P2\n5Nbm3Zit6ZjMKYRCAVz2zi+NxhwqPl6zjsbmVubOmkpmj6yRiqLwyDMvsremjjmTxnH+wjO5/td3\n8eKKz5BSQQkFERoNWmN3aH2w3c1580/hrQ0l8e9RNOBDa7Igg35Ej77JJj0733ruuMoqOBzMJj2O\nlUxs8XGplHL7oc7pQBwJV0FBb9XmfOD0rs//AlYQewoNKV6vl8suu4xXXx3wm0kv9gncUCjEAw88\nwP33399nXUuNRsPEiRMT2m655ZZ+c2iXlZVx1VVX8cQTT6gCfCDs97s50G+2rrETs7m70IDOkMro\nfIHX5ybJaiB/RMFQzfJLOW1O31WcNBoN/+/y7jWYl99+nxc/WoPQaBBoQC+JBHyxGpdBP0o0ikZv\n4NWPP0NntiJ0XW4yXVq6AvRMHhyNRlVvlYNgXflu1pfvHnB/KaVHCPEhcA7Qp/AWQpTRh1lFSjmg\n5OpH4rErgXeFEOuEEN/vasuQUjYASCnrgYNPinwIPPTQQwMS3JMmTeKii7pfKbOysvrt+6tf/WrA\n5++Zn7svnnrqqQFV61GBbJcOd3M9TY21tDRVkZfdv/uhXisTBJUSDdDpDdPcGqaqrpPWtt5lyIYb\nvkAw4aEutDpMIko0FEAqUfSWpNhfkp2Iv0cudxnznspzpVOY0VUZCLj+kguOeBWlY5lZBaO4bv65\n8b++EEKkCSHsXZ/NwFeAHX12jjETmNX1Nw94EHhqoHM6Epr3qVLKOiFEOvCOEGInvZ82/aoAt99+\ne/zz/PnzmT9//iFPpGdB3wNxySWXcNNNN7Fq1Sq0Wi0Wi4UZM2b0qan0t9D47rvvUlFRwemnn86o\nUbE8o6NHj+5VIWd/jqfX2KHEYtZjMmvRaA20NNXyxTY3eyvamTklB9t+uU4mjs9n5ac7afPq0WgU\n7NYA9a1ODAYTCrB2Yw1nnzG8oxO/Mu8kxj73Mjuq6gFQfB0IjZ6Iz4vO3H29QggQgkkF2RTlZpOe\nYsdoMnLVBeeSbLOx+vNNpDsdzJ5+7MYK7GPFihUDilY+gmQB/+qye2uAZ6WU/XpCSClb9mv6kxDi\nc+DXAznZEXUVFELcBnQC3wfmSykbhBCZwIdSyl6uG4Nt816/fj2nn356QoFiIQQGgyEuhM8991zu\nvPNOcnNzSUvrftX+xje+0ad5ZObMmb1yef/hD3/gpptuAmI1K2+66SZuvPFGOjo6WLJkCe+99x7B\nYDD+6rrPbfA73/kO//znP1WzyQD4YGUpOlMmDfVVZGR2V1IXkTrmnTyqzzGKoqDRaFi/qQxfuDsH\nSHt7MwvnZfWbjmC40Ox2c8UNy1izdSdag5FoKABRBa3JHM8sqChR8pLNrH/t2RNOERhONu8Bnq9n\nKSsNMU38OinlgJ6sQ6p5CyEsgEZK2SmEsAILgd8ArwDfAZYDVwMvD+U89jFz5kxWr17Nz372M9at\nW4fD4eDBBx9k/PjxrFy5EqfTye233860adP2zZ/c3Fwuu+wyJk2a1Et4W63WPp/8PetpdnZ2csst\nt/DJJ5/w8ssv89xzz8X3SSlRFIWVK1ei1WqZO3euKrgPEo0m8dU/FOn//u0TZhazFo+/O0mVluBR\nE9wbtmxl7ebtjByRy1nzTjlgX4fdzpbyGgy22MJrNBRCGHREo2EI+pGKQopRy7r/z955h8dVXvn/\n897pXaPeu2XZlo1xYjDdpBCyENhkEwjZJYUUfiTAbgopZEmAFEKyIWGzm0YIm5CQAgkhhOYEML3Y\n2FiWbdmyJauMujSj6eXeeX9/jOZa4ypXucznefR47p1b3jvjOffc857zPY8+csoZ7hOU7894rTKd\neTfbnY922KQMeFgIIafP9Vsp5SohxFrgj0KIa4BeDmLAh8uSJUtYtWrVHusbGxu56aabWLdunb5O\nSkl/f78+yTizknLhwoWsWrVqr6Xee1v3+OOP89prr3HOObt6DwohMBgMhxUKOlVpqHHQ1TeFpqlI\nKbNeF65Z9F5YOL+W6Prt+EMCg0jzliVzo8T3jxde5uO3/hfxlIYAbr32X/l//5r5KfQO+Ljl7p8x\nMDLOBW9Zwi03fIqRkRFUKUBAKhrGaHegGIyk1RRqLILBYuXBH38/H8s+QZBS7r1rzCw5qsZbStkD\nLN3L+klgz7Y2c8yBJgsDgQArVqzg5z//OYsXLyaVSuH3+/F6vTnb3XnnnVx11VU5HXqEELjdx3dc\n9USiob4Cl9PP+KTK+HgvwujAZkmzdPH+J+r7B8bo7A6Q1gTFhfCW0/YeYjkWPLRqdab3JJlJnz8+\n+axuvL9w5908v2ErABu7+3l1XTsbegaQyTiqlkYoCooh8/NVjCYMZislThute+kCfywqRvPMHiHE\n5xMlZYYAACAASURBVPb3vpTyrv29nyX/bc5gNiGL9vZ2Fi5cyJ///GcKCwspLCzEZDJx00036ROa\nl156Kb29vVxxxRX6cW+++WZd1zvPkaG42EtrSx3nnt3GOWc0sOy0pv0aqFQqxcatQRJJC9E4+MMF\ndO0YOIYjzsW1W4s2l8NG38AAk5N+tvX69PUyleCN7gFUCVpaIgwGPYski5aMM5HQuObLt6NpmRvC\n2g0buf6WbzL/Xe+n8W3v4fO3fYdYLEaeOcd1gL9Zccppm+yPK664ggcffHC/2yxZsoT169dTWFjI\n1FRuUWh1dTX9/f36spSSHTt2YDKZqKur2/1QeY4xQ8MjPP/aGN7CUiwWG0ODO2mqtbF82dyIOQ0M\nDvGxr3yTDTv6qC4uoKmylOfat2I1GWmqKGbzQKYxtpaIZfK5k3EUkwU1EkSxWDMd4Y0m0qkESFCM\nRoTRxLP33sUr6zbyn//9C6TRSDqZQDFbEIqBUqeFlcuXsXTBPK658n0n1RzLiTZhebicEnres2Xl\nypV7Nd4XXXQR3d3dVFdX88Mf/pBUKrWH4QYYGBjgySef5OKLM01jhRA0N598Km/xeJyRkRGMRiOa\nplFcXLxHV6LjEYfdhsPhwjqdmVFZ1UA00jNn46murOCp+35Ev8/Hq+s7uOF7P0EoBhKapLNviDPn\n15NU0/zTBWfx28efZUf/IFoyjtHhzhgqoxmpqaRTSSyeIgBkIkqB283PHvormpQogDAYENMTu6Ph\nBL978hn+8MzL+IMhvvCpj87Z9Z/qCCGsZMrjFwF693Ap5TWz2T8fNpnBddddx+23387555/PW9/6\nVj7+8Y+zbt06nnrqKbq6urjkkktYvnz5Hl3aZ9LTs3djcDw/4Rwso6Ojuu55bW0t4+Pjcz2kWaGq\nGiZzblZJgXdu1RwVRaGupoZEKqV7wWlVJZVSea1rgHXdPnqHRnno7m9x62c+SrHNpG8nhEAxmjDO\naECsKAofuPErTPinUEwmtEQc9tIPSAjBc2v3LWKV55hwP1AOvAt4joye96waMUDeeOcghOCWW27h\nueeeY82aNfziF7/Q0wYfeeQRbrrpJlKp1D73N5vNXHXVVTnr7rnnHmw2GwaDgdbWVsbGxo7qNRwL\nds9m2L0v5PFKYaEXs5jUb6Sx8Aj1NUVzPKoMF59/Nk0VmboCqaUwWDIOghCCB558DpfDzvUfvoqW\nlhZS4V0VocnwFMJo1pdTqkbXwBABvx+jIlDMFixSI61paPEoqViYtKaixiL09A3whW/dxYTfT545\noVlKeQsQkVL+CrgEOHO2O+eN9ywYGBjYwyjvzrJly+jo6MjpujM2NsanP/1p4vE4Ukq2bt26Rzef\nExFVVXUDKKXc7w3teGPluQso9UxS6JjgrLeU4dm9S80cUVpSzMM/+g533ngNV110Qc57FpMRmy0j\nKpWKR5FCkgxPkQz6kWnQoiHUWIRUNIRMayBBmEyUO0w0lBTQ2FDLuQsbUCw2TDYnRpsTgLFogvuf\neo6b7vzvY369eQDI/nACQog2wMNBSIWcGC7THJHt4v7UU0/tdZbebrdzzz33cMEFF1BVVbXH+yMj\nI3uoFfb19R218R4ramtr6e/v13t2Vlcff+L/Q8PjTAaiFLhtVFXuyuMWQrBg/vE5eVxWWsJH3385\nH7z0XUyEbucfb3RgNRq47bqP6kVEvtFJzI5dnX8SoQCamoR4DKO7AOO0cqBQU2zv6cPsKcqIWck0\nwrzLmxczsnI2bus+hleZZwY/n5bHvoVM4aJz+vWsyBvvfbBq1Sq++tWvsnbt2r2+b7fbefjhh7no\noov2eYzW1lbq6+vZuXOnvu4973nPkR7qMUcIQW1t7VwPY5/s6B6k2yewWAsZHI8QigzQOu/4u8Hs\nC6vVym/u+ibbdnRT4HZTXrbLGQvF4mQfmNOqigIYPcWktRTpeBRptuqxcIPDhcFiJa2m0FIpjOZd\nczVyRru/RU31x+jK8uzGfVJKjUy8e1ZKgjPJG++98Nxzz3HZZZftITpVVFRERUUFF198MXfccccB\nY71Go5F169ZxzTXX0NfXx2WXXcbXvjYrzZk8h8HAcByLNWPwLBYHg8MjtM5dLc4hoSgKrfNyM5XW\nd2wmHA6j2F0IIVBjEcyugkwIKy0BgRqLYrI7SGuqbqwVowkzaS468zS29vpobayhrqyE9Vt7qK0o\n5T8/8/E5uMI8QI8Q4kkyjWmeOdi86Lzx3gtPP/30XtUCr7zySr0P5Wzxer08/PDDR2poeWZBRo1h\n38snKlu7e1HsLtRoCIlAJhNIKdHiEQxWB4rZghqcZHnLQkbHJ+id3FXhe/nbzuPH3/wqAM+9+jpr\nN27h4/9yCe9552FVaOc5PFqBS4HPAL8UQjwK/F5KuX/t6GnyE5Z7YW8FNZdeeinf+ta3ctZpmsbn\nP/95Fi1axMUXX0xX1+zF2vMcPZrrPcSjGbXNWNRPU93JIUuwpHUeLqsFk8ONwWjE4PKgRqYwWB2Z\nOLYQGN2FtG/dwb9/+ErKC5zIdJrm8kLec+E5SCl5ZNUz/OuXv8137/8zn/jGD/jfX/1uri/rlEVK\nGZVS/lFK+T4yMiJuMiGUWZE33nvhYx/7GEVFuSlkVVVVOZkkkGnucNddd7F582aeeuopPvGJT5Bn\n7qmsKOasZUVUFflZsdRDbc0x6fVx1FnY0sy9t93EP521jDMWNGM0W5Biz59wNJkikkhyzXveiRIL\nsnlrF//2lW/zxTt+wCPPvEgqnX0SETzybL75x1wihLhACPFjMn0vrRxHqoInJIqiUFdXx8TELq10\ns9m8x3bbt2/PWT6ZPO+JiQni8biufFhWVjbXQzooXC4nLpdzrodxxFl59hmsPPsMQuEw//SJf2er\nT6LFIhimGzJo8SiKxcazL77MU69twGj3YBaCtKryy788weUXnJVzvMLjJFXyeOGbqVn3/z1sppuz\nrwf+CNwkpYzsf49c8p73Prjtttt0T3vBggV89rOf3WObCy+8MEcb4mSRdg2Hw0gpqaqqorKyErvd\nPusuRHmODS6nk7u+eCNqNIQajxH3j2XyvhG01ZTxysZtKAaD/v9TMRqRaY2rLn0Hy+fXo6Q1WqpK\nuOXTs6rEznN0WCKlfK+U8ncHa7gh73nvk0svvZSOjg76+/tZtGgRLtcuD2XLli10dHSwdOlSHnjg\nAZ544glqa2v50pe+NIcjPnIEg0EqKyv1ZZfLhc/n288eeeaCggIPJYVexgMhhADFYkUIhca6Grb3\n7fl91RQV8I7zz0PV4JGnn6OitITqihPriepkQkp5WM1T88Z7P1RVVe1RfPPoo4/yoQ99iHA4TEFB\nAQ899BC/+tWv5miERweXy8Xw8DCapuF0OjEYDPvVc8lzbJFS8us//ZXv/eI3TATDGG0ZbRMtHkUY\nTSysr+a5VxQi8QRSRgGBTCXwCw/f+d97+N+HHtfj3tt29vPbH3xrP2fLc7ySD5scJHfffbfeZCEQ\nCPCjH81OQvJEQghBPB6nqqqKdDpNb2/vHhO4eeaOu+75NTfdfS9j0SSK2YoWz/RkTWsaBWbB+s1b\nCSQlGAwoRhMoCsJkJiEFv3nsHzMmLOHZV9dy9Re+xo23f5d+3+BcXdIpiRCiYTbr9kXe8z5ITCbT\nfpdPBgKBAPX19UAmTz0SOehwXJ6jyLNr1+uxbKmmSGsqxKNUFLrxJySr1neClsRgNGdywC12lGnl\nQavZjAxnJqLTyTjSaGLV6xuAjBf+xL3/fVJpfB/n/AlYttu6h4C3zGbnvPE+SG6++WbWr1/PyMgI\nVVVVJ02ceyahUAifz5cps1aU/I/5OKOiuAjoIZ2Mg8GIyeEmraaYjGukReb7MlgdehMHt8NGOKlh\nNxv5+vUfZ8OWbTzx0utMTkzgT+7ywtdt7SYcDufM7+Q58gghWsloeHuEEO+b8ZabGbreByJvvA/A\niy++yFe/+lXC4TCf+tSnuPbaa3nzzTfZtm0bra2tlJaeHDnEWSKRCIWFhXpqYDAYpK+vb6/CW3nm\nhltv+CSBYJiX17ejTvexRMrpFmm7tnvb8iVcffklnLF0MRu3bKOhtpKm+noue+eF3HLjtVz2iRt5\ntbNHF6kyCYnTefKlVx6HzCdTWVkAzBQ7CgGfnO1B8sZ7P4TDYa688koGBzOxwM985jPMnz+flStX\nUl5ePsejOzpMTU3lZJq43W4KCwvncEQnN6lUirvuvZ8NW3ewoLGWmz75kQNODldVlPPg/9zJFdff\nxHPt2wBQTGZS4QBGhwchBFoyzoL6Wppqq9m0tYvlS9v4x0uv8u2f/ooir4fPX/NvtM5r4uX2LTD9\nZLW4uSH/lHUMkFI+AjwihDhLSvnKoR4nb7z3Q29vr264IVMOv23btpMmn3tvuN1uJicndYMdiUR0\nOdLjDSklHZt3ktIUSovsVFeVHHin44zv/+LX/OB3fwXg6Tc6SCRTfPPz1wOZ/2+9/T1UlFXpet5Z\n2jd38kr7FrREZtJSqCmEyZIJpZARo/rf3z3MPX/9BxqCCreV0WAMbbqrzo4+H1+85irWtG9iU+8g\nNcVe7vzSDcfwyk9dhBBflFJ+F/iQEGKPRgFSyhtnc5y88d4PLS0tLFmyhPb2TLsol8vFWWeddYC9\nTlwCgQDRaJRgMIjf78dqtaIoChUVFXM9tL3y0mvb0JRKhBCM7wiiaSPU1Z5Yecsbtu7IWX5za6Zq\nd2xihG/8/AYGg1uxG7z8xwe/w7K2Ffp26zdtJYkBxWRBSU1isUpWLDqDZ9t3ZBQH41FQFN1YD4z5\nMVh23QCeX7OO1WveBClJJ2MUNddRtJv8Q56jxpbpf/euNz1L5ixVUAhxsRCiUwixTQhxXM76mUwm\nHnnkEa677jquvvpq/vKXv7B48eI9thsaGuLaa6/lsssu4957752DkR4+wWAQVVWprKyktbUVg8GA\nqqqk02kGBgYYGhqa6yHuQSC0q4LQanMzNHriZcXMr6/JWW5tyIiiPbTqXoZC2xBCEEsH+PVjP8jd\nrqkOkyLwOv2sODfIGWdHiJuexmMMoiXiKCaz3nQ4i979KJ1GomC02jHaHBidBbyxcTPf/Okvj+KV\n5skipXx0+t9f7e1vtseZE89bCKEA/wO8HRgE1gghHpFSds7FePZHfX09P/7xj/e7zUc+8hH+/ve/\nA5kiHq/Xy/ve97797nO8EQ6Hc2LdNTU1DAwM6BOVoVAIv9+P1+udqyHugdEgd1ueo4EcBl+69qOk\nUipvbtvBgsY6vn5DZr4qnszt3BRPRXOW33raYj5yyXLWbn8Ikznjg5ntClV1YyR6K4irFpxWQTSd\nBqEwr66Gi89+Cy++uYlIMMD2sV2xbcVgRAPG/YdV8JfnIJmWgN1dr3iKjEf+MyllfH/7z5XnfQbQ\nJaXslVKmgN8Dl8/RWA4LKSVr1qzJWbf78olAOp0mPaO7ytTUVM5EpcvlIhqN7m3XY4azfj6uecuo\nXJRpCt3a5CIYGCQc8pOIDLBw/vEZ3tkfNpuNb910A4/d80P+6yufxTWd7bHyLe/BRCbMIdOCC0//\nZ30fKSXf/tnn2OZ/EKMpV3c+lRQse8sIt15zeaaLTiqFGo9hNfj5f//6Xv7+f//LE/f9BAu7vuu0\nmkIxmrj0/BXkOXSEENVCiGeEEJuEEBuFEAeKXXcDYeCe6b8gmYyTlunl/TJXMe8qoH/G8gAZg37C\nIYRg8eLFvPDCC/q6vYVWjncqKirYuHEjZWVlaJpGLBbL8bKHhobmtMqyYOEKbJXNKEYjyXgMe91C\nor2bqastIxqN4nDUHPggJxCnt53BbZ/8JZu2r6OiuIYzTz9ff6+ru5MN/asRQpCIpUFJY3cqjA8L\ntvdWUFGsMB5SCcVTKGYLCrClL8h37/sC3//ib/F43Dz+8+/zpe/9D2OTkyxsqOVf3n0Rl12Ub8xw\nmKjA56SUbwohnMAbQohV+4konC2lXD5j+VEhxBop5XIhxKYDney4n7C89dZb9dcrV648LjM9fv3r\nX/OlL32JoaEhLrvssgN2mj/e0DSN3t5elixZQiwWY3h4mObmZiYnJ/H5fEgpcbvdc6pvYnA4Uabb\nzhmsNiyFxUDm5ulwOOZsXEeTeQ0LmNewYI/1NqsDIQ1omkosKti4tRKDSKFKK8Jg5JJzriKayDyN\np5NxpJQIJK++2cG69jVUVdSweMF8Hv/liS3tsHr1alavXj3Xw9CRUg4Dw9Ovw0KILWQc1X0Zb6cQ\nolZK2QcghKgl04QYIHmg84mDbJt2RBBCrABulVJePL38ZUBKKe/cbbuDbeuW5xAYGBjI6QA/ODhI\nKBTCbrdTUVFxwF6dx4Ki5e/AaNtlpJMhP/71z8/hiOaWB/72c37z+N1YnQbeXOclksxkijSWl/Do\nT7+Hw2bln675FBt3jmMwZbTohUxy+hIfBR47n7z0Ft529iVzeQlHHCEEUspDTlQXQsj3f+W0WW37\n0B0b9nsuIUQ9sBpok1KG97HNPwE/BXYAAmgAPj293yellD/c3xjmKua9BmgWQtQJIczAB4G/ztFY\nTnlmFmaMj4/jcDiYP38+NTU19Pf372fPY4cWDZJWU5nX8RipwMQB9ji5+dCln+KiMz+Awaiw+DQ/\ntRWD1JVN8OAPv0FxoRebzcZt/36jbrgBpDATiRhQifObJ/97Dkd/cjMdMnkI+Pd9GW4AKeXjwDzg\nP4B/B+ZLKR+TUkYOZLhhjsImUkpNCHE9sIrMDeReKeWWA+yW5yhhMBjo7OzE5XIRCoVobW3V37Na\nrZnH7jmuvAtseh17dQvCYqHc7eD5J59g1XPbM+5KtY3mplOvfP/ic9/P2q5/gCVMXWOSty16L9WV\nuyZtW5sbKfM4GZnK2A8DcVzuNCBIaftNZMizG6O9Ycb69mmHdYQQRjKG+/7pSsq9bfM2KeUzu+ma\nADRNPz38eTZjmrPnYSnlk2Rq/PPMIYlEgnQ6jd1uRwhBJBJB0zQMhkzeXTweZ3BwECklhYWF2O32\nORtrdCBTCj44NMamHWms9kw2TLdvioICP8VFx08a47GgtamN2z55L+u2vEyBo5h3nHtpzvulxUVc\n88/L+Nmf/0Rag/LSEHanAZmGi1ZcOUejPjEprXNSWrdL92XLiyP72vSXwGYp5d37OdwFwDPk6ppk\nkcDxbbzzHB+Mj4/j9/txuzMd1svKyli3bh01NTXEYplc42yu986dO6mrqzskL3xsbIxUKoWUEofD\nsUcz54MhEIxhsexKY7TaPEwFAqec8QZoqptPU92+faCSEjttp2VSPFMJiAQ0brnmfznrrefvc588\nh4YQ4hzgX4GNQoj1ZAzxzdOOqo6U8uvT/37scM6XN96nOFm97traWiDjiY+NjeF2u5mYmKCgoIDR\n0VFKS0uprKxkfHyckpKD0xAJBAKYTCZ9v7GxMaLR6CF78aXFbgZGAlhtmRtAPDpBaWu+WcTeOOu0\nt/PUmj8Q0wIYzQrnLLxkD8P9l6d+T9/Qdi4881IWL1g6RyM98ZFSvgQcsFRMCPG5AxznrtmcL2+8\nT3E0TcvpDG+xWDCbzYyOjrJo0SIgU105Pj6OqqqHpDAYiURyJGVLSkoYHBw8ZONdXFTAgsYkOwdG\nMnn2890nZaf4I0GRt4z/+MB36RrYiMvu4eLzc8Os//n969g0/BJGk8KzGx/k2ktv5+ILT8h6uROJ\nrGD6fGA5u5I13gO8PtuD5I33KY7T6aS3txePxwNkYtxGo1EPo0CmurK/v5/i4mLMZvO+DrVPzGZz\njqcdCAQOKzc7mUxS6HVQU31yaakfLqteeIRHX7yf4FSQlrrFGISJN7r/gSZTFJprWLJgOd19bTTX\nZyakn335SdZ1P4fDYyYV10glNe7+w5d5du2jfOcL98z5JPXJipTyNgAhxPPAMillaHr5VuCx2R4n\nb7xPcYQQeL1edu7ciaZpRKNR6uvrc8rjpZSYTCbi8Tg+nw9VVSkpKZm151xSUoLP58Pv9yOlxGw2\nH3ITi/aNO+kbTqMYTDgtfZx3dmveyADbujfzi8e/QSQUxWI38trWQQxGBaNZwYAgkO5n1Ws9rO96\nnu9c/xs2b1/P3Q9+SRerSiU07J7MjXnH5Br+8Oi9fPCyT8zlJZ0KlJFbjJOcXjcr8sY7Dy6Xi4mJ\nCZqamgAYHh7W1QQVRSEej2OxWPS4OEB/f/9BhT2ORCee8fFJhgNW3AWZEEk67WFrVz+tLbUH2PPk\nxzfSixRqpgWaUSEZ05BopBIaAGK6omNiaoSPfvUiZAoMDo1ERMVoVmDG/U8IQf9YT87xw5EQv3/i\n50xMjXJ6yzlcdN5lx+rSTmZ+DbwuhHh4evmfgf+b7c55432KE41G6erqytFjKS8vx+fz5RjcmU0p\nABTl2Nd3hSMxLJZd4RxFUVDVfAUuQGvjYuymAqJyFACDUaClJTZXpkF2MqYSiSRxuM1YXOAfjuJy\nWXB6LUSnkqjJNEIIhACzzcDyRecB0NWzhcee+QOvtj9PzDCK2WpkTddTmIwmLjzr3XN2vScDUspv\nCSGeAM6bXvUxKeX62e6fN96nMKFQSM80iUQieuNZKSXj4+NApoAnkUgwMTHB2NgYxcXFlJWV5SgQ\nHitqqsvp6unC4siU8scio1S3HH73nL6+PgwGA1JKLBbLQWfTHA9UlFXz5av/hwefuoc3t71MLBrF\nU7LrychsMxL2J5BpSMRSFFU5MBgzN2ChQDSYxOYyoWlpik3NrFxxMZ07OvjSD67GYNMwOBS0iCQZ\nUzHbjGzueSNvvI8AUsp1wLpD2TdvvE9hgsGg7l0PDAwQj8ex2Wy6SJUQgq1bt+JyuZg/fz4Oh4Nw\nOMyWLVtoa2vTGzSUl5cjhGBiYoJ4PCOE5PF4jngXcoPBwPlnNdC5bRCJQltTEQWewzvH0NAQFRUV\nmEwZD3V4eJhkMnlIE7NzzYLmNr7WfDd/XvVr7nvsTpJxDbM1k7kmVQMS0NQ0RrNBN9wAWkpSUJox\n9AaDwki8i/d8ahlOuweVBObpEnubw0Q0mMRklZQXnlwqjicieeOdB4Dq6mq2b99OT08PVVVVDAwM\nkE6n9fBINjvE6XTi9Xrp7u6moaEBgB07dlBcXIyiKPrNYHBwELPZfMT7X1osFk5b3DCrbQcGBjLN\neDWNysrKvQpsZSdjsxQVFeH3+w95QnWukFLyj+efYCrsp7F2Pm5nAcFwgFgojcVg55OX38y9j36H\ncDSI3WMkGkxid2eMcjyW0sMrkAl/x9MR0mqcaDChbwegaGbevuRK3nvR1cf6EvPsRt54n8J4PB6G\nh4cpLy8nFosRi8VYunSp7nXu2LEDi8WiZyRkicViNDU16Ya9qamJDRs2sHTprgKPyspKBgcHc7rz\nHEsGBgYoLy/XDfbOnTupr6/fYzuLxUIoFNKfEoaGhnIUFo8XpJQ88OjPWLPlWdyOQj7+zzdRV90I\nZBpp3PjNDzIc24pMQzpq4YYP3sbmnWuwWRy89x0fJRqL8M7hK3jihYfQtDAWm5HQZAI1qWF3m3Rj\nrmlpQpMJvOUZT9ziMBIYjVJQaicaSmK1uvjUFV+ckzmPPLnkjfcpjNPpxGAw4PP5MJvNFBUV5YQL\niouLkVLqyoJVVVWMjIygqmpOep4QAqPRSCKR0D3tQCDA1NQUkKni3L37+dFGUZQcT3umdz2ToqIi\nRkZGCIVCpNNpCgsLj7phGh0dJZVK6eOcTYPnv7/4CA+/8hOEEPim4K7ffIW7v/wHAP7x4t8Yjm3N\nTDgaAHuC19pX85XrMgrL7ZvX8r0HPks8HcLkNnHOvCt55vVHEYYUJosBi91EWksTC6WYGo/iKd4V\nKzcYFBCCyaEINpcZDCmSyeQx/z7z7EneeJ/i2Gw2PdQxMjJCLBbTf5gTExM0NDRgMpno7u5mfHyc\nlpYWiouL6enpobEx4/lt3rwZl8tFZ2cnJSUlaJrGwMAAK1asQAhBT08P5eXlx/QHr2lazvL+Jlhn\nVpgebaampnLy3MPhMBMTEwfsUuQb68m5YfrGe/Sw1h+e/HnuzVSBZGpX+vBjL/2OeDpENJhEiBSP\nv/oAP/nPv+Ib7ON7991MKhnBZDZgNMOChmX0jm3GNmMqwWwxYC+xEZlKsGLBu/KG+zghb7xPcdLp\nNJqmYTKZKCsrY3BwkIGBAUwmE6lUis2bN1NcXKxPUI6OjmI0GjEajbS3t6OqKkuWLMFoNFJWVsbU\n1BQTExOceeaZukFpaGigt7eXurq6gxpbIBAgEolgtVoPugVbaWkpvb29mEwmVFWddeNkTdMYHR3F\nbrfrVacz38uqLR4qkUgkJ5TkdDr1J5T90VJ7Go+9LhBKJoQ1r3oxiqKQSCToH+nCaFVweDJPPcGR\nJB+7flf7REUomWwSpwmhCGwu+PY9n+U/r/0hbzv3Irbv2Eqhp5TL3/lvvLL+WbSUSig6STg5AQKs\njoyZ8DrK+fzHvnVY15/nyJE33qcwPp9PD3mEw2EaGhrQNA273U44HEYIgdVq1T3TyspKtm7dyvz5\n80kmkySTSaLRqB4Tt1qtTE5OApBKpfQQipSSYDC3M/nU1BSRSAQhhB42kFLqBjcWi2EymairqyMc\nDutZITMJBoMEg0H9JjEzL91isRz0zSIejzM8PExtbS3hcFjPdff7/YTDYcxmM/F4/KCqS2eSTCaJ\nxWJs2rSJ1tZWDAYDU1NTOJ0H1mU5561vIxK7ldc3P4PHWcSH/unTAAyPDWKyGrE4DESDSRJRlfm1\nS6mt2jWpe/mFH2H1G48hlF3e+WRoiG/fdwP+eCZ/f8o/xM9/N0SffxM2l4mEqhIOxCmudmIwKmhq\nmqsvuSEf6z6OyBvvUxS/34/H49ENR1FRER0dHbS2turx4b6+vpy4cTqdxul0MjAwQCQSYf78jBRp\nT08PNTU1KIrC5OQkFouF9vZ2Fi1ahM1mo6Ojg5aWFv04WQNfWVlJKpVi69atVFdX4/f7qa2tHmcq\nnAAAIABJREFU1Q1EX18fkPFOdzf+UkoCgYBe9RmPxxkZGTmsEMj4+Lg+qel2uwmFQnR3d2MwGDAY\nDCiKQl1d3UFXl0LGcA8PD9PU1ISUko6ODgoKCrBYLLPObLnovMu56LxdolGqqnLnrz5LOq2BzOh0\ne0psDId28OLapzn3rW8HMrrfZulkZiV2JBrG4FD15bgWZNA3gs1tJBnLpBja3RaScQ2kRqW3iYsv\n2L13QJ65JG+8T1FisViOprbBYCCdTudM7GVDJ0NDQ6RSKYxGI8PDw8RiMc4991x9u4aGBjZs2ICi\nKNTU1OjHXbNmDQaDgcbGxpyUwVgspnvJw8PDeL1eUqkUgUAgJ9PDbDbrcd3dM16CwaAeCskWFQ0O\nDhKJRLDb7UgpSafT2Gw2/e9A7K6REggEdA8ZMumPqqoekvc5Pj6u32iEELS1tTE4OHhYKYm9/TsZ\nDnajptKE/HE9VxuDxq/+9l+68X7m5ccRtihT4ynMFiNSSowWRS+4AYiHNUw2RU8ZjIVTpDWJzWnC\nanBz7RU3H/I48xwd8sb7FKW0tJS+vj49tDA4OIjBYEBVVd3bzjYhDoVCuudcWVnJpk2bcnLAs5OD\nHo8n54ZQXV2tx8F7ejITbJFIRI8lj4+PU1xcrBvWaDSaoz4YDAYpLy/X9cBn4na7GRgYwOl00t3d\nTWNjI9XV1QwMDOD3+2lpacFgMLBt2zacTifbt2+noKCAgoICQqEQhYWFWK3WnGM6nU7GxsYoKSkh\nnU4Ti8UYGxtD0zTS6TRut5uRkZFDMt5Sypx2cocbP58MTPBm5yukQgYKSu3EQqmc96PxMH0DvZhN\nZh5+7peYbEbMqszJ554ai6NNywuEJ1OU1O16mrA5TdR5lvDxK/6Dmop6SoqP3aTuXPL8i7MNtW04\nquOYDXnjfYowMjKCpmlomkZVVRXBYBBVVdm4caPekKGtrU0PlQSDQWw2W0boaDcj4/F46O7upr6+\nnnQ6TVdXF6eddhqvvvpqjnhVKpVC0zQ9ng4Zr3v9+vX6cYuLi/XtKysr6ejooLCwEE3TKCgoYHBw\nEKfTuYeHKoTA5XKxYcMG2tradKNoNptpamrSx9zS0oLP56OpqQmfz0dfXx9er1eflJ0ZF/d4PIRC\nIXw+H5AJS7hcLr1A6c0336S8vJzy8vKD/vzLy8vp6Oigra0NVVXZsmXLIXvdgSk/X/vxJxkO7SAU\njmBy2lEMQvekpZRYFQ+f/dFlSA1SUQVpSJGMqRiMArPNSDycxGI3YHVkjHkimKm+zFZeaqrG1e+9\njmWLzwRga/cmtva0U13WyLK2Mw9p3HmOLHnjfQowPDyM2+3WwwmbN2+mqqqKpqYmNE2jr69P93az\nxjcajeqedE9Pj+41RiIRDAYDzc3NbNiwAU3TWLZsGb29vTidTnbs2IHb7SYWixEIBDCbzTnZFTab\njerqasxmM8lkkh07duSoGTY2Nh4wnpxIJHTtlerqaj2kA5m4/N482mQyycDAAOeee67uxXd1den6\nLtFolHQ6TTwep6qqCq/XSzAYzNEd93q9aJo2q9S+mUgp6enpYeHChaxdu5aamhqWLFlCNBrVuxQd\nDGs2vsBgYDvxcIqiCjvJ2HRapAFKLfPwWEvZ6n8BNZkmGkpOh1NM2FwmJoeiVFCJP9yD0WIgHlYx\nWRQMVsnkUAKXN1OUVV3YyorlmY47r7/5Aj/4402oxCGt8OF3fpH3vP2DBzXmPEeevPE+BchmkMAu\njzUb3jAYDGiaxtTUlN7iLJFIoGkaoVCIgoIC6uvr6ejowOPxMDk5SVFRkZ4V0tbWxujoKGVlZRiN\nRqqqqkgmkxQXF2M0GikuLqavry/H804mk6TTaWpra0mlUmzatAlFUYhGo6RSKd3j3xuJRIKRkRFq\na2uRUtLe3k46nWbBggVYLBbC4TCjo6O0tbWhKAq9vb0YjUZUVWX+/Pk54RePx0M8HicYDOphoWg0\nmhNCmhnqSCQS9Pf366qLmqZRWlqK2Wzer5jV4OAgTU1NpFIp6uvr9W3tdjt+v/+gv0+7xUk8ksJR\nkJlHsDoViFv5j6vu4IX1T/BC+9+wu82oSRWLLTfcZLEZGJnqp9hdw7h/iIJKi359RlOSSCjJ+Usv\n5Ws3fF/f5+k1f8kYbgAlzTNv/CVvvI8D8sb7FGD3ApVEIqG/npiYwG6309zcjM/n0w1wX18fUkq6\nurooLy+noKCA6upqamtr9WrE2tpauru7mZqaYtmyZfp5slWa2fi5x+Nh8+bNOJ1OJiYmcLvdugFM\nJpPU1NTonXuy+irZAiDIpBWGw2EA3VP1+XwUFBTQ1tbG2NgY7e3tlJWVEY/HicViDA0NEY1GGRkZ\noby8nKqqKoaHh4nH43qsOxAI6FkyWex2OzabjXA4zKJFi9iyZQsWi4VAIIDFYqGqqorq6mpMJhOb\nNm3Sq1T350FnpFaF3tBi5vey+0TsbDj7rRdS+7cFTKS69XWKQeJ2eHlp82PItGR8IILNbYL0btIG\nERWn10xEjiAMWs5N0mw3MuoL8+a2F7jyprNoql7IdR/4GhZz7mSvxZQv0jkeyBvvk5hAIEAoFEJV\nVTo6OvB6vaTTaex2Ozt37qSyspLJyUnmzZsHoAtSDQwMUFtbq/+wN23ahM/n0wtXxsfHdU+6qamJ\nrVu3snVrpjx7y5YtGI1GYrGYrosSj8dxuVz6pF02TFJTU8PmzZtzWq5ZrVbKy8tzNFeyoYxsnvRM\n8ats9kdVVRVVVVX4fD4WLVqkj72pqYnu7oyRKy8vZ3BwUL8ZCCEYHh4mlUrpYRBN00ilUoTDYRKJ\nBI2NjXqsOmv0szopbrdbj+urqqofN1uwBOihmM7OTlpbW3E4HGzbtg273Y6qqgediw6Zm8EXr/kO\nX//5Jwgn/CTjGotrzsA30osiBNYCC44CC4HRGK7CjF43gJrSsLtMJKMaJqsBm8uUozwY9idxFVgw\neVRAZcfYen7y4Df4zJVfZ/vARkbDO0knDHjt5aRSqX1KDuQ5Nhy1jHshxNeFEANCiHXTfxfPeO8r\nQoguIcQWIcRFR2sMpzqhUIiamhoaGhpoa2tD0zRqamqoqqqirq6OYDC4R8ZFIpEgmUzmeGQul4uz\nzjoLu93O5s2b9xB4stvtmM1mWlpaWLBgAY2NjRQVFeH1elm7di2qqlJTU0NtbS1lZWV0dnYyPDwM\nZPKzx8bG8Pl8+Hw+JiYmSCQSukfq9/t1Qzg2NkZzc7N+3srKSl3RcGY5/O66K4lEQq9izGa2lJaW\n0tzcTENDA8XFxXR2dtLd3U17e7ueyldfX6971zM/p6zRCgQC+uc8OTmpTwYXFBToOepbtmxBURRs\nNhvr1q2jq6uLpqYmqqurqa+vP+QWbo11LXzuqruwGwuwu81sD7zGo8/dr6f+AVjsRmLhFKlUGmEQ\nuIttGYVABdSklpncTEv8I1GiwSTOAjM2Z64U7shkPzWVdSybdyHxsAoGlbW9T/B/D999SOPOc+Q4\n2uVSd0kpl03/PQkghFgAXAEsAN4N/FjkmxAeFXafuJu5LIRAVVXi8Tjd3d3EYjE9XhyLxXThJMh4\noy6XC5vNRltbGxs3bswxltly+ZnnCYfDOJ1Oli9fTmFhIcPDw4yOjuJyuWhtbaWwsJAdO3boHnBV\nVRVmsxmj0chLL71EeXk5oVCIiYkJvUBn97BDMpnUwxdZVcCsBjlkPPM33niDVCqFz+fj6aefprOz\nk2g0islk0uP+hYWFtLa2YjabKSsro66uDiklIyMj+iTtTILBIGvWrNHnD7L58dmenxaLRf88FEWh\nvr6euro6li1bhtfrxWAw6LH1bMHSwZJKpbjvL98nqYT06x4MdaEmd30vMi1xeMzItEQIiIVSxCMp\n0qk0iYhKYCSG2WbAZDZgd5sxmHZlmmRZUPsWAHaOZCovs9t0DbQf0rjzHDmOdthkb0b5cuD3UkoV\n2CmE6ALOAF47ymM56oz1+9j5j5eRKZWi0xfQtHzpgXc6iiSTuyrqst4hZBoCZysss2lvHR0dKIqC\n1+ultbWVdevWUV5eztTUVM6jvaIoFBYW6pKrFouFiooKtmzZom+TTqdJJpP6JKnT6WR4eBir1arH\nhbOG2m63U1xcjM/nw+v1smjRIioqKhgbGyOZTLJ48WKGhoYIhUIkk0n6+/upqalBCEF3dzdlZWWY\nTCZ8Pp9eeFRSUsLGjRtpaWmhsrKSRCJBe3s7bW1teDwe+vr68Pv9OJ1OPWSTLVMPBAJEo1G2b99O\nUVGRHiZau3YtxcXFxONxnE4n0WiUtrY2IFOktHnz5pzPPvvksLuGuNFoZGpqing8TmVlJeFw+JCk\nc//4xL3sGN6Qo7VtVMyc1ng+r29ZhaqpGIwKqbiGmtQwes0YTQaSMRUQ2D1m1JRGYDgKM3wnq9PI\naG8Yi81ARWED//GR2wGoKmpk69AafbvK4l1zEnnmhqNtvK8XQlwNrAU+L6WcAqqAV2Zs45ted0KT\nTCbp/M0juOOZSbvxgeewFripmjd3/8mrqqro7+9H0zQSiYTep3Lnzp0YDIacqsPi4mKcTic9PZnG\ns6eddhqDg4O63siCBQuAjE52cXExVqs1p89laWmpHu+enJzcQwjKYrEwPj6eU0FpMBgwmUxEo1GE\nELqxLyws1HVXAF3TZHBwkJKSEjweD1JKzjzzTPr6+vR490wKCwv1qk6LxUJhYaEefmlpaWH9+vXE\nYjEmJydJp9M4HA7Kysqw2+1s2LCB5cuX5xxPVVWqq6sJh8OkUqk9cr1NJpOuvxIMBvWnnGzM22q1\n6nn24XAYi8WiT7oeyqTl8GQ/FruRSCCJ3WMirUr+7R038r53Xc2fnvw19z3+nYy2t5RY7EaMpsx4\nzDYjqWQau9uMlJJwIIHDbSYSSJBOSxJRjeJqB1KD6664hVA4RFFhER/55xtJpGL0DG2htqyFa977\nuYMec54jy2EZbyHE38ltVS8ACXwV+DFwu5RSCiG+CXwf+MTBnuPWW2/VX69cuZKVK1cexoiPHiMD\nPlwxTfdirMLAVJ9vTo23yWSipqYGn8+XE6eur6+nvb2d6upq3UCmUimmpqZwuVxs2rQJq9WaY3Re\nffVVvF4vtbW1WK1WJiYmCAQCCCF0DzJr4KWUbNq0SdcaGR0dxWKxYLFY9Jjv0NAQJpOJ4uJi/H4/\nsVgsZ+xZg5ZtSaaqqp69MrPUfl9Virtn2KiqmrNsNpvRNA2v16tXfIbDYf3pY/dtnU4nQ0NDTE1N\n0dbWxsjICMFgUJ+0zGqBZ59gshO68+fPZ3h4GE3TkFLS3NxMR0cHTU1NFBcXMzo6SigUmtX3OZOF\nDct4ufNv2N0m4mGVtzS/jfe9K9Pd5n3vuppEMsafn72XYHgKkyX3M9LUNMmYSiKqYXEYUQyKnnZo\nNKUyHrum8Z0H/h8Om4fr3nsr5y1/p+6FHy+sXr2a1atXz/Uw5ozDMt5SynfOctN7gEenX/uAmQ3w\nqqfX7ZWZxvt4pqSygp1mgXs6VJxIq3grjo+S4mwrsGAwqMdvsznQWf2QrNfd0NCgG/qRkREcDgdO\np5P+/n5dKXB0dBSz2cyiRYvQNI329nY93ps9n8fjoauri0QiQUlJCSMjIyxYsABVVRkaGqKrq4uV\nK1fqmRtjY2NMTk7q8XGHw4HX62VoaIh0OtPZvLa2lt7eXv08Uso9jHKWbHjEbrcTjUaJRCJ6zvb4\n+DiVlZU5Rrq7u5uioiKSySR+vz8nv3tiYoLTTjsNIYRuaIUQBINBQqGQrr44MjLCvHnzsFgsDA4O\nomkaTqdzDy/d4/HoTxmlpaVMTEwc9Hf67gv+hXQ6TUf3WkoLKvngJZ/K+fw/dNm1vPuCD/DJ299F\nUosRj6Sw2I1Ep5LYHCYiwSSeEivRqSRma8YMaKpEKJDWJJGpzPspIvzmibs5b3nmpx6NRfnzqvuY\nDI2zfOEFnLVs5UGP/UixuzN32223zdlY5oKjFjYRQpRLKYenF98HdEy//ivwWyHED8iES5qB14/W\nOI4VVquVpg9eQu/fX4SUivf0hdQubDnwjkeZbS+vIbClm02JGPVvO4t5rRklQK/Xy9jYmJ5dks3d\nnmnQysrK8Pl8OJ1O3ZvMij9li1oMBgNutxu/358TEgkEApxzzjl0d3djtVr1DI1sHnn2JlJcXExv\nby+pVIqJiQm8Xi/V1dW6cdtdBrayslIv4VdVNaccfyZutxu3260XDGWfQBRFwW6352iwDA4OUldX\np3vxdrudNWvWUFhYSCwWY8GCBbohr66u1q9p5vX6/X4ikYj+VFBZWal/druz+9PCzFTJg+GSCz/A\nJRd+YJ/vez2FNFYtpGd8A5qaJhZOIdNgshqwpIwIkSmVj0wlKHHX0Na0gv7h7XSPv4mn2MrkUBSb\n04RD2TVJ/INf/ydv9j4DwIub/orBcDdnnHbuvoaQ5yhyNGPe3xVCLAXSwE7gWgAp5WYhxB+BzUAK\n+LQ8lKDfcUhlcwOVzbNrjnss6F7XztQTL2FRjJQCI0+/qhtvh8PBxo0b8Xg8erbI6OioniUCGY8z\nOzmnKIquvd3Z2ZlzHoPBgBCCrq4uHA4HqVQKt9uNEEI3+BMTE3vobUNmMjN7vplpgPvCZDLt02Dv\njWzBkBBiv70pZxpUq9W61zg6ZAyt0WjkjTfeoKSkRL8pBYPBPSYn9/ffOhKJ4HA4GBkZmZWe96Fy\n/ZW3cc+f7mCHbzOx1DiOAjNOQxmqJaPjbbIYMJgUJiM+Vm98CJvLhM1lJhJIUlBqRTEoGKRFlyDY\n2P0qTH9UUmhs2PZK3njPEUfNeEspP7yf9+4A7jha586TIbjTh1nZ9RXH+0dyNErMZjMmk0k3mkVF\nRXR2dlJRUYGmaQwODlJTU4PJZKKiogIpJd3d3dTW1rJlyxYaGxuZmJjAarWSTCapqKjQPebe3l6k\nlHoBTTAY1D3mvU347Y3u9RuZ7OjCYLMw753n4fQcmod6ILxeb07GR19f3377Strtds477zz6+vp0\nKd2s/nd2nmBwcDDHu59JVVUVExMTTE1NUVBQcNDa4H975o/85bn7CEemqC9fyKc+8EUa6/b+lFdb\nVc83bvwZAJ3bOxgeG+TlN5/h5XWjqI4kFmsmlOL0WoiFUgiREbiyOoy6SFVQ+nj21ce56LzLKfFU\nMBzeAWRuTkWeA/ffPJUQQtwLXAqMSCmXHM1z5SssT1L6+/sJCw2HTKOIzI9QKXDqoYOenh5OP/30\nnLzpgoICTCaTHsNdsWKF/l4oFNJFpLJFK+FwmNLSUqLRqC5ENTExoRfk9Pf369opWYnWWY9/yzZG\nH34Gm8jss35whPNuvOYIfTq52Gw2pJS6mmA2/fBA7P4E4PV6GR0d1bNt9qchvjdhq6GRAbbsaKey\nrJbWpra97re5q51frboTFA3M0Dn0Gjf/6Bo++c9f5S2LV7D6lSdZ9fqD2GwOPvD2T7Bi2QX6vq3N\nbTzyzP28tOlR7CVmUkkFEfLg9Gby6C12I7FAGmGSGJXcLF8tnZlb+MyVt/GzP30Lf2iMZfPO5/K3\nX3XAz+kU4z7gR8Cvj/aJ8sb7JCMSibB9+3ZMJhOli1oYS6RQe4YIp1O89arLKauuprOzk6qqKmw2\nmz5JCLse87Ml7VnN7vHxcQwGA0uXLmViYoKJiQmKi4t17zGZTOo5z6lUSvdiDya8sTtTO/p0ww3A\n4ESO1veRxm63H5FjH6rMa+eODu741Q1EVT9CGvjYxTfz7gv+ZY/thsb6M4Z7GpPFwNjgCP/1wOdQ\n42C2K9jdJojD9//wOW5z/R8L5y3Wt9+w7TU9N9xkNpBMTdFavIwdo+swKzauvfIrjAfGefLFPxBV\nMkVKle4WzlmWmbBsbWrjB1/83SFd46mAlPJFIcTBax4cAnnjfZLh8/lwOBw4HA4KCwv1icVUKsWa\nNWvwTYxRUFCAwWBgaGgIq9XKwMAAmqYxPj7OkiVLMJlMSCnp7OzE6XSSTqf10EBRURF9fX0UFhYy\nOjpKTU1NjjJedt/Dxeh1k5rx1KA5rSd11/IHHvsxUTXzOUqh8bcX79+r8W5tXIJFuEjITNZLNJjA\nXWzFaDbgH45ic80obzdodO1szzHeXncxo/GAvqwIwe2f+Sm+oX4KC4rY1ruJ+566g6gaJjKR4vSW\n8/ja9T/A5ZzRTj7PcUG+m+hJhqZpNDc3I6XMyYc2mUw4HA697NztdjM0NMTQ0BB+v5/KykoqKir0\ncEE2dFJTU0NdXZ0+8QjordEaGxv1prxZksnkEWlSO//s5cjT5zFlUQh6rLRceckh64Ac7/zmkZ/w\nxrbnctaNB0b5wa9uoXNHR876qvIabrnmJzQUnk46aiERkRjNmScUo0UhEd2VOplWJVXluXUGn//w\nHSjJzE0wnZZcctaHsVgsNNY3U1Dg5bl1fyMSDYMQeMttbB9/ndfbnz8al31CkQyME+7t1P+OB/Ke\n90lGNq6c1dHOhi62b9/O/PnzsVqtFBYW0tXVxbJlyxBC6OJQM/VKgBx9E8jkhgeDQZxOZ06j32y5\nvKIopNPp/WZ1zBYhBMve+25472Ef6rhGVVX++tJ9GM0K8UgKq8NEMq6SVqd4eeujvLnjRb5z/W+o\nKN2V+TK/qY3vfuE+AH58/538Y+NvMRgVLDYTUX8KNZVGpiUG1cbYxGDO+Rrr53H7tb/g1fWrmdfQ\nxtnLL8h532Z2ZHpX2jOmwWhW+NuL9/P2sy85yp/E8Y25oBhzwa6uT9G+bXM4mgx5432S4fV68fv9\neL1eCgoK9G432ZJ2yKTCuVwu3ZPNri8pKdGbLGQrGrNEo1ECgQB2u32PDu0ul0sXhspz8AgUzFYD\nakoQGI5iL7BgdWZ+mlHVz7aeTTnGeyafvvpLOP7k4o2tq7F6PHSmXyERU3EVWhBCcu+TtxONR/iX\nizPJX6tffZKf/fVWVOJUds9jXuMCSop2xenff9HHeW7tY8CupymjIS/9epAI9q7rdETJh01OMrJd\nWnp6elj7m4cJ/GU10efXM9Lbr2+TTCb38Ko1TcNqtVJXV6dPNjY3N9Pf34/P5yMYDLJo0aI9Ssfz\nHB5Go5EPXPj/0FKSVFwjM12wa85AagoVJft/kvnIv3ya/775j5yz9O1Y7EZMZoN+YxaK4M2ul/Rt\nf/vUf+tdcQaDXfz12ftzjlVeUsm3bvglDmMmG8aiuHj/2z95BK701EAI8QDwMtAihOgTQnzsaJ0r\n73kfBUZ7+xnr2omlwEXzWw9PWXB0dBRVVbHZbLM2nF6vl74X11I0EgIM4I8ReH0z/fW1emijpKRE\nT+XL5mjvjtlspqamZs8T5DmiLJm/nEdfKiJinEBRDMQjSbSURKDwoXdeT0vjwlkdZ1HLUrR/ZKRg\n02lJPJzJ27bV7noqSqnJnH1ULbX7YWhpXMgPP/8Q3f1dVJXVUlZy8A2XT1WklB86VufKG+8jjG/b\nDnofeAyHVEik06zrH8rEbg+BmQp+U1NTOa22/BOTTE1MUllfq1cRzkQLRpiZVS1i8T0M8b6KSI4G\nI30DhMYnKWusw1XgOWbnPRH4/VM/JZqeRAiBxSEosFexeN5befc5H6Rt/ul7bN+xdT2/feJHhMJT\nlHvrOX3hCi5ccQnNDa18+vJv8suHv8v4+Cie0unOPyObmfCPU+Qt5uIVH+ShF/4HoYDD6OVtZ1xO\nMBjMCaMBFHi8LPOcccw+gzwHT954H2FG12/GITPRKIOiENjYBYdovBVF0ePRHo9Hz/bY/vo6xh57\nAZtU6PdYWXrNB3B5cw2xe14t/s6deoWlrWHuVHe3vbwG/5MvYxUG2i2Clg+/l5LqXP1qKSXbXnsD\nLRKjbME8iipPHW9vZCJXl628uIabrrlzr9vG43G+/9ubmIqPkoiqjER3sMH3NC+8+QS3f+anvOPc\nS2msms8Xf/Z+fR9/YpBXNzzLJSs/wAcv+QTzatuY8A9jszj53v1fYCw4QGNpG1/++F2UFB0fYmp5\nDkw+5n2EUSy5kzvCfOj3x93zpbPLA0+9iJ1MXNMdTND9/J59LJrPWIb3sgvQFtSirFjI0ives99z\nxeNxvRXZ7vKsh8vI82uxThfcOBOS/hfX7rHNmt8/QuzxV0g9v4H1//NbXrn/IdofeYrJ4dEjOpaj\nyfadnTy2+kE6Otcd1H6BgB81lZGwVZMaRiz73Pb/t3feYXaV5aL/fWv3Pr1kSmbSK4F0CKH3DgcB\nsQGConjVq8cDXFTUW45ej0evqCgeEVQEEZTeEiCBQHompE8mmZLpffe+13f/WDt7ZjKZ1Kmwfs8z\nz+z1rfK9q+x3f+v93tLc2kQg0Uk8khpQiOFg5zZ2VlcB4HC4UPqNy6SUPPfW47z5/gsALJq/nMvO\nu4HX1v+V3mgzRrPgkHc3f3vjsZOSW2ds0Ufew8zUC8+hqq4Je0+IkCIpv/KSUz6WyWTKeI60tbVl\nEhjJpEr/312ZOHpa1GlLzoIlg1+7jyQej9PR0ZFxK2xsbKSgoGCAn/iwcsSPkt/ro2V9FXaDCY/N\njoxGsR9oQ9LGnt0HWfCV2we9WYw3Nu/4gJ//7dskZBRUA1+86iGuOO+mE9rX4bQR8KokoikUg6C4\naOiqOqWTyjDHswhE2zAYRaZmpVTBYXMAUFhQxG0XfpOn3/4FKRJEAgnIauG/Xv2fFOWWsmDOYgCC\nEf+AY4eiA5d1xjf6yHuYcXrcrPjGXRTfcxNLHvgyZfNmc6BqJwc/2nXSkYcFBQUYjcZMxZXDRQOy\nF88hoWo+2SEDFC+ef6zDHJeurq4BoexlZWV0dXWd1jH7k3/uQpoDvXQEfPTIBKUrFmXWBX1+dvz+\nGSqz88l3uWno7qDA1WcTd8VU2mpqh02WkWLVxn9oihtASfHWxr+f8L6XLv1UJqc2qpEcx9GVt5SS\nnz3xEHGzF0s6J3c0lEBNwjVLv8isaX35UG66/HM8cNtvCfti2D1avhqUFC+/+3TmOVxThlF7AAAg\nAElEQVRxxhWZ31EhjaxYcMXRutUZp+gj72GmufoAXbv2o1gtuFZ62Pr4s7g6Akgp+XDrLs6589Yh\nIwXDoRAdh5rJKsgjK1fLN3I0H+ozr72M2vJJxH1BSqdXkFt8evbhwx4nhyc+k8kkiqKQSqXY9veX\niRxsxuCyM+OGS8kvP/kAnGBHNwV2NyaDAa/NgD2dHbCzqYWPnn4JtaWLlNFEUk3hstrY39bM1IJi\nDIpCXE2SM0LZBIcTi2ngW4rZZB1iy8F86qo7qW8+yMaDLyMU+OfG35Cdlc1VF9w8YLvWtha2HHyL\nSCCBzWXCnWPF1xmhYsoc7rjpv2W2SyQS/Pov/4f3P3oFu1vLFmgyG1AMgo373+Dpl6dz+3X3ct7i\nK6ht3EcwFOC6C25n+aLz2bZrI23djcydspDJZXqdyvGMrryHkZYDdRz666vYMaAC7279iMmpvnzS\n9voOGvbtp2L2zEH7dre0se9PL+CMJGlVJEU3XEjlgqNnlgOYcox1J0thYSH19fXk5ORkKs1UVlay\nc9VaTHsaMQsB3SH2/f0N8r99cpXsQsEQ0S17caWVW1YkRcOm7Uxedhb7//QC+TEV3FnUd7dT5M7G\n6nRrqWe9XWR5POSds4CymcfP8z3W3Hzx3exv2kFPuAmHMZtPX/bVk9q/zVeHSGfyE0Kyac+7g5S3\nw+EkHhI4PObMtp5CGx3etgHb/fnFX7Fq69/SNnEFh8dCsCeGwaxgc5rYUbuRS7tv5OHf3YM32grA\nC2sTNHXU88zaXyAUiUVx8sDnHjmqt4vO+EBX3sNIT3Ut9n4OempHLxwxey+GyPtR9+4GnBHNdm1X\nBU2r1x9TeQ83FRUV+P1+VFXN1F9MeQMD3hIS3lOziQ4yFwlBR0MjzlhfnUmbyYLV1PdDl1eQz8qH\n7psw+Uwml03h5996lvqmWkoKS/F4Ts5G73HkQk+/ZWfO4G3cHlbOu4Ytja9m2gwGhSl5A/3Aa1v3\nDLpuBpOmuAFyXYVs3f1BRnEDHOjcSntvE0LR7lVMDfL25hd05T2O0W3ew4jitA1QVM7cHAKF2khS\nlSqRKUWUzzj6KFIcUTCXpHrU7UYSt9udsasDuCrLiPXLd2ItP/nE+w6nA+fy+Rkbvd9tpWLZWXjy\n84jQd47x1MBJV8VpmzCK+zB2u505M+adtOIGuOO6b1HqngVxE1PyzuSzV3/tqNsF4z0EvbHMsiHu\n5nv3/XzANmUF2jN22INFTakUuSuxiWxmFS/njhv+Oy7bwKr1BsxwxCNnNo7QhLXOsKCPvIeRWSuX\ns6WpneC+Oox2KzOuvZKS2dOp370XRTFQWVTAtmdeQo1EyZo7jenL+ibuCpfMp7G2GbsqiKlJcpct\nHMMz0Zi6eAFSTeHb34DBaWfhZecBUP3BJsLNHViL85h17rLjKtmzrr+cprnTiYXCzJo5DYvVisPl\nxHft+TSv2QgpSdnKiwjXNxNpaMWU5WLGjZeNximOG8pLKvj5A89kcqgfDSkl+5uqsDlNmgcJcNXZ\nV2bykPf6enjkrw9zoGk32Y4iFNWEx5HFjRfewYXnDJyMzMsp4KOa23i76jlMigmPuYhm334Ug5Zn\npcg1hRsv+sLInrTOaSHGc/lIIcTHpbwlAO//4g+4ezUf6piapOCWy6iY3/fK29nUQnfdIdrqD+FI\ngjk3iykXnk3bvhqEwcjUs+aP+Wh05+r3SKzdjkFRUKWKsmwOC64+dXdInZPjvv/9L3SE+sqQfe7i\nB7j+Eq2azXd+fCd7mzchFLA6TCyacikP3P3TYx4vHo+z6v1XeOLtHwGQiKWIR1P8+1f/woK5Yz+A\nOBmEEEgpT/kLIoSQBSuvO6FtO95/6bT6Gg70kfcoEY1GkW3dYNFGSRbFSKC+Gfop7/zSSbTtrcFT\n04YiFBIHW3hv/TZKzU6klLz0jze4/KGvY7WduCdDf2q376K7ag/CZKTy4hXkFp94NF31B5vordqL\nt6mVYovmb64IhXBt0ynJMhHZumsDVfvWkeMq5PpLbj+psm7Dxdc//UP+658/odffxcKZ53HtRbcC\nWk7wOu82bC4TyXgKb3uYDaHV/PzJ73Pfp7971BQKAPFEjGfeejRTVNhkMWA0K4OKKeuMP/Q7NEpY\nLBZUjwOi2ptEUk3hyh+caCra2IY5XT2mJxSk1K3ZT4UQlCtW1v7xaS7/qpaoLB6P07i7GmFUqJw3\n55ij8uYDdXT84+1MabE9zf/k7G/ffUJf0kN79uN740NsihFfNEH/AECDY2TKko03Nu/4gP94+puo\nQjNX1LfW8K07fnRax9xdvYNwJMAZsxefcEDUzCnz+Om3/zyofcPuVRkPFKPZgMVuwuSQfFj9EgVv\nTuIz19571ONt2L6WMO1EfEkcHjNSSqblLmXOjNOLHdAZeXTlPUoIIZj+qavY8/TLiEQS14LpzFi+\neNB2xiwXNHTSFfDR6tWSFaXUFAWuLCQS1RcCIB6Ls+mxp3B1hVClyodbdnPOHZ8aUoH7GpoG1IR0\nhuK0NTZRWllxXNkDLe1Y0jlS3DY7Ld5uTA471qI85l1z0Ulfi67WNrzNbWSXFpNbNDFyaWzeszaj\nuAGqak6vuszjz/2c17Y8iVBgSt6ZPHzvr7HbHcfcJxAM8PvnfsKh9hoqJ83hnpu/g92m/Xi67Nm0\nB/u27W9u7PS2oKoqtfUHyPLkkJfbV1TAYrIgFIHNaSQSSCAl3POlfxtz85zO8dGV9yghpaR21ToK\nkgoIM927DnJoQS3l07VAiOptO+ioayB/aiXV23djSCRYUFZ52I5HbWcrFpOZSYvPBqB++05cXZoi\nV4SCva6NQzUHmTyEN4u9MJdONZlJVBU0wuwTDO7xlBbRrG7Dqhiwmy0knFbO/rcvHbXCejKZpL2l\nlcJJxUcd1Tfs2kvL86uwqwodQqXk5kuZPG/2gG0ioRC7XlxFqteHeVIBC667bExMFP3JduYNXHbk\nDbHl8enobOfVTU+SvhXUdm3n3U2vcfUFnzrmfn/4x3+wfv8rADT7qrG+ZOXLt94PwBeu+Sbf/9W9\nBONeSBgwu7S3N6nCzLIz+e4vv8z+9k0YsHDnlQ9kQvdXLL6Y9TuuZNOB17E6zFy77C6mVk4/5XOb\n6HS8/9JYi3DCnJbyFkLcDPwAmA0skVJu67fuQeAuIAl8Q0r5Vrp9IfAEYAVek1J+83RkGG9IKVFV\ndYCykVLyxqNPUNgaAEUhFIuipJJ89OhTiLtvpXbTNuIfHSDP5WbrG2uZXVROqwj1JdQXAovZQv7K\nRZxxzSWZtoH9knltPhoV8+YQbO/Gt30fmIxUXr4yk7HweJTOmk7k6hX0VO1DmIzMunTFAMXduGc/\n7Vt2Eo1ECDS1UoiFzWEf2QV5WLLdTL3yAvLLtKyGdavXEenxEjQYSKkpwm+sHaS8dz3/BuaDmg+y\nbPezy2RkwTWXDpKrq6MTk8mEZxTyntx4yeepb6lm+4H15HkK+fLN3z3lY6mqypHT8FIe3zW0uXNg\nmoCmzrrM5217PkC1BXHYtQLQgU7J3PKlXLj4WnyhXmo6NiOEQCXOk6/+jEtXXI/BYEBRFL7zxX+n\nruFuTCYzZSXlR3arM0453ZH3TrQqg7/r3yiEmA3cgqbUS4HVQojpadeRR4EvSik3CyFeE0JcLqV8\n8zTlGBesfvQJkjWNGIUBKou4+L47EUKw+5112OvaiRmM+KMRHBYrdpOZlt5uat96n1BtE8VZ2TT3\ndpPvzMKgKMSSR9SPBHLnTs/8KFScNZ8Nm3fi7giQUlXiM0sonzb1mPLNu3glXLzylM5t+vLFsHwx\nQX+A1uoaIuEIk2fPoONQE03Pvo5dGjABsXCCtkSIUnc2pnAKwr3seeolzrv/XoQQBDt7KcvqC0Bp\n7uwd1Fe0rYvD02tCCGJt3QPWSynZ8JfnEfsaUZE4zjljxD1erFYrD37pZ0gpT9ukUFRYzCVn3sar\nH/4JIQQzyhZwwdKrjrvftJL51HXtIBlTMVoUpk2am1m3t2HbgB97kw0SaoRLzr2Wp18Z8PUkrkZJ\nJpOZZ0kIwZSK8R/FqjOQ01LeUspqADH4ab4eeEZKmQTqhRA1wFIhRAPgklJuTm/3J+AGYEIr72Aw\nyIGqHZhq28h3aZOQakeQ3es2Mm/lclK9ARShUHWolgXlU3BatBHvnEnl1Hd1YxHQ5vNiVAyoUrK3\ntZEsm4Omni7MJhMpNYWUkoaX36FkcjlWuw2TycTyL93Omt/+CaXDi7mjl7baeoqmVGTkUlWV3avf\nI97Zi7U4nzkXrjgtxePr7mHnfz2LK5zEp6r0LKnH4nZil31vGXlON/taGynJ7jMrKN4goVAIp9OJ\nKy8HvH31Ea3OgXbeUCCI3+cjEo6jShWPzYGtYGC04cFtO7DWtKAYtdF/bP1uPnLa8G/eg4xEsc2q\nYPHN19Cy/yB1r61BjcRwzpnKWddfftqKdzhswVJKEsmYFvEoIMuRj912bHs3wMXLruPdLS+RUnyY\nk27OX9yn8AtzytnXuqmvD1UST2hVc1YuuoLVW57HH+9ASskFZ9zI+m3v0tbbyLypi/UoygnKSNm8\nS4D1/Zab021JoL9vWVO6fUISDgbZ+sRz0NRJMJXAlOp7GVaEgr9Vy0Wteux4wyFKsvOwmfpctoQQ\n5M2eSiISpWXDduZOKtdGTQYDgUgYfzRMoTsbRQjsVhueUIqGqp3MXKFVOKleu4H8zjBCsYAvSvXf\nX6fo/q9kjr/9xTdRqg5gFIJ4dSPvNTRy/p2fPuY5tdXV07D6Q2QiSd6S+Vpa2TSNG6twhbVISJOi\n0PDOBqRJochkz4S2+8IhHBYr0UQ80ybzPJl0tkVLziDw5npMigEpJZ65fSO+loZGdr26mlKjHdza\nRFyLiLP06osHyJiKxVFEXyCLSQg6Vm+gQLEAAnVnHXuLNtC+ZjM5SW271Nb91BTlHXWSeLTZs38n\n7+/5R8bMtaNpDRu2vcc5iy845n5/W/UYKXMQIwYSBHj6zV/z4D3/CcAXrv8GTW117KrfiFRVzFYz\nly3TbOilxZP5X195nK17PsRpdfPcW3/kjY1/BcBktvA/vvAISxecO3InrDMiHFd5CyFWAf1dAgRa\nhdSHpJQvj5Rgh/nBD36Q+XzBBRdwwQUXjHSXJ0zNOx/i7gyCxYYbG4d6+goHdEeCzDtnEalUis59\nB7GYTBS4PDR7uylNj0pbQn5WXPVZDGYT22paMqO6PKebeCJBWU4+LqsNk6HvNqlS0tLYSPXbH+Df\nWUOFs8/dMOUPafbOXi/VL79Nz679FNm1jHyKUIh8VMOa3/2F6RedQ8n0wRnjwqEwVb//G4ZIHBC0\n7z2INctDaXpbeURBbIOUlNqzaPf34o9GiAmJPxzEbrHS5jFRnJWDsJmZd/l5mX1mnruUXakkbTv3\nkUilmJzlJuDzs+ultzBVN5MlVZoDvszI3eV2D5qsLJ0/m6p123CFNNNST7YNa3tMm0VJn2v9+io8\n4TiYtUaDohDv8Z3gnR1ZEon4gNriQghS8ug52fsTivgyXiRCCIKRQGady+nix9/+A3UNNVQ37KQk\nfzLzZ/dF8BYXlnJN4S28sOovtEX3ZQo5hP0x1m1/fUIq7zVr1rBmzZqxFmPMOK7yllIOnik6Ps1A\n/4KJpem2odqHpL/yHm+okdiA5DBWp5Nmk4pRUZh60zUUlJaw5blXMNS3k1RTpKRKkSebdn8voWSC\ncx64F3d2FqlUipR5oIIyzionXt9GbWcbU/KLMCoG9vs7KNy0g9AbG1DDQdRolIglnhnNhwwSf6+X\nPc++hr21FxKpAccUCOL76jnU2k3vpcuJ1bcipUrJOQspnlJBR3MLIhyj0KOZKaSU7Hv7fUqnT0FK\nSVKB5lgARxKcNjsmuw2AQrf2A9Lq7WFugZYyNhZKUXrLCkJdPXQ3NOHweLBYLbTVNdCzZguJHi+T\nsnJIrN3OpnVbcERTmEwWwECBO4uuoJ9chwtbxeDc1g6XkwVfuo2mql2gKJy7bCGbf/80dGm+cuF4\nDFsoTjcpnGnlHZUpSqdOPr0bPkycMWchZ5ZfxPZD72gupAWLWbbgvGPuI6UkHkkRDWpKXkjByisG\nl9ernDydyslDe4u09zQPMP0IIXDaPEgpWbXuJXr9HSyccy7TK2cPeYzxwpGDuR/+8IdjJ8wYMCzh\n8UKId4F/lVJuTS/PAZ4ClqGZRVYB06WUUgixAfg6sBl4FfillPKNIY47rsPjG/fup+np17ELzQQQ\nn1vO0luvH7DNB7/4A87eCC3eHoLRCE6bDaPHxYybL0eYTMR8fgqmVdJ5oI6ml9dgSgFTilh6xy1s\ne/xZDI2dHOxsw2I0YchyUW7sC4pp9/diUAx0B/y4bXZynS4CBolQFHKkkXA8hi8SSo/WBB6bnc6A\nj0J3FhEFctKJh3qiIcIlOcy5+iIO/PqZAcUQ2twmbEmVzuZWpmblE00m6AoFiNhMeFRBKhYn2+4k\nJSXxZIIch5Z7XEpJq9NAcTCFEIJAjp0lX/4MVU/9g/oN25k9aWAx5I6Ab2C/WSYmzZvF3ItXDpnr\noz+hQJAPH3sK2dSJUVHIdbrxR0IwuwKb0UTuGTNGNUvj8VBVlfVb16DKFMvOPH/ICMjDvP3BKzz6\nykN9k5IJM0/+aC02m+2kJlE/3Pou//nstxCG9PcqbON3D7/MM68+xju7/4YQArNw8j++8Gvmzlhw\nWuc42gxHePzJbD+hw+OFEDcAjwB5wCtCiO1SyiullHuEEM8Ce4AE8NV+Wvg+BroKHlVxTwTKZs9A\n+ZyRnv11mNxOFpw7uNq2KcsNvREmpT0sGp0KRqedD/75KtndYQyKQlOWkzPu+TSTH/46kXAYt8dD\nZ2MzTe1tJDo6KPZk0xMOEuvxQkGf8lZVSTQepciTjScd4JELtBlSkAS72YLVZGJfVyu5ZjveSIgc\nh4v97S0UebJplSHyXR5MKET31rHnwF8IyAQFaEo0kkwQafNSZM8iajDTEwqgKAp5dhd1na3EbHZK\ns3Op7+rAbjJj6ufX3ZAKMTnoyCgVV0+Yho920XGggQK3h0QqmTEHqVIl7OhTXh02wUVfu+uofuRD\n4XA5KT97IZE3NvQpMouJRbdem0ncNJ5QFIUVS048wMkf6hmgoJMiyup1r/LP9x8jFPVzzrwr+W+f\n/d5xf+jOWXQhieSP2bJ3LVajk8/f8DVcTjfrdr+eOX5cBvnwo7cmnPL+pKEnphphgl4fu55/jXhH\nL13hIMVJrXCwNxSgOCsXgGZvN4UrFrIoXSTY29nN+z/5LXlGC3aLldrONirzCvFGQhgUBbfVTkpV\nafP1UJKdx+7mBpwWG4oiCEbDGLLdOMxWUokkSiSGyWCkMB1m3+73km13YE57ahzq6cBqNFOQXt8Z\n8NIbCuGy2fBl2SgKq2TZHLT5ehECsu1OukMBij05SClp6u3CYDIyyZVNMBahV01QtPQMHJWlhF9c\nq7lNoo3EWzwmRGMHCoJYMk6e04MQgnYZ54zPXs/uPz6PRQWXzYH7kqXMvejk7LBSSjY+9Q+ie2pR\njUbKrz6Paf0yN05kGhpr+d5jdxFJeQGYXrCEQx3VxNByrEspufuKH3DF+TcSiUZ4YfVfCEa8LJ9/\nMfNnHT/B1D0/vApvrCWzfNM5X+XT13xpZE5mhNBH3jrDijPLw/Ivah4ebz70U+xmCx0BX0ZxA5Rk\n5dLe25eJv/NgHZaUxO7Q7LWqlHQGtUIJNpOZAx2tpFSVGYWaPbjIk00oHsWgKBgUI9lJBYdBoTsU\nQUWAYeAzeVhxAyBFRnED5LuyaO7twWIy4fFGaQz4cBRrhRLCsSi94RDFaZu4EILS7Dwacs2k8ouw\nGQSzzluaKcv2/kf7sNW1YxQKh3o6UXoFZTn5+KNhAtEwXUE/oUSMxXfdQu+OGsr7Tb52b9wBJ6m8\nhRAs/+y/ZHyYJ3KId9WujXyw4y2cVjc3XXonk8um8PDdv+P9qjexm+2ce9aVfO0/r8aQvpVCCHxh\n7Rn66eP3s7NJC99/p+p5vnvH7447ir7n+gd45O/fJxjrZV752Vx/0WdG9Px0Th9deY8madeww8UZ\nDru7RZMJKs5elFlnze5LlN8d9FPkycZpsWqjV283FqMRu9mSUU494QDTC/o8Lms6mukJBSh0Z2M0\nGGj392IJh/DY7PjCwcwoHEA6LHgjQbJsmitfLJkgz+VGVSXFnmyKPdnUdrZhM5sJxaPY03Ic7juR\nShHp6qEzIZl17cUD6mlmFxfSvrsWVUrKc/LxRkJ0+L1YzWamF5aQUJP4ynKYvmwRW+sGzlsL5dTD\n4Sd6Rrw9NTv44e/vJZGKIYRg9Ycv8+efrmLq5JlMndxXQu+MinPZ3bwOAIviZNHsFcTjcbYfXIch\nnecqSZSqfR8cV3kvPfM8npj/Nv6An+yswQnTdMYfeiWdUaTi2ovoDgcocHmoaW8hGIvijYYxLpuD\nVTGw5n//ijXf+0+aN1ZhnFuJPxYmqaqZoB4hBAiBeeFMzLMr6AoHaOztQhzx9ua22plZVIpBUWju\n7cRuttAZ8LHx4D7sFiv725po9fZw0N/Fim/cReGNF9Po76Hd76Xd7yUci1Gem5853pT8IhShML2w\nBH84xP72Jm2CNplkZ1Md06SNokCShj++QHP/Su8GhTynmwKXZh5JplKoUsVt1WzQJsWIsU0zA1Sc\nv5SgXVO6EaFScvHykbwV45rNO94jGotklr2xFt5aOzjnxv1f/Cm3rPw6Vy2+g+/f9TumVcwiFA6g\nJM2E/XGkKpFSkuU8sTwsBoNBV9wTiIk9RJlgzFy6kLzKcnZv3MqMSStwezwUFhZhd9hZ86NfkqMa\nwGBBHmjDdcFZFF59CTteXgUtff7JrtIiVt55O6AlgXrnl3+g46O9A0byKVUbtbusNrJsTjoCPjx2\nB7luzSVsRpHmzhdPJtj34SZW3HwdgZoGzAfbaPH24LE7SKkp/NEILquNZCqFUVFoNabImj2N7t37\n2dNyiAK3h/LcfIKxKNl2Jx6Ljarf/pVDM6cw/9ZrmHLOYrbtPoDLGyGmplCnFJHq8tE/sYcwaY9g\nbnERZ933OdoaGinNzyW3oO/Ho7uljQOvrSEViuCaVcH8yy8c0fs01oiUEYNRyfhiJ2IpDjbuRQtc\n7sNmtfGpK+/KLPf6evjub+5GOKLYMRPqSXHp8psGFTLW+XigK+9RJjc/j/OuuXxAWzweR0RimUIN\nQghSwTB5k4o4+7M3sfXJ50keagOXnWnX9UUbbl+9FnNrLwsnT6Wpt5NkSiWRSlGZ3xdTJZHEU0my\n7U4SqSQuqy2zzmw00bh5J9VFRcSDYdriQRQ1SZbNTUNPJ6VZuXQH/LTHw1z4nS8zabIWAfrig/9O\npcWdOU67vy8/iVmV2Jt72PjHZ7n4m3ez9L7P0XKwDpvTydKyEgK9XrY//hxOX5iIIim74oLMvnan\ngylzZw26Znuefgm3Xwv1jn+wg5os94ASch83li08n5eqHs0smywGzObj5/vesP1dOoJ9yars2QrX\nnveZE3K11BkehBBXAL9As2r8QUr5k5HqS1fe4wCz2YxpSgk0a0owgkr5HC3QwuZwcO5XP08ikRjg\nOtfV3ErnW+uZ5NLs12U5BTT1dlEf6MYTdJDnctMR8OINhzizbAqRRIxYMk7YH8tEeEYScXyhIP43\n1+NUDDjNThqNcZq9PUxPT4YWerJJ+BmQ9zurqAB6+/KTpFQVVap0+H140vmlkw1tNO6toWz2dCpm\n99lpXdlZnPPNO6k/UIsjmSTmD7J71XvkTq+gqGJwRrtUKqWN1s3aj45RGIgckajq40Z5SQUOQzZh\nVTMpyZRg4dyzj7ufxXSEz7c04DhOjvBjsau6iqdef4RoPMLFi2/kmotuOeVjfRIQQijAr4CLgRZg\nsxDiRSnlvpHoT1fe44TFn7+Z/Ws+QI3EKZs7nUnTKgesP9Lnubex+UgnEkwGIwtLphCOhPmo8SAm\ng4mS7FwMioLTYiOWSNAY6CKRTKIoBsLxKJNmVmL294Vm+4NBHEeM8iSSN7/3H0QDQWweN4biXAJx\nLy6zlVgyQTdJGhoPsrikEpPBmEmL66trpGz24Gi/1gN1tDz7Br6OLiZlafLVfVBF8rarKJ01cHuD\nwYCxIAe8mg04oabILik46es7kbDZbPzrZ37GX954hFg8wkWLbmDhvOPPAVyw/Ao27HyHrXWrEFLh\n5vO+SnHRqaUOCofD/MdT/0ooqf1QPvlWNZMKyk9Ijk8wS4EaKWUDgBDiGTRbl668P85YrBbmX3Hi\nQRuuonyMZhOtvl6KPdkEohEUAeFoBF8kQpEnh0lZuUQTcdr9XgrdWZiMRhbe+xk6N+7AmEhRPqMC\nW7aH0OvrMSgKXUE/ZsWAQRG0+XpJSZUCl4cuby+FKlRkaSN22eLngN1A9tkLaNm6k/nGQlLuPA50\ntJJl1+ptFrg8mHPcR5W94c33cScFEaMJQ/qV3i4NdHy0d5DyBpj32eupeW0NqXAE9+ypTF185ilc\n4YnF/NmL+MnsJ05qH0VRePBLP6X+UB12m53CghMrtnE0WtqaCCa6+kbxikpj60FdeR+bEqCx33IT\nmkIfEXTlPUEpqphM8ObLqH59DQfb2ilwuYmnUkzy5FDsySGWTGRCzv3RCFJKUlMn4V2zhdxggggp\n4opKVm4WHTMnEdq6F18oiNNiozRHU9IpVaW6tRG72ZJRsqDZ5K2dXg6u24QhmqAnKclxuCh0ZxMW\nKs6cLAxzpw6ZwU+NxdOfBr46KGYz3q5uOg7W48jNoST99pGVn8eSL+iTbieCEILKyYOTjp0sk8sq\nKXBW0BlqAMCAhZlT9IjL8YSuvCcw05acxbQlZ1G/YzeNqz5EaY5lRkqWfoE4kViU5jwXk/JyUZo1\nO6rfH8D53g6866sJiwTZNgdZVjvheCyzn0FRUDxOCgsLibT0ZUyUUhKKxZglLQvPKRAAAAskSURB\nVGCx0B7rpaG7HavJgmlmGSvv/cIx5c45aw6RtdswGYx0Bnxk2R2Ec52UTZ/MnkefxpEEr5rEd9Fi\n5lw48bLdjTRVuzZR07iLikkzRiwboMlk4sE7f8nfVz1GNB7lokXXMWvq+MkNM05pBvpP3Bw38d7p\noIfHf4xY98vHcXWHM8stvd0YjUZUu5kFd99K25ZdiJ21SCnpSCeoOkyzt4eSrByaeroyI+9oKknh\nLZeSU1bCntfXULt2PfkON4FomGJPDva0bbzF25PJ3RJPpci75RIqz5jLsajbsZtwRw+OkkJsHhcF\nxUVUPfsyhj2HMtv4zHDeQ18btuvzceCdD1/jty9/HymSSFVwx2UP6hOJacY6PF4IYQCq0SYsW4FN\nwKellHtPVaZjoY+8P0bMuP4yqp97nUSvH3NZIcXXLMdptVNcUY7D5URRFGr21mJPHJmZG4xlBXgj\ncYx5brpz7LicDnLmTKMirYSX334D2VPLqXv2dSbnFtAdDGA3W4gnE1j7TaaaDQYC9c1wHOV9NOUu\nxBEubbqL2yDe2/4qUqTTwiqS97a/qivvcYKUMiWE+BrwFn2ugiOiuEFX3h8rCiaXUvDte4ZeX16K\n6Uu30lFTh7JrL8lmrfRa0CxYdPsN5E469gTXzGULKZ0zg9Z9B1A7uvDvrUXGBFG1z9SSVFPsW/0+\nhzZWUXTuQqYuXYTL4z6hkPXylUvYV9uEM5IkKlMUX6hPjh2J3eoauGw5dVdA0Fwx+4fEN7Y0sKtm\nC4W5pSyct+y0jv1JJJ0ldeZxNxwGdLPJJ5ja7btIBMMUzpxKVn7u8XcYgo6GRg688i6JQIim2lrm\nFU8mlkzQ3NtFsSeHqFlh8k2XUrlgHp1NLXTXHcKRn4unuABbOl1rLBrD6XISCgRpr2/AlZNDfknx\ncJ3qx4ZDzfX85In/TqvvIHnOMv7t8z9jWsXgwCbQ5iZ2V+/AYDAwe/pge/XmHR/wyLPfIxjrZlbJ\nEm688G4eef5+IkkfUgpuO+/r3HzlnSN9SsPGWJtNRhtdeesMG1vWrEOu2ozZaKLd35upsAPgtQim\n3HgZTc++jjkpafZ2k+t0EVVVAtEIeXYnyqxyln/uZj0i8Dioqkp7RxsF+YWDSsQdRkrJT35/P1vq\n3gQJK+fcxL9ceicvvvtnEqk4ly3/F371t+/TGa7P7FNgnUZH9EBmOctczO9/8PpIn86w8UlT3rrZ\nRGfYmDS1ggOvfYjZaBpkv1ajcdo378CuKrQHe5mcowXaOAGSKawGE8aD7RzYuJUZZy8ZfeEnEIqi\nUFw0uDxcfzZVrWNL3ZvpZGbw/p5/sGX3e0QNWtDN1v3voiYZUEtTSnXAMUzG44fk64wd+hBHZ9iY\nVFaKeelseiMhVDWFL6pFRapSxTF3Chi0x+1IxW41mYgnE1pOl2h80HF1Tp5kKjEwn7mA3nBbZjGa\n8lOWMzOTetiEjVsv/wpFrqmAlmL2M1d+fVRl1jk5dLOJzrATCoaIhMMkgiG6a+oxOu3MPGcJ3S1t\nVP/5RWRvgLiaIteu5RBv6u2iNDuPoFkw955bySo4sRSmOkMTj8d5+Ndf4UDnVgCm5i6hsWcPcRnS\nNpAK3/vcY7T3tNLja2PBrLOZNXUe0WiU2oYaCvKKyOuXFngi8Ekzm+jKW2dUCYdCdDS2oKZSRJrb\nUBUDUqYwSCg+Yw45RR/vvCWjSSwWY33VGhShsGLxRWyoWsszq35NMpXgiuW3cf0lt4+1iMOKrrzH\nEbry1tHROVE+acpbt3nr6OjoTEB05a2jo6MzATkt5S2EuFkIsUsIkRJCLOzXPlkIERZCbEv//abf\nuoVCiB1CiP1CiF+cTv86Ojo6n1ROd+S9E7gRWHuUdQeklAvTf1/t1/4o8EUp5QxghhDi8qPsO25Y\ns2bNWIsAjA85xoMMMD7kGA8ywPiQYzzI8EnktJS3lLJaSlkDg/IccbQ2IUQR4JJSbk43/Qm44XRk\nGGnGy4M5HuQYDzLA+JBjPMgA40OO8SDDJ5GRtHlXpE0m7wohDicdLkGrLnGYpnSbjo6Ojs5JcNzw\neCHEKqCwfxNaCZSHpJQvD7FbC1AupexN28JfEELMOW1pdXR0dHSAYfLzFkK8C3xbSrntWOvRlPq7\nUsrZ6fbbgPOllF8ZYj/dyVtHR+eEOU0/73pg8glu3iClrDjVvoaD4UxMlbloQog8oEdKqQohpgDT\ngFoppVcI4RNCLAU2A58HfjnUAcfaCV5HR+eTw1gr45PldF0FbxBCNALLgVeEEIfzR54H7BBCbAOe\nBb4spfSm190H/AHYD9Skk5fr6Ojo6JwE4zo8XkdHR0fn6IyLCEshxP8VQuwVQmwXQjwvhHD3W/eg\nEKImvf6yfu3DHuwzHoKOhpIhvW7UrsUR/T4shGjqd/5XHE+mkUAIcYUQYl/6PO8fyb6O0ne9EOIj\nIUSVEGJTui1bCPGWEKJaCPGmEMIzzH3+QQjRLoTY0a9tyD5H6l4MIceoPhNCiFIhxDtCiN1CiJ1C\niK+n20f9eowbpJRj/gdcAijpzz8G/j39eQ5QhWabrwAO0Pe2sBFYkv78GnD5MMgxE5gOvAMs7Nc+\nGdgxxD7DKscxZJg9mtfiCJkeBr51lPYhZRqBZ0RJH38yYAK2A7NG8RmtBbKPaPsJ8G/pz/cDPx7m\nPs8Fzuz/7A3V57G+KyMkx6g+E0ARcGb6sxOtSvussbge4+VvXIy8pZSrZV8Zjw1AafrzdcAzUsqk\nlLIeqAGWjlSwjxwHQUfHkOF6RvFaHIWjXZOjyjQCfZM+bo2UskFKmQCeSfc/WggGv6leDzyZ/vwk\nw3zdpZTrgN4T7POo35URlANG8ZmQUrZJKbenPweBvWh6YtSvx3hhXCjvI7gLbfQIWgBPY791zem2\nsQj2Geugo7G+Fl9Lm7X+q9+r6VAyjQRH9jXaAV4SWCWE2CyEuDvdViilbAdNuQCjkYy8YIg+R/Ne\nHGZMngkhRAXam8AGhr4HY3E9RpVRq2EpTiDYRwjxEJCQUj49lnIchWENOjpFGUaUY8kE/Ab4kZRS\nCiH+F/Az4O7BR/lYs0JK2SqEyAfeEkJUo12f/ozF7P9YeRyMyTMhhHACzwHfkFIGxeBYkE+MB8ao\nKW8p5aXHWi+EuAO4CrioX3MzUNZvuTTdNlT7acsxxD4J0q+NUsptQoiDwIxTleNUZDhGX6d8LU5R\npt8Dh39ghqXvE6QZKB+lvgYhpWxN/+8UQryA9greLoQolFK2p81XHaMgylB9jua9QErZ2W9xVJ4J\nIYQRTXH/WUr5Yrp5XFyPsWBcmE3SM9XfAa6TUsb6rXoJuE0IYRZCVKIF+2xKvx75hBBLhRACLdjn\nxUEHPk2x+smXJ9JVc8XAoKORlqO/TXHMrkX6S3GYm4Bdx5JpOPvux2ZgmtA8f8zAben+RxwhhD09\n4kMI4QAuQ8uo+RJwR3qzLzD8zyBoz8CRz8HR+hzpezFAjjF6Jh4H9kgp/1+/trG6HmPPWM+YSm1m\nuAZoALal/37Tb92DaDPFe4HL+rUvQvsC1QD/b5jkuAHNThYBWoHX0+2HH85twBbgqpGSYygZRvta\nHCHTn4AdaB4eL6DZGY8p0wg9J1egeRnUAA+M4vNZmT73qvR1fiDdngOsTsv0FpA1zP3+Fc1kFwMO\nAXcC2UP1OVL3Ygg5RvWZAFYAqX73YVv6eRjyHozmszkWf3qQjo6Ojs4EZFyYTXR0dHR0Tg5deevo\n6OhMQHTlraOjozMB0ZW3jo6OzgREV946Ojo6ExBdeevo6OhMQHTlraOjozMB0ZW3jo6OzgTk/wPK\nC18AwydDqAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1139266d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the results\n",
+    "plt.scatter(projection[:, 0], projection[:, 1], lw=0.1,\n",
+    "            c=digits.target, cmap=plt.cm.get_cmap('cubehelix', 6))\n",
+    "plt.colorbar(ticks=range(6), label='digit value')\n",
+    "plt.clim(-0.5, 5.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The projection also gives us some interesting insights on the relationships within the dataset: for example, the ranges of 5 and 3 nearly overlap in this projection, indicating that some hand written fives and threes are difficult to distinguish, and therefore more likely to be confused by an automated classification algorithm.\n",
+    "Other values, like 0 and 1, are more distantly separated, and therefore much less likely to be confused.\n",
+    "This observation agrees with our intuition, because 5 and 3 look much more similar than do 0 and 1.\n",
+    "\n",
+    "We'll return to manifold learning and to digit classification in [Chapter 5](05.00-Machine-Learning.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) | [Contents](Index.ipynb) | [Multiple Subplots](04.08-Multiple-Subplots.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.07-Customizing-Colorbars.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/04.08-Multiple-Subplots.ipynb b/notebooks_v1/04.08-Multiple-Subplots.ipynb
new file mode 100644
index 000000000..e06195cfc
--- /dev/null
+++ b/notebooks_v1/04.08-Multiple-Subplots.ipynb
@@ -0,0 +1,439 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) | [Contents](Index.ipynb) | [Text and Annotation](04.09-Text-and-Annotation.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.08-Multiple-Subplots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple Subplots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes it is helpful to compare different views of data side by side.\n",
+    "To this end, Matplotlib has the concept of *subplots*: groups of smaller axes that can exist together within a single figure.\n",
+    "These subplots might be insets, grids of plots, or other more complicated layouts.\n",
+    "In this section we'll explore four routines for creating subplots in Matplotlib."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('seaborn-white')\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ``plt.axes``: Subplots by Hand\n",
+    "\n",
+    "The most basic method of creating an axes is to use the ``plt.axes`` function.\n",
+    "As we've seen previously, by default this creates a standard axes object that fills the entire figure.\n",
+    "``plt.axes`` also takes an optional argument that is a list of four numbers in the figure coordinate system.\n",
+    "These numbers represent ``[left, bottom, width, height]`` in the figure coordinate system, which ranges from 0 at the bottom left of the figure to 1 at the top right of the figure.\n",
+    "\n",
+    "For example, we might create an inset axes at the top-right corner of another axes by setting the *x* and *y* position to 0.65 (that is, starting at 65% of the width and 65% of the height of the figure) and the *x* and *y* extents to 0.2 (that is, the size of the axes is 20% of the width and 20% of the height of the figure):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GYAcAyxDsAGAZgh0ALEOwA4BlCHYAsAzBDgCWIdgBwDIEOwBYhmAHAMsQ7ABgGYIdACxD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\nBwDLEOwAYBmCHQAsQ7ADgGUIdgCwzP8AxZhVoScunCkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bd88240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax1 = plt.axes()  # standard axes\n",
+    "ax2 = plt.axes([0.65, 0.65, 0.2, 0.2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The equivalent of this command within the object-oriented interface is ``fig.add_axes()``. Let's use this to create two vertically stacked axes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1A4qLaV4gJgZo0kS/x1AqgXfeAY4dAxYvBpYtAxo1AjZs0O9xmGmSfVIozcWF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\nmMb/A681T5mxFqFRAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e296748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4],\n",
+    "                   xticklabels=[], ylim=(-1.2, 1.2))\n",
+    "ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4],\n",
+    "                   ylim=(-1.2, 1.2))\n",
+    "\n",
+    "x = np.linspace(0, 10)\n",
+    "ax1.plot(np.sin(x))\n",
+    "ax2.plot(np.cos(x));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now have two axes (the top with no tick labels) that are just touching: the bottom of the upper panel (at position 0.5) matches the top of the lower panel (at position 0.1 + 0.4)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ``plt.subplot``: Simple Grids of Subplots\n",
+    "\n",
+    "Aligned columns or rows of subplots are a common-enough need that Matplotlib has several convenience routines that make them easy to create.\n",
+    "The lowest level of these is ``plt.subplot()``, which creates a single subplot within a grid.\n",
+    "As you can see, this command takes three integer arguments—the number of rows, the number of columns, and the index of the plot to be created in this scheme, which runs from the upper left to the bottom right:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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EuP/++0V9fb1tu8ViEePGjRMJCQkdHuNNufrcBUq1tbUICgrqcn99fT3mz5+P\nkpISJCQk4MUXX3TrfAMGDMDjjz+OadOmYfv27Rg6dCiysrJU1di4cSN++uknLFq0CDU1NaipqUFd\nXR0A4PLly6ipqenwi9SgoCAIIVBTU+NW/77EF7MFgLlz5yIlJcVum7+/P6ZPn47q6mr8/PPPdvtu\ntGx9Mde+ffsCaPsJ6Nre+/fvj4kTJ+L777/v8LsUb8rV5wZ7r169uvw0ycWLF2E0GlFaWoq5c+fi\nzTff1PTc/v7+mDBhAn777TfU1tY6/bhDhw7hypUrSEhIwP3334/7778fs2bNgqIo2LhxI2JjY/Hb\nb7/ZPab9kwy9e/fW9GvwZr6YbXcGDRoEAB0ukLnRsvXFXNs/vRMcHNxhX3BwMIQQXp2rzw324ODg\nTv9FvHTpEtLT02EymfD0008jMzPT5XOcPXsWEydOxK5duzrsa2hogKIoqt6zW7JkCT788ENs3rzZ\n9t/bb78NIQRmzJiBzZs34+abb7Z7TG1tLRRF6fQbS1a+mO3vv/+O+Ph45OTkdHouALjtttvstt9o\n2fpiriNGjIBOp+vw0xYAmM1m+Pv72/7hbudNufrcYB86dCj++OOPDq8AVqxYAZPJhHnz5mHx4sVu\nnSMsLAwNDQ3Izc1FS0uLbfv58+dRWFiIcePG2X5Uc8Y999xje6Xe/t+YMWMAtD3px48f3+GbrrKy\nEoDrH630Rb6Y7S233AKLxYK8vDy7jxleuHABe/bswfjx4zs80W+0bH0x18DAQEycOBHFxcU4c+aM\nbbvZbEZxcTEmTZoERVHsHuNNuar7sKgXGD9+PPbs2YPTp08jIiICAHDmzBnk5+fjpptuQkREBPLz\n8zs8rv2TCWazGSdPnoRer+/wSqpd7969sXTpUixevBhPPfUUpk6dipqaGuzcuRN+fn5YtmyZ7Vhn\n6rmirKwMoaGhCAkJ0aymt/PVbDMyMvDCCy8gKSkJiYmJaGhowM6dO9GnTx+7eu1utGx9NddXXnkF\nJSUlMBqNSEtLg5+fH7Zt24bAwEC89NJLHY73plx9brA/+OCDUBQFx44ds32TlJSUQFEUWCwWvP76\n650+rv2b5NixY3j99deRlZXVbajTpk2zXfSwevVqBAYGIjY2Fi+++CLCwsJsxzlbrzOKonT4Vx9o\nu5FTaWkpnnzySVX1fJ2vZjt58mSsX78e//nPf7B27VoEBATgvvvuw0svvYThw4fbHXsjZuurud56\n66346KM9LyAPAAAIyUlEQVSP8Pbbb+PDDz+EEAIxMTF45ZVXOjzO63LV4FM53fLEx+Kef/55t27Y\n9NZbb9ndT8NdWtf76quvRGRkpFfdVMiTta7FbNXz9o87CsFcXXFDfdwRANLT03HixIkOtyR1RnV1\nNYqLixEVFaVJL1rXA9ruUREbG2t31eCNgtnKibn2LJ8c7Hq9Ho888gg2bNig+rEXL17Eq6++itDQ\nUE160bqe2WxGYWGh7erAGw2zlRNz7Vk+OdiBtl9YFRYWqn4FMGLECJfv9dIT9XJycpCcnIyRI0dq\nVtPXMFs5Mdee43O/PG0XEhLSY7cc7UmuXPkoG2YrJ+bac3z2FTsREXXO7TVP22VkZGDdunWaN0ie\nwVzlxFwJcGKwf/HFF7BarcjNzcXLL7/c6Y8dubm5XrNyCDmHucqJuRLgxGA/fvw44uLiALStEnL9\niisnT57Ed999Z7v/M/kG5ion5kqAE4O9uzUUq6qqkJ2djYyMjC7v3kbeibnKibkS4MSnYrpbQ3Hf\nvn2ora3FggULUFVVhebmZtxxxx2YMWOG5zomTTBXOTFXApwY7Hq9HsXFxZgyZUqHNRSNRiOMRiMA\nYM+ePaioqOA3iY9grnJirgRosOYp+SbmKifmSoATg11RFKxYscJu2/V3rAOAmTNnatcVeRxzlRNz\nJYAXKBERSYeDnYhIMhzsRESS4WAnIpIMBzsRkWQ42ImIJMPBTkQkGQ52IiLJcLATEUmGg52ISDIc\n7EREkuFgJyKSjMObgAkhkJmZCZPJBJ1Oh5UrV2LYsGG2/QUFBdi6dSv8/PwQHh6OzMxMT/ZLGmGu\ncmKuBLi55mlzczPee+89bN++HTt37kR9fT2Ki4s92jBpg7nKibkS4OaapzqdDrm5udDpdACAlpYW\n+Pv7e6hV0hJzlRNzJcDNNU8VRcGgQYMAANu2bUNTUxNiY2M91CppibnKibkS4Oaap0Dbe3pr1qzB\nuXPnkJ2d7ZkuSXPMVU7MlQAnXrHr9XocPHgQADqsoQgAy5Ytw5UrV5CTk2P7EY+8H3OVE3MlwM01\nT0eOHIndu3cjOjoaRqMRiqIgLS0NkydP9njj5B7mKifmSoAGa57+8MMP2ndFHsdc5cRcCeAFSkRE\n0uFgJyKSDAc7EZFkONiJiCTDwU5EJBkOdiIiyXCwExFJhoOdiEgyHOxERJLhYCcikgwHOxGRZDjY\niYgk43CwCyGwfPlyJCUlIS0tDWaz2W5/UVEREhISkJSUhLy8PI81StpirnJirgS4ueZpS0sLVq1a\nhS1btmDbtm346KOPcPHiRY82TNpgrnJirgS4uebpmTNnEBYWhqCgIPTp0wfR0dEoKSnxXLekGeYq\nJ+ZKgJtrnl6/r1+/fqivr/dAm6Q15ion5kqAm2ueBgUFoaGhwbbv0qVLGDBggN3jW1tbAQCVlZWa\nNEyua8+gtbWVuUpEy1zb61xbl/43rs1VLYeDXa/Xo7i4GFOmTOmwhuKdd96Jc+fOwWKxICAgACUl\nJXjmmWfsHl9VVQUASE1NVd0ceUZVVRVzlZAWubbXAZitt6iqqkJYWJiqxyhCCNHdAUIIZGZmwmQy\nAWhbQ/H7779HU1MTEhMTceDAAWRnZ0MIgYSEBCQnJ9s9/vLlyygvL8fgwYPRu3dvlV8Saam1tRVV\nVVWIioqCv78/c5WElrkCzNZbXJtrQECAqsc6HOxERORbeIESEZFkNB3sWl4c4ahWQUEB5syZg5SU\nFGRmZrrdW7uMjAysW7fO7XqnTp1CamoqUlNTsXDhQlitVpdr5efnY9asWUhMTMSuXbsc9taurKwM\nRqOxw3a1F6kw17+oydWZeq5k6425OlNPTbbM9S8uXVQmNFRYWChee+01IYQQpaWl4rnnnrPtu3Ll\nijAYDKK+vl5YrVYxe/ZsUV1d7VKty5cvC4PBIJqbm4UQQixatEgUFRW53Fu7Xbt2iblz54q1a9e6\n9bUKIcT06dPFL7/8IoQQIi8vT1RUVLhc64EHHhAWi0VYrVZhMBiExWJx2N8HH3wg4uPjxdy5c+22\nq83BUX/MtcKtemqz9dZcHdVTmy1zbeNKDkIIoekrdi0vjuiulk6nQ25uLnQ6HYC2K+r8/f1d7g0A\nTp48ie+++w5JSUluf60VFRUYOHAgNm/eDKPRiLq6Otx+++0u9xYZGYm6ujo0NzcDABRFcdhfWFgY\n1q9f32G7KxepMNc2anN1pj+12Xprro7qqc2WubZx9aIyTQe7lhdHdFdLURQMGjQIALBt2zY0NTUh\nNjbW5d6qqqqQnZ2NjIwMCCd/l9xdvZqaGpSWlsJoNGLz5s04fPgwjh496lItABgxYgRmz56NqVOn\nYsKECQgKCnLYn8Fg6PQTDa5cpMJcXcvVUT1AfbbemqujemqzZa6dn8fZi8o0HexaXBzhTC2g7T2u\n1atX48iRI8jOznart3379qG2thYLFizAhg0bUFBQgL1797pcb+DAgQgNDcXw4cPh5+eHuLi4Dv+i\nO1vLZDLhwIEDKCoqQlFREaqrq7F//36HX29351KTg6P+mGvXuTqqp2W2/+tcHdUD1GXLXP86j9oc\nAI0Hu16vx8GDBwGg24sjrFYrSkpKMHr0aJdqAcCyZctw5coV5OTk2H68c7U3o9GIjz/+GFu3bsWz\nzz6L+Ph4zJgxw+V6w4YNQ2Njo+0XKsePH8ddd93lUq3+/fsjMDAQOp3O9qrHYrE4/HrbXf+KRm0O\njvpjrl3n6qieO9l6W66O6gHqsmWubVzJAXDiylM1DAYDvv76a9v7XllZWSgoKLBdHLFkyRKkp6dD\nCIHExEQMGTLEpVojR47E7t27ER0dDaPRCEVRkJaWhsmTJ7vcm9Zf68qVK7Fo0SIAwJgxY/Dwww+7\nXKv9kwQ6nQ6hoaGYOXOm0322v7fnag7O9MdcXa/narbelqujemqzZa6u5wDwAiUiIunwAiUiIslw\nsBMRSYaDnYhIMhzsRESS4WAnIpIMBzsRkWQ42ImIJMPBTkQkmf8D8PQ7HlBzC30AAAAASUVORK5C\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bec31d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(1, 7):\n",
+    "    plt.subplot(2, 3, i)\n",
+    "    plt.text(0.5, 0.5, str((2, 3, i)),\n",
+    "             fontsize=18, ha='center')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The command ``plt.subplots_adjust`` can be used to adjust the spacing between these plots.\n",
+    "The following code uses the equivalent object-oriented command, ``fig.add_subplot()``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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q6+vx85//3O51LleoVCr87ne/Q19fH0pKSrBu3Tq89tprNn8uOnLz5k1s2bIF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\niYgkw8FORCQZDnYiIsn8H1BDee8pcOnKAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10be83ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
+    "for i in range(1, 7):\n",
+    "    ax = fig.add_subplot(2, 3, i)\n",
+    "    ax.text(0.5, 0.5, str((2, 3, i)),\n",
+    "           fontsize=18, ha='center')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We've used the ``hspace`` and ``wspace`` arguments of ``plt.subplots_adjust``, which specify the spacing along the height and width of the figure, in units of the subplot size (in this case, the space is 40% of the subplot width and height)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ``plt.subplots``: The Whole Grid in One Go\n",
+    "\n",
+    "The approach just described can become quite tedious when creating a large grid of subplots, especially if you'd like to hide the x- and y-axis labels on the inner plots.\n",
+    "For this purpose, ``plt.subplots()`` is the easier tool to use (note the ``s`` at the end of ``subplots``). Rather than creating a single subplot, this function creates a full grid of subplots in a single line, returning them in a NumPy array.\n",
+    "The arguments are the number of rows and number of columns, along with optional keywords ``sharex`` and ``sharey``, which allow you to specify the relationships between different axes.\n",
+    "\n",
+    "Here we'll create a $2 \\times 3$ grid of subplots, where all axes in the same row share their y-axis scale, and all axes in the same column share their x-axis scale:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1023b3828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 3, sharex='col', sharey='row')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that by specifying ``sharex`` and ``sharey``, we've automatically removed inner labels on the grid to make the plot cleaner.\n",
+    "The resulting grid of axes instances is returned within a NumPy array, allowing for convenient specification of the desired axes using standard array indexing notation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hISRJQmZmJlauXOnw+N7eXjQ0NGDSpEkYM2aMm0+JvOnGjRuwWq2YPXs2IiIi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\nvvmiAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1023b3828>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# axes are in a two-dimensional array, indexed by [row, col]\n",
+    "for i in range(2):\n",
+    "    for j in range(3):\n",
+    "        ax[i, j].text(0.5, 0.5, str((i, j)),\n",
+    "                      fontsize=18, ha='center')\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In comparison to ``plt.subplot()``, ``plt.subplots()`` is more consistent with Python's conventional 0-based indexing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ``plt.GridSpec``: More Complicated Arrangements\n",
+    "\n",
+    "To go beyond a regular grid to subplots that span multiple rows and columns, ``plt.GridSpec()`` is the best tool.\n",
+    "The ``plt.GridSpec()`` object does not create a plot by itself; it is simply a convenient interface that is recognized by the ``plt.subplot()`` command.\n",
+    "For example, a gridspec for a grid of two rows and three columns with some specified width and height space looks like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From this we can specify subplot locations and extents using the familiary Python slicing syntax:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FVF5erv7+/hnHL1y4oP3796u0tFTNzc2eFQrAHvrabXHf3fGnn37S+Pi4WlpadPPmTdXW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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f438128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot(grid[0, 0])\n",
+    "plt.subplot(grid[0, 1:])\n",
+    "plt.subplot(grid[1, :2])\n",
+    "plt.subplot(grid[1, 2]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This type of flexible grid alignment has a wide range of uses.\n",
+    "I most often use it when creating multi-axes histogram plots like the ones shown here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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sbNzWYBSNRhkeHmZmZoaFhQXa29uJRqMsLS3h9/txu92kUimRh1e6Nbce9BqN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Icffoju7m7ExcUNv85Hw/JWoaFDJe7u7li0aNGEv3/p0qWTnOi+RYsWISoqatzba7VatLW1Ka46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\nsbyJiFSI5U1EpEL/A5Irq324HXUvAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ef598d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create some normally distributed data\n",
+    "mean = [0, 0]\n",
+    "cov = [[1, 1], [1, 2]]\n",
+    "x, y = np.random.multivariate_normal(mean, cov, 3000).T\n",
+    "\n",
+    "# Set up the axes with gridspec\n",
+    "fig = plt.figure(figsize=(6, 6))\n",
+    "grid = plt.GridSpec(4, 4, hspace=0.2, wspace=0.2)\n",
+    "main_ax = fig.add_subplot(grid[:-1, 1:])\n",
+    "y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)\n",
+    "x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)\n",
+    "\n",
+    "# scatter points on the main axes\n",
+    "main_ax.plot(x, y, 'ok', markersize=3, alpha=0.2)\n",
+    "\n",
+    "# histogram on the attached axes\n",
+    "x_hist.hist(x, 40, histtype='stepfilled',\n",
+    "            orientation='vertical', color='gray')\n",
+    "x_hist.invert_yaxis()\n",
+    "\n",
+    "y_hist.hist(y, 40, histtype='stepfilled',\n",
+    "            orientation='horizontal', color='gray')\n",
+    "y_hist.invert_xaxis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This type of distribution plotted alongside its margins is common enough that it has its own plotting API in the Seaborn package; see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb) for more details."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) | [Contents](Index.ipynb) | [Text and Annotation](04.09-Text-and-Annotation.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.08-Multiple-Subplots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/04.09-Text-and-Annotation.ipynb b/notebooks_v1/04.09-Text-and-Annotation.ipynb
new file mode 100644
index 000000000..621eeaed3
--- /dev/null
+++ b/notebooks_v1/04.09-Text-and-Annotation.ipynb
@@ -0,0 +1,446 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Multiple Subplots](04.08-Multiple-Subplots.ipynb) | [Contents](Index.ipynb) | [Customizing Ticks](04.10-Customizing-Ticks.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.09-Text-and-Annotation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Text and Annotation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Creating a good visualization involves guiding the reader so that the figure tells a story.\n",
+    "In some cases, this story can be told in an entirely visual manner, without the need for added text, but in others, small textual cues and labels are necessary.\n",
+    "Perhaps the most basic types of annotations you will use are axes labels and titles, but the options go beyond this.\n",
+    "Let's take a look at some data and how we might visualize and annotate it to help convey interesting information. We'll start by setting up the notebook for plotting and  importing the functions we will use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "plt.style.use('seaborn-whitegrid')\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Effect of Holidays on US Births\n",
+    "\n",
+    "Let's return to some data we worked with earler, in [\"Example: Birthrate Data\"](03.09-Pivot-Tables.ipynb#Example:-Birthrate-Data), where we generated a plot of average births over the course of the calendar year; as already mentioned, that this data can be downloaded at https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv.\n",
+    "\n",
+    "We'll start with the same cleaning procedure we used there, and plot the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "births = pd.read_csv('data/births.csv')\n",
+    "\n",
+    "quartiles = np.percentile(births['births'], [25, 50, 75])\n",
+    "mu, sig = quartiles[1], 0.74 * (quartiles[2] - quartiles[0])\n",
+    "births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')\n",
+    "\n",
+    "births['day'] = births['day'].astype(int)\n",
+    "\n",
+    "births.index = pd.to_datetime(10000 * births.year +\n",
+    "                              100 * births.month +\n",
+    "                              births.day, format='%Y%m%d')\n",
+    "births_by_date = births.pivot_table('births',\n",
+    "                                    [births.index.month, births.index.day])\n",
+    "births_by_date.index = [pd.datetime(2012, month, day)\n",
+    "                        for (month, day) in births_by_date.index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Gj1hIRUVRuTIpD64Ykwn47DPgr38Fhg2jiHFFhfJr/fILRWVGjaJkPzmuXgXOnDE32xg8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\njEVEREREADXGIiIiIiIA/B/rXnxzXnFSGQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1144abdd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12, 4))\n",
+    "births_by_date.plot(ax=ax);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we're communicating data like this, it is often useful to annotate certain features of the plot to draw the reader's attention.\n",
+    "This can be done manually with the ``plt.text``/``ax.text`` command, which will place text at a particular x/y value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GIcaRI0DXrkCvXsD+/brXvXGDBJ9Qbq1pU5p4wZDjpKZSZDs4mDzYq1eT3YOm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\nIS3w/wGBo6oVXGYX4gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114c827b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12, 4))\n",
+    "births_by_date.plot(ax=ax)\n",
+    "\n",
+    "# Add labels to the plot\n",
+    "style = dict(size=10, color='gray')\n",
+    "\n",
+    "ax.text('2012-1-1', 3950, \"New Year's Day\", **style)\n",
+    "ax.text('2012-7-4', 4250, \"Independence Day\", ha='center', **style)\n",
+    "ax.text('2012-9-4', 4850, \"Labor Day\", ha='center', **style)\n",
+    "ax.text('2012-10-31', 4600, \"Halloween\", ha='right', **style)\n",
+    "ax.text('2012-11-25', 4450, \"Thanksgiving\", ha='center', **style)\n",
+    "ax.text('2012-12-25', 3850, \"Christmas \", ha='right', **style)\n",
+    "\n",
+    "# Label the axes\n",
+    "ax.set(title='USA births by day of year (1969-1988)',\n",
+    "       ylabel='average daily births')\n",
+    "\n",
+    "# Format the x axis with centered month labels\n",
+    "ax.xaxis.set_major_locator(mpl.dates.MonthLocator())\n",
+    "ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))\n",
+    "ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
+    "ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h'));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``ax.text`` method takes an x position, a y position, a string, and then optional keywords specifying the color, size, style, alignment, and other properties of the text.\n",
+    "Here we used ``ha='right'`` and ``ha='center'``, where ``ha`` is short for *horizonal alignment*.\n",
+    "See the docstring of ``plt.text()`` and of ``mpl.text.Text()`` for more information on available options."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transforms and Text Position\n",
+    "\n",
+    "In the previous example, we have anchored our text annotations to data locations. Sometimes it's preferable to anchor the text to a position on the axes or figure, independent of the data. In Matplotlib, this is done by modifying the *transform*.\n",
+    "\n",
+    "Any graphics display framework needs some scheme for translating between coordinate systems.\n",
+    "For example, a data point at $(x, y) = (1, 1)$ needs to somehow be represented at a certain location on the figure, which in turn needs to be represented in pixels on the screen.\n",
+    "Mathematically, such coordinate transformations are relatively straightforward, and Matplotlib has a well-developed set of tools that it uses internally to perform them (these tools can be explored in the ``matplotlib.transforms`` submodule).\n",
+    "\n",
+    "The average user rarely needs to worry about the details of these transforms, but it is helpful knowledge to have when considering the placement of text on a figure. There are three pre-defined transforms that can be useful in this situation:\n",
+    "\n",
+    "- ``ax.transData``: Transform associated with data coordinates\n",
+    "- ``ax.transAxes``: Transform associated with the axes (in units of axes dimensions)\n",
+    "- ``fig.transFigure``: Transform associated with the figure (in units of figure dimensions)\n",
+    "\n",
+    "Here let's look at an example of drawing text at various locations using these transforms:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jIyIitH79etdtm83muuTeD+Xn5+u2225T+/bt5e/vr8jISO3fv7/GmJdffln3339/rbl6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\nwxHkAGA4ghwADPd/L3FVgxb4YbMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1144abd30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(facecolor='lightgray')\n",
+    "ax.axis([0, 10, 0, 10])\n",
+    "\n",
+    "# transform=ax.transData is the default, but we'll specify it anyway\n",
+    "ax.text(1, 5, \". Data: (1, 5)\", transform=ax.transData)\n",
+    "ax.text(0.5, 0.1, \". Axes: (0.5, 0.1)\", transform=ax.transAxes)\n",
+    "ax.text(0.2, 0.2, \". Figure: (0.2, 0.2)\", transform=fig.transFigure);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that by default, the text is aligned above and to the left of the specified coordinates: here the \".\" at the beginning of each string will approximately mark the given coordinate location.\n",
+    "\n",
+    "The ``transData`` coordinates give the usual data coordinates associated with the x- and y-axis labels.\n",
+    "The ``transAxes`` coordinates give the location from the bottom-left corner of the axes (here the white box), as a fraction of the axes size.\n",
+    "The ``transFigure`` coordinates are similar, but specify the position from the bottom-left of the figure (here the gray box), as a fraction of the figure size.\n",
+    "\n",
+    "Notice now that if we change the axes limits, it is only the ``transData`` coordinates that will be affected, while the others remain stationary:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1144abd30>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.set_xlim(0, 2)\n",
+    "ax.set_ylim(-6, 6)\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This behavior can be seen more clearly by changing the axes limits interactively: if you are executing this code in a notebook, you can make that happen by changing ``%matplotlib inline`` to ``%matplotlib notebook`` and using each plot's menu to interact with the plot."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Arrows and Annotation\n",
+    "\n",
+    "Along with tick marks and text, another useful annotation mark is the simple arrow.\n",
+    "\n",
+    "Drawing arrows in Matplotlib is often much harder than you'd bargain for.\n",
+    "While there is a ``plt.arrow()`` function available, I wouldn't suggest using it: the arrows it creates are SVG objects that will be subject to the varying aspect ratio of your plots, and the result is rarely what the user intended.\n",
+    "Instead, I'd suggest using the ``plt.annotate()`` function.\n",
+    "This function creates some text and an arrow, and the arrows can be very flexibly specified.\n",
+    "\n",
+    "Here we'll use ``annotate`` with several of its options:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XvLg+CSYnwtyXn59u+Nx7r9WVeC+f7yMXQrjW+++/T1aW5y/L6E4kyIUQLrNr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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114622cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "x = np.linspace(0, 20, 1000)\n",
+    "ax.plot(x, np.cos(x))\n",
+    "ax.axis('equal')\n",
+    "\n",
+    "ax.annotate('local maximum', xy=(6.28, 1), xytext=(10, 4),\n",
+    "            arrowprops=dict(facecolor='black', shrink=0.05))\n",
+    "\n",
+    "ax.annotate('local minimum', xy=(5 * np.pi, -1), xytext=(2, -6),\n",
+    "            arrowprops=dict(arrowstyle=\"->\",\n",
+    "                            connectionstyle=\"angle3,angleA=0,angleB=-90\"));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The arrow style is controlled through the ``arrowprops`` dictionary, which has numerous options available.\n",
+    "These options are fairly well-documented in Matplotlib's online documentation, so rather than repeating them here it is probably more useful to quickly show some of the possibilities.\n",
+    "Let's demonstrate several of the possible options using the birthrate plot from before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UgRkI573atdOIjpaUa+5JhKG44AI5P/9c0rnJlVdKZPXkSflp0yb0NrfeKvmv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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117960208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12, 4))\n",
+    "births_by_date.plot(ax=ax)\n",
+    "\n",
+    "# Add labels to the plot\n",
+    "ax.annotate(\"New Year's Day\", xy=('2012-1-1', 4100),  xycoords='data',\n",
+    "            xytext=(50, -30), textcoords='offset points',\n",
+    "            arrowprops=dict(arrowstyle=\"->\",\n",
+    "                            connectionstyle=\"arc3,rad=-0.2\"))\n",
+    "\n",
+    "ax.annotate(\"Independence Day\", xy=('2012-7-4', 4250),  xycoords='data',\n",
+    "            bbox=dict(boxstyle=\"round\", fc=\"none\", ec=\"gray\"),\n",
+    "            xytext=(10, -40), textcoords='offset points', ha='center',\n",
+    "            arrowprops=dict(arrowstyle=\"->\"))\n",
+    "\n",
+    "ax.annotate('Labor Day', xy=('2012-9-4', 4850), xycoords='data', ha='center',\n",
+    "            xytext=(0, -20), textcoords='offset points')\n",
+    "ax.annotate('', xy=('2012-9-1', 4850), xytext=('2012-9-7', 4850),\n",
+    "            xycoords='data', textcoords='data',\n",
+    "            arrowprops={'arrowstyle': '|-|,widthA=0.2,widthB=0.2', })\n",
+    "\n",
+    "ax.annotate('Halloween', xy=('2012-10-31', 4600),  xycoords='data',\n",
+    "            xytext=(-80, -40), textcoords='offset points',\n",
+    "            arrowprops=dict(arrowstyle=\"fancy\",\n",
+    "                            fc=\"0.6\", ec=\"none\",\n",
+    "                            connectionstyle=\"angle3,angleA=0,angleB=-90\"))\n",
+    "\n",
+    "ax.annotate('Thanksgiving', xy=('2012-11-25', 4500),  xycoords='data',\n",
+    "            xytext=(-120, -60), textcoords='offset points',\n",
+    "            bbox=dict(boxstyle=\"round4,pad=.5\", fc=\"0.9\"),\n",
+    "            arrowprops=dict(arrowstyle=\"->\",\n",
+    "                            connectionstyle=\"angle,angleA=0,angleB=80,rad=20\"))\n",
+    "\n",
+    "\n",
+    "ax.annotate('Christmas', xy=('2012-12-25', 3850),  xycoords='data',\n",
+    "             xytext=(-30, 0), textcoords='offset points',\n",
+    "             size=13, ha='right', va=\"center\",\n",
+    "             bbox=dict(boxstyle=\"round\", alpha=0.1),\n",
+    "             arrowprops=dict(arrowstyle=\"wedge,tail_width=0.5\", alpha=0.1));\n",
+    "\n",
+    "# Label the axes\n",
+    "ax.set(title='USA births by day of year (1969-1988)',\n",
+    "       ylabel='average daily births')\n",
+    "\n",
+    "# Format the x axis with centered month labels\n",
+    "ax.xaxis.set_major_locator(mpl.dates.MonthLocator())\n",
+    "ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))\n",
+    "ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
+    "ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h'));\n",
+    "\n",
+    "ax.set_ylim(3600, 5400);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You'll notice that the specifications of the arrows and text boxes are very detailed: this gives you the power to create nearly any arrow style you wish.\n",
+    "Unfortunately, it also means that these sorts of features often must be manually tweaked, a process that can be very time consuming when producing publication-quality graphics!\n",
+    "Finally, I'll note that the preceding mix of styles is by no means best practice for presenting data, but rather included as a demonstration of some of the available options.\n",
+    "\n",
+    "More discussion and examples of available arrow and annotation styles can be found in the Matplotlib gallery, in particular the [Annotation Demo](http://matplotlib.org/examples/pylab_examples/annotation_demo2.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Multiple Subplots](04.08-Multiple-Subplots.ipynb) | [Contents](Index.ipynb) | [Customizing Ticks](04.10-Customizing-Ticks.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.09-Text-and-Annotation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/04.10-Customizing-Ticks.ipynb b/notebooks_v1/04.10-Customizing-Ticks.ipynb
new file mode 100644
index 000000000..b3b6a820c
--- /dev/null
+++ b/notebooks_v1/04.10-Customizing-Ticks.ipynb
@@ -0,0 +1,508 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Text and Annotation](04.09-Text-and-Annotation.ipynb) | [Contents](Index.ipynb) | [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.10-Customizing-Ticks.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Customizing Ticks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Matplotlib's default tick locators and formatters are designed to be generally sufficient in many common situations, but are in no way optimal for every plot. This section will give several examples of adjusting the tick locations and formatting for the particular plot type you're interested in.\n",
+    "\n",
+    "Before we go into examples, it will be best for us to understand further the object hierarchy of Matplotlib plots.\n",
+    "Matplotlib aims to have a Python object representing everything that appears on the plot: for example, recall that the ``figure`` is the bounding box within which plot elements appear.\n",
+    "Each Matplotlib object can also act as a container of sub-objects: for example, each ``figure`` can contain one or more ``axes`` objects, each of which in turn contain other objects representing plot contents.\n",
+    "\n",
+    "The tick marks are no exception. Each ``axes`` has attributes ``xaxis`` and ``yaxis``, which in turn have attributes that contain all the properties of the lines, ticks, and labels that make up the axes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Major and Minor Ticks\n",
+    "\n",
+    "Within each axis, there is the concept of a *major* tick mark, and a *minor* tick mark. As the names would imply, major ticks are usually bigger or more pronounced, while minor ticks are usually smaller. By default, Matplotlib rarely makes use of minor ticks, but one place you can see them is within logarithmic plots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('classic')\n",
+    "%matplotlib inline\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iRGbetM3z2xOQpBFNrCeQmScjYvemw3uAZzPz+cFkTrC2/XNmsPd/LCJeB9wbEb8LPNZk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\nEZCkGWYRkKQZZhGQpBlmEZCkGfZ/oG4thqPtPXkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10346a438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plt.axes(xscale='log', yscale='log')\n",
+    "ax.grid();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see here that each major tick shows a large tickmark and a label, while each minor tick shows a smaller tickmark with no label.\n",
+    "\n",
+    "These tick properties—locations and labels—that is, can be customized by setting the ``formatter`` and ``locator`` objects of each axis. Let's examine these for the x axis of the just shown plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<matplotlib.ticker.LogLocator object at 0x10dbaf630>\n",
+      "<matplotlib.ticker.LogLocator object at 0x10dba6e80>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ax.xaxis.get_major_locator())\n",
+    "print(ax.xaxis.get_minor_locator())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<matplotlib.ticker.LogFormatterMathtext object at 0x10db8dbe0>\n",
+      "<matplotlib.ticker.NullFormatter object at 0x10db9af60>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ax.xaxis.get_major_formatter())\n",
+    "print(ax.xaxis.get_minor_formatter())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that both major and minor tick labels have their locations specified by a ``LogLocator`` (which makes sense for a logarithmic plot). Minor ticks, though, have their labels formatted by a ``NullFormatter``: this says that no labels will be shown.\n",
+    "\n",
+    "We'll now show a few examples of setting these locators and formatters for various plots."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hiding Ticks or Labels\n",
+    "\n",
+    "Perhaps the most common tick/label formatting operation is the act of hiding ticks or labels.\n",
+    "This can be done using ``plt.NullLocator()`` and ``plt.NullFormatter()``, as shown here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GRTecpO6F5mYaYj44qP6x45wukF90k4bdCMq0QWXeeGFkhKLCOXMqO3hknadcvRpNYKtt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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x110a2b7f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plt.axes()\n",
+    "ax.plot(np.random.rand(50))\n",
+    "\n",
+    "ax.yaxis.set_major_locator(plt.NullLocator())\n",
+    "ax.xaxis.set_major_formatter(plt.NullFormatter())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that we've removed the labels (but kept the ticks/gridlines) from the x axis, and removed the ticks (and thus the labels as well) from the y axis.\n",
+    "Having no ticks at all can be useful in many situations—for example, when you want to show a grid of images.\n",
+    "For instance, consider the following figure, which includes images of different faces, an example often used in supervised machine learning problems (see, for example, [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kZU5WRrArBJoix2R2b4whkJcZKFivXoyPmKJi6jz5ENi0LZtdLVjBMIBSFFUuLe5dS9u0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S5tk+s6TG9msoyqhbSOmtQ7slGsNWtEccij/hHttgzQ6+06vVMBeXbPJKFOb2dKGdY1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MqlqjPZxx+/Ye21dbdpiW2NxumI91QpzXu+5LBO9hvwf0/t6g1O7PaQiv5E2qDZVF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\ns7I4JVpHKCj4E5+mEf/76nxFlqQMp0OSKCFYhh3CGQQ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eh6YqKK6La1usNiGb+YY8zdFN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\ntV6C0st6WS/rz2q9BKWX9bJe1p/V+j8F2I3NFISt9gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10dca2b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(5, 5, figsize=(5, 5))\n",
+    "fig.subplots_adjust(hspace=0, wspace=0)\n",
+    "\n",
+    "# Get some face data from scikit-learn\n",
+    "from sklearn.datasets import fetch_olivetti_faces\n",
+    "faces = fetch_olivetti_faces().images\n",
+    "\n",
+    "for i in range(5):\n",
+    "    for j in range(5):\n",
+    "        ax[i, j].xaxis.set_major_locator(plt.NullLocator())\n",
+    "        ax[i, j].yaxis.set_major_locator(plt.NullLocator())\n",
+    "        ax[i, j].imshow(faces[10 * i + j], cmap=\"bone\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that each image has its own axes, and we've set the locators to null because the tick values (pixel number in this case) do not convey relevant information for this particular visualization."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reducing or Increasing the Number of Ticks\n",
+    "\n",
+    "One common problem with the default settings is that smaller subplots can end up with crowded labels.\n",
+    "We can see this in the plot grid shown here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10dc82e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(4, 4, sharex=True, sharey=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Particularly for the x ticks, the numbers nearly overlap and make them quite difficult to decipher.\n",
+    "We can fix this with the ``plt.MaxNLocator()``, which allows us to specify the maximum number of ticks that will be displayed.\n",
+    "Given this maximum number, Matplotlib will use internal logic to choose the particular tick locations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10dc82e10>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For every axis, set the x and y major locator\n",
+    "for axi in ax.flat:\n",
+    "    axi.xaxis.set_major_locator(plt.MaxNLocator(3))\n",
+    "    axi.yaxis.set_major_locator(plt.MaxNLocator(3))\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This makes things much cleaner. If you want even more control over the locations of regularly-spaced ticks, you might also use ``plt.MultipleLocator``, which we'll discuss in the following section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fancy Tick Formats\n",
+    "\n",
+    "Matplotlib's default tick formatting can leave a lot to be desired: it works well as a broad default, but sometimes you'd like do do something more.\n",
+    "Consider this plot of a sine and a cosine:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P/p2v/WhhrPPc9OwJrFtnHE4gJgbo2BE4eTL/+1I1ef/2G/D668a+x35+sh9p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\nTn1sgejMg5O3hrT1aYtDbx0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mCoQxxlje8e3xGnDt2jW1Q9Asbhtl3DbKbKFtivT2+CJ5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\neTPGmBX6f/hhxZChns1rAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1152f8a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot a sine and cosine curve\n",
+    "fig, ax = plt.subplots()\n",
+    "x = np.linspace(0, 3 * np.pi, 1000)\n",
+    "ax.plot(x, np.sin(x), lw=3, label='Sine')\n",
+    "ax.plot(x, np.cos(x), lw=3, label='Cosine')\n",
+    "\n",
+    "# Set up grid, legend, and limits\n",
+    "ax.grid(True)\n",
+    "ax.legend(frameon=False)\n",
+    "ax.axis('equal')\n",
+    "ax.set_xlim(0, 3 * np.pi);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are a couple changes we might like to make. First, it's more natural for this data to space the ticks and grid lines in multiples of $\\pi$. We can do this by setting a ``MultipleLocator``, which locates ticks at a multiple of the number you provide. For good measure, we'll add both major and minor ticks in multiples of $\\pi/4$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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dvniYVlGtnIbeT/kxoccErzP06em3di20b59s6ENDjeFxl6EHqFOqDssGLKN8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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1152f8a58>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.xaxis.set_major_locator(plt.MultipleLocator(np.pi / 2))\n",
+    "ax.xaxis.set_minor_locator(plt.MultipleLocator(np.pi / 4))\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But now these tick labels look a little bit silly: we can see that they are multiples of $\\pi$, but the decimal representation does not immediately convey this.\n",
+    "To fix this, we can change the tick formatter. There's no built-in formatter for what we want to do, so we'll instead use ``plt.FuncFormatter``, which accepts a user-defined function giving fine-grained control over the tick outputs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9MvWxK3wXKnhWAAAkpycjbGkYVp5bmeNjHOm92bMnsH69aQiGe/eADh2AU6eU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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1152f8a58>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def format_func(value, tick_number):\n",
+    "    # find number of multiples of pi/2\n",
+    "    N = int(np.round(2 * value / np.pi))\n",
+    "    if N == 0:\n",
+    "        return \"0\"\n",
+    "    elif N == 1:\n",
+    "        return r\"$\\pi/2$\"\n",
+    "    elif N == 2:\n",
+    "        return r\"$\\pi$\"\n",
+    "    elif N % 2 > 0:\n",
+    "        return r\"${0}\\pi/2$\".format(N)\n",
+    "    else:\n",
+    "        return r\"${0}\\pi$\".format(N // 2)\n",
+    "\n",
+    "ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is much better! Notice that we've made use of Matplotlib's LaTeX support, specified by enclosing the string within dollar signs. This is very convenient for display of mathematical symbols and formulae: in this case, ``\"$\\pi$\"`` is rendered as the Greek character $\\pi$.\n",
+    "\n",
+    "The ``plt.FuncFormatter()`` offers extremely fine-grained control over the appearance of your plot ticks, and comes in very handy when preparing plots for presentation or publication."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary of Formatters and Locators\n",
+    "\n",
+    "We've mentioned a couple of the available formatters and locators.\n",
+    "We'll conclude this section by briefly listing all the built-in locator and formatter options. For more information on any of these, refer to the docstrings or to the Matplotlib online documentaion.\n",
+    "Each of the following is available in the ``plt`` namespace:\n",
+    "\n",
+    "Locator class        | Description\n",
+    "---------------------|-------------\n",
+    "``NullLocator``      | No ticks\n",
+    "``FixedLocator``     | Tick locations are fixed\n",
+    "``IndexLocator``     | Locator for index plots (e.g., where x = range(len(y)))\n",
+    "``LinearLocator``    | Evenly spaced ticks from min to max\n",
+    "``LogLocator``       | Logarithmically ticks from min to max\n",
+    "``MultipleLocator``  | Ticks and range are a multiple of base\n",
+    "``MaxNLocator``      | Finds up to a max number of ticks at nice locations\n",
+    "``AutoLocator``      | (Default.) MaxNLocator with simple defaults.\n",
+    "``AutoMinorLocator`` | Locator for minor ticks\n",
+    "\n",
+    "Formatter Class       | Description\n",
+    "----------------------|---------------\n",
+    "``NullFormatter``     | No labels on the ticks\n",
+    "``IndexFormatter``    | Set the strings from a list of labels\n",
+    "``FixedFormatter``    | Set the strings manually for the labels\n",
+    "``FuncFormatter``     | User-defined function sets the labels\n",
+    "``FormatStrFormatter``| Use a format string for each value\n",
+    "``ScalarFormatter``   | (Default.) Formatter for scalar values\n",
+    "``LogFormatter``      | Default formatter for log axes\n",
+    "\n",
+    "We'll see further examples of these through the remainder of the book."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Text and Annotation](04.09-Text-and-Annotation.ipynb) | [Contents](Index.ipynb) | [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.10-Customizing-Ticks.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/04.11-Settings-and-Stylesheets.ipynb b/notebooks_v1/04.11-Settings-and-Stylesheets.ipynb
new file mode 100644
index 000000000..bc8b6bcde
--- /dev/null
+++ b/notebooks_v1/04.11-Settings-and-Stylesheets.ipynb
@@ -0,0 +1,653 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Customizing Ticks](04.10-Customizing-Ticks.ipynb) | [Contents](Index.ipynb) | [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.11-Settings-and-Stylesheets.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Customizing Matplotlib: Configurations and Stylesheets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Matplotlib's default plot settings are often the subject of complaint among its users.\n",
+    "While much is slated to change in the 2.0 Matplotlib release in late 2016, the ability to customize default settings helps bring the package inline with your own aesthetic preferences.\n",
+    "\n",
+    "Here we'll walk through some of Matplotlib's runtime configuration (rc) options, and take a look at the newer *stylesheets* feature, which contains some nice sets of default configurations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot Customization by Hand\n",
+    "\n",
+    "Through this chapter, we've seen how it is possible to tweak individual plot settings to end up with something that looks a little bit nicer than the default.\n",
+    "It's possible to do these customizations for each individual plot.\n",
+    "For example, here is a fairly drab default histogram:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('classic')\n",
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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k75N8F3gb8Ld91zND7wQ+33cRekGefNeIJJuBLcBj/VYyXUnOS/INYBF4uKqeONW6Mw/3\nJA8neXLZY2/33zcDVNXfVNUrgDuB9826nmlbbfu6df4aOFpVd/VY6mkZZ/ukedJNydwLfOCk2YGz\nXlWdqKrXMpoFeH2Sq0+17szPUK2qN4y56l3AvwM7Z1fN9K22fUneAfwB8LtnpKApm+Dn14LDwCuW\nPd/UtekskWQdo2D/TFXd13c9s1JVP03yReB6YN9K6/R9tMwVy57ewGiOrBlJrgf+CnhLVf2873pm\nrIV59yeAK5JcnmQ9cBNwf881TVto42d1Kp8C9lXVbX0XMm1JXp7kgm75V4A3AKfcWdz30TL3Ar/K\naEfqIeA9VfX93gqasiQHgPXAD7umr1bVzT2WNFVJbgD+EXg58GNgT1W9qd+q1qb7hXwbvzz57iM9\nlzQ1Se4CBsBFwBKwo6o+3WtRU5TkWuBLwF5GOxoL+FBV7e61sClJ8hpgF6P/N88DPltV/3DK9T2J\nSZLa0/vRMpKk6TPcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lq0P8BE4CFs153l+0AAAAA\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1023b1a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.random.randn(1000)\n",
+    "plt.hist(x);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can adjust this by hand to make it a much more visually pleasing plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c2bea20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# use a gray background\n",
+    "ax = plt.axes(axisbg='#E6E6E6')\n",
+    "ax.set_axisbelow(True)\n",
+    "\n",
+    "# draw solid white grid lines\n",
+    "plt.grid(color='w', linestyle='solid')\n",
+    "\n",
+    "# hide axis spines\n",
+    "for spine in ax.spines.values():\n",
+    "    spine.set_visible(False)\n",
+    "    \n",
+    "# hide top and right ticks\n",
+    "ax.xaxis.tick_bottom()\n",
+    "ax.yaxis.tick_left()\n",
+    "\n",
+    "# lighten ticks and labels\n",
+    "ax.tick_params(colors='gray', direction='out')\n",
+    "for tick in ax.get_xticklabels():\n",
+    "    tick.set_color('gray')\n",
+    "for tick in ax.get_yticklabels():\n",
+    "    tick.set_color('gray')\n",
+    "    \n",
+    "# control face and edge color of histogram\n",
+    "ax.hist(x, edgecolor='#E6E6E6', color='#EE6666');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This looks better, and you may recognize the look as inspired by the look of the R language's ggplot visualization package.\n",
+    "But this took a whole lot of effort!\n",
+    "We definitely do not want to have to do all that tweaking each time we create a plot.\n",
+    "Fortunately, there is a way to adjust these defaults once in a way that will work for all plots."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Changing the Defaults: ``rcParams``\n",
+    "\n",
+    "Each time Matplotlib loads, it defines a runtime configuration (rc) containing the default styles for every plot element you create.\n",
+    "This configuration can be adjusted at any time using the ``plt.rc`` convenience routine.\n",
+    "Let's see what it looks like to modify the rc parameters so that our default plot will look similar to what we did before.\n",
+    "\n",
+    "We'll start by saving a copy of the current ``rcParams`` dictionary, so we can easily reset these changes in the current session:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "IPython_default = plt.rcParams.copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use the ``plt.rc`` function to change some of these settings:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from matplotlib import cycler\n",
+    "colors = cycler('color',\n",
+    "                ['#EE6666', '#3388BB', '#9988DD',\n",
+    "                 '#EECC55', '#88BB44', '#FFBBBB'])\n",
+    "plt.rc('axes', facecolor='#E6E6E6', edgecolor='none',\n",
+    "       axisbelow=True, grid=True, prop_cycle=colors)\n",
+    "plt.rc('grid', color='w', linestyle='solid')\n",
+    "plt.rc('xtick', direction='out', color='gray')\n",
+    "plt.rc('ytick', direction='out', color='gray')\n",
+    "plt.rc('patch', edgecolor='#E6E6E6')\n",
+    "plt.rc('lines', linewidth=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With these settings defined, we can now create a plot and see our settings in action:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d680da0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(x);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see what simple line plots look like with these rc parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ty4bjMo24fJmxvhCv/zzMT58O0XYxSiQM+UWCY/faeeBXnTQctG+Kh35ZLIsP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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11004e940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(4):\n",
+    "    plt.plot(np.random.rand(10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I find this much more aesthetically pleasing than the default styling.\n",
+    "If you disagree with my aesthetic sense, the good news is that you can adjust the rc parameters to suit your own tastes!\n",
+    "These settings can be saved in a *.matplotlibrc* file, which you can read about in the [Matplotlib documentation](http://Matplotlib.org/users/customizing.html).\n",
+    "That said, I prefer to customize Matplotlib using its stylesheets instead."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stylesheets\n",
+    "\n",
+    "The version 1.4 release of Matplotlib in August 2014 added a very convenient ``style`` module, which includes a number of new default stylesheets, as well as the ability to create and package your own styles. These stylesheets are formatted similarly to the *.matplotlibrc* files mentioned earlier, but must be named with a *.mplstyle* extension.\n",
+    "\n",
+    "Even if you don't create your own style, the stylesheets included by default are extremely useful.\n",
+    "The available styles are listed in ``plt.style.available``—here I'll list only the first five for brevity:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['fivethirtyeight',\n",
+       " 'seaborn-pastel',\n",
+       " 'seaborn-whitegrid',\n",
+       " 'ggplot',\n",
+       " 'grayscale']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plt.style.available[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The basic way to switch to a stylesheet is to call\n",
+    "\n",
+    "``` python\n",
+    "plt.style.use('stylename')\n",
+    "```\n",
+    "\n",
+    "But keep in mind that this will change the style for the rest of the session!\n",
+    "Alternatively, you can use the style context manager, which sets a style temporarily:\n",
+    "\n",
+    "``` python\n",
+    "with plt.style.context('stylename'):\n",
+    "    make_a_plot()\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's create a function that will make two basic types of plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def hist_and_lines():\n",
+    "    np.random.seed(0)\n",
+    "    fig, ax = plt.subplots(1, 2, figsize=(11, 4))\n",
+    "    ax[0].hist(np.random.randn(1000))\n",
+    "    for i in range(3):\n",
+    "        ax[1].plot(np.random.rand(10))\n",
+    "    ax[1].legend(['a', 'b', 'c'], loc='lower left')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll use this to explore how these plots look using the various built-in styles."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Default style\n",
+    "\n",
+    "The default style is what we've been seeing so far throughout the book; we'll start with that.\n",
+    "First, let's reset our runtime configuration to the notebook default:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# reset rcParams\n",
+    "plt.rcParams.update(IPython_default);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's see how it looks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Fc52jN7k6VR0SFEL7Wu1ZsXdFoecGBZm9uItDksy8hHnc2PhGQoNDrW/8zBkY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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11004ec18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hist_and_lines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### FiveThiryEight style\n",
+    "\n",
+    "The ``fivethirtyeight`` style mimics the graphics found on the popular [FiveThirtyEight website](https://fivethirtyeight.com).\n",
+    "As you can see here, it is typified by bold colors, thick lines, and transparent axes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KNd4lIxdNMsLVq6puBXW9ite5RHikIoBTlz3EkC4bADRzK2z1b3Dz5YmXQEoK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\nhBBCCCHEx/4/P9Gwzw0UxnYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1101a9390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with plt.style.context('fivethirtyeight'):\n",
+    "    hist_and_lines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ggplot\n",
+    "\n",
+    "The ``ggplot`` package in the R language is a very popular visualization tool.\n",
+    "Matplotlib's ``ggplot`` style mimics the default styles from that package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6lT0t3czNiGlIcV07GfSKj6Lf3kTvTx7E+MwXj167t7cX80f3os65hMHMHEjC\nWPa39lJblHbM4xztcS/ItvDSgS4uqRj/l66XrsL83ZME/89VKEf0065T+T4T77Wbe/10DPiZk66j\nPk8k167MsbK1oQuzO8CqM9Pp7++PeazHXzuS986IprYffvhh3G43F1100dHbTjvtNF5++WUg/Aa4\nYsUKAFasWMHGjRsJBoO0trbS0tJCRUVFDA9BCCFODhUVFbS0tNDW1kYwGOT1118/+p45rKCggPfe\new+A7u5uDh8+zKxZsXU5URYL6jN/j1p1FubdX0d7DsT9GBLJ3PwaesfbGF/4SkJnCaZ7CaDjKaVQ\nn/8S+t3N6O1vffiDt1+H9iOoj/110q49XjHykao+2HAz0Zd0lZMH8xeht72RqCFOG8O7tUfrEJQI\nNUVp0KooKraSnTv5fWYmvOKuXbt49dVXKS8v5+tf/zpKKS6//HIuvfRSHnjgAV566SUKCwu58cYb\nAXC73axevZobb7wRq9XK1VdfPS1rmgkhRKIYhsFVV13FnXfeidaac845B7fbzQsvvIBSivPOO49P\nfepT/PCHP+Smm24C4LOf/SwZGbGn1pRSqI99CjOvEPP+28KbWWqWJ+ohxUy3NKF/+QjGDXeg0hKb\nOizNsrNzf3dCzznVVFoGxtVfxXz4+xjffgBTgfmrxzD+/hsoa3I6lmit8fT4KcuaOHOYn2bDZTNo\n6vXjHqPm5DB1enjTDavOStRQp4WNh/q4cnlh0s6fhYW5ppPc+VPT/nTCQLKqqopf//rXo/7sO9/5\nzqi3X3bZZVx22WXxjUwIIU4iy5YtY8OGDcfcdv755x/9/9zcXG699daEX9f4yJnonDzMR+5GfeoK\njCRszIiU9vkwH/k+6pOfTVgbv5HcWXY8J1FGcpiqqEGdfRHm4/fjLS1HLV+FqqhO2vU6h4I4rIoM\nR2SBSVVhGrvahyYOJJedjv7lI+juznCGcgY40u+nbSBAbVHaxAfHaNd7Xnozg+zr9TKnMLG7wiMh\nnW2EEOIkpxYuxrj5n9H/9WvM//zllJUH0v/xCKp0LuqsjyXl/MWZdjoGg6O2jJvu1Mf/BswQga1v\noC77fFKv1dgzcXZxpKoCFzsnqCcJoBwO1PLT0W+9Es/wppVNjX2scmckbVq7sy1IZ0eQonk26iN4\nDpJBAkkhhJgBVIkb45Z/Qb+7Bf2TDejg2D2Sk8F8/X/R7+9B/e3/i3u5k9aa4Ci1C62GojDdxuH+\nyX1sk0FGHeP0AAAgAElEQVQZFozrvkXGt+9L6k58AE+vj7IJSv+MFElh8mFq9TkzqmViuAh5cnab\na62p2zZE9RIXi4tPLEw+WSSQFEKIGUJl5YYLlw/2Yz74j+jBgUm5rvYcQP/mp+F1fU5X3Ofbv8vH\ni39sGzWzWpplp+kk6HAzGpWZhaV0TtKv4+mJbKPNsDk5DjoGg/T6IujEVFkLgwPoxtTaAJYM7YMB\nmnv9nFKcnMC/6WD4C1PpHBvlOQ56fCG6hia/jqoEkkIIMYMohxPj/92CmlWK+S/fRHe2T3ynOOih\nQcxH/gW1/irU7PL4z6c1h973MzQYwtNwYuYxvE7y5OhwM1Uae/24I9hoM8xiKBYWONkdyfS2YaBO\nP3tGtEzcdKiPle4MrEmY1g4GNTvfCxcfV0phKEV1gYv6tsnPSkogKYQQM4wyLKjPfBG1+hzM7389\nadkhrTX6yYdQC2sxVq9LyDm72kMoBWecm8+u94YIBo7NSpaeZCWApoKnxxdVRhLCZYB2RhjEqNPX\nod98BR1hL/npauOhPtaUJb5YPMD7u33k5lvJL/xwz3RNURr1rZO/TlICSSGEmIGUUhgXXIb6m7/D\nfOA2dN3WhF9Dv/RH9JEm1OXXJuycjQf8lM2zU1DkoGCWlb07j23PV3qS7tyeLP2+EL6gJt8VXT3C\n6g92bkdClbghtwB2bo9liNNC51CQgz0+lpUkfre2d8jk/T0+qk85dod2TdHUrJOUQFIIIWYwY+UZ\nGNfdgvnEA5ivvZCw8+oDe9H/9evwukhbdNmtsQSDmsOeAO654fNVn+Li4H4/A/0fZrZKsxw09fqn\nbGf6dNfYG85GRrshamG+k/2dXgKhyH7vavW6k3rTzRuNfayYnYHNkvgwa/d7Xsrn20nPOLY8U0We\ni+Y+PwP+yc30SiAphBAznKqsCW/C+dPTmL//97iDMD3Qh/mjuzE+dx2qaHaCRgmHPQFyCyw4XeGP\nLqfLYMEiB/XbPsxKZjks2AxFt/fknjZNFk+PH3cUO7aHpdstzMqwc6DLO/HBgFp5Jvq9LWjv1Ow0\nTraNh/pYnYTd2j1dQY4cDlBZfWK9SJtFUZHnjHgHfaJIICmEEAJV7Mb45r+g67ain/hBzOWBtGli\nPvED1PLTUaeuSegYh6e1R5q/yEFPd4i2Ix+Ot1Q23MTME0GHmrFUF7oin97OzIJFi9Fvb4rpWqms\nxxtkf6eXU0sSu1tba039Ni8La53Y7KNnjGuK0ia9nqQEkkIIIQBQWTkYX7sL7R3E3HAHerA/6nPo\n55+B/l7Up65I6NgG+0P0doeYNfvYtoAWi6J2mZO6rUOYZjiTWpplx3OSlgBKtsae6GpIjhRpYfJh\nxunr0Jv+HNO1Utmbnn6Wl6TjsCY2xDrSHMTnNSmfP/bzU1uUNunrJCWQFEIIcZRyODCu+yZqdjnm\n3d9Ed7RFfF+9Zwf6f3+P8cWvJ7wPdGODn9JyGxbLiZmY4lIbdofBof3h4NGdbaepTwLJWMSdkWwb\ninxpxCkrwdOA7miN6Xqp6vVDfaxN8LS2GdLUbwuX+zHGKSe0qMDF+11e/JPY3UkCSSGEEMdQhgX1\n6WtQZ5wfLg90aP+E99G9XZiP3ofxhetReYUJHY/WetRp7aPjVYrFy13srvPi95m4sxwnbVHyZPIF\nTbqGghRnxPYlYFaGDVNrWgciWxahbDbUirXoN16O6XqpqM8XYk/7EKfOzkjoeRv2+0nLMCgqGf+5\ncdkM3FkO9nZEtlY1ESSQFEIIcQKlFMb5n8T49NWYD9yO3vH2mMdqM4T56H2oNeeiFp+W8LF0tAax\n2RTZuZYxj8nKsVDitrGnzislgGLU1OunOMMWc19opVRU7RLhg5qSb7x80uyyf8vTxynFabhsiQuv\n/D6TvfVeapdF1hVqsssASSAphBBiTOq0tRhf+hbmTzZgvvo/ox6j//Ar0Br1ycuTMobhbOREJWkW\nLXHSdCiAK2jQ7Q3iG6UftxhbPNPaw8KFyaPY7LGgCkJBaNgX13VTRbgIeWKntffUeSlx28jMHvuL\n1EiTXZhcAkkhhBDjUhU1GDf/M/q/f4P5zC+OyR7pHe+gX3sB45qbUEZkH3TRCAQ0Lc0BSudMvAHE\n4TCorHGyc7uX4nQbh2WdZFQ8vb6YSv+MFE1hcghnMdXqc06KTTcD/hB1rUOsdCduWru3J0DToQCL\nFp9Y7mcsNYUudrcPETInJ8srgaQQQogJqeJSjFvuQe/chn7iAXQwgNneivmTH2BcfRMqOzcp120+\n5KegyIbDGdnH1dwKO95Bk0UOl0xvR8nT46cszozk/FwHh/v8DAYir+OpTj8bveW1mEtOpYrNTf0s\nnpVGmi1xX6i2vtnNgipHxK9/gGynlVyXlYPdk1MCSwJJIYQQEVGZ2eHyQD4v5g++y8CGf0Sdewlq\n0eKkXXO8TTajMQxF7XIX7n4nnm4JJKMRazHykWwWg/m5Tva0R77ZQxUWw6xS2PFOXNeeahsP9bEm\ngbu1Ww8H6O4KMK8y+uC+pshF3SStk5RAUgghRMSUwxFue1g+HyO/EPWxTyXtWv19IQYHTIpKouv7\nXFRiw56mGDwsayQjFTI1h/v9lMYZSEJ4nWS03VXU6nWY07hl4mAgxLstg3ykNDHT2oP9Iba9Ncjp\nH80bteTVRGoKJ68wuQSSQgghoqIMC8b6q0i/4XaUkbyPkcYDfkrn2MetmzeW8hob2b1WvEMSTEbi\nSH+AHKc1IUW0qwpd7IxinSSAWrEWdm5HD0RfBD8VvN00QHWhiwxH/NPawYDmrdcGqKx2Mmt25Gsj\nRxrOSE7GbngJJIUQQqQcbWo8DX7K5saWIZtX7GSfHmLXe5PbLm66auz1UZYdfzYSwh1u9kS52UOl\nZaBqlqG3vJaQMUy2jY2JmdbWWrPtrUFy86zMrYz9+ShKt2E1FM19yV93KoGkEEKIlNN2JIjDaZCV\nE1uGJ91uYa/VS0tzkO7OYIJHd/JJxPrIYdlOKzlOC4090W32mK67t31Bk22HB1iVgN3ae+t9eIdM\nFp/mmrDc1XiUUtQWTk67RAkkhRBCpJzGA37Ko9hkM5ribBsZ5QY7tkbRtm+G8vT64q4hOVJVYVp0\n9SQBapdD62F0a3PCxjEZ3mkeoCLfSZYzurW8x2tpCnBwv48Va9NjWhd5vJoiF/VtEkgKIYSYYfx+\nk9aWALPnxNevuzTLTndakFAQmhund2mZZGvs8VOWoIwkEHWHGwBltaI+cua0a5mYiCLkfT0htm8e\nZOXadJyuxIRmtZNUmFwCSSGEECml+WCAomIbdnt8H1HuLDvNfT4Wn+qifvsQwaBkJUejtaYpAV1t\nRqoqdEVVmHyYWr0OvemlaZNB9odM3m7uZ3UcgaTfZ/LWawPULHORkx9fVnMkd7adAX+IjsHkfomS\nQFIIIURKORRl7cixDPfczi+0kpdvZf+uyGsbziSdQ0FsFkVmAnYcD3Nn2en3h+gainJ9avkCsNlh\n386EjSWZth4eYG6ugxxXbAGgaWre3jRIcakt5o1lYzGUonoSspISSAohxDRnTlIrtMnQ2x3C5zUp\nnBV/ZmY4kASoXuriwF4/gwNSDuh4iZ7WhnAQs6gghultpcJZyTemR03JTXEWId+53YtSUH1KbGV+\nJlJTmPx1khJICiHENOdpOHk6uDQe8OOea0fFUDvyeIXpNvp8IYYCJmnpBvMq7ex8V8oBHS/RG22G\nVRW62BlDEKNWnYV+eyM6kNqv60BIs7kp9mntxgN+jjQHOHV1Wky1UiNRIxlJIYQQE9lT5yUUmv5Z\nSdPUNB1KzLQ2hLNipVl2mvvCAcmCKied7UE62qQc0EiJLP0zUnWs6yTzCqF8Pmx/K+FjSqR3WwZw\nZznIT4t+U1hXR5D67UOsPCM97rXA41mQ56SlP0C/P/Le59GSQFIIIaa5rBwLB/endvYmEq2Hg6Rl\nGGRkJm6t3uxMO54P6hlarYqapS52vDOEPomWA8TL0+unLAkZycp8Fw1dPnzB6JcTqNPXYab47u1Y\ni5B7h0y2vD7A0pVpZGYn7rU+GquhWJjvjHqJQTQkkBRCiGlu0WIX+3Z6CQamd3DUeCD2TjZjcWd/\nuE4SYHaZDYsVGk+i5QDx8vT4cCeoq81ITqtBWbaD/Z3Rb3JSp66GPXXo3u6EjysRgqbmTU/009qh\nkGbzawPMWeCguDS+8laRGm6XmCwSSAohxDSXnWshv8jKgb3RdRJJJT6vSXtrgNnlCQ4ksxw0jQgk\nlVIsXu5i13teAv7pHXgnQr8/hDeoyY9x1/FEqmKoJwmgnC7U0pXoza8mYVTx23FkkOIMG0UZkQeD\nWmve2zKEK82gsibxGeCx1BSlUZfEdZLJeeUIMQMpqxXL/uSUrAjMmg0Z2Uk5tzg5LFrs5PUX+5lT\nYU/qmqtk8Rz0Uzzbhs2W2E0HpVn2YwJJgJw8K0UlNvbWe6lZ5kro9aYbT48fd7Y9rnZ846kqcPGX\ng70x3VetXof5u5/DuZckeFTxi6UI+YG9fnq6gqw9NzNpv+/RLCpw0dDlxRc0cVgT/94ggaQQidLX\ni3/DHUk5teXW+ySQFOPKyLRQXGpj/y4f1adMr+BIa03jAT+Llyd+3LMzw5ttTK0xRnx4Vy1x8vJz\nfZQvsCd0TeZ04+n1JWWjzbDqIhc/3nIErXX0wVPVKdDTiW4+BItqkzPAGIRMzRuePu7+qzkR36ft\nSIB9O72ccW4G1gR/WZqI02owJ8fBno4hlsxKT/j5p9/XViGEEKNaWOvk4H4/3qHpVSuxpytEMAj5\nRYnPbbhsBlkOC20Dx3b3cLoMKqod1G+b2eWAGnv8uLOSN81akGbDblE090XfXUUZlnApoBSrKbmz\nbYg8l5WSzMgC8IH+EFvfGOTU09NIy5iaLy3JLAMkgaQQQpwkXGkGZXPt7K2fXh1cwptsbEmb7nOP\nMr0NMK/SQX+vSevhmduHO1kbbUYK992ObbOHWn0O+o1X0GbqfDnaeKg34t3awUB4c01ljZOCWZOz\nuWY0NUUu6pO04WbCr38PP/ww77zzDtnZ2dx7770APP3007z44otkZ4en2i6//HKWLVsGwDPPPMNL\nL72ExWLhyiuvZOnSpUkZuBBCTCfbtm3jpz/9KVpr1q1bx6WXXnrCMXV1dfzsZz8jFAqRlZXF7bff\nHvV1KqodvPTffSyoCpGWnvpTtqGQpulQgDPPz0jaNYY73Jw6+9jbLRZFzTIXdduGKJhlTVpR6FTm\n6fUnPZAMFyYf4twFOVHfV5XOgYxMgvXbYE5lEkYXHVNrNjb2c+d5ZRMeq7Vm65uD5OZbmVuR3N/x\nRKoL03jg9cOETI0lwa/zCQPJdevWceGFF/LQQw8dc/vFF1/MxRdffMxtHo+HTZs28cADD9DR0cH3\nvvc9HnzwwUldVCqEEKnGNE0ef/xxbrvtNnJzc7nllltYuXIlpaWlR48ZHBzk8ccf59vf/jZ5eXn0\n9sa2QcHhNJhbYWfPDh/LVqUl6iEkzZHmAFk5lqRO+ZVmOTjYPfqO9lmzrTTsM2jY52f+wsnbSZsK\nfEGTzqEgJRnJzkim8dze2Mv4qNPXEdj455QIJHe3DZFlt0S0HGBvvQ+f1+TU1RlTHgdlOSwUplt5\nv8tLZX5i1yJPOLVdVVVFevqJizO1PrFswpYtW1izZg0Wi4WioiJKSkrYt29fYkYqhBDT1L59+ygp\nKaGwsBCr1cratWvZvHnzMce89tprrFq1iry8PACysrJivt6CRU6OHA7Q15u8bhaJ0nggcZ1sxuLO\nttPUN3rdSKUUtctc7K334vOlzvTpZGju8zMrw5bwDNXx5uY4aBsI0ueL7fWoapeHM5IpYGNjH6vL\nJ86eH/b4Ofi+jxVr07FYUiOZlqx1kjGvkXzuuee4+eabeeSRRxgcDM+7d3Z2UlBQcPSYvLw8Ojs7\n4x+lEEJMY52dneTn5x/992jvjc3NzfT393PHHXdwyy238Je//CXm69nsigVVDna/l9prJb1DJl3t\nIUrcyV07Vpplp6ln7BqbmdkWSsttKf/7SrRkb7QZZvmgu8ruGNolAlBShu7vQ3d3JHZgUdJah8v+\nlI//Ja+3O8S7W4ZYuTYdpyt1tqLUFLqoj3Gt6nhieoQXXHABDz30EPfccw85OTk8+eSTiR6XEELM\nKKZpcuDAAW655Ra+9a1v8dvf/paWlpaYzze3wkFXR5DuztTtK+1p8FPitmG1Jjdjk++yMhTUDIzT\nb3hhrZPDngC93amfxU0UT6+PsiSvjxw2vE4yFsowsCxajN5bn+BRRWdvhxeH1aB8nN+Z32ey+bUB\nape5yMlLrQqLwxnJ0WaU4xHToxw55XLuuedy9913A+Fv2e3t7Ud/1tHRcXSa5nh1dXXU1dUd/ff6\n9evJzIy+Z+VksdvtMr44RDM+nyV5f3zJXKeSzHMbhkrZ5zfVX3sATz311NH/r62tpbZ2cmvSHf/e\n2NnZecJ7Y15eHpmZmdjtdux2O9XV1TQ0NFBcXHzMcdG8dy5ZbrBv5xDrPpabwEfzoXiee601TQf7\nWfXRPDIzo8+KRXvt8lwnXUErxfljTEtmwimnGex6d4hzLioc9+95Kl/zibx2y8ARzpiXG/H54rn2\nqeUmv9p2OOb7Bxefir9hL2nnXBTT/eMx/Li37Ojm7Ir8MZedmKbmrVfbmDM/neol0W8sGu/aiZCZ\nCS5bI10hG3NyI1snGcl7Z0Sf2FrrYyLY7u5ucnLCv6Q333yTsrLw7qUVK1bw4IMPcvHFF9PZ2UlL\nSwsVFRWjnnO0AfX19UUynCmRmZkp44tDNOOzhJKXQUn0N7HJOrdp6pR9fqfDa2/9+vVTOoaKigpa\nWlpoa2sjNzeX119/neuvv/6YY1auXMkTTzyBaZoEAgH27t17woZGiO69s2i2pm67nwP7uyhIQo3G\neJ77zvYgIdPEkeajb4z1i4m8dnG6lT0t3bjTxv47nVWq2V0XYO+uTkrcY2edpvI1n8hrN3QOcumi\n7IjPF8+1y9I1u1oH6OrpxRrDmkxXZQ3+//0DoSn4vWdmZtLb28sr+zv4xkdLx/wd7Ng6hGmGWFBl\nJOw5SvRrrbrAyeYD7eRZJw50I33vnPCdZcOGDdTX19PX18d1113H+vXrqauro6GhAaUUhYWFXHvt\ntQC43W5Wr17NjTfeiNVq5eqrr57ynUpCCDHVDMPgqquu4s4770RrzTnnnIPb7eaFF15AKcV5551H\naWkpS5cu5aabbsIwDM477zzcbnd817UoFi52suu9IdaeM/U7R0cK145MXmu+441VS3Ikw1DULnfx\n7uYhikpsKbNJIhlCpuZwn5/SJHa1GSnDbqEo3cqBGHcNW+ZWQkcreqAPlT752eADXeE1tvNyR8+e\nNx7w0doc4IzzM1ApXEYqPL09yAWVicmYQgSB5PHfmiFcEmgsl112GZdddll8oxIpz9LVDp1tER/v\ns1gjzjSq4MwtDixOXsuWLWPDhg3H3Hb++ecf8+9PfOITfOITn0jodd3lNvbv9NJ6OMis2VNXEHmk\nYFBz2BPgrAsmLyBwZ9l5NYKez4WzbGTl+Hl/t4/KGuckjGxqtA4EyHFak9J7eSzVhWnsahuKKZBU\nVivMWwj7dsLSjyRhdON7/VAfa8pH75Hd1RGkfruXNesyUr7PfU2Ri9/UtU98YBRSayWomD462/B/\n/xtJObXj+uiLMAshRqcMxaIl4axkUYk1JbKSLZ4AOXkWXGmT96E7XJQ8EjXLnLz6Qj9l8+wptes2\nkRp7Jm+jzbCqQhdbmvq5pCq2+6uFtei9dahJDiTDu7V7+era2Sf8zDtksuX1AZauTCMzO/UbALiz\n7HiDmraBAIXpiflieXL+hQghhDiquNSGYSiaG1Mj29/YkPzakccrybRzpD9AyJx4LXN6hoU5C+zs\nfPfk7cPt6fHjnqRp7WHVhS52xVoCCFCVteg9dRMfmGAHOocIhDQVecdmqEOhcPvDORUOiktTI9s/\nEaVUwtslSiAphBAnOaUUVUuc7H7PixlBIJVMgwMmPV2hSf/gdVgNcl1WjvRHFkxXVjtpPxKkqyN1\nyyfFo7HXjzt7cjv5FGfYCIbC2bCYzFsIzYfQvsmt9/mX97tYfdy0ttaad7cM4ko3qKyeXh2RagrT\nqI+xFNNoJJAUQogZoGCWFWeagach+h3SieRp8FNaPjUbWSLZcDPMalNULXGx453E191LBZ4e36Rn\nJJVS8dWTtDvAPRfe353YgU3gL+93sab82PW8B/b66e0OsewjaSmxXCQakpEUQggRNaUU1Uuc7K7z\nEgpNTWCktT66W3sqzM6y4+kdu8PN8dxzw1nTpoOpsSQgUbTWeKYgIwnhdZK74uiuohZO7vR2Y4+P\nPl+QRQUfbhBqawmwb6eXlWekJ72YfjLMz3XSNhCkN8aWlceTQFIIIWaI3AIr2TkWDu6fmqxkR1sI\niwWy86ZmU4I7ig038EEf7uUudr47RDBw8mQlO4eC2AxFlmPyn4f410kuRu+dvEDyj7u7OK8yH+OD\nrONAf4itbw5y6up00tJTf3PNaCyGYlGBk50JapcogaQQQswgVUtc7NvpnZLAqPGAj7J5k1c78nju\nLAfNUQSSAHkFVvKLrOzbdfL04Q5nI6cmK7wgz4mnx89QwIzxBFXQsBc9CWXiPL0+Xj/Ux6eXlwAQ\nDGg2vzrAwhpnUgr8T6bhdomJIIGkEELMIFk5FgqKrLy/J/Ip3kQIBjQtTQHcUzStDdGVABqp+hQX\nDfv8DPafHH24wzu2p2aDiN1iMC/Xyd6OGNdJpqXDrNlwcH+CR3ain29r47LqPLKdVrTWbH1zkNwC\nK3Mqpu41nCg1RS7qErROUgJJIYSYYRYtdvL+Hh9+X4xZoRg0N/rJL7TicE7dx06O00LI1FGvDXOl\nGcxf6KB++8mRlZyKGpIjxbPhBianDFB96yD7O7x8fFG4T/2eOi8+n8mSU13TbnPNaBbmuzjU7cMb\njP89QAJJIYSYYdIzLZS4bezfNXlZyamoHXk8pRSlWXaaothwM2zBIgfdnUHaW6f/xpup2mgzrLrQ\nxa54A8kkrpPUWvPTra18dmkhDqtB44FBDh3ws3JtOsZJ0jbTYTWYm+tkdxzrVYdJICmEEDPQwlon\nB9/34x1KflZyoC9Ef6/JrJKpL9rszo68BNBIFquiZpmLuneGprwWZ7ymovTPSFUFLnZ3DGHGWlap\nshr270SbyVlqsLGxD39Ic9a8LPp6Q7z1ehcr16ZPaTY9GWoTVAbo5PqtCCGEiIgrzaBsnp299cmf\nrm1s8FM6x54S2ZzSTAeenth2rZe4bdjsiv27BxI8qsnT7w8xFNQUpE3dZpEcl5VMu4XGGJ8HlZUL\nWTnQdCjBI4NASPPzbW1cubwIQyl2bh+idlkWOXnTe3PNaGoTtOFGAkkhhJihKqodNB0KMJDETSTa\nDNeOLJ/iae1hpdmxbbiBD9rLLXNRt60Xc4pqccarqTfcGnGq1/klZHo7Ceskn9/XRUmGnWUl6XR3\nBOnpClFZlZHw66SCqkIXezq8BOPMsEsgKYQQM5TDYTCv0s6euuRlJdtbgzicBlk5qVFzL5ruNqPJ\nybOSnWvDc3BqOwTFqrHHN2Wlf0YKb7iJY1q1sha9d0fiBgQM+EM8taODK5YXArC7zktljRPLNCw6\nHokMu4XiDBv7O+P7+5dAUgghZrD5i5y0Hg7S15OcrORUdrIZTXGGnbaBAIE4Moq1SzPZt9M3LddK\nenr8lE1R6Z+RqgvT4itMvrAW9tYntH3l7+o7WTE7g7m5Trraw38TU71BLNkS0S5RAkkhhJjBbDZF\nRZWDXTsSn5UM+E2OHA5QOmfqN9kMs1kUhelWWvpjzygWlThxOBWHPdNvB7enNzUykmXZdnq9IbqH\ngjHdX+UXgdUKR5oTMp62gQDP7+3iM0sLgBHZyBRY15tMNYVp1MexxAAkkBRCiBlvboWD7o4g3R2x\nfaiPpelQgMJZNuyO1PqoKc1yxDW9DVBR42RvvTehGbHJ0NgzdV1tRjKUYlFBvO0SE1cG6JfvtnNB\nZS4FaTY624L095kplUlPlpoiFztbB2PfQY8EkkIIMeNZrIrKGic730tsVrLxwNTXjhxNrB1uRioq\ntqKU4khzYoPvZPKHTDqHghRnpMZzEm9hciprIQGB5IEuL+809/Op2jwgnI1cWONIiSoDyZafZiPd\nbom5kgFIICmEEAIon29ncMCk/Uhipmv7ekIMDZoUFqde2RR3jEXJR1JKUVnjmFZZyeZeP0XpNqxG\nagRIce/cXliL3lsf9zh+trWN9YsLSLNZaG8NMthvTmkrz8kWb7tECSSFEEJgGIpFtU52vZeYwKix\nwY97rh0jRYKWkeLduT2sxG0jGNC0t06PrGRjj39KWyMerzLfxYEuL/5QjEXxi93gHUJ3tsU8hm2H\nBzjS7+eCyhwA9uwYYmGtIyVft8lSUxhfPUkJJIUQQgBQWm4jGNS0Ho4vMDJNjScFWiKOZXhqO96A\nWSlFRbWTffWT12oyHp5eH+4U2LE9zGUzcGfbYy4/o5SCypqYs5LmB60Q/3ZZIVZD0X4kgHdIUzon\nNV+3yVJTlEZd22DMfw8SSAohhABAGYqqJS52vTsUV5DV1hIkLd0gMys1akceL8tpxVCKHm/8JY9K\n59gYGDDpak/9rGSqbLQZqaog/untWNdJvnygF7vFYHVZJlprdu/wsrDWOaOykQCzM20ETU3rQGzL\nWiSQFEIIcdSs2VYMi6L5UOxrJQ+l6CabkdwJ2HAD4SUBFVUO9u5MfqvJeHl6/ZRlp05GEqCqMC2u\nDTexdrjxBU3+fXsbXzi1EKUU7UeC+Hya0vLUKVU1WZRScU1vSyAphBDiKKUU1ac42b3DG1PBbZ8v\nvGFndllqB5KlCVonCVA2z053Z4je7uS1moxXyNQc7vNTmpVaz0t1YbgEUMwZcPc86O5A9/VGdbf/\n2t4uDVIAACAASURBVN1FZb6T6sK0o9nIRbVO1AzLRg6rLXJRH2OnIQkkhRBCHKNglg1XukHjgegD\nraaDAWaV2LDZU/sDObxOMjFrGy0WxYJFqZ2VbB0IkOO04LSm1sd+QZoVq1K09MeWAVcWC8xfBPsi\nXyfZ6w3y7M5O/nZZERBeihEIaGaXzbxs5LDaIslICiGESKCqJU721HkJRdlKMFVrRx4vUTu3h81Z\n4KD9SJD+vtTMSnp6/Cm10WaYUiruepLRFiZ/akcHZ8zJpDTLLtnID8zJcdA1FKTHG/1aXwkkhRBC\nnCA330p2noWGfZFn7Xq6ggT8JgWzUq925PHcCehuM5LVpphb4WD/ztTcwd2YIq0RRxN3Pcko1kke\n7vPzckMv/3dJuBVi6+EgoZCmZAZnIwEsRrjTUCztEiWQFEIIMaqqxS727/IRDESWlWw8EK4dqVTq\nZ3ZmZdjoHArGXsNwFPMq7RxuCjA0mLhzJoqnJ/U22gyrijOQZF4ltHjQ3onX+P18WxufqMolx2n9\nMBu52DktXrPJVlPkoj6GwuQSSAohhBhVVo6FgllW3t8zcZbNDGmaDgWmxbQ2hDMwRek2mhOYlbQ7\nDMrn29m/K/XWSoZrSKbmczMv18mRgQD9/tiWBSibHcrnw/7d4x63u32IXW1DfLIq3ArxSHMQrTXF\npTM7GzmsJsZ1khJICiGEGNOixU7e3+PD7xs/y3bkcICMLIP0jNSsHTkad7adpr7EBZIA8xc68BwM\n4POmTlZSax1eI5miGUmroajId7KnPZ7p7cXjTm9rrfnpO618ZmkBDqvxQTZyiEWLXZKN/EBlvhNP\nr4/BQHQBvQSSQgghxpSeYWF2mY19u8bPSjYe8FM+TbKRw0oz7TT1JDaQdLoMSsttEWVxJ0uXN4TV\nUGQ5UjfIryqIc8PNwlr0vrEDybc8/QwETNbNywagpSmAUopZs1N/Pe9ksVsM5uc62d0eXUZdAkkh\nhBDjqqxxcuh9P96h0bNs3iGTzrYQJe7pFUi6sx0JKUp+vAVVDg7u9xPwp0ZW0tOTuhtthsW74YYF\ni+DgfnTgxDJCQVPzs21tXLm8EIuhZG3kOMLT29Gtk5RAUgghxLhcaeG1f3vqRs9UeA76KXbbsNqm\n14dyaYK62xwvLd3CrNlWDuxN/Llj0ZiipX9GWlTgYm+Hl1AMRfABlDMNit3QsPeEn72wr5v8NCvL\nS9IBOOwJYLEoikokG3m8msLoN9xIICmEEGJCFVUOmhsDDPQfu35Kaz1takceb7i7TTx9xcdSUe3k\nwF4fwWDizx0tTwqX/hmW6bBQkG6loTv2JQHhMkA7jrltMBDi1++184XlRSil0KZkI8dTVehiX6eX\nQBTVDCSQFEIIMSG7w2D+Qge7dxyblezuDGGakFeQuuvvxpJht+C0KjqHoi/CPJHMLAv5hVYO7p/6\ntZLhYuSpHUhCeJ1kXPUkF9aij+tw8+zOTpYWpzM/zwlAsyeAzaYoLJZs5GjS7RZmZ9rZ1xn5OkkJ\nJIUQQkRk/kIHbS3BY3pKNx7wUzZNakeOxp2k6W2AyhoH7+/2Rd0dKNEae1O3huRI4Q43sfV7BqCi\nBvbvQpvh12fHYIA/7e7is0sLASQbGaFoywBNGEg+/PDDXHPNNdx0001Hb+vv7+fOO+/k+uuv5667\n7mJw8MMn/plnnuErX/kKN954I9u3b49y+EIIcXLatm0bN9xwA9dffz3PPvvsmMft27ePyy+/nDff\nfHMSRxcZq01RUf1hVjIYNGlunD61I0dTmuAONyNl51rJyrHgaZi6tZID/hBDgRAFaamfgasuTIsv\nI5mZBTn50NgAwK/ea+e8BTkUZYTrRDYdCuBwqGnReWkqRVuYfMJAct26ddx6663H3Pbss8+yZMkS\nNmzYQG3t/2/v3uObru/9gb8++ebWtEnbtCm0SWuBphQiFwUmCorlom7e8HHOmNOzx+YDPSqomz/P\nUDxD3bFedhhTJuqOHnbYHjpv50w2FbcxoF5QB2grWC62gG3TUnoJTdJLbt/v5/dH2tDSQtMm33yT\n8n7+0yT95vN5p/3mm3c+VwfefvttAIDT6cSnn36KZ555BmvXrsV///d/yzL2hBBCUokkSdi8eTP+\n/d//HRs2bMDu3bvR1NQ07HF/+MMfMGvWLAWijE5xiQ6drhBOdYTgrPchM1tAmiF1O7fkmnDTr2Sa\nHnWH/JDGOIkkVk5PAFaTLiVa4AqMGvhFjvaeoTOvoxXed/srNHT68Y/GLvzzhTkAAEni+LqGWiOj\n4bAYcGgUa3qO+O4vKytDenr6oMf27duHRYsWAQCuvPJK7N27N/L4ZZddBkEQkJeXh/z8fNTV1Y0m\nfkIIGXfq6uqQn58Pi8UCtVqNBQsWRK6bA/3lL3/B/PnzYTKZFIgyOoLAUOrQ4/ABH4593Z1ya0ee\nyWbSoskt3zjGHIsaegNDc8PYk6NYNLr9KEyB8ZEAwBiLfbvE0vC+27+rasU/OXKQoQ2P3W2qD0Kf\nxpCTR62RI8lKUyNzFGuOjulrpNvtRlZWVrjCrCy43W4AgMvlQm5ubuQ4s9kMl8s1lioIIWTccLlc\nyMnJidwf7trocrmwd+9eXHXVVYkOb9QKJ2nR2y2ho82f8tvL2TK1snVt97NP16PukE+RHromTyDp\nZ2wPFPPC5PbpONDSjUZ3AN8pDecp/a2RpbSLTdSm5xmiPjYu/RH0jyGEkNhs2bIFt956a+R+Mg8L\nUqkYHBelwTHLBEGd2tf/XIMGbr8IX0i+xcMtE9RQCQwtTYlvlWxM4q0RhxPrwuQ8Oxe/L1yGHxSr\noBHCKY7zmwAM6SrkUmtk1ByjSCTH9FfNyspCZ2dn5GdmZnjLIbPZjPb29shxHR0dMJvNw5ZRU1OD\nmprT2xmtWLECRqNxLOEkhFarpfgG8AvyvSHl/GKSqmWrVCxpz79kf28AwJtvvhm57XA44HA4Elr/\nmddGl8s15Np47NgxPPvss+Ccw+v1oqqqCmq1GnPnzh10XLJcO41Tw//7QECZiSTxPO9smXp0imrY\ns9NHPniMdc+co0bNl17Yy8wxXStGW3ezN4iy/GwYjWljrnOsdY/F7LR0OHc5odYbkKY53b0abd07\najugSkvDYl899MaZEEWOukNeXHplDozGsSXUSl7jlKr7hpkZAKK7dkaVDXDOB307njNnDiorK7F8\n+XJUVlZGLnRz587Fr3/9a1x33XVwuVxoaWlBSUnJsGUOF5DX640mHEUYjUaKbwBBjP+6a/3kbIlJ\n1bIliSft+ZcK740VK1YoGkNJSQlaWlrQ1taG7Oxs7N69Gz/+8Y8HHbNp06bI7RdeeAFz5swZkkQC\nyXXtVPJ/H8+68zPU+PpEJybqomuVHEvdmWaOgD+E43WnYJk49uEAo6k7IEpo6w7AqArC6439mp2o\n//cFWVpU1bdhxoTTiX00dQdFCS9/1oh7J/jg/+pzBOdfifqjfqSlM6SlB+D1ju1Lz3g5z0fLZDJF\nde0cMZHcuHEjDh48CK/Xi7vvvhsrVqzA8uXL8cwzz2DXrl2wWCy4//77AQA2mw2XXnop7r//fqjV\natx+++3U7U0IOe+pVCqsXLkSFRUV4Jxj8eLFsNls2L59OxhjWLp0qdIhntfCO9zIu3A4Ywz2aXrU\nHvLHlEiORrMngLx0DdSq1Poc7l+YfGAiGY1tX3figiwdZpSVQPr77yGKHLUHfbj40tGVQ0ZnxETy\nzG/N/datWzfs4zfddBNuuumm2KIihJBxZvbs2di4ceOgx5YtWzbssatWrUpESKSP1aTF3qYu2esp\nKNLgyFc+uNpDMOfKP17P6QmgMIUm2vSbZjFg+9HOUT2nyy/i/2o68MSyIsCkBYIBNHzVAWOmPiF/\n6/NZ6i7+RQghhMSBTcZFyQdSqRimlOlQezD67ediEd4aMXUm2vSbaknDkfZeSKMYLvRWTQfmFxpR\nmBleM1MsnYm6WgmlDr2MkRKAEklCCCHnOatJi2ZPYFSJy1gVTtLC0ynCfUq+ceb9Gj3+lFr6p585\nTY10rRD1QvEnuwLYcbQT3595evlBp60cpkArsnOoNVJulEgSQgg5r6VpVMjQCmjvlj+5EwSGyVN1\nqD0k75hMIHVbJAFgWm70ywC98mU7rptqRnZaOGkUQxx1gcmwH39bzhBJH0okCSGEnPesmVo4ZZ5w\n0++CyTp0tIbQ5RFlq0OUOJq9qbUY+UBllugWJq/r8OHAyR7cOO30clr1xwLIsmiR2XoQ3DO6sZZk\n9CiRJIQQct6zmeTf4aafWsMwya5DnYytkq3dQWTqBOjVqfkxH83C5JxzbKlqxfdn5CJNE36doRBH\n3aHwntqYMg2oPZiIcM9rqXmGEUIIIXFkTWAiCQDFdi1amoPo6ZZnRx1niu1oc6bCTB06fSG4fWcf\nbvB5czdO9YawdEpm5LH6Oj/MuWpkZqvB7A7w2pqzPp/EByWShBBCzns2ky7qyR3xoNWqcMFkLY4e\nlmcGd6pOtOknqBhKc9NwuH34VklR4vhdVSt+eJEFQt86maEQx9Ej/shMbWafTolkAlAiSQgh5LyX\n6BZJAJg8VYemhiB8vfFvlXS6AyhM0Yk2/c414WbHMTeMOgHzrBmRx76p9SPHooYpq29rxeIS4OQJ\n8J7uRIQ7rnB/9MMuaF48ISmAq1QQjh6Sp3CzBWJ27sjHETKO5RjU6AmK6AmKMAzY41lOOr0K1iIN\njn3tx/RZse+FPZDTE8CSAV2+qajMkoY3DrQPedwXkvDa/nY8vMga2T0vFAy3Rl5WfjqxZGpNOJk8\nehiYMSdhcY8H/IP3gUnRbYxAiSQhqcDrRuDZx2QpWvvQLwBKJMl5TsUYCozhVkl7TnyTunOZUqbH\nh3/zomSaDlptfDoJOedwevwoNKVu1zYAlObqceyUD0Fx8PqefzrkgiMvbdD/6XitH5YJahgzB38J\nCI+T/AqMEsmocUkEr9wG/Ci6RJK6tgkhhBD0jZN0J7Z725CuwkSrBt/Uxq/eTp8IgTGY9KndVmTQ\nCMg3anHs1OlxpJ29Ibxz5BT+ZZYl8lgwyHHsaz/sw+xiw0od4DRze3S++gIwZIx8XB9KJAkhhBCE\n15JM9DhJACgp0+F4rR+hYHx21ml0+2FL8dbIfmVnjJN8/UA7yieZMNF4+vUd/9qPvIlqGE3DDEmY\nPBVoOAYeSMwaoeOBtPNdsMXXRn08JZKEEEIIAKtRm9CZ2/0yTAJy89SoPxqfZMfpCaAwhZf+GSi8\nMHkPAMDp8WN3gxffvfD0UJxgQMLx2uFbIwGA6fSA9QLgeG1C4k11vKUJaDgGNu/yqJ9DiSQhhBAC\nwJapRVOCdrc5U8k0PY597Ycoxt4q6XSn9tI/A/UvTM45x++r2nDTdDNMutMtj8e+9mNCvgYZxrNP\nkOofJ0lGxiu3gS1cBqaJ/vyhRJIQQggBUGDUoqUrCFGKTxfzaGRmCzBlCWg8HnuLaKMnMG66tvPS\nNQBj2F7bgWMuH66bmh35XSAg4XhtAHbHuVtfaZxkdLivB/zTXWCLvj2q51EiSQghhADQqVXI0gto\n7Q4qUr99uh51h/2QYkxkne7x07XNGMM0Sxqe+bAe/zLbAq1wOm05dsSPfKsG6RkjLNdUMg04dgRc\nlG9v8/GAf1YJlM0Ay7GMeOxAlEgSQgghfawmnSITbgDAnKuGIV2FpoaxJ7LdgfBamDmG1J6xPZAj\nLw2FWXpcUWyKPBbwS/imbuTWSABg6UYgJw9oOCZnmCmNcw6+8z2oyqOfZNOPEklCCCGkj02BHW4G\nsk/Toe6QD5yPrVXS6QnAatJC1bdQ93jwbXs2nrmhbNBrOnrEj3ybBob06BaPp3GSIzi8H2AMmDpj\n1E8dP19ZCCGERGRkZER2/ZCLIAgwGo2y1gGEW0u6urpkrwcIb5U4cN3CRMudoIZazdDSFES+bfTj\nHJ1uP2wpvjXimQQVQ7pWgLdvHpTfJ6H+aABXXDWKc6/UAf6PD4CrbpInyBQn7XwPrPzaMV0zKJEk\nhJBxiDEGr9erdBhxkYhktZ/VpMWH33gSVt+ZGGOwT9fj6xofJlo1o/5gd3oC42bG9tkcPeKHtUgD\nQ3r0narMPh381d+ASxKYijpjB+IdrUBtDdjK+8f0fPprEkIIIX1smcqNkew3oUANSeRoawmN+rmN\n7gAKx1mL5EB+n4SGYwGUTBt+3cizYVk5gCEdONEoU2Spi1e+D3ZpOZh+bFuDUiJJCCGE9MnWCwhK\nHF6/cjN8GWMoma5H7aHRd7E7PeNnDcnh1B3yw3aBBmmG0acv4WWAamSIKnXxgB/84+1gV35nzGVQ\n1/Y4JpxqB1xtspTNQsosj0EIIXJijMHaN+GmzDK2Fpp4KCjU4MgBHzraQsixRPdRHRQldPSEkG8c\nn4mkr1dC4zcBXHnNGIc62B1ATRUQQ9I03vC9HwHFdrAJBWMugxLJ8czVhsDTD8pStO7Hj8pSLiGE\nKM1q0sLp8SuaSKpUDCXTdKg96EPOooyontPsDSIvXQO1Kv6TrHq6Jfh6/JAkETq9CoI68bPC6w75\nYCvWQp82ts5UZndA2voqOOeyT0RLBeElf96Favm/xFQOJZKEEELIAEovARSJo1iLr2t86HSFkGUe\n+eM6Xlsjcs7R3SWhozWEjrYQXG0hiCKQYfKhtzsIv49DJQA6vQp6PYMuTXX6tl4FfVr4py6NQatl\ncUnaerpDcNYHx94aCQCWiQDnQPvJ8O3z3bEjQG8P4Lg4pmIokSSEEEIGsJq0qDyu3MztfoLAMGWq\nDnWH/Ji7YOSP6/DWiKOfaMM5h9ctoaPtdOLIVECORY0cixql0/VIN6pgMpng9XrBOUcwyOHv5fD7\nJPj6fvp9HB53ONH09Ybvh0IcOt0ZCaaeQZ8W/jnwcUE4e8JZ86UXRZPG3hoJhIct9I+TZJRIgu98\nN7zkT4yz2CmRJIQQQgawmXRwJkGLJAAUTdGh9pAHXo8Io+nci2873X7MtY7cDS5JHJ5OcUDiKEKr\nZcixqDEhX4Pps/RIM6jO2pLIWLilUasFjJnnjkkUOfy+04lmOMGU4OkU4fNJkWTU7+MQBBZOLtNO\nt27q0hg0Gob6o35ceU10XfznZJ8OfF0DXLYk9rJSGO90gX/1OVS33hVzWZRIEkIISbjnn38ef/jD\nH9De3g6r1Yo1a9bgmmuuUTosAEC+UYO27iBCEpdlvOFoqNUMk0rDu91cdEn6OY91egJYPm1oi6Qk\ncnS6TieOpzpCSEtTwWxRw1qkxcw56pha+s5FEBgM6WzENR855wgGwknnwATT5+PwnBJx0SWZ0Olj\n24Mc6Bsn+fc/x1xOquMf/hVs7uVghtiTc0okCSGEJFxxcTG2bt0Ki8WCd955B/feey8++eQTWCwW\npUODRlDBnKZGS9fYuorjbVKJFjve86KnS8TZ1mYXJY6mvsXIQyGOzo5QX+IootMVQoZRgNmixgVT\ntLhovgE6XXKt/scYg1bHoNUN38ppNGbEZ4H9giKguwu80wWWZY69vBTEQ0HwD/8K1U8ei0t5lEgS\nQsh5SrzjhpjLEF4eW+vOtddeG7l9/fXX47nnnkNVVRWuuuqqmGOKB5tJiyZ3ciSSGq0KF0zRou6w\nHxPyh/4+GOQ41ujDfMGIfZXd8LhFmDIF5FjUmFKmgzknHRotzVIGEB4PWDINvPYg2LyFSoejCP7F\np8BEK5itOC7lUSJJCCHnqbEmgfHw1ltv4eWXX4bT6QQA9PT04NSpU4rFcyZbZnic5CVKB9JncqkO\nu973ordHRMAvwdUuRmZVd3lFCOkMGXoBZTP0yMoJ79dNhsfsDqC2BjhfE8ld70G19Ma4lUeJJCGE\nkIRqamrCgw8+iDfffBNz584FAFx11VXgPPYxcPFiNWlxuK1X6TAidHoVbBdosO2PLRBFCdk54RnV\nF16UhkyzgD8fcUHoAXInaJQONekx+3RIn+1SOgxF8IajQEcbMDt+X5EokSSEEJJQPT09YIzBbDZD\nkiS89dZbOHLkiNJhDWI1afH3o26lwxikbEYapjr0EDQ+qM6YBOT0BGDPGd3+0+etoilA20nw7i6w\n9DjMBE8hfOd7YIuuARPOPdt+NJJrtC0hhJBxz263484778T111+P2bNn48iRI5g3b57SYQ0SXpTc\nn1StpGoNgzlXOySJBIBGdwCFSTCeMxUwtRqYXArUHVI6lITiXR7wqk/Brrg6ruVSiyQhhJCEW7Nm\nDdasWaN0GGdl0glgADx+EZn65P6o5JzD6YnPrjbnC2bvW5h8VnJ9gZET/3g72KxvgRkz41outUgS\nQgghZ2CMoSCJFiY/l06fCBVjSZ/wJhNmnw5eW6N0GAnDJRG88n2wxdfFveyYzrrVq1fDYDCAMQZB\nEPDUU0+hq6sLzz77LNra2pCXl4f7778fBoMhXvESQkhKqq6uxpYtW8A5R3l5OZYvXz7o9x9//DH+\n9Kc/AQD0ej3uuOMOFBUVKREq6dO/57YjL7k/w5wePwpN1Bo5KpOmAs5vwP0+MN15MLZ0/17AlAVW\nbI970TElkowxPProo8jIOD1YdevWrZgxYwZuvPFGbN26FW+//TZuvfXWmAMlhJBUJUkSNm/ejEce\neQTZ2dlYu3Yt5s2bB6vVGjkmLy8PP//5z2EwGFBdXY3/+q//whNPPKFg1KQ/kUx2TneAurVHiel0\nQOEk4NgRYNospcORnbTzPVlaI4EYu7Y550MGIu/btw+LFi0CAFx55ZXYu3dvLFUQQkjKq6urQ35+\nPiwWC9RqNRYsWDDk2lhaWhrpvbHb7XC5XEqESgawmrRwuv1KhzGiRk9yLJyeavrHSY53/EQj0FQP\nNmeBLOXHlEgyxlBRUYG1a9dix44dAAC3242srCwAQFZWFtzu5Fo+gRBCEs3lciEnJydy32w2nzNR\n3LFjB2bPnp2I0Mg5WDO1KTFG0un2o5BaJEeNlTrAaw8qHYbs+K73wC6/CkwjzxqjMXVtP/7448jO\nzobH40FFRQUKCgqGHMPY8Kvr19TUoKbm9DeBFStWwHi2TUSTgFarTbn4/IJ8A6/P9n+lsuUqXL6i\nBUENQwzndrK/NwDgzTffjNx2OBxwOBwKRnNuX331FSorK/Ef//Efw/4+2munEMd14pQmCMKQ15iI\n885uSEdH7zfQGdKhFU63uyh5zg9Xd5M3iKkFZhiN8rZKJtvrjpU0ax48L61HRpoeTH32JCuVXzfv\n6YZnz0cwrt8M1RjKiebaGVOmkZ2dDQAwmUyYN28e6urqkJWVhc7OzsjPzMzhp5kPF1BcNmSXidFo\nTLn4BDEkW31yrq1GZQ9XuHxFi2IopnM7Fd4bK1asUDQGs9mM9vb2yH2XywWz2TzkuPr6erz00kt4\n+OGHB409Hyjaa2eyJ/ejIYrikNeYqPMuL12D2mYXirJOJ2lKnvNn1t0TFNHlDyGN++H1ytt6mkyv\nO24sE+H9qhpsSlni645CrHVLO94FymaiW6MHRllOtNfOMXdt+/1++Hw+AIDP58P+/ftRVFSEOXPm\noLKyEgBQWVkZ2f6KEELOVyUlJWhpaUFbWxtCoRB279495NrY3t6ODRs24J577sHEiRMVipScyWbS\nwulJ3nGSTncAVpMWKjl7RMax8TxOkktSuFtbpkk2/cbcIul2u7F+/XowxiCKIi6//HLMmjULU6ZM\nwTPPPINdu3bBYrHg/vvvj2e8hBCSclQqFVauXImKigpwzrF48WLYbDZs374djDEsXboU//u//4uu\nri5s3rwZnPPIkmrj1fz58/HLX/4SCxcuVDqUc7KaknucpJMm2sSElTog7d4BXPNPSocSf4e+BDQa\nwD5d1mrGnEjm5eVh/fr1Qx7PyMjAunXrYgqKEELGm9mzZ2Pjxo2DHlu2bFnk9l133YW77ror0WGR\nEdhMWuw/2aN0GGfV6KYdbWJinw78fhO4JIKpxs+4YgCQ+lojZR2/D9rZhhBCCDkrq0mX1GtJhlsk\nKZEcK2bKBoxZQFOD0qHEFW9rAY4eAvvWItnrokSSEEKIIqqrq1FeXg6Hw4EHHngAgUDyJWzhtSQD\n8k6mi4HT7Yctk7q2YxFeBmh8jZPkle+DXbY0vPC6zCiRJIQQooitW7fitddewyeffIKjR48O6fpP\nBkadAK2a4ZRPVDqUIYKihLbuEPIzqEUyJiXTga/HTyLJ/X7wT/4OduW3E1If7fBOCCHnqRtfPRxz\nGX+69ezLpozktttui8xQv++++7Bu3Tr89Kc/jTmmeLMawzvcmNOS6yOz2RtEXoYGGoFmbMeClTog\n/fF34JzLPp4wEfieD4Ap08AsiVn9IbneFYQQQhImliQwHvLz8yO3bTYbTp48qWA0Z2fLDO+5PXNi\nutKhDOJ0+2l8ZDzk5AEqAWg9AUwYurFKKuGcg+98F6p/vi1hdVLXNiGEEEU0NzdHbjudTkyYMEHB\naM7OZtIl5RJATk8AhTQ+MmaMsfGznmTtQSAYBKbNSliV1CJJyHmOqdUQjh4a8/P9gvrcuyiZLRCz\nc8dcPhm/tmzZgiVLlkCv1+O5557DjTfeqHRIw7KatKg60a10GEM43QFcXJBcraQpy943TnLhspGP\nTWJ857tg5deCqRLXTkiJJCHnO68HgY0/l6147UO/ACiRJGdgjOGmm27CLbfcgtbWVlx99dW47777\nlA5rWFaTFk1JuLtNo8ePG6ZlKx3GuMBKHZD++kelw4gJP9UBfuhLqH54b0LrpUSSEEJIwn366acA\ngNWrVyscycjy0jXo9InwhyTo1MkxIkziHM20q0385BcCvl5wVzuYOTW/+PIP3ge75AqwNENC602O\ndwQhhBCSpAQVQ36GFs3e5Bkn2dYdhFEnIE1DH+PxwBgDSqan7DhJHgyCf/Q3sPJrE143nYGEEELI\nCAr6FiZPFo3uAC1EHmes1AHUHVQ6jDHhn+8GrBeA5RcmvG5KJAkhhJAR2EzapNoq0enxo5CWuQ2l\n8gAAEXdJREFU/okrZp8OnqILk/Od70K1OPGtkQAlkoQQQsiI+teSTBbhFklKJOOqcDJwqh28y6N0\nJKPCj9cCnk5g5jxF6qdEkhBCCBmB1aSFM4lmbjvdARTSRJu4YoIATJqact3bfNe7YFd+G0wlKFI/\nJZKEEELICKym8GQbiXOlQwHnHE6PH1ZqkYw7VuoAr02dRJJ73eBf7gFTcP1LSiQJIYSQERg0Agwa\nAR0951h8P0HcPhEMQKZOmRao8SzVxknyD/8KdtGlYBkmxWKgRJIQQgiJgjVJJtw0evywZerCS9aQ\n+JpUCpxoBPf1Kh3JiLgogn/wFzCFJtn0o0SSEEIIiYItScZJOt0B2GjGtiyYRgsUTQaOHVY6lJFV\n/wPIsYAVTVE0DEokCSGEkChYk2QtyUZPAIW0hqRsmN2REt3b0q73FFmA/EyUSBJCCCFRsGXq0JQE\nu9s0uf3UIikjZk/+CTfc+Q3Q0gR28aVKh0KJJCGEkMRrbm7GHXfcgZkzZ2LGjBlYt26d0iGNyGrU\noilJWiRpDUkZTSkD6uvAg0GlIzkrvmsb2BVXg6k1SodCiSQhhJDEkiQJP/zhD1FYWIg9e/bg888/\nxw033KB0WCPKTVejKyCiJyAqFkNPQER3QIQlXfkEYrxiaQZgog2or1U6lGHxni7wfR+BLbpG6VAA\nAGqlAzifCafaAVdbXMryC2oI4uBlKVgoeb9NEUKU984bnTGXcf33skb9nKqqKrS2tuJnP/sZVKpw\ne8a8ecrsyjEaKsb69tz2IV+vTAwNnb0oMGqhohnbsupfBoiVTFc6lCH47h1gF84By8xWOhQAlEgq\ny9WGwNMPyla87sePylY2IST1jSUJjIfm5mbYbLZIEplKrCYtGk75kJ+vTNdywykfbDTRRnbM7oD0\n0d+UDmMILkngu96DauX/UzqUiNR7FxNCCElpBQUFaGpqgiRJSocyajaTFgdbu+ALKRN7fWcvCmmi\njfzs04Gjh8Al5YYxDKvmCyAtHZg8VelIIqhFkhBCSEJddNFFyMvLw5NPPokHHngAKpUK+/fvT4nu\n7bnWDDy/pxXbDrXBqBVQYNKiwKhFgUnT91OLCelaaAR5up4bTvmwsNAgS9nkNGbMBLJygMZvgMzZ\nSocTIe18D2zxtUm1GD0lkoQQWTG1GsLRQ/IUbrZAzM6Vp2wiG5VKhS1btmDdunWYN28eVCoVli9f\nnhKJpD0nDZtXXAi3x4OOnhCaPAE0ewNo9gSwv6UHTZ4AOnpCyE1XRxJLa9/PAqMWOQZ1TOMb60/5\ncPOFyTE2brwLLwNUA1yYHIkkP9kM1NeB3f2Q0qEMQokkIUReXg8CG38uS9Hah34BUCKZkgoKCrB5\n82alwxgzFWOwpGtgSddgdn76oN8FRY6TXQE09SWYx0758HGDF82eALoCIvKN4aTSatKiwKiJJJtG\nnXDOlqagyNHa5Ud+BnVtJ4R9OnjVp0pHEcErt4EtWAqmTa4xspRIEkIIIXGkERhsmbphJ8X0BEW0\neIORlsz9LT14v7YTzX0LnRcYB7dgWk1a5Bu1SNOocMIbwASjTrZuczIYK3WAv7kZnHOlQwH39YJ/\nuguqdc8oHcoQlEgSQgghCWLQCJhsFjDZPHj9IM45PH4x0k3e7A1id4MXzd4ATngDyNAKSNeqUJRN\n4yMThZktgFYHqbkRMCk7nIB/VgnYHWA5eYrGMRxKJAkhhBCFMcaQqVcjU6/GNMvgZFHiPDIeszgv\nCwCtEZwozO5A6PB+4FuLFIuBcx5e8ufmOxSL4VwokRyB0O0FQkGEerwQ4rxdEkvBpS8IIYQk1sDx\nmEajHl4vJZIJM3Meen//HLDzPbCCIiC/MPIT2TmJmT195ADAOVA2U/66xoASyZEcPYzA809Ajt1V\ndfc9IkOphBBCCIkH1byFSJ8zH121h8FPNADNjZC+3AOcaAQC/r7EshAoKALLLwIKCoHsXLA4LrYv\n7Uq+JX8GokRyJJwD1HJICCGEnJdUmdlgUy8Em3rhoMd5lwc44TydYH71RTjB7O0BJtqGJpg5eaNO\nMKX2k8DhA2C3/SSeLymuKJEkhJBxiHMOo9Eoax2CIEAU5d/5IxlmzRJyJpZhAuzTweyD9+PmPV3h\nBLO5ATjRCOnIAaC5AejyAhOtYPl9CWZBIZBfBFgmgKmEYevwb/8z2KXlYPq0RLykMZEtkayursaW\nLVvAOUd5eTmWL18uV1WEEJL0orkm/va3v0V1dTV0Oh1Wr16N4uLiMdfX1dUVQ7TRMRqN8Hq9stdD\nSCphhgxgShnYlLJBj/PeHqDFCd7cCJxogPTh38IJpqcTmFAwYAxmX4KZnYPArm1gP31KoVcSHVkS\nSUmSsHnzZjzyyCPIzs7G2rVrMW/ePFitVjmqI4SQpBbNNbGqqgonT57Er3/9a9TW1uLll1/GE088\noWDUhJB4YmkGYFIp2KTSQY9zv29wgvnJznCC6WqDesZc8InJnTvJkkjW1dUhPz8fFosFALBgwQLs\n3buXEklCyHkpmmvi3r17sWhReIkRu92Onp4edHZ2IisrS5GYCSGJwXR64IISsAtKBj3OA36kG43o\n8ssx3Td+4jetaACXy4WcnJzIfbPZDJfLJUdVhBCS9KK5JtJ1kxAyENPqkm47xOHQZJsRqPJt0Nxy\nJ1SMQYr3gG9h+MG1hBBCCCGpQJZE0mw2o729PXLf5XLBbDYPOqampgY1NTWR+ytWrEBBQYEc4cSm\noACYPVe+8hd/W76yAeCq66ns8VL2suvkK1vOuBNR/gjefPPNyG2HwwGHw5HQ+qO5JprNZnR0dETu\nd3R0DDkGSL5rp9wzw6luqpvqVq7uqK6dXAaiKPJ77rmHt7a28mAwyP/t3/6NNzY2nvM5b7zxhhyh\nxA3FFxuKLzbJHF8yx8Z5csQXzTXx888/508++STnnPMjR47whx9+OKqylXx9VDfVTXVT3bK0SKpU\nKqxcuRIVFRXgnGPx4sWw2WxyVEUIIUnvbNfE7du3gzGGpUuX4uKLL0ZVVRXuvfde6PV63H333UqH\nTQghI5JtjOTs2bOxceNGuYonhJCUMtw1cdmyZYPur1y5MpEhEUJIzITHHnvsMaWD6JeXl6d0COdE\n8cWG4otNMseXzLEByR9frJR8fVQ31U11n991M85p7ylCCCGEEDJ6sqwjSQghhBBCxj9KJAkhhBBC\nyJgk5YLk77zzDl555RVs3rwZGRkZSocT8cYbb2Dfvn1gjCEzMxOrV69Oqu3LXnnlFXz++edQq9WY\nMGECVq1aBYPBoHRYEZ999hneeustOJ1OPPXUU5g8ebLSIaG6uhpbtmwB5xzl5eVYvny50iEN8uKL\nL+KLL75AZmYmfvnLXyodziAdHR3YtGkT3G43GGNYsmQJvvOd7ygdVkQwGMSjjz6KUCgEURQxf/58\nfPe731U6rLhS6vxV8rxU8rxT+pySJAlr166F2WzGgw8+mLB6AWD16tUwGAxgjEEQBDz11FMJq7un\npwe/+c1v0NjYCMYY7r77btjtdtnrbW5uxrPPPgvGGDjnOHnyJL73ve8l7Hx79913sWvXLjDGUFRU\nhFWrVkGtTkzatm3bNuzYsQMARn6PybX+0Fi1t7fziooKvmrVKu71epUOZ5De3t7I7W3btvGXXnpJ\nwWiG+vLLL7koipxzzl955RX+6quvKhzRYE1NTby5uZk/9thj/OjRo0qHM+zafk6nU+mwBjl06BA/\nfvw4f+CBB5QOZYhTp07x48ePc87D74377rsv6f5+Pp+Pcx7+Xz/88MO8trZW4YjiR8nzV8nzUunz\nTslz6p133uEbN27kTz/9dMLq7Ld69WrFPpM3bdrEd+7cyTnnPBQK8e7u7oTHIIoi/9d//Vfe1taW\nkPo6Ojr46tWreTAY5Jxz/qtf/YpXVlYmpO6Ghgb+wAMP8EAgwEVR5I8//jhvaWk56/FJ17X9u9/9\nDj/4wQ+UDmNYer0+ctvv94MxpmA0Q82cORMqVfhfarfbB+2SkQwKCgqQn5+vdBgRdXV1yM/Ph8Vi\ngVqtxoIFC7B3716lwxqkrKwM6enpSocxrKysLBQXFwMIvzesVmvS7Q2t04X3qQ0GgxBFUeFo4kvJ\n81fJ81Lp806pc6qjowNVVVVYsmRJwuociHMOrsDc3J6eHhw+fBjl5eUAAEEQFOlpO3DgACZMmIDc\n3NyE1SlJEnw+H0RRhN/vR3Z2dkLqbWpqQklJCTQaDVQqFaZNm4Z//OMfZz0+qbq29+3bh5ycHBQV\nFSkdylm9/vrr+OCDD5Ceno5HH31U6XDOateuXViwYIHSYSQ1l8uFnJycyH2z2Yy6ujoFI0pdra2t\nqK+vT0h302hIkoSHHnoIJ0+exNVXX42SkhKlQ4obOn+VOe+UOqf6G1l6enoSUt+ZGGOoqKiASqXC\nkiVLsHTp0oTU29raCqPRiBdeeAH19fWYPHkybrvtNmi12oTU3++TTz5J6Geq2WzGddddh1WrVkGn\n02HmzJmYOXNmQuouLCzE66+/jq6uLmg0GlRVVWHKlClnPT7hieTjjz8Ot9sduc85B2MMN998M95+\n+2387Gc/G/S7ZIpv7ty5uPnmm3HzzTdj69ateP/997FixYqkig8A/vjHP0IQBCxcuDChsUUbHxlf\nfD4ffvWrX+FHP/rRoFb7ZKBSqfCf//mf6Onpwfr16+F0OmmXrXFCqfNOiXOqfzxqcXExampqFPts\nzM7OhsfjweOPPw6bzYaysjLZ65UkCcePH8fKlSsxZcoUbNmyBVu3bk3oZ28oFMK+fftw6623JqzO\n7u5u7Nu3Dy+88AIMBgM2bNiAjz/+OCGf61arFTfeeCMqKiqg1+tRXFwc6e0cTsITyXXr1g37eEND\nA1pbW/HTn/4UnHO4XC489NBDePLJJ5GZmal4fGdauHAhnnrqqYQnkiPFV1lZiaqqKjzyyCMJimiw\naP9+ycBsNqO9vT1y3+VywWw2KxhR6hFFERs2bMAVV1yBefPmKR3OWRkMBjgcDlRXV4+bRPJ8Pn+T\n4bxL5Dl1+PBh7Nu3D1VVVQgEAujt7cWmTZtwzz33yFrvQP3dqiaTCd/61rdQV1eXkETSbDYjJycn\n0iI2f/58bN26VfZ6B6qursbkyZNhMpkSVueBAweQl5cXmXB8ySWX4MiRIwlrICovL48MJ3jttdcG\n9X6cKWnGSBYVFeHll1/Gpk2b8Pzzz8NsNuMXv/hFQpPIkbS0tERu7927F1arVcFohqqursaf//xn\nrFmzBhqNRulwkl5JSQlaWlrQ1taGUCiE3bt3J2WrqVJjk6Lx4osvwmazJdVs7X4ejyfSDRgIBHDg\nwAEUFBQoHFX8KH3+KnleKnXeKXVO3XLLLXjxxRexadMm/OQnP8GFF16Y0CTS7/fD5/MBCLcE79+/\nH4WFhQmpOysrCzk5OWhubgYQTrAS/WXw448/TvhQsdzcXNTW1iIQCIBzjgMHDiQ05/B4PACA9vZ2\n7Nmz55wJbFKNkRwo2SayAMCrr76KEydOgDEGi8WCO+64Q+mQBvntb3+LUCiEiooKAOEJN7fffrvC\nUZ22Z88e/M///A88Hg+efvppFBcX4+GHH1YsHpVKhZUrV6KiogKccyxevDjpWqs2btyIgwcPwuv1\n4u6778aKFSsi3xKVdvjwYXz00UcoKirCmjVrwBjD97//fcyePVvp0AAAnZ2deP755yFJEjjnuOyy\ny3DxxRcrHVbcKHn+KnleKnnejfdz6mzcbjfWr18PxhhEUcTll1+OWbNmJaz+2267Dc899xxCoVBk\nabtE8fv9OHDgAO68886E1QmEvyjOnz8fDz74IARBQHFxccLGpQLAhg0b0NXVBUEQcPvtt59zghNt\nkUgIIYQQQsYkabq2CSGEEEJIaqFEkhBCCCGEjAklkoQQQgghZEwokSSEEEIIIWNCiSQhhBBCCBkT\nSiQJIYQQQsiYUCJJCCGEEELGhBJJQgghhBAyJv8frw1/iLE4cecAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11006f160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with plt.style.context('ggplot'):\n",
+    "    hist_and_lines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### *Bayesian Methods for Hackers( style\n",
+    "\n",
+    "There is a very nice short online book called [*Probabilistic Programming and Bayesian Methods for Hackers*](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/); it features figures created with Matplotlib, and uses a nice set of rc parameters to create a consistent and visually-appealing style throughout the book.\n",
+    "This style is reproduced in the ``bmh`` stylesheet:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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aGNtRa5cD0DmBzhHg7mxLC8IjlZ0uN1+YDRrHlr3HkEYT0QX5dvdxn8x21Hpb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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11118d128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with plt.style.context('bmh'):\n",
+    "    hist_and_lines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dark background\n",
+    "\n",
+    "For figures used within presentations, it is often useful to have a dark rather than light background.\n",
+    "The ``dark_background`` style provides this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Dv0e1J4NAWUmYadOH1vlcxM1bWH3tLC7/sQtF+QVMhNhgdO7cHpqa6ggIiBdL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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1114bef28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with plt.style.context('dark_background'):\n",
+    "    hist_and_lines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Grayscale\n",
+    "\n",
+    "Sometimes you might find yourself preparing figures for a print publication that does not accept color figures.\n",
+    "For this, the ``grayscale`` style, shown here, can be very useful:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+++/x22+/4cSJE4yOlg2mdY5AxyjqsWPHcPHiRVy8eLHz8fPnzyMvLw87duxg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\nhBJHQgghhBDSI5Q4EkIIIYSQHvl/RVB0HF7HtpYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1114beef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with plt.style.context('grayscale'):\n",
+    "    hist_and_lines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Seaborn style\n",
+    "\n",
+    "Matplotlib also has stylesheets inspired by the Seaborn library (discussed more fully in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)).\n",
+    "As we will see, these styles are loaded automatically when Seaborn is imported into a notebook.\n",
+    "I've found these settings to be very nice, and tend to use them as defaults in my own data exploration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VFa/PO18zcn+ROGATCO9S9szk/pM4b6mf8l6+B4GQXS6sr78KktQ9CxngH3RiOVsQBEEI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\nkUIIIYQQYtgkiRRCCCGEEMP2/wNVB0YyMqd1QQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x111a0f278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn\n",
+    "hist_and_lines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With all of these built-in options for various plot styles, Matplotlib becomes much more useful for both interactive visualization and creation of figures for publication.\n",
+    "Throughout this book, I will generally use one or more of these style conventions when creating plots."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Customizing Ticks](04.10-Customizing-Ticks.ipynb) | [Contents](Index.ipynb) | [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.11-Settings-and-Stylesheets.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/04.12-Three-Dimensional-Plotting.ipynb b/notebooks_v1/04.12-Three-Dimensional-Plotting.ipynb
new file mode 100644
index 000000000..ffdc3d875
--- /dev/null
+++ b/notebooks_v1/04.12-Three-Dimensional-Plotting.ipynb
@@ -0,0 +1,603 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) | [Contents](Index.ipynb) | [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.12-Three-Dimensional-Plotting.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Three-Dimensional Plotting in Matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Matplotlib was initially designed with only two-dimensional plotting in mind.\n",
+    "Around the time of the 1.0 release, some three-dimensional plotting utilities were built on top of Matplotlib's two-dimensional display, and the result is a convenient (if somewhat limited) set of tools for three-dimensional data visualization.\n",
+    "three-dimensional plots are enabled by importing the ``mplot3d`` toolkit, included with the main Matplotlib installation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from mpl_toolkits import mplot3d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once this submodule is imported, a three-dimensional axes can be created by passing the keyword ``projection='3d'`` to any of the normal axes creation routines:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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w+tvf/oYXXngBO3bswJtvvomRI0di4sSJePzxxzU9Rzabxdy5c/Hee++hb9++qK+vx4wZ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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bfc8780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = plt.axes(projection='3d')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With this three-dimensional axes enabled, we can now plot a variety of three-dimensional plot types. \n",
+    "Three-dimensional plotting is one of the functionalities that benefits immensely from viewing figures interactively rather than statically in the notebook; recall that to use interactive figures, you can use ``%matplotlib notebook`` rather than ``%matplotlib inline`` when running this code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Three-dimensional Points and Lines\n",
+    "\n",
+    "The most basic three-dimensional plot is a line or collection of scatter plot created from sets of (x, y, z) triples.\n",
+    "In analogy with the more common two-dimensional plots discussed earlier, these can be created using the ``ax.plot3D`` and ``ax.scatter3D`` functions.\n",
+    "The call signature for these is nearly identical to that of their two-dimensional counterparts, so you can refer to [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) and [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) for more information on controlling the output.\n",
+    "Here we'll plot a trigonometric spiral, along with some points drawn randomly near the line:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VxowZ0+H3CgsL1ewXl8vFvn37WLZsGZIkcfHFFzNw4EDVHSdSHo0yB451gUOq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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e2fd898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plt.axes(projection='3d')\n",
+    "\n",
+    "# Data for a three-dimensional line\n",
+    "zline = np.linspace(0, 15, 1000)\n",
+    "xline = np.sin(zline)\n",
+    "yline = np.cos(zline)\n",
+    "ax.plot3D(xline, yline, zline, 'gray')\n",
+    "\n",
+    "# Data for three-dimensional scattered points\n",
+    "zdata = 15 * np.random.random(100)\n",
+    "xdata = np.sin(zdata) + 0.1 * np.random.randn(100)\n",
+    "ydata = np.cos(zdata) + 0.1 * np.random.randn(100)\n",
+    "ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap='Greens');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that by default, the scatter points have their transparency adjusted to give a sense of depth on the page.\n",
+    "While the three-dimensional effect is sometimes difficult to see within a static image, an interactive view can lead to some nice intuition about the layout of the points."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Three-dimensional Contour Plots\n",
+    "\n",
+    "Analogous to the contour plots we explored in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb), ``mplot3d`` contains tools to create three-dimensional relief plots using the same inputs.\n",
+    "Like two-dimensional ``ax.contour`` plots, ``ax.contour3D`` requires all the input data to be in the form of two-dimensional regular grids, with the Z data evaluated at each point.\n",
+    "Here we'll show a three-dimensional contour diagram of a three-dimensional sinusoidal function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def f(x, y):\n",
+    "    return np.sin(np.sqrt(x ** 2 + y ** 2))\n",
+    "\n",
+    "x = np.linspace(-6, 6, 30)\n",
+    "y = np.linspace(-6, 6, 30)\n",
+    "\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "Z = f(X, Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UCt544w1uvvlmxo4dyz333NOh5+A59+348ePCIXEB4PdHumcjWwndIQ10Rx6u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BIREKZcjFODadJDoODuwmazSQGKMjH9xQxXt+5qrwUAUZkuqQNmt9o0Ry+uVEPwGLr7jJkvpVzU\n9yYmJkZx0aRLAoGATPRNT0+XzPHgwYNSCKR/LtUB+fn56O3tRWVlJZqbm2VnwMKix+PBnDlzUFlZ\nCbfbjfb2dhw4cECmE59zzjn4yEc+IudJhYrNZhOqg7uhYDAouxKPxyNm90VFRVG7LipMcnJy0NbW\nJovT7t27EQgEcPnllx9lwM97bCpDc8ebN2/G1q1bcd999437+bKzs0UvH+t7BlveExIS8KUvfQnX\nXnvtsZ569oLuZHnqAtHTI8YTlJ0RWE3TxJ49e/Dee+9JK3BPTw8qKiqwf/9+VFdXo6GhAaFQCIsX\nL8bevXvh9XqxfPlyhEIhtLe34/LLL0cgEEBrayuSkpKQl5cHm80mrmFULNjtdvT29grfqMGA8iTK\n6pKTk7Fjxw709PRIljQ8PIyysjLs2bMHOTk5qKioQENDAxobG1FcXIySkhLJgBobG5GRkYHi4mLk\n5eXJyCRmfz6fT7rpsrKyRDMdCoVEs6u372zcYDasmyJ4rpxQoAtjCQkJsN6/WqnA7jNNQ1glZZSg\naW6Y0sPU1FTp9qK2mFktPYxpGE/PChYMuWuy2+3IzMwUfwMa82jrzObmZqFQ0tLSZFHKysrCmjVr\n0NDQIO9FT08PSkpKUF5eLuqDAwcOYP78+UL5cKHgNeQ04oGBATEc4nDNgoICGIaBhoYGka7V1dVh\n7ty5qK+vR05ODlpaWoT6oo7b6uqlnb+mMrSB+Ysvvojm5mbcfPPNo/7NeeedJx2dAGSk03333Yer\nrroqCmRzcnLQ2dl51HO0tLSgoKAAHR0dOO+88/Dtb3/7WFTG7AfdiVIDwGHQtdls47anY8bNKQIO\nhwOBQAA///nPRdPJDyZH6gSDQcybNw87d+4UtcLBgwfR0tKChQsXoqKiAsnJycjPzxcaoK+vT5oe\n+CHr7+8Xk5asrCwZpknROg3Gme1u3rxZ3NCys7PR09MDwzBEm+v3+zF//nyZydbe3o6CggIxMSeX\n29bWhtzcXBQVFcmgxkAgIL8nB+tyuWQum27/pRyLbmJcHFhIo+yN7bocv639Fxj6XtZSsXA4fJTe\nlwCurSJjaXIJVvrcuIBy2gXpHC5w5E+pTx4aGoqShbG4SD3w/Pnz4XA40NbWhtTUVJHnkR4oLy9H\nRUUFCgsLAQCHDh3CoUOH0NDQIFTDaaedJtaLNEbnfZGcnCz3BDn1vLw8+P1+tLS0IDs7G2lpaThw\n4AByc3NlNBM70DweD3p7e7F7924Eg0GsXbtWJpjwumvnr6kMzR3/6Ec/QmJiooxKH08sWLAAmzdv\nFnrhwx/+MHbv3j3q39x7771IT0/Hhg0bRnvY7AfdiVIDwJGRPaO5lcUKPcCSnKrmln/729+KBImT\nJDIzM8UxaufOnSguLkZmZibee+89pKamisVeXV0dFi1ahLPOOgudnZ1SILFmNHSSooQuEAjIwEeO\nmWGn19DQEHbt2oVwOAy3242WlhZUVVWJEfnChQtl0nB6ejrmz5+PcDgskwhcLpf46PKDzOnDLPTl\n5ubKUE7TNKPkYpxCYbPZxASHdAMpAH49/PDD6OjoED2nznJ5jZm5WLNcAq7mdK3j2/XEX2a8vK8A\nSObpdrtl+CMAGX9Oz4m+vj7xmeCOic5j9LotLS1FaWkpWltbsW/fPrk2LS0tUvyi9afD4UBGRgYy\nMzMxODgoOwzy6GVlZcjOzkZ3dzcaGxtRX1+Pq666Cg6HQ4CJnrvc6XCxo+McOfzGxkbhkTkhmPcr\nue/GxkY0NzcjISEBn/jEJ6Lub6vz11SG5o6feuopzJkzB5/61KfG/Xy33HILsrOzccstt4xYSNO7\nx8HBQZx//vm4++67cf7554/21LMXdMfjqTtSDA8Pi4v/WCKWz0NfX99RBb1du3Zh586d8kFOT0+H\n1+tFZWUl9u3bJ3rXvXv3Ys6cOXC5XHj33Xdhs9lw+umni96W2z9uy9kJRR8FFqK4TQcO62NZ8WXR\n5/3330dra6uAcWpqKjo7O0Wj6fV6ZYrtwYMH0dzcDI/Hg9LSUumsogtZfn6+jJFheyvlYnQ+4yBK\nbtW1EQ6zMT1vjK9lcHDwKPNyDY7kbUd7fwi4VHVoW0fKfwi8sSYFUwrG8/H7/XIe7ALLycmB2+2G\ny+WSMfJsQEhKShKrzLa2NtmRcVGiRGzBggXYt2+f+E90dXVhaGgILpcLixYtkkWOE5fpmVBaWoqS\nkhKheRYuXCjZNWsJGRkZMAxDZr5lZ2cjHA6jvb1dvB8aGxtl8a6vr0dZWRn279+P4uJiHDhwQLoi\nPR4PzjnnnKjrbDWhmcrQNMb999+PdevWHQv8Ro2uri58+tOflqkuv/jFL8RI/tprr8V///d/48CB\nA/jEJz4hVq1XXHEFbr311mM99ewH3aGhIdlGjzc4CuZYKzYr1cPDw0hISJBsEoAoE/S2NxwO49e/\n/rVkwtSF0n0sLS0NtbW1WLJkiRTS5s2bh/z8fOzatQvt7e1YuHAhli1bhkgkIuYoCQkJ0l3GLJ9a\nUMqauKVmoScYDGLbtm0yC4zG5QDQ1taGqqoqhMNh1NfXw2azydj3lpYWkRmVlpZGfXCbmpqkuyov\nL0+q49YpEcxyWejRX8zCCHz79u3DCy+8IK3V1uGTsUxVrF1nvPbM1jQfTKClNpdAyuckPUEuODMz\nUyYz0B6USgWv1yvNB6RBMjIyZCQR/STYjRYOh2ViAwApgGkT+uLiYlGesIW3t7dXPHaLi4vF8Keh\noQEtLS0oKirCBRdcIKoPGskHAgHRHLNQSEUGtdZut1sy3oSEBHR2dsLlcklG39raCpvNhjPPPFOy\nff0ZnA7QtdIYt956Kz7/+c9j9erVU3rcccbsBV0NgMeTpcaKWBaR1rCaplsLdyOpKF599VXJNAjs\nlO8cPHhQBOl9fX1YsmQJ+vr6sHv3bhQXF6O6uhoHDx5Efn4+5s2bh+TkZMkGOffLbrcL52gYhhRR\nqA0l4IfDYbzzzjty3WjlWFpaKmBLr96WlhY0NDQgJydHbP4Isv39/eIwlp2dLdOGSTX4fD7k5eVJ\nhssGAQCSOVIqxkr50NCQZN92u10mKuguNYKjHsWugzQD/yWtwi898ZftxgMDA+ITS+DlvcW/1yZB\nPA/gcNEtKysLHo8HJSUlyMnJwfDwsMi7mFEHAgH09PSI1WhWVpZoecnRA8D+/fvFC5mL65w5czBv\n3jxZ/FhYS09PlzHwTqdTvBU+/OEPw+fzyQJGn2RSX6ZpoqurS6ZcdHR0IBKJyL3o8XikAae5uVkW\nzaSkJPzjP/7jUbyt1flrqsJKY/y///f/cMcdd2D+/PlTetxxxuwH3bEA5rFitJE91FLSbYrtodYY\nSUXR1NSEmpoacV/iLLO2tjaUlJTA6/XC7/dj7ty5aGhoQH9/v2S2O3fuFKqhqqpKgDUjIwOJiYmy\n6BBsOISS0ipmdDabDQcPHhTFBGVo+fn5OHjwIAoLC5Geno76+nppCU1OTkZbWxsOHToEp9OJ8r9P\nlQ0Gg5L9EmA5x0tPt+js7JQJB3a7XabZMrvlgsFMNBwO41e/+hWam5ujZplZO8n4WmOZlVv/Tz2u\n9sq1Nk2QT6XSgtkuJ01w6oge4U6KpLe3F16vFz09PbJwpKWlyVBQehYMDQ0Jj9rX14f6+nopFHIG\nW35+fpRLW3d3t3g7pKamoqioSOiEnp4eNDQ04MCBA3A4HCgvLxdZWWZmJnw+X5RVps/nQ39/v7wG\nOtG53W50dnbCNE0BWo/Hg+bmZhmMmZCQgIKCAmn71TFdoGvNqK+44gp873vfQ15e3pQed5wx+0F3\nMmacxRrZw1ZVPRZ8tArtSCoK+hwEAoGoLCw5OVk0p6Wlpdi9ezdcLhfmzZuHPXv2wOv1orq6Gvn5\n+Xj//ffR2NiIdevWYeHChdJ2rBUX9IZlvz/Bn4Wk999/X6gY0hAJCQnIy8tDbW2tGKl3dXWhvr4e\nKSkpqKiogNPphNfrFUAuKiqCx+NBdnY2fD6fFJXIE3KCBS0c2RGn3cX4FYlEhAenL6weCKlH/vDa\nEnD5Xli1uvrnBE2t/eX/OaWCnK1evPj+875gm7VuDSblkZ2djZKSEpSWlsIwDJn6Sz8Kgi61yfRD\nrqioQDAYRH19PRwOR5RdJ/nwrKwsrFy5Er29vZLlDgwMoLi4GKWlpaLzra2txcGDB7FgwQKcddZZ\nYmDERYoJCRU2LpdLTNPdbjdaW1vFIS0cDqOhoQHt7e2yIJ555pkjJiTabnGqwgq6H//4x/GHP/xh\nyo3TxxmzF3SBI7TARGecae8E0zTFw3UskycYo6koampq0NbWJrwsFQZDQ0MoKChAXV2dFJ327t0L\nl8uF6upqtLS0YO/evcjPz8fChQtRUFAgHVdOp1MyRL/fL9OHKU8j2BqGgXA4jF27diEUCiE9PR1d\nXV0oKSlBU1MTEhMTUVpaioMHD4pQn9MBDh48KJlUbm5u1LSESCSCwsJCeDwe6Y7jJAkOdwyFQqJ1\npQY1LS1NMspgMCh+DjU1NbL152LK1xDLG9d6/8ZaEK0OVlqjS06X3Dod0AiszKwHBwdFh0wJmj5/\n6o85Xoi0AymY5ORkcYmjQxafLxKJiPE8efTOzk74/X60t7fLQMyCggIpnEUiETQ2NkpjDVuzs7Ky\n0NjYiGAwiNNPPx2meXh6CUGfiQOnUlMdMTQ0hOzsbLS0tCAvLw+NjY0Ih8Oora2FzWaD0+nEueee\nO+LnZjpAN5aB+euvvz7lDRnjjNkNutyCTnTGGXvXU1JSxm2GPpqKore3F2+88YaAiNPpFNs9r9eL\nrKwsJCQkoKmpCaWlpcjIyMCuXbuQlJSE6upqmKaJXbt2we/3o6qqCkuWLBEjbdM8PPZneHgY6enp\nUWDLjit6oDLTTktLQ2dnJ+bOnSsG2tXV1QgEAqirqwMAMdBmgS8QCMjsL85ta2pqirIQ5PggFvto\nkkKvCCoW2NWVnp4uk3y12Qy3wQQxrcsl4FonQ+jfAZDWXaoYyG+TkiFXS/kdDXdYiNR/m5KSIkMq\nOVaImTR9jN1udxSotrS0oLOzU+oAzP55zvPnz0dSUpLMUAsGg+jq6hJtLY9F7W1DQwOam5uRlZWF\n0tJSFBcXy88PHDgA0zRRWVmJiooK2O12AXLejwMDAzAMQ2oK2tmMaouuri74/X6ZEpGQkIDy8nJU\nVVXFvOe13eJUhgZ32jqeoF66wKkAumyQGO+MM/bU09mKGeTxxrGka5s2bRLzFloM0vM2MTFRWjLZ\neltWVga32419+/ahq6sLc+fOlcmtHAhJUGLzgHVMTSAQkBZPFljou0qer6KiAv39/Th06JBMoO3s\n7BS+sLS0VOawkct1OBxR5i7BYFDkYu3t7eIzwIYITjMgHzo8PCyFtHfeeQfd3d2iK+V7SgDVgMp/\ndebLD571X/7f+gVAsl3yunryLwuA5O+Z3XNMEbfo5H71lAh97+Tm5qKyshLFxcXiodDZ2SmG7j09\nPVJYYwbLDLyhoUEek5KSgsrKSjEc7+jowKFDh9DY2IjMzEyUl5ejqKhIaIaOjg7Mnz8fixcvFu8K\nyuPIT2dkZEjrc1ZWlkw13r9/P1paWqJsMM8888wRuz1ZpJ3qbT5pOA26J6iXLjDbQZdvxnhnnJGa\nYFYzEYvIY0nX9uzZg4aGBlExJCcnw+/3Izk5WUaqsMpdUlKC4eFhNDQ0ID8/H6WlpWhvb8f+/fuR\nkZGBefPmYd68eUhMTJRik+7YYsEIOMyHcW5bIBCAx+NBU1OTGJzQRD0lJQV1dXUYHh7G3LlzkZub\nK0MM2SpMs3Jul1taWhCJRKSY5vF4xLmMDRH8ok8EKYaenh4xcUlNTY2ayMtMUnPg/GKwQm+VjjEL\n5msHIJQAf0YfBZ31ktelykEPrtRmNNTlUodLS0u+l2xR7urqkmGeSUlJyMnJQWFhIVJTU+Wey8zM\nFL6WHGxqaioKCgqkSYLcKxtFaIrEJpuGhgaZ5lFZWYmEhAQ0NDSgt7cXF198sRV/QS4AACAASURB\nVNh5GoYhkx78fj8yMjIEhJOTk/H2229LnYDXNCMjI2YBjTGdoMuMOhKJ4GMf+1gcdGcqxuupSxNr\n8qAsZEwEdPlBGqmSGwgE8Ne//lWAIxAIIDU1Ff39/cjJyUF3dzcMw0BeXp6MFS8tLUUkEsHevXul\nsEU3sObmZixYsAArVqw4KrvTpjDUy+rincvlQnNzszQ97N27Fzk5OSgvL0d/fz/27NkDu92OsrIy\n5OXloa+vT4pDqampkuWmpKTA5/OhtbVVOtPsdrtkjeRyyTPTXYxmKyw2cQdgnXWmB07qDDeWkiFW\ngY0Ui540oX9PRQndxTjAk63L6enpQj11dHSIUoG+EVzs2FLNJgkW4fQo9ubmZuHN2WjBBhcCKc1x\nenp60NXVJX7MNL4h10tJGmkGu90u7cEZGRkoLy+Hw+FAS0sLTjvtNOTk5MDv9yMSiSAlJUUkdBkZ\nGTh48CDq6upkAWe3XEJCAhYsWAC32z3iPc928om04I8lNLgPDQ3hn//5n/Hqq69O6TEnELMfdOmp\nOxbA1G27NFfh30x0ZA+BYzS98Jtvvilto9RwJiUlyc1LjpMC+9bWVgwODsoEV3ohFBcXo6ioSLrV\n+F7yQ8OszjAMoRaYhbNzjr3+bHFlYaaiogIejwednZ2yzS0rK0NpaSmSkpLQ3t4uraEpKSkijyLN\nQJUA23/pI0AO1263449//KPItPQUXmvWqjNWDbiaVtA0hJ48oBsprFaQ+m85FcKqcPD7/VHFN76G\nzMxMGYFDDp1TIXp6eiTT9fl86O7ujvJ+KC4uFs51//79klnSspMTnW02G0pLS9HZ2SnXkdNHPB4P\n3G43QqEQWlpaZLdQXl6OnJwcyX7tdjsqKirgcDgQDAZRXV0tC7HT6UR/fz9qa2vh9XpFzUDrT+Dw\n7mDNmjWj3vPTBbr6OM3Nzbj99tvxy1/+ckqPOYGY3aDLrqHu7m7JGmJFrLZdK7hO1Jd3LHrhjo4O\n7N69G6FQSExWdHdYa2urTIWgxy7djxobG1FYWIjCwkL09/dLcau8vFym8ZIzJchwbAw1xuxs4nNn\nZGSgtrYWdrsd5eXlUrWORCIoLy9HQUEB+vr6UFtbi7a2NmRkZEixjJMrOjo60NHRgf7+frGepBkM\nOTgWrAgepBt4HaxZKHCEBrDyu9ZpApqGYGars2V9PUaaOsFjUWdLHTENVnj/9PX1obu7W+wX2ZjD\nQhv9g/W8OA7cpGNcR0eHzFsrKCiQDLOkpASDg4Ooq6sTxzJO/M3JyUF+fr60Gbe1tUl7dlZWFjo7\nO1FbW4tAICCSPmp509PTUVxcjOHhYXR3d2P16tWifmCbsN/vF8UGs9zc3FzMnTt31HveOpRyqkKD\n7t/+9jc8++yzeO6556b0mBOIUwN0R+oGG61t1xoT9eUdi17YNE1s27YNPp8PAEQs73K50NXVhZyc\nHEQiEbS3t6OoqAimaeLQoUPywaFHazgcRvnfx4h0dHSgqakJS5YsQVVVlRTRwuEw2traMDAwIMDO\njjbOvHI6nSgrKxN+tqioSDIscrkej0fAgQMnvV5vVDU/MzMzanwQO83YCUUQ++tf/4pDhw5FTfxl\nWy7BMJYcTIOtBs2RmiSoobU+lqGPozljqhH8fr80lmgLSOp1s7KyZCaa3W5He3u7eFJw4kQgEAAA\naR8GDpu0z5s3D1lZWTh06BD2798vjQzkjktKSpCeni7n4vV6JWPmfDqn04nm5mY0NDQgEAigpKQE\nhYWF4pfc39+PkpISacJpa2tDYWGhjHPnItHe3i7SMVJPzOyXLFlyTK52ukBXH6empgavvPIKHn74\n4Sk95gRidoPuaJ66Vl+GY90YE/XlHWuTBrNGcpUUsjMr7OvrQ2FhIQYHB9HR0YHS0lKZNOHz+VBW\nViacLD0R6D5FE3HgcPbW1NQk2aTf70dubq7MVCspKZECTnV1NRISErBv3z4MDAygrKwMWVlZMiq+\nq6tLwJVOZ8xWCTIcCcNxPMygmHGxaEMVBX0hNCBqMKTxN3l7PfNMT4YAjuhx+VxWWkF7LuhjWjNo\nFtg4mJJ/w8eTI6dJD7lZu92OtLQ0kc3l5ubCNM2oeXJsZSWApqSkoLS0FGVlZcJx0yCernJ0bmNX\nmd/vF71vaWkpioqKMDQ0hLq6Oni9XhQWFqK4uFiaLoLBIEpLS4XzdTgccLvd6OjoQEJCgjiPcXw7\nr1dKSoq02OqdiDXGO5TyeEMf549//CPef/993HXXXVN6zAnEqQG6uhtsrG271pioL+9YmzQCgQB2\n7twZVZH1+XzimVtYWIiuri7JSPv7+8XTlplLU1MTXC6X+CI0Njbi0KFDSEtLQ0lJCSorK2Gz2dDe\n3g7g8PaMvfbMbE3TFDH+gQMH4Ha7UVRUhL6+Pmnxzc/Ph8fjEZClJpfZHkcP8T1g1xmlcMxy9+zZ\ng46OjqP4Um2vaNXcxjIu12PRAcj3DA2e1pE9/KKGWQcfxwyVmTgXBh6bj01OTpZmCm2CQwMcUidp\naWlSTLTb7bLgpKWlybDIjo4OhEIh5Ofni1nQnDlz0NjYiL6+Ppm8wUkinNzMuXVutxuVlZVITExE\nY2MjWlpa4PF4UFRUhMHBQRmCmpubi46ODnlf29vbZaFnAw2vZ1FRkbQk87rqIBDrRWcqQxuYv/DC\nC+jv78cNN9wwpcecQJwaoMstND84Y2nbtcZEfXmtE4FHiw8++AADAwMCDBkZGejq6hKJkN1ul/bM\nYDCI/Px8RCIRNDQ0IC0tTZymDh06hPb2duTl5aGoqAh2ux2tra2YP3++0CpcfKjUaG9vx9y5c9HW\n1obOzk4ZwdPY2Cicb25uLoaGhtDS0gKv14vMzEzhaBMTE9Hf3y/OYTRnoRSM74vP55PWY4IhvV5Z\nyOOugr+nCJ7FrVgjekKhEADEpCJiZbk2m+0oI3N+gAkWuvWXk5RpN0lqgTQEzXJYH9AZe3p6Ojwe\nj1h2tre3i3EMkwGqO3ivzZkzBw6HA7W1taJXZucg9bsOh0MaTJi9FhYWChXV1NSEtLQ0US1wqjPB\nk6PfuYMaGBhAbm4uurq6kJ6eLj68bLThEExqymNx4Xp8kebSAUx6p5g2MP/e976H7OxsfP7zn5/U\nY0xizG7QpWSHxsushI/nTZ+oL284HB5zk0ZPT4+MQKdUiv38oVAIOTk5aG5uRlJSElwulxRuqCAg\nGObn56OgoEDG6XR0dEiFOykpSbbWKSkp6OzsRF5enojuS0tL4ff7cfDgQRkrw+/9fr9YNQaDQWlJ\nHR4eFmNvqkW0hWNfX5+01tKrV49W1x1f7PrSKgOd2VqtGPnBZoeaphI0AI+U6Wq6guBNekI/lkCq\nzXD4gdfUDUcgaQ6d9olstGF3GR3ZwuEwWlpaMDg4KPdcb28vkpKSkJ2dLRLBgYEBKaYODQ0hNTVV\nCpR2u13ka1yAMzMzpdhK6gE4PGnCMAwUFxcLXVVYWCieIuxO0zsIt9uN4uJiAEcP+YylldZgqzGF\n7+1kALE2MH/00UexZMkSXHrppeN+vimO2Q26tMKjKfVE3I4m6surh1MeK0zTxAcffCDFFmp2uXWn\nkoLZUmpqKtxut5iisIgzNDSEQ4cOiWcr9ZjceiYlJUnbbnFxMZqampCfn49AIIC2tjYUFBQgNTUV\nra2t6O7uFjnS8PCw/CwtLU1ae4PBIDo7O+H1ejEwMCAVe3Zwkbd86aWXoiRfGsT0UEo2E2gbRtIP\nzHJ1W64exWPtWANGznStHrp66rDmbjk2njQJFQrUuFJ/S6mfdh4DIJQCJ4j4fD6RzXFnwwYLNpHw\nnuvu7hZrTDqUUYrX3t4uBcrMzEyUlpYiMzNTpkeEQiEBXzau0GC9t7cXnZ2dMrnZ6/UiNzdXFkjK\nJ5m1sukmVoRCITFIpx6ZCxxwxNXNqpfW972W740ViLWB+T333IOLL774KEP1EyhmN+hSd8sP5URA\n91jNDWM5l+PpjGtubobX640qHLFZgtvgjo4OZGdnw2azoaOjQywAA4EAWlpaxKHM5XKhu7sbzc3N\nGBoaQk5ODjIyMmRsO31tk5OTZdCeYRwe8e50OuHxeBAKhdDc3Izh4WHhK6muoAkL23nT0tJENcLJ\nv8xGOCGCM9P0vDFmvez20ubksbLchISEqC28dZurs91YkjI9nFJnusx2dfbGkT4s8nGhSEpKkm40\np9MpbcwEUzZHcNfFLJXeHSxeUU/NLFVTFKQbCgoKkJ6ejoaGBhlymZiYiLy8vCjDGtIMhYWFMvOM\n711JSQlSU1Nld1JcXAzDMNDc3Izc3FwAkGm/PT09cDqdspixKGsNtsoToK11EqsKRGfE1vcsFu7o\nQp0ViLmQE3Q3bNiAr3zlK1i2bNnYPpjTH7MbdCkJG0tjwrFios9hmuaYu9oIsnV1dQgGg7DZbAgG\ng1LQ4PTgvLw8hMNhtLa2ih9ra2srfD4fSkpKkJaWJn4HiYmJyM/PR2pqKnp6euD1eqX1lmO23W63\n0BPkaTs6OmSxoJENv9LT05Gbmwun0wm/3y/TEtgpRR2p3W7Hu+++i9raWlkECaSkfNjxpS0SmS3p\ngZBDQ0Pw+/0ClCx+ERhHu2/5O37I+YGnDIp8LotkzHjZJkwPC1IwHP1OjTfPh6CqZ7wlJiZKp6Pu\n7GK2Ozw8LMdk2zRweGoHxwENDAzA4XAgMzMThYWF4h5HCZ7dbkdubi7cbrcAaU9PDzweD/Ly8hAI\nBNDY2Ai73Y6CggJpoMjKyoLT6URHRwdcLtdR1A6LaKWlpUft9MhFU9Uw1ux0LEBsLYLq0NkyC7MA\n8MUvfhHf/OY3UV5ePqbzmIE4NUB3Mjx1J8MM/VidcToTAyCzs1hRZ098V1cXXC4XQqGQKBqYiZJ/\n5cwszt3iuO22tjYYhiFbUzaPMCvLzMyUuVrs8yfdwK1tdna2vB6Opc7KypJR5PTHPXDgADo6OqRb\niyY32r+VW2vrVl3zhZS1aR7XqmywXlNmR/r3vKf1dSZY60KclqLRX4GLBJ+Xsi56/JJK4Sy0rq4u\neW1sKafpEHXJHE4JQLodh4aGpMEiEomI5jcxMVFUHVQtGIYh1E12drZ0pw0MDMDtdiM/Px82mw1t\nbW1R9BDNifLy8pCWloaWlhZ5j+hqNzAwgJSUFDFFcjqdkhXzGnKhmYiU0nr/jzUj5uP5HrEw/ulP\nfxovvPDCuA2upiFmN+gCk+epOxnAPVIrsRVsgSNqB4KhLsiwC2poaAgej0c+bPn5+aK5tNvt0hmm\nCyvZ2dmw2+2iMPD5fDLZNjMzE16vV+arDQ8Py1DC/Px8uFwu6R5jJxT73bu7u9HS0oJgMIicnBwB\nelbVe3t75Tm5dbdmutwia9UAs35SDhoQdaHMuj0djde1/svfs2FCUxY0RgcgGaDNZhNNsS4C8n1i\nppuamiryOcq9OC1jcHBQtuU04CblkJKSAo/Hg8TERJkqTFlURkaGUBKRSEQyYQJ0ZmYmHA6H7Gb4\n3hmGIQtnYWEhkpOT0dTUBIfDgezsbHR2diIxMRHJyckYGhpCcnIyQqEQDMMQCoO7PGa3LCxPtW9t\nrGKd7jTcvn078vLy8O677+Luu+9GbW3tlHv4TiBmP+hOlqfuZAC3tZWY11jfRJRTAUBycrJU+Jnt\nUdrV09MjDQqUjQ0ODqKtrU141e7ubnR2dsqsrkAggI6ODvFlzc7OFjVGS0sL+vv7YRgGUlNT4XQ6\n4XK5ojJhdv7QuMU0TfF3YIV8//79omQgFaLdw2hvaJpm1IQGv98v18kqEdNqAj0WXXeC8YNvbaaw\nZsCxfBr4cyvPSzWENjfXHhCmacLhcAi4EjRJI9C8h/60AKTbixk/h4iyQ4+LLV3JnE6ncLmkeuhy\nlpqairy8PDidTtkt0O7R4/HA5XKhra0NXV1donAIh8OyAHs8HplG4Xa75XF87fw3OTlZCm2Tnd2O\nJ3iNuPv42te+hj/84Q/o6OjAqlWrsHr1atx1113jchachjg1QHeinrrA8elsRwrdSqw/+FyxqcHU\nxYhgMIjW1tYoYPb7/bL1CwaDMsvK7/cjJycHoVAI7e3tcDqdwu+1trZKS3FWVpYAg9frFRka+Uy/\n3x/VqkuJFzNsnWlzcCSN3ZnZZWRkwGazSRbOxYFZC2kfcoF6uGSsggo1opRhWbvMrGHNcGNlu/p3\nVkqDSgRec+1DrKkJraig3y2B2eFwiAkOfQxIPVA6xsnMfIy24yQgd3V1IRwOi843OTkZAwMDsiDy\nfafBe29vL7q6upCQkCCaYHr9ZmZmIjk5GX19fTBNU1ziIpGI6MBTU1OlUYPz67ijYRZ/PBr3yQor\npWG32/HSSy/hoYcewv33348VK1bg7bffxo4dO7B+/fr4uJ6Ziol66jKOR2c7UnDOF81sGAQgbmut\nNzSzXWp2WUlnMauvr08+4JQzud1uJCYmykQG7fTV0dGBgYEBOJ1O5ObmRumZe3t7kZCQIDO4+LrD\n4bC4gxFQUlJSxNjbMAxRK3CKLmkAqwSM4EWzGj3Bl8Uq4MgcMj3VgYCkgVCrFBij0QixZGVa1aA5\nXGa3GlC1bpXZsW4h5vP4/X4MDg6K7pYFOd0y7XA4RAGiR7WzAEcKhtms1+uVaRQEaTZH9PX1yTgg\ntu7SGD0vLw82mw1er1c4fdIi5HD5PvD6JiQkiDqDO6yZzG5JadAN7+tf/zpsNhsee+yxEzWrjRWn\nBuiOx1PXGscr+bKGaR72TaCGkVQBM4rRuLFQKITOzs6ozIvZrr4RqUnOzMzE8PCwyMFcLpd87/f7\nBSg5w6u3txcAxDoQgHy4BwYGYJqmfOA4KoYf6N7eXlEkaL0tZWGhUEhcoEif6M4tFs40mJF+4bWy\ndovpJgit/dTZbSyA1cBsLcjozJWqAC4AGuytmS7pCCoe9AghBtULwBGv5r6+PrFuJG9OKR/nrpEn\nJpVASiglJQVer1dAmioJl8sl7ycpA1JQHJ3ucrmiZp9xMWXnGblcLjp8HzXNEqtwOVURK7vdvHkz\n7rnnHnzjG9/ApZdeOiNZ9wTi1ADd4/HUHSko+WLl/nj+DjjSFkl+kj9nFnWsm5nbURaQ+NyBQEBm\nmrEYw0kFlJQRsFn44hh0DvPjh5a+ACzaECxIFdAljHwabQ7Z+ECdLbNYbr25hbZ2j9Ezl1QKC2jM\nbKn95L/aH0FzsgRUfe2sma5+P6yAzMdpxYNWSWjQ16DKLJf0AsFaZ8A+n08oFE4KJm+elpYGm80m\ntpD9/f0y8DQ9PV0oH04s9vv96O/vF6UBOwK5u7DZbJLlcgQ8p4EkJSWhp6dHFCimaaK3txcul0u0\nxampqbJAsohIcyJrEUvz6lMFxFY52vDwMO688050dnbi6aeflgThJIvZD7raU3ciJuTHo7PVfzMS\nb8s2XKsvrLVQxPOlHpOvh85g5PAIEmwb5WBK9s9zDExXVxcASFELgFAQpCi4rQyHwzIJgdxuZmam\njOPmhASduVr9Bux2u/gokC4ggOhGCBYKmVXpLjECtvZF0JmXVi7oBUlztda2YP0ecWEg6Ft9HfRs\nNqs2mNk4VRgEZ/LeVCgEg0EARygTdm4RjHmd6FtLEA4EAnA6nTKynsemRwKN4h0OB4aHh+VvCL7B\nYDDKA4PvGw3jSZkRbHkPJiYmSgOMNUZSE0wWEFubLex2O7Zu3YrbbrsNN9xwAz73uc+dbNmtjlMH\ndCdqQg6MfXpELAnYsXhbIPbNDBxx1CIdQVMX/Tf8APE1kjJwuVyS6ZumKdkw5WihUAgul0vcsFh0\npLEOK+02m010p1Ry2Gw2yYYJPpTqsCWUnCabPDSQ6q4uLjb0LbCCnlYwWE3KY32NFlpipr8HjrSq\n6i44ni95bAKlYRjSZaazUWa8bALRTRZ8ndwNEIT1xBJeF44y0tMh6GDGeWCkiEzTlCYH8vDsEmQ7\nLzNbLs7cZfh8PiQlJUVx01RljBXcJguISb9wQQoEArj//vuxd+9ePPvssygqKhrT+ZzAMftBl1kk\ni1gTKQSMZQJFLL3tWHjb0Z7PeiMz27LZjoz0oYIAgGRAGRkZsvWkpwHnkCUmJopBSiAQEDMWbikz\nMzMlu6eEidkuAYfbZQKsdt4iH8hOM26lqfMlbcAskK+P2bH+0gMpNfdrpROsFIPeZWiAJVhFIpEo\nxQS30dYmCSvw83y1lCwpKUn8Gii5AyDXhaA8GhjrRg3y3QR6mp1TFubz+eB0OqXjLxKJyHWleTwB\n2WazCX9LTwVmxtzRMMNmljuaof9Y43iAGEBUdpuYmIidO3fipptuwtVXX41rrrlmyvXA0xSnDuhO\n1A8XOKyzTUtLO8ofdDS9LYtQk+Upyu25BilqXEk5sB2zv78fwOECGdtI+b3mEpkBk99ji6vP55PK\nuS5YUffM5yPokONNTExEIBBAf3+/ADFBSysfCDrac5WgREAmyAGImd3ye13k0u/JSFmVtalipJ/r\nYh5fp6YPeH56SjAXEmvnWnp6OpKTk6WoysWM14bX2jqdmCoIdoaxAYIm8aZpygRi0zRlN0K7TbZP\nk1IgZ8/2ci6iBMGJfk5Gi5GAmO/Vpk2bMH/+fPzmN7/B1q1b8Z3vfAeVlZVTci4zFLMfdJnVTNQP\nF0DMbHmsetvJChbPAoGAgG4gEBATbKoidLWX0iBWwH0+H3p7e5GYmChOWKyoa6AlyOpMlg0BGkS4\nqDGjI+Bwm8ysl4BDgOLioQtPugkiFr9tBUmrjlbzu7rIBiBKaqZ/buWBTfPISB+d+fK6sktLnxdf\nL7ld3iN+v18KjOSO6fPARSolJUWyUAJmKBSK6f3A68b3jobnnNBB/p6OaOSKAcjYHe6Q+P4SrLWh\nz3RwpryX+XrC4TCuvPJKvP322xgYGMDatWuxZs0abNy48WTmcK0x4guZWqv3GYhYgvuJPEcsKoFZ\nCwtSU3GjMFPUoMEblh8WbhvZyw8Aubm5MnwQgEwr4M9sNpu0AwOQhYrG2pzPpfnB9vZ2oR2Sk5PF\nYlKbuxAkBgcHo8CL8ipKkrQ/Aq+rlo/xmuusVj82EAgISFoVCXzsSC5kDP5Oy/r0e629HKzgzGy3\nr68PoVDoKDokPT1dRhnRYW14eFhap1m4crvdYqXI3QY9bbkbyczMFOOdgYEBAW/TNEUaSPUD/57m\nQ2xwYDLAxQPAUW3PUxmRSETuTXpxPPXUU/D5fHjttdeQk5ODHTt2oK6ubjYB7qgx6zLdifrhApAK\nPzk0BjMQtkxOlAsbSzBLoOqBW3c9y8rn84lXBDMtFq1YbDEMQ7J3FnSoQkhJSZEPBAtipExo9MKC\nDjM6brcDgYCAsZZZkXrR2adV86rtFQlS1i40ayPESBIxXquRfqazXP5LUNdFO61UsBruWBcGTQ1o\n6VwkEonqvGOxyuFwRBXkuEtKTU1Famqq0Ea8/omJieKnzPeM1p/cyWiZHgBJBnjfJiUlCafNa2zV\nF09FsCbBe9HhcODAgQO4/vrrsW7dOtxyyy0z1oAxTTH76QWC00T9cE3TFJMSfmiYWUw2b3s856Td\nsLQJDIt3CQkJkilxG0vDbHoFcOKsYRhRW2NeNxbrnE5nFEVAzpVUB7NWZlLAkcIVs1ytStCie4Kf\nVSOrGyas2aVu2dVfsYo2vF7W+1rrcrVrGY8HHOEhdWEt1jHZVEAw0a5kGsw0b035oLaVZCZMWoKS\nNAI0PSvC4bDQQ9RJh8NheRwBnNIxqhN0Uwn/Px2AG4lEoj4vhmHg+9//Pp5//nk89dRTOP3006f0\n+Dp6e3txzTXXYNeuXbDZbPj+97+PNWvWTMehTx3QnYgfLrMfgoXewupW0emurpKj1Ibb/J7Bwpph\nGAKUlApxCwtAFg2tn9VZHQtoLBRFIpEovpHgSlDUhTDNf2oJFq+XBuVYRuJWFYI1442lv7VSBzo0\nd6ubLKy0heaLNTBrzbDenlsXFUrCmLFbdbx8r7gb43XlY3hfUUbH3RQLcwTaYDAoCyJpHdYvqHnV\nkjyer17guGCMdt3GGzq7pVSwubkZ119/PZYtW4Z77rln2n0SrrrqKpxzzjm4+uqrpWYxETOr44jZ\nD7oAoqRJx+OHO5relh9AneVQ7qSLP1PBR3EhockKCye6W4uVaQBCO3BLqxsCuN2lTIlbWz5ONzNo\n+RYbOwgWXIy0zIsgw3Ph+8DH8/xiaWNjZbuaCtDZPYGNng06s7UCMf/VAB6LJuB15DGs8j3NT2up\nG0FYNxfohV9LzwBI8U1fJ03R8Pdc+EhX0NOC9A7pCypNqGihCoFUArN/ni+LanwtvH6xJF3jCWa3\nPDfDMPD888/ju9/9Lh599FGcccYZ087Z9vX14fTTT0dtbe20HvfvceqALm/6saxmscCWPeoj8bax\nNLXkAfWHeqLtkgRH8tM6a+GHlaBkmqZ8kFmlZhbLjIOcIOkBh8MhcjdSFNrzQE9KCIfDAowEGgKD\ntZOLv9fdZQRvnq+164vX0JqJahWC1UtBf3+s93ikTFc/F/9vVSroBYi0Ei0qtZSMel5ebwIs30su\nQhzro2ezMUvldeFCxt9pT4KUlBRZIPUCxr/j6+L9oq+99ZqM1KBzvEDMe4uvvaOjAxs2bEBxcTEe\nfPDBCY3Pmkjs3LkTX/rSl7Bw4ULs3LkTK1euxOOPPz5dHrynBuiO1VM31tbSykMdD28b6wbm9s4K\nxMcKZjMjSdEIEtYtLY/J3zHD0XSJnmarFRhay8ksTYMru9i0x4I2S+Fjec2YGVu7y/R10X+rr5FV\nbaCvLbM3q3ZX/2uNkegJfmm9r37/dLGPgKTpB724aKmXBldyq3wc30/9ODYsWDl2tutqLS1pCV0o\nI9DzHGknqoFTJxWj3cPWnQUz+1jvkb5Xw+GwJAYvvvgiHnnkETz44INYt27djCoSduzYgbVr12LL\nli1YuXIl1q9fD5fLhXvvvXc6Dn/qgC61pCNZM46mt+UNPhk3ykitviPR8jbSKwAAG6FJREFUEppK\nGK2FGIBkYdZtMcGXH3YAQjcw82FWyswVgFACzIL53ACiMjlte8gPs96yaqMaK41gLXTpwpSVe7Xu\nJvgzHVZOMta10tI07gZiAbD+V4OyLkLpBY7/14CsFx5eW03NWIuKVsUBVSC8B7UOOxwOy66L15et\n2dzxEGyZnfO18PzGE6Pt6nivseU4Eong5ptvRnJyMh599NEJ+VFPVrS1teGMM85AXV0dAOCvf/0r\n/uM//gO///3vp+Pwp4ZOV+s1rTESb0u+dLL1trparI/PG5cfJv3h4AfpWFk2Xwc/2FaOl5kQC4oE\ndJq9uFyuKJCnnCwh4cgYdD4nOUtrMYy8cCRypKWUGbHOInkeBHWtBACObGd1pT2WplYvTvp91o+1\nfs+f6X9H+rlVJaEzPZ6XtpvkosjjcdEhwAKIAmC2R2t+XMupqDjhVp3NCw6HI2rHwHtEqygARHG3\nfK8mArh8zlgcON97u92OX/7yl9i4cSOSk5OxbNkyXHLJJWhvbz8hQNfj8aCkpAR79+7FvHnz8Oc/\n/xkLFy6c6dOaXaALHN0cMRJvy64uTkqYjvOy3sDU/fLDRLnaWGkJ3YJKAOQWEYCAOQtfBGPt6UCz\nE253maXq62LV5BKAuKXUAMtClwYJvg7tjTuSzpbZMwFNvy4NlLHANNZiO9bMltllrOYN/X/r7kJn\n85R7aaUJFyi95WcHn17AeH2oQOF11KBKjlf/H4C8B7zWWnUzmcHqPw2SBgYGUF9fj0suuQTXXnst\n6urqsH37dpSVlaGqqmrSjz+eeOKJJ3DFFVcgGAyisrISP/jBD2b6lGYXvcCMoLu7W+iFkXhbzaFN\nd+jCiZVKGImWGIlXA450r2nu0woUBBMCGr/X/B2fX9MU3L7qLTMQndmNRino12VVAlhlYqPxr9ZC\nGoCjimm8l/Uxrf9qKmMkWkMXo6yvSWeSWtHC16czf9INvPc0BaN3BHxenqeWJfKxfC5NJVnBlo+f\n7LAW8+x2O9544w3ccccd2LBhAz7zmc/MKHd7gsapwenyA93d3S2SMWYtlC9NJm97vKGlWWN1Ixur\nWkJnkhrMAESBLx9LukFna8ARQb8GYQKCBg1rlmcFVw1GuiCjs3f+y2Pyuax/r2kDDbCjFdE0OMdS\nKeifaY6c2TwXZCvdEOu16OukC3Kx/CV0Js3fE1D17gA4vKhQAaGpDmumziLoVGW3emqJz+fDv/3b\nv6GhoQHPPPOMDLKcrohEIli5ciWKi4vx4osvTuuxjzNODdDltpb9/5rwJw83HVRCrKAUbTzqCGuM\nppbQ234+zlqh5/fcoloBhSDKa6fpCGumba12a2DXXK2V49ULgZXntYJjrNfPx1mLX/r3Gpj1NYiV\n5erj6ufVXLNeIK1AHEuVYc2IdTGVj9HXX6sjNE2kgZtFMz5W624nM6zZbWJiInbs2IGbb74ZX/rS\nl3DVVVdNSNc73nj00UexY8cO9PX1nbSgO6s43S9/+ctoaWnB8uXLkZaWhvfeew8PPPAAnE6nbIVj\nOVpNZVCeNZlZNoFRA7f+YHPqKzuarFwvwVZvt1lRjwXCukhmzeKY8VL1wGxPZ+hsp9WPB46MNtcZ\nIH+ueVXGSJyu/mJYgdj6fx3a+8EKxpo20OCrC2r6/HThUC9SycnJ8hh9/fh767VnEVYXDHndtKOc\nPsZIXPnxBrNbm80m7mb//u//jrfeegvPP/88ysvLJ3yM8URjYyNefvll3H777XjkkUdm5BwmI2ZV\npmuaJv7v//4PX/3qV9HY2Iizzz4bTU1NqKqqwqpVq7B27VrMmTMHAKK2cjpDmawbV6sD+OGczsxA\nAwC1pJSPAbEr2wQbvcXVz6X5Yb1g6Qza+jjrF4+teXb9NRKgMmIBqZVysP6Nfh36eRg6I4/FJ/Px\n1uugpWhWysRKIcSSpOnz4rWwtpnrv2GRTDfGjJRt834eabcw0j1jNRj/4IMPcOONN+Izn/kMvvKV\nr8xIdsv41Kc+hdtvvx29vb14+OGH45nuiRCGYWBgYABXXXUVrrvuOrEe3LNnD7Zs2YL//M//xAcf\nfICkpCQsX74cq1atwurVq5GZmYlwOCy2gSNtE8cauptsutQR1uCHPBQ67MGqncSY2WkOkn+jAQc4\nwmky42Xw76zbc/1B1wDI7E9nsfpYmhe28q76NelimZXTHY37HQlwrQCvs1vrORIAtcEN/5YAzEKW\nBkruSPR5aB6Z8is+TmfdWpHA68fHjLbbYe0AGFuXWTh8ZHxOWloaIpEIHnvsMfzpT3/Cc889h/nz\n5498s01DvPTSS/B4PFi2bBk2b948Jfz1dMWsynTHEqZ52HF/+/bt2LJlC7Zu3Yq2tjaUlpZi5cqV\nWLNmDRYtWhQltwJGVw8wIpHRu8mmM/QWUbcz6yKQpgv03xFsrCCsMyrrY4CjM02tTLAWrvR9dyza\nYKTv9b8M6/W2fq9BC4g2PLdmqvpvrK/LCsb6eNZFQO+mrNeGHK11sSOokk8fb4ymhuEXqTfes/v3\n78f69etxwQUX4Gtf+9q0u+rFim984xv4yU9+ItRKf38/LrvsMvzoRz+a6VMbKU6NQtp4IxKJoKGh\nAVu2bEFNTQ127twJ0zSxZMkSrFy5EmvXroXH44m6gbV6gBnlsQZSTtdrIfCzYDfauegPn942E2Q1\nn6mzNwCS2elMD4huUqFETfOTI30xjsXFHu+1jVWs4s9jAflISgfrsa1/b82G+RjNSwNHlAh6UbZy\n2tbFcLJC004EW+Bwt9bzzz8Pp9OJnTt34rvf/e50WSAed7z22mtxeuFkD5vNhoqKClRUVOBzn/uc\ncFtvv/02ampqcPfdd6OhoQFutxurVq3CmjVrsGzZMhiGgcbGxqhJAQBkuzidwKs55OOZaKE/3Ho7\nTABhxVw/VtMT1myPH2p67+qOPGuhi8fUIK2zYGvWGAsgGaO9Vr0VHw1AdfarM1LrY3VWzB0Dz4eP\ntwKm5nl5Psx8taJjqrfNmnbSO7KCggJEIhHU19fD4XDgwx/+MK677jo8/PDDU3o+p2LEM90xhmma\naGtrQ01NDWpqavD666+jvr4eiYmJuPnmm/GhD30IFRUVUcWTqSrSWWMkR7KJhgYXXUjS94ymHGIV\niUYCOGvjwrFoA+tzjCWsNMhoESuzthbV9LnHokT064+VuRrGkTllvG7TzU1GIkfG57D77ac//Sl+\n+MMf4rHHHpPs1u/3o7e3F3l5edN6frMo4vTCZMaOHTtwwQUX4KabbsJHPvIR7NixAzU1Ndi7dy9S\nU1OxYsUKrF69GitXrkR6evqYtJzjienmkGMpEXQnlwZeazEqVoY5UnFLRyxwi/U3Vo5Yd3Ixex5p\nQTjWcWKFFZw1GFuvEzNZYOJ+COMN0zx6fE5bWxtuvPFGVFZWYuPGjdNleXiqRBx0JzMikQja2tqO\n6sYxTRO9vb3Ytm2bFOm6urpQUVEhkrX58+cfVcA6XkN0TSVoDe1MBEHFMI74F+gikbXpQet4YxXT\nRluECJzHAsWxgPlIj9d/oymQ0R6rqRZu27WzmKYTxvoeT2ZEItG2pTabDb/5zW/wxBNP4Jvf/CbO\nOeecaT2fxsZGXHnllWhra4PNZsO1116L66+/ftqOP00RB92ZikgkgtraWinSvffee0hISMDSpUuF\nH3a73VFFulgSH34oaJIDYFKphPGEVkjwwwwc3dmlOVurrpSArcFoNAAY6X5lZs3nivU8Y31eq8LB\nShlo7teq2QWOmHrrUTq6IcKqp53Mxgbra7KOz+nu7sZNN90El8uFb33rW9M1uiYqWltb0draimXL\nlmFgYAArVqzA7373O1RXV0/7uUxhxEH3RAnTPDzvipTEtm3b0NTUhPz8fNENL1myRMxNmA3rxgKO\n2TlZFBIMq8xK0w7WxojRwkoFaBvKsYKXFVBjcc/6nDR9MBrwa1PvkaRW1sWHNMxkdksyu41Ejoz2\n+cMf/oAHHngA9957Lz760Y/O2P1jjUsvvRRf/epX8Q//8A8zfSqTGXHQPZHDNE00NjZKke6tt95C\nIBDAaaedhuXLl2NwcBCBQABXX321UBMscFmNVKb6PJk5TTatYX0ea4cbEE3DaE6Z12K0Ilys18J/\nR1NFjCUm47qMpqc9lj7cGtbxOf39/bjtttsQDAbxxBNPIDs7+7hf41RFfX09zj33XOzateu45hqe\nBBEH3ZMtAoEAXnjhBdxxxx0IhUI47bTTAAArVqzAmjVrsGLFCjG+1lvW4x0PNNagYQ8wM7SGVoVo\nkxftdDbdXCkQnVFO1MhIh5aS6S+tiLECsTXTTkhIwP/+7//izjvvxNe//nV88pOfPGGyWwAYGBjA\nueeeizvvvBOXXHLJTJ/OZEccdE/GuOuuu1BaWoovfOELMAwDnZ2d2Lp1K7Zs2YI333wTfX194iux\nZs0azJ07FwAmVKSzBjXLnFg7k7SGtYCoPYRjOXVN5Q4gFl86HTsN3digF1vDOGJ8npWVhUAggHvu\nuQfNzc145pln4PF4pvTcjjdCoRAuuugifPSjH8UNN9ww06czFREH3dkY2leipqZmRF8J7ZR1PIYo\nBBVroWwmYiyZNkFpJKtJnQ1PBCCtfOlMFjOpu2UB9r777sOPfvQjkS5effXVOOuss5Cbmztj5xgr\nrrzySrjd7pPaLewYMXtB98knn8TTTz8Nu92Oj33sY3jwwQdn+pRmLEwztq9ESUmJgPBpp50W01dC\ng5JpmlGFspmasMHXZHW+Oh7A1LTEaK95rNKy6c5uRws9PiclJQWBQAAPPPAA9uzZg0svvRT19fXY\ntm0bPvnJT+KLX/zijJ2nNd544w2cffbZWLx4sSyAGzduxIUXXjjTpzaZMTtBd/Pmzdi4cSNefvll\n2O12eL1euN3umT6tEypG85VYsWIF1q5di/z8/KgMkeoCTv6djiJdrNBTC8YyZWMsoRUPI3GlsV6z\nVes6k9ktF0VtMP7uu+9iw4YNuOKKK3DdddfN6K4kHgBmK+h+5jOfwb/+679i3bp1M30qJ01YfSVq\namrQ0NAAh8OBzs5OLFmyBI888giSk5OnrUgX6xw5cXY6Mu1j0RLMcJOSkk6I7FaPzwmFQnjsscfw\n+uuv49lnn532gZCvvPIK1q9fj0gkgi9+8Yu45ZZbpvX4J3DMTtA9/fTTcckll+CVV15BSkoKHnro\nIaxcuXKmT+uki3vvvRdPPvkkLr/8cjidTuzYsQNDQ0Oorq6WIh19JbThzWR3WTEDZWPBTHba6aKd\nNsMZDy0xWedjzW737NmD9evX46KLLsKGDRumPfuORCIy2rywsBCrVq3C888/P9uaHMYbJ6/L2Hnn\nnYe2tjb5nh+A++67Tyb/1tTU4M0338SnP/1p1NXVzeDZnpzxoQ99CF/+8pejKtyhUAjvv/8+tmzZ\ngieeeCLKV2LVqlVYtWoVkpKSxFFsolMLrMWpmfRw1YBLxQZ/zmyY0qzpMDWyjs8xTRNPP/00fve7\n3+GZZ54ROeF0x7Zt21BVVYWysjIAwGc/+9nZ2Fk26XHCg+6rr7464u+effZZXHbZZQCAVatWwWaz\nobOzEzk5OdN1erMizjvvvKN+ZrfbsXTpUixduhRf/vKXj/KVeO6556J8JdasWYPq6mrYbLaYUwtG\nygw1wDkcDjidzhndvmuVhHXqB/0lGFZaYjIWHx2xiogNDQ24/vrrcdZZZ2HTpk0zWuRsampCSUmJ\nfF9cXIxt27bN2PmcLHHCg+5ocemll2LTpk0455xzsHfvXgSDwSkF3Icffhg333wzvF7vCdXVMx1h\nGAYyMzNx/vnn4/zzzwcQ7Svx05/+NKavRG5ubszMkGDk8/lgGDM31ogRK7sdix0kwVU/jzYIH+vi\nYw3r+BwA+K//+i/85Cc/weOPP45Vq1ZN8BXHY6bipAbdq6++Gl/4whewePFiJCUlTenojsbGRrz6\n6quylYrHYT+IqqoqVFVV4corrzzKV+LWW29Fc3Mz8vPzsXLlSqxevRpLly6FaZqora1FYWEhgMOA\nxInBU12kixXMbg3DQFpa2oSOT67bOheNQHwsWiJWdtva2oobbrgBCxYswKZNm2Sy8ExHUVERDh48\nKN83NjaiqKhoBs/o5IiTupA2nfGpT30Kd911Fy6++GLs2LHjlMt0xxtWX4m//OUvOHToEKqqqnDN\nNddgxYoVKCsri9qmj9bqOtnnNhEN8ESOG0stoQeFdnV1oby8HL/+9a/x9NNP41vf+hbOOuusE6qN\nNxwOY/78+fjzn/+MgoICrF69Gj/72c+wYMGCmT61EyFO3kLaiRAvvvgiSkpKsHjx4pk+lZMuDMNA\nSUkJSkpKkJCQgJ/97Gd49NFHMW/ePGzbtg0PPfQQamtr4XK5JBteuXKltPhONk/KsG7fpzO7ttIS\nBH+/3w+73Y6WlhZceOGFCAaDyMjIwJVXXik0xYkUCQkJ+Pa3v43zzz9fJGNxwD12xDPdv8doKomN\nGzfi1VdfRXp6OioqKrB9+/Z4sW4cMTAwgEAgcNQuwTTNEX0lOKF53rx5UZaIwPgcuGYqux0prONz\nbDYbXnrpJXzzm9/Ehg0bkJiYiG3btqGurg6/+tWvZuw843HcMTt1utMRu3btwkc+8hE4nU7ZKhcV\nFWHbtm3x+VFTGGPxlcjKyjqqq8zawKEBdSTT9ZmIWONz+vr6pLng8ccfR1ZW1oydXzwmHHHQnayo\nqKjAW2+9NekfiK9//ev4/e9/j6SkJMyZMwc/+MEPZsTV/0QN0zTR39+P7du3o6amBlu3bkVraytK\nS0uP8pXQ89lYpOLP2Fgw09mtdXzO5s2bcc899+C2227DJz7xiRk9v/i9OCkRB93JisrKSmzfvn3S\nC2l/+tOfsG7dOthsNtx6660wDAMPPPDApB5jtsVIvhKLFy8WWqK7uxs+nw+LFi2SoZHTMaE5VsQy\nzBkaGsKdd96Jzs5OPP300yeEG9h03Yt33303srOzxdrxjjvugMfjwVe/+tVJP9YMRBx0T6b47W9/\ni1/96lf48Y9/PNOnclKF9pV47bXX8Nxzz6G9vR0XXHABFi1ahFWrVmH58uVISkqasgnNI0Ws8Tk1\nNTW47bbbcMMNN+Bzn/vcCaVMYEzlvdjQ0IDLLrsMO3bsgGmaqKqqwptvvjlbaJW4euFkiu9///v4\n7Gc/O9OncdKFYRhITk7GGWecge985zs444wz8OijjyIQCKCmpgavv/46HnnkkShfidWrV6OyslKa\nIyZSpBsp9Pgcp9MJv9+P+++/H3v37sVvfvObE1rbOpX3YllZGdxuN3bu3InW1lYsX758tgDuqBHP\ndKcxRlJI3H///fj4xz8OALj//vvx1ltvxSvVEwyfzzdiE4H2laipqcHevXvhdDqxYsUKrF69GqtW\nrUJGRsZxFeliRaxBle+88w5uuukmXH311bjmmmtmrJh3otyLL7zwAt544w20trbiqquumk2eunF6\n4WSIH/7wh/jud7+LTZs2ISkpaVKfO27BN3JYfSW2bt0a5SuxevVqLFiwQMzfQ6EQABzVwKEBVI9h\nT05ORigUwre+9S3U1NTg2WefxZw5c2bq5Y4ppvJe1BEMBrF48WKEQiHs27fvhKRYxhlx0D3R45VX\nXsFNN92E119/fdI1wHELvuOPSCSC/fv3Cwi/++67SEhIwLJly6J8JWJ10pErdjgcSElJwe7du7F+\n/XpcdtlluP7662fUY2IsMZX3Yqy47rrrkJWVhY0bN075saYx4qB7okdVVRUCgYDc5GvXrsXTTz89\nKc9dU1ODe++9F//zP/8DAHjwwQdhGEY82z2OsPpKbN26FU1NTcjPzxery3A4jLa2Nlx44YXo6enB\nypUrUVVVBa/Xi5tvvhmf/OQnxW/iRI6pvBetEYlEsGLFCvzyl7884bP/44x4Ie1Ej3379k3Zc8ct\n+CYedEI7++yzcfbZZwM44iuxefNm3HLLLaitrcXZZ5+NLVu2oKysDKtXr8bChQuRm5uLP/7xj3jg\ngQdQV1eHlJSUGX41o8dU3os6du/ejYsuugj/9E//NNsAd9SIg2484jHOoK/E/v37sXjxYmzatAmp\nqanYuXMnfvzjH+PGG2+UohRwpFgVj8OxYMEC1NbWzvRpTHvEQfcUiLgF39TGXXfdFcXTkm6wxkwA\n7qnsAX2iRnxk6CkQq1atwv79+9HQ0IBAIIDnn38eF1988aQfp7GxEevWrcOiRYuwePFiPPHEE5N+\njBMxTtTCWNwD+sSMOOieAqEt+BYtWoTPfvazU2LBZ7fb8cgjj4gG9qmnnsLf/va3ST9OPMYWN954\nIx566KGZPo14WCJOL5wiceGFF2LPnj1Teoz8/Hzk5+cDANLS0rBgwQI0NTXFpWkzEHEP6BM34qAb\njymJ+vp6vPPOO1izZs1Mn8qsjbF4QOvfxePEiLhONx6THgMDAzj33HNx55134pJLLpnp0znlIu4B\nfUJEvDkiHtMToVAIF110ET760Y+KZV88ZjamygM6HqPGiKAbL6TFY1LjC1/4AhYuXDhtgBuJRLB8\n+fIpUWPMluCU4XicGBEH3XhMWrzxxhv46U9/ik2bNuH000/H8uXL8corr0zpMR9//HEsXLhwSo9x\nskddXV1co3sCRRx0T+LYvn07li5dikAggMHBQZx22mn44IMPZux8zjzzTITDYbzzzjt4++238dZb\nb02pVV9jYyNefvllXHPNNVN2jHjEY7IjDroncaxcuRKXXHIJbr/9dtxyyy34l3/5l1Mq66MOdTa3\n1j755JNYsGABFi9ejFtvvXWmTycekxBxydhJHnfeeSdWrVqFlJQUPPnkkzN9OtMWL730EjweD5Yt\nW4bNmzfPSs5y8+bN+P3vf4/33nsPdrsdXq93pk8pHpMQ8Uz3JA+v14uBgQH09/fD5/PN9OlMW7zx\nxht48cUXUVlZicsvvxx/+ctfcOWVV870aU1qPPPMM7j11lthtx/Ojdxu9wyfUTwmI44lGYvHCR6G\nYfwOwM8AVAAoNE1zVoxSPZ4wDOMcADeZpjllEgbDMFwAvgfgNAARAF8wTXPrVB3v78d8G8DvAFwI\nYBjAzaZpbp/KY8Zj6iNOL5zEYRjGvwAImKb5vGEYNgBvGIZxrmmam2f41GZjPA7gZdM0P2UYhh2A\nczKe1DCMVwF49I9wWB9/Bw5/PrNM01xrGMYqAL8AUDkZx43HzEU8041HPI4RhmFkAHjbNM1pddo2\nDONlAP9hmuZrf/9+P4A1pml2Tud5xGNyI87pxiMex44KAF7DMH5gGMZbhmH8p2EY0zH+4bcA1gGA\nYRjzACTGAffkjzjoxiMexw47gOUAnjJNczmAIQDTod/6AYBKwzDeA/D/AZhdlcJTNOL0QjzicYww\nDMMDYItpmpV///4sALeYpvnx0f8yHvE4OuKZbjzicYwwTbMNwKG/b/EB4B8AzFzrXzxO6vj/AR+/\nKbFT0DZMAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e6a7fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.contour3D(X, Y, Z, 50, cmap='binary')\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.set_zlabel('z');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes the default viewing angle is not optimal, in which case we can use the ``view_init`` method to set the elevation and azimuthal angles. In the following example, we'll use an elevation of 60 degrees (that is, 60 degrees above the x-y plane) and an azimuth of 35 degrees (that is, rotated 35 degrees counter-clockwise about the z-axis):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xiWYwGOZVdIWjvoXPd+H7WKVSEY1G5QdbNKkymcw8KSEej8ubkDqEX1hsrtDp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\nFQBQX1+PrKwsjBw5EhMnTkRmZ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9CozYCYtRKAZapB/j2/J7gyq1cDtJzVUqbG4hOBzuVy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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e6a7fd0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.view_init(60, 35)\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, note that this type of rotation can be accomplished interactively by clicking and dragging when using one of Matplotlib's interactive backends."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Wireframes and Surface Plots\n",
+    "\n",
+    "Two other types of three-dimensional plots that work on gridded data are wireframes and surface plots.\n",
+    "These take a grid of values and project it onto the specified three-dimensional surface, and can make the resulting three-dimensional forms quite easy to visualize.\n",
+    "Here's an example of using a wireframe:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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SKVpB5K+//kKv1zNr1iyWLl1a5Hb8/PyYNGkSAQEBXLlypdDyM2fO0KpVKwYOHIggCOzY\nsUNadu/ePTIyMqSuvgC1atVi2rRpjBkzptDj9j8NUUiLijG2tLSUxDgvL4/s7GwpxlgsIG+YAGIo\nxmlpaaSkpJCenk5qaippaWkmMf4bMInuO4wothqNxkhsc3NzSU1NJS8vDysrK+kCfR1s376dXr16\noVKpaNCgAXXr1mX9+vVG6xw4cICuXbsyYMAAHjx4wNmzZ42Wp6enExUVRbt27ZgyZQrz5883Wp6Z\nmcnly5dp2rQpcrmc77//nm+++YaMjAwg3wpu1apVoZvHuHHjsLKyYtmyZa/lWN83npbwIUZZiKGC\nzxJj0cIuaBmLYmwYEmcS49eLSXTfQcS6CMWJrVarxcbGBhsbmyJ9qi+CoXtBEAQ2b97MkCFDpOVf\nfPEFixYtMrJ2Dx48SNeuXVEoFEyePJmFCxcaCeTZs2dp1KgRVlZWjBo1iosXLxIWFiYtDwwMpF69\neqhUKgAaN25M69atJTH19/eX/LmGyOVy2rZtyw8//MCdO3de6bjfFV6HmD2vGAuCgEajKVKMDaMr\nwNilYRLj14tJdN8hDMVW7MAg1o9NTU1Fp9OhVquNxLYon+zLEhYWRnZ2Ns2aNZPea9iwIbVr12bD\nhg1AftrutWvXpHbr/fr1Izo62igK4cyZM7Rt2xbIj2qYOnUq8+bNM1oufl7km2++4ffffycmJoYz\nZ84UKboAd+7coXXr1syZM+e1HPO7wpuIPCgoxnK5HEtLyxeyjE1i/Poxie47gBgyJBYKF8U2Ozub\ntLQ09Ho9arWaEiVKFArUf1UMRXvz5s1SrVtDDK3dw4cP07FjR2miTqlU8umnn7Jo0SJpfXGSTMTH\nx4e//vqLoKAgaXlBUXVycmLixIl88sknWFpaUrly5SLHGx4ezsSJE4mKisLf3/91nIJ/Ha/DTfEs\nMU5MTCQ1NdUkxkVgEt2/EfFxLysri7y8POk9UWwFQXim2L4uS1ev17NhwwZcXFwKLWvcuDHu7u5s\n3LiRgwcP0qVLF6Plw4YNIyoqirCwMO7fv09KSgp16tSRlltYWPD5558zb948UlJSuHnzJg0bNiy0\nn4kTJxIREUH16tWLHGNubi7Xr1+nQYMGzJkzh+nTp0vn7U1QXHLEP5XXKcbi9yIm6Jgs4/9hEt2/\nAUPLVoy1hPykBFFsbW1tsba2fu2WbXHjCQ4ORq1Wc/To0SLX+eKLL/j+++/x9/fnP//5j9EyKysr\nJk2axOLFi/H396dVq1aFrOXBgwdz9+5d1qxZQ6NGjQqlHUN+inK1atW4detWkWO4evUqlStXRqVS\n0blzZ8qVK8dvv/32kkf9bvA2hf1l9/UsMS6qYhsg1ZcA4yJBBYX73ybGJtF9ixiKraGFptPpSE1N\nRSaTSWL7vKmer2rpihfhgQMH6N+/PydPnuTJkyeF1mvatCmlS5emXLlylCpVqtByHx8fQkJC8PX1\nLeSvBVAqlcycOZM1a9bQsmXLYseTnJxMbm4u586dK7QsLCyM+vXrS+P+/vvvWbRoUZHjNfHmEcW4\nqIptgJEYF1UkyDDG+FlibBif/L6LsUl03wJFia0gCFKwO4CtrS0qleql8+pf9Ud44MAB+vbtS+fO\nnY1iZg1xcXGRwrrAWPBVKhXjx48nICCg2Emwfv36kZycXKgWg0h6ejpxcXF89tlnLF68uNDy8PBw\nPDw8pNc1atSgX79+fPfdd899nP9m3oZVbVikp6iEj+ep2FaUGGs0mmLFWLyu3peKbSbRfYOId+6i\nxDYtLQ0zMzNsbGyKzNd/Xl71IpLJZERHR5OSkkLDhg0ZNmwYGzduLHLd27dvo9PpiIyMNHpfjCdu\n0aIFWq222DFpNBr0en2xVcUuX76Mu7s7H374ITdu3ODSpUtGywuKLsDMmTM5cOAAERERz3vIJt4w\nRYn7s1KhX0WMk5OTSU9PJy0tjTNnzrB27dq/6cifD5PovgGKqmWr1+ulOglmZmZFdmn4uzh69Chd\nunRBLpfTpk0bkpOTCQ8PN1rn8ePH3Lt3j+HDh7N582bgf4Vc0tPTyc7OJjQ0lEqVKrF9+/YiHwMv\nX75M9erVCQwMLFQMHf4nqubm5kyePJklS5ZIy8RJNMMJOgA7OztmzpzJtGnT3gsrpyD/tsk6gPPn\nz1O7dm3UajV2dnbY29vj4OCAk5MTFSpUYP369ahUqucWYzHpw8zMjLi4OBITE//uQ3wqJtF9jRQU\nW7GYdUZGBunp6SgUikJi+zqiD15lGzKZjMOHD0vVwuRyOcOGDZPickUCAgJo1qwZPj4+bN++Xfrx\nC4IgWS3h4eF069aNbdu2SRa+uF52djYhISE0aNCAAQMGFGmNGFqyw4YN4+LFi1IK8dWrV6lSpYrk\nLzSkRYsWhIaGcvDgwZc6B/8W3pbAF7efO3fu4OPjw8iRI3ny5AmRkZE8efKEhw8fEhISQrly5Zg2\nbRo7d+5k0KBBJCcnP5dlDJCdnU2XLl1YvXo1fn5+bN68mbCwMDQazXOPe9SoUTg4OFC3bt1i15k0\naRKurq54eHgUMkyeF5PovgaKEludTkdGRoZUJLw4y/Z1Jje8DE+ePOHKlStScRmAIUOGsHPnTqMK\nY2fOnKFly5a4uLhQpUoV9u/fL0UgiDG7ISEhfPDBB9jY2BAcHFyoeEt4eDh16tRh6NChbNiwgaSk\nJKkillarJSIignr16gH5ERETJkyQajuEhYUVci2I3Lhxg7p16/LNN9/84+syvI+kp6czZ84cWrdu\nTc2aNfn0009p27YtlSpVQqFQkJKSQr9+/Rg+fDh9+vThq6++QqvVUqdOHU6dOmW0rYJuCvE3qFKp\n+PHHH/Hw8ECtVrN//36GDh1aZN2P4hgxYkSx0TsAhw8f5tatW9y8eZPVq1e/dFcTk+i+Anq9nuzs\nbLKysorsP6ZUKrG1tS2y/1hBXjUC4WU/f/ToUVq2bGnUhaFSpUrUrVuXAwcOSO/5+/vTqFEjMjMz\nGTJkCLt27TKqZJaUlMSDBw+oWbMmgwcPlixlw+ItkZGRNG3alDp16lC/fn0OHTokxX6mpKRw+/Zt\nXFxcpMmRYcOG4efnR3R0dJH+XJGrV6/SqlUrHBwcJNfHq/I+uiqexts8HkNLd/fu3Xh4eBAfH09g\nYCCff/45Gzdu5KOPPgLyb5j16tXj9u3bLFmyhA8++ICFCxdy5coVcnJy6NWrF/3793/mzVQul+Pm\n5oZKpWLkyJHs2LGDqKgoKdrleWjRogV2dnbFLvf19WXYsGFAfleT1NRUHj58+Nzbl8b6wp8wgV6v\nN3p8FtvHFOw/9jxi+zoe915FdA8ePEi7du0Kve/j48PGjRvJy8sjOjqa+Ph4GjZsiK2tLQMGDCAg\nIIBHjx5J64eGhlK/fn0UCgV9+vThxIkTRr61jIwM7t69i7u7OwBjxoxhzZo1KBQKLCwsuHXrFtWr\nV6dkyZKSmKtUKoYPH86iRYu4dOkS7u7uUklLwxTpiIgIHBwcGDVqFHPnzmXPnj1cvHjxtdU1eJO8\nbZ/u29zXyZMnGTduHO3atWP16tU4OTkRHBxMRkYGbdu2xdfXl/bt22Nubk5MTAwPHjwgPDwcNzc3\nqlevTmxsLN26dePo0aP07NmzyH0UPH/p6elSf77XTVxcHBUqVJBely9fnri4uBfejqlzxAtQVJcG\nQRDIzc2Vmhi+TNFwUTTf9oSKRqPhyJEjeHp6FlrWtWtXPvnkE27cuEFERAQtWrSQ/Kk2NjZ07dqV\nnTt3SsVxgoODadSoEQAlS5bE29ubHTt2MH78eAAiIyNxd3fH3NwcgI4dO/L5558TEhJC48aNiYiI\noH79+tKsdFxcHAEBATg6OvLLL7+g0WhITU0lICBAesTbv38/Dx48QC6XExQURMmSJdFoNIwePRqd\nToe1tTUtW7bEy8uLpk2bSpN0Jt4sgiDg5+fHxIkTcXR0lKxagLVr1zJ8+HC++OILDhw4QLNmzWja\ntCklS5ZEEARGjx7NxYsXGT58OMuWLaNMmTJUqVKFgIAABg8ezKZNm4wShgpeN2lpaVJn6XcVk6X7\nHBiWVxQFV6vVkp6ejk6nQ6FQoFarsbCweOmMn7/DvXD+/HmcnZ05ffq09J7hcfXu3Zv9+/cTGBhY\nKPZ26NChRo/ywcHBNG7cWHotWsriuERLWMTMzIwxY8bw66+/AvmTaKmpqVSoUIGyZcvi7e3NlClT\nmDNnDiVKlADgt99+47PPPmP69On8+eefqNVqRo8eLW3/7NmzTJ48GZ1Ox48//ohCocDW1pY7d+4w\nceJE7O3tsbe3p1KlSnh6ejJ79mxOnz79QpMt7ytv86Z+9OhRJk6cyIoVK0hKSpJu6omJiRw6dAhf\nX19u3rzJvn37CAwMZMiQIWi1WoYOHcquXbtwd3fn/v37KJVKqY5Du3btOHDgAO7u7iQkJBS779TU\n1Ke6CF6F8uXLG0Xd3L9/n/Lly7/wdkyiWwwFC4eLYpuXlyeFSFlZWUlpkO9j2M+xY8fo3bs3ISEh\nPHnyhPT0dNLT0yVf9IgRI9i4cWORBWpatWpFSkoKUVFR6PV6QkNDpXoKgiDQokULcnJypFjbsLCw\nQhb1hx9+yLFjx/Dz82PHjh34+vrSuXNn7OzsyM7O5ueff+bjjz+Wbirffvstjx8/xtbWFnNzcypX\nrkyvXr2Qy+VcunSJ3Nxc1qxZQ/fu3bly5Qq7d+/m2LFj/Pnnn9y8eRMLCwvKly+PWq3m3r17rF+/\nnu7du2Nvb4+trS1ly5alefPmLFiwgPnz55Oamvp2voh/EIcOHWLy5Mls3ryZlJQU2rVrJ1XEmzt3\nLnl5eXTp0oWdO3dy5MgROnTogJWVFQMHDiQoKIiZM2eybds2Fi1ahEajITQ0FH9/f/bu3Uu3bt2Q\nyWT89NNP0v6KsnRfxb1QVLdmkR49ekgx7BcuXKBkyZI4ODi88D5MLdgLIJ50w95U8L8q/WJ5PDFk\nJTs7G71ej7W19UvvMzU1FWtr65eujZueno6FhcULPzp7enqyfPlyvv/+e7p168awYcOMrHVBEKhb\nty4JCQk8fPiwUALH3LlzefjwIePGjaNfv35ERERIyR8lSpRg6dKl3L17l+XLl9OgQQM2b95MzZo1\nyc3NJSIigtOnT7N48WJycnKQyWRERkZSqlQpOnToQEpKCi1btmTFihV06dKFqKgoLCwsEASBTZs2\n4enpybx589i1axdly5ZFq9XSqlUrdDodM2fOxMPDA5VKRcOGDQkLC6N06dLMnz+fli1botfrWbVq\nFREREZw6dYoVK1YQFhaGn58fUVFRyOVysrOzJf/06NGjady48Ru5sYrxzMX1kntdiHGuRYXcvS72\n79/PlClT2Lp1K/Xr12fMmDF06NCBIUOGcO3aNby8vJg7dy7//e9/EQSBxo0b8+WXX7J48WJcXV05\nceIEkZGRWFpa0r17d+Li4jhz5gz29vYAPHr0iEaNGqHT6bh27RolSpQodP46d+7MmTNnXirZaPDg\nwfj7+5OYmIiDgwNz5sxBo9Egk8kYM2YMkF+U6ciRI1hbW7Nu3ToaNGhQ3OaK/bGYRPf/I1q2Wq3W\nSGw1Go3UIlsMfTK8+HJyciT/4cuSlpYmxR6+DC8junfv3sXLy4vIyEh27drF8ePH+fPPPwutN3z4\ncM6dO8fNmzcLLYuJiaFly5Z8/fXXnDt3jjVr1hiJ7oMHD2jSpAmBgYF4enrSq1cvDh8+TEpKCkql\nEo1Gg7W1tSQGMTEx9O/fn0qVKjFv3jwmTJjA3bt3sbKy4vLly6SmphIZGYmzs7PUNWPIkCEcO3aM\ncuXK8eTJEzZv3sy8efOIjY2lZs2a7Nmzhxo1aqDX67l+/bp0McbHx9OgQQPq1avHoUOHpKeaWrVq\noVAocHFxkQTYzs4OtVpN3759GTx4sNFkyqvyTxHdnTt38umnn0ouAJ1OR40aNQgKCsLKyoqWLVvy\n6NEj4uLiUCgUnD17lgkTJpCXl8fIkSOl9N4ZM2YwYMAArl69yubNm2nfvr3RfjZt2sSsWbOYPXs2\nH3/8sdH5EwSBLl26EBAQ8C48eRY7gH+9e6GowuGQL7ZpaWnk5eU9tf/Y3x1n+6JjEOOH9+3bR9u2\nbbGzs6M4lR9pAAAgAElEQVRTp06cPn2a9PT0QuubmZmRmJhYZAnFKlWq4Obmxu7du6VJNEMcHR1p\n0qQJw4YNIzc3l+3bt5Obmys9SVSsWJEqVarg6OiIVqule/fumJubs2TJEsmS6Ny5M+fOnSMtLQ25\nXM6xY8eM9iEIAl999RUPHz4kMzOT0aNHM2LECC5evMjFixcJDAzE3t4eV1dXI+vHyckJCwsLPDw8\npApaJUqUwMXFhbJly3Ly5EkOHz5MuXLliI+PJykpiQULFtC4cWPi4+Ol1O73vfjK6+D27dtMmTIF\nT09PPDw8EASB8PBwnJ2dcXBwYPTo0VSuXJlOnTpJT3Pz58/n0aNHfPPNN4wfP57169fTqVMn2rVr\nR8mSJWnUqFEhwYX8GPKKFSuyaNEiKRutqGvyXeZfK7pFFQ4HpC4NeXl5lChRAhsbm6daoH93Rtnz\nIoqtmIYcEBAgpf7a2tri5eXFkSNHCn0uMjKSihUrGk22GdK/f38uXrxYpOg+evQIf39/QkJCJLdN\nmzZtWL9+PRYWFtStW5fhw4eTkJAglZf86quvjLpifPTRRwiCgIWFBU2bNmXJkiVG5+rq1as0a9YM\nnU6HXC5HrVbj7e2NSqUiKyuL7777jnv37pGUlFRobNnZ2UYVym7fvi11G/71118ZPXo0sbGxmJmZ\n8ejRIzp16oRMJqN79+5kZmYa9RcTU1PFRI93rfjKm5pIy8jIYNCgQbi7u0tZjQAnTpygY8eOzJ8/\nn/T0dOzt7SURvXDhAufOnWP9+vX079+fHTt2UKVKFcaOHcvUqVO5evUqX331VZH7k8lkrFu3jqSk\nJHbs2GF0XO/S+X4a/zrRNRTbjIwMqcqXKLYv2n/sXRDdp31ep9NJBXZEgTUzM+P06dN07NhRWq9X\nr17s3bvX6LNiwsKgQYMKLRNp06YNGRkZODo6Gr2fnJxM27ZtpXOo1+txc3PDysqKgwcPMmXKFKKj\no7G2tqZixYpkZ2fTr18//vvf/xpZ1dOmTUMmk9GsWTNq1KhBcnKylKWUnZ1NXFwce/fuRSaTMX36\ndFQqFW3btuXYsWM4OjoSHR2Nk5MT8fHxPHjwQNruzp078fb25vjx41L/t/Hjx1OtWjWuXr3KyZMn\ncXd3p2/fvvzwww80btyY06dPIwj57ec9PDwk94jYpbeoIt9iSUJRjAt+T+9z7QVBEBg3bhyenp6k\npKTQpEkT6f1Tp05hbW3N1q1bWbduHX5+frRv356cnBxGjhyJu7s73t7eCILA/PnzuXnzJps3byYj\nI4N69eoVWeRexNXVle7duxdK+87JyXnjbprXwb9GdIsqryiTycjLyyu2/9jz8C64F6DwXV6v10ti\nK9bpFUtHBgYGUq1aNcqWLSut361bN44fPy7dhCDfIvH09KRv377s37/fKD5Z5NatW5QuXdoofTI9\nPZ327dtz//59IP8cVatWjTNnzpCdnc3u3bvp27cva9euZdasWdy9excnJydCQkKws7Nj4cKFACQk\nJEgZTQsXLsTX1xdra2tp+Y0bNyhfvjyrVq1i1KhR9OrVi9TUVObMmcPo0aMpWbIk5cuXJy0tjfbt\n20uWvCAIbNmyhTFjxuDu7s6SJUto1aoVFy5c4D//+Q99+/alSpUqBAUFsWDBAkqXLs3FixfJycmR\nLuy0tDRq1aolxWg/b5Fvw1oUBRM93iRvQtyXLl1KXFwcX3zxBQkJCdSsWRPITy2/fv06q1atYuPG\njcTHx1OmTBkqVKjAt99+i1wuZ/jw4QDMmzePpKQkTp06Rd26dfnhhx/44osvnrnvDRs2sHnzZqPj\nSk1NfedjdOFfILrFFQ4X03efpyXOm+Z1FSKH/4mtYVH0gnV6jx07RqdOnYw+W6ZMGerXr8/x48el\n9c6dO0fz5s2pWrUqDg4OBAYGFtp3REQEnp6e7N69G8ifGPLy8uLevXvScQmCwO3bt+nQoQNnzpxB\nqVTi7e1NTk4Ow4YNIy8vj59++onbt28zfPhwNm7cyPnz5xk0aBAqlYrevXtTo0YNevToIVmiV69e\n5eLFizx48ABLS0s++ugjqlevTl5eHvXr12fYsGHExMRw+fJlKSJDtIwiIyNJT08nJiaGmJgYVq9e\njbOzM+PHj2fmzJl88MEHbNiwgf79+9OrVy8pqmP8+PGo1WpUKhWWlpakpqbSsGHDIm9G4rktqsi3\nYcEW8fdpKMbvqovCkCNHjvDbb7+xZcsWIiIiaNiwoXT9iDe3b7/9loYNG3Ly5Enat2/PmTNn+PPP\nP8nMzMTb25vHjx+zYsUKhg0bRrVq1Rg2bBiVK1cuVEmuKAxdCuLf70NiBPyDRfdpYpuamoogCFI7\nnFcR23fFvaDX68nKypJiS59WFH3r1q2ULl260Pu9e/dmz5490uvz589LnYGLcj9AflJDr169CA0N\nJTExkenTpxtFdHh4eFCmTBkqVqyItbU1mZmZ1KhRg5SUFDp16sTJkycxNzcnJCSESpUqMXfuXH76\n6SeGDBlCaGgoNWrUkCyoWbNmcf36dfR6PStWrOCHH36gcuXKlClThmrVqiGTyWjTpg3+/v5kZGTQ\nrVs3nJ2dKVu2rBSz26JFC0aOHEliYiJ79uxh1qxZaDQaoqKipGPdt28fWVlZ/Prrr0RFRTF58mQm\nTJiAhYUF69evl6JbqlatSmxsrFETzuf5rgwLtigUCpRKpeSiUCgUz3RRGEbX/B3cvHmT8ePHs2HD\nBhwdHQslxvzwww/Uq1dPylY8ceIEXl5ejBs3jsmTJ2Nvb0/lypWZNm0acrmccePGERwcjL+/v5TB\n+LwYim5KSsobSwF+nfzjRLe4wuGiIAmCcf+xv1swXxXDOhCGx1ZcnOL9+/dJTU3l8uXL0nviMfTo\n0YMjR45ICSHh4eGSn04U3YLHGhERQZMmTWjXrh1//vknGzduxMbGBrlczqeffioVucnJySE8PJyu\nXbsyY8YMfHx80Ov1XL58GZVKxdq1a+nRowfx8fGcPHmSpKQkXF1duX//vlSvwcHBgYkTJ2Jra8u+\nfftIS0vDzc3NqGdbmzZt8PPz48aNGwiCQP/+/Tl27BidO3emUqVK9O3bl9TUVLp160ZOTg5fffUV\nZmZmxMbGMnbsWJycnNi6davUAeH//u//OHz4MCdOnMDX15e2bdvyxx9/IJfLuX37NjVq1CAiIoI/\n/vjjpb4/UTSetymkYR8y0UXxPFEUr8u9kJKSQt++ffn666+l30ZQUJD0t6+vLwkJCSxYsADI9+1f\nuXIFX19fvL29SU5Oxtvbm/379xMcHIyzszMuLi6MGjUKtVpNjx49XnpsJkv3LVNU4XBRbA39moaC\n9C5YqS+7jYKWrbm5+XP1Vjt27Bht2rTh2LFjhSwmJycn3N3d8fPzIzQ0FDc3N6m1jru7OyqViosX\nL0rrJyYmkpycjKurK71792bJkiXIZDIeP36Mg4MD586dw9nZmSNHjpCRkUFOTg5Pnjxh/fr1/PXX\nX9Ixp6SkkJqaysqVK8nIyOD333+nWrVqpKSkkJmZibOzs7TPiRMnkpWVhV6vR6fTcf369UKie/r0\naW7cuEF8fLyUejxnzhwePHjAsmXLePToEQqFgilTpnDz5k1q1aqFm5sb7du3l9oRmZmZIZPJCA4O\n5siRIwwdOpSYmBhGjRpFpUqV2Lt3r1Qr2czMrNjZ9leloBg/T7cFww69r9NFIdZGSExMxMfHB8h3\nJ4WFhdGwYUOSk5P59NNPpcgUgNOnT1O5cmXCwsL49ttvOXbsGM2aNWPatGk0bdqUnj17Mn/+fMzM\nzBg1atQLP3WafLp/A0XVshUD9A3F1srKijt37rBt2zbJ5/Q6rdS3VTtBLCeZmpqKXq9HrVa/UJGd\no0eP0rdvX0qUKCEVYTbcv+hiMHQtiOv06tULX19f6b3Q0FDq1KmDXC6nQYMGPHz4EEEQKFu2LD17\n9iQkJISbN28il8vp27cvTk5OTJo0ia1bt5KUlESdOnUoXbq0ZOmJxyFGXGRnZ2NpacmZM2d49OgR\ngiCgUqmws7NDEASpp5qXl5c0JkdHR8qUKUN2djZXr16VykEGBwdLlmL9+vVZtWoV3t7enD17losX\nL3Ljxg0OHjyIXC6nTJkyNGnSBK1Wy/Xr12nVqhU1atSgV69e3L59G3d3d8aOHYuFhQX37t2jdOnS\nxMXFFenzfhM8rfWN6KIAjFwUGo1Gikd/WTHesmULkZGRRvGzly9fxsXFhZIlSzJ79mw8PDxo0qSJ\ndPPft28ft27d4tdffyU9PZ3bt2/j6+tLt27duHjxIlWrVmXLli2kpKTw4YcfvvC5KCi6JvfCG+RZ\nYmtmZoatrS0ajYaOHTvi5OREhw4d2Lp1K3369OHjjz+WHslfBVEw3sbssyi2YqSFOPn3vPvXaDT4\n+/vTsWNHunTpwuHDhwut07NnTw4cOEBAQADNmzc3WiYKsrivS5cuSZakWHgG8v3mq1atki6GRo0a\nsWfPHsaOHcvOnTvZtGkTtra2xMXFkZWVxW+//YZcLicnJ0eydMqUKUPVqlUlF4CrqyvlypWjdu3a\nJCUlSdsuyrqvXbu2VCRn8ODBuLi4MHv2bLZs2UJ2djaVK1eWuhZ/+OGH6HQ6yVpUq9UcOXKEffv2\nSQVXlEolH374IUFBQURGRtK1a1fKli3LBx98QOPGjXn48CEymYwRI0Y813f5pniai8Lwd/IyLorr\n16/z5Zdf0qpVK6MaGsHBwTRp0gR/f3/8/f2pWrWqFO6l1+vZv38/H374IQ0bNuTYsWPUrFmTc+fO\n0bdvX2QyGUuWLKF///7UrFmTKlWqvNDxFhyryb3whhDFNiUlhezsbKOWOAX7j8nlciZPnizVZq1Q\noQIfffQRs2bNYu/evTRt2pTIyMi/fYb4aaIpim1KSkohsX1Rzp8/j6urK2XLlqVz586S6Bruv2LF\nilSsWJGzZ88aWZAAHh4eaLVaoqKigHzRrVevHiEhIQQGBlKqVCm0Wq30Pfj4+CAIAoGBgdSsWRMf\nHx9OnjzJvHnzcHZ2pmfPnjg4OPDBBx9QoUIFdDodWq0WS0tLIiIicHV1lUo69u/fH0dHR6lItRgX\nHB8fj6OjI59//jn379+XztWTJ09ISUnh6tWrCIJAWloagwcPBmDv3r0sXryYtLQ0mjRpYpTC3alT\nJ6pWrYqZmRk1a9Zk4cKFDBw4kNTUVORyOXl5eXz11VfcvHkTT09PLC0tKVGiBCVKlCA+Pp5ffvnl\nhb6TtxGnK1rGZmZmRi4KlUr1TBdFXl4emZmZ+Pj4MGfOHO7du2dUTD4oKAgPDw8mTZrEsmXLCA8P\nx9PTE5lMxurVq9Hr9cyfPx/I7zh97do1li9fzokTJyhdujS1atUiOjpamnR72eODfNF9UxXGXifv\nneiKF6ZYJ+Fp/cd27txJWFgY+/bt4/r16/z3v/9l0aJFbNiwAWdnZ6ytrRkwYIDUEuZleRPJDYKQ\nX6A7JSVFStgoTmyfd/+GoWLNmzcnOjq6yDJ5zZs3Ry6XF0p4EF0MYoRDaGgoHh4eTJkyBTMzM5KS\nkhAEgaFDh9K7d28qVaqEmZkZ5cuX59q1a4SFheHo6Ej58uXx8/Ojbt261KxZk4CAAB4/foxCoZAq\ntslkMo4fP46FhQUVK1ZkzZo1lClThsTERL7++muqVasG5E+utWzZkt9//506derg7OxMWFgYer0e\nS0tL2rZty/Xr17l9+7YkFuIEXtOmTYmKipKSI+bPny9NqGk0GmrVqsXp06dZu3YtAwcOpGHDhtjZ\n2eHj48OoUaMICgoiNDSU8ePHo9FokMvlb8y3+6oUFPenuSgM64BotVo+++wz3Nzc6N27t1QXWbwG\ng4ODpZrIbdu2JTIykgYNGpCbm8uiRYvw8vLC3Nyc3NxcKXSsbdu2bN++nZiYGD7//HOCgoKKLVL+\nIsdksnTfEHK5XBJejUZTbP+xuLg4pk6dytq1awkMDKR9+/YMHTqU8PBw0tLSiI6OJiIiQuqtJaZ/\nvgyvw70gft5QbPPy8l4oO+5ZHD16VJp0UiqVRgkDhtja2pKXl1fkMYlRDA8ePCA7OxsHBwciIiKQ\ny+VYWFhgZ2fHhQsX6Nq1K3v37qVy5cpoNBo2btzI8OHDuX37Njdu3ECpVDJnzhypK0BmZqZUwFxM\nJkhJSaFEiRKS/zEzM5Pc3FyuXbsmtVxPS0sjJCQES0tLLC0tGTVqlFRDolatWmzZsgWVSsXVq1e5\nffs2AKVKlUIQBHbs2MGTJ08wMzPDycmJsmXLsmfPHmmiqFy5cqxcuZIZM2awdOlSAgICaNCgAQqF\ngrt373L06FEqVqwoxagqFApycnJeW8ugvwNDMbawsODw4cOcP3+eFStWEBcXJ5XBzMvL49atW6Sm\npnL06FHmzp3LpUuXqFq1KtbW1vz6669YWlpKYio2Il26dCnh4eEkJCSwbNkytm7dSrdu3V6qYFRB\n0TX5dN8QYlaPYdB5wcczvV7PiBEjqF+/PiNHjmTq1Kn89ddfLFu2jD59+qBQKJgwYQJqtZpz586h\nVqvp2rWrUSPGF+F1WbqGdR9edypybGwsDx8+NPLHde7cmSNHjhT6fHR0NDY2NkV2O23SpAkpKSns\n37+fBg0a8N133wFIkzidO3fm1q1btGvXjtDQUCpUqEDHjh3x8/OTst0yMzMZMWIEHh4eLF26FEtL\nS27cuGEUOyz2bIuPj+fs2bP07NmTpKQkRo8ezciRI0lNTZXCqLKzs1m/fj3Tp08nNjZWisrQarXI\n5XLS0tL4+OOPGTBgAAqFgtzcXMqWLSv9btRqNV26dCEyMhKVSsW2bdtQKpWsWLECtVrNiBEjKFGi\nBCNGjCApKYnmzZsTFhZG7dq1sbCw4PLly/Ts2VNKsZ45c+YzvzORt+Xaehk3xu3bt5k2bRrr1q3D\n1taWyMhI6tevL/mLw8PD0ev1LFiwAHt7e4KCgvD09CQpKYmlS5cil8vx9PREo9GwYsUKOnbsKE24\nubi40LlzZ3755ReaNm36Wo7xVWvpvi3eO9EVC2w/rQiNaN3a2tryyy+/UKNGDUaNGsW4ceNYv349\nGzduJCYmhpycHARBICkpiYcPH/L555+/1JheRXRFH7VOpzMqsvOilu2z9n/06FE6dOhg5J7w9vbm\n1KlT0uO1yLlz5/D29ubQoUOFtiOXy+nRowe7d+/G09OTdevWYW5uLpVrrF69OjKZjMuXL5OZmUl0\ndDQHDx7k/v37dO/enWrVqqFUKlGr1cTGxiKXy6lbty43b97ExsaGXr16odfrJd+c+D2fPXuWMmXK\ncOTIESkGt27dupJrY/ny5fj4+HD8+HEEQaBEiRLcuXOHefPmMWPGDFq3bi09fmZnZ+Pk5CQVybG3\nt8fb21uKXTY3N5eqjyUmJkohiGPHjuXatWtERUWxdetWKTPu9OnTjBkzBjMzM3Q6HampqUZJJs/i\nXay9oNFo6Nu3L5988ok0WWrYrRlg9erVODo60r9/f8zNzQkNDcXLy4sffviB//znP6SmpuLu7s6p\nU6d48uQJQ4cOJSEhgcDAQKZOncqSJUuQy+UMGjTopcZY8EaSkZEhhTi+y7x3omvo8ytOaLZu3cqG\nDRvYtGkT9+7d48aNG4wZM4YFCxbg7u5Ojx49OH36NNWqVaNUqVIAZGVlsWvXrlcKcn/R9XNzcyXL\nViaTvbQb4Xku2m3btlGrVi2j98qUKYO7uzsXLlyQxn/v3j1yc3MZPHhwkaIL+REOoaGhJCQkoNVq\nqVChAo6OjlhaWkoW4OzZsxEEgXv37rFp0ybWrFlDZGSklDm2ceNG7t+/z+3bt2nRogWbNm1i6NCh\nLF++HI1Gw4MHDzAzM8Pe3p5u3bohCALx8fFYWVlJF9bEiROJiopi6tSpnD9/nvj4eLRaLQqFgrp1\n61K2bFk2bdrEoUOHWLBgAeHh4ZJ//MaNGygUClQqFU+ePKFatWrSpGpSUhIrV65k3LhxmJmZcevW\nLQBpAvLSpUtUq1ZNqh984cIF6tati5OTEyqVCplM9tI38HeFWbNmERMTYxSVEh4eLonunTt3CA0N\nZcaMGdK1GBQUJLloWrRoQZMmTbC0tJRqZbRt21aaaOzUqROrV6+mQ4cO5ObmSll3L1Ius6DoCoLw\nt6XyvwjvneiKFCe6sbGx3Lhxg27duhEbG8uUKVMoXbo0Xl5exMXFsX37du7du8fcuXNRq9XodDqp\n+HdqaioTJ040SgB43rE8L4Zim5ubi7W1NSVKlHgla+dZlnZ2djYXL16UfJqGdO7c2ahG7blz52jW\nrBktWrTg1q1bRpW5RFq0aEF6ejq7du1CJpMRGxtLw4YNadGiBadOncLb25vQ0FBsbW3p3LkzTZo0\n4eHDhyQkJBAWFsYHH3zAvXv3cHZ25vz583h6erJ//34GDRqEra0tffr0QRAEXFxcePLkCWfOnKFU\nqVJs3bqVvLw8srKykMvlNGvWjPPnzzNx4kSsrKzo27cvCoUCnU5H48aNuXv3Lrm5uSiVSvbs2cOt\nW7ck37NWq8XKyorMzEzq1q0rhbiJCRTdu3dn3Lhx5OXl4e/vLx37jBkzyMjIID4+no4dO+Lj44NG\no+Hs2bOMHj1aigh49OgR+/fvf+nv9HXzIu6FgwcPsm/fPlxdXaWqXXq9nsjISGkyUhRbsVKd6NZZ\ns2YN48eP58qVKzRr1ozjx4/z6NEjqUbFmjVraNq0KXv37kUulzNmzBisra0xNzeX5muKi6J4mhj/\n3RFIL8J7J7riD6c4odm1axfdu3dHLpczePBgbGxsGDRoENHR0fz88880aNCA0qVLM3r0aE6ePImf\nnx+ffPIJgBSuZNi99HnH9KwvXYyPTEtLIycnB2tra6lW75uO8z116hS1atXizJkzhZZ16dJFeiSH\n/xW5USqVdOzYschYXrFpYEZGBlZWVpiZmZGdnY2joyNubm7s3LkT+F/XYDFszM3NTUoPFme/o6Ki\niIuLo0WLFlLVM9G6Etvbp6Wl4ePjw+7du0lOTsba2hq9Xo+/v7/UeHLMmDHEx8dTr149LCwspHY7\nXbt25dChQ8yZM4esrCy6d+/Oo0ePqFatGg4ODnTq1ImMjAz279+Ps7Mzp06dYtOmTUyfPl2qHWH4\n9FO9enVsbW1Zvnw5AIsXL8bS0pLPP/+cPn36kJWVhVKpRKlUsnjx4md+N++aWNy7d49JkyYxbNgw\nKasM8i1bGxsb7O3tOXbsGJGRkZQvX17yoQYFBVG1alUuX77MmDFjCAwMpGnTpsybN4969erRsmVL\n1q5dS6lSpejSpQuLFy9GEARatmyJTCZ7ahSFGNIm1i4WrWKxVKaYoQjvpqumIO+d6ML/ZliLKvzx\n559/0q9fP77++mvUajXXr19n5MiRUrB8QVxcXJgxY4ZUJevhw4fcvHnTqNrW84znaXdgUWzFZpYF\nu1C8yXq6kN+7auDAgWRkZEiPyiJ16tRBo9Fw48YNID+WV0yKMIzlhf9VMDt79iwWFhZAfqRD3bp1\nCQkJIT4+Xipoo1QqefDgAV26dMHCwoLw8HBUKhVdunShSpUqODk5ERsbS61atdi6dSv9+vWTKmwF\nBwdLhcPF0ogtWrRgx44dUjqxq6sr06ZNw8XFBT8/P3x9fZHJZNy9exdBENizZw8KhYJGjRrh6urK\nxx9/LB2fg4MDCQkJxMfH8+OPP5KamsqAAQO4deuWVCayXLlyAAwcOJCwsDCjSVYvLy+2b98uJeX0\n7duXGzdukJSURO/evaXKapcvX36u4jRvQyiex9LNy8tjxIgRTJo0iaysLGrXri0tE10Lubm50g3G\nsLvzhQsXuHPnDrNnz0av13Pt2jUeP35MXl4eCQkJNGrUiBUrVkhteezs7OjZs2exczNFhbQVVYtC\nr9ezcuVKKlSoQExMDB9//DE///yzUW2R5+HIkSPUqFEDNzc3vv/++0LLT58+TcmSJWnQoIHRBPLL\n8F6KLhQtNLdu3eLevXskJyezc+dONmzY8Ewfj7idLl268OmnnwL5scBjx44tskXN846lOLF9Wsru\nm7B69Ho9hw8fplu3bnTo0IETJ04UGnvHjh05cuQIiYmJxMbGShaOt7c3p0+fJisry6jOQ1RUlBSW\nVbNmTerUqYOLiwsHDx7E3NwcLy8vqYGnGJFw4cIFEhIS6Ny5MwqFgnLlyqHVarG3t+f+/ft07dpV\nqrB16tQpyXev0WjQ6/XcvHmTkiVLolarsbW1xdvbG09PT8LCwvjxxx8pVaoU1apV49GjR5ibm5OY\nmEibNm24evUqACEhIUD+b2TWrFkkJSXh7e1N+fLlcXNzY+3atVSpUoUrV64wefJk6fx4e3sjk8mM\nJsbatGmDUqmUUqJ9fHyQy+VMmjSJadOmSe4PvV7PgQMHXvt3+qb49ttvsbOz47///S9XrlwxmgOI\niIjAw8ODFStWSHG6hkkSR48excbGhj59+kjp4YsXL+aTTz6RJh9r1qxJiRIl2LhxIwqF4qVicw2z\n7sT/P/vsM86dO4erqyt16tQhKiqKoKCg596mXq9n4sSJHD16lCtXrrBt2zauXbtWaL1WrVoRGhpK\naGgos2fPfuGxi/yjRHfXrl20bduWKVOmsG3bNqmL6LO2IzJ//nzJIn7w4AE///zzC4/lZcT2VS2d\np1m6wcHB2NvbU6VKFTp16lSoxxjkC8uRI0cIDAykcePG0mReqVKlqFmzptS4Ua1WY21tLZV4VCqV\nhISEoFAosLW1JScnhz/++IPIyEg0Gg15eXk8fvwYjUZDeHg4Dx8+lKzouLg4lEolwcHBDBo0CAsL\nC5RKJQqFguDgYKpXr46rqyuCkN/Dbu/evTg4OJCYmEhOTg5dunTh8uXLzJ49m4SEBNLT0xk3bhyQ\nP4tdq1YtIiMjuXTpkjTJI54rjUYDwNChQ4H8oustW7YkOTkZvV4vnU+ZTEaFChVQKpWsXLlSOl+1\na9ibX3cAACAASURBVNemZMmSLF++HEEQcHR0RBAEjh8/zqpVq7CxsaFy5cpAftuf94GjR4/yxx9/\nsHr1auRyOVFRUYUs3fLly/Pzzz9Lk5Ki6D558oTY2FgpySgoKAh7e3sUCgVqtZr69evzyy+/ULNm\nTZydnalUqRJxcXG0adPmlcZsaL3L5XKcnJyYOHEiv/zyywu5CIODg3F1daVixYoolUoGDhxoVGPE\ncH+vg/dSdAvOWIrs2LGDCxcuMGfOnKe2+yi4LcNtiBenXq9n9uzZRU4+FYUY+pWenk5WVhaWlpYv\nVIzmdSZYGHLgwAG6desGQPv27QkICCgUItayZUsiIyM5efIkzZs3N0rQ6NixI2fOnJGy4fR6PXfv\n3gWgbt266PV6Lly4QHBwMP369aNcuXJcvHgRuVxOmzZt2LdvHxEREdjZ2dG+fXspvCw5ORnIv2AH\nDBggjeXy5cuUKFFC6soASEJ88+ZNMjIy0Ol0eHh4UKpUKQ4dOoS5uTlXr16VHkkBGjduTGxsrFSn\nNzk5GUtLS9q0acOyZcuwsLCQbi4xMTF88sknUqcLsatFcHAw6enp1KtXj4SEBC5dugTkW/cPHjwg\nPT2d6dOn065dO+mR98SJE+h0OqKjoyVf9tNcDG8jDfhZ+4mLi2PChAmsXbuW0qVL8+jRI7RarZSR\nKAj5jSZ9fX35+OOPcXFx4fLly1Ikw4IFC7C1taVVq1ZA/lNNREQEs2bNIiAgAGtra+rVq0dUVBTX\nrl3Dw8ODzp07v1D36mcd06skRsTFxRl1eHZ2diYuLq7QeoGBgXh4eNC1a1fpCepleC9FFwoXmvnr\nr7+4desWrVu3ZuTIkS+0HUOxqlq1qmSl6PV6vLy8ColUQcSatpmZmVhYWGBrayt1K3gbPG0/+/fv\nlxoGli5dmho1anDu3DmjdVQqFV5eXhw7doxGjRqRmpqKRqORHhcPHToknSMx20omk+Hq6kpubi4R\nERHY2toyevRoMjMzuX//PiqViqFDh+Lr60tQUBBmZmZSNty1a9coX768FD52/vx5aSwBAQGUKVNG\nmtGuU6cOWq2WvLw8kpOTcXd3x8HBAblcTv369QkODqZGjRqUKlWKuXPnIggCbm5uVK9enWnTpmFl\nZcW0adOk7S9atIgHDx5Qp04dIiMj8fPz4969e/Tq1Uvys9+5c4fIyEi+/PJL3NzcuHz5MjY2Nqxe\nvVo6jyqVipIlS7Jz50727t2Ll5eXVI5y2rRp6HQ6bGxsyMvLK3S+3yVyc3Pp2rUrffv2larKiVau\n+LuKjY0F8oveTJkyhVu3/h975x0YVZ29/c/MpPfeIb0nQAJGSughVBFpi4CIXaTZXV1/ImvBwoKU\nVcCCdESatNBJo4USUkhIIb0QSO+Zycy8f+S9380QVEDc/fG+e/4hTO7c3Htn7rnn+5zzPM91bGxs\nsLGxobGxkW3btgnlMZVKJSrd6Oho4uPjuXz5MvPnzycpKYnAwECSkpKYMGHCHz7225Pun0kB7t27\nN0VFRVy5coV58+b9oeN/KJPunRpQknj2ihUr7inZ3anClJpoWq2W2tpaRo4cecf3Sp31trY2Yfp4\nv8n2z5hgyMnJob6+nvDwcPHaiBEj7tgkHDRoEAUFBYSEhIjJCj09PQICAtDT0xMiN//4xz8Erpac\nnIy5uTlGRkbo6+sTFhZGenq6+HnEiBEkJycTGxsr3HShw8FXEl5xc3Pjhx9+EMeRkJCAXC6noKCA\n6dOns2TJElG9WlpaEhoaKq7TzZs3cXZ2xtbWFicnJ6Ft+9prr7F69WqeffZZVCoV+/btQy6XM3Dg\nQBISEpDJZDQ3N7NkyRJeffVVrK2tefLJJ5k/fz4eHh5UVVWxcOFCjhw5QmlpKXPmzKGxsZGff/5Z\nVDgODg7k5+ejVqsJDAwkMDCQbt26ERwczLFjx4SqF8CqVase6Od6r/FbTd633nqLyspKpkyZIl6/\nHc+9ePEibW1tfP755xgbG5OcnCyghXXr1mFlZUVUVBTQYYWk0Wj48MMPqa+vJzMzk5CQEGpra5HJ\nZEycOJGLFy8ycODAB3qOfyTpurq6UlRUJP5fUlKCq6urzjZmZmaYmJgAHQ1mlUrVxWH6buOhTLpS\ndE5US5YsITMzU9BH72cfUri5ueHt7S1+f+7cOR3jxfb2dhoaGmhqahLi4VLl/SDO5UG9/8CBA4wd\nO1ZH+vB2XFeqIq2trZHJZNjb23eZrJDGrqCjEaXVagkICKCuro7GxkYMDQ3F37l8+TJKpZKxY8di\nbGzMsGHDSEhIIDAwUDTVLl26RHl5OXl5ecyYMYNr164JFbWzZ89SVVXFhQsXmDZtGoMGDcLDwwPo\nuLHMzMy4efMmRUVFpKSkcPPmTZRKJcXFxfj7+9Pe3o6/vz/u7u4cP36cHj16CAGc/v378/HHHwuf\nNTs7OxYtWkSvXr04fPgwo0ePFrb0n376qRi2HzBgAD4+PgQHBxMVFcWuXbu4fv06jz32GO7u7iQl\nJREUFIRCoaCkpERMXUgNyePHj//HLXag64ro+++/59y5cyiVSuHOAXTBc3/44QecnJzESkXCc+vq\n6sQDRaKXf/vtt1hZWTF06FDi4+ORyWS8++67gr22b98+fHx8fnWa6F6ic6X7RyjAjzzyCLm5uRQW\nFqJUKtm+fXsXB4uKigrxs6TNLBGr7jX+n0m6wH19kL+W7KQuv/S7mTNnUlJSQkNDA42NjYKObGRk\nJIRa/pNxp/NYu3Ztl5GcPn36UF5eTmFhoTgXiXVlZWUlcMvOMXr0aA4dOkRmZqbQM3BycsLLywsr\nKyvq6uoEfz4hIQG1Ws0TTzwBdBAp2traiIyMZMWKFULTuLq6moaGBlasWIFarSY6OpqFCxdiZWVF\nRUUF4eHhotqYMGGCGJxPTEzEwsKCxYsXM3v2bDw8PMjIyKClpQVLS0ssLS158803efPNN1m6dCmP\nPfaYaJytW7eOpqYmqqurmThxImVlZRQXFwvBpLCwMIKDgwkODuaXX36hurqapqYmXF1duX79OsuX\nL8fQ0JCXX34ZR0dHGhoaGDFiBMePHycgIIAbN27Q0tLCSy+9hFwuJy0tTTwIkpKS7vi5/bsw3dvj\n9OnTLFmyhM8++wwnJycdwZnOlW5JSQlnz55l7ty54jilpPvPf/6TIUOGUFdXh6+vLxqNhqNHjzJ1\n6lRkMplI1hEREYLEkpiYeE/w32/Fg0q6CoWC1atXEx0dTXBwMNOmTSMwMJC1a9eKRujOnTsJCQkh\nLCyMV199lZ9++um+j/uhTLoPar71t/bh7OyMi4uL+H9rayuzZ88W4uidhXb+zOO438jPz6e6upqs\nrKwuvxs8eDAHDhwQUwd6enokJCQwZMiQO6qODRw4kGvXron5RXNzc4qLi0lNTRVyjgsXLmTWrFnE\nx8djaGgoJkcyMjLQaDRs2bKFgoIC3nnnHaE/O2vWLMrLy+nZsye1tbWUlZUJ94mpU6eKvz9ixAg0\nGg2GhoZC2ergwYPExcXR3t5OTU0NMpmM3NxcwsLCMDY2JjMzE3t7e3H+enp6REdHo1arOXbsGJ9/\n/jnt7e2cP3+eyspKnnjiCfT09PD39wcQx2dkZISHhwe1tbV4enpiZmaGvb09FhYWxMTEMGDAAI4e\nPYq/vz85OTkMGzYMrVaLubk5bW1tfPTRRwB3nP38d8Xtib24uJjZs2ezdu1aGhsbdapclUpFTk6O\neO29997DyMhIwAcSM6179+6sW7eOESNG0KNHDxQKBUeOHBEOzxqNhsTERObMmcPWrVvFcWg0mvvW\nWrj9nDrHH1UYGzVqFFlZWeTk5PDXv/4VgJdeeokXX3wRgLlz55Kenk5ycjJnzpwRnnD3Ew9l0pXi\n1wgS9xK/lew6L8NVKhWlpaWsXr36T6lMHjS88PPPPzNx4kSSkpJoaWkRxIb6+nqioqJITEwUcphN\nTU1kZGQwe/bsOyZdQ0NDhg0bJizMW1payMrKErO03bp146mnniIiIkKIymi1WlauXMnWrVvR09Nj\n0aJFwiVWrVYjk8nEUn7OnDlUVVXh6OjI4MGDUavVLF26VEySSAQJW1tbURVbWlry6aef0r17d0xM\nTAgMDOTWrVuEh4ezYsUKvvjiC5555hlxzFZWVuzdu5cFCxYINpqzszNnzpwhPT2dSZMmAR2N1Jyc\nHPr06UNqaiobNmzAwMAAT09PSkpKRJLav38/ra2txMTEUFhYSGVlJdbW1gQFBXHs2DEx533t2jXk\ncjmnTp36XwExNDc3M2PGDObNm0dUVBQZGRnCbRk6FOZcXV0xMTHhxIkTYuTO3d0d6Jj0sLS0ZMuW\nLTz++OOUlZWJnsEXX3yBgYEBvr6+HDx4UJAtvv76a3r16sXq1asJDAy872X5naJzpfswaOnCQ5p0\n/4wKs/N+1Go1jY2N2Nra6iy7bty4werVqzl37twd9/G/KXbs2MGMGTMIDQ3lxIkTOtbsY8aM4dSp\nU0I969y5c/Ts2ZPBgweTn59/R72FqKgoQYiQmEJfffUVxsbGREdHc+zYMYEDNjY2MnPmTLZu3Yqz\nszNBQUFi7jEjIwNra2taW1tFM2Xs2LFAB1ustLSU0NBQ/v73vzNjxgw++OADTp8+Tbdu3aipqUGh\nUHD06FGhgdu7d2/UajUe/1cwPSQkRDDQ9uzZQ1NTE4BwGelsqTNw4ECqq6sxMzMTlZ2NjY2QLoQO\nwkBSUhLe3t5iVjglJQULCwscHBw4ePAg/v7+nDlzhqCgIOzt7Tl37hzjx49Ho9Gwc+dOfHx8BG35\nbsVcHmRIla5Wq2X+/Pn4+PiwYMECoGOSJCAgQGwr4bltbW28+eabzJ49m5CQENEXuHLlCoGBgaxf\nv5633nqLy5cvEx4ezvnz5ykqKiIiIgKZTMayZcvw8vLi5s2bZGVlERkZSWVl5R9yiLjTOUnxsMg6\nwkOadKV4UElXCinZdrb9WbZsmfh9S0sLw4cP5+mnn6a2tvaBH8eDqnQzMjKoqakhPDycyMhITpw4\nIYgNkiOE1AACiI+PZ+DAgULY/E4ECqlTa2ZmRnt7O25ubmLOctKkSbS0tLBp0yb09fVxdnbmxIkT\nbNy4kbKyMp566ikuXrxIVVUVGRkZqFQqAgIChJiKNJVQVFREfn4+Y8eO5fHHH+fs2bPk5eXxxRdf\nMHDgQJ253ZEjRxITE0Nubi6GhoYYGBig1WpF8nzttdd0HId9fHzw9/ene/fu4pwkKMHR0VHnXH19\nfcnPz6dnz55MmTKFp59+WjSB5s+fj6mpKenp6fj4+PDaa6+RkZHBL7/8QmBgIKWlpQQHB5Ofn4+p\nqSlXr14VhotLly7tIuYCHd+7B+na+2vx6quvcuHCBZ3V2u2VroTnrlq1Cn9/f0xMTHSaaikpKcI+\nSXLpCAsLY/ny5YSEhNC7d2/y8vLIyMhg3LhxrF+/XvQMNBqNmBn/o3H7tfpv0v03xYOsMG83tJSW\n3pLVNHQsc3fs2MHQoUOZNm1al338mfoJdxtarZatW7fy2GOPodFoGDNmDHFxcV3o0CNGjBDJtbMJ\n5ciRI3UmNaSQGgeSuMirr74qoIvw8HDGjh1LbGws7e3t+Pn5sWDBAubMmUNrayvTpk1j6NChohlX\nX1+v4ygLHYlbEjWRbkx7e3s2bdqEgYEBp0+fFrbr9vb2uLi4EBMTw6VLl+jRo4fQvZVsfAwNDend\nu7eoiAoLCwWEIIWk7lZQUKBz7X18fCgqKuLJJ58kLS2Np556isOHD1NcXMykSZMEndrb2xs9PT0W\nLFjA8ePH8fT0JDMzU4zl9ejRA61WK6Y2zp49KzQEjI2N7+jaez8Sh78XWq2WdevW8csvvxAdHS1G\nn5RKJQUFBfj6+opt09PTcXR0ZNWqVXz22Wekp6cTGhoqfn/+/Hkhp1lRUUFzczNKpZILFy7Q2tpK\neHg469atE8p+P/zwAw4ODpw8eRIPDw+dh94fjc4F08PiGgEPadJ9UPCChHNK+5KS7e3usqNHjwY6\nBsnVajXV1dXExcWJJfPtRI3/VEiGnT///DMzZszA3NyciIgIbty4IdhWUkiQQG1trTBZhH8Jm9+u\nO3E7A+f555/n9OnT+Pn5YWBgQHR0NJWVlcjlcn744Qfefvttbt26JaY8Hn/8cfbs2UNaWhrNzc1d\nHlrSja9Wq3WSgFKppLGxkf79+wtzRUnTVtL0HThwIAUFBQA6n92pU6eQyWTo6+tTW1srSCKd963V\narGwsBDaDNAxMtjS0sLkyZPJyspi6tSptLW1oaenJ5pKUtK9fv26UCTbvXs3165dExMNoaGh2Nvb\ns337dgwMDFCr1ezevVtHzAXQce2V8Os7SRzer326pGfcr18/HZGa3NxcunXrJlYc0FHp7t27l1de\neQUPDw+d8TGtVsvly5eZPn06Tk5OXL58mbCwMFauXMnzzz9PWloavr6+bNu2jaqqKm7duoW1tTUu\nLi64ubl1GcP6I3E7vNDW1qZzHv+b46FMulLcb6KTkm1dXZ24AaQv+53iu+++Ez8bGhqyf/9+evbs\nyfz588USEf4zla5EP1ar1SiVSq5du4a+vr6gQSsUCoYNG9ZF6KZv377k5uZy4MABHnnkEQEVODo6\n4u3trcMSk6pM6Lh2vXr1Qk9Pj6ysLAYPHoxWqxVVszSvKsEzGo2G5uZmRo0axfnz57l69SoKhUIH\nR4QOOjCgk4ygo3HTrVs3li9fLpKNhYUFaWlpuLu74+rqKsbgJNsfgEOHDlFTU4O9vT1KpRKZTNal\nSZiZmQl0iLLv2rVLvC5VokZGRkyePJlNmzbR2tpKc3Mz27dvZ+DAgSQnJ+Pi4sL169eRyWTMnDmT\nlJQUsrKyCAkJoaamBgcHB9zd3blw4YKAPZYvX/6rn6VEOrmTxKEkBnR7Vfx7WrO7d+9myZIl/PTT\nTxQXF+tMKtwOLVRXV1NdXU1+fj4LFy5EpVKRnZ0ttjl9+jQqlYr33nsP6Ji39vPz48CBA0RGRuLq\n6kpMTAy9evXC39+frVu3YmxszPXr12loaBDY/YOIO43aPQyyjvCQJt37rXRvT7aWlpaYmJj87pyt\nra2tGIGSsENLS0vkcjlffPGFzjHdb9xP0u1M0lAoFBgZGbFx40ahjCXFndTFDAwMGDx4MNu3bxec\neSluhxg+/PBDALF869u3LzU1NTQ3NzNu3DguXrzItm3bkMlkWFhYAB3XOjs7G2dnZ7Zs2YKZmRkR\nERGo1WpcXFy6XK9Tp04JbLbzsWZlZeHv709ycrLYb0xMDNOnT6epqQmtVkt4eDgtLS24urqSkpJC\nVVUVr7zyCjJZhw4rdODAn376qWgGQgd7ytTUlPDwcPbs2SO27dxYnTFjBj/++CODBg3CwMCA+fPn\nk5CQQJ8+faipqRFSmePGjRPKaWfOnGH48OHU1NSIRClRyzuvGO5mRlcqCvT19UWDT6qKJV2PX6uK\nY2JieOutt/jpp5/w9PQkJycHPz8/se/MzEydh5/kebZ06VKMjIzIycnB1dVVNJOXLFmCt7e3gEuS\nk5MpLi7mySefJDs7W9g3eXt74+fnR15eHtevX8fU1JT29nYdfd4HGf/pFea9xkOZdOHelvRStdW5\ngy8lW2lfv7efFStWAIibNjExkYaGBtasWUN2dvafJlhzp5CSbWNjIwYGBuIB0NraKhxuO0dUVBQn\nT54USUWK6OhoQcns/LdHjRqlUxVKDTdpqd7S0iIq4ZCQEObPn09QUJCQVJTJZKSnp6NQKJgwYQKr\nV69GrVYTHBxMe3u7zhJXigsXLuDg4ICxsTGbNm0Sr2dlZeHn58c333wjdIhTUlLo378/JSUlOrCJ\nt7c3qampvPnmm9jb22NpaUljYyPQ8Zn37NmTr776SmxfWFhI9+7dUSqV2Nvbc/bsWaBj+qKlpUUk\nipaWFgYMGIC9vT1hYWHMnTsXNzc30tPTKSoqQq1WC3cMNzc3vvzyS6Kiorh27RrZ2dkEBgaSlZUl\nPNR+jShxLyGT3Vn428jICD09PRITE3nllVdYv349vr6+5OXlYWdnh6GhoaiKMzMzdSrdVatW4eTk\nJNwg0tLSBJ6bnZ3NxYsXBUSj1Wq5dOkSiYmJzJ07lwsXLggXlNLSUiorKxk6dKggD93pQftH4r+V\n7n8oZDLZb84+dk62UnUqdfBv38/vJbxJkyaJD9XExEQsc0eMGCHGb/4ovPB7IU1XNDQ0CGKDRNKQ\nyWTs2rWLsLAwzp8/r3Msbm5uODk5dWGb9e7dm7q6Oh1dVOhgrd26dYvCwkJhKwSIFcKlS5c4ePAg\nzs7OfPXVV/j5+VFRUYFCoaC4uJiqqiri4+MxNTVlxIgR2NnZceDAAerr6wF0lrjQcd2Ki4uxt7en\nubmZ+Ph4QbvMzs6me/fuHDt2jNDQUPz9/YX7hNT03L17N3K5HHt7e06ePElqaqpwhzAxMcHV1ZX6\n+noeffRRvvvuO8rKyoRuRr9+/cjJyWHixIkCYigoKMDKyoqioiIxPldZWYmxsTEuLi7s2LGDgwcP\ncujQIezs7CguLkZfX5/BgwfTvXt3Ll26hJubG0lJSRgYGPD444+Tk5MjzleyI3/QIcETx44dY9q0\naaxbt45Bgwahr69Pbm4u/v7+OlXx1atX8fb2FtKbiYmJOo3jznjup59+iqurKxEREUCHw4RSqWTU\nqFF0796dCxcukJKSwrPPPsu5c+eE5VVraytFRUVCRP5BReekq1Qq/7Bi2b8zHuqk+2uwwO3JtvO4\n1J3ibqvUMWPGAAgrmFu3blFcXExNTQ179uz50zDdzsQGSVhHmq7o/P7vv/+e119/HZVK1cUhQmru\ndI6MjAzMzMxITU3VeV0ulzNixAiOHDkiKkOFQiGcGMrKykhISMDT05NNmzaxaNEiSkpKUKlUREVF\ncejQIeLj42lpacHPz4+FCxeyYsUKzp49i4GBgQ6PHRDyh7W1tfj6+vLII4+wbds2oCPpFhYW4uHh\nQUhIiKjCAgICaG9vp62tjf3796PVdugYZ2VlCcqqk5MTbW1tDB48GB8fHxISEnjmmWf45JNPSExM\nRC6XExERQXZ2NhMnTuSXX36hvb2d3Nxc3N3dycvL48iRI/Tt25fY2FiBKffp04ddu3ZRVVWFkZER\neXl54ho3NDQQHBzMypUr8fX1xcnJCW9vb2QyGWZmZujp6d2TK8m9hFarZcWKFSxYsACFQiFYZDKZ\nTGCzUlUsl8spLy/Hx8cHlUrFvHnzcHR0JCwsTPiSpaamEhQUREpKCqdPn6aurk5ABOfPn0elUrFw\n4UKqq6spKysjPz+fwMBA9PX1GTZsGKdOnSIwMLALw/BBx5+tMPag46FNup0TjpSstFotLS0t1NXV\n6Yhu3617xO+F1FCTbnC5XM758+f56KOPWLRokdCIvd/zuf0YtFrtb8IinSM5OZlbt24xatQohg8f\n3uXGjoqK6vLa4cOH6du3L0eOHOnytyWIQVIAk7rvI0eOZNSoURQUFHD9+nU++eQTCgoKMDQ0xNPT\nUySvc+fOoVKpcHFxYezYsVRXV5OXl4ehoWEX488tW7ZgaWlJWVkZUVFRODo6snHjRtRqNTk5OZw8\neRInJyf8/f2F5xp0NDWNjIw4f/485ubmJCYmYmRkRFJSEubm5iiVSlpaWnj88ceZMmUKFy9eZOHC\nhRw+fJgtW7ZgY2ODn58fubm5eHp60r17d2JjY7l+/TrBwcHk5uZy+PBhnnzySaHY1tzcDHSsBkaO\nHElBQQH79u0T1/j69esYGhqSlpZGYGAgMpmMW7du4ezsTHt7O2q1msrKSuHv9aCWxEqlkrlz5/Lz\nzz+zdOlSgoODdfadnZ2tg99mZ2fj7e2NiYkJa9euxcbGhurqasLCwoTgUXp6On5+fkLnQq1W4+Dg\nQHt7O9u2bcPNzY2QkBAuXbqEhYUFTz/9NJcvX0alUjFp0iQxUtarV69fteW537hd1lHqJTwM8dAm\nXfgXrqvRaGhpaRFKVRYWFkJ0+273czdJ19raWjxR5XI5np6eaDQa9u/fz6hRowTP/o+G9PCora3V\neXj8WqUOsH79eqENcSeHiMjISNLS0kQCb29v5/jx4zz77LN3nMuNiooiISGBiooKMcJkYGDAkCFD\nGDJkCCqViuDgYKZOnUpiYiLt7e2Eh4cTHR3NhQsXsLe3x8/PTyx5p06dikqlQqVSUVFRQW5urjjX\nI0eO4OHhgbe3N0OGDOH69esoFAr27duHsbExNTU1NDQ04O/vLzDGK1euMHDgQDHb6+DgQHl5OT16\n9ODgwYNUVVWJ/YSFhTFhwgRUKhWXLl3i7bffJiYmBi8vL3x9fcnJyUGr1TJ58mS2bt2KXC4nODiY\n7OxsEhMTGTlyJH379qW6ulqnSp82bRqOjo5s2LCBL7/8EicnJ5ydnbl27RrvvvsuKSkplJWVkZmZ\nKeamJft4yX3jQTSBqqqqePzxx6mtreXw4cNUV1cL4of0N7KysnSSbmZmJoGBgWRnZ7Ny5Urefvtt\nrKyssLGxQU9Pj9raWiFfmJWVRWhoqLj2ra2tnD59mmnTptHS0kJsbCzV1dU8/fTTHDp0CCMjI86e\nPYulpSXXr1+/Z6PXu4nbk+7DMqMLD3HS7Zwo6+vr7yvZ3mlfvxfSuIxGo6GkpAS5XM6PP/7IX//6\nV/bv33/P9u2dj0Gj0Qh79vb2dszNze/qfGpqati3b5+gWA4bNozExEQd8XVjY2P69u3LqVOngI7l\nYffu3XnssccoLCzkxo0bOvu0sbER5owajQZfX1/RBJNEZL788kvkcjnx8fEolUoGDhyIqakpbm5u\nGBsbC6IC/KsB6e7uzuTJk4VjcGZmJq2trTg6OhIUFETfvn1JS0tj2rRprF+/HkNDQ2bMmEF2djb+\n/v4YGxvj6upKTU0NQ4cOxdXVFa1WS0FBgU4jz8zMjKKiIhQKBY6Ojnj8X3nIf/7znzzzzDM0C45s\nbAAAIABJREFUNjZiZ2eHpaUlZmZmlJWV8cQTT3DkyBG8vb3x9vYmOTmZHj16YGNjQ8+ePTEyMiI/\nP198V4YMGcKtW7cwNjbm5MmTjB8/XlCLQ0JCyMvLo7Kyku3bt6NUKjE1NUWlUiGXy4WWr7e3N337\n9hVC4fcaKSkpDBs2jIiICDZv3oyZmVkXaq9GoxGYrhSZmZn4+/vzyiuv8O6771JbW6vTVJOaaB9/\n/DHvvPMOWVlZ9OrVC0NDQ44dO4ZSqeSZZ55BX1+fmJgYwsLCsLW15eLFi0yfPp29e/cKN4YHOZ8r\nRef79b/wwr8p2traqK2tRavVYmJicl/JVop7Sbrz588XPzc1NdGvXz9UKhXfffcdzz//PJMmTbpn\nYROtViuWnm1tbToi4ncTmzZtIjo6Woy12djYEBgYqDNrC7oC5ocPH2bkyJHo6ekxfPhwTpw40WW/\nneESExMTPD090dfXZ/v27ejr65OamkpDQwOZmZno6ekRHBxMS0sLarWaqqoqfHx8xHVNSEjAzMwM\nY2Njpk6dyo4dO9Bqtezbtw9HR0c0Gg1BQUGYmJjQo0cPPD09OXv2LJWVlYwZM0Zo/QJCs2H37t0C\nT5Xef/PmTZqbm6moqBB0Y6na9vPz4+LFi8TGxmJgYEBSUhKNjY34+vqSnZ2Nq6srDg4O6Ovrc+XK\nFa5du4aBgQGHDh3C2NhYEEaqqqqAjpWPnZ0d7e3tfP7557i7u7NlyxY0Gg1RUVHCMVmpVLJlyxYq\nKyuF+FBubq6wR/rLX/7C8OHDu2h6/FZUV1fzxhtvMGrUKEJCQli8eLFYCUljdlIUFhZibW0tqmzo\nwPOlh9ILL7zA1atXdei+6enpWFtbU1JSwvTp00lNTRUMu88//xxbW1scHBzQarXk5eXxxhtvkJaW\nRltbG1OmTOHGjRvi4SOJKklY8YNi2z0IWcf/RDy0SVcul4vE9FvL7ruJe0m6crlcCJx3DkmPs7Ky\nkg0bNtzVvqQZzs7uE+bm5veEf7W2tvLJJ590EW+/02yulHS1Wq0Q7YaOudzbt4V/6S3I5XJKS0vp\n168fZWVllJSUEBAQwP79+4mLi8PNzQ2tVktwcDCtra2UlZVRVVWFvb29sNzOzMykW7duVFVVERYW\nhlwu5+LFi+zfv1800aRKS/JsMzc3x9ramvr6evz9/cVN1qdPH2QyGRcvXsTW1la8HhAQQFNTE+3t\n7bi7u/PWW2/pVHwBAQH069ePVatWYWRkRO/evVm8eDF+fn6kpKSwefNmKisrOXv2rMCUy8vLeffd\nd/nss88EO27o0KFC+rOsrIzW1laee+450tPTBctNT09PYKNyuZzdu3fr+HBptVomTJjApk2bhNPF\nk08+KWQQfy00Gg0bNmwgIiICrVZLdHS0+ByluL3SvR3PhY6kum/fPlavXo1cLufq1atdKt3U1FTe\ne+899PT0SElJoWfPniQkJFBVVSWsfX788UchmylZoW/btg19fX0qKyuZPXu2IHhI16O9vZ3W1lYd\n2rPEtrvb+/B2eOG/le6/IQwMDNDT0/uPiM0sXbpU/HzhwgUsLS3RaDSsXr0aHx8f3n//fTEf+mvR\nmdhgZGQkBNjvtbHy/fffExAQQEZGhs453MmWJzAwELVaTUJCAmVlZWL8Jzo6uothZVFRkajYvby8\nqKio4PHHHxfnPm7cOI4dO0Z8fDzGxsY4OjoKd9/w8HCMjY2prKzExMSElJQU9PT0sLKyorKykvz8\nfCZOnMiaNWsoLy+nvLyckpISMUo2aNAg4uPjaW5uRiaTdancJJpwaWkp48aNE9csJiYGc3NztFot\nixcvFjoQUgQEBNCtWzfOnDlDU1MTX331Ffv27ePSpUt89NFHLF26lMbGRmQyGba2tsJcc/r06UKg\nR6vVUlhYiLOzM5aWlkRERKBQKNi8eTOtra2CRt7c3Ex9fT3e3t6o1WoqKirENEHn74B07NHR0YSF\nhfH666/rWMdIodV2mFwOGzaMzZs3s2vXLpYtW0ZBQYHOOdbV1VFfX4+bm5t47dq1azrXr76+nhs3\nbvD666+La3m7Rc/Zs2eRy+VMmjSJ2tpabt26hbe3N8uXLycwMFDIOX777bf06tULrVbLyZMnGTdu\nHNu2bcPJyQmtVis+H4lpaGBg0IX2rFAoRHO6M8FDqorvRHvunHT/W+n+m+M/kXRHjx6tMyM4dOhQ\nVCoVJ06coE+fPjg4OAim2u2hVqtpaGigoaFBEBskCvK9nkdTUxNffvklS5cuJScnRyx7ocOCpLi4\nWAerlclkREVF8e233xIVFSXgGKnplZCQILaVWGjQMeer0Wh45JFH2LBhA2ZmZvTu3Rt/f3+OHTtG\nc3OzuKljY2OFW8T58+eRy+UcP35cGEsOGjSIo0eP8uSTT3Lo0CEGDBiAhYUF9fX12Nra0traSq9e\nvbh69aqgWJ85c0YnaUiwi0wm4/jx4/Tr14/Zs2fT2tpKfHw8crkcCwsLcnJydHQc/P39KSsrQ09P\nD1NTUywsLHByciI9PR2tVsvgwYPx9vamR48eZGdnY2trS//+/YXwenh4OFqtFgMDA6ytrWlpaeHq\n1atAxxzrjRs3sLS0xM7ODrlcjp2dndADeP7559m/f784FkNDQzH1AB205by8PObOncvf/vY3oCOx\npKam8uGHH9KzZ08mTpzItGnTOHr0KD179kSj0XR5IElkks6rPwkPl+Ljjz/GyMhIQGUtLS0UFxeL\na9XS0kJJSQmLFi0SDhghISGkp6cLlw4J2y8oKGDChAnExcWhVCoJCAigtbWVyspKAgICcHBw+NXv\nrwT7SGy7zrTnzlXx7WJAkhNI557Of5PuvyE6U4H/TCHzXwupSoSOpZrEvNm9ezdarZbvv/9ex769\ns2ykVPV1dp+4n1izZg0DBgzgkUceITIyktjYWPE7PT09hgwZckeIIS4uTvhdQcf5Dx8+XIeFJmnW\nQseNbGlpyZo1a4SbRmhoKCNGjKCoqIjq6mqhpB8XF4efnx/29vZcvHhRzDAbGhqSlZVFRUUFK1as\n4OrVq2i1WgwNDXFxcSEwMBATExMUCoVwVFYoFEyfPp0zZ87g6ekpxF7y8/OxsrJiz549XL9+nWnT\npvE///M/yGQy3nnnHeRyOZWVlWRnZ3epdDMzM0Uzq3///uTl5WFhYYGBgQHLly+nqKiImTNnolAo\nqKur49KlS1RXV3Px4kWam5sxMDAQ7DN/f3/hAL1v3z6ampqYPn06o0aNQibrcDn29fVFLpejUql0\n9CXa2tooKyvj+vXrFBYWsnDhQl555RV69OhBYmIizz33HOHh4cyYMQOtVsu3336LWq3mueeeE98Z\nyWqo89L69iR8+2vnz59n8+bNREVFiYfXtWvX8Pb2FgSDlStXYmhoKBxvU1JS6NGjB1999RUvv/wy\nV69epVevXqxduxZzc3P69evH2rVrkcvlnDlzRmC2EgRxL3E3VbF0v1dWVhIaGkpsbCxbt25l586d\nYhLlbuPw4cMEBATg5+f3q+4eCxYswNfXl169enHlypV7Pqfb46FNulI8CH+y+0m6a9asET9Loid1\ndXWoVCry8/OZNWsWf/vb3+6K2HA/x1BfX89XX33F//zP/wB3xnCjoqK6jI7169ePmzdvdtFbGD58\nODExMUAHblhZWSkmQm7evImbmxvffvsttbW1tLS04OzsLCq6uro6RowYIcgi0syuu7s7AwYMID8/\nHzs7O2xsbHj11Vepqalh1apVNDc3s3PnTsrLy/Hz8xNVj0KhoLa2FktLS2bPns2NGzfo1q2bEHvJ\nzMzE3d2dFStWYGVlJSYMFi5cyNGjR3F0dCQtLY3CwkK8vLzEOXp5eVFSUoKFhQU1NTWUlpby9ttv\nc/78eZqbm9m1axc2NjZER0fT0NDA0KFD2bBhA5GRkWi1WqEsVlpaKpw3FAoFzc3NqNVqFixYwKJF\ni7C3t0etVmNiYsKPP/6o0wNwcHDQUcMaM2YMYWFhNDU1sW3bNjZu3IiLiwt79+5lzZo1pKamsnjx\nYkxNTfHw8NDB+2+HDaTXbp9ckDDdyspKnnnmGSIjI3XsZjoL3zQ1NfHPf/6TAQMGiO9oamoqLi4u\nnDx5ksjISBwdHZHJZPz88880NTXh5OREbGwsERERwgaqtbW1i5TmH4nOVbGkOWFra8uePXtwcXHB\n2NiYzZs367ga/15oNBrmzZvHkSNHuHr1Ktu2bePatWs628TExHD9+nVycnJYu3YtL7/88h8+l4c2\n6T4oecf73UdgYKCoFLRarWAdSTdUW1sbSUlJIpH9FrGhc9ztcaxYsYJhw4aJGywqKqqLJcyIESM4\nefKkzmsSBi1ZqksREhJCc3MzOTk57NmzR7w+ZMgQ5HI5FRUV1NTUUFVVhZ6eHt7e3nz33XcYGBgI\n6CE+Pp7+/fuzfv16kpKShA6Al5eXYIVJS2Q7OzueeOIJFAoFbW1tnDx5UiiEnTp1CgsLC9FYVCgU\nnDt3TlQ9+fn5uLi4UFhYSG1tLd26daOpqYmXX34ZhUIh5nGdnZ11EpykU1BTUyMq3ZKSEpycnHBz\nc+Pdd9/Fy8uLnJwczM3NCQgIoLa2lj59+ghtiejoaIqLi7G0tOT555/vklABQXc+d+4cMpmM1tZW\nsY27u7tQPYOOUT4TExOuXr3KqVOn2LVrF88//7wYv5K2u33OFu7cILs96UrVsLm5OS+88AKTJ0+m\nqalJB7/tPLmwcuVKbG1tGTZsmPh9SkoKKSkpPPPMM2RnZxMWFsaWLVsIDQ2lR48e7Ny5k27dumFt\nbY2DgwMtLS2Ympr+IR+x3wrpHlEoFPj4+KBUKvnwww/Zu3cvV65cuevVY1JSEr6+vri7u6Ovr8+0\nadOEXKsUv/zyC7NmzQLg0Ucfpa6urguj8l7joU26UjxIHdt73U9nqbri4mL09PRQqVT4+fnxww8/\n8Prrr/Phhx8K6OG34l5ghoyMDJYtW6ZjnOnl5YWlpSUpKSnite7du2NnZycUuqDDxmfYsGEcOHBA\n52/LZDJGjRrFoUOHWLZsmTje6OhosTR+6aWXCAgIIDo6mri4OCoqKlAqlaI6jYuLo76+nitXrvDq\nq6+yd+9eSktLkcvlmJmZCZ+tSZMmERcXx5QpUwSmPXHiREaOHMn27dvZvn07vr6+1NbWcunSJfz9\n/fn666+FYE9OTg7l5eViZtfOzg5TU1OsrKzw9fXl1q1bXL16FS8vL5qamgQOGBcXR0NDA1qtVtj2\n7Ny5E5VKRe/evTEwMKCmpoZz584RHBzM5s2bMTAwIDc3VyTXcePGodFouHXrFjk5Oejp6QkY5rPP\nPiM2NpacnBzMzMyIiYnh0KFD3Lp1S1zrvLw8jI2NcXZ2BqC8vJx58+ZhbW0ttpFw2Y8//lgk7F+r\nan8LSoCOeVw/Pz+WLVtGc3Mz77//fpemmTS5UFpaypo1a7CyshJEiJaWFvLy8jh16hRz5swRGrrr\n1q3Dx8eHPn368OOPP9Le3k5eXh6tra3o6+szdOjQX/3+PojofL80Njbe1/RCaWmpzkSJm5sbpaWl\nv7mNq6trl23uNf6bdLl/daLOEENDQwNtbW14eXmJ7nNsbCxmZmb8+OOPd30cv3cuKpWK5557jmef\nfVYYN0oxbNiwLnBC5ymGhoYGjh8/zuuvv87Bgwe7/C0pGaenp4uku3btWnFs7e3tODg44Ovri6Wl\nJa2trVhbW9Pe3k5zczO7d+8W/Pzg4GBMTU3RarWUl5cjk8kEQUEa7fLy8kJPT4+bN2/y+uuvs2/f\nPj777DP27t2LQqHAzc2No0ePMmDAAKytrTl06JCAbwoKCmhqahIEDGn5CR2QU319PV5eXhgZGaFQ\nKCgpKRG2OQqFgjlz5pCcnIy7uztHjx7Fz88Pa2trysvL2bdvHxqNRiSQ7Oxs8YCTuvZJSUnExcVR\nVFQkjsHd3Z3nnnuOq1ev4u7uTmJiohDmkQR5pMq3s8jQc889p/M5ZGZmkpGRQUhIiCCR3KnSvb2q\nbWxs5ObNm+I6S+8zMzPju+++Y/369VRXVyOTyXQaXFISXrx4Mc8++yw5OTki6V69ehVLS0smTpyI\no6MjycnJqNVqzMzMuHHjhliJlJeXU1xcTHl5OW1tbbz++uv8WXE7fVqtVt/1TPv/hnhok+6DhBfu\ndz/W1tZiPlaj0WBiYkJbWxv19fU4OzuTkJCAi4sLH330kaDf/tFjWLJkCY6Ojnz00UekpaUJAoNM\nJmPo0KFdKL2dk+6BAwdE483U1JTk5GS0Wq2opiIjI7l06RJtbW2CEVdWVoZCoaBPnz4cOXIEfX19\nvL29hcWPNLYXGBhIa2srR44coaysjO7du5OcnIyNjQ0qlYqqqiqRDI4ePUq3bt04efIk1dXVqNVq\nmpubCQ0NZd68eUJmsVevXpw7d47Q0FAWLlzIypUrBfPsmWeewc7OTqdClJpsEyZMQKPRoNFohJzi\nxIkTaW9vFwLrXl5e9O/fHz8/PzZv3oy7uztlZWW89957QmRHX18fd3d3rKysxGSInp4ezs7O7N+/\nn8TERNzd3XnppZcwMjIiNTWVF154gcrKSsLCwkhPT8fJyYmxY8diZmYmDDmlY5PidqnHK1eu4OXl\nxbvvvsvXX3+NVqvtkmAlam/nqjYnJwcfHx8dklBycjLx8fGiCZqenq6jy3Dr1i2USiU3btwgLi6O\nyZMnY25uLjRzL1y4QF1dHfPnz0elUpGRkcGpU6d48cUXhbKYJHspieGYm5sLEf0/Izon3T9y77u6\nuuqM55WUlODq6tplm85swTttc6/x0CZdeLA2OfezH5VKxZNPPin+39jYSFFREV5eXqhUKjw8PDh+\n/DiOjo68+OKLf/gYL168yHfffcc333yDiYkJkZGROs2zfv36kZqaqmOaGRkZSUpKCvX19ezYsUOo\nPY0dO5Z9+/ZRV1cnRnAcHR3FzQYduLSkLvb0009TVFREXV0dvr6+xMbGMmDAAMrLy7G2tqampgZ/\nf3/MzMwoLi7G3d2d48ePi9EmSbkLYP/+/YwbN459+/ZhYmKCu7s769evBzowtI8++giNRsO1a9fI\ny8sjNDSUxx57jIqKCrZv305rayvPPPMMhoaGOsmrqKgIa2tr3njjDaDD6UCr1TJlyhRKSkr45ptv\nsLa2Fuc0ceJEbt26RWJioo7bsKQ1ITlqqNVqUeFptVpCQ0OJiYlBq9VSVFTElClTCA0NRaPRkJCQ\ngLW1NefPnxfjTn/5y18wNjamqamJvn37Ah24tdQUW7VqlTiHmpoaamtreeeddxg6dKho4BUUFOjQ\nqm/cuIGhoaHO53U73NDa2srRo0cZM2aMwGjvZEQZFBTEe++9x/vvv8/169d1PNF27dqFn58fPj4+\nZGRk4OzsTGpqqrg2p0+fFjh5ZWUl+vr6XeaR/x1xP6vVRx55hNzcXAoLC1EqlWzfvr0LZXn8+PFs\n3LgR6MDoraysuhiZ3ms81EkX/jOVbnt7O/X19TQ1NenM40qTFP369aO8vJz6+nq6detGfn4+Bw4c\n0NFUvddjKC4uZuLEibzxxhsCD+wsNi418fr37y/0FaCDvvvoo49y4MABTp8+zbhx42hvb2fYsGEc\nPHgQExMTzM3NxbF3dlaYOXMmlZWVPProo9jY2AAdmKSPjw9xcXHY2dmh1Wpxd3dHLpdTUFBAZmYm\nhoaGmJubc/z4cczNzQkODiYrKwt3d3caGhpITExkwYIFpKSkYGxszMiRI9m0aRO5ubmkpqYSHByM\ni4sLpqamNDY24u/vj0KhYO7cufzwww8EBARgZ2dHc3OzDlVZGhGTqL/5+fnMmTOH+Ph4Fi9ejFKp\nxNHREaVSSWtrK2PGjOH8+fMMGTKEM2fOoNFo+PTTT+nXrx8vvPACWVlZNDU1CXJAZmam6NbX1tai\nUCgYP3485ubm9OrVC4VCwcWLF/H39xcYf0lJiUieVVVVzJkzRzQPpSTaWa9j48aNwodNJpPxyiuv\nsHz5ctzc3AStGH4dz5WqYZVKxaxZs2hpaeGDDz4Q29wJzzUyMqK5uVmH7gsdeG5ycrIoGJKTk9HT\n02PWrFmkpqZia2vLqFGjOHXqFLa2tmRnZ6NUKv9UaAG6Vrr3Cw8qFArBJA0ODmbatGkEBgaydu1a\n1q1bB3RMl3h6euLj48NLL73E119//YeP/6FOup1Vxh7Evn4v6d6J2GBmZiY47ZKVdllZGXK5nOLi\nYgoLC1m3bp2YsbyfYygqKiI6OpqgoCCdJCOZS3Y+/zspjI0YMYL169czfPhwZDIZDQ0NDBgwgLKy\nMioqKnS+wFLnHTrGyLRaLePHj+fy5cu4ubkJxlZ5eTkHDhxAq9Wyfv161Go1kZGRfPLJJ7i7u1NT\nU0NGRgbl5eVMnjyZhoYGXFxcOHbsGI8++iguLi54eXnR3NzM8OHDCQ8PZ/HixUyYMIHy8nK8vLx4\n//33AYS27hNPPEFVVRWDBg1CqVRSXV2toxssJd28vDzxmWzdupUhQ4ZQUVFBWloaBgYG2NnZkZ2d\njaWlJf3798fDw0MIoevr6/PUU0/x2muvERQURF5eHvr6+kyZMkXo9sbExIhqeObMmTQ3N9O9e3ds\nbW2Fo4K3tzft7e2iEpXGCSUdYEBAI9K5QIdaXFhYmPhMpk6dSmpqapcl7W+Ni6nVal566SWamppw\ncXHRgWBur3TT0tK4cuUKS5YsQaFQkJaWJpKu9L2dPHky0AGDFBUV8dxzz5GUlMSNGzd48sknyc3N\npW/fvrS3t2NhYUHPnj27fIcfZHROtI2NjcJO6H5i1KhRZGVlkZOTw1//+lcAXnrpJZ2V6erVq8nN\nzSUlJUVg+n8kHuqkCzwQbEfaz6/t4/eIDW+++abYVi6Xc/r0aUG59fb2pqGhAQ8PD5KSkpg7d+49\nPSQKCgqIjo5m7ty5vP/++2IEDcDT0xMbGxuSk5PF8UtJt/O5DB8+nKSkJMaOHSsMI01NTRk5ciSH\nDh0S26WlpYljk2xzDA0NCQsLIyEhgUcffRQjIyPi4+MJDw/n6NGjGBoaUlxcjJ2dHRYWFqLqiYuL\n49FHHyUtLY3IyEj09fU5f/48+/btE0s4FxcXGhoaCAsL48UXX+Tw4cP85S9/oaCgAA8PD8rLy3Fy\ncuKDDz6guLiYXbt2YWRkRGFhIXl5eXTv3p3KykrxIJKYV+np6fTq1QulUikq7djYWNLS0oSwuuRV\nNmHCBHJycgS219LSQt++fZHJZKLaUavVTJ06lZ9++olFixYRGRkJdFRK/fv3x9TUlICAAMzMzIRw\nkUTDLS0tJSsrS4j1rFmzRqxUJAdjgC+++EII/HRenhsbGxMUFCSSshS/1ljz8/Pjtdde4+bNm8ye\nPVuMgkmNUMk+SIrY2FgCAgKEiFBqaiqhoaE0NTWxfPly3N3dRVI7deoUvXv3FrrDRkZGtLW1CdEb\nSVb03xkPm5Yu/D+SdP+sWd27JTa89dZbOv9XKpVERETg4ODArVu3OHLkiJhJ3bx5M+PHj+8ipXj7\nMWi1Wnbu3ElkZCQvvfQS8+bNExKAnUdWbvcz8/HxQU9PT2gxSF5fUpXV+fjHjRsnRsc6JxnowHcT\nExMB6NatG2lpaTzyyCM0NTVx4sQJCgsLkcvleHl5ERcXx9ixYzl27BiPPPIIBQUFnDx5kqCgILp1\n60Z1dbXAbU+cOCEcOCROvZ2dHba2tqjVahQKBfn5+Xh6epKamkrfvn3x9PTkqaeeYtWqVSgUCuLj\n47l48SJBQUH06NFDjMlJo1ZpaWlkZWUhk8kYMGAA27ZtIzc3lytXrnDjxg3Cw8PFEPyYMWOIi4tD\npVJhb2+vYyIpKZ6pVCpiYmJobW1l7969/OMf/wDQsUry8fERJAkfHx9SUlLQ19dHpVLx9ddfC+2G\nrVu3iu9LeXm5mCL45ZdfWLZsGa6urjqVKMDNmze5du2ajp7H7Um3tbWV0tJSNm7cSFpaGtu2bSM7\nO1sHSsjLy8PR0VHofBQUFFBWViaYWFVVVTQ0NODu7s53331H9+7d6devH9DxMCorK+O1116jubmZ\nvLw8XnjhBTZs2IC7uzuXLl0SFfafHQ+z7gI85En3zyJI3ItjA3RUtxLmKe1HghjKyso4evQoAwcO\nxMrKSjCkwsPDefXVV4VVjfRelUrFqVOniIyMZNmyZbi5uYn36OnpERUVpZNko6OjOXLkiM61kCpY\nSZd327Zt9OzZ846ww9mzZwWk0HnyobGxkcbGRoyMjMjNzSU0NJTa2lqcnJyENZG1tTU+Pj7Exsby\nxBNPEBoaKmQdjxw5gpGRERERERQUFNCnTx8OHDiAv7+/SDRFRUUYGxuTlJTETz/9xKBBg1i3bp2o\ndNPS0hgxYgQ1NTVCa9jQ0JAnnniCXbt2ERAQQFhYmJhDlirdffv2cfPmTSIiIkhISGDixImYmZkJ\nemqfPn1EpWtlZYWZmRlGRka0t7djZ2en812wtrZGLpezf/9+qqur6du3L1ZWVmi1WpqamoTco7u7\nO9XV1eK788svvzB8+HA8PDz4+eef8fLywtHRkbq6OmbMmCHwcKkyLCsr4/LlyzQ0NODj4yMeSCqV\niqKiIkJCQoTwOXSFF3JzczE3Nyc2NpZdu3Zhbm5ORkaGzqRCZzxXq9Xy/PPPY21tLeAASWOhqamJ\nlStX4uHhIUxEf/zxRwwNDYmKihIP41mzZpGYmCgMR+Vy+Z9GiOgcD7PCGDzkSVeKB5V0pdnMe3Fs\nkOLvf/878C/lqNjYWNRqNUZGRhgYGKBQKGhsbMTJyYmUlBTCwsIoKChg/vz5uLu706dPH0JDQ3Fy\ncmL69OksWLCAxMREZs6cycGDB8XfGT16tA7EMGDAAK5duyYG8FUqFQMGDODo0aPi2Lds2cJrr72m\nc9MCgjd//PhxIWMoRUtLC+bm5vj7+5OYmEhkZCQ5OTkEBARQX1/P5MmTcXV1xcXFhYzbiPCKAAAg\nAElEQVSMDAYMGMDUqVPJyMhg1KhRNDc3U1BQQEREBIWFhfj7+2Nvby+W1tCRaEJCQjh48CC7du1i\n0aJFHDt2jOzsbNzd3UlPTyc6Opq2tjaam5sFRjp37lwuX76Ml5eXSLpVVVVCuS07O5t58+YxfPhw\nHBwc8PPzE42vgIAAgoKCRKUrifFIn3vnVYxMJhPXVRr2P3PmjEjyVlZWosqWtALkcjlNTU3o6+vT\nu3dvXFxcMDIyEk0mKZFKUwz29vaiiTlixAiqqqpwcXERIi979uwRdNX169ej1WoFRVvqoqvVaj79\n9FOam5vZu3evKACk8TApOss3bty4kYqKCgErwL+EyyVDy4KCApF0161bR0REBDKZjB9++AF3d3dK\nSkpoaGgQ35s/G8uV4nYB8/9Wuv/GeFCVrlarFbYvKpXqrh0bOsezzz4rfpY66/3798fHx4e6ujqO\nHz9OUFAQSUlJTJo0Ca1WKzRlP/74Y6BDfKO0tBQzMzNCQ0ORy+WMHj2aw4cPi2pYYoNJs7WGhoYM\nGTJEVKlSYyo5OZm2tja2bdtGv379mDRpEjdu3OhiWClBDBcuXECr1aJQKERVaGZmhq+vLwkJCQwa\nNIjc3FwyMzNRKBTk5eVhZmaGUqmkd+/eGBsbM378eKqqqtDX10dfX5+EhASRdCWYQZrgaGlpoaWl\nRbjwenp6CludvLw8DAwMMDQ0xNHRER8fHxoaGpg9ezbFxcW4uLigp6dHeno6YWFhXL58maysLHx9\nfRk9ejRyuZyPPvqInj17YmFhwdatW4mIiKC+vh4/Pz8hVVlZWcmbb77JO++8I869s1KbSqUiNzcX\ne3t7tmzZwvfff09dXR2zZ88GOjDp06dPAwjxdDs7O1xdXTl69Ci+vr5CsvDSpUtUVVVha2vLpUuX\nRPKT3g8dlbqPjw9mZmaYmppiYmLC+vXrsba2ZvTo0RQXF3P58mWuXLkinDyqqqqYOnUqycnJvPDC\nCyIRNzY2UlFRoaM9IVW+xcXFfPjhhwwYMECHpJGamoqvry+rV6/mjTfeEDY9ly9fpqysjClTpqDR\naIiLi2Pq1KksW7YMe3t7IQKzaNGiu75f/mh0hhf+W+n+B+J+k650Q9TV1aFWq9HX178nx4bOITGO\n4F/c+7y8PDQaDZaWluzYsYPhw4djYWFBVFQUqampxMXFiTnOoqIiTE1N0dfXZ+zYsaK69fb2xsbG\nRtin29raEhISQnx8PNCBiw4ZMoSDBw8ik3XY1NjZ2dGnTx9iY2P55ptvxJjSY4891oVbPmbMGI4c\nOcLmzZvFNenevTsKhYKamho8PDy4fPkyffv2JSMjg8bGRpydnTl16hTt7e2UlZUxePBgoKNylslk\nnDx5kjFjxlBZWYmvr6+wcvfy8qKxsZErV65QUFAgZmirqqpE80jyUsvPzxfzopWVlXh5eQnboC++\n+AKZTMaWLVswMzOjrq6Oy5cvc+PGDSorKwkPD0cul9OrVy9KS0uprq4WDy2J7OHr68vf//53goOD\nBQFCImlIn19OTg5ubm7Y2NigVqt57LHHGDhwIDdv3hTVqZQ0v/76a+zs7MRI2rFjx/D19aWqqorm\n5mZhB29kZMS5c+dEcr906ZJQ90pPT9eBDMrLy7ly5QphYWGYmZkxc+ZMduzYQV5eHv7+/qSkpDBk\nyBA8PT3x9fWld+/eQgw8IyNDiAh1hheCgoJYsGABr7zyCqWlpWJSAToq3YyMDEaMGIFSqcTb2xtj\nY2NWrFiBkZER/fr14+TJk7S2tjJt2jQOHz6Mt7e3EG2X8N8/O26HFzpPZzwM8f9t0pUcG1paWsTN\n8EdkFgFRsba1tQlr8/z8fNra2qioqMDb2xs9PT1Onz7NBx98gJ6enugCSzKMWq2WsWPH6kwVdE7C\n0AExHDp0SODOklyjRqPREcXesGEDarVaDMZPmDChS9Lt1q0b3bp1E/vXaDTo6emJf4uLiwkICKCk\npIS2tjaMjY0ZNmwYLS0tYgxryJAhQEen3tzcXCxbtVotFRUVFBQUkJKSwvjx45k5cyYbN27k7Nmz\nQpNCYo5BR+VuamrKzp07CQ0NFaw7yeL76aefZv369YSEhDB9+nSWLFlCr169+OWXXygpKcHHx0dU\nb05OTshkMiZNmsSVK1cwMzPjzJkzXLlyBTc3N37++Wc+++wzMjIy8PDwwNjYGCMjI3JyclCr1cTE\nxIj/Nzc3884772BsbExrayseHh5cvXqVuLg4bt68yfr167Gzs8Pc3Jzi4mLOnTuHs7MzhYWFKBQK\nsrOzsbGxoaamhrNnz4qRNY1GIwRwWltbRRMPOlySAwMDBUQwa9YsduzYQWZmJkqlkilTpvDBBx/w\n5ZdfkpmZKex0lEolly9fxv//sHfmUVXV6/9/nQnOAQ6gMquAoiAIAgKiiKg44DyVww1Ns8m0W1nZ\nYKONlqXXzMoyc8oGcyI1FUUlFRSQQXAAmSdlkJnDePbvD377c6Hpdstb3+66z1qs5ZLD5+yzz97P\nfj7P8x48PGhubsZoNFJTU8ONGzeIi4ujsrKSRx99lNTUVHGumpqayMnJYd++fTz99NMkJyfj7+9P\ndna2sJ/v168f69atw8rKipSUFExMTLh58ybt7e0MHz78d907/078cJD2P/TCHxi/pb3Qmdig1WqF\nlurtkIicNWuW+LednR1GoxE7OzssLS2xtbXl6NGjVFRUcOjQIRYvXoxKpRKT44kTJ4oWQVhYGBkZ\nGUJ/tXMfV5IkwsPDOXz4sJBe7N+/Pz179hS0XuhIuidOnGDJkiXiPI0cOZKsrCyKioq6HPfYsWNp\nbGwUfUl5m6vVaklISGDEiBGsXr2a7t27YzQahbZtQUEBbW1tDBgwQDgqWFtbo1QqSUpKwtfXlw0b\nNtDa2kp0dLRIunv27OHs2bM4Ojry5ZdfEhwcLLSAc3Nz8fLyIjY2Fh8fH9avX8/DDz9MTU0N165d\nIzg4GE9PTxoaGnjqqac4ePAg9vb2XLhwgZ49e1JdXS0qZIVCga+vL66urtTX19PU1ISPjw933303\nV65cwdfXl969ewumldw7fuihh3B3d+eDDz7g1q1b9O3bF4VCgYmJCXl5eRiNRlGpt7W1ERoaSmho\nqGiZyAyuxMRE9Ho9Wq0WLy8vNm7cSF1dHRcuXBCYWqVSiZmZmfgu5O/GaDSyY8cOunXrJlAKrq6u\neHp6smfPHk6fPs3BgweZPXs25eXlNDc306dPHyEGnpWVxaBBg0Slm56ejrOzM6tWrWLt2rUUFBQI\nRpskSVy+fBm9Xs+UKVOEKaefnx8bNmxg1KhRBAQEkJ+fT1JSEuHh4WzZsgWDwSD6uW+88cbvLlp+\nTfzwHv1fT/dPil9DkPg5x4bbiYBQKpUCnynztUtKSgRj6ciRI3h7e1NWVkZhYSEjR47k/Pnz1NbW\nMmHCBGJiYmhpacHU1JTRo0cLlIIMFcvNzRU0XNmNWK4QIyIifmQu2dTU1AXMbWJiwsSJE7s4GMA/\nQfoy4N/Z2ZmwsDBqa2sFrvPgwYP06tWLsLAwEhMTcXFxEWgK+Rzm5+fT1NQkXIcXL17Mzp07sbW1\nFUO5Xr16ERgYSHx8PP3792fbtm08/vjjpKSkUFVVRV5eHsOGDaOmpobKykqio6O59957hS5vnz59\n0Ol05OXlUVZWxtNPP83hw4eRJIkNGzZQXl7eBYfq5+dHYmIi5ubmWFlZCYGa0tJSIft45coVYTUv\nI1EuXLiAj48PdXV1fPrppygUCmxsbIQrw8aNG7GyskKpVFJWVsapU6eEU8ewYcOws7Pj2LFjWFpa\nYjAY0Ol0DBkyBK1WS0NDA7du3erS0pDPoazDcPr0aaFl7OHhQU1NDa+//joXLlygpqaGqKgoUQHL\nA7DOSS8jIwMfHx9Ba75+/Tq1tbUsWbIEPz8/0tPT8fHxEV5lsbGx1NTU8Nhjjwnas4uLC3v37sXB\nwYGgoCA2b96Mi4sLnp6eJCcnC5ifRqP5EWb4Px3/g4z9SSGf+F+qUn+tY8PtQkC8+uqrAGIg19ra\nSn5+Pu3t7bS2tmJlZUWvXr04cuQIDz30EAqFgsceewxHR0fc3NyIi4sDOnqtnVsMo0eP5ttvv8XM\nzAxLS8sfoRjkpCt/hueff56wsLAuEo7QwerqrJcLiNfI21xzc3Pc3d1xcnLCaDSyf/9+vLy8aGtr\nY9iwYcTGxjJ9+nRaW1u7APmvXbtGVVUVDz74IIWFhUydOlVUmVOnThWvW7hwoSA+tLa2MmbMGIYP\nHy40BmQhobVr1zJ//nysrKzw8/NDoVCg1+tJS0vjgQce4OWXX8bS0lLgV0NCQkR/UQ5fX1+Sk5OF\nh5upqSlpaWk4ODiQlJREeXk5V69eJTk5mWHDhuHs7ExJSYnoo4eFheHr60uvXr2Iiori1KlTKBQK\nDAYDAQEB+Pv7d8E+l5aWkp2dLR4YdXV12NrakpyczIQJE4QAd7du3QQho7NCWn5+PgDbtm1j/vz5\nZGZmcuLECfz9/SksLBSDss6fMT09vYuTr1y5dv4/2dHkiSeeEENIPz8/4cqwe/duBg0aRO/evamu\nriYrK4uYmBhmzJhBeno6Xl5efP7559TX11NcXEy/fv2or6+npaWFcePG/S467r8TP3yf/yXdPyl+\nD7Hhl9b4LcfRWTBD7k8aDAbOnz/PgAEDSExMpKWlhe+++47AwECUSqUQX54wYYJoMUyYMIETJ05w\n69Yt6urqmDRpEidPnhQ37Q+T7tChQ8nPz+fGjRucOnWKa9eu8corr7B79+4fsdNSU1O7aLwmJCR0\n+QzZ2dlYW1tja2uLnZ0dJ06cEFqjWq0WX19fXFxcaGtr62KAeOHCBTw8PLC0tBQEDT8/P27dusWU\nKVPE60aNGkVbWxvXrl1j4cKFXT5Pbm4utbW1DB8+nJs3b4qqVX6f7OxsLCws+Pvf/87Fixd59NFH\nRSI6duwYFhYWXQTafX19hSVPVVUVZmZm9OrVi/3791NTU4Ofnx9mZmbodDpefPFFGhoaKCoq4urV\nqxgMBvEQDQwMJCUlhZ49e2JlZUViYiIDBgzg0qVLaDQaHnnkEfR6vbCU6TzYc3BwoKGhgZEjR7Js\n2TIsLCxob2+nT58+XXZaMmssJiaGI0eOcO7cOZqbm8nIyOC7777j4YcfxmAw4OzsLERYgC56CdDR\nWzc1NRVY4JSUFL7//nueeeYZIbKTlpYmIF5ZWVlcvnyZ5cuXo9Vqhb7Gl19+ydKlS8Xg09vbm9ra\nWg4fPkxVVZWgM69Zs+a2aVr/u/G/9sKfFD8kNhgMhl9NbPipNX7PcQBCyKSiooKGhgbxu2HDhqHV\nasnLyxMKWG5ublhZWfHII48QERHBsWPHMBqNWFhY4Obmxvnz57G2tmbSpEmcOXOGxsZGoCNxJScn\nCwqsRqNh5MiRHD16lOeee45XXnmFoKAgVCqVQD5AB6103LhxosWQm5srgO0KhYK+ffvSs2dPbt68\niUqlEtoB2dnZqNVqkpOThVCMubm5wP62tLSQmZlJWFgYJ06cIDQ0lHXr1onjk88DdEzsFQoFCQkJ\n3HXXXUDHQ+b48ePk5uaKai8sLIz169cLai10EDgCAwMxMTHB3Nyc5uZmlixZglarZf369bi5uXV5\niLi4uNDc3Ex5eTl+fn7k5OTg5OTErl27xMDQw8MDHx8fBg8eTHt7O21tbbz44os4ODgI8fKAgACx\nU/L39+f06dNUVVVhYWGBtbU1WVlZPPbYY/Tp04fExER69eqFvb09kiQJN+To6Ghyc3NRKpVUVVVx\n8uRJTE1Nsba2Fhhe6JgNWFpaotPpCA0NZfv27Xh4eLB792769evHmDFj2LVrl1CHk0kNcnTG55aU\nlLBw4ULUarXwPIN/JmpJknj88cdRKpVi4JqcnCyGu/JcYvfu3QwdOhQ3Nzfs7e3FYFWlUglNZXk3\nJ6ux/Sfih5WuPAj/K8VfOul2rhI6ExvkAdOvJTbIa9wuVtuqVavE/6lUKtzc3KivrxeJR6bPnjp1\nivHjx1NZWYnRaOTixYsYDAYuXrwIwNSpUzl+/LjYjvr7+wsFsZ+Sdhw7dixbt24VE3uFQsGcOXOE\nELYc06dPFyiGTz75BOjYrkqShF6vZ8SIEcTFxZGWlsbVq1fp0aMHV69eZfDgwRw+fJhJkyaRnJxM\nUFAQX3zxhUBRVFVVMWvWLKKjo1myZAnJycnExcXh4+PD5s2bBcvq8OHDqNVqNBqNSLCOjo64uLhQ\nVlbGxYsXuXDhAqtXr8bOzo6dO3eSk5ND3759OXLkCAEBAXz88ccCLpSamkpgYCDJycn4+Pj8qHJX\nqVSUlZVhNBrp1q0bp0+fJjo6mgULFmBtbS3oskqlkgkTJmBqasrZs2cZN26cWEdmXVVWVhIaGkp7\ne7vQnggJCSE+Pp6goCBmzZqFp6cnNTU1gpChVqt55JFHhMOtXBBIUoezcGeRIfmYP/30UxwdHYWX\nndFo5JtvvkGlUgmbpkOHDmEwGMjLy+vSU5XtdxobG4mMjGTGjBk4ODgI0oRcDLj+f6GfoqIi3N3d\nhcbCxYsXxUMkISFBPLjq6uowGAwEBQUJcafx48djbm4uKvX29naam5t/5N57uxLxT7Uxfu09/n8l\n/lpH+zMhP11/K7Ghc9yOC2PmzJni3zLZABDVpaWlpYAkhYWFYWVlxaRJk3jttdcIDQ0lNjYWc3Nz\npkyZ0sXh4Yd93p9qMSQlJbFq1SpxIc6ePZtvvvmmy6BxwoQJnDt3jurqaj7//HMAsRvIzc3lypUr\nHD58GIPBwNixYxkyZAitra2cOXOGtrY2PDw8yMvLY+zYsQQEBBAVFcW5c+eADhxxcXExw4YN4957\n7xWuECdPniQ/P19oNyiVShYsWMBrr70mPl9wcDA6nY6bN28SFBSEl5cXr7zyCm+++SZZWVkEBARw\n6dIlunfvzrvvvotCoWDDhg1ER0cTEREBdNBjCwsLRWKrq6sTguwFBQUCN7tz507CwsJobGzEzMyM\nU6dOUVdXx/jx40VfWGaNQUd7o7W1laKiIvz9/XF1dcXc3JyysjICAwMpKirC19cXb29vdDodgYGB\n1NbWCj2JiRMnUlZWxnvvvYckSUL8ftasWahUKqGKptPpaG9vZ9iwYcIaBzpcfPV6PTk5Ofj6+rJo\n0SK2bt3KlStX6NevXxfZR7kHu2TJEjw9PfH29u4yUJXdfevr63nuueeYOHFiF9Hx06dPC/fbxMRE\nKioqeOCBBzhz5gyFhYUUFBSINsX69etRKP7p4KvVaoWVutxqkSRJJOKGhgYMBoPAE8sP4l8bt0vA\n/M+Mv3TSlaUIW1tbxZDlt9p23A7hHPnvlUqlALm3trZy7do1unfvLrRYZfyjXLXV1dWRmppKZGQk\nxcXFArXg7e1Ne3t7F3EWWTwbOpLnsWPHxNZ73bp12NradtnKe3p60qNHjy7MJ71eT1hYGN9++60w\n2aurq0OhUFBZWUm/fv3Q6XQolUo++ugjIXM3ZcoUGhoahOjJxIkTWbJkCTt27ODLL7/E2tqa06dP\nM3LkSDGggg5HVR8fHz788EPq6uooLCxEoVDwwgsvkJOTw4kTJ2hsbBQVlSRJPPbYY0DHtj4kJIQL\nFy4wcuRIqqqqeOONN2htbeWtt94SvWKZACEPruTdwrVr19Dr9UJHwsXFBWdnZ65evSr0JMzNzfH2\n9uaOO+6gT58+tLS00NLS0mXLfv36dTQajbAZKi4upmfPnl2MJi0sLPD29iY9PV20ZRQKBfb29qKl\nEB8fLzzlXFxcKC0txdraWjAMZS0HGU8sJ8uvv/6aCRMmiJ7u1KlTSUtL49SpU12OEzoq3fj4eG7c\nuMHatWsF7VwOubXw5ptvMmrUKG7dukVAQADQwWQrKSkRMofnzp0jJyeH6dOnc+3aNaZOnUp8fDwt\nLS2YmJjg4OAA/LgClXcY8vBSTsQ6nU7sqmR3ZzkRNzc309ra+m8n4j9igHc74y+ddOVEK1eSt2O9\n25F0ocOkEBBtj0mTJqFQKMjMzKSsrIzW1lYaGxvJz88X9tZPP/00ubm5JCYmUlVVhUKhYNKkSYK4\n4O7ujpmZmaBduri4YG9vT0JCArt27SIhIYEVK1aI6lWOn2oxzJgxgw8//BCgC3ROq9UyYMAA6urq\nGDZsGI6Ojpw4cQITExMSEhL4xz/+wcmTJ0U/esyYMZSXl3PixAmcnZ3ZtGkTaWlpjB49mjNnzgjK\nsEKh4JNPPsHHx4e2tjYcHR0xNzfnmWeeYc2aNWJAKFelvr6+Ynv67LPPcuPGDVG9yw7EM2fOxGg0\n0tbWxp49e7CxsSEyMpKbN2+KXvPVq1cFYeDOO+/knXfeoaGhgaSkJG7cuIFKpaK4uJhPP/0UT09P\n5syZQ7du3QQZQI4zZ85gb2+PWq3mwIEDGI1G8vPzGThwoBim5ebm4uzsTE1NjdiR9O/fnxs3bpCU\nlERgYCD79+9Hq9Xi4+ODWq3m3LlztLa2YmZmJj4/dLjyWltb06NHD1pbW9m/fz9ubm4CxaHVahk8\neDC7du3qMkRrbm4mOzubmJgYPv/8c0xMTH6kA5uamkqPHj344osvePXVV0lKShJJd8OGDaLvX1tb\nS25uLnPnzuXKlSsolUrx/0AXRMqvvT9kzWJTU1PhhGxmZiZaL7LbRuf2ROc+cefkLtsv/dXiL510\nAYFDlL+Q3xO3M+lOnDixS89ZfoJ7e3uTkpKCq6srBoNBVIZWVlZcunRJJGsZxvVDNtpPtRg+//xz\nnnrqKbZs2cLs2bM5depUFw2BO++8k3379okqSl5XFmuxsbFBqVSi1WqZNm0aH330EZIkicl9TEwM\nvXv3pqCggBkzZmBnZ4dGo+HVV19FpVIxevRoDAYDly9fJisri5UrVwqK84ABA8jMzOSDDz7A19eX\nwYMHC4WuhQsXEh4eTkVFBTExMYJw4O3tjZmZGaampsJJWKPR8NZbb6FUKiktLcXCwoKioiKKioro\n3r07dXV19OnTh8zMTB588EG++OILKisrOX/+PDU1NZiamrJw4UJGjhxJU1MTsbGxnDt3Djs7O5yc\nnDAxMeGFF16gsrJSJPfOzspnz54VguDbtm1j2bJlNDU10bNnT1JSUhgwYADnzp1DqVRiZ2cnHmTj\nxo1Dq9Xy7rvvEhQUxNmzZ2lqasLNzY2cnBwmTJggbII6m0XGxsaK6jQmJgY3Nzdu3LghEAeSJJGR\nkUF2djb9+/cXf7d9+3aMRiNfffUVtra2tLW1kZGR0UWMJi0tjQMHDrBy5UpMTEwoLi7G09OT5uZm\nPvzwQyF2Hx8fD8DSpUvZvXs3ZmZmHD58WJwf+VqVj+e3VJydWxMmJibodDoBY5O//859Yrk/fP78\neY4cOfK7dReqqqoYP348Hh4eRERE/KyXoaurK76+vvj7+zNkyJDf9Z5/+aQLf4yQ+W/5+6CgIPH/\nR48exdnZmcuXL+Pg4EBNTQ0WFhbs2LGDkJAQ9Ho9R48eZfz48UKwBX6ZnQYdKIYdO3bwwgsv4O3t\njZWVFREREezevVu8pk+fPmJwJ4fMLoMOHVWZcbV06VJKSkpQq9UEBwcLBwhnZ2dMTEyIj48nMTGR\noKAgdu/ezSeffCJUymbOnCnoufv37ycgIABPT0+mT5/Orl27uO+++0hMTMTCwoJ3330XR0dHwsPD\nmT59Om+88Qbff/890CH5KA9qFAqF8J3LzMxEr9fTu3dv/Pz8SEpKIj09nT59+qDVamlqauLcuXM8\n8MADKBQKFi1axK5duwSU7+TJk6jVaqZMmcLFixc5e/YsDg4OgiTxySefMGfOHGG5vWPHDqCj9SK7\n6up0OnJycpg/fz5Dhw4VTLxRo0YJycPq6mrhlFFbWyvaFZcvXyY7OxvX/y/Q7u7uzuTJkzEajdTW\n1opBlkwJluFy33zzDbNnzyY1NVUkz6SkJFEdX7lyRRzvSy+9xJgxYwR64dq1azg6OgqqbF1dHQUF\nBSgUChYvXkxycjKDBg1CrVazadMmTExMuOOOO8R6jo6O9O/fn+joaLHrMhqNaLXa3+0V9kshtydM\nTEy69IllI9TMzEzWr18v7qtp06aJ8//vxOrVqxk7dizXrl0jPDycN9988ydfp1QqOXXqFMnJyT8y\nEv134y+fdH8NQeLfWet2Jt3169cDHS2G6upqFi5cSH19vSBFGAwGUamUl5cLZtWUKVMoKCjg9OnT\naLXaLi6/oaGhXL9+ndLSUqqrq3nttddQKpUMHz5cvP/8+fOFgI0csvOBHNnZ2eJYm5qaxHTe29ub\nlpYWLC0tqampIT4+HlNTU7Kysli8eDHPPPMM/fr1w93dnVdffZUVK1ZQV1eHjY0Nly9fFoOonTt3\n0q9fP5ydnXnsscf45JNPhFWNWq3G19eXdevWsX79enbs2EF+fr4QIW9ra+PixYvi+HJycqipqUGl\nUtHQ0EBpaSkDBgzg8uXLFBYW0rt3b+rr6ykqKhKUWAsLC+Li4mhtbcXT05MxY8YQExMDwD333ENz\nczMJCQmi31tXV8fHH3/M8uXL6dmzJ0qlkq+++oqdO3cSHx+Pv7+/QCSo1WpaWloYNmwYN27coKGh\ngdmzZ3Pu3DnS0tJoaGjA2tqaoUOHkpCQgJeXF01NTSQmJtLa2sqiRYu4evUqgYGB5OXlie223F+X\nIVCNjY00NjZy5MgRZs6c2SXp7t+/n9GjR2Ntbc22bdt49dVXWbNmDSNHjuyCiU5JSemiJBYXF4ck\nSaxdu1bACQMCAqisrGTt2rU0NzczdOhQjEYjJ06cYM6cOcLWKTg4GIPBANDlPeD3eZX92pDXV6vV\nLFiwgDVr1nDfffcRExPD3Xff3WWn8GvjwIEDLFy4EOgg7fxQ/lQOSZJuiy0Y/BckXTluJ+TrdoQk\nSQKGJEdNTY3YtjU1NWEwGNBoNHzwwQeoVCoqKirIz89n8uTJmJmZsXDhQurq6m3nKZoAACAASURB\nVITADSDcVnfv3k1ERAQBAQEsW7aMbdu2ifcJDw+nuLhYDLKgY0p+8OBBMbCRK2no6JGWlJQQEhLC\nzp07RXJTKBQcOXJETPNffPFFMjMzRYW1YsUKJk+eLLR3MzMzGT9+PPHx8ahUKtra2nB1dcXd3Z2Q\nkBCef/55NBoN1dXV9O7dW5Ai4uLiBGZ20qRJzJs3j2+++UYMW/bu3SsMM8eNG4dOp6OtrY2UlBSu\nX7+O0Whk2LBhLFq0iG7dunH27FlaWloEvblv374MGTKEU6dO0dDQIEwkLS0tuXnzJjdv3mTTpk2M\nHDkSZ2dnvv/+ezw9PYVx4cqVKwkICBBU2qCgIGFfdPHiRRQKBT179qS+vp5//OMfKJVKmpqaiIiI\noLi4WIjByD3Qbt26UV1dzcCBAzl37pwQPndzc8PCwkIMQqOjowWJRqPRcOvWLaHqtX//fvr27Yuf\nnx9VVVXs3buX48ePc+XKlS7bX1mlTL4mX3nlFdzc3ARaQU66b731FuHh4VhZWeHk5MSePXswGAw8\n9NBDvPrqq/To0aPLg/yNN9740fX+R8RPKYz169ePO++8E3d39397vbKyMlGxOzg4UFZW9pOvk1tF\nQUFBAmb5W+Mvn3Rvp3bC7ap0W1tbBaZRVuCCDtUoFxcXYc7Yu3dv7O3t2b17N15eXnh6enL8+HGx\nbe3RowcrVqxg4sSJnDhxQoDhvb29eeWVV5g0aRLvvPMOixYt4osvvhAAe7Vazbx587oM1JycnBg0\naJBwj+hMBba3t6esrIwpU6bw3nvvYTQaGTx4MN988w1RUVE0NjZy9913U1hYiEql4sqVK3zzzTds\n3rwZW1tbBg0aRFFREe3t7eTk5LBjxw4iIyPJy8sTtuvLly8nLi5OWPV07p2p1WrxMNi7dy9z585l\n3759aDQabt68KdoiI0eOZOPGjWi1Wo4fP05qairXr1+nvLycUaNGce+993Lz5k1WrVolLNVNTEyI\njY3lypUrODk5kZaWBiAoyHl5ebi5ubFhwwaWL1/OuXPn8PDwYO7cubS0tHDo0CGqq6vZuXMn7e3t\nmJubExAQQGxsLH5+fhQVFdGtWzeuXr1KcHCw0ERoaGhg8ODBBAYGYm1tjVqtJiYmBgsLC15++WX6\n9++PpaUl8fHxQlCnoqJC+LNBhybE119/zezZs4V3mSwmZGJiwo0bN8jMzKR37944OjqiVCqprKzs\ngtntnHQ3btxIQUEBf//738Xvk5KSsLGx4euvvyYwMJDg4GDa29t55ZVXcHJywsLCgr179xIeHi7w\nz2ZmZgK18MPr/z8dne/PX6ulO27cOAYNGiR+fHx8GDRoEFFRUT967c99hrNnz3Lx4kUOHz7Mxo0b\nf1MrQ46/fNKV43ZVqb9nDXn7UV9fj6mpKZaWlmzatEn8vrKykhkzZiBJEnZ2dqjVau68804UCoXA\nQB49epSePXui1+tpaGjg1KlTJCQk4O7uTlRUFA8++CDvv/8+7e3tok3h5uaGt7d3lwFbZGQku3bt\nEnAy+CeK4dKlS4JNJB+XJEmYm5vT1NSEQqHggQce4IMPPhDwnnnz5vHdd98xYsQIamtreeqppxg8\neDB79uwRSmIjRoxg5cqV7Nu3j7vuuov8/HxcXV2BDk0BeYtmZ2fXxY9tzZo1DBw4EFNTU4qLi3n9\n9dfp27cvR48eZfHixZiZmWEwGHj77bextbVlz5495Obmil5rVlYWISEhaDQaTExMqK6uFkMZhULB\n119/zfLly3FwcODUqVMolUrMzc2prq7Gw8MDKysr4TBx+PBhRo0axZw5c4AOqFZtbS3+/v40NDRg\nNBpJSUkRFkmWlpbY29sLK3PokGCUCTshISFUVlZiMBi4fv06U6dORa1WC0EfhUJB7969kaQOi6dF\nixYJ2KEkSZw+fZopU6YIbC10PDC9vb35+OOP6d+/P8eOHSMrK4uvv/5aaAlDB0vw2rVrQn953bp1\nqNVqQbgoKSmhtbWVTZs28cgjj5CRkcHQoUPZs2cPkiQRERHBF198gVarFYNgQLgD34575reEnBir\nq6t/VdKNjo4mLS1N/Fy6dIm0tDSmTZuGvb29aOvcuHHjZ1sUsuOJra0tM2fO/F193f8l3duwRmed\nB+ggP8jT6969ewsgOXR8sW1tbZw8eZLi4mIhEK5Sqbh16xZnz55lwYIFODg4UF5ezh133ME999xD\nTU0N9957Lz169CA9PZ077rhDDHoAFi1axPbt28Xxe3t7Y2try+nTp8VrZsyYwbFjx1iyZAnQIQak\n0+kEuH7nzp0Cgjd9+nQKCgrQarXY2Njg7OwsYGlqtZrY2Fi2bdvGhAkTaG5uRqlUkpGRwYABA9Dr\n9dja2lJSUkLv3r0xGo08++yzBAcH8/333+Pi4sLHH38sLH0+//xzWlpa8PDwYNu2bezbtw8HBwdW\nrlxJamoq1dXV9OnTB09PTxQKBQMHDiQoKIimpiZKS0tpaWmhX79+REZGMnr0aIxGI4WFhdTV1TFw\n4ED8/PzYu3cvqampfPHFF0Khrb29HVtbW65evYqdnR0mJiYcP34cU1NT4fC8cuVKAOLj4zExMcHO\nzo6zZ89SUlKCk5MTt27doqKigoMHD5KYmCjYkDY2NmRkZAiMsaOjI0ajkSVLlghIVFRUFBYWFpSX\nl6PX69HpdAIxICc4a2trLC0tSUlJwdfXl4yMDD7++GNSUlJQKpVs2rQJU1NTmpqa2LhxYxePMnkA\nKl87r776KiYmJjg7OwMdVW7fvn1JS0tj6dKlglW3evVq7O3tCQkJYf369TQ1NXXx1+uMWvjh/fOf\njs7thbq6ut8tYD5t2jS2bt0KdIgMTZ8+/UevaWxsFKJKDQ0NHDt27EfY6H8n/vJJ989sL/yUzsNP\nURJlzrtSqWTv3r3C/LBv375cuHCBhQsX0r17d5qbm4XNuSyKsm/fPuEAYGZmxosvvoi1tTX33Xcf\nW7ZsETfnjBkzSE1NFcpV0FHtdm4x2NjY4OPjIzy+oEN3t7W1ldbWVlJSUsjPz8fd3Z2mpiY0Gg2F\nhYX4+/tz6dIlsrOzmT9/PpaWlmRkZLBhwwaWLl3Kl19+iY2NDQsWLCAjIwODwSA+p6mpKfv376ey\nspLXX3+dCRMmCIWrHTt2sGrVKhYvXsylS5cICwtjxowZPPTQQ0RFRZGTkyOm1QsWLOhyTh944AEs\nLCyQJImhQ4cydepUhgwZwrZt21AqlcI8MjQ0FDMzMwYOHMi3335Lfn4+S5cuxcHBAVNTUzIzM4Xz\n8JQpU8jJyeH8+fOMHj0aLy8vlEol8+fPp6mpiRdeeIE9e/ZgbW2Nh4eH2Cm0tbVx9uxZgQo4ffo0\nAwYMID09nYCAAOHMDB1Sk3q9nmXLlhEfH09tbS3Xr18nMDCQ/v37s2PHDlQqlaiaZdTKxYsXOX78\nOBMnTkSv17N582Z69+6NnZ0diYmJ2NraUlZWJphtgEAmLFq0iGXLlolzJd8ziYmJFBQUCKGfsrIy\n0tPTsbOzIzMzk6amJtRqNb179xYDNBsbmx/h4v8ohbEfvtevrXR/KZ5++mmio6Px8PDgxIkTghRS\nWloqhoU3b94kNDQUf39/ca39Hqv5v3zSleOPTLoyrVF22/2hzsMP19i4cSOAcAnw9PQU7CkTExN6\n9epFbW0tRqNRqGK9/fbbODk5oVAoBG3W0dGRvXv3Av+kzMr9Tq1Wy+zZs9m1a5d43zlz5nDw4EHx\nlG5ubhYaBHI8+eST5ObmYm1tjZOTEw4ODnh6elJcXCyIHR4eHixduhR7e3siIiJwdXVl3LhxNDU1\nERAQwOHDhwkKCiI8PJy6ujpmzpzJc889h7OzM+3t7axcuRInJyeGDh1Ke3s748eP5+bNm6xZs4bT\np0/j6OiIjY2N6IXOnTtX0EdlpmBnajV04IwbGxuRJIljx44REBDA22+/zfbt2+nTp49AZzg6Ogro\nka+vLyEhIURHRwsd2IKCArKysmhpacHMzIyZM2eybds2IiMjefzxx2ltbeXWrVuCtuzq6oqdnR0R\nERH069cPT09PqqurRbvG1NSU7du3o1arSUlJQavV4uHhQWlpKQqFQph16vV6lEqlkEcMDw+nW7du\naLVagUuFjhbBwoULycnJoVu3bsyZM4dFixYRFxcnTCWjoqJEhSZLOEJH0s3Pz6dnz548+uijnD9/\nvgu1+bvvvsPc3Jw777yT8+fPM3jwYNasWcO8efOwt7dn69at9O3bV4j0AF36wX90/PC+qqur+90K\nY927d+f48eNcu3aNY8eOifUcHR0FVr5Pnz6kpKSQnJzMpUuXRGL+rfGXT7o/FL25HWv9Usg2P83N\nzZibm6PX67uwYn5qDUtLyy5KSBkZGZSVlaFWq2lsbOTcuXNMnDgRJycnzp07x/Xr1zE3N+fmzZto\nNBrOnz/PBx98ILCJ8vvcd999fPrpp2LdhQsXdunj2tvbM3z4cPbu3Ssm67Ifl1qtFpRpWUg7Ozsb\nOzs7BgwYwMGDB9FoNDg6OrJ//37S0tL48MMPKSwsxNXVlczMTAC2bNlCTU0Nw4YN49lnn2XVqlUc\nOHAAKysr6urq2L17N/X19SxfvhyFQkF+fj4PPfQQPXr0oKKiglGjRvHVV1+J4UxTUxMPPPCAMJg0\nGAxCNKhzWFlZ0a1bNyRJws/Pj8OHDxMdHc1rr73G5s2bRbIuKSnp8nejRo0SVZv8MNq4cSPPP/88\nly5dIiIiAo1GI9hX0GEYGh4ejoWFBUqlUoDoL1++zGuvvUb//v1RKpVCQ0EWAY+JiWHVqlVoNBqB\n+Dhx4gRDhgwhOTkZV1dX0TOXYYPyQLFzgklPTycsLIx3331XwMdiY2MJCwtDkiQOHDjAwIEDBeFG\n7u0fP36c0tJSNm7ciEKhIC4uTviYVVVVce3aNd544w2USiXx8fHodDqcnZ2pr6/H3d2dsrIyUlNT\nBSMM4NFHH/3R9f1HVrrw1zalhP+CpCvHfxqnK9t7NzQ0oNPp0Ov1XXq1/2qNhx9+GED0blUqFSEh\nIeh0OqKiopg3bx46nY4rV64wbtw4vvzyS/z9/QkLC2PLli0EBQVhZmbGpUuXhLjM3/72N06cOCEG\nAT4+PkL/FjpuhjvvvJMdO3aQnZ3Ne++9J/p5ss3Ovn37aGtrY8qUKRiNRiorK3F3d2fHjh1YW1uj\n0+koLi7GysqKUaNGUVhYiJWVFbm5uTz77LO89dZbODg4kJWVhV6vZ+HChTz33HPCefi5556jtbWV\nefPmAR1SkrLrhFKp5ODBg2RnZ1NVVYWXlxdPPvkkZmZmZGRkiD5iW1vbj87pwYMHBbzns88+Y+PG\njURGRtKrVy98fX3FA0VmlUmSxLlz59ixYwfNzc2EhoaiUqlQqVQ8/fTTeHh4cOPGDQIDAwUQPz8/\nX1iqv/TSS6ICHTt2rKBCOzg40KNHD8zNzbG1tSUyMhKdTse6devQ6/W8//77wu1BqVSyatUqvvrq\nK/bu3UtlZaVIaPfddx+XL1+mpqZGHLtsdJqXl8eMGTO4ePEiGo0GDw8P4uPjCQ0NJTU1VaAWhg4d\nyksvvcSqVavYsmULRUVFfPbZZ+j1eiorKyktLWXgwIFIksTChQuxsrJi4sSJQAd+NykpiZUrV3Lm\nzBlKSkqYM2cON2/eFMfTp0+fP1XR64fJ/a9oSgn/RUn3P9VeMBqNAjyv0WiwsrISOgH/znG8/PLL\nQEdVKVdhWVlZKBQKLl++zOjRo6moqEChUHD16lU+//xzsQ09cuQIVVVVwvl1wYIFtLW1YWVlxfTp\n04WgtUKhIDIyki1bttDc3Ex1dTXjx48nPT2dBx98kGeffVYQBKCD2ii7CE+ZMkW4Gciav2+88Qa5\nubn4+fnR1NREbm4uOTk5ZGVlcf/99zNmzBhKSkqorKzkwIEDwrTwnnvuQa/Xo9fruXXrlrCcLy8v\np66ujrfffpu2tjZ27NghYG4NDQ0cOnSI77//noqKCg4fPsytW7cwMTFBkiQGDRpEeno69fX1PPzw\nwyxbtgytVou1tTVLly6lf//+aDQa2tvbmTBhgqiCk5KS2LdvH6NHj+a+++6joqKC6dOnCwsftVpN\nc3Mz99xzDzqdrguqIiEhQfRWBwwYIHY2I0aM4ObNm/Tv35/Y2FgKCwtpbW2lsrKS/fv3o9FoGDx4\nMA8++CBTp04VO6ERI0agVCp56qmnaGtro7GxERMTE4YOHYpSqaR///5YWFgImKGM121tbSUsLIz9\n+/czc+ZMYaXTo0cPDhw4wIwZM7hw4QJDhgxh1KhR5ObmsnLlSgIDA/Hy8kKhUHD+/HkCAwNRq9V8\n+umnXLlyRaAQmpubSU5Oxt3dnSFDhvD999+Tl5fHmTNnBDMM6CJZ2jn+6EpXjv9Vun9S/KcGaTJg\nXa5QrKysfpVj8M8dh1KpxMnJCeggI7S1tZGUlMTDDz9Me3s7CQkJzJo1C3t7e8rLy9FoNJibm5Oa\nmsqECRPYtWsXwcHBhISEUFZWxhNPPAHwo4Ha9OnTiYmJobCwEAsLC7p164aLiwu3bt1i6tSpoi0C\nHdjDnJwcrKys2Lp1KyqVitbWVuF+8cILL6BSqcjLy2PZsmUsWbKEzMxMUlJSWLRoEU8++SRPPPEE\nVVVVuLi4MHDgQBQKBRqNBktLSyoqKgS77Pr16xQXF2Nubs65c+f46KOPMBgMaLVa0Rt/7rnnUCgU\nfPfdd7S1tbFs2TI8PDzQarUUFBQwbtw4+vfvT319vTB8nDx5MhkZGSxcuJBHH32UZ599lrS0NMrK\nygTJ47XXXmP58uVMmTKFBQsW8P7773P16lW8vb0FoF6hUDB69Gg++eQTxo0bx9dff01cXFwX/LD8\nusbGRiF+/t133wmpQnd3d5qbmwkLC+PUqVNEREQQHR1Nnz590Ov1nD9/nurqakaNGoW7u7u4lpYu\nXYparRbypBUVFahUKpqamkQ7aNOmTezbt49p06YRGxsr3Jb379/PtGnTSEhIwN/fn3nz5gmH5c56\nC3JrISUlhTfeeAMXFxfRPrlw4QKSJPHiiy+SkZGBUqkkJCSExMREFAoFzc3NKBSKLiLof0b8MLm3\ntbX95G7z/3r85ZMu3B5ZRnmdzmLoRqPxVztPdF7j545j7dq1QEdlISde2ejwvffeY968eRgMBoYO\nHUpNTQ2pqalcuHCBu+++m88++4whQ4Zw5coVwsLC2LdvH9u2bSMwMBBLS0uio6MxGAxYWFgwZcoU\nvv32WxQKBZs3b6ayspK6ujoBg5KrZE9PT9ra2oRmwbx584TC1vLly8nPz0ej0Qj9gLa2NtLT0xk7\ndizR0dFUV1eLabgkSSxfvhyj0UhycjKXL1/GxsYGOzs7bGxsmD59OkuXLqWuro6vvvqK0tJSnnji\nCfbu3cv8+fOFzJ+npyf19fVERkZy3333UVhYKLzaZPxzVFSUEEAxGo3o9XouXrzIoUOHuPfee1Gp\nVOh0OmHxc/XqVV577TW2b9/O3LlzOX36tHA9tre3F6y/3bt3Exoaip+fH5s3b2bPnj1C/0FWZIMO\n7K6fnx9lZWXExcXh5eWFTqejoaGBXr16MWbMGBISEggICKCxsZGSkhKmTJkiMLh33HEHjY2NYqiq\nVqsxGAyijXPlyhXGjh2L0WgUOrlfffUVarWa/v37c+rUKdEXbm5uxsHBgaqqKlauXElmZiZPP/00\nVlZWHDp0SCBr4uPj8fHxYeHChbzxxhtcvnxZMOU2btyIg4MDwcHBHDlyRMCiOic1WUfkp+KPqnR/\nSkv3z6iwf3fI6lw/8/OXiObmZqmhoUG6ceOG1NTU9Jt+DAaDVF1dLZWUlEhlZWVSfX39b1qnsrJS\nqq6u/tnfKxQKCZBMTU0lpVIp2dvbSz4+PpJOp5MaGxslGxsbydfXV4qIiJC0Wq3Ut29fKSEhQXJz\nc5OOHj0qmZmZSbt27ZIGDRok2draSidPnpTeeecdadKkSVJlZaVUWloqRUdHS+7u7tIXX3whOTg4\nSPHx8dJbb70l3luj0UhjxoyRLC0tJUAKDQ2VJk+eLO3cuVPS6XSSXq+XrK2tpYiICMnU1FSKioqS\nHnjgAUmv10uA9Oabb0q2trbS2bNnpYEDB0qurq5SaWmpNGLECGn06NFSt27dJIVCIc2dO1cqLCyU\ngoODpYCAAEmpVEqmpqbS66+/LnXr1k2KiIiQevXqJSmVSkmn00l9+/YVx6TRaKRu3bpJSqVSCggI\nkIYOHSqZm5tLa9askZ566inpnnvukezs7KSZM2dKSqVScnR0lNzc3KS1a9dKmZmZkrOzs5SWlib1\n6NFDGjNmjBQUFCT17t1bsre3lxQKhaRQKCQbGxtJoVBI1tbWkoWFhTR16lQpMDBQUqvVkkqlkgDJ\n0tJSnDczMzPJ3d1d6tGjhzR//nxJq9VKgOTi4iINGTJEMjMzk2xsbKQLFy5ILi4uUlRUlGRmZib5\n+flJW7ZskaZMmSINHDhQ0uv1kkKhkCwtLaVly5ZJ8+fPl8zMzCS1Wi0BEiDt379fAsR7A9LDDz8s\n3bhxQ7KwsJByc3Olp556SlqyZIn01ltvSebm5tKCBQska2tr6eDBg5KdnZ10//33S5MmTZJKS0sl\nMzMzafLkydJ9990nRUVFSUOGDJFqa2ulixcvShqNRlqzZo1UW1srubq6SiYmJpKJiYl4X0BKTk6W\namtrf/KnoqJCKi8v/9nf366fyspK6ebNm1Jtba1UU1MjDR8+/M9OPb8UP5tX/1fpgqDtyjCd3yuG\n/kvHIVcMsn5oeXk5vXr1wmAwcPXqVebMmcPly5dZvXq1qLTPnz/PPffcw65duxgwYAA2NjZUVlby\n5JNP8re//Y3hw4dz7tw5qqqqhDRiQ0MDS5Ys4bPPPqNfv37MmTMHSZLQaDRCC1auHBMSEnjmmWf4\n4IMPaGpq4vHHH0elUnHq1CmCg4MZM2YMb775Jv369UOlUrFy5UpsbGyIi4ujvr5eVNtPPPEEZ86c\nEdja+vp6Ro8eLTQSZIlGGWscGBiIhYUFvr6+zJs3Dzc3N0pLS4mPjyc9PZ1vv/0WLy8vli9fjpWV\nFQ8//DAxMTE89thjBAYGEhISQkhICNAB9E9LS+PBBx/EysqKiooKXF1dmTRpklgrPT2dbdu24ezs\njKWlJR9//DFLly5Fr9djYmLCiBEjWL58Oc8//zwLFixApVLx0UcfMWzYMNRqNd7e3pSXl1NdXd3F\n8DM/P5+0tDRMTU3RaDSkpqZiMBj48MMPkSSJyspKfHx8iI+PZ9GiRTQ0NKDVaqmtrSUjI4OvvvoK\nLy8vIW6uVCpZunQpOp1O0LqhAzuamJiIm5sblpaW7Nmzh4KCAl544QWBcXZzcxO7oblz5xIbG8us\nWbOwtramqKiI119/nVOnTjFq1ChaW1tZvHgxSqWSyMhILl26RF5eHqNGjRIIDuig/f4QPfJnxQ8r\n3b9i/FckXfhnsvt3voz29naBSJBpu7frOH4uPvvsM+CfVGGNRkNaWhoqlYr7779fGDWWlZUxdepU\nMjMziY+PZ/78+Rw4cAB/f38uXLjAXXfdxZUrV4iIiGDatGmMHz+e7du3Y2FhwZ49e6iursbS0hJ3\nd3daWlp46KGHAITLRkVFBRqNRlBzk5KSOH/+PMHBwWzcuJGHHnpIJOfi4mLmz5+PWq0W7YLc3Fye\nfPJJSktLOX/+PI8++ijz5s3DzMwMSZKwt7dnypQpbN26ldLSUkpKSggLC2PTpk3U1taSlJTE2bNn\nCQkJoXv37gQHBwtdBB8fH3r16sX169fp168f/v7+pKen8/TTT5OVlcWZM2coKCjAycmJt956C3d3\nd86fP09dXR2NjY2kp6fTr18/gXEtKyujpaUFtVrN6tWrmTZtGn5+fkRERPDqq69SUVHBHXfcQW1t\nLTNmzGDFihWkpqYyZMgQpk+fzpw5c9Dr9bz++usUFRVRW1tLeno6vXv3xsHBQQjcyC4m999/P5WV\nlRw5coQBAwYgSRJNTU3odDp8fHwEXlsWMVepVPTq1YsePXqIB5NOpxP6x3Jf9/jx48TGxuLr68v9\n999PTk4Orq6umJmZ8dZbb3HgwAGmTp3KqVOnGDduHCdPnmTWrFmUlZVRWlrKm2++SXt7OzExMYSE\nhLB69Wra29sZOnQoBw4cICwsDBMTEwwGAwaDQSS4f4VLlf6E9oJ8Pv+K8V+VdH9tdKbtqtVqrKys\nBG33dss7/jDc3NwEZtfU1JTm5maB9b148SJ6vR5LS0s+//xzNm7cSGNjI9HR0Tg4OBAWFkZTUxNx\ncXHcc889HDhwgFmzZqFWq4mLi2Pz5s2sWLGCd955h5iYGEJDQ3n++efR6/VER0cDHQM9eXIeHh6O\nJEnMnTuXJ554QlTCM2fO5KOPPsLe3p7w8HAGDx4srMnr6upoaWnhrrvuYs+ePYKZtXfvXu68805O\nnjzJU089RXh4OJGRkfj7+4u+pAwXa29v5/7778fKyop//OMf5OXlERQURHNzs4C/QYf8pJubGy4u\nLgJBsm7dOp588kkyMzNJSkpi/vz5hISEcP36dXQ6HSqVqkvSHThwIBqNhk2bNgnrmR49euDj4yO+\nAw8PDwYOHChk/Wpra7l06RL33nsv0GG/rlQqhbwmdNgDNTY20rdvX0xMTATqY8WKFURGRgr8bWpq\nKjdu3BAoho8++kg4L7u7uzNz5kxaW1s5efIk5eXlAiGRk5NDz549AYQDdE1NDZ999hl79+6luLiY\nefPmERAQgJeXF7a2thw6dIiJEyfy/fffi75/3759yc/Px8/Pj3nz5rFy5UquXbuGJEls3rwZMzMz\nioqKePvtt2lvb2fy5MnC2umXsLl/RnROureDjfZnxX9F0v21BAnpJ2i7sihK57X+k0kXEMgD2ber\nqKiIBx98EEnq0MKVB1XW1taMHz+esrIycnNzmTNnDsnJySQlJeHs7ExIAMj5hAAAIABJREFUSAjZ\n2dnU1NQwduxYSktLOXHiBLGxsXh7e/Pee++RmprKihUraG9vF1jmzmwvKysroqOjsbW1xWg0Eh8f\nz4ULF4iMjKS8vJxbt25hYWEhoFwjR44UFGAZmvX++++Tl5fH2rVrcXJyoqSkRLQiOp97WQBnxYoV\nlJeXs2XLFiRJoqioiD59+jBo0CAuXbok/kaudBUKBT4+Ply6dInw8HCCg4M5efIkeXl5rFy5El9f\nX9LS0lAoOuxeZJEXmZTi5eXF9u3bWb16NUuXLiU1NRVPT0/hQuDv709LSwtlZWVcv35dcPFlDKvc\nruksKLR161bmzp1LWlqaMHAcOnQoLi4urFu3Dk9PT7p160Z8fDyPP/44ZmZm5Ofns3//fmExVFdX\nR3R0NG5ubtTU1Ai3YujAc8tOyZ2jurqaCxcuYDAYWLhwocB4nz9/np49e1JaWoqzszPl5eUUFhay\nYcMGTE1NiYiIYNy4cdTU1CBJEjNnzkSj0ZCYmIi9vb2wvpFbHPI17Obm9i9NJP+oSrdz/FXhYvBf\nknTl+DmChPQvaLud449Ius8++yzwTxfjpqYmKisrUavVXLp0idTUVEpLS6mtreWFF14AOvRvp02b\nRk1NDS0tLeTn57N48WK2bNmCTqdj7969jBgxguzsbOLi4oCOXtyuXbsE9tRoNHYRtI6JiaG+vl4I\n4CgUCsLDw7l69apgth05cgRXV1fWrl1LaGgoixYt6kK9zMvLw9HRkYaGBuF8XFhYKCQd5aisrESl\nUvHMM88QFRXFl19+iVarpbi4GFtbW0xNTfHx8REtBuiodGWfss6/e/nll6murmb58uWYm5szaNAg\nUlJSaGhoQK1Wk52dLeBrarWa8PBwqqurSUlJESQE2autsbERT09PkpKSmDx5Mt988w0bNmygZ8+e\n4nNqNBp8fX0pLCykpKQEg8HAV199JXRpJ0yYQHR0NEOGDCE2Npbi4mJR+Wq1Wl566SWysrI4ffq0\naGEplUqqq6u5deuWEIFvbW2luLiYvn37AgjHhM5hNBppb2+ntLQULy8vTp48yR133MGhQ4eYNWuW\ngJM98sgjqNVqFi9ezMSJEzlw4ADz58/H2toac3NzfH192bNnDwqFAg8PD1paWtDpdIIaLF/DX375\nJQqF4raZSP6e+GGl+3spwH9W/Fck3V/C6v4r2u5PrfWfvoiUSmUXrytJkti7dy9BQUFdcIeTJ0+m\ntLSUHj16sHv3brKysrj77ruxsLBg/fr1fPrpp2RkZGBmZsbDDz/Md999x913381dd90lWGt2dnai\n+lepVEJj1s3NjaamJuzs7Fi3bh319fUolUpOnjyJqakp7e3tmJmZ8e2333L06FHuvvtucnNzcXBw\noLa2lvr6eqqrq7lx4wYODg6Ym5sLHHNnHV05Ll++jNFoJC8vj8bGRpF8cnJy6NOnD4CoZuWQ2wsA\ngwYNEkn3o48+QqVSkZycTGtrKy4uLmRlZWFiYoKpqSlXrlwRVjcA/v7+SJIkkllBQQE+Pj6YmZmh\n1+sJCQkRUn+yQNCwYcOor68XGNzAwEBcXFw4evQo+/btIyAggOTkZMaPH094eDhHjx4lKCiIxMRE\n9uzZw+zZswkLCyM2NlYch6+vL3V1dQwZMgStViuE0nv16iUGWi0tLWRmZqJQKAgMDOziawcdW/61\na9cybdo0Dh48SHh4OHq9nqioKMaNG8exY8c4ffo02dnZfPbZZ5w9e5bhw4dTXV3N6NGj+e6777h1\n6xYbNmzg008/RavVsn//fmpraxkwYAD19fViJ6hSqYTWb2f/sh+aSLa3t9PS0iISsWwiebujc9L9\nX6X7fyQ6J8y2tjZqa2v/JW33l9b4vcfwS/HRRx+J45QHWj169KBXr14UFRUJGb81a9aICj0kJISP\nP/6YgoICoqKihKNEdXW1uLk/+OADJk+ezMSJEzl+/DivvPKKeE9HR0fy8/NRqVRkZmaKakseJtrY\n2PDkk09y8eJFnnjiCRYsWMDw4cOFTkNJSQkDBw4Uw7KCgoIu9uLff/8969evJz09ncOHDxMTE0NV\nVRXFxcU88MADdO/enYMHDwqKLXT0eeWk27m9cOvWLVpaWoS+qZx0s7Ky2Lp1K4MHD+bEiRMkJyfT\nvXt3XF1duXbtGvX19ZSXl+P6/3V8AXQ6HY2NjWRmZpKQkEC/fv3EcApg4MCB5OXlERwcTGFhIfb2\n9oSGhqLVakUP3MfHB4VCwaFDh/j000+ZP38+R48eJTQ0lH79+tHQ0ICTkxPp6ens3LmTyMhIRo4c\n2SXpfvvtt0CHs8eoUaNYsmQJzz33HKampmzZsgUTExMaGxvFfKEzI07ui0OHGP706dP58ssvmTNn\nDqdPn8bKyorDhw+TkZFBcHAwZmZmDBgwgKtXr1JaWsqsWbNISEjg5s2beHl5ERAQwNGjR7GwsGDC\nhAkolUquX79Onz59xPW7aNGiLtd0e3u7oGR3TsSysaRCoRA7N9nN93Ym4s5/X1NT87+k+38h5C9d\nHrqYmJj8S9ruT63xRyRdf39/cUM1NTVhbm5OXFwcBoNBIApMTU2JjIwUlutOTk7MnTuXe+65h9ra\nWu666y7CwsJYvXq1kBYE2LVrFxYWFsyaNauLtUh9fT2SJFFfXy+q39mzZzN27Fj8/PxYsWIFzz33\nHI6OjmRlZXWxP5GTkdFoxGAwYGJiQnJyMpIkMWvWLDw9PXn88ce5dOkSkiRRW1srxGDk/umgQYPQ\naDRdqtbOSXfAgAHk5uYK7zg3NzfxvXl6epKbm8vf//53QkND8fb25rHHHuPdd98Va6am/j/2vjss\nqnPrfs0wlBmGIlUUBESkOzRpggVsFBWx9xprNMXkWhKTmKhRb0yUGDVGscSCsaDGgiiiglItIEgV\nkQ7SZ4Y6sH9/cM97Ieq9KSb5Pr/fep55Hp1yzjDnPfvss/faa6UiOzsblpaW3e5mfvjhB6iqqsLT\n0xOHDx/uZlkOdLIDbGxscPnyZVZLdXd3h0AggKqqKkQiEQYPHozy8nLcunUL+fn5TCfYwsKCOSLH\nxsbC2NgY7e3tcHZ2ZpkudzzXr1/PXCZGjx6NyMhIBAQEoLS0FHw+H/v374eOjg6rr7a0tKCpqQna\n2tqMzgh0Ko/p6+sjLS0NpaWlWLJkCQoLC3Hq1CmMHTsWJiYmGD9+PM6dO4fAwECcO3cOZmZmCA4O\nho6ODt566y1UVVWhrKwMQqGQKZ21tLQwWyQA+PLLL9nwTEtLS7fAya3zruLmABjzQigUsotHVzff\nl9mq/xZw66G+vv7/lxf+TnANtPb2djQ1NTE91V8ztvuybf0VQRfoNEjkwI0ct7a2QiKR4NmzZ2hq\nasKmTZsgEokgkUhQXV2N8PBwzJgxAy0tLXj//fcBdKqLiUQijB07FlKpFDweDwsWLIC2tjbrQItE\nItTV1QEATExMsH79eowcORLjx49HTk4OlJWVmYMs8O8mFtCZYWRnZzOXXD6fj82bN+PDDz+Ejo4O\n1q1bhydPniA5ORmLFy+GjY0NvvnmG7z77rsQCoXYsGEDzMzMEB8fjwkTJsDY2Bj37t1DW1tbt/KC\nqqoq+vXrh8zMzG77517T0dFBSUkJdHV1YW9vj6VLl+LBgwdITk5mzbTMzMxudjV37txhvmGurq64\nfv06Yy50hZOTE3744QemINZ1GwCYUJBIJIKXlxdu376NUaNGsfFwrvnJ4/Fgbm4OuVwOQ0NDCAQC\nZGdn48KFC6ivr8fChQtx8+ZN+Pr6Ijo6GjY2NqipqWGOBBs2bICvry8SEhKgr6+P9vZ2dty6wsfH\nB3K5HDExMWhsbMSxY8dQVVWFHTt24OzZswgJCcHZs2fh5OSEyspK/POf/4Smpiaam5sRHByM7du3\ng8fjYefOnbh27RqTpXz+/DkAwNnZGerq6hCLxawHwt0pcoGYC56ceBHXU+GcnLlgrKysDDU1NRaI\nf2mrzgVirrH5qvOna3lBKpX+/0z374RCoWB1KK758Hu7qX9l0O0qINLc3AxtbW1W7+UI8QMGDMDO\nnTsxZswYmP3L5HHFihWwtbVFdHQ0jh49Ch6Ph6FDh6J3796YM2cOFAoFPD09mZMF0BnUeTwebG1t\nmWGlra0tPvzwQ3z55ZfIzMzsFnRzc3NhaWkJhUIBmUyG/Px89OvXDzExMXBzc0NBQQEmTpyIadOm\nwdfXl9Voc3Nz0bt3b8yaNQuLFi3C+vXr8d5774HP5+PAgQNwcnJCeHg4Ll68yOQkDQ0N2a2og4MD\nUlNTu9VzOzo6kJOTg6qqKgQHByM/Px82NjZQU1PD2rVr8dlnn0EikSA1NRWZmZmwtbUFAKYn8PHH\nH8PFxQVEBKlU+kJzCgCsrKxw//59jBo1Cnw+HyUlJS+8x9HRkVml37p1C6NGjWLNupEjR+Lu3bso\nKipCa2sr1NTUoKSkhEGDBiEqKgobN25ES0sLZs2aBVNTUxQVFcHIyAjJycnQ1dVlFxhfX1/cvHkT\nvXv3RktLC/r06YPVq1eDx+N1a/xymharVq1Cz549ERERgbfeegt1dXWoqqpC79698eTJE2ar/tVX\nX4HP58PT0xO6uroICwuDiYlJN50P7sICgDE4uDXNaRILhUKWzHBi61wJpqWlBS0tLd1U4V4ViAUC\nQbdArKSkhI6Ojv8YiLsG3f+tCmPAGxJ0BQIBmyr6o/grGmncUAZnAMlBJpOhqqoKOTk5sLKyAo/H\nQ48ePXD48GFYWFhAoVCgvLwc6urqTIBk3bp1SEtLg7u7O+tCr1mzBklJSd2aMJaWliAiaGtro6Sk\nBOnp6SgrK4ORkREkEgnEYjF0dXUBgDXKtLW1WY0xMzMTiYmJWLduHXbt2oXDhw+juLi4WwmipaUF\nR44cwY0bN2Bra4sZM2aw29Xc3FzY2dnh448/xokTJ5Cfn4+AgAA8ffoU1tbWzA3Y2toaKSkpyMrK\ngomJCeRyOerq6rBs2TIMHToUDQ0NyMrKgpWVFQBg5syZKCoqgkwmw6NHj7pluleuXEFDQwOmTp0K\nJycnVg5JSUl54Zjk5uZCKBQiPz8fdnZ2OH36NHutra0NMpkMQqEQ2traSExMRHJycjfTUV1dXejp\n6cHe3h6PHj1CW1sbVFVVMWzYMJw/fx6NjY1wc3ND79694efnh0uXLmHYsGGIiIhgNu7t7e0wNTWF\npqYmwsLC4ODggObmZsycOZNNyHXFsWPHEBERAW9vb0RGRuKdd95BREQExo8fj/379zM6Xnh4OC5f\nvgwNDQ2EhIRgypQpkMlkCAsLw5EjRyCVShm/Geh0K37ZBBo35NHY2Ag1NTWoq6tDVVUVQqEQYrGY\nWT39sqzwy9LEbwnEHPOIU1xrbGzEjh07mL/cH8Hp06dhb2/POPKvAjfk0r9/f2zduvUP7RPAm6G9\n0NHRQc3NzVRXV0fV1dW/W3/hdWyjqamJSktLqamp6YXXGhsbmT5CbW0tNTY2UkFBAZtv5/F4pKSk\nRAKBgE6cOEE6Ojqkrq5OH3zwAU2ZMoU0NDTop59+IkNDQ9LQ0CBPT0/atWsXmZub0/Hjx8nJyYlu\n3bpFYrG429y8q6sr+fj4kLKyMmlra1OPHj1ILBaTWCyme/fu0alTp2j48OEkl8tJJpNRdHQ02dvb\nU3V1NclkMrp9+zapqqrShAkTqKamhuRyOcnlcjIxMaH09HSSy+V05swZsrCwICMjI9q2bRvJ5XLa\nv38/TZgwgQoLC0lDQ4NkMhnbh5aWFm3YsIF4PB6Fh4ezbV66dIk8PT3JycmJfv75ZyorK6N//OMf\n5OXlRWfPnqUBAwaQtrY21dXVse0dPHiQ3NzcyMTEhHr16kWpqanU0NBANjY2dOrUKZLL5ZSRkUEG\nBgZkYmJCPXr0oMrKSrbPiooK0tXVJZFIRJ6enrRhwwaSSCQklUrp+fPnVFZWRnV1dWRtbU0WFhak\npaVFbm5u7PPco2fPnhQcHEzu7u70888/k1wup8ePH5OSkhI5OjrSgQMHSC6Xs9/32rVr1KdPH3Jy\nciItLS0qLi6m0tJSmjdvHtnZ2dH27dtp+vTptHXrVrpy5Qppa2tTz549ux1bsVhMLi4uNH36dFqz\nZg1paGiQjo4O8Xg8CgkJIQsLCyoqKiJNTU1SV1cnd3d3MjU1JX19fcrKyiIej0f6+vqkrq7Otrl9\n+/YX/rb6+noqLy+nyspKkkqlL7z+qodMJiOpVMrOq8rKSiorK6OysjKqrKykqqoqqqmpYY/q6upu\nj5qaGqqtraWamhoqLS2lwsJCeuutt6h///6krq5O5ubmtGjRot8VN7KysignJ4eGDRtG9+7de+l7\n2tvbycLCggoKCqi1tZUkEgllZmb+ms2/2doLHDhJuz+C11Fe+CXoXxkCVwLR1NSEiooK2tvbGWOB\ne5+2tjbjEvfq1YuNY968eRM2NjascWVtbY2kpCQEBQVh+PDh2LdvH1JTUzFixIhuDsC6urrw9PRE\nQUEBzMzMoFAoEBUVBblcDoVCgc8++wyRkZGws7NjGV1OTg769+8PNTU1REdHIyQkBD169MC6detY\nF10mk6GyshI//vgjJBIJlixZgq+++go6OjpMD8HR0REPHz5kTbmu1D4HBweoqqrCzs4OK1asYNYo\ndnZ2ePToEfLy8mBnZ4f09HQcOnQIhw4dgru7O7Kzs9GvXz82diuVShEQEACZTAY9PT1UVVXB3Nwc\n4eHh0NbWZgMOpqamkMvlsLe3Z463HMLCwjBkyBBGA5szZw4qKiqY+aOGhgbjAZeVlUFfXx96enrd\njjHnO5eVlYWhQ4cyG6XCwkLw+Xzk5uZizJgxADr1NzjTzvLycgQGBsLExAQ5OTnQ1NSEt7c3srKy\nEBAQAB8fH1y7dg0SiQQymQxr167tVmaQyWRIT0/H8+fPUVVVBT6fDxcXFyxatAi9e/fGpEmT8NNP\nP6FPnz7g8Xhwd3dHVVUVZs6ciSlTpkBZWRl9+vRhbhpKSkpYtGhRt7Xb1NTULbv9LULmXFmEq+ty\ntE2xWMwa3F1LE79s1gFgwyQAoK6ujq1bt6JPnz549uwZrly5gilTpvzq79MVVlZW7A7wVUhKSoKl\npSVMTU2hrKyMqVOn4vz5879rfxzeiKD7Z2nqvo5ttLa2sgYZpwHb9ZZKoVDg6NGj7LNcl3ry5MmY\nOHEihEIh9uzZgyVLlqCyshK3b9/Ghg0bUFlZCRUVFfj7+6O5uRlxcXFQUlKCv78/O4FEIhEaGhpw\n/fp1eHl5wd3dHRKJBAcOHGC3k0OHDsWPP/6Iffv2YcKECfjmm29w48YN9O7dGydPnsTChQuxa9cu\n1NbWorm5Gbdu3cInn3wCFxcXdHR0oLGxERs3bgQA+Pn5sdovAPTv3x/l5eVITU2FpaVlt99owIAB\niI+Ph7OzMyIiIvD222/j7NmzUFVVhVgsBo/Hg6qqKhYsWIBdu3axYQUtLS306tWLjUsLhUIoKytj\n7dq1yM/Ph4aGBurq6vDFF19g/fr1jLzP4/Ggq6sLbW1tzJ8/n9UsW1paEBoailWrVsHU1BTq6uoQ\nCoUYM2YMLl26xOqX3333HZYtWwYHBwdUV1e/UPMNCwvDokWLUFVVBVtbWxZ0Dxw4AFNTU5iZmTEd\nYyUlJfj6+rILTUdHB/z8/HDjxg3weDx2/LhJssTERKSnp0MoFOL58+cYNmwYY74AYBddIyMjODo6\nory8HBs3bmQNtd27dyMrKwsTJkzAoEGD2FRmbm4u1NXVce/ePbYeR4wYwYIqV88nIqYJ8TrwskCs\nqanZLRBzpQmurqtQKJCSkoLc3FycPn2a8dOtrKzg6+v7Wr7Xy1BSUsIayABgbGz80nr/b8EbEXSB\n16up+zq2wY15NjY2QiQSMQdVTp+VC1itra1wcnJindjW1lYMHDgQcrkcu3btQltbG3R1dXH9+nV0\ndHTg4sWLyMrKYsaQWVlZuHjxIqZMmYL+/fvjwoULADpP7ODgYBAR03jw8fGBs7MzDh8+DC8vL4hE\nIkyfPh0ikQghISGYN28e2tvbcevWLezZswfz589HVVUVFixYgI6ODixcuBALFy5EaWkp5s+fj6Cg\nIGzZsgVBQUGMV6qtrc0caZWUlODg4ID4+PhutV+gM+g+fvwYVlZWcHBwwNGjR/Hee+8hJiYGZmZm\n0NfXx4oVK+Dv74/AwED2Oe4ugfudOVrX+PHjoaqqCoVCgePHj8PW1hYDBw5EY2Mjq1FzDBc/Pz8U\nFRUhIyMDx44dg4ODAyQSCWsWqaioYNq0aczksbS0FNevX8fs2bNhZ2fHJgI5/zWpVIqzZ89i9uzZ\n8PX1RU1NDbKzs5GXl4eoqCjGQOi6rkaOHImjR4/CxcUFly5dYmwGhUKBCxcuwNTUFI8ePYKhoSFs\nbW3x7bffIjAwEFevXsX777/frX9x6dIl2NjYYOfOnUhJScHUqVOxdu1aNDU14d1338XTp0+hoqKC\nr7/+mtmrx8bGor29nV3gOOzdu/eF7Pa36En/EfwyEHMNTz6fD1VVVURERCAkJATLly+Hubk51q1b\nh9ra2v+4zREjRmDAgAHs4eDggAEDBjDO9N+BNyboAv8zMl0us5LL5cxBQUlJqdstP7egVVRUmGUM\n17hpbW3FkydPwOPxIBKJ4OnpiWfPniE+Ph5FRUV49uwZJk6cCIVCgXHjxsHBwQHq6up4/vw5cnNz\nQUQwMDCASCSCgYEBFAoF3n//fdy9exc+Pj4oKipCR0cHpk2bBqCzSSSXy1FbWws/Pz+89957ICLo\n6+tDW1sbtbW1+OabbxAcHIyUlBRMmDCBNey4hhWPx4OzszOuXbv2Qkbr6OiIjIyMF4KuRCJBSUkJ\nzM3NWZPp3LlzePfdd9Ha2oqmpiZkZ2dj06ZN3T7HBYSXHTeOXfDVV1/h888/ZxNnmpqaUFVVRXV1\nNSoqKtDa2orJkyfj+++/x/bt2/HOO++goaEBNTU1zKPMzc0Nra2tSE1Nxb59+zB58mRoa2tDIBBA\nLBYjMDAQERERADobMj4+PjAyMsLIkSMZw2Pr1q0YNGgQWltbAYCxRoDOYPDo0SOsWLEClZWV0NXV\nRVpaGrKyspCQkICQkBBmreTn54fr16/jgw8+QF5eHiwtLdkwAkfX4uRJ9fX1ER8fzy506enpCAoK\nwrhx41BRUYHU1FS0trZi+PDhUCgUKCkpYUGXs2ySSqUgol89UPS60TXoc351169fx6NHj3Dw4EGU\nlZVhw4YN6Nmz50uZKF1x7do1pKWlscejR4+QlpbGSj3/Db1792b8dwAoLi5mQkS/F29M0P27M91f\n1m05XiPXoQU6b2W5cVuObcEteC8vL0b2bm5uhrW1NUpKSjBo0CDo6+tDQ0MDPXr0gEQiQd++ffHz\nzz9DX18fBQUF+Omnn5iKGNBJp1m2bBnOnj0LdXV1Fkiqqqpw+/ZtNqigrq6OkpISGBsb48GDB9DQ\n0MCOHTtQVVWFS5cuQSQSITMzEw8fPoSlpSWam5vh5OSElJQUVvfl4OLigqSkpJcG16Kiom7BmIhg\nbm6OpqYm9OnTh/0WTk5OOHv2LB49eoTy8nIcPnz4Bfm+2tpalJWVvfQYcNY6pqamsLe3Z8/zeDxm\nqJmVlQWxWIy33noL4eHhMDAwgJOTE3g8HtLT06GpqYmMjAwoFApMmDABJ06cwMGDB5k0ZlFREerq\n6jB27Fhm4RMWFsY4135+frh16xZ8fHxw8eJFiMViTJs2DWPHju2WXVVXV7N1MHHiRPz0008YOHAg\n9u/fD19fX/j7+7Ogy90F2djYYPjw4YiMjISJiUm36TEiglAohJ+fH/bt24fKykrU1tZizZo1KCoq\ngr+/P5YvX46Ojg6cPHkSJ0+eZOuSu/v69ttv0djYCKFQ+Idol38EXUsaGhoaaGxsxJIlS3Dp0iVE\nRUVh5MiR0NXVxfDhw7F69epuk3p/BK865wcOHIi8vDw8e/YMra2tCA8PZ3ZWvxdvTNAFfp+m7qu2\n8WtBRKxu29bWBg0NDfD5fDb+SERsIXV0dEAsFr9yaINr7jQ2NqKgoACqqqr4+uuvMWzYMIwcOZJx\nWZ2dnREbGwsVFRXIZDIMHToUurq6aG1tZVk1R6UaO3YsLl++DA8PDyxZsgTvvPMOswsXCATIyMiA\no6Mja6r99NNP6Nu3L6ysrODu7o7Hjx8jLy+PBTEHBwekpKQgMzMTffr0YbxMJycn5OTkvJDpOjg4\nQCaTMQoS91tw9uetra3dfgsdHR0AnYHglxTA+vp6NDc3Iycnp9udA3ccHj58CB6Ph+Li4hdef/jw\nIVxcXNDe3o7S0lIYGhoCANzd3aGpqYmWlhbU1NTA09MTKSkpaG1tRWBgII4dOwaJRAITExM0NTUh\nPj4eZmZm0NXVRU5ODiIjI/H8+XMMHz4cQKfWhYWFBaqqqtDS0oJbt25h2rRpGDNmDCv9AMCRI0fY\nKO64ceNw7tw5RiWbNGkSXF1dUVBQgG+//RZhYWFobW3Fxx9/DCcnJ0RERODp06eQyWTw9PSEWCxm\nE4y5ublMFU0kEiEmJoaJu9+6dQtDhw6FQCBATU0NlJWVGR/c398fampqbCT9z9JPeBV+WdIQCoW4\nefMmxo4di5CQEBw6dOi183LPnTsHExMTJCQkICgoiDVdy8rKEBQUBKCzRLZr1y6MHDkSdnZ2mDp1\najddj9+DNy7ovo5t/NrF9qq6LRcsmpqamGgKF+T+00XBw8MDPXv2ZJ9VUlICj8dDXFwcnj59irCw\nMGRlZSEiIgKmpqbw9/eHsrIyVFRUIJVKoampifb2dgwfPhzLli2DkZERQkJCcP/+fdTX18PY2BjG\nxsaMlA8AGRkZsLe3h6GhIQ4fPowlS5bA1dUVQKeObHJyMh4/fowBAwYwkRaFQoH8/HxYWVkxT7l+\n/fqhvLwcxsbG3QjyXMOHU/TieL/FxcXo0aNHN4GbyspKjB07FmKO/EG8AAAgAElEQVSxGEKhEBMn\nTmT8TAAsu9bX10deXl63366wsBDNzc3w8fHpVq7h8PDhQzg7O8PR0RF3797FxYsXYWBggPv377N6\ntIuLCzw8PPDw4UOoq6vD1dWV6Q4DQGJiInr16gVPT0/ExcXB398fW7duxZw5c7pxaEeMGMG0dw0M\nDGBtbY1BgwahsLAQhYWFaG1txYkTJzBv3jxER0fD3d0d6urqUFFRQXl5Oby9vbFu3Tq0trbi5MmT\nKCgogKenJ6Kjo7F7927cvHkTRkZGMDY2xrJly9gUplwuR0pKClpaWsDn8+Hm5gahUIjg4GBYWVnB\n0NCQ6XJwx4QrfezZs4fJnHIlJ64Wzon+/FmB+JdJSWtrK1atWoVDhw7h8uXLGD9+/J+SdQcHB6Oo\nqAhNTU0oKyvDlStXAHRqlHBNTgAYPXo0srOzkZub+18F3X8N3pig+zoZDMB/tgPhRNA5fQeubsvR\nWrpa/XCNCIFAAIVCwRazXC5nY5RdaW5dmQxSqRRmZmZoa2tDWloaPD09sWbNGpSVlcHLywvBwcHQ\n0NCAtrY2VFVVoaqqChUVFcTExCAlJQUbNmyAra0tKisrkZKSgu+++w7JyclwdnZGYmIigM6gW1NT\ng8LCQowePRpPnz6Fk5MTAMDNzQ0JCQnM7Zf7fW1tbZnTMEeM79evH+tyc/oMDQ0NePz4McRiMRIS\nEtjrKioqyM7OhqmpKdNgkEqlCAkJQWBgIFpaWuDt7Y2ePXvi7bffZseCG3zgqGhdsXv3bqioqGDp\n0qXQ19dnrggcHjx4ABsbGza8sGvXLmzYsAF5eXnIzs5mrhlubm5ISkoCAMTGxkJDQwPPnj2Dmpoa\n7t69ixEjRmDIkCFISkrCqFGjcO/ePYwfP77bMXVyckJ+fj50dHTYlJ9AIEBAQADOnz+Pc+fOMQul\n58+fo7S0FFOmTEFERASbYqupqcGRI0dQVlbG2AklJSVITk6Gjo4OKisrUVpaij179kAikbAslWuw\nSiQSLFiwANnZ2QA6m1HPnz/HmjVr0NTUBENDQ3bODB8+HHp6ei+wCTQ0NNigw58RiLmSHHchFgqF\nSExMRGBgIHx8fPDTTz9BX1//d237fzLemKDL4XXwbF+1De4WqCvf9r/VbTlLHk44hVvMXDbc2toK\nmUzGTlpnZ2cYGRmxfWZmZqK6uhoKhQKTJ09GTEwMgM6s79ChQ7h58yaKi4uhqqoKNzc3+Pr6orW1\nFc3NzTAzM2ONvWXLlqFXr17sVooLLCkpKQgPD8dXX32FrKwsPHjwgAVdR0dHZGVlwdLSslv32sjI\niGX1HDihnpqaGqbqpq6ujry8PPTu3ZtJO0qlUsYtdXBwwMOHD9Hc3IypU6fC0dERY8eOhbW1NVxd\nXeHs7IysrCymyJadnc2CLseLBTrrowcPHsT48ePh4eGBp0+fokePHjh9+jQr/zx48AAODg5wd3fH\nzZs3IZPJMH78eMycOROHDh1CUlIS3NzcIJFIkJubC7lcjt27d2Pp0qWIiIiAQqFAdHQ0/Pz84O3t\njcTERNTW1oLP53eTPCQiPH78GCoqKqitrWVlhra2NowaNQrnz59HeHg45s6dCxUVFfj6+iIqKgpT\npkxBcnIyiAiDBw/G/v37ERgYiIaGBkgkEpiammLo0KFMM4GI8O677+Lu3bt48OABeDwe5s6diwsX\nLkBLSwsWFhaMwnf+/HmkpaWBz+fD1NQUYrEYlZWVaG9vh5KSUreR3654Ga3rdQViTpiKY1B0dHTg\n008/xY4dOxAREYEZM2b8LTXlvwJvTNDtmun+0QEJoHum27Vuq1AoXsm3/TV1W+DFxcwFKM40sqsy\nmEgkgkgkgpKSElJTU/Hw4UP07dsXbW1tyMzMxOTJk6GkpIQPPvgAsbGxSEhIgJqaGgYOHIjp06dj\n5syZUFNTg5GREWQyGXJzcxEcHIzS0lIcOHAAtbW1iIiIQHBwMDIyMpCens60fkUiEfT19Vn9k4NQ\nKGRZPYe8vDzo6uqyUVuuPldQUAAHBwc8fvyYMQnU1NSQm5vLasazZ89mpPe0tDTY2dnBxcUFDx8+\nxPHjx7F161bEx8ez8V9HR0ekpqayfX/00UfQ19fHqFGjoK+vDx0dHcyaNQtffvklpFIpnjx5AhUV\nFfTt2xeurq5IS0vD+++/Dz6fj7lz5+LYsWO4f/8+Bg4cCFVVVdjb2+PixYtITEzEypUrYWJigkuX\nLiE9PR1eXl6MJ7xv3z4MGTIE586dY3QzNTU1nDlzBubm5ujbty8SExNRV1eHxsZGeHl54dGjR4iP\nj0dAQAAUCgVGjRqFqKgopKamor29HePHj8etW7cYswAAuxtYuXIl9u3bh2nTpmHRokXIyMhg48FE\nhNOnT0NPTw8ymQwpKSmQyWRoa2tjlkJfffUV8vPzWUkK6Cwr/JZa6R8NxFx2y/kSikQipKWlITAw\nEFZWVjh//vwfZgf8T8fvs7z9H4xXuUf8FvxSl7exsRFEBJFI1C2z7aofyr3+e1yEOfEQri44bNgw\nzJ49G0eOHEFjYyPs7OwYHezBgwe4e/culi5dik2bNrEBgoMHD0KhUGDv3r1YtmwZM3y8evUqux22\nsLBgrAVTU1OsXr0a7u7ucHFxAQDmSszxbAGw0klXtLS0oKqqimVKQKd2Qd++fXHv3j1IpVIoKytD\nLBYjLy8PkydPxv79+9nfKhAIkJubCzc3NwgEApSVlTFZxbS0NFhaWjIBGkNDQ3z33XeYPXs2BAIB\nrK2toaOjg9TUVHR0dCA2NhYxMTEgIibZOHDgQHR0dEBbWxsXLlyApqYmc8x49OgROjo6mCtz3759\nYWZmhrKyMhZ8Bg4ciH379mH27NkQiUSYNGkSvv/+e7i7uzM2hY2NDeLj47Fp0yZs3LiR+YilpaWx\nSTklJSW4uLiwTFZDQwN9+vSBtrY2hEIhmpub4ebmhpUrV+LGjRusiccF+dzcXEyYMAEXL15kGstN\nTU1MUEgikUBHRweDBg2CgYEBUlNTkZOTA21tbWRmZgLoZFPI5XLo6upi7dq1EIlEbPrL0tISM2bM\n+M3r9ZfgFMa60ss4TnR7ezva2trYOcKtgRs3bsDKygoRERFITEzEsWPHmMj8m443MtN9HUG3vb29\nW92Wc5zomuF15duKxeLfbdv+MnCNDaDTdWHDhg1oa2vDkSNH4OPjA7FYDCMjIxgZGUEkEiErKwvv\nvvsuevbsCQ0NDZw7dw7l5eUQCoW4f/8+oqOjkZCQAHd3d1y6dAlPnz6FiYlJN/pLz549megNh5dJ\nCz579gy6urqsXgh0ljs4hTCu+8zn85GTk4MRI0bgyZMnjF/b3NyM4uJivPfeexCJRJg5cya7e8jJ\nyYGzszPMzMygpaWFJ0+eYMiQIZgwYQKKioqgp6fHMubMzEysXLkSn3/+OaRSKczNzaFQKDBgwAAk\nJyfj448/xvbt21nJpK2tDWvXroWnpyezNAI6R4+7HlcHBwfcu3cPixcvBgBMnDgR8fHxGDJkCHtP\nXV0djIyMMGzYMBQUFDBR9mPHjsHe3h6urq7o27cvLCwscPXqVUYPrK6uRkdHB6uFc8pera2tOHDg\nAAwNDWFjY4Pdu3fj+PHjmDZtGiZNmoT9+/cjMjISffv2xenTp6GlpYXx48ejpKQER48eRWJiIgwM\nDODt7Q11dXVMmzYNEyZMwPjx4/HgwQMUFhaisbERFhYWzOjy+vXrv3N1/nd0zYhFIhGjdnGuE4cP\nH4a/vz++/vprtLe344cffvhL2RJ/J96YoMvhjwZd7hZILpeDx+P9qrrtbxFJ/y348ccf2XfatWsX\ndHV1sXXrVjg7O0NJSQnV1dUoLi7Gt99+Cz6fj3nz5uHw4cMwNjZGYWEhJkyYgBEjRuDKlSsoLy/H\ngQMHQERYvnw5PvnkExQXF3cLJL8c4gA6GQUFBQXdnsvJyYGrqytSUlJYKSEzMxMSiQT6+vrIz88H\n0MmpbWlpgampKSwtLZGRkQGg00Ghvb0d7u7uzO6c+zvT09Nhb28PPp+PgQMHIj09HWKxGOPGjYO6\nujr27t2Ljo4O2Nvb47PPPoOVlRW0tLRgZ2eHxsZGyOVyeHp64uHDh/Dz84OOjg4iIyPh6OiIffv2\nwdjYGBMnTmSOt0BnAG1vb2d17qysLPB4PFZbNzIy6lbTLiwsRGZmJkpLS9HR0cE4u21tbfjpp5+Q\nl5eH+fPnY9asWSguLkZ0dDSamppw9+5daGlpISMjg8luchdXHR0ddveRkJDA3uPq6srqzj/88AOW\nLl2Kmpoa3Llzh/nqPXnyBNu2bUNsbCy2b9+OdevW4cSJE3B1dUVeXh6MjY1RX18PZWVlxhZZvnz5\nC/oRfwa4pnNbWxtTIDt48CAbKS8oKMCHH34IY2PjN7aG+0v8/6D7L3St23JEc1VV1ZfWbdvb27t5\ngv1Z8Pf3x1tvvQWgcwZcS0sLPB4PJiYm2LZtG1RUVDBv3jwIhUL07dsX8fHxOHPmDGpqamBvb49T\np05hzJgxMDc3x8iRI1FWVobQ0FB89NFHGDZsGCtdcODcBDg8f/6cCVZzz1dVVaGtrQ3e3t5ISkqC\nVCpFU1MTcnNzYWNjA2dnZyaTl5uby9x8OcZBeHg4VqxYAScnJ3z++efw9PREQkICgM5pH6FQyDrW\nzs7OuHfvHoBO9kFQUBD27duHjIwMmJiY4MaNG9i+fTsePXoEGxsbdpz69euHJ0+eoK6uDqtXr0ZG\nRgZ69+6Nbdu2YevWrfDx8WFBt6OjA3fu3MHSpUvZYMjRo0dhbGzMLgZZWVlQV1dnPNtvvvkG8+fP\nh6WlJe7cucMEw69cuQJdXV10dHQgKCgIkyZNQkJCAuzs7HD9+nUcOXIEc+fOhYeHB6KiopCfn49t\n27axi3paWhpGjBgBFRUV6OrqwsjICOrq6nBxcYGFhQXu3r2LkJAQLF68GFu2bEF0dDRWrVqFjz/+\nGPHx8XBycsLatWtRUFCAkSNHYseOHQgNDUVBQQFaWlpY+cXOzg7btm37E1bsv8GdTzKZjOn1Pnv2\nDOPGjYNCocD169dhY2MDAwMDNrjxfwVvTE33j5QXflm35QQ2uForl839kbrt78WOHTuQmJiItLQ0\n1NbWQllZGU+fPoWSkhL69euHiIgI7N69GwsWLMDSpUvB5/MRFRWFHTt24Pvvv8e4ceOY0SQR4Ysv\nvsCWLVtgZWUFkUiE1NRUDBgwAESEvLw8CIVCFBcXw9jYmAmba2pqMotvLtPcvXs3SkpKEB4eDoVC\ngba2NkycOBF6enooKCiAtrY28vPz2bCEvb09vv/+ezQ3N2Py5Mksy7K3t0dpaSmqq6tZlsvB1dWV\nKTqlpKRgyJAhCAwMxJw5c9Dc3AxjY2PG9eWE1Dliv62tLVJSUtCrVy/w+XysXr0awcHBsLCwAJ/P\nh1QqRXFxMWpra6GtrY2VK1fC1tYW69evR0hICAQCAaKjo+Hs7IwrV64gODgY0dHRuHz5Mk6dOoV7\n9+5BLBbjypUr+OKLL/Ds2TNmC7Ro0SIoKSlBU1OTCRKdPn0a169fxxdffAFNTU1cuHABBw4cQEBA\nALKysjB27FiEhoZi//79eP/99zF//nwmWKSpqYk+ffogPz8furq6mD9/PjZs2IDRo0dj2bJlCAsL\nw4MHD3D58mW8/fbbCA0NxYIFC3D79m1oaWnBz88PLS0t+PnnnyEWi5lp6Z8FjjJIREz7+cCBAwgP\nD8d3333H2DF/BTi3jvT0dPD5fISFhcHd3f0v2//L8H860+3o6Ojmp8bVbbsGWo7ixKle/RXCH7/E\n7du3oa6ujtraWkgkEigUCsydOxePHj1iNu3cFFJoaCgbsBCJRKitrcXYsWORmpoKFRUVLF++HMnJ\nySgsLER7ezuOHDnCKFW9evXCwIEDcefOHcYN1tDQQGVlJRYtWoTFixejqKiIGWQqKSkhKysLt2/f\nRv/+/XHv3j3MnDkTZWVl+P777/HJJ58gLi4OH374IbZv346ioiLcunULtbW1TIRcIBDAxcUFycnJ\nLwRdJycnZGRkoLW1Fffv34eLiwvGjRuH1tZWaGpqorS0FAqFgk3VAf9u1Lm5ueHRo0d4+PAhJBIJ\nEhISsHLlSrS1taGxsRHu7u6Ijo7GjRs34O3tDaFQiEmTJuHkyZNYvXo1hg8fzsZwr169ioCAACxd\nuhQfffQRpk6dCkNDQ4wePRqXL19Gc3MzRo0ahTt37uDZs2eYPn06+xtmzpyJnJwcXL58Gd7e3jAw\nMEBAQAAuX77MAv+SJUuwYMECREZGoqSkBHK5nDU7T5w4gY6ODkYnS01NhbKyMluHQqEQXl5e4PP5\nePz4MfNV27NnD5qamlBXV4fTp0+zEWQu+PwZ9dOu2a2SkhLU1dVRXl6OSZMmoaysDDExMX9pwAWA\nd955BwEBAcjMzERqauofniZ7HXhjgu5vyXS78m35fD60tLS61W0FAgHrynNGe0pKSkxUpKGhgY3k\ndrUmed2gf6nmNzU1ISUlBXw+H8nJyRCJRNDT02Nye7du3YJAIECPHj2watUqPHz4EPfu3UNTUxM8\nPDzg5eWFjo4O2NraIjY2Fjo6Oujo6EBISAhOnToFb29v/PDDDxgwYADc3d1x+/ZtbN68GZ9++imy\ns7NhZ2eH/v37IyYmBkKhkLkE29jYIC8vjzXoevbsicWLF6Ourg6nTp1Cnz590NzcjEOHDkEsFkOh\nUEChULzgY8Y1ttLT07uVO8RiMUxNTREfH4+ysjLY2Njg008/hYmJCVpaWthk3bNnz5hFDwc3Nzck\nJyfjzp07qK+vh729PdOi0NTUZIyOuLg4eHh4QCaToby8HEDnnY+7uzvu3buH4uJipKamYvDgwQgM\nDERubi6mTZuG9vZ2mJubo6WlBaWlpaysMH369G7sjyFDhjABGe47coMybm5uePz4MUJCQqCtrY3p\n06dj165d2L59O3bu3ImioiKEhoYiNjYWQqEQS5Yswb59+3Dx4kXY2Njg2rVr2LBhA86cOQOpVIr3\n338f+fn52Lt3L95++23MmTMH8+bNY+fGqVOnIBKJWKLxqgGd3wNONa+lpYVN1504cQIzZ87E+vXr\nsXnz5temk/Br0dDQgNjYWKaLIRAImK3U34k3Juhy+E88XS6IdeXbcrfe3Ge6krY5eTmOf8mdsJwy\nGDcCy1GEXue4JFc/bmtrY/5V9+/fB5/Ph0wmQ21tLZOEfPvtt6GhoYHPPvsMFhYW8Pf3R3JyMgQC\nARYvXszkE4ODg3HlyhUUFBSgqakJW7ZsAQBs374dCQkJuHLlCo4cOYIff/wRFRUV0NDQwNmzZ7F+\n/Xrk5OSgra0NSUlJkEgkaGxshLOzM5KSkhAfHw9PT08AncwELS0tuLi44MmTJ9i9ezfKy8uxefNm\nKCsrw9vbmyllcXB3d0diYiIbSe4KV1dX/PzzzxgwYABOnjyJ06dP4/jx4wgLC0NZWRlOnDgBS0vL\nF3QaBg4ciKSkJFy9ehUKhQIHDx7Et99+i6qqKgCdxo53795FfHw8RowYgbKyMty+fRsTJkzA/v37\noaysDIlEgtDQUHh4eEBJSQlHjhyBtbU1zpw5A7lcDjU1Nfj7+yMyMhK3b99mdjtdwTUEBQIB4uLi\nAACbN29GQEAAjh07hpkzZ7JgtHz5chw4cAB6enqYNGkStm/fjtLSUnz55ZcsgEZERGDjxo1obm5G\ne3s7QkNDoa+vj9LSUsyaNQuGhob4xz/+gd27d8PAwABhYWEgIsTExCAgIOAFEXHgxQGd3xqIOfF7\nJSUliMViVFdXY/bs2UhLS0NMTAy8vLz+libZ06dPoaenh3nz5sHZ2RmLFi16qULdX403Kuhy4sgv\nC3icTkJzczOTiwP+rbBERMwMj3v9l/xUbh9dTfo4t1SOIsXVhzly+G9dwFzGwI1Gdv0elpaWCA0N\nZReWxMREVFRU4NKlS1BSUsLOnTtx//59tLS0gIiwcOFC5Obm4vDhw5gzZw78/f1x5coV3Lx5E0OG\nDIGenh4kEgkePHjA6sW6urpob29HZGQkGhsbceHCBTZVduPGDRgZGcHU1BQCgQASiQSxsbG4ceMG\nrl+/DhcXFzg6OkJJSYmN3I4ZMwZKSkoICgrChx9+CHNzc/B4PCxevJhpobq5ueH+/ftMz6ErnJ2d\ncffuXfTu3Rtr1qzByZMn0bNnT3h4eCAwMBA///zzS919TU1N0dbWhsrKSnz99dewtrZGSEgIayDZ\n29szbq6RkRE2btyIlStXYvXq1Th06BB4PB5GjBiBqKgojBo1CnV1ddi/fz82bNiAH3/8kf3Go0eP\nxunTp1FYWAgXFxccOnSo27Hu6OhAeno6Uzo7ffo0Tp8+jU2bNqGlpaUbt9XY2Bh8Ph92dnbg8XiY\nNm0abGxsEBcXx0aGgc7JvBkzZuDx48fo1asXGhsb8dFHH+H8+fM4fvw45s2bB2tra/zzn/8E0Kk7\n4ebm1m0N/yc3By454e7q5HL5S+/quLXKnTOqqqq4cOECJk2ahOXLl2Pnzp3/VXrxzwRn3rl8+XLc\nv38fIpGIJRp/K/6Tlw/9L0NLSwvJ5XIqLy/v5ktWVVVFZWVlVF9fT42NjSSXy0kqlTL/Js4Hq6am\nhvlu/dHHq3yhnj9/TjU1NVRfX99tXzKZjGpqaqisrIx5k71q2xs3biSBQEA8Ho8GDBhAAGj8+PGU\nnJxMDg4OZGpqSuHh4cwLTUdHh22zT58+NHr0aAoNDaWDBw+SsbExqaurk7q6OpWUlJBMJiM/Pz/y\n8/OjIUOG0OLFi8nV1ZV5twkEAlJVVSWBQMB83QQCAX322WcUHR1N5eXltHfvXrK0tKT58+dTdXU1\n1dXVkVQqpdu3b1PPnj1p9uzZtHTpUjIxMaGoqCiSy+VkYWFB5ubm3f7OhoYGioqKInV1ddLT06PD\nhw93e72wsJB4PB4FBQW99HcyMjIiTU1N9v/8/HzS0dGhjIwMksvlZG1tTb6+vhQXF0c9e/ak58+f\nk1wup7Fjx9L27dvp5s2bpKSkRI8fP6aPPvqIQkJCqLa2lgICAujrr7+m58+f09OnT0lZWZl69OhB\nZ86cIWdnZ/rxxx/Z8Tt+/Dg5OjqSp6cnzZkzh3r27ElffvklffXVVzR8+HDS09OjkpISksvldPjw\nYbK1tSVjY2Oqq6sjuVxOc+bMIR6PR6amptSjRw/y9PQkKysrkkgkdOTIEerXrx8VFhaSp6cnqamp\n0dGjR0lFRYUAkJqaGuXm5v7uNSyTyaihoYFqa2upqqqKKioqqLS0lMrLy9m/MzMzqbS0lIqLi2na\ntGk0b948qqur+7tDARERlZeXk7m5Oft/bGwsBQUF/VW7f2VcfaOCbmtrKzU2NlJZWRk1NTVRbW0t\nC6Zdgy334IJcVVXVawu2v2cBc0G5oqKC6uvrf9X2CgsLmQHlgAEDSElJiSZNmkRGRkbUq1cvamho\noDNnzhCPxyMPDw/2ualTp5KKigoZGRmRt7c37dq1i0QiES1evJikUilVVlbSV199RXp6enTmzBn2\nucOHD5Ouri7t2bOHqqqqqK6ujhoaGkhPT4+8vb1f+G4CgYD27t3b7aJTVFREAoGAvv76a6qvr6fT\np0+ToaEhrVmzhnx8fMjR0ZH9VpyBZ15eHgGgWbNmvfR3UFZWJrFYTHFxcd2ev3HjBgkEAhowYEC3\n59etW0eTJ09mQXfEiBHk5+dHO3bs6PZZMzMzunz5MikpKVFcXBwZGBhQUlISyeVyunz5MllbW7ML\npUAgIENDQ6qurqajR4+Svb09lZSUUFlZGdnY2FB4eDjt2rWLXF1dic/n0/3798nS0pKioqJo1qxZ\n9MEHH5BUKiVbW1s6c+YMDR48mMLCwigpKYl0dHTIwMCAVFVVycjIiNTV1SkrK4s2bdpEqqqqtGLF\nCqqvryczMzOaNm0aM5d0cnL6U9Yxt0ZKS0upsrKSvvjiCxIKhdSjRw8aNmwY7dixg3Jycv7uUMAw\nePBgys7OJiKizz77jP7xj3/8Vbt+ZVx9YyhjXUFETLhbQ0ODTZhx4EZ3+Xz+K8sIrxu/HPUFwDiw\n7e3trCwil8uZDCT3/pcxJnR1dVFWVobp06fj559/Bp/Px6lTpzB8+HCUlJTgxIkTCAkJgUgkQkpK\nCtzd3SGXy1FRUYGOjg6Eh4fD1dUVHR0deP/992FtbQ2ZTAZVVVWMHDkSH3zwQbdb0lGjRqGmpgYD\nBw7sJixuZGT0QoOE08RVU1Nj0o70L11hrpTT3NwMLy8vXLt2DStXrkRqaioMDQ2ZWI9AIEBTUxOC\ng4PB4/GYHGFXFBQUQCAQYNSoUZgzZw7u3LkDDQ0NVFVVYfbs2dDX10dJSQmIiNUUV65cCYlEgpSU\nFBQXF6OxsRFKSkqYM2cO2y7XFNy1axfMzc2xY8cOeHp6sibf4MGDGaWsoaEBAoEAurq6UFNTQ3Bw\nMLZu3Yo7d+4wQfBhw4ahuroaK1euxODBg7F+/XqoqKjAw8MDpqam8PT0hLm5OfNEA4DPP/+csRSm\nTJmCtrY2JkwzevRouLm5MSrW+fPnUVpaihMnToDH42HLli14++23f/3i/JXgSmdcQ0omk6GgoADj\nxo3DW2+9hfz8fKSkpLBhmP8JCA0NxYwZM5gGxcGDB//ur/Rm1XS5ui3wb6EY4OV1W66W9VcE3F+C\nCzrcAubqaRoaGqy5B7zY4PhlXY3P5yM8PBwRERFMkDouLg6FhYVYsWIFDAwM0NjYyJpvSkpKrPZm\nbm4OALh16xY0NTWRnZ3NJobS09Ohra3NhhaAztFfoVDYTd0L6GyccV5hHJ48eQKhUMiEWoDOi05W\nVla3fWlqasLMzAxnzpyBUChEXl4e7ty5Ax6Ph6dPn8LPzw+WlpawtLTs5ozB4ebNm3B2dkZdXR18\nfHzwzjvvoL29HQsWLEBQUBBkMhl69OjRTQZSQ0MDa9euxcX6ncoAACAASURBVKpVq9CrVy82jsw1\nlehf9cwlS5bgxo0bCAgIwKVLl/Dhhx92+1uWLVuG7777Dl9++SV69eqF6upqZGZmgsfjYfXq1diy\nZQu2bNmC9evXQywWIyoqCqampqirq8PVq1cxZcoUyOVyaGpqYsqUKVi/fj0++OADEBFGjhyJkpIS\n3L9/H6tWrcL69evh7e3NdIZnzJiBGzduwMnJCVZWVigqKoJCoYCxsTGKiopee8ClLgJGQqEQQqEQ\nd+7cwZgxYzB8+HAcP34cvr6+WLhwIfbu3Yvg4ODXuv8/AolEguTkZDx8+BBnz55lLhx/J964oMtl\nVr/MbjllI06I5e/yfmptbYVUKn2lGtl/UyB7GVvCz88PaWlpTFRcQ0MDra2tWLhwIQoKCrBlyxZY\nW1ujV69ekEqlsLS0xNWrVyGXy3H06FFMnjwZkZGR7HtERUXBx8enm9X0nTt34Ojo2M1yprGxEcXF\nxSgpKUFlZSV7Pj4+Hi4uLi/M9sfGxsLLywuxsbEA/t3QqaqqQmNjIzQ0NDBz5kzs2bMHY8eOxdtv\nv43evXvDy8sLkZGRaGpq6taUjImJQXBwMJKSkrB582akpqZi1qxZaGxsxODBg+Hm5sbYBV0xd+5c\nFBQUoKOjg2XlAJjeRltbG2xtbaFQKJCWlgaFQvFC5sZJMWZmZmL//v1YsGAB9uzZAwAYN24cysvL\nQUQYMWIE6urqsHnzZhw9ehTt7e0QCAS4c+cOxGIxxGIxHB0dUV9fD3Nzc0ilUly/fh3V1dVQVVXF\n8+fPUVxcjHfffRdnzpzB8ePHsW3bNlhYWEBDQwOPHz9mKnPZ2dmv3V3hl/Y5CoUC69atw+7du3H+\n/HlMnTr1L2UmdHR0wNnZ+Q9b5vydeKOCrqqqKgQCAQQCAeMhyv8lWkP/mo7hJOj+anAndEtLC8vC\nf82gxa9lS2hoaODq1atwd3dHdXU1xGIxDh48iGPHjmHu3Ll48OABUlNTcf78eZSVleHjjz9GdnY2\nIiMjmRlleno6iAhRUVFYunQprly5gra2NgBAXFwcJk6ciJs3bzLBlHv37sHe3h5+fn7dAltCQgL8\n/f1RWFiIiooK9nxsbCzGjh2LsrIyVFRUsN/k2rVrGDZsGIYOHYqAgAB88cUX8PX1xeLFi5Geno7A\nwECoqqqyQQmZTIa6ujrExMRg1KhR6NOnD7Kzs7FixQpcuHABn376KRISEuDt7c0YG12hrKwMkUiE\nJ0+eYPXq1Th16lS3i7K6ujpOnjyJ4cOH49atW3B3d3/BiUJNTY3Zxbu5uWHBggU4e/YsampqmMIW\nd3y3bdsGf39/mJqaoqKiAmpqanj69Cm+//578Hg87Nu3DyEhIdi+fTtycnIwffp0eHh4ICkpCRkZ\nGfDy8sK4ceNgamqKTz/9FMHBwUhNTUVUVBTMzc2Rl5eHDRs2/L6F+Qp0zW450Zr79+8jMDAQEokE\nZ8+e7ab7/Fdh586dL3Cy/7fhjQq6S5YswYQJE7Bjxw4cOnQI7733HpqamhgZXSaTvVZC+K8BNxLZ\nNcv+o2PEXa3HOcdbrkRx7tw5pKenY/Xq1dDW1sa6detgZmYGHo8HQ0NDODo6IiYmBi0tLRgxYgQT\nIx8zZgwuXryIjIwMqKioYPDgwTAzM0NcXByICLGxsRg9ejQcHR0RHR0NoDO4enh4vBDYEhISMGjQ\nIAwZMoS9l9M4GDJkCLy8vHDjxg3I5XKoqKjg5s2b8PX1RWNjIyIiIhAeHo709HQsW7aM6dz6+/sj\nJiaGZf+cM0OvXr3g7u6O8+fP4/PPP8dbb72FVatW4fbt2/D09ISXlxeePHnSLfg/ePAAxcXFbJuP\nHj1CUVERK690dHTg2LFjyM7Oxrhx49DU1IQDBw50OwZ3795FRUUF9PX1ERYWhp49e8Lf3x+HDh1i\nPnNtbW0IDw/H0aNH8cknn2DNmjUICQnB999/D7lcjs2bN2Pfvn1obGxEaGgoYmJiEBQUBD6fjyNH\njqBv376wsbFB//79ceHCBfj4+EBNTQ0XL16EqqoqvvzyS8TFxUFLS+u1Wun8Uhua/jU+vnHjRoSH\nh2P+/Pl/y2RmcXExLl++jIULF/7l+36t+E9dtj+7vfe60dHRQXFxceTk5ET6+vo0YcIE8vDwoFmz\nZlFoaCglJSVRdXU1VVdXd2MPcDSuhoaG18Zi6EoBq6qqIqlU+qeyI17FloiIiCB9fX3W1e7Zsyd5\ne3uTm5sb8fl8srKyIpFIRBKJhIyMjGjs2LHk4+NDe/bsofHjx9PQoUNp48aNpKurS1FRUbR69Wqa\nOHEiyWQyGjVqFB07doyePXtGmpqaVF1dTUVFRaShoUH19fX07bff0pQpU0gul1NiYiL17duX6urq\n6NNPP6W5c+cyWp26ujr16tWLhg8fTrq6ulRXV0cVFRXk4+NDampqVFxcTOfOnevGwti4cSMtXryY\n5HI5HTp0iHr06EHr1q2jmpoa8vf3J4FAQPn5+VRRUUHjxo2jXbt2UUNDA0mlUrK2tmYMAzs7O/Lw\n8KBNmzaxbZ87d44MDAxo/PjxVFNTQ3Z2dqSjo8MYElKplPr06UMSiYQePHhAenp6dPfuXYqLiyMT\nExPq27cvXb58mfbu3Uu6urr06aefUkREBJmamlJFRQXJ5XJasGABubu7k5qaGm3dupVu3LhBWlpa\npKKiQgYGBrRp0yYKCwuj3r1705MnT8jPz480NTUJAAUFBVFlZSXV19dTTU0NPX/+nMrLy6m0tJQq\nKiqoqqqKamtrGS3yt6wZjjVSW1tLMpmMkpKSaNCgQRQaGkrt7e1/6/k9ceJEevDgAd28eZPGjBnz\nt36XX4H/G5QxIqLIyEjauXMntba2EhGRQqGgjIwM2r9/Py1cuJC8vLxo2LBhtGrVKgoPD6f8/PxX\nLlyOX/pbA159fT3jMjY0NPylwfZVgT8yMpKMjIwY39bY2Jh0dXWpX79+NHXqVLKysmK8W3d3d5o+\nfToFBQWRmpoa9e7dmywsLMjDw4P69OlDAEhZWZn4fD6NGDGCNmzYQLa2tnTs2DE6ffo0DR06lORy\nOWVmZpK+vj5JpVL65z//STNmzKDy8nK6ffs2WVlZUVxcHDk4OJCamhpFRkaSXC4nd3d3RlX78MMP\nSSKRUP/+/SklJYU0NDSosLCQ5HI5+fr6Unh4OJWVlZGPjw8JBAJ69uwZyeVy2rlzJ6mrq9PGjRup\nvr6edu/eTYGBgVRWVkabN28mVVVVOnnyJFVUVFBSUhJpamqSlZUV+92GDh1KGhoaVFBQQHK5nOLi\n4kgkErELyLfffkuqqqp0/fp1ksvldODAAfp/7Z15WFN39offCxEEBQVBqQwuCFhEUIFEtCpU6zaK\n22gttWWsUkftjFpxnanruONSbetedera1k61LuO4IGiVgGiltFocsSKi0CJuKBpC7u8PvfcXEFwD\nCXjf58kfwAPfk3Bzcu5ZPsfLy0vMzs4WPT09xVdffVW8c+eOuGnTJtHW1lbs2bOn6O7uLu7atUs+\n48qVK2LNmjVFNzc30dHRUXRwcBDHjBkjenl5ibt37xb79+8vCoIgtmrVSmzWrJloY2Mj9uvXT25b\nK+1RVm/4b7/9VqxfurTfvXXrlty+ePv2bfHmzZvirFmzxLCwMPGXX34x87taFHfv3i1+8MEHoiiK\n4uHDhyuy3/Z5KdOvCuLjb0mqnKqwKIryOpOEhAR5qqtBgwYEBwfTunVr/Pz8sLKykpXvgUfauErL\nC0uFLqmgJwk2mwO9Xi9via1evTrW1tbcv3+fRYsWsXfvXvLy8nj33XfZsGEDS5Ys4fz588ybN4/C\nwkLc3d25ffs2/fv3Z9u2bTg4OJCcnCwXKdu3b4+bmxu2trb07t2b5ORkOVfs6OiIp6cn8+fPR6PR\nEBgYyOrVq1m4cCE9evRgwIABxMbG8u677+Lo6EhgYCABAQFyTnLt2rUcPXqUdevW0axZM7Zv305C\nQgLz5s3D09OT999/X85vHjlyhMGDB6PRaMjLy6Nt27aMGDGCrl27MnDgQGJiYpg8eTLdu3enRYsW\n7Nq1i/DwcGxtbUlNTZWLrevXr2fq1KkcOnSIunXr4ufnJ6/FkZgwYQKrVq0iJSWFkJAQPD09i6l1\nffDBB2RkZHDy5Emsra3ZuXMnAwYMYN26dYwePZq8vDyOHz+Oh4eHLFiUlZVFWloaRUVF2NjYcPPm\nTby9valfvz5paWkYDAZu3LiBh4cHq1atKtbC97QYb3AoeT1LD0klTrpmz58/z5gxY+jatSvjxo2r\nUFW9svj73//Opk2b5FZCaZHpF198YW7TyqLMN/5L53RLw2AwkJGRQUJCAlqtlpSUFHn9S3BwMCEh\nIdSrV6/YBWxlZSU7YamgpdPpsLGxMVuxTnoukuO3s7NDpVKVasvJkydZtWoVO3bs4P79+9StW5fd\nu3cTFxfHihUr+PTTT9myZQtffPGFvGVWo9GgVqtl/YO0tDRZovHs2bP07NmTO3fuMHjwYA4ePMjt\n27epXbu2vHOsTZs2JCYmEhAQgF6vR61WEx8fz+LFi2XtBkkLeNWqVcydO1d2bPv37+edd97B39+f\nKVOmMGnSJG7evMmQIUMYN24cR44cITo6mi1bttClSxfOnTvHxYsX6datGx9//DExMTHk5OTg5+dH\ns2bNmD17NoAsBdmyZUvq1KmDjY0Nly5dIjk5+RHH1KhRI9zc3MjLy2P16tV069ZNfj1PnTpFWFgY\nI0eOpFGjRsyYMYNBgwYRHh5OVFQUUVFRrF69WhYjz83NZejQoQwfPlwWj2/YsCFOTk6yAxw3bhwd\nOnR4RFfiRRBFUb6OJWcLDwql27Ztk+U+16xZY3YJxLKIj49n0aJFsr6xhaI43WdBfNir+cMPP6DV\natFqtWRkZODi4oJaraZ169a0bNkSGxsbrly5grOzs9xlIDnispxdedqs0+m4f//+Mzn+3NxcJk2a\nRHp6OmfPnqV9+/ZyL/O9e/cYNGgQe/bsIS8vj6CgIE6dOkVKSgpWVlYEBwfzyiuv4OzsTO3atfn0\n008JCAigRYsWXLlyhV9//ZWMjAzu3bsHQN++fZk6dSo+Pj5kZGTw2muvIYoiGRkZxaKpiIgIrl69\nyptvvsnIkSPl7x86dIg+ffrg6+vLhQsXWLZsmSyjKIoirVq1IjAwEDc3N+bMmQPAiRMn6Nu3L9Wr\nV5d1BbZt2yYv35Q4fPgwvXr1QhAE1q1bR9++fWXHJAkijR8/ns2bN9O4cWNOnz4t3/FkZWXRqVMn\nhg0bxtKlSxk9ejQxMTGEhoaSmprKwoUL6d69O7t37yYyMhIPDw80Gg3bt2/H2dmZadOmERERUWFt\njMbXirSt+vTp0yxatIjc3FwKCgo4c+YMI0aMYNGiRRVi07NQ2Z2u+e8bLBBBEKhevTpt2rSRIzBR\nFMnJyUGr1RIXF8fMmTO5ePEi1apVY/z48bRt25bGjRvLDlsaSpAiJZVKhZWVVbk4YimVIAjCMw98\nuLi4yEsj8/LyOHjwIHv27GHnzp1Ur16ds2fPEhERwfbt27l69SqDBw9m06ZNBAUFsWvXLgYMGIDB\nYECr1WJtbc2lS5cIDw+nffv2NGjQgBo1ahAaGsobb7yBvb09YWFh9OjRg2HDhuHt7S0PiBjTu3dv\nhg0b9kibVmhoKK6urvz88880bdqU119/Xf6ZIAhERkYye/Zsjh8/Lv8fbGxssLe3Jzc3F1tbW5yd\nneUNCsZIAylFRUVUq1ZN/r8Zb809cOAA1apVIzMzk88++4x33nmH+/fv07t3b6KiohgzZgyFhYVM\nmzaNNm3asH//fmrUqCG3yG3bto26deuSmZlJZmYmM2bMYMSIERXaMy6J1ACywPjmzZvZsGEDH3/8\nsRzdSmp8lkhoaGixNVOVDSXSfQ5OnjxJ165diY6O5o033uDkyZNotVrOnTsnr1fRaDQEBwfj4OAg\npyQMBkMxJ1zWiO/TUp45ZEnEPCEhgePHj/P999/LEfCoUaPo27cvhw8fZvPmzWzfvp3w8HCWLFnC\nl19+yd27d1m7di0Gg4GRI0eiUqk4fPgw8fHxODg4sGnTJtasWcNvv/2GKIrF0hQAa9asYcKECaSk\npNCgQQP0ej1ff/018+fP5/fff8dgMPDBBx+wdu1aYmJiGDBgAPBgKeSIESPkDRjHjh1j+PDhfPTR\nR7i6uvLnP/8ZZ2dn0tPTi71O69atY/r06cCDZaPOzs507NiRBQsWUKNGDaZNm8Z3332Hl5cXrq6u\n/Pjjj+Tl5eHg4ICVlRVt2rRh1qxZxMbGMnz4cOrUqUNubi4+Pj5cu3ZNFoy3tramV69eRERE0KlT\nJzlHXhGIoihv5bW1tcXGxoacnBw+/PBDPD09mTNnTrHxboUXRkkvmBKDwUBOTs4jzeHiQ80HSWM2\nMTGRvLw8GjdujFqtJiQkhKZNm8oFHKmoYVygK6tIV/Ic6fZQml4r71SGKIqcPXuWr7/+mtzcXE6c\nOEF6ejpOTk5kZ2fL2rNeXl707duX9u3b4+XlxZIlSzh48CDLly8nISGBjRs3otfrGT58OFlZWVy9\nepU7d+4wZMgQoqKi8Pb2plOnTjg6OqLRaGjUqBHz58/H1dWVqKgooqOjadSoEe+++y5qtZqoqCia\nN2/OkiVLGDZsGLm5uYSHh1OzZk3mzZvHhg0bCAsLY//+/QwePFheXLl+/Xrq1KnD+PHjOXr0KIGB\ngfK009atWwkICCA+Pp4mTZqQn5/PH//4R7799ltiY2O5ffs27du3x8rKipycHEJCQvDx8WHDhg3U\nqlWLiRMnMnDgQFQqldw7K8ljuri4PNX/2JQYr8+Rhmq+/fZbli1bxoIFCwgNDa1Qey5fvkxkZCQ5\nOTlYWVnx/vvvM2rUqAo7v4JQnK65MBgMpKeny0W61NRUrK2tadGihZwfdnFxKVakM3bAUjQsvSkk\nkRxA3mhhLm7evEliYiJbtmzh3r17pKWlkZmZiZeXF7/++it6vZ7Zs2fz+uuvU79+fTnv+dVXX1Gv\nXj2WLVtGSkoKQ4cO5U9/+hNbt27FycmJrKwswsLCOHDgAM2bN2fOnDmEhoYyaNAgAgIC6NOnD126\ndOH777/H1dWV6dOn8+WXX3Lnzh3++c9/MnPmTOrWrcs333xDkyZNuHjxImFhYWzatIlbt24RFRWF\nIAjUqlULX19f3n//ff7617+SlJSEk5MTAwcOxN3dnePHj3Pu3Dm6dOlCfHw8b775JpcuXSIlJUWO\nGF1dXcnOzqagoIBx48YxduxYBEGgoKCAatWqYWtrKxfrjPPDj/sfmwrj6FbK81+/fp3o6Ghq1arF\nwoULzbJJITs7m+zsbFq2bEl+fj5BQUHs3Lmz2DaRKoDidC0F8aHojpSSSEpKIisrCzc3N9RqNRqN\nhoCAAFQqVbFoWEpDFBUVyUUhS+yQyM/PJzU1lQMHDvDDDz9QVFTExYsXyczMpFatWvz++++4u7vT\ntGlTOcKXtgrrdDp5RLegoIC6dety/fp1wsPD6dGjB1OnTpVb12JiYjh69Chbt25l5syZrFmzBniQ\ni3R0dMTa2pqFCxfSq1cvOnXqxKBBg4iIiODnn39m7Nix/Prrr9jb28udA127dsXBwYGrV6+SmZkp\ni/VIyy59fX3p3LkzAQEBBAQE4O7u/sjrL70uRUVF8utSGpITlh6SiFHJO54XTT0VFBRgMBjkkfP/\n/ve/zJ07lxkzZtC9e3eLWXnep08f/va3v9GpUydzm2JKFKdryYiiyOXLl+VOiVOnTqHT6WjevDmB\ngYHcuXMHnU4n77uSWtZK5oYrIsUgRU7PmtbQ6/VkZWWh1WpxdnYu1rokieX06dMHJycnRFHk1q1b\nxMXFsWPHDnbt2kVBQQHe3t64u7tTo0YN7OzsiI2N5datWwiCQI8ePRg8eDC+vr7s3buXDRs2kJqa\nio2NDbVr10YQBPLz82nevLlc8HR1deXMmTPcuHGDsLAwXnnlFfnh4uKCu7v7U+31epHXReJx/bRP\n6g8vSWFhIQUFBXJ0e/v2bSZPnkxhYSHLli0rJvJjbqS7kJ9++kkeSa8iKE63sqHT6fj666/56KOP\n0Ov18u6woKAgWrduTVBQEHZ2do8U6Z6kw/u8FBUVyfulKjqtIYqirAEhLVXMz88nMzMTW1tbRo4c\nKXdtGDulzMxMuXUrODiYBg0amPyDyTiifFx0+6wYfygZP4w7Yko64pKRtrW1NUePHmXKlClMmDCB\n/v37W0x0Cw/uisLCwpgyZQq9e/c2tzmmRnG6lZGpU6fSoEEDhgwZgiAIXLt2jcTERBISEjhx4gS3\nbt3C29tbzg17eXkBvFCRriRS65VOp5Or3uZ645YsINrY2JTplMr7DqC0fGlF3GkYDzYYf9gKgiAP\n6Dg5OaHT6Zg+fTpXrlxhxYoV1KtXr1xte1b0ej09e/ake/fujB492tzmlAeK062KFBUVkZaWJhfp\nzpw5g62tLYGBgXJ+uHbt2hgMBvR6/SMFHCkXW5azkJyKlZWVXPU2F08TaUtOSXJIZbXpPe45Pw0l\n86XmLGZKfbdSAXbWrFl88cUXcuvie++9R7t27XB1dTWbjaURGRmJi4sLixcvNrcp5UXVdbqffPIJ\ny5cvR6VS0aNHD8vY9mkmytKV8PDwkJ1w8+bNS9WVMHZK4sPNFlKhzByC78bPSYq0n6cXWXy4Jqgs\n7QFjR/w0f6uio9vHYbw+x87ODp1Ox9y5c0lLS6NPnz5cvHiRpKQk+vfvz9ChQ81mZ0mOHTtGhw4d\n8Pf3lz8A58yZU2ysugpQNZ1uXFwcc+bMYe/evahUKnJzc4s12Ss8XlciKCiIkJAQ3NzcikWI0kYH\nGxubcp2kexLSpJ21tTXVq1c3SaQtKT2VdMRPmh4s2etqzuhW+lAsLCyUPxR//PFHxo4dy6BBgxgx\nYoRZ70oUgKrqdAcOHMhf/vIXOnbsaG5TKg1l6UrY2Nhw7do1AgICWLx4MdWrV6+wIl1pNhYUFFRY\npP2ktIQU4dra2lpEdCt9ENnZ2aHX6/n44485cuQIK1eurPCFkPv27WPMmDEYDAaGDh3KxIkTK/R8\nC6ZqOt1WrVrRu3dv9u3bh52dHTExMQQHB5vbrErHjBkz+OSTT4iIiMDe3p6TJ09y9+5dXn31VblI\nJ7VZSUUcaXvFixTpSiJFoNJgQUVM2j3OFqloJxptE36etISp7CkZ3aalpTFmzBh69uzJ2LFjKzz6\nNhgM+Pj4cOjQIerXr49arWbbtm1Vbcjheam8gjedO3cutmpFegPMmjULvV7P9evX0Wq1nDhxgjff\nfJMLFy6Y0drKSdu2bRk+fHixCrder+fnn38mISGBZcuWFdOVUKvVqNVqebWNTqd75iJdSUoWp8yp\n4VpShct4U7AUDUutWRUhamSsjSytz5EWQ65YsUJuJ6xokpKS8Pb2pmHDhgC89dZbVXGyzORYvNMt\nbfW2xMqVK+nXrx8AarUaKysrrl27Rp06dSrKvCpB586dH/meSqWiRYsWtGjRguHDhz+iK/H5558X\n05Vo3bo1r776KlZWVnKxCZ4cGZaUpLS3tzfr7btxl0RJxTZBEIpp25ZMS5jiw8eY0oqIGRkZjBo1\ninbt2hEbG2vWImdWVhYeHh7y13/4wx9ISkoymz2VBYt3uo+jT58+xMbGEhoayrlz5ygsLCxXh7to\n0SLGjx9Pbm6uRU31VASCIFC7dm26dOlCly5dgOK6Eps3by5VV8LV1bXUyFByRvfu3XsuSUpTU1p0\n+yRHKWkoG9ttnIJ5lg+fkhQVFcnyoNKk1r/+9S82bdrE0qVLUavVL/iMFcxFpXa67733HkOGDMHf\n3x9bW9tyXd1x+fJlDhw4IN9KKTzQg/D29sbb25vIyMhHdCUmTZrElStXcHNzIzg4GI1GQ4sWLRBF\nkfT0dOrXrw88cEjSdubyLtKVhhTdCoJAzZo1X+h8KdctpUekbgnJET8pLVFadJudnc3o0aPx9fUl\nNja2QiUhH4e7uzuXLl2Sv758+TLu7u5mtKhyUKkLaRXJgAEDmDp1Kr169eLkyZMvXaT7vJTUlTh8\n+DCZmZl4e3sTFRVFUFAQDRs2LHab/rhRV1Pb9iI9wC9ybmndElZWVrKDzsvLo1GjRvz73/9m+fLl\nLFy4kHbt2lnUGG9RURFNmzbl0KFDvPLKK2g0GrZu3Yqvr6+5TbMEKm8hzRL47rvv8PDwwN/f39ym\nVDoEQcDDwwMPDw+sra3ZunUrS5YswcfHh6SkJGJiYkhPT6dWrVpyNBwcHCyP+Jo6TypR8va9IqPr\nkmkJyfnfv38flUrF1atX6datG4WFhTg6OhIZGSmnKSwJa2trPv30U7p06SK3jCkO98koke5DHtcl\nMWfOHA4cOICDgwONGzcmOTlZKdY9B/n5+eh0ukfuEkRRLFNXQtrQ7OPjU0wSEZ5Pgctc0W1ZGK/P\nkUat9+zZw4IFCxg7dizVqlUjKSmJCxcu8M0335jNToVnpmr26VYEP/30k7zfS7pVdnd3Jykpibp1\n65rbvCrL0+hKODk5PTJVVnKAw9ihGrdemVtLorT1Obdu3ZKHC5YuXYqTk5PZ7FN4YRSnayoaN27M\nqVOnTP6GmDBhArt27cLW1pYmTZqwfv16s6j6WyqiKHL79m2Sk5PRarUkJiaSnZ1NgwYNHtGVkPKl\nkjC48fekwQJzR7cl1+fExcUxffp0Jk+eTN++fc1qn3ItmgTF6ZoKT09PkpOTTV5IO3jwIB07dsTK\nyopJkyYhCAJz58416RlVjbJ0Jfz9/eW0xPXr17l37x5+fn6IolhhG5pLozTBnLt37zJlyhSuXbvG\n8uXLLUINTLkWTYLidCsTO3bs4JtvvmHjxo3mNqVSYawrER8fz+eff85vv/1G165d8fPzQ61WExgY\niK2tbbltaC6L0tbnaLVaJk+ezOjRo3n77bctqjNBgBJsZwAABUFJREFUQrkWnxvF6VYmevXqxVtv\nvcXbb79tblMqLYMHD8ZgMLBkyRJ0Op2ckkhOTi6mK6HRaPD09DRJka4sSq7PuX//PrNnz+bcuXOs\nXLnSontblWvxuVGcriVQVofE7NmzCQ8PB2D27NmcOnVKqVS/IPfu3StziMBYV0Kr1XLu3Dns7e0J\nCgpCo9GgVqtxdHR8piJdaZS2qPL06dNER0fz3nvvERUVZbZinnItljuK060MbNiwgTVr1hAbG/tU\nCxGfBUWCr2xK6kokJiYW05XQaDT4+vrK4u96vR7gkQEOYwcqRbeSWpper2fhwoVotVpWrlxJkyZN\nzPV0n4ryvBZfEhSna+ns27eP6Ohojhw5YvIeYEWC79kxGAycP39edsI//vgj1tbWtGzZspiuRGmT\ndFKu2MbGBjs7O86ePcuYMWPo168fo0aNMqvGxNNQntfiS4TidC0db29vdDqdfJGHhISwfPlyk/xt\nrVbLjBkz+M9//gPAvHnzEARBiXafgZK6EomJiWRlZeHm5iZLXRYVFZGTk0O3bt24ceMGwcHBeHt7\nk5uby/jx4+nfv7+sN2HJlOe1+BKhjAFbOv/73//K7W8rEnwvjqSE1qFDBzp06AD8v65EXFwcEydO\nJD09nQ4dOpCQkEDDhg3RaDQ0a9YMV1dX9u/fz9y5c7lw4QJ2dnZmfjaPpzyvRQXF6SooPDeSrsT5\n8+fx9/cnNjaWGjVqkJKSwsaNG/nwww/lohT8f7FK4eVGcbovAYoEX/kyderUYnlaKd1QEnM43JdZ\nA9pSUVaGvgSo1WrOnz9PRkYGOp2Obdu20atXL5Ofc/nyZTp27Iifnx/+/v4sW7bM5GdYIpZaGFM0\noC0Txem+BBhL8Pn5+fHWW2+ViwSfSqVi8eLFcg/sZ599xi+//GLycxSejg8//JCYmBhzm6FQAiW9\n8JLQrVs30tLSyvUMNzc33NzcAKhZsya+vr5kZWUprWlmQNGAtlwUp6tQLly8eJHTp0/TunVrc5tS\nZXkaDWjjnylYBkqfroLJyc/PJywsjClTptC7d29zm/PSoWhAWwTKcIRCxaDX6+nZsyfdu3dn9OjR\n5jZHgfLTgFZ4LGU6XaWQpmBShgwZQrNmzSrM4RoMBgIDA8ulG6OqIG0ZVrAMFKerYDKOHTvG5s2b\niY2NpVWrVgQGBrJv375yPXPp0qU0a9asXM+o7Fy4cEHp0bUglEKagsl47bXXZD3aiuDy5cvs3buX\nf/zjHyxevLjCzlVQeBGUSFeh0iL1oVbl0dpPPvkEX19f/P39mTRpkrnNUTABSqSrUCnZs2cP9erV\no2XLlsTFxVXJnGVcXBy7du0iNTUVlUpFbm6uuU1SMAFKpKtQKTl27Bjfffcdnp6eREREcPjwYSIj\nI81tlklZsWIFkyZNQqV6EBu5uLiY2SIFU/CkljEFBYtHEIRQIFoUxXJrYRAEoRawFmgOGIAhoigm\nltd5D8/8AdgJdAMKgPGiKCaX55kK5Y+SXlBQeDqWAntFURwgCIIKsDfFHxUE4QBQz/hbPOiP/4gH\n708nURRDBEFQA18BnqY4V8F8KJGugsITEATBEfhBFMUKXWwmCMJeYL4oivEPvz4PtBZF8VpF2qFg\nWpScroLCk2kM5AqCsF4QhFOCIKwWBKEi1j/sADoCCILgA1RTHG7lR3G6CgpPRgUEAp+JohgI3AUq\non9rPeApCEIqsAWoWpXClxQlvaCg8AQEQagHJIii6Pnw63bARFEUwx//mwoKj6JEugoKT0AUxRwg\n8+EtPkAn4IwZTVKoxPwfRbN8xzdvuqAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e6a7da0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.plot_wireframe(X, Y, Z, color='black')\n",
+    "ax.set_title('wireframe');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A surface plot is like a wireframe plot, but each face of the wireframe is a filled polygon.\n",
+    "Adding a colormap to the filled polygons can aid perception of the topology of the surface being visualized:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MdpKuItCNa3A0lJSPFh5QRQjIiAjm/HHYyqSobKIyhimKUP0Qrv57rMjN5+24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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ecb7860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plt.axes(projection='3d')\n",
+    "ax.plot_surface(X, Y, Z, rstride=1, cstride=1,\n",
+    "                cmap='viridis', edgecolor='none')\n",
+    "ax.set_title('surface');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that though the grid of values for a surface plot needs to be two-dimensional, it need not be rectilinear.\n",
+    "Here is an example of creating a partial polar grid, which when used with the ``surface3D`` plot can give us a slice into the function we're visualizing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qsdzpWLOKXoiiiHa7Ta/Xo1wub2jZzno/ZulY9H2ftbU14jhmYWGBer0+INzl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\n4Q348Pm8KD4ZxaugeGQ8Xg+yT8bjVZA8Ium4Rqw/TqI/zi8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h7+D4wjUlpEsVErPKiC2p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Oe5+A+EY6R7pOP/t3c/L61cUQDHvxfShX3SUlDMThpIIUkDGvwFllTsJoLVjQXduPAfkFDEgFVw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\nde+rhRf0HQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10eae2128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "r = np.linspace(0, 6, 20)\n",
+    "theta = np.linspace(-0.9 * np.pi, 0.8 * np.pi, 40)\n",
+    "r, theta = np.meshgrid(r, theta)\n",
+    "\n",
+    "X = r * np.sin(theta)\n",
+    "Y = r * np.cos(theta)\n",
+    "Z = f(X, Y)\n",
+    "\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.plot_surface(X, Y, Z, rstride=1, cstride=1,\n",
+    "                cmap='viridis', edgecolor='none');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Surface Triangulations\n",
+    "\n",
+    "For some applications, the evenly sampled grids required by the above routines is overly restrictive and inconvenient.\n",
+    "In these situations, the triangulation-based plots can be very useful.\n",
+    "What if rather than an even draw from a Cartesian or a polar grid, we instead have a set of random draws?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "theta = 2 * np.pi * np.random.random(1000)\n",
+    "r = 6 * np.random.random(1000)\n",
+    "x = np.ravel(r * np.sin(theta))\n",
+    "y = np.ravel(r * np.cos(theta))\n",
+    "z = f(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could create a scatter plot of the points to get an idea of the surface we're sampling from:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RDqs89aCXXVui3P9HD8VFRn75gMwp14+gaWcHP32ijX27gtgMUW652c78540E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\nONjCewrgEsX/B15cARoDcRRCAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e7947b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plt.axes(projection='3d')\n",
+    "ax.scatter(x, y, z, c=z, cmap='viridis', linewidth=0.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This leaves a lot to be desired.\n",
+    "The function that will help us in this case is ``ax.plot_trisurf``, which creates a surface by first finding a set of triangles formed between adjacent points (remember that x, y, and z here are one-dimensional arrays):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wIt3tquG897yavETPnaVjRuSSYX1IgMUqT0MXGQArtkOdGg29zjnTQ0RxOTvD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\ngXJnk5pDg/DdvfmUo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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ef33e80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plt.axes(projection='3d')\n",
+    "ax.plot_trisurf(x, y, z,\n",
+    "                cmap='viridis', edgecolor='none');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is certainly not as clean as when it is plotted with a grid, but the flexibility of such a triangulation allows for some really interesting three-dimensional plots.\n",
+    "For example, it is actually possible to plot a three-dimensional Möbius strip using this, as we'll see next."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example: Visualizing a Möbius strip\n",
+    "\n",
+    "A Möbius strip is similar to a strip of paper glued into a loop with a half-twist.\n",
+    "Topologically, it's quite interesting because despite appearances it has only a single side!\n",
+    "Here we will visualize such an object using Matplotlib's three-dimensional tools.\n",
+    "The key to creating the Möbius strip is to think about it's parametrization: it's a two-dimensional strip, so we need two intrinsic dimensions. Let's call them $\\theta$, which ranges from $0$ to $2\\pi$ around the loop, and $w$ which ranges from -1 to 1 across the width of the strip:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "theta = np.linspace(0, 2 * np.pi, 30)\n",
+    "w = np.linspace(-0.25, 0.25, 8)\n",
+    "w, theta = np.meshgrid(w, theta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now from this parametrization, we must determine the *(x, y, z)* positions of the embedded strip.\n",
+    "\n",
+    "Thinking about it, we might realize that there are two rotations happening: one is the position of the loop about its center (what we've called $\\theta$), while the other is the twisting of the strip about its axis (we'll call this $\\phi$). For a Möbius strip, we must have the strip makes half a twist during a full loop, or $\\Delta\\phi = \\Delta\\theta/2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "phi = 0.5 * theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we use our recollection of trigonometry to derive the three-dimensional embedding.\n",
+    "We'll define $r$, the distance of each point from the center, and use this to find the embedded $(x, y, z)$ coordinates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# radius in x-y plane\n",
+    "r = 1 + w * np.cos(phi)\n",
+    "\n",
+    "x = np.ravel(r * np.cos(theta))\n",
+    "y = np.ravel(r * np.sin(theta))\n",
+    "z = np.ravel(w * np.sin(phi))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, to plot the object, we must make sure the triangulation is correct. The best way to do this is to define the triangulation *within the underlying parametrization*, and then let Matplotlib project this triangulation into the three-dimensional space of the Möbius strip.\n",
+    "This can be accomplished as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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00Moo0XVNYwXzVbJTEVlrlGU3pP61y9jSY+DePsIwxDjZu+n9lb9vGAWUWYkG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\ngQIA/w9ULKic9q2iRAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e69c358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# triangulate in the underlying parametrization\n",
+    "from matplotlib.tri import Triangulation\n",
+    "tri = Triangulation(np.ravel(w), np.ravel(theta))\n",
+    "\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.plot_trisurf(x, y, z, triangles=tri.triangles,\n",
+    "                cmap='viridis', linewidths=0.2);\n",
+    "\n",
+    "ax.set_xlim(-1, 1); ax.set_ylim(-1, 1); ax.set_zlim(-1, 1);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Combining all of these techniques, it is possible to create and display a wide variety of three-dimensional objects and patterns in Matplotlib."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) | [Contents](Index.ipynb) | [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.12-Three-Dimensional-Plotting.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/04.13-Geographic-Data-With-Basemap.ipynb b/notebooks_v1/04.13-Geographic-Data-With-Basemap.ipynb
similarity index 100%
rename from notebooks/04.13-Geographic-Data-With-Basemap.ipynb
rename to notebooks_v1/04.13-Geographic-Data-With-Basemap.ipynb
diff --git a/notebooks_v1/04.14-Visualization-With-Seaborn.ipynb b/notebooks_v1/04.14-Visualization-With-Seaborn.ipynb
new file mode 100644
index 000000000..21817be21
--- /dev/null
+++ b/notebooks_v1/04.14-Visualization-With-Seaborn.ipynb
@@ -0,0 +1,1799 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.14-Visualization-With-Seaborn.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualization with Seaborn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Matplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users will admit it often leaves much to be desired.\n",
+    "There are several valid complaints about Matplotlib that often come up:\n",
+    "\n",
+    "- Prior to version 2.0, Matplotlib's defaults are not exactly the best choices. It was based off of MATLAB circa 1999, and this often shows.\n",
+    "- Matplotlib's API is relatively low level. Doing sophisticated statistical visualization is possible, but often requires a *lot* of boilerplate code.\n",
+    "- Matplotlib predated Pandas by more than a decade, and thus is not designed for use with Pandas ``DataFrame``s. In order to visualize data from a Pandas ``DataFrame``, you must extract each ``Series`` and often concatenate them together into the right format. It would be nicer to have a plotting library that can intelligently use the ``DataFrame`` labels in a plot.\n",
+    "\n",
+    "An answer to these problems is [Seaborn](http://seaborn.pydata.org/). Seaborn provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas ``DataFrame``s.\n",
+    "\n",
+    "To be fair, the Matplotlib team is addressing this: it has recently added the ``plt.style`` tools discussed in [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb), and is starting to handle Pandas data more seamlessly.\n",
+    "The 2.0 release of the library will include a new default stylesheet that will improve on the current status quo.\n",
+    "But for all the reasons just discussed, Seaborn remains an extremely useful addon."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Seaborn Versus Matplotlib\n",
+    "\n",
+    "Here is an example of a simple random-walk plot in Matplotlib, using its classic plot formatting and colors.\n",
+    "We start with the typical imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('classic')\n",
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we create some random walk data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Create some data\n",
+    "rng = np.random.RandomState(0)\n",
+    "x = np.linspace(0, 10, 500)\n",
+    "y = np.cumsum(rng.randn(500, 6), 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And do a simple plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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H3PzFzWwq28SW8i2UmEoIVASSGCx5ESgVSqakTOFPa/5EdnW2X/pXairF7DLz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\nSKoIUuBLJBJJFeH/OyKinWS1qMsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1023b2cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the data with Matplotlib defaults\n",
+    "plt.plot(x, y)\n",
+    "plt.legend('ABCDEF', ncol=2, loc='upper left');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Although the result contains all the information we'd like it to convey, it does so in a way that is not all that aesthetically pleasing, and even looks a bit old-fashioned in the context of 21st-century data visualization.\n",
+    "\n",
+    "Now let's take a look at how it works with Seaborn.\n",
+    "As we will see, Seaborn has many of its own high-level plotting routines, but it can also overwrite Matplotlib's default parameters and in turn get even simple Matplotlib scripts to produce vastly superior output.\n",
+    "We can set the style by calling Seaborn's ``set()`` method.\n",
+    "By convention, Seaborn is imported as ``sns``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's rerun the same two lines as before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hJGWVk9eYtpea8HujHDvQm7fdUT692tTOSitqjYq25kGe+fcPee2FY+OOa28e\nYs/O1jEuf1dvYEyhl6xnYumqci420skwUf8pAPz9u8ikY1McAfFwN4moi3i4m0wqSsj9Ea5TP8PV\n/BiJSP98i1wwLoyplUAguKCRZZlXnjkC5K/LVtXauev+9Xlj9QYtn/78WvbubKO2oZjaxSWUOqdn\ndY/GUW6hqNhI++khEvEUrt4Ag/0hGpY6cFYoyjarvAf6FOs8G4U9EeVVNvq7/STiaZatKqerzYvF\npufyzXWTHpdFrVZRXmWjt9NHOJhgyBUi4IvmXPdZfvvsUQCc5Zace739jJvfPXsUe4mRO++7DL1B\nq0TVt3ooq7JOut7+cSXiPTrqm0wq4UdnnLiTWiadwHX60dx3vWURKvXI+GjgDDpTxXyIWnCE5S0Q\nCOYdvzdK0B+jepGdT3xq+ZTjyypt3H7PWtZtrJuV4oaRNeZUKkPLyUFOHFJc3ZdtqssFpY0o7+Dw\ndSe3oEd7ANZurOXer2/kzvsuw2SZfL17NJs+sZj1Vy1i/dX1ALSeGszbPzqYbd+77TnrO9vtzOeJ\n8v5brQDs3ak0Yll/Vf20r/9xIhZqB8BcehkA6eTk6/OxYGve93iog3ioE0mlHd7fUngh5wmhvAUC\nwbyTjYaeKAd5vlg6nAp16mg/Q64QeoMGZ4U1F1x2tvLOWuQTYTLruPnOS9i8pZFSpwWNRj3j+ymr\ntHHFNQ2suqwKSVLKlI52hY/OI/cORXJV07JR7nqDhhOH+mg7PUjHGTcV1TbqFk8dif9xQ5Zl4qFO\n1Do7enM1oASvTUbENzZ1MJOOYixagc5YSTzcRSZT2EyA+UIob4FAMO90tSnKu3rR9FKZCoW1yEBV\nnZ2+Lj9+bxRHuQVJklCrVZiteoL+GJmMzGB/kOJS07RymhuWOlm7YeZr8GdjNOlYua4KnyfKkQ97\nctuzjVmWr1Hct+1nlOYgnqGwEidwnZK+9bvnlPXydRtrL6oo6yzJ2ACZdBSDZRFqrTLp8nT+hsHW\np0nGxpahTUT6z3Kzj2CwLkJrqgA5Qzoxf/XnC4lQ3gKBYF4J+mN0nHFT7DBNmIY1n2SVIICjbMQF\nby0yEArG8Q6FSSbSU7rM54MN1zYgSdB2esR13tnqQaNVccU1yr6OFjeZTAa/J0pxqYmK6qLcWItN\nP60Uuo8j0WErWm+pzylvgKj/JK7Tj+YFn8WC7bjO/ALI4Gj4XN54AJN9JRqtEu+QEsr748+hwWPs\n6ftwocVTUqAUAAAgAElEQVQQCM5rPnyvnUxGZt2GhbEQG5eP1OYudphzn21FBmQZWk4qinOqYLX5\nwGDUYi81MTQQQpZl/N4IPneEmvpiLFY9pU4LQ/0hfJ4omYxMcakp7x4al5fl6q9fTMhyhpD7AJJK\nh8m+PF8ZS2oy6Sj+vrdzm4KDe5HTcYprP4XJvpyqlX+Ks/H3ATBYG1Cp9ah1yt9/qnXz8wWhvOfA\nI0d+wRMnniaZSS20KALBeUnLyUFOHu6nxGnOK8V5LtFo1Fy+eREqlZTnts8GrWUD2aZbnazQOMut\npJIZ/N4oHWeUPPRFw/XES8rMpFIZdg93NauoKUKlktAMNz5ZvOzitLrj4S7SyQDm4lWo1HpU6hGP\nTkntp1Bri4hHunPBfsm4G0ltwDIc2CapNBisjTgXfx7H4nsAUA9b3heK21ykis2SeDqR+9wX7qfO\nOnFNY4HgYuXYAWUt98Y7Vo7bsONcccU19Vx6ZS1a3cgrL6u8I+EEZquOYsfCpFqVllngmIsXf3Uw\nVyCmtl4JQHOUWTiNi642Lza7gWXDbUrvvO8yXL0Byqsuzj7bqeE1bZ1Zee+O9ujoTJXozdVEfMdJ\nJ/yodVZScQ86Y2XeOEmSMBYtyX3XDFveKWF5f7xxRUaaGnQFeyYZKRBcnEQjCXo7fZRX2SguNU99\nwDwiSVKe4ob8Gua1DSULFvSVrRwXCSVyZWKz0fClo9bo119dn5sAlZZZxtQmv5hIxhUPhUY/EmWv\n0iiTL63Bic6kFOEZaHmSeKgL5Axaw+ReCmF5XyS4wiMBJl3B3klGCgQXJyeP9CPL569r11FuwWLT\no9WpueTSqgWTo6quiMs21fHR+505ubJKubRsZNJzdsvOi410MoQsp9Do7KSGlbd2lPKuXP515EwS\nSVKhNyvZAKm4m4EzjwOg0U9ePlal1iOp9RfMmrdQ3rOkf5Tl3RG4sGriCgTzTcAXZd877RiMGpau\nOj8rVhmMWr74x5sWWgxUKhUbr1uM3xul5eRgXkS80aRj623LsRUbF3TZYaFJxoZwNT+GJGmpXvUt\nUnEvkkqLSjMyuVFrRz7rzDWU1t+Jr+f1nDKeyvIG0GiLSCV8yJk0kko9K1lTCT+ZdJzgwG60xgps\nZVfO6jxTIZT3LOkPK8q73FRGZ7CHoagHh/HiK5QgEIxH+xk36VSGq7Y2TdjoQ5DP9bcsw1lpZdVl\n1Xnbz9fJz7kilQgwcOZJMimlSE06FSWV8KDRT7zUIUkS5uJVGKyN+PveJpXwYbDWT3ktg7WB4OBe\nwp5DWByXTUs+WZaJhzvRm2uRJBX9p/4tJyscnjflffFO5eZIe6ATi9bMJ+uuA+CNzp2kznHUuS/u\n5+jQCYaintxkQiA4H8iW8qyqLZpipCCLTq/h0o11aLWzs/g+rni7f0s66c+tScdDHciZZN5690So\nNUZKam+hrPHzeTXMJ8JWvhlJ0uDv3zmtJicAUd8JBpp/gbvjBeRMapTiVpDl9LTOM1OE8p4FnpgX\nX9xPY1E9l5atxqI1s6vnfR46/BjpzPz8ocbjJwd/xv87/O/8f+//L/5+7z8RTUXP2bUFgolIJtMM\n9gfRaFUUXYTNMgSFIxX3EvWfQmeqwlZ+FQDBwQ8A0JuqJzt0Vqi1VmzlV5FOBvH1vAGQ19ltPGJh\nZdk04j1KyH1wzP5U3FtwOUEo72khyzKdwW4SaaXmbauvHYDF9nqMGgPfu+JbrChZygnPaXb1vH/O\nZOoLu/K2vdf7wTm5tuD8JRHpQz6HE8iziceSPPp/3sU7FMFeYrooC4gICkdwaD8AVufGnKUdH25G\nYrKvmJdr2iquRmNwEHJ/RMR3iu7DPyTsHb91K0A64ct99vZsB0BvqcsFyCXjY0u1FoKPvfKOpWI8\n+NFDHBk6DsDevv388/6f5uVpT0ZGzvCzo7/kf+/7Mds73gSgNdABwOKiegCKDXa+sOIuAE56mgt8\nB+MzOmAui6j2dnGTiA7Qf+rfhstALgwDfUEyGcVSqawRLnPB7JFlmYj3CJJaj8m+Au2oaHGtsRyN\nvnheritJaooqrgNkhtqeQs7Ecbc/S/osd3iWZGwISTUc1zHsIi+pvQ171VYAfD07pmyYMhsKory/\n//3vs3nzZm6//fbcNr/fz/33389NN93EV77yFYLBhQm/P+45TbOvlYcOP0ZGzvD4iado8bfT6m+f\n8ti9ffv52ZEnODio9CHuH7Z0OwLdqCQVtZaR9BK7vohivZ2j7hO82PJbEtOcHMyWZu9I6zqtSkO1\npZK+sItIUrjOL1ZSMaWBRSLcTTzcvSAyDA0oL6m6xSVctml6Pa4FgvGIhzpIJ4OY7CuRVJpc+VIA\ne+WWeb22yb5yTP1z16mfExzYSyzUkduWySRJxb3oTBWYilcBYCxahkZfmptspOJu/P3vFFzGgijv\nO++8k5///Od52x555BE2bdrE9u3b2bhxIw8//HAhLjVj/PFA7vOJUVZxT6gv9zkjy7xzuJfdR/tI\nDbfmS2fSPH7iKQ4NjbhLwskI6UyanlAv1eYKtGpt3rUaipSX1Wsdb7G3/6N5uZ8s7YEuAP7s0q/x\nvSu+xSWlSo/kjmDXvF5XMDcSkX4G254hFuoYtz3hXBidnzrY+hSerleRJwmiTCfDRANnCiqDe1h5\nX33Dkhn1uBZcvCQifTn3eDLuIR7uJuB6j7DnMACmomUASJKK0kV34Kj/vbzKaPOBJEkYbSPXsJVf\nTSrhxduznYEzT5BJx4FspTcZrcFJcc0tOOp/D0fDXUiShMbgwOrcOHyPha8FUhDlvX79emy2/DJ9\nb7zxBtu2bQNg27Zt7NixoxCXmjGuyEgxlZ8eGplgjK6KdrjFzb+/epKfvXyC/3y7hXAykueCXlrc\nhEVrJpAI0Rd2kcykqLONLYdaZR5J6Rg9OSgkhwaP0ervoDvUi06lpdFeT4W5nAabMnFo9wvlfT7j\n7f5tLjp1qO1p0skwoDRa8PfvylWOmgnpVISAazdh38hEM5MKExr6kFiwlVTcy2DLr/H37cw7ztP1\nEoMtvyroJGLIFUKrU2OzTx3ZKxDIskz/qX/D2/UKiahLWfY5/Si+3jcIe5TgL62pMjfeXLIGU/HK\ncyKbcXhN3WRfib1qC87Ge4eFzpCMKl7YrIdLZ6xArTFiKl6JJClqVZIkimtuQmsoIxkbQJYzY66R\nSSfG3T4d5m3N2+Px4HAoSfFOpxOPZ+YvpUIwWnkD3FK/FYNaz4eug3QEunjwo4f4bdvIxOKN/d38\n/PB/8KtTzwJwz7JtfH3NH2DTWQkkAnQOK/3acWqZX1uzmaurlJnWOz3v82LLb8nM8g8zHh2BLh45\n8gt+tP//0hPqo9pSiWr4h1I/bPULy/v85ux2g6nhYJeI9xj+vrcZaH580uNlOUMi6kKWZWRZJpOO\n03/qZ/h6d5AYfpEU134qNz4e6WWg5UmigdP4+3fmpa1E/acBCA7uKci9pVMZfO4IpU7zRVu282Ik\n4j+ViwCfKdngM4DgwPvIwxZtFpXaiFpjYSEw2hopW/IlSupuz30vqfsMoMSXAMRCbQDorQ0Tnkdr\nLEcedq+PJpUM0nP0n/F0vTIr+c5ZkZbp/mN2OufeU7fT18Oe7gM0ldQzFBvCbrDx6eU3UFtUxZry\nFXSEOzk+2MwPP/xX5QA1mI238+VPL+Wht1/llP9U7lzXL91AicmOw1JMb7ifvoTi/lhTuwRnab6s\nTqx8s+rLHP/NKTxRH691vMXKqsVsrltPIp3kl4ee4/r6TSwuGVkLzMgZ/vc7P6Xc7OT+y++e9L4+\n9OZHl1dYKjnY6uGGjYtwyBb0Gj2hVHBaz7AQz1kwOWc/40TMR2cykLfNbIhT7LSSDikvrXQyMOHf\nRpYztB1+Eq/rMJKkRqXWYi1pzIt2BWhYdg01Des4susfCPTvyttn0vmw2OvJZFJ0SiqQM8RDnRRZ\n0+gMc+uq5eoLIMtQVWs/Z78v8Tuef6Z6xvsPPAVA3ZJNaLQzSw1s6x+1LDnsJl9y+QP0t71J0HMG\nrd5MWdkCNl9xrsr7atY34OkENR4cDjM9RzvQGuxUVtdNqOMy4UVEvEcwaP2UOOtz29uP/RY5kyDs\nPgD8/oxFmzflXVpaytDQEA6Hg8HBQUpKpld9bHBw7oFtP93/BK3+DiQkZGSWFy9hY8lGovEUr+9u\n49P1t3J88F/yjllZX8SRyJvoFp0AoEhnY33FOtJhNYPhIAZJaTl3qFfZb0haJ5T1irLLcpHpD77/\ncz5oP0y1pYrfNb/Nns4D/MNVf00gEaQr2IssZzjQp/yAb6+7ddL72t2sRMyr0gYy6hi79nh5s/8g\nTpueaoeZIq2VoYh3ymfodE4su6AwjPeMQ24l8NFedQMafQlDbU/hGeonpV6M39OfGzcwEBj3RRAN\nnMHrUl5wspwmnUrjGziWa8QQ9hwCYMgdQZZVSJIGWU4BKuzVW/H1vE5/13GKkqXKGtwor1Bvx3HM\nJavndM8tpxVrxGjWnZPfl/gdzz9TPePREdh9Xc3oLQ2E3QcwWBej0U8+GZQzabwDx1BpTLnCJpJK\nSyzlIC0rk4BMRjqv/sZyxgSoCHi76e1qI52MYLA2MTQ0cTR5IqM8B7erjbR6sXIeWcbTe2BOshTM\nbX52IvuWLVt47rnnAHj++efZunVroS41JdmmITKKTPU2pUj9v796gn997gg9XWruXf57eceUNfjZ\n51IeppzUcnPJPdzZdFtuv02nzD7dMQ8OQwl69cQlH2+p38rfXvmX3NZwEwC7+/bxTPOLAAQTIRLp\nJP99zz/y00M/54kTT+eOm6oYQE+4BzmtJnxoM5dbryE1sAiAviFl3bRIbyOUCJ/TQjGCEVJx36R/\nw4j3KAAm+3I0OiWNKus2Tw5Hiivbxi/qkBx21RmLlo+qFiVRUnd7rjViFkmScmOsZRsxl6wFJCK+\nE0o5x4gSk5HtbxwfFUE7W7xDygu4xCkKs1wsJMIjsUOJSB/xUDuerpfpPf7jXDzHRMSCrcjpOObi\n1WiG647byq9BUmmwV30Cnakq57I+X5BUGrRGJ8lIf87lr5uiWIzWqMRCJaIjntN0Mjg8sZ49BbG8\n/+Iv/oK9e/fi8/m4/vrr+dM//VMeeOABvvWtb/Hss89SXV3Ngw8+WIhLTcqRoeP8puV3hM/Kx1tR\nqkQrfnhKUeqnu/zcsGhR3pg3h14C4DOV9/DrF724dLAXF0++fpp1SxwsWjXiOqq0TF5rWKvWUm5y\nckvDVjZVred/7P3nXPUzrUrLzu73iKaU0nuhUT/wcCqCQWVk38kB1i8rQ6sZmVuFkxFCGS+ZcDGk\ndHQdLYWMMiPt8yj3W6S3ISMTSAQpnqMLVDAzvD07CA7sRm+pw7l4xAWWbaAAMrFgGzpzDRp9MZnh\nv38q4UeWZZKxkbz9ZKQ/r1tSbntM+f3aq7agNThIJ8Okk0F0pgrkzNjUxJJFnyHqO4G98hNIKg3G\nomVE/ScJew4TGtwHSFjLriTsPZqX/jJTMqkY6XQEz/AkcqHbfwrOHfFIz6jPvXmWeMh9gKKKq8c9\nTpZl/P1KAKWp+BLMJWtIJfyY7ErWjEZXRMWyr86j5LPHZF+Bv+9tfL2Kd1U3KqBuPNQaE2qtLRfk\nBiMTdJ2pmkRkdi2lC6K8f/SjH427/bHHHivE6afNQ4dHrrfGcQmHh9O8Gmx1uRQwgLa+AOWmsakG\n11Zv4pr6NTwl7eJ0t4+3D/YSjafYd2KA1ZeNrLtUmsunLZNdX8SfrvsqDx9+DH8iSCwdy1n4WXQq\nLYlMkkA8yL7TXp547TTBSJIbr6jNjdnZtRskyPiUtoAdriAqSSIjy/S7RyxvAF88IJT3OSQR6SM4\nsBuAeKiTqP8kVFyFLMv0HlfiKhwNnwNkjLalAKg0BqX9YMKHu+MFMukYKo2FTCpEwPUeRvvyXNRq\nlmRsECRVrtKUWmvOdVLSW+oxl6zLvfxACbAx2hpz363OK4j6T+LpVLxA5pK1aA0O9JZFxALNpBI+\nNLqZ/276T/+cVNxNwLMVnV6DySIakVwMZNIxwu6DgLLEEw+2k1CNpM8GXO8Qdh+gtH4b+mHPUCad\nYKDll0iSlkSkF1Pxqty+qZTg+YK5eDX+vrdzE2atcWp9oDWWEws003XwB1Su/GNSccXjZi5Zg24a\nx4/Hx6bCmi+eH8W7vGQJl5at4db6T6JWqekeHFmT6HQF8YeS3Ff7NWLHlI4vxXo7dzbdhlGvodZp\noaUnQDSuuDXiyTRl+irUktIwIJuWNV0W2Wr5h6v+hpsXKYUFekJ9lJkcXFW1gWK9netqlJq9gUSQ\n/acHAZnDXaMKAcgZ3up+FzmlodEwsi553boqNGqJPrcy27UPFzHwJ/KDogTzS9YiNhYpijMbVZpJ\njfzmsvnUo6tCaXTFJGODw1WkDDgXfw5zyRoS0T7c7c+TinuR5czwfzLJ2BBavWOMUodsDuynMRYt\nnVBOg7UBR8Nd2MqvweK4IlcBKpszG/XPvDqgLMukhss/phIeShymSYNTZTnNYOtThIbmtw6CYGKi\n/tP0HP0XEsNLJ1Mt101EwLWbdDKAreIaiiqvJ5OOkk4GMFgblXiLTJJUwovr9KM5izwe7lKKCA1H\naRdVXl+QezqXaPTFmOyX5L6rVNpJRitk25HKcoqQ+wDJmGt4eykldbdNdujEcszqqPOQI0Mn8r6X\nGR1cV7OZUCBGMpmmuUtR7ktqimju9vOzl49z5SXlyGE7Nxd9kZvWLc8VXbmkoYTO4WITRr2GaDxF\nIqznn679O/rCLurGSRObCkmSKDeX5b4vLqrn88s+i4zM7uGa5INhH6c6Q2hqTtNmb+OUu4JlpU10\nBXuIpCKkPTWsbajgxsuMPLnjNNetq+J0t48+TwRZlnOW9+jCNIL5J5ubbbAtJuo/mXOJJWMjNY3D\nbkVZabQjHhyDtZ5kVAlUK676JHpzDVqDk2TcQ8R3jGigGbXWikptwNHwWeRMYlo9iSfDZF8xpia0\n0bYEL8pL3eq8YkbnS4+Knm9a3InRGqDv5IdYHevHbakYD3UR9Z8i6j81ZcvFufRUFoyPLKfxdm8n\nnfTj6f4taq2VdDJIedN9SKqZqYOI/ySSSout/CokVMTD3UiSiuLqmxhsezr32wYlDcxetTWvWIla\nZx93eehCoKTuNjKZRF4hl8kwl6wdrnzYRWBUtTWNbvb3/7GxvLuC+eUgHcZSopEET/x0D7/9zyMc\nbfNQAtTH0qysLuJEh5eTHcpLdnFpNbpR1dKuWzdS9vS6tcrnnqEwOrWORbbaWeewrnaswKhRgoia\n7IuVoCJJlQuGO9XbTzojo61SZqX7e5Q83FNexWrLBEqpKTNz6VIn//THV1FXbqXMbiSeSBOMJinS\nK0FQZ3shBPNL1tI2WBoAKecSS41TcGV0icfRStRgU6JQVWo9ZU1fxFS8CjmTIBV3k4j0EPEqmQY6\ncy2FRqMrQmusIBZqz1WOmi6jX8YV5W6KTC0ko/0EXO+NOz42Kq93IotPzqQYbH2arkM/wNf39ozk\nEUxO1N+sTC4lNYlwN1HfCRLhbtydL5FOhif8m2RS0bzfRiruJRUbwmBpQKXSIqnUlDV+Hufiu9Ho\n7aiGa31r9KWoNRaCg/vIpOM5a99gbcTZ8Ln5v+F5QqXWU9b4eazO9dMarzOWUb70D4YDR0cY/T6Y\nsQyzPvIcEIom+W///gEfnHBNObY31I9KUvGdy/+Eu5fegdNUSt+wtd3T4aOrw0sjKnxDEWp1ygzz\n/WPKeR22/GpQZcUmNq4sp7LUxOXLnMr5hyaPnJwORo2Rv9/8V3xp5T2sL1+X2561mA+E30G/9u3c\n9uOdijv2lEdR3ulACdWO/IIFJVZFdm8gjtPoQCWpeK93r+jvfQ5JxT3Da9HFqLW2kQjycboJja6X\nrDPVIEka1Fpb3lqzSqWluPrGvOP8LmW2brIvm49bUCwIOU3Ye2RGXcliwfb8DapStMYKUgkvsVAH\nIffBvApS8WBb7vPocq6jCXuPKnEDyAT6d80pmE6QTzYwsrTu01idG9FblMDdiPcIPUd/RO+xBwkM\njC3a033kn+g9NpJeGw0oSyyGCcqUSsPZOFqDE4vzCiWf2XOERKQXlcaCs/H30ZkmD/z9OFJceyuV\nK/4YS+nlmEsvG3cJbLqc18p773EXna4QD704cTs2UNaEe8L9VJjKaCiq49qazQD094xYoHXpkRll\nZCA/J6/ENrYG8x/evpL/8dWNVDmUgKB+z/gdZWaKUWNkQ8VlaEe5qLKWN4BKP9IAfjDi4cCZflr8\n7ajiNswaM/azgoGysnuDcYr0Vu5eegfhZITXOt4qiLyCqUnFPWh0xUiSCo3eTjoZIJNJ5daCDdaR\noLHR/1glSaJq1Z9RufyPxpxTrbVgsC7OfZfTcbTGilkFlE2H7Fq5t+tV+k78NC91bSKSMTch90d5\nExJL+SdyrveB5l/g6fzNcBEKpcjM6OjkVGxojKUnyzLBwb2ARHGtUvcgFmyd072dT8hyOueZWQiS\nMcUbpDNXU1xzE+VLvoSz8V7UWsVrl04G8fW+kbfko1jcMpl0jPRw1kw2hsNoaxr3OiU1t2AsWkpx\nzc1YStcBEt7uV5U1ccvEBU0+7qhUWrQGByV1n6J0lmvduXMVSKZ5we2PTT0I8MS8JNIJqs5K4err\nHlHeeiTUWjVNK8uIhZPYtcqt28w6tJqx62oqSVKK0+s1WIxaBr3z163LprNiVlvIhIrYzH38xeXf\nUGTQx/iP3ftIZpIkvMXUlVvH/OiLrYry9gSVZ3VV1UZKDMXs7d/PE8efzqWoCeYHxZ0YzUWAa3RK\nQFoi6iERHUClNuSsm/HYv9vFwX3je0mUJgcjrkVzyZoCSp6PzlSFsWgpap2dVMI7rXKXwaF9IKex\nj/ISFDkWYypakTfx8HS9Qs+xf6H36IN5hWECA+/TdfDviYc6c9sivuMkoy5M9pW59cRUbGFKK88H\nAdf79B7/Ma7mx4kGzpCZ5+6DZ6NMKFV5k0CjrZHyZfejUhvR6B0gp3M1CZRjRuoOxIKtZNIJ4sF2\ntIayXL2Cs9Hoi3EuvgeNzoZaa6Wo4lokSYPWWEFxzS3zdn8XE+e18m7tGwmGSWcmrhHePdwEpNo8\nkmrgHggx2BekvNpGWqcoZ2eFhZJhS3pjo5NSi44Ny8vGnvAsyoqNDPljZDIysUSKjCyTTBWuEIpa\npWZ14veIH7+SyxqrWVy0iGK9Hb05jk9S1hTTgVIuXTI2WKlk2OXvDcZz/w/1Ka7+Pf0fcsx9aswx\ngsKRDVbLBt5oDUobwKHuD0gnfBhsTai149dmHuwPsn93B3t3tuF1j/XsqDQGDKNSvSwl68aMKRSS\nJOFcfA+VK74O5BeNCQ59iKfzlTwrWZZlYv5mJJUOo20Z+w6s41Trpag1BlQaA2VNX6Du0r/NWeXp\nhJ/0cPS9YVgpx4JKW9vRE4VA/y6Q1NirtqDW2pAkzbixAxcq2frz8VA7gy2/ou/k/yOV8JPOyLzX\n7yVSwPfKeKTiHjT64jHuWo3WSvWqb1PWpNQoiPhO4un+HelULK9okLv9WQbOPI4sp2bU2auo8jpq\n1vwXKpZ9NZfeKJgb563y7nOHae8fUd5Dvomt8NPDAV0NRSMWzntvnEGWwVhl5WgiSUyr4uqtTViH\n3cyek4MsDqVYXTx1Nagyu5F0Rua3ezv4xj/v4ke/Psgf//MuntvVMuWx0+VYqx+DTsOSGmUmW2yw\nk1RF0Di7kWUJOVjM+nEmGjnLO6Ao7/2nBvG3V6HJKEp9MDJ23VVQOLJWSTYFLBtQ5uoYKUBhsq9A\nZ6qmtP7OvGMP7h2xOA/u6WQ8VCot9qobKKn7DCrN/HfqUqm0qHV2ksMu7VQigK9nByH3/rwqbKm4\nh1TCi8G6mGRSZmDARjIz1sNgK78KSaVFPyrQzmhrUiy8LMOKRJYzJGND6E1VwwpGQqMvIRl3zzqd\nab7IpvDNlGTcjUptpHzJlzEVryad8DNw5pfs7HXxStcQz7W5OOYN8djpHhLpwjU1Akif5SU6G0ml\nQa0tQlLpSMZchAY/4OCb/5WhtmfyxilBitKMPUGSSj2nNV5BPuflk5RlmbffeYEVzj70w1Zzr3vi\ngLGTnmZ0al2un7bXHaanw0d5jY1n9nWRAEovKcNZYcVyVnDauzvOcGCCF2cWh12pa/7szlZk4ESH\nl3RG5uXdHXgC03PtT4bLE2HAG2VlfQkatfInyQaxSbo4qe4l1JYWYx+nP3J2m3fYbX6iwwtJA+bu\n6wAYjE69dvnBrjae+fcPiYRmFmksUHKbgdwLUW8ayVRQacwYrU2o1Hoqln0Fc3F+kwNXTwCjWUtx\nqYnTx1wEJ1gmspVvwlK6dtx984FWX0omFcLf/za9xx7MFaMIDOwmk0kCIwFLxqIlBP3K78ZSNHZy\nYXVuoGb1d7E4Ls9t0+iKMJeMPIvU8PpqOhUG5Lw1dI2+BDmTIJOae8BooUgnQ/SdeIiBM0/M6Dg5\nk1YsX0MpeksdjvptWMs2k4q7ae5XPGS9kThPnunjtD/CUe/E9bJnQ7b2/WTpWZIkTZiOWL7sq5Qu\nugPIFvhxFlQ+wcw4L5V3MBziqkUtbFvdzNdvVizR/nHciqCsd7sigyy1N6IZDgI7cUhxo5cvVn6k\nFqOWO65R1uAso4LTqursGIxaDu7tIj3JLLdsWHmPR1vf3HOqD7cqL681jaW5bZeULMOoMpNoXU2q\nbzEVpeN7CLQaFTazDk8wTjqT4VSXYgm6XDIqScVAZGrlvX93B0OuEDteOjHlWEE+WZduVnmPzpW1\nOjdMmKecTmcIBeMUFZtYd2UdmYzMkQ+7xx17rsm+vHP5qJIaraGMWOAM7vbnAYhlo41tTYSGJ7BW\n2/ieAUmlRqMf+W2rdUXYyq7CXn0jao2FZFyx8tPDxYXOVt4wfuR+Ici2Vk1E+pVgssTUwWTujhdJ\nxVPVei8AACAASURBVIeIhzpmZH0r7mcZ7Sivg71qK7aKa/HJytLK6IiWgagyacrIMpk5eh7C3mP4\nel5DpTHlTaTGQ6UeayQAaPUOTMWrcTbeS3GtWLdeaM5L5e319OU+F6M0ZO+bQHl3BZU14UZ7PQB+\nb5RjH/ViNGvRDlsCt2+ux2ZWorTN1pEfZl1jCUtWlhGLJuluH78ZBChr3lnuv3UFjiIDX7pZSdlp\n65t7x5sjLcqLaVXDyIx4U9UVfGvln5MeUoreV5RM7N532g24/THOdPuJxpU1M1lWYdMUTWl5x2Mj\nxfH7uvxkMueXe3I6hGPJBbu2suYt5QXulC7ahq10GVbnhgmPCwViyDLY7AaWrCxDq1PTfub8WOIY\nbVHpLfXUrPku5Uu/jNbgJBpoJp0MEwt1KNHvWiuh4SUbyzhZGyPnHFHeGp0dSaXGVnYleksdciZJ\nOhnMpY6pRxWyyaYTFaJxytmu92TMTfeRH9J77F/oP/UIPUd+RO+xfx3Td3k0qYQvt1YPylo+QMR3\ngr6Tj5CMDk54bC6CW1eKL678ZiVJQiq5Cj/DpY0TqZwCbw9FSWVk/ulwO8+0Tp0uOxFyJoWn8yUk\nlY6ypvumLPQjDTe0MdpXcOkn/ydqnR21zo5KrVOCeG2N06oqJphfzkvlHQ6OVOYh0YtOLdM3gdt8\nIKL8Yyk3KS+c/e+1k0pluGprE/6I8g+keJTCVqtHbrnEYWbJJUpd2SP7Jy4O31ht4+YNdfztl9dz\n9ZpKfvj1zVyxXDnubMvbPRDid88dxTfN1LJ4Ms3JTh81Tksu+CxLqW1k0jCZ8q6vsJHOyLy2rwuA\ntcMWvCppIZQME06ML0vAF+W1F0bS8DIZmWj43Ea/ns2AL0rPUJhILMnjvzvJ6a7JLaGjrW6++eA7\nvLqng30nB3j01RMT/lYKjSxnhtPE7EjSiIVtLlnNksu/OqEFAxAYjuEoshtRq1VUL7Lj90YJ+BY+\nO2B07q7R1oRKpUWlNmCwNYGcxtv9O5AzuWjw4BSWNzCqC1q+ZZftJpWM9o8o71Gpk0bbEpDURLzH\nSSdDZNKzW6YKuHbTffh/Ewt1kIi6iIU68HS9jJyO56rEKeeWiQbGj2VJxoYYOPNLRcbhCUYy7iER\n6Weo7RmS0X6iwTMTypAto/u8x8kPD7cTTCoT5zOBkX+f8vB/AN2hKG3BCL5EikOeIF2h2d17Mu5B\nziQwFV+Czjh1gG5x9U2YitdQUnMLKpWGyhVfp3L512Z1bcH8cV4q72whgYTkRJZTXFKToM8dGTdo\nJesWLhtW3v29AXR6DU0rynIR2MUTWAQlDjNllVaqF9npavXw0q8P8fJTh3JuwCxqlYrPbWmivmJU\ndSyDhooSE+39gZxLKxFPceJQH22nh/iPRz4gEZ+65dvJDi+pdIbVjWPXoUyGERfsRG5zgPoK5WV3\noFl5Fp/b0oRaJTHQr/x5d545gjs6NmJ33zvtOY+DeTh/PBScet17PoOHfvyfh/mvP9vLn//kPd4+\n2Mvzu8bm+PYMhuhzK9Wg/n/23jM+jvM8+/3PbO+7ABa9gygEeyfFIkpUtbrkJtmxY8WOk/hYceL0\nxDk+r98Uvzk+aY6d4irLsmxLsmyrUJ2iKFEkxd4JoncsdrG9z8z5MFuJQoASJco/Xl9IzM7Ozu7M\nPHe77uv++a5uFODxXd18+6kT7Dk2yt//6CCJ1OUfi+ob+BVyOjptHOd8kDXSdqdq1OoyWZfB3vef\nWa3V2ahs/10sJSuwlOYZ7kZbEwBR/0kQNPjDTTzy7bdy99BckTdAWfPHKGv6SNG2bEtZLHAuZ0QL\n0+aixoDR1kQqPsHwif+P0VPfWtD9J8sppoZfwj/yEoqcZKLrh4yd+S8mun44azQfD/fOuD04sZd0\nwoeoMWKvUOcRpJNTRZH4XFF71nj3RNXYejiiPmtnAqrxXimcKj53BF4ezmfO3hiffuxTU2GO++bO\n/mX1BnQFpYu5oNXbKWu8O9cloTpvV4fNXGm4Io23IKkLmGhVF4628jDRRJrgDFGhJzaJgIBZsLP7\n0BABX4ykRiCZlnO9z1kVsixuuruT5etqsdoNCILA5h2LMJq0DPVNMdg7xVuvzU8UoqnKTiwhMe5T\nxyF+95/3FEXwhX3ms+F4tt7dPPeDVTEHK76pKu9U1LqtVJVaqC23IofVXs5nxp7gb/f+47RFr9BQ\nN7Wpzs+FjkshJEnmse/s56VfqbXxZCI950I6MB7iq9/fz8D47ItLz0iQr35/Pz0jQfzhRE7JLplW\na4neGc7nK9/dz1//zz6OdE0yOBHGZTPQVGVncYOLjnonkXian716npN9sxvCbz11gv/85YlZX78Y\npHSUiO84OmM5JRkxkYUgG3nbM3yKqkyXweT4u0tSulTozZWUNtyFRpu/7wyWekSt2uZjc69n3+se\nQoE4k+NhHC5TUUlqJpgd7dN01Q2WWkStmWjgLOlMzbtQ/x3AUbE1J3QjpcMLIq+FJw/mJr4JogFR\nY8TsWoatfCOljfdSu+IvcTffT+2Kv6Ru5d+g0TtIhHpnrGUno2pGsGbpl3OToNKJKRKRPFdhrra2\nVHwCmXy6+eGuEb51aoDzgShObZrFYt4JqEQ19AOZNU8nCpwLRJEKnre0LPPI+VF+0j2GNz57xizb\n9qc1zs94X8UHA1ec8VYUGbPoZSpqwJGZw11lU43g9549UzTa8/WjI4yEJigxOnnj2DhPv6BqgY9G\nkzyztw9fKIEoCDgsxV5jS0c5m3csygmelJZb+a0vbOKBz6+nxG2h6+TEvNKXTVVqhNA7GmSwZ/pD\n6524+EJ8ZsCPQa+hpWZmsYMHP7SY2zY1YDLMPjSgssSMOfP6XVsaAfjEjW3UXaCDHb8g5Rj0xxAE\nuP1jy6muVw19tn4JIMsyEwVlgYFuL1OTUc6fnuD1F7r47j/voefs7DW+f3viGAPjYX79Rh+9o0Fe\nentwmrF/+s0+BsbD/N3Db3PkvLrI3L21iW9+aSsd9U68gTjxZD6DEYrmF6mHXziLIMCXP7aSr3x6\nLX96/yru2KxGh68eGuYbjx3hmb19084rkZJ4+8wE+09PMH6JynnxUC+gYHYtmTM9PhsujLyzRjw4\nR0vk+w1Ro6d6yUNUL/kSzuobivgSa65puCTVLEEQMTnakdORjCRqceQNYLDWUb7oE9jcG4DZZVVn\nQlZspKz5Y9Qu/zNqlv0pZY334Kq5CYtrKaKow+RoVfW5BRGTvQ1Zik+bsKYoEqn4BHpzdYaAlyHS\nxcaJh3rR6OyIWsuskXe2DS5iKG6nG4okSMgynTYRO/n1olnMd8BUmw2sLrMTl2QGwnG88SR7xqbo\nDubXqFdGZnca8pH3OxtqcxVXFq44452IDKLXpOj2unA5XCozVa+yNI/3eNl7UvV+Q9Ek33/+BBEp\nTIdoYLRrhKbM14misHPfIIMTYRxWPaJ48UVFq9XgcJlp7VRrQlOTMy/qiXianrMezh4fI57RTu8d\nCRURvbLzjL0Tc0cIkiwz7otSXWrJtYhdiC3Lq7jv2pYZX8tCFAX+5P6VfO131rOmXT3/RTUO/vYT\n20ARECUtKBBKhkmnJRRFIeiPEQ4mqK53UtdUkkt5Fkbjb77czRM/PERvJh1/+liei3DikJphOHu8\ngJ9QgKGJcK73vG8sxNd/fIhHX+rieIGTk0hKnOpX/1aAh3eq7TJLGkswG3XUldtQgCFP/ncsJC4G\nwkk2L6vKSdgCuT75LH6xu5c9x0Y53OXhj765h3FftEinPns/zRepuBf/yCv4h18AKBJRWQiC/jha\nrYgp41jq9BrMFv27VvNWFOWykA9FUYdWbyccTBAJJXCUmNi4vZnWJRevpc4Gs0N10hU5hUbvnHW6\nVdaop1MX7/CQUmGS0VGS0RGMthbMjnaVHHYRB8NaugqAkGdfUS93KuYBRUJnUgl0otaCVu8iHupB\nkZPqRDhDCemkP6cNL6WjjJ39Dv6RV9SIXJHwa2aeWb21plolgwmqc6onxRbxbZYYp/jEoioWO9V7\n/KVhL0/0jvPs4CSP9eTv3VP+CNIs11slyqnSvVfxm4MrbiTo8OAxdEDCW0bQH0Nvrkbyn+avHmjj\n7x/t4vVjo2xdXs2QJ4JoDlGnFdliCJNo28VLw5sQNSLXbWzkiTf6SEvQUGG72EcWwZZhqAcDMy+i\nu58/y/nT+WjTIQj0jgWpL2jycDhNpFPSRSPvyUAcSVbmJKPNF4X1+EIsHt6GMGokrUvyfPc5EmGZ\ndEoiGwA7M7X0nPEuiLyzJYCxoQBNrWWMDgZwlJhoaC7lWKatSaefuRWqeyRfMihMff/6zV6Wt5Ry\nfjjA8/sGSKZkblxbxyuHhpBkBadVT0Omhl9bri5YQxNhFmUyE4WGV6sRuSsTaRdu+9TN7UTiKWrd\nVv718WN879l8C9zBcx6spnzq8sj5yVwb4XzgH36RWFDN8Gj1LvSmmRfjuZB1nmxOY5ExsTuNjI8E\nkSS5iFh5KXjtyAiPvdLF135nA+45Wh0vFeMjqgHtXFHNyg3vbNJZoZSqtXT2MaFZkpg0w7x6RZGQ\n0zGS0RES0WFCE/ty/emWkmXzPhe9uRKDtYFEuI/Bo/8AioSj6rqc46DPGG9BEChv/RTB8TeQUiFs\n5RsJTx4kERkkHu5DEDQEx98gGR0hGR3J1Y+nUCP2OosRg0ak0WaixKDFYbKRsDVzT3AnJ+VW2nUT\nCFIEnTaAy7Aeh15Lp9PCKX/+/k9IMhathk6XhQOeIAOROE224mutKAqpxCRaQ0kRqfIqPvi4ooy3\noigEvOdxGQWCHiev7TzHdTtqiPlPU2ULsqSphJO9PiYDMYY8YQRLgBJRXeQM+jQllV7ue+Amoikj\nT7zRB6htYgtB1njPJJgRiyaLDDdAvUFL91gIn7agv9dhRAHGhwMM9fnQG7SUV003rtne9dnIaJPj\nIcaGgyxZVX3JQv6aEdUA6pMmwr7pLVWWjMiL2aJmKLI178L0djKRJhFPk0ykqay1s2F7E3qjlrf3\n9BHKGPtTR0cYHw6y/VY1wslGy7df08jTb/bljtU9HORY9yT/+vgxFAUW1Tq4a0sTTVU29hwf5bdu\nas9lIerL1QVzsMAJGs4Y77ZaBxuXVlI6gzDI9lU1uf9/6SPL+ZefH8v9fWZgqog/MOyJkJbkWTMf\nhZDTMZVNLIiUt3xSTaEu8LpM+mNY9BqSCYmquuKF1u40MTYcJBJK5NLol4oDZyZIpmSO93i5fvXC\nCXUXQ/b5cM1BpJwvBFGLpXTVRWd8Z1noM6XNA6OvERzfM+P7TI6FTWJzN32MqeGdJGMe0nEP4cmD\nmJwdAEWTsLR6RxHfIUuA83T/eNoxp4Z2AgI+nECST7ZWYdMVL7/W0lXEQ4+zzTJCWdOn8A08TSIy\nhCwlEDUGPtZSyS96Jzg+FabOamQ0kuCTi6qISRIHPEHOBSLTjLeU9KNICfS2mQeIXMUHF1eU8e4b\nDeA0hgmHLciyyNRkBIO1EYCI9whtdZsxpU/Sf/oxntzTiNjgx1Ww6G5acYqRk6ep7PhdPnVLO+m0\nzJKmhQ07zy6aMxnvbPrYajeg02uIR1MQTbEEgZEBtaVp1cZ6Vm6o4+ThEcaGAvz6sWOIosBn/3gr\nR/YP0tLhxlliRlGUXBRZNUvk/dwTJwgHE1is+hyhbCEoJJ+ltQm0TQrtZW1ERkKEFIXBgSneHPDl\n6pWOEhO+yQiyrDBVEOFOeaN5IQ6HEa1Ww7otjZw9Ppbb/tpzajS6bksjcUWheziAANy2sYEjXR6G\nPBHu3tLEU3t6+eHOsygKLGkq4Q8/vBytRmTjkko2LikeLFNdZkYUBAY9qvHedWSYlw+qEf8ffmTF\nnDyALJa3lFHmMDKZuZ7nBvx4A3EEATZ2VrL35Bgnen3sPjJC71iQ379rKW11M6cXo4GzoMg4qq7H\naGucxxUoxu6jI/zguTNsz8jcZuvdWWT/DkzF3pHxTktyLvPRNRS4LMY7kimvmK3vDgu5pO42lJqb\n52Q1Z4ls0gxp83BmclkWosaELMUwOdoXzEkQtcackthk31NEp44RmTwECOgyRLWZYLDWT9tWUnc7\nvsGnATC7ljIRVDBrNVhnGIZkdnVSY/1jRK0FQRDUDEBkkER4AJOjFZ0o8tGWSu6VZTSCQFpR0AoC\nSUlCLwoc9Ya4oaYUTYFDmZ2frTcvPEN0FVc2rqia92uvHESrUYjE1fatWDSFqKvAYG0iHuqmwenn\n9s5u3IZB1tUNUO0IUKa9UCxAIew9zPaVNdywduHpPJNZh1YrzkgcCmYmi91wx2I+/tn13Pup1Tiq\n8ml5s0XPxu3NGE06lq+tzaWUZVnh6Z8dY//uXt54We0D/dUbffx8l8ourSwxE40kefbx4/Sey0f2\n2QXyyL7BBX8PgLFhdZFLl0t0LdvN4cggj+zt48GHtuAzajiLwuG+KXoyKVB3pY10Sibgi3L0QJ5B\n6/dFCQam9/Ja7QYioSRSOk8iHB8L8df/s4++sRAldgMGvYa//OQa/vyBVdx2TQNOqz7XwrdjTe2c\nEa9Oq6Gy1MzQRJhzg35+9PxZREHgxrV18zLcWdx7rZqWrXCZSKZlRr1RNiyuoLVOTcX/2+PHOHJ+\nkkA4yfeePU1yljaz7FCJ2cYgzoW0JPPU62oXw9EzaiukP55m74mx3Lz6rMEOXOIEu6A/xvf/dQ9v\n7ukjmVKvyblB/2Vp7YtkpHQvxjCfLwRBvGg7Uq7mnSyOvBVFyU0rM7uWUbfyr6lZ9idUtv8upfV3\nvaPzyrLjFSWN1lg6pziJSmZTv4OgMeCo2o61bDXu5vtx1d6KpepGphIpKk36WTM2Gp0195oxE7jE\nw31F+2hFUZUxFUU83Y8wcfIbLLEk8CfTnPUX82ySsavG+zcVV5TxToyqDMvalkU4MtFo0B/HUbkV\nAGfy2dy+N7T18xm3hjKtjrSkYXwiH2HHpk6RToXwDe0k4ju+oHMQBAGbwzhj5J016LbMImt3mtj2\noXZGMrIK0YJWNp1ew4d/ew0tHWrEnI3Mw8EEsqLw0tuqQdZrRVwWPc/+/Bj9571FrWaGTG12bDh4\nSUSm4X6V+bp+XS2SLoWgS6Ct6ub0RDfDnnwqevfREaLxNPZMCvTAnj7OHh+jrMJKbaOLWCSFN9PC\nZCtIU2cNebb+CTA4GCCVMeYWo3r+JoOW9noXGlFkTVue2DQfPkKt20I8KfGTl7tQFPjT+1dy/w3z\nn2YEaoT9jS9s5iufXseadjdVpWY+fkNr0effvL6OHatrmZiKcWKWXutkbAwE8ZI0nY93e/GHk6xu\nc2PPRF0vnxjlf54+xX/+8iSKolDiVksc3gKC3uk+H//wyMEZ+90vxKmjo8RjabqOZRZsnchUKMG5\nQf+73vceCSURRQGT+b1T2hJELaLWPC3yltORTJTdQVnjPQiCBkEQ0Jsr3/Ewl0JHLTvuddbzEzS5\n6Nvd9FEcldvUYzhasbnX4U1pUYAK0/yyFXprHQgi8WD3jA6YIqeJh3pR5ATtaTXzsGdsip92j+UU\n3HKRt6ly2vuv4oONK8p4O62qR11aUZ9LIQb9MQzWBnXBVKaLnjhIk0xaOHGqFaxrMbuWIqXDjJz4\nZ8Ke/fiGnlvwedgcRhLxNF5PMeEsFIgjaoScoAlAdakFQ0Y+VbbpCYQTuQfNWWJm9ab6acfoHg4Q\niadZ3ebmS3cu4YUnT+AZUz9rYlRlrktpWU3LZ9DfvTDpTFmW6Tk7icmso22R+uDqy0fQ1XXxyKFf\nMT4Vo6PeSYndwP7T4/z9Iwf5yR5VnKL7jAeNRmDH7YspzRiUrHhIofHOktwKhUU8BX3KMxnZlW0F\nus7zSLnWlatEn/6xEC6bYdaU9sXgshkwG7V84Z5l/N3nNmI366mvsLJ+cTn372jlo9ctYm3G0Toz\nML3dR1FkUrEJdMbyWfXKL0QskSYcU6/hqYyQyU3r6lhRrxqBQjkcbyBOSZkFURQY7J/in35ymHAs\nxXeeOU3XUIBn9vaz68gwkzMQKfe82MXeV7s5d0KN4JORFGbgw5kuha8/epj/9yeHp73vnSASTmC2\nzh5BXi5o9U6V0V3Qh52KqZkMnendH5QhiBosGRLdfNTJnFXXYa/cNuMM9/GYesUrTPPLVoiiDpO9\njVR8gmR0ugJkod67Sx7DpBHpC8c56gvxyoiPE74Qu4IuNDoXsmjgka4RnuobJy5dfgGjq7j8uKKM\nd0vzILIiojdVYncU972mnMuIo+PZSS2vh4sFFKIxI6LWRn3rh7CWrS16TZHiSOmFRa0dy1Vj9/yT\nJ4s83mAgjs1ezBAWRYG/+fxGeowih0Nx/uibb3CwoPe5xG3FZjdQVeegpcNNKilx4LjqDTfpNLz6\n5EnGhoO0dLhpW1pBKinh80RyacmaBtVY9XV5c0MU5oPhfj/xWIrmDjd2g2oAZUF1fgb8mc+vtrOh\ns4J4UmJkMkJQkklnsghbbmylxG3JsdGzKfiiyDvjYBVqcmdLC1+8bxnt9dMjlfY6Jy6bgc1LK+e1\n8Hc25jMqHfXOBRsLRVHo7/aSSk5fsDSiyO/dtZQb19UhCALN1XZ0WpGzA9MlWdMJL4qSzolzzIZR\nb4SfvXqe/SfHeOhfX+cff3wIRVE41e9DrxNprrYTnYqh0YoU5nb6xkJotCKuMgsBX5TT/VM8u7c/\nV2KQFYWHd57lP35RLCwTj6U4fnCYI/sGiYQSOYfKCVy7spoNner5do8E37X0uaIoRMPJd63evRCo\nTrxUJIaSjI9nXrv0drW5UFJ7C66627BXbL3ovnpzFc6q7TOOvhzPDBqpNM//d7O51fUsNLEP/+iu\noj7yrGIbgCJFqbXknYLD3iCPdo9xRGpjWN/JUCTBKX+E/Z4gu0ZmV4G7ig8Orijj3e11oS+/F1Fj\nykXeYx4ff7L7/+afjj/L97r1HBf9vJmK8rqQTwMNDJTm9i+aG5ypV6Wio8yFdGIqN+oQVBGXptYy\nAlOxXN05lUwTj6aKjFchWppKyLoUbxzPf54oCnz8c+u54+MrcmnRs11ejDqR8W4fRrOOO+9fwU13\nL6E6E1WODQdy/dYV1XYqa+0M9U3xn19/jUe+/RY7nzxx0bpodrJaa2cFGlGDJaOUpaS1JIQQxjUv\nUlIVKlJ2k4HjKBxCZvEKtUbmLCDTaXUiBqOWHzx3hrfPTFCRYdD7CtK8yUgCASifRRFOqxH5P7+/\niQdvU69NMpHOkd5i0WTRsUBVj8sOlWmunlnIZi6MDPh59ufHc61tc0Gn1dBSbWdwIpyLmLPIph/n\nIiwBfPPJ4+zcN8DXvrcPSVZJiT2jQZLeKO1VdpLxNIGpGCUV1qL39WdU6OIiiAiYgJ371TJSVv4W\n1AyEJOed19HBYhW/ilY1s1EuihzdN8hnb1vMykXqttg85Hrng1g0hSwruU6F9xLZkkVWNQwgntEi\nN1yCTO18IIhabGVr3rFE6FjGeJfPM20OYLA2odE5iPpPEhzbjX/01dxr2SEoola9lxzavHMmFfhp\ne6I1HPXmeQL94fdfP/8q3jmuKON9611fRnnyefq/+hXMDgOxGgtdfUEEv5H2o9fRcH4N5pAaie31\nnac3lUY2L2N03J0j+wiCQEXbg5Q1fSQ3PzkemX1ed8R3jJFT/45/+KWi7WWV6gNx6ugogalobmbx\nhQzhLGoKhEIuFIXR6jRoNCIlmX1S0STLqxwk4mlaF5dT06BGqFUZAtVw/1TOabDYDGzeka+7hYMJ\nes9NziqOkkqmefbnx+g+46HUbaGyRjWw22o3saVyM5JPNcqCRuLNqZeLlN1uWV+P3qBFIt+e5Sxo\nBaqotjPkibD76AjfeuoEKVFAq1NvIaNJp2YsZLAA5bP8TqBGvIIgMDke5vv/9gY/+tZbHHt7iEe+\n/RaP/+BtEhdMCfvr31rDzevr2LJs4aSbycz3mI/aHUBjxiEZvqBkEguqRMMsiWg2TMzgVD37SjfN\niJhHI4wPq8a2vtGFWJBF6B4O8PLBIU6OqRmOuoIF/pYN9VgKdO67h/M13+ELUvw/OzhIHAWdrGrX\njwz4sVvUunQw+u5MX8vdm++L8c4MMslEnXI6RjzUh95cXTTZ7UrEeCyBU6/FqJl/v7UgCJgcbbm/\nY4FzyHIKWU6RyBDZsn3yK+yqU7fdOo4xk9fRkmYypWGfJ+/kDUcSswq6zIbvnBniR10jpOX5j0C9\nisuLy268d+/ezS233MLNN9/Mf//3f8+57/EHHiBy7CiJkWEe7h5hssOJr9lJy+lr0EjqAnRL6S2A\nqsj183CC0bHlALmoFlQP3OxcjN5cDUBwbDcR3zEuhCwl8A78GoDw5NtFacVSt2q8D77Rz6P/tZ++\njHTnbC086xdX5JjT4zMs4KFoEnuJ+l4TAvbM6M7m9nydzuEyYbMb6Dk7yZ6XVGNhsRkor7Jz5/0r\n+Mhn1vKp/2sTAL7JmdXb+rt99HerKcUVG+pyaebbm2/m/s67uLYjvxAYtAa0GpEb1tRSVWrm3mub\n+cRN6utff/QQw5ORIkJSdZ2T3rG84dh5YJB0htVc2+TKSay6DVp0M7TCXIjxkSByJkR446XzpFMy\nkqQw2FtskNxOEx+7vhXDLIIwc8Gf6aWfmmWk7IWoyjgrowWyqYoiEw+eR6Ozzxl5S7JclNbfvFTN\nDvUPqWl4OSXxxstqlFjXVEJFiQmNKFBfYeXMgJ8fv3iOlEmLVqehTM7Pdu6od/H137uGL9yjio28\nciifRTjflSlZaEUmUZCBwvg6Fk1hy6RpZ5oNcCnI/pY2x/sZeavGOxbqAeQF93K/14imJUIpad5k\ntUIUkuYUOUnMfxb/8IuqGqWlNtd7XqWN8FdLy2mPv8YmfTdV+jS/V3aelaX5zM3qUhtpRWEsdvEB\nRFmEUml6QjFO+yP87cFuXp1DivUq3jtcVuMtyzJf+9rX+O53v8vTTz/NM888Q3f3zOP2ALBZJ7Dn\nFwAAIABJREFUiZqtxEwWBjNpy6RLR8KuQ8msZPq4GWOmb7OEMo7tG8JqN7BkVfW0w6kiCrcTFpz8\nR49Et1/1PqV0lFR8Uh0ooGRroQrpglRcabml6Fj7XuvFaNLSvmxm1mZFiZlvf3kbTVU2JqaiRdKU\nwUiSv/ivvfw/jxxERqEMgcB4mJoGZy7aBtXLrmlUo/AsWc2RIcPVNLgoq7BitugxGLUzGu+hvinO\nn1bJO9fd1kHbkumGprM6/ztNxtQ6+gM3tvF3n9uIViOyrLkUAYglJPYcGykyRpW1dvrH8um3E71e\ntt6kktJWrq8jlTGuZQk5J586F7KjJJtay9BqRZrb1agq6yhdiGgkydjwxYe9FCJrvAO+6LzkQrNS\nq6MF8riJ8ACyFMfkaJuz5j7mi+W09y0mHXdtaUIjChTmIEKBOGuuaaCq1sEnb2rnc3d0snlpPqPw\nR/evZvnaGlKJNPctr+bmtnIGz01iNmpZ1VpGc7Wd/acnON0/hSTJRAIxIigcSKeZNGnY0FmBryDz\nEw0nsL/LxnsgM0yntnFu9vXlgEbvRNAYiId6kFKxXA34SmdTZ+vd8yWrFcJob8FWvomS+jsAEf/o\nK0T9pxE1JsoX/Vau/93b/xSB899FQGJTTR1fXLGY6qY7iox3k111TgdmGS+qKAqD4XhRZD4cKd73\njH/+g2Gu4vLhsoq0HDt2jIaGBmpqVMWr2267jZdffpmWlpn1oH/88TtIic3U9Z3LbZM1GibWlbPe\nZmb0qS4Cvhg71m/jpPcsndE1DMgJlq+tRT9L36+1bDWnPSL+lIUfnR/li+5dRKaOgyLnBh2YnZ1E\n/aeIhbpzjNULa9tWu4Htt7ZjtszuOWtEkYoSM72jISaDccozUfqR85PEMpF2HIFsIvq6D3VMMwYt\nHW7OHBujqa2MJauqc6n2LARBwFVmYXw4QDotoc1EuIl4il8/djS3X3Nb2YyGprO0gy0N6zkycpJw\nKkIwGcJhyKu/WU06/vmLW/jSv+9h74kxzg36uX5TPWN9U1TWOuh7tRutRmRFSykHz3lw1jn47B9v\nQafX8tyRYYIo2BF4/YUuLDYDTa2zD0PItuNtvmERFpsBQYAf/cdehnpnJtS8/sI5es5OsvmGRSxf\nO3d9MxZNotGITGUiaElSCAfjRZmTyfEwRpMWa0HvelWJ+nu/+PYgNW4L21ZU5+RQs/OrZ0N2etrH\nr1/EndsXcfztQe5aVcPRQyP5Ic3A2i0qE3lxplwSCCd4ak8Pa9vLqSu3UmrRc/TAEP0ZLfnd5yZZ\ntNiN3qBl+6Iy+kaC/NNPDuPQibQhEEXBatLxmUx9W7lDoefsJC88dZJIKIm9Wl28g9F3ZrwPvzWA\n2aJnsMeHxaantNx68Te9yxAEAXv5NQRGX2Wk5yWktHoPXTjM5ErDWDTLNF945C0IIq6aGwG1zh3y\nvAWAydGGKOpy0quKkkZKh9EZy4tGubbYzDTZTCyym6mzqPf6YCTOphk+qzsY43vnhmmwGvlcRy2+\nWJLdY2rmaFuli91jU8SustWvCFxW4z0+Pk5VVT6qqKio4Pjx2fuuk1QgAIONaurWbdThydQ/z8aT\nVFr1+L1RPtl0I2uNG3KjKWubZo4Awqk0OlFEb3JDOEpSEYn48gYu5NkHgKPyWqL+U8SD3djLNwLq\nInHtLW3IskLnymoEgXkxnbM65Y+91IVGIyBJSm5a1pc/tpKuvQOMDvgxGLUzkt/qm0v57YeuwTQH\nI7WkzMzYUAC/N0ZZhvjkvYDoNZszo9foeGjjZ/j+vsd5ru9lRsJjOeOtKAqvD79FrbUKg15DMJoi\nGE3x3dEQm5dVkkjLDHnC1FfYWLGojIPnPBw4PcE925pJpiT2nZ7AaNXxmftW8MQPD3Lm2Oicxjuc\nUTqz2PLDY0orrAx0+0jEUxiM+ZS9oqgGCWDvq90sWVmNRjtz4miob4rnnjiOLCu5tDyo6d6s8U4l\n0/z8+28DcOt9S6msdWA06YpmqP/guTN01DtJB7oQRF1unvVsOJupPy+qdfLMT49y7uQ4oijg1ohI\naRmrXeUviGLxeTusBv7p9zdj0KvbzRY9qzbU8fYb+XnTAz0+dHoNx3f30Y7IaWTElAyIdLSWcc9d\nnblShSAIOa5DJJygzqzyRGaKvB/eeYbXj43yrT/eNmepIxpJ8taufK9558qq97xNLAt7+SYi3iNM\nDOzJTcrKGrArFbnI2/zOSg32imtyxttgUdtQCx0Xk6MdR9V1RTrmGlHgcx2qsysrCkaNmIuu45KM\nRZffN0tm6w/H+Un3KL6UxGgmSt9W5aI7GGUslkRWlCLOxlW897ii5FE1GguFmc3VVQ6e71UXbEmA\nsgobA91e9r/Wy/FDwyQTabQ6kfbF09uO4mmJf9h1khKjHrdFD0SB6TebyVZDdX0zU0OVxEM9mHQ+\nrE41Mlq2NAYClFTMPPRjJly7pp6nXu/NGewsmmscbF/fQKDbx+iAn5b2ctzuS4sW6hpLOHVklImR\nEPFIipXr6+jvyrdr1TWVXPTYnTUtPNf3Ml7Zg9u9BoD9Q0f46blfAKBbYiPV24rsV9tv3jg+xog3\niiQrLG4q4ZYtzTyxu5uXDg5x46ZGBsbCxBJpbt/SypLl1TxvO4HPE5nzPCLhJHaniYqKfOmgqtrB\nQLcPQRGK3lsoUiNLCoqk4C5QtwuHEnSdGmfZ6hoeefatXC0eIGLzYgmV8pP9T/Plzo/Q9VaA4wfz\ndePnnjjB2msa+dB9ak15w5JK9mWmjT26cx/3dnhxli+hvGJmJzGWSPPTF8+y++goVpOOxgo7z588\npJ6rrICs4Co188W/2jHXJSnCh+5dzsZtLXjGQ/z0ewcY6Q/Q0KwaYSvqnWzO3M/r19RSXVXc/15a\nakUQIJmQaKhTzzuckCgrsxKPpXLO4a4jIwBIoobqOa7ViaHiMsjqDQ2XfP++GxiP3k7i3GMQnwBB\npKKqYsb2rCsFvvMjiAJ01page0dDZ2wER1sJebuorOvE4rChKBYS/rXYS9soqVp10SM0uyycmgzx\ng55RhoIxvnZtJ2adlifODLMrU8+usBg4OZUPCDZWl9BQ5aRqzMdwNIHebsRlfO9bBa8ij8tqvCsq\nKhgZGcn9PT4+Tnn57L2YsgKteoWupLoolQlBFCWBIBgIJ9MYys3Q7eXg3nxEsm5LI5OTxczgtz0B\nftk/gaRAKJmmP1hQv9S4KC1pJezZD4De2obHE0JnbiQeHuPs/m9isNQhpcKkk2oklaIGUTM/pSa7\nYfqDecv6eq5fXYPHE2LlpnpkRWHtlkY8nvnPJS6EJsPwfu15dYSmIij096rGe92WRhavqJrz2G63\njRLU8sDJ0fNscav7/ujwL3L7SLoQhrZDxA5vxyBYsJt19GaU1CqcRsLBGLdvauTHL57jj/75Ncoy\nEe3K5hI8nhAl5RYGun0M9HunZRGktMyvHztKKBCnus5RdK56k3pL9vV4c/8HcnPDrXYD4WCCrjMT\naA35iOGVp09z9sQ4z/3yGOmEgt89RFpMIZni+FzDLD58A+KUmZ/9cg/BI9MXnfNnJ3Ln8YkbWrlr\ncyPfefoUpTo1U3TeU459lt/0sZe7eOHAIGagXYJnH1ezOzfd3YnPE+HtN/qpaXBd0vV2uc3YnUa6\nTo+jN+a/7/3r6xnr8hKailHutsx4bLNFj98XJZ1R23rl7UHSoTixs14qauw4XSaaEehFobvfh3EO\nm3L6eHG7pcVhuOT7951gOBLngCfAfk+aGmErd2heQaO1MDkLgfNKgKIoDAVjlBp0+H3v/DwdtR/G\nXDpGNOkkmrkGlooPIcG8rkmlXscpoDtjnP/slROIArnAyaQR+UJHHU8PTDAQTfDbi6qx67V4PCHM\nGfJR14h/2hCUq7h0XIojfFld1WXLljEwMMDw8DDJZJJnnnmGHTvmjj7aXHbWvvUyTadfYzzSTzD8\nMEudatQlNzvYemO+7njHx1ewckNewSyWlnhrws+TfRNFfY6FkKvuw1Vzc+5vs2sJAJaSFbltA+Eo\nB+MVubGZUf9p5gtBEPjCPctoq3XQUmOn1G7knm3NOeNmtujZcmMrRtOly0pe2K729E+PcfKQ6iSt\n3FA3L71pp8GBQ2+nLziAJEt4Yz7GoxPT9jOt2sWfPdjETevyv3NTZvzo9atruGdbM5KsMO6L0lhp\no6pUrRm7M9KjY0PTCWajQ35GM9vFC6KQLEHP7ytmh09kiHKdK1XC3WSBkpssy3SfUY17OpGRqq2Y\n4PqbO/mLDz9Is7uWuDGMOewi0/GVw6qN6veKhpO5bgOrSUdliZm/+MQqtrYGSKZFXjw5c5Tx6zf7\nePGAKnVbg4CYlBjsncJqN9DYWsa6rU187stb2XzDpc39FgSBxtYyUkmJsyfy7YFd+4cITcVoW1Ix\nKw/DYjMQDSeK2sz6M6WH8eEgZ0+MU4qAi+KxrYVIpyW6To1z/vQEOr2GuuYStt7YOq0d8r2Aoig8\ncn6U/R7ViRxWKvAp9iu63p2QZP7hSC9xSb4kstpMEEVdkZ7FQrGx3EHNBen7woxnTJLRigJ3N1bw\ntWuXYNfn7x+XQV23phLvTtvhVVw6LmvkrdFo+MpXvsKDDz6Ioih8+MMfnpWslkWN00bZsX0IisIr\nrjGwwJaKCgYjUXaPT/G77bXIL4MoU8TUBtg5NMmBzINdYtBxbZWLU1NhzgbyhqA7pqNdEHBW70BK\nhdEZ1FSk3lRB/aq/ZbLvV/zCowqItFqTmOM9RLxHsJZOT0dFp06RinvQmSqZGn4em3s99vKNrGl3\ns6bdTVqSkWUF3Sy12UIoikJoYi+ixoildOWcKcDZhGJcpWa0uvm3UzU66jnqOcFDu/4yt82kNRJL\nFy/ko9Ex1rav4McvqsStqjK1ri8IArduqOeF/QNE4mnWL86z27O1+J1PnmTzjkVM+aKUlVtYsqqG\nob68gllWDCaLrCjMhSI0w/1TCIK6/4HXe5kcDxGPpTi0tx+700Q6LRMsGSOpiyEoIvetv4mV5Wqf\n/y2NO3ji8F5KPPUQhup6B2aLHp1ey8btzQSmYvSc9RAOJop+W0GOIcpBxiIVnB+OEk+mMRYsZN5A\nnF/s7qHUbuCL9y3nrWfO4J1Qo5kVa+tyM7kXck1mQlNrGccODBGLqAtmVa2D0aEARrOOa3bM/jyZ\nrXomRhWS8TQ72tzsP+chm/ifQCGlFalJK5QizCi7CrDnxfM5wZ8b7+pk0eLLo2I2H0wl0wSSaSxa\nDTtqSvhVv4c35dXcKZ+7+JvfJwyEY4TTmbZQ+5URqVp1Wn6vsw5/IsU3jvdPe/1DdbPzVEozxnt8\nAa1mV3F5cNlr3tu2bWPbtm3z2vdTy+ppMOg5VepAN+ln9cu9dN9TQ63NzX1Ncb53bpj/PDOE5YY6\nPlNdnlscQTV+2RaGpS4rH2muQCeK2HQazgaiLC+x0h2MsWfcj12vZXPFNaQVhX853o+Cwp0N5bTY\nzURKrwdPJkVYdQ/GyaeIh7pJRkdzk3nioT7C3kNEp4qlKv3DL2Fzr88ZXq1GhIus28HxN0lEhzFY\n6vGPqEIx6VQQZ9X2ov3C3qNo9XaMtqYcwzwLQVDlTKtqFyZSsaZ8OUc9xd9hfeUaXht6o2hbJBXF\nYTVw+zUNaEURjSgST8f5dc/z3FB/LZuWVvL60VHWFyzs9S0lNLWV0XtuMjdJDdTJZcP9U4iiwINf\n2oxOf8FMY7sBjVZkqoCAl4in8IyGqKixY7boqai2MzYc5MmHDxUZ+anyQUJ2D/e338sK95Lc9o6S\nVj5+rZ0XHlczKKZygRtvyL9eVm6h56wH70S4yHhnB2CIRjOSkubcYIDlLXlFusNdarR/y/p6+o+O\n5Qx3Za2DDduaicXfndasyloHeoOWZCKNyazjutvaOf72MCs31M1JbHSVmunr8nL66CjBc146Mom2\ncRQGUCAt4UTAAXhmkIRVFCXXtnf3J1ZSdYm68u8W+kPqtd5e5WK928GJsR56ElUcTgS4PNpq7xzD\nEdXI3dtYzuqy+XNnLjc0gkCpUU+dxchgQSvYX65smnFcaRYNNiNGjcgRb4ibasuKxo9exXuLK4rh\nsbVObW/SfeYBkloBZ1iiU3YjCiKLHGa2VqpxQ0SS6VJSxCWJ/zo9yKHJIOOxJKGUxIoSGw8sqkKX\nYfR2OK18vqOWuxvKWedWH55nBycZjyU54QszEU/iiad4rHuMaFpid4EIiSeexFautpP5R17ODUOY\nGtqZM9wanQ1LyXJEjQmQiYd65/19FUXBP/oKMf9p/MPP57YHx3Yz2fsE6ZSaKk6nQvgGfsnE+R9N\nO8bS1dV86gubWLq6ZsGtO2sqVvLn6x5iY1VeD35D5Wo+v+zT/P3mv+HP1n5RPZ/MCMZ7t7Vw5xaV\ncX1o4hi7ht7glcHX+eh1i/inP7iGkoKWK61Ww833LJmWXn3ih4eYGA1RWeuYZrghz5T2eiL88tEj\nDPb6GOrzoygqUQ9gVWbYS2AqhqhRj68ziISsk2yp3sCWmo3TCIzNLXnHYsxQHG2UZVL8Pz+wk7O+\nvKOR/f27pDPoGk7z+K7zRRHq4cx8d7dWk+trX7Wxjns+uQrruzQqE1TFvqzGvSTJOFxmttzYWtTi\nNhMqqtX7fd9rxfekr6BvrR8FCZCGQoRC8Vzp4MyxUf7z668Ri6RoW1LxvhtugL4ME7rRZkIUBO5v\nEtAgcVZuuCxjTxeKlCzTE4wWnctQxjC2OsxXJDv7wfYa/nBpviRm02nn7CLQiSIrS22EUhKnpuan\nWngVlwdXlPHOorZ5Ka+tUQ3RNf48w/fWujK+sqoZgyiybyLAockQ/eE4j/eO050hpbU5zMjxOFIo\nT9xosJkwajVcX13KUpd63PFYkgMZycD1bjuRtMT/PtzDaX8ES8bz9MSSGG0tGG0txEM9BMffVA9Y\ncHNXtP0OpQ13U9Z0HwCJGYy3nI4xcvLfCYy9fsH2SG4OcRZZpaio/ySBkVcAiAfyacFUwkdgbA86\nnZpCNRh1JAOvM9H9KOnkwgRMAOpttXykNT/zuNJSwXL3EhwGO3Z9pj84Y7wVRWHf6EHeHj9Ct78P\ngK4pte/bOkMNXxAEHBlVuSWrqnNRrU6vyYm7zISGRWp0OzLg57XnzuaEWaozBqyhpZTl62rZdF1L\nrmZtqxVBVKixziyhKggCH//cOgJNvRyRDpCS8zpklbUOQEHwmfm3I/+d+77phPq5YUVBWz7E0NQU\nf/M/+xj3RZEVhe7hALVuC/5M/b2swsqyNZcnBnRnyhDJxPx7bMur85Fetq0uiUIY+NQt6n0WAYZR\nEID/8629/HDnGTxjIXY9dzb33rrmEq4EjEQSaAWByky9tqFpCy1mGZ9kYjh6aWncpCTzzIDnHddw\nFUXhx+dH+c7ZYc4E1AxMOJVmMBLHqtVg111RjT05GDQiFSYDO6pL+EjT3Lr9WWwsdyIALw57Fyyz\nehXvHq5I423Wmalbvx1FFNDtPYQUzdesTVoNTTYT/mSatybyqb7JTD94pdnA8Df/ld6//gvkeHEd\nTysKrHerqeXzwSh94ThNNhN3NJTTmGFO6kWB32mvQUSNvAVBoKzxPgRRT3jyAIoiI6ejantK+2fR\n6tUFUqt3EVWM7PTZmEqkSEbHCGUkV8Pew6STUwQKhgpAfpShzb0Bg6UOR+W12Nzrcq9HfMdIJ4NE\nC4y3p/snBEZfYft2lSTV3FFKcOJN4sHzjJ/7vvpZqYWxgI1aA5ur17PSvQxDwfAFq141GMGkapx+\n3vVLHj79U75/8lHeGlN7pIfCo4RTszNob7hjMY2LSlm7pZFb71vK1htbue/Ta6aJzxSicVHBsBRZ\nYTJDVssaMEEQ2LxjESs31LFkZTX1LSXQrGZMqmcx3gCuUgv1K2wk5CSeaL6VT2/QkLCGMUeciJKG\nwxMqw9wfVQli4cwCtWxNjGRa5lSfD18gTjItUyXDycMjaHUi935q9bzIgpeC1oxa3vptc/eaF6JQ\ne/zmu5fQtqSC9dua+JeHtnDtirzSXtykJY1CqQJHz08y1DeFokDnqmpaOtxF1+P9gqIoeBMpSoy6\nXKpWEEQ21ahtnd86Ncg3jvURTS9MQOTNcT9vjPt59Pz04UWxtMSj50cvGmHG0hI/7BrhXIZb89Z4\ngKQk8+1Tg4RSEu1Oy/vWEz9f7KgpZdU80/rlJj1r3XYm4ym6glcuy/83HZqvfvWrX32/T6IQ0YwK\nVHtlJ8gKkaNHUBIJLMuW5/YZiyboD8eJpvNRa0KWiaZlrtem8D/+U5RUCm1pGcbG4sVOKwrsGfcz\nmvHUt1a6qLeaaHOY0SBwd2M55SYDh70h/Mk0WytdiBodUipIItyHN57mrZCNRosOV9VWhsJxomkJ\nm8HIcyMRTqZreHPcT3DqNOnAcZ6atNMdCNIkqKnasO8oosaALMWZ6FbT4Lbyjbhqb8Joa0RrcGGr\nuAaNzko8eB6twUXEeygXocuS6pDotCmuvevj6DUBwt6DAChygljgHIqULBpmcCEsFkPud85iWVkn\naypWFG3TCCKvDb7BWHQcb8zHmyMHsOmsGDR6knL+/U32eiotM3vtZquB1s4KdHoNZoue8mp7kV76\nTDCadNjsBgZ7fCQSEpFwEmeJmRXrpzNs9QYtbUsq2DXxGpMxL/ctugOdZvbjj0c9nPF10VHSSqVF\nTaUPhUfZ13MUS6iUiM1HTB9iQ9Uahkf3oE+HUFzLOR8aodbpZrjbSondiMWko/fUOI6YaizqW0po\nW5KX6JzpN34nMBh1rN5YT/UCx6IaDFoMJi2rNtbT3O6mrs6JQadBEAR+mZnf/tCHl6NJyoS8MYZT\nEjU6DX5fjJvu7qRzDjGc9xLRtMyroz7qrUZWZOQ+LRYDJllBAXpDMWKSjE4UMGk12GaIdA9PBtk5\nOEmjzYQpk107OBlkNJoglJLYUZN3UrLM9jOBCAPhOJsrZ5eC3TvuZ58niNuoRyOo08MSkkJXMMo6\nt507Gtwf2NrwbPexABz1hSk3Ga62jL0LsFgW7vS//0/lHCi97Q50bjeB3btIefMiJBUFJB1ThrQ2\nGU9h0WqIvrQz91pg92vTjmnTadAUPEedmTS6Tafl5royyjLCA812EzFJ5nSGBGcr34gg6nly0sFR\nZTEnpCYkReG/zwzxbycH6A4l6VbytaND6RZ+Id3EcELDOamamKIeV0r68Q38ionzD+f2zQ5byEIU\ndRhtKos4OPEmipzCUroajT5fd1TkFBqNQDKqtoi5am/B5OgAIJWYWRv8UmDLRN/7xg6ioLCuchX3\nd9wLQINdNaYvD77Oi/273tW6Y8fyKjoyTHRZVnJT3mbDSHgMl8GJWTf3QlJuUpm0E1EPiqLwfN8r\n/OOBfyFsV++vqngjXf4eXuzfxURQnUa3pHw1AHFCCAIMecKMToapQkDUCGy5cVFRC+PlgkYrLjiC\nW76ulhvv7JzxfV/++Eo2dFbQWuekNiPVuhSBvi4vesPMCoDvF7wJ1YBk2c6FuKGmlD/ozNyLIz7+\n/eQA8gX3oqIoPNU/QVcwyn+cGsxl7UYL0u2xgqh9KpmmK1OKC6WkWadpKYrCIW8IjSDwe4trWe92\noAB7J/wYRJFba8ty/JvfJGTb3hYy4OQq3l1c0XeVoNVScvudKOk0gd27ctsL+yW3FnjEzlSc4J7X\n0VdXY2hsIjE4gJwqrmUJgpDzyleW2nDMQJoC2JxR03pjTBVq0RlKqGj9NFOoBnRccnDCFyadWSS+\nd26YFDo6xD6WOqYfc9I0s/KRRmdHZ5yeltQaStDonUiZOrbRWo/Z2Zl7XVHSpGITJKIqUcpgqcfd\n/FFErRlpjjT2QnGhOV7kbGKFeyl/se4P+eLKz6ITdfQE+niq+1lGIjOPKS3Ew6d+yg9OPgZAOBWZ\n0+AXptbdlbP38oZTEQLJINXWiw+nKDerjtLxyVMc8ZzgVz2qs1dfX4ooCthCbiRF4pddOylN2Eim\nReodjdj1NnxxH5UlZoY9YUbGQugQqKhzsmxN7UXJY1ciljSW8Pk7l6DViDlCnJhRbdMZtdOu/fsJ\nb6YsVmqcOatSfUHf8uQFY2WHIwlSmfKHpCj8qt/DqyO+IuPTG4rN+H9JUXKs8QvhiaeYiCXpcJox\naTVF2uU1FgPGeUzX+yDCqddiEMWc7OtVvPe4oo03gG3NOgStlsixvCZ5WcEDfE1FPho1T6h1q+ov\n/CHGhgaQZZKjeYW3LO5uLGd7lYv7GmcnaJSb9NRaDAxE4jmvW9HnGctn4hZ+2jPdWC0RzrAl8iPs\nqHXauxoyqVl5eu+ks3oH1Z1fLNIhzkIQBCyufDuTwVKHvXwTBktdLsKOBc+RCPUhiAZ0JvVzNDo7\nUir4rkXBFwq3tDjUMkSdrQaT1oS+IEU9HvXMeSxJltg3dpAD44fYP3aIv9rzv/nZuadm3T/Lli4t\nt9AxyzQ3UKNuYFayWiHKTCr5qifQz3dOqGWLNmcLn1l+PxU1dpJTAk2nNtJ8ahMmY5Jk3IAoiJSZ\nSvAl/FS7zcQSEscy09uqa66c9p93AmepmaqCGQHdgRh7T1zcGXuv4M0QykoNM7fGiYLAtQWO/IWT\nsE751br1JxdV8dCSeuw6LS8Oe5EVcnyXwmlZvZmo+7oq9X7JMt0vRJZN3mxT9QnKCwKLCx2K3yQI\ngkCFSc9EAfH3Kt5bXPHGWzQaMbW1kxgcYOx730FOpdCJItdWuri5thS9RqQkk0pLRmMY6urRV1Rg\nqFFZv8nhoWnHbHNY1B7Fi6hEVZuNyApMZLzLkVm8zI3l+f7qMtRI/U7NK3yyVmGt245eFBhJmXP7\nlLd+GqN9EZbSVQji7J65o+o6LCUrMdoXodE70egsVLR9htKGuxBEHYHRXaSTUxhtTfnecp0dRU6h\nSGrbjyKnZz3+fHBj/XYAbm3cwX2td2DVFxPNPtx6Z+7/Y5HxOY/ljefnAP/w1GNIisSdmkM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KqqJJ3z+NReVWDndvyvvYL/1Zen3Cc+NDjn2rlBqeCra2tZadEzGI3jDsdIi2JOXjHbdgLkGJop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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11a0d73c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# same plotting code as above!\n",
+    "plt.plot(x, y)\n",
+    "plt.legend('ABCDEF', ncol=2, loc='upper left');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ah, much better!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring Seaborn Plots\n",
+    "\n",
+    "The main idea of Seaborn is that it provides high-level commands to create a variety of plot types useful for statistical data exploration, and even some statistical model fitting.\n",
+    "\n",
+    "Let's take a look at a few of the datasets and plot types available in Seaborn. Note that all of the following *could* be done using raw Matplotlib commands (this is, in fact, what Seaborn does under the hood) but the Seaborn API is much more convenient."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Histograms, KDE, and densities\n",
+    "\n",
+    "Often in statistical data visualization, all you want is to plot histograms and joint distributions of variables.\n",
+    "We have seen that this is relatively straightforward in Matplotlib:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MxM/IYDTLsjQwMPkJRJzOmKLRqFze7D7do9GoXJZb0ejZT4gSHYnqU2cWBoCsIsBhtIGB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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11a191080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n",
+    "data = pd.DataFrame(data, columns=['x', 'y'])\n",
+    "\n",
+    "for col in 'xy':\n",
+    "    plt.hist(data[col], normed=True, alpha=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rather than a histogram, we can get a smooth estimate of the distribution using a kernel density estimation, which Seaborn does with ``sns.kdeplot``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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EaRaTY+j29qBYUwRJvPOr8I+ODqK3P4zqchHLFpszVGHu0GhErF6uRyQKnK+X\nIQoiPMH+bJdFNO+kfa7LW2+9BZfLhaGhIfzgBz/AwoULsXHjxoTnOZ35/4uxENoAFEY7ErWhc7QH\nETmKUrMTNtv0T9QXr46hvtEHp13CI/c7oNZktt/bZMqNhWHWrtLielsYFxsDKH3AhsHQEIodRohC\ncuNi58O/qXxRCO0ohDbMRsIAd7vd6O7+eqEGj8cDl8uV9A0mjrXb7Xj88cdx4cKFpAK8vz+/R7U6\nnea8bwNQGO1Ipg3ne68CAEwwY2Rk6h24AsEYfvuHHkgisGG1FqFwBKFwJOX1Tsdk0sHrDWbsfoms\nXaHDZ8e88A3pETYO4WpHB4r1ideQny//pvJBIbSjENoAzO6PkIR/LtfV1aG9vR1dXV0Ih8PYt28f\ntmzZMu3xN88HDQQC8Pl8AAC/34+jR49i8eLFMy6SKN06Jgawmaf/4/Tgl8PwBWSsWKSC3ZYbT8LZ\nVObWoNSthm8o/saii2uiE2VUwidwSZLw3HPPYffu3VAUBTt37kRtbS327NkDQRDwzDPPYGBgADt2\n7IDP54Moinj99dexb98+DA0N4dlnn4UgCIjFYnjyySfxwAMPZKJdRDPSPt4JAQJKzVOPQL/e5sel\na/E1zlcsNWW4uty1fpUBfzgT/9+jbbgDq50rslwR0fyRVB/45s2bsXnz5lu+t2vXrsn/djgcOHz4\n8G3nGY1GvP/++3MskSi9ZEVG53g37Bob1NLt65cHQzIOfD4EUQQ21unzYk/vTLGaJVTa7fAAuNDT\niic5k4woY/ibiOa9/sAggrEQiqfZA/zT48MY98WwfKEKjmK+Ov+muho7lJiIbl8/ZC6pSpQxDHCa\n9zrG4nOY7VMsodrWFcT5Bi+KLAJWLeer86mYTWqoY2bIGi/OXed0MqJMYYDTvNfunRjA5rzl+7GY\nggNHByEIwIY6LV+d34HDUARBlPHeifpsl0I0b/A3Es17HWPxAC+zlNzy/RPnxzA0EkXtAgku5/xa\nbW2mHIaJNdH7cLVjJMvVEM0PDHCa1xRFQYe3C0UaKzTS1+uZj4xF8OWZUei1AlYvN2axwvxgVsWX\nVBX1XvzuaFOWqyGaHxjgNK8NBIYQiAbh0Nw6gO3gl8OIxhTULVVBp0v7goV5z6q2AQD0Vh8ut42i\no8+b5YqICh8DnOa1jhv93zdvIXqt1Y/rbQG4ikXU1nDgWjJ0oh4aUQvJFF8Ri0/hROnHAKd5rf3G\nCHS3KT4QritDAAAgAElEQVSALRyR8fEX8Tnf61fqIAhCNsvLG4IgwKqyISR44SiScObaIPpGAtku\ni6igMcBpXptYQrXMEt9x74vToxjzxrCkWoLdzjnfM2FVx99iLF2igqIAh061Z7kiosLGAKd5S1Zk\ntI93wqq2QKvSorc/hK/qx2AyCKhbNj93N5qLiQDXWcag10o4eqEXkaic5aqIChcDnOatfv8A/NEA\n3DoHZFnB/iNDUBRg/Sot1Br+aMzURID3BXtQV1OMQCiGU419Wa6KqHDxtxTNW61jHQAAh7YYX9WP\nwTMQRk25hIoyzvmeDbPKAhEiBqP9WLPIAQA4eLIty1URFS4GOM1bLWPxPlqz6MTRU/E532tWMbxn\nSxREmNVWjMZGYDWpUV1iRkuvD139nFJGlA4McJq3WkfbIAkSTp+REI0pWLNcBYP+9t3IKHlWVRFk\nxDASGcLaG0/hh05zMBtROjDAaV4Kx8Lo8vXCqBShozuCcreImirO+Z6riQVd+oK9qC23wqhX4dil\nPoTCsSxXRlR4GOA0L7WPd0FWZIx4TFCrgfV1Bs75TgHbjYFsPb5OSKKA1QsdCEVknLjiyXJlRIWH\nAU7zUtNwKwAgOm7FhpVqmE2aO59ASbHcCPCBcDyw19QWQwAHsxGlAwOc5qWjTVcAAOVmOxZWc853\nqmhEDQySEcOxIQCAxajBwnILOgcCaOsdz3J1RIWFAU7zzldXPBiI9AJRDe5e6cp2OQXHqi5CSAnC\nF42PPp8YzHaQK7MRpRQDnOYVz7Afrx08B1EbhF2yQ6ORsl1SwZnoB/cEewAANSUWmPRqnGrsRzjC\nwWxEqcIAp3kjHInh5+9dQlgzCABwaO0JzqDZsKnj/7t2++IL5YiigJXVdoQiMs5e689maUQFhQFO\n84KsKPjlh5fR5hmHs8wHACjWObNcVWEqUhcDAHqDXZPfW7UwHuqfne3ISk1EhYgBTvPCu4ebcaqx\nHxUOHdS2EUiQ4DSWZLusgqSVdNBLBgzFBqAoCgCg2KJDWbEBVzvHMTjKbUaJUoEBTgXvwPE2/P54\nG+xmNb51bwkGI/0okoohCez/TpcidTFCShDe6Njk91bWFENRgE9OcjAbUSowwKmgXWoZwku/OQ+D\nVsJ37q/CCOK7YxVJxVmurLBN9IP3Brsnv7e8ygZJFHDgROvkkzkRzR4DnApWV78XL713AaIAbN9U\nDmeRGV2B+IIiTj1fn6dTkSb+B1KX7+unbZ1GhcUVVniGgmjp4ZxworligFNBGvWF8bO36xEIxfDU\n/ZWoKY8HSmegLd7/beD873SaeALvC/Xc8v1VNfH/P3AwG9HcMcCp4IQjMfzv39RjcCyIe5YVYePK\nSgCAP+rDUGQAdskBkf3faaURNTBKZgxFB255XV5dYobZoMaphn5EopwTTjQXDHAqKLKi4P/7/RU0\ndY9heaUJD66tmvysKxh/ncv+78wo0tgRQQSjkeHJ74migHVLXAhGZJy9OpDF6ojyHwOcCsr7n7fg\nqyt9qHDosHVTzS07jLH/O7O+ng/efcv31y2Nd1/wNTrR3DDAqWAcv9SLD75sRZFJjSfvr4ZGrbrl\n885AOySo4GD/d0ZM9IN3eW+dNua2G1BiN6Cxcwyj3lA2SiMqCAxwKgh9w378x4FGaNUinrp3AcwG\n/S2fj0dGMRwZRLHkgCjwn30mxANcQF+457bPVtXYoSjAlxdv/4yIksPfZJT3YrKMVz64jFA4hofX\nOOF2WG47psV/HQDgUPHpO1NUogpWlQ1DsQHElFsHrC2rKoIoCvj8fPc0ZxNRIgxwynsfftmGpu4x\nLKswYc3isimPafFdAwBUmKum/JzSw65xQIaM/lDvLd83aFWoLbOgdziIdg/nhBPNBgOc8lpT1yg+\n+KIVFoMKWzZOHc6hWBCdgTZYxSIYNeYMVzi/FWvie4HfvKDLhFU18T7yz893ZrQmokLBAKe8FQhF\n8fIHl6AoCh7fUAqjXjPlcU1j1yFDhlNyZ7hCsmviO751+ttu+2xhqQU6jYTjl/sQk+VMl0aU9xjg\nlLf2HLqG/pEgNiy2orbCMe1xV0caAABlxspMlUY3GCQjtKIO/RHPbZ9JkojlVUXwBWO41DKUheqI\n8hsDnPJSc/cYPq/vgcumxea10/dry4qMa6NXoRcMsOunD3lKD0EQYNc4EFD8GL9pZ7IJE6/RD3NO\nONGMMcAp7yiKgv/6JD4o7cE6F1Sq6ZdF7Ql2IhgLwKUqvWVRF8oc+41+8G7/7SFdYjfAbtGivnkE\n/mAk06UR5TUGOOWdM1cHcK1zFIvKDHd8dQ4ATb5GAIBLy9XXsqVYHe8H7/C23vaZIAhYVWNHTFbw\n1ZXbX7MT0fQY4JRXojEZb392HaIA3L/qzqEsKzKuea9AK2pRZq7IUIX0TTaNHQJEeMJdU36+oir+\nGv3Iuak/J6KpMcApr3x6tgt9wwGsrrHAXWy947EdgRb4Yz5U6hZw97EskgQJNnURhmNDiMq3vya3\nGDWocpvQ6vHBM+zPQoVE+SmpAD9y5Ai2bduGrVu34uWXX77t8+bmZuzatQt1dXX41a9+NaNziZLl\nC0bwu6Mt0KlF3LuqPOHxDeMXAQBV5oXpLo0SsGscUKDAE5p66dRVC+Mbnxw+yznhRMlKGOCyLOOF\nF17Aq6++ig8//BD79u1DU1PTLcfYbDb86Ec/wp//+Z/P+FyiZO37sg2+YBQbF9tgNurueGxYDqHZ\ndxUm0YxSy9Srs1HmFN+YD97ubZny8yUVNmjVIr640Ms54URJShjg9fX1qKqqQnl5OdRqNbZv345D\nhw7dcozdbseqVaugUqlmfC5RMgZHgzh4ugNWowp3rUzcn93kbURUiaJUVcHR5znAoYmvQd/hb53y\nc7VKxPIqO8YDUVxs5pxwomQkDHCPx4PS0tLJr91uN/r6+pK6+FzOJbrZgZPtiMYU3L3UDvUdpo1N\naPDGX58vMNekuzRKglbSwayyYiDad9vGJhNW18Zfo39y+vZlV4nodhzERjnPG4jgyPluWAwq1C0q\nTXj8eGQUnYE2FEsOWLS2DFRIyXBonIghil7/1DuQuYv0cNn0uNQ6glFfOMPVEeUfVaID3G43uru/\n/oHzeDxwuZLbknEu5zqd+b/pRCG0Ach+Ow5+1IhwRMbDa91wFJsSHn+260sAQI15IUymeF/5xP/N\nd/ncjnK5Ai3+62geacKD5VMva3v3yhJ8+EULzjcPYsejSzJc4cxk++ciVQqhHYXQhtlIGOB1dXVo\nb29HV1cXnE4n9u3bhxdffHHa4xVFmfW5N+vvz+8tBp1Oc963Ach+O0KRGH53pAl6jYhllcUYGbnz\nNKOYEsOZ/lNQCxqU6Krg9QZhMung9QYzVHH65Hs7TEr8bUjz6HXUGe+e8pgatwmSKGDf0WY8uKok\nZ8cvZPvnIlUKoR2F0AZgdn+EJAxwSZLw3HPPYffu3VAUBTt37kRtbS327NkDQRDwzDPPYGBgADt2\n7IDP54Moinj99dexb98+GI3GKc8lStbR+h54AxFsWloEnVad8PgW3zX4Yz5UqxdBLSY+njJHLxlg\nlEzoCXZDVmSIwu09eHqtCosrrGhoH0FT9xgWld95rj/RfJYwwAFg8+bN2Lx58y3f27Vr1+R/OxwO\nHD58OOlziZIRk2Uc+KodKknA+qWJ+74B4MLYGQBAjWlROkujWXJo3WjzN2Eg3Dft8rZ1C4vR0D6C\nT890MMCJ7oCD2ChnnbzSh4HRIFZWmWE2ahMePxweRGegDQ7JBZvenoEKaaYmp5P5pp4PDgBVbjMs\nBjVONw4gGI5mqjSivMMAp5ykKAr2n2iHIAAblrqTOufi2FkAQKVm+u1FKbsmAny6BV0AQBQFrFpY\njHBUxleXucEJ0XQY4JSTLrUMoaPPi6UVJjhsiUeeR+QwroxfgFbQYYGNS6fmKoPKCKPKBE+0B7Iy\n/YprqxcWQxCAA1+13TIwloi+xgCnnPTRqfje0esX3Xm70AmN3ssIyUFUqqq5cUmOKzGUIqKEMRCe\n/unaYtRgcYUNPUNBXO8azWB1RPmDAU45xzPkx8XmIVQ4dKgoKUp4vKIoqB89DQECaiwcvJbrSg3x\ntelbvXfeF2H94vgfbwdOtKW9JqJ8xACnnHPoTHxHqpXVyY1A7gl2YjDch1JVOUxaSzpLoxQoSTLA\nK10mOKw6nLs+iOHxUCZKI8orDHDKKcFwFF9c6IFJr8Kq2uSmjtWPnQYALNCx7zsf6FV6WFQ29Ec9\niMrTjzIXBAHrlzghK8CnZzoyWCFRfmCAU045drEXgVAMq6rMkMTEq3B5o+No8jbCIlpRYk68Rzjl\nBpe2BDJi6Aneef/vFdVF0KpFfHauG9EYtxkluhkDnHKGoig4dKYLkihg7eKpF/n4pstj5yFDRqW6\nOmeX3aTbObXxqYGt49fveJxGJaFuYTG8gShONXAnQ6KbMcApZ1xpG0b3gA9Lyo2wJLFph6zIuDR+\nHiqoUG1bnIEKKVUcGhcECGgPNCc8dt1iJ4D4lDIi+hoDnHLGodPx16mra5ObOtbub4Y3OoYyVSU0\nkiadpVGKqUQ17BoHhmKDCMYCdzy2yKzFwjIL2jw+tPXm/6YVRKnCAKecMDASwLnrAygt0mJBSXJ7\neF8cOwcAWGDk4LV8NLEWeoe/NeGx6288he8/Pv0KbkTzDQOccsKnZ7ugKMDKGktSfdne6Dha/ddh\nE4vgNCa31CrlFueNAG8ev5rw2JpSM4otOpxsHMDAyJ2f2InmCwY4ZV04EsOR890waCXUJTl17PLY\neShQUK7muuf5yq4uhlrQoCPYmnC5VEEQcM8KNxQF2HesNSP1EeU6Bjhl3YkrHviCUaysMkOtSrwM\nqqIouDxef2PwGldey1eCIMKtK0VA8WMw3J/w+OVVRbAaNTh6oRejXi7sQsQAp6xSFAWHTndCEIC1\ni5N7Fd4VbMd4dBSlqgoOXstzbm18VbYWb+LX6KIoYNMKN2Kygv1cXpWIAU7Z1dQ1hnaPF4vKjCiy\nGJI6p2H8IgCgXL8gnaVRBri18S6TpiQCHABW1dhh1Knw2dlueAORdJZGlPMY4JRVE+ue1y20J3V8\nRI7gurcBBsGIEhNXXst3WkmHInUxBqJ9CMWCCY9XSSLuWuZCOCrj45PtGaiQKHcxwClrRrwhnGro\ng9OqQW15cVLntPiuIaKEUaoq58prBaJEVwYFCtr9yU0RW7vIAZ1GwsFTHQiEpl9LnajQMcApaw6f\n60ZMVrCqOrmpYwDQ4I2/Pl9g4tzvQuHWxt+kXBu7ktTxGrWEDUudCIRlfHb2zmupExUyBjhlRTQm\n47OzXdCqRaxelNzUMX/Uh3Z/M2ySHTZ9cq/cKffZ1EXQijp0htoSTiebsGGJE2qViP3H2xEM8ymc\n5icGOGXFmav9GPWFsWKBGVqNKqlzrnovQ4GCUhX7vguJIAhwa8sQUoLwhLqTOkenUWHjUhe8wSgO\nnGBfOM1PDHDKiol1z9feWCIzGdd98VesCyx8fV5oyvQVAIDG0UtJn3P3chf0WhX2n2jHmC+crtKI\nchYDnDKupWcM1zpHUVNigLPInNQ53ug4eoJdKJacMKiNaa6QMs2lLYUkqNDsv5r0a3StWsL9q0oQ\njsp47/PEu5oRFRoGOGXcga/irzzX1Cbfj93kbQAAuFXJ9ZdTfpEECSXaMnjlcQyFB5I+b01tMWwm\nDY6c74ZnyJ/GColyDwOcMmpwNIhTDf1w2bRYXJnctqEAcN3XCACotNSkqzTKslJd/DX61bHkX6NL\nkojNa8ogK8DeT6+lqzSinMQAp4w6eLoDsqJg9cLkp475ol50Bzv4+rzAlejKIULE9RtvW5K1tNKG\n0mIDzl4bRHP3WJqqI8o9DHDKmEAoiiPnu2HSq7A6yV3HAKDpxtO3S1WSrtIoB6hFNZxaN0bkYYxG\nhpM+TxAEPLQ2vqb6noONSfehE+U7BjhlzJHz3QiEYqirtkCVxK5jEyaeyCrN1WmqjHJFma4SAHBt\nfGZP4QtcZtSWWXC9exynGxPvbEZUCBjglBExWcbBUx1QqwSsW5L8k7Q/6kN3sAN2yQGjJrkR65S/\n4v3gAhrHLs743EfWlUMSBbz5cSMXd6F5gQFOGXG6sR+DYyGsWGCGyaBN+rxW/3UoUOCS+Pp8PtBK\nOri0bgzFBjASGZrRuXaLDncvd2PUF+G0MpoXGOCUdoqi4MBX7RAArF/qmtG5zb74yOIyU2UaKqNc\nVKmvBgA0jM78KfyeFW5YjRp8fKoTnX3eFFdGlFsY4JR2VztG0NIzjkXlRjhtyb8Gj8gRdARaYBYt\nsOqK0lgh5ZJSXSVESGgYvzDjAWlqlYjHN1ZAUYDX9l+BzAFtVMAY4JR2v/uiFQCwfgbLpgJAR6AV\nUSUKJ1+fzytqUY1SXTnG5TH0hz0zPn9hmRVLKm1o7hnHFxd60lAhUW5ggFNaXe0YwZW2YVS79agq\nndlTdMuN1+elem5eMt9U6KsAAFdG6md1/pb15VCrRPzXoWvwBiKpLI0oZzDAKa0++LIVAHDXspk9\nfcuKjBb/NWgFHZxGdxoqo1zm1pVBLahx1XcZsiLP+HyzQYMH6krhD8Xw5seNaaiQKPsY4JQ2TV2j\nuNQyhCqXHjVlxTM61xPqRiDmh1tVAkHgP9P5RhIklOkXIKgE0BWY3XahG5Y4UWo34MTlPs4Np4LE\n34yUNhN93zN9+ga+Hn3u1LD/e76q0se3ja0fPj2r80VRwLfvrYIkCnht/xWM+bnlKBUWBjilRUvP\nGC40D6LSqcPC8pk9fQPx/m8JEsrMC9JQHeUDu8YBk8qC1uB1BGOBWV2j2KLD5jVl8AWjeH3/FS6z\nSgWFAU5p8cHE0/fS5HccmzAaGcZwZBBOyQ2VqEpxZZQvBEFAtaEWMmQ0zGJltgkbljhR4TTizLVB\nnLgy81HtRLmKAU4p19Y7jnPXB1Dh0KG2YuYBPjH63KGe2aIvVHgW6GsgQET96OlZPz2LooA/2lQF\nlSTgjQONGB4PpbhKouxggFPK/eZIEwBg49LipLcMvVmL/zoAoNxcldK6KP9oJR1KdeUYjQ2jLzT7\nOd1FZi0eWVeOQCiGVz+8xAVeqCAkFeBHjhzBtm3bsHXrVrz88stTHvOTn/wE3/rWt/Cd73wHly9f\nnvz+o48+iqeeegpPP/00du7cmZqqKWddahnCxeb4yPPFlTMfvBaKBdEd6IBNtHPvbwIAVBtqAcx+\nMNuEtYscqCk143LbCA6cmN3IdqJckjDAZVnGCy+8gFdffRUffvgh9u3bh6ampluOOXz4MNrb2/HR\nRx/h7/7u7/C3f/u3k58JgoA33ngD7733Ht55552UN4Byhywr2PvpdQgA7l9VMqun7zZ/M2TIcKo4\n95viXNoS6CUDrvkbEIoFZ30dQRDw7XuqYNSp8JvDTWjqGk1hlUSZlzDA6+vrUVVVhfLycqjVamzf\nvh2HDh265ZhDhw7h6aefBgCsWbMG4+PjGBgYABDfyEKWZ74QA+WfY5d60dHnxfIFJlS4bbO6Rov/\nxuYlxopUlkZ5TBBELDQsQQxRXBo7P6drGXVqPHFfNWQFeOm9C/AFuUob5a+EAe7xeFBaWjr5tdvt\nRl9f3y3H9PX1oaSk5JZjPJ74aE9BELB7927s2LEDe/fuTVXdlGNCkRjePdIMlSTg/tVls7qGrMho\n8zdDLxhQpJv54DcqXNXGWkiQcG7k5KxWZrtZlduM+1aVYHg8jF/t49Qyyl9pH8T21ltv4be//S1e\neeUVvPnmmzh16lS6b0lZ8PHJDgyPh7Cu1ooi8+z6rnuCnQjJQbik2b1+p8KlEbWoNFTDJ4+jzd+U\n+IQE7ltZgkqnEWeuDeDTs10pqJAo8xJOsnW73eju7p782uPxwOW6dXqPy+VCb2/v5Ne9vb1wu92T\nnwGA3W7H448/jgsXLmDjxo0JC3M6k992MlcVQhuAxO0YGQ9h/4l2mPQqbL1vMQx6zazuc2K8GQBQ\nXVQFk0k3q2tMJ9XXy5b53I5V6jq0tjXh/OhXWFe+Zs41/Mm25fjXt89hz6FrWLe8BEsWzGyznfny\n850PCqENs5EwwOvq6tDe3o6uri44nU7s27cPL7744i3HbNmyBW+++Sa+/e1v49y5c7BYLHA4HAgE\nApBlGUajEX6/H0ePHsWzzz6bVGH9/eOza1GOcDrNed8GILl2vPFRIwKhKB5a7UA4FEU4FJ3xfRRF\nwZWhS1BDDZvKBa939oOVvslk0qX0etky39uhhgEOjRsdgXY09bWhWDPzWQ7ftP2eKrzzWRP+7pfH\n8Le7N8FqTO6Pz/n0853rCqENwOz+CEkY4JIk4bnnnsPu3buhKAp27tyJ2tpa7NmzB4Ig4JlnnsFD\nDz2Ew4cP4/HHH4der8c//MM/AAAGBgbw7LPPQhAExGIxPPnkk3jggQdm3jLKWc3dY/jsTBeKzRps\nWDb7bT/7wx6MR8dQrloAUZBSWCEVklrjEgyEPTg1+CW2ln5nzterKbXgwTWlOHK+By+9W4//+Sfr\noZK4PAblh6TWqdy8eTM2b958y/d27dp1y9fPP//8bedVVlbi/fffn0N5lMuiMRn/8YcGKAAeWVsy\np198zb74lo9OTWmCI2k+K9VVwKSy4Jr/Cu6LPAyz2jrna25a7oZnKIDGjhHs/eQ6/uTxJSmolCj9\n+KcmzdrHpzrQ0efFyiozFlbMfMOSmzV5r0KEiApzZYqqo0IkCAKWmFZAgYJTQ8dSds1tmxag2KLF\nwdOdOHaxN/FJRDmAAU6z0j8SwPuft8Cok7B57dzmbI+EhzAUGYBLVQK1NLsBcDR/VOqroBcNuOKt\nRyDmT8k1tWoJf7x5IbRqEb/afwUtPWMpuS5ROjHAacYURcEbHzUiHJVx/0oHzIa5jYxu8l0FADhV\n3PubEhMFCYtNyxFDDOeGv0rZde1mHZ64txqxmIJ/efs8hsbyf8AgFTYGOM3YV1f6cLF5CNVuPdYs\nnnufdbOvEQIElFu4eQklp8pQC42oxfmx03NaXvWbasuteGR9Ocb8Efzz3nMIzGJGBVGmMMBpRsZ8\nYbx18CpUkoBH1pXPecGV0cgwekPdKJZc0Kv0KaqSCp1KVGGxcTkiShhnho+n9NobljixbrEDXQN+\n/Pz9i4hxKWjKUQxwSpqsKHh13xWM+SO4d7kdzqK5L55w1Rvfua5UPbvlV2n+WmhcAq2ow7nRkynr\nCwfig9q2rK9ATakZF5qHsOfQtZRdmyiVGOCUtI9PduBC8yCq3Xrcs2ruo8UVRUHj+CWIELHAujAF\nFdJ8ohJVWGpaiSiiODX0ZUqvLYoCnrq/Bg6rDodOd+Gjkx0pvT5RKjDAKSmtvWN457MmmHQqbNtU\nlZK1ygfCHgxHBuGWyqCRtCmokuabauMi6EUD6sfOwBtN7WpcWrWEHQ/VwqhTYc+ha/jiQk9Kr080\nVwxwSigQiuLn719CTFbw+Ho3LMbU9FU3jt94fa7l1qE0O5IgYZl5FWTEcGLwSMqvbzVq8N1HFsWn\nl/3+Cs5c7U/5PYhmiwFOCf3nR1fRNxzAhsVWLK5yJT4hCbIi46r3MtSCBhVWjj6n2VtgWAiTZMFl\n7wUMhlMfsE6bHv/Hw4sgiQL+/b2LuNI6lPJ7EM0GA5zu6PdftuDYpV6U2rV4aF11yq7bHeyALzaO\nUlU5JK59TnMgCiLqrOsAKDjS93Fa7lHmMOKPN8fHafyv39TjavtwWu5DNBMMcJrW+esD+MW79TDq\nJPzRpsqUbvJwcewsAKBctyBl16T5y60tg1NTgs5QW0r2C59KdYkFT95XjXBExnO/+BLXu0bTch+i\nZDHAaUqtvWP4+fuXoJJEPHFvBRy21O2364t60eRthEW0wW3i9DGaO0EQUGddDwA43PcxZCU9c7eX\nVNqw/d4qBEJR/ONbZ/k6nbKKAU63GRgN4F/erkc4EsMfb16AqhJ7Sq9/cewsZMioVFenZDQ7EQBY\n1TZUG2oxGhvG+dFTabvPimo7/nTrMsRkBf/89nmcuz6QtnsR3QkDnG7hD0bws7frMeoL48G6YqxZ\nOvs9vqcSU2K4OHYWaqhRY1uc0msTrTCvgVrQ4Pjg4ZRPK7vlPjXF2HGjT/x//6YeX13xpO1eRNNh\ngNMkbyCCF/eeR/eAD+tqrbhnVer7p5u8jfDHfKhQVUEtqVN+fZrftJIOKy1rEUUUR/rTM6BtQnWp\nBd99ZBFUkohfvH8JH3zRAllR0npPopsxwAkAMOoN4ae/PoPm7jEsrzTh0Q3VablP/dhpAECNhU/f\nlB7VhloUqYvR5G9Eu78lrfeqcJrwvS2LYdKr8NvPW/DSby9yAxTKGAY4YXA0iP/nzTPo7PdhTY0F\nT9y/CFIKR5xP6Aq0oyfYCbdUCquuKOXXJwLiA9rW2u4CAHzS93tE5HBa7+e2G/D9bctQ6TTizNV+\n/OT1U/AMpW5tdqLpMMDnud4hP/7hzdPwDAdw1xIbvrVpYVoGlimKgmNDhwEAi4zLUn59opvZ1HYs\nMi7HeGwMxwYPp/1+Bp0azzy6GBuWONEz6MffvXYSxy71QuErdUojBvg8dqF5EP/366cwNBbC/Svs\neGRDTdpGhbcHWm48fZfBZZr7HuJEiayw1MEomXF+7BR6gp1pv58oCtiyoQLb76lCJCbjlQ8u419/\ncwHD46G035vmJwb4PCTLCt77vBk/23sewXAMj6134f416VvOVFEUHL/x9L3EtDJt9yG6mSSosN62\nCQDwce8HiMqZ6ZteWWPH7m8vR6XTgHPXB/CjXx7H5/XdfBqnlGOAzzNj/jD+ee85/O6LVliNanz3\n4SqsT/FUsW9q8V9DX6gXpaoKOIzOtN6L6GYOrQu1xiUYjY3gi8FPMnZfm0mLXVuW4Ft3VSIWk/Gr\n3zfg//31WbT0jGWsBip8qmwXQJlz/voAXj/QiOHxEBaWGLB1UxXMBl1a7xmVI/h84BAECFhiXJHW\ne0KLQA8AABaHSURBVBFNZYV5LTzBXtSPnUaVYSGqjYsycl9BELB2kQMLSy34+FQ7rnaM4IX/OIVN\nK9zYsXkhHLbU7OpH8xefwOeBUW8I//7eRfzLO/UY9YVw33I7djy8JO3hDQAnh7/EWHQE1epFsBsd\nab8f0TepRBXutt8PESI+9nwAX9Sb0ftbjBrseGgRdj26CC6bFicue/DXrxzHnkPXMOpL7wh5Kmx8\nAi9gsqLgaH0P9n5yHf5QFGXFWmxZV45SpzUj9x8M9+PMyHEYBCNWFK3OyD2JpmJVF2GlZS0ujJ3B\nR57f4TtluyAKmX1+WeA24/vbluNy2zCOnOvCRyc78OnZLjyyrhx/tGkBrCZtRuuh/McAL1CXWofw\nzqdNaPOMQ6sW8fAaB+5aXpGxtccVRcGn/X+ADBkrDGugUfGXE2VXrXEp+kO96Ay24djQYdxf/EjG\naxAEASur7VhaaUN90yCOX+65Jci3bVoAG4OcksQALzCtvWN457MmXG6N71e8rNKEB1eXochizGgd\nZ0e/Qk+wEyVSOSqt1Rm9N9FUBEHAhqL78Fn/H3Bm5Dhc2hIsNi3PSi0qScT6JU6sri2+EeS9+Ohk\nBz4504nNa8rw7XuqYLekv4uL8hsDvEC09o5h37E2nG7sBwBUu/W4f1UJyl22jNfiCfbg2OBn0Ak6\nrCnamPH7E01HI2pwj/0hHB44gI89H8KmLoJTW5K1em4O8ovN8SD/5EwXDp/rxv11pXji3ioOdqNp\nMcDzmKIouNoxgg+PteFSS3xf4lK7FveucGFRZXYGjIXlEA70vQ8ZMlYbN8CgyeyTP1EiFrUVG2z3\n4cTwEbzf/V/4/9u79+CoyruB499z9n7LZbPJ5gYBAuEiCSDWC32tCCggICCoM307OtDWdt4Zo5YO\nHUHbzoC2omM78/7hyLTqW19feb1RX+uMWqMkAnKHgGIaETAkIZvL5rKb3eztPO8fKwEUyAXN2cXn\nMxOy2Zyz+T3s2ed3znnO+T13Ft9Dpknf0r5Gg8r0CbmUl3o4eqKDjz9toaa2me1HTnNjRQGLbxhD\nTqY8IpfOJxN4GkpoGgfr23l3bwNfNCXvKx2dZ2NmWQ7jiz26zbF9Zty7O9bJOFMZRRnfXXEYSboc\nhbZiKrSZHO7ez9am/+HO4ntxGJ16h4VBVSgv9XDV2Bw+a+hkx5Fmqg81s/3waW6cVsjiG+Spdeks\nmcDTSDgS56PaZt7f30h7dx8ApQV2rpmYR0mB/pODHOzeQ33wKNlqDlNzpusdjiRdUqljItFEhLrg\nJ/y9+WXuKPp3bAa73mEBybKsV41xM3l0Np992cn2I81sO9jE9sPN3DyjmEU3lJDhMOsdpqQzmcDT\nQGtXmKp9jWw/0kw4ksBkUKgYm8HVE/PIy3bpHR4AX4a+YGfHh9gUOz/ImoVBlZuWlPomucqJiijH\ne+t5vfFFlhX9GKcxNT5T8FUiH+tmckk2n570s/1wM//cd4qa2iZu+cEoFlw7GrvVpHeYkk5kL5ui\nzoxvv7f3FIc+b0cATpuRGya7mVGWj9OeOrea+KPtvON7EwWVq53X47CmTgcoSZeiKAoVGTNRUTnW\nW8frjS+yvOjHZJhG/uLPS1FVhfJxOUwuyab2i3Y+/qSFf+z8kg/2N7HgutHMu6YYq1l259838h1P\nMfGExp7PfLy39xQNvmTFqIJsCxXjsrhqnBej0aBzhOcLxLp5s3kLUS3CNOs15Ln0u6JXkoZDURSm\nZszAqBipC37CK43/xe2Fd5FnSb1Z84wGlZlleVSM87C/vpU9R328UXOcf+47xaIbxnDzjEJMKdZH\nSN8dmcBTRDAcY9vBJqoONNIdjKIoUFbsYHqph5KCbN0uTLuUcCLE309vIZgIMMk8lXHuMr1DkqRh\nURSFyRkVmFULh3v283rTfzPfu5RxjtTcpk1Gleun5DNjfC576nzs/1crW6o+5909Ddx2fQk3VhRg\nNslEfqWTCVxnPn+I9/adYsfh00TjGhaTytWlmVw9yYt7hIuvDEUo3subp/+XrpifcaYyJntkqVQp\n/ZU6J2I3Otjr38HbLa9zg3s2M7OuT8kdaACL2cCNFYVcMzGPXZ+2cPBYOy/9s55/7DzJ/GtHM3tG\noTy1fgVTRIpOUtvWFtA7hMuSm+u6aBvOjG+/u+cUtceS49uZDiPlYzOZUVaAzZI6F6VkZdnp6gqd\n91xPrJs3T79MV6yT0caxXJ2buh0cgNNpJRjs0zuMyybbMXI6o352dVTTJ8KMd0xibt4izOrZq74v\n9LlIBaG+GHs+83HwWDuxuMBpMzHn6iJmzyi6YInWS/VT6eJKaAMk2zFUMoF/Ry60UUVjCXYd9fH+\nvkYa25Lj24VuKxWlWUwdl4+qpl4S/HpH1Rbx8dbpV+lNBCg1lVHumZnSyRvSI2EMhmzHyOpLhNnt\n344/1obb5GFh/nLc5mSBpFRN4GeEI3H21fk48Hk7kZiGqir8YFIe82YWM64wo/8zeyUkvyuhDSAT\neEo5d6Nq7QxRXdtMzaFmevviqApMKHIyrTQnZce3zzjTUQkhONJzgO0dVSREgknmciZ7yvUOb1DS\nJWEMRLZj5GkiwZHugxwP1WNUjNycu4BJrvKUT+BnROMJPj3hZ/+/fPgDMQBG5TmZNTWf66Z4mTDW\nc0X1telMJvAU4sq08e6O42w/fJq6hi4AbBYDU0tcTJ+QT3ZGetQ3zsqy09DWzEcdVRzvrcesWKiw\nz0yrCUrSKWFcimyHfprCDRzo3EWcOGXOKdxeupTIyE4rflmEEDT4guyta+FkSxBNgKLAjLI8ZozP\nYdp4D05b6gzdDYVM4CkoHd+QaCy5t3ugvo2Dx9oJ9cWBZJnTicUurhqXn1ZXhobivXwa3see1t1o\nJMhRc7k66zqc1gy9QxuSdEwYFyLboa9gPMBe/3a64p04jU5mexYy1jFe77CGLBSJ89lJP5+caMfX\nGQFAVRTKRmUyfbyH6RM85GWnRkW6wZAJPAWlyxvS3hWmrqGL2i/aOXK8g2hMAyDLaaKs0MnUUg+e\nLP1rLA9WVItwKvwldYEjnOw9hoaGXXEwwTqZsVkTUvp0/8Wka8L4OtkO/WlCoz54lH8FPkFDY4y9\nlH/LmUu2OUfv0IYljsKeI00ca+qi5atkDpCbZWXKGDdTxriZNDoLlz11y7bKBD6AmpoaHn/8cYQQ\nrFixgvvuu+8by2zcuJGamhpsNht//OMfmTx58qDXvZBUfEOisQRN7b00+ALUn+qm/lQnHT1nN3q3\ny8S4fAfji7OYOrGQnu6wjtFenBCCcCJEbyJIMB6gO+anM+anNXKatogPQXKTyFSzGOcspdheijGN\nS6Omc8I4l2xH6oiZQnzctIOOeBsKKlNcFUzPuhZ3miXyc8fyg+EYxxq7+LzRT3NHH5GvDkYUoDDX\nwYTiLCYUZTK+OBNPpjVldua/zwl8wF5Z0zQ2bNjACy+8QF5eHitXrmTu3LmUlpb2L1NdXU1DQwPv\nvfcetbW1/O53v+OVV14Z1LqpRhOC7mCUtq4wrZ1hWrvCtHaGONUapMUf4tzdHZvFwIQiB/nZVsYW\nZuN1O/s3alXHjTucCNEZ7aA71kl3vItgPEBvPEBvopdwopdwItSfpM+louI2eMhS3RQ5RpFjz7si\nOltJ+rZlW9zcmDuP5r5GPuk+wKeBQ3waOESJvZSJzqsY6xiPWU2dcseD4bSZmD4hl+kTctE0QYs/\nxPHmThp8AVr8IZraetl2sAmADIeZEq+LknwXY/JdjPY6cWdYde33vo8GTOCHDx+mpKSEoqIiABYt\nWkRVVdV5Sbiqqoply5YBMG3aNAKBAO3t7TQ2Ng647kjQNEEoEicYjhEMxwiEogRCMXp6o/SEonQF\no3QFInQGInQFIyS0byY3s1GlKMdKToYZt8tCsTeLfLdD173QM4naH22nPdqKP9pOR7SNPu3CR/5G\nTFhUC9kGN2bFikWxYFYsOE0uMq1uMsyZGJT0GaOXJD0pikKRbRSF1iKa+5qo7/mEL0Nf8GXoCwyK\ngSLraAqsxeRbi/CY87AZ7Clz1DoQVVUo9Dgo9CSLSSU0kTyQ8fVwqjVAa3eEI8c7OHK8o38di8lA\nvttOocdOfo6D3EwrOZlWcjKsZDktKXmbbLobMIH7fD4KCs7WBPZ6vRw5cuS8ZVpbW8nPP1sDOz8/\nH5/PN6h1B0sIwfHmHjoDEaLxBNGYRjSuEYkliEQT9EXjRKIJwtEEob4Y4UiCUCRGqC9OqC9+gePN\n8ylKcrIQb5YFh81Aht1Eht2EO8OOJ9tJht00oh++7lgngXgPUS1CJNFHOBEilOglEA8QiHfRHeu6\nYKJ2qE7yjYXYFAd2gwOXJQOXJQOrasOopudVppKUyhRFpcg2iiLbKHpi3ZwKnaQ5fIqG8Akawif6\nl7OqVjJN2TiNGTiNLmwGO1bVhsVgxayYMalmjIoRg2LEbnBgN6ZOJUaDqlCQ46Agx8G1U5J9eqgv\nhq8zTFNbD21dYbqCURrbgnzp++bpbIOq4LKbyLCbcdlNuOxmHFYTVosBq9mAzWLEYjJgMqoYDckv\nk0FBVRUURUFVFBSFZD8ukmdKIZkXmjv78Hf2ktDE2a+Edt7P2pnnRPKxEMl1haA/Nyhf/ZP8e2BQ\nVVT1q8cGFYOqJL8MCkZVxWBQMKgqRoNCToYVr3vkL/z7TgY2v4vr4lr8IR57cf+glzcZFawmAzaz\nSrbThtWsYjGpWEwGrGYVu9WMy27GZbfisJlxWI0ps4fYFfPzYsOzF/29iopNdeA1FOBQnTgMLrJt\nbrKsbgxK+o5VS1K6yzBlclXmNK7KnEYk0UdHtI32kI9APECvFqA14sMXOT3g6ygo3FvyH7iMqXvH\nh91qYmyBibEFZ2PUNEF3b5SOnjD+7hA9vRGC4TiBcIxQRKPFH6KhVdMx6u+GosB/PnDjiE/tOmBv\n7/V6aW5u7v/Z5/ORl5d33jJ5eXm0tLT0/9zS0oLX6yUWiw247sV8fUA/N9fF/z11O3X1x5Lj0IqK\nwWBAVVUMavJ7aonhyhvemzkKL08UPsoXx4+hKQJVVVENKiaTaeTbmQC3LT3uWb+oK6ENINuRSgbV\nBhsTyIaLTPIjhCAejxOPJxCahtAEQtMABZPRyLjCkbggbvj91MWZgcHdeSOEQNM0ElqCRCKBpiW+\n+oWWPBAUov+AUAiBoij93/t99VhRkkfrguQRe/J5pf95VVWTjzm7/Lnfzz3w7P+biP4YRX8sZ96n\n5BKapmE2mykZ5R76f9VlGjCBl5eX09DQQFNTE7m5ubz99ts8/fTT5y0zd+5cXnrpJW677TYOHTpE\nRkYGHo+H7OzsAdcdCkVRmDxxwrDXTzdjSwr1DkGSJElKUQMmcIPBwKOPPsrq1asRQrBy5UpKS0vZ\nsmULiqJw9913c9NNN1FdXc0tt9yCzWbjD3/4wyXXlSRJkiTp8qRsIRdJkiRJki4u1QaOJUmSJEka\nBJnAJUmSJCkNyQQuSZIkSWkoJRN4XV0dd999N8uWLWPlypXDLv6SCl588UUWLlzIkiVLeOqpp/QO\n57I899xzTJo0ia6uLr1DGbJNmzaxcOFCli5dyv33308wmD5zQdbU1LBgwQLmz5/P5s2b9Q5nWFpa\nWrjnnntYtGgRS5Ys4W9/+5veIQ2bpmksX76cX/7yl3qHMmyBQIDKykoWLlzIokWLqK2t1TukYXnh\nhRdYvHgxS5YsYc2aNUSjUb1DGpR169Yxa9YslixZ0v9cd3c3q1evZv78+fz0pz8lEBhEfXeRglav\nXi0++ugjIYQQ27ZtEz/5yU90jmh4du3aJVatWiVisZgQQoiOjg6dIxq+06dPi9WrV4ubb75ZdHZ2\n6h3OkO3YsUMkEgkhhBBPPvmkeOqpp3SOaHASiYSYN2+eaGxsFNFoVNx+++3i2LFjeoc1ZK2treLo\n0aNCCCGCwaC49dZb07IdQgjx/PPPizVr1ohf/OIXeocybL/5zW/Ea6+9JoQQIhaLiUAgoHNEQ9fS\n0iLmzJkjIpGIEEKIBx54QGzdulXnqAZn79694ujRo2Lx4sX9z23atEls3rxZCCHEs88+K5588skB\nXyclj8AVRenf+wgEAni9Xp0jGp6XX36Zn//85xiNybv13O6Rv9H/2/L444+zdu1avcMYtlmzZvUX\nwZk+ffp5hYdS2blzEZhMpv75BNJNbm5u/wyFDoeD0tJSWltbdY5q6FpaWqiurubOO+/UO5RhCwaD\n7Nu3jxUrVgBgNBpxOtNnyuNzaZpGOBwmHo/T19c36EJhervmmmvIyDi/yl5VVRXLly8HYPny5bz/\n/vsDvk5K1t18+OGH+dnPfsYTTzyBEIItW7boHdKwnDx5kn379vGnP/0Ji8XC2rVrKS8v1zusIauq\nqqKgoICJEyfqHcq34rXXXmPRokV6hzEo3+Z8AqmisbGRuro6Kioq9A5lyM7syA7q9GaKamxsJDs7\nm4cffpi6ujqmTp3K+vXrsVqteoc2JF6vl1WrVjF79mxsNhs//OEPmTVrlt5hDZvf78fj8QDJHV6/\n3z/gOrol8FWrVtHe3v6N5x966CF27tzJ+vXrmTdvHu+88w7r1q3j+eef1yHKgV2sHQ8++CCJRILu\n7m5eeeUVDh8+zIMPPpiyR0+Xasezzz7Lc8891/+cSNHSAZfapubMmQPAM888g8lkOm/sSRo5vb29\nVFZWsm7dOhyO1JmsYzC2bduGx+Nh8uTJ7N69W+9whi0ej3P06FF++9vfUl5ezmOPPcbmzZuprKzU\nO7Qh6enpoaqqig8//BCXy0VlZSVvvfXWFfPZHszkWbol8Esl5LVr1/LII48AsGDBAtavXz9SYQ3Z\npdqxZcsWbr31VgAqKipQVZXOzk6ys7NHKrxBu1g76uvraWpqYunSpQgh8Pl8rFixgldffZWcnJGo\n1Tx4A+3kvfHGG1RXV6fVBVSDmYsgXcTjcSorK1m6dCnz5s3TO5whO3DgAB988AHV1dVEIhF6e3tZ\nu3YtmzZt0ju0IcnPzyc/P7//bOD8+fP5y1/+onNUQ7dz505GjRpFVlYWALfccgsHDx5M2wSek5ND\ne3s7Ho+Htra2QQ25puQYuNfrZc+ePQB8/PHHjBkzRt+AhmnevHns2rULgBMnThCPx1MyeV9KWVkZ\nO3bsoKqqig8++ACv18vWrVtTLnkPpKamhr/+9a8888wzmM1mvcMZtHPnIohGo7z99tvMnTtX77CG\nZd26dYwfP557771X71CG5Ve/+hXbtm2jqqqKp59+muuuuy7tkjeAx+OhoKCAEyeSU53u2rUrLUtc\nFxYWUltbSyQSQQiRdu34+pnMOXPm8MYbbwCwdevWQX3OU3IMfMOGDWzcuBFN07BYLGzYsEHvkIbl\njjvuYN26dSxZsgSTycQTTzyhd0iX7cxsQOlm48aNxGIxVq9eDcC0adP4/e9/r29Qg3ClzCewf/9+\n3nrrLcrKyli2bBmKovDQQw/xox/9SO/QvpceeeQRfv3rXxOPxxk1alT//BXppKKigvnz57Ns2TKM\nRiNTpkzhrrvu0jusQVmzZg27d++mq6uL2bNnc//993PffffxwAMP8Prrr1NUVMSf//znAV9H1kKX\nJEmSpDSUkqfQJUmSJEm6NJnAJUmSJCkNyQQuSZIkSWlIJnBJkiRJSkMygUuSJElSGpIJXJIkSZLS\nkEzgkiRJkpSGZAKXJEmSpDT0/yrJsHWX4UslAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11a195a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for col in 'xy':\n",
+    "    sns.kdeplot(data[col], shade=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Histograms and KDE can be combined using ``distplot``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TzDM4YDA5UfwDx6V7KJAnT67M0Qmx8ZSUwI8dO8ZDDz3EgQMHeP755+ccv3Tp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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11acced30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data['x'])\n",
+    "sns.distplot(data['y']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we pass the full two-dimensional dataset to ``kdeplot``, we will get a two-dimensional visualization of the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OZPnO1SSmHAcgxCeIcX1GM6hLfxztGu+xVhqq2HFgL78d3MLpglQAlHIlvcO7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7/q8iXmAa4097JYLBhMBsID+79k6a2z1huuaa0n0FM/qD55ubgzKSBqUNvYqrqljj2lB9ldcrB3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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11aebdbe0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.kdeplot(data);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see the joint distribution and the marginal distributions together using ``sns.jointplot``.\n",
+    "For this plot, we'll set the style to a white background:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VTaLLkRUDiQi8QJO6DEkKBwD8eLBYcCXyYiCRV/Omruhc3vq5PcHQAeHQANi6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\nSBH+H3jVAOxlSfUyAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11ad139b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with sns.axes_style('white'):\n",
+    "    sns.jointplot(\"x\", \"y\", data, kind='kde');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are other parameters that can be passed to ``jointplot``—for example, we can use a hexagonally based histogram instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PsX///pc096U7JAAwdQWm0fhf4bPPORfjRw7j6OQkhoaH8R8//AHuurv+cfirXvUafOLe\ne8A5h+95+M1vfo23vu0dyKTT0DQN8UQCjuPg8cd+jj/4v/4IAKoL/y233FI33iOPPIJcLgfDMPCf\n//mfuOeee+qOOZYd0uTkJG677Tbce++91XdEjVhYWMDAwAA8z8MXv/jF6uOzV77ylfjqV7+Kv/iL\nvwAA7N69G2eddVbdvB5++GHcd999uP/++2EYi+938vk83vOe9+BDH/pQzfsszjlyuRz6+/vh+z4e\nfPBB7Nq1C8TxQYJErCu2b9+Or33ta7jjjjuwY8cO3HzzzdB1HX/zN3+Dj33sY8jn8xBC4B3veAd2\n7NiB2267DW9761vQ3z+Ac847D6ViEUD9nf83vn4/nnziv6GqKnbs2IErrrgCuq7jqaeewnXXXQfG\nGG6//XYMDg7WCdLSsVaqtFdVVXzoT+/ErX/8HkghcO31N2B7+THVd779zwBjeNObb8Ip20/Fyy/b\nhZt//81QFQU3vOlGnHrqadi39wV8+C//HEIISCFw9ev+B3a9Inx/c+DAAVx88cUNz3v++efj1ltv\nxfT0NK677rq6x3XHyt///d8jm83iIx/5CKSU0DQN3/72twEA7373u3HXXXdheHgYX/rSl/DTn/4U\nUkq89a1vrT5ted/73oe77rqr+ghu06ZN+MIXvlB3no997GPwfR/vfOc7AYQ7rg9/+MO4//77cfjw\nYfzd3/0dPve5z1XLuy3Lwh/90R+Bcw4hBC677DL83u/93nH9rgTApGzw8LlHGR8fx1VXXYUf//jH\nNVtnguiEiYkJvPe978UDDzxwTD9nuwH8oP1jl3hUh7JCgjI1X0LAW5+z1Q7pRBGNaLj1j9+Hz33u\nc9C02nN/97vfxbPPPos///M/P6lz6mX8gMN2ed3Xkx1U2VXWuw995O9w0+/8VtPHlWsJ2iERBHFM\nNNphEMRKQIJErBs2bdp0zLsjADA0BULKliW7pq4AEsAKmen7EkZdld1SFAYkoka1yq4ZmsrKVXYc\nK/EsxHUDwNSgqfWlDTfccANuuOGG4z9JGSklXJ9DCgnTUI+p80KjsbyAQ3AJw1ChHsdYx4KqMJi6\nclyFDZlMGqvoQdZxQYJEEG1QVQVRhYFzCdsLahZ2VWGIGCsfvhcxNIwNqCjaftWHVCEZ1RGL6lAV\nBVJK6KoC2+U1pd+MAZahQVVZOaNJgR9wON7xVXxxGfqXdJUdt0i0wucCnserpercCcvXDf3YIz04\nL3uAymMFdgBdV2C+hLGOFUVRYBoKNO2l+5CEqH/kt1YhQSKIDmCMQdMYYqoO3+dwfQHLDCPLT9Si\npigMiZgBy9SadmpgjEFVGWIWq+nUsDyLiTEGQ9egqgKuxxEcp0HT5/K4RKIZQoTz85fNT0jA9QUC\nHu6WGu3QliOlhOMF8IPasSTCXWUQSJiGAl078bHyqqIgarJqp4ZjYWBgaH30sQMJEkEcEwpjMA0N\nuiahKCdnkdA0BYOpsLNAs4WJMQZdU6EqrOWuRVUURAygaAc43odAFZHQtZVLxLVd3tDAW4ELCdfj\nUCPtbwRsN2gpvEJKOB4/aYm+4d+IQVVo2W0GXRmCeAmcLDGq0OmC2ckjtJVefE/22w2G1R1+eKIe\nc64F6MoQBEEQPQEJEkEQBNETkCARBEEQPQEJEkEsoVO/RyfHreRYK83JPuN68dEQxwcJEkGUEVLC\n8zn8QDRdQGX5mHzRa9nap2LE9APecjHmQsB1A/BjCHo7HqSUHbVBUhhgaICqti4e0Np8Hwh/x0LJ\ng+MFbYVJ1xS0qxdRFNaRwOma2raVUyfzJ04eVGVHrHuWJnmGy5yAqoaG16WOfs4F8iUfBdsHAORK\nPgYSJiIRrbrwSSnBhYTtLhpoFSZgmbXmWSFl1c8EAJ4dwNRX1tOzHC4EnDZl1QCgqwwRU6tGc/iB\ngOdzLP0xRQFMTYWuN/fwSBmWaC/knPLP+rBMFamYCU1rfC9s6Go1v2m5f0hTGUxdhdqBBwkIxU1T\nGVyfw/dFza5QVVjHfqZuk8mkkc1mq/8/mUyu6irDVpAgEesWKWXVi7LcQc+5RLEsEpqqwPU50nm3\nrv3OQt6FVvIwkDShKuFxy70vQoYhfoauwNAUcIEl4reI6wt4gajpsLASCCHhB7xt+5pGi3RoqF0U\niYCHnSFMo7lwVgQ+k3fh+rVdBmyXw3ZLSMUNRCNawxY+jDFYpg5DE3AqrYP01uLXDMYYIoYGXRVh\nHLuQMFbYzHuiMQwDj+9Jg7EMSqUirn312Wu20SoJErFukRI1kd6NcH2BbMGD1+IxV8AlZtJO2w7O\nni9a9p2rzKnkBohZGtQVM5s2TotdisrCTt6tjLeWqVcj0VvBhcT0QqnlMdmCB1VliJotTLzllk2V\n8x8Pqqogqiodzb/XGBwaRTyxNgVoOSRIBEGAsc52ZCd7MV/p8602MVpv9P4DVIIgCGJdQIJEEARB\n9AQkSARBEERPQIJErF9YWFnWjk6O6fTVhK6ythl+DJ3l/B2cyCJXcFseE2YBtff/KKy9t4cLiSPT\n+aahgRUYgIjefmnpJCSPcwHfXz95QOsdKmog1i0KY4hGtGUepHr0csmx4wUNq+QsU22bqaMwwDK1\nsqkT8ALecCxTV6DraktByuQd/HL3NI5MFxAxNezc0oeXnTFcs8BLKVGwfRRtHwGX0FQR/h7LfDcM\nQKSDXKfJuQIOTeaQL/kYnyng1E1JjA7EGh6rqgoG+yw4ZQ/Scp2LGCr64s29SJX5ux6vVjf6XByT\nB4lYnZAgEeuaxYwavalIVIgYGgxNoOiEplddCz0u7Sq3LFNdFqqH0FejKqFRVcowedZsHa3NucCT\nz89i75E0bDfcNZScAE/vncPkbAHn7xzGKRuScD2OXNGr8QAFXCLgAbimwNCVMMm0LH6tuhkUbB97\nD6cxm7argp0revj13jkM95dw+tY+RCN63c+FZeIaxgaiKNg+8iUfCgMGkpGWHiYAoWE44FjavCLg\nEpyfvKRXojuQIBEEwnY0pq5CAeC0ECVFUZCIGhBCtM21URhgtTB/qipD1GLgQkBVWofEeT7Hvz1y\nCPM5p+H3ZzMOfvrEOC4+cxgDKatuV1IdJxAIhMBIvwVNbb2rOzpXwJ4XM/AaPDITEpheKCGbd3Dp\nuRtgmY2XElVVkIwZiJoaFJW1fUxnO35dWmyFStKrEBKW2f5GgFh9dFWQPM/DLbfcAt/3wTnH61//\netx6663dnBKxjmGMddx0tNMgvHYLMGOsrTAAoZCk843FqAIXEkKiqRhVEAJQWPv5F2y/oRgtxfFF\n235xjLGOuyy0eT0VIteXnyi9MA9Zft1v20VIubXLMzpxdFWQDMPAV77yFViWBc45br75ZlxxxRU4\n//zzuzktgiCInkGIAEKE/RMFb91ZZLXT9Ud2lmUBCHdLQbC2LzZBEMSxMjg0isHhMQBAsZBb07vD\nrpesCCFw/fXXY9euXdi1axftjgiCINYpXRckRVHwL//yL3j44Yfx9NNPY9++fd2eEkEQBNEFui5I\nFeLxOH77t38b//Vf/9XtqRBrEM5FOaOo+VvzSvZPO+NnZbx2MLaCSalSIhU3Wx6iaQxCyLamWoUB\nQYsQwgrJqIGI2boYwTRUeH57420nSCkhOygrEVJCnKRAQ+Lk0lVBWlhYQD6fBwA4joNHH30Up556\najenRKwxpJTIlzzsHc/i4GQOR+eKDZ3/fsAxs1DC4ekCjs4X4biN32dyIZAveSg6AUq2D9FgIQ59\nRsqKlCZzIbCQc3B4uoAztvXjlA0JRCP1r36HUhG8bOcwhvujkEDT1FXGwkq2mYyNbMFrKayjgzH8\n9jlj2DAUw/JiQVUBBpImThlLIFv0kc47CPhL66ggpUQQCBRsH53ojJBAwQ7grpAQEr1DV4saZmdn\n8Wd/9mcQQkAIgWuuuQavetWrujklYo1QiRqfni+h4CyKS6bgIVv0MDYYRSpmAgzIFz1MzhWr5dJS\nAnNZB4bOMJCwoGlhjs7yFNNASBRKPiKGCqNc1qyrYchdJ2Xh7eZfcgNMzhSqvhzGGDYMxTGYimBi\npojZjI2IoWHLaAxbx2pTRIUstyAqC1C4W6stCS/YPoqOj/6E2VQ8I4aG83cMYTYdxf6JLLIFDwlL\nx2BfBDFrMf+p5HCUHDsca0mCbrvfUQgJx68PSOwE1wvzpSxTg6qsXKAh0T26KkhnnHEGvvvd73Zz\nCsQaZT7rYCZtN/yelMDRuRLmsw5UBthe49tyz5eYWiihL2609Mc4HofrcQz3W1VhOl6m5ktI5xv3\nqTN0Dds3pTA6EEV/0oRpNP7XWCL8XRmae5OkBBZyLuJWgFQ80nRRH+6PYrDPwv7xTMvuFOm8i6Lt\nY7jfaisQnt8+xbYdUobdKpbeFBCrl555h0QQK4nttbcQeL5oKkZLCTp4XySBmujv46Xo+G2P6U9F\nmorRUjrwwCII2iepKoxhINFctCq0S6et0OFhHdHJuyei9+m6D4kgCIJozvJODdlsX833k8nkmnlc\nSYJEEATRwyzt1GCaBh7fkwZjGQBAqVTEta8+G6lUqptTXDFIkAiCIHqYpZ0a1jr0DolYVUgpOyr1\nXckHGJ08DZFSduRN6mTunZYyC9HZtehorA6va6My9+V0+jfqhJUci+h9aIdErBo8n8MLOCAZTENp\nGYq3cSiOZDQs52708twyVaTiJhiAXMlD0a4vgmAM2DAYRSJqwA8EFnJuwwW5ZPuYy9o4dDSHbWNJ\nbBmN1z3Tl1LC9Tn8QEBRGCJNwua8gCNb8GAZGlSFV3OPlo9VcsLwPdNQMZiymmQSAdFIWBLtB6Lp\nWLYbYNb2MZN2sHkkhr5EpO44zgWyRQ9+IKol5M2QAGYyDpJRveG8KkSMMCeqWTiiX06LFVLC0MK/\nd6N3Jabe+rNArB5IkIieh3OxzKsiYbvh4m4ajUPtFIUhGTcRtTTMZx3MZ8MSaoUxDKUiMIzFBaw/\nEUHM4pjPONUKsf6EgaE+q7rQqaqC0UEFRdtHrhg+zw84x1zaRqbgVX9u96EFzKRL2LG5D32JsLOC\nH3C4nqiKGecSRR7A0JRqWJ2UEpmCB9vxqwKqa2ES7dKMIM8PykmwoYC6vkDJCZCKmxjuj1bj1peX\nQetaefFf4qXyAo5iyUfRWRzrhcMZDCQj2DqWgKGr1eTZQsmvqZ5TKr6mJdd8qdcpKAt4ODejoWBU\nwxFVPbzZKJeACxH6iyppsQDgeAIBlzWpsarKYK2A54voHUiQiJ7GcYOahWkpAZfgTmVhb/xR1lQV\nI/1RJGMmsgW3qQHU0FSMDUbhuByJmN7wOFVRkIyZsEwd+46kMZOx4TTYdcxnHWSLM9g4FMXWsWRT\n02clLE8IoOQGCJr8nlZEh8E5js6VULD9urLqgEvMZx2U7AAbh2MY6rcaGlPDFFcdusYxNW+j6Ph1\ncxNlU3C+5GGkPwpDVxp6hSpTYAyhKjXZNTkeh5exEbd0JGONWx8pLAxH1DUFmbwD1xcNOzZUUmNN\nXUEqYdak8BJrAxIkoqfxmyzSFWR5MWxFJU6bc9HS+8IYQypuINIk/bSCrinIl/yGYlQhCES4q2jT\ngUAIwG4hRtXjZBgd3mo02wsjvtt1SVAVBaUGYrQU1xcouT4kmj9yA8Lrr7DWniIh2ocGMsagMgbO\n0bJ9ULlnBT2iW6PQXpcgXgK9el/e+bx69Tcg1jMkSARBEERPQIJEEARB9AT0DokgCKKHWdo6aDlL\nWwmthRZCtEMiepZODZFStD9WStlRM08u2gfXSSnhd2SCbX++Tg9kYNWS7pU4ZwdDdT7/DujUxNvJ\ngiqxvsyyldZBjf5TaSX0f376HHK5XLenetzQDonoSbgQcDzetodzwAVyRQ8xS0MyajQ0m/oBx1wm\n9AsNpiKINCkRLzkBjszk0R83sGU00bCU3HED/OqFWTz+3DS2b0giamkNq8JScQMDyQhKto+IqUFp\nogCez+FzWS2fbmgQDTgmZgsIhITZpAxb1xQM90WQbJMqC4SL/paxBGbTJeSLft05VQVIxkwMpiJh\nrlTQuAxbVRh0TYGmMri+aFoRqWsMYGFpezNDcIXBvgiyhdC/1EhzdJVBU5UwcsJUobC1n4PUSeug\ntXINSJCpGDpRAAAgAElEQVSInqISrNcuJ0cICdsJwMurVtEOULID9CcXw+aEkMiVXBydK1UXt7mM\ng4iuoC8ZqcZFBIHA9EIR2bLhdS7rYj7n4pQNCQylLKiqAi4kXjyaww9+/iLccuLsgckcYhEVW8aS\n1VLrqKlhIGUiGTPBGAtD/Gy/zqjKhahZdCv/u7SEWsrQXzS9UKr+nOsLaAoDK3dfAMLk1s2jCcRa\ndEVYjqmr2DySQLbgYiHnwi4n5MYtDUMpC1FrcSxdk3C90IgsEXqPdE2BqS92TrBMBZoWdlYIyuXk\nqsqqHRaARUOwaSgwmnRdUBgLjcomR7boVa+1qpTHKl9DLiSKdlDt0tBM8InVBQkS0TMEAYft8dZt\naYSAF8jqQlXzPYRhc7rmIRrRMbNQaihsji8wNV9CMqYj4BJT86X6sSRwcDKPydkihvos/PdzU3hx\nqlB3XNHh2HMojbFBCzs392NkINpwcayE+EVMFUEgqp0XllMRo2LJw+GZAkSD54yBkICQiEU0jA3G\nOgrDa0YqbiIRMzCbtqGrDP3J+rwjxhgiphYKTsBhaI13ObqqQFMYXJ+DATD0xqJTSXqNmlrT3ZJh\nqBjSIyjYPmw3qBG/mrH8cmpsRFvRPCqiO5AgET2D64u27y24aCxGS/ED2VSMlpLJeyjYrYPwXF/g\nF89O4ch0vRgtZWrexuXnbWx5py4BuB7v6F3WdNpuKEZLGUhFMDIQbT9YGxTGMNrBOJqqtF30GWNN\nH4kuRcpwl6u28LcyxhC39LafCYlwx0mCtPqhvyBBEATRE5AgEQRBED0BCRJBEATRE5AgEQRBED0B\nFTUQJxTOBVyfQ1FY00opKSX8QLR9iQ+EL9YtEw3D5ioEAQcXEpVkhEYwAEN9EQwkTYzPFpue2/PC\neIv+hIl03m08FgNO39KHTMFFKmZAa9KJOld0cXAyh/6EiS2jiaaVcRFDxY7NKRwYz6Lo1gcHAoBW\nLp7IF13Eo0bT65rJuyjYPgaTkZpS7qUEXCBXcKGqCpKxxmMBQLbgIlf00FeuzDteHD/0mS0th18+\nf9drXcAChKXya7mgoVWnhgpLOzZUWI2dG0iQiBNCZTGpeFfAZTlgrTbdsz58rw2MLYbN+QF8f/Hn\nOBcIuKiprlMVVpcfFI9qSEbNakXczi0aFrIOZjNOzVglJwymC4REzNKRiOqYmi/V5DONDVjYtiEF\nRWGwXY4gcBG1NMQtvboYBFxg/3gGR+eKcMsl53NZB9s3JtG/JJ1VU1jVRGvoKs7dOYT5rIMD45ma\nyryhvgiipg4JIFv04Xgc8agOy1wUHNsNMJexkS+FVYQlx0cyZmK436ou3lJKFEs+Co5f9g6Fpelx\nS68RL9fnNSbagu0jWTIw0m8dVwyElGE5vM9FnWHW9zncgLeMogBC8da1tZ2LVOnU0IpKxwbGMgCA\nUqmIa199NlKp1MmY4opBgkSsOH7A4fr1i4kQi0mvhq7AD0Q1vfRYYYzBMnSYmkDB9ssJo/Xn5EKi\nEiiqMIbBVKRuEVUVBcP9USTjJiZn81jIhrsKZ9nduZDAhuEYPI8jnXdw5imDsJZlJ/lcIFvw4Hoc\nsYiKhbyHw1N55IpezXGzaRuZvIsNQzHs2JxCKm7W3eUrjGG4z0JfzMD4bAGFko/+ZChgS6+a6wt4\nWRfRCEfM0rGQc5AtejUizwWQzrsoOeEYlqmhUPLrSui9QGAh78L2AiSiOrIFD5m8V9MqSUogW/BQ\ncgL0JwwMplr7oFrtVIHaBF1NY/B8UTXXNkNXw7Td9WCI7aRTw1qBBIlYUYSULR+nAWHyZ8DbP4rp\nBEVRqnfaTedUXktHB62WbWtMXYWmqpjLOk2P4VxCVRVcsHO4ZXS243Es5Bwcmsw1XYz9QODwVB47\nNqdaPnLSdRVbRxOYmi81HUsCKDoBMgW35fWv7NBScaOlv8d2OfJFH6Umjwwr859JO+hLRKCprf1X\nneAFAl7z01VRFYaI2fjxL7G66aogTU1N4fbbb8f8/DwURcFNN92Ed7zjHd2cEnG8dKHnZcfrUgcH\nig6bdqqq0t6wKWVHl6NdwisQXtZebSd6smWBYe30biNq6aogqaqKO+64A2eddRaKxSLe9KY3Ydeu\nXTjttNO6OS2CIAiiC3S1NGV4eBhnnXUWACAWi+G0007DzMxMN6dEEARBdImeqZUcHx/Hnj17cP75\n53d7KgRBEEQX6AlBKhaLuO2223DnnXciFot1ezpEE3o1FK3zaXVwYMeheis4VDeua0fzP/nzojdD\n65uuC1IQBLjttttw3XXX4bWvfW23p0M0QEpZ9eVw3jpRlTHA0JUVWVgcN0C+6LY0zNquj/0TGRRt\nr+kxQJjNky244C2MLX7AYTs+TENtWjXGGJCM6eCQLSvLGAur0KyyT6bVvB5+agLzLSr7gLCyLBnT\nW6bGMhYG/ult58Uxl6mP3FiKwkKDsa6ypn9LhYWeqIihtkyg5VxgdqGIQslrKYSaymDoStsij0BI\nuH7QszdIxEun62Xfd955J3bs2IE/+IM/6PZUiAYIEQbmVcygRSf0ixh6Yw9IJX5A1wRcj7f1kzQi\nCAQW8ja8suk1Vwo7DUSWeH64EBifzuOXe2argjXcb+GUsSS0JQKgKmFQH+cSJc5RckoYSJqIRjRU\n7sellJhN2/j1/rmqd0dhgGmoNZ0CopHQ8FpJkg14KEpSomq+ZQBcP8CBiVzN1yxTheMuJuDqmoJ8\n0cNM2Yy7f2I3XnH+GC48fQSmUW82ZYwhGTMRNTXkSh5KzuK8lLLIFMtf4wi9Vwpjddd/Jl2E44Z/\ny5mMg21jCUSXBPsxFl7/nFOpv5ZQWHgdl46VsHRsGomhr2zslVKGJtdgqV8pDCecmCmExt6si5il\nYsNgvKY7g6IApqZWw/cMPYwY8X3RVL8qmUqWqUFV1nZqbCedGpbTqHNDhV7u4NBVQXriiSfwwAMP\n4PTTT8f1118Pxhg+8IEP4IorrujmtAiEi0nARUNPS+gXEbDMsGNCow+3qiiIRhT4AQ+jyDvQJSFC\nk2uuWOtKlxKYyzowdQWpuImi7ePx3VPIFWqPm03bmEvbOG1zCsP9USgK6ro0AGGIX67oYSAZgedz\n7D40j4Vc7Q5LyDC7yNAUKApDNKIhGtHrftfKIq2rYTDd0bkSMoXaFkMSoa/H0BSAAZ4vcGAyV3dN\nfvbrKfz37ln87q5TsHUs0VDwNU3FQNJCxPCRL3rwuUS24NUt3EIAoryL40KiUPSwsKz1kRASBydz\niMd0bBqKQ1NDkVx+yYQERFl8VYVhpD+KjcOxmmvBGINlajC0MHq+5Pg4OltEaZk/rGhz7BvPYqTf\nwkAqgoiuwjTUurEihgZdbd3FQ8owdl5XWdkk2/UHPieETjo1LGd554YKvd7BoauCdPHFF2P37t3d\nnALRBNfjNS1yGmG7HHGr9d2prqkQUsL12vSAQSgUrQyuri+w93Aazx1KNz1GAtg3nq1rpbOcgEtM\nzBaw70imZWCeFwhsHIpCbZUkB8DnEgcmsi1DAb1AwPM5phbspse4Psf/99P9+L/fdG7NzmU50YgO\n2w0wn2s+FhD+npm8U20h1IhC0ccRnsNgqnVIX8AlztjWj0S0eR87VVWgqRL7J3Itx5pJ2+hPmDW7\n3kZjRRXWcu5AeO0VLmCuUUFaT50a1uZfkCAIglh1kCARBEEQPQEJEkEQBNETkCARBEEQPQEJ0hoh\nzB8KWvpsjgVVZW17kXIeVsW184MojLX0qQBh2XLJaT+WkBKxSPsMnkzebVhhtxTbDTqKLyiUfAS8\n9XV1HL+tjVRKibmMDSFadzq3DBUTs4WW10JKifmMDd9vPZaqMGweibf9WyYso6V/CQhLwm03aBuk\n6AVhDEa7sQAJ0cHnVdfa/406aVBL9D5d9yERx8/SMDMvENA1pWk6a6dUQvC8gNdVyEkp4QWhT0SU\nox+SMQMRo/HHqTKWH4i6KjopJeazNtJ5L8xJ0hRougJtWcWUH3AUbB9FO8BgykIqLjE1X6yrkEtE\ndZi6Wq3YG0hG6lJQ/YBjPhvmEQkZhrw1qu7TVAZVZciVfLiBQNzSYZlazVgBF0jnHKTzbmiENUPv\n0vJ5FWwfM/NFTKdtxCwNCcuAsqxkngE4dVMC8aiBAxM5ZPMuTt2UwkDKqhkrk3exfzyDuawDTWVI\nRA1ELb1uUR4bsNCXiEApl2rvHc9gcrZYc0zEULBlLAlDU8u5RbKu7B4AUjEdiZgJzxeYSduIR3XE\nIvXXIluOv+hPmIhbOmYzdp2ADSRNDPWFUSBLfW2NPq+LvjYJ2w3qSuVVlSFiqFDXaIXdeoMEaRVT\niQdfaliUMvS5cC5hGCr044h2ZozB1DVoyqIfxPdD82OwZJHxfIG5jIOoqSEVNxpmDjEWpqCqKoPn\ncfhcomB7mM84KDqLITgVj1O4yDAoCkPRDpNbKyFxUgKKwrBlNIGi42Mu40DXGJIxA34gq+XqJSdA\nySmgLx6GyJmGinTeRTrn1AiQ4/Fy6mj4uwChSC019rreYppqzNKgayryRQ8LeQdFe3H+tsuhqwoM\njcFxObxy2urRhRL88thFO0DRDjCQNGEaGhRFwWh/BGODMfCyiRcA5nMussVZjA5GcfqWfigKw97D\nGRydL1YNqAGXSOdD8Y1bGkxDQzJmYGwwWhNEGDE1nHvqIDaPxPHM3jk4HseW0TgSMbN6TMXG2xc3\n4AYctsNh6gwDKatmwQ+4QCbvwnYDpOIGdFVBvuSjaPs1u1JdU7BxKIaS42Mh50LXFGwZideUeksZ\nlvMHLT6vjDFoKkPc0qs3Naz8O2nq2jbFrjdIkFYpfsBrnP/L4ULCdgKwiNYy/K0TKn6QdM6F3cIn\nVHIDeAHHSH+06aMwVVEQMRnmZ/KYnCs19QA5HoemMBSd+uTWClxIRAwNGwYtOH7z9NlMwUPRCaBr\nCkpO4wS4ygIfPh5iTc8ZJskG4CI0pDYciwv4PFy4XziSRr7BjgMIfVe66uGiM0fQn4g0fMQYcImJ\nmSIWsg4YY03nb7sBbDfAOdsHsHk00fAYxhj6ExFcfv6Gll4oIQFdVRFL6S19Qq7HMZe2oSj1HSGW\nEo3o5R1VvbG4QuXzqkS0piGKlZsaTWUAY/SYbg1CgrRKER2Gv63Uv7KMsY56hwnZPgePMQafy5aG\nVCDsWdZJ6yFFYU3d/BX8QKCzpzqspv1Nw3lx2fY9CgDYjt9UjKrz4hLRiN72b2m7vKO/ZaO2Q8tR\nVQWqwtq+Y9M6eHcjZOW/WqM36ejxUlirHRma8VJaBzWjVUshoPtthUiQCIIgepiX0jqoGc1aCgG9\n0VaIBIkgCKKHodZBBEEQBHGSIUEiCIIgegISpFWKpihtX9Iz1j71U0oJzw/gB62D96SUUJT2Blce\ncJSc1uFpQcBxdK7YtjCgksXUCoUBAUcY7dACQ1MQcNnymjEA2YLT1qypKEDR9lsG9AGApilIxlob\nRJMxA6rC2s5fUdoXi+iqgqLtd1RwYeitz8dYWBHZSf1AJ4UnnaZiBW0CIIm1Db1DWqWoqoJYRG8a\nE9FJRgznArmSV/XRJGM6YpZe4zkJ02IlbC+AqiqwTA2eH/qIllOwfWTyLibmStg4FMWGoViNF0ZK\niSPTBdz/g904MJFD1FBx9cu3Ybi/NvaAAZjP2njhSAZSAsN9EYwOROuKuRQGzGXtalhdfyI0wC5d\nIFWVAUJirpzIGjFUxCytrkScC4Hf7JvFvvEMFMZw2fkbsXEoXnNOhYXepmf2zyHgEomohm0bUvVm\nTYVhYiaPg0fzAEKzrhBA0Vl8Ma2pDKduSuHC04eRjJnVThX5Ym3HB11VULB9zGbCMu2hVATxqFHX\nOSIR0xE1dZRcjr1HMtg8GkesSXwFYwyDKQsl20fB9us+P6auIhHTETE0CCHKsRn1nzEuRPWzY6gM\nkQbn0xQGXVdqPgetcH1RNhhrUNZ48B5RDwnSKoYxhogZprNWjKuqwtoaYqUMXe/pvFuzmOaKPgql\nimFThRCyznirqgosVYFWTpHl5V3MXMauWbwn50qYXrBx2qYU+hImCraPHz3+Iv790Rerx5Q8ju89\nfACnbU7i5eduhGVqcLwAew7Nw3YXF8DZjIO5bJhumojqABhKjo/ZTG3sdzrvwdDDzgV+IKGrDLmS\nVyM+jsernSV0VQEXAhMzefz8N5PVayGkxCNPT6AvYeCy8zaXc4kkXjiSwWx68Zz5UoDf7J/H5pE4\nBlMWJCTyRQ/P7J+v2aVU8nxScQNFx8dwn4VzTx3Ctg3J6jG6piIVVxExtNDr5AYQEnhxKldzXeey\nDuZzDjYNxaCqDIauIWZpiBiLXRO4kHjxaB6JqF5nkF1K1NJhRTTkih5Kjg/GGOIRHbHool9IURRE\nDAWaGmY5hZ8FiULJr5mXxyW8ogfLVKFrYaS5rithB4hjFBUhsayDA0iY1gkkSGsAVVUQUxUEXHQU\n5zyXtuE28doIGe4m4hENSgtR03UVmqbgyHQeBbuxWZOLcBGfSxfx748drokDX8r+8RwOTORw+fkb\nkVmWalpBSuDQ0TwSVmicbPZUyvMl5rMuYhENuWLjeQFArujB9QM888IM0oXG58zkPfz7owdw7mlD\ndYmySxmfKWBytghFCc2uzcgWPFx05jAu2DHc1PxpGhp0XcULL6aRbnEtxmeL2DIax0Ay0tSEnC/5\nyJey2Lkl1VSUGGNIxcNI97AjQuN5aWXvUjrnoNQgRbiC7YYtpYb7raa/Y6dUunaENyHEeoDeIa0h\nmsWJL8fvoKFl0MFzfMbamysBYD7rNBWjClICJbu918It989rP7n2h0gB5O3mQlOhWXeEpQgpG8a9\nL6cvbrZdqJUOr2ultVJbOrhelX6DrQg/W+3PJ4HjFiNifUI7JIIgiB5mJTs1tKJdF4d2rESXBxIk\ngiCIHmYlOzW0olUXh3asVJcHEiSCIIgehjo1EGuaTnbVsgM/iJSyruS54fkUpaNzqm18PUBYatxp\nY9O2Y3EB3iZ4Dwjv/tohpUQQtH/XVCzZHV1Xz2s/ltrB+yMpw0rJdvAu+H+obo5YDu2Q1iEj/VEU\nbb9hEBsQCpYXSHAZlNv919+3uB5HvuTBLOcWFZu8+NdUhjNP6ceW0QQee2YS+ydydccMpUy85uIt\nSCUiyJc87D64UPdSX0oZ5vDYPlTVRX8iUi7HrsVxfYzP5DE1X8SGoTg2jSQaBgdOTi9g78GjCNwA\npmXBF1rd82/fc1GaeRYv/OIgNm7cgs2nX4JovP4Zu+fayGSycFwfyWQCTLPqxhKCw85N4Z//5Uk8\nvnkEV19xEbZuGqkb6+hcHv/93BRmFkoYHYxhIFkf5cEYcNEZw9g2lgRjYWBfo8o3LkLB3X1oAUN9\nFraOJuqKDaSUYd6U7UNhDPGo3vC6chGWfWuagriioWgHDWsl4paGRMxo8J1arHKWEReNg/eAcvie\n3pl/iVgbkCCtQ1RFQTJmwjJ1ZPJu9Q6aIVzsKloQcAnOg7KfRIGiKOBcIF/yUXIWfSiqqqAvbsB2\nA7hlA2Xo9F80qVqmhtdcshXnnFbCfz72Ikoeh6owvOaSzThlw+Jz50TUwKXnjGFytoAXp/LleQi4\nXlBTUp0vehjqs5CMGjAMDUJKTM8VcGQmV80pOjCRwULOxqbhBDYMxcEYQ75QwgsHj+LAkbmq6LGC\njeH+BIRiQjIVUko4CwcwN/E8ZmfnAAAvvLAHMzOTOOW0s7Hh1JdBVVUEgY9CPofpuWy1o0S24GBk\nMIGIFQNTw/A7r5hGZuEoZubCZ/PPPH8Yh8ZncPH5O/A/Xn0JIqYBxw3wi2cnsefQAmw3FPfCeAa5\nPhfDA1EkohEAwLaxOM7bMQxrSU7RQMpCPAiziYQsm5mFqAkYnJovIVf0MDYQxchAFIyx6k3FYvaT\nxEIuDN5Lxgzomlru5BGGMlb+3oqiIBEz4Pu8mo+lKQyDfZG2BtiKt6gispXgvaXJxIwBlqFBpfC9\ndQcJ0jpG1xQM9UVQdHzkCl55Mas9RiJMUQ24gBQSJZc3zAsSMvTQGHp4x8t54yyjkf4o3vL6MzE+\nU8DYYBRGkzvgjcNxDPVbeOyZo0jn3LrHTlwA0ws28iUfEUPFfKaEo/P1j9YyeReZvIuFnA0EDg5P\nziNXrPX3SAnMLOQRjTjQNYHMxHM4cvhQ3bXIZHJ46onHMDd9BJt3XoKSB2TzteZcAJiZz8PUbfSl\nLDiFNI5Oz9Y9GswXHfz057/BvkOTuPD8czGd5ZhN14fmzWZspPMONg0n8PtXn4GRgVjD62VoKjYO\nxzGbLiJbDBqW2ZecAAcmc1jIuxjps+D69VHrQOgl8nwn7NqhNs+a0nU19KOpDNGIhlYP4RSGpt0X\nliYTcy6h6yuXnUSsLkiQ1jmMMWhKc6NpBSHCx3TtwuukBDhv3UFPURh2bE41bHm0FENTUSh5Ld+B\nlJwARdttKEZLOTpXhPQLdWJUO5YPaR/F4RcPtRxrfHwCg5vPRbbU/BjXDzA3t4DMwkzrsY4uoG84\ni4LbfGcRcIlMwa1rsdQIIdHW85XJu0hEWz9W40Ii4BxA+0dmVhsxAsrG2jbeJFVVoNITunVN14sa\n7rzzTlx++eV44xvf2O2pEARBEF2k64L0pje9Cffdd1+3p0EQBEF0ma4/srvkkkswMTHR7WkQBEH0\nJCerU8OxErFMsPKj2k6sEZ3QdUEiCIIgmnOyOjUcC3aphCsuPKOmM0MymWzxE51BgnQS4UJAYT1Y\nyso6M0SKjhqudnbK5Xk+zcdrP6CQYf5To4ymCrqmIAjaj6WoBiwrAtuur56roGkqOuhPC9MwELNM\nFO3mhRQMgOe1Lx4wdQVCyI7Mw+3QVBaGN66UD1airctVSAkpZe999lcBvdipoVjIIZVKHXeroOWQ\nIJ0EpJTwAwHH41BVBqtNcN7JnFfRCTA5W4AQEvFy1lAjQi/KYuJqowW5aHv49b45eD7HmacM1nhl\nKigK4AcC2ZKPqKkhYmoNO1vbjo9f7plGvuQjEdPhNCg3N41wEZ9Ne4hHIwAk0g3KsEf6Q++NaYxg\nfGIGh4/Oo1Cq7fIdMTREDIaZWR39G89AqjiNqampurE2bdqA08++GCObdyKdTmNqJo1sofachq5i\ndCiFeCKJwB/D3PQ4xien6zpMDPYlMTA8Cs2MI26o8IOgmp1UQVWA7Rv7cP6OYRwYz2LLhsZGXwDw\nA46IoWEoFQkzlRpU2yViOoaSFiKmCj8IPUbLr76qANGIjmTMQBCEgXlBg7+RqoZJt+1EJuACuWIA\nU/PRlzQ77kpPrD96QpDWamRxaFCU1bA1ICyJLtgBTOOlhZetFJ7PMZMu1XRryBV9RHQVhrF4ty6E\nqMk7qgiRqjAIEZZ3cy5wYDKLA0u6MDz1wizGBqPYNpasmiArXQUqf+6SG6DkBkjFDSiMQcgwtnz/\nRAa/en62Ola+6MPQFCSiOvIlH4wB0YiGhYxTzXXyeXgHPpiy4HoBCraPZEzHyEAc/UkLSvk6n7J1\nA4YGUzg8MYPDkwsQEuhPmMjnc5jJhjsZqUSB+CnYsr0PxcxRLKSzSCbj2HnGOTj17N+GpoWdDAYH\nB5GIxzE3n8bE9AL8QGB0MIG+vhQM0wIAaJqOTdvOQCo1gOmpCczMZ2FFDIyOjiKSGIOmh2N5gYQU\nYZJrvuTC8wVGB6I4e/sgtm1IgTEGjwvsH8+iP2litD9aLaMWQqLo+JAy3FFaER2moaFgeyjaPgIu\nYRoqBhIm+pOR6mfONEJR8QNeDTG0TBWJmAGjbHCt5F55QZhzJGToKdKr4XnNP79CSNhOAF7+g7uB\nwPSC3TCZmCCAHhCkP/mTP8EvfvELZDIZvPrVr8b73/9+vPnNb+72tI4bzsPYZ7/JoynXC78fjWgn\n/V/MhZyNqfl6EyYAOD6H43NEy1Hlje6MAVR3NTPpEn69d67hLmdqvoTphRLOOqUfEV2D08RPlC14\n0NSw/dCjT0829CdVwtpMQ4HnCRydqzcBMcbg+hKAgs0jcQz3xWA02E3EY1GctXMbUokY9h48gumZ\n2YZjBVo/9P4YztjgY+dZFyLRN1x3nGGa2LhxDMlEHLlCCfFEfQt+xhgS/SOIJgYQT45DjyShR+L1\n51QUuL5E1DRw5rYELtg5Ak2r/2ykcy4yORdbxuLQVbXh9VIUhmTMRMTQEAQcQ30WtAZdFCrBe5oq\nEDG0alDf8vlXjKs+F9DbeIokJDxPNPWPhcnEPob7rY6jzYn1QdcF6VOf+lS3p3BCkFI2FaPFY7qz\nO5zLNH8/UsHxeUdNTA9O5lqGyUkZLqD9yXaPdSReeDHd1izrugK5UptQPcawdSyFVpefMYaBvgSK\nhdbVQYpq4LyLLoFu1gvIUuKJOCKRSLWVTiNUTcPWU05te/0DAbzsjNGW4XsSYfukeNRsOZahqxjq\ns1oewxiDZWiIWq2TWdUOzK1AGHzYrqGrkAjFjQSJWALtmQmCIIiegASJIAiC6AlIkAiCIIiegASJ\nIAiC6Am6XtSwVlEUBsvUqtk2jdBVVi1HPhlIKeF4AQZTFuazdsN4CABwPR9FOwzni1l6w9JeKSUK\nto+NQzEILpArNXaSR3QFhqFCCAnGGhtdpZTIlzxELR19cR2ZQnNXeiyqwYqomF5oXCUopYSTm8KD\nDz6NU089FZs2b2t4Ts8LMDWfx8jYGKaPTpU7W9ezc/tGjAz2QUJBptC4mML3XDzz6PeQSc/hjEt+\nB8mBjQ2P40GA2VwJiqJBoHHJtMIYXnXxJvQlTNhu0LS7uh9wHJkuIB51sXE40TQ9VlFCT5ehKVBb\nFBAIKeG4fjmr6KXfpzIAMUtDxFCxkHebFsboGkMQSLgsaFs+vt7pxdZBtl1ENrsYVplM1leXvhRI\nkE4QiqJAUQBVCcPHPH9xYWHlbBi1QTbMiaBizPXK+Te6pmBsMIaS49eE3nEuULB9FG0PXABFJ4Dj\nBSMrsVQAACAASURBVEhEdZjGYgWW4wUolnyUymK7dSwBx+M4MJmrLkAMwIahKHRdLXuvwtwiTWU1\nC57j+UjnPKTz4Tz6EhH0JS2MT+drBDNuhQbaYtkTNTYYhedxLOQX5+87eWRnDmJiYhJcCExMHMVp\n28dx5tnnIVl2lEspMZfOY3w6i3QuFLWRDZvBfRvT04tREfGYhVdcei6i0VjVQzY2YCFTcOGUg+Sk\nlDj03GN45r9/VI2sOHpkP844/zKcduHroelhxEMQcHieg/lMCU45mnywLwpdMyDZokicua0fv3X2\nKMxyqXrc0iEkkC0s/o6hcIQGWj8QyBQ85Ao+NgxFMZCqraZTlTCRVUDC5xymkNA0pcZmwBgAGVa9\neYFEIAIYmgL9JXjkDD301ikKg64BY7qKou0jW1wU8oqHrDIH1xdVn1SjZGKiN1sHmaaBx/ekwVgG\npVIR17767BXp2kCCdIJRFAZTV6GrCmyXQ9fZSTXEci7g+LxhyFo0osM0VOTyLmYzNgq2X018rWC7\nHK7HEY9ymIYKx+MolPyatjNChuXFZ2/vx0LWge1ypOImAi5qzut4HAzhgiREuKtayLk1u4CwVFti\n24YkbMfHfM6Boal1HQxK5cj0DUMxzKcLmD+6H1OT48gXF/1JARd4ft9BTE3PYOfO07Bxy05Mz+cw\nPlMbo16wfQAatmzdilwmjbN3bsamjaMIOGpyohyPIx7VkYgy7N23D0/97HvY8+xTCILF3VU6ncZj\nD/0bjr64B2dcdBUGN5+DbL6EzLIOEvOZEgzdQX8qhphl4XUvPwWDywSlcu6BZASO6yNTcFG0g7q4\n+FzJQ/6Ih+Gciw3DMSSiBoSQdeX4lcVf1yQMXamakWvOKQDHC49rFl+/HJUxREy1LnxPURgSMQMR\nU0Mm74IxNAxk5EKi5ATQVQazR7qY9BK92DroREGCdBJgjEFVGWLWye9j53i8pU9IVRQwVcF8rnm/\nNSFDM6Ph1e706o4TQDJmQlP9pr3qJMKdVyg2zc/p+QKqqkJhrE6MllK0feTmDmPv3heaHpPNF/HL\nJ3+NM4IoCk7za5EtBjh9xykYHR1E0MRGEwQSgMQvH/w29jz366ZjvXjoADxpYbuyoekxni8wPZfH\nH12/s06MlsKFBBjDfNZp6q2SEpjJ2BgesFr+vbmQ4B6HoastQxkDLqGpspN8PliR1iKiawoSMb3l\nZwcIu220jg0k1jp0K3ISWe3PyVdy+q1Mn8vO2n6sDhuOdnL9O70776zJaWem504bpnbioV7JT1in\nf+9OfsuO57U2u4gRHUKCRBAEQfQEJEgEQRBET0CCRBAEQfQEJEg9SK/GccjOMvU6otWL99qTtj+O\ndxj210mz2E4vPW9W9bB0rM6Gguz0WnQy1oqNRBAnHxKkHiL0C/3/7L1ZjCzZed/5Oyf2jFyrsrZb\nd+/tdrPZbFKkaNEUPWCTmNFIGlEwX/wwGIie8cMM9EBAwDx5bECGBWgM+8WAYMAeyoRhCGNDMAfQ\nWKZJa0RJlswWSbNJNnu9fffal9wzYzvzEJlZmZWRS3ff7qqre35AA111T504eSIyvjgnvv/3j2n3\nIuJFLEkXwLGNqaJJSG/SAkWl4GBlWB0MyDkGlinwnOlpV1KmKdTtbjQzXTiKEl6/fQikZnBZWKYk\nCCM6QYzvTk8GNeiyv/uApZKH52XnaOU8h5WVJQ73tyl608dfKbrsHbfpdAOsKYkGhoT68R6Ju8LF\ny1enJkpsXLjIxSeeZ7mSo+Bnj8s0BFcvlLm93SAIpguoAY7qXRIFzoxztFxyMaWYeb6FSAN4rdmb\n+eBjWxLPMabOw4BFxd22ZWDPGDuk8/FQszI0jxw67fscoJRKzcxGjPxanQjHmm+CNg/TkBiuIAhj\ngigZWwF0exEH9S5KQT5n49oGrW5IY0Rn5NgGtiGRI5HDkJIwioeaJSEgDBNu3q8N/ZMO6l02V/IY\nkmGqshBwe6vBT2+lwWj3qMtK2WVzJT+0Hx981DduH9LupauQeitgteKhgF7f3sExYevuW/zld/+c\nwbog53ksl8scHreGrtor1TKtAGqtBDimXjtm8+JlLLcw1PP4noXn2LSDtJrEK2/tUfRtnr1WRSCG\nq44k6vDn3/0eb739DmAjS09zLbdM4+Aue3upqLZQKHDl6Y9TferzWI5HNwClDKoVn1qjM9RcrS75\nXFwrUy76RAl87/U9Lq7mubiaHzvf7W7IqzcPx6xMlosOCobC4bxnsr7sUy17CJGKYQVpJuPplWij\nFQyvsWY7ZLnsjjnQSinIexaF3EmFDjNOCE7JBwwp3pWYVQiB65hY5qQuzhAC2zZmPhA9zpzHSg2j\nnK7aMMq7reCgA9IZkySpUV+WD1Cv/3vPNjGM965hEkLg2OnNoBfEdHoxh43uhC7ENA1KeSN1G20H\nSClSndKp4xqG7KvxUzfZB3tNjhrjZXWUgnu7TXKOwVLJo94KePnV7YlyRXvHXfaOu1zfLJL3bPaO\n25nme7tHHRxTUspbNGv7/Mmf/n90OuNeRu1Oh3anw/LyEpbtIQyLo1Y8Mf779+5gWRYXL1/FcX2C\nGDrheFp4vRXwX378gCsbRdYqLndu3+ZP/uwvxlYVQghiu4qzUuRqaRkhJJvPvoRX2Tw1/5JOAPmc\nhyEV1YrPWrU0Ycx4b7fJg70mN64ukXNNbm832DuaLJF0UO/hWJJywaFScLmw4k/4CinSbVFDpjqy\nXi+ie+p8K1JvLMsQrFQ8fM+ilHcmgoxlSMz+Q00YKSxTvOcHJcOQ5KQgjBLCMMEwU+H4oy6J+CA5\nj5UaRhmt2jDKe6ngoAPSGdMN4qk15SC9sXeCiPwc87RFkFLiuZK7u82Zx3RtE8OQdLrTt5FSsa/B\n/b1jalNqvAG0ezF7tw+5tdWYObab9+tUCvZEYBulFyW8c/sOP3r5j2b2dXBwyNUnnmb/uDv1RheG\nIXdu3+LaUx+dKRC9vVXn7dde4ebNm1PbGKaNMi/z0Z99iW44vbMghuevruB70031EgWvvnM4tw5i\nL0wo5h2ubBSnD550ddoLo4kKHKOEcVrj8PL69L4GDzXOQ1CuCpEGtKyqDZpJHqdKDed3HajRaDSa\nxwodkDQajUZzLtABSaPRaDTnAh2QNBqNRnMu0AHpDImTZCGB6CxdyYCB+d48kWizE8wV3iaJ4vaD\nGq3O9AQDgFYnmGogN0ql4LBacWe2iYIud+/cIp5ilAfpZ4zCHuXy0sy+pJC02y1Qs8Wr62tr5GYk\nGECahr6yfpF8vjCz3dUrV1gu52a2MaTg4koe3539Mr+ct7mwkptb3NS1jIVEwdYCqdlF33loguww\njAnCeGZ/SZLQ7c2/XjWPFzrL7gxQStELY8JTuqDTCMHclO+BmDYIk9RkLUywTYljj6fShlHMzmGH\nRjtAqRPzttPsHLW4vdXgqN7DsQxWKx4bK/5YinIcJzzYb7F31KYXJuRckyhOJtLIPdsgn7PphTFP\nX65waS3ih2/uj2X4JUlCt7HN/t4O9UaH6tIRS9V1rNzS2PijoEXjcIsH27uYTpFLVyts379DGI6n\nw1arywhpcXzcoFzKY5iSens89dv3czz9zHMkwiZRsFEtcFhr0wvHA9jakk8+lzq3/vXPvcT+7n2+\n/72/HLvRuo7Lz3/u58kXl4gS+GjJ5+5OjePGuLXGc9eX+cQza1imgZTQ7kS8s1UfO/9SCp67ukTR\nt1FAtZTj9laN7VPuuNWyy/ULpTQTshcNxc+nK5UbUqRjlZJy3qYbxHSD8c9Y8EyubpbIOeb7Tr0+\n7b0VxgmuZWCMBESlVD99PL1ewyjBsqRO/dYAOiB96IQZIsMsHFvONfKL4yTT7yiIEqIkwTENTFNy\nUOty1DhthDcunmx3Qm4+qLF90BoKWXthzN3dJrVWwPpyjkrB4bDeY/uwRaN1Egja3QhDptbVrU6E\nAJaKDkk/8KbHA9sy+bnnN9g5bPHG3Rph+5jjw2129g6Hfe0f1jg8rnNhfRW/soE0HLr1LfZ2d2m2\nU5O7MEo4aiRUN66ioi7bD+7i+zmKxRIHxy0gHdtxrQlAdblML5IEkeLGMzfwS8up62t/2hrtkILv\nUhKwe9Qi55hsrBTo9OJh6nUvgsLSJl/8b1d5/bWfcPvWbT71qZ/h0uXrhIlgMLVBpNhcLbG+nPDG\nnQOKvs3nP3mZUv5khZgk4DomH7m2xN5xh53DDpfX8myupquwwdmUUnBts8x6Nc9rtw6IYnju+hK+\neyIBSFRqomgaEsuk78ibVk8YvS4GJoqOZdDoBIDgic0ilYL7LqxAslFK0QviCS1dHCtacYRlClw7\ndfztnbpeFelDVBQlOLYxoafSPF7ogPQhEoTR0P56FjnHxJyjWg/DmE4wfUsqSaATxBzttyYcRgcM\nxJOtTsD3Xtul08vur94KaLQCCr5FoxVm1kuLk5PqEnnPGlZeOE2UKJbLOby7t3n97dcJM2rCJYni\n3oMd8rUapmkOA8tpGq0eIFjbuEyn0+oHo0n2D47xXJsXPvFZYuFmnoPByuHCSgHHMqbORZBYPHnj\nRV742MdB2mTJe9IVoOBTz13guWvTtxcTBcslj4urhZmaHM8x+fgza1P/PT1mQhQn+G660omnLL0V\nUMjZPLFZemg6oFY3mlknMIwUYTRb2DkIrFKIsRWV5vHizAPSd77zHf7hP/yHKKX4m3/zb/J3/s7f\nOeshfWAsWp5OLPB9TBYso7nIO6puv3rDLBTpk/C83oJosfdiqDgzGI3SbHXJ52a/4wEwLZPu8ewb\nXqcbYNsOnTmCdynFTBEppME37+dodmbXn1v0xuo6xsLXxjwWOeZgy/ahcU6LAf9V4byXDsrC9Rw6\n7cmKK/M404CUJAm/+Zu/ye/+7u+yurrKl7/8ZV566SWeeOKJsxyWRqPRnBvOe+mg03TabT734jOU\nSlcpFmdXEjnNmQakV155hStXrrC5mdb++sVf/EW+/e1v64Ck0Wg0fR610kGtZp1SqfSuatgNONN1\n4M7ODhsbG8Of19bW2N3dPcMRaTQajeaseLQ2JjUajUbzV5YzDUhra2s8ePBg+PPOzg6rq6tnOKIP\nFtOUyDkzLiVzbT8Tpag3A7pzTN2SRKGUYt7761LeZrXizWzj2gZRpHDt2ZlZnjPQTU1vY0hQwmSp\nnJ/Z14X1KqVyBceeXunc9xwM22NluTKzr6ILD97+AfaMTWrLksSJmmlCCOBasLd/hG1O/5CGFNim\nnJs8YEhBrdmbK4I1Zd/AbgaWIbFNOTdD0zIFvTnC1UWJ49launfDw8yz0DyanOk7pI9+9KPcuXOH\n+/fvs7Kywh/8wR/wj//xPz7LIX2gmIbEd61MzQaQKWg9TasT8NqdI/aPUk3OlY0Ca5XcRHZVpxdx\n2Dffg2whrCEFiVKYhsGLT6/wYL/Fne0G9dZJhQbTSP1qRlPHfdekF47bZniOSaIS2t2IdjfC90xy\njjmWsSYENFsdvvPyG9QaHRLyrK26NOp12t2TYy6V8iytrOPkVxBCUChWqR1ts7WzP2wjpaC6VCZI\nHEIMlJtn40KeZv2ARvMkuyfvWTT33uI7f/jvUCrh8lMv8tKXv4rhXxiL+4WcRb3Z4aiWfs4LK3k8\nxxrLPrRNQbvV4M9/8CZJklAs5Pj4CzcwTHfMwqKct1kqOeQci6NGD9+1cGw5Nl9SpOnh9/ea/ay3\nFtczUrFNIz1H6eWS+hsJIYlGKhwIoOBbrFRyOP3qDY12SLsbjo3LkALbkpiG5LDew7UjSvnZTsHT\nUEoRRDG9YHwc7zU2DbRKWhz7eHOmAckwDP7u3/27fOUrX0EpxZe//OW/8gkNWc6ZhiEmFO2jKKWI\n4oR7u03euDNugnV7q8H93RbPXC5T8G2iWHHU6BCc8uWJk9QNVYpUvS9OCSeFEGyu5Fkte9x8UOPB\nXgvTTI3UTuuYWt1U7JhzTcIwwbIE7dNtOhGtTkSl4CBFqsH64Wt3+enN7WEbKQ3akYGTXyKf71Kv\nt1hbW8UrbWBaJ8Y7pldkyS3g50vs722nWhXbpx2dBG8hJIEo4hRd/Hydo4N9jLjGD/7Tv6N5vDfs\n686b/5Wv/db/xM//91/hI5/5EpZbII5jtvbH/Zoe7DUxpeDSeimdpyTktdduclQ7aVdvtPnjP/s+\nT1zd5PrVS5iWxXLRpZi3x2y9W92QVjddiQ5+e1Dr0OqeBLs4Ubx595hy3uHCio/RtyI/redK41CC\naaTn0bZMlooOpfxJerxhpOZ9nmPSaAf0ghjbSi3oR6s5dIOY7mGbkm+T8yykYG5AUEoRx4pOEE2s\njN5LMDKkwLWnX/uax4sz1yF97nOf43Of+9xZD+NDxzAkviGJ42TulzEIY/7ix9sTZV8GRHHCT945\n5OKKj5ixJ6gUJ4LJKfsslmXwzJUlUHBre7qpXip2jHBsSbs7XU901Oixf3DMX/74naniySgxCFWO\nZ569Tiyza94JIXCLa1TNHNu7+4hYZm5zKWkTyiqNrW/xk5e/NXVcf/L//l/88L98k1/+X3+HaXKo\nKFG88+AYSzW5c3dral9v37rPO3e2+J//1hexrenbi7VmgBDMNDQ8bvY4bvb4yPVlkhkmilGsqBRs\n1pb9seA3imMb2JZLoz07ZbjWCgjCaG4tPoBuEBFGD2ePzjZT0z+9KtIMmPtY8sorr3wY43hsWfTJ\nMJgjIoX0BroIi+zV23PeFQ0wFlDxhlE8U8kPacDx/Pk3RNOyEQscU6rZ79cAwqCz0PsPtYBqNUmS\nhSofPKwCppBuAU8LRgOEEHPfPcHi2taHqYE1pNTBSDPG3BXSP/pH/4ijoyN+5Vd+hV/5lV9hZWXl\nwxiXRqPRaHj0KjWoBR4GpzE3IH3961/n/v37fOMb3+Bv/+2/zcbGBr/6q7/KSy+9hDVje0Kj0Wg0\n759HqVJDp93mFz77zLuu0DBgoXdIm5ubfOlLX8I0TX7v936Pr3/96/yTf/JP+I3f+A2++MUvvqcD\nazQajWY+j1KlhkGVhve6FTs3IP2bf/Nv+MY3vsHe3h5f+tKX+Nf/+l+zvr7Ozs4Ov/qrv6oD0oeC\nwDGNmdW9gbnvE6CfJaXU3PcwrU44zMab1Vc4w1BvQG+OXmrAImZtyYJVSJMFnrUMwyIKuwhjtnmg\nYcx/N2RISRQlczVAtmkwsMeYhiBNkZ/3vmbR8z3v/V3aGXPPN/1xLcL7SQHXPL7M/da+/PLL/Pqv\n/zqf/vSnx36/trbG3/t7f+8DG5jmBNuSfOZjG9zbafL6qbRvAMMQXFkv4jnp6Wx1ArLu7XEcE0aK\nOElwrGzjv14Q8f3X93jr3jE5x8R1TMysxIv+DbPWDPE9kzhOJgzi4jjh/tYudx7sUy66RGFMM6Pc\ndjHvsXlhjTASlHyTWmsyC00phSFi4jhidWWJMAg4yrKliLuI3i525Tqf/uL/yE++++9p1vYnmj37\nM19g5crH2b/zIyrVTYz8xsRcWKbgqUtVECtcu3KBH/3kTfYPa5N9PXWZZ5+6wlEzpJAj02xOSsGV\n9QI51+JalPDOgxr7te5EXysVj6sbRSxDkiiVmSEnBFyo+hR9e+LfRklNE2MSNTvAlXwb31ts+921\nTSxT0elNpn1Deq3apgQEYRRnVk4fNZ7UaEYR6mGm/XzA3Lt3j5deeolvf/vbXLx48ayHcya0uiFv\n3D5i9yh1Ed2o5lgquBOPrnGS0OrbI8RxaglxOm3cNiWmJTGlRCnF63eO+OnNAw7qJ26nliko511M\nMxVUJkkaeJqdcEzo6Xsmjin7eifBwWGN2/d32R7R95iGoJR3OW50iJM0A+yJqxcQ0h4TCvt9Tczg\nZixUTBgFHByfCF6FSG+kx7Um3V6AShLMaI/a4TaNxogwNmfTPrrDj//i36NUwsqFJ7jxyV+gFVpj\nT/AbG2v45YtINy0IeW2zTCHnjN1QLVPQqNV4+QevEicJlVKen/vkR3Acb0Jkm3PNodnc+nKO5dJk\nJYxmO+Cntw6JYoVtSm5creB740FGitQocSDQXSo6VMte9kNCnyRJ3XtPi6+FSFcugwWTaxuU8vZ7\nMsU7LYyVQuA5BlKePOQopUiUots7MeVzLImt3WEXYnC/+z/+z3/5SG3ZfeFnr7ynwqpwDnRImneH\n71q8+PQKe0dt2n2n0CwMKSn6NseN7pit9ChBlN60kiThu69uc3e7ObHNEkaKveMOec8in7NQCNrt\nyRVMqxPRAvKewRs373Fv63AsYEGqnTmodch7NsWiT7lUodWLJ4yiWv1VVMm3OK43OWq0J+zRlYLj\nZoCXy2HR5Wj7Lbb3DybG1WwH4Kzzc7/wvxCFXcz8Ks0Mw8KtrR3cw0OuXn+K5z/6Ar1QTTzdh5HC\n9Yt8/m/8NaKgw/JSiSie3JpqtENa3ZDloscLT1anbuPlczafem6deiuYutpJFFimgWMaVErucBU8\njXRVEmf6KymVjlUKqBSd91UZQQiBY5mYMiFJFKY5mcIthMAQgpwriOIk02ZdoxlFXx2PIEIIcq41\n8yl5QJyQGYxG2T/ucCcjGI3S7IQgJisynOaw1uX+9tFEMBrvK6CQ99NgNINaK6TVCSaC0SidXkTY\na7CXEYzG+mpHFKqX6MwYf7cXYtsmvXD2fIUxrK8uTxXUQhpjpRBz3ylBuuqZh5BibjACSGI11+wv\nUeBYD0eQahgSa86KRwiBZRo6GGnmoq8QjUaj0ZwLdEDSaDQazblAv0PSaDSac8yjVKmh02lRq5Uz\n/61YLM7dJtYBSaPRaM4xj1KlBsex+e5rRwgxLk9pt1v8D//Nc3Oz73RAmsFAVDiayjqtnVLqoby0\nVUoRJ2puwsKiufqdXgjMfioxpMCxZKZmZIAQ0OlGcwWPrmPguTZhc1JjM0qr1cHJ5WcmBniOQac9\n/8V7nCg816bTnV5FO+e5LLJDHQQBtikz/aoGmFLMNUcEUCwm4lULFcVVw5T7WcgFKucK0qrvcs51\nsQgP89rXZPMoVWp4v+iraApJouiFMa1uRLsXZVYIGPgUNTshzU5E8D5cOJVShFHM/lGH7YM2jXaQ\nqbBPEkWt0ePebjNNaZ4SHprtgD/8z+/wf3/rTd68ezR1XLe3jvjd/+f7vHFrG8sg0+E055gEQchP\n39mj0ergWJOXjSHT/966e4S086xXy7gZ9qxF38Y1An766ivs3nsHz54clxRQyJnUmh0iZVCt+OTc\nyb58VyJ7u9x5+zUUUF2efPoSAi5cuMDqtY/hrtzg0pXrlAr+RLuCn+Pq9SeJ7DXeuX+IZYjM27Vr\nG9RaXX789gGtdoiVMReWKVld8ijnHbb2mlMrUBhSIAXEKv3Ms55BjpsBb96tZQqLB5/TsSRLJZel\n4nTTPSnSK2b3oE27G76v6zWOU61bsxPRC6OHWslc83iiV0inSINMqkQfEMeKZif1/rFNo29ulxAE\n8ZiBWjeICcIY1zEx5qyqRomThFY7pD6iyq81A5rtkErRwenbGnR7MQ/2m8OVjALqrRDXMoZ2EVGc\n8NqtQ7713TtDAeQPXt/jJzcP+PkXN4cCzVqzyzf/85u8cedweMxX395ibSnParVEN0hwLIMoiXn7\n3uEw7N3fa/Jgv8lTl5ZxbJMgSnAswdZ+k72jVJBqGAYhBoWSQT4K2D9q4NomthHz4P6t4YrgwfYW\nWzvbPPPMDQqlZTpBQt4z6fQitvbTKgxCCDoBmKZDtWxzWGtjSIFFhzde+R5BJz1mtxvQ7QZUyvlU\no1RrslwpU6heBG99eC4SZwV/pUC+uMfu7g5JrNjY2MAtX0TaaaBqdkJ+/PYumysFlis5ekGCa0m6\nYczb9062Im5t1zF3BU9fqWAaqSi4UnCGGh9ILUEe7LfI5ywqhbRE0cAIb9QgMVHpCTVk+v9KnaxK\nB6u1WCnubDcoeBZr1Vy/DBFYhsCxT9Kqc66F56QVL9qd1DVWirTfwSEVcFjvYZkBlYKLlaEjmkaS\npKLY0XT8XpCKcQcVGLTwVfNe0AFphDiO6QbJhNX3gMGXzjanb28lCtrdCNuSmSVkTtPpRhw1umQd\nMk4U+8ddXDu1rD6sZ29JdcOYbhhTa3T5jy/f4bgx2S4IE7798l0uruWRKuLb330ns6+dwyY7h02u\nXaxyWG/RzdAKKQVv3Dkg71kslz1ev1XP7CvBRBkGpXxA7XCXveZkqR+lFK+99lNyOY+nb7zA9kH2\nVl+soB2AZwt27r7G3tadzHZHx+kxLly8ilm8grAmBafCdMG8xOpmEdsyMf2VzPN0f6/Bg/0G1zeX\nuHPYzDSmixLFq+8cslR0+MQzq5QLTmZfzXZIsx2yvpxLKyNMWU0MFlNxHI85yo7S6IQ07ta4slGg\nWvawMpZWQgjKeQffNdk/7k69psNIsXvUoeRb5HP23Os1jOIxW/dRlIJ2L8I0Us2UDkqad4sOSCNE\nCVO/uAOUYua7lmFfscK1538h290wMxiNt8muAXea1+8cZwajUe7tNNnenS0iBWi1e3R7sz9nsxPO\nNRgUQiCEopERjEZptzvEc17cCiHoddtTg9Eo1Y2rNILZ9dmEU8Yr+DNFvOlNNpjrknpY71Epzi7S\nCulq21rgWxcs4MoaRUlmMBrFMg1MQ8y9rntBTMGff72GM96tDZh3LI1mGvodkkaj0WjOBTogaTQa\njeZcoAOSRqPRaM4FOiBpNBqN5lygkxpGsAyJdMRYyvdpHEtimnIi5XsUIVK9yiwHTqUUQRhjWQaJ\nSj1vpmEYgkLOotkJM5OzlFLUWz0qRYcbV8u8fus4U50kBVxaK7C54vPTm1vsHLQyj1f0HaRhUMqb\n1JrZSRKGFHz2xYsUfZuf3Nzn5v1J4zro+9/kquQ8j7fefJ14isPsUinH1p238YsrYBcy563o2zzz\n7Ef4+DMb/Ps//A80GtnZfR954ZMsV9fJBSE7+9lt8p7NZ168gmka/PDNXY4bvcx25bwLSTr/Uj/8\nUAAAIABJREFUWWZ5kOqOfva5tfR8mmKqG2/es4aSgGkv/gfaNqXUMFV7Wl8516TTDcdSvk/31Qti\nHNtEERJMqWIuBZimpN3vy5ghcnVtI9NracDAfE/z8HiUSge5nkOWgq/dzr7XnEYb9GWQpbMwDIE3\n8sUfVFQ47Zzp2sZcTUcYpxqmMR1Kouh0I+KRzgZdjPYfxcmYBUS3F3FQ63LcHL+h3rxfY+fwxKhu\nfSlHPmcNMwRNQ9BodXn5x3eJ+rnGhpSsLhdo9+Jh5lnJt4nihNbIMT9yfYkbV6sMKkAIAfVWjz/9\nr3dpj6Qql/MOnV44PKbvSOpHu9y9e5Il5+dsHNvmsJ+ubRiStdUqVq6apmeT2jh89MkV8jl7OC5D\nJLz95uv8pz/6o6Egc23jIs88/0kCdZLt5rsGuwc1mu3ecKyffv4Sm2ulYYq1YQgOjtt876c7JP2+\nbEuyVPL7Vu5pu6Kfzl9vxOjw+etLXForDAOH2dcECRheK4YUrC3lxjISDSn6Bnbpz4OqIEGUDDPZ\nRL+/0QcfKQWXVv0xIz8p0qA4anwXhjG9aNwXKUnS8zh6PeUcc8wiQwDWHMnCYKydXjycL2BMp6d5\n/wzud//b//7blJeqZz2cuXTabX7hs89MLQ+0SC07HZCmMPjSdXtx+tQ4Rew3qLAQxWru02WSpDez\naSsrOAk4s56OATqdkAcHLY4bvcynVSGg14t5+/4Ry+XcVE8h0xDc2Tri4LiNkMZY4BkgZbpqMiR8\n+vlNPCc7nVoKuLVV47Vb+4CYuqLwbcWdW2/hmIp6s0OQsTos+B7lpSpXr13n8nppamp2Erb5iz//\nC4rVi9j5amZ6tm1JBAmGUHzs6XWYsoKRAt64fUCrG5NApgbLMlMvKseSvPDkytS0d8eWWKbBStnD\nd62p5ZYMKQjCmChRY4HudBtBaqq3VHKnXmOGFFimIIrVzFT2MIqJkgR3hieSFGL4GaYxWM0FUYJr\nGXMlAJp3x6PmGPt+3WJBv0OaihACw5B4rpnphjnazrZMPMecGYwgdWidFYwATEMO1fqzqHd67B51\npm6dKAW2bbC5WpxpcBfFimLeox2ozGAEqdnccaPHp57bmBqMIB3zSsWn20umBiOAViAoF4vsHzUz\ngxFAo9Xh7t27XFzJz7y5SivH85/4DMJdnqoVCsKEXggv3rgwNRgNxl8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pSxWWim0e7LdR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\nKwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11543ac18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with sns.axes_style('white'):\n",
+    "    sns.jointplot(\"x\", \"y\", data, kind='hex')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pair plots\n",
+    "\n",
+    "When you generalize joint plots to datasets of larger dimensions, you end up with *pair plots*. This is very useful for exploring correlations between multidimensional data, when you'd like to plot all pairs of values against each other.\n",
+    "\n",
+    "We'll demo this with the well-known Iris dataset, which lists measurements of petals and sepals of three iris species:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal_length  sepal_width  petal_length  petal_width species\n",
+       "0           5.1          3.5           1.4          0.2  setosa\n",
+       "1           4.9          3.0           1.4          0.2  setosa\n",
+       "2           4.7          3.2           1.3          0.2  setosa\n",
+       "3           4.6          3.1           1.5          0.2  setosa\n",
+       "4           5.0          3.6           1.4          0.2  setosa"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris = sns.load_dataset(\"iris\")\n",
+    "iris.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualizing the multidimensional relationships among the samples is as easy as calling ``sns.pairplot``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VAW1dnR5yXyjixd90ovtLdSEBDBcMpWiFW+gw3LZYuIUMwy98KGrrYc6niUeS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\nqkIzN7UlPwmtkMybNw933nkn1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f+kx2Mh3fuE9zERUWwFmqd6nnlFyB4Pls2UbFghiaFA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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11ae9d160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(iris, hue='species', size=2.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Faceted histograms\n",
+    "\n",
+    "Sometimes the best way to view data is via histograms of subsets. Seaborn's ``FacetGrid`` makes this extremely simple.\n",
+    "We'll take a look at some data that shows the amount that restaurant staff receive in tips based on various indicator data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>smoker</th>\n",
+       "      <th>day</th>\n",
+       "      <th>time</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16.99</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.34</td>\n",
+       "      <td>1.66</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21.01</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23.68</td>\n",
+       "      <td>3.31</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24.59</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   total_bill   tip     sex smoker  day    time  size\n",
+       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
+       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
+       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
+       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
+       "4       24.59  3.61  Female     No  Sun  Dinner     4"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tips = sns.load_dataset('tips')\n",
+    "tips.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AbgeayWSyrXMiInIqu3fvBgAcOXIEwO0/FBZj9QKLhoYGrFixAsnJyXBzc4NK1fFOkJ0f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\n4jGsiIhI8f4/8A46ouL3SiQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c3a3fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tips['tip_pct'] = 100 * tips['tip'] / tips['total_bill']\n",
+    "\n",
+    "grid = sns.FacetGrid(tips, row=\"sex\", col=\"time\", margin_titles=True)\n",
+    "grid.map(plt.hist, \"tip_pct\", bins=np.linspace(0, 40, 15));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Factor plots\n",
+    "\n",
+    "Factor plots can be useful for this kind of visualization as well. This allows you to view the distribution of a parameter within bins defined by any other parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9d955B/v374eLiwtcXV2xYsUK3Hvvve0eq1arMW7cOPj7+9u5SutYc03kmLp9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\nYImIBGHAEhEJwoAlIhLk/wApsnpaZFKY5AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c6f0518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with sns.axes_style(style='ticks'):\n",
+    "    g = sns.factorplot(\"day\", \"total_bill\", \"sex\", data=tips, kind=\"box\")\n",
+    "    g.set_axis_labels(\"Day\", \"Total Bill\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Joint distributions\n",
+    "\n",
+    "Similar to the pairplot we saw earlier, we can use ``sns.jointplot`` to show the joint distribution between different datasets, along with the associated marginal distributions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+Le0cYmEV4TpYnEcf12UNC5Yrarr3M7roTpbqSrSQ8JVN8yqXt5GfZREaTQgg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\nGWRbiq0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d25ef98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with sns.axes_style('white'):\n",
+    "    sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='hex')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The joint plot can even do some automatic kernel density estimation and regression:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SUhIFQlG+9fg/CUfjABgNOuZNzsag+pha6MBigsb6GjbW1wzqfK2tTZgcLmyyB58YJcGA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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c3b0550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='reg');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bar plots\n",
+    "\n",
+    "Time series can be plotted using ``sns.factorplot``. In the following example, we'll use the Planets data that we first saw in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>method</th>\n",
+       "      <th>number</th>\n",
+       "      <th>orbital_period</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Radial Velocity</td>\n",
+       "      <td>1</td>\n",
+       "      <td>269.300</td>\n",
+       "      <td>7.10</td>\n",
+       "      <td>77.40</td>\n",
+       "      <td>2006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Radial Velocity</td>\n",
+       "      <td>1</td>\n",
+       "      <td>874.774</td>\n",
+       "      <td>2.21</td>\n",
+       "      <td>56.95</td>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Radial Velocity</td>\n",
+       "      <td>1</td>\n",
+       "      <td>763.000</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>19.84</td>\n",
+       "      <td>2011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Radial Velocity</td>\n",
+       "      <td>1</td>\n",
+       "      <td>326.030</td>\n",
+       "      <td>19.40</td>\n",
+       "      <td>110.62</td>\n",
+       "      <td>2007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Radial Velocity</td>\n",
+       "      <td>1</td>\n",
+       "      <td>516.220</td>\n",
+       "      <td>10.50</td>\n",
+       "      <td>119.47</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            method  number  orbital_period   mass  distance  year\n",
+       "0  Radial Velocity       1         269.300   7.10     77.40  2006\n",
+       "1  Radial Velocity       1         874.774   2.21     56.95  2008\n",
+       "2  Radial Velocity       1         763.000   2.60     19.84  2011\n",
+       "3  Radial Velocity       1         326.030  19.40    110.62  2007\n",
+       "4  Radial Velocity       1         516.220  10.50    119.47  2009"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "planets = sns.load_dataset('planets')\n",
+    "planets.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VFRVFozwAAGCAqISgb37zm9q+ffsd7fHx8Vq3bl3kCwIAAMZh2wwAAGAkQhAAADASIQgA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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11ade0dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with sns.axes_style('white'):\n",
+    "    g = sns.factorplot(\"year\", data=planets, aspect=2,\n",
+    "                       kind=\"count\", color='steelblue')\n",
+    "    g.set_xticklabels(step=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can learn more by looking at the *method* of discovery of each of these planets:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Pf/5j/SciIiIiIiIiIlIWBoOBvn37snbtWq5c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\nERERERERERGpcf4XgBnpZwZT4DcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bd8d668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with sns.axes_style('white'):\n",
+    "    g = sns.factorplot(\"year\", data=planets, aspect=4.0, kind='count',\n",
+    "                       hue='method', order=range(2001, 2015))\n",
+    "    g.set_ylabels('Number of Planets Discovered')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more information on plotting with Seaborn, see the [Seaborn documentation](http://seaborn.pydata.org/), a [tutorial](http://seaborn.pydata.org/\n",
+    "tutorial.htm), and the [Seaborn gallery](http://seaborn.pydata.org/examples/index.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Exploring Marathon Finishing Times\n",
+    "\n",
+    "Here we'll look at using Seaborn to help visualize and understand finishing results from a marathon.\n",
+    "I've scraped the data from sources on the Web, aggregated it and removed any identifying information, and put it on GitHub where it can be downloaded\n",
+    "(if you are interested in using Python for web scraping, I would recommend [*Web Scraping with Python*](http://shop.oreilly.com/product/0636920034391.do) by Ryan Mitchell).\n",
+    "We will start by downloading the data from\n",
+    "the Web, and loading it into Pandas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# !curl -O https://raw.githubusercontent.com/jakevdp/marathon-data/master/marathon-data.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>split</th>\n",
+       "      <th>final</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>33</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:05:38</td>\n",
+       "      <td>02:08:51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>32</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:26</td>\n",
+       "      <td>02:09:28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>31</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:49</td>\n",
+       "      <td>02:10:42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:16</td>\n",
+       "      <td>02:13:45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>31</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:32</td>\n",
+       "      <td>02:13:59</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age gender     split     final\n",
+       "0   33      M  01:05:38  02:08:51\n",
+       "1   32      M  01:06:26  02:09:28\n",
+       "2   31      M  01:06:49  02:10:42\n",
+       "3   38      M  01:06:16  02:13:45\n",
+       "4   31      M  01:06:32  02:13:59"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('marathon-data.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, Pandas loaded the time columns as Python strings (type ``object``); we can see this by looking at the ``dtypes`` attribute of the DataFrame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age        int64\n",
+       "gender    object\n",
+       "split     object\n",
+       "final     object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's fix this by providing a converter for the times:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>split</th>\n",
+       "      <th>final</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>33</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:05:38</td>\n",
+       "      <td>02:08:51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>32</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:26</td>\n",
+       "      <td>02:09:28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>31</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:49</td>\n",
+       "      <td>02:10:42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:16</td>\n",
+       "      <td>02:13:45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>31</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:32</td>\n",
+       "      <td>02:13:59</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age gender    split    final\n",
+       "0   33      M 01:05:38 02:08:51\n",
+       "1   32      M 01:06:26 02:09:28\n",
+       "2   31      M 01:06:49 02:10:42\n",
+       "3   38      M 01:06:16 02:13:45\n",
+       "4   31      M 01:06:32 02:13:59"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "def convert_time(s):\n",
+    "    h, m, s = map(int, s.split(':'))\n",
+    "    return datetime.timedelta(hours=h, minutes=m, seconds=s)\n",
+    "\n",
+    "data = pd.read_csv('marathon-data.csv',\n",
+    "                   converters={'split':convert_time, 'final':convert_time})\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age                 int64\n",
+       "gender             object\n",
+       "split     timedelta64[ns]\n",
+       "final     timedelta64[ns]\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That looks much better. For the purpose of our Seaborn plotting utilities, let's next add columns that give the times in seconds:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>split</th>\n",
+       "      <th>final</th>\n",
+       "      <th>split_sec</th>\n",
+       "      <th>final_sec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>33</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:05:38</td>\n",
+       "      <td>02:08:51</td>\n",
+       "      <td>3938.0</td>\n",
+       "      <td>7731.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>32</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:26</td>\n",
+       "      <td>02:09:28</td>\n",
+       "      <td>3986.0</td>\n",
+       "      <td>7768.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>31</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:49</td>\n",
+       "      <td>02:10:42</td>\n",
+       "      <td>4009.0</td>\n",
+       "      <td>7842.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:16</td>\n",
+       "      <td>02:13:45</td>\n",
+       "      <td>3976.0</td>\n",
+       "      <td>8025.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>31</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:32</td>\n",
+       "      <td>02:13:59</td>\n",
+       "      <td>3992.0</td>\n",
+       "      <td>8039.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age gender    split    final  split_sec  final_sec\n",
+       "0   33      M 01:05:38 02:08:51     3938.0     7731.0\n",
+       "1   32      M 01:06:26 02:09:28     3986.0     7768.0\n",
+       "2   31      M 01:06:49 02:10:42     4009.0     7842.0\n",
+       "3   38      M 01:06:16 02:13:45     3976.0     8025.0\n",
+       "4   31      M 01:06:32 02:13:59     3992.0     8039.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['split_sec'] = data['split'].astype(int) / 1E9\n",
+    "data['final_sec'] = data['final'].astype(int) / 1E9\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get an idea of what the data looks like, we can plot a ``jointplot`` over the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GG2/EsGHDMoxa2JvpqRYqhmHgu9/9bhBSveCCC/Cxj30MSqlA6PHFL34Ru3btwtKlS4N5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0XN7XaTnp7O8uXLAWhpaSErK4uEhASee+452traPI8tLi4mPj6epKQkvvzyS097XV0dKSkpJCQk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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b646ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with sns.axes_style('white'):\n",
+    "    g = sns.jointplot(\"split_sec\", \"final_sec\", data, kind='hex')\n",
+    "    g.ax_joint.plot(np.linspace(4000, 16000),\n",
+    "                    np.linspace(8000, 32000), ':k')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dotted line shows where someone's time would lie if they ran the marathon at a perfectly steady pace. The fact that the distribution lies above this indicates (as you might expect) that most people slow down over the course of the marathon.\n",
+    "If you have run competitively, you'll know that those who do the opposite—run faster during the second half of the race—are said to have \"negative-split\" the race.\n",
+    "\n",
+    "Let's create another column in the data, the split fraction, which measures the degree to which each runner negative-splits or positive-splits the race:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>split</th>\n",
+       "      <th>final</th>\n",
+       "      <th>split_sec</th>\n",
+       "      <th>final_sec</th>\n",
+       "      <th>split_frac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>33</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:05:38</td>\n",
+       "      <td>02:08:51</td>\n",
+       "      <td>3938.0</td>\n",
+       "      <td>7731.0</td>\n",
+       "      <td>-0.018756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>32</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:26</td>\n",
+       "      <td>02:09:28</td>\n",
+       "      <td>3986.0</td>\n",
+       "      <td>7768.0</td>\n",
+       "      <td>-0.026262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>31</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:49</td>\n",
+       "      <td>02:10:42</td>\n",
+       "      <td>4009.0</td>\n",
+       "      <td>7842.0</td>\n",
+       "      <td>-0.022443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:16</td>\n",
+       "      <td>02:13:45</td>\n",
+       "      <td>3976.0</td>\n",
+       "      <td>8025.0</td>\n",
+       "      <td>0.009097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>31</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:32</td>\n",
+       "      <td>02:13:59</td>\n",
+       "      <td>3992.0</td>\n",
+       "      <td>8039.0</td>\n",
+       "      <td>0.006842</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age gender    split    final  split_sec  final_sec  split_frac\n",
+       "0   33      M 01:05:38 02:08:51     3938.0     7731.0   -0.018756\n",
+       "1   32      M 01:06:26 02:09:28     3986.0     7768.0   -0.026262\n",
+       "2   31      M 01:06:49 02:10:42     4009.0     7842.0   -0.022443\n",
+       "3   38      M 01:06:16 02:13:45     3976.0     8025.0    0.009097\n",
+       "4   31      M 01:06:32 02:13:59     3992.0     8039.0    0.006842"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['split_frac'] = 1 - 2 * data['split_sec'] / data['final_sec']\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Where this split difference is less than zero, the person negative-split the race by that fraction.\n",
+    "Let's do a distribution plot of this split fraction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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i1atXa/HixVq0aJEkKSMjI9L/0EMPadWqVZL69sDPnz8f6WttbZXf7+/XblmW/H5/1Pee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\nAAxBqAMAYAhCHQAAQ/w/HaCoKnHAZlAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bb754a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data['split_frac'], kde=False);\n",
+    "plt.axvline(0, color=\"k\", linestyle=\"--\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "251"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(data.split_frac < 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Out of nearly 40,000 participants, there were only 250 people who negative-split their marathon.\n",
+    "\n",
+    "Let's see whether there is any correlation between this split fraction and other variables. We'll do this using a ``pairgrid``, which draws plots of all these correlations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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r1y6+/OUv8+yzz/LMM8/geR4/+7M/y7Fjx7jnnnt47rnnuPvuuzl69OhaT9Uw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UUul6OtLUoPuH37dl5//fXyMXfccQcAW7ZsYffu3dPZndkn8LBP7cX71X9dWAMQeNgf/ILUvpdI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\nRCKRSCSS3/h/L/d2XtJO7wgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11aeb5908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.PairGrid(data, vars=['age', 'split_sec', 'final_sec', 'split_frac'],\n",
+    "                 hue='gender', palette='RdBu_r')\n",
+    "g.map(plt.scatter, alpha=0.8)\n",
+    "g.add_legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like the split fraction does not correlate particularly with age, but does correlate with the final time: faster runners tend to have closer to even splits on their marathon time.\n",
+    "(We see here that Seaborn is no panacea for Matplotlib's ills when it comes to plot styles: in particular, the x-axis labels overlap. Because the output is a simple Matplotlib plot, however, the methods in [Customizing Ticks](04.10-Customizing-Ticks.ipynb) can be used to adjust such things if desired.)\n",
+    "\n",
+    "The difference between men and women here is interesting. Let's look at the histogram of split fractions for these two groups:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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v8lV9XyIvYxAT1bg/vGDNhs+cM6n4sURhxbQmVW/rElA4+MHwwQCDmKhSGMRE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\nrbfeKv7hH/6hWuWVpZwxdHd3i61btwo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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bdb3a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.kdeplot(data.split_frac[data.gender=='M'], label='men', shade=True)\n",
+    "sns.kdeplot(data.split_frac[data.gender=='W'], label='women', shade=True)\n",
+    "plt.xlabel('split_frac');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The interesting thing here is that there are many more men than women who are running close to an even split!\n",
+    "This almost looks like some kind of bimodal distribution among the men and women. Let's see if we can suss-out what's going on by looking at the distributions as a function of age.\n",
+    "\n",
+    "A nice way to compare distributions is to use a *violin plot*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MTAoLCykrK+OOO+6gqkoLLshH6+zs4I03XuO5535NQ0MdiRk5LLztTlx5RePa3R4eE8vs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\niMUp7EVERP7/DXMAAPmmNk33GAG5AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d2aeeb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.violinplot(\"gender\", \"split_frac\", data=data,\n",
+    "               palette=[\"lightblue\", \"lightpink\"]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is yet another way to compare the distributions between men and women.\n",
+    "\n",
+    "Let's look a little deeper, and compare these violin plots as a function of age. We'll start by creating a new column in the array that specifies the decade of age that each person is in:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>split</th>\n",
+       "      <th>final</th>\n",
+       "      <th>split_sec</th>\n",
+       "      <th>final_sec</th>\n",
+       "      <th>split_frac</th>\n",
+       "      <th>age_dec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>33</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:05:38</td>\n",
+       "      <td>02:08:51</td>\n",
+       "      <td>3938.0</td>\n",
+       "      <td>7731.0</td>\n",
+       "      <td>-0.018756</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>32</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:26</td>\n",
+       "      <td>02:09:28</td>\n",
+       "      <td>3986.0</td>\n",
+       "      <td>7768.0</td>\n",
+       "      <td>-0.026262</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>31</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:49</td>\n",
+       "      <td>02:10:42</td>\n",
+       "      <td>4009.0</td>\n",
+       "      <td>7842.0</td>\n",
+       "      <td>-0.022443</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:16</td>\n",
+       "      <td>02:13:45</td>\n",
+       "      <td>3976.0</td>\n",
+       "      <td>8025.0</td>\n",
+       "      <td>0.009097</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>31</td>\n",
+       "      <td>M</td>\n",
+       "      <td>01:06:32</td>\n",
+       "      <td>02:13:59</td>\n",
+       "      <td>3992.0</td>\n",
+       "      <td>8039.0</td>\n",
+       "      <td>0.006842</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age gender    split    final  split_sec  final_sec  split_frac  age_dec\n",
+       "0   33      M 01:05:38 02:08:51     3938.0     7731.0   -0.018756       30\n",
+       "1   32      M 01:06:26 02:09:28     3986.0     7768.0   -0.026262       30\n",
+       "2   31      M 01:06:49 02:10:42     4009.0     7842.0   -0.022443       30\n",
+       "3   38      M 01:06:16 02:13:45     3976.0     8025.0    0.009097       30\n",
+       "4   31      M 01:06:32 02:13:59     3992.0     8039.0    0.006842       30"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['age_dec'] = data.age.map(lambda age: 10 * (age // 10))\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zz/36eiiwOioOg4MDi6J3X1ZWgvv378HLPxAxmbNfV2YjFonwteVrEOXnj6qq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\nhBBCCOMo2BNCCCGMo2BPCCGEMO7/A2wATbzbo2s4AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bd81c18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "men = (data.gender == 'M')\n",
+    "women = (data.gender == 'W')\n",
+    "\n",
+    "with sns.axes_style(style=None):\n",
+    "    sns.violinplot(\"age_dec\", \"split_frac\", hue=\"gender\", data=data,\n",
+    "                   split=True, inner=\"quartile\",\n",
+    "                   palette=[\"lightblue\", \"lightpink\"]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at this, we can see where the distributions of men and women differ: the split distributions of men in their 20s to 50s show a pronounced over-density toward lower splits when compared to women of the same age (or of any age, for that matter).\n",
+    "\n",
+    "Also surprisingly, the 80-year-old women seem to outperform *everyone* in terms of their split time. This is probably due to the fact that we're estimating the distribution from small numbers, as there are only a handful of runners in that range:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(data.age > 80).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Back to the men with negative splits: who are these runners? Does this split fraction correlate with finishing quickly? We can plot this very easily. We'll use ``regplot``, which will automatically fit a linear regression to the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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5xMfKpiraGn14XCpVHg/ORfyBQQLiCjM6KzB6Ymh1OvlmU1vZKt/pGl2zuzsV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ULs7PNj/9FO7D/aiR538qU+AcBabcS0eWX4j7IwujWFstnnyMIApmk0cKsqVADTdXp8z1SCQwEf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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11e0dfba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.lmplot('final_sec', 'split_frac', col='gender', data=data,\n",
+    "               markers=\".\", scatter_kws=dict(color='c'))\n",
+    "g.map(plt.axhline, y=0.1, color=\"k\", ls=\":\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apparently the people with fast splits are the elite runners who are finishing within ~15,000 seconds, or about 4 hours. People slower than that are much less likely to have a fast second split."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.14-Visualization-With-Seaborn.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/04.15-Further-Resources.ipynb b/notebooks_v1/04.15-Further-Resources.ipynb
new file mode 100644
index 000000000..4aed29225
--- /dev/null
+++ b/notebooks_v1/04.15-Further-Resources.ipynb
@@ -0,0 +1,97 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) | [Contents](Index.ipynb) | [Machine Learning](05.00-Machine-Learning.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.15-Further-Resources.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matplotlib Resources\n",
+    "\n",
+    "A single chapter in a book can never hope to cover all the available features and plot types available in Matplotlib.\n",
+    "As with other packages we've seen, liberal use of IPython's tab-completion and help functions (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be very helpful when exploring Matplotlib's API.\n",
+    "In addition, Matplotlib’s [online documentation](http://matplotlib.org/) can be a helpful reference.\n",
+    "See in particular the [Matplotlib gallery](http://matplotlib.org/gallery.html) linked on that page: it shows thumbnails of hundreds of different plot types, each one linked to a page with the Python code snippet used to generate it.\n",
+    "In this way, you can visually inspect and learn about a wide range of different plotting styles and visualization techniques.\n",
+    "\n",
+    "For a book-length treatment of Matplotlib, I would recommend [*Interactive Applications Using Matplotlib*](https://www.packtpub.com/application-development/interactive-applications-using-matplotlib), written by Matplotlib core developer Ben Root."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Other Python Graphics Libraries\n",
+    "\n",
+    "Although Matplotlib is the most prominent Python visualization library, there are other more modern tools that are worth exploring as well.\n",
+    "I'll mention a few of them briefly here:\n",
+    "\n",
+    "- [Bokeh](http://bokeh.pydata.org) is a JavaScript visualization library with a Python frontend that creates highly interactive visualizations capable of handling very large and/or streaming datasets. The Python front-end outputs a JSON data structure that can be interpreted by the Bokeh JS engine.\n",
+    "- [Plotly](http://plot.ly) is the eponymous open source product of the Plotly company, and is similar in spirit to Bokeh. Because Plotly is the main product of a startup, it is receiving a high level of development effort. Use of the library is entirely free.\n",
+    "- [Vispy](http://vispy.org/) is an actively developed project focused on dynamic visualizations of very large datasets. Because it is built to target OpenGL and make use of efficient graphics processors in your computer, it is able to render some quite large and stunning visualizations.\n",
+    "- [Vega](https://vega.github.io/) and [Vega-Lite](https://vega.github.io/vega-lite) are declarative graphics representations, and are the product of years of research into the fundamental language of data visualization. The reference rendering implementation is JavaScript, but the API is language agnostic. There is a Python API under development in the [Altair](https://altair-viz.github.io/) package. Though as of summer 2016 it's not yet fully mature, I'm quite excited for the possibilities of this project to provide a common reference point for visualization in Python and other languages.\n",
+    "\n",
+    "The visualization space in the Python community is very dynamic, and I fully expect this list to be out of date as soon as it is published.\n",
+    "Keep an eye out for what's coming in the future!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) | [Contents](Index.ipynb) | [Machine Learning](05.00-Machine-Learning.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.15-Further-Resources.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.00-Machine-Learning.ipynb b/notebooks_v1/05.00-Machine-Learning.ipynb
new file mode 100644
index 000000000..caff9877c
--- /dev/null
+++ b/notebooks_v1/05.00-Machine-Learning.ipynb
@@ -0,0 +1,91 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Further Resources](04.15-Further-Resources.ipynb) | [Contents](Index.ipynb) | [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.00-Machine-Learning.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Machine Learning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In many ways, machine learning is the primary means by which data science manifests itself to the broader world.\n",
+    "Machine learning is where these computational and algorithmic skills of data science meet the statistical thinking of data science, and the result is a collection of approaches to inference and data exploration that are not about effective theory so much as effective computation.\n",
+    "\n",
+    "The term \"machine learning\" is sometimes thrown around as if it is some kind of magic pill: *apply machine learning to your data, and all your problems will be solved!*\n",
+    "As you might expect, the reality is rarely this simple.\n",
+    "While these methods can be incredibly powerful, to be effective they must be approached with a firm grasp of the strengths and weaknesses of each method, as well as a grasp of general concepts such as bias and variance, overfitting and underfitting, and more.\n",
+    "\n",
+    "This chapter will dive into practical aspects of machine learning, primarily using Python's [Scikit-Learn](http://scikit-learn.org) package.\n",
+    "This is not meant to be a comprehensive introduction to the field of machine learning; that is a large subject and necessitates a more technical approach than we take here.\n",
+    "Nor is it meant to be a comprehensive manual for the use of the Scikit-Learn package (for this, you can refer to the resources listed in [Further Machine Learning Resources](05.15-Learning-More.ipynb)).\n",
+    "Rather, the goals of this chapter are:\n",
+    "\n",
+    "- To introduce the fundamental vocabulary and concepts of machine learning.\n",
+    "- To introduce the Scikit-Learn API and show some examples of its use.\n",
+    "- To take a deeper dive into the details of several of the most important machine learning approaches, and develop an intuition into how they work and when and where they are applicable.\n",
+    "\n",
+    "Much of this material is drawn from the Scikit-Learn tutorials and workshops I have given on several occasions at PyCon, SciPy, PyData, and other conferences.\n",
+    "Any clarity in the following pages is likely due to the many workshop participants and co-instructors who have given me valuable feedback on this material over the years!\n",
+    "\n",
+    "Finally, if you are seeking a more comprehensive or technical treatment of any of these subjects, I've listed several resources and references in [Further Machine Learning Resources](05.15-Learning-More.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Further Resources](04.15-Further-Resources.ipynb) | [Contents](Index.ipynb) | [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.00-Machine-Learning.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.01-What-Is-Machine-Learning.ipynb b/notebooks_v1/05.01-What-Is-Machine-Learning.ipynb
new file mode 100644
index 000000000..1dd061dae
--- /dev/null
+++ b/notebooks_v1/05.01-What-Is-Machine-Learning.ipynb
@@ -0,0 +1,512 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Machine Learning](05.00-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.01-What-Is-Machine-Learning.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# What Is Machine Learning?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Before we take a look at the details of various machine learning methods, let's start by looking at what machine learning is, and what it isn't.\n",
+    "Machine learning is often categorized as a subfield of artificial intelligence, but I find that categorization can often be misleading at first brush.\n",
+    "The study of machine learning certainly arose from research in this context, but in the data science application of machine learning methods, it's more helpful to think of machine learning as a means of *building models of data*.\n",
+    "\n",
+    "Fundamentally, machine learning involves building mathematical models to help understand data.\n",
+    "\"Learning\" enters the fray when we give these models *tunable parameters* that can be adapted to observed data; in this way the program can be considered to be \"learning\" from the data.\n",
+    "Once these models have been fit to previously seen data, they can be used to predict and understand aspects of newly observed data.\n",
+    "I'll leave to the reader the more philosophical digression regarding the extent to which this type of mathematical, model-based \"learning\" is similar to the \"learning\" exhibited by the human brain.\n",
+    "\n",
+    "Understanding the problem setting in machine learning is essential to using these tools effectively, and so we will start with some broad categorizations of the types of approaches we'll discuss here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Categories of Machine Learning\n",
+    "\n",
+    "At the most fundamental level, machine learning can be categorized into two main types: supervised learning and unsupervised learning.\n",
+    "\n",
+    "*Supervised learning* involves somehow modeling the relationship between measured features of data and some label associated with the data; once this model is determined, it can be used to apply labels to new, unknown data.\n",
+    "This is further subdivided into *classification* tasks and *regression* tasks: in classification, the labels are discrete categories, while in regression, the labels are continuous quantities.\n",
+    "We will see examples of both types of supervised learning in the following section.\n",
+    "\n",
+    "*Unsupervised learning* involves modeling the features of a dataset without reference to any label, and is often described as \"letting the dataset speak for itself.\"\n",
+    "These models include tasks such as *clustering* and *dimensionality reduction.*\n",
+    "Clustering algorithms identify distinct groups of data, while dimensionality reduction algorithms search for more succinct representations of the data.\n",
+    "We will see examples of both types of unsupervised learning in the following section.\n",
+    "\n",
+    "In addition, there are so-called *semi-supervised learning* methods, which falls somewhere between supervised learning and unsupervised learning.\n",
+    "Semi-supervised learning methods are often useful when only incomplete labels are available."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Qualitative Examples of Machine Learning Applications\n",
+    "\n",
+    "To make these ideas more concrete, let's take a look at a few very simple examples of a machine learning task.\n",
+    "These examples are meant to give an intuitive, non-quantitative overview of the types of machine learning tasks we will be looking at in this chapter.\n",
+    "In later sections, we will go into more depth regarding the particular models and how they are used.\n",
+    "For a preview of these more technical aspects, you can find the Python source that generates the following figures in the [Appendix: Figure Code](06.00-Figure-Code.ipynb).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Classification: Predicting discrete labels\n",
+    "\n",
+    "We will first take a look at a simple *classification* task, in which you are given a set of labeled points and want to use these to classify some unlabeled points.\n",
+    "\n",
+    "Imagine that we have the data shown in this figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.01-classification-1.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Classification-Example-Figure-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Here we have two-dimensional data: that is, we have two *features* for each point, represented by the *(x,y)* positions of the points on the plane.\n",
+    "In addition, we have one of two *class labels* for each point, here represented by the colors of the points.\n",
+    "From these features and labels, we would like to create a model that will let us decide whether a new point should be labeled \"blue\" or \"red.\"\n",
+    "\n",
+    "There are a number of possible models for such a classification task, but here we will use an extremely simple one. We will make the assumption that the two groups can be separated by drawing a straight line through the plane between them, such that points on each side of the line fall in the same group.\n",
+    "Here the *model* is a quantitative version of the statement \"a straight line separates the classes\", while the *model parameters* are the particular numbers describing the location and orientation of that line for our data.\n",
+    "The optimal values for these model parameters are learned from the data (this is the \"learning\" in machine learning), which is often called *training the model*.\n",
+    "\n",
+    "The following figure shows a visual representation of what the trained model looks like for this data:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.01-classification-2.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Classification-Example-Figure-2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now that this model has been trained, it can be generalized to new, unlabeled data.\n",
+    "In other words, we can take a new set of data, draw this model line through it, and assign labels to the new points based on this model.\n",
+    "This stage is usually called *prediction*. See the following figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.01-classification-3.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Classification-Example-Figure-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This is the basic idea of a classification task in machine learning, where \"classification\" indicates that the data has discrete class labels.\n",
+    "At first glance this may look fairly trivial: it would be relatively easy to simply look at this data and draw such a discriminatory line to accomplish this classification.\n",
+    "A benefit of the machine learning approach, however, is that it can generalize to much larger datasets in many more dimensions.\n",
+    "\n",
+    "For example, this is similar to the task of automated spam detection for email; in this case, we might use the following features and labels:\n",
+    "\n",
+    "- *feature 1*, *feature 2*, etc. $\\to$ normalized counts of important words or phrases (\"Viagra\", \"Nigerian prince\", etc.)\n",
+    "- *label* $\\to$ \"spam\" or \"not spam\"\n",
+    "\n",
+    "For the training set, these labels might be determined by individual inspection of a small representative sample of emails; for the remaining emails, the label would be determined using the model.\n",
+    "For a suitably trained classification algorithm with enough well-constructed features (typically thousands or millions of words or phrases), this type of approach can be very effective.\n",
+    "We will see an example of such text-based classification in [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb).\n",
+    "\n",
+    "Some important classification algorithms that we will discuss in more detail are Gaussian naive Bayes (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)), support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), and random forest classification (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Regression: Predicting continuous labels\n",
+    "\n",
+    "In contrast with the discrete labels of a classification algorithm, we will next look at a simple *regression* task in which the labels are continuous quantities.\n",
+    "\n",
+    "Consider the data shown in the following figure, which consists of a set of points each with a continuous label:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.01-regression-1.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "As with the classification example, we have two-dimensional data: that is, there are two features describing each data point.\n",
+    "The color of each point represents the continuous label for that point.\n",
+    "\n",
+    "There are a number of possible regression models we might use for this type of data, but here we will use a simple linear regression to predict the points.\n",
+    "This simple linear regression model assumes that if we treat the label as a third spatial dimension, we can fit a plane to the data.\n",
+    "This is a higher-level generalization of the well-known problem of fitting a line to data with two coordinates.\n",
+    "\n",
+    "We can visualize this setup as shown in the following figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.01-regression-2.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Notice that the *feature 1-feature 2* plane here is the same as in the two-dimensional plot from before; in this case, however, we have represented the labels by both color and three-dimensional axis position.\n",
+    "From this view, it seems reasonable that fitting a plane through this three-dimensional data would allow us to predict the expected label for any set of input parameters.\n",
+    "Returning to the two-dimensional projection, when we fit such a plane we get the result shown in the following figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.01-regression-3.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This plane of fit gives us what we need to predict labels for new points.\n",
+    "Visually, we find the results shown in the following figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.01-regression-4.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "As with the classification example, this may seem rather trivial in a low number of dimensions.\n",
+    "But the power of these methods is that they can be straightforwardly applied and evaluated in the case of data with many, many features.\n",
+    "\n",
+    "For example, this is similar to the task of computing the distance to galaxies observed through a telescope—in this case, we might use the following features and labels:\n",
+    "\n",
+    "- *feature 1*, *feature 2*, etc. $\\to$ brightness of each galaxy at one of several wave lengths or colors\n",
+    "- *label* $\\to$ distance or redshift of the galaxy\n",
+    "\n",
+    "The distances for a small number of these galaxies might be determined through an independent set of (typically more expensive) observations.\n",
+    "Distances to remaining galaxies could then be estimated using a suitable regression model, without the need to employ the more expensive observation across the entire set.\n",
+    "In astronomy circles, this is known as the \"photometric redshift\" problem.\n",
+    "\n",
+    "Some important regression algorithms that we will discuss are linear regression (see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)), support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), and random forest regression (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Clustering: Inferring labels on unlabeled data\n",
+    "\n",
+    "The classification and regression illustrations we just looked at are examples of supervised learning algorithms, in which we are trying to build a model that will predict labels for new data.\n",
+    "Unsupervised learning involves models that describe data without reference to any known labels.\n",
+    "\n",
+    "One common case of unsupervised learning is \"clustering,\" in which data is automatically assigned to some number of discrete groups.\n",
+    "For example, we might have some two-dimensional data like that shown in the following figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.01-clustering-1.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Clustering-Example-Figure-2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "By eye, it is clear that each of these points is part of a distinct group.\n",
+    "Given this input, a clustering model will use the intrinsic structure of the data to determine which points are related.\n",
+    "Using the very fast and intuitive *k*-means algorithm (see [In Depth: K-Means Clustering](05.11-K-Means.ipynb)), we find the clusters shown in the following figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.01-clustering-2.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Clustering-Example-Figure-2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "*k*-means fits a model consisting of *k* cluster centers; the optimal centers are assumed to be those that minimize the distance of each point from its assigned center.\n",
+    "Again, this might seem like a trivial exercise in two dimensions, but as our data becomes larger and more complex, such clustering algorithms can be employed to extract useful information from the dataset.\n",
+    "\n",
+    "We will discuss the *k*-means algorithm in more depth in [In Depth: K-Means Clustering](05.11-K-Means.ipynb).\n",
+    "Other important clustering algorithms include Gaussian mixture models (See [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb)) and spectral clustering (See [Scikit-Learn's clustering documentation](http://scikit-learn.org/stable/modules/clustering.html))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Dimensionality reduction: Inferring structure of unlabeled data\n",
+    "\n",
+    "Dimensionality reduction is another example of an unsupervised algorithm, in which labels or other information are inferred from the structure of the dataset itself.\n",
+    "Dimensionality reduction is a bit more abstract than the examples we looked at before, but generally it seeks to pull out some low-dimensional representation of data that in some way preserves relevant qualities of the full dataset.\n",
+    "Different dimensionality reduction routines measure these relevant qualities in different ways, as we will see in [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb).\n",
+    "\n",
+    "As an example of this, consider the data shown in the following figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.01-dimesionality-1.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Dimensionality-Reduction-Example-Figure-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Visually, it is clear that there is some structure in this data: it is drawn from a one-dimensional line that is arranged in a spiral within this two-dimensional space.\n",
+    "In a sense, you could say that this data is \"intrinsically\" only one dimensional, though this one-dimensional data is embedded in higher-dimensional space.\n",
+    "A suitable dimensionality reduction model in this case would be sensitive to this nonlinear embedded structure, and be able to pull out this lower-dimensionality representation.\n",
+    "\n",
+    "The following figure shows a visualization of the results of the Isomap algorithm, a manifold learning algorithm that does exactly this:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.01-dimesionality-2.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Dimensionality-Reduction-Example-Figure-2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Notice that the colors (which represent the extracted one-dimensional latent variable) change uniformly along the spiral, which indicates that the algorithm did in fact detect the structure we saw by eye.\n",
+    "As with the previous examples, the power of dimensionality reduction algorithms becomes clearer in higher-dimensional cases.\n",
+    "For example, we might wish to visualize important relationships within a dataset that has 100 or 1,000 features.\n",
+    "Visualizing 1,000-dimensional data is a challenge, and one way we can make this more manageable is to use a dimensionality reduction technique to reduce the data to two or three dimensions.\n",
+    "\n",
+    "Some important dimensionality reduction algorithms that we will discuss are principal component analysis (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)) and various manifold learning algorithms, including Isomap and locally linear embedding (See [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Summary\n",
+    "\n",
+    "Here we have seen a few simple examples of some of the basic types of machine learning approaches.\n",
+    "Needless to say, there are a number of important practical details that we have glossed over, but I hope this section was enough to give you a basic idea of what types of problems machine learning approaches can solve.\n",
+    "\n",
+    "In short, we saw the following:\n",
+    "\n",
+    "- *Supervised learning*: Models that can predict labels based on labeled training data\n",
+    "\n",
+    "  - *Classification*: Models that predict labels as two or more discrete categories\n",
+    "  - *Regression*: Models that predict continuous labels\n",
+    "  \n",
+    "- *Unsupervised learning*: Models that identify structure in unlabeled data\n",
+    "\n",
+    "  - *Clustering*: Models that detect and identify distinct groups in the data\n",
+    "  - *Dimensionality reduction*: Models that detect and identify lower-dimensional structure in higher-dimensional data\n",
+    "  \n",
+    "In the following sections we will go into much greater depth within these categories, and see some more interesting examples of where these concepts can be useful.\n",
+    "\n",
+    "All of the figures in the preceding discussion are generated based on actual machine learning computations; the code behind them can be found in [Appendix: Figure Code](06.00-Figure-Code.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Machine Learning](05.00-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.01-What-Is-Machine-Learning.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.02-Introducing-Scikit-Learn.ipynb b/notebooks_v1/05.02-Introducing-Scikit-Learn.ipynb
new file mode 100644
index 000000000..8d3ecb877
--- /dev/null
+++ b/notebooks_v1/05.02-Introducing-Scikit-Learn.ipynb
@@ -0,0 +1,1587 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introducing Scikit-Learn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "There are several Python libraries which provide solid implementations of a range of machine learning algorithms.\n",
+    "One of the best known is [Scikit-Learn](http://scikit-learn.org), a package that provides efficient versions of a large number of common algorithms.\n",
+    "Scikit-Learn is characterized by a clean, uniform, and streamlined API, as well as by very useful and complete online documentation.\n",
+    "A benefit of this uniformity is that once you understand the basic use and syntax of Scikit-Learn for one type of model, switching to a new model or algorithm is very straightforward.\n",
+    "\n",
+    "This section provides an overview of the Scikit-Learn API; a solid understanding of these API elements will form the foundation for understanding the deeper practical discussion of machine learning algorithms and approaches in the following chapters.\n",
+    "\n",
+    "We will start by covering *data representation* in Scikit-Learn, followed by covering the *Estimator* API, and finally go through a more interesting example of using these tools for exploring a set of images of hand-written digits."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Data Representation in Scikit-Learn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Machine learning is about creating models from data: for that reason, we'll start by discussing how data can be represented in order to be understood by the computer.\n",
+    "The best way to think about data within Scikit-Learn is in terms of tables of data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Data as table\n",
+    "\n",
+    "A basic table is a two-dimensional grid of data, in which the rows represent individual elements of the dataset, and the columns represent quantities related to each of these elements.\n",
+    "For example, consider the [Iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), famously analyzed by Ronald Fisher in 1936.\n",
+    "We can download this dataset in the form of a Pandas ``DataFrame`` using the [seaborn](http://seaborn.pydata.org/) library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal_length  sepal_width  petal_length  petal_width species\n",
+       "0           5.1          3.5           1.4          0.2  setosa\n",
+       "1           4.9          3.0           1.4          0.2  setosa\n",
+       "2           4.7          3.2           1.3          0.2  setosa\n",
+       "3           4.6          3.1           1.5          0.2  setosa\n",
+       "4           5.0          3.6           1.4          0.2  setosa"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "iris = sns.load_dataset('iris')\n",
+    "iris.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Here each row of the data refers to a single observed flower, and the number of rows is the total number of flowers in the dataset.\n",
+    "In general, we will refer to the rows of the matrix as *samples*, and the number of rows as ``n_samples``.\n",
+    "\n",
+    "Likewise, each column of the data refers to a particular quantitative piece of information that describes each sample.\n",
+    "In general, we will refer to the columns of the matrix as *features*, and the number of columns as ``n_features``."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Features matrix\n",
+    "\n",
+    "This table layout makes clear that the information can be thought of as a two-dimensional numerical array or matrix, which we will call the *features matrix*.\n",
+    "By convention, this features matrix is often stored in a variable named ``X``.\n",
+    "The features matrix is assumed to be two-dimensional, with shape ``[n_samples, n_features]``, and is most often contained in a NumPy array or a Pandas ``DataFrame``, though some Scikit-Learn models also accept SciPy sparse matrices.\n",
+    "\n",
+    "The samples (i.e., rows) always refer to the individual objects described by the dataset.\n",
+    "For example, the sample might be a flower, a person, a document, an image, a sound file, a video, an astronomical object, or anything else you can describe with a set of quantitative measurements.\n",
+    "\n",
+    "The features (i.e., columns) always refer to the distinct observations that describe each sample in a quantitative manner.\n",
+    "Features are generally real-valued, but may be Boolean or discrete-valued in some cases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Target array\n",
+    "\n",
+    "In addition to the feature matrix ``X``, we also generally work with a *label* or *target* array, which by convention we will usually call ``y``.\n",
+    "The target array is usually one dimensional, with length ``n_samples``, and is generally contained in a NumPy array or Pandas ``Series``.\n",
+    "The target array may have continuous numerical values, or discrete classes/labels.\n",
+    "While some Scikit-Learn estimators do handle multiple target values in the form of a two-dimensional, ``[n_samples, n_targets]`` target array, we will primarily be working with the common case of a one-dimensional target array.\n",
+    "\n",
+    "Often one point of confusion is how the target array differs from the other features columns. The distinguishing feature of the target array is that it is usually the quantity we want to *predict from the data*: in statistical terms, it is the dependent variable.\n",
+    "For example, in the preceding data we may wish to construct a model that can predict the species of flower based on the other measurements; in this case, the ``species`` column would be considered the target array.\n",
+    "\n",
+    "With this target array in mind, we can use Seaborn (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)) to conveniently visualize the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1ICc4Ty+XT+7pp2qjJejc1eV0Y3d24+r28OQbR4lSyFidMnxDL0NOWnQqn5gO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3/+E4B169axaNGiVucvLS1l4cKFrFq1iiuuuKJFedpTTOZEdoXvf7PV0ehaSUUt2YU1JMXqCQkM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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1170288d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import seaborn as sns; sns.set()\n",
+    "sns.pairplot(iris, hue='species', size=1.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "For use in Scikit-Learn, we will extract the features matrix and target array from the ``DataFrame``, which we can do using some of the Pandas ``DataFrame`` operations discussed in the [Chapter 3](03.00-Introduction-to-Pandas.ipynb):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(150, 4)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_iris = iris.drop('species', axis=1)\n",
+    "X_iris.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(150,)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_iris = iris['species']\n",
+    "y_iris.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "To summarize, the expected layout of features and target values is visualized in the following diagram:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.02-samples-features.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Features-and-Labels-Grid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With this data properly formatted, we can move on to consider the *estimator* API of Scikit-Learn:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Scikit-Learn's Estimator API"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The Scikit-Learn API is designed with the following guiding principles in mind, as outlined in the [Scikit-Learn API paper](http://arxiv.org/abs/1309.0238):\n",
+    "\n",
+    "- *Consistency*: All objects share a common interface drawn from a limited set of methods, with consistent documentation.\n",
+    "\n",
+    "- *Inspection*: All specified parameter values are exposed as public attributes.\n",
+    "\n",
+    "- *Limited object hierarchy*: Only algorithms are represented by Python classes; datasets are represented\n",
+    "  in standard formats (NumPy arrays, Pandas ``DataFrame``s, SciPy sparse matrices) and parameter\n",
+    "  names use standard Python strings.\n",
+    "\n",
+    "- *Composition*: Many machine learning tasks can be expressed as sequences of more fundamental algorithms,\n",
+    "  and Scikit-Learn makes use of this wherever possible.\n",
+    "\n",
+    "- *Sensible defaults*: When models require user-specified parameters, the library defines an appropriate default value.\n",
+    "\n",
+    "In practice, these principles make Scikit-Learn very easy to use, once the basic principles are understood.\n",
+    "Every machine learning algorithm in Scikit-Learn is implemented via the Estimator API, which provides a consistent interface for a wide range of machine learning applications."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Basics of the API\n",
+    "\n",
+    "Most commonly, the steps in using the Scikit-Learn estimator API are as follows\n",
+    "(we will step through a handful of detailed examples in the sections that follow).\n",
+    "\n",
+    "1. Choose a class of model by importing the appropriate estimator class from Scikit-Learn.\n",
+    "2. Choose model hyperparameters by instantiating this class with desired values.\n",
+    "3. Arrange data into a features matrix and target vector following the discussion above.\n",
+    "4. Fit the model to your data by calling the ``fit()`` method of the model instance.\n",
+    "5. Apply the Model to new data:\n",
+    "   - For supervised learning, often we predict labels for unknown data using the ``predict()`` method.\n",
+    "   - For unsupervised learning, we often transform or infer properties of the data using the ``transform()`` or ``predict()`` method.\n",
+    "\n",
+    "We will now step through several simple examples of applying supervised and unsupervised learning methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Supervised learning example: Simple linear regression\n",
+    "\n",
+    "As an example of this process, let's consider a simple linear regression—that is, the common case of fitting a line to $(x, y)$ data.\n",
+    "We will use the following simple data for our regression example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFVCAYAAAA+OJwpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11acab828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "rng = np.random.RandomState(42)\n",
+    "x = 10 * rng.rand(50)\n",
+    "y = 2 * x - 1 + rng.randn(50)\n",
+    "plt.scatter(x, y);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With this data in place, we can use the recipe outlined earlier. Let's walk through the process: "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### 1. Choose a class of model\n",
+    "\n",
+    "In Scikit-Learn, every class of model is represented by a Python class.\n",
+    "So, for example, if we would like to compute a simple linear regression model, we can import the linear regression class:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Note that other more general linear regression models exist as well; you can read more about them in the [``sklearn.linear_model`` module documentation](http://Scikit-Learn.org/stable/modules/linear_model.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### 2. Choose model hyperparameters\n",
+    "\n",
+    "An important point is that *a class of model is not the same as an instance of a model*.\n",
+    "\n",
+    "Once we have decided on our model class, there are still some options open to us.\n",
+    "Depending on the model class we are working with, we might need to answer one or more questions like the following:\n",
+    "\n",
+    "- Would we like to fit for the offset (i.e., *y*-intercept)?\n",
+    "- Would we like the model to be normalized?\n",
+    "- Would we like to preprocess our features to add model flexibility?\n",
+    "- What degree of regularization would we like to use in our model?\n",
+    "- How many model components would we like to use?\n",
+    "\n",
+    "These are examples of the important choices that must be made *once the model class is selected*.\n",
+    "These choices are often represented as *hyperparameters*, or parameters that must be set before the model is fit to data.\n",
+    "In Scikit-Learn, hyperparameters are chosen by passing values at model instantiation.\n",
+    "We will explore how you can quantitatively motivate the choice of hyperparameters in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb).\n",
+    "\n",
+    "For our linear regression example, we can instantiate the ``LinearRegression`` class and specify that we would like to fit the intercept using the ``fit_intercept`` hyperparameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LinearRegression(fit_intercept=True)\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Keep in mind that when the model is instantiated, the only action is the storing of these hyperparameter values.\n",
+    "In particular, we have not yet applied the model to any data: the Scikit-Learn API makes very clear the distinction between *choice of model* and *application of model to data*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### 3. Arrange data into a features matrix and target vector\n",
+    "\n",
+    "Previously we detailed the Scikit-Learn data representation, which requires a two-dimensional features matrix and a one-dimensional target array.\n",
+    "Here our target variable ``y`` is already in the correct form (a length-``n_samples`` array), but we need to massage the data ``x`` to make it a matrix of size ``[n_samples, n_features]``.\n",
+    "In this case, this amounts to a simple reshaping of the one-dimensional array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50, 1)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = x[:, np.newaxis]\n",
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### 4. Fit the model to your data\n",
+    "\n",
+    "Now it is time to apply our model to data.\n",
+    "This can be done with the ``fit()`` method of the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This ``fit()`` command causes a number of model-dependent internal computations to take place, and the results of these computations are stored in model-specific attributes that the user can explore.\n",
+    "In Scikit-Learn, by convention all model parameters that were learned during the ``fit()`` process have trailing underscores; for example in this linear model, we have the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.9776566])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.90331072553111635"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "These two parameters represent the slope and intercept of the simple linear fit to the data.\n",
+    "Comparing to the data definition, we see that they are very close to the input slope of 2 and intercept of -1.\n",
+    "\n",
+    "One question that frequently comes up regards the uncertainty in such internal model parameters.\n",
+    "In general, Scikit-Learn does not provide tools to draw conclusions from internal model parameters themselves: interpreting model parameters is much more a *statistical modeling* question than a *machine learning* question.\n",
+    "Machine learning rather focuses on what the model *predicts*.\n",
+    "If you would like to dive into the meaning of fit parameters within the model, other tools are available, including the [Statsmodels Python package](http://statsmodels.sourceforge.net/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### 5. Predict labels for unknown data\n",
+    "\n",
+    "Once the model is trained, the main task of supervised machine learning is to evaluate it based on what it says about new data that was not part of the training set.\n",
+    "In Scikit-Learn, this can be done using the ``predict()`` method.\n",
+    "For the sake of this example, our \"new data\" will be a grid of *x* values, and we will ask what *y* values the model predicts:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "xfit = np.linspace(-1, 11)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "As before, we need to coerce these *x* values into a ``[n_samples, n_features]`` features matrix, after which we can feed it to the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "Xfit = xfit[:, np.newaxis]\n",
+    "yfit = model.predict(Xfit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Finally, let's visualize the results by plotting first the raw data, and then this model fit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cLiMwwMzc6YMZN7QHgQHmS34feveuAH5sJ+rdu7LTfL/87Wd/ofZ+GHEqkP/whz/w/PPP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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c4d9b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x, y)\n",
+    "plt.plot(xfit, yfit);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Typically the efficacy of the model is evaluated by comparing its results to some known baseline, as we will see in the next example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Supervised learning example: Iris classification\n",
+    "\n",
+    "Let's take a look at another example of this process, using the Iris dataset we discussed earlier.\n",
+    "Our question will be this: given a model trained on a portion of the Iris data, how well can we predict the remaining labels?\n",
+    "\n",
+    "For this task, we will use an extremely simple generative model known as Gaussian naive Bayes, which proceeds by assuming each class is drawn from an axis-aligned Gaussian distribution (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) for more details).\n",
+    "Because it is so fast and has no hyperparameters to choose, Gaussian naive Bayes is often a good model to use as a baseline classification, before exploring whether improvements can be found through more sophisticated models.\n",
+    "\n",
+    "We would like to evaluate the model on data it has not seen before, and so we will split the data into a *training set* and a *testing set*.\n",
+    "This could be done by hand, but it is more convenient to use the ``train_test_split`` utility function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.cross_validation import train_test_split\n",
+    "Xtrain, Xtest, ytrain, ytest = train_test_split(X_iris, y_iris,\n",
+    "                                                random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With the data arranged, we can follow our recipe to predict the labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB # 1. choose model class\n",
+    "model = GaussianNB()                       # 2. instantiate model\n",
+    "model.fit(Xtrain, ytrain)                  # 3. fit model to data\n",
+    "y_model = model.predict(Xtest)             # 4. predict on new data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Finally, we can use the ``accuracy_score`` utility to see the fraction of predicted labels that match their true value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.97368421052631582"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "accuracy_score(ytest, y_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With an accuracy topping 97%, we see that even this very naive classification algorithm is effective for this particular dataset!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Unsupervised learning example: Iris dimensionality\n",
+    "\n",
+    "As an example of an unsupervised learning problem, let's take a look at reducing the dimensionality of the Iris data so as to more easily visualize it.\n",
+    "Recall that the Iris data is four dimensional: there are four features recorded for each sample.\n",
+    "\n",
+    "The task of dimensionality reduction is to ask whether there is a suitable lower-dimensional representation that retains the essential features of the data.\n",
+    "Often dimensionality reduction is used as an aid to visualizing data: after all, it is much easier to plot data in two dimensions than in four dimensions or higher!\n",
+    "\n",
+    "Here we will use principal component analysis (PCA; see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)), which is a fast linear dimensionality reduction technique.\n",
+    "We will ask the model to return two components—that is, a two-dimensional representation of the data.\n",
+    "\n",
+    "Following the sequence of steps outlined earlier, we have:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA  # 1. Choose the model class\n",
+    "model = PCA(n_components=2)            # 2. Instantiate the model with hyperparameters\n",
+    "model.fit(X_iris)                      # 3. Fit to data. Notice y is not specified!\n",
+    "X_2D = model.transform(X_iris)         # 4. Transform the data to two dimensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now let's plot the results. A quick way to do this is to insert the results into the original Iris ``DataFrame``, and use Seaborn's ``lmplot`` to show the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wa3u/dhBgQsq5nW53WGBDez/2Xs+af+RumKCczNxD8lZ5QW+ohFKpgNbPGyGBLTCodwiG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\n0CcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c6676d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "iris['PCA1'] = X_2D[:, 0]\n",
+    "iris['PCA2'] = X_2D[:, 1]\n",
+    "sns.lmplot(\"PCA1\", \"PCA2\", hue='species', data=iris, fit_reg=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We see that in the two-dimensional representation, the species are fairly well separated, even though the PCA algorithm had no knowledge of the species labels!\n",
+    "This indicates to us that a relatively straightforward classification will probably be effective on the dataset, as we saw before."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Unsupervised learning: Iris clustering\n",
+    "\n",
+    "Let's next look at applying clustering to the Iris data.\n",
+    "A clustering algorithm attempts to find distinct groups of data without reference to any labels.\n",
+    "Here we will use a powerful clustering method called a Gaussian mixture model (GMM), discussed in more detail in [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb).\n",
+    "A GMM attempts to model the data as a collection of Gaussian blobs.\n",
+    "\n",
+    "We can fit the Gaussian mixture model as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.mixture import GMM      # 1. Choose the model class\n",
+    "model = GMM(n_components=3,\n",
+    "            covariance_type='full')  # 2. Instantiate the model with hyperparameters\n",
+    "model.fit(X_iris)                    # 3. Fit to data. Notice y is not specified!\n",
+    "y_gmm = model.predict(X_iris)        # 4. Determine cluster labels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "As before, we will add the cluster label to the Iris ``DataFrame`` and use Seaborn to plot the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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elWKRGhdPwtSraz3m6NtLuQf2O9cd+vZq1j0G8/uD0OL3BNCAAQO0YcMGXXnl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\n6tru5al3TjBOAwPc8ZgAuum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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11a932828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "iris['cluster'] = y_gmm\n",
+    "sns.lmplot(\"PCA1\", \"PCA2\", data=iris, hue='species',\n",
+    "           col='cluster', fit_reg=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "By splitting the data by cluster number, we see exactly how well the GMM algorithm has recovered the underlying label: the *setosa* species is separated perfectly within cluster 0, while there remains a small amount of mixing between *versicolor* and *virginica*.\n",
+    "This means that even without an expert to tell us the species labels of the individual flowers, the measurements of these flowers are distinct enough that we could *automatically* identify the presence of these different groups of species with a simple clustering algorithm!\n",
+    "This sort of algorithm might further give experts in the field clues as to the relationship between the samples they are observing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Application: Exploring Hand-written Digits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "To demonstrate these principles on a more interesting problem, let's consider one piece of the optical character recognition problem: the identification of hand-written digits.\n",
+    "In the wild, this problem involves both locating and identifying characters in an image. Here we'll take a shortcut and use Scikit-Learn's set of pre-formatted digits, which is built into the library."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Loading and visualizing the digits data\n",
+    "\n",
+    "We'll use Scikit-Learn's data access interface and take a look at this data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1797, 8, 8)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_digits\n",
+    "digits = load_digits()\n",
+    "digits.images.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The images data is a three-dimensional array: 1,797 samples each consisting of an 8 × 8 grid of pixels.\n",
+    "Let's visualize the first hundred of these:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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uXboY2qKjow1tc+fONbS5XC7L4pDGmZQwBcj9r5tA09Bkg7riUCXVSPuiSslL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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11acadd68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, axes = plt.subplots(10, 10, figsize=(8, 8),\n",
+    "                         subplot_kw={'xticks':[], 'yticks':[]},\n",
+    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
+    "\n",
+    "for i, ax in enumerate(axes.flat):\n",
+    "    ax.imshow(digits.images[i], cmap='binary', interpolation='nearest')\n",
+    "    ax.text(0.05, 0.05, str(digits.target[i]),\n",
+    "            transform=ax.transAxes, color='green')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "In order to work with this data within Scikit-Learn, we need a two-dimensional, ``[n_samples, n_features]`` representation.\n",
+    "We can accomplish this by treating each pixel in the image as a feature: that is, by flattening out the pixel arrays so that we have a length-64 array of pixel values representing each digit.\n",
+    "Additionally, we need the target array, which gives the previously determined label for each digit.\n",
+    "These two quantities are built into the digits dataset under the ``data`` and ``target`` attributes, respectively:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1797, 64)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = digits.data\n",
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1797,)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = digits.target\n",
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We see here that there are 1,797 samples and 64 features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Unsupervised learning: Dimensionality reduction\n",
+    "\n",
+    "We'd like to visualize our points within the 64-dimensional parameter space, but it's difficult to effectively visualize points in such a high-dimensional space.\n",
+    "Instead we'll reduce the dimensions to 2, using an unsupervised method.\n",
+    "Here, we'll make use of a manifold learning algorithm called *Isomap* (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)), and transform the data to two dimensions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1797, 2)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import Isomap\n",
+    "iso = Isomap(n_components=2)\n",
+    "iso.fit(digits.data)\n",
+    "data_projected = iso.transform(digits.data)\n",
+    "data_projected.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We see that the projected data is now two-dimensional.\n",
+    "Let's plot this data to see if we can learn anything from its structure:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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J83zHi8Pt/bHzz3x+0ueGJxx9qD/RP5xEYWh1nIHkIMUcfWFyIc53kkiFOI45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MnVcyQ6SWzie3iFVzFuEzq+FPG7EYnV6sFtiHHqkV+wtd8CPlxYqNf+FUZXVze/+7v/bt5o+b1g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\npJZlWZY1Dv8fbutNhc9gqb8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11acadb00>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data_projected[:, 0], data_projected[:, 1], c=digits.target,\n",
+    "            edgecolor='none', alpha=0.5,\n",
+    "            cmap=plt.cm.get_cmap('spectral', 10))\n",
+    "plt.colorbar(label='digit label', ticks=range(10))\n",
+    "plt.clim(-0.5, 9.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This plot gives us some good intuition into how well various numbers are separated in the larger 64-dimensional space. For example, zeros (in black) and ones (in purple) have very little overlap in parameter space.\n",
+    "Intuitively, this makes sense: a zero is empty in the middle of the image, while a one will generally have ink in the middle.\n",
+    "On the other hand, there seems to be a more or less continuous spectrum between ones and fours: we can understand this by realizing that some people draw ones with \"hats\" on them, which cause them to look similar to fours.\n",
+    "\n",
+    "Overall, however, the different groups appear to be fairly well separated in the parameter space: this tells us that even a very straightforward supervised classification algorithm should perform suitably on this data.\n",
+    "Let's give it a try."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Classification on digits\n",
+    "\n",
+    "Let's apply a classification algorithm to the digits.\n",
+    "As with the Iris data previously, we will split the data into a training and testing set, and fit a Gaussian naive Bayes model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "model = GaussianNB()\n",
+    "model.fit(Xtrain, ytrain)\n",
+    "y_model = model.predict(Xtest)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now that we have predicted our model, we can gauge its accuracy by comparing the true values of the test set to the predictions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.83333333333333337"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "accuracy_score(ytest, y_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With even this extremely simple model, we find about 80% accuracy for classification of the digits!\n",
+    "However, this single number doesn't tell us *where* we've gone wrong—one nice way to do this is to use the *confusion matrix*, which we can compute with Scikit-Learn and plot with Seaborn:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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a1qB4uaKMWzKSj6f1w9HJkXFLRvJak2rk8737C6i01DT2bT5A0bLP5Xr+vVQ+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\nLzLMGdy6nsSyT5eSeieVln1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oXbv2Px4/OjqaadOmAVC7du2s78lseL/99htXrlyhS5cu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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11f230ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "mat = confusion_matrix(ytest, y_model)\n",
+    "\n",
+    "sns.heatmap(mat, square=True, annot=True, cbar=False)\n",
+    "plt.xlabel('predicted value')\n",
+    "plt.ylabel('true value');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This shows us where the mis-labeled points tend to be: for example, a large number of twos here are mis-classified as either ones or eights.\n",
+    "Another way to gain intuition into the characteristics of the model is to plot the inputs again, with their predicted labels.\n",
+    "We'll use green for correct labels, and red for incorrect labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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x0a8+sjs0JX/sZ+01zLr6OgBAdUM1AKCmvgbBjmBrorLAor8uQnR4NNKT0u0O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\nxpcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x121637240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(10, 10, figsize=(8, 8),\n",
+    "                         subplot_kw={'xticks':[], 'yticks':[]},\n",
+    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
+    "\n",
+    "test_images = Xtest.reshape(-1, 8, 8)\n",
+    "\n",
+    "for i, ax in enumerate(axes.flat):\n",
+    "    ax.imshow(test_images[i], cmap='binary', interpolation='nearest')\n",
+    "    ax.text(0.05, 0.05, str(y_model[i]),\n",
+    "            transform=ax.transAxes,\n",
+    "            color='green' if (ytest[i] == y_model[i]) else 'red')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Examining this subset of the data, we can gain insight regarding where the algorithm might be not performing optimally.\n",
+    "To go beyond our 80% classification rate, we might move to a more sophisticated algorithm such as support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), random forests (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)) or another classification approach."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "In this section we have covered the essential features of the Scikit-Learn data representation, and the estimator API.\n",
+    "Regardless of the type of estimator, the same import/instantiate/fit/predict pattern holds.\n",
+    "Armed with this information about the estimator API, you can explore the Scikit-Learn documentation and begin trying out various models on your data.\n",
+    "\n",
+    "In the next section, we will explore perhaps the most important topic in machine learning: how to select and validate your model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.03-Hyperparameters-and-Model-Validation.ipynb b/notebooks_v1/05.03-Hyperparameters-and-Model-Validation.ipynb
new file mode 100644
index 000000000..3edcada26
--- /dev/null
+++ b/notebooks_v1/05.03-Hyperparameters-and-Model-Validation.ipynb
@@ -0,0 +1,1176 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) | [Contents](Index.ipynb) | [Feature Engineering](05.04-Feature-Engineering.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hyperparameters and Model Validation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "In the previous section, we saw the basic recipe for applying a supervised machine learning model:\n",
+    "\n",
+    "1. Choose a class of model\n",
+    "2. Choose model hyperparameters\n",
+    "3. Fit the model to the training data\n",
+    "4. Use the model to predict labels for new data\n",
+    "\n",
+    "The first two pieces of this—the choice of model and choice of hyperparameters—are perhaps the most important part of using these tools and techniques effectively.\n",
+    "In order to make an informed choice, we need a way to *validate* that our model and our hyperparameters are a good fit to the data.\n",
+    "While this may sound simple, there are some pitfalls that you must avoid to do this effectively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Thinking about Model Validation\n",
+    "\n",
+    "In principle, model validation is very simple: after choosing a model and its hyperparameters, we can estimate how effective it is by applying it to some of the training data and comparing the prediction to the known value.\n",
+    "\n",
+    "The following sections first show a naive approach to model validation and why it\n",
+    "fails, before exploring the use of holdout sets and cross-validation for more robust\n",
+    "model evaluation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Model validation the wrong way\n",
+    "\n",
+    "Let's demonstrate the naive approach to validation using the Iris data, which we saw in the previous section.\n",
+    "We will start by loading the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "iris = load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Next we choose a model and hyperparameters. Here we'll use a *k*-neighbors classifier with ``n_neighbors=1``.\n",
+    "This is a very simple and intuitive model that says \"the label of an unknown point is the same as the label of its closest training point:\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "model = KNeighborsClassifier(n_neighbors=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Then we train the model, and use it to predict labels for data we already know:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "model.fit(X, y)\n",
+    "y_model = model.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Finally, we compute the fraction of correctly labeled points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "accuracy_score(y, y_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We see an accuracy score of 1.0, which indicates that 100% of points were correctly labeled by our model!\n",
+    "But is this truly measuring the expected accuracy? Have we really come upon a model that we expect to be correct 100% of the time?\n",
+    "\n",
+    "As you may have gathered, the answer is no.\n",
+    "In fact, this approach contains a fundamental flaw: *it trains and evaluates the model on the same data*.\n",
+    "Furthermore, the nearest neighbor model is an *instance-based* estimator that simply stores the training data, and predicts labels by comparing new data to these stored points: except in contrived cases, it will get 100% accuracy *every time!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Model validation the right way: Holdout sets\n",
+    "\n",
+    "So what can be done?\n",
+    "A better sense of a model's performance can be found using what's known as a *holdout set*: that is, we hold back some subset of the data from the training of the model, and then use this holdout set to check the model performance.\n",
+    "This splitting can be done using the ``train_test_split`` utility in Scikit-Learn:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.90666666666666662"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cross_validation import train_test_split\n",
+    "# split the data with 50% in each set\n",
+    "X1, X2, y1, y2 = train_test_split(X, y, random_state=0,\n",
+    "                                  train_size=0.5)\n",
+    "\n",
+    "# fit the model on one set of data\n",
+    "model.fit(X1, y1)\n",
+    "\n",
+    "# evaluate the model on the second set of data\n",
+    "y2_model = model.predict(X2)\n",
+    "accuracy_score(y2, y2_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We see here a more reasonable result: the nearest-neighbor classifier is about 90% accurate on this hold-out set.\n",
+    "The hold-out set is similar to unknown data, because the model has not \"seen\" it before."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Model validation via cross-validation\n",
+    "\n",
+    "One disadvantage of using a holdout set for model validation is that we have lost a portion of our data to the model training.\n",
+    "In the above case, half the dataset does not contribute to the training of the model!\n",
+    "This is not optimal, and can cause problems – especially if the initial set of training data is small.\n",
+    "\n",
+    "One way to address this is to use *cross-validation*; that is, to do a sequence of fits where each subset of the data is used both as a training set and as a validation set.\n",
+    "Visually, it might look something like this:\n",
+    "\n",
+    "![](figures/05.03-2-fold-CV.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#2-Fold-Cross-Validation)\n",
+    "\n",
+    "Here we do two validation trials, alternately using each half of the data as a holdout set.\n",
+    "Using the split data from before, we could implement it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.95999999999999996, 0.90666666666666662)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y2_model = model.fit(X1, y1).predict(X2)\n",
+    "y1_model = model.fit(X2, y2).predict(X1)\n",
+    "accuracy_score(y1, y1_model), accuracy_score(y2, y2_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "What comes out are two accuracy scores, which we could combine (by, say, taking the mean) to get a better measure of the global model performance.\n",
+    "This particular form of cross-validation is a *two-fold cross-validation*—that is, one in which we have split the data into two sets and used each in turn as a validation set.\n",
+    "\n",
+    "We could expand on this idea to use even more trials, and more folds in the data—for example, here is a visual depiction of five-fold cross-validation:\n",
+    "\n",
+    "![](figures/05.03-5-fold-CV.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#5-Fold-Cross-Validation)\n",
+    "\n",
+    "Here we split the data into five groups, and use each of them in turn to evaluate the model fit on the other 4/5 of the data.\n",
+    "This would be rather tedious to do by hand, and so we can use Scikit-Learn's ``cross_val_score`` convenience routine to do it succinctly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.96666667,  0.96666667,  0.93333333,  0.93333333,  1.        ])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cross_validation import cross_val_score\n",
+    "cross_val_score(model, X, y, cv=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Repeating the validation across different subsets of the data gives us an even better idea of the performance of the algorithm.\n",
+    "\n",
+    "Scikit-Learn implements a number of useful cross-validation schemes that are useful in particular situations; these are implemented via iterators in the ``cross_validation`` module.\n",
+    "For example, we might wish to go to the extreme case in which our number of folds is equal to the number of data points: that is, we train on all points but one in each trial.\n",
+    "This type of cross-validation is known as *leave-one-out* cross validation, and can be used as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
+       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
+       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
+       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
+       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
+       "        1.,  1.,  1.,  1.,  1.,  0.,  1.,  0.,  1.,  1.,  1.,  1.,  1.,\n",
+       "        1.,  1.,  1.,  1.,  1.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
+       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
+       "        1.,  1.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
+       "        1.,  1.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
+       "        1.,  1.,  1.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
+       "        1.,  1.,  1.,  1.,  1.,  1.,  1.])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cross_validation import LeaveOneOut\n",
+    "scores = cross_val_score(model, X, y, cv=LeaveOneOut(len(X)))\n",
+    "scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Because we have 150 samples, the leave one out cross-validation yields scores for 150 trials, and the score indicates either successful (1.0) or unsuccessful (0.0) prediction.\n",
+    "Taking the mean of these gives an estimate of the error rate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.95999999999999996"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Other cross-validation schemes can be used similarly.\n",
+    "For a description of what is available in Scikit-Learn, use IPython to explore the ``sklearn.cross_validation`` submodule, or take a look at Scikit-Learn's online [cross-validation documentation](http://scikit-learn.org/stable/modules/cross_validation.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Selecting the Best Model\n",
+    "\n",
+    "Now that we've seen the basics of validation and cross-validation, we will go into a litte more depth regarding model selection and selection of hyperparameters.\n",
+    "These issues are some of the most important aspects of the practice of machine learning, and I find that this information is often glossed over in introductory machine learning tutorials.\n",
+    "\n",
+    "Of core importance is the following question: *if our estimator is underperforming, how should we move forward?*\n",
+    "There are several possible answers:\n",
+    "\n",
+    "- Use a more complicated/more flexible model\n",
+    "- Use a less complicated/less flexible model\n",
+    "- Gather more training samples\n",
+    "- Gather more data to add features to each sample\n",
+    "\n",
+    "The answer to this question is often counter-intuitive.\n",
+    "In particular, sometimes using a more complicated model will give worse results, and adding more training samples may not improve your results!\n",
+    "The ability to determine what steps will improve your model is what separates the successful machine learning practitioners from the unsuccessful."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### The Bias-variance trade-off\n",
+    "\n",
+    "Fundamentally, the question of \"the best model\" is about finding a sweet spot in the tradeoff between *bias* and *variance*.\n",
+    "Consider the following figure, which presents two regression fits to the same dataset:\n",
+    "\n",
+    "![](figures/05.03-bias-variance.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff)\n",
+    "\n",
+    "It is clear that neither of these models is a particularly good fit to the data, but they fail in different ways.\n",
+    "\n",
+    "The model on the left attempts to find a straight-line fit through the data.\n",
+    "Because the data are intrinsically more complicated than a straight line, the straight-line model will never be able to describe this dataset well.\n",
+    "Such a model is said to *underfit* the data: that is, it does not have enough model flexibility to suitably account for all the features in the data; another way of saying this is that the model has high *bias*.\n",
+    "\n",
+    "The model on the right attempts to fit a high-order polynomial through the data.\n",
+    "Here the model fit has enough flexibility to nearly perfectly account for the fine features in the data, but even though it very accurately describes the training data, its precise form seems to be more reflective of the particular noise properties of the data rather than the intrinsic properties of whatever process generated that data.\n",
+    "Such a model is said to *overfit* the data: that is, it has so much model flexibility that the model ends up accounting for random errors as well as the underlying data distribution; another way of saying this is that the model has high *variance*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "To look at this in another light, consider what happens if we use these two models to predict the y-value for some new data.\n",
+    "In the following diagrams, the red/lighter points indicate data that is omitted from the training set:\n",
+    "\n",
+    "![](figures/05.03-bias-variance-2.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff-Metrics)\n",
+    "\n",
+    "The score here is the $R^2$ score, or [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination), which measures how well a model performs relative to a simple mean of the target values. $R^2=1$ indicates a perfect match, $R^2=0$ indicates the model does no better than simply taking the mean of the data, and negative values mean even worse models.\n",
+    "From the scores associated with these two models, we can make an observation that holds more generally:\n",
+    "\n",
+    "- For high-bias models, the performance of the model on the validation set is similar to the performance on the training set.\n",
+    "- For high-variance models, the performance of the model on the validation set is far worse than the performance on the training set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "If we imagine that we have some ability to tune the model complexity, we would expect the training score and validation score to behave as illustrated in the following figure:\n",
+    "\n",
+    "![](figures/05.03-validation-curve.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Validation-Curve)\n",
+    "\n",
+    "The diagram shown here is often called a *validation curve*, and we see the following essential features:\n",
+    "\n",
+    "- The training score is everywhere higher than the validation score. This is generally the case: the model will be a better fit to data it has seen than to data it has not seen.\n",
+    "- For very low model complexity (a high-bias model), the training data is under-fit, which means that the model is a poor predictor both for the training data and for any previously unseen data.\n",
+    "- For very high model complexity (a high-variance model), the training data is over-fit, which means that the model predicts the training data very well, but fails for any previously unseen data.\n",
+    "- For some intermediate value, the validation curve has a maximum. This level of complexity indicates a suitable trade-off between bias and variance.\n",
+    "\n",
+    "The means of tuning the model complexity varies from model to model; when we discuss individual models in depth in later sections, we will see how each model allows for such tuning."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Validation curves in Scikit-Learn\n",
+    "\n",
+    "Let's look at an example of using cross-validation to compute the validation curve for a class of models.\n",
+    "Here we will use a *polynomial regression* model: this is a generalized linear model in which the degree of the polynomial is a tunable parameter.\n",
+    "For example, a degree-1 polynomial fits a straight line to the data; for model parameters $a$ and $b$:\n",
+    "\n",
+    "$$\n",
+    "y = ax + b\n",
+    "$$\n",
+    "\n",
+    "A degree-3 polynomial fits a cubic curve to the data; for model parameters $a, b, c, d$:\n",
+    "\n",
+    "$$\n",
+    "y = ax^3 + bx^2 + cx + d\n",
+    "$$\n",
+    "\n",
+    "We can generalize this to any number of polynomial features.\n",
+    "In Scikit-Learn, we can implement this with a simple linear regression combined with the polynomial preprocessor.\n",
+    "We will use a *pipeline* to string these operations together (we will discuss polynomial features and pipelines more fully in [Feature Engineering](05.04-Feature-Engineering.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "def PolynomialRegression(degree=2, **kwargs):\n",
+    "    return make_pipeline(PolynomialFeatures(degree),\n",
+    "                         LinearRegression(**kwargs))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now let's create some data to which we will fit our model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def make_data(N, err=1.0, rseed=1):\n",
+    "    # randomly sample the data\n",
+    "    rng = np.random.RandomState(rseed)\n",
+    "    X = rng.rand(N, 1) ** 2\n",
+    "    y = 10 - 1. / (X.ravel() + 0.1)\n",
+    "    if err > 0:\n",
+    "        y += err * rng.randn(N)\n",
+    "    return X, y\n",
+    "\n",
+    "X, y = make_data(40)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can now visualize our data, along with polynomial fits of several degrees:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
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4vA6cPhdOrxOdGRpaW3F6/ducPieOM753ep3YvU48Z1wbvRQGrR6zrrM33nnK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\nsKmpQPsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d3faa90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn; seaborn.set()  # plot formatting\n",
+    "\n",
+    "X_test = np.linspace(-0.1, 1.1, 500)[:, None]\n",
+    "\n",
+    "plt.scatter(X.ravel(), y, color='black')\n",
+    "axis = plt.axis()\n",
+    "for degree in [1, 3, 5]:\n",
+    "    y_test = PolynomialRegression(degree).fit(X, y).predict(X_test)\n",
+    "    plt.plot(X_test.ravel(), y_test, label='degree={0}'.format(degree))\n",
+    "plt.xlim(-0.1, 1.0)\n",
+    "plt.ylim(-2, 12)\n",
+    "plt.legend(loc='best');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The knob controlling model complexity in this case is the degree of the polynomial, which can be any non-negative integer.\n",
+    "A useful question to answer is this: what degree of polynomial provides a suitable trade-off between bias (under-fitting) and variance (over-fitting)?\n",
+    "\n",
+    "We can make progress in this by visualizing the validation curve for this particular data and model; this can be done straightforwardly using the ``validation_curve`` convenience routine provided by Scikit-Learn.\n",
+    "Given a model, data, parameter name, and a range to explore, this function will automatically compute both the training score and validation score across the range:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NdiGAkhKp6RHxLheMn6+EO7sdXH37h644IopKXDueQiFmg726Gqivb3rVOcO3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\ni4iIWMgPEUMpSKY0y3EAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118d05f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.learning_curve import validation_curve\n",
+    "degree = np.arange(0, 21)\n",
+    "train_score, val_score = validation_curve(PolynomialRegression(), X, y,\n",
+    "                                          'polynomialfeatures__degree', degree, cv=7)\n",
+    "\n",
+    "plt.plot(degree, np.median(train_score, 1), color='blue', label='training score')\n",
+    "plt.plot(degree, np.median(val_score, 1), color='red', label='validation score')\n",
+    "plt.legend(loc='best')\n",
+    "plt.ylim(0, 1)\n",
+    "plt.xlabel('degree')\n",
+    "plt.ylabel('score');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This shows precisely the qualitative behavior we expect: the training score is everywhere higher than the validation score; the training score is monotonically improving with increased model complexity; and the validation score reaches a maximum before dropping off as the model becomes over-fit.\n",
+    "\n",
+    "From the validation curve, we can read-off that the optimal trade-off between bias and variance is found for a third-order polynomial; we can compute and display this fit over the original data as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YOnUq/vSnP3n8OpMp2pu3CzlKjlNtQys27y1CbJQB35s/ClHhesVq8QS/pzzD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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d3ecdd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X.ravel(), y)\n",
+    "lim = plt.axis()\n",
+    "y_test = PolynomialRegression(3).fit(X, y).predict(X_test)\n",
+    "plt.plot(X_test.ravel(), y_test);\n",
+    "plt.axis(lim);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Notice that finding this optimal model did not actually require us to compute the training score, but examining the relationship between the training score and validation score can give us useful insight into the performance of the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Learning Curves\n",
+    "\n",
+    "One important aspect of model complexity is that the optimal model will generally depend on the size of your training data.\n",
+    "For example, let's generate a new dataset with a factor of five more points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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S3XffbWmhvGrt2tnKyoquYR4//oSkTh0+XKBQaHtP93WyLeHYHcSYMAYA3mC6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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11944a278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X2, y2 = make_data(200)\n",
+    "plt.scatter(X2.ravel(), y2);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We will duplicate the preceding code to plot the validation curve for this larger dataset; for reference let's over-plot the previous results as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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aUFofqx+afmZ7OCkUghdecFFYCOefn3xv5t8/Ae++9gxqjWZ6t++USXMTkOHA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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119451f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "degree = np.arange(21)\n",
+    "train_score2, val_score2 = validation_curve(PolynomialRegression(), X2, y2,\n",
+    "                                            'polynomialfeatures__degree', degree, cv=7)\n",
+    "\n",
+    "plt.plot(degree, np.median(train_score2, 1), color='blue', label='training score')\n",
+    "plt.plot(degree, np.median(val_score2, 1), color='red', label='validation score')\n",
+    "plt.plot(degree, np.median(train_score, 1), color='blue', alpha=0.3, linestyle='dashed')\n",
+    "plt.plot(degree, np.median(val_score, 1), color='red', alpha=0.3, linestyle='dashed')\n",
+    "plt.legend(loc='lower center')\n",
+    "plt.ylim(0, 1)\n",
+    "plt.xlabel('degree')\n",
+    "plt.ylabel('score');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The solid lines show the new results, while the fainter dashed lines show the results of the previous smaller dataset.\n",
+    "It is clear from the validation curve that the larger dataset can support a much more complicated model: the peak here is probably around a degree of 6, but even a degree-20 model is not seriously over-fitting the data—the validation and training scores remain very close.\n",
+    "\n",
+    "Thus we see that the behavior of the validation curve has not one but two important inputs: the model complexity and the number of training points.\n",
+    "It is often useful to to explore the behavior of the model as a function of the number of training points, which we can do by using increasingly larger subsets of the data to fit our model.\n",
+    "A plot of the training/validation score with respect to the size of the training set is known as a *learning curve.*\n",
+    "\n",
+    "The general behavior we would expect from a learning curve is this:\n",
+    "\n",
+    "- A model of a given complexity will *overfit* a small dataset: this means the training score will be relatively high, while the validation score will be relatively low.\n",
+    "- A model of a given complexity will *underfit* a large dataset: this means that the training score will decrease, but the validation score will increase.\n",
+    "- A model will never, except by chance, give a better score to the validation set than the training set: this means the curves should keep getting closer together but never cross.\n",
+    "\n",
+    "With these features in mind, we would expect a learning curve to look qualitatively like that shown in the following figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.03-learning-curve.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Learning-Curve)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The notable feature of the learning curve is the convergence to a particular score as the number of training samples grows.\n",
+    "In particular, once you have enough points that a particular model has converged, *adding more training data will not help you!*\n",
+    "The only way to increase model performance in this case is to use another (often more complex) model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Learning curves in Scikit-Learn\n",
+    "\n",
+    "Scikit-Learn offers a convenient utility for computing such learning curves from your models; here we will compute a learning curve for our original dataset with a second-order polynomial model and a ninth-order polynomial:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+vOrO/a4869cr85nPyQqH7HEmTFMyLcmyZJimva727fn9ZmG/ZR+rbLZwTCik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\ncPKYnE9+qb/wa4DRo0cPffjhhxowYIAkKTMzUyt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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1194c3f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.learning_curve import learning_curve\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
+    "\n",
+    "for i, degree in enumerate([2, 9]):\n",
+    "    N, train_lc, val_lc = learning_curve(PolynomialRegression(degree),\n",
+    "                                         X, y, cv=7,\n",
+    "                                         train_sizes=np.linspace(0.3, 1, 25))\n",
+    "\n",
+    "    ax[i].plot(N, np.mean(train_lc, 1), color='blue', label='training score')\n",
+    "    ax[i].plot(N, np.mean(val_lc, 1), color='red', label='validation score')\n",
+    "    ax[i].hlines(np.mean([train_lc[-1], val_lc[-1]]), N[0], N[-1],\n",
+    "                 color='gray', linestyle='dashed')\n",
+    "\n",
+    "    ax[i].set_ylim(0, 1)\n",
+    "    ax[i].set_xlim(N[0], N[-1])\n",
+    "    ax[i].set_xlabel('training size')\n",
+    "    ax[i].set_ylabel('score')\n",
+    "    ax[i].set_title('degree = {0}'.format(degree), size=14)\n",
+    "    ax[i].legend(loc='best')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This is a valuable diagnostic, because it gives us a visual depiction of how our model responds to increasing training data.\n",
+    "In particular, when your learning curve has already converged (i.e., when the training and validation curves are already close to each other) *adding more training data will not significantly improve the fit!*\n",
+    "This situation is seen in the left panel, with the learning curve for the degree-2 model.\n",
+    "\n",
+    "The only way to increase the converged score is to use a different (usually more complicated) model.\n",
+    "We see this in the right panel: by moving to a much more complicated model, we increase the score of convergence (indicated by the dashed line), but at the expense of higher model variance (indicated by the difference between the training and validation scores).\n",
+    "If we were to add even more data points, the learning curve for the more complicated model would eventually converge.\n",
+    "\n",
+    "Plotting a learning curve for your particular choice of model and dataset can help you to make this type of decision about how to move forward in improving your analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Validation in Practice: Grid Search\n",
+    "\n",
+    "The preceding discussion is meant to give you some intuition into the trade-off between bias and variance, and its dependence on model complexity and training set size.\n",
+    "In practice, models generally have more than one knob to turn, and thus plots of validation and learning curves change from lines to multi-dimensional surfaces.\n",
+    "In these cases, such visualizations are difficult and we would rather simply find the particular model that maximizes the validation score.\n",
+    "\n",
+    "Scikit-Learn provides automated tools to do this in the grid search module.\n",
+    "Here is an example of using grid search to find the optimal polynomial model.\n",
+    "We will explore a three-dimensional grid of model features; namely the polynomial degree, the flag telling us whether to fit the intercept, and the flag telling us whether to normalize the problem.\n",
+    "This can be set up using Scikit-Learn's ``GridSearchCV`` meta-estimator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.grid_search import GridSearchCV\n",
+    "\n",
+    "param_grid = {'polynomialfeatures__degree': np.arange(21),\n",
+    "              'linearregression__fit_intercept': [True, False],\n",
+    "              'linearregression__normalize': [True, False]}\n",
+    "\n",
+    "grid = GridSearchCV(PolynomialRegression(), param_grid, cv=7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Notice that like a normal estimator, this has not yet been applied to any data.\n",
+    "Calling the ``fit()`` method will fit the model at each grid point, keeping track of the scores along the way:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grid.fit(X, y);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now that this is fit, we can ask for the best parameters as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'linearregression__fit_intercept': False,\n",
+       " 'linearregression__normalize': True,\n",
+       " 'polynomialfeatures__degree': 4}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Finally, if we wish, we can use the best model and show the fit to our data using code from before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VQRCAG67IgZzNRIiIRMFgJgBArbkTXx5tQlZqHGYVGMQuh4goYo0qmFtbW7Fo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\nzERERBLCYCYiIpKQ/w/RYVdtHvVc2QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118bc2c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = grid.best_estimator_\n",
+    "\n",
+    "plt.scatter(X.ravel(), y)\n",
+    "lim = plt.axis()\n",
+    "y_test = model.fit(X, y).predict(X_test)\n",
+    "plt.plot(X_test.ravel(), y_test, hold=True);\n",
+    "plt.axis(lim);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The grid search provides many more options, including the ability to specify a custom scoring function, to parallelize the computations, to do randomized searches, and more.\n",
+    "For information, see the examples in [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) and [Feature Engineering: Working with Images](05.14-Image-Features.ipynb), or refer to Scikit-Learn's [grid search documentation](http://Scikit-Learn.org/stable/modules/grid_search.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this section, we have begun to explore the concept of model validation and hyperparameter optimization, focusing on intuitive aspects of the bias–variance trade-off and how it comes into play when fitting models to data.\n",
+    "In particular, we found that the use of a validation set or cross-validation approach is *vital* when tuning parameters in order to avoid over-fitting for more complex/flexible models.\n",
+    "\n",
+    "In later sections, we will discuss the details of particularly useful models, and throughout will talk about what tuning is available for these models and how these free parameters affect model complexity.\n",
+    "Keep the lessons of this section in mind as you read on and learn about these machine learning approaches!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) | [Contents](Index.ipynb) | [Feature Engineering](05.04-Feature-Engineering.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.04-Feature-Engineering.ipynb b/notebooks_v1/05.04-Feature-Engineering.ipynb
new file mode 100644
index 000000000..7315fb277
--- /dev/null
+++ b/notebooks_v1/05.04-Feature-Engineering.ipynb
@@ -0,0 +1,911 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) | [Contents](Index.ipynb) | [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.04-Feature-Engineering.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The previous sections outline the fundamental ideas of machine learning, but all of the examples assume that you have numerical data in a tidy, ``[n_samples, n_features]`` format.\n",
+    "In the real world, data rarely comes in such a form.\n",
+    "With this in mind, one of the more important steps in using machine learning in practice is *feature engineering*: that is, taking whatever information you have about your problem and turning it into numbers that you can use to build your feature matrix.\n",
+    "\n",
+    "In this section, we will cover a few common examples of feature engineering tasks: features for representing *categorical data*, features for representing *text*, and features for representing *images*.\n",
+    "Additionally, we will discuss *derived features* for increasing model complexity and *imputation* of missing data.\n",
+    "Often this process is known as *vectorization*, as it involves converting arbitrary data into well-behaved vectors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Categorical Features\n",
+    "\n",
+    "One common type of non-numerical data is *categorical* data.\n",
+    "For example, imagine you are exploring some data on housing prices, and along with numerical features like \"price\" and \"rooms\", you also have \"neighborhood\" information.\n",
+    "For example, your data might look something like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "data = [\n",
+    "    {'price': 850000, 'rooms': 4, 'neighborhood': 'Queen Anne'},\n",
+    "    {'price': 700000, 'rooms': 3, 'neighborhood': 'Fremont'},\n",
+    "    {'price': 650000, 'rooms': 3, 'neighborhood': 'Wallingford'},\n",
+    "    {'price': 600000, 'rooms': 2, 'neighborhood': 'Fremont'}\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "You might be tempted to encode this data with a straightforward numerical mapping:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "{'Queen Anne': 1, 'Fremont': 2, 'Wallingford': 3};"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "It turns out that this is not generally a useful approach in Scikit-Learn: the package's models make the fundamental assumption that numerical features reflect algebraic quantities.\n",
+    "Thus such a mapping would imply, for example, that *Queen Anne < Fremont < Wallingford*, or even that *Wallingford - Queen Anne = Fremont*, which (niche demographic jokes aside) does not make much sense.\n",
+    "\n",
+    "In this case, one proven technique is to use *one-hot encoding*, which effectively creates extra columns indicating the presence or absence of a category with a value of 1 or 0, respectively.\n",
+    "When your data comes as a list of dictionaries, Scikit-Learn's ``DictVectorizer`` will do this for you:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[     0,      1,      0, 850000,      4],\n",
+       "       [     1,      0,      0, 700000,      3],\n",
+       "       [     0,      0,      1, 650000,      3],\n",
+       "       [     1,      0,      0, 600000,      2]], dtype=int64)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction import DictVectorizer\n",
+    "vec = DictVectorizer(sparse=False, dtype=int)\n",
+    "vec.fit_transform(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Notice that the 'neighborhood' column has been expanded into three separate columns, representing the three neighborhood labels, and that each row has a 1 in the column associated with its neighborhood.\n",
+    "With these categorical features thus encoded, you can proceed as normal with fitting a Scikit-Learn model.\n",
+    "\n",
+    "To see the meaning of each column, you can inspect the feature names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['neighborhood=Fremont',\n",
+       " 'neighborhood=Queen Anne',\n",
+       " 'neighborhood=Wallingford',\n",
+       " 'price',\n",
+       " 'rooms']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vec.get_feature_names()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "There is one clear disadvantage of this approach: if your category has many possible values, this can *greatly* increase the size of your dataset.\n",
+    "However, because the encoded data contains mostly zeros, a sparse output can be a very efficient solution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<4x5 sparse matrix of type '<class 'numpy.int64'>'\n",
+       "\twith 12 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vec = DictVectorizer(sparse=True, dtype=int)\n",
+    "vec.fit_transform(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Many (though not yet all) of the Scikit-Learn estimators accept such sparse inputs when fitting and evaluating models. ``sklearn.preprocessing.OneHotEncoder`` and ``sklearn.feature_extraction.FeatureHasher`` are two additional tools that Scikit-Learn includes to support this type of encoding."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Text Features\n",
+    "\n",
+    "Another common need in feature engineering is to convert text to a set of representative numerical values.\n",
+    "For example, most automatic mining of social media data relies on some form of encoding the text as numbers.\n",
+    "One of the simplest methods of encoding data is by *word counts*: you take each snippet of text, count the occurrences of each word within it, and put the results in a table.\n",
+    "\n",
+    "For example, consider the following set of three phrases:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "sample = ['problem of evil',\n",
+    "          'evil queen',\n",
+    "          'horizon problem']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "For a vectorization of this data based on word count, we could construct a column representing the word \"problem,\" the word \"evil,\" the word \"horizon,\" and so on.\n",
+    "While doing this by hand would be possible, the tedium can be avoided by using Scikit-Learn's ``CountVectorizer``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<3x5 sparse matrix of type '<class 'numpy.int64'>'\n",
+       "\twith 7 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "\n",
+    "vec = CountVectorizer()\n",
+    "X = vec.fit_transform(sample)\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The result is a sparse matrix recording the number of times each word appears; it is easier to inspect if we convert this to a ``DataFrame`` with labeled columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>evil</th>\n",
+       "      <th>horizon</th>\n",
+       "      <th>of</th>\n",
+       "      <th>problem</th>\n",
+       "      <th>queen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   evil  horizon  of  problem  queen\n",
+       "0     1        0   1        1      0\n",
+       "1     1        0   0        0      1\n",
+       "2     0        1   0        1      0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "pd.DataFrame(X.toarray(), columns=vec.get_feature_names())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "There are some issues with this approach, however: the raw word counts lead to features which put too much weight on words that appear very frequently, and this can be sub-optimal in some classification algorithms.\n",
+    "One approach to fix this is known as *term frequency-inverse document frequency* (*TF–IDF*) which weights the word counts by a measure of how often they appear in the documents.\n",
+    "The syntax for computing these features is similar to the previous example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>evil</th>\n",
+       "      <th>horizon</th>\n",
+       "      <th>of</th>\n",
+       "      <th>problem</th>\n",
+       "      <th>queen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.517856</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.680919</td>\n",
+       "      <td>0.517856</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.605349</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.795961</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.795961</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.605349</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       evil   horizon        of   problem     queen\n",
+       "0  0.517856  0.000000  0.680919  0.517856  0.000000\n",
+       "1  0.605349  0.000000  0.000000  0.000000  0.795961\n",
+       "2  0.000000  0.795961  0.000000  0.605349  0.000000"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "vec = TfidfVectorizer()\n",
+    "X = vec.fit_transform(sample)\n",
+    "pd.DataFrame(X.toarray(), columns=vec.get_feature_names())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "For an example of using TF-IDF in a classification problem, see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Image Features\n",
+    "\n",
+    "Another common need is to suitably encode *images* for machine learning analysis.\n",
+    "The simplest approach is what we used for the digits data in [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb): simply using the pixel values themselves.\n",
+    "But depending on the application, such approaches may not be optimal.\n",
+    "\n",
+    "A comprehensive summary of feature extraction techniques for images is well beyond the scope of this section, but you can find excellent implementations of many of the standard approaches in the [Scikit-Image project](http://scikit-image.org).\n",
+    "For one example of using Scikit-Learn and Scikit-Image together, see [Feature Engineering: Working with Images](05.14-Image-Features.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Derived Features\n",
+    "\n",
+    "Another useful type of feature is one that is mathematically derived from some input features.\n",
+    "We saw an example of this in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) when we constructed *polynomial features* from our input data.\n",
+    "We saw that we could convert a linear regression into a polynomial regression not by changing the model, but by transforming the input!\n",
+    "This is sometimes known as *basis function regression*, and is explored further in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb).\n",
+    "\n",
+    "For example, this data clearly cannot be well described by a straight line:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/Bn9r+FJ1FDktZiTtl/T16jG+KenWrRZvN9Q/lHTJaR/vq+7DLlGdjR2S9JcR8TdNzzMp\n1V8rD0u6tulZxuRqSddV57h9Scu2v9nwTGMVEa9U/ywlPajBUeu0eEnSixHxverjQxqE+6y2G+o1\nSR+y3bE9J+kGSdP27PO07lZO+oakoxFxR9ODjJvt99q+sLrdkrQiaSqeKI2I2yLikoi4VIPfd49G\nxBeanmtcbO+t/qYn2+dL+qSkZ5udanwi4oSkF21fVt11jaSjW60f+g0vQ77YVH8zjO37JGWSft72\nC5IOnDz8nwa2r5b0W5Keqc5xQ9JtEfFQs5ONzS9Iuqd6ddIeSasR8e2GZ0I9F0l6sPpfU8xIujci\nHm54pnG7WdK9tmclPS/pxq0W8g0vAJA4nkwEgMQRagBIHKEGgMQRagBIHKEGgMQRagBIHKEGgMQR\nagBI3P8DWfnC9JC1xVsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114836550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = np.array([1, 2, 3, 4, 5])\n",
+    "y = np.array([4, 2, 1, 3, 7])\n",
+    "plt.scatter(x, y);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Still, we can fit a line to the data using ``LinearRegression`` and get the optimal result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eqqzT6bBhwzQLCwf3FjNMTDTodDoGtWrTbrdpt9sDP67qjnqa3o761UdY445axXBHrdWg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\nH1pY4+tXGp0xAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1143bf2b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "X = x[:, np.newaxis]\n",
+    "model = LinearRegression().fit(X, y)\n",
+    "yfit = model.predict(X)\n",
+    "plt.scatter(x, y)\n",
+    "plt.plot(x, yfit);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "It's clear that we need a more sophisticated model to describe the relationship between $x$ and $y$.\n",
+    "\n",
+    "One approach to this is to transform the data, adding extra columns of features to drive more flexibility in the model.\n",
+    "For example, we can add polynomial features to the data this way:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[   1.    1.    1.]\n",
+      " [   2.    4.    8.]\n",
+      " [   3.    9.   27.]\n",
+      " [   4.   16.   64.]\n",
+      " [   5.   25.  125.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "poly = PolynomialFeatures(degree=3, include_bias=False)\n",
+    "X2 = poly.fit_transform(X)\n",
+    "print(X2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The derived feature matrix has one column representing $x$, and a second column representing $x^2$, and a third column representing $x^3$.\n",
+    "Computing a linear regression on this expanded input gives a much closer fit to our data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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aXgDqgLcxq2Dr1qP5/OerQseTPEU6Pc85tx841cz6AYvN7CTn3NrW1zU0NBx8nEqlSOmO\nRl7M4N57/Vl2EyfCE0/4RTIibclkMvToUUNLS13uIyfQs+cdvPvuGBXqGEun06TT6YK/ruAFL2b2\nfWCnc+7eVh9XR91Ju3b5cesTTvDTq3RDSNry6Y56D716VbN58+tUValQl4rIlpCb2UAz6597XAmc\nD2ieQhH07On3sV65Em67LXQaibOqqirmzr2fysp6KiqeoaLidn7yk/tUpBOq3Y7azE4G5uGLejdg\ngXPujsNcp446Ih984FcvTpkC3/pW6DQSZ83Nf2Ls2H68+upfGDp0YOg4UqB8O2rt9RFTGzb4fUHm\nzIFLLgmdRuJo715/T2PECL0CK1Uq1AmwerWftrdwIZx7bug0Eid/+IM/6b6iwv989OsXOpF0hLY5\nTYCRI/32qF//uj/dXAT8PYzTToOzzoJnnlGRLgcq1DF33nkwezZcdBFs2hQ6jYT20EN+5eGPfgR3\n3KFpnOVCu+eVgAkT/A3GA6sXB+qeUdn5+GN/qv2vfw0vvQTDh4dOJF1JHXWJuOEGGD/ed1M7d4ZO\nI11p0yZ/Y3nHDmhuVpEuRyrUJWTGDL9F6uWXw549odNIV1i2DM4807+qWrAA+vQJnUhC0KyPErNn\nj98ataoKGhu1ejGpnPMHAPzHf/gbytqNIZk0PS/Bdu6E0aOhvt532ZIsO3b4XRXfe89PvRsyJHQi\nKRZNz0uwo4+Gp5/2p3nMnh06jURp7Vo44wwYNAhefFFFWjzN+ihRAwf6LVHPPtv/Uk+YEDqRdNbC\nhfDtb8Pdd/uOWuQAFeoSVl0NS5b4YZCBA/2cayk9e/f647OeeMIvYPnyl0MnkrjRGHUCrFgBl13m\nTzcfOTJ0GinEoUvBm5rgmGNCJ5KupDHqMnLuufBf/+XnWG/YEDqN5Kv1UnAVaWmLhj4S4pJLfHc2\ndiy8/LIft5b4eugh+N73/J/jx4dOI3GnQp0g3/oWvP++3xcknYa+fUMnkta0FFw6QkMfCTN9Opx+\nOlx6KezeHTqNHEpLwaWjVKgTxswfNtC3L1x9NezfHzqRgJaCS+do1kdCffwxjBnjZ4Hcd5+Wmoei\npeByJFpCLmzf7l9qX3UVTJ0aOk350VJwaU+Up5APMbPlZvaWmb1hZpOjiSjFNmCAn1v9wAMwb17o\nNOVFS8ElSvmcQj4YGOyce83M+gCvAOOdc+tbXaeOOqbWr/cvuefOhXHjPvm5bDZLJpOhpqaGqqqq\nIPmSRkvBJV+RddTOuW3Ouddyj/8KrAO+0PmI0lVqa2HxYl80fvObv328qWkB1dW1nH/+P1NdXUtT\n04JgGZNg71645RaYMsUvYFGRlqgUNEZtZjVAGvhSrmgf+jl11DH3i1/Atdf6OdbHHJOlurqWlpYX\ngDpgDZWV9WzatF6ddQdoKbh0RL4ddd4LXnLDHguBG1oX6QMaGhoOPk6lUqR0iztWxo2DmTP92Ytz\n5mylR48aWlrqcp+to3v3ajKZjAp1gVau9HutfOMbcNttOnBW2pZOp0mn0wV/XV4dtZlVAE8Dzzjn\nDrsDsjrq0nHXXTBv3l7eeedEPv74KdRRd5yWgktnRN1R/wRY21aRltJyyy3w/vsVPPdcM5nMqfTo\nMYg9ezYxd+79KtJ50lJw6Ur5zPoYBawA3gBc7m2ac+7ZVtepoy4h+/fDxImwY8cuvv/9NRx/vGZ9\n5GvTJj/UMWyYn0mjVYbSUVrwIu3atcuPW1dU+KI9dix87nOhU8XbsmV+AdGUKXDTTVrxKZ2jQi15\n2bnTL21+5hlYvhxOPNHvvnfhhX5zJ90Y87QUXIpBhVoKtns3/OpXvmgvWeK3TB0zxhftcu62tRRc\nikWFWjptyxa/BL2cu+21a/2WsfX1MGsW9OwZOpEkiQq1RGr3bn9yzDPP+Ldy6La1FFyKTYVaiqp1\ntz18uC/aSei2Dz0VfOFCnQouxaNCLV0mSd22loJLV1KhlmBKtdvWUnDpairUEgul0m1rKbiEoEIt\nsRS3bvvQpeCLFmkpuHQtFWqJvdDdtpaCS2gq1FJyDnTbS5bACy8Ut9vWUnCJAxVqKWltddsXXeS7\n7Y7uH6Wl4BInKtSSKFF021oKLnGjQi2Jdbhue+zYv41tH67b1lJwiSMVaikbW7b8rWgvXw4jRnyy\n2160SEvBJZ5UqKUste62t26F/v21FFziSYVaBHj3XV+o+/YNnUTk01SoRURiLt9C3S2PbzTXzD4w\nszXRRBMRkUK0W6iBRmBssYOIiMjhtVuonXMvAX/ugiwikctms6xatYpsNhs6ikiH5dNRi5SkpqYF\nVFfXcv75/0x1dS1NTQtCRxLpkLxuJppZNfBz51zdEa7RzUSJjWw2S3V1LS0tLwB1wBoqK+vZtGk9\nVR1dfy4SsXxvJlZE+Zc2NDQcfJxKpUhpIwUJJJPJ0KNHDS0tB3qLOrp3ryaTyahQSzDpdJp0Ol3w\n1+XbUdfgO+qTj3CNOmqJDXXUUgqinJ73KPArYLiZbTaza6IIKFJMVVVVzJ17P5WV9fTrN5LKynrm\nzr1fRVpKkha8SKJls1kymQw1NTUq0hI7WpkoIhJzkQ19iIhIWCrUIiIxp0ItIhJzKtQiIjGnQi0i\nEnMq1CIiMadCLSIScyrUIiIxp0ItIhJzKtQiIjGnQi0iEnMq1CIiMadCLSIScyrUIiIxp0ItIhJz\nKtQiIjGnQi0iEnMq1CIiMZdXoTazC8xsvZn9zsymFjuUiIj8TT6nkHcD/hMYC/wd8A9mVlvsYHGS\nTqdDRygqPb/SpueXfPl01GcAv3fObXLO7QEeA8YXN1a8JP0HRc+vtOn5JV8+hfoLwJZD3n839zER\nEekCupkoIhJz5pw78gVmXwEanHMX5N7/LuCcc3e1uu7I30hERD7FOWftXZNPoT4K+C0wGngfWAn8\ng3NuXRQhRUTkyCrau8A5t8/MrgeW4odK5qpIi4h0nXY7ahERCavTNxOTvBjGzOaa2QdmtiZ0lmIw\nsyFmttzM3jKzN8xscuhMUTKznmbWbGav5p7f9NCZomZm3cxstZn9LHSWqJlZxsxez/3/Wxk6T9TM\nrL+ZPWFm63K/g2e2eW1nOurcYpjf4cev3wNWAROcc+s7/E1jxMzOBv4K/Ldzri50nqiZ2WBgsHPu\nNTPrA7wCjE/K/z8AM+vtnPsod6/lZWCycy4xv/RmdiPwZaCfc+5rofNEycw2Al92zv05dJZiMLNH\ngBedc41mVgH0ds7tONy1ne2oE70Yxjn3EpDIHxIA59w259xrucd/BdaRsDnyzrmPcg974u/JJGas\nz8yGABcBD4fOUiRGQqcQm1k/4BznXCOAc25vW0UaOv8fQYthEsLMaoBTgOawSaKVGxp4FdgGPO+c\nWxU6U4TuA6aQoH98WnHA82a2ysy+GTpMxIYCfzSzxtzQ1YNmVtnWxYn810oKkxv2WAjckOusE8M5\nt985dyowBDjTzE4KnSkKZjYO+CD3ishyb0kzyjk3Ev+q4V9zQ5FJUQGMBObknuNHwHfburizhXor\ncNwh7w/JfUxKRG5sbCHw/5xzT4XOUyy5l5UvABeEzhKRUcDXcuO4TUC9mf134EyRcs69n/szCyzC\nD7UmxbvAFufc/+TeX4gv3IfV2UK9CjjBzKrNrAcwAUja3eekdisH/ARY65ybHTpI1MxsoJn1zz2u\nBM4HEnGj1Dk3zTl3nHNuGP73brlz7h9D54qKmfXOvdLDzI4GxgBvhk0VHefcB8AWMxue+9BoYG1b\n17e74KWdvyzRi2HM7FEgBRxjZpuB6QcG/5PAzEYBE4E3cuO4DpjmnHs2bLLIfB6Yl5ud1A1Y4Jxb\nEjiT5GcQsCi3NUUFMN85tzRwpqhNBuabWXdgI3BNWxdqwYuISMzpZqKISMypUIuIxJwKtYhIzKlQ\ni4jEnAq1iEjMqVCLiMScCrWISMypUIuIxNz/B0W/Syq+z56RAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114819c50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = LinearRegression().fit(X2, y)\n",
+    "yfit = model.predict(X2)\n",
+    "plt.scatter(x, y)\n",
+    "plt.plot(x, yfit);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This idea of improving a model not by changing the model, but by transforming the inputs, is fundamental to many of the more powerful machine learning methods.\n",
+    "We explore this idea further in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) in the context of *basis function regression*.\n",
+    "More generally, this is one motivational path to the powerful set of techniques known as *kernel methods*, which we will explore in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Imputation of Missing Data\n",
+    "\n",
+    "Another common need in feature engineering is handling of missing data.\n",
+    "We discussed the handling of missing data in ``DataFrame``s in [Handling Missing Data](03.04-Missing-Values.ipynb), and saw that often the ``NaN`` value is used to mark missing values.\n",
+    "For example, we might have a dataset that looks like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from numpy import nan\n",
+    "X = np.array([[ nan, 0,   3  ],\n",
+    "              [ 3,   7,   9  ],\n",
+    "              [ 3,   5,   2  ],\n",
+    "              [ 4,   nan, 6  ],\n",
+    "              [ 8,   8,   1  ]])\n",
+    "y = np.array([14, 16, -1,  8, -5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "When applying a typical machine learning model to such data, we will need to first replace such missing data with some appropriate fill value.\n",
+    "This is known as *imputation* of missing values, and strategies range from simple (e.g., replacing missing values with the mean of the column) to sophisticated (e.g., using matrix completion or a robust model to handle such data).\n",
+    "\n",
+    "The sophisticated approaches tend to be very application-specific, and we won't dive into them here.\n",
+    "For a baseline imputation approach, using the mean, median, or most frequent value, Scikit-Learn provides the ``Imputer`` class:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 4.5,  0. ,  3. ],\n",
+       "       [ 3. ,  7. ,  9. ],\n",
+       "       [ 3. ,  5. ,  2. ],\n",
+       "       [ 4. ,  5. ,  6. ],\n",
+       "       [ 8. ,  8. ,  1. ]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import Imputer\n",
+    "imp = Imputer(strategy='mean')\n",
+    "X2 = imp.fit_transform(X)\n",
+    "X2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We see that in the resulting data, the two missing values have been replaced with the mean of the remaining values in the column. This imputed data can then be fed directly into, for example, a ``LinearRegression`` estimator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 13.14869292,  14.3784627 ,  -1.15539732,  10.96606197,  -5.33782027])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LinearRegression().fit(X2, y)\n",
+    "model.predict(X2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Feature Pipelines\n",
+    "\n",
+    "With any of the preceding examples, it can quickly become tedious to do the transformations by hand, especially if you wish to string together multiple steps.\n",
+    "For example, we might want a processing pipeline that looks something like this:\n",
+    "\n",
+    "1. Impute missing values using the mean\n",
+    "2. Transform features to quadratic\n",
+    "3. Fit a linear regression\n",
+    "\n",
+    "To streamline this type of processing pipeline, Scikit-Learn provides a ``Pipeline`` object, which can be used as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "model = make_pipeline(Imputer(strategy='mean'),\n",
+    "                      PolynomialFeatures(degree=2),\n",
+    "                      LinearRegression())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This pipeline looks and acts like a standard Scikit-Learn object, and will apply all the specified steps to any input data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[14 16 -1  8 -5]\n",
+      "[ 14.  16.  -1.   8.  -5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.fit(X, y)  # X with missing values, from above\n",
+    "print(y)\n",
+    "print(model.predict(X))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "All the steps of the model are applied automatically.\n",
+    "Notice that for the simplicity of this demonstration, we've applied the model to the data it was trained on; this is why it was able to perfectly predict the result (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for further discussion of this).\n",
+    "\n",
+    "For some examples of Scikit-Learn pipelines in action, see the following section on naive Bayes classification, as well as [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), and [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) | [Contents](Index.ipynb) | [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.04-Feature-Engineering.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.05-Naive-Bayes.ipynb b/notebooks_v1/05.05-Naive-Bayes.ipynb
new file mode 100644
index 000000000..f5d492a42
--- /dev/null
+++ b/notebooks_v1/05.05-Naive-Bayes.ipynb
@@ -0,0 +1,749 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Feature Engineering](05.04-Feature-Engineering.ipynb) | [Contents](Index.ipynb) | [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# In Depth: Naive Bayes Classification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The previous four sections have given a general overview of the concepts of machine learning.\n",
+    "In this section and the ones that follow, we will be taking a closer look at several specific algorithms for supervised and unsupervised learning, starting here with naive Bayes classification.\n",
+    "\n",
+    "Naive Bayes models are a group of extremely fast and simple classification algorithms that are often suitable for very high-dimensional datasets.\n",
+    "Because they are so fast and have so few tunable parameters, they end up being very useful as a quick-and-dirty baseline for a classification problem.\n",
+    "This section will focus on an intuitive explanation of how naive Bayes classifiers work, followed by a couple examples of them in action on some datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Bayesian Classification\n",
+    "\n",
+    "Naive Bayes classifiers are built on Bayesian classification methods.\n",
+    "These rely on Bayes's theorem, which is an equation describing the relationship of conditional probabilities of statistical quantities.\n",
+    "In Bayesian classification, we're interested in finding the probability of a label given some observed features, which we can write as $P(L~|~{\\rm features})$.\n",
+    "Bayes's theorem tells us how to express this in terms of quantities we can compute more directly:\n",
+    "\n",
+    "$$\n",
+    "P(L~|~{\\rm features}) = \\frac{P({\\rm features}~|~L)P(L)}{P({\\rm features})}\n",
+    "$$\n",
+    "\n",
+    "If we are trying to decide between two labels—let's call them $L_1$ and $L_2$—then one way to make this decision is to compute the ratio of the posterior probabilities for each label:\n",
+    "\n",
+    "$$\n",
+    "\\frac{P(L_1~|~{\\rm features})}{P(L_2~|~{\\rm features})} = \\frac{P({\\rm features}~|~L_1)}{P({\\rm features}~|~L_2)}\\frac{P(L_1)}{P(L_2)}\n",
+    "$$\n",
+    "\n",
+    "All we need now is some model by which we can compute $P({\\rm features}~|~L_i)$ for each label.\n",
+    "Such a model is called a *generative model* because it specifies the hypothetical random process that generates the data.\n",
+    "Specifying this generative model for each label is the main piece of the training of such a Bayesian classifier.\n",
+    "The general version of such a training step is a very difficult task, but we can make it simpler through the use of some simplifying assumptions about the form of this model.\n",
+    "\n",
+    "This is where the \"naive\" in \"naive Bayes\" comes in: if we make very naive assumptions about the generative model for each label, we can find a rough approximation of the generative model for each class, and then proceed with the Bayesian classification.\n",
+    "Different types of naive Bayes classifiers rest on different naive assumptions about the data, and we will examine a few of these in the following sections.\n",
+    "\n",
+    "We begin with the standard imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns; sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Gaussian Naive Bayes\n",
+    "\n",
+    "Perhaps the easiest naive Bayes classifier to understand is Gaussian naive Bayes.\n",
+    "In this classifier, the assumption is that *data from each label is drawn from a simple Gaussian distribution*.\n",
+    "Imagine that you have the following data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
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/EkTvg4do3DDjw71ffDSOMvMXs/PwGeLikyhTNJCX+nShdo3M660CvDpkAJHR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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116167550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_blobs\n",
+    "X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "One extremely fast way to create a simple model is to assume that the data is described by a Gaussian distribution with no covariance between dimensions.\n",
+    "This model can be fit by simply finding the mean and standard deviation of the points within each label, which is all you need to define such a distribution.\n",
+    "The result of this naive Gaussian assumption is shown in the following figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![(run code in Appendix to generate image)](figures/05.05-gaussian-NB.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Gaussian-Naive-Bayes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The ellipses here represent the Gaussian generative model for each label, with larger probability toward the center of the ellipses.\n",
+    "With this generative model in place for each class, we have a simple recipe to compute the likelihood $P({\\rm features}~|~L_1)$ for any data point, and thus we can quickly compute the posterior ratio and determine which label is the most probable for a given point.\n",
+    "\n",
+    "This procedure is implemented in Scikit-Learn's ``sklearn.naive_bayes.GaussianNB`` estimator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "model = GaussianNB()\n",
+    "model.fit(X, y);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now let's generate some new data and predict the label:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "rng = np.random.RandomState(0)\n",
+    "Xnew = [-6, -14] + [14, 18] * rng.rand(2000, 2)\n",
+    "ynew = model.predict(Xnew)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now we can plot this new data to get an idea of where the decision boundary is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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C/SI7u0AIwcXkNEZ4VdWYxZlxeqRGM1s8qElx9LVFC9EqpJ2ugfAeP5r2qdgl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hAAAg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\nqVV54uXPoVGvE3b6ub0dGb1kjdb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tFLpwYHT9F+kHKTLKGAwC0iihvrxIIAY0jRJhnJ/r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\nCiussMIKK1xnWAXnFVZYYYUVVrjO8P8DYfQSJAwZ8HAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118a16d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu')\n",
+    "lim = plt.axis()\n",
+    "plt.scatter(Xnew[:, 0], Xnew[:, 1], c=ynew, s=20, cmap='RdBu', alpha=0.1)\n",
+    "plt.axis(lim);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We see a slightly curved boundary in the classifications—in general, the boundary in Gaussian naive Bayes is quadratic.\n",
+    "\n",
+    "A nice piece of this Bayesian formalism is that it naturally allows for probabilistic classification, which we can compute using the ``predict_proba`` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.89,  0.11],\n",
+       "       [ 1.  ,  0.  ],\n",
+       "       [ 1.  ,  0.  ],\n",
+       "       [ 1.  ,  0.  ],\n",
+       "       [ 1.  ,  0.  ],\n",
+       "       [ 1.  ,  0.  ],\n",
+       "       [ 0.  ,  1.  ],\n",
+       "       [ 0.15,  0.85]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yprob = model.predict_proba(Xnew)\n",
+    "yprob[-8:].round(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The columns give the posterior probabilities of the first and second label, respectively.\n",
+    "If you are looking for estimates of uncertainty in your classification, Bayesian approaches like this can be a useful approach.\n",
+    "\n",
+    "Of course, the final classification will only be as good as the model assumptions that lead to it, which is why Gaussian naive Bayes often does not produce very good results.\n",
+    "Still, in many cases—especially as the number of features becomes large—this assumption is not detrimental enough to prevent Gaussian naive Bayes from being a useful method."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Multinomial Naive Bayes\n",
+    "\n",
+    "The Gaussian assumption just described is by no means the only simple assumption that could be used to specify the generative distribution for each label.\n",
+    "Another useful example is multinomial naive Bayes, where the features are assumed to be generated from a simple multinomial distribution.\n",
+    "The multinomial distribution describes the probability of observing counts among a number of categories, and thus multinomial naive Bayes is most appropriate for features that represent counts or count rates.\n",
+    "\n",
+    "The idea is precisely the same as before, except that instead of modeling the data distribution with the best-fit Gaussian, we model the data distribuiton with a best-fit multinomial distribution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Example: Classifying Text\n",
+    "\n",
+    "One place where multinomial naive Bayes is often used is in text classification, where the features are related to word counts or frequencies within the documents to be classified.\n",
+    "We discussed the extraction of such features from text in [Feature Engineering](05.04-Feature-Engineering.ipynb); here we will use the sparse word count features from the 20 Newsgroups corpus to show how we might classify these short documents into categories.\n",
+    "\n",
+    "Let's download the data and take a look at the target names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['alt.atheism',\n",
+       " 'comp.graphics',\n",
+       " 'comp.os.ms-windows.misc',\n",
+       " 'comp.sys.ibm.pc.hardware',\n",
+       " 'comp.sys.mac.hardware',\n",
+       " 'comp.windows.x',\n",
+       " 'misc.forsale',\n",
+       " 'rec.autos',\n",
+       " 'rec.motorcycles',\n",
+       " 'rec.sport.baseball',\n",
+       " 'rec.sport.hockey',\n",
+       " 'sci.crypt',\n",
+       " 'sci.electronics',\n",
+       " 'sci.med',\n",
+       " 'sci.space',\n",
+       " 'soc.religion.christian',\n",
+       " 'talk.politics.guns',\n",
+       " 'talk.politics.mideast',\n",
+       " 'talk.politics.misc',\n",
+       " 'talk.religion.misc']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_20newsgroups\n",
+    "\n",
+    "data = fetch_20newsgroups()\n",
+    "data.target_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "For simplicity here, we will select just a few of these categories, and download the training and testing set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "categories = ['talk.religion.misc', 'soc.religion.christian',\n",
+    "              'sci.space', 'comp.graphics']\n",
+    "train = fetch_20newsgroups(subset='train', categories=categories)\n",
+    "test = fetch_20newsgroups(subset='test', categories=categories)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Here is a representative entry from the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "From: dmcgee@uluhe.soest.hawaii.edu (Don McGee)\n",
+      "Subject: Federal Hearing\n",
+      "Originator: dmcgee@uluhe\n",
+      "Organization: School of Ocean and Earth Science and Technology\n",
+      "Distribution: usa\n",
+      "Lines: 10\n",
+      "\n",
+      "\n",
+      "Fact or rumor....?  Madalyn Murray O'Hare an atheist who eliminated the\n",
+      "use of the bible reading and prayer in public schools 15 years ago is now\n",
+      "going to appear before the FCC with a petition to stop the reading of the\n",
+      "Gospel on the airways of America.  And she is also campaigning to remove\n",
+      "Christmas programs, songs, etc from the public schools.  If it is true\n",
+      "then mail to Federal Communications Commission 1919 H Street Washington DC\n",
+      "20054 expressing your opposition to her request.  Reference Petition number\n",
+      "\n",
+      "2493.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(train.data[5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "In order to use this data for machine learning, we need to be able to convert the content of each string into a vector of numbers.\n",
+    "For this we will use the TF-IDF vectorizer (discussed in [Feature Engineering](05.04-Feature-Engineering.ipynb)), and create a pipeline that attaches it to a multinomial naive Bayes classifier:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "model = make_pipeline(TfidfVectorizer(), MultinomialNB())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With this pipeline, we can apply the model to the training data, and predict labels for the test data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "model.fit(train.data, train.target)\n",
+    "labels = model.predict(test.data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now that we have predicted the labels for the test data, we can evaluate them to learn about the performance of the estimator.\n",
+    "For example, here is the confusion matrix between the true and predicted labels for the test data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hXYsObNmyhccff5wFCxbQq1cvVqxYwaVLl4iPjyc2Npa9e/cCN765/F5CQgI+Pj7MnTuX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\nkBERkar9AwXLSNBV/8O+AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118a3d1d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "mat = confusion_matrix(test.target, labels)\n",
+    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
+    "            xticklabels=train.target_names, yticklabels=train.target_names)\n",
+    "plt.xlabel('true label')\n",
+    "plt.ylabel('predicted label');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Evidently, even this very simple classifier can successfully separate space talk from computer talk, but it gets confused between talk about religion and talk about Christianity.\n",
+    "This is perhaps an expected area of confusion!\n",
+    "\n",
+    "The very cool thing here is that we now have the tools to determine the category for *any* string, using the ``predict()`` method of this pipeline.\n",
+    "Here's a quick utility function that will return the prediction for a single string:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def predict_category(s, train=train, model=model):\n",
+    "    pred = model.predict([s])\n",
+    "    return train.target_names[pred[0]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Let's try it out:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'sci.space'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict_category('sending a payload to the ISS')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'soc.religion.christian'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict_category('discussing islam vs atheism')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'comp.graphics'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict_category('determining the screen resolution')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Remember that this is nothing more sophisticated than a simple probability model for the (weighted) frequency of each word in the string; nevertheless, the result is striking.\n",
+    "Even a very naive algorithm, when used carefully and trained on a large set of high-dimensional data, can be surprisingly effective."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## When to Use Naive Bayes\n",
+    "\n",
+    "Because naive Bayesian classifiers make such stringent assumptions about data, they will generally not perform as well as a more complicated model.\n",
+    "That said, they have several advantages:\n",
+    "\n",
+    "- They are extremely fast for both training and prediction\n",
+    "- They provide straightforward probabilistic prediction\n",
+    "- They are often very easily interpretable\n",
+    "- They have very few (if any) tunable parameters\n",
+    "\n",
+    "These advantages mean a naive Bayesian classifier is often a good choice as an initial baseline classification.\n",
+    "If it performs suitably, then congratulations: you have a very fast, very interpretable classifier for your problem.\n",
+    "If it does not perform well, then you can begin exploring more sophisticated models, with some baseline knowledge of how well they should perform.\n",
+    "\n",
+    "Naive Bayes classifiers tend to perform especially well in one of the following situations:\n",
+    "\n",
+    "- When the naive assumptions actually match the data (very rare in practice)\n",
+    "- For very well-separated categories, when model complexity is less important\n",
+    "- For very high-dimensional data, when model complexity is less important\n",
+    "\n",
+    "The last two points seem distinct, but they actually are related: as the dimension of a dataset grows, it is much less likely for any two points to be found close together (after all, they must be close in *every single dimension* to be close overall).\n",
+    "This means that clusters in high dimensions tend to be more separated, on average, than clusters in low dimensions, assuming the new dimensions actually add information.\n",
+    "For this reason, simplistic classifiers like naive Bayes tend to work as well or better than more complicated classifiers as the dimensionality grows: once you have enough data, even a simple model can be very powerful."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Feature Engineering](05.04-Feature-Engineering.ipynb) | [Contents](Index.ipynb) | [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.06-Linear-Regression.ipynb b/notebooks_v1/05.06-Linear-Regression.ipynb
new file mode 100644
index 000000000..ccecf6292
--- /dev/null
+++ b/notebooks_v1/05.06-Linear-Regression.ipynb
@@ -0,0 +1,1397 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) | [Contents](Index.ipynb) | [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.06-Linear-Regression.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# In Depth: Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Just as naive Bayes (discussed earlier in [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) is a good starting point for classification tasks, linear regression models are a good starting point for regression tasks.\n",
+    "Such models are popular because they can be fit very quickly, and are very interpretable.\n",
+    "You are probably familiar with the simplest form of a linear regression model (i.e., fitting a straight line to data) but such models can be extended to model more complicated data behavior.\n",
+    "\n",
+    "In this section we will start with a quick intuitive walk-through of the mathematics behind this well-known problem, before seeing how before moving on to see how linear models can be generalized to account for more complicated patterns in data.\n",
+    "\n",
+    "We begin with the standard imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns; sns.set()\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Simple Linear Regression\n",
+    "\n",
+    "We will start with the most familiar linear regression, a straight-line fit to data.\n",
+    "A straight-line fit is a model of the form\n",
+    "$$\n",
+    "y = ax + b\n",
+    "$$\n",
+    "where $a$ is commonly known as the *slope*, and $b$ is commonly known as the *intercept*.\n",
+    "\n",
+    "Consider the following data, which is scattered about a line with a slope of 2 and an intercept of -5:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
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Yg81VA0A8sDSoQ6GQNm7cqPfee09JSUnasmWLbr75Zis/AnHGhN4sW4cCiGeWBnVFRYXa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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115e58cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rng = np.random.RandomState(1)\n",
+    "x = 10 * rng.rand(50)\n",
+    "y = 2 * x - 5 + rng.randn(50)\n",
+    "plt.scatter(x, y);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can use Scikit-Learn's ``LinearRegression`` estimator to fit this data and construct the best-fit line:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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G4PcuFbCllbV6a2WetuScUHCQXT+/pbfGD+2h4KCGm2i09tL12VXogX6PFs1DUAPwew0F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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115f34470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "model = LinearRegression(fit_intercept=True)\n",
+    "\n",
+    "model.fit(x[:, np.newaxis], y)\n",
+    "\n",
+    "xfit = np.linspace(0, 10, 1000)\n",
+    "yfit = model.predict(xfit[:, np.newaxis])\n",
+    "\n",
+    "plt.scatter(x, y)\n",
+    "plt.plot(xfit, yfit);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The slope and intercept of the data are contained in the model's fit parameters, which in Scikit-Learn are always marked by a trailing underscore.\n",
+    "Here the relevant parameters are ``coef_`` and ``intercept_``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model slope:     2.02720881036\n",
+      "Model intercept: -4.99857708555\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Model slope:    \", model.coef_[0])\n",
+    "print(\"Model intercept:\", model.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We see that the results are very close to the inputs, as we might hope."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The ``LinearRegression`` estimator is much more capable than this, however—in addition to simple straight-line fits, it can also handle multidimensional linear models of the form\n",
+    "$$\n",
+    "y = a_0 + a_1 x_1 + a_2 x_2 + \\cdots\n",
+    "$$\n",
+    "where there are multiple $x$ values.\n",
+    "Geometrically, this is akin to fitting a plane to points in three dimensions, or fitting a hyper-plane to points in higher dimensions.\n",
+    "\n",
+    "The multidimensional nature of such regressions makes them more difficult to visualize, but we can see one of these fits in action by building some example data, using NumPy's matrix multiplication operator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5\n",
+      "[ 1.5 -2.   1. ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "rng = np.random.RandomState(1)\n",
+    "X = 10 * rng.rand(100, 3)\n",
+    "y = 0.5 + np.dot(X, [1.5, -2., 1.])\n",
+    "\n",
+    "model.fit(X, y)\n",
+    "print(model.intercept_)\n",
+    "print(model.coef_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Here the $y$ data is constructed from three random $x$ values, and the linear regression recovers the coefficients used to construct the data.\n",
+    "\n",
+    "In this way, we can use the single ``LinearRegression`` estimator to fit lines, planes, or hyperplanes to our data.\n",
+    "It still appears that this approach would be limited to strictly linear relationships between variables, but it turns out we can relax this as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Basis Function Regression\n",
+    "\n",
+    "One trick you can use to adapt linear regression to nonlinear relationships between variables is to transform the data according to *basis functions*.\n",
+    "We have seen one version of this before, in the ``PolynomialRegression`` pipeline used in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) and [Feature Engineering](05.04-Feature-Engineering.ipynb).\n",
+    "The idea is to take our multidimensional linear model:\n",
+    "$$\n",
+    "y = a_0 + a_1 x_1 + a_2 x_2 + a_3 x_3 + \\cdots\n",
+    "$$\n",
+    "and build the $x_1, x_2, x_3,$ and so on, from our single-dimensional input $x$.\n",
+    "That is, we let $x_n = f_n(x)$, where $f_n()$ is some function that transforms our data.\n",
+    "\n",
+    "For example, if $f_n(x) = x^n$, our model becomes a polynomial regression:\n",
+    "$$\n",
+    "y = a_0 + a_1 x + a_2 x^2 + a_3 x^3 + \\cdots\n",
+    "$$\n",
+    "Notice that this is *still a linear model*—the linearity refers to the fact that the coefficients $a_n$ never multiply or divide each other.\n",
+    "What we have effectively done is taken our one-dimensional $x$ values and projected them into a higher dimension, so that a linear fit can fit more complicated relationships between $x$ and $y$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Polynomial basis functions\n",
+    "\n",
+    "This polynomial projection is useful enough that it is built into Scikit-Learn, using the ``PolynomialFeatures`` transformer:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  2.,   4.,   8.],\n",
+       "       [  3.,   9.,  27.],\n",
+       "       [  4.,  16.,  64.]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "x = np.array([2, 3, 4])\n",
+    "poly = PolynomialFeatures(3, include_bias=False)\n",
+    "poly.fit_transform(x[:, None])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We see here that the transformer has converted our one-dimensional array into a three-dimensional array by taking the exponent of each value.\n",
+    "This new, higher-dimensional data representation can then be plugged into a linear regression.\n",
+    "\n",
+    "As we saw in [Feature Engineering](05.04-Feature-Engineering.ipynb), the cleanest way to accomplish this is to use a pipeline.\n",
+    "Let's make a 7th-degree polynomial model in this way:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import make_pipeline\n",
+    "poly_model = make_pipeline(PolynomialFeatures(7),\n",
+    "                           LinearRegression())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With this transform in place, we can use the linear model to fit much more complicated relationships between $x$ and $y$. \n",
+    "For example, here is a sine wave with noise:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1DePbnHtRUHAfsrLSsWNHttSh0U0wactYXmkzVEqWxm8lUOMJYdCIoMhWeHgP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\n5koAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1188dcd68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rng = np.random.RandomState(1)\n",
+    "x = 10 * rng.rand(50)\n",
+    "y = np.sin(x) + 0.1 * rng.randn(50)\n",
+    "\n",
+    "poly_model.fit(x[:, np.newaxis], y)\n",
+    "yfit = poly_model.predict(xfit[:, np.newaxis])\n",
+    "\n",
+    "plt.scatter(x, y)\n",
+    "plt.plot(xfit, yfit);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Our linear model, through the use of 7th-order polynomial basis functions, can provide an excellent fit to this non-linear data!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Gaussian basis functions\n",
+    "\n",
+    "Of course, other basis functions are possible.\n",
+    "For example, one useful pattern is to fit a model that is not a sum of polynomial bases, but a sum of Gaussian bases.\n",
+    "The result might look something like the following figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.06-gaussian-basis.png)\n",
+    "[figure source in Appendix](#Gaussian-Basis)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The shaded regions in the plot are the scaled basis functions, and when added together they reproduce the smooth curve through the data.\n",
+    "These Gaussian basis functions are not built into Scikit-Learn, but we can write a custom transformer that will create them, as shown here and illustrated in the following figure (Scikit-Learn transformers are implemented as Python classes; reading Scikit-Learn's source is a good way to see how they can be created):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PRUbePGR+5cqVWLly5egqG6Gqeg8ERnWjMi8EgMJh1uzZsgfn6HEkuwp7ThTj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\nC2FzAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118997668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.base import BaseEstimator, TransformerMixin\n",
+    "\n",
+    "class GaussianFeatures(BaseEstimator, TransformerMixin):\n",
+    "    \"\"\"Uniformly spaced Gaussian features for one-dimensional input\"\"\"\n",
+    "    \n",
+    "    def __init__(self, N, width_factor=2.0):\n",
+    "        self.N = N\n",
+    "        self.width_factor = width_factor\n",
+    "    \n",
+    "    @staticmethod\n",
+    "    def _gauss_basis(x, y, width, axis=None):\n",
+    "        arg = (x - y) / width\n",
+    "        return np.exp(-0.5 * np.sum(arg ** 2, axis))\n",
+    "        \n",
+    "    def fit(self, X, y=None):\n",
+    "        # create N centers spread along the data range\n",
+    "        self.centers_ = np.linspace(X.min(), X.max(), self.N)\n",
+    "        self.width_ = self.width_factor * (self.centers_[1] - self.centers_[0])\n",
+    "        return self\n",
+    "        \n",
+    "    def transform(self, X):\n",
+    "        return self._gauss_basis(X[:, :, np.newaxis], self.centers_,\n",
+    "                                 self.width_, axis=1)\n",
+    "    \n",
+    "gauss_model = make_pipeline(GaussianFeatures(20),\n",
+    "                            LinearRegression())\n",
+    "gauss_model.fit(x[:, np.newaxis], y)\n",
+    "yfit = gauss_model.predict(xfit[:, np.newaxis])\n",
+    "\n",
+    "plt.scatter(x, y)\n",
+    "plt.plot(xfit, yfit)\n",
+    "plt.xlim(0, 10);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We put this example here just to make clear that there is nothing magic about polynomial basis functions: if you have some sort of intuition into the generating process of your data that makes you think one basis or another might be appropriate, you can use them as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Regularization\n",
+    "\n",
+    "The introduction of basis functions into our linear regression makes the model much more flexible, but it also can very quickly lead to over-fitting (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for a discussion of this).\n",
+    "For example, if we choose too many Gaussian basis functions, we end up with results that don't look so good:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wFKgaZwvjLLtep+Urd87jK3fOi8LIUo9GUbhhQQ4vfNDM17/zATb/ANXVq1Oi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\n0CYiIjIIBm0iIiKDYNAmIiIyiP8Dl2lHrVvT3fgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115f34f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = make_pipeline(GaussianFeatures(30),\n",
+    "                      LinearRegression())\n",
+    "model.fit(x[:, np.newaxis], y)\n",
+    "\n",
+    "plt.scatter(x, y)\n",
+    "plt.plot(xfit, model.predict(xfit[:, np.newaxis]))\n",
+    "\n",
+    "plt.xlim(0, 10)\n",
+    "plt.ylim(-1.5, 1.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With the data projected to the 30-dimensional basis, the model has far too much flexibility and goes to extreme values between locations where it is constrained by data.\n",
+    "We can see the reason for this if we plot the coefficients of the Gaussian bases with respect to their locations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ObJyqyoA+BiYO68OgaBP9woMJMQScUzHybD39i6rz9+OWq4aw4f0M3th2jN8ljZGEyh7k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\nCCGEEEIYTQKEEEIIIYwmAUIIIYQQRvsXnmrfJfygsuYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118ad2c18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def basis_plot(model, title=None):\n",
+    "    fig, ax = plt.subplots(2, sharex=True)\n",
+    "    model.fit(x[:, np.newaxis], y)\n",
+    "    ax[0].scatter(x, y)\n",
+    "    ax[0].plot(xfit, model.predict(xfit[:, np.newaxis]))\n",
+    "    ax[0].set(xlabel='x', ylabel='y', ylim=(-1.5, 1.5))\n",
+    "    \n",
+    "    if title:\n",
+    "        ax[0].set_title(title)\n",
+    "\n",
+    "    ax[1].plot(model.steps[0][1].centers_,\n",
+    "               model.steps[1][1].coef_)\n",
+    "    ax[1].set(xlabel='basis location',\n",
+    "              ylabel='coefficient',\n",
+    "              xlim=(0, 10))\n",
+    "    \n",
+    "model = make_pipeline(GaussianFeatures(30), LinearRegression())\n",
+    "basis_plot(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The lower panel of this figure shows the amplitude of the basis function at each location.\n",
+    "This is typical over-fitting behavior when basis functions overlap: the coefficients of adjacent basis functions blow up and cancel each other out.\n",
+    "We know that such behavior is problematic, and it would be nice if we could limit such spikes expliticly in the model by penalizing large values of the model parameters.\n",
+    "Such a penalty is known as *regularization*, and comes in several forms."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Ridge regression ($L_2$ Regularization)\n",
+    "\n",
+    "Perhaps the most common form of regularization is known as *ridge regression* or $L_2$ *regularization*, sometimes also called *Tikhonov regularization*.\n",
+    "This proceeds by penalizing the sum of squares (2-norms) of the model coefficients; in this case, the penalty on the model fit would be \n",
+    "$$\n",
+    "P = \\alpha\\sum_{n=1}^N \\theta_n^2\n",
+    "$$\n",
+    "where $\\alpha$ is a free parameter that controls the strength of the penalty.\n",
+    "This type of penalized model is built into Scikit-Learn with the ``Ridge`` estimator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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4aejX3YcHpoXJ3O2twJIlY4G6sbWeFaX4+3YgLuUclZU1JH6XQVYrXgxDtA4J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\n4QshhBBtgCR8IYQQog34f7r12UT2+ByXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1189f3390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "model = make_pipeline(GaussianFeatures(30), Ridge(alpha=0.1))\n",
+    "basis_plot(model, title='Ridge Regression')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The $\\alpha$ parameter is essentially a knob controlling the complexity of the resulting model.\n",
+    "In the limit $\\alpha \\to 0$, we recover the standard linear regression result; in the limit $\\alpha \\to \\infty$, all model responses will be suppressed.\n",
+    "One advantage of ridge regression in particular is that it can be computed very efficiently—at hardly more computational cost than the original linear regression model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Lasso regression ($L_1$ regularization)\n",
+    "\n",
+    "Another very common type of regularization is known as lasso, and involves penalizing the sum of absolute values (1-norms) of regression coefficients:\n",
+    "$$\n",
+    "P = \\alpha\\sum_{n=1}^N |\\theta_n|\n",
+    "$$\n",
+    "Though this is conceptually very similar to ridge regression, the results can differ surprisingly: for example, due to geometric reasons lasso regression tends to favor *sparse models* where possible: that is, it preferentially sets model coefficients to exactly zero.\n",
+    "\n",
+    "We can see this behavior in duplicating the ridge regression figure, but using L1-normalized coefficients:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AQ9vxWYAQ4sqUVzfw8/4itmcUcrLkzDTmsAAvYqP9iAzzpXuQN0Hd9AT66vHWX/gzX15e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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1194f3208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "model = make_pipeline(GaussianFeatures(30), Lasso(alpha=0.001))\n",
+    "basis_plot(model, title='Lasso Regression')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With the lasso regression penalty, the majority of the coefficients are exactly zero, with the functional behavior being modeled by a small subset of the available basis functions.\n",
+    "As with ridge regularization, the $\\alpha$ parameter tunes the strength of the penalty, and should be determined via, for example, cross-validation (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for a discussion of this)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Example: Predicting Bicycle Traffic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "As an example, let's take a look at whether we can predict the number of bicycle trips across Seattle's Fremont Bridge based on weather, season, and other factors.\n",
+    "We have seen this data already in [Working With Time Series](03.11-Working-with-Time-Series.ipynb).\n",
+    "\n",
+    "In this section, we will join the bike data with another dataset, and try to determine the extent to which weather and seasonal factors—temperature, precipitation, and daylight hours—affect the volume of bicycle traffic through this corridor.\n",
+    "Fortunately, the NOAA makes available their daily [weather station data](http://www.ncdc.noaa.gov/cdo-web/search?datasetid=GHCND) (I used station ID USW00024233) and we can easily use Pandas to join the two data sources.\n",
+    "We will perform a simple linear regression to relate weather and other information to bicycle counts, in order to estimate how a change in any one of these parameters affects the number of riders on a given day.\n",
+    "\n",
+    "In particular, this is an example of how the tools of Scikit-Learn can be used in a statistical modeling framework, in which the parameters of the model are assumed to have interpretable meaning.\n",
+    "As discussed previously, this is not a standard approach within machine learning, but such interpretation is possible for some models.\n",
+    "\n",
+    "Let's start by loading the two datasets, indexing by date:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# !curl -o FremontBridge.csv https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "counts = pd.read_csv('FremontBridge.csv', index_col='Date', parse_dates=True)\n",
+    "weather = pd.read_csv('data/BicycleWeather.csv', index_col='DATE', parse_dates=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Next we will compute the total daily bicycle traffic, and put this in its own dataframe:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "daily = counts.resample('d').sum()\n",
+    "daily['Total'] = daily.sum(axis=1)\n",
+    "daily = daily[['Total']] # remove other columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We saw previously that the patterns of use generally vary from day to day; let's account for this in our data by adding binary columns that indicate the day of the week:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
+    "for i in range(7):\n",
+    "    daily[days[i]] = (daily.index.dayofweek == i).astype(float)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Similarly, we might expect riders to behave differently on holidays; let's add an indicator of this as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
+    "cal = USFederalHolidayCalendar()\n",
+    "holidays = cal.holidays('2012', '2016')\n",
+    "daily = daily.join(pd.Series(1, index=holidays, name='holiday'))\n",
+    "daily['holiday'].fillna(0, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We also might suspect that the hours of daylight would affect how many people ride; let's use the standard astronomical calculation to add this information:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8, 17)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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bA7KOZTzixaW5DKpgMKjweZN7zgBOa0GvQrSWI621w7MXFwEoQGvDXlycW0YV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\nIiLJ2HyJiIgkY/MlIiKS7C94s4p0653Y7QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1196796d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def hours_of_daylight(date, axis=23.44, latitude=47.61):\n",
+    "    \"\"\"Compute the hours of daylight for the given date\"\"\"\n",
+    "    days = (date - pd.datetime(2000, 12, 21)).days\n",
+    "    m = (1. - np.tan(np.radians(latitude))\n",
+    "         * np.tan(np.radians(axis) * np.cos(days * 2 * np.pi / 365.25)))\n",
+    "    return 24. * np.degrees(np.arccos(1 - np.clip(m, 0, 2))) / 180.\n",
+    "\n",
+    "daily['daylight_hrs'] = list(map(hours_of_daylight, daily.index))\n",
+    "daily[['daylight_hrs']].plot()\n",
+    "plt.ylim(8, 17)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can also add the average temperature and total precipitation to the data.\n",
+    "In addition to the inches of precipitation, let's add a flag that indicates whether a day is dry (has zero precipitation):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# temperatures are in 1/10 deg C; convert to C\n",
+    "weather['TMIN'] /= 10\n",
+    "weather['TMAX'] /= 10\n",
+    "weather['Temp (C)'] = 0.5 * (weather['TMIN'] + weather['TMAX'])\n",
+    "\n",
+    "# precip is in 1/10 mm; convert to inches\n",
+    "weather['PRCP'] /= 254\n",
+    "weather['dry day'] = (weather['PRCP'] == 0).astype(int)\n",
+    "\n",
+    "daily = daily.join(weather[['PRCP', 'Temp (C)', 'dry day']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Finally, let's add a counter that increases from day 1, and measures how many years have passed.\n",
+    "This will let us measure any observed annual increase or decrease in daily crossings:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "daily['annual'] = (daily.index - daily.index[0]).days / 365."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now our data is in order, and we can take a look at it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Mon</th>\n",
+       "      <th>Tue</th>\n",
+       "      <th>Wed</th>\n",
+       "      <th>Thu</th>\n",
+       "      <th>Fri</th>\n",
+       "      <th>Sat</th>\n",
+       "      <th>Sun</th>\n",
+       "      <th>holiday</th>\n",
+       "      <th>daylight_hrs</th>\n",
+       "      <th>PRCP</th>\n",
+       "      <th>Temp (C)</th>\n",
+       "      <th>dry day</th>\n",
+       "      <th>annual</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-10-03</th>\n",
+       "      <td>3521.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.277359</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.35</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-10-04</th>\n",
+       "      <td>3475.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.219142</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.60</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-10-05</th>\n",
+       "      <td>3148.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.161038</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.30</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-10-06</th>\n",
+       "      <td>2006.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.103056</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.85</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.008219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-10-07</th>\n",
+       "      <td>2142.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.045208</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.85</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.010959</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Total  Mon  Tue  Wed  Thu  Fri  Sat  Sun  holiday  daylight_hrs  \\\n",
+       "Date                                                                           \n",
+       "2012-10-03  3521.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0      0.0     11.277359   \n",
+       "2012-10-04  3475.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0      0.0     11.219142   \n",
+       "2012-10-05  3148.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0      0.0     11.161038   \n",
+       "2012-10-06  2006.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0      0.0     11.103056   \n",
+       "2012-10-07  2142.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0      0.0     11.045208   \n",
+       "\n",
+       "            PRCP  Temp (C)  dry day    annual  \n",
+       "Date                                           \n",
+       "2012-10-03   0.0     13.35      1.0  0.000000  \n",
+       "2012-10-04   0.0     13.60      1.0  0.002740  \n",
+       "2012-10-05   0.0     15.30      1.0  0.005479  \n",
+       "2012-10-06   0.0     15.85      1.0  0.008219  \n",
+       "2012-10-07   0.0     15.85      1.0  0.010959  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "daily.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With this in place, we can choose the columns to use, and fit a linear regression model to our data.\n",
+    "We will set ``fit_intercept = False``, because the daily flags essentially operate as their own day-specific intercepts:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# Drop any rows with null values\n",
+    "daily.dropna(axis=0, how='any', inplace=True)\n",
+    "\n",
+    "column_names = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun', 'holiday',\n",
+    "                'daylight_hrs', 'PRCP', 'dry day', 'Temp (C)', 'annual']\n",
+    "X = daily[column_names]\n",
+    "y = daily['Total']\n",
+    "\n",
+    "model = LinearRegression(fit_intercept=False)\n",
+    "model.fit(X, y)\n",
+    "daily['predicted'] = model.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Finally, we can compare the total and predicted bicycle traffic visually:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ts2UcfWNIWkbXtchrilAauGIpoAS4QqFYcGqZ0IPUSqU6nHU05VzJlJaZKVRgE4LmdHSm\n6IQrwOuUx8qHbXlgE7LYTZgXSoAr5oVRsWNpQ4qVDaeBR5jQ4+RCD37HJTtTxkuvDeD0xXGkktHD\nm6uBxxnIuWUAJcEbnlyxgn98pQedvVOL3ZQ5owS4giNXrODK4ExsL9sXftSLfznRt8CtUjQ6rIma\nIijBeXE8H61oKm8AAIYmCrH6cFV+xzKhs9UpE3rjMzpVAgB09jWuAJ/XbmS/8iu/gkwmAwC46aab\nsHfvXjz44IPQdR2bNm3CgQMHAADPPfccnn32WaRSKezduxc7d+6EYRh44IEHkM1mkclk8Mgjj2DN\nmjXzvyPFvPi30wMAgHUdzVjT3hT7e5ZNkEyo+aBCDCfA5+HEJvuOiDgi1l8Djy5L6giFUzQAy+AZ\nzlmAVyoVAMC3v/1t79z999+Pffv2Ydu2bThw4AAOHz6M973vfTh06BC++93volwuY/fu3bj77rvx\nzDPPYPPmzfjsZz+L73//+3jiiSfwhS98Yf53pJgzrMZi2vVpQYWyhVVt6WvdJMUyweYEeDCMTF5W\nikyC15HxDWDXwJUGvpwZny5hYrqE2zas9c4th2c4Z5Wps7MTxWIRn/zkJ/Hxj38cb7zxBs6fP49t\n27YBAHbs2IFjx47h7Nmz2Lp1K5LJJDKZDDZs2IDOzk6cPn0aO3bs8MoeP3782tyRYk7MFiowLV9o\nVyr1JbcoluVORQpFrTXwIDaheOPKBC70TErLaBIJzprn4+Rf1zV3DTx6MFcaeOPyypvDuNg/vezG\nqTlr4M3NzfjkJz+JXbt2oaenB5/61Ke4F6atrQ35fB6FQgHt7e3e+dbWVu+8a353yyoWh5Jh4cXX\nBtDWnPLOGVZ9Atyy1YimkFNLAxeVdfcKZzUmlqDWLoLTmAnF8fMjaGtJ4X3vucE7X08YWT0bsiiW\nJvyuePGfoWUTdA3N4pa3Z2JFN1wv5tySDRs24F3vepd3vHr1apw/f977vFAooKOjA5lMhhPO7PlC\noeCdY4V8Ldavj1fuerIU21QPM3kDbW3Oerf7b0trU6z7csuvWtXS8L/DUqeRf9/xfMXrK0HSqQSX\nnCWTaUZbm+OMFrxnt4516zK4YXVLqK6xXAVtbc64smp1i98/V7eiaBIUTYOrs61/Bm0lC60tqcjf\nt1Ayl31/X473BPj9Zv0NGWRanaW+kRkDbW1OBj72vruHZpArVnD7e9Z7597qyqJ3vICSRfFzd91y\nHVtemzkL8O985zu4dOkSDhw4gNHRUeTzedx99904ceIE7rrrLhw9ehTbt2/Hli1b8Oijj6JSqcAw\nDHR1dWHTpk244447cOTIEWzZsgVHjhzxTO9RjI/n5trkBWH9+vYl16Z6KZRNFAoGd258Io/x1c3R\n361+LztZQEdTYkHap2j8fjY5WQj1MU3TQCmFlUrAYAT41HTRKxu8Z6+/ZfOgZjh8cZr57uSkf3y1\nd1JY58yMU8Y2benvSymFTZwMcH7dBYxn0sgVK0joGloZ61Wj0uh9rBZsvykVnGfl9jNN07z7ppTi\n8Ks9AIC3dzR5SywjY7MoFAwMVqzr/hvVmlTNWYB/+MMfxuc//3ns2bMHuq7jkUcewerVq/HQQw/B\nNE1s3LgR99xzDzRNw3333Yc9e/aAUop9+/YhnU5j9+7d2L9/P/bs2YN0Oo2DBw/OtSmKeSKyJMVy\nJGJQuzMpalGzf4Sc2HxfDEIoJmfLWLuq2RtMgXhr0Ox6+GyhImlX9d8aFZ6+OI6B8Tx23nFj6Ppu\n1MYvvf/W6AYprivj0yW0Nie5pUG2s7kmdLb7FZmcFrZNoSedT92+t9TGuTkL8FQqha9//euh84cO\nHQqd27VrF3bt2sWda25uxmOPPTbXyysWmHo7ar0CX7GysAUCUoPjNB50SLMZf4qLfVO42D+Nn9iw\nFptvXu2dD8WSV5GFq8miKlzBbdWIuhgYd5YAC0z2N7UGvrSxbIJX3hwGIJ9ceU+Q6X6sI69NCFJV\nP293w52l9txV4K5CyMB4HmYdjmxLbWaqWFoI+0cMR7SL/dMAgAvBbFmS7mYTigotgVDCOSlZVm0B\nTgiNTCDjOrwBygt9qSMbjy72TXkb13gaOGPZYb/HOua6j36pPXclwBVSb8yzV+VhPEGW2sxUsbSo\nZaGJ51FOMTFT8v6Widrpygz6yTmM0S5usJVp2FQyYIvbyQz0qr83JL2jOZy8MOr8UX2EbPdjBTjb\nZ91n746VZ8ffQufk5QVtaxyUAFdIExIV6oiZrFcDp5TiysCMdG1SsbwQ9Q9pLhZJV8qzG5hIChVM\nx8GoQKd4DZwRznw4mP/d6ZyBs1ezcmFPxXUolh5xHo/77NkJJDsxs5l+wFpfCCXozw3i6nQ3LLK4\n+0AoAa6QSvCEroFQx4koSuOoVyOZzldwrjuLF18bqOt7isakpgY+h/pktbGXIRINnFL/b3ZicbJz\nDF1DM7gyMFNX3YqlR5zxiIhM6Gyf4TRw/7xh+9EUuYofIj1brOBU5xi3jr7QKAGukA6GyYSOi71T\nOPrGELqGZsPfY2erdTu9+Z3cJBZeGfoRRovjddWhaByE46k7KgZs6DIHtUCh6POcBu73t+6RWfzT\nsR4MZwsBLd0pM5kri6sO7EamtPClS9SjmZgueZM3TgMnYg2cra9k+f2DUIqJ6RJyxQpOXhjDwHj+\num6OogS4QtrbkwkdI9Ude9j1R9HX4mwGQSlFxXbMoGxI0HB+BNPlGZwaOVNHoxWNhEgoa4F/mcJC\n2EmiTMPitCnJdy/3Oxp2/1ieu1QiYjMewl1/WeyFsWyJmgS+/OawcFmHN6GLl0w4AU4IXn5z2Asn\nBBDaXplSiuFsAeXKtTe3KwGuqKGBa17H1QWeRuxLEsdkdWW6G//a+xKyJX6GGkvjUjQ2tR5xTBu6\nzJOcr4qP87VoxUvE4uJGVyR0nZuEuhqXrCuz5ylVGvhSJt4auBsHLvFCJwRj0yUMThQ403rR9JUZ\nNuV0OuWIUzYpEQB0Dc3ilbf6cfrqtV8uVAJcISWZ9Ac4oQAP5JqOonumF5cHZ/Bq16WaAv98zyTO\nXp2ou72KpYvoccu8z2U9w5SYNAO1ekczlWn0kjcwRYc5bcoV5omEuAEyJzZ+Qxa65EKKFD717i4n\nOkcIcOzNYZy8MMrVNzbjLyeapu1N5JpSTibKisn3n+FsEb3kDbw+dfqaT/qUAFdIB6Jk1YkNcBIc\nBDsf580bQ4DrNAXbJuibmAKhgEGLINQOaeCX+qeFa+6KxqWWlSWuExvrHETh+FEEzZWEuc5kxZkE\nztBRmLaJSTKIMi14nyd1mQAXm06D24kqAb50qUcDZ7EJwQwZhUnLKNlFFOhUtT6/bNEykCuasGwC\nw7LRRU5hmFxGIgHk6RSMaopfd8ykcDRyTQMKRgVj0+HlyLmydLZVUSw5dF3z4mRHp4r40flRbP/J\nH/M+J4EBLYqmhLOhgEVNFCoFDJC30KS14Q56u1eG9xam3JqmonGp1T3iPuPgmuQP3xjGdN7Af/qZ\ndyGVTFTPM5m0qscadMzaU5iiQ5iiQ9iYuBOAE2UhmliwzkuydXdKVSjZUiaOQkECyypvdk1i2prA\nBO1DijajPKljjBRxi347CAHKNI8J2oeOmQTGc3kkEzpuTDvr4UU6jd7yZYySQSRsCqNyM174US/e\neUMbDOIL7MOn+gCawC/cdQtamuYvfpUGrggNRKmk3y3Y92Bkshj4on8Y54VpTjibo1gwULScugxa\nAKW+3lQyLJi0DEpJaPAcmyqqQbNBqeexyZ4xN0mkwHTeCecpGTZThsmjTp3zOjRYJJzTIJHQUSJ5\nDJALsKifj4ANH5LFfhPCa+CqXy4t4jwPtj91Dc2ibzSHrnEnyYuFijem2TBBCMUguQCDFrw1cMsm\n6Mn3eHXkLCdrYIkUvJwFQxMFEFjVNgEWcfqkaREUzRKypfjJskQoAa4I4WpEFZPU9JzkNJIY9SZ0\nR0sisMEmvSoaFi72TWEyZ2C8MI0+8iYGaScf+jM8i2PnRnC+5/qFaCiuHUIv9Go/i2tk4TRw9jwr\ncJlP3PMadBCE0wIndA3D1hUYNI8pOowZMoo8nYTFLBfJ/Dwo5a+l5PfSIs7kyj3NOjlacCZySaS9\njU0oCDcWseNeyfK1awo/Xzrbp21YTJlqPRrwUv8P8erwKdhVoT6XSaAS4IrQ4OP2vcsD07G/F2/G\n6x9XLD8ZwkR1TWhsqoiy6ZikDFqAZfuevvmiM6PtGhIn2VAscYQOQ66AjSwKILy8IjwGO9AyxwIB\nrjMm9Fk6hgnah1FyFYRSb5Bm21JrDVxFUlx/KKW42DeF2WI4m6PMd8Gloy3t58Gn1HPS1VFdigFF\nsZqJkoDAtP3+Ixv33Ake2+8AZ8nQqdOpK9RWUIwVxvH8xf+NsWJWer8ilABXhAef2MvOcg18fLqE\nmTy//zOXJIHp5BXivxwW9Y/Hp4v43svdGM4W0NrsrBcFE8ZYNvEmAIqlS03xFlMFD2ZTc7EJRe/Y\nFM50DwasQq4QJrDBDsDOeV3ToEmGQFFe9OCkIUpIKBaWsakSLvRO4aXXBkOfFcq+1ivyz8m0pDz/\nHkL5VKkAPxGkoDAsf5Ige+5+WlV+cmdSyyvs1UuBYtnCZM4ApRQ/uPo6ekZm8Vr/pZr3HEQJcEWI\n4PaOMggFLFrBGAnnBH7lzWG8dIZ/sVgHI7a8aTHHzEz3Qr8zGz3XNYmyVcY46UWF8sL69csTePnN\nYW+HIcXSRGShkQo9yXlTstmIZRP83fn/gxd7X+Yy/LnHNmxvHdKpPjqe3Gsvp3Xzbef/VhL8euNG\nJQR/+6GJAs5c9rM6yhK2sKdrC3CCss1mX2PL+fj9zULn9EVMEWc7U9f/ggKYoH0o0hkQStE3lsPY\nVBGGaaN/0lkLJ2Y64q55lABXhAbMuGuSlFKM0CvI0QmMmf3ceRH8Vn2+oK7YbpiF5jl5AIBp+7Pe\n8fIEZukYBkknCKWYKTgJOkanHGe4yVlx+kvF0kDUI9xzcU3o7Pa2rNVoeNr3i3DNlQC8tUUCS7gO\nSUFD5k6vjCe/ea3bohWUaT6k1anc6IuAZJwKhmkJI2Qoc54xoXvfCWrgtlgDZ2eh7vhm0QoG8gOY\npAPO+jq1vaIGLWCYXOImmhWmX69qacVM3sBUjrdeylACfAXTN5rDdN4IDZhxLegU8DTioElTBPsi\nsaZyd2DWNH/QBeClXaUAilVnEQKrajYbQP+YE8oBxMvSpVg8hHM692TMDsfFgTP1dQ76SX9Yy47N\n9jHKbkVau69QSoSDPqEU/eQtDJILMIkZCCurDt42wZWBGRiV8Jq7YnEQpXmmgGdCpwD0kCRkny1B\nmdnAJBAMwVzHd4Rzjyso+suFzBcrlj/RdOPGnSIUL50ZxJHXw8sCIpQAX6FUTBuvXRrHD84MhjWe\nGCo4pRQlw/K0mYSW8D6zJaZONsmGafud1tW69YAGzgr5suftqWFw3DGXj0+XPAFuSrJnueSMvFTb\nUlwHJCb0Es3hYukUDCbBSpwFZW7CCEbrpqwAZ8zpIg1cchkCwngoM+cpPFO8YZchUsR6RnI4153F\nic7RyHtQXB/kGrj/p6uBi5wRy8ijQnwBHswHwFTpfA7LU2JKNOeFM7KjjxtGCwCXB31n4Xodg5UA\nX6EQiRkIiGdCf+NqFsfPjXh/s4OlZduo0FI4cxvTg7mBlrBr43a4DKXeFn4aNBikgCHSCcM2vJj1\nWlv4dWWH8a3j/4wfXHk9+sYUC4LIKEMBjJKrsKiFGTpWs2z4u6xDpC/ATcI6L4m14KCDUpAcneA3\ntaAmSnTW09gAoELK3Hvj9nW3H2Zn1JLOQjOfFE+k6oRInXjAmuRp1nneVaQREGyfcQU4cp7iwH5e\nYUzyo0x+DXYcjbMtqRLgKxSZGQiI92L0DPOpTm1iYWA8D9Mi6J7tRz85xw3KAB9ewQpqd7C0bIKx\naV8TY0N/bC+sh6DP7ESJ5jBWGUSyms9alr8aAC6OOOaos8NdMe5McT2xYULTnLjbuoilgYsFeJQJ\nPUv7OWHdQ97AELnIrYNWaEXozNSc9i1RisUhrmAvkjx6yBnMkglmPBRL81k7Ov9EMFc+AJRpzrMk\nsmNuhVjPZQlAAAAgAElEQVSe1l+CP5ZyQl4JcIUMWkOCzyV9acl0NrN/48qEt693AYFdxzhzZLiz\nA8BsiTVVkao2ZHPtNT2NS4euaSCBrG1BXA0/oanuvliIvdCZPiAI82JJVL2Ey7SAScKHi7ECmV12\nsWUOahBrTSxud3KWeqoTTCabG4ENUThROqUE+FJDFgExavWAwMYsHY+M45dacySGTC8mHLbnjMuN\nYbbpWQ8naC/XVs9ZMsb2o2pEW6EITZqUYoCcx5jVV3d9bpxtdrbMTAiCnrqs0GZzVvsvR5H6iVqm\n6BDGSDdG7V7OzO6+CAktgRIpoJe8jlHTfwmCWNXvakqALwijU0XhfvEsUct5nAAXFahOKgfJeUzR\nIRSsvLgeyjoEyQR47TXwFFrwVvckBsby3KA7kM0x7SXSCaliaSGOgKCoUMcsnkSzMOpAliMgWI93\nzI1vfhlLIPwrtsllgvO/R5DDBHrJG3j50uXI66sRbYXCpYVkjgxaQNYailUHO0Cyg6KfxcqvebQw\nhuGSXy/nkW4zJlD4ZkrXYahE8kLHER0JTFbGQGAja/nr8UHcyUJCbYyyIBw/N4KXzw7XLCMTb+4g\nyTqZiQqHNg6j4mdpc30yujXiMhQTMyWcujjGZ3bjrAREpVJdbGLHu1b/oQQlmqtquexyi6wXxMkX\nIDvP+lCEBXj/VFZ4VUKBWepYMGdItCOk2o1shSJzvgAQ+8XgBjDWMYg43zeo75xxavT1gImJcdag\n4Y0m+Obo3Dvm1UM1lKveobU0IFcD18OxIorrheT5aNX/+EQrsiqknwjLyMpPkkEUtRlQrBNeTWYN\nYNfaCeU1cD/xi5LkSw33iczScWenMbSg1fpJ5nNx2KAYDbL+xsIqSEQQxzYwO4JUIjweEUqQQApA\nYFIrQY1oKxSZVzAQ3wlEtG5JKWVeBorJMrMOLjE52lECHHpgmKVeHeXqVn2E0lA2OK9+tQa+6MiW\nbAgIoAXyRwsGRV3TYMCfEBKJph0+DvfmCkqeliNsqyBvOsD3U8eEHtbAlfhegrgRAjCq/5YwZg74\nH4MiW57AOOmNpXWzyFxvuLS/Ug1f/D29KpZlS0AsakRbofAzRF/g1oMoHIcCnHkzV8mHygDyGF0R\nboSm6PomcUzuhFAuhIjFHex1JcAXDYuaGCSdGCM9zFkK/7m6kzI7MLmzkCUDsGGjTP2+xGtMEodM\nGpEWmIoHV0cjI6HrsIJdqoErFpXRySK6AxEyLik0eccW5fMCXM1fxiwd43xwwmih/iR77oYZnchH\n5HhrU+qNh7oWbSBXJvQVCjdIzjEPJB+OQ2BSAwnSJF1rnqvTj0UtJAQDsRvL6dRNuSxu3HWr7Yyb\n411x7SnSWZRpDmXk8DZsAOD3H8coSTFNhpGlA+gw12A93gMAmKSDmKVjgF1BAi1efYQS5OlkaMDl\ndyajiLInSbeahI2gfsMJcC4tEbusU/NyimuI6MkefyvsCyN6NMEoBnuOE7D5zNtEfc+2ibc2H2e8\nUirJCoXtPMGdlzQtnkbBauAmDPSRs+i3LgTq0pjy4utH4ayPCjQlzlzPb4rCl/MaE/uaimsLm8nK\nhTdXUmSpY9Ys0hlYtAKLmt4gVqAz/Ho0oRglV5GjEzDBJNlga/Q0cPFzrzWJdE3kXGIY1uJEgyZ0\nJbmXKqIYbzYsjIIgEVOXDQrVax19YBNfA49Tt9LAVyis0m0LnCwoaOQMULReVKYFTBh+5VwdnNYf\nf63JphaSNBxfS8Fv6ViRJHNxvUA1quari4UbssPCO0Hy68m95A0AwA3aLV4JVlDza+ASQVpVwDWq\nQWgqr5bVNC0kgAls9JO3QIdTTHW8Bi5K5BLU8oKbZCgWBpsQXOiJTrbiEpx8samgZYjGw/jym3d+\nk2ETwjh0RpdXI9oKRbQG7g5yGrR4IRSy8zQswCnCg1tcKKgwFMMNB/HrlAhwwmdCutAziUv908Ky\nioVB5Kjo9TGthuMYcz5H2U1L4vTP2pPQK/mL0n4+QN6CicCuVkGnTc5cz5wXHCuuPezcqG80jyuD\n4vVr0XPgdxsjsWK+q1etWbesnrjLdyYx/ZDcGOXnJcCz2Sx27tyJ7u5u9PX1Yc+ePfjoRz+KL3/5\ny16Z5557Dr/6q7+KX//1X8cPfvADAIBhGPjd3/1dfOQjH8GnP/1pTE3Fnzkprg18UpXAh8yASqgd\n6SXOIel1faM5bsvPegc3NkWmfynKaVZEsgbueqe7Av5i/zTO90zWdX3F/CCCjuH2MQ28cGzW2rxj\nA+J93okgsY+IWlkFJwwn1W9c50Z20A8mcnH/MGwDQ6QTZVoQ7oLlUiv1r6J+auUNF5nQeV+JesLI\neJzv1WdlqTVZYHOkL6gGblkWDhw4gObmZgDAww8/jH379uGpp54CIQSHDx/GxMQEDh06hGeffRZP\nPvkkDh48CNM08cwzz2Dz5s14+umnce+99+KJJ56YazMUc0TsUBb2uO0mr6GHyDYBEXnw8rgDaMng\nBXD0CyNeO+fr4MWCJdHA3T2iCSFKK1okuKQ/Ec+A/dSsmt6Dcph3Oqpdn6/98JWMV/eNjps6mFs3\nDXjLuwyVBlGiOYyQS9L7vDIwg3861qP2sL9OeI6uzLmgBh5nXAj7oLN+Fn4Z2XejygB8TowF3Y3s\nj/7oj7B792687W1vA6UU58+fx7Zt2wAAO3bswLFjx3D27Fls3boVyWQSmUwGGzZsQGdnJ06fPo0d\nO3Z4ZY8fPz7XZijmCJeJLWQGkms+0dCAJ1E9DkSSTk7FQ7RBSpxgEK2rE0q8kBFbss+zYuHh16Aj\nBDjrmFhd9w72Ik4Dr1GfBs2T/rJhU68xoLITT34NnHegpBQ42TmGkQmnvTYsaV+72O9YHAfG5/qO\nKepBNK0P+lBEbXDjEzahsxq1XDhrkmMeNpfFgpnQn3/+eaxbtw53332397KxL1RbWxvy+TwKhQLa\n29u9862trd75TCbDlVVcXzgTurvfPPN5iYpjKeMQZ61btF4t6/yyOmbtKT62XCDALeJrSoQSWJYS\n4AvJcLaAqZzA4zwU3sUK3sCgGOM6dsAEKsPxQddElwmUEh0DI8xWj0GtjQ2/HJ8pYXA8D4O5dZkJ\n3d3D3lZm9DmTL5nIFX1ttZYRJWojHRqYjMkRRzRoMYRzPPHNW6rivAlz8kJ//vnnoWkaXnnlFVy8\neBH79+/n1rELhQI6OjqQyWQ44cyeLxQK3jlWyCuuD7LdwADnZSjQ6RimTvHn7AAuq8NNwMJdFxoz\npPvH7oGTkS1giuWWAsJr4Da1veUCAgJTDZoLyo/OO/mbf+n9t3rnnIxrtdapg2cEkzstqPnE08Ch\nadAi+jFn3tTknsV8ilXC7W//2uAlVIiBZi0T2S5XgFtzzL+gAA6f6o9dVhhGFuhjcbKeAWLhG888\nHk+Ec5k1KMFgfhjr18vl45wE+FNPPeUdf+xjH8OXv/xl/PEf/zFOnjyJO++8E0ePHsX27duxZcsW\nPProo6hUKjAMA11dXdi0aRPuuOMOHDlyBFu2bMGRI0c803scat3MYrEU2xTFRN5EW5szuWpvb0bb\nrAGTakiVk2hqSsGwKFqbU0iVnS5ipmbQqnd499rW1gRqp5GqiLtQKuWcX7W6BetvaPf+dtF1QCe8\nASihJWBTDZrmbFRiM+b3VFKHrvOaezqdAHTiLQdkOppCz6JQSSCZdEJE0skEVq1uRVubk5Gp0Z7b\nUm0v+3uKfltKKdLpBAhx+kBrcxoJLQnYBlKVJJqakiib/nNNJZIgJMVN/tLpJNh0Bc0taaQKTn0J\nTYdOxf0wnUyCEAKNUOi6xi0duTSlkyAVRzjLyvj4fTbdpmE8eRnvSN2KaWMISAIdqXakTKcta9a0\nYVWmKVTD6lUtIJqG1tb0knumS609Mtx+5rJmdRva2sRLEqur73zJSnvPhu0zmqYh3ZQMjVFBkloS\nGnRoAWGf0lLeOfY4WMas9uekloIlmSUmNR2a266UjUuFS3gfNsvbVLPFdbB//3588YtfhGma2Lhx\nI+655x5omob77rsPe/bsAaUU+/btQzqdxu7du7F//37s2bMH6XQaBw8ejH2d8fFcdKHryPr17Uuu\nTXGYnCqgUHA05enpEgoFAxatwCQWDEOHaVrot7pgVteP+8xLADSMj28EABQKBorV8rXITuaxiuZg\nmsFy4bhIAjetqrOqxG5wQWwdlGicFqSBwrR8B5TJqQLGm/lnka8U/WvbOkbHZr37bqTntpT7mft7\n/u3/voBC0bGssG0lhMIwTJjV51mwy0hoKZSowfU3F92yUKF88p6kDq5MPl/y/racFWlh23SScNLs\nUgtJPQFLEKmQRNKrK5lIwLKj02ACwLnhTswa0ygY57zkG5NW1ntnxifyqJTClqZSsYJCwcBkUltS\nz3Qp97Egbp9zmZ4uhs65ZCedsa5EKt6z4fuMhlLJYPqXOGabANCQgBVK/Zz0+jZ/7EOh+9/TEl47\ngtjwxz0NFEXJPflXniff/va3veNDhw6FPt+1axd27drFnWtubsZjjz0230sr5oHIic2LA68qvjM0\nuJ1d/SY/d4N6wSehM7qmw6ZiM5RThx44x5exCYFpEaSSOnfOhVASyjqnuHbkimFhBYTD/YKm5ZBn\nr7CfyTNg1VwD1zRvPHamhWHhzPY3XVJGhF4dPtktcAvUX0oU9ftcseK9X7bqi9eOWmvggr0egn2M\n7U/+8l04vjZqhXu+Xuj1jrEqE9sKhXXAmYtn9iC5wG0XKoNQKk2wEkTTNH+9mzkGHGEd5XF5aXAK\nPV09+LltNyPTUt2Sj/AvrXIcuv443ct/DjmaRYFMIaOtlZSnCA5kwTXw+Lmr/eFYnhWNEeCSmHCn\nluDEo3b4WdAUP5M38NKZQe9vFQs+N9iJUZ5OohmZGqVl4xsfFRH00RA+b00TRtVoNf4SnQ+ObXyr\n2EFPUhWDysS2QrGFu5E5xImKLdN8rGxthFKJRhVE4wZpsVYWOBt4MfNVc2V2hkkYw01UCCq2iTHS\nDZPWNk0prh2Or6HfVybpAAwUUII40kHUX4KhXqI9lkVoYIS/RIDrWgztSHA6ytQelBtj03xmN2UN\nmhvu2FWiOYySqxgkF0KPx6QGsqQ/dhKqoBObKNmKXDQzYWQxJom1RljeUhWN0sBXKJZtY5IMoklr\nA6Et/IfXMH1zcNOHIKxnuTt4app4GNU1jXNkcj3K/TooDFrEy+NHkGrbhqFBHR2ruNagK9eFHJ2A\nBQPAbfO6N0VcxFM471xwXiYoHfZCjyf8NGY9M06aS1kZp+/x17Qk65guQc2vWObLlyu1v68Q4yoc\nNhzhbCG8dJOjE5imIyjSGRD6jsCnAv8bRoBrwpQtcg2cry5OGNm1M6ErDXyFUrErmKJDGCGXmXhV\ncVxukPDgKS9vU4psWZ4qlxXafC8XvUC1B2AKilk6BkopXuk7i4HxPF6/PM6VLVrFarsslZVtHlBK\nkS/F1W4A8cAk628CAR7UwOsY6NzvyhK2sF1NqkGJNPAIB04S2C2PMJpjv30OFhX7DChqI9z+uPp8\nXE3afeYVlDBWGgWhdsjHhyU4FIj6gaxv1BtGdi03uFECfIVSYQYfmxAU6Qwm6RAARO7MEzS518ol\nTUFxelSWipUR4NyxbAYsr8W9mgXTydwmGd99r3ltXnv5rnQuD8zg8Kl+DIxFJ2FyTOjhJRB/8xwI\nz7OE1sDj7man+ck3pEMr039lg6vorMijnWV4ooB/eLkbgxNOeJMreEbJVVRQwhQdqpnDWyFGKMDh\nbEPbRU4jTydRgL9Z0cXZTm+rWhlylST45MWR4P6R2FTO5xpQAlwxT0zL155MWsEwuYQ8zQKonVoS\ncFKSxiV6rVLz/o3q1lE+oI7JbFq0Wu7h7Uw2jw0MFMDguCO4B8bzmCFjyFX7jghHIFPBBEz2+0ev\ngcfzq3AnhtVjqQVH/peLaFIbpYF3Dzu7Y10emK6Wd96FBBwHSwumMqPPAV+A8wLS3a1ulFyFQfmY\ncPZv4ROWaOD15jmPI7Tj7kwWR8NQAnyFwmrgQU0iaoYYcnqrUT7KTM1q75w2LjJhxTBP1T7H3itV\nGvg8SFZD9UybYIL2Yox0Scv6GnjgvEQIC88H+sNcnNjimEClGjgjwN11ckuQ+Y/FSxlbfV8mjQmU\naR5JrRohQU0YlXghawof2/N94UmjVfwF6k8ihV+EILRRJMA1sWWQM49LBbi4abVQTmwKKSazbZ1J\n+LVMmabiEvQ+16FJI2ejtFxd1wA78E5JXpQ4A3C4rQHHI08Dl8WnK+LgxtpbMU3AFBQ6AoMSGzIY\nKBskuEwT23qiadC8cdvVx+UT0OhIX99pUpS6l8W9D0KdiWxP+SJKxEKHth6As2FQsWICaKlRiyKI\nSAN3/hI/u2B/0jU9tP98aA2csQwGPghJVj0g5EUObeFcA9cGJcBXKGwGNYPw4S1RJnQroP3U0sAj\nBTgT4uPXU6+gju+hzO4rrsS3nJm8AZtQrO1oFn6equbzrsQQ4I6HL3XUEC7M1V0Dj3ZiC4WRxdw9\nyhlva/fnOBq4yM8jaAUIxw5XBThxQildueOa0AEgVy4C6KjZvpVGySqjYBZxQwufJ2B0sohyxUZz\nU3g5w+la4jfa3Ta2Qp1/4yTr8YalwGo4L9hdpziNmYzKdiaLE2pWP8qEvkJhBXiF8DHRkSb0wBp4\nLa/KqLVK1xzJO7GJTU5yDbwW/PVd5yelgdfmpTODOPrGEHeuZFie53lCr+6oFVhOydEsSpRPxymf\nxDEDYBSBInaEA5noi7qmR/erWJ7G1TYEJxGBr2bpgJeJkFLqmdLZd6JkOr+n6os+L/X/ED8aPoWy\nxY9LR8514/SlUeHMu2Dl6/CLCD/j8JbK4jVwsQFdZjZnnSNrX3+uKAG+QmF3A6sEdgaLCnMIJ37x\nyyfBbzIQNdDqrNbtzXrFnbyeNXDDNpCnk6FXmt2ZaNbI48r4MGYLKpwnDv9yos/bBcod8IJ9ZYx0\nYYh0cudkwskdcKMsPqIydTmxacEzwTLR5k3+Pp3jqARIOToBAwXHhA4/pIzN8W8TG9N5A997uRu9\nI42Rh3yhcfsL6yQ4bcygn5zDGO0SPvlLs52Ia1MTTRjDXugya6B4bdyF7Sfy9fB4FsY4d6ME+DJl\ntpLDbEU+ILBJKCq0zH3GJ7MId7ZgQotanpp5U7xDkFfecxaRe3PKz4iv6TpNjZKrKNN84DNXCwKO\nDb+K7711FC++xoeYVEwbA+N5pRXVwNW84yjPwdhc5pNqHdGVBP0y4mQBdOtmB+Mo34pYsb7VtoRS\nBEuco5xshP7kkU08YlOCvlGnj57rlnvyr3QKppO/oUCnvPcyLPDqSNkcQPaq+34T1b4UER/OL8eI\nzebSPhaUxjGGHyXAlyGUUvxw4Dh+OHBcWsZGPBO6qK8FBy5NoJ245ExnEqFL3C38Tu6/HLJMbPHS\nFFb7fbXzG+AFOGE0cNn7cbJzDKc6xzAwXnvysRKQmb/d87Jnwk5+PFNzoKj/BOaigQcHa/nyCp9r\nQFB3HCc2QZrf8FKSKP2msz0pZZK6WEx6T9ZCdS3XRpcbaT1d83NN02Lq39EmdAoqiVyQaNQQj5cy\noR2nj8WloQQ4pRS9IzkVOxmBO1utBTuIBDNCRZnQs+VJ7u9aOcxN27lOEuIX0NONIsycwevwpYMa\nuJ9/PWhd4F5UiXBynV7iZhpbboiyhwURaeBuHC7AKw/yfGtiM7yPfG06KMDlOcy12n8jOACL6+HD\nHR2CkxvZso+7oY9nGmY0cMt18FPUhH0slDq5zoMpVA3EnXDHsfi4Ajy6f/CmcrETW6xQxchWhWko\nL/TOnimcuTyOhK7h/7n73YvdnCXLUGFY+lm2NImmRJOXRxgIJ2YJOuwEh5cLk501ygdNneEy3HeZ\neEt21ameOHDRzmX+9f09f9nYb8oeBwZiXXMG3ZVqQme3ubQJRVKQmM+2CUxqQNf8idkY6faOKaXe\nqEslk4Co1L1s3wtqt3bVi1jTNOY5hXurBg1uHFkcC4403WrQhE4Fliix3QiEUM5jnV8DV8pIHNjf\nmgLoI2e5z4tWAWUaT4CLnnF4Mub+6/xHIV/i4zzPuTmnzCM9ngCP4+fRUALczWwkS6W3kiGU4NLU\nVdzU/k5MG7PIl0zhBhCvDp9yjoU6kgPvlRv6OIQep7zwPL8m6R1L4sB5U6e/CYoeEuAUwdfBDfHh\ndvuRL3xVkz+sTNhtLmUa+IQ5ij5yCQn7x4Wfx5n7+MJZpj37hYIlSNWHQ9P8a4m2gOSri07RK91O\nVLCsJJr4yZAvRTiauUGLSNHa22KuFLKzZczkK3j/O5mJJPP7ufsZzJU4a+Ce8NW8/1Wfe209WWaN\n5Bzd4lqLYtBQJvSxmRnveKVqRzL6c4O4Ot2NEyOvgVKKgfE8BsacGak7e42716woOxpLyGOzxhq4\nPNbX1bTDfzhaU7hdbB0tWrvk+rL5g+t45LeLPQ62y63oUv80hiZW1lo4O0EWTZYppZg2HYercWsw\n9LlThjmWXkneN4IEhaOrxYoyZgVhN7sRroFDrEHJru/XF7hOjQRIMqXDpjamzCwGyFsYJ73S7y9X\n3HF8pDDmOd2OT5dQsWzkDWaZj5lUlkw+b0X9CB5yUIBX/40TIRHUX1x0mTl9pcaBzxJ/jU1p4Twl\ny1nrLVv8mu9ocRwvdB/GUH6kzi0YvT/ChDp7DQcN6n9S61pB3Ug4IEsmCiGticozK/m/AeWO+cs4\nZSumjfM9kzhxYVTY9uWKHUMDT8FJ8BJ0gHTh+prE4c3fHSrapBgUjq4JnY+XEGvXflIOMTLHI1lr\n3DL16BCy/QNsQpCznH3Rc2RSWGa5cmW6G9/v/lfkzQJOj74ecrrlfDHYPin4LeuRibpQftPA+rVb\nryz6m722WOvm+wx7Vlxfm94OttSy80I3GWcrdp1O4cdMJvUk1/F7Z5243a6ZHm4LxlqmRl3aId3v\n8sjiILkyYpVaOjMVOotIPDnjxU+GB133dwrnQXb+nV6h8eFWYA08CIX/28k0FEqB2WIFFdOW9rVa\n1pnqB/5hqD9Uv8uNxlqoLOeTIbVcsgk3ouPA/fYGTeii7zplZBMhArkTG6FU+r3lwMXJywCAseKE\n8HPWb8BkvPXnuwmRfFObcNgXazbX2Nkg9z123BLvbBcrDjxg+Yxzlw0lwC1aQYnmkKeT8bcTXCHY\n1bzMSS0hfel7Zvpi1VXLxFMomyHVQxb7CDhDkztMSy7G1INaJaUEJw3FssnEe/MbH/Be6Gwr2SY5\npfPFClN2+Q6kQSK90CmYPODiIcQmBC+eHsC/nOyPNKHHyX4m38tbC5UOOZ5HDJ6Rlp/A9eXpVuXL\nTSKt0TlvS3+fwyf78Q+vdEs+XU74v4Bhi985dryvZzdEEfJnzE72BP2KO2brE3wNwTjw6C1r55Kt\nraGc2Cxa8bI8mfaWRW7N0sLdpCOhJ7l0oSyXpq54x7XkkS4wJbn868l+DGm8E4keo3PKDZOMNiP8\nroaotdLgecsmyM6W3Vq5Mpxwqh7naCAszvvcP2daBOmU7449Pl1CpiWFlqaGeoViwXYNznmIzsCG\nCYoN3mYQsmfiavGsOV6GHhoOw51THt7FlWLKiqeN4h4WY32yhp+Hf1augcsUjpJhIV8WW3qKxsrw\nUGffSXZjJXbSw+6/IJpU1jO/lvtMsjvOuWU1cM9buLIntljK4sPj9Z94ArzBNHAm+Yi1Mjp3XFgN\nnO3s8plcLRO6aCbqY1rB7Ucl33UvQ52WMN/w/i9cG2JM61ITe5xBFwBcDbzaLsHSLLI0YJnQ3M8Z\nzcD073lsuoRX3hzGa5fGa1y3gZFo4MPkEsZINyhlNhOh4iGEcJaOmjYY+XII268iJm/s/znTpSbW\nmli4CaikVEISKsS3RY7MKlYo+wKLvffpvNi3YNnD/EwnLozhyMXzeGP8HDcBEu0CNzET37EtSgN3\n1sOZMSpCB+eX/sTZ1zgLTqQ1Kb4G3lACnI2fNKwKLk1dwVvZi4vYoqWDq4HreoJZt6QhLZxH1pHq\nu7bM1JlAygvdipWtiPlXpouLqL2ZCn+dOIlcRC9Pidm3ebjqle5q+csNTgOXrd1GbKUZjNsFaq39\nRVttpOuWXiH/qYXymUd0aFnsrqyNcULNXGboGAi1Q+mHXWTv5w/OiL37lyPBLGguhBKcGn4TA7kh\nVGwmdl5gQrdiWHpcxJYS8Mt5gmP5BFT8hyY0yQf7mEQpgbb8nNjYQaFimbg81YWemd5FbNHSQ4PY\nXBfsCxS1Bk6xSUh6TbZzMjbNBLNCI6yT0bSDsdv+SyPWfNpb08z5Wt246mwVI/ZTdB2XUtkKfZ5M\nNNTrExtKnZzTeTopnOQQQj0TujS+mRX8/jqGEH4/5TibifjIVl08DYrRwGV9Pl6WrOg3QSTY8zSL\nCdoX2npUwSJzrmUtQbW90OtBGiooshJqzFikRWvgvK+E2LIj80gPbpiz7JzYWMq2CQpnP+L4Wwsu\nXzwNE0CBSQEqd76i0kEplMgl5rUBgeOGF9IlMY3WYTYSeYlGfa+Wp3NQ+AxOFDCdN2BTEzk6wf12\nRcbU6bZ5uXoIF8wCRsgVjJKrwkkOBfFzC0gGU4t9J6NM6JIBUJOUkX3XN6Rr3pc1pqI4JnT5Wnt9\nJlCWCkoRW6q64XSSIgzD2QKuDs1EF2wgOJHNaeM+JqOBiyZDuig2TII8K1rYl4dS3jLol5RkX2Nr\njhk1Iy8fPb40rAeOYVWQnSljYqaEnvYZbHzH2ugvLWM8LYNSTtjIQ3jkA1pCDztzsKTQDBO++Vge\n3uVnSwt3YH4C4WhH7LEW+p7kMNZ+5CLtiAspIxQnq/He4/plzJIZQAcIsZFDFjcaW/1ruwJ8mXqm\nG7a//irTwL3PpSFQbOrQCBWcgX/eMkuQP7iJel7tjAKCNrATSuaPdCqBiummbK3tFxK8kq5r/u9E\n5dw/S3MAACAASURBVJ7Ttd5DET867/TRje9cVce3ljrRGrjFeaGH+1xC10FiKnLBfOW1wxnZJ8T2\nMUmyK9lkVKrkaEwmwXCdUSNMw2rghmVhphqnOzw1u8itWXzcB947OouZaviTBq12+NM8zIWy8vF2\ndvKPRfssRx8FZqs1VRcqLTOV8ychrDDOVxNrmNTABO2DQQuYNWfw5sR5DOaHvRd+uWrgUSFzBMT/\nDWokKAEcz/V/630ZQDzNI06fFA2Y7P958yZXiZCg05uouGwwltXTykQnUNAaJnRWZInrFT2PpR7W\nWI+Zm3csFQvzKCe2eoizdMj3Ce7LNeuT9SVZ+Vom9Dg0rAAvmWXvybvrcSsZdzu9oZkJGG5Sf63G\nDI7WMqHL/vC+zP2lyzownNmlY4aSGlBrXjeOSUrmVMRdJcI+KUrwwjJrzqB3ZgBnxt6M5VzSKIwX\ns7g81cWdY4dekZzgfx/xj+HuQjdDR73d3aRrj5LUvbLQG9nmEX59wX4i0+TDZ2UDcJxQSd6syvZJ\nCtlcL86GFaISiym/oyYPRbOIF7oP4+LkFWmZbGnKry/Gb8AukwodAqvnZLsessjsM6IwLhooy9sM\nw3Ww6JJ+KougidoFUniNur+xRChZZe8nZPe2XqkMjRdwsW8qdL7WJveytbxaceBOnTxS8xB05EuV\n6oAv6LQau9ZEA+ufEZ1ZYk6XEZXTOGpQylk5XOyfwsB4vma5GSOHbKlxUmKeGDmNS1NXuAQaoLzD\n0Hgxyw2ghPhDrmzwPTFxEtNkOKBf1qf5SHNJC8vzgroeZF7osmgJ6TSAKbMquYb7rLYTW20fAdFP\nvFjLN4PjeXzv5e6aYW7Z8jQA4Mp0l7TMq8MnvWNugyHJscVlYgsrbP4iTXQf0CO9wCmznMeMS5pY\nEQj2R3cioAvW1AG5MBd6wUc85sYW4N7NLSOVaI4MT0p26JHO/OVEzwSDGrjcuSxfMjGSLQpfLA01\nOrDwqD5TlayNIvg7cmffjBNbxTG3F0pmzd/u5cHj3o5vjYTFbGvJiprh0jBOjJzGW9lO5nM//adU\ne6LANOVzyNfjiFbrmAtV1ATJNyR9JtoGVMtKwF4/ugx7TxWUULbDAo9NOFML0W+8WMs3b3Y5k9Oe\n4Zy0TFqvz7VKHtopXgMXTV6iliFaNd9nQOp0FrFMI75e2HL4Y/p7sFr7MXQkxX5ZvGBnzq8kE3rZ\nMnzng7lNvBuKqfJ0aKMSQgmKZo2t9Si4/OdB5DGv0UkruPLcd8OdsGRYvCYmeTf4S7kmdJlJ1See\nCb3251EaOPvbW8twD2eR1zgA5EzHH2CilPXOEUKYVLUyJ0nH6hLHPMoNaDKtRSbMdT1wJvBXSDHX\nqtdhBL/kOutSbxeXibPlaKBFF2c6g8UBBFfAxZ00ehnj+sFqpjKSeqquOkU5A4LwFiCBNcNzBIue\npsm90AUKArP8xyscFK3aagBAirlfXXMmC+v0m5HQmDBamZITsFgGWxL1/szZC50Qgoceegjd3d3Q\ndR1f/vKXkU6n8eCDD0LXdWzatAkHDhwAADz33HN49tlnkUqlsHfvXuzcuROGYeCBBx5ANptFJpPB\nI488gjVr1kRc1ceybd97T1veGrhpmzg2dAK6lsCH3v0fvPMnR85gopTFz950t/B7FLxgMkwbo1Ml\nvGNtKxwvcBZfG4jSwMMmdL/jJQTrmbquwaQGc579rkiCi7d95F+wOicZURq4sAsxTjSM2W62XIRF\nK0hqaVBKYdkUqaRTf6liN2SefotLYUmZYzctr0+tveS9s174Db+KGEWc8ED2fEJgDq1Vh/tXq7Ya\neZpFELbvJ2RxvJK211on98cq37E0pgJe9SOhePnssHdusbqYe1fFsgWbECR0/55tYsMMTG4JpRgc\nL+Dta1q4VMQsnNlc6sRWezMTv2x0n5GHdwkiVcBoyZwJnWKddjMy2lqsTb4dwCWvDrclUrO51JeH\nb3Ec5qyBv/jii9A0Dc888ww+97nP4Rvf+AYefvhh7Nu3D0899RQIITh8+DAmJiZw6NAhPPvss3jy\nySdx8OBBmKaJZ555Bps3b8bTTz+Ne++9F0888URd17e4dbq53kVjYBBnfTK49uNqRTkzD4mrC3d+\nZLKIYtnE6JTrVCReY6wZlkX5OoPlRTtBJXQNzWjzzrExm2y4hGjnKFnWJJnpizWVcW2UzrqdCYY4\n3lncsa7OXkEveQMlmsPrVybwz8d7vJSYvSOzGBirvU6+FGGtCkKvZ+a3YNfAo4j2sa4xoEqd28K7\nh8mmCbqmeVq9zNlVZmaXbUYhM/nLNTv/dwgO5LGc2CiFYdpc5r9FC2Gs3sD4dAmvX+YnQEcGXsG/\n9R3h/G56R3I4fXGMSzsctGDZnAbu39cY6WK+U9sL3Q/FijFJlOQcjyPk2WlpWmtGu7ZOnplP0pY4\njpJxI4HmrIH/3M/9HD7wgQ8AAIaGhrBq1SocO3YM27ZtAwDs2LEDr7zyCnRdx9atW5FMJpHJZLBh\nwwZ0dnbi9OnT+NSnPuWVrVeAO2Y8/zhvFqBDQ2uqda63tGSJSlRDa3iUi150mxDwbmXuzDEcsy24\nWuhMVBiZrmtoI2uQ0daCwMaMfsVrm5+AhnUcYerWNO9EnBdFJvCDGnvwZwmaMmsNrFMVZx0wR7Po\nHXH28J2cNdDWXJ/pcCnBak58Okvn2LB8CwqBHekGLYo8iDWgyZZMJGV8DZB6fSV4HTcjoC0JP5KF\nJOq6WKMOTh5F+5ongpNgTsCwTmvM7yiZODuWNP7copnQmeP+sRy2/vh67+9SdZmJHa/cUN+pvIGS\nVcLZ8fPccgwQDDkT35dpR3mhMw2sHrdrNyBHJ6qna49RwWO/Xupll3Qc2gTtZL6ma5o3fZEl/5H7\naERbn4LMaw1c13U8+OCD+MM//EP84i/+Ijdzb2trQz6fR6FQQHu7v1F5a2urdz6TyXBl64FS4vV3\ni1Ac6X8FL/W/PJ/bWbKI1lynDT4bUyIhe/n9Z5JwOyINC32uszGJXIJCs4Bp1NLARSb0hO4EYKS1\nFjRrGfxY800AHE1OupOP4HZkn8dxYqvpzETF8ihKOzLhb6AQNJsv9TjdIO4uUEP5EUwZvhe9KNMa\nodF6IwWFCcMPaYT8Ocn27xYPaPzQlhBpxgENWa86ujkauCaohW2LfywzifNtZJePuE7J1UsFp93Q\nz0hEfXORzI4lkscAOQ+TGkhIUgmzGnWhUkC3/RpKmMGRgWMh4Q3wfUz23hgWayESpYp2J0XRFhGZ\nBSWVYvqYqK9Sv7zs1w9OWUXXlO4ZLlgWjBpG5p2J7ZFHHkE2m8WHP/xhGIY/Sy8UCujo6EAmk+GE\nM3u+UCh451ghX4tUymlyMq0jrSVACNDSmoLd1gQAWL8+Xj3XkoW+ppkronWWv7+Xzh1Ba/We16xp\nRXNzCiUj/Djb2poA00amuQktzWkYFkUiqSNFgaamFCrV7zQl0jCqW0DesLYNqX7nfHNTGhVmo4BJ\n9EJPADrTdTKZZqRmkv5x3jluSTahYCXR1JREazKNJt1pb+vqdvRWy2Ram5CaTSKpJ9Da2oRUMYmm\ndBItLWmkjCSa9CQIqdbXmkaq4By3tjZ518lknO8BQHMijYot+B1a00iZzvmErnGbdOg6sHZtG9Kt\nQJHMIm0mQShBczKJkiV/RXSNoq3ZuaeOjhasX9/u9c+169qQTIjX/ObKQvSz1GASFZMg09GE9evb\n8dLoEcyg7N1HS2saeoszeLrnVq1uQTqdAAS/s0tzc8orz54rCn7P9kyLf73mNErl6nFTE0pV7+32\nTDNSqSQ0DWhOpmBVn2WmrRmp6SSSWgJNehK2nURLUwpl6owNra1paHYKqUoSqbQGXU/C1JJoSiRR\nrra/va3Zuz7blvY2vy93tLcglXX7ntM3ASCppWBVu1JbG9Mn25qRyvn3qid0pFJJpJI6NMt5n5pS\nCSRJAkk4bWlqSmLWsPFj69qc97bK2nVtXv0ua9a2Yd2qFunvP1ei+th4ogskaaCUnMCNHT/OlW8d\nddq3anULWgvO8WxlHImUhulkP9a0ZMCKHEopRrJFrG+HP5atbQv1m9a2JhAt751PtySQKon7XpOe\ngl0dL5qTae/9bU6kYVSfd1trkzeONOspoDp5zbQnMDWbREpLO8+4kEQ6nURTIoUiTaKlOYkETSFl\nO/2NffdT09Xn3t6Miu2e9/tAa5s/drUz42Vzcwpl6p5v8so3p9KomEm0ttaOa5+zAP/e976H0dFR\n/NZv/Raampqg6zre+9734sSJE7jrrrtw9OhRbN++HVu2bMGjjz6KSqUCwzDQ1dWFTZs24Y477sCR\nI0ewZcsWHDlyxDO9R2GazmBS1kyYlg1CKHK5EqA5L/r4uDy8YSFYv759wa85lp9BscDfn/s3AExO\nFWBUTO+3YZnNlWDYBpJWGaZpwTQtEKLDtgkqsL3v6HbKM6NWyhXvfKViCetlKRX9a5eK/ncNy4JJ\nLRgVDUWjAquqCSVa/fLFklOeaNSrx6Cm83xNC7rm1BGsuxhxzSDlsn8fRNc5jVnTNExM5HEx9xq3\nTlqyzGpdjF2OgUJHoSpgJrIFrG1NedcYGZtBU9I3qRfNInRNR3OyueZvKWOh+tmbl521yXdnZrBe\ny6FYMFBinn+hYCBBnHt0z2UnCzAqVk1v/NlcOdRvjDLbl/zftFhg+pvm90mD6Xv5vFNG0zRUbNvL\njV2p1kmgAXoSJrFgGDbMig1CKcolE5qtwzQtlAwNsDWYpoWy92yde/SuwxyXSib3O4jOUyRgwT1v\nMX2Sfx9HJqpKDE142/HqoLAtExY0mNRC3jTw4oletLWkuP0MJqrfLTDv/Ph4HqRybaMhavUxmxBo\nmgbTcJ7PpDmOSvpWrrw7Jk1MzqJQMEApRS5PneeT0DA7W8bIZAFvW9OKdFLHbLGC4YkCpieG8a6b\nnbFhIpsL9Zt8voxcqegt8RWLRqiMS0WzvefKjgXsMftsdN3yxr1CuVgdc5pQro4pBjWBhNt/TCRI\nojpead67n2tO+ONSoQKj+ju0pjVfXjHXzOULfn/X/T5TZMYxkzh9vFgU7xXvMmcB/sEPfhCf//zn\n8dGPfhSWZeGhhx7CrbfeioceegimaWLjxo245557oGka7rvvPuzZsweUUuzbtw/pdBq7d+/G/v37\nsWfPHqTTaRw8eLCu61NKvPUkm1AvKOTs+Fu4ff1PzvW2liRRYUuOOVz+me8B658D5E4bTcwMOLQ1\nowDO8UcPm0CDMavJZJwEB4LrSLIWSRMjSL4rXuqiAicn3/EoysR+vmcSm29e7X8WsPK5yzv/6dYP\nCtu32BimFem8xp/jz6fRigr8kMapnCjumTlmauD6D7usI+3VYccfCgrZfhYJxLeEyDzcZWZPvbph\nT/i8pB9yfwRN6M6nrPAGxGbl670G/o+v9KC9Ne29RzZMlJjlERbTtnF1cAa6rqG9OpQ0JdIoFXTk\nSyYqVh63vqMD5YooIYugv1FwjnGyvPJhZL4V4vMWdX73JNKQ+UVErU3L+jKLxSgYUdvURj3lOQvw\nlpYW/Omf/mno/KFDh0Lndu3ahV27dnHnmpub8dhjj8318k4yieq9E0q8V7Q/N8gJcEIJTGKhKRGd\nYm8pMFmeQkJLYlWTb5oKhmYEoZCvL84WDfSMTWHzO5pCQoj9SqLqHU4I5b3EY8RYy8NnqqFVhsXF\nSCS5zVL8NSV+cBNdSbaOLfYq5dtYYw0ctQdEsf6N0Fm2DjuQzexac2nqKvJmAT/1ttuvSX0Wtfx2\nMrcl+l0IsQWhhFrkaBOaRFHRecmaINW8r7H9jQ1j8ta3A05Fad2xeqS1JojS8Mi3MJVNNAPHAidL\n+fanfD8UCaxUUodp1Y6NnosAd59vrbwJFyYvwSIWttzwE6HPcsUKwPjamKQcKgMAs8Wysz+3DVR0\nR4NM6SloVUuFu3e3e4/ptOa9/3HelVr+JfXv+uX/Frc0vwezxfNYp90EaE5WSwqxgzA79ZIngGGv\nCazR3okpOoT2FJtURjxh9Zx7I3wdGjaRCxvNVOseXxs7i8O9P0DRLMkLLSGOD53Ey4PH6/wWhWS8\nwPiMoxWNT5egVac5fmICfrB8z42rsPnm1VLnNhkyxx+Z41hCFw+GATc15x/m2UpzUEdo7sHrhOqh\nEbGlkkpDgy/zJ2uir9i8RjVfbGLj8tRVDOdH+BSoc8K5uYpli2O+RQJc6PUX3U/kLRAPYuyxZ2PS\nAlpy0As90BZdSyKjr8J6fQNubWYtc+LwNm5Sy3mhS0IuwTp8yvo1mDLBIdcfyAhs9NlnUdb5lMiU\n+bkdixqdUya218bO4oXuwzUtel3TPeibHZB+zt6XLCyPnby6CYKSetJzenPDEN3QS0opLvVNY2ii\nIBTgwTutLeQlWrekX7FPKZNqxy2J9yKl8ctc/KhUFaziOSf3RzAMca1+I96t/xTaEm3itggcMntH\nay+bNawAB6XMACrvzKOFMQBArnJ918ZrYRILJ0fOYLIczl0uIjiIhv4GlSazcQWJLrBZ8ppENWZW\n0yB7CXjEHVUWOwtu0JXUWYcMkO9GFmfg5Ls9hWxQCC818J8GNXBG6yYEo1NFjEwWUSHzFbI8eSb7\nXr4yv5hz995mS2Vh1r64aTzjxd/y/c0/FsdY8/kC/Dr4cC3fhC5qQ1JLQtM0dGjrkdabQp/XaqM0\nDEi2lMMJiRjZ2gJWCxsmTBgYrcY/V2gJZZp3BHb1nR+kF9BFToFQoGLauNAziYmZeMqJOxbWsuhR\nAKZNuDGGPWZ/YSLZZ0Gc951yXvrDEwWvHxFqg4JitlgRTqSNgKlddl2nfRKtW2ZCjzCVU8bznFWU\nZPEDTUwfE63a6VpCajbnFRHnD8uubZFoWAHOxkbKzEklw8LYdAl2HYknrgcDuUGMFcdxfOhkdGEA\nwQmKSPOTdVx3Nqxp/u/EDoYQHvvEib2Wd0IxooGZXcenYDq/5pfi6xMPrjJqmzfFGk1UrwkLcMoc\nA8fPjeDVt0auuQYOUJQMC7miiUKtVLoxcJ9j98i0MEFGrmgiO1vmFO7/n703i7HkOM8Fv4jMPPs5\ntVd3V/XG3rhITbJNUqZtiZZ9pXul8Qz04CHGoqUnw7AeDBsmYMiwvMCwARkwCEEPEmCAb5SgKz0a\nF/fOjHU9pmRLvpZkWZQokuLezd6rutaz5hLzkNsfkfGfk6e6W81q+SfAzsoTGRGZ+ee/L5HlyZSR\nvUoRV0KSqKuFqjyUOTqWmAsqq3pGXW7bDniCyhRyIddKwWngloWMvU+qN3Ah+hEuRi/FI5JhaVqe\nihQuXN/FKxc28W+vXGfnmBY2doZ4/eIWLq3nLZrpDulzMPFlMAqx3fO1Ilvp/SnoZZ23SXAWPW9r\nvnThmq58jW8MQ/Zqs+hh3Pu2DjeQZjymVzQGbieGpuJk31c5beam08jeHWD/CL73ynXcSKsXHXo3\nsfDpYLM7xJuXt3F4Kc6bv7h7RftdxRzcCilj8jHMXA0Zbyxh8htHdFX+RwZmNzIbWHN3GRAghI4x\nW5XBdU7gSMFuQs+FHzsojFQfu+oG5sSKRoypGXF002Zuc1WVmdZ6B2/N3ApRvmfyKHYHIwz7fY1R\nKhWi+M1NYT5B+j7iObjgLyroLczUgWvATKsC1afWnFwDt4Er3IlbYwu5UOLOuH34PN6y9dIVekqv\n6TAchZpqNRgG+NeXrmljIqUwHIUYqh6CwWQyTourjPMhp9Xe3lnfweriTDZ+K7oKV1S178i0Wr11\nJWX6FwrzKhWx6yotXmQynR7b34Fj2pwGzpynMFLxM5FCZrEYHEP2ZAXAwBwyxtc92U0zDvatBg6A\n+MDtL3SQpFkEwbhX/tOHMoVHKLz01gaGfogbO0P0/D5euP6j4pzJ/z3ouaH5s1F5JSqRjp7OrOSB\nmiAZolvi3iRnQtdmzz+U7FjZP7wyHJxvjBGD3fc2GWsuRC9iQ11CD1uaFk+1hJv3U/OwvnNzsR3p\nPUYqIj5wzQsOQDflhar4PbXqJfowl9A8dP92rt0uzzZwcmUGS7N1TTCkY2xoQOdWxLRD71HTmjS8\nBjnmrFX2Qi58q179W/ExLPiSTXp2fXNQMKXu9H0MRgHeiV7E29EPJhYO4irtmZDisJAhvv/qdfzo\njXUoBayp87gSvardFY2mputre01991Asnabn/WiytWovPnD2PPOOU+uCgMSCGze1OVg5QubT72VR\nHEVLzMMT9mqMnBKvxQ+VECZM2LcMPE4tSM0zdsRIGUUYqXdVZayy0lU2PnlLGzsDfPcnVwq/q+Q/\nYZmZNk4wnwFXBY0ClQrromMd77BSpF2irLiU2NveC+MysH93Rgw6YzGYYG24YmnHmld3GgdpAFKg\n1WumhWJutQZOYTAqEjw/CiYQuRzybyi0X2N5PTaXQL0yWQssY+WhGolruFo8NyZxWuU/axBbDqZJ\nNjVh0/MVWSG/53M0vRazR7I+o4HzAZc6ttpMxibYGN/G9hB9P8crGrVuB92nzWvD6bcW4Y3LG3j1\nnU3NfaJp4FHRVA7oDDzDryzwbvw76vt8n/Fs3TG4XYZp67ENxAVCXk0qVEk4WHAP4bg8h4bM6Z++\nJjAjD+CAPMlaajgLIGX4XMzQONi/DFxDyBg93766gxsk/zR9mN2Bj+7g9hHRaWFaDZy+/MvrxaCl\n9P7T0TagDTuUKjImFtnYFC3GpCh5lJJC4IHj82h5uZVgpOL34sJjGHRO5CihBUP0x62dHVue0ZuX\ntwvnMsJS4n0pKC3ynGrjw+AWm9Bph7nAx6sbb+CH118CEJtK/9+3/qFUT3KzMU2mgROCnT4Duubr\n228UylmWaumqvVVynnk31GxPNW2uHziFVefeuK1jddnYQzxeY+CCaOBSYkXei4PyNJoeiRZmNfDJ\n5nRt/RIuJhNShkjL0vYGPgajHK+G/oR+CeR4FI3w39/8e7y68QY7/npvHW9F38dl9RP9anKPoSEU\npGBLgVNQbM8GWqO+PyrDwPl7pTUsyuAb9/7SNSQcSCGy1qDT0G7OzUePqzKPeKfBvXe9D1wLqlER\n/CBCfxjEOccJUOn47avbOL3409whD2U/3Gw8eZcRio1IAGVJ6zHXtPi+LP644tp2cyGH+LZa6AAw\n267iVx46BkdKbHZzbXeU5JK6wh4hTKM9awTZeQv6ZM3H9hEGKBIOW81lMM9JERM0oPvA+0kjkElM\nLlJRKUZIYegH+JfzL+LaRh+Hzh1Hqxnf28Zgc+K1Zo/2cW4E2gkru4Y0wCkDOhGVQHItZzqk+FN1\n7GZuRwrMtWuoeg6GRLatiw4OyXYSCJfThHStCBEOytMYqh6EIloQRGZpYtMjWa27jAY+vZk0DBVC\nFeCd6MfZuaEfQor8vqbJKruR4MZPNl7D6bkTzJhYoO2rbfiBJtHla0ZF4Q+wm9AjNZlGAchK544D\nC5XKzi7N1LGWxPTpUf52Zk4FQPo+wsyE7hiKhSjMUdxLcR3T2tkUc4WCMbz5n4f9q4FTnGJMQtSc\nQbvZ6PMovLV9Hv3gp5cnzn3crJmfpIgpS8Q01aPGvfhMGrYsw/skJzWXMDVw+xjPcTKNqpaY0Gui\njYVGXL2sidm87SNlJAJYFMfgoYoOLYDAEn07TNKONtSlwjlrC0hmBYWIBPHo95D6wF3Jy8uvb76F\n//Hm17Hr26tbcTAKA1zb6AFQuLQ2XUQ6tRhIKawVrqwpOwkCuWAibllg3hkTOEbfk1aC1jA1Hpir\nY7ZV0TMXMq+Lvq90TqUiNMUs5uUKegPK4MtUCbRbALTxtHAR7Cb6srrcKAgRQndbBGGIYUDOKYVB\nMBxjXrZryxz0WO0+37UWfEYDOG0ZHUkqnO37oYJkmXgRc/taZD/DEI8st61j9JiE/MpIROmBfV02\nnsO6vH7fQuCgPIVFeZQ37ZdEjn3LwDWEhLIyJcpMOMS+3L2KF9dexr9e+f4t3yEHNiQOoiDp6z1+\n/Ka6YrnX3AfOUQWaimLz7fKFJzipkBAoYuqsOVUAAjXR0sbQbmmuK3FCPoIVcS9+7uD9OChPoyOW\ns2CiCKH2JczIZRx1HoRD/EXxq43HBGryRz+JyZuRwIA9pYVj4NtqDT94583s7zBU8NUQvhpkvsJx\nTO7lGz8BAFzrxerDOCKrRbgHATw3ZiLnNy+jO0XBIrODmrWQi+UZ5MJiDqXywDWCRs8zJk0qAEp7\ned80x96TVIvW18yr/SniA8/vsjfIGaE0zJhp4Gbbowwgn9/VNDjC/DVGzaSjsfggdJN0GBYC3RR0\n18woDPA/zz+Pf7r4vwAU6Z3uFhkTxZ3sKa01DwBDEmeh1TrgjiOdNqf/xr784j1HNxkvIjihi4zh\n6JgiypEuVOYmdIo/djy3v0c2jUwbkx9r7qCSlrh9zMBziCxRsYD+0kIVYmt3iBcunNcqEaUaz80W\nxJgGbB/uv1z+Lr75zrc1bTofn5/pq60CcX9p/ScYRJM1N2UccKZnDfE5rZuRHOtOHcflw1gR92lj\nKJOXUkAICSEE2o0afvH0KTzx0KoWDZxp40y1JyAnroPQXtKRQqF+dQmwB7GZ7y4heBjghsorWAVh\nhPPRCzgf/RCXb/Rwca1bMpBS4JUbr+G/v/n3GAR2cyJtDx+QP97ov4x/uazXFhhXCMIMNspLqVIC\nPC6wbTqTH2fZ4euf63MenzmGEzPHtXmGSQ/qulfTI86tM1C8yu/r5Cpn2QFW5H04Jh9ChZRiZq1P\nXCYH7Gb28SmaeiW/K9Gr2pghuloUeCrI7Ix2MApH+B9vfh0vrr9MrihmFpiglMrugfqZu0Qbp4Fr\nVJCkaWqhZlpX2djUB26rmGebnwPzOyqjgXPlesPEvSLhasLbyc5JAEBLzOfDlX3PFDjslQZe2fal\nR6SXg7uCgXMmdBoUEEQh/tu//Qh///q38e13/j07n0p8VIK/3WD7cLeGsfn1jYtbOG+UzzMZSzLP\nEgAAIABJREFUvjVnOTlVce1BPboEnu+keAS40h4ZqfvuCeGSugTsJNWvKIwL0LjnUAcLMzU0ZBz1\n64pKJo1SQmYKtE0xBwCoOTR1jrMkTJ9jqSySjs0AaAPKWNe3B9jpjSZWVUrhtc04uGhzaPdjBxqx\npMf6/D9+6wb+27fewnbXrtXQqPk4jKLYpmScBl7x7OZjDsoEfJkaqkt6Tr9n4V7cv3BGI4atJFL8\nQGNp4vrxHvQo9P/9F4/j6AGiXRt7dEUFrqgYRJepHMe6AoglqjQDz9/Chr+WMZoULkUva0yWMr7N\nhJa8tXXeOj+XzkVrLVDBmaZ20YAz00pgO58HRuZpZOlzspvTpwdaTMd022XnNetIDlEiBDnQ6f89\nM0dxQj6Cqmhq523KF2s2Zxk1pZ32MS6TjmbCXcHAI0RWpJQaA48wVLGP8OLO1ez8KPTjyHX103sU\n40ypfhgH42nlC43hnCInpUSbzcctcnAuSpM2fmGjb62pPIBw7Nhs9sb+lXOr+M+PHdXO1Zx6rPG4\n77EzcDpYCMyLVRyQJ3GkcQw2oHnreuRyuYjprJCL8UsZ0AN54gc+mLL9I0fgqckxUGE2fxTpubY/\nuRALANc37WZ1avZMGffrF7fy4kcsxGvYGomYUBNt64gydQccIXEyqc9PgQqSp+dO4tzygzg9e3LC\nnmPIu5dFyT3o+9a66TEEWHLnOQGF0RDHmdAHyIV4JnwHQ+RWNzOo14QyjFGRdBb63Q1DysDtqWOj\n0I7bebXMtJCLyOiylYGXqLGvFG/VoMePnMkzEByGjs04cWTzrDygve+K52RMPz2vucALu7LBdEIt\nDdQsk/kC7OModApKodC+EdAZeBgFGVLSD+L61i6ubfQw2HWBe273TmMoowFSf4v5Lsd2zmID5JB9\nTdarBUUkYi5kchPpB0QRz2WD2PR9zbTsUed10YYrXATZunpAW76v2BTXwjyrXddEG74qRoCXCrgS\n9ij0YhwAp4HTAJ8Y4UYTc3WNtZh9hkb0L91BGCpI17iOuV092EhBISplJbAGQXK1vxmhr0wWQ8Nt\nYYi1wnPQcsWFg5XWwcKaeT6zwaCN1qKFuTXL0jjLS3HvNHCN84GbeeA2EBC4Er2W/c0VN+mqWEBz\n4GougUlFiTgfuCL9JagGHlCCSdx5ERKzuBDwA044VdncQPy88mdsuX9ja7ZyszFtdDJ6LplIcs/R\nswvy4xzm3CVANuEKXfHpNCs4tNDEoYUG3rm+S661aODGflPQCwHRY/t30HAbWBBHUBNtSEtWjA32\nKQPXCWeECDd2iloDfVBBFGWXUR9iP22sHpSRUW8VTF5LKYU3r2yjWXMLKLOXmjSbu0Ot6AXAIxJ1\nJxTK/lm0dxrI41pqUwOA49hN+xpkkq7KCa2WCpID51MCgHmxiggR6qKNHbUWj58QhV6YXwiEpAhO\nYZOIhRI/ZBg4IXpRYv40TdwU1rYGcB0JsUBXsu8zJAgQRpFmR7u03sXKQhMe03OdAjXB0iDHsjDu\nHeRgt8hwQYWUabrCRafaGVuASP/BzrQpzLsHsSG2MCcOWX+nAkTVo0FF+SFlbmwKJSPIckFWQtDA\nNX3/PqPdpnilYNbhH1+Qh4vFiLvMFc/TmKEUh+tVF9GIauDjK6hFUBCI+x2k76+MCV0r2UwG8TEU\n+fkKCXzUfeA6jTKZNxC/p59/IK7AdvF6Un9er2jDRXPajzF5jBQCszIWRpW4ixm4EAYTU0rL/waA\n1y9t4fWLuQ8x9hWmJhHy1G4D377Wu45L3at4cPGBqfN6UwhVhB+8FjMf0dB/i5QqPITYOCXGapcm\nAykTFcy2UaSMlVzsuvb7pb5MDlKCORyFqDeKqMkVmDEJwZxcARB3c8rGS46h2bVolkfoZgD7IAAj\nwsDDhOCb6X+RitDz+2hVmnlHKeINGGtNyeYItb/7wwBvXtnGmcOzCFWQxCPY92hacsq2/MleO5uh\nAHLeDnz1NerPBN6/8vOFa7l66bY1Y4aRb9wRLg7J08yu9LmrFbvQSf3AentN6u5hcFWzBOn75ALv\n+EDOFJTV72yMIMfMLMqOARvDvGuihjPJeAHAnxB8ppSKBawJGvi4NjlCyLimOgAHkjwVu4BURgMX\nhC6wLVozxaJowRkHHI7T9UOVBtE5rMtmHOxLBm6CLQb9h6+vaz6iQJG6QaSmdtbgo9zzKgXfSVLS\nDrdWsFifL/xu7vafLv5LYYzGbI29KRWnw2gfdtLNyx1TCW0cCCHwwSPvL0Z4sv29dQK1Ku9HgBEq\nLkUp+jFN3lejml+bEnIuFkBIg/pZgObfcsU3bCY6c0rtvqXQrPoc+JpGkpgljWf7k43X8frmm3jk\nwEPWOVhXiya4FWtMp4Torej7WBRHIWAP8KLXUfOpHQiLyb6Z6TRwLkpb12Kp1msXSLl0xjxrQL8m\nK6lcwnRF/fpUAxcA2mIRO2oNDbdJzgvMiVXsqDXU3VzS1oPVSEU5NuIJLGedVCzHxAEzn38QDPDv\n13+oXcHNY4PLg4vZcVYjXCQxIgkHDxgrQX5dBKlEnNaX3LYVZVTMzFLaZtKc1FLEpo7RQDAj84XO\nkx/nsDBTQ7Pu4aGTxAxGxyig4yygLRaxUllFkgDBzldGAw+QBtGZXfPKMaR9GcRWDOqyI/hA5b6L\nMAoJgpKP/jYw8HxNBqkNQpJGoOvXEsZludwqoRlRu5OgUB3Ia6BV0aMu6cdx+nAeTGRqEjXRQkvM\na0FBv/pzefF/tgc4gXotR+IZdw4CEstOrpLSe+P6Let7t/vGyjwhLoezbCEOm9lTKYWe38N3r3wf\ng2CAS924rv2VLu00VYYpkjlhz8BIYU3FkciRinBx9zL6Qe5qUoaf/qX1n0xeEDyjtEGZ2tS0HrRT\nwu88Sds3U8FS4TEoEYNAgy0pvrmOxJI4jmPyYTQdwsCFwLxcwTHnQbhctTZNiCmTDaGfn6SBKyhc\nWe9hpx8LjaYG/uMbP8H2cAfbvREurnXZVC0axGYsQHYmsx0q5LjAmfmzeyBpZPlcNg1cZ862Z6eg\ntOeoBdEygrpkLB90C4szdXz40SNYnjNMnkQDd4TEsrwHDSevk08L3dCpOTpD7ylI4hukcEu1ozVh\nfzJwE8EJAXOsGqhAGEV2Bp7gctnKNyaMq35kq2w1Dui34wchemo7LvVobC6OwozHm4zedfZ2IyxR\npB8Kk65BgUq6VOsu04FssVPDymITj963DM/xcMJ5BLPyQL6+Y//4Bfm/CUvyODxU0STakamB24Bz\nL2gukTG3NLIIb6FS+NH6y7jau44X1n6MShJrMGKClNiqb5PytM15RFwr4Pk3v4eX1l7X9qPvr2ST\nCJsGzjJVO1GixIoyTa27WAmc1L1hWYCGBikjDph4BQqOFHjo1CLee4+uhbUbHo4eaKPqVFAjjVu4\nIDaH+l+5aoYajvGCSxkT+jtrO7iYBFtRE//l9S62+nH2zaW1LnZ6IwwIw4lUhB+tvYTt0Q5rhYkM\nfMsLRkWZLhKMqU8OxPnWCiZjsjBwxVv69GMqXNnTGbnI8/FpoeWg1HVkHbNscQqhyjXwvZjQ9ycD\nNzVwJlc4pTRCJEidBiVpJvR0zPSvchT6+LuX/x7fNopnpBBEAfpBH2v9dWO/JeYOAlyOXsE70YuW\n4CeFSAFX1nt49Z1NjEi7VGcaDdw04U0Yo/ud7WO482XbjL7v/gM4vNTK3gclHppAYHBYWi2JQkcs\n4ajzoBYp7zB74dJ9tDGaNs77w2wmxShSmRDUC/pZsKBmbif3e93AGxuU8VsLIXBxcx2X17v43ut5\nbrB2reKDm0wYBml63WT8WVnQzc35nnSmlrbBNdMNbcC16kzBvA03icr3S0TYO9LBPYc6OHV4Rjvv\nuQ4euXcZ/9svHEOduHo07U/bI8h5+h042i8c0M5/kxm4TgPToDMF4H/9+CpefMvEo/wBXeut4e3t\nC/jmO99mBTgt3AgRUv4dN/AJoZSaqIGHUYRQ+RCYEIUOs9oZxRmdIc+2qji00NStNlyaHicslaBL\n2Xguf14LbuOEAzsDD1T87Tvw9iRY7EsfuHlzRjwb+kEfW9E1YupLk2Siwgy5CX36aLZhUnh/c1As\nwwnEmtU/nP8mAODDxz6oVXNKYXdEKqiRLfT83NRppvYoAK+8vZEVd9jt+ck9COJHTnV0HkppUFrA\nkF37ZMuwMkUKyoC0fDPUpMlp0c2aC1jqlvBNJPLjeqWC3miQjKF7p6MnR3cDerGVFCIVoebGgsQw\nGKJTiXOkqQauyH5e3Xgdp2dPFJ6drV/3OBACGAbxfvyACK9aO8jysJ4E3HHaUVssYUddT9bmhD66\nP4HD8n4oRFrJVF6I4p673bSfCn5hGQZuBJ1+8Nyqtp5p4fMciexL5cpyav3LGSJN/vAxhCB1Kcq0\nhqUBdGnamStcrEVXMFS7AObIfEQoJs+by5KwCXZCSITw8Xev/j2OzB5CGKX7tdOdKFKIRARX5FHl\nVvatAEwwoQOxoHVwPraqdX27a01KoII6Ruhr3+qMN4tZcQhtsViKUaaXpgF72knoNErHa3JflIGT\nUYfrx3BpYxMdsaTXFyjJwvclAzdVcCp9Kij8w/lvYk1tkFQkQKkQeRBOkZinjzeNxCzbzm0cUM3K\njwLCwPOXmdbAVkrXELqjPIJ6fZQXnskh398oCJMpBaqegxMrM9ja9bG+Pb65xQRrVuF0MXrbMp5j\ndlMG12XvhbpHHPvHTGFpto7BDVnoj1wmIlSLgOaEElYQ0MG3mNAjpbKWqEEUaD69FEKjagetB9Af\nBqh6zvRphCovkyoAbA13sDncRKTo52/vJzAOynRSYsuLGv6+WDN1WG3KWFm7NgWbi0wIUSr+IgWT\nQc9a6hVw9zdX62BeHEZDzLDfClfFzdzhCPn3n2vgvFBull4F4uY56+pCPIeZepjtP98PF0tk4oVA\nzCgVIpy/toOLa12cWz2V/GYPCgWAIFKouIJyQeta+pOzp4vFglaY3KcebEivPSzfA4UoU7bi8S4W\n5GFuCyzQUu6RpVwsYAr+kydfrB7ACfloYexdHYVeuDVGIaGRjArK+oGbF/5/37+IkR/hIz9/1DJm\nOkjrEwM6IwojhbWtATrNCq4mzSsuXN/Vmip0iQZeIKzG336Ym9CFEKi4cur0NVbiI6drjr34Cts6\nUStsMR1kUq9mnTKZRO4iySJUBQrMG9AjUrlIYN3Hz6xbwmoBGMUvElBRzIQHfoia5+QuHTLPxa4u\nrKmEovUGPv6ffz2PxZk6lg5M5rSFOudRjvvfvvyvCKMQS96qvhY7m8ifLz2rCWv66BS4AjpUU6Ja\nL3UBsXEWjLDAAS35OokulnFBcdkQFc/BnIzzyytMChPrlx0LadtVyZrT9cppI1xa62Kp7cFDDT4G\nWVU+QC+/S+FGvxhMC1joj9DfaxjllTCtedvZPKrI4LLB8fccC6zSOsbUwDMGTn3gmgAYXy/goObG\n76MhZtlYDA5SoS6MVCYMVlwq+EycYmxlzfS7KCe86rAvfeAm3usauGW4iMf0VYKgShSQOEXA7e6o\ndMnLSWZnWtyASqVXb/SwttXXap5T5g0AfY2B63flh5G+tiV2h0NMtmIWOx44IE/ikLwXVSdv6ciZ\njQCBX3zvQfzcmaWpfU22/VDplipS1JyuFdyAwspis7CenkZir4ZlfvzWfbGGYB18C5FUUPjuK1fx\n1uVtKNjTH80+3umYbj/GybWtfilf9TVSPjWIQo3AprhP83vjDU6ngmuZAMyjKFOKly3Ly85px2Fa\n7YvC6mITR5bb+MCDK/YJEeP4gjg8lbYO8OVT625en58TaMrCeMUjGUO0581uD9u9Ec5f3bbSqDCK\n0B0ECCI9aO1Gb6MwNp5bxwvdjx1D/p2Of34CdlqQxbAYkW68CT0/9pjgVgrzlQWsyHtxQOgld9sN\nrvR0DmlRJD8ItXNpNUnue+QMlmwRo58ZH3jBhE6OGRpEJddIRfi/3/qfONo5nF0wvQccY8zOscZP\nNXCt4X2iDY0rWzkgrQLNUTe2B1qOM13XdlzcW3JcKoAj7sgTjyc/0AdmML40DWNE+hVP65GgdLTd\nqGCnN9LyxOMPOH6nzZoL+jIeu28ZQajw7RevZHW9XSYATv+A7Cb/vXxkNg08ihTycr5R9uHTd1Xo\n9Z6MsWcX8CbVkU/wTeUMXEHBkR7CKCi0bpzehM6c50zo2rV2walMsw+dGuaH3PalFHjk3vHNTlpi\nPtEsxw4r7Mue9RLf3zH5ECKEueKAsmlkOpgmaZuZOvWB7/Z9eDJ/17Y1ugMfF67twHMdvJ8YYcoE\nsaVgCiI2q5cN8gh2+/1f3+yjihY5Y08F03K82XQxff40MJCuO9OczMArGQOPNHyreqkLLAfeQkd9\n4HYoo0CYsD8ZeOEMRR6Ft66M7+Y1iHoAaji//Q5U0vBiFPWxObQHo3FgM0VFKsqYtU0D3xntliIS\nAdPdJ4VJH38Z4seZPctAveKi50vMtqpsVPDU9cfpfsj4Jx46hO4gQKNG8oVpzeyal+8gMZt5rsB8\nu5oxcL3PM6OBayZe7p7KWRW4Ll4pHoz8KNeayDyhycCT8XT/uQDGM11qRQqigAiQceTwpfUe2lU9\n35VjgGUCHMuMoaD7M/Njt4QGzgWCKWJqnjQHBzeDp/TdOTIv0dlXOT1i85HHQCHv3iK3pff+zvVd\nHFluk7NFPEzLR1ONMt5/ySA2UXzfqTIykS4JWpynODZ1GeVL2b9b6vfmhCLPoQGR+v6za6XA4+85\nqJnETfC88VZLrjdF/HP8stiCVIxmXlYH358M3Lg33yj8bprAzfFaNUDyJcSFLBaSMYr9mEfhCM+/\n8y2MwhEaTd0vTDVtGsSmlMKl3Sv4/rUXEAxzqW+rO7JKgZoP04oflpsq4UMpk6KhX2AfI6XAqbSP\nMnvp9BJlNj+5wHMdzLb09CIpJR69dxnbvVFi4kpNcPnDmmuT1DEu1URb057zrj+DchqUjXACOb75\nYZSZxyUJqhz6YWJRSMZPKItZBmIGnl+ztjXAdneIbtcImJtSBedzl+0aKtuGk8YnaJYPO+HktF7N\nnbpHKGNC15qWCIH7js7h/NUd1Ks5znSaFdxzqIM3L2/rflxp//7GP3kFR0q0qh62+r5VcKM0MAxz\nDdwmSOpR7Yo5T0ZYgthMepEz//IM3FQgbEZ4Lo3VYUojN6pxnv5Cu1la2E6j2Tmwpa+GkcqCatnP\nRrsRLZjHOlwaeFUG9icDn/ILjQkB1WiLY+LI8/wDPH91F9u9Ec6eWCiMvdK7VjA/0nlS8FUuSERK\n4Vo/rm3eC/PUscvrXTTrnlY+EAB6WcUsMYGwCjRkGyNs6sxojAndfp4DLvjCjmxcgMj0ms343x0h\ncHi5VTivrU8/iDIaOCPQ8H5cfn9sRG9mQle5yZLMQ6s6xePj52/WoE7vI5xQQANIGHhCYIUAesOk\nYleZmrAFyNU/PYgtzoAY+iHrymHbcGo+cPt4CqxlJ30uY7B5EhaWKTik3ZMUuO/YHO47lvSmr7gY\njAJIEReEefOy7od29miV0hlfUQXfVXmud2oJiNm3zZXDFJ6aJo3MeJLpmmXuSGYMkdM4Kf7YGTXV\nwGl6brNSx2+c+xW0KzptmLwSDzXiukvjbQajEO1GbBFktWukwZ96rAG3JiPbjYV9GcSmF0MoM15/\nGrrPXGUnqYT3/Vev4/WLW1akHstQLYQ2nj7KCItJI157Z7PwQez4sd+sTIUriFh61c0xjNmHNS8y\n65TwYXJNH/R5Jg5h17VBUQtLpGHydh2Wgdu/FM+N78N1JJ/uUzKK2BotrPL9BVGEMApw5UYPGzu5\n9mT6EreHO3h7+4KGtCmxLCul+8rPmbVUhMAb9QUYtHbZyHB9/eOHOrjnUAc62Mdzwp0eBW6/P05L\nzmIKSr4jG5Rh4Np44zv70KOH8V/edzSb5wMPrqBVzy1BeykYlYPQ/uEgpVnx+y2+VLMPvO08BVOo\n1M3gMaS555PuTwoBJ7sNzoJD1tKEH3vFvjQ1M4WF+nyx5sYemGMKSzM1nD4yi19+eBW1hNYN/TC7\nVzbuipaOZRRwLhbkrk4jK1OtiULhI7aY0BUUoIpMLwgVzMyScYUVONYeqQj9YIChH+LqDVt+tr5H\ntjPOxCuTc8z75yKwy+ALR9w8hsCPy3OdBBM1cCOoSxj/AkZwFNurOYeq5+L4wQ4qnsTWFol+L9E4\nQd+J3feoQDTwSGHg+9jcHaK3mwc5mfW6v3v13wEAB2bzntc/eH09WancUw2iIPN3SsG/C14w1Rmi\nIsf5D4KYViczarYdJKONa7vhotAt60wLZYQiDceM8a4jNYFnYaaGh08t4o0fJOMZ68SkVYtx3zyY\nAppIcraz3xkT+rh2t7b9UEgrsUnScth+HX1m02ngwghiOzp7HJ1KG5fWcosmb3kcs6EJIITAe47H\ngbzXNmLaPfLD7FItD5xMGBcOShULfT4b/NSi0IMgwB//8R/j4sWL8H0fn/rUp3Dq1Cn80R/9EaSU\nOH36NP78z/8cAPC1r30NX/3qV+F5Hj71qU/hgx/8IIbDIf7wD/8Q6+vraLVa+Ou//mvMzc1NWDWH\nyrQM3NTAtS5M6QEACPgq1oY8EUvNYRgBnr7eeB+k/bdIKQQqwIVru9bocy4tw/z46BU2SOfhPndH\nONa0Mza9jF7LNpooQfSmJKqTCGkxHcemgXOMlxNiRCZhs1LyhECT1D+pjBoE+Z4EEMZ4lQY5am6X\nJKe/MLMq/sG9MxP80IefBEU6joDP0OkyrjydOdOzCd4JHff0ACOQ84wGrtWa53CY0cDNYK/i5lk8\nPH6og83dcj2YuSh7Dqi5d1qhWVvX4ju2gS3ti9O0v3Pl38n58kqDed8p85cTuvUJIdBpVnFlc8f8\nxXJkaOCGG+z++TMAgMukvj/3cDirzLSi3uJMHcAGDi007Rq4iJ9NpJQekEqr9JVYp6x1bU8m9L/7\nu7/D3NwcvvzlL+PZZ5/FX/7lX+Kzn/0snn76aXzpS19CFEX4+te/jrW1NTz33HP46le/imeffRbP\nPPMMfN/HV77yFZw5cwZf/vKX8bGPfQxf/OIXp1rfEdPJHeZHa7ZRBJAQTQfnoxdwPnohGRdiZEmP\nGIfoCsC1jT5ePr+RpYvF18TR6ZR50xZyBQZu8Sk1xZx9vEpM6JwUqxFRu/DD4YsiiOeOMW8eXGgk\npmf7PNM2i5mEvyaCZ38xJildG+eElcmMp0xgnhAiN6GbDCSZNowUKaGq46PN+mLzqZdlAH6UM3Cl\nIva69a2B9ve4fN30L/Oo+F44nGQIaoluZGa50xQyBj7uwTA/PXxqER98eNX+45g5pikEAugldstE\nbNNFy+rgJvoU/NWKptTq6bVlwBTSgLyZyaQ69QKxkHzmyGwpQUgfQ03o9u+QdR0K+/G0sDBTwy8/\nvIpH7s1Ln5oCU2odDKPchK43ILLvpeJNFl5N2JMG/tGPfhQf+chH4k2GIRzHwY9//GM8+uijAIAn\nnngC//zP/wwpJR555BG4rotWq4Xjx4/j5Zdfxve+9z389m//djZ2egZOohHhIYS9m1MKJlLpze1z\nH7iI8nFdtYkr0au4vFvFbEuvyjYJ0W/sxIRwMArQSlKfIhUV8ixjZhpkf+lrpHtk/I8C401VZKgj\nBIJM6zaJaDwRhy5FCbg4XgB4/IGDGAc3k55jA84HXiXpIJzFgA1Kg908rDPzyVHS2q4EtfIoKEGC\n2DJhTn+R5msNIoVvvfI6gBqGqkcYVTn5exiNMsFxXOBa1ygmJIQoVM9ypES6bSuDFzYci0Eyx1yg\nElf0hCvLm+fVk/2Y196UDzqdkwock98B1cA9xMWQZsVBK5HuNKvwgxD9YaB94kKUo+kOKhZXiMnA\nywercWDiXppSm78bbff5cbIVKQR4Tdj+Bind1zIX9lAcJ59+enxIs1uoBl6tOBiOQvhBBEdK+IgM\nSyt1VdCgN7tiUVbh2dOd1+t1NBoN7O7u4vd///fxB3/wB9rLbzab2N3dRbfbRbvdzs6n13S7XbRa\nLW3sNCBLtLWkUNTA7ak5lC2tRW8DAP7hlR9hq6tHnNuuf/nGq7i4e1mXtMiwCFHhOs5UFK9RJEbz\nnbp2hXatgmam0dYZg6R5bikjAVPi7Ujr8y7XEWvikFLj58QKPNSypiApzHdqqHoOmnV7a0FWSme0\nKY7Jl/FTjXuvKX6EkdKKq+iD9D+v3ujhxbWX0FWbeCd6EevRhbHrmzAI+5mJs0zUego2bWpSUGOh\nprMpdFrmoUGQelEO+x2yaWQlOgveAv6t76XEhK1K3pGtIVs4Lh/GonPU+v5szzQ9X4rWwW5CpxAx\nOFDWhC5QfMYZA0/O6xkH+TEtMPTQqSUyhtPAzfKpyXHJmvK3E9JllVL45YdWcHiphSPLrVwDD4kG\nTpVGo14AM3upPew5iO3y5cv43d/9XXziE5/Ar/3ar+Fv/uZvst+63S46nQ5arZbGnOn5brebnaNM\nfhzUqy6W5+poVWrwduOte8KDmKAR12seekF+q67jZPnb3S7gjeLfRAXwvHRcBA8u2m4LXT/CqaV8\nj5fCGhp+zkAazSou+5dw2Qf+j8MfyuaoVF3U6xUEUYSZmTrqI4/Mj7gfdIL4VelBRXrlMqXiMSKK\nzZ6nj87j8stXk/EuosiFEAL1ugcvclD1PHQ6NeyKKlzhorLtxtKh62WKfs3xMArjPTSbVVR2XUQq\nQqtZxRK5x3SfrVYVg+RZHVhuo1KJxzcbVTSa8aQLCy00K/ZcynSepcW2lo4xCfqhQjNZl+7rSPsE\ngBNYXupgrp2Xdr3/xCK2hzV0ZurZ+MEw0OZI9zLbqWfHNaeCKGl00G7VMXRj64kKZYZjjUYFXi95\nHo0avH5yvl7JcCcFKWKCkkrZriMzSdxzJTzpwEtcQNVqjA+ucDUcbjQqcGmt5fU4cEZGbux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PYvuUgHUicN17a0JmvgILXslElv1eskpB38jF4CBgN3HImFTo3V3snk2m9ph8BOtYkj8j04It/D\nuh+5t8GViU3z8u8U3KwxYJ+a0O2Rrax5b5zfx6KBp4FC6b/bvRG2eyMsuVvauHE0IF3R7LHrOnkV\npTJBPc1aBd0+ClJ1mfeefygG0VXm2XIghYAjPNThTX/xuwmE3ZzmMI1KHMYHbiVuymCsNqJr8Yca\nUwCIMxwKDUaQm+TpzGxhC9iKtOhnOEZs69vM9+jOz68sNbLAYb73uoP5dg31MQSU62w1bWc7fZ/x\nvzejgVNw5HStjXVg/LKEaafuGIiSGriKWbjQvnP6fnnhxqqBs1aC4l8CyCMkRawxA3Gk9cnVmRh3\nyNw0BodTuJQCVpea2O6OcGZ1EZeuXSyMKaOB0++NauPzHV6guZXA7ZD/nsrNuz8ZOKeFUEnTczFI\norTHpXqM80nSgCEAuLi+idpsyT0mezEJNRc1z3ZeIn4vztqQmZi4vdB1pACiVAtR5ANi4M4Gb952\nKGWS5jTwlFYJEQtmQVx1SnuXBQKbapBjHuyYYGp2X5zAKiYzK2vFNWJEF4Xzxe1xWps0CHY2Rgos\nz01ww0xdiW0ypM9mmt7XhfXJ/XnOTTBwyxb0CO88lU9k/9MvYy13mhA32e0DmFHo6bWWbRdM9HTu\ndA6CxlJoBXqmAaUUzszfA8zr7Ywp43PGpqnFoGUEMWl0dwIqnoOzJxcw09QzKu5qE7pkCARFqlbd\nI2PGa0SFspzCPtYspDGeMHLExx49yUbcJmPMX8sQsfy+dKGBVnpKQTGdzNi5pxr9bgN7tD71D3Ln\nKVD8aGSNWnS7h4lDZtTBtAE1bIEZWhzCwPdJvErfT/6vjj3J/6VdOGFdPSV6r3MwrQm9DOQ9nPc8\nhQauuAkdiNVui+9Se17UBWTgZsY0OXpiLBophYtrXfSGQUbP6HNvuXk8waQ4h9himR4D9UrS1Mml\n3SMZ0JSy/I9IKTywcC8eWLhXV9y0KouT8aGZ8IPlufqdYeBjtnhyZQaLM3uLKdqXDJyrUy3KMEfz\nQSrTjMGFixRLWU7Ypf0sFyjFELpsb4IXVuw6jn0HUkgiyRN2whA00zc+LcO57+gcTh8paba4A8D5\nurXzsOOV7UnEBXfyalQaqPh/kyqojeMtrLkbjJUANA98skbbcmaysYJy8wQcK96Z+GgXMmgCehks\n4sbcnAk9vvimTOhk/WnTKaedX3K4lP5uCoi2FEOKv8bD2+n52OmNcP7qTm6NZPDKui/ygyDrCyFw\n5sgslmbrmG1XtLE2emVS4BQiLfjMjlcaTjK4sbrYxPvuP4DH7lvOsnXKBiDeCShLZ/e9CZ0zI/LB\nNrr0TSXW2KSMDJvMZxiJ8gycJz4MAZ6ggZtzlooUpea37Jy5z/RmSyanpM+vJO7fN6Ym8LsB9BKr\ndmau55zSi22CoS4A2shVwWzPMRPLacmYQ/m89dznF/vDi5NybiibKZWrL8Dn2e+dwU1yK+0Fch/4\nnqdg8++nBy43ufj8BOzfPGvhIM+uVnGxPUznM8fR3SQaOPteKQ7n1zlCILSY1auemwSt0fUE2pU2\n+sFAq6jJ7Yl6IHW6n/9hBgrb5xRYWYz7sqca+Lhgx1sN06JJ2fH7VAMnSCWZ86zWVHw08ceR/0EO\nNTC7QY3VlJgPq2xtaOv5EgULdLB98CI/ryng9rvhIpZvT/TtTwvsUj2HJ3rtfTv+zHkL2bHWkUkb\nXySQdhVcGbskw1nB1MhnJwtM9IFbjllzqbTvnbsnnsGVEUC58zfDwONrb8YHfqv2UoqBT8g2sQex\n6c9uaZamXOnz2fKQ2YwGjWxQGpzThIg0RMmsHdCD/c4tn8XZpQdwvHM0n9QQQNN96fnbdN/2eyjz\nWufaseBwZLk1YeQdhJJotf81cNgJl8MSOmOyOCIjUUCNKExjsEII7ZFNyAO3AWfqZE3oXBlPXRVk\n9pANJuuT2mskD7x00ISw9RXaX0BLiZYRqMqkKpIZiVWDeS+MGboMlNov0/LTHpFuvz+q8SmFrK62\ny9aF5/ZiDzDynAoQAAebyzBhSR7HUPV4ofYW+MBvBm7GB0+BvouKqCNAt2CxsfnAqbBtSyMDdDrD\n0cJ4nKaCF84VLTIKUMr67vUMF32/UkiECeVwpYuj7cPgIMW9CCrrkGbOaX4HK4st7PZHqFcna+NH\nllvoNCvoNMs1IbklMKWgV7ZE7/5k4JyJh2Fw4whmnjNpRxR9rFEQY8weS5nE2cCjvBFBzmD13ZfS\nvy0mdE0DJ3WTytbjzssclhr+rgTBSPW8djtZuxTk30lpOJp5UzaxG+5ov48NjWQEC+64Wa+gl9XY\nL8EQsxuhdQRERnwrjkeGFrWwwtxkv9QS4EDio/d8yPqtdcRSsg9uTuvpUrC61MJLb2/g4dOLe59E\n28vNfwgeahOFRFPrRUKzClUmUyZcQgBN5y0cc4qCdmifnwaxZWMUE4wH+t3o35tMNAVeA9fHdxoe\nOg2vtGtxdoomJO9m2JcMnAtg4LRxyVHd7JTIo2+RI9PNfJpsxSpq+oH9PrROQmRjvAbO7MGiXQth\n/4RKM/BSo97doKUzaXhCz1O8shMrGM81PTfJ7zvRdZJs0Gb61n3g5DzrD7drU9x+uBSiRs1Fb+Bj\npp6bY3X8LVdUhu59cjEi+/mb0cBbdQ8fe/89N2X6lpBwUUFdzNzUB0G/OZWrrtRGBiezeNhb/5ZJ\nI+OEu3Re85gva0pGWWiRGc9BlYP0PmwKVPF8nj5Hy2hw+L4faNK0eyw7fn/6wDVCMGVQj00josdE\n89A/JotmNKFmtfU8I2TojIQvymHb9XuSjlvFHRb3IKlmoxScRIbzpF2WM5nIzfn87izMiRUAwEw1\nj4rXbkfa8QoMsbA9CREjTQJ2/NAF0GKRmJvXwBkGXsakn6F+HoynoHBkqYWTqzNo1UgrX8rAS8R8\nTAssA79JHLxZHJZS4Kh8EMvy+E0xD5X5i8ksqkiPAF0TraKRjSn2eUhTwcrQEIN9kwjyfH2yFzLW\nZmYXANykZ4LrSO3idD9hicqXkigrXA1z/bb3L01ioSSO7ksNnGfUHNPmpbXYNCUhiGdXL8SBHMvN\nmtJj9shH0Nr3pUU6WyqCxelJVCskc06RymKOXRTH4MDDPU2+uxOFfcy/MS9XMadWtEYIXJQ/l5qi\n55+S81QDJ77jFLTueIwAmkIYxT3m7f5qO0OW0v4dOCaTt0W2c+4m4xl4jkRF5iZ0aOswAqvBnGxz\nc8CX8Zx46W0HTevcI2SlS00rhaZpJkKdirLzAo4+noCtJzqlLeM6M4YWgUI/lgAimFUhZb4xnDk8\ni8HlG1jo1HMNXEWQiYJgFs6yv2ORfWdcACbPzN+dMG3K2s+QBm4nhpyJpfDBpciofZCicF0ydE97\npCAZBqDvt4Q5UuTn0u+zoH8L/d98neTjEIArPCzJY6gwKR0m3ArCdSfB3Pf4gJ3isR6FXsSxWG8t\nCgWeqObCGENQ03UurXXx5qVte6UuxrRfr3ioV114rmOkvU0mdLZvRRA+ohTQqcYFPdrVtv26UhXB\npoPbpYHfLNyq1bPSpWNmzDRwGB3AksMy2SvcMaCjWBgabrsxx7bPQABoVD0cmm8kKVoiWyMVJLnK\nl6ZSlu5Ta0LCPKZb11nu9kGTFBYrA3d1HjhXyL5s4IYJ5gfEkKIiQR2DOJyfjjN76lJyrinRWThT\n2CAcxHMYr1OvCZasI6iHzV5IgUIxjYwZuI+AE+joMfU4VrUGEJZ3kMtEMfue8JA4nzp9p34YMRq4\nnfk7QuLogXYskKrJ3wQFW9CUafF5/NCj6Ad9VNEojAV4DcMRAgudGta3B5jv1IFLxTU5uB0+8HcT\naP23M2ZufPOpW0X7EPmA20wD53DMZOBk3rTAiYaflmNlnLfRGUBkbjk/CtDMTOjjclgEAAUhgJ87\ns4QfvLaGBzL34Dicefdz8NvVNGVfMnAaCaaZDhnNfJwPPEqKt2gaVBoxKbLECdhScMqa0NMiMeYe\nKR2a2DBDZP8r3MhycxYzrSrOLT+g7cH2WUkiGWu1W0rSxDut/dwS0B6jXSiiL7flxfmiEg6bkkiJ\nmK6N6vMrc03TfGoBFxUAxQY4Zu53iiJaDIW0CwgUuO+DjvekC6/SxsgnLWs1F5B9bgGJpdk6FmZq\nWnnjclg0Wbu8M3Br1k8ZuBQChxYaeO1qF4uzdVzf7GdjMhM6Ih1vgQIuIRlJrwPG4ZsuoE+jgVst\nn1SxgkDFidO0wijQmDkF3Q2VZPVCYK5dxQfPrepjYYd3M/t+74kFhGE0Nc6WTevdlwy8jKZdOhJX\nJQgoyIjUTAwJiDAJLJlcEIPbY/qxFfdr37sr3UKytYARhU7Q9vTcCRxoLmGu0GklZf52QqvGSPIc\npFPdqm5Odxq4qGtaAUoKgePyYQBj+rZnOCPYLlqZ4KThJl1HIrREFVVEAz52s71k48lWHCGzSv1s\nlgangWu3QTXwdBtM2h2j5Wlzy3xPnFuCA47m3Wn+favWpyVuZ9oV3FudQ7tRwdrmIBuT1jqn7yDX\n1ZUm7FPgm/GM0cCjVEikSlEOkyv26QFnVSfPs06Z+SgcWfdbDh8mC6DvNji1au9/PgnK4ti+ZOCa\nT5fRuh0O2Qr8O/Yt6XqQKFxnk//MMCPPdeAHYWEvNL2CDzbKjyuOkzFwrrqcyj5jAUc6FuZtR3hX\n0kIjdGxhaLKOfc59zb8N5mwdYrjqHBFrj1xhEhsTjI+Lc/M17Q3iajnP4QMvyE52JWl5vFpiT3E8\nt18uCl0TeKakszwDv9Ma+K0BM2fbdle52VrltK4E/vJpkPp4KqhmKVumWmzOYxZyIcMpTaQM3EuC\nH0eR3hBK33P8TCI13futuzWcXXwAM9W9Mcv9DPuSgZfKeeW61Vhww0TG7IPKtIbYzF5gWsbfnisz\nBs6ZWrl8WZe0JeRaFFIiGREGPgnoCJ3O8lyYmv3188mV+5iD63ng5JjgjNbGnb4/+gBVcQhNvypc\nnADn944Fvfi4ovmaJ6eISUnjJuh5nnjn6+4tBUyY608YM06Qtl57i0zV71ZIA7qKzyJn2rY0sizX\nTJlBlTloWjfjRnFR0TX7zM033vxeTFa1C3qzSbrmPTPHMg2ck/xFNr/aE2052uEru+1HKIv7717b\nwxjQCZQdUSd37oI2VjMdJqP0ghwiS+3xwwgvn9/AjZ2hvi/NMsB9BFwQEmXmVK6y30dac3g8kS3+\n5hANnDONAsBC0ui+WTMD45BcO2bZdzmwDJw8r6XZuL3fgfmG9hRt5joaNwHiguGJ1WStqSLq9oAk\nDd/tmjbL5Fliz+ylqPDpOEv2wqaRaQr4eMGmuIeJQ/Y15O07pVUYBPJnXDShp4O5d2qnOdSFNiMO\n6I2dLCloXJYGR19ToUQKiYrj4aP3fAgPLNyLhht/T26h3oTI/slm2ce05VbBXR2FriEY242sXBBH\nclY7okEZIsMvgZ3eCFdvCFS8WEPuDXx4nkuutVsAeJ8kHZ9r3RXCwNNeuhVXD6CKUn/VmBxw253y\nwUb6+ffdv4yrG32sLjX1cVaNYJ8BUxyCPt9a1cG9R+fip6JpmvmxjeAJxvTMxRvops5JPkbeHOow\nsRXTmtDzNem6jA+8hAbOBciVCSQfR8Q+/NiROxbMdquWteeBAxR/0u81VBHRgPOUsjLFediWy9Ct\nbEM/tR4yOMMIuzSAMzK0+HSuA40lnJk7Za19n85Rxj33xEMr8Nx9qXdOBWVRbH8y8BK+R14j4TSo\nXBJMEd6mKW3uDjPttDCPRqCYvTBWgoaTN3SnDPzYwTbe7tUw265qH2IY5ZIuB1ZRRRAGQwYMfT1q\nruI51m49E5TLfQFjg+8TR5xCRMhSDlwQm66lj7f4sPnmhrl5tz/K/rKN0RtWOOSY0cC5zAgyJs9N\nzv+vWyzs83E+cHp/ZYW+Dzy4gu6A95UCQLM2XV7trYRbZdqnAiCNadE18OQdRGTdOC0mHs/4i3X6\nl5/X85HtzW3KCFqc5SrXwIUxXuD03An7ZMac47BknqG9dxuUjcvblwy8jNm8TBi+EiwAACAASURB\nVG/wDIj5Rs9/tRPaIGJMo2Rqp4QAQYkhLWXqufmxK2VmzqX3mhY4GJsTm/ny6SkBmVRyknBw75FZ\nvHJhE+1GOYKYVxm7Wzi4/o7/89EPwo8CXO+vWS+1+cBjq00u9GnpV5ZexZwGZ7psNhIXTblgNYbJ\ns5Yoe+lMRQZkqMW8ay4NkoIet0ELW/MUamGmhoWZu59Q59oqr4GnsTFU6y6TPaIpNgkNkVLEAbLp\neSbOhTO/U3uMPZhTxRXjjOtKAaHBJaqt/gcksC8ZOK/tEGTjKp5Z3P6C2MrzwLVYurXpUmm+pHWe\nbM3JPknDJoVFcRQjDFBhGs3T+VMNnPM90tmVYQKdFyuIEOJ0+z7cf3weRw60C75uDrImA/uYgXMa\nuBACnuPBczyovt1sTBlVVuxFiEyQEqSdqAJQq9gCEhmtW0MHuzmdwyXab7mMDzwNGNJ3MzkKncK4\n9KR8nfw4JJGBt0aHvUNwizafWTvGWGzsghkpM8QJgzSmRsrMamjWHbDpIpyFqFn3sNkH2nWPtTKl\nboHy7g0i+N4FtOWWQclHsD8ZOGfGYzXw8X5AURhPTIdEs0qBUcBLmdA1jYRMJCExIw/EY9y8kIPO\nfAkDV5NN6LavWwoBR3g4IE6g7sSRzq0pyvyly3HPYD+ApnUY1gnbGC5YjH5lNLaA4spcu4q31/X1\nOYLNRqcnx51mVXtXPDNnhEdpX1fn4DlTpxqXDbQ1OaFTw9n93kn+1sJcrYO13g3M12cA0IBY+3Mt\nY2LOIb/O5Urrwm5J4yw7My0PXqWDquewDDozoe8hPvpucM/dKiibSXdT0QA/+MEP8MlPfhIAcP78\neTz11FP4xCc+gb/4i7/Ixnzta1/Dr//6r+M3fuM38I//+I8AgOFwiN/7vd/Db/7mb+J3fud3sLGx\nMdW6XAlHLtpb06AsvrpC6o+wkS6R/csX2M+POcJJNbhGNSbGrbqnXTvTiAn1fKem1Q62auDjgtgs\nSDBtKo8Jd4UJnYCugefHVHBaTMy57UZFDwIkJvT0vQqIzLyoFFD1HJw5MouFjr2LV9ljKYCVhYYu\nDDpUeM018GaVVDxjvgkutYhWBxMT3rUW3VzCZBpGpIrbPtbBb9XOP3zfOfzi0XP40L0P5/hmfJSu\n9T2NjeIAYLg3HPu7jmmZ7Vo77QQEahUHQnCWRBLENqUJ3SJD/kyDKulH2DMDf/bZZ/Enf/In8P04\n2OSzn/0snn76aXzpS19CFEX4+te/jrW1NTz33HP46le/imeffRbPPPMMfN/HV77yFZw5cwZf/vKX\n8bGPfQxf/OIXp1q7jL+vTL3pfA6dZ9vSbdLrHKkj/ay7pI3K1mS0Npd8TBXPwenDs1hdamkE1XMl\nDi+1sDxbZ9vplUkjs/1ysxG0eZ/e/fuVaQq44QNPgT53R0p89PFj+MCDhzRTdZRfCMfJiVgaJRtF\nKmG+ujlaj0nIj/lKaXmw0dj6+emxlFicqWOmVZ3CnB6DIucmMVkpBQ7Ik5gTK+T+i/DYwXPoVDs4\n0l6x7mW/wa3aer1SwS+cOINahbeAuZoGngiGND+cofN6ZceiHzs+5mo9MAhKoua5Ln65D3zKhyTy\nawI1PoDxZwOmtbNMCceOHcMXvvCF7O8XX3wRjz76KADgiSeewLe+9S288MILeOSRR+C6LlqtFo4f\nP46XX34Z3/ve9/DEE09kY7/97W9PtTZldtRfrDFNJo1rch5vjkgqKayfDAIQM1dlXJ1fR/bIBhhR\nISMWCARMPycxs2saeH5+aS7WChdn8uh1Dug3OrasbAnI/VRTX/quAaUUDshTaIo5tJw80t4kbkB+\nv1XPQcUzOn2Rf9PzAgKuzAOPrJMbK2VHLNET2ffMpY55JHNBCIHFmRoOzTfYdCJOWKA9qieZbCUE\nWmIe83J1LMFebizhA6uPwyOVufZiYr2rgbouyHOfb9fQaVRwZLmlaeBeQve02vRM7QndhK4Hn9m0\nXS7mgv6uB88Sy2Ap157tSoG5RvwtVmr/4Wopa+Hcsw/8wx/+MC5evGhdsNlsYnd3F91uF+123n6w\n0Whk51utljZ2GqDIUSXSK5tHyzBWMhhS5eSYBiGZTE5KgSC0m7UP1g7hxvabhT2yBWa0D0tfIwVF\nInelEJjv1OC5EoszNZzxZjFTH9MGVHMFCJikOGCC8cZBuuX9rIG7jkRLzKEl5qBX0Mif+9H2Km4M\nNnBq9h7tWhpnoYijijJwa54qtaQwTFsaxJWOV+Q4H58fVyhz1OYEObYLuxT3IuT1sPO0JS4KXeLs\nyQVs7gxLadR0xDQ97N99cDutB/rcjpRYWYxrMYjETa6g4uyGETAYhVk0uYSD0NL0RrfU0JVyWkY7\nGXJVJOmV9vetcvzZQ33yBw4dhnQDnFianzz4LgclbjMDN4F+kN1uF51OB61WS2PO9Hy3283OUSZf\nBlxixtQ0Io0oTVHMQqhCgQMAhZq/8Smlm2AZrYZqR65wrGO0soY0vcPqk5QQQmA5SSlTUDAbRBRu\nKxNEFKj5q1Hz0Bv42O1Pb6razz7wX/25w7i41sXyXG61EIzbxXM8PHbwXGEOaUsjE4L4xoVeCjc5\n7Ye0i1fh5wnneQ0q26+knb7sAkKZojE0N3mSGVQI4OShvdWffjc3oLgTYP2alGLfZavhYas3QLvp\n4eoofm8xA0++afLqXEfiYHMZV7rXUHP19Ly0/kNHLGMX1wCYNKoYtGma0KHhD5cax4HI/v/exfsw\nU21jtXWo5LV3L1RlrJjFnQh5uGUM/IEHHsB3vvMdPPbYY/jGN76Bxx9/HGfPnsXnPvc5jEYjDIdD\nvPHGGzh9+jTOnTuH559/HmfPnsXzzz+fmd7LwNJSG27Vyyqgzc02sbLUxmAUYKZTz863Gg0yJj9u\nNmrwdvTbVpUhGqhjZ9RHteKi2azC67uoVF04SiFUCkvtJq7tDlGtuBBBlFFbAZHN3W7X4d2Ijzud\nOrz1ZC/NKrxufDw7o+/rmopf1OGDs2g243DlhfkqGn58vt2poeFX4UoHS4ttNG5Uk/uroi+r6DTr\nWFqyC0C1mgcvcFGtevCG8Zrzsy00MYMLV3dQa1TYazmYW+/h2vYQniunvvZOw9IScPL4AgCg2Yyf\n40zHyd7HwkILS3Pj76nWr2TjGw0PXt9FteriwFIbncs1PLiyjPl2E9478Zjjy4fwk/UBtnaGqFRc\nhApoNqoZDjabFVS6HpRSqNcq8Pz4fM3Lj+teFZs+0GhWMdvIcXxurpkdLy600BjG9zQ/10JjkNxf\nJce3VivHw3q9gn7SGardqmVjKhUXXuii0ajG+9x04QlHe9fZmvMtFgfSMfT3IArJs25jaWF/4U8K\nfhBl+HOrvoH/1Hgc3734Ah4/dhZXN78Lb9dFpeJibq6JRj9eq1qJ370jHKwe6KDZ9PDQ6gFc+Ld3\nEIQRarICJO06O80cT5aXOjh95P0YhT6ubG7mtLBShTdKcayCXT9+b61WNaNjGk1t1hDAhyddLC60\nsvP1upvRmUbTA/wqZmebpZ5NteoiEC4a9QoOLM/gwPLPXkMSK7gu7nnrAdRksZiWNuxWrffpT38a\nf/qnfwrf93Hy5El85CMfgRACn/zkJ/HUU09BKYWnn34alUoFH//4x/HpT38aTz31FCqVCp555pnS\n61y/voPt7gi+HyPq5mYPnbqLTt1Fd3eYnQ+HCr4foFWvYHu7n50f9IPs2EUVAYYIfBcqis8PEaLf\nS46FjwghfD/AYBDA90MMRfwBZ+ZnF9l8fbKvXjffiz+IsuPdnfz89lYfvW5sF9ve7qObHG9shtn5\nDWcXve4QnuPhxno3Hx/20RsMsRsNcf36jvVZDQZ+snc/39fOCKeWm9jdGeD4UpO9loOd7QG63SEc\nR0597bsJ0me9syOyZ3NjowsnGK8ZDkb5s+z24vc9ksDOzgAr83XM1D24EVBxBWaaVSzLFVzBBkaq\niqujFxEhQL9P8KTvwx+FUIi09zQIfPgqOQ59wIlxajfU8Sc93tocZLixudnLjneGgxw/e/n8/ijM\ncZLgal/EexsOfPQQH0cQ2rvO1tzq43rFjgPpGHpdGOVrbm/0UYn2ZyBbEEYZ/ty6b6CCR+ceRX87\nQi/BK0c52NzMaUTox+dDKHS7I0il0N0ZIAhC+EEIKaIMZ7rknW7c6MGvxM96c6uXv+vQz/pzD4Ig\no2X9rk/o1YDg+xCh8uE5EXZcglcJTR0OA2zv9DEKR9hxBixuUBgOA/hBgF5/tK/pya2GG9sDyEET\nownBbDfFwFdXV/Ff/+t/BQAcP34czz33XGHMk08+iSeffFI7V6vV8PnPf37vCzP+YmpuOro4iyPL\nLTSqHriAsrZYwIa6BClyn04cRELMxCJfUiA2VXEmdMFEntPIZb3wyvhgESAPhJJCGqZOro6yfW/Z\n+o6Hqufg0fvsNYknQRZgs4994BRslabGgWY6VBT30n/jNLIjS7Hk7EkXjxx4CN+8eomM1c32mY+b\n8XtxZlRH2Iu3cH5yhw3stJnQJ4c43owZfD+nkd1+KOIVwH/rIqNRdhci37LWfp7r4zBXncXa4DoO\nNJbZGAZXuhiFI82l8x9w+2B/FnIhx1yhfs/xsnrJbA9jxL87UkBEaYpGPj89TjOBFEz/LyWoBPHJ\nR0B99oIQXc6NTCsU15zYZ9WptLX7m6adKN2vJ+ytSsvC3VCJjYMyDImmS3E5tDZhjH3XAqB+wOy8\ndixwYnUGiCI9JZGU35VCxpMpI55D86szaWdaGhnNA7fv2Ta35VeMS4W5W3p63xZIBEMlIuM50ZiW\nGEIVaspHPpJhzgwt5IQ+On61dRCn5o5jttrBtd0N65hHDzyM1zbfxMmZ45PusrDn/4Ac7vJuZFyw\nmn7TVTduACKZgDZHeHHet6RpZHoBi0KNa6W0FCrbBxbPTXO/Het59iWR0ydmjsGVLlZbB7UfSmng\nlt+K7fymg7G11/cjTHk7wsLsaCEgLn+aCjx6BP/kfOv7j89iqxq7UkxiPCMOoqc2UZEePnT0lzEK\nR9ge7WhjUuAaXNgq0GlNbxhGPC7y/P965AOFc//BtMuBm8TFCKEHsaXKioLCUn0BV7pX4UqXCFIU\nN4CTKzPJMZ3DDtYgXpg442ChPgeAK4gFtCstnFs+O/EeC3D36QM3Bbc9jexOAhetaxKUXz0SE5EL\nW1fItUQzJhp4bhs2mwboNvSY9to1cE5ydY061ccOdtAb+LGFgLh93v/gIfQGAQLcyM450sE9M0cB\nIPNXJRtNVh9nQk9H5vutujdn2rrbaDDX9IaDdETcEEbHjfiQcYUQlAkJAxfk4jCy41Wo8vdulsJc\nlEcAHAEgUHU8VJ0KdkZ55gdXEZBLbYwyBp4T773Q1sPt8ZHE+5mZO1Kg06zgwFzjtsxfQZw25krH\nKKqSHig8vPReXKjP4WDjAIT4YfKzjhtpOqO9yiRvTufSyOixranPXmC8iPizC2UtnPuTgdPjMYVJ\nUgLE+ftcVDAnVvDQwj144fJryVmla+DIPwKBomRUphSmbkIXqFcc1CtOYb+LM3VgBnhjyyiene6d\njOd7Ceu7i+8IeOSeo7i6ewPzzZuLmt3PFbT+//buNTaO6uwD+P/MzN6vXq/vjnFiQpxLcy+X5lJI\n4W1SXkojKCkVSSQilVKJAkkRUWkJl9KQVEkr5VIVVNSEQAkNpaUfWlFQRRTaCpoKIkrJiyhVQgiQ\nxAHba8de7877Yb27M+uZvXq9O+v/70uc8ezurPfZeeacOec5hsz6rc12FwKd0gIICMRxPvWw5Gdp\nWiNak5xjMW0CT0zzicXM5+XHkJ6CltmDo8gSRmLxVGGPTGbFW8x6glRNXKU3m88DL5YVpyEmCSGw\nYmF72Z5fjjvRJHWhw1dv2v0tSzI6/YkL+/R1mUlXuVlCNq2bYV6PIEl7TiuJSJ+jKC3fr4clEzgg\n0CHNxTAG4dAUsDBr/egHuumDNiS1IeyqhxDvAUicwORUUOnLmApkr0Bm9qXJXMLP6Gct8/ulY/fP\nNt9Se1pe3nEZovER2OXSWuC10oXusMsYGo7BoVnuM9+5q7JIfG1SeVh3UjQucqFNWJkNbUWSMDQm\ngQvUiVacVz9EwFaHQZwZfc2Ybp+rF7cjMjgChz13jJmuUa853PQa4VLOammlhEItjqEYLyOxOLwi\nBJ/Nq1+W2OTzSNY6L/Ret1Zm6z31mmbxY/DhF1fZcfQHxoNOvn8NS1ZTEAKwCQc8IpjRejAOILO1\nuZOhmiiIMrbO8Jg/ohBjTjymFw2a7T63diGL3I9VTT4+7f4LGj6HkLMOM0IXG+4LpL9k8bgKSUi6\ni51iJVfDCvqyVICzgBUL2rFsbitcDu0FTX4nIJsiIeR3pvbW9wgZf6W0SVvfApdSYyRGMgpbh6Q2\ndEoL4JG9uLRtHtw2NwIOv+5onXZlzNrZ+uNJ/2zanW4wCl3SPFFmPIZEG+xwwaEUFgNGBWNorOSF\nnKJIqHfVocPfjstbPm/au9MQcCHgccBl15TTzaM1bta6ThYictjkjPgxW7ym+A9TW/WS0mr6Hri2\ntWETud9CrqtRoZlGpqr6amPaRpYAskaa7nUkgYagC7IkQdYMHMtnoXvz1Z/Szx9w+HFF6+ezPk/y\nPlXcbMWDIjQEXbh8djNCFk/gDrsMh11G37ne1LZ8exdWXX4RBIBDr38wuiU9EC1z4ZIkXRe6dl1s\nAbSFvRg4HUF7vRf/98mno8+YIAsFIzEVnXVT4BkJ4uPe87rHGjPuDjUbzKl9Hu0gNrMLzDqpFXVo\nzboSXi5W7kIvN5/bjp7eC/C5bYlyteFZAIA6X+LWWub6B4os0FLvRv95Jb0qqTbxmt7m00r/z+lQ\n0Nnsh90mZbkQSH/2al6384ylhx4xHrRqugtdP7dV03VosoiqWV3yVAtc0tY/z7wHrmlnCaPWsVmX\nFFDvT7SMlDxG/+qNTzAn5/3GxvnL0Rwqz+CdSsisDZ3fY/StBpHxHEbPopv6kzGIze20oastALcw\nvr2hq70vzE7AMNyuq4WuW9bWuDu9JezBibMXEAo4MTQ49laS7nUKPGELg5Y+jfX57kacPjeAjib9\neBW3w4YZHXVZPnfj2MivBa7vpXSONpKEyWBHs0WhisV40KvpFjiQSCIf9QzAYcunBa752WDghiS0\nI27TK5BlnriMwlMg0a2sqkDQo2mVmhRyySfI65yJqRpTfG05980mebHC+43ZZD8pZaUt5CKS/wrD\nprF+CVP9z8mTqr5Ba9zdbTZy2IzZ6mVmJ/VwwAmXuw5uhw3RC9l7bkoZz8iYNOdyKJjW6h+zPVdx\nHdOBa7oYN35sZ7MfZ88YPVb3CunngUA44IIiC8QHDAaD5Ek/44eSgt5ELpnWmr20rGUT+KWzmgBk\n3NM2aYHnWh4vcYJMblfTyTzzfrfJGas55IEiCzgdmsIamt/LknHFLLOrrLArhC9OWQK3Mnap0KVt\nl+c9lzvVAq+RqmllYTJCNx+NdU6cHAR8bhtSvTkml2jaLvTujhBefS85tVGkujiNuvCntQbQ2Zxu\nienuSZqcjbWftlmraXgkPQ1R2xWujo4k0T+z2ZiMwk2R5kCCwhZXEVo9zXj/sxOYUWc87sV8VLmu\nBWOoo9GHf4wmcCkjUad+zrgADI+OvegZKL4LPT3OgrRcDgVfXTI15209yyZw4wEduTsVjQI7UUo1\n2QLPrH6U3cLuJhw/azTtK/0cdk1ZQf1SoeZh67V5DLdrBzHlopThHnit0d9QKewE1BCyoUsEdHPr\n9QVQ0pIXayG/E16XdrBROg6Nwm5uV33GFuOTq+61MmZPJCmaKnLDw4mYsNskfXlfNd2aKseUQbsY\nXU2PGbxgdtmOq6YsNf29WR0KLWEyblkx6SrX3Us3m8UgpS9eC2Xl1Q3LLZ8xOZYchW7O7B54Yh3t\nhqBLF6ja3ydHAsdiquGJSz8vNk03RUyzXXvf0mdPryijHYBX7qAtZZDRpFFYj7TOhdgF2GQJLpt2\nRLpxGzzZAB/zG6EdAGeceLW032ltQtbSxpX2aWyaKXPtDT7YZAnN9R5dD5F+0Ob4x8/UlsQFaJ3F\nB0FWo3wuQM3GfDi0I9i1idokBrSPndrqg89tR0vYuNGRTaoLnQm8KJZtgRvJNn86uY62UQECIQDn\n6MlNO4jN6HkyaU+K2tNzdCSOZm8jFEnRLTqhGJwsy0VJFffgl8NMKQNvkpXx3IoLDe4wPhvqRcDh\nR+9w75h9vS4b4n0jUBQ7nIoTiQVMVKiqCvvo9D5bHnP0taN/ZZPiLfoWeHof7cWm12VHV1vi/po+\ngWsLueSKm8L/dnO76jF7akgTmzRe9LdOYPK1Nzu3aX82GX9h0sJ32GS0hT26mgqF4hmqOLX1Lcrj\nKs7oxCFJAnab8X3qXLQJXAikFlCxKzIWNc3HvIY5puUIk0VV5PGqapQhHHTC57ajsyX/bvfJJo/b\ng6bm1M9E2FWPueFZCDoCWNy8wLRQzhWzmzE13Iigx4Fmd2NqUZSRERWz67sxxdeGOeGZ2iMzfB5t\nCyqfHhbZpAWuu7dp0lVndiE7rTUARZZSI5ULkaweR+NHSjYQtD04ecSP2a1FbWzY8yh0pJ9sWxjB\nLvSS1EQLfFqgE+cu9MA2uqKYLeNKUNsVaNQCT0ybUHT/Tz/W+Ockh6IvntAa9qB/MIqW+nR3khAC\nNmVs+VSfzYOFTfMQLOC+diFkSUJb2ANbiQuY1DL9mIjCHuu1e3BZy6Ix22WDFd/cTgXXzlyCCyMX\n4LV7IEsCIzEgGovDqTgwt2F2fsebxxVHXDVugSdrYwP6e5jaaY767nfjF5jbVY/PTQtZup55LZEl\nCfFYTJ8EhTBs1hotn+t320330TZsTAMuj4WVzKSHDlMxauLMPrP+EgDAqf7TaKxzw+fSt4K0cWV0\n9S8E4Hc7EA644HEqpnMcjaa+2BX9n1CWBAIeu37+rRDoMpgWogJo8TRle2slGr265dfDlNm82FIE\nHQFMr5uGRneDbrtDtqeq4cmyBERjGBkpdIR3PsdofA9c1nWHGk9zTL+KyDooicm7eshCRhRRqNDU\nCzDb2eAXrWGP7he69SI058vMz/wLrZdClmS80/Nu4nEltMA5LaE4NZHAtYwqhOnLBI59y2J0FHpy\nWkQh9VW0S4VqDcWHDV/faOnGcuHVbW7aj1rOsxJbzucUApeYTPVJMlt8JBddF7rJvHWzUehmpTAl\ng6paiQcXdYg0wZKfn5DSn50sS4DB5BPz5US1P2t7HTXbMwKizhkEoDmPFREvqXMUT1JFqakEblYc\nQhuQTsW4HrhZ0f70PZpEF+TgaF4O+ZwI+R26+5ACAnXOIM5f+DTnQhBA+VvGgik8J5dDQUPQBbfT\nNqELtTQEXYirKqa3mxVqMDsWbZwaXzxqLwwz31JXawAQQJ0j/bq6C4HkuVgztZKqW/KWjXZIhF1R\nsDA0z6CRkLvHKbMB0xB0YSSWe3ptMbMW0utO8BxVjJpK4KrRJSf0V45Om3ECFybdRkpqsFEcDSEX\negcSxYaFlBiMo7vfKYDPNy/ER5GP0eZNr4dsFvhlr0bF3qmchBCpkrcTSZEF2sIeeJyZ9x9F1p4Z\nXSEXkwSrL7ih7wJN3gf3O3zoCk6F1+aBohi1wM1roVN1SV6AaWcNOBTZ8PactnGyeEYjomcSU1z1\ng9j01RGS3w+zeEgtbVzEsTOBl6a2ErhZCzyfBK7Zp97vQtDrQMBjx8BAYns0po4OWBudn5FadlE/\nyM0mKWNKoGYOhHMoDgyNDJW8tGcubIFXv8xrO7dDQeRC1HR/RZYRDrjgtMumXeitnmacu3Aenf4p\nuDByIbU9c1GL7tB0AMBnQ324qMkHRZF044lrZOXYmidJxi1wQ5qAa23wwhcZew7KZzGljAdk/31W\n6R5OKlxNJXDzLvT0zw7ZDr/HDqdNAfo0+2QM4kgu2DE8lHjORGGWxF5eEQbQP7pv7m6jzIFSy9uu\nwMDIIFzKxLT8eHVrrvKtTP3rt9R7cOpsP0TU/LiSYzXMYk+WZMxvmAMA+GhkKLVdmLTMJSHgSpUB\nTpdYTU4h4oC16iYb3AN32IxvrwgAFzX5IMuS6aCzqDqi29/oZy1VEzOFSo9h4zmqGDWVwJMjfD12\nfUUgbWDZJRtaR6d4RbUJ3KRIgXv0xOZ12SDECCAAGUoqJRqtbpaNEImSiPZxWJs752ulRqGTmUpf\n3Hhs+pXdFFmgo8kH/7AXn2sbu5hNKanUbHqkbhCbpgke8DjQHHLD7SxvTxGVRjaYB25UcTIpebGm\nu9WiiQdlNC1krvdulqBVTiOrmJpK4C2eJkTDM9GUMX1He9qTJRk2yYZ6VwizLu0w3Ef7c8DrQGvM\ng65QAD3DY2ueSwbzKrOJxsy7R8db6vvEq1tTlU7gAbtvzDYB4OL2IOq8RuVGCztJ6qPaOFb1J+Z0\na0oIkVoViarXjLqL0R+NoMPfhf87/TEkSWQ0LNKyLXLSGvbgTM8Amn0h2O1zEXIG8dlwn/H+GqW1\nwDlQshQ1lcCFELjIP2XM9szKatdcdOWYYNS3TjTPCQG/254Y6DM8NkAzV+gx0+5rwwd9p8a0uMqL\nXZ+5VKrrbknbZYhEB+DMuI0iSzJi8RjiqtmAzALlmF2Rud1sChpVL6/dgy+2fwGqqqKl3gOPy1bU\nDIL6oAsumwQhSWj1NgOAPoGbtcBLGcSWfA6TeKfsaiqBm8kMvJzTIbL8PjGETU31+eT7RflceCbm\n1HeXrWyqEZ5+c6tUCzzoCCDoGDuF7Mr2Jfhv70k0exqNH1hgUtXubdZlKnTzwNO/r/z4ACqEEIki\nUoD5lC7zVcrMtud+bJO7Ab1DfWhwh/M/2IznZCdhcSZFAs/npGd6stJsViQZEEBcjae+HtqRwJHo\ngOnzS0KqQEblCTiXahs841ScqdHhRgr9RPULXBj3FikG88nZ+La2UpaCKCVcewAADyVJREFU1X8n\ncj/PxcFpaHI3wm9wOyiXZE//RNZgqCWT4gZEPiX+tCc0o1rWAKCM1hRXEUs/t5AwO9wNAAi7QqUc\n5rhjF2huZZ+LX2ED0cHUz8JkEJssyVjadjm+1PFF3Sg2xo91FdrSHomnz2mF9kpJQkLA4S8qXlpC\nHvg9dkxtLTz502RpgedBG3qN7jCm+NowxdeGN8/+K7XdJhQICMQQQ3JcrhACnf4OtHlbq27REHaB\n5uYfXat9WrCzsgeSp0I/0yZ3GJ3NfnQHu/XzwDNOtoGMBXVEEa9F1SOfSpC6/QtM+ONFkSW01nuK\nWtmOJkkCz+fKMHNaTXJ1KO2C88kWeBwxzPNdiplTgqnHVFvyBtgNmg+bbMNXpl5jmdZm8jhtUn5T\nu9w2N27oXgUAGNQUdTFjk22IxqKQJSVVaMgqfxtKM22BZ2xf3v4FnLvQA5/di+Wdl+HdUycnrD6F\n0fFQYaov65RBXleReQSSIimAANR4DLJkg9vmGoejKyd+OfJhpZOIJCRcNWUpbEXUEchnwOXlLYvx\n/mcncJGvHZKQMKu+Gw3u+mIOlSoo38G1PrsXvtFeqGZvA+SQPnlP2Fejtu9klc3kSOAlDGLTTrBR\nJBsEgBhilgg466QlKoS7yKmI+YwF8dt9mKdZm3xqoCPL3lStsjVa5oRnwq1M5HTW3Cpdj8GqKpbA\nVVXFAw88gOPHj8Nut+ORRx7BlClj53BPlHxa6Z3+DoR8/4HobUFLuLq+AMaYwimNq4tNHtlGoRvV\nyjBX3nMIx1mUpmIJ/KWXXsLw8DCeeeYZvPnmm9i6dSv27t1bqcPJ0krXVGVz+HDLvP/FSCwOm1L9\ngy4s1DNME8BKtwqoNON3sTYxLWO2v4tTsUvyo0ePYtmyZQCAefPm4a233qrUoQDIfZ2ZmlwjhCWS\nN8CrW9JjPEwe4/VZx8pcIU17i5IKV7EE3t/fD58vPfdPURTE45Urp8fWCdU6xvjkMV6fdUwzP5yq\nT8W60L1eLyKRSOr/8XjctAC/VkNDcRP+3R87sj5eDETh/mzsPv7PXIhdGIbP5zB9bLHHVG59sgfu\nwezvm6xjPD7DXN8DsrZSP9/Mx0WUT+GOlC9m/IMu9AkHvE7z8yuZq1gCX7hwIf7yl79g5cqVeOON\nN3DJJZfk9bgzZ/py72RgprcbslBMH//Z0CAGIkNjXiMSGcLAhSF8Gh8wfGxDg6/oYyq3vv5hDESG\nIAmpao+R8jNecWYU41Q7BgeGoapqUZ+vUYyd7e0ra8z09V7AQGQIctTGmDSR7cKmYgn8mmuuwauv\nvopvfOMbAICtW7eW9fWaPU1Zf++3+9AZ6BizFGmyolHcgvdomj2N6ApORZu3pdKHQkQT4H8uusp0\nJbtiJAsGFTt1MbfRQlllevZaV7EELoTAgw8+WKmXH0MIgdn13WO2J0dzxlXr3QuShJR1YQyafK6a\nsqykhS6ouinjXBGy2dOIWfUzcjaAqDImRSGXUsipBM71asn6qr96IFUTSUiYGriobM+fWk6UbfCi\nsLJDDtLo+t21vmoVEVHF8PRaFCbwHJLlJ9kCJyKiasIEnkPyHnjMgvfAiYiqWWq1RzbBi8IEnoPE\ne+BERGXB4ZSlYQLPQRa8B05EVBacEVESJvAckqMkrTiNjIjICtiFXhwm8ByS08hUtsCJiMZVqv3N\n82tRmMBzsMt23b9ERDRe2IVeChZyyaHd24r+6AA6fG2VPhQioprE9ndxmMBzkCUZs+tnVPowiIhq\nTrL9zXvgxWEXOhERkQUxgRMRUUUITiMrCRM4ERFVFGf5FIcJnIiIKkJwFHpJmMCJiKgi2rwtAIBL\n6roqfCTWxFHoRERUEXXOIFZNvTq15gQVhn81IiKqGCbv4vEvR0REZEFM4ERERBbEBE5ERGRBTOBE\nREQWxARORERkQUzgREREFsQETkREZEFM4ERERBbEBE5ERGRBTOBEREQWxARORERkQUzgREREFlRS\nAv/zn/+MTZs2pf7/5ptv4qabbsI3v/lN7N69O7V99+7d+PrXv46bb74Zx44dAwCcP38eGzZswC23\n3IKNGzdiaGiolEMhIiKaVIpO4I888gh++tOf6rZt2bIFO3fuxNNPP41jx47hnXfewdtvv41//OMf\n+M1vfoOdO3fioYceAgDs2bMH1113HQ4cOIDu7m78+te/Lu2dEBERTSJFJ/CFCxfigQceSP2/v78f\n0WgU7e3tAIClS5fi1VdfxdGjR7FkyRIAQEtLC+LxOHp6evDPf/4Ty5YtAwAsX74cf//730t4G0RE\nRJOLkmuHQ4cOYd++fbptW7duxapVq/Daa6+ltkUiEXi93tT/PR4PTp48CafTiWAwqNve39+PSCQC\nn8+X2tbX11fymyEiIposcibwG2+8ETfeeGPOJ0om5qRIJIJAIACbzYZIJJLa3t/fD7/fn9o/FArp\nknkuDQ357TeRqvGYqPYwzqjcGGPWMm6j0L1eL+x2O06ePAlVVXHkyBEsWrQICxYswJEjR6CqKj78\n8EOoqopgMIiFCxfi8OHDAIDDhw9j8eLF43UoRERENS9nC7wQDz74IL73ve8hHo9jyZIlmDt3LgBg\n0aJFWLNmDVRVxf333w8AuP3223Hvvffi2WefRV1dHXbs2DGeh0JERFTThKqqaqUPgoiIiArDQi5E\nREQWxAROZbd27Vq8//77hr9bsWIFhoeHJ/iIqBYxzqjcqi3GmMCpooQQlT4EmgQYZ1RulYgxJnCa\nELt27cLBgwcBAP/5z3+wdu1aAACHYNB4YpxRuVVTjDGBFyhbFwqZy7w6ZYvIHGOseIyz/DDGildN\nMcYETmUxMDCAWCxm+Du2hmi8MM6o3Ko5xsZ1Hvhk0dPTg23btiEajeKTTz7BXXfdhS996Uv46le/\niksvvRTHjx+HEAJ79+7VlZedTDZv3oxbbrkFixcvRk9PD5YtW4ZPPvkEAPCvf/2rwkdX/Rhj+WGc\nFY8xlp9qjjG2wIvwzjvvYMOGDfjlL3+Jhx56CE8//TSARJnY6667Dk8++SQaGxtTleYmo1tvvRXb\ntm3DTTfdhFWrVuHaa6/FK6+8gnXr1uHf//53aj92cRpjjOWHcVY8xlh+qjnG2ALPw8DAABwOB2RZ\nBpCoLPf444/j0KFDAIBoNJrad+bMmQASK69N5mkr8+fPx3PPPafblvx7ab388ssTdUhVjTFWHMZZ\n/hhjxanmGGMLPA+bN2/G0aNHU0uhPvroo/ja176Gbdu24bLLLqv4fRCyPsYYlRtjrPawBZ6HW2+9\nFQ8//DCEEFi5ciW6urqwbds2PPbYY2hsbMSnn34KQN+Fwi47KgRjjMqNMVZ7WAudiIjIgtiFTkRE\nZEFM4ERERBbEe+AGRkZG8P3vfx+nTp1CNBrFt7/9bVx88cXYvHkzJEnC9OnTsWXLltT+PT09uPnm\nm/GHP/wBdrsdg4OD2LRpE3p7e2G32/Hoo4+isbGxgu+IqlGpcZb03nvvYc2aNfjrX/+q2040HjG2\nfPlydHZ2AgAWLFiAu+++uxJvhQwwgRt44YUXUFdXh+3bt6O3txfXX389uru7sXHjRixevBhbtmzB\nSy+9hKuvvhpHjhzBjh07cO7cudTjn332WcyZMwff+c538Pzzz+Pxxx/HfffdV8F3RNWo1DgDEnN2\nt2/fDofDUaF3QdWs1Bg7ceIEZs+ejZ///OcVfBdkhl3oBlatWoU777wTABCLxSDLMt5++20sXrwY\nQOKK9G9/+xsAQJZl/OpXv0IgEEg9fv369bj99tsBAB9++KHud0RJpcYZANx///3YuHEjnE7nxB48\nWUKpMfbWW2/h448/xrp163DbbbexfnqVYQI34HK54Ha70d/fjzvvvBN33323bo6kx+NBX18fAOCK\nK65AIBAYM4dSCIH169fjqaeewtVXXz2hx0/WUGqc7d69G1deeSVmzJjBObxkqNQYa2xsxG233Yb9\n+/fjW9/6Fu65554Jfw9kjgncxOnTp7F+/XqsXr0a1157LSQp/aeKRCLw+/26/Y3mS+7btw8HDhzA\nHXfcUfbjJWsqJc5eeOEFHDp0CGvXrsXZs2exYcOGCTtuso5SYmzOnDlYsWIFgETltjNnzkzMQVNe\nmMANJE+G99xzD1avXg0gUVrw9ddfBwAcPnwYixYt0j1Ge9X62GOP4fe//z0AwO12p0oXEmmVGmcv\nvvgi9u/fjyeffBLhcBhPPPHExB08WUKpMbZ7927s27cPQKJ2ektLywQdOeWDg9gM/OIXv0Bvby/2\n7t2LPXv2QAiB++67Dz/60Y8QjUbR1dWFlStX6h6jvWq94YYbcO+99+LQoUNQVRVbt26d6LdAFlBq\nnGVuZzc6ZSo1xpLd5q+88goUReG5rMqwEhsREZEFsQudiIjIgpjAiYiILIgJnIiIyIKYwImIiCyI\nCZyIiMiCmMCJiIgsiPPAiSapU6dO4ctf/jKmT58OVVUxNDSEGTNm4Ic//CHq6+tNH7du3Trs379/\nAo+UiIywBU40iTU1NeH555/H7373O/zxj39ER0cHvvvd72Z9zGuvvTZBR0dE2bAFTkQpd9xxB5Yu\nXYrjx4/jwIEDePfdd3Hu3DlMnToVu3btwk9+8hMAwJo1a3Dw4EEcPnwYu3btQiwWQ3t7Ox5++GGu\nvkc0QdgCJ6IUm82Gjo4OvPzyy7Db7XjmmWfw4osvYnBwEIcPH8YPfvADAMDBgwfR09ODnTt34okn\nnsBvf/tbLFmyJJXgiaj82AInIh0hBGbNmoX29nY89dRTeP/993HixAlEIpHU7wHg2LFjOH36NNat\nWwdVVRGPxxEMBit56ESTChM4EaVEo9FUwv7Zz36G9evX44YbbsD58+fH7BuLxbBo0SLs3bsXADA8\nPJxK8kRUfuxCJ5rEtGsZqaqKXbt2Yf78+Th58iS+8pWvYPXq1QiFQnj99dcRi8UAALIsIx6PY968\neXjjjTfw3//+FwCwZ88ebN++vRJvg2hSYgucaBI7c+YMVq9eneoCnzVrFnbs2IGPPvoImzZtwp/+\n9CfY7XbMnz8fH3zwAQBgxYoVuP766/Hcc8/hxz/+Me666y7E43E0NzfzHjjRBOJyokRERBbELnQi\nIiILYgInIiKyICZwIiIiC2ICJyIisiAmcCIiIgtiAiciIrIgJnAiIiILYgInIiKyoP8Hp1hizYUg\nfj0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1195dc208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "daily[['Total', 'predicted']].plot(alpha=0.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "It is evident that we have missed some key features, especially during the summer time.\n",
+    "Either our features are not complete (i.e., people decide whether to ride to work based on more than just these) or there are some nonlinear relationships that we have failed to take into account (e.g., perhaps people ride less at both high and low temperatures).\n",
+    "Nevertheless, our rough approximation is enough to give us some insights, and we can take a look at the coefficients of the linear model to estimate how much each feature contributes to the daily bicycle count:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Mon              504.882756\n",
+       "Tue              610.233936\n",
+       "Wed              592.673642\n",
+       "Thu              482.358115\n",
+       "Fri              177.980345\n",
+       "Sat            -1103.301710\n",
+       "Sun            -1133.567246\n",
+       "holiday        -1187.401381\n",
+       "daylight_hrs     128.851511\n",
+       "PRCP            -664.834882\n",
+       "dry day          547.698592\n",
+       "Temp (C)          65.162791\n",
+       "annual            26.942713\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params = pd.Series(model.coef_, index=X.columns)\n",
+    "params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "These numbers are difficult to interpret without some measure of their uncertainty.\n",
+    "We can compute these uncertainties quickly using bootstrap resamplings of the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.utils import resample\n",
+    "np.random.seed(1)\n",
+    "err = np.std([model.fit(*resample(X, y)).coef_\n",
+    "              for i in range(1000)], 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With these errors estimated, let's again look at the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              effect  error\n",
+      "Mon            505.0   86.0\n",
+      "Tue            610.0   83.0\n",
+      "Wed            593.0   83.0\n",
+      "Thu            482.0   85.0\n",
+      "Fri            178.0   81.0\n",
+      "Sat          -1103.0   80.0\n",
+      "Sun          -1134.0   83.0\n",
+      "holiday      -1187.0  163.0\n",
+      "daylight_hrs   129.0    9.0\n",
+      "PRCP          -665.0   62.0\n",
+      "dry day        548.0   33.0\n",
+      "Temp (C)        65.0    4.0\n",
+      "annual          27.0   18.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(pd.DataFrame({'effect': params.round(0),\n",
+    "                    'error': err.round(0)}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We first see that there is a relatively stable trend in the weekly baseline: there are many more riders on weekdays than on weekends and holidays.\n",
+    "We see that for each additional hour of daylight, 129 ± 9 more people choose to ride; a temperature increase of one degree Celsius encourages 65 ± 4 people to grab their bicycle; a dry day means an average of 548 ± 33 more riders, and each inch of precipitation means 665 ± 62 more people leave their bike at home.\n",
+    "Once all these effects are accounted for, we see a modest increase of 27 ± 18 new daily riders each year.\n",
+    "\n",
+    "Our model is almost certainly missing some relevant information. For example, nonlinear effects (such as effects of precipitation *and* cold temperature) and nonlinear trends within each variable (such as disinclination to ride at very cold and very hot temperatures) cannot be accounted for in this model.\n",
+    "Additionally, we have thrown away some of the finer-grained information (such as the difference between a rainy morning and a rainy afternoon), and we have ignored correlations between days (such as the possible effect of a rainy Tuesday on Wednesday's numbers, or the effect of an unexpected sunny day after a streak of rainy days).\n",
+    "These are all potentially interesting effects, and you now have the tools to begin exploring them if you wish!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) | [Contents](Index.ipynb) | [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.06-Linear-Regression.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.07-Support-Vector-Machines.ipynb b/notebooks_v1/05.07-Support-Vector-Machines.ipynb
new file mode 100644
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@@ -0,0 +1,1044 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) | [Contents](Index.ipynb) | [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.07-Support-Vector-Machines.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# In-Depth: Support Vector Machines"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression.\n",
+    "In this section, we will develop the intuition behind support vector machines and their use in classification problems.\n",
+    "\n",
+    "We begin with the standard imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import stats\n",
+    "\n",
+    "# use seaborn plotting defaults\n",
+    "import seaborn as sns; sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Motivating Support Vector Machines"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As part of our disussion of Bayesian classification (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)), we learned a simple model describing the distribution of each underlying class, and used these generative models to probabilistically determine labels for new points.\n",
+    "That was an example of *generative classification*; here we will consider instead *discriminative classification*: rather than modeling each class, we simply find a line or curve (in two dimensions) or manifold (in multiple dimensions) that divides the classes from each other.\n",
+    "\n",
+    "As an example of this, consider the simple case of a classification task, in which the two classes of points are well separated:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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gkfH8fCA/vye8vAqOcUt5DpVNJhj55cndu3fxxhtvYPjw4Rg4cGCZnpOUlGHM\nS0mGp6eLxc8B4DykxBrmAFjHPB7PYe/eFQgPHwMnp+KP2bvXBR4ep+Du7m7+gGVkCfvi2rXjuHTp\nLfTrdxJubsCVK2rs3Nkd4eG/wcnJySLmUBbGvrkw6hNzcnIyxo4di08//RTt27c36oWJiKSoRo3G\nuHbNAS1a5BTblpzsCR8f2/0kZyq+voGoVy8Ou3atQE7O36hRoy0GDuwsdizJMKqYZ82ahfT0dMyc\nORMzZsyATCbDnDlzoFKpTJ2PiMisGjRogfXrQ9CixfYi41otkJbWnf/PmYhCoUCnTsPEjiFJRhXz\npEmTMGnSJFNnISKShPbtf8bcua8jJGQfGjbU4OhRV5w+HY7evaeJHY1sAE+nJiJ6iqdnLTz33Gqc\nP38Ux4+fQePGnfD88w3EjkU2gsVMRFSCpk1bo2nT1mLHIBsj7XP+iYiIbAyLmYiISEJYzERERBLC\nYiYiIpIQFjMREZGEsJiJyCJlZ2fj6NFtuHz5tNhRiEyKxUxEFmfnzu9x8mRHBAcPRtWq3bBpU1/c\nuMGCJuvA65iJyKLs3x+Ljh2noW5dLQCgenUtmjXbg9jYcahVK45LZpLF4ydmIrIo2dmrCkv5SQMG\nnMaBA4tESERkWixmIrIoavV9g+MuLkBe3m0zpyEyPR7KJqJiNBoNdu+eDqXyAGQyPTSaQISEvIcq\nVaqKHQ0aTW0Axb9PTk6WwcGhsfkDEZkYi5mIitDpdPjzzyiMHbsDSmXBmF6/B/PmHUS3bmvg7Ows\naj4PjxE4e3Yf/P0zCscEAVi7ti369h0sYjIi02AxE9mIy5dP4MaNpbCz00Clao8OHYbAzs6u2OP2\n7YtFTMw/pQwAcjkwcuRhLF36M8LDJ5gxdXGBgf1x8OADnDkzF35+55CW5oyEhBC0b/+1wfmYgyAI\n+Ouv36HTbYOdnQY5Oc3Qrt2/Ua2apyh5yLKxmIlswI4d36NRo+8RHZ0JAEhO/g3Ll69A//4LoFar\nizxWpzsMF5fiP0OhAJTK4+aI+0zt24+AIAzH3bt34OvrjIAAV1HzrFv3FgYNmo9q1QQAgCDswKJF\nuxAYuByenrVEzUaWh8VMZOVu3ryE+vV/QFBQZuGYh4eAsWO3YPny71GjRjskJi6CUpkErbYOkpPT\nS/xZer29OSKXiUwmQ61atcWOgXPnDqJTp2WFpQwAMhkQHX0aCxdOR69e34mYjiwRi5nIyl28uATR\n0Q+LjavmoKsWAAAYo0lEQVRUQHb2cnh5zUCPHv98X7t7txvWrVPgued0RR6fnCyDUtmz0vNamoSE\nTejSJafYuEwG2NtL4wgDWRZeLkVk5eRyLWSykrYloHnzjCJjXbqk4fZtL5w65Vg4dvWqCqtXj0Bo\n6PDKjGqRBEEBQShpm9LwBqJS8BMzkZWrXj0cV67MQoMGuUXGBQFQKjUGnxMQ8BC3bi3A2bO7AOSj\nevUIDBjQufLDWqBmzWJw4MCv6NjxQZFxnQ7Ize0oUiqyZCxmIivXsmVnrFo1GB4ei1D10WXIggDM\nmeOLoKBrBp+j0SjRpElruLry0PWz1Knji507/43jx79HYGDB0YfUVGDZsh7o0+d9kdORJWIxE9mA\nAQNmYtu2YOj1OyCX5yAnpwVCQ9/CsWMvIDg4vtjj795tj5YtxV9MxFJ07/4uLl/uhoULl0Ch0ECl\naosBA4YVuXwrPz8f+/cvhla7G4AMKlUXhIREQS7nN4pUFIuZyAbI5XJ07foygJeLjNeq9Qk2bHgL\nffrcglwOaLXAqlXN0LjxZHGCWrCGDQPRsGGgwW35+flYvfpFvPDCGlSrVjCWkrIEy5Ztx6BBc1nO\nVASLmciG+ft3R0pKHBYvngWF4j70eh+0b/+y6Kt7WZt9+xYgOnoNXJ+43NrdHXjhhZXYtasHT6qj\nIljMRDbO3d0T4eEfix3Dqul0u4uU8mPVqgF5ebsBsJjpHzx+QkRU6Uq4nuqZ28gWsZiJiCqZnV0o\nMjKKjz98CCgUvAyNimIxE0lQZmYGDh7ciIsXuXKUNQgJGYkFC/oWKef0dGDhwucQEhIjXjCSJH7H\nTCQhgiBg27Yv4ea2GJ06/Y3ERBU2bWqHpk2/Qf36/mLHswkajQYHD66ATpeD4OBBcHNzr/DPVCgU\nGDAgFlu2zEd+/l8QBBkUis4YNGikaHfEIuliMRNJyJ49v6JHj/+gRo2Cdard3LRo0mQP5s9/DXXq\n7IBCwX+ylSk+fiWysr5C375XoFYDO3d+i6NHxyAsrOitLi9fPo7r1xdCqUyDVuuH9u1fhaurW6k/\nW6FQoEuXMQDGVOIMyBrwXzmRhOTlrS0s5Sf1738cu3atQKdOw0RIZRvu3LkFhWICBg9OLBwLC7uH\nmze/x+HDjdC27SAAwP7981Cr1qeIiSlYglOnA1asWIfGjRegTp0GomQn68LvmIkkRKW6b3DczQ3I\nyblh3jA25tCh/6FHj8Ri497euUhPXw0AePAgDcD3aNPmn3WxFQpg2LBzOHNmqrmikpVjMRNJiEZT\n1+B4QoId3NxamjmNbVEo0kq8C5dGcxnbtvXFypX+6N37psHHODkdgVDSbaaIyoHFTCQhbm7Dcf58\n0VW3BAHYuDEEwcERIqWyDXZ2zZCVZXhbbu4VREfvQcuWmShp9UyZTM9iJpNgMRNJSOvWg3D58tdY\nsiQYBw86YfPmGpg3bwi6dJkHWUkf58gkevT4F5YubV3s3spr19qjVy8tACA0FNi50/Dzs7KCuOY1\nmUSFTv46efIkvvvuO8TGxpoqD5HN69BhJARhBO7fvw8vLycEB3PdanNQq9Vo334hYmOnwNHxIBQK\nLbKzA/HgwQk8//zfAAAXF0AmA86dA5o1K3ieIABr1jREo0YfipierInRxTxnzhysXbsWTk5OpsxD\nRABkMhm8vLzEjmFzPD1rIiJiFgRBgCAIkMvl2LEjDMDfhY8JDwdOnACWLgXS0lrByakrgoNfg6dn\nTdFyp6YmIz7+D8hk2fD3fw41a7YSLQtVnNHHXby9vTFjxgxTZiEikgSZTFZ4WFoQIvDgQdHtAQFA\nfn4LPPfcTvTu/YWopXzwYCyuX++IYcM+Q1TUt3BwCMPq1S8hPz9ftExUMUZ/Yg4LC0NCQoIpsxCR\nBTp//iAOHtyPrCwV2rUbBWdnF9GyJCRcx9mz86BQZEGlCkaHDkMrvLJW9+7/xurVd+DruxKhoSlI\nSpJj69ZANGjwDZRKpYmSGycp6R7U6s8RHv7PZV4NG2pQq9YyrFvXDD17vitiOjKWTKjAaYQJCQkY\nP348lixZYspMRGQBdDodFi0ahXbtVqNx4xzk5QFbt/rA3f0btG8/xOx5du6cDZVqEkJCkiGTAamp\nwNq1PTF06BqTfOV29+5txMdvgJtbXXTo0EcSJ3qtXfs5+vefbPBM8TVrumHAgBLOVCNJq/DKX+Xp\n9aQkA7dXsSCeni4WPweA85ASS57D1q1fIzJyERwcCv6sVAJ9+17HunXjce1aB7i4VDFblrS0FGi1\nk9G9e3LhWLVqwMiR27Fw4YeIiJj2zJ/xrH2hUFRFu3YF901OSSnhuiozy85OLfHyrfz8DIv9u2XJ\n/y6e5Olp3NGjCr/l4yUcRLZJqdxZWMpPioi4hcOH55k1y9Gj8xEWdq/YuJ0dYG+/36xZzMnNrRNu\n3TJ8OD0np6mZ05CpVKiYa9euzcPYRDbKzs7wp0alEhCEdDOn0aKkr5Llcq15o5hRUFA4Nm7sjdzc\nouMbNvihadM3xQlFFcabWBCRUXJymgA4XWz8yhU1vLy6mDWLn19/HDv2XwQFZRbbptFY71KmMpkM\n/fv/juXLv4FK9Rfs7HIABMHbexzq1WsidjwyEouZiIzSoMFr2LHjIHr0uF04ptEAO3b0waBBoWbN\n4uvbDOvXD4O3929wd9cXjq9d2wBNm75j1izmplKp0KvXx4V/ruzvZ9PTH+LIkRWws1OgXbuhcDD0\nfQZVCIuZiIzi5xeMK1fmYcGCmahS5SI0GgdotV3Rv/+EZz+5EvTr9z127WoOnW4L7OwykJPTFC1b\nvo5atXxFyWON4uL+iypVfsGQIXeg0wGbNv0HSuUHaNcuWuxoVqVCl0uVl6WfZWdNZwpyHtJgDXMA\nrGMe1jAHoPLmcfz4Vnh7j0LjxkXPLdi71x1q9SbUr2+6Q+fWtC+MIf6FeEREJHnJySuKlTIAdOqU\ngkuX5pk/kBVjMRMR0TMplamlbHtQ4jYqP37HTERmp9FocODA7xCEU0hOzoJWq0KNGtVRo0ZfNG8e\nInY8MkCj8YMgFNxd60laLaDTNRQnlJViMRORWT18mIrdu19AdPShwgVKTp4E7t4Fateeg3XrYtC/\n/3SLWbwoPf0hDh2aAzu7VKhULdChQ2SF1+eWooCA17B+/RY899y1IuNLl7ZAp06viJTKOrGYicis\n9u37EmPGHCryyatVq4K1rd3cNOjb93fs398BISFDxQtZRqdPb0V6+vsYOvQ6FAogORlYteoPdOu2\nEFWrVhM7nknVqOGN7Ox5WLDgOzg5HYNeb4esrLYIDPwUzs68Z7gpsZiJyKwcHQ8XOxwKAF26ABs2\nAM89p0dOzhYA0i7mvLw83L//KYYNu1445uEBvPzyPsyf/zH69JkpYrrK4esbAF/fBdDr9ZDJZBZz\nVMPS8OQvIjIrmUxfwjjw+OJNhSLX4GOk5NChtejT51yxcZkMcHLaV64b/FgauVzOUq5ELGYiMqvs\n7ACD40eOAIGBBScT5eUZfoyU5OSkoaQjuEqlBnq94TcgRM/CYiaiCktNTcKmTR9g164wxMX1wubN\nk5GVZfgmFwEBH2DpUn88+YEyIQG4fRuoUweIjW2PkJDXzJTceIGBzyMuzsvgtqysFlZ5AhiZB79j\nJqIKych4iIMHB2HEiCOF3x3n5x/A3Lnx6Nt3FVQqVZHH16xZHyrVWsTG/gyV6gzu3PkbeXmAr68H\nFi5sjW7dxsPR0VGEmZSPh0d1HD0ag3v3fkSNGrrC8b17a6BWLem/sSDpYjETUYXs3/8ToqKOFDmh\ny84OiIn5C3/++Qe6dXu52HPc3asjIuJzM6asHL16TcbevfWh1a6HSpWGnBxf1K//Mvz92z3zuYIg\nIDMzA/b2DlAqDd9TmWwTi5mIKkSlOm3wXshOToAgxAMoXszWQiaTITR0NIDR5Xre4cNLkJ7+Gzw9\nLyIrqwpSUzuja9ev4exs3NrKZF1YzERUIfn5Jd/2T6+3N2MSyxAfvxr164+Hv//jmzSkQa+Pxa+/\nJmLQoBWiZiNp4MlfRFQh9va9cf9+8Y/MFy86wMtrkAiJpC0tbf4TpVxALgfCwnbh1Kld4oQiSWEx\nE1GFhIS8gB07XsHZs/+csHXkiAuOHn0TLVp0FjGZNKnVNwyO+/pqkZh41KxZSJp4KJuIKkQmkyEq\nagb27h2CRYvWA7BDgwaRCA9vKnY0SdJq3QFcLTaemgo4ONQ1fyCSHBYzEZlE48at0bhxa7FjWIC+\nSEs7Aje3oiuDbdgQgPDwISJlIilhMRMRmVH37v/GunWJqFNnJUJDE3H3rgK7drVG06bfclESAsBi\nJiIyK5lMhj59vkZKynisWbMF7u71EBERyrWnqRCLmYhIBO7unujWbbjYMUiCeFY2ERGRhLCYiYiI\nJITFTEREJCEsZiIiIglhMRMREUkIz8omIqJSZWZm4sCBWVAorkOr9UBAwMvw8qotdiyrxWImIqIS\n3b59ERcvjsaQIWehUgGCAGzdugR3736PgIC+YsezSjyUTUREJTpz5jNERRWUMgDIZECvXneQmjoV\n+fn54oazUvzETEREBmVlZaF69cMGt3Xtehrx8TvQpk24mVM9m06nw/79C6HTHYJer0L16gPQsmVX\nsWOVGYuZiIgM0uvzoVDkGdxmbw9otdlmTvRsGo0Gf/4Zjejo7ahSpWDs8uWF2LTpVUREfC5uuDLi\noWwiIjLIxaUKEhMDDG6Li/NDcHBvMyd6tr/++g/Gjv2nlAGgYcNcBAbOxsWLlnG/axYzERGVqHbt\ndxAXV6vI2LlzztDrX4G9vb1IqUqmUByAUll8vEWLbNy6tdr8gYxg1KFsQRAwZcoUXLx4ESqVCl99\n9RXq1uUNvomIrI2/f1dcv74CCxb8Cnv7W8jNdYeX1zCEhvYUO5pBcrm+lK06s+WoCKOKefv27dBq\ntViyZAlOnjyJadOmYebMmabORkRkExISruPMmdlQqe5Aq62F5s3/hdq1fcSOVcjHpzl8fP4rdowy\nyckJgl7/F+RPHQ++dk2F6tWld+jdEKOKOT4+HqGhoQCAVq1a4cyZMyYNRURkK06f3o78/DcQE3MH\nMlnBdcLbt69GaurPaNFCmp9KpaxTp/GYN28/Ro48DMWjhktJkWH79qEYMKCrqNnKyqhizszMhIuL\nyz8/RKGAXq+H/Om3KEREVCJBEHD37jeIiblTOCaTAWFhd7Bo0Tdo3rwHZDKZiAktj4uLK7p1W42l\nS3+GSnUCOp0KKlUYBgwYYTG/S6OK2dnZGVlZWYV/Lmspe3q6PPMxUmcNcwA4DymxhjkA1jEPc8/h\nypVLCAgwfKZwixZHkZGRCD+/huX+uba+Lzw9XeDjM9WEaczLqGIOCgpCXFwcevfujRMnTqBRo0Zl\nel5SUoYxLycZnp4uFj8HgPOQEmuYA2Ad8xBjDikpmXB2NrxNJgOSkzNRpUr5MnFfSIexby6MKuaw\nsDDs27cPw4YNAwBMmzbNqBcnIut09eopXL36B1SqVGg03mjT5nW4u3uKHUtyfHwaYPv2YPj7Hyq2\n7fTpYPTo4SdCKhKbUcUsk8nw2WefmToLEVmBQ4cWwd39I8TEpAIA9HpgzZp1qFPnd/j4tBI5nbTI\nZDJUrz4ecXFvoVu3e4XjcXE14Ok53mK+EyXT4pKcRGQyWq0WWu10dOiQWjgmlwODBl3BggVfw8dn\nsYjppKlVq964eXM9FiyYA7X6LnJza6JJk5fg7d1Y7GgkEhYzEZnMkSN/omfPSwa3Vat2FDk5OXBw\ncDBzKunz9m4Mb+9vxY5BEsHrm4jIZARBQElHXwuu0RXMG4jIArGYichk2rTpi+3bDV/ek5oaBEdH\nRzMnIrI8LGYiMhm1Wg25/C0cPVq1cEwQgLVrfeHn96GIyYgsB79jJiKT6thxFC5ebIaFCxdApUpF\nTo43goJehZdXbbGjEVkEFjMRmVzjxm3QuHEbsWMQWSQeyiYiIpIQFjMREZGEsJiJiIgkhMVMREQk\nISxmIiIiCWExExERSQiLmYiISEJYzERERBLCYiYiIpIQFjMREZGEsJiJiIgkhMVMREQkISxmIiIi\nCWExExERSQiLmYiISEJYzERERBLCYiYiIpIQFjMREZGEsJiJiIgkhMVMREQkISxmIiIiCWExExER\nSQiLmYiISEJYzERERBLCYiYiIpIQFjMREZGEsJiJiIgkhMVMREQkISxmIiIiCalQMW/btg3jx483\nVRYiIiKbpzD2iV999RX27duHpk2bmjIPERGRTTP6E3NQUBCmTJliwihERET0zE/MK1aswB9//FFk\nbNq0aYiIiMDhw4crLRgREZEtkgmCIBj75MOHD2Pp0qX4/vvvTZmJiIjIZvGsbCIiIglhMRMREUlI\nhQ5lExERkWnxEzMREZGEsJiJiIgkhMVMREQkISxmIiIiCTF6Sc6y2LZtGzZv3mzwOudly5Zh6dKl\nUCqVGDduHLp27VqZUYySm5uL999/HykpKXB2dsbXX38NNze3Io/56quvcOzYMTg5OQEAZs6cCWdn\nZzHiFiEIAqZMmYKLFy9CpVLhq6++Qt26dQu379y5EzNnzoRCocDgwYMRGRkpYtqSPWse8+bNw4oV\nK1CtWjUAwOeff4769euLlLZ0J0+exHfffYfY2Ngi45ayLx4raR6Wsi90Oh0++ugjJCQkIC8vD+PG\njUP37t0Lt1vC/njWHCxlX+j1enz88ce4fv065HI5PvvsMzRo0KBwuyXsC+DZ8yj3/hAqyZdffilE\nREQI7777brFtSUlJQr9+/YS8vDwhIyND6Nevn6DVaisritF+//134aeffhIEQRD+/PNP4csvvyz2\nmKioKCEtLc3c0Z5p69atwoQJEwRBEIQTJ04Ir776auG2vLw8ISwsTMjIyBC0Wq0wePBgISUlRayo\npSptHoIgCO+9955w9uxZMaKVy6+//ir069dPeOGFF4qMW9K+EISS5yEIlrMvVq5cKUydOlUQBEF4\n8OCB0LVr18JtlrI/SpuDIFjOvti2bZvw0UcfCYIgCIcOHbLY/6dKm4cglH9/VNqh7NLW0j516hSC\ng4OhUCjg7OyM+vXr4+LFi5UVxWjx8fHo3LkzAKBz5844cOBAke2CIODmzZv49NNPERUVhZUrV4oR\n06D4+HiEhoYCAFq1aoUzZ84Ubrt69Sq8vb3h7OwMpVKJ4OBgHDlyRKyopSptHgBw9uxZzJo1C9HR\n0Zg9e7YYEcvE29sbM2bMKDZuSfsCKHkegOXsi4iICLz99tsACj7pKBT/HDi0lP1R2hwAy9kXPXv2\nxBdffAEASEhIgKura+E2S9kXQOnzAMq/Pyp8KNuYtbQzMzPh4uJS+GdHR0dkZGRUNEqFGJqHh4dH\n4WFpJycnZGZmFtmenZ2NESNG4MUXX4ROp8PIkSPRokULNGrUyGy5S/L071ihUECv10Mulxfb5uTk\nJPrvvySlzQMA+vbti5iYGDg7O+P111/H7t270aVLF7HiligsLAwJCQnFxi1pXwAlzwOwnH3h4OAA\noOB3//bbb+Odd94p3GYp+6O0OQCWsy8AQC6XY8KECdi+fTt+/PHHwnFL2RePlTQPoPz7o8LFPGTI\nEAwZMqRcz3F2di5ScllZWahSpUpFo1SIoXm8+eabyMrKAlCQ8cm/JEDBP44RI0ZArVZDrVajffv2\nuHDhgiSK2dnZuTA7gCJlJsXff0lKmwcAjBo1qvDNU5cuXXDu3DnJ/gdkiCXti2expH1x9+5dvPHG\nGxg+fDj69OlTOG5J+6OkOQCWtS8A4Ouvv0ZKSgoiIyOxceNG2NvbW9S+eMzQPIDy7w9Rzspu2bIl\n4uPjodVqkZGRgWvXrqFhw4ZiRClVUFAQdu/eDQDYvXs3WrduXWT79evXERUVBUEQkJeXh/j4ePj7\n+4sRtZgns584caLImwU/Pz/cvHkT6enp0Gq1OHLkCAICAsSKWqrS5pGZmYl+/fohJycHgiDg4MGD\nkvn9l0R4aqE9S9oXT3p6Hpa0L5KTkzF27Fi8//77GDhwYJFtlrI/SpuDJe2LtWvXFh7aVavVkMvl\nhW+8LWVfAKXPw5j9UalnZT9t3rx58Pb2Rrdu3TBixAhER0dDEAS8++67UKlU5oxSJlFRUfjwww8R\nHR0NlUpVeHb5k/MYMGAAIiMjoVQqMXDgQPj5+YmcukBYWBj27duHYcOGASj4emHDhg3IyclBZGQk\nJk6ciDFjxkAQBERGRqJ69eoiJzbsWfN49913C49adOjQofCcAKmSyWQAYJH74kmG5mEp+2LWrFlI\nT0/HzJkzMWPGDMhkMgwdOtSi9sez5mAp+yI8PBwTJ07E8OHDC88037p1q0XtC+DZ8yjv/uBa2URE\nRBLCBUaIiIgkhMVMREQkISxmIiIiCWExExERSQiLmYiISEJYzERERBLCYiYiIpKQ/wcFt5VhxUFP\nYAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11602a4a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "X, y = make_blobs(n_samples=50, centers=2,\n",
+    "                  random_state=0, cluster_std=0.60)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A linear discriminative classifier would attempt to draw a straight line separating the two sets of data, and thereby create a model for classification.\n",
+    "For two dimensional data like that shown here, this is a task we could do by hand.\n",
+    "But immediately we see a problem: there is more than one possible dividing line that can perfectly discriminate between the two classes!\n",
+    "\n",
+    "We can draw them as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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x97PhNXEy5tLmAqQH8501Bzf+8nek/rAdgZkZKDE0RErASAz67B8wNjZu/Qm6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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1188979e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xfit = np.linspace(-1, 3.5)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
+    "plt.plot([0.6], [2.1], 'x', color='red', markeredgewidth=2, markersize=10)\n",
+    "\n",
+    "for m, b in [(1, 0.65), (0.5, 1.6), (-0.2, 2.9)]:\n",
+    "    plt.plot(xfit, m * xfit + b, '-k')\n",
+    "\n",
+    "plt.xlim(-1, 3.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are three *very* different separators which, nevertheless, perfectly discriminate between these samples.\n",
+    "Depending on which you choose, a new data point (e.g., the one marked by the \"X\" in this plot) will be assigned a different label!\n",
+    "Evidently our simple intuition of \"drawing a line between classes\" is not enough, and we need to think a bit deeper."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Support Vector Machines: Maximizing the *Margin*\n",
+    "\n",
+    "Support vector machines offer one way to improve on this.\n",
+    "The intuition is this: rather than simply drawing a zero-width line between the classes, we can draw around each line a *margin* of some width, up to the nearest point.\n",
+    "Here is an example of how this might look:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fQXv2C4DjYJ4yHfP//fsDaqDE7Px12HnkEDYfPiiVX9kA/OOB+Vj+7PPBXBpBBASO49Da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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11889a710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xfit = np.linspace(-1, 3.5)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
+    "\n",
+    "for m, b, d in [(1, 0.65, 0.33), (0.5, 1.6, 0.55), (-0.2, 2.9, 0.2)]:\n",
+    "    yfit = m * xfit + b\n",
+    "    plt.plot(xfit, yfit, '-k')\n",
+    "    plt.fill_between(xfit, yfit - d, yfit + d, edgecolor='none',\n",
+    "                     color='#AAAAAA', alpha=0.4)\n",
+    "\n",
+    "plt.xlim(-1, 3.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In support vector machines, the line that maximizes this margin is the one we will choose as the optimal model.\n",
+    "Support vector machines are an example of such a *maximum margin* estimator."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fitting a support vector machine\n",
+    "\n",
+    "Let's see the result of an actual fit to this data: we will use Scikit-Learn's support vector classifier to train an SVM model on this data.\n",
+    "For the time being, we will use a linear kernel and set the ``C`` parameter to a very large number (we'll discuss the meaning of these in more depth momentarily)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=10000000000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC # \"Support vector classifier\"\n",
+    "model = SVC(kernel='linear', C=1E10)\n",
+    "model.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To better visualize what's happening here, let's create a quick convenience function that will plot SVM decision boundaries for us:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def plot_svc_decision_function(model, ax=None, plot_support=True):\n",
+    "    \"\"\"Plot the decision function for a 2D SVC\"\"\"\n",
+    "    if ax is None:\n",
+    "        ax = plt.gca()\n",
+    "    xlim = ax.get_xlim()\n",
+    "    ylim = ax.get_ylim()\n",
+    "    \n",
+    "    # create grid to evaluate model\n",
+    "    x = np.linspace(xlim[0], xlim[1], 30)\n",
+    "    y = np.linspace(ylim[0], ylim[1], 30)\n",
+    "    Y, X = np.meshgrid(y, x)\n",
+    "    xy = np.vstack([X.ravel(), Y.ravel()]).T\n",
+    "    P = model.decision_function(xy).reshape(X.shape)\n",
+    "    \n",
+    "    # plot decision boundary and margins\n",
+    "    ax.contour(X, Y, P, colors='k',\n",
+    "               levels=[-1, 0, 1], alpha=0.5,\n",
+    "               linestyles=['--', '-', '--'])\n",
+    "    \n",
+    "    # plot support vectors\n",
+    "    if plot_support:\n",
+    "        ax.scatter(model.support_vectors_[:, 0],\n",
+    "                   model.support_vectors_[:, 1],\n",
+    "                   s=300, linewidth=1, facecolors='none');\n",
+    "    ax.set_xlim(xlim)\n",
+    "    ax.set_ylim(ylim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9vQWXTxzDqVvJiOzeE0kBgdYOi9gJrVaLBw/uG565lk4fMwzDeRepVCqwcOHv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\nAH6sncTGlP8LAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119058128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
+    "plot_svc_decision_function(model);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the dividing line that maximizes the margin between the two sets of points.\n",
+    "Notice that a few of the training points just touch the margin: they are indicated by the black circles in this figure.\n",
+    "These points are the pivotal elements of this fit, and are known as the *support vectors*, and give the algorithm its name.\n",
+    "In Scikit-Learn, the identity of these points are stored in the ``support_vectors_`` attribute of the classifier:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.44359863,  3.11530945],\n",
+       "       [ 2.33812285,  3.43116792],\n",
+       "       [ 2.06156753,  1.96918596]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.support_vectors_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A key to this classifier's success is that for the fit, only the position of the support vectors matter; any points further from the margin which are on the correct side do not modify the fit!\n",
+    "Technically, this is because these points do not contribute to the loss function used to fit the model, so their position and number do not matter so long as they do not cross the margin.\n",
+    "\n",
+    "We can see this, for example, if we plot the model learned from the first 60 points and first 120 points of this dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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5RaW+A4rF04j/oigKp14fx6Dz57KmDZkCNaKj2Hv7FpXyuUPIs+5BpZatOe7j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\nc+fOasiQw622jx0rbdggXf9n8PTpcB09+qiSkoZ06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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119060cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_svm(N=10, ax=None):\n",
+    "    X, y = make_blobs(n_samples=200, centers=2,\n",
+    "                      random_state=0, cluster_std=0.60)\n",
+    "    X = X[:N]\n",
+    "    y = y[:N]\n",
+    "    model = SVC(kernel='linear', C=1E10)\n",
+    "    model.fit(X, y)\n",
+    "    \n",
+    "    ax = ax or plt.gca()\n",
+    "    ax.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
+    "    ax.set_xlim(-1, 4)\n",
+    "    ax.set_ylim(-1, 6)\n",
+    "    plot_svc_decision_function(model, ax)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
+    "for axi, N in zip(ax, [60, 120]):\n",
+    "    plot_svm(N, axi)\n",
+    "    axi.set_title('N = {0}'.format(N))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the left panel, we see the model and the support vectors for 60 training points.\n",
+    "In the right panel, we have doubled the number of training points, but the model has not changed: the three support vectors from the left panel are still the support vectors from the right panel.\n",
+    "This insensitivity to the exact behavior of distant points is one of the strengths of the SVM model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are running this notebook live, you can use IPython's interactive widgets to view this feature of the SVM model interactively:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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aZqe7u4vS0jKuuOJqv3HJySnv+Tgnn7yFu+56gE9+8lrOOec8duz4FOvXb/Ab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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11907e7f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from ipywidgets import interact, fixed\n",
+    "interact(plot_svm, N=[10, 200], ax=fixed(None));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Beyond linear boundaries: Kernel SVM\n",
+    "\n",
+    "Where SVM becomes extremely powerful is when it is combined with *kernels*.\n",
+    "We have seen a version of kernels before, in the basis function regressions of [In Depth: Linear Regression](05.06-Linear-Regression.ipynb).\n",
+    "There we projected our data into higher-dimensional space defined by polynomials and Gaussian basis functions, and thereby were able to fit for nonlinear relationships with a linear classifier.\n",
+    "\n",
+    "In SVM models, we can use a version of the same idea.\n",
+    "To motivate the need for kernels, let's look at some data that is not linearly separable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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k5IeYMePsbaXrWLPmDGr1WgIDw+wWmxCNkSRuUSG9Xs/mf36A8uc1uKWmUhga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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11909f048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets.samples_generator import make_circles\n",
+    "X, y = make_circles(100, factor=.1, noise=.1)\n",
+    "\n",
+    "clf = SVC(kernel='linear').fit(X, y)\n",
+    "\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
+    "plot_svc_decision_function(clf, plot_support=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is clear that no linear discrimination will *ever* be able to separate this data.\n",
+    "But we can draw a lesson from the basis function regressions in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), and think about how we might project the data into a higher dimension such that a linear separator *would* be sufficient.\n",
+    "For example, one simple projection we could use would be to compute a *radial basis function* centered on the middle clump:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "r = np.exp(-(X ** 2).sum(1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can visualize this extra data dimension using a three-dimensional plot—if you are running this notebook live, you will be able to use the sliders to rotate the plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nrnAkzWNAb2/6yFK388tf3o+HHvo1VFXFmDFj8LWvfR0XXHBe9F37o0KNSFse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\nkJQsgUAgEAgmQVKyBAKBQCCYwP8DzZfIxLKdt6oAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1188cc780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mpl_toolkits import mplot3d\n",
+    "\n",
+    "def plot_3D(elev=30, azim=30, X=X, y=y):\n",
+    "    ax = plt.subplot(projection='3d')\n",
+    "    ax.scatter3D(X[:, 0], X[:, 1], r, c=y, s=50, cmap='autumn')\n",
+    "    ax.view_init(elev=elev, azim=azim)\n",
+    "    ax.set_xlabel('x')\n",
+    "    ax.set_ylabel('y')\n",
+    "    ax.set_zlabel('r')\n",
+    "\n",
+    "interact(plot_3D, elev=[-90, 90], azip=(-180, 180),\n",
+    "         X=fixed(X), y=fixed(y));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that with this additional dimension, the data becomes trivially linearly separable, by drawing a separating plane at, say, *r*=0.7.\n",
+    "\n",
+    "Here we had to choose and carefully tune our projection: if we had not centered our radial basis function in the right location, we would not have seen such clean, linearly separable results.\n",
+    "In general, the need to make such a choice is a problem: we would like to somehow automatically find the best basis functions to use.\n",
+    "\n",
+    "One strategy to this end is to compute a basis function centered at *every* point in the dataset, and let the SVM algorithm sift through the results.\n",
+    "This type of basis function transformation is known as a *kernel transformation*, as it is based on a similarity relationship (or kernel) between each pair of points.\n",
+    "\n",
+    "A potential problem with this strategy—projecting $N$ points into $N$ dimensions—is that it might become very computationally intensive as $N$ grows large.\n",
+    "However, because of a neat little procedure known as the [*kernel trick*](https://en.wikipedia.org/wiki/Kernel_trick), a fit on kernel-transformed data can be done implicitly—that is, without ever building the full $N$-dimensional representation of the kernel projection!\n",
+    "This kernel trick is built into the SVM, and is one of the reasons the method is so powerful.\n",
+    "\n",
+    "In Scikit-Learn, we can apply kernelized SVM simply by changing our linear kernel to an RBF (radial basis function) kernel, using the ``kernel`` model hyperparameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=1000000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf = SVC(kernel='rbf', C=1E6)\n",
+    "clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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EoQ0RiVvQW48/vpSdO3eweXMoAwcOrnM7W1s7PvvsvzzyyBOMHz+cmTMns2XLX9W2OXhw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\nAwBgI/8FOMqL16Jtqf8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118bb3e80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
+    "plot_svc_decision_function(clf)\n",
+    "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n",
+    "            s=300, lw=1, facecolors='none');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using this kernelized support vector machine, we learn a suitable nonlinear decision boundary.\n",
+    "This kernel transformation strategy is used often in machine learning to turn fast linear methods into fast nonlinear methods, especially for models in which the kernel trick can be used."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tuning the SVM: Softening Margins\n",
+    "\n",
+    "Our discussion thus far has centered around very clean datasets, in which a perfect decision boundary exists.\n",
+    "But what if your data has some amount of overlap?\n",
+    "For example, you may have data like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+8yHWIf25vW0LNR56Hrylugctxo0vp8gLysrKwnLB3PtJ+C7vzAyOzJ+L5rkJ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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1197ec860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X, y = make_blobs(n_samples=100, centers=2,\n",
+    "                  random_state=0, cluster_std=1.2)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To handle this case, the SVM implementation has a bit of a fudge-factor which \"softens\" the margin: that is, it allows some of the points to creep into the margin if that allows a better fit.\n",
+    "The hardness of the margin is controlled by a tuning parameter, most often known as $C$.\n",
+    "For very large $C$, the margin is hard, and points cannot lie in it.\n",
+    "For smaller $C$, the margin is softer, and can grow to encompass some points.\n",
+    "\n",
+    "The plot shown below gives a visual picture of how a changing $C$ parameter affects the final fit, via the softening of the margin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fnqtxsTHs/GU2ltQU7rl/CBWqVC3QeO2hpL9/sqN6sU31Yi2nbYtcJyKGDBnC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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119ed0eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X, y = make_blobs(n_samples=100, centers=2,\n",
+    "                  random_state=0, cluster_std=0.8)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
+    "\n",
+    "for axi, C in zip(ax, [10.0, 0.1]):\n",
+    "    model = SVC(kernel='linear', C=C).fit(X, y)\n",
+    "    axi.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
+    "    plot_svc_decision_function(model, axi)\n",
+    "    axi.scatter(model.support_vectors_[:, 0],\n",
+    "                model.support_vectors_[:, 1],\n",
+    "                s=300, lw=1, facecolors='none');\n",
+    "    axi.set_title('C = {0:.1f}'.format(C), size=14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The optimal value of the $C$ parameter will depend on your dataset, and should be tuned using cross-validation or a similar procedure (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Face Recognition\n",
+    "\n",
+    "As an example of support vector machines in action, let's take a look at the facial recognition problem.\n",
+    "We will use the Labeled Faces in the Wild dataset, which consists of several thousand collated photos of various public figures.\n",
+    "A fetcher for the dataset is built into Scikit-Learn:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Ariel Sharon' 'Colin Powell' 'Donald Rumsfeld' 'George W Bush'\n",
+      " 'Gerhard Schroeder' 'Hugo Chavez' 'Junichiro Koizumi' 'Tony Blair']\n",
+      "(1348, 62, 47)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_lfw_people\n",
+    "faces = fetch_lfw_people(min_faces_per_person=60)\n",
+    "print(faces.target_names)\n",
+    "print(faces.images.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot a few of these faces to see what we're working with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MISKFWAk+vdFS+MaZAjX4AgDkggyg5BaR18xI6fq7ubMx11oDhWofSknG6Zhh3AKAknPd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\nxZQS4kbDrmmu3bcKnBk5NzpIPfWUSvsDhgAloo9RNg7gJsMlJyGCBB1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Ah6oM6D08HrT/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Z0jpVGWHZRlJxpM5xysKWGtgbUGUhI0oHUGrTNI2dvi6f6BEfQNDqLR30BeyyOpyjkHVwb4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\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8LHWmYUTGkPNr7z1gSReWLOIs/bK07woABkF7W0vQCoACX54XBOUxIzkfFRCPVmJTJpKK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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11906eef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(3, 5)\n",
+    "for i, axi in enumerate(ax.flat):\n",
+    "    axi.imshow(faces.images[i], cmap='bone')\n",
+    "    axi.set(xticks=[], yticks=[],\n",
+    "            xlabel=faces.target_names[faces.target[i]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each image contains [62×47] or nearly 3,000 pixels.\n",
+    "We could proceed by simply using each pixel value as a feature, but often it is more effective to use some sort of preprocessor to extract more meaningful features; here we will use a principal component analysis (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)) to extract 150 fundamental components to feed into our support vector machine classifier.\n",
+    "We can do this most straightforwardly by packaging the preprocessor and the classifier into a single pipeline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn.decomposition import RandomizedPCA\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pca = RandomizedPCA(n_components=150, whiten=True, random_state=42)\n",
+    "svc = SVC(kernel='rbf', class_weight='balanced')\n",
+    "model = make_pipeline(pca, svc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the sake of testing our classifier output, we will split the data into a training and testing set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.cross_validation import train_test_split\n",
+    "Xtrain, Xtest, ytrain, ytest = train_test_split(faces.data, faces.target,\n",
+    "                                                random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can use a grid search cross-validation to explore combinations of parameters.\n",
+    "Here we will adjust ``C`` (which controls the margin hardness) and ``gamma`` (which controls the size of the radial basis function kernel), and determine the best model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 47.8 s, sys: 4.08 s, total: 51.8 s\n",
+      "Wall time: 26 s\n",
+      "{'svc__gamma': 0.001, 'svc__C': 10}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.grid_search import GridSearchCV\n",
+    "param_grid = {'svc__C': [1, 5, 10, 50],\n",
+    "              'svc__gamma': [0.0001, 0.0005, 0.001, 0.005]}\n",
+    "grid = GridSearchCV(model, param_grid)\n",
+    "\n",
+    "%time grid.fit(Xtrain, ytrain)\n",
+    "print(grid.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The optimal values fall toward the middle of our grid; if they fell at the edges, we would want to expand the grid to make sure we have found the true optimum.\n",
+    "\n",
+    "Now with this cross-validated model, we can predict the labels for the test data, which the model has not yet seen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model = grid.best_estimator_\n",
+    "yfit = model.predict(Xtest)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at a few of the test images along with their predicted values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RjcKTHWqWE2XtSwnBOUWYWgMw0Iy8T8a4MwyMMTKWo+7RbWIGyNMcwhP2PQqH\nq4hgyfAyziB8iSAKyQhYen0y0NVDtLAXePBDohMLylH4HSoWhe+lDAwrvDD6h3MG4UkEnkTge/Cl\nhOQUdSqtIZRCmmXgjCHnDDq3a6tYmzYVw0dFs8bOcecQo/NZlFYQHapYv3w08FpDSh9a55a9kM5Q\nuqhzVOTpWAvBx2zoDCDHQHTuGYCL4oUULq1QvL54HeOdIID+hQsMyDmjzzDG0NpPKSWQ23SK0gpZ\nlkLKbBJHjfZgITxsv+Mu2PMtb8K2O2+PkRfriJsx0lYCPwzQt00/+redgp6pPfBDH0HgQwpigKIo\nQCUMEXoepBCQNk1XRJncMpBaG+RaI81zxFmGXClUgwCR76MdheCSY+NQnVIkgyPwfA9BJYDve9h+\n++mo7LsY3d1V3HVrDase/COazREY/fLB0oSuVmVzP7mvYJIMWZbbfFPgNmtttDN8xWZsbERZ5EKA\njoHMs9xukKPpYO4mYPFcmLGvLx432oAZvUkeRo2hao3W1iiQ8TGTuFDZqPya1gZsVM5XO9qV2Vyn\nNRQGFAHmyo6b6UQHdnPi4GNoNnp/DQ4+xtEoaG+lNVhOY8NsxFqwBHmqxkbyRdTKOTwvmLSxkpLy\nSlpocCHcxs44IyNnvzuXL7eUfcE0MLtBeULAs4uS2bmjDTkq3OZ7mX2L0ZESYGk2gMaL2SjM92C0\nRpZa5w30uS7tIChyQQiXj5XB5BjOIqoJqiF83wMTNkqymzbYJi8Y/XthP62jEfk+KkGAwCPDCQC5\nUhB5DsEAkXNknBOzkStidXJL17LOei0ic2ccRlsD2HEffWkM0Hqsoz1R4ExAw6Z0pEfMjo0KmZ0U\nwgYCzqBaA0pOCOs4AYy7VBOtubGf5ehvN+eKCU0enLI5dlY4YqOobnJ4NZSN9PMkQ55m5MAZA6Vy\npGl7UsaMHCFyOKdO3QG7L1qAmbvNgjFA2qYgJKyECLsidE/tQbW3Cj/wKO0UBfClhC8lAikReN4Y\nzYExBkmWQWkNzhg8IchhsAyR0hqCcwQ2Py85t+kBho0MaDdiaKUpAAPQU6lg2916MWNKH6IwRNJO\n8djD9yNJXifDWVCDAJAlGfIsJ3qoGiKsBCiWgRMJaIM8z0lIkCkwwVwyl7z8nDxXodzrisnHRkUb\nnbnWmbBFDs+gQ88WKDbVgj7hgkMrhTTOyEhkctJynJxZr5UVi67YcOHybsx0VpvWHeOVtBIYYzqC\nBFDOUhuN2j6bAAAgAElEQVQNzjjAzJhFX+R/nRjD5ipHG22jDfJUUSTCOqIFILcUkH1OljtKUkrf\nRRITDdqsJKRHm3lxP7SBUXSic+2iqCzLAcbgGcCzEZ6wniwv5ssoOpYx5mTnxhjk1oHSow0nYzCM\nIXOiFhJgocgmuNzfqF3SZhWKSOMlxmoiYY27kALSJwrUD32IQlC1ycUwtsl1g5wFTwqEnoeK7yMo\n8st207K3TSwIY1CeRuYJ8IzDcEMsLOdgxmoQijd2hqAQBo5awTYC5pxB+h59l5MBxpBnKbI8hbBM\n0EvGiI+iWmVHR8CF2OR5Nr9p9QFKaXDNSF/ANbgsRH+sQ+ujYzw5B1G4wv7VOhlFoEB/oj0ut6kU\nBgbp+eCJgFKTM2aMkSCqWu3BrnP2xM57vBHS9zD43CDajTa8wEPUFVEOs6+GIAqcWFFaJ1ZyYnkK\npodSRxQYMMWAjKJnyek1hTYhkNKtT22d+cjz0FurggmGQQPErQQqV+AMCD0P3VGE2vYhWnvNxVNP\nrcbapx9HkrQxev5tigkznCpVMIFNamvdoYQqAbQ2yFJSvRZcvIs4cwUYQBSRkosWYIUclIMpRD4F\nBcIAK6KB9UgtDas7ec3RLl4RIRUeGVEiRPdqxax4KQGQTBqNJqSEFB6k5zsqsTBgEOhseF4nD1lw\nahSJd4QCo71Siszt+1tPS+UKeZZDq466cbSiD4CLtLM0czS28CSE0jA6hzGUawEronIGKT2nxpxo\n+FEAaSMoWMfJUe9cg+kOna+yHLD5EOkJ2mfs+CitkSo1iqaFy51wGzEqm2NiYG4huyh2VCSrco0s\nzWnMrEMCBrcpahsVFJsa44DwpHNkJhqOFuSUO4xqEfzIh+d7LqcPdOjTTUGpAkZ0mpTwpIAs5ps1\nmoKTGEhwASPgxD25IOOqAVrPmjnnl3OOIobknAPCRv7Kimus4yY8CekrBGpymA1jFOK4CaUyhGF1\nrIjLsTqF095Jn7jIcRTbAVYYPBIxGqWhbNRKuUtlH+sYQfdRfKwwssh9OmemMNycEwOlFRlKBngy\ngO+HyPPJoWqJopXYdrudseuCAXT1dZHKt96C0RphNUS1u4Kou4KwFlGwUrBkNooMLD0rLHWd27x5\npqxuQWt6DeCMrS+l29ML45krhdyyE6Hno1KruMeUZdaUMZCMYfr0Puyy+y6477ZtMDz8ImC2vCYn\nNOIsJlORT+GCU66slSJLrZDCEF0lPQ9cMPg2ihBF4l1K936cc3iB5xb16I2+oMQK1zTPiSbO0gxZ\nnDpBi1PugiZjEe1SiUJBo9DmlsYJsjQfk6uYSHAuIKQPPwwgfY+8U0X5ROEJeIHnvDM9isKVPkVY\nwqN8WTFGeUrSdMrRGopmpXAOC9FPNgoVzEXuHUNgXJkFACvmsvkFycGsAyukGLVxCHA+OYpHz/fo\n+q2akNkoRSsFlQFgdG2FYKJQExdef54ppEnq8pYFSyEYg7CeLAOgDD0mGaPcszHIihxncd9WVaky\n5Zy4PCdH0KlLOW2gepRopnDgpDc5zhkX3M0Rz5eujMKVJo1hbjblEjv/FjSlMZamtg/qgjazURAD\nbB6ZNnZNL3COggvON2WBzCgmaJQxB2AFg5OzJrMsRZbGNjUiwLgg2tjqB0aXwrm8ZUEwFGOkaT0V\nMNqmYRgjNbHkYJwMpsoUcpN39BdGOyZM2HSEyuy8UoqcQc6Awqm2OVjGSDyntYKUPnwvmERVLUMQ\nVLDjLm/E1O2mg3GGpBUjS2yUaPcy6UnAECOpcoWskgOhTX9w3mkyYJ3TVCmoLIMstBxaI7cshy8l\nAqUgrJEtok2jNXKlkSmFLM/hSYGoGtLe6HKiObiUqFZC7DxrJma+4Y149tnHqERlC5iw1VpEAdzK\nyPMsRzwUI4tp8Azoxqq9FUS1CiXXpQAMoJRyxqyIKkdvipyPorcKD43b/I31ykZLvguhi1aWxkhz\nirZyTSpcA3CPuxyfskY3aSVoNZqb9bwnClJ68MNODZ0bx8KTtNeWxik4Z1A5bcphLaL8RkqUuB9S\n1BpEgaNvdK7BBA2co1MZRZ9ZnDm1c7GphRWShKssR54pUtQmWScPO2qjFYKMt5Qe5e8mCYyT98lG\neffGgBShSlPdr7QLUQg3L/JMASy170GOQyC9MSIgYNSmbucU5xxZnkONovyVVh2FtuCQvgfpZWNo\nThfVW2PAtb1eTeM/WfQ2qY8ttSU6zIU2BtzSx50IfpMX28BJKRJitNOU0gOFk2EdkCzLkKqO0E5Y\nxafwJIwBckNrr0hDFGuZvjcb8VuHlj6X1r5SyqYlYqRJMvGDBSBJmlAqhy8jUtPacZMeUd3O8fBG\n15MX+gMAlvlSuXpJGoBUuRLc0vWOjbAsXOd1sIpdSuMopVz9pvtq7LzyQg/VrgryJEPSjknoYgwC\nP4TvR5MyZgBDrdaHGTtti6ASII1TxM2kU6cP5pxywM5/yRG3YqhKBK01EkfFCicOCoxBO8uQ5vS6\nxM5BxhhCz0Pk+2AAcrsWxag1VTBLSmt4UjqVcvE3rTV8KbHdttOw26Ld8PAfV2LDhme2eIcTR9Vm\nFCkVqtQiB1ftraLaXYUf+Y4iKm5MCOvNJ2QY0jhFFmedRgaMgYu8k1PwBMnkOQMHUazGdHKnBV1S\nJO0BMrKZSB19ZwBXDkB0GshAKY0szdBu15Flk0VxcGs4aWykJ51gSlipu18hYZX0JRmyjMpDQlNE\nfjT2qRW/FBSXlAJacEe5ckEGJksyJK0EieX9i8i22PRJncshPVBeoquCPM/RGm45pSQA13xC+hJS\n+pMyXlyQQ5FlGTjGRkwulIGB7/tUpgM4SqyoL6SolCP0fJcfKRZSYTcKaoeNEmNwxqAKqlcZoocV\nqXEZ60R2dJ0ciY6hjLGbrueK/JmNSLJ0cuZY8Vmcc6jAGnytwWwVpYuMR8H9pgFw2xAiV2izFGme\nQ3IOT0oIxpAbjSTNkGvdodpA1K6QgijqvFMTqgta0imPyGjmuYK2QkFjm5ZoW58dt9sYHt4wKeOV\npgmStA0uBIyhZinCE+Qc+R68kNarF3qWOegYw+K7LYyaUcatK+lRvXhBkdM8onu2A+KMp1YaQnPL\nMI2uF6a8vQswGEMQ+RQ8AMiyDEkSI46bYIwjimqTMma+H2LKlO3Q1dOFLE7Rqreplt+KPou91fre\nkNUQQgi0hlrYYICsX0FIgTTLoLWBZ1MCjDFkeY7cNnIpGmYIT1gNAtW8K022RHKaX8oyiNko9ie3\nqaoXGHcioVoYor+/BzvP3hnTps3Eiy8+u8V7nDDDmbZThLXIdUthgqN3ag96pveQ6tAURbusQxdy\nbgUuHvzIh85pgEm5SV6GKRRpBU1rO3MApACFQicBr401sp1JqJRyBefMGm3OiTKhyUo0CKkAaaCz\nbMsh++aglNqqPB/nAkFQ6XQA8iSyFODawI8CRF0RwmpASlKtkcYp8oy8z3a9BWMoX0WLHNBZ7lTI\n0icqXOXUcST3OxS41hpe4KHSU+mUCRU5glShNdJEEickNLC1j2E1gNYasY4pQs9z15BAiMmJOAtV\nLBXhdzohaa2dwEp6pBwdo4SV3DknfkBiJuoOpGBA9asFikYInDFwGJcvGVPuVOQ5DdU+GkMRvZRU\nKmOkhg48GAAq77x3kV8HgE75/8SiXW+76KhQ+46u+QVstD6KIgXr5MvBjFMWF/VyhTdPIinl6jiV\n9eKLXLEUAkoIaKkhICBs0whXPsUYeM6RJzmylFiOIh9cNJFQSiFNYwwNrZ+U8UrTNtrtOoSQyHPK\nW6NojuKTsCqoBKQ7YGwUVco6361gMKbTZKUY6yLfLKVwLAcD5eWajTbyYdrzClpdW6fH1bYLDtVS\nzlEs1icTHBVjkMbdaI3U0W7XkSTNlzIIEwQhPPRNnQYvCJC2MzSHm2g32q5ELkszNwfzTEFaVT6z\nud80zxEIjsD33dxRWqPeaCFpxbSvCw6jNIQvITxygNM8t6kR06HOTUcwlSWZU9kXDFCjHcP3PXhS\nwreR7dSeXkzbdjs88siWWaAJzHF2vCs/Io9MeALtehv1tA7GmYuqKCkEgHc2JGEVoAVfT8XqsrMx\njjK4Y/IkdtPShgZKZQpM2YJrS/84AY2NXAsBiLbtwgqjLD3PRk+vrii2wJvf/Gbce++94x4zKTyE\nlQqCkCI2Eu9oV0aSpzlaWkOIhMoIpED3lG4YbWxLshbiZoygEiCskIH1fN8p71Ru3OR1+WLOXMF0\n4eUqRbRs3IjR2NjA8OAwYKyEvBqi0hXBC31Ue6sAgOZQ09WECsFJxTsJ8ELPCgq4y98CIGM1Svyl\nc+0iwKASkIrUo/Z6ruC8yJGDqEWAygWLVEGR5y5qaXUhHrIUpUGnFrloDiB8AaGEq3EdTU8Vuc7c\nljx5weTsas36CCq1mi21sXQtL+htMuSaM0dL0/gW98pBikmKoCTnCKR0pTxFbavgDNqQUEgZDc6E\nU94WVlratIw2GirTUCJHxnJHWZLdoWgYdm4BQJ5lSNP2pJVWNJtDaLXqkDJAEreQpgnyPCLqHmzM\nXOeCg3vcpZy0mw8cQjLbrUzZ+8jBU5qDRc7YicdyheZwExvXbUTcjF3KRNqa6tFzaLQAkP5GZS+e\n76HSXUHP1H7kmUKzOWKVohMPKT30Tu+FVhr1jXU0h5uIGzHyNENRBsYYEERBp7uXEIibMfRgA1mS\nI6gGqNQiVKOQjJp1wGLfo7yvHa80TpG2M6St1OlgqNa/Q39nKVVIKKVpXTJh03HkhMS+D12tgoO6\nDvV01zBt5nQEwZap7QkWB5GAI+qKXA9ZAPBD2sAKNWHSTmCUce2pACDnuau7VFkOrTWEJ92AUV9R\ngdFJeYC8O+FJJ1YolKha28L3wHM5wtwqLZlVBcIYokMDH0EUIKpVkGcZ8nx8+ZQZM2bg1ltvxeLF\nixEEr17953k+KrWIvvjR9aOGCuuLHCONhUAQBujqz9yiYgxIkwxxk6JAz7b/4lJYA0L1qoX6VOW5\nK9sg6lq5TUtw7lr5zXjDDJcvMLZZhVaW8rMRH9dFn1vaXCcDReRdCDFcXS/g2IxC6MJtvaL0OmOr\nLS9fUEbCCn8MOaoQgBUejRFk030aA88alkwpKCmhfCvYstGG0d4YRTuVZBXF7cyVAhV568lAmqao\nsqKkoZPWKGhmU9QfMhu1CwZPeACHS3sEvo9K4CMKfEQelWgkeY7U9qP1pQdPGOdchJ4HTwjkWnUi\nT02CDWqgAajcCqe0RtKK0RpuIW7FzgEyiuZvGqfIsmTS5lizOYIsS6BUijSL0WyM2BpdWmeteov6\nqfbVqMxOdvY5MOacM+pMplwXtTxX0E1SmuZZDpXanCWjCNPYuZHFmV37KYIKCQOVbdhSpKKK+s3C\nwSicm7Aaom9GPwAGPMfQbA5PypgFfgQ/9NGqU+/v1kgbzeEm8jQDlwJB6CNLcrTqLQB0v0iB+uAI\n8kwhqkVOp1ErUnuhR+pjY1wahjGGLM2RtlJinaSACK1Q0gYbELZe1hpn6XUa6Bi7rzbjGIPNJgxA\nHYo8gek7TEet1rfFe5www5kkLQBTIHzh2qExzhBUAlRqFYp0slGRjtbQlDe2Mn3mNvssoXwpl7kL\nv7mwESfjHTWt9Yo9T5IXrTUpVb2O6lNa5WlYDZ0XQipT2ri44PAjH5WuyE3k8ZYKrFy5EkuXLgXQ\n8SQZYy/pjrQp/CAih8I2G8+1sh5VQsY+yaDyHEJIiuAFR32wjqgWwQs8or5c2Y5wVGYQBS5vWdwT\nA+VCiyg+y0h9rLXp5GkEh85zcAioNEfcjBG3bJLfRkt5msEAzigVlNVkwBl73WkWzkynsb3rylJ0\nChKdUgFh71FKCU9IG0nST5ErAQq7Z0jlCYAK0UnuXnxmnKRIElJuF3NaWSfFGONSDlpR71fhCfCM\nAQnlkONWa9LEQcW9M2abj+QK2tVBU49YG2pDFUGM1pYBUqR4L1SMQkJwcsoK2b8xgG8bKRROGmPM\nOV5SCCR57iLcNMlcY/LEGqKN64fQGGxQVCa4LR8ySOIUeUaOoz9JLfeyLLGlHQpxuwGlcgwPb4DY\nIOEHAbq6+zBj+x2w3S7bwfR3wdgqgUIMNboxPuPclaaoXCFJMjpYohFTNy4rcotqETXajwI3Lzot\nJY2bU8ooa0iV0xuQk0g0uOd7QBc5bHGzjSybHEFVEFbBbQTZrlOD/qRNznwoQldrrjKFpJ3ACz10\n93eh0lVFY7hBlHxMByYUJV7CE04XIAMJz/MQVANnV7TWkFxa8V+nzJH2XGqxF3o+lNGI26nborIk\nxfCLVgzam6GnRhqOsBahEnVt8R4nzHDGcYuS6Y5CoKLftJ3CKJs/y3LXQ7Bo3VacVOEFVJZStHPT\n2sBkneYHNIFgFZ+5bcpOXVsoUW/VozYyLXju4sQTL/Bt3kDbsg2GzGSAFSkF1ZC8FE+MUWe9GmzY\nsHXChSCIICylxYUAs2KIxlAdjfoI2q0mjNEIwgqCILQ5J1qLXHL4fgDPC+D5vluw0pOo9dZQ66/B\nD33neBQF5My25mrX20jaccew2ugrjVO3+RVq4zSmciJjqY+ouwK/34cQY4VFE42klTjDKYsevdiE\nZnSMhC04z3KaG7YWVmsSumhj4IlO27hcqSJFBQAv6V6S2QLzpJ2gvrFBp+okZByJCaF8ZhqnaNdb\nxKpokKrZ2KiAF9eVI44nZ1NzdbYGthWeBuOqI6rSo7pCpSQ883wPcTOBkAJBJUDSU0XcVUVUCeF5\nVGucpNTNRXpUUweQupHGuDCsGs12jFY7pmgqydEYamD4xWE0NjaQtBJkSYqkndrDBDTAGNI27Aaa\nw2gFY/Sk5dG1Vu57T7MEucqciC9LqYuWJ62hq5CDWqRWJDoqZWZFaFoJV1eucnLAuOTwuLdJGonR\nXmbXUyE0y9MMwNgaeNe/1jrNYMwe1ECpk7AaotbXhbg9Pq3G1iIMq27/KRrKZGkKMAYv9FHpriCo\n0P4Vt2IABrXuKsJaSEarGrr17FcCdHVXIT2JZqONoReGMLRhCDBAtaeKqCuitF/oo1KNnAAtd716\nDao1au4eeh6aSYIsoROM0jh16buknaAVxPB9sjlBFKBa7dniPU4cVasyt4HpXCGx3W3oyC4NIShZ\nnrQSt2EzxuGHFA1Gtcj1uM3z3OWtAFuSkeV05FCDNiUA8HwfQeTDC/xRFJ0AOGBsKYr0JCrdFVR7\nqgirAaRHBtoHTTpqU2UgpQSr0MQbbwPuNE1x8cUX45FHHsFll12GL33pS1i2bBl8/+XVpr4fkdG0\nuVtjDPI0Q6vZRH1ko2tAnCQtCCFtxKORpgmM0fA8OnEjCCP4PhU9V7tqVG/mCycqAArRCwmo2o02\nGoMNxE3a0PI8Q5bGlM/J6DglT9KpEJwL5FnmVKBhFMKzOVnpS/f/yUDcbLtyHM/3YPyxZxoUTbGT\nVkKiFk5shB8FRPHEWacNIwOCMIBvRTxgnTIVIQS0FBCMQdlOSUmSIm2naAw1MLR+CK2Rzjws3iNp\nxmg12siSFNYGwAt8+KHvcnZFLi+OW5MyZlJ6Tieg8hx53ml6UDi6hZAijUkMk8V0n0WpU1gLUe2u\notpbRaUWkWKZURemIPIp5zyqns4YAw5ySOJ2glajjbjZRqvextD6IWxYswH1oSFoZSCE5wwG51YQ\nY+v8nGBP5Y4hmmhopWyJlQ/P8+F7ISrVLtR6euCHATjjiCpVO2YpstTv9OG1VK30JIwV7DEw65QZ\nFOr4go4GOvXqRUeponazkzft1HWSOjTvBBaagpOih21hsL3AQ6WrgkqtOiljVqv1wvN9MBC1msQx\n8jxDEFJP5K7+LnRP7QHjDPVB6iGbpRmirooV+Qk6TtEU7TKp8qHWW4XnSTSqDWRpboUtQKUaoWID\nncy2dgUMPN9DVxRham83uqLIqcNHfM86JtIxQ1lKR1vGtk40qkbo6pmyxXucOLfN0qKFiqo+2MDw\nC0NoDNeRpombKGmcIE0Sy9cL+EGAqFpFpbuKancFvj3ii87PJO8qSzJ75lwTzXodSRzT5un58IPA\nGh3dKYpnlM9hoCbNle4aeqZQj0Q6litypxlopcBsmzbnNY3TcH784x/HtGnTcM8990BKicceewwn\nnngi/uM//uNlXxdG1TGnJBTnima2Xs73qCm44BJK5chUCqUy5DnlfeK4Bc9rIEgqCIIqqtVu+AH1\nX2TDTSQ2aiiOuCpo6HajTTmlZhtpmiBN24jjJtptinB9P4TgAtJuHEJ6buEXnXYYo/HyA3/S8k9x\nM4FSClIKqCjodFwp6CxF52gm7cSdcCNti7mCLgIoz2GMQaW7gqgWgnMBL5BUbuBLW5dJ1LdS1AeU\nmJIU7QZJ7eNW7HLLMECaJGg1GrbeVjiD5dnj7qRlU6j+AuNWbm8tSE0LJ0Jhae7y+0p12pulsW2T\nKYj+T1oxWvU2UWWDAiOVENWeKrr6asQ4hGMdgqJfr9LFQQWWmo0TxM02Rl4cQX2wjpEXaOM0xtB7\n+AExR/Y4v6J8itrI0RFv2mhk49QdbC2UzuExckK7u6agu28KpsyYhinbTkO1pwKA1PBFnaXO6Xxa\nBqqp5Iw5YYvgHJkUtFH7HtzpJUWkCIzqi01zWHqeE7gVY+Fy7qP0HSSIpP7aRZBRnP7j1uYkObWV\napfrXx03YrSbTRe5M8YgAw/VngrCaoSeKd1oDDUR1UL09nbDkwJ5rsA5Q2wDrSzOENUyqrYIfUyp\nToWUnBw7pegoPF8ibidIswySC0Shj+5aFf1dNVSDAEmeoxHHVHvMAC/04YPqueNWbEVEOZX3WF2I\nlFtu5DJhhlNKUtJqpZC0UtQH69i4YRD1YYqctO34kOcZcpXROXecmoS3WzW0my206xWElcgpYIva\nSzq0tY2k3UKcNJGmMdE6TuygofIMyn5ZnAt4ng8pA3jSR7PeQNKMURvpIu+5t4pKFzVhMKPazzEu\nwEE1W+PBPffcg3vvvRe//OUvUalUcNVVV2HPPfd8xddVatTsuPAmiw0DIBrX8wKEYQVRtQrGDTJF\nTkcSt9EcqVtDR155lsWIYwnRkDBQaDeo7EL40lHlRX6EjECMJCa1YpYlyNIEeZ5Su6s8gwKNJ8AQ\nCjpaiXMBLmRHeGPl+ZPVci9utQAwcG6PfeKdlmfGdKispBWTF2oVfUQPEYo+xAXdRVEV5YX9KIDK\nPUilIFLhHAVKHxgXCVDnHXqfLCanLonbUCpz6mU/DK0yd3SuuzhBQmKMiugVcMUVV+Cf/umfcN55\n52328X/7t3/b4muFLf4mw6nBhAJXlBdiWkO7buqUsvBtnk1IDi/wXV63SH8kcUoOiCk6IAlkvHO4\ngLLjWrTATG0km2fKNkCpotpTdYdBA3A1xQYGWZwR1WdLCbQ1NONpH/eXjJcNE+F5Prp6+jF9u+0w\nbeY0TNl2Cmp9NVdWwQBktmlKkcqgvCUJpALPgycFUk9Ca4U0o5Kd4nxNZY0oCqW/rVGWUlIP79g2\nm5ACuT19x9h55PkSRhrkGXMRlCpSEihSW3DjO9HwQg+tegtaaTSGh5EkMTiXFEANDUOu4Wg3mqj1\ndKHWWyMnSdDeW+2pIm5Tx7a4EaM50iLqPs1c0/wg8tHVU4PwJNXeA0hzBSEkeroChIGP0KejArU2\nWD84hHUvDGLjSIPEbnY/Z5zDD0ncmLZT9z0ApD0oOnxtDhNmOIOgAs/3XR6jvnEY7UYDaRojSSii\nMUYjzzOXsxBCwPdCJGmMJGkhblcRhBWrdO0kyrM4RZrEyPLU5TspUoqRJE2iLrU9VksQzeLbzhm+\nH0JpOm4nbsUkBBoizzmohJCSCpOL1nZjDn1+lWCMIbUdLQDghRdeeFV5v0JpTFon7ZoKFPkcKX2E\nlQjdU7tR6aaayyylOqmNz2/ExhfXodkasoXPbTvODbSbXQijGvyAFLvcFv0qrZHGsXtulsVuYzRG\nIwgiep1fUN8cDNwZzaKNWjFORW56shogNJsjCMNqx7O2itDO8WrFCTdEvXS6TRm7ueV0zXYh5UXd\nK/dIvFIoIXPeKW01HYVuUXsIFCeeUAMIMCCshXTAtm9V2hVaxO1G2+ZWjFOH+2EAKV+9+rrYCLem\no1Wh8FR5busohaP/mAEYp9IdP/QtLRu5tSClPSS92JxVJ7opnDxX72kdGeSmcxhBmiNPqPC91ltD\n/4w+O18kgsiHYUASp6MEJQlawy3bqq3dadPHxnc6yl80Xly61xU0Y62308AlrNDB36HnQRmDxnAT\nw4Mj7kgvqilm8IUAGIkZ05wof200cmU7dyVZJzK3ueJKNYLSAOOwjTM0jPaI/tYGqWUuChaJtRM6\nXQpksNM4g5Da0Z2T1XO7cKRaIy2MDA9BqRyMccTtJhqNjVj79FMwRqFa7UFXXw+6+7sR1SrwAh9d\nfd0IK6ETA2WpbdDStmyZojXphwG6p3Sja0oXPN8DY9SLNpQSXBvUhxt4vhWj2WxjeHAEG9cPIY1T\nhJUA02fNQKWr4lqORtUQgnPErQQ6U/Cq5AR7L5Nam7CRrFRrtLEnKdojLbRbLRgYeF7glGppGiPP\nEyhFVJngAp4Xwg/aCAKKfrx2SGUalR4EQQAv9F2rMK0V5eOyFGnaRpK00GoNW2GSQRBEiCKPDiEG\noDUJDojGyxG3W0jiNrI2CV2irpySzIxZI2ajiXFStSeddBIOOOAAPP/88zjppJNw/fXX4zOf+cwr\nvs4LPVssTfVexuYkfT9AlrFO/imldloAbKRtkKsMWZ7aOrPUNW/QinKUte5e9M/oQ62PlGJxI8YL\nz67H8PAG1OsbkSRtFD0mfT9EEFYRVWro6Z2CqIvqmVSWo92IEbdbVqJPp2sUXXMYIyq0Uu0e13ht\nLVqtYXge0TTS9zA6f1tQ81xwVKskaS/UjkoptEZaaNruR0UzgKJxhss35QrKdk0q6jVdxJlrxK0Y\nzROLy0IAACAASURBVCFb3K0U0T9WwVyUYBVRqRCcDLUvXT5U51QH6mk9rnZoH/7whwHgVc2pTcGs\nB+AOPigKxI0mQQngTjEKogDVngqirgrCKEAU+NCAO3kjy3InshjTC7goCyrSJNaA5jbyDIpDirur\nCH3KB3MwJFlmFaHC9e5NWsmY9IUQ5PiNx3D+RePFOZTqlMMRLUiOlus5DOaOsmI9QJ7naAw1HRUL\nFD6bFYNpTWdkFkr1Row0Th07smHtC2DMoG/GFARRgFpf1dY8kv6BaHDbFN02UUjaVKLDBYfQAlme\nufxn0RN2slCImUhJm5MgjQukWYJmcwSN+iCM0Wi1RjA4+BzEUx4YEwiCEP3TZmDKNlPR1deNoOLD\nKI1WvYXGUIPEV6GHtJVi8PmNGBkcQe9wL6Ja5MSLxZGQrXoLIy8OUxlMkgHgiLoi6qqkDYKKb19j\nT5+ya1TZE46yOIM2r0PE2dXfhbDSOdSYMZr0lNxX1lhqa0DbRLdqAyE9+H6AIKgiiqrw/QhRVKPm\nAGFIVCbniGOBPE/Rao2g3aojjhtI0pj+TchwZhklpVWlB1FUI5GHVq5MxfN8EiOEvmtPV9CNhScn\nfemOoHq1OOGEE7DXXnvht7/9LZRS+OlPf4p58+a94uuKzYKaPlu5rO3Eo42ByjOMDA2hMTJMkbRH\n9Ac5DCPI8xRKkbgHxkBKH5VaD/qnzsA2O26PmQMz0T2lG0opbHx+I4ZeGESWpYjbDSRpC5xLazwj\ndPf0o9bT7TZBKSnvp3KFuG2Q5xkYyyET4Y6N00pDSImuni2r0V5LxHETfb0cfuDZMhibI2IGzCqs\nhSdQ66shqkW22wpt6F193UgTUnTnSY48JfpQa424GSNLUluaFHQiME+AG47WUBND64cx/MIw6hvr\njuYxjt70kSaJrSelzdYPfaeU9EPaEBRXtptQDt8f/2kfX/7yl3HuuedieJjq815N2VMRlTv1cXFE\nFWwZkz3kt2h2EVRCRNUQXSEVoudaI2MMKYPtOCVtBxiKsBiznZxsLaHihcNnD5znjAr6axX4UlLf\n2yyHLyTy3J73muQdMZCtL5WeQBAFyNIUXEjrME78eBUHPmhNeTetNVUGGIO40caGtS8gqoWo9XZR\nWZgUAJg7fajdShBElI+lUz5y5EohaafkvI20XFcdIQTazTaefewZjIwMYvq2O6B7Sjemz5xO+2kt\ndE485S25Kz0p0juF2K1oyMEYoDnpJcw4y+q2FllMauw0jR3LV5wio1SVVOdWtR/HdQwNbSDxUBCh\n1RpBc6SBSrVmz0Ymi+9HAVUHiBqkJzH43CDqg3UMPvcigkoIxqgrVqvepBKnLHWiyTCqoNbVg97p\nPRBSoL6xjqgrQtQVUTogzWzJGAlOM6Ww7unnsX7d69Crtn/bflR76Qy0Wm8NWZJheGOOkZEXsW79\n02i365QjSVr4/7y9eaytV103/llrPfMezj7DHTtZsBRaTY0Yi2CAUiAKxCAYMMSXAkUKgkCwEhM0\nqdEQI6gxJoKgYEqIAYNCQqwGgRLESBEZfvKCQFuhve0dzrDHZ1zD74/vd629D/aee8/pKyu5cO/t\n2eeevfZ61nf6DE1TwpgOSiooFaOuJaScUKsw62E43MLa2jH0Rj26+NkZYLGY8rygQlUvUNcLtE1J\nMz7doarmmM/HKIopBoMNpGlBQ/s4wWCwiY3NUxhtbKG31kMxLNAf9Rltm4XMxGoLG11eunb33Xfv\n+/NgQNXdV7/6VXz1q1/FK1/5ygNfHycRV0kKUSKRFSSz55yDLBssuhaTyQXU9QxCUOIwHp/HYjHB\ncLiJkyefgLW1Y5jP9uCcxWjjJLa2TmG4NcRwaxj2zzmHZt4gTlPk+YBmzGWMrqt5Ptpg+8IjOH/u\n+5AyQq+3FhIf31631kApPytg/iOrEuW9Hw7HzjmLOEkCktcZC8GXSZxG9N/YIDdOYzRljWbaUIbJ\nYtl1WaOe1eTUI5ZCEMYYQuVtOPSGBVRMwdN0Bk3dYvfsDs5//wKqRQWjCSleVTSKkFKh6xokSYbB\nYJ3+f2OAjdObKAZFaG23dRuAb70jVOl/8id/gq9+9au4+uqrL/s1+8zemXb0g5evb7t6Rw5rPSCF\nQER126GuCdbvgyFxqREqLK9TC6wIt9uliMFsMsecK/jZ7gxN2WC+N0c1r5D1slBFVbMKnUfz9nIY\nbZDO8iPRUY6yXx5417Z0KV/4/nmMz42p8qkbnP3eI5BK4viVp3D6CacwOrEOIQWqeQkwpzPJkiDj\n6AsJwmk0rF5DyTwihXxQ4KonXwPdXYFjVx4LZ9fr3hpOUIi2okJ3RIBELSKeYQvQ86AZSURauJcf\nOK+99trHHC/5ZOOBBx646GulEoB1iKMUMo9CkBRCIsv6GI1OIMlSWGtQLeiOns/HDHBcYHv7Iai9\nCGlaIMt6SJIsgMe6umN3nwg7Z3dQlbPAs+3aGnWzCAIZvd4aNrdO4eTVp3H6uiuweWoD1jhMd6cY\nn9sjoFsSsZsS08vKBuMLYzz6wKMHJmf/a4Fz7RjN4ZxzWDs+QtdpjMcXUJZTdF0dHlCtyfIqS3so\niiFUFBE4pWv3wc7TrIfR8XUMt4ZhrtI0Jbq2YTh3hCiKYfI+W1vJIOibpgV6vTVuh7HyjbOYzXao\nNaUk0jzZJ33luM1n3X7j64PWZz/7WQDA/fffj+9+97t44QtfCKUU/vEf/xE33njjJQOnZ+VaQ22v\nOEuQFVkwijYmR9EMSQAhTgKfrN9fR78/wonTV6HoD1DPS0AAo2Mb6I8ow4uTGBGLwFvroBKF3rCH\njc3jWBttYjGfoFxMUQyGGAxH2D73KL73vW9gPh9ja+s0hsPjgQOoVMSZZE6wc7F8oIQQxIH9Iayu\na9kVJ+LZsGPAEtnRefssupynGJ8bo2S+ZbNoUC/IZaNtGyglkff6iOMIaS9DlEasQsRC0cz36toO\nzaLGfLKA6TTNL0d9xFmC2d4Y5x45g8ViQnO8/hqyvMBkvAPrLPJ+jqKfoxjkSPMkIHOtNocGoAHA\nDTfcgBMnThxp76T0VAk2Swhnb4kKBQhxHKcxdrodLCZzLMYLQAjkA7rQfQuQvk8EIVISUAACktQZ\nmilXi5o1QhEqrDiNUc0qfOffv40z338QSkU4/SNXY7gxCnzSKI6gcqrIhRCYjQeI48OfsaPslxAS\nxnSYTraxe+EcrAbyokB/NKQ5ZH9Az2oSU4twSqCYuqwJOJYnaMomzOb8XdI1LZ25Ac32jDZB+H3r\n9BbSgjRwJYO1PC+xq1u0dQerljrH/vOMYgXvwys54TEtIc/9uOBy17333nuofVpdi8mcwZxAFMds\ndt8BDkizDGtbayjWeqGirOYVyukci+kCVTWD1gQWTdOck3PiOuuWOkPGkBBH0evBGo35fAytO2R5\nH6P1E8iKnOUG17BxcgPrJ9aRDwsoSZ2ReBGjmlcQUiLrpQEf4W3IrLG46slX48Zn3HjR9/i/FjhV\nRIinmOXjrLFQMsHW1pU4tnUlpFJomhLT6S7atub5Zkqq/m3NSFvKOoZrm1g/to6142vor/VJOKFp\nsTYjHcY4TjEcbsLoDpaBRlGUMIhFIs1yFH2iuCR5wh8kqbzA0sPR1h2qWbVs0bIfKB4j67rY+uAH\nPwgAuOWWW/D1r38dW1tbAIC9vT28+MUvvuTrBf+PfxDJPYZQqxNLl/FgYxhAL1kvw9X2WnSdpsH6\naIAkpQvZWoesl9E8BARNF4LE7OOIeFLFWoGu7RBFETaxia4jyLeSRCJumhptW6Iohuj319G2Ndq2\nohZwr480J1H5rJft83NM8h8OOMg5G1CP9BDawM8iVGwSWlvTnRnOfe885pM5uq5BuZhiMR/DGIM8\nG2Dz2GmkGV3OvbVeUHCSEbW5LNtCCQjmGyqsn9pAf9RHkhHHdT6mxG98YRcqVtg8fgzDrRGKvR4p\nFGUJg2ps8Fs1emk3dtj15je/GT/+4z+Opz3tafus3D7wgQ8c+DrS86QRRcpkcw9Q6eqW6DFMuSE6\njcT2mW089J0HMR3vYbR5DFdddw3WT6yHVmcURUhyIu+3TYQ2I1s1UlmywezBa4lG3F2J0wj99T76\n6wOsL06gP+pTpTUo4AwJSJjOsJMNna/+aIj8/OGdPo6yX0mSoqoEymqGql5gM4ow3FjD5ulN9NcH\nuIbF3n2V7JwLgCbJtnFeNYpQtmSLSMFMIuHZvDWWOOz9HGmWIE9TJLHnajvUHVX5NoqQ5OIH0Lgu\nzD6VojZtVEWQUqDTFnA6UFIud33uc5878L8fVATsjc/DOJJSjJOU+ZzeHIHOfL2oAze14wSS2BEI\nBZUQAlFkYQx3uCKFfJAjksTXrMsSumuhFFW1klWsyPGpRTmpIMSY6HiC9sw719BnMaeEtpdDRQQm\nhBAYbNI9Otm+uEThZQfOb37zm9je3t5XfT3zmc+8+DdmcXaPcsx6GU5dewWuSq5BmiVwDqjmJeaT\nGapFxYLsjEQzmlQmkgRRHCPNcqyf2MBg1CfHFTZo9kEky/PA3evajtoEKakDJWlMcxp2uveADecc\n0zJc4JzG6TJYeQWifb6el7keeeQRbGxshD/3ej08+uijl3wdzXVsoDr4KjFwLrUhx3QpUU5J4zLJ\n4iAp5xOU2BFhOCuyMDQnc/CIkJ+KUIy9QQHbGZZgk2HOZ7TBxrFN9Nf6gHCIkxSAQD2voHXHItKU\nEed9mhWkRcr+oC6gVA+7DnvGesWQuFb85b7KLIYFikEeCOK6pVZYnMboj/oQcoBsmkEKmnkP1zdw\n/IrjBB5QEqPjo2DbZrqlRrDJaWZEe0ugpNGxNcQZ0TSSPEHez1AtTsFoi7zIkA9ybF25GQBtzhL4\nSDHYyJu8H4Uq8OY3vxm/8iu/gmuuueayX2O5+khzEgvJehm1oIPDDjkHJUXKaGAyC2jKBpsnj6O/\nNsTa1ggbJzeCyL/VBpTD0JxI1S3azCOTHUxn+WssFJPxsyKFYbJ/f9THj/7kj+L0dacDrUkpTt4W\nNVkSgrtBxqE37CM/QA7t/+V+pWmBKIqD8IKKJLJ+jt6oj7Vja0jzhHmHMvCig9Qj88B9q9r7aLrI\nd5VIIMCyj66QQNwlsLFFo7vQASM8lwtIZu/6FEUKyi6BXgG85qgFqeIIUnWhcpeHqDh99+yxlhDi\nwMC5eewkTGfRmRpNTfPhqppCa400LdDUFXF0rYbWpBleLqYoyynzPSWUpDtJMf9ZqRj94ZAF73N0\ndYftnTOoqxJ1TVKI4GobcMTXLwYYDNcxXNtAmvUA5xCnCdY2h2ysEIfn2CN2nXOophXOnDuDB7/5\nHeDX/s9jvsfLCpyve93rcM899+CJT3xiyIyFEPjMZz5z0dfkg4LcRRKD3lovKGT4Xr3uOsTsPpJm\nFZpFg7Zugj5hnMTIehTs0jzF2tYaEq4CnHOI4whZL8XAEJTZX5LltIRuqHLyJrOe7O6sC/qOURxB\nJEtnde+r5x/crEhJ+klFzF+8/PXCF74Qz3ve8/CSl7wE1lr87d/+LV7+8pdf8nVdo1nDMQmouShN\nMNwaQsWk/RjFFNzzQY62ahElCklOWV1d1mirdukAEknoluaOQlErRykVXEC80o8n9wPERfNE6zTP\nQ/ULgCraLAm0jYTls5IsBiAI8MKB+LDrSGcsH3IGaxiYpIIOcVqk0NoAmoLd6NgoIIrBSdtiepoI\n1FkWAkjez5APCpqhs2qNbsnmalWEO+vn4QLzwJ+syNAf9RnNbAMSNOU2Y1M11Abli9Xr2sZpcqQ9\ny7LsYA7iYy1HILS0SOlXTv/vA3qctoiSiGXRKGimaYLhxgCnrz3JxPoYKo0hCITMwgYtWT6t/Bms\n7+BtwXxCpJRCv1cQ2MZYdGmC4bAHrQ3alpRfDBun+9ausxYOCM/pUUyZj7JfaVogjjO+p4hXHtx1\n2BPYGBIo8IHTeLQto4HDeWbXGQLcyYBqVlz9+iS3bTsWMxeh3W357pRSwPLXksqYCL6U3noNksZP\nvmNiWupQHWb57plfe3t7WF+/uOj56ppOdgAnwmzTORsoiGU54xhAANG2a3iObgPvWcoIumuhTcfc\n1Rj93ggOFlmfhOu7hqr3+WwX88UEWreMv9CwlvS883yIcjHDZG8XWdZDFMWQSqGaH8fa5ggnfuQE\neqM+kjQOojpt2WK6M8WjD5zBuYcfuuh7vKzA+elPfxr333//JSXjVtdqFi2EQFImdHE0BJPu6g5N\n3aIuKaPULOFGSEhqfeS9DFEaI83TwIvzJbflPn4IEoqIsFmRoprXYQ6qtYZsKTh6L7ZIRCGbk2op\nORZmPgyHT9MESRSFgf7lrj/+4z/Gxz72Mdx7770QQuDOO+/EL/zCL1zydd6nzoOfSDkk4qQDYTYk\nmC7jA70QgukNFlFClyKA4KQS5N2CfRZ3OFfEAuJYQYg0CE0LIcJnGCXU8k5yktFqqgYQZGyd93MI\nCLSsnhPg74dcRzpjKSk+eZ9GqWXQp4RYGmvDkWqKd4IB/AWnYViRRbcaSikUQ/qe5bQLQdMq0m3V\neul9KCW5onjVoTheClcEqzL2m4QATEtVSZzFIRFp0YTL9zDzJ7+e+9zn4jd+4zfw8z//8/v27aAq\nHWATZZb+SzgQEQJYBqu/YlAg62dIogh5mgTrMN9+bXSHuu3Cpe27PR4FSUISS2FyjxA3mnRLrbNI\nVAwNIJLkTJOkgEoiyLpF7Wq4ituQSkJEEkGaTiy5uv/b+5UkOdI0JyWu/hDFgNr4QggSoW8ki6Zg\n2eKuGm4LqqVCFf/sIhb7AqlUElEeQYgsJBbeXNxoAysAxxrKSUIiMcZQ+5s6FxJCsrJQp4GOQFrB\nMlFKdHb5OR12fe1rX8PLX/5ylGWJf/u3f8Mzn/lMfPSjH8VP/uRPXvQ1Dz/0bSRJzsC4AllWQIh1\ndF2L+WwXTVuhrku0LYmEFPkA6xunMBxuEQtCdzDQEJbeX5b1kOV9pGnOOA0yoe/3R2ibGg7AbLaD\nimlynoqYpjkGg00Yo1FVM2RZD6512LlwLgicSCmXHSWmWVXzEqazB/LRL+uGu/rqq1FV1aEuNSkF\nV5R0KXj7MA/T9vB1/6BFbNzsL/68lyFiw+s0T5mE7RVhbNBdTdIk0ABUHKEY5uhVXVDw8OjImCkL\nZEAbI2HHdi+n5wOv7kggGA5IkpgVPw4fCE6ePIkbb7wRr3rVq3Dfffdd1mt0R2Aoyxl6mN8puvh1\nq1GXdfiQvfN5W7dBtqwYFIjTGPWc2t/hIhd8mUtyRg8TNcFi08aFBKRJSQ4sSqIgSSiZ/+hRbVmP\nFJdUpKCbDl0rA1bhsLxX4GhnjMQPGFHLNnbeIs0aG+bDS09TLH1cVxCf3r5JKprP6s6grTu0DbWR\nlFOhqoUDjDdZ1iZ8jXeycD7D4cTMGPoaL8XnqSmhPed5jkeYcX7lK18BgH3er5eq0qmbQP+cEGSG\n7FG+KduG0Sxc8DNMXohpFC3VkTRRKgTvuzdDp/cCHoXwzyO97+eKp+yswixfQBcZyf+xSMCSJ0ut\nXc0JpOcHOwdI1cDL0f0w9itJMuT5AP3+OoreAEm27HqRMAP2tWTbpuX55nJffXIq+ExACAjrKUGC\nKW8xBOj+sY5cdHzC7hWElFIwaqkBrAS1a3UQ+lj6qrqQMIvlfFUdPnD++q//Ov7+7/8er3jFK3D6\n9Gm85z3vwetf//oD77TZbJf41SpCHBMVJY5TQpvrljAsugl8fgDY3DyNjWMnUC0WqMp5QO6TRnCK\nvOhh4+QxbJ7exGh9COcstk6cRByn6PfXkWUFqmoOL6ZTFANsHT+NzeOnGBldI04y6LZF12jGZVDS\nDXo0aSbNLfGsl2Nz6+JAsgMjwqtf/erQAr3pppvwzGc+87KH6nRhCwCUUScZ9ZM9/N67AMRxTNWC\nV55Jls4oXkXDzyfpAFImZjpNWZcHpfBDTyokNNP0GbAH+9BBViFArx5qvzTPtJqSHlApJfLicBy7\nP/3TP8XHP/5xnDlzBi972ctwxx134Pbbb8edd9554OtMR1ZdXg5NM5fN6+ZmvQwQ3PJjdJkHrkgp\nkfaJsK75+5AKDlXY3okDAKKVbF2ALivdaVJRWisQZzEWk5JalHUbWo4e+VcMC1IuSmPiQHaaBbjZ\nyPgQgfPxnLG86IWK2zHIS8XUnpLKG9oK3lO6bASrrjjhwrnxLX2p6M91Sdq2oSMBhDMLeKcOUs1p\na9LSdAPyk7WWTK7B7To6q8vugVc5CgLW/L29lu5h1kFzqIutjkE65MxhQ+JKWbxCBLYbY+3OOIrY\nBsxBGaq4W/beNF4bmJ9pHxS8T6SvUL01mJQCTU1KV0pJdJ2mjoEX0AAYHEjuFV71SfL5d+AghcMn\nGUfdrzhOURRDDIdbyHu94PbhgU6+ZSrUMkB5OzWfRDk+c6vKVeH3TB2Jo4i9Oz1nVAZbOiko2ZVC\nQHO71jmi/MRKoek6VCtb4s8mwnl1Yfxy2FWWJZ7ylKeEPz/vec+75D1G4CfNkqeSgTtkMBDHKZRS\nGA63gmRnfzDA6auegP7agBMP4qNbo4PIfzbIcOrak7jqiVfg2PoaNo6vY/vhbUgZYbi+gfVjW8S9\nltRNyYuCZuGDgvn4pNBltUE5q9Ab9TDYHKA3pDFfU7ehINENIYDXNo8o8v7sZz8bAIK35GGWdWQZ\nQxcqmUXng5wUGmKFpE3IscRYCAnKfFn5wh8Mow3Jl3mbGSzblR37+HlfReKYARTsqIVbDApuhyxV\nTACq4FRMWaK/2LwijG+TGm0wH8/hnEN/43BAhL/+67/GF7/4Rdx8883Y2NjAl770Jfz0T//0JQ+c\ns6yX2hlyIGk6zrSpoo6SCJkgOaq2bNlPU5EAgNeJjSN07SIkAF5Bx7d+tDGwSrG/ogycPVLbAbKC\nZnxCSdQzog+U0wWkksh6OXprfXIpyBLi5JUtVV+dCa2gw7TRHs8ZW5VDDO/DuKB+488MoVfpTEkp\nQvKkuIUmFLl5ePcOT3CP0yXi0YNTVEzaxT6R8PO8rulIhi2jLgkY6RhFK4GS2+Rdw3tmTLiEffVy\nmHXLLbc8Jhr3oArKapoD+W6P13+NBAMkFM2pHV9YJYBGtkxD4svbLStMxaLl/pL2bWraeIT99yh7\ny4CfBYt8JHkKb2FmOkpuvDdlsKnjqtWjpo9acR5lv6RUSJIcvd4QeVEEsF7wXNUGXddBdIBuTbh8\no4jcUIyxAI8PVCRDV8InbQ4s2sF/FykJpySUELCK3mfKVb8UAjEcjOU7QbILja/YVxMWr9vM+7VK\nhTnM2tjYwNe+9rWwbx/+8If3AR8fa6VpEXjeSkXIez0kaQoIF977cLiBojdElhVIewnyfgGAQU3r\ng9CtqRc1qnmJ4eYAmyc3sL42wEa/jyJNceWTrkRTt0jSBFIKLKYl2roNKOecsQ5JTiDGYlgQsLRs\nMFzvY31zDVEcoaybIILRNRqL6QJN1WDz9NZF3+OBgfO2224Lv3/00Udx6tQpfP7zn8fXv/51vOpV\nrzpw88rJAjPOwLzqh0euCiGQ6CRcdIpVXwT9TwiCnujrLXaklEsiNrc5lOIKgw+Jb1UoSYCfiOkw\nRM524XL3WWBTNmjKGkb7S3/ZWtKdoez4kDM7pdS+lmOWZZclfE5zIq54q5ag01IGOycVq2AZlGQx\nkCU8k6LLXCkVxKWbskE5LUO7yPKsSAoRPAB9GzP84h6eb2NUWbW0bEtj9IYFimEPWU6VL7VdSIBZ\nM3jGYVlJXc56PGcsUERCZejIkJt/Fj8P9he3B5VFkUIcL1uPXvuz0V2wVgt6tBEZ6vr9T/khTPIk\noBeNpgfcV64Jo2wBH2xlEIZfiuo3BMH3qkv68G20u+66K/y+6zp84hOfuCSAw1JJzJ0KBpM4R+Ax\npojoiAFixqJZkLSj8GATr8nLhtZmVfSe3y/9wr5gSq/lYMIYh67RQSox8Ke5Ha6UJFWiOFqiSu3K\nM3wE5aCj7pcQghTMegUy/uw96FBIEeaLbdPxXWLIuUQudX2pYxEhSjigccfBv+/OaFjO7X1AjCUF\n0JixHYL31+PI/FMWSRmM5Dv/2SQRVMfm7WAxF3P4rsZ73vMe3HbbbfjGN76B0WiE6667Dh/+8IcP\nfE1RDNB1LT9fDgkbVSc5VZxaa/QHa0HcIS2S4NxC4v+0Ed7pJYojbJ7aQm9QIE9iZDHNeq/60StR\nLUhxLsliTHem2D27B+eIijfcXEN/vY+0lyLLCb9RTkskWYwTxzexNRqiNTSW6dqlN3RXd2jb+kCs\nxmVFhDe84Q2QUuKNb3wjXvGKV+D5z38+PvOZz+BjH/vYRV9TL5rAA4tTIgd74WiaWSg4q4CYkHJ+\nCeHbYkAsGFwEMK8ygrJsP1QkkC1zwiImq/uDyKonPutSigAFvpVoGf2pO1Lgb+omtNMUo0gjfjCS\nPD20Es6znvUs3HnnnVgsFvj4xz+O973vfbj11lsv+TrnSK7Kc5z8XEdIRtfFUUAyRpEidC0DqIiL\np1EviIQ9vkAekcUghxsUnFhwhekIDWo4MeiaDv31fgDMOOaURUmEnu2RDqlcwuubpoX2KDR2uvBt\nYwAr1kiXv45yxsAZewCtaJJDqxM69OQlSqRyj7gdFAwyW91zY1AZqvB9AFOspsNfhcQHxYSc57Mi\ng+kMYn64fNVorUUUqZCgAIBhtKTTHRuBs5FutWwPHWX9YJX+3Oc+FzfffPNFXUDo/dogTehbeo6p\nDBG3C6NYhdlv23TwQ1GaVUXI0gSRUtDWouWkybBIh3MytKFXk4fVQs/TVHynQ0YSMQi85WemAAFA\nVgOqY46fow/9h7Jf1upAkYiTmNH2OSKWGgRIoccaGYK6F1XxnTPHe+lnweSAxKR7JcPMXQA8nnJw\nkgOiUlBSQNuVz4kDq7eAk0IwpWI5V1Vc+QsGPx5WOcivvb09/Mu//AsWiwWMMRgOL61wlcQZoqF9\nOwAAIABJREFUlmGdOjxZj2zosiLnYkSxWD49S3FCzyt559Lz3PJIKuuR/2uSpYgVVd9KSmwNB9g7\nvYmdc3uIkhjrJzYACMzHszBvrhekhlZOSyzGc0ipcOLJV2F92Kc2N48hdKtRs/Rhf30AOZPoDYuL\nvsfLCpz33Xcf/v3f/x2/+7u/i9tvvx133XUXfuqnfurA1/ggZtjOyXQaNWfsaS9DVlCve7Vd4ewP\nAF2GBXrDXmjfQgoIt3RviCKvViLIlLc18BqOYuVyoL8TgaTv1VHIIaMNqEBnLBy300iIQARFlMOs\nd73rXXj/+9+Pm266CR/60Ifwghe8AK9//esv+TqqcinzSTJCIVtrAzpOdoZ79SQzlw8KJHlCACdL\nH/58MsfOoztYjOdIexkGG0NCTQpBcz44aENVj26JvuNb0r1RL1xOMlJQTHFpFg0Ba5RE27RLsIG2\n5MRSNehYpJpmM4cPBEc5Y2alRexBZ84SKKiOKNtFlkAlEdI4QZFl6GU5EkaIOufCg5PGMUxqgmpO\n13UwHTMqGESmlEKkFLueeHEEBwdC9Ya2vyFB8CyOEXErrWKJuramDkdbtzTPqVsYrQ/twAMA3//+\n98PvnXP4xje+gZ2dnQNf41v24eddbeG5ZcswjqMApPNo1iyJkScJIkWVZtm0xC30ZsrGwTlNVVMc\n0QXIwQGgDoHVNnCudaehGx1au1Ea7a8+V0j+Pvj6wHmUtuNR9msJYHHBYJ4SWRnal84n7TwSIl51\nDClV+JkNa6F2dQdXWGT9HCohgZg0iZEmybKCx/6gaZd4M0RSIlaKcApCoO26lRnqErFr7Qq/M2A5\nDh84f+d3fgff/va3ccstt+BFL3oRnv/856MoLh5QAFJ5S7MemoZQswR4tOivDzDYHKJZNKjmFWQk\nkeUp0l7KCPiYrOvgMN+bY7I9gbUOo+MjFGsFqW6lKfoZ0e8arbF7Yh3j7Qmm21NsXrGJa268BtOd\nKaY7UxhNAbNaUMdRSomTTziOk5vrUFJStak1CeK3nCQKYPP0BtbaIY5fc0RwkF/eHucTn/gE3vve\n96IsS5TlwY71mvmYAJjQ7+hDT0lGzhPmdWcIpdh2qMsGO2d2sHduD1ES4eS1J5HmKWIZwxgHmKXu\npZQSkPRwaU0ag3XZBPmyfJCjN3RBxxSgjMzD/mUsmWdFLvOa+YuQAokgoXUVRUcSQJBS4hnPeAba\nllC9P/uzP7sP8HLQ8gGuazuISnCGSrQQoUSQl0sy8tfUnYHkCq+clth7dBfz3RmSLMHa5hppMbIb\ng+4IPtZ0Hbq2Q5zF2Di9gapaYDGdoSlH8C65QgqmuxCitJxQ399XeAR8WJk3d5rnOFR9HXYd5Yz5\n+ZeD45mYDPNE7+quIgaZGQvvjCMES8EBiLkCB4BGqdBK7Zge5S+iKFaAtciSGGUa0dngxCJUWNz2\n9BJnAF12iVLQMSm5mM5QS69qmLvcwprDBwFgfwUlhMCxY8fwZ3/2Z5d+4Wq15vWYefZpHScViroc\nUomgOxsubAd02qDtluL+AAKy1HQaaDqmuQg+g25flemXMSa0xiUkz95ZTL0xDDyzIfgYD/Q6JLf6\n8ewXgVu8hi+9B+sshONAxeMTZyh4+Vk37YdnARBK2IPMHBB8SmOme8VKIYkiAv0wdxMAYimQRRGS\nOCZ0s5Qw1qBqO3TWotY6PH/Asp3tQNV8mqfQjQbqwydn99xzD+q6xmc/+1ncc889eNvb3obrr78e\n99xzz4GvS9MCdb1AVc2xmM7RHw6DaMSJa05QkcDWawICSR6jnFWoZhX2zu1h+8w2cdJzQjHnvQy9\nLEUex4gkndF+mmKrP8DOxhDjCxNMd6Y4fXITT7n2KlRNi535HGVFSaoQAsWgwLDIMakqbM9mPEe1\nmM8rLCYLLMYL6LZDVmQYbOQYbV3crOKybvNXvvKVOHXqFJ7xjGfg5ptvxlOe8pRLVlDTnRmyfhYU\nU8LAdpAHSTbdUYBoyxbzyRyTCxNceOgC6nmF0QmaPXinCoLIE4CI+HeGW4giSIhFnUHD5X7XUuut\nGBbI+zngHKxdctFIzSiFFAJ1SXqavpJN8jQIhCdpjPSQSjgf+tCHcNddd+HFL34xrLV4yUtegt/+\n7d/Ga17zmoNf6Ft73kWeLzjh57mRYiTkMnMkxRGq7Cc7U2w/soNqXmOwOSDEIhPx4Wgvp0IGWSop\nJda21jDbmWJyYYLp9gyD9SGLzVO7UkqBYkjINDlZkNYrk9s97UO3+9uNXdMear+Ao52xYljQRd3S\nhaSKNLS9nHNEaeCLXWsDzfw3P9dzIE9SYwzqrkPXUMCUigT2hRShSuhaIuUrVmwxTKmKsxgJCxho\nTUExzhOoyEILDUTRPvqPdyexxhHCVS8r5sOuBx988LL+7jGXR3tyZUcdGwEZOdZoFgzW4+eFz2Jn\naK5suO0YKh0pAWPRaY1m0QTR9yiKIBS91nOmrfbv14URDMBJo7Mw7dKEnKpLC9NZ6KZjEJE+lJH1\n49kvCpZMUYMLiF6BZXXn55kqIkWplMU0pJIwzgS+pmCBAtMtDa8X0xLVrEJ7rMPaxhC9JIGSIlDg\nHAAlBJI4puAZRRACaDqB0rVouw4Nc4w9zU9zheerLM8i8MngYdaFCxfwuc99Dvfeey8+//nPY2Nj\nAzfeeHENV7/6/RHqeoGmWWA63UOynUJI4rmWmyVGx0ZY21pDnERo2w7ltEQ5KVHOSpjOBCW0riUp\n1MnOFNubYxRpShgNIbBoGhhnEaXUFVlMFtgbz7G+voZja0NcubUJrQ2mVYl53WC3XODh753FbDxn\n4REacdVljfl4zgGWgIXrJ9Zx7OQRUbV+ve1tb8Nb3vKWAKb4/Oc/H3RYL7YmFyakxpIT+jKKFBnh\ncj+Z+so066nmNSbbE4zPj7GYLlAMchy/+jjWttbQNi2qOQWRiLP2claiXlCmn2QJCyLTYS3TCBVn\nLsuM3sD0iDwrmZriHJHSfRsoTmNEiILMnVTU8vVcycOsP/qjP8J9992HTYYzv+Md78Czn/3sSwdO\ngOdLtEcBlQhqM5uI5m4+8AF0GTljMZ8ssP3wNsbbOwE30VYNAWM4gJaTEuWUqji//0KQBVw1r0hN\nZ7JA3s+DkaxvbfsZoaoU2qoNGW4wM2a4vYwImXbYdZQzlvUzQv5aQ1wsni+F1hRYtJx1Q40x6LTZ\nd+l1WmNRN1jMS9RlQ0jlRCHLs9CablRNLWhrUXcdieQz0M37VnYtobzreR0c7YUADBxaIFCLgKUu\nrR9PHJFdgeFwiA9+8IN46UtfGv7upS996T6e4v9Y/t9emRd6EXtlLYQVcAIQQpGBMrjylEtXFRuq\ndLGPN+vHDG3dQDBtg6TqmCvN7e6u6RjERpxSr67jZ5paL4UUpBSwQsI5Q8IprYaxRwMHHWW/yDVn\n2RoWEqGd7D9HKSWgCMTo1ZhiDpwqcMcjKJ5nezSytRZN2aBaVIE65tb6yHieGSuFOFJIVIR4ZaZp\nnUNnDAlwcOLnAXpebYk+arEEFeFo7e0TJ07gxIkTeOtb34p77733stWDimEPySzFfL6HxXxMfpx1\nQ7Zf1SaEEGRe7jK0Dc37BTMopJRkwFBksMaimle48NAFxEmEpmqxvbmGKFKo6wZaW5ZuFOgajdl0\nju3JFALAMCfZTaUUkpgqeWMJ1zHbnaJruqDaVc3IKD3vZxis93HympNsEffY67IC51Fg3Hvn9jA6\nMcJwYwiXeC1VElNHRSLkuiO+5GJaYjFeYD6ew3QGg9EAW6c3MdwYYLw9QTUrKZhEdJG3NR2OKFZB\nZNxrRepWo5GkRNRULbQf/naGyecxnIsB69DUDdqmC4pCik10Vy8Xa2kmeJhljAlBEwC2trYui6Lh\nNWUlXFCzESDelo4VZKt4diwBJ8JDqDuNvXN72D27CyGIs7R77jzmizGSJMPJK64mBGhEACJnqa1W\nLyq09VIxQ2sakNdlHXiuUpG9E5yjeVznZ3lL1R1vYq0Y/SePMK87GlVA7nuNvxgIFU0XhrXUgowM\nez9qs4LMdmhajXJRUSVdtei4zUyCz0tBdIA4vmVVU0acRJTkaIOmrINUoXWWlYAocEorw2ervRax\nXs5kw4jgCGtrawvvfve78eUvfxnvfOc79+3BxdbqZRoqTueCown992Vg8EpBtK+MdreWSPd+BqlN\nADp13L6NVcJjDsFKNpT4emS4B3FlRRbUinwAJek4sncjcQ0dTBmsp9Hg8EHgKPvVNHMoRZZ75FIU\nRJCWFCPuaMA5ntNFpBCVxuG9Gpao9Px2x+LvRhssxgssxnP0Rj1kPbIR1MYgTxI4F0OJJe1E894v\nmgZl26DpyJbMcOLijZh9lSsVderEKk3oEOu//uu/8OlPfxqf/exnccstt+CGG27ALbfcgl/91V+9\n6Gu07mgeGxG6vGlKzOcSRnesqBQjKVJG6vfoGW01dWOMC4lVnMa0P5M5ybE2HXYe2UF/NGCONgAh\n2A+1j/H5MXbP7jEwsMOsXyBWFDAFgDyOsb414va5wYWHL6CtO2S9FF5XIC0y4qgnEXZ2HqfI+1Fg\n3Oce/T7Wjg3QX+uxePZyfuS9z3zLopxXXEVWgT7gs9fAFdNduKBJ/zNBlMaIkyiAfeqyZvCKCeIG\nbdVivjsPfX9rLeKOzHG7VnPVacKDu0/tg2efh53Z3XTTTXjrW9+K22+/HQDwV3/1V7jpppsu+Tpr\nHaQjIr0xCBdqaPV4kJTPPHleVE4WOP/QeWpxH19Hmqc4/8ijqM6VmHZ7xC9rW2RFHi6Kpqxx9pHv\noSrnGAw3kaU9RHGMumrQlE3wqVOMTrWazHeD0wOjkj2lIWTgLkJySMEI4GhnjPZm2f70pZsQyzms\nr4b8w1mrBtrSnMg5h7ryvohkLODnlovxnObHXC2keYKuYcAWV7H1osF8PIduOkgWTjcdoXaTLCZD\nZ0Yie66eF5vwcn5GL9WzDrtGoxE+97nP4bWvfS1e8IIX4G/+5m8uSXvy++GVbPw5soxkxwolzPmW\nLIN5pCObPc1JgA/8RK1pAlWKkJTLFiHNyRH+TkUqaNHWZU1ymjHJYIKfb6GJ5tFUnhDvg5P/mA+f\nnB1lv+q6hFIKi8UE1YIQ6EmWQEY26F/rTqMtG1jrkDP6OmO0qNUmJOaewqIicghZ7Zx1zGP1n1Gj\nNZnXc2WtrQ1nttUai6bGnBN/zWME/2+B70wCR6pABzqKotd1112H6667Dk9/+tPxqU99Cu9973vx\npS996cDA2bZEY4sUOS11ukXTkIGAimL0yiGqWYXZ7ozBSyKMfuqyps7jeEEI/rrCeGebbCAnY2RF\nQXxab2yhJIabQ6xtrcE5YL43C2I3re90MmddCAERyeAP7ZXYCPeyTPCaqsX2ozuY7c0u+h4vK3Ae\nBcZ94cJDGD28iRPXnER/vQ8IsoKpZhU9CDww96Aer38apzG6psW5753DZGdKfoqc1Rp4Jw+qBtuq\nwXxvhnJaoqlqOEtovjgh82KlFGqGNFezMiBtdcItSLvM/uEQLkkpKUvzCjHdIVVd3v/+9+Ouu+7C\na17zGlhrceutt+LP//zPL/k6rxIkGNFovEFr0zFqbwkx9xdbW7fYO7uH2c4Eeb/A+ol1pL0UklHE\n0/EYbdXi7EMP89+RWXhT1ZhO9vxYFVmRQ0URGTVXHDi5AnbOwbCKkJ/teb4oSXtRK886Fy6Ww66j\nnLEkjdFUDbeylwhtYKWSsDTD816aEMTbjCIF3ZGRddd25DahREjqFtMSTVkDjuhSxaBHFAJFsnHz\n3Tnme3PM9qYkQ5jFAHqcJbOIBY8k/Mw1/Ehm/5mTdok8PcxyziFJEtx9991497vfjac97WnouoPn\nWM6ZIErig49PKo2mRMHB7WuVCingpAKECcArz/3VWi8lICMFJUkljOybKMHNsxRtR21Wr4/biSWY\naJXGtFpJhuev1SEJ8sjRo3S3j7Rf1qAzGvP5HmbjCap5FeaX/nMlrAZJZcZZHLowBCJCSNq8T6dX\nR6Nu0jKoBQqPoJl4ZwxcS+cmtQYR22bVXYeyaVD7wNktkdFCSSjL3E2eV/vAeRQN6V/+5V/GF77w\nBTz5yU/GC17wAnzyk5/E9ddff+Bruq5BUxP/18t8AmQKXtcLlIsF8mkRVKBUopCkCXTbYbozxd6F\nXZTTBYzpUFYzTMcXUDcV0t0c/f468rxPRVFHqN21jU1c+YRrEcUxurrj8RslJzELkhDiXwSwZ5SQ\nnV3M89GuadEIAmztPrqLCw+dx3T3cVacR4FxLxYTTPZ20dQNQ6kdjNbM/WuDYomP/M46IhgXVDbv\nnd0D5Dh4SupO0yWW0o/ctRrldIHx9g7GO7tomwZ53sPmiZPYunKL1IaSpfeaEARG8nSKVcUZZy10\n5wIXVApBMwUWlj6ss8Cv/dqv/Q93gctZmh9ipQhd6CUH/c/Q1R0HPjpwutVYTBaYcoKxecUmemsF\nIAS1M+IYo9kmJttjnD/7CHbOnYWzFv3BBvKih9NX/Qjyfi8oe/iH3XNvIRgR7VxoNXbNSrXElbji\nKgIOLPT+w6EK9NcHZI6s7b4uQWBXWAcn3VIhhxGfXg3IizsLIRAlFEiFoICc93PESRz4qW3dYrY3\nIyPhpiNfz7YjlHOPLiWPfl4qWS2RxyFA+AAAyrQjGRHv+Ajt7Z/7uZ8Lv7/zzjvxYz/2Y3jLW95y\niVetdCusXYK8GLQCcMXHlSdxAxWMsKG9zd9m36wWQHDj8F6mvirIshRRRDZtZRoHCyfJyakHcBEq\ndXnmfYXuWLnJ/7ure/i/v1+AdRZlOcVkdxfz8Sx4bwKeH7lMKIi6JWAtdTOcc2jmNet0k153kifo\nr/XC2YiZL64iBa01FFN/tKUWbA26F1xEqF6SPDRMBTJLq7o4gvI6tY5Q5hEHZS/gcdj1spe9DH/5\nl38Z3udoNLr0flmDspzCtxCUitl43KHrGixmE8RscG21QdrLYHvU0ZpuT0nYBA6TyTZ2d89iNttB\nWc4Qxwk2Nk6i11tH25TY3TuLtq0wHB5DXZa44pprkRY5ykmJ8/ocCS2M+pBSoq1IRlOtjAuEEMh6\nZJkoBM1ITafRVC0m22M89MB3L/oeD1Vx+nbc1tbWJWHc1hrUdbkkRluHrmHwQNUSks8sA6eKyFyZ\n3ghIVWXRoJ7XkFKgbRpYQ1QTax3aukG1KLGYT1HXCwBkOku6hFFAk+UDGZxSPPFbd3qZfXDmS23Z\nbh9SUEYyHO7DrP/8z//EfD5Hv384s92mKQEU7NDCMzSekTm44A3pAUI+cBptsHXFFkbH1hElMQcD\nIGWFkyRLAAEY26BtGwxGI2xuHcfoxAbyfh6AHZRAEBexrVsICJjI8KzQhbYcCQXQrHDVd9B0mobu\nujnU+waOdsZ6az3snd1Do5sVhOhS1stXToYTglUQi7NEV3EAV8jUuvcycJ6/SAGX0LJd3WG+N0fL\nFZZ3j5E8vyM7s4yNA9y+s+UD51IblhIka92+YH856+zZszh58iTe8IY37Es4brjhBnzqU5868LU0\nv/Qc5/1KT9Y6iJWfEQCcU5B8yXgeNORSg1Yxr1FIel6SNAkgH0pWNZqmY+4mwnsN6mCdJrBZQc5A\nXuVmdfa6yt101tGs+BAqOI9nvyxTmOp6gfHuBUx3T6K/NuDxkQkdY5/4dG0HOzFsAagAkHqQp5b4\n57aa10xdsogzwmkIIaAbTTM5kSBWEeB0cP+xXC0RnWq5/1IIOBaWscwlhrCQERuW8wz6KK3am266\nCc95znNw//33wzmHa665Bh/5yEfwpCc96aKvMUZjOt1FHBMnk8Q1EgZ+taiqGeRYApa6L33uCpXc\njRxujhAlCc6f/z4mk/OYzfZYFD6BUhHatkFVzjCb74ZzMt45jmuedB2OX30c1azCdGeKnTPb6OoO\neT+D7gyqOQGAIsbcRFz506bSZ0OOXSXGexfw8MPfvuh7vGTg/Na3voV//dd/xalTp/AHf/AH+MIX\nvoCnPvWpQWP0oGVMR/BynlcuPJ2harmKWaJDsx4Ni731UwqwTmqJpqKNapuaJ/OU/ZAUVoZeb4go\njlH0ehis9wNiDSCFHcTRPjK1sw7GOgjpWDyaG0Rc2TVogsYt0sOjHqWUuPrqq3H99dcjz5e+gQcB\nXQCq0gW8+ocK8xAAIQDQfExBt9RSreYV8n6O0fFRACNQBRXDa1bGKfHohutrACOIsx5pOPo5sJQC\nMk4gFbXPu5oMwb34g7OWAoYHaDiujFm82xqLcl7ikYf+GzvbZw61X0c9Y9TassFlZF+7k2fljvfO\nGQ6CwkEw+d9ye5G8MTUEEBRdnCIfQQCI4hxCSbRVi3KygKwa5CLfF6QFk/69zFpbd2yrtZybL380\nL+uo4LCsHC53vfa1r8UnP/nJx9T3FULggQceOPD1BKpadlsElr60HmS3Kopg9dJqTsVLQJaH7kdx\ntBTEEB1cQ5V2F3M7fEGJlO82AdRSpCSWKGlaGyQriQ9VwlTf0s+JMLZoqhpaXz7l6fHsl/98jekw\nne1isreH0dZWGCF55OxqG163HaxhGUNGX6d5uuQdc2JitYH0NoaM1fCUiCgiJKiUJBVmGEmrJAm+\nx1FEIvspAo1NRjLMnknBCYhSgUhHwT/0sOv1r3893v72t+OXfumXAAAf/ehH8brXvQ733nvvRV9j\njUZdzwD0/C4ScyFO4ZxF17WoqgWUimlEkhCYp5rVyAc0boqTGP3BEL3eCEp5WUYJaw2aeoEoTnD6\n9I8iSTMkaYIrf+SJuPYp1+KqJ12FxazEQ99+GLuP7mL7zHaQ9vOAy67pghm9PwNegnKys4eHH7wf\nZ8/+N3Z3H7noezwwcL7zne/Ee9/7XkRRhGc/+9l48MEH8Yu/+Iu49957cccdd+Duu+8+cNOtJWGD\nckao2fH5PZRTQsgKKcKsM81SCCHDQ0P8saUJLPGoWjRtBa1bCCERRQmK3gC9/hBpRuCCNE+C9iVV\nl16HlPU57bIqWdWsXRWl9ibE9Efi2x0WjfaHf/iHh/p6v6bTbcQRSY+RcwApiwACTixb2wB92B5C\nPdwc0DCcW0NihbslGPDivTPzPsmFJWkCoQTDwAWsiblq9LxCynQlC0zQxch0CimhpJcVkyxv1WG6\nt4fvf/+bePjh/7rs9/x4zhhdoN5LU0JkVP15ugmBDlzgDwLLysALPQAk1uGR2gDPHeMlqlRxZloM\nCuSDPLQVvTWeTwwtB2eqZJcJF/GZlwmRr9IdHIT+gYB/GeuTn/wkgENwNn9g0c/AFQoDhbx/qG95\nB66rnw9FEtLJwGN0/DrFoKi2alAvlnMt/3pKrCQbKtjQ7SEHDS8Uv9pmXyoGUfBeVpseXNjWLfQh\nKs7Hu18eT1BXM0z3drGYzlGwHJu3YQvVcKvRNjSK8hzVNEvD5U3ng+49SEH+kmwY4JyD0mo5Fy/S\ncPcYBmolUUQ0FaXQKYPVK1ywqIDmTorX+/Zz1Cg5/Ixze3s7BE2AWre///u/f8nX+UpQMASZZAup\n8tRa031eL8gxZR6Hef9wY4BiUEAqiWt+9Mno9dZRlgzSEUBTzTGfTTAYbuD4yaswWB9isDHEFddd\ngSc86WqsjwaoN1qIiO6lR+4/g+nOGFk/R2/QY+QgYBsbOmmKedld22Gyu4Pvfe//YmfnzIHjgAN3\n8sMf/jC+9a1vYT6f4wlPeALOnz+Poijwxje+ETfccMOBG0eDc8oy53sLTC6MMT6/h67tEMXUOqzL\nBaFcUybnSsqaPPlVSoEki2FNzpeaQNfVkDJCnvfRGwzIjFSRX13gTkUqqBK1DUnLrWpdes1I/ohJ\nXcbfp5zm+raa53geZj3rWc/CN7/5TWxvbx9qFrO7exZ5PoCKErbHYRNpsaQJ6M5wq9uhrRpk/ZyU\nQdjaSymi6MRpjKRIwkgqbWmf+qN+mMNZa8lhJlqidT3goWEzXq9XGqo6Kdjnki4MD54qZwtMdndR\nVTMcRtrr8ZyxalqR9JkkXVjPcfNycl4lKlRIUnALloEwrA5leO9MpyEjBeEEpCTEnlJeEF7CCfLT\n9N/TOYemrFGxxqU1GrrpiOdoI3gOWZzFoZ0t2LoMflTgTKgYDrsuXLiAN73pTfj0pz8NrTWe85zn\n4D3veQ9OnLi4VJi/zFbt0uwPtGfD19qleLszFk6RApV323HOm1+nSIuOqJUCQdu5WpB3bNO18FZj\nAIJIheKfxXc1TGtCa9RfpFS1dzDW8FyavBUPU3E+3v3yq2lrTKY7mE32MNxYg1f5Mp3e52vqxwRS\nkr5tWiRI8pi1eB2MUTCdXfJ4uefljIOLXJhZOuf2iesLR0hoJUVQclKcpDiAkbvRfu55h5A8H2Wl\naYr/+I//CMbVX/7yly8puefgoHWHrmsQRSklaJbAcBFXj8ZoNG0DVS8gxhJRnODYlVtIi5TsIiOJ\n0fERkiwha8NYIUpjlNMF9s7vYrg+wjU3XoPNU5so1gr0hgXSIg3z3yxPMToxwoWHz+HMfz8IISQ2\ntk5isN4PSU9bt9xdidG1GvWixmI2R13PWZnq4nt2YOCM4xhFUaAoCjzxiU8MG6aUuuTmeU+2elEj\nUnPMxlOUizmEUIiiBNZodG0D6wxM14MW9GHHMV0y4EMlFWW0QgJKRTCGHBV6wx67eFA1oRicESUR\ndNtBxYQK1QwA8e4fbqXqXJKY92+QB8l4pOCqCP3lrDvuuAP/8A//gCc+8Ykrba2DDXMBMoBtmgp5\n3kDrCG3DQJMA1RcBUAEBRAkR8J3DUhCBL6g4iQKJvxgU6I16sNrSnq08WCpSRDZmUQNrLSKj0NYC\nmgXPfRUaeak5sSRwe1usuioxnVyA1hpZdvmz3cdzxkYn1hGlMcyDhroX4TKyQYLPL6WWCZlpKSBI\nQRrHbUNoYS+CTVqj9KD67oV3z/FOO0IKuug8Voa5o7oDBU8+O/6s+c8miG+L5ezThOqlHCa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mAqC8ElAKX0HlxjLQM8yGl4Rx8IydxQJJsRosTMQ+w6MUbuge77is9qv/RLv4Qf+ZEfAQBMJhPE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+65B9/4xjfwvve9D9dffz2+8Y1v7HmdaITNsJWgA8uAu5RUcFc4lpIEvZCoO9xhkKrVnrgt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\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1ssu9XC977SuZLWcsDpcTOzaygnoXSB5nMeV5RXkucMcz3/gjzk5vUVVrjBmI44w8XwhD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\nX8m/P6cIOwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118d01320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(4, 6)\n",
+    "for i, axi in enumerate(ax.flat):\n",
+    "    axi.imshow(Xtest[i].reshape(62, 47), cmap='bone')\n",
+    "    axi.set(xticks=[], yticks=[])\n",
+    "    axi.set_ylabel(faces.target_names[yfit[i]].split()[-1],\n",
+    "                   color='black' if yfit[i] == ytest[i] else 'red')\n",
+    "fig.suptitle('Predicted Names; Incorrect Labels in Red', size=14);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Out of this small sample, our optimal estimator mislabeled only a single face (Bush’s\n",
+    "face in the bottom row was mislabeled as Blair).\n",
+    "We can get a better sense of our estimator's performance using the classification report, which lists recovery statistics label by label:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                   precision    recall  f1-score   support\n",
+      "\n",
+      "     Ariel Sharon       0.65      0.73      0.69        15\n",
+      "     Colin Powell       0.81      0.87      0.84        68\n",
+      "  Donald Rumsfeld       0.75      0.87      0.81        31\n",
+      "    George W Bush       0.93      0.83      0.88       126\n",
+      "Gerhard Schroeder       0.86      0.78      0.82        23\n",
+      "      Hugo Chavez       0.93      0.70      0.80        20\n",
+      "Junichiro Koizumi       0.80      1.00      0.89        12\n",
+      "       Tony Blair       0.83      0.93      0.88        42\n",
+      "\n",
+      "      avg / total       0.85      0.85      0.85       337\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(ytest, yfit,\n",
+    "                            target_names=faces.target_names))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We might also display the confusion matrix between these classes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bv9lhmNe5e3vWx30HQNb1bDZt2Eavvv8HwONPP0bLtk+xcuMSPotdwFMtmhst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\nIgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119354160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "mat = confusion_matrix(ytest, yfit)\n",
+    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
+    "            xticklabels=faces.target_names,\n",
+    "            yticklabels=faces.target_names)\n",
+    "plt.xlabel('true label')\n",
+    "plt.ylabel('predicted label');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This helps us get a sense of which labels are likely to be confused by the estimator.\n",
+    "\n",
+    "For a real-world facial recognition task, in which the photos do not come pre-cropped into nice grids, the only difference in the facial classification scheme is the feature selection: you would need to use a more sophisticated algorithm to find the faces, and extract features that are independent of the pixellation.\n",
+    "For this kind of application, one good option is to make use of [OpenCV](http://opencv.org), which, among other things, includes pre-trained implementations of state-of-the-art feature extraction tools for images in general and faces in particular."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Support Vector Machine Summary\n",
+    "\n",
+    "We have seen here a brief intuitive introduction to the principals behind support vector machines.\n",
+    "These methods are a powerful classification method for a number of reasons:\n",
+    "\n",
+    "- Their dependence on relatively few support vectors means that they are very compact models, and take up very little memory.\n",
+    "- Once the model is trained, the prediction phase is very fast.\n",
+    "- Because they are affected only by points near the margin, they work well with high-dimensional data—even data with more dimensions than samples, which is a challenging regime for other algorithms.\n",
+    "- Their integration with kernel methods makes them very versatile, able to adapt to many types of data.\n",
+    "\n",
+    "However, SVMs have several disadvantages as well:\n",
+    "\n",
+    "- The scaling with the number of samples $N$ is $\\mathcal{O}[N^3]$ at worst, or $\\mathcal{O}[N^2]$ for efficient implementations. For large numbers of training samples, this computational cost can be prohibitive.\n",
+    "- The results are strongly dependent on a suitable choice for the softening parameter $C$. This must be carefully chosen via cross-validation, which can be expensive as datasets grow in size.\n",
+    "- The results do not have a direct probabilistic interpretation. This can be estimated via an internal cross-validation (see the ``probability`` parameter of ``SVC``), but this extra estimation is costly.\n",
+    "\n",
+    "With those traits in mind, I generally only turn to SVMs once other simpler, faster, and less tuning-intensive methods have been shown to be insufficient for my needs.\n",
+    "Nevertheless, if you have the CPU cycles to commit to training and cross-validating an SVM on your data, the method can lead to excellent results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) | [Contents](Index.ipynb) | [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.07-Support-Vector-Machines.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.08-Random-Forests.ipynb b/notebooks_v1/05.08-Random-Forests.ipynb
new file mode 100644
index 000000000..f567f238e
--- /dev/null
+++ b/notebooks_v1/05.08-Random-Forests.ipynb
@@ -0,0 +1,756 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.08-Random-Forests.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# In-Depth: Decision Trees and Random Forests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Previously we have looked in depth at a simple generative classifier (naive Bayes; see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) and a powerful discriminative classifier (support vector machines; see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\n",
+    "Here we'll take a look at motivating another powerful algorithm—a non-parametric algorithm called *random forests*.\n",
+    "Random forests are an example of an *ensemble* method, meaning that it relies on aggregating the results of an ensemble of simpler estimators.\n",
+    "The somewhat surprising result with such ensemble methods is that the sum can be greater than the parts: that is, a majority vote among a number of estimators can end up being better than any of the individual estimators doing the voting!\n",
+    "We will see examples of this in the following sections.\n",
+    "We begin with the standard imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns; sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Motivating Random Forests: Decision Trees"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Random forests are an example of an *ensemble learner* built on decision trees.\n",
+    "For this reason we'll start by discussing decision trees themselves.\n",
+    "\n",
+    "Decision trees are extremely intuitive ways to classify or label objects: you simply ask a series of questions designed to zero-in on the classification.\n",
+    "For example, if you wanted to build a decision tree to classify an animal you come across while on a hike, you might construct the one shown here:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "![](figures/05.08-decision-tree.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Example)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The binary splitting makes this extremely efficient: in a well-constructed tree, each question will cut the number of options by approximately half, very quickly narrowing the options even among a large number of classes.\n",
+    "The trick, of course, comes in deciding which questions to ask at each step.\n",
+    "In machine learning implementations of decision trees, the questions generally take the form of axis-aligned splits in the data: that is, each node in the tree splits the data into two groups using a cutoff value within one of the features.\n",
+    "Let's now look at an example of this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a decision tree\n",
+    "\n",
+    "Consider the following two-dimensional data, which has one of four class labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jQJ0sHpMKry8RiF9j3b6ayX4jI4z27sYpLJQoVzfSe/Si+xczSrprerOzs+di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\nx7fzqr/V11bxzvBMfMrHmrU3CuX0fOsIc568/7Ib7hU2icuOcdckLm3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s7KmtVrN6RSaL\nFrdU7Wlo0HPubC5BvbpQW1uDk5Olm7wjGI11qNWWK0uZTEbPXl5mut5VlQ04OKrJuFJCr5AWqVOd\nzkBmuh1jI259ayF61jCGxtQSu2obuhqR0TFB9Bu8oDmCuCCviLXP5eOTtYjmNf0O+CZ9Pf+z3QVX\nV9dW+26LisoKXty1hdzJk5B9764/mHaJxft289TEzhEm8fXtgnrwaTgywuLYxZB4lH1HI0kSytNn\nWIKcAL+uVnq5f5HL5TdV9WzuM+PYaDxIyroTiFn+SO5leI+t5MW/dp7kp427h80Q/wSQJImkpARK\nSrNxc/Nj5IhxtxQQ1Br5heeIDLOMEHZ21lBTl9Hqdc7OLkyZ8BviDqwB4ftVqeTNzKkvt1o8XpA1\nAJYGo4ufC5cuNrmfq6sbSUkuYNpM8zSb7Ztz2bzhLC4uGkRRQi4XmDG7P2WldWRcvcSggcM7+InN\ncXXxp6K8EHcPyxepwWAy+/funaksWDSEC+fz2RubikqlwGgUycup5WdPfgJAXt41jp/cgiAvR5Lk\nKOT+TJu8tEMF7B0dnVjwrPX8052fJeGdZRnI5335Ibb+dz2P/+7WXuL/PrSfvJkzkN2wkhN7h7Hm\nxElmlJbi7dX6fmd7XM66ysGLKTTWVMPYBlJy/0t49rPNohtlPnFELTBQe+QYKlFi/uBhBHa1LiH6\nY2Peshjm/MxEeXk5zs49sbOzXlbTxv2PzRD/yCkvL2NH7AcMHqFiWJgj5WXXWLH6ABPGLcPPr3PS\nUITWXIYAQtt60l5e3syf+0uzNlEUiY/fRVVNFpIkp0/YGIKDm9yckmjdQOfnVeLt07SiPXX8GjET\nLYUops/ux8F9ly2OFRXq6B96688ictREvl15mGmzzd2KFy+UU1XuTcLBGpBkyAU/3FwDkMtlDBxs\nnjN8aI8ONzc38vOzOXnmM8ZM9ITvU1sM+gpWrvkbTyx5/baCrOquqVBbieSWIaf22q3vFV8UJKvj\n0g0byubEYzw3bdZN9ylJEn/esJo4X2+qKktRBgSgmdwP0+hrXNv1JyIzuuHV1Y6lT/ajW69Ftzz2\nBx25XP59IRkbDzI2Q/wjZ8++z5gyy635Renh6cCUWQ7s2/k1jy18vVPu4eTQjdraVJyczGfkBoMJ\nldz6Xm9r6HQ6Vq55k6hxasLcmoKcLqWuJONqb6ZOWYyHa39Kii/i7dNikCVJYu/ObAYM9qO+Tkd9\nnd6q0Ml1FawbkSSJ0gInfMZYinx0FJlMxpwZL7N371fIVUWoVBLaeie6B03khZ9Hm5179txREuO3\nEBHljUwmQ6czELe/nLGRzwNw/ORWRk8wV4xSqhQMGyXjxMnDjBwx7pbHqdeUUkc6bvSwkK9Uut66\nbnOrUy1BwCS2PRFrjeX7dnMoYji1iYk4TZnS7PJWBAfT8FIw2TtieWXR/FsbsA0b9xk2Q/wjpqqq\nElfPKgTB0sj4B2nJzs4iKKjtwgsdYeyY6SxfeZpJ0xXNaTlGo4k9O8pZON/6Xm9rxO5dweQZjihV\nLV/NsHAPLpxPJTs7k/Ex84jd3cil1BQCusmoLDdRWerKM0++h9Fo5PLl86gUrUtV5lwzcPFCGcGh\nrmRerSH7qpqZ026//rKbmzuPzP8tBoMBvV7fqmt90MBRBPiHcCR+KzJBi0Lhy0OzXmzeHxTklYCl\nm9/bx5HMy1eAmzfEV6/k8MkvEqhJDEeJgctsR40zPWiqr1vmfoyFS1pXFWuPEAmshbspz59nxqCh\nVo60z7GGWnBwQFCpmo3wjVzpHcL5S2kMCLv1cduwcb9gM8Q/YqqqqnB2tZ4e5OGlpryipFMMsUKh\nYPGiV9m7by16Ux4goZR14ZF5z99UAAqAyZSPUmUpVNC3vyfH4w4SFNSDqVMWo9frqasrQ/JX4+HR\nErHs4+OLR6o3Vy5tIuQH1YYyLlcyddIvcHJ0I+1cCj26hzF6eOgtfebWUCqV7aYEeXp6Mmfm01aP\nSVJrJSMlRNH6sdLSUk4lHcTOzp6oyMlme8kGg4EPHj2C5+nHms27N30o5RJZHEbTs4rIX2kI6dPX\nat8d4dmRUVzeu4/SCeObc3mlggKmVtbc8n5tg0xAbGxE5mS9sIcUEED62RSbIb7POHMxhYSsq9gh\nsCAiEnd3j/YvsmEzxD9m/P0DOHUWQqzYmvRLWibHdJ5msEqlYsb0JWRlZZCcchCJRs6cSSAyctLN\nBYYJ1lezgiAg3LDfrFKpCA0NpbS01uLc8PBBHDiYydGEkwwb2eTmTTpehoN6KKOGNq3QunXreROf\n7u6hUXenoT4HewfzVeDZpBJGDH3E4vwt279AbZ/B4FGeaLVGtuw4QvfAqQwbGg1A7NojOJ2eaXGd\nF2HU99/PKzsXmgmT3ApBfl35JHoy3xw6RLZMwF4UGefThamz5t1yn4EmyHZwwFRVZfW48uJFRtzG\n5OFe0djYyOf7d3NRMiEBvQU5z46ffNMT1vsNk8nEK+tWcbx3MIwZhWQyseXYEZ5xcmNelHXFNxst\n2AzxjxiFQoGr00AK8i/h17VlZVFW1oCc0E7/8e/dtxa5/XkiopuMX0V5Et98d5THFr7S4Ze9JHoh\nSTqL4J+ca9UEBY5u9TqdTseOXV9ikvKQyYxIoisBXadw+mgBJoOR8eOf7XSt4DvB5IkLWb32H4T0\nraJbd1dEUSTpRCn2qpF06WK+uoyL30FwnyK8vg/WsbdXET3Rh2MJsZSV9cXT05OKLL1Z0YYbcRb8\nb9sIX8fLw4OX53Rsz1YURRoaGnBwcGg1+GzJoKEkHztOvUaDoagIpW/L9oqo0zGsoJiA0RM7Zex3\nC71ezwvrVpAxczrC916TS0Yj5zau4tMFix/oqOev9sVydNxoZN9vyQhyOY1Ro/js6DGiSkrwsQWU\ntYn8T3/605/u1s0aGjq3kPePEQcHdac+p549+nIlrY6UC9fIya4iK0PE2NiHqVMe67R7AOTn51Ba\ntZ0Bg1pSVTT2Srr3VBB3KJ2w0LYr1xiNRrbt/AqtLpPLaTlkZZZSV6fDt4sLtbVazp1UM2mCuYLY\njc9q5eq/MWaCSM8QR4J6OOLqrufQ4b1oHCtxdKkgJfUUNTUGAvzvz5XwdWQyGf37RVFR6kRqchUF\n2Y6MGLKUvuGWe61JZzfRp5/ly7trgD3HE3MIDR1E9rUcyvb5o8Ay9UkacJ5RD9290nmiKPKvHVv4\n58Vkvi7OZ2fyGcquXWNorxALg+zp6kZfQU5NQT75J0+iTc9ArKzA/Uo60dn5vDpn/i1VWGqLzv7t\n/ZAVB/awf/Qosz1vQSajont3SExkSHDnbpPcSX74rD5NPU9lqGUOvLFrV6RjxxgRYpnF8FPAwaFj\nE13bivgnwJjRM4BbU2zqKEln9jFyrGW+qFKlwCjmt3v9ug3vExljQqPxBZpWP2mpxaxenklY8Fge\nXdi6mzP5winCB5maA7xEUSTu4BUWLRls9oJPv3yE02fsGTI4qsOfq66ulriErZhELZ7u3YkYGXNX\ndJr79R1Kv75tBzrJ5HqwYmBlMhnIml6SUxdF8dbKHajPmecP12gyGD6/9YIPd4J3tm5k14ghzfu+\nJcDqykr0O7fy0ow5Fuf36xnMP3o2TRREUaS+vg6Nxr7dOtYdISX9ClvTLlAvyAiSyVkcPR4vL+ue\ng84iVduI7AdVugBkajVphgd7kaKVWf9NCDIZjQ+erPld55a+0UajkT/84Q/k5+djMBhYtmwZMTEx\nnT02Gw8QgiC2bqBa2fe9TnZ2Jn5BlWg05mk7vcN9KC6oY/KktiX7snNSGDGmxe18JimH0dHBFuMJ\nDnUlbv+RDhviM2cTyczZSsRoL5RKOWWlR/nmu8MseOh3ODo2BZQ1NjZiMhmt1vS900hGJ8Ayh7u2\nVou9pkmXWaVS8cKKEXz6ixUYTvRBoXXHEHaGAUuVxMyJvmtjra2tId5OaRF8JXNz46DJwDKdrk03\nuUwmu2Xlsx+y6vB+vlLJMI5r2rtM1Os5tG093z68ALXcerR7Z9DWy1Yh3Zw2dl5hAeuTjqOVyRjg\n6s6UiKhOFem5WQINIrlW2sXCQgZ73Xpq4E+FWzLE27Ztw83NjXfeeYfq6mrmzJljM8Q/cboFDSTn\n2g4Cu1nZhzV5WrbdQErqcYaNtn6OTFGNJFkXjLiOWumIVluCnV3TvltdrQ53D+svVJm8rs2xXEev\n15OetY1xE1teIp5eDkyZpWH33q8YNXIOCYkrUdtXIFdAQ50Tob0mMnBA+1WcOosB/Sdz5tRKBt9Q\nI1mSJOL317Dk0RYPSGh4d/6w3pOc7ByqKwsJC4++68Ue0q5mUN2rJ9buWhrQlfz8PHr0uPPbBrW1\nNaxsqME4pCXeQFCpKJw+jbf37OG1aQ/dsXuP8fIhsagIwdfcMIllZUS6tf0buZF1CYf5Ql+PbmwU\ngiAQW1bGthVf8cHD926f+fEhw0lJSKR6dGRzm6TX0/f4KcYveeqejOlB4pYM8dSpU5kypUlDVhTF\nTnEV2WidhoYGYvd8jUgBgmBAktzpGzaF3r3vn5qj/fsNY8Wqg7h7aHH8XthDkiQO7y9j7Ki2C9q6\nuHhTWZGJm7tl8JhoVLbrCh49egbbYl9vNpoKhYyGBj329pZuW9HUsT2bhMTdjIi0LNoul8to1Gdy\nMO4jJs3wAVoES86d3sGVdGdCgsM7dI/bpVfPPui0DxG3bw+CvBxRlIHoy+wZL1v9TQYGBcLt1Xa4\nZQJ8u2B35QImX8vVkVNpGZ697s4z23z0CHUjR1roiwmCwN6yMl4xmTp97/k6k0dGcnLDag6GaaFb\nNwDE3FzGJKcy65HFHeqjrLycr+pr0EdGNH8GmacnaTOm8e/dOzocMNfZhAR14x1J4ptcucU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VODe/cl6vOFZHTqVO/arvsPMKdny9T63bgnnb+fOkb+8GEoBgNeWzdwt0bP45NubYnVO4HsI25l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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11616a908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_blobs\n",
+    "\n",
+    "X, y = make_blobs(n_samples=300, centers=4,\n",
+    "                  random_state=0, cluster_std=1.0)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A simple decision tree built on this data will iteratively split the data along one or the other axis according to some quantitative criterion, and at each level assign the label of the new region according to a majority vote of points within it.\n",
+    "This figure presents a visualization of the first four levels of a decision tree classifier for this data:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](figures/05.08-decision-tree-levels.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Levels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that after the first split, every point in the upper branch remains unchanged, so there is no need to further subdivide this branch.\n",
+    "Except for nodes that contain all of one color, at each level *every* region is again split along one of the two features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This process of fitting a decision tree to our data can be done in Scikit-Learn with the ``DecisionTreeClassifier`` estimator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "tree = DecisionTreeClassifier().fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's write a quick utility function to help us visualize the output of the classifier:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def visualize_classifier(model, X, y, ax=None, cmap='rainbow'):\n",
+    "    ax = ax or plt.gca()\n",
+    "    \n",
+    "    # Plot the training points\n",
+    "    ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=cmap,\n",
+    "               clim=(y.min(), y.max()), zorder=3)\n",
+    "    ax.axis('tight')\n",
+    "    ax.axis('off')\n",
+    "    xlim = ax.get_xlim()\n",
+    "    ylim = ax.get_ylim()\n",
+    "    \n",
+    "    # fit the estimator\n",
+    "    model.fit(X, y)\n",
+    "    xx, yy = np.meshgrid(np.linspace(*xlim, num=200),\n",
+    "                         np.linspace(*ylim, num=200))\n",
+    "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n",
+    "\n",
+    "    # Create a color plot with the results\n",
+    "    n_classes = len(np.unique(y))\n",
+    "    contours = ax.contourf(xx, yy, Z, alpha=0.3,\n",
+    "                           levels=np.arange(n_classes + 1) - 0.5,\n",
+    "                           cmap=cmap, clim=(y.min(), y.max()),\n",
+    "                           zorder=1)\n",
+    "\n",
+    "    ax.set(xlim=xlim, ylim=ylim)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can examine what the decision tree classification looks like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ax3p6erD4+8+5eDEOJInFTUxShWcvPki8lbonXCUp7TadfL0BcAjpTtaajbjrqu0n7OnB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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118b21e80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_classifier(DecisionTreeClassifier(), X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you're running this notebook live, you can use the helpers script included in [The Online Appendix](06.00-Figure-Code.ipynb#Helper-Code) to bring up an interactive visualization of the decision tree building process:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QuXrzxPNTKC4s4cDxg0yf2Lx2cu9RDVGxU1tt4/j+bayYW9K0PMnDTUGgVwEK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\nVZJV3OI2c0lWcafb2XdgH4YxPlZXmtW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WaMBPi2ZoKHVerjdHo2LAIJlocFhb/IHOhXjd\ngaBTSHh8JHVBlZTKBVxVZXLu+Bl+9+wvWfvBxzgcLT8w2Ow2tn60BblYiyTIaMxelOypZOs3mzpt\ny8Q5U7D5NTkpRVGITAgnMDC402PeyMChg7CoXDXJrb5GRo8d06FxHn72ccJnBmL2N2DyqycoyZdH\nftTyPvn4+ROw6ppel01nZuy8ce2aJzjW9buvKArBse57Pzx0L7dt5fv5sT23ayoPXYDFbEEQQNWF\nGaz/seYf1ElNz4OCIDBg3nj++MTPumxOd/Hh2jWkxdipnp2HtNWAt9Ubk2gkYkkEf3x9f4tJPefy\nivnDoodQu3F11xEEQSA0IhRdnR4fmzOxSalTKKq7ij1LBD5l5ZOrASguKUSlUhEUGEJRUT6GHBPe\n163KJEGm6HJRS9O0i8FD4ln2y2Xs/3YfDTVGIgZGsOoZ59xnTpxk26ebqSyswj/Cnzmr5pE4rn0O\n7HpGJiRwcuExLm2/jKpBi9XXSMIDozu8T63VaPnxb3+KwVCPoijo9a3rnCfNmomX3puT+46DAqOn\nJTJ2woR2zbPs6ZW8X/4WhnQziqjgN8ybFT94uEO2eui5eBKuPNwUo8HAe1+uIY8aBKCfFMRTK59A\nVrUdmuwoOkHFjUE1L8H983QFK5cs55+fv4ttRhTlseUYz1ez6L4FTF82v13ZtA6Hgx2bN5OXlofO\nX8f8BxcTHBTS5XafP3LOpfetIAhoFGeCT+bxTMoWlfDuf/+L8vNVCLJAZEI4T/7kGTRhKrhuO1ZR\nFHxDfG/JlsTx40gc7+pUDYZ61v1tLXKxFg16jGU2vir+kgFvD8LPt+Xa5Zvxg5d+xJWFmaSdO0/C\nuLFERPTqtL3e3vp2nTdm/HjGjB/f4fGjonrz21f/yLnU00gqmfj44R51ubsIj/P1cFPWbPiMvBE6\nBNFZGpFpsfHFN1/y8HL3PIHbrFa27thKSUMlUo0ZW3oDcpyzg4ySV82EviPcMk9Xo/XS8ZMf/JiK\nklKs4y2E9+7Yaurdv79J9uZ8ZFQoisKlY3/lp//7S/z93VtqcyOS3HzVreBAQEAQBD5/82MMJ614\nCT5ggYqUWjaFfc2YxWM4+slx1CYtdsWOeggsWn6/2+3bs2MnYpHaZR9dLtGyd9tO7l+xvFNj9u03\ngL79BrjJwq5FFEVGjGquye3hzsfjfO8ByktKuJqXx5Chw9DoOpbck2+tRhCb2qmJaplcU7nbbHvt\nw39SOMILUaNC6ReAek8O/ZRgBEkgcWASI0aOdNtct4OgsI63nrPb7WSlXEGDsxxFEASUKyq2bdjc\nGPZtCYvFwrspn3BFKkdCYIgQxeqpDyGK7U/lGD97ImsPrUWucyYi2RU7ViwoosLAsQNJ3ZWKKDQl\nKYmCSNGlYh7/xzMMGjaYM0dO4Rvox+xF8ztV29sW3t7eOLAjXpee4sCOzrt9dbIePPRUPM63B2Kz\nWvn62/XkmyrQKDJJw8YxbHjHV4CKorBm3cecE0pwhHmjXbeH+f0nMGXy1HaPoRZkLM2OuWeP8nxq\nKvkxArLGGVoWJBHrpF70qe/FzNlz3DLHnYDiUFDqXMOJgiDQUNNw0+ve3/8paeMFRNmZaXvMUIfu\n0DesmPwAAPWGej5+432KLhah0WtInDWGuUsWuowxYvRoLL+ycGBLCgVX8mmwGogOjSFuXBzLHl3J\npWOXMBe4Jl7p/Jwdj+KHDSd+WNfWnE6dMZ2Ub/ZhS1caQ67yQKVdKlQePPRkPM63B/LuZx9weYiE\nqHauhD65tJun1BoGDR7coXGOHD7I2dA65KBQJMAe4M2W04cZmzC23SvghPCB7CnLQQpx7m8pBTWM\n7zu6Q3a0Rn5BPlK4676Z5KWhsqTGLePfKUiyhK6fGuVy0zGrbGZQws0/7ytSBaLctNKWvLVk2Jtq\nSN/5yxtUJNchCDIm7Oy9lIzeT8+kpCSXccZMGM+YCS3vSY5fMIFdObtRGZ3fF1uAmaT7OtdjtzPI\nsooX/vAy33z8NdVF1fiH+7P4kaV3nHSlBw834nG+PYz62jouy9WI6rCmgwOC2J96pMPON6soF7m/\nq3Mz9/HlfGoqCePbly26YN5C9Mn7OJuWiSgIJPYZy9hxHU8eaYmJEyax97u3YXjTa7VnV5Iw9OY6\nuXcbgiCw7EfL2fD219RdNqAKlBk+ZxiTpiTd9Dq5hUpB+ZpwhqHBQGFqMVqh6fNXWTSkHjzD+MmT\nsdvt7QpPz1o4j+DwEE6mHEeURabMm3ZLkplHDx5kzxe7qCmtJah3EIsev4+4+PibXhMSEsYzr7St\nCAXOBK3tmzZjMpgYM3U8AwYO6rStHjx0JR7n28OwWa3Y5eYF2PZ2qg5dj5/aG4elAlHd9DGLpQai\nJsd0aJypSdOYyrQOz98WvgH+LIgdy85Tx6gLlfGusDMlNI6+/fu7fa6ezojRoxn25kiKigvw9w9s\nlwD/ULE3h+qqEH2cq1JHcR2Jvs5OQqIgILQgXFFsL+M3yX8n6OezMGaWsPXkDuYn3DzEPzIhgZEJ\nrkk/iqJgMhnRanXtzsAtKSliw9/Xo6r0QoUXtSVGPitfw+/+9Se3rGSLi4t4/Tf/QLkiIwoiqRvP\nMePZ6cxdvLDti9tBeUUpRqORqF7RnqxjD7fMHel8z589S1FhARMnTcHbp33p/ncK/kGBRNRruD6l\nyV5ax9DeHU88mjNzLqkfvUptYjCiWsZWaSDeGkRYRMtqPLeK3WZjy7bNFBgq8BG1LJw5D/82hPGT\npkxj4riJFF8tICQiHK2XrktsuxMQRZFekb3bff6qKQ+iO/IdaearSIpAon8cs0Y7leR0Oi9iEntT\nuKO8UYnJ5FVP/cRAVBND8QF8hkezOf0cQwoHERPZ/geyA/uS2bNuF3XF9fhG+jDn4XmMmzSxzeuS\nt+9BrtC5ZC7bs0UOJO9jhhv2+Ld8sQkhW833flFt0HFo40FmLZiHdAu11CaziX/+1/+j8FgxigUC\nh/nz5M+eJiKy82VKHjzcUc7XarHw2of/pCBWRAzxZvf6N1gycBITJ0zubtPcypOLV/Hp1q8otNeg\nFWQSwwczaVLHX6PWS8fPH3+J7bu3U2Mx0Dd0KJNWTekCi528ueYtcuM1SL1VKIqFS1++w68efRGd\n981XcSq1mt79+nSZXd1BSWER36Zso8ZhJFDSc/+sRQQEubdsSBAElk5YzNJW/v7MT1/gc7+PKEgv\nROOtQTU4mEvjnLW4DpudhnOFyMF6DmUdb7fzrSgv47vXN6Gq9EKLHkuNwobX1xM3PB5fH7+bXqvS\nqFBQEK7zvg7Rjlcb34/2Ulde3+xYQ5kRk8nY7prclvjig08p31fbGMI3nrGx9l+f8JM//rLTY3rw\ncEc53++2fUfRKD3ytTCqMjKcbacOM27MeCT5jnopNyU4NJSXH2/fHldbaL103LfY/fWXN5Jz+Qo5\nQRZkrfMGJQgCDQkh7Niz87bM35MwG0288e0azOPDAW9KFAf5X7zDr5/92S2twDqKWq3m8eefafz9\nZNoJLlYdw5xfi+PtbIJzfDDpSjgzKJflk+5vly5zyq69zVavUrGG5F17WLy0tccAJ3MWLeDk1pOQ\n5wwxK4qC91ANY8e3T/GpJc6ePs3xfUcRJRHZR8ChOBpX+gC+0T543WIP3eKsEpcxAUqyym5pTA8e\n7iiPVWqqdtm/BKgJECgrKu6wqIEH91JSXAxBriFjUSVRbzG6dR6b1crGzd9wtaESL1HFzMQp9Ovf\nswQT9uzbg3FUUOO+vSAIVA/14+ihQ0yc0nWRh7YYHZfAlu+Syd6cQ6/cUBBAY9JhP2Pn26/Ws/Sh\nFW2OoffzwY4N+TppSYdgx8//5qtecCpCPfN/n2Prum+pLa0jICqABx9f2aG65OvZu2Mn21/bgcrg\n3PM2+xlQxUmYsxREq4QcA/c9ufKW92d1vlrqcP0ee/ndu9sjHtzDHeV8/SQvFIfJJZFEX+MgMKTr\nZfg6w/FjRzmalYoNB3HBMcyZPe+uTdQYlZDApk9TsCU23ZRs+dWM6D/NrfO8/dn7ZA+REdXOG272\nsY28oH6IqOj275V2NSarCUHl+q8l6tTUtxAWvZ0IgsBTCSv4y+v/7XJcEiQKL7Wvzd20WTM5+O3+\nxrpbRVHQDZeZNPXmmdnfExPbh+d++VKHbW+Jw1sONTpeAE2NN4FDfZj34gIqyssZO36iWxK5Ziyd\nxSfnP0Yud85l1ZmZvKB9r9eDh9a4o7oaLZq9EP2REuwmp+yD/UoFE8PiUWs1bVx5+zly5DBflh7l\naryGongd26XL/O1v/8PF8+e727QuQa3V8MDIWehOlmHOLkM6U8IUJYahI9wnD1ldUcllTY1L9MMR\nH8Keo8lum8MdTJ0wBS6UuhxTnS1lSjsdVFcSEhyGd6irOpSiKOiD2rcnqlKpeek/fkr/ZTEETfSl\n//IYXv7T7Q2nf09DdXMREmO1kbghQ5k8dZrbaoHjhw3nh//9I/otiyZ2cS8e+uMKt2VQe7h3uaNW\nvnpfH3711E/Yu3cXNcZ6EoYupt+AnhVy/J7Dl05Say1HKSjFWl2P5K2lcHwf3snfQ/jBHfx49Y/u\nuszehIRERo0aTWlhEQFBQR2WsmwLo6EBm1Zs9qW1YnfrPAClxSUcPHoAHy89SUnTW+zmdPjgAS4W\nXsFL1DB3+hz8AwMACAoJYdmAqew8eZAKq4FgyZvFY+ah8+p+SUS1Wk3igkSS39mPD/4oioLQ18qC\n5YvbPUZgYBBPvPhM2yd2MWH9QinJrWqMJimKQtiAsDau6hyxffryxAt9u2RsD/cmd9TKF0CtUTN3\n3gJWLF3RYx2voihkXLqEPr43fuP6I2jUBE6JQ9ZrkSP8KBsTwPrNG7rFtqqKCj798jPe+eIDdu7c\n3mrP1s4iiiLhUb3c7ngBwnv3IrTcNWxvL6llaO/Oiz60xP4DKfx1z0ccjqljmz6XP7/7N6orK13O\nWbt+LV+ZUrk4UOFkXyP/+/VbVFZUNP69wdRAg2jDEetLrdZBVt4Vt9p4KyxdtYIrfudJ0x1n+FNx\n/PwfvyYkpGucVley6ker0SeqaVDXYdTWEzDZm4efeYyamioajIbuNo/z51L56tPPOXXyOIrSfX2b\nPfRM7qiV753C8SNH8Jo+EEmnxm6yovJ3XfEIkkip9cbmeV1PdWUVf/v6HcxjwxEEkYy6bPI++4Af\nPPqDTo3XUF/P1l1bqbEa6e0fxsyZszudPNMeBEFg9axlrNu7iRLRgM4uMSZsEONuIVs24+JFdp1M\nwaBYCJV9eHDBUnZnHkMY7XRGkpcGw8RwNu3awmMrHgWcr/u0IQ+5n/McQRQwjwln49aNyGo1RcZK\nMnMvo5/YH22IL4TDwdxchqalM3BI3K2/EW5A0og4NFZWrH6ku03pNMHBofzqL/+HwqJ8QGD9mnW8\nsuzH2Mw2HGoHidPH8MxPn29XFre7ee/Vf3FpSxZqi47j8ikOzzzI8794+a7N+fDQcTzOtwsoKC1C\njnHuoYkaGXvDja0JwFdy/8qwLbbv24F5THjjDUDy0ZGuKqWitIyg0I4lrZkajPzl4zcwjAtFkETS\n6rK5/PG7PPf4s11heiPRsbH8/MmXsJgtyCr5lpx9SVExHx7fhGN4GKCh3KHw2idvURfgcPnHEASB\nGnvT/mJ1RRVmX4kbMw1OZp3H677hCKIfgSNHU7k/HT9vDZKXBjkmkDMZ53qM8+0odfW1qGQVWm3P\n2yqJjIjig9feomhbOcFCJAA2k5XTW0+zPnwdKx67vQ8YmZcyyNiWicbifOhW27Rc3V3I6ZknGJ04\n5rba4qHn4nG+XcCYkYkcPP4l0oBgBEFA9tFSn16APq4Xit2B+lQJC+Y/dtvtanBYmkkO2gPUlHfC\n+e7cu4P6xGBEyen8JB8dWT4V5OfmERUTfcu2njl9ir3nj2BQLITLvjy08EEEQOfthSTLqDW3nkyz\n59Be7MNCG0tWBVGgNEpGnVEFQ5rOUxwKAVJT9CK8dy8CdjpoiG06p+5sLtqJsS7vr/+EgdSevIL/\nuAHYDWYC9V2jLNYWVquF4pJCQkLCO9z2r7yslA/+9i6laWWIapH+E/vy1EvPdUuC1c3ISc1FvK7b\nliyoEBSBqxeu3nZb0s6eQ2NyjXapbBquXMzyOF8PjXicbxtcuZSJ3e6g/+CB7Q4ZRcVEM/V8fw6e\ny8DayxtftAw2BaG7rEUjq5jz0PJukcUcFN6HCxWpSNdltvrkmek/o+N7pjUmQ7OaayXUi/yrrs7X\n4XCwbv060uvysaMQowrk8WWP3nRPODsri8+z9sHwIMCb4islnHzjj3j1CcHLJDKxVzzz5yzosM0A\nJ08c59Tl8wiCQG1JOUL/Gx46NBJjoodw/EwuyrBQ7HUmAtPquP+RHzWeIooiS8fM5qvjO6iL1iBV\nWQjLsVE/8IY6Z1lCcSg4rHYCUquZ9vSTnbL5Vth9Zh/bqo5TEynhc8VOkjaeJWPb/96tee0Dao40\nNKo7ZW/KZ0PoFzz4yKquMrlTSLJIS9kLav3t7340PGEUh7yOYDDUY8UCCNhEC0vj2p/U5uHux+N8\nW6G6spJ/ffUBJZESSALBKd/yzH2PEhrevtXLkoX3MaOmlsuZmfS7byB6X58utrhtJkyaRO7XeZwp\nKsDkIxJY7uC+xNmdUgeLjx3I6ZLDyGG+jcc0WTWMXOEqwP/Z2k84HWNEHuBsfZdld7Bm/Wc888hT\nLY5bXVnFe1+ugQX9AKcMormgisCFwwCwAHvyMom9cKHNbjg3sjd5D1vqzyEO9gfALgk0pGTgM7Wp\n801grpllzzzE3Jpa9h9MIcA/gHE/nNgsvD18xEji44eSeTEDXV8t5iEm1hz9FuuYiMZzTOcLGSKE\nElsYwPwnHkZWqbidVFdXsdF4DCaEowWsMbAj4yIjCoa0eS04H5xKMkpQC00KUZIgk3sut4ss7jxD\nJsVz/NJp1Nc2A0xKA4IXJC28fe0Pv6dPn374jvDCeshGgOB8uLM77Jzcf4LRCWNvuz0eeiYe59sK\nX2xdT+XYINTXVru1kfDlrk288Gj79zT1fr6MSExo+8TbhCAIrHrwYZbU1VNdXkFETO9O75mOSkgk\nY/1lTpflYg7W4FNoZv7gSS7lU5ezMtlfcA7/+KFNNkgiuZaSFscsKSzitS0fUeJn5XsVZMOlInyG\nuYaxxWh/Tl0822HneyTnHOJI/8bfpf7BBOQb8DpdSZ3DTKjkw/J5DyEIAj7+fixYePOViiTLnL54\nlqO1WSjheurz8tGXVaGK8MPXoWHBgAkkTZnWIRtvFUODgV0Hk5EkCYvYgDIm9HolSMRBwRw+dqJd\nYwmCgNpbDTcoKWr1tz9foS2WPboSlUbN0e2Hqa+pwy/Sj6ee+ylxQzr2HXEXetkXk9CU4SwJEleO\nX8HhcHRpUuLNKC0tITPjIiNGjUav7/7FwL2Ox/m2Qqm9DkFwFcIvsdV2kzXuxdtH75aw98oHHmJR\nTS1F+QXETu/XrBZ25/EUlBbCflIrFW5b9+/EOiYCbY6MIasY7/7hqHx1WKvqXTLGFbsDndzxcKLR\nYW12TBfgy28e+7d2Xa8oCkcOHySvtJDo0EhEQeSYbxmqmEhqU3ORevlTdbWcFSGTWLhg8W3PbL1w\nKZ13du7BHBCDotiwXz2Fqm84cmjTjdZmMBGki2zXeIIgMHLmSI5/dBqVzfl+2wPMTFnY/WIhNyII\nAvetWMZ9K5Z1tykALZYWKY7uKzda86/3uLAtDaFG5tvQb0l6OIn593nC4N2Jx/m2greg4cZiIL3Y\n85S0uhu9ny8D/Hxb/JtBMSP7aDEVVqGNdApQWGsbSPBvWQqyXjEDarxiQ6hLy6fyYAaCzY5cYsQR\nFYSocibUaE6VMvvBBzpsa6TsxxVFaRJlsDuIUge0+/o3P/gXV/ooyH28OVZ5FiU5G3nBICr2pRE4\nNQ5Jq8IUE8zGHZtZtHBJh+3rKPX1dXz31TfUl9cTNag3J8uLsQT3RQAEJITYCZi37EZ8OB5RLaPY\nHQQdrmb6/Gm8yf/XrjmWPbKSwNAg0o+lIWtkJs9PYsh1kQwPLTN0wjB2H9mLfO2hxaE4iBkV3S2r\n3jOnTnJhQzpqq5ezIUYZJH+8j4nTJuPn1/7vvwf34nG+rTBj+EQ+S98Fcc49GyWzgqRBbfcs9dBE\nmOxLSX8d9ekFGHPKQAB9sZXlv3+uxfMjdYHkWpzNM3yGOBtl+Jyo4Oe/e4FNWzdRbK5CL2hZsOAx\nfNoh5H8jDy9ewbtff0S+txHBAdEmPStXtrz3fCNnT5/hSpQNOdD5oCEHemMZH0XJ1tOELRjdmHym\njQjAOiySirJygkKCO2xjezEaG/jLz/+M/aKEIAhc3pJHUUw1XvOaeswKgkCYMIDRZwModtQQhJ77\nZ67ocN3r9NmzmT57trtfQrdRU1vN0TMn6B/dl76xXaNaNWv+PIwGI6n7zmAz2YgZFsWjz93+hDuA\ntFPnUVtdkwGlCi1HDx5izoKOyWRaLBbWvv8xRZeK0PpoSVo8nZE9aGvtTsLjfFthxIiRBPoHRZEp\nWgAAIABJREFUknL8IAoKk0beR5/+/brbrNvC8RPH2J9+AqNioZc6gJVLVnRKCvPBRcso+extbCFe\nEOKLX46Zxx9b1mo4dsmCJRR8/DY5/iYc/hqEYwXYff34z7WvESR5s2TiXPr06/zN0tffj1d+8BLV\nFZWIoohvgH/bF10j+2o2cozrCl8d4Y+tqElrWlEUas/kYKtr4J/r3mXuuBmMGdM1CTbbNn6H7aKI\neO29lJHxK5AxVJej9m9y+sF6HQ9Ovnmrv3uJ7fv38u3ZDGwBveBiCkO8U3hx1WNdsiJd/OBSFj/Y\n/e99aK8wzinpyEJTwp9NZ2ZA3OAOj/XWX16jeFcloiBSj5l159ai/Q8tg7tpb/1ORlBuk+7Z3qKM\n2zGNh1sk7cJ5PszYidDPud+t2B30OmPg5ade7PSYeVeyMdTXM2hofLtucldzcrmak8OmnMMoo5qy\nh9XHivg/j7/Sos5yV3MlK4vXz21E1bfJsdlzKhlWpid1gBWVvzfVx7Kc+9SBzv105WoNDwQnMn58\nyxGTc3nF/GHRQ6gliQ1rd3fInjVvvkfmVzkuxyyKmYqJEnL8RHA40Ffn8eP77yMmqnnd9cyFiZhM\nJkLvQFnJ9qI0COhN/kiKhEFVi9m7Ae/4aQTGjW88x9pQR/GO95AVWzda2rUoioK62ot+lmHIgoxJ\nMXJFdx6Hb/MciJvhcDiIqOhLiOKaM5CuPYHdt7mQkAcoKi5o9W93nLazh67lyIWTjY4XnNnJeV4N\nVFdU3uSqmxPdtw9xw4c1Ot629KR7x8ZQWlt5TXmqCePwIA7sT+m0HR3hanYORw4cwGw0AaDVajGk\nXqX+YgGK3UFdegHCqWIe+8HTxOWpsV2pwGG2NTpeAKG3H0eyUrvEviGJQ7GoTC7H5Cj431/8gkVh\nKu7v7c2fn/1hi473XsBhUuhbF08fWxzR9oEMNiagrvPGJ9Z1haby8kHU3937noIgYPFvIM3nKOm6\n42T4ncTu03FnqSgKktI8WCoqPUtw5U7BE3b24IIDBXANCysiOOy33jmoIP8q63ZtpNhei05QMT6q\ndbEM8VqvWBdL7EqXJ6zY7Xbe+vgdLvs3QLAXm9Yd5L64JDLysvBfOgpLWS01J6+g6xOKEidTW1nN\nM6uf5syJU/zz8tpm41m6oOMSQOLYcWQ9dIkz21Kxltnw6qNl0ZNL8PMLYNHMeW1e7+vjh6+PX4dX\n3HcKb/zp7xTvbXpgFASBPt4Dqa8rA11T9rfDZuHJFY9x/+zOibbca/zPr/6D2qOmxq0jm9bMz373\na8ZO8OTDdBSP873LaKivJ/dKNrH9+3WqhV1C/2FcKjiI0MuZ0KQoCpG1GgI7ID957txZtp9KoVpp\nIEjUs2j8TPoPGMiHW7+gdmwwAj6YgN1FWYSdOMHoxMRmY8xImsmRr97EntAkamJKyWLks8s7/Jo6\nwo4d27gySET2CgLAPkrH5pP76e0VDIioQ3xRhzj3fs0GC/W1tVjtVr5K3Yld5Swn+V5i0m6y0Ne7\n68K6K59azaIV91NSWkRMdB9k+faKePRkhBYe0iRZYkxEIMeqK5B8grBbTIQbCliwvGv1yO8mnvr5\nD/nsjY8ovlSCzlfHxLlTPI63k3ic713Ed9u+40BZGqZwLbozW0mKGMa82fM7NMao0QlUJ9dw5Mw5\nGhwWImU/Vj2wut3X11ZV89nJrThGhQM+FAMfJW/gB45llIXi0oxAivDjzKW0Fp2v3teHB4fN4J/f\nfoYQ4o1iteE9Oop/fvkBv3z2J11WQ1tQX44U6VpSVhMmkWj0JaO0COm6mtngcoiMjebzrz/HkhCG\nvzGAyuQ0JC81DpONRJ8+LH2s4yVRHUGv92mXYILNbmPXlq0UXSkiuHeIM6rQxXXIdrsdi8WMTnf7\n+xgnzhjD+kPfoDI6P0uH4iBmdDQ/eHAVCedTOXvlMiERfsyeuMjz0NIBgoNCeOn//Ky7zbgr8Djf\nu4SrObnsM2QgDQ9DAzhC/dh18TyjikYRFtExQf/pSTOYnjSjU3bs3b8P+3BXVSXziGDOnk9FaiF0\nLV9LO7ickcGV7GzGjRvfmIWclZ9DwMIRLs0KSh3VZFxIY/DQrsmu9JG0KPYGBKlp5aSttDFr6Vwc\n+3Zx4tQljLKDUJuOh6YvRRAETIoNQRCQvDQETY93SmIWVjF38Mwe0YBAURRe/dNfKUuuRhZUZCrZ\nqNQ6rP7GLptz/c6tHMrMxuAQCNdJPDJzJv1jb1+1wNgJEzG9YuLojiNYDBZ6D41l5VPOh8iRQ0cw\ncuiI22aLBw8t4XG+dwknUk8g9XetKxUHhXDs+BEWL7n/ttkhS85mAtc1mEGxOfALDCA2V0+e1d4o\nliFcKCNpwjLe+OCfZIdYECJ82PndW8yNSmDm9FnYFXuzLkxoZBoaGugqFsycT/pnb2JIDEFUy9iK\napjg2wcvvZ77F93PYrsdi9nsEtIfGB5LWsW5xoYVoiwRVGwnemHbZVE2wQF2SC1uWXKzPYwIv3lo\nO/X0KUoOVaAWnLKQkiDTxzKEi+b2yUx2lGOpJ9iZV4kQ1AcBKAHe27aNP//w+duq+jV15gymzuzc\nQ6QHD12Np9TIjTTU17M3ZS+SKDE9acZNO/e4m0MHDrDefg7pOhlGW3k9j/iNva1tzIwGA//5+Wsu\nDQa0h4v43Q9+isOhsP679RSYq/ASVMwYNZn8gqts1VxG8mmqIxbPlPD75T+muKiIN05vQBzQ9FCh\nO1bC7576aZeuKI0NDezcvYNai5FhfQYxYvToNq9Zu34dZwy5mL1FAqsEHpwwr0VdYYfDwboN67hY\nm49DUTj/5R5qz+YSEBbaaXttdgeWegOiIODr01x8xFBhYKxtVjPHd0TZiTrY/d9R0S+c3rMfdzlm\nrCyhaOd7XfK5KYqCIqkRZDV2Yy2CJCM47N2moezBw/dUlBW3+jeP83UT6WkX+PjIt1hHhIJDQXum\njGdmPUR0nz63ZX6Hw8Ff3/o7ZQl+iBoVdpOFiDMGXnn2ZbetNhRFIfXUKbILcokfMISBrRTpZ1++\nwtYju6lyNBAoeXPf1PlERvVq8dwPv/qE9AGu4WhLZT1P+k5keMIo9h9IITnzJHWYCRH0PDB5Hn37\n93fL63E3pgYjddU1BEeEtfqer9+0nkPBZUjeTqfnsNjI+PVavGs6nxVttdmpLi0DhGZ1uxa7neqK\ncuKVMYQJUY3HK5VSLmpP4RPYcaWwtnBo/Og15wcu70Ht1QxqTmx0u/N1OBxIYQMITZhHbf4lJLUG\n75Bo6vLSqcs6hmQ1uHU+Dx46QnFBfqt/8zhfN/G3NW9QMsK1WUHMeQvPP/LMbbPBYrawY9c2yhqq\nCfMOZM7suW5rY6coCm9+8C+ye9uRQn2w5VUx0hjM6hWP3tK4327eSEpouUtvYOVCKb+Z9zR+gXdf\n/eV/ffwaVcNdlbJK1h3hwz//s9Nj3kysI7W4hN//9BGoF4jTj8Je6EAMExi9ahxLnu6anrwlRUX8\n7f2vsfo5a4wdNit95QpeetH9WcVr1nzG6RpvTFXFgIAusCm/QanK59c/XEZI6N0rJOKhZzMtvvU8\nB8+er5uocjQArs63Sum6vcmWUGvUXSbof/zIEa7EOpCDnJm1cnQAqZdLmZaTS+/YmE6PO3fmPM59\n8CqVI/yQvDXY8qsZq4u+Kx0vgEQLK2I31FC3xMGUFPZuP4Sm0gujrp6fb/gTl9MziB3QHx8/9694\nvycsIoLnH1nE9l0pGMw2ekX68cDSrtE1rjJYEAQ9ltoK/GJvaPjg34ujR46xaImne4+HnofH+bqJ\nANGLG1NmAoTbX2LRVeSU5CP3cX24EPsGcv7CuVtyvmqthl88/W/s27eXitIqhvabQfywYbdqbo9l\nRMRAdpRlIoU430trtQHj2dYl6DrLrs3b2PX6HlRmDQMZQZ25muO7DpJ031y3z9USMbGxPPt0bJfP\nE+SjJafKgajWYjPWI+uavqMOQwX9+4+/ydUePHQfHufrJhaOmcGaQxuxDA9BcSjoUstZNLdrwnrd\nQd+IaI6Wn0AOvu7mdrmC4WNm3vLYskrFrNlzbnmcO4E5s+Yg7obUc5nYUTixZjNilantC4GykhJ2\nH9iLTXEwflgC/QcNavXcE7uOozI31Sv74M+pbw/fNud7u1h6/0KyX3sXW0Ao1TnnCeg3EkmtxW6q\nZ4CPjcFDhnS3iR56CMn7krlwKQeVLDJ1QiKD4uK61R6P83UTg+Li+G1MDPuS9yJLMkmPrUat7Z7+\nvxfOneN4+mlEQSQpcRIxbkj6Shg7lhMfnSHTWoMc4Yc9p5IEJYJe0S335vXQOrNmzmEWzoeNVa+u\nh3YkxGVlXuK9w984a6gFgdQLm1lSVsKUyVNbPN/S0Fy711J/94nfe+t9+M0vX+bIocOU99eDIFBd\nbyQmKpIpU1t+b+5mFEVh/dffkJ5TAijE943k/qVLbmuJV09kw4ZNJGfVIuoCwAIZ36TwmMXK8BHD\nu80mj/N1IzovL+bP71h/THezL2Uvm6tTEQcFAHbSjq3nkbpZDBt+a6ICgiDw3BM/JO38ebKysxge\nP5nYTrRYzMvOZuPBHVQ6DPgLOhaMm8GAga2v4Dw42XEsGceIsMYdY7FfEMmnT7bqfCOHRJJ9KR9R\nuNbMQnEQOeLubLIgiiITJ0/qbjN6BF9/vYH9eRYkrbPzUHJ2A8r6TTyw7L5utqx7OZGeg+gb2/i7\nwyeClCOnPM7Xg/s4mH0GcWRTVyJlcDB7zx7utPPNzrrM7hMpmBQrMb7hLJy/iCFDh7Z9YQtYzBbe\n3bkO87gIQEsD8MH+DbyoeYQth3ZRbq/HR9Awd9x0+vcf0Kk57lbqFTPg2kqx3mFu9fxHn3uSdw1v\ncuVkHjXVldRoy/nlK3/qYis7j9lkYt2X6ymsqMdbq2L21PEMHtK9YcE7kbTsYiRdU0mZqPEiLbuQ\nrhU57dkoioLJ0jyp0Wju3jaSnir0uwyD0jy02KB0rG/n9+Tl5PD2sQ1kxolcHaIhJaCYDz//qNO2\nHUhJpmFEkMsxy4gQ/vrJ61yKE6ga5kveUA3v719/Sy0M70ZCZR8Uh2tVYKikb+Vs0Gq0vPjrV3j4\n769QHlaALcCCWtM92yDt4Y23PuB0lY5SOZxsWxDvrd/L1dzc7jbrpjQY6rFYWn8A6g5aqhx1diq7\ndxEEgchA1+RXh9VCTHj3VlR4Vr53GeGCL9fnzip2B+Eq31bPvxl7jqbgiG/qZiR6a7ioFGOoq8fb\np/Ubf2soioMb2xU2ZBajGdfbZU/KNiKUPQf28sB9yzpl9/ecOXOa3ecOUaeYCBb1LJ22kF5RUW1f\n2ANZvnAZb3z+NsWRIopWxv+KkQdnP9jmdSq1psfv95UUFZJTC3JAkwCH3T+KPSmHeHx15zPpu4qS\n4mI++nwDhbVWVCIMjQnmsdWresT7PCg6lCOFJkS1U8TFbjYSF+Opc3585VLe/+QrCuocyIJCXISe\nZR1oGNMVeJzvXcZDc5by/nefUhoGWBV6V6l56OGnOzWWGRvgqkhk1YoYDYZOOd8pSdPZt+ZvWMY2\nSU+SUYHY23U1jChgtd9aSKi0uITPL+yC4aGAnqvAu1s+43dP/6yZ7ODV7BwsZjN9Bw3sETfQlvD2\n0fOLZ1/hckYGxgYjQ2YMv2vkE80mMw6huRiM3dEzV2xrPt9AsaoXYhDYgVPlRoK+28Kixd2b7wGw\nYsUy+GI9F/OuAjAkNpxly25d211RFLZt20H65XxEQWDMiEFMuoP22YNDQ/nFK89TU12FWq1G5+Xd\n3SZ5nO/dRlhEOL96+hXys3NQqTWER0V2eqxBoTFkVl1CCmgK2YRWSwS3IeTfGmqNmqdmrODbwzuo\nsBsIEL14/MGnWHd4M6axTdrOwsVypk6+tTKtfYeTUeJDXNbZtQP1nD5+nIRx4wAw1NXz5tp3KQqx\no6gkglO+4wcLVxHeq/PvWVfT7yblRXcqvWNjCVM1UHXdMUddGYmTE7rNptYwGY0U1FgQr+thIql1\nZOQUsqj7zGpEFEVWrmw7ItJR1q/fSEp2A5I2BBTIPXgJu93B1KQpbp+rK/Hz7zniPfe08z1/7iw7\nzxygVjERIupZNmMxYZERbV/YwxEEgd59b728KGnaDEq+KSc1JwezSiHUrGXVrFtL3ejTry8v9XvO\n5dgTOi++ObiNMkcdvmiZGT+FiFa0oNtP8xWsw+HAam7aE//iu68oS/RHda1zUm0UrNv1DS8//vwt\nzu2hIwiCwFOPPMDa9VsoqjSg16mYlDCY4SNHdrdpzVCpVGgkuDGLIu1iFhfT0xnczbWjXUVq5lUk\nfdMWgOAdxLGzGXec8+1J3LPOt6ykhE/ObEMZHgZ4kwu8teljfvts87DkvYogCDy09CEesFgwm8zo\nfdtu2t4Z+vTry0/6udfhjY4bwe7kNfiPb8qarj2dQ0HfplV7ib0WQXSVWSxR6txqh4f20Ssqip++\n5H7tZ3cjyTLD+4VztLAeWevceqkvzkYd1o/Nuw7ctc7XanO065iH9nPPOt+9h1JwDHNt+l49SM+Z\nEycZPfb2teC7E1Cp1ajUrmUuySn7OJufAQiMjhnCpEmTOzRmRloaqZcuEOwTQFLSNCTZvV9Fs9mE\noFFReeAioizhsNrwHd2HuoomNSlvQeMS6gTQCz03I9hDz+DhVSs48spvqBa9QVHQ+IeiCwynsjav\nu03rMmJCfckwORCuLUzsVjP9o4LbuMrDzbhnnS+0/NTmzMj1cDO279rOTiUTKd65Es4tOo1ln5np\n01ylJk0NRtZv3kCJtRYfScv8iTPp1bs3X238isNiPnKfAGyGSo68c5qfPfmSWxXBBsYNJviUDuvk\n2MZj9ioDfUOaFLlmjJzEx6nbUK5ldDtyqpjcd1S757CYzFSVV6CgEH7LYfK7G7vdzpGDh6ipqyMp\naQre+q6JotwOBEFg4MD+ZNtdnU+gz9374PbE6pW8/9HnZJfWIgkwODqEZcvuHvnc7uCedb7TJkzj\n1O41KEObmpj7Xapj1DOeVW9LZF7M4GT6GXw13pwszEAa03TjkSJ8OX42jem4Ot/XP3mL0kQ/BElH\nMZCz8zN+NH0lx+uvIA91hn9lby1ViTLbd21l8aJbz8r8HpVazaK4KXx3IgVDrDdSuZF4RwhTViY1\nnhMfP5QXffzYd2w/DhyMGzKHQe3QAi4rKeH99Z+QUZyNblAEkkqmV7Wap5euJijYsxq4kZrqav7x\n5gdUasIRZS37Tr3PQ/Mnk5DY8xKq2suS+TN4++MNGPS9QRTR1uayaPn87jarGTarlc/XfcWVoipU\nkkjCkH7MnTe7w+NodTqef+6pxjrinloVcCdxzzrf0PAwHh46m11nD1LrcNaBPrDwUc9+bwt8t+07\n9pkzkfoG4jDXUX3iKr71emS9tvEci+KqIJOVkUFhL1BJTe+nZWQoG7dtwjLQy0WrSVTLlJvcv9c6\nfvwERo9KIONCGpHDehEUGtLsnKjo3jwa/XCHxl2zeR1ZtjKC70tAuPb6KhSFtVu/5oXVP3SL7XcT\nGzZuodqnL9K1G7YtoA9vf/oNITsPE+yrZenCWcTExnavkR0kJjaW3//iefbtTcbhsDNt+nNodbq2\nL7zNfPTx55yr9Ua8pnq19WwxKvVeZsyY3qnxPE7Xfdyzzhdg+IiRDB/R8zIqexIWk5nDJWlII50r\nVVGjImDxSKoOXiRwijO5xGG1E6NxXfHVVNeA3nWfWJBE9AH+6PKLsAc3hR1tBhO9fLtGTEGtUTNs\ntPs+Y6PBQJG6AcEiNTpecN6Uimw1bpvnbqK8zogguNaF29U+1HtF0qDIvPPJBv7vL19EVjWv9e3J\nqDUa5szrud24FEXhUkElYoB/4zHRy48zaZc77Xw9uI972vl6aJvK8nIMfiLX72YJooC+ToDTRQgK\nDFAFs3LFCpfrRiaMZuOH+zCPaSpmt2dXMmHkAiKLrrLj7EmID8VeXEtssczMxzseCusOZJUKlU1A\nsTfPDfAW7949v1sh0FtDQb3ismpyWM0IolPApc6rF4cOHGDg4MHs2LUPk8XO4P69mZqU1NqQ9wx2\nu52U5GTyi8qJjghhyrSkm0bnFEVhw4ZNnMvKx2Z3UFNTh9eNpa2d0C5RFMWz6nUzHufr4aaERITj\nVwWm68qGHRYbU+PHsGTBEoAWM5UlWeaRSYvZcHQH5UIDeoeayTHDGTB4EAMGD2JMZQJHjh4iNmYc\ngxbcOT1XVWo1cbpIjshW6tML0Mc5E61s2RVM7Du6m63rmSxZOJfstz+mzjsaQVZRl38JlZdf481c\nUBSqKiv5+3tfYvGLQRAELhy7SkHRV6zqAsGIOwVFUXj19bfIsQYgab05UVTM6fNv8/KPf9iqI/zu\n280kZzcgeTkTC41FZWjtNkTJ+T/qMNYyfGRsu204eOAQuw+fodpgJthHy31zpxI/NP6WX5sHj/P1\n0AoOh4Njhw9TX1/HrH6JbDl9FNuQIBzlBqIKBBY9vrpFp3vy5AkOpJ/AhJUoTSCvrPoRACq1yuWG\n4RcYwNxubr/YWVaveJSgrd9xOus81ZcvEhEcytwJMxl6i20beyqbNn3HibRsTFYHUUHePP7wg/j5\n+7d94TWCQ0P43S9eYN+efVRUVHK0yIYY3qTU5WMqpKwmCKt/bGPpn6Tz5XRWDsvM5h7dEKI9mIxG\n9ienoNVpmTh5MpIkNfv7R59+QV5JLSpZZHRcDEuWLOLk8RPkmH2QrkkhSlpvsg1Wzp4+w4jRLWfl\np10pRNKEN/4e0G8khoxDREb3QZZFRg+JZdbsWe2yu6SwkK/3nQb/3qCDcuCTDTv5w4D+d/xn0hPw\nON97CEVR2J+8j+zyAvzV3sydNQ+tV/MkkZqqal5b+zZVQ3wQA9WozpbxQPw06mrqiIyOZPCClp98\n086fZ13ufoRhgYCOcquZmrXv8/zjz7V4/p2KIAgsXLCYhSzublO6nOS9+9h9sapR3SjbrvDeR2t5\n5eWOfaZqtYY58+YCMCo9na17DlJTbyHIV8sDjy9nw+Zdza4xOWQM9fV39I3+wvkLfLxhJyaf3ij2\nKnYe+H+89Mxqgq9L/nvvw8/IsgYh+AVgBHZn1OC1cxe1tfVIXq4iMJK3P9m5ua063xsXxKIkExYV\nwx9+9UKHbU8+cBjFL8pFC8Goj+JAyn5mtNOBe2gdj/O9h3jv0/dJ721BHuCFw1rF2Y9e5RdPvIxG\np3U575sdm6idEIp87T/ZnhjBrtNH+Pcn/u2m4x+6cAJhcFMvYVElkS3XUF9b12XqWB66lnOXcpC8\nmj5TQRDIq7bQYKjHy7vjzTUABsfFNVOCio0IJiurobEbD0CI1oF/YOCNl7uVrt7L/G7nfiwBfZ29\nW2UVdep+rN+0lWeffgxw7uleKa1DCGpyxpLOh3OX8rh/XhLJF3cj+TWtZB3VRSQumdfqfCMGxVJ4\nrhRR5+xkZreYGBzdPMu/PWjVKhSHDUFqchOKzYxPJ5qqeGiOp67mHqEov4CLumpkf2eTBFElUZsY\nzK69O5udW+EwNLshVToacDhuLkBib0GgxC4L2K3d27T6VrFZrRw5cIDzqakt9ku9m5FbSO6RBJAk\n9z63L1i0gHjfBqjKw1xVhG9dDivvn9NljnHz5m387s+v8bM//J2//uNf5Ofnd8k85XUml98FQaDi\numOCICC18BIlUaBPv35MHhgM1fnYLSaovsrkwaFERbdeGTB33hzmDgkmxFJEoKmQiZECq1Yt75Tt\ns+fMxKu2qaeyoigE2UpJvNaYxMOt4Vn53iPk5eaihLm20RLVMlXG5vW1foKOkmbHtG3WQI+MiSOz\n+ARSuPOpW1EUetVr8QsKwNRg5GJaGtGxsQQGB910nJ5E5qUMPk7eiCHOF0ptBL+1ixdXPYOPX+d6\nJN9pTBgznMxtJ1D0TjEah81CXLgPGq22jSs7hiiKPPP0E9TWVFNXU0Nk72i3Ol6Hw8H+5GTyCsqw\nNNRwtkJEulbeVgh88OkGfvuLF93u7AO81ZTfcMzfu6kETxRFBvYK4HydBVF2HlcMlSRMHgjA8uUP\nMKOsnPS0NIYMnUFgUNsiLvMXzsMd6RQ6L2+ef3I5W7btoarBQoivlmWrn/RkPbsJj/O9Rxg+aiTf\nfLEfx6imPV5bWR2DeiU2O3fR1Lm8/t1HmEaFIKgkSCtjZtzENucYN2ECVTurOX46HaNiJVLy45H7\nH2Vvyh52XDmOOUaPtGsPI6VePLK8Y8IW36MoChfOnqWsrIwJEye1uGftTr45tB3z2DDnP4oPVAXr\n+XrrNzyx8rEunbenMGLkSFZabBw4norZaic2LIDly7uuCbmvnz++fu1P5movr7/5DpdNPkhaPTaz\ng5qC8wQOCkYQBBSHncv5pbz77vtMnTyBQXGdy76vr6tl46atVNWbCPbz4v77FjE3aRyfbz2E3b83\nisOBV20Oi59wXYk++fgjfPHlei4XFqKRJcaNH8ykSU29coNCgpmcNPWWXn9niYqKagyR90QUReGr\nrzZwIbsIxQEDegezauWDzZLaeiIe53uPoPPyYkG/8Ww9dQRjrB65pIFRYgSJM5uHkMIiI/jNoy+z\nZ99ujBYTU2bMJ7SdPXznzZ7PPJpk9gx19WzPPo4yKhwVQKCeUyUVDDl5glEJzR3/zTAbTby65k2K\n+6gQ/XXsWHuCB0fMIqGD43SEMkc9Ak1JL4IoUO6o77L5eiJjxiYyZmzXvcddzbnUVLLqNch6516l\nrPHCt/dgDCU5eAVHUZV1Cv8+w0k36zi/4TCTz19k+fKOtc60Wa389dV3qfHtiyDouFxq58qrb/Hv\nv3iZvn1j2bs3BY1aw8xZP2qmhCXJMqtWrWhl5K6lvKyUnVu30W9Af8aMn9CuVa3JaCT9wnmiY/t0\nu5zqNxs2cSDP3FhadbzUDJ9/yaOPruxWu9qDx/neQ0yZksTYxHFkpKUTNSz6puFfrZfQyf63AAAe\n5klEQVSOBQtuvT346ZMnsA0K5PrnUDnMl/TLmR12vhu3baI00R9Zdo7mSIhg88lkRo9O6LJQmK+g\n5cbAvI+n81G7MTYYEASxW6UXs7NzkbxdlSZU3r40lOVRV5BJQP/RiLJTXUvyDeHIxTzmVlfj24Fy\nquR9+6jSRSEJzq0ZQZQokUI4fvQIY8dP4IFlS933gtzEv/71DkfOXUEfPYSDBZf4fP02fverl24a\n2t63N5mtB1Jp0AQhm48zKtafx1Z3LorlDtKyi5A0kY2/SyoNl/LvjO5SnoSrewyNTsvwhFG3bd81\nJrYPFLm6L7vJSsANJRTtocxSiyi7hpOq9Q5qK6tvycabkTQgEUdGGQCK3YF0spj542d02Xx3C4b6\nOv7x6lv8+i/v8ev/eYc3/vkeFou5W2xJHJMANYUux6zVRfQLUuPlMDQ63u+xqP3Izcnu0BzVNXWI\nKteHMlHtTVlZhcuxBkM9yXv2kH35cofGdzcX0y5w5HwOgXETUHv7ofYJhOgEPv70q1avMTYY2Hwg\nFWtALCovH4SAKE4U2jhx7NhttNwVsYVnboE7Y0/a43w9dCm9Y2MYWOeDrcYIONWx/E9VMXN6x+sE\nfQRts2xjvQH0fl1XxjRl8lR+POZBRl3WMi7Ph18t/SHRffq0fWEPwm53YFVEqior2j7ZTXz82Vfk\nCmEIQbEQFEumJYC1a7++bfNfT2SvXkyN7wVVV69lDRcwqX8Qv/73n7F49hTsZqPL+TpLFf0HDmpl\ntJaZMGEcVBe4HJOqrzJlSlOf65Tk/fz+f99nfWoF/1i3l9feeBu73X7jULeF1HPpSFrXBExBEMgt\nqWz1mlMnTmLxct1+kr39uZiZ0xUmtouhA3rjMDVtAznMRuJi27dF1t14ws4eupxnVj9NSvI+crIK\nCNT6M+eJR1Br1G1feAOLZs4n68u3MSaEIqpl7JfLmRw9okWlLXcS3SeW6D6xXTpHV7F1x2b8xyzG\nO7wf//HGWqYOi+a++7teHCSvrA7Brym6IkoyuWW1XT5vayxduoRp0ypJO3eewXEzCApxhlZnzp5J\neuY7XKk3IHoFItRcZfa4If9/e3ceFtWdJnr8e86phQIKKDZlEwRFxT0qIijGLW4xms09JpnOnult\nnrndc5e+d+4zc+8807fTM3kmT2emuyedpWOSNkmnE2Nco7iLJopxjaKCigqyb7WdOvcPErUEVLCo\nQn0/f4XDWd7qfuSt3+/8fu+LLTy8S/dPTklhTt4gthQfoUE3EWPyMnPK6CtT1263izXbvkaP7d/2\nCsacyElnKxvXb2Dm7M737faUvolx6B5Pu+OOyM5fD2T074+y7ShYr57j87iJjQ7dvt8HH5wDxhoO\nnjiLYcCg9EQefTRwrUl7kiRf0eMURWHy/VO43TL5sXFx/LcVP2HTlo00uVrIHbGA/gOyAhLj3ajk\nyEFOuc3YUwcDYDjSKDp0lvz8SyQk9uzoIMxiwnn9MXNoV6A6HLEUFPqvGtY0jR//8AWOHj7MmdNl\n5BUsw+GIxeV0Un7mNKn9+mELj+jkjv5mzJjG1CmTqautISY2zm/FbdmpUzSqUX4NSjSrjbMXgzcb\nca2JhYV8vvZLas+fJDI5CzBoPXuY557ufKFZSloaOYkWDtU1YrLZ8Xk9OJxnmf7AS8EL/DqKojDv\noTuz1pwkX3FHCQu3MXfOnfhPLfgOnSrFZPdfPGNEp7C3eC9zHuz6Yjrd62Vr0Vbq6xuYOKmA+ITO\nKyflDh/A2oMXUL9vqdN0mfxJHZclrbp0EYvVSnTM9e13gmfI0KEMGdoW37p1G/iy+ChNmp1wfQOF\nI7OY99CtbZzVTCbiEhLbHU9KScWmN+Pj6myAoevE3GCk2RHDMFi16mMOnqzAo/tIT7Dz9IrFXR6p\na5rG//nHX7Dqgw/YvmszTo+PqIQk1m7aQUxMNH2Tkju87tlnnmJb0VZOlVfgiI5k1swXsVhkAWJ3\nSPIV4i6VEp+AfvkCmu3qtKDRVEX2oK7vGW2or+fXr/0ntWEpqJYwtv77BywoHEnh5Ekdnj9r1gNE\nRe7g68MnUBWF8dNHMGas/+r2Sxcv8sY7H1LRqqGhkxVn5YXnnsRs7vorie64XFnJ7t17SO+XxvBR\nbT2fL1dVsa74W3BkYAV04th08CwjR5yhX0ZGt58VabczLjuJnaeq0exx+DxuHK1nmTv3+S7dZ83q\nL9hZ7kb9rkDICbePN956j5df/EGXYzKbLYwcOYq95S7Cotq+MJQDv3/7I/57JwVHFEWh8P7JhGbX\n8d1Fkq8Qd6nC3HzW7HuVWiUNc1gE3tYGcmJVBgzM7vK9Pvn0c+qjstC+/4Ps6MeGnQeYOKmg08pn\n+RMLyJ9Y0OHvAN5d9SlVtn6Yvxv8lXq9rFr1Z5YuXdTl+LrqizVrWf9VKcSk4jtaQkbRTn708nPs\n3LkLI8a/mYAak0zx3q9vK/kCLFr0KDklJRw8/C2O6AimT3+py00jjp6uQA27OrJWVJUzlQ3drlFd\n/PUhlCj/kfolTxjlZ06T3j+zy/cTt06SrxB3KVVVWfLIIn7xs79CDYvkb372c8bnT+jWvWoaXSiK\n/8KaBo9KU2NDtytSVdS0cM0sLKpm4mxV56ttu2PL5iJKjpaCAqNzBlA4uZDWlma+/OoEiqNt9KhF\nODjjtrFx4yZSU5LwHTuKFnH1M+muZhITOp6G7arhI0cyfGT3W0+aNBWuKy+uad3ftNLxVh39ju4k\ndauKdxezYfs+6lrcJNhtzJ89mUGDBwft+bLVSIi7mKpqmAwvamsdeQX53S5GEh9lw7iucUa0xSDS\n3v0a1+GW9t/9wy2B+5O0bu16PtlbTpkvnjI9no/3nGb9+g18e/w4Tov/+2XNEsb5yhpGjxlLqqkB\nn9cNgE/30sd7iYmFHU+vB0JrSzMrV/6JV19/k7feXkltTedfQEYN6U9D6de46tsqRvvcTnLSE7v9\n/+v9hRNQG65ukTJ8PtIidJKSU7p1vztF5aWLfLChmOqwVPTYTC6ak3jrw7VB3YsuyVcIcVML5s/B\n0XQa3dWM4fOh1pQxc+LomzbbuJHxIwbga6m98rPaUMGUieMCES4A+w6fQr2mmIsa7mDfoVIyM7Ow\nuP0Ls/g8bhIdUSiKwk9+9BzTM8MYEtHE/Wkaf/uTF2/rc96IYRi88m+/Y+9lC2V6LAfqI/iX19/s\nMAlsLdrG5zu+IaLfUHweJy3HtjEpzcTyZd2fpk/PyODJByeSrl0mzn2B4VFN/PXzT9/OR7ojbCna\ngS8mze9Yc0QKO7ZtD1oMMu0sAmr3nt3sOXkALzqZ9iTmP7igx/5wieCJtEfxP37+Y3Zu305tbT2F\nK5bf9urkuXNnkRC3hwOHT6BpCpNnTmPAwIEBihjcXh2umz31eH3Yo6PJz0lj2/FLqFF90J1N9PVV\nMXPWi0DbQqQHH7r90qq34qviYqrUeLTv/o0oikJ9eBqbN2322//rdrv4Ytt+dEcGGmCLT8UbGcu+\nr/YzZ+6sLq92vtbwEcMZPmL47X6UO4rFbAKfD67ZDmboHsKDWAZVkq8ImOLi3XxcVYwyrG20cbH5\nMs0frmT5wuUhjkwEgqqqTCwM7DrX3Lzx5Ob1TH/Y9MRovrpQQ2v1eUAhPDGN9KS2d7mPPjqf0SdP\n8NX+b+ibmErBpMWcLS9nT/FXREdFMnXalKCsuq6urkG1XNfq02ShoanF79iZ0pM0XLdP2BQWziV3\nGG/98QNeeO7uH60G0owZU9nzL7/H5WirE2AYBnHuS4zLC15DBhmSiIDZc/IgSto103wRVo40nb/n\nGtCL3iFnUBa+xiqiM4YRnZ6D93IZuWOuLnbKHDCQxx9/hEmTC1m/biP/+u5adleaWXO0gX/85Ws0\nNNy4IldTYyPNTe37YXfFxMKJmBquawRQd56CfP8vJMkpaYTp/t20DF1HURVOXwpd5bA7VUSknRee\neJgB5hoSPBfJsdXzw+efCOosnYx8RcDo+Do85vP57oj+muLusnl3CbaU72o0Kwrh6SPYtHUPOUP9\ni314PG6KvjqGEpMBtHXGaYjKZPXqLzrc9tTc1Mhv31hJWa0bRYH+sVaef2YF1rCwG8Zz7MgRdhTv\nB2DC2JHkDBtGRKSdx2dM4PMte6hzq9hNPqYWDCM5xX/BU2RUFLnZfdl6ohJrTCI+r5va0hJiMkdg\ndl7s5v9C97b0jAxeev6pkD1fkq8ImEGx/ThXX44W3fbexPAZ9NMcknhFl7mcTv7w9nuculiPpioM\nSU9g+bLFXRqZNLS44bqZ47oWd7vz6qqraTIsXNvbSFFUaps6Xvn6zsoPKVf6oMa1xXLap7Py/Q95\n+qnOX6/s2rWLVVsOgb2trOfhz3bxWH0j+QUTyM3LZWzuWJoaG4iItHf672XRosfQ33mHjcV7UWyR\nOLJGge5heGZgtkGJ4JLkKwJm1szZNH6yikOlZ/AYPtJMDp58RN73iq57648fcKzFTlPzZQzdQ2Vd\nC1bLxyxa9NhNr91atI0PP11HbZMTc20jimYiKq1t/2ZiVPvRaWxCItGam2vfshq6Th9Hxw0DzlU3\nodivblBWVI3yqhtPP28r/gbsfa8esPdha/FB8gva9l2rqnpL+6WXPvEEGZk72XPgGLqvliFZycx5\ncPZNrxO9jyRfETCKorDw4YUsDHUgoldzu128u3IVZZX1WDSNscOyeGDmDL9zTl2so/bit8RkjkSz\nhOF1tbCpaBcLFz56wz2tx48d452PN2BNyiahf1sy87Q2UVe6n4zEGB5dvKzdNZqmMbtwLH/+ch96\nTBp6axMpWh0PPfRch88Is5houe6YzXLj2Z1mpweu68/Q7GrfVehW5Bfkk1+Q3+HvWpqbKNqylYiI\nCAomTbwyim5taebYkaP0z8okxhHbreeKwJLkK4ToNsMw2PLlZk6UXcBmMTF75tQbNlzweNz83396\nhbqYwajhbQU61pRcICxsK4WTr66kbqmtIiptCJqlbaRqsoZjThnKwQMHGDl6dKf33128H91kwRJ5\ndRRptkUSbjL4xd/9qNNp64KJ+YwcOZztW7fRp28/Rt13X6dJPm/EIFZ/VY4a2ZbEjMYqCjppGvG9\nJEc4Da6rJSANwyDZcWvdkm5Vyf4DvPvZFtzR/TC8DWza8So/fmEFe/ftZ2PxUZzWOMxf7CF3YB8W\nL775DILoWbLaWQjRbW+/8x6ffH2RYy12vq4N45X/WMnlysoOzz129Cj/659eo7zBQNWufu9Xw2PY\nf+Sk37lxUWGYI/yrZ1mi4igrO3vDeFRVwfC1X13f6vbhcbd/33utSLudWXPnMHrMmBuOrqfPmMqS\n+wczwFpHtrWeJ2aMouAGNawBli5+lGTveby15/HUVNDXc45liztv39cdn3+5C29sJqpmQrOG0xCV\nxbvvfcS6PcfQHRmYw+3gSGP36XqOHDoc0GeLrpORrxCiW5oaGykpq0b7rkayoig4ozP4Yv0mnli+\npN35n6wtojUmE6XhSPubXZcvly9dyK9XbsQal3r1YH0FYx956IYxTbu/gHXrt+BNzcb0XdN3n9eD\nT9Eo2lLEA7Nmdu1DdmJ8Xh7j8/Ju+fyoqCj+y09fourSRTAMEvomdfvZO7Zt56tDJwAYN2IwEwom\nYBgGlxudfkVFFEWh9HQZSnquf6MIewLfHD5KzrAbj9ZFz5LkK4TolvraGlxY/Ao/KIpCi8vb4flV\n9a0o8Sq6x41P914Z/fpa6hg9foDfuVkDs7k/5xC7j53FEx6HueUyk4ent9uCc73k1DQmF4xh59Hj\nKJoKKBi6TnT6EKB79Y8DKaFP35ufdAPr121gTUkFanjbgq9T24/T6nQyddoUHBFWaq87PykhlgvN\n1Sj2q68CdFcrCXHxiNCS5CuE6Jak1DTiTS6uXefrc7WQmd1xgomOsFIHxGSOoP7MYRRVw6a4eWhG\ngd/73u89/vgjTK+p5ttjxxmcM7NdOUuX04nb5cIeHe13fPkTSyl95be4v6teBBBWW8r9UxZ0+7N2\npL6ult07d9E/M5PsIHXD2XuoFDX86tYiNSKW4oPfMnXaFGZOGsuf1u9Bd6RheL1ENJfz1DPL+NNH\nn3HS2YJmDcfnceM+s4+1Tcls2nOYof37sGTJwm43ZhDdJ8lXiB7idrrYXPQlLreLwvxCYuLurlWm\nqqqycN40Pvh0I9V6OFbDxch+MUyfMb3D86dNGMlHW0pQolOI6T8Ma/0ZXl6xjLT09E6f4YiNY3y+\n/8pen8/Hm2+9y5GztXgMlaRIlaeWLqBvUltSsoVH8INFc/l8wzZqm1zERlqZt3R+QNvkbdiwkbW7\njuKLScMo2UGWvYiXXnymx/e0uzqoV+3y6ADk5uUyMDuLLVu2YbNFMnXqy1isVl5+8Rk2b9rM2QtV\nHD9yGCMzF6/ZihfYc7EV2yef8vDD83s0btGeJF8hesCF8xW8vvodWkfHoZg0dqz+HQuHT2PMfWND\nHVpADR02lL/PGULF2XKiYxztRqHXKphYQP+MdLZt343FYmLG088Qabd3+ZmfffY5B2utqLFtTQYq\ngbff/ws/++mLV84ZmJ3NT7Kzu/GJbq61pYUNe45gxGagAIo9gRPOFn75z6/QRBge3SAjwc5TTywk\nPKLjvcLdlRYfyZFmHUVtS/KGrtMv8erCNEdsHA8/4j/CV1WVaTOmAfB3//Aqmvlq9tYsNr4tkwpZ\noSCrnYXoAau3rsWV1xfVam579ziqLxsO7gh1WD1CVVVS0zNumHi/l5yayqLFj/HwIwu6lXgBTp2v\nQrX4F8u4UO/B5XR2635d9e3x47Sa/afAWyrLuGBJpTUqA6+jPyfcsfzh7Q8C/uwVyxYxwFyDUn0a\ntfo0A601LFvy+C1fb9LaTy+bNEkDoSAjXyF6QK3eAvgnl1rf9aUZRHfYzCa4rvKjVTMwmYLz5yyj\nfwYW9w6MiKtfNnSvG1PY1VGuoqqcqWzAMIyAvk+1hoXx8os/uNLv12Lp2lT60Mwkdle0oFnaWhD6\nmmsYlzcoYPGJWydfeYToAXGm9tONsWpgiyrcq+6fOA6tseLKz7qziZEDU9CClHyjYxzkZiehN1a1\nPd/VisXb/ouVpqk9tpDJYrF2OfECLF70GFOzIuirXyLVqOTRvCwKJ0/qgQjFzcjIV4geMH/qHF77\n6A0aRzpQLWZMByuZM1Zq8AbC4CFDeFZRKdpZjMujMzg7hWmdLPLqKYsWPcrwQ4c5ePgo8bFx2MbN\n4cNdJ1Ei2rYA+VytDM1IDGpMt0JRFObPnxfqMASSfIXoEfGJifzi2b9lx/ZttNS3MnnRImwRMvIN\nlOzBg8geHNrp0pxhQ/0LVagqew4cxeP1kd2/D/MXSJITnZPkK0QP0UwmCu+fEuowRJAUTCy4aZlJ\nIb4nyVcIIcQ9y+vxsG7tei5criMuKoI5c2dhDWvfejLQZMGVEEKIe5JhGLz62m9Zf7KFI812is75\n+NWr/46u6z3+bEm+QggRAF6Ph9rqanw+X6hDEbfom5ISyt2RVwqPqJqJSlNfthUV9fizZdpZCCFu\n09ov1rP162M06SYcZp150ycwdtzdVc3sbnTu7DnUcP/iMJo1nKrquh5/tox8hRDiNpSePMG6/WU4\nozMwxabSaE9n1drtOFtbQx2auIkJBRNQ6s75HfPVX2Ts6BE9/mxJvkIIcRuK95WgRPXxO+aKTGXX\njp0hikjcKkdsHLPzBmOuPYOroRqtrpwpQ5PoP2DAzS++TTLtLIQQtyHSFoZPd1/pTwzgczbSp6+U\nbbwTzJgxncJJEyk/c5qUtLSAN8PojCRfIUTAeD0e3nx7JScr2tq6D0yJ46kVS4JS+vHShQrCbLZ2\nfX+74vTJkxw6fIQRI4aT3r//LV0z44Fp7HnldZqjs1AUBUPXSbU0kzNseLfjEMFlDQtj4OAhQX2m\nJF8hRMC8+94qDjXZUR1tCfCbBjfvvf8hy5cv7rFnnjt3jjdXfsJFpwkTXgbGh/H8s09iMpu7dJ8/\nvPkOJRVO1Kg+bDq0gbHpdpYvX3LT68JsNv7mxRWsXrOBuhY3fRMiWLDg2e5+HHGPkOQrhAiY0xdq\nUSPSrvysmiyUVlwK+HPcLhcHS0pISUnhvQ9XUxPej+8a9XDC7eHjj//CwkWP3fL9jh05zIELHrTv\n3t2q0X3ZW3aJ/JMnyBww8KbXx8bFs+KJmydqIb4nyVcIETBaB71hTZoW0Gfs2rWLv2wspjksAbV1\nH62NtUSmJ1/5vWoyU15Z2aV7Hjn6LZo93u+YFt2Hb745ckvJV4iuktXOQoiAGT0oHV9rw5Wf9ZZ6\n7huS0aV7XKyoYNWqj1nz+RpcTqff7zweN59tKsbt6I/ZFonqSMHjbV+NKNzStXFFVv8M9OZav2N6\n02UGDer5Va/i3iTJVwgRMA/Om8OcEX1INipJMSqZNzqZ2XNm3fL1W4u28cv//ISdFzXWn2jhH371\nGy5XVl35/ZnSUurVqCs/K4qCZrbibrh85Zip/jzTJud1Ke4Ro0eRbXfjba4HQG+uJSdWYXDO0Jtc\n2XvU1VTT1NBw8xNFryDTzkKIgHpg5gwemNn16wzDYNOuEnD0QwEUk4WWmAF8umY9f/XUMgD6JiVj\n1ZuBuCvX2VOzSXGeIspmxmzSmDJnJhmZmV16tqIovPTiM3y9bx+nzpxlYNZwRt13X9c/RAjU1tTw\n+u//SHm9jqF7Mbde5ocvPMmgITmhDk3cgCRfIUSv4HI6qXf5UK/bZlnX5Lry3/boaMZkJVB8tg4t\nIgaf7iWq6Qwv/OhZIu1R3A5FURgzbhxjxo3j+LFjrFr1MSlJfZhQkI+iKLd17570zvsfUWVLJzy8\nLUZDH8D/+7c/8M//++fEJcTf5GoRKjLtLIToFaxhYcTa/BdnGYaPhBib37FlSxexfPIgRsW0MjlN\n47/+9IXbTrzXeu+9Vfzm4x3sumTig11l/OrXrwWly013nblY5/flQNE0fLYYNm7aHMKoxM1I8hVC\n9AqKojB3ah6mmtP4dC96ayOxzad5eP7cdueOyxvPiuWLePiR+djCwwMWQ1XlJYpLL6NFJQCg2SI5\nRyKbv+y9icxER12UDDy6dFfqzWTaWQjRa4wZO4acnCFsK9pKjCOZceOXBnXK9+iRo/gi4rl2/K1Z\nbVysrAlaDF01JW8kq78+hzUmEYCWyrNYVMgff2e8s75XychXCNGr2MLDeWD2LHLz8oL+rnX4yBFo\nTf57hL3OJtJT+wY1jq6YPWcWM4cm4Cotpv7IduzeGpbMLZT9yb2cjHyFEOI7Dkcsk4f3Y8s3ZyEm\nBV9TDdlRHgomTQx1aDf00MPzeejh+aEOQ3SBJF8hhLjG/AXzyMutoHjvPrKycskZdufs9RV3Dkm+\nQghxnT7Jycyb/1CowxB3MXnnK4QQQgSZjHyFEEL0OF3X2bFtGw0NjUyeXIg9OjrUIYWUJF8hhBA9\nqq62lldff5MaazKq2cKWA2+xaNYExuWOC3VoISPTzkIIIXrUn//yOXX2TDSrDUXV8MVm8MXmPRiG\nEerQQkaSrxBCiB5V0+Rut2e7plXH43GHKKLQk2lnIcRdxdnaysr3P+J8dSM2i4nC3JHk5uWGOqx7\nmiPSwrlGwy8BO2waZrMlhFGFlox8hRB3ldd/9xbfNEZSG5ZChdqH9zeXcOTQ4VCHdU9bMG8W9oZS\ndLcTw/Ch1pbxwKQxvbpbVE+Tka8Q4q5RX1vDmVovWtw14wp7H3YU75diGSEUGxfP//zZDynasoXG\npmYKly4hNu7ebncoyVcIcdfQdR+G0n5C7x5e19NrmMxmps2YEeoweg2ZdhZC3DVi4+NJjTT8VtEa\nzdWMGzUkhFEJ0Z4kXyHEXeW5p5cywHwZW0MZsc5zzBubwegxY0IdlhB+ZNpZCHFXiXE4ePmFH4Q6\nDCFuSEa+QgghRJBJ8hVCCCGCTJKvEEIIEWSSfIUQQoggk+QrhBBCBJkkXyGE6KUqzp/j0MESdF0P\ndSgiwGSrkRBC9DJej4ff/McblNaBTwsj+rPNLFvwAEOG5oQ6NBEgMvIVQohe5tNPV3PKG48Wk4TZ\n7qAlOpMPP//ynu5/e7eR5CuEEL3Muap6VJPZ79jlVmiorwtRRCLQJPkKIUQvExnW/o1guOYjIiIy\nBNGIniDJVwghepnZM6ZiqTuNYfgA0JuqyRueiclsvsmV4k4hC66EEKKXSUpJ5ucvLWfdui9p9XgZ\nNWGENIe4y0jyFUKIXsgRG8fiJY+HOgzRQ2TaWQghhAgySb5CCCFEkEnyFUIIIYJMkq8QQggRZJJ8\nhRBCiCCT5CuEEEIEmSRfIYQQIsgk+QohhBBBJslXCCGECDJJvkIIIUSQKYY0iBRCCCGCSka+Qggh\nRJBJ8hVCCCGCTJKvEEIIEWSSfIUQQoggk+QrhBBCBJkkXyGEECLIJPkKIYQQQSbJVwghhAgySb5C\nCCFEkEnyFUIIIYJMkq8QQggRZJJ8hRBCiCCT5CuEEEIEmSRfIYQQIsgk+QohhBBBJslXCCGECDJJ\nvkIIIUSQSfIVQgghgkySrxBCCBFkknyFEEKIIJPkK4QQQgSZJF8hhBAiyP4/4Wv2MpBRy3AAAAAA\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118894860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# helpers_05_08 is found in the online appendix\n",
+    "import helpers_05_08\n",
+    "helpers_05_08.plot_tree_interactive(X, y);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that as the depth increases, we tend to get very strangely shaped classification regions; for example, at a depth of five, there is a tall and skinny purple region between the yellow and blue regions.\n",
+    "It's clear that this is less a result of the true, intrinsic data distribution, and more a result of the particular sampling or noise properties of the data.\n",
+    "That is, this decision tree, even at only five levels deep, is clearly over-fitting our data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Decision trees and over-fitting\n",
+    "\n",
+    "Such over-fitting turns out to be a general property of decision trees: it is very easy to go too deep in the tree, and thus to fit details of the particular data rather than the overall properties of the distributions they are drawn from.\n",
+    "Another way to see this over-fitting is to look at models trained on different subsets of the data—for example, in this figure we train two different trees, each on half of the original data:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](figures/05.08-decision-tree-overfitting.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Overfitting)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is clear that in some places, the two trees produce consistent results (e.g., in the four corners), while in other places, the two trees give very different classifications (e.g., in the regions between any two clusters).\n",
+    "The key observation is that the inconsistencies tend to happen where the classification is less certain, and thus by using information from *both* of these trees, we might come up with a better result!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are running this notebook live, the following function will allow you to interactively display the fits of trees trained on a random subset of the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xItv2fMKidZvve6+C1mtoXHQ2jI3GNq5+cKPGTeBU+m6mT2wZYr9VaMPbP6Jb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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118c4ca58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# helpers_05_08 is found in the online appendix\n",
+    "import helpers_05_08\n",
+    "helpers_05_08.randomized_tree_interactive(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as using information from two trees improves our results, we might expect that using information from many trees would improve our results even further."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ensembles of Estimators: Random Forests\n",
+    "\n",
+    "This notion—that multiple overfitting estimators can be combined to reduce the effect of this overfitting—is what underlies an ensemble method called *bagging*.\n",
+    "Bagging makes use of an ensemble (a grab bag, perhaps) of parallel estimators, each of which over-fits the data, and averages the results to find a better classification.\n",
+    "An ensemble of randomized decision trees is known as a *random forest*.\n",
+    "\n",
+    "This type of bagging classification can be done manually using Scikit-Learn's ``BaggingClassifier`` meta-estimator, as shown here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bZE4OqCl77rkVMAG0Mshes5nAoADsGihR2VZkMhlz//1Xtn33MyZJKahcXRj9\n1BKDLq8RBOHRIYJvKzKNuEbVelgWQHH4GSJHDKFnj26tMg0d+tk3TFq9kYrJzqSIa4QZGXH/6g3m\nhu6u7E/GrdsctLZk0uJ5dV7Ly9MDr6cfFEZwd3Pl5qu/YseajXilpHHH0wPHlYtxqVIRqiopM7vG\nMiInjYZ7MhmuVVINrgR0Zk752toxC2ez4Ug41jeimUz5Wl4JpFPn2PDPz1jyjz826edhSDbWVsz/\njeGmhe/eTSf8y+8xi09C7eJMlyXzCGlHSWiCINRNbKzQinSmNZ9R6mJvY/X0q6x99nXS0+/p/Z6K\n0+eo+pTRp1RF7uHjmF6KqPZBwFmrQ3nqfJOvP2rWFKau+x6X7WuYvv47Rkyvu4C/y6B+JD28/aKX\nB0cXzuaYvR23gS0Bnen8+q8qp+KtLCyY/fXH3PPyqCyiAWX1lM0jb5CRk4u2yrT040qSJA6/9wEL\n9x5mZswt5p04TeafPyQ1tfZHAIIgtC8i+LYi07GjSK/yfPc24AX4a7UsvxLJic+/1f9NtTV3bZHp\ndEi1PGeWjJr37NnY2AhPVxeMjOqfOBk8YgiXly/iiJMj8UCovy8eb7zAkt+9Ts+tP1O6/nvmrP2W\nPkMGVjvP2tIC54CaW8/lZmZze+ZSdi37FSf3HGpW3zuKSxcjGB15vVrb2PuZnN++u416JAhCU4hp\n51Y09cklHLa25OyJM6RFXKNnQSGjq3y/6u45+qLq3xtV4p3K6d57wK2bcaiNjRgHVDwxjTM1wWnC\nmCZdOyX9PlqdFp9O7o0+Z86rz5H75GJSUtOZ3tkP4/KRsKOdLY52D5eUeMBrxmSunr9CSEEBAOmA\ng0rFEJUKbt7m6Kf/I71/b9xcnZv0GjqKulYIPioZ9YLwuFO8//777xvkTulJBrlNe+PfPZCgKeNJ\njrzO5CpLdwBuBHah27SJer1f18H92V5YxM1SFSdLSshXa3iyWMnwwiK+MzclPrArt7v6o35qKSPr\nmTKuqqCwiI1vv4/5p/+jdEMohy9fxWNQPywaWV3JzNQUFydHFIrGT7R4+HiRGtiZczqJMJkMVXYO\nU6FyizhfZQlhTg4EPqYbX3Tq5Mausxfpde9+ZVuYsxM9f/s6NjbW9ZwpCILBuPvW+S0RfA1EZW9H\nzLlL+BQrkQEnHexxevlZOum5SIJCoSB42CB0wUF03rqLITodMsqC1iCNlrgp45jz13fxDWz8fq87\nPvqCxfv4JO4NAAAgAElEQVQP46HR4K7V0isljd1ZOfRo5X1RO3l50m3cKJT2toQcPl5ti7g8IHP6\nJPxrmZ5+HMhkMpwG9GFfTi5xcgXXuwXg8dpzBHQPrDwmv7CI3IJCrCyaX4FMEIQWqCf4imlnAwkZ\n1I+k7z5lx859SDqJkBmT8Pf3rfecgsIi9vz7v5hfvY7OzAzjCaOZ9vSyRk0tyuQydLUd1oxpSbPY\nuGrJATLA/ObtJl+nuYaPHcnP/UJYeTECOWU7Gn3SyZ1+WVnk5hdgp4eRnk6nY8eXP0D4GWRqNSV9\nQ5j921cxMzVt+OQ20snDnYW1ZH9rtVo2f/gpTidOY1lczNGewYx49w08vTzboJeCINRGBF8D8vHx\nwufV5xt9/O5/fMKi/UcqA9+9uAQO29kyoRH1fLsFdmV1714EXLhcOVV7wsmBkJlTmtxvjV3NtbWa\nep7X6ptcLmf+Jx+w/ae1ZN+IQRFzmz+k3UX+6Tfs3byLzn99h+A+Ldvcfs+q9Yz9aW3lxgbqpGS2\nyWUseu/Nlr8AA9vzywZmbNtVOVMw5NxFNvz7SxZ//mGb9ksQhAdEtnM7pdFosL5yrdovyFWrpeh0\n45YHyWQypn7wHptnTmZHtwC2jhiC9V/ewc/Pp8l98Vkwi3MOdpVfX7W2wnVuywr6S5LEru9XE7ry\nJbYv/xVbP/+23iVE1laWzH31eeztbHkqPx8Tyj45zkxNI2bV+hb1BUB3/nK1HYWMAbPLkS2+bluQ\nIqN4eB8f2+hYSlWqNumPIAg1iZFvOyWXy9E+vEYWkGppq4uTkyML33+nxX3pN3wwsf/9F9t3HwCt\nls6TxzGshYlOe3/ZwNCvf8RRV5a1WxQVyw5Jx9zXX6j3POO0uzXbUmu2NZXOpObPVWdsXMuR7Z+2\nlnrSpdZWGDewNEwQBMMRI992Si6Xoxo5hKIqbVGWFnSaPL5N+hPYLYDZb7/K7HfeoKceMozVp85V\nBl4AS0B+9mKD56lqeW6p8m7cln31cZgwlrgqRVGy5TLko4e1+LptoduCWZxwdqr8OtXICKOpE5q0\n85QgCK1LfBRux+b++iV229jClavozMxwnz6Jwe0wINxNu8vp1RsxzshC29mPKU8vazBRSaot8asR\nwcFn9lS+OHKCJ1UqFMAmuQyjBhLXGmPUjEmckMG1w8dBrcZ0yABmLlvQ4uu2hW49u2P02YeEhu5G\nVqzEfuhApk9pmw9tgiDUTgTfdkyhUDDr+Sfauhv1yssvIPz1d1lYXjBEfTScdbfiWPnJB/WeZz5q\nGPcuR+Ja/pw3H2DYoAbvd+foCZ5XqTgBaIFFOon9J8+ie+mZFo/sRk6fBI1c+9zedQ3qStff/7qt\nuyEIQh1E8BVa5PiWncytUqnLGBh4+gJRUbF0r7Lm9GGTlszjgE5HybFToNMiHzyAmc+uaPB+xvez\nMAGqjuNs0zMoUpZgbdm4oh8PkySJQ6F7UEZeR2Nrw/AlC3B1ezwrZwmCYBgi+AotoisorPEmclGp\niMrIBOoOvjKZjMnLF8LyhU26n8bfB01YeLV7Zvn7tKiQxOZ//5fxG0NxkCQkIPTkOYZ/9REuLiIA\nC4LQOkQGhtAiAeNGcfWhEWeYvy+Dhg6s44yWmfLMCtYMH8xtE2PygK1+PgS8+FSzaxpn5+XjciAM\nh/JayTJgTkISJ9dv1V+nBUEQHiJGvkKLdO8RxJFXnyd0y07s72eQ4e9L0EvPYNJKy3TMzUx58vP/\nI/LqDS7fz2D6qKGYmtTcurGxMrNzcM3LrdYmAxT5BS3sqSAIQt1E8BVabNzC2WjnzaCwWImNlaVB\ndtbpFRKsl+t08fFiS1AA3aJiK9vSFXJs+obo5fqCIAi1EdPOgl4oFApsra0euS3t5HI5wW++zKYe\n3Yg0MuKQizOnly9k9NQJbd01QRA6MJlU18ag+nbluEFuIwjNIUkSiWnpONnZNjtr+nGRr04nRVfa\n1t0QhHav++C6l4qKaWdBoCz72s/Dva27YXD56vQmHZ+iKyUzB277zWqlHglCx9G9nu+J4PuI0Gq1\nnDh0jKKcPIZPm6CXbfQE/VCp1ag1GizN29++uVVHqZ5yU2yM3ap9LyKrlLTAPk26ZrGdN0El4v0n\nCC0hgu8jIDs7h51v/ZHZEdexAg6u2Yjzb19jwKj2V2rycSJJEp9/GsHNo53QFVth3+MaL//eAw83\nB73e59KVZLb/XERBmjW2Pnkses6B7gFuDZ9I2Uj1vGUgRXn5jFakEVyehF4RlG/7zSIoXwRSQTA0\nEXwfAWE/rmVlxPXKfXmn3r3H1p/W0X/k0Ecuwak1pd/LIDE+kZA+vTA3q7+2tD6s3RBJ+pqVuEj2\nZQ3h8KXsc/7xqf6Cb05+AT/+yRiX1F9hD3Ab/nfnBz5erWpwiVWe6i7rP08g+agzZrkuXA+M5onf\nXGNIr7KNMbJ79oVkvXVV0LPi4kL2f7Ef5S1TFPYa+iwOILCvfrL8hbYngu8jwDg5lYdDrHVKKqUq\nVYMbGDwOJEli80f/xXPvIbrkFXDYywOHF55iWCtvJhB/yQKzisBbLuO6N5fzbmFm1vy1x1Xt3BKH\nU+qfqrXZ3lrMt3v+ybipXeo9d/vGBEo3voSfrrxSV2Q/vv/XZ9h8n0hWroy0e1KTp48z0+9x/Mdw\n1OmmmHqrGPfcOGxs7Ro+UWiyTe+G4n58OdYoADh79ShW3yTh4dv0PbmF9kcE30eA2sMdCaoF4AIP\n9xYVl+hITh45wfBN2+mk1QEwIzmVXV//hHLMiNYdAZvn12gqtZa45DQHhZ72zr1jeRAndFD+BxhA\nQke8VT8s7UbXe25c4gG8ddVLZBrHDWdfiRkOfp5Nnm4uLS1h22+O4Be9vLwfEhujV/HMdytavKlF\nXl4O109fwr9HAO5e3i26VkeQlpyI0bmeyKv83jvdH8vF0K14/FoE345ABN9HwKinl7H6ejRzrkdj\nCRxxccbziSViyrlcTsS1ysBbYVByKhFXIhkyZECD5yfdSSFi/2GMLK0YPXdaoxOnps51ZvXZozhk\njQVAKc/AbWAk1hZd8M5veHowxqyALgk7cLKHSJc+FKtqJjJ5TZ/AhvW78b4zp7LtbtAunhozD6OS\n+quIRVvUDIha20xCTAZj2YyEqdM7w/CInlv5tQwZzlemc+nYKQaMHdHk61U4tvEwCT/IcMkYzmHr\na1jMOMvctxY81u/v0pISFJqa70NJ/fj+TDoaEXwfAU5Ojiz54b8c23eIkrx8Bk+dgLOjfpN6HmUy\nZydKgapj3Nu21vj5NzxCOLnnEHz8BTNz81ABobv2M+o/f8fdve6EJq1Wy4kjx1EWFLHg76Wc3BeN\nutiEkAElTJ0VSOr9K0S6NNzvLrFX6O1YnoF8/wqZOVe4E9inWuC2tLJhzIfdOPfjZlTpJph6lTD1\nuSEYNaJ855DFA9l7chded2YAoJRnYjcxD0vL5iVYlRSoMKZ6QDCVbCnMaXopzsvHz5J44h6l5FEc\n5o53zkQA3AoGkbfJlatDz9O7EVtMdlS+XQIJ67UeLnerbMu0jqDvxM5t2CtBn0SRDeGRV6wsYfNL\nb7H46nVMgbsKOeELZrPwt6/Ve54kSYSueIG5VUpLAmxbOJu577xR6zkZGZnse/vPzIi8gQWwz9sT\n//d+Q68Bfasd19j1sw8v/dnr1L3WEXBzpSencHbjBTR5cpz7WDByzvhmjyjvp99l74oYPDLHVbYl\nee5g6cYxWFhYNfo6R1YfIPuLAOxUnbnNAXwZjRHVHw+UPrWd6a/NbFY/O4r05FSOfHaKkptmGDlo\nCZjnxJAZzZ9hEAxvaO+6H8eIka/wyLMwN2PB/z7i8OYdaO9nYN+7JwvGjWzwvFKVCqv0ezXaje7W\nbKtw/LvVrIi8Ufn8ffadFLZ8v7pG8K0aVJvC01XGTT1mILt5eTL7LU+9XMvFzZ0ebydyffVWNGkW\nGHkXMvBZvyYFXkmSSNpVjJeqbATnSAD3uU4n+lUeU0Iedj6iypiblwfLPmralpvCo0ME38dAkVJJ\nWkYWvu5uGBt3zF+5hbkZ055Y1KRzTE1MyPfxhuwHuxrpAI1f3Qk/pskpNTLPTe+kNum+j7L+E4fQ\nb4KERqPGyMi4yaNonU6HNvdBoqA9fqRwDktcsMWLEvK5P2wLU6at0HfXBaFd6Zh/iYVKe1etQ7Zl\nJ75377Hb3xf351cyeMLotu5WuyCTyej6/EpC/+9TpiQlk62Qc6Bfb+Y/W3c9VlUtz4JVnZo3yn1U\nyWQyjI2bl2mvUCgwCyyEjAdtQcwmedKXmLp3xtbbnCkzVmCkp2xxQWivxDu8A7sWcR2f71YTrFQC\n0C0ugd2ffU3hsIFYWYhpPYCQQf0IWP89xw+FYefkwJODB9Q7mhv89DLWR8Uw52YcJsBhF2e8VzRt\nxP24G/fmEA6UrMX4anc05gUYD0/kmfdfaVQS2eNMkiS9ZICnJCaQlpBMr8H9MTMXfwfaiki46sB2\nfPEds35cW62tGDj19/eYILbMazZlSSnHdu1DVVjM0JmT9ZZ5nq9OJzI4mJvJXh2+drIkSaTfTcLM\nzBJ7B+c6j7sUdoaEsHvIjCSCp3chqG9PA/ayfcjOzGTfPw+hvGGF3EqLx2RjJj09rcnX0el0bPzr\nenSHu2Nd5E+GZzghrznSf8KQVui1ACLh6rFl7OSIEqotDkkyNcGznmeaQsPMzUyZsmB2s859OAu6\nuYlZjzqZTIZ7J996jzm+8QgZ//HDtrSshvnlsIuU/uUiISP7G6CH7cfuvx3C7cRSZOXZBgUJqZx2\nOcbQ6aObdJ3w7Yex3DEd87JCpXinzOTq19sIGa1q9mMEoflaVpZGaNfGzpnG5l7d0ZZ/XQxcGDWM\nbt0C27Jbj62o0iQiskrZ69SdvU7dicgqbfKWfo+TxD0F2JY+KKHpnNufqO2PVzHqwoI8tFfdKwMv\ngLXGg9RTeU2+VnZUSWXgrWAV35vE27F1nCG0JjHy7cDMTE2Z/d9/sWvtZuT37qPo0pmli5o3YhOa\nr2IHoYp9cCvKOsb4eeOUu6PePT8fZ5r8Wip05T8otxh3I5Yrm2+gyTfCvpeM8SumolAoapzzKDM2\nNkEyLa3RLjfV1XJ0/UxdJLRoUFT5s1/sHIebZ796zhJaiwi+HZyNtRWzX3iqrbvx2KoIvJEufcQ+\nuE1k0b0YKUmqHPVpUGHdQw3AnVtxnHzzLp3uzQdAFVbE1pTNLPzD4jbrb2swNTPHemQOqi3FmFCW\nHHXf/jz9Zta/qUZtRi0dy7qTa/G4tghjzMg1uY3zrEKsrcXGGG1BBF9BaGXZPftS3MGSqNRqFecO\nnUBhJGfA2JGtsjRo6lsT2V68FumyN5KRGrOhd5n30jwALm2NpNO9eZXHmmDJ/WPOFL6eh5W1rd77\n0pbmvrOAAy67ybwqR26lofdsv2YlnllaWbPim3mc2HoQZYaWwEGd6DV0Viv0WGgMEXwFQc8qRrsV\nUu4ZZkGBody5Fc+BP17APXYGElp+6r6JaR+OoJO3l17vY+vgwMpPl5KXm4VCYVQtqGqVNaekFcXW\nKJVF7Tb4xt2I5fxP11Glm2DmVcqIXw2kk2/DyY8KhYKpz+knSJqZWzBx+XS9XEtoGRF8BUEPKhKn\nqj7brdDUrfvau1PfXME3dknl175Rywn/ZhOLPtBv8K1ga+dYo81jsB1396ZirfGobNP2iMPZZWCr\n9KGlCvLzOPb7WLyTF5Q13IA9SWt56md3sb75MWWw4CuyOoWmeFSW4FSMcrN7ltV2TrkntfmzXaWy\niN0f76b4ugVyKy1+U20ZPneM3q5fklRzWUpJUivum1yLIVNGsS95F3f2nYd8C4y7ZTPp7eEG7UNT\nnNl+Ao/k6iNOt+gZnD98gqFTxtVxltCRGSz4qkMer7V5QvMZX71Y+e/6PrQZMkDX1o9qo9zyFTCG\nCroatZpTO8MoTC+hyzA/Ans/eAa47S/bcTqwFLvyjdjv3rjFJbsz9Burn2IKJu5quF1Lm4FNeX4G\n2me0aNQqTM0atwdzW9HppGrLhQBkyNHpmp61LHQMYtpZaJcqgl1EVilpgX1qfL9T7BWc7JPobtrw\nnr0t7UdFkK2tH20xyi0tUfLLKxtxv7QQU6y4suYG8U/tYsrzM1Aqiyi96IqcB0tu7Eq6En/kGv3G\n6uf+/Z7oyqm43XimTUFCItVrN6OfDG74xFagUChQKNp34AUYMmcEW7fsxSt1RmXb3YBdTJo4pw17\nJbQlEXyFdkcd0h/jqxdJ0ZUF3qqby1eI8fOGhB1E2SdVa/eUN276szGj5nx1emXwL7bzNtiz25Rr\nMdzck4qRpYxhC0dia2uPJEncOLgJ3cVjXEgywvPSfzCibPrXsSSY1K1pFCzJxdjIBOS1JHi1vCRw\npaD+PXFf68GZ0F3I5DIWzhmFtU3rJzldDjtL3JF0ZDLoOsmDkOEDWv2e+mJra8+wv/py6ZetqNKM\nMPVWMelXA0RlqceYCL5Cu6QO6U+2Rklxcu1JPEEl1sT4zao2+xnglYyrUeNGQflXL9YbgCtGvGmB\nffS6uX1DTv4YQfq73XAtnIsOLZv3bWfqf/qTtv1L5q3+BA+tliImUUj1P9qW9wNJTUwkqGdvzAdm\noN3zoJhCjkU0ARM76bWftnYOTH7KcJvdnww9xt1/e2KnHApATNgNSv9wioGThzXrehq1mtN7jlGY\nWUyfSX1w92r9kquBfYMJ7Ns2MwRQlgtgYmLWKoVIDq7aS9phFVKJHKs+Jcx8c0a7fxTQ1kTwFdoN\nrVZLxKUILK0sCeoe1ODxVQNijFmBXvtiY+yGpzod7l8h0gViaL0AfPHaae5eTcG1pwe3v/fFq7Cs\n4pAcBT4J8zjy7Q/ojsRwW/s+JuSj4zim5GOGTeU1Crwj8Otalrgz949z2W2zlcJrpiistHSd4Uzv\nke03GakxEvbk4qZ8MG/uUBTMrZ3bGDi56dcqyMtj/evb6XR1PiZYcWjNafxfi2f43NH663A7Eh91\nk/DPrqK96QCOhfjOMWfssol6u/7xLYcp+qIfHlp3ALRxGrarN7Ho/Y5V8ETfRPAV2oW423GcOr6V\nkN7WZGeq+embvfRa0PgN1YNKrIlJ9iLF5A6erjK61TECjtaUba9IcDAO1y7ro+stsuWbQ8zauJkZ\nylIiTE2QmAQsrXZM0tl0BpSGVj7HzSeGezyBhfzPOOh6kOZ6mG7P2lSONExNzZj32wWGfimtSltQ\nc7SmK2zeCO7oz0fwuboSeXlpe/e8YdxcF8rgGeoOt+xHp9MR9o8reN8oXxqWCxlfxHC9y2V6DOqr\nl3uknSzCqTzwAigwouCihd62QOyoRPAV2oXzp/YycfKDqVFfPx279+3CdWi3Rl8jqMSaGLyBZKI1\nyhoBOFqjLFsKpCqfYrRr3LrU1nrWa34xhtkbt9FLWVaQo3epildlB1jFOewZBICKYuxKAqslUNkQ\nhDkWyJ/ei6lHKgvGDjPIM9fGyLp3nxOrw1FnmGDZRcuElVMwMWn5MiSL4GJ0N3WVAVOLBoseJc26\nluquERYP7SljnOZBbm4mTs7udZz1aLoddR3rqEHV2hxKgog7Hqq34Cur5TOQTNGxCsu0BhF8hXZB\nLi+EKjuuyOVyzORFjT6/YtrZwuQOUPvIt5uRObgqqVwXVI9qQbqVZFw8Ri9lcbW23pKKdNv/YJL3\nX4q4z03THTjJ/Wucm+TtxwsvvdOuRhZFRQVsey0Mn5uLkSFDc1DF+pi1rPx4ZYuvPe3NaYQWrkV9\n0R1JrsNs0H3mvda8TGELH12NDQbU3snYO9TMZn/UWdvZoTLPgOIH7yEJCbm5/pY4+U90IunMLeyU\nXYHyD4zDVO3qvdkeieArtAsSNUdHaqlpU4ABXsl0M7Ko95iKoFw5/VzXMa5K5MZyYuJbbx2mY+/h\nRJma0730QV9iTMyQbAPIyruFOfYML32XS6pvUFOCMWYAZFtEMeiN6e3uj9upzcfxujm/cj2rESaY\nnhpEXHQ0nbs1fgajNpaWViz/1zKKigqQyWRYWFhV+352xn3Obj8DwODZQ3BwdqnzWuNWTmRN5M84\nnJ+MhdaVNLeD9HrarcPtiATg7umNNOI4mgO9MCr/P5bivYuZC4fq7R4DJw1DqzpBwoFr6Epk2PWT\nmP38XL1dv6OSSZJkkPmBrNI4Q9xGeESdOn6M0pIIuvdwQpIkToXfxSZkKsVmo5DO3EaSJLr37Vtn\nwIkxKygPvg1nWNaYfn6IRflz45vlmdatmel87oOXmLpzFYGqUmJNTFk3bDp5Yf/CngcjFS1qrnf7\nCFfzbijMdXSZ7tzsLN+6SJLEsY0HuXuqBJlCwm+iI4OnjmjSNXZ9vgOzn6qPRpXk4vbxFQaOHaXP\n7lYTe+k6p/6YgsfdsuyrVPf9DPubJ4H9etR5jiRJRJw8S9bdLAZOHo6NTcfd2UetVnHwx70UxMgx\ndlQzaGk/PP1927pbj4WhvWvWIK8ggq/QbtyMuUnU9YvIUDBkxFhiS0rYsDQe94gxyJCT2/M4M/8x\nDhf3ms/lGgq+cbfjOHbuICayErKVFtj1WMlgm9pHY1Uzpw2xxCj+cjg5EaexCxmCjUcX9i1Ixr3w\nQTUqHTpUz4Qy45XWK8iw/4fdlH41GAtt2Ygxz/Q2nd5NZujMxgfNW9ducPEFHU7FIZVtSX5beWLD\n1GY/95UkiaPrD3D3eNlzcfeRpoxdOqnah7B1b4TifHxetfMyRm1h6adi9CW0rfqCr5h2FtqNgKAA\nAoICKr/++ukjBF16rnIa0zbiCY58voElHy6s9fyUexIpFNfIdi4oKOTcqc1MG+8JlAXTbVu+QjP2\nk1q3wjN0xSr/viOg74NRpvGYMFS7Qir3b03x286cxa27VCj9qAYP7YOpWtvSLiQciGRoE5bydu0Z\nTMorh0jYnIDings6/xQGvdi5RQlXR1bvp/Dz/rhqXQEouHSPQ5p9TFw5tfIY9b2av0P1vY6VtSx0\nPCL4Cu1WSbRtjXq4pfG1j2yDSqyhJLh81Fo92zn86GFGjKo+Wh43wYrLlw7Qt/+0Vul7Syz40yKO\ndtlPbpSEkYOaGcsG4+DkrJdrq9UqwtYfoOC2hImbljErxmFlbYOutOZ0vqRq+jPlMUsmMGK+moKC\nXOzsB7X4uXRamAr38sALYKl15e4xNVTJ4TLzLYWY6ueZ+jYvE1oQDEUEX6HdKSws4uyabejUiaiY\nUTkCBDDyzUHXNarOcyvGzVVHvlqdFoWiehAwMVGgdkyo91oNqXhurO+RspGREROfaJ09V9f9fj3O\nR5Zggxk6dKw/t5qV3y3Eum8J2jg1CspGjKXk4zCgeYHTyNgYewf9fFhAW8uHAk31r0e8OIi9KWtw\nvl72zDejx36mvti059WCYGgi+Artyu0LV0l65i3m3ExgFvCNURh3NDuwJoS7bofp/eKDQNeY5CqA\nIcNHc+LIDwwd8WAd8anw+yxaugxTo8ZNidaXHR1jVkBQiTUlymL2fLaHwhumKKy1BMx0qZYYdeva\nDZKi4uk9egBOrobfMjH26jXMw4dVZk3LkdMpcgGntocx881ZbNdsJv+SGTKFhONwLbOeNcwz06LC\nfExMzWqtc2w/UIvqWhEmWAKgogiHQdUz0Dt5e/HUqkVcCT8NwPQRizpk5rLQsYjgK7Qrcf/6iqU3\nEyq/fl1zh991fpG0ca/TZW4PTH0GcjO5LCPZ4f6pyo0U6tuy0snZkS6BEzgedgqZrBidzoKBQ+Zg\natpw4K0IunVlR1cd9W79SyhOB5ZiU14QI+HaNcxtLhE8uA/r/7wWo0ODsC+dyZ5vj+P59FXGLZvU\nuB9KM+l0OjIz0rC1c8LU1Iz0pFSsVeOrHWOCBcWZKkxNzVj0p9YvB5gYe5vLW66hLZJj7F1MboQc\nXbQr2BThMlnDtJdmVk5VFxXmY2QhIzrkc6zz/TEyNsFhiIbpL86ucV2FQkH/0c0b7cZevU7UvtvI\nZNBjagBdenZv0WsUhMYQwVdoV8zjaxbACDHT4PtieZJV+aO8GLyJdIHIioNSayZaVdWrTx969Wla\nEYWqS5IamlouLMhDdb5TtUpUDoU9iT2wjcK8Aiz3TMVSKpuK7ZQ9muRfDpM3PQdbW/u6LtkikScv\nc/F/CRjH+aN2icRrnoyhC4az4ZuDeKc9mNLOsIhg4OiurdKHhyXG3OLEr1NxTy/LTC4igzzC6c5Y\nyIfCn+5yyjuM4TPGkpZwh71vXsQzYTY9UJAgP0xe0DXGTJ6u11HtxYNnuPmBGc75ZaP8c/vPU/Dn\nC/QZ/ejsmCQ8mkTwFdoVpb833Iit1pbs7UVJLRsnVB2JllW20r/GVrmSkECq5RmpBBnR+ZWBt4LD\n/f7EXo5g4JiR+ugmx1PCUSbux8lISUGhJbGf9aBzSnk93xTI/iaaO73iCXndnmvfh2IS3wVVpzt4\nL5DTpXvrjsArXNpyvTLwAljijBGmaCjFCFOstO6knz9LzrAMdn4eil/C65XlJP11E4iKyufQny7z\n5FrvBrfiO77pCEn7C9Ap5Vj3LmX6GzMwNTWrcVzM1nRc8x/0ySV3IFFbtorgK7Q6EXyFRomPiyfy\nyllkyBk4dDTunVrnmWXn373IxpvxzI6NQwds6N4F//dX4O7VUEnIuke9zdXNyBy8kkm5JzW4q5G1\ntR1G/VPRHX5QfzjHIpqA8Z3Iy8ilgAJMeXB+rv01hgUH6qWfl9UpGKeuZuZUL8CMs4fvkZ9Sfdca\nh5Ju3D4Ryow3ZtF7rIZ76Xdwchpt0G3fdAU1/9yYYI0aJUaYokNHXHQURXMdsM2bRhRbcKY7rpQV\ny5BjjPutSZw7eJzh0ybUeZ8ze0+Q+UlX3Et9AdDGqNmu3FzrLjua3Jp9qq1NEPRNvMuEBp07fYq8\nnPMMHuKMJGk5e3I13XtOJ7CFJQNr06V/Lzqd2c7OtaGgkDNx6RzMzFpemL+5KkpNpty7w50G9j0f\n8GDob1MAACAASURBVFFXwj9Yi3GkNXJrLV1nOtJ75Eg0ajW/HF+Dw8mZWErOZJndwH5eBo4u+qlS\ndefiXpaNe5BM5uptSoR5PBZKx8o2LWpM7Mo+FBgZGeHhWbNedGtz6mtMwaFszCWHyrZcEvGlbPR/\nxeZzuse9hHl5jW8nArjOJlwIRoYMLaVIaFAY1T/tnHQ0G8fS0ZVfKzCm4LwVGo2mxrpu88BipJtS\n5ZI2CQmLwLqT6wRBX0TwFRqUEHeR0WPLpk1lMhlDhrlz8nh4qwRfAAsLc8Y+t7ThAw2km5E53Twa\nc6QFAT9ZcTPZq9oo2cjYmCc/Xcn5Q+FkJ+bRd4g/Ab1qJg01m0xG1Tp1fgF26EZvQL2vB8aYIyFx\nJ3ATyxa0zvKlxhq9cBLbU7aSfNgKeaE1BCfj211HTsp2FNYa3DMdMT9R/Rm4I13IIIoMonAmmPTg\n3UwZV3uRlUq1rZCS1b693eQ3JrAtazXGl4KRZDq0A6KZ9/qMFrxKQWgcEXyFBsllpTUba2sT6iSX\nyxk8qXXqG/sMmEbYifeZOO7BFomdJudwJ2g7lrEWGDurWbBiApaW+q/clZWRwdGvjlOSYIqJi4oB\nK3vi3z2g1mNlMhlz3pxP6ctKlMoi7Oyrj/x3fLINCalaYZVC60Tyva7gJgvByPcaM341staqZFX5\nj3PiTvhtbEu7AKChFOtBRbUmatk6OPDUl8u5m3YHmUyGk1Mv4m9Go3Zzx8bWvsF7CUJziXeW0CCt\nrvoOMpIkoZMs26g3wsPMLa2x7zedE8dOUSorJqfYDOvgV1g5sner3leSJLb9bh8+V1ZiVx4ww6J2\n4vCzI3YOjnWeZ2pmXuuz5mFLh7H91Fa84+chQ0aBIhWvBTDj1b80qV8DJw9HVXKMxL2R6ErkWPdW\nMffV+tcsu3fyJuL4RXa/coHs+CIU8uuYmJpgN1jN1N+Nw9G17l2SBKE5xMYK/8/eeQdGlZ13+7lT\n1XtvSEIUCQSIIoG6QHRYOsuyu+x6d23HjuPETs/32bFT7NhJHNtfYsdre3uDZVl6BzWQ6FUFCSGE\nKuplJI2m3fv9MYvEIKE6QgLm+W/u3HvOmZHmvve85ffaGJSK8ntkntzJrDlO6LpNFBbo2LD167i5\nT4wG7hOJIqO2j9t5rHnQVAJ665FDmgVy92Zh1JtYuC4BN/fHG8ORcj3vPHe+E4KzGIieLu5yGhEj\ndqvu8I1//d6IxmxubODsp2cxtgkExLoTt8w62eCDYTDo+eClQxjuuOJLNE6YJS0lJO4nf8KOX730\nRNZh49nC1ljBxqgICZ3EK2/8FdcuX8PRRc3r34iacL1kbZjp0ofgWNDIx39zmaCy9ciQs2fXUeJ+\n6M+MhX13wpIkcfT3B7l/2oSok+E0p4t1f/MCdvYD90UG0Ot0yEU1OjooYg8zeREFalqP3GWfzx7W\n/fnwFbI8vLxZ+2dWjIcPkdLCAlzuLKCWKz2GF0BAwHjDl85OzZi47W08vzzeLNuw8RAymYy5C+Yy\nc9YMm+Gd4OS9c4XQsq0oUCFDTnDtaq68X9bvuZk7j6P/XTxBxRsJKV+P696t7PvZ/iHNE5MYT0Pk\nce5ymmhe6mnW7iaF0bLPk5bmRqt9prHGNzCQLtdyJMS+b9rrUChsXZJsWBeb8bVh4xlDX9u3NMtQ\n03+dVG1uNw5irwCIHAXtl+0ZSjRKoVCw7MexaANu9zRkeIBTSwS1lWMjfDIWeHj54LiyGgc8qeJ8\nz3Ed7bimaPoV6LBhYzTY3M42bDxjqEO64eojxyb1n50uU/Q1sjIlQ/ZuhEwJJ+Fbs2n8YSMOklfP\n8bZJl4mIfDLKWdZi099sJS86k/xjF8mvycPLJwCfBWpW7dg83kuz8QxiM742bDxjpHw9nr2lH+Jf\n8AJyVNSEHSL1rf6bBQiBbVyXfYBaNCfPTSIZzyRjv+c+jvjVaey69hntxyJw6YigMfgMs77ljUo1\nfuIoI0EQBOJXpRG/Km3Qc2/mXqb4aAWSBBHpAcSkxA14fmenWR7VFje28QBbtrMNG1akyKglN0ei\nK+MOU2KmETbdOhKSA/FwtvODTGuj0cjFk9nou/UsXJnar9v0WvZFiv+vAx6aaAB0dFAy6//xF+/+\nNTLZ8CNStVWV1NytYOaCuSOSrcw7kMPtvY0YW+XYT9ey8vvpuHlaN0tbkiQuZZyl4XYLwbP9mRk3\nb9g5DBeO5XL3X117vrdWh2IC/qaGhHV967i7tV188U9foj/vD4ByQS2bfrQee/uhlepVl5dz5cA1\nECTiNsbhExAw+EU2Jgy2bGcbNp4At4tLePuHR1Ef30ygdh3n7fO5tOZTtvzDky9TUSgULFqxeMBz\nSg5X4aHpbSqgxgmX5qghxXv7wz8oGP+g4MFP7IeCC1e59zMP/DvNBkwqk9jX9jGv/bf1lM4kSeKD\nv3sPl5OrcBL9KVaWcWvjTrb83fBaKZbsr8NXk9jz2q1rGmUHCkhY1/fcg788iNfR7T3drsTjJg65\n7Gbz/9ky6DzXsi5x45+78W/aiITE4UMnWPSvLUybO2NY67UxMbElXNmYcNTXNVJbUzfeyxgW169c\n4Xz2LpwzNhKkTUJAwFMbjbB3ETfPXx7v5fWLqO/78xcMciSxn4zfftBqOzl75CRlRbdGvZbi4/fw\n7IzuXQcCwuWpNNRXj3rsB1zOzO0xvACuhnCM+2dQXlwyrHFETV+lLGN7/7fSznw7izaTMuR05A/N\nHZ//aSX+TeY6ZwGBwPvLuPrJ8NZqY+JiM742JgwdHZ188Idfc+Xie+Rf+5AP/vhLmpuax3tZQ6Lk\n1gW6G9V4t1uKQrgawqm8Zj0DMhgOqgpu9dN+sT8CEh3plN/veS0iYjenFYVy8LKaSyfO8cmWDFr/\nIYHzb4h8+HcfYDQOL1b8MIK8725bkhuQy63nnGsoae4xvA/w1M6i9FrxY67oH8cZOkz0flYREceZ\n/TdjkDuZ+hxTOA/t4cbQ0PezG+ptzspnBdtf0saE4fC+nSxZ5oZcbn4mnDlL4vjh3Wx79RvjvLKh\noCcy1p6Tjhfx7IztOdour8YxposKl4IBr+7SD9yycCg83IFpsBaIAEkblnCs+RBFu5oxtSpo4R6y\nXBNvb/+IwCQnVn7zhX5jv0ajketv3yek2iyi4dU9E/2xMDJnHSN9++oRrX3mmmlcOHEBnxbzdydi\nQoi7g4fnohGN1x9BcwK4pbqDm35yz7E6l3MsTYgZ1jhr/nwNu9s+Q3/eDyQBxbwaNn7/hX7PnbLO\nm/L8Qjy6zAlvLQ63mLx2aHFsdbgWHirPlpBQT7Z1XHpWsBlfG0NCp9MhCAIq1SB99UaBILQjl3s9\n9FpAJrSP2XzWxZXQKQKOmz6l9TNn3PSRtMvuId+8n+/uWPnYpJ4io5aqOuvlPD4wwFDJrUFkLgVB\nwD3IlcD22bgYJgEgGSRuFn2CWLSEw+xnzbfMalP3aytQKlV4evlRW30X+9LpFmOpcKS9pO8ub6hM\niY5C++PL5O/eg7FVhlOUni3f3QBAwblrXH7nDroKFapAPTFfC2NW4txhzxEdN4/ijTtp2K/Bq2s2\ndS7n8Nxeh1/QwJnKj6K2s+fln2yns6MdSZJwcn58dnTcqkTUzhcpPbEHJJi6NIA5yUlDmif1zxZx\nuP4j3PNTEAUDbTE5bPrOymGt1cbExWZ8bQxIR0cn+3Z/gJ26DUkCg9GTzS+9jnIIrsnhIop9x5R4\nOpSFVq/fyu5Pfs+sdXruTf8Jxbn2pGxYxJrtgxveLn0IU7scydxzjKabOhTuRhJfTsLT27vf6wYj\nUmFPkXFoO6SK7GbcdL1ZugICTvgDAk150LihjgM/OoniahSSUgeLTrD271eg978Ktb3lSyImVL4j\nN74As5LmMStpnsWxzo52zv1LFSHVXyUo1cHFmkNM+qwFV1f3fkYZmM1/+yLlG25TenU/SxNihm14\nH8bRyWVI581JWsCcpAXDHj8gJJg33t1GweUrKJQKps9+xaYu9wxhKzWyMSCfffh7klPVPe5Hvc7I\npUtKNm592SrjGwwGTh45jF7fSk11I6FhEjHz/AAoLWlGkM0iISXVKnM9Ce7X1qPX6QkJDRrS+Q8M\n8JG/v4z/rnXY446ExL2IXWx9ewmu7h6DjvGg1ChS0VveM9QGD7v/cS+u+y01mEs5RghJNEYfRemn\nw+vEtp42fyaMaF/Zg6OHA82/D8NTOwMDWqqjd/HS/6zDyXloBmmonPzsIMafrbRQ0BIxwfcOsHxH\n/65eGzYmCrZSo+ec+7V1VNyrIHp2NPb2w5PJE2hFJutNUlGpFZiMTVZb20fv/oa0JS7Y2SkxGr3Y\n83kZHR0eKBQywicnMXve8OJx442f//Baz0Uq7GmiEuWxcOwx7+QEBEJKt5Dz2d4et29/6PU6crN/\nhdyxglaFkSK7YNZv3jasGt2oNWFcz7qCV5vZjWugGy3NmAQdXgkC9w87WPTXlaOg65aaDb9fxa2Y\nG9zO+RJ7TyU7Nm4eUW3vYNg529GC1sL4GunGyfnpEvCwYeNRbMZ3AmIwGDi09wtMpmYkUcHUqFhm\nzRl+b1ZJktj96Yc4OdUTPMmJg3syCAyOIz556E3dpX7/Razzb3P9yjWiZymxszPfWBUKOavXhlBe\nHsiS5U+XNOFoaK5uxqnN8u8rQ4ahdWAXY27Or1m1tgul0tyFR6Pp4vD+L1mz3ly7q+3QsPNfDtOV\nb4/cSSR0jQvJmy1rf6MWzMbwT5co2LubxpI2NMY6AvymoErIZvlbL/B+7l54RKJZ7mrO9J0+ZxbT\n58wazUcflIXLUnj3s51Myn+15yGgOvJLXl+9aZArbdiY2NiM7wTk80/eISFJhVptduHduJaBSqVm\nelTksMbJO3OWadM78fE1u3GTUp3IzjyHVhs35B2wh9dUamsq8A8wuy/L7rQSFDJ7WOt4HNVVVcye\n42RxzNFJTVdnm1XGf1pYNXcGOdFncL8Z3nOsXVmBfUrngFnSSqcylMpexSNnZzu6tbU9r/P+7wkm\nH3sNj68qCutvlXLR7SwL0hMsxpmdPJ/ZyfP7nSNivTvVpQV4dJmFHeo8zzJ3S3i/544FCqWSDf++\nlMw/fI6uSo1dkJ71b6Y9ddKVNmw8is34TjDa2zQ4ObWhVvfeVGfN8eZszrlhG9+G+nuEL7SM+c2M\nduP61WssjF84pDGWrVxNdkYGZTm3ARnBk+YRu8g65R+LEhPJPPk2ixJ6P+utwkYiZ4ysXOVpRaFQ\n8OLPvdn/fz5AuDGHbt97eG+vZcVr8RbnPRzTBdip6Cv2wFe7wy5NJ8qLgcgeKuV31UVQnnGDuWkm\nmpvqcHf3HrSmN3FDKvn+V7h96ksEhUTCuijCpk8d2QcFrmZe5OaHVehrFdiF6Vn4zSgiZg38f+3l\n58vm/zu05gadHe3kfJ6JoUNiRvo0wiOnD36RDRvjgM34TjD0ej1KVV93oyAMPy9OoXBEr2tFpe79\nM1dVdhA9Z9KwxklOSwMGF5sfLu4ebvj4xpKVcYGQSSpqaww4OU0jYtoUq8810ZmfNpW5ZyLIuFNK\np+iNpEqmpLL3fQdVBfhqLQywvX0wba1tuLqZj1VXtePta9aSFmQCkrxv9nE7BVzIyyIw0Ej5HTly\nRSJzFwxs2GYunMvMhZalPZIk0d3dhZ2dw5AzcOtra7j+rx0ENn7lMq6FzPpdhHwcbpWdbH1tLfu+\nm0Nw6SbsUJK36zLVf5FB0ibr/O82NtTSrdUSGBxmyzq2MWqeSuN7/ep1amuqSUhKwtnl2eoS4uXt\nSWOD5W6kpkaDr//MYY+VtnQ5n334a9KX+qJSK2hs6KClxQP/QP/BLx4BRqOR44cOotM3I0lqUpes\nxMNz4GzdhJRU9Pp4Ku5VM3ueHw4O1k/aeVqQyWQEhAX3yVJ+nGLV2o1bOHboAJr2SkDA1386KYvN\nMV17RwfE+EJM+ww9yUq1TrnMXHmPZavM+svRs+Hq5RxqqmcTEDj0B55Lx/O4+UEtUrUbsuBWZr8R\nTEzq4KU0lw5cJKDRMoHMv3Q1F05kk7h66ZDnfxxn388jtLRXp9mnYx63d+4hfr0Jubw/L8HQ0HVr\n2fWD3Ui5U5HrHdDHfMaqHybjGxQ46jXbeH55qoyvXq/n4/d+Q/QsFTNmOHL80G8JDIlnYULi4Bc/\nRSxbtZ1Txz5HIddgEuV4eE5j+eqhFeY/jIODPdte/S4ZJ45iNHTi6RXF1peHP85Q+eT935GUYo+9\nvQpJEjnw5e/ZtO07ODkN3MFFpVIRMSVszNY1HtRW13A2+yiC0A04kpq+Bk+v4XfouWWnwUFVQZCv\n0MftLAgCK9Y8vtwm4Z9WUer8JR35KmSOJpSzCklbanbxGwwmbl5sxMvPjvzCjCEb3+aGem7+RxdB\nDV/tllvh6s8PMXluKy4ubgNeq7CTIWK0yFzWCx3YOzsMae7B0Nf340Kvc6Nb2znkmtz+OPq/h/E+\n+TLyB7fLi7M5+YvPePkXW0c8pg0bT5XxPXboIIvTXVGrzT+yhOQAMk/nMT9uIQrFU/VRBsTXz4ft\nr/2pVcZycLBn9boNVhlrIO6UlhEaasTe3qyAJQgCi5f4knXq+BOZfyKh1XZz4sgHLFsZBKiQJIl9\nX/yB1976q2HvwKZ3O3OLEKrq+rqdB0OpUrPhL3tdykX5njQ0HKSxXOT0P0/GteRbaB3u0hh1irh4\n45B+QxcOnSOgwdLgB9Qu58Lhw6RvWzPgtQkbU/h07z4m3TWvSUKicc4R1iW+MuTP9CiFl65RfLwc\n5BI61yZMGHuNJCCENeAwyh66HUUqHB65VWpLnl8PjQ3r8FRZLKOxFbXaUt7QP0BOTdX9IYsa2Bgb\n6mpq8fWzvCEpVQqMQ1RaGioGg4EjB/ZiNDQhoWJ2TCIRU0eeADQWZJ0+SXKab89rQRCIT/AgN+cs\nSanJA1zZPw8MsEwpA6l+xOuaPiOJPUe/pOOzSQSVmB/unLv8cb8Uw6mPj7D8tbWDjuHgbk87najp\nNWg62nDzcBrgKjOOTi6s+I+55L3/Ofr7KtQh3Wz5k9Uj6h0M5v6/937ujmeHWSSkzT2Dkpn/jV/x\nKuwMPtSHnST+T6eNOj4rdzP0OaZwH3kTCRs24CkzvgL2iKLB4sfaWG8iLmFkMnxjzcVz57h39wYg\n4uo+ifTlK57ZRI25sfPZu+sMqUt6XYh3y1oIDbeum3vnR38kKUWNWm2+2V84tx+VaishoSFWnWc0\nGPQ6VCrLHa6jo5J79zrGZL4i49B2xIIg4DP/Ddp/bKmXrcSelqKhddpZtCqFd3fvJDR/BwICEhL1\nMftZs2Rou9eg8FC2/Dh0SOcOxu29Tfh39NasB7akoZ7TzOy/1tLacIUVSautksgV8+I0zl/NxL8h\nFYBmh0Imrx/YxW7DxmA8VcZ38bLV7P70t6Qu8cbBQUVRQQPObpHY2U28mr8LeXkYdJdITDarFt2v\nvct//eznLF25mujZw0+emujY2amZGrmYjJNZ+AfKaGo0Yu84hUVJwxcHeRyNDU14eGh6DC9A7EI/\ncs9mERL6qtXmGS1x8cnk5bxH3KLexLbcM/dZs9G6MUKLpgxDdEm7+fojBZXCQ2qvEhIq76HpMiuV\nKrb81yoy392NrlqFOkjHi19fN6qEppFibOmn41KLnGmzovs5e+RMmzsDh9+UcXnvF0h6gajFwUQv\ntH72v43ni6fK+Lq4uvDy1/6CzFOn0Gk1RM5YzZRR1ByOJXdLryCXN1JZUU9TYwdOznasWRdOa0sW\n7/7uOC+++q1nLrN37oIFzJk3j+rKWuISPIctZTkYnR1dODr1vckLjE7Qvz9qa+5z8dwZHBycSV6c\n1m83p9ycHOrv3wVUJC9ejoen+UHLx9cb/6BETp/MRRI7kSQnZsWswNHROolFD9Olf7DjrxzwvAco\nVWqCXmuj7ae3cNVOR8RERcRuNrw8dHe4u5cnG/56/BWm7Kdqke5KPcpXIiYcpuvGZK7giHCC/+rJ\niYvYePZ5qowvgFqtZvmqVeO9jAGRJImS4lu8vGMO9g4qDh+4yfJVZoUgFxd7AoNEjh78ko1btz/x\ntTU2NJKdcQwBPc4u/ixetmzEMbf+kMlkBE8amxKMkNAgsjMkpj2km1BV1Y5/kHX1n3Ozs2hqvMCC\nWD+6Otv46J1fsH7LNyzKpvbu/pTQ0FYWxjsjinoO7/8dK9a8hZe3uSVit1aLQm4ifJobVVU6KivL\nmBUzOi/ArTIRMVg7rK5F/ZH+/VjaF5SQdegsrTJHXty0bNBM5YnIsu+nsb/tI5RXZiDK9bCwhC3f\n3UBbaxNKlRoHh8Hj0GNJ0dXrlF++R9DMAGbGzXtmQ042RsZTZ3yfBs7nnmPt+kgcHNVotXo8PC1L\nbeRyGUhPvk9tc1MLRw78gfRlgQiCkra2SnZ9/C7bXn1zRONpNB1knDiCaOrCydnX6ob8UQRBIClt\nE6dP7MfOrgu9Xo6bx1RWrBm54lZxURE3ruYgk+kRRSeWr97IvfKLpC0xu4wdndSsXBNIxolDbNpm\ndm1rNB1IYiV+/uayHZlMxpKlgZw4sg+VWoVB30pVRSnpy6fi5+9KUDDcLq6kML+QqJlRj13LQEzv\nduaWnYaqOokqugDzrvfB8eESlzYDlyRz5yOXQTofTVQ8fXz42m9fprb6HhISp39XxG+XfIGkU9Cl\nvs+U5b5s/j8vjkslxO6f7cS0Zy4e+vUUK+9yc+VHvPQjW0tAG73YjO8YUF9fw4IF5huanZ2Sjo6+\nrjBJsq5LdijkZBxnydKAnhuAq6s9rq73qbvfgK/f8JLWurq07P7kf1i6wg+FQk5bWyWffvB7Xn79\nm2Ox9B5Cw0IJDfsuOp0OpVI5KmNfW11L4c2DJKX4Aw6IosjOj/6XwEc27oIgIAhdPa+bGprx9Opb\nU3rvbgE73piDTOYOLODY4QKcne1wdFIzZZonF87dHLHxBbMBpnvGiK9/UDMMwzcAGk0rSoUKO3vr\nu85Hi3/gJL74+ed4HtiOL+b8j25tO2V7T3DM7xCrv7nuia6nrOgWhn0z8NKb1cZcDWG0HxHIX3GZ\n6EX9a2jbeP4Yu23Kc8zsmPnk3zCXhAiCgKurPdevVAFgNJo4daKaRYnLnvi6JPR9jJWPr5qG+uGX\nr2SePM6Spb4ovtIXdnW1x9+/i3vl96yy1muXL7Pz49/w+Se/YufH79DW2kZbaztGo7nEQ61Wj3qX\nfT4vk4Xxfj2vZTIZ06NUlJZa7iRFUUSUeo1OSGgQlfcsS00unLvL0hXhFmtavHQ6Fy+UA9Ch6cbB\n0XVU6x0pBoOerPvXUIgl/Yp1DERTXT3vf/dTdq+9wSfrz/D5T3ZiMlk/xj5a2i+pUNCbeGmHCwJy\n2m48+Z1m6ZUSvLSW3Z5cDKFUF9Q+5gobzyO2ne8glNwqwWQSmR419HrBSaGTKMqP4FxeCRERToii\nHffr3Ok+p0Ams+eFjd8eF1lMb+9Q6u4X4OvXGwsrLtKy9ZVpwx5Lr++w0IwGCAx2ovJeJZNCe7Wj\nRVFk3xc70XdXgSAhSe6s2/zqgMlYpSWl1N3PJiXVvBu/VVTLu7/7J6ZO80XTIeDhFcXSFSNrvnDp\nwgUqygsAgfs1jQiCn8X79vYKfP2jOJtdQVy8H+1tOvLOtrJl+5/0nCOTyYies5TTJ44zLdKehnod\nN2/oiZ71SJ2zUo5okjDojWRlNLPjrddGtObRcOPaAbRdJ4kKl6g4r6fq9kwih/HdHfn5afxztvck\nNek/7+S4zyFWvjWxGtkLyv61z+XOT74ed1psFGccr9DVqUFLMwIyumQNrJxp3SxsG083NuP7GJqb\nmtn/xbtMmaZAIZfx/u8PsmLtK/j5+w1+MbBizTraWtu5XVzC4mXTcHEd/7hafHIie3dXUl5ejZen\ngnv3TMyYlT6imFhwyFSqKi8SFNwr23f9ajNrNsyzOG/Xxx8yd74BFxdzDNVoNLHvi4/Z9kr/cebm\nphb27n6fV14zPxAYDCYqypvZ9kpvUlVpyR0KbhYwI3p4LtjsjNMo5AXEJ5izkgtuyjhysJCVa3pd\nwYX5Wl59cxttre3k5mTj6ubG176Z0GeXPStmDlHRMykqKCZ8qh0Bwd3k5R5gydJesZdLF6rQar25\nft2F7a9vQzlIB6GhcMtOY6H7PBAdLU2oOc6yFT4AREyF/BvF3C2bQVh46KDXi6JId4FTj+EFUOFI\n4/URLX1M8UuV01XYgAPmB7ZWKtA7NBC9aXDNaWszaUoEh+b+J+45LxCKuQ7ZIGopPr2XWQuf/Hps\nTExsxvcxnDiyh+WrfHp2u5PCIOvUPl58ZegxTVc3F+bHTZwYjyAIbNiyHU27hoaGJhYlh4zYdTsv\ndgF7d5dSU11FQKAdd25rmRSeYFE+VVpSQnNTPi4uvf1/FQo5AnX9jllbXcPpEx/g59fr1sy/Uc38\n2FCL8yKmenD+3PVhG9/a6hukpHn1vJ4R7UPp7U6yMhoQ0GESnUhN34ogCLi5u7LqhYEVnxQKBaUl\nNzAa7hAS4kzF3Uo+/bCF4EluGAwqQiYtZM2G1GGt8XE8SKqaGlxJRZ3Uk2z1KBpNB19kXKBRMNFd\nI/K1lV4W78+c5cOF8xeHZHwFQUBwNvLon0vuPPHczsvfWsNJuyPcPlCPtlWHIkjDuu+tYkr0yGPs\no8FDNRlPej1KSuxpy7NHFMUxTUociIa6Wu4WlBC1YA5OzuMTArHRi834PgaZrANBsHSNyoThZ5VO\nRJxdnK3i9l6/+SXaWtupqqxiw4sRfWphr13OxtW1b30s9C/IkHfmJEuWBnG7uI6igloiZ/jj7uFA\nY2OHRca4ySQikw0/YU2grwvSw9OFLdv/fEjXS5JE3pmzNDXU4OkdgCCTERLShLd3EOfz7hIa7k7Z\nnUYcneJZseYFq2a2Ppzt/DjDeyn/Nj++Vk5rYhqSKKK6+EdW3YeAwN5zNZpu2h3chzSnIAgEB4//\nyQAAIABJREFUrVCi+d9qnI3mLLR6j3PM3TDZOh/KigiCwNJXV7F0omitiP387U3jl+m875df0LbP\nH7fWGAp8zxP+hoyUrUvGbT02bAlXj0WU+hoNSZp4SlrjjaubCzOio/oVoRAEPa5u9lTca+451trc\nhZ39Y3S4BXNW+JRpvnR3GzhxrJDC/PtknLyHQd9rOLNO15KUOvwWdKLkgiT1xgaNRhMw9B3Ah+/8\nFmenfBbE6XF2yuf0sT14ejly6MBN5sdNIi19OstWRpF1+vCYlJRM73YmpH1Gj+Ht0LRz6H/3kvfD\n42S9c4E/XC6hLW0ZglKJTK3G8I1v8f6eavQ6Y8/n3XOkHa/IJEt1rAFY/uZq/P6xmNaVX9K+4QsW\n/MKRyAWzBr3ueScoxQWNslf4xIQR59jOcdn15l+4gv7TGPxbE7HHjaC65ZT9Adpamwe/2MaYYdv5\nPobIqEVcvniaeQvMAvk3rzcQGrFwnFf1lCG4EDPPiWtXKrldXIcgCJSXdfP3P/73fk9XKt3R6TSo\n1Upi5pmVm06dqOdvf/htjh3ch8nUhiSpSVv6Km7uw3ebrVjzIvv3vI+Xtw7JBM0t9mzaNrQa52uX\nrxI1A7x9zMlq3j5OLF0xid07L7Nl27yeTlvBIR7ExgVSX9eIj6/XQEOOCq22k0+/tZ+QgpcJQk7H\n561UJf4npPeeIwgCFWHpnDoJgtCIKLqyPOWvuSeKlHxlF4YSP45fkwoDNyx6qmhrb+Z8fh4RgRGE\nTxp+suFQSFyXxqmOY1Qdv4jYLcN5Tjfrv/dkS54eUH6hCje9ZfjLvyGVq5nHSV2/clhj6fU6Dv/m\nAB2FKhQuJqI3hzFjofUkZJ8nBOnhrcAY0qS7M/hJE4x75fe4eukskiQRPTuOiKkR472kJ8LlCxe4\nc/sSgmAAXFm9/sURSWF2dWnZ9fHbhIdLODopKMzvIiF1M+GT+5fpMxgMfPbB2wQF6/D0suP0iSqc\nXZxxcVEiSo7MX7iMyRGjd3k2NjQhl8tx9xi6qtOBvXtYsKCrz/Gf/NNR/uGHKwCzW/pcbhktzV0Y\nje7MXZDG/Li4Ya2tyGgWvhjMKB577wDir1aioNfjUGGXy5mDJhRhvYXKkfuy+X7ajmGt4Vnm6OWT\n7JfVo1s4D6HsLtH51fzZ0q+NWxz2SZCz/ySt/xiH3UNengb7ayS878CkKcO7p338Dx/jeWRbT0/m\nOo9zxP3Sadxi6xOd+DmP/7+S/+hHP/rRk1iE1tTyJKaxKm5ubkyPiiZyxiwLacFnmYKb+TQ1ZDM/\n1p1JoQ4EBcPh/eeYHRM77LGUSiVz5i3EJPliNAaQvmL9gN+jXC5n9txYBJk/NTVK5IpGli4PYlKo\nE6FharJOnyNyRuyoRfwdHB2GrTstFxSUlV7Fy7s39lxyqwmFIgIPTx0OjiqyM28zPdKP2THBTIt0\noaHuDrU1JoKCg4c8T6NopKndFS/jwCGOglO3UN+wbNChNDpQ7PAhzJ+BZDDgevg0X5uWhpvz0GK8\nzxpZu06R9e/XufpJCbeLruM3w4u3W2+gT0tAUCgQvL2o9XPD+Wo+4YETL45tLQInh3A2/zPsq6ai\nQEWn7D7imnMkbEwZ/OKHaG9rJv/fdbjper8rJ20QlYocIpMirb3sZ4Jgv8eHn57dxz0bI6Kk6BJR\nMz17XsvlMjw9tTQ2NI14zPDJYcyOie7ZXYjiwO3rQsNC6ehoJiHR3+J4QpIPOZlZI17HcLhbVs6Z\nrDNotd0A2Nnbcz7vHjeuVWEyiVy/Wkl2VhVvfuvr3Lwhp6igAV23ES/v3h3r5CnuVJSPTV1O6CJ/\nWlWW3qTGkBy+veNNNhTdJDGviJ8mvM6kgOezGcCFY2do+M/JBFzfRNDtDbh+uZVPfrCLzlnTLc6T\ne3pQpmsdp1U+GRQKBa/96mUc/yEb7at78ftpEVt/8NKwxzEY9MgNfR8KJYNNMnMk2GK+NizoLwYh\nlwuYTEPr9zoQVRWV5GTsQybXIJoUePk+XixDJgiIosTDm1zRJCITxrZ1nclk4pP33yY4pBv/ACcO\nfJFLaEQSVRV3eO3NBdyvbeNMdilTp/kyd56C5qYWXtrxdS6dv0Rn565+RhwbkYfZ8bFUvbaP6n1l\n2NWH0jn5JjHf9sfB3ZM181Zyq0xE1f38JgiWZzTiru/t1CRDjn3JVJQFxUgpvbF4sbsbb/rLyH+2\nUCpVpG1ZMaoxPL38EGNOI51d2FP73WpfQkSa/yBX2ugPm/F9xtBoOigrLSNiasSIWtiFhs+k7M45\nwieb46GSJFFXpxiW9vPN69cpuJGDTKZFlByYOz+dKdOmcvr4Tpat9APMY1eU3+XShYvMj+0rPJCU\nms6R/b8hZXFv/PLg/hJ2vLlx2J9pOJw4eoT4BBWOTuYdbHKaPRmnzqBUegIq/Pxd8fM3x840Gj1t\nbWbJy9LiU6jVgkUdZ1eXHrXad8zWuvrb6+h4tY36+9WEhK5GoVRyCw23ykb/oPS0098zmkwJcU0m\n8u7eQwibhKmjg8ADmaxa+vUnv8CnlLX/uJRj//Ep2iIHFG4ioWuciUm2lSyNBJvxfYY4fmg/nZ3F\nhIbZc+zAUVzdZ7Jk+fDaL86dP5/sjHayMm4ABiTJlTXrh56w09LcSnHhMVIXB/DAyJ46/iUmcSOh\n4ZZ3xJBQN87lFvZrfF1cnQmLSOWTDz7FP8AJvd5Ecmow+3a/y+vf+P6YdYfRaZtwdLLcMYaGKamu\ndqamutGiZraiXCQpbRJ7d39KyuIAujq9OHIwH0cnNV1dBhSKUF5+fWz73jo5u1oIJjyoB340Ycto\nNHJm3ylaS3Q4hSpI2ZyOUjm2Oz6TyYRe3429vePgJ1uZKcsDKM4sxKPLnAhkRI99XDMvpe9gXtEl\nbhRexEflRPqyb6KwgvLY84Kntzfbf7ZtvJfxTGAzvs8I5XfLkclKWZRgdgH5B7hx5VIRtdUx+AcO\nzy2UnLYYWDyidZzNyWBRgqUEZ0KyL5cuXsfJqa8ykiiZd4nFRSXcLSslblF8Txby/Zp7bHtlrkUm\nqtHQQsHNQmbOGnl3n4Gxw2TSmds+fkXdfQPLV68kJ/MEt0tuo7aT6OywIz5lg1kFCgOCIMfRSc3q\nF2ZhMJi4V96Mj9+SUSeHWQNJkvj47z/C8+QWHHFGRxcfnP2Yr/36tTHL8t1z7gA5YiNdjnb4NXfx\n6uQkIkKmjslc/TEnORb9D85ScuALxE4ZLrOMbPqO+UFoTuR85jBxlOdsPJ/YjO8zwo2rl4iN87E4\nFjPPh8sXz7EmcMMTW4dckCGaRAvjZTSY8PD0oLqyHoPeiFJl/re7eL6OmPkb+PCd3xI+2ciMGS5k\nnXobd8+5pCxOR8LUxzg4OCro6uocs/Wnpq9kz67fsCTdD5VaQUV5K3JFKM7OTqxauwGTyUR3t87C\npe/lHUrd/aKehhVKpZzyMiPxySPPoB1Kj97+ypEeXPfw7rfg4hUcs9JQY36twgHP3LVcOnWG2KXJ\nfcYYLRfzz3FkiguEmRsJ1AB/2HucnwZPeaL9bGNXJBA7ujCnDRtjhs34WhGNpoMzmRkIMhkpi5cM\nu5xlNHh6+dLUWIinV6+Lr+5+B37+0we4yvokLU5n/xf/zeL03ljtmZwmXn79NUQxkSP792ASW5FE\nJdFzVnG39A6xCxW4ftV4YmF8AGeyr9DVlcDM2bHcvL6f6Nm98earVzRsf21en3mthaubC1tf/i6Z\nJ49jMHQxKTSetRvm9rwvl8v7xNITU1PYu7uGstJKXN3k1NbCvNhV/e4qH3R50nVXgyQik3uzfsvL\nFgphU4Mr+1z3KFV1EhUq+khN9ud2rrldjYvBcqfnKPlwqzoDlyEY+eFysr0E4i2Neu2sKZysvkhw\nhPVLUowGA5cvZKHRdTE5IIy29lYips3EyfX5LLGyMXGIH0BBzyayYSWK8gu4cfUA8Un+iKJITlYd\nCSnbhiRgbw1EUeTd3/0XS5Z5YGenpKtLT+bpNr72jb+w2m5DkiSuXrpMddU9IqZGETmj/xvpndI7\nXLlwCkHQIooOJCSvIjA4sN9z9+7+kIWLLNfXUK9Bb4hjXuxccrOzqLh3BZlMh8noyIL4FURMmWKV\nz2Nturq0tDa34h/o99jv/MDe3Uyf3oqzs/nBTK8zcv4cbNn++rDneyAR+Tit5wfktdymaHMtwc2p\nPcfqXS6RmKlg0gzr17e+s/MLcuOTLL+D69f4yZwZeAcGWHWutpYWfrpzD/UpaXTm5SF3dMRu1izs\nCgpYaq9iw+rlVp3Pho3hEK96/O/StvO1EtevZZK6+IFmsZz0ZUHkZB0nLPwbT2R+mUzGK298h9PH\njqLTt2Gn9ubVN161quH98J3fMjMaFsS6cOf2CXZ/donN2/oq2U+OmDxkJSqVygWdrrlHnhHg3t1O\n4lPCAIhPTiGe4YkBjBcODvaDKoF1d9bi/JDohUqtQBTvj2i+SIU9+GqpqqvgFv0b4Ft2Gtz9/Qj7\nQQnl/3UYl4o5aAIKCPi2hkkzhq+PPRRWJS7iWs4ZtIlJAIg6HVH3a/EOtP58u4+donH1WgwlJajD\nwlB/9WBmWLiQo5cvkVB7H58htgG1YeNJYjO+VkImdPGoSL8gaJ/oGtRqNStfGBv92PO5ecyJEfDx\nNd/gJ0/xQKdvpPxuOaFhoSMed/GyFXz87q9ISfPAydmOu2UtiFIwHp7PpsuwXzeTNPIHpAcGuKQf\nT/Wlk7lcy6jBxVlg8kYPtl6J5O7V24RET8fFfejSmsPFLzCA78fGcDA7kw5BIESpYOuO4Ys6DIUm\nQUAQBAyVlTinp1u8Z5o7j7zz51m3fuDWkDZsjAc242slRKlvTa0kDV8PeaLSUF/FgljLnVVklCdX\nL98clfG1s1Oz463vkX36NJ0drYROTmFR0rPbNcfLZyq1NWX4B5i/y7ZWLQ6OIVaf58yXmdz/eQiT\nuhMBKDtYgv5XN1n0arzV5+qP0PAwvhMeNubzeEsSJaKIzNkZY3MzCo+H5EvL7zJ18tivwYaNkWAz\nvlZiztw0Mk4dICHRF5MokpNVT8riZ6cezscvhNqaqz1GA6DgZgMzZ4/eJaxUKlmy/PmIzS1eupyM\nk8e5U3obSZJwcg5mzYYXhnTt/do6zp05DYJI5Iz5TIt8fEeeu4da8evuLRdz65zKnU+us2ii9Lu1\nEltXLePOR7uoXhSP5sQJXFatQu7sjKm5mZklxUS+aWsqYcPM6YxsbtQ1oAQWR0cSOWN8m0HYEq6s\nSGdnFzkZp5HLFSSlpWFnNz7yfjev36C0+BoSAjHzEgmzwtO/JEl8/N7bTJ1qICTUjVtFjTQ3+7Bx\n63YrrNjGYNwuLuHm1b0sSvRHEAQK8xuxs48hPtn88PNw8hVA5qaTTC60FPiomf8Fr51d9sTXPtaI\nokjumbM0NLYAIs0ihHm4k5qa9Ex3K+oPSZLYtfcANzu7AYlZTg5sWbfmiZZ4TUQ+33+Io/7ByPzN\nmgeK69f5k0AfZs8ZWy+bLeHqCeHo6MCKNePb+DQnMwPJdINFCebmCFcvf4lGs4RZc2aPalxBEHjl\na9/k5vWbXL5UyrSohaSmD7/F4t2yci7mHUUQuhAle2LmLWbq9LHpqfosce1yJkkpvZnCUTO9yDx9\npScZ7UHsFyqRKSeRn6zFVGhE/tVP3IQBp7ju8Vj6mCOTyUhMThrvZUwIPtt7gJNTo5C5mvNPjrW0\nwL6DbH3O497nWjXI5vaKDRlnz+ZUTuaYG9+BsBnfZ4yaqmukpPXWxcbM8yE7M2/Exrf0dinXL+eA\nYMDB3pfla9YSPTt6RGPpdDpyMj5j2YogwCxIkXlqLyr1S1w8dxKZ0IkoqomZn0rE1CenhvQ0IMj0\ngGVegUzQWbyOVNhTZDQn+a39t2V80f4R2kxvECQcUhvZ+JP+m1hMBHRaLR/vO0SlCE6IrJwdTdRj\nStlsPJ6bndoewwsgc3fnRoeWreO4pvFGkiS6+tn5dzG+3gCb8X3GEDD0PSboRzRWeVk5hTf2kpDk\nByjp0DTw+Sfv8eIrb4xovJzMLBKTLVW44pN82fXJ//DyazMRBHOGc3bGl7h7fB1Pr+ejh/JQkEQn\ni6YNACbR6bHnq+3t2f7uJnTd5t2u2u7JCb6MhP/6eCel6csRFOZbUtn58/ytgz0hYaHjuq6B6NRo\nUKpUqNQTp3tUfzHE573NhiAIhBgNlD10TNRqCVeOr/mzGd9njEdvyOZWgC4jGuvKhSzik3prJJ2c\n7bCzq0HTrsHZ5fGxjMchSRKPPoAW5NeQvnySRUwqIdmf3JzTrN2weUTrfsD1K1e4VZiLXK7HZHIg\nIXkNQSFBg184AVmxZhNffPZ7pkyX42CvJP9GB4lpg+9nJrrRBbhfWcXtgGBkit7bkT4ujhO52bwZ\nFjpu63ocdTW1vH30JJUurqj0euYIEm9u2zwh4qoz7NRkaDTInM2/T7G9nWiHifNwMF68uXIpvzt0\nhHueXigNBmZoO9m6fXz9ATbj+4yRmr6BY4c+IXyyDL1epKpKweaXRtYyTZCZAMvGAI6Ocjo6ukZk\nfJPTUvn8k1+SvqxX7er6lQbCN3tZnCeTCYimvk0YhkNtzX3K754i5aFeo8eOfMKrb/xVnyScsjvl\n6HU6pkVOnRA30P5wdnHm9W98n+KiYro6tbz8tVnPTDKRrrsb0d6eRz+NYYL+LX5/9CQVy8yi0Tog\nr70d70NHWbdm5fguDNi+8QX4cj/5WnNIItrBjhetEO+VJIlDx05wo6UduSQRHxJIUuKTKVuzBj5+\nvvzgzR20NTWhVKtxcHq81+hJYTO+zxj+Af7seOv7lN0pR61Ss2TFyOX83D0m0dhQipd3r150ba1A\n+sqR9ahVq9UsStpMVuYJZEInkuTAmg1vcOH8YZYu741nXrlUz7zY0ZVpXczLIjbOUtlo3gIXLp67\nSFx8HACadg1f7PwjYeESKpWcD/94gPQV2wkIsq4EojUZqLzoaSUkYjKBmWepe1g2tLSUhU+gTni4\ndHd1UeloeeOWubhQ0NrO2MjbDA+ZTMYrm9Zbfdxd+w5yIjQCYab5QfnO3bsYs86QlpJo9bnGEldP\nz/FeQg/PtfG9ef0GhflnkAndiKITyWlr8Ley9ux4IAgCkyNGf+NKWbKYA3saKSwox94e2trVJKWN\nrpl9f9KT9g4OZJ4+ilzWiSjaMXlqAkEh/WtBD52+uyaTUUSn601SOnrwC5at8OjZQYaFQ8apvby0\n49ujnNvGcBAEgW8tX8z7p45TLZPjLEFKgC9z5i0a76X1QaFSodIbeDRvvODuPYoKi4iMejaTxC5r\nOhG8ej1UUlgYudmZpI3jmp52nlvje7+2jtLiY6Sk+vMgJnrk0Ie89tZfPzPuvNEiCAIvbHoRvV5P\nt1aHi+vwXc1DwWyQ/9SqY0ZMm03GqY9YnN67UzyXW4ZfQO8NRBA0yGSWMpZyWYdV12FjaASGBPMP\nr7083ssYFIVCwTy1gqzmJhQe5l1U1+XLyBMT+fLKjWfW+Or7yeTqm9ppYzg8t8b3Qm4WC+Mt3ZLz\nF7hy6cIlYhfGjtOqJiYqlcqi5R1ATkYG9XW3kSQICIokPml4dZaF+QXcKSnA0dmd5LQ0FArr/ivq\ndd04OCg5cbQQpVKOTmckPnEyd+707nwlSdXnOom+x2zYeJjXtmzg7D//jPagYJAk1OHhqKdMobGm\neryXNmZMRuS6yYQgN+eAiJ2dTLOz/VZGw3NrfPsT9hIEkMQnIvj1VHP6+FHc3O+SMNW8E664d4Os\nDD0paUsszuvq0nL04JcgtSNJamIXLSF4UggHvtyNu1sNC+I80Gju8d7bv+CVN/7cqopgUdGRFBce\nY+mK8J5jjY2duHv0ZjtHzYzn0vnjzI8zx7BvFTURFDxnyHN0d+tobGhEEiF40mjd5M82JpOJ3Jyz\ntGo6WJySiKPLyDLwJwKCIDAtPIzi1MUWx72lZ7eo5+ub1/O/u/dSKsiRiyIzlXJe3Da6aoTnnedW\nXrK25j4X8j4ibmHv7vfo4Wp2vGlzO/fHrcJbFBddQ61ypKmphCVLLZOusjNb2Pryn1kce+/tX5K+\n3B2Fwvy0nHmqmriEbdy8tovYhb1ZyDqdgfyb7qxeZ91EkQt5eZTdziZyhjM11V1oNF5s2f6aRUZz\nRXkFVy7lIEki0yLnEjVzxqDj1t2v4+CeD2moL2d2TABKlYLqKgWr1u3A28dr0OvHkiKjFply0riu\n4VHampv52c493E9MRubkhF1uLjumhhO7YO54L23ElJfd5dcZObQlp4JMhlNOFn8SN4/IyOnjvTQL\njAYDH3yxj9smEaUksdDHk1XL0ge/8DE8MBcTtSpgojGQvORza3wBbly9RlHBWXOjdpMDSalrH9v0\n/Xnm+KEDqO3KmDbdk+5uA7s/u8YLG2fi4tLbtSknq5Et2/+i53VxYTFNTccID+8VyjCZRA7ub2Ph\nIjm+fpY7n3N5Eus3W1/1X6fTUXCjkMDgIHz9vAe/YAh8/O6vMRhqWLoiCrnc/KAmSRKZpzt5acef\nWGWOkTIRje/vP/uc8wkpFjds3aef4h3gh68ksTU+ltAJmNk8GLrubk5nZCGKIkvSUrBz6NvZbLz5\n7Uc7ubQoAdkDIZCaarZqWlm6JHVc1/W8YNN2fgyzYuYwK2bobsbnke5uHe3tt0icY96p2tkp2b5j\nHieOFrJ81UwADHojgswyhb+1rRVXF8uYkFwuw9PLjdLbtRbGV6PpxtFxbB561Go1cxfEWG28jo5O\nnFy0dGoUPYYXzDsBmazdavM8SzQg67NTMgQG0hoXR7tazX8fPcy/BQehUCrHaYUjQ21nx8qVE7cb\nlyRJFIlSr+EFCAjkUvZtlo7fsmx8xXNtfG0MTkNdI17elkIbMpmMpkbIyapCkkAUPdn4omV3o3kL\n5rHzoyyWLO2tibxV1MTMWcupu1/FudwrLIjzo7KijZJiGS+//nR021GplBj0wlfKYX3efeLreRrw\nkETKJMnCAItdXQhfJfE1JySRk5lDZNR0Dp3NQysJzPDxIi0tebyWPGEwmUxkZGRT2drGJHc3UtOS\nBwyLSZLE5/sPcVXTiVGSaNN0WOW/Unrk72dj9NiMr40BCQjy42y2yPSHKih0OgNTpseycq25D21/\nmcoKhYJ5sWvIOHUctbobvV6Jn38006OmMz1qOs1N8zifm0tI6Dx2vDl4nHWioFKpQPDD3VPP9auV\nzI4JBuBWUQNBwdbbYT9LbFqcwp19h2hOW4JgZ0dnbi4KH5+em7kANDc38ZPMXLqSkxEEgat1dVTt\n3surm60vGPG0IEkS//HH9ymOT0I+fSY5LS1c+uP7/PVbrz/WEO47dJTjoREIX4lJdO3bh1Kv73nQ\nEWtrmes9dKGJnNxzHLlTTosgw1c0sSlmFtHRT8/vdSLzXMd8bTweURTJO5NLR4cGBwd7aqvPE7vQ\nh/u1HdwqEtn+2rf6lB8BXLl0kTsllwEDMrk7a9ZvAcxG61l5cpYkiWMHD1B+t4CO9jbcPX1ZsHAx\ns2JG17bRGoxFzHfPwaOca2lFi0CoaOKttStw9Rhe0wu9Tsep05k0NrdwpkWDuL7XqLodOUSYoz1X\nky2zh+1ysvmPDaufCn3qgeju6iIjMxt7tR1JqUnI5fI+7/9hz37KJBkqRGLd3di4ZgUX8i7wO4Vd\nTw9aALG6mu/IRGIek6z2408+pyo5tee1pNfT/e67BEybggqI9XTnhZVD8zLVVlXz44vXMC3oLb10\nPHmCn23b+NT/TZ4UtoQrG4DZaGSdzqS1pQqFwpHFy1bi4GDf57yW5lb27HqbRfFuODmpyD1TR9jk\nZDQaDX4Bgcyc1f+Tb/6Nm9TXniZqpvnJ2qA3kp3Vzcuvj28S0vOEtY3v6YxsPnF0RQgwK79JksTk\nE8f4+zdGnhxXVFjE/is3aJbJ8DGZ2JaSwM68CxQlWLqZxatX+Y+4GNx9rJMoNx7cvFnAH65cpyMx\nGbq78crJ5q82rMHbt7e713+99zEFaUt6amilujq2NNfT1tHBiQV9Vb6WX8xj84YX+p3vnz/eRUWK\npe6US8Zp/vPVF4e99k++2Mvp2HjLcIFWy0t3iklfYYsaD4WBjK+tpuY54rMP/4iP920WLhKYPbud\nzz78NVpt3wbrp4/vZ9Uafzw8HVCpFaQuCeRu2XnSVyx7rOEFKC661GN4AZQqBc4uGtrbNGPyeWyM\nPdfq6nsML5gTy8qdXejUjPxvGhkVyd++8iI/276Fv3x1G4EhwYQ72CM+MqZ/Yz1u3mNbujXWe48v\nr96ga8lSZGo1MldXmlavYeepzJ73TSYTpQplj+EFEHx9udbYxPyZUVBYaDngzRvEDtAAfq6XB9L9\n+73jd3QwUzWy6KKdQgEGSx0rqaMDZ+fxb0rwLGCL+T4nVFVU4+3Tjoen+YlbqVKwZKkPWSePs2Kt\n5VO0IOtEECyf2GQybZ9+so8i9NNNVKkAg+HpFqIzGAyczz2Ps7MTs2JmPzPu86Gg6Oezyk0mqyuS\nvbB6BTUf7eS6ozM6Nzf87paxI2nhmH3X+48eJ6euiQ6ZjCCTkdcWJxMUEmz1eeoFSxezIAg0CJZZ\n8kI/DwByBMKnRLA4v5Csy5fQT5uOqqiQVEEcsMfx6uXpiEePc6m4CJME0+1UvDTCuPmKJank7PqS\njq86OEmShP+5XGL/5M0RjWfDEpvxfU6oKC8nOMTSoKrVSnT6vjsY0dQ3niOK6kHFRwKCplNZcZXg\nEFfA/GNtbFDj6eVBV5eWwpsFTAoLHXchiuFQcquYC3n7WBDrRmenkXd/d5JN276Bq9vTq9A0HJKm\nTKawqBBTZBQAYnc3Ufpu1PZ9wxWjQSaT8e0dL9He3Ex7SyuBixOsanhFUSQzI5vyllYMDY1ciJqJ\nbIk5Qa4c+N+jR/jnN1+1urH3kEzUPnLM8yElLJlMxgwkLnV3I/sqjircvUtcsNnb8NLvVhY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TCxqZEP33z1ucUmLp6/QP6FSwAkTnmJaTOm4+HtzboJ4Ww/fIg6X198GhvJDBnHuFDrNpZeo0YR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\nVwghhLCz/w+BfjfvUVnKbQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118c0a748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import BaggingClassifier\n",
+    "\n",
+    "tree = DecisionTreeClassifier()\n",
+    "bag = BaggingClassifier(tree, n_estimators=100, max_samples=0.8,\n",
+    "                        random_state=1)\n",
+    "\n",
+    "bag.fit(X, y)\n",
+    "visualize_classifier(bag, X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example, we have randomized the data by fitting each estimator with a random subset of 80% of the training points.\n",
+    "In practice, decision trees are more effectively randomized by injecting some stochasticity in how the splits are chosen: this way all the data contributes to the fit each time, but the results of the fit still have the desired randomness.\n",
+    "For example, when determining which feature to split on, the randomized tree might select from among the top several features.\n",
+    "You can read more technical details about these randomization strategies in the [Scikit-Learn documentation](http://scikit-learn.org/stable/modules/ensemble.html#forest) and references within.\n",
+    "\n",
+    "In Scikit-Learn, such an optimized ensemble of randomized decision trees is implemented in the ``RandomForestClassifier`` estimator, which takes care of all the randomization automatically.\n",
+    "All you need to do is select a number of estimators, and it will very quickly (in parallel, if desired) fit the ensemble of trees:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bZE4OqKh477kVMAE0MshZu5mg4EDsmihRaSgymYx5//4r2775EZPEFJSuLox5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\nP8ctGsamB3K3AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118cfeb38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "model = RandomForestClassifier(n_estimators=100, random_state=0)\n",
+    "visualize_classifier(model, X, y);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that by averaging over 100 randomly perturbed models, we end up with an overall model that is much closer to our intuition about how the parameter space should be split."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random Forest Regression\n",
+    "\n",
+    "In the previous section we considered random forests within the context of classification.\n",
+    "Random forests can also be made to work in the case of regression (that is, continuous rather than categorical variables). The estimator to use for this is the ``RandomForestRegressor``, and the syntax is very similar to what we saw earlier.\n",
+    "\n",
+    "Consider the following data, drawn from the combination of a fast and slow oscillation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Od48kVn2+ZclvCJsfeSKAeJ4yqGsI1XK7frklduULRZRMKHvdCy0gb79pRWC7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\nAZmIiEgCDMhEREQS+P8p5hEpezc9PwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10babb0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rng = np.random.RandomState(42)\n",
+    "x = 10 * rng.rand(200)\n",
+    "\n",
+    "def model(x, sigma=0.3):\n",
+    "    fast_oscillation = np.sin(5 * x)\n",
+    "    slow_oscillation = np.sin(0.5 * x)\n",
+    "    noise = sigma * rng.randn(len(x))\n",
+    "\n",
+    "    return slow_oscillation + fast_oscillation + noise\n",
+    "\n",
+    "y = model(x)\n",
+    "plt.errorbar(x, y, 0.3, fmt='o');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the random forest regressor, we can find the best fit curve as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lef4veKfTSXdHC3YpjtFqRpFlLPkMl21sxmIvaJEV1pCn5kk/8ryPyMQ4Nkcd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W0LsZgM9Id6AFBdbnSDg6V60GP3Xd6iza0OhULE4zEWGwxgMECFK3RNlb/QBx6OlH8xhlxymECd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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118cfe0f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "forest = RandomForestRegressor(200)\n",
+    "forest.fit(x[:, None], y)\n",
+    "\n",
+    "xfit = np.linspace(0, 10, 1000)\n",
+    "yfit = forest.predict(xfit[:, None])\n",
+    "ytrue = model(xfit, sigma=0)\n",
+    "\n",
+    "plt.errorbar(x, y, 0.3, fmt='o', alpha=0.5)\n",
+    "plt.plot(xfit, yfit, '-r');\n",
+    "plt.plot(xfit, ytrue, '-k', alpha=0.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here the true model is shown in the smooth gray curve, while the random forest model is shown by the jagged red curve.\n",
+    "As you can see, the non-parametric random forest model is flexible enough to fit the multi-period data, without us needing to specifying a multi-period model!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Random Forest for Classifying Digits\n",
+    "\n",
+    "Earlier we took a quick look at the hand-written digits data (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb)).\n",
+    "Let's use that again here to see how the random forest classifier can be used in this context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['target', 'data', 'target_names', 'DESCR', 'images'])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_digits\n",
+    "digits = load_digits()\n",
+    "digits.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To remind us what we're looking at, we'll visualize the first few data points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Hl946Ie3vrVjRTqXjaOSeNH36dC+r8R6f+IiIyFHY8RERkaOw4yMiIkdhx0dERI7Cjo+I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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118c22128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# set up the figure\n",
+    "fig = plt.figure(figsize=(6, 6))  # figure size in inches\n",
+    "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n",
+    "\n",
+    "# plot the digits: each image is 8x8 pixels\n",
+    "for i in range(64):\n",
+    "    ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n",
+    "    ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')\n",
+    "    \n",
+    "    # label the image with the target value\n",
+    "    ax.text(0, 7, str(digits.target[i]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can quickly classify the digits using a random forest as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.cross_validation import train_test_split\n",
+    "\n",
+    "Xtrain, Xtest, ytrain, ytest = train_test_split(digits.data, digits.target,\n",
+    "                                                random_state=0)\n",
+    "model = RandomForestClassifier(n_estimators=1000)\n",
+    "model.fit(Xtrain, ytrain)\n",
+    "ypred = model.predict(Xtest)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can take a look at the classification report for this classifier:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             precision    recall  f1-score   support\n",
+      "\n",
+      "          0       1.00      0.97      0.99        38\n",
+      "          1       1.00      0.98      0.99        44\n",
+      "          2       0.95      1.00      0.98        42\n",
+      "          3       0.98      0.96      0.97        46\n",
+      "          4       0.97      1.00      0.99        37\n",
+      "          5       0.98      0.96      0.97        49\n",
+      "          6       1.00      1.00      1.00        52\n",
+      "          7       1.00      0.96      0.98        50\n",
+      "          8       0.94      0.98      0.96        46\n",
+      "          9       0.96      0.98      0.97        46\n",
+      "\n",
+      "avg / total       0.98      0.98      0.98       450\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "print(metrics.classification_report(ypred, ytest))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And for good measure, plot the confusion matrix:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IC5iISJBHuidcRbJnXzoWLVuJ4uJiNPD3x2ydFi4uLjaby2zbf68nRoahY9d2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\nCfL/ADUiXKng20C6AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c1ccf60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "mat = confusion_matrix(ytest, ypred)\n",
+    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)\n",
+    "plt.xlabel('true label')\n",
+    "plt.ylabel('predicted label');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We find that a simple, untuned random forest results in a very accurate classification of the digits data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary of Random Forests\n",
+    "\n",
+    "This section contained a brief introduction to the concept of *ensemble estimators*, and in particular the random forest – an ensemble of randomized decision trees.\n",
+    "Random forests are a powerful method with several advantages:\n",
+    "\n",
+    "- Both training and prediction are very fast, because of the simplicity of the underlying decision trees. In addition, both tasks can be straightforwardly parallelized, because the individual trees are entirely independent entities.\n",
+    "- The multiple trees allow for a probabilistic classification: a majority vote among estimators gives an estimate of the probability (accessed in Scikit-Learn with the ``predict_proba()`` method).\n",
+    "- The nonparametric model is extremely flexible, and can thus perform well on tasks that are under-fit by other estimators.\n",
+    "\n",
+    "A primary disadvantage of random forests is that the results are not easily interpretable: that is, if you would like to draw conclusions about the *meaning* of the classification model, random forests may not be the best choice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.08-Random-Forests.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.09-Principal-Component-Analysis.ipynb b/notebooks_v1/05.09-Principal-Component-Analysis.ipynb
new file mode 100644
index 000000000..065b1f4a7
--- /dev/null
+++ b/notebooks_v1/05.09-Principal-Component-Analysis.ipynb
@@ -0,0 +1,1108 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) | [Contents](Index.ipynb) | [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.09-Principal-Component-Analysis.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  In Depth: Principal Component Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Up until now, we have been looking in depth at supervised learning estimators: those estimators that predict labels based on labeled training data.\n",
+    "Here we begin looking at several unsupervised estimators, which can highlight interesting aspects of the data without reference to any known labels.\n",
+    "\n",
+    "In this section, we explore what is perhaps one of the most broadly used of unsupervised algorithms, principal component analysis (PCA).\n",
+    "PCA is fundamentally a dimensionality reduction algorithm, but it can also be useful as a tool for visualization, for noise filtering, for feature extraction and engineering, and much more.\n",
+    "After a brief conceptual discussion of the PCA algorithm, we will see a couple examples of these further applications.\n",
+    "\n",
+    "We begin with the standard imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns; sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Introducing Principal Component Analysis\n",
+    "\n",
+    "Principal component analysis is a fast and flexible unsupervised method for dimensionality reduction in data, which we saw briefly in [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb).\n",
+    "Its behavior is easiest to visualize by looking at a two-dimensional dataset.\n",
+    "Consider the following 200 points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/jxHYwOAjqDHkRDMKOPT5Z0/PF/JueOGQ1K6DB0eqqCj8M9LQz06blidpjd55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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x101eb9f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rng = np.random.RandomState(1)\n",
+    "X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T\n",
+    "plt.scatter(X[:, 0], X[:, 1])\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "By eye, it is clear that there is a nearly linear relationship between the x and y variables.\n",
+    "This is reminiscent of the linear regression data we explored in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), but the problem setting here is slightly different: rather than attempting to *predict* the y values from the x values, the unsupervised learning problem attempts to learn about the *relationship* between the x and y values.\n",
+    "\n",
+    "In principal component analysis, this relationship is quantified by finding a list of the *principal axes* in the data, and using those axes to describe the dataset.\n",
+    "Using Scikit-Learn's ``PCA`` estimator, we can compute this as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PCA(copy=True, n_components=2, whiten=False)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA(n_components=2)\n",
+    "pca.fit(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The fit learns some quantities from the data, most importantly the \"components\" and \"explained variance\":"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.94446029  0.32862557]\n",
+      " [ 0.32862557 -0.94446029]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(pca.components_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.75871884  0.01838551]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(pca.explained_variance_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "To see what these numbers mean, let's visualize them as vectors over the input data, using the \"components\" to define the direction of the vector, and the \"explained variance\" to define the squared-length of the vector:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OR+h135AGgIceeghGoxEAsHjxYhw/flxRUGdlmYb9DLGeRoJ1pcxEqacTJ9qg1xdCrw+8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\nY1ATEREJjEFNREQkMAY1ERGRwP4/bV+C7ucCrxYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118c75cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def draw_vector(v0, v1, ax=None):\n",
+    "    ax = ax or plt.gca()\n",
+    "    arrowprops=dict(arrowstyle='->',\n",
+    "                    linewidth=2,\n",
+    "                    shrinkA=0, shrinkB=0)\n",
+    "    ax.annotate('', v1, v0, arrowprops=arrowprops)\n",
+    "\n",
+    "# plot data\n",
+    "plt.scatter(X[:, 0], X[:, 1], alpha=0.2)\n",
+    "for length, vector in zip(pca.explained_variance_, pca.components_):\n",
+    "    v = vector * 3 * np.sqrt(length)\n",
+    "    draw_vector(pca.mean_, pca.mean_ + v)\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "These vectors represent the *principal axes* of the data, and the length of the vector is an indication of how \"important\" that axis is in describing the distribution of the data—more precisely, it is a measure of the variance of the data when projected onto that axis.\n",
+    "The projection of each data point onto the principal axes are the \"principal components\" of the data.\n",
+    "\n",
+    "If we plot these principal components beside the original data, we see the plots shown here:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.09-PCA-rotation.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Principal-Components-Rotation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This transformation from data axes to principal axes is an *affine transformation*, which basically means it is composed of a translation, rotation, and uniform scaling.\n",
+    "\n",
+    "While this algorithm to find principal components may seem like just a mathematical curiosity, it turns out to have very far-reaching applications in the world of machine learning and data exploration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### PCA as dimensionality reduction\n",
+    "\n",
+    "Using PCA for dimensionality reduction involves zeroing out one or more of the smallest principal components, resulting in a lower-dimensional projection of the data that preserves the maximal data variance.\n",
+    "\n",
+    "Here is an example of using PCA as a dimensionality reduction transform:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "original shape:    (200, 2)\n",
+      "transformed shape: (200, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pca = PCA(n_components=1)\n",
+    "pca.fit(X)\n",
+    "X_pca = pca.transform(X)\n",
+    "print(\"original shape:   \", X.shape)\n",
+    "print(\"transformed shape:\", X_pca.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The transformed data has been reduced to a single dimension.\n",
+    "To understand the effect of this dimensionality reduction, we can perform the inverse transform of this reduced data and plot it along with the original data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6XS5oaZVMpDEF9datWzn33HMBOO2003jjjTd6n6utraWiooJgMAjA6aefzpYt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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11920e438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_new = pca.inverse_transform(X_pca)\n",
+    "plt.scatter(X[:, 0], X[:, 1], alpha=0.2)\n",
+    "plt.scatter(X_new[:, 0], X_new[:, 1], alpha=0.8)\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The light points are the original data, while the dark points are the projected version.\n",
+    "This makes clear what a PCA dimensionality reduction means: the information along the least important principal axis or axes is removed, leaving only the component(s) of the data with the highest variance.\n",
+    "The fraction of variance that is cut out (proportional to the spread of points about the line formed in this figure) is roughly a measure of how much \"information\" is discarded in this reduction of dimensionality.\n",
+    "\n",
+    "This reduced-dimension dataset is in some senses \"good enough\" to encode the most important relationships between the points: despite reducing the dimension of the data by 50%, the overall relationship between the data points are mostly preserved."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### PCA for visualization: Hand-written digits\n",
+    "\n",
+    "The usefulness of the dimensionality reduction may not be entirely apparent in only two dimensions, but becomes much more clear when looking at high-dimensional data.\n",
+    "To see this, let's take a quick look at the application of PCA to the digits data we saw in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb).\n",
+    "\n",
+    "We start by loading the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1797, 64)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_digits\n",
+    "digits = load_digits()\n",
+    "digits.data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Recall that the data consists of 8×8 pixel images, meaning that they are 64-dimensional.\n",
+    "To gain some intuition into the relationships between these points, we can use PCA to project them to a more manageable number of dimensions, say two:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1797, 64)\n",
+      "(1797, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pca = PCA(2)  # project from 64 to 2 dimensions\n",
+    "projected = pca.fit_transform(digits.data)\n",
+    "print(digits.data.shape)\n",
+    "print(projected.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can now plot the first two principal components of each point to learn about the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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62XbkD8PoD6vbdkVXwcw4S3oO0tOxjxEjwtiyNogE8CslgkIQNSz0UDNhexK/\ncQ1W9GpAg/Ig4IGbBn8GQp3g5kCPEWmMVmvZEgNZj5tthvIoGhm01ncT7PsfZ/4BOTkoHIJchKam\nVef9eZ6JlJKD2SIV32dlPEzwEs2kXSy/e6dajG1+I7vkgTk1NcUDDzzA5z73udpC0dWrV/P888+z\nefNmnn766XktIAWYPEM5sdezpqbYomiz5/i88L+OUkzZRCIWh7aPsumjy2pl6+ar++Ymum+uTgDJ\nVyrkJ8+8M4hT9jAC1WUdz3/1KPnJMkK4BIvPsG5TmZLbTSof58XH+ln77tm1UjrYuRJmvUGkI4QH\nFAp2dfeRoMAHPHt2CNeEsnRq773vS4Z3pChnHZpWxEl0zi1zZ9qNmIVjtct2oAEvVSI4Vl2ioVWG\nEF4eP9CJkR8GPYSbncbI78SJbqoWG8h8H3smT11XPc3LCkwNmkSSHks255mayiL1LYSWuYS7p4m3\nbWDSXgaTOZA+oeFv1ma3aulfoJXG0O08Qnq0+kWus+/h6WKBoBtnSyGOcLJIArQ7SSynjbTXjVHc\ni2a/8n6b+NY6tNwgerkfP9iLt+dLeGYHtufjFsfRveWYRgMgEJUk7hk+p8LLERz7GsLLEYkEyGjr\nsJN3nHbc+fxkPMXeXHV7sgbL4L7OZgIXufD6YvndO9Via/PlEO6XPDC//OUvk81m+ed//mf+6Z/+\nCSEEn/3sZ/nLv/xLHMdh2bJltXOcysIoTFfm1HEtpmyKqQqxlt/vAnTP8dn974OkBwtYYYPV7+yo\nrbNsTT5LILQP09BJxIfxpIX0N572GA3LYgzvSNUWy7euTpDwHY7+eoxiqkJjX4wrPtiLf0rRg0O/\nGGV0VxqAkZ0prvrwEuJtJ1+bk3graAE0ZwIv0IsbvQIj92ItxHyzBbO0H+GmEW4KN3Q9tRWQAjR7\nDL10GLQQUguz8pYG1jizheZjG7GNJFJKSj+bRBx4DrF6Em4LE/R+g+ZMohcP4AX7QAh8I4bu5UGY\nSAReoJtrtavZqK0jlPpXHPc6Rr39JJwc8fCNCL+IPvMUXnAZWuUFhF/CNxuxmz5E0PkuXrjaK7Qy\nv2XGWEeFBNIrUhH1hIXEoETFXHrmCkSlI7WNsKE6McpO3H5By2zKnl8LS4Bp22WgWGFFVBU3UF7/\nLnlgfvazn+Wzn/3sadd//etfv9RNUc4iEDXRTa1WfFw3NawLHJKdj+EdKdKDBZySy/j+DBMHMsTb\nQ2iGRth2Cx9gAAAgAElEQVSaxAyYaEb1yzgWnaL1+sbTHqO+N8rKOzvY/eggQhMIHY4+NU7ruiQt\naxKM7Z3h5e/1E6v3WHrLEtqvaGT6yMkvfd+TpPrzcwIToePUzd3h3T+l4o3mjIOewAuvQVZOILwZ\nfKsZJ349CAPNOYQfaGdmOkJuyiC6pJVg77uq5witZgDkt76B9uP/iWYWcUefJih+jnbDW6pPIH2M\n/IsgDHwjgTQbq8OzWhCEhm82o5ePI7w8llFPj3EDpr0VRwTRS4cQbhatfBy0EAgP4RUJTP0QvbgX\n3+pC6tU1qq6QCFGmem5TUgyuQwQ7MCJrz/jFILXIqy6HzxuWWuUEenkA32zGC6/A1ASmJnBO+QMm\npHYpURYJVbhAOU0garD2PZ0c+80E0ViAZVfXVZdq/J65to+UkvF9Gdyyh1tyibYEqesMoid7aO/2\nsKIGTsmjsW0zWsOZeyEjL6VqtWIP/HKEzHCRcEO1sLtfyrG8/XHq6vN4+yOI1X9KuMGaMwM3XH/u\nc6vS8/CGPGynGyt0APwKbngNUo/ghVfhG3VUmj6ENJJozjhm6nGmDkyy64koUoJ8QWfdfbE5e3rK\nPc+im7M9LU8ihgcQfhmpBZF6BCFtfLMZKXSkrOCGV5+8zkyCO/fn4VntaPZQtdC60NHt0eqem0g0\nexTNmcQLdKIX9iDNepBlDHcS2+xE4CFEGN9sxgi3E5STSL8VtLnvixdegRvbWF1yY8SoNJx7JEgr\nHyM4+b1qoXjATt4KsWt4R3M9P5tI4UjJpkSMrtBrKzmoKJeaCkzljBqWxmhYGruo51Fa1yU48cIU\nbtkDUe3JDu9IYRdd2t5/J0bPXnCm0FqWoUWvqN3PtX1GXkrhe5K29QmK6ZPDx5ouCDe88gUs6e7d\nT6wuBwgMvYiV+RWr33k3hx4foZxxaF5dR/Oqsy8LkZ6HfOQ7yBNH0VtfRraGkUsb0ZxxnMkG3Gf7\ncc1lyFsmECvr8a1W7KY/YPB//28kRaRZh2t0Mrajn7bA8+AXceNvwu9oR0yU0YQNBJCJ9tpzClnB\nCy6rBSfOVG3dpzTqkHoMaSSxEzdj5p5DagHKzfcSmPoeAvDNJvTifnR7BGnUIaSHcKaQkSvxAxWk\nHqViLWHbxC854h6gJdDCneEokcoQOMHZ50lQarkfXrWptZ28AztxO5HmOP55PhdG8UAtLKuX9+PG\nrmFFNERfpB0JaGq3EmURUYGpXHJSSkozDropuO4/9+E7ksJUmclDWYQmMEMGh381RePHbiSQME+7\n765HBsgMV3tnoy+nSXSGmZodZtUMjY0fWUpuvIRdcGGwrvalXN8bBd8lGDPZcE/P/Bo7NoocHMAI\np9DMIkwXkT29eFJSfnIG6bbgWzH48Y/QmpsRiTqM/C6MSBgv1I5vNgGSaOmnBKaeBulh5l9E37IE\npjTkjIPZreO/5RrKzfchvDT4NxJI/QK9dBAQ2Mm3gQAz/wIeEiv1E+z6d+DGr8ONVyfImZmtVJwx\nHHcY3UxCoBNvII03ITFb42gdIUDiCh1Nj/NMfpQX/Do05wQZmSdpLeX24nA1qI0Ewp3BzO88bWga\nmPc5S6nP/UPE10/OfhdCoKJSWWxUYCqXlJSSvY8NMXkwixCw7K2t3PDxlRz46TClGYdYaxAzqCN9\niVv2CbxqJUgl59bCEqCcdVh+SxuJrghj+2ZoW5ZAtzQ6rpwtkrDxnWjHpzB0GyNoUYlfe2ENtqq9\nVemf/FURsohvh/C0Znhl1NLzIJPBkr/DyO9k9ZUa5bEYM5UNxLpaWbFyK95EETmVQ2+cJNA8gbh7\nOZoziSEq2JX9lI0EMrwCvBKB1M8ADaSHlXkK32zAx8LKPos29X2CY1+j2Pl/4da9Ca18jJmZRzgs\nDhGyCgTcF2kfeg+B7aMIL49z2GDinV18q2MvOR3emT7BkBNGCgPfbEQadUz5EhBIceowrAbSRxYK\nyG2/hYqN2LgJ0T6/vUCd+LUId7o6M9dswn5VHVtFWWxUYCoXTTFtk58oE20OEk5Wv4hT/QUmD1aL\noksJR389TvsVSdbd1Y1r+2SGqmFY3xsl3HD6uUUzpGMEtFo9WCEgnLTInCiQHy8znE9x9IUJNt63\nlEhjAE9votz8x4SsaZxAA/IMlWvKOYehF6YRQtC5qZ5A9GSvVjQ1IW7cgrf1aZxCK6E1Y2j2btzg\nGoyojZuffbxoDFpa0WYeByAY9bn+ngyVSBmvfgnicZ/KLwcRXhlHkwTf66JFjWpFH10D6WLNPEml\n+YM49ijTrke9FsIs91fbYY+juWk0Nw3SQy8fJzTy/3DA0hj1DpAVB2nxLHyRJG04OEdOsDSyAeHO\nUBQ238sO80S4BMBMQ5AVRPGy3YBALx9lSaiBSvzNmKV9+B74gRaMzNMEJr6FsydF8egq8E3k0cNo\nf/THMJ8lBMLAbnj3PD4pirI4qMBULor0YIFd3x/AdyW6qbH+7m6S3RGknFsAXPoSKUHXBVe8v4ep\nQzmEBo198TNut6WbGuve283hJ0bxPUnP9U1EGgOM78tQnK4wsWuGUsEh2hSg79Y2dj0yiF10CTcE\nuPIDdQReNdnXtX12fqufcqa6ldXk4Syb/3DumlPtTTciN1+LzO9AG/4LkGDYxwndvIbC+FVIgogr\nr0KEQviFFjQndfIJgq0gDEoj69A5DMIA6eHsF+ibKtUeXagN19qAcNMMTj/Dj058Gz/7PB3C5iMh\ngRXswjdbMUr7EF6pWqVHjzDs7uGR4l/jRK5gJpjmtnwLfW6citmKn0xAGqTZwIwcIZMQCCmRQnDI\nqrCycxPvnKhnNL+Ptvp3sTZxJXr6J/h6HeAj3Ey1Dq/noQeHkM02pbGNYNswPg7Lz793oKK80ajA\nVC6KoRen8d1qOHqOz/COFMnuCPW9UZLdEdKD1U2ee65vqu15qRsaLWvOPgHnFcmeCNc8sHzOdb7v\nM7wzRSlt43s+L39vgOxoqTbnpDhdYfC5Kfpubp1zv+J0pRaWAKX06WtOZ04UOPqbcSKlF1m/BsJx\nwCti+i8RWLsZJ74JaVWXndiJW6prOO1J8HKY+R1o9jiVxs0QPlBdn4kPegA3sgE0A6NhDZOFSWbc\nl/nl2FPI/DHQQgz7Gi84Za6NtyL1GL7ZjsZ4dS2kdHgppIEsVSfPBDaxzx2gxw/jmevouOUOhP4M\ncnqacHOGNzcMsGkow/5IHb+r7+XmUoK17mEImuAPISb3I/VYNdABM7+jOgSgaWAYGOHp6pthGNB4\n+vIeRbkcqMBULopXVwXSZ0NR0wUb3t9DbrSEbmlEm4JnuvsFa12XZO+PhtA0gRkykUBmqEi8/WQV\nH9/1T7tfIPaqNaeWRiB2shvqlDx2PzqIW/EpFuo5VOll3fVHsRjDs7qrmzyXj1NuvJtg6scId6Za\nb9Zqw8wPg5etFlm/+mrs6dvQDk2hN1TQr1mBF1qBF1rC0XqLb+q7wJ1gV/00vaUiupuhYDWSDa/E\nC69AalHQgggnhZn7HVIzIbQcKdIgNMJejuVujF4/ydL8YbzoNWh33YORe4HeE78iWAkxojl0FQJ8\ncOd62iafQCamEH0rIRZH+IVqYM7ygsswCrvAdZENzXjeKsSSpYhN1yDqVUF25fKkAvMyU/JKTFdS\nJKwEUSNy/ju8Rkve3ExurEQxZRNuCLDkxubabZomqOuYW47OtX2yw0XMsDFnb8v5cG0fzRDUdYZx\nCy6u4xOss2i/MkkxZeO7EjOk03n16V/0gajBuru66H9mAulLlr6lBSt88teikndq50u9QDfDo+vo\nyYzQGPerk0X9MgIITv8Q4VV3PdEqQ2ilo6CdXF+oB3Jof/Rf8Ct3YU19D98rINHxzFae1nbimUl0\nd4JEQHA0brM8LTAMF68hQaXpQwQmv4NReAnQSEWWMXHsME27DlO3tof8FXE6XYubKu0YaOgaGO4J\nHFahza7HbPOjtPlR3BM6+R2jeA1BzGAZefAAYtM12HVvRXOmqus1gz2UzS0EdnwGgxO4TiuVmz6B\n1rbkgn4uAMKdQasMVycXWS3nPb7s+bycLSCRbIhHCF+iOrOKMh8qMC8jKTvNtwe/S8EtYmkmf9D5\nXrrDF+dcVKjO4poHluOUPMyQfsbzka9wSh47vtVPcbpaem75W1vp2nzuXoxws+jlI7h+mJ2PmuQn\nysRagpSnbMy4Qde1jax/bzeu7VFK2USbg1iRM3/cE10RAjGTyUNZ9v94mHV3dVHXEUZmM4ROHCXk\nVyhpcaRRh5VsINS+As/Oo1WGsNwUbnQTbmQ14uTuY0gzifCKs21Ng5evBkegg3zjH2KMfpVg5kni\nk9/ELJgYRhCBSwt5ltYlGWpIkotFeMJq5JaZpzBzL1aLu8sKg/snGDxSHVpePpqnN7qevsYeTPEb\n9MpRkD6VcBcO4Ae68K226tZegGdH8c0m/HyIivDRQxlk7DrcxJbZ86t+dbeVX/6c0rHV6MEWrLoT\nBF78O9zbP4Nvtc33I4CwxwlOfKNaUlAIKvXvwYusOevxnpR8Z2SSiUp1iHxPrsj9nc1YF7nOrKLM\nlwrMy8jzqRcouNUvcdt32Da1ne7uizd5Qwgxp7d2Ks/xOfj4CJmhIk7Zwym6aEb1i/H4tolzBqZw\nMwTHv4bwCnhpm4jdRZ7riDQGae6Jc8VHl9QqE5khnUDM5PhvJ8iMlIi3hVhyYzOafjLAx/bMMHmo\nOnPXLroc+PkI19zdgP/1/wWlIle4gqHkZsTqdSw1htAGTyBDOQjrgIHUAnjBVWju76qBo1lUmj6A\nZo9iZn5brS1rj6JPfJ18/fsp5WfQKhpFbSNRD26b2Mu3GnXywQ7aZSNTgRwH6nSQZSJekWxlkGZ3\nphpkIkjk2AiarMPTqr0vOXCcyrI/wJp+DKmFkEY9uXKacmGYWPRKyq0PYORfABGk3PlWGPghFPI4\nuQ7cJe9AS958yps7G06+ROhlgk37EcIHXUOf/B6l9v9aO895PmZ+Z63+LlJi5p8/Z2CmbLcWlq9c\nnrQdOoKqEtDlrmw885rv+/scR1OBeRnRmPuXuiYW7i/3gWcnGd+XASA/UaacdWhcXj2Hpp2ntqhe\nOoTwqpOGNFOjPnqA8ZnqAn7N0GrnS099roHtU0B1I2pNF3OGiF/ZiPoVbtlDHjgApeofF5YlWco+\ntI5O+Nk4sjmNSGbx/Rhu1/VIow7faqbU8kfVPTJlBSO/E2kkGNWKjBlDCH+YXrEUvTCA70dwCx6W\n7lAxOukoPMef+h1kG64kojfwHetljlHPBns9HW4H42aUsDFO1D2M0CyMpi7keDXww4Rpbd6AECZe\nqFoN6Ml8gu0zBl5uP1e1aNzSsBw3sr5W6k67/w+Rhw4iQmFYXQ0wKSXb0zmOFcs0WCY3Xb2Z4OCz\n1bDUdERnF3gFhJdHnlJXF0COjyG3bwMhENffiGiq7lAjtblD63PXeJ4uYmhz6szqQhBTQ7IKkFpy\nhgIa8/T7POOuAvMycm3DZvoLx5lxMoT0EFsab3hNj1PxKoyWx4gZMRoC9bXrfb+62fPZepWnKp0y\nMzXSFKgtN9FNjRW3n3vY79Qi4IGoQby7CTEi0AzBhvd012bdviI3Vq792ym5TB3JzQnMljV1DL04\nXa0MBHRubECES8xZABMKIYeHkHac4ugmpDTRHB+xpA6ph/GD3dWSdX6J4ORjICUVyoy426nM9gQP\nyv30idvxHR/P0UETaLKE44XQPIOY3gRCYEU2c32uTF8qSClYxohfRSb2LqzyzxBmHQ3vvpVlzx/E\nn56kMdSFaYbxKzpueA3Z3CF+l3UQ2Gj2OLuPH+TafJamgEm58Q/wg0sRsThi4+Y579GuXJFnUtVe\n9nDZxouFefsH/0/8gf+bMWuYvDVK0AjQpM/dq1YWi/jf/TaUq2s85YkTaH/8J4hAACd+HXplEK0y\nhDTqzlu4IKzrvLulnqemM/gStjTEiZvqK0p5/VCfxstI3IzzR0vuZ8bJEDdiBPQLH+oquEW+NfAd\n0s4MAsGdrbexPrGOzEiR3f8+iFPySHSFWX93z2nBdaqmvhgT+6s9TCEEV9+7hMa+OIalYQTO3avw\nwqtxKwMYhV1IPUJ083vZ8qYOhIDm5vhptW8TXWGmj+ZI9efJjhQppmxiLUFWva1asSYYN9n00aWk\nBwoEYmZ1vahfjxgYQB7cD9Eo2tvfCTMzSEB6QYrDmzE3BAkYYTyrtdrz0mPo5eO8stdYRdrkdYu8\nGSXmFJgyY6yp7MDwW8kQwClpyCM5HPsK4hua8eu24AW6uC0zSO6Rf8ButzBFFqtnG1pjCGk1U0m+\nHSu3jb7lL+NGXYrP5ZBHjyNjcSr33U9Z70FO7UBqIYSXr+7b6RXAD2Olf0G57WNnfE8nKvbcy7aD\naGnh6eWbGM8N4Ykgx6M6N8mdXCU24o2P4/96KzKXQxbyiFd6goU8ZDLQ3AxakHLL/dWdVoQ1r5J6\nyyIhlkUuzlZfu7IFtqWzmEJwS2OC3vDvZ4a2cvlQgXmZMTWTpsBrX0e3J7OXtDMDgETy26ltrE+s\n4/AvR2tDmzMniozsTNF97dmfp3lVHbqlkxkqEGsN0bQiftZjTyMEdv3bsJN31r6EzzWI27W5Aafk\nMnkwS+Myi9XrniGcmsQ/vBpt6QdADxCImrSurQ41VvIO+388TGF6FfVrNrHyjnaELtCSPtptV+Id\ny0KyEWPNBMI+juHOYJSPUmr5T/jmyZ5rRERwrHZeaOgFYGVZEEu7CG2YcLwEhx6nkgkSSUawX/Jx\nW7sRXd2En/wnAkMTFELTOIkmGBwi3FiHhk9k+Ivo5QGEm8JyMxjLW8kdu4MdoVG2Tf85gfZOlkQ0\njpXCgGSdOUGrGUICQrqnvzmzukMBdmYKtcs9szuIHDMynEgsq10/JIe4cmYpxe9/E5nKIh0H+o/B\nipWzLzoKda9aS6st/DnIybLN45Pp2r6pPxqb5r/0tmGqCUXKBVCBqVwQXczt/RmzE0AqeZfcWAnd\n1Ag3WLj26WseAYqpCuWsQ6w1RMPSKA1Lo2c8bl5mw7KSd5ASgrEz79kphKBtQz1NK6dpr3+aeOgY\nmj1BOLULXd9Nsedzc9YgHvrlaK2wwtjuaSINOstW7MbMPQ8N4HX0UGm8CWP4b08+ie+gl44iZAVf\njwECGeziqrqPYbnPkywcYYUTwyhvxzeTmJE4vtuOTjeyGMNORxCDA+hNYFT2I90U0f0/xQs3okVC\nBK/eAoCWPoKwJxFGtfarHshTrB/gVz0psFZQEkWCXXk+krLQvTKdUiD1EAiBE7/xrG/lymiYd7VA\nf6lMg2myOVH9ubSKNk7IwdpxbaINhoaQlepkHmGayI5O6FuBMAzE9TdS8ExGBl2ScY2mxOsjkDKO\ny6lFpiq+pOz7KjCVC6ICU7kgGxLrOJg7xHBpBEszuaXlrdWwHC0yfSwPUpLsjdK2PnHafcf2znDg\nZyNIXxKsM7n63qX/4X02+7dOcHzrJACdG+tp+uCZa5yGkxadG+vRT+TQ3BliDRUCYR/fHsfMbCUV\nvJ3hKZ9kTKtV/tEqQ+iV49CfI1h/FC9QnVGslwfQKgP4RgOaM0VmuMjM8TziwP8kGbcJr2tBW9uL\nU3cTdfmd3DL2zeo+lV4BhIkuXfxAKyVrGW6xAUsEgAqiqRmj8DJi/Qa0/mn8kQy67+G/+Som5SRW\nqkD8RIRg/Sj4NugGvh+loIFsakI0Vnu3Y8Eogfa30zz1E6QdB+lTbvoAfmj5Gd8bgBPFIabKA7SZ\n9cStXqZtl6aAyRbtJjQ0xuUY3aKHjWIzNIzOWSYk2tvR77oHgOmMz7d+WqZUkWgC3v6mAGt6F/5r\npjMcoM7UyTjVUZDOkEVUTShSLtDCf5KVRcXSLD7U/X6yTpaQHiKgBxjZlSYQt2i/IolT9gjVWQTr\nTu/tHd86iZydAVnOOIzuTtN7fdNrbks559TCEmDoxRSZm4pwlu/BvlvacIbeQnR8J0HLBiHwzSay\nBZt//VWZku1RCaS4NiEIjJXRK8cRQtLcbVcD0mxEarPn14RBpfFu5OBPGDk8iLe9RMvRXVQcD23v\nEMH7DfT4gdllJRNozjh4pep+lloE0DDXxPH0ILrTgWjvRaxYiUyfACuCfvc70PMlconl/L+NKTRn\nnFJpihvsJdxUmsKKTCE9HW/1HcTb/wf1+ndIyzQA3aKH+sxLCCddWwJiFPdinxKYUkp830MIwUDp\nBN8/8QPKvsbufAttoSyd4Q7ubEqyPh7hLfpb576Rbe0E3/1uCk/8BhEMIm69o3bTy0dcSpXqz9iX\n8Nw+54yBWXEkMzmfuqhG0Lr4G30FdZ17O5rZkyugC8EV8cg51wYrl5evfOUrPPnkkziOw4c//GHu\nvvvuMx6nAlO5YJrQSFgne5CvzIq1IkbtvzN9GZ269hGqFX/+I6QnT7vOd+VZAxPA7LwWL/qf8Sa/\nj9Sj+FYrO0avolhx2Nfz/7P3nkFyXWea5nOuyZveVFWWL5SD95agA+gAihSNpJZaogxFqqVRt9TR\nszs9ip7e3eiRYic2FKHVzG7EhCJG22p1b3MlkaJE0chQJEiCDoYACBK+UADK+8rMSp953dkft1hA\noQCKZKubpFTvv1t58ua5ps57Pvd+j5ENDdLjV/mrrmtpP1eittUi3giO2QV4bmY7vAEQGOmnKFVL\nTGU303j6n6FcBeHiDmewjw7BNTFwLUR5EighZBUsB9tfQ0ntxtZb0K5pJrTqM5RmE5Ws6A0o1WGM\nzNMgbGacLGrWoObAFNp0jgnfJBfUIFWrmbpQG7VaBL8vxud5gJPucTShsU5sQJFPzrtuIS9mJUsp\nKRbz2LaJEIKTuZNIJBNmiKqrMl1N0xJo4UAmz7rolavY9E2bUFsXWqw+7e2PwbNCH3muQqEsCRiC\nT9/qp6HmvblGCyWX6aykLq4QDrz9+xTWVK5NvItY+SL+KPDaa69x9OhRHn74YUqlEj/84Q+vOnaR\nMP8AkLO8coCofuXFwJUueydf5EKxnzqjltsbdhPU/mWZiP3FAXrz54j5Ymzt3kzrlhpG38igB1RW\n3d16xe8sva2RE48P4Zgu0aYAzRsTb/sbjuWSGSii+hQSSxYu3IG4j6b1CcaOeZZV3bII8ZYg09OF\nuTGKOYaeeQ4hLazo9TjBFdjxWymGN3k1k04RvedlsmqBvHEaiKOqLvuXvs7Opk60ci/gEVm15l4E\nDlLxERj9HsKtEgpKutoHKasuFauGQLAAqoId3QiVJMrLFxBuFUV3UdvDCL+PrH8HFWMlSAVXthJz\nHOTkJO6Tj5GauYCzpkTjqg5CShSfHOPe/c8g8xJHUxgZNBlv8GPWBRiLWmyggCEUggTZpnq9PqVt\nY0WuQa1c8Fy3ig8rcrEPqGWZ2LaXFSulJOAauI6DY1Vw7BC6NmuVXmVDYzuSbMHFdeWCTc/WVTp9\nYw5jKZeQX3Db1oW1lwdOWhTK3manXJXsO27yiZvefcbq6LTDo89XqVoSQxd8+laDprpFN+si3h1e\neeUVli9fzte//nWKxSJ/8zd/c9Wxi4T5IcdLU69wIPUaAFsSm7it4ZYFY45kjnI4cxTw5PFUoXJP\n810Lxo2Vx7CkTWug5W1FDYZKwzw69BhytlIxY2b4yG27WXpr49u6uWo6wlz/teVYZQcjqr+thelY\nXtut/IRXQ9myuYblu+bXZ7quZOUdzTRvSOA6klhLYP7vSxtj6qdzIgdG6nHK+leQei1SS+CoMYIj\n/43tHQ4vl3302FkMn5/OpjBSVnF8TSB8OMGVOIFlntIOIExPcxUANUrzugjjH9+Buu8wut9EbVdR\ndmzHfnkvp4pRIEH7mEFIdqDuXEdR245WOYtEg+oY5tCvCBz4PjPGBAOqIDpYolDjo6vxehpMnYKS\nZVTRMBQfrW0qB+Rq9rtrsGY0XqtbwV9KCakU7uGDcOg1MAzkipWU7vwyqjONqycXiA1cio3RdczY\nM6jOMIoIUGM04xOSXbUx9Mwe1Mo5pF5HNXEnU/kAjz5fQQobv2bzmdsMwsGL74rfJ/jCR/yUq+D3\nXdmLcFmHtwXH7xSvnbKoWrMlPJbktdMWH9vx+yNM03XpL1XxKWKxBOUPGJlMhtHRUb7//e8zNDTE\n1772NZ5++ukrjl0kzA8pXOlyvtDHi5Mvz9VTHskcZX183YKykYyZmXecvuz4+cm9PD78JFPVaZZH\nlrI6upo/bfuTq5Jmf3FgjiwB+or9AO8oJlSeMZEuV4xxzptjf2GOLAFGXk/TtaMezVCRUtLz2zHG\nj2fQAxqr72kl0b7QAhVOaY4sAZAOip3BeauJtLTBNfHr8D8vLWKPOozrM5AV3K7bGOZL3nlkBSc4\nWzYhJXr2RdTyOYRTxPXV0xOK8qsHba7tzLHVzmAmJSXx9xyorXIoVkU4OtFSngfyQYL1n0ZN9+Jq\nnuCDYk2jTh/FsStUtAwtdS6Dk/UkzCrTTFMki6OE8M/eX7+m8avY9aTDcSSCgNnB+b5+Op/4GfLg\nAWS5hOjs8qba0Ulp9Ubvfl9yX3Tdh6bp2LaFEIKQP8KuYhfqvlGkHCB3TROJZd1ErRPoeW8zhpXG\nQLD32EcplCWhIKRyLgdOWuzaNr9sRAjB2/HLtWt1+scdShWJ3ye4ft3bvwtXg3oZGV9+/C+B6br8\naGSKqVmpvs2xMLuSV990LOLDi3g8Tnd3N5qm0dnZiWEYpNNpampqFoxdJMwPIRzp8OjQY/Tkz3I0\n8wZdoU4aA54YtyudBeOXhrt5c+b4HMktDV+sq+vJnWXf9EEGSkMAnM2fI6SFGSwNsSTYxmuTRxia\nnmRFZDkNfi8L81J1H4Ba3zsTnzr77BgjR73myskVUdbc23pVkr28PZiiCsRsDHTqbH7ODWuWbE7/\napjrv75iwTksJ0gl00jQmMAISmzFT16PMdcnRfFhh9aiFU9w+tk426dvZqq+lWA1z9prf4kWv4BU\nY86ZHqcAACAASURBVJ4J5FZBMRDWJGrPM1ijBVR/kVyrygsNcVZmTxBcnqKv3MeMKnHtYZ5vNQkO\nLkMlRDYRo2f7x9nobyOi7qNsO0hUdPc0RTmA0xBDG1TRVJuyCPBm9yZ2RrZwml6WiBJ1A9Motku1\n5Xp0/yqibpU6kSQhahGn3wTLAsers5QT44jmFk6fKfCrkyWkhGvX6Ozc6LlHhRCEQlEcx0v6UUwT\nfvsbnHIZASSeeRpj6QoU92Ij7FJVcuTMOL98zUQRsGmldy7rstJOx5HkSpJwQKBrV362dTGFr9wT\nIJNziUcUAsbFcSNTDhdGHRIRhbVdGrYjee2URbYgWdGu0dV80YK8fp3O8JRDviSJBgU3rH9vxHsl\n9JUqc2QJ8Hq2wE210cUylD9AbNmyhYceeogHH3yQiYkJKpUKicSVw0WLhPkhxNl8L4OlIQJqgHqj\nnr5iP43+BlbFVlJv1C8Y3xXu5JOtH6e/NECdr5Z1sbVzn+XtAgoCAUigOiuWrQqVp8efoc86R7Fk\nciT9Ol/o+BxJo47V0VVkzSw9+V7ivhi7Gm6b93tDpWFmzBmWhNqI6V4RezlrzpElwFRPjtxYmVjz\n/DZfb6GmI0zd8gjDh1IYEZ0VH21BnRVnt8rzV2mrsnCTUC3YHP1JH+X0ZnR3hPqPTPH8qgFy8u9Z\nYa/kHvXjcPgQ5VfPolAiPb4KN9FJbVGnOf4MsjSFiNieFalGQHiLsRwbh+FBQOCUwpQnptkQt2gu\nT2LYVc6rZTT8KFIifYL02hrqrQ4IhQgEl3jniG0jPv1zcuYUx+QJdL+K1VKkO7iacVPl7K5ltNau\nZ7nyUU47j7GvxaKhZhpL1Vkbvp/PzSzjQMZLEqr1abTU1uBKyEUa0UYGCMZ8mHqQPflu5Gyo+sBJ\ni5XtGvUJ7x4KIdBmY5WyUECTAkWf1ZpVVJRikVP5DkoXDgAS24GBajctdQqnBxxGJm1a6gRbVl4k\nqULJ5eE9VdJ5l6Bf8Ke3GDTUXNlF6veJBfHGoUmHR/ZUmE2kZibvki1KTvZ5z/tEn819u/y01Xvf\nq40p/Lt7A+RLkkhQoKm/Pwvz8g4puiJQF7Nq/yBx8803c/jwYT71qU8hpeSb3/zmVTfyi4T5IYEj\nHaarKULqfIJZGummOdDI59s/S3Og6aoPuivcSVd4YT/DpeEu9mkhOkId9Bf7SRpJ1sXW0BZs5bHh\nx9FmXWuWtOkvDsy5e6+ru5br6q5dcL4j6aM8N/kCAH7F4HPt91Fn1CKu4C670t/eQrqvQKaviC+k\noYc0os0Xk5TqlkYZ2D9NNe9ZAC0bF7pORl5PUc6YIHQstYPHj47ArOvvuH2MxKTK9heOo6HgEiQ6\nPUY61g0qGH4TX10rKF4vSYSKnnsZK3ItsurHzLXiiw4DEJu0qLemcZQyZtxhLK7TCjiKj6AS5UWj\nh2rwLOvEBv6KdgBcfxfFye0M/vZ7FHDIb23EbbCYiXdxc8e3WH/JM7xdvZNngIyepkssZYOyBVEr\n6Az6KTsu7UEDrbGWfXsGcRwXGtuwtlzP5k9uprJ3vsVl2Rfd6FJKKqYXZySRQNTWoaY8gXricYrB\nWh55MQLaJ2jTBjl3IYpWs5F4RGHTcsHmlX5u3si8zNSDpyzSeS+TuFSRvPC6ycp2DduB1R0aQf/b\nE07vkDNHlgA9g85cjNKbMwyOO3OECaCpgkRk4Xkt1+XpyQz95Sr1hs6XElfemF0NnUE/G2Mh3sgW\n0RTBHckEyiJh/sHiG9/4xjsat0iYHwKYrskjgz9jrDKOKhRub9hFW6CFofIIAsE9zXfTEmx+T+eO\n++Lc3/E5egvn0YTK0lA3UZ+XbRvTYxTJXhyrx652GlxHMnhwmn1njuM2aygdNhW3yqncaXYmb8Qf\n0em4Pkn/Pi9ZpnlDgmjj1TN1+/dP4VguiqZglx2Gj6RZvttL+jHCGlvu7yLdV8AXUqntuoJYwbzF\nTeIKBwWYqkzTM9NLemKU3niBz2RXIssqtpjmtG3QWuOnYftW9IBDMRfDMN+EgIN//IcExn5ANbCD\nslyFNdKMnJ7GnyiSLCvkK6DV6rTEtvP88klknZ9j/jQ1ShgkpJjmN+ZTfMS5AyoVjOdexTV9iNQ0\ny//v80yuaSZaC+7tr6Ns2jI387AI8yfioxipJ1GsEzhGHrPmLlpVgdz/CmQyZBqXsb/zbrhkP7Q+\n4GdVu83pAc86a29Uaar1rKbcyfM88k9nSZc1apY18ZmvrCZy3+eRR4+AlIhNmxksCV430tgixCFW\nUxMM02KDIiSVqmTdMh/hgMWlcC4Vd5KSAydtBie8P75x1ub+O/0Y+tVJJxaa/1ksLJBSULikm8w7\nVQ46OJPndKHMTApOT5mUMxM8sDr8rmovb08muLk2hioWrctFeFgkzA8BTmZPMVYZB8CRLi9OvcLX\nl36VqeoUftU/5/Z8r0j4ElxTs3XB3+9tuZtXCy8yZqVYE1vNssjVlWJ694wx+mYGN2tgnwmi3V5C\nabUJqBdJsfPGeprWexmtwcT8coPB0hA5K09HcAlh3ZNlM4s2iibQDHUufvkWjLB2RTUh6+RJnJ/+\ngqaKZLK0gb6ATU7PEtkhmDFtjveewS0Iyn1RhvUix/1TaGfqGIt2UOhsoVeYrI/sYOKojZreRyTQ\niD9aItI8xqPhaQaC0zR8op27Jx8gcOAA1tAkijqKoihkpgXn0tezu3ALXfd18Kb1FSrSc0P7XYNw\nNYglTCgXEaUiLSdncKwSSqlKcGyGjviNyIP74RLCBPDNPDfXAFornULqCUp7U8hTJxCKSuBMLzUB\nSDcsB7y9gqEL7r7Bx7puDVdCR6OCogik4/DSj4+QLkYBh3TPMC8/n+Cue1oQN+68+DyKaUJhyBY9\nzeBgl8mnumM8sqeK4RP88uUS21bANasvWrGbV+j0DDqUKi7mrDSiaUl8uiCddxmddulsUqmYEl0F\n9bJnumm5Rjrncn7EIRFVuGO7D6EInj9ski26rFiisXzJ2y9Z7qyJmrMdZqbh1CEVCbwyadFlWXNx\n3HeKxebVi7gUi4T5IYREogiFBn/Dv+rv1PgS/NmK+xd0/wAoTFUYPpxCqIL2a5NkBrxs1O5QJ2fy\nPdhjJt0r29gU3zDve/7owsSMA6mDvDT1KgAhLchnmz9DOWMyfnIG15a0bE6w5JrfnVgkSyUqjz0G\npRI+oNV4kgN3x6m2+sDvEj+fZO3gFvKZCsJVmQl3oKxcxcnyMqabVrAu9CsafWcI9+v0nd+M495M\nZ8NTWJMp9i2ZYcDnkFcscsowL7YeZXfvi/BGL6YoM1znI+WE0BOP4Js8gb/vP7B9yXU87fyaUrpK\npFiDKBhcaL5ALBSlvr0d482j1JhhzKDKknItvrIDPgOl0oeR/jW4JnZ0O8LOzbtOuzxNtf8CwvKE\nB3Sfwc6GKX6tdrHc/wJbOyaot9ow5W46mrz7fcDZz2HrNYJVgVEJAxdrdkdHy/z9kyVcF25Y72Nt\nl4ahKqzuVJme8Qhja5OPYgbikYsE8nqPNUeYUzMuvUM2y9tU9p9wOHnBYXTapS6u0N6gIhTB6JTN\niQs2p/ttdBXuudFgaevFJUhRvLrN69ZKAgZcGHWxHPjIdh8SOHLG4qWjVZa2aTRfod7y8BmLF4+a\nCAHLVhlkpipzudz1fh/nhh12bvydr9EiFnFVLBLmhwCro6s4lj3BRGUSRSjcUn/TezrPRGWSN2eO\n4VN8bK/dNs/6ezcwSzZvPNw/150k018kWGdQnjEJaSG2JDazYnMTza0LY4tXwpHZGlHw2ocdPnIS\nvZigdUstjungj/gwwu8gA7JSRjrenIoUOc2b9PsjGP5uwlaIqr/IXcqdvMw+ekaHaAwm6bzhLsau\nDSDGT9KonISxIqKYYmnNa4yX7qQvs4FqYC/7A3kOBi0UYeHIBP1HfknTK3XUVBpIxAdIlE1GupsJ\nBzWq/hzuSy/yFw/+JYmjUY71nyMp2hhbOkZmJkU46ad892aG3T0cC09xIV6gbajErmw9G3d9hfD0\nLxCuV1Kjz+zFCm9Crc4KoAtBWetANpYR2awncec4dG5r4T9GD+CbPoAIBKCQQgofVmIXw+4QL7le\nXLlkQMO6IdRDTThSIAyDUSeGPuNZdi8eNfnULX52Xx9mqGKiCJOorrKrPs5gcf7t9s9mt07NOPz3\nR0tUTBiasLFsieUIwgFBrig53GOxfbXOE6+YFMuS9kYVy4Ff7zf59396cQmaybs8+kKFTF4yPOlQ\nF/dk83QN+kYdBsYdQgHBui6N++8I0HpJLHMm7/LC6+ZcTeeZk7C7O8beiSohVaElYJCILEwOA6iY\nktEph3BQmUuKeguOlLyUyjJaMWny+7hp1kW7iD9OLBLmhwCGavD5JfcxXZ0mqAWvqujzdshZOR4e\n/ClV11N4GSoNc3/H597TfIbHxjmgvAohwcryGpgJs+ruFnS/SnnGpG5phOY174wsAfyKnyKluWND\n8eHilZIoAY230VCYj3gCrb0d91QPp9wTZJIqg/UVHPcUa+RalqorUScMNkxsQ3m2mc6lrfT8fJzb\nH+hmuMZFP1kmFM5RE+7DLLuEy8eZ9qvs61hPql5hxH2JiOJHSxnUDeUwrAKOpVMoxlFqsvgTXs9H\nv/QSTKxf/Yb2/z6IbyrOgdtP0SMEnTWt1IXCTCSnmPrkMg7lhrCy7YzVBciZa9l7pkT9aDf1jRPc\nvHqQWhlkMNuGqnfSEpnGMZZg2zGcW5ogGKSSHWPvylH66n/Mztf3sSkLZcMl3LGOUMBL4skz30Mw\n8dk4D6xqJpURGEs7+PlBlXPDNtMznhv1SI9FY63CF9fVU3Zc/IpACEFiqWRg3KF32CESUti92XNv\n/refFHn5TYuqKcmXPTm8ZELB0AW1IYGiQFOdykTaZSrj0t7oEZ1lS6SUc3HFV45ZZPIS0/J+p1SV\nLG/TOHTaolCSCAHFsmQ05dI77MwjzIop5wkgSAk3LvcTV3UujDp0tPq4bvXCDjqFsuRHvy2TLXrn\n373Nx8ZlFzdn+zM5Ds14ylEjFRNNCHbW/stCIIv48OJ9I8w333yT7373uzz00EMMDg7yt3/7tyiK\nwrJly/jmN7/5fk3rAwtN0eZqLd8pevPn+O34HpxZ9Z63yBJgrDJOxangV+dXmB/NvMFLU68ghMKu\nhlu4KXnNvM/LTpmnKk/SFxpCOpJx3yj3VD9BqM7Pqo+2vKdru6Ppdn4x/CQlp8TyyFKu69rE8b4h\ncqNlhCLovsW7brNok7qQRw9q1HUvTPQRikLgC19gYu8eeqxxplcmaTPGSTspumQXm1PXUEiaFCdM\nmqINUFWwqy7ldJXlq9YweTZMgBEQYJMg2TJDqWMSdWmSGM3U00ZExKiZaaJzfBi/nCZUzuKTkqrR\nQnLIYsbuxuwocEEeILTPYcnyMtFujbQleHpSYPqqDJz3cX1gK6bmo5yqQ06qCE3Q9/RaZup1zsdh\negherJtmtVDJ9DRgySBbVnRy21YDv2lSOXUSt7aWl28s0BcKIXt7OOovUQpPkagGoHiAupadtOGJ\nsYeJUJglzmXqChq2b6ABL+bXdK7C8fPePQwYnmWYznnsYyhwUp7AlFVWKKv4xE0hHEfS2Og16t5/\n3OTwGZtsQVKqSjQVbAeGJ10iQUFrvcLSJo/YaqKCYvmidbZtlT4vCcee1QZWFVAEuLP8JqU3r4op\n547j4flWXn1Coa1eZWjSsyLbG1XqYgq3blW5FUgmw1cMLZzss8kWL55333FrHmFOVeeXME2a8xOd\nFvHHhfeFMH/wgx/wxBNPEAp56izf/va3+eu//mu2bt3KN7/5Tfbs2cOuXbvej6n9waDqVPnl6K+x\nZpsGH8ueQBHKnBs2qkcwLmvsmzYz7Jl4YU7g4Ddjv2Vb+9p5Y1LVNFW1QsOqGDPDJaQwWXJNHM33\n3pMjWgLN/OXSP8eRDprivZKbPttJcbqCHtDwR3WqBZsjD12YKyVp3VrLslsXbiCErhPccD2OcxpH\nTtFCK93aUj4m/4SyYZGSefbU7iH1sUkazRYanY8iwgY2OoXaB5k68wLN8edwK0WamzM0qz70Sh8E\nulmurCQuEqiNPppnIObMIMIRgjJLpBDCqb0RZcnzVBt0Sr4SgdX9JFM+ojkDvZSgWmmlz6ghka7j\n2v6bKZ4TBIxz5ESWpqFOzHItoqgyXm8gnFqm80t5MpZhk+pg2HCkx+aGdTq+x39GYKDfuw9nBrlw\nXwuOEBwOu0w5YTaWA8zoDUQqM7Q6KUI1tdyvPcBp9zR+YbBGrJu7X4oi+PRtflzpxQgbahRUVdDd\n4pHcU87j9MgzABwSB/mi+mf0D+mcGSmTCDoMTzlEgoJc0SM0VRXUxQSGLti0XCUSVFjaqjGdlTTV\nKvzZXV7dZMAv5pWHAGxdqdM/5mAiWNmhYegecd5zg0H/uMOZAY8Mb9/mY+Oy+UuXogj+9FaD3iHH\ni2G2qu9I3F+77LXVL1sRlwQMeovleceL+OPF+0KY7e3tfO9735sTuT158iRbt3pZmjt37mTfvn2L\nhPkvRNWtzpElMBtb3MR4ZQJNaKyPrSVv5+e5d0t2aZ7knSNdynYFuLjjjvvi+BQdYtAY8+FX/TQ3\nzZfiAxgvj5MyMwyVhpixZmj0N7IjecOCBtRvQQgx14waPHdspOFijDV1Pj9HlgCjR9MsvaVhfl9G\nKwX9D6MNj3BPtZUj7a0oYdikbCWpJ3FXSZ6t7qWSzOArCnL+CX594ggH/2uI4NokH7uthZqaj2ON\nmDS1v4wvEaPB6OArpRCj4btp1pqxpY1dYxO54QhOWqLnUzABA3YNqdO9LImPoo6qZJL16HUudm6C\nSNWgpOjcaHWz+s07ONv5Jo/HHqFkwF2vfB5z3MGuStRGQaZwgXypSDEcItKmkcpdzOpUFVBzGeQs\nWUoknVNhjk6UyLa24e/vo1oOc1qL0n0+yMrfvspoepKjsc2kNt3Czg1baEmqqJeVdhi64IE7A2xd\nqTORdlnS4JFcRVbmyLJqSs4WMvywZ4jiUAuhoKRcNuloUmmsVRhPueiaoKNBQdcFLUmVurj3rHds\n8M25T58/UmX/CQtNgZs2+1jRpnKy3yHkF6zt0vjSXQGmZlyScYVoSOC6XjbtWyIGDTVXbwemqYJV\n77L35vqlGr1DDoOTDoYu2H2ZzN+WeBhNEQyXqzT7fWy8SveWRfxx4H0hzN27dzMyMjJ3LC8JPoRC\nIfL5ha6TRbw7RLQIHaF2+osDANT6atiRvAGB4KdDP+epsV+jCIXbG25jfdyzOBr9DdQbSSarXq3k\nkmArCSNOKl9ESkl/aQBXuny85V4Opg4hhGBn3Q1zWrZv4djMcX47voeh0hAj5THWxdYyWBpGILip\nfse7ug676lBKm3AJkQNofnUeWUrXxXfux2Qr04z35IBhmg7cQvfHbydc77mdFUUQXq/SLuuoZC3O\nHcpQNGdIjOYolS32xJbw7+6No3bdiJGaRpoTKOYodf5riSgr6XMv8JTzBFUqrNtQx67TrUiriWPD\nOuPTQWqrE9gvOQQjRWrbLSY+5kNR64joUVoNH5QMpjZnmF51HqfqMB7PYu+wuOb529DGe9ns7MNS\nApw9N8prn29CtKu09VyHa0coBIeJbD/A44bLTiXDK8keDreOEyXGjuhfEQ93klj+ZV4tP0Mq10/3\ny+epq7RzetQhOnqIZ9nEU68EuHaNzi2bffMUet7Cmk6NNZfUcvrwYWAwU61w7LyN7cCFl3wsCTss\nb/f6Xfo0iaoIulpUAoagoUZhVYfGyJTnT12xRKUl6Zlxj+2t8H89UiKdc/H7YP9Jk2IZgn5BfULh\n1i0+7tvlJ3ZJU/G3ejzHIwrxK/cGZ6pqMVCuUKPrdIXenUi6rgk+s8ugWJYYvivL+W2IhtiwSJSL\n4AOS9KNcUutULBaJRt9ZUksyeZX/oA8w3umcR4qj9GR7qTESbKhZ956a3f5F3f0cS5/Alg5rE6sI\naAHeSB1jRkwTCnqWy8Hifm5bdv3cd/593Zc5kTmNgmBdzRoUoZBMRvhZ3xOcSJ8CoDPSztc3P3BV\ncfYzUycJBnWsahVFk+TJkAzGKPty867fci105erZr8V0lQM/7aWSM1F9Cm2ra8gMFdEDGps+1UE4\n4Gd8rIruF8R+8zDu8RfIjufxrWlHNsYJ+iqUelK06Qqipga1ro7rzW1Ml8Yw0zaqolI/2Y1PV6Fs\n4zMMb37J7eAehqkzoAcJ6CkIZfhH+7eorksQH+e7c2z84rUYPx7lYGM3dm6GjvM/ImsnkXoae7LI\n49EGVmhVOnWL68Y20b1uFdnlBZR/OknpXJTJ2jD9N5ms3N3C8sljuGerWK7NkuYOdpaWozd8lNrm\nWqavLfA/Ks9gC5NJ4Lt3HaecHkVIGO8IYjSc4O9inyCoBNnIOpyxMYqR73NqyiRfMinrISayGkJX\n0H0+Dp4R7NgaIhT43W70L1n38197f4ZOge7cdnJGklTOI8NQ0Eci7mNlpznvO/ffFcOVnq5sQ623\nsXFdyU/25JkpSEzLS/g5N+wSDijEIyozBegdERjBMLHwO3fvj5aqPDY+hS0llMtsd1WarBDNSY36\ny2T53sn/3lCxwm9Gp7Fcyc76BOsS4Xc8l38NfBjXuD9kfCAIc/Xq1Rw6dIht27bx0ksvce21CyXX\nroQrBfE/yEgmI+9ozmPlMX48+AiO9BamszWD77mUpBWvc0UhY1MgTzpboli6uMBZilwwpyV44uyZ\nVJlkMkLvyBAHh9+Y+/xEqZc3jbO0Bq+c5GOWJMWyid8NYlqTWKakWDKJh+qYmsqTtbL8bOgXpMw0\njf4GPtn6CULaQumys8+OkRqb7W1ZBDWisuXPu1EUwfipLM/878cZGLOJu5PsCJ9lfXMCRWYRxwao\nJBKkR0LEj/8j0/sVUBSUez9B2/IV3Ol+nPOin85zktGJOqqWgxr3s67DnbsX/kIJRVs5+9sm1vAR\nUuEsLhczLafq66jWdXKmXtBbybFaeZZYOYd/qo7JNXXUZGs5N9nLOdelrmeEto+tp+bH/8DhV1ow\nywZKH6yym9j8rRrGH3iR0ql+kDBZ20asYz1q2scUecblGFnbm5cjHY53VBGdSRSpUSKPUzzOeXOI\nRmVW7UkLc0Z0MjZ2jFzR5XB8JamKSnNYUq2aVKswNp5/R8R0+Eic4v77ccYd9DaVlqRkMi2JhBQa\nYjabuh3ePGtRqngegEREYFaKaKpABaZn1fbSORfTsrFcF9MFRYLhkygCTMuLTVYqFoVcAbO8cHPo\nSMmrA0X2HnQIS431SzXKrQWenZ4hbdosDwWY7lf58bNp6tQCDQmF/3BfkFUd3obs7f738rZNwXaJ\nayp/PzRBZVayaDBT4MG2Bup8vz9R93eDd7pefFDwx0DuHwjC/E//6T/xd3/3d1iWRXd3N3fcccf7\nPaX3FecKF+bIEqAnf/Y9E+blWBlZzrGZ44yUR1GEwq31N//O72iKjkDMi2++nWW4u+FWfj78OE2B\nJpr8jSyLLKMt2MqWxCbA6+GZMj0FnPHKBPtTB9jVcOvvnry42F/xzLNjDI55MVonZzOUN2hJdlLf\nEiXdl+XCxL3UVQaI180uvq6LPLAPsXwF9aIeWefS9VGDXJMgbyss3ZmkufHiNUk1BtZFsXipJdis\nbOGwewiAmIjRLZYx2JJGbT1F1oUXUrezwTrLTNxlpm2AvFqC5hawbYxUJyI7g54xSFSWUJZ52qbP\nknhlnJn/eJxCXkGqBqpdRVSKDLGMVbO/XUMtURElJ3OoQiVRWMpQn05N+jUa3DLq5iJP1/6a+8WX\n5mLER7rvJCM2UbEFFTPBkiosa/M+W92hzZHl6LTDs4dM+sccltRLkjEXFI2tK/2UKpLDZyySMUEm\nJzg75HDbFh9fvTfA2hXxucX8s7v8HD5jISWs7lCv2N+yVJEUsLFwEapA80l2bjNQbIWxlEssLPji\nnQF8V5DOk1Ly2FiKR5+1qFYgpmn0vaESqlYQMcjbDhdKFQYORJGOAipMZFx+uc9E1lqMVKqs1aDp\nsvPmbZv/b3iS1zIFGvw6bX6Dgu2gzXpzXAk5y3nfCHMRHzy8b4TZ0tLCww8/DEBHRwcPPfTQ+zWV\nDxxil9VZvp2G67uFrujct+RPma5OE1AD76imM6yFuLX+Zl6YehFXulxbe81cq68rocHfwNe6v4rp\nmgvimwBlp/K2x2+h7ZpaUufzVHIWml+l88ZLfvOSVbkYqafqREGk8dUuof5jt1K3Yh3KAQd5YPCS\ni9eZklP8xHqIiqigtWp8vP1P2KQslPwza+7Cl34KtdyLkC6KOcFO5UZCw3VY/iqbl6zHj5+jO/aS\nip1G6atyaG0jK8514o+M03Tb9fRnXkQBrpvsJmlGQdXwb11PfN8hVg7tJ1waxa2tR2UZvswIqaY1\nIF2sUA0ieDHhySd8fFb9AofcgwBE33yA/Iv/D+GKRJdR4q+7DH9tkHRNmiRJwOvkMRL17lc7cMN6\nnaYaTx6vvdEjS9OSPPp8hddOWeQKDs8ftlnTIdm9FZ54yWXTCj+OI6mYEsvxyjzqE4Js3uXsgEnM\nL1EUQW1MYdsqnX94qsxjeysE/YIv3+3nZL9LoSRZ3alyut8hUOtSY0ukK9my0+VzNym02yGqlmRJ\ng3rVdmApy+ZCsYJleoSftW0UQDeh2e+jaDuULIkuBYFL0l7Hqya/nPDUFs7aFtcFg2yOXXSxfq9/\njMfHUlhSEi2r+GoEmqLMEWZYU2n0L5LlIi7iA2FhLmI+1sXWMm2mOJvvJa7HuLPpI7/X86tCpdZX\nO1fC8U6wpWYT6+NrcaW7gAQnKpMMlYapM2rpCHkdOYQQVyRLgM3xjQyWhnCliyZUNsbXLxwkJWGt\nh+s+ladQ7cCoaUAPXIxJLbu1kaHRYQbGbGQijHLPZ6lZnSXU2UzF0VEAuW07su8CTIxDMIS4bv5X\npgAAIABJREFUfj17J7/GkNKDpYYJhW7goDhA1xUIU2pRzPguAtVhpHRQskeZfOEEpQt3AT6GNs4Q\n360yJAZZuSaEZeg49cepS0RZ1xChOeGww7wNceYQOlPYG7qw2zswOjpZs+81iuUgWiGCEXUw7TKj\n6wSZyiBGNYmy4RZarzc47/aSFPVERYyYiLNL9d6DMavE8iGb8UQS4ZgEpjPUvT5IaNfFxJRbtviw\nHZjMuHQ0qly3Rl9QZlGqSGYKkkJZYrsuriuYKUCu6BI0TKazOm/02oxMeS7T1R0aT71qsuewSSLq\nEA85fPVjfsbTkucOmxw4aWLPiun8L/+jyI6NXp3lP//GZDTloMYVmjs8r0AwKonrGq1Xaf91KQzF\nEz+ob5GMD3mt6JbEdPxJE4Gg2Qli9oSIBhVOT9lEgpLaqErH5iol5GzzOrhQqswRZtlxOJjJ43qv\nGlnLYcq0+UJrEgWBJSUboiGC6u+e3yL+eLBImB9ACCG4tf7md+QufbcYKY/y+MiTFO0SKyLLuKf5\nrqsm71yOK7lhh0sjPDL06JwLeXfDrWxKvL1g59JIN/e3f46J4hQt4aYFDakBfJmn0QqeZF5AMaho\nDyK5qCfbvD7BJ//XEOlpE59boGbgIPQbiDVLYbb8RAQCKF/8EpnCIE/ovyE887/xqnIOgUBxcoxX\nD7MhMCt0Lh303H6ENY0T6MYJrUM1x2C2IXd5xkS1UuBpEDH2Zoa6G2phahRVOmzqakBpddnVlSAu\nvUzNUPwC7g2rwXVQfQ6Ue3CCKwl11hP0rcV5w0SWijxTd47nVodx/GEi62q4s1Xh/3T/C6PWKEGC\nfEP/W1YoK+eufdMKH+O+OpLpJbj2a8T8sPo1DaPwa+SyFdDYhFFXx903vH3NYDQkaKpVvHIVBXRN\nEvB5Oq5VR3D8vMO6bo1yVVI2JQE/TIy5VKqSaNikajocP2+zulPljV6byYxLTdR7l7IlL7nnVL9F\nz4CNECAnFVqWasSSLruXBlgTWRi3dl3J6QGHquk1jA75BRFNY1ddnMllU+SCkg2BCH+5JUpJDTJQ\nrnLgDFSEQk2LZ1m3NSvIVXlOlApMzVisDAcI4aP2kiJLV0JIVYlrKqnZLtiNhs6WWAS/uii4vogr\nQ/3Wt771rfd7Eu8VpZL5uwd9gBAKGe/7nB8d+jlZyxPzTplponrkbUXcf9ecD6Rem+ukAlBySmy4\nksV4CRzbpe/JFKnnTbKnq8TbQhjh+Xs3I/XEHFkJ6SD1OJMEyNt5QmrIEx33q0T0Cv6f/b8wPATD\nQygjQ1grL4otCCF4Uv01o2KMcPEcI2rOE69HwdICPFj8EuHhDL7qPvTyYRRrCq18FlerRfqSTGZe\n5aeZsxwojlOYDpM/uZLscAnXcViX2Y/RP8KgHEBMp9htbqL9NyewT4yB5aDW+7DcINP9ZSampxl2\nHILJFbgBA/ou4Mbi2JrBwysMMqEQTtWkmirR09rLeaUXE5MiRfplP7erF+P69QmFUFcrDSeOszxl\n0ZZYQUzE4cUXoFREHnsD0dyCiF+5a/yl92Zlh4auCWxbsKzVZdtKaKrTaKwLceiUg1AgHBQUSpJE\nRCGVk7guGD6FUtmlYkpqYwqRoELvsFdPqWuCljqFZELhSI+NbUvqEwo+XbC83se370+woeHKOsY/\n2VPhH39Z5oXXLV4/Y3HjBh2fLhitVOkZtYj5VAJJB2EpFMd1oo5OOuVJ3IHXmNpKVKnGq/gVwdli\nmb5ylWTA4BPJBP5Zi9GnKNhSMmPb1Ooa1yYi/M3SVgIfIIvyg7BevBuEQv96og7p6nPv+bu1xu+v\npn/RwvwjQ8V9Z/HDq8GRDgPFQTRFY0mwjbA2vz7t8uMrYeT1NKkLXvZrJWdx9plRttzfNW+Mq0aR\n7iQv6YNMKAUyRZPxkQgCwYrIMu5tvtsrtRkdhcrFa3BGRpDlsidAPovSrE6t4+8kYk8gRJCIkuDm\nmVuo/+mvcF0X0XoMNnVByHPZqdVBqsFV/LhqUJF+ZChC/3gzLWMpQkqQaJ3CyOtptjS3sHGsEXn6\nFPmxl5m0svi74hh5KPiXkc9dYMrpZzg8wHOnR0jXPk9rVxfBz5W5dWITDaP1+Ab2ACMI10VWTERF\ng0u6UJXkZarnQMOaNuSf34/7qycBkKdPITXdcz46DsUDRziabUHXBJuXa1dMpgEI+QX37fKzsl3l\nucMqfROSI+ckfaMVZvIuhs8TA9ixwce1a3SmZ1z++ekKCEFrvUoq5+LTBNGQ4NYtPlqSCjVRhV1b\nfYxMuZy44JDKgk/3ksbq4oKrCfCYluQ3+6rkSx75nR91eO6wyb07/Pz85Qon+z0yC0ckvXaF5bNl\nMW9ZyY7rSeg1dkr6XBiqmARUlQZNJenXOThT4CP1FzcRn2qqY0M0hCUly0IBjMVWXh9Y7HNffs/f\nXfZ7nMfbEqaUkmKxSDg8vxZpamqKZDL5e5zGIv6tsCWxaa6VVlANsiq6Yu6z8fI4VdekNdhyRUUe\nV7o8OvQYg6UhANbGVrO74TaOpI/Sk++lI7TkHWW72lUXYWdQrClQDKxy14Ix1dqP82ruuxxUJqhq\nNewvHqHTt5Rms42efC/D5RHagq1QUwuKMic8qkSj4J9fvL5R2cSzzm8Zi3TQXI2wUnbS6N/C8mfO\ngetdi1vyU+ofpT/UhRAQ767HcKsUlBCE1gAQ0kPUbQqR9NeC45If9AMVlHSG6YEMpVQFXAUlk8NM\na+RSMdLlDgZuOEaf3cBQoMz0TB96IUXbMLwgz/Bl6wF29q/j1aJF0ZeiQ1nNDZGP8l/Ef6boFsmL\nHI008UvnST6i3IkuLnGLr16DGB1GnjwBiTgi4iWHWTbsPaVyyvFc0xdGbD6723/VWt5iRbLnkIkr\n8Uiuzybk9yxLnwYNNYL/6dPBuaScZUtUDp4RlEomQQNO9Tuc7LNZsUTjI9sNVsz2rGysVfn2X4T5\nzz8ocPycTbEikdJkcNzlW18O0Vo/f/nRVHAuy7C1HUhlXbLjF8lsdFDQmVBgdk80U5B86a4AM3mX\nhlqVgtAYG60yMwOj44KKptJrW7Qumf9OK0KwIrzQLbyIDx5OlN6d4Mk8LGyb+55xVcI8cOAA3/jG\nNzBNk1WrVvGd73yHhgbPdffVr36VX/ziF7+/WSzi3wzX1m6nOdC8oFnzi5MvczDtlUy0Bpr5dNun\nFiQFDZdH5sgS4ET2FEE1SNmtsCTUhotkuDzC6t+Redu0tMjU3hNYs96mzu4KsHLeGOmrZzC+AUvG\nsV0LitPktRzMfkcgkP19yLNnoLkFmc0iYjEC932S8mXEsEnZQi11HHD30WNUOUyeGxQHofvmCmXy\nU92cqAhybS1MW51MTa7kq/catAeXMFDyMm39zSqx/iggQFVJ3LMTxvZBtcqIbMYQEzT6LqAKi4oi\nKNTfSO5IGydGazHbTCy1RFmmOa0WcGP1dGQTBM6e4c7P3E77Hh/SSNDy2R0YAYNvO9/ln8r/wODA\nOIFqDQdqDxFtjbIiuwPXhcZaBSEEYvcdsPsO3MlJ3O/8H8gL5yk0LqV35UUxiuEpl2IFQuYMlEqQ\nrEdo3rNNZV1eesPk/IhDS1LBlfOFz326oDU5P4N1TafOzdd4NYKZvMvUTIlYSEXX4Ff7qrTVqwT9\n3vhQwJO86xt1cKUknQO/z+XJl02+/smFerD33mjw2N4qjgut9QpGZ5V/Hs2QsVRqdA3TlQQDgrbA\nRRM8EhTURJW5+GkIH19qrefcgQxlXx5VgdFJh0Ty3akALWIRl+OqhPmd73yHhx56iPb2dn7wgx/w\nhS98gR/96EfU19fPk7JbxPsD27V5ceplRstjNAeauLl+51V1Wt9C2SlzOP06lmuyPr5+jixN15wj\nS4Dh8ih9xX6WReZnj/rE/KQfgWCgODjvb+cLF1gdXcXVIPM5wtNH2H7nJDMzQQJRl3hzmfIVxraI\nVgblAD5Fpy3Yip7yTIpV0ZU0p13cnz2CzOWQp04i4nG4YQciHIbSwjZOdSLJoBzAwEAiecV9ie5b\nPkHd9BTkchQjTbwc+hh2YdbikC65dIk/af0YR2fexHRM1nx8Fbk3HAqTZWJNBk2De5HpFLKhEdQJ\nQv4cUhVU/WFkSCcZe4Psho9xc08nr3X2kJdptIKfvBtjIJTn5oFOVKkQvG4bnZvnd4XpVLuInWoi\nUCphYpPOFfjNMZMDOc/9vHKJxj03+i5aja+8SKm2hX1iMwVLQ06MQ7v3/Pw+gb/nTdw9T3tM2NCI\nct/nKUkfP362QqniUjXlrJWoUjVVLEdSKElWdWgsX6LxxMsV/D7BjRt8hGbJUErJqT6b105ZFMre\n+MZahdu2+FjSqJLJS/IlF+eSx+E4EseVFKRNwXYIa/Pf2ft2BVi/VCdbcNFrbZ4vprE1idNdYnhM\n4XpfgluvC2DZcKrPIRIS3HX9wthZQFFpk0ESVR9FxaKGAANZk6cnTdZFg7T4F0XUF/HucVXCdF2X\nzk5PWPKrX/0qPp+PL3/5y/zkJz95TzJti/j94tXp/XONl8cq4/gUHzuSN1x1vJSSnw39Yi5B50Tu\nNH/W8UXCehgFBUUo5K08eStPSAvNpeIDnMye5nTuNBE9wrrYWo5nTyAQ3FJ/E2kzzUR1cm7sdDXF\njwceJqJHubX+ZnyKjiIUVKEihwZxf/5TrNI4mt1D493rUOrCOL4rKwbdoOxARWVCjnNrZBfd/mW4\nSGqNGtz9r3qLf38fWCbMzMDUJOa+fbBxoVKUhTlPpQegmgii/PlfQrlMUAng+1UZuyLxF1JsPvZz\nYsNl1OYmtn3qM4igR6SJ2VO7+15Bnj8HgHjzKF3GNGk1ipabRAnr6JhYR45hN95N6zWfQTt+gZeW\nP0WxsQk1M0y0mmPjZDPi2us9N/IVFF2SQ22cq/X6bpkVP5lTbSRavc/ODNpsS2k0WhPIF1/AeW4P\nPxW7SGm1SAlTMxo1zZKWeoXd2wzEw3svmo0T48jTJ5ls2EC56vWjXNGuks5J7r7eYPkSjVROEvaD\nRPDQ0+U50pvMuNx/RwApJU+8XGXPoSqDEy6FsiTgg+ms5NHny4SDKoonsITjeCUpR3ospJBU4lUK\nnUW+P5Dno/UJVs1my6ZzLq8et3BdyTWrdCZ0E1mQnC6UqCQcAjEI1+hs6AwT1zV2bbva2+4Jsa/v\n1njzHIQcjTHLRPqKjOXgVKHEF1vr31aQQErJM1MznC6UiGgq9zbUkjQWazL/2HFVwqyrq+NHP/oR\n9957L5FIhAcffJDJyUm+9KUvkc1m/y3nuIgrYKo6fdnx1NuOLznledmsFafCaGWM5foyNEVjTXQV\nf3/hh9iuQ9WpMlgaoiPUzu2dN/GzC09xodgPwIbYOv5q2deI6lEM1aDqVKm6JpOVSVShMl6Z8MpU\nyqO8njlKQA2gCoXbG3ax5uBJME2kVoPjtmOd/v/Ze88oO+4r2+/3r3Bzvn07B3RCDgQJkgAI5ihK\nFEWKWYmSRp739GSNtTx+Y9lr1ozHbySv0Sx7Se95vJ6W5QkaSZQocshhTiBIMIlEIHI3Qgd0Tjen\nin9/uM1uNBEV30jExhdUdd26VdXVteucs88+VdRbL8eM33zGY3akQ2aqwEwlh+qbZl3jhoXeTlGX\nqqVTPyCBQABTcRhmFOQcCZFcsq8IUZaLFRyV/UAtem0WLbWXv0CAAHD/jT7eO2LR9NpOljeVURSB\nnJyAd17Du8FCsWZxfF1Y0WuhsjjwmmyWUFxBxHrwHRxELRdRvS4VvYGAk6FYijJe6KY6+THc9Ufw\n690k/VE67n2AseM6h779Lt6DO2juVglsuQRlUy3avDx4OeKYRiY4SzDdxEhwqZpZ2Bbuz38GlTIV\nV2N2pgyNMY6Zdcx6kqwsSzZEVVpSKs6Hrq0QgkRYoCoS266RZluDyoZeHV0TfOA1fuCEtSRCnJhz\nKVdd/vGZAo+/ZmBakkS41tMZDqg0JARDk5LuFpegX8F1obdN4ZJejWs36lTDBsP+Kr6AwJGSHXM5\nVoUDWLbkp69UFwQ/e/ptLMXmgN9mNmlSF1Vo8OlIpTbIOfbhOVxnwC1XeOhpValUJa9oZYz5YTe2\nKxmtGOckzIOFMvvyNbHVnGnzzHSah9vOria/iI8GztpWsmXLFh577DF8Ph9dXTVRxlVXXUW5XObN\nN9/kK1/5yu/yOM+I3yfJNfxmZeJlp8LgPIkBbIxdQrP/w+Zfi9CEyr7cfiy39tRQhMLm5JULHq5H\nC8dQhIrE4XjxBFkrR8Eusju9l7xZxJlv8bClTVdo2YJBgaZorAj3cmn8EspOmZFKbQpNwSpwKHeE\nlkAzeSvPztk3WTZhkSzWGsmlGsbt3Iq7/pOgeM5wxLUoem92H1XXYNacw3ANukO1e1Ekk6B7wDBq\nhLG8nR9tOsreywW73PcJiwgNYvEBJ4RguVhJmDARolyjXE9QWaroDfoEvW0aDSf3oZXyC+t130k8\n9XMIp4hqjCDVAG6sB3n4UI2wi3lEIonekEKcnEC4KlJLYGVNCqF2qvFWnJKFeTxOpHU1yXIPX+q8\njYDt5fBTo/j3PUFxbC/pkVESxgxaQwsimSTRGSJqxmmqtNDR0Egg5We6XKvTXdKrsT5VQr77DgBq\nPMrhQpxsrI0BTztKwE9bg0q2KFneriGDQUbePkom7+Bb1ornpptQVIeIr0ymYBMNSu7YFjyDv6zg\nwAl7odbbkFCYmJUcGXI5NmLOD3UW2E7N8aenRcNxa4YIQ1M2ZVNy2XINwxRMpl2ypoNMmgtTSLyK\nwuWxMJm8y5M7DebyknzR5Z2DNrNpiZr24cZN4h6NNXV+FCHYHAuflso9E4So1TbrEyqj0iZTMefX\nw+Z4mIh2dtIdLFcZrhgLyxK4Iv679Uq92FayiJcKv3pbyS3h30FbSSqV4jvf+c5p6x9++GEefvjh\n39gBXMTp+KBGfK7U96bEpeiKxnhlghZ/88KIrrNBEQr3tN7Nq9M7MFyTKxKbSHkX51j6NT+6opG3\nC0jkQj00a2bRXR+WtPCpPvyqH89ZCK4t0IbgHSQSRzrEPFGKVpGDuUO4SF7ojiLG5liptEAshtiy\n9Yz7+QBZM7tkOfOhZeWKK+GKK5GZNIeLr5JJeAhYJq5R4Y3Q66xTlvaDTjHJDnc7BgYHnf18mvto\nVzpO+16x6Qrk+FiNDL1etBU1NezC91oziMZNKJ//EnJkGHH3vUz963tk9wxR0a4m4c/TGksj2upR\nq2Vm+vOUZw1agx7iwzE23VVP4y+eZHLfLOZYgrniQZBVKAv2DRyhY/sR6soGmlejeW0XEwcyVHNF\n/GT52KUp2q6sIxZWkLYKiQSk06iqwj2XVnmuew1D+yTNdQp+77wnqiP5p9Fl7IjeQnbcQj1Rx7cH\nJV0NJToaoaNRAC6BgAUsJaKGhMJd13p5/5iN3yu4ZoPOf32yjO3oNNcpjM+6NKYUbrpcQ9egt01j\nbMbmJzsrZKoOekUS7LNpUmqCG7uskXF16ldZqEJwbbKm7H33iMXotIthSaYzLlKCrikoDnScrKMp\nZdDkU7k0GqTRd+b771x4cFkDP6valB2HDdHgeWuYvUE/v8gUMNza32LKo7M7W6Td772Ymv0I42If\n5r8xHMod4aWpl3Gkw1V1W9mcvOKs226IrT+vScCpaPDV80D7fWf82dbkZqarM4yWxwioI8T0KI50\nCOpB/AQZKA4SUP1sS21lffTM5NwWaOXTrZ+ir9BPUA0yVBpid2YvLpKUtw4nHGP7nSlWN30WwhHE\neZrEe8Ld9BWOLiwv/5AI6QOIeAIl1AIHf45VKSJNG6WhDB/SHu2ce5O8UsQb0rGExXvuL5YQppSS\n19xXOd51lIbP1nFDZiP+qsAtvgXKDIRrEYbjq01zEckkumcIY/hljmfDOGuuRx0cRvS/Q0NDBF9L\ngmjdCrI/L+PYLvGISrBSIfzGK8ixQWJ+gSxWqVo+fI4BBYXKhE3upbdQXniKhDLDcLaBE5470BqS\nJLtDZA7Ose6mmkes0DSUBz5TizIdh7rLLufziQjtbSZv7TepGpJrLtFBCIa2P0PPwRHyepg3Ktv4\nLy+P8L/dFSN0Zv8AnMwcNqCGI3S36HS31B4VT71p8PYBm2LVoT4uWd+t4kjB4IRDU1JhYMzBFC7B\nFgufA8Wq5I2jFVb4FFa26JiWZIUMc0erRlBTCM9HeQPjLmu6NE5OOVQMSTwsMKyabV1E0fj3q6K0\nN9TuF1dKduWKpE2broCP5Wc7iVMwXjGI6iouksmqRbvPJnKOtG7So/O51npOlKuMVgyOlWoRp6YI\nHmiuo/miaOgjiYuE+W8IFafC85MvLNjMvT7zBl3BTup9F9bzars2QojzqmXPBJ/q44H2e7m39W6e\nHn+WX6Tfw3ANGiN1SFNlfXQtilB48CyE+wG6Qp10hWpisa3uZpr8Tbw6/fqCgXwikDqvA80HWB1Z\nhVfxMlIepcnXyIrI8rNuu34kxpExl9kEaK7K9Ts15LJFA4P+F8YZys8yGktjVRx8MZ2AN4W7SaKo\ntUhsn9zLu24txZlOQnDoRa59XWIgwXcCz+Y4btsmHF+NZBVzAk/2RcoVFeEIvJk91M0OI9QCVCxQ\nFMpta9G9R9Bys5QPWWQmh1HurNU/vR7JpvVFdgTizBhR9AEbGYjTMZRBGT9OKR7Cnkwj1BEqwsus\nI+nYXLfkvEUojLhhaQ04FaulVYVSE+GETh6hp+9dylWN+uoMHtdiqHQzUvgx7TKHBiWKonL5Gg+6\nlDjPP0vRqiA1DTqW4e9egc/nZzbr8sPnKkxnbdJ5mJqThP0eJuYcHLfWorKhRyMQrX13Nu+SzruE\nUg4TUw6Hjjo4CZNYwWY66OPO7hgjEwbxiEIkAOWqYEW7Rlu9iiLAdiQBn+Azt/ppq1+8p1+by/Fe\ntmZ8sT9f4q6mJL3Bs5Pm63M59lYrvDGRwZWSdZEgJ8oVvtTWgH4Os4KERyfh0TlUWKxX267ktbk8\nZcfBdCVXxMJcFvtvOzPzIn53OC9hvvnmm1x11VL15Ysvvsgtt9zyWzuojypM11wy1gtqJHoheGfu\nF+yceWveh/ZaLp0fpXUqBoqDGK5BV7DzrMboqqJyZ+sd3Nl6B4fzR3g1+wol00QIhbD+y9VwdEXn\nlsabiOhhDuf7CGthbm385eoJ3aGuhbrlueBRfTx4eD1mXELexefqNYkmNR/Y8X0ZVns2cqI4QFXL\nESFC+9AaxuQUrVc2IIQgIzNL9qn29QPLUcxJrEwRjobwNM7gybyImbwDYWexpUO6IQM9Fp53LRTH\nILAshb4yggCcfIW4N0/GspBA1FfGpyzWxuJ+H7d89laesUfI/1ijuxKDgcdQFLBsBc0j8fmgZDgU\n0zbL1jYgpTxnuv7l90w0TaABA+MO03PTNNdLRiwX11FoUqZR64M0JHz8+CXJZNpFEYITEwafWzuN\nNTmGbJifDDM8hNHQhM/n562DBv3DFtkS6KqgDBwbtYmHVRxTUq5Kqibcud7HlFrluQNltHobr6JT\nykuMoIXdXmTMhR8dNnj1kMFN8TiKIuhtUfHUQ77ssmWtzrb1OqYN3jM4FA2dUlsEGC4b5yTMg4Uy\nJSEx59OradMmoKpkLId67/ndfYKqCtRq/xLJrlyBBk8tLfzKbJYWn+dXShNfxO8fzkqYzz77LKZp\n8r3vfY+vf/3rC+sty+L73//+RcL8LSCiRegOdXKiOAjUUqjnEvJ8gDkjveDeI6XklakdLA/1LvRZ\nArww+RL7sgcASHoSfLbjwQXStF0bRSgLJuyWcoKqtp3WpMNKJcae8gxNkWmuaQxSVd/A62xFcOE2\nYpuTV7I5eeUFb/8rob0Ddc16EkNHKbsm4vobEd55Re18BBk0w2x94XZKlOjsrcc7PIDR/wvcvTrc\n8wlWh2cJzL6DlIJjkWUkI13IKTjcH2Zm5hK8mTAbu1VindO4rmRsKMgz7nGK9VPwCdjYvZrlzyfw\nx+Yt6sIREisTRGKSUEvtxWdFbwlR38rRdR/n2HsjWA0t3NG4ngepMH5bhtFdc5jqzQQPprELJQa1\nVQTiYfo9KWRbnJ0jOuXdJjduOvMLz2ilyh5tjpIjSZUCtHt8VBuXsbkzhp8iBwt1HK3bzDp/gvf6\nbKazoMy/WMzmXOayDlF7qaZWCOgbsvnBU1XyZTCsWj9lOADFsuCK1SpHT9aMCS5bobGyQ2PzaJhC\nncqxEQetojJuu/ibHLLz74O2LclpFpmCJBkVzOZdvvLJpa47ZysV1nl0Zj6QvAJ1nqWPMcN1mTMt\nIppGSFMJaSoGLqqoKXM9isCnKkQuQDgEcHMqxpOTc8xZNq0+72nzPvO2Q+MF7eki/q3i7rvvXnC0\na21t5Vvf+tYZtzsrYRaLRfbu3UupVOIXv/jFwnpVVfnGN77xGz7ci4CayOeuljvpLxzFkS7Lwz3n\nHNT8AQx36Ru3RGK4Jh/QpemaC2QJNdP1odIwKyLL2T69g93pvWhC5WNNt7I80k5Z/xckJgi4tHGW\nStUmHBzEVAyq2hxg4XOu/5XOcao6xdHCcaJ6hHXRtb+xnl4hBOLjdxDSHarZKiK4qID1hXWWbU0x\n9NYM0foQQcOPNzeHWspQ32niME155puEInmWJ0zMTAOrcxC+4WEmBnYyXZxDTeRx4jEO7pBsXtHO\nwcdPsi9zgPd7QviKKk1rvOzanGBb4hOI3buhWkVWKzTs+Ht8a2LMBi1isTw+v810spnnxuuxGmqP\n2R+/UORzNys0r4/TvD4O9HCw2k7foZ/jTGpUx3sQ042o7TWPr75hhxs3nX4NTNfl8ck0hs9mYNpm\nUJpUc0laPt1GPnYD05Pb2bdvDfXRNipVl9f3WQjkwtgvVYFAdzue4wnsXB47GkFpbCIQTfD2OzWC\nSkQE0xmJokAqrtBUJ2hIQGvS4fKVLg3JKoblIRVXafV7CTW7ZPKS9nrJBA45KghR24+tUcQ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K5QJ0cLtaiwYOUp22UCWgAn6TLePsCtE58CoP3KRUeb0d1pTrw2BcDciQLSha6r61GFyifVu3jB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\nGGOMWeE/TqIe1RXjFs8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1195ba048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(projected[:, 0], projected[:, 1],\n",
+    "            c=digits.target, edgecolor='none', alpha=0.5,\n",
+    "            cmap=plt.cm.get_cmap('spectral', 10))\n",
+    "plt.xlabel('component 1')\n",
+    "plt.ylabel('component 2')\n",
+    "plt.colorbar();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Recall what these components mean: the full data is a 64-dimensional point cloud, and these points are the projection of each data point along the directions with the largest variance.\n",
+    "Essentially, we have found the optimal stretch and rotation in 64-dimensional space that allows us to see the layout of the digits in two dimensions, and have done this in an unsupervised manner—that is, without reference to the labels."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### What do the components mean?\n",
+    "\n",
+    "We can go a bit further here, and begin to ask what the reduced dimensions *mean*.\n",
+    "This meaning can be understood in terms of combinations of basis vectors.\n",
+    "For example, each image in the training set is defined by a collection of 64 pixel values, which we will call the vector $x$:\n",
+    "\n",
+    "$$\n",
+    "x = [x_1, x_2, x_3 \\cdots x_{64}]\n",
+    "$$\n",
+    "\n",
+    "One way we can think about this is in terms of a pixel basis.\n",
+    "That is, to construct the image, we multiply each element of the vector by the pixel it describes, and then add the results together to build the image:\n",
+    "\n",
+    "$$\n",
+    "{\\rm image}(x) = x_1 \\cdot{\\rm (pixel~1)} + x_2 \\cdot{\\rm (pixel~2)} + x_3 \\cdot{\\rm (pixel~3)} \\cdots x_{64} \\cdot{\\rm (pixel~64)}\n",
+    "$$\n",
+    "\n",
+    "One way we might imagine reducing the dimension of this data is to zero out all but a few of these basis vectors.\n",
+    "For example, if we use only the first eight pixels, we get an eight-dimensional projection of the data, but it is not very reflective of the whole image: we've thrown out nearly 90% of the pixels!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.09-digits-pixel-components.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Digits-Pixel-Components)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The upper row of panels shows the individual pixels, and the lower row shows the cumulative contribution of these pixels to the construction of the image.\n",
+    "Using only eight of the pixel-basis components, we can only construct a small portion of the 64-pixel image.\n",
+    "Were we to continue this sequence and use all 64 pixels, we would recover the original image."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "But the pixel-wise representation is not the only choice of basis. We can also use other basis functions, which each contain some pre-defined contribution from each pixel, and write something like\n",
+    "\n",
+    "$$\n",
+    "image(x) = {\\rm mean} + x_1 \\cdot{\\rm (basis~1)} + x_2 \\cdot{\\rm (basis~2)} + x_3 \\cdot{\\rm (basis~3)} \\cdots\n",
+    "$$\n",
+    "\n",
+    "PCA can be thought of as a process of choosing optimal basis functions, such that adding together just the first few of them is enough to suitably reconstruct the bulk of the elements in the dataset.\n",
+    "The principal components, which act as the low-dimensional representation of our data, are simply the coefficients that multiply each of the elements in this series.\n",
+    "This figure shows a similar depiction of reconstructing this digit using the mean plus the first eight PCA basis functions:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![](figures/05.09-digits-pca-components.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Digits-PCA-Components)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Unlike the pixel basis, the PCA basis allows us to recover the salient features of the input image with just a mean plus eight components!\n",
+    "The amount of each pixel in each component is the corollary of the orientation of the vector in our two-dimensional example.\n",
+    "This is the sense in which PCA provides a low-dimensional representation of the data: it discovers a set of basis functions that are more efficient than the native pixel-basis of the input data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Choosing the number of components\n",
+    "\n",
+    "A vital part of using PCA in practice is the ability to estimate how many components are needed to describe the data.\n",
+    "This can be determined by looking at the cumulative *explained variance ratio* as a function of the number of components:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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uLoeIiOiGHJ5j37NnD5577jlERkbCZrOhqqoKK1asQI8ePRzuXJIkLFq0qMG6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\nJCMMdiIiIhlhsBMREckIg52IiEhG/j/X1c1dl6Mg0gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11942e3c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pca = PCA().fit(digits.data)\n",
+    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
+    "plt.xlabel('number of components')\n",
+    "plt.ylabel('cumulative explained variance');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This curve quantifies how much of the total, 64-dimensional variance is contained within the first $N$ components.\n",
+    "For example, we see that with the digits the first 10 components contain approximately 75% of the variance, while you need around 50 components to describe close to 100% of the variance.\n",
+    "\n",
+    "Here we see that our two-dimensional projection loses a lot of information (as measured by the explained variance) and that we'd need about 20 components to retain 90% of the variance.  Looking at this plot for a high-dimensional dataset can help you understand the level of redundancy present in multiple observations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## PCA as Noise Filtering\n",
+    "\n",
+    "PCA can also be used as a filtering approach for noisy data.\n",
+    "The idea is this: any components with variance much larger than the effect of the noise should be relatively unaffected by the noise.\n",
+    "So if you reconstruct the data using just the largest subset of principal components, you should be preferentially keeping the signal and throwing out the noise.\n",
+    "\n",
+    "Let's see how this looks with the digits data.\n",
+    "First we will plot several of the input noise-free data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAADsCAYAAABwrnycAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11969c908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_digits(data):\n",
+    "    fig, axes = plt.subplots(4, 10, figsize=(10, 4),\n",
+    "                             subplot_kw={'xticks':[], 'yticks':[]},\n",
+    "                             gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
+    "    for i, ax in enumerate(axes.flat):\n",
+    "        ax.imshow(data[i].reshape(8, 8),\n",
+    "                  cmap='binary', interpolation='nearest',\n",
+    "                  clim=(0, 16))\n",
+    "plot_digits(digits.data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now lets add some random noise to create a noisy dataset, and re-plot it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+fftkTbly5SLX/NMWLlzo5mqQq5keqtqjRw9Zo96fZmYNGzZ082LFismaRH36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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119676828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(42)\n",
+    "noisy = np.random.normal(digits.data, 4)\n",
+    "plot_digits(noisy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "It's clear by eye that the images are noisy, and contain spurious pixels.\n",
+    "Let's train a PCA on the noisy data, requesting that the projection preserve 50% of the variance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca = PCA(0.50).fit(noisy)\n",
+    "pca.n_components_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Here 50% of the variance amounts to 12 principal components.\n",
+    "Now we compute these components, and then use the inverse of the transform to reconstruct the filtered digits:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AACSPDQ8AAEgeGx4AAJA8NjwAACB5bHgAAEDywpSWmt+xcOFC2TN69Gi3rmbSmOnkSPRL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\nAEgeGx4AAJA8NjwAACB5bHgAAEDy/gcjsffkWE6IPAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11976a358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "components = pca.transform(noisy)\n",
+    "filtered = pca.inverse_transform(components)\n",
+    "plot_digits(filtered)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This signal preserving/noise filtering property makes PCA a very useful feature selection routine—for example, rather than training a classifier on very high-dimensional data, you might instead train the classifier on the lower-dimensional representation, which will automatically serve to filter out random noise in the inputs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Example: Eigenfaces\n",
+    "\n",
+    "Earlier we explored an example of using a PCA projection as a feature selector for facial recognition with a support vector machine (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\n",
+    "Here we will take a look back and explore a bit more of what went into that.\n",
+    "Recall that we were using the Labeled Faces in the Wild dataset made available through Scikit-Learn:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Ariel Sharon' 'Colin Powell' 'Donald Rumsfeld' 'George W Bush'\n",
+      " 'Gerhard Schroeder' 'Hugo Chavez' 'Junichiro Koizumi' 'Tony Blair']\n",
+      "(1348, 62, 47)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_lfw_people\n",
+    "faces = fetch_lfw_people(min_faces_per_person=60)\n",
+    "print(faces.target_names)\n",
+    "print(faces.images.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Let's take a look at the principal axes that span this dataset.\n",
+    "Because this is a large dataset, we will use ``RandomizedPCA``—it contains a randomized method to approximate the first $N$ principal components much more quickly than the standard ``PCA`` estimator, and thus is very useful for high-dimensional data (here, a dimensionality of nearly 3,000).\n",
+    "We will take a look at the first 150 components:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomizedPCA(copy=True, iterated_power=3, n_components=150,\n",
+       "       random_state=None, whiten=False)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.decomposition import RandomizedPCA\n",
+    "pca = RandomizedPCA(150)\n",
+    "pca.fit(faces.data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "In this case, it can be interesting to visualize the images associated with the first several principal components (these components are technically known as \"eigenvectors,\"\n",
+    "so these types of images are often called \"eigenfaces\").\n",
+    "As you can see in this figure, they are as creepy as they sound:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2nG544liMyEv71vue3z0380r1rnzpJgUQPC1V4s4GAc8Cc/x3+znwENr42U0B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lAB/bdyHVYWz156czNTnGEl8T0wjjrQYaLkqfk84Xw54XKecRlHGFAD5TztsGspSPuKfV+w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\nrdv6F1v/F1E+s8isuOd8AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b4fb5f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(3, 8, figsize=(9, 4),\n",
+    "                         subplot_kw={'xticks':[], 'yticks':[]},\n",
+    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
+    "for i, ax in enumerate(axes.flat):\n",
+    "    ax.imshow(pca.components_[i].reshape(62, 47), cmap='bone')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The results are very interesting, and give us insight into how the images vary: for example, the first few eigenfaces (from the top left) seem to be associated with the angle of lighting on the face, and later principal vectors seem to be picking out certain features, such as eyes, noses, and lips.\n",
+    "Let's take a look at the cumulative variance of these components to see how much of the data information the projection is preserving:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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G0Wj0ZEnkJqcQyD9ajm92FWNfYQUAwKAPwC1XxyJpeC/ERBp8XCEREUnxaMjn5uYiKSkJ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\nMsWQJyIikimGPBERkUwx5ImIiGTq/wMjrvJM/BfyVgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b7c3630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
+    "plt.xlabel('number of components')\n",
+    "plt.ylabel('cumulative explained variance');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We see that these 150 components account for just over 90% of the variance.\n",
+    "That would lead us to believe that using these 150 components, we would recover most of the essential characteristics of the data.\n",
+    "To make this more concrete, we can compare the input images with the images reconstructed from these 150 components:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# Compute the components and projected faces\n",
+    "pca = RandomizedPCA(150).fit(faces.data)\n",
+    "components = pca.transform(faces.data)\n",
+    "projected = pca.inverse_transform(components)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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q8K0BFGAX56/spPolgkHVX0AWLG7aWyX0dS86ZwAYLeqGaTO7Jkr6/X7qGNGJ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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11985a5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the results\n",
+    "fig, ax = plt.subplots(2, 10, figsize=(10, 2.5),\n",
+    "                       subplot_kw={'xticks':[], 'yticks':[]},\n",
+    "                       gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
+    "for i in range(10):\n",
+    "    ax[0, i].imshow(faces.data[i].reshape(62, 47), cmap='binary_r')\n",
+    "    ax[1, i].imshow(projected[i].reshape(62, 47), cmap='binary_r')\n",
+    "    \n",
+    "ax[0, 0].set_ylabel('full-dim\\ninput')\n",
+    "ax[1, 0].set_ylabel('150-dim\\nreconstruction');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The top row here shows the input images, while the bottom row shows the reconstruction of the images from just 150 of the ~3,000 initial features.\n",
+    "This visualization makes clear why the PCA feature selection used in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) was so successful: although it reduces the dimensionality of the data by nearly a factor of 20, the projected images contain enough information that we might, by eye, recognize the individuals in the image.\n",
+    "What this means is that our classification algorithm needs to be trained on 150-dimensional data rather than 3,000-dimensional data, which depending on the particular algorithm we choose, can lead to a much more efficient classification."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Principal Component Analysis Summary\n",
+    "\n",
+    "In this section we have discussed the use of principal component analysis for dimensionality reduction, for visualization of high-dimensional data, for noise filtering, and for feature selection within high-dimensional data.\n",
+    "Because of the versatility and interpretability of PCA, it has been shown to be effective in a wide variety of contexts and disciplines.\n",
+    "Given any high-dimensional dataset, I tend to start with PCA in order to visualize the relationship between points (as we did with the digits), to understand the main variance in the data (as we did with the eigenfaces), and to understand the intrinsic dimensionality (by plotting the explained variance ratio).\n",
+    "Certainly PCA is not useful for every high-dimensional dataset, but it offers a straightforward and efficient path to gaining insight into high-dimensional data.\n",
+    "\n",
+    "PCA's main weakness is that it tends to be highly affected by outliers in the data.\n",
+    "For this reason, many robust variants of PCA have been developed, many of which act to iteratively discard data points that are poorly described by the initial components.\n",
+    "Scikit-Learn contains a couple interesting variants on PCA, including ``RandomizedPCA`` and ``SparsePCA``, both also in the ``sklearn.decomposition`` submodule.\n",
+    "``RandomizedPCA``, which we saw earlier, uses a non-deterministic method to quickly approximate the first few principal components in very high-dimensional data, while ``SparsePCA`` introduces a regularization term (see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)) that serves to enforce sparsity of the components.\n",
+    "\n",
+    "In the following sections, we will look at other unsupervised learning methods that build on some of the ideas of PCA."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) | [Contents](Index.ipynb) | [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.09-Principal-Component-Analysis.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.10-Manifold-Learning.ipynb b/notebooks_v1/05.10-Manifold-Learning.ipynb
new file mode 100644
index 000000000..7ec547ba9
--- /dev/null
+++ b/notebooks_v1/05.10-Manifold-Learning.ipynb
@@ -0,0 +1,1063 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) | [Contents](Index.ipynb) | [In Depth: k-Means Clustering](05.11-K-Means.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.10-Manifold-Learning.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# In-Depth: Manifold Learning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have seen how principal component analysis (PCA) can be used in the dimensionality reduction task—reducing the number of features of a dataset while maintaining the essential relationships between the points.\n",
+    "While PCA is flexible, fast, and easily interpretable, it does not perform so well when there are *nonlinear* relationships within the data; we will see some examples of these below.\n",
+    "\n",
+    "To address this deficiency, we can turn to a class of methods known as *manifold learning*—a class of unsupervised estimators that seeks to describe datasets as low-dimensional manifolds embedded in high-dimensional spaces.\n",
+    "When you think of a manifold, I'd suggest imagining a sheet of paper: this is a two-dimensional object that lives in our familiar three-dimensional world, and can be bent or rolled in that two dimensions.\n",
+    "In the parlance of manifold learning, we can think of this sheet as a two-dimensional manifold embedded in three-dimensional space.\n",
+    "\n",
+    "Rotating, re-orienting, or stretching the piece of paper in three-dimensional space doesn't change the flat geometry of the paper: such operations are akin to linear embeddings.\n",
+    "If you bend, curl, or crumple the paper, it is still a two-dimensional manifold, but the embedding into the three-dimensional space is no longer linear.\n",
+    "Manifold learning algorithms would seek to learn about the fundamental two-dimensional nature of the paper, even as it is contorted to fill the three-dimensional space.\n",
+    "\n",
+    "Here we will demonstrate a number of manifold methods, going most deeply into a couple techniques: multidimensional scaling (MDS), locally linear embedding (LLE), and isometric mapping (IsoMap).\n",
+    "\n",
+    "We begin with the standard imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns; sns.set()\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Manifold Learning: \"HELLO\"\n",
+    "\n",
+    "To make these concepts more clear, let's start by generating some two-dimensional data that we can use to define a manifold.\n",
+    "Here is a function that will create data in the shape of the word \"HELLO\":"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def make_hello(N=1000, rseed=42):\n",
+    "    # Make a plot with \"HELLO\" text; save as PNG\n",
+    "    fig, ax = plt.subplots(figsize=(4, 1))\n",
+    "    fig.subplots_adjust(left=0, right=1, bottom=0, top=1)\n",
+    "    ax.axis('off')\n",
+    "    ax.text(0.5, 0.4, 'HELLO', va='center', ha='center', weight='bold', size=85)\n",
+    "    fig.savefig('hello.png')\n",
+    "    plt.close(fig)\n",
+    "    \n",
+    "    # Open this PNG and draw random points from it\n",
+    "    from matplotlib.image import imread\n",
+    "    data = imread('hello.png')[::-1, :, 0].T\n",
+    "    rng = np.random.RandomState(rseed)\n",
+    "    X = rng.rand(4 * N, 2)\n",
+    "    i, j = (X * data.shape).astype(int).T\n",
+    "    mask = (data[i, j] < 1)\n",
+    "    X = X[mask]\n",
+    "    X[:, 0] *= (data.shape[0] / data.shape[1])\n",
+    "    X = X[:N]\n",
+    "    return X[np.argsort(X[:, 0])]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's call the function and visualize the resulting data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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4hvhdWFLSVFJNwpQBALh6uVPUVh3E5O5+9efN/1JBwaGMue1uAPZu+4qE+q3c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IhcFGyGYRjt3YiTtAd2dtpTdZ9q5R7VyX2qlXtUJ/e4e0StAU9EREQsTEEtIiJiYQpqERERC1NQ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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1157d5b00>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = make_hello(1000)\n",
+    "colorize = dict(c=X[:, 0], cmap=plt.cm.get_cmap('rainbow', 5))\n",
+    "plt.scatter(X[:, 0], X[:, 1], **colorize)\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output is two dimensional, and consists of points drawn in the shape of the word, \"HELLO\".\n",
+    "This data form will help us to see visually what these algorithms are doing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multidimensional Scaling (MDS)\n",
+    "\n",
+    "Looking at data like this, we can see that the particular choice of *x* and *y* values of the dataset are not the most fundamental description of the data: we can scale, shrink, or rotate the data, and the \"HELLO\" will still be apparent.\n",
+    "For example, if we use a rotation matrix to rotate the data, the *x* and *y* values change, but the data is still fundamentally the same:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ftzlGq46dWDlyDI3WxBAEVLRpS78lMXj5+PyjmDQaDQE+PpwqLeEpYBvKlK+mNjb4d+yE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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1184a4278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def rotate(X, angle):\n",
+    "    theta = np.deg2rad(angle)\n",
+    "    R = [[np.cos(theta), np.sin(theta)],\n",
+    "         [-np.sin(theta), np.cos(theta)]]\n",
+    "    return np.dot(X, R)\n",
+    "    \n",
+    "X2 = rotate(X, 20) + 5\n",
+    "plt.scatter(X2[:, 0], X2[:, 1], **colorize)\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This tells us that the *x* and *y* values are not necessarily fundamental to the relationships in the data.\n",
+    "What *is* fundamental, in this case, is the *distance* between each point and the other points in the dataset.\n",
+    "A common way to represent this is to use a distance matrix: for $N$ points, we construct an $N \\times N$ array such that entry $(i, j)$ contains the distance between point $i$ and point $j$.\n",
+    "Let's use Scikit-Learn's efficient ``pairwise_distances`` function to do this for our original data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 1000)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import pairwise_distances\n",
+    "D = pairwise_distances(X)\n",
+    "D.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As promised, for our *N*=1,000 points, we obtain a 1000×1000 matrix, which can be visualized as shown here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9Pmx5JYWMUjgfX0/F419jl337a2t87XLEJx6ti9f8peSTOR4Y6UwhFQ/7rzzb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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1190e7be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(D, zorder=2, cmap='Blues', interpolation='nearest')\n",
+    "plt.colorbar();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we similarly construct a distance matrix for our rotated and translated data, we see that it is the same:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "D2 = pairwise_distances(X2)\n",
+    "np.allclose(D, D2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This distance matrix gives us a representation of our data that is invariant to rotations and translations, but the visualization of the matrix above is not entirely intuitive.\n",
+    "In the representation shown in this figure, we have lost any visible sign of the interesting structure in the data: the \"HELLO\" that we saw before.\n",
+    "\n",
+    "Further, while computing this distance matrix from the (x, y) coordinates is straightforward, transforming the distances back into *x* and *y* coordinates is rather difficult.\n",
+    "This is exactly what the multidimensional scaling algorithm aims to do: given a distance matrix between points, it recovers a $D$-dimensional coordinate representation of the data.\n",
+    "Let's see how it works for our distance matrix, using the ``precomputed`` dissimilarity to specify that we are passing a distance matrix:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LS0MGLAttQ+Sva7B3dkarzcPZ2YXK8nJObtmI3NKSmswM1I6O9Jo0tcksZflP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8aJ4QHejB9w++8BRwVH8lJQhCiR2kOUSD2feR5VsDMAfgMiaa1uJGJSx9jL3YzsyzuO1xwxqE3t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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119e931d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import MDS\n",
+    "model = MDS(n_components=2, dissimilarity='precomputed', random_state=1)\n",
+    "out = model.fit_transform(D)\n",
+    "plt.scatter(out[:, 0], out[:, 1], **colorize)\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The MDS algorithm recovers one of the possible two-dimensional coordinate representations of our data, using *only* the $N\\times N$ distance matrix describing the relationship between the data points."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MDS as Manifold Learning\n",
+    "\n",
+    "The usefulness of this becomes more apparent when we consider the fact that distance matrices can be computed from data in *any* dimension.\n",
+    "So, for example, instead of simply rotating the data in the two-dimensional plane, we can project it into three dimensions using the following function (essentially a three-dimensional generalization of the rotation matrix used earlier):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 3)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def random_projection(X, dimension=3, rseed=42):\n",
+    "    assert dimension >= X.shape[1]\n",
+    "    rng = np.random.RandomState(rseed)\n",
+    "    C = rng.randn(dimension, dimension)\n",
+    "    e, V = np.linalg.eigh(np.dot(C, C.T))\n",
+    "    return np.dot(X, V[:X.shape[1]])\n",
+    "    \n",
+    "X3 = random_projection(X, 3)\n",
+    "X3.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's visualize these points to see what we're working with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ziYIs+yihb7agAJJZwDg/MFC6Vjbf+ehHb+OTn/wkPT3LALfALN9DozLmYKe2\n+IosOXPpNFMdf6Yp5/G3jqWGpVEXf/+d2Tq29n7b0cJzQ7599vn8xONhJAkCgdrUsvF4GNM08flq\n0vyZsVgY0zQIBGpzXlOaFndpmb6kxubPm3biJpGIJiNmJQKB/ELPvU+x2CSyrOD3B0kkYkkfrOrq\n3ZmOYejE4xEUxZf1MmCaBrFYOHlcgoBSUjDRQqetrT7n91XrwywXXV1d9Pa6fZdCs7TIDo6YepTk\nVPxd5Y4UrjxKL4JRapRqJR7rdLdEZgRruj+zsJnV9mdapfO0nMvkw3mBMEkkYp5TXOy5p/fuzO3P\nLGQmtioRic4mM40QmCWSXe3HppwPj0pI67DHNvDemmq+BOJUwlwXYh3bTJ9kbgGSGQzj1G714pO0\nImXt3Eyvx8U9F3fDai/L2/8Xq1dbrKqRuxKRVaS9El9wqgshMEuks7MrTz3ZhXEx5tcabbz10Ky+\nh3NlkF3H1v0gzVc6cb4d/9LvtdwRrMWD5DJrtpZqwLDNt07D6tzk7mySmRtq5Fkn/2M8Peo3juds\nFUFOhMAskcxG0tX3sPFOaRVxbGTmXzeU8lCaSRXmPp2pcrBK46U+5fzd9gXqeiKZ8+jt/pVlp3WW\nrsc9mf2dsntKzs4kOdbIOXenqIGZo6BC8UCk7EpEcUDLEr4CbwiBWSJdXd309maaZMvrP/Naoi8f\nxX2NxcuVOZeSVKVay0ww9fM/9QpRSuq76tYap0+uNBE3bm1R02IlbdvepmmaHisIOecqV2eS7Lnn\n90e6fanusUtrB2bvdxxNi5LdNEDghapNKykXTU3NjIwMl3saU2bquXXW58p9ENv5sJXN1I6/83d2\nLmHl7/PskS8IKf816q7IA6WYWB3fYrEKQtZ204WZqvqx+27mKvBeOIDHSVVxj13Kubc03QCJRCy5\nTc+rClwIDbNErAs682qrhKCbfFGqMxUIUsykVyl3YOUIkOlHqU6lrnA5mOt55TrHkmeNS1V9qbZY\n3q+XUioI2fdhrmhdK4gnOwiosLDPFe1rj1FK+T17+4aREEFAU0AIzCng86nE494LO8822Q11i6Vv\nzGYgyEK+Cc0pvZzMj0CccmAfbz3ZPDp/UE0mlvCwIk2LRbBay5nJ9ZRUzmMxn2TmOXQiX0mmmjgC\n14uwt7VEa/1IzjG8YhV0EJ1NSkUcrimwZEknZ870u76ZOw0zfyBOagmKay0LIxBkNsivNdqUnlta\nvczNy1H7TyOYAAAgAElEQVT28XbXsHVq1zr5iqUd02LaYnIWqe0qii9pEjVy+kILCb98QTxezavW\n2L4ZMMVbmq5pis4mpSAO1RTo7OzMKF4w85QeiGNjRakKrWX6TC1KeL7kls49pRW6AEuAWcdblt0V\nnrwIEzuK1fJDFtYWrbml12wNIElynkLphc2ruYN4vJtX7aIGpWJbotzpKpZPUwQAeaXqg35M0+Sr\nX/1XDh06iN/v5zOf+Ue6urpTv+/du4e77voaAC0ti/jc576Izze98mlWX0x3Lub0o1Sd9acWiOOu\naVueB3O5/bjT2+fplutzSrWJd1AvTK+wu63RK65EfwVJ0lMmVkXxFbwP3CZWSSJZrDxXvdnc2p87\nECezULqjYebZmxxBPHYdWS9IkoSq+lJmXcPQXT7ZQjiab3Yrs6Ao0u6Bqr+7n376KeLxOHfffS+3\n3fZR7rrr39N+v/POL/H3f/95vvGN77Bx49V5ig6URmdn95S3M/uBOAvZhwjF9r88TY0XNtO55nMf\n73yBMbbA8uaXtMdztMVEwfWyKwjJRXIsi+dH2uvm2r5X8je8Tiez0IGl6arJ4xUTRQ08UPUa5s6d\nO9i48RoALrjgQvbt25v67cSJ4zQ0NPHAAz/myJHDXHPNtSxd2jPtMbu6uvj9753+mrnaW5X+Bm3/\nnzt9QFA6c9XU2Dn/C5typyy517dKweVvj+X2M6Zri1EkKZS2nrNfuXIkVQzDh64nSCRi+HwBz9G6\nmakepVxDTmEEFcPQshpeF1rHXX7Pbkem6xqSlBCdTYpQ9RpmODxJXV1d6rOiKCkH/ujoCHv27OSW\nW97H1772TbZv38rLL2+f9pidnd1p1X7SUzmmWkd1ulpLeU2i5RTwma3BcmuNIkp1JskVmV3O2sHW\n+nbqh5XjWEplHUtbzBf9WlgAphdK1zwLTHD8mRbeJZVjUlZTpe+KFVTIXX4vvZiDaep5TcmCeSAw\nQ6FawuHJ1GfDMFJvh42NjXR1LaWnZxmqqvK6112dpoGWimmaDA4OcODAXiRJ4o47/om//du/YXT0\nnHspFvaDeXYFdv4oVfthUyxKWJhUSyW/STW1BOW45vM1hHYq6+SOYnUv695EevRrdkWdQvPIFDrJ\nXzzthx14ZJpm3s4k2Tjm1fTSd4VM0bk1ZbdpWdOiSJIwl+Sj6k2yGzZczHPPPcONN76Z3bt3sXLl\nqtRvnZ3dRCJhentP0dXVzauvvsK73nVzyWMYhsEXv/g5tm3bklbl5+DBA7S2thKPuy9yBWFSnRlK\nb2psh/5bb+xzfw6q+0FTuknV/m2ur/niWqOq+pNVdTRkOZHKu8y1rBu7+XKuaj6FcyQtoeP0wSzF\npO/8bQUQyUWDeHKblCM5TcrOOvm7m7hNy5oWRVFqME3xDMuk6gXmddfdyLZtW7j99g8B8NnPfp7N\nmx8hGo1y000383d/90/cccc/AHDRRRu4+urXlzxGIhFn377XCAZDXHTRxaxevYaHH/4td975Vbq7\nu5FlOflWWbjdzmySy48691gP0cwQ/GLMRFNjp6lwec3D5aO0fZ5elKp9vdkFzMtvqHKuAedzvihW\nyK815vJnejWxuoWONYa3ubvNq7pu+SP9/sJNpzPn7y75Z60fzKGBpwf9ZOIu3ydJcVQ1gO69FsSC\nQDIL2BsGBsbnci5Vxf/4H3/FZz7zWbq7rRQWJ61Dxu5sMNc4D7Dppc1MfXwN66Gr5r3ZZ+JBnX+7\ndmrH3L8HluvYO/ud/7orXVP3eszLtc/Z46qqD02zWmDV1DgxDXYwjpV76AiRWCyctawb93qyrKDr\nCXy+moL1Y625mcRik8k5+bNqxuYikYiltm/XmrW7nOQ79tGoNUZNTW3GtqLJtBo1rS2Ze58DgdqC\n91E8HsY0zeT46oIMAmprq8/5fflfDauUjo5OTp+e3eIFpWPdBJVSkHtmU2iEmdsL3lJm5oOPPX1e\n1jTNrO/zVeXJrPWaiXu9UppHu9Ne8jV+zsbRYJ0AIj2vP9MpQJA9H3dBBbuFmXu9Yuc0M93Fuo4q\n43lSCQiBOUVmunhBNWMH4jjoRXIbq/lBXRk4x9wpE7cQ8kkLV+PJXSwgvSqPHRRT3G1gr1dq70j3\nZr3kSGb6I60qPFJRgVuoswmkN612rAzFz3N6zdoouj6JYXjNa53fCIE5Rbq6lqalllQGsy+0C3fg\nyDT3zVYKTTbV8sCfCoVL9GU+zBeqpu4O+sr4JavThzfHnHs9C2/3lSW4pWSgUf4emM7y6QFIVuSr\nJbByCdxiPtXcBRUK+y8zsTVsez1Rb9ZCHIYp0tnZyenTjsB0Lt75o2GWblK1j4Fdz7YcD+rqPv5T\na+Rt/b2QNXVvQsTWmmIFl81cz14ukYh71BYtwa2q/lSOZOF0key5K4qaKtKeKXAdjbeQSTm9abWd\nm17K9aCq7jKBuhCazIMo2XKRWbygMpjaw3GmmhpbkcJm2neC3MxU8JNjli3X8a6ecRXFl0o1KW0b\ndvS3kbfebNYaqXSPALFYBE2LJysPZQdm5Ysqt+drFyVwxvUWteuOei0WwJVvH+x917QEPp9dJMHz\nJuYd4p1hiixatIhz585lfFspQTeFzT+z19R4/mnZ3imcAjAzx3xhaY25yb62rGLiqU8F17a0JnsZ\nr09+x5xpFQfIry3mMq/anUEs86pRcHk3tkk4syhBsfSQzPVBSvkgp3PtWDVvF7C0RAjMKWPlXpZ7\nFoWZ6aLXC/tB7Y2Fcswr4aXQPs6GkUhFhBY7XHYkKlgFSbwE9Ng+SccvGCvQPzNb+3P3wLQ6jJgF\nl8+cb7o/0vCcF2ovYwtse1+84m4H5jYPy3K5z335EAJzGiiKjKa5o8fKo2E5EZP2uF4jJudbU+O5\nm3em1ugc30LHvFgXDkEm2Zq5HbBj4j7OpTSPdh/rYlGsbp9k4Xqz7uWzhZldMzbTn+lF+NlFCZz5\nFvdhZq5va6O6nijhZcfRZN09PHU9vmA7mwiBOQ0WL17C2bNnXd/M/kNvZiMmZ/pBXRkP/ZnWfryZ\nVG1KNWMLIPcxLtw8GuyXvtILVTiCILNubD7sc5YvrzNz29l5oo55VNPiWebVYveOVWTdzgudekUx\nr/vrnpu7/J5tHjaMxIIMAlqAuzxzdHZ25SleMDMP7KlHTFJm8171mmyyj7lXf6ONUqZjXj1Mz6dr\nH08lWevVKnlnFzB3dwvJP35yC4qa5R/MsbRrHhbpeZ3ZxQEgf46kbR61mz+7fyuEHUBkaYpTv7+c\n/S1e5D13O7AgYM9/4XU2mVdRsqZp8tWv/iuHDh3E7/fzmc/8I11d3VnL3Xnnl2hsbOK22z4yrfGs\naj/uSNm5jFLNjpi0trWwnfJemel+pdZxr94Xhdkis/Vaade2mTIlWi8flrCzAlj0lJAEMAxnjOKR\nrI6G6S5aLsuhrECaQi2xctWpLSasbX+mpsWJx6OpffDqj7THtcfy+kJmL+vzBV3zLlzkPVexdneR\neU2L4vOFWEhF2ueVhvn0008Rj8e5++57ue22j3LXXf+etcyDD/6co0cPz8h4VmpJX45fikWp2m/W\nsxExWSmRupXDzESpVqq/sXLO89QsItnXtqIoKIqc/F9CVa3PVjSsc/w1LZb6526pVazNlVsIZvsH\nvR3PYv7MQteI2x/oVNDxdk25t1usIIJNth82s6hBofWyo3HdOZ4LrR3YvBKYO3fuYOPGawC44IIL\ns3pf7t69k337XuPd737vjIzX2dmZJxfTzPmQnu9NjctfvMEZf/ZeSATpWiNF/I2ZQWaK69pWXcLR\n+qeqSkooWhqlkWw5ZQvH7OIBlqakIsv+lPDLNHkWwm7CnMu/V8jEmsuf6SWIx+0PLNRyqzDWutlR\nt7lIF3yZRQ3yrV9oX9xFGUwzsWCCgOaVwAyHJ6mrc7oPKIqScpCfOzfIvfd+h7/5m0/P2Hh2ebxE\nIkEkEiFdUJTykBYRk1MhW2u0H5Dz84WkHOTXzt3RqvmubSV1jG1/o+1ztISjnKYx2gn6iYQjHHVd\nSz6U7YIYtnB0OpVYAkBFUWRAQlHypXA4++TMN3++Y65lM8n2Z3oL4pl62T1rOeu4yRiGVlCbdq/j\nvr69VCEqFI1r+1TtICbD0BZEENC88mGGQrWEw5Opz4ZhpHwLTz75O8bGRvnbv/04584NEovF6OlZ\nxtvf/q6SxohEIhw8uJ+DBw9w8OB+hobO8Y53vAWAn/70pzQ1NbmWLuxvnB0s349tgpl77PFnjtL8\njfYcZObumM8PSvM32khYAjLb32j/LkkktSkzOY6BYdhCOPcY1jZsYZr7hUaSSGqfiWSXDwlJMpOC\nxBEG2S22CvklIyQSsZRAyrVs5jzd/sxSrjkr3UNKanpx/H6l6HqORirj8/mIxcJoWiwZ+FSovVuu\n/XWqEFmpI5niwH5JybfvVlEG2wfs94eYZzpYFvNKYG7YcDHPPfcMN974Znbv3sXKlatSv91yy/u4\n5Zb3AfDww7/lxInjJQvLWCzKrbfexMjISOo7WZZZtWoVF198MXV1Da6l8/eEXAiUEpCQuV7yL4o/\ntDNfSOx8SJlMv4sgnewemcVeQJyXPuelSE/5FYGkYHQHv5ipNAhbS8259ZSGbwtIb9q+4we0yt2p\nqrs/pj8ZmJK/JF2u7dkBOYlEDJ+vxpOP0PZnWgKztJdFe/Pey+455lU7aCmRiBZsOp3PtJou8Nwv\nCY5lodh9lNm42ucLYhjz97k3rwTmddfdyLZtW7j99g8B8NnPfp7Nmx8hGo1y0003T3v7Pp+fd77z\nv6HrOqtWrWb16rV88pMf4+6770FJGvGt4APbFFguDa86mPpDO/cD1YrWWzgBCF6Y3guI+4EpZZnc\ndN3OBzaSla+MpPnUyCto3Pm/bo10qsiymhTIOobhaFmmSfJBHiUejxIIOMKkmF/Srt/qNlUWm2N2\nndrSsMvuWabr/I/lzLkrioph+JINr6M5m04XMq1mRu06TbaL+2JtLD+unuzBGcVqZK6Qr6F5NSOZ\nBV6hBgbG53IuVcltt/0Fn/vcHSxZsgRwC0yl6NvZbGDdHDqWljX3F6zl4zJxa9hTf2hbn72Hztv7\nPvfH3jnvc2tZcJ9vxxw2PeGYz6Rqjef2aRYqLSelaY2z6Zt3B+vYDZit7yVMU0sJIluYxGKTmCbU\n1NTm2Z5JLBbGOiYKpqknBW7ha8owjFTKh88XSLb3KjRvk1hsMqUp2uv6/aGU1p5JIhFD1xP4/UHX\ny4HdkURHVf1ZJmirp6WWd7vW+jEMQ0NRfPh8AQxDJx6PpD4XwzRN4vFI6npQFD+SlGkKrx7a2upz\nfj+vNMxy0NHRSW/vqZTAdEdqVhuDA0P89I4d6MP1BHvG+JPPv55g0ApM2L/jOL2vRpEDOle8s4e6\n+roiW9NdGl/6sRgaHOH4gX6W9Cyio3sxM+/jrb5jXyqOdu40kM5fGDu/L724cCzub7SRZbUsAVR2\n6TY7Id96WNv+TLfZNpHs4FHYXWAXGLAEgJOuUnwezt+Z+ZmF15PSzLqWeTWYZ47ZQUW2H9XyZ2b7\nI4tF7tr+zHhcT75clO7/z8wRtV42HJPzfEEIzGnS2dlVYW2+pi6wf/Tpl+g6cQsDkeOc2TnIPzz1\nSy55+xKaVoL5ygZa/OsxTZPfndjCTZ9cn7yx8mkzmZ+th/Xh13p56muDjB+pJaEN0vamXXzwn97C\n5MQku184huKXaGirQZZlVq5d5umBYzMyNs72g4doravl0nXr0n7buu8QL54Zw2do3HrJGhY1NXLP\nQ5vZdS7M+pY6/uptN6CqM3E7zLwpfrr+RvuhJ8vuh+zM+BvtQgL28qWcr5nESXEwUpqSjaL4MYxo\nSphYFD5HblMlePPJu/MWrZSPQoIvl3nVMetqWiwjijb3OjZuIZ/LH1mMzKIG9vEr5cXHDjyyX05k\n2U81uYi8IATmNOns7OLUqeOub6pXw9T7WpjURomMaETDMTpG3sXhH54gIp+ju1ZipLuXxm6F/dvD\nhKNbWHddM5devyrP1pxIVfdNt+fREcb21dOhXQnA+GMn+c/aX3HkIZXFk9dwZHg79SxBC4wTD+6i\ndbXKhnc38dZbr8578+q6zlf+8yf8dMxH6Io3sigc5b1nt/Knr7+cR17ezZ5TfTxf08Xxmg402cfP\nH3iK9fIEj7Zfjnn+Kh6PjPHSd37CDReuY8eRE6gdPTSg8cFLV7OosYG7ntjKwbjMiePHWdbdxcqQ\nwl9fs4GXDx7hh68eZs+ZIZb4Je78wzeyqmdZScfc/SCemunaWUaSfKnf3HLLrfGlC0cv/kZbOOb2\nN1qC14emWSY9K+2jPEJTUXzJFAcddxUby5/pT+VyWvP24pvzpQSmlTbhzcRojSsXFHwW2dqiqgYw\nDANd15DlRJZZt5C2mM8faV9jxfbZreXa/tvSXRvOGJoWRZazfarVjBCY06Szs4tt214s9zRS2G/9\nUxLYjaOMj/bj0xdRkzCYYIDF8avYazyIGu9kPDzOnh07WMwlDJ422LbtLIHQCdZfuYL0FwXbh5p9\ns0UjUWpiy1LlVw+e28K5r49zYfxPOM5OWricSc5Qz3pCcoLmiaWcOHGWXww8w7v+aiOBQLo/xTAM\n/uzb9/PspB/9hluRElHG1BA/P3mS3T/+FWcueyu9iTAHAo2Yde3IwNiS89n32haMDaswdWBklEdr\nlnPkjEnfyjcjnT1B4tBOfrT7BJ3aOHVvfT9n9rzC0Mb3cnB8mOfGBvnBXZuI1i9iwt8EN76b46bB\nTb/4FU+8P0jn4iVZ+51JOBzmW0/8iMlFwFiCW1a/kfNXOC8fvWf62HvyEC21DVx2/iXkD8Yxk35j\nKWnaSxeOpmmm8hjzl+/z7m/UdZ14PE4wGHTWlqQ0k6gk+cvykJQkCVX1JfM3E679MTFNGVn2YRiW\nIPBiKnTvg5doW7cws7Ta/IIvc3n3mPnK7tnrFKsi5G46bbfl8pruYa1vpARmqSZVx9KgeB6zmlDu\nuOOOO/L9GA57q2q/kJFlmV/96he8613vBuyL386VKleUmLfx0/1gBovWmrzy0g7OnjmDFAuhmD7C\nxjmW8jp6fc9zMvwqLcZaQnITkh7g7Lleek+eJRydpHNtIz6fiqPxSDkFpqaOs+epszQZKxhIHEEf\n8zOpDdNqns9JXmQp1xBhiDiTtJsbmIyOciq8i8ShTk7uG8KoG2FJT0tqe09ve4kfxluZ0E3MzpWY\nqkpk4DTDB3ezt20tvUot4y/9nsiyCzFr6jB0HX3vdszIBKy8GFQfnD6C0b6Usb5eJuqaiOx9hfjG\ntxFbsYHRuMZ4TQOx0WHGauqJ1zYT6z1KONhA7OxpzGtuwlRUUFRiHSv5/ffv4kDYYMeBw1y2rBNV\nVbOOMxh8+7EfEXzf+bRc1I3SXc+mTT/j0PApBnr7kZF5IrEX/7XdbD/5Gk9vfZ6hkWF6WjqpqQmk\nzG2yLKEoiksQWk83O5ndMpXqaYLSLgdnPYjVVNK/JQxyd7AZOjfAK79/kOc3/5zR1x4ifvQZnnz6\naYb6T3Di8F5aO1YQCNRga66maXpK45gNnJcFI5UWYftp7TQU64VDSktDmZyc5OzZs2z6h7/jpW9/\ni1HDZMWGDUkNU2Lbpk08/JlP8+x3/i9H+0+zduPrUpHxNragco6nkuzsoSf9u1LB5d37YBclsM3L\ntqao65ZZ2T33zP23Ioe1pKYtJccvHH3rxp63tT05az8LYR8vKyjJXSy/uqitzR3oJDTMadLa2sbA\nwEDGt5YWMNVcxOmTPb4XP9iai5bx2U09fPEv7mf4KQ1Jq0E2/DSYndTXNjAc68M0DWrMJvq0V2jT\nLiU4cZLgweW8+OB2bnjf+qIzu+SadcT+eSfbfvJLRifO4hu/kkC8jhM8QxPLOcTDhGhHRkUnwZB5\nhKXa9ciJERYbTeze9ArrroylNE3NMEFWMWUZ8+gupJUbMI7sJXb+1aiRMTQlQFyqge2/g2Xng6lD\n31GIReCl38HqS+D0EWhoITY+AtEoNLdDqBFjcpRoJEx0dAIGB2HZxaDrGLIKI2dgcgzC49CQFOCv\nPMnRje/lbMd56CMDbP7Kt7n/Y39KY2ND1nEYCcToDgUwDJO9/7WFyz9xE7WBIEePnGHn7x7hor94\nI7u2voK/p4G6DUswQm3c/9BD/NW1f5QMpsj2N2ZGrU41v9HN+Ngoex++i9evUNgTPM5lq9oYikiE\nh49yVWOcUF0jd975UdpDOo1BmX6tmff8P1/wnPs4G9iC0TY7O4LCRJZVdD2eOn6yLLPlJ/eh/9uX\neeHYMT5tmtQD2559hh8cOsDiZUs58vzztG7axBqgE1i8ayc/uv9+/vCpp2lsbnGNbN1z2zb/jsjI\nMFe+8yaCoZoCgTzZJlmb9HSRWFqkarFz6C7CYJuUSznv7mWt3pfezqX9jHH7t+cbQsOcJrIsc999\nP+R97/tj17f2Q2vuy9xZF609vv23Ozc0MxDH3djYKjt26OkJmsMXoCoqUWWQ08pWfDUSdWYHYX2I\nmDRGWB/FVGPU+GoZGxsnzAAXvakjuV2DfBomQPeKxVx10wquesdKHr7/eQLhdqLGJIPsxcBkgtOE\nGWSc00hIBGhgZOIsfa/F6N07xr7Du1lxWSN1DSGWLm5n8+ZHOdPUiTE5ATt+jzzch3rFm9EH+9CG\nB619DtVD72FoaAZNh3gUahtgbAia2uHoLuhYCb0HYWQA2nsse1RbN/zufmjthF3Pga7BiX3W54kR\n2L8d/EEYOAXHXoNLb0Q5c5JINMrZpRt4af8B1gYMlrS0uI6zxAs7tuFb20JkZAICCvVLWpCRCLU2\ncOSFPagrGxkeHKJ9w3ISkRixyQimKrFkPEBtbShH1KpznSmKP01znE7k6kvPP84fdA1wqvc0jTUy\nixsD/OyZg7zr8g5MI8ELu45iREf40Bt7uHBpHRuWSPx088usu/SaVCWbucbR0Gzt2tIwJycneOXJ\nJwlHI7S0tXHu7Bme+se/J/4f/4frBwZImCYXA6eBF4HIlhe56NFHOfbaa7QB7cAbkr81j43y1GOP\nsOydNxFKluPUtAS//sTfcO0Xv8DFv/0Nv3nqSZa8+2ZqgkEMwyqq4dbyrFZkBqrqy3mvOIFMevIc\nkjTxFtcW7XNujUtJGqalySawryld11Ot0IqsmZZLalRx46R8GqYQmDPAAw/8hFtv/SPXBWU/yGZX\nYKZrjQbFUwvShWNmHVV7riPRAUZeraOnaQMdjSupu+Q0jcslNCNG0FdHf/gQYbmfZa0XUhvrRhur\nIaz2E+wO097dhGMSLuzD8Pl8DJ4ZYnRokiHzCK2sYdA4yBLzEmpZxDKu4yhPgClTTxdNLGfYPApH\nl7Pn5HNc+bZV+P1+br70fLY/+Rj+lnbaW5ox6prRTBNj6RrMEweQVJWAZKCHxyChQWsH+AJw+igE\nghAIQP9x5NYOpP5jmC0dsPMZSETh6B5oWQxrL7eEYiIBnefB3m1Q3wyXvwlefQYSMZgYwVy/kUTv\nYYzOlZiKytm6xezetoX3XbouKcCsFIKLutfyyC9/w9m+s4THJ1i8vgdJlUlE45x8ZAcJPximSd2S\nZuucnBnixJ5DDNcn2LX/NVY19xAMhpIFwH3Jh6GZ8ouVYkYrxMCZPmqHd7CstYbn9/aztrOe517r\nY8PyZnTd5Nk9Z1jd2cCKxfW8cvgcD28/gRzuZ9dzjxBV6ulcet6cCk3bb+t+aTRNg4H+PrZ+4M+5\n/t7v0nffffxq7z6GH/wlNz3+OBOTkywHXgU2AJuARcA1wHFgKLml1cBO4ArgUuB1587x252vsvZ9\nf4wkSfzkC5/nDffey2rTRAUu7u/n26/uYPye7/DsPd/hqcceZfzwYQKLF9O4qNUlMHP7fC1fqGPW\ntV8CvAo/y7Rq5UVLEkXzQt04gs+XevHIZVZ2YxXK15LWBSEwBXl4/PHHuOqqq1yF391+vJl5WGT7\nwTK1xlz2D0s4OoIxWzjmYsW6TiKLDnEmvhd5/SFu/fvLGe8zWd12FWcHzqLqIVa0XMap8d0YapwB\nYzfdzWs5dvQY59/Yiqra0bHFnf51ixWk4RYuW/4mxmJnWRToYXxijJgZZpTjhBkgzgQJJokwRJBm\nND2BeaqDHVtfY/FFEm1LFvGeqy5hSWKM8aGzLL7yegLH9jC8ezv6uX58l1yLvGQZ5tG94PfB2VNQ\nEwJ/DWgJCNXRJBmEJANTUZG1ONQ1WEd08XLrXSMyCeMjYBpw5R9Yxzs6Af3HLW0zWIe0pBtp1/OY\negKWrECqCSGF6oiNnGOlNsK6nq7Uca8J+Ll+7VWc7DvJRCTMcN8gk4NjHH3kZXpaOplQ4oycGGD4\nWD9qwM/Aayfouf5C2ruW0LS+i8PbX+OC7jUZASNystmAkdKypsuSrmU88NOf0h7SCfpVNr1wkpb6\nIFsPDjIRiXPF6lZ2HxtmeXs9Ww+c4dKVrbz54g42rqznyK4XGA2tpK29eBDUVHAHNRXy2wK8+L//\nN+/97W/ZEo8T1TTU/ftpPHaM5brOY6bJNcB9QA1wFmgABoEbgCex7qQDQAtwmb1lVWVwfJxXohGe\n/sZdrP/ZT2k3DBYlx/wpcNXx4xw7049x7hyfPHaMDc8/x/bHH8e87nrqW5owTTOvwIRMf6ZdFMDn\n2dztjoS2/dfFj6st+NRk3qqR0lQLCWrHJ2u3L/M0xYpkQQlMu5H0D37wPR577GE2bLiEhgbHj7R5\n8yN8+cv/Hw8//Fv27n2Na655w7TG2759K52dnXR0dLi+zR/4Umzuzvr5hKNNbq3RWX/qNVV7Vi/h\n4jct5cLXLyMQCNC0XOGlbdsZ2lNDRBqijjZq1DoCRgOdTWvQDJ2+k4MceHGASGKU7rXNaQ1+89Hc\nWs/iS0HrOMaltzQwGDnCsYN9hOJL6JGvIWIOE2eMejpYxBqijLFYupBhDpPob+TI1jH6x46w5rIu\n1kHd+A4AACAASURBVC3t5KL2BnYdO0XjhtcxcGAX8YtvQD97ksT2JzBrG2BkEDpWwPILob4RWVVR\n111Bw7JVdNVIvH78IFpjG1zwOmL7XsIYG4LVlxIa6Uc9ewxt5BwoqiVsQw1Iw/34ZJnghqtp7FmF\n78xxpDMnMLpWodY3IQ2fYVFsjEvrZdZ3taW0BdM0kCQ4NHiCNTddRQ0+lAmNZVIrYS1KaN1ixs4O\nsertV2BoGopPpa6zhbHxMRRTQhmIc+GS1WnH0k6AdzSC6ZtEJUkiZvhIjJ6ipbmZay9dxZMHInQ1\nSoSSMR0nByZ4YlcfIb/KdRcuIehXkGWJnrZafvHUHi5+3ZumPQ+niILhKajJ1sIkSWHP5kfZ8oUv\n8OZEghew7pT3YgnA802TUeAhLCEZAvYDbwWeANYAzyW3HgReBt6MdceZsszPJIkPv/gCQwcO8FZN\n49vAlcAYsDm5vfOAS4DW5HYOjwzz4m9/w8DIMN1XXUVNTajgvltlB81UEQWrW4tXgenkyNraqZeK\nRVawkW3WdwcR5U8bcgtMKxfV0xQrkgUlMJ9++imOHTvCl7/87/T0LOeee77Fm9/8VgBisRif+9xn\n+c53fsC73/0eHnvsYXw+Pz0l5s+52b9/H5IEq1evSX5TOFLUJrdwdLelKi4c82uNM2sSDtUGObSj\nn9ieLjrMyxkO93M08iKR2Di+eDMnx/ewrv1qgmYLi0LdnI0eoHNlq6dtB4M1LOlupXlRE7oGF9a9\niyNndzM5OYHfaEBC5Sx7GGQ/cjJU3W80oCYaGD43gn5gOS/8fhsrrqqns7OFK1pqiO7ayuGIzoSk\nYsTj6DW10LkCrnobnDoAh15FCtVDZJL6E7tplHSunDjOVz54C8vlGL07X6KpbTGr9DFaR/tZun4D\n65Yto2XgCKO9J5EmRugYPsmnNyxmRW2Akd7jBAeO89YGnWVdXfQfOQixCLWxcTa0BHnfeS00N1gW\nCPuhI8sq7f5mXt3xKlJAoWYM3tB2IfsjvZw4eJRoOEp0eIJl117Aqa37qV/STG1HM2PDo5x57iDX\nXbAx6/zmixKdDl3LV9M7GeBMWObJ3f3UMcap3n4mYxrH+seZjOn89dvWsmX/IB0tIRbVBzBN2Hl8\nlMmapZx3wZWehXdmnqidBO9oSm7h6BxH+8Ge6beNRqPsve2vCfX3MwTEsATjiuRWNmFplE2AHysK\nciD5fx2W1ikBG7ECfj4M3A/s8vl4etUq3jI4SCQa4xHT4CpgAsuM+wSW4DwOrAPCwGLg18DrgTdO\nTrJ2+3Z+dfo05ycj7AvhmFftyFVv/khdTyS12ECGQMt/LjKjd+2XEPslJZ9p1hrLSEbwzk+BOS+j\nZAs1kvb7/XzrW/fi91tJyLqup/6eKla1n17XN9nFC2a60Lg3ZvaKHT3oQ67V6Dt5GD2iENS7CQXr\nCMdGqZU6GD9tYLaNgxkiPja1S2vxigae+vE+Lm+5hV3GY4z3T9Bjvo7zpZvYYzzIGKdYxCpqaGKI\nw3RoV1FzrgbkBn715S3cdtc1NDXU0966iPMWrefkgWNWcE8sCvUtMNSPfMkNGNsewzxzEunKtzCB\nQbz/KEFTZzIc44aLL+LGSzZgH/ZEQuN0fz/NjZ00veWTJBIJxsbGqK+vSxUddx/r0bFxfv7iK7x0\n+hhrupfw9rVtrF7Wk1N4dSzu4EPt7yESiWC2mWza+zsGxobwt9URP5tgcmicI4+/SnwyxuCB0wzu\nO0UwEKT5+mUcPnaEVStWZh1Dd5SoFTAy/dv84o03cPTwAVbXnObUcZnYqJ/b3raGaELn/z6yn0N9\n49x67XK+88h+XreuHdOUGDYaWfuWt2IHg2TWOHWEo5OO4q3CkPei7SMjw/QMDzMBHAEOYWmNfiw/\nZQS4CkgAW7FMrj5gV/LvdmApsBdLYH4DOAnc9PMHOX9xKyPXXMNRTELAN4EOrNfZPwF2YAnnXViC\nM4D1KtyE9drrN01aduxIOx4jI8P4fP60vr72vlpam54s/+dNy7RfylXVh92M2466zV+BKDsGwarF\nW7h0X3qlo6JTq0rmpcDM10jablbb3GwFUmzadD/RaIQrr9w4rfE6O7t5+eVtgFswgpNUXqxaS3YZ\ns+kxS0EWoSh1Uivx0BhDsXMsNtZTL7Vy1Pg9hiYRUOpoDNdxcNdJes4f5cVfHMM0oefyIF3LFzM5\nOcl/fecVjIkA511Ty1VvdNJQeo+doXd3GHwavotPMXDOwFefQI3EODjyCM3GSiY5Qw3NHOdZQrRR\nSzt+aokb49T5ahg52kA4HCYUCnFeexO1/XFW6KO8eiYMsSjS2VOYho6x4ykr0b5tKebRPWixMFpb\nN5vUNfheOswnN65JXT+maeLzKfR0d2J3uAeThoZarPNrmcms82aVCmxsqOdDb7mOv1bzP5TcSJJE\nKBTi+Ve30Hrdamp+e5y6ngZObdvPotVdjJ0aJDYWpv2CHjBMQo11DB/uZ3h4CHIITLuQgKbF0HUt\nTchMh+GBPta3BDl5XEJVZTY9d4zu1hCL6vxsfrmX5UvqWd5ez66jw9SG/EyElvCG7uXJFAMjWUxA\nShOQOY5GWirMdDXktrZ2dq9dR19/P2/DEnYvAo1YATy1WNrfFVgm0zEsDfB5RaFe11mKJfj+GNgH\ntAHdLS2svugiRkdHeMXno0HXUbAE6nuAUSy/54PNzdwyOsqwYXAaeDw5p1TJCcNgLGQVgI/FYtz7\n1jdz9YH9jAeDJD72cd74/34i/chITiGBzA4s+XCnlqmq3+XvlXP0CXXWSY6Y9r27dF8uoesuxCAE\nZhVRqJE0WCf2m9/8OqdOneBLX/rKlMfRNI1jx45y6NB+BgbO8PGPf4Ta2lq+8IUvuKIU3RdfuoCc\n/ejBmb1qL39PO48d3Iov0o0xOUFUVqmPdxBMLEE2/QxFeokPNjEivURo+1pWtK3j4OF9PHH3PhqW\nHKW/r48rGt6PT/Wxf8dxTP01Nv7Bep78+csce7CJJn8ndZ0GR3u34482soI3cci/BaPZIDZ2joBW\ni4mOj1o6uZyDPEQTndQGa1EDKnpolETCSrhe3tnJeyeO8kijn4G4RDxsENXD+E0dLTZK9Iq3MjF4\nFupbYela0DXGz56gNxjiB489yfvf9AZqQ8Gcx0GSZA6eOMW58TAbzltGfV1d2rnUtARWuymtpMjE\ngBpAjyUI+mqQ64K86Yt/ztNfeoCOy1aRiMQ58exrrH7HFeimwYkte/ne6Z0kFJPzl6+hubEpbVuZ\n1XfsguTTYc0Fl/DcY89RV9tAXc05BkbD9A2H+fBb1/DTZ47yhvWLOdI/xs1XL8PvUxiPSzzzm29z\n3a0fB0gFjrhmmSUYZ/qe2LnlCY7URAF4EEvgXY+VOrIM6MPSPNuBo8CtQFySaO/o4IEVK7h52zZq\notHUuolAgNCnP0NjYxPBYIimCy5A27mTVYkEp4DHsDTMnaEQH/7+D1nywT9jfSLBf0WjnNA0aoC7\ngPVAP/DiuUH2LGklHI/zZSyfpxmNsvUrX+bYO29i+WrHT+1oi3bPztztvNKXd6r82PVm7abR+XNl\n85ffs0v3WUJXSRVQsF+C5nsf2nnpw4xGIzz//LNce+0N7N69ixMnjvGWt7w99fudd34JSZL4x3/8\n5ymF38diMT75yf/Jv/3bv/CLX/yUrVtf5MyZM/T399PS0sLb3/4OnGtNLTlKdWYonAs5FRYvbcG3\neILRgQhKSGN8JMxQ4ijD5jFaWUOHsoF6XyuDxn6W1l7MwMQJ9j4xyuKh6wiPJjDPtCJN1BOZiCHH\nQpwOv4amTnDkV3UsGruU8ZFJ9r9yCvNUB9pIgOHJAQI1fgbrt6LHJNZoNzNg7sXE4DTbCPpDjNUc\nQfdPcGb0JIahc/zVMRZvkGloaqKnfRHXruxmeHyCZVe/kZ7GOpb6DJbo4/RqPqKHdiajOepBj6Of\nPkw42MCwL8QPn9rKz/ae4nd7j/Pcawd5fN9xnnppF1vPTvLtF/bwYLSRgbbz2Lb3ILVjAyS0BE31\n9a6oRoOJiQm27T/CkZMnOXhmkL6+fp451s/OU2do8cs01KW3l+poW8zO57fTdFE3rz7+IsNH+/HV\n1XBu/ylqWxsZPz1IbCzCmV3HiAxPsPjGdfTVT/L8Sy/Se7afWDhCV6sTkWoXAbcenFLeYA2v+AM1\nSM0r6B8K09vXz5FTZwn5FUIBlbdf3s03fruXYEBhbXcTiiQR9MucHo7Qfv71JBIa8XgMn8+Xlgrj\nVBia+Xti367tdA79jtBrR+g8fJbTpsnlwDBWPuUklom2FiulZAxLmzwKbL3sMm574Ge8Wl/H8UCQ\nYDBE88aNtH/6s1z7/j8FLMuVdOllDJ3p55Qs09fYyNGmZsY3vo4//dVvWXn+erbE45w6cIA+w8Rn\nGLzLMLgBqJUknpAkgqOjfFzX6QWuxYle8Gkaj9bWsuGGG1P7YxWPl5KNrY1UJZ/8ptn0vEhIj7q1\n8iuz/Zl2tR6fL1sDzUx1cQcR2Z1OLE209PNVSeTzYc7Lfph2lOzhwwcBq5H0/v17iUajrF27jg9/\n+ANs2HAJYF0At976Pq699gbP2w+HJ/nIRz6MoqisXr2G1avXcv/9P+Kee75HbW1tcg7ZfSHnEtO0\nS1t513C8MjYywbHdAzz8/ZepPXwlx08fQo000cBSpFAUmsZo6QrQd2gC/3A3TcYK+pStxOJxFssX\nElDqGJdOMFD7Mu1dzdTIdXSoF3Po5B7aY5exK/Ig53MzSg2M+44zmRghdNEpDr16BsJB6tXFtNZ3\nEqk7jRlRGI2epTG6hkXKefT7tzFRc4zLbm7jvZ+6kqamBoZHx/iv144QQ2ZDcw0DQ0N8futxjh85\nAj1rIVhrFSgYPkvj4k4WRc4xGmxBbWknfHAnBhIt6y4icvIopj9AYnIcc8kKmuJjNI6fpW5xN6ta\n6jl/cD/tDXWYssKahiA/PjbEqUAzB0bjtNQFGT3Tx5KVa1na2kzg+F4+dXEnbS3NGefNZPe+PWw+\n9xJSawhjSYDjew9z7mAfWjzBRX/0Bs4dOk2gvpbOy1dx/Pe7WXbVOuqiKk1akI7DElevvyJte3bB\ncUXxT1loZvobtz70PczeLezYd8zSJidjLGoIsr6nkT+4pAtZlhgNJ/j1oSBRXxvLg8Noms6hQYOV\nl17H5a9/2wx1h8nP84/cxzs6TrLp+8/wBz96nq9j+S27gBNYseeXYWmFq4APYmmHYeA3ra3cuHsP\nhqETCNQWvIdN00j2z7QiWP3+9GLr4XCYRCLO8W3bePKfP8+6PbtpwIrSDQJ/DdwNXIiV+ykDD2BZ\nSfj+D1h7xVW4e2cGAqHkmBHATOuN6aZQT0tNi6e0TLeWmjlOPiyzbBSQUvNxj6VNrY92xbCg+mFK\nksSnPvXZtO/cUbC///2WaW0/FKrle9+7L+27//zPe9MKUs9nGprq2PCGOvx1Ms99bZIl5hLGT+uo\npkJzSzt6Z5Recwvjo134EhIhaTFhfYIoo5w19hE0GhlkP2t9b2Hw5G58NU08H/kloUgPqtFH1Bhj\n0hxG1hQGOc0ieQ2T+yO0S60E/M3UaksIREP0J/ay4f9n772jJLure9/PSZWrK3TOuacnZ2k0QhFJ\noIAAGyFkMBiDjf38njHOzzzfhcHxXgPG93K9lhMYjAgWScAFCUlIYjQazWg0uXs659zVFbvSSe+P\n0xW6p3umJ2nEaL5raUnqqlPnV7/6nd/+7b2/+7s9D9OtPUu5to0+9Skq0pupjO8n+wOdfx74CZ/4\nyl143Xbet3cjAOl0mqfnVWrLypjLmiRlGwyfhdAMSmMHgt1BNGMjJdvJplU0JAhUklVlWExgSA5M\nyYFgcxHvO8V4xw48oRlSpsiB0RQ7d20i4PfxtRefomz37Uz2dJMqb6G79yTShj0k4zHqgga9ixp/\n+cwRbq4r5z17NueJZ4IgsKVzMydOjmBrKuXM2TNER+YoqS/DWx2g7yevEWyvBhN0VcNW4sSQIJ5J\nUuJxM2tGlv1WlyKMfi4Z59x8Y/Pe+/lZ92vsbq9iPpqktcpL12iY2Uiar/6sH7/bxqnhCB5fgAd2\nRBEEgalwmt++pZpQ5jUOfHeI23/5d65qZxOHr5K5aB++jgBfUUTqVIMMFvlGxRIfuHPpvYMsldti\nbYqxSIT+147SsmPHBe9T3I/TKuNYLonpcrkAF9vuuZcf/uEneGjpHq8AESzm7k1YdZsRIAvcAlQv\nLPD4c88xePIEkaxKQ0Mdm27Zn29mnWvnZeUznedEk87X2WSlSPtKg3qhNSJJMqZZCA3nSsiu5/wl\nXKch2WuBZ555iltuuSXvYV4N8YKLQ27VXj21obIqP77OFAlxCt0TxtMZQW6bwrVznKmXHXgTbdj1\nEobNA8SYoJa9mGgkmKWcTiTTTlIPM5sYJZmO4TTKSJtR3FQxxUk0MjgIkJCmCBhtTKmn8KgNxMwp\n4uos09lT2I0gk6kzVLKdObpp4FbAREQkmUqx7VEFp9OVLzmYmJnniFLBdBakknJiC7MYlY3Ie9+K\nkAgj2l2YM2OkomHklk3oZ49CRR2GN4Ax1IVpc2Bm05jZDNjsYApkfeXMDPQSqWpjcnyUmG7S399P\nryozPj5GvLQeVTcQ7TYymkF28AwLNRtwllURK2tgrOsEe5pq8vMqCAIB3IyPjJEcDzPY3UNiIUbL\nW3egJtOEeidw+N04/R5ikyHc5T5MQWA+uUDvK6fIpDM0ldbm0w3WRlpgoBZ7I8s7muh5we9zW38J\n5FpmiaKEx+Nj4777GZyOUVFRyXQ4zZaWCkamQqQzKhndwOO0sanGydbGAH2TUfa2BVlMa2i6hkdK\nE7a34PMv97CvJKrqmnniqYPMT/RjT2eRQglKDYsFq2F5eLuwvM0zWDlFPxardRqo6ukhffPNBNcl\nvGDmc7Smaa5Z9nH0s/+De1Mpji3dfwHoWnqtF7gdeKsg4BMEntM0hk6fQn3ySW5+9hnav/tdDr3w\nIs477sLr9+cN5FpKPGuJu8NqIu1invF9MfJ7hTZx1n53PdRgwpusDvNa4NVXD1NXV0dVVfHDdfXl\n8dbG1dezNU2TQJmPzbfUc/M7mtl8VxmiYjLxsoR/eh/j0TMoeEgYszjwoZPFxMTEQMaOaUBWClNi\nNiDLNpxiEMPQyJIgTRgDDSc+fK4AEWGIFGEWbRM4RB+LwjQ+oZ6QPoBkOpjmBGmiVLAFEQnBlJmR\njvG2/6cFm82Rzx/Losgr4/NMpnR0h5MFzQRdwzR0TAOMkwcQkzGysShmPIyZSsHcBDjcEJqCyCyS\ny4050mOFcqdHLIk8SYFklEzdBsJnjpKUHaixiKUzKylQWoXe/SqioRFdCFFW4qWxPMBMJM7M1CTv\n6Kxf9jv5PCVsrmpjb9NWtIBC33Afod4JbB4nC4MzCKLA+EtdZFMqatLSml0MxfA1VdCws4PB4920\nVTXlP6+grVqocVxZ/L/cOEp5ibO1OpqIokhT5y6qOm+hom0v3V1duM0I9+yopr7MQzie5o6tVbza\nP4+umwQ8Ch6njE2WGB6f5kT3AJK9hNLKGq4GBEEA2cGusggTgoq+sEjPQoo2LBWfDixDeRCrznIC\ny1AqWEarWZZ5raqKxl17LvgM5YxTjhG8Vm7xtYMHmRjox4llLBuwSlwCWAze2qUx9QIDgkBLJkMl\nVs5VATpmZ/lOXy/b3vvo0ncsGK2V91wuvXeuJ7+yvtLyGs38f1+obKXY6BaXr/yi12DCDYN51dHT\n082liBdcPVxZg7m2bu1ykYXh58FIK4RHVTx6I5PaMQxdx08Ts5zGTz0LjKCSYFGeQFWiuBU/ggAB\ns51p4SQBoYkG+26i0gB+qZGAvY6MdxKlRMVHPY3SfuLGFA3CW4jIvZga2PEjYBJmAJ0Mc2Y3c8ox\nUjGD+IyGPWDiKXFht9sJqHEGBoeY1SERT6C4vdjHzpLpO4F0z/uQ2negz46jpxahog7K6xDSSSSH\nAzGziGt2hKzNCdtvh57XLIMarITpYbA7MWIhq+7T5bWM5WIcYvMIwSoaS5yUpcKY5TXMZgyizgCL\nC3Nk5ybZ1Vhzzm8lSRJVcoBwNExUS0JKwy7aaL9nF66aAM4yL3X7NhAZnqVqZwvpTIbek93Mzs6y\nGIpRG6hAFFcyVAu/10plnOLi/4sh47jcHjp238nzL7xAfGGGWzdWMD6fwDCgKuDicO8s4/NJ6svc\njM/GmY+l2FsnIWYXmFV9BMoqLmt9roWyimpeOnIaUlMobQF8fXNMZXT2mVaZyCBWick4lnd5K5Zm\nrGKaLESjfP/UKZofeICSZV1JzkWxccoZz9UK/He+69389PArnJmfY0xV8WHlLE8AaSwt27cBG7CU\nglqxDGUllmyfzTTpGhlmtr6Bxq1b80bLMnrLlXhyKj/nY0hbe5OQHzMImObqXunq1xeMLuSk824Y\nzBu4ACYmJpidnWLnzl1Ff73yTNX149JDwhenW1tQIDJNkbHjSYLuSoYHh5E0FzOLPbj0GlKEEBBQ\nyVAjbEZQTHBkcQVlpEAMIy2zyDSBgJ8p6SjOMpNApYuQ3oPeMkjLPidb3iszPNlHMqqSFeIggCbF\nqTH2EzWHrFCm1IRKkpQyTYf4AMlRJzbNx1DXDM37PCiKQlXAx4NbWrFNDjCZSFPSthlFVoiZInLb\nNpibJCvJVkeToS6r3ZfNgZiM0+CUqKmrIxGaJxtbgIlBy/vcfDMkY6DYIDSJUFFveaS77gLBhNA0\nDi3DnnIvLRs3MXbgp+B0Yw9N0NlYT9jpZ7uQwLuiYB3A43Kzf+Me9tVto9ldRZ2znNPjPYRnQsz3\nTiDbFcJDM/gbK0gtxLGXe4mm46iNLn7+2kFaXFV43e4V61BY6mqiXHZHkxzisSih7qeQ9BSyBFsa\ng7zcPcPRgXlEQaK0xMHobALBNLlzaw2go5gZBsISdW1bL/m+54MgCDRvvomf/J8fsafRTu2Wahyx\nFIMOhRd8Tt6dyHLYNNmGVWbybaxwbQrL4/u1xUW+f/AlNn/o1877HOfCorJsWxbqXNnlQxRFbnrf\nY9z0f/8OodOnmRkY4P1Y8nkpLIP5CvATLOGEYawSmK1YBKFXgRrTpHtygi0f+nD+OxY8xcI9Cyo/\n589bW15qrqa4EFpdb35ZEEQ0TV2aB+MXug9mDjcM5lVGLBbj+PFXuf32O4r+ur6uHVcHF+6Ycum6\nteLS58pLG62EJFmNZjPESM/aSKlxpkfnSabT1Bk3YceLLDiQJRGb7MIjVGKz2zAqJjAqpmh/JEnb\nfQKNd+nc8eFGXLUqnoYs+z4c4KZfqmPH/dVs2dvG7Y90YtaP4G5OEZdG0GNuFrLDdJTcRTBQQV/y\nWWptO3EZ5WSyaeTFctQ5J3rExamZ5whUuPGXWuUfHrtCLFCPJxlmMbJAbG4GVVRQMTEREZIx8Pis\nnpd1bYheP/7YDA/LC+hldTjUNEqwkuT4AOLCFILLixSepUw0EXQVfWYEJgawqWn8s4PcuWcnFc2t\niNk0m7UFKrftpa62BrvLjZaMc5tfwuNeXmpSDJfTRW1FNbPxEIORCbKSTtOdWwn1TpKOLmLqBqlQ\nAiOr0nDrJvylQRw1PuZ6J2kLNuBwOJdUgKzfVhSv7GFuuP8sydGjdFY7ODUUQjNMbDYZw4T339nG\nawMhTEx8bhsuh0ywxAmCwMEhjY0791+11IEgCERicfpOHcZX5sK+rYbhoIuGCg9lZ6bxmVZ4NIrl\nZerAvVi1kjIwEo9T8zu/fV7DU+xh5uZ4tZZeOWiaSuO996CZAk+fPEkUgTnD4C4sqb5HgSPAXVjy\nfVNYZS8VS+P6eiqFHI0wNjRI3fYdeW+wOG9ptegyzyH0rDY/hVZi1p51ISO72vexYK2tK6EudS1x\nw2BeZQiCwA9/+CQPPvhQ0V+vpYcJxSHhy9OtFZbVkkqSmA/XWXVXBaNcVuvF0ZAgyhhzXSb2UBMx\nbQYTyLCAIjuxmW4Uw0MoPYw70UJd6i7C0wmqNjlp21bHpj3NdN5Uy+a31FHfWk1ppR+ny6Lqi6JI\nc2ct225tZtf9dRiLMq3b65hSTxDWx0gyj5wqISaOUaI346UKXUwxFD1Gpr+U0ZcNTp45zo47myj1\n+ZAWpkjpBq5UlHhZA8yOkRk4g6GpVu/Lho3gK4fxXsx0kuhwD90129A0FX86yjsaA/zZHdt5pLmM\nR8pgd5mLsm03Ued10SrrPFIh8ivNQf720QfokNJ4FsbZ79J4cOcmjnWdJeurwEgvsj02zP7OtmX0\n/kInjuVknMHwOK137SB0dpz5yVkyi2my8RSb330rejKL7LZjL3Ej2mWGj54lpEWJ2VXGBoZoq2pa\ntjnmfscrAUGUmOk5yKZaN6PzCar8TmrLvLx0do5UWqWh3MXxgXkq/A4ii1nG5hc5M6Vilm2mqX3j\nuvVRLxamadLcsYVTXWeZn5lgc4OfRCpLWcDF0NFREprJr2KFP1/E8jD3Ya18HXgeCIfDlN18M3b7\n6kIBK7253ByvVSupaVlsNhud97yN1CuH+ICq8mQsxhAmc8B2rBpRBSvHqQKbsXKe/wj8SibLvqlJ\nqg4d4tnwAi2337HCUyR/77UUfYpR8FItw2cJvK9v37L6Z2bz4WCLif2LLWBww2BeZTidLr785X/l\nvflkvEDBw3x9O8+vrlt78cbRErYW8kQPURSWGUfrXsY5G7rDaUNEID4hMNA9TqWwFZvoYNo4ScKc\nRsTGorGAHR/l5hbm1X4yIQfhk05ScwoJbZa6zuAFN3KbzUZCXUCJlqMky5AdIKVKWAiHUFQfccbQ\n0Zg0j+HV6imlgzKpnexgBVMcZcPuOhrKg+yrK+PWxkq6J+dwu12I/lI03SCzMA/hGUu0vWkz9BzD\nvOleMuWNmIFKPNX13KdEuW/fHhpqq2msr2d7Ux1N6Xm2O3Q+fPse9m/fSmdzIzabjcqAn/bKByfB\noAAAIABJREFUIJWBEux2B3ur/PhmB9mtpLhn28Y84WLtNlXWb5NMLBKS03Ts2YLN7SQ1Fcahywwe\nPoOh6YwdPEvVlkYiI7PYy7y4K/wEfX6cDQFiPdNUl1XlySkWQeXKNHp2udzEdCfdJ1/jrTvqmEnZ\n+PGRIXY2+emoLeHx5we4e2sNg7MJtjUGMQyT7vEoJdoMP3v6SXzVGwiWlV/WGNbuamKwec/tyOUb\nefboAGPDAzy0r4FQ9yw9c4tMYK3+LixPcwRLhP0Q0Fxby32xGC8uhKi/Zf+quT3Lwyp4c8UGaGU+\ns9jAyLKCsGEDh86epbXES1c6TXM6zRCWwawGQlilJl7gGJYB3Q8YsozbZmcsEafusfcXGWptRePo\n9dViC4KwJFrAUu5zfTXkuYbTkiRhszkxzV9sYwlvMvH1awFZltHOqdYVyNWzXU2m6oVF3S8sz2fl\nr5beJSwPIxc2oWIN0HPvk9NTFQSB5o31nG59lRJnKbq+wKzaR6ncSFqP41VrmKELGRcZLUVUC1PF\ndkw1TeKwl2PT3bS/JUR5+YW7ney6u4XJthlGo6P45ssY6zZwEURFx0EZiqzgEL2Img1ZsJFMpEjL\nSY58ZxK0o9z8zkYqa8pwuVxsDjjx2vykDDehk8fw7rwVNRoifeD7UNEAigJuP6YoocoysUyaiUic\nVCqVr8EVBIG2hvo1x2sRNKyaSJtNZu+GFqyShOVrZ6WeanEuelvrFoz+05w6eYakN0OgspySdzYi\nyiLmWIKIGCB+Zpr56Rmqbm7H8DnpjU1Qo5XiVB1Lny8uY1heKe9ux813ktp6E2e6jxPRwtQExylx\nKZSXOGivKWFzU4D9myp5+tgEkwuLtFX4aKxUeHC7nx889XdMjL6H9o3bqKlrOO99LlW4fcv23cQn\nThPW+vjZqSmCGyu4fzjMwaRKI5Zn90vA57EM1ruAaCTCi/E44te+xouzs+z5b5+mxL9cijCnplQM\nURTPK1ie+++mbTto+vo3AWgcHWFw9w46dZ1DWO3ENmDlLlNYHuYoltEUwmEypkmmKIyfUwLKZlNL\n4zrvNK6JnG7y+UTai99r3fsXn+xzIdzwMK8gvvGNx3nvex8tWmBXjql68fnG4vtJ5+QbLc+xEFIV\nxZzhZFkocO3WSjl25bmtlazPlGjbVcUrhw5iZu245QCmYFh1ldoCCm7CDGKnBI0MboI4JT+SKJOS\n5snYZwETd8B+QUUYr89DbD7D5Jkk8lQjmq6RNqLYcbMoTjEvdaMZGarYQWIxzlj8NO2uO/Gl2zn+\nUh/N++04nA46Am5SC7NEh3oAWAhHUT1+9HgUPH6obITJfiivQ8ZEPPYCldXVdCcFjMg8DeUFJuVq\nbaqKexMWfsv1talauX6qghUIKR25PcBsNoqvpQJJkdCyGvO9Y2REjdq3bCQ8NEWwrQZ7iZPJ7iHc\n0zqbmjfkDUihN+eVC80qikJlTT3x6ALueB/xVIYDpyYAk4xq0FpTQiiWxmGTcTtkdrSWk8lqhKIJ\n2r1R7Jlpzg7PUtXYcd65XF4rutpcKqvOpSbYCfe+QF2pk4zPwYlwmtZIkhnDJGRaBJwqrHDoNqBH\n13mLrhOTJMSzZ3nmuWfZ/Mh7l61LS7ZOzGur5pDrHFPcgDnnka1W6zg3PcP0v/0Ld5kmvVjMWRdW\nq7ERLFm/diyGr2yaPG2azD7wIO7yCgJlZfnftfgAcaF2XjnkxpWbJ6vTjXhBtmwxK/gXvXF0Dm/K\nkOyFGkkfOPAin/nMn/PjH/8A0zTZsKHzsu7305/+hP37b11S9oBLZapeiXyj9fdcobq8zDgWvMrl\nxnF9m9D6NnSwSiI691eRMWOoLOKvU0jMa0STM4CImwrCDJNhAUEAr1JBnEn0wAxNFZsokasY6Z+k\nos15Qc3f0kYHZ/u6mOtVkTM+yqUOTMGgxr4dSZTQxBizWj9T+nEaxVuRFNDTEnIyQMR3ktbNdVZO\nqbqc+zqbqE6H6ZqcwalIROemMREhNGEd2fuP4Tx7mOb6GuK+ag4cO8FTYwv0DQywrbwEh0PJz2U2\na3U3KXjvuQJxln4bKU8UuVimaonby8+PHMTRFiQWjpBYiBOZDxMOR0iGY6iaiuJ2EBudIz4TxuH3\nMB2eowwPlf6y/L1yh6Er0XC6GC6Pl+7jh7i52YYsCozNxxmeijMRSqIaBj3jUWpKXdSXuTnaF2Jf\nZyUoLiorKslEJ8l4WyzS0KrrcrmQQk6fNvf/55vLQGk5J3rHMBOTRBIZHnp4K5H2FrxnpxlXdeKG\nQTnQhJXTzIoiIVmmLJlkZzJJeHKCb//Hl5CDQSo3bkIUxWU6qiuxPJ8pIgisKQ7g8nh44d//lXg6\njY51JJ7Fks17DUvC721AMxYZSNU07j30Ml2P/yeHR4bZev8DS79pQUgBWFcEISdaIElWK6/V9GJX\nQ47wZH3368PLfFMazPM1ktY0jT/909/nn/7p33jHO97N5z73t9x2252XJW935Mgh6urqi8QL1stU\nzRnGlQbyQsax0ER6Zb5RksSiz19e4Ly6msvlbUJrweN10XlLNW1vceNwS/T0daFlJDxqHZKgIJsO\nSqgiJYfQxSQR51naWjZSv82N3WnDbvpIO6fxBVfXdsxBsSls3d9ERJ9kcnSKhDlLSpyD0nmiwijt\njnuotm1Gdc2jJMtwKiUYhkEsnGQ4cgybQ6GmNZD/fk6bTLZuA9tbmjBSKdKBCvRUCnuwDL/TjlsR\nGS9vZ2JshNiGfSTdAcZTBs8fPcbtjeU4bXY+/6MX+PuTUzxxdpLo+DABlwunouTZqjkhgbUM1UIk\nwsDEFC6bkpfOC8eiHBscIxyLUVdRTr2znOMnjjM/MEnazBKdmKfhtk2k40nS4QSiQ6FicyPBxgok\nXcDlcZFJJNlU1lIU7l1dBehyYbM78NVv4cc/+Qkj0xFKbCKVQQcmkNUMKoNODvXMUxV0MxdXqSoP\nYDqD2BxOkukMaU/rknJW8bpcW0jhYrBp5358HXdhq93LtNiAXL+VeOsmAq+8gpnJcBBLBWiTovCC\nJOHVNHbrOt/E8kDfk0ziPHSIV+Zmabz77qU2aqur4yzPZ2p5z361WkdZlpG2b2fg5y8yH4/ze1hi\n8BNY3mY91pM6irUL3I0VOr5H0wh2d/F0NMKmt95zTl1lcX3mWsjlfXPzmxNpv1DT6WKGsHW/9f8O\nb1S8KQ3mD37wPXbu3E1LSysVFRV88Ytf4H3vez8AQ0OD9Pae5aGH3okoioyMDGGaJk1NLZd8v+7u\nLmRZoq2tveivK5mqF1/fuLZxFJaIONJSiUAhFLzcEJprEkiu5Ca0FgRBwO6wUdXhpmqrg9SUnZnI\nMBkjzoLQi+FcRLVFMOyLOB1OHJkqdHuUyvoAqp7F05TG61u73CIHURRp312FXBGnqrSO6tYA9b7N\nGO4Ec9MhhJSbRCLBrNaNqDmIJsLMmCfZGLiL7KSbmH2IulaLdOJ0OOgbGibrK6OhtprJYwfRJQVn\neQ3Opg2EZ2fI2uyohoHhK0ebHiPpCTLtCPC95w7wg5eP8mNnM3PNuwgZMq/EYSAU5amxKMOTk1TZ\nBCLxBGo2i9NhP2e+f/raKT7dFeGnuo+nT/fRLmV46lQ/Xzgb4pitihnZQ3iknz3tLbT56ujuOUv5\n/lYWJueo2ddBOp7E31zB6M/PEBuZRzIEfDY3/uoy5rvH0VWdrr6zhFIRFiIRPEsC2rmNMoe50Dxz\nC/O4na5zvPz5hRCTM1O4HC5EUSSTyZwTPnd7vGipOHe2iBw5O4rXrlBb5sKuSGxtLOWOPZ2MJj0c\n6EuwEE1Q5pFZTCT40aF+fG4naU2ktKLmooUU1gOn00VFRSW1DS3U1LfQuHsP/aEQUk83H1SzHAWe\nMgy2ezy8kErhwiJ93Ib1FEmGTmp+Ht79S9jttiUPc3VPzhpzTiAgFypdXRyguqmZeImPwKuvMpJO\nEzVNarBKX2axDOfzWEpFUawSGBvgN2Fhfp6SX3k/smyFghXFnj8kryakUIyVoVUrEmLmD9ZrXW8R\nhcwbBvMX3WA+//yzdHR0UldnkTCeeOIbvOc970MQBMbHR+nv7+Ouu94KwOnTJ7HZbHR0XHpYdnx8\nnPn5GbZvzwk2XypL9crmG3NYK994JTehtZAL/ZWWl+CuACPmotLeiVcpY4P/DuLxCI3iHZQ7Wkib\nUWYnoriqNOYWh3EobmSPgXONRbz8O4o0bqiipE3FXp0iIY+Rimg4MlUMJQ6CpuA0ytFIkdQj1Ip7\n8FRCwFfOTGKQzv3leYZqR8BFdmqEoJognU4hdewilkqS9NeQGe9HTaUxYmHQDfCXY/pKERejpBxe\npubm0Lbfjqk4yEbm0SQbmieAv2UDsymN53tHiJQ1ciqWZba/m8dPDPLkwAzDQ0Psbmng04d6yXbs\nQXR60EprefH5nzHhrSJSv5mMbCccDoPNwR6fjL/Eh4zIUGySjKChZrKYpkmgoRKX0422mMHfWoW9\nzE3/iycJ1pZjltgYdC0QcakMxyc5NN1FT2SU8MQsqqrRMz/Mz0++wny1TqzMpKu3i3p3BTbFxsDE\nEE8ef5YT8gSpcpEfP/9T+lOTjElh+ob6aPBVI4kiqqqSyaRxldXzzFNPopBmZiGJy66gGwbtNSXM\nhBc52DVLozeDV1E50TvK4Ogk77u1Hm1xjvG+Y/T1DeAO1uD2rB5lUFWVeDyOzXb5PT/dGzdy+MdP\nMKJq9AjwgGbQnskwAPRgSddVY+USx3Wd3nCY3p8+jVlfT0Vre94ArjYOK6pgUJxbXOn1haanOfO9\n7zI+OkLr2AhCLMpbDYNXsI7XIazcagCr/CWC5QkbQAKTOQFKPvob2GzKktdnRxTFZfWZa81R4T2F\ncRXnYNeS+9P1Qjsw07x0otEbCW9Kluz5Gkm73Z5lryWTi3i95w/7rQZN0xgdHaa3t4ejRw8zNTXO\nE098i61bt/LXf/3XK969kqVqFp3mBYqfnWIjtpz0sH5GYO5Ua9WIGaxkv76eEASra4ZpZuncV4lh\nT3P8q9MoWRN1MYksuZBUN4aqUhNs52TmCQzJxZaOXZCG0Vem8b5dRVHWR5GvrC2lsraUbbfA8RcG\nOfGdMMFQHRkNvNSTNiIkzBCmKhPq1okmeimtyyxjqzocTu7Y1IZpQldUJaRn8Gtp5k4cJK2qCHYF\nwenFPHUQtu23FH08JWjxMJSUw+QwWstmEETMZIyMvwRMmAuF8LTuwBBAl538xavjePbdhxCe4ZWp\nKX7+2X8hVNmKkUySNgRETNSMiRRXmTBmyYoydkSS0yNobdaa3btxJ0MvjjMnakwd6AaXDV9lEDOj\n0XH3Dk594wUUjwtHuQejzsUrB47Q+fA+Jk8N4ar14PIHCXiDHHzuCK2SRnV9LdESG6JDI+B149jd\nzLFjXdT7qzhsDDJWncZZYeeVoZPo5Vm0qiyiQ6V+bz2HXn0Nj+LglUgvhmTSSCk1u9/F1IEv8+tv\nayQUT3N6ZIGnj08hKnYWoyFu293Kj46Mcff2apw2CS2zyNjQCDdtqEYXBvnxVz9J9dZ7yIguzPAQ\nXo8bZ9VGnCVBpo//CD0+TSQtULP3l7HbFPylldTUNS5bE4lEguTiInMTfRCfREWhY899uIpYpuH5\nafZuqOP2oId/fMUifxlYSjvjwGmsEOktWEICDwoC9sFBej77Wb7/2jEqT5xElCTGS0spvXU/nbff\nSW1dQSdYUexkMtYaW/kMz06MM/aHv89d8TgZTePvUmlKEOgzzfyR2g2UYan+jGGpAD2J1RRbAk4p\nCps9Xqz2grlwsJLXDta0DIqyvP1YDsVs1xxyrNtMJpknNhV70bl64dw114OxPB+ua4O5bdt2Xnrp\n59x11z2cPn2K1ta2/GuNjU1WJ4l4HIfDwfHjx3jssQ9e1OdPT0/zkY+8n2g0uuzvdXV1bN26FWuJ\n5x6KwsmuuITD8ihZZhwLYRDLM129ZamwzDCeL4QqSQqalsnnWa62N7kWrHCyReLYsK2BqW0DlGRa\niEwnUbNTzC2colxqYJYRKpq8lDgL+qI2zcdiIok/sP7+nrmDRuOWIAMvxfFV2lk0JKKREWTTgV0s\nISaOIKQEnDMS3nkHA8fm2LC3bmm8hXnye1zs8pbyalcPDmeAtOpBVzUUtxtx560Yg6cxhC0Yugpq\nFqobYOgMpBYhOocgCujVdcyH5sgmk0RGhyn1OzkzOMR8oJ7k7AxqKgUlVUy7yjC7D6EHWrH7yzBn\nx1CiMbx1EpnZCcz2HaRCk4wkVP79peN84oE7EUWRR+94mKauo4xUhDk71sfi0Uk0Q2W6e5SSDTU0\n3baFue5R4qEotjofpmCRkcQSO8gCCAKO8hJSToPkYgLZYUNVDExMBnv7WExEOTZ7FnOjH1OUiYWj\n6JIBHgWlxsvYQpj5gTBVEZFBRxj/vgYUu0Lf6AIl46U4Oh/kPw98nwd2VrGvsxpBtnHg9CQum4Sm\nGciigE0SiSRUUpkUu1pKCThFft41yQf21/CtQz/Bq+i8dWcDqmBnLhrjxIkYDc4FdnV6OHBmkoWf\nf4G9O7cxN+3kmedV6uqbEXwNgIk9dIzQ3CS1HhNN8pBJL/L0fxzmgY/+RT5HXFPfwuGOZs7OvkYF\nVi3mBFYfzVmgXJLo0XUCwH1AiWmimSZnRkfZ/E//mwpBoF9VaRAEWn/4JFF/gAMf+CBv+aM/xjRN\nRvp60U2D2sZ6NC3LYiJB/1NPMdjVzfSX/51fT8Q5aJpkJYkthoFPlkkCDwP/hcWYPYslxu6RFe4z\ndNoMg6OiiCZJ1G/ZiizLZLPqsjUsy3YMI9eJRF2VnLRWOzBBEPKtxFQ1k0/ZLF2Vf7atz1j34/kL\nies6JNvY2MThwy/z1a9+iSNHDvEHf/D/cvjwIbq6TrNx4yZqamr5u7/7DD/84ZO84x3vZNeuPRf+\n0CIYhkF/fx9bt27nwQcf5ld+5YMcOfIK3/jGE2zbtmNpERXIFJK0dr5xPQXrl5pvLBhjI08yuVYQ\nBGHJ04eGLX66ek/gtgVp2VRD2juGGQzRequH8jY7JSUeHA6LhJUSFqjd5F6TLbtSGSc3l4ahY7PL\nlG2QmZgaQ0RmXhtAVwXaPPtZVCZxKh6cQYmGzUEk007FRvncXJyR4YXTvfS6qnGVVZKdn0a55X6k\n0CQufynBTAzn/ChaZA7R7kLQVYzFCMgyPreLytpGhOM/o1SC6blZ4q4APdEUM6ITEmHSmQxa0xaM\nuXHE6kbUhTnU8BzpgVNoWRWtuglt+CwZbwBjehS3XaGkfQtSKkG7XafcV4IgCNRV1NBRUs8mXxOz\nczPMhGbRXAKiU6GkqZxMNMnU6SH8zVVMd4/gcbmJTC8QKA3icjgYOdKN6HeSdsP42SEUlwN9IUnC\nqdPU3EgynSJWYpCOJTHQiU2Hkew2XEEvrgofGUPj9NOHCb6lGW9NKZpgMDs8wdGzJ6lu20BT6110\nd/Wi6zpBfwn9o1NUBxy81DVDidPOQiKDyy5xZjRMwGtHlizG58hMlMTiIje1B3EpJg5BZTG2wGu9\nkzy0o5Tu0Xl0TefOrZXEomHGR0fYVJqh3JlFj48Tmexjb2ctkflZFDOJV0rSVmmnviTL4e5x6jt2\nkUqlcHs8KDVtnFKyjBw+wVbdIG2atAEJm406u51aXSdjmnlv76RhMJ3JsEvTGFNVgobBFsPA1DT8\ni4s4e3ro37CRgf/4Eh2Pfw352Z9ybGYG34YNHP3Yb3Lzs89y4pmnKU8m2YiVq7zDNHkVuM0weH5p\nDbqw8pZ7gVdtNlL1jcxHIyQAt2ky5PHS/tnPU1bfgKZlKFb5OVdI4Vzm60rxheXPbaGVWHFot7gd\nmCjK143BXCskK5iruy8AzM3Fr9qArlfcdtvNPPHEE1RWVi0ZB21JyV9YqsG6UEh17YL1y0FOXcTq\n1adcU6NpmsYSUcDK33YdmESPudBJI3lUvCVeShvtRGeTJCYkkA1qNjsorfLlv0vxPJ5PSKEwh6IV\nPu+fIhKKcOJHUUIvlSKkXJTJrQhVc7Te4cBeprLtEQcOx7lhq67ePn731SmkLbcw1XOaeCyGLRGm\nZsMWdpV7ETIJpqIpZlIZ5kJhopqG3ePHW12HJx2lMTmLLxjkpKseLZVk+NghjEAldl+QdNeraLvv\nxjYzim/bzYRf+AHm5pshGcdZ30764I/w1jaRnp3E3HYbdtGkPDzB/XUl3GFb5I6NreeM96f9BznK\nCIlMkoWpOVwVPnz1FQgIzLzaz9b7b2G+Z5zEy2NUb21iUcgycLoHX2c15Z31ZGfjhA8Ps9HbQMPb\ntuNxucmkM3zj29+kck8rQz8/jd3rpOPhm5g80k9JdRA0E/+swDRRWu7dwfjxfmSXHZvLjkOykXh5\nmI+0vZ3h488y0n0EIzbOL9/SwHMnJxmcSVDiVGiu8pJVNabCae7bWctUOAWmidMhY5oCb9lUgWHC\n+EKaz37vLH/y7k5mwoskMxotVSUYpsHYXJLtLUEESWE6qjIUFth/816OnOhCSc+xpd6NQ5E5OxHl\npbMLOPzVCNoiGjYo7eDtj/4OZ557hr7/75OkQgvIskTdbbcxEAqx58QJxhYXiWLlEqewQqLzWFq0\nYOU6c9ILGVHkLzZt4k/9AdSpSaaSSQYEgeONjfxJdzcLmQyPp1L8BvCfWE2tO4AvYoVeS7HypxVY\nIdmMzcatpaX8RJb5aDjMsGFgM02mSstoePEAPp+fTGYRUbSUd4qh6xqqml7yGl3L9pZ0OoEgiNjt\nLlaDaZqoajqf58yVnqhqBlm2LzXRXvXSXziUl6+enruuPcxrgY6Odr7whX9AliWee+5ZyspK87Wf\nV6q+8VKwslD9StfcXexYcjlZURSpbglStcFFbaeP6pYApbVunG47/nI3Fa0uypucOFzK0uHj0oQU\nrByqRLDcT01jFQ1bfMSNaZJ6GM0Zpm5jCRVNXuSGOWpbS1cdd3lpKampUfpHx9CjC9QGfXzw1p3o\niQh1PjcVaJQm58g6vNT4vdRKOqIkUh6d5n4/bKgMMJlUmXWWInlKiE+NIW3cjTMTw1Ndh/ja85TZ\nRdSSUozIHMQiCLqGbLejaGnKHHZK41MwPYQrm+TOSjflJV5u9okEvOd2ORkeH2XSnsDudVK6sQ6H\nx8XIC6fw+LzUbWlFm4giLhrcU7kDe8IkkomhlLmpu30zekajtLqcYGUZlQt27GUe7F4XAjA8Ooye\nyCL7nMSmFtCSGfzNFQRbq5GyJjvat9D1zBFUxWS+d5yyzjp8tWU4PC40RUQcTXH3vY/gq9/MQvfP\nmIos4lAktjYFmI2maavxEU/peB2WF3NmNIJhQk3QxXQ4SSKlMzyboHsqTcDv4+XuaVLpDH6XQiyp\nYlck5mIpWqpLEAQRWTD51osD1PkgG57k2aODbGvyE09lGZmNo2UzlNozBOw6O+rt1NliPPfSYe54\n72+y+Tc/Rvtv/Ca7/+CPqH/oHWx75D0cGhqidGCAB02TnYrCjGmimiZprHjScSCOVTtpYsnrzUky\nnZOThOZmUeJxbkom6Z2ZpUXNUqHrPGUYbAROAotY4gSnsAzvHiyvsgcrDLxH13EmErwUj/MWQaRK\nUQhKEsdFkdaPfwJRFJYECM4tc1nOfC2UNBXEFFavJS08Y8ubTuc+63qqwYQbHuZVxfj4GI8//hV6\ne3sYHOwnmy0cND72sY/x2GOP5f9fFJX8Bn4tUBySWa/G5NWAaZp5OnqhX9/FS53liE2XOp+pVIqF\n+TCx2Qx2p0RTZ90F69Wi0SiZbJa5xQxxzaDapSCIAgG3G6/Xy8mhcY7GNAxJpkaLc++WdmRZZiq0\nwHfPTvC9vkn0jj1o/SeYSWYJtGzAGZnht8o1vDaFH/ZPsoDC6XAKatrQBRGfnqQ0EOCOtnq0ZILR\ngX72NVSwzW9jZ0vjOWM0TZNsNsP/+PG/ELh3AyMnenGX+0icnqDh3h24giW4VQllJssutY6pTJgx\nW4TRxVmEWg/emiCyIRA7OMydzs3YFQeTZphsKsN4Zp4FNUHUm8XAJD61QHwmTM32VnbXb8a5YLBV\nrOffDn6bqeQs7Y/eiq+2DEE3mXl1kL2peu7ffgeGYfCNv/kQtR6VRDLLfCxJhd+Jx2Hn/l01/OvT\n3bz7libmYioDEzGcDpnusQVu6ijD7XTR2FjP4wdn2NhYzvFXX8bnUrArIjZFABMaKryYpkhGNzk7\nkWAhEqO92oskC3gdNnweG/FklqxmEktmeXBvPbIoYCAyHjHo9d7H/rsfzIf7c6mT5OIi//Xoo/xS\nVxdVosjXBIEd2SxnAJeq4sYi5GzHYjG0iiJ/ESzjHaE5gqbJTcCsIPCqLDOl6zwmCDyv65zAYsJu\nw2LhjmIZzt3AS1ie7MNAHfB/sPRlA0CpopDxeBjbtZt3/dd3lgQzUnkvcPW1kcqzaGVZwTAMstkk\nkiSvSQrKIff5UBBlsDxZCWP1R/YXDmt5mDcM5hXAV77y7/zzP/9vFEWhubmV+vp6wuEQH/7wR9i+\nfQeSJL0hDdVandhfjzGAia7r5DorrI71E5suF1f698ltsiuNr2EYhEILHOoZxOGwU+f3MDq/QEd9\nLc21NZimSSaTIpGIk8roPHW6F4fbS7VTxuZwMaJJ2NC5o76MymAgT9TIedwrDxqqqvLMiQMsLCYw\nHQKNOzo4fvgool2mxl9Jh7OWHS2b6RnrZ8hjGcyJ6SmUUheORWjRg7y1+SZKvP783P+s+2WUrRU8\n+e3v4N5WjRpKYkNBGU1y/5bbaa5uwOf1MTU3zTN9r3Bo+hRVu1pRkHBlRO62bWFLs1W+deAn30QY\nfYGAW2EuNM+tWxs5NrhAPLKAy2lnNCJQ4VOYTYpU+hyYGPSPztPc3IjhqGTXfR+g7/CP6D/0JD67\nwY6WAImUzsmhBapKPWxsLOdI/zxNVQFCoXlaqrzUBN281DXNztYgzxyfxuuUSGV0Hr2CZVJ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EfM//XCxbCJl8/n2j1bL3U+zzeWXM30ao2bV2Ov5sa7Gvt1JXN1rbHkemDabK6lz0oiSfKSaMHa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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1209d3eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mpl_toolkits import mplot3d\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.scatter3D(X3[:, 0], X3[:, 1], X3[:, 2],\n",
+    "             **colorize)\n",
+    "ax.view_init(azim=70, elev=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now ask the ``MDS`` estimator to input this three-dimensional data, compute the distance matrix, and then determine the optimal two-dimensional embedding for this distance matrix.\n",
+    "The result recovers a representation of the original data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LS0MGLAttQ+Sva7B3dkarzcPZ2YXK8nJObtmI3NKSmswM1I6O9Jo0tcksZflP\nPj+z2UxRUSEajUW9e/lnDkSTFnOWbiNG4+pR/zNYP3wg9x85BEApsH76qwx44eWb3v6/0mirgwmC\nIDQn29ctI/P4KuQyE2qfrtw56l4Ob/gOK7mOvBpbfMwXmNzWkmPhFszfloLZDH6u1ozo4sfhBC16\ng4mknFLOZ5WQWONLtxtI2hcTL5A1/Vle0OmQAaWZF3G+4nVvwOG++1hcUoHMZCLi/odwdnenqqqK\norw8CrKyyHvmCTokxHECmII0fWzp3t3c9fX3t/VV5bWQyWQ4OUnvzMXzCSQs+oGkk8cYfeY0fXQ6\nNsz/htJvfyA4Qlq+s7KyEq8rBqnZAcr42MZo+nURiVsQBAFIiDnBgZ2rsS86hp+tCkcbDQbdIX55\nfwevj2+FTCZj8e6TjIqSlqcIcrPG0UpBoJcj4b7SlVNWYTXtglwIcrNkfZyJIROm/9Up/75N0Xvx\nr03a+4F2wK/ARKQpS7+GtKT/Qw8h11we9XzxwnniH7mfbjFnWWdhwdPV1ayp3QfAHuixbg2p018j\nMCiY5iT3Yjqp905mfFIiK5BqkQOMSknm56++IPib7wGwtLQk19sHaqfs6QCdrz+FWi1H3n8bTWkJ\nmqj+9Lj7nkaJ4++IxC0Iwr/esb2bcM1dg11+ChYWCnq28cDL2YqSiho06vy6K1NrjfSVqS2pYsep\nbN6bFsnaw2kcuVCIrYML6lYjqQrrwZ68LHpM64aNzfV1hV7i374DJ1QqjHo9BUgDzvKA34BkKys6\nfPoVu2fORJ6WQVWbcAbOeIPYjz9gasxZAFTV1YB07/tKBrm8ya0K9k+c+W0lU5ISOQf1ljgFKDgf\nV/dvmUxGyw8/5ue33sCyIJ/iiPYMfOk1Nk8YzUOHDiADLmzZxGGVkq4TJjdkCP+ISNyCIPwrnT0e\nTV7ySWQaByiIYUiYBQdPm8jX6vB0kr72bSyU5JdU1+0T6mvP0gPZWMp0TOwdiEwmY1S3ADLzKzjn\nci8duvQEoEWrsJvSxhYR7UmdMZt3PnwHVVUVI00m3IAxwHI3d5LnzuGBbZuRARW7trNSBpZVl9ur\nAHYCXYGlwAQgXybj+NgJjPLzvyltvJ2oHZ0oRVoNrRDQAq7Ad0BSUhLzJ45h9GfzcHF3J6h9R4JX\nX64ml5OTTbvTp+qKr7SoruJE9D64DRN30xidIAiCcBMdi96MY8oiJvknMcz+EDkZUrWtMd0D0JZU\nsjw6BQCnSXdhAAAgAElEQVSFQk5OmYkfD5Wx5lQp+/I8CRn5NmfL6o8Sj0kvJjV6IXu/f5pNSz7l\nZo351ev1lBiSmf7TWNyHh/KNQk4WsMPSEs1j/8UpPq4u0VgDVnExaAYNIa52gJYNUj3zH5HmeW8E\n9pnNWHn7NJv721fqNeUelo4Zh1yppAA4C/wPqV75Z9VVvLxrBz93i2Rr764c6BDG6kfup6ZGWjLU\n3t6BLBeXumMZAJ2TUyNE8ffEetwNpLmsCfxnmnN8zTk2+HfGl7BvCXcGSbXF1CoFx5OKqDSqaetr\nRW6piexiA8eT8tl1oYauE1+n+/D/4BU5nDZdBuDg6EyLdt35beM22nrIyS6q4lR6BQ/1caWNuwwf\nVR6740oIaNnuhtt+YNdGxngmEHMul9Y/HKW7wUQKUKNUYfHoE2hPnSAiMwOQusOPdutBvxdfIT6k\nBcfc3KmM6s+JhDh8qqqYhrQOdxsgoaaGgLun3XD7GsK1/H3KZDJCh4+k+K7xZBYWoomLJRF4qvZ1\nHVCh1zMxP5+8slKM8XEk/DCfxMJCWvbtT76rGyfiYsiUK9jZqzedX5mJjY3NLfuRI9bjFgRB+If0\npvqdja7Ozlj3fJFlMccIGXk/g1qGU1NTg5WVVd02V94Ttndwot8DH7J672a0xcVEBR+oe83ZVo1J\nm31T2mk2m5HJICetkC41UnGXToC5uool587S6p0PWDbrVdQZmRSFtiZq9jsAdBg+CoaPAmBzRhqy\n5cvqx39FXM1RwvIlZB46QLhcjp3JRDVggdR97gNkIA3uGw9sLSok/su57PjuKyrbd6TL0lXkpSZh\n+8YMinp3ZYObOy7d78A5sgM97552W/RUiHncDUTMlW26mnNs8O+M72JaEgkb59Db30BSgZFCt8Hc\nMWjCdR2/qqqKw4ueZXx7qcJWXnEV3+0tpPfE6YS27XxDbdfr9az5ejp32OdQ9dZ2upbqANjr7oHl\nr2sICG39t59f7sV01k8YTcukRDoAe62sKezdm1ZjxtNpzLgbal9DuNa/z52ff0LpW2/gBeQizdHO\nByYDxcAeCwt6VVcTXvv6LuCxK/b/YcgwVAX5TD1ymGikUfhtAa1czvoHH2H4Ox/enMAQ87gFQRD+\nMV//YJymzSE2/gwJ2dF45J1j46Ikeo56DDt7h7/dPyHmBBlntmM0QVjvCXj1fITP1/wPHytppbBX\nR/izbN98gkMjUalUf3O0P6dSqRj56AdEb1tN+lRvkmKTkCuVON/3AK1DW/+jY7j7+jFt72Hi42P5\n/q1ZPL57B56bN5G4ezf7S0u4494Hr7t9t6OK48dogXR1/UDtc1pgdptw2j/wMJ1CW3Nk+EDSAVuk\nufBXkqekYGWQbqMUAD1rn3c1mbDfsgnz2x80+lW3SNyCIDRbF+LPcXBnAh7+4fj4SQuAXEqk1tbW\naNPOMiUwhcpqPdGxuWz94hh2Le9kwLhH//TLOSUpnurj3zApVBp5vmz9+3Sc9C5ugW0ZE5xTt12Q\nQw0FBfl4eFx/ARaQFsDoN2wiDJv4p9sYDAZ2zHkPdUoKhlah9H/mhXqV0VQqFeHh7ShNvFC36EZI\ndRUnt2+DZpa49V7eVCAN1rvEFejdshV9p91PSUkxZmcXUgrySQX8kBYjUSEN4CsNC0evUKJLPI/x\nd8c2aDSNnrRBJG5BEJqpgztW45W/iREBlsz9cT6ejtbI5ArKnLowatrzAKjKL+LorWLj0XSm9g0B\noLD8LNvWLaTfyPuuetzE03uZEnp5lvCocCUbju5FZuNNQVk6zrZqAC6UWNLfxfXWBol0H3zB2BE8\ne3A/VkiV0X4rLmbom+8CUFpaglwux8bGFp1N/aU9dc1wqc9hb7zF/Og9OMbH0Qtpac+T1jbYDhkG\nSKPHy558BsOH7zKpqpJtSNPF1EBmh048/NnXmM1mlnt6UHz6NIvPnGRoYSGxDo5oHnnsz0/cgETi\nFgShWapO3kWnCCuW7E5ibFdv/NxsSM0tY8fp7Wybe5xqCy+yc/Mp9rbA1+VyAnO0VpG8bxeyskyq\n5Tb0HfOfeitEqS0dKKnUY28lXbmnF+hwDvQmrF0nNv1ahsXFFKpNaloN/A9K5a3/iv3lqacIrU3a\nIN2TtTlxDLPZzLrpz+K/djVGuZzcCVNwe/ZF1s+eSUh2FkfD2xLewLW5G4JGo+GJvYc5c/gQn37/\nDd52dngNHEKngYPrtunzxFPkT5zClk/n4HzmNDkKJYH/fYZRffvXbTP4tVkAFBUWsO/wIXxCW9Mj\nMKihw7kqMTitgfwbBwA1F805Nmi+8e3+7ila2haxIjqVt6Z1BGDZnmQm9ZG+fPfH5qBQKEjIKEan\nN/LI4FAAVh9MpVsrdzydLNEbTPx4zo7Rj7xdd1yTycSaBW/RWpNKlR5yrTsyeOKTDR1eXVuOdmlH\nVXo64694/ueBg7EePZaeT/4H19oFSdLUauIWLiWkU2e0ubn4+gegVqtvi67fv9Jc/z5BDE4TBEGo\nR+bdlVNnfqF7a1d2nMqif6QXVhaXv/IKymoY2dWPbq1cOZ1cwI/bL2Bh705+ubqucppKKcfFnFM7\nLUtKcHK5nDEPvUFBQQEqlZL2dvZXPX9DkMlk1KjVtAOWAZ7AaQdHerw2i/Pbt9QlbQCfmhoOpKVi\n338AFhaWbPrvo7gePUyVvQMuL71GZG1X8r+FyWQi5thRkMkI79T5tv8BcyVROU0QhGapz9C7KbEI\nZkgnX44l5vPL3iRi04swGqVkptMbqayWRg9HBDnj4uKObYf7kdn61jtOldniql/qzs7O2DVi0gYp\ncdv+978U29kxBEhwcEQW0Z7YZUsI7j+Azb5+ddsuc3ImojY5757zPvf+tpLRmRlMjj1H8ezX0Ol0\njRRFwzMajax+cBqBwwcQMHwAqx6+D9MVP3Jud6JyWgP5N1anai6ac2zQvONLTUsn9vRhpvYJZtPx\nDFr5OHAyqYDk3DIOnS8mr6iczIIK9sTkkZAP/R3jsDAUsfywFoNZxsFUE17dpuLm6ff3J2skbfv1\nJqvXnSwuLmTYqRMMTUuhzbHDbCkq4lBRAQWFhSQDblVVHK2pIezOgaT/uoyI2oVIAMp0NZjuuRcb\nG5vGC+RP3Iq/z71Lf2Lc53NxQyqH2iIhnt0BgfiHtb2p5/k7onKaIAjC79jps7izkw/HEvPxd7Nh\n7B0BAJzPLCHE045wf0eMRhObjmdwT5QbFmolwR62BHtUsl8xnL53D2sSq2gFtgqldUkJrWofqwD1\n8aN0zMrk7iu2S1q7Gt6fg7pzVy6uXoFvbZ3uuDZhDHV1a+hmN5r0Eye4snacLVBTdvk+elFRIZmp\nKQS0aHVb/pgRXeWCIDRbOpMcBxsN/SK8CPVxYOneNGLSi5m3LZ3yKqmbXKGQYzSZsVBfvo5xsVOj\nkJmaRNK+pMrBsd5jnYMjOln9r/jq2nh6TrufQzNm8eugIfw0bhKdv1lQb953c2d3/Ci/INV3NwNf\n2jvQZaw0vO/E2t9IjOpB0KC+HB8UReKJY43Z1Kv693xSgiA0W0WFBezctILTxw/Uez609xR+Pa0n\nNbeMIr0GRZtJHDT24cVRLTmVXEBCRjGllTWcSi1l8aFiQJoXveSkiY497myMUK5ZYvw5tq36Fn3v\nLnzftTu77B1Y0qo1rWa9hXbAALYgVQBbo1Dg+OyLdftFPfokUT/9wuCvvsXd9/a9FXCzmUwmnMvL\n6A+sQVrb3LpdO2ztHdg17wtSpz/D0Ows/IFxF86T/Mmcxm3wVYiuckEQmrSLaUkkbv4IF1kh2Weq\n2b/WiQdemYeFhQV+ASG0fmYehw8dx7e7D+1dXNj22w94OmpwslVTVWPkeGIBL4wO5btjCpakhWAy\ny+g1dfJt2UX6e3FnjmI+u4ApLTQUldewfOQdeC1cyoXZMyh4/hn8bW058dCj7K+owNbZiWA7O0wm\n0x+urmP27uHIFx9jefEiVq3DCH7iKdp06NSkRlr/nYSD+0l9/VVs83I4YzQxHBgN5CoU7OrVl83v\nzGboF3M58LtBaurqqkZp718RiVsQhCbl8J6NlGnT0cs0qMw6Mi6coL1zEUEedvSP9EJvMPHtdzOY\n8F/pSsnKyorwtpF1+7eK7MX2vYcwmsxEBjnXPa/RZTFg/GcNHs/1MJlMLPt+DkUXopk1TpqX7mij\nJkSVwsHvvuLupT/Xlfz8qaAAs709U+NjKQWWbdvMXfMW1CXlmL17SL//boaUlVIOZCUlUrNxHSu6\n92TwwsXYNvLI+Zsl9fVXmXr6JAADgffahNG6TRjyyA70e/gx9g0bgIvJRBVQhDRoLcXCAvoPaMRW\nX53oKhcEocnYtvIbIirX0sawH9+izUz2iydQk0ulzkigh1TMQqWU46vOp6bm6iOR/QJC0LR/hNjM\nSmr0UjXqsko9JaVlGGoXl7jdfTX7IXrZnKGdV/2vcJ0BlNnZ9ep0V2Vnck98LHLAAYhavZLjh/bX\nvZ67cR0OZaW0AbKAscBAk4lH9+8l+t03b30wDcBkMmGTl1v32BJo4+tH36/m0+eRx5HJZFTb2QEw\nATgCfOjpRdzcL4l6tHGK6/wVkbgFQbhtxJ49zt5tayktKa73vNFopKSkGHXhGXydLUjTVtC3nbRc\nRoVOz/nMEi4VgTwYl0dyZgE7fv2crMwMzp05TXl5eb3jhbbtSFC7nqw7cpG1h9PZeSYLf2+vJjEY\nraSkmAALLR2CXegY4sLqA6no9EbisypJqPYhU1ZE0hXb5/5ufzlwat5XdY+ziwuRISVt599tR3bW\nLYqiYcnlcrRh4VzqBC+UydBHtK+3TdCLr7A4tA2nlCrS/QOxCAqmev43rPvvo1RWVjZ8o/+C6CoX\nBKFBXVmF7Epbln9JJ/UpOjtpWLNkI2EjX8XT249Th3dScHwp7tYGtLlFQACG2iIqcReLiQx0psZg\n4tO1MThYq4kIdOaZ4cEcv3CSmKX76drCgRM5Ktx6PExo205154u8817iN86hs08NFwrMyHyGNYl7\nukqliku3Yf3dbLC1VPHG4hNofHtwh2ce/Se4MHe1ihaVenRI3cLfIuMRzJQBS4HqHVtZM3UCLgOH\nYucXgBb4FmlaVG+kpJ0KZDZArfWG0u/r+fw0eyZWWi36yPb0f/ZFTCYTqSnJWFpZYevmTqWFBflG\nAyXZWTyfloIMMB47wiKFguFzv2zsEOo0n09FEITbmk6nY9PCd3AxZ1FltsCj0wTaduoNSFeR7hXH\nCA2Q7qdO7qhk8e5f8JjyAvnHlzG5k9T5qzBWsvV0HuF+jszfloSdRsbYOwJQKOR0DHHhl33JtA+W\nrhtTcsuY0jsQgGBPWHJkRb3E7eMXhNO0OSQmxuPRxQc3t6Yxj9na2ppcZSBHErR0aeVKSm4ZnUJc\niC/OYmAbV8xmM/5OVoysLKnbZ72LC7PztXQH/IHJNTVotm7m4u6dLB09lgkqFQf0ejoBKwENUnJo\ncRve3/071dXVnDt5HEtLS1q3i6wbiGdn78DQjz+v266mpoa1D0yl964dlKg17PH15ZX4OACqanRc\n+gmnAGyTkxs4ir8musoFQWgQu377jnvDihjT3oYpHZRoj/5MYkIM29cvJfF8HJa/u4xQyM3odDrs\n1fq653qHe7LvbAYbCtrSdvLHVARPYGuMVDhDLpehLa2pK2mqVtXv9tYo/nj/2srKirbtOjSZpH3J\nQ9M/5beTJcxZdRad3shdPQIwKK3RluiQyWS4j2/HRnsNF4BFYeEM+OkXXF1cGYjUHX6pXpdvTQ2B\nJcVEP/E06qBgltrYEiiT4S9XkD5mLL0mTW28IK9RUVEha//3IQtDA/AZNQTfgVH8evd40tJSKS4u\n+sP2e7/9ige2biZMr6dHRTne5xPqXqu4YjsTUO53e02XE1fcgiA0CLWxtF4yLS/KxXTsEyYGW3Pw\nwk72pClo56vHzkrFVxvi0VYnU2mQU15mi9FoQqGQc1FbTq827sRkHyQo6DmCglrw4ydnqYiOR6WU\nE+pty/urz9MvwpOYzCq6FVfj5mBBVpEOnV1YI0Z/c8lkMgZMfZmsgz9TYqhh/gkV9zw9m6/nPE5P\nPwNGJyvy7u+CxskS1/bP0qp9B3YqFFQC1b87VrWtLYNffZ2L4ychv2ciTsllFJuNmM3U3To4s2Mr\n2j27kXt60ec/j982xVpO7dqBNj6WysJCXH5eSElBPvcBPkANYLFjG8buHUi1taPgkcfo//xLAGjz\n8sg+e5p6BUdNJmKAMKA78D7g6emFqWt3+rz7YYPG9XdE4hYEoUEoHQPJKrqIl6P0dVljhDtaSHOl\n7wixJq1cxiebU3BSVdI91JVOLVwxGtN5J7mcd5cnExHkhJVGwcAO3mTvvTxoyt/NmnEtAi+fSF2M\nosdMJo92Z9/e9ahytJisvBhw110NGu+tFt6hJ2Ht78DBwYKSEmmBkFadB9NKFY29lQobSxU748rw\nDQhBq9UyoLSULUAcsAgIBWKAGndpkF/CwgVMTr48rE22djVpr8xEe/IY3tOfo39pqTSdLPYcYz6f\n18DR/tHOT/9H+48/oldVJRvkcjqZTOQBTrWvbwbuATQGAxQVcviLuaSPnUBhbAzGl5/njpxs1snl\njDCZMANJwS1wTbpAEtJV9nPAynETuHPm7TeyXiRuQRAaRK/Bk9i1TociIYkqowp7V32912WY6Bjs\niKlaTqcWroBUjnRoSxMrj9kxINILS40SvcFEvtmdmpoaNv74FoUpxyn3CcTGUgVAZpU1kQGByGQy\neg+Z2KzXc5bJZKjVakBK3FHDp7L2hzTcjSlUGWuwbDmMtu7SexXr6cno5CQsgX5I1dQ6AqtSUwAw\n1I7Kv8RoMnFg6U94JiURUVoKgB3gtWcXer0elUrVUGFelWL5UlpWVVIAuJtMeCGVL12BlLCBelfU\nPhUVxOVkk//VZ0zOyQYgwWTiIy9vPMeMZdR9D5F493gm1HaZn7K3x6NXVIPFcy1E4hYE4ZbT6XT8\ntuBtyjNO4mqrptrSHwffjsRmHaaNlyWxWdVofKPITTmKvb4Qk8mMXC510+ZVyJn60nf875uZ2JBH\nmcyZac/9j12/zWdaWDFbdRZ8vi4WJzsL9Fa+dB/3YpMYHX4ryOVyRj84E6PRiFwur3sf1Go1zm++\nx6/vv0V5QjwavR4vwAhUXrq/36UbC7/7mruRknoCoN+ymQxLy3rnqLS0RHkbjTZ3Ai4AXYGJwHfA\ni3b2WLRshX1yEn0KCzADG9t3RL17J6YL5+v2bQUE+vjS5423ATB88wOLv5iLRqfDesQoOkX1a+hw\n/pHb590XBKHZ2rnySxxKT/LEyGBkMhlms4kfz6aQ3eFeTqfF4ebfml4delBYcCerv53JuyviGNzB\nnfwqFdXeA3B1dcPV3ZM+rgYUMulK28nRnqPntbQLdGJEV2nw0LpTZVjbOv1Na5q3+JiT7F7zHS2d\nTBjlFnh0uIu2nXrTbuBg2g0cTNz+aH6ePQPrfC0FbdvRv7Yr2MvXl1RgC9KV9RhgRlERk+Jj+Q4Y\nCpyVySkdNvK2+GFknDiFhI8/olVlBX5u7rwZHILPyeNEVVcTVlrCIi9vTC/P4Je1q6nRaNDnaZn6\n8YdsATIBbyBVo8E4aAggzWzwCmlB4NfzGzOsf0QkbkEQbjlLYzEWVuq6L/xD8VpKMtLJtvOgz8gH\nUSqV6PV6Lpw9yNBWZsI8QzmRUkSyuQXjBk9i3cqfGBesxdlWqm7l5VTKlydk6HWV9AzzqDtPt0Al\nxy7E4Ozcu1HibGy71y2k/NxKHuvsiYu9dKW87tTPlLaMwK62dGnrO3rSeuvuP8ynD4toz3obG2zK\nyzEBc4HWMsgxmxkFXAQizCbyd23H+NobjV6spt9Tz3E6oj3H42II7hVF+DdfMfHg5YpwbXbvwP3D\njwnvHQXAvs4RWCLVJ98NLPb3J+zVWfQcNoKV906h9YF9FFnbYH7yaXo+9GgjRPTPicQtCMItV6V0\npqpch9lsJjomF2c7Dc8MdaNGH89rr0ykawsH1EoF6QXVDO8vdd12DnEm5VgiK79+FbSncRgYUnc8\nK40S38BQMlIgXavFz1Wa530iw4T/sNBGibGxmUwmZJn7cLZR1iVtgDA3AxkX07ELa1tv+99fNZeV\nlTJMLqcSOAd4AMUF+bgDbrX/AQSkpxF36iRZS39CYTTiO+UeWnbuegsj+3MRffpCn74AXNCo671W\namGBn8ai7rHOzrbu31FARmRHuowZy7Y573P/pvXS/fCSEnZ89D7aUWNxdXW99QFcp9tjTL8gCM3a\nneMep8ypMx+uTeFoUhFt/BwxGk3MWXWWB3o60D1ATXmxFlOFtt5+8el53Ne2jCm9A1i440JdWdN5\nW1PR2HszeNJ/mX+wikW7kvl2ayJnix1xbWJzsm8Ws9mMSm4mu7CSi1qpxOtvB9OIjs0je+/n7Nu0\nFJDKx6anp1FaWlJvf2trG/LtHJADjwMPAP1rajgtl3Pllmd9/ch75nGGLPoB4+JFpI8cwqqh/clM\nSrzhGPZ9/y277x7PlofvIz0h/pr2jXz6eRa1bUcucNjahsqHH8Pa+nLVdpcXXmGVfyCnVCoWRbQn\n7KXXAFAUFdYbxOZXXERhXs4Nx3IriStuQRBuOZVKxYT/zAJg00/vYzZns/VkJi297WnpZc+v0alM\niQomNr2IVQdS6R/hxan0SixdgrDUyLDUgMlkZs2hdHR6I2aDjpbaRezZXcBTUb642LkAEJ9VTMzp\nY4RFdPqL1jRPCoWCYptwHKy1nEouZMX+VO7q4Y+/m3SleSp9F8cO+pD5/rt0PnaEBCdnjNNfRe3j\nQ3F2Nn6R7bloNGAJDK89ZgCgkMl429uHUIUCdctWmDt2YvQH77IamALIjAY4dpSFr76I9y+rr7v9\nh35ZQvtZrxGok0bIL7hwnpNh4bilJFLk4kaHtz7Aw9//T/d39/Gl99otnD5xFCcfP/oGBtV7PXLI\nMCr79CU/X8udnl51o+Jd+t3J8eVL6VhSghmI7tCRgS1aXXccDeG6ErfZbGbWrFkkJCSgVqt55513\n8PX1rXv9xx9/ZMWKFTg5SYNE3nzzTQICAm5KgwVBaFyHzhzhcH4MmMxE+XYmolXbv9/pCl2GPMiC\npW9iKKpkVFdftp3KwspC+irKK67G1c6CU8kF6FFj6xLIqkPRjOzshYeTFcO7+PHbwTQeGdwKpUJO\nck4ZLnaXr5eCXDWczkr9VyZugCGTn2HJR/E809WStYfT65I2QFtvCzZ+OodXDu5HBkTk5vDO668w\ntboaT6OR9x2dmFlUyEqgEmkFrdXAy0Yj8swMLlhYkvDiK/j4B5Cm+R9WustlQQFsMjOuq806nY4N\njz9E9fZtjKhN2gA1sed4KPZcXZJaWFXF0F/X/OWxrK2taf8XU7isrKzw86uf/Nv1H8jJT79i+aYN\n1Fha0u35l2qn2N2+rqurfPv27dTU1LBs2TKef/553nvvvXqvx8TE8OGHH7Jo0SIWLVokkrYgNBPx\nyfGsl51ANTGY/7N31oFxVVkD/41PMhN3d2vSpm3q3qbubiyl6MLiy7ILCyzLAgsfC4s7ixQKdXd3\nSyVp0rRNI427TDIZycj7/piSNhRpSzV9v//mvXvvO+fNzDvvnnvuOYpZ0fxQs5WyysurIOXl7cPo\nh94maOgzHC0VCPPVkl1Uj81mp6G5hf5J/gzsGMDQjl60lB8lv7yRzzec5vjZOgTBsU1MLnM8ujqE\nuLMt4/z112RbSO4+8KrqfCshkUjoO/XPLDpmwl2jZP+pqtZz23JMBDq7tBrbMmBAczNhNhtKoFN9\nHeAI3loJfAWESaWtRiLGZMSwexdJvfpw8IGHOa1S8eNOfDuQXVt7RVW0tr/xGnevXkmk0dAm1ahV\nrmgzs5QdPYLZbGbPimUc3Lge+4+VVi5AEAQOrF3N5s8/oaqs9JJl6DJ6HIPf/4QRb7yNt5//b3e4\nwVyR4T5y5Aj9+/cHIDk5maysrDbnT5w4waeffsrs2bP57LPPfr+UIiIiNwWr9m0gcEgHAGxWG0aZ\nhbfmv09VTfVv9GyLXC6nZ5+BePZ5jAxLR7wTh/PRYWcq21bfxNisJ8xbjZuzggdHJbB4TwHltQbW\npTlmd5H+LuwrVvD9aW/m5/gSkvokXt43b1DR9SA8Mo7ud/wHY9IjFLiPZdEpLQtOuiBNnIPfhMkc\nd3HMwg2A+oJ+A4A3FUoEYLhEgmHCZOou8KTagKpzMQYjX/gnPTZu532FglXAYuBPtTXsfvfNy5ZX\nWV2FEsd2sxXnxvo2Np4qD3cuzC5fZjaxZvpEBj4wl8Y7Z/DewN4c27ubze++xdYvPsVisbDmmafo\ndt8cZj33V05PGUfhuaIh7Y0rcpXr9XpcXM67YORyOXa7vTV/7ZgxY7jjjjvQarU8/PDD7Ny5k4ED\nb9+3YBGR9kKzoRmhvA6NrxvHv91G0swByMcqeX/BfP7UcQYB59JnXipR8R2Jiu/IhoXvE2Q9zdkG\nO7uya+mf4MnRQiN4xuDlUkBWYT2eLiqm93esW76xIodG7zAsgpzZf/kXLue2Ook40Gpd6NqjHwA1\n1ZXsW/x/aI7+jwarM2VP/pnNWzdgUEux7kwjwWpFCxwEKiMieTW5M7F9+zNt5h0cXbaEH159Ce+K\nMvJtNpJXr2BLRARD//IMCqmMMTYbF64GNxcV/mLZVrPZTEFuDt5+AXh7e7ceV/fsxdnliwk3m5kN\nvOGsQRUVTag9kiUb1+GM4yVDp1LxxP69fAfMAgaePsmmqeO522bDCHywaQPdj6QRYLMBMDkvl++/\n+JSwN9+5Jvf4RnJFM26tVktz83mnxoVGG+Cuu+7C3d0duVzOwIEDyc7O/v2SioiI3HCUcgVn1h/m\n0IdriRjSCYWTColEgv/MLmzM2HHZ4wmCwLyPXqan6hijOrnz0IgIVHKBl3eoMHd8hLse/zfpxkjk\nMuZH30sAACAASURBVBlL956lvM7AjuPlOIX2JnXGk4yc+ahotH+DQ2s+5Z4UG+M6u3FnNwUlRVt5\n5C/xpIRYCLBZWQusBo7JZDySd4bnFi/A/vEHrHz5RXTpRymLiyPFZuMBoL/JiPuXn9PU1EhEdAw7\ne/XhR4f1biBg+VJWPPJga/Q/OOxDYe4Zto0bQcDgvpQM6MH+eV+2nu9zxxyO/vNV3omM4n/AXEMz\nj69fQ37GMbShoYwBOjo7o+6SQi7QD8f6+2Hg7nNG2gkYsmMrgq1tBTgJbdO4theuaMbdtWtXtm/f\nzsiRI0lPTyc2Nrb1nF6vZ+zYsaxfvx61Ws2BAweYOnXqJY3r4+Py241uYUT9bl3as25wafrV1dVh\n7uaKU5OASWfE1mJrc97ZWXnZ92ndws8JbD5MpN/5gKGesd5U+nSiz4C+APz55Q85uGc7p3ctYkF6\nLb5xQ3n0gT9f1nVu5+/PQ21pnQGbLTaSg9WoFDJatuXyoAA7gUKgtyAQfm7d2Hb6JHeePokT8KVC\nwYW56NzNJlxclPj4eDBr/Vr+O20akZs2EQ7U2O04L/6Bb60mHl+0iIMLFlDxr39RXFzMEyZHXbLo\nmhqWffgunk880prEZfIzT/HeR+9y3wXXGVlRjubAATZkZBDcoQMPRUfz/cCBDMhxpCy148hN/uPc\nXiuTsX/kSDquWIGX3c5iDw+a3V1wdVWiUrWpA3bLc0WGe9iwYezdu5eZM2cC8Nprr7FmzRqMRiPT\npk3jz3/+M3feeScqlYrevXszYMClZTFqr4UAgHZd6ADat37tWTe4dP0KC0uR+WhISO2AIAhkzNuG\nk7sGlbuGym+PMrv/PZd9n5pLTjCscwBr04qZ0MthvDefNBI4oEebsSLjuhEZdz5S/HKuo1TaqarS\n4e7ucVmy3Sr81vfXKAugobkOd40CKVDb3HYWOhD4AXA6Z7QNQCCOWSxAisXCFpWKoWYzBiB9UCpR\ngurcNSUE9urHpE2b+B5HmlRnoHD5ch728GSgycRsq4VVP5HJWa+ntLQWpwvyoDdJpdiAH/Ox1QEZ\nL/+bR1avoKZGjwDEvfIG386ZiavJRA/gvyoVj5jNHAR2yeUEFRTxzuhxaHbv5K76erzfe48vs7KZ\nOH/xDc/09nNc6QulRBCEm8aXID4cb13as37tWTe4dP1sNhuvrHwHr/u6IpFKOfr+OszFDThJ1Dw9\n5WFiI2N/c4yfsv6bV7gzvpqzlXrS82vJq7GRPPFvdO7W70pUuYi18/9LqCUTCQL5Qhzj5j5zU+TZ\nvpr81vdnt9vZuuILFM2lGCRaXH0jURStoyrtFN3XnERhsaIDTnHe8C6USrnjnCHfDuz19ETu6obT\ngMFMf/1N5HJ5qzu8prKCzKnjMeWc5i4cdbDfAaYD9UAXYA/gDiThqAf+zYTJTP786zZy7ln0A8cf\nfZABgkAh0AT4SCQYhwyhx5vv4xsUzMbZ07hjy0YOAzpge1g4YSXFuNlszDg3zr+kUv5xQcR5HlCy\naScdOne54nt8rbhSwy0mYBEREbkkZDIZTw3/Iwu/X0FGbhYx9/XANcQRwf3O29/wQfjLbWJdLoXu\no+/nf4v+TZTWzslKC15aBfb0T1l+cClD7ngBN/crLxiStm8bqZ6nCfFyrIF3bCxm9/Y19Bsy7orH\nvBWRSqUMm/xAm2M1NQM5ZXiXDb4xnDmSz/uH0+kObAaqgH0hYfjWVmPR6wmUSplbV8feujqMZaUs\nM5tw8fNHs2o5dqkU7pxLh/lLWDOgJxgN7MKRwCUYxz7wzjjWpVcAC318Sbjvj4x75AnA8VKx8eV/\nojmaht7dnZa4BLqeyqYMeASQCAJs3cq7TzyC/6QptJwtQAJ0P6fHkbo6Otps/JgMdw2OcqR2zgdw\nNQBOLu1rqUQ03CIitwmr9q7jqCUPgHh7IDOHTPnZdk36Rtbt34xMImVsv1Go1ec3DWm1Wu4d+Qce\n/+4frUa7Lq8ck7Od57e/i1ezmoeG3oWzs/MlyeTt48f4P73D0q/+Q5hHDXcMCgdAEOx8u+YLRv3h\nrxf1ObRzDc1n92Gzg3/yGJK69v3ZsXV1lQR5n5fd21WJsazmkuRq7+xb+SGPd9OhkLuT3yWS1fln\nmVrXQDBgkUj5qLCAw0olSzok8n/ZJ5gP3AHQ0sLWhd8TLZUSdm5Wm/nm/7GrpBgXq5UvcKw9W4EO\ngA+wFFDimHF39vFlwJNPt8qx9e3/MOHDd3AD5uOIFl+HY5vaj36RE0Dwvt2M27mNlyUSagEvHDPy\nMgScJRJOCgJe564bD8zD8fJQA2x0deX+qPN57tsDYq5yEZHbgKycLI6H1VGvNlGnMrHdnMm81fMv\natfYpOON7Z9TPdOL8qluvLb2A8wXZLP6EaHZgrXFkX6j9FAO3f44iuCZKajmxvP19gWXJZtEIsHd\nlIOPq7rNMbXE1Kbd8SP7mPfhS/hVrGB6goFZiQasmd9QUfbzGbuSewxmxfHzY2w4YaJDyuDLkq29\n4i5UopA7Hv+Rwe6UzxjID2PGs9k/gLGC3TGrbWnBt6yMArmcC6MDmqHVaAMkNOtxWbGURywtJAJu\nwCqlipU41qmnAONxFPZoCgpqI4f81EnccNT/DsHhSh8JHMGxbxwcLvwpFgtKoJsgkIYjCn4n0C8w\niIxHniAtMJgv1GqMUiluQH8c9cQbAb+xE67OTbuJEA23iMhtwKnCXBrq6wnoGkX8+J4kzxnCUWUh\ner1jbVSvbyIz6zivf/Nf/OamIJVKkSnkeMzpxJYD2y4a745ekzj63jpOrjwAF6wZS2UyTJrLD5tp\nabFSXm/Abnf0PVvZRG75+S2nO9Z8R1DJPGLsGXSL0LYeHxSjJjvjwM+OeWzncirrG/nv6jz+u60Z\n994PExQSftmytUcMNnWbz25hEQz96jv8e7f1XkQ7qfkmvgP5FxwLB3Ze4IXZEhSEs8KRIrQ3MAPo\n3rsPps++wva35/g8dRjLYuP4OnUY3V5rm6DFFBKKCYch+vFVYAPwZxyJWJYCeRe4ue1AD2AckAo0\ndevJqBdeYk56NpOKqrA8+AidFQoKgSwnJ/ZOn8mYt967gjt0cyO6ykVE2jFllWUsOLqGOkMDdbU1\nRA5JBqAquwgjFt5b+wX9YjuzoT6d0qoyXDp5EmAXWl/p7VY7MunF0bg9k3ug0WhIO5vJkZLjrUk3\nLEYzrsbLz/NcZvGmm2czC3fl02iwoFJK8ZbpaWpqxMXFFcr3E5+spqZOSXG1nhAfh/FOLzYR1qXD\nReOl7d1Mf81xQgf7AX6kn22k0Wq5qN3tSuygu5m39RO8lQYqW1xJmeDYiBUw5262HtxPalkpJUol\njVNmkFhYQErWcRbhcGGfVKmQTZlB5vYtWJRKEl79D2e/n0fl2lX4AfuAMwcPMOR4OgqNFtmjT9Ln\n7vt+Vo6wYSP44H+fEms0kgFE4giO8wZmnmvzXnAwByur6FlXS0+ZjA969CI+KBhLWBijnnqmzXij\n/vkKB7qk0HQ2n86DU4ns1Pla3L4bjmi4RUTaMZ8dXIjvvSkEAsUfrKa5qgGz3oS+vI7kPzjcxt99\nvAHnMC9SJo7Earaw781l9HpiAnarjePzthHp//PbOZOiE0mKTmRU1SDmz1uJVSPBvVnJ3OF/uGw5\nh055gHUf/om+cR4MSQ7Ez8OJer2ZPRlp9OqXyo95NPol+rPyQCFbjleh8QxGFTmMPrEXG+6GyiJC\ng8/v3e0U6sIra+aTkNT1smVrj0TGdSQy7kNMJhPdL5g9d+jbn6KFy/l+6ybcwiMYOXocOz/7GNWG\ndUy3WLADR8IjmbtkARFmMzbgi68/JzA2jmwc2deOAi+ajKhMRqivZ/N//k3NuIltsqUBNDbqyPry\nc/5iNAIOd/oCiYTSoGAoKW5tpwkJYbHZwuqmRsxqNf3uvo8+E38+PgOg14RJV+0+3ayIhltEpJ2i\n1+uxhpx/KPd4eCxZr6zFIrfT9dnxrcdVAW6tmSzMumZC+yVydkcmUpmUlAdHkflFOhN/5Tr+vv48\nNfqPv0vW8MhYXKMG0DO2EhdnR7nFvOoWfFOCAThZpySjoIZO4Z74ezhhtjRTbg/Ev7aQnet+YMCo\nmW22eYUndGPXgS0MSPQDYEdmOe4S7cUXvs25MPDwR0Lj4gmNi2/9POD+B9ktlWA7eACjpydh+XlE\nnHbkAM8EzLt3UdbSwlggB8f2qwvTnYTV1lJVVdHGcJ/YtQPdU4+jKSxoEwEeotGgee0/fPLMX4iq\nruKknz/NDQ3MyM91RJJbLCx77CEqu/XALziE2xXRcIuItFM0Gg3SqvPBWXarjS5hSbhKnCjTNaN2\n0wBgrNQRPbobmfN3EJGaTGNRFR1nDwLAYjRTUVXROkZ9Qx0b07ahkMoZ13/0VS1/OPvB5/nhy5eJ\nlJ2lpNZIvUlKmO6/nNjqjqWhFEFQsfpQMQnBbpTWGpgVVYKvu5qqhjw+eXkbCeF+GO0quo2+n5iE\nZD76Xx11jSYEQSDS3wWhtO6qyXo7IZFIGHDfg3Dfg5w9mc2OMUMRcMysBeAxkxHTzu28HBiIW3UN\nKksLx4FO586vcXZmelRMmzHL33mTWYUFNOKIAE8FKtROFNz7R2QnTjC2sgKt1UpySTEfNdS3bv8C\nGG8ysWTHVvz+MPd6qH9TIhpuEZF2ikQiYUp4Kiu/24nVWYJrnZT7R9zDh5u+5NTyTLxig7CaWlBU\ntRB3UIGLPILSLzIxe5k4sXgPMpUCc6MBM2YMBgMGk5F3DnxDwJwUbC1WXvvyff4+4TEUCsVVkVcq\nlTLpvhdpamqk+Pt/8eQAR95pQTDxjxw9tU0wvmcoAGm5Nfi6O2aLR3Kr+GMvb/zc9QhCE18ufp3x\nD7+D1sMfd42JpDAPDuVUI5dfHTlvZ3K//Zp79Xrm4Ui0cv+542ogtrYWiWBHA+iBVefaOMUlXJRy\nVHnOPe4KzAY+iI2j/7wFjIiMYuf4EQRbz+UcFwQa7XZKgR/j0dPlcoITO11LNW96RMMtItKOSY7r\nRHJc24ecyV1C58mpNFfrkCnkNFfZqTLWgVLGkF6D2HZsF7LoANwj/MjbdIyYOX3ZsH8zRruZgDkp\nSCQS5CoFmhnx7N2zl0G9B11VmV1cXHGVGQGHoZVIJGhd3cguqqGxuQWJRILBdL6YhMUm4OfuhKnF\nyvL9hajsEtZ88hdUXpGE+BSSU6ajZ5wP5XliMZLfi10mwwOYg2Pf9YW5wmtkMp4ym3kFGIpjW1gu\nsLVHj4vGMQ1Opex4OoEWC7VyOb5jJxAeGQVAi1PbHAApnTrxma6R2Lw8WhQKVI/9mWFdbu9YBdFw\ni4i0YyqqKth1fC9ualeG9x2KRCJB1eg4p/V1x26zcfJsEZ5/TUYqlXIouwxtpgazXEZlRgHRI1OQ\nqxTYLA0go03JRnuL9ZrNYnWCJ3Z7I1KpBIvVjm9YJ3SNjfSOq8Pfw4mvd5Xz1c5yekaoyS6qZ3S3\nYFYfKmZq34hz+5Ot/O9QLXsNybgrazhTomLgjD9dE1lvJ5IffJjv9+xk2okskqVS3vL2ZkJ1NaWu\nbgjDR1GwahmJZjNbcRgXN8B5396LKkgO/+vf2RsUgik7E1VCIsPvmNN6LuSJp1laVEiXvFyyQsNI\neOEFhnfr15pitb2lrL0SxFzl1wkx3/Wty62qW35RAV+fXYPf5E6YapuQLS/hiYkPUltfy5d7F2Fy\nFbAU6ZD08cMzOZSTS/eidHHGnFeHq6Am6MHeAGTM20aA1JMHu03n8yNL8JnTBYvBTMuCM/xt8qOX\nneb0UtA3NbJz2ftohUb0Mi9Spz2GSqVi/851NNdXEdu5L1n719HXKQMPrYr1R0owmK38cVQ8+05W\nUlFvpL7ZgiRiJPc8/vdb8vu7VK7377NR18CRdWtw9fMnqW9/zubm4O7ti5+fH1vffYuaD9/jkYb6\n1vY73dwIOJSBh8elp6/V6/UUF+QTFBZGVFRwu/3+xCIjNzm36sP/UmnP+t0Muq3as5Y8ayUys53J\nnUYSHBD8m30+2/It1lnny2VW7j/DQ9rR+PsHtB4zGAy8nvkldeYm4sb3RKZwOOHOvL0NncyIe5w/\n4YM7IVPIUc0vZnb/yWw9uB2VQsXQPkOuidH+JfJOZ5GXtpa8vNP4BUWC2o1RntmEejvWT99amcP4\n7n5U1Bnpn+QPQE5ZI3Wx9xHVoc91k/N6czP8Pi9k85uvM/aNf+N67vO85C6M3LTjimfKN5t+VxOx\nyIiISDvCZrNRU1fD/LQVnK0uxm9CEh4xjnzLn3+zmOe9H7kqQWHOzs6McO3M1yfWtRptAKubjLBu\nSfh0Om/4BYWj/bjBY373dS+X0uKz1O17H6GqlCcHhuHiXMPB/EIWn/EirKwZg9mKURXAG8uyee/+\nlNZ+sYGuLD6TeZHhFgSBwrMFSKVSQsPCr7M27ZshTz7Ncl0D2sNpGNzdiX/uRdG9fZURU56KiNxE\nlFWW8fKad/nHwQ95ZvUbqObEI4lzwyMmEIDcTUepUjTx8ub32Xp4x6+ONTyuHxVLMxAEAUO1Dq8T\ntjaz7R+ZOGAUfbWJmHSG1mNedmfk+2qxmh3Zxqq3nKJXyI3LQpV9eDtDYhT4uKpb93n3jNQQ4q2l\n79y3sSHj+ZEudI9248DpqtZ+J0t0hMYmtxnLbrez/LMXURx6BcmBf7Hyy1e5iRyPtzwymYzRL7/O\ngPVbGfnDUiKSbu8I8GuBOOMWEbmJ+P7IKrzu7oIgCDRJzEgkEuw2OxZTC1WZZ/GI8CN6uCOids+e\nXCIKQ4kMi/zZscJDInhYPZ1dP+zF3dmNoePv/9l2AHOGz+KrlfOpcTYibxa4v+dMfDx9WLpsFWas\nzIgcSHxk3DXR+VJQaz3QGawYW2xtjlvsEgoK8kly1wFe/GFQNO+vyeZUSRMyhYoWn+48MmBYG1fr\n7i0rmBFbi7vG4cwN1pWwf+dGUno7Msn9dOuSiMjNhmi4RURuIqwah0tRIpFgMTiSp8SO6c7x+Tto\nLqmj3/PTW9t69oogc8mJXzTcAH4+fkwbNvk3ryuVSrl31J0XHZ81dOrlqnBN6Dd0Aqu+PoFFV8Ke\n7EriglzZmishbuR09q34gHC5HvDCWS3niQmJLCqJZ+SMh392rBZDI+6B55cZvLQKjm1ciuzMQkBC\nnSaZUbMfvz6KiYhcAaLhFhG5iXDXKzEZTCid1QR0ieL42+vxDwsi1upHSvIQDp4sxy3B4e6u31/A\n2JhLL1O55eB2SpoqifAIYmBK/2ulwjVBIpEw4e7nqK2tpbyshMPNDfS8syvNzc309GtEsDqxeE8B\nTkoZx8ol3PPiW784Vqdew/lm3iruGuRI5vKfZZncOyiGEB/HPu/i2pMc2ruVHn1Tr4tuIiKXi2i4\nRURuIu4bcSffLltEg8qIr0HG01P+gZOTU+v5xt1ryT6RDVaB/m6JRMRHtOlvMBjIycshyD8IHx+f\n1uPztyymtK8cbWgg+/MqqN6xkqmDbr06xV5eXnh5eV1wRMLZZgljk33pKQhYrXbqPCJ+NRWrn38g\nLVINK/YXIggCQZ4aQnzOJ/0I9lSxu6LsGmohIvL7EA23iMhNhFwu5+4Rs3/x/MT+Y36x4EdBcQFf\nnV6Fuk8QpjMH6Z0XyahewwDIV1TjFZoEgGuUH2eOnbzaot8QtFot1tDhbMzaRKCLwP4KF1Ln3H1R\nO5vNxsKPX8Rcno5SIcdoV3D/WEfE/NmKJhbvL2Fab8cWuw3ZJhKH/XxFNBGRmwHRcIuIXGdMJhNl\nZaX4+wfg7Oz82x0ukdUnt+F/RxfHhzBf9iw8ykjBkS1NYm3bVmJpP1HUfUfMpKFhBDqdjnFBwcjl\nFz/WNi//ElfdMeaOj0YikbD1WBlvb2sizNeF42db6BOsYMX+QvQmK9UuKXQLDvuZK4mI3ByIhltE\n5DqSlXuChUVbUCR5Y0mrZYJ3P7olXl7eZbvdzupda9FbTfSKSSHqXHCaXfmT3Z1OMux2OzKZjH4e\nHdmx9SQuKcE0HixipG/7yPWcdWw/lWcOYZdrGDh2ThujbTQaW5cZasvyGBHv07qfOLVLIPnH3eh/\nz0vYv/oLwxPPv9kszaxHRORmRjTcIiLXEL1ez+GsI/h5+pAQ24F1+bsJ+HFWnBjCwk83XJbhFgSB\n/674GNmsKFSubny7cTNTWvrSMSaJeHUIR7NKcU8Kwqw34lUhRyaTATCoa39iK6I4vSuHDlGT8PP1\nuxbqXlfSD+7A7ez3zIxwxmyx8dUX/2DyQ69RXJhL5rr38FPqqWrR0m/m03iGJFBUkUtCiDsALRYb\nSq0jBsAitH3habGL6S1Ebm5Ewy0ico2oqKrgoyPf4zYuHkNJMSGbMrCp2xoFgzus37+ZUb2HXdKY\n1dXVNHVU4+vixJkNR7C1WPksbT7/CXmRkb2Gok3fx+mss3ihZur4B9r0DfQPJNA/8Krpd6MpPr6J\nYcmOpQaVQkacupT6+jqyt37J3G4yHCUuYNHqTxh117/56s3j1O4qwNNFyclGL6Y95tjX7pc8jg3H\nv6VHqIyjJVY8E6dTUphP5saP0Uj16AQvBs14GhdXsbqYyM2BaLhFRK4RK49txO9ORxlMJw8tuZXZ\nBOXLaaqoR+vvgbFeD1I4bS5h1M/0r62vZfuR3WjVzozoOwyJRIJCIcdmspCzNo3gnnFofNywjbLy\n/tf/4+mJD9Ovcx/60X7zcl9IRVEuQqeAVvd3ja6ZKCdnnCWmNu1a9LVsWfk1EYk96TnoFaxWCyku\nrgiCwN4tKzHqynGOmMI+hZzwEQn4+Qey7tOnuKuLDVBhtzcxb/kHjLnruRugpYjIxYg+IRGRa4Vc\n2iZHs8xFyYSeozj91S5OrTpIyYFTdJjaF6nZflHX8spy3jk8j4oZ7mQPsvLWso8QBAEPD08iSrVY\nGo1ozu07link6LwuHqO94x8YzPwdeZytbGLPiQrO1llxcnKi3KhprdddUNGIsbGGmf7HGKPZxdov\n/4lGowVg3fdv01vYwFDXYzSkfYZeV4ffOY+Es13Xeh2pVIJGaJ9FLkRuTcQZt4jIVWbH0d3srDrK\n6eMn8HeqInpCd1oMJhrX5bA93oXunh0oVZhRRnpQ+d0x7u805aIx1mdsI+AOR7EMtYeWmv7u5Obn\nUlxXRoPKhD67uk17ufH2M9yuYd3o2NJEk9FMgKcTnji2uzlLDGw8WoJMJiW7qJ5npjlylTur5QwP\nqSH7RDqJSV1w1WdTjIm6JjMjO3mz9/RiMo/409xQTUlpCULXOCQSCS0WG80yr18TRUTkuiIabhGR\nq0hFZTlbhCwqWqrp+a9p1OdXsOfZ7wl18sPvzhR00X40HLfQ8bg7nVwTiRgcwYn8k2w8uRulTcL0\nAZNQq9Xwk2pKEgmcPptDelQdnqNjSazw4fB7awhMioAKExOCb799xwNHz+bAdi2Gyhzsdg/GzJkL\ngLvCwKRu4QDYbHYEQWj1fFisAhVl5SQmdcEqSCms0jOlr6Pt5F5OfHd4OWqhkTmDwlm4qwAnlYyM\nKiX3vPDmDdBQROTnEV3lIiJXkdMFZ2gw6kiaOQCFkwrfxDB6vzwDc4gK12hHJLd7p2DylTXEx8Zz\n/EwWa+XHaJkZjG66H/9Z8xF2u50RHQdS9sNRBEHApGumZPFR9ucdwbO7Y3+xi78HXR4eRXy+K/9K\nfZzuHVJ+Tax2S6/B4xky8y8MnXRvawR9g+DRWu2rV5wPH2wqxmqzU9NoYmdmGdH1S9m6/AvkYYMx\nWdp6KiSCBVeVHS9XNTMHRjKhVxjxCR3FwiMiNxWi4RYRuYp0iE7AeKYGifT8jFkiAX1z2zVS6Tl7\ncbg8C68+UYBjrdqc4kZ5eRlB/kE83uUPFL+0jfz1R+j4wjiM3VxoyKtoHUOXWUrnhGSkUvFvfCED\npj/NN9leLMlWsrEmhpnPz+elDc0cL6jjgZFxdI90xbNhH8l9R9Pg2YuiGiMARTVmZP6dKTD7Y7Y4\nqpAdLzaiDbk9X4pEbl5EV7mIyFXEx9uHu5Mn8f4b39H32alIZVKOf7cDaZON2kMFeKSEUrc7nyFe\nHQE4U3CGKCG81ZVrqG1Ep2kgKCgYHy9vPDoGEzrFUU4zekQKR19bRWBMGBIBOssiSOqfdMN0vVlx\nc/dkzN3/aP3s4+NCUnwcQ6LO5x/XyKwUFuQy/b6/c2D7avYVFqL1jSR19GhaWlpYuvpr5LZmPEKT\n6d5ryI1QQ0TkFxENt4jIVaZf175sKztM/uZ0BLudhCl9sCzPZ4LQm+zFpxgbm0p4SDgAbiotGd9u\nI7RvB/QV9RjqmzCrLK1jSX4ScR4eFs7zAx+5nuq0C/w7DGRX1pcMiHHCaLZypqQWP9un5Cofodfg\ncW3aKpVKhk154BdGEhG58Yg+NhGRa8CUhGG41klxc3WjfukJJsYPJS4ylkmp41uNdkVlBTVuZtwj\n/LAYzbiH+6Kpl5AQk9A6zojQPpQvyaAuv4LyVccZ4tf9Bml0a9MhuSeW+Lt4Z00OW9LLuHNINKOT\nnDl7bN2NFk1E5LIRZ9wiIteAxKgO/DMinsZGHW4J7m32c//IoROHibmrH9UniqjPr6SmpYQkeaAj\nqvwcyXEdiQqK4GxRAaGJI3AVs3ddMZFxnTAGejK2q3frMaH91FoRuY0QZ9wiItcIqVSKu7vHzxpt\ngJjQaHRZZQR0iSJ+fE/CencgJjDionZarZakDh1Fo/070dXXcLqkjrJaA4IgsHx/EXYXR5S+Xq8n\n4+ghKisdwX/lpUVsXvEN+3duaI1QFxG5WRBn3CIiN4iGZh3GHQXkZZbgpNUQafBi6MhfqrYt8ns5\nlZnGY6OjOXi6iiO5NfSM9WaHsYnCghwyV72BvamUY3orNc2QEuXGrN5BVOpaWPtdJmPvfPpGiy8i\n0opouEVEbgCr9qzjRHwTwUMHoS+qxX+3meGdB1JWVkpAQOAvztKX71rNCXsxEgG6OccyoufQDzEd\naAAAIABJREFU6yz5rUtEXEcOH9pMv0R/APKrTHgGRHF69wIUpgrG9Ylge2Y5McDE3sEA+LurCC7N\nprFRJ3o8RG4aRMMtInIDOGktwS0hHgBNiCfb8peTF9kMUgluK8w8OfHBNsbbZrPx4v/+jXx8OL4d\nHVvADh0oIDw/h7jI2Buiw62Gl7c/65ujyUvLR62UY/PtyZDeqezI24XKWUluRSPdYrw5fKbmRosq\nIvKriGvcIrcVgiDQ2KjDbr84t7cgCCzYsoNXl6xhx9H0aybDvoyDlFSVtn4u3H2ChPsG4dc7Br+e\n0TAtjHW7N7bpM3/bYurjZPh2DGs95tEjjKy87GsmZ3vizMljZC76GzPD8ghyseIUMwKtux9bF79H\niU5Clc5MiLeG0yU6EkPdWZdWjCAIlNUZKVUkibNtkZsK0XCL3DZUVlcz6aP5pCw/yoBPlrHtJ8b5\nxR+W8ySxvBs6nPvOSli8Y89Vl2Fv+n62eeTg1juMvC3ptDSbqNibg8bPvbWN2kNLk1nfpp9ObsIz\nKoCq7KLWY3X7C0iOFhOwXAqFh5YzKdkJHzcnBsdrObD6YxIalzEzLJ/pkWXkmQP54WA9Rwqa2J5d\nT0mzmudXN3LEeTIjZz1BaWkJ9fV1N1oNERFAdJWL3Ea8sm4n+7pNA4kEHfDqwZUM6dq59fzGJhm2\naMdWocbgBFac2sC0qyxDZl0unsMcs+aminrSP9+Eq0LDqeX76TClLwDly9KZ0LFtxTB3qxPWGHdK\nDpymNqcUc1kj08OHEdAhgKKiQgICAlEoFFdZ2vaDXGJr8zlAayPSz1HeM8jLmRiXMsb9ddFF/Uwm\nE8s//hs9vGspNEK6V38Gj7/nusgsIvJLiIZb5LahQaJqU3WrXubUpnKUUrCeb9xiouxUFku3aLlv\n2pirJoO0RcBqtyOVSnHx96ClyUT8c2NpKqsje9k+GgoqcG1WstW4i9ne05DLHX/R2UOm8sXSb/HU\nKpEbFIzqOJrCymJey/gSRbgbtk01PNB1OsEBwVdN1vaExS2ezMItdAzzoEZnwm5vu8WrVmfgi1fu\nx6IrItzHGbvSnYAes6gpK+C+ri0o5A5X+d6c3ZSVDicwSLzPIjcO0VUu0i5pamrkkxVr+GL1OgwG\nA28vW0P12TNI6sodDWxWkgVdmwCw+6O9cDl1AOqrYNXnnBj1MA/Jk5n8xhdYrdZfuNLlMbPvBE69\nuYmKjALObDyKrroeq6kFt2BvBJudrvePIOkfY6mb6MWn679p7SeTyfjj6Lk8N+BBYjUhfHR8AStq\n9xA4IRmf5HD853ZjacaGqyJje8TTP5yymmZWHSziWH4tZouNdWnFlNUa+G7bGRqb9HR0r+XRUZHM\nGRTG3D5u2E8uwmKoRyE//5gMcJGyecXXP3sNvb6J7BPHaWzUXSetRG5XxBm3SLujsVHHtG9Wc6z7\nVLBZefeFN6mc8CiMGIZ093IS7Y30Dfbm73Mmt+kX7O6K+9YNNJ08BpMeApnj77E6ZiSjd+xiytDf\nX2zC1cWNyLAojK5OVJ0oZMhLszmxcDdRw7sAoHbVAKDUqKl1M1/Uv6qqiu3NGbh3CcFY13Yd3K4W\n38N/idCIOEqyPRif6Li/Xq7OrD9lJbtOT7NBSfcYFYIAHtrz5TsT/aDKGsqe3Hz6RTsjCAJ7siuY\nlCRl/7aV9B4yobXt6awjVO79jC4BFrL2ydB0uZNO3W6/Guki1wfxny7S7vh49UaH0ZZKQaGkMrQj\nqDUgkWAfMBkf/wBenj0ZJyen1j5frN/CvaUqigOTIDAcJBf8NWRyLDbbxRe6QuRW8IjwR+XihJOH\nC8l3pdJYUosuv7JtO9PFfWvqqjFaTYT26YCpXo/F1AJAQ24FETK/1nYVVRUs3ryMrfu3iZm/AB9f\nX4S46SxIF1h63MYJaQ/u/+d33PXC97honFHIJMikEgoqzpdfPVgqY8DQceRrBjFv6xmW7StkXM9Q\novw0NFfmsmPjUjav+IbamiqKDy1mShcnThXrwFRL5pq3yD2ZcQM1FmnPiDNukXbH6uM5EHmBsbK0\nnblqbRfPZJeWN9OcFANFeRAUDTuWwKCpYLcxKGsVk+6ZftXkG58whC+/W0mToR77ufXu4J5xmPaV\nUjHvCIRpoKiZWbGjLuobHRlD7coSmmt0JM0cwJl1hzHUNNJXiGXiBIeMuWfzmFe8Hr9ZHSms1HFq\nzf94eNx9V03+W5WufYZBn2EXHZcq1Lg526lpMrNgVz5KhRSkCqTOXvicOU7fwWPIrt7OuE4aBEHg\nu+25nKnI4qnxcbi4y1m0ZD9IlezKqiIpzINQX0fQ25JdH+Mf8iZarfZ6qyrSzhENt0i7wGKx8OLC\nlWRbVZS6BcLKj2HcA2CzQHkhsh1LsIUloM45jF1rpaWlBaVS2dpf/uOstOdISN+Fl66clK0f0i8p\nDmWUHw8v2YSn0MJz41L5eONOjhileNjNvDCyLyEBAZcla3hIBH/3eZDM7Ew2fLobs58CZbPAvb2n\n0yEyAZ2uAdd4N6TSix1iSqWSyMQ4Dn+yAZ+EEOw2G8oaK3ffM4eCs/nUNtRxoPw4/nM6AeDs7055\nVAW1tbV4eXld+Q1uh1RUlNNQV4vKLRBX50JqmswoZFKeGJ+ITOa491/v/BKt5wsoE2eyIH0VRcVF\n9Il0oXuMD64aRxT/jK5q/rKgGPxbGJDk3zp+SoCFosJ8OiR2uiH6ibRfRMMt0i54efEavggfDko1\nVCwCd19I3wFSOUx9FNXqTzDEpWAaMou1CLy2dC0vzprU2n9ujC+5uYepjeiCt4szr4zqx+QBvfl4\nzSaeFxKwxviB3c7u198if9j9CFrHvuuKZUtY9fDsy5ZXrVbTvWt3unc9X6ZTr9eTnpVOsH/wzxrt\nH/ESNEQ8Nx2bxYpgtyP/oYh5mxeQH2NC3cGVM4dPkEL0+Q4SRHf5T9i28ksCG3cT4Cpla24ZGT4y\nXJyVqJTSVqN9NLcGpaUR2YGXqKp1psPoJ5AcXIW3PBP7BfdTEAS0rh5UNxVS12TC08VR3e1IkZHO\nA0NviH4i7ZsrMtyCIPDPf/6T06dPo1QqefXVVwkJCWk9v23bNj766CPkcjlTpkxh2rSrvRtWRKQt\nOVa5w2jnZzpqNTbVI6mvROETSPyBRRSFxGIIOF9566yt7U9/yoA+JBXkc/DETnr2iiUuIhKAtAYz\n1thza8dSKcXOvq1GGyDHyRe9Xv+73aGnC3L4rmA9zn2CMZ46SL+CGIb3SKWispzSijLio+PRaByB\nVff3nc28bxZj1kpw0cuZ0Hk4nzauJ6C7I4Vq+LTunPlhH9Eze2OsacT3jATvBO9fu/xtRV1dLZ51\nu+mf6Nji1SVUzYRejr316w8XU15nIMDTmdzyRmYPdPwOukTAt9u/xdk/EQ/5GTYcLsbP3Ql3jZLv\n0oykpM7Er2wxW9LLUCvl1DRZcO40Gzc391+UQ0TkSrkiw71lyxZaWlpYsGABGRkZvPbaa3z00UcA\nWK1WXn/9dZYtW4ZKpWLWrFmkpqbi6el5VQUXEbkQRX0F2GxQVgCpMwAQgD4ZK1l0/yzGfPQDaT82\nNhmIVl6c8jQuIrLVYP+Ih90Mdrsj0A3Q6GtpsbSAwuFmDzDWthrU38PanB0E/MERWe4W4sOeBcdo\n2W8lzaUQdbw3K3fvZm7sBCJDI/D29OLPYx5s7VtwNh+5x/lAO6/oQJy31eKzoAZ3JzeGievbbWhu\nbsbb6YLPJit2u4BUKmF4lyBeXldLYqw/ZkHZpp9aaqH/yOlsXFSDxlfNBzuqMMvdifDRYsjfzmFb\nFF5enhgECR4deuPpH4bBYMDZ2fk6ayjS3rkiw33kyBH69+8PQHJyMllZWa3n8vLyCAsLa52BpKSk\nkJaWxogRI66CuCK3I42NOvKLiogOD0erdbnofF1dLdm4wuK3wTuozTmD3OG2fGdMX/61ZSW1UjWd\n5Gb+Nqtt+UydroFDJ04SExJEeMh59+bz41OpWrKCo3jg1dLIMxMGsfLEKo4LWjwFM88P7PiLlbwu\nC5XsvMx1TVSWlrM30EbI+G4AuM3yZvV3W3k89GIjHBYajnTZCqzxgciVCqq35zCxwwCS4zqSX5jP\nks3LiQ2NJjleXGsFCAoKZnmNJx3DLMhlUnx8vHl3r0CIp4Im3Ljrb6+i1bqw6qtXMZjKcVbLKa0z\nYXNLRiKRMHLGwwCE5edg3P8WA2LsQDP78mqRpTxK5qHtOJ38ltBmF7ZvspI44e+ER8bdWKVF2hVX\nZLj1ej0uLucfoHK5vDU69qfnNBoNTU1NPzeMiMhvsjntKA/vOUNDkx659BC9nG18fOck/Hx8Wtsc\nPnmaYosAUx6FgxugqR5cPJDVVzDQ1dEmJiyUb+/9+fXG42dyeWDDEfKbTMikuaTY61j85AM4OTnh\n4e7Bhmfvp7i4GpVKhUQiYXSfXlddz1hFEJmnytHr9TRXNhA6uSsVh/PatBEuMO4XIpVK+eu4h1m8\nZCUWiY0p4b1Jik5kb/p+tiqz8ZodTU5mFvm7i5jUf+xVl/1WQyqVMvreV1i45htkgonAlD78oVOP\ni9qNmfMMK1d9hcxcj8IrgtSJU1vP7duylPTNX/OPyec9NH2i1Hy4ZyPOlduZkBoFQIQ/fLb+M8If\nfuvaKyZy23BFhlur1dLc3Nz6+Uej/eM5vf58Yojm5mZcXV0vaVwfn4tnU+0JUb/L5+mth2lQekLq\ndKwKFXsEgb+uWcW6vzryRdfV1RHk6Yza0IBJoYK+4+Hodmis4+lwOf9++J7fnBF/sfAU+UYbDJ6O\nTSbjkM3KP5av5+vH72ptExLi8ysj/H7unjiNzQd28FXGGuL+OBiAor0nMTU2o3bV0JBVymC/2F+5\nxy48OfveNkfS9CfxnhQDgEenYDJOZ/DAL/S//X6bLtzxp6d/s9+sB5686NjqRfNwL1nOuGQ3zpQ1\nEhvkWCvPrzJjtxnw1PzksWquv+b39/b7/m5vrshwd+3ale3btzNy5EjS09OJjT1fDzgqKorCwkIa\nGxtRq9WkpaVx7733/spo56mubr8zcx8fF1G/38But/P4J9+wp0WFnxz+3q8jdRInUChAcS6jlUTC\nSYua6uom/rdxO/8ttVLv6od3TTFV2Qewd+gFXQfjtPx9ug3uT02N/tcvCjRZBHDSgOzcjFYm57hJ\n3qrP9fruOkel4F16sPVzx9kDyXp9HT0iOzPIO4reyT0vS47mZgMXrtJWNtWQl1+Cq0vbEpXib/PS\nSdu1juoDXzOodzAuzgrWpRVzorAenVWJEDiA6KREijbvo9HQgquzkpwSHQanqGt6f8Xv79blSl9I\nrshwDxs2jL179zJz5kwAXnvtNdasWYPRaGTatGk8++yz3HPPPQiCwLRp0/D19b0i4URuLx567zOW\nJ08FjSulwKM7luJeV0qFzd4mQCzIbsBoNPJeYTPVnYcDUHHHP+i67HWOZe1H8A/HOGQWszetZ4eP\nL9EREb9yVRgX6sXG/CwuzEYeIBivkZa/TrDRndpKHc5+bhhKGxgY2YNZqVN/u+MFpGdnsHD7MvJK\n8olNcCIwJYa6vHIU3hq2pe1k4pDx10j69k9zURrjuvmzJ7uCUd1CGN09hB/2FKOQKwm3H+HEkTNI\nw4bw0abtKGXQ4hrLvU8/f6PFFmlnXJHhlkgkvPTSS22ORVzwcBw0aBCDBg36XYKJ3F58t2UHayub\nQXN+WaXcP5Z3Yz14cWc6DYvfQebpRydngdcnDaG5uZkmZ4/zA0gkVBuMCKPmgrcjIUrLsDt5a9XX\nfPz4H3/12pMH9MFqMvJ/Gz7B4OpLJ2cJr4wfeC3U/E3mDp/F2r0bqTSXEKXxZUTq0Evu29LSwrOf\n/4tqdyPdnh2NbpUj//apVQfR+rsT1qcD2j1Ovz2QCODY9mq1WtuUS20RZPi4ORHirWXZvrOU66xo\nNC7M7efYbtdHEPj2jJ25L68CHMVhRESuNmICFpEbzvwtO3jWFESL/SjoasDN8RB0zs9g8lN3MG5A\nP8rKywgOCm7dWiMIAj0bzrDV2hnkClyLsohzc6LY2nJ+YEHAS3tpW7WmD09l+vDUq67b5SKRSBjb\nb+QV9f1hx1JK5PX0f2waUqmUhEm9OfzpemJHd0Ow2BCWFpI66U9XWeL2SdquNTRmrUSrsFEmBDFm\n7gsolUri+8/kh/Vv0T9MiYenF9boVNyrNrf2k0gkqCVm0WCLXFPEIiMiN5w9VXrMvmEQFAVpm+Hg\neqRbvueeQBUqlQqtVktsTGyb/bASiYQv753BAzmrmZ61nA9C7Hz70vOE7FkAulqwtBC0+X88NeXq\n1dK+0djtF+89vxCDwoJEKmkNxpPJZSRO64vHkhomlifx1KQ/XZ2ta+2cpqZG7DnLmdlNw9hkV+5K\nbGDHqi8BCA2PpvecN9lmSeVEtQpZxX4O5jZgszm+m/wqEyq/xBspvshtgDjjFrnhuAstjjXsXqPg\nxAHccg7xwYQBjOh18RadC3lj5QYWSgMwK9WYj51kWLcupL3+HCu27aC5wcykJ+9qFwUeKqoq+OLg\nQoxeEpQ6gRlxI4n/yb5gQRDwsrkQ2ieBtI/X0f2h0dharGS9v5kv//QOcrmcJn0jq/ZvwI7AiC5D\n8PW+tpHytyo1NTWEuZ2vBqeUS8k/dYzNK76hS9+ReHn7QuluHunvCP0bFO7Lvzc2Ehsbi5NfB/qk\nTvyloUVErgqi4Ra54Tw7LpXcbxaS4RKCh7GeZ8f0/02jnZaZyRfqWMyRju1OK02x9Fy/mfvGjmTy\nVaibfTPx/eGVeNzdGc9zs+Ul327i+QsM98n8UyzI2UBpfTm6IzVIVXLWP/4pbmF+xE/pydYjO+nf\nsTdvbP0Mv3u6IZFIeO/773g85U58vMRUqD8lODiETeu1dAxzvBB9sPY0jwyMwF17lMXL9hE86DEC\nnAyAIzrfy9WJuJggBs/++40VXOS2QTTcIjccV1c3Fj96F42NOpydNcjlv/2zLK2pxexxQSYwtYa6\nausvd7iFsWgkqC9wcVs0bd3dy89spdHbTtSgHmj9PcjdcJS4cY4XH0EQWL9+M9sP7yTgmQGt+RYC\nZndl8/fbmD386pUrbS8oFAq6TXqG7zbPo6G+ir4d/PBwcWxHnN7FiflHN2Mxno+d0BstWNV+vzSc\niMhVR1zjFrlmHMrIYPX2nW2S9fzIvoxM7vpmOXfMW8XiHXsAhwH/LaNtt9spLy+jV0ICSVmbHQVF\ngKDsXYzpnHD1lbgJ8DSoaWk2AWC32XCpb/u31UmN2Cw23EJ8kMpl2Fosreeyl+4l8J4e2Ab50qI/\nv8XN1mJFKVUg8vP4BQQzYs7f6TXhMaSSnwSaSaDj6Mf5NtOZpVlSlhRHMmSimA9e5PohzrhFrgnP\nfbeEb1ySaHFLIvmrFXw/ayQ+Xl7knC3kscUbSVd4Ye8zDoBDxdnYN26h0mDCR+PEzGFDfjaISqdr\n4K55KznqFY+nvpp7/dX0LtiARSJlRs8YEqOirrea14V7ht/BvOULqVcZcTJI+dPQuW3O1xdUII9x\nVKGSSCQ4e7uSuyYNz/hghGYrzt4uGGubOPLpepLvTEXupMSwJIcHxz96A7S5tQgNi2DFlnBCmirw\n1CpYdMxMx3GTCQgKJfTef99o8URuUyTCTVSot71mx4H2nf0H2upXXFxE391lmKK7Ok4KAn8qWM8/\nZ05k1ueL2Cp4QnwKqM+5G8vycSs6ga7XOCR6HdPzNvHefbMvMt5//345X0SOak3EEnxsI/vuGoZa\nrb5uut2MvL3+M4435yNTKQjtm0BFej4dKjwZ0nUQC0o2U6cyEjWsM0qtEyWHcmhem8t/H3q11btx\ns+v3e/m9+gmCwN7tazHqG+jaZ7gjOO0mQvz+bl2uNHOa6CoXueqYWlqwKi8oZSiRYDnnbqyRqsE/\nFApPnz+dcwxdL8fsW9C6sdopgqqqqjZj1jfUs/pUUavRBtBpPNvkxb9d6ewajZOnC/ETe1J9qhiJ\nTIrK35UO8R3o55qERWdE5eKMRCIhpGccrp2DLimOQMSBRCKh35CxDBv/h5vOaIvcnoiGW+SqExUR\nydDKo9DiWJdV7V9NN1/HtqxgQzV4BYJJD3tW4bJrEfHNpW36y+zWixJY/HvNdioju0LROYNvs5FS\nfwYvL69rr9BNTse4TmhUTpQfycM7NpiEyX2QtkBNXS3NTXpcywUudKwpLwg50Ov1vLP0cz7a9g2b\n07bfAOlFREQuF/G1W+SqI5VKSfBxY8PhLSCTY45M5qOTh+kSW0q23MNRelOuwKMkm47BARzzDEW+\n9Qesg6ahaKhimr2MhVurWH2mBO+gUIYEuNEgUUKHnnAyDQ6uR11RwEdP3iEmFAG8vb3pcjyUU94N\nyBVyqr44zLQOI3gvYz4+UzuiPRFN5utr8UoIQdloZ2rsMMDhAv7vxs/wvL8LUpmMtJPlcGgbw3q0\nr+10IiLtDdFwi/xuTubl8cPWAmL8A+iW6MgaVWhVQJ/hrW3O1pX8P3tnHRhXlf3xzxvJZJKJW9O4\ne5O6pJY6dUlLhULL4uwu7ALLCrDAwi66sOwPWaClFOru7m5JmqRtrI27T2RmMvb745WEUKRAavA+\nf2Xeu+/dc2cmc96995zvYdXhE+T37ihwUW80cHjgRJArQFuH/bJXGebtygmDhcUqDxj9KAgC+05s\nwb84A7ljBOaovmA2k5i6FndJQKSduSOSKS8vo768nrCJ01l0cAXd5iYA4BEfiLG2lT/6zMTVtWOF\noqGhHlOUBtnV1Q3nKG9y0vMZfUtGICEhcb1IjlviZ7H1xGmezTdSHTIEx6xL/K30AAvHJBGssoKu\nGdTiEnlgSwVe7i6gbxGD0poboKpYdNoA5QW09B3PdgBbNbS1gSDAofWYQ+PJHzgJzuzB5+x27gr3\n46/3TrtVQ75tadW1YjSZxFWIbyxECEo5ZrOFL/asolzZiEJnpb97HA211XQjEhBT7RSG2yZWVUJC\n4juQHLfEdWOxWFi8Yw9lOiOJgd0Z2acXn2eXUx19FwBav2i+PL+dhcBTU8dTt3w9aUYVTmYDz43p\nQ3RQEEc/Wc4293jM6ccgsi+U5IJvGNRVwMDxkHUW7ByhIlPstLlB1DAH6DuauiNNvDp3mrRE/g0+\n3v455T1A7qFmw4Y9TIkeycYdp/C6Kxp9fTOul0wcdD5GxSg1Gk9vmirqWbp9J85h3cjafAo7Fwds\nLjTx1OgHb/VQJCQkfgDJcUtcN898vpovAkeBhyNLCy/xr+ajfHN+9lUZDLlczmvzZ15zj+EhvuzL\nK6LF0QViBkDmcSjNQ1F4EdPA8RDWE/atBO9AOLoJ9K2db9Cmk5z2N8jOy6a8p4yG0mpMBW0IPjKW\nHl7LY+MWsOv/9uHr6M7MqY/wp5WvEuQ5FIDS0znE3y+WDDXq22gqr6NXZQBOjs63cigSEhLXgRRV\nLnFdWCwW9rfZt9fL1vpFsa24ntlB7jgXpgNgV36Z5G7fXe/ZarXy1tnLtPQeA3KluGweOwj6jqW/\nlyPhqVtB14wmOJIptef5Z6gdw5xlcHg9FOfCmT30o/GmjPdOorq2mqbaBhz93ImaNoim8npy6wp5\nYe9/uORZy66G07y85HVatU0Y9WLZU0EAk0FUWFPa2mCjssHBzvH7upGQkLhNkGbcEt9LeWUVjy5a\nTonBSp3crtM5G4uJ5GGJaA4f5fnlL2JW2dPQMwqLxdKuiQ2QdyUPvcHIpbIqyvRXqy55B8Gmj0Hj\nhE9LFZ89J6p4Hc+4QMgAbyKCnwRg3mgdz6/cRG7+UaJcNfzjocdvzsDvIM5WXOBKXipJr8wna8sp\ndHVafPuF4+DtiqabCx6z/NA3tpD1tzVkrjqM2sWBtmYdB1/4kr6/nYhFZ0R9sJ7BU6fc6qFISEhc\nB5LjlvhOLBYLk99dTGG/KZBzDgQ5pB8Dv1DC8s/yxOge6PV6nth2lPrZL4BMxn+aG2HNJv529zSs\nVit/+nw1K+zCMSptCTp5BJQucP4INNXB3X8AoLRFyxeHTvC7KeMZP2RwJxvUajVvLZx9K4Z/x2Bw\nFuj54BiyN52kLq8c9wg/ZEo5hiYdQUliIRZbJ3tUQa7IleK/vLasFq+EYAoPZmLOqee1e56TtiAk\nJO4QJMct8Z1UV1dT7OQDTfXQYwi4eUNNOZTlM9vVQkxICJ9t2UG9b1SHopnGiV0pdfwNOHLuHMs8\n+mDy8APgSo8xqOrKMeRfgOivle20dySvwnytAXcger2exsZGPDw8Oq063Ehsm8E+qBtmg4ny8/k0\nV9TRrWcINReLO7XTNTbT44FRKO1sMLboiZszDBC3MF5/4/944/4Xb4q9EhISPw9pj1viO3F2dkal\naxTTuuzF2sO4e0PMAEwKscyhIAig76g6hdWKRq8FoKZRi8nBteNcWALjjCUMcLVFmZfacVxby+ET\nJ2hqurP1iDcePcXgz3fRf+8Vpn2wjOra2pvS78Khd6NfcgnZJS19g+NxKrFSeDATbVktZz7cRmNJ\nDfkH0vFPiiHr37spWZmC2rVDI1kQBBrVbTfFVgkJiZ+P5LglvhOVSsXTcT4oa0rg0FqwiDHjoanb\nSB7YG4DZo4YT01IKh9bDyR04r3+XRQ/MBWDsgP6En1rfXnpTfXgdj49L4s9jBhFrJyBb9Tac3A4Z\nxymd/Rzj3vz41gy0C7BYLLyeUUJRz7toDevNib6z+Nf2gzelb0cHJ56e9CgvDH2MP094nP/88U2S\nA0aQ+KcZ2Lo60lLZgHuUH0FJ8YTHRvP6uKepP1uI2SSuclRnFSOX8rclJO4YpKVyie/ld8nTuE/b\nyInUVI5mb8RGbYenq5kj5zOZ5uyMWq1mx58fZ8uJ42ib9cx76AkOn8/kjztOYBEEdLpWOLEdZAK6\n8D68tGYDqfa+tA6/H/augAHj2/sqsfe6hSP9eRgMBhptvlbpRxBoktnctP6LyopYnrZFMUw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8aJ4QHejB9w++8BRwVH8lJQhCiR2kOUSD2feR5VsDMAfgMiaa1uJGJSx9jL3YzsyzuO1xwxqE3t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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1228007b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = MDS(n_components=2, random_state=1)\n",
+    "out3 = model.fit_transform(X3)\n",
+    "plt.scatter(out3[:, 0], out3[:, 1], **colorize)\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is essentially the goal of a manifold learning estimator: given high-dimensional embedded data, it seeks a low-dimensional representation of the data that preserves certain relationships within the data.\n",
+    "In the case of MDS, the quantity preserved is the distance between every pair of points."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Nonlinear Embeddings: Where MDS Fails\n",
+    "\n",
+    "Our discussion thus far has considered *linear* embeddings, which essentially consist of rotations, translations, and scalings of data into higher-dimensional spaces.\n",
+    "Where MDS breaks down is when the embedding is nonlinear—that is, when it goes beyond this simple set of operations.\n",
+    "Consider the following embedding, which takes the input and contorts it into an \"S\" shape in three dimensions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def make_hello_s_curve(X):\n",
+    "    t = (X[:, 0] - 2) * 0.75 * np.pi\n",
+    "    x = np.sin(t)\n",
+    "    y = X[:, 1]\n",
+    "    z = np.sign(t) * (np.cos(t) - 1)\n",
+    "    return np.vstack((x, y, z)).T\n",
+    "\n",
+    "XS = make_hello_s_curve(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is again three-dimensional data, but we can see that the embedding is much more complicated:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SCOXyMGaVxaaDSIToC/90gUgw5mNdCTQ2t9DQ3MJv3FZa7Kn4bfGYhgF5ZQRa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\nYAKWgRwGBwcYHtCoP9+HLdHkwEObpg3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qCs0Xv1l5fQOcjrdhm5XGoD/IYFstFbkThZPJ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5LNEk2nsThKKK9dR7i0hvPDfbibi2guKr1t130tuDKJwurzqfKP//gdfvCDH1BcXIzZbMFoNFJX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\nBUEQBMEKZEtWEARBEKzg/wFMIO7l5jtiWwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bc36668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mpl_toolkits import mplot3d\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.scatter3D(XS[:, 0], XS[:, 1], XS[:, 2],\n",
+    "             **colorize);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The fundamental relationships between the data points are still there, but this time the data has been transformed in a nonlinear way: it has been wrapped-up into the shape of an \"S.\"\n",
+    "\n",
+    "If we try a simple MDS algorithm on this data, it is not able to \"unwrap\" this nonlinear embedding, and we lose track of the fundamental relationships in the embedded manifold:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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B7auNR4xsp39M7H7M236Y6aOGmxdSvtVzQBo9B6SZHUOkQXTNV+S//ObKGcyN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\nKl8RERGT/R+73qklHmTvMAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x122800fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import MDS\n",
+    "model = MDS(n_components=2, random_state=2)\n",
+    "outS = model.fit_transform(XS)\n",
+    "plt.scatter(outS[:, 0], outS[:, 1], **colorize)\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The best two-dimensional *linear* embeding does not unwrap the S-curve, but instead throws out the original y-axis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Nonlinear Manifolds: Locally Linear Embedding\n",
+    "\n",
+    "How can we move forward here? Stepping back, we can see that the source of the problem is that MDS tries to preserve distances between faraway points when constructing the embedding.\n",
+    "But what if we instead modified the algorithm such that it only preserves distances between nearby points?\n",
+    "The resulting embedding would be closer to what we want.\n",
+    "\n",
+    "Visually, we can think of it as illustrated in this figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![(LLE vs MDS linkages)](figures/05.10-LLE-vs-MDS.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#LLE-vs-MDS-Linkages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here each faint line represents a distance that should be preserved in the embedding.\n",
+    "On the left is a representation of the model used by MDS: it tries to preserve the distances between each pair of points in the dataset.\n",
+    "On the right is a representation of the model used by a manifold learning algorithm called locally linear embedding (LLE): rather than preserving *all* distances, it instead tries to preserve only the distances between *neighboring points*: in this case, the nearest 100 neighbors of each point.\n",
+    "\n",
+    "Thinking about the left panel, we can see why MDS fails: there is no way to flatten this data while adequately preserving the length of every line drawn between the two points.\n",
+    "For the right panel, on the other hand, things look a bit more optimistic. We could imagine unrolling the data in a way that keeps the lengths of the lines approximately the same.\n",
+    "This is precisely what LLE does, through a global optimization of a cost function reflecting this logic.\n",
+    "\n",
+    "LLE comes in a number of flavors; here we will use the *modified LLE* algorithm to recover the embedded two-dimensional manifold.\n",
+    "In general, modified LLE does better than other flavors of the algorithm at recovering well-defined manifolds with very little distortion:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zJyCkKZw/ji4+jjKtE7ryElpHhlNeXkVMeAjdLLn0tWTzzqCeuFcUsz4gGrQO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\nT51xdgy7a4QAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11871f128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import LocallyLinearEmbedding\n",
+    "model = LocallyLinearEmbedding(n_neighbors=100, n_components=2, method='modified',\n",
+    "                               eigen_solver='dense')\n",
+    "out = model.fit_transform(XS)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.scatter(out[:, 0], out[:, 1], **colorize)\n",
+    "ax.set_ylim(0.15, -0.15);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result remains somewhat distorted compared to our original manifold, but captures the essential relationships in the data!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Some Thoughts on Manifold Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Though this story and motivation is compelling, in practice manifold learning techniques tend to be finicky enough that they are rarely used for anything more than simple qualitative visualization of high-dimensional data.\n",
+    "\n",
+    "The following are some of the particular challenges of manifold learning, which all contrast poorly with PCA:\n",
+    "\n",
+    "- In manifold learning, there is no good framework for handling missing data. In contrast, there are straightforward iterative approaches for missing data in PCA.\n",
+    "- In manifold learning, the presence of noise in the data can \"short-circuit\" the manifold and drastically change the embedding. In contrast, PCA naturally filters noise from the most important components.\n",
+    "- The manifold embedding result is generally highly dependent on the number of neighbors chosen, and there is generally no solid quantitative way to choose an optimal number of neighbors. In contrast, PCA does not involve such a choice.\n",
+    "- In manifold learning, the globally optimal number of output dimensions is difficult to determine. In contrast, PCA lets you find the output dimension based on the explained variance.\n",
+    "- In manifold learning, the meaning of the embedded dimensions is not always clear. In PCA, the principal components have a very clear meaning.\n",
+    "- In manifold learning the computational expense of manifold methods scales as O[N^2] or O[N^3]. For PCA, there exist randomized approaches that are generally much faster (though see the [megaman](https://github.com/mmp2/megaman) package for some more scalable implementations of manifold learning).\n",
+    "\n",
+    "With all that on the table, the only clear advantage of manifold learning methods over PCA is their ability to preserve nonlinear relationships in the data; for that reason I tend to explore data with manifold methods only after first exploring them with PCA.\n",
+    "\n",
+    "Scikit-Learn implements several common variants of manifold learning beyond Isomap and LLE: the Scikit-Learn documentation has a [nice discussion and comparison of them](http://scikit-learn.org/stable/modules/manifold.html).\n",
+    "Based on my own experience, I would give the following recommendations:\n",
+    "\n",
+    "- For toy problems such as the S-curve we saw before, locally linear embedding (LLE) and its variants (especially *modified LLE*), perform very well. This is implemented in ``sklearn.manifold.LocallyLinearEmbedding``.\n",
+    "- For high-dimensional data from real-world sources, LLE often produces poor results, and isometric mapping (IsoMap) seems to generally lead to more meaningful embeddings. This is implemented in ``sklearn.manifold.Isomap``\n",
+    "- For data that is highly clustered, *t-distributed stochastic neighbor embedding* (t-SNE) seems to work very well, though can be very slow compared to other methods. This is implemented in ``sklearn.manifold.TSNE``.\n",
+    "\n",
+    "If you're interested in getting a feel for how these work, I'd suggest running each of the methods on the data in this section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Isomap on Faces\n",
+    "\n",
+    "One place manifold learning is often used is in understanding the relationship between high-dimensional data points.\n",
+    "A common case of high-dimensional data is images: for example, a set of images with 1,000 pixels each can be thought of as a collection of points in 1,000 dimensions – the brightness of each pixel in each image defines the coordinate in that dimension.\n",
+    "\n",
+    "Here let's apply Isomap on some faces data.\n",
+    "We will use the Labeled Faces in the Wild dataset, which we previously saw in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) and [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb).\n",
+    "Running this command will download the data and cache it in your home directory for later use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2370, 2914)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_lfw_people\n",
+    "faces = fetch_lfw_people(min_faces_per_person=30)\n",
+    "faces.data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have 2,370 images, each with 2,914 pixels.\n",
+    "In other words, the images can be thought of as data points in a 2,914-dimensional space!\n",
+    "\n",
+    "Let's quickly visualize several of these images to see what we're working with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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X15HYx3gjqPV6Pe7krNfrC0dmIUM4hCyo3WlSL8TBwIJYmf/BYBDRMGez+vm4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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c0ff518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(4, 8, subplot_kw=dict(xticks=[], yticks=[]))\n",
+    "for i, axi in enumerate(ax.flat):\n",
+    "    axi.imshow(faces.images[i], cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We would like to plot a low-dimensional embedding of the 2,914-dimensional data to learn the fundamental relationships between the images.\n",
+    "One useful way to start is to compute a PCA, and examine the explained variance ratio, which will give us an idea of how many linear features are required to describe the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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w+ctQ3WjBusID0IWqcN/kgZDLZP4uiYiI6Bxee/JXQgiBBQsWoKKiAmq1GosX\nL0ZS0ple7+7du/HSSy8BAGJjY7F06VKo1Z17GlghBFZ+XgG7w4X7Jw9AlF7j75KIiIjOq109+ePH\nj+Obb76B0+nEsWPH2r3zgoIC2Gw25Ofn4/HHH0deXl6b9fPnz8eSJUuwZs0aZGZm4sSJE5dWvR98\n/3MV9h5pREZaDEb1j/d3OURERBfkNeT/85//4MEHH8SiRYvQ1NSEGTNmYOPGje3aeXFxMTIzMwEA\nGRkZKCsr86w7dOgQIiMjsXz5cuTm5sJgMCAlJeXyfgofMZhtWLf5ADRqBXJ/1w8yHqYnIqJOzOvh\n+n/961/44IMPcPfddyMmJgYbNmzAvffeiylTpnjduclkgl5/ZtS5UqmEy+WCXC5HY2MjSkpK8Pzz\nzyMpKQl/+tOfMHjwYFxzzTUX3WdcnP9GsS//fCfMLQ7MmToE/dKCd9Ibf7ZxV8J2lh7bWHps487N\na8jL5XLodDrPcnx8fLuntdXpdDCbzZ7l0wEPAJGRkejVqxdSU1MBAJmZmSgrK/Ma8rW1xna9d0cr\n2V+H70oqkZYYjpHpsX6rQ2pxcfqg/dk6E7az9NjG0mMb+8aVfJDymtZ9+/bF6tWr4XA4sHfvXjz3\n3HPtvkHN8OHDsWXLFgBASUkJ0tPTPeuSkpJgsVg85/iLi4vRp0/nnDHO2urAqi8roJDLcE92f46m\nJyKigCATQoiLbWCxWPDPf/4TP/zwA1wuF0aPHo2HHnqoTe/+Qs4eXQ8AeXl5KC8vh9VqRU5ODrZt\n24ZXXnkFADBs2DA8/fTTXvfpj0+N73/1CwqKj+OWsSm4NbO3z9/fl/jJ3DfYztJjG0uPbewbV9KT\n9xryK1aswM0339xpZrvz9S/UkZNG/P1/dyA+Kgx/v+9qqJTBPbUA/9P6BttZemxj6bGNfUPSw/XV\n1dWYPn067r//fmzcuBFWq/Wy3yzQuFwCK7/YByGA3JvSgz7giYgouHhNrXnz5mHz5s148MEHUVpa\niltvvRVPPvmkL2rzuy0llThUZcTogd0wMCXa3+UQERFdknZ1TYUQsNvtsNvtkMlknX5Wuo5gMNvw\n0ZZfEapR4o4JnXNAIBER0cV4vYTuhRdeQEFBAQYMGIBbbrkFzz77LDSa4J/Kde3m/bC2OjAzKx0R\nuuD/eYmIKPh4DfmUlBRs2LAB0dFd53D13sMN2FpejZQEPcYPS/R3OURERJflgiG/du1a3HHHHTAY\nDHj//feTrAL3AAAZ9UlEQVTPWf/www9LWpi/OJwurP7qF8hkwKzsfpDLeU08EREFpguek/dyZV3Q\n+m53FarqLbghowdSEsL9XQ4REdFlu2BPfsaMGQCAxMRETJ06tc26NWvWSFuVn7TYHNj4/SFoVApM\nuS7V3+UQERFdkQuG/IoVK2AymZCfn4/KykrP606nE//3f/+HmTNn+qRAX/pi+zE0m22Ycl0qB9sR\nEVHAu+Dh+uTk5PO+rlarsWTJEskK8heDqRWfbzuKcK0av7s6yd/lEBERXbEL9uTHjx+P8ePHY9Kk\nSUhLS2uzrqWlRfLCfG1j0WG02p2YPqEPQtReLzogIiLq9Lym2YEDBzB37lxYLBYIIeByuWC1WrF1\n61Zf1OcTVfVmfFtyAgnRYci8qru/yyEiIuoQXkN+6dKlWLRoEZYvX445c+bg+++/R2Njoy9q85mP\nvjkIlxCYNi4NSgXnpyciouDgNdHCw8MxevRoZGRkwGg04pFHHkFJSYkvavOJg5UG7Npfhz49IzCs\nb+e40x4REVFH8BryISEhOHToENLS0rB9+3bYbDYYjcFza8HNP7mvHJh6XSpkMk58Q0REwcNryP/1\nr3/F66+/jvHjx+PHH3/E2LFjMXHiRF/UJjlLix07K2oQHxWK/slR/i6HiIioQ3k9J3/11Vfj6quv\nBgB8/PHHMBgMiIiIkLwwX9i2pxp2hwuZV3VnL56IiILOBUM+Nzf3osG3cuVKSQrypW9LqyCXyTB2\nCEfUExFR8LlgyD/yyCO+rMPnjpw04ki1EUP7xCKSs9sREVEQumDInz5Ev2PHDp8V40vf7T4BALg+\no4efKyEiIpKG13Pyb7zxhue5w+FARUUFRo4ciVGjRklamJRsdie2llcjQqfGkLRof5dDREQkCa8h\nv2rVqjbLx44dQ15enmQF+ULxL7WwtDoweXgyFHJOfkNERMHpkhMuKSkJv/76qxS1+Mx3pe5D9ddx\nClsiIgpiXnvyf/vb39osHzx4EOnp6ZIVJLWaRgv2HW1C/16R6BYV5u9yiIiIJNOu6+RPk8lkyM7O\nxrXXXitpUVL6bncVACCTA+6IiCjIeQ35qVOnwmQyobm52fNaXV0devQIvJAUQmDbnmqEahQYkR7n\n73KIiIgk5TXkX3rpJaxbtw6RkZEA3EEpk8nw9ddfS15cR6tpsqLO0IIR/eKgVin8XQ4REZGkvIb8\n119/jW+//RZardYX9Uhqz6EGAMCgFF42R0REwc/r6Pp+/frBZrP5ohbJlR9uBAAMTGXIExFR8PPa\nk58yZQpuuukmpKenQ6E4c4g70Oaud7pc2HukEXGRIYiPDPV3OURERJLzGvIvvvginnnmmcsaaCeE\nwIIFC1BRUQG1Wo3FixcjKSnJs37FihX46KOPEB3t7ln//e9/R0pKyiW/T3scrjLC2urANQPiJdk/\nERFRZ+M15PV6PW699dbL2nlBQQFsNhvy8/NRWlqKvLw8LFu2zLO+vLwcL7/8MgYOHHhZ+78U5Yfd\n5+MH8nw8ERF1EV5DfsSIEXjkkUdw/fXXQ6VSeV5vT/AXFxcjMzMTAJCRkYGysrI268vLy/HOO++g\ntrYW48aNwwMPPHCp9bfbnkMNkMmAASlRkr0HERFRZ+I15K1WK3Q6HX766ac2r7cn5E0mE/R6/Zk3\nUyrhcrkgPzVf/OTJkzFz5kzodDo89NBD2LJlC2644YZL/Rm8srY6cPBEM1ISwqENUXn/BiIioiDg\nNeSv5GY0Op0OZrPZs3x2wAPA7NmzodPpAAA33HAD9uzZ4zXk4+L0F11/Ptv3nITTJTBqUMJlfX9X\nwzbyDbaz9NjG0mMbd25eQ37ChAmQyWTnvN6eyXCGDx+OwsJCZGdno6SkpM2c9yaTCTfffDM+++wz\nhISEYOvWrZg2bZrXfdbWGr1u81s/llQCAFLjtZf1/V1JXJyebeQDbGfpsY2lxzb2jSv5IHVJt5p1\nOBz46quv2n3dfFZWFoqKijBjxgwA7qMCmzZtgtVqRU5ODh577DHk5uZCo9Hg2muvxfXXX3+ZP8bF\nlR9ugEalQFpihCT7JyIi6oxkQghxqd902223Yf369VLU49WlfmpsaG7BE8t+wFVpMfhrToZEVQUP\nfjL3Dbaz9NjG0mMb+4akPfkdO3Z4ngshsH//frS2tl72G/rantOz3PHSOSIi6mK8hvwbb7zheS6T\nyRAVFYUlS5ZIWlRH2nP49Hz1vHSOiIi6lnadk6+vr0dMTAysVitqamqQnJzsi9qumEsI7DncgEid\nGj1iA/8GO0RERJfC6w1qVq1ahf/6r/8CADQ0NGDOnDlYu3at5IV1hOM1JjRb7BiYEn3eKwSIiIiC\nmdeQX7t2LdasWQMASExMxPr167F69WrJC+sIh0+6B4SkJ0X6uRIiIiLf8xrydrsdarXas3z21Lad\n3bEaEwAgKV7n50qIiIh8z+s5+YkTJ2L27NmYNGkSAODLL7/EjTfeKHlhHaGy1gQZwPPxRETUJXkN\n+SeffBKff/45duzYAaVSiVmzZmHixIm+qO2KCCFwrMaE+KhQaFQKf5dDRETkc15DHgCys7ORnZ0t\ndS0dqslkg7nFgf7JvHSOiIi6Jq/n5APV8Vr3+fiecTwfT0REXVPwhnwNQ56IiLq2oA35Y7WnR9Zz\n0B0REXVNQRvyx2tM0KgUiI0M9XcpREREfhGUIe9wulBVb0FinBZyznRHRERdVFCG/Ml6C5wuwfPx\nRETUpQVlyJ85H8+QJyKirisoQ/7MyHoOuiMioq4rOEO+1gwASOTheiIi6sKCNORNiNJroAsNnJvp\nEBERdbSgC3mT1Y5GYysH3RERUZcXdCHvOR/PSXCIiKiLC76QPz2ynj15IiLq4oI25Hvy8jkiIuri\ngi7kj9WYoZDLkBAd5u9SiIiI/CqoQt4lBCrrTOgeo4VSEVQ/GhER0SULqiSsbbLCZnfxznNEREQI\nspDnPeSJiIjOCKqQP1bDQXdERESnBVXIV56azpY9eSIiomAL+TozwjRKROrU/i6FiIjI7yQNeSEE\nnn/+ecyYMQOzZs3CsWPHzrvd/Pnz8dprr13RezmcLtQ0WtEjVguZTHZF+yIiIgoGkoZ8QUEBbDYb\n8vPz8fjjjyMvL++cbfLz8/HLL79c8XtVN1jgEgI9Ynl9PBERESBxyBcXFyMzMxMAkJGRgbKysjbr\nd+3ahZ9//hkzZsy44vc6UW8BAHSP4eVzREREgMQhbzKZoNfrPctKpRIulwsAUFtbi7feegvz58+H\nEOKK36uqzj3orkcsQ56IiAgAlFLuXKfTwWw2e5ZdLhfkcvfnis8//xxNTU344x//iNraWrS2tqJ3\n79649dZbL7rPuDj9eV+vN9kAAIPT4xEXxUP2V+JCbUwdi+0sPbax9NjGnZukIT98+HAUFhYiOzsb\nJSUlSE9P96zLzc1Fbm4uAGDDhg04dOiQ14AHgNpa43lfP1RpgEalAOyOC25D3sXF6dl+PsB2lh7b\nWHpsY9+4kg9SkoZ8VlYWioqKPOfc8/LysGnTJlitVuTk5HTY+7hcAicbLOgZx5H1REREp0ka8jKZ\nDAsXLmzzWmpq6jnbTZ069Yrep9ZghcPp4qA7IiKiswTFZDgnPIPueC6eiIjotKAI+apTl8/1YE+e\niIjIIyhC/gQvnyMiIjpHUIR8Vb0ZSoUMsZEh/i6FiIio0wj4kBdC4ES9BQnRYVDIA/7HISIi6jAB\nn4qNxla02pwcWU9ERPQbAR/yPB9PRER0foEf8p4b0/DyOSIiorMFfsizJ09ERHReAR/yVfVmyGUy\ndONNaYiIiNoI6JAXQuBEnRlxUaFQKQP6RyEiIupwAZ2MRosd5hYHevB8PBER0TkCOuR5Pp6IiOjC\nAjrkq+pPhTyvkSciIjpHQIf8ibpTl8/x7nNERETnCOyQP9WT7x7NnjwREdFvBXzIx4SHQKNW+LsU\nIiKiTidgQ97SYofBZOOgOyIiogsI2JA/2WAFwOlsiYiILiRgQ95gagUAROo0fq6EiIiocwrckLfY\nAAARWrWfKyEiIuqcAjbkm03ukA/XMeSJiIjOJ2BD3tOTD2PIExERnU/AhnyzmT15IiKiiwnYkDeY\nbZDLZNCFqvxdChERUacUsCHfbLZBr1VBLpP5uxQiIqJOKWBD3mC28Xw8ERHRRQRkyLfanGi1OXk+\nnoiI6CICMuQ5sp6IiMi7gAx5z8h6ToRDRER0QQEZ8gYTZ7sjIiLyRinlzoUQWLBgASoqKqBWq7F4\n8WIkJSV51n/xxRf417/+BblcjptvvhmzZs1q136bLezJExEReSNpT76goAA2mw35+fl4/PHHkZeX\n51nncrnw2muv4X//93+Rn5+P999/H01NTe3a7+mb07AnT0REdGGS9uSLi4uRmZkJAMjIyEBZWZln\nnVwux2effQa5XI76+noIIaBStW9im2aLHQB78kRERBcjacibTCbo9fozb6ZUwuVyQS53H0CQy+X4\n6quvsHDhQowfPx5hYd7vDR8Xp0eL3QkA6J0cw6CXQFyc3vtGdMXYztJjG0uPbdy5SRryOp0OZrPZ\ns3x2wJ+WlZWFrKwszJs3D5988gmmTp160X3W1hpR22iBQi6D1dyCVkurJLV3VXFxetTWGv1dRtBj\nO0uPbSw9trFvXMkHKUnPyQ8fPhxbtmwBAJSUlCA9Pd2zzmQyITc3FzabexBdaGgoZO2cotZgsiFc\nq+aUtkRERBchaU8+KysLRUVFmDFjBgAgLy8PmzZtgtVqRU5ODm655RbcfffdUKlU6NevH6ZMmeJ1\nn0IINFts6B6tlbJ0IiKigCdpyMtkMixcuLDNa6mpqZ7nOTk5yMnJuaR9tticsNldiOCUtkRERBcV\ncJPheK6R55S2REREFxV4IX9qSlv25ImIiC4u4EL+9JS27MkTERFdXMCFPKe0JSIiap+AC3nenIaI\niKh9Ai7k2ZMnIiJqn4ALeU9PngPviIiILirgQr7ZYoNSIUOYRtJL/ImIiAJewIX86Slt2zsFLhER\nUVcVUCF/ekpbXj5HRETkXUCFvKXFAbvDxZH1RERE7RBQId9kct9WliPriYiIvAuokG9sbgHAkfVE\nRETtEVAh7+nJ85w8ERGRVwEV8o3NPFxPRETUXgEV8qd78hx4R0RE5F1ghbyRPXkiIqL2CqiQbzSe\nGnin1fi5EiIios4voEK+ydgKpUKOUI3C36UQERF1egEV8o3GVkRwSlsiIqJ2CaiQbzK28nw8ERFR\nOwVUyDucnNKWiIiovQIq5AGOrCciImqvgAt59uSJiIjaJ+BCnj15IiKi9gm4kGdPnoiIqH0CLuTZ\nkyciImqfgAt59uSJiIjaJ6BCPlKvQXQ4p7QlIiJqj4AK+feevQkqJae0JSIiag+llDsXQmDBggWo\nqKiAWq3G4sWLkZSU5Fm/adMmrFy5EkqlEunp6ViwYMFF96dSBtRnEiIiIr+SNDULCgpgs9mQn5+P\nxx9/HHl5eZ51ra2teOONN7B69Wq8//77MBqNKCwslLIcIiKiLkXSkC8uLkZmZiYAICMjA2VlZZ51\narUa+fn5UKvdA+kcDgc0Gp5vJyIi6iiShrzJZIJer/csK5VKuFwuAIBMJkN0dDQAYNWqVbBarRgz\nZoyU5RAREXUpkp6T1+l0MJvNnmWXywW5/MznCiEEXn75ZRw5cgRvvfVWu/YZF6f3vhFdEbaxb7Cd\npcc2lh7buHOTtCc/fPhwbNmyBQBQUlKC9PT0Nuufe+452O12LFu2zHPYnoiIiDqGTAghpNr52aPr\nASAvLw/l5eWwWq0YNGgQpk2bhhEjRrgLkckwa9YsTJw4UapyiIiIuhRJQ56IiIj8hxeeExERBSmG\nPBERUZBiyBMREQUphjwREVGQkvQ6+Y7ibQ58ujwOhwNPP/00KisrYbfbMWfOHPTp0wdPPfUU5HI5\n+vbti+eff97fZQaF+vp63H777Vi+fDkUCgXbWAL//d//jc2bN8Nut+Ouu+7CqFGj2M4dyOFwYN68\neaisrIRSqcQLL7zA3+UOVFpaildeeQWrVq3C0aNHz9uu69atw9q1a6FSqTBnzhyMGzfO634Doid/\nsTnw6fJ9+umniIqKwpo1a/Duu+/ihRdeQF5eHh577DGsXr0aLpcLBQUF/i4z4DkcDjz//PMICQkB\nALaxBLZv345du3YhPz8fq1atQlVVFdu5g23ZsgUulwv5+fn485//jH/84x9s4w7y7rvv4tlnn4Xd\nbgdw/r8RdXV1WLVqFdauXYt3330Xr776qmf7iwmIkL/YHPh0+SZNmoRHH30UAOB0OqFQKLBnzx6M\nHDkSAHD99dfjxx9/9GeJQeGll17CnXfeifj4eAgh2MYS+P7775Geno4///nPePDBBzFu3Di2cwdL\nSUmB0+mEEAJGoxFKpZJt3EGSk5Px9ttve5bLy8vbtOsPP/yA3bt3Y8SIEVAqldDpdEhJSfHMQXMx\nARHyF5sDny5faGgowsLCYDKZ8Oijj2Lu3Lk4e9oErVYLo9HoxwoD3/r16xETE4OxY8d62vbs3122\nccdobGxEWVkZ3njjDSxYsABPPPEE27mDabVaHD9+HNnZ2Zg/fz5yc3P596KDZGVlQaFQeJZ/264m\nkwlms7lNDoaFhbWrvQPinLy3OfDp8lVVVeHhhx/G3XffjcmTJ2Pp0qWedWazGeHh4X6sLvCtX78e\nMpkMRUVFqKiowLx589DY2OhZzzbuGJGRkUhLS4NSqURqaio0Gg2qq6s969nOV27FihXIzMzE3Llz\nUV1djdzc3DaHi9nGHefsfDvdrjqdDiaT6ZzXve5Lkgo7mLc58Ony1NXV4f7778eTTz6JqVOnAgAG\nDBiAHTt2AAC+/fZbz7TDdHlWr16NVatWYdWqVejfvz9efvllZGZmso072IgRI/Ddd98BAKqrq2G1\nWjF69Ghs374dANu5I0RERECn0wEA9Ho9HA4HBg4cyDaWwMCBA8/5GzFkyBAUFxfDZrPBaDTi119/\nRd++fb3uKyB68llZWSgqKsKMGTMAgAPvOsg777yD5uZmLFu2DG+//TZkMhmeeeYZLFq0CHa7HWlp\nacjOzvZ3mUFn3rx5npszsY07xrhx47Bz505MmzbNczVOYmKiZzAT2/nKzZ49G08//TRmzpwJh8OB\nJ554AoMGDWIbS+B8fyNkMhlyc3Nx1113QQiBxx57rF03duPc9UREREEqIA7XExER0aVjyBMREQUp\nhjwREVGQYsgTEREFKYY8ERFRkGLIExERBSmGPBF1KoWFhVixYoW/yyAKCgExGQ4RdR3l5eX+LoEo\naDDkiTqx7du345133kFISAgOHjyIfv364dVXX4VS2fa/7ooVK5Cfnw+lUolx48bhiSeeQH19PZ55\n5hmcOHECSqUSc+fORWZmJt566y2cOHEC+/btQ2NjIx599FFs3boVpaWlGDBgAF577TVs374db775\nJpRKJaqqqpCRkYFFixZBpVLh448/xooVKyCTyTBo0CDMnz8foaGhuO6665CdnY3i4mIolUq8/vrr\nSExMxM8//4y8vDy0tLQgKioKf//735GYmIjc3FxcddVVKC4uRmNjI5599ln06NED+fn5AIDExEQk\nJCRg6dKlkMvliIiIwKuvvorIyEh//FMQBSZBRJ3Wtm3bxLBhw0R1dbUQQohp06aJwsLCNtuUlpaK\nm266SZhMJuFwOMS9994rysvLxaOPPiqWL18uhBDi6NGj4rrrrhP19fXizTffFNOmTRMul0ts375d\nDBgwQBw8eFA4HA5x0003iX379olt27aJjIwMcfjwYSGEEH/5y1/E8uXLRUVFhcjKyhIGg0EIIcTC\nhQvFyy+/LIQQol+/fuLrr78WQgixZMkSsWTJEmGz2cQtt9wiqqqqhBBCfPfdd+Kee+4RQghx9913\nixdffFEIIcTmzZvFbbfdJoQQ4s033xRvvvmmEEKI3Nxc8fPPPwshhFi1apUoKirq8DYmCmbsyRN1\ncunp6YiPjwcApKWloampqc36nTt3YsKECdBqtQCA9957DwCwdetWLFq0CACQlJSEoUOHorS0FAAw\nZswYyGQy9OjRA/Hx8ejduzcAID4+Hs3NzQCAkSNHIjk5GQAwZcoUrFu3DiqVChMmTPDc/Wr69Ol4\n+umnPbVcd911AIC+ffti586dOHz4MI4ePYoHH3zQc/tMi8Xi2T4zM9OzvcFgOOdnv/HGG/HQQw9h\n4sSJuPHGGzFmzJjLa0SiLoohT9TJnX0TCplMds763x66r6mpQWhoaJt7UgPuWzQ7nU4AgEql8rx+\n9n2sz3b26y6XC0qlEkKIc/Z7ep9n1yqTySCEgNPpRK9evbBhwwYA7vtk19XVebbXaDRttv+t2bNn\nY8KECSgsLMTSpUuRnZ2NP/3pT+etl4jOxdH1RAFu5MiR+Pbbb2G1WuFwOPD444+jrKwMo0ePxkcf\nfQQAOHbsGHbt2oWhQ4ee8/3nC1cAKC4uRk1NDVwuFzZu3Ijrr78eo0aNQmFhoae3v27dOowePfqC\ntfXu3RsGgwE7d+4EAHz44Yd4/PHHL/rzKBQKzweH6dOnw2QyYdasWZg9ezYH5RFdIvbkiQLcwIED\nMXPmTEyfPh0AcNNNN+Haa69FWloa5s+fj48//hhyuRyLFy9GbGzsOd9/9tGBs5/Hx8dj3rx5qK6u\nxtixY5GTkwOZTIYHHngAM2fOhNPpxKBBg7Bw4cJzvvc0tVqN119/HYsXL4bNZoNOp8NLL710we0B\nYNSoUXjqqacQGxuLxx57DE899RQUCgVCQ0M970VE7cNbzRLRObZv34633noLK1eu9HcpRHQFeLie\niIgoSLEnT0REFKTYkyciIgpSDHkiIqIgxZAnIiIKUgx5IiKiIMWQJyIiClL/H6oVvXKpw8oaAAAA\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11871f860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.decomposition import RandomizedPCA\n",
+    "model = RandomizedPCA(100).fit(faces.data)\n",
+    "plt.plot(np.cumsum(model.explained_variance_ratio_))\n",
+    "plt.xlabel('n components')\n",
+    "plt.ylabel('cumulative variance');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that for this data, nearly 100 components are required to preserve 90% of the variance: this tells us that the data is intrinsically very high dimensional—it can't be described linearly with just a few components.\n",
+    "\n",
+    "When this is the case, nonlinear manifold embeddings like LLE and Isomap can be helpful.\n",
+    "We can compute an Isomap embedding on these faces using the same pattern shown before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2370, 2)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import Isomap\n",
+    "model = Isomap(n_components=2)\n",
+    "proj = model.fit_transform(faces.data)\n",
+    "proj.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output is a two-dimensional projection of all the input images.\n",
+    "To get a better idea of what the projection tells us, let's define a function that will output image thumbnails at the locations of the projections:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from matplotlib import offsetbox\n",
+    "\n",
+    "def plot_components(data, model, images=None, ax=None,\n",
+    "                    thumb_frac=0.05, cmap='gray'):\n",
+    "    ax = ax or plt.gca()\n",
+    "    \n",
+    "    proj = model.fit_transform(data)\n",
+    "    ax.plot(proj[:, 0], proj[:, 1], '.k')\n",
+    "    \n",
+    "    if images is not None:\n",
+    "        min_dist_2 = (thumb_frac * max(proj.max(0) - proj.min(0))) ** 2\n",
+    "        shown_images = np.array([2 * proj.max(0)])\n",
+    "        for i in range(data.shape[0]):\n",
+    "            dist = np.sum((proj[i] - shown_images) ** 2, 1)\n",
+    "            if np.min(dist) < min_dist_2:\n",
+    "                # don't show points that are too close\n",
+    "                continue\n",
+    "            shown_images = np.vstack([shown_images, proj[i]])\n",
+    "            imagebox = offsetbox.AnnotationBbox(\n",
+    "                offsetbox.OffsetImage(images[i], cmap=cmap),\n",
+    "                                      proj[i])\n",
+    "            ax.add_artist(imagebox)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calling this function now, we see the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QdR2PPvoo7rvvPo6soqqbxWLB+vo6FhYWsLS0hKGhIRZSDocD0WgUlUoFFosF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drYtChQVWo7aG/jdrIl6e7uZisLyvOklrfVasWZM2dgMBhw/vx5pNNp7N69Gz09PchkMlCr1ahW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Rj8cxMzODhYUFHDhwYMXf0VhSJpNx1/CJJ55gW5lyuYzR0VFotVqoVCrMzMzwoq/T6ThyhwQe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XV1eXqlUgkLCwvI5/PYunUrOjs7ZfNlJFepVILFYmno0wagjjdmNptRLpfrNm02bcpYMZ5K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fj3Q6jc2bN8Nms+HXv/419Ho9hoeHYTKZUCgU4PP54HK50N3dvA3q8/mwbdumRZ+BCkEq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\nYdG+SqXC2bNncfbsWdx+++3vwjO2Ob70pS9tqWaLdHbDw8O49957t31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hPPDAAxgcHITf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\ndW320AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119104d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "plot_components(faces.data,\n",
+    "                model=Isomap(n_components=2),\n",
+    "                images=faces.images[:, ::2, ::2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is interesting: the first two Isomap dimensions seem to describe global image features: the overall darkness or lightness of the image from left to right, and the general orientation of the face from bottom to top.\n",
+    "This gives us a nice visual indication of some of the fundamental features in our data.\n",
+    "\n",
+    "We could then go on to classify this data (perhaps using manifold features as inputs to the classification algorithm) as we did in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Visualizing Structure in Digits\n",
+    "\n",
+    "As another example of using manifold learning for visualization, let's take a look at the MNIST handwritten digits set.\n",
+    "This data is similar to the digits we saw in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb), but with many more pixels per image.\n",
+    "It can be downloaded from http://mldata.org/ with the Scikit-Learn utility:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(70000, 784)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_mldata\n",
+    "mnist = fetch_mldata('MNIST original')\n",
+    "mnist.data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This consists of 70,000 images, each with 784 pixels (i.e. the images are 28×28).\n",
+    "As before, we can take a look at the first few images:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2JOFEo1EcDgeDg4N8/fXXmM1m0tPTOXDgAGVlZUkp/oB4ful36bU6NTXF/fv3\nuXTpEgMDA7jdbmQyGWlpadTV1VFUVLTtO3mlUklmZuYzGbqJRIKZmRk6OjpwOBzU1dVRU1PzzOej\n0Shutxuz2czk5CSBQID5+Xlu3brF1NQUPp+PeDxOZmYmZWVlYoP7ZDq7MBAI0NvbS39/P6urq6Ko\ny+VyiouL2bdvHzk5Odvqqttu5HI5RqMxaTfe3wXhqES9Xk9BQQHl5eXb6rnYUktyfX2dsbExFhcX\nWV9fR61Wi0XM0WiUaDRKOBwWU+bv3LnD9evXuXbtGuFwmKysLI4dO8YHH3zARx99tJVDe2WOHTtG\nbm4uU1NTdHZ2vrStmkwmo6qqiqqqKvHYnaNHj27TaN8MgkiurKwwOTnJ3NyceIwZPFmAQ6GQKBrX\nrl0jNTWVxsZG2trakvZECPiTay4ajYoxq5ctKkIccmBggCtXrtDR0SHWbglt7WpqanbE1SXkA+h0\nOoxGI8FgUOyIMz4+TigUYnp6GofDIcYun47DBoNBJiYm6Ozs5PLly4TDYZxOJzMzM2LGbkpKCmVl\nZZw9e1Z0eSWLQAq1qkKWsbBRVyqV6PV6qquraWtr27ZWZjuFcLbk2yKQQoes9fV1jEYjeXl5FBQU\nbGvZzpaIpHBDhMOWHzx4gMlkYs+ePWRnZ6PValleXmZubo7R0VFWVlbEzEmHw0FKSgqlpaXU1tby\nzjvvJEW3mZycHHQ6HZWVlRw4cICf/OQnL/2McLSQUFe42xFOsc/IyCAWi4l1jwJCd4/f//73XL9+\nnXg8zuHDh7lw4QJlZWVJnV7v8/mYmppiZWWFYDCIRqN5qUXp8/lYW1tjYGCAoaEhAoGA2Nqsvb2d\n9vZ2jhw5siNZk1qtlqqqKt555x3W19fp6OhgamqKSCSCw+EgGAxiNpvp7+/nN7/5DefOnaO5uVkU\nOofDwa9+9Sv6+vrEGkihx208Hken03Hw4EHOnj3Lhx9+KC5UybIYCx2HbDbbBjerUCv6dD9eid2F\n0+nEbreLG8Ht9l5smSUpk8nE1kFDQ0PiQb15eXnodDrRZTkxMYHVahV7KKanp1NVVUVrayv79u2j\nqalpw3FSO4VOpxMtpmQqkt5OjEYjhYWFVFRU4HK56O3tpaysjPz8fLGhfV9fH52dndhsNmpqajhx\n4gTHjh3DZDIldfag0D3GarWKVofQlSYWi4kZyUKrt7W1NcxmM9PT0/T29rK8vIxKpaKwsJDGxkbO\nnTvHoUP9aXdGAAAgAElEQVSHKCws3BHrSihv2LNnD7FYDLPZzOLiItFolFAoRDAYxG63YzabGRwc\nFMMdOp0OuVyO2+3m9u3bLCwsPDP+jIwMysvLOXv2LO3t7TQ0NCRdrDkYDOJwOFheXsbpdAKIZ2Ge\nOnWKlpaWb13GtFsQYszxeFwsaxI6XCXDsXRbQSKRwGq1srS0RDweR6FQbPuzt+XposKp6MIBp0Lc\nZ2VlBbfbLZ6arVKpqKiooLq6mrq6Oj766CNaW1u3rMekxOujVqvJzMykpaWFzs5OvvnmGzEhZ2lp\niUuXLvHFF18wNjZGXl4eZ86c4Z133qGpqSlp3HCbIZx9KvS5VCgUyOVyNBoNoVBIPCXE6XSyuLjI\nvXv36OvrY2hoCKvVKoYHDhw4wIULFzhy5EhStN4TzkW8cuUK/f394tmCwiZWGN/du3fp6uoSPydY\nYs9z1QkCeeHCBRoaGpLy3rrdbiwWCxaLRbQkTSYTjY2NnD17Nunj/98V4Yg04XlNTU0Vz6l9W1hc\nXGRubm7HTkt6bZFMTU2lvLycsrIypqenWVtbEy1Kh8PB+vo6SqVSrFfSaDQUFhZSW1vL2bNnxRhO\nRUVFUlse/xcR+pqeOHECt9vN4OAgv/vd7+js7BQPLrZYLJSWlnL06FGxNVmyWRkCWVlZNDU1MTs7\ni9PpJJFIcP36ddbW1sjJyaGwsJCioiImJiZYXl4mGAwSCATwer2srKywurqK3W5HqVRSV1fHBx98\nwMGDB8U6wWSYt1wuR6vVcvr0afx+P3/84x9xOp0bTq2RyWQbrGUBpVKJRqMRvSiFhYUcPnyY+vp6\nqqurKSwsTIoyrOchdGYRNuEymYza2lpaW1tfuT50NzEzM0Nvby8DAwPIZDL27Nnz1iUn/fnRdtvN\naz/xBoOB0tJSWlpacDgcjI2NYbfbWV9fF3fkMpmM1NRUsf9qY2MjBw4c4P3330/q5A6JJ7WdjY2N\nmM1m7ty5w8jICF1dXTidTtRqNdnZ2Rw5ckQsLk/mPphFRUW0t7eLheY+n4+hoSEeP35MVlYWJSUl\nlJeXMzw8zPz8PH6/f8NJCnK5HIVCQUVFBceOHePjjz+msrIy6WrSFAoFbW1thEIhlpaWxBNZNjva\nSqvVotPpSE1NJTs7m8LCQgwGA9XV1Vy4cIHCwkL0en1S31shKVDoJyuTySgrK6O2tpb09PS3cgOe\nSCQwm81MTEywurpKZWUlbW1tb9WpJvCnpLTtbGr+NK8tklqtluzsbC5evEhFRQWdnZ3cunWL/v5+\n8Rphh3PkyBEOHz5MRUUFBQUFUhB9FyD0u2xqauLv/u7vuHTpElNTU5SXl1NfX09LSwt79+6luLgY\ntVq94+7GFyEkkmk0GhKJBENDQ2KGq8PhwOv1Mj09TSAQEHeugjDCn3rsfvTRR3zwwQfbXtT8bZHJ\nZOTm5nLo0CGUSiVffPEF165dw+l0PtdllZeXR21tLTU1NbS0tHD48GEUCoXYXk+tVid9xqTP58Nu\nt2+YX0ZGBtnZ2Ulh4b8pvF4vsViMqqoqjhw5wqlTp966dns6nY78/HwMBsOOxJVfWyTlcjlqtZqi\noiLxpaqoqGBubk68RiaTUVNTQ3V1NVVVVaSnpyfl4iLxLEJpRF5eHocOHUKj0WC1WjEajRQVFVFS\nUkJ+fn5SNDB/GUJ8/N133xUzkMfHx8UEF+EUe3jiejQajTQ3N9Pc3IxWqxW7JZ08eZLa2loMBkNS\nLsAymQyNRkNubi4HDhwgGAySmpoqiuTzYo41NTWiJV1ZWZnUgvg8jEYj+fn5ZGVlia35nu6V/LZS\nXl5OIpGgsbGRiooKqqqqdsW7+G2RyWS0traSk5NDSkrKc2t83/gYEm/7UyQh8RwWFha4fv06ly9f\npquri2AwuCFGp9PpyM7O5qc//Sk//vGPSU9PT5rOMq/K8vIyS0tLeL1eotHoMwKYl5cn7tR3q5tO\nyNb953/+Z+7fv4/D4eBnP/sZFy9epKmp6a2NSUq8eSSRlPg/id/vZ3V1lZWVFRwOB7FY7JlDvjUa\nDaWlpeKZg8loNX4bAoEAwWCQaDS6aUxSq9Vu6xl9W00sFsPn8zE+Po7D4SAcDlNeXi6GdXbrvZPY\neSSRlJCQkJCQ2ITduW2UkJCQkJDYBiSRlJCQkJCQ2ARJJCUkJCQkJDZBEkkJCQkJCYlNkERSQkJC\nQkJiEySRlJCQkJCQ2ARJJCUkJCQkJDZBEkkJCQkJCYlNkERSQkJCQkJiEySRlJCQkJCQ2ARJJCUk\nJCQkJDZBEkkJCQkJCYlNkERSQkJCQkJiEySRlJCQkJCQ2ARJJCUkJCQkJDZBEkkJCQkJCYlNkERS\nQkJCQkJiE5Q7PYCnSSQSxONxurq66OzsJCUlhbq6Otrb21EoFMhksp0e4rdmaGiI7u5uhoaG8Pl8\nGAwGLl68SFtbGzqdDoVCsdND3FL8fj9ut5vf/OY3zM3N0drayr59+6ivr0cmk+2qe/c0oVCIQCCA\n3W5neXmZhYUF1tbWSCQSlJSUUFFRQUlJCTqdDpVKtSvmmkgkWF5eZm5ujrGxMaamplhcXNxwzf79\n+6murkav11NaWkppaekOjfb1iMfjRCIRLBYL8/PzzM3NiV+JRAKDwUBpaSn79++nra0NvV6PSqXa\n6WFLJBFJJZKhUAibzUZXVxf/8z//g06n4+TJkzQ0NJCZmYlOp9vpIX5rlpeXuX//PpcvX8Zms5Ga\nmkpFRQV1dXVotdq3TiRXV1fp7u7m888/Z2RkhLm5OdRqNbW1tcjl8qQXDoBYLEYsFiMcDhMMBvH5\nfFitVpaWllhaWmJubo6ZmRmsViuJRILKykpqamqor6+nqamJ/Px89Hr9Tk/jGSKRCKFQCI/Hg9vt\nxul0Mjs7y9jYGL29vTx+/Ji5ubkNn5mdnaWxsZGMjAyOHTtGeno6KSkpooAk+/1MJBK43W7sdjtL\nS0uMjY0xMjLC5OSk+AWQmppKZWUlNpuNWCxGY2MjOTk5KJXKHZ9jIpEQ/xuPx3E4HNhsNux2O6FQ\n6JnrZTIZKpWK7OxsTCYTmZmZyOVy5HLJYfg6JJVIulwu7t+/z8OHDxkZGUEmk6HVajlw4ACtra27\nbjcrvGShUAi73Y7b7SYQCBCPx3d4ZFvP2NgY//qv/8r4+Dh2u50rV67Q2NjIhx9+iFqt3unhfSui\n0Sg+n4/V1VUsFgtTU1M8ePCAnp4e7HY7fr+faDRKNBoFYHBwEJVKRV5eHv/4j//ImTNn0Ol0O764\n/jk+n4/l5WUGBwfp7e0V5+NwOHA6nc9dcHt6ehgdHSUtLY1IJEJBQQFlZWWkpaXtikU3kUgwNTXF\nvXv3uHLlClNTU6ysrBCJRIhEIuJ1Xq+X0dFRXC4XU1NT/MM//AOHDh0iLS1tB0f/JxKJBNFolEgk\nQl9fH1euXOHmzZusrKw8c61CoSA9PZ3333+f9vZ2Tp06hUaj2RX3K5lJKpF0OBzcuHGDkZERwuEw\nACqVisLCwqTcob8K8Xhc/HqbCIfDWCwWRkZGGBsbw+PxoFQq0ev1aLXapBOM5xEKhVhcXGR0dJS+\nvj6sViurq6s4HA4sFgvLy8sEg0FisRjwpx1+KBRCJpMRiUT48ssvkcvl/PSnP02aRSkSieD3+7l7\n9y63b99mYmKC+fl5lpaWCIVChEIhwuEwCoUCrVYL/GljF41G8Xg8BINBrl+/jt/v52//9m9pbm5O\n2k1PLBbD7/czMzPDwMAA3d3dDA4OMj09zfr6OgAVFRVkZWVhNBqx2Wyip8BqtTI0NMTly5eRyWSc\nOXNmx7098Xicx48fMzw8TH9/P5OTk0xPTzM7O4vX633meplMhtPp5Nq1a8zNzXHnzh1OnTrF3r17\nMZlMu8aNHAqFMJvNWCwWlpaWmJ+fx+FwAH969wBSUlJIS0ujoKCA4uJiysvLSUtLQ6vVbqkFnTQi\n6fF4mJub48GDB8zNzSGXy8nMzBQnn5qautNDlHgOkUiEhYUF5ubmWFtbIx6Pk5mZSWNjI4WFhUkf\nS47H4/h8PgYHB7ly5QqXLl3CbrdvWIQEN1ZaWhomkwmZTCZanevr6wQCATo6OsjKyuL8+fOkp6eL\norOT+Hw+pqenuX79Or/73e9YXV0VrUatVotWq8VoNJKRkUF6evoGF6PVasVut7O+vs7AwAArKyuc\nOHGCysrKpBTJcDiMx+NhdnaWu3fv8oc//IHR0VFsNhtarRaTyURRURH79u2jtLQUk8nEzMwMw8PD\n+P1+nE4ny8vLPHz4kKKiIk6fPr2jIilYkKOjo3z11Vd89dVX+P1+ZDIZCoWC1NRUDAbDhlCG8JmZ\nmRkmJye5evUqPp8PlUrFgQMHksKF/DJisRgej4fe3l56e3sZHR1leHiYlZUVVCoV4XBYNKBSU1PJ\nycmhtraWPXv20NbWRm1tLaWlpeh0urdPJPv7+7l9+zZWq5VwOIxOp+Ps2bO0t7eTmpqKUpk0Q5V4\nikQiQTgcJhKJiLu83NxcfvSjH3Ho0KGkF8loNIrdbuf27dvcu3cPq9W6wR0HT9xYmZmZHD9+nL/4\ni79Ao9Hgdrvp7e3l7t279PT04PP5mJ2d5fbt2xw4cICKioodmtGfsFgs/PrXv6azs/OZeRUXF1Na\nWkpeXh779++npaUFg8EgWhtffvklV69epbe3F5/PRzQaZXZ2FrPZTHp6etLdU5vNxvDwML/97W/p\n6elhbm4Ov9+P0Wikrq6O06dPc/LkSYqLi0lLS0OpVLK+vk5/fz82m43R0VF8Pt8GS2UnERKOFhcX\nMZvNRKNR0eI3Go3U1NTQ2tq6IU4cj8dxu910dnYyOzuL2+3m9u3bKJVKysvLN1ybrASDQSwWC198\n8QUPHz7E5XKRSCTIzs4mNzcXq9XK4uIiMpkMv9+PxWLB4XAwMDDAF198wSeffMJPfvITiouLt2yu\nO648Ho+H1dVV7ty5Q2dnJ263m5SUFIqKimhsbKS8vBylUpk0Lqxvy8rKCo8fP36uW+RtIh6Ps76+\njs/nE7+n1WopKCggPT096e/bwMAAN27c4MGDBywsLBAMBgHEBam8vJyamhpqampoa2vj2LFjqFQq\nnE4nKpWK+fl5uru7iUajeL1eVlZW8Pv9OzyrJ+h0OsrKyigtLRXdVSaTibKyMnHHbTKZqKyspLS0\nFK1WK25GBXfz7Ows4XCYUCjE8PAwZWVlVFZWbrh2J4lGowQCAfr7+7l8+TJdXV2iQBYVFdHU1MS5\nc+fYv38/9fX1GI1G0RLOzMzE4XCQlZWFVqsVn+FkEMpoNMr6+jpms5nl5WXkcjnV1dXs2bOH8vJy\nKisrqaioQK1Wi/chHo/j9/uprKzk4cOHdHR0YLfbxUS61NRUTCbTDs/sxQQCAZxOJ16vl4yMDBoa\nGsjNzSU3N5f8/HyWl5fFTGybzcbk5CSrq6ssLS0BPBMa2Qp27CkXHkSbzcaDBw/o6Oigt7eXcDhM\naWkpDQ0NVFZWkp2dnXS71hcRj8eJxWLMzc3R09NDJBJBJpOhVCpRKpVJb1m9CoJ7x263izs+uVwu\nzjOZBVLIGOzs7OQ///M/WVxcFMVNpVJhNBrJycnh9OnTfO9736O1tZXs7Gzx80qlkqqqqg2LTiwW\nIxQKbekL+jrk5uZy/vx55HK56P6tr6/nnXfeobi4GJPJtGliR3V1Na2trdy5cwe3200wGGRwcJCS\nkhJOnTqFQqFICpEMBoNYrVbu3bvH119/jdVqJRgMIpPJKC0t5fjx4/zoRz8iOzv7mfEqlUo0Gs0G\nCytZskEjkQg+nw+Px0M4HCY9PZ2jR4/yySef0NraSlZW1nM/l0gkOHbsGNXV1bhcLgYGBsRyn6Ki\noqQXyXA4TDQapaioiJqaGk6ePElZWRn5+flkZGTgdDqx2WxiItPvf/97QqEQPp8PtVqNRqPZcqNq\nR0UyFAoxOTnJ7373O6ampohGo6hUKvbv38+Pf/xj9uzZQ25u7q4SFZ/Px+LiIisrK4TDYeLxOBkZ\nGaJ7Kysra8cTAraKp+/hwsICADk5OZSXl1NcXJzUceRIJCJafhaLRYzVyWQyGhoaaG1t5fDhwzQ2\nNlJVVfWt5pJsz6lGoyEnJ4f33nuPAwcOAGA0GsnKyiIlJQW1Wr3pmE0mE+Xl5ZSXl2Oz2VhYWGBh\nYYHp6WksFosoLjuN3W7n1q1bDAwMsLq6KsarEokE09PTjI+PE4/Hv9U7J5fLMRqNGAyGNz3sl6LV\nasnNzeXChQtUVlYSDAY5dOgQLS0tGI3GF35WqVRSWlrKBx98gMPhwOPxbNOoX5/MzEwx0UipVJKd\nnY1Op0Oj0YixWIDR0VGxJMvr9WIymdi/fz+HDh2ipKRkS5/NHRPJUCjE2NgY3d3dYkq6QqHAZDJR\nV1fHwYMHyczMTIoEiFfB5/MxPj6O1WolFottCKq/bayuropZrcvLywCkpaWRk5NDWloaGo1mh0e4\nOXa7nfv37zM2Nia62VQqFVqtlv379/P++++zb98+srOzX7ooPS00ydRMQKFQiC7XsrKyV/qsTqcj\nLS1NvI+JRAKv14vL5cLr9T4Tt90pHA4H9+/fZ2ZmhkAgAIBarUav11NYWEheXp7Y5OHPiUQiYj1s\nOBxGqVRSUFBAXl7ejt9DIUO8tbWVsrIyQqEQxcXFG7wZz0N4/jIzM9mzZw8ZGRm7SiS1Wi0ajYas\nrCxkMtkzFqFKpUIulzM9Pc3Y2BgrKytoNBqqq6s5f/48ra2tpKambun92zGR9Pv93Lx5k5s3b4pW\nSHp6OmVlZZSXl5Ofn79TQ3stvF4vY2NjWK1W8UZ5vV7m5+dZXl7G5XKJu6LdzsTEBJcvX2ZkZIS1\ntTXgyeJqNBo3XZiShbm5Of793/+dR48eid/TaDSYTCZOnDjB2bNnxexBieTF7Xbz6NEjMSYFYDAY\nKC4u5vz585w5c2ZTyzAYDOJ2u3E4HAQCAdRqNdXV1ZSVlSXFfZfL5ZSUlFBSUvLKn9VqteTk5Ow6\nIwN4rjgKxGIxMeFqcHAQp9NJY2MjBw8e5KOPPqKgoED8HVvFjoikz+djaWmJvr4+JiYmxO8XFBTw\nN3/zNxw9ejSpF9gX4XK5ePjwoSj88CRVuaSkhKKiIjIzM5MilrMVrK+vbygrgCd1aA0NDUlfIxmN\nRnG73YRCIeRyOXq9noMHD3Lx4kX27dtHSkrKCzsFud1uHjx4wOzs7DaPfGeQy+UYDAYyMzNJTU1N\nmjKQ4uJifvrTn3L37l0mJyfJz8+npaWF48ePU1RURH5+/qZjtdvtLC4usrq6SjweJz8/n6qqKoqK\nipLm2f2u4/D7/SwvLydNEtmrstm8b9++zRdffMHdu3dRKBScP3+ec+fOcfToUTIyMt7IfdvW1ToS\niRAIBBgfH+f+/fsMDQ2xurqKXC6ntLSUw4cP884777yyayiZCAQCLCws4HQ6xe8ZDAbKysrIzs7e\n9U0RniYUCrG+vk4sFkMul6NQKKiqqqK+vj6pXa3wxP0di8VQKpWkp6dTUVHBsWPH+PDDDzGZTC8U\ngVgshsvlYnBwEIvFssGVnkgk3grXeiwWE7sLJRIJMRSSl5dHVlZW0tzf7Oxszp07R3p6OpOTkxQV\nFbF3716OHj266WeE+zMxMUFfXx92ux29Xk95eTlFRUVkZGRs1/C3nFgsJpYj3bt3D7vdjkqlEhNa\ndhtC3sP6+jo2m42bN29y6dIl4vE4e/bs4YMPPuDkyZPU1NS8sY3Ntv7VAoEAs7Oz/PrXv+azzz4T\ni8/VajUXL17kk08+ITc3961wRT5NSkoKBQUFu6r37KuiUqnQ6/VUVVVRW1ubNIvoZiiVSjElXqfT\ncf78eU6fPi02QHgR4XBY7H8quJnh7Yo7CxvaQCBAJBJBqVRSVFREeXk5eXl5SXN/DQYD1dXVlJSU\niON8mZUrbGRu3LjB559/jsfjoaWlhdbW1qRpR/ddiUQizM7OcvPmTX7+858TCASor68Xe+/uRpxO\nJ8PDw1y+fJnOzk7W19c5ceIE58+f5+OPP0av179Ry39bRFLYtS8sLPDb3/6Wu3fvYrVaiUaj4g7+\n9OnTYvPvt4309HQaGxtJT0/f6aFsCfF4nGg0ytraGmazmUAgIHakMRgMYluoZKasrIyf/exneL1e\n1Go1VVVV37oAeX5+npGREcxmM+vr62KJRV5eHnV1dbvaEhHo7e3l2rVrPH78WKyxVCqVqFSqpKpb\nlsvlqNVq1Gq1WNbjdDqZmJhgeXkZn89HSkoKeXl55OXlYTQaWVxc5P79+/T09LC2tkY0GsVoNFJQ\nULCr1x+hrvXzzz8XG7NUVFRQX19PSUlJUmebPw+bzcbs7Kx4r4aGhigtLeW9995j79691NfXi72E\nd71Iwp/ckH/4wx+YnJwUs+NKSkq4ePEie/fuJScnZ7uGs+UkEgl8Ph9ut1ss/RDameXm5tLU1ERm\nZuZOD3NLELqB2O12zGYzwWCQ9PR0CgoKMBgMu8ITUFBQwA9+8IPv9Nnx8XF6enpYWVkhGAyiVqsp\nKyujsbGRhoaGXSOSgks1FAoRDAbx+/0Eg0HC4TBXrlzh6tWrTE1N4fP50Ol0SW0p+3w+vF4vHo+H\nmZkZRkZGGB8fx+VyiSd9VFdXk5+fz+joKJ999hmPHz8mFouRnp5OYWHhlpcObBdCfe7k5CR37tzh\nq6++YnJykkQiIVYK7Kb+18ImfH5+nuvXr3PlyhUeP35MIpHg/Pnz/M3f/A05OTnb5pnbNpEUmkXb\nbDYx5V6pVKLT6TCZTLt6BwdPRHJ0dJSenh5sNhvBYBCFQkFOTg4VFRXs2bPnrXG3Ck0EvF4vTqeT\naDSKyWTi4MGDL01Rfxvo7+/nzp074nOs1+v5/ve/z/nz5ykoKEgaV+TLCIVCrK6uYjabmZ6epr+/\nn6mpKZaWllhbWxNPrYHkdyVPTU2J5WTj4+PMz88TCATEdm4pKSkYDAby8/PxeDyMj4/j9/vJyMjg\nyJEjtLe3c/DgwZeW+yQjfr8fs9nM119/zWeffYbVakWpVJKRkUF7ezvvvfferppXNBrFZrPR39/P\n7373O8xmMxqNhkOHDtHW1iaW9WwXb1wkhV3ByMgIfX19rK+vE4/H0Wg01NXV0dLSQmlpaVIU8H5X\nhDMIBwYGxD6e8Xhc7OghlEUki4vqdYnH4wSDQYLBoOgRyM3N5cSJE+Tl5e3w6F4Noa3Z4uIi0WgU\nnU4nnpARCoVYWVlhdXV1w2fu3r3L0tKSOHeVSiUeTJzsrmbhfVxfX2d6epqOjg5mZmYwm83MzMyw\nsrIiulefJlmyPf+c9fV1LBYLN2/e5MqVK8zMzOBwOAiFQmJ3q0gkgtvtJhqNYrFYxDICeJJ5vm/f\nPmpra99YduSbIpFI4HK5GBsb449//CPXr19nbm4OpVIpHlbf2tq66xqyBAIB+vr6ePjwIVNTUwQC\nAdLS0nC5XDx48ID19XWxFlvIQjcajRQVFb2RDeobF0nB4ujp6eH+/fsEAgHkcjlpaWmcOHGCEydO\nUFpauqtu4p8TiURYX19naGiIR48eiUcoCe7WZG8q/KqEQiGsVqu40MCTLMPDhw/vusQHv9/P0tIS\nHR0dBINBcnJycLvduFwusQZvaGhow2dcLpeYWq9WqzEYDKSlpSXdWZJ/bv0lEgmCwaDokrx79y6/\n/OUvWVhY2FAqsNkcBPdsLBbb8baDQvLN2toa9+7d49KlS1y9elU8PaiiokL0Tnm9XqxWK2tra7hc\nrg2/R6PRUFhYSFpaGvF4fNccEA5P/gYWi4XOzk5+8YtfiGdMpqWl0drayt/+7d9SWFiY1Ju25+H3\n++nu7mZgYAC32w086fE9PDzM2NgYOp2OpqYmiouLyczMRK1WU1hYyKlTp8jOzt5yoXzjIrm2tsbI\nyAiPHj1iZmaGSCSCyWSisbGR06dP09zc/KaH8MbxeDxMTU1hNptxOBxi707BWi4vL9/hEW4ts7Oz\n/Mu//AudnZ3i94T+tLvthRwYGODq1at0dHQQCATIzc3F5XKJTdvdbveGzQA8yW5NJBLIZDLq6+tp\nb2+nqqoq6WI+gltcEMtoNMr4+Dh3796lq6uLwcFBMab8MqLRKIuLi8zMzLC0tEROTs6Oe388Hg+T\nk5N88803jI+Po1KpyM/P59SpU/zgBz9Ar9fj8XjEY9CezkQWWF1d5fPPPycajZKSkrKtsa7XJZFI\n0N/fT1dXF16vl0QigcFg4MSJE5w8eXLXxlhlMhkajWaDcRGJRHA6nWKjgfX1dbHnrlwup6CggLm5\nOdrb2zly5MiWjueNiWQkEsHhcDA4OMj169cZHx9nfX0dlUpFdXU1p06dorGxcVcn6wiEw2Hcbjde\nr3dDYb3Q5ionJ2fX7E5fhFCztLKyQk9PD2azGZlMhlarRafT7Yrz6uLxOAsLC6ytrREMBrl27RpX\nrlxhfHyccDiM2WzekMAiiOHT/HldJCRXOzrBYrRarYyNjRGNRonH4zgcDvEQW6FtonDs1fNOq1Gr\n1aSnpxMIBPD5fKytrYnuWCGTeaeIxWIMDw9z584dHj16hMfjIT8/X4zBnTp1Cp/Px8TEBB6PR4yt\nymQyseWesBnq7+9Hq9USj8dpbW2lvLw86d/ZQCCAy+VieHiY0dFRQqGQWPPb3NxMQ0MDRqMxqeew\nGSkpKTQ1NeHxeFCr1eKhAUqlEqfTidPpJJFI4HQ6xfCdzWZDo9FQVVW1e0QyGAwyNjbGjRs3+M1v\nfsPa2prYj7CtrY2PPvqI/Pz8XeXeeFUUCgVpaWm7Kmj+MjwejxjzERpHP31ob7JbktFolAcPHvDg\nwQOsViv9/f2MjY0BTxZQwap6Wvz+nKe/Nz8/T0dHB+3t7VRWVu64dQVPxu52u+nr6+Pf/u3f8Pl8\nRCIRxsfHycnJ4fDhwzQ1NdHQ0MDCwsKmR7rp9Xpqa2tZWVlhcnJSvPfCyRQ7STQa5caNG3z55ZdY\nLNQT43kAACAASURBVBbxWKW//uu/pq2tjZSUFMbGxrh16xaffvqpGFdWKpWYTCYaGhoYGhpicXGR\n5eVlLl26xODgIP+fvTcLbutM7/Qf7CAIEgBBgOAK7jspUtRCa7fstlvWtNuTpLuTTjrVU101qdxN\nTU3VXM/t5CqVStVkrjKTmXRn0um4bUW2bEuyKEvcJIoUd3AHCZIgARAAsRD7/0L/c1q0RFuySRF0\nn6fKZVsEj74P55zv/b53+b0//vGPeeuttygsLMzqLO1AIMD09DRTU1MsLS2RSCTIzc2lsLCQuro6\nSktLD3uI35j8/HzeeecdWltbOXPmDB6Ph1gsRm5uLo8fP2ZkZASZTIbH42F+fl7c0K6urj7jTt8P\nDsRIZjIZotEo4+PjTExM4PF4SCQSVFZW8t577/HGG2+IroCvin/4fD7kcrkYUM9WY7q9vc3CwsKu\nhUYQ6RV6Yh51hISPR48e0dfXh9/vJ5lMotFodsl/ZbORFGLH09PTDAwMiHEqoc6xvLycxsZGysvL\nicVi3L9/H5fLJcZFBJ4+SUajUdbW1rh58yYajebQOtoLNYJer5elpSXu3LlDX18fMzMzmEwm7HY7\nb775JlVVVZSUlOD3+1ldXSUWi7G0tCReR6vVYjAYeOONN8Skj9u3b7O1tUUwGGR5eZn3338fjUZz\naPrKPp+PpaUl5ubm2NjYIJVKUV9fz/nz57FYLLjdbmZmZvjoo4/44osv8Pv9aLVazGYzXV1dnDhx\nglOnTjE2NsbY2BiTk5MsLy+zsbHBzZs3USgUlJSUUFRUlLUb3EAgwMzMDD6fT3SzXr58mX/37/4d\nnZ2dR74mWy6XY7FYOH78+K6TZEtLC5cuXWJ6epre3l6x1EWtVmOz2Q6kFvRAjKTb7WZ8fJzBwUFm\nZmbEgt7S0lIuX75Me3v719YMCgIEQoA+Gw2kYDjcbjejo6OiGwCetBqqr68Xe2IedXZ2dvD5fDx4\n8ICBgQHRzaFQKLBYLJjN5qw/SQr1nR6Ph5WVFVZXV1GpVFitViorKzl27Bjd3d1kMhmWlpaeyVTV\n6XSYTCb0ej0ajQaZTIbb7cbn83H//n0MBoPYY/JVnihjsRihUIjV1VUcDgejo6P09PTgdDrJzc2l\nrq6Ozs5Ojh8/Tm5urhhrXV9fZ2tri1gshkqlwmKxUFpait1u59133+X06dNoNBpSqRRut5uRkRH8\nfj9ffPEFXV1dtLS0oNfrX7ncmdPp5MaNG0xPTxONRikoKKChoYHm5mZCoRBjY2N8/vnn3LlzR1xE\nKysraW9v5+233+b06dO0tbVRU1NDY2MjQ0ND3L17l8HBQRwOBzqdjrKyMs6fP09jY+MrnduL4vV6\nGRkZwePxoNVqqa2t5cKFC3z/+98/kt2TnkY4EOXl5T2zSQkGg+h0Oubm5ojFYiSTSeRyOUajkcbG\nxgPJrj+Qp3twcJBf//rX3LlzB5fLBTzpY1dYWEhBQcELBZMFrcivUoQ/bNLptJhK//nnn4vZZfCk\nae3rr7+O1WrNarfNiyLEl+/du8fw8LDobhPSr7MtaeV5KBQKtFotWq1W7KVotVpFDciTJ09SW1vL\n3/zN3/Dhhx/icDh2ZX3abDZee+01mpubxVPUtWvX+M1vfsPIyAg5OTnY7XbOnDlDfX39K5tXIBBg\namqK3/72t/T19TE5OUk6naayspKrV69y+fJlmpqacDgcfPHFF9y9exeHw8H6+jqJRIJ0Oo3BYODC\nhQucOXOGrq4uqqqqxAa9r7/+Omazmb/927/l0aNHoou2ubmZpqamV+5iHhkZ4W/+5m/w+/3k5eVx\n7Ngxmpubyc/Pp6+vj56eHj755BN2dnZIp9NotVra2tr44Q9/yLlz50RXZGlpKRaLhY6ODrGl1PT0\nNMPDw3g8HnHhzUbW1ta4e/cuGxsbu3qGflfWm71YXFzkzp07/NM//RPj4+MkEgn0ej2lpaVcunSJ\nurq6ff8799VIxuNxscPH8vIywWBQjMu99tprXLx4keLi4hcykkK2ZDaTTCbxer2sra2xtrYmJgfA\nk9q5o5LM8lUIGZKTk5P8n//zf5iamhKTk/R6PXa7nbNnz9La2pr185TL5Wg0GlpbW5mfnxdblzkc\nDjKZDOPj45SVlXH37l1WVlaIRqNiuYOQFPDuu+9SUVEhekJCoRCbm5tMTk6yvb2N2+3e9Ry8CpaW\nlujr66O/v5/19XUsFgv19fWiwozH46Gnp4e7d+8yOjrKwsICW1tbpFIpCgoKqKmpobW1VTSmNptt\n1wmxsLCQtrY2/uiP/oj6+nqcTqco4XcYG9hoNCp27tBqtYRCIUZGRlheXmZsbIyZmRm2t7fJy8uj\ntraWy5cvc/r0aVHVS9B2FST2NBoNJ0+eZHt7m7//+79nbm6OlZUVHA4Hs7OzlJWVoVQqxWQoQcf2\nMI1RKpUiGo2i0WgoLy/nzJkzVFVVZf2a+U0JBoMsLCzw8ccfc/36dWZnZ4nH4+Tm5vLmm2/y5ptv\n0tLSciCqZvvyjQouRiHeI8QK4vG42Nfs9OnTnD179kDqWA6LRCLBysoKKysrYpmAYCi0Wi15eXlH\nfleXTqeJRCI4HA6uXbu2q1zAZDJRW1vLiRMnDmQHt9/I5XIxu7q+vp6bN2/i9/tFQ5mXl4fZbBbL\nP1KplFgHabfbOXXqFBcuXCA/P190ZwWDQUKhEIFAgJ2dHZaWlvB6vaJc3UEakWg0ytbWFiMjI9y/\nf5/p6WkUCoUowVZSUkIgEGBhYYHl5WV6e3vxer0olUqUSiXFxcW0tbVx6tQpTp48SUdHB2az+Zm/\nRxDof/vtt2lsbMThcFBZWYnRaDyU51sIc8Dvanb9fj+pVIqVlRUSiYTo+r5w4QI///nP9xQsETbj\ndXV1yOVy7t69i8vlIhwOMzo6Snl5Od3d3SgUCvx+P06nE5PJRElJyaG/20JzZUES8btQKfBlBLnP\nxcVFbt++zY0bN/jiiy9Eu1JSUsKVK1d4++23KSoqOpAWbvu27UilUiwuLvK//tf/or+/n/n5eWKx\nGIWFhZjNZmpqaqisrPxOFdZHo1FGR0eZm5t75melpaUcO3bsyIkKf5lEIoHb7WZjY4NIJLIracVq\ntdLQ0IDBYDj0BeNlEeIeMplMjHlHo1HW19dJpVKk02ngiUhCS0sLP/3pT+nu7hazeAXq6+tRKpX0\n9/czODjIRx99RGVlpdg/9CBjQ8vLy3z44Yd89tlnPHjwgEAggEKhYHp6mo2NDTF9XtBm3d7exmQy\nUV1djU6no7m5mR/96EeUl5djMBi+0mUuxJ6F05lGo3mmlu0w2NnZweVyIZfLyWQyJBIJbDYbzc3N\nXL16lbNnz75QvaBQI3n69Gl8Ph/9/f309PTgdrtZXl4Wsyfz8/Pp6uoSn4/DprKykoaGBnJzc4/c\nO/giZDKZXVUS8/PzqFQqcfNy5coVOjo6sFqtB3aK3perptNp5ubmGBgYoK+vj8XFRWKxGDk5OTQ3\nN/Puu+/S1NT0nZJmg9/FJAUNz6cxGAxHSsdzL+RyOTk5OajVanFhkMlkKBQKqqur6e7uxmQyHZn7\nKpPJKCwsFJtDOxwOscg8nU6Lsdbc3FxKSko4deoU58+f58yZM1RUVKBSqXa5lfV6vViAHovF2Nzc\nZGRkhKqqqgNPoHC73Xz66aeMjo6KWY6pVIpUKkUoFBLbRgkNk48fP05DQwP19fXk5uZSXl5OW1vb\nC4nSy2QysdtGNm380um0WCOo1WqxWCycPn2aH/zgBxw7dozKykp0Ot3Xzk+hUKDX66mvr2dycpL+\n/n42NzdJJBIkEgm0Wi0qlYpLly5ht9uz5nn3+XxMT09z+/ZtSktLn7k3Qg23y+UiHo+Lmdx2u51j\nx46hVquzzkUrbMRXV1eZnJykp6eH+/fvMzMzg1arpb29nTfeeIPu7m46OzsPvL/pt/52kskkkUiE\nhw8f0tPTw9zcHOFwGI1GIyY6/PznPyc3NzfrbsZ+oFAodr2AQnKI0Wg88mnY8Lu6MpPJhEKhEDVp\n8/LyaGxsPHJSdHK5nOLiYtrb27l06RIymUys+RReTkHmqru7m6tXr3Lp0iVyc3O/8tQkyLQlk0nm\n5+eZmJj4ysa/+8H29jaTk5N4vV7xz4QYskajITc3l6KiIjGx4b333qOjo4OKigpx0T+KCDFiweWq\n0WjIz8/HYrFQW1vL97//fX72s5+91DWFeLXdbqe8vByNRsPOzg5er5f+/n4qKys5fvw4p06doqOj\n49DXMuE7cDqdbGxs4PF4qK2tpaysbNcmbnt7m5WVFQYGBsSDS1FRkZjkolAoDn0uXyaTyRCPx5ma\nmuKXv/wlvb29LC4uAtDc3MylS5f40z/9UxoaGkR93oPkW387q6urjIyM8PHHH9Pf3y/qlhqNRt55\n5x0uXrx4ZNonvSwqlYqSkhIxCxAQYzfHjx8/xJHtL8JJpKSkBJ/Ph06n48SJEzQ2NmIwGI7cYiuk\nzP/Zn/0ZDQ0NPHr0SOzcIpPJaG1tpampierqakpKSr7WlaVUKikqKqKoqIhQKERbWxunT58+8KzP\n8vJy/uRP/oQvvviCiYkJQqGQ2PWiu7ubkydPcuzYMTHkYbPZdolCH1WKioo4ceIEDocDhULBiRMn\nOHHiBG1tbdhsNsrKyr7RdZVKJRUVFTQ1NdHU1MTCwgLhcBiLxcJrr73G1atXqaqqygrvUFFREWfO\nnKGvrw+n08ng4CDj4+NotdpdRiOZTJJMJkkkEjQ0NHDixAk6OjpEz142vrvRaJSxsTFu377NzZs3\nCYfDmM1mqqur+cEPfsDVq1cpKyt7ZUI039pICu2u6uvrUalUorvKbDbz9ttv09LSkpU3Yj/QarU0\nNTURCoXEBVEQ2n2VJQAHieBaraur44//+I/FwuzW1lZaWlqyYsF4GYREDZPJhMlkEkUEvF6vuMFr\nbGykoqICg8HwQrtsrVZLd3c3ubm5uFwu3nzzTZqamg68Vs1ms3HlyhWKi4txOBxispFCoeDkyZO0\ntbVRW1uL0Wg8MnqkL0JNTQ0/+tGPmJ+fR6FQ0N7eTktLC7W1tej1+m+83gjCJUIm78rKCpFIBLPZ\nLJ4iLRZLVpy8ysrKeOutt0gmk6L6jNDf1Gg0iuERofSuoqKC48ePc/LkSerr6yksLBTLoLIBIRnL\n6/WysLDAp59+yv3793G73TQ3N9PW1kZHRwfnz5+nqanplY5NlvmWjeJe5Nez5UbsN1839+/KvL9q\nnkd9jvsxt8P6fl7m1T3q9+lpDuq9e9HvMxu+S0Fh6fr16/T09IgdMtxuN21tbRgMBhKJBHV1dbS0\ntHDq1CmqqqooKiradZ1smAs8SRAMh8MMDg5y69YtfvOb3+ByudBoNPzH//gf+eEPf0h7e/tXqrQd\nFN96S5QtX/Jh8Psy9+/yPPdjbof1/XyX78tXcVDzPkrfpyCy0tHRIcoMCk2yTSYTKpWKTCZDXl4e\nRqNRVIHK1jkKDcDv37/PJ598glqt5sqVK7zxxht0dHRQU1NzaCffw/cbSEhISEi8NDKZjPLycsrL\nyw97KPuGTqejpKSE0tJSzpw5wzvvvINerz9Umb1v7W6VkJCQkJD4NqTTaVKplNimTsje1el0h94p\nSjKSEhISEhISe3B088AlJCQkJCQOGMlISkhISEhI7IFkJCUkJCQkJPZAMpISEhISEhJ7IBlJCQkJ\nCQmJPZCMpISEhISExB5IRlJCQkJCQmIPJCMpISEhISGxB5KRlJCQkJCQ2APJSEpISEhISOyBZCQl\nJCQkJCT2QDKSEhISEhISeyAZSQkJCQkJiT2Q+klKPEMymSQWi7G0tMTy8jLJZFLs2m61WsnLy0Mu\nl1NQUIDJZBIbwMLRalwr8fuDx+NhfX0dt9tNPB5HpVLR3NxMSUnJYQ9NIsuRjKTEMwhdwn/1q1/x\nT//0T2xvb5NIJJDL5Vy+fJmmpibUajWnT5/m5MmTqFQqlEqlaCglJLKNiYkJrl+/zieffILX68Vo\nNPLf/tt/47333jvsoUlkOZKRlHgGmUyGTCYjkUgQCoXw+XzEYjEABgYGmJ+fR6FQMDQ0xEcffYTd\nbqelpYUTJ06g0WhQqVSHPAOJ33cymQzBYBC32838/Dy3bt3i888/x+VyUVpayqVLl6RTpMQLcWBG\nUnDPxeNxotEoyWSSdDoNQCqVIplMkkgkUKlU5OXlodPpxMU1G112mUyGeDxOLBYjFosRDoeJRCIv\n9LtKpRK1Wo1GoyEnJ4e8vDwUCsUBj/ibI5fL0Wg02Gw26urq0Ol0BINB4vE4KysrzM/Pi5/VarW0\ntbXxxhtvkJubS0VFBWazGYVCkZX3USCRSBCJREilUiQSCYLBIIlEAgCdTkdOTg4qlUrsiq5SqcQT\nczbPS+J3eL1exsbG+PTTT+nt7WVqagqTyURbWxs/+tGPqKysPOwhvhA7OzsEAgHC4TA7OzsAmEwm\niouLD3lk2UEgEGB7e5tQKITRaKSwsHBf158DPUlmMhncbjdjY2N4vV7RqGxvb+P1esVd3YULF2ht\nbaW0tDRrF6BMJoPL5WJpaYmlpSXu37/PyMjIC/2u2WzGbreLJ64LFy5gMBgOeMTfHLVaTUFBAd/7\n3veoqKhgdnYWp9OJy+Xi4cOHOJ1O8bPxeJypqSnC4TAOh4Of/exnXLx4kby8PJTK7HVUbG1tMTw8\nTCAQwO1289lnn7G2toZcLqezs5PGxkbKyspQq9WoVCqKioqw2WwUFRUd9tAlXoBMJsPKygpDQ0Pi\nCVKpVFJVVUVLSwtNTU3odLrDHuYLsbq6yr/9278xMDDAzMwMCoWCd999l//6X//rYQ8tKxgcHOTO\nnTv09vZy9epV/sN/+A/k5ubum0frQFaxTCZDLBbD6XQyMDDAZ599RjAYFHdBkUhENJSFhYWsr6+T\nTqfJzc0lPz8/a04hiUQCv9+Py+ViYWGB6elpFhYWWF9fZ3x8nLm5uRe6Tn5+PrOzs9hsNmZnZwkE\nAnR1ddHU1HTAM/hmCCdJu92O0WiksrKSzc1NNjc3ycnJYXBwkPX1ddFDsL29zcLCAj6fD4PBQDQa\n5ezZs1gsFnJycg57OiLpdJpkMsnS0hIjIyPicxkIBBgdHcXv94tu5rW1NQoKCoAnno2SkhI6Ojq4\nePEier0ejUZzyLP5elKpFPF4nPn5eebn59na2kKv12O32ykuLsZkMqHRaJ4bSw6FQqytrTEyMoLZ\nbKa6uhqLxXJkDEsmk2F2dpbh4WFWVlawWCw0Nzdz6tQpTpw4gcFgyIo15qvIZDKEw2EWFxfp6enh\nwYMHOJ1OtFotx48ff+nrJRIJEokEmUwGhUKBRqPJ+u/gq8hkMmQyGaanp+np6eHhw4e0trbu8lru\nBwdmJLe3t+nr6+P999/nX//1XwHELEjBFatWq1lbW+Phw4fYbDbKy8upqakhJyfn0G9eKpUiEokw\nPz/P7du3uXbtGg6Hg83NzZe+VjAYJBgMMjMzw8jICI8fP+YXv/gFdXV1yOXyrE14yc3NJTc3V4zd\npFIpVCoVarWa3t5e1tbWCAaDAESjUaLRKP/8z//M0tISFosFrVabVUZSuKe9vb1cu3aN69evE4vF\nyMnJwWw2i/MMhUKMjY0RjUYJBoNEo1HMZjNXrlyhsrISu91+JIyksMm7desW//Iv/8LU1BQVFRV8\n//vf5+LFi7S2tmI2m3c9f8LCs7m5yf379/nrv/5r2tvb+fGPf0xXV9eRMJLCZmhycpLHjx+TSCTo\n7Ozkj//4j+ns7DwybspMJoPX62V+fp6RkRHcbjdyuZzc3Nxv9F5Fo1ECgQDJZBKdTkdhYaEYTshG\nhGdRyJF43s/T6TSLi4vMzMwQj8fFz+3nnA7ESAYCAWZmZvjoo4948OABACqVisLCQhoaGtje3mZ7\ne5vq6mp8Ph8DAwN8+OGHhEIhfvGLX1BTU0NeXt5BDO2FGR8f59GjR0xPT/Po0SMcDodoEL4N0WiU\n+fl57t27R1lZGSdOnDgyLjy5XM7p06exWCx0d3dz48YNbt26RTgcJplMAk/iJ8L9jcfjhzzi3QSD\nQRYWFrh79y4DAwPEYjFqamro6uri0qVLu+7D1tYWExMT3Llzh8HBQYLBII8fP+b//t//y09+8hPM\nZvMhzuTF2Nzc5M6dO3zxxRdMT08TjUZxuVxcv34dAIVCwcmTJ3e5xYX4129/+1tu3bpFKpVCr9ej\n1WqzOo7+NJubmzgcDubn50mlUjQ1NXH8+HHa2tooLCw8EhsceGLsNzc3cblcYsxcp9PR0dFBfX39\nS19vfHyc27dv43K5qKmp4cc//jEFBQVZu/GJRqNsb2+Tn5+/56ZAOJCFw2FMJhNGo3FP78g3ZV+N\npPCCTU9P09/fL8Z8KioqKCkpoba2ls7OToLBID6fD5vNxtjYGIODg0xMTKDT6XjvvfcoLy/fz2G9\nFPF4nHA4zIMHD/joo49YXl5mZWXlhU+QSqWS3NxcYrGY6F5+mkQigdfr5dGjRxiNRioqKo6MkZTJ\nZNjtdqxWKzU1NWxsbIg7dcFICjWWsVhM/LNsIZ1Ok0qlxASjiooK2tvbOXPmDK+//vquE4bH46Gi\nogKPx8Pk5CSRSITV1VWGhoZ48803D3EWX08mkyGRSLC+vs7du3dFV3J9fT35+fmiNycWi4leHQG/\n38/ExAT37t1jfHyc+vp6qqqqsFgsqNXqQ5rRiyGcPFwuFz09PTidTnJycjh37hxdXV2Ul5cfGWOf\nSCQIh8NMTEwwMTFBOBwmlUqRk5NDe3s7tbW1L3wt4WQ9NzfHZ599xvz8PKdOnRKT7bLNSKZSKTGE\nMzExQVFREaWlpaIH58v3b2dnh1QqhclkwmAwoFars9dI+v1+Hj58yKeffsrdu3fx+/1UVVVx8uRJ\nLl26xLFjx7DZbMhkMsLhMDMzM6yvrwNPdrUqlerQ45GhUIi5uTnu3r3Lxx9/LGbhvig6nU40ICsr\nK3t+zuFwEI/H+eEPf7gfw36lqNVqrFYr5eXllJWVsbW19cKZvoeJwWCgoaGBv/iLvxC9AiaTCbPZ\n/IznIj8/n/b2dlpaWhgYGGBpaekwhvyNCYfDLC8vMzAwgMvlwmQy8Yd/+Ie0trai1WopKCigqKjo\nGcO3trbGzZs3mZ2dRaVScf78ec6ePUt9fX3Wl/YIm4OlpSVu3LjB2toa1dXV/MEf/AEtLS1ZEcZ5\nUSKRCCsrK9y6dYs7d+4QjUaBJ9nkzc3NVFVVvfC1UqkUoVAIl8uFw+Fga2uLUChEJBLJuo0sPKnT\nnp+f59q1a/zv//2/KS8v5+zZs/ziF7+gpKTkuZscpVKJ0WhEr9fv+2ZuX41kNBrF6XSysbGBSqXi\nvffeo6WlhYaGBqqrq7HZbOTk5CCXy9FqtczPz4sB1uPHj3P58mVKSkoONY4VCoVwOBysrq4SCoWe\n+XlOTg4Wi4X6+npqa2spLCxEq9WKP9dqtZjNZkKhEG63m4mJCSYnJ5mcnNx1nXg8TigUIpVKHfic\n9gvBteF2u5mZmRGNh/ACZzvCKb+8vFz83oUY65dfPKVSSV5enuhqzNa48fMQypVCoZCYVW40Gikp\nKaGhoQGTyYRKpUKr1Yqu1nQ6TTwex+l0cufOHVZXV7FardhsNgoKCo6Ui3Jra4vZ2VmKioro6OgQ\nE9CO0j0UapS9Xi9bW1uk02lsNhstLS1UV1eLSWUvQjAYZHh4WAwZ5efnU1RUJOYNZBuJREJMllxe\nXiYej1NRUUEsFtuVkBMMBlldXSUYDCKTyVAoFAcSY91XIykEUo1GI62trfzkJz+hvb39mRuayWRI\nJpP4fD4CgQAymYwTJ05khZGMxWJ4PB6SySQajYZ4PI5arRaTWKxWK9XV1Vy8eJFz585RXV29Z/w0\nEAjwb//2b7z//vtMTU3tcm0plUo0Gg3JZFKUycqWXa4wplQqJY5ZqCd0Op2Mj49z9+5d+vr6WFxc\n3PW7wrw0Gk3WlYAISVIvciKSy+Vi+cdheze+CcK7mE6nyWQyYsKH2WzGZrM99/OxWIy1tTWGhoZI\nJBIUFxcfmUxeePKMbm1t4Xa7cbvdtLe309jYiMlkynpX8ZeJx+MEg0EikYjoyRLCM4WFheTm5r7Q\ndQRRhYcPHzI7O0ssFqOsrIySkhKsVmtWJdbBk7UnHA6zsLCAy+UiHo8Tj8fF7+Dp91BIhnw6V+Qg\n3tN9XcVsNhs/+MEPeP3118lkMhQXFz/XgCSTSQKBAH19fUxMTKDRaCgqKqKkpOTQF1a9Xk9dXR0N\nDQ2srq7idDqpqKjg3LlznD9/XiyuN5lMmEymr3zIVCoVFRUVz82mM5vNVFZW4vP5WF1dpaKiImsW\n4u3tbWZnZ3eV7fj9frEkwOFwsLa2xtbW1jO/W1hYSFVVFZWVlZhMplc99APjy7G7bEYmk5GXl4fV\naqWsrOylXOFC/Go/U+hfFdvb23z++ecMDQ0d9lC+NUKS2dPerPX1dSYnJwkGg6RSqRc6GafTaTGE\ntLm5KQpjaDSa53pQDpvt7W0WFxfp7e1lcnJSLL+qrq7GYDDs2uB+WQTkoDaz+2qRdDodFRUVX/u5\nlZUVBgcHGR4exuPxYLPZKC4uxmw2H7qRzMvLo66ujsbGRgKBAN3d3VRXV9PZ2UlnZyelpaUvfK2d\nnR3GxsaYmZl5ZpHVaDQYDAZyc3OzZpebTqdZXV1lfHycO3fu7JKj297exuPxsLCwwMbGxnOTPuB3\nZRZutxubzfbCO95sY2dnRzyV+P3+rIzdfBUajYbi4mJee+01otEobrebkZERysrKnnuSfBrh9HmU\nEDxA/f39TExMIJPJ0Ov1YqLS1+H3+9na2hK9RoddR6lQKMjJydllxEKhEKurq0xOTlJcXPxCccl0\nOs329jbz8/Ni8qHgUcnG8o+trS0WFhaYn5/H4/EAUFRURFlZ2TNJV8JJMhAIoNVqKS8vfyk3VcUT\nwQAAIABJREFU9IvySizS0y9cJpNhdHSU3/72t4yNjQFQW1tLUVERer1+Vx0lvHqJOuEkubCwgFKp\n5Ic//OFLGUaBTCZDIBDgV7/6FXfu3HnuZ5RKpej6yAZSqRRjY2Ncu3aNX/3qV2Is5GXw+XzMzMzQ\n29tLfn6+uCBn28v4dYRCISYnJ8VTs1CDdRTmIZPJUCqVlJaW8s4777CysoLD4eD69esYjUbOnTsn\nfu7LCBmiwn8fFSKRiOgqFkQ+zGYzVqv1hU5LwubQaDRSVlZGfn7+od5rg8FAVVUVeXl5yGQyMUS1\ntbVFb28vVqtVlNX7qnEKSTsLCwt4vd6s2ZB/GeFZ83q9LC4u4vV62dnZQS6XY7FYsNlszxygvF4v\nDx48wOPxkJeXJ6q27TcHbiQFnVZB0s3hcDAwMMDjx4/Z3t4mlUoxOTnJ//t//4+1tTU6OzspLy/H\nYrEc6k6npaWFioqKb+wy9Hg8zM3NHYmsz6eJxWJEo1FRmeN5mM1mOjo6UKlUhMNhxsbGRNdrOp1m\nfX2dDz/8kHg8TiaToamp6UB2eAeJ3++nr6+P2dlZkskkpaWlNDY2Hqm61vz8fFpaWkR3/9raGrOz\ns0xNTVFcXIzRaHzmd4Qs82QyeSQ2BAJCQplQsyuTyaipqaGpqemFYqojIyP88pe/pKqqinPnzolC\nH4dFXl4e1dXVNDc3Mzc3h9PpFMur5ufnxYQWpVL5lZsAITs/2zc8Qgzd5XIxNTVFJBJBpVKJ5Vrl\n5eWHll19YEYylUoRjUbxeDyii2B8fJyxsTEWFxdxu93Ak5cyFAoxMDCA1+tlbW2NlpYW6uvrKS8v\nx2AwHIqx/LodiaBesby8jEwmo7CwcNfPJycn6e3txefz7XmNdDpNOBwmHA6j0+kOfVGSyWSYTCbK\ny8upq6sjEAiQTqfRarXodDrRdWqz2eju7kaj0RAMBiksLGRqaorl5WWxVnZkZERMfJHL5TQ2NmI0\nGg99js8jnU6TSCSIRqOEw2F8Ph/Dw8P09vaKpR9FRUXU1dU9NxEtW9FqtZSUlGC327HZbGxubjI7\nO0tvby+nTp0SE6yE90vI/rVYLM+NN2czGxsbzM3NEQqFSKfTKBSKPU8gz8PlctHX10coFKK6uvrQ\njYqQRX/8+HFWV1fZ3t7G7/cTj8dZWlpibGyM4eFhKisrv1I5x+v1sr6+Lsbt5HK5mLWdTe+i4C53\nOBxMTk6Ka2JJSQmVlZVfm6+iUqkOTBjhwIxkIpFgeXmZe/fu8dFHH4llFbFYjEQigUwmE12sarVa\njCcMDQ1RWVnJ8ePH+fM//3OOHTuWdRlY8ETV4+HDh/yP//E/UKlUzxSY3717l56eHgKBwJ7XSCaT\nLC8vU1paSk1NzaE/tAqFgq6uLrGGbmNjg3g8TnFxsbgrhyduYqHmLJlM8u677/LZZ5/xd3/3d7hc\nLvx+P+l0msePH4vJAplMhtOnT2ddogA82dD5/X6WlpaYnp7m9u3bPHz4kKWlJSKRiBg/ttlsVFRU\nHLk4a2NjI5cuXeLTTz9lfn6ef/mXf9k1J2GBzcnJwWaz0dbWxujo6GEP+6VYWFhgYGDgK9+3o4ZS\nqeTChQsAosvc5/OJ9ZN+v5+f//znnD17ds8a0MnJSR4+fCiWaalUKqqrq7Hb7Ye+3jyNz+ejr6+P\nvr4+xsfHicfj2O12Wltbqa6uFjt77IVer6etre1ouVtjsRhzc3M8evSIBw8ekEgkKCgooLq6mvz8\nfPLz87Hb7ZhMJrRaLYuLi2KnCa/XS39/P83NzeTl5dHc3JxVNxRgZmaG27dvMzk5STKZ3JWFJpPJ\nWFhYEAPPz0NQJvr1r3/NwsICZ8+exWq1UlhYiNlsPpTYgUwmE90bFy5cIBQKkUwmMRgMFBYWPjfh\nI51OYzAY6O7uZmtriw8++IDh4WHRk7C+vk5/fz82m4329vZnkhGygVgsxvT0NL29vdy9e5eZmRlW\nV1cJh8Ni+YQQo97a2qKuro6mpiY6OzvR6/WHPfyvpaGhgTfeeAOHw8Hc3BxjY2OMjIxQUlJCQUGB\n6MZKpVLEYjHxvh8FBDddJBIhGAy+9LiFbN5snK9MJsNqtdLa2spbb71FJpNhYGCARCLB6uoqiUQC\ng8HA+vo6DQ0NlJWVUVhYiEqlEt+/qakpJiYm2NnZQaPRYLFYaGtro7a2NqvqRpPJJMFgkFAotKsd\nmKAv/PSaIcTNhVK1dDotlnYdxNpyYEYymUzi9XrFDMmysjKam5s5f/48JSUlFBYWUlFRgdFoRKvV\nivHKoaEhbty4wdDQEMPDw1RUVNDY2Jg1N1Rwy42OjoqqQtvb26ytrb3UdQKBgGgop6am8Hg8dHZ2\n0t7eTn5+/qEG2IVY1osgdAwRJM9mZ2dxOByEw2HS6TSxWIzR0VFqa2uJRqMH9iB/G4SuH1NTUwwN\nDZHJZMjLy8NkMiGTyUin0+JO98GDB9TX13Px4kUqKip2FeRnK1VVVcjlcm7duoXL5WJ5eVnMdG1o\naBBLARKJhPgsH5VY+tOGXZBuEzJDX+S+JBIJtra2xN8VOmUkk0mxOP2wEE73drudt99+m+XlZUZH\nR9nZ2SEUChEKhfjwww+Zm5sTpfeampowGo2i/OXU1JRYH6nX67FYLDQ1NVFZWZlVB49MJkMqldqV\nKKjRaDAajYTDYdbW1sQSD6FRhtBHUrhf4XCYeDy+756eA3u79Xo958+fx2azcfr0acrKyigtLRXF\nAtRqNVqtVtzFWq1WEokEbreb/Px80um0+CVkExsbG3zxxRfcu3eP+fn5fVlMXC4XH3zwASaTifb2\n9n0Y5atHp9NRWlpKc3OzqDcZjUZJp9N4vV68Xu8zihnZgvCslpeXc+HCBXZ2dlCr1ZSVlaFSqQiF\nQty7d4/e3l4GBgYYHx9Hq9Vy7tw5sTl1NiPEobq7u9nY2MDpdDI6OiqWbHV2dlJWViZ6RNxu93N1\nh7MRQbTd4XAwPT1NJBLBbDZTV1dHQUHB1xo5v9/PvXv3xGSR9fV10ZuVLeLfer2ehoYGGhoaqKio\nwOl0Eg6HAUQd6KWlJb744gvq6+s5deoUCoWCpaUlHj9+jNfrJZVKoVQq0el06PX6rAxhfZnJyUn+\n7u/+jqqqKoqKitDpdKLdsNlsjI+PixvycDjM1NQURUVF+16ffWBGUqVSianUdrudgoIC8vLy9lRo\n1+l05Ofno9frUalUorRWtmVmCXJlVquV4uJilpeXv7WhFG6yy+XC5/Nlpevn61CpVKhUKmpqamho\naGBubo5oNCoquQgixNl0LwVUKhUlJSUYDAbsdjuxWAyVSoXNZhONpFarZXt7mwcPHoj1WZ988gkA\n3d3d+9rkdb8R3OgnTpxgfX0dh8OB1+tlenqawcFBTCYTFotFzISMRCJZuZl5HkI7u5WVFTEeKWj0\n7pUoFolECAQCZDIZlpeXxZ6TwulrY2ND7L2ZDUZSrVajVqvp7Oxkc3OTsbExsbet0EzA6/XidrtZ\nWlpifX0dpVKJ2+1mcXFRrHXW6XQUFBS88Cn7sBEUhzwejygPmZeXh9FopKioiOXlZfx+P/A7gZqD\nkMg8sG9KUHYwm81Hoq3Qi2K1Wvn+978vPmQffPDBvrmmhIe8o6NjX653GFRWVtLQ0MDNmzcPeygv\njU6ne+6iqNFoaG1tZWZmBqPRiN/vZ319nX/8x39EJpMdeor6i5CTk0NHRwehUIilpSV6enrY3Nxk\ncHCQ6upqMaRx1OokBa3lp+P/eXl5u2oMv0wgEGBiYoJ0Os3y8jLT09N4PB7S6TR+vx+v10sgEMi6\nUp/XXnuNmpoaHjx4wAcffMDCwsKunwt9a4U60afvJTz5XoqLi7NSr/V5CNnWgkscnrjXNRoNLpeL\njY0N8bNPa7fu+zj2/Yr/P9/W361Wq2ltbaWhoSFr4pGA6BNvaWlBr9dz9uxZVldXWV9f39WEGBBP\nI8XFxaJLbmZmhr//+79/buut9fV1lpaWxJ3fUUKI54yOjjI4OHhkRM+fZq9nVqFQYDQaRVksuVxO\nPB4nEAiITZmzXaheeG5ramr4yU9+Qjwep6+vD4fDwfvvv4/T6cRut4tC/Hl5edhsNsrKyp5bT3nY\nZDIZQqEQTqdT7HQiEAqFWFxc5OHDh6ytreHz+VhaWmJ1dVX8udfrFa+xtLSEx+MR3dImk4n8/Pys\n2/QIiTfHjx8nFArh9/tRqVRiUb3wzu3lBbBarTQ1NWVlsll+fj6tra0kk0laW1uBJ/XYX5brzMnJ\nETWvb926xfj4OJlMBr1eT2tr64EIs2TVmTudTouF7CqViqqqqqzSNH2a0tJSSktLOXPmDB6Ph6Wl\nJZxO5666SLVajd1ux263U1FRQTQapaenh1//+tfPNZLxeJydnZ0jsYOH3wnVh0IhfD4fGxsb9PX1\nMTY2Jr6wgqsvLy8vq0TcXwa5XI5OpxMzc59WQEkmk1nrRn4eRUVFGAwGZmdn2djYoL+/n/7+fhYX\nF6mvrxefy/z8fKxWK1arNStLXjKZDOFwGLfbLbqPBYSEuGQyiU6nw+12i+5J+F1fUeGkJWxwhFCK\nyWQSwz7ZhHCyqqysJBaLkUqlUKvVuFwuUVknlUqxsrKyq8PQ0wpMra2t5OfnH/JMniU3N5eamhpM\nJhNdXV0AWCwWUXrv6XUjlUoRDofFNovwpCa4rKzsQPSis8pIxuNxNjY22N7eFrsWZEOR/ddhNBrF\nPpJPnyhkMpkYT4DfnRT36k9ZVFSE3W7PWumoLyOIIYyNjdHf309PTw/j4+Osrq6KcVWhLqu6ujor\nF55vikKhQK/Xk5eXl5VlLXshZH52d3cTDAaZn59nfX2d2dlZVlZWxPuWn5+P2WzO6ibF0Wj0uX0R\nvV4voVCIiYkJ5HI5iURCjN09zZc3NjKZTBTNyOZ5y+VyqqursVqtyGQyfD4fDQ0NwJNT8v/8n/+T\nkZERMblHpVJhMBiora3N2rpzQQzAYDDs2rRkA9kxCmB2dpaBgQHu3btHOp2mu7ubkpKSI+E/VyqV\nYubYVxGPx78yw9Nms71yIxmPx9nc3GR+fp6ZmRngyQ6uvb39mZY8mUyG9fV13G43Ho8Hj8cjdiaY\nnp7G4XCIqiDw5CRdWFjIa6+9xvHjx/dM2sp2EokE6+vrrKysiK7VnJwcqqqqsNvtWK3WI9NOSkif\nr6io4MyZM/j9fqanp1lcXGRxcVH0ANTX13Ps2LGs3qQK2sB2u51gMCgahadP+Dk5Oej1epqamigt\nLd3T+AlqYH6/H4/HQyAQIDc3N+sMinAvtFqt+MzpdDq0Wi2ZTIaNjQ2xzlr4PgRPiLCpy0Zepo3d\nq2ZfjOTTgX6hsPNFF0Oh593IyAiffvopfX19HDt2jHfeeYeKioqsXnwEF4fg+oAnhsFoNO56EROJ\nBDs7O/j9foLB4NcayVc1ZyEjbGxsjM8++4yPPvoIgKamJn76059SX1+PxWIRP5/JZHj8+LGYXTc/\nP8/CwgJra2vPxCDlcjkGg4HKykouXLjA8ePHxXjeUSMejzM7O8v8/PwugYW6ujpqamqyLsHj6xBk\nFDs7OzGbzYyMjNDf3y9mWafTaZqamujq6so6IyEgl8sxm83U1NTQ1dVFOBx+5hksKCigsLAQq9XK\n6dOnOXHiBAaD4bmb0H/4h39gcnISr9fL6uoqXq836xMOBYOZk5NDeXk50WiU7e3tZ1pgCbHMvRKZ\nJL6afTtJJpNJ8Sbl5eW9sN87FosRDAYZGhpiamoKu93OuXPneOONN7BarVm9qPp8Pv75n/+ZoaEh\nVlZWAOjo6OA//+f/jNVqFT/ndDrp7e3ls88+o7+/f09dTMHd+qqMpNA+6R//8R93zUEoVDYajbsW\nyUwmg9PpFF3i4XCYSCQinhwFhNhOW1sbFy9epLm5GbPZfGRf0GQyycbGBpubm2Ipi1Dkne0L6Vch\nLK5CfLW3t1e8RwUFBVgslqxxee2FzWbjvffeo76+fle2p0wmE7uAlJSUYLFYMBqNKJXK564pBQUF\nR/b5FNjc3GR6ehqXyyUmECoUCsrKyvjZz35Gd3f3IY/waLIvb0A6nRYzxFKpFBUVFV9rJOPxOGtr\naywsLOBwOHj8+DE7Ozt0dnbS3NyM3W7Pyn5nT7Ozs8Pk5CR3797F4XAAT4QBysvLdwmeLy4u8uDB\nAx48eMDi4uIz19FoNOTn51NYWIjBYHhVw2d1dZWRkREePnzI3NycGCsVRMqfd/ITsjm/jOAqKSgo\nEHvdnTx5ku7ubsrLy7P2RPJ1CAYkGAyKXWsUCoUomZUtbc6+CUqlkvz8fDGkkU6nyc3NFe+hwWDI\n2ricQF5eHk1NTRQWFu5SiRIaTxsMBgoKCr7W2D8d1olEIqysrFBSUrJrs5vtRCIRfD4foVBI3LjK\n5XIKCgo4efKk2Frru8CrlBLcFyOZSqV48OABc3NzWK1W8vLyKC8v/8rfiUaj9Pb2cuPGDT7++GM0\nGg3V1dW0tLRQWlqa1SfIr2JiYoL/8l/+yy7jLmTQ7XVT8/Pzqa+vf+Wp9qurq0xMTLC1tbUrmSiR\nSOD3+/fsN/g8VCoVeXl5dHR0cPr0ac6fP09tbS0lJSVH9l7C77Q9d3Z2xO8oJyeH4uJiuru7vxML\nj9vtZnJykq2tLQoLCzl//jx2u/1IbGwUCoWoHPS8Neeb9AD1+Xw8fPiQyspKqqur92uoh4JcLker\n1VJYWJiVpR/fBEHu8sserIPiWxtJQTvR6XQyNTVFKBQSM62+jKDmMTMzw/DwMJ988gnT09MoFAq+\n973vceHCBVpaWigvL8/qE6SA0Onk6aLWdDr9wpJeubm5vPXWW5w8eZLGxsZXLknX0NDA66+/Lip3\nCOoVAs8ziEJZgNVqRavVotfraWxspLCwEI1GQ3FxMSUlJaKCTba569LpNE6nk/X1dbGptEqloqio\nCJvNJsYXd3Z28Pl8LC4uMjY2xqeffsrExAQAx48f59KlS0fipPVVCHHyoaEh7ty5w/b2Nm1tbbz5\n5puUlpYeiXdQGOO3HWttbS3f+973GB4eJpFIsLCwkHWSmF+Hy+VieHh4V622oFzm9/uJRCJZWc7z\nsgiSpUJTCYVCgVKpPLDn9VuvYEKyTiqVEgdeUlLy3JsRj8cJBoM8fPiQgYEB5ubmUCqVnDhxgrff\nfptLly6JcYOjxDepkRP0Jd99910uXLhAZWXlKz9xVVZWkslkmJ2dxWAw7DL2z0Mmk1FeXk5FRQV2\nux2dTofRaOT06dNiOno2Cpg/TSaTIRgMsrCwwOPHjwmFQigUCiorK6mqqhJPhpFIhNXVVUZHR3n0\n6BFDQ0MEg0EsFgunTp0SpeiOMrFYDJfLxcTEBGNjY6hUKsrLyzl16tSuhK3fB+rq6rh69Sr5+fkE\nAgHMZnNWJw0+j1AohMfj2XXCSqVSbG9vMzs7+0y2+lFF8Mw9XSpykEmB39oaCXVXx44dY3V1lQ8+\n+IDx8XH+4R/+4ZnPCs1tg8EgqVSK7u5uzp07x6VLl45MDORpcnNzOXfuHH6/nzt37rzU73Z3d/Nn\nf/ZnnDx5kpKSkkPZtavVaiorK/nLv/xLsaP7i/yORqMRM+iEekEhdTvbTx8ymYzi4mJcLhdLS0vM\nzc2xtbWFWq1Gr9eLMeFEIkE4HMbr9RIMBlEqldTW1lJXV8drr71GQ0PDkVtEv0w0GmVlZYXNzU0S\niQQWi0X0BByVWt39oqqqCovFwqVLl0Qx8C83Us92bDYbzc3NDA0NiX+WSqXY2Njg5s2bGI3G70R4\nQKlUUlRUJCbNKZVK1Gr1N3Ktv9Df920vIKg5NDY2kkgk0Gg0LC4usrq6itvtxu/3Ew6HKS4uFkXO\nhZ6Jb7/9NidPnqSpqenQ29J8E/R6PRcuXKCgoICuri5mZ2dFxZLl5WUxW9RisVBbWws8iT/W1tZy\n5swZuru7RbflYSCXy8UMx98XhISOmpoa3nrrLcbGxhgfH+fx48csLS2Jxl6tVovPanNzM8XFxVRW\nVlJfX09bW9szZT5HESE0EI/HxdO0UHZ11N7Fb4ug23uUEnW+jNlspqqqCqPRiEqlIpFIoFAoMBgM\nNDc3Z323mhflaZEWwbtVW1uLVqvNTiMJTwZdVVVFaWkp58+fp7+/n3v37jEyMsLCwgJut1ts9VJY\nWEhhYSElJSWim+6ovpC5ubmcOnWKU6dOkU6nuX79OuPj4wDcu3dPFD5vamriypUryGQyysrKeOed\ndw5EPkni65HJZGi1Wurr66mvr2d0dJQ7d+7g8/lEYWhAlP86efIkXV1dYqu3oxKrexmEzVJTUxPV\n1dXfufn9vmA0GrHb7ZSXl7OxsUE4HEatVlNfX8/Vq1ePfBLS0wjvcWFhIceOHaOzs5Pc3NwDeXZl\nmX0QnRQuIcQmfT6fqFoRDofZ2dnBZDKJrbLUajU5OTkUFBSILquj/mIKajRC0Nzj8Yh6koIyiNBE\n1Waz/d65s7KVQCDA5uYmTqdTTASAJ9m6QjmEIDuYk5NzYLvVw2B1dZW7d+9y/fp1pqen+U//6T9x\n7ty57+RG4PcBoeZ8dnaWra0tksmkKLrQ2tpKbm7ukT2QPI1QlrWyssLY2BgWi0VsJCGcLveTfTGS\nEhISRw+fz8fExASjo6Nsbm7yox/9iLq6OlHEXUJCQjKSEhK/twg1oKlUinQ6jUajkQykhMSXkIyk\nhISEhITEHhx9B7WEhISEhMQBIRlJCQkJCQmJPZCMpISEhISExB5IRlJCQkJCQmIPJCMpISEhISGx\nB5KRlJCQkJCQ2APJSEpISEhISOyBZCQlJCQkJCT2QDKSEhISEhISeyAZSQkJCQkJiT2QjKSEhISE\nhMQe7Es/SQkJCYlsJpPJiC39UqkUiUSCTCaDTCZDoVCgVCqPfBNtYX6JREIUrlepVOLcvgvC9U/P\nMZVKkUwm0Wg0YrP0rG26LCEhIZHNRCIRwuEwmUyGyclJbt68SSgUQqfT0d7ezrFjx2hsbDzsYX5r\nwuEwfX19DA4O8vDhQ958801ee+016uvrycnJOezh7QvhcJje3l6Gh4cZGxvjpz/9KWfPniUnJ+dA\nNjqSkZSQkPjO43K5mJqaIhwO8/jxY27cuEFBQQE1NTXE43HS6fRhD/FbE4/H2djYoKenh5s3bzIy\nMkJDQwOnT5/mu9LsKRwO43Q6uX37Nv39/SwtLfHGG28c6N8pGUkJCYnvPCMjI/zyl7/E6XTi8/mI\nRCK8+eabXL16lZaWFgwGw2EP8VsTDAZZXFzk888/Z3p6mqKiIpqbm2lqakKtVh/28PaF9fV1hoaG\n+Oyzz/B4PDQ1NVFUVIROpzswd7JkJA+IVCpFOBxmamoKn89HIpFgZ2eHRCIBQENDAy0tLahUqiMf\nC5GQyFYSiQSRSITFxUUmJyfJycmhtbWVpqYmLl++TENDAwaDQYxpHUXS6TSpVIrp6Wlu3brF2toa\nZWVl/OAHP6CxsRG1Wn3k45HCfezr6+PXv/41a2trVFZWcuXKFSoqKpDLDy4HVTKS+0QmkyGVShGP\nx4lEIoRCIdxuNzdv3mRpaYmdnR1CoRA7OzsAvPvuu1RXV6NQKI60kUyn0+zs7BCPx0kmk+h0OjQa\nDXK5HJlMJn4vyWSSZDJJOp0mmUwSi8XIZDIolUry8/PRarWHPRWJb4iwSKfTaeRyOUql8tAXZSFR\nZ3t7m5mZGaanp3G73Zw8eZKzZ8/yzjvvUFZWhslkOtRx7geJRIJgMMjjx4/p6ekhGAzS1tbGv//3\n/x673X6gBuSgEe5jOBxmYWGBe/fucfPmTXQ6HbW1tVy+fJni4uIDHYNkJPeRUCiE0+lkeHiYmZkZ\nZmdnGR8fx+fz7VpIAI4dO/adiIXE43EcDgculwuv10tHRwfV1dViED2dThMIBNja2sLr9RIKhfB6\nvSwuLpJKpTAYDLz99tvU1NQc9lQkviGxWIxwOEw4HEaj0WC1Wg/dSAIkk0lmZmb427/9W/r6+lAo\nFDQ3N9PR0UFVVRUajeawh7gvBINBhoeHefDgAZOTk2i1WoqLiykuLkan0x328L41iUQCp9PJ+++/\nz/DwMOl0mtLSUmpqaqioqDjwDfaBGMn+/n4ePXok/r9cLqe7u5va2lq0Wu2R3tk8j83NTRYXF3E4\nHExNTTExMcHKygrr6+t4PB7S6TS5ubmYTCbMZjNWq5Xq6mpUKlVWLCYvSzqdFh/cyclJHjx4gNPp\nJBAIMDU1RW1tLcXFxcRiMba2tggGg/j9fra2tohEIgQCAdbX10mn0xgMBmKxGGfOnOH48eNH2u31\n+4TwDITDYba2ttja2iKdTmMymbBYLIc9PAD8fj+Li4s8fPgQj8eD2WymoqKCkpIScnNzj+S79zSZ\nTIatrS0mJye5fv06w8PDJBIJzp49S1dX15F3Iwv4fD4cDgf37t3D6XSi1+vp6uqivb39ldzHfTWS\n6XSadDrNb37zG/77f//vAGId0l/91V9RVFSEWq1+ISP5vGysbHqoBTdAKpVibm6Oa9eucefOHSYm\nJggGg+IJUaFQYDabKS8vp7GxkdbWVk6cOEFNTQ16vR6l8ugc5oU5x+Nxtre3uXfvHr/5zW8YHBxk\nY2ODTCbDp59+SlFREa2trWxsbDA/P088HicWi7Gzs7PrvsrlctRqNXNzc2xsbNDa2nrkXuqn55PJ\nZEin0+L3JPxMJpOhVCqPxOZQGPPT//7yP/Bkdx8KhVhbW2N1dRWv14vJZEKr1WZFJmUmk2FjYwOn\n08nm5iYymQyr1YrVav1OJOnAkzmurKyIcbrNzU2Kioq4cuUKFy9eJCcnJ6vWzJdFeN5cLhfj4+OM\njIwQiUSoqqriwoULdHZ2vpJx7OsKvbm5yfDwMHNzcwDiovBNFodMJkMkEiGdTqNQKFCr1Vm1gO7s\n7LC2tsaNGzfo6+vj8ePHrK2tEQ6HRQOp1+s5f/48J06coL29ncLCQsxmM2azWTSQR+3UlmWTAAAg\nAElEQVQhXllZYWxsjFu3bjE8PMzU1BSBQEBcGGOxGBsbGwwNDYlxWMHV/GUymQyJRILV1VXcbndW\nLK7fhGQySTAYZHNzE5fLhdvtZnNzU/xeDAYDV69epa6u7rCH+rXEYjHxnsViMXFebrebjY0NfD4f\nwWCQRCJBPB4nHA4Tj8dRqVS8/vrr2O32w56CiNfrxe12k0qlUCqVaLVadDrddybTM5PJ8OjRI+7f\nv8/29jYWi4W2tjbq6uqwWq2HPbxvjbARu3HjBteuXSMcDtPc3Mzly5dpbm6moKDglYxjX42k4Btf\nX19HJpM988/LEI/HmZ6eZmtrC6VSSU1NDcXFxYeuHCEs5CsrKwwODnLt2jWGh4fZ2NgQDbper8ds\nNlNVVcWVK1fo7u4W07CP0snxacLhMF6vl/7+fnp6evj444/FTQH87pSfSqUIhUKEQqFdf74XwmIs\nJPJkGzs7O+zs7BCNRlGpVBiNRuLxONFolGAwKMZbNzY2cLlcOJ1OVldX8Xg8RCIRlEolRUVFdHd3\nZ52RFDw/gJhktbq6Km5wIpEIfr8fj8fDxsYGa2trogHNzc1Fp9Oh1WrRaDQYDAYKCgrIy8s79I2f\nYLzX1tbw+/0YjUbKysro6uqiuLj4O1FUv7Pz/7F3Xs9tXmf+/6IRvQMEAYIkwF7FJpKiKImSrGLZ\nsTzeOOtsNmU3yexkJrN7s3/Ezl7tzc7eZzOJs3Z+jiNLtpopWZVi772BIAqJ3jvwu9CcE1KiYokS\nCdDBZybjGCDoA77ve57ztO8Thc/nw+TkJGZmZpBOp1FRUYGenh6UlZVBLBZne4l7huwDHo8Hy8vL\nGBgYwNzcHIRCIZqbm3Hu3DmUlZVBKBQeyHre6I4di8XgcDjoxvk6hEIhXLt2DRMTE2AwGPjJT34C\nuVy+b6oKLwsJqQ0PD+OTTz6hBjKVSoHBYEAgEKCkpAQnT57EmTNn0NHRgaKiokNfhm2z2fDo0SP8\n6U9/wpMnT2hby+vCZDIhk8kglUpz8u/j9XphNpthNpuhVCrR1tYGl8uF9fV1TE5OYnx8HNPT07DZ\nbAgEArSKl8PhQKPRQCKRIJVK5eQBgFQmA0+vQyqVwtDQEP7zP/+T9hKm02kUFBSgoKAAqVQKhYWF\naGxsxJEjR1BXV0c3ZC6Xu6OyOZsEAgGsr6/DbDYjGo2ivr4eFy9exHvvvQelUnnow5DA0zzd7Ows\n5ufnsbW1BZFIhM7OTly+fPk74UWm02mYzWbcuHEDJpMJBQUFqKqqQnd3N3p6eg60Gv6NGslMJkPL\n/AkFBQUQi8Xg8XgvbdwcDgdmZmYwOTlJq5neeustxGKxrLcK+P1+eroZHx+Hx+MBk8mESCRCa2sr\njhw5gtraWlRVVaGiogKFhYVZX/Pr4Ha7cePGDQwODmJ2dhazs7NwOp1IJpPP5eMA0OIkrVYLnU4H\nnU4HiUSya6g8HA4jHA5DJpOhubk5p8LpJNTY19eHr7/+Gl6vF2KxGHfv3oXb7YbD4YDdbofL5YLf\n7weLxaLXXCqVQiaTQaPRQCgUQiwWo6SkJNtfiULydUtLS3j06BGkUikqKipQX18PLpcLLpdLi1s0\nGg2Ki4tRVFQEABCLxbRysrCwEHK5HFwuN6famOLxOLxeL7a2tuBwOBCLxcDhcKBUKl9pH8pl7HY7\n7t+/j42NDWQyGcjlchQXF0Ov1x/q/QZ4ev1sNhuGhoZw/fp1bG1tQavV4uLFi2hqajowD5Kw77E/\noVAIvV5PK61e5gTncDgwNzcHk8kEu90OFouFUCj03MacDXw+H0ZHRzE1NYX19XUAoKHVd955B2+9\n9Rbq6uoO5Y1KvOREIoFIJIJIJIKlpSX89re/RX9/PzweD/1ZJpMJgUBAvQcSBi8pKUF5eTnq6urQ\n2NiI+vp6qojxLG63Gy6XCwKBACKRKCeMJCnG8nq9mJubw40bN/CHP/wBAMDlciEWi6lnyGQyIZVK\nUVxcDJ1Oh6amJvT09KCoqAhKpRIymQwcDifrntV2SDHE+vo67t27h9/+9reoqqrC+fPnUVlZCY1G\ng7a2Nsjlcuh0OtTU1KCyshKlpaXZXvq3Qu5dr9cLi8UCh8MBt9tNC8dI7+5hJp1OIx6Pw2w24+HD\nh9ja2gKHw6H1Doe9KCmdTiMcDmNiYgKPHz/G4OAgVCoVqqurcfbs2ay0iu27kdRoNDh+/DhKS0sh\nEAheasNIpVKIxWJIJBKQSCSorKxEcXHxS39+P9ktpNzQ0ICf//znaG5uhsFgyInNfq+EQiGYzWaM\njo5ibGwM4+PjmJqaojlGALQIoqWlBZ2dnaitrYVCoQCbzaahU4lEArFYDJFI9MJCCRKmI4IKubKB\n+f1+TE5O4je/+Q2ePHlCw8qk8KOlpQUVFRU011VSUgKxWEw9SDKVIBdbfEi0Z2ZmBmNjY4hGoygp\nKUFbWxskEgmkUil+/etf7yh0OSy9dolEAmazGUNDQ7h58yY2NzfBYrHA4/FoPUCuXY9XJR6PU/Wg\n+fl5+Hw+SKVS6HS6Q28ggafX0Gaz4bPPPsO9e/fAYrHQ2dmJ8+fPo6KiAhKJ5MDX9EaMZCaTgdfr\nxcbGBpaWluB2u8FgMMDhcKDVatHe3g4mkwmz2YxUKoW1tTWYzWZwOBwUFhbCaDRCpVJBIpHQBvRU\nKoVUKgWhUIjKykqoVCpwudys3+Qkj5NMJulrKpUKbW1tKCoqQjqdxuLiIm2uJn8LUtigUqmgVqtz\n0pCm02msra1hYGAAt2/fxvT0NNbW1hCJRGjOVS6XQ6/Xo7GxEe3t7WhtbYXRaKTXjhiIlzm1E0OS\nK2wvOZ+cnMTg4CCCwSCqqqpQVVWF4uJiKJVKNDQ0oLS0FGKxGGq1GiqVKuc8xhcRiUTgcrmwsLAA\ns9kMsVgMvV4Pg8EAPp8PHo8HmUyW7WXuiWQyCZfLBYvFApPJBJVKBZ1OBy6Xi+Li4pw6iO2VcDiM\nkZERjI2Nwel0QiKRoK6uDqdOnYLRaMz28vYMefbMZjOGh4cxMTEBh8MBuVyOo0ePoru7GwqFIiuV\nya9tJEmIzmq1UhfZ7XbTE5xWq0VTUxM2NzexurqKWCyGP/3pT7h69SokEgmOHTuGDz/8EB0dHaio\nqNhReZbJZMDn86HX6yGTyXKudJuEfgsKCiCRSJDJZGCz2XDv3j2sr69jY2MDExMTYLPZqK6uRnNz\nM1pbW9HZ2bmjCjBXHtxUKkUnJHz11VcIh8M7wtsMBgN6vR5nz57FP/3TP8FgMHwnTq9kruD2vtfJ\nyUnY7XbodDqcOHECP/vZz9DU1EQLxw6DQdwNn8+HxcVFLC8vw+v1oqysDHq9HkqlMqcOLHshnU4j\nGAwiEAggHo/DaDSioaEBSqUSNTU19Jq9KGWTK8/hi8hkMggGg3jw4AFGRkYQi8VQUlKCEydO4MMP\nP8wZEYe9kslkMDMzg9u3b8Nut6OgoAA6nQ7t7e1oaWn51s/u9v/fxB772kYymUwiFAphYGAAAwMD\nSCQSdMOJRqMYHR3Ff//3f8Pj8SAYDNJwAXl/enqaCoEfP34cPT091EvbS+vIfkNCwds9SZfLhbGx\nMfT392NmZgabm5sIhUIIh8Nwu91gMplwuVyYn5/H/fv3UVFRgZMnT6KnpwcymexQ5C/ZbDb4fD4q\nKipQVVWVE179mySVSsFut2NhYQFffvklhoeHUVRUhHPnzuH9999HeXk5Lfo4zN/b4XBgYGAAJpMJ\n8XgcGo0GMpnsOxGKJESjUTgcDoyOjoLL5aKjowNKpRI+nw8mkwmZTIa2qxAFsMMwdDkcDtNeXBKh\nKisrQ2VlZc45EK8K0Z+dnJzEo0ePEA6HUVdXh8uXL8NgMHzr55PJJILBIObn5zE1NQWbzQapVIqm\npibU1NRAp9PteW2vbST9fj9MJhOGhoYwPT29w0gmEgmsrq7C5XIhFAohGo3u0C8l8WebzQa/30+H\noJLG81w0ktFoFBaLBX6/n75mtVrR19eHGzduYHZ29rk1ZzIZOJ1OrK6ugsfjYXh4GC6XC8lkEs3N\nzSguLoZIJMqJ78pms+mG+eyJjMlkIpFIwOFwYGRkBFarFUqlkubjpFIpmEzmofOykskkFcK4e/cu\nBgYGEAgE0Nvbi97eXrS2ttLNNBfvyVfB7/djZWUFLpcLTCYTBoMBarU65w3Eq0Cu0ebmJtbX1xEK\nhbC8vAyr1Yr5+Xmk02mqvCMSiajerEajoXnyXMTv98Nms2FrawuBQAAAaKHci6IAyWSS9lTGYjGk\n02lwOBwIhUIoFIqcuZ/JNZqbm8Pa2hqUSiXq6+tx/vz5bxUwj0ajcLvdWFpawoMHD3D//n2YzWYo\nFAqYzWbweDxoNJo9F269tpHc2NjArVu3MDIygvX19R0eFvC00MXlclG5rhextLSEUCgEn8+Hmpqa\nrCRoXwaPx4P+/n6YzWb62tLSEm203u0iEIPDYDCQSCTgdDpx7do1jI+P4xe/+AXeeust1NfXZ/1m\nJX2eu+khJhIJBAIB3L17F0+ePEFBQQFKSkpQVVWFpqYmtLe3o6Oj41Bq85JIyNWrV/H555+DyWSi\ns7MTv/71r1FUVIRIJIJkMgk+nw+RSJTt5b4W29u0ZDIZWlpaXuuUnYtIJBJUVFTA7/dja2sLV65c\nweLiIkZHR6kiFpEJ5PP5UCgUOHnyJC5cuIDm5uac7TMkdR9E8YjFYkGr1aKkpOSFRjISidBipq2t\nLUQiEcjlctTV1eHEiRM5U1zmdDpx9+5drKysgMvloqKiAk1NTXSc4F/D4/FgYmICn3/+OQYHB7Gw\nsIBEIgEOh4Pl5WXU1taitbV1z73qr20kiY5nOBzetbmc5HnkcjmEQiGYTCZqampQVlaGpaUlWCwW\nbG1tIRwOY3NzE0NDQ7Db7ZDJZHC5XFCpVK+7xDcKKUjaHnYjuqTAX/KTdXV1zymsmM1mrK+vw2Qy\nwefzIZFI4OuvvwaTyQSPx0NhYWFWDwdMJhNGoxF1dXUoKipCKpVCOBym75OcD6l0DQaDVNx9ZmYG\nAwMD6O7uRn19PeRy+aFQF4pGo7Db7fjmm28wMDAAl8sFiUQCp9OJW7duIZVKwePx0OuqUqkglUpR\nWFiI0tJSWnCW65DnMBqNIhAIIJFIUDk9n89He1k5HM6hDb2SZzMWi2FjYwMsFguxWIzOWLRYLNRA\n8vl8CIVCRKNRrK6u0oJB4l3mouCA3W7H/Pz8jsp6DoezY/MnLSLr6+sYHx+HzWbD+vo65ufnqXEV\nCARUKLy8vDyruUxyX25tbeHhw4ewWq1QqVS4ePEiuru7/6o6ElG9unXrFu7evYvBwUFYLBa6P3E4\nHPB4vNcuqtu3XYzJZEIoFNKHjvRgcTgcXLx4EceOHUNfXx+Gh4cxPT2N9fV1uFwurK6uwmq1oqCg\nAJFIJOeMJI/Hg06ng9vtpoYR+IuItUwmQ3l5OS5duoQLFy7s+OzQ0BCePHmCVCqFzc1NRKNR9Pf3\ng8lk0irgbBtJg8GAlpYWNDU1gcvlwul0IpFI0DA5mRtJRmD5fD4sLy9jeHgYUqkUPp8PLBYLjY2N\nEIvFOR/GCwaD2NjYwJMnTzA/P09DMna7Hf/v//0/eL1euFwuaiQVCgUKCwtRU1OD3t5eNDY2Hhoj\nSarGSbQnHA5jYWEBXC4XwWCQCh9IJBIIhUKa58o1Y7EbJErFZDLpDEkSYrNarUilUmCxWNRz1Ol0\nkMlkiMVimJycxNzcHCKRCI4dOwaj0ZiT0nVEACIcDtPvsn1gBElxbW5u4smTJ/i///s/mEwmbG5u\nUvUk4Klj43K5oFAowGQyIRaLX3rwxH4QjUaxubmJ0dFReL1e1NTU4MyZM2hra9v158n33NrawsLC\nAr744gvcvn17x2AJBoMBmUyGuro6qFSq1zr47ZuRFAqF6O7uhsFggEKhQHNzM0pLS8HlcqFWqyGX\ny/Huu++ipaUFKysr+Pjjj/Hw4UNEIhHE43HE4/GsCwfshlgsRn19PVXz2A6p5D137hy6urqea3wl\nYY6Ojg5cv34dt2/fRjAYxMLCAr766iuo1eqsl3Hz+Xw0NDTgX//1X+l3JLMivV4vlpeXYbFY4PP5\nEI/Haf6YjMX64osv4Ha78fOf/xzV1dU5P9SWzLYkOR4y0kyr1cJgMOzIRRLPeWVlBQ6HAzKZLCeu\n2ctADnEqlQoNDQ2wWCxYW1vDZ599huvXr4PP50Mul6O0tBS1tbU4ffo06urqDlXoPJlMwul0wuv1\nIpVKwel0AnhalMXlcunz19nZid7eXoTDYSoKsrm5CbfbTYsLSXokl4hEIlT6UKVSoaamBqWlpZBI\nJLReYHNzEx9//DHu3r2LsbExqjZEZrym02ksLS1hdXUVv/nNb5DJZMDlclFZWZmVflhSr2G326l+\nM5PJpNG63UgkEtjY2EBfXx9+97vfYWlpiWoKA0/vdRKy/eCDD1BRUZGd6tZ4PA6Hw4H5+XlMTk7S\neXIAoFAoYDQa0d3djYaGBhQVFcFoNNL+QLJgkUgElUoFvV4Pm82GSCSCkZERBAIB+scKhULY2NiA\n1+tFPB7P+sghpVKJnp4eOBwObG1tIRqNUvmujo4OdHR04OjRoygpKXmuPYIInxcVFcHpdGJ2dpaO\nGVpaWqJ/w2wl07dvpO3t7fShtNvt8Hq9CAQCMJlMdFbmxsYGXX8kEkEsFsPS0hKNHLBYLLS2tua0\n0glpUzp9+jQaGxupp6FWq1FUVLTDo3K73TCbzfjzn/9Mp2K8CZ3ig4DcU0VFRejp6QGfz8fc3BxN\nd1gsFiSTSczPz2NxcXGHtN5haA0hYthPnjyB2WyGWq2mB7mCggLU19ejra0NTU1NaGlpQVtbG5xO\nJyKRCAQCAXQ6Herq6qDRaMDj8XLyfk2lUkgkEshkMigsLERXVxe0Wi0YDAYCgQAWFhYwNDSE27dv\nY3FxEQBQU1OD8vJyVFdXg8/nw+/3IxaLYXFxEfPz81hfX4fH43muluSgIDMxSSEjj8eDXC5/YYiU\nCNf39fXh5s2bePLkyY7DOjns1dTU4PTp0+ju7oZGo8mOkQyHw5icnERfXx+uXr26w+srLS1FT08P\njh8/To3kixYpFAohFArxve99DwUFBVhcXKSVo+l0Gm63G6Ojo+jt7UUkEqF5zWxRWFiIS5cuweFw\nwGw2Y2trC2VlZTh58iTee+89HDly5IVGjoQ2xGIxGhsb0dDQAL/fj3g8jmAwSFtLsp0TIpNMRCLR\nDk+JXGNS0Xz//n2aC7Db7Ugmk1Qy6+rVq5DL5XRGZK6GXXU6HZRKJVpaWpDJZMDhcCASiXa05ZBr\nQQZGLyws4PHjxzT0fJjQ6/UoLi7GhQsXYLFYcO/ePQwODmJsbAxTU1NYWFjA4uIijEYjVXE5DEbS\nbDbjwYMH+Pzzz+H3+1FbW4u5uTl4PB6IxWKcPXsW//zP/wy9Xk8ryWOxGFWEamhowAcffIAjR44c\n2Aim14EomWk0Gqp1evXqVfzhD3+A1WqFSCRCTU0N3n//ffT29qKmpgYAYDKZaKEhqXbdbYzdQZHJ\nZOgknVQqBYVCAa1W+8IKY5fLhYmJCfzv//4vhoeHd6S8ANA95x/+4R/Q09OD8vLy17YXezKSNpsN\n09PT+PzzzzE0NATg6UZC5pmdO3cOvb29KCkpgUwm+6uLJBsQSbKSRGsikQCTyYREIkFVVRXUanVO\nTBgguYCzZ8+irKwMkUgEIpEIWq0WZWVlf9UYbDd8RNKMz+fD4/HQop7NzU06nPqgIbF+m82GkZER\nBINBMJlM6PV6lJWV0X4loVCI0tJSvPXWWzAajaiursbjx48xMDBADf78/DwGBgZQXl5OZ2nmIkT0\nYvvs0xcZdTIrk+gIH0bIAY7JZEKlUqG7uxvRaBRWqxVisRhCoRBGoxHHjh1DbW3toejhBUCVoAoK\nCuD3+7G6uopgMAi1Wo3Tp0+jq6sLOp0OfD6fXmtS8epyuSAUCrG0tIS2trac9CKfhYyYI+1Ld+/e\nxejoKBwOB3g8Htra2vDDH/6QqkOx2WxYrVbMzc3RqB2Z0ZvtQ/l2B4vD4ey6zxMP8uuvv8b169ex\nurqKeDxOP0NqQU6cOIFTp06hpqbmtdo+trMnI+l2u7GwsIAnT55geXmZNuNqtVqcOnUKZ86cQWdn\n5yv9ToFAAIVCAY1GA4/HA4/H85xHlgs5StIHWF9fj/r6+tf+fQwGA/F4HE6nE06nEz6fD0ql8sCN\nZCqVol7g2NgYvvzyS3g8HnA4HBw5cgQnTpygRrKgoAAKhQIKhQIGgwFyuRyZTIYOYCaJ+KWlJczO\nzlLZwVyEXM+/VolLKvBIg7rD4QCLxYJarT6Uc/vI9yEFWPF4HIlEAiqVClqtFidPnkRbWxuKi4uz\nvdSXRiwWU3nAcDhMvXytVovS0lJotVqa/ojH4zQSRopFhEIhNjY2EIlEsvxNXgyLxaLpKo/Hg+np\nafD5fMTjcdy9exczMzMIh8N0wMCxY8fA4XDg8/mwvr6Oubk5DAwMwGKxgMfjwWg0oqysDDKZLKuR\nnu292aTncWVlhRq4UCgEp9OJlZUV3Lx5E3fv3kU4HAafz4dUKoVSqYTBYEB7ezt6e3vR3d39RmUi\n92Qko9Eo/H4/bftgMBh0HFBra+u3Nn/uhlqtRk1NDVpbW+H3++nECbvdjr6+Phw/fhxHjx7Nek7y\nTWG1WjE6Ogqfz5ftpQB4ek2dTieuXr2K27dvY3R0lOZr/H4/NBoNzp49+9zneDwempubsb6+DrVa\nvaMdhkwTyWY4502QTqcRiUQwNzeHjz/+GMvLyygqKkJnZ+ehKNrZDfJ9Pv30Uzx69Ajr6+vo7u7G\n+fPncfny5UMnN8hisehGS6aBEBm3ubk5tLe3I51Og8lkwuPxYHFxEUNDQ5iamqJazNFodMeYv1yD\nGAU2m425uTnY7Xb09/eDzWZjZGSEtipVVVWhsLAQVqsVGxsbWF1dpbnm1dVV+Hw+HD16FL/61a/Q\n2tqK8vLyrAoo8Pl8CAQCMBgMWK1WPHz4EJlMBlqtFmw2G/Pz89jY2EAgEIDX60UwGASbzUZZWRk6\nOzvR2tqKxsZGVFRUQKFQvPHezz0ZSaVSiSNHjuCDDz7A2toa/H4/Kisr0dLSgrq6uj1VNJK5k0Sn\nlZBIJODz+ehmmwve5OuQSCRo6MBisSASiVBR6aKiIjpN46CZnp7GlStX0N/fj7m5OTidTvr3ZjAY\nLzxpklmaZNTV9gMMORFu77XMBslkkhYgWa1Wuom8jBB7KpVCMBjExMQE+vv7MT09TYcvl5eXHxpj\nQsJzXq8XTqcTQ0NDGB0dxfj4OCQSCd555x309PSgra3ttQsdsgERBtDpdKivr4dMJoPZbEY6nYbL\n5aK9kmq1Gl6vF7Ozs5iZmaE9deXl5Th58iSUSmWWv8mLKSsrQ1tbGxYXF+H1emGz2ZBKpcBms2nh\nC4PBwNraGlKpFObn5+Fyuejs00AggFQqhebmZvT29qKzsxNFRUVZbXdhMpkoKipCdXU1Ghoa6JqH\nh4chFArBYrFo4SA5+PB4PNTW1qKnpwdvv/02jRTsV2/2nn4jSehXVFTAbDZjY2MDLS0tMBqNEIvF\ne/b0eDweiouLc75tYK+QDXd1dRXr6+u0B1Emk8FoNKK0tBRqtTorRnJqagr/8z//Q0NVBGIEeTwe\nVfkg1zeRSNBQXTAYfO4Q4/V6sb6+nvUQViwWo9KJQ0NDuHDhAhoaGqgEGcmDbD8IkL7CSCQCm81G\nxQZ8Ph/Ne2i12pzrpyPKVkTliSjskOkfq6urmJ2dxZUrV7CwsACVSoVTp07h8uXLqKyshFQqPXQG\nEnjqSYrFYjQ0NECv16O0tBRff/01TCYTEokE7ty5g6tXr6K2thbxeBwLCwtgMpk0ddDW1oZLly7l\ndNFOeXk5enp6MDY2hkAgAJfLRQteiAdM+j4nJycB/MXDJnk7vV6PCxcuoLe3F2VlZVkvymIwGLR1\nrre3FywWCzMzMwgEAgiFQnS/kUgktIKejOk7d+4c3n333X2/X/e0G7PZbAiFQuh0OsjlclRXV0Mq\nlVKXea8oFAqcPn0a4+Pje/4duYzH48H4+Dg+/vhj9Pf30/AO6SMtLCzMWm6AXNNYLLbj9Xg8jtXV\nVSwsLMBkMu1QBTKbzZidncXi4iIGBgbgcDh2VJuRYakHPUn8WdxuNz777DM8fPgQKysrGBkZQVFR\nEbRaLe2HbG1thUqlon//WCwGn8+HpaUlTExMoK+vD8FgECdPnsQ777yDU6dO5aREXSwWQygUAofD\noUbeZDJhfn4eo6OjWFxchMVigVgsxrlz53Dx4kXU1dWhpKSEyhHmYo/gt0EmRvz4xz9GIpEAl8uF\nxWJBLBZDeXk5FhYW4HQ66T3qdrupWH9lZSVOnTpFQ3W5ilQqxZEjR/Dv//7vePDgAa5fvw6bzQaH\nw7GjkX47arUaJSUlqKysRFtbG1pbW1FUVPRcO142YTKZKC0txY9+9CMcO3YMGxsbSCQSEAqFVA3I\nYrGgr68PY2Nj8Pl81Hs8CPZkJMkJjIRI3xQ8Hg8lJSUoLi6GQqFAMBjcVerusEEKBYaGhtDX14e7\nd+/CarUCwI5TVHFxcdbyrRKJBAaDAbFYbEd4NB6Pw2q1YmBgAAKBAGq1mhoHk8lExy6ZzWYqtUfU\naRoaGugcuGxC7leS+wgEAggEAlhdXYVYLIZGo8HKygqUSiX9+8fjcfj9ftjtdmxtbYHH46GyshJd\nXV3o6upCSUlJTvZ/bmxs0IpzEtrf2NjA+vo6bDYbotEo5HI5WlpacOzYMZw+fTrnjcPLQDxJUkyX\nSqXQ2dlJvSgS6TAajXA4HDCZTKitrcWpU6fQ2NiIqqqqnK/kJUIsMpmMSueRXuXNzU36z2AwSCN9\n9fX1qK6upmPDyMiwXKnrIM+PRCJBfX09iouL6T4iFApp+Ht1dZU+j263Gz6fjyrADncAACAASURB\nVAqA7Dc5Ka5JqiaXl5dp6O8w5yJDoRCsVis+//xzXLt2DQ6Hg2ookpaZy5cvZzV5XlhYiPb2djgc\njh1KQslkEltbW/j666/xzTffPFdtTEI9JDwJPG1vqaqqwsmTJ/H2229nPSQpl8vx3nvvoby8HJOT\nk4jFYrDb7ZicnMTCwgIGBwfx1Vdf7Tq9Ra1Wo6qqCu+++y5OnDiBlpaWnC4eGx8fx3/913/RUW2R\nSAQMBgMikQj19fU071hZWQmdTncopAP3ApPJxJkzZ1BUVISHDx/CYDCgrq4OR48exczMDBYXF3Hi\nxAlcvnwZGo0mZyd/7AaHw0FTUxPq6urg8/mo0b9z5w7u3LmDpaUl1NTU4Be/+AU6Oztpr+BhmIMq\nlUrpbN7t3Q1isRhGoxEKhQKRSASDg4MwGAw4e/ZsboZb95uSkhJ0dnbC4/FQYYF4PI5IJJK16rN0\nOo2NjQ2aA9iOXC6HTqeDxWKB1+ulr0ejUSwtLWFtbQ0bGxsYGBiA2+1GMpmkm+/58+dx9OjRrGon\nAk/zzL29vTCbzQiFQnSTJVJRyWTyr/YGErWempoatLW14cyZMzh69OiOvrRsQSaWiEQiVFZW0tww\nUZqxWq10QgKDwYBQKNyh06rValFVVQWdTrfnSQIHhdFoxOXLl5FMJpHJZMBmsyESiah6TmFhIQ2Z\nk8KIXP4+r4NcLkd9fT2kUimSySQ4HA4Vpi8uLkZLSwtUKlXWn71XgVwrYvBkMhm4XC6kUikUCgW6\nurpoG1lDQwMKCwvpASCXr/P2te22TqlUivb2dojFYrz77rtQKpWvLTf3suSkkVQqlaiqqsLDhw/p\nzbu1tQWz2QyVSnVgYRFiHLxeL+x2O6anp2GxWJ4LAWu1WlRXV2Nubg6bm5v09XA4jJGREayurmJr\na4uGjzkcDioqKnD69Gn09PSguro66w+pWq1GW1sb1tbWwOVy4fF44HQ6qWYrKZPfXpxD/slmsyGV\nSqHRaHDq1CmcPn2ahvFyYRIIm82mfZ1ETzedTtNGbDIpgUjMSaVSqFQqFBUV0dBWLnuP2yktLcU7\n77wD4KnHIRAIIJVKIRaL32jvWK5Dxr4RybntlJaWfuuk+8MAg8GgaS+pVAq9Xp/tJe0bQqEQFRUV\nz+lhHwTZ38FekuHhYahUKlRUVBzY1IVUKoVAIID79+/jk08+ocNqn/WoJBIJlEolHA4HLSkHnm7E\noVAIsVgMiUQC6XQaBQUFUKlUOHnyJH784x+jqKgoK8LCz8Lj8VBUVIQf/vCHePvtt+H3++mInXv3\n7mFlZQVut3vX6leBQICWlhZ88MEHaG9vR1VVFSQSSU6H8Yjnq1arIZVKUV5eTiMEpLmZqJHkYu7x\nRchkMno/EWUdFov1nfYY8+TZT3LSSBYUFEAoFILL5YLNZlMP5qClwMhJTa/Xo7u7G7W1tbuGfAsK\nCsDj8RCJRL610IjD4UAsFqOrq4uWYOeCMSEbqVarpaIAOp0OBoMBpaWlsNls8Hq92NraoiFw4On3\n0el0aGhoQGdnJ4qLi3f0ueYqJN9BTuLfFchMyDx58rwZctJI8ng8KBQKKJVKSCQSBINBmhs6yA2A\nCH13dna+sszeYYZ4UkKhECUlJejo6EAikaADat1uN/1ZMmZHLpfnvZU8efJ858hJI1lYWIi2tjY4\nHA5otVosLCzgwoULeOuttw7FgNvvIkQIvKysDFqtllafEbGBv5VcV548ef62YGRytLciFotRvcGV\nlRWcPn0aR44c+ZsqPsiTJ0+ePNklZ43k9kkFqVQKBQUFtFIyH9LLkydPnjwHQc4ayTx58uTJkyfb\n5OOWefLkyZMnzwvIG8k8efLkyZPnBeSNZJ48efLkyfMC8kYyT548efLkeQF5I5knT548efK8gLyR\nzJMnT548eV5A3kjmyZMnT548LyBvJPPkyZMnT54XkDeSefLkyZMnzwvIG8k8efLkyZPnBWRtCsh2\nbdZnZzAWFBSAy+VmaWVvhnQ6jVQqRQcUk/FTuTA7Mk+ePHnyvBxZM5KxWAxLS0vo7+/HjRs3sF1C\n9u/+7u/wox/9KFtLeyNsbm5ifn4eV65cAZPJRGdnJzo6OmA0GrO9tDx58uTJ85JkzUgmEgmsr6/j\n8ePH+Oyzz5DJZMDn86HRaNDT05OtZb020WgUHo8HY2NjuHfvHv785z9Do9GgqqqKepV58uTJk+dw\nkLWcZCqVwtbWFjweD31NqVTi3LlzqKury9ayXhufz4eRkRFcuXIFn376Kex2O5RKJVpbW6FUKrO9\nvDx58uTJ8wpkxZO02WyYmZnBvXv3MDMzAwBQKBSorq7GiRMnUF5eno1lvRECgQAmJiYwPz8Ph8MB\noVCIoqIi6HQ6CASCbC8vT548efK8AgdiJEmRTjQahd/vx/T0NB4/foz+/n6srKyAwWCgpKQEzc3N\naGtrQ3Fx8UEs642SyWSQSCTgcrkwOTmJjY0NAEBVVRVqamqgVCpRUFCQ5VXmyZMnz+Elk8lQexKL\nxRAOhwEAyWQS4XAYyWQS6XR6x2eEQiFEIhHEYjHYbDaYzFcLoB6YJ+n3+7GwsIA7d+5gcXERq6ur\ncDgcyGQyYLPZaGtrw6lTp6DVasHj8Q5qWW8Up9OJpaUlTE9PIxgMwmAw4Mc//jHOnDmDgoKCV744\nefLkyZPnL2QyGcTjcfh8PqysrGBychKZTAZutxsjIyNwOp3UcBI6Ojpw/Phx9Pb2Qq1Wv7KzcmCe\n5Pz8PL755hvcunULNpsNPp8PwWAQSqUSVVVV6OrqQlNTE0Qi0aFsk8hkMlhcXMTY2Bg2NzfBYDCg\nVqtRX1+PsrIyMJlMMBiMbC8zT548eQ4N243ixsYG7HY7Njc34XQ6YTabsbKygkwmA7/fj8XFRXi9\nXsRisR2/w+/3w+Vywev1oqurC0eOHAGLxXppp2XfjWQ6nUYikcDAwAC++uorDA8PU0vPZDJhMBjw\n/vvv4/jx44c2F0lCAFNTUxgaGkIwGIRCoYBUKoVYLAafz8/2Ev9mIdcmnU7vaDPa/v6z4Zlv49kD\nTyaTAZPJ3PF6/kCUJ8/eIM8peTYDgQCWlpZw69YtDAwMYGpqCg6HA9FodNfPP/vszc/Pw2w2Y3Jy\nEsFgEBUVFRAKhbljJM1mM0ZGRnD//n3Mzc0hFoshk8mAy+WisrISJ06cwKVLlw5lHnI75GL6/X6k\nUinI5XKUlpbmDWSWSSaT8Hg8GB0dhdfrfe79tbU1mEwmBINBJJPJ594nDyyDwQCbzQaXy0V9fT10\nOh0AwO12IxAIoLa2FpWVlaioqMiLRuTJ85pkMhnYbDYsLCzAZDJhamoK9+7dg9VqhdfrfU6A5tuI\nxWKwWCwYGhqC0WhEd3c39Hr9S31234xkPB6Hw+HA8PAwrl27homJCTgcDgBPFXUUCgWOHj2K7u5u\n1NXVHep8XTweRzAYxNbWFtxuNxgMBgwGA9rb2yGRSLK9vJeGJMTj8TgikQgymQySySQCgQC8Xu+O\ndp29UldXh5KSkjew2pfD4XBgdnYWN2/ehM1me+795eVlLC8vIxAI7PrgPWskeTwempub6XdwOp3w\n+/1oaGhAU1MTjhw5gsrKShQVFe2pSCBXIR65z+eD1WpFLBYDg8GARCKBQqGAXC7f9zXEYjFsbm4i\nk8lAKBQC+IuyFY/HA5vNht/vRzQa3fVaplIpuFwuelAnMBgM6HQ6lJSUgM/ng8Ph7Pt3eRGJRAJ+\nvx+xWAypVAoAEAqFsLW1hXg8/lzUQ6VSQSKRoKCgAAwGA0wmE1KpFDweDywW69BFNEgBpNPpxOjo\nKPr6+mhYdWZm5rlQKnkuCwoKIBAIEI/HqYfJ4XAgFAoRCAQQDodpyHZxcRFNTU0vvaZ9M5KhUAgP\nHz7El19+iS+//BKBQIC+JxKJYDAYcP78eXR0dBy6C/ksoVAIZrMZq6ur2NzcBIfDQVtbGy5fvnzo\n2j4ikQhcLhfMZjMSiQTC4TCmp6cxNDSEx48fv/bv/4//+A/85Cc/eQMrfTnm5ubw5Zdf4tq1a7sa\nyUQigUQi8cKQ6/Z7M5VKIRwOY2RkBBMTE/S1dDpNc+4lJSX45S9/iUuXLkEikXxnjCTw9G81Pz+P\nP/7xj7Db7WCxWGhtbcXx48fR2dm57/99n8+H27dvI5PJ0NRMIpFAKBSCTqeDWCzG5OQkLBYLfD7f\nc58PBoN4+PAhrFYrNUAMBgMsFgsffvghfvazn8FgMGTVSEYiEUxPT2NzcxORSAQAsLKygmvXrlED\nT2AymTh9+jSOHDkCpVIJFosFPp+P1tbWQ9tylslkEAwGMTAwgKtXr+Kzzz6j0qXPirEwmUyw2WwI\nBAKoVCqUl5fD5XLRzgKlUony8nJMT0/DZDIhnU6DxWKBy+W+0nO5L0bSZrNhenoaN2/exODgIDwe\nDzKZDDgcDgQCAXp6enDx4kW0trZCpVLRzyWTSYRCIUSjUcTjcXo6EIlEOW1IXS4XxsfH4XA4wGAw\noFAooFAoct6LjMVi8Pl8mJubQyAQQCaTgcfjgcViwdzcHOLxOBKJBDY3N2E2m2Gz2XZ4Vrvxbe8/\nW3m238RiMQSDQfo/AODxeOBwOHSNxEDutuZn85iZTGbXXEgsFkMsFoPf78eDBw8gk8lw/PhxyGSy\nrN+7MzMzmJ6eht1up2vPZDIQCATQarV0fTKZDFwuF5lMBgwGA6FQCBsbGwgEAojFYkgkEjCZTBgc\nHEQgEACfz0cqlUJpaem+G8lAIACTyYR79+7BbrdTz5VEPiQSCbhcLux2O3w+367XKB6PY21tDYFA\nYMc1Z7FYcDgcCAQCu4bcDwrSP37lyhWYTCZqEN1uNxYWFmiLA4HBYKC/vx9ra2sQCARgMpng8/lY\nWFhAc3MzNZ58Ph/xeByhUIg+AyR1IBKJciolFI/HsbW1hdu3b+PRo0fPpUgKCgogFApRVlYGHo+H\neDyO9vZ21NXVQafTIRKJ0AOSQCCAQqHAyMgIFhcXkUwm0dbWhu7u7lcSdtkXI2kymfDw4UM8ePAA\nKysrSKfTYDAYEAgEKCkpQW9vL77//e9DLpdTIfNkMgm/34/V1VU4nU4Eg0GIxWJotVpUVFSgoKAA\nbHbWVPR2hYSgNjc38eTJEzidTnC5XGi12pw0kGSDj0QiCAaDdBO8ffs2DWM9ayS3QzZTsonutvmT\nApkXvX/QBoMU1JD/NpvNhl6vh0KhAIvFgtvtfu5BTCaTtDXpRWsmfVqJRALJZJL+eywWw5MnTyCV\nStHY2AipVJo1I5lIJBCJRDA4OIjPP/8cc3NzOzwsuVyO2tpa+vcpLi7e4X14PB6Mj4/T55H0OgeD\nQWQyGVqJfuzYsX3/LtFoFC6XCysrKxgfH0ckEkEqldq1GGs7JETOZDJpKI/FYtHPcTgcSKVSiESi\nrHv9NpsNIyMjuHHjBpaXl6m3ux1SHMbhcMDlcuFyueB0OhGPxxGPx8FisbCysoKNjQ1Eo1FUV1dD\nqVQiGAzCbrfDbrcDeHpQlEgkqKiogFarzZnUQCKRgNfrxcTEBJaXl+nrJJSsVqtRVlZG79utrS10\ndnbi+PHj9P591k5UVlZiZWUFyWQSlZWVaGxszL4nSUJPLpeLXmgej4eSkhKcP38ezc3NkMvlO8Ia\ngUAAU1NT+P3vf09LeeVyOY4dO4aPPvoIer3+QPIerwLJ2a2treHGjRtwOByQyWSoqalBYWFhtpf3\nHKlUCgsLCxgeHsb9+/epd7W2toZQKATgLx7RqybGcxVyWmaxWDQX/tFHH+HYsWNgs9nY2NiAxWLZ\n8RmbzYZkMkl7qnZ7oHw+H2ZnZ2GxWLC1tbWj9Jxs5D6fDzqdLmubj9vtxujoKO7evYtHjx4hFArt\n8ERIaTwx4lwulxoQBoOBRCJBc7Xk4LC9SjgSiWBlZYXWGuwnUqkURqMRXV1dyGQyWFlZgdfrpSHJ\n3SgoKIBarUZdXR14PB4SiQSsVis2NzextbUFAFCr1bhw4QLeeust1NXV0VxnNojH4wiHw0gkErsa\nSDabTSMger2erjcej8NkMsFkMsHhcGBlZQUejwfDw8NoaWlBUVERvF4vTCYT1tfXAfzFy/rBD36A\ns2fPQqPR5ITYCYfDgVgshsFggMlkgtlspq9LJBJcvHgRZ8+exczMDMbGxjA0NITNzU1MTEzgo48+\nQnV19Y7oJAAYjUYUFRUhnU5Tj/tVDq5v1EiSfNb8/Dzm5uaoa89gMFBbW4vjx4/j7NmzqKqqoh5k\nIpFANBrFysoKRkZGqApPKBSCSCRCKpWCSqXCuXPncs5IAn+pajWbzXStUqk0p0IYhEQigUePHuH6\n9esYGxujxjAUCtFTeSaTQUFBAVQq1a6hmO1hqt1utI2NjV0LfMhDKRKJ9ufLvQCtVouGhgaUl5cj\nlUqBwWBAJpPBaDRCq9WiqqoKLpeL/nwmk4HL5UIymYRCoXjhCTsQCKChoQGzs7OYmprCyMgINRaB\nQAAulwuJROJbPZ39gPSWra+v4+rVqxgaGnrOkGUymR2KJc++99dCz+Q9DocDtVoNsVi8D99iJwUF\nBdBoNHjrrbdQUlKCjY0NWqTzIjgcDhQKBSoqKsDlchEOhzE4OIjR0VFqJElYvLGxETKZbN+/x1+D\nx+NBLBbTIqRnQ79KpRIajQZCoRBarRZGoxFsNpt6kkRtJhAIIBQK0UpQmUyGUCgEp9MJp9MJ4Onf\nUyQS0XDsuXPnoFAosvG1d8Bms6mUp1wup0YSeOpFE0+TFNxtbW0hGAwikUiguLgYXC73OSMpFotf\n6x59I0aSPDx+vx+Tk5NYWFigbj3ZTI8fP44PP/wQR48ehUAgoJ+JRqNwOBwYHx/H4OAgzGYzNa6B\nQIBWNBkMBjQ0NNDfmStsn4uZ68Tjcdy6dQtffPHFXz1NiUQiVFZWorS0FBqNZsd7z1YFPvv6zZs3\ndzWScrkcLS0tBy7yXllZCQ6HQ1WQVldXaVOywWCAwWDY0/gy8n1HR0dx69YteoonZPMezWQyCIVC\nWFhYwKeffkpz5dvf3/7vL3rv2z4jlUrR2dmJioqK/fw6FJlMhrfffhvnz5+nB5BvO4SwWCxwOBww\nmUx4PB6w2Wy63wBPN9CmpqacaEGTy+U0FSAQCOD3+3e8X1xcjLa2NhQVFVFVMr/fD7fbTSu0CWRf\nejZkSa5hPB6H2+1GX18fUqkU2tvbc8JIslgs8Hg8KBSKHYYtnU4jGo1ieHgYKysrmJ6epmmScDgM\nk8mEvr4+6PV6tLe3v9E1vTEjub6+jsnJSXzzzTdYXV2lDxVxn9VqNeRyOT2Vp9Np+Hw+DAwM4PPP\nP8fS0hJWV1cRCoV2nFj9fj+Wl5cxMTGB6upqGI3GnPHSkskkXC7XjjwPl8tFYWHhgXtMbwIOh4Pz\n58/j6NGjMBgM9DT3MphMJoyMjDzXH8jhcFBaWoru7m5cvnwZNTU1+7H0F8JkMiGTyXD69GkEg0FY\nLBYMDg6Cz+dDr9dDp9PtKcQWDAZhMpnw9ddf4+rVq9jc3KTvlZWVob6+HmKxOCuh1lQqheHhYfT3\n9z9XMv+mEAgEMBgMuHjxIhobG/flv/EiyL7yMl76iyIeuYhSqUR7ezv+7d/+DePj45icnMTKygqs\nVit8Ph8tOuLz+fQ5Iy0jxKNmMBjg8XiQy+U05cPlciGXy1FZWQmdToeJiQlMTU3R4RK5RCKRgM/n\nw/Ly8o5q9FQqhUgkAovF8pz0HIlotLe3o7S09I2v6Y0YyXQ6jZWVFQwODuLx48ewWCz0RtbpdKit\nrUVFRQUtliAXdmJiAn19fbhy5Qp8Ph/NL5AinXg8jlgsBofDAYvFArvdDr1enzNGMpFIwGaz7QjX\ncblcaDQaMBgM2Gy2Hb1NQqEQfD4fXC43K4lyFouF8vJy1NXVweVy0Wo4cjLlcDi4fPkyOjs7adj4\n2wwIada32WywWq305iWFL8TbOHfuHE6dOnXghwcGgwGRSITm5ma4XC5YrVak02kkk8ldxZBflkAg\ngLGxMTx58gSjo6M7jFFhYSEVkjjoaxyNRuF2uzE4OIjh4WHEYjFwuVyIxeIdhTkvqkLeLVJgt9th\nsVh2hI+lUinKysrQ2tp6oH2vAHKiwGQ/EIlE4PF4UKlUqKiogNFoxMTEBJaWluheEovFYDKZdoSZ\nSShaIBCAz+dDJBJBp9OhsrISAOjvbGhogF6vh0AggNfrxezsLGQyGdRqdVbbXraTSqVoCm574SAp\nutrukJBintLSUrS0tKCzs3Nf7sU3YiRJQcjg4CBmZ2cRDAbBYrEglUrR1dWFn/70p6ivr0dhYSHY\nbDaNKf/ud7/DgwcPdhT4AE8fQJlMBpvNRgtKcpF4PI7V1VVYrVb6Go/HQ3FxMQKBAB48eLBDPol4\nwlqtFkKhkOZlDwoul4u///u/h16vx61bt8Bms1FeXo7vf//70Ov1tDmcnFRfRjUmEolgaGgI169f\nx5UrV3ZEAgQCAcrKyvCDH/wAJ0+ezFoFIYfDgUajwblz51BTU4NEIgGxWIyysrI9XwMSBVlcXEQ0\nGt1hXAQCASQSCdhs9oF7MU6nExMTE3j8+DGmpqYQj8eh0WjQ2NiIn/70p3sKjf7xj3/Eb3/7W/j9\nfrpxqdVqlJaWQiaT5UTBx3cFFosFkUiEuro6GAwGdHV1wWazwW63IxgMwmaz4caNGzv2HJVKhZaW\nFlRWVkKv19OcJTGSTCaT9gfGYjEIhUIqPlBTU4OjR49mtWBpOyQnaTQasbCwQHsed4P0hZ45cwbf\n+9730NXVtS91K3s2kul0GvF4HF6vF1arFVNTUzRcmkwmIRQKUVVVhebmZjQ3N0Mmk9HTyujoKK5c\nuYKBgQFYLBYkk0nIZDIUFxejqakJLpeLxtJzOVRCih+2V4JarVb84Q9/AAAq4k7eLywshEajQVFR\nETo7O9HZ2QmBQHBgpzgWi0WLGEjVpVKpRENDw55vrkwmg3A4TCX5nu0/43K5UCgUWS2KIFENpVIJ\ngUCAVCq1ozXgZSG559XVVTx58gSTk5O0dQb4SzFEfX09jhw5AoFAcOD3r8vlwvT0ND1gptNplJeX\n4+TJk2hpaXmlk3Y4HIbVaqWtJKS4i8FgUG/5Vf+GuURRURGMRiMkEknOeFLkueHz+eDxeLSIzmg0\n0j5cg8Gww6MSiUTQ6/VQqVT00CISiaBQKHbcf6TNjgiEZzIZqFQqFBcX58z3J4pBXV1d2NjYwODg\n4K4/x+FwUF5eTqNUpGNiPw5sezaSqVSKKq8PDQ1hcnISNpuNVhDy+Xw0NjaiqamJ6lwSRkdH8ckn\nn8Dj8dCTqVarxbFjx/Dhhx+ir68PExMT9HeR8v3tDeC5isViwe9///vn9DtJxSExTE6nE4WFhTAY\nDJBKpQeyNvLfViqVaG5ufu3fR8TriQDE9vwP6YvdfjjKNlwu97W893g8Dr/fj8HBQfT19WFhYWFH\nj6VEIkFlZSWOHj2K5ubmA1U8ISHkzc1NTE1Nwe12U+H1iooK9PT0QKfTvdK9Fo1GYTKZYLVaaYSA\nFMIUFxfDYDAceDTkTaLT6VBeXg6pVJqT3jBJFTybouju7n6l30Nad0gbyOLiImw2G83XkyruXIDJ\nZEIoFKKxsRGjo6O7/gyDwUBBQQEMBgMuXbqEo0ePoqysbN/WtOe/TCwWw9raGvr6+vDpp5/CbrdT\nvU82mw2FQoFjx47RitTtRCIReDyeHR5YU1MTTp48ibKyMrDZbPo+qXQqLS1FcXFxTt7M2+HxeCgs\nLERhYSGtFiP9lLOzs9jc3ITb7cY333yDdDqNX/7yl6+kI5hLhMNhWCwW3Lt3D2NjY8+9f+TIEXz0\n0UfPHZIOKx6PB1NTU7h16xbu3LkDj8ezo6q5srIS//Iv/4KOjg5IpdIDFTmPxWIwm82YmJjA4OAg\n3G432Gw2+Hw+tFotSkpKXnlOaygUwtzcHC1KIuH40tJStLa2oqGhIWfqA/YCi8XKSkj8oCH6y3fv\n3qURPJ/PB6VSCZlM9koTMfabdDqNSCSC9fX1HcVwu0FmFNfW1u7rml7bk9zY2MDs7OyO9yorK3Hy\n5Ek0Njbu2lSfTCZpoQO5QVOpFDweD0ZGRrCyskLfl8lkaGxshNFohEqlypkTD/DUszCbzTsupkAg\nQGVlJVpaWmglJxFh/vLLL+kGZjabMT4+/lyZ92HC6XRifn4e8/PztOUHeBp2lEqlqK6uRmtr64F5\nyvsFKZYYGxvDgwcPMDw8vCMnxOFwUFZWhs7OTvT09ECj0Ry4h0U0P6emprCxsYFYLAaZTIb6+nrU\n1NS8cnEGUbchESISJdBqtejt7UVjYyM0Gk3ORAn2AvlOh6kCdi+QYROTk5O4f/8+tra2IBKJUF1d\nDb1eD5lMljP7ajAYxPr6Ou7du4fJyckX/hyJmgwNDaGlpQUVFRUQCAT7cjDdl7/M8ePH8bOf/QxV\nVVUvfdJcWlpCMBiEz+fb0UCqVqtx/PhxVFRUHEjT8qsQiUQwOjqK+fl5+ppYLEZ9fT0uXbqE3t5e\n+jrpWwoEAhgeHqbqGrspaxwWrFYrJiYmsLW1taPajs/no7y8HAaDAYWFhYc6JAc89Zjv3r2La9eu\n4fbt28/J9fH5fPT29uLs2bMoLS3NyoYTCoUwNDSE6elpWuym0Wjw7rvvoqWl5ZUPKiaTCcPDwxgZ\nGaEHAg6HA6PRiA8++AC1tbWH2ov8WyIWi8FqtdKxcMDTNqWjR4/SroNcwe12Y2pqCp9++ukLjSSp\nBdnY2IDb7caJEydoWi8njeT2hl6ilEBaPl5UuEA+w2Aw6GfX19fhcDioEC+Px0N3dzfOnDmDd955\nB6WlpTl32iNxca1Wu6MNhJQmkwtGpLxisRithCTaiblygtsLa2tr6O/vf077VCQSoaGhAQaDAUKh\n8FDPVrx//z5u3ryJ/v5+LCws0DFLJN9XW1uLrq4uvPvuu2hubs5K+C4S/eov1AAAIABJREFUicDh\ncGBubm5HNaBCoUB3d/crPTukBJ9IFxLlINLvSuZmZlOTNs+rEQgEMDExgY2NDXode3p68P7778No\nNObEdQyHw3A4HLhx4wauXbsGq9X6rX2wZCBGJBJBPB7fN3Wr16puJYsj8Pl8lJSUQK/X0zArCTUS\nTUIAO3rpyAVyu9309xBDe+nSJZw6dQrNzc05udGSAga1Wk1f2+3vEg6H4Xa7sbm5SfNYSqWSTsg+\nrDidTiwvL+9o+yADtYuLi6FSqV45D5ZtiAEkY8KePHmCTz75BBaLhd63pDBNrVajq6sL7733Hjo7\nO6HVarOyZq/XC7PZDJPJRJ8jhUKBsrIyKnD9spDNanx8HOPj43Q6DJ/PR1tbG44ePUpnZebJXch9\nbLfbMTU1hYGBAZjNZrDZbBgMBrS0tKCjoyPrUR6yb/h8PkxMTODevXu4d+/eDrEAoVAIkUgEsVgM\nj8dDHZJ0Ok2LB/czIrfnOz2RSFBhZwIRkN5e1UeqH7cLIRMlhRedYM6fP49f/epXKC4uhkKhyJmk\n8rOQXtDtYeBoNAqbzbZDIsput+PRo0cYGRnB+vo6EokEKisrce7cuR0G9jBCHkbS+pENrdI3DVFS\nWlxcxPT0NL1mBBaLBY1GgwsXLtCwejYPO1arFbOzs/B6vUgmk2CxWGhvb8exY8de+ZDicDjQ39+P\nubk5bG1tIZlMgs1mQ6lU4oMPPsD58+dz8sC6F74L9+qLIM4JkaF8/PgxXC4XCgoKUFRUBI1GkzNF\nS+l0Gna7HdevX8fU1BRtXQJAB9g3NTWhubkZd+/exY0bNw50fXs2kqRoZbsuZCgUwtLSEm7dukX1\nO0nubXl5mYrrTk5O7qoPyePxUFRUhPLycpSWlkIul+e0J8Lj8dDQ0EAH8AJP/wbz8/O4evUqtra2\nIJFIYDab8ejRIywuLoLD4aCqqgodHR1oaWnJyZFar8J3qfCBhBoXFxcxPj6OBw8eYGRkhBaR8Xg8\n2ujd3t6OM2fOoKmpKevC2BMTE7h16xbcbjdtRm9tbUVbW9tLV4MTbUwyVHlpaQmpVApcLheNjY04\nderUa/XT5iLxeByRSIQqL+XqYXwvRKNReDwejI2NYXh4mO69crkcDQ0NqKysfOVpGG8aUvU/NjaG\nO3fu4MmTJ7BYLDsUypRKJTo7O9Hd3Y2Ghga43W5MTk4+N4B6P9mzkSRyZNs9JiJw7nQ68fDhQzAY\nDCSTScTjcdhsNlrJ+aITHBmnxefz4fF4cm4g6LOQXtC6ujpIpVJEIhGEw2Hah3Tnzh3odDo6VgkA\nSkpK0NPTg66uLqqIcVghPazbZzaSG5zkYb9tCHMuQNYZCoXgcrnQ39+Pmzdv4tatW/SeZbFYkMvl\nKC8vx7vvvovTp0+jsbExJ6a/T09P486dOwD+srGQ+/JljSSpFhwdHcWf//xnGjaXSqU4fvw4/vEf\n/xF6vX4/v8aBEwqF4PP5qHTkd8FIkuctEAhgeXkZCwsLtFiHCKgfOXIEFRUVWf++JK1x7949fPHF\nF5ient5h+BQKBZqamtDT04Pjx49Dp9NhcnISer0eoVAo942kSCRCR0fHri0gbrcboVCIFuaQU+q3\nEQqFMDMzg9T/b++8gtrM0vz9SEhCCQkkJEQSiJzBgN0OONCm3Z4O02FS9aSdqa2au63a3dr7vd6r\nvduqra3a2Z2wPdu90zMdptse241xagO2AdtEkxFBIoggoSz+F67vtGnbHZwQ/f+eKl8Yg3wO+nTe\n86bfG4/j9Xp56623qK6u3vG4+cOQhvhWVVXxi1/8go6ODoaGhgiHw6KY4t5p56mpqTgcDlpaWp7Z\n5ISniU6nw2QybStaAkQOOhQKEY/Hkz48Jw2j7u3t5aOPPuL69esMDw8LLeGUlBTR9/vWW29RVlZG\nfn5+UvbsZmRkUF5eLrQ8v+7lZGNjgz/96U+cPXtWHLQ6nY7i4mLKy8txuVxJcSF4kszPzzMxMSEM\n5bclz5pIJJiZmeGTTz4RnQIpKSm4XC5aWlqSRv9aMpIjIyMMDg6KlIYkFlBeXs4PfvAD9uzZg8Ph\nECLnz7or4JGfCq1WS2lpKdXV1dy+fVtoC8JdV/9eVfov4nA4cDgcwN0P58LCApFIhGg0yvLysph8\nvhvCeNLD9+qrr6JWqzEYDNsGwgaDQZGrLS4u5uDBg9TW1mK325N+b1+F0+mkubmZlZWVbRGFQCDA\n4OAgtbW1bG5uotPpkrafTiqqGh4epr29ndOnT+N2u0Wu3WAwYLPZaGxspK2tjcOHDwt922REEt/4\nJnKH6+vrTE1N0dnZue3CK42RKi4u3vGQ8tPgi57kt4FoNMr09DTXr1/nwoULeDweNBoNVquVhoYG\njh07llSFV4lEgpWVlW2Fm5LzYTQasdlseDweZmdnxRBxqeL6WfHIvylJNLquro7FxUU+/fRTRkdH\nv9bP1tbWcvz4cQCGh4f59NNPxaBbgKqqKl577TWcTqcQ4k1WlEol2dnZQmGnoKCAP//5zwwODgpP\nJC0tDZfLxWuvvcbx48cpKira1VWtEg0NDSgUClFeLrG8vMynn35KaWkpra2tYqJ6MuLz+bh16xa/\n+93v6OrqYnJyctuBKY3g+fnPf05zczM2my2pn8dHwePxcOvWLcbGxrZFBcxmM3v37qWwsHDnFifz\njdjc3OTy5cucOXOGa9euEY/HSU9Pp7S0lEOHDtHW1pb0kR2463xsbm4yMTFBb28vQ0NDzM7Osry8\nLCqunxWPbCSlirfGxkaysrI4fPjwNtHdh003h7t5OUloed++fbS2thIKhcTtoKysjLKyMoxGY1If\nSPcWHykUCmHUCwoKWFlZEZ61FJYsKioiLy8PrVab1Pv6uhgMBnJycmhsbGRtbY07d+4AnyfkJUHs\nZCQejxMOh7l58yanTp0SYuWSgZTmoB4/fpwf/OAHVFZWkp6enpTRjVdffRWj0UhnZydOp5OTJ09+\nLSHzcDiMz+fj0qVL/PnPf2Z2dla8ZzU1NRw9epSGhob7Bm/LJCebm5ssLCzQ09PDyMiIcDqsVisv\nvPACNTU1SXtZvRcp/TEwMCBaknw+H36//6n2Qz6MRzaSKSkppKWlkZaWRlFR0ZNc065EoVBgsViw\nWCxUVVXt9HKeCRqNhoyMDCorK5mYmODOnTvbHuB7p0ckG4FAgJmZGbq7u7l48SJTU1NCqcZgMGC3\n2ykpKeH48eOcOHFih1f75Rw4cIDCwkKysrKw2WwcPnz4a6lTSdPp3W43c3NzQsdTes3Dhw8LAXCZ\n5Gd1dZXx8XEGBwdFZEcaCXfgwAFcLtcOr/B+pCHRWq12m1BHJBLB7XY/dFSWNFIrPT0dq9X6VB2P\n5AhMy+xKJAHtzMxMTCaTqGiFu7kGn8/HzMwMVqs16SQFFxYWeP/992lvbxczIeHuh1aamvHyyy9T\nU1Ozwyv9alQqFQ6HgzfffBOVSoXJZPpaITVp3F11dTVms3mb519VVUVJSUnS5l5l7mdmZoZr164x\nMzNDIBAQilD79u0jLy/vmQ88/yqk6nhpfKDb7d42MOBhpKSkYDKZRGvS/v37cTgcT81Llo2kzCMj\nPeRqtVqMMZMqmhUKBVNTU1y6dAmn0/lAofudQKqok+Yuut1uoe5hNptxOBwcOXKEY8eO0dDQkFS6\nlg9DqVSSmpr6jcOi0qxCnU53XzuSNAM0WQo8ngRSlbIkqZesqYBvyubmJh6Ph6tXr3Lu3Dkx41Sj\n0fDcc8/R1taG3W5PulCrVMV67NgxIpEIn3zyCQsLCyKicy/SM15QUEBJSQllZWVUVFRQXV1NUVGR\n7EnKJC/Sg24wGLBYLCJvADAxMUFHRwcvvvjiDq9yO8FgUExikfLoSqUSh8PBwYMHeeGFF9i3b19S\nzdl7Guh0um9d7+OXoVKpyMnJ2XZhk8J7u9FgSmuW+tMlSTdAzHI9ePAgra2tO7nMhyKdHSdOnMBi\nseD1ekWnxBfRaDSYTCZaWlo4ceIEBw4cwOFwPJMipG/vCSDzTJAUWYxGI01NTfzxj3+ks7Nzm/Zi\nMrG1tYXf72dxcZHZ2Vk2NjZISUkhPT2dxsZGfvKTn1BcXIzZbN7xZmuZJ8uDjGQikWBzc/O+yS67\nhXA4zPz8PJcvX2ZyclJ83eVyceLEiV1RL6JSqSgrK+Pv//7vWV9ff2BPvVKpRK1Wk5mZSVZWlpAr\n/bIC0Se2vqf66jLfelQqFdnZ2aKARyrP7u/vT7oqUAlpbJmUg0tNTaW4uJj6+nqampqSuq9T5tGR\nVJPsdjs2m421tTUx4Dc/P/8bCcEnC+FwmMXFReGBSXrS5eXlHD9+POkHnkspmszMTDIzM3d6OQ9E\nvirLPBYKhYLU1FRMJhM2m43XX3+dv/mbvxFSfcmITqcTh6Ver0ev19PY2EhtbS1Go/FbHWL9/xml\nUolWq8Vut1NRUUF6ejobGxv09vZum2G7m/hi6kCtVlNUVER9ff2uyaknO/JpIPNYfFGo3m63s2fP\nHtHfpNFokqrPTqFQYDAYqK+v5x//8R9ZXV1FqVQK6TU5xPrtRqVSkZ+fz4kTJ8SQd4vFsmvFPdLS\n0sjJyaGqqkrIJL7xxhscOXKEjIyMpJRO3G3IRlLmiWI0GikoKECtVrO2tiZmZyYLUl9WcXHxt0I/\nV+brI4X2bDYbzc3NYm7hbjWS0oXPbrdTXFxMLBZDoVDQ0tJCQ0MDOp0uaVMeuwnF1m4s65KRkZGR\nkXkGyLElGRkZGRmZhyAbSRkZGRkZmYcgG0kZGRkZGZmHIBtJGRkZGRmZhyAbSRkZGRkZmYcgG0kZ\nGRkZGZmHIBtJGRkZGRmZhyAbSRkZGRkZmYcgG0kZGRkZGZmHIBtJGRkZGRmZhyAbSRkZGRkZmYcg\nG0kZGRkZGZmHIBtJGRkZGRmZhyCPynpEYrEYGxsbjI6OMjU1hdvtZnV1lc3NTeDuYF+TyUReXh4u\nl4vS0lL0ej2pqak7vHKZe0kkEoTDYRYWFpibm2N2dpaJiQmWl5cByMnJoaSkhH379mG323d4tTIy\nMs+ap2Yk4/E4m5ub+P1+/H4/8XicRCIh/l2hUKBWq0lLS8NisZCSkrIrBt4mEgni8Ther5eJiQku\nXrzI9evX6e/vZ35+nvX1dZRKJUajkczMTGpqamhubmZjY4Pi4mKysrJITU3dFXv9IltbW2L/0WiU\ncDhMKBQiEokQiUSIxWIolUp0Oh1ZWVloNJqk3ae0D5/Px/z8PP39/eLPjRs3xKT6qqoqjh49SkFB\ngZiLGQqFCAaDbG5ukkgk2NraQqFQkJqaSlpaGqmpqajV6p3cnoyMzBPiqRnJYDBIT08PV69e5cqV\nK6ytrREMBj//j1UqHA4HR44c4a233iItLQ2tVvu0lvPEiEQirK6ucurUKf7yl78wMjLC4uIigUCA\ncDiMWq3GaDQSi8WYm5tjbW2N27dv88EHH/Czn/2MEydOUFBQsCv2+iBCoRCrq6t4PB5GR0e5c+cO\n09PTzM/P4/V60Wq11NfX83d/93c4nc6knYwejUZZXV3lr3/9K6dPn972Pm5sbIjvW15eZmxsjOXl\nZfx+P0qlkpGREQYHB+nt7WVjY4NEIoFKpaKoqIiWlhaKiorIysrawd3JyMg8KZ6IkZQ8jM3NTZaX\nl5mYmGBkZIT+/n5u3brFrVu3hBGRvl+lUmGxWIjH42RmZrJ//36KioqexHKeKn6/n8HBQbq6uujs\n7GR1dRWTyUR1dTV2ux2r1UpGRoYwpuPj47jdbnp6erBarYTDYY4cOYLL5cJms+30dr6SWCyG3+/H\n4/EwMzOD1+sVoUm3243b7cbj8bC8vMzq6ioajQa/309dXR0tLS0UFhZy584dNjY2UKvVFBQUJMW+\nl5aWuHLlCufOnePSpUssLy+TmZlJc3MzGRkZ6PV6ABYXF4nH48zNzREIBBgfH2dqaorJyUkmJyfx\n+/1Eo1FSUlKYmpoiHo8LT1rm2ZJIJIhEIszOzjIyMsLS0hLhcBidTkdaWhpmsxmj0Uh6ejoWi2XX\npz+2traAu2fS7OwsXq+X5eVlfD4fGo2GzMxMSktLycnJ2dXRq3A4LCJVXq+X+fl5lpaWiMViInqT\nlpaGwWDAarVitVpRq9VPbL+PbSS3trbY2toiFArh8Xjo7e3l/fff5+zZs2xsbBAOh9na2kKpVKLR\naFAoFOJnlpeXuX79OpFIBJvNtiuMpM/no7u7m4GBARYWFkhNTaWkpIRXXnmFpqYmysrKSE9PB+56\nIe+99x6nT59mfn6es2fPMjU1RSQS4YUXXiAzMxO4G3pORqRDx+12c/XqVc6ePcvY2Bhut5vl5WXi\n8Thwd/0KhQKVSkUsFsPtdvPBBx9gMBiwWCx8+umnTExMYDQa+e53v5sU+56fn+dPf/oTnZ2dzMzM\nCA/4V7/6FRUVFTgcDgA+/vhjzp07x8zMDJ9++invvvsukUgEhUKBXq8nEomIsOvGxgahUIjq6mrq\n6+t3fI8S0uctkUiIPw9DoVCgVCrFe3rv15KZra0t4vE4a2trdHV18dvf/pabN2+ysrKCzWbD5XLh\ncrlwOp2Ul5dTU1NDXl7erjWSkoGMxWJ4PB46Ojq4du0aN2/eZHh4mLS0NOrr6/nxj3/MsWPHyMzM\nRK1WJ8Xz+HWRntXV1VVWV1cJBoN0d3dz+fJlent72dzcJCMjg4KCAvLz88nNzaWuro76+nqR9rj3\nOX5UHttI+v1+lpaW6Ovr4/r161y9epXx8XHW1taIxWLo9XosFgvV1dW4XC6sVispKSmsra1x+vRp\n4vE4er0elWp31BD5fD6uXr3K9PQ0KpUKm81GfX09r7zyChaLBZPJJEKMmZmZnDhxgkQiwdTUFF6v\nF6/Xy6lTp8jMzKSyshKtVpu0e19aWmJ4eJh33nmHnp4eZmdn8fv9BAIBEokEarUag8FAbm4uNpsN\ni8VCf38/GxsbrK2tcfXqVebn5zl16hRLS0vY7Xb27t1LNBpFpVLt6AdWOlS3trbQaDQ4nU6qqqqo\nr6/HbDaL9zAnJ4fq6mrS0tLY3Nykrq6OvLw88vLycDgc3Llzh+vXrzM6OkogEGBqagqPx8PGxgYG\ng4GUlJQd26NEIBAQYePe3l66u7sf+r3Z2dlUVVVhs9lIT0/HYDCQk5NDTk7OM1zxN0eqE3jnnXdo\nb2+nr68Pn89HJBJheXmZUCjEzMyMeF7Lysr48Y9/zHPPPbfTS38kIpEIgUCA8+fPc+nSJbq7u1lY\nWBDGJBqN0tvbi1KpZHZ2ljfeeIOcnBwRIdkNeDwehoaGuHjxIoODg8LWeL1efD4f8Xic1dVVvF4v\nAwMD6HQ6Ll68SFlZGQcPHqSmpoaioqKdM5JSYY50SHR3d9PT08Pt27eJRqOkpqbidDpxOp0UFRXR\n3NxMWVkZdrudlJQUsdn19XVKS0sxmUyPtZGnzdbWFsFgEI/Hw/DwMEtLS6jVavGBq66uvu9nVCoV\nFRUVLCwsUF1dTTgcZm5ujps3bzIxMUEwGEStVietkZyYmOD8+fOcPXuWiYkJFAoF6enp5OXlodPp\nsFgs2O12CgsLyc7OJjMzE4fDwejoKLFYjMHBQRFuj8VixONx/H7/l3oyz4ovekxKpXLbH8m42Ww2\nSktL0el0GI1G1Go1xcXFFBQUkJWVxY0bN4hGo+IC5Pf7CQaDRCIRdDrdjhrJYDDI8vIyU1NT3Llz\nh9u3b3P16lUuX7780J/Jy8ujvr6erKwsrFYrJpOJ+vp6FAoFGRkZSZdLlzyqpaUl+vv7OXfuHJ2d\nnSwtLYnDMRgMEgwGWVxcZGtri+npaaampqitrcXlcpGRkbErCq0SiQTRaJT19XXcbjcjIyN88skn\ndHZ2Mjk5iUqlwmAwkJ2dLXLufX19qNVq9uzZg9FoRKfTAckR4XgQ0WiUQCDA9PQ0AwMD3LhxgwsX\nLjA8PEw4HMZsNmO1WsnOzt5W7xCJRFhbW2NgYIDR0VHW19fZ3NzEYDCQnp4u9v0oPPLpLIXhzpw5\nw69//WtWVlbw+/1EIhE0Gg12u52XX36ZQ4cOUV9fj8ViETdrhUKB1WqlpaWFaDRKbW0tubm5j7yJ\nZ8HW1hZer1e0ekQiEUwmEy6X60tv2SqViqysLPbu3cv09DTT09Oi4jcYDGIwGJ7hLr4ZUuh8fn4e\npVKJyWSiqamJ6upq8vPzKSsro7i4GKPRiEajISUlhYaGBm7dusWZM2fo6enhzp07hMNh0tPT0Wq1\nTzRX8DhI4f+UlBTC4TCTk5P09fXx2Wef0dzcTH5+PolEAqPRiNPpFNW6ra2t4mKjUqlYX1+nurqa\nzs5OVlZWMBgMYp87fRAtLy9z4cIFLly4QHd3tygk+zIWFhbw+XyoVCrUajV6vZ7W1lZisRj79+8n\nOzv7Ga3+67O1tcXQ0BBnzpxhaGgIn88HIC47KSkpItwcjUbx+/1MTk5y7do18vLyOHjw4K4wkrFY\njPX1dW7dusWpU6d45513WFtbIxQKsbW1RU5OjshBrqys0NPTg1KpxO/3MzExQVZWFpmZmUkR3XgY\nm5ubjI6O8utf/5ru7m7m5+fx+/3EYjEMBgPNzc0cO3aMPXv2iLQN3H3We3p6uHDhAl1dXXz88ccs\nLy+LsLPT6XzkNT2ykQwEAvT29tLb28vMzAzRaBStVovT6aS0tJTa2lqOHj1KWVkZDofjPo/JZDJx\n8OBBEokEdrsds9n8yJt4FmxtbRGLxYjFYqLsH/jSmPcXczrS379ObmgnCYVCrKysMDk5yfT0NMFg\nEJ1OR0ZGBlVVVRw4cACXy0VWVhYWiwWVSiUMX0FBAQqFgo2NDRYWFujr6wPuvt9lZWWi3WenDYjd\nbufkyZOEQiF8Pp8oyHrnnXdQKpVotVoyMjIwmUyiOODeg1RqIZmfn+fWrVtsbGyQmpqKxWIRldo7\nfRkIhUIsLCwwMTHB2NgYgUCAeDz+pb97KUIEd5/b9fV1urq6UCqVImKQTAQCAebn57l8+TKffvop\nXq8XvV6P3W5nz549FBcXYzabicfjLC4ucvr0aSYnJwmFQoyPj3Pnzh2ampp2ehtfi9nZWW7evMnp\n06e5evUqs7Oz4vzcu3cvzc3NVFdXYzKZCAaDtLW1ief7s88+IxAIEI1Gxe8kmYjFYgSDQS5dusSZ\nM2e4fPkys7OzRKNR9u/fT3V1NdnZ2RQVFVFUVERubu620HF2djYWi0V4mZcvX2Z4eJh3331XXHQf\nlUc2kpubm/T29jI8PCxaO1JTU3E4HDQ0NHDo0CEqKipIT08nFovdF1LUarXU1NQ88sKfNVJfp9QD\np1QqicfjbGxsEAgEthlNCanid2VlBY/HQzAYJCUlBa1WK3KRO20sHkQkEsHn87G2tsbm5ibxeJyU\nlBQ0Gg0Gg4HMzExcLtcDc24GgwGbzUZeXh5ms1kYnNzcXJqamsjKykqKm6zNZqOtrQ2v18v09DSj\no6OiQjA3NxeTyUR5eTkZGRlkZGTc9/ORSISlpSURwvT5fJhMJrKysjCbzTtaECIV0q2srIjq4/X1\n9W3fIz3HW1tbRCIRotHoA18nHo+LFIPf739WW/jaSJeba9eucePGDQCcTie1tbW8+eabHD58mKys\nLKLRKKOjo4yPjzM/P084HMbr9YqDOJmJx+NEIhHu3LnD+fPn+fjjj5menkapVIp6j9dff50DBw5Q\nWVm57WdnZmbo6Ojgvffew+fzkZGRgc1mSzojKeXzz507xx//+Ef8fj9arZacnBxOnjzJyZMncblc\nD/1c6XQ6zGYzKpWKRCLBwMAAw8PDnD9/nhMnTjzW2h7ZSIbDYVGkICGFAqLRKEtLS0xOTpKdnU1a\nWhqVlZVJdwv9JigUCux2O/n5+WRmZrK2tkY4HGZkZISpqSlisdh9HlIwGOTatWucOnWKDz74AK/X\nS1paGjU1NZSVlWE2m5MyzKPX6ykoKCA3NxeLxcLi4iIbGxtMTEzwf//3f3i9XsxmM4WFhaSlpW37\n2Wg0yszMDP/3f//HjRs30Gg01NfX88ILL/Dqq68mRfsHgFqtxmw2s3fvXjY3N/nkk09EqO79999n\nbGyMI0eO0NLSwp49e+57bxcWFnjvvfc4d+4cbrebUChETk4OLpdrx/PriUSC4eFhLl68yNmzZ5me\nnt727wqFgqKiIhwOB1tbW6Kd5YtIOa6jR4/y2muvUVhY+Gw28A3w+/3cuXOHpaUl8TWbzcbevXsp\nLS0V4UUpXSAVI33x0pDMSOHhzs5OPvvsM1ZXV0Uo/Hvf+x4vvvjifeFHiUuXLvHhhx+ysLCA3W5n\nbW0tKS8FMzMz/OY3v+HixYv4/X7y8/N57rnnOHHihKhE/rKzMhwOs7i4yNmzZ3n77bcZHx8nJSWF\n3Nzcx05pPbKR1Gq1lJSUMDk5ycrKCrFYjGg0is/n486dO/j9ftxuNxaLBaPRSHl5OXa7HYVCgdFo\nFAlYqRBAirdPT09jNBrJzs7G6XTu+IEjoVAo0Ol05ObmcvDgQaLRKIODgywsLNDf38/ly5fFHre2\ntpiamqK/v5/z58/z2WefMTk5KcLRJ06coK6uDp1Ot+MhuQehUqkwGo1UV1dz5MgRrl69ytzcHH6/\nn9HRUZRKJTabjeeff56mpiZSU1NFpGBqaorr169z48YN5ubm0Gg0VFRUiLxAshQpSTnJ4uJiFAoF\nKSkpZGZm0t3djdfr5erVq2xsbODz+VhcXKS0tBSbzYbRaGRqaoquri7OnTvHwMAAoVAIu90uUgw7\nWQm6urrK3Nwc58+fp729nYmJCcLhMGlpaaK6XK/XU1dXJwo8Tp069VAjKXnU+/fvf6BHvdNI54bU\ngw13Pa97ozYSer2e4uJi3G43KpWKlJQUURsQjUaT8sIKd52PmzdvcvPmTcbHx4lEIpSUlHDo0CFe\neukl9u3bR2ZmJiqVSnj/i4uLQhGsp6eHjY0NotEokUgkadI8kofDJiwhAAAduUlEQVTc09MjntdA\nIEBFRQVHjx7l0KFD7Nu3D5PJ9KWFN9J+z549S3t7O7du3SISiZCXl0dhYeFj25BHPrHS09N5+eWX\niUQiTExMCOURuFtpJoWilEolKpVqm6JOQUEBFRUV7NmzR0h9bW5uMjY2xkcffYTT6aS1tZXvfve7\nSWMkJbKzs3njjTdYXFykv7+ftbU1enp6+N3vfsfPf/5zMjIyiMfjdHZ28oc//IHOzk68Xi8KhQKz\n2UxlZSVvvPEGxcXFSWMwHkZzc7No15HEICKRCIODg0xNTREOhyksLMRms4niiL6+Pjo6OpiZmWFz\nc1M0NBcWFiblhUCq4iwtLaWgoIBQKMStW7dE6HVkZITu7m5+9KMfsXfvXpxOJ1euXOHjjz+mu7sb\nn89HamoqlZWVHDt2jNdff31HD9uFhQUuXbrE+++/T3d3N8FgkNTUVFFIV19fT05ODkVFRZjNZgKB\nALOzs5w5c+a+11KpVKSnp5OVlUV2dnbSVbY+jOXlZfr6+mhtbd32da1WS11dHeFwGKPRyOLiIktL\nS/h8Pmw2m/g8JlsKZHV1lWvXrjE8PMzy8jJarZbnnnuOf/7nf94mfCH1w4bDYYaGhnjnnXe4ePEi\nExMToqc5mZDSVb///e/585//jNfrpbm5mZMnT/LTn/6U4uLir3yNra0totEok5OTojdWyqmnpaVR\nUlLy2KHlx/Iki4uLefnll8nMzBRtDT6fj1gsBtx92DY3N4UknZS7jEQieL1ebt++vc2T3NjYYGZm\nhvT0dILBYFK+sXq9HpfLRUNDA6Ojo4yOjuLxeLh48SLRaJSzZ88SjUa5ffs2fX19rK+vk5aWhsPh\n4Hvf+x4nTpwgJycnaW+t95KRkUFtbS2//OUvycvL48MPP2R1dVVol3Z2dmIymdi/fz+pqam43W4+\n+ugjurq6CIfDHD58mJdeeomjR4+Sl5eXdIePhKS1u3fvXtLT07ly5QqdnZ309vYSCATo7+/n97//\nPe3t7VgsFlFmvrGxQUFBAfX19bS1tbF///4dvwgMDw/z+9//ntHRUSKRCFtbWzQ1NdHW1kZraytO\np1Mo0HzVWqXewsHBQfr7+ykvL8disTyjnTw6sViMQCAgziEJrVZLU1MTaWlpZGZm8uGHH9Ld3c2/\n/Mu/0NbWxvPPP092djZGo3GHVv5gNjY2GBgYwOv1otPpqK2tpbq6moyMjG1tEOvr68zNzfHZZ59x\n5coVLl26hMfjSRrP8YtIF7SFhQWCwSBWq5WmpiZaW1u/1nMmFc91dHSIaEggEADu2h6tVovNZnus\n9g94DCOpVqux2+0iuVpYWMjo6Cher3ebcVteXmZ6eprNzU0heL62tobX6xWb+SLBYJBwOHxfFWky\nkJqaSlZWFo2NjSwtLZFIJESVnN/vJy0tTYSdpVL0goICnn/+eV5++WUOHDiwrdI1mTEYDKSmpmIy\nmVAqlfh8Pvr7+5meniYQCDA0NCT6sVQqFZOTk6Js22q1cuDAAd54442kPHjuRSoukhRZLBYLNpsN\nk8lEb2+v0G6VCq6kfLRGo6GsrIwXXniBw4cPJ4W3vLCwQGdnp6jChruiFuXl5aLSXCISiYiws06n\nE585iVgsxurqqpAhLCgoeOb7+SqkKNW9BR2hUEhEs9bW1jAajaIVxGKxkJ6eLi7wkshHWloaVVVV\nIj2UTIRCIebm5tjY2ECj0eBwOIT0pSTXtrm5KS40n3zyCbdv3xYFStIZmixIHq9k0GdnZ9FqtTQ0\nNLBv3z5qamq+1nsQj8cJh8Ncu3aN8+fP4/V6xTOdmpqK1WrF5XLdVzfxTXnseJ/BYMDpdGK322lt\nbRUqJhLz8/OMjIwQCARYWFhgYGCAnp4ehoaGHvqasViMcDhMLBYTExaShZSUFHQ6HXv37hU5GoVC\nQW9vL4uLiywvL4u8gER1dTX/9E//hM1m2/FD9JuSkpKC2WzmwIED5OTk8Pbbb3Pu3DlRLLG+vs7Q\n0BAKhULkPKxWK/v376epqQmn07krvGaJlJQUampqyM/P59ChQ/zbv/0bo6OjItQsTbSRQpj19fW0\ntraSnZ392DfWp8XCwgJDQ0Ps3bt329clKcGMjAyys7OZn5/fNoRgN6DT6SgoKBBSkHDXoxobGxPe\nb3V1NSkpKUSjUaanp7l48SK/+c1vmJ6eFv2TsVgsqfJ1DyORSBAKhfB6veIMlfY7MDDAwMAAg4OD\nxGIx8vLy8Hg8rK2tJdW+pHa6mzdv8h//8R/Mzs5SUlLCL37xC5qamkRV/Fch5Z7HxsYYHh4mEokA\niPqC4uJiGhsbHzv68dhGUrqhPaw0V1I8CAQCTE5O4vP5GBkZ+dLXnJ+f59q1a0K27UlICz0ppL5I\nk8lEUVERx48fZ2Njg6GhoYeW0k9OTvLOO++wb98+KisrhY7ibkAqasnIyCA1NZVXXnkFm81GR0cH\nQ0NDIvcokZWVRXV1Na+88gp1dXW7ShtTutxMTk4yMjLC8PAwY2Nj4t+kAxU+NzB6vZ60tLSkGQum\nUqnQ6XRCTxbueiKScb8XyYPOy8ujqqqKjY2NXWckpUhWeno6SqVShOCCwSAdHR2srq5SVFREamoq\n4XCY6elpbt++vU3xymw2i2hYMsq2SWLlXq9XzLCNRCLiHA0GgywtLbG4uCgGLkg58vfee4+uri5h\nQJKBSCTCzMwMIyMjTE5OkpaWRlFRETU1Nd+oRUxSH/L7/aJwKyUlBaPRSFNTE42NjfeFpB+Fp145\nYjQa0ev1YnrEvQcN3H3ITSaTcJ03NzdZWFggEAiQl5eH0WgUKvbJ0F8noVKpMJvNNDc3MzIygkaj\nEZ6vJKwLdw+ogYEB/vVf/5W33nqLSCRCVVWVkPjaLXM0NRoNarWaI0eOkJWVRTAYZHV1VcxdlLBY\nLFRUVPD8888nvYqShORJSPnzy5cv09HRQWdnJ4uLi6SmpqLX61EqlUSjUYLBoLgNSyGvZLmpS7Jk\nc3Nz4sImrTkSiYieV/j8gisptdy+fZvFxcWdXP43RqvVkp2dLYQtpBaHRCLB1atX6evrEwIWsViM\nlZUVgsGgeL+kS3xeXt7XKhTZCQwGA8XFxaysrBAIBIQ4hIRSqRQtIRaLhdraWk6ePMlbb73F0NCQ\nkE5MFqT2Qbfbjd/vp6CggIKCAhwOxzcKdQeDQbxe77aLnU6nIzs7m0OHDtHY2PhERD2eupGMRCKs\nr6/z3nvvcfbsWZGAlsKotbW1/PSnP8Xj8TA4OMi5c+dYXV1lc3OTc+fOoVKpKCkpwel0Jl0Jejwe\nZ2VlhZWVFdF0bzabqaiowGAwEI1GuXXrFqurq6ytrfHhhx8yODhIY2Mj+/bto7GxEZvNlpS314cR\nCASYm5ujr6+P+fl5gG15Y8nj2k1Eo1Hcbjc3btygo6NDlNqvrq5iMBgoLCyktbUVrVaL2+3ms88+\nY2JiAq/Xy9TUFOPj4xgMhqR4H4uLi3njjTd4//33GRgYAD4v6PB4POTm5ibd5+hxkARMWlpaWFlZ\n4fTp08zNzQGfh+Ok6nKpEjJZLjRfF4fDwWuvvYbZbEav1zM2Nsba2pqIDBiNRqGP3dzcTGVlJYWF\nhbtiPJZCocBisTySXJ4kvbeysiK+VlJSQmtrKwcPHnxiEcinbiSl0Gl7eztXr15leXlZNN5brVZq\nampoa2tjZWWFkpISTCYTN2/eFFWjfX19tLe3c+TIEXQ6XdKEteDu4To/P8/i4qLYk6QQYbPZiMVi\nVFRUMDo6ytTUFEtLS3R3d7O4uMj8/DzT09PU1dVRVFREdnb2Nnm3ZCMajRIOh4UhGRkZEYVJ9yIV\nZ+2Wg2hubo7x8XH6+vro6uoSzyiAy+WirKyMmpoaDh8+jFarZXZ2ls3NTTY2NlhZWWF4eJgLFy5g\nt9tJT0/fcRUli8VCTU2NuMSsra2xvr4uwlvSYaTVar8yDCWpTOl0uh0Xa38YkoZudXU1oVBIKIHN\nzMwQDoeJx+PE43FxiZFa1aSLnFqtFrrSyYrZbGbPnj1oNBqys7MZHBwUvemS8HxFRQV1dXVUV1fj\ncDjEhS1Z0lT3IqUqJIGOQCAgHCO9Xv+VqahYLMba2ppQ1Jmfnxeyl0ePHuX48ePCljwJnrqRHBkZ\n4b/+67+4deuWCOUoFAoxh7GqqoqCggKKiopEKf3bb7/Nr3/9a9xuN1NTU/zv//4vZrOZvLw8MjIy\nksaQRCIRpqenWVhYAO7eaouKivjJT34itAL9fj+dnZ288847fPbZZ4yNjdHX18fg4CBnzpzh5Zdf\n5sUXX+T48ePo9frHjp8/LcLhMEtLS7z//vt8+OGH28J5934QV1dXxUDU3cDt27f58MMP6ejoYGpq\nSlQol5SU0NbWxuHDh2lqahI5r3A4LIa+Xr9+nf7+fjY3N2lsbKSgoGDHKyMl/ViXy8Xs7CyDg4Os\nr68zNTVFT0+P8EYkwfYvIyUlBb1ej9lsJj09Panz6AUFBZhMJvR6PZmZmXzwwQcsLy+LfLnNZsPh\ncDA2NiY0mOFueM7lcj1QrSZZ0Ol05Ofnk5eXx9GjR3G73WxsbAgjaTKZKCgo2JbmSWak81+j0bC1\ntcXExASDg4NCkPyrnjOpL7Krq4tPPvmEUChESUkJb731Fq2trdTW1j7R38NTN5KS6v69MXGtVktW\nVhaHDx9mz549QgtVqVSK3rzW1lYxrHh8fJyPP/6YWCzG66+/njTSZtLAU8ndl4pc7hVzNxgM1NTU\noNfrqa2t5caNG/T09IjZbxcuXECtVlNQUEBhYWHSfVil8Vbd3d385S9/4cqVK8LTOnLkCIcOHUKj\n0TA8PMyZM2dQKBQix3dv/ivZWFlZ4c6dO7S3t3Px4kXm5uZIJBJYLBZ++MMfcuzYMQoKCsjOzhby\ngVLRljS0d2BgAJ/Px/LyMjdu3MDhcNDU1LSje87IyKC6ulqoO3344YeMjIywtrbGxYsXmZ2dpbOz\nE4fDIQzf2traA/OqGo2GrKwsCgsLKS4uTmpvS1IHamhoID09nYaGBiGAAXdTAtJ7dW94TlKWSuZ5\nmfcOSpDCy1arlUQiIbz9L0bYJAU0qU5ia2uLjIyMpChOkooxy8vLcTqdYsTVf/7nf9LW1sZzzz1H\nWlqaGOI+MzMjolbLy8u43W6h1Su1LaWmppKdnS0us0+Sp24kJemheyvrMjIyKCsrY9++fZSXlwu3\nW1LnKSsr4/Dhw/T19TE7Oytu7SaTiba2tqQykisrK2L80L2zCaUHW6PRiCG9ktSe1Wqlu7ubW7du\nMTY2RlpaGmVlZeKDnixhV0ngYWBggPb2dv70pz/h8/lISUmhrKyMtrY23nzzTbRaLZcuXWJiYkK0\nEaysrOBwOB67R+lJIx0YS0tLXL58ma6uLoaHh0kkEmRnZ1NZWckrr7zC888/j0ajue9GqlQqKS4u\npqqqCr1ev62Ywu1209jYuEM7u4sUVpT6PKUZiv39/YyNjeF2u+nv7982IFzyfkOh0LbXkio/bTYb\nWVlZO7Gdr41SqUSn0+F0OsWQhWg0Ks6d0dFRrl69ilqtFpcBrVaL3W6nqqpqW/9oMpOSkvK1wohS\nr6g0+QXuqqRJYhI7iVqtJisri8rKSg4cOEBnZydzc3N88skn4oKdlZUl+lu7u7uZmpoCEHUAt2/f\nxuPxEI/HxcSep3Xe7IguWmFhoZgS8iCDl5OTQ1NTk+g9C4fDBAKBbcnqZCCRSAjtx6+DZCD37NnD\nH//4R9EWMz4+zn//93+LKfDJElIOBoNMTU3x29/+losXL7KwsIBKpaK8vJyf//znHDp0CJfLJbyr\no0ePcurUKSHZl5GRkXRGEu5GNxYWFrh48SJjY2MiZ9XU1MQvf/lLampqvjTk43Q6KS8vx2w2i1B7\nso0+kw6igwcPAncvcLdv32Z9fV3k0e8dMg0ItZLdjlqtFjKJUu5RkqpbW1sTX8vKyqK0tJSysrKk\ni+A8Lj6fj5s3b+LxeITyUlpaWlLJC1ZXV/OrX/2Kzc1Nzp8/z+zsLO+99x4XLlwgNzcXp9NJfn6+\nmBMKnxcH3iuSkJWVRVFRETk5Od8eI1lWViYkoB50GKWmpopBvtIHOB6Pi9BBsiAdRFarlTt37nzl\n90vjiUwmE8899xxLS0u89957TE1NMTY2xvz8POvr65hMph3N/8RiMUKhEF1dXaLgyuPxiGkuLS0t\nHDx4kMLCQvGBk6aCGAwGMZz6QYU9O83W1hZ+vx+Px8Po6CjLy8toNBrKy8tpampiz549X3lJWVlZ\nYWFhgVAoJAzjvcZmp5EiGmazmdraWoxGI8XFxUJj+UHE43GWl5cZGxujt7cXuGv4pbasQCCwTcg+\nmZHSHvfyIKk6vV4vpoIkc771UZBE+j0ej+gldTqdSZVbTk9Pp6qqih/96EeUl5czOzvL5OQk8/Pz\nDA8Pi5z6xsYGeXl54qw1Go2sr68zMjJCV1cXGo0GvV4vWuqeNM/0iZcMnMvl4tChQ8/yv34qaDQa\ncYOBz8V219fXCQaDwoDcG7KTDtOysjKCwaDwZjY2NlhbW9vxylDplubxeGhvb+ftt9/G4/Gg1+tx\nOp08//zzPP/881RUVKDX68V7Ko1VUqlUYtZiMs4fBITU2vz8PIFAAKvVSl1dHXV1dV/a2ylVSg4N\nDdHb28vq6qrIuxoMhh0PY30Rqb/O6XSyf/9+MXT3QUii2B999BF9fX2iD9Tv97O6usry8vI2EfDd\nxhf7s+Hu51fqv06WC87jIn0epUr6paUl0tLSqK+vF3nlZCnuSU1NJTU1lR/+8Ie0tbUxNDRER0cH\nV65cYWZmRhQBNjY20tDQQHV1NQUFBZjNZiYmJvjoo4/o7u4WKa6ndVF9Jk/843h/0s8mY/+dVM0q\nHayRSITx8XF++9vfcvLkSQ4cOPDQloC1tTVmZ2cfGKrdqX1Kv2Ov10tHRwd9fX0sLi6iVquF93/s\n2DGqqqq2VUbGYjGWlpa4devWtqKIZCSRSDA7O8v09LQI3atUKhwOx1eG3Px+P/Pz85w5c4YzZ84I\nLU2r1UpjYyOVlZVJedhKLR/SKKUHEQqFMJvN20JxkUiEhYUFxsbGGB0dFa0gu5FIJCJ6mb/NSEO0\npbaKSCSCxWKhqamJgoKCpDGQX8RoNFJRUUFmZiZHjhzB4/GIfKpUQJeWloZerycej+N2u5/Z2nbk\nWuh2u+np6aGwsPCBOn3hcBi/359USiYPQq1Wk5ubK+Ylut1uFhYWOHv2LIlEgs3NTXJycjCZTEIW\nSwpf9fX1cfnyZVZWVkRFbFpamkhW7xRS72d7e7soaCkrK2P//v3Cg7zXmITDYe7cuUN3d7cYHZWe\nnk5hYaEYg5ZsqFQqUa0Kn8+jm5qawmq1irmRgMiFr66uMjU1xeDgIJcvXxbSYIWFhUJuMCsrKykP\nIemG/VVe4BfDqfF4XBy2Pp8vqVRbvinSpfSLxUnfNra2ttjc3MTn8wk1GqlK2WQyJeXzCXe9eovF\ngsVioaioiI2NDXH+S21LEoFAQJyRW1tbWK1WsrKynlqU45kYyS++MdevX+d//ud/+NnPfibyAfd+\nTyAQwOv1brv5SXmWZEKlUmGz2WhubmZ9fZ13332Xnp4eent7WVhYoKuri6NHj1JSUoLFYhEPrtvt\nFoOJpYc4PT0dh8OB3W7f0ZxBKBTC7XbT3t6Ox+PBarXS0tLCiy++SEtLy30XGr/fz1//+lc+/vhj\nLl++LPJ7kuJFsqFUKiksLKSkpERUr/r9fq5cuUIoFMLn83Ho0CGxdrfbzdDQEH19fdy4cYNr166x\nvr4uDtva2lq+//3v43K50Gq1SfeMytxFGq6QzJfuJ4E0o3FxcZG5ubld6TmnpKSQnp7+tSdAuVwu\nofP9NHjqRjI/P58TJ05w6dIlBgYGxOisTz/9lFAoRE5OzrZhp1tbW0JGa3h4mGg0KsaoHDp0aMeb\nte9FoVCg0WhEri4YDGI0GkW+amhoiPX1ddLT09FqtcKL9Pv9LC4uEgqFUKvVVFZW8p3vfIf6+np0\nOt2OHbSSwLfUX5VIJIRgwvXr14VIciwWIxgMMj8/z8TEBD09PYyPjxOLxXA6nRQXF5OZmZmUoTmF\nQkF6ejp1dXX87d/+LVeuXGFgYACPx8OVK1cYGxujvb1dDGqVlHXuFZCOxWLY7XYOHTrEd77zHfbs\n2SPGL8kkF1KfdiAQ+NYbSIkvivHvNu7tC/06zMzMMDo6+tTqXJ66kczJyeH48ePCE+zu7mZlZYX1\n9XWWl5cxGo33eSeSIQkEAhgMBhwOBwcOHODAgQNJZySlsSxms5lIJCIKOCYmJlhaWmJyclLkCAAx\nvy81NRWbzYbdbqelpYU333wzKcZKSSFh6RYXDocZGxsjGo0yPj4O3D14NjY2GB8fF9MUpN9DXV2d\nMBo7vZcHoVAo0Ov1lJSU8L3vfU+EtycmJvB4PIyNjd13uEgN21qtFqvVisFgoLy8nNdff519+/ZR\nWFi4M5t5gkiN6nq9HoPBQCgUEpWgUt/rbgy3SkVoD6rqVavV90WxZHYXCoUCj8fDzMwMoVCIeDwu\nbM2Tel+fupG0WCw0NDSIhuTJyUnC4TDRaBSPx8Pi4uJ9m5FCBAaDgaqqKl544QVaW1upq6vbcbWI\nByEZvrq6OgoLC/nOd77D6dOnOXfuHHBXH7S/v3+bMkReXh4ul4uDBw/S0NBAaWnpjvcvSfmMe2/d\nkpGcmZkRxTqSxynNWFQqleTm5lJfX8/3v/99WlpahCeWrBiNRsrLy9HpdJSXl9PR0UF3dzc3b94U\nmp8S0rDeoqIiqqqqqKmpobq6mvLy8seeVZcsSJccp9NJUVHRNpWT6elpurq62Lt3b1IOXv4yAoEA\nY2NjLC0t3fdvaWlpZGRk7NqKXZm7SAIE0gU/JSVFaMM+CZ760yGNWCooKKC5uZlXX32VyclJoVKz\nsrLC1NQU0WhUlKxL4stlZWVUV1fT0NDwRCZMPy2kW4tUeONwOIjFYkLFw+fzMTMzQyKRQKPRkJGR\ngcViwW63U1paisPhSIrSbGkPBQUFtLS0cPv2bdxutxCOltYnzQ/NzMwkOzub4uJiysrKqKiooLGx\nEYfDkZRVnvciCWM7nU6MRiMmk4nq6mqmp6fvmxShVCoxmUzY7Xby8vLIz8/H4XBgNpu/NQesNFxb\nuswuLS0JI7m0tCQGp+82EokEwWBwmxesVCpF247BYEj6Z1VmO9JwDKvVikajYX19ndu3b/Puu+9S\nWVmJy+XC5XI9sYv6Myvc0ev1lJaW8pOf/ITp6Wm8Xi9wVwC9o6ODzc1NsrKyOHnyJOnp6ej1eg4c\nOEB+fv6uEe6Fz0OwDQ0NNDQ07PRyvhEKhQKr1UptbS2vvfYaer2eRCKxTdoK7lZBSiPB9uzZQ2tr\nq1At2S3vk4R0UObn5+/0UnYUpVIpZitardZtw7LX1tZwu927biAzfB71kNIHkmi7yWTCZrM9sLp+\ntyP1DarV6l0ZIv8qVCoV2dnZ5ObmYrFYWF1d5caNG8zPz9PS0sKJEydECuyJ/H9P5FW+JpKnmJ2d\nLaoDDxw4wEsvvUQ8HhfivZJAuNVqTcq81rcZpVKJ3W7n2LFjVFZW8tOf/vQ+paN7W1bS09Ox2WxJ\n6+XLfDOkCMFOh/6fFJFIROjrqtVq8vLyaG5upq2tjYqKClwuV1ILt39TlEqlGCBRVVXF9PT0Ti/p\niaNUKklLS6O6upof/vCHnDlzhvn5efLy8kTk8Umme56pkVSr1WRkZHyrhr5+m5C8QMm7+jYUpMh8\nM6Qagp6enp1eyhNBq9WSm5vL/v37sVqt5OXlsWfPHo4dO0ZGRsa3ykDC51G7srIyXnvtNWZmZjAa\njUkxxu1JoVQqSU1NxeVy8dJLL6HT6Zibm6OsrIympiby8/Of6CVPsZVsMjYyMjI7RjAYZH19nX/4\nh3/gD3/4A3A3cuB0Ovn3f/932tradniF34xEIkE0GhUTQaQwpDSeb7elB74Kqf3j3ulLUuVySkpK\n0o6uexQkmUhJdECKcEnRxyf13spGUkZGRiANEmhvbxeTF6Tw1vHjx8UwcRmZ/1+QjaSMjIyMjMxD\n+HaVdcnIyMjIyDxBZCMpIyMjIyPzEGQjKSMjIyMj8xBkIykjIyMjI/MQZCMpIyMjIyPzEGQjKSMj\nIyMj8xBkIykjIyMjI/MQ/h+NGlY9L5UZIAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b3d9588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(6, 8, subplot_kw=dict(xticks=[], yticks=[]))\n",
+    "for i, axi in enumerate(ax.flat):\n",
+    "    axi.imshow(mnist.data[1250 * i].reshape(28, 28), cmap='gray_r')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This gives us an idea of the variety of handwriting styles in the dataset.\n",
+    "\n",
+    "Let's compute a manifold learning projection across the data.\n",
+    "For speed here, we'll only use 1/30 of the data, which is about ~2000 points\n",
+    "(because of the relatively poor scaling of manifold learning, I find that a few thousand samples is a good number to start with for relatively quick exploration before moving to a full calculation):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QPH8Z2z/t/9iLAEH4X+bg4EBqaioRERHk5+ezaNGiJ5YXnwLh/4V9p0+zKKCdtQWWnMjC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\nG6NRi7W1CYMhC9AAZvL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b3d2egD9xWT83cCqrDmwmW8jFzWDNrMH1HhKz2cy+1G9o87jo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Ny5c4HSm4NiYmKw2WyEhITQqVMnAIKDgxk1ahRKKeLj4wGIiooiNjaWDRs24Ofnx5IlS5wV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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b08efd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# use only 1/30 of the data: full dataset takes a long time!\n",
+    "data = mnist.data[::30]\n",
+    "target = mnist.target[::30]\n",
+    "\n",
+    "model = Isomap(n_components=2)\n",
+    "proj = model.fit_transform(data)\n",
+    "plt.scatter(proj[:, 0], proj[:, 1], c=target, cmap=plt.cm.get_cmap('jet', 10))\n",
+    "plt.colorbar(ticks=range(10))\n",
+    "plt.clim(-0.5, 9.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The resulting scatter plot shows some of the relationships between the data points, but is a bit crowded.\n",
+    "We can gain more insight by looking at just a single number at a time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZWLVqFf773/+iW7du+OijjzjpgIiI6P8xtJHDOH78uLwzw4wZMzjpgIiIqBSGtmowGo1q\nlyAzGo3w9w9UuwxFCCFw7NgxPPvss+jSpQsSEhIAOMctXSIiInthaKuioKAWSE8HsrJMdj+3Vqsp\nd15//0AEBbWwey1Ks+zOMHDgQJjNZkyYMIFhjYiIqAIMbVXk6ekJvd6gyrkDA+vCaLyE5OQkhISE\nQqPRqFKHrUycOBE6nQ7R0dHc/J2IiOg2GNqcgMlkQvfuEUhNTYHBEIydO/c6RXC7efPmHZ8XQmDz\n5s04dOgQpk+fXmarqlsVFRUhLS2tRvUYjUbk5OTW6BilBQW1gKenp2LHIyIiuhOGNifw66+/IjU1\nBQCQmpqC5OQkhIV1LPc6RxtzN3PmNHz33Z47vs5sNuPPP/8EcOcxbGlpaTAajdDpdFbXVJP33spo\nNCI9Har1vhIRkfthaHMCrVu3hsEQLPe0hYSElnuNmmPuKnL+/GVcvPj7HV8jSRJGjhyJkSNHVumY\nOp0OwcHBClSnDEf5rImIyD0wtDkBjUaDnTv3lhvTVlRUhPT0MypXV5bllmHDho3QvHlQpa/npAMi\nIqKqYWhzEhqNptwt0fT0M8jJuarobb+aKH3LUKPRYM2aWLVLIiIichkMbU7OkW8Z+vn5qlgJERGR\na/FQuwAiIiIiqhxDGxEREZET4O1RsimlliGp6XIfREREzo6hjWzmdsuQmM25MBrToNPp7zjurfT2\nXUouins7QgicP38ea9aswTvvvIP4+Hj06dPH5uclIiKqCoY2spk7bf11//3tKn1/YGBdXL16Q+my\nKiSEwPbt29G3b1+EhIRg165d6Ny5s13OTUREVBUc00Zuz9LDNmvWLPj4+ODNN99E586duYYcERE5\nFIY2qpAQAgcPHoRWq4W/vz9Onjypdkk2IYTAxYsX0adPH5w8eRLx8fHo378/AxsRETkct7096si7\nCTiK+Ph4ZGdnAwDS09PRpk0blStSlhACcXFxWLBgARo2bIg9e/aga9euDGxEROSQ3Da0OfJuAo6i\nd+/eWLZsGZo3b47HHnusyu8zmUzlttxyVM899xxatmyJ5cuXo02bNgxsRETksNw2tAGOvZuAmoQQ\nyM7Oxvvvvw8AmDBhAnx9q7a7gdmci+7dI+TN7Xfu3Otwwc0yhi0mJgZ16tTBJ598wsBGREQOj2Pa\nqEJxcXH46quvAAB6vb7KgcZoTENqagoAIDU1BcnJSTar0RqWMWxz5szBW2+9henTp3PSAREROQW3\n7mmj8oQQOH78OKKjowEAzz//PHr37l3l9+t0ehgMwXJPW0hIqK1KrTYhBH777TcMGDAAKSkpmDx5\nMmbPns3ARkREToGhjWRCCBiNRsybNw9//PEHwsPD8dZbb1VrcoSfny927tzrEGPahBDIy8vDggUL\nkJJS0vu3efNmSJKEwMBAzhIlIiKnwtujBKAk4Pzyyy8YOnQovvjiCwQGBuLzzz9HQEBAtYONRqNB\nWFhHhxnL1rt3b0yePBk3bpQs1Nu2bVvs378fXbp0UbkyIiKiqmNoc3NCCBQUFODgwYPo2rUrDh48\niNq1a2Pt2rW46667nLonSpIk1K5dG82aNcO6deuwe/duRERE4MMPP4TBYHDqayMiIvfD26OEr7/+\nGv369QMAGAwGvPPOO+jVq5fThxohBAoLCzF27Fjs2LEDffr0wbZt2wDA6a+NiIjcD0ObGxNCYPXq\n1Zg5cyYAwMfHB5999hk6derkMqHm6NGj+PrrrxEYGIjVq1e7zHUREZH74e1RGxJC4J133oEkSfDw\n8ECLFi3w888/q10WgJLaVq1ahWnTpiEzMxMBAQFITU11qcAGAAsWLMDdd9+NzZs3o0GDBmqXQ0RE\nZDX2tNmIEAKXLl3C//73P0iShObNm+OTTz5Bu3btVK+rqKgI27Ztw6xZs/DHH3/AYDDgxx9/dPox\nbBZCCKSkpKBfv374/fffsWPHDnTp0sUlro2IiNwXQ5sNCCEwa9YsfP311zhx4gSAkt0XHGVfy2nT\npmHp0qUAgPbt22Pu3LkuFdh++eUXdO/eHZcvX8aRI0cQFhbmEtdGRETujaFNQUIImEwm7N27F/v3\n78eJEyeg1+vRqlUrbNq0SfXgYNkgfdmyZQCAwMBALFq0CN26dVO9NiUtWLAAGRkZePrppxnYiIjI\nZTC0KWzOnDl477335Mf9+vXD4sWLVayohBACMTExeOONNyCEQN26dbF7926X2nNTCIHvv/8e3333\nHZo2bYoFCxa4zLURERFxIoJCLL1smzdvltseeughTJo0CZIkqRoeLOPYNm7ciKtXr6JOnTo4evSo\nSwU2iy+++ALXrl3D+PHj0aZNG7XLISIiUgx72hQghEB6ejpGjx6NixcvAgAeeeQRfPPNN9XaFeDc\nubMVtmdna5CVZarw9Vpt6yodOzQ0FKdPn4afnx/i4+OrtQm8s5AkCe+++y7effdd+TEREZGrYGir\nISEEsrOzMWbMGCQkJMjtEydOhEajqVZw8Pf3hVZbccirqD0nx7dK9R0+fBi///476tati88++8wl\nFs69HVe9LiIiIoa2GrAEtn79+mH//v0AAG9vb7zwwgvo0aNHtQOETqdDcHCw4nVmZGQgLy8P8fHx\n6NOnD4MNERGRE2Jos5LlluiYMWPkwAYAL730krychiOQJAlPPfUUCgsL5cdERETkfBjarCCEwJw5\nc7B///4yt0THjh2LN9980+GCkaPVQ0RERNXH0FZFQggUFxfj9OnTWLlyJWJiYpCfnw8AaNy4MQ4c\nOICAgAD4+lY+zoyIiIiouhjaqmHz5s2IjIws0xYeHo4uXbqgcePGANirRURERLbB0FYNpRfNBUqW\n9VizZg1atGjBsGYHRqNR7RJkRqMR/v6BapdBRERuhKGtEkII3LhxA7Nnz8bRo0cBAE2aNIFer8e2\nbduqvawHWScoqAXS01HhenVq8PcPRFBQC7XLICIiN8LQVgXTp0/HypUr5ceTJ0/GpEmTAPB2qL14\nenpCrzeoXQYREZFqGNqqICYmRg5nnTp1Qt++fR0mrPGWIRERkXtgaKuGjh07Yu/evfD29la7FACA\nXq+HEALvv/8+li9fDgA4depUpYHSaDQiJycXzZo1r/Y5zeZcGI1p0On08PMrO1OWtwyJiIhsh6Gt\nGt577z14e3s7TC+bp6cngoOD5cAGACEhIVV6b1aWqdq3G00mE7p3j0BqagoMhmDs3Lm3WnurAkBR\nURHS089U6bW323NVaUFBLeDp6Wnz8xAREdUEQ1s1SJLkMIENKJkk8fjjj8uP9+zZY9PzJScnITU1\nBQCQmpqC5OQkhIV1rNYx0tPPICfnKnQ6XZVef7u9WJViNBqRng6OlyMiIofH0FYJSZJQXFysdhnl\nWALb3r175baIiAibnjMkJBQGQ7Dc0xYSEmrVcWy1x6q1HGVGKhER0Z0wtFWBI/WuASWBbe7cuWUC\n2549e2xep0ajwc6de5GcnISQkNBq3xolIiIi6zG0ORkhBPbu3Yu5c+fKbdHR0TbvZbPQaDTVviVK\nRERENeehdgFUfaXHsUVHRyM6OtrhegOJiIhIWQxtTkIIge+++46BjYiIyE3x9qgTmTdvnjyObc+e\nPYiIiGBgIyIichNuHdocbTeB2y2DcetMUQY2IiIi9+O2oc2eG5Dv2vUtOnZsf8e1yXQ6HfR6fbn2\nWwNbREQEA9sdCCGwYMECREdH45VXXsF7772ndklERESKcNvQZs8NyM3mXDRt2qDaa5PdurQHx7Dd\nnhACN27cQLdu3XDixAm1yyEiIlKc24Y2e7p1j86qsAQ2y9IeERERigY2szkX+/fvAwC0b9/BJdZc\ni4uLw+HDh9Uug4iIyCYY2hzQrWuxRUREKL547qhRQ3H+/DkAgF5/H779dp/TBjchBObPn4/58+fL\nbREREXjnnXdUrIqIiEhZXPLDwVgCm2VpD1sENgByYAOAtLTTSE5OUvT49iCEQEFBAZYsWYKFCxei\nqKgIANCjRw9s2LABXl78m4SIiFwHQ5sDsdwStXVgA4CmTZvJX+v191m9j6jaVq5ciWnTpiEvLw9A\nyWcWFxeHBg0acOwfERG5FHZFOIiKtqey5X6ia9eux/XrOQCcb0ybEAK//PILNm3ahLfffltu9/f3\nx44dO+Dj48PARkRELoehzYGU3gDe1rNE/fx8cf/97Wx2fFsRQqCwsBCrVq3C8uXL5fYmTZpgwoQJ\nDGxEROSyGNoclL02gHdGH374YZnABgDff/89mjVrxsBGREQui2PaHIQkSXjjjTdQXFyM4uJidO3a\nVe2SHI5l4dyZM2fKbZ06dcLKlSvRuHFjBjYiInJp7GlzIAwdFbPcEl25ciUWLFiAgoICAEDt2rUx\nd+5cPPnkkypXSEREZHsMbeQU0tLSMHHixDJt7733Hp588kmGXSIicgu8PUoOTQiBixcvYty4cWXa\np06ditGjRzOwERGR22BPGzm8wYMH48cffwQAeHt7o2nTphgxYgQ8PT1VroyIiMh+2NNGDi8xMVH+\nul+/fkhJSUFoaCh72YiIyK2wp81OjEaj2iXIjEYj/P0D1S6jUkIIzJs3D/n5+XJbcHAwwxoREbkl\nhjY7CApqgfR0ICvLZNX7tVpNld578uQveOGFkfLjDz/8CLVr14ZOp4efn6/c7u8fiKCgFlbVYm+Z\nmZkQQsiPX331VRWrISIiUg9Dmx14enpCrzdY/f7AwLq4evVGpa9r2LARDIZgpKamQK+/D0uXvo20\ntNMwGIKxc+dep9qq6nY+/vhjDB48GM2aNav8xURERC6EY9pciEajwc6de/HNN7vx9tvLkJZ2GgCQ\nmpqC5OQklauzzq3h7O2338bly5dVqoaIiEg97GlzQEVFRUhPPyM/zs6u2u1RADCbc3Hhwnncc8+9\naN48CGfPpqN58yDUquWNtLRUq2sKCmph99makiThtddeg1arxeLFi5GamorHH38cHTt2tGsdRERE\njoChzQGlp59BTs5V6HQ6uU2rrdqtTa1Wg6ZNGwAA/ve/nYrUYzQakZ6OGt3itZYkSRg9ejRGjx5d\npo2IiMjd1Ci0Xbt2DQMHDsTatWvh6emJ6dOnw8PDAwaDAdHR0QCATZs2IT4+HrVq1cJLL72EiIgI\n5OXlYerUqbh27Ro0Gg0WLVqEgIAARS7IVeh0OgQHB6tdhszaSRRKYEgjIiKqwZi2wsJCREdHw8fH\nBwCwcOFCTJkyBXFxcSguLsauXbuQmZmJ2NhYxMfHY/Xq1ViyZAkKCgqwYcMGBAcHY/369ejbty9W\nrFih2AURERERuSKrQ9tbb72FIUOGoEGDBhBC4LfffsODDz4IAAgPD8ePP/6IEydOICwsDF5eXtBo\nNAgKCsKpU6eQmJiI8PBw+bUHDhxQ5mqIiIiIXJRVoW3r1q2466678Oijj8praBUXF8vP+/n5wWQy\nwWw2o27dunK7r6+v3G5ZfsLyWnJ8JpMJiYlH+P0iIiJSgVVj2rZu3QpJkvDDDz8gOTkZUVFRyM7O\nlp83m82oV68eNBpNmV/wpdvNZrPcVjrYVSYwsOqvdVbZ2Y63nlrt2hJ69Xocp06dQsuWLXHkyBGr\n1n1zxGvTajUO9+/K0eqxF163e+F1uxd3vW4lWRXa4uLi5K+fe+45zJ07F4sXL8aRI0fQsWNH7Nu3\nD507d0bbtm2xdOlS5OfnIy8vD2fOnIHBYMADDzyAhIQEtG3bFgkJCfJt1aqoyiKzzi4ry1Tl2aL2\nkph4AqdOnQIAnDp1Cvv3H0ZYWPWX3nDEa8vKMjnUv6uqLqbsanjd7oXX7V7c+bqVpNiSH1FRUZg9\nezYKCgqg1+vRo0cPSJKE4cOHIzIyEkIITJkyBd7e3hgyZAiioqIQGRkJb29vLFmyRKkyyEZ0Or28\n24LBEIyQkFCrj8V9WImIiKpPEqU3dnQC7pDU09JSodVqHGbJj5SUFGRlmdCwYSMkJychJCTU6i2x\nbl04+E6quudqTamxcPCduPNfpLxu98Hrdi/ufN1K4uK6bkYIgQ8++AATJ04EULIG2uLFi/Haa6/d\n8X0nT/6Chg0bWXVLtLTq7MPqKv+RVyeoAtXbAcNajhZUiYiocgxtbkIIgZMnT2Lp0qXYtGlTmedy\nc3Mrff+T/FJTAAAgAElEQVQLL4x0qY3n7amiHS4qY8txf2rucEFERNZjaHMDQggkJSUhIiJCnuUr\nSRJ0Oh1++umnKocwy8bzNe1tc0fc4YKIiGrK6sV1yTkIIXDs2DEMHDiwzLIsAPD888+jfv368PKq\nWnav6QQEIiIish572lyYEAJ//vknevfujYyMDLm9Xbt22LJlC5o0aVLlfT1XrfoUTzzRjbdGiYiI\nVMLQ5uLi4+PLBDYAGDZsGFq0aFGt47Rp05aBjYiISEUMbS5ICIGioiKsXbsW06ZNk9t9fHwwY8YM\nvPLKK1XuYSMiIiLHwNDmotauXYuxY8eWaVu4cCEmTJigUkVERERUEwxtt1HdtbWUdO7cWWi1ra16\nrxAC//73v8v0sHl5ecHb2xtjx45lDxsREZGTYmi7DWvW1lJKTo6vVe8TQuCPP/7Atm3bkJOTI7cP\nGjQI69evV6o8IiIiUgFD2x042tpaVXHhwgVs3ry5XDt72FyHEAJ79+7Fhg0bEBYWhhdffJHfXyIi\nN8B12lyEEAKnTp3CU089Vab90UcfxaeffqpOUWQzs2fPxqpVqzBu3DgUFhaqXQ4REdkBQ5sLWbVq\nFc6dO1embfbs2ahVq5ZKFZHShBDYtm0bTp48KT8mIiL3wNDmAoQQuHHjRrn12EaOHInw8HDeOnMB\nQgjk5ubigw8+wHPPPSePWWzZsiW/v0REboJj2lzEsWPHsGHDBvmxv78/nn32WdSuXVvFqkhJ3333\nHSZOnCg/btmyJb799lt4enqqWBUREdkLe9pcROlf5gEBAYiLi0P37t3ZC+MChBC4efMm3nzzzTLt\nkyZNQuPGjfk9JiJyEwxtKhFCoKCgAOPHj4enpyfi4+NrdLzSY9lefvll9OrVi7/MXYAQAmazGXff\nfTcOHDggt0+cOBGDBg3i95iIyI0wtKkoKioKH330EQDAz8/PqmMIIfD+++8jNze3TDt/mTs/IQRy\ncnLQv39/3Lx5U25/+OGHMWPGDGi1WhWrIyIie2NoU4EQAocOHSozBu3BBx+06liSJOGVV15BnTp1\nlCqPHIAQAtnZ2Vi6dCl27doltzdr1gxbt25FgwYNGMyJiNwMQ5udCSFgNBoxYMAAXLlyBQDw+OOP\nw9/f3+pjSpKEGTNmKFUiOYiJEydi3rx58uM2bdpg/vz5aNiwIQMbEZEb4uxRO7IMKF+4cKG8PEdg\nYCBmz54NHx8fq48rSRKmTp2KqVOnKlUqqUQIgevXr2P58uX44osv5HYPDw+8/fbbnFxCROTG2NNm\nZ2+99RY++eQTAECDBg0QHx+Pv/3tbzX+RSxJUpn/kfO6cuUKZs2aBbPZLLdVdTawZfHdHj164Pvv\nv7d1qUREZEcMbXZi6WXbt2+f3Pbhhx9y8VuSWSYe/OMf/yjTPnHiRAwePLjK/07y8vKwc+dOPPbY\nY7Yok4iIVMLbo3ZgWbahXbt2SE9PBwCMGTMGPXv2ZGCjMhYuXIijR4/Kjzt27Ij58+db9e+kuLhY\nydKIiEhl7GmzMSEEjhw5AoPBIAe2tm3b4v333+eMTyrn4MGD8tf16tXDzJkzodForApt3JeUiMi1\nMLTZwYIFC+SJB1qtFp988glq167NXjYqp1OnTvLXjRo1wsMPP2z1v5PBgwcrVRYRETkA3h61EcuO\nB82bN0dGRgaEELjrrruwa9cu3H///ZX+IjYajXaqtHJGoxH+/oFql+EWxowZg02bNuHs2bOYM2cO\nGjRoYNVxdDod5s+fr3B1RESkJoY2G7BMOhg1ahSuXLkCSZKqFdj0en25NqPRiJycXDRr1txWZd+W\nv38ggoJa2P287kaSJAQHB+Pw4cO4fv067r33Xqt62Xr06IGuXbsiMJBBm4jIlTC0Kcwy6eDFF1/E\nli1bAJTcEp03b16VAhsAeHp6Ijg4uFx7VpYJer1B8ZrJcUiShMDAQKsDlyRJ0Gg0Vo+DIyIix8XQ\npiAhBEwmE8aNG1dmA/hvvvkGYWFhTv1L1GQyITk5CSEhodBoNGqX49KUWLOPiIhcDyciKEQIgbS0\nNHTr1q3MnqJxcXFo164dioqKcPHiRRUrtJ7JZEL37hHo2fMJdO8eAZPJpHZJREREboehTQGWW6Jv\nvPEGDh8+LLe/8sorqF+/Pnr27IlPPvkEiYmJKlZpveTkJKSmpgAAUlNTkJycpHJFRERE7oehTSH/\n+te/yvSwAcDu3bsxYMAA9OrVC08//TT69OmjUnU1ExISCoOhZIydwRCMkJBQlSsiIiJyPxzTVkNC\nCFy9ehUxMTHlnmvVqhViY2PRtm1beHh4OO1YI41Gg50793JMGxERkYoY2mpACIH8/HyMHj0a169f\nl9sHDRqEmTNnolWrVvDyKvmInTWwWWg0GoSFdVS7DCIiIrfF0FZDtWrVQtOmTQEADRs2xJw5c/D8\n88/D09MTgPOHNSIiInIMDG01YAlkK1aswIoVKyp8jgjgDhdERFRzDG01xHBGlQkKaoH09JLFkatC\nq9VU+bXW4A4XRETOiaHNxXARXMfj6elZrZ0sAgPr4urVGzasyHUVFRUhPf2M2mWUERTUQh4uQURU\nEwxtTshkMuHnn48CANq37yCHM8siuKmpKTAYgrFz514GN1KFNeEpO7vmPYznzp3FjRtZaNKkSbnn\nmjVrZvfwZDQakZ4Obj9HRIpgaHMyJpMJTz4ZjrS00wAAvf4+fPvtPmg0mgoXweWMT1JDevoZ5ORc\nhU6nq9b7tNqa/ZGh1bausN1oNN52T19bs+WtbiJyLwxtTiY5OUkObACQlnYaP/98FHXq1EGTJs1g\nMATLPW1cBJfUpNPpVAlJRESuiqHNyYSEhEKvv08ObjpdC0ydOglpaadhMARj69YduHDhHMe0ERER\nuRiGNiej0Wjw7bf75DFtADBgQMn2WKmpKbhw4RxviRIREbkghrY7cNS1tTQaDbp0CQdQMsaNt0SJ\niIhcH0PbbVR3ba07KSoqxu+/n7f6/f7+vsjJyYVGU4S0tNRyz8fErIHRmAadTo+MjEvIyKj8mFyG\ngIiIyLkwtN1GddfWupO0tFT4+/tWeyZdVWm1GjRt2qDKr+cyBNVnz/W/qrr0BYM3EZF7YWizE0eb\nScdlCKrH2iUsrFXZ0hcM3kRE7oehjaiKGLzdixACV65cwT333ANJkrB+/XoMGTJE7bKIyI15qF0A\nEdGdCCGQlZWFt99+Gx4eHvjwww9VqWPMmDHYv3+/KucmIgIY2ojICfz3v/9FVFQUAGDBggV2O++x\nY8cgSRIAoFatWvD397fbuYmIbsXQRkQOSwiBlJQUzJo1q0ybvXz11Vfy1/369UObNm3sdm4iolsx\ntBGRQ1u+fDnOnj0rP+7Tp4/NzymEwLlz5/DZZ5/Jba1atZJ73YiI1MCJCG6iqKgIaWlpAEpmHubk\n5KpcEZesoKpZt25dmcczZ860y3mXLVsGs9ksPw4LC7PLeYmIboehTQEmkwnJyUmq7fcphMDixYsx\nc+ZMfPrppxg+fHi516SlpcFoNEKn09lt2Yo74ZIVfxFCYMKECdixYwdmzJiBF154Qe2SHIIQAt9+\n+y1MprKzZH19fW1+3kOHDuHf//633HbXXXfh0Ucftel5iYgqw9BWQyaTCd27R8jbSO3cudeuwU0I\ngYULF2Lu3LkAgG+//bbC0AZwyQp7E0Lg/PnziI+PR2hoaIW39YQQaN68OTIyMlBYWIiFCxcytKHk\ncyksLMSbb74JIQRatWqF06dPIz8/3+bnzc/PR1RUFK5evSq3r1u3Dj4+PjY9NxFRZTimrYaSk5OQ\nmpoCoGTD9uTkJLud2zLuZu3atSgsLAQAPPnkk3Y7P1Vu2LBhWLhwIfz8/Mq0CyGQnJyMmTNn4sKF\nC/L3r1mzZmqU6VCEECguLsbUqVPx/fffw8/PD2FhYcjPz8fdd98Nb29vm503Pz8f7dq1w/fff1/m\nuXvvvZfj2YhIdQxtNRQSEgqDoaT3yp4btlsCW//+/XHmTMn2SvXq1eMvfQchhEBcXByOHTuGRx55\nBBEREXK72WxGVFQUunfvjsWLF5d538svv6xCtY4nMTER77//PgBg0KBB+Pzzz1GvXj188cUX0Gq1\nip9PCIH09HR069YNKSkpih+fiEgJDG01pNFosHPnXnzzzW673xr95JNPcPz4cflx165dER4ebrfz\n052NGDECwcHB+Pjjj8v00rz00ktYsmQJzp8/DwDo27cvACAoKAjt2rVTpVZHYemB7N69OwBgwIAB\n6NChA3JzcxEQEIBHHnlE8R4vy63Y6dOnl+thIyJyJAxtCtBoNAgL62i3wCaEQGJiIj766CO5LTQ0\ntFw4IHVZbvPt27cPX331FR577DF4enpi/fr1EEJg6NChKCgowJdffgkhBAYPHgyDgRMz9u7di5yc\nHAQGBmLUqFFo27at/JwtAlteXh6GDh2KzZs3y+f45JNP0KFDB0XPRURUU5yIYAO2nE0qhEBmZiYe\nf/xxeTkCjUaDKVOmIDAwUNFzUc1IkoQTJ05g1KhR8PLyQm5uLp555hm0a9cOzz33HLRaLY4fPw5J\nktC8eXOMHDnSbUO3EAImkwmbNm2SbxF/+OGH6NWrF0aOHAkAmDZtmk3OvWPHDmzZskV+PGnSJIwY\nMQJGoxFHjx61yTmJiKzB0KawimaTKkUIgZycHPTo0UMObL6+vli2bJlb/8J3VD/88AOOHz+O9evX\nY9iwYWjXrh06deokP3/48GEMGjQIANC5c2e37WUTQuDEiROYPXs2tm/fjgYNGuDjjz/GU089he3b\ntyMuLg4AbLZZe/PmzeHr64vc3Fz06dMH0dHRAAC9Xm+T8xERWYuhTWEVzSatX79+jY8rhMCff/6J\nN998U94P0dvbG8uXL8fw4cNVDWxCCBw7dgxz5szB119/jczMTJsMFncmkiShc+fO6Ny5M8aOHVum\nHSj5zN59911cvHgRQMksU3cO3b1790ZWVhaCg4OxceNGeWxfRkYGhBBo166dTZbckCQJYWFh+Oab\nb5CSkoIRI0bICz4fOHBA8fMREdUEQ5vCLLNJLT1tISGhyMi4VKNjWnrYunXrhsTERLl9yJAhDhHY\nCgoKsHTpUuzYsQPe3t5uHT5Ku93nYJmpeOLECQBARESE208g+fTTT+Hv748HH3wQQMlnJ4SQt6/q\n2bMnateubZNzS5KELl26oEuXLmXO3bBhQ5ucj8ieioqKkJ5+Ru0ykJ2tkdfm5G441mNoU5hlNmnp\nMW0ZGTU/7ldffYWffvoJQMkv/QcffBBLly5VPbDl5+fjxRdfRFxcHHx8fBAfH69Iz6Kr27hxI5KT\nkwEAu3fvVrkadUmShCeeeEL+Gvjrj4Ht27cDALp3727Tf+sVHbtFixY2Ox+RvaSnn0FOzlWH2AlH\nq9VwN5waYmizActsUiUIIbBu3TqMGzdObpMkCV9//TXq1aunyDlqYuPGjfKm2i+99BKeeuop9rTd\ngRAC27dvx7x58wCUfC/5eVUcmnbs2IHjx4+jadOmaNWqlQpVuSd79cyU7nmpDHtmaoa74bgOhjYb\nscwgbdKkGU6e/AXh4Q9X+xiWmaIrVqxAfn6+/Itt1apVCAgIUL2X7fvvv8drr70GoCSovvrqqwwg\nVZCUlIT8/Hz4+PjIg96pvK+++goAULduXdx9992q1dG6dWsEBQWpdn57s2fPjFZb+ex69swQ/YWh\nzQZKzyCtVcsbBQX58q2wqhJC4MCBAxg2bJg8rgcApkyZglGjRildcrVYVvWfMGECMjMzUa9ePcTF\nxaFx48aq1uUsli9fDgAIDg622TIWrkaNPwb69u2LmJgYnDx5Ev7+/nY/v5rYM0PkmBjabKD0DNKC\nAus3uF6yZEmZwHb//fdj8uTJqvewmc1mjB8/HsePH4e/vz/WrVuHPn36sJetEkIIJCQkICcnBwAw\na9YsfmZ3EBERgTNnzuDxxx+3+7klSUK9evXw4osvyo+JiNTG0GYDpWeQWnraqkMIgY0bN2LPnj1y\nW/v27fHll1+iUaNGSpdbbdu3b0dsbCwAIDIyEk8//TR/qVXCEtgee+wx+bPiZ3Z7kiRh+PDhGD58\nuKo1EBE5EoY2Gyg9g7RJk2Y4ePCHKr/XsrzHnDlzcP36dQBAhw4d8Pnnn6NJkyaq97K98cYbWLFi\nBQCgf//++Ne//sVfbhUQQiAlJUVe1iM7OxvTpk0rE9jGjx+PV199FUBJKB82bBjq1KmDPn36qFa3\nI+G/KyKishjabKT0DNI2bdpW8uqynnvuOZw589fsrVmzZqFp06aqB7Zdu3ZhxYoVyMzMREBAAObP\nn+92Y30qIoTAwYMHAQDp6en44IMPAACXLl3CuXPnyr3e8n28du2a/P4LFy5g+/bt8Pb2RocOHfDD\nD1UP+kTkXOy5dtq5c2eRk+Nb6ev0ej1n6DoBhjYHlJSUJH/t5eUFLy8v1QPbmTNnEBkZKQe2devW\noVWrVuwN+X+PPvpouc9CCFGurUmTJvDw8JAfS5KExYsX26VGInIM9p2h27rS1xiNRgBwqMknVDGG\nNoUpsVn8li1b0L17d1y9ehUzZsxA7969Fa6y6oQQ+Pnnn7F48WJkZmbioYcewuuvv86JB7dYsmQJ\nTpw4gfr166NZs2Zlnnv//fdx7tw59O3bF59//rlKFRKRI3G0Gbq3EkLA09MTkiShX79+eOGFF9Cj\nRw+1y3J7DG0KysjIQK9ej+P8+fPyZvHVDW6SJOH+++/HpUuXyrSpQQiB3NxcTJ48GQkJCahfvz6W\nL1+OsLAwBrZSJEnCpEmTbvt86ef4uanH0ptg73P6+wcCUOYPOiJ7kyQJ27ZtQ05ODkObA2Bo+381\nHWNgNudi2LBncflySdhKTU3BqlUxeOKJbrh27WqVuqgtHOEXuxACN2/exOjRo+XAFhcXx8B2G/xM\nylMjJN2O0WhETk6u3df78vcPRFBQizJrN1r7B52zEEIgLy8PEyZMwM8//4zdu3ejbt26apdFVrL8\nbDMajfj555/Rvn17lStybwxt/6+mYwy0Wg0SEvZW+FxhobkGlaln37592LRpE4CSDbt79erFcEJV\nEhTUAunp1VsUVaut+rZG1eXvH4h27dTbCqn02o2pqSlITk4qt9WdK/XEvfnmm1izZg0AYMGCBXjr\nrbdUrois8emnn2LSpEm4ceMGzp07h549e+K7775Dy5Yt1S7NbTG0leLoYwzsRQiBP//8U/5B279/\nf3z88ccMbFRlnp6e1d52KDCwLq5evWGjitRVeu1GgyEYISGhZZ53lZ44y5CK3bt3y23x8fEMbU5I\nkiQMGzYMa9euxf79+wEAmZmZePvtt+VATvbH0EYVKi4uxo0bJb9A09PT4efnp3JFRM6rTp06iIlZ\nA6MxDTqdHhkZl5CRUfJcdrYG+/YdKNMTt3v3/6q9VFB12WoT9n/84x84dOgQDAYDLl++DCGE4ucg\n+5AkCWvWrMF9990ntxUXF6tYETG0UYVu3ryJxMREAIDZbGYvG1ENpKefQWGhGeHhD1f4fHj4w9Xe\nn7gmbLEJuxAC27dvx/bt23H//fdj9OjRmDx5MoqLi3H8+HG0a9dOsXOR/UiSJC9TJEkSdu7cye+n\nihjaqFKpqamoV68exo0bx9scRFZytOEXSo4fFELgwIEDiIyMhI+PD5YuXYrMzEwAJbd+U1NT+Ute\nIUIItGzZEp6enti1axfuvfdem56vfv36CA8Px/fffw8hBC5fvox169bh3Xfftel5qWIMbXai9kw6\no9FYrUkWWq0WV69exd///necP38e0dHRePnll21YIRE5I0tgmzVrFnJzc3H48GF06NABmzdvVrs0\nl5aUlIT4+HhMnjzZZueQJKlMaLPYvHkzJkyYgKCgIJudmyrG0GYHer2+2u+xLFHQrFlzRWbV5eTk\nVvm1lluhd911F44dO1aj87oStYN3aaXX/yJS23vvvYfLly/js88+Q4cOHQAAvXr1QqNGjcqsOUnK\n+vbbb20a2iyefvppxMTEyFvvWb7Xc+bMsfm5qSyGNjvw9PS06rZIVpYJer1BlVl1HMNWljVLWFir\nKiHdsv4XkSMYNmwYAJTZKcXPzw8eHh6ciGADDz30EFJSUrBnzx4cPnwYDz30kM3OJUkS2rdvjzp1\n6qC4uBhCCBQVFdnsfHRnDG1EVWDNEhbWcuWlL8j1SJKEPn36yF/f+hz/AFRenz59EBcXh7y8PBw6\ndMimoQ0o+T5GRUXhlVdeQXFxMTeWV5FH5S8pr7CwENOmTcPQoUPx7LPPYs+ePTh37hwiIyMxbNgw\nzJ07V37tpk2bMHDgQPzjH//A3r17AUBeLXvo0KEYO3YssrOzFbkYRyaEQHJyMgYPHgwPDw+MHj0a\ner0eZnPNF941mUxITDwCk8m+q70TkWuo6c8QhjP1fPnll3Y5DxfUdQxWhbZt27YhICAA69evx+rV\nqzF//nwsXLgQU6ZMQVxcHIqLi7Fr1y5kZmYiNjYW8fHxWL16NZYsWYKCggJs2LABwcHBWL9+Pfr2\n7YsVK1YofV0OQwiBgoICrFixAt27d8emTZtQu3ZteHl54fTp0/D19a3R8S2Lcvbs+QS6d49gcCOi\narn1Z4jZXPXxr6SOHj16yENurly5gitXrtj8nPXr10fDhg1tfh66M6tuj/bs2VPeOLaoqAienp74\n7bff8OCDDwIAwsPD8cMPP8DDwwNhYWHw8vKCRqNBUFAQTp06hcTERLzwwgvya101tAkh8Mcff+D5\n55/H1q1bAZSM81i9ejVCQ0Or/JdpUVER0tJSK3zu5Mlfyi3KqdPp5UU8/fxKQuG5c2ertf8pEbmH\nW7fYMhrT0LRpA0WOLYTgmDaFSZKEevXqYfLkyRg3bhxOnjyJLVu2YPz48TY9Z/v27dGtWzesW7fO\nZuehylnV01anTh34+vrCZDJh4sSJmDx5cpn/MP38/GAymWA2m8tsFGx5j9lslrdosbzW1VgGay5b\ntkwObBqNBqNGjcLgwYNx//33V/lYaWlpyMm5Cq1WU+5/lkU5Lf8LD38YTZs2kP/f8jp//5r16BGR\nuoQQuHLlCgYMGAAPDw/FbotZttgCAIMhGDpd9We7307piQmkrGeffRYhISEAgI0bN9r8fJbdESy/\n2+bOnYuzZ8/a/LxUltUTES5duoR//vOfGDZsGHr37o23335bfs5sNqNevXrQaDRlAlnpdstYrluD\nXWUCA6v+2urIzlZ+n7+1a9di3rx58uNFixZh/PjxVf4hptVqEBhYF9nZlxRZmNPRlqzQ6XSVfj9t\n9f12dLxu16LUz5cdO3bgiy++AAB88MEH6Nevn9XHsvx8CQysi6NHE/Hrr7+idevWuHjxoiK1AoDB\noMzkHUutrkKrrdm/B0mSEBAQIHd+HD9+HIcOHUKnTp2UKO+O5w0MDERWVhYkSUJmZiaaN29e7eO4\n2vfTnqwKbZmZmRgzZgzmzJmDzp07AwBCQ0Nx5MgRdOzYEfv27UPnzp3Rtm1bLF26FPn5+cjLy8OZ\nM2dgMBjwwAMPICEhAW3btkVCQoJ8W7UqbDWrLivLVOP/kG5VOrDNmzcPQ4YMqdZfnefPX0FAQCNF\narFmrbjbKb2GnLX8/QNRr16DO34/3XUWpa2uu6ioCOnpZxQ/bk2U3v/Slb/ftvj50qxZsxq9PyvL\nVObzbtGiFW7eFDap9ejRoxg0aJDV77+1VmdjMpmQnJyEkJBQ6HSNFP+Mr1+/jj/++EOx493JokWL\nMHbsWABAVFQUdu3aVe1jOPv3szqUDqdWhbaPPvoI169fx4oVK7B8+XJIkoRZs2ZhwYIFKCgogF6v\nR48ePSBJEoYPH47IyEgIITBlyhR4e3tjyJAhiIqKQmRkJLy9vbFkyRJFL0ptQgg88sgjuHDhAgCg\nbt26+Pvf/46AgIBqHWfMmOH47rsfFanJ2rXibseyhhw5j/T0M8jJuVqtnTFsyRb7X7qTtLQ0tUu4\nLSEEcnNzMXDgQLz66quIjY3F2LFjreqVcXaWiR6pqSkwGIJx9GiiYsdu3LixvEe0vTRq1Ah169bF\njRs3cPDgQXz++ecYOHCgXWtwZ1aFtlmzZmHWrFnl2mNjY8u1PfPMM3jmmWfKtPn4+OC9996z5tQO\nTwiBdevW4dChQwBKxv/FxcWhc+fO1R7bcfZsOpKTk9CiRRNblEpuyJX3v6TqUXqWqBACFy9exNmz\nZ/HNN99g9+7dOHToECRJwsWLF3H06FG3DG23TvT49ddfAdRS5Nivvvoqdu3ahdzcXFy+fFmRY96J\nJEno0aMHli1bhjFjxiAvL0+RZauUVrpn03IL2VVYNRGBKiaEwIYNGzBx4kR5Ysbf//53PPXUU1YN\nxm3ePAghIaFKl0lETuro0aOKHWvMmOGKTwKLj49Hly5d8K9//QuHDh2Sfw4+/PDDbjto/daJHq1b\nKzOLX5IkdOnSRZ7UNmXKFPz000+KHLuy8w4fPhz5+fnIz8/H8OHDbX7O6nD1ZbC4I4IVhBD44Ycf\n0KVLl3LPzZ8/H9evXwcAPPnkk9iyZYvVs6fWrImFRqNBdrZ73PsnojtTci9gS09+WFhHxY7ZoUMH\ndO3aFWFhYQCAMWPGQKPR4K677kKdOnUUO48z0Wg02Llzr016fiRJwjPPPIODBw8iKytL0UkklZ3X\nHqwZh1vRMlht2rRVrKbS43DVwNBmBUmS8Oijj8qPhRAoLCxEWFgYTp06BQCoVasWZs+ejVq1rO8G\nt6yxRuQKStYb/GsclmVCi0V2duV7ripN7R/AalK6J1+SJHTt2hV79uy57fPOQunbaxqNRtFwXFq7\ndu3kr7/88ks8/fTTNjmPGqwZh2tZBssWHGEcLkOblSw/gIQQSEtLw6uvvopffvkFANCxY0csXboU\njzzyiFP8oBJCoFOnTvjzzz9x5MgR1K5dW+2SyAWlpaXJS70AqPAHsdKzFu/EEX4Aq+nDDz+WA0np\nkPqSeugAACAASURBVFITzvDzrjK3ThzYuXOvQ4+LCgkJQXBwMFJSUhTtiXUUHIdbFkObAk6ePIlt\n27bJj3U6HTp27OgUP8AsC3YCwI0bN5CVlYVGjZRZZoToVvwB7DguXy65lZaRkYFevR7H+fPnYTAE\nIyZmjV3Ds6O5deKANbeQ7TUQXpIkNG7cGDqdDikpKSgsLERBQUGN7vCQY+NEhBoQQiAuLg7jxo2T\n22bNmoVPP/3Uqf6j6devH3766SfUr18f99xzj9rlEJEd6HR6mEwm9Or1BM6fPw/gr22snJ3JZEJi\n4hGrBqHfOnGgur2PagyEnzlzJoCSDgR7bSBP6mBPWw2dPHmyzFRrT09P+Pj4OE0v21tvvYVjx47B\nx8cH0dHRTlE3ORYhBLKyshAYGIjBgwdjw4YNapfksho0UGZPUKBkzGxychLOnz8ntzVt2lTRbazU\nUNPbmzWdOHBrT93PPx9Fly7h1TpGdQUFBeG+++7D6dOnrT6GvXbMMRqN8PcPtMu5XBFDmxWEEMjL\ny8OUKVOwefNmACU/TJcvX45evXopGnxMJhOOHz9eZrCpEoQQ2Lt3L9544w0UFBSgd+/e6Nu3r6Ln\nINdnub1u2U5JyZ03HI0jrP301FNPydtYKcHSq5SamoKmTZvh6693w2S6rtjx7cny/bl586Yiswfr\n16+PjIxLyMio3vtq1fJG06bN5DA8ceJ4rF27Hn5+vggKalHtOiojSRKaNm2KlJSUGh3n8uVrGDv2\nJZw9m47mzYOwZk1smclwZnMujMY06HR6qybJabUlE438/QNt8jm4C4Y2K2VlZSEmJkZ+HBAQgC5d\nuig6rd1szpX/YlRyNowQAqdPn8bo0aORn58PAHj99dfZy0ZWSUxMxOHDh+Hl5YWePXuqXU61nDtX\ntbXDzOZcjBkz/La/0Kpynpyc8q/X6/VWz15t3LixVe+zMJtzkZychK1bd+DChXPybcCDB39A06aO\nM0yiKj0zpXvX9Pr7oNffh7S00/jvf/9r9x1AtFoNdu36tly7ZeLLPfd0UPycSvzsDglpie+++7HC\nP0yUmJzhytvU2RNDWzUJIVBQUICJEyfKbaNHj8a8efPQsGFDRYOP0Zgm/8WoFEv9r776Ks6dOwdJ\nktC7d2907Gib6ejkuoQQyM/PxzvvvAMAeOWVV/DII4+oXFX1+Pv7VmnQvVarwf/+t9Pq82i15RdU\ntdyOqs7EjNzcv5ZI6dGjh9X1AJBDqOWXsNlslickNGjQEN7etXDhwgWrQurtlA6/LVq0wMcfr6v0\nuFXpmSl9SzIt7TS2bt2Oa9cyOfGlmm63NIkSkzNIGQxtVvD09ETdun9tAnvx4kXce++9ivdU6XR6\n+daFkl5//XXs2LEDAHDvvfdi7ty57GUjq6xcuRIJCQkASgZD2/PfkRACZrMZzz33HCRJQmxsLHx9\nqxcsHO2XemU2btyo2LHOnk0H8Ne4q4kTx8sTEq5cySjzuoKCfOj1ZYdoWHO7ODHxiHzeM2fOVHhc\na5S+zWswBKN9+w7IyLhU4+NSiVs/X+7Uox7OHrVCYWEhkpKSEBAQgGXLluHzzz+3yS8rPz9fbN26\nQ7ElOIQQOHnypNwzcu+99+LLL79E+/btFTk+uQ8hBNLT0zF37lwIIRAeHg6tVmv3OhYtWoT//Oc/\n+PLLL+WFrV2ZZVsoAGX+cLRG8+ZBACDPlCw9IaG0in5JWztDsvTMzJYtWyr2y98yeeCbb3Y7/Lpq\nzsjRP18hBN544w14eHjA09MT+/fvV7skm2FPWzVJkgRvb2/8+OOPdjlfamoyLl2q+V+MQghs3rwZ\nL774otw2atQodOjQgb1sVC1CCOTk5ODpp59GTk4OJElCmzZtVPl39Ouvv0IIgbCwMDzwwAN2P79a\nvL290b9//xodY82aWBQU/B97Zx4XVb3+8c8BBkXGQBJBnBmWYc0NNU1MUXM3TUVNKzPK63LLCjPz\namqaZm64lKVG5nZ/mZaomQuKC4iKIYaKC8I4wCCCGIoOkgzw/f0x9xxnhoHZzswc8LxfL1/CcOZ7\nnlnOOc95ls9TyThOdCTFyckJVVVVkEoDsXLlWoSHd651kTa3Q1KzM7Nnz26oqCC1tjG34cOaUwfY\norz8seGNOEpDeH8dHBxAURRef/117NixA/369bO3SazDR9rMgKKoWv+sRUVFhcVr0N2uBw8exMOH\nD0EIgVQqxbvvvss7bDwmQQhBRUUFAgICcO3aNQBqB2LSpEk2t+P69evYv38/KIrCqFGjGvV3mRCC\n4uJi3Lr1dA6jpa/X1bUZunTpCqFQqBVJ+euv6zh8+DiOHUtGz56RtQrS09PTIBJJIJUGMo/PnPkR\nUlKSjYq40Rd/zWkMtKaabgSvuLjYbL01LtIYNPC4Sp8+ffDcc8+BEIKioiKMGzeOKd1oTPCRNg5T\nXv4Y8+bNtmgN+iL78ccf47///S8oioKbmxuOHDkCPz8/dgzleSYghODBgwcYN24cHjx4wDgNERER\nrEvSGLKjvLwcY8aMYdKFtORIY+a5556Dp6cnAgIC0L49ewOwaTQjKV5eXrX+rttB+OWXX+Ott8YC\nAOTyW4iKGmZyZ6HumsuXr9aK4NGNEWKxGIcOndBrV0NCUwPPVrpoxtDQtdPoube7du1CdHQ07ty5\ng7KyMowePRq7du1qVBE33mnjMHK5DHL5LcMb1gEdYfv444/x008/MY/v3bsX/v7+jToywcMuhBD8\n+eefmD9/Po4fP671t/bt29v8u7R3715kZWWBoiiMHj0aYWGNuzCaoii4uLggIyPDbjbopkRdXFxq\nNUrV1VlYV8pTd82KigoIBM5QqSrh5OTENEYoFAoMHdoPSUmptRxCLujnGQvdKevnF4DcXNt0kxqj\nr9YYtNMoikL//v2xc+dO9OnTBwBQVlaG6Oho7Ny5Ez179rSvgSzBO20cxt9fyugNmcuZM2e0HLZl\ny5ahd+/evMPGUy+0k/bXX3/h5Zdfxv79+7F06VL8888/AAA3NzeUlZUhJCQEa9eutbot5eXl2Ldv\nH3JzcxEWFsZ0jBJC0LJly2fi+2yt12iM06NUKlFRUcGcj+gOzYSEU8jIuIhZs2Igk+VAKg1ERUUF\nlEqlVvqzLo0v3a5EFxcXqFRq7ciqqiq0bNkS9+7dA6BulNB1CHXnprJZJE8IwbVr1/DKK6+gpKQE\nKSkprEnaODo6QioNYmWt+mBDX60hQVEUevXqhVOnTuH111/H3bt3cfv2bezYsYM1p83edYl8TRuH\ncXVthmPHkrFt2zaz11iyZAnzc9u2bTFt2rRn4gLHYz50GnTEiBH44IMP0LFjR3zxxRd48uQJvLy8\nsGjRIri6uoKiKLzxxhs2+T4tW7YM77zzDpYvX47o6Ghmn/x32TI0a8gGDIjUW5dGbxMVNQwAEB//\nB3PxFwqF6NkzEseOJSM+/g8AQFTUMK2OUn0aXzS6XYnh4Z21auUePChjfpZKA7W6TfXNTT137gzz\ndzYurpmZmbh7965W1661sWRuqi71vfeNFdpx69WrFxwcHODg4IAtW7Ywzr+lTJr0tl1rLPlImwZc\nrDEQCoXo3r27yc8nhGDVqlU4deoUKIpCkyZNsHPnzkZ9l8XDHlVVVUzTCqAe6fPyyy9jwYIFEAgE\nzJzapk2b2sSekpISEEKgVCpBURS2bt2K6Ohom15MaQghWLt2LWbOnAlvb28cOXIEHTp0sLkdbKAr\nShsVNYwZZ0XXj2VkXNTaxsXFpdZ5RCgUwsXFhckKaKZJDWl86XYlrly5lnEQq6pUWo/rplZ1ZUqi\no9/ExYvX4OrqikmT3rZIENkesB0Ze5b11TZt2oS///4bp0+fBiEEAwcORHx8vMW13Hl5uXYVF+ad\ntv9h7RqD+uoK9I3IcXPzRMuWrZCengYvrxZwdXU1el+EENy/fx9//PEH0926bNkyvPDCC1aJTDSk\nmhIew1AUhZYtWyIxMREJCQlo164devfuDU9PdaHy7du3mW2jo6NtYlPPnj0Zu0aNGsU4axRF2aUJ\nYd++fSCE4M6dO/jvf/+LFStWWHV/1dXVkMnY6zyUy+UoK3uMZs2EcHISaDlHCkU++vaNwKpV6wAA\n//nPp8zfvL1bo7i4CCdPJqJNG5HWCK6qqmp4e3ujqKgIYrEEMlkOBAJnuLo2w4YNm5nzHz3P8/59\nod7zrUDgzKxD20anYzUJCQmrVT6iUqmQmJiA0NAwRsTXEpYtW2bxGobQPH+yPXlAU2LlWTo/UxSF\nFi1aYOLEiTh//jyePHmCjIwMjB8/HqmpqRat7evrZ1fnl3fa/oc1awwM3T1pqoTT6uNCoTdeeeVl\nKBT5CAgIwOHDh03a53vvvccIDDZv3py56LHNs1Yz8axAURQiIiIQERGh9RghBPv37wcADBo0CC1a\ntLCJLRMmTMCECROYx9LT0wGob1BoZ9IWEEKQnJyMlJQUm6ZmZTIZ5HI5a3M0Nde5ejWz3m2Tkk7V\nekwul0OpLNVax8NDWKfEgoeHEGJxK72P63uMXkculyMnJw9Dhw7XG93TjMoBgJOTE/r3HwRXV1dG\nPNgSNMeGWQPd82d8/EHWI2MNQV/NGlAUhYkTJ2LRokVMCr2kpMTidTdv3mHXaxzvtNkAQ3dPuiFs\nkUjCFNcC0NJmMgStVH/hwgUAQNOmTbF161Z07lz/kGJT7uTpu3QAyMy8ovXajh8/inbt2JEj8PCw\nnYwET210nRK6IWDt2rUghOCzzz6Dk5NtTiGatmhG2dSq+iE2sYGmsLAQhBDGjjFjxpi9lrElGbTD\nxrWRW7awZ+rUaRg6dLjev4WHd2bOnS1aeCA+/g8mrbt58w6r22YputeGgoJ8kyJj1dXVyM01X2HA\nGvj5BWhFYO0JRVE4ePAgRowYgZycHMjlcowePRrbtm0z2/FiYwavJfBOmw0wpqZD80BV12oomL/7\n+PgYvS+KouDn54fBgwdjy5Yt6N+/P0aMGGEwKmDKnbzmNpGREcjKyjLaPmORy+WQyWRo0YKdEV48\n7JCWloZbt27ZRFi6PuLi4kAIga+vr8nzRi2BoiiMGzcO77zzDlQqdUrRzc3N7PXomx+F4m69sgz0\nds8i9dUQCYVCxMcfZG5yp0yJZqL99r64GoO+a4MpkbHc3FsoKythLQJrKXK5HLm5sElnrDFQFIWw\nsDCMHDkSsbGxcHBwwIEDB5CVlYUuXbrY2zyz4J02G2BMXYHmgSoSSRitIkdHJ5O7RymKQlxcHOLi\n4pjfjYGLd/I83MMexf+67Nu3z26TECiKQrNmzfDw4UOL15JIfOHj44Nhw4bzJQZ1QNcQ0bVfIpEE\nBQX5zLm0oCBfq4PUnkXipsJGzRnXztu20J4zBYqisHz5csTGxjIlHrNnz0ZiYqK9TTMLXvLDRuiO\nbqmPgoJ8RquouroK9+/fN3l/9o6E8PBYE1vLMOjSvXt3rRSpJVy9evWZk2UwBTrNSUuTdOoUpjWo\nXnMIPR2tUiqVyMy8Yk+zjcaUa0NDgk3pEkuhO86bN28OAEhNTdWSw2pI8E4bB9E9CQUGBhp4Bg+P\nbejatSt+/vln9O7d26S0PdvQNyT2moRw8uRJUBQFV1dXODs7W7RW27ZtazkdPE9xdW2mVftVVVUF\n4Omg+qys64iPP8hovQFqB2/y5Gg7WcyjO0PW3o4b3cxEj9t78uQJvvzyS/z44492tcsceKeNg+gK\nTpoi98HDYy1oJ2XcuHE4ceKEXW4mCCFYsmQJE+USi8VW7/DTR01NDQCgc+fOFus+6R7vjS3iwgYh\nIWEQi8Vaj7VpI8KsWTEYMqQfoqJeZdKLmg4ej33goqgvRVHYtWsX2rZti+rqalRVVeHMmTOGn8gx\neKeNozTWkDlPw0Yz7W7t1DshBHfv3sWQIUPQtWtXdO3aFd26dcOKFSuY/b/00kvo1q0bhg4dypri\nuSGbtm/fjurqagBg7X3gj/f6EQqFOHToBMRiCQBALBZjxYo1WmK++/fHQ6lUQiSSMNtZimY0WVOf\nkKd+9KWs7Q1FUfD09ER0dDQEAgEcHR2RkJCAS5cu2ds0k+CdNg7BpRoAHh4usG7dOiQkJODChQtI\nT0/HhQsXUF5ezkTa7t69i2vXriE5ORmnT5+2iU20w9YQIYRgzpw5cHR0xPr16622j2+//RaOjo74\n4IMPWFvXy8sLSUmpOHz4OA4dOgEXFxdm5JVA4IwZM6ZjwIBIjBw5BApFPry9vS3e59SpU5mfY2Nj\n+XOzkXA1ekxRFD766CP4+PiAEIKioiKLxkTaA757lCPwIrU8PLWZO3cuKIrCvXv3cP369Vo1bJMn\nTwYANGvWDKGhoTaxacuWLczPtmiGKC4uxu+/70N09ATDG9cDIQRz587F2rVrAajnEt+/fx/z589n\nw0wtfvvtN2afbFNRUYGRI4dAJsuBv38AJk+ehri4jQCgNR2hqKjI4n316NEDEokE+fn5OH/+PGbN\nmoUNGzZYvG5jhp75ylVRX4qisGLFCrzxxhsAgN9//x2rV6+2s1XGwzttdsSU8SXWnovKptq6JoQQ\nHD58GMOGDQNFUYiMjMS+ffss0rbieTagpTUWL15s9Pa2xhJhXWMoLi5G585toVJVWuy0AcDy5cuZ\n96mkpAR///23xWvqg01njXYCNG9saeTyW4zDBqgnItCNCpamSCmKglgsxnPPPcc8lplZ//SIhgIt\njp2WloZTp06xuvakSW/j5MmznA46tGzZkvl50qRJdrTEdHinzUboKlfrzhtdv/4H+Pr6Mb8LBM6Q\nybIBAG5uLigre4zdu/di/vz/1Fp78eJlEIvFUCgUEIvFcHFx0WtDRUUF5s79DIWFt+Hj0wZbt/6M\n5s3VB5atxDtzc3Px+PFj3mnjMQouSdYQQnD79m0tMWl3d3er7jMxMYGR/7EGo0aNstrabDFhwlgc\nO3YaBQX5BhsMaIcNAGbP/pyV/Y8aNYpx1iorK/HPP/+gadOmrKxtT3799Vfcv38fCQkJGDRoEGvr\nsjFQnQ5oCATOekedWQJFUejTpw8jjt3Q4J02G6GrXO3hIcTRowla2+j+rkl4eFuEh7fF66/XfZKN\niHjRoB0nT54AoI6s5eXJMWJElDHmmwUhBA8ePMD69euZi+97773HSq0JD489KC4uZuYXOjs7IyrK\nescPAPTvP4gR2rYGkZGRVlmXTYqKijB0aD8cOnScmR4glQbiyy+/xrx5syGXq2+GfXzaQCAQIC8v\nF1JpIEJDX2Bl/9HR0di9ezeysrLw559/IiYmBt98841ZUi/l5Y9x/vx5tGolsXskKiIiAr/88gvm\nz5/PqtNm6UB1zYiqr69fvddFc+HSzaCp8E6bDeGacrWTk/WkROhC8R07diAh4elBN2/evAZ9wDR0\nrJ1mN7RvrozbMZedO3cCUJ/0Bw0aZHU5Hi8vL1y8eBU//7yd9bXbtm3bYI5FhSIfBQX5iI8/iMTE\nBPTvPwheXl6IiHgZ586dwWefzcDt2wVwchKwul+KouDv749Dhw7hlVdeQV5eHjZt2gRCCNavXw+B\nwLT90dkV3bplzVIZWzlz7du3xy+//ILLly+zuq6lA9U1S4Xy8nJZsqrxwDttzzDWns33ySef4Jtv\nvgFFUQgODsa1a9cazEWiMeLnF4DcXPuNmWkM8zP//PNPAOqbEpFIZJPvs5eXF157bSTr606ZMoX1\nNa1FUFAwRCIJoqJe1WrWAoC7d4tx+3YBAKCqSp3ykslycOPGNYjFrSzeN+24LVmyBIsXL8bNmzfx\nww8/wMHBweSmBNoJ0axbtlcT2rRp07Bz505kZmZixowZWLNmDSvrWnpd0ZzH6uvrx4pNjQneaeNh\nHUIIYmNjsX370+gA3QXIYz8cHR05M8i5oUJHJSiK4ieV2Ii4uK3o129grWatHTu24ocfvsPt27e1\nZjVXV1dBKg38X1S5Dys2UBSFt956CxKJBL179wYA9OvXz+R16LplTe0y3deVkXERPXtaN21NURRa\ntGiBIUOGIDMzE9u2bcMHH3zAie+05jxWgcCyaSONEV6njccqREREoKysjPm9VSvL73h5eOwN3RVJ\nCGEu3g0JumzBnnNbTcXfXwqhUAgPj+fh6Pg0zvDFF3MZwVuVqhKenq1QXV2FNm1EqKmpQWzsMlbt\noCgKvXr1Qk1NDWpqajB69GiT19i8eQdOnDiB5ctXo7y8HOnpaRCJJIzeHADMmhVjMz24IUOGAADu\n37+Pu3fv2mSfxkDLhVg7G9QQ4SNtPKxCjxnasmVLg7ow8PAYQ2hoKMrKyhAVFYWQkBCr74/tLjo6\n2m2rqHdKSgoKCwstmlM7adLb+OOPYxgz5jVUV1fVuV1JidrpoFOl1oCN9+3999/HjRs3mOhgUFAw\nvvzya7z11lgA6tSupd2XxuLu7o5mzZrh8ePHmDhxIrKysuDo6Gj1/ZqCPetwdZHL5XBz87SrDbzT\nZgOUSiUyM68gMjLC3qZYFUIILl26hPXr1zMddoQQLF26FAMHDrSzdTxcgGsnYFMaIyiKQmpqqhUt\n0kaz1kksliAx8Rgr69rCYaOjeZmZmSgtLbXIacvLy0ViYgIUinyjtvf3D0BlZaVVnTdzkctluHHj\nBgAwHcF0alQslkChyLfZ2CeKohAeHo5Zs2Zh0aJFkMlk2LlzJyZMsFwPkC3sXYeri5ubJ/z8Auxq\nA++0WRnNE6+mvlNjgxCCa9euYdCgQcwMSIqisHTpUnzyySd8PRuPwROwh4fQqifn/Pw8uLk1Yxw1\nf39/SKVSk9Zg+3ucn59X5+vOzLzCXNCNdVi4Apvvk6+vH/r3H8Q4NTTLlsVCLJZgwYI5kMlyGBmQ\nBQvm4PbtAk5KCz158gTBwcG4efMmE2mTSgOxYMEcKBT5EIvFiI8/aFM5kJEjR2LRokUA1GlSLsHX\n4daGd9qsjGaRqbUghGDmzJnMeJrdu3dbXaldH/fu3UNJSYnWEO3AwECT2+J5GieGTsCens1RUvLI\nqjZ4eAg5Jbvj5qau2dGX+oyMjGBu9NiKUDbEkoXNm3fAy8sLhw4dx9Chr0ChUCAoKBivv/4GhEIh\nIiJe1posQ4+yYmOMFdtMnz4VwcHBiI//A0FBISgoyEdFRQWiooYBABQKBQoK8uHl5WUzmzTHv8XH\nx+PDDz+02b55TId32qyMZvsy2xBCcO7cOXzzzTfYvXs3HB0d8dJLL+Gll15ifV/GEB8fX+ux0aNH\n81E2Hp46sLV2o61r2gDg6NGjaNeundnPp4vR1QPjz9fSMxMKhYzDJhJJOC8XcfPmTbi4uMDLywte\nXl5QKpVaosEVFRVQKpV6o226em7l5Y8trnV0dnZGTEwM1q5di6SkJJw+fRq9evWyaE0e68F3j1oZ\nun05Lm6rVdYXiUTIz8+HSCRCVFQU2rRpw3RUGYKe6WcphBBkZ2fj22+/ZWpZ3NzccOLECd5h4+Hh\nAIQQps6UPibpMgZrc+DAAdbWorsKNR0augRlyJB+iIp6FfHxB3H48HFs3ryDtf2ySWhoqFbNmlAo\nRHz8QSxbFouamhpERQ3DoEF9anWQar5O+u9yucwiW+isyPjx4wGovyf79u2zaE0e68JH2myAUChE\nu3btrbL2+PHjkZaWhtGjRzNq7cbCxmBfQgg2bNiAlStXAnh6Eli3bp3FI3LkcrnNC1D9/AI41z1l\nT3Rn5lqT+/eNq2njPyPzWLp0qdbvS5YswcKFC62+XzZTsvomB+jqnCUmJmDEiCgUF9/hXONLXNxW\njB8/GhUVT98TpVLJiAbTaIrv0ui+zqys6/D3N60msy66dOmCjh074tKlS7hz5w4ra/JYB95pa4AQ\nQqBQKJCamopz586BoiicPXsW58+fR/fu3Y1eh43BvgCwbt065Oc/LRBu27YtevXqxUqUje1hwfUh\nl8uRmwu+8FUD3Zm51sbQ501/RuqmBtOcyfz8PJSVcUf3yVZjvegZwBkZGVoOVEOYO6qJUqnEgAGR\nTNPBsWPJTGqUTi8KBM6YMWM6vv/+Gxw6dBwAoFDcZcZHOTkJUFWlgrd3axQVPXVO4uK2MjfW5eWP\nme19ff2wefMOi/XC7t37G2fPnsG4cW9BKBSiouJp7aa+umd9HaSar5P+e3Gx5Q4WRVFwdHSEk5Pa\nHbh9+zZUKhVfi8xReKetgUJRFDMiytHRET169DC5lo0e7GupJIm+O2k2HDZ7zGrlSms5l+DazNzS\nUqVZzqSbWyjy8/OtEn25cOECXnzxRZPsMad71VweP36MvLw8rZo2c8RhjWXlypWIiGBH4ig/Pw8A\nkJ5+gWkykMlyEB//K9zc3ODvL8WGDZtx/PhRfP31YgDqSNSpU8fRrl17yOUyZnwUPeaqqOgOxGIx\n09TQr99ACIVCKJVK7N8fz2yfl5f7vw7PjmbbX1xcjMjICKhUlVi1ajny8/Pg6Ph0Zq2mMyaVBmLl\nyrUID+9cKwOiOSmAjjQWF5ttVi1iY2PRt29fJCcnY+PGjXxDAkfhnbYGypo1a3D27FkQQvDSSy9h\n586dJjtKdM2HuZIktC7bgwcPtJTi4+PjIZFITFqLh8dUzHEmw8Ksp3/FNeeWhqIo+Pj44N1338XC\nhQttUmfq4uLC2lpubs3g4SHEgAF96j1HRUdPQHR0bY0xugtXLpdj2LDhqKpSISgoGPHxB1FQkM84\nQJqRPDoix4ZmWmJiAqPJplJV4uDBg3jttdeZv+tzxuqCruljG3rag0QiQV5eHvbt28c7bRyFd9oa\nKA4ODnBwcEBNTQ0A8yJbrq7NzJYkoXXZBg8ejL///hsURaFt27Z48803IZFI+AYEHh4OQVEU5s2b\nx9SwTZs2zaodgj4+Pvj++++xa9cufPzxxxatxaYzvHfvQTg5OUIkkmg5bACQkXGRieRVVamwbFks\nIytiDnT9XY8ePRlNNoHAGa+++mqtba3ljJkCRVEYPnw41q9fz6laQB5teKfNQvQVxloLupZtv/BS\nzwAAIABJREFU/PjxTC2bSCRimgDMwRxJEjrCFhUVxXSkOTs7Y9asWZxS02YLWxbjA8YV5PPF+I0X\nQgjS09MxbNgwnDx5krXooIODA6qrq1lZqz4oisLzzz+PqVOnYurUqVbfnym0bPk8vLxaM9mFoKBg\nJCSc0nvuDg4OMemcrnktAKC1j5SUP3H2bAr69x8Eb29vq+sRWkpD1PN7VuCdNgvQnHZQ38HPJqmp\nqUhLS9OqZTOl+UCXp5IkG0x63ty5c5GXl8f83qxZM/j6+jbKCJuti/GB+gvy+YaJxgshBNevX8er\nr75qFUkOWx2fXD4P6OvC7NKlK8LDO0MqDWQaHcLDOxtci3bURCIJ0wEaFBSM5ctXa+2jtPRvvPXW\nRKu+LjZ44YUX7G0CjwF4p80C6jr4rcm4ceNAURQIIaipqTGrlo0mM/MKvLxaQygUol8/42eDUhSF\ngwcPmrXPhgrX6pX4honGByEE5eXlmDdvHu7evYsLFy5oqdXzsIO+LkxAfQO7b99hJCYmoH//QQZv\nwJVKJQYO7I2cnGytbtTs7JsoLLwNX18/pgNVIHCGTJYNwHhpG2NQj0Fry8paFEVh2rRpmDZtGivr\n8VgH3mmzgLoOfrbRnHxAR9heeuklxMTEWHRHO3lyNBMhNLWlnct30jyNm/LyxxZLMHCVGzduYP/+\n/YzeIX+csU9dhf+aemn6GhV0ycq6jpycbBw5ckRvFH7AgD512sCWlBHbEjb894378E6bBRjq+tGt\ncbCEgoIC5OXlgRCC6upqZgqCpdARQnd3d4vX4uExBCEEsbGx+OyzzxATE4PVq1ebvIZcLmNVrJoQ\ngv3792PUqFGgKIpp7rEX9FQRHuuhr/BfN3NCzzkVi8U4dOhErXmgISFh8PX141wUnqdxw4+xshB9\nY1WA2iNHzBkZRQjB2bNn8cYbb2D8+PG4cOECKIrCzJkzsWvXLlZmjAoEzhCJuC/PQQhBYmKi0dt+\n++23cHDgv95cghCC0tJSbNigrp/cvn27Vl2ksbClAq/JP//8A4A7Bdh8xMP20JkTABCLJVAoFADU\nQ9yHDu1Xa6yUUCjk7KgsnsYLf1WzErp3bebOiNONsNEzRrt3787KiV2lqkRBQb7hDe0MRVHo16+f\n0dv/9NNPVrSGx1QIITh9+jSioqIYOYHS0lI8emR6Fx2bqVFCCDIzMzFjxgzW1mQDrjiPXGDy5Mk2\n2Q+dOYmP/wMLF34FT09P5m8KRT6ysq7Xek5jTdPzcBc+PWoldOvdTI0OEEKwevVqzJo1C46Ojkwt\n265du1hz2ACwOg7F2hjzmgkhUKlUUKlUeP311w1uz2NdCCF4/PgxDh8+jLfffhtPnjxh/iYUCtG0\naVO72zZ//nwUFRUBALp162Y3ezTR913nknaWrWyRy+U4ffo0Fi1ahC+++MIm+5w1K4bRa3NyckJV\nVZVVa5Z5eEyBd9qshG69mylOEa3HtmfPHq0IGy3vwZbDFhe3lRnfwuY4FHtz8OBBXLt2zax6KR72\nuXLlCuNAP//881CpVHj48CHGjx9vszFOdfHVV19h//79zO+zZ8+2ozVP0Y20mfo+yeVyDB48GIA6\n1bdly/9pRYU052u2auWFzz6bi/DwzmjatClu31bUu/bNmzcxf/5/tB7z9vZmHF8AmDNnPkJCQuHt\n/bxZUjmEEBQXF2PIkCEAoOXsW4KhMpWsrOuMwwYAVVVVWLYsFsHBIazsn4fHUninzYpoFrua6hTp\n6rGxHWEDgHbt2mvV4nHtTt5cXbQVK1agSZMmaNmyJctW8VhCy5YtMXjwYOzatQve3t5Yu3at3Wq3\naKfgt99+03q8VatWdrFHF933xdHR0exi93XrvkeHDrVnZ548eRYZGRcxa1YMPv30Y6Zj8tGjh/WK\nhXft2h3bt/+kNSszKChEq/Ny8uR/o7j4Djw8hGbZTQiBq6sr8z5kZ2ejqqqKGWpuLnK5TO97QRMS\nEsZotQGAv38A4uI2QCbLsZkWp6lw7bzt5uZpeEMes+GdNo6yZs0aVFdXgxCCn3/+mXWHTRc/vwDk\n5gIKxV3I5TJ4e/vg/ff/BYUiX++del1o3sFrEhe3len4Ky9/DLlchurqGvj5+eh1zswZpk0IwalT\np3DhwgUEBQWhU6dOJj3flhBCsG/fPowePRoikQjHjh1DSEjjvJsPCAjAjz/+iDfffBPvvPMOVCoV\nvL29WZ1PaQqEEFRVVeGdd95Bdna2lp2dOxsWVLUFbNW0BQUF1ykSKxQK4eLiwjgo6o7JflAo8ut1\nUOrqmqcfE4kkyMq6DoHA2SJpiyZNmkAkEkGhUCA+Ph4VFRVo3ry52esB6iaW+qbYCIVCHDuWjIyM\ni8xjUVHDANhOi9MUdM+RcrkcZWWPIZH42sUeNzdP+PkF2GXfzwq801YPbI4vMlUEMSYmBm+99RZq\namrg4OBgFYdNM1Xg6OjIKOzTd6JJSakmjehSKpXYvz++lsMWFBTMpGFpOnToCJks2+w78bqYNGkS\nqqursWHDBs524BFCUFFRgW3btjGpcLlc3iidNoqi4OnpiXfffRf379/HyZMnAQCff/65XT+fefPm\n4ejRo1qPOTs7282RpLl+XV3szsZ7s2rVOgQE1H/jo1l7KxaLoVCom5IMOSj6JDOEQiFCQsKYKTG+\nvn44ejTBLNspikLLli3x2muv4bvvvgMhBD/99JPBOabV1dWQyfQ3fcnlchQV/c3cVPr6+mHz5h16\nb0Zbt24NQH2OrEskFzBf3JYQgk2bNuHChQv48ccfTX4+jb4IbGmpkp+W0ojhnbZ6YHN8kSkiiBRF\n4fXXX7d6If2kSW/j5MmzdTpkpgwxViqVGDAgEjJZDgQCAVQqFQCgTZs2iI8/aJO5rImJiZDL5Rg4\ncCCno2wA8NlnnzG1VG+88Qb69OljX4OsCD3B48yZM7h37x7Cw8MxfPhwu9p07NgxAOpxbLt370ZO\nTg7c3d3t7uinpKQAYCfS9p//fIqqKpXRUTPdUUzmFN5rds3r3rxZyq1bhm+gZTJZnaUV/v7+8Pf3\nN8mR9PAQ1ru9JeK2CQkJ+OOPPwDAIseN59mCd9oMYC/hRFtcPPLycpm7aVMG3+vbNiPjIpNmoR02\nALh9+zays7NqCVOyCSEEZWVleOeddwAA27ZtsziNYi0IIYiJiWG0ylxdXbFjx45nQlPuwIEDAIBO\nnTpBIBDY1ZaUlBT89ddfiIiIwJ49ewAAUVFRdrVJEzaO/6oq9XFoTNQsJCQMWVnXDU4BMIRm5M7X\n188S8wGoP5PvvvsOgPq8U11dDUdHx3qf01DEbmfPno3ff/8d27dvR3h4OKZPn25vk3gaAI3/SsFT\nJ76+fggJCaslBKxUKqFUKpGenlZLUFLftvpo3dqH+XnWrJg6t2OLM2fOoKioCJ988gk8PT3tHjHR\nBy1H8uDBA9TU1MDZ2RmLFy+2Wvqba/z666/Mz/Z8vRRFwcXFBT169MCZM2eQlZUFgBvaaHfv3gUA\nSCQSiMVii9ZyclI7xrpRM91ju7i4GL17d8eQIf0wdOgrEIkkZkfG6cjd4cPHWRGe1YyYb9u2DcWN\nqM39ueeeA6BO6S5dutTO1vA0FHin7Rlm1qw5AGoLAWdkXKzTMdPdlhacDA/vDKk0EAAglQZi1ap1\nzHNkshy9wpRsQAjBpUuXMHXqVDRr1gyff/45p6NWd+7cYVIiISEhFs+PbUjQTpGrq6udLdHvNNpT\nM46Gnjvq6elpcffz3r0Hcfjwca3UqO5NV3Fx8f/GNalr2epS/zcFuqyCDeFZOo0NcMOpZhM/Pz9G\n0oR21nl4DMHdqxuP1Zk+fSr69u2BqqpqJpXh6+uHwsLbWo7Z8eNHIZNlQybLhkDgrLUtXZhbXHwH\nmzZtQVzcVmzatAVeXt7MdmKxBDJZDi5fvoTLly9h//54XL58CdXV7Mx4XLx4MQoLC/Hpp5+iRYsW\nnHaCtm3bhtLSUrRo0QIjR47ktK1sQQhBcnIyMyrq3XfftbNF+nnzzTfttm+6MJ2eO9qrVy+Lvxst\nWz5fa8Se7k1XYmICM66Jpi71f2OhI3nmjO7TJSkpCUDjm8dKR3s//fRTAOrXt3jxYlRXV9vZMh6u\nw9e0PcMcOXKEKdjVLbalU0a61FeY6+EhhFj8VOeqru3E4laQy+XIycmHp+eL5pgOQH2i27ZtG+Lj\n49GpUyd88cUXnHeC6OaDXr16YdGiRXa2xnYcOXIElZWVCA8P52yXrL2/OxRFMf9GjhzJ2rplZWVI\nSDjEOM3quZr58PX1Q2BgMNMd6ejoiOrqar1dkoagZXy8vX0wffoU5OXlwsenDX76abPe7aVSqcHa\nNODpZ0K/L40NWrKDEIKFCxciJiaGs/W4PNyAd9qeYexdsJuWloGICPOcNkII7t+/j1WrVsHV1RUL\nFizg9EmdEIKdO3fiypUrAABfX19O28sWtKTJtm3bAMCu+mz6OHToEAB1qqpJkyZ2teX06dNMNKlX\nr16srKlUKvHKKy8jLu4H5gZt+PDBWtuYK8uhieYNm6H1aDFYY8494eHhFtvGZTw8PNCvXz8kJiaC\nEILffvuNs5FoHm7AO202hGvK1WxImViCpYXWK1euxNWrV/Hee+9hxIgRLFnFPoQQZGdnIzo6GiqV\nCiKRCB999JG9zbI6tMzHpUuXmBFHbdq0wejRo9GyZUv88MMPdrYQjF0dO3a0e63djRs3tKJtbJCV\ndR0KRb7db9DMZfTo0czA+AkTJmgNcW/oUBSFZs2aISQkBImJiQCAjIwMO1vFw3V4p81GaIbBc3Nz\nmbmAX331FcaMGWPUGvQ8QU1RSDot4e8vrfW7XC7D5MnRzPM106HmTBxgG3MjLoQQpKenY/PmzXjx\nxRfx/fffcz5qlZ2dzUihjBgxwu7vPdsQQlBaWopff/0VW7duRXl5OQB1mr2qqorZbvNmdbrMUoe9\nvPyxWWr7hBA8efIESqUShBBUVlYCAE6cOIFvv/0W//73v+0iR0JRFNLS0lhfNyQkDGKxhPV1bYXm\nce3u7m7xGCuuc+7cOTx8+JDpLOXh0aVxHwEcglauJoRg6dKlzMmodevWJt8B5+XlQqWqhJeXlFEf\nF4vF+O23A1CpKpnpAwEBUi3NpIZ6t62Pn3/+Gc7Ozli6dCmcnZ3tbY5BaCcGAAoLCznvZJrD/Pnz\nsXHjxnq3adWqFbp27WqRuC49Ks2UtB4hBH/99RfWrVuHrKwsnD9/Xuvvjx49QkxMDO7fv48vvvjC\nbNsswRrfCaFQiO+/bxzCrb///jsWLFiA559/3irrE0Iwc+ZM7NmzBykpKRbfWJiyX/r/Cxcu4I8/\n/rBrUwwPt+GdNjug2d59+vRpk2sYaN0lzU4whUKBl19+EVVVVRCLxTh06AS8vLwYtfOqqsbRlUQI\nwY4dO/Ddd9/h/fffR//+/Vm/2JkiNGwstIArwC0RVzYZO3Yso1pPURSioqLg5eWFNWvWICkpCW5u\nbkhPT4ePj4+BlepHLpeZpLZPCMF///tfTJkyhSnG10dQUBAmTJhgkW1cpKioEMHB9plFySYKhQJP\nnjyx6j4+/vhjrFmzBuPGjcPZs2etui+aBQsWMNkCiqKQmJjIO208dcI7bTZEX9v63LlzTVojLm4r\nE0lTpz7ETMs+nYaitZaSklKZ7fr27cFKwTGb3L//wKzn/fnnn/D29saiRYtYd9jKyx8z0Ut6/A8b\nXL16lfm5Xbt2rKzJJSiKQp8+ffSO4xKLxZgzZw7eeOMN+Pj4WPyZ+ftLTVbb9/X1rdNh69atG1av\nXg1fX1+0adPGItu4BC254e/fOFLxrq6uRnWcmgtFUZBIJBCJREhNTbXafnSxdwMMT8OC12mzMfSg\naoqi0LFjR7i7u5v0/Hbt2jPRH6FQiEOHTjA1K46OT31wTa2lrKzrrM8BZIMPPphi1vP27NmD//zn\nP1ZpjZfLZXrFg9nCz88Pvr4NP+qhD80ies1/4eHhOHz4MCZOnMiKk+3q2swktX2KotCrVy/U1NTo\n/ZeamooePXqgTZs2jSptLZerB6ezIXJrLyiKYsofoqOj0apVKwPPsJyYmBim69kWuLq6YsoU886F\nPM8evNNmI+gGBM3apqlTp1pcn+Hl5YWkpFQcPnwcZ89eYBw4zdE1ISFhrMwBZJvq6irDG+lAURQK\nCwsxbdo0q1xg/f3VdYBA7fE/5kAIQUlJCR48UEcVp02bZrKj3tBhuyMSMN0Rqcuh1P3XmGjoETaK\notC8eXMcPnwYffr0wauvvmr1z4iiKMyYMQMAGOFba+/P0dERLVq0sPq+eBoHvNNmQ7Zs2YL169cD\nUBdkT506lZWTED02xt8/gHHg6LReerq6I42NOYD6oJW8HRwc4ODgwCiYG4NmZNAUrHmBdXVtxsxO\n1Bz/YwlFRUV48OABHBwc4Orq2uicA1tTXv4YmZlX7G0G52E7wkYIwdy5c/HNN9+wum590Gn348eP\nY8CAATbbJ2C/sVn37t1DRUWFXfbNw334mjYbsmTJEuaEMHfuXKt1i3Xp0pWZMUjXZm3YoF+Z3BII\nIdi6dSuWL18OBwcH1NTUmPSavvvO/jpd+qDfQ7bo3r07Kioq8OKLLzKaUzzmM2nS28jLy61zaoe9\n4JoOo5ubWtPMXHkUffz666+YOHEiK2sZi71ucnQ7jG3FoUOHkJ+fz9nJITz2hY+0WRlauf+VV15h\nGhEmTZqE6dOnW3W/ujMG6foWtsnLyzO7o6tp08ZbgEsIwbp169CjRw8QQhAQEID58+dzQp6Eng1p\nyVBwe8LF+kyJRIKysscoLVVq/VMo7mLgwEEICQnR+pecfA4KxV0kJ59DeXl5reclJ5/T2v6HH7bU\n+fzLl2+gbdt2CAkJQdu27XDzZh7c3Dzh5xcAAKwe+/fu3cOHH37I2npcZcyYMTaradOlMc1Y5WEf\nPtJmA5KTk5GcnAyKouDl5YUpU6ZY/e4xJCSM0WgLCgpmvb6FEILExER8++23AICwsDAcOHAAXl5e\nRq/x6acf48yZM6zaxSUcHByQmpqKQYMG4eeff+bEMHvdCCxbKWBb4u3tzUwy4AqOjo6QSHwhlQZp\nPZ6enlbLyQwKCmY6wDt06AhPz+YoKXmkJTXj5dWaOX6l0kDEx+9mni+VBqJ795cxcuQQyGQ5kEoD\nceZMGs6eTUH//oNqHYNsHPuEEKSmpuLRo0dwc3OzeD2uM2bMGPz2229QKBQ20WsbPnw4li9fDgBw\ndnaGgwMfT+HRD++0WRFCCA4fPoypU6cCUCt6b9myBV26dEF1dTVkMtPugOVyOcrKHhu9/YYNm5np\nCAUF+aiqKq93e2OHOBNCkJKSgnfffRcPHz4EoC7aNbUr8t69eyZtby/MSS9RFIXp06drRVTt7bAB\ntSOwWVnXWU0F24KioiJ4e7e2txm1yMq6gczMK8x0EgAQCJyZgexisQSzZ3+O0NAXUFx8B8XF6ucV\nFFA4ezYNy5d/xQxy37x5B3P8PnnyBNOnT2X2M2PGLCQlnYBMlgMAkMlykJZ2Ht27R6C4uAh5eXK8\n8EI7xhlnq7YtPj7e5BKIhkr37t0BAGvWrMHq1autui+KohAREYElS5ZgyZIl+PHHHxEYGGjVffI0\nXHinzUoQQpCUlIQJEyagrKwMADBx4kQMGjQIFEVBJpOZPP/T1FmhmkOc6f/rwpQhzgCwbds2FBYW\nAgD69OljtpwD1+qAFIoieHm1Zi54SqXSZPV9Gi5e3HQjsMZ0x3LtMwKAoqI7drakNt7ez9c6Rj08\nhEZ9dwYM6IMBA/rUepw+bvXV7+l7rKysBMOGDbZKFPX555+32ZQAe0JRFPM69+zZY3Wnjd7nnDlz\nMGfOHOZ3Hh598E6bFbl06RKcnJzg5uaGt99+Gz179tQ6GBviWClCCO7du4effvoJjo6OcHd3x+ef\nf272SWbYsOHYu/cgWrZ8Kn1Cz0/t0qUDnjwhKC9/jOjoN1BQUACRSIQNG37C9OlTkJeXqzWH1RD0\n+CP6eevX/8Cs4+PTBhUVFbh/v1TrgsdVjTtzEQqFzJQMYyY++PkFIDcXKC21fv2bh4eQ2Y/uZ0V3\nP0+dOg0A4OvrxzlnkkvHszWiqEVFRc9MVyN9PrN0eoc5++ThqQ/eabMCtAzGwoULQVEUgoODsXbt\nWnubZTG01tzo0aOZxz788EO88sorZq9ZVaWCk5MjUwukWXMVGhqKQ4dO4Ny5MygoKAAAFBQU4OHD\nMpw8edasUVOaz9N0yAoLbzPbaF7wuKpxl5+fZ/Zz/fwCjL6YOzo61qrTYhPNOi5//9YoKXkEQLsW\njJ6126VLV+bzCwwMxr17d1FaqmScfM20pL7HdDFmG0Pk5+fBza0Z/P39IZVyRxeNDY1BXdzc3KBS\nqVhZy1oOt6nZC0PYq4OUh6cueKeNZQghuHTpErZu3QpAHdmIiYlpNHdRR44cwZUrao2sfv364aOP\nPrLotQkEzhCJJMzvmjVXN27cQFbWdeTkZGs9R6HIx4ABgxjHS9dxKy4uRmJigt6ibE05D81UoSat\nWnkxFzyhUGg1jTtLcHNrZpaMg1wuR24uWHHELJ3RqtsUcfFiOvO3utK4mp+fZkF8hw4d61xXX5qQ\nzYYMDw8hZyJsgPaoOzbp06cPfv75Z1bWomtz64vg6ou2GnKuTan5NQa+k5OHa/BOG4sQQnDt2jVE\nRUUhL08dCVmzZo3JA+G5CCEE+/btY2ouevXqha1bt1rcSaZSVaKgIJ9xrkQiCQQCZ6hUlXB2doaH\nx/PYuvVHZnuBQIBXX32tzotucXExOnd+ASqVCgKBABcvXmPW1nUy6FThuXNn8M47b6KqSgVHRycc\nOJCgdcHj4hggS1JxbKQ69b3/Li4uyM29ZfQamZlXtJoiDh8+DLH4abRKs5FGs3Df1HX1pQkbQ0NG\nXWiOugPY1WkztXmqLiQSXwQHBzORVX3oi7ZKpR3r3J5tYmJiGkWGhKdxwTttLDNq1Cjk5uYCUKcO\n33333QYfZdOXFg0ICICXl5fWazOlI5ZOj/j6+kEgcIZMpo6mZWZegUpVCQCorKzE7t2/QC5/6gjM\nmbMASuVDpKae0broHj9+FO3atcfPP/8fk8JRqVQ4cGAf/vWvqXU6eUKhEAMGDMJff12rMzqnaS8X\nYDsFZA76nB53d3eUlZUYbVtkZES9ArmajTSmQK8rl8sxdeo0vWlCcxoyGiKWNNLokp+fb9PIkzmf\nEZsOKt1BysPDJezmtBFCsHDhQmRlZcHZ2RlfffVVg+1MokVzw8LCoFAoQFEU5s2bh+nTpzd4h41m\nxYoVjHYQRVGYPXt2rddmSkesv7+/3gu2vgv5lCm1I5V1XfCnT5/KyCPI5XJkZqq3MRRZ8fLywltv\nPVV614zK+fkF4I8/rmDw4MHM39ev34SVK782mLrRrHliCy7UT+m7oBYX3+FUMT7wdHxbenqaVhq3\nroYMS1O+XIPNRpq//vqLlXWMRd9nZOjzkctlZjn6ulAUhbFjx2L8+PHYvXs3Xn/9dYvX5OFhA7s5\nbYmJiaisrMQvv/yCS5cu4euvv8b3339vL3MsZuPGjcjJUesmtWrVClFRUWjZsqWdrbIcQggyMjKQ\nkJDAOGmvvfZanSNWuHbRzsnJg1KpNOmuXV9Url+/gVrP9/FpY3Tqhms1T2yg74JqbPrS1tRVu6Y7\nrqwxCA/T0M6NSCRhrZGmT58+WLduHStrGYvmZ2TM58OWiDghRKv5qT64FoWnx5fxNE7s5rSlp6ej\nV69eAICOHTsiMzPTXqZYBCEEGzduRFxcHPPY6NGj6xxqTm+fmpqKzz//3Kp2Xb58GZGRkYxOnLkM\nGjQI9+/fBwC89NJL2Lp1a4OJIE6fPhVBQbFISDjFOBkikaTeu/W6onKaTgqAep1A+qIpEDizlq4x\nB92UtakCzbrodly6u7sz9Wb5+XkoKzNc/2esiDMbyOUyo2vX7FnnRgtxDxs2DBRF4cSJE+jdu7dZ\na5WXP9ZybtavZ2fGb35+PivrmIsxnw+b9afnzp0DAKSmpta5jaGIN328SSRPhcc1pW3YRnN8GU/j\nxG5Om1KpRPPmzZ8a4uSEmpqaBje+g6IoTJs2DdOmTTNpe0tkMgxBCEFZWRlGjBjBdLFawr1795iL\n7Pvvv9/gog+6Eh513a1rRicMReXq0zvTjAj4+vqxUk9kLropa0vTtPXVmXl4tDX4fFNFnC3F319q\ndITV1Ggs2075tWvXmJ/37t1rttOm66gWFRUiONi0aSX6uHXL+CYTU6iurjaqgUVzuoRuLSyNsTcO\nhpBKpUx6NCYmps7tHB0dDX6XS0uVWt3a9NgyHh5zsJvTJhQKUV7+dKySsQ6bp2dzg9uwxf37xp2M\nTY061RWFYwN6QH14eDj69++PkSNHWrTWe++9B0IIqqurAQA9evRoMFE2mtDQUPTs2Q1CoRC3bl3T\nuqDl5t5A165dkZaWhmnTpuHmzZvw9fXF8ePHce/ePbRt2xZCoRBFRUXo2zcCeXl5CA0NRVpaGjw9\nW8Pf/+k4paKiIhw8eBDu7u7MPrggzMu1lLUtEYtb4eLFdFy9epX5LOvC07O5UdsqlUpERr6CGzdu\nwM/PDwkJ7DjlcXFxrBxbXbp0QGhoKG7cuIHQ0FB06dKBBeuAbt26sbIOAMbR9fRsjps3bxrVwGLM\ndAljbhwMoXljUVNTY/F6Hh7CWtctW17HuMSz+rrZxG5OW+fOnXHy5EkMHjwYGRkZRl9UbHmHUlqq\ntGtqy1QIIaioqMB7772Hdu3aYdOmTWZfBOhatmPHjoGiKDRp0gTvv/++SQPhuUBc3FZ07/4yUlL+\nhEgkQWHhPeZuHQAmTfoXAGh1qObl5aFv31eQlJSKigqCkpI76N27OxQKdXroxo0bSEn5Uys1o5Ya\nact0vtKIxRKwDT0ibcyYMQ1mfqu9KC1VokULgoCAF1BRQVBRYfj8YWjb9PQ03LhxAwCw++SGAAAg\nAElEQVSYTnFLoD/PkpISi9cCgCdPCA4dOsFEgYuL78DV1fJ1W7dW36DIZDKLG2Ho9GBJySOUlio5\ne2PBhhNdWqrUum49q5G2Z/l1s4ndnLYBAwbgzJkzGD9+PADg66+/tpcpjYqtW7ciOTkZf/31F5yc\nLPt4Hzx4gOL/VZf7+Phg1apVbJhoU/z9pYiKehXZ2TcZ/TfNeipNZ00ThSKfkRHJzLzCOGwA4O3t\nXSs18/vv+2o5bAAQHf0vFl/NU2JjYxtcxNNesN0RqplGZcspv3z5slbt6Zo1ayxaT7OAn60GEaFQ\nCCcnJ1y5coXV7mU2ZTp4eBo7dnPaKIrCokWL7LX7RgchBGlpaYiJicGECRMgkUhYuahr6jI1RCdB\ns76Hdqqqq6tx5MgR1vTEACA6egKioyfo2T+7nWV0BPTo0aN47rnnWF27MaJblM9GR6hmPaNA4MyS\npdpw7VijKApdunSBn58fMjMz0adPH7i7u7OyNlsyHTw8zwK8uG4jgBCCu3fv4pNPPoGXlxc2bNjA\nykk/NDQUPXr0QEpKCgtWmgchBAqFAn5+fhg7dix27dpl0vPd3VswURE60ubkJOBsOsYYHj58iMrK\nSgwfPtzeplgNQgiOHz+OUaNGISAgAJcuXTJrHVO6R3nqh6IovPzyy1i8eDFGjhzJmtPGlkwHD8+z\nAO+0NXDoOrbly5fjzJkzmD9/PpydLb/7pygK3t7eSEpKYsFKy1i7di0IIfW23tfFhx9Ow7FjySgo\nyIdIJEFBQT6USuu029uK2NhYiEQixMbG2tsUq7Jo0SIolUqLuhZ1u0dFIkktoV1T0ewOFoslSEw8\nZrZ9NHv37rV4DWtDURS2bNmCLVu2sLouF8fE8fBwFd5pM4C1hBPZHEW0ZcsWrFmzBm+//Tbmzp3L\nWmqFKyma3377DYBaI85UioruoKAgn4muuLq6om/fHnaV4TAXQggOHTqEAwcOoHfv3qxFOrhKQEAA\nUlJSMGTIELPXqKio0NLnGzlyCGSyHEilgTh2LFnvIHlD9W+aemGatY7mQAhBTEwMTp06xZnjrT4a\ngo1swNZ5nxe75WEb3mmrBz+/AOTmsjNgWxdLBE5pCCG4c+cOFixYgPDwcGzatAlNmjRhwTpuQAjB\nuXPnoFAoAABjxowxeQ1fXz8tvS02x/rYA9qBnTlzpl0uoIQQ7N27F++99x4OHDjACGRbA/pzt2Sy\nyKhRr+Kvv66hS5euSElJhkymnloik+UgI+MievaMZLbVjKBJpYFYuXItwsM713Le2GpEoGf67tix\ng/ksKYpC27aWy1bwWEZZ2WNWzvu82C0P2/BOWz04OjpqiSJykf3798PJyQm7d+9G06ZNG92dMN1d\nDJg3wHnz5h1aF12RSAInJwErttkSesLFnj17EB4ejkGDBtnFhgcPHmDFihV4+PAh9uzZYxWnjW6q\nOXnyJABYNPexqkqFzZs34aOPPql3O6VSif3745kImkyWg6ioYXqbF9hsRFCpVEzXKH3sTpkyxaI1\nGxpc7B6VSHw5f+7neTZpWOMHeBgIITh//jxmz56NkSNHIjAwsNE5bIDl3au69TIFBfmoqlJZbJc9\nuHDhAh49egQ3NzcIBPZxPM+dO4c///wTANC+fXur7WfPnj0A1JNSLO32XLt2FXr16oY2bUSQSgMB\nAFJpIMLDOwN4GmGbMWN6LScsO/smzp07g/T0NK1aSKFQiJCQMMjlMliK5nf8WcTS95AQAqlUarDD\nm4enMcA7bQ0QQggKCwvx5ptvQiqVYuPGjY3OYSOEYPXq1cyw5lWrVkEkElm8bkOMtBFC8OTJE6xf\nvx4A8O2339r983ZyckKnTp2stn5aWhoAYPLkyejSpYvF692+XYCoqFexb99hHD58XKueTbNGTaWq\nhFCoLYYZHf0mhgzph0GD+qC4uBgpKck4diwBAwZEYvLkaIvscnd3R4cOHUAIYf49C5SXP8b58+eh\nVCpZ6R4lhODXX39lwTIeHm7Dp0cbKL///jvkcjnmz5/P2gXc0uJbNpsrAGDdunUAALFYjLFjx7Ly\nOtmOtBFCkJycjL59+yIsLAxXr15lbW1NEhISkJGRgSFDhqBdu3ZW2Ycx0N+RgIAAqzht9Bi27Gy1\ncLFIJGLt+3379m2tphQazRo1AFAqtVXbVSr19yU7+yYGD+6L27cLWLGHoih4enqid+/euHz5MvOY\npZSXW14va20mTXobeXm5CAoKxoYNmy1er3Xr1jh//jwLlvHwcBveaWtgEEJw6dIlxMTEIDQ0FHPm\nzGFt7TNn/sTgwYOZ3+PitjITATQjCvTjurDRXAGoX+PMmTOZQvRffvkFYrGYlbWtEWmjRxrduHED\nP/zwA6KioiwqntcHPS6pa9eudo2yrVq1iokIWcsOpVLJRFhHjBhh0VoikYhZSyoN1Cv5IRQKER9/\nEEuWLMSuXf9Xaw1a308slljcLaoJIQS3b9/GgQMHALDXmSmXy9ChQ0edx6zTBW8OcrmcaQbKzr7J\nirju2LFj8emnn+L69esICwsz/AQengYK77Q1IAghUKlUWL16NTw8PLBx40Y0bdqUtfV79+6LX355\nqmnVr99ACIVCeHm11tK6oh+3BrSYLt0l2b17d0RERLB2QbNWTRud1po2bRpefPFF1pw2QggqKyuZ\n96Nz586srGuOHb/++ityc3NBURTGjh1rtX0dPXoUANCpUycEBFjWeTdv3pfM5+3r649hwwYgLy8X\nvr5+2Lx5B1xdm6G8/DET+dFlzpz56NGjF4qKCuHt7YP33/8X47i1aSNCRUWFQYdIKpVqjU7TxNXV\nFb6+vqzMMKXRTTdaowte8z3TfC+NucFzcnJFYGAQcnKyERQUzEp6NDIyEtXV1dizZw/mzZtn8Xo8\nPFyFd9oaGCdPnsSOHTuwceNGREZGshrtcHVtxnTF6UYiEhJOISPjImv7qo9x48YxUbZdu3ax+hpD\nQsLg6+vH2no01ox+bdq0idEre+2116y2H0P8/fffzM8eHh5W209GRgYAYPjw4RbflPj5+Wil7PXp\n83l4CA3q9gUH+wKAyUK6tEOnb/oGRVFwd3dH+/btcerUKebxjz/+GNOnTzdpP5roNt+Y2gVvjFZd\nenoa4+Tm5eVCpaqEVNrR6Bu8o0eTcPduPlq1kqC4+I7xL64ORCIRvLy8mFQ2D09jhXfaGgiEEKSk\npGD58uXo2LEjJk6caBVHQXPQtC6zZ39iUMPKEmhdNnryQffu3VlLi9IIhUJs3ryD1TUBMOlCaxSS\nx8fHA1Dr1NlLm62srAzfffcdAKBdu3b497//bbX9HTumdowCAgIsfr0NYVwZRVGc0WnT1Kqrb1ar\nZh1gUFAwo4WoKYdSn9MnFArh7/8SSkoeWTzQnq4NdHNzw4EDB/iZ1jyNGt5pawDQtS/R0dF4/Pgx\nTpw4wWpatD7ou+6KigqjNKwsRVOXLTY21ipOCttjc27cuKF10WULQghycnJw8eJFODs7o0OHDqyt\nbSrbt2/HtWvXAKidNmt8/wghuHLlChQKBQQCgUWTEBoiXNBp0+ykrW9Wa33OWX03ftYmNzcXhYWF\n8PHxscv+eXisDS/50UC4e/cuFAoFFi5ciNDQUJtEXOi77iFD+mHWrBhG44qGPqmzAV3LRkeq6Fo2\nLkMIQXp6OtatW8fYLZFIWI0Obtq0CY8ePcJ7773HivSFuRQWFjKv0ZrTGA4ePIiKigrMnj0bnp7P\nxvifUaNG2dsEKJVKpKenQSSSIChIHZnUjKDpg3bOaIeNXsOes33v37+PkpISu+2fh8fa8JG2BgBF\nUejUqROePHnC/G4LNO+6ZbIcxMf/AQCYNSsGMlmOwZO6qYwbN47p9GO7ls1a0IO+6RRXZGQkq52j\nFy5cAKAe5WSv1GhJSQm2bdsGiqLwwgsv4IUXXrDa/lasWAEA6Nu3b4P4/C2Foij07t0b1dXVdrNB\nNyUaH38QBQX59aY3Da3BdgTeGIYMGYKbN2/adJ88PLaGd9oaCPa4gOnWrdA1bMeOJRusWTEFWkiX\nrmWbMWMG67VswNNUr0DgzNrYHDplSEehevbsycpnRafEL168CCcnJ4wcOdLiNc1l+/btKP5f4dEH\nH3xg1dT8P//8Y7W1uYq9nVPdlKg+LTtT16grrWpNmjdvbngjHp4GDu+08dTL8uWrAUCr6YDNmhU6\nLfrpp58CUKdFrVHLphkJ8PX1M9gtaAhCCOLj47F//34ATyNtbGlEURSFNm3aICgoCF27drWb1Aeg\n3TVqb504Hvapq6nAFEQiCaNnJxA4QySSWMFSHh4e3mnj0Yu+dIe1oB02gP20KK0OrxkJ0KfHZQ7L\nli3T6hr19PREz549WVkbUDtudHrUno4S3b3q7+9v9bo6iqIgFosRGRlp1f3wPMXYjs/6yM7OgkpV\nCUA9CqygIB9eXl5sm1ovXO8S5uFhA74RgUcv+tIdbEMIwe7du5mZgWfOnGE9LUoPow4JCavVSGEu\nhBBcv369VtfonDlzWHeuNOUgbA0hBA8ePIBSqQQhBD169LCJLY8ePWJqG3lsg25TgSkolUrMmhXD\n/C6VBrJa62osL774IgQCAaPzx8PTGOGdNh690CkTQLuLrL4OMXNmHq5duxYA+5MPaGi1daFQiJUr\n17K27v/93/+hvLycqWXr0qULPv74Y9bW5wpnz57FnTt3bOY8Dhw4EA8ePMCtW7esvq/6IIRg6NCh\ncHBwwPvvv4/k5GS72sNF6HNBRsZFyGQ5zOMrV641qYEhM/OKxbZQFIXQ0FAMHToUixcvtng9Hh6u\nwqdHefSiL2ViqEPsxo1rJs0QpCgKZ8+e1fqdbTQ12cLDO2sNBjcHOsr29ddfA3gaCdu+fXujrPVK\nSHha+9e+fe15s2xCURTTjWtPCCFISkpiZsrGxcXh1KlTTNNJY8eYiQia5wKpNBBSaSDTUR4eblz9\npeYaWVlZrNjeqVMnXLliuRPIw8NVeKfNjnBtiLO+ge/u7u4oLr6D4mIgM/OKVsr0+PGjzFzB8vLH\nSE9Px4ABfUzary0dHdoRPX78qFnPJ4SgvLwcY8aM0aplmzp1KkJDQ1m2lntY22kD7N9JSXP58mVm\nlBoAPHnyBCUlJWZpx9nyOJfL5XBzM1/fzljpDn1yQC4uLibVxF29etWiGyh9JCUlsboeDw/X4J02\nO2HKEGcPDyGrw54BtZMll8sgEAhQVVUBkUiE9u1D6xxsDQCRkRF13hF7eAgxblwUqzZykb179yIr\nK0tLl+1f//oXZ5wNtunSpQuee+45+Pv727WD1R5ojiTLz8/HsmXLEBsba/I6ubmFmDp1mtZwdUBd\nb+nvL4VY3Erv8V1e/hg3bqije6GhLxg1ycPNzRN+fgHM78ZEzTQxVrqjLjmg+tC1pW3bthZHvnVx\ncXFhbS0eHi7CO212wpQhzp6ezVFS8oh1Gzp06AiZLBseHsJnovPKknQMLTK7dOlSEEKYC/r333/P\nmswH16AoChMnTsTEiRPtbYrd8fHxwaRJk8x6bnb2zVrD1bt06YoOHToCqP/4prcxB3MEb42V/zC1\n41SfLZ6erZGQcAqHDh0w+zXq0q1bN2RnZ7O2Hg8P1+CdNp5GTX5+HvOzZnrXUMpKKpXWijouW7aM\ncfYoisLo0aMRFhZmMMpmzzS4XC6Hv7+/2c9vrBHEuiCEIDs7GzExMczv1dXVcHFxMds537Xrv1o1\nX6Z0VpoaKdPEHMFbU5wxU/Qa9dni798aQqEQTZo0MfIVGWbw4MF8tI2nUcM7bTyNGje3Zsz0g/rS\nu5rQThYdfSSEYM2aNVi7di1Ty9alSxd8+eWXBp0aqVRq1P4GDx6MmTP/A39/f/To0RVPnhC922Zm\nXsHkydG1Hv/ww08wcOBgFBUVwt9fyqTS9NUp8hjGwUHdWF9TUwNHR0eLumcLCgoM1nzpc840o1Ni\nsQSHDh03SfvMXNFcawx8r88WusPbUiiKQrdu3dCtWzdW1uPh4SK808bTqPH392cl9Tt69GiUlpYi\nLi4Oc+bMMVrew9HR0aj9OzkJEBu7DEFBwXjnnXRUVOh32ry8WjMXPycnJ1RVVUEgEODbb1fj0KHf\nsXLlWgQESG0+99EUbBV5NDfKOGXKFKbJhA3EYvV0gPocNn1pTM3olEKRj6FDX0FS0nmjP1s2RHPZ\noj5bjKnVM5ZnLTLM8+zBO208rMK1jlhLUoM0FEVBIpFg8eLFjAYU2xeHqioVAHXq6OrVqwgI0D+U\nXfPiJxJJkJiYgBkzpgNQd/FFRQ2z28BuYykre1yr8L68/DGmTHkHt27dYpwchSIf3t7eKCoqYrZb\ntWodli79EqWlT0drxcVtZbqY8/Pz4ObWDP7+/vD39zcq0qlLYWFhrcdee+01k9fRpL7PRbOLUjON\nGRIShjZtRLh9Wy00rFAoTJ7paY2ombmwbUt1dTVkMhlr69HU1UlvDH5+AfU2c/HwWAovrsvDGlKp\n1CwnSS6XW8XZM/eirQ86PWYtkVlfXz8AaiHjtm3b1rstffHz8vLCiBFRjAgyjeYEC3MEj62NROIL\nqTRI659KVckI6ioU+VAo8gEARUVFjBMXFBSMqKixSEpKZSZnBAUFo1+/gcw6zZoJmehqcHAwKxfQ\nadOmYcGCBWY/n34tmp+Lpkg13UVJvx7N1KGzszPzszmTBnTFsOsTxzZ2Da4gk8msdt4ID28LDw+h\nSf/KykqQm2tfUWiexg8faeNhDWNTgXXxLHSw1sXmzTugUlUyqaOKikcoLi5GYmICevToidLSv2vV\nO9GppoSEU8jIuIhZs2K0it2VSiUmTXobR48mGNi7/RGJJHB2dkZlZSWcnAQQi8WQy28hKCgY8fEH\nUVCQz7x+oVCIpKTzemvAJk9+B0lJp8yygRCCmJgY5OTkaD0WEhKCZs3MT+H5+vohLy9X63PRTIde\nvJiulToEgD//TEVurhxy+VMnYMaMWYxmYl3QUj50ndikSW8zUiPr1/+A6dOnaEmPGEpNlpc/ZtYI\nDAzC0aNJnIrgslX+wBZsSzPx8OjCO208rEMIwapVqzB79mx8+eWXmDdvnr1N4jxyuQz9+g1kLojF\nxcXo3LktM4QbAJNeA1CrBqpnz0gcO5as5cikp6chLy+XcylrfeKvBQX5qKxUv9aqKhViY79hCveN\nJSvrOoqK7lhsI92EAKgbEQDL0uG6Dnl6eppWOvTq1ato1UodTSwvL0dU1KvIzr6JI0eOmCxN4+Eh\n1JpKouuwm+rAe3gIcfRoAtMsY2p6loeHh114p42HVQghUCgU2LJlCwDtCyBP3UyeHK2hX9UciYkJ\nWg4boJ1e01cDJRQKERISxjhuIpHk/9u787goy7UP4L9HtpBRCAU3VkdFzZRE33LNUkPTMre0xZbj\n0bT0VdNCzdxzyWzFTM3MXFL0UJ1cjqXH3SzEyEwlHQdZFEREZAZivd8/eOdpBoZt5hmGgd/38+kT\n88xwP/cNyFzcy3XBz88fgwYNqtLMil6fg0OHfsDy5X/Xbtyw4UsEB6vl2RZ//wBERLxlkuy1oiSw\npU+7btjwJbp0ebDMvUNCOqB9+/a4dOmSSbJWcycoPTw8zG7cDwnpIC8zW8MQoBkOIixbtgxPPvkk\nAgICLGrPw6Mh1Oq/862VPkkZGBiIvn0f/v8x+suVGGrbLFJgYBD8/AIQGxuj+MEGS/6wUGrPKpEj\nYdBGinvppZfkuo1Udcb5qwYMCIeLi2uZmTbDzJO59AnGAU5wcGvk5+chJSUF/v7+2LPnxwrTRRh/\nruG+hv1iKpUKhw+fMnvyr3QNSsPpVaBk5uuhh3qZ9LV//8fM7jNTqVSIiYnBiRO/mNzD3AnKjz5a\nW27Qaqg2oKQOHTrAzc0Nf/5Z/cz9Wq0WqakZOH/+d5NULO+/H4kjR/6LFi1a4IcffjAaYxKaN2+B\n1NQbFuUStKXIyPXyLKC5QxWW5pSrTnUYY0xnQ/URgzZSnFKpEiy9d3FxMaKiorBkyRLEx8dj0aJF\nDrFEaxyANWvWDGfP/lHunjZz6ROMAxzjvVBJSUlITk6sMGgz/tyCgnx88EEkhg0bIbdtfPLP+M25\ndA3KESOGQq1ug+LiYmi1V6FWt8G33+432ZNWHSEhHeDvHyBv5jfMQpWX80vJ9BEGw4cPx507d5CQ\nkFDtmR3DKdbSSqqQvCw/ru4yaOlcgjUhNfV6ucl6Lam+YFCd6jBE9R2DNqpz0tLS8Pzzz8uPT58+\nXe02KpvlKCoqQmJiYrXbNSc5ORlLlqxAjx69kJZ2AwUF2fKsw0MP9UBxcRG8vLzkTejGm82NN6a3\nadPObC1Hf39/+PkF4MSJYwBgtk6kn1+AvDSnVrcpd5lRp9Nh4MC+0GiuQK1ug+3bd5eZEdRorph8\nfPlyPHr37lvh10Cn06Fv30fl5VHDm75KpcK+fYfw+OOPIikpSV46tVX+MUMFBOPHQMmSaW1brqxp\nzZu3LDdYtqT6AhFVH4M2UoRhhuvDDz/EmTNnAAADBgzAxIkT7dwzYMiQIdX+nJMnf8GgQYPkx4Yl\nK8P/AeA///mPIntqypuJKU/pzebA30Fm6ZOkrVr5YeHCd/Dkk+Hy7Jta3QY//njM5NTliBFDkJSU\nhFat/FBcXFxuXrG4uLNyUKbRXMHhw4fK7L1r2bIVrl9PkR/n5uZW+jWIj78oL6mXftNv1qyZ2dOi\ntggKJEmS92FKkoT27dujT58+it/HEU2ZMhF79vxodta0KtUXrCnJRUQlGLSRYr744gu88cYb8uOV\nK1eiSZMmNd6PNKOcCB06dMCoUaOq3cbDDz+CHTv+fhMypJ3w8wuQ9/XUtpmXpKSbUKlU8klSQ/A2\nfrxpwXeN5opJUGQ8S2JI5Ar8HTwZH24ozd8/QH6zNuxpy83NxXPPjZZfU5VakKUPIpS+V00miTU+\nKRoYGIjOnTuzCDlKit0nJyea/T5UVn3BfMH4RjXV9XIJIeDn54dOnTrhwIHanxqHiEEbKWb//v0m\nj22ViLY8hqWt5cuXy9ceeOABNG3atNpteXg0LPMmZNgTduDAERw69INi/VbK+PHjcPjwKXlZ0d3d\n3WSp0sA4SatOp0Nubq5c0FytbgMAcr43P78Akzfb6Oi9Jq/t0aNXma+TTqczeU1oaNdK+17eQQRD\nH2tqhqZPnz7YsmULsrOz5WssjVQiMDCowhQs5gJrw/cuNzfXbMF4exJCYOLEiUhNTUWnTp3s2hei\nqmLQRjYxaNAg+Pn51fh9169fj3/9618AgMaNG2PatGkWv+mWN7ujUqnkskm1ybVrCSYzaMZLVmp1\nGyxevBzu7u5m02mo1W0QHb1HDrDMHTS4fPlPJCcnlskHB6DMIQVLDh+U96ZfOmg0blfJgE6SJIwc\nORIFBQV44YWS2cmFCxda1WZdsnHjlmp9jUv/fBkC+eoUr7cVIQQ++ugjfP7553btB1F1MWgjqwkh\n8NZbb+H7778HAAwbNgxRUVFwdq65Hy8hBC5evCjXBgWAoUOHIiwszKL2zJV/Mg4QaqPSMyEqlQrR\n0Xtx8OABDBgQXub0aOmTn+7u7mWCMHN7ldzd3U0ORhgYZ8835IWrLIO/scxMVZm0D+fP/24SNA4c\n2BepqTfkDP+vvvpPJCUlonVrNQ4ePA69PqfC/YCVkSQJY8eOxdixYy1uozYQQuCVV17B559/jmXL\nlmH27NlWt5mRkQ6NpurLxMbfO43mCiIj18HNzU0+QGN84KYytqjpeevWLUXbI6oJDNrIKkIIREZG\nYtWqVfKpu6lTp8LZ2bnGl5Uef/xxpKWlyfcdNmyYxX3QajXo3PnvhKilZ3zefz/SquDAFjZs2GwS\nJJkLooxTYri4uMollgIDg+Di4mr2TXnt2o3yadWrVzX46aeT6N49tMwBCkP2fGuU/pr27dujwnQY\nBw/+KGfr/+mnk5g79w2r+1BXlkMNhe8/++wzTJgwwer9pZ6eDav1M1/Z9w6o+MCNgVarRUICoFa3\nrfV/OBHZGoM2spgQAtevX8euXbvkgC0kJAQtW7a0yxtfZmam/LGXlxfatGljcVvNm7c0eVx6mXDC\nhBdw9OhRi9u3hczMDBQX58rBVGVBVFWDrNKnVQsLQ2vdIQygJPnutWsJ9u5GrTFv3jzs27cPiYmJ\n2LRpE2bNmmVVe/b8nt++rSvzh9PatRtr3R9ORLbGoI0sIoTAZ599hg0bNiAuLg5Ayf6f0aNHIyQk\npMb7snbtWuh0fy+1DBs2DF26dKngsyr26qv/xIoVq+U9YMbLhMbJXmub2hhM1QR//wAMGfIk1q79\nxN5dMUsIgdu3b2PZsmXYv38/3nzzTbz00ks2vWfLln//4bFv3z6rgzZ7K/2Hk1arKZP6hqiuY2FI\nsphGo5EDNgC4//770b59+xo/MRobG4vZs2dDCAEhBJo1a4aNGzda1Y+kpEQ899xojBgxFAMHliSG\nPXDgCKKj92DFitXw97esDmVFhBDo2LGjRSlK6rtNm7b9//dd+TJWSjl16hRiYmKQkJCAtWvX1sg9\nDf8mjhw5UiP3syXDH05ASUWM4GC1nXtEVPMYtFG1GTb9b9u2Tb724osvol+/fnZZFj116hT0ej2A\nkv1I4eHhivZDo7mC776Lhl6vR0TE6yY5yJQihMALL7yAS5cu4dtvv7Wojaoksa2rDHv1bFHGSgmS\nJGHo0KE4cuQIgoOD8csvv9g8kHJ1dbVLnkRbMeSC27//EA4cOFJrv9dEtsTlUaoWIQQOHz6Mr7/+\nWk5iO3z4cKxfv75GT4sa+hITE4MFCxYAKHljbNGihUmCX0v5+fkhObkk0ayTkxNmzJhSqgam8suj\nFy9ehBACvXv3tujzZ8+ehaNHjyjbKQdUWQkyS9u0tvpF6T8k8vLyrGqvsns1a9YMjz76KHbv3m2z\n+9Q047QwVT2VXBW3bt1CRkZGnQpyqW7iTBtV28mTJ7Fx40b58ZAhQ+xyWhQAPpGyXKMAACAASURB\nVPzwQ9y9e1d+/Nprr6FDB+tPln355dfYtm0XfHx85UMWSUmJaNFC+YSgQgikp6fj1q1bkCQJc+bM\nsagdQ3mt+iwoqDU8PX1w+7bO7H9JSTcxYMBAhISEYMCAgUhKumn2NY89Fo6QkBA89lg4kpJuIjU1\nw95Ds4ihdqqj0+tzEBsbY7JvVUlnz57Fnj17bNI2kZI400ZWCQkJwUMPPVTjAZthY/fZs2dNro8e\nPVqRvnh4NIS3tzfS02+aXJck2/yd89FHHyExMRENGzZEYGCgRW00b27fDPO1gZOTE9TqthW+5ujR\n0xUm5I2NjZFPoV67loC7d7OwYMFbVqcSsRchhMOnMTGkrjFXD1cpq1evxujRo9GwIZddqfbiTBtV\nmRACN2/eRHR0tHxt6NChisxsWWL79u0mNSHnz58PtVqZzcl6fQ78/ALKHDi4fj0F/v7+itzD2PLl\nyyFJEubOnWvx6dsVK95TuFe2I4TA1atX8e677yI0NBTOzs44fvy4Im3rdLoKZ2UMS2zlvfGX3vAO\nQPFUIq6urmWSHduCPYI1IQQSExMRFRWFBg0a4M0337S6TcPX31ACSwmDBw82Sdj7+++/47vvvlOk\nbSJbYdBGVSZJEnx9fTFixAgAwPPPP49FixbZZZbt7NmzmDdvnsn1mTNnKtaXS5cuYMSIIUhKSkSr\nVq0QHNwaQMmb+L59/8WGDV9afQ8hBHQ6HUaOHAkhBJo2bYqnnnrK4jFUpTB7bXL58mVERETgt99+\nQ1FREQYNGmT18pchl9fgwf0RHt7PovZKb3gPDe2KwMAgq/pVWsOGDa1KSVMZIQTS0tJw+PBhuwRu\nY8eOxXPPPQegZAuDtQxff6VKYEmShJ49e2LgwIFWt0VUk7g8StUiSRLmzZtXJmCqaXq93uQN+b77\n7oOLi4ui9zDkhEpJSUF09B64u7vLS2pK1R69dOkSvvvuO0iShK+++sous5ZCCBw6dAi+vr7YtWuX\nfD0gIADBwcFITU1FaGio4kW19+3bZ/I4JyfH6j1YpXN5GddirY7SdVCVSCUihMCFCxdw8eJFDBw4\n0ObBVH5+PjIy7LMX76effoIkSRBCyHtCrbFx4xYUFOQrUmPW2MyZM3Hs2DHk5JQtW0dUGzFoo2qr\nDftjIiMjTR5/8803cHV1Vaz99u07GiXT9UfbtiE2Wc5atmwZhBAICAhA165d7fK1lSQJ/fv3B1CS\na8+cjIwMjBkzBjt37lTsvoMHD8bHH39scu3atWtWBYfGSZDV6jbIzc2FTqeT3+gtLTCvVHqJ2NhY\nCCHQtm3F++6UYgiC+/XrVyP3M5AkCQ0aNEBxcTEaNLB+QcfDoyHUamVnJiVJwqOPPgovLy85aJs+\nfToWLVqE3bt3K/5HCpESuDxKDsk4uBg3bhyCg4MVDXg8PBoiOnrv/6f5SMKIEUMUPbkmhEB0dDS+\n/fZbSJKE999/H02bNlWs/eqSJEn+DwCSkpLw0UcfYceOHQCA9PR0xMTEKH7P0rKzsy1q6/z53+Xg\nzJAEGQBGjBgqL5NWtnRa2V44JVy4cAEA4OHhYbN7GNy4UXKaWJIkdO3a1eb3MyaEQHFxsfz/2qr0\nz+DNmzchSRK8vLzs1COiijFoI4djWKItLCxEYWEhNm3aZJMZquTkRDkfm5IboA1u3bolZ6wfPnx4\nrZjBNNi2bRtmzJiBF198EYsXLwZQM+kj7rvvPos+b8KEl+RATKVSwd3dHRrNFQB/f+9KL53Gxf19\n8liJvXBVkZeXh0aNGmHixIk2ad/Yjh075J+psWPH2vx+xgwzbZIkYebMmTV67+qaNm0a3Nzc5MeT\nJ09Gq1at7NgjovJxeZQcUk0EOH5+AXBxcUVBQT5cXFzh56ds6SrDLNvIkSNrVcAGAN9//z0AoKCg\nQLETuaUdOFA2hYY1XwfjPWzGy6TGm9fV6jZyMPfGG9Px44/HoFKpzAZ0xnsYlRIdHQ1vb2+0bt1a\nsTarwhb5BStiPNN26tQpk+eKioqg0Wiq3JZWq0VWVtk9Z4mJ15CVZf2y9ZNPPolt27bh3Llz8rXa\n9u+RyIBBG5EZ58//jiZNmqKgIB8AUFCQj+TkREX2tQkh8J///AcHDhxAw4YN5Zms2sBQp/LXX38F\nAHh7e8uHI15//XXF7vH5558jKirK5PqDDz5o1WES4+DMsExqPJumUqmwePFyuQyZRnPFbJCnVrfB\nG29Mh0ZzBf7+Adi375DFfSpPTQUFQgg8/fTTNT5zZLynrTSNRlOtChPlvc7b27JZ2dKuXbuGHj16\n4Ny5c/Dy8kKvXr0UaZfIFhi0EZkxYcJLCA5ujVatWiElJUWxVAMGhlm2Dh06oH379oq1q4SVK1fi\nr7/+AlCSrqFr167Iz8/HCy+8oNg9Fi1ahJSUFPlxgwYN8Prrr5ssU1XHhg1fon//x8rMikVEvC7P\ntkVH78X8+X9Xm1Cr25h8T1eufF/+eMSIoQBKqmA8/vij2LRpO7y9LZ9xE0Lgp59+QmJiIp5++mmL\n26mOffv2QZIktGzZ0i5peQwzbdOmTSvzfHBwMNq1a1ejfarIjBkzsHbtWnt3g6hSDNrIJrUaq3t/\na+s62oJWexUA4O8fgOjovYoukxnqjAK1bynm1q1b8se+vr6QJAmurq6KnM4VQuD48eMmpccAwMXF\nxapqFp063V/m+1N6yfPgwQPy0igArFr1IVQqlbyfzTi4M60zmwStVgN/f1+L+mZw5swZALDZcnNp\nf/75JyRJqrEg0ZjxTNvHH3+M0aNH13gfqsP4EA5RbcagrZ4LCmqNhATg9m3bnZirjLn9KrVJUlKi\nYkujxpR+k/jzzz8RGmr5kpEhIWtWVlaZ55TqqyRJ6N27Nxo1alTmpKjSX4/S+9oGDAg3eRwaWnKi\nsnRwd/lyPFasWI0335yBlJRktG3bDsHB1gdaFy8qe5ClMl27dkX37t1r/OQoUPGeNiKyHIO2eq4q\ntRrro8jIdfjgg1XQaK6YXRrV63MsWi4TQuDYsWM4fvw4JEnCP//5T6W6jLffno2nnx5uVRvbt2/H\nlSsls1FeXl7w9vZWomsmzAVnnp6eit/HsK/NOC9b6cdA2dxuhv1sanUbREfvQWhoV6Sl3bC6P5cu\nXYKzszOGDh1qdVuVkSRJntmzB6XztBFRCQZtVCvYe4nWmFarRVhYN/z44zGTN3hDYlY/vwC8/PJz\nOHjwR4vav3Tpkrwc07FjR4V7b50vv/xS/rhTp07o1q2bou0LIVBYWFhmg/rcuXOtalevNz9bW7qy\nQenHhmuGYC43N1fez6bRXIG7uztUKhXS0izvm2HMcXFxeOKJJ9CjRw/LG6sGey73OUqeNiJHw6CN\n7K68JVpvb1WFy7bnz/+OCRNekh9v2PClIuWlPD19EBTUGk5OTvIbvPG+p1at/JCSkmxx+4bcbADQ\nu3dvq/urBMMMYEJCAgCgcePGePXVV+U3fiEEUlJS4OfnZ/W91qxZg9TUVJNr1gYYhw79gNat1Wb3\nHRYVFSEh4WqlbXh5eaGwsAjNmzdHamoqAgOD4OLiCo3mMhITr1l1WvHgwYNo06YN5s+fXy/2TnGm\njcg2GLSR3ZW3ROvj0wjp6eVnyG/WrIXJHiVzpweVYrzvyZqADQBGjBiBjz76SN4orpQlS1ZY9fmp\nqanyPrOwsDA5IasQAsnJydi7dy8mTZpkdT9Pnz5t8njEiBF45ZVXrGpz+fIl2L17Jw4cOFLmZyAh\n4SqystKrdNjF21uFo0ePlrluTT4wSZIwaNAgDBo0yOI2HI0tZ9qEEPj111/Ru3dvLFiwABEREYq2\nT1SbMWgjh1XeHiVbKL3vKS/vL4vakSQJPj4+cjkjJVmbQsHcqVED4/qdlhJCYPXq1WXql44aNUqR\nk6kVFYi3d4qJ+jC7ZszWM2179+5Fbm6uvP/SWkpsz9BqtfD09FGgN0TlY9BGDs3cHiVb3cc4QLx6\nteoZ3UurrW/gn3zyCQDA3d0ds2bNMnnOxcVFkaD4zp07Zq8r8TVROpceWc7We9qUPmSRlZVj9Ql6\nw7YKIlti0EZURcYBooeH9eVzlJabm2vV5xv22X3wwQcICwuTr0uSBLVabVV+MSEEioqKTGbzlNS8\neXNs3RolV0AIDe1q05nX2qAmD+9UN5eio+1pCwgI5Cl6cggM2oiqwXCC1MXF1aoM+bYwd+6bOHz4\nv9X+PCEEfv75Z9y8eRNAyX620jNfSsyEpaWlYd26dSbXnJ2dLa6CYCw1NRUjRgyV9xuq1W3kuqJ1\nVUxMHJydPar9B4Ren4NDh37A8uVL5GvGh3ji4y+hefMmJkFacHBwtYL248eP45lnnkFSUhJPjxIp\niEEbURUZnyANDAzCDz+ULXhuT9evp1T+onJ8/PHHuHPnDu6//35FToias2TJkjLXOnXqhOHDrcst\nB5TMtBkfEDGuK1pXDRgwsNqzQ8Y/w87OzigsLDR7iMfbW2XxHkBJktCzZ0/s2LEDvXv3tslMm2FW\nmAEh1TcM2oiqyPgE6bVrCRa3U1RUBI3G8j1x5mi1Wvj6+lq0ZGbIWt+gQQNMnjxZ8coPQMkb+dq1\na83Wd1RiFm/Dhq8wffqrcpmq4ODWyM3NhU6nszgRcmm1LZegJZvejX+GCwsL4evra1KiTafT4fz5\n39G3r3W55CRJQo8ePVBUVGRVOxW1D8Ahll6JlMSgjaiKjE+QBgYGWdyORqNRvN5qcHAwjh8/btHn\nJiQkoLCwEKNGjcKkSZNsdlDClgcwmjZtgh9/PIa4uLPIzc3F/PlzMGLEULRt2w7Tps20um6okvVC\ntVotsrJyEBAQCODvfIR6fQ7Gjx+Ha9cSEBgYhMjI9bh2rSRQbN++o8kyqKWb3kNCOpjUVb1586Zc\nos14Fi4+Pt7qcdbEgRt/f3+b34OoNmHQRlRFxidIXVxcLZ55MQRs9kxBUdqhQ4fQrl27WnuytSpU\nKhV69+6L2NgYecbNMKtkLScnJ7Rr1w5CCLz33nvIycnBggULLG7v9m2dvLRpnI/w8OFTctWNp54a\nLI/D2j16hr2YISEdsHv3v/HEE4/h5s2baNu2Hfz8AhAbG4Pc3FzFvl41xRblz4hqMwZtRNVgOEFa\nVFSEP/44j8ceC5dnRjZu3FKlTeFZWeZLLtmToayWo7p1K0MOgkoXim/fXplSYYb0FdHR0Yolyk1L\nS8O//x2FBx98GM2aNZN/vowDT6Bkj95330Vj2LAR1Q7cjGfQ1Oo2AEpm2Pz9A7B1axRGjBgiP6dW\ntzG5b213/vx5e3eBqEYxaCOygJOTEzp37iLPjNg6uS9VbMKEF3Dq1FmoVKoyOfWUKPZu8N133+H0\n6dOYPn261W2lpaWha9f7UFCQDxcXV5w9+4e8nzAkpINJAOXi4oIZM6bg008/Nlv1oSLG+9iMA7Kk\npEScOnXC5Lno6D2IifnZ6rHZGg8iUH3FXZxEVjDMjDBgs6/U1FTEx1+UHxt/X8orJm8JwwGSkJAQ\nq9s6ePAACgryAQAFBfk4ePDv08gqlQo//ngM0dF7sGLFahQUFAD4u+pDdRhmHgHIs2lASTLiAQPC\n5efatm2H0NCu6N//MavHZms8iED1FWfaiMjhOTu7wM8voMx1nU6H8ePHKZae5dSpU/Dx8VFkA/yA\nAeFwcXGVZ9oGDAg3ed6wRy80tCs2blwnL/dWt+pD6ZlHACazw6VLwdXGxNFEVIJBGxFVW21Lf1FY\nWCCfgjQWH3/RqvQsBkIIFBQU4PTp03j00Ufh7e1tdZvNmjXDiRO/4LvvojBs2NPlplqxpsau8QEE\n45x1xh+bKwVny++vkienz5w5AyEEDh48iIEDByrSJlFtxqCNiKr1Jm3IvVXZ5yQnJ6NRI285tUXJ\n5xYjJSVJfpybm4u5c9/E9espcrLXli1bYdmyd+Hu7o7MzDvYs+c77NnzXZn2X355Ig4c2Ivr11MQ\nEBAIFxdXaDSXTV7j4uKKli1bVXlsFdm7dy9SU1PRsmXLah3aKJ2Xz5DywzjFx7Zt2+WDLHp9DrRa\nDYKD1SazXl5eXkhLu4G0tKrdt3QKEeODMkFBreHk5GT284KCWiMhAVWqxVleXyui5EEcQ+m2mTNn\n4tNPP0Xv3r0Va5uoNmLQRuTADDNAAwYMgLe3N7799luL2nnppZfh4uKCpKREOUfYq6/+U87nZax5\n8xbYujWq3Dfpv9/IQ3DffZ1MggON5jI8PRuazLRUVnrrkUd6YfXqd80+N3v2zAo/19tbhS++2Fjh\na2ytdF4+47GbW7b19lZZnVfO0I659rVaLRISUG41BScnpypVWjA+ldq2bbtqH5CwhuEgQq9evQAA\n+fn5WL16NYM2qvMYtBE5uBMnTuDEiRPo0qWLxW3Mm7cQU6a8AqCk2oOzsxM++uhTjBgx1OR1/v7+\n2L37e9y+nYFmzdRl3qSr8kZe23LUVYezszNGjRpV7c+rbWOuyixaZYxPpRoOSNRU2TDDTOc333yD\nyMhIjBw5EhcuXKiRexPZE4/eEDm4mJgYAMCzzz5rcRvt23c0OUUYEtIBoaFd5WutWvlh27Zd2Lfv\nv3j22VEYPLg/Bg7sC53O9M3f3Bt5XfHrr79CpVKhRw/rSjzVFcanUi05IGGN5557Dq6ursjMzMTK\nlStx5syZGrs3kT0xaCNycDt27AAAdO9u+SyHh0dDHDhwBPv3H5Jnx1QqFaKj98Lf3x8pKcmYPXsm\nfvnltJzrS6O5gri4sybtGPKLASXpJWryjdzWNBoNAgICHDoJsZIMBySMf2ZqgiRJGDVqFB599FEA\nwLVr15Ceno7FixfXyP2J7InLo0QO7q+//kJgYCDCwsKsasfcKcLk5EQkJZUcHEhKSkRExAyr7uGI\nhBBITk7GN998gzFjxti7O7WKuZ+ZmiBJEu6//35otVoMGTIEkiShU6dONd4PoprGmTYiByWEwN27\nd5Gbm4vQ0FCbzHQYCowb3Lp1C61a+QEomUkLDe1q8vr4+ItlZuJiY2PKLKM6mitXriA3N5e1LmsJ\nSZKwcuVKXLx4ESNHjsTGjRs5A0r1AoM2IgcWExODxMREm9UOValU2LfvkJxMtm3bdvjPfw5j//5D\nZguYl86+/8Yb0zF4cH+Eh/dz6MDt1q1bAIB+/frZtyMkM/zMO3rdXKLq4PIokQPbvn07AMj7eyxV\nkkvscrnPb9q0Xc7HpdPdrTBn2Nq1G6HVapCXlyefSL18+U/s2/c9MjMz0b17aJX6pFary80lVtNO\nnjwJAHjggQfs3BP7M07Yy/JtRDXLoqBNp9Nh1qxZ0Ov1KCgowJw5c9ClSxfExcVh2bJlcHZ2Rs+e\nPTFlyhQAQGRkJI4ePQpnZ2fMmTMHnTt3RmZmJmbNmoW8vDz4+vpi+fLlcHNzU3RwRHXd119/DZVK\nhfDw8MpfXIGUlOQy+dOMVSd3mPFr4+PjLeqPIXFveWkyhBCYOHEivvjiCyxYsADz58+36D6VEUKg\nqKgI33zzDR588EEEBJQtlWVrQgjExsZi4MCBuHv3Lnbu3GlR2hEl2DM3GxFZGLRt2rQJPXv2xAsv\nvACtVouZM2ciOjoaCxcuRGRkJPz8/DBx4kRcunQJxcXFOHPmDHbt2oUbN25g6tSp2L17N9asWYMn\nnngCTz31FNavX4+vv/4aL730ksLDI6qbhBDYs2cP/vrrLzzzzDNo06aN1W3Wtlxildm4cSOcnJxw\n584dFBYWwtnZNgsHv//+OxITExEWFma3Zbhjx47h7t27AIBz587ZLWizZ242IrJwT9vLL7+MsWPH\nAgAKCwvh5uYGnU6HgoIC+PmVbFLu3bs3Tp48idjYWDlrdYsWLVBcXIzbt2/j7Nmz6NOnDwCgb9++\nOH36tBLjIao3Vq5cCQAYPnx4vd7TExkZiZSUFHt3o8Zs3brVbve2Z242IqrCTNvu3buxefNmk2vL\nly9Hp06dkJ6ejjfffBNvvfUW9Hq9yTS5h4cHkpKScM8998DLy8vkuk6ng16vR6NGjeRr2dnZSo2J\nqE4TQiAjIwMpKSno1asXHnvsMXt3ya4MJY1s5ffffwcA3H///Ta9T1UVFBQgOztb/v1Zk6wpXk9E\n1qs0aBs1apTZqfj4+HjMmjULERER6NatG3Q6ncnpML1eD09PT7i4uECv18vXdTodGjduLAdv3t7e\nJgFcZXx8av4XVW3AcdcdmZnWv9GtWrUK165dw9tvvw1XV1er2/P0rFqx7/qoffv28PHxqTU52q5f\nv46nnnoKhw4dsrgNb2+Vxf+2fHwaITi4hcX3VuLnX2nWfD0sURd/r1VFfR23kizaBHLlyhVMnz4d\nH374IUJCQgCU/AXm6uqKpKQk+Pn54cSJE5gyZQqcnJzw3nvv4R//+Adu3LgBIQS8vLzQtWtXHDt2\nDE899RSOHTuGbt26Vene6en1b0bOx6cRx12H3L6tg7e3dW9cmzZtAgCkpqbi1q1b8PHxsaq9rKwc\nqz7fHgyHBGxJkiR0794daeaOydYgIYQ8o9i4cWO8/fbbVrV3+7bObv+2bt/WISsr3S73Nker1cLT\n06fGvh519fdaZerzuJVkUdD2/vvvIz8/H++88w6EEGjcuDHWrFmDhQsXYtasWSguLkavXr3QuXNn\nAEBYWBjGjBkDIYR8ymvy5MmIiIhAVFQU7r33XqxevVq5URHVUUIIbNy4EXfv3oUQAvPmzUOzZs0w\nfvx4e3etRggh8Ndff2HZsmWQJAlOTk7w8vKy2SEEALViv6ChD5Ik4d5778XDDz9s5x5ZLiioNRIS\nSj5WonC9tTw9fRAU1Nre3SCqEot+03366admr3fp0gU7d+4sc33KlCly+g+DJk2a4PPPP7fk9kT1\nkhAC2dnZWLp0KfLy8gAA69evxz/+8Q8796xmnT59GitWrJAfT506FS1btrRjj2qGcfBo/LEQAmlp\naRg5ciS2bduGoKAgO/Su6pycnKBWt623My9E1mBFBCIHcuLECbkCwrhx4zBq1KhaMRNkIITAjRs3\n4OTkhNatW+PGjRs2u4+x2vQ1sIdGjRph+vTpyMrKsndXiMiGWBGByAEIIXDx4kU888wzAIBevXrh\ngw8+qJW1MNPT0yGEwLVr13Dr1i20aGH5pnWqnCRJaNiwod1ytxFRzWHQRuQgMjIykJ2djcaNG2PX\nrl3w9vaulTNMW7ZssXm/iouLIYSAv7+/1ZvyHcHMmTPlr2lxcXGZ52vjzwERKY/Lo0QO5J577sHO\nnTvRrFmzevtGvWrVKjRo0ACSJMHX17defB2MDyI0aMBf20T1FWfaiByAJEno3bu3nPOwPgQqpQkh\ncOTIEfz888/yNUNViMoYapnag1arLbema3UYvueBgYFWt0VEjolBG5GDcJRATafTQQgBNzc3ODk5\nKdKmIdXHli1bcPfuXUiShNGjR+N//ud/Kv1ctVpd7nNarRYxMXEYMGCg2ef1+hyMHz8O164lwN+/\npFh8UlIiAgODsHHjFnh4mE9KrNfnQKvVIDhYrXgOvNdee03R9ojIcTBoIyJFCCGwb98+rFu3DpIk\noV+/fujQQbnalKmpqdiyZYv82MPDA/fcc0+ln+fk5IR27dqV+7yzswfU6rblPn/48Cm5bBOASks4\n6XQ6hIf3w+XLf6Jt23ZYu3ZjpX2sDkcJ3olIeQzaiEgxS5culT/OyMiATqdTtEamcQUEIYQiAUx5\ns2UGKpUKYWHd5cfGH5sTH38Rly//CQC4fPlPaLUa+Pv7WtVHW9dXJSLHwB2tRIQ///zTqs8XQmDP\nnj349ddf5Ws3btzAnTt3rO2aiQYNGqBBgwZwcnJCRESEom0rJSSkA9q2LZnZa9u2HYKDy1+erYwQ\nAosXL4YkSXJJrccff1yprhKRg2HQRkR4++3ZVrfRokULuLi4AAB8fX3x73//G35+fla3a8wQvACQ\n6x7XNiqVCgcOHMH+/Ydw4MCRSmfyyiOEQF5eHi5duiRfc3V1hbu7u1JdJSIHw6CNiBTRoUMHuLu7\nQwiB5s2bIzQ01Kb7rzZuVHavmJIMS6rl7Xurqps3byIqKkp+PG7cOO5pI6rHuKeNyA7smYKiNKX6\n8tlnnyEjIwOSJGHIkCE2Dy7Gjx9v0/ZrAy8vL6xatQqzZs0CUFLfmYjqLwZtRDUsKKg1EhKA27d1\nZp/39laV+5wSjNNYBAYGITJyPdat+8Li9oQQyMjIwKeffgoACA0Nxbhx4xAdHY0+ffrAx8fH6j5L\nkoSgoCDk5+ebXLO1oqIiJCRctaqNxMRr8Pa+r9qfJ0kSGjVqhBkzZmDGjBlW9YGI6gYGbUQ1zMnJ\nqcIUEz4+jZCenm3TPhinsVCpVNBoLlvVXmxsLBISEgAA/v7+ePnll7Fs2TI0bdpUgd6WsMeyYELC\nVWRlpVuVHDcry7I9bQDTexCRKQZtRPVQ6TQW1lq3bh2Aklm377//Hl26dMHDDz9cJ4KO4ODgCvO8\nERHVFB5EICKLCSFw/fp1/Prrr3IusWbNmmHr1q11ImAjIqpNONNGRFb56quvkJiYKBdwP3jwIDp2\n7GjvbhER1TmcaSMiq1y8eFH++Ntvv0XHjh0dapZNr1e2NigRka0waCMixQQFBTlUwAYAWq3G3l0g\nIqoSLo8SkSJGjx6t6GnRmshlp9VqrSozVZ371BZarRaentanYSGimsegjYgsJkkSNm/ejM2bNyve\ndlZWjs3y1en1OdBqNQgOVuO++zrZ5B4GarVyQaFWq0VWVg4CAgItbsPT0wdBQa0V6xMR1RwGbURk\nFVsthwYEBFaYz85anTvXTHWBBg0a4Oeff8aLL76IefPmYfHixVa1d/u2zqZfFyKqvbinjYjIxho0\nKPlVu3fvXjv3hIgcGYM2IiIbW716NYCSvHZLlizBvn377NwjInJEDNqIiGzs5s2b8scXLlzAsGHD\n8MgjjyAnh+lGiKjqGLQREdWwoqIiHDt2DEVFRfbuChE5EAZtREQ2IoRAS2S70wAADXJJREFUfn6+\nXOILAMaPH4/w8HCTa0REVcHTo0QEgLnEbCUuLg463d+pS/r3749evXrBw8PDjr0iIkfEoI2IEBTU\nGgkJsFletKrw9lbJ969LucR++eUXZGdnm1xzc3PDnj174OLiYqdeEZEjYtBGRHBycrJ77i8fn0ZI\nT8+u/IUOpvS+NUNeu8GDB9ujO0TkwBi0ERHVMEerz0pEtQMPIhAR1ZBWrVrZuwtE5MAYtBER2dCO\nHTvkj//3f//Xjj0hIkfHoI2IyIbi4uIAlOwbdHJysnNviMiRMWgjIrIBIQQOHz4sH0RYunQpHnnk\nETv3iogcGYM2IiIbMQ7awsPD5QMIQgjExsYiPz/fnt0jIgfDoI2ISGFCCOh0OqxZs0a+Zhyw5efn\nIzw8HHl5efbqIhE5IAZtREQ2UFxcjDt37phcE0Lg9OnT6Nu3LzIzM+3UMyJyVMzTRkRkY56ennBz\ncwMAfPLJJ4iJibFzj4jIEXGmjYjIBqKiouSPx4wZg5CQEABgoXgishiDNiIiG4iNjZU/njdvHqsg\nEJHVGLQRESlMkiSsXbsWRUVFKCoqkishSJKE7du3y9dVKpWde0pEjoR72oiIbKC8mTXOuBGRpTjT\nRkREROQAGLQREREROQAGbUREREQOgEEbERERkQNg0EZERETkAHh6lIioAlqt1t5dkGm1Wnh6+ti7\nG0RkJwzaiIjKERTUGgkJwO3bOpvdw9tbVeX2PT19EBTU2mZ9IaLajUEbEVE5nJycoFa3tek9fHwa\nIT0926b3IKK6gXvaiIiIiBwAgzYiIiIiB8CgjYiIiMgBMGgjIiIicgAM2oiIiIgcAIM2IiIiIgfA\noI2IiIjIATBoIyIiInIADNqIiIiIHACDNiIiIiIHwKCNiIiIyAEwaCMiIiJyAAzaiIiIiBwAgzYi\nIiIiB8CgjYiIiMgBMGgjIiIicgAM2oiIiIgcAIM2IiIiIgfAoI2IiIjIATBoIyIiInIADNqIiIiI\nHACDNiIiIiIHwKCNiIiIyAEwaCMiIiJyAAzaiIiIiBwAgzYiIiIiB8CgjYiIiMgBMGgjIiIicgAM\n2oiIiIgcAIM2IiIiIgfAoI2IiIjIATBoIyIiInIADNqIiIiIHACDNiIiIiIHYFXQptFo0K1bN+Tn\n5wMA4uLi8PTTT+PZZ59FZGSk/LrIyEiMHj0azzzzDM6dOwcAyMzMxPjx4/H888/j9ddfR15enjVd\nISIiIqrTLA7adDod3n33Xbi5ucnXFi5ciPfffx/bt2/HuXPncOnSJVy4cAFnzpzBrl278P7772Px\n4sUAgDVr1uCJJ57A1q1b0b59e3z99dfWj4aIiIiojrI4aJs/fz5ef/113HPPPQBKgriCggL4+fkB\nAHr37o2TJ08iNjYWvXr1AgC0aNECxcXFuH37Ns6ePYs+ffoAAPr27YvTp09bOxYiIiKiOsu5shfs\n3r0bmzdvNrnWsmVLDBkyBCEhIRBCAAD0ej1UKpX8Gg8PDyQlJeGee+6Bl5eXyXWdTge9Xo9GjRrJ\n17KzsxUZEBEREVFdVGnQNmrUKIwaNcrkWnh4OHbv3o1du3bh1q1bGD9+PNauXQudTie/Rq/Xw9PT\nEy4uLtDr9fJ1nU6Hxo0by8Gbt7e3SQBXGR+fqr2uruG46xeOu37huOsXjpssZdHy6IEDB/DVV19h\ny5YtaNq0Kb744guoVCq4uroiKSkJQgicOHECYWFheOCBB3DixAkIIXD9+nUIIeDl5YWuXbvi2LFj\nAIBjx46hW7duig6MiIiIqC6pdKatMpIkyUukixYtwqxZs1BcXIxevXqhc+fOAICwsDCMGTMGQgjM\nnz8fADB58mREREQgKioK9957L1avXm1tV4iIiIjqLEkYIi4iIiIiqrWYXJeIiIjIATBoIyIiInIA\nDNqIiIiIHACDNiIiIiIHYPXpUaXodDrMmDEDOTk5cHNzw6pVq9CkSRPExcVh2bJlcHZ2Rs+ePTFl\nyhQAJfVMjx49CmdnZ8yZMwedO3dGZmYmZs2ahby8PPj6+mL58uUmZbZqo+LiYixfvhx//PEH8vPz\nMXXqVDz88MN1ftwGGo0GY8aMwalTp+Dq6lrnx63T6TBr1izo9XoUFBRgzpw56NKlS50fd3mEEFi4\ncCHi4+Ph6uqKd955B/7+/vbultUKCwsxd+5cpKSkoKCgAJMmTUKbNm0we/ZsNGjQAG3btsWCBQsA\nAFFRUdi5cydcXFwwadIk9OvXD3l5eXjjjTeQkZEBlUqFFStW4N5777XzqKomIyMDI0eOxKZNm+Dk\n5FQvxgwA69evx3//+18UFBTg2WefRffu3ev82AsLCxEREYGUlBQ4OztjyZIldf57/ttvv+G9997D\nli1bkJiYaPVYy/vdXy5RS2zevFmsWrVKCCFEVFSUWLFihRBCiGHDhomkpCQhhBATJkwQFy9eFH/8\n8Yd48cUXhRBCXL9+XYwcOVIIIcSSJUvEN998I4QQYt26dWLTpk01OwgLREdHi0WLFgkhhEhNTRWb\nN28WQtT9cQshRHZ2tpg4caLo2bOnyMvLE0LU/XF//PHH8vf46tWrYvjw4UKIuj/u8vzwww9i9uzZ\nQggh4uLixOTJk+3cI2X861//EsuWLRNCCJGVlSX69esnJk2aJGJiYoQQQsyfP1/8+OOPIj09XQwd\nOlQUFBSI7OxsMXToUJGfny82bdokPvnkEyGEEHv37hVLly6121iqo6CgQLz22msiPDxcXL16tV6M\nWQghfv75ZzFp0iQhhBB6vV588skn9WLsBw8eFNOnTxdCCHHy5EkxderUOj3uDRs2iKFDh4oxY8YI\nIYQiYzX3u78itWZ5tF27dnJFBZ1OBxcXl3pRz/TEiRPw9fXFK6+8gvnz5+ORRx6pF+MG6mf92pdf\nfhljx44FUPJXqpubW70Yd3liY2PlsXTp0gXnz5+3c4+UMXjwYEybNg0AUFRUBCcnJ1y4cEFOIt63\nb1+cOnUK586dQ1hYGJydnaFSqRAUFIRLly4hNjYWffv2lV/7008/2W0s1bFy5Uo888wz8PX1hRCi\nXowZKPk93q5dO7z66quYPHky+vXrVy/GHhQUhKKiIgghkJ2dDWdn5zo97sDAQKxZs0Z+/Mcff1g8\n1tOnT5v93X/q1KkK+2CX5VFz9Uznz5+PkydPYsiQIcjKysL27dvrXD1Tc+P29vaGm5sb1q1bh5iY\nGMyZMwerV6+u8+OuD/VrzY17+fLl6NSpE9LT0/Hmm2/irbfeqnPjrg6dTmdSws7Z2RnFxcVo0KDW\n/D1pEXd3dwAl45s2bRpmzJiBlStXys+b+14CQMOGDeXrhp8Jw2tru+joaDRp0gS9evXCZ599BqBk\n+4dBXRyzQWZmJq5fv45169YhKSkJkydPrhdj9/DwQHJyMgYNGoQ7d+7gs88+w5kzZ0yer0vjHjhw\nIFJSUuTHwijNbXXHmp2dbfZ3f3JycoV9sEvQZq6e6dSpUzFhwgQ8/fTTiI+Px5QpU7B9+/YaqWda\nU8yN+/XXX8cjjzwCAOjevTsSEhKgUqnq/LjtWb+2ppgbNwDEx8dj1qxZiIiIQLdu3aDT6erUuKtD\npVKZjLEuBGwGN27cwJQpU/D8889jyJAhWLVqlfycXq9H48aNzf5bN1w3fF0c5XscHR0NSZJw8uRJ\nxMfHIyIiApmZmfLzdXHMBl5eXlCr1XB2dkZwcDDc3NyQlpYmP19Xx/7ll1+iT58+mDFjBtLS0jBu\n3DgUFBTIz9fVcRsY/66yZKylA1XDayu8p8JjsJinp6cccRrejOpDPdOwsDAcPXoUAHDp0iW0bNkS\nHh4edX7c9bV+7ZUrVzB9+nS899576N27NwDUi3GXp2vXrvLPf1xcHNq1a2fnHinD8IfIG2+8geHD\nhwMAOnTogJiYGAAl37ewsDDcf//9iI2NRX5+PrKzs3H16lW0bdsWDzzwgPx1OXr0qEN8j7du3Yot\nW7Zgy5YtaN++Pd5991306dOnTo/ZICwsDMePHwcApKWlITc3Fw899BB++eUXAHV37Mbv240aNUJh\nYSE6duxY58dt0LFjR6t+vsv73V+RWlPG6ubNm5g3bx5ycnJQWFiIadOmoUePHvjtt9+wbNkyuZ7p\n9OnTAZScqjt27BiEEJgzZw66du2KjIwMREREICcnR65natgvVVvl5+dj4cKF0Gg0AICFCxeiQ4cO\ndX7cxvr374/9+/fD1dUV586dwzvvvFNnx/3qq68iPj4erVq1ghACjRs3xpo1a+rV99uYMDo9CpQs\nHwcHB9u5V9Z75513sH//frRu3RpCCEiShLfeegtLly5FQUEB1Go1li5dCkmSsGvXLuzcuRNCCEye\nPBkDBgzAX3/9hYiICKSnp8PV1RWrV69GkyZN7D2sKnvhhRewaNEiSJKEt99+u16M+b333sPp06ch\nhMDMmTPRqlUrzJs3r06PPScnB3PnzkV6ejoKCwvx4osv4r777qvT405JScHMmTOxY8cOJCQkWP3z\nXd57XnlqTdBGREREROWrNcujRERERFQ+Bm1EREREDoBBGxEREZEDYNBGRERE5AAYtBERERE5AAZt\nRERERA6AQRsRERGRA/g/YHDGvgsdgV4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d47fcf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import Isomap\n",
+    "\n",
+    "# Choose 1/4 of the \"1\" digits to project\n",
+    "data = mnist.data[mnist.target == 1][::4]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "model = Isomap(n_neighbors=5, n_components=2, eigen_solver='dense')\n",
+    "plot_components(data, model, images=data.reshape((-1, 28, 28)),\n",
+    "                ax=ax, thumb_frac=0.05, cmap='gray_r')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result gives you an idea of the variety of forms that the number \"1\" can take within the dataset.\n",
+    "The data lies along a broad curve in the projected space, which appears to trace the orientation of the digit.\n",
+    "As you move up the plot, you find ones that have hats and/or bases, though these are very sparse within the dataset.\n",
+    "The projection lets us identify outliers that have data issues: for example, pieces of the neighboring digits that snuck into the extracted images.\n",
+    "\n",
+    "Now, this in itself may not be useful for the task of classifying digits, but it does help us get an understanding of the data, and may give us ideas about how to move forward, such as how we might want to preprocess the data before building a classification pipeline."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) | [Contents](Index.ipynb) | [In Depth: k-Means Clustering](05.11-K-Means.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.10-Manifold-Learning.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.11-K-Means.ipynb b/notebooks_v1/05.11-K-Means.ipynb
new file mode 100644
index 000000000..8907d80cf
--- /dev/null
+++ b/notebooks_v1/05.11-K-Means.ipynb
@@ -0,0 +1,1007 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) | [Contents](Index.ipynb) | [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.11-K-Means.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# In Depth: k-Means Clustering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the previous few sections, we have explored one category of unsupervised machine learning models: dimensionality reduction.\n",
+    "Here we will move on to another class of unsupervised machine learning models: clustering algorithms.\n",
+    "Clustering algorithms seek to learn, from the properties of the data, an optimal division or discrete labeling of groups of points.\n",
+    "\n",
+    "Many clustering algorithms are available in Scikit-Learn and elsewhere, but perhaps the simplest to understand is an algorithm known as *k-means clustering*, which is implemented in ``sklearn.cluster.KMeans``.\n",
+    "\n",
+    "We begin with the standard imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns; sns.set()  # for plot styling\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introducing k-Means"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The *k*-means algorithm searches for a pre-determined number of clusters within an unlabeled multidimensional dataset.\n",
+    "It accomplishes this using a simple conception of what the optimal clustering looks like:\n",
+    "\n",
+    "- The \"cluster center\" is the arithmetic mean of all the points belonging to the cluster.\n",
+    "- Each point is closer to its own cluster center than to other cluster centers.\n",
+    "\n",
+    "Those two assumptions are the basis of the *k*-means model.\n",
+    "We will soon dive into exactly *how* the algorithm reaches this solution, but for now let's take a look at a simple dataset and see the *k*-means result.\n",
+    "\n",
+    "First, let's generate a two-dimensional dataset containing four distinct blobs.\n",
+    "To emphasize that this is an unsupervised algorithm, we will leave the labels out of the visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AnAS6f/WKqLLXWU9zc3hhDqNWa26uhT//eVf3HGZjsbwL7MHtno7d/pmm1WhG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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11581b588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "X, y_true = make_blobs(n_samples=300, centers=4,\n",
+    "                       cluster_std=0.60, random_state=0)\n",
+    "plt.scatter(X[:, 0], X[:, 1], s=50);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By eye, it is relatively easy to pick out the four clusters.\n",
+    "The *k*-means algorithm does this automatically, and in Scikit-Learn uses the typical estimator API:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "kmeans = KMeans(n_clusters=4)\n",
+    "kmeans.fit(X)\n",
+    "y_kmeans = kmeans.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's visualize the results by plotting the data colored by these labels.\n",
+    "We will also plot the cluster centers as determined by the *k*-means estimator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UeKCgRybHuLFt0x907dmNhT8vYNmU1cipWlK5ihd+6CR3LKQ47IjkQwAXOYWE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5BVJ4U6AGxFJcQFlv8cWnhEsIvy/fw7xFCcmkne5iMoJVZuCGnL1IH3EdekeaX9mzVsgnp0MzRy\nLDziXaqptZaPGVmZfPjLXA6XnsOGnWaGcKb1HUPLpi1q5XxXmyRicd26ecANLNn5ObYAx7viuAYh\nTvYQdVVwcDDhJQac1XLzSCmi2/CuDsvLysowmYzc2KMfA3rewNn0s/yybT22RlGs27Gfou7nX8NR\nVZXwPUWMn1a9vpqvr5hDRreAig9Yr0YhlJzIQh/oUzHsvd9sZcaXz/POnc8Q7OR93bKyMh774iVO\nd/NDiTRQVgDOUraqqthLKvdY9m4WTuG+k/h3iKm0XaOkMkbOuBmAeweO49jP71HQKRhFo+DdJBwv\nLy+GlzXnL2OmYTKZuO+1xzlqzcTuocW/TMd9vcbSJF1H2gUtmAv3nMBqKsXPouO5Be8wttMgurR1\n3XP0kpISnvjuDXK6B6Mo5f8vk1D5x4ZPecf7rzSMbHiJI9R9kojFdathw4bc1K4xK47moniVf8NX\nVZUA02mm3X23e4MTl0Wj0XBTw878J3MvSvj5bjx2UxndNY0JDTn/xcpoLOL1RR+TZDtHqadKRKkn\nw2K6MW7QaKaNKp8VfXP6WeasW8ix0nQ0ioZWng144M4HHF7/cSbx4D5ONrBWeu5XllGAIdQPr8bn\n49AYdGR1D+LjlfN49s5HHI6zcO0S0jp6o/3vMLPGQ4e1qASdX+UYynafIjJHh1lVK2aIe0YFo0kp\nIGJzHvmeFvSKlraeUTw6+cGKqlmNoxrz/u3PMHftIk5Z8vBGx4DmQ7mxez8sFgtj/3kv1htj8Alt\nC5T3X3732GrGBcVj33Gck43sFKScxa9VQ/Qh5XfC+7Bx6MBCHi8trdSp6kosWLuY7E4BlerZAxjj\ng5m7fglP3/mQS87jTpKIxXXtsXun0nX7FpZu3EFxmZXGIf5MGfsgkRER7g5NAPuPHmDBHys4ZcnF\nQ9HRJbA594yc5LQE46Sht6Nfq2fNnp1k2osIUDzpEdySGXfeXWm7Z+a+SXIXTxRNGBogC/g6fQeG\njQZG9y9vXdgwMornJ/61RjGnpZ9GDamcLIuTMwjs6fh6n6IoHC1Jr7TMbDazffs2lq5ZRm6YBUWv\nRR/mj2+HGPK3HMWzURA+zRug2uwU7E5F66nH3CEUv2UnsTT0waLYiNEEM67vVPp26nnRWEOCQ3js\njvsdls/7eQFFMV4EhlZ+HuwVF8GPa35nxVNzWPfbWt7wXFKRhP9kaRbIwsTVLkvEJ01ZaKIcU5Wi\nKJy11Y9n0pKIxXVv1PAh9O7mmg8N4TqJh/fz0s65lLYJAsoTwpLSE5z88g1evecZp/uMGzSKcYxC\nveDu8EI7EndyvJEZjaZyolQi/fgl8Y+KRHwlesZ3Z86K9VjbVO/xRnp+Ftv2bKdbfBdWr/6F/fv3\nYbNZMeUUYFbKn/WWnsnDuD8N76aRGA+dpSy9AI1Oi1/7aLQ+HgCY7PB+v4dp1KAROp3jR3tZWRkr\nfl2FqayEYb0GERJSdXy/HtyOX7/GTteVRHiQm5tLdlE+ugTnw8In7XmYzWaXdGHyQg+UOF3nrVyd\nLk+1TRKxEKJO+s/25ZS2Daq0TOtpYE9IPokH9xPfpr3T/U6dPcWnaxdyzFx+pxlniOT+wRNo1KAh\n+04cRuNkohFAlr2IpKMH+X7HL2TaivDXeDK4WbfLbj8YGhJCD000m0y5aP6bJL2bhmM8dAa/No0q\nbauqKgVaMy+cWozXR/+ia2w7dDodGo2G6MBIzhSnovE2lE8cU8F09CzWgmLChw91nEzWMoSlf6zl\nkdvucYhp1dZ1fJG0goI2/mgCdXy/fDuDfFrz8FjHbQH8vHw5bSypSPIXshQWo9VqCPANwF5chtbP\ncbjeYNc4/TJQE7d2HcRvO+Y4NJlQM4zc2GSAS87hbvL6khCiTkoz5zpdrokOZPNh552ICgryeWrp\ne+xua6GoYwhFHUPY3dbCUz++Q0FBPo1DGmDLd97KUCm28o9dX7OzVRlpbQ0ktbbzTu5avlz+7WXH\n/tDIqfQ85E3gzlzsG1Ox/JFGaUoWpel5FdvYrTZyNxzAPz6awrRMjuvzSDx+oGJ9s8ZNiDH5oBad\nn4ilFltQckvJ/+O4Y/yKglV1nC2dkZnBR0dWYOwUitbTgKLVYG0bygrfEyzduMJp/BMHjMaYmOaw\nXFVVrGYLH62Yy+DeAwg54ninqqoqbTwaoNG4Jr20ataSyaG9MezLxm61odpVNIeyGV7SlCG9Hds1\n2mw2Vm5cxdyfF3Au45xLYqhtkoiFEHWSp+L8jspuseGtc7xTA/hmzSLyOwU5LM/rHMTcNYsY1OtG\nGhwtc1hvN5VRmlmAuUXl2tNE+rI8YxfFxdXrQ6yqKm8v/Ji7F73Irw2zyfe2UlZgxPfWDoTd0hFr\nnon0H3eQt/UoBduTCewRh8bLg+KUdLQGPemleZWO17dDd4ZFdCAm14OYXA/6+7ciNrABxSnpDr2E\ni/ekkZGZwf8tmsOJtBMVyxdu+pmyNsGUns6hOCWjvJsT5UU4fk3b6/Q6enTsRoeyUHI2JFW8GmUt\nLCZnXRIBnZqy23QSjUbDjG5j8NyThd1aHoutqITIbfk8NmJ6tX5e1TVu0Cjm3v4CE7PiuO1MNJ8P\neZq/jHU8xx/7dnLXnGd4j8182yCF+9a9xavz33NauKUukaFpIUSd1N43mjWWbDT/09faKymX28Y/\n6nSf02W5Tot6KFoNp8py0Wg0zLr5Ad745XPSGtggxAuP5CI62xqwrVkQzt4qN7b0Y+3WDYwceOnn\nxx8u/oI1kelofEPwBIyFZ9H3PP/usW/rRlgKSgjqcX7iVkHiCVRb+XvLVictBBtHNSI44PyrVP6+\n/mQe3krRwVMExMeiqio565PwjQ4jqaOe/Womv2x+n+E72/LgmKkknz1B/qk0PGNC0XgbyN5wAJ2f\nB0HdW2DEXOW19O/cl7TgwxTuS0O12dF66gm5sS2KVkOxrojS0lL6dOxJfPO2LFj3IwXWYpoHt2fE\njGGX7GdcE76+fky6ZVyV60tKSnhr67eYuoRWvE9ubxnCb0W5NFj2LVNH3OnymFxFErEQok56aPQ0\nTn7+MkdirWjD/VBtdjz253Bv62FVFo7wUvRQRfUrb6W8LGPzmKZ8ev8r7E3aS1rGGXoP74G3tzfj\nvn3W6Z5qqRVfr0s3mbfb7WzOPYwmNpiS0zmUns7FnFFAxK2Vewjr/L0oyyjAI6L8WbUlu6jieW+g\nturiHn/y9/VjaOtepO4/h7emlJy0c1h7NEEfUv7alqIo0CKEZaeP0mrLBg7bswju17pif8/IIEzJ\n6aQv3UnXFlU3kujTqQfzN+0gsGszh3XhNu+KV7n8/Py5d9SUS8Zd2xavX0ZR+wCHYV6NnyfbUg8z\n1S1RVY8MTQsh6iSDwcC7D7zIrLDhDD4Ryphz0Xx123MM6z24yn2GtO6FerrAYbl6qoAhrSsnnYR2\nCYwcOJyQkBC8vLxooThv8hGaXMYN3S/dW9tkMpKjlJC9bj+qxUZQjzg8Y0IwXVA6suRkNmWZBRTs\nSMZutpbH9t8hZn1GCR2iW2E2l5F4NIldhxPJy89zei5fH18Gxvfh1RGP0Lxp84okfCGlkT+fbVyE\ntpdjNTCfZpHovD2IMjgO4/8pumE0HUtDHat2ZRoZGtvN6ax0d8orK3Sogf0no1r1nX9dIHfEQog6\nS1EU+nbpTd8uF28B+KeeHbsz5tRRlh7Yi7V1CKgqusN5jAzsSI+O3S6678ODJzHr5/8jJyEAjUGH\nalfxSMphTEw/3ln0KVk2I/6KB2O6DKZ1XGuH/X18fClJziRkdHzF8HhAQhOy1+7HOyYM1WanJC2b\n0P5tsVttFGxPBqA4JQPfLBv92/cgIy+LxJQUrOFeoCgcObOTpmeD6NHm/F21xWJh88GdFATCQv0B\nSk5m4tO6rdNrKlYsTofqAbS+nhwrzXC67k8vTJ7Jez98xs7CFIoUMxH4MjS2G7cPuvWi+7lDmwbN\nWZadgjbU8UtJpNZ5fey6QhKxEKJeuWfkZEbl3MzS31eCCiOHP1CpslZVohtGc0/8CFZuWYtXmDfB\n3gG0bdGT9w/9TIalAP47yemXBdt4su8kRvS/udL+qzevw2guxrrtGGg1qFYbXrFhBPVpSeaKPXia\nFQKGl79ypdFpCepVXidZ42NAWZ2CXqtjT2EqaqTP+WfVwV4klxnxTT5Eu2blyX/9vi1khSv4d4pF\n0yqMspxcvG12h4RrK7UQjS8nLDaH5+wAqs1O8UWeEQPodDpmjptRPlvaakWvd37HWRfc2KMf33+4\nmpNBlX8WutQCxnQY5cbILk1Rr+J0sqysoqt1qqsuLMyv3l5ffb42kOu71lXn+k6cOsmqHRvRa7WM\n7nczQf9T23nz7m28++s8Clr5ofgY8E02MSQknr0ZR9mZdYzgG1qj9SwvHmE1lmJansT6l7+tKFix\nafdWnvj+TYJvSUDreT5ZFew5gSHUD0OIL823FpM60HEo2G6xkffvDTQ0BHE80OR0yDc4087Q+H5k\nZmexJjsRJdCTBuN6odFpsZpKKdiRTPANbSr2Ve0qkX/k8+aEp5j41dNo+zetdDzT8XQUrQbf/fn4\nx4SjVTXE2gPRG/ScUvPRotDWtzH3jpiMh4fzGequ4OrfTaPRyFtLPiWp5DSlipVobTBj2w9gQNd+\nLjvH5QgLq14TDEnELlKfP+zq87WBXN+17mLXp6oqby34iI1KCvbmwWBXMRzIYULjvowbNBqAD7//\ngvknNhA4oE3lfbNMpC/fRfj4npWSK4CloJhByUE8ee/jANz95mOcbKrBOzaM/5W5cg8NYhpzV1g/\nPtPuQBvsOPHLZ+FRis/lcjrU4rAOwC/DyoiE/iQe2U+STx4+LaIIuqBkpiW/GOOGw8Q1jUOraGjl\n1YAZwyfj5+fP3oOJPLJgNrQIReOho+RkNh7h/lBUhqFJKB4RgViLSsjfmUJI//PJ3G6x0XRXKf9+\n4MVamQUNtfe7qaoqdru91uKuruomYpmsJYSot37asJx1YedQ40JQFAVFq8HSIYy5WZs5nprM4vXL\nmJe6Hv++ju0FlTAfFH9PhyQMoA/w5njJ+eerJwrSnSZhKO/321VtyJghI4k5Wl6QotL6EwU8ePt0\nWsU2x1bi+I4zgL+mvEeyv7cvel8vArtXbk+oD/SmSUwTvp72Kl9MfZknx/8FP7/y56IJbeJ5dtB0\nNAeyMJ/NwzMqCEtyFhabDY+I8vemC/elEXLBHfWfcR9vo7Bs0y9OY6rLFEVxexK+HPKMWAhRb/1+\nZj+aNo6vBNnjgvlxx2pSTRngY3D6DBVA7+VZ5bF1Wh2fLPmaUJ9AtCU27FYbGp3jcRStlmHxN6Ao\nCm9Ofpp/LfmUA+ZzlOpsNCaQ0a1von/XPvRO6M6RxyeRRhFc0A9Zn1lC2+hOKIqGW4ePwpL8G+f+\nt7xliZnuQc57857LOMdnKavwuS2BinvxllEU7U+jLD0fj8hAFK3GoWQmgM7fmw+XL8TX25eB3W+o\n8mchrowkYiFEvVWKFWcfc4qiUKJayFdL0ei12ExlTusqh1u9MJeY0XpVbi5gyTexr/QUJ6N9sRWn\nYtFD8dajBPetPJvabrZit9qwWMuHnP39A3jprr9hs9mwWCx4ep5P9Hq9nncff4VpHzxNAcVYjKXY\ny8xYjGUcsaTjGdkA//AQnmv/ILOXf0pqhAVCvTGkFtFTbchDE6c5/RnM3/gjJe2CHYqV+LWPJnfz\nETwiA8Fe9RPKoiAt755eiUaj4caul36NS1w+ScRCiHorShtAimp0mABlKy6juX8c2SX55Mb7krvp\nEKED2lXe5mgWz4y4l7m/LuFEFzs6n/Kkack3kb/9OKGDOwCg9fbA7/ZOnP1mE/keegI6N0XRaihL\nz6dofxpNA6PoGl+5qIdWq3U6dPrpr9/jO6EzvpSXlMzfmUJw39ZY9Vp2YWHHmZ8YebIlH933MvsP\nJ3HibBrdh3QlPMz5sDhArt35BDCgYnaxxlA+6evPa/xTcWomng2DsccE8OO+Db0EOTsAABLKSURB\nVJKIa4kkYiFEvTW5/xgSf3kPY/z5WdKqqhKVWMzY+0YSsN2f41m/EpAQS87Gg+j8PMuLQpzM55kB\n0+jTuSc94ruyeP3P7DudAsD2Q0mEjuzgkNz8EmLQl6oU7EhGBQwhvoS3jOX2gJ7V6kRksVg4Ys0E\nwgEoTDxZXlLywue2Uf4sP5LEyHNnaN+qHe1btaviaOcFKl6oqtlpMlZzy2to+8XHkvfjbvx6NcfQ\nsHxmt+nYOcw5xopynOfshZc8l6gZScRCiHqrcVQjXh5wP19tWkyyOQsdGlp5NuDhyQ9iMBi4uc9N\nFK41sSxlK2qTcDR5ZpoV+PHMPc/SOKq8ZaFOp+OOm0Zzx3+POerTR7E4eZ7q1yGGXjt1WAP05FiN\nBNi8uKVpP7rHd61WrFarFav2/BCxotU6TZ72FsEs3bqaGWOqV7RxXJ8RbFn/byxtK79LrTuWzz/6\nTOXEibPotVrG/G0Gn/3wFT8lJ6LotHjFhhIU16Biex/qR+/fukgSsRCiXouLbc4rsU9WuX78oNHc\nbhtJWtpJ/P0DCLlE8Y+G+iBOOFuRnMe4m+6neRPH2szV4eXlRSxBpP65wEUVJGMaRfPXNqP4ctdy\nzkTYQKPQIF3hzraDGdprUKVtZ059hH2fP0NB18o/A1upmS6BlWdqC9eRRCyEuO5ptVqaNGl66Q2B\nMW1u5N0TK7HHBlQss5Wa6WoKrXES/tOEhCH86/ASzC0CUa12VFV1uCtWjuUxvN+kyzruDV360K9z\nbw4ePojNbqPd8HZO+wXr9Xoe73sn72z6DzmtvdH4eqKk5NHVFMYDU+66omu7mjKzs5i7bhFnLHl4\nKQYGNO3CwJ793R1WlSQRCyHEZRjY/QY0Gg1L9m3gnK0AX8WDzoFNmTHlyvv79O7YAz8vH77fsYo0\nfTgnfzmI54AWYLOj8TJAppGh+pZEN3Rs5HApiqLQtoqa1Bfq0rYT37SKZ+Wm1WSdyeWGbuNpGlu9\nLyl1QUpaKs+u+oDCjsH//RJjYU/GGo4sTuXBag7nX22SiIUQ4jLd2LVvrc0g7tCqPR1atcdoLOLl\nb//N7p0nMHtq0eWUMjC8A49Mv6dWznshrVbLLTcOq/Xz1IbPN35PUaeQSiP7SoQvKw8lMTYzk4jw\ncLfFVhWprCWEEHWMqqo89c3r7EtQ0Pduik/nGDxuasnvYdn8smWtu8Or046VZTpdbmsZwrItq69y\nNNUjiVgIcV0pKipk4YofWLpuOWZz3exTu3XPNpJjbY7Vrhr6sezoFvcEdY3QVNUnWVXROnkuXhfI\n0LQQ4roxZ+l8lufsprRNEKrFxvyvN3B3myEM6z24xsdcsWkVv6btxWQ3E6ULYHL/MRWvPjmz99A+\nFuxc+d+JRHq6BMVxz8hJlSZP7T95FG208x66GdaCGsd6PWjpEckOtcxhkpvhQA6jRj/opqgurm5+\nPRBCCBdbu3UDS7QHMXcIRaPTovUyYOwUwkfJqzh97vRlHctkMvHl0vlM/OeDvGfcSFJrO6ltdfze\nwsjMVe9xJOWo0/12HtzDi7vnsb+Nndz4AM508GZJSArPf/2vSttFBoRiM5Y6PYa/xuuyYr3ePDhk\nEiHbc7FbbBXLlNR8xkb2IDDQsQ1lXVCjRKyqKs8//zzjx49nypQpnDp1ytVxCSGES609vh0ifR2W\nW9sE891vy53uY7PZWL9lI8s2rKSkpASALXv/4O7//IP5+iRONVbRRZy/c1UUBVOHYL78fYnT4327\nfSXmVpWTgcbbg53+2Rw6frhi2fB+Qwg7YHLY324so2doq0te6/UsIjyCj+96kbEZMXQ86kHvY768\n2nYyk4fdcemd3aRGQ9Nr167FbDazYMECEhMTmT17Nh9++KGrYxNCCJcxqmaqagBhVB3vPjfs+I05\ne34mu5kBxaDjq2/XcUt4Z9ae2U1xlzCKtx1zaEf4p+NlGU6Xn7LmAo4FQzSxQWza/wetm5cnWZ1O\nxzM3Tedfa7/mbKwWJdAL/bE8+mpjmT5hYvUvugqqqrJ+20Z2HNpLo8Bw7hw1wel7xdcqHx8f7rl1\nsrvDqLYaJeJdu3bRt2/51P34+HiSkpJcGpQQQrhapM6PVEocltvNVhp5VU6O6RnpvHfgRyydQys+\nJEviPfh84y94d4pBBygaBdVmR3HS+lBbxWCjp6LD8T63PAY/j8rtGts0b83nzWZzKGUfB4+l0m9o\n74s2d6iujKxMHvriBfITAjDE+2POOcAnL0zgkd7juX3I6Cs+vrh8NfoKZDQa8fPzq/i7TqfDbre7\nLCghhHC1cb1G4HUwz2F58J4CJgweW2nZgk1LMbd1vHO1e2nR+pZ3KPJr15jCPScctlFVlVaeDRyW\nA7T3bozdanNYXrjlGGfzMlHVyu0IFUXhhh59uG3oKJckYYDnF76LaWAjDKHlQ+qGED/8b03g7a3f\ncjw12SXnEJenRnfEvr6+mEznv9fZ7fZqDWuEhfldcptrWX2+vvp8bSDXd62rzvWFhSXwmn4an/y6\nhGNlmWhUaOfVkKemzyK6UeUiD1YPu+OrQ4BPq4aUJKbh3TEGrbcHGoMO46HT+LYunyVtK7XQcL+J\nF2a8SFioY0wv3fcY6W/PIjGqCI/IQOxWG4W7UtA1CGBdZAZxm5dxz+g7a3R91ZGVlcUxnwI8Fcfj\nGVpEMG/Dd3zQ7VWXnKu66vvvZnXUKBF36tSJDRs2MHToUPbu3UuLFi2qtV9WVlFNTndNCAvzq7fX\nV5+vDeT6rnWXc33NG7bizTufwWKxoNFoKnoC/+/+gaoPdnMWGkPlj0h9gDe+aWZKmpSgCfTCPz6G\nsvR88pfvo31wLL2axDNu6mgU1aPKmHpGx7P97CqKT2ShKODXPhqttwcAK/ft4NasETW+vks5diwN\nNdD5rGt9oA8nD2Re1d+V6+F3szpqlIgHDx7M5s2bGT9+PACzZ8+uyWGEEMIt9Hr9RddPGDSG9d88\nT0G3ysPThqN5vDRpJvtSDrF5/wGK7GU00AUxdtQddOvQuVrnzjDl498hxum6AieTxlwpNrYJ2p/z\nIdpx2L0kNZMmEZfubyxcr0aJWFEUXnzxRVfHIoQQdYKPjw+v3PIQ76+dz1E1C5tOoYktgDsTxtCu\nRVvatWjLndxWo2O3iIjh59xktME+DuvCNbU7TKvX6xkSnsAvGafxiDjfPcqca0STXcKEm0fW6vmF\nc1JZSwghnGjSOJa3ps6iuLgYm82Kn5/zSleXa1CvG/n+ozWc7la5xaFypohbWtW8wld1PTHpIco+\nf4vVe/Zg9tFiN1sJNmp54ZYHiGtSuz2H8/PzSDp2kJioaBo3bFyr57qWSCIWQoiL8Pb2vvRGl0FR\nFF6b8Dfe/OlTDtozMHtAZIkXo+L6MLjHjS49V1VmTZ/Js6pKckoydtVOXLM4h5KQrmSz2Xh9wfts\nt6VRHOWFLrmUlkV+vPvAM4Ch1s57rVDU/50vX4vq+0P5+np99fnaQK7vWnctX19xcTElJSUEBwdX\nmQiv5ev707vffcIvUelovc4nXVVVab3fyltTn3NjZLWrVidrCSGEqKyoqJCPln3DkdJ07KjEGcK5\nd+hEwkJCq9zH29vb5XfcdY3NZmNbwTG0zSpPEFMUhcNBxRw4epC2Ldq4Kbq6of7UNBNCCDcpKyvj\n0a9fZmPzQtI7+JDZwZffW5p4fOFrFBZe392SiotNFHk4FjEBUKP8OJB86CpHVPdIIhZCiCu0YPVi\nznXyrVQERFEUcroE8c3q790Ymfv5+PgSZHb+HFg5VUCnVglXOaK6RxKxEEJcoWPGs2g8HN9NVrQa\nUkuz3BBR3aHRaOgX1hZbQeU636rNToIpmOZNmrkpsrpDErEQQlwhD6Xq6TYeMhWHe0dOZkR+LL57\ncilLzUa/P5vOBw28O2OWu0OrE+Q3RAghrtDAFt3ZcuYnNFGV3zW255ro3aiPm6KqOxRF4S9jpnGv\n2Ux6+jmCg0Pw9fXF29sbk+nanhHuCnJHLIQQV6hXpx7cXNYMknMrOiipJ/PpnxnBzTcMcXN0dYfB\nYCA6OgZfX193h1KnyB2xEEK4wMO33cPNqcks370BVVUZHD+KNnGt3R2WuAZIIhZCCBdp1qQZj9SR\nyUepJ1NZuXMDWkXLrb2HEBkR6e6QRBUkEQshRD3z1sKPWE8KarMgUGHZmjcZE9iZqbc49joW7ifP\niIUQoh5Z8esq1gSegeblZTMVjYK9dSiLyhJJPLjP3eEJJyQRCyFEPbLpVCLaUMcWi8QGsnzfr1c/\nIHFJkoiFEKIeKcVa9Tq16nXCfSQRCyFEPdJYH4Rqd2yqZyu10Ny/gRsiEpciiVgIIeqRKYNuJ2B3\nbqVlqqoSuaeIOwaOclNU4mJk1rQQQtQjYSGhvDHiUT7f8D3HSjPQotDKK4oZk2bg6enp7vCEE5KI\nhRCinmkc1ZgXJj7u7jBENcnQtBBCCOFGkoiFEEIIN5JELIQQQriRJGIhhBDCjSQRCyGEEG4kiVgI\nIYRwI0nEQgghhBtJIhZCCCHcSBKxEEII4UaSiIUQQgg3kkQshBBCuJEkYiGEEMKNJBELIYQQbiSJ\nWAghhHAjScRCCCGEG0kiFkIIIdxIErEQQgjhRpKIhRBCCDeSRCyEEEK4kSRiIYQQwo0kEQshhBBu\nJIlYCCGEcCNJxEIIIYQb6Wqyk9Fo5IknnsBkMmGxWHj66adJSEhwdWxCCCFEvVejRPzll1/Sq1cv\npkyZQmpqKjNnzmTx4sWujk0IIYSo92qUiKdOnYrBYADAarXi4eHh0qCEEEKI68UlE/GiRYv4+uuv\nKy2bPXs27dq1IysriyeffJJZs2bVWoBCCCFEfaaoqqrWZMcjR47wxBNP8NRTT9GnTx9XxyWEEEJc\nF2qUiI8fP87DDz/Mu+++S8uWLWsjLiGEEOK6UKNE/OCDD3LkyBEaNmyIqqr4+/vzwQcf1EZ8Qggh\nRL1W46FpIYQQQlw5KeghhBBCuJEkYiGEEMKNJBELIYQQbiSJWAghhHCjq5qIk5OT6dKlC2az+Wqe\nttaVlJTw4IMPMmnSJKZNm0ZmZqa7Q3Ipo9HIAw88wOTJkxk/fjx79+51d0i1Ys2aNcycOdPdYbiE\nqqo8//zzjB8/nilTpnDq1Cl3h1QrEhMTmTx5srvDcDmr1cqTTz7JxIkTueOOO1i/fr27Q3Ipu93O\ns88+y4QJE5g4cSLHjx93d0gul5OTQ//+/UlNTb3ktlctERuNRt544416WQ7zu+++o127dsybN48R\nI0bw2WefuTskl/qztvjcuXOZPXs2L730krtDcrlXXnmFd955x91huMzatWsxm80sWLCAmTNnMnv2\nbHeH5HJz5szh73//OxaLxd2huNzSpUsJCgpi/vz5fPbZZ/zzn/90d0gutX79ehRF4dtvv+XRRx/l\n7bffdndILmW1Wnn++efx9PSs1vZXLRE/99xzPP7449UO7Fpy1113MWPGDADOnj1LQECAmyNyralT\npzJ+/Hig/tYW79SpEy+88IK7w3CZXbt20bdvXwDi4+NJSkpyc0SuFxMTU2/rFwwbNoxHH30UKL97\n1Olq1Bagzho0aFDFl4szZ87Uu8/M119/nQkTJhAeHl6t7V3+r+usNnVUVBTDhw+nZcuWXOuvLV+s\n9vZdd93FsWPH+OKLL9wU3ZWr77XFq7q+YcOGsX37djdF5XpGoxE/P7+Kv+t0Oux2OxpN/ZkWMnjw\nYM6cOePuMGqFl5cXUP7v+Oijj/LXv/7VzRG5nkaj4emnn2bt2rX8+9//dnc4LrN48WJCQkLo3bs3\nH3/8cbX2uSoFPYYMGUJERASqqpKYmEh8fDxz586t7dO6RUpKCvfffz9r1qxxdygudT3UFt++fTsL\nFy7krbfecncoV+y1114jISGBoUOHAtC/f382btzo3qBqwZkzZ5g5cyYLFixwdygud+7cOR566CEm\nTZrE6NGj3R1OrcnJyeH2229nxYoV9WLEdNKkSSiKAsDhw4dp0qQJH330ESEhIVXuc1XGO1atWlXx\n5wEDBlzTd4zOfPrpp0RERHDrrbfi7e2NVqt1d0gudfz4cR577DGpLX4N6dSpExs2bGDo0KHs3buX\nFi1auDukWnOtj7I5k52dzfTp03nuuefo0aOHu8NxuZ9++omMjAzuu+8+PDw80Gg09Wa0Zt68eRV/\nnjx5Mi+99NJFkzBcpUR8IUVR6t1/nLFjx/LUU0+xaNEiVFWtdxNj3n77bcxmM6+88orUFr9GDB48\nmM2bN1c8269vv5MX+vPuoz755JNPKCws5MMPP+SDDz5AURTmzJlT0Qf+WnfTTTfxzDPPMGnSJKxW\nK7Nmzao313ah6v5uSq1pIYQQwo3qx1iAEEIIcY2SRCyEEEK4kSRiIYQQwo0kEQshhBBuJIlYCCGE\ncCNJxEIIIYQbSSIWQggh3Oj/AZinj0vqJNrbAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1189265f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')\n",
+    "\n",
+    "centers = kmeans.cluster_centers_\n",
+    "plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The good news is that the *k*-means algorithm (at least in this simple case) assigns the points to clusters very similarly to how we might assign them by eye.\n",
+    "But you might wonder how this algorithm finds these clusters so quickly! After all, the number of possible combinations of cluster assignments is exponential in the number of data points—an exhaustive search would be very, very costly.\n",
+    "Fortunately for us, such an exhaustive search is not necessary: instead, the typical approach to *k*-means involves an intuitive iterative approach known as *expectation–maximization*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## k-Means Algorithm: Expectation–Maximization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Expectation–maximization (E–M) is a powerful algorithm that comes up in a variety of contexts within data science.\n",
+    "*k*-means is a particularly simple and easy-to-understand application of the algorithm, and we will walk through it briefly here.\n",
+    "In short, the expectation–maximization approach here consists of the following procedure:\n",
+    "\n",
+    "1. Guess some cluster centers\n",
+    "2. Repeat until converged\n",
+    "   1. *E-Step*: assign points to the nearest cluster center\n",
+    "   2. *M-Step*: set the cluster centers to the mean \n",
+    "\n",
+    "Here the \"E-step\" or \"Expectation step\" is so-named because it involves updating our expectation of which cluster each point belongs to.\n",
+    "The \"M-step\" or \"Maximization step\" is so-named because it involves maximizing some fitness function that defines the location of the cluster centers—in this case, that maximization is accomplished by taking a simple mean of the data in each cluster.\n",
+    "\n",
+    "The literature about this algorithm is vast, but can be summarized as follows: under typical circumstances, each repetition of the E-step and M-step will always result in a better estimate of the cluster characteristics.\n",
+    "\n",
+    "We can visualize the algorithm as shown in the following figure.\n",
+    "For the particular initialization shown here, the clusters converge in just three iterations.\n",
+    "For an interactive version of this figure, refer to the code in [the Appendix](06.00-Figure-Code.ipynb#Interactive-K-Means)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![(run code in Appendix to generate image)](figures/05.11-expectation-maximization.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Expectation-Maximization)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The *k*-Means algorithm is simple enough that we can write it in a few lines of code.\n",
+    "The following is a very basic implementation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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pMmPnrSFgTaEsazOmhHR0CVYCW1egKkrIRCBVVXAVZiPJMuW5O7CkNsTvLMO+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YS6T/vSSy+9dKVOVlJirnqjq5Svr1e9vb76em1+/oHsTMkn0Hsvgf8bbVNVlQXLImie+AKBgaEX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\nf+kjNKir5vNWAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118a43588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import pairwise_distances_argmin\n",
+    "\n",
+    "def find_clusters(X, n_clusters, rseed=2):\n",
+    "    # 1. Randomly choose clusters\n",
+    "    rng = np.random.RandomState(rseed)\n",
+    "    i = rng.permutation(X.shape[0])[:n_clusters]\n",
+    "    centers = X[i]\n",
+    "    \n",
+    "    while True:\n",
+    "        # 2a. Assign labels based on closest center\n",
+    "        labels = pairwise_distances_argmin(X, centers)\n",
+    "        \n",
+    "        # 2b. Find new centers from means of points\n",
+    "        new_centers = np.array([X[labels == i].mean(0)\n",
+    "                                for i in range(n_clusters)])\n",
+    "        \n",
+    "        # 2c. Check for convergence\n",
+    "        if np.all(centers == new_centers):\n",
+    "            break\n",
+    "        centers = new_centers\n",
+    "    \n",
+    "    return centers, labels\n",
+    "\n",
+    "centers, labels = find_clusters(X, 4)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=labels,\n",
+    "            s=50, cmap='viridis');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most well-tested implementations will do a bit more than this under the hood, but the preceding function gives the gist of the expectation–maximization approach."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Caveats of expectation–maximization\n",
+    "\n",
+    "There are a few issues to be aware of when using the expectation–maximization algorithm."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### The globally optimal result may not be achieved\n",
+    "First, although the E–M procedure is guaranteed to improve the result in each step, there is no assurance that it will lead to the *global* best solution.\n",
+    "For example, if we use a different random seed in our simple procedure, the particular starting guesses lead to poor results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UKgWkkojQKzS5pxFvffo2wcHBLsvyL4PBwJD/G+ryXJXwcJ5/rWgK179zGc8e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\n0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GxejyuKgk5jQ6+5gJ9BISfXRWCAZwvqYm4k1rwsLL/bJCI900mgcSvx/faQa3WzL1Hl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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118926390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "centers, labels = find_clusters(X, 4, rseed=0)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=labels,\n",
+    "            s=50, cmap='viridis');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here the E–M approach has converged, but has not converged to a globally optimal configuration. For this reason, it is common for the algorithm to be run for multiple starting guesses, as indeed Scikit-Learn does by default (set by the ``n_init`` parameter, which defaults to 10)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### The number of clusters must be selected beforehand\n",
+    "Another common challenge with *k*-means is that you must tell it how many clusters you expect: it cannot learn the number of clusters from the data.\n",
+    "For example, if we ask the algorithm to identify six clusters, it will happily proceed and find the best six clusters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8vZF1W1bQt/tg5i6fyRbHHMzd9Bxdl0PcgAiSzEZSF+SheFSv7RVVRSV7nxVZ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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118bb2278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels = KMeans(6, random_state=0).fit_predict(X)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=labels,\n",
+    "            s=50, cmap='viridis');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whether the result is meaningful is a question that is difficult to answer definitively; one approach that is rather intuitive, but that we won't discuss further here, is called [silhouette analysis](http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html).\n",
+    "\n",
+    "Alternatively, you might use a more complicated clustering algorithm which has a better quantitative measure of the fitness per number of clusters (e.g., Gaussian mixture models; see [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb)) or which *can* choose a suitable number of clusters (e.g., DBSCAN, mean-shift, or affinity propagation, all available in the ``sklearn.cluster`` submodule)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### k-means is limited to linear cluster boundaries\n",
+    "The fundamental model assumptions of *k*-means (points will be closer to their own cluster center than to others) means that the algorithm will often be ineffective if the clusters have complicated geometries.\n",
+    "\n",
+    "In particular, the boundaries between *k*-means clusters will always be linear, which means that it will fail for more complicated boundaries.\n",
+    "Consider the following data, along with the cluster labels found by the typical *k*-means approach:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_moons\n",
+    "X, y = make_moons(200, noise=.05, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cxJ6JQ4frMC5duon1K39n47fb8pI25FZXh1OeWPUiXjjPi512m4ZkNG3RjIc/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\nQgghFCIHtxBCCKEQObiFEEIIhfwBiIgLgfNhbMUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118dd6550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels = KMeans(2, random_state=0).fit_predict(X)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=labels,\n",
+    "            s=50, cmap='viridis');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This situation is reminiscent of the discussion in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb), where we used a kernel transformation to project the data into a higher dimension where a linear separation is possible.\n",
+    "We might imagine using the same trick to allow *k*-means to discover non-linear boundaries.\n",
+    "\n",
+    "One version of this kernelized *k*-means is implemented in Scikit-Learn within the ``SpectralClustering`` estimator.\n",
+    "It uses the graph of nearest neighbors to compute a higher-dimensional representation of the data, and then assigns labels using a *k*-means algorithm:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9rYiqVg2Hw0HCqUR0uE7j0mWZWLPsd9Z9u7kwaUPB4+pwKpGgnscL57rYmTdo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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118ddb6d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import SpectralClustering\n",
+    "model = SpectralClustering(n_clusters=2, affinity='nearest_neighbors',\n",
+    "                           assign_labels='kmeans')\n",
+    "labels = model.fit_predict(X)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=labels,\n",
+    "            s=50, cmap='viridis');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that with this kernel transform approach, the kernelized *k*-means is able to find the more complicated nonlinear boundaries between clusters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### k-means can be slow for large numbers of samples\n",
+    "Because each iteration of *k*-means must access every point in the dataset, the algorithm can be relatively slow as the number of samples grows.\n",
+    "You might wonder if this requirement to use all data at each iteration can be relaxed; for example, you might just use a subset of the data to update the cluster centers at each step.\n",
+    "This is the idea behind batch-based *k*-means algorithms, one form of which is implemented in ``sklearn.cluster.MiniBatchKMeans``.\n",
+    "The interface for this is the same as for standard ``KMeans``; we will see an example of its use as we continue our discussion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examples\n",
+    "\n",
+    "Being careful about these limitations of the algorithm, we can use *k*-means to our advantage in a wide variety of situations.\n",
+    "We'll now take a look at a couple examples."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 1: k-means on digits\n",
+    "\n",
+    "To start, let's take a look at applying *k*-means on the same simple digits data that we saw in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) and [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb).\n",
+    "Here we will attempt to use *k*-means to try to identify similar digits *without using the original label information*; this might be similar to a first step in extracting meaning from a new dataset about which you don't have any *a priori* label information.\n",
+    "\n",
+    "We will start by loading the digits and then finding the ``KMeans`` clusters.\n",
+    "Recall that the digits consist of 1,797 samples with 64 features, where each of the 64 features is the brightness of one pixel in an 8×8 image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1797, 64)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_digits\n",
+    "digits = load_digits()\n",
+    "digits.data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The clustering can be performed as we did before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 64)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans = KMeans(n_clusters=10, random_state=0)\n",
+    "clusters = kmeans.fit_predict(digits.data)\n",
+    "kmeans.cluster_centers_.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is 10 clusters in 64 dimensions.\n",
+    "Notice that the cluster centers themselves are 64-dimensional points, and can themselves be interpreted as the \"typical\" digit within the cluster.\n",
+    "Let's see what these cluster centers look like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcsAAAC1CAYAAAAwXdRCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYwkAgMBYAgAg/AskS+OmduoezQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118acb780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 5, figsize=(8, 3))\n",
+    "centers = kmeans.cluster_centers_.reshape(10, 8, 8)\n",
+    "for axi, center in zip(ax.flat, centers):\n",
+    "    axi.set(xticks=[], yticks=[])\n",
+    "    axi.imshow(center, interpolation='nearest', cmap=plt.cm.binary)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that *even without the labels*, ``KMeans`` is able to find clusters whose centers are recognizable digits, with perhaps the exception of 1 and 8.\n",
+    "\n",
+    "Because *k*-means knows nothing about the identity of the cluster, the 0–9 labels may be permuted.\n",
+    "We can fix this by matching each learned cluster label with the true labels found in them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.stats import mode\n",
+    "\n",
+    "labels = np.zeros_like(clusters)\n",
+    "for i in range(10):\n",
+    "    mask = (clusters == i)\n",
+    "    labels[mask] = mode(digits.target[mask])[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can check how accurate our unsupervised clustering was in finding similar digits within the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.79354479688369506"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "accuracy_score(digits.target, labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With just a simple *k*-means algorithm, we discovered the correct grouping for 80% of the input digits!\n",
+    "Let's check the confusion matrix for this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P5TQYDEydOhVXV1f27dv3P1/W9qQDsodNmITfM8/875ehPeaA7Hv3H2D25/Mx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\n6lQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11a2d2ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "mat = confusion_matrix(digits.target, labels)\n",
+    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
+    "            xticklabels=digits.target_names,\n",
+    "            yticklabels=digits.target_names)\n",
+    "plt.xlabel('true label')\n",
+    "plt.ylabel('predicted label');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we might expect from the cluster centers we visualized before, the main point of confusion is between the eights and ones.\n",
+    "But this still shows that using *k*-means, we can essentially build a digit classifier *without reference to any known labels*!\n",
+    "\n",
+    "Just for fun, let's try to push this even farther.\n",
+    "We can use the t-distributed stochastic neighbor embedding (t-SNE) algorithm (mentioned in [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)) to pre-process the data before performing *k*-means.\n",
+    "t-SNE is a nonlinear embedding algorithm that is particularly adept at preserving points within clusters.\n",
+    "Let's see how it does:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.91930996104618812"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "# Project the data: this step will take several seconds\n",
+    "tsne = TSNE(n_components=2, init='random', random_state=0)\n",
+    "digits_proj = tsne.fit_transform(digits.data)\n",
+    "\n",
+    "# Compute the clusters\n",
+    "kmeans = KMeans(n_clusters=10, random_state=0)\n",
+    "clusters = kmeans.fit_predict(digits_proj)\n",
+    "\n",
+    "# Permute the labels\n",
+    "labels = np.zeros_like(clusters)\n",
+    "for i in range(10):\n",
+    "    mask = (clusters == i)\n",
+    "    labels[mask] = mode(digits.target[mask])[0]\n",
+    "\n",
+    "# Compute the accuracy\n",
+    "accuracy_score(digits.target, labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's nearly 92% classification accuracy *without using the labels*.\n",
+    "This is the power of unsupervised learning when used carefully: it can extract information from the dataset that it might be difficult to do by hand or by eye."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 2: *k*-means for color compression\n",
+    "\n",
+    "One interesting application of clustering is in color compression within images.\n",
+    "For example, imagine you have an image with millions of colors.\n",
+    "In most images, a large number of the colors will be unused, and many of the pixels in the image will have similar or even identical colors.\n",
+    "\n",
+    "For example, consider the image shown in the following figure, which is from the Scikit-Learn ``datasets`` module (for this to work, you'll have to have the ``pillow`` Python package installed)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zVhNJ3QgAIA38CE2UM8c7A91jNf7tvU/vUtpUIHwok9F4iy1lNL4WQ2nYVSc5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HbmPPlsCmN2npHLV1CkZPpcH1ohjT5XYvT6Nda0tHLyo4Ns2bAWdbHM9av93HvzzRx98x1uu2MX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VXiEARJRC9JyKsSZyBuKxCCIPDZrkVegGjyZY2hrkpv6xAe8l9VOXVdM51CGIWcjA+eZ2rO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\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YPDRFoVxg78QBAoEAdXODYnkex6rQaBQo1TZJ9iSxHJdKKU+5mCXVE2d1bRHTaiBxsD2T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\nFVf8FFwVzCuuuOKKK674Kfjf+C09CqNJKM8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11ab6eac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Note: this requires the ``pillow`` package to be installed\n",
+    "from sklearn.datasets import load_sample_image\n",
+    "china = load_sample_image(\"china.jpg\")\n",
+    "ax = plt.axes(xticks=[], yticks=[])\n",
+    "ax.imshow(china);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The image itself is stored in a three-dimensional array of size ``(height, width, RGB)``, containing red/blue/green contributions as integers from 0 to 255:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(427, 640, 3)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "china.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One way we can view this set of pixels is as a cloud of points in a three-dimensional color space.\n",
+    "We will reshape the data to ``[n_samples x n_features]``, and rescale the colors so that they lie between 0 and 1:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(273280, 3)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = china / 255.0 # use 0...1 scale\n",
+    "data = data.reshape(427 * 640, 3)\n",
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can visualize these pixels in this color space, using a subset of 10,000 pixels for efficiency:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def plot_pixels(data, title, colors=None, N=10000):\n",
+    "    if colors is None:\n",
+    "        colors = data\n",
+    "    \n",
+    "    # choose a random subset\n",
+    "    rng = np.random.RandomState(0)\n",
+    "    i = rng.permutation(data.shape[0])[:N]\n",
+    "    colors = colors[i]\n",
+    "    R, G, B = data[i].T\n",
+    "    \n",
+    "    fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "    ax[0].scatter(R, G, color=colors, marker='.')\n",
+    "    ax[0].set(xlabel='Red', ylabel='Green', xlim=(0, 1), ylim=(0, 1))\n",
+    "\n",
+    "    ax[1].scatter(R, B, color=colors, marker='.')\n",
+    "    ax[1].set(xlabel='Red', ylabel='Blue', xlim=(0, 1), ylim=(0, 1))\n",
+    "\n",
+    "    fig.suptitle(title, size=20);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qbBVFURRFURRFUZT1GhW2iqKsN6xIXJ/y/qD3W1kV9G9WURRFWVNojK2iKIqi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cbG5ui22+RyDRrjs2TO6EAceH3aO0tkClUKtWcDQ3FiGqdQwsxmicOA3tY9dDuHlSkTeQMAIt89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kJF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Xkfwe5HAIKeBHlqSTNOn/e1DWL0NxcDXRjAlEcqaduMI3wIAr8n8QZ5WVk5KQyLxZF/a7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\n3YJSG528CQeD7Hn8IXrbDhH2efHZrbjM7Rx88ylCQS9hhQ+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ft9PZqeeBh24AHDgc9YCu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\nQgghhBCiSvt/Nw9T+IjeT4cAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118fcd400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_pixels(data, title='Input color space: 16 million possible colors')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's reduce these 16 million colors to just 16 colors, using a *k*-means clustering across the pixel space.\n",
+    "Because we are dealing with a very large dataset, we will use the mini batch *k*-means, which operates on subsets of the data to compute the result much more quickly than the standard *k*-means algorithm:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PpbDYpnU5wNZCKGYbob/tIjOqh7FN1QqtNCYyDa8tzmGT3Nr2vNgoCDFhnK9s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yz9dw1i6ve75VLAUVNP/u36w5zmh3I0hGDHY3eRsPwsb11XPdCgarg+rH/phk9ztkJi4hmPPu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CWcOHGU41Vd6PUyX/jsY1jt6WhawlA7/3qfOLaDmqoDzJh10zAlgAuprzvM0fffoLh0PuXT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\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vIU9Rn8+LKIoIgoharUWpVOLxuFCrNYSHH3FraGisJXBIOLG5uYnurqAlUHVNJQ/+9W6u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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117e3d438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import warnings; warnings.simplefilter('ignore')  # Fix NumPy issues.\n",
+    "\n",
+    "from sklearn.cluster import MiniBatchKMeans\n",
+    "kmeans = MiniBatchKMeans(16)\n",
+    "kmeans.fit(data)\n",
+    "new_colors = kmeans.cluster_centers_[kmeans.predict(data)]\n",
+    "\n",
+    "plot_pixels(data, colors=new_colors,\n",
+    "            title=\"Reduced color space: 16 colors\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a re-coloring of the original pixels, where each pixel is assigned the color of its closest cluster center.\n",
+    "Plotting these new colors in the image space rather than the pixel space shows us the effect of this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ls+h9awnjQQcdhJtvvhmrVq3CEUccgYcffhirVq0CY8ysawTwnLw4DjroIHz/+9/HLbfc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\n9lPIrjD2w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oImEphMZqQENNTVM3L7FqM3Wyj1xvCaIDkzTnfPDXqn+uge66O9s5v23l76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WLPfv99kuzVsXLw3u0em0BuOiH/wGYGN9hYfOnWNt5S6maZBMJVgMfMTcFAB7uxXozd9nf/Q8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vG3/hzBUGhuqmdxOYzL00idu401XdtpbezCAERR4mY0GckEEBFvULdWbY33HH5uBY64I5AF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\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3rnH+ozcY+jFhp09sU3l0rVbBuoaHTz3G+z99k4vf+1N0Yx+joVIucnJugdmxWd57+ScU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cxLb5FegK0JTMMiXAbUXAfLcbBM/dxJBmbNIs1zMA0sxyaKo9LFZFgUCNIsRROK2WKCrhmovCxS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RwTSqxHUNr2mQiqcwShUy0TiV4hKZRBLPreF5FpoiE1Yk0tEQgmeQjAaJSQLpYICkLLIuHCVi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\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DQt8nlJIwkhiJBKqqx1MSAUKGZFQdTdVpuQPQdaJIEPk+BD6GpiGjmB4ayoMppUCGUZxL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dc4ag5p9VwOD9vMLc7yyisvc+3aJAfp/IcXUeMVvvDqC1xev871my2WFmdYemWBmfwU/9sffo1e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\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ffXTE2mqROPKpVsps793BX5yRL9T47nc+QFSK6HrAw82HvGWZDCWB/PoBaTTDquwgEGFV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\n/gy7N5H+QY8w6QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11fd2ef98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "china_recolored = new_colors.reshape(china.shape)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(16, 6),\n",
+    "                       subplot_kw=dict(xticks=[], yticks=[]))\n",
+    "fig.subplots_adjust(wspace=0.05)\n",
+    "ax[0].imshow(china)\n",
+    "ax[0].set_title('Original Image', size=16)\n",
+    "ax[1].imshow(china_recolored)\n",
+    "ax[1].set_title('16-color Image', size=16);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some detail is certainly lost in the rightmost panel, but the overall image is still easily recognizable.\n",
+    "This image on the right achieves a compression factor of around 1 million!\n",
+    "While this is an interesting application of *k*-means, there are certainly better way to compress information in images.\n",
+    "But the example shows the power of thinking outside of the box with unsupervised methods like *k*-means."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) | [Contents](Index.ipynb) | [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.11-K-Means.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.12-Gaussian-Mixtures.ipynb b/notebooks_v1/05.12-Gaussian-Mixtures.ipynb
new file mode 100644
index 000000000..f5c4d7358
--- /dev/null
+++ b/notebooks_v1/05.12-Gaussian-Mixtures.ipynb
@@ -0,0 +1,1075 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In Depth: k-Means Clustering](05.11-K-Means.ipynb) | [Contents](Index.ipynb) | [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.12-Gaussian-Mixtures.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# In Depth: Gaussian Mixture Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The *k*-means clustering model explored in the previous section is simple and relatively easy to understand, but its simplicity leads to practical challenges in its application.\n",
+    "In particular, the non-probabilistic nature of *k*-means and its use of simple distance-from-cluster-center to assign cluster membership leads to poor performance for many real-world situations.\n",
+    "In this section we will take a look at Gaussian mixture models (GMMs), which can be viewed as an extension of the ideas behind *k*-means, but can also be a powerful tool for estimation beyond simple clustering.\n",
+    "\n",
+    "We begin with the standard imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns; sns.set()\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Motivating GMM: Weaknesses of k-Means\n",
+    "\n",
+    "Let's take a look at some of the weaknesses of *k*-means and think about how we might improve the cluster model.\n",
+    "As we saw in the previous section, given simple, well-separated data, *k*-means finds suitable clustering results.\n",
+    "\n",
+    "For example, if we have simple blobs of data, the *k*-means algorithm can quickly label those clusters in a way that closely matches what we might do by eye:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# Generate some data\n",
+    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "X, y_true = make_blobs(n_samples=400, centers=4,\n",
+    "                       cluster_std=0.60, random_state=0)\n",
+    "X = X[:, ::-1] # flip axes for better plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NHX/LXduMjAxeeOEFZsyYUatgDDffuDvZlNH3MWV09Wt1q7tPZ5LP8f3mZRQ7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\n2qkiyS239RV69fnKf69Z8TZ9O8wntpv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/Pz2PffpPjre1u3zW5ISZbx+aVftC3SJZmWmc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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116370940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the data with K Means Labels\n",
+    "from sklearn.cluster import KMeans\n",
+    "kmeans = KMeans(4, random_state=0)\n",
+    "labels = kmeans.fit(X).predict(X)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "From an intuitive standpoint, we might expect that the clustering assignment for some points is more certain than others: for example, there appears to be a very slight overlap between the two middle clusters, such that we might not have complete confidence in the cluster assigment of points between them.\n",
+    "Unfortunately, the *k*-means model has no intrinsic measure of probability or uncertainty of cluster assignments (although it may be possible to use a bootstrap approach to estimate this uncertainty).\n",
+    "For this, we must think about generalizing the model.\n",
+    "\n",
+    "One way to think about the *k*-means model is that it places a circle (or, in higher dimensions, a hyper-sphere) at the center of each cluster, with a radius defined by the most distant point in the cluster.\n",
+    "This radius acts as a hard cutoff for cluster assignment within the training set: any point outside this circle is not considered a member of the cluster.\n",
+    "We can visualize this cluster model with the following function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "from scipy.spatial.distance import cdist\n",
+    "\n",
+    "def plot_kmeans(kmeans, X, n_clusters=4, rseed=0, ax=None):\n",
+    "    labels = kmeans.fit_predict(X)\n",
+    "\n",
+    "    # plot the input data\n",
+    "    ax = ax or plt.gca()\n",
+    "    ax.axis('equal')\n",
+    "    ax.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis', zorder=2)\n",
+    "\n",
+    "    # plot the representation of the KMeans model\n",
+    "    centers = kmeans.cluster_centers_\n",
+    "    radii = [cdist(X[labels == i], [center]).max()\n",
+    "             for i, center in enumerate(centers)]\n",
+    "    for c, r in zip(centers, radii):\n",
+    "        ax.add_patch(plt.Circle(c, r, fc='#CCCCCC', lw=3, alpha=0.5, zorder=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WLVrM/v37kGWFEyeOkZIyiQmjVIlLIBAIBsOQ1fKee+4hLy+PzZs38/Wvf50XXnjhC1PZ\nqZvDhw/Q0tKC1+vBbDazYsXKHs/OarUy0WPrd41a08a8iSPfTEHTNH557G2a5oVhjgnBHB1C89ww\nfnXyXRRFGfHxr8aKaVDHR4L09AxiYmJRFO+V0p8fjclcCAQCwY0Ysvj6+/vzs5/9jNdee40tW7aw\nfPkXK8RXUXGZ06dP9WTSLl68BLu9bwjhq/MeJOBYHWqnBwC9uIklTRHMyZg14vZ9duY4jan9Szy2\npgWx7/ihER//ahZHp6PXOvoc0+scLJowNC98KEiSxMqVqzAajSiKh6amJo4cGf25EAgEghshMlIG\nwOv19gs3T5kytd95KfFJvDjhb9j52V6aO9tZPHUlSRNHJ9Gqs6sTKaj/2yeZjbjcnaNiw+dZPW8p\n7fud7Dl5lhaLQrDHyIqINNYsv7k14+EiKCiIhQsX9Qk/T5qUyvjxE0bVDoFAILgeQnwH4PTpU9cM\nN1+N2WzmgWVrRtlCWJK1kC3vfUbX7Mg+xy3nmlh935dH3R6ATcvW8ZC+FpfLhb+/f08OQGNTI/tP\nH2FCWBTzZ80Z8Wpg6ekZFBUVUVVViSwb2LdvD088sfmOr0ImEAi+OHyxMqSGgc7OTk6cOIqmqWia\nyvz5C/qFm28HLBYLTyQuwZhdh65q6KqGIaeOx+MWjmrHoauRJAmbzdYjvL//+E3+4shv2JZQx4vq\nZzz36v+ltqF+xG1YsWIlsiyjKF4qKysoLv7iV2ATCAR3DkJ8r+LEiWN0dXWhKF6Cg0OYNi1trE26\nJquylvDLe/+c+8siuK8knBdXfpc185aj6zqHTx/llY/f4sz5nDGz78z5HD71L0efFo4kSxjC7NQv\nCOO/924d8bGDg4NJS0tH01R0Xefgwf1o2uhkoQsEAsGNEGHnz9HR0c7p0ydRVRVN05g/fz4Gw+1d\nGCQgIJAn7tnY87vL5eKHb/6CiilmDJPs7KjZzaTX9vH3j30Xk2n0Mo8BDpXkIE8L7nNMkiRK5Fa8\nXu+I25OVNYe8vAsoipfGxgby8i6QljbymegCgUBwI4Tn+zk+++wIiuIr1B8REUFycspYmzRo/mfX\nm1QtCMYwzrfn1jA+iMJMP17f9e4YWHONNdZR6j5ktVqZNSvzyhKCxpEjB8XWI4FAcFsgxPcKLS3N\nnDt3FlVV0HWNBQsW3pEJOsWexn4NEmSzkcLO2iHdz+l0sv/YYYpKB9+wYFnKLLTylj7HdF0nSQ8d\nNS985syZ+Pv7oyhe2traOHs2e1TGFQgEgushxPcK2dlnerYWxcTEEhc3caxNGhLGa3ibhiG81f/4\nv//Jk+/8iJf8zvC3l7bwN3/4CR0d7Td9/fQp6dynJmM8W4/m9qLUtDHhaCvfXf3EoG0ZKmazhays\nOT3eb3b2adH3VyAQjDlizRffvt7z58+hqr7knMzMzLE2acik22Op7mxA9u+tNqa2uMgcl0pdXR0O\np5PEhIQbevX/79X/IieuE1tCEgDGAH8qE3Ve3PEqLzz2pzdtz5OrH+KBjna2fbqdsvYugsPCKSgt\nYkFI6KhFFqZMmcqxY0dRFIXm5mbKykpJSLizGl8MF91z4HC043Q6cTgcOBwdPX/3en3VwXRdQ9d1\nZNmALMsYjUZsNht2ux27PQC73X7l9wBCQ8NGPZ9AILjTEeILFBTk0dXViaoqBAUF3bFeL8DT9z5M\n47b/Icdcj3u8FWuli8yuMI5pF/ij6zSqn4Go4zpfmraKBRlZANQ31PPesV106l6mhsezNHMBh+ry\nCVgwvc+9JVnirKsSXdcHJZzH83PYZS5FWzwOSfJwtO4Ix97O5S8f+cawvvZrYTabmTJlKmfP5qDr\nOjk5Z+4K8VUUhcbGBsrLL1JQUEJtbQ2NjQ2oqtrnPF8kQO+JCFwdGOh+q33vudTvvZdlmbCwcURG\nRhEVFUVU1HjCwyOEIAsE1+GuF19d1zlz5jS6rqFpKunpGWO61uvxuNl2YAflrkaCJD82LVhD+Ljw\nQd0j1D+QwLYmnHktpJgjqHA0Ubc8HKMUiBFoiYPfnN5JekIq+WVF/NelHXinRyBJEkdbctj128N4\nrtGG14s6KPHVNI1tpUfQZ4f3BMQNkQGc8raRW3CejMmjs5UrLS2dnJxsVFWhqKiQ9vY2AgO/eD2o\nm5ubKC4uorjYV2REVVVsNgsOR9eVz7je49VeLbo3S68IgyTJSJJEXV0N9fV1nD+fC/gEOSpqPMnJ\nKSQlpTBu3Lg7ModCIBgp7nrxra2toa6uFlVVMBqNA5aRHC26urr42ovfozlc9vXE9Sp88tpJ/vm+\n7zAtefJN3ePFd1/m+CQXstXXQzdP12n8qJJx2jgw9H75eWZE8P6RT8luK0OZHdkrjCE2ymYpmA9r\ndNW04Dc+pM/9pRrHoLpXVVRcpi4crm40KccEcaLg3KiJb2hoKLGxcVRWVmI06pw9m8PixUtHZeyR\nRNM0qqurKCoqpLi4kKamJoArQuv7cTi8uN3enmuCgoIIDAzCZrNhs1mx2XwhZKvVhtlsRpYlZNnQ\ncx9V1fB6Pbhcrp7wdPff29vbaWtrRVF8Ai5JMrIsIUkylZUVVFdXcfDgfoKDg3uEOCYm9rbfwicQ\njDR3vfjm5GQDOqqqMnnyFPz9r+HyjQL/99Vf4podQejEXk+3q6qZn+94hd/8+b/c8HqHw8EZqRrZ\n2ltyUpIkQlZMpT23nKCZCXRcqERpdYJBZl9VERXeZoyeULjiCQXPTcEUEUSAbKN4Xx5BM+OxTZ6A\n0uai7VQJQRNDqKuvIzIiEkVR2LL7PQo76zDoMvMnTGX1vL6CFhgYhMVxVZhT1Wg9UcQxh4XGd3/D\nkvjpLJg59xZn78akp6dTUXEZTVPJzT3LwoWL79g2mE6nk3PnznL2bDZtbW0APVXZVNW3Zgu+YiMx\nMRMICgolIiKSiIgILBbLsNri8bhpaGikvr6O+vp66uvraW1tQVG8SJJPyJubmzh1qpVTp05is9mZ\nPn0G06fPICAgcFhtEQjuFO5q8dU0jcLCS1e+rHTS00e2AIOiKLx/YCfFzlr8dSOPLVpDRFhUz7/n\nd9VgndjXw/WLDqU6p/Km7t/QUI8zxNDPyzTa/NDdCm2nS/CLDiVgWgwAnZoOu3IJmpOEbDSgeRSa\nD+YRtjyNWOs42hZEgQStx4sw2v0IW5EGEuw6fYgn12ziH994kaJMP2SLL7mroOEM1Z808OV7H+4Z\nOyQkhMmuIApUDckgo+s6TXvPE7JoMp3+Zs4BuVWHqNxXz6PL7x/8pA6ChIREbDYbXV1unE4HVVWV\nxMbGjeiYw4mu61RXV5GdfYaLF/OvFINRe/7UdR2TyURCQjwJCYnExydc8W4tOJ3uEbPLbLYQHR1N\ndHRv7+TOzk7Ky8soLS2lvLwMj8d9RYhlOjpUPvvsMMeOfUZycgozZsxi4sR4EZYW3FXc1eJbWVlB\nV1cnmqZit9uJjIy68UVDRFEUvv/6Tymb5Y/BakHXPZw6+jLfjF3BwulzfCeZBw7Fyeabe5uio6PR\n97VA3Lg+x90N7UiFzagJQViieitOSbJE6KLJdOReJmhWArLZiP/EcJQDxayb8zD5bXsxTgxFNhmQ\nDDKSLKF2ebFZ/DmRe5rCJDBYepNqpHA7Byov8mhnZ58Iwl898DV+tv0PXKSelrpGAmZNxPC5bGwp\nOohdp8+xwXPviPaENhgMJCYmcv78eQCKigrvCPHVdZ2CgnyOHz9KfX0d3ZEaX7tLDX9/fxITJ5OQ\nkEhsbOxtkejk7+/P5MlTmDx5CkVFFyi8mANyCNXV1TidDiRJxmAwcvFiAZcuXSQsLIysrLmkpWXc\nsdEIgWAw3NXi211sX9NUEm5i+82tsOPwbkpn+mG0+kJ+kiShTAlj26mDPeKb6hdJ0QDJTBOUm2uU\nYDKZUZucfdZqNY9Ce3YZs8bFkx/Y3/sx2Cxont6qT/6xYSxoiGJOZhbBv3yP8rJazOGB6IqGt7mD\nCM3Guqe/xh/3vo9hUv+GE+3jzZSWlzF18pSeY1arlecf+TYej4ff7djC4cj+djRHyFRVVY54FnJC\nQiLnzp1D01SKiwtZvnzliI53K+i6TmlpCYcOHaCurhZd11AUpcfLjYoaT3p6BikpKRiNt99/5Y6O\ndo4f+AELZuYzf73K4ZOBBFrvIzJ6Pbm5uVRWVqCqvrB0Q0MDO3fu4OTJ4yxatJRJk1KFJyz4QnP7\n/Y8dJXRdp6joUs8X2Uh/6Re112CMvTogDDUGFx6PG7PZwp/d9zTPvfOfaMvifSFaVUPZV8T3N/3F\nDe9/ubqSLUd34LUbcWWX0nq8CEtkkG/Nd0EKF/cX4XX2X+tTu7zomkbzoQIkowxOD4Sk+5pL2E2E\nLe6bgOa/uwKLxcKEwHEo7bUYA/uukfs1eIhOG7h3rtlsJjo4AtVVhMHa1xZri0r47MFldQ+FmBif\nZ6hpKs3NzTQ1NREWFjbi4w6W7kSly5fL0XUdRfGiaSpGo5HJk6eRnp5BRETEWJt5XU599h9847E8\nZFkCDKxb4eT8xbcob03noYc20dzcxLlz58jPz8Pj6cJgMNDY2MD7729j/PgJLFmyjIkT48f6ZQgE\nI8JdK77Nzc20tLSgaSomk4mYmNgRHc8um9F1b7+neZtqwGj0hQnDw8L57y89z5sHtlOvdBBmCODR\nx79PUND1t8TUNzbwTwdfpjMrkhB82cOu4jo0r4J9cjTN+y4QfM8UPNmleJodmEN9dZ91Xaf5QB66\nqhF+7/SespRHutzk/+Ifcd4bzdWB8NZJNgqKLrJq3lJ2vHqUhkV+Pa9J7fSQpUZc1961C1fyyRun\naV3YKxxql5dZWsSotG40Go3ExU2kpKQEo9EXeh5r8c0+vYem2k+QJAXNMB1np51Lly6i6zqq6kVV\nfaI7c+ZsZs3KxM+v/0Pc7Yau6wRbL1wR3l7SUjVOf7AHpmYRGhrG0qXLmD9/AWfP5nD69Ck8Hjey\nbKC6uoqtW/9IfHwCq1ffS0hI6Bi9EoFgZLhrxbeoqBAAVdVITEwc8bDdxnn3cHT/b/DM7BUdzekm\n0z+uzxqX3R7AV9c/Pqh7v3XkY1yzI/oUlrQmRdJ8qAA1uJ2wNgO62Ujw3BTasktx5FchGWTctS14\nnV1ErMroUw/a4GemzNBGsDWh31h6gIXm1hYMBgPPLd/MP775Ii1mL8ZOjZUpWXxzw+br2moymfj+\nmq/z+wPbKNNaMGMgwxrD1zYM7jXfCgkJiRQXF6HrGsXFhcydO2/Uxr6afbv+i+kJL7Nqhi87ub5x\nHz/+RQRG6wJUVUGSJNLS0sjKmovdbh8zO4eCJA3cwlGi73Gz2UxW1hzS0tI5ffoUublnr3jCRsrK\nSnj55d+xePFSMjOzRCha8IXhrhXfysrLeDxdnGwq5ERQI29vzSZRCuVbKx4lYpBFLW6G8HHhPJv2\nAFtP76FCa8OGkbnBSfzJfY/e8r2bdBeS1D9Zy6oZ+cuA5ZTPqOLtrssY/MwEzewV1JYjF7FEBGOJ\nDO53rWV6NOq5GowZfUPIgcVOZm+cyeXqSv7lyKu4H5yE7UqIPPd4JS2tLYwLG9fvfp9nfEQkzz/y\n7SG+2lsnPj4e8D141dRUoyjKmKyZOhwOrNLbpCT0ilHEOJknH6zl12/WMitzPnPnzickJOQ6d7k9\nkSSJVudkdP1UH8EsLofA0EX9zm9oqKOhoZqsrDlMnz6d48ePk5+f15NUtnfvbgoLL7FmzTrhBQu+\nENy1aYW1tbV81lyI8qU0tIWxeGZHkT/LxD9v/+2INV2fMTmdf330L3j1sR/w68df4M82fXlYMjtD\nJf8BqxQl2SPJypjFA4vvJfJkK7rWe05XZRMGux+W8cF0VTb1uzagA1bJyejF3UUbdKQL9TwUNx+z\n2cIbx3bgnBvZ4zFLBpm2+eG8fvijW349I43VaiU4OPhKAQmVxsaGMbGjIP8ks9Ob+x2fM8tI+rSw\nK0Jz5wlvN+mzn+W3W+KpqfNt5Tt03MT+M+tJn76k5xy3282u7c/jbfgK06P/kktnnqbg/HZWrVrN\nY489QVhYGB6PG6/XQ0VFOS+//DtOnTohmmMI7ngM//AP//APozGQy+UZjWFuCoejg+07P+JyugVT\nRO8mf0mI9ZBTAAAgAElEQVSScARKhFV4SYgZufrO3Z6A2WzE61VvcLZvP/LJs2eoqK4kJDCYpqZG\nrFZrj3DHhU3g0NHDKBNsvWOUt/FI1FwSJsRhMBhYlDSTthNFFJw5h6OuGckoE5AWiynERsv+fPzj\nw5GMPu9Za3Wx0BHJMw8+RboUhZZfT1KjhT/NeohZUzIAeO34DupqanDXtGIKC0A2GpAkCanGweop\nIxfGvdk5uxE1NdU0NTVhNBqv1CQePwzW3Txer5d9+/Zj0o8TH9v3AayuQaPVvYnxE4YnCXC45myw\nWK12ElLu49SFOI6djSc87s9Iy1jd55xDe/4fX33oMxLidIICDUxO6kL1nKO0KpWJ8SlMnToNSZKo\nrq5CURR0XaesrIza2hoSE5NHLGJhs1luq++s2x0xXwNjs127oM1dGXaura2l3eXAMKF/oo0h2EpN\nYeMYWDUwuZcu8N8n36chwUJbbhmWXD/MUUGEtxm4d/wMNi5ZS1R4BN9fsJmtJ3dSpzkIwMzqpMUs\nmtFbNSogIIBvP/g0Cy9l8o/7X8ac6KuC5SyuI8xl5J6SUIrcDRiQmBU2iQfvXwPApMRkJiUm97Hp\nnf3bqfXrJDgzGV1RaTtVgiUqCGtiJDZGbp/ucBIeHsGlS5fQdZ3a2qH1Oh4qHR3tvPvuO9TW1vFh\nSQgLZrdjMvkeyHRd5/3dCSxft3xUbRopJElixsxl1/z3QL+zGI1913GnT1HJfn8XqVNmYzAYmDdv\nPomJSeze/SmNjY0YjUZKSop5/fU/sHHjw4SG3n7Z6gLBjbgrxbeurpbxoREUnK3AtKSvsOjFTcyf\numqMLOuLqqq8dPJ92ueF4zxWSOjC1J4tOh3AWxUXiM6NYE5GJvExcfxtzDdveM/GliaMqeG055Sh\nazp+MaGwKY2Ogk5+vOnPbnh9Q2MD77ZmY8vyrR1LZiMhCybRfDAfq2xhVeKdUS85IsL38KHrGnV1\noye+1dVVvPvuOzidHXi9HtzaPH70yzKmJjcjyxrtnZOYueDP7ppCE7KsDHjcIHn7/B4REcFjjz3B\nsWOfcfr0aTRNp7Gxkdde+wP337/hruhSJfhicVeKb21tDSaTkYgyD80JrRhjfQlHarOTeR3hJMb1\nz/IdC45mn6A51ep7k1St395YKTaIvbmnmZNx/f7DvuL6Dmw2G8eq8zFmhBAc03ctMd9d1/N3j8fD\nzs/20trZwZK0OcTH9obgPz65H21a38xqAGtyFMtqI1mwcs5QXuqo071HVtM0GhsbRiXp6vz5c3z6\n6cd4vV68Xl+hkZUrV5ORMbadtMaS9s4U4EyfY7X1GiZr/8+0wWBg4cLFjBsXzp49u/F43IDO229v\nZdmyFcyePeeunUfBncddKb4NDfVoms6UsIlMM00n+3wJGjqzImey8qElN77BKOFVFPC74gEZBvaE\n3NL11/K2HdjBrtqztPorBHYa8FS3QkZKv/O0K9s/CsuK+fcjb9A2PQTZz8TOvDdYkh3NMw88BYCf\n0Yyuakhy3+xqg1dj+ez+Way3KxaLheDgYDo6HKiqSlNTE5GRkTe+cIgcPXqEQ4cOoGkaXq8bi8XC\n2rXriY0d2f3ltzuTp/8pv9v69zx0byUhwTK5+RJvfxxOasphDu4+T2Lqw8TEJvW5JjV1MsHBIWzf\n/iFOpxOTycy+fXtob29jxYrVQoAFdwR3nfjquo7D4ejp+rJ07mJWGm/P9bVFmfN44+2DOOf4ow+Q\nMKO5vaT4X7se9b6Th3nbUIA0exwy4AC8DRacxwoJntcrwLquk2T0bQ/63fEPcMyL7C2ukRzG/spa\n5pzPYVbaDNbOW8GWt36E/9K+Ah5bI5O84s4K/QUGBtHe3gGA09kBDL/46rrOoUMHOHbsM1RVRVE8\nhIaGct99D/QrRlJdVUxh/hZslgZc7lDiUx4lbuLNtZK8HdF1nZzsfTjay4gaP5OU1Jn9zomMjCHs\nnt+y8+RO2tvKaKrdzw+fbcRg8GXZ7zp4nKKu50lOmX3VdZE8+ujj7Nixnbq6WoxGE6dPn0JRVO65\nZ40QYMFtz10nvi6X68pWIh2LxTIq+zu7urp4Y8/7XPY04YeR1alzmTV1+g2vM5lMfHnqan538lOs\nyZE07r1A6MJJyBYTaquLhPMeHvnStTsBHajIRZret2qUKTwQ28lqtNJm5IRQ1CYnE/I6+ebG7+Bw\nOCg3O5DpW8zBEBPM4QtnOV9ZxEdlx3EZvbRtO4E1JQpTkJXOc5V8e/71i2vcjlit1p4tKw6HY9jv\nr+s6+/fv5eTJ46iqgqJ4iY2NY926dZjNfZcQqiqLaa16nq9ubO05tuvgWcrUfyA+cWS7bY0EHR3t\nHN33t2xYVcT4SJn8wjf55INMVq3/p369fI1GI3Pm3cf+3b/ib7/djuFzfadXL3Hyyntb+okvgN1u\n56GHNrFr16cUFvqS586ezUZVVdauXS8EWHBbc9eJb/eXrK6DzWa7wdm3jtfr5YUtP6V6XgjylW4z\n58o+5an2Fh5aee8Nr184fQ6ZqRns/GwP7pgUlDKVdt3NpLBJrHh6yXW/YDrxAv073IyPGs+fJm7g\n5MWzTIxIZ+GX5yJJEp2dnWhdnn6bv3VdZ9+pQ1g2ZmBKmUR3bmnTvgvIFiOh62eQff4ii5h/k7Ny\ne+B7/0dOfA8fPtgjvF6vh4SEBNauXT/gA1/JxTf4kw2tfY6tXuLg5fe23pHie/roL/nWE8U9iWNT\nUnRiJ5xg2/4tLFzy5IDX+Jsq+whvNzbLtVtqGo1G7r13DQaDgYKCfADOn8/FYDAID1hwW3PXia8v\nvOgTFJtt5Mv1fXT4U6oyAzCYep/29fhgtp88zkb9npu6h5+fHxtWrB/02LGGYCo0N9Ln6uvquk6o\n20R9SwO6pBNoD0CSJLxeLz9++yVa21sI02P7fGm1515GTQjEelUThdAlU2g94esM1SkNnLV6O2O3\n29F1HV3XcTqHV3yPHfuMo0eP9Hi8iYlJrF27rp/X142fqW7A41bT6G6DGi7slov96jrbbTIGLRcY\nWHy7PIEDH/dev7a5LMusXn0PBoOBCxd87SLPns3GaDSINWDBbcuQxFdRFJ5//nmqqqrwer0888wz\nrFixYrhtGxGcTifgEyGr9eZa9d0K5Y4GDHH9N1o3+nlwOp1I0q33XnW73XR1dfVbQ/zyio189vt/\nQl2ZgMHPjOZRaD6Yx5FqB3kpGsYUO9srP2bqmb1MDIqkONOPYG8qzfsu4BcThjHYirOwFoOfCWOA\nf79xJYMMkoTqcpMSMHCGeNnlMoory8icOoPg4P5lLMeS3vdfH1bPNz8/j4MH919Z4/USHx9/XeEF\ncHsHLpnY6b0z97Bq+sBfLfo1jgMkpm5i9+ETrFrk7DlWWiFhtK6+5jXdSJLEihUrUVWVgoJ8JEni\n9OlTBAeHkJmZNfgXIBCMMEMS3w8++ICQkBB+8pOf0NbWxoYNG+4Y8e39ktWx20c+7Bxk8EdX3X0a\nFwDY3DL+/v50dQ3dY/R43Pzs/Ze5INXjNktMcFl4dOoK5l3ZehQQEEhQSDAXd2QjSRKaR0XXNCZs\nXtDjDcsxQVwI81D0yXHk1MnIFhNhK9Jw17fR8tlFIu7LRDLItBy52G981ekGVSPhTCf3P9k3hO52\nu/mXt18i395OW0MzcvY7yC1dpERMZEVqFvcvuWdEPBJN0ygpLSUwIOCGLfe6lx26k/CGg9raGnbu\n3I6maSiKh5iYWNauXX9d4QWIjn+YA8dzWTrX1XPs2BkL4RM2DItdo02nMovOzsv4+/d+7iuqwWK/\ndkZ8bFwKRV1/xyvvvoHNr5oubxBG6ypmz334psaUJIlVq1ajqipFRYVIksS+fXsICxtHfPztsX1Q\nIOhmSOK7du1a1qzxVUDSNO22bOR9Ldxu3/5KXdf7Jb2MBA8vXstnH76Ia25vJq3a1sm8gKQrX8hD\nF9+ff/AHstN1ZFMUBqAOeCnnY5ImxBE+LpzishKKLpcQvn5Gj+fafLigTxgawOBvpstP6vNhsEQE\nYYkIRr5SctIcZsd5qQbbJF8ZRs2joH5yiWey1rJ+8T19xEVVVX75/isUpBtoO9ZI2PK0njFLC6r4\nTf0BLr1dxl898q0hv/aBOJR9nC0X91IXJWFwqSS1+vNXa/+E0GsU4u9uzafrvr3Nt4rD4eDdd9/B\n4/Hg9boJDg5h3bqB13ivJiEpnaKiv+cP772F1VxHpyeMsPEbmTz1zlpH72bhsm/xhw8amRx3ktTE\nTs6cD6LBeQ+Ll6+97nXJk7JInjR0T7U7BN3e3k59fR2SJPHBB+/x1FNfFg0ZBLcVQ1JNf3/fF7nD\n4eDZZ5/lueeeG1ajRpLuTGfght7IcBAQEMj3Fj3JH098TKXWhr9kJCs4mS+tvTWPxuNxc16vRzb1\n3R7jzYjgvWO70IFdrgIsqVF9QsbX8jbHWQJpqXMgRfaugxusJrw1rZjGB2OfEkNneSNNB/OJdJlZ\nlTSbx579ap8HmK6uLl786A/kq/U0uDtwvF/H+Efm9xF7++Romg8VcCa6laKyEpLjh2d7UlNzM/9T\n/ClKVhTdFpXpOj/d+Qo/euIvBrxGknq9Mk27tdrHiqLw/vvb6Ohox+t1Yzabue+++7FYbv4BLzl5\nFsnJs27JjtsFo9HI6nU/pLGxnuNFpSRMncrkUejX3D32+vX3sXXrFlwuFyCxbdvbbN785UG9HwLB\nSDJkl7Wmpobvfve7bN68mXXr1t3w/JAQK0bjyIvdjQgM9MNqtaBpXqxWy3ULXw8XGVMmkzFl4P2a\nA42v6zrbD+7mdF0RBh2Wp2SycFbfylGq6sZt1NDbXRgC/HtEVZIlatsbyBvvprNTwT4lpt/9NbcX\n2dK71qw1O9k0YymNjjY+zTlPS4QBW7PKvaZJhLmD2JWTT0e0BatLY0FgKi88850Bvbl/ffslzmXI\nSIYogohCM4Bs6v+eyyYDJISQU3KO6dOmXH/ybnLO/rhrH96MvpW3JEmi2O7E7XYSGtrf6/F6/TCb\njfj5mbBazYSHD10cPvjgA1pa6pFlDZPJwMaNDxITc+092KPNaHzOBx43lokTb76QSHlZAYV5b2I2\ntuFWYpiz8E8IChp8ZyebzcIjjzzE1q1bAY3OznYOHdrF448/ftPLHbfyebgbEfM1OIYkvo2NjXzt\na1/jBz/4AfPm3VwHm5YW141PGgVaW504nW48HgW3W8HpdI/4mBcKC/j4wmEceIkxBvH4sgew2+3Y\nbJYBx//3t37DqQQXhsm+NckTVbu5771inlz9EODz3n+9fSuuljYwqHhbnZiCrASkxaI2OnC1dSLP\nDcfPItFV0YgtpbdjT/C8FJo+ziE4fjx6TCB+l50sNcUTMDGAM1VFxEuhzK3x59F7HyQoyJcg9aDr\nXi6VFBGbMoGwsHG43Spud19PsaWlhXPmZiRD7zqrrmromt4vzK0pKmqjkwnBUYOe/2vNmcPdf10d\nQDFLNDe3YbH0X9/v7PTi8SjouoeOjk4aGjoGZUs3Fy8WcOjQURTFi6J4Wbx4CRERE0bls3UzXD1n\nTqeDk5/9FpulCEW1YLQuImvuQ2NooY+iSycxdv0rm+/zJVxp2in+8M4JZi78OQEBfZMJLxWcoqFm\nJwapE1Weypz5j2Iy9U1eDAwMZdGiZeza9Qler8aZM7mEhY1n5szrl2MFn5AM9fNwNyLma2Cu90Ay\npOrtv/71r2lvb+dXv/oVTz31FE8//fSwrJmNBrIs0/3gO1J9ez/Poezj/Fvx+5xKcXOio4RtLdk8\n9V9/w/mCCwOef6mkiNMhLRhCesVCjg5kV1teT6b2yx+/ycH4NgKWTiZgWgyhC1OR/Ux0ZJeTUWwi\nPmYiuq5jiQyms6IJzdO7rqzrOpkhSfx83jd5jvn8YsV3CbUH89OmvWSnaeRPN7A7pY0fvPqzngIU\nVquVGWkZhIWNu+brbG1twR3Q18sNmBZL6/HCPse66lqR/c1EF3Qxf+bw1YFePDkTrbR/b9wJLcZr\ntgvsff+lITcycLlc7Nr1CZqmoaoKqampzJjRv5LT7YKqqhze/dd8ZcPHPL6+iM0PXGDxtF9zeP9v\nxto06iu3snJhb6azLEt8eVM1Z46/2ue8MyffI8r/hzz9wCGevP8UDy9/md3bvzdgj98pU6Ywc+Ys\nFMWLpqkcOLCPtrbWfucJBKPNkDzfF154gRdeeGG4bRkVfOt8PvUdDfH9oOgI3mkBtBzMJ3T5tJ4E\npn88+xbPywozJ/WtdHX8Yg7ypP4hUle8ndyC88zPnMsZRzkGW99zbMlRRO2t5+++/qfU1tdx+Pj/\nok0L9+3FPe7bi+vn1Fk5Pp1vPPYVLBYLUZFReDxudlSdosXbhlRRBxLoXpWupCCe+c+/4wdPfJcP\nT++nXe9igjmYTUvX96z5f564uImEn9DpiOs9Zgq2YvIzo719nq4gA4qiYvHoLI6dyjMbnxjWbOcp\nyamsKIhlX/FlpKQwNK+Kf04jT2Xcf81xPv/+D1V89+z5FJfLiaJ4sFptLFu24rbeV3rm1Mc8tq6o\nz+uNHg+B5t243WO7Jupv7l9MQ5Yl/M1VPb+rqorq3MbMtN4HSqtVZtM9uRw+vYtZs/vvnZ8/fwHl\n5WW0tLQiyzIff7ydxx770m39Pgm++Nw5acrDxNVZuSOJruvUqB20Z9cSunRqj/ACmKdH82bOvn7i\nGx0SgdJahTG47x5kQ4OL2OnRAHReI0M6JHwckiQxPjKKpyMX8vapIzTFmrD6+WMsbSM1MQWz0URX\nV1fPl2xJWSmXG2sIXzOjxz5d12nafY7ycBPPvPJjAjbNRJIlsj0NHH7933j+nq8QFzexz9gGg4EN\nE+fx2sXj6Km+valqo4NFWhx//bfDm9V8Lb5x35dYXlbCwQsnsJot3H//U9etYtb9/ksSyPLg8xEu\nXiwgPz/vilelsXLlyts+ocfTWUpYaP8HjaTYZurr68e00YPbGwK09Dve5endH97Q0EBSXC1c1Vdr\nfKRM54k8oL/4Go1GVq1azVtvvYnX6+Xy5XJycs7cVPhZIBgp7jrx7fbaJEm6kgk5ckiSRJBkoRkn\nsrn/VNeo/ddIls1ZxHuvHqZxYW8Sla5qpLbYiJngS56KMwRz6arrNLeXJGtveHX13KUsz1zIzgO7\neUs9gXdTGsWSRJHq4MyHv+DH675NWGgYXq+CPSmqz4OBJEkEzIin9XghcnRA755gs5H2JeP5xlv/\nSow1jBC/AExBVhJMYTy9ehNr5i0npXwiH589hBeNmVFZLH1k4a1O46BIjk+86Qzqzs7u918adMGV\nq8PNU6ZMvSP2kpr8JtLcohEa0leASypDmTTr+vuiRxqT7R6Kyn5DcnxvRGLXQTuJqZt6fg8ODqa4\nLABfm5Beuro0dOnayyJRUeOZNWsWp0+fxmAwcODAPhISEgkOHnwyl0AwHNwdHbs/h/3KdgdJknrW\nUEeSRWGT0RxdA65HBcl+/Y7Jsszf3/cNpmYrWE7VYT1Vz+xzBr73UK/3+OSctViP16IpPs9NaXcR\nd9LBpmV9S1AajUbOtpWhzBnfmw1tkGmfF8Ebhz4CoKmjFUtM/zC3JTwQc6jdtwkW6KpupvV4IZ7G\nDoxRgXTdE885pYbyDD/2pzr4/pafoWkaSRMT+e4DX+a5B77CsjmLbuvQnsPhe/8lSRp0qdFDhw70\nCTcvWbJ0JEwcdjKz1rNleyKa1vt5rKmD1q4VY+61Z87ZyJnib/H6B4m8vSOUP7ybgRbwAjGxyT3n\n+Pn5Udc+nw5H3/9Pb+2IJHPOpqtv2Ye5c+cTGhqK1+vF43Gzd+/uEXkdAsHNcNd5vnZ795fs6Ijv\nE6s24Gzv4MOj57Ev6P0S0ZqdLIkYeJtNeFg433/kO+i6jqZp/fYjJ8cl8tMHnuPdQztpVV0kByVw\nz+blA+5brtbaAd/Tva7paB7fNqNqtR1d10mOmYj5+H60GX3XcZ2FNZjDAvC0Omnccw5rYiRBWcm4\nimpxltYTNCeZgKkxuIrrsCVHUZHuz55jB1m9YNmtTdgo0l3PWZKkz30ubkxTUxO5uTmoqoKmaaxY\nMfbCdbMYDAYWrvx3fv/eb7BZilBVC7LfQhYvf2RU7bhw/hCt9bswGV24PInMXfgV/P39yZyzAbj+\nHvhlq/4Pb+22YjWcwGjopKMriZRp3xgwF+HzdIef33xzK4qiUFRUSGVlBTExd3dPZcHYcNeKry/s\nPPLiC/D1h55izqULvHVmD9VaB3bMLBiXytMPbBpwO4rX6+VXH75KrrsKt0EjjiCenLWGacm9e4Xt\ndjtPrR247F5TczN7Th0k2BqI0aWg6zptJ4rQFQ2D1YLi7EKv8vLs1p9Qb/XgKqnBEmnBPN63tuZp\ndeI+W8UkaxQFxi5Cl03pCZvbJo3HEh1C26liAqbF4q5rA8AYbKWkoGpAe25XXC5Xj2c+GPE9fPgA\nuq71tAhMSLiz+hjb7QFkLXiGMyfexCg3omq+xhpXb9Xpxu12c+zw77GaLqKqFsz2xczKGnyjj25O\nn9jG1NjfkTbfl7ugKLn8ZksuK9b94po2fB5Zllm68jvAdwY9dlTUeFJTU7l06RIGg5GDB/fzxBOb\nb+sIjeCLyV0nvt3hxe6ws67ro/IfL2PSNDImTbupc3/23u85k6Yim31FGsqB/zjxFi9G/UVP2Hwg\nPB43f/XfP6Y8QsE/I4aWw/tQHF0or+YTsX4W5rBegXEW1nJZ8mBLjiJgWhTt2aXYs5uYGBXNOI+N\np575CUFBQWz+7d+hX7VebbT5oasaHecvEzgjHgDV0UW0PY7hxOPxoGlaTxnI4cbX2ML33t9s2Lmm\nppqLFwtQFN9DzYIFI7OmfTH/FI21nyLhwWCZSVzCLAoLDhIUEkvG9MW39Jmtrb1Mybnn+dJ99ZjN\nEh0OjVff282Se37az3vUdZ19H/8133y8AJPJN2Z5ZQ4HDtawcMnXBz22pmmorg9Im9SbNGg0Smx+\nsIgPP/uAeQuuHzoeDubNW0BhYSGq6qWysoKSkiKSklJGfFyB4PPcdeJrNpuxWCxXWr2pdHV13TBc\nNdJ0dLSjaTqKqvBfn77BMUcxnDKiKypBc5Ix+JnonBXOu4c+6fF2C4ou0tLWSmb6TMxmM7qu83/+\n+5+5nGTAnhpP465zhK3wbW1qOXKxj/AC2FKiaDlyEVuyT+ADZyZgURv5h0f+rM95EcHjGKjZnbfN\nhd+EEGSLCV3XichuZ83mlYDvIWDbgY+p7GwmWPbj4UXrbqqj0bb9OzjacJE2t4O2mkYMMcFIJgMT\n1UC+Pn8DiXHxg5/c6+ALO9+856vrOgcO7EPXdVRVISVlEpGRkTe8brCcPPYmU2JeZt0c35p+Q9Nh\n/rDVxV9+2051rc5H25OYteBHhIaGD+n+F3N/x1c3NdD92gPsMt98vIRXPnqZpSu/3efcnDN72HRv\nPiZTb3rIxBidgAuf4nY/hcViobW1ibOnXsHfXIXbG0RM/AYSkgbuQdzR0cGEcf0/UYEBBnRv6ZBe\nz2AJCgoiLS2dc+dyMRh0DhzYT0JC0pC3mwkEQ+GuE1+AoKDgnkzXpqYmYmL6l2AcDSqqKnjh5Z9R\nH6BgGmfHdakW+8YZhEhTAV+Wc9P+C4xbmY5sNNChdVFTX8t/fPoKFTGg280EvbuHhycuwG7yp8jS\nRtjkdBwFVQTNjO/NYB6g8tNAxzvx9jtlsl8UNZ72PtnaqtPNZFcQ/moY7uw24g0h/MmGP8VoNOJy\nufi7N39K/Zwrwqx2cGznL/m7BU+SFHftbOBXP32HHSEVyLF2GveUErYxrce7Kwf+/eDrvPj43wLD\ns7aq6zrNzc09PWevbsc4EGVlpVy+XI6qKkiSxLx5w9/0wOPxYPC8y/QpvdvgwsMMPLjGQm6eh+nT\nLHzz8RL+952fs+zefx7SGHa//iJnNEr4m0r6HXd1XCIqov/nJzWhkerqKsLCQjl3/Dm+sqm25/06\ndOI0F/P/htQpC/pdZ7PZKG4OBvoWRHG7NVR9aA8TQyEra07PNrHGxgby8/OYNi1t1MYXCO7KR73I\nyKieovoNDfVjYsPOY/t4+vUfU5thR40JoPZ8CZalyX3CiZJBxn9iOO66VtRGB2mRifxizx+pmR+C\nMTYEU4gN1+xwXqs5THbpBWSrBV3XUVpdmMN7G5Pr3v77mXVVA61vxmic3N87/cqaR5mc7UErbUbX\ndPSiJmYWGPj5X/4zP3n4OX7+8F/x3MavEXLFs31977s0LBjXUztaMsh0ZkXyxxMfX3MuNE3jcMtF\n5FAb7rpW/CeG9wurNmUE8unRfTcxszdHW1sbbrcbSZLx97cSGHhj8T116kSP1zt16jRCQoZ/m8rl\nyyXMnFrb73hKopnySt/DkSRJBPoXDJhBfzN41YG3VXmV/hEgk18szS39i9EUXw4hIiKS7BOv8fRD\ntX3er8VzOmmsfnPAMYxGIy5tOXUNfW3f8lEUs+aMXtKXzWZjxoyZaJqKrmucPHl8yPMpEAyFu9Lz\njYyMRJIkJEmmvn6goOrIcrHoEj89vAVTbAhKqwul3YUp0Io5rP96riUyCEd+FXO1CUxbM4mXavch\n0/c8NT2Cul11+MUH4rhQiSHQH0+zw7dVCLBNnkDzoXxCFqQiGWTUTg+uD89hXeZb59JVDf8zDXwp\nq/+Xn8lk4odfepai0mJyC/PInLaGiTHXXtutUFqRDOZ+x6v09mte43K5aDeryIC31YV5XP95MFgt\nNFW1XfMeg6X7fZdlmaioqBuuoTY3N1FaWoKq+tYqZ88emQbtYWGRlF+2kZzQNwrR4dDw9+u1UdeH\nvuarGxdR31hCeBjkXfIgAYruT0ikr01oc3MDRYVniImdQmbWOt7c/iHf+lJZzxw1t+jUty9mqs2G\nv7m6J3rweex+Ndccf+HSb7DrsA1JOYzJ4MLpTiR1+lcHvdf6Vpk5cxY5OdkoikJ9fR3V1VVER49N\nFCpTxBsAACAASURBVExw93FXim93rV9Zlqivbxj18X/0/q8JezCzT8OBpn3nac8tJzCjb+Uo74Ua\nvh65mAdXrKOxsRHV0D9cIUkSMTGxdNZVUmDvQnN76CxrIPze6UgG+f+zd95xUeRp/n9XVSdockaS\nJAMKAioGFCPmMKOOk+PmdHd7u7d7d7t3u/e7vbTh9m7z3u7OTp5xTDPGMecsoCIoQUAEybmbbrq7\nqn5/tIBtoyCCOju8Xy9evqyu77e+XVVdT32f7/N8HvTBPiArSB8WMjk1nUiPMFZ883MczjlBcWEV\nPhoPnlz6zD1drwmx8STExvf73TzVvqNVjdw9itVoNBJo09ECeMaG0J5b7vYiIpQ2k5W8pN/jD5SG\nhvqeF7DQ0P6rD124kAeALDsYPXo0Pj4+/bQYHP7+/uScmsxs2yl0ut77Y9seE+tXOc+Joqi0WZIG\nHXQ1fdbzvLXpGp7iARbOlpAVOHbSg3GpPhza+9/EhR9mxXQLhcU6Dn4ymfTMH/HD//kBvsZr2O0S\n6Gezdv1fA2C1+fcZtGix3b12riAIzJj9PPD8oMY/VBgMBhITx3DlSiGgkpeXO2J8R3hofCaNb3Bw\nyK0CCyItLc3YbF0udWmHk8bGBjrjvPC4Y7bgnzmOqjePYBgV0DPzU2raWR00mScXrgAgJCSEqFYd\ndzol1eJGFqY8zUsh4by+90NK7HU0dTqwvJWDFOGLXtCwInISL3/v2y4PySWZCxg6c+Zk0dhpFFTs\nRRnd68KWm81MDxxz1zaCILA4Ip0PblxEjPJF9NDRcbUa73FOOU25po25toh7zrjvl7q6+p5zcbfC\nC93Y7XYuX85HlmVUVSU5OWXIxtEXWQu/x9vbf4G3PhdRtFN+Q2JUsIYum42r10RO5o1n+txvubSp\nvF5MRdkFomNT0Wg8CQoKumuUuCzLhPiV8tKaXjfz+EQzv3rjH3lqWSehIRIgMi3dwaSkU/z7r0v5\n8vONjAoTAIXcy8c5f2YjU6evJ3HCenYdOsPy+b1qbcVlIjqvob6zhofk5BQKCwuQZZmioivMm7fg\nnpKkI4wwVHwmja9WqyUwMIi6OqdrrL6+4aEFXbW2tiH4u6+tiToNkl7L2NwuvMK9EAWRzMhMMme5\nlmz8/NRV/M/pjbSl+CEadIhXGlnmMZ74GGeu6ddWvwzAO/u2cLTpCo0aC9ZGM4eu5eCx34P1C1cN\na2pVetIkXupoZef5szRIFrwdGjIDxvLUopX3bLdyVjYBeT4cupSHRQjFs17G0yog6bRMj5lPxqyh\nKzKvqioNDfU90a39RSxfvVqI1WpBlh34+voSEzN6yMbSFzqdjrmLvt3z/zScEfHbTp4gODiGhSuS\nej6z2+0c3vNDMpJzGeXdQWOZSmKcjtK8QBrNWcxZ8A23/nNzDrByfi13+lDWLjNz7bqd0JDe+7Pi\nhoMnF9cwKqx3KSF9okx51VZsticID4+hy/ID3tj6HkZ9FV12Pwy+i0mfumLoTsgwEhoaSmhoGA0N\n9ciyhvz8S8MSSDfCCHfymTS+4Jzt1NfXIQgC1dVVD834xsbG4nfahvUOUR1z8U1emriI15568Z7t\nxyeM5Vcx32XvqUO0dZrInvkkwUGuUaJbD+9ip28lYlwI3Y/Rjqom3mk4S83WJv5mzWtD+I3cyZ42\nh+xpc7DZbGi12gEb+8y0aWSmTRvWsYFTnL+rqwutVo+np7HfYKsLF/JQVQVFkZk4MfmRCDJ4e/sw\nbfpSt+0njvyBV548S86lLhLjdMTFON376ckdNDXvYOcxXxYt/aJLG7utE88+suu8PEUsVtego+Iy\nG6sWu6dhTZlQx+VrVxk3PoXRccmMjvsPl8/NZjM3b95g1Kiox34mmZycwv79e1EUhYsXc5k2bWA1\nykcY4UH4TEY7A8TGxvWs+ZWXu6dYDBeiKNJV20L7pcqe6ErrzWbIqeGVtQNbA9NqtSzPWsRzS9a4\nGV6AUw1FiIGuDzyPyEDkzi5ypBqampoe/IsMAJ1O52KoTCYTv9v2Dt/b8kv+detvOZZ7+qGM407K\ny8sQBGcN3+774G60tbVSU3MTWZaRJImkpIEJpTwsPLX5GAwi9Y1yj+HtJjBAQKO4n+O0yYvZddg9\nsn3Lbg3J4/UcPN7Ju5vb+dWfWsi5YKG9wz1avvKmgcAg97VyVVU5euBXVF5+kcSAr3Cj4EUO7//F\nYx1JnJiYiMFgQJYdtLW1UVt792CxEUYYKj7TxleSJCRJor6+no6Ou0fjDiVnLpzDMTsaQ7gfradK\naDlVjNxpw7BkHCeGyBhZhb5LDiKJdEUaKa4oHZLj3A82m43vb/oFR8eZqZhk4GqKht+ZTvDRsT0P\nfSxO4ysiCAIJCfdWNiotLQFAUWSioqIeuSDL3bi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dOokgCGg0GsaMGcuoUf2fW7PZmcal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\nKT9Z+gJrZjzCT77zE468cRqlW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PcFt1FfX4/DYSc+PgFBEPjpl7V88et2GjQxCC4baeoV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\nEEIEmQRfIYQQIsgk+AohhBBB9v8BSm5J8FHUoFEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118e9e438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "kmeans = KMeans(n_clusters=4, random_state=0)\n",
+    "plot_kmeans(kmeans, X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "An important observation for *k*-means is that these cluster models *must be circular*: *k*-means has no built-in way of accounting for oblong or elliptical clusters.\n",
+    "So, for example, if we take the same data and transform it, the cluster assignments end up becoming muddled:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Bslln8KIY1FCAvLT4blLh8XhiLc0rqVymqiotLWdpbKynvb2d3t6eWCv6XA3w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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118ee6550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rng = np.random.RandomState(13)\n",
+    "X_stretched = np.dot(X, rng.randn(2, 2))\n",
+    "\n",
+    "kmeans = KMeans(n_clusters=4, random_state=0)\n",
+    "plot_kmeans(kmeans, X_stretched)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "By eye, we recognize that these transformed clusters are non-circular, and thus circular clusters would be a poor fit.\n",
+    "Nevertheless, *k*-means is not flexible enough to account for this, and tries to force-fit the data into four circular clusters.\n",
+    "This results in a mixing of cluster assignments where the resulting circles overlap: see especially the bottom-right of this plot.\n",
+    "One might imagine addressing this particular situation by preprocessing the data with PCA (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)), but in practice there is no guarantee that such a global operation will circularize the individual data.\n",
+    "\n",
+    "These two disadvantages of *k*-means—its lack of flexibility in cluster shape and lack of probabilistic cluster assignment—mean that for many datasets (especially low-dimensional datasets) it may not perform as well as you might hope.\n",
+    "\n",
+    "You might imagine addressing these weaknesses by generalizing the *k*-means model: for example, you could measure uncertainty in cluster assignment by comparing the distances of each point to *all* cluster centers, rather than focusing on just the closest.\n",
+    "You might also imagine allowing the cluster boundaries to be ellipses rather than circles, so as to account for non-circular clusters.\n",
+    "It turns out these are two essential components of a different type of clustering model, Gaussian mixture models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Generalizing E–M: Gaussian Mixture Models\n",
+    "\n",
+    "A Gaussian mixture model (GMM) attempts to find a mixture of multi-dimensional Gaussian probability distributions that best model any input dataset.\n",
+    "In the simplest case, GMMs can be used for finding clusters in the same manner as *k*-means:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KlKKAmq/Kpfdj3fH29vnXumMuXOTTVz9BMSqkkkCMOEOyiCeTVDzxJeucAYNwnkOcRzZW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nMPq3btp8YFsQY1F5w1nLGnndbm129NosXIgRBTtvp2i8ccrmFlbjkYY/C5XVidJdx+y5Ud9tpC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\nImIAFbKIiIgBVMgiIiIG+A9xzEUuTUgoxAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1198d1e48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.mixture import GMM\n",
+    "gmm = GMM(n_components=4).fit(X)\n",
+    "labels = gmm.predict(X)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "But because GMM contains a probabilistic model under the hood, it is also possible to find probabilistic cluster assignments—in Scikit-Learn this is done using the ``predict_proba`` method.\n",
+    "This returns a matrix of size ``[n_samples, n_clusters]`` which measures the probability that any point belongs to the given cluster:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.     0.     0.475  0.525]\n",
+      " [ 0.     1.     0.     0.   ]\n",
+      " [ 0.     1.     0.     0.   ]\n",
+      " [ 0.     0.     0.     1.   ]\n",
+      " [ 0.     1.     0.     0.   ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "probs = gmm.predict_proba(X)\n",
+    "print(probs[:5].round(3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can visualize this uncertainty by, for example, making the size of each point proportional to the certainty of its prediction; looking at the following figure, we can see that it is precisely the points at the boundaries between clusters that reflect this uncertainty of cluster assignment:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DiIh/nenz36dT68OYDG7+3FEXtekWevTqe7GrV2UqlYro+g+xeNXrDOpVhEql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jP7UHNgMxEZPVHpWiNLq/dtJCw+jbCUrtgAmwTPfbUZh9PFpZMuBCAmPAyl3hfQb0VRiGojmCYY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\nIBMREckAA5mIiEgG/g99v0ZXnVp78wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119918748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "size = 50 * probs.max(1) ** 2  # square emphasizes differences\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', s=size);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Under the hood, a Gaussian mixture model is very similar to *k*-means: it uses an expectation–maximization approach which qualitatively does the following:\n",
+    "\n",
+    "1. Choose starting guesses for the location and shape\n",
+    "\n",
+    "2. Repeat until converged:\n",
+    "\n",
+    "   1. *E-step*: for each point, find weights encoding the probability of membership in each cluster\n",
+    "   2. *M-step*: for each cluster, update its location, normalization, and shape based on *all* data points, making use of the weights\n",
+    "\n",
+    "The result of this is that each cluster is associated not with a hard-edged sphere, but with a smooth Gaussian model.\n",
+    "Just as in the *k*-means expectation–maximization approach, this algorithm can sometimes miss the globally optimal solution, and thus in practice multiple random initializations are used.\n",
+    "\n",
+    "Let's create a function that will help us visualize the locations and shapes of the GMM clusters by drawing ellipses based on the GMM output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from matplotlib.patches import Ellipse\n",
+    "\n",
+    "def draw_ellipse(position, covariance, ax=None, **kwargs):\n",
+    "    \"\"\"Draw an ellipse with a given position and covariance\"\"\"\n",
+    "    ax = ax or plt.gca()\n",
+    "    \n",
+    "    # Convert covariance to principal axes\n",
+    "    if covariance.shape == (2, 2):\n",
+    "        U, s, Vt = np.linalg.svd(covariance)\n",
+    "        angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\n",
+    "        width, height = 2 * np.sqrt(s)\n",
+    "    else:\n",
+    "        angle = 0\n",
+    "        width, height = 2 * np.sqrt(covariance)\n",
+    "    \n",
+    "    # Draw the Ellipse\n",
+    "    for nsig in range(1, 4):\n",
+    "        ax.add_patch(Ellipse(position, nsig * width, nsig * height,\n",
+    "                             angle, **kwargs))\n",
+    "        \n",
+    "def plot_gmm(gmm, X, label=True, ax=None):\n",
+    "    ax = ax or plt.gca()\n",
+    "    labels = gmm.fit(X).predict(X)\n",
+    "    if label:\n",
+    "        ax.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis', zorder=2)\n",
+    "    else:\n",
+    "        ax.scatter(X[:, 0], X[:, 1], s=40, zorder=2)\n",
+    "    ax.axis('equal')\n",
+    "    \n",
+    "    w_factor = 0.2 / gmm.weights_.max()\n",
+    "    for pos, covar, w in zip(gmm.means_, gmm.covars_, gmm.weights_):\n",
+    "        draw_ellipse(pos, covar, alpha=w * w_factor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With this in place, we can take a look at what the four-component GMM gives us for our initial data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/y6bNc5k9d/mI2jUtD0k7T1NLB1MnNaBppfUdkEhKwRErvs3bmxFxpcihyHBN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SeeuHplfLrDQ0tbcNQ1rP+0p7ljXFWSqdSfjOEFuP/mB5A3y2w9vZ84GaqEj/tnf4q50+bR2t6J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\nEVGJMXyJiIhKjOFLRERUYgxfIiKiEvs/+3X3PJI8PucAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1198f5320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gmm = GMM(n_components=4, random_state=42)\n",
+    "plot_gmm(gmm, X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Similarly, we can use the GMM approach to fit our stretched dataset; allowing for a full covariance the model will fit even very oblong, stretched-out clusters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SYwkMx6F3Bf/mZ3fw8HceRM/VuuR33L2D7e9/iU/+xZ/WHafpOkXLYmAkQm+X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\n3ClIquFiZFyUoBSEN8IhB99bb72Vvr6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6WlvaEsmxinQ7SyRHCNN6OrDKk5egNE2DjrCP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\nsBUHw6MxbLt1EpThcDEwmqJY2v/aS4493rHJOjQ0xBe+8AVuvPFGLr/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sh8OB0xCcd/Ip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rebYr+dTCY3EnG6XNg/krgiWFLIri1vcuMli5gyIbej2OFQ+ewnfaxanzvPOn6sjdfezJ+WXrw0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\nhBADTIKvEEIIMcAk+AohhBAD7P8D+Q4pr3c2pGMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119cc9ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gmm = GMM(n_components=4, covariance_type='full', random_state=42)\n",
+    "plot_gmm(gmm, X_stretched)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This makes clear that GMM addresses the two main practical issues with *k*-means encountered before."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Choosing the covariance type\n",
+    "\n",
+    "If you look at the details of the preceding fits, you will see that the ``covariance_type`` option was set differently within each.\n",
+    "This hyperparameter controls the degrees of freedom in the shape of each cluster; it is essential to set this carefully for any given problem.\n",
+    "The default is ``covariance_type=\"diag\"``, which means that the size of the cluster along each dimension can be set independently, with the resulting ellipse constrained to align with the axes.\n",
+    "A slightly simpler and faster model is ``covariance_type=\"spherical\"``, which constrains the shape of the cluster such that all dimensions are equal. The resulting clustering will have similar characteristics to that of *k*-means, though it is not entirely equivalent.\n",
+    "A more complicated and computationally expensive model (especially as the number of dimensions grows) is to use ``covariance_type=\"full\"``, which allows each cluster to be modeled as an ellipse with arbitrary orientation.\n",
+    "\n",
+    "We can see a visual representation of these three choices for a single cluster within the following figure:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![(Covariance Type)](figures/05.12-covariance-type.png)\n",
+    "[figure source in Appendix](06.00-Figure-Code.ipynb#Covariance-Type)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## GMM as *Density Estimation*\n",
+    "\n",
+    "Though GMM is often categorized as a clustering algorithm, fundamentally it is an algorithm for *density estimation*.\n",
+    "That is to say, the result of a GMM fit to some data is technically not a clustering model, but a generative probabilistic model describing the distribution of the data.\n",
+    "\n",
+    "As an example, consider some data generated from Scikit-Learn's ``make_moons`` function, which we saw in [In Depth: K-Means Clustering](05.11-K-Means.ipynb):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IbsADuHwGpojXO8SJZv4Q3IAHpHP5DN3qSFc2PzP0DrmP4AY8IJ2Z5xw4ka5s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\nAwS7zXKJEUjLAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119c24d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_moons\n",
+    "Xmoon, ymoon = make_moons(200, noise=.05, random_state=0)\n",
+    "plt.scatter(Xmoon[:, 0], Xmoon[:, 1]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "If we try to fit this with a two-component GMM viewed as a clustering model, the results are not particularly useful:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JayeBMArp6kuRKXiYVrzuPcXFLpKLCH3k9g/ffA8rtLGFQ5IWPpTv0Se30Su3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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1197ec048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gmm2 = GMM(n_components=2, covariance_type='full', random_state=0)\n",
+    "plot_gmm(gmm2, Xmoon)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "But if we instead use many more components and ignore the cluster labels, we find a fit that is much closer to the input data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QHHzuCK6vJKK0DBGN4hjPC1DmHXtg3yH+5St7+fZdNZ7cfZDe/oi+gdmuYVEU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AEeeeAuivkYqXnFa+BVODctL6/o174kymGKJf+QuRIOc2SiyJrlQ2dE3rYTsUiEWAS0ZhPDsLAd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\nWITgBgDAIv8EN8YuyZrcp7YAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118e21588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gmm16 = GMM(n_components=16, covariance_type='full', random_state=0)\n",
+    "plot_gmm(gmm16, Xmoon, label=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Here the mixture of 16 Gaussians serves not to find separated clusters of data, but rather to model the overall *distribution* of the input data.\n",
+    "This is a generative model of the distribution, meaning that the GMM gives us the recipe to generate new random data distributed similarly to our input.\n",
+    "For example, here are 400 new points drawn from this 16-component GMM fit to our original data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Zj3QbKMrgJvIhHzTY1dWCL3zhRr8SP8We/0Y87lrlm3A6f4CeHvEamUwtkI48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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119b5ae80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Xnew = gmm16.sample(400, random_state=42)\n",
+    "plt.scatter(Xnew[:, 0], Xnew[:, 1]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "GMM is convenient as a flexible means of modeling an arbitrary multi-dimensional distribution of data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### How many components?\n",
+    "\n",
+    "The fact that GMM is a generative model gives us a natural means of determining the optimal number of components for a given dataset.\n",
+    "A generative model is inherently a probability distribution for the dataset, and so we can simply evaluate the *likelihood* of the data under the model, using cross-validation to avoid over-fitting.\n",
+    "Another means of correcting for over-fitting is to adjust the model likelihoods using some analytic criterion such as the [Akaike information criterion (AIC)](https://en.wikipedia.org/wiki/Akaike_information_criterion) or the [Bayesian information criterion (BIC)](https://en.wikipedia.org/wiki/Bayesian_information_criterion).\n",
+    "Scikit-Learn's ``GMM`` estimator actually includes built-in methods that compute both of these, and so it is very easy to operate on this approach.\n",
+    "\n",
+    "Let's look at the AIC and BIC as a function as the number of GMM components for our moon dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JczLnJffOucn9Gz4FJS28/vlJzhl6iA3z5vs3JhMZrONsi6H/g4dgKH9gDSioy8rKiI6O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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1197c1438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_components = np.arange(1, 21)\n",
+    "models = [GMM(n, covariance_type='full', random_state=0).fit(Xmoon)\n",
+    "          for n in n_components]\n",
+    "\n",
+    "plt.plot(n_components, [m.bic(Xmoon) for m in models], label='BIC')\n",
+    "plt.plot(n_components, [m.aic(Xmoon) for m in models], label='AIC')\n",
+    "plt.legend(loc='best')\n",
+    "plt.xlabel('n_components');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The optimal number of clusters is the value that minimizes the AIC or BIC, depending on which approximation we wish to use. The AIC tells us that our choice of 16 components above was probably too many: around 8-12 components would have been a better choice.\n",
+    "As is typical with this sort of problem, the BIC recommends a simpler model.\n",
+    "\n",
+    "Notice the important point: this choice of number of components measures how well GMM works *as a density estimator*, not how well it works *as a clustering algorithm*.\n",
+    "I'd encourage you to think of GMM primarily as a density estimator, and use it for clustering only when warranted within simple datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Example: GMM for Generating New Data\n",
+    "\n",
+    "We just saw a simple example of using GMM as a generative model of data in order to create new samples from the distribution defined by the input data.\n",
+    "Here we will run with this idea and generate *new handwritten digits* from the standard digits corpus that we have used before.\n",
+    "\n",
+    "To start with, let's load the digits data using Scikit-Learn's data tools:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1797, 64)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_digits\n",
+    "digits = load_digits()\n",
+    "digits.data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Next let's plot the first 100 of these to recall exactly what we're looking at:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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V0GEv8DDVQP1RgEkXmb29PTs6Oooq3+l0Ggzth4eHYBwAmKozSUI5Pj62Wq0WMjlrtZpV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jtRcoUaTN2M8xjM4XBonU4nFG9fXl7OlA5QRE8k4jmEGTsQvtRh2ZAs4XKQJfWCviuSf7+s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\nEJgAsCAwAWBBYALAwgu8y3hsplqZLgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11a7a9518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_digits(data):\n",
+    "    fig, ax = plt.subplots(10, 10, figsize=(8, 8),\n",
+    "                           subplot_kw=dict(xticks=[], yticks=[]))\n",
+    "    fig.subplots_adjust(hspace=0.05, wspace=0.05)\n",
+    "    for i, axi in enumerate(ax.flat):\n",
+    "        im = axi.imshow(data[i].reshape(8, 8), cmap='binary')\n",
+    "        im.set_clim(0, 16)\n",
+    "plot_digits(digits.data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We have nearly 1,800 digits in 64 dimensions, and we can build a GMM on top of these to generate more.\n",
+    "GMMs can have difficulty converging in such a high dimensional space, so we will start with an invertible dimensionality reduction algorithm on the data.\n",
+    "Here we will use a straightforward PCA, asking it to preserve 99% of the variance in the projected data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1797, 41)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA(0.99, whiten=True)\n",
+    "data = pca.fit_transform(digits.data)\n",
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The result is 41 dimensions, a reduction of nearly 1/3 with almost no information loss.\n",
+    "Given this projected data, let's use the AIC to get a gauge for the number of GMM components we should use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WtBDt6TeD1d+P1Kt6UFVTxxd5pWaXIyLSLOeqanl5eTbvbzlCRHgQT81IUuD7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\n6gXOOmr4ZGcxG3Z9/Xh9d24c2UvH60U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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1197ba208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_components = np.arange(50, 210, 10)\n",
+    "models = [GMM(n, covariance_type='full', random_state=0)\n",
+    "          for n in n_components]\n",
+    "aics = [model.fit(data).aic(data) for model in models]\n",
+    "plt.plot(n_components, aics);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "It appears that around 110 components minimizes the AIC; we will use this model.\n",
+    "Let's quickly fit this to the data and confirm that it has converged:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "gmm = GMM(110, covariance_type='full', random_state=0)\n",
+    "gmm.fit(data)\n",
+    "print(gmm.converged_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now we can draw samples of 100 new points within this 41-dimensional projected space, using the GMM as a generative model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100, 41)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_new = gmm.sample(100, random_state=0)\n",
+    "data_new.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Finally, we can use the inverse transform of the PCA object to construct the new digits:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xcWKtNxqNXIlN+ixiz2WTJ633HNsP+qxfw1giykhB62EwQV93d3d2eHgY1kGr1QoKGQSu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\nrMd6rMd6rEeF8X8A/YbZ39aNw3EAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11ab089e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "digits_new = pca.inverse_transform(data_new)\n",
+    "plot_digits(digits_new)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The results for the most part look like plausible digits from the dataset!\n",
+    "\n",
+    "Consider what we've done here: given a sampling of handwritten digits, we have modeled the distribution of that data in such a way that we can generate brand new samples of digits from the data: these are \"handwritten digits\" which do not individually appear in the original dataset, but rather capture the general features of the input data as modeled by the mixture model.\n",
+    "Such a generative model of digits can prove very useful as a component of a Bayesian generative classifier, as we shall see in the next section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In Depth: k-Means Clustering](05.11-K-Means.ipynb) | [Contents](Index.ipynb) | [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.12-Gaussian-Mixtures.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.13-Kernel-Density-Estimation.ipynb b/notebooks_v1/05.13-Kernel-Density-Estimation.ipynb
new file mode 100644
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@@ -0,0 +1,1094 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) | [Contents](Index.ipynb) | [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.13-Kernel-Density-Estimation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# In-Depth: Kernel Density Estimation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "In the previous section we covered Gaussian mixture models (GMM), which are a kind of hybrid between a clustering estimator and a density estimator.\n",
+    "Recall that a density estimator is an algorithm which takes a $D$-dimensional dataset and produces an estimate of the $D$-dimensional probability distribution which that data is drawn from.\n",
+    "The GMM algorithm accomplishes this by representing the density as a weighted sum of Gaussian distributions.\n",
+    "*Kernel density estimation* (KDE) is in some senses an algorithm which takes the mixture-of-Gaussians idea to its logical extreme: it uses a mixture consisting of one Gaussian component *per point*, resulting in an essentially non-parametric estimator of density.\n",
+    "In this section, we will explore the motivation and uses of KDE.\n",
+    "\n",
+    "We begin with the standard imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns; sns.set()\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Motivating KDE: Histograms\n",
+    "\n",
+    "As already discussed, a density estimator is an algorithm which seeks to model the probability distribution that generated a dataset.\n",
+    "For one dimensional data, you are probably already familiar with one simple density estimator: the histogram.\n",
+    "A histogram divides the data into discrete bins, counts the number of points that fall in each bin, and then visualizes the results in an intuitive manner.\n",
+    "\n",
+    "For example, let's create some data that is drawn from two normal distributions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_data(N, f=0.3, rseed=1):\n",
+    "    rand = np.random.RandomState(rseed)\n",
+    "    x = rand.randn(N)\n",
+    "    x[int(f * N):] += 5\n",
+    "    return x\n",
+    "\n",
+    "x = make_data(1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We have previously seen that the standard count-based histogram can be created with the ``plt.hist()`` function.\n",
+    "By specifying the ``normed`` parameter of the histogram, we end up with a normalized histogram where the height of the bins does not reflect counts, but instead reflects probability density:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
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Gj7UGAADEzzK0XS6XAoFA9Hks4TudGknKz3dbrpPOTB7/yIgr1S3cVV6ea9b/2872/mbK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\nNgAAhiC0AQAwBKENAIAh/h+zvw1TQgOgbwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115809b38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hist = plt.hist(x, bins=30, normed=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Notice that for equal binning, this normalization simply changes the scale on the y-axis, leaving the relative heights essentially the same as in a histogram built from counts.\n",
+    "This normalization is chosen so that the total area under the histogram is equal to 1, as we can confirm by looking at the output of the histogram function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "density, bins, patches = hist\n",
+    "widths = bins[1:] - bins[:-1]\n",
+    "(density * widths).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "One of the issues with using a histogram as a density estimator is that the choice of bin size and location can lead to representations that have qualitatively different features.\n",
+    "For example, if we look at a version of this data with only 20 points, the choice of how to draw the bins can lead to an entirely different interpretation of the data!\n",
+    "Consider this example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "x = make_data(20)\n",
+    "bins = np.linspace(-5, 10, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117b17eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 4),\n",
+    "                       sharex=True, sharey=True,\n",
+    "                       subplot_kw={'xlim':(-4, 9),\n",
+    "                                   'ylim':(-0.02, 0.3)})\n",
+    "fig.subplots_adjust(wspace=0.05)\n",
+    "for i, offset in enumerate([0.0, 0.6]):\n",
+    "    ax[i].hist(x, bins=bins + offset, normed=True)\n",
+    "    ax[i].plot(x, np.full_like(x, -0.01), '|k',\n",
+    "               markeredgewidth=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "On the left, the histogram makes clear that this is a bimodal distribution.\n",
+    "On the right, we see a unimodal distribution with a long tail.\n",
+    "Without seeing the preceding code, you would probably not guess that these two histograms were built from the same data: with that in mind, how can you trust the intuition that histograms confer?\n",
+    "And how might we improve on this?\n",
+    "\n",
+    "Stepping back, we can think of a histogram as a stack of blocks, where we stack one block within each bin on top of each point in the dataset.\n",
+    "Let's view this directly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.2, 8)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11819d898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "bins = np.arange(-3, 8)\n",
+    "ax.plot(x, np.full_like(x, -0.1), '|k',\n",
+    "        markeredgewidth=1)\n",
+    "for count, edge in zip(*np.histogram(x, bins)):\n",
+    "    for i in range(count):\n",
+    "        ax.add_patch(plt.Rectangle((edge, i), 1, 1,\n",
+    "                                   alpha=0.5))\n",
+    "ax.set_xlim(-4, 8)\n",
+    "ax.set_ylim(-0.2, 8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The problem with our two binnings stems from the fact that the height of the block stack often reflects not on the actual density of points nearby, but on coincidences of how the bins align with the data points.\n",
+    "This mis-alignment between points and their blocks is a potential cause of the poor histogram results seen here.\n",
+    "But what if, instead of stacking the blocks aligned with the *bins*, we were to stack the blocks aligned with the *points they represent*?\n",
+    "If we do this, the blocks won't be aligned, but we can add their contributions at each location along the x-axis to find the result.\n",
+    "Let's try this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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G6XQ+bqGeRU1B4NYyNq7vm9SPP1vpM820GVBfBK6x79SjbfU0UrWBy/VIDQ0E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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117bcfa20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_d = np.linspace(-4, 8, 2000)\n",
+    "density = sum((abs(xi - x_d) < 0.5) for xi in x)\n",
+    "\n",
+    "plt.fill_between(x_d, density, alpha=0.5)\n",
+    "plt.plot(x, np.full_like(x, -0.1), '|k', markeredgewidth=1)\n",
+    "\n",
+    "plt.axis([-4, 8, -0.2, 8]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The result looks a bit messy, but is a much more robust reflection of the actual data characteristics than is the standard histogram.\n",
+    "Still, the rough edges are not aesthetically pleasing, nor are they reflective of any true properties of the data.\n",
+    "In order to smooth them out, we might decide to replace the blocks at each location with a smooth function, like a Gaussian.\n",
+    "Let's use a standard normal curve at each point instead of a block:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kn89vOgZ2wLIsjc+uKpvNmo7iOhQuAFeanJrWSiZgOgYugzdSq7ZOznLfjsIF4ErtvcOK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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11836efd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import norm\n",
+    "x_d = np.linspace(-4, 8, 1000)\n",
+    "density = sum(norm(xi).pdf(x_d) for xi in x)\n",
+    "\n",
+    "plt.fill_between(x_d, density, alpha=0.5)\n",
+    "plt.plot(x, np.full_like(x, -0.1), '|k', markeredgewidth=1)\n",
+    "\n",
+    "plt.axis([-4, 8, -0.2, 5]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This smoothed-out plot, with a Gaussian distribution contributed at the location of each input point, gives a much more accurate idea of the shape of the data distribution, and one which has much less variance (i.e., changes much less in response to differences in sampling).\n",
+    "\n",
+    "These last two plots are examples of kernel density estimation in one dimension: the first uses a so-called \"tophat\" kernel and the second uses a Gaussian kernel.\n",
+    "We'll now look at kernel density estimation in more detail."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Kernel Density Estimation in Practice\n",
+    "\n",
+    "The free parameters of kernel density estimation are the *kernel*, which specifies the shape of the distribution placed at each point, and the *kernel bandwidth*, which controls the size of the kernel at each point.\n",
+    "In practice, there are many kernels you might use for a kernel density estimation: in particular, the Scikit-Learn KDE implementation supports one of six kernels, which you can read about in Scikit-Learn's [Density Estimation documentation](http://scikit-learn.org/stable/modules/density.html).\n",
+    "\n",
+    "While there are several versions of kernel density estimation implemented in Python (notably in the SciPy and StatsModels packages), I prefer to use Scikit-Learn's version because of its efficiency and flexibility.\n",
+    "It is implemented in the ``sklearn.neighbors.KernelDensity`` estimator, which handles KDE in multiple dimensions with one of six kernels and one of a couple dozen distance metrics.\n",
+    "Because KDE can be fairly computationally intensive, the Scikit-Learn estimator uses a tree-based algorithm under the hood and can trade off computation time for accuracy using the ``atol`` (absolute tolerance) and ``rtol`` (relative tolerance) parameters.\n",
+    "The kernel bandwidth, which is a free parameter, can be determined using Scikit-Learn's standard cross validation tools as we will soon see.\n",
+    "\n",
+    "Let's first show a simple example of replicating the above plot using the Scikit-Learn ``KernelDensity`` estimator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.02, 0.22)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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iBLZv3768/OLFizh16hTMZjOeeeYZHD58GADw9NNPL/8xuv/++3Hy5MkCfQt0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\nRERkYCxqIiIiA/v/FdwuAWtQn+YAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10072ca58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KernelDensity\n",
+    "\n",
+    "# instantiate and fit the KDE model\n",
+    "kde = KernelDensity(bandwidth=1.0, kernel='gaussian')\n",
+    "kde.fit(x[:, None])\n",
+    "\n",
+    "# score_samples returns the log of the probability density\n",
+    "logprob = kde.score_samples(x_d[:, None])\n",
+    "\n",
+    "plt.fill_between(x_d, np.exp(logprob), alpha=0.5)\n",
+    "plt.plot(x, np.full_like(x, -0.01), '|k', markeredgewidth=1)\n",
+    "plt.ylim(-0.02, 0.22)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The result here is normalized such that the area under the curve is equal to 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Selecting the bandwidth via cross-validation\n",
+    "\n",
+    "The choice of bandwidth within KDE is extremely important to finding a suitable density estimate, and is the knob that controls the bias–variance trade-off in the estimate of density: too narrow a bandwidth leads to a high-variance estimate (i.e., over-fitting), where the presence or absence of a single point makes a large difference. Too wide a bandwidth leads to a high-bias estimate (i.e., under-fitting) where the structure in the data is washed out by the wide kernel.\n",
+    "\n",
+    "There is a long history in statistics of methods to quickly estimate the best bandwidth based on rather stringent assumptions about the data: if you look up the KDE implementations in the SciPy and StatsModels packages, for example, you will see implementations based on some of these rules.\n",
+    "\n",
+    "In machine learning contexts, we've seen that such hyperparameter tuning often is done empirically via a cross-validation approach.\n",
+    "With this in mind, the ``KernelDensity`` estimator in Scikit-Learn is designed such that it can be used directly within the Scikit-Learn's standard grid search tools.\n",
+    "Here we will use ``GridSearchCV`` to optimize the bandwidth for the preceding dataset.\n",
+    "Because we are looking at such a small dataset, we will use leave-one-out cross-validation, which minimizes the reduction in training set size for each cross-validation trial:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.grid_search import GridSearchCV\n",
+    "from sklearn.cross_validation import LeaveOneOut\n",
+    "\n",
+    "bandwidths = 10 ** np.linspace(-1, 1, 100)\n",
+    "grid = GridSearchCV(KernelDensity(kernel='gaussian'),\n",
+    "                    {'bandwidth': bandwidths},\n",
+    "                    cv=LeaveOneOut(len(x)))\n",
+    "grid.fit(x[:, None]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now we can find the choice of bandwidth which maximizes the score (which in this case defaults to the log-likelihood):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'bandwidth': 1.1233240329780276}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The optimal bandwidth happens to be very close to what we used in the example plot earlier, where the bandwidth was 1.0 (i.e., the default width of ``scipy.stats.norm``)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Example: KDE on a Sphere\n",
+    "\n",
+    "Perhaps the most common use of KDE is in graphically representing distributions of points.\n",
+    "For example, in the Seaborn visualization library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)), KDE is built in and automatically used to help visualize points in one and two dimensions.\n",
+    "\n",
+    "Here we will look at a slightly more sophisticated use of KDE for visualization of distributions.\n",
+    "We will make use of some geographic data that can be loaded with Scikit-Learn: the geographic distributions of recorded observations of two South American mammals, *Bradypus variegatus* (the Brown-throated Sloth) and *Microryzomys minutus* (the Forest Small Rice Rat).\n",
+    "\n",
+    "With Scikit-Learn, we can fetch this data as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_species_distributions\n",
+    "\n",
+    "data = fetch_species_distributions()\n",
+    "\n",
+    "# Get matrices/arrays of species IDs and locations\n",
+    "latlon = np.vstack([data.train['dd lat'],\n",
+    "                    data.train['dd long']]).T\n",
+    "species = np.array([d.decode('ascii').startswith('micro')\n",
+    "                    for d in data.train['species']], dtype='int')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "With this data loaded, we can use the Basemap toolkit (mentioned previously in [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb)) to plot the observed locations of these two species on the map of South America."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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pXMmYcjXOv7HAmh99HaY2JybwdzyIopotHGINvqTSQSl2yHAmAEde4jNKrE/I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\nxC0QzFD+P0Qmdryva36JAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117f3dbe0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mpl_toolkits.basemap import Basemap\n",
+    "from sklearn.datasets.species_distributions import construct_grids\n",
+    "\n",
+    "xgrid, ygrid = construct_grids(data)\n",
+    "\n",
+    "# plot coastlines with basemap\n",
+    "m = Basemap(projection='cyl', resolution='c',\n",
+    "            llcrnrlat=ygrid.min(), urcrnrlat=ygrid.max(),\n",
+    "            llcrnrlon=xgrid.min(), urcrnrlon=xgrid.max())\n",
+    "m.drawmapboundary(fill_color='#DDEEFF')\n",
+    "m.fillcontinents(color='#FFEEDD')\n",
+    "m.drawcoastlines(color='gray', zorder=2)\n",
+    "m.drawcountries(color='gray', zorder=2)\n",
+    "\n",
+    "# plot locations\n",
+    "m.scatter(latlon[:, 1], latlon[:, 0], zorder=3,\n",
+    "          c=species, cmap='rainbow', latlon=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Unfortunately, this doesn't give a very good idea of the density of the species, because points in the species range may overlap one another.\n",
+    "You may not realize it by looking at this plot, but there are over 1,600 points shown here!\n",
+    "\n",
+    "Let's use kernel density estimation to show this distribution in a more interpretable way: as a smooth indication of density on the map.\n",
+    "Because the coordinate system here lies on a spherical surface rather than a flat plane, we will use the ``haversine`` distance metric, which will correctly represent distances on a curved surface.\n",
+    "\n",
+    "There is a bit of boilerplate code here (one of the disadvantages of the Basemap toolkit) but the meaning of each code block should be clear:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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HB5FIRPXq1alUsTKBgQHcu38PHW0datRvTJ0GjWnfpSfGpmZFuqaFpiTX/UHJirnuFxD4\n2ljpFn8l/KOUExkZGQT4vebVC29ePvci9OVDvHxe8DYwiArlylK9amVKa2lx78Ej/AMCqFazHnUa\nNMa2aQtatOmYY9Krz8FSKfqTfX6Z+rmkFBD4+uRXRuQbGCgSiUr0y22hKWH9z+u4cP483j7eiMVi\njIyMefnSF11dXerWqUudunUZOmQYlStXlp+XlZXFC98X3L1zh5u3bnLypDtjx45j9qw5xCtoy/OG\nolfmH4sDQRQIfCusdIt3RElJLycy/e+yZNVavF/48uqNH+amJgSHhpGWlkb1qlWoV7smdk0aMWLw\nwByzdb57l8C9Bw+5fc+TE2fOIpVK+dlpGa2aN5NX4NmVe1Eq9I+FgSAKBL4V+ZURP7QIEIf50qKl\nHX/t3kO1atW5dPkSCxY40rlzF0xNTXn69Cnu7rLZ7F6/8qNs2bK55hMUFMSSpYu5cOE8Cxcsol3/\nsVQo849yKkoFLogAge+FIALyx2nqQFRVVRkzbDCltbRwXObE46deDOrXh/SMdPYcOERYeAQOvXty\n8K8/c81DKpXicswNx2UrsalahcmLN2BduaogAgR+CPIrI37owMBDzgfp17c/5cqVZ8TIYfz++1aO\nu51g+7Y/WbJ4Ke3atnvfFWCDr+8LoqKics3HwsKC3bv+4rjbCY4ePUI3uxqcOnVS/tDycvl/Dhaa\nkhybgIDAt0eclobbyTPMnzmdm3fu0bhtZ6pWqsSzO9cYOmcdC2bNoEzp0igqKlKhfFmeefvkmGs+\nG5FIRP/ePXl+/yYt7ZoyqKsd62ePIPJ9mWKpFJ2ry/9zyM7ja+QlIFBYfjhPQGxMNFNH9ef+rWtk\nZmbSrFkzXrx4wXzHBUyYMBFFRUV5q9tCU4Kn533+/vtvvLyf4eXlhZqaGjbVbbCxscHGpgY2NjZU\nrVpNvkiHVCrl7NkzzHOch6GhAWvXrKNuXdkqXh/mW1DLvqCKXvAMCHwtBE/Apxzc8yd//raOQH/Z\nsrv169RGQ0OdbRt/pmrlSoCs5W2pFI1YLOaPnX9x8+49nnk/JyAoGGvLCtSoVpUa1atSo1o1alSv\nSlkLc3kQZGxsHE7rf2XvQRd+mjye6RPHoaGhkaOboKCWfUEVveAZEPhalNjugNiYaPwfXcXQwIDa\ntevkWC0rr0rS884N+neWzT3dsGFDmjdvwYTxEzE3N/8k7Yd5WGhKkEqlBAcH4+X1DC9vL7y8ZNur\nVy8xNzOnuo1MHAwZPJSyZcuyZ89fLF+xjNat27B82QrKli0rVN4CJY5/swjIysrinfdl3gYGUrdW\nTcqXKyuviPOrJLvY1eKF91P0dHXp1K4N7Vu3pEmfCVgpx3yS9sN8LJWiSU1N5bnvS555P+eZz3Oe\nefvwzOc5ySkp2FStQo3q1Whi24AhA/rh5/8Wx2VO3L7nidNCR4YM6IeCgoJQgQuUKEqMCBCnpeF5\n5wY3rpzn5pXzBL19Q+NGjYmOicHHx5vKlavQoH4DbG1tadC+L1ofLTeZTV797QX1w+fVOs/IyODV\n61d4eXlx/do1bty8we1bd1BTUyMxMZFffv2Zbdv+YNSo0fw0YyYpaoZFfQS52iIIC4Ev4d8mAoID\n33LjsqyMuH39Ekb6ulhVKM/DJ0/JzJRgW68ODevXo23L5hjV65RnPnn1uefXF59f6zwmJlYuCn7f\n+RfzZkxl2CDZ0rq37t5j1oKlpKalsm75Eso37ZkjyLCofEnMgYBANsUqAhICnnL2zBkuXrzAnbt3\nsLGxoU2btrRt0xZb24byNazT0tJ48uQxd+7cYf4CRw67HKFz5y6fVUF+jYrVXCMThwH9KV+uHGvX\nrpfvDwkJYemyJRw5chgtLS0qVqxIpYqVqVy5MmPGjCWW3AWLgMC35kcXAWmpqfheceH8paucv3yF\nhMQk2rZsTrvWLWjbsgXmZv+siBkcEspdzwfs2LOPsIhInty68tkV5JdWrJZK0Tz18qaNfW/uX/Gg\nfDlZwLFUKuXocXeWrl7PG/+3WJYvR+WK1lSytqRDm9Y5RhUICHxPik0EhIUE07lpdQYOGESbNm1p\n2bJlgavj7d37Nzt2/sm1qzcQiURFEgFf0rK20JQQHR1N/QZ12fv3Ppo3z7nMr1QqJSQkhFevXvLy\n5UtWrV6J8yEXzGo0y9emL7VLQCAvfnQRsHTuFF48uEm/Xt1p16oFNapXy3c8fkZGBjUbt2DdiiXY\nd+rw3UVAdh7rNv7GaY8LXDrp+om9KSkpvPbzx/fVa85dvIzf2wAunXTN85ofeiAEoSDwtSk2EXDs\n0F7uXHTn4P8OFSp9UlISNjWq4eJ8GFtb2RrQxVVxel8/Sb/+fdHW1sbU1AwzMzPWrV2PlZVVjnT1\nG9SjcaPGODg40LBhI5SUZPMv5dZFIYgAgW/Bjy4C2tpWxvXv7dSqYVOo9L9t28GJM+fwcDuMSCQq\ntkqznCiCjr36c/vefUyNjTE1MaZ965bMnzUjR7pTZz2YNGseS+bNon3rVpiZmgB5d0MIIkDga1Ns\nImDWxGG0adqQsWPHFSr94iWLCAwMZM9ff8v3FVfFaaEpITMzk4iICEJDQ9i9ezeKiops2bI1R7qH\nDx9w9NhRzp8/z9u3/rRo0ZJ2bdth06wTFuUqFIvtAv8tfmQREBYSjH2L2kT7+RRqNr7Y2Diq1G/C\nRfej1KheDSjeStNSKZqEhERCw8MJCQ2j79BRPLpxiXJlLeRpUlNTOXjkGOcuXubC5WtysdChTSss\nGtqjqqZWbPYL/DcolnkCpFIpt69domXLVoVKHxAQwJ9/bsdpxcpvZdJnEZSsSJhYFTMzM5SUlTl/\nwYPq1at/kq5u3XqsdFrFvbv3efbUGwtzC6ZMnUzbBpVQSQr9JL0wT4CAwD/cvn6JRs1aFXo63mVr\n1tO7e1e5AChu/DL1KV1ai4pWlpw5fxFdHW00NTVypFFXV2fkkEE479lJpN9zNqxewcmzHnTo2Y/N\niyZ8kqcwT4DA96TAtQOKylu/1yiKpFSsWLFQ6R3nz2Py5Cm5DvUrTi5evEDvPr2wsrLG39+PadOm\nIk4XIxb/s6WLxYjTxfj5+aGkqMTSpcsYMngoRkZGBCVDSnIy7kcPcmz/dt69S8DY2IjS+qYYGBpj\nYGSMvqExhkYmGBgZY2BojI6e/leJLBYQKOncvn4J+5a2hUr73Pcl/zt8DJ/7N76xVZ/Hmww9Zgzu\nxMmzHowcMhCndb8iTk9/Xz6kvy8v0hGL04mNj8PPP4CWdk35ZeVyOrZrTeD7fHx9nrF/1+/cunQa\nXR1tjA0N0TQoi4GxiayseF9eGBjJ/tbQ1CzW+xb4d/DNRIDv3Qu0bNmqUCuMvX79miNHDjNr1uwS\n12+uoaHBjBk/oaqqiqqKquxTVRVlFRX5d9X3342MjSllYYOCggJSIChZFsTUzrYy4WEhzJg+g0GD\nhxAbE0N4RATPfbyJjQgg9I03EeHhhEdEEBERTnx8PPr6+vzyywb69O5T4p6JgMDXQCqVcv/6BVbN\nGl+o9D9v3oqujjZvA4JI1K7yja0rPFlZWdg1aUSDunVQUVGWlweqqiqoqqjK9r0vJ9TV1ahZvTqx\npWSxRdkCwGXfLhynjaZC+XL88es69PX0CI+IJCwigrueD4l6F8qzm5Hv90USFh6BkpIidWrWwMPt\nMGpqakIsgUCR+CYxAeYamfTo0Y3evfswdOiwAtNLpVL++ms3q1av5PLjgM++3rfkc133uVXYr174\ncO/CMVxcXDA1MeHkydMAnD59CgMDAxo0yNkSysjI4Pr1a4waPZJVq9bw7NlTug6aIMQYCOTKjxoT\nkOBzmS59BxL84mmhGgsRkZF0dxhK14FjGDi8cHFG34vPcd/nVllnD5M8fuoMLq4nOHvsEM2bNgFg\n6ap1LJ0/J0d6qVRKYmISg8dMoKy5GdWrViE0VZkRE6Z/2Y0I/CvJr4z46p4AC00JK1etJCQ0lJ49\nexXqHJFIRHRMNF06d/3a5nxi2+e2qr9GK7xilWpUrFINiURCfFycfH9mpgSl9/MkfIiysjKtW7eh\nd6/eDBs2BIBOHTthUa3sV7NJQKA4UY/2ou3gEaxYOK9QAgBAT1eXV2/8aN3+25YThZny92O+tBWu\npq5OrU7DqNpmAK4n9eVTGwO5Ph+RSETp0lpsWL0C69qyRoSmpgbLJw8q1IyKAgLZfNXAQAtNCTt3\n7mDf3r2cdD+FlpZWgedcuHAed3d3Tp06SZcuXb6mOZ9Q3JWnnq4el69cIT4+HoBMSaZ8SGFuLFiw\niJEjR6Gjo8PKlU7s27+PpKQkIbBQ4IdGL9mPjr0cGD10ECOHDCowfXp6OguXr+L8pSuUL2eBsanZ\nN7WvOCvPCopR6Onq4up+ulDprSwr4HZwL40a1KeUpiY/OS7i4eMnxeoZEvixKNATkFvrOTjwLY8u\nn+DUqZPc97xP40aN6drVHhUVFVY4LefihcsYGxsXePHAwEAGDR6Inp4+UVGRtGjRksjMot/Mt6Ao\nExBdu3iOOROHoKysjK6uHvr6eujp6aOnq0dYWCg9enbj4oXLZGZmoqSY818glUrlSl5HR4dtf2xn\n44ZNnDx1kv379/HTT9NZvGgJU6ZMzXO65I8pbvEj8N8jPT2d+7eu4enhwqlz55FKwb5Te7p2bM/I\n9b/SomljHGcWznW9eOUa1m74jcoVrRnQp3Dexe/N505AJE5LY8mk/py/fBU9HR309fTQ09VBX0+X\nmtWrMX76LIwMDejepdMnFfqHZQRA9y6d6N6lEy9fvWG/82H6DB2FpoYGF92PkqRTNYd9eSF4Df67\nFCgCMjIykEjg0f07XPY4yaVzJ4mOiqBr586MGj2G7dt3cOv2LU6edOfx48e4uR7H2tq6wAvfuXOb\n4SOGk5ycTFxcHD179kJVVRVKiAiYN2UUL597kSlOITUlhZTUFJKSU6hU1Ya1v+3GsmLlXM/Lyspi\nxtiB9OnVG0fH+cTExhAbE0N0jOxT30AfPz8/rl+/RmpKitwTkJmZyeQpk9i3by/GxsaYmZlhZmaO\n+ftPUzMzJk+azOPHj+QjLgSPgEBJwVIpmnthWVy9cIZLZ925efUC1StXpGvHdhw7sAepVIr7mXPM\nX7aSmjbV2Lh2ZYHdABKJhEkz57J9t2zeEN9Xr7Hv1P473E3heHT/DqsXzyIrLYGUlFRSUlNJSUkl\nQ5LF2KlzGDt1Tp6jfA4f2M2pcxe4fNIV7TJliImNJTo2VvYZE4uJsREvXr6iknXOyckePn5C136D\nSElNxdzUFHNTE8xMTWTfzUxpbFufjMwMrly/hXaZMhgWsmujKF0gAv8OCgwMrF7dhoiIcExMTOnS\npQtdOnehQQPbQo/rzY3bt29x6fIlxoweS3+Hvrx+/YZlS5cxcuSoEtNqvXx0N5MmT2T5shV069Yd\nDQ0N1NXVOXToIMuWL2XJ4qWMGzee4JRPddTL594smDIMXR0dataqRVBQEEFBgQQFBREZGYmxsTEa\nGpqAFOdDLlhaWuEwoD+ZGRns3bufhIQEQkKCCQ4JISQ4mJCQEIJDggkJCWaAw0AmTZpc5PvK7fkW\nJVZCoORQEgIDbevVxff1a9q2bE7Xju3p3L4thgYGRc5TIpEwb8kKhg9y4I3/W/oPH0MlK0se37qC\nv6To+X5N4uNimT6kC7Fx8ez54zcMDfTR0FAnPv4dY6fNJD09nb3bt2JZofwnFaxEIsH1j+Ws37SV\n4YMcSExKIjAomMDgEAKDg5FKpZSzsCAiMoo2Lew4tGcHl65ex2HEWLZtXE/LZk0JDg0lOCSU4NAw\ngkNCCQmTfaZnZHBg5zZMTQr2xubFx/YKIuHH5otmDDx6xJVatWpRtmzZQl/wydMn3LyR+1jexMRE\nMjIzWDB/ISKRiFatW7B82Qrs7JoDJcd1baEp4fr1awwaPBBTUzMUFRVITEwiKyuLoKBAMjIyuHnj\nFvqVGuR6vrFKGjt2/ElSchIWFmUpa2GBhUVZTE1NUVJSIisri7HjxhAQ8JbKlaoQGhaK8yEX+YJK\n34qS8nwFvh4lQQScP34EuyaNZN68QrJr735SU9NyPfbKz48Jo0ZQpVJFrt+6jeNSJ254nAJKluva\nPCuUCTOCskXxAAAgAElEQVRmc+7iZUyMjFBSUiI6JoY0sZjgkFC6dmyPu8uBXG22VIrG+/kLDh1x\nxcTYiLLm5pS1MKOsuTllypRGJBLx5JkXHXr2Y+Hsn1i+9hec9+ygVfPc1yn5mpSkZyzw5XyRCEgX\nf75/ftnypUyamHtrdeeuHYwZPRY9PT0AqttU4+iRY2hafDobX0lAPTWCgIC3SCQSNEuVAsDI0Ahd\nXV0UFBS+qFI1VUtnzNjRXLx4gUcPn5CsWrQWTmFiAooS2yB4CH4MSoIIkCZEffZ5sxcuZe6MKbke\n27h1O06L5wNwxO0EB48cY/2ek19k57dCKpWS+uoGKSmpZGVlUaZMadTV1N57BmSzB35JpZrgc5nW\nXXsxY9J4Fs2dWaS8ChsT8LmxDYKH4Mfguw0RFIvFeHt7ERISgr5+7j8MBQUFND+Y6SoyMgIjIyOS\nvqYhXxF9ff087+VLUVRUZOeOXSQmJlKmTBmSk7/JZYCcQkBAoDgJCg4hNCwcDXV19N83Bj7mw770\niMioL+pa+NaIRCJsqlX9ZvnXrlkDv6eelCkjLFcu8PX5qiIgJSWFBQvmM3jI0DzTpKWlyV2GYrGY\nlJQUtLW1SUr5smt/q1brt24JKygokKCkS8IXCIDC2lhQOqHlL/A9uHH7LgdcjrB90y+5Hv+4xRIR\nGYXRVxAB37LV+q1bw9raZb7oGoU9t6B0Qsv/38dXnSdAR0eH48fdefnSFw+Pc3mmy44K3r17F40a\nNSowSrgwi+78iJVXULJiibL7Y1tKkm0C/x4G9O3Fknmz+eW330lM/NQHmJ6ejqqqCgDJyckcPHKM\nxrb1C8y3oIV3ftTKyy9Tv8TY/rEdJcUugaLz1VcRVFFRYdnS5URERrJx44Y8+yFev37N8hXL+H3r\ntq9tgsAXInQbCHxrGtSrg+PMacxf5oTvq9c5jqWkpKKhrg6A41InGjWoT4e2rYvDTIE8EFY5/PdQ\noAgIDg4mPT39szMeMngIevr6eHre/+RYVlYWY8aOYt5cRypXzn28vUDxkR0/8OEmIJAfoWHhn32O\ngb4+G9Y4sW3Xnhz7U1JT0dDQ4PK1GxxzP8Xmdau+kpUCX4sPAwk/3AR+PAqMCahbrzYWFmXZv+8A\nVavmHfxy9uwZ7t67m8O1HxcbR6eOnT5J+9tvmwGYMmVqnvlJJBJio6OIjAjDKz6YkJBQwsPDeRsR\nQ6NmrWjbqZuw3K6AQAmhmm1Tpk8Yx8I5P+U5FbZUKmXB8pWoKKvk2F+5Ys7JxVJSUhCJRIycNI3t\nG39GR0c7z+umJCcTGRFGVEQYnlG+hIVH8DwsEWVlFfoPHY2peeGHNgsI/BcpcIjg65gsnPfu5Fcn\nR1Y6rWLEiJG59uEvWDiflU4FK/ajx46y7Y/fefDwAbVr16Z+/QZoamoSHhZOWHgY4eHhhIeHERkZ\niXYZbYyMjTE2NsbY2ARjY2O0tbVxc3MlPj6OyZOmMnjwUBKU8i4kBIrOhx4AIT6g5FIShgje9gll\n4UQH0tPT2b/jD8pamH+SzvPhY94GBtKnR7d884uPf4fj0hUcP30WBQUFmtg2oG7tmkRFxxAWHkFY\nRIT8UyxOx8TYCBMjI0yMDd9/GhEVHcPegy50bNuamVMmUq9OLaH/+hvxoQdAeMYlky+aJyB7idBX\nL3yYNdaBypUr88fv29DW/qfijYqKYt++vfz008xCG/XC9wXzHeehp6+Pubk5JsYmGBubYGIiq/B1\ndfVRUVHJ9VypVMqtWzfZ/NtG7ty+Tb/h4xg6ejIGRkWfIUsgd4oyv4DA96UkiIA3sVKysrI4smUJ\nG7Zu548N6+jVLedqf8vX/MysqRPlY+cLIisri/Ubf+Pm3ftYVSiPqYmxvJLPrvi1tcvkGVj87l0C\nO/7ex+ZtO7AsX47Bkxxp2a7zF812KpA7nzu/gMD35auIAJAterFlxSxOnT7J3r/30bixbL3rnTt3\n0Lp1GywtLQs0RiqVcsj5EK9fv2LWzNmovw8A+pCMjNz7oDMzP93/5s1r/vhjC0eOHmblyjUMGzqc\n8PTcxYPA5yOIgJJPSREB2UQ+PMvA0ePp0KYVv65ajrq6OlKplPnLnFi9dFGh8oyMimL1L5vo1a0L\ndk0af5F9GRkZHHY9wS9bfkcsTuf2hTNoaZUSKqyviCACSjb5lRGfJYlV1dTYuHETv/y8gb79+rB6\nzSokEgl+/n6FEgAAu3bvwtDAkEULFxdaAGRmSnIVAABWVtb8/PNGVq9ax6ZNGxGLxZ9zSwICAl8Z\nw7odeXT9EvHx72jQsj1ePs+5e/8BDevXK9T5iYlJbNm+i2Xz536xAABQVlZmYL/e3Ll4FhHw5569\nX5yngMC/hc+eLCgoWZE6bXpy53Zdho8YytmzZ2jTum2hzn337h0hIcGMHjU61+MfC4DcKv4HD+7j\n7HyIrl27IZVKuXPnFuJ0MZoamoSEBCMSCa6+b4HgERD4HGI0Lfnf7u38/b9DtOrSE9t6dTiyb3eh\nzt3x9z5GDxtM6dJaRbq2VCpl+Zqf0dTUoFO7Nty+dx//gEAUFRSxrFCelJTUIuUrUDDCZEI/HkWe\nMdDc3JxzZ88zZ+5stm3/gzp16mJvb5/vOTt37aRrl9zTFCQAMtIlhIQEc/ToUZYvW82Fix6oqaox\nccI01NTUAPjrr134+/tRpkKNot6WgIDAV8JfYsDwQQNobNuArn0HMmj0BHb+tgFdXZ08z0lOTiYw\nKBgzU5MiX/eX336nS4d2mJoYc+HyVVo3t8OyQnkADjgf4fjpM0XOW0Dg30aRm81ByYqEpqnwy8+/\ncuTwUX6aOZ3p06eRlpb7qmDx8fGoqapy5cplfv99KxJJ4ceeZ6RLSEpKYtPmX3GctwhFRUU6tO9E\nixat5AIAwNDIiKCgkKLekoCAwFfGL1OfyhWt8bp7nXIW5tRu1oprN2/lmd715Gk6tm3N9LkLeO77\n8rOvd9j1OBXKlaV+3dqYmhgzdGB/uQAAMDE2KtKcBgIC/1Y+KzAwN7LdxPHx8UyYOB5vb28GDRpE\n+/YdqFWzFgoKCrx584bNmzexZMlSdHV1efrsKbt37WLo0KFUqGCJiopajpEAmZkSxGIxbm7HePLk\nCTo6OkRFRTFhwgz09XOfQzw9PZ0aNSrg9ewlWTqmRXkWArnw8URBQndAyaOkBQbmRnbg2Olz5xk1\neTrtW7ekc/u2tGvVEl1dHaRSKZv/+BMDfX0G9utNZmYmv23biRQpA/v2opSmJhoaGp9E9r96/YYD\nLkeJjIqmYf26xMTF8dPkCXnasXbDZsLCI9i4dqXgtv6KCMMESzZfbXRAQUilUnxvn+X06dOc8zhH\nQsI72rZth6KiIsuXrcDMzEyeNjMzkyNHjxAVGcm7dwmIxWIWL15KZqaE4OBg1q5bxYhhY6hRoybP\nn/uQJVXAyqoiAOnpn3oRvL0fM2fuVM573ESsWabQNgsI/Oj8CCLgQzRjfXA5dpxzFy9z7dZtqlWu\njE21KjSoU5tRwwbnmGzoxctXXL52g6TkZJ56ebNxzUr09HQBWL9pC2VKl2Zg315oaGjgfuYc3Tp3\nzHctkh4DhjKgT0/69+4pVFYC/xm+mwiAnC1Hf39/zp/34JzHOa5evYK1dUXatWtH+3btqVixEkFB\ngQQGBeHp6YmXlxeGhob8+ssmkpIS+d///se4sRMBSBPL8syt8s9m91/bCHj7htWrfxVEgMB/ih9N\nBMA/LUexWMyN23c5d/EyZy9cIjgklLatmtOhTSvatmxBmlhMUHAIgcHBHDziirmpCZ3bt6Vvz+4s\nXbWOJY6zC1yALBupVIqRVTUeXLuAhbmZIAIE/jPkV0Z81aWEIae7WMnQmk6DrOk0aCLp6emEPLuB\nx3kPZs2exaNHDwHo3r0HZmbmtGzZkj//3MYzr6f4eHtjYiJz6ecmAFLTMj+5rqfnXTp26JSvANBX\nSCU669NhiQKfjzCboMCXIK+AFaFCs16Mb9aL8UsgPDQE32tHOXfhMqMnz5CnHzawP40b1CcxKYkd\ne/bTvGljQsPDyczMRFlZuVDXfOPnj4qKMhnGtfD7tAiRI0S4fz55PTOhm6Dk89U9AQWRXXm8ffuW\nFy+e07FjJ9LTMxkxchgXL5yncZOmjBkzjmZNWgAyEZAtAD6s/FNSci5qZG/fkAMnr1LeMuc85AIC\n/3Z+RE9AfmRXHBKJhOOnzmDXpBEG+vqcv3SFRU5rePn6DYvmzmTMsMGUKlWq0PnuO+iC+9lzrNl1\n/KvZKiDwI/BdPQEFkd1qVDSworqBFUHJYKwiwdrKmtOnT7J40VKsLCsB/3gBPuTjyh8gPDyUtLQ0\nylWwyvWaqsnvAIRuAgGBH4APW4y1Ow8nEUjMhIpWlkTHxNClQzumTxxX6G6AbG7du0/jBg3yPC54\nAAT+i5SImXXC01VYuHAxixcto1fvbjx/7iM/lpsXACAlNVO+eT64R/XqdfMsFPKLJSguMjIyWL/c\nkba2lbl9/XJxmyMgUOIpX64sNzxO4vX8OVNmzSMrK+uzzr997z5NGuYtAkqiAHjh/ZRureoxZ9II\nkpOSitscgX8hJUIEgEwITJ48hWVLnejVuyv37t35JE22FyAlVSYIUpLTSUlO58ljTypVqpVn3lId\nXcSaZRDFxSKKi/02N/AZBAX4M6RrM954P2TxgkXMGjeQvZtWyI/rksygzk14cFc2nlpfITXH9r34\neHiggEBx4pepT4peda6cOo7X8xcMHDmO9PRPPYO5kZCQyKs3/tSpVfBEYh/2YxcXUqmU078vZlj3\nlkwf3AutrAT6tqqJxNtDnsbr+B9M799a7uatkOKbY/seWCpFl4jnJVB0SowIAJkQ6Nu3P1u2bGfo\n8AFcvCT7weeIBUjNlFf+AClJYry9HlChfPUC888toPB7c8rVhT7tbOlm3xMX52NkZmSQlZVFQMBb\neZpbt27y5Mlj1i+eUax9vULAn0BJpEyZ0pw95ow4PZ2ufQeRVIgW8r0HD6lT0ybPlUk/pLg9AvFx\nscwa2J6/Dh7m5qEddGpUh6R38cS/SyA6LFiebuueA3hcu8llj1PFaK3Aj06JEgEA0VnqtGndjgP7\nXJg1azKurs6AzAuQ7QEAWeWfkiRGLE4jIPA1psYVC8xbzcQQqY7uN7M9P1JTUnCcNoYNTvM5dPAY\n3bv1pEWLpsxznMOcOY5s2rhFntZx/hxSU1O5f/8el4/uIUqiRnSWunwrCGOVwrWOBAR+RPwy9QlV\nMufw3l2UK2tO6669iI6Jyfec2/c8adLQ9jtZWHTu375Oj2bVqGBqyE3nnVy4dZ/K7fsQFhnDQ7d9\nNKot82REhIXicfUmYnE6YwbYYxR0HX+Nyjm2/Pha3gK/TP1iF00CX8Z3DwwsDO+USlGvXgNcnN0Z\nOKgXEZFR9O07EkDuAZB9F+P78glmphVQVVXLK7tiYd/OrTx75EkprdJolS7D2RNHqFWjJmdOX0FF\nRZUNv67C9+ULvJ+9RltbG4Aw77ssWLaMgLdvsbNrwfXrV5k9ZyaayxZRp6Ed9Rs1o17DplSpXjPf\nNdGVlRWxUJYILXmBfzVKSkr8uflXFixfSbP2XfFwO0xZC/Nc0966e48xw4d8ZwvzJzw0hF9XLkRF\nVZVSWqVJSU7iorsLOxbNoHOzhnj5+LD5rwM4dGjJn4t/AiAtNgr3HZNYvfsgdapYkyZO57l/IIaN\n2lO5em3qNWpG/febjq5eMd+hwI9AiRQBIBMCFStWxsXlNH37dubNm9eMHDULBZGazAuQLFsyWE1V\nm6joMPzevKIJ9YvN3szMTP7YsJonD+5SxaYW+3dsYdasJaSkJJOUlMT0qXPo3KkbUVER/P7HckaN\nHIfPc29OnT5B0yZ2rFm7kmvXLzPzpzkcPvA/VFVVAVnfoJ+/H3du3+L2nVvs3LwGhxETmTDDMU9b\nClP5f+6qgBaagqgQKFlkt0BXLVmIVqlSNGzdAZe/d9KkoS2Kijl/qxWtLHF1P02vbl2Lw1Q5r174\nsG7ZXPT0DUkJ8UVdTRW7xg14FxNNmnIGa/dvxcRAj12HjpIqTucvxwn0W/QrqTERHLlyh6W7nKla\n3pzT6x2pZV0eUSnZYkwpqWnc8/blps8rXLevYcHk51x8HIBW6dxHRBXkKcgm21tQ2PTCCIsfj+8+\nT8Dn8uL2dbp0aU6LFh2oVq0u8fHxZKRLUFRQwdjICgvzSvzPeQNh4X5cfOgjrzy/JynJyYx26IKi\nkhIDh4/n4fXr6OkZ0K/fcAA0NGT9kOpqSmzZ8jODB4/C0FCfGzeu4jCgO9raOowdM4EJEyajq6ud\n77UePHjIsOEDufTIP09vgFQqzXOkhEQiIS42Bn3FFFJTU0kTpxEQm45YnEZaaipicRritDSq2tTC\nunLVoj8Uge/Gv22egM/FUikah+FjOOx2go1rnIiJjZMvUGZlWZ66tWqipKRI2259+NlpGY16jSsW\nOz1OubFg+hgmzlyIqqoqIQ8uMGf0EMppa8rTSJPiiEtMYuuxsyzs2QaACiPmERQVR6Mqlqwa3pPm\nNpX+ybT0PysyZgsCgL6zl1O3y2AGDB+bqy35lREASYmJ6Ec/Ji0+hlSRMmliMW+kRu/Lh1TE7xeK\na9elR45pngVKJiVqnoDPpXKjZjg5bcTZeS8OA8bKggKTxMREx/Dy5XOu3zpDk8Y9uHzlIBtWLWLe\nsnXf3cafV8xHVVGTObN/RlFREau+NdHQVMkRwwCQlJRImjgDbW0d0tMlVKhgCcCggUOYMzvvln02\nGekSataoRZnSZdi8dCYqKqpERUUSHR1FTGw0kZGy76qqakyZMo2eI6ah+dFkKi7b17Jw4QL53zY2\nNVBTU+XduwRevZKt2mZmZsa6dT/Tqu4/hY3gBRAoqfhl6jNn/S783ralfLmyTBk/BoCsrCz83wbw\n8MlTgkJCObJvNz0GDONI/Q6Yly3/XW0MCwlmwYyxnPn7DxrUrgEpCWBfD2nSO3kaaVIcAH8dPc2I\n5u+9mgnxVDTUIygqjpMzh6KtqQ4J8bJjpbUhIe79dx35+aJSOgzv1gHHzatRfHuPyNh4IhOSiYyJ\nIzImlsjYOKJi42jdqD5jFm6grm3jHLZKJBJqlSst/7u8uQk62jqoKCvj5fuK5JQUFBQUaNfCjqEt\nqlOqlEzEiNQ0BC/AD0iJFwEikYiyZSugoKDItWvnQKpItSoNUVcvRUXr2piaykYFlNYy5rffx2HX\nugNNW7T5bvbdvXmVEy4HWeV0kLjoZDQ0c3oissWAhroShw7to2fPAaSmZaKupsTixXPp3r0Ph5z/\nx8yf5lC6dBky0iUoq+Rf4S5atIxz586goaFJjRq1MNDXx8TUBAMDA/T1DQgNDWHd+jVsrW/NiIkz\nGTxqIsoqKty7dY2I8Ah0dXXR0tJi9qw5jB07Dp/nPtSrV4eePXsxbuw4WrRo+Yk7VUCgJKOto4ue\nrg7JySksdlqD48xpqKurY2VZASvLCvJ0s6dNYv74/uw+cfO7tWClUimO00YzbfgAmQDILc37Cjwt\nOpKYxCTMVESQEM8Tn5c8Cwylo40VG9zOs6xbi09PzkUMdGhcn4t3HxITn4iJvh61KllhZGaGoZ4u\nhro6aJfW4n/u55g1qjdlK9dk6pwl1LVtTHhoCE+O/kbrRvW5/uAxLRrUZe/6pRiXs2TEjHn4BQax\ndOZkBvTtg6mx0bd6ZALfkRLfHQBw7bgrmzevxcjYgrNnjlC7VhNGj1qCsqIWySkZAKSmZvDqtSdH\nj63n8mPf7xYU06VxQ+rUbkPL5vYAaGgqv/+UiQGNUrJPRSUp+/dtZsoURzQ0VLhx3YO165Zx3uMm\njo4zsLCwwHHeQoA8RUBGPpMe5XaOr+8L1q5dxdVrV5FIMrG2tqZrV3u6drXHprqN3B0olUqZOnUK\nT54+ZruzB6W0tNDOikdVVVU+pErwBJRc/uvdAdmM792KsSOG0HfoKAAO7v4Thz49c6TJysqiXfc+\ntLJrysCfVn8Xu7yePGTqEHteXj8nW+cgJQFA7gXIFgAkxLH73HUaWRhSrYwakqws7NbtZWS9SrSp\naEHDTS54zR6IYSkNWfrs/n6t95+l33clvu8i+LB7QPb3p/EB6ekZ7Dl2klXb/kJJUZH4xEQ6N29K\n19bN6GDXmDJa7z2JGqV5/uoN7RxGsHbBbJoMnoVEIkE/3gftMrJ8BU9AyeW7riL4LfC+cYPBg7sy\nbPg0nj19yJUrJ9HXM2H82DXo6pYHZCIA4MTxLcQnRNKn+wLSE2TBg+PX9Mwra6Dg/rH86NmiNZUr\nNaRxo04AaGpki4B/xIBGKVWuXjtBpUrVqWFTAw0NFbp2bcrCBU506NCRoKAAOndpye2bD9DXN8jX\nE5CbEMgrvZKSbL+f3xvUNTTyjJwGWeE4evQoTp0+iVQqRSwWY2hoyPr1v9C9W3eCU0q80+g/iyAC\nZKyaPpiXr9+weO4s2nXvA8CUcaP5dfWKHK3+kNAw6tq14fjBvTSylbndC1N5FbWcCArwZ2jXZry9\ne0m2IzcRkBCHRJLFgh2HWGPfFIBjNx6y+pInd6f2Q0FBxFSXCygrKvJLhwaQ3c33oRAoggjIJj09\nA69Xb6hZ2Tp3D4mGrHvA5+Vr7HoNpFSpUkRERqOoqEBf+y6sWTQPk3LlBRFQQsmvjChx8wTkhk31\nauzff5Ib189x/fpZ5szeSP9+k9n42zRevbqVI20j2z6Ehr3k1r3Dhcr7/u3r2FY2onZ5bVrVs2bC\n0F5cvXC20FOSqorKEBUZRmxkMqmpGSSnyLaU5Ax5mqSEVF76PsfKShZol5KSTo0adXj06D5izTJU\ntLbEytKal68KHrerrKL4yZYb2QIAwNLSChNjk3zzVVBQoEnTpsTFxeHj/YL4uAR2/LmTJUsW06VL\nJ0KCAgrzOAQEio3tm36hT3d7+gwdScP69Qh+8RTfV6/p3HsAcXHx8nRmpibMmDSObg5DSEwseKKh\njIwM5k4eSTUTNRpXM8W+RR1+W7ec8NCQQtllaGxKaEQkmZmZeXsBAPcrt+ha930wbsI7apnqExib\nyKPuiwkYtYUeVcvhGfp+dr6kJNmW8D6mIPHdP7ECRUBFRZm61avkKwAAqlWyJi4+gSpWVrx740W4\n9wOMDA2o0aI9v23bUeTrCxQfP4QnIBupVEpw4FssylXg8cVneHk9YuOm2TRu1I0mjfsjEom4dvUI\npTR1OX1+CwPtVyISKTBsgT2m5hY58vJ/84oxnQbhH30fG6NOVNJrQcK7OKLT3uCfeJuMrFTKazXi\nrzt/oW9g+IktawbuxjvwEg9fH6dL6+lYlq2Lahk11LVUUFeXeQE0NZTR0FTG+/lttEqXopldKwA0\n1JWIj4+kb9+2OB86yb37N1m3zokb1+5iZmZeYExAYfhQBGSjrJx/vomJiejp6/D6lR9ly5YFZAXg\nuPFjMTExYaXTKqFboAQieAJyEvM+OLaUlhZlCWfWgiWcOX8Rd+cDVKooW2Rs0k9zSElNRSqVsvv3\nzRy+6Utd2yY5YmEyMzO5f3wng8dMQE1VlQCfR2RkZBIQFMS+Q4c5dNSVVnbNmDBqOBWa9cp1tI7I\n9yKjZi4gUyLh0p7f5Pl/7AWQSqXM2LKXDT1bIEqUCQWSkvjp7D3SUtMYXr0cq+6+QFtVmb861kdU\n+n3F/KFH4CNvwIeegPy8APnygQDIxqBGI2Li4smK/Kdh4BsYQu2mrYh885xojQqfnCNQvPzwnoBs\nRCIRFuVkP7DabWpgWaEay5bswef5Ta5d3w+AmoYa5mZVadF0MG4ea9nvNhe7mmWZM2kEr32f4/Xk\nIVNG9KNfxyb4R98HwDviHJ6BR3ka64Z6hgGNNSdQS6Mf75LD6dysBr4+XjnsCAkKwP32Ol4G3aJb\nowWYaVdHnCBG/C6N1MR0eddENjra+vj6PuH3rWu4eeM8AKam5jg4DKWrfUvu3b3N7l3/w8wsb3f9\n51AUAQCgpaUFwLbtf3xwnjJWVlbCMCCBHwY9fQNKvf8tB2LMxrUrmT1tEnYd7QmPiABAX0+PLT+v\n4fY9TwaNGo9Dl+Y0qGjAwT1/8i4ujn07t9KmfkV+3Sp7FxQUFGjWvis/zV+E81E3tvy8hkDvx7Rv\n3ZLp8xbiOHV0jkJWKpXivHcntl360qGlXd4CIDv9uzisjfVZ5n6dqa7XkCTIhMD8OhU47BvMoFP3\naGKqxy8ta8rSJ/wjFL4IjdL5b7nw08RPhx1WqVQRkUiEsrKSsJbAD8YP5QnIjVsnPLl37zp/73Oi\ndatBKCqqUaF8LVQVdfB+cZXomCDUFUuTmpbA0+cXyZJkYVO+LRW0G5EQF8PNgF3Ep4VipWWHqqQM\nr1Mv00BrJKppssDCcMlT3kgu0LjMWNQUtAlOf8DLVA+sy7TAxrw9aqXVUCutiloZNVRLq+bwBmR7\nAj4MEtz91zpmz1qCpqYqykqyYYOGhvqoqf5TSX+JJyA3AQCFEwEAKqqyyv7QIRc6deyEuro6S5Yu\nJi01lSFDh5GZmUmtmrWEGIEShOAJKBhLpWiad7RHQUGB/Tv+YOff+1k6fw5+/m9xO3WGsPAImjay\nZeff+4mLf4eBvh7zZkzFtn5d/tj5F5NnzaNm9WrMmjqJvYdcMDMxZvfvm1FQUCA5OZk2XXvSvHFD\n1i52JDTwLWNmLyI8Kpq/N67Bpqyx3I7cggFln/Eylz5w7fFz4mPj6ValLNKEBKJTxeiqqaDwUTxC\nDm/Ah56Aj2ICPvEC5FG5FwoVdTwfP8W2vT1b1qygUf061LNtiFQqRVHbiJcP7xAX/47y5SxI1K5S\n9OsIfFV++MDAgvjfbwdYtGQwAF07j6d6tSaULm0KQGqibJph8bs00t7JVuATJ6TzLjaW6z67EUuS\nqa87GHUlbcQJYsLEz/BJPklVpR5ISMdAVIXgrHu8lVzDQrER4ZKn1C09AH3dcgColVZBRUulUEJA\no0gcQ+gAACAASURBVJQqnp6XMDExonHjpqirySpSFRXFAkWAkpIimZn5r+qXlwCA/EVAamoq7ifd\nWbDAkYCAf1x8R4+4Ym9vz5Ytv7Fq9Ur09Q1ITU3B0MCQ5Zv+olLVghdtEvj2CCKgcMwd2Y0jbu6o\nqKgwb8ZUls6fk2ugX2ZmptzzdeL0WUZPnsGiOT8xedxoRCIRycnJdOrtQLUqlenZtTNm+roYGxpQ\nr21nenRoy/FzFxjUqxtLxg1FWVmWT27zAQAyEZDdl5/4DhLeIZVKmeN6hfXtG/zT4s+FwoiAHALg\nSyp/wDcwlG1/H2DT9l059ksTopBKpZS3qQuAvp4u/m8DGTdyKMPmrC/Uok0C35Z/TXdAXlhZVaJr\nF5kIuH3Hje07ZuK0ugdb/xjPkRMr+D97ZxndVNaF4SdN2qZeoI6UUhza4u5epPgAg7u7u7vL4Da4\nw2CDDO4OM7gUr7dQt8j3IyRNmqRNS2GYjzxr3VVy77lKcvd79tl7n+u3DmBuJ0ZsZ4FAYAIWEo7/\nPQc7a2dq5RuEhSilSp+ruRezVq3kkWQfzyXHkSMnt7ACVgJH/KXnEAvssRG5kPgl8yCjVK5SlwsX\nTundnloAiERClXFX/lvfoveYOgSAVCrl8OHDuLg6YWdvQ4cOv/L27Vs6duzE0SPHiIqMoUkTRdrj\ngAEDCfgYxN8P/uHxo6e4uLjSrlEVFo4fgFViqHHKYSP/CXp16YRfwwYkJSWx5+AfmDvkJG/xUlRr\n0ISOPfvx5JmiWJZSAKxct5EBI8ZweNdWBvbpqRIMVlZWHN2zg7fv3tOgRRtOX7hEjuzZ+HPrOn7f\ne5APgcHUL18ybQEQ9SnFC5AKgUBACZfs3A1Me1IkIEUApD5GqswAgwWAmYXGEvQpmn4TZmCSqzBF\nKtVi6ZoNZM9mz5KZU3h+/QLyqFDVNb99dI+3j+5x5+IZzh8/xLZd+/CrXITHp3fgIQw17PxGvjv/\nFyIgWw47+g+YyL49D3BydqNQoRLMmLKHrp3GU7FCI27ePcinmLeERL8iShKEqVCMmZkYF8f8WNhZ\nIrZVKFVzW3OaTa+Jr18rRJhTRNQUE4HCgOY1qU52gSef5W+Jj443+NrUvQCWVmYIhUKEQqFKlZml\n4fpPy7CnR0hICAMG9GX5iqXExcUBinHKkydPYGNrhYWlOa1atyAiIoKhQ4by94OHJCYks2H9RurV\nq49YrHtCpn379+Hr68vDfx4jlUrx8i7G/gP7M32dRox8L+rWqsEfu7Zy89wp3rx7z81zJzl75ADT\nxo/B0SEHI8ZPJjIyij0HDiGTyXB2csTSwgIfL22PV5ilB9Wb/Eq5UiUY0L0zJMVTpIAnrRs3wN7G\nmjPXbiGPidRw/2sNAYCmF0CNxgVzc/pVgN57UXkBlKgHBSrbKL0AqQXAFwMvNxVz5NwVWvUewv1n\n/or1QFR0ND2GjsLEyR03r7Ks3ryNvHlysW/jaqL8HxP27AGDenalQNGiOq8tPDyCrbv2cuqPvSyd\nO5MREybTsGVboqMidbY38u/yfzGwW7B8SnnbQzVuIBAIVEE4Vw/fJjo6mPUbhyMQCMjnUZIWjcfR\nsNYg9h2fjkcDHyYfGahxvIT4eECARJ5IrDwUEeaEyZ9SWNiEUPkTwLBcYWXNAHUsLUSYmJikm2+c\nWQGwcNECJk0ar7FuzJhRWu2mTJnKgP4DsU39MtGDVCplztzZlCpZinZt2wGwfPkKunTpQtNmfmxY\nb6WoeWDMHjDyg6LMYc/hU5d7byJVbuo8ucswq2xp8pcoh4dXaZKSk/D08KBl0ybsPXiYCdNns3DW\nNJ058AkJiSQmJnHzwT1iYuMo5pGbfpt/I/jje0DH2D9op/JFaxvH+0ERlHLNoTL2ymEBDeOvywuQ\nKitAgy9GPiw8gkIVa/Dpc8p5Dxw7odW8eJFCrFkwmwplSmm8rwRiS93HBx49ecq+Q0eYMHIYdna2\nFClUkDo1qzNo1DgGd2jIiQO7CRBlTQC0kazh/8IToI5IJNJI86nkV4aGvm0ZOngBE8f+zrv3jxBb\nm+KcOy+2No48ePWn1jHEFhaUtm3Pc9lx3kmu8Fp6AXdhVcQmduQWVsDCJu1pi9XTBFMjl8uJj49V\nxQMAqniArwkIjIiIoGGj+kyaNJ6CBQpy7dotYmMS+fwphnXrNmJvb0+/vv2JCP9MUqKEcWPHGywA\nAObOm0Od2nXw9W2osb506TLs2b2Xrt26cOvWzUxfvxEj35PU49RisZg/9+/i/pVzdGjTmsvXrgPQ\nqlkTFv+2mpuB2nVDWnfoRinv4rTt0YdZy9fg//IFgzu3xSePM3W9PFXpfxrBf+o9f+Wig/ufYvHI\nljLvh8DWVjMGQF0ApIoFAB1egC8C4NjpMzgVKcmnz5FMHT2M2LfPkIW85c3dq7T2a4SbizNnDuxE\nFvKWvy+comLZ0ioBIBBbpikA4uLimD53IRNGKQSAElNTU1YsmIOzkyPte/RRTe5k5Mfg/yIwMCOU\ny++Cra0DoaEfKVy4EvXr9qDbKO2KgsNLLeRN6G0exR2mqt0QzE2sNLab2yoi/tUDA+1y26UZFGhp\nZYalhYgbN87x5s1zBg0chomJiZYIyKgX4OrVK3To+CvBwUEMGjSE2bPm6mxnaIaALsLDw5k2bSpz\n587TOVSwcOECHvz9gC2/bwWMZYa/J8bAwKzl0J5tLJw6EkeHHHz6HMmyebPwqt9RZ1vXiL8p3aAF\nvtXKs2DMkC9DADoi/0GvwVeh5i6PTUpm4p/X6V6+KMVcdJRA1yMANIIB1QSAVCql/+iJrN2yHYC3\n966RxzN/2teTCTZv30k2e3uaNvLV2paYmIh7sZKcOXKAYkUKG6sLfkf+07MIZjVbD5/myrFrWIpz\nYGHpqLed2NaMwraViHsbwoP4XVR26a2KD1Bvo54ZoI4+AWBpaUbNmvXxf5WbyVPGYGdnj0hogkhk\novpPEgqFFC5chJYtWxl0TyNGDkMgEFCnTl1mTJ+lt11ysjTTQuD9h/dIJBKuXr1CrVraEzQFBATw\n4cN7Pn78SM6cOTN1DiNGfgR8/Vrh5SLG3s6OYkUKYWlpib9Ed1ux2JzTuzZSvlFrqnsXoVGpQooN\nqaP+QcPIp4eVmSnzm1Rmztm77HnwEhOBALmZouMhEIDczByZTE7/lr44pyMAAE6dv8i+I8ewtLTg\n8K5t30QAxMXFIZfLOXjkmE4RkJSUTHBIKA/+eUTRwoWy/PxGMsdP5wnIKGOrL+Xym3VYmmWjTM5f\nNLZlJDVQXQToSg1M7QXYum0Lbm5u1K5VJ83re/jwIeUrlCZPHneuXL5O9uzZ072njAiBp0+fsmHD\nOkqWKk3LFi0xNzfX2c5cbIpcLid37twcOnQYr+JeRm/Ad8LoCfh38Yh7xrW/TtJs6GTOLp1EkexW\nGnn/KuOfXmEf9Wm/dUT8AxqTBcXEJzB2+zEWDuyCeTZHvQIAoGWXXhw8fpI5UycyeuigDN5h+ixf\nvY6w8AjatGym18CfOX+ROn4tsba2omv7diyeMwOhUGj0CHwH/u9TBL8l9rnsqFa0B/4R1xBYyjGz\nMVMtujwA6QkAJepZAbpiATp26MSVy5d5/do/zeubOGkcANu27sTW1g6JRKpa9JGcbPiY3J69u5k+\nfSa/tvtVrwBITExELpezbu16Zs6cTYMG9Th37qwxddDIT8Fry0JU8C5Kzya1WLv3qLYA+FLnXx4V\npXNRoU8k2NilLLb2qiwAa2dXhnZtz8ydx9IUAJFRURw8fpIaVSszasjA1Ef/aj59+kxsXBxTx49O\ns4e/ZOUaAF7ev8nDJ09p3ak78fGGZ1oZ+Tb8dMMBGWX09i7Excay130U1vY2GlGySg8AoPICpIeu\ngEAlqWMBxowZx5gxI/Ft2IiE+Hji4uOJj4sjXu3fp06dZNiwEfj4lNA6l1IIZDbTQCaTkZSUpDdd\nUImpqSlbt26nzS9tAHB1caF9h1+ZP38h7dq2M3oEjPzf8yZvHeSijeSwVevNfxEA8qgoZJH6vQDK\nnphW2h9oTxMMGuP/ntbZqFqpPPPWbaFY8eLExScQlyQlPiFB8Y5ISODvp4r6B3/s3Jrp2VLT4o/j\nf9KsccN0200dN4r5M6bg7OTEiQO76dZvMLWbtOTw7q045Mhh9Aj8SxhFgAGEBAdibZUdC/sUda1u\n/AGDhgGUpOcFSGlnxqRJU3n69AmOjk5YWlhgaWmJWGyBmZk55ubmfPoUweRJ09K8folEqiUEDIkP\nePjoIZ75PNNsA4q66koBAFC9eg1OnjxN06ZNuHvnNu7ueQmLkyGRJCORSJBKJIq/UknKZ6nib4Uq\nNfFt2krnZCxGjPzIvEgQU88qSVX5L7UAkHyO1dpHZG+FLDIGE7sv4iEmRnNYAPROEazs/derUgGX\nXO4kJCZiaWuPpYUFFhbmWFpYYJUtB1t27GbEwP7Y2tp8g7uGB/88omPbX9JtV6qEj+rfZmZmbF23\nkvHTZlKpTkMG9u5BSLKF6r2QnJys8V5Qf2dY29jya9c+5Mzt/k3u52fDKAIMICQoABvbHNjm1h6n\nU/b+9QkAjbYZ8AIosbe3p0KFihrrlD38V69e4uNT0qB7yIwQ8CruxY4d23n16hWenumLAXWKFS3G\nxQuXWbRoAf7+/ohEQoQiEaYiERYiESKxCJFIjEgoQiRSLDKZjMUzxrB64TTGj59Aufq/GMWAkf8M\nwUEBuBS10xIASuMvidTt+lYJAVJ5A2zU3jf65gP44v73LmqrMQQAKfn8r9++o1un9l97e3rp3qk9\n0+Ys0FuGWR8CgYBZkydQrHBhrt+6rXoPiERCzJXvBbEQkcgckcgKkVCISCTi2s1bNKnqTZuWzegw\nZJpRDHwlRhFgAMGBAdjbO+h096eeNlg5WZCStLwAmUF9rP/CxfPUrVMfgOQk7fH31F4GXUIgLQQC\nAdOnzWDUqJGMGzceR0f92RS6CI8IZ8iQYeTKZVhxkH3797F61VqSkhKZPmMa0TOm02f4JHybttKo\n/WDEyI9ISFAAbpVyaQkApfFPiErQaC+2Fau2ieyttI4HaAwD6BMAgIYAUM/lVwaDfYthACXFixYh\nPCKC5avXMaiv9gyD6ZEjezaWL5hjUFupVMqzFy95ce8GS1atoVmNktRt0op+w8aRK0/eDJ/biDEw\n0CBCgwPJls0JCwtTrcXKUvdsgcphACVZXRwI4OPHD7g4u+kUAKAQBqm3pTcJUWpMTU2ZPn0GgwYN\nYOrUKQwZMoigoCCD9r139y7JycnpNwRiYmK4ffsWNWvWpH79Bly6eIX58+azY91i/Kp6cfXYDmOR\nESM/NCFBAbgJtYsKgbYA0LdOJ2pVAHUGAOoRAAD+r9/gmS+vYef5CqpXqYyjowMde/Zjyqx5LFj2\nm8H7Xr91x+C2G7Zsp1vHX3F0dGDmpPE8v3ud/E7WNK9ZitlDO/L+7evMXP5PjVEEGEBwUABuri4q\ng6++KIy/tgBQkjolUBeG9s7VDbhMJkMmNSwtS59IAMMyBaytralduw7169enRImSJCUlpdn+zz+P\nM3HSBF6+emlwzYDVq1cxaOBg1WeBQEC9evW5eOEyCxcsZPnypfTt2PxfTYUzYkQfiYmJxERH4SCX\nankBlMY+OiZZtWSGtDIAQHc537MXL1OzapVMnS+jtG7mh4WFmCnjRhETox3/oE5oWBjzl65g/LSZ\nlCtt2JBmQkICr16/oYS3l2pdjhzZmTFpHC/u3cDFyYkWtUpz48qFr7qPnw3jcIABhAQF4O5SDEsr\nHXMBqBl/xWeFANA3DJBVXoAnTx5TuLDuCTyymuvXrxEeEU6FChW5efMmNjb6A4zkcjnnz59jzpx5\nGXJBOjk78znyM25ubhrrBQIBdevWo0aNmlSvUZWjW5bTr19/wFiV0MiPQ1hIEDkcnRVFfdAdBJgl\nZEAAALx594687nm+zbWoIZVKGTd1JiMH9Teo/c69B2jTohl5chs+j4C5uTn6XinZs2dj+sSxVCxX\nhr79OnD/8jmyZbM3ZhwYgFEEGEBIcCAli9bQGu9XkpYAUB8G0EVm0/fEFhZ8iogwuH1yklQlPNKL\nDUhKSuLatatcvnKZ+Ph4PPJ6MGrkaABKly7Nnj276dWrt04j//HjRwoUKJjhMUi/Jn6sXbeGokV0\nCxtTU1N+37yV6jWqcvPWTaKiopiyZBPZsusoqWrEyHcmODAAZxc3zWyAVF6ADKFWDlijEqAO0qrn\nL0BATEwM1qkzDrKADx8DOHX2HK9ev8FEYEK3jr9SIL8igFg5dl+ogO7KhOERnzIkAEDRIbCxtiYq\nKlpvpkPD+nXx861Pl74DefbiJRNHDadiyz4Zu7GfDKMIMABrG1v+OLwJF1dX8uYtqLVdw/2vIw4g\nq70AAHly5SUg8CNxcXFYWup/CagzdtwobG1s8fNrho+Pj8a2oKAgDh06SEBgAKamplSsUJFhQ4dj\nYaHZ46hcuQpCoZApUyczaeJkrYC9Z8+eUqhQxkuC2tvbExmZdlnVAgUKMGvmbHr17kmFChXYt34h\n06ZON3oEjPzrWFha8sb/BQutYumXz4Wv+kbqqxaowwuQlgAA6NmlI2s3b2XYgL4GnfrZi5dMm7uA\nejVr0LJpYy3xcPbCJS5euYZEIiGnmyv1atWgW0ftzIPJY0cyccZsWvo1oUwp7RommcWvYQOOnjjF\nr7+01Ntm3vTJlKleFwsLMX2GjuCjb33CLD2y7Br+3zDGBBjAik17qVylNgMGNEdOoiLoT22BL73/\nLx4AQ+IAsoJOHbuyddtmg9vndfegVatfePjwHyZMHMeaNasICwsjOVnK8hXLaNLEj2lTpzNxwiTq\n1KmrJQCUVKhQkWpVq7Fv/z6tbS6urty5Y3igjzqFCxfh8ZPHabYpUaIEA/oPYOKESVy8aBz7M/Jj\nUKS4D/tOXmP/k/eMvfJYbzoggI21YlhRbJtShMvEzlqRHpiBHnt6AgDAPU9uPn+OJCoq2qBj5s/n\ngXvuXJQpVYJFK1YzbuoMLly+gkwm4937D1y+doNxI4YwY9I4+vboimc+3cZVJBIxa/IEVm/crHO7\nVColKDjYoGtSp3jRIvzzOO13hIWFBVUrVmDtskWULuHDles3MnyenwmjCDAAU1NTnJ2cKFzYGysr\nTTeU0vgDaQ4B6PIC6HLJG1rX39RMiLt7XiIiwomOMewH7ufXlL/+Ok2bNu2YMX0Wvr4N2bJ1MxMm\nKkoPZ2Tin5u3blKvbj2t9cWKFsPe3p7z588ZfCwlTRo34ejRI3q3JyYmsnnzZhYsWESlSpV58OAB\ncXFxGT6PESPfgjweniRIZdR01O7J21ibqhZIEQAiOwvN9EBbu7SHAsx0C/O06N2tM2s2/W5QW6FQ\niEgoIn8+DyaNGcG08WOIjY1j3NQZDBs3kQ5tWmlNw6yP6OgY8uhJDx47fDAz5y9GItEzM5Me1IcE\n9HHg8FH8GtanbKmS1KhSmQtXrmboHD8bRhFgABHhYSxZMp3hwyen9PhTGX9dAiD1MEB6KAVARib4\nKVOmHBfOnzWoraurG0HBKel9efK4M2zoCKZPm8mkiZMNPmdCQgLx8fFky5ZN5/bOnbtw8tRJAgMD\nDT4mgKWlJZ8/f9a7ffGSRQwaNBgTExOsrKzw9vbm+vVrGTqHESPfivUrFuBoKqSOhcLA60sBTC0A\nDPICWGqWFDbEC6DEzdWF5y9fGdzet25tTvyleKeIRCIa1q/LnKmT2LZuFfk88hp8nG2799Khje6Z\nUK2srOjXsxvzl64w+HhKHB0c+Kjn3RIcEsKd+w9oWL8uADWqVub8JaMISAujCDCA6WMH06BBU7y8\nSmmsVzf+kL4ASM8LkFFe+b/k6tXLNGrkZ/A+efPm5fUbzVxagUCgd3IgXezZu4fWrfWXCRUIBEwY\nP5E5c2ZnSOlv2rSRjh066dx2/fo1nJ2cyZcvn2pdtWrVuWAcEjDyA+D/4hkbflvIkvw50wyKTVMA\nfAMvAMDshUvo062Lwe0rlCvD9Vu3ta89nTlE1JHJZLx++y5N0VCkUEHy5XXn2IlTBh83MTGR5y9f\nUaSQdmyWXC5n7uLljBmakmpcoWxpHj55Qky0Yd7SnxGjCEiH08f/4N6NKwwbNl5l9FMbf9AtANTJ\n6DCALm+A+n5JSUn8tnIp48dNzlAkvl+Tphw58ofB7XXx5MljvIp7pdnGysqKvn37sWDhfIOOGR0d\nzfsP7ylSpIjWtri4OPbs2UOXLl011lerWo2LFy8YZys08q+SlJTEmEHdGZtDjLuFQkzrqg6YUQGg\n4iu8AGs3baFUCR9Kl/RJv7Hy+AIBFmILYmMzn+Z45foNqleulG67Ni2bc/3WHd68fWfQcddt3krP\nzh11btu+ex9+DRtgY5PiURGLxZQu4UPA7eOGXfhPiDE7QAcP7tzk6IFdXDxzgvDQEJYs2YCFhe4f\nXurxf3UBkNFhAF3rUxfzEYmESCRShEIhPt4lWLJ0Pr169sNWX0Sx8lhfritHDgdCQ0Px93+Fo6MT\n1tbWGU7nMzXVrpegi8KFC+P5MD/Hjh2lUaPGgCKFcOfOHcTGxSIQCJBKpXjm8yQoKIg+vXVHMC9Y\nOJ/hw0doXWfFipW4f/8+cXFx5LayNGYJGPluJMTHc2DX71w4c4Lrl85RQyygh5eHziEAdeMPpCsA\nlAis7fTWBTCUWtWqsGLtBgRA/Tq1DN6viW89Nm7dQevmfjjkyIFIlDFT4ejgwNt3HwxqO2HUMIaN\nncii2dNVHslDR4/z98PHqiqhyZJk2rRoTnBIKAULaM9j8v7DR176v6ZD29Za25RxAQ3q1jbWDdCB\nUQSkwjw2kinD+1DCpzTz5y6jeDEfkpK1q9TpCv7TJwCyehgAFAE8PXr0IiQkmKnTJ7Jg3lK9xjx1\nSmLXrt25dv0aoaEhfP70mSlT0p6FMDVOjk4EBwfj7OycbtvWrVozZepkihYthoeHB0+ePKZkyZLU\nrl1H1ebGjevs2rWTBr6+hIaFsnPHdqysrSnhUxI5cgoUKKgzaNHa2hovLy9u3LhOzZq1yG0lNQoB\nI9+FN92qsPbiQybnc2VNiTxYJ0hJik7UaqeeAQDpCIAvpDUMkBEvAEB+z3wsnjODfYcOs23XXp1G\nUhclvL34EBDIwSPHOXX2PGuXLcTRwXADWqhAfrbv1s4e0oW5uTkjBw9g5vzFTJswBoBbd+8xddxo\nlfhISEhg0KhxuDo7Exsby6oNm/n8ORKHHNmpVrkim7fvYu60STqPX6NqZcZNnQlAPlEYgFEMqGEc\nDkhFUpIUCwtLGjRogo93KYRCoYbBtxCL0hUAGSG9IEBd2589e0pcXBwfPnxg9JiRFMhfkPcf3mBq\nJtQw+Kk/KylYoABt27RDJBJRpkyZDF9z1WrVuHjpYrrtnjx5wtWrV/Bt4MuYsaN59+4tjk5OfEoV\n/Fe+fAUOHz7KtatXuXnjBnPmzGP8uAnkypWThIQE2rZpq/ccDRr4MmToEHbv2Z3hSGMjRjKLeUIS\nrmamNLG0xDpBqlESWFdhoHSHAEA7DuArhgEAbt6+C8CGLdt4/PQ5T5+/MHhfgUBAE9/61KlRDSdH\nB7LZ26e/U6r9DSE+Pp6LV64SHBKKpaUFG7dsJzo6RjW7qBKxWMzaZYvo2LY1U2bPp4lvfWZMGkf7\nNq148+49vbt11p/SXLY0L/1f02/oKPxfv8nQffwMCORpFGMXCAS8ivh5arVHhIdx9sBOli9fwJQp\nc6lVsx7xCZqGRV8FQEO8AKDtCUhLBDx+8pjgoCBkMhnJEilyuRy5XM6Rw38QGhqKRz5PhgwZxtSp\nkylevDg9e/Q26D4DAz+yZMkiunTtjldxrwxlI4Ai6Kd//35MmDBRZw/92bNnbNq8kUIFC5ErVy7i\n4xOIiY0hJCSEmJhoGtRvQJkyZTN0Tn3I5XL+/PM48xfMIzAgkFHTF1G3YdMsOfZ/Bc/sgn91ToWf\n7T1x58ZVTnRuzPXoOA7nzU1srKbRt/pSXtzG2hSxrVj3MIBbzpSpgnUFAhpYGCg+Pp5rN2+r3g1y\nuRyZTIZcLmfEhCmUK12SDm1a8+bdO67euMWapQsNmpFTLpezadsOgkNCGTGov8FDgOocPHKMsPAI\nenTuoCUKJBIJW3bu5vnLV9SqVhWJREJcfDyfIyP5HBmFRCJhzLDBeo6ccYJDQli2eh1rNm6hbs3q\njFm4CZt0hlD/n0jrHWEUAWpMGT2Qe9cu0bZNJxr4ttAZB5BREQBpDwekZYAHDhrAL61/QSAQYGJi\nglQqV/27SJGiWFlZIRAIOHjwAIf+OMiG9ZvTvceDB/fz/Pkzhg8fqYr2zagIAEWw3tx5cyhVsjRN\nmyqM7sePH1mzdjWuLq5069Y9QxkHWUHPXj0wtXFg7DTDghH/XzCKgO9HTHQ0Pu629MhuR3VTC7zN\nzYlOSInbsRErfktWVqYaIiBDmQAZqAy4dtPv2Nna4urijImJieL9IFD8dXZyxNXFGbFYjEwmw69N\nByaMGkaFsml7/8LCw5k5fzEt/BpRtVLFr3hacPXGTfYePMz4kUNxyJEDuVzOwSPHuHL9Jh3bttaY\nDOh78OFjAPlLlOPi3+9wcHT6ruf+N0nrHWGMCQAEnxQ1+D3dchGS053nL55Tq3aSThEQnyBJdz6A\nhESpwUGBaeHo6EjVqtU01uma9a9Zs+asWvUbkZGR2NnpVreRkZEsWDiP2rVrM378xK++NktLS6ZO\nmcbhw4eZNHkiYrEYM1MzRo4YleYEQ1mN8nlIJBJ+/30zQ8dN/27nNvLzkGtEPRLehmEdlYCTSIhA\nIueNJAkHSUoP10YoJDpBqhICqclqAQCKIN0KZcvgnid3mtdvYmLCiEH9+W3thjRFwMm/znL24mWm\njB2FnZ2t3naGUql8OYoVLsz0eQvx9MjL85evaNbYl4WzMhaHlFUcPXGK5ORkZMZpyVX89CLghgQc\ntwAAIABJREFUs/9Lbt68yo0bV7hw8Sx583rSp88wVq9eRN26jSlVqpzWPoYIgawgKjKSqdOmYGVp\nxYgRIwHdGQMCgYDt23cxY8YUBg8eTq5cuYj4FMHZM3/RvHlLbty4zpEjfzB69FgcdAT3JCdLM+UN\nAPDz86NcuXIIhUIcHR3TbW/I1MWZ4cOH9wCEBmesQJERI+khk8m4+fAd5wM/czYsis8SKUUxJVIu\nY11MJC3EVpiru7sTUoYElJjYqRUCSi8VMAOBgHly5WLl+k2IREKaN26UZp3+GlUrExAYxPLV6xjQ\nuwcAd+49IDk5mRLexZm7eDlFChXQG2CXWezsbJk/YwqPnjylT/cuGc5Gykqu37qNTCZTZR0Y+cmH\nA+aNGcj2HZspXao8pUpXpGzZihQt6oOpqSlyuZx9+7YRGxtLx449tcbRDAkONCRDwBDjO2XKZCZP\nnqLx49FlTBMSEpgxcxpCoRA7OzskEikfP36gVMnSdOrUOc0fX2ZFgCF8K8Ov5J+H/9CiRVOGDhlG\nix7Dvum5fkSMwwHfDtPRDSn5+2mymZhQViymoExEAaEIczl8ksj4JJPyZ3I8NURiipmbYS0UYiMU\nYiMW4uJsqagP4O6gGApwc9PvBciEAFAnJiaGFWs3GDSOfvnadXbuPYCdnS2lS/hw5M+T2FhbM3Lw\ngAzP7PdfQSaTMWriVP48fYaTB/eQ5Oz9b1/Sd8U4HKAHodCUrl360KfvSI31cXFJADRq1IYXL54w\nZ84EWrXqgEQiISrqM1FRitnu6tdroDFkkJQkzXCWgL5e+OQpk1TCw9LSArlcrmHEdXkExGIxM6bP\nUn2OjIwkIiIcD498fG8MNfwSSUq7zKRPnj9/ns5dOjBv3kJatmgFxBMmy1xOtREjqbFLTCRBKmO/\nRx6iPyURLZUSI5XySSIj7Mt3t46JmHvSJPzjJVQSi5FJJUjkYBKWTCkbM0rzZSgAslQAvH33nokz\n5uDhngeAalUMG7+vUrECVSpWUH3On8+D4kWLGBQw+F8kOTmZbv0G8/rtWy6dOEL27NnwNyYSqfip\nRYCPTyk2b16j+qw0/uoUKFCEPn1Gc/XqX1hZWWFnlw1Pz4IkJSWxfMVixGIzmjf7hdy53bX2VY8N\nSE6SqrwBEolUw+DpEgJCoTDdev66hIA6dnZ2emMEUvM1QwLqx9CHurH/mjbqHD58iKHDBrN+/SZq\n1kgphOJgYhQCRrIGUUwCBcVm3PgUQ26pUEsAAJgIBJQWmBMtkPE0OYk8pmY4m4jwtLbgYVQce24+\np1xkIs1rV8SQGHtDPQCRUVE0bdSAlk2bZPLuFPh4Ff+q/X9kYmJiaNWxG2ZmZpw6tFc17bqxXkAK\nP60I+BQRzqLFs2nQIP10MgsLS2rX9tMqFVy0qDcxMZ9Yt24FiQnx1KnrS5XKNTA3z/hjzawRTk8I\nfKtryExPX7VvUvr76qpvoM7evbuZMHEs+/cdokSJkuzatYNPnz7h7OKCs5Mzzs7OiBw9sMpERUQj\nRpRsfvKBwEQJDmJFSRWlAAhN9R12NBPiITQlm8gEVzMzbMyFuIjN8MqdDTN3Zw5HJNBn258UKuhJ\np6a+uKbhBTAUiUSSqdS9n4WEhARqN2lJ8aKFWbN0IYFBwSxbvQ5Hhxy4ODnh6uJMYo4i5HB0ynBF\nxP8nsuzO9dVv/xEruEVHRdG5RV1q1KhDz56KMTRdXgBDEJqYEBDwnvHjZvDixVPmzp3KxInTVIbH\nUG8AaBphgUBAaGioQcF26kLg3v17tGrVnKCg9APkfHxKsH/fQVxd3TSuIStIbfz1Gf6ERMX61NkU\naQkFiUTCpMkT2PL7LooV9ebPP/9k+oypNKjfkKtXrxAcHERISAjBIYoZE52dXejZozf9+w80egj+\nZZQ9sNT8iD2yw/t3Mt8/kK153LCIkRGYZPg7wsrKVKNa4J0PofiW8aJauRIs2nOU1o3qUbbcl6Dj\nTMYBuLo4c/zUX/g1bGDwPqAYHx84Ygwr128yqP2yebPo36s7Jib/rdpyO/cdwM7WlvUrliCXy+nQ\nsy/OTo5YW1kRGBRMUEgIgUHBhEd8Inu2bOTO5caf+3fh6ODwQ34fvxXfXP78iKVcP38K563/S+bO\nXPzVvcTs2XOQP38hrKytqVatFra2tqxe8xt9+wxQtcmMEBg0cDCzZs1g7lzDct6VQuCff/4mKCiQ\nQgUL4ezsQmJSIklJSSQmJpKYmEBiouLfSUmJPHhwn/wFPKhSuSp79uw3eOhAH+n1+pUGXxf6tulK\ntTz+51Hy5M5DqZKliYmJYeSooSxeuJyaNWtrtJPL5cTExtC5869YWFhw4cJ5ChUqhNQuJxtXLqJl\nuy44ubgaentGfjLu37qOl4U5jskQ+CWaXJcXIC1E9lYIbG0Z6FuZDbee0SqPO3NG+DDqt9/JWdgL\nN7WqgBmtCOjq4oKjgwO3795PMysgNYmJiWzboyjpW7VShS/rkkhMSvzyV/m+SCI8IoJBo8YxaNQ4\n9m/bRAu/xhm6xn8LuVzOstXrmD15AgKBgPW/byMhIZGdG9dqxT5IJBIuX7tB2649iYmJ5eWr12Tz\nqs3t65f5FB5Gw2at/6+9iVmeHZDejG7/tiCQSCTsWrWQxYvnMnXKPOrUVUzDa4gnIPVwACiyBAID\nP3Ls2CF69OgPwJEjBzA3N6dxY80pfjNaSfDkyRPExcfTvFlzg+4to714qVTKggXzmDZ9CgDt2rXn\ntxWrDCryk974vT7jn2TgCzStAMuWrRrStUsvGjVqypo1y5kxcxLv3kbo7KnEREdQrkJJFi1cyqjR\nw5HL5YjF5sjlkJSUSO9efRkxYgRHL9yiYrVa/8ngqP9adoA+b4CSH6EXljisHgOP3+JjdDzbHJwJ\njUsmRirlRUKyThHgaCbEQSQkt7kpNkIhLjnEOLrZIHZ3QJjbDdxyMu3C3wxq35JsrrlIEJkzYuFq\n5k+dhKWlwhOQUREAil794FHjWDBz6jcrzvX6zVuKV6hGXFwcAFdOH6NSee3U6R+Jy9eu063fYJ7e\nuUZQcAgFS5Vn1aL5dGynewr00ZOmIZfLuXL9BsEhoQSHhiIUCslmb49jjhwsmDkFsUd5goMCKOZd\n8jvfzdeT1jviv+XfyQJ6/erHyZPH2LP7GE2atDB4P10CQImzsysBgR9Vn5s0acEr/xfcv39fo526\nMUxOkmoYSi33ebKUWrXqcvv2LcLC0n5pKsloTIFQKGT06LFEhEfRo3tPdu7cTvYctkydOhmZTKZx\nbakXfajfV0KiVHXPSUlSkpKkxCdI0l3U26cWDefPn+Hjx/e4urrRrHl9Zs5SBE9u3LQGXezctZN6\n9Rowf8Ecli1dif+rd+zdc5Azf53jyuXrvHn7hsJFCtK5ZT2qeufhU0R4hp6hkYzzIxj5tLh/+wZV\nd12glpMdfxX1wDSDvUAbsRAba1NEdhaK+gDWihoB3nlzce/FawAsxGLGD+zNpLkLFZk/mRAAoCgC\nNKRfb5as1P39zwo88roTG/SWB1fPA1C5biMEto48f/Hqm53za5BKpUycMYc+3bswZ9FSCpepiFQi\nZdDocSTpGNJJTk5my87d5M7lRkxsHM/uXufdo/sc2rGFl/dvMqRfbzr3GUj5wi741SjF72uXf/+b\n+oZ8UxHwPlaotfzbvHz2mAXzl5M/fyGN9WkZeX3blLUCDh7cjV8qQdG71yD2H9jFx48BGoZM3TAC\naQoBAL8mzbh0+VIad/T1mJubs3TpCkKCI6hfvwHz5s/BxtaCXbt2GtTjV1+U6DP+oPC66FsAnYLg\n8+cojh8/SoeOLREIBPTs1ZHWrTtQv35jHBycaN2qg5ZwiIuLZcvWjQQGBmFrY0v9er6YmJjg5eVN\nrlx5yJPHnfXrNvLHH8cACA4MoE/bBnhmF3Dxjy2EPb9FcGBA1j5sIxr4Sxy0ln+bd29eUcfdiSEF\nc2Jqkr4AUHoBsolMsBEKVfEAInsrVZskCysuPnxOzapVVAGBbi7OtGvRlIUr137V9Xrm8yAyKuqr\njmEI3sWLIY8K5fzxQwAUKl0Bh7yFiI2N/ebnNgS5XM7zF68YNHIs5y9dYemqtdy6e5/b50+TlJzM\nrEnjMDPTfpfv/+MoNtbWzFqwhBkTxyo8ANnsqVG1MkKhkF9/acnT21eZOWkcAGsWTcczuwDP7AJc\nk99z8czJ732rWco3EwE/gsHXhVAoRCqV6dymy9inJwBev37Fu3dvKFFCsxSniYkJw4eNY/mKBSQm\nJmj1ag0VAk+ePqFwoSLp3FUKX5PmZ2VlxZ7dB3j7JgB397x079GFM2f/0rhOfUZfSereP6Bt/OMl\n+hc1QZCcnEybtk3I55mD4l556NW7AwC+DZpy+tR1fmndnn79hhEeHkpsbKxq8pRLl8/Ts1cnSpcp\nQnBwEFeuXODW7ZsUK55f5zX7ePsQ+Tma2Jh43NwUEyJNnTKZWrVrMmGYYZMyGfn/wcREiFSm23Xq\nIBLiaKa5KLHWMZQksLVFbmPLnBPX6NW6iebYspkFpX288PD0ZN+hw191zSLh94tur16lMvKoUPZu\n2UB4RATWrnl19rC/F5euXkNg64iJnROFSldg5fpN5MienQ0rlnBwx+8ULJAfr6JF2H3gD9VMo2Hh\n4cxeuIQSlWvQZ+gIXrzyJyg4BL82Hbh09ZrWOczNzRk3Yihxwe/Y8/t61Xrflm3p2roBd25c/W73\nm9Vk+TfnRzX+SkyEQuRyGWZmQsW0wWKRxkyBaXkElCgFwJOnjzh0cA8jRkzQahOfIEFgYk6f3kNY\nvHgOo0dPRiAQqAyjmZlQIzJeX8Dga39/2rX9NUPpe5lNG1QKEHt7ex7cf0Qed1datPDjzF+X8PFO\nO/AodWCfLgEAcO78GcLCggkJDsbDoxBmZua4uubB2TknllZmxMVLkMlkbFg/j23bFC7ORo1aEhsb\nzfnzpzjz103Wr/8NnxIeADg6Oivqp1csqhrzUv51cnKhefPWFCpYGH//V3Tu1Fnv9SunId23dz83\nb96gZq0a7Nyxm5Ejh+N/+wz5ytTWu6+RjPMj9Pj1YWJigizV+KmNUFEjABRCQFknwOHL71TpBUg9\nFCCztGL6qVvULl+Swu45U+YIUKNVMz/mL13BnXsPKF3SJ8PXGxsbq4or+J60aubHrfOnKVujLvWa\ntebs0YNZkkEQHBLCidNnCQ4NxdXZWRHRb21FmZIlNHryr9+8JZ93Suerb4+urFq/Ce/ixdi/bRMV\najUgPEIxL4y9nR3/PHqMWQ43BAKBasZFGxsbLMTm3Ln4FyvWbqBuzRpULKd/hlMLCwuqVa6EPCqU\npm07EhkVxarF81k8eQhb/7zxnwwg/OmSI0UCNOpGP3v2hIIFC6cZua5EvVTw3bu3OH/+NKNHT9b4\n4qeeejhbdmdq1vJl3fpVdOzYS3WMiIjPPHhwG6FQSIUKlbG1sdApBORyuer431IIpPZACAQCrl65\nQ3GvAtSuU5Xr1+6S37OAQc9JnwBYs3Yp69ct0GgrEpliaWlF8eJlmTBhOVev/smMGUMBqF+/GdOm\nLcXaWoxEIsHHJydBQQEUK5ZS8jM0NJgRwycgl8twz5uPnG65ePvuDcOG9cHbuwTjxk5RBfsZOqlT\n2bLlePzoKbly5WL3nl2s37CeWUYR8NMgFAqRfhEBYlsxsshEAmMTVT39TxKZyviDQgBYfxEA6kMB\nUmtrJp64Tus6VShVsWzKPAGW2hPzjBjUnxHjJ+Pq4oybqwvwxb398hX3HvxD+TKl8MirXZAM4OHj\np3gVNdxbmJWUKVWCkYMHMH/pCgaNHMvyBXO+yhB+DAikVNXahISGaqwvXLAA4RGf2Lr2N8qXKU2V\n+o159OQpADfPnaJsaUWw3u2797l19x4e7nlUAgCggGc+fOvVwdXZCfc8uShcsADFy1cjOjqaI7u3\nUqqEDxtXLsvQta5dthBbGxv837xl4MixxL+4jGXBqpm+93+Ln2ruADtJDHXqVadO7Xr07z+cO3du\n0vqXxqxdu53ateoD2kYctOcJuHT5HA8fPqBP78EaX3hd+yo5fvwgwcGBxMfHk5ychJ2dDWXKVACk\nXL9+heSkZIoULkSdOvVwcXUGFC+BOXNmMlGtcmBqERAdHc2tWzcJDAoiKDCQoKBAAoOCMDExwd3d\nHQEmDBs2AisrK/SRVmrfnj076TegFy4ubhw5/JdGTYHUKI2/RCKheYv6BAV9oFmz9jRu3Jr165dx\n7NgezM0taN++Pxs3LtB7HEdHV37fchJHB8VLU+mdKVbMmQIFCnHo4F8UK56b6tVqc+HiGZ48/oi5\neUpOdj7PHAD88/dbsmVL6XkpRYBSaKU1h0NCQgKtWrXg1OlTALx5/Q5XV1eWrNvKri3r6N6pAzVb\ndEVs8e/XHfivZQf8yLifmMeps5fpv+cM+2p54xmVQLMbzwlISmaDvRMxMpnKI6BEOV+ASw4xNtam\nWOfOhjynA+NuvqBXw6oULl1au0ywmXZGQFxcHCMnTMG7eDHef1AEGhcqkJ8S3sW5fusOb969w0Is\npnb1apQrU0olbtds/J3GDeqR001/uuuTZ8958cqfwKBgAgKDCAwOJjgklAKe+TAxMaFm1cr41quT\nqWeWlJREwVIVePvuPVPGjmLy2JHp7wScOX+ROn4taVCnFr27daZY4UIULKVIWezZpSPXbt7m4eMn\nevfftGoZXdq301h349YdKtRuQELoBwaPHs/Dx0+4cv0mVStV4OKJI6p2R/88RZM27Vm/YjHdO3XI\nxF0rOHH6DK06dSM2No5uHX9lw29LuRkgZfLI/uSwEDB8YF/sitVK/0DfmLTeET+VCHAwief5sxeM\nHDWU4JBgoqIiKV7cB5HQlOXLN6S7f2xsDDt3bcHMzIxOHXtobNMlAFKnHQYGviF3bnfMzBSpPEpx\nYWam6PF/eP+KU6dPEh4eRuXKlfHy8ubU6ZP07NFL4zhKgxUWFoZbTpd0r7tEiZKcPnVWVTIzNWkV\n9olPkNC1W1suX76Ie5687Nt3nGzZsqU+hEYwXnGvPDrP4+fXke7dRyL58liGDmvBu/cvAbC3y0GR\noiVp334Anp5FsLRSGH5LC5FKBAwZ0p3Tp4/i/0oRwS+Xy6lSxZv9+0/i4qIQJwcP7WH48L6c+esm\nHh6eGqmGGREBUVFR9O7Ti/3799G7Vx9mzpzFgIH9uXP/byZPGM/OnTu4desm7br2o333fuRwSL+o\n07fCKAKyDvcT85C9f8uGS/eY+NdtSliJCYtPJjAxmbl2OcgvMiM6lQhIPWHQIytTtgZEMqJeOdyL\nF1fNFWDiklexgx4RABAR8YmY2Fi9E/nExcVx5sIlbty6Q7JEwuQxI5izaBlTx4/W2wPv2ncgm7fv\nSvfeD2zfTPMmjdJtp4s79x5QpnodrK2tmD15gmqWQn2s3rCZvkN1i4Vb50+r6h78de4CdZu2Um3z\n8SpG62Z+DB/YD7FYrHN/ga0jc6dNYtSQgQBs2raDS1eva/T0BbaK36s8KlTnMQzl8PETNG3bEYD7\nV84REhpG5z4D6NqhHfZ2dixdtZbCBQvQof84qtaq968NFxhFQCpsk6PZt38PAQGBPH/xjPz5C9K9\nW3+97ePj49i9eyvh4WG0bduJnDm15+5WFwHp1RxQjztQFwKgMFR/HD6Iq6sL0dFRODo6Ur58Ba1j\nCARyLK0UP4L4uESt/HaZTMbly5dYvGQxx44dpXbtOuzZvV/nD0ddBKQOnEtIlBIYGEAD32oUKFCI\npKQkdu08hEikfZygoAAqVfYC4Ny5Bzg5uRAXl0RYeCTJyUmYihTeiLiYRORyOa1+8cHZORcrVxxP\neTbWCoGkLgKUz+zDh7fUr1+Ou3deYm+vLUQAOnZqwZUrF1RCQf25KjFEBADs2LmDY8eOMmzoMDp0\naE/NmrVYsGChSkw9ffqUpUuXsP/APhwcHJFJpYppSmVSpMp/f/lraWnJ0CHD6N27D0FJul9emcUo\nArIe96Vd+PjsNXNuPaeeyIwuLz5w0i0nkqRUsQJixffGysqUABsh+z5FU8rTlXZliyLMlVsxYVAu\nD72TBWU2NVAmkzF0zASWzJ3J5JlzmTZhjM52G7Zso8eAoaxcNI++PbpqbQ8LD2fHnv0MHj0egCO7\nt9PYt16mrmn8tJkcO3mawKAQFs+ezq+/tNTZrt/QUazasIkObVqzdd1KQCHoPwYE4ubqojG8unLd\nRvoPH82hnVto2sjXoOswxMALbB3ZsGIp3Tr9aujt6aVYuSpsW7eSPQf/YMvOPWxdu5Ja1RXDAklJ\nSezad5AFy1cS8ekTFmIxUrX3Qspfxb/LlynFvOmT8SpWNEvjZoyzCKZCIBDQulUbIj5FUbZcUc6e\nuaEyFhoR/Anx7NmzjeCQINr80pE8efLqPF5awwDqLFgwCalUSqVKNTl4cBuXLp1h166jlCubMvtX\ncnIyt2/fZPq02SxbsYi+ffprVRaUy+VYWSsMSUhwmM4CNyYmJlSrVp0KFSpSooQ3ly9fon2Htuzc\nsUdnmowulOP/oaEhhIeHER6uqFdQsFBOVRsbG1sKFy4GwK1biqja27dfa8yuaGlpBVgRF5sijqKi\nPgHQuFFHvedXFwAAuXK54+lZgJiYaL0iQD1KWlfBofTmJFAnOCiIe3fv4te0CUuXLqdVy1Ya2wsX\nLsyqVauZOXMWYWFhmJiYIBQKdf59//4dEyaMZ+WqlcyZPZcStZr9J4OIfibcXHOwwCcf8x+8oXF2\nWxxszImNTdZoY2VlyrOERI59isDHMjuzKxfBLJttyqyBtvbaB05jnoAXL1/RtlsvJo8ZycPHT1iw\nbCV5cufk/pXzGu127z9Em5bNNGKGUnPrzj16DBhKmxbNdAoAAIccORjUtxfOTo607dqLTr37s3Pj\nGurXybgLOzExiQf/PAKgfY8+tO/RR7WtYH5P8ufz4PgpRbbRgplTGT6wn2q7QCAgV07tocZTZ88D\n4FvX8Jicw7u3sXXXnnTb2dpaG3zMtAgKDqFDz364587FvctncVIr9W5mZkanX9vQsd0vvPJ/jVwu\nV70TUt4PAtU7fNe+g9Ru0pJmjX3pPnoejs7pe3q/lp/SEwDw8P4d/ti6hjdv37Lld+0vzOHDB3j4\n6G/atu1IPg9PQH+1O0O8AAEB72natJLObS9fhGJiYoKZmZBt2zZRuVIlihQpyrz5MxkxYjROzroN\n3u3bd/H2Sn9e7DNn/sJXrb64/6t3ODs7qz7r8wQoRcCYsUPx93/JmzevCQj4AEChQkWJiAgnNDRY\n1d7buyT7951EKBRqBQXGxUs0RMCVy2eZMbMvmzZcwNb2y9h/Gl4A0I7N0IXSE/Dh/WfVusx4AQDm\nzJ3N0aNH2LplOx4eHumeOz3kcjknT55g9JjRODo6MGzKIrxKlP7q4xo9Ad+Oy829GHf9Gas93Chn\nY0l0TIoI+CyVsjrsE0VtLejk4YxFNkvM3J0VAsAtp/5pg9PwAtRs1Izzl65orR8zbBCzp0wEFGV/\nx0+bxYKZU3nx8hXXb90hMiqKgSPHau1nZWVJTODbdO9TLpdTx68lZy8oapLMnjKB0UMHGSxUQ0JD\nyVu8NE1867HnwB+q9d7Fi/HPo8ca38+TB/dQr3ZNg44rsHUkV0433j95YFB7QxHYOnJwx+80a9zw\nq44jl8txyFuIcSOGMLR/nyzJjvj06TMz5i/i9x276dx3GN36DsVCz1CuoRg9Aal44/+S0f06IhQK\n2bfnMGJzoUbUe2TkZ/z9XzJu7BSN/ZRpheoYOgxgampK8+btadmyA/nzF8HGxoKkpERKlsxD/gKO\nVK9Wm3ye+Xn/7g3eXsVVFfu8vAsDUKtWbYRCoWoZNXKUQQIAoHbtOjRs2Ii87nlZueo38nkqxuyv\nXr2Jj7ciJSk5OZn2Hdpw8uQJrl+7S57c7hw5cpi+/VJ6EN2796FF83YUKaI59ahMJiMiIhyHdMbF\nLa3MVEIgJPQdAC5umVe6unr6Mpnm/09apZrTY/SoMYwZrf1izSwCgYAGDXypU6cumzZtpM+vjalS\ny5dpC1Zirmd808i/g1wu50j7Coy7/JjFPh5Uy2ZLQlQCNtYps/b9HhjF4DyO5MlhjcjOIqU4kLVa\nD1NdABjAr61b0rxxQ379pSUOORQBrgNHjGHOomWcPHOO4kWKEBUdTYM6tfgYEMj9fx7i7ORIp979\nsbW1oWLZMqqepod7HhbNnm7QeQUCAcvmzaJ+818oVrgQY6fMYOyUGQzs3YP5M6aoShLfvnufsjXq\n0r9nN5YvmMP7Dx+ZMH22qucdEBjExpVLadXUDxsbzZ52TEzMl8eTsR74YgPvIaNkRYlwgUBA2Jtn\nWerVy5bNnoWzptGvR1dGT55G3fKFmbVkHdVq18+yc6jzU3kC5HI5B3ZtYe6kEYweNRZ/f39mTJ+j\n2q4UAuvXr6Rho6a4uebUOkZmRUBqlL3bS5fO0qdPO63tZcuUo7iXN5s2rWfBgsX07dNPY3tmiwIl\nJ0s5evQwbdq2Vq2bPn0WEyeO07vPzBkLaNeuU5rDCLq8JLq8AQBxsUnI5XKkUgkiUcpLVekBgLS9\nAGnNK7B79zbOnD3Fls3bNdarCwBd8zT8G0RFRdG3Xx8iwsPZt+8AEWinjhmC0ROQtYSFBDO5WTnC\no+PoUNANb0tzypmbIomMByAhKoEEmYylYZ+ZnN9NJQBM7KwVXgBra7C1g5zu2l4AyFQ8QJnqdbhz\nT7s3PLhvL5auUlQclEWGZJkxiomJoVXHbpw8cw6Ajm1/4e9Hj1Tu/tRULFeWjSuXUrhggSw5v5Lk\n5ORvMl2yQ95C3L5wmrzuuoOYfyTOXbxM2669WLloLiUb6R7WSQ9jYCCKtLXhfTry6vED1q/fTHBI\nMDExMTRs0ESjXXJyMtNnTtPyAihJShU5r05mRIDSuH3+HMaevTswMzXj9p1rHD9+FGtU9N5sAAAg\nAElEQVRra2JiYnB0dOLN6/ca+3+N4UpOlhIbG4uTc3bVOkdHJ+7e+RsLsRVXrl7mwoWzdOvWC3v7\njEe963pGqYWAPpTGH7SfkT7jn17+/48oAJRIpVJ69+nFq1cv+ePQESKFuod+0sIoArKOW9cuMbR9\nI7pU9GJiNS9G7j7NbO+8CGMSAFRCYFtAOKVsLfHOqfgNaYmAnHl0DwVApqcOlsvlnDpzjotXr5HT\nzY3+w0ZpbP/47B9VjYGsYvSkacxbklIrf/Oq5XRu35bk5GQmz5pLhbJlaFS/7n9y4q3/Evce/E3D\nVu1YMGMqFVv2SX+HVBhFABASFEiNkh68exuAWGxB5y4dKFa0OEKRkK5demJjbQPA/gN7yZfPkyJF\ndFfu0icCMiMAQJE2ePDAbuzs7WjbpgOmIgEXLp5jxW9LePjwHzq078iKFasxNdUcuflaEQBQrHgh\n4uLiWbJ4GY0apYghXRkCGeVrnlN6vX9Di/4YMlPjj4BMJmPIkMHcun2LY0ePE2ueMeFlFAFZx6JO\nNbEUwOzGlTh79xFrL92niK2YCnZW1PiSjSOXyxn38C2zvfICqIYBVCJAXzwAaMQEgGEiIDk5md37\nD3H/n4fUq1WDWtWrcvf+3xw8epw5i5YC8ODqebyLF8uy56Bk176DtOvWi1bNmrBq8XzVEIWR78+j\nJ0+p16w1U8eNolb7oRna1ygCvtC4cjFW/raacuXKq4LhgoODWLZsCdWq1qRmzdpMmjyO6dNmA7qN\nX1aJgAcPbnPu7J/kcc9Li+ZtsLNTiBClgROZmmhE/malAVOKgJs3b5A3rwdOTk5p1gpQR58gSB1X\nocQQz4m+KZrBcOOf3nh/6ucHP44IAIVhGTt2NKf/+os/j5/AycnJ4BLcRhGQdfy9ejTLdv3B6THd\n4OM7iIlBFhnJgRcB3H0fwqA8Ttz9FIMMOXWds2kLAOVQQBaIgJiYGDZt28mHgEDatGhKqRLaHROZ\nTJYlwWj6iI+P5/K1G9SpWd2YzfID8OLlK+o0bcXwAX0Z1LeXwWmERhHwhRXTR2BmasakSVMAzaj4\nw4cPcezYURr6NqZhw5Resb6a+JBxESAWi7hw4TQ3b16hXNny1K/fGKFQmGYuO3wbA6arpLChQsBQ\n1J9dWrEUoB35n57xNzTIT9ezgx9LACiRy+VMnzGNHdu3U6tWbcTZXHBwcsbB0RkHJ2dy5s6LWy7t\nGhVGEZB1ONzaSs5mPXm3fBy20kSIioSYGORRUYQGR7Don7d8iE1gc/XiGkYxPREAGBwXEBQczIYt\n20lMTKJrh3Z6ywUb+Tl5++49tZu0wKtYUbLnLoSDkzM5HFPeEwUKFdUKNDaKgC+8vHmG0WNGcuXy\nddU6dcMXExODlZUVkuSUWQYNFQGQthCwtDRj8eIZVKpUg+rVUlR1egIAvp0b2xAhoGr7FYIgPQ+B\nktRj/qmNf2Z6+6n5EY1/as6ePcOLFy8IDgkmJDhE8TckmKdPn7Jy5Wpatmip4SUwioCsw/3hXnwH\nT6FX9dI0L5JbJQIA5FFRyCJjiE6WYKM2PGdip4h21ykCIEPBgU+fv2DNxt8ZP3Ko0fVuRC8hoaGc\n/OscwaGhBIcolxDefwzAxtqa03/sw9bWRuUpMKYIfqFChYq8ffuGwKBAXF0UdbZFIqHK8OlKXdHn\n5jYUdVe3SGRKjeo1VJ8zIwC+NerPQ52MptcpSU6SahnzhERphoL89J07I8/mv2D8ldSqVZtatbSL\nozz4+wGNGzckV86cuBWv/C9c2f8/AutsNKxajuOPXtC8nFoq7BchYGJnjR0gi0z5rJPoSMVfXcWC\n1JAnxGkIgeu3bjOgV3ejADCSJk6OjnRs94vWerlcTr9ho/ilc3dOHEy/YBLAtxtM+gFJTIzH2tqG\nF8+fa6zX6mln0uBZWpppLUrCwkJwcXYCFMY/swIgK42ZvmOJRELV8tXnMBOqFiVic6HeJa39MnNt\npqbC/5QASAsfbx8GDhzErl3p14E3knmEJiY8/RCkudLaOqUKIArjr1cApEVcVJqb/V+/Nbr/jWQa\ngUDA8vmzuXnnHoH/Y++sw6Jq2jB+L42AdCkhYHeigGJhd3dhd74ioGKCnSDY/dmNgYmChV0YdCnd\ntcDu+f5YQWKbLWB+13Wu1z0zZ+bZV/fMPTPPPE98PO8HUMNEwPwF82BhUQ89elSMVsVNCJQenErP\nYPmJYFdcLzzsO5o2bclxr7u8D4CkHNl4DZKlB11hrjJ9lRrYeV2cbOD3+1Snwb80vXr1LslsSBA9\nPyNjsHT/CayZPpY1i9fQZC3v/6W0ECgNp/scKchje5tbGGACgR8UFBTQs1sX3H/kz1f9GvWv7b//\nViI9PQNDhw5GQsKfCuXCrAjwEgLF5d9/fEWrVv8i/JXOaMfLCRAQ/3K2uNrnJgoq+0z5Ab86Dvrl\nadWyFTIzMxATFSFtU6ol9Rs3htvsyZi8cR+O3Atg7aMWCwH1f3v/5S9RwGQyiQc+QST06dm9JNAT\nL2qUCGjdqjVePH8J644dYd2xPU6fPlHBWYLToMNpNQCoKARUVRRKLoAVECYlJRFqaupllr35nf1L\nanCTxGAq6OqBNGyUZeTk5ODg0AsBj/2kbUq1REFBAa4zJuDR4Z048vAl+mw7gcikVFZhsRDgFPa2\n9H2NimGCqewMto9R+bkAAP+A52jdsjnbOgSCIPTp2QP3H/uDweDtz1ajREBMjjwSClUxebEb/Pwe\n4NDhQ3CcPoWrEOBnWwCoOPCXrnf0mBfGj5si0BFAQLrObOxm2cJeorSDwNoSeEZEgFiI0OuAyHoO\nUHeYibPPfsChTRN0XOOJF39YWS9LtgaKxUDpqxL8iY/H3QePMGzQgEp+AwIBMDWpC0MDfXz79J5n\n3RolAkqjZdEaz54G4MePHzh//myFcmGEQHmUlOTx4MFdmJvVQ6tSCp+fGADVacCTloCorjj0dEBQ\n4BMUFhbyrkwQGgUFBax0HIdDCydj2sHLyFH8G+SnNpdkQNzKiil2DvzrF1BYWIhN23ZhnfN/ZDuA\nIDL69OzO12ShxooAAEgoVMWxY8exytkJf/7EVRh0OAmB0pT29C9/LyIiHJ+/fMSokf/y0Is7CBCh\n+mNgYAAVFRUoZMZK25RqT1TzUWi9/ABsGlth1bk7/5b5a2uyv4BSdbgfDyzGfY8XFs+YAjU1NTF8\nA0JNpXnTxkgK+wxLhWSu9Wq0CAAAbcs2WLx4CWbOnF6SvpcX5Y+zAf8G/tKC4OCh/Vi5wqnkM69E\nNkQAEPihoKAAqampMDY2lrYpNYY9K2bD9+MPPAj7zRrk2ez5c4PKTmN7/4F/ABpaWaCBpUWJbwCB\nIArifsfD1KQOz3o1XgQAwPJlK5CblwsfH2+BVgN4JbKxtLBARESY6AwlEADExsbCyMhILClWCezR\n0lDDQad5mLXvFNLl/sb/KBYD5S8ulDgH/t0SaN3IEh84pOclECpDVEwMzE0rhhkvDxEBYO39HT1y\nHJs2b8SvX78EFgKcrnlzF+LoscPIz8+XWDrbwkIGz4tQtYmJiYa5OQkoI0lo6trobd0KA+w6YOmh\nC3/zAnBZ7udzK0BfVwd9unTCmUtXAYCsBhBERnRMHMxNTXjWIyIArFMDDRs2xH8rVsJ1tQuAigNz\neSHATwwBBQUFLJi/GPs9d7Fth10/wiLIAE/EQNUmJCQEZmZEBEiSyHoOiGo+CltXLID/1xC8iUth\nFRSLgfIXG4q3BMqvBvTsbIPouN/4FRbOKidCgFBJmEwmQsLDYW5GVgL4JiZHHrNmzUZgYAAiIyMB\ncBcCAO9gQopK8rC0skDz5i1w546vSO0tjbADOhECVY+kpCRs2rwRUyZPkbYpNRL1WqpYMG44vK7e\nA2prsy5eZP7zB+AkBP6bORn7Dh8HnU4Xuc2EmseOfV4wqWOMhvWteNYlIqAUqaiNyZMmw8fnQMk9\nfoQAr9C3gwcPxbv3b5GYWDaWsyhWASo7kBMhUHWgKAozZjhiwoSJ6N69R5lMggTJEFnPAQ6LtsD3\nxXvEp6SzbgooBNihqKiI5TMmY+t+HwBkNYAgPG/efcDO/d44e8QHUZRhSSZBThARUI45c+bh5KmT\nyMnJKbnHSwjww5jR4+Dre4tjm6KmqIjB9iJUXTw99yM5JRnr3NYTASBFtLR1MLp3Vxy+F/jvpgBC\ngNNqgIWZCVLS0kqivBEhQBCUzMwsjHOchQO7tsKMD38AgIiAClhYWMDOrjP+97+yAYTYCQF+xEBx\nvf2ee9HBuiPy8tgnDikmMTGRb1vZzeJ5DfbsyshqgOzz4eMHeGxxx+lTZ8mpABlgoeNEHLzii0Jl\nASMFchECP0LDUUjPR0JSckkUU3ZCgMlkIjklRXjjCdWW+cud0LObPUYMGcT3M0QEsGHB/AXw9PKs\nEE6Y3eyd3zj48+ctRFhoKLwOeGLhonmIiYkp0w5FUTh16iSGDB0stN3lB/isrKwS/wZu9QiyTXZ2\nNiZOnICdO3fD0tJS2uYQADRrYIVmDaxw8f5T0NT/rgKUXw3ITP93lbnPXgjUra2KYX0dcPP2Hazf\nvgebd+9nlZcSAvEJCViwfBUOHD4uku/BZDIRGRWNrKxskbRHkB6nz13Eu4+fsNtjo0DP8ZcLtwYR\nkyMPy/Y9IScnh8ePH6FnT4cy5YqK8kLNnJs3b47mzZtDUVEeoaGhePjwASZMmAgGg4GsrCzs3Lkd\nAwYMRL9+/YS2/fnzQAQEPAWDwQBFUVBTV0d4eBj27zvA89nCQgYJViSjLFu+FJ06dcK4seMAgGwF\nyAAReh0wZuEa7N+0AhP69+T9QLEQ4HJ0UENdDb3s7dDr72eX7ftQUFAAiqLAzMvH48DnePH+E1Ys\nmoe7Dx4JZXd2djaOnzmHpGTWSgKNRgODwUD7tq0xdGB/odokSJ+Q0DAsc1mLR7euoFatWjz9AEpD\nRAAbaDQaFi5YCE8vzwoiABBeCBQPslZWVvDzu4ddu3dCSVEJcnJycHJyho6ODoKCgvhqq3T/BQUF\n2LVrB+o3aIgVK5ygoPDvr3XPnl3sHidUES5euojAwEC8fsX6d0EEgOzQrVd/bFk5G6+/fEdHSwGj\nN2amAbW1QWWngaauDSo7AzR1TZZ/QC1WauKRfXpim6cPtGrXRk5uHpo1bojNTsvwIyoWhgb6Atv7\n8fMXnL9yHfNnOsLUpG6Z+7FxFVOrE6oGBQUFGDd9NtY5/4eWzZsJ/DwRARwYO3YcVq9xRVhYGKys\nKh6zKB7Q+RED5WfYNBoN8+cvYFs3N08wZ6CIiHDs378X8+cvhLm5RYXyPAHbI8gOERERWLp0MW7d\n9IWGhgYRADKGvLw8Fkwei33nruGs6zz+HspM/7cawEMItG3ZDG1b/n2pK6mWNJH85w9qqyjzbSdF\nUTh0/BTy8vLg7uYKObmyu8BMJhMMJtkirKqs3uiBusbGmDfTUaAVgGKITwAHatWqhalTp8Hb24tr\nPWllwmMwGLhy9TLOnz+HLVu2w8qqvlj6IUiHwsJCTJ4yEf+tWIm2bdsRASCjTBsxCPdfvsPv3L+D\nKD+nBLhQ/sRACQX/HIpT0tKgo63F1+mB6JhYOK3dgJbNmmLJ/DkVBEAxJHth1eT+oyc4d/kqjnrt\nEfrvkIgADsTkyGPI5AU4c/YMsrKyJNZvTnY2MjMzS5wS09PLOhVlZGTA03M/3NatgZmpGZydXaGk\npCQx+wiSYeOmDdDS1MKiRYuJAJBhUi26Y+DoyTh4uXLBwEonGCojBEqLgYI8oCAPKWlpoCgKdDod\nVH4uCgsLkZ1d1rHv9Zt3cFq7AVdv+mLNymWw6diBc98UxyKCDJOQmIipcxfi1EEvZGo2EmoVACDb\nAVypY2KGbt264/TpU5g3b75E+uxg3RHHjh1FVnYWKIpCYGAAWrZsBQ0NDTAYDCgpKWHSxMkwNzcX\n6dE+4hQoO/j7P8HJkycQ9Potx5kbQXaYPHMhJvTrBJfp46Fc+C++CGprVTwZUJ6/WwIASrYFWH/+\nuzUAlPETAIAeHdvi7rNXuO//DHR6AXLz8vDm0xd079IZAJCXn4+2rVpg81qXMv5B3CArAVULJpOJ\nqXMWwnHieHS374zwIuHbolHlz8GVLqTREJZas2ViwvdXGD5iGN6+eQ8DAwOJ95+VlYUtWz2weZN7\nhTJOcQJKs337FkybNh16ev8cidjFNyAiQDZITk6Gdcf28PE+iCZ2/HlrW+nQKhxnlSTkPQGsGNkF\nTSzMsGHKUNaN0hECywuB8icEym0hlBw5BP4JgWJKiYHSfgIHjp1Ct+7d0KxJY4FtT01Ng+eho1i7\naoXAzxKkwy5Pb1y6dhMnfF/wFTeE2zuCTDN40L59B0yYMBFz582RyotWQ0MDjRo1xps3/J0aKD/A\nT58+E4cPHxSHaQQRQ1EUZs6agdGjRqN37z7SNocgAF4bXXD0+l28CvvrZV96YOeVXKhcSOHyWwMl\n2wNAxe2Bv8ycNA6Hjh4X6h2lo6ONWrVUERUdw7syQeq8+/AJHjv34n9HfUQSOIyIAB7E5Mhj+opN\niIyMxKlTJ6Viw8QJE/G///2Pr+BFQFkhoKOjC01NLYSGhnCMckhWAWQDHx9v/PnzBzOdPIgfQBUj\nr0k/uO06gqlrtyJXXoV1sxJOglR2Gns/AYCtEFBUVES/Ht1w6+a/0OSCMHf6VHgeOirVFSUCb7Kz\nszHOcRbWbPUCZdJOJG0SEcAHysrKOH78BJxdVrGNwCdu5OTkMGrUKJy/cL5CGTchUHzNnj0Hly9f\nxJUrl8VtKkFIPn/5jI2bNuD0qTPE0bOK0mfQcNi0aob/dh8qG0WwEgmGBBECfXp0xePAF8jP4J6s\niB1qamoYOWQQlq5ajfiEBIGfJ0iGhf85o4ttJwwcPkZkbRIRwCctW7TE8mUrMH3GtJIEH5LE1tYO\nnz5+QH5+vsDPKisrY/XqtdDU0oSL6yrc87uL4ODgCh7FBMny8+dPUBSF3NxcTJw4Adu27UCDBg2k\nbRahEuxb74y7gUG4+zyozN5+iRio5BFCXsydOhGHz5wTKvlQxw7tsGmNM/YfPAKfoyfw/NVrxMb9\nlsr7jsAiOzsbMbFxAID/XbyCl0FvsW9bRf+wykAcAwWgjkoBevXuiYEDBmHZsuUS73/9hnVwdVnN\n1uOX35MC2dnZ+P49GNHR0YiKjkR+fh5ysrMxbdp0NGnSRNQmEzjw6fMndOpkjUOHjuDK5UvQ0tbG\nieOs7SZBtwKIY6BsEenrjUkr3PDxvA90tViOfKVn9MLA0VmwnKNgdnYO9h89AefF/04z0VRqCdxf\nWHgEwiIiERUTi99/4sFgMFC7tgaWzJvN94kDQuWZOGMu3n/6jF3uGzBp1nzcv34RbVq1FPg4ILd3\nBBEBAlKUGAq7zjbw83uAFs1bSLTvtW5rsGE95+QQwoYyLioqwt69e6Cjo4OpU6eR40ISwG3dWjx8\n+ABv3rzB1KnT4OV5oMTJh4iAqs/+ZeMRl5CIc5ucyvyehBUDfIkAAA9evIWiogK62dlUbEMIMVCa\n4B8/4X3kOFYsmg9zM9NKtUXgDUVRUNKtgzYtWyA8MgrXz51EZ5tOACBSEUC2AwREwaA+3Dd7YNq0\nqaDT6RLrNysrC+rq3NOWCuPgV1jIgIKCApYvX4GGDRti5coVSE1NFdZMAp9kpGdg7JhxePLYHwd9\nDgktAAiyySz3Y/gWEo5zT16VGbRp6tpltwkqS7nIgq/fvEHHtm3YVqXyc4XaJiimaeNG2L5pHU6c\nPY/L128K3Q6BP7Kzc6CsrIRLp47ifcAjoQUAL4gIEIIeI6cjLS0VX758llifr169RKeOncTSdvEK\ngp1dZ7i4rMaWLe64cPECYmJiiLewmMjIzICmlhbs7DqXzBSJAKg+KKuoYNqydTh17TYA1uy9vBgQ\nB/l0OlTlqTLHB8tTGTGgoqICN+f/UEtVFc7rNuL1m3fIy+PcF0F4MjIzoaWpCXMzU5iZmgAQvQAA\nSMRAofj+9RNoNBratGkrsT7fvH2DpUuWia394lTC2tra2Lp1Oz58eA8/v3uIjYsFwEqWYmdrhx49\n+EibSuBJRkYGNGv/GxSIAKh+PPHzxbCB/VhL9n9n7DR1zRIv/3/RAblvEfArGOj0AiiW3q8vFgKl\nggqVhsrPFXqLoH+fXujUoT1evH6DPQcOIi+P5bCsraWFyeNGQ1dXR6h2Cf9Iz8iAZu1/2z3iEAAA\nEQFCERH6CwYGBhLdO8/Pz4eqKvsfczGVDSNc/Lyiojzatm2Htm3/nUMtKiqC27q1RASIiIyMdGhq\nsn7gRABUTyLCfsFwoD3rQzkhAKCCGCimWBTwvVrwN6zw289f0aEVGz+l0qsC5QRB8YqAMGJAR0cb\nA/v1xsB+vUvu3b3/EJ+/BaO7fWeB2yOUJSMzs0QEiEsAAGQ7QCj6Dh4BeXl5HDlyWCL90en0Sp0d\nLypicLzYwU5MKCgoQFmZ//SlBO5kZGRCU4tN9DhCtcF5ww4sXb8FWcVHcWvVLuPIV36L4N99zn4D\n7OoDAHIz8eLtB9i2Z+8PUMLfJETlqYyvQBkz8vKgR1YBREJGRia0NGvzrlhJiAgQAnl5efj4HILb\nurWIiRF/qM13796iXVvu0aE45RHgNNDzqiPK5ESEimSW2w4gVD/sujmgRxdbOG/bV3YGzkEMcBzg\nwVkwlCYjNRlaiviXfbD0VR4xCYHklFTo6hARIArSMzLLbAeICyIChKS2eUs4rVyF/gP64ffv32Lt\n6+XLl7CxsRXoGV6DP7v65Z8pLQQYDAbJaCdC0jPSka2oQ7YCqjmLNh/E/SfPsHWfd8W9+XJiACgr\nCPgRB8Xw/H2yEwNiEALJKSnQ1RFvQKSaQkZmJjQ1a4t1KwAgPgGVYsTMFSgoLICDQw/4+T2Aqal4\nzs5mZGZAi8vScflZe4XBvIC7IFBU+jcQFRUxyuQXKHYYTEtLg5YmWb4WBRRFISsrCxpkJaDao6ml\njVO+zzFtiD3oBQVYs3wRy5eo9ABcLATYzdj55MvPMLRoaMW7YnEfxX0W5LH1ExDWYZBOLyDbhiIi\nPSNDItsBRARUknHzXKCkqAQHhx64cvUamjdrLtL2eSl8bgKA1+Bfvl6xGGAnBLS1tZGQSGKKi4Ls\n7GyoqKjAQpMGgEFWA6o5hsZ1cMr3ORyH2iM7JwcbVy2HsvLfgZedGOCXUqIh8N1HjOzbo+RzmTwD\nYONL8NeZsMQGEQkBM9O6CAkNQ4P6fAgSAleKHQMtFZKJY6CsM2LmCri4uKJv396YMnUyQkJCRNb2\nt+BvaNxY8HC+5QVAPp3B8WL3TPnVBHl5eSgoKAiVu4BQlvT0dK4rO4Tqh56BIY7fDMSPkFA07NQN\nh0+fQ2FhIWvwLb4EpZRo+J2YDCN91kBRXgBwulcGLnEFBGHE4EG4ctNXJG3VdDIys6ClKf7VQiIC\nRESPkdPxPfgnGjVqBPuunTFr9kyRZBy0tLDEi+fP+YpOyM4PoPxAzw5+hEBhIQP9+/XH3Xt3+TGb\nwIWMzAzUJlsBNQ5dPX3sPf8QF08exaUbvmhi2wOnLlz5l6CntCAQUBi0btIA1+4/4TrYVyjjsf0g\njH+AtrYW0jMySJAxEZCenkEcA6sa6XJacHF2RfC3HzA2NkYnG2ssXLgAcXFxQreprq6O5ctXYN16\ntwo/LE4e/Jy2AQoKGBWuYjgJgdK0b98Bb94ECfoVCOXIzMgoiRFAqHl0sm6PB77XcMx7H46ePY/m\nXXrhwvVbYDKZFSvzEgV/VwNG9++FnxHR+PAjlGvfXFcEROQo2K51K7z/KLloqtUV1naAhtj7IQmE\nxIhqXgJ27NyO48ePYdLESfjvPycYGhoK1dbdu3eQlp6O8ePGl9wrLQLY+QKUHtgLuPgHKJVyDFRR\nZv25tLNgaf+A6zeuIjY2BllZWWVECY1GQ3R0NA54eVcqpkFN4M6d2/Dx8cbNv8umlfUJIAmEqi4U\nRSHs2WWs2bQFebm5WO+0DEP69eYeiKz8YP13Rs9kMjHNaT2Or13K9fkK/gHlMhGyfUYA/4C8vDx4\n7NwDOTnWv+vy74k6xkaYNW0y3+3VVOx69ce2jW6w69Sx0j4B3N4RxDFQjOSpGmKLx1YsXrQE27Zt\nRavWLTB9+gwsW7ocurq6fLeTk5ODu3fvYv36DULZwU0AFJcXC4F8OgMqyvIoLGCUEQLFjBo5imM7\n6zesK0mEQ+BMRmYmaktgr48g+9BoNPTu2R29enSD7737WLPJA+57D2CDqxP6dLFhP5grqbKdtd/2\nfw5767aVi2TKxklQUFRVVbFhtTPbMgaDgc3bd1eq/ZoCiRNQTYjJkUdRbRMs27Qfb4LeIS01Dc1b\nNMWGjeuRkcHDWQesaIGuri5wdV0NbW3xnb/lJRT4gUajkTTEfFAcKCgmR56cDCAgvEgPEQx9NOs1\nAZeffIbT0oVY7uqGLoNHwT/oPfuHygUfevQiCBGxvzF91BC+4goIiqgiCqanZ0Bbiwhgfig+HSDu\nOAFEBEgSnXo4cMAbzwNfIioqCk2aNsLWbVuQXRxWtBxMJhOurs5YunSZ0NsIBNkjPSMdmuRFSGCD\nnJwcRg4djC+vnmHeDEfMWrQMPUdOwMtPXzk+8+bjF7z+HopFk8eU3OMkBMQhEAQhJTUNOmKczFQn\n0jMykKZmKfZ+iAiQMDE58lA0bIB1e0/i8SN/vH//Hl272SMpKalC3eDvwWjXvgPMzc2lYKng6Gjr\n4OXLF9I2Q+bJyMiAJtkOIHAgvEgPUZQhbEbMwe1XvzBxzEiMmjwdB06dK1vx72rAjfuP4LxgdoV2\n+Io8KGhsgkqiq6ONoHfvWccjCRwpKipCfj4daurqYu+LiAAp0rhxY5w/dwH9+/dH7z69kJiYWKb8\nxfPnSElJQXJyMs+2SjvvSYv58xfg8ZPHCAp6LW1TZJrExESSN4DAFwoKCnCcNLoQdLUAACAASURB\nVAGBfr7Ysf8A9h07XaacUlQBKAr7j51mG4aYKxIWAACgq6uDRXNmwmntBhQVFUm8/6pCYlIyNDTU\nJbK9SkSAFInJkUdsrgI2rN+IoUOHwqFXT/z586ekPO53HAb0H4CrV6+wfV5RkfvAX+zpr8TGwa88\n/NThBY1Gg4uzK+753cO7d28r3V515NGjh/Dzu4d+/fpL2xRCFSG8SA+USTv4376OfT5HsMPrYElZ\ndGwc6llYIDEl9d/smh8xIAUBUIyVpQXmOE7FKreN/2IkEEpgMBhwnLcIU8aN4V1ZBBARIAPQaDS4\nrV2HMWPGwKFXT4SHh+PS5UtQUFCAhYUFIqMiBWqPnVc/t0Ge3RFBYaHRaFizei1u3rqJ79+/V6qt\n6kZISAimTJ2MM6fPVpktHoLsYG5miqd3b+DQ6XPYsu8AQsMjsd3rILrZ2qCPQ0/4PQ0s+0CxGGB3\ncaKSJwP4pWEDK8yYMhFrN2+RSH9VCae1G1DEYGDH5vUS6Y8cEZQBij3EXV1WQ1FREZ272OHqlasY\nOWIkAFbUwNDQUNSvX59rOwoK8hWiBqooy5fECxBkts9OSPALjUZDq1atkZPD3uGxplEcz2HRogVY\n5eQMy/Y9EZMjZaMIVY7wIj3AUA9P796EXe8B+PbjFzy3bGDFl69nBpfN2zCwf3/hQwBLSAAU07hh\nA7ISUI6gt+9x8doNfAx8gmgYSaRPshIgY6z8zwka6urQ09Mv2Q8aMWIkrl4TbEtAUYjZPbt6wvoa\nvH37Bu3atRfq2epCYSGjTECn0LBQ9OrVR4oWEaoDdYyN4OG2GgUMRsk5cjk5OWioqyEjM1OwsMOV\nyV1QSb7//IXGDRtIvF9ZJjQ8AjbW7aEjwXTMRATIEMXnxk3NTBETE11yX1tbG2lpacjKymL7XGkh\nUHrQLi8ESl/s7rN7jlM/3EhLS4OWlhaJGVAKiqKQmJgIAwNy1JNQOcKL9CBv3AzRMXGgqdQqieY3\nclB/HD93qWxldvkIhBj4hU0tzI2rN30xfNBAkbdblUlITIKhvr5E+yQiQAYxNTVDdHR0mXvLl62A\nq6tLGcdBTpQXAuwG9fIDP7u67FYBGAwG2+OMpbl27SqGDxvB087qTPm8DtnZ2aDRaFCXwJEfQvWn\njokZomNjSz7TVGqhcfMWMNTXg/fx01yeFBxhBEBaWhrXpGcURSErOxu1JRAbvyqRkJQIQwPJigDi\nEyCDmJqaIjqmrAjQ09PDli1bsWTpYhz0OVRhlq2oKF9m4CkewIt9BMoLgeL8Apxm/Sz/giL8+PED\nHz6+R3R0JCiKgry8PIyN6yA6OgpKSkqw72IPW1u7MuGCQ8NC4eg4XchvXz1JTEyAgYGBtM0gVBMM\njIyRnJIKOp0OZWXlkvvjx4+D38PHOHH5OqaOHFrpfvgRACkpqXj/6TM+fP6C7GyWs4uurg7S0zNA\nL6DD3NQU/Xr1hJmpSckzL4PewLajdaXtq24kJCahvqWFRPskIkAGMTM1QxCbbH21atWCvr4+x2X2\n8kIAYO8sCHAf/AFg+45tKCwoQKNGjdHV3h6WllMr9Jufn4/AwAB4bHFHYWEhrCyt0KJFS5iamPL1\nPWsSt+/4omnTZtI2g1BNkJeXRx1jI8T9/gNLi3plyhQUFGBaty7HAZxbCGBBZv3hEZHYse8AGta3\nRNvWLTF3+jRoaFRc6YqKjsGd+w8RExcHZSVldO1sg/uP/OHm/B/ffdUEsrKy8fhZIGZPmyLRfokI\nkEFMTU1x5crlCvdjYmJgUteEzRP/4CQESsNOFJSuk52dDYrJhIvLaq5+ACoqKnBw6AUHh14AgNDQ\nUDg6TsXly1e52lgTKP33EBoagh3bt+HJk2dStopQnTAzqYvo2LgKIuBp4AuscVrO8TlR7e9fvnEL\nm9Y483RiMzczxZzpUwGwJg5Xb93G1+DvJNtoOZzWbkDPrl3QyVqyDtVEBMggurq6SE1Lq3D/5s0b\nGDp0GM/niwfu8mKgGF4e/7fv+KJ//4F8OwIWU79+fdjY2CAzM5MsfYP195CfX4A5c2dh1SoXWFnV\nR3wBefERRIOujk6F9wSTyQSDyRB7Nk+KopCaliawF7uKigp6du2CJ88CeVeuQTx+GoBb9/zw5eUz\nsScMKg9xDJRB1NTUkZNT8SD57z+/UadOHb7bUVSUF3ggB4Dv37+hdeuWAj8HAEuWLMO9e3eFerY6\ncuiQN+Tl5bBw4UIiAAgiRV1NrWQPvpg/8QkwlkCysS/fgtGymXDbW4YGBjA2NCT5A/6SnZ2N6QuW\n4OCeHdCSQmIxIgJkEHV1dbaBdtq1a4+3b98I3F6xGOAkCkqXZWVlQFtLW+jjfcbGxohPiBfq2epG\nSEgIPLa449DBI4jLE+/MjFDzUFdXQ05u2f39unWMEcfHCaLKcvPOPQzuL3zMC7tO1nj+qqLfU03E\nae0GdOtsi8Y9x0l8FQAgIkAmUVdXZ5teeED/Abh953al2+cmCq5fv8bXlgM3FBQUwGQyK9VGVYfJ\nZGLW7BlwcXaFsnEjaZtDqIaoq6khm82KYR0jlsOguKAoCjm5uZU67tq1sy0CXrwSoVVVkyfPAnHz\nrh92e2ySmg1EBMggampqLOc8iipzX1lZGUwmU6zLaGHhYTzDE/ODnFzN/qfl5eUJGo2G+fMXSNsU\nQjWF3XYAAIwePgQXr94QW79Bb9/Dul3bSrWRmZUFbSksfcsSxdsAPru3S2UboJia/aaWUfLz86Go\nqFhBBABAn9594Od3Tyz9xsfHw5BEtKs0+fn5cFu3FocOHqnxYoggPugFdLbbdoYGBkhISmT7/hAF\ndx88Qr9ePSvVxq/QMDRqUPnJRlXG6/AxWLdtgwF9e0vVDvKGkkH8/f1hZ2vHdgCxtbXDy1cvOT6b\nlZWFvXv34NWrlwLn67569QqGD6/Zkf5EgbKyMuTl5aGlpVWSHIpAEDWP/APQtbMt27JWzZvj05ev\nHJ+9/+gJzl64jN9/BPPfYTAYKCwqhIqKikDPlednSCga1reqVBtVHXU1NdSurYHwIj2p+AIUQ44I\nyiAPHtwvOXtfHhqNhtq1ayM9PR1aWlplyuh0OlavdsWiRYvx48d3uHtsRmFhIfR09dCte3e0aN4C\ncnJyiIyMhLe3F9RK7enR6XQUFBTAxIR7HAJe5OTkICE+AafPnGbNRCgKTCYT8vLysLbuiEaNpL8/\nzunoJMB/fgROUBSFy1cug6IofIlORf1GZGWFIHrSUlPw/dcv2HbswLZ8yIC+2L7XC61btqhQ9uCx\nP0LCwtHdvjOu3vRFfEIiaDQa2rRqgW6d7UqO/XkfOY6ExH8hwimKAgUK9rY2lbY/IioaTwKegwaA\nAsVqm6JgoK8Ph272lRYZss6f+Hg8e/ESBQXSPyFBRIAM8vDhA1y4eIlj+fBhI3Dt2lVMm+ZYco/B\nYMDV1RnLl6+AmZkZrKysMGAAKzlHUlISnj57in379uKAlzdOnT6J1avXQkPjX9zuhIQELFg4H1lZ\nWWXuC4qamhpWrXJGUVERaDQa5OTkQKPRwGAw8OTJYxw/fhR2dl0waNAgofuoDNwEQHG5sELA3/8J\nXFydwWAwcfHCJdRv1ESodggEXjz3fwh7W5syIYNLU6tWLdAL6CgqKoKCwr/XfNDb93j74SOcly8B\nADRtzBLlDAYDHz9/xbEz/4OGujpGDBmI9IwMrHNZWabd1RvckZKaWmn7nZYsQmJSEmg0WpkrPiER\nm7btgoqKMqZNHI+6dYwr3ZcskZmZhe17PXHgyHE4ThoP52WLkS5lm2gUl40jGo2GsFTx7CsR2BMV\nEYYJA7sgMiKa6zE9F1dnuG/2AMBS6GvWrsaE8RPRpAnngef9+3f48OEDfv/5jTWr11YoT0pKwsaN\nG+Du7iG2RDcURWHHzu0YPWoMzM3NxdIHJ3gJgNLwEgIfPn5A61atS/6OfHy8sWvXTmzctBmjRo6C\nnJycxLYCrHRoYtv/5QfynpA8TgscYd+6ARbOmcmxzuOnASgsLEQfhx4AWKl7z1y4hE1rXLi+W5zW\nboCerg4mjhkJY6OKOe1Pnj2PWrVUMWrYkMp/EQ5kZGRi6559cHdbLbY+xE16egZS09JKIjoyGAxY\ntmyPbp1tscF1FczNWOHVJbEVwO0dQXwCZIyAx35wcOjF85y+pYUlDhzwwrt3b3Hnzm0MHTKUqwAA\ngLZt2+H8hXOYNHEy23J9fX2sXr0Grq4ubIMViQIajYa+ffuxzY0gTgQRAKXrUxSFFy+eI/h7MAoK\nCsBkMrFm7Wp07NgBvr6+CA0NRWRkJJ4FPIOLiytsB4xHXJ4i8QUgiA2KohDwxA99evbgWq9bFzuc\nu3wVN27fRUxsHK7duoMNrqt4vlscutkjNDyCrQAAgCkTxiI7JweXr98U+jvwQlOzNuTlqs5vKDEp\nCU8DnyMhkeWQ+SskDNbde8Nx/mL8CglDeEQkPnz6AjqdDjevC2DUaSN1X4BiyEqAjDF7whBMGjsK\n48aO41qPoijExcXh/fv3ePvuDYwMjTBv3nye7WdnZ/Oc5SckJMDdfTM8PLagVi3R5xFnMBjYtHkj\n3NauE3nbnBBUBACAnBywdOkS3L13B4qKSoiJiYaOjg4sLCzQqFFjBAYGglFUBAaTASaTiX0nrqJl\nG8nG/QbISkBN42fwV8ybMADhn9/yHNDz8/Px5dt3vPv4CfcePsaFE4c5biEUQ1EUcnNzoaamxrXe\n0VNnoK2lheGDBwr8Hfhhj5cPJo4dBT1dXbG0Lyo+f/2GgaMnwFBfH2ERkaBAgQYaFs+dhUvXb4JO\np4PBZKKoqAg21u3hceS6xG3k9o4gPgEyhKFiHoKe++Pk4YM869JoNJiYmMDExATy8nLQ0dHhqw9+\nlvkNDQ3h7OwCZ+dVYhEC8vLyMh9MKDc3F47TpyAnOxtv37yHpqYm6HQ6IiMjYWFhUZL8hMz4CZIm\n+OlV9OnZna+onioqKujQrg06tGuDqJgYngIAYL1beAkAAJg+eSKOnDyN6753MHRgf75sF4SO7dvh\n9Zt3Uj9Cx41H/s8wznE29m1zx9iRw0BRFJJTUpCfT4epSV24Of8nE7N9bpDtABkiIiICerp60NfX\nF+i5V69foWPHTiK1xcjICKtWOcPZeRXy8vJE2jYAKCoqoqCgQOTtioKkpCT0H9AHmpqauHnTF5qa\nrEAeysrKaNSoEREABKny6cs3WLdrI9AzMbFxMBEg7wi/zJgyCYlJybh5R/SxS9q0aoEPn7+IvF1R\ncfrcRYyfPgeXTh3B2JGsKKs0Gg36enowNakrZev4h6wEyBC6urpIS6+YPZAbBQUFkJeXF0tQGmNj\nYzg5rcKqVU6w7tgRgwcNrtTJgdK0bNEKgYEB6NLFXuwZz7jBYDBw6JAPAgMDkJqaiuSUZMTFxWLO\n7HnYsGEDYnMVAOmf4iEQStDV0UZaeoZAz1y7dRsjhw4Wiz2zpk3G4ROnsd5jOwb374PWLVsInXuk\nNCoqKsjOyUFmZhY0NNRF0qawhISGYdteT/z+E4/klFSkpKaiiMHAk9vX0LRxI5mf7XODiAAZQkdH\nB9nZ2cjPz+frnGxSUhJ27tyO8RMmis2mOnXqYO/effjy9Qv2e+5DdnY2unSxR88ePSuVD7xHjx44\nc+Y0Xr1+VRIGOT8/Hx7uW0RlOk+Cg4Mxb/5sqCirYMaMWdDT04Ounh4M9A1gaGgo1ZcOgcAJYyND\n/IlP4Lv+rbt+SElNQx1j9o5+omDm1EnIycmB7737uHD1OvR0dTF0QD/Ut7KsVLsD+/bG4ZOnkZmZ\nBQBISU3F+NEjYNvRWhRm84TBYGC3lw+27NqHJfNmYUj/ftDV0Yaerg5MTepCRUWlSgsAgDgGyhx2\nzU0R4P+U6/E5JpOJkydPIDomGosXLakQNEicUBSFV69e4X/nziIvLw/r121A3bqiWfpat94N69zW\ni6QtdhQ7BxYWFmLXrh3w8tqPtWvXwdFxRslKSlVM90scA2sW1y6cRsBjP1w/uodrvajoGOz1PoRe\n3buiX28HCVnHIjU1DVdu+sL33n2MGT4U40YNF4moPnvhMtq3bS2RkMPfvv+A47zFqFVLFUf274aV\npUVJWVUb+IljYBXCpI4R4uP/cBQBHz5+wMkTJzBp0qQywYIkBY1Gg42NDWxsbJCVlQWvA55QUlTC\n3LnzoKqqKnS7KSkp0NbSFqGl7Pn9Ow4jRg6DoaEhnj9/BVNTM7H3SSCIEn0DI2QlRnMsLygowIHD\nx0AvKMDmtS6V+l0Ki46ONmZOnYSZUyfhxesgLHFyxfhRI9CxQ7tKtRsSFl6y/y5OvA4dxTqP7di0\nxhkzp04qs91a1QQAL4gIkCFM1RgwNDTCn3jO8bwPHTwIT08vyMtL3ylNQ0MDq5ycERERgfXr3dCu\nfQeMHDFSKMX/+fMntG7dWgxW/oPBKMS48aMxcMAguLisZkUoq4Izf0LNpm1dFWzhsh1w8n8XYGPd\nodIDrqiw7WiNTh3a4+yFy7jmexsLZ88UOhJgcQhycXLzzj147NqLN/73Uc+cNUmobgN/acjpABnD\n2MgYCVxEgJGxkUwIgNJYWFhgy5ZtMDI0xPLly/D+/TuB2/j06RNatmwlButYUBSFufPmoF49C7i5\nuUFJSaHSeQIIBGlgbGiI+IREjuUG+npQUpKesy075OTkMGncaKz+bxlOn7+I7Xs9kZubK1Abktjy\nCv7xEzMWLMWV08dLBEB1h4gAGSImRx5MJXWBTwjICl262GP79h34+vUr1qxdLdCPNj0jHdraot8O\nmDN3NpSUFdC4cUN8+fIFhw8dAY1GQ0yOPDniR6iSJCiZIjMri2OsDZM6dRD7+4+EreIPdXV1rFq2\nGKOHDYGb+zY8e/6C72dj436L5ehdRkYmaLX1Qautjz7DRmP7RreSVRRZieonTogIkDHev2SlEa6q\nyMvLY+zYccjLzRVoW+Ddu3csx8D1boiO5rzfKSibN7nDxMQEEZERuHnjFl9BUAgEWeb186ewsW7P\n8ViwSV1jxMb9lrBVgmFuZoq2rVoKtKp55/5DvH77Dms3bcGx02dFZoumZm1cPHkEADB+1AhMmTBW\nZG1XBYhPgCyRGono6CjY2LDPEQ6wBtnymcFkjSNHDmP6dM6JTcqTl5cHW1tbOK9ywfv37/D58yeY\nmYlmKY6iKBQWFuL1qyDUqVOHzP4JVZ43fhcxqF8fjuX6enpITEqWoEWCk5ubi/efPmPcqOF8P5OS\nmgrPHVugoqKCde7bRGpPSFg4+vd2wJb1awBUbx+A8sjuSFIDuXPnNvr06ct1gDc2MkZ8fDxMTEwk\naBn/pKSkIDEpkWcyo9J8+/YVzZux8p7LKyigsKhIZPY4rVqJsWPGok2btkQAEKo8FEXB1+8+/K5d\n5FhHTk5OqkdG+eHAkeOYP1Ow0035+XS+4qcISlh4BHZ5+uDt0weg0Wg1SgAAZDtAprh92xcDBw7i\nWsfExASxsTESskhwPL32Y+GCRQI9c//BfbRpwwqDqqioWBI8SBQEBQVhyJChRAAQqgXfv36CspIS\nGjdsIG1ThOb3n3jQ6XSBHO9SUlLFlm/k87dgNGnUAPXMzWqcAACICJAZcrKzEfg8EL17cU+WUdfE\nBLFxcRKySjB+/PgBfT196PKZ9YuiKOzbtxctmrcsWdlQVFBEkQhXAqZMnoITJ0+IrD0CQZo8unsT\ng/r1qdLRLD0PHcHC2fxvF8bG/Yab+1asXLJQLPb07+2AX6Hh+BkSKpb2ZR0iAmSEQP8HsO5gXZKs\nhh1MJhMMBgOxMbK5EnDs2BHMmMHfj5vJZGKz+ya0bt0agwb9W/1QUFBAkQhXAqZOnYabN28gLTVF\nZG0SCNLikd8trv4AACtdeFJyskxuCbz/+AlWFvVQuzZ/OUh+hoRi5/4D2LF5Pd/PCIqysjKmTx4P\nn6MnxNK+rEN8AmSEx/duYcAAznm5w8LCsH3HNnTq2AmjRo2WoGW8YTKZ2Lx5I/zu++Htu7fQ09OH\nkaEhDI2MYGRoBD09fejp6UJXVw96enrQ0NDAuvVuGD16DNq0/pcNraCgADduXEeLFi1EZpuenh46\ntO+AoOdP0WcQ/05IBIKskfDnN6IjwtDZpiPHOpu374KioiIG9u0tc6sFiUlJcJy/GAoKCrh07Sb0\n9fVgbGAAYyNDGOjrQ09XB3q6un//q4PgH79w9ZYvtm9aV8ZP6sevEJFnIB0+aCAmzpyLhZtE2myV\ngIgAGYDBYODxfV9sWuPMtpzJZGL//n3Yu2cfX/nAJQlFUVBRFTzq3tevwWjYoGFJG1euXkHQ61eY\nOs0RTZs0FamNISEhaNC4mUjbJBAkzWM/X9j37Msx62bAi5cwMzHBpHGyNUkAgONn/gfHeYsFesZx\n4jgc8dpbImbiExLgdegYjI0Msc5lpUjtCwkLR7MmjUTaZlWBiAAZID74JYwMDGBpyT7j1rFjRzFx\n4kSZEwAA8OzZUwDA88AXqF1bEwoKCpCXly+5yn+Wl5eHoqJiyYssIOAZrl27hmHDhmHbth0ity8x\nMRHpGemoZ1V1HakIBAB4df8Kxo8awbYsNzcXV274YvcW2ZzKFguA729fQF5Ortz74e9nubKflZWV\nQaPRkJ2dDa/Dx8BgMLBi0XxoatYWuX2v376Ddbu2Im+3KkBEgAzge9sXAwYMYFsWGRmJpOQktG/f\nQcJW8ceEieMBAB06CJba8/v37zh+/CisO3bCzp27xLZ0+eZNENq34xxYhUCoCuTl5uLp8xc4ddCL\nbfnO/d5YsWi+zG0BAChJA3zn8jmBTjUUFhbi+JlziIyOxrwZjjCpW0dcJiLo3Qe4u7mKrX1ZhogA\nGcDX1xc+3j4V7lMUhb1798DDY4sUrOINk8lEYmIiXF1X8/0MRVHYuXMHNDQ0sGmTO5SUxJvAJ+hN\nENp3kE0BRSDwy/OnD9G+TWtoa1dMG/4q6C1M6hqLdZCsDKs3ugMA+vbqyfczv0LCsP/gYcyaNhmz\npk0Wl2kAWL5In75+Q7vWrZAk1p5kEzI9kjIxURFISkpkO5O+c+c2Bg0aJJYAGaLgytUrAACnlav4\nqk9RFLZs9YC9vT1mz54jdgGQnZ2Ne/fuwVrAVQoCQdZ4dO8WBvVlfyrg4rUbmDphnIQt4p/9B4+g\nUYP6fK9SREZFw/vocezesgktmonWP4gdvvfuw7KeOTQ01MXelyxCRICUeXTvFvr16882hnZAwDN0\n795DClbxx4S/Lx5+RcrevXtgZ2sHa2vO3s2iIjIyEl272aNFixbo06ev2PsjEMQFk8nEk/u+GNSv\nYgyRdx8+oUPbNjK5DQAACYmsbIfFsfl58ftPPHZ5emPrhrViD41OURT2HjiIectWwmunbK62SgIi\nAqRMbFQEGjSouE8WEhICKyv+1bOkSUlhnbv38T7IV30fH280adIE9vZdxWkWAODpU390sbfD1ClT\ncfjQEY7e1ARCVSAvNxeZGekwNzOtUHb1li9GDOF8tFjaDJ8wFQDQsjnv0zlJyclw37EbWzesFfsq\nIZ1Oh+O8RTh25hxePboHezvO+VqqO0QESJm21rZ4+fJlhfsXL13A2LGyu8Q3fMRQAKxgPLw4ceI4\njI3rSGRG7ut7CxMnTcCJ4ycx1HEpYnMVSMhgQpVGTV0dlvUb4d2HT2Xu5+bmQllJWewDprAkp6Tg\nxes3WLWMdxjxtLR0rHPfhi3r10BVVVWsdjEYDPQZNhrZOTk4e/c1mHXb1oiUwZwgIkDKdLDpghcv\nnoPBYJS5r6KsUuGeLPHy5UuYmZnx9Lo/d/4cVFRVMWTIELHblJOTg8VLFuF/Z8+hYSfuUdUIhKqE\ntW1XPHtRdrKgoqIi0hDboiY+gbUVsHjuLK71srKysXqjOzavdYW6uvj35Q+fOA0mk4kLJ46gFkkt\nTkSAtNE3NIKBgSG+fP1S5n7Hjh0RFPRaSlbxh4GBAdfy6zeuo4BOx9gxos/PzS4k6patHujcuQvq\nte0u8v4IBGlibWuPp4EvytyT9WyBWVnZAIBaqrU41snLy4Pzuo1Y7+oELS3OIdOFpfz/n5SUVKzd\nvBWrth5EJJP7+6umQESAlPkTF4vU1BRoqJeNi922bTv8CgmRklX8UVuDc9COy1cu49OnjzAwMMD5\nC+dFPmP577/lUFJWKBFPISEhOHLkMBau3i7SfggEWSDm8zPo6uhUuG9ooI+cnBwpWMSbrGyWCODk\n10Sn0+G0dgN6drPHk2eB+PDps0j7ZzKZkNM0wILlTiVhhl03umPsiKFo3KylSPuqyhARIEVMahXB\nY9VczJ07D1ZWVmXKoqOjIZsugf/QqM1eBFAUhXt376BN67bQ0dGBSd262LLVQ6R9L168FADQvn1b\nuLg6Y9GiBfhvxUoYGsvmWWkCQVgyvj3G0VP/w/ZNbhXKfsfHo6hINrcNM7NYQYI4+Tafv3yNdTRP\nXR1NGjXElRu+CA0LF1n/cnJyGDqwP7wOH0Nru+7wOnQUN27fxQZX/o401xRIsCAp8uDBfYSFheH8\nuQtl7jOZTPj4eGP7dtGH0RUltTmIgNDQUHTpYo/BgweX3MvMzMTZ/53FhPETRNL3+Qvn0Lp1Gzg5\nrcK4cWOgpqaGmzd9ES/avCIEgtRZsmo1PNa5wrDc9tvjpwFo1byZWMLoioKU1DQAnFcCQsLCsXGN\nc0m5m3N9LHNeg81rXUWSMTA5JQWfvwbj9KED8Dx0FAtWrMKxA3uhpaWJVNl1pZA4ZCVAisQnJKB9\n+/YVcgIcOXIYU6ZMkdmjbbt37wIAOE5zZFseEPAMXbrYl7nXv/8ApKakICjoNSIjI+HpuR95eXlC\n9X/jxg0cOOCF7du2Y+7c2QCAy5euyOz/LwKhMsQnJKKzTacy93Jzc+F77z5GDx8qJau4U1hYiDlL\nVgAAatXi7BNQWiAoKipinfNKrNnkgaKiIpy/fA3PXwnnF0Wn0zF8wlSM4kxJWQAAIABJREFUHj4Y\nX4K/4/Xbd2jZvBmmjBe9f1JVh4gAKaKiolJhIIyKikJqWiratJHNZBaXLl+C06qVmDd3PuzsOrOt\nExEZAQsLiwr3FyxYiLv37uLq1StwcOgFV1cXvHjxXKD+P3z8gLnzZsPL8wBmz56F/Px8DB8+Aj17\nOgj1fQgEWUdFRQX5+fll7u3YdwDLFsyVyTgiDAYDSrqsbbnUqBC2NsbExrENc6yrq4OZUydh3rKV\nMDc1QVh4JNa5bxPI74GiKMxd+h90dXRgqK+PXZ7eUFRUxFHPPSSHCBvIdoAUUVVRrfDjPnzkEFa7\nrpGSRdx5+tQfEyaMQ7du3bBnz162dYq9cdn98Gk0GtzWriv5vHPnLpw+cxoPHz3E8mUroMbjuM6f\nP38wcuRweLhvwYaNGxAeEQ5bW1vs27sfAEg8AEK1REVZGXl5/94TKSmpkJOjyWSuACaTCU0TVjbU\n0I9BbHMdAMDTwBfo2pl9gJ7mTZvg0D7WaqNNxw74/Sceqzd6YHD/vuhuz37iUZod+7zw4fMXLJ47\nC47zFkNBQQFeO7eifdvWNTYWADeILJIiKirKyC+3EmBsZIz09HQpWcSZDx/eo1dvB+jp6cHv3gOO\n9SIjI2FRr+IqADtoNBomT5qM6Y4z4Oa2Bk+f+nOsm5eXh5GjhmPSpMm4dOki3r9/h3lz5+O+30Oe\nRxUJhKqMqqoK8kpNFnR0tJGfT5eiReyhKArtuzogJycXgfd9YWXJ+T3w41cI3xkF6xgbYZfHRsQn\nJGLNRo+So4fsuHnnHvYcOIgVC+dj9uIVMDYyxLO7NzFz6iSBv09NgYgAKaKqqop8etmVgK7dusGf\ny2AoDYqKitCxEysJT2RENNclyIDAgAr+ALyoW7cutm/fid9//sBt3Vpk/fUqLs3//ncW2lraiI6O\nxrOAZzh69Dj27NkLJSUlxOTIk1UAQrVFRVm5zIqhLG4BAMDm7bvw4dMXnDnsDbtOvPODCPI9aDQa\nxo0ajgWzp2Odxzb4PXzMtt5iJ1es/m8Z5q9wQsf2bfHu2UN0sm5foyMC8oKIACnC8gkoKwKaNW2G\n4OBvUrKIPcXJjbZt3c4zRGlYWCjq168vcB80Gg3jxo7DvLnz4erqgri4uDLl/k/9kZySgoCAZ3jq\n/wyTJhJlT6gZqKqqllkJAIB65qaIiIySkkXsSU5JBQBMGDOSa70/8fEwMhRu9c7QwAA73TcgMysL\nuzy9y5RFRkUjOzsH7jv3YPLY0Xh06yqMDA2F6qcmQUSAFEmn1JGVU3Y7gEajyVwUsGLFfujwIZ51\nlZWUKzVTMTQ0xLZt27Ft21bEx8cDYC0zPnv2FLt37caboHfQa9ihZPZPVgAI1Z0ipdqIzi0rvrt3\n6YwnAYFSsog9M6ZMBIAKfk7lefn6DeztbCrV16hhQ9CiaRPs8/73TnoSEIieXbvgwY3LWOJxGDE0\nY7ICwAdEBEgRZRUV0OkVfzBmpmaIjo6WgkWc6dChA0JDeUcwLGJU/gCuiooK3N094O6+GRkZGQgJ\nCYGCggI6dbKBlhZ7RyMCobqioqIKjYLEMvcsLeohPEK2VgKaNWkMAPB79IRrvbDIKFjWM690f716\ndIOVRT0cPXUGAOAf8Bzd7Tvz7WtAYEFEgBSx1FFCIb3iWfmuXWXPL2D27Lk868TFxZVsHVSWYr8A\neXl5PHv2FPb2XUlGQEKNRF8VFRwBi1fbZGnVsNgm7yPHOdZJS0tHVna2yDIFJiQlQUNdHRRFwT/w\nBaxsBpKZv4AQESBFVFUrHhEEgAYNGiAk5JcULOLM6FGjAbCClLDj2bOnsLA055osRBDc3TfDw2ML\nkpKS4LHFHaNGjhJJuwRCVaP86YBiGta3Qkio6MLsioIWzZri01fOPk065g1w+94DkUwWngY+B41G\nw+jhQ7Fhyw7o6mjDon7DSrdb0yAiQIqwCxYEyKbKV1FRQUR4FMfoX+HhrJeRqBS+lrYWUlNT0bdv\nb/y3YiX69x8gknYJhKqGiooy28lCd/vOePwsQAoWceb143v4+oq7TeVPRAmLkaEhFBUUsW3Pfpy/\ncg13r5yX2ZMTsgwRAVIkJSWFY6jb+lb1ERYWJmGLuFO3bl2OZcnJyQC4hwgVBHU1dfTq7YC5c+dh\nzpy5ZBuAUGNJSU1j+54wNamL2LjfUrCIM6qqqtDVrZjtsDSiWi1sWN8Kl6/fxKHjp/Dw5hXk6DQV\nSbs1DSIC2GCqJpmsXD4HvTF50hS2Zd26dYe/P3cHG1kiK5u1h5+SmlLptpKSknD2f2cxZcoULFmy\nlAgAgsxhqZAskX6yMjNw8eoNjB81gm25LJ4m4kVqWhqYTGal2zl84jQ+fv2GR7eugm7QQgSW1UyI\nCGCDJAad3JwcHD9+DAsWLGRbbmZmhrBw2VoJ4AY9Px+NGzfBtm1b8enzJ6HbSUtLQ/8B/TBkyBA4\nr3IhAoAgk0jK+ezCqSPo07M7TE3Yr8JZWpjjZ0ioRGypLHQ6y7lRWVkZsxYtA4Mh/GTr9LmL2Lht\nJx7euAxGnTaiMrFGQkSAlLh+8TRsbGxhZWXFtvz06VPo07uPhK2qBDQaDAz04bRyFfr374ulS5fA\n1/cW2+h/nMjMzMTAQf3RvVs3rF+3gQgAQo2mqKgIpw7vx9L5c9iW5+bm4t2HT6jPJTyvLFFQUAh1\ndTXMmDIRoeER6D5gKHZ7+uBr8HeBVjMuXr0OJ7cNuH/9EuTMrcVocc2ARnH5v0+j0RCWWrWWmqoC\nTCYTA2yb4oDXAdjbd61Q/vXbV/jdu4fly1dIwTrhKCgowPYd2+DpuR+jR41Bnbp18PjRI7x5+wat\nWrWCg0MvOPR0gJVVfURHRyE8IgIREeGIjIhEREQ4IiIiEBMbg1kzZ2Pnzl2IzSW5rfjFSke6S8Lk\nPSEe3vsew24vHzx/cIdtucv6TZg/czrq1jGWsGXC8zX4O6bPXwI5OTkMGdAXEVHRePDkKfLz8+HQ\nrSt69+iG7vadQafTEREVjfDIKIRHRiIisvjPUVBUVIDftYtQb1zx3UlgD7d3BBEBUuDJgzvY7+6K\noNdvKniz5uTkwNXVBTt37hLZmXtJEvw9GHPnzoacnBy8DxyEmZkZAgMD8PDhAzx89AjR0VGoV88C\nFhYWsLSwgIWFJerVqwcLC0uYm5tDRUWFrAAICBEB1ZNJ/ayxZN5sjBo2pELZ/y5egY62Fvr26ikF\nyyoHg8HAPu/D2LxjN1YuWYBlC+YiKjoGD548xf3H/nga+ALq6mqwMDeDZT3zv1c91mcLcxjo64NG\no5F4AAJARICMMXlYLzhOnsA2/v2mzRsx3XEGjI2rjrovD5PJhI+PNzZu2oC9e/dj9KjRFQZ2Ts6X\nRAAIDhEB1Y9P74KwdPpIhH4MgoJC2VWx5JQU7D1wCBvXOEvJOtEQERmFWYuXIy09HYF+vlBRUakw\nsLNzwCSDv+Bwe0cQnwAJ8zP4K8J/fcOY0WPYlsvJyVX50LhycnKYN28+pk6dhs+fP7Ed2Pm9RyDU\nRI777MHC2TMqCAAA0FBXZ3u/qmFRzxw3z59G8I9foNML2A7uZMAXP0QESJhLR3dj9uw5HLPxdbTu\niKCg1xK2Sjz4+/ujl0MvjuWlkwARAUAgsFBK+IzAR3cxY/JEtuXKysooLCyUsFXiIfDla7Rp2Rya\nmrU51ilOAkSSAYkHIgIkSHJSIq5du4qZM2ZxrNO6dRu8fPVSglaJB4qiEBLyC3fv3YUWM13a5hAI\nVQbPQ0cwaeworgMjg8lAQUGBBK0SDz9DQpGQmIQnz2QrI2JNgogACXLuxEEMHz4C+vr6bMtzcnKw\nceMGjgGEqhI0Gg0f3n9CQkICmjVvgtjPshXelECQRXJzcnDk5FksmjOTY5279x/CyMCA42piVWL+\nrOnYvNYFjvMXw2XGMFjIJ0nbpBoHcQyUEHQ6HT3a1MPdu35o1rRZhfKsrCysXu0KFxdXGBoaSsFC\n8bFh43rk5uZi/uod0jalWkIcA6sPZ495492TW7h+7hTb8hu37+L3n3jMnTFNwpaJl/z8fOhbNkZM\n8CekqrOPnUIQHuIYKAO8uP0/tGjegq0AKCwshIuLM9asWVvtBAAAWFnVR2xsrLTNIBBkmnpyiTjj\nsxNL589mW37H7wGSkpOrnQAAWAnKTOrUQUxcnLRNqXEQESBmKIpCfPBLbNu2FYsWLWZbR15eHnr6\netDTq35OLwwGA8ePH0Wb1iS0J4HAieysLHjs3AM1tVqwt7NlWyc0PAKD+/eVsGWSwT/gOTIyM1G3\nCh+NrqoQESBmHId1w8SJEzDdcTp69erNto6cnJxIEmrIItu2bwWNRsOSJUulbQqBIJP8fHweXVuZ\n4v2nzzjhvZ9jOtwObdvgzbsPErZO/KSkpGLSrHk45rUXOjra0janxlH1D5vKOKmpaThz5iw6dOAe\n41pBQQGFhYUcUwtXRV69egkvL0+8ehlUJaMfEgiSoIjBQPs2rXHlzAmu9Vq3bI5dnt4Y0Jf9ZKIq\nQlEUZixcilFDB1fJ6IfVAbISIGbsbO3w4sULnvWaNWuOb8HfJGCRZMjIyMDkyZPg5ekNExMTEgeA\nQOCArXUHvH77DkVFRVzrqaqqIj+fLiGrJIPP0ROIiomBx7rVJA6AlCAiQMzY2NrixUveIqBD+w54\n++aNBCwSLxRFITAwAEOHDUbv3n0wZMgQIgAIBC7o6urAtG5dfP7K3yRAmidBREVGRia27/XEmk1b\ncO7YISgrK0vbpBoLEQFihrUS8JznD9fExAQxsTESskr0FBYW4tr1a7Dv2hmzZs3EuLHjsWvXbiIA\nCAQ+sOtkjeevgnjWs6hnhsioaAlYJB5iYuOwcs16WLZsj4+fv+Kx71UoWnQiKwBShPgEiBkzMzMo\nKCggLCwM9evX51ivsLAQOdnZErSsIhRFISwsDGZmZhwDkVAUhbi4OHz58hlfv33F16+sKzQ0BK1a\ntcKyZSsweNBgyMuTUMAEAr907tQRt+8/wEIuQYIAoJZqLcT+/g2LeuYSsqwiqalpoBfQYWxkxLFO\nfn4+gn/8xJdv3/El+Du+fAvG52/ByKfTMWXcGLwPeARzM1MAQDj3XRCCmCEiQMzE5irA1tYOL16+\n4CgCKIqC27q1mD9/oYStK0tKSgqat2gKeXl5NG3aDK1atUKrlq2grKzMGuy/fcHXr1+hrKyM5s2a\no3nz5ujRvQcWLVyEJk2aolatWiVtEQFAIPBHeJEezKz7IXD9JlAUxfF0QPCPn/gZEoqxI4dJ2MKy\nbNm9D9v3esLQQB+tWzRH65bN0bhhA8TE/saX4GB8/hqMqJhY1Le0QMtmTdGiWRMsmjMTLZo1halJ\n3ZLvR2b/sgGJGCgBTh32RMzPj/DxPsi2fM+e3bCxsfl/e/cdX9P5B3D8k0UGIgkRWSQi9giC2Cpo\nFEWVaq0OlJoxWrP2FtTetar2CDWK1B6RlEQICbGisoNE5s39/ZG6PyRG5d6b4ft+vfJK7jn3nOe5\nIuf5Ppt69eprOWdZdezUATtbO3r06MGVK1e4fPkyqWmpqkK/atVqWFpavvU+EgRoj6wYmP8plUoa\nVynNmSMHsq3lR0RGMmPeQuZNn5zrM21uhIRStV5jTh3yJiomhssBVwm+GYK9nQ3VKmcW+hXKO711\nWWMJArTnTc8IaQnQgpp16rPt12XZntu+Yzt29vZ5IgCIjo7mwIH9AHh5zX9pWuPzQj0FuJ+YG7kT\nouDS0dGhvmsdzl7wzRIEJCUlMXnmXGZNnpDrAQDAynUbSE9PZ/uefcybPpl2Hq1V554X7A8ApJk/\nX5CBgVpw09eHSpUqZTmelJSEv78fn3X6LBdyldXjx49xKOtAVGRMgdivXIj8wjr9ARcu+VGpQvks\n535ZvoqxIz0pUqRILuQsq7j4eBbPncn0n8fmdlaEGsiTXsNK6CYwf4EX3t4HspxLTk6mtFXeWSYz\n8VkixiYmmJqaSnO+EFriqB/N0nW/4VK9GrVq1shyPjk5BevSrx+Ep22Jz55hYW5O4cKFpUm/AJAg\nQMPWrFmNq2tdalTP+setUCjyRPPec88SEzExMX77G4UQapOamsrM+b+wff2abM/ntXUBnj1LkudE\nASJBgAalpKQwz2suO3fsyvZ8enp6ngoCEhMTMTE2ye1sCPFBWf/bVio5l6eea+3czso7SXz2DGMj\no9zOhlATGROgQTt/W0e1qtWoVSv7P25ttQT4+Bznp9E/EhAY8Mb3JSY+w9hEggAhtCUtLY0ZXgsZ\n/+Pw3M4KnqPHs37z7zx9+ub1ShITn2FiLC0BBYW0BGjQioWz2LBu3WvPKxQKrQzAO3HyBD4+PuzY\nsZ3ixc3o0b0HXbt+QVpaGgEBVwi8GkhgYCC+Fy/S/KOPNJ4fIUSmg/t2YFPaikZuuT876Jflq/Bo\n2YIhP42l3cet6fVlV9zq1iH4ZihXrl4l4Oo1rlwNIiDoGiUszHM7u0JNpCVAgypWrcFvv/2WpU8v\nIyODjIwMrbUEJCcn07lzZ475hzF3zlwCrwZSuUpFGjZyY+nSJcTHx9OmzSfs3LWbxYuWyKBAIbTE\noZwzN0JuERh0Lcu55xsKvW7xIHVKT09HqVTivW0zN/3P41qrJj9NnIKZfXm++WEIPifPYGtjzZjh\nQ7lz1Q+nco4yKLCAkJYADZq3bCPd2zai/4DvsbS05Mrly5S2tmbt2jVs2bKVGtVroKuFICA1JZXC\nhQqjq6tLs2bNadasOSuWr8w2AJEAQAjtqVazNgtmTuWjtp2YNGYUf50+Q7GiRVmzYTMAyidRWslH\nSkoKhoaGmQW7WQkG9y/J4P59X1tRkQCg4JAgQIPiYqMJDAwgMDAAT8/hVK1WjTlzZtO8WXM6dezE\nzZs3tdYSYGhoCLxYyEthL0ResGbjZqJjYli6eh1DB/SlzyBPAC6dOKq1PCQnp1D4hRX+XirkZdGf\nAk26AzTI0sqaE3+dJCU5jZkzZnHJ1xdn5wpMmzYdHR0dFBna6Q5ISU3h2bNnqtcKhYLD+3cTE62d\nWoYQ4vWWec3hQXAAVy+cokrFigCMHj6ECuXLAdqZIpiSmgpA0gvPiYcP7uHz5x8aT1vkLmkJ0BA7\nEwWY6OPk1kB1rEuXrixfsZyGjRqwfNkKbty8wUAtbBrU/avu9OzVg0cRj3B1rcvkyZN4+DCcIUOG\n0mvoRI2nL4TIylE/OvOHfwt7gMoVK1DbpQbeB4/wKCISy5IlcHYq95o7qE8py5K0aeVOx6bV8Jo+\nhSPHfdi8bSdp6WmEBwcSZVRW43kQuUM2ENIQOxNFlmMmRYxIS0sDYOzYcfw8YaJW8pKWlsbatWsY\nNHggAL//vg0Lc3OGDhvKvlOBWsmD0BzZQCh/UgUBL7j/IBz7yjUBWL14Pnp6evT+qptW8hMSeotv\nBw7l1NnzOJQtw/ljB+nRZwC9v+pGvY59tZIHoRlvekZId4CWZGRkYG5uTq1atVmxfCW6urooFFkD\nBU24HnydMWNHY2JiQtWq1Vi6dDHffPs15ctnXadcCJF7jhz3oVbN6pQtY0+Xjp9y73641tKevXAx\np86ep1bN6sTGxdF/2EguBwZhblZca3kQ2idBgJb8/bc/pqbFOX/uAl9//Q22NraEh2vnD7x6teqE\nhtymSeMmXL0aSJkyZflt8xa2bd2ulfSFEO/mwOGjDO3fj7BAP4oWLaqaJqgNqxbN548dWwi6fgML\nM3Nca7kQevkird1l7ZCCTMYEaMmBPw7QxsND9drBwYGwsNvY29trPO2nT58yfvw4Aq8Gsm+vNx9/\nnJkPmQ4oRN6RkpLCsRMnWbFwbq6kf+L0GQaOGE3XTp+yYOY0zP5tAZDpgAWbtARoQHbjAR48eMDT\npwmqfhkHB0fCwsI0npczZ05Tu7YLKakp+PtdlgBAiDwqKjoGpVJJVHSMVtNNS0tj8MjRfPVdfxbO\nmsb6FUskAPiASBCgZtkFAACzZs7m0OGDLFmyGABbW1vuP7iv8fyMHDWCceMnsGrlap4aWHA/UU8C\nACFykaN+dLaDAm1trJk6bjQtP+3M48dPADA2NiIxMVGj+Tlx+iw+p84QeO4kbT1acTu9hOpLFHwS\nBGhBeHg4X375Baamxalbty4Aenp6ZGRkaDRdpVLJzZs3aftJW42mI4TIubUbNzNlthedP22HsXHm\nLn2OZcsQdveeRtO9ERJKI7d6mJkVl4L/AyRBgJplV8uOjIrk1q1blCxZguTkZNXxxIQEQkJCNDa9\nKzIyEgMDA8zNZbMPIfKK19WyA4Ouo1AoMCtenISEzNq/Y9myHD7m89ad/XLiZugtnMtpfi0CkTdJ\nEKAFLjVdCAq6Tu3adXBv2YK//vIBoF+//szzmotzBSc2btqo9nRDQm7KNEAh8on5M6dy/tgh1mzc\nTJvOmWsD1KpZHcsSJWji0Q739p8RGxun9nRvht7C2clR7fcV+YPMDlCz7MYEZGRksGLFctav/5Wl\nS5bRtGkzjhw5zPQZ0xj90xhmzZyNqampWvMRGRnJ/gP7cXauIGMAhMhDshsPAHDn7j36DB6GQxl7\nNqxYglKp5JPO3ajvWodNq5ZRqYIzurrqq7elp6cT8O/WwM5O5aQr4AMlQYCGxcfH8+WXXxAbF8ep\nk2dwcnICoFWr1ujo6HD06J/UqlU7x+mEhYVx8uQJzpw9w5kzZ4iKisStvhujR4/J8b2FEJq1c683\n3w0axk/DBjNi8A+qPUW2rV/D7AWLCLoeTOWKFXKURmpqKqfPXVB9nb90CTsbG7p26gB2ddTxMUQ+\nJMsGq9mrLQGhoaFUrlJR9fron8do0qSp6vXdu3cZNOgH9u3b/95pHjlymJ69etCyZSsaNmhIg4YN\nqVqlqqrWIC0BBZssG5y/ZNcS8M2AwazbtAUAG+vS3Lt2+aVa/48TJtPIrR7tPFq/V5ppaWm06/IV\nkdHRtGzejEZu9WhQ1xULi8zxQtIKULC96RkhLQFqplQq0dHRUb32veSr+tnjYw+srEqrXgcHB7N6\n9Up+/XXDe6cXEBjA19/0ZueOXTRs2Oi97yOE0I5XnxEAFy75A5kb+fTs1uWl8/MWLaWOS433DgCU\nSiU/DP8RXV1dLvocQV9fHvvi/+R/g5r4XzzH5x9n7hg4fvwEpkyZzJLFSzE0MsLKyooSJUqye/fe\nl6L7Gzdu8FX3Hu89ev/hw4d06tQBL68FNGzYSGr8QuRxnVu58fel8xQvbop7sybs2ONNxuNISpaw\nAGD+jKl0+7zTS9c8efKUzzt++t5pzl6wiIt+/pw6tB99fX2p9YuXyOwANalYpTqensMBmDJlMgA/\nDBxAOUdHipgUYfas2VkG9dja2vAwB/sHrN/wKyVLWvJ558/fP+NCCK3xHDcN11ouxMc/ZscebwBa\ndfic73p2x7WWC10/65DlmldbDf6L9PR0ps2dz4QfR1C0aBEJAEQWEgSoSajvUby85mFsbEzPnr3Q\n19fHvYU7bT7xwMnJCXf3llmusba2ydEmQsOGemJoWJhBo8ZKK4AQeVzC06dsXzkHX/+/mTttEkWK\nmFDOoSwPwh/Sb+gIvGZMznb0f07Ge+jr6/P72pUM8BzFsRvqn14o8j/pDsih0BvXae1WWfW6Q4eO\ntGvbnk/bd6Bt27YMGzaU77/vn+21ZmZmPIp49N5pGxoasnPHbpo0bYy1rT09+wx873sJITSnf89O\nHNm/W/W6epXKrFgwjwb1XImKjmH3/gM0cquf7bU6OjokJydjaGj4Xmm3ad2SKeNG800XD7YfOkuJ\nkpbvdR9RMEkQkEOWhmkAWFlZsXDhIry85rJ+w68EBFwhLT2NBQsWZnudUqlk0uSJ9O3TL0fpm5ub\n471vP82aNyE5KYmGzdwpX7EKhQoVytF9hRDqkxIfAcANv/N4jhnPzPm/ULhQIWZ4LcTv5FFca7tk\ne93Va9fR1dV97wDguT69e3D33n36dGvL4FE/U7maC5ZWpXPU1SAKBpkimEN2JgqioqIIfxhOzRo1\ngcwCfvHiRcydN4fQkNsYGBhkuW7u3Dk0b96c2rXVMz/3SsAV5s/3wtf/Mvfv3sbRqQJfft2fbr37\nquX+Iu+SKYJ5n6N+NGcvXKRG1SqYmJgAmQP+ajX+iIF9v2XoD99nuebhP4+Y+8sS5k6bpJZFgpRK\nJXMWLsb7+DmCAvzR09enao3azFq8TloHCrg3PSMkCFCzJ4/j6da2KcFBAf++TsgSxW/dthUTY2Pa\ntm2nkTw8e/aMpUuXcMnvEr9v2SrjBQo4CQLyn60bVjNmaB8A5s+YkiUISElJwXP0eOZOm4SRkZHa\n01cqlTwIf0jzTzqwY+NailVurvY0RN7xpmeEDAxUEzsTBXYmClwczFQBAICdvQ1Hj/750nuDg69r\nLAAAMDY2xrJUKYyNjTWWhhDiv3PUj+bu2T2qAABg2OjxdO393UubBF29FkzLj5ppJACAzMDNztYG\nPT09jaUh8gcJAtSsQ4eOmJiYsG3bDqIiYwBo84kHw4YNVb1H07W26Ohodu3aiYXsHihEnuNQxh6A\noQP6EXj+JPu3bWbbrr0Us3HgUcS/YwdCQqnorNnNv7wPHuafiAiKmxbTaDoib5MgQM1aurckMTGR\nLl06U9LSgoyMDADq1c8c+ZuUlJTjQT6vo1QqWb/+V2q6VKe8kxMTJkyUrgAh8hirUpn97wuWrqBa\n/SZ0/Ko3AGXL2Kv6/m/fuYNj2TIaSf9B+EM+696b4WMmsHvzehLNK7/9IlFgSRCgZn369CUwIIgK\nFTL3C+jX73tmz5pDxw4dAQgJDcHJSf0R/vXr13Fv+RHLVyzHe99+5syZR7xucbWnI4R4P8/3DDA0\nNCTjcSTTfx4LgLGxEasWebFm8QIsS5YEIC0tXe0zfBQKBb8sW0nNhs2pWqkSAedO4NCo09svFAWa\nTBHMoey2Dj5wYD+hoSGMGTOWiT9PeulccHAwFStWzHLN+0pKSmLAptRmAAAONElEQVTmrBmsWrWS\ncWPH06/f9+jp6UkLgBB5yKubBj1+/IS1G3/DxMSY0MsXKWFhoTqnVCpRKLI+V3LC7+8r9Bs6nCIm\nJpw+sp+KzuVl9UABSEuARgwaNBgHB0cauDV46fiu3bsICrpK5UrqaX67dv0atWu7cOPGDXwv+jFg\nwA8SAAiRDxQvbsrksT9R0bk85mZmquNxcfGMGPszbVq7qy2tCVNn0qZzNwb1/Q6fA3skABAvkSmC\nOZRdS8CIEZ78sugXWrdqzZ49+0hOTmbGzOnUr1dfrbMC+vT9Dnt7e8aPm6A6JgHAh0emCOZ9r7YE\nJCcnY2RpB8AfO7bg0cqdU2fPsdv7D8aN9MTc3Cy72/xn4Q//oVr9JtzwP0fJEpkFvwQAHx7ZSljL\nnj1Lolat2kRERvLT6B9JS01j1Kgfsba2VlsaSUlJ7Nu3l7/9r6iOSQAgRP4QFZ05c2jkkIH07j+Y\nIf37YlWqJPOmT1brKn6/79xNx3ZtJAAQryXdARrQqVMn/P39CAm5ybJlSynv7KyWAMDDozWLFy8C\n4MAfB6jlUkutgYUQQjvsbG1o2qgBC5etJDYujn1/HKJnt645DgCuBd/AyNKO5ORkADZt3U73rrLL\nqHg9CQLULC0tjRYt3OnYsRP9+vYjJjqOhISEt1/4DpydK+A5fBifd+nMuLFj+PKr7mq5rxBCu9LS\n0vhl9nSKFS1K8KWzTBozitPnLuT4vnY2Nqquhp59fyAmNo6mjRq8/ULxwZIgIIdebYI3KWJEYUMD\ndu/ehdd8Lw4dOkRychL//PNPjtPy9BwOwOnTp1i8eAndv+rO/UQ91ZcQIm96sRn+3AVfCllYU6NB\nM6JjYvisx9foG+hz5PhfOU6nSBETfv5pJAC1a1bn7J9/cCfDktvpJaQrQGRLggA1eLEATk5K5czp\ns3Tv3gOAr7/pxbZt29i2bavqPU+ePPnPA7n8/C7RtFlj5s314p+HEbi7t+TBMxnSIUR+8bwQdqvn\nSuzdENYvXwzAlcAgWrbvTGDQNVJTUwFIT0//zy2ICoWCwSNHs2OvN3eu+jNkQD9SS1VX74cQBY6U\nImqmq6tLnTquGBZeB0BCQgJxcbFcD77OpMkTAbjk68u6despUeLdInNvb2/6fd+HZUtXUMu9E/cT\nNZV7IYQ2mJkVp1njhqrXmWsDZDB64lSKmJigp6dHbFwcC2ZNe6f7JSQk0O2bfiQlJ7Ppj3MoTItz\nO11TuRcFiQQBGpCSkoLvJV8AXF1dOX7sL1JTU7lw4Tzu7i2ZOOlnLF5YHORNdu/ZzcCBA9i315tS\nleprMttCCC06d9FX9fOGFUvo0a0LFy/5Y13aChNjY9Zt3vJO91EqlbRo9xnOTuXYuWkdD2SlUPEf\nSBCgAYaGhvhevMTWbVuZ+PMEjh8/xqcd2gOQmpKOjo7OO48CdnJyQldXlwfh4ZSqpMlcv1l26yG8\niYxREOLNun7Wkc4d2lOnqTtRMTH0GTSM1es34b11MxbmZlR6xw2EdHR0qONSg8uBV0lJSYVc2hTw\n1bUQ3kbGKOQNMiZAg1asWMbw4SNUAQDAlKmTiYqKUm0s9CaJiYnUru1CREQEXbp05pbvUU1mVwih\nZWcvXOSfRxE8efKU1es3AdCu61dc+vvyO+8dsGLtepauXsfZC74Us3HQZHbV6r8GDUIzJAhQk+xG\n6Ldq1Zqp06a8dGzKlMlEREQwcuSItw78MTQ0ZNSoH+nSpSuNGjVi8JDBFE2LUXveNeG/thwI8SF4\ntfZrb2tLCQsLZi9c/NLxwaPGcOyvk3gfPPzWe9Z3rU3nDu3o3vVzTE2LcXDtrHxTwOaXfBZksmyw\nGr1a8IWFhVGhYtYmPV9fP0pblWbDxvUkJv5/lF8RkyLY2Npia2ODra0d1tbWZGRkcOLEXxgaGrJq\n9Sqio6JYsuWg2ncYe5v3KdSlS0A7ZNng/OXVgu+7gUNZs2HzS8faebRm7+8b2bl3P4HXrqFDZveh\njo4OVqUssbUuja2NNbbW1pibm3E77A4BQdfQ1dXlmwFD2LhyCRVbdNPaZ4L3L9ClW0Dz3vSMkCBA\nTbIrJG/evEm7dp9w8uRpfv99CyNHjaBx48YcO+qT7T2ePn1KeHg4IaEhHD58mDNnThMaGoKlZeb+\n49HR0SQlJTFmyjy+/cFTo5/nVe8aBEjBr30SBOQf2RWUvb8fSO2aNfj0Ew/KVHEB4EFwADbWpbO8\nV6FQEBkVxf0HDzl70Zcjx3zwvxJAQkIipa1KERf/mNi4OIyMDPG7Ha/VysK7BgFS6Guf7B2QS4oU\nKcLjJ49Zs3Y1S5ZkNvedOnUKYxND9PT00NXVpXDhwuzcsYuGDRuxz3sfO3Zsx8fnOOWdylO+fHlq\n1qzJo3/+ISwsDCMjI9auWUubNm2JfvuQAiFEPlDExISLfn9z+vz/VwwsU8UFXV1d1XOisVs9Du3e\nxt1791m9YRNbd+7haUIC1atUxr1ZE1JSUwm7e4+Y2Djatm7JorkzUGi5tVDkTxIEqMnzGvCLNWZz\nc3OsrEqzccMGoqKiAHiWmExGRgYZGRkoFApWr17FmrVrePLkCdOmTqH5Rx9hbGyCg6Mjjg4OODg4\n4vDv9z179zB4yGCaNfsIOwsjrda6pYYvRM7dTi+RpcZcqYIzG7Zs46KfPwCrFnnR68svVM+J9HQF\nlVwbcC34Bt8NHEaNalVQKBQ4OTpQxt4Ox7JlcCxblnIOZbG1saZus1b8vnMPPw6z12qtW2r4+ZME\nARpkaGjI5b+vsHjxIjyHD8PjYw/09TP/ye/cuUNQ0FWqVavGjJnTycjIYNCgwejp6YESlixZmuV+\nLVu25Py5s/Tu3ZM9e/YBUjALkd/90Pdbun7WgcqujYiLj6eic3kMDAxISUnhwiV/SlmWpHKFCixf\n8ys3Q29x4uBeHA8d4fd1q7C3s81yv887tmfB0hW41nKhbMOOufCJRH4iYwI0JLs+dKVSSUhICO3b\nt+V22G0AzMzMmDljFqN+HMnVwGvs2bObqdOmULVKVYqZmmJqakrDBg3p1as3/v5+3Lt/n0WLFtK0\naTO+9pys7Y8l8iAZE5A/ZdeHrlQq0dHRYcykqSxYupKkpCQA3Js3pXqVyiQkJrJi4TxsK1bH1tqa\nEhbmmBYrRnHTYvzkOQQ7WxsmTp9N4wb16d6nPzuO+lHaJmugID4sb3pGyBRBLdLR0aFs2bIMHDhI\ndezbb79D38CAOrXrUKpUKXr16s3OHbvw9BxOGXt71q//VbUtaEZGBoUMDNi8aQvr1q0l6NT+3Poo\nQggNeL6IWLfOnfi8QzvV8VmTxnP85Cm++CyzZn/68H5mT5nAtz2/4sIlP3xOnaFQIQPV+1s0a8Lg\n7/sw4puOqv0IhMiOBAEa8rqd/QoVKsTAgYNITkrlwP4/CA4Opk+fb/nii8zpPIaGhtStW4+o6Gg2\n/7aZXTt306/f90BmEKCrq4uVlRWbNm6mZ68efN2hKWuWeHE37JZWP58QImfetLNftSqVWb9iCRG3\nrjH957F0+LIXkVHRNGnoBkDZMvZUq1yZJavW4uxUjnNHD1Lq31lEz/04bDD2djY0r2HH6CF9OH54\nP8n/tiwI8ZwEAblEV1eXli1bsWvnbu6E3aNKlaoUKqxPs+ZNePToEXPnzsHY2Jiga0GqbYgVCgW6\nupm/soYNG3H3zn2GDfUkIuw6Xds04mO3KsyZPBr/i+feaUVCIUTeZlmyJKOHD+V2wCX8Tx2jhEMF\ndIqVZOOWbRw7cZKTZ85RtGgRTp09j0Lxchekrq4uW39dzalD3tSvVIbVS+ZRr6IV/bp3YPumtURH\nRebSpxJ5iYwJ0LIXxwrcu3cPH5/jHDt+jL/+8uHRo0eqc87OFahXrx4bN24AwONjD/r3H4CBgQEt\nWrhnuW9GRgZ+fpfY572P3fv2ExsTxUet2tLCoz0Nm7pjZGys+Q8ncuRNazG8aXaGjAkoWF4cK5Ca\nmsp530sc9TnJsRMnOXvB96X3Dur3HYtXrkGpVFLaqhRTxv3E/QcPmThmVLb3jomJ5eCfx9hywIdT\nPkcoX7EK7h7tafFxe8o5V3znPU1E7njbWgyva1mSxYLykOcP+sCrgdSpU0v1iyldurSqxv8iIyMj\nkpKScCrnxJgxYylevDiffNL2jX+sT548Yfv2bUydNoXw8HAsLCwIu30XQ0ND1Xtkyl/e8y4LMmX3\ne5MgoGB58UHf/bv+bN62A8j8dy5lWZJHEa+vwc+cNJ7IqGhmTBz3xoWClEolQdeDmTFvIb9t3wnA\n9J/HMnr40JfeJ9P+8pb3XZBJgoA8yqpQMnFxccTGxhIXF0tMTAyxcXHExcYSExtDXGwcjyIecfLk\nCcqWdSAyMoL4+HhSUlIwMzPDzMwcCwvzf79bEBgYiEKh4NatUFxcXKhXrz7167vhVt+NUqVKvZS2\nBAH5x4vBgQQBHxalUkmplLvExsUTExtLbFw8sXFxxMTGvfT97AVf9PX1USqVPIqIICHxGYUKGWBh\nbo65WXHMzcywMDfDQN+AvwMCiYyKxsTEGDfXOrjVrUODenWpWb0qBgYGL6UvQUD+8GpwIEFAPvRf\n1uZPS0sjNjb2heAh87uVlRVmZmbUqFGTiLRc2k9UaJ0EAR+Od60JKpVKEhIS/w0U/h88mBYrxtOE\nBNzqupJiWU3DuRV5hQQB+YS6dt6TWv6HRYKAD4s6dt6TGv6HRfYOyCfepfB+W6AgAYAQBdu7FOBv\nChQkABAvkimC+cybCnkJAIQQ8PqCXgIA8aq3dgcIIfK+3O4OEELkbe81JkAIIYQQBZd0BwghhBAf\nKAkChBBCiA+UBAFCCCHEB0qCACGEEOIDJUGAEEII8YH6H+m+5dwrcLxQAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118332ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set up the data grid for the contour plot\n",
+    "X, Y = np.meshgrid(xgrid[::5], ygrid[::5][::-1])\n",
+    "land_reference = data.coverages[6][::5, ::5]\n",
+    "land_mask = (land_reference > -9999).ravel()\n",
+    "xy = np.vstack([Y.ravel(), X.ravel()]).T\n",
+    "xy = np.radians(xy[land_mask])\n",
+    "\n",
+    "# Create two side-by-side plots\n",
+    "fig, ax = plt.subplots(1, 2)\n",
+    "fig.subplots_adjust(left=0.05, right=0.95, wspace=0.05)\n",
+    "species_names = ['Bradypus Variegatus', 'Microryzomys Minutus']\n",
+    "cmaps = ['Purples', 'Reds']\n",
+    "\n",
+    "for i, axi in enumerate(ax):\n",
+    "    axi.set_title(species_names[i])\n",
+    "    \n",
+    "    # plot coastlines with basemap\n",
+    "    m = Basemap(projection='cyl', llcrnrlat=Y.min(),\n",
+    "                urcrnrlat=Y.max(), llcrnrlon=X.min(),\n",
+    "                urcrnrlon=X.max(), resolution='c', ax=axi)\n",
+    "    m.drawmapboundary(fill_color='#DDEEFF')\n",
+    "    m.drawcoastlines()\n",
+    "    m.drawcountries()\n",
+    "    \n",
+    "    # construct a spherical kernel density estimate of the distribution\n",
+    "    kde = KernelDensity(bandwidth=0.03, metric='haversine')\n",
+    "    kde.fit(np.radians(latlon[species == i]))\n",
+    "\n",
+    "    # evaluate only on the land: -9999 indicates ocean\n",
+    "    Z = np.full(land_mask.shape[0], -9999.0)\n",
+    "    Z[land_mask] = np.exp(kde.score_samples(xy))\n",
+    "    Z = Z.reshape(X.shape)\n",
+    "\n",
+    "    # plot contours of the density\n",
+    "    levels = np.linspace(0, Z.max(), 25)\n",
+    "    axi.contourf(X, Y, Z, levels=levels, cmap=cmaps[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Compared to the simple scatter plot we initially used, this visualization paints a much clearer picture of the geographical distribution of observations of these two species."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Example: Not-So-Naive Bayes\n",
+    "\n",
+    "This example looks at Bayesian generative classification with KDE, and demonstrates how to use the Scikit-Learn architecture to create a custom estimator.\n",
+    "\n",
+    "In [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb), we took a look at naive Bayesian classification, in which we created a simple generative model for each class, and used these models to build a fast classifier.\n",
+    "For Gaussian naive Bayes, the generative model is a simple axis-aligned Gaussian.\n",
+    "With a density estimation algorithm like KDE, we can remove the \"naive\" element and perform the same classification with a more sophisticated generative model for each class.\n",
+    "It's still Bayesian classification, but it's no longer naive.\n",
+    "\n",
+    "The general approach for generative classification is this:\n",
+    "\n",
+    "1. Split the training data by label.\n",
+    "\n",
+    "2. For each set, fit a KDE to obtain a generative model of the data.\n",
+    "   This allows you for any observation $x$ and label $y$ to compute a likelihood $P(x~|~y)$.\n",
+    "   \n",
+    "3. From the number of examples of each class in the training set, compute the *class prior*, $P(y)$.\n",
+    "\n",
+    "4. For an unknown point $x$, the posterior probability for each class is $P(y~|~x) \\propto P(x~|~y)P(y)$.\n",
+    "   The class which maximizes this posterior is the label assigned to the point.\n",
+    "\n",
+    "The algorithm is straightforward and intuitive to understand; the more difficult piece is couching it within the Scikit-Learn framework in order to make use of the grid search and cross-validation architecture.\n",
+    "\n",
+    "This is the code that implements the algorithm within the Scikit-Learn framework; we will step through it following the code block:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.base import BaseEstimator, ClassifierMixin\n",
+    "\n",
+    "\n",
+    "class KDEClassifier(BaseEstimator, ClassifierMixin):\n",
+    "    \"\"\"Bayesian generative classification based on KDE\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    bandwidth : float\n",
+    "        the kernel bandwidth within each class\n",
+    "    kernel : str\n",
+    "        the kernel name, passed to KernelDensity\n",
+    "    \"\"\"\n",
+    "    def __init__(self, bandwidth=1.0, kernel='gaussian'):\n",
+    "        self.bandwidth = bandwidth\n",
+    "        self.kernel = kernel\n",
+    "        \n",
+    "    def fit(self, X, y):\n",
+    "        self.classes_ = np.sort(np.unique(y))\n",
+    "        training_sets = [X[y == yi] for yi in self.classes_]\n",
+    "        self.models_ = [KernelDensity(bandwidth=self.bandwidth,\n",
+    "                                      kernel=self.kernel).fit(Xi)\n",
+    "                        for Xi in training_sets]\n",
+    "        self.logpriors_ = [np.log(Xi.shape[0] / X.shape[0])\n",
+    "                           for Xi in training_sets]\n",
+    "        return self\n",
+    "        \n",
+    "    def predict_proba(self, X):\n",
+    "        logprobs = np.array([model.score_samples(X)\n",
+    "                             for model in self.models_]).T\n",
+    "        result = np.exp(logprobs + self.logpriors_)\n",
+    "        return result / result.sum(1, keepdims=True)\n",
+    "        \n",
+    "    def predict(self, X):\n",
+    "        return self.classes_[np.argmax(self.predict_proba(X), 1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### The anatomy of a custom estimator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Let's step through this code and discuss the essential features:\n",
+    "\n",
+    "```python\n",
+    "from sklearn.base import BaseEstimator, ClassifierMixin\n",
+    "\n",
+    "class KDEClassifier(BaseEstimator, ClassifierMixin):\n",
+    "    \"\"\"Bayesian generative classification based on KDE\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    bandwidth : float\n",
+    "        the kernel bandwidth within each class\n",
+    "    kernel : str\n",
+    "        the kernel name, passed to KernelDensity\n",
+    "    \"\"\"\n",
+    "```\n",
+    "\n",
+    "Each estimator in Scikit-Learn is a class, and it is most convenient for this class to inherit from the ``BaseEstimator`` class as well as the appropriate mixin, which provides standard functionality.\n",
+    "For example, among other things, here the ``BaseEstimator`` contains the logic necessary to clone/copy an estimator for use in a cross-validation procedure, and ``ClassifierMixin`` defines a default ``score()`` method used by such routines.\n",
+    "We also provide a doc string, which will be captured by IPython's help functionality (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Next comes the class initialization method:\n",
+    "\n",
+    "```python\n",
+    "    def __init__(self, bandwidth=1.0, kernel='gaussian'):\n",
+    "        self.bandwidth = bandwidth\n",
+    "        self.kernel = kernel\n",
+    "```\n",
+    "\n",
+    "This is the actual code that is executed when the object is instantiated with ``KDEClassifier()``.\n",
+    "In Scikit-Learn, it is important that *initialization contains no operations* other than assigning the passed values by name to ``self``.\n",
+    "This is due to the logic contained in ``BaseEstimator`` required for cloning and modifying estimators for cross-validation, grid search, and other functions.\n",
+    "Similarly, all arguments to ``__init__`` should be explicit: i.e. ``*args`` or ``**kwargs`` should be avoided, as they will not be correctly handled within cross-validation routines."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Next comes the ``fit()`` method, where we handle training data:\n",
+    "\n",
+    "```python \n",
+    "    def fit(self, X, y):\n",
+    "        self.classes_ = np.sort(np.unique(y))\n",
+    "        training_sets = [X[y == yi] for yi in self.classes_]\n",
+    "        self.models_ = [KernelDensity(bandwidth=self.bandwidth,\n",
+    "                                      kernel=self.kernel).fit(Xi)\n",
+    "                        for Xi in training_sets]\n",
+    "        self.logpriors_ = [np.log(Xi.shape[0] / X.shape[0])\n",
+    "                           for Xi in training_sets]\n",
+    "        return self\n",
+    "```\n",
+    "\n",
+    "Here we find the unique classes in the training data, train a ``KernelDensity`` model for each class, and compute the class priors based on the number of input samples.\n",
+    "Finally, ``fit()`` should always return ``self`` so that we can chain commands. For example:\n",
+    "```python\n",
+    "label = model.fit(X, y).predict(X)\n",
+    "```\n",
+    "Notice that each persistent result of the fit is stored with a trailing underscore (e.g., ``self.logpriors_``).\n",
+    "This is a convention used in Scikit-Learn so that you can quickly scan the members of an estimator (using IPython's tab completion) and see exactly which members are fit to training data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Finally, we have the logic for predicting labels on new data:\n",
+    "```python\n",
+    "    def predict_proba(self, X):\n",
+    "        logprobs = np.vstack([model.score_samples(X)\n",
+    "                              for model in self.models_]).T\n",
+    "        result = np.exp(logprobs + self.logpriors_)\n",
+    "        return result / result.sum(1, keepdims=True)\n",
+    "        \n",
+    "    def predict(self, X):\n",
+    "        return self.classes_[np.argmax(self.predict_proba(X), 1)]\n",
+    "```\n",
+    "Because this is a probabilistic classifier, we first implement ``predict_proba()`` which returns an array of class probabilities of shape ``[n_samples, n_classes]``.\n",
+    "Entry ``[i, j]`` of this array is the posterior probability that sample ``i`` is a member of class ``j``, computed by multiplying the likelihood by the class prior and normalizing.\n",
+    "\n",
+    "Finally, the ``predict()`` method uses these probabilities and simply returns the class with the largest probability."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Using our custom estimator\n",
+    "\n",
+    "Let's try this custom estimator on a problem we have seen before: the classification of hand-written digits.\n",
+    "Here we will load the digits, and compute the cross-validation score for a range of candidate bandwidths using the ``GridSearchCV`` meta-estimator (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_digits\n",
+    "from sklearn.grid_search import GridSearchCV\n",
+    "\n",
+    "digits = load_digits()\n",
+    "\n",
+    "bandwidths = 10 ** np.linspace(0, 2, 100)\n",
+    "grid = GridSearchCV(KDEClassifier(), {'bandwidth': bandwidths})\n",
+    "grid.fit(digits.data, digits.target)\n",
+    "\n",
+    "scores = [val.mean_validation_score for val in grid.grid_scores_]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Next we can plot the cross-validation score as a function of bandwidth:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'bandwidth': 7.0548023107186433}\n",
+      "accuracy = 0.966611018364\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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/hjz5hE27i7H9wFlcmzkWK65L83Q5NAiebGtrtdnx05km/HiiHkdO1TuvBRASJEVmShSu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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1206d02e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.semilogx(bandwidths, scores)\n",
+    "plt.xlabel('bandwidth')\n",
+    "plt.ylabel('accuracy')\n",
+    "plt.title('KDE Model Performance')\n",
+    "print(grid.best_params_)\n",
+    "print('accuracy =', grid.best_score_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We see that this not-so-naive Bayesian classifier reaches a cross-validation accuracy of just over 96%; this is compared to around 80% for the naive Bayesian classification:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.81860038035501381"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "cross_val_score(GaussianNB(), digits.data, digits.target).mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "One benefit of such a generative classifier is interpretability of results: for each unknown sample, we not only get a probabilistic classification, but a *full model* of the distribution of points we are comparing it to!\n",
+    "If desired, this offers an intuitive window into the reasons for a particular classification that algorithms like SVMs and random forests tend to obscure.\n",
+    "\n",
+    "If you would like to take this further, there are some improvements that could be made to our KDE classifier model:\n",
+    "\n",
+    "- we could allow the bandwidth in each class to vary independently\n",
+    "- we could optimize these bandwidths not based on their prediction score, but on the likelihood of the training data under the generative model within each class (i.e. use the scores from ``KernelDensity`` itself rather than the global prediction accuracy)\n",
+    "\n",
+    "Finally, if you want some practice building your own estimator, you might tackle building a similar Bayesian classifier using Gaussian Mixture Models instead of KDE."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) | [Contents](Index.ipynb) | [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.13-Kernel-Density-Estimation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.14-Image-Features.ipynb b/notebooks_v1/05.14-Image-Features.ipynb
new file mode 100644
index 000000000..47ddff7da
--- /dev/null
+++ b/notebooks_v1/05.14-Image-Features.ipynb
@@ -0,0 +1,695 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) | [Contents](Index.ipynb) | [Further Machine Learning Resources](05.15-Learning-More.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.14-Image-Features.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Application: A Face Detection Pipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This chapter has explored a number of the central concepts and algorithms of machine learning.\n",
+    "But moving from these concepts to real-world application can be a challenge.\n",
+    "Real-world datasets are noisy and heterogeneous, may have missing features, and data may be in a form that is difficult to map to a clean ``[n_samples, n_features]`` matrix.\n",
+    "Before applying any of the methods discussed here, you must first extract these features from your data: there is no formula for how to do this that applies across all domains, and thus this is where you as a data scientist must exercise your own intuition and expertise.\n",
+    "\n",
+    "One interesting and compelling application of machine learning is to images, and we have already seen a few examples of this where pixel-level features are used for classification.\n",
+    "In the real world, data is rarely so uniform and simple pixels will not be suitable: this has led to a large literature on *feature extraction* methods for image data (see [Feature Engineering](05.04-Feature-Engineering.ipynb)).\n",
+    "\n",
+    "In this section, we will take a look at one such feature extraction technique, the [Histogram of Oriented Gradients](https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients) (HOG), which transforms image pixels into a vector representation that is sensitive to broadly informative image features regardless of confounding factors like illumination.\n",
+    "We will use these features to develop a simple face detection pipeline, using machine learning algorithms and concepts we've seen throughout this chapter. \n",
+    "\n",
+    "We begin with the standard imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns; sns.set()\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## HOG Features\n",
+    "\n",
+    "The Histogram of Gradients is a straightforward feature extraction procedure that was developed in the context of identifying pedestrians within images.\n",
+    "HOG involves the following steps:\n",
+    "\n",
+    "1. Optionally pre-normalize images. This leads to features that resist dependence on variations in illumination.\n",
+    "2. Convolve the image with two filters that are sensitive to horizontal and vertical brightness gradients. These capture edge, contour, and texture information.\n",
+    "3. Subdivide the image into cells of a predetermined size, and compute a histogram of the gradient orientations within each cell.\n",
+    "4. Normalize the histograms in each cell by comparing to the block of neighboring cells. This further suppresses the effect of illumination across the image.\n",
+    "5. Construct a one-dimensional feature vector from the information in each cell.\n",
+    "\n",
+    "A fast HOG extractor is built into the Scikit-Image project, and we can try it out relatively quickly and visualize the oriented gradients within each cell:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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3aysyZQG/lWskn89jZmYGMzMzKBQKIuj5zXqTxX5o0Mt1rAW/vo/8oP9ubss2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NCTJ08sboJjeI0kmYHe7XZ1cnJi8phgRZQW5urV1ZUVXGFfqtVqlkWG8bq5uVG9Xtf+/r6Vs47H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gCov5P5TUMGPtSqKn1ANQCEjVto2OypNgFzApx1Op0QplzHquOucx+4a/iuZaeaN9/g/x67\n4tq+J2Cx+aRsHI9r7A4MPPHLJxdCpbSLW8sgWGmqyXi1A8z8uI49mxBAmlrMiCtj/rvdrqRpzAlz\ngGIwHk9CBSjjhBtqfX09wDz9K5VKYTm9ubnRw4cPNRpNSzWtr6+HhW9+fl6DwUBv377Vmzdv9PTp\nU+3u7urBgwdxkhVB+2xESVEqBwWGsltkaC4tLWl3dzeY++XlpZ4+fap79+4F4yNUwBnz0tJSZOK6\nJgpIxEosTc8rPzo60v7+ftBlsVjM1JSVFOV8AIAwKwQgtO5aYr/f1/X1dcTftlqtsFoDVi8vL7W1\ntRUKDPQOzbu73sNA2B9uQYAW+Te1bHrCVRoH6nuJ5x18unfA55VnecY9Bx6jmobk8E5fHwfMqWUu\ntQq4EpvGn80SCikA/ik1DydyTxfKlc+Z8zef00KhoI2NjYww4R3enFfR8qw6DoTz/ubC0Y0e6XvZ\nL6mXIFXmEOaeXITgx5WLVXV1dVVra2sZ0D0/Px9JlT7+w8PDd/q1sbGRCSFivrFyevOa2H4vQIXr\n4/E4vDa+D+fn51Wr1QLQMq7l5WV99dVXYXGVJvL74cOHsW/4FgezeDgHXqJ0z7s31NvOzk4o8fRh\nYWFBjx8/jnvYq5TColGPPaXPYnGSZ+DXFxYWQjZwDTn3ySefZAxKKFZ8m3/TE7h83Z2XAHwxpvha\nbG5uqtPpRIUHZLrTDHKY51IF3VtqCHAeB9BPAdzW1pbS5om6fr9Xm8j7uzS7fqv325/Bu5Hu4RSo\nuiHzrjm461t57e4jS+wlXlcPYYSgwqqHm0lSxnpKfTov2CtNAaP/y2S4q4p+MDgXbPw/jedw7RnG\nDWhGKDOBmOCZeL7jLkziFYvFSX1SLJO4kNC419fXtbGxEdnz/BAOAXgF6Kcgg0Qu5tE1LzYIm5Vw\nASzRWBuJrUUjHo8nmZOj0TSeFg2OeRoMBqpUKup0Onr27Fmc4HRxcaGzszPd3Nzo/Pw8gDjHvM7N\nzanZbOoP//APtbq6GpZESk65pQSNmvjPubk5VSoVlUqlsOKUSiU1Gg11Oh29ePFCh4eHQTfeT2Jm\nvXwIgfCsL6Enbq2n9qsLg7m56aEJL1++1NzcXABW3GwIuouLC21tbalcLmt1dTViYrEe4jpcW1uL\nd5OJDS2wN2Co0LF7DFLg5hZkt36mVkp3TblXwd3uDpBT0Mr7XRlk7lIQ4rGyaRhDqtgyhvSbvMfH\nm2f9S6+lwiBldGmffyot5YvpnKVz4p6g9F54jTT7qMW7+jGr+fqn6+wttbrkgd+8a97cKOH7zcGM\ng18Hu2nLc2v6vQ4OeG9ef7jX97bvffqRloby5z0mslAoROgB8ZeS3jFKpON1oOJlxVxpQeH3tQEA\nfvbZZxlgC5jxeUZGOn251dLHS798HZlbauLe9S0AWt7cOkZgbpmXvDX3e5EhqbU23WMp+Ezv9XV0\nfp2nwKVKTto/f/esvZbu51Rx95Ki/gxGqfS9Kc6a1Yc8Ws77Pn/7EKAqvQesXl5eRlIOAjaNQfRk\nEReA8/PzoVnOz8+HBZJ7sTb5JHBvKlB9sAzMM6URTL6RHWRyzX9IdnLh7MLUzea4ozHH05c0mcst\nRGg8rVYrYnPcgutxOfQPoM7m5pseTsAmJTu+UCiE5ZO4SmnKfNw1u7e3F/3CNcM9Xhh7f39fH330\nURDdl19+qUajoeFwqPPzcx0dHWk4HEbRaxK2Xr9+HdaMZrOp4XAYJaFYB8qheFmstbU1/d2/+3f1\n/Plz/eY3v9H8/HzUiOWY1s3NzXCBAIolZeKuXEN18DgcDmPdCoVCuP0AxE+ePNHp6akGg4HW1tai\nliGaMmAWS0Kv19P29na42Cl9Bc2m14mrorYr97HWvnFpDircGpkytjxB4MoX11GIvNoCiqc0tZQ6\nSM2zhvo8u1XVFS9XaFNmmLY0rMeV09TrMgss5TG6WbFRP4XmbjcHR9K7wgGB7dfcc5ZaR9Ln86z2\ns+7l//7MrH651d3Bjbe896aAIV3/lN58DOl8pQI3HW9eX51O+R1Xejovee/1MlXcQ4UV75d7QViz\nYrGYOY3QvYU+N/CcZrMZtXh5F3zT+1UsFjP98moqPq+8w+eNw1NmreMs+vB19H+dRplbl/vpmvMc\n8aBYRqH7NPlNmloQPXQDmZLup1lj8DbrWh5fzKMZruXR9Id8y+/1uaV5KIDPt99LiGP6Lf/X54Dn\nXWFLMdyH9pt2J1h1MOZgEAHu5Z5IcEotLRS/XVlZya0vCWOE+ACwDMBd/6l275PldQF947sF2IEr\nYLBYLEaNVIpjcwoKREtSEcHtuHk58x6gVygUtLKyovv370e9OB8zc4eV1RffXVRuBQQgcQQdQAD3\nNoyEOcTSCLBttVpaXl7W6upqJAehPWEZhVgBjyRaEWdKRubx8bGkiZb76NGjiAMdDAb64z/+Y/2t\nv/W34kSS1dXVYHyAPpKS3DLMaSvNZlPr6+taW1vTX/7lX6rRaKjVauns7CyqCBwcHGTW2AEWSoVb\n8lAKcOVj3RyNpoX5Ad3uZuY6YB7FCyVrYWFBOzs7unfvXliQOTGoVCrp/v37ajabkVAFYIcJUWcW\n5Yy5cbDtFgP+zbNIuveBMXgcNP93ZcyVGE704X3Mp+8ZD1lwEOl7PXX/e31VX4+8/qeAKQUJ7mnx\nObgLvDqN/JSar6U0zYz2xCZpanlGYHvpJJIfEWDsB+etznvTWLQUvPkapCDDw1WkrPXSAVh6PCy8\nw3m9pABrPDscDvXq1Ss9fvw4BK6ftIQ3hvvpF5alPCXRx+WgCmDmYJhrbt1EsTs7O9Ph4WF4bsja\nd6BEEirHo7IvSYCBr6FEU6FleXk5jBjIFp9TP7oaMIlMYc2hIeInXfnk+9DO//yf/1MPHjzQ+vp6\n0AF/IzSB38k0p7mnKI/OHOC4FdyPp2VtnJ64dzwe64/+6I/0j//xPw76Yb79GFjwDLG8pVIpszYY\nMGiOJ9xjnOfRSWnGQyNTvkc/PLbby2aWSqXMCYq+7xib73UHwP491sgb9J2e0Mi6pcetMgcpLx4O\nh+9YglNQy7W/Ecsqbgg2X14MGvfB9HCdU0oJbQZAKL3rmnEQmueeBHAyKamwcgbiAhHBDThzgc1z\n/I7QxU1M4DIudVzzHM3JNbdAUfi+1WpFYD8MkzhVZ26uuQA6AZQEkeP6p48wOr8G0AEcUIri+vo6\nk+AFICXDE0AxNzcp0E/xfwcrfmwjBaWbzWaUSalWq/Fze3sbNVdvbm700UcfaXd3N5gh4Bsmi6C4\nurrS3t5ejIMEghcvXmhhYUEnJydqt9sRfgFjnWXBcIbgFqRCoRBHS0pSr9fT3NxclAVDMWEDOhiW\nprX+1tbWoh/9fj+EPmPq9Xo6ODiI+eUwCSwXgHk8EDBc1tIBIPTvm5v+wdhgIAiS1Drv4SPOFBw4\n8HzqJXBmAnPlOWgxtdQyBk96ob98B/DE3uCb9NEZMy2PoaVgyO/5IW7rH1NDmUXxkqZJmKwhWdCp\nt0dSAMTLy8vMKUfujfJYUHgPfN35NQCDfkGXbhEkVhyvw3A4jIM5iMeUJgmT3W5XxWIxcxpVvV7X\naDQJ08JS+Pr1azWbzUyc6c3Njf7kT/5EhcIkufOTTz6J5zlJj9h7Kp+MRqM47pR5ZQ7YV7e3kwok\n0C9zQjUM5I+kiL2fm5vT5uZmjOH8/FzX19cRTwog5XQ+LIKEUnHgCrydsCz6LylOLxwMBjo4OIia\n141GI95LnVHPzt/Y2Igk0G63q263G6dYFQoF1ev1UMa3t7dVLpe1v7+vP/3TP9X5+bm++uorffHF\nFzHebreb+RaJqOPxONYMmepzy9ygOHDNE0pLpVLMLcYpt0oOh0M1Gg1tb2/H9WKxGNVtxuPp0a6D\nwUCNRkPSNOTOvWoYFwqFQlSjgZYB5c6LeC8hfdI0tpdxdbvdyHlBdgMK4etuSfeQQPCH0xdryT2+\nz3iX80i3LNPyPBiunM6yEr8PcOaB1R/S3ptghWAkAQemJykW07U7gK0LLDQqj4FyIYRQcrdjGl+U\nCk++5S5LgC5WQRaBY2G73W6mLBR95XmuOxG4BRZgi6Dld0pzjceTzHCAEj8Az3R+/D1sSE6DQgHw\nklzF4iQ7kjqlzWYzQCZAaGVlRfV6Xd98840ePHigR48ehYaN277dbkuSarVaMMZOp6P79++r3W7r\n/PxcBwcHcXYxfVtfX9dwOFStVtOrV6+C6bTb7ZgrrBZbW1tqtVoBxohfJS6Vo1Xn5uYiiWs4HGp3\nd1eVSiVinRCa4/E4SknBoBFQKEK+Th5zlII4hCprnWrzrJHTCMJ5bW0tzt+mn5z3TbymF8K+vZ0c\nyCBNLNIrKyuxb1zhiA05ly0llWqhqTUTJcoTygDH7CUS+aApAKF/2xkiwNotBqkSCCiZ5fr3fqbj\nkPLDXnycPn63CM8CrPybgtefYvNwFOaCdU/nBr4HSGTfANiwkMDnXHGRsoc0sF/yYhL5VuodkxRl\n5JwW19bW3nFZFgoF7e7uZq71+33t7e1lDCjj8aTY+4MHDwJIS9K3336rP/zDPwy6kxRAa2NjI2oo\nS5OkUPhZ6lFM5wAZ4XHonqHtFirCrNzqNB6PtbW1lbFGobRjEKC/8Mvb29sAQ7e3t9ra2tLS0lLG\n+ler1VSpVDKWTE5uKpfLkVQmKVPCieZGCr5VLE6O2aZKgleN+af/9J+qXq/HtWKxqFqtFt5F1sGt\nrw7kMGr4+nqsrdOBh4HRWANfGwwxXiFgPB6HgQADDOuEQcQNBMgXD2fw0oOMxY+k9ebZ9v5OSom5\nhRTPY2qVxNJKwyOcAlmexQiEgSo1GNBSwyPzk97n4/Rxz+K3swDpD72euWd8B2f/N//m34SGiXaB\nRoFG46DQrXEAL8Dm6upqFHx3Nzt/Bwy4S9fdl6kAwxLgf3MBSp9dkDrgdSHKDxYBwKSDA6xeZLLz\nDUD59fV1HK/JhltYWIgkHECK12P1HwgNoUH5EdwU9A2NsNfrBfhEy8Z1QUJXsVgMIIsriZAM3k92\nPklAhG5QBonKA27F5T3ERhGPDF10Oh1dXFyENg4DoGxLpVLRxsZGzBHMgsMYbm5u9OzZM3399de6\nvr7W06dPdXx8rFarFYcHAPCJJ1pcXNTe3p5qtdo7QoXxo1nD0FIrZuoiSZMSqtWqHj9+rO3t7Uzs\n7NXVla6urgLoz81Nat+iSLx580aSdP/+fa2trWlubpIpvLm5Gd/EwgX9sZ9SGnGm4ZZ2lEL+77Tu\nABWB6h6T1KuR7kdPDKQPWOX4JrUMoU9qJjKv0JvTv1t90z2f7v80fjb9f8r0/N/9/f33MsIfU3OX\nswsYb+n1vPhWtxxxPVUaUotLKtD8Xlf+0ufz7k3XctYY8t5LS605vr+gew9DcWux/5t33T07fo1v\neehF6gXyPs3qA0CGPcw3XPm+6173OLlruN1uh9ePawB4DEv0DTnolnR32ac0A09yyybvTWND03W8\ni2bcc8p7aWksrM9hKh953stMMgbonT75t1jjNJ44pXvaLFrMA4sOJr1UWUpzbkx5X0vpPlUCpGnh\n/5S+xuNxhNOkystwOHxnHdM5cM9f3t7NM0x8CFi907IKMCMZyq2F4/E4o3FLyggwB6sAnXa7HXGO\nABxPBErdlG4JK5fLkaiCuxa3vJu43ZXJO1jg1NrjTMYrBbgF1DcPwp77SHzCAkHciwNkgAZaHITP\nmHgvVQAWFhYibAKLZ6/XmyzW/+uSoC5ouVzW+vp6gKdms6m3b9/q9evXwSAArLhbyuWyjo+PYxzc\nW6vVwk3DmInVlKQ3b95E3M7e3l5orIA+XHMcw1qr1YJBwuwA7l4nzq3QWBpvbydnV//BH/yBnj59\nGhsJy6DH+wDMOOWrWq3qyZMncS+bzAHZ4uKiLi8vA3w7A3DG4NpqqVTSRx99pE8++SSs0ViUAYzN\nZjMKcLOmzWZTl5eX2tvb0+LiYoB/mKi7XpmTlEFAI9Cl07IrXoyPefRrqYXSmQf7wN37qaXK3+me\nDnf/51lY85gQ36EfHkbhyqP/QJfva3lM/qfW2Nve7rJouLDyez0O1a/nXZv1/5Sff+jzqYHirjHM\nshrlXcvrV6qkvq+P6b1Ob+977yygkQIUaXoUbvp9j+++6163xvp14u9THuEyneaeFX/vLJpxxTJ9\nb3pv2mbRTB4gzJuDvLWZm5vL5AzQUmDPO6XZHoFZ3/oQupt1LX1v3nz73H4ID8z7Vh7duXXYnysU\nCu8AVd6Rejry/p+nODJfef38UD59J1gFECDQ07hLgAIWGOLZ3JW+sLCQKVh+dXUVoIcST15vFCuZ\n18hDiyO2MB1kOlEIXLceOVD1d7qF1S1NDkIABy6kAebMEQsPkCJ+BpcIABtLqbsMCIL3xBiAa7lc\njnuvrq5UqVR07969sEb1ej2dnJzo22+/jc1HkWQsmTy/tLSkdrudiVMjUery8lLPnj3LxK/t7OxE\nbC7XBoOB6vW65ubmwlXHGJrNZoQxuHU31VwB9CQ2DYfDsCJjvca6SzWEw8PD0IyXlpbidC3uI0Si\n1+vpxYsXcbqKNAWorDVxptCPVw5whQVXWbVa1f7+vh4/fhyufCwQvV5P4/HktJnT01M1m82gx4WF\nhbAi7+7uqlarBTjHu8Dc4Q4rFAqZWKNUELtlBcXQrbLQMfQEo8Va6QyP/cAPeySYg7k/fR+5qw26\nTt3/9DXVqLnGu7y5hWxWex9wncVMfyqNfY9g8bX20BCnJ+gG5Qwlo9frRRy2K/hYV5wHo9z4+vn3\n0j4iFPMsLrOsMKl1Le1/+o302+xzv+YywYELPJmcBZR2ZNvFxUXwAr5FLVCvgILyhtykr/5e7qXu\nNPyaNet0OlFZB5nEXHh5Knjn2tpaGHWwkjkPxJ2cx5sZu8fRI9M8hpKYWmLv4UUotoSwvXr1Sltb\nW5lT5PBopV4a1sf5lNOXG5G4Jk2VKuTmixcv9OjRo0ySWLvdDnf+eDyO0DLCIqB/DBh4lKAv/gaY\nd+sh/7px666W3iJo7L8AACAASURBVOPP5SlobsF0TON07nsn3SNunXZ8wxwSWujv48hZvw8MlPIO\nPJbOa9LveMvbrx/S7jxu9Ze//GUmaz01Q6eA0YUXDJOOYwH1hQcAra6uRmkiLIowXXe1I1TzNLqU\nCeUxQybK/+7uR6xSvlFSrQYLkM+B/83jlpgzNrIDEmfsWD339/d1cHCQOauY+Bj+trS0pMvLS52d\nnenk5ESvX7/W5eVlZPDPzc1pa2tLo9EoYiZHo0kBZGJQC4VJ+aZOp6N6va7r62sdHR2FG7fX60Vs\nGIWDGVO73Va73dbl5aVKpZJ2dnYywBILOAWX+QanQvmY/Tg33nF+fh6MlmPvdnZ2tLKyIkkZS6pX\nRADkwyR5Z7E4jRkbDofhmk6t7AS3QxeFwiRGd2trS59++qkePnyY8QhICjc/MaKnp6cqFAqRlAJY\nrVQqWl9f18rKSihmnCQGEHRACR3hNueH6/zuSpgz1Tw6T/dKuh/SPZK6HdN7+ZaHILBfCZGhL17j\n1T0dHv7C736de9MfH1ceSEk1ejwGP6XW6/UylVdckXZ+fnNzE4lLVBHBsPBXf/VXWl9f1+Liom5u\nbuI4ZJRoF2RU7Ehj/90a4wAOGoB+U6Gfl5zngCbPEOGCGlrEolcoFCKsCXqjT81mU81mM3I0SqVS\neLB++ctfamdnJ2hoPB7rL/7iL3R1daX9/f0Yw3A41J/+6Z9qd3c3DBfD4VAvXryQNDEUOKj71a9+\npb29vdjHw+FQ3333ndbW1jIepmazGW57SkZeXV3pzZs3wU+Y66dPn4axB6/T1dWVvv32W1Wr1VjH\nTqcTCWXsscvLS7XbbZ2dnUVoH3L65ORES0tLGQstibfIS3IhOAGKfvV6Pf3Zn/2Z7t27FwB1OBxG\nJRpkoaQIMUuT98APKX3hxXM+Qczs1tZWrM3t7eQwmK2trXheko6PjyMcjXuvr6/19u3bmFu+dXZ2\nFhZi5CKeRfile55d8XaZI03ANUnc0ISHU4B30j3iyhbzglGQOWAvEyfLvklzZFhHFBfP8aAPHg/M\nGjsN8G7nKTT2Udp8T/M773pfe++hAIVCIQAkQhmiYWAwN5gMbmuAAoPG6ra5uamNjQ2tra0Fem+1\nWgFovNxOGpcKUWDlTCeCe1LNB6JzUO2Em6fpOHCAITlopq8wb4AdoMOrCTB3ZOlubGxoa2sr5mA4\nHEZ8MCd8bG5uhlCAkfT7/XAll8vlyHKEiOfn5yOZ6ebmRtVqVYeHh8E8nj17pnq9Hoca9Ho9vX37\nNlzSnNxSLBa1srKily9fRshBv9+PE5ygA2JbKpVK/J3N6LGgjEWagEjOqMZyytg5sWUwGGhnZyfA\nT61W08nJSXxnZ2dHc3NzESMqTeOQsDgjwBCa0Ck0AoODGbDO0iQ54eHDh9rb29P29rZqtVqGgbHp\nisViAHySJ9bX18OyCYMDnLKxPX45ZSgIW5I7+I7HtKHkkUBB9QpolGeckUP/ME63yKZW1ZSBuGWN\n98BA/SAAf+csq7AD1nTvOQCdZaF4nzvMLXDvs3L8GBvWULKKmQtCd6ApeHqxWAxrnDSJZXv16pV+\n93d/N+Od8PJ5rCOACDAjZatopFZM6N+NAPyNVigUgl/B66EXhHuqkKQWJfYX4yMJdmFhQa1WS+Vy\nOQDk+vq6BoOBWq1W7PHLy0s9f/5c/+Af/IMA/Ry3en19ra+++ir63ul09Pz5c/29v/f3Yr7IrueQ\nGO5tNBp6/vy5fv7zn79z78OHDzPJZhwL/fHHH2diQC8vLzPHoqJoF4vFOCFPmgDK8/NzffTRR2E5\ng2/U6/WQRQ6+AHSsSb/fV61Wy1jSAZt7e3uZvbq0tKSzs7NQ1guFSUJco9HQ6empdnd3Y83X1tbU\n7XbD4gpdpMomY3MrH31wIwrz9ed//uf6Z//sn2XubbVaun//foYWU0XY1wGFQ5oCaIwO3Atte8UZ\nxpy+15VD+BHVLMjvcUs2ezXlm24kgw97LoOkmE+O9cYglFqqaVQOSlse38zjuRiWPqTdZVH9EGvr\nnWDVs6v5v8diMqmASYRmv9+Pd8zPz+vg4EBPnjyJEhWU3wC9w0Rck8ljSAh1t9imf3cm6ILRtXd/\nzq1GriUxVu7h3WlGHlqRJ4wwJ5QzIeHm/v37+vzzz7W/vx+JN69evZIkPXjwQE+ePFGlUlGj0Yii\n+ljYADOj0ShKnpBRD7F5LHG329Xm5qYePnyoXq+no6MjHR0daTQaRTbpaDSpO9put4N5AIwLhYK+\n++471et1FQoFPXr0SLu7u+r3+3r58mVUCahUKtrZ2YmyJ+vr68GUWH/KoGBdw71IiRXiOjc3N0OY\nVCoVDYfDiPPs9Xq6d+9eWIR3dnbCuss4JEXIAsITgcf8AFxT1wVte3tbxWJRGxsb2tnZiWxWFwzQ\n1fLyckaL3draigxcACVKnJfBQQmC9lDK+N0TrTy4Hzrm/7yHigt5CVYpsIP2XVv3eFO+zX4nAYpn\nAczsU569ubkJL4gzr9T66Vq59437HMDmMf4f0v667qb/mxs0Miv2zePUAC5uqZQmRoUnT568E6dc\nLE4ywd0d6G5vmisp/v08JSMFren9KcBI73EQm2el8THwrx9nyTy4NV+auOK/+OKLTB1PLK6///u/\nnxnD/Py8Pvvss0x/5+fn48Q6/3a1WtXf+Tt/JzMe9i/eI/42GAz0ySefZN5LaJIDOwwTT548iX4y\nBkKWeB6+s7u7G4YE+rWxsZGRk8ie9CStXq8XR9FyL3Jvd3c37oWv/c7v/I6++eYb3b9/P8ZWKpWC\n9nwuKIEIOIfPprTiBjP+9vTpU/39v//3lTas0n5vo9HQ/v5+pp5ouVzWzs5ORnHCU5hm/8N7HUCm\nAJNvuXWf6/QnjeXlnT5ez3vxa84v/f7xeJzxJnGP3ytNXf3e+Ab7yd+ZZwX2caX7Ejnp701b3rOz\n2p3VAP71v/7XsZkHg8kJSc1mM2NO9lg4d//j+qxWq7q5udHJyUm4eDc2NsKy6pnpqbvQF46BubUo\n1ab9mk8AE+fC3wVuOulcA+S4mymNzQNYu/sTsz/JUpRGubq60uvXr/Xy5UtJk/IwT5480c7Ojq6u\nrnR2dqZWqxVWDLfkSdNzpd3NAIC+vLxUq9UK1/Hm5qZKpZLevn2ri4sLSQrXLPG/lF0ql8s6OTmJ\n8+y3trb08uVLnZ2daW1tTb/3e7+ncrkcGjJAutfr6eHDhzo8PMwoGbgUKfIPI8M1w/oCkBiXnw7W\n7/cjuQ/3/NnZWdT9+7M/+zO9fPlSz54902AwCMuQx2pSakSaAFUsq+5S96Q4sv3J1Kd6BSEWAEKs\nGyhegDZcYNSRLZfLEWeN4HBLKbTF2ri1k/87uGRunEb5u4cKzGJgKQB2Fz70Sy1OaeoZ8Sx+XMUo\njlhVPQyAOWIPsrdJemNueCf04Yoe9zgDd4D7PubmguHevXt33vtjbLPcay5A3ndvnhCZJVh+yL3/\nN7UfMoY0RO2u5/Ouz1qb1HV61/N5ewNDwfu+NavNWlvpXZpxAPy+e3/IHHzo8z+kX7Pm60PX8Ye4\nsH9I+yHj/d9tyJ90zvMOAPB8H2+z5jaPvv53+MSdltU0TpRTmch+H4/HUb4KVyku4NPTU52enur7\n77+P2AysNQAQLDgEPruVdparL9XEGKz/69ZS17h5nmuptdWFdDrZqcbvQBXraalUiux8NMR6va7X\nr1/r17/+tUajkT799FP983/+z9XpdPTs2TP9+Z//ecRRERMJqKEMUK1W03g8jlq39Hd+fj7iTNFq\nd3d3I/7o/Pw8xtlsNlWr1WIcm5ubUdy/2+3q4OBAP//5z3Vzc6OjoyPNz8/r448/1ueff65SqaTX\nr18HENrc3Axge3h4qKurK3W73QBAKysrERIgKU7CGo/HERvmJ8c42C8UChHmgPWAWCtJURtwb29P\n9Xpdq6urajaboQHj2mMDeOB/mkyHpaTX62lzczPWbW9vLxKrPIaZcAhon+OIAVJe4aBQKITnAMDl\n2f+eyEJ/PQYXMO3g1UGqg9nRaFpSzAEl/fKQBL6JxTdVxFI3PvPH2PyUHuYXb0ua7MVY6IPvnzwr\nSV4CmIdr5AmWuxj7T7UhVC4vLzOJE+wJEhi5Lr17pKc/Ax+E/rHcSu+eUpValvKsKx8C4H4IOLgL\n4CC70ve2Wq0otM+1ly9f6sGDB++8p9FoaGtrK/Mtfx4rLV4d5pb23Xff6ZNPPsn0q91uR3IS75UU\nJ/n5tfPz8/i+K6t5x4c+f/5cjx49yjwPf0jX7OLiIrxbPO+hI9wLf0ifdwuo7zcHL6enp5HwyzUO\nFCDpLLXeOVBibZAnvva3t7f69a9/rZ///OeZfuXRctov7vXjaWdZCol9Tukzfcab3+t7JC/0yZ+/\ni+7Tb80quXYX8MvbY25J5hvEPDsITefWr6f7XMqC/rv69aHW1fcetypNYy2wsmISX15e1uHhYVgN\nW61WuPchZKyr3hHfDAgyrFx8j7/5v/yfH0CjNC2XkALTPKDqk+dWV67zTicErnuMzGg0ivJOlUol\ngNT5+bm+/vrrOEGlWq3q888/V6VS0ffff6//8B/+g05OTgKcANa9LmWj0YijXzudTmSOkog0Go3U\n7Xa1vLyszc3NYFpv3ryJYv1Yu0ulUrjvsNi2221tbm5qOByqUqloZWVFr169iuNWy+WyarWaOp2O\nJGl1dVU3Nzc6PDzU8fFxZOhjzaOvxWJRZ2dnWl5e1t7entbX199J9CJcAfBNvJWkELDEYuIOg1nN\nzc1lwOXJyUlY+FPFxBOAmDOSD3CHIRxIgNrZ2QmLf6VSiaQL3kPFA2gJFxy0MRqNIibOgRYJhn4y\nCe9NgZ+DafcyAAYdfDv9Mn4KkpOoRzUPfx/MFwUh9TikANkVM2iK5gLN9xZ7yOfBwasDVbeY+r1+\n3T0qvn/9d9qPwar312kOSFGA3MPw6tUrbW9vZxQfjv/FLQjf56QlajATXlSpVDKKviuBngfgwpQ2\ny5CQp1z4N6DzVCbkCVzn+4T9+AE3z549i0onyJx6va5ut6uvv/5an3/+eYzpP//n/6wvv/wyIxca\njYbK5bI6nU7EoRPnixWf73/99dd68uRJJtlrPB7H9w4PD+O9z58/1+HhYcaCxQEuADhkxq9+9Sv9\n7u/+bvSrWCzq6OgoTgL0mN+Li4sIHeNbJycnqtVqMQeFwrR84ng8LbM4Hk8qnTBOvgffoF/8DRAO\nXWxuburk5CTkD17X//E//oe++uqrcME70IReHewNBoN3QNK///f/Xv/qX/2rzL0e5oWxYzweR0UF\nnwPonj2Cp88xCWvhtOLr6wCSZ/N4D+8BwHkYSrqPpHdP53TDBooR/fIwrbyEKU9KlPQO74bfex4S\nSdve/LTE9HqqoA2H7x636uuUzuGHANY7wSqE6Bauvb09SRPN8OLiQt9++21MIgISlyQWR0f+TCCu\nxdFoeiKWE4MXq2WBUmuqg1oXXv4dJilvgrjHBb4zUV9c/xZxNMQOdbtddTodff/99+r1eioWi3rw\n4IG+/PJLtdttnZyc6Fe/+pUajUYANOaLjUW8aK/X0+XlZSaulGoJo9FI5+fnEStKOaRut6vXr1+/\nk415fX0dcZ+sJdZaXPS3t7cRfsC6cMa0JHW73bCYl8vlCFN4+PBhxAI5c4MpULKMecPC2O/3I2GP\nNYduisWiVldXMwlpbCBKvPT7fa2urkY/VlZW4uhYmIuvuR/kwDcAUBxP+PDhQz169CgS/4g5dQsj\ntMTpJhTB58i829vbTLLgcDiMk1ugMWeAg8H0TGZnctA1QNtpFLc8zBsmI2U9DdCU04KkjOUWuqZ/\njNWzVpnHVJFLLVVuRfN9x5gdfDtQ9RAin+MUWKdM3b8x6/d0v/9UGnuk0WjEKUPSZB+8fv06U4ED\nfkKiCyDj6OhIvV5PGxsbmp+f1+rqatAs+8itbCjGzr/zki4Quqn1nHaXIJOmBglAML/7HmC8ZP5z\nfDRz8Pz58wgvQTb9xV/8hf7Lf/kv+pf/8l/q8ePHkiZW5P/4H/+jHj16FCCHrPJ6va6HDx9GItFg\nMNA333yjarWaMWbU6/XYTwCKVqul//bf/pt+/vOfa2dnJ8Z4dnaWqVcOYDg6OtK9e/cyJaa+/vpr\nraysZPYbNZ05iYv3EnrnFtxmsxkhYdx7fX2t4+PjSA7lXg7AcVBE+Un4Nvf2er3MPgVMwYOx8heL\nRT18+FB/+Zd/qa+++ir4AjwtxQvNZjMTzzsajfTtt9/q937v9zK0hNcX7xpzQCUB51PI2Uqlkkmg\nw8PHqZPQG7LMTx30MAKnRWjWk+K43+NRMSx4yJPfiyGI38FTGBvcCOOx2fCAUqkUYYMA1kJhavln\nTGm4pVvMveWByRTEv6/NskR/SLvzK8ViMVz8HPH55s0bNRqNTDIHkw0RU/rCs/kBJb640lSAMhC3\nmHoyUyrouCcPqKaC1C0w3lzrh1BgYG6tYi5w0S8vL+vm5ibOlCamcnl5WY8ePdLy8rKeP3+u7777\nTu12O1NFgXnButjv9zOA2TPqt7a2tL+/r6urKx0dHWk8HqtSqejw8FDr6+sajUb6zW9+EwlK0mQj\nEq4xGAzi+vb2dsSgEo95fn6u169fa3NzU5eXl+p0OkG8vV5Pt7e3Ojg4iDnAUvz5558HYyEGmXhO\nMmsHg0FUHJAmVhrc4N1uN0q0YC0FXLGBKNHV6XQiZAAwiMt/f38/6q2iFTMH/BByAjByt3yn09GD\nBw90cHCg3d3dWFuvYEH8LbTCukmTBLd2ux0nNqXAi+xiaBHlBquHM1R3nTMGH4tbKt0yyj3OSJyJ\nEkPq1mTmCvrGMu5auytw3icHJ+yvVPFzYJ7Gm3p8qs9XnsXVAW66z98HQlNe8FNqi4uL2tjYyFii\nbm9vtbu7+05WNRVJXIG8d++exuNsORrWEQurAxJ+d/pzyxDNwaQ/n97H7+kaO33539hP/g4UWU+Q\nury81OHhYRhgpMke3t/f17/4F/9CT548iXtfvHihP/iDP9D9+/cz++zBgwfBK5iv4XCovb29OHRF\nUtTUvnfv3jv1Sb/44gvt7u5Gf6mfTZUQ2tzcXFQhYZ6Gw6Hu3bv3DqgkPGt5eTl+H4/HoTAzhuFw\nqNXV1aiD7d/a3NzMVHUA2CC7GYMf6uJzwBHZDnyohILyjxV3c3NTx8fHGeu7730aPMhpEXn65MmT\nd+bAy/rRX8chtNXV1TCscS+VaXwMrjSn+yGl3RT0+dw6T5byk54Yu68Vz6feI5QtEnb5G30kXI3n\n8yyiecfb8h1/p/fFm9+bXmcc7zMwvM+amrl/fAfX/3f/7t9F6SXOP/fjRHFbshgIQHfpOzjwBCqu\nLS8vZzYPzI+4OIjb3aGpJcYnKZ1IF6Z3/esmdF8EP2+ZagdY6gAg1WpV19fX6na7uri4iBOmPJ6P\nxfYSP1gqWNzFxUWtr6/riy++0NbWlq6urvTb3/42NMvPPvtM+/v76na7UbsUQMppXh43SQgCmnet\nVouDAbDGAvzo/+rqarjBFxYW4rhW3DfSBMDd3NxEOQ8Ij/F6+S63sKKlA1I9sQcmyTxLCre9W2hH\no0n4w/n5uer1uv7qr/5Kf/Inf6J+vx8WXTZPsTiJLXOmjUZKktg/+kf/SJ9++mmMBYbjZVHoDxbQ\nwWCgdruter2uRqMRNSgpJYXGfnt7G3SNaw4LltcyTC2tjBPhBkB2K1QeQ6c5I/V94swQRdPvYV96\nKSwH574GbgVgz/B+4maZb4CrJ1UhVOAVHvfLGrD3fayzmgMY/3+xWNTh4eF7n/8xNZQTV7ZRiNyy\nI02PBHVBmgcSJWUUqjQjODUY+Dv8Wiqg3mdpyRNms57JU9jSPrjbmmvEn6bPAzi9Ee/r97ry5l6H\ni4uLKDjvz3v9W5qXqeM61kssavSL9fV7OaRAmvIGDxPyfuFq9/XycpCptZLv+zXG6/QFjfm9bjRw\nxZ1+wGt5bwqSPKTB+3t6ehohZrRZMZzwTp8D7vV1eB8tO4idtUfuote0X3nP+zMf0i/nc3nfvKtf\ns8Jq+HE6wAKcNy8+Lt7LO+/a+7P6NavdaVk9OjoKgCgphGv6UQQa/3q9R9dqiKFjEnBJefY2IAGh\nBujyhXJh7BPsv6cLkN6btzi822Mwr6+vdXFxEaAKIUqh+nq9rufPn4e2B+ggBsYTcqgDilvBLU21\nWk1PnjzRvXv3dHR0pF//+tc6OTnR3NycHj9+rK+++krn5+f61a9+FX3B1Q+4kBSF5kejyXGgjUZD\ny8vL+p3f+R29fPny/2nvTJojvbK6f54cJKVSY2pWleQauul2uxq7GR1EE8Hbe/YsWEDwCdiyYcFn\nYAELggURsGLJhoBFs4CAiDaTwXa7BperSnNKykEppaTMdyF+J//P0U3Jhd0dpitPhKJUj57hDuee\n+z/jtcePH1uv1/OaeicnJ/b48WObmJiw9fV1L3Wys7NjExMTubAOsyvrqpl5mSlcEAhT5gaXjI4p\nWiCWVw3i5khYgDIxdMTNUf7q/PzcqtWqJwcsLS3Z7Oys7ezs2Pn5uQPGsbExazQaXuuQdmEFbbfb\n9vM///O2sbFhk5OTntRgNijZRmmsYrHoc0dVjIODA3claWUAYl2xkpRKJU8wU2Cn4RFsciguyq9a\nGUDBIWMNP/JedSMBKHUt0D/WYQTB8D5JbRrHBt/pd/g/AgvLEuEOqqBGa2tUPrmeWt/DAGtKe4+C\n9k0jtYxBrMNIGqd2G92kOPxvx3rYZnrTpp+iKNeHgQK1pEG4+aMVGFCq/Ad4PTk5cfAV3b2MMzJF\nk5t4PmZW60lU8d44j6k+8C29ruE+SjF5Se+J8xstnal7tG3RJQzfqWUPBSkqAuqtoV28jxMdoaWl\npWQfUnyY4v3UWtD7UxT7fZOCFf8+7N6bQOpt94KHIr75ou266TvxbzG5Lj4f195tYFyv36awQjeC\nVY5nYwGijeFa1KL3CmABrqBxYpywlKlbsNvt+rGbZAiiDapbNwoS1dxu07TRGHkv//og/A+wQOPG\n4tdsNn0zrlQqnmhwfn51tOjnn3+eGx+dLNUSVePEilYoXMWsLi0teU3DnZ0d++EPf2itVssKhYI9\nevTIarWaNRoN+4d/+AfXvgEAgAHc5miah4eHHi/14MEDu7i4sH/+5392ayJt3N3d9dhbsytX/d7e\nnmXZVZ1SyjmRMUrc0szMjLXbbQ8V4J38TQsRkyQViyejbWfZVZwsCQAa5E7sULFY9DEpl8u2v79v\nS0tLnuCwtrZmn3zyiX+LpKfd3d0c+Dk+Pvbkr+985zv2ve99z5PjcG8SjoBVkDEDTNFuhDghCisr\nK26J1KNycRkybwhqgC+WaFyTWmkjAlXWFbyripyCvRhSoOuGucA1pCEE8YeSchpOAmhVj0HK8oCF\nRjdMwLuWq1LAGoFsSmjG9Z2iNxWkmuUVd/6Fb5WQ0woOmDOVqWpJUk+aglx4IGaGR7mtf9P/p6wv\nPM81BQqpGFnNe1DjhlrvaA+ySPeRuMkjv6leosBqa2vL9vf3bWNjw2Wj2VXS0+Liot27d8/7wPHL\nmvmfZZlXMtGC7J999pnNzMy4NZa9iIojerTr7u6ux6eicBK+g0xlzjVHgjFA9rIGNdaSceR59ncs\npsw5XjLlEcK29IS9TqdjFxcXNj097eFX//Zv/2bvv/9+LhP/8vIyt8cxXr1ez/7+7//e/t//+38e\natDv991KzffZh9Q6q3sJfMt9GAmU7zX8D2MKfdawwVhlgnFm3KIFNlpH1cuha4H+pqyQ7B36XHxG\nE3QhcFjK5a9hFvqtRqPhnkb9vsY56/WopNwGmv831tVbDwXAwkU9SV5KzJxqnRqnoK4R/q6ZhrhH\nK5VKrmwVi42NWjdX3YyHac4wHZMTtQ7+zuTgHj49Pc2FOZhdMT3JTefn57a9ve0bdUqDVbCqoRE6\nbizGhYUFe+edd2xqasr++7//2549e2YnJyc2NjZmm5ub9u6779qHH35o//RP/+QhFCy6iYkJ63Q6\nrvnXajVbXV21fr9vjx8/ttPTU5ubm7ONjQ37r//6L1/YChhI4oKRS6WS7e7u2tjYmM3Oztru7q5N\nTU1ZvV63ra0t6/evQh4mJibs6dOnHv/WaDTcYk52KadnAKTV2nx+fu5AlxNBiGNlw0SgzczMWLPZ\n9BCGbrdrr1698rquWZbZw4cPbWdnxy22gMlWq+V8cnR0ZK1Wy0tcvfPOO344Q7VadbcY44PgLBQK\ntrS05CEWjUbD+QpwWSgUPP62UCg4D42NjbmVut/ve/wc/IfVEgCApaFarXo9WPgsWlY1RIC/aRwo\nY6FW0xgOAP/G8jFqjSVMQF358KBZ3uprZjmQwNgoiI7W09QPbfkiAiz1dzYVlQVvGqEQd7tdj3/n\nGuOjijnXUZoADVrnFl7iutlA9qYypeHNWBA9WmYiYFYjhPIV1/r9QXY63+r3+7a9vW3T09MOHlnD\n29vbnhQMb3N8KfKTfQmABHgjDOvtt9/2d56fn9sPf/hDW1pasu985ztmNqjh/Gd/9mf2R3/0R97W\nXq9nz549swcPHuRAcbfbtZcvX9q7776ba+t//Md/2A9+8AO/9/Ly0ur1uu3t7dm3v/3t3Nz++Mc/\ntvfee88BaLfbta2tLSuXy7a2tmZmg0SoFy9e2Obmpt97fn5uT58+tenpaVteXraxsTHrdrvWaDT8\nGGz1AHIsqZbUOjk5sU6n4ycwokhvb2/bW2+9levXwcGBzc/Pe7+mp6ft448/9moACuyOj489cQ3+\nOjk5sf39/WtZ71qCjfCw09NTTwiGbzmcR4Ee4XzlcjkHrglV05CFVqtl/X7fk8KLxaKdnJzY6emp\nLSwsODgsFq9yPRRowx8qF2kvyYoKLuEbs7zXg3knDh3ZizeOdc6hTIuLi76eyuVyziCo607BOPfD\nTxFMYxxTDBE43AAAIABJREFU0rWrc6aKpyqgKaDK2ryNbgSrlEDCbWw20LioCxrrZbKZAfqi5ZNN\nVQcSt6PGt+rGOsztH4FqHJQ4GHxPgTZnQOumTgF3mOHFixe5c4CjxSfGEiqYUGtusVj0WqhmZi9f\nvrSdnR13l3/729/2+qd/93d/5wATixzH1Zpd1RvNsszLLh0fH9v+/r6VSiVbX1/PCXDqb05NTXni\n0tLSkrXb7ZxWfvfuXcuyzD7//HO/F6DFHMMTL168sEqlYrOzs14GhpPJYGosioA2DiKYnJy0Wq1m\nhULBqyNwahYLZGpqyprNpnW7Xa9a0O/37cGDB34wwOTkpB0eHnr5tO3tba92cHZ25pZwjl5dWFiw\nX/3VX7Xvfve7LqAAiapoIdA1Tofxazab/v9C4ep4Q+b/7OzMpqam/OhVvoEg7fV6nnTG35rNpgse\nlBUt7s+GHS2Z8KCCWeWz6AYHbLKeUET5Xa0RzDXWX7V24/nglDoNXUBJ4736Lz8aU62hAYAEXVv/\nGwvpsHX5ppAqKHo8pNlAmVDrt9lAGUHGMh9Y9LhfrS/Md5ZlDnYUmEaXbpTDUPw9yvUY/3h6emqz\ns7M5A8jZ2ZnVarXc0Z2np6eetc/z7GXT09O5tqoVEMXx8vLSDg8P7b333vO2kLfR6/XsN37jN/y9\n29vb9pd/+Zf2x3/8x34vsnJtbS13UtPZ2Zn96Ec/svfffz/Xh52dHfv+97/vllaebzabXuuaPrRa\nLXvnnXdyey9x/4An3tHtdu3BgwfXjg9dXV21mZmZnOcjnkTGPr62tuZ7NqE/Z2dnfsiLtuvevXs5\nUMl8qwW5VCrZ7/7u79qf//mf2+/93u95W5GPmtne7Xbt93//9+1P/uRPrhmF1MJXKBRsZmbGKpWK\n86J6bFQWoJiYWQ5Y0l72Bb6FQUMxiOYn0L9C4SoJm3h/VfYUCyBzCW0zG4SBMF7R6o9FU/cojCaK\nlyhDScIvPDI+Pu6hilFZjFbR09NTX0tK0TLLvKcqB+g6V56K6/916Eaw2m63vV5fTNxhcWsiFYsD\nrcxswPCaVYglFU1XS/SoVYtJ4EcFakT0kRgghCYAAY0KrYJ3AFIBd1reQwWtMh0CmwlXUzyLg3vm\n5+ftW9/6lp2cnNjHH3/s5Y3Oz89tZWXF3n33XXv8+LF98MEH3jaAr2biXVxcWK1Ws8vLS3vrrbds\nYmLCHj9+7EDnrbfectAGFYtFW1hYsLm5OWu32zY9Pe1WPU5b2tjYsEajYU+fPrXNzU2fAzS24+Nj\nB35YbHq9ni0tLXnCGTGbhULBy2FxshQJW5eXV3Vdd3d3ffNBEHBi18XFhR0cHNjZ2ZnNzs7mNL1O\np+PnT7daLT/beH193RqNhjUaDbf0UEqlUCjY/fv37b333rPNzU2vL6v8gScAbZe5J26W8BWsT7i3\nFOgBvokb1sUKAKYOMbw2Pj5uR0dHdnJy4lYXlDpAtJnlyrqwRuibWhDhTfVEwAOESPA3QLGWp1Me\nhgeVz9msAPlZlvlYqfVUgWnqR62uKpzVS6Mgh3G8SdB9GUH4s0LIZwAY46FWb8ZU+SNuYvCFbtrE\ni3NWO89FYwKk3zQbPn/xWqFQSG6CyGj91tjYmJfii+/keE+I0Cl9XpU+BQh4mDBsAFr+6q/+ygEW\n1Ov17Hd+53dy/SiXr078u3//fu56s9m0X/u1X8uNBfkN1DJlXVxeXto3vvENX+tm5jJBY17L5bJ1\nOh1bXl7OuZcxuvBOnie2X8Ek8kBdush+rdOLp3NpaSkHXtQ4ofP12Wef2f37913xZ7zK5bL94Ac/\nsFevXtnm5qZbJSldCDD727/9W/vTP/3Ta+FKvV4vV+lBx1JDRPhdZQP8ipKl97KvQch2KuxE17+C\ncPgIgJhSFPVexi0+Hy2a9Cu+L77LbIA3tHwXpH1DbhPqqQoO46N9oMyjEgYNJTXoxWvD6IvK7Bur\nAfzhH/6hN14TaIrForsY2NT4OzF1bOYAQzZELEtYWsbGxmxubs41AuLk1Dqk7s1ofYkdVuGsGyE1\nR2F6us3miTAmvkato1Hbj+9h8ejfYZrZ2Vm3DG5vb9vx8bEDj7m5OVtYWLCLiwt79uyZl47S2Cqs\nHP1+34+wnZubs1qtZoeHh3Z4eGinp6dWrVYdaBMHzELkqFCUj0Kh4AWRKXGzu7tr+/v7fjDB4uKi\ng0sUDtwQMDp/B1zhLtf2VqtVb8fk5KQDNhiYuaamXb9/FfdEuEK/33cghyCYmZnxo1fL5bI9ffrU\nPvjgA3v16pW9fPkyF7uKa/Cb3/ymfe9737O5uTlXnBAM5fLVEalYRClyjNar34dncIcyNvCmZtij\nDPE9Yt8Al1r1glCbGIYCbwKasVyb5RUyVeZ4DjCqFgZApFpwFTwChFGSSOBjvZNUxrrm+2qVwHpe\nrVad94jh5fuaeKmgW5VT1vMwzTxeV8VTr+MWfZPoddxrwyhlSUldM/vyR1Tepoh8kfemnn+de38S\n9DrtSo2tei2UCDWKz6f2xmHv/TJzM+zeYd9K3ZvimWH89UXHaxh9Ff39Mvz5daDXGYPU3Lzue7/q\n8brRssrxnABRTXhSqxhmdzZhLDsATwAnG5J//H/uiZsL2pxqvHQKBmVTUm2LzRIrJ/EnPJvreKnk\n2fCNRiOX1Uw7zPJlQHRg0Xw1cFktFnNzc3b37l3b39+3p0+fursUkPJzP/dzdnZ2Zk+ePLGTkxO3\nUNBfXOlqGV5eXrbNzU13JQGSqcV3dHRk/f6gxBaZ9cT8QMfHx/6+Uqlkn376qX9/enray25hQcGd\nbmaedIYLBm0L91y1WnUARwxslmVeqkrBbZZl7gY/ODhwflhcXLTz83NPEru8vLSjoyN78OCB9ft9\ne/Hihbvc+/2+x4fWajV7/vy5g8+pqSmbn5+3jY0Nd4VFywQueviH06ewtGL9pv/EShHnpjyARRJg\nxlyi2JDogBIFCATQEaurMauAO0B9sVh0i3K0lLEmoutWQSvKWSwbpRYDlDX4C2tqtMSxZvCYYHXm\nd74dQap+L8qEm4SWKoapTWrYJvemUarPajWP1hs8C+q+5V/dSDqdjo2NjVm73b5WhkgtfXo9lXyR\nssik7mXNaPY6zyODYx9TikzqHpIz9V4SN+MYxBOgsizzAvx6L6FiMcv9L/7iL+y3f/u3c9c+/vhj\ne/jwYS7+N8uyXIwt7yXmNPZra2vLFTHmam9vz8PMuBdlmm9h5UwdNUocKOPI+u92u7kQhUKhkKuI\nwPPsuYwtcrTZbLr1m/A2aorDdxgHPvnkE4/RBVuoW5020D7WPTJK+Uv3dMUS0S0fr6nSS3+H8ZfK\nrFgCTJ/Xe7VN6qGG9DhfSBPIeDbKS12bSnr8OUQbGo3GtW+ZDXhB749VGVKk+1K0+kLR8vpF6Naj\nB3RwGVhcmtF9j/WGzQjrjG5omN8BDiRZqXtQN99oTY0CEYCqDAf4i/3A2sa9u7u7uTqSw0gZS/sb\nhSWnXnBowCeffJJL9CmXr44wnZyctK2tLavX6+5ShbDWmQ2s2NVq1d566y2bn5+3ra0te/bsmYOi\nWq1m5XLZtra2HIixqRQKV8lFWEuzbHCC1OrqqnU6HT/WFateu932uCHaDgDB8qrF/nWjwzrNZsJh\nA8w12aMXFxfWaDSs3++7tY5yTxwFC4g9PDy0fr9v8/Pzdnh46MB1bm7OTk9PbWdnx4VLuVy2paUl\nd2/cuXPHQwtqtZrt7+9brVbzYHPieAqFgh0eHjrPYmFVLwIWYwQ3ChGxQQj/YrHo9VVjjKCGHKir\nhbGnogNhKBcXFx5XXC6XPQGrVCp5goO6pjQmVd3wKdAYrarRxYT1X8FvjD/VWFm1UrO+9XfN8FVr\nr357mGDjb/x8FVbDn1VCRqNsFotXtay5zlpVmaZxmvBvvz84KQqXLDHWMzMz15QZddNCWu81btJR\nwUcu6bwjG9XKgzIeQZaGMehY8C/fAtDVarXct/CenJycOCjr9/v2L//yL/bo0aMczzWbTZucnHRw\nzXvr9boDRZ7/m7/5G/ut3/qta+EHVDdRevbsmd2TSgJm5tn1MSziH//xH+373/9+7t56ve5udJTl\nXu/qJK3FxUVPeAU8cuogY9zvD5KJOKmv3++73NQxZo+lbi39/fTTT+1b3/pWbo4PDw9tamrK2zU+\nPm57e3vWbDZzyVjFYtF+9KMf2Xe/+90c6KvX626YwOMIj8PL/BCapUYE+B75o/yNgUETazEo0Qez\nfA1YM/PqKFpe7OjoyHq9nh9XzjfwQmnohbY3KjfwYLRuskbol7YP2Tg5OWm7u7t+OhptV0+t3p+q\nO2x2Vbd3cXEx1y7GKVJKHqtiHOkmhfImmX4jWD04OMgBTxUwCBIWABuZui5Z/FiJiDli81RLjP6w\n+WrMasr6qhnygOe4qfE7rth2u+2BxlED0H9Twi812ADxWq3mgOjFixfuPqfty8vLNjMz465qFgwL\nQMGLTv7CwoI9evTIut2uffDBB57gUyhcxcGen59bvV73rMy9vT0rFAoO+vr9vp8c1etd1UR9++23\n7cMPP3TgAFjCktput213d9cKhatYz8vLS3evc8jA0dGRA+Pp6Wmbnp52Aby4uOgHB9RqNbu4uLB6\nve6hCLSJAwU4WrZarXoiEkAbZabX63ldW6yQe3t7br0lqB2r/Pr6um1sbFixWLQ7d+64kHz16pWX\n3qJqApnTuPbVeknMF2EO3AuwZrwoOaW8TyKHLlwN8TAbZHzCv1iyS6WS7e3tuRW30+lYq9VyFzvh\nDHwXsK4glTXG7+p+VyunKoMIbNYz6zwqorrxwUfIA/2+glzWbQSqKbAKRUVRLSP8rp6XlLXhTaLz\n83M7ODgwM7t2ypEaF8wGiqZ6cczMga7G/rGpM8bILXg5WrDiYQPwla4R2qVHIUN6HKjOuRa0h1JA\nWY0V+q1Wq5VLPOv3r86zf/LkiT169CiXrf3v//7v9s4777ilEaCMwq4A+uXLl7a0tJRrw87Oji0v\nL/saZwz++q//2n7zN3/zWhsAGPqtJ0+e2Ntvv52zFD59+tR+4Rd+IdcvPUabdp2entrW1patrKw4\n+KLKATJDwefW1patr6/nAEmj0cgl/6DM1+v1XH97vZ5tbW3ZxsZGzkpI+FC1WvV24a1SAA+oRhnn\nOrKkXq/nEroYH4xfyuOATaysWGx1vEm6JcEb0hwVJQ3NMjMHePV63ebm5qxUujqAp9vt2tOnT21m\nZsZWVlbcW8gplCsrK57fQ2Ug/R5eNV0PaqDR/iveUR6r1WpudFIjHd4AHXMO01G+TcWA883U/6OR\nYZjxL2VZ1XfcJrdvBKutVstrrOkRdTEYX7OqVdNUDUiBrloa1eqqm61ugAginjEbxARq8pEKUAQq\nYQjEcUahyrvUUpAaVNWqGINSqWRTU1N+FO3jx4895pDvcyIUxfcZT5iL96gVgb6vra3Zw4cPbXt7\n254+fepgrFgsWq1W8xJNCwsLNj09bdvb2w7WiH/FJYJ1s1Kp2H/+5386YCTTETcP5T8AQ3t7e55Y\nMTU15aCORU3tPMDr9PS0HR8feygEsa7FYtH29/etXC7bwsKCL+ypqSlrNBpWrVa9buvExIS1Wi0f\ndxIIWq2WTUxM2Pb2thUKBX+uXq/nNj2qDczMzHhMNDX5GAtidtvtts8zri0smZy3DW/GsBMVyrQX\nIal1HZVnNKYVkKqAguN5q9WqJzHU63UPO2CupqamvILGxcWFFxNXKypgVUGrgkcFjtHipWOvfMf6\n1LhuZADvV3Csazkqnbq+Um4jtRrEZ+JajID3JmXzZ5nK5bK7kZWYI02mMLMcH+g1TaoxG4wnm7SO\nf0xgwZIZxz8Vk8xa0/uRWapIaf80ZIX3xr4qkIzjowCY79+/fz8Hok9OTtwzE8cAmQddXl56AinP\nkyS6traWa2uz2bSNjY1cG3i3gkQUidXV1ZxFFesWuQO8G+MFY8a8rKys5NpVKBRyeSFcHx8ft42N\njZwiQL94F2M7MTHhoEtd8NPT07nn1ZAT+4Z1XuVOsVi05eXlnIJjZh7zroBRDVq0S3lD9+3oli8U\nCrmDIFTOqFLAvSlQxrtqtZrvJawPwtVYB2rYUGUrzgFrM4ZGkpSsPKdrUUnHRAnQHA2OUfGLRgFt\nQ0qeRrmdktW8N94T5/k2Q8ONYDW6+nUhoPkqiGRTBJBGN6jGzRDXWC6Xcy5WJbUEaU281GDAdDAm\nJndNrEoNcEoTiAJSGZrnFhYWbGJiwg4PD217e9vdpozH2NiYLS0tWavVsq2tLT/9hwniG7rZY6Yv\nla5OrZqdnbWPPvooV5jezLzmaLvdto2NDatUKvbs2TN/p5akAjTOzs7awcGBn0rW6/VsZWXFjo+P\nHSDCyABVKhYwf+fn53Z8fOxhBuPj455wAzA/OjqyYrHop36h3e3t7dn8/LxNTEx4nUPc2OPj47a7\nu5s7EAKrLO9iwdbrdXdXVSoVOzw8dMsOyg8gVY9GxOVfKpW8CgICjvbjetITtswGgpr4K8CXFtuG\n71XJIrxFCV5TJQuhDmGpnZmZceG9s7Pj/IN7N8uuMpQps8ZaY72wHgEfEQAoiIwCQxU/jW0FrLL2\nuUfd/vH+GK8VLaq6znRNpgSXWnNT46q/v4lW1gjkdcz5v/4MU8z5f7Rep+YOr41+K7WJ6r+Qfkuv\npeKSmffY5hSYSBHGC+VtFEvN6jYzL90Xn+/3+zlLI/3XOqJcW19fz1moAHrvv//+tT4QBqV9HR8f\nzx1nTRtWV1evjRdrPLZB12ocW51HQLzKexQW5pYf9nFdX6x7PTaa8QLIqRI8NTWV22fL5fK1azzD\nfqU8rN7IOBY6XpF/tV2pZ5Q3lKIMMxvIFwA97VJgH9/BvSlAqThJKe4hsT2xvQBe/b+ZeTiQ8kLq\n3TEsgXcMMzSkKMqWYddue4/SjWBVNzsAKZs6lk02MxZK1J65XzcvdUVmWeYlgjDZo0lpDB4WrxTz\nIcAQRAAQXTg6MDHOIxVsHTdwvocb9vT01La3t68lo6D1kmGvhymwENQqzPgBtqrVqt2/f986nY59\n+OGHnkjDOBD7c3p6apubmzY/P29PnjxxaxeuEbMrJl9eXrZKpWI7OzvWbDYdnCBc9/b2chrX7Oys\nTU9PewwnLnfAGbGV5XLZA/fpx+7urgNvsvY5CWxzc9MKhYID4GazafPz89ZoNKzdbrvbo1wuW6vV\nsvHxca/lySlKFNxXPms0Gp7YgPY4PT1tMzMzXt+VRADCBQC+uCVRgrBmIGzUkm82iOXUTHYA7sTE\nhG8sqsQRq6vgFJ6CsAojlHHrcXAD7q/9/f1cIgvtV68G8x4trLp+h2nI9DluTvCeWjN0XFQGYEng\nfn2OdTpMYMW/DXMbxfal1vcwRfRNoGGbAn9L0U0btD4L38W/vU67UlaWCDjV8hnbnppzDCRxo4+g\nV61K8DDrJiZ+3QQoiLPX9ughGmZm8/Pz1u/37eDgwBO0ABHECirgxdAQj3HVRCbWGn2jPak+6Hil\nxjx1r9l1Kxh7uHo16a9aTDHS6PPIqlRYBtZhSJO01TIOYL2NP19HOVVjVYqf4r1RSdLnv8i3bmpf\nHC+z4dU1tH0pJW2YbOS6jpm+J4LdeP02YJr6f+qdX3R+UnTrcatsQprhTMCy2cBVVygUHEQRr2k2\n2DjVRcjvbPq6AQLYAL+ACe5JMZLZQAgQ0Bw1D6XIeCkAzLvVFYGb/ODgIJeYxb+zs7M2Pj5urVbL\ndnd3cxnnCrDi/2nn+vq6LS4u2vPnz217ezvHjFmW2czMjAPShw8fWqlUssePH1uWZbljb+nL4uKi\nFYtFe/HiRe6UGlwSWFkvLy8dlM7Oztr+/r71+4PDAHg/1sxCoeBF+dEk6/W6JwGRfHBycmLtdttm\nZma8fYzJwsKCbW1t2fn5uc3MzNjJyYldXFx4TC5UqVT8W1jIx8bG/F24qIk9ItwBZWd2dtbDEogz\n4xACKh9o8f9CoeCFnlVJ0LhQdfEwv6wLxkcTjyiDpa5z3bTjZgevn52d2d7ens3Oztr8/LyVy2U7\nPDz0dqi1FKBOe2P8qlp8VRilYrRSSiebJO44rY6hYFattwD6KDgZK7OBlYP36b26yelajOtG1y73\nabbwm0RxQ43Zu6nwCK7FyhbwqiomeFU0Q9psUBxcrVYocrF9umdAmnQYN+NUH+OGqnWp9b3El2sf\njo+Pc7LNzFzxMxsAv2Hg4Pj42Ne49u/58+fXarv++Mc/tuXl5WuAYH9/39bX13PXHj9+7Ae6cJ3w\nJzxBtOPo6Ch3hCtjyLzpelAAzNxyepPyA/s6oJN5Ojo68iOpuZfYfdY53yIJSHkDj5a2CwVdLbJZ\nlnlCboztV1CtSU8x5lP5kjlSvlA8gOGBvyELFWewBuDllDINH7TbbTf8cK9iBOSS8iL10XUeMQJG\nXk6B3ZQyp7kX9EENY/qcll3UdhKeqPcOA/jcP2ytp5S+SAqyh9GNYJXNSQeejVQ3cWLxLi8vveg5\n13kHIIIBUysLi4Mj8AjiVusrP7oIYKrx8XFPslLGiy4PHRilCGh1QgDPlFLSjRqmGh8ft+npaet2\nu3ZwcDBUg4qLhXGtVCp29+5dGx8ft+fPn3uSlLpvJicnfYHfv3/fKpWKffbZZ26J1P6OjY3ZwsKC\n9ft929ra8nED0K2uruYqCtAWTt+IIRzERKJcYPHEgk2oBcCo2Wy6gOD/WL5ZmAcHB3ZycuJgst1u\nu4UQHlF329nZmZfNKhQK7ipnTCuVils6mDP4ihO+zMwPRCAxiY1XE4mw6qoLjN8jeFUrJ+Ok5bEY\nFwSw/phdTyCCPwF+VE7Issz7pJZ6KmqoF0TBIqCVdqd4MsZpR4qgVd37jIPGijGO2i/u0fWF8NZ3\np6yr8V5tU/yd8VOl4E0jwlg0iRF+Rk4APgAdWZa5wso1Ev7USEHIjFrUWP8RFKsXwGygHJnlY4vj\nppZaEwpS+Jta+NTjoQYVrUfK9zVOHB4hvCzGVtIubW+r1cq5/fv9vieGqmUWwwmnDdKfVqvlRhqe\nR5nVKgcAzdgm8ga03BAJO1oZh/fGdUO7UDB5nrwOfe/Z2ZmdnJzkktIwKhQKhdzxn+fn53Z0dOSV\nBxjvs7OznFWPMC9OVjS7whrUmlblot/ve5kxTV5Vj6b2QfmA/oILNImQd0S5iHKDHIXvIi8DbHXO\nCZ1DLtMHalWbWS4Gmprjyp9gqagEKFZSvon8qWMc5a32Qd+RWmcRI7FOVVYzBjqOPMuYx/BN/p2e\nnnaDH9eipT3SjWBVLYHESNJwgAAbk8aw0uAYKwpg0g1tbGzMYxN7vUEpoJvcQLoR4zLVQVcNMG5i\nupHGyVDCld3pdDyuUzde3CJo6NTHjEKYCY79ASRw/Orh4aG9evXKWq2Wbxi8a2pqyusbPnz40Hq9\nnj1//tyyLPNEKgWqq6urfiQp1wGeKysrtrW1ZZeXl14ShViuLLtyQ/GMaqnEejIOZByyUTDXh4eH\nPq9aoYEFhSZJPOnR0ZHH2NLvs7Mzu3v3rm828BbglHu07BfB/cSjAqjoH/Vm2VDHxsZcgGi4CAAW\nXgQwwmtat5frCH3WiZl51Qs9TIFqCPoteEXBL3yqmy8nklWrVRcAWG3xemgGPjzGT8pqxXdUGKYo\ngkEd2whOo6s/Wqf096ixQ2p9Uwu1av0RqKogfZPBqipYnLFuZjn3bJxLwkV0TIdlScNvZoP540Q2\ntSjx7ihrI6hlPmO8JRSVEp5ROWs28ODpe9vtdg5Usj4Bj6oc4eGJfKr38a1er3ctxvX58+f29ttv\n59p4enrqtViVPvroI/ulX/ql3LcajcY1q2yWXdVdffTokV/DYLG0tJTbU1iDMQaRetQKsszMZUZ8\nr1YTYO3VarUccMJ4oIl8GKuWlpZy71WPi36LUCwNaSDcS41MY2NjNj8/n6tWA39iBdZvMW667rGe\nQjpe+jzGEryQ+t6YYMTvMRwKL6KuQ/ZOPXGLvS1WOIDf4jwqL8Z7I5GfwTt4X6vVulZ6KvW+QmFQ\n3ksxV0pOY6yD1IAX+dPMkgmLt4FU6EawqrGkgBIGm42YEAEWClYk1XywkmmpKmILi8WiNRoN10bY\nbGFUBi+GC2RZ5ht4NOGnNqg4uXET1U0X6x5F9lUrIbmF4HcsfApI9Xu0TzfSLLuK61xbW7OJiQn7\n/PPPfaxVW2NxUh7p3r17lmVXyTalUsmazaa3ncW+sbHhtUt1Hnu9q6NRKd3FgkbbnJiYsN3d3ZwV\nXcecub68vLTZ2dncOcNorVoJAcsMfQF0AeayLPN4VRYTLnmy9wGLgGgAEoKLGNR+f5D0EN1CPMPm\ncnx87FZO5gt+QxvkuyQcQGNjYzmLE+2G/1W5w4KFO//09NQBKxbiqNTF2E7ldQAn16MFWl3++g54\nVzeASNyb0qijVwKKSqBayLRfgG94RS3DaOc6xjp++i59tyqB2g7mgPmPccJvAqmiYpZPAlKZxLru\n9/ueVcwzClyhUqmUc3PCF/HoTr6tLnKu62Ei0RIU+VLbA6UMEMim2ObLy0sH61wHdMTkE0ryxc1Y\nZTrj9emnn9rDhw9z7221WvbgwYNrz3700Uf2i7/4i7l27+zsXAOq7GWLi4u5NlBOK45XPAY3yzJr\ntVq5uFbGAGOKziO1PaP1bW5uLrf+kek6Z+yLa2truTnSakHa1k6n4yFcGvPKtTiP5GSohRmFSnlF\nLZq6pwMMU+tA2wUv6mEYChR1zthb1UofvaTaLlXc4jzQHvY1VbTBF7Hu6U3yl29Gr5m+l/8Tnqel\nvgD9EbSqchEBqvYhUqoOawp8D7v3JrrxuNU/+IM/cDO+WiVBzgpOGXzVPnVTArBQykmtUGzEuDQ1\nvhUQzCLFLR03VaVoMeIbqd91Mjn/t91uu2tM36nJLJeXl15sH9INFZcE39P3TExM2Orqqh0eHrpL\nnWOfC6u9AAAaDklEQVQ0GTO+T6b3nTt3rNPp2N7enlv4dEMvla7KlPT7VwH9vAdAzxGtxK/SXrOr\nRIB2u+2uHQ3h0GoKGlMJX6g1GTDK4kFoACYBg9Vq1cMlGB+EKFUAOPa2Uql4XA2gFTc9AAxeUQ2e\nc605USsCSbJkaSduK9qI8OBfjfuEvxBqCHPu1zjRLMu88gW8jdWbsAH4QmM+NYsfMM17bzu+NLrh\nWSu8J1pTlWfVksnvp6enXv+P0lnMPRuOtg2lVGu7AtwjAIGv1M1EmyKPK1hVAK+gQjcn+vfrv/7r\nQ2XFzyqlLNpf1b2p+1LXh137ot96HfpJteuruPfL9PWraFdKSf1JjcGwb32Zdg2jn+Y8vg79X/rW\nV3HvT4tutKxiRWIzZyMCXbO5sumzUQMoAQf9/tWJGAsLC9brXRUP7vf77tJkE9fNVwemUCj4aRsA\nXMXYcfCiaT/G7OjzWXZl5STeRMGX3ocFgnJNBJfH90ERMAPal5aWbGFhwZ48eeKxoYQxAOoBxZOT\nk1YoFGxtbc263a7XPFW3P2O+vr5unU7HGo2GA03mhROrnjx5knOzqBZIELxaIXDLo92amQNYbafO\nNz9ofIwFlni0x36/n9OeAX2ANOaOb1HtAD6hhitu+2gBUdc9mrSWVaKNaOPwr7qlIiBiLGkz4QIa\nc6T9Z+5pB1bg8fHx3JGtjCvfUCup1iul7bFKRwSD/KiCGSsSKJjTeTO7Hp4Q1w1AkjAPQD7WZl0L\n3E/f4CWuKyjm/xDhJTEUAN6ISRsK0qNl4U0ixlvlcpyTKLeoUaz3ojAj81VGMM6MP1Yn5Suz9HGr\nsQ3aDrO8GzfVB1W+9Dm8LXpvPKZTFbQIlPReHZfj4+PcEab9ft+rAei9hF3pCVi9Xs+PQNV7j4+P\nbWZmJqdUEp9OvgHX9/f3/cAAHdeU7NF9Td8RFUH2RVXiUzyj46WyUeVLvJc5j99K8aLuk7Q1NQ8q\nj5V31CCW4q9h34rfpe+8S5/vdDpujY/vZAyjV1WPrdX1FPkz9guKbdDr+h2dA+WPWG3C7Iq/O52O\n15jV9+m34nhGkDps7cZrKfkb26vPx3dEuhGsYv4GSBCAn2WZb35MFiBSg5gLhau6Yhw/ure352BQ\n4yn4V61RCg4KhYK72nmvDkYqcUQHPMYyKTDE1YGlTxcDQhGw3Ov1HAymmEk3XWV6M3PA2G637eOP\nPzazq4BsLIi6iPjbxMSEWxk5GlYXP/1cWFiwk5MTz6jXmNMsy2xtbc2f10WBVZzkI4S/Wt/Uhc/f\n2bBoL3FojC3zr6AHCy9xO+o6ZJPR5wEiCsDMzI/dgzcAOyr0UXZioD3fUHDHeKUEPUCVPiigRdFg\nTBB23KtWW3ibCgmVSsUtvgAC3gVA1UL+sYKGWlV1caslmH5Eqz4/Kfd+FOwKKKN7n7YDuFkvCi5R\nclUZYO6ipVR5SYWcHizCfZoYohTXRWoT+1knVSRw82kcHmuFtaOKKjxtNgBJlFwyMw/VYQ0zviiN\nKg8BtzE5CIqbq27a/B0FWEmBgBKhAPFetcRDWoYJgm/ivf/6r/9qv/zLv5y7TnhW/Fa9XrfNzc3c\n9YODg9wRrBD9Vdre3ra7d+9eA+AaH8s6azQaHo+r+08ETawxszy411rN3Ks5CTGpLvZXPY8xsUYV\nevhAvWDwCjJS20yspY4Bc66hWTyvWIK5JpZV9yQSryJ/RaCnSpvOE2FcOn+0Nx7wcHBwcA0Qsv4Y\nA50z5UXtt35L91XlGwXdrEfaFvlrZ2fH7ty549/Fk1qtVh1HkQOj5dNUCdHQT9pKLkmlUvFrGODw\nDNIH9lsMOGaDnJFU+JHSjWC13+87aNLTqdCqsHz0+4NYREqbTE1N2Z07d+zs7MwODg48+zAOuII7\n1e6oDkCdUrPrJ6AoKNLrGncZhSLXiN8gU10nnU2P3/VYS2UaFbRsDto3+jQ5OWnr6+v22Wef+ZnC\nJBkhkGkD/ZqZmbHV1VXb39+3g4MD/zbv5Wd1ddX7QMkuBdJra2u57E0dS00yUg0bcKhxlwh43YCw\n2Clo5HADVV6wqBJQrxmQKhywVJZKJa9MgHBC+LB4AHi0QS09qojQLxYIyheAGGEB32l8p/KZWucB\nX4wBc8JihDf4lh4D2263rd1u546o5Z0IY4Aqa06BKu3muvJ33GhViNFeBYXa/mjVZD4BPwpOyTQn\nNID54Z5KpeKACUGua0KFtVrf9VoEznFtRatYlA167U0i5ECn0/FKAGaDBJhowSEGXDfMs7MzPzoz\n8k20ZPG9uJGrEhjbZ3YdrA4DWcrjAKdYxknbpGCZkk3x3ghQ8NgpSDEz+/TTT+1XfuVXrr0X5Vzv\njWWrAEjxncPubbfb145r7fevQrq0nBVjMDs7m3ShA4YYL90buUfzBlQpRMbre7Fy6jeQkzGuUb2p\nrHHu1f2a/VLlAOMVjU9ab5q2EmIXk7bYWyPY5N4I8iIwLBaLnh+C7CU8sV6v+7tpQ7PZ9GQ9na+5\nublcNQMSgjXRjTboXs04RnDKv3qSILyo4w9pchVtaLVauaQ8QGq327VXr17Z4uKiTU5O+r68t7dn\n5XLZlpaWrNvteu1zPV3r5OTEut1u7uALKhzouxgv4p95ftgeMYxuBKskhiiQUcBK7CZAjUBizsp9\n9eqVx7vqpqnvIx4RBi0Wiy5k9/f3c2CACeAd0RIWF3r8O9/JssxjRPk715WJAWGcIxw1F934WYx8\nF6vcwsKCFQoFe/78uQNDAJ1aImCgfv/K6riwsGD7+/t+lCjfVo1kdnbWjo6OvD+0gTZS7uP58+f+\nLQXkk5OTdnh46P9X7Qxgq+5/xlAth5OTk7maezAsLipiQwnoR2uPFuxUgDgbDcCaYHgEIL/jWiFs\nwWwQQ0mIAHOvsaf0ERCongLmUfkqKjQAALWuwhOaFckGARgl3hOBiGULFz9hMXgyNHEKPlHFTPld\nrSiMI6RKVrSyqhKqa0gtqfxL+xFW8D9jimBGIeNvjLO2LQpdvqvCWwGulpXhe7xX17H+/iYR65q1\nQ//ZZON4FItFr9epY766upoDLYwna04tsGxgPK+KnH5P26PXUr+bpRM4IkhU2U57zPJewdQYxf0i\nViO4uLjIAV2IDVevd7tdm56evnYvXhT9FkdZx7FQpZ/rrVYrB4Yg9VLo36IMZa2pFVctjvosMkZB\nklk+PInxjh4rs4FHS0GpKtbaX70W+xUJGa73RkWddqDERJmX4gP11kVKKRh6PCzfmJ2dzSX0kmwY\n+TbLsmuWad2LU22L99JvXaP6LxTXpuILHV+wBIZFFBjkxPr6uu8FlMLUueZeze7nG1ptg/vBCpFf\no4f9JroRrALSms2mW3diDB0bBtnxxWLRXeXRBaMuPzqByR63bq1Ws/39fdd0C4XByTto2uoejhs2\nEwbxPUAA5ZZSTKLgaWJiwssjpTZP+qNWYQWEWZbZgwcPbHd3146Pj33iKRcFqdDBGrexseFlrKLw\n4NuU88CNr/1EQ11cXLQXL17k3EMAEkIQVLuLY6Hme+7RUAIEMRo5LlvuIc44y7LcJoeLn7FSsEMb\no7sG8Ma4U7gfEAxvqBAtlUr+jIIl5kcFDYtXF1h01dE+tEHmHZ6MfJGyogNI4atKpeLriPam4lWH\nue21H+oq1xgxXRu0U+NGh/GA2cAih1KKlQBFNpZpgf+owED7mBvcQrpO4vgq8I0ACJCqsZMqhzTe\nWTfON4WGgfSo3POvWmC4Piyj1yx/LrvZQImNMjKuLbPrcXE39SHF7ynPnPZB35sKFUnxw7C2lkol\nW1tbu3avlumBKL8Y76VeqfZjYmIi5y7Ve+P6U8u49jeeCT9MAWCvSikEw8COUvTSxXfodTWS6H2p\nvWuYAqEySOcxyiY1DkT6onyf+ha/DwPN+s0UcIynd/G3lNKVauttgC01l8NAuGIgvJoxZ0Epteaj\nZTc+mxqn1NzofnPb8zfRjWB1d3c3Z8lg02ETpRj+8vKylctlB4IMGKAkIn11U/C++fl5KxaLnu3O\nM6rZMWiA18jg+g0dJBiNws0KKvQZQDCxWTG+RO/VKgB8i/ZwrvTnn3/ugIV2R8AOw+FaWl9ft729\nPY8dUQFAu6la0Ov1HNAyVty7urpqR0dHHlukjI0gxwWtfaCPGlvE+7UagBb7ZYywNqIYKEAHiJfL\n5VwxfwWrKuh6vV6uDqwG3Pf7g9O1sODqnMM/uBciwNH7ogUSgK5joq595Rkz8+oHKpB03PBM6Klb\nWuYFd4yCa9YE/Y68HhUItYwrrwKO6asKaV2HWkOYsceiqpZV3DZnZ2fW6XS8vBjPFwoF5wfKp2ll\nADb6qFQoL9BX5kstqCo8kS/0SRUR5jEqem8SYQjQBBz+jW57s+tFxLmWAozNZtNj8vS9Knv1+m2b\nsLYhFQ4QjRGp96KgDQMGkTRulXelYmGH9UGPW4Wi0g1pySMd22jN7vf7uaNVoWGep9vaeNP1YX8b\ndj/KL+2J8pNnX/e9sU/I22i5Td077L3KR8N4hjCV2MZhhMyN/Y1GgTi/2t7U+1OhHKk+pvoQv6Hf\nSfEMRjLFY6lv6VqKbYgn4unfblOAho3BF6UbV3az2bRSqeQFzmNc2eTkpK2srPgJRBp3h4szbh6g\ne6y0FLHX4vZs7tHFx4amsS9R8+v3BwlAJEWR3BM361KplCsdpFmvbHSqHUbwxnv4we1/cXFhOzs7\nuaQD2q0glwXf71+5UO7fv2/1et0tpdpnGJMfivdrGa9SqeR1Si8vL+3o6ChnwaTv3BeZNWqe/A7p\n2CJUAJuA0H6/7+EhWTawRLCoCUgH4CAANAwD176OEW1Xq5y6vAGDLESu8e7omoxKjfaRv8eNkx8N\n/QDE6UlgZuZjA1hlDfEcmfO0UxVBzfRXoKrWC/hGrZooGNrm1Gk9SsOUL303llWsq8SuYl3VZxkz\nlQXUCoZfNZkMMK6gVJULBayMQeQbvqtutdQcvgmkiiVzqBv348eP7d69ezk+wPNCfKLyBGPM/QcH\nBzY/P38NyKpc52+aFQ5FRZB2AehSm3HclBU4Qfqt1HpWoiap/o1EwVi9QMcEonxeJJKelEhOiu2I\nyVBmZi9evPAEK+4dlvgV24lcSIEfXQf0I8bmQzHcTUG8ghLkWNx7FcwoVkjNYwxZYrzBGtrmYaA0\ntoF3xv6pcsDfIs8x/xqqRB9Q/GK7kH2RZ1JGrth2s3yoTKo/cS7j9WF8z3G6SltbW3b//v3kOxkT\n+tPpdGx6ejp3LzkkMY7Z7Kq6hcau0h+t66qAN1pydTyG0a3HrSo4YbOoVqu2vr5u1WrVdnZ23Jpi\nNrBEabA0jWYzBYTWajWbmpqyRqNhZgM3j4IMtZiwcfIdZYAYJ1MoFHLJWXFx6e/6fj2VJ2q9EItf\nN+nx8XGbmZnxGpo8R5uKxWIOIOpCGh8ft+XlZTs+PrZms3kt+QUqFq+CvVutlid9McZ8q1y+Onnm\n4OAgN/7a7vHxcWu1Wte0Kf4ezf9seqqwxOxsyjKpix+ewJrI9UqlkqvfCtjVRCRtL7yogJX2AFDo\nlyoOLKhogacPel0VEO2zzjl9RkEAdAPYsFYp78BXGqZQKAxCW6KLHK+FWhMjLyqwV+DMGLHGUCRi\n1qv2U+ea+VDrfwStrG1+AOtxrLIs83hl1rR6LjggQUOKtL/q2o9jwByqIkkfUjLhTSIAA6fVZFnm\nYSfHx8e5dYAyg/zGqkgcMjzLemPO1WoDD5jlkw15PgKJKBt0XWkf4LeocKgyxr36E9c57dHn1fjB\nt0gMVuCkQE7fe3FxkauIYHYdHPDeCLQY80ha1ij2IfJxXGtxLLS9OgepeUgBL+1zrIGscxYNR8yX\neiQ1aVX3A9qgyhDX6POweVS5FGV4BMDaB42PhJdVbvIe5RHeGRU31hn7kfIX/dL3RayBvFXLLDJd\n7419jmBV51vbkJKXaint9Xq50on0DeMeSWbsxZeXl7kDkczMvWzn5+fuDej1ern625OTk87zjAXA\nnnmBF287JOBGsKon84Dg19fXbXV11Q4ODuzo6MhjcLSUDoOuRb3NLGdp2dzctE6n47Xp9Fm1sqi7\nVClqIUxGpVJxF74yoIIxXdRxU4dJ1EoVhYBqlkwIhe61DqwCLYS8ClSA6t27d61er7tLVRmVZ+hb\nq9Wycrns4QUK1vr9vq2srFij0ciBSQVOxI7qM9qfFKCPWrEqDYBwZTY0L56DNzirPGrBKpwAgAqy\nscqh+SkIAhwTJsC7FMDrJso9zFGc4/hjNsg+1YoPvBewxiEFWvMVnuQd1OmFxwGmUTnTeExVFKPy\no9YzVXB07FTJ42+pTYDvAUoiWKWfamVNfd/MckqcuuPx0lSrVZuamvJjceEVftSKGjdEKIKulMIR\nf38TiHVYLBavZcJfXFzYvXv3cuPJBo6FyWwQJxgtd+ohgPBOqeXNLJ8EqNf0X9rF9+O9EQhwv34/\nynG9Fjd2Nsb4rSy7Kr8Vj1vl+ymAENtFxnpsl3oXI9jVuTk7O0smU6lSraQyLe5NCjZVxvM+XVvx\nWwq4FUTovSov9FqcXwWCKnd4XueBdrHvR4rALYI5JZ0znUMdF6zlsV3s+ypLVHlW4sQt3sveHSsq\n8I04VyrDVbZHjMD9ca5Sfacdsa3sP1AEh7yLEDUSY3kfBhZ9Bq+BhsOcnp5ey+5XHtbnI4Ae1h/o\n1tJVbDyLi4t2584dKxQK9vLlS58UtQwBGBR0KpjKsquzc+fm5uzVq1dXDfifzZnnVXMBCAEmza4m\nU88E5l8GEld0JAVIMIICoAhU4+BFxmFjJ5N2f38/dzIUgIF/Y5t4x9LSkieUAewjQIFRsDyqlZgx\nwDJ3fn5uJycn19wVtB2gy7yoZS4l6KKVV0EQY4AwUDDJu3B9s8mwuWkNRQUl8JuGOJCog3CLoEaF\nXhRMKih0zOijAh21DERAqwdkKP/Aj/Ay7dF4bU2SYgyYU10/2i7tR1zMOrZm6bJE2me1ckShOWyt\nMEdYUhWsqhKkPykrDvNEHPP4+LhNTk5apVLJnXAVLaj6o3yugNUsb6VRiv18UyjLMs84V4W5WLyq\nsqLjixKtB72YWVIGFgoFazab144w7fV6uYLp/KvyR+/V9ch7UzyuG67K4RiTyvqL6ye1mavxQPmU\nslGRV9S6xr2Hh4cOSrWtWleTZ9m49Xn1wGh7AdF6DRdq5G0drzjesd+pMdAQBJ0bVVjMBjI43osn\nLFqeIwCmXRqqw78RkDKPqVq50aWumCLeG8NOoqFC20VSsI57BMsauqTfvLi48FrhjDl7XRwDNQzF\nv6WS++hL7F9qztlnoheT0EclDfGJfKHAMssylxVmV+vl5OTEraTcG8MEzCxXE1jfnVL6brOkRroR\nrBKI//DhQ5ucnLTj42OPzyMOTQ8K0ESsqFEVCgWr1Wp2fn5uW1tbDlY0fk1j1NjgMZNHgKKAdnJy\n0kvpxA0+JbTUhUzSC4PKBKl2pcAFMJNlV8X4m82mJ9mY5QUcjBStcfxtY2PDdnZ2nLFU4CjQpM8s\nGgXkCowrlYodHR1dA7M6DuoCj+4V7b8CZdqtriNVKszy5Wr4XS1u9A1woRY/LXlyeXnp8c5Y9BTE\naNwQ11Qpim1XLZy5U9IxUUsqbQbk6fzpmOpYdLtdX6woDqpIoIzpv/QhFrtOgYXUJgUfq9tVlTCe\n0/HW+VJhq++l/3qKFD/KN5EHdA60YkhUahW4p0KNlHTd6f9RplXBSK3ZN41SMZKA1Xg9Zjmb5cN/\nlDhZKb6Xe+P1Ye+NvJYClxF0xOeUYkyp3hvfm8rYxjKUalek+fn55BimvhVDBegX9yqlqgFEgKTt\nSo1t6t7U76m5UUtcah5Sz79Ou5SGzU0Eqtx7k/FJaRh/pX6nXalqGPH31BgMA37RoqnvSrV5GJ+l\n2vJF5py5iTHVY2Nj1zwHqbGlPVpP1Wzgzh/Wxtvode4dRjeC1W984xu2sbFh3W7X9vf3HSzgzqfw\nq24mClbMzMHH9PS0dTodB7wMPJoI4FXjV3C/mg0sXGo6Jqai3W6b2fW6frqRAkrYRNmII8ikzfqs\n2cDET38UGMZs6tuERLFYtKWlJdva2vJYJ1zpulkDakulknU6nWvufwVe8/Pz1ul0clZn3qEakgLZ\nlCWBZ2h3dFUoiIrZhYCmLMs8HIJ3ALiji0Xd94BugEycF3grxirRHhUKCsIUWKf4IzUOwxQMfY5v\nalgC31drsyZQqQcBIKcAFcCl2nLsX5wvBZdKWOHj/POjQnAY0VdNsIqANQpz2hq1ad6FBUXjl2Jf\ndG4YD20L/cUSr+swNadvGg3bHG7bGH8a9/60nv8q3vuT+tYXvffrOo8/qW99mfte9x0/qTH4svRV\ntCt1PXolbvpWNBjcdO9Pi24Eq9/85jet1WrlgseJaWADx31tNgBXGuA7NzfnxeeJhVMrKRYmM0ue\nd242iJlgYyf2jRI6Ztc3OQUcCthi4HOcWAULeo1/q9WqFQoFL9YfXZ83TT6uluXlZavX636ql7Y/\nxhRyytT4+LgnQsR+EdNJSaiUW5/xBRTzzWEaqoIHTaxSoIkrG6CliodaBZgzDX7nGwp64Bm1vAHq\neE4BNz8pDZd2DrMS6bzSRnhDQzFom/JtBLIaYwnIVFBKrCr8rd4HQl0YM8IkUhnPyifabr6tSpaC\n/Dg+Gm4yDBCmrmlogCp+jGUEnKooEIzPsYnwDYCVNsbvssbU6hyBKyCVtqXGYEQjGtGIRvR/l7L+\nm26CGNGIRjSiEY1oRCMa0deW3rzaLiMa0YhGNKIRjWhEI/o/QyOwOqIRjWhEIxrRiEY0oq8tjcDq\niEY0ohGNaEQjGtGIvrY0AqsjGtGIRjSiEY1oRCP62tIIrI5oRCMa0YhGNKIRjehrSyOwOqIRjWhE\nIxrRiEY0oq8t/X+gMq/LscTgkgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1023a1278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from skimage import data, color, feature\n",
+    "import skimage.data\n",
+    "\n",
+    "image = color.rgb2gray(data.chelsea())\n",
+    "hog_vec, hog_vis = feature.hog(image, visualise=True)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 6),\n",
+    "                       subplot_kw=dict(xticks=[], yticks=[]))\n",
+    "ax[0].imshow(image, cmap='gray')\n",
+    "ax[0].set_title('input image')\n",
+    "\n",
+    "ax[1].imshow(hog_vis)\n",
+    "ax[1].set_title('visualization of HOG features');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## HOG in Action: A Simple Face Detector\n",
+    "\n",
+    "Using these HOG features, we can build up a simple facial detection algorithm with any Scikit-Learn estimator; here we will use a linear support vector machine (refer back to [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) if you need a refresher on this).\n",
+    "The steps are as follows:\n",
+    "\n",
+    "1. Obtain a set of image thumbnails of faces to constitute \"positive\" training samples.\n",
+    "2. Obtain a set of image thumbnails of non-faces to constitute \"negative\" training samples.\n",
+    "3. Extract HOG features from these training samples.\n",
+    "4. Train a linear SVM classifier on these samples.\n",
+    "5. For an \"unknown\" image, pass a sliding window across the image, using the model to evaluate whether that window contains a face or not.\n",
+    "6. If detections overlap, combine them into a single window.\n",
+    "\n",
+    "Let's go through these steps and try it out:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Obtain a set of positive training samples\n",
+    "\n",
+    "Let's start by finding some positive training samples that show a variety of faces.\n",
+    "We have one easy set of data to work with—the Labeled Faces in the Wild dataset, which can be downloaded by Scikit-Learn:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13233, 62, 47)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_lfw_people\n",
+    "faces = fetch_lfw_people()\n",
+    "positive_patches = faces.images\n",
+    "positive_patches.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This gives us a sample of 13,000 face images to use for training."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Obtain a set of negative training samples\n",
+    "\n",
+    "Next we need a set of similarly sized thumbnails which *do not* have a face in them.\n",
+    "One way to do this is to take any corpus of input images, and extract thumbnails from them at a variety of scales.\n",
+    "Here we can use some of the images shipped with Scikit-Image, along with Scikit-Learn's ``PatchExtractor``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from skimage import data, transform\n",
+    "\n",
+    "imgs_to_use = ['camera', 'text', 'coins', 'moon',\n",
+    "               'page', 'clock', 'immunohistochemistry',\n",
+    "               'chelsea', 'coffee', 'hubble_deep_field']\n",
+    "images = [color.rgb2gray(getattr(data, name)())\n",
+    "          for name in imgs_to_use]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(30000, 62, 47)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction.image import PatchExtractor\n",
+    "\n",
+    "def extract_patches(img, N, scale=1.0, patch_size=positive_patches[0].shape):\n",
+    "    extracted_patch_size = tuple((scale * np.array(patch_size)).astype(int))\n",
+    "    extractor = PatchExtractor(patch_size=extracted_patch_size,\n",
+    "                               max_patches=N, random_state=0)\n",
+    "    patches = extractor.transform(img[np.newaxis])\n",
+    "    if scale != 1:\n",
+    "        patches = np.array([transform.resize(patch, patch_size)\n",
+    "                            for patch in patches])\n",
+    "    return patches\n",
+    "\n",
+    "negative_patches = np.vstack([extract_patches(im, 1000, scale)\n",
+    "                              for im in images for scale in [0.5, 1.0, 2.0]])\n",
+    "negative_patches.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now have 30,000 suitable image patches which do not contain faces.\n",
+    "Let's take a look at a few of them to get an idea of what they look like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BYIBgMAi/3y8PtVQqiefHhAYyp/Y6rcLD6tFa4zWHoU6ng93dXYRCIdRqNVSrVdTr9aGN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\naN1aLoVU6c5abDVq1IkqLy+X80sUZbFkx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/3qV1i3bh3q6+slQCIBlb2zNRfk5Zdfxo4d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s8Oe8J/oKXNXYlEIjbn+Ojo6NIsZL7n+Pj4d86XfivO1n2QfABXjo7BzAyX5++ZWuN+H/cB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\n/wB45kixx1m61wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11a5d7710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(6, 10)\n",
+    "for i, axi in enumerate(ax.flat):\n",
+    "    axi.imshow(negative_patches[500 * i], cmap='gray')\n",
+    "    axi.axis('off')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our hope is that these would sufficiently cover the space of \"non-faces\" that our algorithm is likely to see."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Combine sets and extract HOG features\n",
+    "\n",
+    "Now that we have these positive samples and negative samples, we can combine them and compute HOG features.\n",
+    "This step takes a little while, because the HOG features involve a nontrivial computation for each image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from itertools import chain\n",
+    "X_train = np.array([feature.hog(im)\n",
+    "                    for im in chain(positive_patches,\n",
+    "                                    negative_patches)])\n",
+    "y_train = np.zeros(X_train.shape[0])\n",
+    "y_train[:positive_patches.shape[0]] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(43233, 1215)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are left with 43,000 training samples in 1,215 dimensions, and we now have our data in a form that we can feed into Scikit-Learn!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Training a support vector machine\n",
+    "\n",
+    "Next we use the tools we have been exploring in this chapter to create a classifier of thumbnail patches.\n",
+    "For such a high-dimensional binary classification task, a Linear support vector machine is a good choice.\n",
+    "We will use Scikit-Learn's ``LinearSVC``, because in comparison to ``SVC`` it often has better scaling for large number of samples.\n",
+    "\n",
+    "First, though, let's use a simple Gaussian naive Bayes to get a quick baseline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.9408785 ,  0.8752342 ,  0.93976823])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.cross_validation import cross_val_score\n",
+    "\n",
+    "cross_val_score(GaussianNB(), X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that on our training data, even a simple naive Bayes algorithm gets us upwards of 90% accuracy.\n",
+    "Let's try the support vector machine, with a grid search over a few choices of the C parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.98667684407744083"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import LinearSVC\n",
+    "from sklearn.grid_search import GridSearchCV\n",
+    "grid = GridSearchCV(LinearSVC(), {'C': [1.0, 2.0, 4.0, 8.0]})\n",
+    "grid.fit(X_train, y_train)\n",
+    "grid.best_score_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'C': 4.0}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take the best estimator and re-train it on the full dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearSVC(C=4.0, class_weight=None, dual=True, fit_intercept=True,\n",
+       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
+       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
+       "     verbose=0)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = grid.best_estimator_\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Find faces in a new image\n",
+    "\n",
+    "Now that we have this model in place, let's grab a new image and see how the model does.\n",
+    "We will use one portion of the astronaut image for simplicity (see discussion of this in [Caveats and Improvements](#Caveats-and-Improvements)), and run a sliding window over it and evaluate each patch:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YvPh65dDzQP/XO++53lys8qZAidveSj9W/xcHMoQYK7HiIcTou47H39ZKaIGXlae1QlkA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rxkci6Xc4BkfScRIxbxsqQIIPv48aPlcvfr5nSHHLMHHwoll1mVAD3WDURgerECxuLioleUZSnV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\nVqvZH/7wB69UCqNk0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HcRNUgMJStTwzmPKcLx+BnUFxDD3zSuQ2VOEhI1n4nnYuABRMLk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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d140fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_image = skimage.data.astronaut()\n",
+    "test_image = skimage.color.rgb2gray(test_image)\n",
+    "test_image = skimage.transform.rescale(test_image, 0.5)\n",
+    "test_image = test_image[:160, 40:180]\n",
+    "\n",
+    "plt.imshow(test_image, cmap='gray')\n",
+    "plt.axis('off');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, let's create a window that iterates over patches of this image, and compute HOG features for each patch:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1911, 1215)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def sliding_window(img, patch_size=positive_patches[0].shape,\n",
+    "                   istep=2, jstep=2, scale=1.0):\n",
+    "    Ni, Nj = (int(scale * s) for s in patch_size)\n",
+    "    for i in range(0, img.shape[0] - Ni, istep):\n",
+    "        for j in range(0, img.shape[1] - Ni, jstep):\n",
+    "            patch = img[i:i + Ni, j:j + Nj]\n",
+    "            if scale != 1:\n",
+    "                patch = transform.resize(patch, patch_size)\n",
+    "            yield (i, j), patch\n",
+    "            \n",
+    "indices, patches = zip(*sliding_window(test_image))\n",
+    "patches_hog = np.array([feature.hog(patch) for patch in patches])\n",
+    "patches_hog.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can take these HOG-featured patches and use our model to evaluate whether each patch contains a face:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33.0"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels = model.predict(patches_hog)\n",
+    "labels.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that out of nearly 2,000 patches, we have found 30 detections.\n",
+    "Let's use the information we have about these patches to show where they lie on our test image, drawing them as rectangles:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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i4gInJyc4Pj7GxcUFLi8v0Ww2HaPhRE6SxJlMKAtSRT2fK3ix/GRH3W4XjUajZA7BvqO+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\nZkHCE++2AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d19f198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.imshow(test_image, cmap='gray')\n",
+    "ax.axis('off')\n",
+    "\n",
+    "Ni, Nj = positive_patches[0].shape\n",
+    "indices = np.array(indices)\n",
+    "\n",
+    "for i, j in indices[labels == 1]:\n",
+    "    ax.add_patch(plt.Rectangle((j, i), Nj, Ni, edgecolor='red',\n",
+    "                               alpha=0.3, lw=2, facecolor='none'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All of the detected patches overlap and found the face in the image!\n",
+    "Not bad for a few lines of Python."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Caveats and Improvements\n",
+    "\n",
+    "If you dig a bit deeper into the preceding code and examples, you'll see that we still have a bit of work before we can claim a production-ready face detector.\n",
+    "There are several issues with what we've done, and several improvements that could be made. In particular:\n",
+    "\n",
+    "### Our training set, especially for negative features, is not very complete\n",
+    "\n",
+    "The central issue is that there are many face-like textures that are not in the training set, and so our current model is very prone to false positives.\n",
+    "You can see this if you try out the above algorithm on the *full* astronaut image: the current model leads to many false detections in other regions of the image.\n",
+    "\n",
+    "We might imagine addressing this by adding a wider variety of images to the negative training set, and this would probably yield some improvement.\n",
+    "Another way to address this is to use a more directed approach, such as *hard negative mining*.\n",
+    "In hard negative mining, we take a new set of images that our classifier has not seen, find all the patches representing false positives, and explicitly add them as negative instances in the training set before re-training the classifier.\n",
+    "\n",
+    "### Our current pipeline searches only at one scale\n",
+    "\n",
+    "As currently written, our algorithm will miss faces that are not approximately 62×47 pixels.\n",
+    "This can be straightforwardly addressed by using sliding windows of a variety of sizes, and re-sizing each patch using ``skimage.transform.resize`` before feeding it into the model.\n",
+    "In fact, the ``sliding_window()`` utility used here is already built with this in mind.\n",
+    "\n",
+    "### We should combine overlapped detection patches\n",
+    "\n",
+    "For a production-ready pipeline, we would prefer not to have 30 detections of the same face, but to somehow reduce overlapping groups of detections down to a single detection.\n",
+    "This could be done via an unsupervised clustering approach (MeanShift Clustering is one good candidate for this), or via a procedural approach such as *non-maximum suppression*, an algorithm common in machine vision.\n",
+    "\n",
+    "### The pipeline should be streamlined\n",
+    "\n",
+    "Once we address these issues, it would also be nice to create a more streamlined pipeline for ingesting training images and predicting sliding-window outputs.\n",
+    "This is where Python as a data science tool really shines: with a bit of work, we could take our prototype code and package it with a well-designed object-oriented API that give the user the ability to use this easily.\n",
+    "I will leave this as a proverbial \"exercise for the reader\".\n",
+    "\n",
+    "### More recent advances: Deep Learning\n",
+    "\n",
+    "Finally, I should add that HOG and other procedural feature extraction methods for images are no longer state-of-the-art techniques.\n",
+    "Instead, many modern object detection pipelines use variants of deep neural networks: one way to think of neural networks is that they are an estimator which determines optimal feature extraction strategies from the data, rather than relying on the intuition of the user.\n",
+    "An intro to these deep neural net methods is conceptually (and computationally!) beyond the scope of this section, although open tools like Google's [TensorFlow](https://www.tensorflow.org/) have recently made deep learning approaches much more accessible than they once were.\n",
+    "As of the writing of this book, deep learning in Python is still relatively young, and so I can't yet point to any definitive resource.\n",
+    "That said, the list of references in the following section should provide a useful place to start!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) | [Contents](Index.ipynb) | [Further Machine Learning Resources](05.15-Learning-More.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.14-Image-Features.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/05.15-Learning-More.ipynb b/notebooks_v1/05.15-Learning-More.ipynb
new file mode 100644
index 000000000..17d8cc77c
--- /dev/null
+++ b/notebooks_v1/05.15-Learning-More.ipynb
@@ -0,0 +1,127 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) | [Contents](Index.ipynb) | [Appendix: Figure Code](06.00-Figure-Code.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.15-Learning-More.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Further Machine Learning Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This chapter has been a quick tour of machine learning in Python, primarily using the tools within the Scikit-Learn library.\n",
+    "As long as the chapter is, it is still too short to cover many interesting and important algorithms, approaches, and discussions.\n",
+    "Here I want to suggest some resources to learn more about machine learning for those who are interested."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Machine Learning in Python\n",
+    "\n",
+    "To learn more about machine learning in Python, I'd suggest some of the following resources:\n",
+    "\n",
+    "- [The Scikit-Learn website](http://scikit-learn.org): The Scikit-Learn website has an impressive breadth of documentation and examples covering some of the models discussed here, and much, much more. If you want a brief survey of the most important and often-used machine learning algorithms, this website is a good place to start.\n",
+    "\n",
+    "- *SciPy, PyCon, and PyData tutorial videos*: Scikit-Learn and other machine learning topics are perennial favorites in the tutorial tracks of many Python-focused conference series, in particular the PyCon, SciPy, and PyData conferences. You can find the most recent ones via a simple web search.\n",
+    "\n",
+    "- [*Introduction to Machine Learning with Python*](http://shop.oreilly.com/product/0636920030515.do): Written by Andreas C. Mueller and Sarah Guido, this book includes a fuller treatment of the topics in this chapter. If you're interested in reviewing the fundamentals of Machine Learning and pushing the Scikit-Learn toolkit to its limits, this is a great resource, written by one of the most prolific developers on the Scikit-Learn team.\n",
+    "\n",
+    "- [*Python Machine Learning*](https://www.packtpub.com/big-data-and-business-intelligence/python-machine-learning): Sebastian Raschka's book focuses less on Scikit-learn itself, and more on the breadth of machine learning tools available in Python. In particular, there is some very useful discussion on how to scale Python-based machine learning approaches to large and complex datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## General Machine Learning\n",
+    "\n",
+    "Of course, machine learning is much broader than just the Python world. There are many good resources to take your knowledge further, and here I will highlight a few that I have found useful:\n",
+    "\n",
+    "- [*Machine Learning*](https://www.coursera.org/learn/machine-learning): Taught by Andrew Ng (Coursera), this is a very clearly-taught free online course which covers the basics of machine learning from an algorithmic perspective. It assumes undergraduate-level understanding of mathematics and programming, and steps through detailed considerations of some of the most important machine learning algorithms. Homework assignments, which are algorithmically graded, have you actually implement some of these models yourself.\n",
+    "\n",
+    "- [*Pattern Recognition and Machine Learning*](http://www.springer.com/us/book/9780387310732): Written by Christopher Bishop, this classic technical text covers the concepts of machine learning discussed in this chapter in detail. If you plan to go further in this subject, you should have this book on your shelf.\n",
+    "\n",
+    "- [*Machine Learning: a Probabilistic Perspective*](https://mitpress.mit.edu/books/machine-learning-0): Written by Kevin Murphy, this is an excellent graduate-level text that explores nearly all important machine learning algorithms from a ground-up, unified probabilistic perspective.\n",
+    "\n",
+    "These resources are more technical than the material presented in this book, but to really understand the fundamentals of these methods requires a deep dive into the mathematics behind them.\n",
+    "If you're up for the challenge and ready to bring your data science to the next level, don't hesitate to dive-in!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) | [Contents](Index.ipynb) | [Appendix: Figure Code](06.00-Figure-Code.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.15-Learning-More.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks_v1/06.00-Figure-Code.ipynb b/notebooks_v1/06.00-Figure-Code.ipynb
new file mode 100644
index 000000000..73940a1c1
--- /dev/null
+++ b/notebooks_v1/06.00-Figure-Code.ipynb
@@ -0,0 +1,2786 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Further Machine Learning Resources](05.15-Learning-More.ipynb) | [Contents](Index.ipynb) |\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Appendix: Figure Code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Many of the figures used throughout this text are created in-place by code that appears in print.\n",
+    "In a few cases, however, the required code is long enough (or not immediately relevant enough) that we instead put it here for reference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "if not os.path.exists('figures'):\n",
+    "    os.makedirs('figures')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Broadcasting\n",
+    "\n",
+    "[Figure Context](02.05-Computation-on-arrays-broadcasting.ipynb#Introducing-Broadcasting)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ojg529uxZ1tHRwQoLCynrCbJyc3OZm5sby83NFTSLV9Z+L/OaZY05inZ1cqz/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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115e7f908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Adapted from astroML: see http://www.astroml.org/book_figures/appendix/fig_broadcast_visual.html\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "#------------------------------------------------------------\n",
+    "# Draw a figure and axis with no boundary\n",
+    "fig = plt.figure(figsize=(6, 4.5), facecolor='w')\n",
+    "ax = plt.axes([0, 0, 1, 1], xticks=[], yticks=[], frameon=False)\n",
+    "\n",
+    "\n",
+    "def draw_cube(ax, xy, size, depth=0.4,\n",
+    "              edges=None, label=None, label_kwargs=None, **kwargs):\n",
+    "    \"\"\"draw and label a cube.  edges is a list of numbers between\n",
+    "    1 and 12, specifying which of the 12 cube edges to draw\"\"\"\n",
+    "    if edges is None:\n",
+    "        edges = range(1, 13)\n",
+    "\n",
+    "    x, y = xy\n",
+    "\n",
+    "    if 1 in edges:\n",
+    "        ax.plot([x, x + size],\n",
+    "                [y + size, y + size], **kwargs)\n",
+    "    if 2 in edges:\n",
+    "        ax.plot([x + size, x + size],\n",
+    "                [y, y + size], **kwargs)\n",
+    "    if 3 in edges:\n",
+    "        ax.plot([x, x + size],\n",
+    "                [y, y], **kwargs)\n",
+    "    if 4 in edges:\n",
+    "        ax.plot([x, x],\n",
+    "                [y, y + size], **kwargs)\n",
+    "\n",
+    "    if 5 in edges:\n",
+    "        ax.plot([x, x + depth],\n",
+    "                [y + size, y + depth + size], **kwargs)\n",
+    "    if 6 in edges:\n",
+    "        ax.plot([x + size, x + size + depth],\n",
+    "                [y + size, y + depth + size], **kwargs)\n",
+    "    if 7 in edges:\n",
+    "        ax.plot([x + size, x + size + depth],\n",
+    "                [y, y + depth], **kwargs)\n",
+    "    if 8 in edges:\n",
+    "        ax.plot([x, x + depth],\n",
+    "                [y, y + depth], **kwargs)\n",
+    "\n",
+    "    if 9 in edges:\n",
+    "        ax.plot([x + depth, x + depth + size],\n",
+    "                [y + depth + size, y + depth + size], **kwargs)\n",
+    "    if 10 in edges:\n",
+    "        ax.plot([x + depth + size, x + depth + size],\n",
+    "                [y + depth, y + depth + size], **kwargs)\n",
+    "    if 11 in edges:\n",
+    "        ax.plot([x + depth, x + depth + size],\n",
+    "                [y + depth, y + depth], **kwargs)\n",
+    "    if 12 in edges:\n",
+    "        ax.plot([x + depth, x + depth],\n",
+    "                [y + depth, y + depth + size], **kwargs)\n",
+    "\n",
+    "    if label:\n",
+    "        if label_kwargs is None:\n",
+    "            label_kwargs = {}\n",
+    "        ax.text(x + 0.5 * size, y + 0.5 * size, label,\n",
+    "                ha='center', va='center', **label_kwargs)\n",
+    "\n",
+    "solid = dict(c='black', ls='-', lw=1,\n",
+    "             label_kwargs=dict(color='k'))\n",
+    "dotted = dict(c='black', ls='-', lw=0.5, alpha=0.5,\n",
+    "              label_kwargs=dict(color='gray'))\n",
+    "depth = 0.3\n",
+    "\n",
+    "#------------------------------------------------------------\n",
+    "# Draw top operation: vector plus scalar\n",
+    "draw_cube(ax, (1, 10), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid)\n",
+    "draw_cube(ax, (2, 10), 1, depth, [1, 2, 3, 6, 9], '1', **solid)\n",
+    "draw_cube(ax, (3, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid)\n",
+    "\n",
+    "draw_cube(ax, (6, 10), 1, depth, [1, 2, 3, 4, 5, 6, 7, 9, 10], '5', **solid)\n",
+    "draw_cube(ax, (7, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '5', **dotted)\n",
+    "draw_cube(ax, (8, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '5', **dotted)\n",
+    "\n",
+    "draw_cube(ax, (12, 10), 1, depth, [1, 2, 3, 4, 5, 6, 9], '5', **solid)\n",
+    "draw_cube(ax, (13, 10), 1, depth, [1, 2, 3, 6, 9], '6', **solid)\n",
+    "draw_cube(ax, (14, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10], '7', **solid)\n",
+    "\n",
+    "ax.text(5, 10.5, '+', size=12, ha='center', va='center')\n",
+    "ax.text(10.5, 10.5, '=', size=12, ha='center', va='center')\n",
+    "ax.text(1, 11.5, r'${\\tt np.arange(3) + 5}$',\n",
+    "        size=12, ha='left', va='bottom')\n",
+    "\n",
+    "#------------------------------------------------------------\n",
+    "# Draw middle operation: matrix plus vector\n",
+    "\n",
+    "# first block\n",
+    "draw_cube(ax, (1, 7.5), 1, depth, [1, 2, 3, 4, 5, 6, 9], '1', **solid)\n",
+    "draw_cube(ax, (2, 7.5), 1, depth, [1, 2, 3, 6, 9], '1', **solid)\n",
+    "draw_cube(ax, (3, 7.5), 1, depth, [1, 2, 3, 6, 7, 9, 10], '1', **solid)\n",
+    "\n",
+    "draw_cube(ax, (1, 6.5), 1, depth, [2, 3, 4], '1', **solid)\n",
+    "draw_cube(ax, (2, 6.5), 1, depth, [2, 3], '1', **solid)\n",
+    "draw_cube(ax, (3, 6.5), 1, depth, [2, 3, 7, 10], '1', **solid)\n",
+    "\n",
+    "draw_cube(ax, (1, 5.5), 1, depth, [2, 3, 4], '1', **solid)\n",
+    "draw_cube(ax, (2, 5.5), 1, depth, [2, 3], '1', **solid)\n",
+    "draw_cube(ax, (3, 5.5), 1, depth, [2, 3, 7, 10], '1', **solid)\n",
+    "\n",
+    "# second block\n",
+    "draw_cube(ax, (6, 7.5), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid)\n",
+    "draw_cube(ax, (7, 7.5), 1, depth, [1, 2, 3, 6, 9], '1', **solid)\n",
+    "draw_cube(ax, (8, 7.5), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid)\n",
+    "\n",
+    "draw_cube(ax, (6, 6.5), 1, depth, range(2, 13), '0', **dotted)\n",
+    "draw_cube(ax, (7, 6.5), 1, depth, [2, 3, 6, 7, 9, 10, 11], '1', **dotted)\n",
+    "draw_cube(ax, (8, 6.5), 1, depth, [2, 3, 6, 7, 9, 10, 11], '2', **dotted)\n",
+    "\n",
+    "draw_cube(ax, (6, 5.5), 1, depth, [2, 3, 4, 7, 8, 10, 11, 12], '0', **dotted)\n",
+    "draw_cube(ax, (7, 5.5), 1, depth, [2, 3, 7, 10, 11], '1', **dotted)\n",
+    "draw_cube(ax, (8, 5.5), 1, depth, [2, 3, 7, 10, 11], '2', **dotted)\n",
+    "\n",
+    "# third block\n",
+    "draw_cube(ax, (12, 7.5), 1, depth, [1, 2, 3, 4, 5, 6, 9], '1', **solid)\n",
+    "draw_cube(ax, (13, 7.5), 1, depth, [1, 2, 3, 6, 9], '2', **solid)\n",
+    "draw_cube(ax, (14, 7.5), 1, depth, [1, 2, 3, 6, 7, 9, 10], '3', **solid)\n",
+    "\n",
+    "draw_cube(ax, (12, 6.5), 1, depth, [2, 3, 4], '1', **solid)\n",
+    "draw_cube(ax, (13, 6.5), 1, depth, [2, 3], '2', **solid)\n",
+    "draw_cube(ax, (14, 6.5), 1, depth, [2, 3, 7, 10], '3', **solid)\n",
+    "\n",
+    "draw_cube(ax, (12, 5.5), 1, depth, [2, 3, 4], '1', **solid)\n",
+    "draw_cube(ax, (13, 5.5), 1, depth, [2, 3], '2', **solid)\n",
+    "draw_cube(ax, (14, 5.5), 1, depth, [2, 3, 7, 10], '3', **solid)\n",
+    "\n",
+    "ax.text(5, 7.0, '+', size=12, ha='center', va='center')\n",
+    "ax.text(10.5, 7.0, '=', size=12, ha='center', va='center')\n",
+    "ax.text(1, 9.0, r'${\\tt np.ones((3,\\, 3)) + np.arange(3)}$',\n",
+    "        size=12, ha='left', va='bottom')\n",
+    "\n",
+    "#------------------------------------------------------------\n",
+    "# Draw bottom operation: vector plus vector, double broadcast\n",
+    "\n",
+    "# first block\n",
+    "draw_cube(ax, (1, 3), 1, depth, [1, 2, 3, 4, 5, 6, 7, 9, 10], '0', **solid)\n",
+    "draw_cube(ax, (1, 2), 1, depth, [2, 3, 4, 7, 10], '1', **solid)\n",
+    "draw_cube(ax, (1, 1), 1, depth, [2, 3, 4, 7, 10], '2', **solid)\n",
+    "\n",
+    "draw_cube(ax, (2, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '0', **dotted)\n",
+    "draw_cube(ax, (2, 2), 1, depth, [2, 3, 7, 10, 11], '1', **dotted)\n",
+    "draw_cube(ax, (2, 1), 1, depth, [2, 3, 7, 10, 11], '2', **dotted)\n",
+    "\n",
+    "draw_cube(ax, (3, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '0', **dotted)\n",
+    "draw_cube(ax, (3, 2), 1, depth, [2, 3, 7, 10, 11], '1', **dotted)\n",
+    "draw_cube(ax, (3, 1), 1, depth, [2, 3, 7, 10, 11], '2', **dotted)\n",
+    "\n",
+    "# second block\n",
+    "draw_cube(ax, (6, 3), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid)\n",
+    "draw_cube(ax, (7, 3), 1, depth, [1, 2, 3, 6, 9], '1', **solid)\n",
+    "draw_cube(ax, (8, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid)\n",
+    "\n",
+    "draw_cube(ax, (6, 2), 1, depth, range(2, 13), '0', **dotted)\n",
+    "draw_cube(ax, (7, 2), 1, depth, [2, 3, 6, 7, 9, 10, 11], '1', **dotted)\n",
+    "draw_cube(ax, (8, 2), 1, depth, [2, 3, 6, 7, 9, 10, 11], '2', **dotted)\n",
+    "\n",
+    "draw_cube(ax, (6, 1), 1, depth, [2, 3, 4, 7, 8, 10, 11, 12], '0', **dotted)\n",
+    "draw_cube(ax, (7, 1), 1, depth, [2, 3, 7, 10, 11], '1', **dotted)\n",
+    "draw_cube(ax, (8, 1), 1, depth, [2, 3, 7, 10, 11], '2', **dotted)\n",
+    "\n",
+    "# third block\n",
+    "draw_cube(ax, (12, 3), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid)\n",
+    "draw_cube(ax, (13, 3), 1, depth, [1, 2, 3, 6, 9], '1', **solid)\n",
+    "draw_cube(ax, (14, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid)\n",
+    "\n",
+    "draw_cube(ax, (12, 2), 1, depth, [2, 3, 4], '1', **solid)\n",
+    "draw_cube(ax, (13, 2), 1, depth, [2, 3], '2', **solid)\n",
+    "draw_cube(ax, (14, 2), 1, depth, [2, 3, 7, 10], '3', **solid)\n",
+    "\n",
+    "draw_cube(ax, (12, 1), 1, depth, [2, 3, 4], '2', **solid)\n",
+    "draw_cube(ax, (13, 1), 1, depth, [2, 3], '3', **solid)\n",
+    "draw_cube(ax, (14, 1), 1, depth, [2, 3, 7, 10], '4', **solid)\n",
+    "\n",
+    "ax.text(5, 2.5, '+', size=12, ha='center', va='center')\n",
+    "ax.text(10.5, 2.5, '=', size=12, ha='center', va='center')\n",
+    "ax.text(1, 4.5, r'${\\tt np.arange(3).reshape((3,\\, 1)) + np.arange(3)}$',\n",
+    "        ha='left', size=12, va='bottom')\n",
+    "\n",
+    "ax.set_xlim(0, 16)\n",
+    "ax.set_ylim(0.5, 12.5)\n",
+    "\n",
+    "fig.savefig('figures/02.05-broadcasting.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Aggregation and Grouping\n",
+    "\n",
+    "Figures from the chapter on aggregation and grouping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Split-Apply-Combine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JKisrOXLkCGFhYXTr1o20tDR69+7NwIEDnUcOVFdXc+jQIecypb+/P/n5+bzxxhssWrTo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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b1c5f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def draw_dataframe(df, loc=None, width=None, ax=None, linestyle=None,\n",
+    "                   textstyle=None):\n",
+    "    loc = loc or [0, 0]\n",
+    "    width = width or 1\n",
+    "\n",
+    "    x, y = loc\n",
+    "\n",
+    "    if ax is None:\n",
+    "        ax = plt.gca()\n",
+    "\n",
+    "    ncols = len(df.columns) + 1\n",
+    "    nrows = len(df.index) + 1\n",
+    "\n",
+    "    dx = dy = width / ncols\n",
+    "\n",
+    "    if linestyle is None:\n",
+    "        linestyle = {'color':'black'}\n",
+    "\n",
+    "    if textstyle is None:\n",
+    "        textstyle = {'size': 12}\n",
+    "\n",
+    "    textstyle.update({'ha':'center', 'va':'center'})\n",
+    "\n",
+    "    # draw vertical lines\n",
+    "    for i in range(ncols + 1):\n",
+    "        plt.plot(2 * [x + i * dx], [y, y + dy * nrows], **linestyle)\n",
+    "\n",
+    "    # draw horizontal lines\n",
+    "    for i in range(nrows + 1):\n",
+    "        plt.plot([x, x + dx * ncols], 2 * [y + i * dy], **linestyle)\n",
+    "\n",
+    "    # Create index labels\n",
+    "    for i in range(nrows - 1):\n",
+    "        plt.text(x + 0.5 * dx, y + (i + 0.5) * dy,\n",
+    "                 str(df.index[::-1][i]), **textstyle)\n",
+    "\n",
+    "    # Create column labels\n",
+    "    for i in range(ncols - 1):\n",
+    "        plt.text(x + (i + 1.5) * dx, y + (nrows - 0.5) * dy,\n",
+    "                 str(df.columns[i]), style='italic', **textstyle)\n",
+    "        \n",
+    "    # Add index label\n",
+    "    if df.index.name:\n",
+    "        plt.text(x + 0.5 * dx, y + (nrows - 0.5) * dy,\n",
+    "                 str(df.index.name), style='italic', **textstyle)\n",
+    "\n",
+    "    # Insert data\n",
+    "    for i in range(nrows - 1):\n",
+    "        for j in range(ncols - 1):\n",
+    "            plt.text(x + (j + 1.5) * dx,\n",
+    "                     y + (i + 0.5) * dy,\n",
+    "                     str(df.values[::-1][i, j]), **textstyle)\n",
+    "\n",
+    "\n",
+    "#----------------------------------------------------------\n",
+    "# Draw figure\n",
+    "\n",
+    "import pandas as pd\n",
+    "df = pd.DataFrame({'data': [1, 2, 3, 4, 5, 6]},\n",
+    "                   index=['A', 'B', 'C', 'A', 'B', 'C'])\n",
+    "df.index.name = 'key'\n",
+    "\n",
+    "\n",
+    "fig = plt.figure(figsize=(8, 6), facecolor='white')\n",
+    "ax = plt.axes([0, 0, 1, 1])\n",
+    "\n",
+    "ax.axis('off')\n",
+    "\n",
+    "draw_dataframe(df, [0, 0])\n",
+    "\n",
+    "for y, ind in zip([3, 1, -1], 'ABC'):\n",
+    "    split = df[df.index == ind]\n",
+    "    draw_dataframe(split, [2, y])\n",
+    "\n",
+    "    sum = pd.DataFrame(split.sum()).T\n",
+    "    sum.index = [ind]\n",
+    "    sum.index.name = 'key'\n",
+    "    sum.columns = ['data']\n",
+    "    draw_dataframe(sum, [4, y + 0.25])\n",
+    "    \n",
+    "result = df.groupby(df.index).sum()\n",
+    "draw_dataframe(result, [6, 0.75])\n",
+    "\n",
+    "style = dict(fontsize=14, ha='center', weight='bold')\n",
+    "plt.text(0.5, 3.6, \"Input\", **style)\n",
+    "plt.text(2.5, 4.6, \"Split\", **style)\n",
+    "plt.text(4.5, 4.35, \"Apply (sum)\", **style)\n",
+    "plt.text(6.5, 2.85, \"Combine\", **style)\n",
+    "\n",
+    "arrowprops = dict(facecolor='black', width=1, headwidth=6)\n",
+    "plt.annotate('', (1.8, 3.6), (1.2, 2.8), arrowprops=arrowprops)\n",
+    "plt.annotate('', (1.8, 1.75), (1.2, 1.75), arrowprops=arrowprops)\n",
+    "plt.annotate('', (1.8, -0.1), (1.2, 0.7), arrowprops=arrowprops)\n",
+    "\n",
+    "plt.annotate('', (3.8, 3.8), (3.2, 3.8), arrowprops=arrowprops)\n",
+    "plt.annotate('', (3.8, 1.75), (3.2, 1.75), arrowprops=arrowprops)\n",
+    "plt.annotate('', (3.8, -0.3), (3.2, -0.3), arrowprops=arrowprops)\n",
+    "\n",
+    "plt.annotate('', (5.8, 2.8), (5.2, 3.6), arrowprops=arrowprops)\n",
+    "plt.annotate('', (5.8, 1.75), (5.2, 1.75), arrowprops=arrowprops)\n",
+    "plt.annotate('', (5.8, 0.7), (5.2, -0.1), arrowprops=arrowprops)\n",
+    "    \n",
+    "plt.axis('equal')\n",
+    "plt.ylim(-1.5, 5);\n",
+    "\n",
+    "fig.savefig('figures/03.08-split-apply-combine.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## What Is Machine Learning?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# common plot formatting for below\n",
+    "def format_plot(ax, title):\n",
+    "    ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
+    "    ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
+    "    ax.set_xlabel('feature 1', color='gray')\n",
+    "    ax.set_ylabel('feature 2', color='gray')\n",
+    "    ax.set_title(title, color='gray')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Classification Example Figures\n",
+    "\n",
+    "[Figure context](05.01-What-Is-Machine-Learning.ipynb#Classification:-Predicting-Discrete-Labels)\n",
+    "\n",
+    "The following code generates the figures from the Classification section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "# create 50 separable points\n",
+    "X, y = make_blobs(n_samples=50, centers=2,\n",
+    "                  random_state=0, cluster_std=0.60)\n",
+    "\n",
+    "# fit the support vector classifier model\n",
+    "clf = SVC(kernel='linear')\n",
+    "clf.fit(X, y)\n",
+    "\n",
+    "# create some new points to predict\n",
+    "X2, _ = make_blobs(n_samples=80, centers=2,\n",
+    "                   random_state=0, cluster_std=0.80)\n",
+    "X2 = X2[50:]\n",
+    "\n",
+    "# predict the labels\n",
+    "y2 = clf.predict(X2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Classification Example Figure 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IirQNaHve+eRTxIyfCa3u5h50c30trMV5WDRv7t39Yk7EWd/L/QnHWBmcNU1OIT4uHvFx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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118eae860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the data\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "point_style = dict(cmap='Paired', s=50)\n",
+    "ax.scatter(X[:, 0], X[:, 1], c=y, **point_style)\n",
+    "\n",
+    "# format plot\n",
+    "format_plot(ax, 'Input Data')\n",
+    "ax.axis([-1, 4, -2, 7])\n",
+    "\n",
+    "fig.savefig('figures/05.01-classification-1.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Classification Example Figure 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Yza24du2qO6m2J9aIiEiMGzdRcnxNzU3s379HkljVavlkmJGRjZUrf9Lt3qj2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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b2131d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get contours describing the model\n",
+    "xx = np.linspace(-1, 4, 10)\n",
+    "yy = np.linspace(-2, 7, 10)\n",
+    "xy1, xy2 = np.meshgrid(xx, yy)\n",
+    "Z = np.array([clf.decision_function([t])\n",
+    "              for t in zip(xy1.flat, xy2.flat)]).reshape(xy1.shape)\n",
+    "\n",
+    "# plot points and model\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "line_style = dict(levels = [-1.0, 0.0, 1.0],\n",
+    "                  linestyles = ['dashed', 'solid', 'dashed'],\n",
+    "                  colors = 'gray', linewidths=1)\n",
+    "ax.scatter(X[:, 0], X[:, 1], c=y, **point_style)\n",
+    "ax.contour(xy1, xy2, Z, **line_style)\n",
+    "\n",
+    "# format plot\n",
+    "format_plot(ax, 'Model Learned from Input Data')\n",
+    "ax.axis([-1, 4, -2, 7])\n",
+    "\n",
+    "fig.savefig('figures/05.01-classification-2.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Classification Example Figure 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zFkXyipRbVKbOT/aoItvQ3bfdmrT+w2DFxSV65Nt3a8tbb6k7HJWiUS2YUaXL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\nAAAAAIBjCCIAAAAAAIBjCCIAAAAAAIBjCCIAAAAAAIBjCCI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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bbbacc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the results\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
+    "\n",
+    "ax[0].scatter(X2[:, 0], X2[:, 1], c='gray', **point_style)\n",
+    "ax[0].axis([-1, 4, -2, 7])\n",
+    "\n",
+    "ax[1].scatter(X2[:, 0], X2[:, 1], c=y2, **point_style)\n",
+    "ax[1].contour(xy1, xy2, Z, **line_style)\n",
+    "ax[1].axis([-1, 4, -2, 7])\n",
+    "\n",
+    "format_plot(ax[0], 'Unknown Data')\n",
+    "format_plot(ax[1], 'Predicted Labels')\n",
+    "\n",
+    "fig.savefig('figures/05.01-classification-3.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Regression Example Figures\n",
+    "\n",
+    "[Figure Context](05.01-What-Is-Machine-Learning.ipynb#Regression:-Predicting-Continuous-Labels)\n",
+    "\n",
+    "The following code generates the figures from the regression section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "# Create some data for the regression\n",
+    "rng = np.random.RandomState(1)\n",
+    "\n",
+    "X = rng.randn(200, 2)\n",
+    "y = np.dot(X, [-2, 1]) + 0.1 * rng.randn(X.shape[0])\n",
+    "\n",
+    "# fit the regression model\n",
+    "model = LinearRegression()\n",
+    "model.fit(X, y)\n",
+    "\n",
+    "# create some new points to predict\n",
+    "X2 = rng.randn(100, 2)\n",
+    "\n",
+    "# predict the labels\n",
+    "y2 = model.predict(X2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Regression Example Figure 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MF8/QXG3jlccfop+LEcTHT5xi3fZfCPD1ZvrE8Wi12np7FkGoS25LxFlZtdse\nrzELDva+aZ5PURTm/Pg2FwOO4tPGA8WuULDPyi3BtzJu0GQ3R1ozdfHzKykpYcGG5eSai+ga24p3\ndy4ntUNYhWscFivT83155vYHanWv6vjt2cxmM0/NfptDnjoUP18Um42AlFSeHTKKoX3qZgT51d76\n4jOWmBWX/Z2DHGZe/cPjFY6ZTCam/vFJMjReKIqCPiwarY8/hvwMXpk6ju6dOgFlLQ0vvfc+uzKL\nUfxCcFhLCTRl8MfJYxjYu3e9PIu73EyfLU1RcLDrQYaiaVqolaUbF5De7hi+/r+OllVL+Pf2YNOh\nRXRO7U5URIxb4jKZTOTn5xEaGoamgfsUtx3YzVs7FpHVOgjZS8MXa7/Eo2O801xBWathV47zZhkN\n4Z3vv+ZAWBDSr1OtJLWavLgY3l6/kgg/f+LjW1Z4385fSOLHTRswWizE+Qdw57gJGAyGyop3Kc9c\niiR5uD5XWraTlsVi4cflyzmdnkluVib5vqH4RFYc1V7sF8q8dRvKE/HH333PdpMGlV/Z4imyxoM8\n/xjeWbCCHp06odfrqxWnIDQ0kYiFWjmWcwBda+cmQL9OOlbvWcIDE59o0HiKi4v594IPOEYKJT4S\nUnIR/oUaxnQfxuQhE+o9KVutVv6zYzE5HcLLE6+iVSF7uk4GRkflmy7Up/2ZaUjRzgOBsqMimfK/\nd4iPiGJ0i1Y8Ou0OFq1bwwd79lAcGoak1qLk5bPm32/w3oMPExURWeV7hnp5ohgtFRYj+U2wXk9e\nfh5/fPs9LniHIGs9wCcCu/Eipoun8YptVeH6lIKi8n/vPnsJlT7UqcwC3wgWLF/B3VNvq3KMguAO\nYkEPoVYssuv9giVJwnKNvYTryz/mvc3RDkUonQPRNQ/AY1A0+QN8+ODMIh74/AXOX0qq1/sv3byG\n9JYVtxXUhAViSXa9lnW09tqbQtSXEofrHaNkrQbZ05OM6Ejm5qTzyU/z+GznTkrCwsublCW1mtSo\nGP678Kdq3fPuceMJyLzsdNwzK50Zw4Yz+7t5XAyIKkvCv9JHxqKgYDNXXNrToLmyaEqxtbL9sDXk\nV7IkqCA0JqJGLNRKAKEU4rw5fanRSqyP67m6B4/t55fTO5CQ6NdmIB3bdqqTWM5fOM+ZgDxUqorz\nT7V+BmSgoJcvs9fP5b1ZL9fJ/VzJNRYgB1RsfvWIDqFw80E0YUFImit/ch6X87it4+AK1yZdTGLJ\nL1twKAo3V6SXAAAgAElEQVRjevSjTXwr9h4+wOIDv2CyW4jW+zBr9EQCAgJrFWczLx9cbbJXmnIZ\nbWhZX7bi7c1PB/ZijHVuVgdIzMlBUVz3+boSGBDIv6ZPY86yZZwqMuOQZOJ1au4bMZQ2LVuS+PU8\npADnWrpnXEtMZ07gE58AgKPUTO8WceXno/28yXU4308x5dGt3cAqxSa4n8Vi4atPvuPsyRSCwwOY\nfPtEdDrXq401NSIRC7Vya+/bmbPrFXx6X/lVUhwKtu0GRs2aUOFaRVF4Z95bJEWewdClrH8x8fwh\n2h7twOPTnqp1LPtPHESKdT0YQuOjw15i4ZzBSEZGBqGhzk2ZdWFwl958u/UrrLFBFY579++AYd1R\nfKIjMDmsxHj4Mq3jIEb2GVR+zXvz5/JTzgXMUcFIksRP6+fT/OsCkmL8sYYFAjoUh5mtX77Le7c9\nQERoGPNWL+VyYQFhXj7cMWp8lftt7xk0hFPrVmIMv/I+OMylWNPT8erWrfyYEQUk1w1ntl/X1a7O\nYhOd2iXwSbsEcnJysNlsFX4OdsVFNgWQJBRbWRO+nJ9Jv0ADD864vfz0tGGDOLlgDaU+VzbYUOx2\nErQW+vbsVeXYBPc5d+Ycr/95NsUXdahlDXblEusX7uTZ1x+hbft27g6v3olELNRKbFQcD3R7jqU7\n55PhSEaFmig5nqduf8RpzeXVW1dysdU5DEFXkoVnc09O6Y+xdfcmBva6pVaxJLRoi+PUDuRY56lT\nNpMZlU6D2SBRVGQEyhKAw+Hg48Vfsyv3FIVKKWEqH2Z0G8LATjWrSbVs1oL+G/zZWFKKpL9SM/a8\nXMDfpz3EwG59XL5ux/49zCu6jD06pHwLBWtEMMc8NNjy8tCHldWAJVkmrXUkr837nCxJISU2DNlL\ng2LNYskHr/HarXfSoQrLSvbq1JU3ZBXfbtnAiexMLhcWInlo8bxq/mqEtzemzHRMEc67MbUJ8EeW\nZRRFYcP2bRxJOo+f3sD0MWOvuzpXYKBzjb5loJ/LWromJ537+3RGrdMzeMJttGlVsb+4d7du/NXh\nYP66zaQUmNBKMl1iw3h61p+v+z4IjcP/3vgSS7I36l+/86kkNbbLPnz81te89/Ub7g2uAYhELNRa\nq2Zt+HOzl6973eGMA+h6Ojc16cP17N2/u9aJuF2rdsRuMZASU7GWZi+x4nCApJIJzdIQG3tlAf3X\nvnufrbH5yDFlyTsJeOPSWrLzjEwePLZGcfxz1lNELvqWnRfPU+SwEqXxZkaPcfTtXPl6wauO7MUe\n7O90XB3oh+WSc7/q3vQUtAN7lzcZSxoN6fHRvL1yIXNbV21f6O4dOtG9QycURWHmm//keGRYhfdN\nKSqimazBVFzE4dTLyL8bmOWXnsoDk6ZQVFTEk+++zTGdF7KPL4opl4VvvcnzY8cwsGf1pg49OPFW\nTn8+l5yAyCtxFBsZGRvK4/dfe4pXvx496NejR5Of/tIUZWRkkHw0Fx0BTucuHzOSdP48zZrfOOvB\n14RIxEKDsUuVb15vo/ob27vy4pSneX3RbE56ZaEO88J0PguL0Uxg/9Yol02Mi+1bXlNPz0hnl5yC\nbKhYO3OEe7Hs0E4mDRpTozV+ZVnmsSn3UJ2tGcyKnUrHTl4Vgy2vECLCXF56XO0g6UISzeKqvluP\nJEm8OesRXvrmC47aLZg9DRguXgKrje0t4iEkGHVyMp4H9xLXPJ4oLy/uufte4mJiefnDORwPCkf+\ndSS0pFaTHxnDuytX0qdLt2qNUo9v1ow5f3iYr5Ys5WKBEU+NikHd23LryFEUFxczd+HPnE7PRi1J\n9G4bz+QxNfv5NBSLxcLmLVtxKAq3DBqIh4frqVs3u6IiE/ZSCVxsWqZYVRQUFDR8UA1MJGKhwUR6\nRJNrzUalqfgXZzPbiDXUzTZvQQGB/Of+f3Ap5RI/rVnEeZuKEl8HAcdUjIwfyfA+Q8qv3bJ/B9Z4\nf5e76aarSygqKqrXlaZ+r5VvEBst2cjaiolLsdvhqr5Ta3oW1rQcbKYiZL0Oj5bNyqcE2bRqjEXV\nrxGGhoTw0TMvkJySzIXkS/w7O5/MuNjy98YeF0dhaAi9QiK4f/LU8tcdzMxECo92Ki89MIRl69cy\neXT1WhUiwsP5yyMPVzhmMhn5w6tvc9EzHFlV9vPYu+ccB068y7+eebpRJuOlK9bw45IdFFr8AYlv\nftrOlLE9mTyxZq0sTVlcXDMCm3tgdjGl3jdGJqF91bfkvFGJRCzUq583/Mzu9L0UUYy3w4usH3MJ\nnRGEJJd9eDrsDuTtaibdXbdzPWOiYnjm/ieveU10aCSOzAOoXKx2o7dKdTZi02q1olKpymuNrtwz\ndjIbZ7/KmVZh5UlVURS89p1AHe7Pb6m4+Mgp1LIOXd9eSJKE3VRE0c59GHp2QdZqiDGW0q51zQe3\nREdFs/3QAdLDw5zr53oDW8+f4fcrPZttrqdBSVoP8ox100T86Q8LuOQVWeH9k3UGduTls33XLgb0\ncd3v7i4nT55k7oLdKJpI1L9OsS9Bz7eLD9GiWQydOnVwb4CNjCzLjL59EAve24yq9MrYAru2mDHT\n+jb4gjzuIBKxUG8+Xfwpe0KPoumpAzwowIomJAD7YhldpAYJiRhNHHfNuM8tzXZ9uvYi6uPFpAVX\nPK7YHXT0iKz1GtAb9+zguwObOW8twEOR6ewZygu3zcTP13l7P51Ox8cPP8P7i+dztCALBwoJ3kE8\n+tTLJKelsHj/TjIL8zigaJF+t0m8yssTz17dKDl6HL/YKKYndKt13JkF+ci//jysObnYCwvQhISi\n8jSQb7FUuDbO14dEF2V4ZKYzfOwoF2eq72RaFpLKuf9Q9vJj26GjjS4RL16xCUXjYlS+Nohla7aK\nROzCpOm34h/oy5aVv5CRnIdvkCdDJoxh+Ohh7g6tQYhELNQLk8nEHvNBNEEVm3Y9mnviyLTz6rR/\nu71JUZIknh8xk1fXfE5Gaz2yjx5HaiEdcww8N/2R8usUReGHtUvYknIco2IlUuPN9B7D6NquY6Vl\n/3JoH/88to6SloGADyXAJkUh9Yv/8NWT/3BZO/b19ePFex9xOh4aHEz3jl2YPW8uByJKnJ9DpcLL\n5uAfCf0Y1ndAjd6L32sf1xzbjs2Yky+jDQrFIyCU0uRUSkqMdI6JrXDtXYMG88q69RQH/W7qkLmE\nQUEBxEQ5N1nXxLV+T35rWWlMjEUWwHVrStk5wZXBwwYzdcb4m3KwnUjEQr3YeXAnSivXTUp53oVk\nZ2cTHBzs8nxDatO8NXMffoPV29ZxOSWDzs2HM+b+wRU+DP4z/1MWe2dBKwOg5wJwaO+P/N1ipl8l\nI6F/2LeZkmYVB4FJksSpOE9WbdvI2EHV/6ZvUxyVJqXokPA6ScIAQ/r2R/XV53j37Fd+P0OLljgs\npajzsitcO6BHT/6lUTN/82ZSiorx0qjp16w599821VXRNdI+MpRTKSXl62KXM+YybOTQOrtPXQkJ\n9OJ4sgXpqvnXiqIQ7F+99bmFm4NIxEK9CPYPwpFjBW/nJmeVWa72hgH1SZZlxgwa6fJcZlYWq4uT\nICqkwvGi5oF8u299pYn4stUIOD+75OPJifSL1GTIzi0du/HT1uXYQpzn4Lbydm7urqnDxxJRxbdx\nGq0taz1IsTtwOBwVavS9OnelV+eudXb/qz1w+zSOvPomp9SBqLRlNU2lqIDhkd706NrtOq9ueHdM\nu5VdB2dTKlVch9tDSWfG1AfdFJXQmIlELNSLzgld8Jmrx3LVioWKohBjjbjugg+NxaqdGymJD3I5\nsvpsaa5TUvqNj+x6L1zFaiNAV7Nn79K+I7ds28jaYjOS4deEpCiEX0jjodtnVausbXv38P3OLSQV\nFmA0FoLJRGhUNK0DggjXeqD4B7h85nyHQklJSYP+/HQ6HXP+/hcWLF/OkYuX0cgSg/v3ZMiAumkB\nqGvBwUG8+KcZfPHtMs6nFKIo0CzKm3tvn0JkZNU3yRBuHiIRC/VCkiQe6DuTD7Z/hKObBrVegyXf\njOGgxGOTH3V3eNdks9lYtWUdJaVmdCoNSqm1wipZv/FArrSpeHBUGxKNZ5G8K9b8g05lMv2Bh2oc\n26uPPEnrpQvZmZJEfkkxUl4B47r3Jiay6v2xW/fu4sXN6ygND4PAspq0YrNx8sBBUqOj8D6aiMov\nGEeI81zlYLW6QmtGaWkp3y9dQmJ6OjLQt2VLJo4cVef9/xqNhhmTJjGjTkutPwnt2vKf19pSXFyM\noih1+sXFZDKxceMW/Px86N+/3zVH4ws3BklRFMUdN27KHfJNfXWf6jxfaWkpyzYvJcecQ4x/DCP7\nj2rUHxxrftnMZ0fWkRLjiaRR43c2l8ILqTC24tKPiqIw6By8NvNPLstRFIW3vv+ENaZkipoHoxSb\nib5QwDODJtO3c/daxehwOHj5szlsKsqlJDIUikqIzczj/8beRvf2lQ8gA1i0aRWvLlyIqrfzGsyW\njEyw2dCGh+Ozez+mjl0r9MsqJhOzIsN5cOp0AMxmM4+9+QYn/UOQNWUtAI7iIgaq7Lz+pHvm996I\nf3sWi4Xjx4/jH+BP7FWD4a42f/4Cli49iNUSiN1hIcDfxKz7x9O3b9NYU/tG/PlVR7CLqZIgEnG9\nuBl+mRrz8ymKwubd2ziUchKDyoOpg8ZVabeiy+mpzFw2m+I2V009Sc2FY5dRhrRDUqlwmEpoccrI\nf+99lgB/52k1v5eZlcXaX7YQ6OPLiAFDnNbfrok5P37Ll+YcJH3FkbkR55JZ8MxLlc67XLJhLW+d\nOEzh5csYEhJcXlOceAzPhARap2cSqfdif04ORpWaUEVhRMuWPDJ9RnmCnfPdN3yfW4R01XQpxVjI\nP/p0Z9iAht/5qLH/bl5t7tc/sH5TIvlGHbJkIS5Kw5NP3Enz5s4L3KxYsZa5X+1Hlit+mKtUqXz4\n0fP4+DivsX6judF+ftVVWSIWTdNCk1JaWspzn7/KqRZ2VM28UOwOVi15lQfiRzJugOsBWb+Zt3kF\nRa1DnPtGIwLoWqinTU4IhXYzrQJacesTo6qUVEOCg7lrQt0sVmK1Wvl57Sp+PLQHqYtzIk2JDObn\n9auYPnqCi1fDsqMHsQUFoFxKdnneYbHAr8/kpdPz2uNPUlxcTEFBPsHBIU7zkxPTM5EMzh/+krcP\n248fd0sivpEsWbKSJauSUKnD0enLjqVmw7/e+JxP/vey0/u9deshpyQMYLWGMn/+Eh566J6GCFuo\nByIRC03KnMVfcbqrtnwZTUklY+0YzOcH1zCocx+8vX0qfW2BvbTS5tQSFTw2xX0fdCfPnuGvP37N\nxfBgSlQSrsacyzodWcbCSsvIKLkyB9lhsTotp1ly4iSGtm2huJj+zVsAYDAYKh3hfq2W5/pulLZY\nLGg0GrfPRXdFURTMZjMeHh7X7IbZtPUwKrXzF5k8oz/LV6xh4q0Vx9abTBZw8ZOXZRVGo7nWcQvu\nIxKx0KQcLbqErHFeH9rcIYCFm5Zz34Q7Kn1tlMEPhzUNWeP8ZxHuosyG9Mbi+SQ3j0ambGCVK1JO\nPl26d3F5DiDAw4NMwJDQjqKDh9BGRKCNCMdRWkrJyZOoA4PQ5OUz1NOb28e4rlX/XoewUA7lmJCv\nbpouLGBgv8p3mqqNZevWsWj7XlILS9CrJbrGhPLsg/c3mg3k5/20mHXbj5Cdb8FTJ9MtIZInH53l\nsrsgv8AMOCditVrP5dQsp+NBgZ7kZDsdxmYrJSoqwvmEcMNovKNmBKEGShWry+OyWkWxrfSar71r\nxCTCEp0/6bzP5HBnv9F1Et/vFRYW8PZ3n/PgR2/x2MdvM3fpAhwOh9N1J0+f4rjmSs1P7e+PJTm1\nwjWK3U5nk4V+3StPgCPbJCCbTGUrcXXvBmoVRYmJ6Hbu4oH2XbgvqhmfjpvMK488UaWa5qwpU0nI\nz8BReuV9VYpMDNarGNyvf1XegmpZuWEj7286yEWPEKzBsRT6x7ApX83z/363zu9VE/N+Wsy8NWfI\ntYYhe8ZQoopi6zEb/3jzfZfX+/vpXR63WYuJiXZeIvPWibegUuU6Hffzy2HSpHG1C15wK9XLL7/8\nsjtuXFzcdJd68/T0EM/nJvuOHSQj2Hn8oeNSPrPajCIs2MUawL/SarX0imxFxqFjZKekI2cbaZMj\n81SP8XRuW7c7wOTl5/HgJ2+zLVxPuq+Oy95adptzOb15GyN69KuQCE+eOcPK3PTy9Z/Vvr7YcvIo\nTbqALTObkIIibtF486/7/3DNBfI7tGyNKv0ySafPYkRBa7XRWe/FO48+xZjBQ+jdsTPBQUFVfga1\nWs3ovv3QZ15GW5BHHHbubN+WR26/o16ajN/+9gdydFetVibLZBaZ6RDsTXhYmNt+NxVF4b2Pf8Is\nVXz/JFlFRlYufbs0w8+3Yu3XbivmwOEkJLlibd7fO4s/PfWAU7N2eHgYzZt7c+b0UfILcpAlI/Hx\nWp5/4X78/OpuQRd3asyfLXXB09P1mvqiaVpoUu7sPY7Tv3yFOcG//Ji9xEK3XH86tbv+YvtxUbF8\n8aeXSUnJxm6319sKYB8v+4nzrSMqJCxJr2Obj4Utu3cwuPeVGmW3Tp0J3rKKXJ8rA3V0cbFALNEX\nU/nxz3+r8mjs52bO5PZLmRw6doSQwGBaNKvdhusajYa7J02pVRlVlVZQBIEuvih4B7Iv8RhdO3Vq\nkDhcKS4uJqvAiuxqurBHCLt27yM2JqbC4bFjRlBYWMSa9QfIylWhUdtpEWfgqSceqfTnOWLEILp0\n6YrRWIhGo200TfJC7YhELDQp7Vu245/STObtWsElay56NHQLaMGs++6sVjn1vRvUCWMOkotajOLv\nw7YzxyokYr1ez4Tmbfg6Lw3H75KxJiePqR27V3tKlF6vp0/3yued5uXlsnb7VoL9Ahjcr3+dzfs2\nmUx8PP8HTqRlAwptQoN4eMa0aw6g+z1vnZYiF8ft5iIigmNcnCljs9nYvXcfKlmiR/fqv19VodPp\nMHhIuBoy5SgtoHmzHi5fN+P2SUy9bTwXLybh4+NX5fXXq/qeCTcGkYiFJqddfFv+Gd/W3WFck+oa\nTbcqyTnxPTb1DoLWLGf1iURyraWEeOiZ2LknYwbeUqdxvfPVF6y+kExRUBjKqQtEb9zI0+PH07uW\nazqbzWYef/0tLnpFIWnLmpfP5To4+vp/+OhvL6DXu+4v/b1e8dH8fLEIlbbil6Tw0hxGD3W9+cPS\n1WuZv3onWQ4vUBRCf1jN3WMHMnJo3b5vKpWKLm3D2XHShqyq+LEa5VdKj+6Vv39qtZoWLVrWaTzC\njUUkYkGoQw6Hg8UbV7Mn5SxIEv1iWjFu8AinPtOuAZEctuYjXTVCW5Wew7j+E12WPW3kOKaNrL9B\nOfOWLeXn7EIIjUQCJE8vLnt68drixcxr3aZWyzR+v3gJFwzhFWrXkiRz0TOcbxct4sE7Kh/N/ps/\n3HM32bPnsCs1DZtfGI4SE9H2Ap67b4bLWu6RxEQ+XbEXuyESDWX9uJfzzbz52U/ERkXQpnXrGj+P\nK0//4X4KXp/NsYsl4BGMvbSQKL8SXnhqZp3eR2h6RCIWaiQ/P4/VO1ehkmRGDxiHl5d7p/c0Bna7\nnafnvMHOCA1yeFnf8obsQ2z58DD/fvTZCsn4oUnTOfLBG+wL90T2KrtWzshhqmcEHet4YFhVbTp5\nErycVwrLDo7kh5UrmDV1Wo3LPpmaiax2rvXKajUn05yn6riiUql45ek/kpySwuZffiEytDW3DBhQ\n6cCwRWu3YDeU7ZpVkpdOSV46Bv8IVAEtePyf/+PeCQO5e1rd9W9rtVpee+nPJCUlsWvvflo0606P\n7t0a5VxnoXERiViotk8XfsbqjPXoOhlQFIWNyzcyImQkk4Y0zKCdxuqHtUvZGe2BbLgygEby8WQL\nRSzduJpbh16ZAqXVavnwyb+ydNNa9qUkoZVlRncfTc/O7tvWr6DUAi6+T8lqNblFrnpnq06jksD1\nzDK0qur1QUdHRXH31Ovvd1xYbAG02K2lmPMzCYzrfOWkoT3zt50jNGgzI4YMrtb9r3vfQiOXkrM5\nfS6d02eSmDplQr2PORBubGIesVAtO/ZvZ52yGUM3L2S1jEqjQt/TkzWlazh++liDxbHvyH4++Plz\nvlz6Xdk2fpQ1Pa7fsZHPF3/LoeOHGyyW3+xJTaqQhH8j+XiyM/m003GVSsWkYaO5redACkpKeHPz\ncu6Z/RqfLJzncj5xfYvwcj1C3FFcRMuI2i0Y0aV5DPnH91BwLpGCc4nknz6MtdiIUmxkYId2tSq7\nMkE+ehRFwZh2Fr8oF/fQ+bN2x4E6vecXc+fz8n+WsucEHDmnZsHaNJ545l8UFhbU6X2EpqXSGnF6\nejqLFy+msLCQNm3aMHLkyPJvdR9//DEPP/xwgwUpNB7bz23Fo7tzsjG09mTtgTW0a+V6M4G6YrPZ\n+Mvnb3AkvAg5xgeHzc6yH15mfGBXNqclktzCA1WEJ/NPHqfd1sW8fs+z9TYF6WrKNVogHZVsrbLv\n6CFe2LSUgugQoKzp9nhJBhc/mc2rjzxV90Few9T+Azi6dj1m/ysjdxVFoYUxh/HDhte43KKiIhbu\n3It3+x4VmmkLju5lbEIrRg0ZUqu4KzNjwih2vv4xJQUZ/La3jeKw4xkYje7XJvh8U90tDZmamsry\nDSeQteHlx1RqLdkl4Xz46Xc8/8xjdXYvoWmptEa8cuVKRo4cyeOPP45KpWLu3LlYLE13orVQNWap\n8g+u0mucqysfL5nLkfYO5Iiy6RuyWkVJpyA+OriM1O4BqPx/HVAU5cexTh68teDjeo/pN52DonCU\nOv+NOIpK6BHuvJsOwFfb1v+ahK+Q9Do22Qs5de5MvcRZmb7de/D8wP60ys9Em3oJ79RL9LcW8e4T\nT9ZqCtM3ixaR6hfp1Ffqk9CN4ODAeutDjYiIQIuZ0A634BfXHr+49vg370RxfhqlxWWtKMG+dfcl\nbdmK9aBx3sNZkmROn3exNqUg/KrSGrHVaqVZs7IPj7Fjx7J27VrmzZvHXXfd1WDBCY1PoBRIvlLo\n9OHpsDkIVodU8qq6cyA/CTm24ujd4vOZeHRzTnSSSuaQ+TJWq/WaK07VlbtGT2LH+//iUEu/8g0V\nHOZSelwyM+WJsS5fc664AFcds9aIEDYe2E3rBp7WMrz/AIb3H0BxcTEajab8fVu5cQMbjiRislmJ\n8fbm3vHjiIqIrFKZSdn5SCoXTfayzIXc+muy/WnJMoxeLZymivnGtCM/6QiGkEjGTx1WZ/dzOJRK\nv1Q43LPbrHCDqPRrrlar5cyZM+VNOiNGjMDb25sff/wRq7WSURdCkzd14HQc+51rffYdVqYNnV7v\n9zfjvOGBNa8ITZDrBQ6KPRSKi68MNCotLWXR2uX8tHoxJpOpTmPTaDT87/G/8pgjgl4ppfS+XMof\n5Tjef/z/Kl1EQldJTVOxWvHWXX9ubX0xGAzlSfi9uV/x+u5D7FF7clznxyqLzB8++oyTZ6pWY9dI\nlSchXTUHalXH2ZRMVBrnQVKSJKGTbTw8oQd9e7peaKMmhg3th7000+W5FjHX3w9buHlV+lcwbtw4\ntm/fzpEjR8qPTZw4EX9/f/Ly8hokOKHxCQ0J428j/kLAbj+Kd5oo3mkiaE8gz418AS8v15te16Vo\njb/TMUOLUIpPpLi8PrTUo3zD9CVb1jDti5f4j5zIfz1OM/27f/LVih/rND6tVsusW6fz/v3PMHvW\nM9wzforTvrK/1y0gHMVudzoefDGDKcPG1Glsv+dwOLh8OYX8/Gv/Laelp7H83EXwvrJOsiRJ5IZE\n8tmKFde9T2FhAYeOn8Ccne50TjHlM6xbZxevqr6kC0ms2bCe1LS08mN6beXve/f2rRg7ou5qwwAt\n4+Pp3y0Eu/XKFzxFceApp3Df3ZPq9F5C01Lpb2pwcDAzZ1aciC7LMqNGjWLgQLHh982sbcu2vOj3\nEopSeVNcfbmj1zhO75mLue2VhKz29MD3TAHW+FJkw5UakJRhZHzzXkiSxJmkc7yftBlLh5Dyb5/G\nhFDmph2jxb5fGNC9T4M+x2+emX4vF+e8xaFAPfj5oNjt+Cel8VTf4VVabaomflq1kgV795GMhM5u\no723Fy/ceRcR4eEVrrPZbLw6Zw7mkFiX+wufynbeCehqc76fj6l5Z6znT2AvKcYQVdaFUHL5PKNj\nQxg6YECtniU/P5+X//sRJ7Nt2Dx8eX/pL3SK8OKlp//ApJG3sOW9eTi8KvbbOsxGBg6qn5Haf37q\nEdqsXMPOPccptdiJDvflrjueJihQ1IiFykmK4p7Oi6wsoztu2yCCg73F89WjIycTmbd3JRctOegk\nDZ194nh4/N18uvx7fsk5TYFSSpjKmzEtejH5lrJa5Wvff8jqmBKX5fU9L/HGPU+X/7+hn09RFDbs\n3Mahi+fw0mi5c9T4ellL2Gaz8e4XH/H9/iOoImPQhVxJULHpyXz9t7+XN6FbrVaefPNNdqSk4922\nA5KLpvWgzBQWvfLyNe9590uvkuJZtuOVxVSIOf0SALqQSKbHh/DH++6t1TP96ZU3OWYOqPCF0GG3\nMTDMzotP/YH5Py/h+w37sXhGgCSjKsog0qMIL+8gSkrthAd6MWPSaFq2qN3mFw3F3X979e1meD5X\nxIIewg2nY5v2dGzjvPrU41Nm8nglrzEpZf3aitWGce8pFFlGkiUUm53TloaZ3lQZSZIY1m8gw/rV\nX0vTwWNH+dfCn0gJDEbfsyeWjAwKDu/FO6ELslpNkk8Ay9avZeLIskVHvln0M0cMAXjF+1F0/jRe\nLSuu3a0oCgkh16/l/f5rvtbLB238lZ9bbQcwXbp0ieNZViSfivV1WaVm3/k0SkpKuH3yrYwaMpCf\nV6zBarORmqpjb4onslL2gZicCkff+Za/PTqFju3rd+qdIFRGLOgh3BQitb4oNjsF249h6N4G717t\n8NytwzQAACAASURBVOrRFu8+7UmL8WT1zk3uDrHe2O12Xlu4gLTIGFS/DgDThobi1bkLplNli7DI\nBk/OZ2SUv+ZQymVkjRaVhw5ZraYk5WL5OYfVQnRWMk/ePuO6947x1eOq0U3Oz2RU39p1B5w5fx6b\n1nUNw+RQl/d/+/n5M+vO2xk/fAgHLpQge1R8TYlHON8uWlOrWAShNq5bI87Pz2fZsmXk5+dz3333\n8fPPP3Prrbc2mY2ohZvDPaMms+zdZzE3C0W+eqOFZqEsTNzOqL51uyNPY7F680aS/QOcvnVLajWo\nymqTjtJSwiPK9vp1OBykZ2ZBZFnzuGdcS0rzcjCeOIIkycSrFb58483r7oX74TffsediBgWFZ/Bt\n0wXp1xHiSnEhw6IDar3pQod2CXgs3IJd57wZhb/GTlBQxS0F12zcgsMQ5rK/+1yq60FrFy5e5Ivv\nFnM+ORdZlmgVF8Rj999BQIDzmtyCUFPXTcTLly+nb9++rF+/Hi8vL9q3b8+iRYucBnIJQmPm7e3D\n4Kj2LI9znv4EkGxruv1S6Tk5yAbXOydJKhWKohCZm8GURx/kwqWL/PWzLzidV4BXqA351xHfHv6B\nePgH4ig2cV/39tdNwhu2buWnU6kQ0RLvQDPGM4kgy0ilxTw8rD8P3nNPrZ8rJCSYbjF+7MqpuPWg\nw1LCwPbNnOaO6zy0KA47ksr5Y08tO6fnjIxMXnz9c0xSJBABDth7TuHPL73L//79V3Q6HYqi8PW3\nP7Jr3xlMRVaCAw2MHdmHoUPEgFah6q7bNF1cXEyLFi2Asr6sbt26UVpaWu+BCUJdS2jWktKULAp3\nnaRw90lKU67s+uMt1f+CH+4ysHsP1JkZLs8pxcW0ys3glTvvLNs96NvvSA6Kxqt1RwqPHsBhvTJn\n3GEuoadUyuhbnJektFqtrNm4gZXr11Ja+v/snXd4FNXawH8zW5Pd9N4pofdeBUGQIl1EVCyIem2I\n7aqfvV+9dq+KvaHYUASkCEjvodcAgfTeN2X7zPdHMGHZhRRCCDi/5+F5yJk557xns5l3znveYmXV\nzn1grPJsV+v0+LXpil/rzvh27Et+hWenuYbwzIP3cmWkjHdZOs6SLAJtWYxv78v9t7sr+onXjMHb\nlu3WLssynVq6J6OZ99PvlOGaY1sQBPKtYfz062IA3n7vU35flUV+WTBmKYK0fD/mfr2JpctXN9IK\nFf4J1Loj1mg0mEym6p/T0tLOGRepoNBcyTWV4sitwKtHJwRBwJaWTen6Axj7taOff9zFFu+C0aZV\nawYYvFhvsyFqtdXtYl4ODwwdyh3Tq2oBJ6ckk2iRwFhVccm3c08qk48hSxKitYL7Rw7n1uuud0t3\nuWT1ar5eu4k8rwAQRT5dsxmV2QJhnrOCVTjc46Ybikaj4ckH7sFqtVJSUky7di0oKfGcatXb25tb\nJw7m84VbcHhHIggCTpuFCE0es2c97HZ/Vl4ZguCe9Uyl0pCcnk9BQSFbd2Wi0pxREEMdyJLlWxk7\n+iqlBKJCnahVo44aNYr58+dTXFzMxx9/jNls5ro6lCBTUGhOJCYd45vCQ+i6xFe3aWMjUAX4ErHm\nJA++9OFFlM4zJ1KSWbD+LyySRPfoGMYNvxrVKVNyfR/wr943h//Nn8eO7ExKzFaiDQamXjGEqwfX\nmFDzCgqw67z42zYgqtUY21TF2zoKchnWr7+bEj6WlMT/1m7DFhjN3wFOJboYyvdtwyc03kMqVAdx\ngbX7l8iyzKLlK9h64Bh2p0R8ZBCjrhiI0WgkLMw9n7NOpyMsLPyUOfrsOc/Hj76aXl0788uSFVRa\nnbSOiWXyuLs9pkDV6zxnQwPQa1WsW78RSRXm0ayYW2ChoqJCqdOtUCdqVcTl5eXceeedFBYWIssy\nwcHBZ03Xp6DQXPltx1rscSFu7SofAxGtWzc7K8+3ixfy+dEDWCLCEdQCS1OO8eGcXwmMiSXfZiVQ\np2N4i3juvu6GOilllUrFgzffds44zS4dOxGyaBklp2XR+ptIHERHx7i1/7L6L2yB7opR36ojjpSD\naFp2qW6TZZnwsixmTK79fPiFt//HphwJld5IZX4mm/Yn8eOmY2i1GloHaLjj2jH06dmj1nE8ERkZ\nyZx/3V7rfVf07cyBn/eg0rrGdMuWPK65egqlpaVIjuOIHjy3dRpBqUGsUGdqffqsXr2atm3bEhp6\n4RP6KyhcKCzy2c2hFhrPVNoY5Obm8tXhfVhjoqo9fM2pqTjat6M0oGo3WQ58VZpP2bdf8Nitd1T3\n/X3lCtYmHqHS4SDW6MPMcePrXJzB29ubUe1a81N6PhhqlItQVsr47p09vqyYbHbA/cVc7eNLC7sf\nIeoyEnOKUIkCHcKDeOChe2stS7lj5042Z5pRGYKwmYpwWioJbFWjdFNlePWbRXwUEU7EGdnAGpMx\no0Zw9EQq6xIykHVhgIxozWbKyK506dwJWZb5Zv5Kis2uiliWJdq3CW6SQiMKlwe1KuKAgAAWLVpE\nVFSUyxerW7duF1QwBYXGpHNwNCsqEhENrmkjZVmmla7xQvHsdjvzly9ib14GoiAwILo1U0aOrVcZ\nwV/WrKQiKqJaCctOJ7LDgSbAVU7B25vV6RncW16G0ejDa198yuLyCjiV8/ugLJPwxWe8NeMW2rSs\nW+ao+2bMwP/3haw+lEiRxUqot54xPbsxZcwYj/dH+BiQC+0Iguv6ZFmmbUwUT95d/7rl6xL2Ihqq\nkoVU5qcT0ML9WVPpHcX3C//g0XvvrPf49eHBe2cxJT2N5avWoxZVTBw3jeDgKtkEQeCBe6bxxnvz\nMVmDUav1OGwmokMqefiBh2oZWUGhhloV8d9vr5mZmS7tiiJWuJSYctVYlv8vgcOdddXxrAARR3KZ\nNW12o8xhs9m49/1X2R0XgBhaZZZcW5jIlo8O89Z9/67zua5dklxkdJSWojlLruIif1/2HjxITFQU\nK3JzIaxmhygIAgWR0Xy2dAn/vX9Onddx06TJ3DSpbvfOmDCB9W+9T3GQq9natzibGdMbFuJ4+qck\niJ4fUYIgUFDWeN7X5yI2JpZ/3X6zx2tdOnfki4+eY/Efy8kvKKFD284MGTJIcdJSqBe1KuKJEyc2\nhRwKChcUtVrNh//6P95fOI99phwcSLTzDuLOKXcT7sH5pyF8vWQBu1sEVtciBhCNBjZI5SzbsIZr\nhl5Vp3Gu6tGLn1cuQQqpSrCh8vbGWuC5sLyYm8fGPbs4+vuvWOPbeS7OcAGrpQUHBfHKrTcwd+ES\nEotMyAi0DTQya9pEYj2cKf+NxWJh3oLfSEzPQyUK9G7XgqkTxiOKIlcP7sfqL5eCMRhZ8nxsIMsy\n/gatx2tNjUaj4drJEy62GAqXMLUWfXjvvfc8ts+ZU/c3bAWFfwIz3niZjYGelcPEShXvz36kzmM9\n8PprLJGcCKcqMJXt2o2xR3eXnXLl/kPoVTo0LeKpOHIQ746dXK7/TUx+Dmveer2eq3Fl3abNLN+6\nE0mGwV3aM2H01W67vsrKSiRJwmAwsGz1X+w7dpJAHyO3XjfZpZKU2Wzmloef45gUUp2Iw2kzMyjY\nwUf/eQZBEHjujf+x+EABVpsdp7kCY2gLl7l05hy+ef4u2sS3Pq91KSg0B2pVxCUlJdX/lySJI0eO\n4HQ6z7sU4uVeYUNZ36VLQ9d37ydvsSPCsyPS1UUyr952b53HkmWZeYsXsiX1JBbJSYRaS0ZxIcf9\nfJCDArEeTkRrDEITXOVE6TCbsWam4d22nds4oxx2nr/73gav7ZUP57IqtxzBpypBh1RZRi+tlTce\n+7dLBIUsyyTs2sGbX35HbmAcKoM/ksNOgCmLx66fyIDevQGY+/U8fk2qcMmGBeA0l/HY6G6MGl6V\nMGT95s2sTdjHyeQU8svtVRWUkIjSWbl13DCuGupeQlH5bl7a/BPW54laTdNn5pQeNGgQn376qVKT\nWOGy4nDSUb7ZuJxkSyl+Wh09/aO5a9IN9QrV6xESyTZbvotpGkA2lXNFq571kkcQBG6ZOIUzA30S\n9u5m3/GjbNEZORpcE8mg9vLCKghY0lLRx1YlJ5GsVloX5DLnPByH1m/ezMrcCkSfmvrPorcPO60a\nfli0iBlTpgCwbssWvli6mhSbGskYiSUrDY2hBO/IFpQGxvHugiX07dEDlUpFYmYeoso9RErl5cO2\ng8eqFfHQQYMYOmhQ1VokiT379qESRbp26VIv5zcFheZOrd/m1NTU6n8pKSkkJCTgcHjO16ugcCly\n4OhhHl71HeuiVaTGB7I/1sCX2hye+Pzteo1z67hr6ZlSjGSpSQErl1VyZYnM6Cvc00I2hD7de3LH\ndTeg0rnneja0bovK20jxmlWodu3gWrXIV088TYB/gIeR6sbavfsRfdy9ylU6PbtOVlVkSk1P443f\nV5NliEQbEIrePwT/+K4AWIvyAMjVBbFizZpTvc/uyHS2B5IoivTq0YPu3bopSljhsqPWHfG6detc\nfvb29mbSpDq6VCooXAJ8vWkFxfGucfKiXscmryL2HT5At45dztLTFa1Wy9w5T/LTiiXsyk1HJQoM\njOnI5BtHN7oXbVFuNrJ/kNuZsMbPH01oOM5OXdiWmcb5enI4pbOfXDlOXfth6QrMfhFu6tUQ0YKS\n4/vQBYYi6LwoLK465urSIoJDh4sR1a7n6VJlKYNH9D1PiRUULj1qVcRjxoxxS+aRkZFxwQRSUGhq\nTpiLAfeENVJEIGsP7qqzIoYqD9oZ46cwoxHl80RwZBTHDu3Dt0sP7OVlWNNTkZGxFeSjCQik4ugR\n0qOi+e3P5Uwf3/DIhx6t41i/MwmVl2v1Jlly0j6iKlNZcaUNQXDdoTstZioyT2ItysNRWY6XtYyR\nQ8YBcOu0qex96XWO2PxRaav6Oc0mBoeruXLw4AbLqqBwqXJWRZyWloYsyyxevJgJE2pc8yVJ4o8/\n/mD27MaJvVRQuNjoRc/nwLLTiUHduCEysiyTlpaKWq0mKiq6weNE+Pmhb92agg2r0UVG4d2pIxVH\njqALC8e7bXtkpxNz0jH+Ki6osyLevmsnK3fuwiHJ9GwVx/iRo5g4ajRrdr3GAbsGUVP1WciSk1hT\nJjNnPwFAsEGHbK7Jf12WchRBFvCN7YBvXAfKM5KI1FqJCI/gREoKX/22hAKrE70pGZWtkk5t23Ll\n0O5cPWyYEn+r8I/krIr45MmTpKamUl5e7mKeFkWRXr16NYVsCgpNQi//KE46yhHOSOHodyyX62+r\nW1IKSZKY+8v3bMpOocxpJ0Zv5Ia+QxjSp3/1PSs3b+Drres5LoIoS7RHzf0jr6FP1+71knd/4mGO\np6VgLirC2KkL2rAwLCkp6MLC0Qaeyvokihg6dOJweionUpJp3aLlOcd884svWJJeAP5V/dfsPsaK\nHTt5/4kneOeJx/hmwQL2pmUhSTLtw4OZdf/j1cl+Zkwcx8a3P6HCPxpLQQ5qvRHvkJq0mj4xbSko\nL+aXRQv5aeM+Sn2iwTsKvKOQJYmC0ixGDB2qKGGFfyy1hi/t27fvgmTRutxd1JX1XTpYrVYe/Pg1\ndoerIdAXWZLwOZ7DnC7DmDD06jqN8dQn7/Gnr4ygrzHRemcX8Fyv4QzvN5CDiUeYs/xXysNdTeCB\naRl8c/tsQkPcC1KkpKawYP0a7LLMgLbtGdp/IDl5udzx6VyKomIo3bYZvwFVXsXlB/bj06mr2xiy\nLDNBhP+7vSoVpErt4PXPviGpqASdSqR/q1a0i2vBwwuXI/sHu/SVHA5ujPDhnptuqnX923ft4rMl\nf7Ln6EkCOvb3eI8uZRe2WPeXeKelgtmD2zD5mrG1zlMbl9t380yU9V3aNDh8KSoqiuXLl2OzVRUI\nl2WZ4uJiZs5sWPo6BYXmhk6n46MHnmXd9s3sTD1KiI+BCTfOIDDQc1rJM0lOTWG9rQRBH+bSXhkR\nzPwdGxjebyA/bvjLTQkDFEZH8u3yxTx6yyyX9s9/+4Vvjx/GGh6BIAgs2rmVvps3EOEfQGFkNAKg\n9q0JAToz13NNu0C5ww5AfkEBc+Z+SGpgBIJ3Vd+dSakErPgTua17eJWoVrMvI8ulLfHYMRav3YDV\n6aRjXBSTRo9BpVLRr1cv+vXqxZyX/8uhs7zaV0pqjw8cld7A4ZQMJnvupnCRyMvN4+dvf6W0oIKA\nUB+m3XotwcHBtXdUqDe1KuIFCxbQrl070tLS6N69O0lJSUolJoXLDkEQGNZ/MMP6D673W/lfO7di\niQr1GJSTXFmKLMsU2K2A+3mzIIrkV7rmTD6Zksy3x45gi4ysGdPPj202G8EH9iN0O6U05apzbEGl\nQpacHusUyw4HLQKqwpc++e1XUoMiXe4RvLzJcDhxj+qtwnGa1/RXvyzg+52JSAHhgIrVO06wYutL\nvP/U49WZs9rHRnDwZCXCGfHXsizhrRGxeZhDlmV0aqW0anNi+5btfPj0PORCHwRBQJYL2b7ieeb8\nZxY9ezes/KTC2ak1IE+WZYYNG0Z8fDwRERFcf/31bgUgFBT+yQT7+SNbPBej91apEQSBIK173C9U\n/X2dee23DWuxRrjnvxa1WipOU1he8fGU79+LLMvoY+OoPJboNnZEZjo3jatytjxaWOjxHFYVFILD\nVOLWLssS7UODKCwsZOu2Lfywff8pJXyqn5eBJO8IPvxufnXbzVMmE1qRyZknXoFlmUy6og+StdJt\nHnVpDteOapw46/OlvLycz7/8jude+YDX3/6YQ4cPX2yRmhxZlpn3/gIo8q3+vgiCgJzvyzfv/nSR\npbs8qVURazQaHA4HQUFBZGVloVarlYQeCgqnMe7KkcRkFLm1y04nfQKrqiFdN3AoXrl5NddkGUtK\nKtLmbYR4ebv8Tdkk953t3wT6+6PNrxpH1Onxim9LxYH9WFOT8bea0e/bjXz4IJZ9eyjftpUis5Xn\nP/sEk6kU1VnM1/qYOPwzjyNZa14mZEkiLCeFE2mZTH/tfzzy21oKS0opSz7i0lcQVezPyK3+2Wg0\n8vYj99LOkY355F5Kj+2h9NBmvHDQo0tnhoYKCKa8U5+BhKY4nVuHdqVlLc5kTUFmVhb3Pvoaf2wu\n4UCKmu1H4OnXF/DTgkUXW7Qm5fixY+QeqfB4LetwCZmZSvhqY6N6/vnnnz/XDQ6Hg3Xr1jF48GD+\n+OMPkpKS0Gq15+3AVVnpyUh1eWAw6JT1XcLUd32iKBJn8GN3wg7KfPQIKhVCYQk98yp4aeZ9aDQa\nwkNDCbLYSTlyiIIyExX7D6CNjUbVvg0JlaWsWrmCzuGRhAYFY6uo4K/Ukwg6ndtcV3gbGNumHceP\nHqFCp0NQqYiSZeZcOZw37n8QjcPBTrMTTVQc+qhYpMBg0kQN+zeuo2NEBIetDvckIIW5vH/nHUQ4\nzTgL8whymBkcZCS/oIiTPjHIBj/U3kb0QWEIKjXm3Ay0fjXn5wZrGdcOr0l5a7FY+HHtNqTIduiD\nI9CHxlKmD2BrQgJPz7qJ4V3i0Zqy6RykI0yvYsvBFOYtXs2GrTtQyXbiWzVcKZ/Pd/PN974gozjQ\n5bxdUBlIPHqUsSP6oNW6/z6amqb428vOzmLdr7vQCO5WHLtkZsS0AfifR7a2c/FPeLZ4olavaajy\nKtXpdJhMJjIzM2ndujVa7fnFV17unnHK+poXsiyzavM6DmalEKQ3Mm3kOJeKQKfT0PVZrVZ+XbWM\nwspyerZqx6De7lmiJEniX689z94WkW4KsU1qNt899iwAs9/8DzsC/RE1NXmrw9Mz+PD2fxEVEYnV\nauXP9WsRBLh6yDB0p5T2za++SnKguw+HUFLEa8OG8P3av9ir9UU8tXahqIBpcZHMnuGa1Xrtpk28\nsHIHosHXbayi/Vvxa9Mdlb5qjCu8rbzwwH3V19/+7EuW5jjcHMhkWWZksMwT99wFwPNvvMeWXBUq\nTc3DSTAXce+YnowbNdJt3rpwPt/N6bOexia6x3ZLkpPrRoZx0/SpDRq3MWmKvz1Jkrh7yiNYUtz/\nPoxtrHz485sXLNTsUny21IezeU3Xapp2Op3s2LGDhQsXotPpyMvLq1cifAWFi01ZmYmZ7z7HU5nb\n+dGvgv8JGUz9+EW279/dqPPodDpuHDeZ2dNu9qiEAex2O8mC7LFc4XEfPQl7dyMIAu8+9Bi3evvQ\nsaCI+LwCRlvt1Ur477kmXD2a8SNHVythgNxKzyZF2T+QIykp/PD6yzzYNo5hgp1RKifvjhvtpoQB\njqWmeVTCAGpvH8y56ZQmHSCwJINZU1wThuSWVXr04hYEgbyyqjPi1LQ0ElJNLkoYQPYKZNG6HdWf\n1eEjh8nJyfYoR2MjnyWdpyAISE7PdZEvR0RRZNLto5AM5S7tkk851866Ron3vgDU6jW9dOlSDAYD\n2dnZiKJIUVERixcvZvJkJdhA4dLgPz9/yaH2wdXKT9RqyO8YxRtrfuPnzt0bvYiAzWZDrVZ7HNds\nrsSs8vwgk4wGsvKqzlvVajX33XAzB44cZsGG9ZSYbcxbupSbr7mmWhl7IlCnw90dCuQyE226dkCt\nVjNt3ASm1bKG1tFRSMd2I3q7v8HLkoQxri2OsmLGdYklNtp1F+mr1+BRCMBHX7XD37B1O05jmEdP\n86wSM199/xN/bj9MvkWPBhttQ7U8+q8ZxMbE1CJ5w2kVF8RRD36ooiOHa8Zcf8HmbY6MnTCaiMgw\nli1YhamgAr8QI+On30CXbnVP96pQd2p9AmVnZ3PVVVehUqnQaDRMmjSJ7OymeUNVUDhfJEliT1mu\nxx1oarQvKzeu8dCrYazZtoXb332Nq998gTH/eZbHP3qX4pJil3v8/PyJETUe+/tm5zG0b00yjIWr\n/mTOb7+zGg0JOgOLHXDXp5+x+8D+s8owNL41ssXs1h5vLmPIgIF1XstVQ4YQ5yh1a7eXmxBP7WLV\nPgHsTnF33Jk6cjja0hy3dlVZHpOHVZ0lR4aFIlk9794txTn8sjWdMm00et9gVL6RJJmDePC519l/\n4ACSJNV5HfVh5k0T0OPq8S3ZihkztF2dY8ovJ3r07sFTrz3G65+/wJP/+beihC8gtSpiQRBwnmaW\nqaysVEwTCpcMTqcTM2d5cBv05JW4ezs3hK17dvLitrUcCgvE3DKOklaxrPX3Ys5H77ooDkEQuK5b\nHzQFZ8xbWcnV4TEEBAQCVWbZb7dsxXpazWFBECgJj+LTP1ecVY67p9/IBKMeQ04GzsoKhIJcOhbn\n8codd9Tr71YQBF6570462POx5aRhMxVTduIw5pw0fFq0q77PZHF3rGnXpg33Xt2fQFMGDnM5DksF\n/qXp3DWkO927VD3M+/fpTbAz1y3MSZacaDU6BH1NZHN5QTpFafspVkfw0IdLmPnvl1i1dn2d11JX\n2raJ552X72dwVxWtwiroHGfloVlXcMfM2jOLKSicD7Wapvv168e3335LeXk5K1asIDExkaFDhzaF\nbAr/QGRZZvXWdRzPSSc2MIyxQ0ael+lYo9HQUuvDIQ/XDCn5jLq2YSbHbft2M2/TGk6Wl+KtUmNK\nz6Syd3cXU6sgCCSG+LFs3RrGDR9R3X7tyNHotToW7t1BtqWSAI2OK1vEM2vyddX3rN+6mWwffzx5\nYySWVVBWZsLHx/0MVxAEHr/jTu4uLWHX/n3EREbRpnV8g9YYHRnFh0//H3O/+pKvdyVhiImvLvzw\nN+G+3h77jhs5glFXDmXDls04nRJXDh6MVqslIyOTB59/hUyTHack4aw8gS4oDr/odsgVhXQIlMny\nC+bv00lLeTGS00Zgy5oojXzgg183EBsdSbs2bRq0trMRFhbGIw/c1ahjKijUxlkV8cGDB+ncuTNt\n2rQhMjKS5ORkZFnmhhtuICws7GzdFBQaTG5+Hk/8+B4nWusQww1IplR+/GgdL0+6mxbRcQ0e98ae\nQ3j58FrMUaeZF8srGWmIIiy0/t/lhP17eHLdMsoiQyC06gxVjgqmYsdufPu6OmkJRiOJWemMO2OM\na4YO45qhw846x7n2rgK1Bjrg5+fP8CvcX5h379/Pqg3biY2I4KorhtRpl3zHjJvZfOxFMtWuJnW9\nKZdp08eftZ9Go+GqoVdW/1xZWcm0OY/j3XYAvpFV5m17uQlT8l56egdx7fSx9OzWjfueeo1y66k+\nRRn4x3V2G9vuHc6vy/7iyTmNq4gVFC4GZ91qrFu3DkmSmDdvHiEhIfTt25d+/fopSljhgvHq759x\nsmcAon9V7VvR15uMnsG8+sdX5zXuyP5DeKnbKPqkVhKdlE+HEyXco2nBkzf/q0Hjzdu0pkoJn4ag\nVqOLb4klPd2lXXY4CNB73jWeiyEDBhFR5p7tCqCt0ehxN3wuzGYzc/7zGrd++jPfZJfzwqbd3Pzc\nCyQlJ9faV6PR8NZDs+mjKce7IBVNfiptHQU8MeEqenZ1LzRxNp54+T8Y2g928ZTWGH3xietCWl4B\nPU/lJhgxoBuypep8WhDFs74sFFdY6zy3gkJz5qw74piYGF5++WVkWebFF1+sbv87n+2zzz7bJAIq\n/DMoLi7ikKoUQXB/0TvqY2Hewh/IsJnQCSqmDrya2OjYeo0/pFd/hvTyXBWovqRWlgNGt3ZNaDDm\nvYfhNMfe4LQspj94a73n0Gg03DZwIO9tT8AaUvWZyLJMQG4md0+tfzzr6198wR5dAIJXlbFb9PYh\n3duHl7+Zx1fPPVPrzjgsNJT/PvoQDocDh8OBXu85Zee5OJlfhhjh/sjR+gaQcfxY9c+Tx42hxFTG\nn1sPkWcp95xDW5YJMtZfBgWF5shZFfHEiROZOHEiP/74I9OnT29KmRT+gZhMJizeoscvpMNPzzsn\nN+HdIx5Zlvlj5VxmRvbm5tHXXlCZ7HY7FosZo9HHRREYzhJHL9sdaEtNyJKEbLYQU1DEo2MmYTAY\nGjT/xJFX0yY2lgXr11NstRBhMHDz3XcTEeaeh/pcOBwOdmflI4S6h/6cFL3ZsXsX/Xr1rtNYoYp1\ndAAAIABJREFUarUatbpW1xKPaFQq7Ge7pnE1zs28cRozrrOzcdNG3v15HQ4fV9n15hyun3Bzg+RQ\nUGhu1PoXpShhhaYgKiqaiFKBfA/XzElZ6DtXORwJgoCtTShfH01geM4AosLPHlPbUCoqKnjy0/fY\nacqjQhSIFbVM6dSb60ZW1cvtHxHLUWspoq7GcUmWZcTNCbSNbUnukWOEq7U8Pv0WurbveF6ydGzX\nnmfbtT+vMSwWMxVnO1Y2+pCamVFnRXw+DOvViT9S7G6VmWzlJQzv4n7Wq9FoGD5sOF4GH776bSXJ\nxVXRG3H+IrfcOLJZ5KdWUGgMGvZqq6DQyKjVasbH9uKrvIPIoTVJJOwFJmRJQNS5OgpZ2oby68Y/\neeC6xq+LPeu/r7A10q96B5kEvJ2yD+1aDROHjeS+624ife67bKIUR1gIks2OvGEbQv8+JHl5ARGU\nAY8uXcB/5Wvp3qFTo8tYHwwGIxF6DekerumKchncd0KTyHHXzTey+ZGnKPKLRzzl+OUwl+NfeoKn\n3/rorP0G9O3DgL59yMzMQJIkoqNjlBBKhcuKxk0ppKBwHtwyZiqz/frS9kgF/gfyaXWoDK+EdAx9\n3HeEgiBgkxs/7eC2PTvZocctAYgjJICFBxIAUKlUvHH/I3w+8lpukwzcaFajb98O4Yzc1SVR4Xy9\n9s9Gl7G+CILA+F7dEc9w/pJsVgaE+hMZHtEkcuj1er59+1Vu7BxIpCWN0LIT3NE/ht8+/6hOijUq\nKpqYmNh6K+HExES+m/8T23fscItbVlBoDig7YoVmxZRhY5kybGz1z898+x5rPSTkkHNLGNRmcKPP\nvycpESnYc2WZrDMyQXVq14FO7Trw4Y/zcBg9v9MeN7lnp2pMzGYzv65YRlF5Bb3bt2dgH885rq8f\nNw6VSmTlvgOkFpvw0+kY2CqW2Tc33jnrvoMHOJmSSr9ePYk8SxpOrVZLdEQosdkFOCUJEHA4HGg0\nnrONnQ+VlZU8/8r7HEtzoNIF41yRQmTgcp567E6iIhv/SON0HA4HsixfkHUpXH4oilihWTNr+CQO\nLPmYgs41GaYki43++ToGTO3T6PPFBIUhZ+chGNxDjvzVnkuYeWt1yJVlCB6cmPQXsEDK5oQE/rt4\nCQVB4YgaLT+vWk/wV18R1yoemwRx/j7MnDSJsNCqz27qmLHcc8v15OWZGtW0m5mdxYtzv+SEWYPs\n5cuna3bTO8qX5+bc7+LYJcsyL739AZvSbKi8qsKvduRksnHnq7zz/BMuxStkWWbDli0cTDxBSKA/\nk64ZXe+Kb2++9xlJ2T6odFW/A5XWj9xyP15/6wvef+uZeq8zIWEXK1ZsobzMQnCwkWnXX0NcnKv3\n/omkk3z56QJOHM1HlmRaxAdx8+0T6dzl/HwFFC5vaq1HfKG43GtOKutrHPx9/RkQ2RbTnuOQW0J4\nkZNrVDE8fuPdjV6sAaB1bAs2rFlFUYBreJJcaWaSfzR9O7nHzbaJiWPJqhVYAvxc+zidXKk1MqRH\n/RyhklNT+OiXn1i8bRt7DuyjVWQkPkbX4gt2u52HP/uMwvDYauen8qSj2KJak+/lR75az3E7rFv7\nF/3atMbfr0o2g0HHDwsX8/mipSzauJkDBw/SNi62wZ7dAI+8/i7JmggEnTeCqELS+5BmhoLj+xnU\nu1f1fVu2bee7TSdRedd8TqJKTZHTC2teEr27V3225eVlPPjsayzZncuJUjW7Txax4s/ltI4KIqKW\nPAZ/fzctFguffLMCWeXvdk9xqZkeHUMJDg6u8xp/+WURn326kYI8b0pLNGRlSqxdu5GWLQOJiKjy\nYi8pKebJh9+nKMMXUfJBlH0xFarZunkrfQd1wNe3frHf51rf5co/YX2eUM6IFZo9cVGxPH/zbL69\n4zk+m/UUd02eUa9SnIlJx1i4cinpmZ7clVxRqVS8c+udtEnKhqISZIcDQ0oW481a7pl6o8c+RqOR\n+wdciU9KOvKpvNJyWRld0nN5eLp7icFzsXrzJu769luWOkW2qvUsssOsjz9h+17Xko2LV60k17/G\nSmAvLkLjG4TGp0bJCYJAQUgMny9aXN32zFvv887WI+ySDBwW/VhhErj37Q9ITa/9s/FEwp7dnLS5\nx/OKai3bkzJc8myvS9iH6O2uGEWVmkMpNUUi3vzoK1Kd4ai8qtai0ugw6WJ496vf6lzwoaysDIvt\nLI83lZHk1LQ6jZOQsIu33prLt1+vQhQCq9sFQcDpCOP775ZXt/3w3UKsJnfl7qgM4cfvF7u1Kyj8\njWKaVrhsKSgq5P++m8sBbyf2YD+8luygHz68OnO2ixn0TDq3a8/3jzzP9j0JpGVnceVN1xMaEnLW\n+wHGX3kVA7p054eVy6iw2+jWpjuj7xheqwlYlmU+/eVHNiSfxGSzkZuVhSM4HK9T/QRBwBQRzScr\n/qRf957V/YpNJkRdjQK05GThG+/ZO/toXlWBiRPJJ1l8NAvBv2ZXKQgChYExfPbbIl6ec/85ZfXE\n8RPJCAZ35VqZm05Zfjpvf/w5U8deTYsWLc45zt9OVJIkcSAlH8HgnrAl1+nHug0biYyIYNvO3USG\nhTJi+JUeLSOBgYEE+oqUOdzn0lBM75493S+chiRJvPDCWxw6aMFkKsLXx3OoVGqqiYqKCgwGA3k5\nJkTB/QVREATycy7fYvcK54+iiBUuW56e/zF74gMQBAERsMaFsd7h4JXvP+XF22efs68gCPTv2Zf6\n5OIKDgpi9g31c3566dO5LJMkhFM5r1XRMTiys6hMS8E7tkX1fUetNnJzcwg7lczjyn79+H7eDziC\nwk7JCyDjKUu1SqyqoPbKh3MpLrFCXj7ITrxj4lF7V5mkj+c3rApVv149+XrrfGS/U9m/JImSI7sw\nhrbCp81AVmXIrHlrHlMHtKV/tw6sP5FQfT78N7LkpH1M1e7ebrdjdXj2bBa13sz9ej5mTSyidwhO\nazbzl6znsXtuoGN7V896lUrF8MEdWbgqDVFTc8zgdFjp0zGYkJBzm6V//PFXDh8SUKsDEChGlj3v\nxAVBrn4R8DZqAM9mVYOxfufbCv8sFNO0wmXJieST7NXZ3XakglrNtrJcLBbLRZKshuzcHNYUFiCc\nkS5SFxGJo9zV21pGcDHLxrdsxUB/A7KtKt+yProFFakn3OaQZYlOYUE8/t+3OOHXAr82XfGL74xv\nfFcqUo/jqKiqcyQ20HmrdcuWdAvWIZ8qlVqWkkhAXFf0vlUFNgRBQPKN4Jdtx4mJjKB3mIxkq6zu\nLzkdRElZzLyhqvKUTqcjKshzbm5rwUlK1a0RvausEyqdgSKieGPuDx5N1rfMmMZ1Y1oRoM9FMqdi\nELMY3teHJx69t9Z17dlzEpWq6vfi5x9HSYnnnNytWgXgdSpsbcKkkUiqArd7HJQwckzda0Er/PNQ\ndsQKlyXH05KxB/p6fNMs1YmUlpag19cvVWRj89eWTVjCwj1WWhK9dEgOB+Ipr+M2WjURZ4QEvXT/\nA3zy43y2JKdRYbdjt5Vjys+GkKq4YMlmpUVpLj2HDOLNTQdRneYoIggCfm27UXp8P77tutAp/OyF\n7zOyMvn618Uk55ei1Yj0ahXNzOnXV5/Tv/zwbN745At2pmRRaq5wKerwN7JPOEv+2sDLTzzMwqXL\nSDh4Arsk0SE2jBnXzXTJXT15xAA+XLgNyatm1yrZzDhKsjHEtXYbO9fmx5r1Gxgx7Eq3azdOn8KN\n06dgt9tRq9V19ha32Wpi1EVRhUZroKQ0FX+/qipgsiyh1ecw8/aaPOLt2rdl+u2D+HX+BmymAARB\nRO1dyKTJfeg/wHNYmYICKIpY4TKlZ8euGH9ZT2VLd2UbboWgoLp7zF4owoNDIDMDDO4FJGS7A0EU\nkWUZn7wcZo2+2u0eURS558YZ3HNa28Ejh1m0cSNmu5P2MeFMG3cnb3/1LYLBz62/IAgIAsSUZDD7\nXw97lDEtI4NH3/2cYt9oUAeDDEcTizn6+lu88eRjQNUu9ukH7sVut/OvZ/9L1lnWa3NKiKLItePH\nce3Zqycy+qph6LRaFq3eQk5xBT7eWgb0acWisiiPBSDVWiNZ2blnHxDqHc8bGxtITnaNRcXfPw5z\nZRE5eXuJbx1Ez57tuOHGRwkICHTpN3HSWEaNHs6fy1fhcDgZPfZeF4/0xMREtmxMwNfPyPiJY8/p\nq6Dwz0FRxAqXJaEhIQzSBLLSZkfQnvYQLqtkZFSbBhcuaEyGD7qCuPVrSTtDEcuSRKTNQju7mRAv\nPTfeMoNWdcyr3LlDRzp3qIlZlSSJpOQTEOC+kwSI9Nby+XNPnVUhfPXroiolfBoqjY7dpZVs2b6d\ngf36VbdrNBraRAaTme1eLclpKadHuy51WgPAsCsGMeyKQS5tO/adIMvDEaxszmVgv6vqPHZdmDFj\nCgcPvIfZXJN1TKf3o2vLKN5997lzhs7p9XomTnZ903A6nbz83Nsc3lWChkAczgz+WLCNO+6fxOAh\nA85bXrvdTnp6GoGBgfj7e05Io9B8ufhPIwWFC8SLM+/H+/vP2FKcSYkGQh0iV0e15Z5rb7rYogFV\nO9onJk/lxV9/Jis0tMoLurSELmYz7772Vr1ieyVJYvmaNew9mYxOpWLy8GG0jIvj0df/yyGHFjk3\nHe8w1wpGksPO2N7dz7krS84vBW2oW7toCGDLvoMuihhg5rRJ7H3tQ0q9a7yeJaeD9l7ljLpqeJ3X\n44lxV/Xl0993ga5mFyo57XSL9SK+tecXjYYSFhbKq/+5n2+++ZWU5CJUapH27SO4666Gxa9//sk8\njuxwoFFVya5WaXGUhfLZ+7/Rq0/36nPmhvDVx/PYtHQXJekOtEZo1TOUR56/n8DAwNo7KzQLBPki\nJV/Nz7983flDQnyU9TUj7HY7ZWUm/Pz86xR/3NTrs9vtLFy5gjxTKd1axTO4b796Zb6yWq3Mef2/\nHNb4IHgbkWUZbWEOXbWwU/ZF5WWgLOkQaoMf3mFVu1tHRRnd1JW8/X+PndNse+dzr5GiqjLjSw4b\nstOJSueFLMuMj1YzZ5Z70Y3MrCy+XrCYEznFaESBrq0iuHPGDfXOjOWJP1as4o81O8gtqsCg19Kz\nQzSz77q1eg3N9bt536xnKc1yPx5wSg4m3NKKG266rk7jnLm+H7/9mSX/S0Al1Zyxy7JMUDcH7371\n2vkL3sQ0199fYxES4uOxXdkRK1z2aDQaAgPP7ozU2JhMpUiSVGcToUajYdo15zg0rYWP5s/nsDEE\nQVX15ywIAvbgCLamJSEaDagAn/hOWAvzKE06AIJAG2+R9995q1aF361FBMcO51ORnYxKo0dUa3BY\nKtCJEpNvf8Jjn6jISJ564O4Gr+dcjBs9knGjRyLL7ubv5oy53HMlZpWoxlRS3uBxNy3f5aKEoer3\nn3PQyo6tO+irOIldEiiKWEGhkTh0LJH//bGYw5XlSIJAW52eWcNGMKhX4+fEPp19mTkIPu4JRzQx\nrak4cghtfGcAdEGh6IKqzMxRqoo6KbLbr7+On26/n4B2A1zudxZlkJmTS2xMzDl6Nx779h9g996D\nxERHcNWwoZeUEgYIj/Yj7bB7u1020b3XFQ0etzi3DBXuL5laycjhA4mKIr5EUBSxgsI5kGWZbxb/\nxl8nEim0WQnV6mml1tG9QydGDh6Kl5cXx5JP8MGiX1mfloJTrQanhL5NWw57efHCquV8EBBI21aN\ne4Z5OqUV5eBBEQuCgORw34k5bVZ6dKibAl2+eg26lj3cFJ8qMJrf/trIgD71y6NdX8xmM0+99A7H\ns0CtD8Jhy+KH39bwfw/dRnzrVhd07sZk0rSRvP/KQrDXWEkkyUlse5F+/RuuLH2DDVR4KPBlFypp\n0+7CfecUGhdFEStcFBatWc5fqQeolO3EaP24Zeg4Wsa0uNhiufHeD98yv7IIIkOwJqeSmpvNwVat\nWZJ6nLkJWxkVFcvqzDTyY6PRnyqfKMsy5TsS8OneE1NEBPNXr+T5u+6pZab6s2HbVp7/8hvySk0E\nhXvwqi4tooufjlRLJaK+KkmG02qhq1zK9An/qtMcJ7NzEXWeHYlyTeYGy15X3nz/c04W+KPWV53t\nq7VGihxG3vjfPD5+59lLZmfct18v7nvCye8/ryYrvQS9l5oOXaO5b86c8xq3/4hurDyxHxWulauC\nO6oYOERJInKpoChihSbnzR8/43evXIg3ABoSsbNz+ae8PuwWOrZpX2v/pqK8vJxl6ScgLhpHSSmS\nxYaxS031paLYaL7Yvg2vvn1cknIIgoChezcqk5IwtG9PrrXxFda6rVt5ZtFyxI698S0rpSzxAMZ2\nnasVk2S1MNCg4j9Pv8lvy5aRkJSMLMOQnm0YPXREncO3Ao0GJGcJosr9fj+vC1tr1263c+B4HoI6\n2u1aVomenbt20+e06k7NnQED+zJgYOOaim+58yYqyirZtuIgljwV6O3EdQvg4ecfumReUhQURazQ\nxGTlZrO88iREuYbEFHcI4fONS3i7GSniXQf2URjohwqwpqRh6OQeBysbvD0+8ESdDlmqqjjgr2n8\nPMM/rd+IGF4VIqTx8UOIiqPsyH4khx2nxUwLDbz4yaeIosjUceOYeqpffb1Sr584jmW7/kuZ7xlF\nGMxlDB/auZFW4xmLxYzFBh7eARC1PqSmpV9SivhCIAgC9z5yF7feXcHhQ4eIiIgguonO7RUaDyXX\ntEKTsnTLGizxnisZHTPnN7E05yYiJBR1ZdVuVhBFzzsM6RzRf5KMtqCQif3qUzqibqSVuipTtdEX\n3w7d8OvcC60xgNI2fbj5+VdIPH78vOYxGo38+8aJhFZm4Kg04bRb8SrNYFKHECaPHXNeY5+J0+lk\n4eKlvP7up7z/8VeYTGWE+nvedYv2PAYPavzP9VLFYDDQp29fRQlfoig7YoUmRafWgFMCtXs8r8ZD\nCbmLSdv4NnRywMFTP8tOJ8IZcciitxfO0lJUfq4xoubkZCIEgVkdOtO/R+M7NHmpVXgKepFsVkSN\nFpVOT54uljd++Jkvnn3qvObq37sXfXv2YPO2bRSVlHDVkFswGj3HQzYUk6mUfz/3NpkVgag0Xsiy\njTUJn9E5Tk9+SgmitqbUotNhoW/7IMLDLm6ucAWFxkLZESs0KVOGjcU30X3nK8synbzdMzhdbJ66\ndgYtk9PRxURScfCA2/UwbwPDrBLa7BxkWUZ2OtEmnWCiTwBLX36D6WPHeRx3/+GDfLPgF3bs2dUg\nuXpGhVdXPDqdihOJGGJqvGWTLAJHjx9r0BynI4oiVwwcyMSxYxtdCQN88On3ZFsjUGmqHMMEQUDW\nR5KYXsGNY9oRbihAtKTir85hdF9/nvz3fY0ug4LCxULZESs0KUajkVlthzD3+CYs8SFVITZmKy0P\nl/DwrY9dbPHcaN2iBT88/gLL1v7FLknHoWMnKNBpcAgCbfXezBo5lgE9epOWkc7SzRvQqFRMffDx\nsybzKC8v4/EPP2C/JCAFBcPJNbRftoxX7/wXYaF1fxF5+LbbyHrrbfY51Ih+gUh2G+UnEtH6h7rs\n2h1aPUXFDas13JQcPpmLIES5tVvVEdiddua+dX67egWF5oyiiBWanKlXXUPvtE78vOVPKmUn8b4x\nTLt3QqOkQLwQiKLIuKtGMu6qkUBVbKvT6cRorCnWEBsdwz3X157D+sUvPmOPXxDC3/mK/QM4Isu8\n8PWXfPSY50xVntDr9Xzw1JNs3ZnAriNH+HXtNnza9q4um/g3IVYTPbp2r/O4Fwu7U/b4NBIEFWaL\ntekFUlBoQhRFrHBRaBHbgsdi6xbL2txoaIL+sjITu4tLESJ9XdoFQeCA1U5KWiotYuPqNeaA3n0Y\n0LsPIQGBfJJwBMmnZicuVJoY27W9S63fxuJYUhLzflvGydwStCqRLi3Due/2mxtc1q9lpD9H8jxc\nsOQyctitHi4oKFw+KGfECgpNRHFxMRVqz17ANoOR9KzMBo99/bhxPD6sD50cxYSasmlvK2J2nw7c\ndcP0Bo95NpJOJvPk/75jZ5EXRZoIcsQwViY7eOTF/9LQGjIzrhuD3pHj0ibZKhjcNYQWcfV7OVFQ\nuNS4aNWXFBT+adhsNrrOuhupvXs8sm9eFmteegZfX18PPZsXj77wFuvS3EO5nNYyXrppIOPGjGzQ\nuEePHufL7xeTll2Kt5eGK/t3ZMYNUxolMYUsy5hMJry9vc9ZbUpB4WJw0UzTl3upK2V9ly4Xan2L\nVq6gzOZAZzKhPk3hOs2VdNSosVqFC/65NsbajqYVAO6x4CqdDxsSDtGvd8PiewMDw3l09l0ubQUF\n9atM5Gl9P//0O6tX76Gw0IHeS6ZTpwgefvjOC2Kyv9Aof3uXNkoZRAWFi8zaw0cwdOpORVIi5rRk\nRI0WyW5DEFT4d2x3QeeWZZnP5v9IwolUisrMhPsZmHhFP64eOrTeY3lp1WDzPIfOQ3z4xeTXBUv4\n5ZfDqFQhaDTgdMDePU6ee+5tXn/9yYstnoICoChiBYUmw+yUEAQBY5sOgGuCEKvkQbM1Iv/58GNW\n5zkRtcHgB6XAW8u3YbPZGTdyRL3G6t0+jqRdeYga1x2luiyL68bXOOCt27iZFRsSKK2wEuTrxaRR\nV9C7R4/GWE6dWf3XblQq1zKBoqjiRJKNI0eO0KFDhyaVR0HBE4qzloJCE9HSz9fFmelvJSxZrbSP\nuHBZonJzc9mYnI+odVWcTmMQv23cXm8Hq1unX0fvYBtyRSEAsiShKcvgttF9iAiPID09jXc++Ig3\nftjAwSID6dZA9uZ78dLny1m5Zl1jLatWJEmioKDS4zW1OpiEhH1NJouCwrlQdsQKCk3EzImT2PHB\nB+RF1OQDliWJ1sW5TLvnzgs279otW7D5hePJ5SnTZMVsNuPt7V3n8URR5JX/e4R9Bw6wYcdutBo1\n1417gMzsXO75v1c5WejALolYSvNQ60rxDavK9OXUh/Dz8k2MHDa0SSoDiaKI0ajB7EEX2+1ltGzZ\n/OOrFf4ZKIpYQaGJCAsJ4Z077+CzxYtILChCJQh0CQ3hgYcfuaDJTMJDQpCtyQhe7h7ZXioaHPvb\nrUsXunWp8gCvqKjglbk/UqaPQe1b9WDx8g+jsiib8sIMjEFVpQwzSiRycrKJiIhs8HrqQ8+eLdmw\nvhSVyvXzDQ0zM3jwoCaRQUGhNhRFrKDQhMRFx/DyvfdfsPGLigrZsWcPcVHRdGhfVVJy6KBBRC1d\nTTauiliWJLpGh6BSnb+D1Q+/LaJUE+F21uUdGEFRyv5qRaxCarDibwj33nsbptIP2Lc/F+QgHI4y\nIqOcPProLKVer0KzQVHECgqXAZIk8drcT9iYmkelIRDRnEAbvcTTd9xKbHQ0/755Gq98/SN5ulBU\nOj1SeQnttGYeu/OhRpk/v6QC0VPhYKhJ5wnER3gTGBjk8b4LgUql4qmn55CTk83WrQnExcXQs2fT\nOowpKNSGoogVFC4D5n73PSsLHIiBUagAdF4kAc9+/AVfvfQsXTt25PtXn2Xtlg0cT86ia7vBDOrX\nr9F2hSH+BqSTpR6VsSxJSE4H/o5s7rv7lkaZr76Eh0cwefKEizK3gkJtKIpYQeEyYNPRZEQf93PX\nFMGHDVs2M3TQYNRqNTdeO/GCJEy4YcpEVm9/nTKVa2F6mymbnq0C6NnFj+lTbsFgMDT63AoKlzqK\nIlZQuMSRZZkSsxU8JO0RDb6cSMtgaCP7JUmSxKHDh1CpVHRo3wGDwcDT997Ah/MWcrLQgQM1kd52\nJo/vw5TxYxt38suEsjITP837jdLCcsJjgrl2+qRLMtuXwvmjKGIFhUscQRAINXqR7uGabCqkR6e+\njTrf8tVrmL9sI9lmHSARY1zAzMkjGTJwAHP/04G0tFQqKitp26ZtoziCXY4kbNvJBy9+h7PIH1EQ\n2S3lsmbxdp55aw4tWra42OIpNDGKIlZQaMZs3LaV1bv2YnE4aRcewo0TJ3rcNY3p1ZVPdx0Hb7/q\nNlmS6Kh30qNr10aTZ//BQ3y0aCtOr0i0p3bgucA781fSKi6W6KgoYutZyvGfhizLfPnuz8jFgYin\njuhVogZ7dgAfv/ktr3347MUVUKHJUTJrKSg0U97+8kueWb6R9RYN2x16vk4u5K6XXqWszOR27/SJ\nE7itawtCTZnIhVl4FaYzUG/m9YcfaFSZFv65FqdXqFu7xSuCHxcta9S5Lld279xNUbLnbGYphwo8\n/n4VLm+UHbGCQjPk6PHjLD2ZDQFh1W2iRktqQDRzf/iJx+5yz8R1y9RrmTFlMvn5+fj6+uLl5dXo\ncpVU2gD3OGBBECgptzb6fJcjJlMZguzZZO+0g8VixcdzkR6FyxRFESsoNEOWbdqE8zQl/DeCIHIo\nJ++s/URRJCzMtZ8kSXz5w09sT0zB6pQI8/Xi+tHD6d29/ikeg4x68OB0LcsSgT6Nr/gvRwYM6s83\noYuQCt3Tioa3MhIcHNygcWVZ5vsvvidh1V4qS8yExAYxbsYoBja2p55Co6OYphUUmiHnqsMg1a9G\nAy+88wE/HiokVQwhRxPGPrMvL3y3lK0JO+st19RrRqI157i1e5uzuGnKuHqP909Er9cz4tp+OFSu\ntZYlvYlJM0Y0OLb7nRffYelrmyjaacOSpCJ9TQkfPvQNa/5c0xhiK1xAFEWsoNAMGdG/L0Jxvlu7\nLMt0CA2s8zgnTp5kW2aZW8lCqyGMn/5cV2+52rdtw4PThhNBLvbSbByl2cSo8nni9kluO3GFszNj\n5nRuf2Y0MX1U+Lex0Wqwjgdfv4GrRg1v0Hi5ubkkLD6EWnbNqS2W6ln89fLGEFnhAqKYphUUmiFd\nO3ZiZNQG/swvQzBUHRjKkpOo4gzunlX3tJRrt2xDMoZ6rLyUUlDaINmuvGIQQwcPJCMjHZVKRWRk\nVIPG+aczYtRwRjRQ8UqSRGlpCQaDEa1Wy5rlf6Eq9sbTLzr7WD52ux2NRnOeEitcKBR/ljR+AAAQ\n00lEQVRFrKDQTPm/u++m219/seHgYawOJ/FhQdx2z2MYjXX35PHzMSI5ilFp3B2s9OqGx/gKgkBM\nTGyD+ys0nJ+++Zm1v2ykKLUMnZ+G9gNb0W1QZxyCDY0HRzqtQaPEczdzFEWsoNBMEQSBa0aM4JoR\nIxo8xoTRo/hp/SuYNK5KU5acdG0Rfr4iKjQx33/xI7+/vga1TYcOPzDD4V+zKC0y4dNehSXR9X5Z\nlmnftyWiqJxCNmeU346CwmWMTqfjvmtH412aiuR0ACBVltJGzuOhWbciyzIJu3ezYNFicnLdnbAU\nmhfL569FbXPd9YqCSNrWfK6+YSiqFhacctXv2S5aCOyvZs5zF67spkLjoOyIFRQuc4YNGkifbl35\n5Y9lOGQHbWPbM2TgIJJTU3nlk29JtXoh6H34cs1n9Ivz5+k59yqmzGaIJEnkpRYhekgqrrEasFhs\nfLz0fX77YSGlBaW07dqG4VcPV+ouXwIoilhB4R+A0Whk5vRphIT4kJ9fhizLvDT3G7K0UfxdudDp\nG8HGPCsffjWPB+647aLKq+COKIr4Bhsod3emxy5aaNE6Dr1ez40zb2h64RTOC8U0raDwD2T95k2k\nO913ViqNjm2JaRdBIoW60H9MD5w43NqDuukZfOUVF0EihcZAUcQKCv9AUtIzUXn5erxWarYinyuj\niMJF4+FnZtN1WiwO/3KcsgObuoKA3ir+/cZDign6EkYxTSsoXMZs3LKVldt2Y7E7aBEawCP33AJA\n725dmL9jMYIxxK1PuJ93kz3UKysrsVot+PsHKIqkDoiiyBOvPk7unFy2b9pOXKs4uvXodrHFUjhP\nFEWsoHCZ8uFX37LoQC6CIQDQsc9kZvuc53nj4Xvo3LETnQKWcMjqRBBPc8wylzBmaP1zUNeX7Oxs\n3vvwO5JOluBwCESE6Zk8YRBXjxx2wee+HAgLC2PCtRMuthgKjYRimlZQuAzJyMzkj70pp5RwFYKo\nIkcTxcfzFwDw6r/nMDjYjqEsHaE4nTBbFjMHt2Xq+GsuqGwOh4NnXviQpFQDgjoajT6KgtIgPvtm\nM1u27rigcysoNEeUHbGCwmXIklVrcPpEuGU8FASBo1mFAHh5efHsQ/djt9sxmyvx8fFtEvPwosXL\nKCwNRK05Yy4xiD+WbWTggL4XXAYFheaEoogVFC5DzqVPz7yk0WjQaPwuqDynk5aej1rjuWRiQaG5\nyeRQUGguKKZpBYXLkPEjhqMuy3Zrl2WZtlENq3fbWAT4eyNJ7iE4AD5GpTCBwj8PRRErKFyGREVF\nMb5HK+SKouo22ekkwp7J3TdMvYiSwXVTJ6BXu6fTdDrKGHJF14sgkYLCxUVRxAqXNE6nk3VbN7Fi\n3V9YLJaLLU6z4p7bZvDsdUPoH2ilm7GSyW29+em95wkLC72ochkMBh6aPRV/Qw52WykOhxU12Ywe\nHsnE8WMuqmwKChcD5YxY4ZJl9ZZNfLRxNakBRlCrCd++gemdenDzuEkXW7Rmw+AB/Rk8oH/1zz4+\nPlgsZRdRoip69+rx/+3de3SU5YHH8d87mcllMkCAJBAiEI0IVTxRpFwMNzVcLMhuQMSKbLFUUnGF\n7qls3fa0u9X2uOXItusiZ3e1u+BqwVYtQVZgsUrVwxFiAyhykSAESICE3CCTkLm9+wctNc0EFSfz\nvBO+n/94Xmbm9yaEX5739uiWETfpvR071dDQpAnjb5XP5zMdCzCCIkZCqjpZrSff3apzg3P1p7tg\nawd79R9HDyivbIfGf3W00Xz4bJZlaewYvk8Ah6aRkF7cuklnBw7oMB7I7KMN5TsMJAKAy0MRIyE1\nBts6vee1IdAW5zQAcPkoYiSkXF9P2eFw1G05qd44pwGAy0cRIyH9zddmqn9ldYfxnidPa95tkw0k\nAoDLQxEjIfXo0VNP3fsNjaxpUtrR4/JUntDwU3X6x/FTNeza60zHA4yp+LhCL/7yBb277R2Ws0wQ\nXDWNhDX0mmu1askyNTc3KxwOqVevDNORAGMCgYAeX/q4Pt76iZLOpijkCehXt6zVoyu+q2uG5JuO\nh0tgRoyE5/P5KGFc8Z5+4mlVvHJc7nOpsixLnlCKGnec11OPrmBm7HDMiHFJb5Vt17o//F6VbU1K\nd3k0qs9APfnwEtOxAHyKbdv68K29cllJHbbV/qFJ29/ZrsIJhQaS4fNgRoxObSvbrh9/+H/ane9T\nw/W5OjEsW6/09qtkxU9MRwPwKYFAQC0N0R/x6g4kq/Lw0fgGwhdCEaNT68rfVuvAvu3GLI9b7ya3\naPe+Dw2lAvCXkpOTlZnXJ+q2cEZAt05iNuxkFDE6VdnWFHU81L+3tu/bHec0ADpjWZYm33eHQqmB\nduNhO6zrp12rvKvzjOTC58M5YnTK5/KoPsp45HybMn3xW0gezlBXX6//XL1OhyrrZNvSkEF99eCC\ne5SVaXZ9Y1wwa95sSdLv1r2p2iN1Su/j1Y23F+iRHzxiOBk+C0WMTo3uO0hHA2flSm6/WPvAykb9\n9aJphlLBBL/fr0d/9HPVh6+SZfWTJO04bOvgj36hZ5Z/Tz5fD8MJIV0o41nzZisYDMrtdnf6GFg4\nC4em0anvzFmg8ceCclfXSZIirW3q/1G1fnrXfUpOTjacDvH0q1+vV10op91/7JZlqTGSq/9Z+1uD\nyeLn+LHjeuJ7y/XAjKX65ozv6CePLVd1dcenuzmBx+OhhBOIZXODGT7D7o8+1JvlZcrulaG5d94l\nj8fz2S9Ct/LI3/9M5UeiH0ArGBTUqhWPxTlRfNXV1enB4sfUeqz9msneq5u1esO/qEcPjgjg8hk7\nNF1ba35x8q6SldUjLvt39myTPqk8osFXDVLv3tGvmIyF3Ow8zZ+WJ0lqbDyvrCwP378Eddn7dqnf\n1+2IY75eXfW9e2bFf6ul0qu/nGQ2f5Kmf13+nEqWfCvmnxlNd/63KV0Z+xcN54gTUDAY1JPrVqos\nVKXm7GR5dwVUEMrSD7++RGlpaabjoRu6vXCEyg6+raSU3u3Gw21NmjB2tKFU8XPyaJ0sq+OZPJeV\npJNHzxhIhO6Ec8QJaPlLq/TO1X4FhmcpObuXQl/J0vvXR/T42qdNR0M3Na5wrKaN7q9I6ynZti3b\nthVpPaXJI/vqjkkTTMfrcqnpnZ+OSU1PiWMSdEfMiBNMS0uLytqOy5WS1W7cSnJpj6dWp2tq1C87\n21A6dGcPL/qGpk85qte3bpNtS3cWzdc111xtOlZc3Hbnrdr3+1fkDrU/Rxxyn9Mdd800lArdBUWc\nYGpqTqupl6Vo1yyfz05TxbHDFDG6TF5enhY/uMB0jLgrnFio/fcf0lu/KZeruZckW2Ffk6bOG62v\njhppOh4SHEWcYPr166+MJqklyrbUmlZdN+LauGcCrgTfeniBZs75ml4v3SJLlqYXT1MmDzNBDFDE\nCSYtLU2jUgfrzfONcqX++byVHQrr5lC2srKyLvFqAF9Gdna2Fjw433QMdDNcrJWAls39tm6r7KnU\nvWd0vqpeyXtrNeZgsn74dZYnBIBEw4w4Abndbn3//iVqbm7WiaoTGpCTo549efYzACQiijiB+Xw+\nDRs6zHQMAMCXQBEDcLzW1la9tPa3qjpWpzSvW381e4ry8/NNxwJigiIG4GinT5/WD5b9XM21fZTk\n8kgKaec7v9TcBwpVPGu66XjAl8bFWgAc7d//7QW1nMn+YwlfkBTJ0ssvvC2/328wGRAbFDEAR6s4\nWBN1Sb9Ia6ZeK91kIBEQWxQxAEeLhCNRxy3LpWAgGOc0QOxRxAAcLS8/+kNqbM8Z3TljSpzTALFH\nEQNwtPsfmCm3t0b2p9ZEDtnndNu0YcrM7GswGRAbXDUNOFgoFNKadb/R7ooTCodt5ef01sL75igj\nI8N0tLj5yvXD9MSKh/TSixtUc7JZaV63Jtw+TkVTbjcdDYgJihhwqEgkou/++J91oLWPXO4LxftJ\nZUR7Hl+hlf/06BX1NLVBgwZp2T/8rekYQJfg0DTgUJve+J32n0uXy/3n23Ysy6Uaz1X6r7UvG0wG\nIJYoYsChyvcdVlKqr8O4Zbl0qKrOQCIAXYEiBhzKndT5j6c7KSmOSQB0JYoYcKgp40fJ9nec+YaD\nbbr5uoEGEgHoChQx4FC33HSTpg7PUsR/5uJY+Pw53djjnObfM9tgMgCxxFXTgIP9XclCTdyzR1vf\n3aFQ2NaoG29W0aRJUR/5CCAxUcSAw40oKNCIggLTMQB0EQ5NAwBgEEUMAIBBFDEAAAZRxAAAGEQR\nAwBgEEUMAIBBFDEAAAZRxAAAGEQRA+hStm2bjgA4Gk/WAhBz9fX1eubpNfp4/2kFgxENyuuje+6b\nqpEjbzYdDXAcZsQAYioUCun7y57SvnJL4fM5coVzdeJwmn7xs1e176P9puMBjkMRA4ip0vWvq/5U\nrw4LU0QCmXr511sMpQKciyIGEFNHDlfL7U6Nuq3m1Lk4pwGcjyIGEFPpvmTZdiTqNm+6J85pAOej\niAHE1N33zJTcNR3GQxG/xo4bbiAR4GwUMYCYysrK1De/PVXutJMKhwOy7YgirtMad0eWimfNMB0P\ncBxuXwIQc5OnTNKEiWP1+v9ukd/foslT5qlfv2zTsQBHoogBdImUlBQVz5ppOgbgeByaBgDAIIoY\nAACDKGIAAAyiiAEAMIgiBgDAIIoYAACDLJvFQgEAMIYZMQAABlHEAAAYRBEDAGAQRQwAgEEUMQAA\nBlHEAAAYRBEDAGAQRQwAgEEUMQAABlHEAAAYRBEDAGAQRQw4WGlpqVauXKm9e/d+4ddu27ZNx44d\n64JUF+zatUulpaVd9v7AlYIiBhxsz549Wrx4sYYPH/6FX1tZWamuWNMlFArpjTfe0ObNm2P+3sCV\nyG06AIDo1q1bJ9u29eyzz2r+/Pk6dOiQduzYIdu2lZOTo+nTpyspKUk7d+7UBx98oGAwKMuydPfd\nd6uqqkrV1dXasGGD5s6dq02bNmnSpEkaPHiwGhsbtWbNGi1dulSlpaVqaWlRQ0ODioqK5PP5tGXL\nFgWDQXm9Xs2YMUMZGRntclVWVkqSJk+erKqqKhNfGqBbYUYMONS9994ry7JUUlIiv9+v8vJyLVy4\nUCUlJUpPT9f27dvV1tamgwcPasGCBXrooYc0dOhQlZWVqaCgQAMGDNDMmTOVnZ19yc/xer1avHix\n8vPztWHDBs2ePVuLFi3S2LFj9dprr3X4+/n5+SoqKpLbze/xQCzwkwQkgCNHjqi+vl7PPfecJCkc\nDisnJ0cpKSmaNWuW9u7dq7q6OlVUVKh///5f6L1zc3MlSXV1dWpoaNDatWsvbgsEArHbCQBRUcRA\nArBtWzfccIOmTZsmSQoGg4pEIjp79qxWr16tUaNGaciQIfL5fDp16lSn7yFJkUik3bjH47m4vXfv\n3iopKbn45+bm5q7aJQB/xKFpwMH+VJ55eXk6cOCA/H6/bNvWxo0b9d5776mqqkp9+/bVmDFjNGDA\nAFVUVFx8jcvluli6Xq9XtbW1kqT9+/dH/azMzEy1trZevNK6vLxcr776alfvInDFY0YMOJhlWZKk\nfv36aeLEiXr++ecvXqw1btw4hcNhvf/++1q1apXcbrdyc3NVU1Mj6cK53I0bN6q4uFiFhYVav369\ndu3apWHDhkX9rKSkJM2ZM0ebN29WKBRSSkqKiouL47avwJXKsrvi/gYAAPC5cGgaAACDKGIAAAyi\niAEAMIgiBgDAIIoYAACDKGIAAAyiiAEAMIgiBgDAoP8H6WlKEwmWiOcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bbc6cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot data points\n",
+    "fig, ax = plt.subplots()\n",
+    "points = ax.scatter(X[:, 0], X[:, 1], c=y, s=50,\n",
+    "                    cmap='viridis')\n",
+    "\n",
+    "# format plot\n",
+    "format_plot(ax, 'Input Data')\n",
+    "ax.axis([-4, 4, -3, 3])\n",
+    "\n",
+    "fig.savefig('figures/05.01-regression-1.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Regression Example Figure 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8fMrTq7wwcurewueejOM4WOUqnuuSMHWctTL6gEahUNgwqriT1B3Bw4mwqu8h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86Pi+tdU1HJYk0nEAjQ2tp6ugY4m0VRAEVHsapEU0lsFsh2xc+41rn3Ze78Obz1yj6I2bErVhIp\njWg0wqIrptHb18/Ow83o2Qz+YNDIn+fD4/Fw4y3X0d3djcfjIRAoHg7ORYTDAeOZzoeoDfX5fAWN\nVLl5S4G47mbjms9wiuOY86zlGFdVVUkmk0Y6QXzeWKRTzkekwRwCYTTyTWgo9fehdIMpQj6nvYnR\nbalnPj/RLQVOe7NmD1AgwnKAkdcTZQFCbFPovPItKqKcw7y4F6uxFOcu1IuFOgLl5oELUejf0uk0\nb7W8Qcajcrivm5C/gnmNyznYvI+Qp5ZQIJQ3fCu+h1VXsLrtYAVbcmAB8vl8LF0xhf17TgFedHQ0\nS4Sqhrq85yW8mI7+XqKZOKR1nF43f3X3J2hububUqVPGGLBEMkE4EUMDQj4/VY4q7HY74USclmyc\naCaDR1GwKzbcdqfhiZo3JOb7IwyneE5EnjoWi5FIJLDb7aRSKSOcV1UTore9BbCRVbOoFgVFU9EC\nVuPa5F4rcUxFUfjwrRvY9PQbqJ0qiivL6uvms2rNxby+9xBZbxA1mWT3oSYmTzxTIJXvHpa7iRTi\nNnPv56EMh7jXpWgJ8j37ZsOaW8dqzmUOdexi/+U7h3zGNbdud7SbnpyvyKsyBPkeuNEIyQoPQHht\nYsEtx1iejYdpbiwgvEpzmYjY7SYSCUPMYm5AUKqIIN/PCa/F6XTmbVwgFm7h5ZgNUjGjKtr1iZ8V\nuV+zUTUvWrnnl0gk0JwqsXiEuB6nvbedtJYmEkjQF4+i9FqoDJ7u8pL7/4mVjfTG+4glEvRnUxw6\n1sTyhUu4bPVKamoP8c7+AyguhcYpU7Glzmw3aB55llSTqHYFi6LTHg8bxkxw8NQxOhJhIqhUunzE\nwr1MqKsnkUjQm4zhqA4RymTJ9vRR7Q/hdg72zIVYS3i24nsIL1eEi80GVGxsxOJeVV1BR00nnafi\nWLETjYfxT3Pir6hk/9GjTM1kmDx5IDRtHhsm7kEoFGL9dZeRSqWoq6ujoqKC/v5+7BYLdrcPXdOx\nFzBg5ms/nPybaACi67ohKir2XJezHpiff7MRFrl4s1bAPCu2WM611GObjaSIIInvLc7D3DBCepj5\nkQazBM7GGMHQ3o0YF2UOgwrV30gfy4womRC/43Q6jZyFufMKMEj8I8LU+a7JcBaqofJRYjEplKsU\nx8tdTMT/hUExi2hyMbd6E6H3WCxmLDCZVoXWRBdOu4eT9jbsVgVfjQ/Vo9Lb0UtlsPKMzxRYrVa8\nDi9HU91E7CkOdnaQ3q8xu2YKTpeDiRNriWRTaFkNn81lfCcRfhUejtVqxaIqqBYVdFA09Yya26iq\ngsWK7rQTR8OnKOw71kR/Ok08lcDtULBpOgGXD4/LfcZi39rVRVdfHy67zWgCL8qCRLjc6XTi9XpR\nVZWTnZ3MfDcCoqqq8bMzZ09j+gyNrs5uVlw5h754jK6sRp9uYXvTcRbOm4fdbjdEPLFYjNe27aCr\nt5dZkyfi93qMayfy4ZfMn807zcdQ7SoXL1hwxrmfrYdp7k9brMHGSKLrp5t7iHffvAktRa9QzKDm\nRmwKrStCvGSxWAgEAiPWnepCQxrMIuQm5EfKwxQehFj4zI3Szd1iRuJYZoQ3IBoviGObhyaL71ms\nrV25xy52XuY/mydYFFPAFtoNi9KWbDaLw+EYyEHmLCji/pqbHwgDATChehKqzY7NYyfS2o8/HSQa\nj5Ltz1JvrTfyXUL4Ye5IAxBLJcg4Qfc6UftS9GoJOnq7OdzfCnYFW8bCRdVTjGttLn8Q59/a0w0e\nJ/RnQdNRQh6eP/Q2i4Knw7iVTheZTApbJIbf66fO66c1kcDqduMP+KjGht9nI2FNDLpGWVXl4LFj\nHG1qp8rjwWq10NfWx+r1lw26rslk0pjs8tTOXeBy0bx3H0tra3CavFNFUQiGgoQqQsyYMYMDhw8T\njaawaCopS8JoZCCev/1HmjkQTmLBwdYDR1i/bOHAeZkmmbjdbpZfNJu+vj4SicQZuW7Rgk+8O7lT\nTgo9n7kGq1zx2HA2iAJz+FcMdB+OoM98HYbCvLkUjTgAo/GGeB8sFkvZaaD3EtJglsFwX5BcY5Bb\n2yhKVfL9XrkP7lDnmNv4wOFwGMKO3DFQ5gYEpe66R8LDHC0FrFhUzAvLnsN7eP3Ym+hpjaUTFjOp\nfpIRKhQ9YJfMWUTnti5SsTRz/NOZUjOZ/kg/qk0lnUoP8l7FIt/e3UV3sh9Xt5M5E6fha++ko7sL\nj9tNnS3AyY5WomRwY0XVsxxoPU4qk8GPnYba2kGLVltXJ13JCL6KCnx+SMbiuL0etCo/u/YeIJtN\n47I7cPm82NI6KybPxKIoaKpKQM3Sj4aSzDJ9xhQiff2GJxWNxcjqGplslr62MM6+AFitKE6FzqMZ\n3nxtJx++6RoAdh86xMH2dizt7UTTaVIOFy6HExx2IrEYzneFXuI6m/9rrK+nu6mZvkSC2dWVRthd\nhB1jiQQWlxdLNouWShhdq3w+n+GJimdAhITNC7wwAOKZFoYgHo8bee58IjLA6CWsKIqhBB/Oc1Uu\n5vCv0AGMtoESny9EPnBmYxHzqDlJfqTBLIHhPkC5op+hGrWf7fGG+h3hseWWiojensJ7ME8ZEZ8p\nmnKbF8Ryjl3quYs8bjkK2HLIZ8wP9zcRmhIim8rS1NJMdXCgREJ43OJ7rV14mbHQRyIRQqEQkUiE\ng4cOkVGzVFVVGbvzdDpNZzqMxe0g5bLS1tvJmnkrWW9a7B89dJA+UvRpUBEDQj6w2Qn3x2h89x5k\ns1m279/LKTVOBp3eeJgK7LgUK1pWg7ZuUm47ziR0ptPoEQ01neboO7uoqa3DalGYXlFFjWIhGAgQ\n8geI9A003gjHYnQnEoTVLLG2DpSklWxKA10B3YJisdJ2vMu4Ny2xKI6AH7w+2hJxHH29tESjzGqc\nQFVtjfFzrZ1dRGJRJioKfp/PuM6rFi00anW7urqorq42WtnVhQK09Z4gqevMmTXV+CzRx9Zms+Fw\nOHA4HMZ98Zk+W2zuFEUxnhlN0wZ1ZiomItN1nWg0OsjQDzUJx5wyKPfZN4d/S+mENVLkRm3Mm3Rd\n13E4HKNemnUhIA1mGZTrQZkNplmFWmqnoOF4bLm/YzbSIk9iHp8kjGG+RUUYsdzvlLugmAu4hcKw\nnEVAlDUIQclY1FUCBK0BOmxdpCJpKi1B4/qU4k3vO3qYt2MnUBSFfW8cRXVZsWsKC+umY1es4HWA\nouC1+oxJLm63m0gkgi3kp8FZRSYaxZlNkkXB4hgoyjS31etNRMBmx+cPEOvqpqa2kqw1Q9AdZM6c\nOfxp+xYy6KjZNG3JCC5fgKzHTX8yTm11Nb2JBLPrG87YlKXTaTIWhXBaJaOquHUr6Do2pxWrFZLZ\nLJlYkpb2diZNmEC108XxRAKSSSKpNKlAJRWBSnxOK+1dXaTTGTp7etnX2oHdaiF6vIWL580G3h3c\n3dbJn3/1DD1H4yT7stTNDWD1QNehCKkujcoZXm783NVMnjxxULlSKYhnUBhYMclENDgQDRPEMy56\n/gKGOjtf7q8UlbbIdee2sMsthTJvCIXCvFBUaTTIDT3netMul0uKfEpEGswi5OYwh4PIA4qHtVjY\nYzjHy/088ZLkKxUxS9w9Hs+gjjlC3CE+o5CIwKzQE7+f6w0WalZgXkzEjtvcfm6kdtzFvO5L5qxk\n1/7dqFmViTMb8Xg8BRdKszeh6zodiX4cgYHF+HBbC9OmzoasxqFTR+ns6SHRlWFm7UTqZ1cPypO6\n3W4mOfyciPbiSGhMqKpFs1rpifQTyWocaDvJxFA1LpeLhZNnsGn3drq6uvEFg7SE+6hxeYxxaO60\nSmc4RiaVJJnJkHVlsek6qVSGVDxBoz9If38/bx0+zPGeHpxWK24g5HKBqtLX2YXd6QTi6LjQsoAO\nqqZBtYNNh5u5xu1m+UUXUXvqFA0NDbxzqhW7NmBgdx/ch9/txmKxcWDbLoL1jaRVjXQqgqZp/Gnj\nq/TFUzRv3kW1Uotb8WHRNZo3dpP09jG9fiZOl0L2FPzq+3/i3n/93KD7lpvKGIpoNEp/OEYoFCr4\nM8lkctDGsVDphPn9KCQoM/95qPIP8X3M5y9U6MKAmd+LkaZQrlQ8zyMZxXkvIA1mCQznQS6kQi3l\ns87mxTGLesz5mXylIubSBfHylJrDMC8YVquVZDJphHUKKfSKfWexCx8qLDYc8uVJRb3rvBlz0TTN\nOG4pn6MoClOCtbR1HAIFqm0+cNo4dvgw0ZPdOKsDWD1BWiI9LMv5TE3TmDdpGjPTjUSjUcLhMHV1\ndfQm4lj8HlSnjfZoPxdZLNRUVRFUnJxw2omqKg6Xg+y7E1a8Xi9Zh4NARZD+qJWAy4mSzeLWVNbN\nuQhFseD1eNh5+DAdyST9gQDuZBKrqhLTdQI2G5XVtcQUhWxWJ97VRSBbhYJO3JVg6tLl4PbQ2dWF\nx+WiurKSqooKFtrsNO/YTVYHn82GxeGhq7+P3pRKuuUUFaEQtX4X3b19RG1eVKtGvE1DrVXRNdB1\nyKSy6HbQebdBOqD3Oti+ZQeLly8s+/52dnbx9Es7sdi8HDjSwidu+sCg+2W+36UIbMzP2lCel/As\nzY0L8hnY3PKPoQZcDxUKzg0JF0NEl3JzpeKdHSs18IWENJhlUIrHJ3IFwquE0zmx0TiewCxkMCtv\nC5WKnG2+UHhMgGEozcczf4fcBURM8RCfI+oh87WXyz1mscUkd3qDuIbiPHL7z7rdbmKx2KDfKYVp\njZOpDQ3kLm1zbLyw7TXswQosKvQkovj9fjyR055HrtjLbrcPyl85LVbCegasVpzK6Xq41kwSiwYx\nLYu/P4Wzssr4zAqHk454BpfNTo2u4K+oYHZFFbVV1UaI226z4QwF6W7vIKhq2FIp7ECgpoaqVAaL\nqmH1eJi1bj6xVAaLRcEaS6DF41TqOhNmTzdyX7FYjPqqSj57xRo0TePI0WO8tG0nnW0d1NU3YLHY\nCRFl0eyZaLqOHgmj2B3oqoqCgsUD9szABBbFomBxgcUK2SjouobdkX9+qPm+5LtHR0+04vRUoVjt\ndPZ2D9qgmXOGQ5VEDRcRDs6HKBkTxxalGuWUgRSiWK7VLO4xi/Z0XTfywtJYlo80mCVQaohUqN9E\n2Ef0XS33wRzOgyzCQuZdtJj1aJbXmxsQjFS+cKjrk+8lzj22eLHFvMqhQmH5duz5sFgsxgxDGLg3\nkUjEOEdxbPO1NhvXUjAvOjMnTaMz1UXXqVZifTHssRQXL7jE+DxRd6goA6PE9jQ30dLThVeD2tpa\nZjVOwnr8GDbdQXXd6f6sajKFIxjEp6pM8boHzaWcN20armPHcFU6WLRwIf3hMG8fOsTBo8dxu11M\nqa1lRkMDjt4ejvdHcDld2BQbWipDMBBggtdLJJmkYs5MTvX0cLKvH0WHeocdm67QHU3x3Bvb2XDJ\nskGjzcTCG4lF0TIqtmyG2sbJWGL9zKsdKHWxWiysXzSL5pOnCM+thP7TLWYdbgeqAxRdQdcBHex1\nGkuWLzI2cYqi8PyLr3HkaBsN9SGuv3pd3nug6zozpjZyoGkHqhJgcr3fCDmKuaUw9jlDs8BmqPZ6\nhX6/0HswnHdBaAxE6ZqssRw+0mAWoZTwR6F8oTksOxxKWbhzS0WsVqvRPzPXqzS3tRuN3XYhcheQ\n3Fyl+H+5o5GGMqpmta85FC1IJBKGQT3YfIRDJ44yb8oMKoIho9TGnO81k81maWtvJxgIGHmwBTNn\n884zB7EG/MxuaMCR0agKhIwckiiXaO3sIJNMsSfai8PtJNbTRzQWw+V0UltZRSAQMDzeA0ePkrE5\niHb1kQpHsFdXE1AU6uvr2X+kieZjx6jwnO7f+8yOXYRR6I6nqNOg9+hxqh12EqoGdhfdkQS1oQBe\np5Pj7e1cvGgR1dXVRKNRlsyZQ+/mV2jv7aXX6aUlHsHn9TJ5wkSOHDvBrGlT8Hg8g7ox7TxyCvyV\nNLh86O3HWbVsARZd42DTURQdZsyYQSjgZ+bERv78m2fpbY2QdmaZc00dLv8kTu1MCXoAACAASURB\nVB1sJ5XNEpzu5/q/uMaYSgLQ1tbG9nfa8foCHD6e4uDBI9TWVpGPiooKPvmRK1AUhbq6OmPItxjP\nNloDzPOpZIXAT2yah1OuUmpIWBzPbDzNNdzid4XHKjxLyfCRBrMM8hkwEWYTZQVChQqnZ+cNV11b\n7FzMoV+r1WooVMWxxQtdagOC4TKUh2nOH42kR2sOvxZCbGLEfbDb7WeoIju6OvlN0zbsFX6e2/gH\nLqpqZMX0OSyfO5BLS6fTRh7IZrMRiUR49I1NhL12/P1JPrJiFW73wPSRVQuW0ntyP7aKAGpLp1Hb\nKpSb//f1zaQCPhyn2tACXnojYYhE2HHkMCtmzTauoVjgWnt7cVVV0p9Kkvb5aOvqJma307plC9YZ\ns3FpKl3HTzB/5oyBxRLIKApZl5d4OkY0nkCtqqEjHEOvbsCpt6HbrDg9Lrp7+9nf1MziuRex70gz\nPdG9hKMxNKuDiGJDdTnoSmSYkEpQXTEp7z3IxKNErR48vgCLpzYQ8Hn5/ZMv0dMbxubxs+MHv+T+\nO2+hYUIDN3zyOpLJJA0NDVRXV9PR0UEkEsFqtdLQ0EA0GuWV17ex91ArTjtcc+WlaFoGVbeTSoV5\nadMWYjGNujo/N3/0WqOmNhyO8Pzzr1JREeSqqy4nk8kQjUYNA+Hz+UZlUyiedfNni81rqbnSkcCc\n00ylUsYa4PV6jWfdPNdVcnZY/+mf/umfxvskzmVEbs3ICb272IuEunlOn8/nG2SIhFErd2cnCrFF\noXYuqqoadW0wEG4SLfWE6Ed0rBHdWWDAqxyNwc7CixK5UDjToAuhSr5ji7zecPK8+RDXoKenx6jJ\n83q9hEIh4x6KnFLTyeMccCcJpxO0ZGN4gkEO93awbuZCw3NLpVJG+PXkqZO8menHEfCRtuiEkhrO\nd5uQB3w+6I9iiSSoUC20dfdgUxR6IhE27txBq9+DpyJEJpNhns3LnsPNOK02+lQVe1Yl6PXS3d/P\n7uPH2dfWRmMoyL7DRzjV3kF3axtOlweXxUJnPEmrlqWuogI1FqGhsnKgtjGVJBwOo3Z30hgKYlM1\nuqNxOnvDhNMZpk+cgKW/h1QshsPpol+30d12iq6UTlxxEIvFcTht9MXSeF0uZvsdzKoKADpOh52K\nigojR/3Cy2+w52Qf3Z291FiTfPS6q/j9M5voiGSJqnZSySQWXz0TK+zU19UaTfKrqqrw+/2GJ+nz\n+aisrOT119/imY078AbrsTv9oCW5bOVFZJL9TK5zc7g5jdMVoqsnS221jVAoiKZp/Oynf6S3x0ZL\nS5hksoeGhhrjfXU6nYPeB/FfbtRgOAb1zde28+dfvcje3QeZs3AGNpvN2FiN1Sg+gTDUYpOQayzF\nPFaZszx7pIdZArl5rkLzIgsxUh6myMsIEYMwQkI0Y7PZjHrIfKTT6UHhxkJlH+WqUnM9zHK9ylzZ\n/dkglL/mLkVD3ZvFF83ntaf309HTgTWexj6/iuzxTmP24t6mw7y6/x0aQ1VcfellTJo4iZpjB+nz\ngy+cRPEoPLL9DTJWK5fU1LN4+kwisRiP734LJauyY0czNp8fb+MEmva8g7JkAY3xFPOWLuT5A/vI\nBv1ELQqdfT34XS62NDVj9/txBYO0RKL43T6mTAtysKWNzmQCT0WAFBa6j56gXdPxZDJsP3QEt8fL\ngeMtxLI6syY0sGrRAl7eup0OLY2rJkhVopf3NVSxN9rP8ZY2ItEketaC1ZYGd5BwIkE2GsGasbNk\nQj3XXbWe/U0nePmdo7SceJug18OyuUf57Cc/CsCRk514K+vwBqvxWKID70U6SzSlEk6pqLEkkwJx\nJk5oIJFI8NLGV6mrrWLiu5NGzPf7jTff4s3dnSSSOkdP9jFjSoi6uioWL5rP4kXz6e/vZ9eeP2Kz\ne7GnE0yaNJFAIICu66iqBZvdi6Jk6e2NAIOFOKUqtIu9E7nq1M1PvYUt7SUVhef+tJn3X78WwBgk\nMFaYuwaZ0yxSCTs6SINZBPPDpqrqwA4+Z15koQeyVLFQIcy/lzurUtRzDiwaquGhmVvLweAwZLmC\ngXJUqeJ8zerbcvKkI2EwzaFnq9WKz+djT9NBnj68CwWFm1esZemc+YN+x2azcff1H0fTNP7n1Rdp\n6YywYvoyHA4Hqqryix1vYKut5Eiqh9o97zBz0mTuvvoGjrW0EJju5ultb2KZUIfdYedgaxeL9Bn0\n9veRttvp6unBWlVJuK+PqQ47M6fP5HNTLsIxc+C+TayopN+i4LJYqA1V0NzWQdZiJ6Vp9HV1Mauh\ngX4LJINBMgcPoVmtxDI6KZudtTNnoDicWKwWNAVe2raVTO0Uwn1hXj54gsNHjpLRNBKBWnSni2l1\n01BVaOtPojtDZNQ08ZYWugNePGmF1hMtzJ09G5fdTsDvoaGujue3vENnTxg9MBnVmuJwS7+Repgz\npZ7m7UdA11g8s5pYLMaC6Q0cam7Fo1jx11Ry+ZKp7N+9n8cffgKP1YfqUHn92a3cfs9f8szzW7Db\ndK67ejV9/VGsdi9Tp17E8eZdXLZkPsuXLDDuUTAY5OaPvI+9+5qYPXsFNTXVxvO5es08XnpxN06X\nhcsvfz9+v98YuC2aiA8lohlOwwKLxYLNAdmIQjqTwu0diB6Jsqx8uc3RILdrkLl+WrQSlMZyZJEG\nswzMoc1SQi4jUTcoRAS5TdrFLtJs9MyiCZvNZjRzz4cwUKUIZ4oZVrPwSBxP5EzEBISh6sfO9qUW\noishfBItx5LJJDtONaNNqwVgc9PeMwymwGKxcOOaDWd8L9WioPtcaCmFVOb0/Z87cyZHjhyh2u1h\nf7yfrOJmsm0ghL5w7jy2HTxAu82O1+VCDYVwtLRx1ZIlTJ8+nUf+/GdsikK1zUGst4eJoRBzpk6j\ne/c7BOsb6Gw+jDObYfrixTSGQrRHo8xeOI+Nh4+S7uggqyvYg9NxWRXCuoKeSTO1rp6DkTAt0SSp\naAJHfT1q+0lctgjhrk4+8qFPs/nVN+jp70fxVaEpkMlkUd0VJCwKWd2Bw+kmq2bwegY2OcvmTGXX\ngWY6ulqomTKJSt/pWYqXr7qYi2ZMJhyOIG7fzBnTWd0bpbk9ToXHworFC/m3z38HZ78H3WnFnrHR\nuSXCN//lP5m9Yi2pbIZXX9/FLR//ICdO/pH+SIZbb76KeRdNP+OZmDNnBnPmzBj0d5lMhgULZjN/\n/qwzvCtBOZGSXKNa6H1QVZWb/vJqXnnhTQKVNVy2bkANnUwmB3XGKta4o5yaytzzlG3uxgdpMIuQ\nSqWIRqPAwMsnmkKXw3C9JzFVQOzqhVeZ24Agt2F5KQ0ISlXhifMfypiad/ACMZop33FzFxBzjqnc\nRcQcksod+qsoCiGbi+OWge9Q5/QV+bTB2O12PnLRYl4/2UyDw8P7Vi4jHA4D8MPf/47Xjx0lGYlS\nabOxYsYMulNpvvHoozTUN7B63gL8J47TkkywqLKGuz76UZxOJ9987Fe0WW3EIjGinT1MmthIz7uq\n2OkNdexsPobDX0FKzfLd519lSnUVa2ZP5aIFE3n7YBPh6gYq/EHasHDL/FnsO3wEj8vLwnnzmB9P\ncOyR35KqnkI0lcbu8uDy+Ll8+mRqqirpTabxut2caD6E3e7Aq6TojyVxeHyk0yl6TzaDptHrHijJ\nuXjpQhbOnUXT0WMcPXaSSy9ealwbTdOMdEAkEsHr9dLX18fKpfO5eepUYrEYT//hOawxJw5HhlRG\nRbFa8DhddJ3sQb/YhaapOF0D9bt3fmYg1BuPx41rXIizLdsoRKliMhHuv+6m9xuDl4cytKV6raWE\nhc2bw3wqXNH7WTI6yCtbBHM5gln0M5qI4wmP1uVyGUrM3PKI0WxYLii0kIiaTvEzLpcr7witYguI\nGI8lNia5xyy0eJj7ghaaqHLNytVMOnYEq9XCuisuLfu7X750BZcvXQFgfFdVVXm7v4+OdAbP5MmE\nrVZebGrG0dhIuKGe3u5u0vv2c+f7N6BpGn6/3zivtK6RdbrIJNJkdR3V7kQnNtBjVlFIJdPE9Qzt\nkRjWqjo60yo/+sPTVE+eSXtfggqbmw5bmlnuAbXzpPo6kskkTqeTuro6vvbFv+YfvvMjuvtj+P1e\nFtf6+MwnP4qiKDRWBtn61i40TwXBugZivR3Q14WiqqDq4PATi6fZ1hyh/ae/4b47bgGgoa4Wr9tl\nbMyEkAYwJqscPNTE//z5FbKawqqV7axcvmCgEQHgcjvw+63oOqS6s8ycOZEpDVnsNjtrV19c1v0o\np2xjNMKR5c7OLMdrLQdFUbDZbMbEFdE4RCphRxdpMIsgwp/Fdr35KDeHqeu6UcYgft+ckxEvnKKM\nTgOCUsnd4YsQtWhmXcpsPvPiIYx+7k691MJss5dtNqpiNuWVK1eNyOIpdvjZbJYaFPZm0iR1jaku\nHza7najLSU8SPMCJ48f51188ijcU4tKpU/jYtVcDcPP73sfjmzczwargmzaZaDrBkdY2/tDZgxIN\nkwjW4PC6iZ9sRVMcZBVIeirxW+3QMB1bOoK74xR/fe+dNDU18ewrW1BcPt4+2spnb/4wdbW1XLd6\nFS/tO0mss53n3jrMRXN2csXqS9jbdIqKupm0HmujNZzCo1uosmmk00kaK7wkkhky9iAWRacnoRqL\nbyaTobu729gUmRECr9fffAfdXoFVV9j5djOL5s/kiqvX8ur/vIHe+673p0NWSbP2g+u47qZr6e7u\nLihQy3e/zGKyoVIOIyUgy/3M4TRPL9VrLZQiMb8HZqNq3rQ4nU78fr/sCTsGSINZBEVRzmrXVupC\nnTv6C07nAc+VBgTAGYZaGMpyNhTmkKtoui5Kc3LztzB4ETEXZovPKdRWT6hlxbmdbU7JbJS/eNNH\nuWT7dpq7u5laX8+ydev41SuvcPDEScLxBBHdSryihqTFwu6WVj727mfMmDqFvw590FA7t7a2suNU\nFygW7K4MfSpU251cMn8Ox3sTOP0hjh5pwmqzYTlxhIapE7nssiX4vF5++8LrZB2VZONh4rqd7u5u\nQqEQPo+LtrZOHBUTcWppNu04wGUrlxJOQaCimsqYTvjkHiZMqCTUsAjd6uTkwZ241F7CsQ4ap0xn\n4bR6o0fqf/7kNxxviWNXUvz1HTdSVVVpiN1EfWpjQyUn29tQLE4aqlxks1k8Hg8f/l/X8uLvNhPt\njeH02llx9UJWb7iMSCRiGD/zRkd4TOaUA5Tn2cXjcVpbW6moqCj5mRwKs1ebG/YfKYZKkZhb7An1\nNmC8g06nUxrLMUIazDIYCbVrvn/LLRVxuVxEIhHDQMDAiyT6oIp83Ug3ICj2Hcw77FxDfTalIcVU\nxqIpgzCWZkNtPr9c71R4rGYPvdycktlbNRelV1RU8KGrrhr0e/fffDMAD/3sl+zs6KIjkUBzu6n2\nFC4zCAQCBFE5pYIlpRFuO46ts4X161Zx6dwKdh9rZe26FcyfPoVJn7iKVCpFMBgcUEw7/CQzabD7\nsMd6DAPx4Q+s5423dnMwnCBYXUO1f6B5wsz6IIfaurmo2soX/vb/5evf+RGdvVFcAQddkSzLFy8g\nEI9z1coprL9iHTCw6Wg6HsXtq0bTYPuOPdx047XGvXn66U3s33+MRCJMvDuJrme57i/+ApfLRTgc\n4eixdpZtWM6MmZOw2+3U1NQYqm2RtzY3I4/H48TjcSPcmBulESPgREg2twTq1IkWvvvQT1HjFhrn\nbeG+f7hryHtdjFyvttQBCiOFWciXm6sV4h6phB07pMEsgbMpDxnKkOSWiogyFbGgF6qpNO/GzSrU\ns1HeDYXZqxwNQ51Op4lEItTW1p5x3iJMndswPffn8oW+hBdibogwEjklVVWJRCIF86xXLphL26tb\ncHV2UJuKY/VNYueefaxcunjQefzqz8/RFokzuSLIhGSCUxkbKbuLCbMX8frxCJ/bMIsPb1g3SNQU\njUYHNgC6Qk/LcXD5sSX7+NLffNoIEVosFr72D19g82tbCEfjXHPlWja/9iZHjnahqBk+fsu1/OZ/\nXkCzNhJpaSbWeZyGUBAVBwoRGhsbjedWURQCHo14Fsj0sGLZ6fB2e3s7zz9/AL+/gt27DjBr1jyc\nThcbN27jmmvW8LP/+hN6OkRTthVF0Vi4aN6g9nqiM43P5zOuvXk2qqgvNjNUnbHFYmHzC69hiQaw\nWnSad7eTSCQMo5LbmL8Yuc3TxzKSY96g5m4QpRJ2/JAGc5TJZzCF0i2396x5Tp2oqcxdIMx5tGLH\nLaUxQTHBwlBepWDngXf45evPoKDw6TUfZO6MWSVfn4PNR/j35/5A0qqwet9k7vzQR41/Ex2ExIai\nnDxtPB7nh0/8gURG5WNr17JwzhzjupSaUxJ1neL3xCIlGpEXyrPOnzmDf505g1179/Jfr+wmlrby\n9p+fZ8bkibjdbpLJJO/s28+RBDh9lbzdcoKPXbqQD02dwvee2AguH5qa5URLG7OnTwMGjL/dbjeE\nUTvf2UvDjEW0NR9Ct7j5zo8ewR2spb7Sx9/f+1ksFguXX3Za5LTp9b3gakABXnxlF9mMCjYvEycv\nYEIgwYZ1S9i6Yy/TJy+grrYGXdeNzdx9f/MJdr+9n/nz1tHQUG98psViGZifmXWSyWbIZq0opKmt\nrQEgEVexaTaOnWgmmmilraWbT93+MeP3xb2wWq2DJt+IkKvYtIh8ZaENjvnP8xbPZssze7HEPFTN\n9BrNOswUezdg8NSfsW5YnhsCFmpkgRz4PH5Ig1mEkQg55pZbiNBebqmIaEAgXlyhHoXTtYXiPIby\njspR3hVaNEQ4WJzPUAN3n3j7NWIzgqDA/+zeXJbBfOmdnSSm16FoOm+eOsWdnGmsitWU5uP/Pv0U\ne9xObG74r40v8O13DWYp5G4UxLUXnq7b7Ta81qHqWSPROKrLR8riIKVbSSQSRqgx5PehpOMcPXkK\nVXHx1LaDrEumWDVrIm8dbWGC287aS64ABoyI1WrlN398lk1b9+J2WLnthitJbHyDmOahKuimIxyn\nMm0j3mtn6/YdXHLx8kHfqTLgpDWio2sq9dUBli66iF/85kUArv/AembNnEZDXQ3PPruZt7YdYNGi\nGbzvfcux2WwEAgE2XFl7xnWqqanhxo8sY/fuZpav2EA2o1FXX8WKFQtJJBJc+YEl/OG3L4HqIeit\n59ihKM3NRwflFnM3X8/+6UWOHjnF4hVzWHHp8kHPfSksWrKQv/8/tTQ3HWPx0oXGxma4qtR8kZx8\n78xIYe5Haw4Bi/dQDnweX6TBLJHhvhTi94RgJF+piDCUAnPewmq1ntH/NVc0U4iRMqxix1vIuHqx\nk3SooOsErZ4hPyuXhZOm8tKBN9Aq/cx0eM9obZdvDFcp2CwWknYbFquVCqX0BcacsypF4DHUfbjm\nyis41PIYJ/v7WLlsLo2Njei6zqtvbOVUWwc3XryAnz25GWttPR0nj/DsljBrVizkn//qtkGfI7yk\n59/ci8VbRxgLr7y5i9s/8gF+8JtX8PoraG/vRK2oxZqJMKGh3hCEiPO76zM38+enX8TpcHDVhrUo\nisK//OPpjU02m2XHjrfZvr0Tq8XN4cNvsmrViqI5u/XrL2P9+ssG/Z0QWW24ag3LVizgn/+fn6Gr\ndhRrlFAoVPCztry6lY2/2oPL5eLwrpe45LKVZbeZUxSF+oZ66k2ecD7yvRu5+VQhKCtFqT2UkKzU\niE6xNncOh0May3FGGswyGI6HKX4nHA4bD77X6zWEKOJnhLDE3NZuuMZCUI5hhQHv1zy3UTRsLlbm\n8ReXX8/vX3kOq2LhxrVXEYlESu5Tu2rJckLeAMfbTrH2xpVEo1FDqFOuVznonK69jujvfkskm+YT\n7/9A0Z/PzZUOlbMq9TmwWCzc95efHPR3z296lV+9cQSHy0NTyztcsWwev968E2ewFkfIw4s7j3L1\n5VGqq6vP8I6qvE7adRvZZITG+onU1tbwsQ2LOdB0khVXLiCrKiyat4SA30ckMtBX1bx4b7jiMiwW\nC+l0+oz7ITybbMaKqtgAiyGwORvq6mr5izuvZMe2PcyYvYKqqsq811FVVdo7OtBSVjKqgjbKucLc\nd0M0CYEz8+Sl5LyHIyjLvf7CWDudzkFDDOTA53MHaTBLRDzU5SDqAGHgwXe73caLkFsqYm5AcLbG\nYjiUkqssFH50OBzc9oEbyqqfNC8YUyZMYFJ9vVFXJiaJnM0C4XA4uO8Tt5T0s2eTKy2X5lNt6O4Q\naSCc1LjpuvdTHfTx6zePots9WC0RQqFQXs/qH++9nd8/+QKTJkxlzfsupqOjg+VLFtDV0U1tZRXv\nf//lBRf2Yvcjm82yePF8Wlp66eyMsnLlUkOZPZzwo7kkZNHi+UxorDsjlwjvjgl7V1yzZt0qjh5q\noftkhPWXr8LnK68z03DJ7cma++yJzWMxRiqiIyYMORwOfD6fnGF5DiENZhHMOUzIPzQ2F10fPNEE\nwO/3G7lK8VIpyvg2IIDyFLCleKuCYovGUAu5WahhDmUV81qHg7lh+1hsVD60YQ3v/OBR+lMqKxdN\nJZPJcOW6NcRTaU529LJ2/fqCY858Ph+fuvnDAIZx/8F/Pk5nj49MpgtFsXLttevP+L2h8qzZbNa4\nB4qicNNNVxmGytwX1cxQ96G7q5uvf+X7JGMaF6+dy6f/1y0FPXLh1Yv3xOv1cs//vqOMq3l2DKVE\nHQ7lRnRUVSWZTA4alACn75Mc+HzuIQ3mCJNbKmKxWIw/m0U9MLgBQaFyidGiVAXscCm2K889vlCf\nDrfUA4ZeyHMNq1AqC6/2bMPfpVJfV8d3HrwPTdM4ceKEISK6/qr1JJPJsovt+/ozZDU7WOw0H2vL\n+zP5FnGRlxYbNa/XSyaTwe12D2pvWK6n9LvHnyTV6sNqV3j56d187NYPGl2hxJxXcc1PnDiBoigE\nAoGiI/JGGnNUIZ8SdTQR0SphLM3DpsX6INvcnZtIg1kC5p1jIQ+zUKmIyEnGYjGjbZxQgYrPOpe9\nyrE4fimdU0oJdZWTRxK7fEVRBg0FN5cW5N7nkTKmQtyRzWaNod7/+m8/pKM3wYI5E/jrO24r/iHv\ncvGK6Tz34gE8LgtXX/WJso4vcuoej2dQ96Ry2hvmhoBnz53G1qeO48j4CEx0GM0JNj33Cm+/vh9P\nwM29D9zFk797lk2PvwkWuPbT6/jAhzaUnPc+W8T7KLy4sW5GYD6+eaOs66cHPktxz7mJNJglMtQL\nlVsqIhoQiK4wgLFw5GJWoBZaLEaqIcFoe5XlHr+U5tUC8/UpdoyhDGquIlmEBfORey+EKErUCJZ7\nT/IJi9xuN888t5EDbQ5cTi8vbDnBzTd2UV1dXdJnfvC6Ddzy8RtKCkvnHt98/YfT/Dvf/bjyqnW4\n3G6aDx3j+o9cRSAQIBqNsuWpndjjPtoPxHni109y4M0m7PGB0O/u1/bz/uvXl5X3LhamL0Rum7mx\nHrBc6Phi8yIHPp/bSINZJubFVpRAmGdVejwe49/EDjYQCJxRBC8UiOWKM0oJOeZbxHPHYI21V5lb\nrjFaxy+0kJsnbQivXvTpzWdYzfdDGJP/84Ofsf9kL0GnwoNf+KyR6yvlnojNgmjp5vF4SCaTKIrC\n5EkTUDPvkLRW47ZrBXOYhSg1p2weCzWaUY3L1lzCZWsuGXR+dreNVJtGMpmisrqC2cums2nXDhQr\nLLx0BYFAABiZ+ayQv8xDNAKBsQvBC3I3K+ZmCFIJe/4gDWaJmEOyIqQqwiq5pSJmUY9YqMSLOtSu\ndihxRq6XVMr5miXrYpGx2WxGyKcUAdPZIgRQ5skmY+3VmjcrubniUj3W/v5+tjX346mcSIem8exL\nr3DzjdeXHAY2Y7VayWQyRmOIeXPncMfNvew9cJQrVt9g1OeOFObNijlfZiYSifDUH59jweJ5XLxy\neYFPGh66rvORz17DGy9sZ+K0eXzopuuwWq0svXQhDqeDhYsXGD97tvNZSw3PJxKJM6bbDLcjVinn\nO5S4yG63S3HPeYI0mCVgfmGEV2kWi4hSkVxRj1munq8BQb7jiJ8dinyGtdTFIl+rvdFaMHIHW491\nrjZfE4RyFybx3QOBABVOjT7NgiXWxbJFq/L2qM29B2L6hhlxPqKZfDweZ+XyRaxcvggYqNktdk9+\n95sneem5t/B4rXzloXsMD81M7mZhqHKhf7z762Q6vbz0m9188at2lixbVNZ1KkQ2m+VnD/+cpj3H\nmb1kBrfdcYtx/OUrlw3rM8sJz4t8uYisCENVbtefYiHgQnlWkXIp1ObO4XDIgc/nEfJOlYgwUqKX\np81mM5R9uV5lbgOCctt7FaOYYc2nQBUjtIYjkhHHLNWw5oafxloBDPmFLWcjpLBarfz7P9zFH5/d\nyNIFV7N40fxB/25eLMUzka9jkNmwRqNRQ+RR7j357WOv4LI0EO7U+Ml/Pc4dd916xv0QytRim5V0\nOk3niQRezQtZHzu2vn3WBlM8Azvf2sXzP3yNoDvIs69tYv11lzNz1syz+uxSEc8AFG9EUcxrLSdl\nYjbmYnNksQwM2hafIzr3SCXs+YU0mCUg5PACr9c7aCaduQGBua3deDQgGE6uspQFoxzDas7zinKR\n3F6co2U8z0ZYVIz6ujru+NTHi/6cObKQu1kwf3fRxD13KLP4HlA4PO/xWol3K2TUJFU1kwc9n/kw\nhwRzDavdbmfx6insfOkYgQY7193w/rKuS75zF16VzW5Fy6jEo2mybm1MDMRQ+cJ85G52hvpcGF6e\nVWygYGD9ONsNnGR8UHTz6iY5A13X6ejoMF4Aj8czaKeYz6tUlIEmyWMtKhgtQ2E+xlCGNVeBOhSj\nEQoeK2FRIczCGhh6oe7p6eUHD/8cm83K3fd+puy8ZXPTMX7z+DNMmFjFLbfeaNyXdDptHN8sOCrm\nGem6TiQSMTrLlBN2NJMvX/rYT37N26/sZcnaBXz80x8r+LsjQa646WybiSIbvAAAEnNJREFUEQwH\n84ZJhFzNRjQYDMow7HmKNJglEI/HjSYDQtxjFvWYRS1iAPRY7h7N3WpKrWscSXJzhS6XK2+uKN//\ni1GKYYXTfXBh7IVFUH7T9r/9wtc42WxD03QWLnfxjw/efVbHzzXWHo/njBBsqUKZUsh3LzRNG7dG\nHDC4GcFIhOHLJXctEGuFwGazyYHP5zlym1MCDoeDbDZrtPEy5yjMXtV4eJXmRXK86sqEsc63SI3F\nNBUz4nj5wo8wcs0HzN+h1KbtZhIJlUTMDijEYvnrQEulVGOtKOX1RD0bFaro6FNK9yVxbmeDORUx\nHsba/C7m3gNdv7AGPotNldgkvZdCy9LDLAER5hLNCYbjGY30QjHeXmWusR5pYVPusfIt4ua6ulIZ\nyQXc7FmXqwLetm0X//n//Q6rzcL9//s2Zs6aUdb3EOSG/8a6ZEekIkQeX3hUuYKZYpzNfSnWPH20\nKebZXkht7r797W/T2dlJMpnkwQcfxO/3D6oMuNCRBrMEzKUBYpEIh8MoinLGUOdyQ46lLBTmh/Fc\n9CqLlcuMNLn5WrFIin8rxTsqRrH7YRZxjFf4z3wNhlMyc7aYvbqhWsyVek+GsxE1d9AyR3jG6n0Y\nqs2dolxYPWG/8pWv0N3dzb333stPfvITTp48yc9//vPxPq0xRYZkSyCf1yAk4WLxNL/0+dR0InSb\nK1MvNewoFmNhuM0vozjeaC8SYyEsKkaxPrSlqh2HWsTLuS/ieKJrT7mt2oaD2aMZj+gCDPbqij0H\nuaUWhci9J+WGgZPJpBEaHw1RWS5DtbkT5UIXUrgylUrx4IMPUltby913380DDzxAOBzG7/e/ZzxM\naTCHidmIWq3WkuXohQyr+b98i0auGEMYLzPmhWm4KsdCDKdh+kiSK6g4m/BjOWUE5nuhqqrRnQcw\nuiUV6hNsptxIQiHMi/R45+rKDUMXo1TDmls65XA4hrUJFccs17Dm5q3NAivhbV9obe4ikQgtLS10\ndnZSW1tLJpPhxIkTJBIJAoHAeyYsKw3mGGBenMv1fMSfo9EomqYNmsReaCdeSnF1OU0IxrO1HZw5\n4HmsSgXMC7i5lV1u+HEkRDKCoe6HeTTWeIRgcz3bsRyJJSinefpIisrMz4L5HRPHFxtJ0STkQjIe\nuq7j9/v56le/amwMYrEYNTU11NXVAdDU1MSMGcPLw59PSIN5DjGU5yNeTGHEcj3VXI9VVdWC3qrw\niko5H3NeyW63Y7VajSYEo6U8NWMO/Y1HI4hC+VLzdx5uyPFsFvFUKkU6nT5rj7VUzHnr8fJszRu3\nfGUzuZQbSSiUVx1qMyrOyWazEQqFLhglrBlxDSdOnGj83cGDBwmFQsBAbrO5uZkf//jHF+T3NyMN\n5nmCeTFWlNLLAwoZVvMCkBsqLtSEwOzh5J7bSIQbc899PAY8mym3trIYwzGswrMVvy/CwGfrsZZ6\nb3IN1XioUHX9dOcgRVFGfNh0KYbVLO4RY7jM98HpdF4wxkKofc3khly9Xi+apnH//fcD8NOf/vSC\nETcNhTSYFzClGNZC+dV0Ok0sFjO65ZiNaKGd93AW73yhx9w+sGOtwoWh29uNNuI4ovYX8huq0QoF\nm++FaBAPZxbijwWaphnlXOOhRobBOVNzSkIYkbFuVDKamI3l73//eyZNmsTEiROpr68fVHPZ0dHB\nSy+9xN13383f/M3fjOcpjymyrERSEPOuciSES+afLwWLZWC6RCHDOhrk82zHO1d4tp5tqYa12L0Z\nTVFZPgoZqrHEvHHKnWF5oQ58jkajfPGLX6SxsRGn08nGjRt54okncDgchkGNRCK88MIL3HDDDeN9\numOKNJiSEaWQcCnXsOYrsynWQNxMvsX7bMsGSpkbOdqUWts4Goj7IPKjMLBpEaVLY2VYRYSj1Obp\no0FuKNrsXYt7c6EoYcX9FN/lV7/6FZlMhptuuom7776bOXPmcPvtt1NdXQ0wyNN8ryENpmRcyGdQ\no9Eouq7j8XgMYVIxEUYxSs3fmXvRjkd96bmSKzQLnAoJa0rxWEu9N/nuRyaTMcLApYh7RhpzzjSf\nhy+M5YWAOYq0detWstksr7/+OuFwmKamJlavXs2dd97J/fffzzXXXMOGDRvG+YzHF5nDlIwL+fKr\nFRUVZxgI8wIs9na54iVhWIfbFMKM8KYKjcIaDTRtcIu98ZiwUU4YeDjipeEaV9G7eaxCwebrkC/K\ncKENfBbX7NVXX+Whhx7i5z//OX6/n89+9rN8+tOf5s477wQGcpZTp04dxzM9N7hw7rzkvCffgicM\n60g1hshdrHPDwMWaEIy0Ijg3BDvWZTP5zmGkwsDlGFZRtgKnc9fleq1na1iHanMnGiRciErQjo4O\nHn74YWbPnk11dTU1NTV8/vOf5yc/+Qkej4dNmzYxYcIEZs4cm8Hf5zIyJCt5z5DPgEaj0UFlAiMl\nXMqXUzX/HwaHgccrBGvOFY7HOUDpzdNLVQQXI59hNW+enE4nDofDKOG5ENvcCf7lX/6FO+64gwMH\nDvDII49www03sH79eux2O0888QSRSIRMJsPtt98+3qd6TiA9TMl7BnOtnfAU8i3O5QqXChnWUiep\nWK1WVFUllUrlLe0YDQNmVgOPVxi41JypYDj1x8MJBYtcssViIRgMXlBK2FzBTjqd5p577uEXv/gF\nJ06c4KWXXiIQCPC+972PD33oQ+N4pucm0sOUSIZJPoNaiiJYGIhSKTXMWOqifi7UNhYT1owFmqaR\nTCYNz9JcMiKuTWVl5QVhLOPxOB6PB4C2tjY0TWPChAlomsbf/u3fkslk+M53vsN3v/tdTp48yW23\n3cbChQvH+azPPaTBlEhGkXz51VgshqIog/qQDqUINguehqKQQTX/WYSBx7O20Vy+M9alMwJxH/L1\nxRXX5kLp3HPs2DE2b95s1Ex++ctfpr6+nrvuuovq6mqSySS33347q1ev5q677uLhhx/mU5/6FBUV\nFeN85uce0mBKJOcAwxUu5Qs3loKorxwJ4VI5lNM8fbQoZrAvNCVsNBrly1/+Mr29vaxdu5Yrr7yS\nr371q6xevZobbriBUCjEj3/8Y775zW/y8MMPc/nll4/3KZ+zXDhPhURyHpMvv5qPcgxrblMIkbMT\nXm0xgcxQTSHKLeXIrTMdj2YEULzN3YU08FmUxvh8PlauXMl3vvMdVq1axYwZM/j85z/PN7/5TTwe\nD0uXLuXkyZN861vfksayCNLDlEguQHKFS0KJKvKVI9XKsJRSDsDIFSrKyDdPL5Wh2txdaEpYYSxV\nVaW9vZ1wOEwkEuF73/seH//4x7n22mt54YUXeOqpp9i/fz9XXnklX/jCF8b7tM95pMGUSN7DDFe4\nVG4ph0B40GcrXCr3OwrvNlcRLGpPL5Q2d2ZaW1u5++67qa6uprq6mgceeIDnn3+exx9/nAceeIBA\nIIDdbieTyRhzLSVDIw2mRCIZkmJhYPFvucKlfI0hilHMWxUeYDmh4GJt7i60gc8A7e3t3Hvvvdx+\n++243W7+/d//nUsuuYQvf/nL/Pd//zebNm0imUzyox/9iMrKyvE+3fMGaTAlEsmIkC+PGovFjHCn\nMLLFvNVSG0MUM67CWOZrc3ehKWFFfaXIxW7dupVf/OIXfPvb3+bhhx8mm83S0tLC9OnTueOOOzhy\n5AihUMhoqC4pDWkwJRLJmDJc4dJwFcGKomCz2YzGBw6H44IS92SzWSPEfPz4cSZPnkwikWDTpk0E\ng0H27dvHzTffzDe+8Q1ee+01PvOZz/DJT35ynM/6/ESqZCUSyZhSjiK4UI4VCs9g1TRtUJcloRKG\ngRBsIBC4YMQ9qVQKp9MJwIMPPkhvby91dXU0NjZy++238/nPf56FCxfi8/lobW3lS1/6EldfffU4\nn/X5y4Xx1EgkkgsOEVoVeUbhGbpcLlwuFx6PB5/Ph9/vN/4LBAIEAgFsNhter5eKigqCweCgf78Q\njGVPTw/33nsvBw8eRNd1vv71r+Pz+XjooYfYvn077e3t6LrO2rVr2bJlC9dffz3Lly+XxvIskR6m\nRCI5r8nXY7a6unqQkEd4pReCuEfTNB555BECgQALFy4km82iqipr1qzha1/7GosXL+aee+7hZz/7\nGcuWLWPatGnous7KlSvH+9TPe87/rZZEIpHkkGsYR6tkZTywWCysX7+eX//611x++eV0dnbi8Xi4\n7777aGxs5MEHH6Snp4cXXniBOXPmcPHFF0tjOUJID1MikUjOM+rr62loaKC3t5eGhgauv/56Tp06\nRSaTIRqN8uMf/1j2gh0FpEpWIpFIzkN0Xeehhx7izTff5E9/+hN79uzh4YcfBgb64f7Hf/zHOJ/h\nhYc0mBKJRHIec+uttxIIBPj+978PQF9fH6FQaJzP6sJEGkyJRCI5z7n00ku54oor+OpXvzrep3JB\nI3OYEolEcp7z5JNPsn///vE+jQse6WFKJBKJRFICsqxEIpFIJJISkAZTIpFIJJISkAZTIpFIJJIS\nkAZTMm7EYjG+/vWv81d/9Vfs2bMHoKwpFBKJRDKWSJWsZNw4duwYTz31FI8++qhhJAs1xjYbUqvV\nSiaToaenh4qKChwOx5ids0Qiee8iPUzJuBAOh/nCF75AZ2cnW7ZsYcKECWzZsoVf//rXbN++nWg0\nOujnLRbLoHFQ7e3t/PCHP6S9vR3gDK9U07S8fy+RSCTDRXqYknEhEAiwbt069u7dy5o1a3j22Wd5\n88038Xq9vPzyy6xcuZLbbruNU6dO8fjjj7Nz504CgQDXX389V199NZs2beKdd94hlUoBZzbbFp5q\nvobbwlsV46MkEomkFORqIRk3uru7ueSSS6iqquLRRx+lra2Nj370o6xZs4Y33niDffv2sXHjRlRV\n5Xvf+x7Lly/n5ZdfZu/evTQ1NfH222/zyiuv8Nhjj/HAAw/Q398PwB//+Ec2btxINBpl9+7dHDx4\nkJ6eHuO4wlsdylhGo1F++ctf8uSTT476dZBIJOcH0sOUjBsnT55k9erV7Nu3D5/PRyAQ4Lvf/S7R\naNTIU956661s27aNHTt20NbWxpEjR/D5fMydO5fPfe5z3H777fz0pz/Fbrcb4dqnnnqKpUuX4vf7\neeihh7BarSxfvpxPfvKTbNy4kSeeeILa2lpuvfVW1qxZc8Z5vf322/zbv/0bTU1N/N3f/d1YXxaJ\nRHKOIg2mZNzo6emhrq6OhoYGuru7efDBB6muriadThMOhwmFQtx777243W5WrFhBMBgkFArhcrk4\nceKE4SF2dnZSUVGB3W4HIJVKMXv2bPr7+6mvr+fv//7vmTp1Kl/5yleYNGkSjz32GLt27WLz5s00\nNDQwc+ZMI0Tb0tLCli1buPjii2lsbCQQCIznJZJIJOcQ0mBKxo3jx49TVVVFKBRi+vTpfP/73+ey\nyy7jt7/9LY2NjaxevZpYLMYXv/hFpkyZwo9+9CNsNhvBYJCOjg6WLFkCQG9vL/PmzTPylX19fdTW\n1vLWW28xe/ZsAoEAyWSSQ4cOsWnTJt566y08Hg/PPPMMS5cuHWQwJ0yYwOc+9zl2797NY489hs/n\nG89LJJFIziGkwZSMG5s2baK2thaAe+65h0ceeYQnnniCadOmcc899+BwOHj00Uf50pe+RHV1Nbqu\nc/z4cRRFweFwGApZp9NJJBLB4XCwdetWent7qauro7e3F5/Ph8fjIZvNkslk+O53v0soFKKjo4MN\nGzawbNky4LRISBjOZDJJKpWSQ3glEomBNJiScaOurs74c21tLffdd98ZP/Ptb3+btrY2Ojo68Hq9\nHDhwAIfDwQc+8AG+//3vs23bNm655Rbuv/9+Xn75ZZYvX05bWxt+v58TJ04wd+5crFYrLpeLcDhM\nPB5n0aJF2O123njjDdatWzfoeMJLjcViAHKuoEQiMZAGU3JO43A4mDx5MpMnTwZg7ty5AKxatYpV\nq1YZP/foo4/S1dWF3W5n8eLF2O12Iw8pcpvf+MY3+Od//mdcLhfZbJYrr7wSt9ud97iJRAKHwyFD\nshKJxECO95K8p+jt7aWjo4NUKsWiRYvy/oyu6zzyyCNs3bqVb33rW2N8hhKJ5FxFGkyJJA+JRIJw\nODwobCyRSN7bSIMpkUgkEkkJyE4/EolEIpGUgDSYEolEIpGUgDSYEolEIpGUgDSYEolEIpGUgDSY\nEolEIpGUgDSYEolEIpGUgDSYEolEIpGUgDSYEolEIpGUgDSYEolEIpGUgDSYEolEIpGUgDSYEolE\nIpGUgDSYEolEIpGUgDSYEolEIpGUgDSYEolEIpGUgDSYEolEIpGUwP8PQWyaYVLOHGEAAAAASUVO\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b926198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mpl_toolkits.mplot3d.art3d import Line3DCollection\n",
+    "\n",
+    "points = np.hstack([X, y[:, None]]).reshape(-1, 1, 3)\n",
+    "segments = np.hstack([points, points])\n",
+    "segments[:, 0, 2] = -8\n",
+    "\n",
+    "# plot points in 3D\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.scatter(X[:, 0], X[:, 1], y, c=y, s=35,\n",
+    "           cmap='viridis')\n",
+    "ax.add_collection3d(Line3DCollection(segments, colors='gray', alpha=0.2))\n",
+    "ax.scatter(X[:, 0], X[:, 1], -8 + np.zeros(X.shape[0]), c=y, s=10,\n",
+    "           cmap='viridis')\n",
+    "\n",
+    "# format plot\n",
+    "ax.patch.set_facecolor('white')\n",
+    "ax.view_init(elev=20, azim=-70)\n",
+    "ax.set_zlim3d(-8, 8)\n",
+    "ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
+    "ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
+    "ax.zaxis.set_major_formatter(plt.NullFormatter())\n",
+    "ax.set(xlabel='feature 1', ylabel='feature 2', zlabel='label')\n",
+    "\n",
+    "# Hide axes (is there a better way?)\n",
+    "ax.w_xaxis.line.set_visible(False)\n",
+    "ax.w_yaxis.line.set_visible(False)\n",
+    "ax.w_zaxis.line.set_visible(False)\n",
+    "for tick in ax.w_xaxis.get_ticklines():\n",
+    "    tick.set_visible(False)\n",
+    "for tick in ax.w_yaxis.get_ticklines():\n",
+    "    tick.set_visible(False)\n",
+    "for tick in ax.w_zaxis.get_ticklines():\n",
+    "    tick.set_visible(False)\n",
+    "\n",
+    "fig.savefig('figures/05.01-regression-2.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Regression Example Figure 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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X7oQdN7x+il3pCYhofoKBAOebj/PDt4e5ePIEui7HhCWSaMg5YolkhXD3Zjut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1w/qU1deiSEEnPMrOVLz7zsaEZtko8V3fBBMXEsc9pMt6ToXncfbKXxa5H9uaVou3kNdavXwa7a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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bbd3400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.collections import LineCollection\n",
+    "\n",
+    "# plot data points\n",
+    "fig, ax = plt.subplots()\n",
+    "pts = ax.scatter(X[:, 0], X[:, 1], c=y, s=50,\n",
+    "                 cmap='viridis', zorder=2)\n",
+    "\n",
+    "# compute and plot model color mesh\n",
+    "xx, yy = np.meshgrid(np.linspace(-4, 4),\n",
+    "                     np.linspace(-3, 3))\n",
+    "Xfit = np.vstack([xx.ravel(), yy.ravel()]).T\n",
+    "yfit = model.predict(Xfit)\n",
+    "zz = yfit.reshape(xx.shape)\n",
+    "ax.pcolorfast([-4, 4], [-3, 3], zz, alpha=0.5,\n",
+    "              cmap='viridis', norm=pts.norm, zorder=1)\n",
+    "\n",
+    "# format plot\n",
+    "format_plot(ax, 'Input Data with Linear Fit')\n",
+    "ax.axis([-4, 4, -3, 3])\n",
+    "\n",
+    "fig.savefig('figures/05.01-regression-3.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Regression Example Figure 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IMkxEEBEREREREZFkmIggIiIiIiIiIskwEUFEREREREREkmEigoiIiIiIiIgkw0QEERER\nEREREUmGiQgiIiIiIiIikgwTEUREREREREQkGSYiiIiIiIiIiEgyTEQQERERERERkWSYiCAiIiIi\nIiIiyTARQURERERERESSYSKCiIiIiIiIiCTDRAQRERERERERSYaJCCIiIiIiIiKSDBMRRERERERE\nRCQZQRRFMdRBEBEREREREVFkUITqxBUVllCdOiT0+hj2OQKwz+Ev0voLsM+RQq+PCXUIARGJn1sk\n9TnS+guwz5GCfY4Mkdpnbzg1g4iIiIiIiIgkw0QEEREREREREUmGiQgiIiIiIiIikgwTEURERERE\nREQkGSYiiIiIiIiIiEgyTEQQERERERERkWSYiCAiIiIiIiIiyTARQURERERERESSYSKCiIiIiIiI\niCTDRAQRERERERERSYaJCCIiIiKiEHK73Th58gQqKipCHQoRkSQUoQ6AqLsxm004daoYmZlZiI2N\nC3U4RERE1I2t3LISG4q/RXVqPeT1AnpY9Pj9JXcjJzMn1KEREQUNExFEPrLb7cjLWwSDoQwqlRJ2\nuwOpqemYN28+VCpVqMMjIiKibmbLjm+xyrURygujEAM1AKAGDvxr/YtYePtLUCqVIY6QiCg4ODWD\nyEd5eYvgctmRlpaKxMREpKWlwuVqTE4QERER+WvTsa1QZkV5tDeMUmDl5pUhiIiISBpMRBD5wGw2\nwWAog0LRehCRQqGAwVAGs9kUosiIiIiouzIJFq/tSp0KBusZiaMhIpIOExFEPjh1qhgqlffhkSqV\nEiUlJRJHRERERN1djKjz2u60OZCsSZQ4GiIi6TARQeSDzMws2O0Or6/Z7Q5kZGRIHBERERF1dxdn\nj4e9tN6jXb7TjqsuuToEERERSYOJCCIfxMbGITU1HU6ns1W70+lEamo6V88gIiIiv00bPw1TG8ZB\n/LkO9YZa1B83QfeDiIcm3Q+1Wh3q8IiIgoarZhD5aN68+cjLW4Tycs9VM4iIiIg64qZpN2GOfQ4O\n/rofcT3j0Wd631CHREQUdExEEPlIpVJhwYIHYTabUFJSgoyMDI6EICIiok5TqVQ4f9ioUIdBRCQZ\nJiKI/BQbG4fcXCYgiIiIiIiIOoI1IoiIiIiIiIhIMhwRQUQhZTabcOpUMTIzszjVhYiIiDpl98H9\n+Pj7zSiutyBarsTF2X1w51XXQhCEUIdGRC0wEUERiTe/oWe325GXtwgGg2fxT5VKFerwiIiIfGa3\n27H++69Ra6vFlDFTkaJPCXVIEemnvbvxty1fw5yeAiRGAQDyTWU4mfc6/veeP4Q4OiJqiYkIiii8\n+e068vIWweWyIy0ttbnN6Wz8fBYseDCEkREREflu685v8fnhzyAMl0OhUWDrti0Y5h6G++bcH+rQ\nugWbzQaDoRzJyXrodLpOHevDbd82JiFaELQafFttwMmik8jJzunU8YkocFgjgiJKy5vfxMREpKWl\nwuVqvPml9pnNJuTnH4DZbOr0cQyGMigUrXOhCoUCBkNZp49PREQkBaOxCkuPL4FqnAbKKCUEmYCo\nYdE4mFmAlZtXhDq8Ls3tduNfH+fh6rf+gTnr/g+z857D3959DXa7vcPHPG6p8dpuS0/Bhp0/dfi4\nRBR4HBFBEaPp5rflE3ig8ea3vLzx5pfTNLw710iSjjh1qhgqldLrayqVEiUlJVyZhIiIurwV338B\nzcgoj3Z1shp7CndhNq4OQVTdwwtL38NnaguEfj0gADAB+NrphP391/HeX//eoWOqZXKv7aLDgdgY\nz8+JiEKHIyIoYvhy80veBXokSWZmFux2h9fX7HYHMjIyOhMuERGRJOrdVsjk3i+nbYJN4mi6j4aG\nBmypKIKg1bRqFxQK/GSvRrnB0KHjnp+YAtHt9mhPLTXgqkund+iYRBQcTERQxODNb8e0N43CZPJ/\nGkVsbBxSU9PhdDpbtTudTqSmpnNkChERdQtZsVlwWLxPJUgQEyWOpvuorKxAhcb7bUitPhZ7Cwo6\ndNxHbr4DQ4oNEM0WAIDociHhxCk8PHE61Gp1h+MlosBjIoIiBm9+O6a9kSTFxcUdOu68efMhl6tQ\nXm6A0WhEebkBcrmqw9M9iIiIpDZr4hVQbJdDFMVW7Q0Hbbj8/CtDFFXXl5iYhASby+tr2qpaDOrb\nt0PH1el0ePfRJ/CPAefjOqcSdyvjsGzBI5hy4fjOhEtEQcAaERRR5s2bj7y8RSgvD0ytg+6mI8uW\ntjeSJCsrCx2pK6VSqbBgwYMwm00oKSlBRkYGk0FERNStKBQK/O26J/H212+iyFUEp+BEuiwdNw+9\nCef1HxLq8IKuutqITzevRYPbhSlDR+O8Abk+7afVajEuLh1rHE4Iyt9uR0S3GyNFLbIzM1FRYelQ\nTIIgYNrFl2Bah/YmIqkwEUERJVJvfjuzbOlvI0nsraZnNI0kiYuL6/DFQtPxWZiSiIi6q8SERPzl\n5r9CFEWIogiZLDIGHH+6YQ0WH/kRtf1SIchk+PTnzzHpuxg8c/dDEASh3f3/ess9sL+/CD/aymBO\nioa2pg6jEI1nbl8gQfREFGpMRFBEirSb35bFJps4nY3JiQULHmx3/0gfSUJERNQeQRB8ugEPByWn\nS7Ho2I+wDUhHU49dPROxsc6Gfqs/x51XXt/uMZRKJZ6d90dUVVWh4Nhh9MnOQY+0HsENnIi6DCYi\nKCJ1ZIpCdxWIZUsjdSQJERFRe4zVRryz7h2ccBbDDRd6ynvgxguvR/9eA0IdWtB8+t06WPum4ey0\ni6DT4MfC47jTj2MlJSVhQtK4QIZHRN0AExEUUTozRaG78mXZUl9Hh0TaSBIiIqJzsdvteHLZU3BN\nUkIuaCEHcAZmvPzzq3hM/Wdk9cgKdYhBYXM72xz9YRWdXtuJiFqKjElsRP/VcopCYmIi0tJS4XI1\nJifCFZctJSIiCo4Vm5fDPtZzSoZslBbLvl8WoqiCb1jP3nCb6ry+1ksTL3E0RNQdMRFBEaNpikLL\ngotA4xQFg6FxikI44rKlREREwVFSXwqF1nPUoSAIqBSqQhCRNGZOmIKhxfUQXa2X4Ew+YsAdk64I\nUVRE1J1wagZFjEBOUQgUqWpVsNhkYEVSjREiImqbyq2CKIpepymo3WrJ49m5bxc2HdoJN0SM7zUU\nF48eH5QCmjKZDK///jEsXP4f/GIqg110oX9UEu6acQf6ZOcE/HzhrK6uDq988CF+PVOJugYH+iQl\n4LZZl2HoIN+WQiXqrpiIoIjRlaYoSF2rgsUmA8Nut+P5559HUVFxl6gxwoQIEVFozRo9C/v2/gua\nwTGt2u0VNozuMUnSWJ7/+HVs1JVB6NX4e7Cp4huMeWcbnpn7aFCWFNVqtfjL7+4N+HEjidvtxh+f\n/zeORfeAoEsDdMBuETjy4TL8+86bMKBfP8liEUURW7f9gO93H4Qoihg1uB+mXzo5YpajJekxEUER\n47cpCvZW0zNCMUWhs8tpdhSLTXZOqD63s0Vi0VUioq6od3ZvXHFiGtbsXAflCB0EuQy2fAtGuoZi\n5pxZksWxdcc2bIgzQJby22+8TB+NHRobvti0GtdNnS1ZLOS7tZs24agyweNm35LQAx+uXYd//FGa\nRIQoinj6pdfwQ5Edcm3jd+iHogPY/NMu/PN/HoFcLpckDoosTHFRRJk3bz7kchXKyw0wGo0oLzdA\nLldJOkUhUmtVdHdd6XOLxKKrRERd1exJV+Oly/+NCYWjcMGvQ/D02Ccwf84CSWP49thuyFJiPNpl\nMRpsLz8saSzku4MniyHT6ry+VlxjkSyOTVu/a5WEAAC5OhoHanT4dMVKyeKgyMIRERRRusIUha5Y\nq4La11U+t6aESMtRGUBjQqS8vDEhwmkaRETSiomJxU0zbw7Z+Z2Cu+3XxLZfo9CKUsohinYIguez\n4SildLdpP+zJb5WEaCJXafDL4SKE7ptN4YwjIigiNU5RGBySG7auVKuCfNdVPjdfEiJERBRZhiTm\nwFXf4NEuOl0YoEsLQUTkixtnzYKm6rRHu9hgxbj+vSWLw+UWO/QaUWcwEUEkMS6n2T11lc+tqyRE\niIio65gz5Qr0P9gAt+O35TRFlxs9d9fgtunXhTAyOpcUvR7zp46HrqIYblfj9YWs2oDJcQJunTNH\nsjhGDMiBy17v0e52OTEwK9XLHkSdx0QEUQh0hVoV5L958+ZDp9OF9HPrKgkRIiLqOhQKBRbO+xuu\nLU9HvwI7+hTYcPmpJLxx5/9Ap/Neg4C6hisvnYpPnngUDwzrgevTNVh894144g/3B2XZ1TZjmDkd\nA6Nr4XLam9vcbhcyhHLcev01ksVBkUUQRTEk420qKqQrwNIV6PUx7HME8LfP4bCcZqR9znp9DI4f\nLwnp5yb1qhmR9hkDkdvncBCJn1sk9TnS+guwz+HObrfD5XIhKyslpH12OBz4eNkK7D9WArdbxMDs\nFNx+w7XQarVBO2ckfc5NIrXP3rBYJVEIcTnN7sVsNuH06UJERychN3dwyOLoCkVXiYiIqONOlZZg\n4cefouBMDZwQMDAlDjdecjHGjR4dkniUSiXuuOn6kJybIhMTEUTkldlswqlTxcjMzIr4m1ypRyD4\nioksIiLqLsxmExZ9+RnyTRWQCQKGxKdg/jU3ISoqKtShSc5ms+GR197GmYQsQN/4O35QBJ5dsR7P\nR+tw3qDcEEdIFHxMRBBRK131pjuU8vIWweWyt1oy0+lsfJ8WLHgwhJERERF1fXV1dbjnzRdwrF86\nhNhEAEC+y4b9rz2Pdx76n4i7vvhk1SqUR6d5FOurj0vDp99sZiKCIgITEUTUSmduujs7iqIrjsIw\nm00wGMpavR9AY2Gw8vIymM2mLhMrERFRV/Te6mU41icNguy3W29BLkd+TjKWrluF26+81ut+RmMV\n3l+7CgZbHeIVKtwydSYye/q+OpTL5cKXG9fj0OnTiFGpcNO0mdAnJ3e6P511qqoaMqX35Eu5xXP1\nCqJwxEQEETXr6E13Z0dRdOVRGKdOFUOlUnp9TaVSoqSkhNMjiIiIzuGwqQpCqucUDJlahQMVp73u\ns/9QAf6yYikqsntCiFZBFEVsXPIOHht/KS4dd1G75zSZanD/qy/hSGIyZFFREG02rHrzNTx88STM\nnHhJp/vUGTFqFUTRAUHwXMAwVu39moMo3HD5TiJq5stNtzctR1EkJiYiLS0VLldjcsEXnd0/mDIz\ns2C3O7y+Zrc7kJHh+5MZIiKiSKT0csPdRC3Ivba/vm4VKntlNo+iEAQBlsweePP7jXC73e2e84Ul\nH+JojwzI/luDQpDJUJeRiTe++xY2m60DvQicm6+4HFHGUo92oc6Ey0YPD0FERNJjIoKImnXkpttk\nahxFoVC0HmClUChgMDSOojiXplEYHd0/2GJj45Camg6n09mq3el0IjU1ndMyiIiCwOVy4cSJQlRW\nVoY6FAqACb0GAJY6j3ahqgZTcz1vvI1GI/IbvE9RKIqLwvZfdrV7zn2Vla2mgjSpTE3Dyo3rfYg6\neFL0ejx85VQk15TAZa2D2+lATE0pbhjUE9MmhXa0BpFUODWDOq0rzuunjvntptveKjFwrpvukydP\ndmrqQneY+jBv3nzk5S1Cebnn1BEiIgqs1Zs/wd7KdRB6GuGyKBBlzMHNlzyEnumZoQ6NOujqS6dj\nT94RbLAZ4dY3FquUl1ditjYFk8aO99je5XLBLRO8Hsstk7X50KQlexujJgSFEpZ6qx/RB8eUiy7C\nxLFjsXHrFtRZrbjluvmwWsVQh0UkGUEURX7jqUPsdjteeukllJSUQC6Xw+VyISMjAw8//HDI5/VT\nx/n7uZpMJvz1r3+FXq/3eK2iogLPPPMM4uLaTiR0dn8pmUwmFBcXIysrq8vEREQUTtZtXYXN9YsQ\nl9X6WVnFxmi8eP/HkMu9D+On7mHbzh34evcOyAQZrrpwPEYOHdbmtrOf+Cv2/zdp0VJGSTm+feY5\nj5GUZ7v1qafxkzbao11rKMPah/+IzJ49/e8AEQVMyEZEVFRYQnXqkNDrY8Kuz2+88QpcLnurG8i6\nujo8++w/sWDBg2HZ5/aES5/nzl0As9mEkpISZGRkIDY2DiZTA4AGj231+jgkJ6d6HUWRnJwKu13W\nznsi6+RUxBNZAAAgAElEQVT+UpJhyJAhqKiwBDWmrjbKKFy+1/6I1D6Hg0j83MKpz9/mr0LcRM/L\nU93oaiz9cilumXNLWPXXF+H0GQ/IGYQBOYOa/91Wv/T6GNx+4WQ8uXUdLD3TmtvVZypx09BRqK5u\nf0TDzRddgoKvVsOU9tv+Yl0tpupToVHFdrn3NJifs8lUg/c/XY4igwkqhQzjhw/A5dOnQRC8jzqR\nSjh9t30VqX32hlMzqEN8WV0hXC5qI1VsbJzPUyI6O3WBUx8adeXVQ4iIpGATqhHrpT0qTgnDoSLJ\n46HQmTBqDN5MTMLH336Dcls9ElVqXDNxBkYPG+HT/qOGDsMLahU+3rQBp+rrES1XYGK/frhx1pVB\njrxrqaioxMPPvopKRU8IssYRInvXFyD/6An85YH7QhwdRTImIqhDfJnX36cPVxMIpK72lLwllUqF\nBQse9BhFEYj9u3K/A63l6iFNnM7G5MSCBQ+GMDIiImloEAfgjEe7zeJATx2H0gdaRWUl1mzbBK1K\njdmXTINWqw11SK30790HT/Xu+M3yeQMG4bkBg1q1iaKI7bt3wlxbi4vGjO1yfQ60vCWfo1KZ0Wr0\ng0wdg61HKnHloUPIHTgwhNFRJGMigjqESxpKpzs9JfdnFEV7+3enfgeCL6OMwj0RQ0Q0JHUSDpxe\ngtgerS9RjT9FY/6Ns0MUVfA5HA7U1dUiNjYOMi8rPQTDK5/9H1ZVH0ddbz1Epwsfvvs07h0yCVdO\nvEyS84fCT7t3YeHatShSR0FUqZC85TtcNXgQ7r72+lCHFjRHS6sgCKke7YIuGRu++4mJCAoZLt9J\nHRLJSxqazSbk5x+QbFnJlk/JExMTkZaWCper8SY9nEVav30ZZUREFO6mTbga2eUzcPo7GWrKrDhz\nyArjxiTcNuHxdosTdkcOhwPPL3kNN3/wF9z81dO47b3H8N6aJQh2LflVW9bjM6EM9X1TIchkkKmU\nqBqUjpd/3YpTpeH5e2M2m/CPVatwKqUHZHHxkGujUJ3WEx+cKMbXWzaHOrygaasOhCiKEATeClLo\nhN9fdJJMpM3rD8UT+mA8Je8OUx0icXQARxkRETW6bvpduLLhd8g/tA8JWcnodXHvUIcUNE999DJ2\nD2yATJUMAUA1gM+rfwW+Woq7Lr85aOfdePwA3Dmetbzq+6bik+/W4ZGb7vbreJVVVfh627eIidJh\n1qRLoVR6T6yH0kdrVqM6tSfOvi13x8Zj3d59mDFpckjiCrZBmXqUFbs9kg5CnQFXTL0lRFERMRFB\nndDZugDdTSjm7/vylNzXqRDdaapDIPvdXfw2yshz9ZBwH2VERHQ2tVqN84eNCXUYQVVaVoq9mjOQ\nqZJatcsSorBpz17c4b4xaNM0LG47ALVHuyAIqHXb/TrWi0vew+qKk7Bkp0KsduCdhT/gj+OnYeqF\nEwIUbWBUW20Q2lj+tbrBc1WwcPH722/EoadeRImYArmi8XpPrDfi8pHZ6JXTK8TRUSTjeBzqtMZ5\n/YPD+kap6Qn92cNCFQoFDIayoE3TCORT8u401SFSRwfMmzcfcrkK5eUGGI1GlJcbIJerwnaUERFR\nJNuV/wtc2d7WCAGMUQ6YTDVBO3eGyvvKZm6bHb1jkn0+zrINX+ETVyVqe6U3TvHQqFHevyee/2kd\njMaqQIUbEBkJCXDbvSdZ0nRREkcjnZiYWCx65nHcMjoFI/UNGJfuwpO3TcGCu24LdWgU4TgigsgH\noXpCH6in5N1tqkOkjg6ItFFGRESRrF9WbyD/eyAr3uO1aKsM0dHBWwb9tokzsXP9+6jpm9LcJooi\neh2uwo0L7vf5OBuP5kPM8IyzpncPfLRhDR644faAxBsIN866HGuefQane2S1atcaK3DdzOkhikoa\narUat954XajDIGqFIyKIfNDWE3qbzYby8nLExXl/ohEIgXhK3h0LIUby6IBIGGVERBTpcvvnovcZ\nlUdhSpfNgWyLFiUlp4J27n45ffDcxddjVKEVMfklSMw/jUuKXHjttoegVntO2WiL2e199KIgk8Hs\n8G+KBwDU1lpQXW30ez9fqNVq/HvuXJxfWw11aTFkZSXoW30Gj04Yj9HDRgTlnETUNo6IIPLB2U/o\nnU4nDh06BK1Wi7S0NCxe/DpSU9Px+ON/Dvi5A/GUPBhTHZqKXo4YMRjByGl2pdEBLQt86vXBe0JF\nRESR5Ylr/oCnl7+Oo3orZOmxMG4uAOwidg7Nxl0730LvDWr8ecbN6J85KODnHjFoCF4fNOS/qyd4\nX1mhPT3VOhzx0u6ut6JfUl+fj3P85Em8tPJTHKyvhUsmYKBOh5tHT8DkC8Z1KK625GRl47U/PYLa\nWgsaGuxISkpqf6cgqKurw7uffI5DZRUAgMEZqXjsAf8KhBJ1d4IY7PWB2lBRYQnFaUNGr49hn7s5\nu92Ot956DUVFhaiursbw4cM9pg3odDrMnbsghFG27Y03XoHL5TnVQS5X+VVs8+yily6XC8nJqV2y\n6GVneSvwmZ2dhVtvnRd2fT2XcPt/2ReR2udwEImfWyT1ORz7+9HXy/Bu8SaYK2ugzkqBbkDPVq/H\n5ldh8ZWPIjlEN83nsv/XfDy0+QuYMvXNbaIoou+hUnz8p6d8WnK1vr4ev3vlOZzuldmqPbr8DF6a\ndjWG5Q4OeNyhZLVaseB/n0eRtgcEWWPxTLfLifNkVXjxL492yRVHgiUc/39uT6T22RtOzSDykUql\nglwuR3x8PGJjY70WriwpKQla4crOCtRUh7OLXur1+i5b9LKzvBX4rKurC8u+EhGR9KxWK1aUbofq\n/BzIdRqPJAQAmAYl4qNNK0IQXfuGDhqMp8fNxIhTJkQfLkLi4VOYUt6A1+9+yKckBAB8vHYVSjPT\nPdpr01LwyfebAx1yyP3ni+U4qU5vTkIAgEyuwAFHPD5fvTqEkRFJi1MziHzUVPBREIDkZO8VpeVy\neZddWjIQUx26W9HLzoikvhIRUWhs2fE9TH2jIQcAuffng4JMgNFVJ2lc/hg3YhTGjRgFl8sFmUzm\n9zSPUosJgsb7LUl5gzUQIXYpR05XQqbQebTLVWocOFkagoiIQoMjIoh81FTwMS4uDtXV1V63cblc\nXX5pyc4UQuyORS87KpL6SkREoaFVaSA6XI3/cLq9biO6RSTJPW9cuxq5XN6hWhNxSjVEt/e+xynC\nb5pCG/kmAIBCxlszihz8thP5qKngo0ajgdVqhdPpbPW60+kM++UWg1H00ldmswn5+Qckm/oSyr4S\nEVFkmDBmPFJPNAAANJlJqD/i+UQ8/lcjfjflaqlDk8wt0y9H/KnTHu0KYzWuGD46qOc+WXQSn61e\niYO/FgT1PC1dMKgv3LZ6j3Z3vRkThuVKFgdRqHFqBpGPWq6cMXDgwOZVMxISElBdXY3evfvh4Ycf\nhsnUEOpQg+bs1UOaOJ1OpKamByUJ461gZGpqetCLY4air0REFFnkcjnmDpuJhQVrIA5KRt2vpajZ\nVgBNlh5yJ9DXrMWfZ93ZJQtVBoo+ORn/M3kmXt28DkXx0RCVCvQwmnB1//MwZdxFQTmnzWbDX19/\nHbtrbXAmJkPY+yty5Svwj3vuhb6N6beBcs3MmdiT/zJ+rjZB0DVeS7hrqzGrTxymTpoU1HMTdSVc\nNUMikVohNdz6fPZNscVSC41Giz/84U9ITk4Oyz6fTepVMwK12kdHcNWMRpHwvT5bpPY5HETi5xZJ\nfQ7X/pYZyrB06yqYXTakqeNxYd9hiI+LR3Z2Ttj2+Wxutxs/7twOS30drr9yBiwW76MSA+GJ11/D\nZrcKgvy3gpGiKGKopRKLHnssaOdtea7vfvoJ2/YehEwAJo4cjtmzpkTE59xSpHy3W4rUPnvDERFE\nfghEwcfu7uz3YPjwQbDbgzPLK9QFI7193n36ZETcDwgREQVXemo6Hr7+3lCHEVIymQwXXXAhAECj\n0QQtEWG1WrHTUAEhLatVuyAIyLe7cfxEIfr06h2Uc7c818Rx4zBx3LignoeoK2ONCKIO6EzBx3DR\n9B7ExQXvPegqBSP5eRMREYUHs9mEWpn3awuHLgaFRUUSR0QUmZiIIKIuiwUjiYiIKJCSkpKhh8vr\nazpLNYafN0TiiIgiExMRRNRl/VYw0nOFEhaMJCIiIn8pFApMHdAPYn1tq3a3w44L9YlBL1ZJRI1Y\nI4KoGzGbTTh1qhiZmVl+3YT7s19HzxEs8+bNR17eIpSXe66aQURERB3jdDrx/upl2F15Cm5RxOC4\nFNx9xfWIiopqd9/aWguWrl+DGpsNQzNzcNmESRAEweu2oihix97dKK+swMTRYxEfnxDorvjt9zfd\nDPlnn2LDoSOocLgQJwPGZ2fg4dvvCXVoRBGDiQiiDpD6Zr2jS1j6s1+olslsDwuEEhFRuDt2shDv\nffcljlkroRTkGBLdAw9cdbtPSYGOcLvdeOCN57E9OxqyDB0AYLfLgu2LnkPe/MfOed7N23/EP7eu\nRVWvHhC0cnxauAefbN+K1+c/Ap1O12rbA4d+xXMrP8PRGA1EXRRezduOaakZeOTWuW0mLqQgCALu\nueFG3O12o7bWAp0uGvIWK2gQUfBxagaRH+x2O9544xW88MKzWLHiU7zwwrN4441XYLfbg3revLxF\ncLnsSEtLRWJiItLSUuFyNSYOArVfR88hFRaMJCKicFRUWoz/983b+LGviDNDklB6Xjy+zqzFA+88\nC7fbHZRzfrl5HXb00ECm/u1BgyCX4/CAFLy3Zlmb+9ntdry09WsY+2Y2L30pxEbjQK9U/Gvp+622\ndTgceGL5EhzLToeQmACZWg1LZg8ss1vwwarlnYq/pqYaJ04UwuHo3MoaMpkMsbFxTEIQhQATEUR+\nCMXNetMSlgpF6wFMCoUCBkPjEpad3a+j5/A1/vz8A506BhERUbj6YPOXqDlP36pNkMtwpL8Gq7es\nC8o5d5UWQoj2HPUgKOQ4aDK0ud+qTetRlpniuZ9Mht01rfdbseFrnOqh99gW0TpsOn7Y/6ABGKuN\nePClf+PqVxfihiVLcP3zzyDv8087dCwiCi1OzSDyUdPNelpaaqt2hUKB8vLGm3W9Pibg5/VlCcvc\nXM9RAv7s19FznEtXnepBRETUlRQ5agDoPNplsVocKD4ZlHPK0Pa0CIXQ9nPKmrpaCG1cL9hcrVei\nKDPVQKbReD+O0/+RpKIo4pE330CBPg1CbAIUAAwA3i85jahVX+J3V17l9zGJKHQ4IoLIR77crAdD\nR5ew9Ge/YCyT2dWnehAREXUFGsH7c0FRFKGVeb/u6KwpA4YBVZ4jFd3WBoxOzW5zv6ljx0NTesbr\na310rR9Y5GZkQzSbvW7bQ+N/7Ysfd+7Ar2qtR20JMSYG6wry/T4eEYUWExFEPgrGzbovOrqEpT/7\nBXqZzGBO9SAiIgonF6b2h1hr82hXHavEnAsvC8o5J10wHrPsMRBaJiMsdRhXYsUtM69uc7/sjCxM\n0SZCrLO2ao8pPYPbx09u1XbpRRcj11gLURRbtasqjbjm/Av8jrngZCEQF+/1tQqb5/tHRF0bExFE\nPgr0zbo/5s2bD7lchfJyA4xGI8rLDZDLVe0uYenPfh09hzehGj1CRETU3fxuxhxMKtVCVmwEAIgu\nN9QFBsxNH4uczLZHJ3SGIAj4+50L8O9+EzGjAphWAfw9ZTgW3v+Xdgs3Pnn3AtwX3QO5JVXIKjLg\nojN1+OfEKzBuxCiPc7zy+z9iorEOsSdPQXmqFH1LDHh44AhMu2ii3zH36ZkB0eJ9hEWiWu338Ygo\ntATx7DSlRCoqLKE4bcjo9TFh2+e2lrIMxz63V/cg2H3u6BKW/uzn7zm89dlsNuGFF571qKcBAOXl\nBvy///d4t139Ihy/1+1hnyNDMGrchEIkfm7h1ufqaiNWb1sBh9iAYTkjMWrob0/Pw7G/TQqO/IpN\n+3+GSqbAtRNnIikpCUB49Nlms8FqrUd8fIJPy3Z667Moirj9mf/FsbSerdvr6zC3Rzruvvb6gMYs\ntXD4nP3FPkeGtq4vWKySOiwSixGqVCosWPBghxMCndW4hKX/5/Nnv46e4+xjNI4esbeaniHF6BEi\nIuq+1m1bjXXlXyBmlBoyuQy/ntqBde+vwl9uecpjul+4ye0/CLn9B4U6jKDQaDTQtFG40leCIOC5\nufPw9Icf4KDDCbs2Csm1Flyak4O5c64LUKREJJXw/otOQdWyGGETp7MxObFgwYMhjCz4AnGzHu7m\nzZuPvLxFKC/3TFQRERGdzWiswteGLxB/gba5TZephVV/Bh+tfQ93XHlPCKPrWhoaGlBVVYnkZH3Y\nPvzxpmd6D7z16GMoKi5C2RkDhgwaDJ3Oc8URIur6mIigDgnVUpZdTVvTUvzdJhyFevQIERF1L2u2\nrUDsSM+5/gqNAsfrC0IQkbRcLhfeWP4f/Gw8AYvbjgxlLK4dMgE3Xj6zeRun04l/Ln0b28wlMGoF\nJFtFTEzIwZ9umNtubYdwkp2Vjeys4NTPICJptJmIKC8vx5dffgmz2YyBAwdi2rRpUP+3EMzixYtx\n7733ShYkdT2+FCPs0yc4q0h0BW1NS3n88T+3u004T13xhqNHiKglXl9QW+yiDTK59zrqDsH7qlVd\nncVihlKp8mlawt8/WIjNGXbI0hIAANUADhduhG6rCmNzG+tk/OPDRVib7oCsZxoEAFUAltlMcH3y\nDh77Hf/fIaLuo81VM9auXYtp06bh/vvvh1wuxwcffAC73S5lbNSFhWopy2Axm03Izz/g87KSLael\nJCYmIi0tFS6XHS+99FK72+TlLQpWN4iIujxeX1BbhmSej7rT3pdhTBbSJI6mc77duQ3z3vk75ix7\nEnM+ehz/7/+ew+ny021uf6LoBLYpKiHTth4RYs+Ixwc7NgFovFb53lYG2VkPgmQaNbaailBfXx/4\njhARBUmbiQiHw4FevXohKioKs2bNQk5ODpYuXQqXyyVlfNRFhXIpy0Cy2+14441X8MILz2LFik/x\nwgvP4o03XjnnRXHTtJSzi2YpFAqUlJTAbDadcxuDocznhAcRUbjh9QW15YLzxyGmIBXOhtbfhbpd\nDlwx6toQReW/Xfm/4MXja3FyqA7i4DTYh6Zh32A5Hln2ChwO7w9xtuzdDkdOstfXTlhrIIoijhQe\nhylJ63Wbilg5yspKA9YHIqJgazMRoVKpcPToUTSt7nnZZZchJiYGn332WZt/RCmyzJs3H3K5CuXl\nBhiNRpSXGyCXq7pVMcKOjFo417QUuVyOkpISn6auUMf5O4KFiLoOXl/QuTx+y9PofXAEnD+oYPsR\niP4xHfOG/QkDenef1SS+2LMJDb3jPdrLhsRi2cZVXvdJjUuCWGv1+lq0TAFBEJDdMxO6Gu8jRuIt\nTqSkeC6ZTb6pra1F3tIleC7vHXy8fDlHaRFJoM0aEZdffjnWrFmD+vp6DBs2DABw1VVX4ZtvvsGx\nY8ckC5C6ru5ejNCXgpve+nOuaSkul6t5Wko4TV3pKlh3g6j74/UFnYtSqcTcq+4LdRidYnBaAHiu\n5CDXqnCy/IzXfaZNmIz33/wWp4dFtWoXnS6MTcoBAOj1eowU4/CD2w1B9tuzRNHlwgWqFMTExAas\nD5Fk1759+MfSZahO6AlBroC7ogyrd/4Dz993N3KyskIdHlHYanNEhF6vx5133tl8kQAAMpkM06dP\nx5/+9CdJgqPuobEY4eBulYQAfCu46c25pqU0JWPCZepKV8O6G0TdH68vKNzFyjxX/gAA0eVGvDLK\n62tyuRx/nnwDUvcZ4KpvHPUglNVgxK/1eOrOBc3bPX3L/Rh3zAZloQGu2npojhtw8Qknnvhd907e\nhIooilj4xUrUJGdDkDc+n5Wp1ChPysKLSz4JcXRE4a1Dy3dGRXn/I0rUnXSm4Oa8efORl7cI5eWt\nn8w//PDDMJkazrlNd5q60pV0dARLR88ViUuuEoUary8oHEzJOR/5FT8C+uhW7boDFfjdjW0nDEbm\nDsMn/XKxass6nKmowcgBEzDm+lHQaDSwWBqvV3Q6HV7+/V9QcroEhwqPYfAFA5Cemh7U/oSzX/bt\nw0lRA28Ln/5aXReQa4u6ujq8+Z8lyD9VAZfbjX7pSbj7hquRnta9CrASBVqHEhFE4eC3UQv2VkUl\nfRm10Na0lMbpAQ3n3IY6xpcRLJ1dJpRTP4iIqLOumDgdp1dW4KsD+1HXLx6i1Y60Ew2Yf8F17V4H\nKJVKzJl6RbvnyOiRgYwenObZWWaLGaLS+++7Q5DDarV16trN6XTiwaf/jSJ5DwjyFEAOGCqB/H8t\nwmuPPwB9svcCpUSRoM2pGUSRoLMFN32ZltJdp650NVIsGcupH0REFAj3zr4VS274O/7QMAxPxE7B\nR/c8i4tGjA11WHSWsaPHIMla4/W1bI0MKSkpPh1HFEUYDAbU1FS3al+++iucEJNb1fQAAGNUJj5Y\n9mXHgiYKE+2OiKipqcHq1atRU1ODO+64A8uXL8fs2bMRH+9ZDZiouwmXUQuRMJWgMyNYWmrrvZJy\n6gcR8fqCwl90dDSumnp5qMPokBUb12HTkXzUu13I0kbjjssuR05m+BVu1Gg0mD1iMD4sKIIY/dvf\nHqWpEjdcMg6CILR7jE3ffY+Pv/kOxRYnZBAxMDkKC268GgP69UNB0WnIVRqPfQRBwMkz3hMgRJGi\n3UTEmjVrMG7cOGzcuBHR0dE477zzsGLFCtx5551SxEckicZRC93vJjNUUwla3szr9TFBO8/ZOlN3\no733SoqpH0T0G15fEHVN//rwHSxz1EBMafx9PwBg+6fv4sXZNyO334CgnPNk0Ul8tnkTbC4XzsvM\nxN03zQnKeby56/rrkLZpM9bt3oMaqx16nRZzrpyCcaNHt7vv3gMH8PLq79EQnQohERAB/OoG/rb4\nQ/zfU3+GWumt+kSjc71GFAnaTUTU19ejT58+2LhxIwRBwMiRI7Fz504pYqMwFAlP7qXUcipBE6ez\n8YZ7wYIHA34+bzfz2dlZuPXWeZLUUOjMCJb23qtzTf2wWGpRW2uB2WySNPFCFM54fUGBYrGYsWXH\nFiTEJGDcqPGQyTjzuKNKTpdidVUpxIzWhRQrc3rgnU1r8VIQEhFL1qzC23v3oiE1HYIgw9rDR7D+\nz4/hpfsfhk7nuQxqMMycMhkzp0z2e79lG7agIdpz+kZVVA8s+XIlZk0chy3vrAJiWm9TbziJap0T\nT7y4CL3Sk3Dj1VcC4PUFRZZ2ExFKpRJms7n538XFxa2GRRP5gkUAAy8UUwm83czX1dUFLfHRFn9H\nsPj6Xp099cPpdKKgoAA6nQ7ffLMGa9askDTxQhTOeH1BgfDOynfxQ/1uCIO1cNU6sOSDL3DnqN9h\n9JD2n2aTp69+2Aprz1R4m5Dwq8kY8PNVVVXhvT17YO+R0XxOWZQO+zVaLFz6ER6/+96AnzOQKixW\nAFqPdplcgXKjGUPPOw/XjjqA5TuOwhWbDkEQYD66C9rYVJRpslFWCWwvr8KWXc/iPy//DQCvLShy\ntJsynjZtGpYsWQKj0Yi33noLX3zxBaZPny5FbBRGWAQw8HyZShBITTfzZ98oKBQKGAyNN/Ndla/v\n1dnFS/fs2YPc3Fz06dOn+XvblHghos7h9QV11uotq/FD8n4oR8ZCoVFCnRwF53gN3vrl/1BbWxvq\n8EJCFEW43e4O769WKIE29lf4UC/BX8s2rEddWg+PdkEmwz7DmYCfr6XOvlcAEK9Vez+22414XWNt\niHm33ITFj9yJK3urcHGSFfHxSVAn/VZgW6ZQwqDKxD8Xvd+pWIi6m3YfPdTW1mLevHmoqqqCKIpI\nTk6GXM45TeQ7FgH0rrPTVKRYRaKlUNZQkOq9ajn149ChQ2ho+MJr4qXl95bTjYg6htcX1Fk/lu6E\ncrTn02hhpA5fbFmO2y+/LQRRhUa5wYAXVn6M/bUVcIgiBuoSMfeiaRh13jC/jnPt1On46M1/w9Sr\nZ6t2URQxLNG3FST80eB0eqwo0cQhigE/HwCcLivDK0s+QUG5ES5RRD99PO6YcRnOHzrU72PNHD8a\ne1dug1uX2KpdZynFzbMfaP53VmYm7r/rdrzz4RKI8SqPESeCIODgiXIAQGlpKb746hvYHC4M6Z+D\naVMmc7oRhaV2ExEbN25E//79fV6+huhsLALYWqCmqTRNJaitNaOurg5qtRoNDQ3Q6XR+rSLhK38S\nH4G6OQ/0e+XrihuxsXHQ6aKgVns/h0qlxIkTJ7Bt2xZONyLqIF5fUGfVoR7eLmVlSjnMdrPnDmGq\noaEB9/9nIU7m9oQgZAIAdgE4tnU5XtdGYUCffj4fKzo6BvcMuwAvbtsAY7kBco0GsmgdkqotmP/E\ncwGPfeqYC/D58i/gSvb8O9CvxQo6O/ftxcebN6PIZEGUQo7RWRm4/+Zb/J7OZbVa8fCri2BIzAZa\nFON8cumXeCk6Gn179/breBPHj0PpmQos/+EXVMrjILidyJRbce+Ns5CUlOSxvcvtBrxOfAFcbhGf\nr1yD/3y9Gy5d4zSOzYcOYM2mH/DC3x+FVuuZdCPqzuRPPvnkk+fa4NixYygsLERtbS0qKipgMBhg\nMBiQlpZ2rt3aVV9v79T+3Y1Op47YPms0Gnz//VZER0d7bGMymTF9+iyo1Z5LG3VHvnzOb731Glwu\nO2JjY6HVahEdHQ2Xy4FfftmDMWN8X2Pcbrdj586fUVpaAp1OhzNnzqCiogJ2ux3x8QkYMWJUQJ8u\nqtUaFBTkw+VytMrMO51OxMTEY8KESbDb7XjrrdewYcPXOHr0V3z//VYUFORj+PDzOxRLe++V2WzC\nsWNHodFo2v0ODR9+Pn75ZQ8MhnLY7XaYTGbExMT/dzqGZ2ztfW9Pny6FILg7/Tn6y58+B0ok//2K\nJDqd9yHGwcLri8CItO9qy/7uyN8BSw/Pvjtr7bgAQzCw10CpwwuK9j7jj9auwPoEN2Rn3ZTbEqJR\nm/eSUmwAACAASURBVH8UU0Zc4PO5amsteGfdapxUCNAOGQwo5HCZzBDPH4Ld277D9BGjoVIF7m+F\nPikZJ/fvxfEGBwTlbw+tUs6U4bE51yExPgHb9+7B31avRXFMEup1sTBpo5FfZ8PhH7/DZePG+3W+\nj5avwLY6OYSzfvft2hiYTx7GhcOG4pNVq7Bu2484UXQSA3r3bjfZMWTQQFw1aRz6RgOzRg7A/Ftu\nQFYbo1JTkxPx1ebvAbVnYcrBegEbdhbCFd2zedlQmUKFamcUqooPYtzokX711R9GYxW2fvcD7A47\n9Hp90M5ztkj7+wVEbp+9aTeNGBUVBaBxmFBLw4b5N9SLIpe/T6PDWSCnqTTWKXChT58+AIDExEQ4\nnU4cOnQIgCsoBSS9LZ/ZVLyxKaaOruJx9iiKc79Xp/Hii8/DZKr2eTSCvytunOt7m5CQhOrqKkmn\nG7HgK4UbXl9QZ80cfBnePPIhFP1/W1lBFEVod7kw667LQxiZtE6YqiBL8v47UNLgX62Mv72/GNvT\nE6CQNT7NV2dmQNWzB+p378XRkcPx9pfL8PAtgV1i96n77kf/VSvwQ2EhrC4XcmJi8KcHFyAmqnG6\nw9LNW1Cf1Pr3VqZUYUetGfsOHsCw84a0eezqaiOOFh5Hr6wc6PV6FFUZIVN6f6+OGypw29+ewZno\nNMiUKrjLyrD652fwv/fchgF9+56zDyqVChMvuqjdvmZmZGDqkJ5Y92sNZNpYAI3f2ThbCfQxPeDQ\nRnlO25DJcbDQ0O6xO8LtduOFl97Czn1lcLgTAffPyEwDHnnoDmRlZQblnERN2k1EzJ49W4o4KMx5\nu4FtuomKJIGapnKum3StVgun04nKyqqA3xB7u5nv0ycDFRWWDidZ2rrBvuiii8/xXqlQU1OF9PT0\n5jZfEx7+rLjRVuJl5MgLsWbN8jZiC850I6mXaiUKNl5fUGeNHjoGFlstvtq+HgZ1NeQOGXqJPXHf\nVQ9F1AosMXIlRNHW/BS9pVi5999Rb4zGKuyyWSDIYlu1CzIZ5PFxcNdbcbAu8FNeBEHALbOvwS0t\n2vT6GFRUWAAAJ01mQBPrsZ+YkIxt+/Z5TUTY7XY8/eZb2FFuRK06GlENa3F+cgwSonQQRdHre3Wq\npBTy/mOaK/nLlGpUJmTh5SWf460nHgtEVwEAD907F4M2bcbWXQdQ3+BEZnIsbp1zP77avAmCzHvi\nyOF0N/fLbm9AdHRglvp8+92P8NPeBsgVPaCQA4AGZdXAM/96B2+99qTX94koUNr9K71w4UKv7X/8\n4x8DHgyFL3+fRoerQBWYPFdCIyEhAWazOaj1N7zdzHc0ydLWDfbGjd+0+V5VVlYiKyurVVswRiO0\nlXg5fryk3c8xkEUsWfCVwhGvLygQJo+ZjMljJsNiMUOpVEGjCY+pnv64efJMfPX/2bvvACfK9A/g\n38kk2c1utmV7pSwdpImogAiIFKUIFpqInq56cDbs/q54trMgx6mgJ3rKWQFFKdKrogLSRJCmtG1Z\ndtmw2c2WJJP5/bFHWZLtyUyy+X7+0pnMzDOb7PLOk/d9nsVzUZZZs/uEprgUwzr0bvB58s1mlIaF\nwtO/5GJMNCSrFaI2rJnRNp6hlqSSy+FApCHG475//Hs+tlTqoYlLgw6AIyIa21wSuubnI9QuoCr2\nkmKcpWdRLovw9Hh/xGpHXl4uUlJSPextmhHXDcGI64bU2Hb9wCvxxXcLIYTFub0+OcaAZ16YjUMn\ni+FwCUiNNWD88H4Ydt2gZsWxY9cxiNpEt+2ni8Px7Xff49qB9c/yIGqqehMR06ZNO//fLpcLBw8e\nhCRJzb5wfLx3MnmBhPdc/f+Zmd7t5uBv6nqf4+Mj0KpVBmw2m9t0/1atMhr8s+nVqyu++MLz76HF\nYkFqaipKS0vRs2dnREX5/nMXHx9RZ0ySJHmMpaSkBEVFBW7rEbVaLc6cKUBychIcDofbz8pqtXoc\nbOr1OpSXW7z+Gbv0c5uZmVbr+5ienobFiz9GTk4ORFGEJElIS0vDzJkzm7yEIi/vWJ1JHl/c86X4\n94u8jeML7wm2e/Z0vy39Z1Df2OK5ghvw2pbVyE03QdBpEX3yNCa17oo/3Dq+wde4os9lSFq1GGc8\n7HOeLoQ+IwP9E1IU+1mfu87ADq3xcUGZWw2MBIsZ9z/zp/PLvM6x2WzYaS6CxlQzcSBoRBywOfDI\n9Vfj4627UBgWD0EUEVFSgGsyYrG8quYXHOc4RB20WpfP7zs+vgcGdFiJb49XQNRdKEwZ6cxHvrkE\nJboOEMKMEADkVQHvfLkNKSmxGDywX5OuJ8syymx2wEMJL1EfgaKiQkXe65b+u+tJMN6zJ/UmIqIv\nqlgLAP3798e7776LgQMHNuvC56ZbBYuLp5gFC96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pPkkQEbU09c55ys/Px3XXXQdRFKHT\n6XDTTTchPz9fidiIAlpjCyH6WnPrMbz22r9w8OBhHDlyBMXFxThy5AgOHjyM9957zyvx+fLnpURL\nVSJqHI4viFqGVsZIyC73hIPsdCIz1n0m5cWuufJqLPrLX9HHVYnwX/egk07AA0MG4Z1n/+yjaL3n\nL6/Pw6+VJiAqFSERJtjC07DicAne/3Sh2qERBYR6Z0QIglCjXV15eTmzfEHOm9PbSTnNrcdgNBrx\n9tvvIzc3G7t370bv3r2RmpoOo9GIiopSX4ffZGq1CCWiunF8QeecPWvBwk2LcdpZhDCEYthl1+Oy\nTpepHRY10F2jx+KHeW+i4KJaErIsIyM/F7fffke9x7dr0wZvP/t8jW2aJtaHUMq2n37CsQoDNGE1\n/2Zp9OH4du8R3DNFpcCIAki9iYgrr7wS//3vf1FWVobVq1fj0KFDuPbaa5WIjfwMp7cHPm/UY0hN\nTW/WUgylNWdJChH5DscXBADHTx3HyxtfB64yQBA1AEpw4Og7uCFnMG4ZWnenBvIP8XFxmHPX3fj3\n8qX4tfgMNIIGl8XG4oEZDyEsLEzt8Hzi1yO/QxPmuW1qcVl1xxEmVonqVmsiYv/+/ejWrRvat2+P\nlJQUHD9+HLIsY9KkSUhMTKztMGrBLu24AABOp73ejgvBzN9mjwRjx4dAbBFK1JJxfEEXW7D1Ywj9\nw2ts07c3YvVPGzHSNhLh4eG1HBmczp614KM1y1Fsr0BqeBQmjxjjFw/7rdMz8I/pD6gdhmK6dWyH\nhT9tguAhGWEyhjAJQdQAtSYiNm/ejC5duuCjjz7Cfffdh/j4eCXjIj/D6e2N4++zR4Kp40Mgtggl\nask4vqBzZFnGSWcOQuDecUHoEYZV363ELSNuVSEy//Tj3l14dv0SFGUmQzCIkB2nsfytF/H6bXej\nXeu2aocXVPr26YO2X63CMVfNmQ+uyjIMvqKTipERBY5aExHp6el44YUXIMsynnvuufPbz001+utf\n/6pIgOQfOL29cTh7xL8o2SK0IfxtpgyRkji+oIYQNNWfCaomyzLmbFyOMx3ScO6xV9DpkNspDa+t\nWIh//+lpVeMLRi8+OgMvzXsf+/NtqEQI4nVVGNwjE3dOVD55ZrFYsODjL3D81BmIogaXdU7H1Cm3\n1vgChsjf1PrpHDt2LMaOHYvPP/8cEydOVDIm8kPBOr29KQ+MnD3if/xlSYq/z5QhUgLHF3SOIAhI\nF1NwGuVu+6SfyzFy1EgVovKtQ78dxuJtm1DucqJNRCymjrgJBoOh3uN279uLo9F6j+3u9jttOHvW\nguhozzULyDdMJhNm/flxnDlzBoWFhWjTpg1CQkIUj+Ps2bN49KnXUWJPgSBUzy46nl+IA4dewWsv\nPcNlIuS36k2TcZBAQPBNb2/OAyNnj/ifixNKXbp0VS0OzpQhuoDjCwKAKf0m4vWtb0C4Mvz8A5P9\nhA1DowfAaIxQOTrv+nTNUrx9ajeqMqqXI7nsZqyb9zzemvowEhMS6jy2rLwcss7zsN2h1aCqqsrr\n8VLt8vLysHD5atgq7WiTEo9bx45W7QuFD/67+H9JiAtpKlHU42i2HuvWb8Sw669TJS6i+vh3bxzy\nK1lZ0yGKepjNBSguLobZXABR1Ks2vd2XLn5gNJlMSEpKhCRVPzDWJ1hnj/gju92OuXPnYNasl/DV\nVwsxa9ZLmDt3Dux2u+KxnJspc+k0Sa1Wi4KC6pkyRETBpmObjnhhxN/QbV8GkvYa0XpPLP6YcAcm\nj5isdmheVVpqxX8O7zifhAAAjV6HE12TMWfFp/Uef1XvPkgp9Nwqu52kQ0ICC70qZcWadbjvpXex\n5pgT3+eLWLAtH/c89TxOFxaqEs+J7DM1khDn6PQR2PXzURUiImoYLhyiBvOX6e0N1dR1+M1dWhEZ\nGYWYmFg4nc6gmD3iz/xpBgJnyhAReZYQn4AHbvmT2mHUq6TkLBZuXIEyZxWuyuyGfr2vbPCxX25c\nhZLMBFw6SV4QBPxirf8BVqPRoINLh4L8QiD5QjIjPK8Id1x+DaffK6SiogIfrvgWTmPG+fdS1IXg\ntJyONz74FC888ZDiMYli7d8r6+rYR6Q2JiKo0fy940Jz1+E354Hx3DfwRUWnkZ+fh8jISMTFxcFu\ntyMxMaVFzh7xV/5Wq4MzZYiIAtc3W9fjzf3rUNopHoIoYsmJVej141q8nvVkg8YWTkkC9J6TBRLq\nLsq5cduP+L+vPkdOWhzs+aeB73YiOiQUV2a0w4R+o9C3e68m3RM13vLVa1Aakuw2pVwQBBzMLlIl\npm6d0nA8vwiiWPNzKFUVYeiQMarERNQQTJNRi9OcZRVA8x4YZ8+eDUmyIzU1BX369EFGRgaqqqoQ\nFWXCjBkPsyChghqSUFLShTorzhrbOVOGiMi/lZZa8dYv61DWNQmCKFZvjI/Crs6heGvJggadY/SA\noQg/ftrjvi7GuFqPq6iowDNLFyKvfRo0hlCEts1A6IArUNGtPdqbEpmEUFhlZdWFz8AlnJKsSqeX\nO26/DZkppXDay85vk6qKMLhfMnr26K54PEQNxUQEtSjeWIff1AfGc0tWLr52aGgokpOTUVJiYQ0A\nhfnjDIRgqrNCRNRSLNr4Dayd4t22a3Ra7CrJbtA5EhMSMC6uPTRnrOe3ybKM+CNm3FvHt9aL1n2D\nvAz3QpaCIRRb84436NrkPSOHXgedzexxX7sUkypLZLRaLV576RlMn9YLl3cBrrpMg789MQYPzrhb\n8ViIGoNLM6hF8dY6/Kys6Zg/fx7MZvflHXVdW6wlS84aAMrzx04vgVZnhYiIAJujEoLW87/vlbLU\n4PM8dNs0dNq6CWsO74XN5UBGaCTuunU6UpJSaj2muNwGweB5XFPq8pxsJ9+Jj4/D9T1a4ZsDxdCE\nXujqElZhxtTJ41SLSxAEDLv+OnbIoIDCRAS1KN76FrwpD4zp6RmQJM8DEtYAUEdTEkpK8Pc6K+Rd\nTS2cS0TqqaqqwtmzZxEbG4sBnXph8YElkFNi3F7XWu++rS7DBwzG8AGDG/z63m3a49Nfv4Nscv/b\nkRZibNS1yTsezLoTbVatweZdB1Be5URSTBgmjZmMDu0yz79GlmUUFhbCYAhFRESkitFSS7R00df4\nceV2VJZWIikzARPvn4TWbVurHVajMRFBAe/SQb43vwVvzANjZGQU0tLSYLPZ/OYb+GDXkIQSHxLJ\nV5pbOJeIlGe32/GPz97BjvJcWMMExJULGBLfEX2LQ/FjrB2akAu/u5FHi3BHv0k+jWdg36vR96ct\n2BYl1ahNEJ5biClXj/Tptal2o0cOx+iRwz3uW7VuI75c8wNyLU5oBRc6pEXg4XsmIZ1fSJEX/PPv\ns/Hju7uhdVb/LTq9tQS/fvssnpr/BLpc1kXl6BpHkNWoqgKgsJZeyC1VfHwE79nLahvkT5t2DxYs\neE+VwX9UVAheeumVoHrwCNTPdlMfEgP1fpuD99w0c+fOgSS5J0VFUa94+9iGiI+PqP9FAYCf1ZbN\n1/f79Huz8G1bGRrdhd9bubQCE8qToYEG24uPo0KW0Fofhan9bkD3jr4f+BuNWjz+xhvYaclHuSQh\nMzwKU/oOxLVXXO3za6slUD/X3/+4Ha9+tBGukJoFSGOkU3j/9b9Cp/O8zAYI3HtuDt5z4+RkdOWq\npAAAIABJREFUZ2Pm9U9Cawl125d5Uyqe//fzzQ3PJ2obX3BGBAWsi7tjnON02rFgwXuqrcNnDYDA\nMXfuHIgi3D4/8+fP88uHRAos/tY+lojql1+Qjx3iGWh0NYtDChEGbDp5FIuynsMMrfJDZ4PBgL/9\ngUWNA8Gy9d+7JSEAoMiVgK+Wr8Rt48eqEBW1FGuXroVYHAJ4qIl6Yt8p5QNqJnbNoIDUkO4Y1csq\nuqoy2Ffz2sHMai3BgQO/1NmhxG634/XXX8bJk8ea1V2FqC7+1j6WiOq3+9d9qEjxvJ6/yOBCcXGx\nwhGR2mw2Gz77cgk+/GwhCguL6nztuk1bsH3fbx73ibpQnMqv+3ii+oSE6iHD82IGrS7w5hcEXsRE\n8F53DAoM9dVxaMwyi/nz58FiKUJcnOe+7fz8kDf4Y/tYIqpbx9btoPvhW7gy3Kc9R1YAUVH8d6Gl\n+PGnnVi0djPyLTaEhehwRYcM3Dd1MjSaC9/RLlmxEh+v2QZbaDIEjQZffv8WRvRuixl/uMPtfAcO\nHsS8Rd/BIXt+tHJJTsRGmxodpyzLsNlsMBgMtXZmo+AxduJNWPn2Wsg5NecSyLKM9n0zaznKfzER\nQQHJ3wf5LIDoHQ1NMNS2TOfSZRbnZtKYTCbk5ubCZHIfFDT188P3nC7mj+1jiahu7dpkotvqUPws\nyxCEC3OfXQ4nrjKmISQkRJW4HA4HXvnvfGw7nYsyyYEMQwQmXN4Pw/oPVCWeQLd123a8vHgDHOEJ\nQFg0LACyD5cg7/U38Pzj1WOG348fx39W74RkTD8/fdxpTMXyfQXI3LARI64bUuOcS1ZugjM0EboQ\nG6rKihFirDm+MDrzcNtN7gmMuny26Cus3/IzzlgcCAvVoEeXZDzy4D0ttuYY1c9ojMCtj43HZ88v\nhqYoBIIgwCk7EHNlGP74f39UO7xGYyKCApK/DvLtdjvmzp3TIopVKv1g7el6DUkwNGYt/rmZNKGh\noaioqIDT6Wz254edEag2/to+lohq9/zEGfjL53Ox31gBR3wEwvKs6Is4PDn1PtVimv7ay1gboYXQ\nJhkAcBbAkZ+3AkBAJSNsNhs++vprnLKcRbhOi/GDBqFzx44+u97RY8fw8bKVyLWUIUynRf9uHXDb\n2NFYtPbb6iTERTTaEPyUdxZHf/sN7du1w5JV6+EMT3Jbii+ERmPTjn1uiQhLaSUAHSIS2uJs7kGU\nl5hhjGsFyV4JufQEnn36jwgPD29w7J8v+hoLVxyFqEuCaACqAGzb78TfX/wXXvz74037gVCLcNOk\nceh1dS98/dHXqCytRJtubTBu0vg6C6H6KyYiKGD54yB/9uzZDfpm3p8p/WBd2/UmTJjSoARDY5bp\nXDyTplOnTjh06BAMBgNiYmJQVFSEVq3a4v77G/f5aehsDAo+LF5LFHhMMSbM/eNfcOzEcRw99Tt6\n3NgNSYlJqsXz6+GD2OywQdDVXE5YmRiLhbt+CJhERG5+Hh556x2YY1MgiCGABGz8ZDGyruiBiaNH\ne/16+w8exF/eX4yyiBRAEwpIwIEdx3E89984VWQFomPcjpEjErFl2w60b9cOtkoHBMHz2KK8yum2\nLSYiFPhfeano1M5wOR2wFWdDow3Bjdf2weU9ezQ4dlmWsX7LzxB1Ncc/GlGLA79X4Nix42jbtk2D\nz0ctT6vWrfHQXwJ/jMlEBAUsfxvkn4sjPj6+xvZAq5Kv9IN1bdd7++03G5RgaMwynUtn0nTr1g2V\nlZWwWCxIT2+NRx55olGxszMCNUR18Vp+DogCSdvWbdC2tfoPe9/9sgeORM81jU5VBE7bwzcXLkZB\nQkaNGQbO2CR8tG0nRg0eDKPR6NXrfbh0VXUS4iKakHBs+j0foY5yj8dIjirERFUnnVonx+HHvCJo\ntDXHIbIsIznGfWbD+BsGY/ebS+AMqZ5podFWz44IqcrF5PE3Nir2qqoqFFmqIBrc92n08fhx+04m\nIqhFYNcMCnj+0qEiO/tUrYWEAqVKfkO6kSh1Pbu9EqWlZR6PuzjBcCG5UPMbitqWWWRlTYco6mE2\nF6C4uBhnz5YgKSkVDzwws9HxszMCERH5UmJUDFzlFR73GcXAmYr962nPHSNKY5OxdO0ar1/vWKHn\n8YoUmYRwwQFZktz2maoKMHr4MADAhJtGI0HKhyzX7FBgrMzDlPGj3I7t2rkzpt86AHHIR5W1AHZr\nAZK1BXj0zhuRnJTcqNhDQkIQbvA8npTsVrTLbNWo8xH5K86IIPKS9PQMSB7+YQP8o4BmQyjdjaSu\n64WE6KHRiA2q49CYZTrenEnj70VTiYgosI0afD0+eWM7TrSq+fW47HDgqoRUlaJqPJfsueUgBA0c\nTvelDs2lEz1/1yq7JAwdcDX2Hj6Gg2V6aMJj4HI6EFWehwcnjT6/BNVgMGDWUw/gzQWf4VB2ESSX\njHYpJtx550S0Sk/3eO7rB1+LoYMG4rfffoNWK6J16zY1ip42lCAI6HVZGr7fa4fmkmRTsqkCfa+4\notHnJPJHTEQQeUlkZBTS0tJgs9n8qoBmYyj9YF3f9R588HEsXPhxvQmGpiQXvDFd3l+LphIRUcug\n1Wrx8oTb8djHC3AqJRaaMAO05kJc5dJj5v3qFdBsrA5xJuzysD3sTD7GTLvZ69frlhaPLcUuCELN\nhISxNA+3jbkdfzAa8cP27dhz8AiiwqNx86g7YTDUTPYkJibghSceatR1BUFA+/btmx3/QzP+AOvL\nb2L/kWJAFw/JXoLUODuenPmHJiU3iPwRExFEXjRz5ky89NIrflVAszGUfrCu73pxcXGNSjCosRbf\nH4umEhFRy3FVz15YnNgaKzatw+kSC/pddw26deqidliNcu+Y0XhywSewxKWef5AWrBaM7dwOJlOs\n16/30J1TcOIf/8RxmCCGhkGWZehL8vCH4QPO16Pod+WV6HfllV6/tjfodDo895eZOHXqFH7cvhOZ\nba9Cn8t7qx0WkVcJ8qWLnxRSWBg4BXa8IT4+gvccBM7ds78U0GyKxnbNaO77HGjtL2u730B+z+sT\nzL/LwSQ+PkLtELwiGN+3YLrnYLtfoOXcc15+PhYsX47cUhvCtVoMv7wXhgwY4PG13rhnSZKwdPVq\nHD6VD4Neiwk3Dm90vQYltZT3uTF4z8GhtvEFZ0QQ+UAgV8lXuhuJv3U/aapAfs+JiIh8LSU5GU/f\ne69i1xNFEeNvbFzHCiJSDhMRROSR0g/WfJAnIiIiIgoObN9JPme1luDAgV+83vqRiIiIgpPdbsfe\nX/bixMnjaodCRERNwBkR5DN2ux1z584JmLX/RERE5P/+u2oRVuTuRGGyCK3Nicy1BswcOhUd2jS/\nW0GgkWUZubk5EAQBqalsGU1EgYOJCPKZ2bNnQ5LsSEpKPL/N6awuTDhjxsMqRqYOq7UE2dmnkJ6e\nEZA1EIiIiNT25boV+FTeB/Q0IeR/204AeHb1u/jw7heD6ouOb3dsw/xv1+OwIEEA0BFa3DdoGPpf\nfoXaoRER1YuJCPKJc4UH4+Pja2zXarUwm/NhtZYEzcN4oHWFICIi8lfLfv0R6GB0217Y1YglG1dg\n4ojxKkSlvGMnjuP5b9fCmpoE4X/bDgP4+4aV+E9yCtJSUtUMj4ioXqwRQT6RnX0Koih63KfX65CT\nk6NwROqZP3/e+ZkhJpMJSUmJkKTq5IS3sR4HERG1ZEWSzeN2MSwE+WVFCkejnk82VichLnU2LQkf\nrV3ptevk5udh1gfv4y//fgdzP/kIpaVWr52biIIbZ0SQT6SnZ0CSJI/77HYH0tKCYx2j1VqCgoL8\nGstTAO/PDGmpsy64nIWIiC4Wrw2H2cN2yVaJtMgExeNRS2FVOWAId9suCAJOV5V75Robvv8er65f\nj7KEFAiCFvLZcqyfNQsv33EHOma288o1lLZr714s3/g9SivsSIwOx5Rxo5CakqJ2WERBiYkI8onI\nyCikpaXBZrNBq73wMXM6nUhMTA6ah8rs7FPQ63Ue952bGeKNlpUXz7o451w9jqlT7wq4h/mWmlgh\nIqLmGX/ZQOw//g3k5Iga25MOlGPcvaNUikp5Jn2ox+2yLCO2ln2NIUkS3lm/DrbEtPNLPwRRxOmk\ndDz95r/QqX0nuGQZPdLTcOuNo2qM9fzV4qUr8MHGfZDD4gHocaBUxraX38Vfs25Bz8u6qR0eUdDh\n0gzymZkzZ0IU9TCbC1BcXAyzuQCiqEdW1nS1Q1NMenoG7HaHx33emhlybtaFp0HATz9tw2uvvYiv\nvlqIWbNewty5c2C325t9TV9TcjkLEREFjjGDh2OaoQ9MeyyoOlUE+XAh2u+twvOjpwfEw7C3TLx2\nKMLzC9y2R+YVYMqQ4c0+//c7tiE3PLLGNlmWUbp3F/ITW+M7hOB7IRRv/p6DP738D78fW1RWVmLh\nxp/+l4SoJggCKsJT8cGSVSpGRhS8gucvNilOr9djxoyHzxeuTEtLC5hv5L0lMjIKiYnJcDrtPpsZ\nUtusi0OHDqF3796XXNf/u5YotZxFSVxiQkTkPROHjsOt0hicPHkcRmMkEhKCZ0nGOZ3atceTfa/B\n+z9swTGDHpBlZFY6kTVgCNq0at3s81dWVUHW1Kz1VZl9EoZWmdBe9O+YGBKKX8Q4LFjyJbImTmr2\ndX1l3abNsGrj4Kl62VGzFeXl5QgLC1M8rqaSZRkbN27Br7/+DoMhBDfffANiYkxqh0XUKKolIuLj\nI+p/UQsTrPccHx+BzMzgqAkBuL/PzzzzJGbPno2cnByIoghJkpCWloaZM2d6ZZlBr15d8cUXNetx\nVFZWwmAwuH07pNVqUVhYAL3ehago7z0Qe/OznZd3rM7lLOXlFtU/Tw29X7vd7tP3XknB+veLAk8w\nvm/Bds/n7jcpqZfKkSjH03t8+7jRmDz2Ruzdtw+iKKJ7t24QBMHD0Y13y5gRePu771AYdqEOhVRW\nhrDU1m6v1Wh1OHSm0OufQ2+ezxQTDkD2uE+rEZCQEAmDweC16zVVQ+65oqIC0//4LH4/poVOa4Qs\n27Bp4+t44IFRGD1mmAJRelew/f0CgvOePVEtEVFYWKrWpVURHx/Bew4Ctd3z3XfPcJsZUlJSBaDK\nC1fVIC4uscasi5KSEsTExHh8tSiK2Lv3ILp06eqFa3v/fTYaY+tczhIWFqPq56ox9zt37hxIkr1G\nG1ubzYaXXnrFr2elXIq/y8GhpQyMgvF9C6Z7Drb7Beq/5/TUTABAUVGZV697S8+emH/gMJzR9X/T\nXlnp8Or74u33+YreVyL68/UohfsXGZlJkSgrc6KsTN3PVUPvedast3HiRAR02ur5HYKggSQl4803\nl+Gy7t0RHu5exNRf8fc5ONQ2vmCNCCKFREZGoUuXrj6Zmp+VNb1GPY7KyipYLBaPr/X3riUXlrM4\na2xv6nIWtVqa1la7Q6vVoqAgny1WiYjIr00ZPRYvDLkW/R3l6Fpegp4GPVweHtZlyYluye6tRP2J\nXq/H7cP7QbRd6Lkiyy4Yy7ORdWvjipyWllrx3oef4rU33sNni5agqsobXyo13MGD+dBo3BeZOByJ\nWLrUe61biXyNNSJIUVwr7xue6nF89NEHPq1N4UtZWdMxf/48mM3uXTMaSu3OG0p1TCEiCmYrv9+A\n5Qd/gFkqQ6QmBAMSOuKeMZO9tkQh2A3oeyUG9L0SQHVdgsdnvYYfK8qhMVTXU3A5HehgKcBd996p\nYpQNM2bkcHRo2wZL1mxEaYUDiTHhuH38Q4iLi23wOXbs3IVZ736NCm0KNBoR0q+5WLXlBbzw5L3I\nyEj3YfQX2Kskj9s1Gi1stkpFYiDyBiYiSBFqPxQGi+pZF9UPt954mFeLNwqd1tXSVIllEb7qmMJk\nHhFRta+3rMLcoh8gdY0CYEAJgE/KjqPo03l4esoMtcNrcQRBwKuPPoYvVq7Ajt+PwynL6JaShKn3\n/wEhISFqh9cgnTp2wDMdOzTpWFmW8c5Hy1GlTz8/pVzUhqAE6XjjvYWY9dxj3gu0Dmnp0Th+zH27\n01mIa69tWscUSZJw+PBhREQYkZ6e0cwIiRqGiQhShNoPhcGoJXQtuTix0hj+0HnD2x1TmMwjIrpA\nlmV8dfgHSJfV/FuqMYZiS85J3FNUhPi4OJWia7k0Gg1uGzUGt6kdiAp+2rkT5jIDdB5qWh7NKUFZ\nWSmMRt/X2pkwYTheffVLuKQL9ackqRI9e0ajQ4f2jT7fksXLsHLJjyjOAwRRQnr7MNz/8CR06tzR\nm2ETuWGNCPI5rpVXly9rU/irhiyLUMKltTvM5gKIor5Js1IuTuaZTCYkJSVCkqqTE0REwcZmK0Ou\nWOFxX2VmLLbs3KpwRNTSlVhLIYieZ35ILg2qquyKxNGzZ3c888wEdOhoR2TkGSQklmDU6FQ8838P\nNfpcmzZ8i8Xv/4TKM7EIC4mFQZuAouNGvPK391FR4fn3i8hbOCOCfI5r5UlpvloW0VjempXiDzM8\niIj8SUhIKAxOATZPO0tsSEnx7+KJFHgG9Lsa7y3agkqEue1Li9PDZKq/u4i3dOvWBd26dWn2edat\n/AGiy338UFkcgy8XLcXt0yY2+xpEteGMiACiVvX/5vKXh0IKHt7uvOGNeJozK8VfZngQUcsiyzK2\nbNuEOV/OxhtL5mDH3u1qh9RgOp0O3fVJkF2y276MbAlX975ShaioJTMYDBh5TVfIVWdrbBftRRg/\nsn9AFkgtKfY860HU6HDafNbjPiJv4YyIABDoa8O9vVaeqCGULtbpyyKSTOYRkbe5XC68+NHzyO1o\nRmiv6kXvB3IOYetn3+GRiY8GxEPV07fei0c/fBWH0wVoEiLhKqtE0mErnhx+V0DET4HnzttvQ1LC\nBmz4fi+sNjviog24acQIXNGnt9ev9eOPO7Bhw3ZUVjmRnhaLKVPGeb0GRZTJgJJc9+2Sy4GEpBiv\nXovoUkxEBICWUOgxkDs4UGBSqlinEolCJvOIyNuWbliC/B6FCI28UHnPkBaGw+Jv2PrTt7im77Uq\nRtcwRmME3pnxHL7f9SN+OXUUyZGxGHX/CLeaVETeNGLYdRgx7DqfXmP+ex9j9eoT0GpjAOhx+PBZ\nbN/xD/zjpYeRmJjgtesMH9Uf7/y6DqIUWWN7qMmCWyaM9dp1iDzhX2o/11LWhreEDg4UmJraeaOh\nlEoUMplHRN603/Ir9JnuyVJDchi2794eEIkIoLql5IA+/TCgTz+1QyHyCrM5H2vXHIZWe6HOiUYj\noqwsBfPnf44///lBr13r2sHX4EzRWaxc8gOK8wCNVkJ6hzDc99DdCA0N9dp1iDxhIsLPtbRCj75+\nKCRSkpKJQibziMibXJBq3ye4FIyEiC624psNEDSJbtsFQcDvxwq9fr3xt47G2PE34OiRIzBGGJGW\nlu71axB5wmKVfo5rw4n8lxpFJIOxHSsReV+6Ph0up3vCwV5mR/vIdipEREQAAPf6qxd21bGvOURR\nRKfOnZmEIEUxEeHn/K36PxFdwEQhEQWqicMmQ9zsgku6kIyQ7BIitodi9OCbVIyMKLjdcMMQuFwF\nbttlWUZm23gVIiLyDS7NCABcG05K82UHiJaERSSJKFCFhYXhhSn/wCdrP8JJ+0looEGmIROTp93O\nYo/kdQcPH8bna9bBbK1AZKgeI67sjeuuuUbtsPxSSkoKhl7XDuvW50CrjQYAyLIL4WH5uOeeB1SO\njsh7+C9NAODacFJKoLeKVQMThUQUqMLDw3HvuPvVDoNauB937sRLX65GeWQSIIYCDmDvmu04lWfG\nXRNuVTs8v3T//dPQtev32Lx5NyoqHEhPN2Hy5KmIiopWOzQir2EiIoCw0CP5WktoFas0JgqJiIhq\nt2DV+uokxEVc4dH4etcB3DbqBoSHh6sUmX+75pr+uOaa/mqHQeQzTEQQEYCW0ypWLS0hUcglOURE\n5E3l5eU4ZrEB8XFu+6zGBKzdvAnjbhylQmSkBFmWsXblWuzeuh8A0Kt/Nwy/cRgEQVA5MvIHTEQQ\nEYCW1yqWGo5LcoiIyBdEUYRWEDw2i5WdDoQbwhSPiZThcrnw7GMv4ujGYuhgAADsW7UGP2zYjmdf\n/zM0GvZMCHb8BBARAHaACGYXL8kxmUxISkqEJFUnJ4iIiJoqJCQEneI9f4mRaLdgyMCBCkdESvnm\n65U4usFyPgkBADqE4uiGs1i+ZIWKkZG/YCKCiACwVWywOrck59Iq+VqtFgUF1UtyiIiImurBybcg\nznIKLmf1lx2yLCO0OAdZNwxhh5YWbPfW/dAJoW7bdZpQ7P3hVxUiIn/DRAQRnZeVNR2iqIfZXIDi\n4mKYzQUQRT07QLRgDVmSQ0RE1FStM1rhg2efwpR2MbgmwolRCSLef+x+DOVsiBZNluVa97kkl4KR\nkL9iGpIoCDS0CCE7QAQfLskhIqLGslpL8OXq1bA7nBg5cCAy0tPrfH1YWBjumTRRoejIH3TunYkj\nG3ZAq6lZa8rpcqBj77YqRUX+hIkIohasqUUIW0IHCGqYC0ty7DWmyHJJDhERebJoxQos+O4n2Ewp\ngKDB4nkfYljrRDxx371qh6Yal8uFyspKGAwGdoT4n/ETx2H75l0o2OmEKFSPLyTZicQ+Gtw6+WaV\noyN/wEQEUQt2cRHCc5zO6uTEjBkPqxiZetii0l1W1nTMnz8PZrN7woqIiOicEydP4L3vd8MRl45z\nj9uSKRkrzWVot3IVxt8wUtX4lOZ0OvHWvA+wa98p2MplxETrMHhAV9w++Ra1Q1OdTqfDK++8gM8X\nLMLBXb8DMtCxdxtMunMCdDrPS0IpuDARQdRCnStCeHESAqguQmg2VxchDKYHcbaorB2X5BARUUMs\nWrse9phkXPqdvxBmxLcHDgVdIuKlV+diz2EBopgKTShQUgl8ueo4JNciTLv9NrXDU51Op8PUe6YA\n96gdCfkjFqskaqFYhLAmtqisX/WSnK5MQhARkUcVDmetSw9sdqfH7S2VucCMfQctEMWaX2aIughs\n/u5XuFwsyEhUFyYiiFooFiG8gC0qiYiImq9DajIke6XHfRkxRoWjUdfOXXsgCSaP+4qtLlgsFoUj\nIgosTEQQtVAXihDW/IYiGIsQcnYIERFR891yww1oXX7arTVjlCUXd4y+UaWo1NG2TWvIktXjPkOI\nCxEREcoGRBRgmIggasGysqZDFPUwmwtQXFwMs7kAoqgPuiKEnB1CRETUfDqdDm88MRODwuyIs+Qi\n+kw2+oo2vHLXZLRKz1A7PEV16dwZGUmy23aXS8JlnRKDvv4UUX1YrJKoBWMRwmpsUUlEROQd0VHR\nePZPM9QOwy88/dg9ePHV+cgp1EHURcNlL0SntqF49OEH1Q6NyO8xEUEUBKqLEAb3wzZbVBIREZE3\nJScn461//hU7d+3G0aO/44orhqFdZqbaYREFBCYiiCgocHYIERER+UKfy3ujz+W91Q6DKKAwEUFE\nQYWzQ4iIiIiI1MVilURERERERESkGCYiiIiIiIiIiEgxTEQQERERERERkWKYiCAiIiIiIiIixTAR\nQURERERERESKYSKCiIiIiIiIiBTDRAQRERERERERKUardgBERLWxWkuQnX0K6ekZiIyMUjscIiIi\nCmDHTxzH8tWbIUkuDLiyJ67oc7naIREFLSYiiMjv2O12zJ8/DwUF+dDrdbDbHUhMTEZW1nTo9Xq1\nwyMiIqIA8+4Hn2DFlt8ghCZCEARs3LkWvdttwbPPPAJBENQOjyjocGkGEfmd+fPnQZLsSEpKhMlk\nQlJSIiSpOjlBRERE1Bi/7D+AFVuOQWNIOp90EENN2HNcxOeLv1I5OqLgxEQEEfkVq7UEBQX50Gpr\nTtjSarUoKMiH1VpS7/EHDvxS7+uIiIgoOKze8D00hgS37RpdKHbtP17nsbIs49vvtuLLJUtRXHzG\nVyESBR0uzSAiv5KdfQp6vc7jPr1eh5ycHHTp4l4vgss5iIiIyBO7wwXA8/ILu0Oq9bg9e/fhrXe+\nQFFJOERtGD5f8i/075uOhx64h8s5iJqJMyKIyK+kp2fAbnd43Ge3O5CWluZxH5dzEBERkSddO2ZA\ncpS7bZdlGa2Soz0eU1VVhdlvLkJJRTJ0+khoNFrIYjK2bLfi84VczkHUXExEEJFfiYyMQmJiMpxO\nZ43tTqcTiYnJHrtnNHc5BxEREbVco28YjoyoErhcF2Y/yLKMSCEXd0we7/GYJV9/g7KqeLftojYM\n328/5LNYiYIFExFE5HeysqZDFPUwmwtQXFwMs7kAoqhHVtZ0j69vyHIONbFuBRERkXpEUcTrLz6F\noT1CkBx+BvGhhbiqvYzXnp2B+Lg4t9fLsoyiIgtE0fPSzrKyKl+HXCu73Y5/vjoPd9/2JCaPegQz\n7/87NqzbrFo8RE3FGhFE5Hf0ej1mzHgYVmsJcnJykJaW5nEmBFD9kG+z2VBaWgaTyeS2v67lHL7G\nuhVERET+ITQ0FA9Ov7vO11itJfjXWwvw65ECWEvLUWq1IcyYCGNkSo3XxccZfRlqnZ59+lWc3C1A\nozFBBFB4FHh/1loIAjBk6CDV4iJqLCYiiMhvRUZGeSxMCbg/5DudDuzbtw9dunQ5v0SjruUcSri4\nbsU5Tmd13DNmPKxKTEREROROlmU8+efXcbokEYKQBkMEYIgASiwnYSs1IzwiqfqFUjFGjxysSoy/\n7NuPYz/boNPUrGshOqOwcsm3TERQQGEigogC0qUP+SaTCU6nE7t370a7du1qzD5Qw7m6FRcnIYDq\nuhVmc3XdCrUSJERERFTTmrUbkX8mAlpdzZXrUTGtUJS7DaE6J5ISQzH2xv4YOLCfKjH+tG0PdLLn\n4poFOVz+SYGFiQgiCjh1PeSnp2dg2LDR6NSpk6oP+k1tQ0pERETKO/LbKWh1npdctM1sizdmPQqj\nUb0lGQAQn2CCQzoGnRjqts9g5JJPCiwsVklEAaeuh/yQED2MRqPqsw2a2oaUiIiIlBefdWatAAAK\noElEQVRhDIHL5fS4zxiuUz0JAQAjRw2HMcnmtl1yOdD9irYqRETUdExEEFHACYSH/Ka0ISUiIiJ1\n3HrzGIQIZrftTmc5rurTUYWI3Gm1Wsx4fApC4s/AKVUAABxCMdr1FXH/A3epHB1R4wiyLMtqB0FE\n1Fgvv/wybDbb+cKUQPVDfnh4OJ566ikVI7vAbrdj9uzZyMnJgSiKkCQJaWlpmDlzJrtmEBER+ZlN\nm7/H7De/RHFpFERtKDTyaQzql4G//flBCIKgdnjnSZKEZV+vhDm/CNcOuRpdunRSOySiRlMtEVFY\nWKrGZVUTHx/Bew4CvGflqNUasyn325A2pP6Mn+vgEB8foXYIXhGM71sw3XOw3S/Ae1aa0+nE6jXr\nYTlbguuHXoukxCRFrsv3OTgE6z17wmKVRBSQ9Ho9Zsx4OCAe8utqQ0pERET+Q6vVYtSNI9QOg6jF\nYyKCiAIaH/KJiIiIiAILi1USERERERERkWKYiCAiIiIiIiIixTARQURERERERESKYSKCiIiIiIiI\niBTDRAQRERERERERKYaJCCIiIiIiIiJSDBMRRERERERERKQYJiKIiIiIiIiISDFMRBARERERERGR\nYrRqB0BEDWe1liA7+xTS0zMQGRmldjhEREQUoGRZxppNm7B1369wuoDO6YmYNHYs9Hq92qERURBg\nIoIoANjtdsyfPw8FBfnQ63Ww2x1ITExGVtZ0DhiIiIio0Z5/Yy42n7ZDExYJANhxoBDf7X0Jb/75\nSRgMBpWjI6KWjksziALA/PnzIEl2JCUlwmQyISkpEZJUnZwgIiIiaowfduzAlvzy80kIANDo9Dge\nkoz3Fi5SMTIiChZMRBD5Oau1BAUF+dBqa05g0mq1KCjIh9VaolJkREREFIi27P4ZQoTJbbsgivg1\n57QKERFRsGEigsjPZWefgl6v87hPr9chJydH4YiIiIgooMlyHbtq30dE5C1MRBD5ufT0DNjtDo/7\n7HYH0tLSFI6IiIiIAtk1vbpDtp112y67JHRJTVAhIiIKNkxEEPm5yMgoJCYmw+l01tjudDqRmJjM\n7hlERETUKP2vvBL9Y7WQKsrOb3M5Hcgoz8XdE25VMTIiChZMRBAFgKys6RBFPczmAhQXF8NsLoAo\n6pGVNV3t0IiIiCjACIKAv898CI9c0xl9DBXoobdhcocYvP23ZxAeHq52eEQUBNi+kygA6PV6zJjx\nMKzWEuTk5CAtLY0zIYiIiKjJBEHAmOHDMWb4cLVDIaIgxEQEUQCJjIxCly5MQBARERERUeDi0gwi\nIiIiIiIiUgwTEURERERERESkGCYiiIiIiIiIiEgxTEQQERERERERkWKYiCAiIiIiIiIixTARQURE\nRERERESKYSKCiIiIiIiIiBTDRAQRERERERERKYaJCCIiIiIiIiJSDBMRRERERERERKQYJiKIiIiI\niIiISDFMRBARERERERGRYpiIICIiIiIiIiLFMBFBRERERERERIphIoKIiIiIiIiIFMNEBBERERER\nEREphokIIiIiIiIiIlKMVu0AiMi/Wa0lyM4+hfT0DERGRqkdDhEREQUwp9OJpStW4bcT+TAa9Lhl\n3A1ITEhQOywiUhgTEUTkkd1ux/z581BQkA+9Xge73YHExGRkZU2HXq9XOzwiIiIKMBaLBY//dTYK\nKmMhag2Q5Qps3PEWsiYMwohhQ9QOj4gUxKUZROTR/PnzIEl2JCUlwmQyISkpEZJUnZwgIiIiaqw3\n//0RCh2pELUGAIAgaCDpU/Hh4k2oqKhQOToiUhITEUTkxmotQUFBPrTampOmtFotCgryYbWWqBQZ\nERERBaqDxwshCILbdpuQiOUr16gQERGphYkIInKTnX0Ker3O4z69XoecnByFIyIiIqJA53C6PG7X\naLQoL+eMCKJgwkQEEblJT8+A3e7wuM9udyAtLU3hiIiIiCjQtUmJ8bhdsJsx/PpBygZDRKpiIoKI\n3ERGRiExMRlOp7PGdqfTicTEZHbPICIiokabfMsw6CVzjW0uhw0De6ciOSlZpaiISA1MRBCRR1lZ\n0yGKepjNBSguLobZXABR1CMra7raoREREVEA6tWjO56bOQm9WtmRHG5B29hSTBvVHjP/lKV2aESk\nMLbvJCKP9Ho9Zsx4GFZrCXJycpCWlsaZEERERNQsnTp1wN+e6qB2GESkMiYiiKhOkZFR6NKFCQgi\nIiIiIvIOLs0gIiIiIiIiIsUwEUFEREREREREimEigoiIiIiIiIgUw0QEERERERERESmGiQgiIiIi\nIiIiUgwTEURERERERESkGEGWZVntIIiIiIiIiIgoOHBGBBEREREREREphokIIiIiIiIiIlIMExFE\nREREREREpBgmIoiIiIiIiIhIMUxEEBEREREREZFimIggIiIiIiIiIsUwEUFEREREREREimEigoiI\niIiIiIgUw0QEERERERERESmGiQgiIiIiIiIiUgwTEURERERERESkGCYiiILI0qVL8dZbb2H//v2N\nPnbz5s04deqUD6KqtmfPHixdutRn5yciIiLv49iCiJqCiQiiIPLzzz9j+vTp6NatW6OPPXnyJGRZ\n9npMTqcT69evx+rVq71+biIiIvItji2IqCm0agdARMr4/PPPIcsy5s+fj6lTp+Lo0aPYvn07ZFlG\ncnIybrzxRoiiiB07dmDfvn1wOBwQBAG33HILcnNzkZeXh2XLlmHChAlYtWoVBg0ahFatWuHs2bNY\nsGABHnroISxduhTl5eWwWCwYOnQojEYj1qxZA4fDgbCwMIwaNQrR0dE14jp58iQA4Prrr0dubq4a\nPxoiIiJqAo4tiKipOCOCKEhMnDgRgiDgvvvug81mw+7du3H33XfjvvvuQ3h4OH744QdUVVXh8OHD\nuPPOO/HHP/4RHTt2xE8//YQePXogJSUFY8aMQUJCQp3XCQsLw/Tp05GZmYlly5bh5ptvxr333our\nr74ay5cvd3t9ZmYmhg4dCq2WeVEiIqJAwrEFETUVfzuJgtDx48dRXFyM9957DwAgSRKSk5MREhKC\n8ePHY//+/Thz5gx+++03JCUlNercqampAIAzZ87AYrHgs88+O7/Pbrd77yaIiIjIb3BsQUSNwUQE\nURCSZRldu3bFiBEjAAAOhwMulwtWqxUffvgh+vbti/bt28NoNMJsNtd6DgBwuVw1tut0uvP7Y2Ji\ncN99953//7Kysv9v725VVQ2iMAC//oCwq4KgxeglCApewxe8NS/ACxCDwUswiEYNVotmsSjucDgm\ny9ngd8J+nj4MK83iZdbMp0oCAP4jvQXwL4xmwC/y94Dv9Xo5HA65Xq95Pp9ZLpdZr9c5nU5pNpsZ\nDAbpdDo5Ho+vNdVq9dUYfH195XK5JEn2+/3bvVqtVm632+s17O12m/l8/ukSAYAS6S2An3AjAn6R\nSqWSJGm32xmPx5nNZq8HpUajUR6PRzabTabTaer1errdbs7nc5I/85bL5TJFUWQ4HGaxWGS326Xf\n77/dq1arZTKZZLVa5X6/p9FopCiK0moFAD5PbwH8ROX5iT9zAAAAAN4wmgEAAACURhABAAAAlEYQ\nAQAAAJRGEAEAAACURhABAAAAlEYQAQAAAJRGEAEAAACURhABAAAAlOYbHhkKGzXeYrYAAAAASUVO\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115e829b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the model fit\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
+    "\n",
+    "ax[0].scatter(X2[:, 0], X2[:, 1], c='gray', s=50)\n",
+    "ax[0].axis([-4, 4, -3, 3])\n",
+    "\n",
+    "ax[1].scatter(X2[:, 0], X2[:, 1], c=y2, s=50,\n",
+    "              cmap='viridis', norm=pts.norm)\n",
+    "ax[1].axis([-4, 4, -3, 3])\n",
+    "\n",
+    "# format plots\n",
+    "format_plot(ax[0], 'Unknown Data')\n",
+    "format_plot(ax[1], 'Predicted Labels')\n",
+    "\n",
+    "fig.savefig('figures/05.01-regression-4.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Clustering Example Figures\n",
+    "\n",
+    "[Figure context](#Clustering:-Inferring-Labels-on-Unlabeled-Data)\n",
+    "\n",
+    "The following code generates the figures from the clustering section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "# create 50 separable points\n",
+    "X, y = make_blobs(n_samples=100, centers=4,\n",
+    "                  random_state=42, cluster_std=1.5)\n",
+    "\n",
+    "# Fit the K Means model\n",
+    "model = KMeans(4, random_state=0)\n",
+    "y = model.fit_predict(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Clustering Example Figure 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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EUI3PCACqQxDDV6ZPn6FYLFbwvlgspnh8uu0xmOQFwEsYI4avRCIRtbV1TBgj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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11a705208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the input data\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "ax.scatter(X[:, 0], X[:, 1], s=50, color='gray')\n",
+    "\n",
+    "# format the plot\n",
+    "format_plot(ax, 'Input Data')\n",
+    "\n",
+    "fig.savefig('figures/05.01-clustering-1.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Clustering Example Figure 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KTWvFv4sXj//pqTsac9syqhWv/fu1ig8UQlQ76axVx7WK6kLs5J/YeuyPvPOR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\nv9AAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b365198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the data with cluster labels\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "ax.scatter(X[:, 0], X[:, 1], s=50, c=y, cmap='viridis')\n",
+    "\n",
+    "# format the plot\n",
+    "format_plot(ax, 'Learned Cluster Labels')\n",
+    "\n",
+    "fig.savefig('figures/05.01-clustering-2.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Dimensionality Reduction Example Figures\n",
+    "\n",
+    "[Figure context](05.01-What-Is-Machine-Learning.ipynb#Dimensionality-Reduction:-Inferring-Structure-of-Unlabeled-Data)\n",
+    "\n",
+    "The following code generates the figures from the dimensionality reduction section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Dimensionality Reduction Example Figure 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Xi8V8c1IBtSTfTTy1h7T01RKkPT33uL6vsObHpQ9T0UZcBTJ7+EnS66//X8Xj\nX62K5aeTCqglVuNOJSkSmbZtd2xtXWv5YGyaJtPOesCPSx+mIoirRGYPv82bt/j2pAJqSWvrWnV0\nbNCZM6fTXh8bO6t43Ewu2JDZGcuPE0fUM78NWUpFEFcpP59UQK1paWnLCuLUddoTUhda8XsprF74\ndchSKoK4ihVyUvlpyS+g2liVboPBYFqJWMou8fLA7L7Ue93o6LDtkCU/3RMJ4irn5GTy8/g5oBrY\nlW4l5S3x+rUUVotS73WGEZBkpiyO81XTQa6A9gJBXKUikSkdPXpYFy9ekGnGbU8mu6EXTCoAFMau\ndEuJ1x8y73WpNRUJ0Wg0LYQlf9wTCeIq1N9/SENDJ9J6TaeeTJKSN4ZcQy+4aQCFsSrdUuL1h1yz\nmiWEQiGZpnx3TySIq0ziqS81hBOi0aiOHj2sS5dm0iacp+cmgFpn1Y5vGIYMI6B4PJZsOujs7NbI\nyKCv7okEcZXJ9dQXCAQ0M3MxGdKxWFRjY+e0fv0GjY2do+cmUAZ+6uSDr9i141s1HfitNztBXGXs\nJhcIBAIKh1t04cJ02uvRaFStrW3q7b2PmwdQAqf9MuAdu3b8zHue33qzE8RVJvOpLxgMavXqNdq5\n82FJUl/fq5ZVLrRjAcXL1y+Da8s/nN7r/HRPJIh9KlH9tXlzp65cuZH25Jbrac5vVS5AtZqYmNDQ\n0KgaG5ty9sug4yNKRRD7UOpYuPffP6LFYXBm1ly2Vhe/36pcgGrU339IIyODikYXx6NaDYWRvO/k\ng9rA6ks+kz0WzpSUPiA933KHiXlup6cnWRoRKFDiGoxG7cejSos9cqlxQjlQIvaZfGPhnFSFMZMW\nUDwn41ElfVlTBb+rhl7uBLHP2PWKTshXFRaJTGlw8FPbFWEA5JbvGvyKybXlI1aBWy2FEoLYZzJ7\nRRuGkWwjdtL56ujRw1lVaXQoAZxLXIOJNuJgMKhYLK5EE1Equ2urGkphtcQqcDs7u303laUdgtiH\nUjtcWfWathOJTGlm5mLW64FAgA4lqFuJUGxsbNL8/C1H4bhr1yPaseM+DQ2NJlfxSa1pSrCqoaqW\nUlitsJtP349TWdohiH0q0Ss6HG5WU9Oc5YmT+dQ9PT1pOcQiHG7x3YkHVFrmBBwJTsOxvb1dTU0r\nJC1ej52d3Tp69HBy9jqrGioWWXGf3Xz6kqpmel+CuErZVcVYrZmamOwDqBdWE3AkFBuOra1r9cwz\nz+esdmZraPzAAAARGUlEQVSRlcqyOvZWbfqhUEhdXd0yjPzLVPoBQVyFcj11M6EH6l2uhVESSgnH\nzDH8qeFgFwp+LIVVG7sqf7s5phN/p2qYV4EgrkK5nrqZ0AO5JGaLcvvccLPzktPl8MoRjlbhwMNw\n+eWr8s913/PTVJZ2COIqlO+pO9cTu99PSFRO6mxRbnYiytV5qRLnppMhgOUIR7tw2LNnHw/DZeak\nyr8aAtcOQVyFclXFZKIHJ6TcJQpJFQuNXNsdHR0u27kZiUzp5MlhSVJXV7flwiibNvU47jWd+d1W\nxydXOPT2bq/aUPBCvgeyWq/yJ4irlJMqaHpwIsEuNI4ePaxLl2Yq9qBmt93UEJZKOzf7+w9pcPD4\nl9PBSoODx7Vlyz3au3dfyQ8YuR5kaz0c3GLX8TT1b1dI4aMaEcRVLF9VDD04kWAVGsFgMG1oTywW\n1fDwiZIe1DJLNnZhdePGDdtzU3JeQo9EpjQ0dCIZwotMDQ19qs7O7uSc61L2mrT5jI+P53xYqPVw\ncINVYWFw8HhyzHbqw08t938hiGsYT+xIyJwtKhQKadWqsC5cmE57XywW09Gjh/XMM89bfk+uKkS7\n0mNmWC1fvkLnzp3J+u5QKKRIZFoffPDel7PKBbRhwzf0+ON7bH8vu7Hz8Xi85NL+2NhY3gfZWg4H\nN1gVFqwWukl9+KnFYxz8xS9+8QsvNnzz5rwXm606S5c2FX2sli1r1s2b1zU7OyvTjCef2Ht6tpZ5\nL71XynGqFx0d39CWLZv0B3+wTPfd902tX79BJ08OZ5QmpS++uKn29vVatqw57fX+/kM6cuSwxsbO\naXR0RDdvXldHxzckLQb0u+++kwxF04zr8uVZrVt3u3p6tqq9/XbddttK3XHHnTp16mRWeAaDQXV0\n3KGxsXMpN2ZTV69e1uzsjO68s8vydzIMw/J3WPw9vrDcn8zfy86yZX+g48fTZ9MKhUK6775vpn3H\nsmXNamtb5/h7a00p155hGBodHbFd4UpafKi67baVNVGAWLq0yfJ1lkGscbt2PaK9e/fpgQce0p49\n++ioVefa29uTHYkWZ25bk/WeeDyerM5NsOtvkFhm8+jRw1nhmmgLHhj4UJLU27td8/O3LHszb9q0\nRS0tbZY/O3v2jO1ynq2ta7V58xYZhpF8zTAMtbS0We5P5u+VS3t7u7q7exQMLlYcUvVcfomamsQx\nDgaDkoy099RDLR5V03WgVqtzULqdOx/Wa6/9a1apL/PGl6u/gSRdvHjB8vsz2/qsZn9LzIIkSYYR\nyCodmWY8Z7+GRPXw6OiwTFPJ7+rre7XkZhmqnkuXr0d05jFO7chXLw8/BDFQx1pb12rlypW6fHk2\n+dry5Suybny5+htMT0/aVi1aLceZq4PThg3f0Nmzp9O+w0mAWj1slqsjFQ+yxXM6fDL1GFfLbFjl\nRBADdSwSmdLnn3+e9tq1a58rEpnKmp2ou7snWcINBAJpweZk/V4ns789/vgevfXWAZ09+5lM09nS\nn3YozbrHqtRbyvDJenv4IYiRFzNz1a5Ch7h9tT72V+14VsN41q/foPPnz9lWDee60T722J6ynXP1\ndkP3wsGDB3Xs2EBWqZfhk84RxMiJmblqm9MhbpkLKcTjsbxz/aaeO4WWbAnQ6hCJTGlgYMCy1Mvw\nSec8C+JwuD67+hfDq2M1Pj6ukZH0qqWTJ4e0Y8d9am9v92SfcqnXc2piYkLnz59XR0eHo79L6nEK\nh7s0Pt6rgYEBRaNRNTQ0aNu2bbr77vThQqOjlyxLN3Nzs8n3hsNdaZ975pmnNTFxn8bGxrR+/Xpf\nnjP51Os55dTo6KXk2r8JifPiwQcfdHRuwcMgnpmZ82rTVSUcbvbsWA0Pn8q6yBYWFjQ0NJpcMN0v\nvDxOXiq0xsLqOG3f/pBuv31jWmk28z3Ll6+2LN00N6/KedybmlborrvullR913y9nlOFWL58tUKh\nUNp9IvW8cHJu1RO7BzvGEcNWomopFVVL/pFvbG8hWlvX5lyoIHO8Z70MK0Fui+dNb87zIt+5BdqI\n65aTzjDMpetvbneGoRcyrDzxxBNZpV4UhiCuQ4VUZ3Lz9S8vOsPQiQpWOC9KQ9V0nSmmOpOqJX+i\nuhioDZSI6wxj+2oLNRbw2sTEhIaGRjn/SkAQ1xnG9tUeqgXhlf7+Q8mlNZlnoHhUTdcZqjMBFGNx\n8o4Pk81YiWauxNClUnrt1ztKxHWI6kwATkUiUzp69LAuXryQtpJWc/NymrnKhCCuU1RnAsinv/+Q\nhoZOpK3tnCj57ty5m2auMqFqGgCQJXN+8VTRaFTz87e+bOYKSpKCwSDNXEUiiAEAWaxGWCSklnzN\nxeW4FI+bru1brSGIAQBZrKa4lZRci1rSlyXmuCTJNON01ioSQQwAyJI5wiIYDKqlpU1PP/3DvOsN\nozB01gIAWMo1woI5CcqHIAYA2LIbYZEoMScm9GBOguIRxKg4Jys9AfA3q+t4165HtGPHfUxxWSKC\nGBVV6ML1ACqrmAfjXNdxe3u7mppWVHKXax5BjIqxW+mps7ObJ2fAA8U8GHMdVx69plEx9KoE/KOY\nJVAlrmM3EMSoGKtxiPSqBLxRbKByHVceQYyKYaUnwF2ZKySlKjZQuY4rjzZiVBQrPQHuyNf+mwjU\nxHsKCVSu48oiiFFxrPQEVJbTDlWlBCrXceUQxHBNYthEY2OT5udv8WQNlEmu9t/Ma4xA9R+CGK5I\nrTZLYFwxUB5MN1nd6KyFisusNktwOnwCQG50qKpulIhRcbnWNY1Gozp5cpgbBlAiOlRVL4IYFWdV\nbZZqePiEDENpVdSlzk/N/NaoR7T/VieCGBWXOWwiUzweS+vhWer81MxvDaCaEMRwRWq1WSQyrbNn\nT6f9PHWGn1LmtbUbxrFqVZie2gB8iSCGaxLVZpHIlMbGzln28CxkGIYVu8/39x+SaZqUkAH4Dr2m\n4apE221HxwbLHp6lzmtr9XlJMk1TEj21UV65ppQEnKJEDNdktt2uX79Bra1tadXFpUzDZ/X5QCCg\neDye9h6nJWw6fCEX+iKgXAhiuMKq7XZ8/Jx6e+/LCrlSh2Gkfv78+bOamppI+7mTEjY3WeTCGr0o\nJ6qm4YpCl2BrbV2r3t7tRd/UEtXcFy5Esn62WBK3/95i121F/ajUGr1UddcnSsRwhRdT8NlNJNLS\n0lbw5wrpMIbaV4nzmVqY+kWJGK7wYgq+Yjt+sRA68in3+UwtTH2jRAzXuD0FXzEdvxIdtNav35Ac\nYsW8vbBSzvOZWpj6RhDDVW5PwVfIzTKzarCjY4NaWtroNQ1b5TqfWT2pvlE1jZrnpOOXVdXg2Ng5\nQhglc9IBK1F7YxiLt+RAIEAtTB2hRAyIqkHkV8y48kI7YBmGocW5Z4zy7DSqAkEMyL9Vg0wq4g/F\n9GguZKxx4r3xeExS9kIoqG2eBXE43OzVpqsOx8qZUo5TONyl8fFeDQwMKBqNqqGhQdu2bdPdd3c5\n+vzExITOnz+vjo4Otbe3F70fqQ4ePJjcn1AopN7eXj3xxBMlfy/nk3PhcLPGx8c1MpIeqCdPDmnH\njvty/q1HRy9Z1rLMzc1mnVeFvNePOKdK41kQz8zMebXpqhION3OsHCjHcdq+/SHdfvvGtBJoru9M\nlFYjkelkD+tyjf+MRKZ07NhA8uYcjUY1MDCg22/fWFIJifPJuXC4WZ9+elIfffS+otH0kFxYWNDQ\n0KiamlbYfn5hIVHVbCZfC4VCam5elfU3WL58tWWNjNV7/YZzyjm7BxaqpoEU+XrBJsL3woVpnT9/\nLqsUU66pDp20WddKtbXXv4fd9g8ePJj2MJQqX7NFoio7M4TtOmCVOsc6qhtBDDiU2k6YSzk6eeVr\ns3ZjFiY3AtLr2aTstr/Y09k+hHOFZGbbsLTYC3rnzt3q6bnHdl/cHmcP/yCIAQesbq52ytHJK1cJ\nyY0FB9wKei8XTsi1/enpyazqaGlxnvL77vtmzv2zqs2Ix+Oan7+Vd5/cHmcPfyCIAQfs5q3OVM4q\nRbsSUqWHWrkVkG4PGcss4efaflvbOoVCobQwDoVCeUNY8m8PfPgXQQzkEYlM6dq1azKMgEwznvXz\nUGhxbeVKzMJlVUKq9I3erYB0M7CsSvidnd2221+cBKY32UZcyAMW7b0oFEEM5JB6AzcMQ4sTLZgK\nhUK6/fYNam11fwrMStzoU0uLbgWkW4GVq4Sfa/tPPPFEVi96p2jvRSEIYsBG5g3cNE0Fg0Ft2rRF\nXV3eTrRQzhu9VWnRrRKdG4GVq4Sfb/ultNnS3gunCGLAhtUNPBaLafny5a51JsoVUOW40duVFvfs\n2edaia7SgZWvhE9gwmsEMWDDy043bg3ryVVazLdQRrHcHjdMmy38jiAGbHh1A3dzWI/bDxtejRum\nzRZ+RhADOXhxA3dzWI+bDxtejxumChp+RRADebh9A3e7lOrWwwZLTQLWCGLAZ7yoEnfjYYOJLgBr\nBDHgQ7XYpkmnKcAaQQz4VC22adbiAwZQKoIYgKtq8QEDKEXA6x0AAKCeEcQAAHiIIAYAwEMEMQAA\nHiKIAQDwEEEMAICHCGIAADxEEAMA4CGCGAAADxHEAAB4iCAGAMBDBDEAAB4iiAEA8BBBDACAhwhi\nAAA8RBADAOAhghgAAA8RxAAAeIggBgDAQwQxAAAeIogBAPAQQQwAgIcIYgAAPEQQAwDgIYIYAAAP\nEcQAAHjIME3T9HonAACoV5SIAQDwEEEMAICHCGIAADxEEAMA4CGCGAAADxHEAAB4iCAGAMBDBDEA\nAB4iiAEA8BBBDACAhwhiAAA8RBADPtbX16e/+Zu/0YkTJwr+7DvvvKOxsbEK7NWigYEB9fX1Vez7\ngXpBEAM+9sknn+jFF1/Uli1bCv7s+fPnVYk1XaLRqN5++2299dZbZf9uoB6FvN4BANZ+85vfyDRN\n/f3f/73++I//WKdOndL//M//yDRNtbW16cknn1QwGNQHH3yg48ePa2FhQYZhaN++fZqcnNTU1JQO\nHDigP/zDP9Sbb76p3bt3q6OjQ1evXtVLL72kn//85+rr69PNmzd15coVPfroo1q2bJn+8z//UwsL\nC1qyZImeeuop3XbbbWn7df78eUnSd77zHU1OTnpxaICaQokY8KnnnntOhmFo//79unHjho4dO6Y/\n+ZM/0f79+7V06VL9/ve/161bt3Ty5Em98MIL+vM//3N1dXXpww8/1NatW7V27Vrt2bNHa9asybmd\nJUuW6MUXX9TGjRt14MABPfPMM/qzP/szPfDAA/r3f//3rPdv3LhRjz76qEIhnuOBcuBKAqrA2bNn\ndfnyZf3DP/yDJCkWi6mtrU1NTU36wQ9+oBMnTmh2dlanT59Wa2trQd+9bt06SdLs7KyuXLmiV155\nJfmz+fn58v0SACwRxEAVME1TPT09euyxxyRJCwsLisfjunbtmv75n/9Z999/v+666y4tW7ZMkUjE\n9jskKR6Pp73e0NCQ/PnKlSu1f//+5L+vX79eqV8JwJeomgZ8LBGeGzZs0MjIiG7cuCHTNPXGG2/o\n/fff1+TkpFatWqVvfvObWrt2rU6fPp38TCAQSIbukiVLNDMzI0kaHh623Nbq1av1xRdfJHtaHzt2\nTP/2b/9W6V8RqHuUiAEfMwxDktTS0qKHH35YL7/8crKz1re+9S3FYjF99NFH+tu//VuFQiGtW7dO\nFy9elLTYlvvGG2/o+9//vnbu3KnXX39dAwMD2rRpk+W2gsGgnn32Wb311luKRqNqamrS97//fdd+\nV6BeGWYlxjcAAABHqJoGAMBDBDEAAB4iiAEA8BBBDACAhwhiAAA8RBADAOAhghgAAA8RxAAAeOj/\nA532xVzn6tbEAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119c48c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_swiss_roll\n",
+    "\n",
+    "# make data\n",
+    "X, y = make_swiss_roll(200, noise=0.5, random_state=42)\n",
+    "X = X[:, [0, 2]]\n",
+    "\n",
+    "# visualize data\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.scatter(X[:, 0], X[:, 1], color='gray', s=30)\n",
+    "\n",
+    "# format the plot\n",
+    "format_plot(ax, 'Input Data')\n",
+    "\n",
+    "fig.savefig('figures/05.01-dimesionality-1.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Dimensionality Reduction Example Figure 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JO26muCKb2KBr0SjRRPhGU1GbhyG4jF/97tkmu364O5nOIEQrpOzcgH2g49Je\nwX0jKDlcgM5HT9DQCDQ6DQG9Q4mf0QOPIC/iJifiE+mHNtmT/Yf3AXBt8iBqTzo2fZadKME73Id4\nr/hmnx0eHk58VQIWY2OSMu2pY+bIW5qcu3VPCps1a/AaoyFolA+WsZW8u+G1C5arV/gAjEVmh2NV\nxdWE9Q4gb38xfe+NJWCSinlEPme8M6gqPC85m3XN9jW1RllZKf9dOZ9Pl7/NqdzsNt3D3b3z2sec\nPeGLYjfg5xHhUMMP8I6h4KSZv/3ZOdNqrmaq2vaXs0jiEy4nODAEW6Vj7Ui12jEV1xDa13FFFe9w\nP8xljdMYrFkmrunRH4B+Pa+hX2kfqo9XAnD2SBGl3xXTp7QX866784LPf3bu7xiUOYyg/SFE74/j\nV6N+S3RkTJPzvs/bh1e0YyKqiCwmPz+v2ftOH3MjPc+MpmK7jeJdNRz/Io/YweGU5VQR0ScYRdP4\nRpo4JZbjmxrvY62zEV6VTEBAYHO3vqgTJ4/y6sYnKB20ncphu/nw0G/YtnfDJd/HnVksFnJPlqAo\nSn3PcjPN2oqicPp4FWVlpVc8PlfmivP4pKlTuJwR147kqw+WUhumNiSD/O0niRwRR/mJEkLOSX7m\nyjpUe/1HQ9PJaoYykMiIxsEFj895koyTJ9j3415ig2Lp+1i/Cw5W+IlWq+WuG+5rMU6t2vTPR6nT\nXrRWdveMh1DVB7Farby17GVM4acpOl6Gh5/B4TxFUagpreP4pjw0GoWqHAt/nPMbFq/+jCF9R9Kt\na0KL8f1k7XdfEDUOflpxJHyQntStyxg3dGqr7+GujEYjf3/hTdLTCqkoq6TOdIqIgF6UVp8kwCu6\nodZXaSzEyxCERqdKU+dVQBKfcDmKovDCHX/ko3WfUGQ/S/HpIhL9uhJYGkRlYRnlobV4RnpjqTQT\ntN+DW3s8QN6P+QxOGkz/3gOa3C+xWxKJ3ZKaedLlmTpgBv85/C+8+tYnLZvZRkxlPMHBIS2WT6/X\nc+Pg25i//W+EjPDnREouAdGNu0AUHikhNMGf6AFh+IR4cvTjAj4//ReCrvXk4NE1dD8wnAdvfrJV\ncRo1ZZzfO1mrKbuksrqrN//5Pif2qmiVSIK9IrF72CiqSifMP5m8igNoVD1ajQGDzocAryiSB+rw\n9fXr6LBdigxuEaKV/Pz8eXLOE02Oq6rKrgO7SPsxjZiAGK5/6PoOm+fWM7E3v/T+FYtTF2NSaumq\n68q9tz+z8Q/FAAAgAElEQVTc6uuTuvXk9wGvsmrnEjJy1vDj8iw8AwyoNhWfYA+Cu/mTuS0PWwXo\n/UEbVJ8Yg3t5cjJ9D8czp9AjoXeLz/GzR2JXSx36pPxsLS/CLSDjaAEaJajha41Gi1+gnmGTw5hy\n/fOUlVWwbtl2aqvNJPQK45Ff3NuB0bomVxzcIolPXFUURWHUoFGMGjSqo0MBYEj/ocRH92rz9cHB\nIdxz4yMkRfdktfk/+HVtbPJM+/g0PWZF4xVUP9Anc3s+AP6R3gQle3Lguz2tSny3T/wZb634PV7X\nlqHzVqj41ou5Q2Qngdbw8NRz/kJ48d1jefo3jSuzjBnbdH1W0UhWbhFCNGvEoLEUbj7D/u1bMFFD\ngCWC4NjyhqQH0H1MFBmb8/CP9KbyVB2TuvVv9l6n83LY/N1KFBSmDJ1FVGQMf7z3PfZ8t4Oa8mrG\n3TZF+qFaaeyU/nzz8cH6VVwAq1LFuGnDOziqq4s0dQohLmjWxNuZRf26nLm5p3kr/VcO31cUBUWj\nUH2mjoCMZAbcOajJPb79YQerTr9L6JD65t9/79nJ7KSnGNhnKCMGj21VHCaTifLyMiIiIi86Kd8d\n3DZvNj6+PuzefgiNAqMmjmPadY6bCdtsNrKyMomIiMDfP4DDh3/k4PdpjJswmtjY2A6K3HVI4hNC\ntEpMTCwe20KgZ13DsdoSMyFFSUzpMYcR8xqTmKqqbN29kYLSU6Tl7SFyZuOfdfhwHZtSlzCwz9BW\nPfeLNe9ywrwNTUAtytZwZvR7hAGtvLazmnHTdGbcNL3Z76Vu383H7y2n9Ax4+NjAUIa9OgK9Esii\nD/6BT7CJx5+9n1GjL74BsbiyJPEJ4YIURWHu0J/zxeZ3MEeVQKWB7vZreeSppx1qYWazmb9//hy6\nYYV4d9Nj0ZSRmWohYXTj4JVapbxVz9y5fwv5URuIjjUAXtC3ihUpb9Ov58eyUHYzLBYLH72zDHN5\nGD4eUF5+Gg99FF4eAQAEeMdRWpzDv/7yFR4vGhg8pGkN3R24YBefJD53o6oqq7etI604HX+NN3dM\nnE1wC/Pa2iL3zGm+Tl2OEQvXRvdh2iiZM3apeif144XEdzhzJh9//4Bml0xbsflLvCcUo/eqHxTT\n5dpgMncWUldtwcO3vh8vwNa6Jc6O5n1LwEjH+YRevcpJO/IDA/q555v2xaQdOkRFkYH//egxW2sJ\n9HVcRDzQN5aSyizWr0p138TnhKZOo9HIxo0bKSsrY86cOWzcuJGpU6fi5eXVqusl8bmZV7/8Nzsi\nc9H18Ea1l/Pt4hd4bc4fCGlh7tmlyMzJ5M/b38R2rR+KonCweD3Zy0/z6MwH2+0Z7kJRFIdtlsrK\nSlm7cwl21c7UYbMoqctD7+X4ZxyW4E/ewTLCEvyp+yGIx6575IL3NxqNrN/xTf3QO7Meu11Fc84K\nMnUlWsJ7y9qgzYmOiUHnYW6o0ui0BuosNXjoG+djVtUW4OsZhtVi66AoXYATqnwrV64kISGB/Px8\nPDw88PX15ZtvvmHevHmtul6WLHMj5eVl7LadQBdcv3CzolGoHhbEws1ft+tzlny7Evsg/4YmOV2Y\nN6nlBzGbzS1cKS7mSPpBXk15kqKB2ykZlMobO5/GWGrBXGt1OM+YZeCm8GeYbHuSP9/7HlERTZdb\nA0jPOsrflz3Emd5LKej7DTnmbzn6tQn1f+PPzbVW/Ar7EB3V/PXuLjIykv4jIrHY6tdUDfTpSklt\nGnWW+vVha0wlGM0V6D0MDB7RpyND7VDOWLKsvLycQYMGoSgKWq2WSZMmUVlZ2eqYpMbnRs4UFmAK\n0nDuglqKRqHS3vxOBW1VZW+6EafJy0pNTTUGQ/s3q7qLdQcXET6m8c0gYoSO6q0lqNsiMQ/MwyfC\nwNmDZgb738TEMS03La898Amxk61Aff9d16lQvD4Ozc44TFQQ4ZnIzbff46zidAq/+8NTLPlqGelH\nTuMf6MXtd77Llk3b+WrhamxWlW5JUYyZmMSMG6d1dKgdxhnz+DQaDSZT4wzLkpKSSxqBLInPjSQn\nJhO6Q0P1OSOsbRVGeoc0XebrciT6dOFE7WF03o39RRHVfi2ukSkurlYpw/+8YyZtOX++72N27d/G\n6QNZzB44mdjo5rdFanI/TTE+5x2ze1fz4M2/bpd43YGiKNx6280Ox26bO5vb5s7uoIjcw/jx41mw\nYAEVFRUsWrSI3NxcbrrpplZfL4nPjWi1Wh4aNJsP9n7N2VjwLLcz3BbPrHk3tutz7rp+HqcXvkKa\nxyksPhBe4MljY6V/73L52SOAkw7H/NX67XFGDRkPjL+k+3nZwoAah2Pe1tAm56mqys59myitySEu\ntB8D+rn39AZxaZwxuCUxMZHo6Ghyc3NRVZUbbrjhgptEN0cSn5uw2Wys3LqG7PIzzEueTnFhISFh\nIUyfML3dJylrtVr+392/oaSkhPLyMrpfn+D2E6Hbw61jHuK99S/gO7wajRYq9nhyz8j72ny/6669\nly9SXiRijBlFgTM7DNx+7d0O56iqyr8++zX6gccJ6G7geOYSFr8eyUtPfXSZpRFuox0T37Zt25o9\nXlBQAMC4ceNadR9JfJ2YzWbDaDTi7e3NM/P/SFY/sAVaWLxzI4GDu6FB4ev5m/nTTU8QF9O65rFL\nERISQkhI+40WdXcxUV34013vsW13ClabhQm3T7uspcd6JPTm11H/YUPqMlTVzt033oyPj2Pj5449\nGzEMPo5/eH2zdXiCN5a6XN5f+E8emffcZZXH3ZSXl2G12ggNbVqr7szas49PbaebSeLrRGpra3ln\n5SfkmIs5m5OPOUiPxU+LoaiO8mgFX78YyrZnE3H9NQ3XlIXCe5sW8tI9v+3AyEVrabVaJo5u/UAJ\nq9XKgpWvU2g/gkbVkuA7nFunPdBQA/f29mbW1AsPAT9dko5/kuPcvuhefuzdsRmoT3xGo5GKinJZ\n4uwCjEYjL/7pdU78WIbdrhCf5MPv//hLgoPdpM+7HRPf+PHjG/5ts9k4e/YsWq2W4OBgNJrWT1KQ\nxNeJ/P7zv5N5rY7akyUofb3x7lr/ydIM1G49gk+PKDT6pitw5Nllx+jO6qNlr2Abtp9wz/rfe97Z\nNSxLMXDzlLtbuLJeYlQ/duWuJTi2cWLw6UMVBEfU1+QXrnqbHOtWdEG1qFsjmd7/51zTa3D7F+Qq\n9s5bH5P1ox69EgVayM9UefPVj/jTX5/t6NCuCGf08eXk5LB06VJ8fHxQVRWz2czs2bOJjo5u1fWS\n+DqJzJOZnAitRqsNpq6okuCRjhuv+vaKoTqzCNXadCJtsHL+2D7RWRSoh4n2bPyw4xuqI+vIPuDi\nic9isbBkw4ecNWew/9sy+s20Epnoy+m0SsoLTPQNGcL23euoiF9P1ygd4AF9y1i94Q369lhwSZ++\nO7vsjLNolMbNaRVFITurpAMjuvqtX7+eefPmERFRvzRffn4+q1ev5uGHW7cfpvzv7CSqaqqwNbzB\nqah2x/YFtcKEttSMITyA8j1ZqKqKqqpoD5dx6zWynFhnpdD007bSij/7dxf/AeOAdQSOOcnUJyM4\ntKKM1PeLqUz3JNlwHXfd8CQZxfsJiHL87OydXMzx9CPtFn9n4OXTtB/W19ejmTM7KfUyXhfxU9ID\niI6Oxm63tzokqfF1Ev379Cc6dSFno8CvdyxlezIaan12i43+FaH8csa9fH/sIN37xLPj+LeowKwJ\n99MlusvFby6uWtG6/tTV7sbgXf+nXlVoITm4+f3kamtr+XL92xSbMzhVfpjuZ/wJj/dBq9dw3TOx\neP4wgTlTHm8431hlxUNVHfr1ygvq8B8Q6NxCXWWuv3E077+xAcVe36dno4KJ09xn3c72bOrMyckB\nIDQ0lFWrVjFw4EA0Gg1paWnExLR+haEOS3xhYX4tn9SJXIny/mPuU/x91UecrKshyOqH//YyQmIi\nSPKN5MnnHsJgMDDk2r4ATJ7gvB3M5XfrOp578Hk+WPwa2TU/oEVPv4gx3HFb87uv/7+3fkXQhJNE\naxWiieJY6lkMXloCIzxRFAWranQoq7+3H2kpxfSbHIaiKJhqLJSdqeF05I8MGND2XeldSXv8bufc\nfj2xXUJZuWwbNpudyVNvYMq0Ce0Q3VWiHQe3bN261eHrlJSUNt1HUdtrfOglKi6u6ojHdoiwML8r\nWl71vE/hV9KVLmtH6yzlPXkygyV5TxHZs7EJTlVV0jYVc83kcMpOWZno+1v6JDfuK/fV6g842/Ur\ncg5Xo9EqaLUKXXr60bf614wcevW/sXeW321rOPPDW/ynf2/ztdn3/KYdI2l0wRpfQUEBy5Yto7Ky\nkp49ezJt2jQ8POr/KObPn8+jjz7qlIBE62zcvYUtmfuxozIufiDXjZ7S8D0ZUi4uVU1tNXpvx8/A\niqJQXWjj9FYDyX7XMX7aVIdEMG3Mbfx73UYGTqkf8amqKidXhzDinvFXMnSXkp+XR0ZmFkOGDG71\nFjmdnhOqVqdOnWLXrl2YzeaG8Qrl5eU89dRTrbr+golvzZo1TJs2jYiICLZs2cKCBQu47777MBgM\nF7pEXCHLt61hfkUq9Kz/lHaoYCvVKbXMmTyzgyMTV6s+vfuzYmE4IXGNK9wXp9u4Z9TfGDJwVLMb\n0fr7B3D70L+wcetn1GnP4m2L5uEbHnPLD16qqvL3l9/mwJ5C7FYvvPxWc9f9U5g2fVJHh9YprVix\nglGjRnHw4EGGDh1KRkYGkZGt3z7rgonPYrHQrVs3AGbMmMGGDRv44osvuOuuuy4/anFZ1mXthX7n\nNE1E+rIx7TvmIIlPtI2iKNwx+nd8s+ltarS5GOwBDIiaQW1dGfNXPIcdG31jxjKs3/V8s/E/VKmn\nMdiDmTH6fh6d/WJHh9/hVq9ez3c7q9DrwkAPNpMvn3+SwvgJoxtaytyWE2p8er2egQMHUl5ejpeX\nFzfeeCPvv/9+q6+/YOIzGAycOHGCxMREFEVh6tSpLF26lMWLF2OxWNoleNE2JtUCOP4xGe3yOxGX\np1vXZJ7p+mbD1xt2LCHd5z+EjK2f/pBRnMmKtz9h8N0QqNegqir/WXeIJ2/+oMlSZ+7m6OGT6HWO\nP4PaCm9++OEHhg0b1kFRuQgnTGDX6XQYjUZCQ0PJzc2lW7dul5SXLjih54YbbiA1NZVDhw41HJs1\naxZBQUGUlZVdXtTisvTwjMJ+zkR01WYn2RDegRGJzuhY0WaCujS+RVSWGomfYEKrrz+mKApxk6pY\nu31RR4XoMoJD/LDbHReH0BmMdOvWvYMich2q2vbXhQwfPpyvv/6a5ORkDh48yDvvvENUVFSrY7pg\njS8sLIz777/f4ZhGo2H69OmMHTu21Q8Q7e+pWx6ietEbpNkKUIHehPOrO2SwkWhfdswOX1eVmOk2\nIMDhmN6goc7qHiMfL2buvJvZs+tFqs6GotHosNiqGD6uC+HhYR0dWsdzQlNnnz596N27N4qi8Mgj\nj1BSUtI+fXwX4+3t3ZbLRDvx9PTkxft+g8lkQlVVGT0mnCJM35e62o14eP9vh/Z+fhxJqWXgTY39\nywVHbEzrObmjQnQZPj4+vPHvP/DlomWUnq3kmgF9mTxlYkeH5Rrasalz69atjB8/nuXLlzf7/Zkz\nWzfOQVZuuYp5enp2dAiik8rNy0Gv8SZzSSy66LOgqER7DOaGPsPYlfJfNCHF2Cv86RN8Iz2T+l7w\nPkfTD/LdsbWgKoweMJv4rolXsBRXlre3N/c/cOGdLi5EVVX27/+O4uKzTJo0QQbDXMRPzZldu3a9\nrPtI4hNCOFie8jFZylJiRkBUNxuV+xN5Yt6rREcHU1xcRf9ewzl9+jRJ45Ivuh/gjm9Xc7ju38SO\nqW/rWrlvB2Oqfs+AviOvVFFcXk1NDb//7T/IO6VDwZNF/93Gzx+fzfDhnWeHC6Udmzp79OgBQFpa\nGnff3bodRprT4mq15eXlfPbZZ7z11ltUVVWxYMECysvL2/xAIYTrqqqq5LhxBbEDFRRFITBKR/iE\nDNZtXYyqqnz49YvM334nG4uf5s2vf0bmyaMXvNfB3G+I7df4rhc/xMae44uvRDGuGh9+sJDCvCD0\nugB0Og8spig+/WRVu2246hKcsEi11WqloqKizSG1WONbtWoVI0eOJCUlBV9fX/r27cs333zTZOCL\nEOLqdzT9EMFJtZw7XcbTV0epKZvFKz/CY/BOuvtrAQP0K2T5hn/xTLcPmr2XWWn6AdmikQ/N58rP\nr0BRHBcFKSowYjQaO89YCidMZ6itreWNN97Ax8cHna4xjT355JOtur7FxFdbW0tCQgIpKSkoisKg\nQYPYt29f2yMWQrisHol92Z7qRUB44xYvdbU2Aj3iyK04iG+P81ZwCTlNaWkJwcEhTe7lZesKNG5R\npKoq3rbL65vpbAKDPMnBcTudgEB95+q/d0Ll9c4777ys61ts6tTr9VRWNi5jdOrUKYcMK4ToPAIC\nAumum0FeWv2ctMpiC2dSunL9hLkYlKYLGdtqvPD2bn7y+k2jn+D46ggKT5rJT7eQsbort05p3VqK\n7uLOu2Zi8M5vmANYa8rDw9PM7t3fNpxTXV2F1WrtqBAvnxOaOn19fTlz5gw5OTnk5ORw8uRJvv/+\n+1aH1OLuDPn5+axYsYKysjKCgoIwGo3MmTOH2NjYVj+kOe6y6jm43yrv7lJW6LzlzTqZzr4fNxMV\nEs+Y4dNQFIWyylw+2fEEXUfUz++rKrGhPTKZu2b+6oL3UVWVo8fSMBgMVFQX88PJ1ahYiAsaztRx\nc65UcdrkSv1uq6ur+OyzxaxYtgWtNh5fnwgs1nL69/fgbImJvNxavL21jB3fi4cfds6SkU7dneHd\nV9p8bfZjzzZ7fOHChVgsFkpLS4mLiyMnJ4cuXbowZ07r/k+1WHWrrq7m4YcfpqSkBFVVCQ0NbXbB\nWiFE59G9WzLduyU7HEtO6MXNZX9n6/bF2DW1RPsPZNpNsy96H0VR6N3rGr49sIm0uleJHl9fsykp\nPMTSdSXcMv1nTivD1cLX1w8FLf5+Q1CU+kY4nTaAzVv2Exk+BL0uCIsZ1q89RZcuKUyffpXNm3RC\nU+fZs2d5/PHHWbduHQMHDmTq1KksXtz6gVMtJr6UlBSSk5MJD5clsYRwd/Fdk7iv6/9d8nWHclYS\nM6FxSa+ACA3ph7cAkvgAyspqG5IegMlUho+X447iOq0f3+0/dhUmvvYf3OLr64uiKISGhlJYWEj/\n/v2x2WwtX/g/LSa+oKAgli9fTkxMjMOcnf79+7ctYiGE27EppibH7IqxAyJxTd0TIvluXyY6Xf2g\nFp3OE1NdaZPzPD0vPG/SVbXnPL6fhIWFsWbNGoYMGcLSpUupqqq6pMTX4uCWn4bU5uXlkZ2d3fAS\nQojWsFqtFGZZ2b/hLId3lmGz1m8c6mVJ6ujQXMacObPo3U/Baj2LxWLE07ucrt0M2GyN66Vq9YXc\nMntaB0bZRk4Y3DJjxgz69OlDWFgY48ePp6qqitmzL97sfq4Wa3ytXftMCCHOp6oq/174JNfclomX\nbzC1VVbWf1hEz7ix3Dn90ptMOyuNRsMLLzxLRkYmWVknGT16JB4eHixYsIiM9AK8ffXceuvddOsW\n38GRdqzFixczcOBAEhMTG5Yt69GjR8OKLq3VYuJ74403mj3e2omCQgj3tWf/ZsKHHsXLt/6txttP\nx8ibguleczNBQcEdHJ3rSUxMIDExoeHrBx64vPlqnU2PHj3YvXs3q1evpl+/fgwcOJDg4Ev/f9Ri\n4rv33nsb/m232zl69OgltaWKS5d9KpsN36XSPSKWSSPGoSjt3zksxJWQV3SCoCTHUeBBkRrydp0A\nJnRMUOKKas8+vv79+9O/f38qKys5dOgQX375JV5eXgwcOJA+ffq0eo55i318gYGBDa/g4GBGjRrF\nsWPHLrsAonmfrFnMQ9ve47/hBbxQvp2fv/1HzGZzyxcK4YIG9ZnEqTTHY6cOKQzqK1v2uA1Vafvr\nAvz9/Rk9ejSPPfYY06ZNIycnh1dffbXVIbWYHnNychrjV1WKi4uv7lUEXFh1dRVf5e7H2jcCBVAC\nffixr4UvNizj3htu6+jwhLhk3eKTCD02m8xvVxCeXEtRujfRzOzU2xOJ8zhpvW2z2cyRI0dIS0uj\nqqqKUaNGtfraFhPf1q1bHb729vZm1qxZlxykaNmxjHTKI704d8CyxkNPTuHZDotJiMt1y/SfUVo6\nh2MnDjJ56ADp23M37Zj4bDYb6enppKWlcerUKZKTkxk3bhxxcXGXdJ8WE991113XZPJ6bm7upUXr\nxlbt2Mjak/s4a6whyRDCc7c8QEBAYLPn9kzsQeA+EzWh/g3H7HUW4n27XKlwhXCK4OAQRg6T5k13\n1J59fK+88goREREMGDCAm2+++aL7QV7MBRPfqVOnUFWVFStWcNNNNzUct9vtrFq1iscff7xND3Qn\nu77fy2t527AmBAEenLHbKV/4Fm8/9odmz/f19WVOzCA+P3EAS2IoalkNfbPt3PHozVc2cCGEcEEP\nP/xwm0Zxnu+CiS8rK4ucnByqq6sdmjs1Gg2DBg267Ae7g/U/7sHaLajha0WjcNiriqKiogsuAXff\n9bcx/vQwUvbvID6yC5NmjJVRnaJTsFgsfL3mNarUwyiqJ0nh1zF+lHyo6/TascbXHkkPLpL4xo8f\nD8DBgwdlebJ2pKhqi4ksvktXHuoi+5aJzuXz5S8QM24bUR71g8kLsk6QuteD0UOv7+DIhFO54Gby\nLfbxxcTEsHbt2oYh9aqqUlZWJjuwt8J1fUey89gyLF3qa32qXaVvnT9hYWEdHJkQV5bVaqVGtx+D\nR+MMqsjuKhlbNjIaSXydmTPW6szMzCQhIcHh2NGjR+nVq1errm8x8X399df06NGDU6dOMWDAADIy\nMmSnhlYaPmAwv6qpZHXmt5SYakkyBPPcnRdf8cZut3Pk2BGCg4KJjoq+QpEK0T6qq6s49OO3JMT3\nISIiyvGbir3J+SqyGEan1467Mxw+fBibzcaWLVuYMKFxAQSbzUZqamr7JT5VVZkwYQJ2u52oqCgG\nDRrERx991PbI3cx1oyZyz6yZrdrQ8uCxw/wtZSHZ4VoMNVaGWYN46f5nZMd7cVVI2b6QrKoFxPWr\nZN0RLwx7pnLHzN8CoNPp0Nf2xW7bj0arYLOq7FlbgUd1BZu2L2XC6FloNC2upyGuRu1Y46urqyM3\nNxez2eywWYKiKEyc2PpRwy2+o+r1eqxWKyEhIeTn5xMXFycT2J3ktS1fkds/DB1gB3bWWfjPii/4\n2S13d3RoQlxUWVkpx0v/Q21dMUf3K9jt1ei0X7L/wAgGXzsOAC0Gdq8qweClITfDxA33hOLjl0Vl\n2St8sGgHj857rYNLIVzdoEGDGDRoEFlZWXTv3r3N92kx8V1zzTV88cUX3HLLLXz44YdkZmbi5+e8\nberdVUVFOdl6I9A4x0/joedYdWHHBSXEBVRUlFNUVET37glotVr2HthEWXkB42YGo9XVN21lpNWy\n88A3DL52HHa7HYvn90yYHMSR/dX0GRSMj1/9Gp7+QVrCr9nHwcN76N93eEcWy2WtXr2BDZu+o9Zo\nIaFbCE/88v6GLeNcnTP6+Ly8vPjqq68wGo2oauMDzl1b+mJaTHxDhw6lf//+eHh4cN9995GXl9ek\nU1FcPm9vHwLMGs7fejJI49kh8QjRHFVVWbT8JYxem/CPqCJlaRdG9HyasOAuhNk9GpIeQGI/b7b/\ncKbx4v/18VWX2wgMdZx4HB4HJ1N/lMTXjG3bdvLRf/eh0dYPktt/0MaLL73NSy/+uoMjayUnJL5l\ny5YxaNCgNo83abFR3WazsXfvXr755hs8PDwoKipCq9W2dJm4RHq9nuuj+0FRJVD/BhN4uJC7x9zQ\nwZEJ0Whr6jKCrllF75F1xCYYGDi9kD3H/0V0VFcUpenn6JjobkD9/F8P8wBsVpWoeA+yjznuvp5x\nQMeQAVOuSBmuNpu37W9IegCKRsvxjHKqq1seN+AKFLXtrwvR6/UMHTqU+Ph4h1drtZj4Vq9ejdls\n5syZM2g0GkpLS1mxYkWrHyBa79FZd/LnmIlMyfVgVr4f79/yFN27xnd0WEI0KCg/QGCY49tGeI9c\nysrOYisa4NDslJ+h0KPL1Iav77jxz3z9usrpEya+21rJD6lVVFfa2LOhAgqnERN9aestuo1mE4Di\n8LN2aU7YgT0hIYFvv/2WkpISKioqGl6t1WJT55kzZ3j00UfJyMhAr9cza9Ys3n333VY/QFyaicPH\nMHH4mI4OQ4hmaVQ/7HYVjaaxSbOyyJeIgdHMve5vLNvwMmZdOho1gO6hNzJ0TONIOy8vL0IjtYy+\nrn4t2twsI6s/KaTvEG88/L7hvYUnuP26V2QR6/OMGzuQw8dT0Wjr+/9V1U5Sgj9+fv4tXOkinJCf\nDx06BMCePXscjrd2g/QWE5+iKA4bz9bW1soSWkK4qcmjHmDR+lSunX4WRVGoLLWjLR9HcHAIAPff\n+spFr7fWeQP1TXTZR+u484mwhvcTte8RVm74B/fM/ptTy3C1mThhLBUVVaRs/h6jyUL3+GCefvKX\nHR1Wh2ptgruQFhPfsGHD+PTTT6murmbdunUcO3aMcePGXdZDhSO73c6a7Rs5Wnia5NAYbpwwTeY0\nCZcUEhLK3Ekfsmnbx9iUCkJ9r+GuW1q/V+T0kc/x3ZZnGTTBGw8PHD5EK4qCVZ/hjLCvejfPmsHN\ns2Z0dBht4oxRnUajkY0bN1JWVsacOXPYuHEj06ZNw9OzdYMBL5j4Dh8+TN++fUlKSiI6OpqTJ0+i\nqip33HEHERER7VYAAc/N/zu7YlQ0kd6oVT+w+d3veP3n/yc1a+GSQkLCuO3Gto0oHDF0Ep6H/s2a\nT/6BiaaDMxR781t2CXGulStXkpCQQH5+Ph4eHvj6+rJ06VLmzZvXqusvWK3YunUrdrudzz77jLCw\nMPh2zdsAACAASURBVIYOHcqwYcMk6bUTk8lEcXExuw7sZU+oGU1A/Zwcxc+LfdEqW79N7eAIhWg/\nVquVlG1fsXT1a3gYPPm/x5cxY8TfOZlmaDgn86CB3l3mdGCUrmvTlu389v+9yjO//gcffbwQu73p\n8m8uywmDW8rLyxk0aBCKoqDVapk0aRKVlZWtDumCNb4uXbrw4osvoqoqL7zwQmMZ/re7wPPPP9/q\nhwhHry/+iA0l6VQZwDO3HHNyKIZzvq8E+3EsP5sJyCAXcfUzmUz858uH6D/lGPH+Wo4f+5ITa+9k\n5nWPE3A8koNblgMKA3rMpGfygI4O1+Vs2ZrKOx/uQNHWD/o5mVdAecV/eOapRzo4stZxRlOnRqPB\nZDI1fF1SUnJJLWSK2sKY2EWLFjF37ty2RygcfL1+Nb/P2Q7Bvg3HKtbvxX/qkIZfnC63lC+mPEj/\nPn07Kkwh2s0XS97Et887DjszHNntwwMzUggICOjAyK4Ov3zqJdLSHesoXrp81i5/7aroDun5p7Yv\nRXfsT083ezwjI4NNmzZRUVFBXFwcubm53HTTTSQnJ7fqvi0ObnFW0mvNos2dRViYX0N5Nx45CF18\nHb7v0SMO2+aDaEb3xjO3nJm+iUSHd70qf0bnltUduFN521rW0qosgj0ce1XCupazb/939L9mSHuF\n1+5c5XdbXV3H+W/VpjorhYUV7baYSFjY1bUMZWJiItHR0f+/vfsOj6pMHz7+PdPSZtJ7oZPQe+8i\nSEeKCKJiL7i6lnV1V9ddXX3V1bX93NVVcVdQUUCQEjoqgiC995YE0kjvmUw77x/BhCGUAJlMwtyf\n68ofc+acZ+4zSeaep5OamoqqqowdOxYfH59aXy9DB+uZpaikxsRTe0k5Q8MSeF5pz9djnuCp2+93\nU3RC1D1/rwRKi523H0o/HkrrVu3dFFHj0rljM+y2sqrHquqgdfOgxrOClgv6+D7//HN8fX2Jj48n\nISEBPz8/Pv3001qHJPvd1LMhCV1ZvX0Rxl7tALCXVWAvKMHUPIgJwxvncGUhLvTDhrmk5i1F1ZSi\nmBMoXdODJt12E9FE5fAWX+JM9zaaRZbd7c47JlNY+AVbtp+kwmKndYsQnn/2EXeHVWt12cc3e/bs\nqu2IXnnllaqmXkVRSEhIqHU5kvjqweZdO/jipzVYVTtN9CZ0Z/LJy9yEotOiMfkREhfD6C793B2m\nEHXi120rsYd8QLfulbU8hyOdJbP8sGzuSvbeZowf9SAhIaFujrLxUBSF3828j9/NrB5c2KjUYeL7\nbfeFlStXMmrUqGsuRxKfi23YuYVXDq6kLC4YULCePYOteQSBXVoDYFm1nSdie9OlXSf3BipEHUnJ\nXEv7m6ubNjUahWYdcojvuJWUw4fJODtYEl8tJS5fxdZdRzHotYwa3p8e3bu6O6Sr5opRncOHD+fw\n4cNYLBagchGQgoICp13ZL0cSn4t9t2cjZS2q1x7URwRhPn6a4i0HUbQaVD8vCnPz3RihEFdHVVV+\n3bqKrNyDREd0oWf3m51rIUrNoQN2uwONRqFdr1L2rv2aDu361mPEjdN/Z3/L0h/OoNVXDjzZdySR\npx+x0q9vLzdHdpVckPjmz5+P1WolLy+PJk2akJKSQlxcXK2vl8EtLlbqsNY8qIKxdwf8erTDOKAz\nX57YQmlpaf0HJ8RVUlWV/879PY7wF2gz5GvKA57ly/nOq7i0jh1DytHK/fa2/1zCxpXFlOTb2fJj\nCblZVhwa+aJXGz9vPlyV9AAc2jCWr9rkxogajpycHGbMmEGbNm3o378/Dz300FVNYJfE52LtTRE4\nLNXJT1VV1Au+ApV3b8F3Pyyv79CEuGo7d6+nda9fCD23gFNEtEJU2x85eGgHh4/sZvW6b4lv1Z1g\n2wskfh5GXFMDQ0YZuXm8P8PGmdizuQy9o3ZzrTxdWbmtxrFSc81jDZ4LRnUajUYURSE0NJSzZ89i\nMpmcNlO4Ekl8LvbE5BmMyjLgeyQD3bF0fFbtwbttC+eTGllftfBc6WcPEB7tfCyupYPZ3z7HGcuD\nRHV7kyUbxmG3W2nZoiMxzZx3Wo+K9aZPlxn1GHHj1aJJgNPUJ7vdSkKLMDdGdG1csRFtWFgYK1as\noFmzZmzZsoVffvlFEl9Dotfr+fczL7Lk7hdZMuU5VrzyMTHpzs2aoUeymDx0tJsiFKL2WjbtS8oJ\n54+No/vN+AadJDLWjsFLoceQIpKyPsFhqznPTLWaCAyUhahr45kn7qVpaD42cxqKNY1uCQ4eeuAu\nd4d19VxQ4xszZgzt27cnLCyMIUOGUFxczOTJk2sdkgxuqSdGY3Vb/eujZvDJhkTSrSVE6fx4aPjd\nGI3Gy1wtRMPQvl0PfvikO+VlG0no6MXhvWbysmxMnuHPri1l9B7sB0B0qzQc6Y9yaMcm2vWo/KJX\nUeGgIrcPgYFB7ryFRiMiIpz33nqBgoJ89HoDfn5+7g7p2rhorc6mTZsCkJCQQEJCAsuXL2fMmNrN\nhZbE5wYd4tvxYXy7K56XfCaFLft2MaBrL2KjY+ohMiGurHXzgXiZNvLrD6W0amugY1cvACwV1Z9w\n2WdCGTdgGClnotm77htUpRBvpQPTJ/3eXWE3GkeOHOWb71ZRUFxBXFQAMx+6q/EmPVwzneFi9u3b\nJ4mvsXt77mcsLTtNRVwIHyXuYnJQK56cco+7wxKCgf0msHj9LAYOL6g6tnuLldhz/XnJx7QYlckY\njSbat+1N+7a93RVqo5GcnMzX85eTlpHDqdO5+Aa3A7w4nWvnzMvv8v5bf2Hjps0cO5ZEzx6d6dyp\no7tDbtQk8bnJoWNH+N+G5eTYK4gxGHlizDQiwsMB2HNoP4usaTiahaMAFS3CWXD6JKOTTtK6eUv3\nBi48ntFoIj7yebas/g8B4akU50YQbpqKI0/D4Z8yiW8xlPYjerg7zEYjPz+PF//f55QrseSmZxMc\nWb2GqaJoOJWpMPP3z5NeFILOEMCyn5ZwU+8tPPX4Q26M+irUU43vakjiq2c5ebn85auP+LUkA4cW\nVKuNAz3aceKr95n71GtoNBo2H9qDIybE6TprkzB+3r1VEp9oEHr1GE2PbiPJyckhuHcwOp18lFyr\n+YtWUEZ01eDuC5ckU9Fx7IyNwLDKLZy03qGs35bOhORkmjVrVr/BXoO6XqvzUmy22k/1kL/Wevbq\n/FnsahOAt1I5ss1RZqZ87zH2l1sY/9YfiQ4KpanGD9VRjBLsX3WdcraAzh36uytsIWrQaDSEn2ul\nENeu3FyBcm61Gx+/MEoK0jAGVvfpq2UpBIQ6N20qhjC2bNvZKBJfXdb4Bg8eXCflSOKrR6qqcsic\nh6LEVh3T+HpjzyvCOKAr2d4GsoEDWQU03ZNJSg8tir8fakEJg4u96dm5m/uCF+Iifptn1ugWTm5A\nbh7ch/XbvkfjFYZvQCSF2SfJS99JcHAocVEmugzsz/c/ZKAznLdnniWLfn3Gui/oq1GHia+uEr0k\nvnrmo9Fx4cI6qt2BxttQ9dgeHkhEmRf3hHTl2NlU2kW1Z8Tk2i2+KkR9yMxM5/sVL2AMOQ6qHwGG\nEYwb9YwkwGvQsUN77pqQROKaHRSUWElo7s/9d95B925dUBQFVVU5lfwue0/mozMEYa/I4pb+zWjS\npIm7Q6+VhvgXIYmvHimKwtCIVnxTnAWmyr3ILCdS0dkdNc61A2MHD6/nCIW4shVrPiQ192NiWlnI\nOmunRbyBoJDZ/LA+jGE3yaos1+K2iWOZPGEMFRUVeHt7Oz2nKAqv/vUPbN+xkwOHjtKvz80kxLd2\nU6Q3Bkl8LnQ69TR7jx5i4ohh/PZWP3n7vYSuXMzWM6fwUjTc2msiibpN/GS3o/y2o3JRKf1jZD1D\n0fDs2PkDgU0+peNABaicv7d6WRk3jdZzomQzIInvWimKUiPpna9nj+707NG9HiOqIzKq0zOoqspr\ncz5ijeUs5REBvPrGKkILLXRv3Y6Hhk/grtETOX/hoR7tOqP/5lP2lWTipdEyNDqBO0dNdFv8QpxP\nVVXW/vBfKuw7OHL0ANMecm6h6NDFwMkjFlSHl5siFA1ZfU1gvxqS+Fzgp62bWGYohKjwysVQO7fi\nzP7jnPUvZ/83/+brR//itBKDj48Pr93/pNviFeJyli7/B627zSEwCArLy3E4fNBoqntu8nLsFBaY\naNei9mslCg/SABOfLFLtAjuTj0KIv9Mxr4RmWE6lktYmgvnrEmtcU1paynOfvceo919kwgcv8cH8\n2U4rswvhDqqqUm5by2/La3bpaWD9mvKq581mBxvXmUlPCsagv3Qznag9i8XC4qXLWbBwMWaz2d3h\nXD8XLFJ9vaTG5wKxASE4yk6g8a3+ILCmZaGLDAWdluLSmn/Mf/3yI9ZUnEUx6DA0j+Kr0iwCEr/j\n3nFT6jN0IZyoqoqiqU50wcFaOnUx8Ml7BcTE6QgI1DDzaRM6XQY/Lv8rrVslymT265CUnMzf3pxF\ngS0cFA2LV7/G80/cQaeO7a98cQMlTZ0e4rZhY1jz4avsb6mg8fHCll+ENTMHY9/O+J46y4Sxtzqd\nf/TUCdacOoC+RztUm53STbvx6RTPlowk7nXPLQgBVE5St5k7oqobqqYq+PoqGI0Kt97mvHBy515J\n7N69kZ49ZepNbR04dIgv560gK6+cyFA/SktKKCYO7blP5jLimP3tct5pxImvIZLE5wJ6vZ5Pn3iJ\nBesS2XL4IKdy0ykOCiLySBZ3dhlEk1jn+Tf/t2YR3kN7VT3269+Vsu0H0Ee1qu/QhYexWCzodDo0\nmkv3eowe/jorlr+Aj2k/RYUVWGw5xDbRYberaLXVfX2F+XoiAis3Sj12fC+Hji4CFDq0vY1WLTu4\n+lYanZKSEl57by5mXRxgIj8TCtOOEhgd4XReZk7pxQtoLKTG5zn0ej3TR01k+qiJhIWZyMjIv2QT\nULKlGKj+9qwoChoVRrVphEOXRaNw7Ngh1v/yKoEhx0g6XoGfXxQtmk9k2NAHMRgMTucGBYVw55RP\nCAz0Jju7mHmL/khwyBrWrynj5lGV81FtNpUTB3oy4M4ObN+xkkLrS/QZVvmBvW/nCoqK/x/dusi8\n1PMtTlxFmSbKaaCFxV5zund4sG/9BeUC0tTpwc5Penn5eXyxejF5FjNdI5sQpvMm64Lz2+pNjB4o\nTUaibpnNZr5f+gzGgDW0aG1jxzYLE6b4Eh5xGrP5febO38o9d35x0RVY9Ho9er2eu6a+z9FjeylI\nW8dPS0/jYyxBsbdi6qSnAEhK+4pBI6prKZ26F7Nx9VeS+IDDR45w5kwqgwYOQHU4uHBdE9/AKKwF\nB9H6J6CgYLBnMH1yI5/aJIlP5OXncd/nb5PWNhpFo2FV/iG65JXgRwUlzcNBVQk+kckLE2UisKh7\nK1a/ybDR69DrtYCW7j0NJC41M2a8D97eCr36b2Xnzh/p0ePmy5aTEN+ZhPjOVY8LCvJZ//NXeHv7\no6o5Nc5XtLl1fSuNitVq5cVX3+Vgqg20Rj7/7mfumTgIX8dezJrqro8wo5V/vfE3lq/6AZvdzqTx\n0wkICHRj5NdPanyCOauXVCU9AMXfj2NBRbzffyLrj+xBp2iYdsc0wsPC3BypcDe73U5hYQGBgUGX\n7YO7GhrdAfT66lqGoij4eFc/Do9wsPXICeDyie98e/b9yKnUl+g7KIcKs8rueV7kZNkIDa/8eFFV\nFYfFs1ci+vLbhRzKMqLzqdys10wc8xI38fzjU5n73Rqy8kqJCDVyz9Q7CQ4O4e7pt7s54jokiU/k\nWcqrkt5vikxeaDQanpl6X9Uxs9lMXl4uUVHRsvBvA1RSUsLmjV/g612I3qcLvXqPrtPf06+bvyEv\n9wtCgjPIyW1KTOzv6NptJBUVFeh0OrS/LW93lRx2/xrH7PbqT6YdW/zp0W38VZV59MTHDB2dCyjo\njAp33W9h1r/9GTi8HFWFpMNdGTfihWuK90aRnJqDVuvcd5pVrCE0OJC3X33WTVF5Lkl89ax7TAtW\nZO9DCagezNI0t4IObauHK/974dcsOXOYfB8NzcrhyUFjGdCt18WKE25QUlLMusS7mDY+CYNB4Uz6\nPFYu28Xo8S/VSfmpZ06hqG8zZmTZuSPHWbH6ZQ4dnE14+HEsVhNa3VhGjvxDrcusqKhg3749eOsG\nc2DvXjp0Lqks+aid0ylaUpJtJJ+MJdj0IBERUbUuV1VVtIbTTscURaFd2zZEG19CUTQMusOza3sA\nwf4+qKrN6cuRyctGaKgHtOxIjU+MHzqCg18ms/pkKkVGPU3zrTw1YHTV4JcN235lTmkKjvhoAJKB\nt9YvpXfHruj1evcFLqps2vA50yckodNVfojFRStEJC3n268KCQvOxGoPp02Hh2jWvO01lb9//2JG\nDivl/IEPw4fm8+PPGQy9yRso5uzZWWzcGMfAgVduEluz9lPO5rxDfIKFwlI7W7aGcyb5Znx9vImJ\nGsFjDw3gzJlkRg5tgY+PzyXLUVWV1Ws+pqxiE6qqJzJsHP36TsJuiQMKnc6122Jp2bLNNd1/YzV/\n0TJW/LyLolILTSP9+f2DU2l+bv+4u6ZNYNdL75HviEaj0eEw5zJycLvLvt83CunjEyiKwgszHmVm\nfh4ZmZkkxCc4NVttOLYfR6hzZ/aZSCPbdu+gf6++9R2uuAgN2VVJ7zctmxbjb1pG106VCzUvWbUH\nf//5BIeE1qrMwsICzGYzERGReHmHUFau4udb/Ro5OQ4CA6ubyCMiHOzZ9wtw+cR3+vRJFN0/mTAJ\nQE+79noCA7PJTC+lV/ffcez4LwSfjaNNmytPkJ634DVad/qYwHN/nslJ29n8q0p8y5n88tNL9B2U\ni9mssmFNa0YM/X2t7vtG8evW7Xy5+gCKVxT4wskiePS51+nXsxND+3dnYL++fPz2n5m3aClFxeX0\n73VL49xp4Vo0wMQna3W6SVBQMO3atqvRVxNg8D43zLmad3E5MRGR9RmeuAwfYxeycp3/m7ftMtO+\nTXUfzrhbstm+9csrlmWz2Vj43R/Yv2sY6SnDWLRgGi1b9CVxRXzVWq12u8qCRWV07+Zc48/OKWTu\n17/nn/+cwIoVF1/bdd+BZfTu63y83wADx47/TF7xVAbc9A45hVNZtPjlK8ZaXLaqKukBNGtuJSNr\nGV0738yQPons2vgMp/a9wrTJ3xMREXPF8m4kP23eieIV4nTM7hPLLwcLeXv2z3y/bCV+fn7cf/cd\nPPXY/Z6T9ABFVa/5x1WkxtfAzBgxnnWfvUVa2xgURcFRYaG/w0izps3dHZo4p//A20j8fjctotfR\nvEkp6zcHEBxYisFQXUPTaBTKy/Kcrjt2dCcnj32LRmPGz9SfAYOmsXbNh4y9ZTk+PpXXdu+6l0VL\n3+CWEV+QuOpfwGmSkzcRFqahrEzFz6/yvB9/0lJY+DNdu8BttxlISzvAf/6ziAcf/M6pSdzkF01R\noUpAYHVs27ZYmDBJS5u2FgDatqtAo1nAsWMTiI/vcukbVyw1Dmk0lceCgoIZPfKRq3sjbyAGnZYL\nqzYOmwW9jz94BbJywy4mjhvlnuDcTWp84koCAgL5dMZTTM7TM+SsjZlE8OZDT7s7LHEeRVEYN+l1\nQpouIqPsC3oM+p6UVOemz607zBSVVE/iPnJ4K6W5jzNh5ArG3/Ij7Vu+yppV72Cr2FGV9H7j53MI\nk38g48a/TGDQrcy4y8HkST5s2mxh1Wozy1eU88smlfjWKn36eKEoCrGxWqbfcYj1P812Kqt//0ks\nXdy0auSm1aqy/icL7do7f+dNaGPlxMlNl71vLb2w2ao/xYoKwUsvze8AE8fcjL4iveqx6rBTXpSF\nl29lFbm0rOaXBuE+UuNrgCLCw/nz3Q+7OwxxBZFRsYSFtWXHjv1ERmhZtKyy1mezq0SEaok6b8nF\nU8fnMnFUUfW14YB1JSmn82uUm3m2iLUrb8ehBuHlPZQTJ33o0rmCW4ZX7vZRVuZg63Ybbdo4N336\n+mqw2k44HdPpdMy4czGLFr5Cadk2Skv96NppNMlJH9Gsub3qvKQkHU2b9Lzs/U6b8jb/m21Bo98B\nGNBrhjJ+7OO1fLdubC1btOAvj01iQeJP7Dt0gvxSByFxlRP8VVWlWVSAmyN0HxncIsQNKC6uCft3\ntGTarWeqjhWXODi9obp5WqstqXGdVltIdFQJm7dY6denclBMapqNosIy7rnrMAA7du1n994eREdt\nIjwcystVli3vTHy8L8ePbyAmprqP2GxW0Wia1ngdo9HEjLv/6XTsm3mn0OlWEhvn4MxpDYf3jWbq\n7ZefMuPr68vtt71bi3fkxpWWlsb3K9YCMGnMLURHR1c917VzJ7p27kRZWRl/ffNDjqRmYUOhVYSe\np2c+6q6Q3U8SnxA3Ho1GQ2Z2KIuWHaZXd2/SM+2cTLJg1x6vOkfRdqW49Fd8vEBRQKtVyC2Ip0Wz\ng4SFOFi+shyNBkKCNLRoUV2T69GtmIzsCJJOv8Xe/bvQamOYNHkG+flnmTt3GiZTJt27G8jJsbNm\nTXem3XF/rWKedvu77N5zKxt/2k10ZHem3j6ozt+XG82vW7fz9v+WY/GuTHY/7vyU5x4cR58LBqr4\n+vryz78/T05ODna7nYiIiIsV5zGkxifEDap5XDGjh/qx/7CFmCgtvbqZWLL6WNXzffvfwyez5tGy\nWTqqQ+XYqVBun/4av/7yOr26/UKrc8lu524LzZs5d70r2OnX71ageh/HqKimPPPMZjZuXMN3C7fQ\ntGlX7p4xttZLmymKQreuQ4Ah13nnN6bi4iK+XbSQ3Nxixo28mSZxccxLXI/VJ6ZqdqXVJ4Z5y36q\nkfh+Expau6ksNzxJfMJVzGYzc1ctJa24gA5Rcdw6dESdre8orsxq90evV+h2bh4fgM1ePfb/h7Wv\n8PTMXLTayn668vJiVm+Yx8gx77F41et46Q7hUAM4eSqdJ2ZWN5keOeZFbNyYi76moigMGjQCGOGa\nm/JQJ04l8Zd3/kuRPgZF0bBm5395bMogcovKQeM8xzavyOymKBsPqfEJl7BYLDz4wWscbhWOEqBn\n8dlDbP30MG8++oy7Q/MYkbF3sHPfEbp3qlxm7MARAwFht1U976vf57Rpq4+PBi27MBqNjJ/wetXx\n3JyzfJ/4d7wNh7E7AgkKmUK//oPr70YEcxYso8SrSdWQd7tvNAtWbSI23J+CCzaeiAk11nt84vq5\nLfGFhZnc9dJu4cr7/fy7+RxqHorm3PwtxeTLzyXZZOel0y4hwWWveymN/Xf707o55GZ8j1ZThkXt\nxPjJf7/s0lJhYSaGj7iNA/tjWbFhPqgOmre6lXE3Ve+nqNH61bhOqzfVeK/Cwky0aTun7m6mjjX2\n321tFJttNY7lF1fw/suP8fTLH5JabEJRVGKNJfzlmSc94j25LlLjq5adXeyul653YWEml97vsfRM\nNH5eTscqQgLZuG0XYcHRl7jKNVx9r662Y1sizYPfZPDwyg8/m+0UX83OZ9zk9y56/vn3GxHZnojI\nV6qeO/990BhGkJZxgpioylV5jp4wYAoc26jeq8b+u62tUJM3xwpUpwWlI4P9MPoF88k/XuKXzZvR\nKBr69e2DRqO5Id4TVybvhtjUKZ1AN4A+rduh5DjPBws8k8WwfgPdFFHjlZ+1hvjm1d/4dTqFMNNO\nzObr68sZOuxRjiT/mSWr+7N41WBySv4fvXo38p21b1Az751GlJKK3VyM3WrGZDnNg1NHA5UjeAcN\nGMCA/v2kD722VPXaf1xE+vgagSMnjjFv0w+UO+wMaJ7A2CHDnZ4f0KM3U47uZ1nyaUqCjIRlF/Fg\nh774+3vupNmGaMCg6cB0d4chriA4OJjP3nmFfft3k3Imk5HDH8FgMFz5QnFRDbHGJ4mvgdtz+AB/\nWPcdhU0jAC0/nt7N6YVZPDb5TqfznrvzQe7NzubIyWN0n9QFP7+afUriyoLCb+FY0taqWp/VqpJd\n3B1vb283Rybqk6IoDLt58A3RjOl2kvjE1Zq7+adzSa+SI9DEihNHeNThqNHUEh4WRniYB2xs6UI9\neo1ly6YS9hxZhlYpo8zakeFjPHv3cCFuNJL4Grhim4ULf01Fqh2bzSbNLy7Sp/80YJq7wxBuMu/7\nZfy47QAOVOJjQnny4Xvlf+06KI4rn1PfpHe2gesQFI6jwnll93i9n/wjCuECS1euZvaPR0m1hZJu\nC+PHU3be+OATd4fVuKnX8eMiUuNr4B6ZdAdnZn3IZnM65ToNba1aXpg8w91hCXFDUFWVL76Zz7ZD\nSWgUhZzsbBRT9dxXjVbH3lNpOC7StSBqRwa3iKum0+l489GnKSwsoKysjKio+p2XJ8SNqqKigidf\n+Bt7ThfjZQrBLzSO/JwUgi+c0qZc9HJRWy6clnCtJPE1EgEBgQQEBF75RCHEFZWVlfH4S2+SRhzB\nLQyYC3PIT9mPzscfS0kuBmMIAA67jY7NwqS2dx2kxieEEA3A3IWLSVei0GorPwK9A0KxlBbgExSJ\nLvcwGnMmFrtKlL+O5373dzdHK+qafI0RQnic7IJSNFrn7/3eAWGYCzKx2yqwBiegjexAhr45L739\nf26K8gbRAAe3SOITQnic+KZR2C1lTsfMOckkBNlxRHapSopavReHcrRs27HDHWHeEBT12n9cRRKf\nEMLjTBw7mp4RDtTSLOzWCgwlZ/jjjHF06dQBvZfzqkeKdwDHTyW7J9AbgazVKYQQ7qfRaHjtT09z\n9Ngxjh4/wU2DZmAy+XPg0EESdy1G8QuvOldfls6IobddpjRxOTK4RTQ4JSUleHl5oT+3l58QniQh\nPp6Y6CiWrFyNv9HIqOHDmNy3Jct/PUyRw4cgXTl3jOpNaGiIu0NtvCTxiYbidFoqry74kiMVpfii\ncHNUU/549wNOe5AJcaPbvG0778xZSqlvDA5bOgvWbOLtPz3BtAljKCjIIiQk+rKbEIvGSRKfw7w4\nTgAAFcVJREFUh/r7/DnsjQ0BgjEDC0qLiU78nrvGTXJ3aEJcl8TVa/lx+37sqkqvti2YPnnCJb/Q\nfbF4DWXGJiiA1uBDlhrHp3MX8NLTv6N582jZnaEONMSmThnc4oFKSoo5ait3Pujny/b00+4JSIg6\nsnjFKv61ai+Hyv05ag5gztbTfPbVNxc9V1VVMvKdR3YqikJmQdlFzxfXyKFe+4+LSOLzQHq9AZ+L\nrJjup5V+PtG4rdu+H3yrVzhSDH5s3H/youcqikJkQM1mzPCLHBPXQebxiYbAy8uLmyLjUMuqa32m\n9Cym9B3kxqiEuH4Wq73mMZvtkudPH3MThuJUVNWBw2YhsDyF+26f4MoQPU5DnMcnfXwe6k8zHiJ6\nyUJ2Zp7BV6vj9mET6Nq+o7vDEqJW9h08xBeLV5JZUEZUkB8PTBpNuzZt6NwiiqQjJWj1XgCoDjvt\nYy+9OfNNA/vTsW0bFq9cjZ+vDxPHPIC3t3d93YZnkEWqRUOhKAr3TLiNe9wdiBBXqaSkhFc++4YS\nU1PwDiC3HF7+z9fMfuMFHr3nLko+nsX24yk4HNChSSh/nPnwZcsLDQ3hwbun11P0oiGQxCeEaFQW\nLV9JkW+MUz9Nvk8US1euZuqkCTz/+CNA5eAVmZ7jfg1xVKckPg/3y87tfLtlA/nmcmw5OXSOb8uo\nnn3p2qGTu0MT4qIumcyUWp4n6lcDTHwyuMWDHThymL9uWMW2UBPHY8M51TGBrw7s4nc/LGP2su/d\nHZ4QFzVpzCgCytKdjgWVZ3DrqJFuikhcjqKq1/zjKpL4PNjCXzdQEh1R9VjRatGFhmL29mLBoT3Y\nLjMaTgh38fPz428PT6ODdyFhlkw6+hTxysy7ZFBKQ+W4jh8XkaZOD2Z3OACt87GSUqxZWaRYrOw7\neIBunbsAkHk2k8yss3Rs1wGtVnuR0pypqsrufXvRG/R0bNveFeELD9axXTveadfO3WGIWnBlze1a\nSeLzYLd06sYPW3/EGla5AK855QyKToupd09UVeXZtUt42VLBqh1b2FiST5m3F00TF/Lc6An06dzt\nkuUmp57mhS//y3E/HzSqg3ZLF/LOw48THBRcX7cmhBCXJE2dHmxAz948Fd+ZFmcyse/cS8WpJHzb\nJgCVAwNKYqP4x/ffss5bwRIbjS40hLRmsby7ainqZb7Fvfv9Ak42iUETEgyhoRyMieTd+V/X120J\nIRqSBrhyi9T4PNyUW0Yz5ZbRAIx89c/kXfB8nsOGckHfSbJOIT09jZiY2IuWeaqkCIL9qx6rFgvr\n9+3mgQ/eJsTgxYyht9AhoU2d3ocQooFqgE2dUuMTVdoFO69woaoqgVZ7jdpdoNVO0GWaLUO8nBNl\n6d59WHr35kBIID+bfHh24Tdkns2su8CFEA1WQ1yyTBKfoKysjFkL5+GlqoTuPYgjNw81N5c2KRm8\ndf9jRCanojoqh1gphUWMimuBr6/vJcub1rMvPhmVic1y9ixeMTEomuo/tby4GL5Zu8q1NyXqjc1m\nY+Hy5bz/3y/Y+Ouv7g5HNDSqeu0/LiJNnR6utLSU+99/g1NNolFCTTh0MfTLK+O+cRPp0qETiqLw\n38go5qxcSlF5BX3adWfkoJsuW+aogUNoFhHFki0bSc4rZnu48+7ViqJgttecKuFwOPhkwbfszkjD\nW6NlfPdeDOs/oE7vV9Qtq9XK439/neNKMBovH5ad2MTw3fv402OPuDs00UAoLpyWcK0k8Xm4OYmL\nOdU0GkVX+aegCQxkT3EZMZHRVStfhIaE8Mxd911VuW3jE2gbn8BT77+NOfkUxuCg6idTUxk9fkqN\na/4x+3MWW8woQZXbyuzZ8gsajcLQvv2v8e6Eqy1asYJjSgjac83bil8gPyZlMP3MGZrExbk5OiEu\nThKfh8sqL0Xxdv4zKDH5cTLlFOFhl17VvjZUVeVgUQFecU0o2b2nMrk6HDRTFDq36+B0rsPhYGPq\naZQmTaqOWUJDSNy5XRJfA5aanVeV9H5j8wtm36FDtU58h48e5V/zFpOSW0SwrzcTB/Vi4qgRrghX\nuIMMbhENTeeYOCgpdToWWVBM1w6d66R8H60OQ2gopi5d8GvfHmOnTrRq2qLGeQ6HA8tF/kEqHA2w\nnURU6diyGY6yIqdjPsXZ9O/Vs1bXOxwOXvt8LscIoSKkORk+UXzyw072HzroinCFOzTA6QyS+Dzc\nrcNGMtKuw5Ceib2klOCkM8zsM7hOln9SFIWhzVqglpZWPfbOzuHWHr1qnKvT6ejgH+A8grSslD7N\nml93HMJ1hg8ZwqAwHUphFqrdjj4/lWn9Ol121O/5tu7YTrri73TM7h/Oqo0ySOZG0RDX6pSmTg+n\nKAp/f+QJHkw9w4mUJPre2RMfH586K//J6TMITVzMttPJGDRabh04lIE9e1c9r6oqny2cz8+nTlBh\ntRJ04jiOkBB89HpuataSu8bJbtgNmaIovPzU7zl6/Dh7Dh7g5oG3ERoScuULz/E3mdDYLU7HVFXF\noNPXdajCXRpgU6ckPgFAk9g4msTW/WAERVG4a9xE7rrE87OXLOLzs+koEeEAOMLDmKDV88L9l988\nVDQsCa1bk9C6dY3jqqry86ZfSDqTxqihQ4iMiHR6vn3bdsT7LOS4w1E15cVYlMrUsTPrI2xRHxpg\nb4U0dQq3+iXpJIrRWPVYYzCwPSP9MleIxsJisfD4y6/x95Xb+epkMfe99R/mLU2scd7rzzxObP5R\nlKQdROQc4rUHp9VIkELUJUl8wq0utuZnw2sYERfauOVX3vxkFv+ePYf8/AsXuqv01cJFHNaEovH1\nR1EUrMGxfLthO2azueocm83Gn975P1L8m+No0YM0YxO+XbG6vm5D1IOG2McniU+41eCWraCkpOqx\nw1JBn+gYl79uckoy787+Hx98+YUsn3aV/vP1XF5eupG1ebAw1czDb7xPRmbN9/B0TgGaC/rqchRv\nklOSqx4vXb2a4wSjNVQOptL6GNmSWc6hI0dceg+iHsnKLdXCwkzuemm38KT7vZp7feaBe/CaO5fV\nh49gs9vp2ySOFx9++KJ7/iUlJ1NYVMS+EyfYdPwkPloddw0bSo8uVzf1YuX69bzw/QpKQiub09Z+\n+gkf3Hsnfbp1vapyfuNJv1t/fwNrDxwH/8ovJ4pGQ25gHAtWr+LVZ59wOrd1bCgb9ueg0VZ/zIRp\nrXTv1r5qybu8kmI0Xs6DqVRjCEmpSQweWLspEa7kSb9bl5HBLdWys4vd9dL1LizM5DH3ey33OvWW\n8Uy9ZXzV47y8Mqfny8vLee7fH7DLaqVCo8Wckox3i1YYgoJZP+cb3sgpose5DXNr4z/L11IaFoVy\n7nF+eAwfLlxGy7hWVxU31P/vtqKigmVr16CqKuNvGYGXl1e9vbbJpOeNDz4h5fRpdKF2/CIrFxtQ\nFIW03BKn96GiooLT6VmYD2/D0LILWm8/tIWZTOjdntJSO6Wllee2a94Cddd6FGP19AdDYTq9ukxy\n+/+Mp/3fukwDHNwiozpFg5V59izvL5zHxr17sffohaLVogf0YeGU7N2NISiY0vAIFmzaeFWJL7vc\nDAHOx3LKy3E4HMxe+B37MzIweRmYPmw4CS2vPhm6ytGTJ3jx89mcDYgEReHbza/x2n130TY+oU5f\np7S0lM8XLCCjsJSYQBP3T7kNVVW5/6W3OWUIx79DXywFuRSe2E9Aq444rBbim4U6lfHXD/7Ntgpf\nvDv0ozT1FEpxDn+5czJjR41yOq9fr16M2rWXdcczqPAJxLcsl2n9OxMW6lyeaLxkB3YhaklVVf7w\n6UecjI6lzGTC74KmT0WnR1XVyg1zrdarKrupyUTOBa/V1N/EKx9/xDobKF5+oML2r+fy4Yy7aXkN\nk+itViv/+vorDp7Nxlen5dY+vbn5Ohfc/nTJMrLDmlZ1zOeEN+XTpct579m6S3x2u50nXn+LJGM0\nisYLNb2cPa+/RfeEVpzyiqxqtjQEhmApzMGWmUSf6CDumVI9/aSgIJ89Z4vQhFROTDfGtQJasfNE\nMmMv8prPPvoQ0zPS2XfoEH26dycwMOgiZwlRd2Rwi2iQNm/fygmjCUVRqrZEOp/qcFQ+V1FBp8ir\nG/r++4mTiMs4g6OkGEdRAa2y0rhv5Eg2ZeegnLfuZFF4NN+uXXNN8b/y8Ud8V2DmqF8wu70CeOOn\nX9i0fds1lfWbtKLSmseKax67HsvXreWkPhhFU/lFQ9FoOaENZPeR4059dQCG4HCeHdGffzz/LDpd\n9XNWqw1rVUNyNetllp+Ljopm5M3DJOndiGRwixC1Y3c44NyEZkNEJGXHj+LTKr5yWHxGOnpzOca0\nM/QLD+ehKdOuquxWzZsz969/Y8Ovm/AyeNGnR0/S0lIpUzQ1vgmW2uxXHXt5eTnbz+aiiapecLsi\nMJTErVvp37Pmcm21FWn0IaPGsUvvi1hbm7dt4+fd+zB5G7CZzWi8nctUfHzxKcnFbi5F6+1XdTzM\nVsLwoUNrlBcWFkaCv56j52rkAJTkM7jftd+7aMSkqVOIK1u3+RfmbNyAJSMdm6JgiIjEEBaOZduv\n3Ny2PZNGjaJN85Z4eXlfdkPcy9FoNAzpP7DqcUxMLK20CqfOO0ctKaZ3p7ZXVW5BQT5mcwU1dxsE\nq+P6PgDuHz2Kl778hvyQWAACclK5986a2ztdjVnz5vP1/iTwD0HNN+OfnQQVNoirbj71zkvnT08/\nxqxFC/n5dBZWH3/8S7O4b/iAS67p+vLMB3n7f19yPKsAo7eeUT06MXzI4OuKVTRSkviEuLyMzAze\n/PEHSqNj8A6rXMasfNcOOsc1Ycb9DzGkd1+XvK6iKLwwbRpvzZ/PKbMFowaGtWzBrbeMrNX1Z7Oz\neeXzWRwqNWNQQc3OQI2MO6/GU8TA7u2vK8ZO7drx9YvPsWDFClRgyiN3YDL5X/G6S7FYLCTuOggh\nlUvVKRoNReEtaJ53iqKCVLIdOsI1Nqbf1IeoqCj+7+U/s3nLHo6fOsmgvvdiPG/FnQtFhIfzz+f/\ncM2xiRuIjOoU4vIW/biOkqhopx4irw6dGB0XV2dJT1VVNm3byrGUZIb3H0BcTGUNqk2r1vz3hRcp\nKMjH19cPg8FQ6zLfnDOHfcZQFJNCOWA1+hN2bC8EBOOn1zG8fVtuHX79e8wZjSbuu33qdZcDUFRU\nRKHq3LirKAqhEVF88vijZGWdJSIiEr2+ehJ665Ytad2yZZ28vvAMMqpTiCvw0hugtAzOG8Wp2KyY\n/K6/Lwsql8h65t232anoUU0BfDnrc2Z06sB9EydXnXMtAyyO5hWgRFfPkdCbAoiKa8Knzz1XJ3G7\nQkhICLFeCqnnHXPYrLSKCcFgMBDrgkXLhWgIZFSnaFCmjRpNRHpa1WNVVWmRl8vwQTfVSfkLViSy\nzcsE/oEoioIlMoZ5e/dTXFx05Ysvw09f8zukr/76t9apqKjg7VmzeOD1f/DUP99jw9Yt113mbxRF\n4XcTxhCadxprSSFK3ll6aku4//bb6+w1hJBRnUJcgdFo4p177uOzFcvIKjcT5+fL72Y+gUZTN9/R\nTmRlob1gQEa+0Z99Bw/Rv0+fay53RIe2zD6ZCsbKPjfvvCwmjbj5umIF+MuH/2Kr4ofiW7nH3eGV\nP+Ln7UP3zle3TNul9OnenW86d2bbzh1ERUTQXDb+FXXtOgd1uYIkPtHgtGrWnH889nuXlN0kOBhH\n2lk0huqlvgJKi2nf5vomgT942+1ErF3DxiNHMWg13DpuJD27dLuuMgsK8tmdW4wSUd2Eag4MZ9nm\nX+ss8QHodDr69b72pC/EZUkfnxDuNX3crWz+x5vs9zWh+BnRZZ9lYkLrOpk4PW74LYwbfksdRFnJ\narVxsVmEl5sILkSDI4lPCPfS6/V8/MKLrF7/E8mZGdw0dAptWse7O6yLCgsLo43RwMHzJoJrigsY\nMlgmgotGRBKfEO6n0WgYNfT6+9/qw8sPP8Tbs+dwLK8Qk0HP6K4dGT5okLvDEqJRk8QnRAMWERbG\nP5+VieCiEZPBLUIIITyK2vD6pCXxCSGEcB3p4xNCCOFRpKlTCCGER2mANT5ZskwIIYRHkRqfEEII\n12mANT5JfEIIIVxHEp8QQgiP0gCX2JPEJ4QQwnWkxieEEMKjNMDEJ6M6hRBCeBSp8QkhhHAdmcAu\nhBDCk6iyVqcQQgiPIjU+IYQQHqUBDm6RxCeEEMJ1GuA8PhnVKYQQwqNIjU8IIYTrSFOnEEIIT6I2\nwKZOSXxCCCFcR2p8QgghPIpMZxBCCOFRGuAEdhnVKYQQwqNIjU8IIYTLqNLUKYQQwqM0wKZOSXxC\nCCFcRmp8QgghPEsDrPEpqtoAJ1kIIYQQLiKjOoUQQngUSXxCCCE8iiQ+IYQQHkUSnxBCCI8iiU8I\nIYRHkcQnhBDCo0jiE0II4VEk8QkhhPAokviEEEJ4FEl8QgghPIokPiGEEB5FEp/wGEuWLOFf//oX\nBw4cuOpr169fz+nTp10QVaXdu3ezZMkSl5UvhKgmiU94jL179/LYY4/RoUOHq742JSUFV6znbrPZ\nWLduHatWrarzsoUQFyfbEgmP8O2336KqKp999hl33303x48fZ+vWraiqSlRUFGPGjEGr1bJt2zb2\n7duH1WpFURRuu+020tLSSE9PZ+nSpUydOpWVK1cyZMgQmjZtSkFBAbNnz+bJJ59kyZIllJWVkZ+f\nz7BhwzAajaxevRqr1Yqvry9jx44lMDDQKa6UlBQAhg8fTlpamjveGiE8jtT4hEeYNm0aiqLwyCOP\nUFpayq5du3jggQd45JFH8PPzY/PmzVRUVHD06FHuvfdeZs6cSUJCAtu3b6dz585ER0czfvx4wsPD\nL/s6vr6+PPbYY7Rs2ZKlS5cyefJkHn74Yfr27cuyZctqnN+yZUuGDRuGTiffQYWoL/LfJjxOUlIS\neXl5zJo1CwC73U5UVBReXl5MmjSJAwcOkJuby4kTJ4iMjLyqsmNiYgDIzc0lPz+fb775puo5i8VS\ndzchhLhmkviEx1FVlfbt2zNy5EgArFYrDoeDoqIivvjiC3r16kXr1q0xGo1kZmZesgwAh8N5d2m9\nXl/1fFBQEI888kjV45KSElfdkhDiKkhTp/AYvyWrZs2aceTIEUpLS1FVlcTERLZs2UJaWhohISH0\n6dOH6OhoTpw4UXWNRqOpSnK+vr5kZ2cDcPjw4Yu+VmhoKOXl5VUjQXft2sWiRYtcfYtCiFqQGp/w\nGIqiABAREcHgwYOZM2dO1eCWAQMGYLfb2bFjBx999BE6nY6YmBiysrKAyr64xMREJk6cSP/+/Vm8\neDG7d++mTZs2F30trVbLlClTWLVqFTabDS8vLyZOnFhv9yqEuDRFdcUYbSGEEKKBkqZOIYQQHkUS\nnxBCCI8iiU8IIYRHkcQnhBDCo0jiE0II4VEk8QkhhPAokviEEEJ4FEl8QgghPMr/By9NPij/DsO5\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b8d2908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import Isomap\n",
+    "\n",
+    "model = Isomap(n_neighbors=8, n_components=1)\n",
+    "y_fit = model.fit_transform(X).ravel()\n",
+    "\n",
+    "# visualize data\n",
+    "fig, ax = plt.subplots()\n",
+    "pts = ax.scatter(X[:, 0], X[:, 1], c=y_fit, cmap='viridis', s=30)\n",
+    "cb = fig.colorbar(pts, ax=ax)\n",
+    "\n",
+    "# format the plot\n",
+    "format_plot(ax, 'Learned Latent Parameter')\n",
+    "cb.set_ticks([])\n",
+    "cb.set_label('Latent Variable', color='gray')\n",
+    "\n",
+    "fig.savefig('figures/05.01-dimesionality-2.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Introducing Scikit-Learn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Features and Labels Grid\n",
+    "\n",
+    "The following is the code generating the diagram showing the features matrix and target array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3vgOF11VWVqpPnz5au3atrrvuugs27pIlS5Sfn6/58+dfsDEBwFPsgcLr/vvf/6qiokLd\nunVzuhQAuGAIUHgkNTVVixcv1s0336yEhAStXr3ao+fddtttrt/Q9e3bt84h3BMnTuiJJ55QUlKS\nhg0bprVr17qet2zZMg0bNkyxsbEaMWKE1q9f75r3+eefKy4uTkuXLlVmZqbi4uIUHx/vutJPampq\nncPE9W8Cnpqaqrfeekt33HGH4uLiNHXq1GbrkaQDBw7o3nvvVZ8+fTRo0CAtW7bsXFYhgADD3Vjg\nsd27d+uDDz7Qtm3b9Nvf/lZ33nlns3dc+PDDD3Xo0CENGzZMu3btqnPpsCeffFIdO3bU1q1bVVhY\nqIkTJ6pnz57q1auX2rdvr+XLlys6OlrZ2dl6+OGHNWDAAHXo0EF9+/ZVXl6ex4dw3V2ubPXq1Xrh\nhRfUtWtX18XC69eTnp7uqkc6e9HuHj16aNWqVSotLdV33313rqvQSHZ2tutapwAuHuyBwmNpaWlq\n1aqVUlJSVFZWpuLi4nN6fu2v24uLi7Vt2zY9/fTTCgsLU5cuXTRixAht3LhRknTnnXe6LlidkpKi\nSy65RPv27TOq293X/HfddZe6deumFi1a6IYbbnBbz8033+yqRzobxMXFxSosLFR4eHiDezZ6y/XX\nX6+FCxfWuc4uAOexBwqP1dzBoeaehLXvanGujhw5IunshamlsyFXVVXlujnwunXr9Prrr+vHH39U\ndXW1ysrKGtx79HzUhLOn9UjSU089pRdeeEFpaWlq27atpk+f7rqId3O2b9+uuXPnGt2hwrZt/fDD\nD8rNzdWbb75Z52bPAJxDgMIRUVFRat26tXbs2NFg3uHDhzVr1iytXLlSN954o6SztwarvyfZWBi1\natXKdcPg2vd1rK1ly5Ye11Pj8ssv19y5cyVJa9as0TPPPONxgCYkJGjdunUePba+iooKzZw5U088\n8QThCVxEOIQLn6gffh07dlS/fv00f/58lZeX68yZM8rLy9OePXtUXl7uuiFvZWWlli9frtLS0gZj\nduzYUfv373eFZY3o6Gjt3r1bkrRhwwaP9vqaqqfGpk2bVFRU5Pp/X4VZZmamZs+erSuuuMInywPg\nGQIUHnF338Tzeb4kLViwQEePHtXw4cOVlJSkjIwMVVdXq1u3bpo0aZLGjx+vwYMHq7y83O3dHW65\n5Ra1a9dOgwcP1pAhQ3Ts2DFJ0rRp0/T+++/rzjvvdPs9bWO1N1ZPjS+//FJ33HGH4uPjtWrVKs2b\nN++c1oGpcePGqXXr1j5ZFgDPcSEFAAAMsAcKAIABTiLCeYmLi6tzSNS2bVmWpV27djlYFQB4H4dw\nAQAwwCFcAAAMEKAAABggQAEAMECAwq0hQ4bIsiz+AvRvyJAhTrfYRc3b/c/6DwycRAS3LMtyexF2\nxmf8YMD6hyfYAwUAwAABCgCAAQIUAAADBGiAWrZsmetqQBs2bFD//v2VmpqqnTt3OlwZ4H30P3yB\nk4gC1KBBg5SZmanw8HCNGDFCkydPliStWrXKo/tS+vtJFIzv7PhOC/b+h2+wBxqgTp8+rfDwcBUW\nFqqsrEwTJkzQhAkTlJ+f73RpgNfR//AFLiYfoLp3767Zs2eruLhYgwYNkiQVFRWpTZs2DlcGeB/9\nD19gDzRAzZ07V8ePH1dYWJimT58uSdq9e7fS0tIcrgzwPvofvsB3oHDL378DYnxnx/d3rH94gj1Q\nAAAM8B1oAPv666+1ceNGHT16VM8//7z27Nmj6upq9ezZ0+nSAK+j/+Ft7IEGqLVr12rq1KkqKyvT\nhx9+KEkqLy/XH//4R4crA7yP/ocvsAcaoJYuXaqVK1eqS5curt+99e7dW99++63DlQHeR//DF9gD\nDVDl5eW64oorJJ09YUGSKisrFRYW5mRZgE/Q//AFAjRAJSYm6rnnnlNpaalr2uLFi5WcnOxgVYBv\n0P/wBX7GEqBOnDihJ598Utu2bZMktW7dWv369dOCBQvUvn37Zp/v76fxM76z4zst2PsfvkGABrji\n4mIdOXJEUVFR6tixo8fP8/c3EMZ3dvyLRbD2P3yDAA0yJ06cCIpP4Izv7PgXq2Dpf/gG34EGmZtu\nusnpEgDH0P+4kAjQIHL06FG1aME/OYIT/Y8Ljd+BBpCJEyc2Oq+qqkr79+/XHXfc4cOKAN+h/+Fr\nBGgA+fLLL/Xcc8+5nRcaGqquXbuqV69ePq4K8A36H75GgAaQli1b6vbbb79g49X8AN1bGN/Z8QON\nv/U//B9n4QaQoqIiXX755RdkLH8/C5HxnR3fCfQ/fI0ADWCFhYXKyclRSUmJIiMjlZycrKioKI+e\n6+9vIIzv7PgXg2Duf/gGp6QFqPfff18jR45UZmamvvvuO2VmZmrUqFGuC2sDgYz+hy+wBxqgUlNT\n9eKLL+r66693Tdu9e7ceffRRbd26tdnn+/sncMZ3dnynBXv/wzfYAw1QZ86cUbdu3epMu+aaa1RZ\nWelQRYDv0P/wBfZAA9TChQu1a9cuTZgwQZGRkSopKdGaNWvUp08fDRw40PW4xMREt8/390/gjO/s\n+E4L9v6HbxCgASo1NbXZx1iWpc2bNzc6z5/fQBjf2fGdFuz9D98gQOGWv7+BML6z4/s71j88wYUU\nAlTNmYclJSWqrKyss7HOnTvXwcoA76P/4QsEaICaNGmSxo8fr+uvv14hIfwzI7jQ//AFOitAxcbG\nKj4+Xl27dlXLli2dLgfwKfofvkCABqjWrVvr4YcfVocOHeq8gTR14gQQKOh/+AIBGqB27typ3Nxc\ntWvXzulSAJ+j/+ELnIUboNLS0hQWFqarrrqqwU2EPTmJwt/PQmR8Z8d3WrD3P3yDPdAAlZ6e7nQJ\ngGPof/gCe6AB7NixY9q3b1+D0/gbu/pKbf7+CZzxnR3/YhDM/Q/fYA80QK1fv15PPfWULrnkEh07\ndkzh4eEqLS1VVFQUJ1Eg4NH/8AUCNEAtWrRIS5YsUUpKivr166ft27dr+fLlKi8vd7o0wOvof/gC\nd2MJUIWFhUpJSZEk10kUv/jFL7Rq1SonywJ8gv6HLxCgAeqyyy7TgQMHJEmdOnXSv/71Lx05ckRV\nVVUOVwZ4H/0PX+AQboC6++67tWPHDkVHR2vSpElKT09XixYtNGnSJKdLA7yO/ocvcBZukDh8+LDK\ny8sb3GS4Mf5+FiLjOzv+xSbY+h++QYDCLX9/A2F8Z8f3d6x/eIIAhVuWZTldAryMTb9xvuh/1r//\n4ySiALVs2TLt2rVLkrRhwwb1799fqamp2rlzp8dj2LbttT/Gd378QOYP/Q//xx5ogBo0aJAyMzMV\nHh6uESNGaPLkyZKkVatWad26dc0+398PYTG+s+M7Ldj7H77BHmiAOn36tMLDw1VYWKiysjJNmDBB\nEyZMUH5+vtOlAV5H/8MX+BlLgOrevbtmz56t4uJiDRo0SJJUVFSkNm3aOFwZ4H30P3yBPdAANXfu\nXB0/flxhYWGaPn26JGn37t1KS0tzuDLA++h/+ALfgcItf/8OiPGdHd/fsf7hCfZAAQAwQIAGsfj4\neKdLABxD/+N8EaBBjENICGb0P84XARrEuNoQghn9j/NFgAIAYIAABQDAAAEaxPgOCMGM/sf54neg\nAay6ulrFxcWqqKioM71Lly7NPtfffwfH+M6OfzEI5v6Hb3ApvwC1evVqzZ8/X2VlZXWmW5alb775\nxqGqAN+g/+EL7IEGqISEBM2ZM0cpKSkKDQ095+f7+ydwxnd2fKcFe//DN/gONEC1b99egwYNMnrz\nAPwd/Q9fYA80QC1evNh18ez27dvXmZeYmNjs8/39EzjjOzu+04K9/+EbBGiASk1NdTvdsixt3ry5\n2ef7+xsI4zs7vtOCvf/hGwQo3PL3NxDGd3Z8f8f6hyf4DhQAAAMEKAAABghQAAAM8B0o3OJOFYGP\nTb9xvuh/1r//40pEaJQ/n0TB+M2Pj6ax/tEcDuECAGCAAAUAwAABCgCAAQIUAAADBGgQ+Prrr50u\nAXAM/Q9vIUCDwO9//3unSwAcQ//DWwjQIMDvzRDM6H94CwEaBPjNGYIZ/Q9vIUABADBAgAIAYIAA\nBQDAAAEKAIABAhQAAAMEKAAABghQAAAMEKAAABggQAEAMECAAgBggAAFAMAAARrAsrOzdfLkSUln\nL6i9ceNGhysCfIf+h7cRoAEsOTlZS5Ys0cGDBzVjxgwNGDDA6ZIAn6H/4W2Wzb1+AtqpU6c0fvx4\nLV26VNHR0R4/z7Isr94GivEDe/yLRbD2P3yDAA0CJSUlioyMPKfncAuowBcsm/7F2v/Bsv4DGQEK\nt/z9EzjjOzu+v2P9wxN8BwoAgAECFAAAAwQoAAAGCFAAAAwQoAAAGCBAAQAwQIACAGCAAAUAwAAB\nCgCAAQIUAAADBCgAAAYIUAAADBCgAAAYIEABADBAgAIAYIAABQDAAAEKAIABAhQAAAMEKAAABghQ\nAAAMEKAAABggQAEAMGDZtm07XQQuPpZlOV0CvIxNv3G+6H/Wv/8LcboAXLy8uYFblsX4Do+PprH+\n0RwO4QIAYIAABQDAAAEKAIABAhQAAAMEKAAABghQAAAMEKAAABggQAEAMECAAgBggAAFAMAAAQoA\ngAECFAAAAwQoAAAGCFAAAAwQoAAAGCBAAQAwQIACAGCAAAUAwAABCgCAAQIUAAADBCgAAAYIUAAA\nDFi2bdtOF4GLj2VZTpcAL2PTb5wv+p/17/9CnC4AFy9vbuCWZTG+w+Ojaax/NIdDuAAAGCBAAQAw\nQIACAGCAAAUAwAABCgCAAQIUAAADBCgAAAYIUAAADBCgAAAYIEABADBAgAIAYIAABQDAAAEKAIAB\nAhQAAAMEKAAABghQAAAMEKAAABggQAEAMECAAgBggAAFAMAAAQoAgAECFAAAA5Zt27bTReDiY1mW\n0yXAy9j0G+eL/mf9+78QpwvAxcubG7hlWYzv8PhoGusfzeEQLgAABghQAAAMEKAAABggQAEAMECA\nAgBggAAFAMAAAQoAgAECFAAAAwQoAAAGCFAAAAwQoAAAGCBAAQAwQIACAGCAAAUAwAABCgCAAQIU\nAAADBCgAAAYIUAAADBCgAAAYIEABADBAgAIAYIAABQDAgGXbtu10Ebj4WJbldAnwMjb9xvmi/1n/\n/i/E6QJw8fLmBm5ZFuM7PD6axvpHcziECwCAAQIUAAADBCgAAAYIUAAADBCgAAAYIEABADBAgAIA\nYIAABQDAAAEKAIABAhQAAAMEKAAABghQAAAMEKAAABggQAEAMECAAgBggAAFAMAAAQoAgAECFAAA\nAwQoAAAGCFAAAAwQoAAAGCBAAQAwEOJ0Abg4paSkyLIsry6D8Z0bPyUlxWtjBwJv9z/rPzBYtm3b\nThcBAIC/4RAuAAAGCFAAAAwQoAAAGCBAAQAwQIACAGCAAAUAwAABCgCAAQIUAAADBCgAAAYIUAAA\nDBCgAAAYIEABADBAgAIAYIAABQDAAAEKAIABAhQAAAMEKAAABghQAAAMEKAAABggQAEAMECAAgBg\ngAAFAMAAAQoAgAECFAAAAwQoAAAGCFAAAAwQoAAAGCBAAQAwQIACAGCAAAUAwAABCgCAAQIUAAAD\nBCgAAAYIUAAADBCgAAAYIEABADBAgAIAYIAABQDAAAEKAIABAhQAAAMEKAAABghQAAAMEKAAABgg\nQAEAMECAAgBg4P8BcxeynoHUQewAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118e76748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(6, 4))\n",
+    "ax = fig.add_axes([0, 0, 1, 1])\n",
+    "ax.axis('off')\n",
+    "ax.axis('equal')\n",
+    "\n",
+    "# Draw features matrix\n",
+    "ax.vlines(range(6), ymin=0, ymax=9, lw=1)\n",
+    "ax.hlines(range(10), xmin=0, xmax=5, lw=1)\n",
+    "font_prop = dict(size=12, family='monospace')\n",
+    "ax.text(-1, -1, \"Feature Matrix ($X$)\", size=14)\n",
+    "ax.text(0.1, -0.3, r'n_features $\\longrightarrow$', **font_prop)\n",
+    "ax.text(-0.1, 0.1, r'$\\longleftarrow$ n_samples', rotation=90,\n",
+    "        va='top', ha='right', **font_prop)\n",
+    "\n",
+    "# Draw labels vector\n",
+    "ax.vlines(range(8, 10), ymin=0, ymax=9, lw=1)\n",
+    "ax.hlines(range(10), xmin=8, xmax=9, lw=1)\n",
+    "ax.text(7, -1, \"Target Vector ($y$)\", size=14)\n",
+    "ax.text(7.9, 0.1, r'$\\longleftarrow$ n_samples', rotation=90,\n",
+    "        va='top', ha='right', **font_prop)\n",
+    "\n",
+    "ax.set_ylim(10, -2)\n",
+    "\n",
+    "fig.savefig('figures/05.02-samples-features.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Hyperparameters and Model Validation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Cross-Validation Figures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def draw_rects(N, ax, textprop={}):\n",
+    "    for i in range(N):\n",
+    "        ax.add_patch(plt.Rectangle((0, i), 5, 0.7, fc='white'))\n",
+    "        ax.add_patch(plt.Rectangle((5. * i / N, i), 5. / N, 0.7, fc='lightgray'))\n",
+    "        ax.text(5. * (i + 0.5) / N, i + 0.35,\n",
+    "                \"validation\\nset\", ha='center', va='center', **textprop)\n",
+    "        ax.text(0, i + 0.35, \"trial {0}\".format(N - i),\n",
+    "                ha='right', va='center', rotation=90, **textprop)\n",
+    "    ax.set_xlim(-1, 6)\n",
+    "    ax.set_ylim(-0.2, N + 0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### 2-Fold Cross-Validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1alSVJgMADhZhVkWrVq1KU1NTkuSBBx5IXV1d6yuT/mbDhg156qmnqjAdAHCwCbMq6tq1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\nnOjGf5sKAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bdaa6a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_axes([0, 0, 1, 1])\n",
+    "ax.axis('off')\n",
+    "draw_rects(2, ax, textprop=dict(size=14))\n",
+    "\n",
+    "fig.savefig('figures/05.03-2-fold-CV.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### 5-Fold Cross-Validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UeXm5Vq5cqWXLltmeBGCaefjwob766iu53W45jqPdu3frypUrun//vuLxuNatW6fCwkJ1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\nwACiCwAAwACiCwAAwACiCwAAwID/A+awRviS8pn9AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bbe4908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_axes([0, 0, 1, 1])\n",
+    "ax.axis('off')\n",
+    "draw_rects(5, ax, textprop=dict(size=10))\n",
+    "\n",
+    "fig.savefig('figures/05.03-5-fold-CV.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Overfitting and Underfitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def make_data(N=30, err=0.8, rseed=1):\n",
+    "    # randomly sample the data\n",
+    "    rng = np.random.RandomState(rseed)\n",
+    "    X = rng.rand(N, 1) ** 2\n",
+    "    y = 10 - 1. / (X.ravel() + 0.1)\n",
+    "    if err > 0:\n",
+    "        y += err * rng.randn(N)\n",
+    "    return X, y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "def PolynomialRegression(degree=2, **kwargs):\n",
+    "    return make_pipeline(PolynomialFeatures(degree),\n",
+    "                         LinearRegression(**kwargs))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Bias-Variance Tradeoff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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goaEB0dHRmDlzJh5//HFs2bIFzzzzDGpqalo974mcoRDMsaFOaNeuXRg/fjxC\nQkIA2JZdzJ07F7///nuL6/+JiIioa+KcgYjI9zmdMZGRkdGsiMkXX3yBW2+91e2DImrLG2+8YV8u\nkZ2djZdffhmTJ0/mBIOIqBPh3II6AucMRES+z6muHO+88w4+++wzh7Sr7Oxs/N///Z/HBkZ0Pq+8\n8oq904VSqcTkyZOxaNEibw+LiIicxLkFdRTOGYiIfJ9TGRP9+/d3WJel1Wrx2muvYcmSJR4bGNH5\nJCYm4r333sPBgwexb98+PP/8883aTBERke/i3II6CucMRES+z6nAxFVXXWVPd7NarXjmmWewaNEi\nhISEsA0MERERtRvnFkRERCTxX7Zs2TJn7mgwGLBz504kJSVh69at2L9/P7Zv3478/HxUVFTg0ksv\nPe/jzWaLvfczEREREecWRN712muv4eeff8Yll1zi0uMbGhrs9TtSUlLcPDpy1e+//441a9YgKirK\n5TanvqqqqgovvvgiKisrodfrsW7dOvTs2RNxcXFtPraoqAirVq2CyWRyuf18Z7N//36sW7cOPXr0\nsHe281VO1ZiQCCEwYsQIfPHFFwCA4uJiPP7441i8eHGbj9Vqa1wboZfEx0egrMzg7WHIFo+v5/EY\nex6PsWfx+HpefHzzFrkdrSvNLQCe157G4+u8+vp66PV69OnTv13H7NxjHBERidLSszzubuKOc9hg\nqAMAGI31sntdjEYjAKCuzmTfz6qqWqf2U6drvGbI7bi0xmi0HSO93rlj5A6uzi3a9TWDK32eiYiI\niFrDuQWRd1RWlgMAunVr+5vm84mJ6YaammrU1dW6Y1jkBl1jOZz44x8A8DoiB04HJnr37o2NGze2\neRsRERGRMzi3IPKeigr3BCZiY2MBAFpt5QWPiagtUjBbCNs/221eHBC5DRdmEhERERF1Me7KmIiO\njgEA6HTaCx4TUVsaAxPMmJAbBiaIiIiIiLqYiopyKBQKxMTEXtB2oqJsgQm9XueOYZFbdY0P7MyY\nkAcGJoiIiIiIuhAhBCoqyhEVFY2AAOUFbSsqKhoAoNczY4I8r7EukWhSS4ORCTlgYIKIiIiIqAup\nrjaioaEesbEXtowDAMLCwhEQEACdjhkTvkLexS8bl3KwxoS8MDBBRERERNSFSPUgLnQZB2D7Bjsq\nKgZ6vVbmH4g7Hzl+YJf2yXaqMWNCThiYICIiIiLqQqQOGu4ITABAdHQ0zGYzqquNbtkeUeuaLuXw\n6kDIzRjoE/zgAAAgAElEQVSYICIiIiLqQnQ6W2AiOto9gQkWwKSO0rRdqJQxoZBjakgXxMAEERER\nEVEXotVKSzli3LI9qWUoC2D6CvmmEjQu5WgsfsnAhDwwMEFERERE1IXodJUICwuHUhnolu1JnTmk\n2hXkG+T4gb1pVw6SFwYmiIiIiIi6CJOpAUajwW31JQAu5aCO1LQrh5Qx4c3xkLswMEFERERE1EW4\nsyOHJCQkBAEBSlRV6d22TXJd1ysKyciEHDAwQURERETURUgdOdxV+BKwpddHRkaiqqqKLUPJoxqL\nXwqw+KW8MDBBRERERNRFSB053JkxAQCRkVEwmRpQX1/n1u0SNdVY/LIrZobIGwMTRERERERdhNSR\nQ+qk4S4REVEAwOUcPkSemQSNxS/ZlUNeGJggIiIiIuoitNpKKJVKhIWFu3W7kZFSYKLKrdul9pPz\nchrHpRwkJwxMEBERERF1AVarFXq9FtHRsW7/llkKTBgMzJggz2l63jYGJ9p3LjOo4ZsYmCAiIiIi\n6gKqq42wWCyIjo52+7YjIyMBAHo9AxPkeU2DC87H2Ljkw5cxMEFERERE1AXo9ToAQFSUJwITzJgg\nz2vMmGCNCblhYIKIiIiIqAuQAhORke4tfAkASmUggoNDWPzSp8j3A7stJsElGXLCwAQRERERURfg\nyYwJwLacw2AwcA2/18n7+CsUCgghmrQLlW8ApithYIKIiIiIqAtoDExEeWT7ERFRsFotqK42emT7\n1D5yXeEgBSYaf/fiYMhtGJggIiIiIuoCqqp0UCqVCAkJ9cj2G1uGcjmHN8k/YaWxzgTAGhNywcAE\nEREREZHMCSGg1+sQFRXtsQ9yjYGJKo9sn9pLnh/YFQr8sZTDtXah5JsYmCAiIiIikrmammqYzWaP\n1ZcAgIgIW8tQduYgz2usMcGECXlgYIKIiIiISOYaO3J4LjAh1a7gUg5vk/daDluNCaBxPxmZkAMG\nJoiIiIiIZE4KFngyYyI8PAIAYDQaPPYc5Dz5ZhIo4ErwRb7HQx4YmCAiIiIikjlPtwoFAH//AISG\nhsFgYI0Jb5J78UspY0KqMcHil/LAwAQRERERkcx1RGACsGVNGI0Gh3aORO7UWPyy8Xfq/BiYICIi\nIiKSOb1eB39/f4SFhXv0eSIiImC1WlFTU+3R56Guy5YhIcAaE/LCwAQRERERkYxJrUIjIz3XKlQS\nHm7rzME6E94k/2yVphkTJA8MTBARERERyVh9fR0aGuo9vowDsGVMAIDBwMCE98kzk6AxuMYaE3LC\nwAQRERERkYx1VH0JoGnGBAtgeov8MwkUrGEiQwxMEBERERHJWGNgIsrjz9WYMcHAhLfJNZGAXTnk\niYEJIiIiIiIZkwITkZEdmTHBpRzkSaJJYMLLQyG3cDowkZGRgfnz5wMAcnJyMHfuXCxYsAB33XUX\nKisrPTZAIiIikifOLYg6Rkcu5QgODkZAQABrTPgEeX5it2VMNF3K0b795DIQ3+RUYOKdd97BM888\nA5PJBABYuXIl/va3v+H999/HVVddhbVr13p0kERERCQvnFsQdRy9Xgc/Pz9ERER6/LkUCgXCwyNZ\nY4I8xvUMCXkGauTCqcBE//798cYbb9h/X7VqFYYOHQoAMJvNCAoK8szoiIiISJY4tyDqOFVVekRE\nRMLPr2NWcUdERKCurs4eeKSOJveMAMUf7UJZY0JOnHp3uuqqq+Dv72//PS4uDgBw6NAhbNiwAbff\nfrtHBkdERETyxLkFUcdoaKhHbW1NhyzjkISHswCmL5Dr5/VzAxEMTMhDgKsP3L59O9asWYO1a9ci\nJiamzfvHxIQiIMC/zfv5kvj4CG8PQdZ4fD2Px9jzeIw9i8e3a+kKcwuA57Wn8fg6On3aCADo0SPe\nbcemre306BGHnBzA39/M18MFF3rMwsJsGWeRkSGyPP7+/rbv1oOCbB9lu3ULR1RU2/tpMjXWPZHj\ncWlJeHgwACAqyvfPBZcCE5999hk2bdqEDz74AJGRzq1V02prXHkqr4mPj0BZGYv2eAqPr+fxGHse\nj7Fn8fh6ni9NUrrC3ALgee1pPL7NnThRAgAIDAxzy7Fx5hj7+dk+GBcXlyIqqscFP6erzGYTMjN/\ng1IZCLU6pVN8s+6Oc9horAcAVFXVyfL/g9VqW8ZRW9sAAKioqEZDQ9sLAZpeM+R4XFpiNNYBAPT6\n2g7bZ1fnFu0OTFitVqxcuRIJCQl44IEHoFAoMGbMGDz44IMuDYCIiIi6Ns4tiDynqqrjOnJIpCKb\n3u7M8fPPPyArKxMA4OfnB5Uq2avj6TjyrjGhUChgtVqb/O7FwZDbOB2Y6N27NzZu3AgA2L9/v8cG\nRERERF0D5xZEnteRrUIlUo0Jo9F7gYmammpkZx+Gv78/LBYLMjN/w/DhIzpF1oS7yH9X5R2A6Wo6\npjQvERERERF1OCkw4ewSKXcIDw8H4N3il3l5uRBCYNy4S3HRRYOg1VZAp9N6bTzkPgqF1JWj8Xfq\n/BiYICIiIiKSKb1eh4iISPj7u1zzvt38/QMQGhrm1YyJoqJCAMDAgYPQp09/AMDp08VeG09HEjJP\nJJACEWwXKi8MTBARERERyZDZbEJ1tRGRkVEd/twREREwGg0OtQA6ihACp08XITIyChERkejZMwEA\ncOZMSYePxbvk+4FdOERfnNtPxi98GwMTREREREQyVFWlB9Cx9SUk4eGRsFqtqKmp7vDnrqgoR319\nPRIS+gAAYmO7wc/PHxUV5R0+Fu+Qd8qELUNCMGNCZhiYICIiIiKSIW8UvpR4swBmSUkRAKBXr94A\nbB05YmJiodVWnPNNO3VOUo0JKTDh5eGQWzAwQUREREQkQ94MTERESIEJY4c/d3n5WQBAjx697LfF\nxnaD2Wz2akFOcg+FQqqjIQWZGJmQAwYmiIiIuhCtthL/+99ubNjwnreHQkQeptfblnJERnpnKQcA\nGI0dHwioqCiDv78/oqNj7LdJdTYMBn2Hj8db5JtJIC3l+OM3+e5ol9Jx5XmJiIjIKywWC06cyIdG\nk4Hi4lMAgNDQMC+Piog8zReWchgMHbuUw2q1orKyArGxcfDza/wOVsrg6OjxkPtJGROsMSEvDEwQ\nERHJlNFoQHb2YWRnH7YXoOvduy/U6hQMGJDo5dERkadVVekQGhoGpVLZ4c/duJSjYwMBOp0WFosF\n3brFOdwuZXB0haUccq+jweKX8sTABBERkYwIIVBUVIisrAwcP54PIQQCAwMxYkQaVKpkxMZ28/YQ\niagDWCwWGAxV9laZHS04OAT+/v4dHpioqCgDAHTrFu9we0RE1wlMNJLrB3bFH0EJFr+UEwYmiIiI\nZKCurg5Hj2YjKysDOp0WABAXFw+1OhWDByd55RtTIvIeg6EKQgivLOMAbN9ih4dHdHiNicrKCgBo\nIWPCe11CyL0UCgWXcsgQAxNERESd2NmzpdBofkde3lGYzWb4+/tj6NDhUKmS0aNHL07YiLooqb6E\nNwpfSiIiIlFUVAiz2YSAgI4JjkqB2aaFLwFAqVQiJCSkS2VMyPvtXzRZstK+HZX7UpfOioEJIiKi\nTsZsNiEvLxcaze84e7YUgK3ivEqVjKQkNUJCQrw8QiLyNm8WvpQ0ZikYmwUKPEWv1yIgIABhYeEt\njCcSlZXlEELIOmgr98/dtowJV2pMyPc1lwMGJoiIiDoJnU6LrKxMHDmiQX19PRQKBQYMSIRanYy+\nfQfIeqJNRO3jW4EJQ4cEJoQQ0Ol0iIqKbvH9MCIiAmVlpaitrekinYnkeU2QunI0/i7P/exqGJgg\nIiLyYVarFSdOFECjyUBR0UkAQEhIKEaOHAOVKtle0I2IqKmqKikwEeW1MXR0XYeammqYzaZWgyBS\nMKLrBCbkyrErB8kDAxNEREQ+qLraiJwcDbKyMlFdbQQAJCT0hkqVgosuGgx/f38vj5CIfJler0Nw\ncDCCgoK9NoaO7oQh1ZeIimo5MBEcbFvmVltb2yHj8R55f2B3fSkH+TIGJoiIiHyEEAIlJUXQaDJw\n/HgerFYrlMpAqNUpUKlSmlWZJyJqidVqRVWVHvHx3b06jo7OmNDrWy58KQkNDQVgy5igzk/utUK6\nGgYmiIiIvKy+vt7e6lOrrQQAxMbGQa1OwZAhwxAYGOjlERJRZ2I0GmC1Wr3akQPo+MBE2xkTUmBC\n3hkTcl/h0DRjgoEJ+WBggoiIyEvKy89Co8lAbm4OzGYz/Pz8MHhwEtTqFPTsmcAJFxG5pKpKD8C7\nhS8BW4vO4OBgGAwdFZiw1dWIjm55v6WORV0lY0Ku15DG3RKQa4HProiBCSIiog5kNpuRn58LjSYD\npaWnAdjWYUutPqVUYyIiV/lCRw5JeHgEdDpth3y7rddrERQUZK8lca6QkK6RMSF/tvPIdk55eSjk\nNgxMEBERdQC9Xmdv9VlXVwcA6NdvINTqFPTrNwB+fn5eHiERyUXjkgZfCExEory8DPX1da0GDNxB\nCIGqKj1iY+NaDYB0nYwJea/lkF5fq5VLOeSEgQkiIiIPsVqtKCw8Do0mA4WFJwDYqsKnpY2GSpWM\nyEjvtfEjIvlqbBXacq2FjhQR0VhnwpOBiZqaalgslvO+rwYFBUOhUKCujhkTnZkUjBDC2q7ABGMY\nvo2BCSIiIjerqalGTo4G2dmH7W3yevZMgFqdgsTEwfD35+WXiDxHp9MhMDAIwcHeaxUqkQpgGgwG\nxMV5rkuIVFdDalHaEj8/PwQHB3eBjImugRkT8sKZERERkRsIIXD6dDGysjKQn38MVqsVAQFKDB+e\nDLU6BXFx8d4eIhF1AbYlDbrzLmnoSB3VmUMKAreViRYcHIqaGqNHx+IrfOH19wRXMybItzEwQURE\ndAEaGhqQm5sDjeZ3VFZWAABiYrrZW30GBQV5eYRE1JUYjQZYLBafqC8B2GpMAIDRWOXR56mqsm3/\nfBkTgK3OhFZbAYvFAn9/f4+OyVuE3PuF/sFqtYJdOeSDgQkiIiIXVFSUQaPJRG5uNkwmE/z8/DBo\n0FCoVMlISOjDb3GIyCt8qSMH4LiUw5MMBttSjrYyJqQCmPX1dQgNDfPomMgzGjMm2JVDThiYICIi\ncpLFYkZBQR40mgycPl0MwDbpTksbjeHDR3CSS0Re1xiY8H7hSwAICwuDQqHosKUcUrHN1gQF2epu\n1NfX8z2702oamGBkQi4YmCAiImqDwVCFrKxM5OQcRm2trZp73779oVanoH//i9jqk4h8hl7vO61C\nAVvBybCwcI8HJqqq9AgJCUVAgPK89wsMtC2va2io9+h4yHOa1pjgUg75YGCCiIioBUIIFBaegEaT\ngZMnCwDYvmlLSUmHWp3sM99GEhE15WtLOQBbZllp6WlYrVaPBHKtViuMRgPi43u0eV+p7k99vfwD\nE3JNJpD2i1055IWBCSIioiZqa2tx5IgGWVmZ9vZzPXr0hEqVgkGDhrT5bRwRkTfp9ToEBgbaayn4\ngoiICJw5U4LqamObxSldUV1dDavV6tS2u0JgQv7FLxszJvz8+HFWLvhKEhFRlyeEQGnpaWg0GcjP\nz4XFYkFAQACGDVNDpUpB9+5tfwtHRORtQgjo9TrExnbzqW+SGztzGDwSmJAKXzqz7a61lMN3zgF3\nkk5tV2tMyD9w0zkxMEFERF2WydSA3Nwj0GgyUFFRBgCIjo6BSpWCoUOHIzg42MsjJCJyntFo/KNV\nqG8tNZM6c3iqzoTUKrStjhxA04yJOo+MhTqCLRhhaxfa/seRb2JggoiIupzKygpkZWXg6NFsNDQ0\nQKFQ4KKLBkOtTkHv3n196ptGIiJnVVX5VuFLidQpQ+qc4W7ty5ho7MpBnZNju1Ber+XC6cBERkYG\nXnnlFXzwwQcoLCzEokWL4Ofnh8GDB+PZZ5/15BiJiIgumMViwfHjtlafJSVFAGxt7FJS0jFsmNr+\njR51HM4tiNxLp/O9wpeA41IOT5ACHu3JmOgaSznkicUv5cmpwMQ777yDzz77DGFhtl6/L7zwAh57\n7DGMGjUKzz77LHbt2oXJkyd7dKBERESuMBgMyM7ORE6OBjU11QCAPn36QaVKwYABF8Hf39/LI+ya\nOLcgcj9f7MgBAOHh4QA8uZRDyphoO8DcFYpfAnKvodBY/JKBCflwql9P//798cYbb9h/z8rKwqhR\nowAAl112Gfbu3euZ0REREblAavX51Vef4cMP38HBg/thNpuRnDwSf/rT7bj++llITBzMoIQXcW5B\n5H6+GpgICgpGQIASBoPnMiZCQ8Pg79/2d65dqfilXD+0cymHPDmVMXHVVVehuLjY/nvTSqZhYWEe\ne5MhIiJqj7q6Whw5koWsrEz7BD0+vjvU6lQMGjQUSiVbffoKzi2I3E+v10KpVCIkJNTbQ3GgUCgQ\nERHhkYwJq9UKo9GAHj16OXX/gIAA+Pn5yTpjQu5NJxxjEQxMyIVLxS/9/BoTLaqrqxEZ6f62P0RE\nRM4qLT2DrKwMHDt2BBaLBf7+/khKUkGlSkb37j35jUonwLkF0YURQqCqSo/o6FiffM8LD4+AVlsJ\nk6kBSmWg27ZbXW2EEMKpZRyALUgSFBQk68CE/DWe3754rpNrXApMDB8+HL/++itGjx6NH3/8ERdf\nfHGbj4mJCUVAQOdKmY2PZyE0T+Lx9TweY8/jMfas8x1fk8kEjUaDX3/9FadPnwYAxMbGYtSoUUhN\nTUVISEhHDZPcoKvMLQC+b3haVz2+VVVVMJvN6N49zuPHwJXtx8XF4tSpk1AqrW4dX01NJQCgR494\np7cbEhKChoYGnz1XLnRcoaG2wE90dKjP7uOFCAlpDGwFBPg5vY8WS7X9Zzkel5aEh9u60ERFhfj8\nPrsUmHjqqaewdOlSmEwmJCYm4pprrmnzMVptjStP5TXx8REoK2Maqafw+Hoej7Hn8Rh7VmvHV6ut\nRFZWJo4ezUJ9fT0UCgUGDkyESpWCvn37Q6FQwGg0e6zImpz40iSlK8wtAL5veFpXPr7FxacAAMHB\n4R49Bq4eY6XSFiwuLDwDINht47FtD/D3D3Z6XAEBSuj1ep88V9xxDtfUNAAA9Ppan9zHC1VXZ7L/\nbLEIp/ex6TVDjselJUZjHYCOPRdcnVs4HZjo3bs3Nm7cCAAYMGAAPvjgA5eekIiIqL2sViuOH89H\nVlYGiooKAQAhIaFITx+L4cOTnU7hJd/CuQWR+2i1WgBAdHSMl0fSMqkls7uDxkajrVVoRITzy7+C\ngoJhsVhgNpsREODS97TkRU2Xb3Aph3zwfyIREfms6mojsrMPIzs7E9XVthTMhIQ+UKtTMHDgIHbV\nICL6g05nC0zExMR6eSQtawxMVLl1u42tQp0PTAQG2pYCmEwNsgxMCJlXv2RgQp7k9z+RiIg6NSEE\niotPYffuLBw5cgRCCAQGBmLEiFSoVCmIje3m7SESEfkcnc5Wa8FXMyakzDaj0ejW7UoZGO3JnJOK\nbzY0NPhcBxNqHwYm5IOBCSIi8gn19XU4ciQbWVkZ9m/+unWLh1qdgiFDktxaxZ2ISG602kqEhIQi\nKMh99RvcKSzMFjgwGNyfMRESEoqAAOfbQUuto00mUxv3JF/kmDHhxYGQWzEwQUREXlVWVgqNxtbq\n02w2w8/PH0OGDMOECeMQFBTFb0OIiNpgNptgMFQhIaGPt4fSqoCAAISEhLq1xoQQAkajAXFx3dv1\nOCnQbTI1uG0svkme108u5ZAnBiaIiKjDmc0m5OXlIisrA6WltorqkZFRUKmSkZSkQkhIaJeurk9E\n1B46nQ4AEB3tm/UlJOHhEaisLIcQwi0fKKurjbBare2qLwE0DUwwY6Izcjx3GJiQCwYmiIiow+j1\nWmg0mThyJAv19bYWVv37XwS1OgX9+g3gNx9ERC6Q6kvExPhmfQlJeHgEyspKUVtbi9DQC6/tYDBI\n9SXaG5iQlnLIM2OCxS+pM2JggoiIPMpqteLkyQJoNBk4deokACAkJAQjR47B8OEjEBkZ5eUREhF1\nblJdHl/PmGgsgFnlpsCErSNHZGT7AhNSV46GBnkGJiRy/czOwIQ8MTBBREQeUVNT/Uerz8P2NcW9\nevWGWp2Ciy4aBH9/XoKIiNxBq5UyJnw7MBEebgsgGI0GdO/e84K3J2VMSNt1Fotfdm4KhV+Tn704\nEHIrzgqJiMhthBAoKSlCVlYmCgqOwWq1QqlUQqVKgVqdjG7d4r09RCIi2dHptPD390d4uPMtM71B\nGp8UULhQrmZMdJ3il/J0oTUm5L7UpbNiYIKIiC5YfX09cnOzodFkQqutAADExnZDZGQvbN9ejy++\nsKJPn1+wcOFgpKcP8fJoiYjkQwgBrbYSUVEx8PPza/sBXtS4lMNdgYmqP7bLjAlH8v7g3TQwcfiw\nFjt3funU/ILZFb6NgQkiInJZeXkZNJoM5ObmwGw2wc/PD4MHD4VKlYKSkmrcdZcRJSWX2++/Z8/3\nWLcul8GJcxw8mIt1646hqCgQffo0MIBDRE6rrjbCbDb5/DIOoDFjwmiscsv2DIYqBAcH2zMgnNVV\nMibkWn+htFRr/1mn64EtW27l/KIFBw/mYtu2bCQkAG+/fRA33yx8+vgwMEFERO1isZiRn38MGk0G\nzpwpAWCbbKpUYzFsmAqhoWEAgOef/xIlJbc6PLak5HKsW7fRpy+MHe3gwVwsXGhwOFZ79nCCRUTO\nkQpf+npHDgAIDQ2Dn5+fWzImhBAwGKoQG9ut3Y+Vf8aEvGVkaNG9u+NtnF84kuYWffuORELCDuzd\nOwE7dpT69NyCgQkiInJKVZUeWVmZyMnRoK6uFgDQr9+AP1p9DmyWQlxU1PI3WK3d3lWtW3eMARwi\ncplU+NLXO3IAtm/ww8Mj3FJjora2BhaLpd3LOAD5d+WQewmFqqoAe2BCiMasEM4vGklzi75999tv\n8/W5BQMTRETUKqvVisLCE9BoMlBYeBwAEBwcjNTUUVCpkhEVFd3qY/v0aXnC19rtXRUDOER0IXS6\nzhOYAGz1IIqLT8FsNiEgQOnydhrrS7S/5XRjxgSvR51RRITF/nPTwATnF40649yCgQkiImqmpqYG\nR45okJWVaZ/89ejRC2p1ChIThyAgoO3Lx8KFg7Fnz/cONSYSEr7HwoWDPTPoTooBHCK6EI0ZE76/\nlAMAoqKiUVx8ClVVri3DkDQGJtrficTfPwAKhULGSznknTKRnt4NRUW2QttSYILzC0edcW7BwAQR\nEQGwrdc9c6YEGk0G8vOPwWq1ICAgAMOHj4BKlYL4+O5tb6SJ9PQhWLcuF+vWbWRRx/NgAIeILkRF\nRTkiIiLtyxN8XWSkLcOhqkrnpsBE+zMmFAoFlMrALpAxIc/il717x6OoKBcAEB19BrNmbeT84hzS\n3AIIsd/m63MLBiaIiLq4hoYG5ObmQKPJQGVlOQBbSrBanYKhQ4cjKCjI5W2npw/hRKENDOAQkatq\na2tQW1uD/v0v8vZQnBYZaVsCqNfrL2g7rrYKlSiVStlmTMi9xkTTbiNjx3bH1KnTvDga3yTNLbZt\n+wUAMG7cT7j55pE+PbdgYIKIPIYtEH1bRUU5srIycPRoDkymBvj5+SExcQjU6hQkJPSRbZsxX8QA\nDhG5orLSls7erVucl0fivKYZE646eDAXe/bkITISWLZsL+64I6nd76FKZSDq62tdHkNnINfLOOcn\nzklPHwKl0oiffjqLu+9OR2Ki72ZLAAxMEJGHsAWib7JYLCgoOIasrAyUlBQDAMLCwpGWNgrDhqkR\nFhbu5RESEZGzpCy3C1kS0dGioqTAhGsZE9L84oYbQhEYaMamTXPx00/tn18EBiphMFxY1gZ5h0Lh\n1+RnBinkgoEJIvIItkD0LQZDlb3VZ21tDQCgT5/+UKtTMGDARc1afRIRke+rqJACE50nYyIoKBhB\nQUEuL+VYt+4YTp+ejZiYn1FWFg/AtfmFUhkIi8UCq9XKa2An0zQY0b7ABIMYvoyBCSLyiM7Ypkhu\nhBA4deoENJpMnDxZACEEgoKCkJKSDpUqudNUcCciopZVVlZAoVAgJqZzvZ9HRkajsrIcQoh2f+Nd\nVBSI8HADlEoztNoYh9vbQ2pVajabEBjoei0l3ybPD+KO54w897ErYmCCiDyiM7Ypkova2lp7q08p\nVbZ79x5Qq1MxaNCQC+ob3x6sMUJE5DlCCFRWliM6Ogb+/p1rSh8ZGYWyslJUVxsRHt6+dp99+jTg\n9GktAKCyMsbh9vaQ2l6bzWYZBibkW/3y4MFcbNuWhYQE2+86ndG7AyK36VzvYkTUabAFYscSQqC0\n9DSysjKRl3cUFosF/v7+SEpSQa1OQffuPTt0PKwxQkTkWUajEQ0NDejbt/Ms45BERdk6c1RV6dsd\nmFi4cDDKy/cCALTaWACuzS+USluQXq6dOQD5Fb+U5ha9eo1GQsKXAIB9+8wYNIhzCzlgYIKIPIIt\nEDuGyWTCsWNHoNFkoLz8LADbhE+tTsXQocMRHBzslXGxxggRkWdJhS87U0cOidSZQ6/XISGhT7se\nm54+BHPmHEVJCdCzpwazZuW6NL9omjEhN3JtFyrNLXr2PGi/raamJ9atO8a5hQwwMEFEHsMWiJ6j\n1VZCo8nA0aPZaGioh0KhwEUXDYJKlYI+ffp5vUo1a4wQEXlWZ+zIIbnQzhxhYbZila+/fiUiIiJd\n2kbTGhPyJa+UCWkOIYRfi7dT58bABBFRJ2GxWHDiRD40mgwUF58CAISGhiE5OQ3Dh49odzqsJ7HG\nCBGRZ3XGjhySyMjGpRyu0Ov18PPzu6AW140ZE3IOTMiLNIewWhsDLkIo0KdPvbeGRG7EwAQRkY8z\nGg3Izj6M7OzDqKmpBgD07t33j1afifD39/fyCJtjjREiIs8qLz8LpVJpr9fQmYSFhcPPzw9VVTqX\nHl9VpUNkZNQFtflsrDEhv6Ucci1+Kc0thGgsehoSUooFC0Z5bUzkPgxMEBH5ICEEiooKodFk4MSJ\nfBD+kcMAACAASURBVAghEBgYhBEj0qBSJft86i5rjBAReY7JZIJWW4mePRO8vnTPFX5+foiMjIJO\np213y9D6+nrU1dVdcFFnOdeYkHTCU+O8pLnFli377bdNmKBs99xCyLUIRyfHwAQRkQ+pq6vD0aNZ\n0GgyoNfbvkmKi+sOtToFgwcn2b/h6QykiYLUMnTdumMOtxMRkWsqKsoghEB8fHdvD8VlMTGx0Om0\nqKurRUhIqNOPk7IsLjRTRM41JuT8uTs9fQgiIwW++cbWlePQISN27/7SqS8/5BaokRsGJoiIfMDZ\ns2eg0WQgL+8ozGYz/P39MXTocHurz874jRhbhhIReUZZma0LU3x8Dy+PxHXR0bEA8qHVVrYrMKHT\naQE01qlwVWNgQr4ZE3Irfik5fvy0/eczZxLx5ZfTOL+QAQYmiIi8xGQyIS/vKDSaDJSVlQKwtVBT\nqVIwbJgKwcEhXh7hhWHLUCIiz5CuGZ09YwKwdZlqT8tQrbbS4fGuUirlXPxSxikTAL777gz69bP9\nbLHY6oxwftH5MTBBRNTBdDotsrIycORIFurrba0+BwxIhFqdgr59+3fK7IiWsGUoEZFnlJWdRUBA\nwB9ZB51TdLStgKFOV9mux0kZEzExF1ZrqWtkTMhTeXmAPTBhtTYWQOX8onNjYIKIqANYrdY/Wn1m\noqjoJAAgJCQU6eljMXz4CJf7sPsyV1uGHjyYi3XrjqG0NBQ9etSwaCYRURNmsxlabQXi43tcUFcK\nb5OCKlIGhLO02goEBCgRHu56q1CgsfilySTHjAl569bNYv+5aWCirflFVpZt/vXppyewfr2B8wsf\nw8AEEZEHVVcb7a0+q6uNAICEhN5QqVJx0UWDfLLVp7u40jKUdSmIiM6vsrIcVqu1Uy/jAIDg4GCE\nhITaMyCcYbVaodNpERvb7YKzC5kx0XlNmZKAo0eLATQGJpyZX/ztb9W4+WagpGQAtm9nXQpf41Jg\nwmw246mnnkJxcTECAgKwfPlyDBw40N1jIyLqlIQQKCk5BY0mE8eP58FqtUKpDIRanQKVKgXdusV5\ne4gdwpWWoaxL0bVxfkHUNjkUvpTExMSipKQIZrPJHig4H4OhChaLxS1LWBrbhco3Y0ImK0ObGTy4\nD44e/RUAkJCQi1mzypyaX5w9OwnAAfttnF/4FpcCEz/88AOsVis2btyIPXv2YNWqVfj3v//t7rER\nEXUq9fV1OHo0G1lZmfbU1G7d4qBWp2Lw4CQEBna9tY/p6UPadcFnXYqujfMLoradPXsGQOcufCmR\nAhM6nQ5xcfFt3l+qR3GhhS8BeQcmhJz7hQJQKBqXb8yZMwijRl3c5mM4v/B9LgUmBgwYAIvFAiEE\nDAYDlMq2I5xERHJVVnYWWVkZyM3Ngdlshp+fPwYPToJanYKePRNkU8yyI7hal4LkgfMLoraVlp5G\nQIASsbGdP/tOynyorCx3KjCh1UqFLy88MCG9v8h7KYc85x9N51VNgxTn06dPA/LzW76dfINLgYmw\nsDAUFRXhmmuugU6nw5o1a9w9LiIin2Y2m5GRkYG9e/ejtNTWTzsiIhIqVTKGDVO3qyc7NXKlLgXJ\nB+cXROdXX1+HysoK9O7dt1MXvpRIwYiKijIAw9q8v1ZbAQBuWsphC0yYTHIOTMhT08CEs/8PFi4c\njJycX/94vO02zi98i0uBifXr1+PSSy/Fo48+itLSUixYsABffPFFl0xTJuoqpE4JztYKkCu9Xoes\nrEwcOaJBXV0dAKB//4FQqVLQr98AWUwUvalpXYqzZ0PRvTu7cnQlnF8QnV9pqW0ZR8+eCV4eiXsU\nFVUBAL755gg++KDtLglabSUUCgWio6Mv+Ln9/PygUChkuZRD7lwJTKSnD8Fzzxlw+DDQq9cJzJq1\nkfMLH+NSYCIqKsq+LisiIgJmsxlWq/W8j4mJCUVAQOeqPh8fH+HtIcgaj6/nuesY799/BHffXY2i\nosaihPv2/YgtW4oxdmySW57Dl1mtVhw7dgwHDhxAXl4eACA0NBTjx49Heno6YmJivDxCebnmmnRc\nc026t4dBXtDe+UVnnFsAvP55mpyPr0ZTDgAYOjTRq/vpjufev/8I7ruvATfdFI2QkAZs2TIb+/b9\nr9W5hRACFRVliI+PR8+e7rnu2pZzWH3unLnQ8QQH27JBYmPDfG7f3KGurrFVbGRkiNP7eOmlKhw+\n/ANuuGEApk2b5qnh+ZTw8GAAQFSU88fJW1wKTNx22214+umnMXfuXJjNZjz++OMIDg4+72O02hqX\nBugt8fERKCszeHsYssXj63nuPMavvPK7Q1ACAIqKLsMrr2zEm2/2dstz+KKammrk5GiQlZUJo9F2\nLHv2TIBanYLExMHo2TMGZWUGnssewvcJz/O1SUp75xedbW4B8Lz2NLkf34KCEwCA4OAor+2nu46x\nNLc4c+Y0hg07gogIw3nnFjqdFiaTCdHR3dy27/7+Aairq/epc8Ydx7e21pYFotVWQ6HwnX1zF52u\n8b2/urrB6eNVWVlt/9mXXnNPMhpt2b16fW2H7bOrcwuXAhOhoaF47bXXXHpCIup8ulIlYyEETp8u\nhkaTgYKCY7BarQgIUGL48GSo1SlOFedyFpfHEDni/IKodVarFaWlpxEdHYvg4BBvD+eCSXOI06d7\nYtiwI+jV6wwMhshW5xbl5WUA4NbrsFKplF3xy4MHc/HjjyfRrRuwfPmPmDdPLbu5RdOCl1xCKx8u\nBSaIqGvxdqeEjvgA39BQj9zcHGg0GaistBXXio3tBpUqBUOHDkNgYJBbn+/gwVwsXGhASUljJsqe\nPd9j3bpc2U0giIjowpWVnYXJZEKvXvKoLyHNIc6c6QUA6NnzNHJzh7Q6tygvPwsAiItzT5vUgwdz\ncfZsLfz8LLj//i//P3t3HtzWdd8L/IuN+74TBClSJMEFEEEZ8irLphd5q524sZMoSZ02D9NMk7aT\naeNJ8pJpnPa9vkza12XeJGmSFvVk8YuyOMnzUtuKLdGL6BWWIAFcQFIURRDcxZ0Esd33B0xSlCku\nIICLe/H9zHgsHUDAD0eg8Ls/nPM7svhyYDW3OHiwFoWFNpw48QBOnXLKLrdQKnffY4ISHwsTRLQt\nMU9KiPUF/OTkxNpRn36/H0qlEnV1DTAaTSgvr4jZUZ9Wa++G1wQAHk8brNbjskoeiIgoOoaHLwEA\ndLoqkSOJjtXcYmTkOgBAefnolrnFamGisHDvKyZWc4sHHihGaekYfv3rY7L4cmA1tzh48DkAgCDI\nM7fYeFyoPI9ETUYsTBDRtq48KSHe2w5icQEfDAbQ398Lh8OO0VEPACArKxsHD96A5mYjMjIy9xz3\ndpJpewwREe2d2x0uTFRUyKMwsZ5bPI+VFTXq6vrwx39cu+lnuyAIGBsbQU5OLtLT976NZTW3CAQu\nQqMJQKEQZHEBnyy5xcZTOaTXAJk2x8IEEe2I2awX5cM6mh+yc3Oza0d9Li8vAwAqK/fBaGzFvn01\ncV0OKPb2GCIiko5AIICRkWEUFBQhIyND7HCiZjW3ePHFZ3HhQi/0+rJN7zc9fRkrKyvYt29/VJ53\nNYfw+8OnV6hUAQQCGslfwK/mEFcvIpBbbrGxxwRXTMgFCxNEFDWx6AWx1wv4UCiEoaGLcDjOYXDw\nAgAgNTUNra1mGAwtyM0V56hPMbfHEBGRtIyNeRAMBqHTVYodSkyUl1fgwoVeeDxu5OTkbrjNZnPh\nN795FxUVwKlTl5Gfv/ftFqs5RCAQvhTSaMKFCalfwK/mFusUsswtNq6Y2P2XSoIgRDMcihIWJogo\nKmLVCyLSC/jl5SV0dTnQ2Xkec3OzAIDS0jIYDK2oq6uHWq2JOKZoEHN7DBERScvQkLz6S1xNq9UB\nAEZGhtHYaFgbX80trr++HBUVY3jmmT/E8893RS23WC1MqNUBWVzAr+YWL7xwFgBwzz3P47HH5Hgq\nx5U9Jtj8Ui5YmCCiqIhVM8fdXMALgoDR0RE4nXb09bkQCgWhVqvR1GSE0WhCcXFpxHHEgljbY4iI\nSFouXuyHSqWSTX+JqxUWFiE1NRVDQ4MQBGHtwjOcW3wStbX/gsXFDIyPF0MQSqOWW7z44hkAwIMP\n/haf/axBFp/JZrMe8/OD6Ow8jyeeaEN+foHYIUXdXldMUGJiYYKIoiKWDZe2u4D3+31wubrhcNgx\nNRU+5zwvLx9GowkNDc1ITU3bcwxERERimJ2dweXLU9i3bz80GnFX+8WKUqlEVVU1ent7MDU1iaKi\n8MkbbncKysrGkJMzD7u9BYKgXBvfK7NZj8XFITgcdjzxxBEUFBTt+TEpPliYkCcWJogoKsRo5nj5\n8hScTju6uzvh9/ugUChQW1sPg8GEiopKHiFFRESSNzDQDwCoqakVOZLYqq6uRW9vDy5e7F8rTOh0\nPmg0vQCA3t66tftGK7dQqcKXQoFAMCqPl2jkmgddWaCT62tMRixMEFFUxKuZYzAYxMBAHxwOOzwe\nNwAgMzMTra1mNDUZkZWVHdXnIyIiEtPFi30AgOrq6JxIkaiqqqqhVCrhcnXDbL4RCoUC/+2/1eGl\nl04hGFSivz9cmIhmbqFShY+aDAYDUXk8io+UlNS1X/O4UPlgYYKIoiLWzRzn5+fR2XkOnZ3nsby8\nBCDcBMxgMKG6ev9ackFERCQXCwvzGBnxoKxMi4yMTLHDianU1DTU1TXA5erC0NAgqqqqUV6egfz8\nJczMZMNkeiHquYVavbpiQl6FCbmfOnFlzsfjQuWDhQkiippoN3MUBAFDQ4NwOu24ePECBEFAamoq\nWlqug8HQIsuGTkRERKt6erogCAIaGprFDiUuWloOwuXqgs32NnS6KthsbwMAPve5B/D1r1dE/flW\nt3IEg/LcypEM2GNCPliYIKKE4/Uuo7vbCYfDvnbUZ3FxKYxGE+rqGmTb/IuIiGiVIAjo7nZApVKh\nrk76p0XsRElJGWpq6jAw0Ief/cyKhYV5VFVVo6xMG5PnU6vD37zLbcVEMuFxofLBwgQRJQRBEDA+\nPgqHw46+vh4Eg0GoVCo0NhpgMJhQWlomdohERETwepfR1eXApUsXMTs7g1AohNzcPJSXV6ChoTlq\nq/lGRz2YnZ1BfX1DUp0udccd98DnW8Hw8BDKyytw1133x6zB4fqKCXkWJpKhLyRXTMgHCxNEJCq/\n34/e3m44nXZMTIwDAHJz82AwmNDY2Iy0tHSRIyQiIgoX0O329/Hee2/C5wufCpGVlQ21Wo3RUQ9G\nRobx/vvvoLZWj5tuuhW5uXl7er7z588CAJqaDuw5dilJS0vDRz/6cfj9/pivkGSPCelLptcqdyxM\nEJEopqcvw+m0o6enEysrK1AoFKipqYXR2AqdrorHPxERUcJYWVnB73//PC5duoi0tHTcfPNtaGho\nRkZGBoBwkf3ixQuw299Df78Lg4MXcMstt8NgaIno82x2dgb9/S4UFRWjoqIy2i9HEuKxbVPuKyYA\n+edSuykqMbdMbCxMEFHcBINBXLzYD4fDjuHhIQBARkYmDh1qRVNTC7KzedQnERElFq93Gc8++xtM\nTIyhsnIf7rrr/rWCxCqNRoP6+gbU1enR29uN118/iddeewWDgxdw11337Xr133vvvQVBEHDw4PW8\nmIqh1R4TbH4pPQaDCU6nHXl5e1uZRImDhQmiK9hsLlitvdsed7nT+1HYwsI8OjvPo6vrPBYXFwEA\nWq0ORmMrampqedQnERElpEDAj+ef/y0mJsbQ2GhAW9vRLfe0KxQK6PVN0Gp1OHnyJQwODuAXv/gp\n9u1rwS9/ObWj/OKpp86jtnYQXm8qZmdZlIil1RUTctvKkQxuv/0uHDlyB3tMyAgLE7QjyXAhbrO5\nYLHMw+M5tjbW0dEOq9W14bXu9H7JThAEDA8PweGwY2CgD4IgICUlBQcOtMJgMKGgoFDsEImISESJ\nnlsIgoCTJ09gbGwUen0T7rjjnh2vXsjKysZDDz0Cm+0dvPPOaTgcHRgZuRtvv30L3npLcc384vOf\nn8VDDy1BoQB+8YtP4qc/HWR+EUNy7TGRLFiUkBcWJmhbyXIhbrX2bniNAODxtMFqPb7hde70fsnK\n6/Wip6cTTqcdMzPTAICiomIYDCbo9Y3QaFJEjpCIiMQmhdzCZnsHfX09KCvT4o47ju56S4VCocCh\nQzfi+PFLKC6extGjL2PfvkH87ncPXyO/cOGGG1JQUjKBd945hIsXawDUML+IIfn3mCCSDhYmaFvJ\nciHudm9+wXz1+E7vl2zGx8fgdNrR29uNQCAApVIFvb4JRqMJpaXl3CNLRERrEj23GB314N13O5CZ\nmYX77//I2gVsJFyuPDz99Cfxh3/4W+j1vfjCF/4Np061YXh4/TGXlpZQVDSKsrI5XLpUiRMn7lm7\nLdnzi1ha3UoaCMizxwRzL5ISFiZoW8lyIa7T+XY0vtP7JYNAwI++PhccDjvGx0cBADk5uTAYWtDY\naEB6esY2j0BERMkokXOL8Akc/wVBEHD33ffv+bNMp/Phrbcy8dRTn8Hhw6dx++2v4iMfeQ7BoBLP\nPLMAQQhhbGwEZWUBDAxU45e//AQCAc2GP0+xsbqVQ24rJniEJkkRCxO0rWS5ELdY6tHR0Q6Pp21t\nTKtth8VSH9H95Gx2dhoOxzl0dzuwsrICAKiu3g+DwYSqqmpW6ImIaEuJnFt0dLyK+fk5mM03RuWo\nzivzhjfeuBVnz5pw9Ogvcf31U3C7BwEA+fkFyMnR4t//PR/Ly+sneCRbfhFv7DFBlDhYmKBtJcuF\nuNmsh9XqgtV6fMtGXDu9n9yEQiEMDl6Aw2HH0FA4kUpPT8d1192A5uYDyMnJFTlCIiJKRJOT4xge\ndmNpaREaTQpKSkrxuc/VJmRuMTw8hK4uBwoLi3Ho0E1ReczN84ZWmM16+P0+AApoNOEVEmVlyZdf\niGm9x4Q8t3IQSQkLE7StZLoQN5v1O3pdO72fHCwtLaKz8zycznNYXFwAAJSXV8BoNGH//ro97bsl\nIiL5GhoaxFtvvYGJibEP3Zaamob/8T+q8eKL/xdDQ2kJkVsEgwG8+urLAIC2trujepT1tfKGqxtC\nJ1N+kQjU6tUeE1wxkUy41SUx8YqCdoQflMlFEAR4PO61oz5DoRA0Gg0MBhOMxhYUFhaLHSIRESWo\nQCCA1157Bd3dTigUClRX16KuTo/s7FysrHjhdg+ip6cTQ0PduPXWAtxzz4MoLCwSO2zYbO9gZmYa\nBw60orS0XOxwKA6UynBhQm49JlZxay1JCQsTRLRmZWUFLlcnHI5zmJ6eAgAUFBTCaGyFXt+ElBTx\nm5IREVHiWl5exn/9128xNjaK4uIStLXdg+Likg33qa7ej+uvvxnvvNOB8+fP4umn/y/uvfdB7Nu3\nX6SogcuXp/D+++8gMzMLN954WLQ4KL4UCgXUarXsChNcEUBSxMIEEWFychwOhx0uVzcCAT+USiXq\n6xtgNLairEzLijsREW1rZcWLZ599GpOT49Drm9DWdnStueDVUlPTcOTIndBqK/HKKy/ghReewd13\n34+6uoY4Rx2+iHv11ZcRCoVw2213IiUlNe4xkHhUKhW3chAlABYmiJJUIBBAf3/4qM+xsREAQFZW\nNgyGG9HUZEBGRqbIERIRkVT4/X4899xvMTk5jubmA7j99rt3VNSura1HRkYGnn/+t3j55Reg0aRg\n376aOES8rqvLgZGRYdTU1KGmpi6uz03iU6nUbH5JlABYmJAwm80Fq7VX9g0pKbpmZ2fQ2XkOXV1O\neL3LAICqqmoYja2oqqqGUqkUOUIiIhLTbvOL1RUHY2Mj0OubdlyUWFVeXoEHHngYzz77NF566Vl8\n5COPoqxMG42Xsq2lpSW8+eZr0GhScOTIHXF5TkosarWaKyaIEgALExJls7lgsczD4zm2NtbR0Q6r\n1SVKcYJFksQWCoVw6dJFOBx2XLo0AABIS0vDwYOH0NzcgtzcPJEjJCKiRBBJftHV5YDL1YWSkjLc\nccc9EW3/02p1uPfeh/DCC/8PL7zwDB599NNwuUZjnlt0dLyKlZUV3HrrHcjKyo7qY5M0qFRq+Hwr\nYocRI9yKS9LBwoREWa29G5IGAPB42mC1Ho97QeCpp17G17+ehuXlxCiS0LqlpSV0dTnQ2XkO8/Nz\nAIDS0nIYjSbU1uqvufeXiIiS027zi8nJcbz++kmkpqbi3nsf3NMRm9XV+3H4cBveeOMUnnrqZ/jH\nfzRgfj52ucXQ0CBcri4UF5fCaDRF5TFJesIrJuS1lYPNL0mKeFUiUW735qcjXGs8Vmw2F/77f/fA\n6/3zDeNiFUko/GE0OurB6693wul0IhQKQa1Wo7n5AIxGE4qKSrZ/ECIiSkq7yS98vhW89NJzCAaD\nuPfeh5CdnbPn5z9woBU9PX2YmBjCvfd68etfC1j91jeaucXqkaYKhQJtbXdzG2MSU6lUsjuVYxV7\nl5OUsDAhUTqdb1fjsWK19sLrrdz0tngXSZKdz+eDy9UFh8OOy5cnAQD5+QUwGExoaGhGaiq7jBMR\n0dZ2ml8IgoBTp36P2dkZHDx4CNXV0TnqU6FQoL09DdnZmTAanRgdLcUbbxxZuz1aucX777+N2dkZ\ntLRch+Li0qg8JkmTSqWCIAgIhUIsUBGJKOLCxI9+9COcPHkSfr8fn/70p/HII49EMy7ahsVSj46O\ndng8bWtjWm07LJb6uMYRThD8m94W7yJJspqamoTTaUdPTyf8/vBRn7W1etx6683IyCjgUZ9EJCnM\nL8S10/zC4bCjv9+F8vIK3HDD4ajGMDSUhvPnK/H5z3tw110nMTFRgp6e8DGi0cgtJibG8P777yIz\nMws33HDLnh+PpG11+1EwGGRhQuaYEye2iAoT77zzDs6cOYPjx49jaWkJ//mf/xntuGgbZrMeVqsL\nVutxURtOhhOEVgCvAbhtbTw9/YW4F0mSSTAYxIULvXA47BgZGQYAZGZm4eDB69HUZERmZhaKi7Mx\nMTEvcqRERDvH/EJ8O8kvxsZGcfp0O9LS0nH06AN76iuxGZ3Oh7feasXPf54Fi+UsPvax38BqtWB+\n3rbn3CIQCODll19EKBTCnXfei5QUru5MduuFiQA0Go3I0RAlr4gKE2+88Qb0ej2++MUvYnFxEV/5\nyleiHRftgNmsF72HQ/iblVF4PGUAngagQVraEP7X/9KKHpsczc/Pwek8h64uB5aXlwAAlZX7YDCY\nUF29n5V+IpI05heJYav8wuv14sSJ5xAKhXD33ffH5CSL9dyiFr/97Rg+8YkhfPrTP0RlZSPM5lv3\n9Nhvv30a09NTMBpNqKzcF6WIScpUqvDlUDAopwaYbH5J0hNRYWJ6ehoejwc//OEPMTQ0hC984Qt4\n8cUXox0bScD6NytnP/hmZQkWy00sSkSRIAgYGgof9Tk4OABBEJCamgqTyQyDoQV5eflih0hEFBXM\nLxKbIAg4efIlzM/P4dChm1BVVR2T59mYW+RhfHwJJSVTyM6eQzAYjHiFRn+/C3a7DXl5+bj55tu2\n/wOUFK7cyiE33LpAUhJRYSIvLw+1tbVQq9WoqalBamoqLl++jIKCgmv+mfz8DKjV0V3qF2vFxTzP\neifuu8+M++4z7/rPcX63trS0hDNnzsBms2F6ehoAoNVqcf3118NgMOxouSHnOPY4x7HF+U0uu80v\npJhbANJ9X3d0dODixX7U1NTg/vuPxnSV3pW5hSAI+OUvf4nu7m50dJzEww8/vOVzbza/4+PjOHny\nJWg0GnzqU8dQUnLtnJW2J9X38GYyM9MAALm5aSgqSozXtdf5TU0N54iFhVnIzk6M15QIlMr1HjVy\neg9vJStr9f2dnvCvOaLChNlsxk9/+lP8yZ/8CcbGxuD1epGfv/W3ttPTSxEFKBY57c+32VywWntF\n7UVxNTnNbzQJgoCxsZG1pmLBYBBqtRqNjQYYjSaUlJQBAGZmvAC8Wz4W5zj2OMexxfmNvURLUnab\nX0gttwCk+74eHh7Cyy+/jIyMTNx++z04ceJMXHOLI0fuxszMHM6fP49AQEBb29FNvw3ebH7n5+fx\nu9/9An6/H/fc8yAUinRJ/h0kCqm+h6/F7w8BACYm5iAI4p9gFo359XrDjemnphbg3TpdTCozM4tr\nv5bTe3grCwvhN8Ds7HLcXnOkuUVEhYm2tja89957ePTRRyEIAp544gkuFUpQNpsLFss8PJ5ja2Md\nHe2wWl1RSyASsfAhNX6/H7294aM+JycnAAB5eflrR32mpaWJHCERUewxv0hM8/PzOHHiOSgUCtxz\nzx+gq2s45rkF8OH84o//+ACCwSC6uhzw+/248857oVZvncrOz8/h2Wefxvz8HG644RbU1TE/oY2U\nSvlu5SCSkoiPC3388cejGQfFiNXauyFxAACPpw1W6/GoJA/xKHzI2eXLU2tHffp8PigUCuzfXwej\nsRUVFZVMyIko6TC/SCyBQAAvvfQMlpeXceTIHdBqdfif//P5mOYWwLXzix/8wASVyoG+vh7Mzs7g\n7rvvR37+5tsy3O5L+P3vn8fy8jIOHrweZvONUYmN5GW1x0QoJL/CBPNIkpKICxMkDW735sdgXWt8\nt2Jd+JCjYDCIgYF+OBxn4fG4AQAZGZloabkOzc0HYtLhnIiIaLdWm12Oj4+hoaEZRmMrgNjnFsC1\n84uf/OQ4/s//eRSvvvoyeno68ctf/hSNjQY0NR1AYWERAoEAPB43HA47+vp6oFAocNttd67FTnQ1\nOTe/JJISFiZkTqfz7Wp8t+KRnMjFwsI8OjvPobPTgaWl8B63iopKGI0mVFfXRv0ceCIiokgJgoDT\np9vR19eDsjItbr/9rrVvX2OdWwBb5xdqtRp33XUfampq0dHxGpzOc3A6z33ovsXFpbjttjtRWloe\ntbhIftYLEwGRI4keQeBxoVvh/CQmFiZkLnwWeDs8nra1Ma22HRZLfVQePx7JiZQJggC3+xIcP+9m\nawAAIABJREFUDjsuXuyHIAhISUlFS8tBGAymay4/JSIiEosgCHjnnQ6cO3cG+fmFeOCBj0KtXj8J\nKta5BbCz/GL//npUV9diYKAfly4NYHZ2BqmpGmRm5qC6uhaVlfu4lJ22pVKFL4e4YoJIXCxMyNz6\nWeDHY9KcMh7JiRR5vcvo7u6E02nH7OwMAKCoqARGown19Y07OuqTiIgo3gRBQEfHa7DbbcjJycWD\nD34MaWnpG+4T69wC2Hl+oVQqUVtbj9ra8LjcToyg2ONWDqLEwMJEEjCb9THr9xCP5ERKxsZG4XTa\n0dvbjWAwCJVK9cG+3PBRn/zmhoiIEpXX68WpUycwMNCHvLwCfOQjj1yz71Esc4vVx2d+QfEg78IE\n806SDhYmaM9inZwkOr/fj76+HjgcdkxMjAEAcnJyYTCY0NRk+NA3TURERIlmbGwEJ048j/n5OWi1\nOtxzz4PIyMgQNaZkzy8oPuRZmGAPBZIeFiaIIjQzMw2n047ubidWVlagUChQU1MLg8HEfa1ERCQJ\ngiDg3Ln38eabryMUCuHQoZtw6NBNUCqVYodGFBdybH65iqkoSQkLE0S7EAqFMDDQD6fTDrf7EgAg\nPT0DZvONaG4+gOzsHJEjJCIi2hmvdxknT57AxYv9SE/PwNGjD0CnqxI7LKK4YvNLosTAwgTRDiwu\nLqCz8zw6O89jcXEBAKDV6mAwmLB/fx2P+iQiIkkZGxvBSy89h4WFeVRUVOLo0QeQkZEpdlhEcSfH\nrRw8DZOkiIUJibHZXLBae9kIKg4EQYDHMwSHw46BgX6EQiFoNCkwGk0wGk0oKCgSO0QiIqJd6+py\n4NVXX4EghHD99TfDbL4RZ870Mb+gpCTHwsQ67uUg6WBhQkJsNhcslnl4PMfWxjo62mG1upg8RNHK\nihc9PZ1wOM5hZuYyAKCwsAhGYyv0+kZoNCkiR0hERLR7giDgrbfewJkz7yI1NRX33PMgKiv3Mb+g\npCbPwgSXTJD0sDAhIVZr74akAQA8njZYrceZOETBxMQYHI7wUZ+BQABKpQr19Y0wGltRVlbOZpZE\nRCRZgiDgzTdfx9mz7yEvLx9/8Ad/iNzcPADMLyi5ybMwEcbUdSPOR2JjYUJC3O7Nv6m/1jhtLxAI\noK/PBafzLMbGRgEA2dk5MBha0NRkRHq6uEelERERRYPTaf+gKFGAj370UWRmZq3dxvyCkhmbXxIl\nBhYmJESn8+1qnK5tdnZm7ahPr9cLANi3rwZGowmVldU8Jo2IiGRjZGQYb7zRjvT0dDz00Mc2FCUA\n5heU3FZXTIRC8ilMsPklSRELExJisdSjo6MdHk/b2phW2w6LpV6skCQlFAphcHAADsdZDA0NAgDS\n0tJx8OD1MBhakJOTK3KERERE0eX3+/HKKy9CEATcc8+Dmx5rzfyCktn6Vo6AyJEQJTcWJiTEbNbD\nanXBaj3Ortm7sLS0iM5OBzo7z2FhYR4AUFamhdFoQm1t/doSPiIiIrl59903MTc3i9bWQ6ioqNz0\nPswvKJnJs8cEl0yQ9PCKTGLMZj0ThR0QBAEjI8NwOOy4cKEXoVAIarUGBkMLDAYTioqKxQ6RiIgo\npmZnp2G325CTk4vrr795y/syv6BkJc/CxCp2eyTpYGGCZMXnW0FPTxecTjsuX54CABQUFMJgMKGh\noQkpKakiR0hERBQf7777FgRBwM03H4FGoxE7HKKEpFTKuTBBJB0sTJAsTE5OwOm0o6enC4GAH0ql\nEnV1DTAaTSgvr+BRn0RElFQuX56Cy9WFwsJi7N/PXhFE17La8FyOhQmmvyQlLEyQZAWDAfT398Lh\nsGN01AMAyMrKRnPzDWhuNiIjI1PkCImIiMRx9ux7AIAbbriZxXmiLSgUCqhUKja/JBIZCxMkOXNz\ns3A6z6G724Hl5WUAQFVVNQwGE/btq+FRn0RElNS83mX09nYjNzcP1dW1YodDlPBUKrWsVkzwuNCt\nCZyghMTCBElCKBTCpUsX4XTaMTg4AABITU1Da6sZBkMLcnPzRY6QiIgoMXR1ORAMBmEwmLhagmgH\nwism5FOYWMef/404H4mMhQlKaMvLS+jqcsDpPIf5+TkAQGlpOQwGE+rq6qFWs5kXERHRKkEQ4HSe\ng1qtRmOjQexwiCRBfoUJrggg6WFhghKOIAgYHR2Bw3EW/f29CIWCUKvVaGoywmg0obi4VOwQiYiI\nEtLo6Ajm5mah1zchLS1N7HCIJEGlUsHv94sdRtRxwRRJCQsTlDD8fh9crm44HGcxNTUJAMjLy4fR\naEJDQzNSU5lgERERbaWvrxsAoNc3ihwJkXSoVCp4vV6xw4gatlAgKWJhgkR3+fIkHI7wUZ9+vw9K\npRK1tfUwGk3Qaiu5P5aIiGgHQqEQ+vpcSEtLR0VFldjhEEmG3JpfrmMOTdLBwgSJIhgMore3B07n\nWXg8wwCAzMxMtLaa0dx8AJmZWSJHSEREJC3Dw0NYXl6CwWCCSqUSOxwiyVAqlQiF5FiYIJIOFiYo\nrubn59DZeR7d3Q4sLi4CAHS6KhiNJlRX1/KoTyIioggNDPQBAOrq9CJHQiQtKpUKoVAIgiDIZKUu\n93KQ9LAwQTEnCAKGhgbhcNgxOHgBgiAgLS0NJtN1MBhMyMvjUZ9ERER7IQgCBgcHkJqaivLyCrHD\nIZIUpTK8wigUCslqtZEsaiyUNFiYoJjxepfR1eWE02nH3NwsAKC4uBRGowk333wIMzPyaTJEREQk\npsuXpzA/P4e6ugauPiTaJZUq/DMTCgVlUZhg80uSIhYmKKoEQcD4+CgcDjv6+noQDIb/gW9sNMBg\nMKG0tAwAoNFoALAwQUREFA2DgxcAAPv21YgcCZH0rBYjgsEgNBqRg4kqLpkg6WBhgqLC7/ejt7cb\nTqcdExPjAIDc3DwYDCY0NjYjLS1d5AiJiIjka3BwAABQVcXCBNFuXbmVQx64ZIKkh4UJ2pPp6ctw\nOu3o7u6Ez7cChUKBmpo6GI0m6HRVMmkgRERElLh8Ph9GRz0oLS1Dejq/CCDarStXTBCROFiYoF0L\nBoO4eLEfDocdw8NDAICMjEy0tLSiqakF2dnZIkdIRESUPEZG3BAEATrdPrFDIZKk1b4sPDKUSDx7\nKkxMTU3hkUcewZNPPomaGi4dlLuFhXl0dp5HZ+d5LC2Fj/qsqKiEwWBCTU2tLJoFERGR+Jhf7I7b\nHf6SoKKiUuRIiKRpfcWEXLZyEElPxIWJQCCAJ554AmlpadGMhxKMIAhwuy/B6bRjYKAfgiAgJSUF\nBw60wmAwoaCgUOwQiYhIRphf7N7w8CWoVCqUlZWLHQqRJK33mJDXignuqCYpibgw8Z3vfAef+tSn\n8MMf/jCa8VCC8Hq96OnphNNpx8zMNACgqKgYRqMJ9fVNH5yqQUREFF3ML3bH613G5OQEKioqoVbz\ns5koEqvHhcqlx4TA80JJgiIqTPzmN79BYWEhDh8+jB/84AfRjolEND4+BofjLPr6ehAIBKBSqaDX\nN8FoNKG0tJzNLImIKGaYX+ze8LAbALdxEO3F6ooJuRQm1jFvJ+mIuDChUChw+vRpdHd346tf/Sr+\n7d/+DYWF117Wn5+fAbVaWj0IiouTo4mj3++H0+nEu+++C4/HAwDIy8vDoUOHcPDgQWRkZMTkeZNl\nfsXEOY49znFscX6Ty27zCynmFkB039fvvz8BAGhu1vPn5QOch9iT2xxnZ6d/8P/UhHhte40hJUW9\n9jhqNc86WKVWB9Z+nQh/z/GQlRXeFpmbm57wrzmid+rPfvaztV8/9thj+Lu/+7stixIAMD29FMlT\niaa4OBsTE/NihxFTMzPTcDrPobvbgZWV8FGf1dX7YTSaUFlZDYVCgcXFIBYXoz8PyTC/YuMcxx7n\nOLY4v7GXaEnKbvMLqeUWQPTf1wMDF6FUKqHR8OcF4L8b8SDHOfZ6wxesly8vIDtb3NcWjfn1+cKv\nZ3JyHioVCxOrZmcX1n4tt/fwtSwseAEAs7PLcXvNkeYWe36ncmm/tIRCIVy8eAEOhx1u9yAAID09\nA9dddwMMhhZkZ+eIHCERERHzi50IBPyYmBhHUVExez8R7cHqqRxya35JG/FzJbHtuTDxk5/8JBpx\nUIwtLi6gq8sBp/McFhfD1cLy8goYjSbs31/Poz6JiCihML/Y3sTEOEKhEMrKKsQOhUjS1ntMyOO4\nUPa+JCni2h4ZEwQBHo8bDocdAwN9CIVC0Gg0MBpNMBhMKCwsEjtEIiIiitDoaLgvVFmZVuRIiKRt\n9VQO+a2Y4AoBkg4WJmRoZWVl7ajP6enLAICCgiIYjSbo9U1ISUkROUIiIiLaq5GR1cJEuciREEmb\n/E7l4JIJkh4WJmRkcnIcDocdLlcXAoEAlEol6usbYTSaUFam5b4qIiIimRAEAaOjHmRlZSMrK7Ga\nmBJJzXqPCXls5SCSIhYmJC4QCKC/3wWHw46xsREAQHZ2DpqbW9DUZIzZUZ9EREQkntnZGXi9y6iv\nbxA7FCLJk9+KCSLpYWFComZnZ9DZeQ5dXQ54veFjYKqqamA0mlBVVQ2lUilyhERERBQrq19GlJay\nvwTRXvFUDiLxsTAhIaFQCJcuDcDhsOPSpYsAgLS0dBw8eAjNzS3Izc0TN0AiIiKKi/HxMQBASUmp\nyJEQSd/qF3pyWzHBbdwkJSxMSMDS0hK6uhzo7DyH+fk5AOEO3AZDC2pr9VCr+ddIRESUTCYmxqBQ\nKFBUVCx2KESSt7piQi6FCYHnhZIE8Yo2QQmCgJGRYTiddvT39yIUCkGt1qC5+QCMRhOKikrEDpGI\niIhEEAqFMDk5joKCIqjVGrHDIZK81R4TbH6ZHFi4SUwsTESRzeaC1doLtzsFOp0PFks9zGb9rh7D\n5/PB5eqCw3EWly9PAQDy8ws+OOqzGampqbEInYiIiBLU1fnFpz5VikAgwG0cRFGiUslzKweRlLAw\nESU2mwsWyzw8nmNrYx0d7bBaXTsqTkxNTcDhOAeXqxN+vx9KpRK1tXoYjSZotTruESMiIkpCm+UX\nk5PH0dYGFBezMEEUDesrJuRVmOD1A0kJCxNRYrX2bkgaAMDjaYPVevyahYlgMIALF/rgcNgxMjIM\nAMjMzMLBg9ejufkAMjIyYx43ERERJa7N8ouMjGwAbHxJFC3rPSa4lYNILCxMRInbnbLj8fn5OTid\n4aM+l5eXAACVlftgNJqwb99+HvVJREREADbPI7TaEYRCChQWFokQEZH8rObeclkxwR4KJEUsTESJ\nTufbclwQBFy6dBEOhx2DgxcAAKmpqTCZzDAYWpCXlx+3WImIiEgars4vVKogyspGsbKSApWKaRxR\nNMjtVA4iKeInWpRYLPXo6GiHx9O2NqbVtuOzn92HM2fehdN5DnNzswCAkpIyGI0m1NXp2U2biIiI\nrunq/KK4eBxqdRBabYWYYRHJCk/lIBIfCxNRYjbrYbW6YLUeh9utQX39HG64IQi7fRTBYBBqtRpN\nTUYYDCbuCSUiIqId2ZhfpODAgXEAQHNzg8iREcnH6qkcctnKsYrNL0lKWJiIopaWavz5n3vhdNox\nOTmBqSkgLy8fBoMJDQ3NSEtLEztEIiIikhizWb/WSLu9/ffo7BzjlxxEUbS6YkI+WznYY4Kkh4WJ\nKLh8eQpOpx09PZ3w+XxQKBTYv78eRqMJFRWVrFYSERFRVExMjEGlUiE/v1DsUIhkY7XHBLdyEImH\nhYkIBYNBDAyEj/r0eNwAgMzMTLS0XIfm5gPIysoWOUIiIiKSk0AggKmpSRQXl6xdSBHR3q1+iSif\nFRO0GX5ZnNhYmNilhYV5dHaeQ2enA0tLiwAAna4KBkMLqqtroVKpYLO5YLW+Brc7BTqdDxZL/doS\nTCIiIqJInD79PkKhEN55x4dXXnme+QVRlCgUCqhUKtkUJnhaKEkRCxM7IAgC3O5LcDjO4uLFCxAE\nASkpqWhpOQiDwYT8/IK1+9psLlgs8/B4jq2NdXS0w2p1MXkgIiKiiNhsLvzgB2M4cgQ4c+ZWnD17\nkPkFURSpVCrZNb8kkhIWJrbg9S6ju7sTTqcds7MzAIDi4hIYDCbU1zdCo/nwUZ9Wa++GogQAeDxt\n+Md//BGOH2fiQERERLtntfYiMzMTAODxaD/4P/MLomhRKlUIBtljgkgsLExsYmxsFB0dJ3H+/HkE\ng0GoVCo0NDTDaDShpKRsy/1JbnfKpuNvvJEGm43fahAREdHuud0paG0dgd+vxuRk8do48wui6FCp\nlLJaMcF+CiQ1LEx8wO/3o6+vBw6HHRMTYwCAnJxcGI0mNDYakJaWvqPH0el8m477fJmwWnuZOBAR\nEdGuVVZ6UVIyDrdbh1BIuTbO/IIoOsIrJuRRmBDYZIIkKOkLEzMz03A47OjpcWJlZQUKhQI1NbU4\nfPhmZGcX77raaLHU45lnXoTPd98Vo68BMMDt7oxq7ERERJQcPv7xEnR29sLjKb9ilPkFUbSoVCr4\nfJt/wUhEsZeUhYlQKISBgX44nXa43ZcAAOnpGTCbb0Rzcwuys7NRXJyNiYn5XT+22azH4cOncerU\nIgANAD8AA4BG6HRno/kyiIiIKEkUFIS3ino8HgDPgPkFUXQplUoEgwGxwyBKWklVmFhcXEBn53l0\ndp7H4uICAECr1cFoNKGmpi5qZ4J/5SuH0dMzD4+nbW1Mq22HxVIflccnIiKi5DI+PgoA8PvLATyw\nNs78gig6lEoVQiE2vyQSi+wLE4IgYHh4CE6nHRcu9EEQBGg0KThwoBUGQwsKCoqi/pxmsx5WqwtW\n63G43SnQ6Xw8a5yIiIgiNj4+Bo0mBf/0TyX4z/9kfkEUbUqlUlaFCTa/vDb24EhMsi1MrKx40dPT\nCYfjHGZmLgMACguLYTSaoNc3QqPZ/PSMaDGb9UwUiIiIaM98vhXMzFyGVqvDoUMNOHSoQeyQiGRH\nToUJXniTFMmuMDExMQaHw47e3m4EAgEolSro9U0wGk0oLS1n9ZCIiIgkZWJiHABQUlImciRE8qVU\nKiEIAgRB4PUCkQhkUZgIBALo63PB6TyLsbHwHsycnFwYDC1obDQgPT0jps9vs7lgtfZyWSURERFF\nxZW5hck0ivJyoKSkVOywiGRLqQz3mguFglCpZHGJRCQpkv6pm52dgdNpR1eXEysrXgDAvn37YTS2\noLKyGkqlcptH2DubzQWLZR4ez7G1sY6OdlitLhYniIiIaNeuzi10ul+jvHwCY2PLqKsTOTgimVq9\nbgiFQohSP3yRcdUHSYvkChOhUAiDgwNwOM5iaGgQAJCeno6DB6+HwdCCnJzcuMZjtfZuKEoAgMfT\nBqv1OAsTREREtGtX5xZarQdLS+l46ik3Dh9uFTEyIvlSqcKFiWAwBI1G5GCIkpBkChNLS4vo7HSg\ns/McFhbmAQDl5RUwGFpQW1sv2pIrt3vzJprXGiciIiLaypU5RHr6EgoKptHXVwu3O1XEqIjk7coV\nE9LH5pckPQldmBAEASMjw3A47LhwoRehUAgajQYGQwsMBhOKiorFDhE6nW9X40RERERbuTKH0GpH\nAAAejxY63bhYIRHJ3nphIihyJNHB/p0kNREVJgKBAL7+9a9jeHgYfr8ff/Znf4Y777wzakH5fCvo\n6emC02nH5ctTAICCgsIPjvpsQkpK4nxjYLHUo6OjHR5P29qYVtsOi6VerJCIiIgkKdb5hVRcmVto\ntcMAgOXly8wtiGJovfml9FdM8LRQkqKIChPPPPMM8vPz8Q//8A+YnZ3Fww8/HJXEYXJyAg6HHS5X\nFwIBP5RKJerqGmA0mlBeXpGQR/eYzXpYrS5Yrcd5KgcREdEexCq/kJorc4uSEg8A4GtfK2duQRRD\n8trKQSQ9ERUm7r//ftx3330Awj+8anXkO0KCwQD6+3vhcNgxOhr+8M3KyobBcAOamozIyMiM+LHj\nxWzWM1kgIiLao2jmF1JnNutx3XX1ePLJf4NGk4Obb24ROyQiWbvyuFCSq8T7kpvWRfSJn56eDgBY\nWFjAl770JfzVX/3Vrh9jbm4WTuc5dHU54PUuAwCqqqphNJpQVVUTl6M+iYiIKHFEI7+Qk5mZaXi9\nXlRW7hM7FCLZk9eKCe7lIOmJ+KuIkZER/MVf/AX+6I/+CA888MC298/Pz4BSqUBfXx/ee+899Pb2\nAggnIbfccgvMZjMKCgoiDScmiouzxQ5B1ji/scc5jj3OcWxxfpPPbvKL/PwMqNWqOEUWPTt9X7vd\nfQCAurr9/FnYBc5V7MlxjrOy0gAAOTlpor++vT6/Wq2CQqEQ/XUkmpSU9aJTsszN6vs6Nzc94V9z\nRIWJyclJWCwWfPOb38RNN920oz/zyivtcDrPYX5+DgBQWloOo9GE2lo91Go1gkFgYmI+knBiorg4\nO6HikRvOb+xxjmOPcxxbnN/YS7QkZbf5xfT0Uhyiiq7dvK97ey8AALKzC/mzsEP8dyP25DrHXm8A\nADA1tYCUFPFeXzTm1+8Pb0eR49/TXszPL679OlnmZmHBCwCYnV2O22uONLeIqDDxwx/+EHNzc/j+\n97+P733ve1AoFPiP//gPpKSkXPPPvPXWG1Cr1WhuPgCDwYTi4pKIAiYiIiJ5iiS/kLPRUQ80Gg0K\nCorEDoVI9lQqeR0XSiQ1ERUmvvGNb+Ab3/jGrv7Mrbe2oaGhGampaZE8pWhsNhes1l6euEFERBRj\nkeQXUrST3MLrXcb09GXodFXsu0UUB3I6LpRIiuLW7rql5bp4PVXU2GwuWCzz8HiOrY11dLTDanWx\nOEFERES79vbb3TvKLYaHhwAAWm1lnCMkSk7yan4J8AQKkhqW4LdgtfbC42nbMObxtMFq7RUnICIi\nIpK0737XuaPcYmjoEgCgsrIqXqERJTUeF0okLhYmtuB2b76n9VrjRERERFsZHNRsOn51buF2DyIl\nJRXFxaXxCIso6clrxQSPCyXpidtWDimx2Vx46qmL6OubBvA0AAOAxrXbdTqfWKERERGRBK32lejp\nWcR2ucXc3Czm5mZRU1PL/hJEcSKvwgSg4E6OaxIEFm4SEQsTV1nvK/HIFaOvffD/Rmi17bBY6sUI\njYiIiCRos55VW+UWbnd4G4dOty9+QRIludXCRDAo/cIEr7s3x2JNYmMZ/iqb9ZUAbkNR0X/h0UeP\nw2rNZuNLIiIi2rHd5haDgxcAAJWVLEwQxYv8ekzwKpykhSsmrnKt/hF1ddX4/vePxjkaIiIikrrd\n5BZ+vx9DQ4PIyytAXl5+PMIjIgAqlZy2cnDJBEkPV0xc5Vr9I9hXgoiIiCKxm9xiaOgiAoEA9u+v\ni3VYRHQF9pggEhcLE1exWOqh1bZvGGNfCSIiIorUbnKL3t4eAGBhgijO5FaYIJIabuW4itmsh9Xq\nwlNPPY2+PgV0Oh8slnr2lSAiIqKIrOYWVutxjI9noKRkadPcwutdxsBAP/LzC3lMKFGcrRcmpN9j\ngs0vSYpYmNiE2azHffeZMTExL3YoREREJANmsx5msx7FxdnXzC9cri6EQkE0Nhqg4Dpsorhab34p\nlxUT/DeEpIVbOYiIiIhEFgwGYbe/D5VKhYaGZrHDIUo6cloxweaXJEUsTETAZnPhi198Hh/5yO/x\nxS8+D5vNJXZIREREJGEnTpzC/PwcLlzIw+OPn2JuQRRnqysmgkF5rJjgoiuSGm7l2CWbzQWLZR4e\nz7G1sY6OdlitLvahICIiol17+20HOju7oFJp8PTTj2FuLpe5BVGcyan5JXtMkBRxxcQuWa298Hja\nNox5PG2wWnvFCYiIiIgkKxQK4dSp00hP9+PVV2/H3FwuAOYWRPGmUsmnMEEkRSxM7JLbnbKrcSIi\nIqJrefvtN5CTs4j+/v14882bN9zG3IIofuS0YoJIiliY2CWdzrercSIiIqLNdHaex5kz72FlRYNf\n/epRhEIb07KMjEmRIiNKPuuncrD5JZEYWJjYpcOHU6FUntgwplSewOHDqSJFRERERFIzMuLBa6+9\ngtTUNNxww2FkZZ2+6h6v4dy5TDbBJIoT+a2YYPdLkhYWJnbp9OkVhEJVAJ4G8AyApxEKVeH06RWR\nIyMiIiIpCIVCeP31kwiFQrjvvodw+PB1OHCgE1fmFkAJJic/wz4TRHEiv8IEkbTwVI5dCu/3bPzg\nvyvHO0WJh4iIiKSlv78Xk5Pj0OubUFFRCQBYXtYB+NiH7ss+E0TxIbfCBI8LJanhioldYo8JIiIi\n2ouenvCXGWbzjWtjzC+IxCWvHhNE0sPCxC5ZLPXQats3jGm17bBY6sUJiIiIiCRjcXERQ0MXUVxc\nivz8grVx5hdE4lo9LjQYlP6KCYG9L0mCuJVjl8xmPaxWF6zW43C7U6DT+WCx1MNs1osdGhERESW4\n3t5eCIKA+vqGDePML4jEJbetHGx+eW0CKzcJiYWJCJjNeiYKREREtGtutxsAoNVWfug25hdE4pHX\nVg5eeG9GwcYbCY1bOYiIiIjiZHh4GCqVCoWFRWKHQkRXUCgUUCgUslkxwWtwkhoWJoiIiIjiwO/3\nY2xsDMXFpVCpVGKHQ0RXUSqVsihMcKcCSRELE0RERERxMDExBkEQUFpaLnYoRLQJuRQmwrhkgqSF\nhQkiIiKiOJievgwA3MZBlKDChQk59Jggkh4WJoiIiIjiYHZ2BgCQm5snciREtBmlUiWTFRPcy0HS\nw8IEERERURzMzbEwQZTI5LWVg0haWJggIiIiioPZ2VloNBqkp2eIHQoRbUKlUiEY5FYOIjGwMEFE\nREQUY4IgYG5uBvn5+VDwHD+ihCSnFRP8d4akhoUJIiIiohhbXl6G3+9HQUGB2KEQ0TXIpTAh8LxQ\nkiC12AEkMpvNBau1F253CnQ6HyyWepjNerHDIiIiIolZ7S+Rn5/P/IIoQcmlMEEkRREVJgRBwLe+\n9S309PQgJSUFf//3f4/KyspoxyaK1WSht3cOLlcllpePrd3W0dEOq9XF5IGIiCgG5JzrWNAdAAAN\nZUlEQVRfnDvnAgD84AdOvPhiI/MLogTE40KJxBPRVo6XX34ZPp8Px48fx5e//GV8+9vfjnZcorDZ\nXLBY5vHrXx+D3V6I5eX7N9zu8bTBau0VKToiIiJ5k3N+8eMfzwEAXK5c5hdECUo+x4USSU9EhQmb\nzYYjR44AAEwmExwOR1SDEovV2guPp+2D32k2vY/bnRK3eIiIiJKJnPOLQKAcALC4mLrpfZhfEIlP\nqVRCEARZ9Ghg70uSmogKEwsLC8jOzl77vVqtlkV1cWNS4N/0PjqdLz7BEBERJRk55xdZWYsAgIWF\nze/D/IJIfEpl+NJI6v/uyKGwQsknoh4TWVlZWFxcXPt9KBRa+0G+lvz8DKjVqkieLm7q6gS89dbq\n7wwAXgNw29rtOt1rePzxVhQXZ2/yp2m3OI+xxzmOPc5xbHF+k8tu8wsp5BZAOL8IBMKva3HRCOYX\nscV5jD25znFaWvhLyoKCDKSkiLeKaa/zq1IpIQhK2f49RSotbf3XyTI3WVnhF52bm57wrzmiwsR1\n112HU6dO4b777sPZs2eh12/frGl6eimSp4qrz3ymGidPtn+wnaMRAJCW9j3U1eWisVENi6Ue+/dX\nYGJiXtQ45aC4OJvzGGOc49jjHMcW5zf2Ei1J2W1+IYXcAgjnF8888yqCQSWWl1sA9DC/iBH+uxF7\ncp7jQCC80mBsbBZpV17FxlE05jcYDCEUEmT79xSpxcX1JWvJMjcLC14AwOzsctxec6S5RUSFiaNH\nj+L06dM4dizcUVouzanMZj2sVhes1uMYH89ASckSLJab2CWbiIgoDuScX5w9+woWFpS47bZnmV8Q\nJSiVSh5bOYikKKLChEKhwN/+7d9GO5aEYDbrYTbrZV0NJiIiSkRyzS8EQUAw6IdWW4BXX/0I8wui\nBLXeY4JHhsoZe3AkpoiaXxIRERHRzvj9fgQCAaSnZ4gdChFtQakM96zhigl5UvCokoTGwgQRERFR\nDC0vhxtfZmRkihwJEW1FLqdyALwIJ+lhYYKIiIgohpaWwk06uWKCKLHJpTDBrQokRRH1mJAzm80F\nq7UXY2MZKC1dgsVSz+ZUREREFLGVlXBX9BdeuIj//b+fYX5BlKDkUpggkiIWJq5gs7lgsczD4zm2\nNtbR0Q6r1cXkgYiIiCLS0zMIAHjnnRvx/vtmAMwviBLReo8JNr8kijdu5biC1doLj6dtw5jH0war\ntVecgIiIiEjyXn/dAwDwetPWxphfECUeOa2YYI8JkhoWJq7gdqfsapyIiIhoOwsL4QuElZW0DePM\nL4gSi5wKE0RSw8LEFXQ6367GiYiIiLaTnx/OI65cMQEwvyBKNCpVeCtHMCjtrRxsfklSxMLEFSyW\nemi17RvGtNp2WCz14gREREREktfcnAUA8HpT18aYXxAlHq6YIBIPm19ewWzWw2p1wWo9jvHxDJSU\nsGs2ERER7U1OTjomJoC7734RIyM5zC+IEhQLE0TiYWHiKmazHmazHsXF2ZiYmBc7HCIiIpK4lZUV\nAMC//MsfoLw8n/kFUYJaL0xIeysHwOaXJD3cykFEREQUQz7fClQqFdRqfh9ElMjWjwvligmieGNh\ngoiIiCiGfL4VpKSkbn9HIhKVXLZysPklSRELE0REREQxtLKygtRUFiaIEp1cChNEUsTCBBEREVEM\nccUEkTSwMEEkHhYmiIiIiGIkEAggGAyyMEEkASxMEImHhQkiIiKiGPH5widycCsHUeKTT2GCPSZI\neliYICIiIooRn88HAEhJSRE5EiLajnwKEzwulKSHhQkiIiKiGFlZ8QIAt3IQScBqYUIQpF+YoGvj\nqSWJiYUJIiIiohjx+/0AuGKCSAoUCnmsmOB197VwFUkiY2GCiIiIKEZWCxMajUbkSIhoO3LaykEk\nNSxMEBEREcVIIBAuTKjVLEwQJToWJojEw8IEERERUYxwxQSRdMipMMHmlyQ1LEwQERERxchqYYIr\nJogSn1yaX7K5I0kRCxNEREREMbK6lYMrJogSn1yaXxJJEQsTRERERDGyvmJCLXIkRLQdOW3lIJIa\nFiaIiIiIYoQrJoikg4UJIvGwMEFEREQUI+wxQSQd8ipMsPklSQsLE0REREQxwhUTRNIhn8IEm1+S\n9LAwQURERBQjfn8AAFdMEEnB+qkc0r+w52mhH8Y5SWwsTBARERHFCFdMEEnH+qkcQZEj2RsZ1FUo\nCbEwQURERBQjfr8fSqUSKpVK7FCIaBvrWznkcGXP5QEkLSxMEBEREcWI3+/nNg4iiVgvTEh7xQSR\nFEV0qPbCwgIef/xxLC4uwu/342tf+xpaW1ujHRsRERElCbnmFoGAHxpNROkWEcWZfFZMSD1+SkYR\nfVI++eSTuOWWW/DZz34WAwMD+PKXv4zf/OY30Y6NiIiIkoRccwu/34+UlBSxwyCiHVhvfin1UznY\n6JGkJ6LCxOc+97m1D9lAIIDU1NSoBkVERETJRa65RSDgR2ZmpthhENEOKD64mpf6caFsfklStG1h\n4te//jV+/OMfbxj79re/DaPRiImJCXzlK1/BN77xjZgFSERERPKSLLmFIAjsMUEkMUqlSvKFCSIp\n2rYw8eijj+LRRx/90HhPTw8ef/xxfPWrX8WhQ4diEhwRERHJT7LkFsFguIEejwolkg6lUsHChMwJ\nXFKSkBRCBH8zfX19+Mu//Ev867/+KxoaGmIRFxERESUR5hZERETJK6LCxBe/+EX09PSgoqICgiAg\nJycH3/ve92IRHxERESUB5hZERETJK6LCBBERERERERFRNCjFDoCIiIiIiIiIkhcLE0REREREREQk\nGhYmiIiIiIiIiEg0LEwQERERERERkWiSujAhCAKeeOIJHDt2DJ/97GcxNDS04faTJ0/i0UcfxbFj\nx/CrX/1KpCilbbs5fu655/CJT3wCn/70p/Gtb31LnCAlbLv5XfXNb34T//zP/xzn6ORhuzk+d+4c\nPvOZz+Azn/kMvvSlL8Hn84kUqTRtN7/PPPMMPvaxj+HjH/84fv7zn4sUpTzY7XY89thjHxrnZ130\nMb+ILeYWscf8IraYW8Qe84v4iGpuISSxEydOCF/72tcEQRCEs2fPCl/4whfWbvP7/cLRo0eF+fl5\nwefzCY888ogwNTUlVqiStdUce71e4ejRo8LKyoogCILw13/918LJkydFiVOqtprfVT//+c+FT37y\nk8I//dM/xTs8Wdhujj/60Y8Kly5dEgRBEH71q18JAwMD8Q5R0rab38OHDwtzc3OCz+cTjh49KszN\nzYkRpuT9+7//u/Dggw8Kn/zkJzeM87MuNphfxBZzi9hjfhFbzC1ij/lF7EU7t0jqFRM2mw1HjhwB\nAJhMJjgcjrXb+vv7sW/fPmRlZUGj0cBsNuPdd98VK1TJ2mqOU1JScPz4caSkpAAAAoEAUlNTRYlT\nqraaXwA4c+YMzp8/j2PHjokRnixsNccDAwPIy8vDk08+icceewyzs7Oorq4WKVJp2u493NjYiNnZ\nWaysrAAAFApF3GOUg3379uF73/veh8b5WRcbzC9ii7lF7DG/iC3mFrHH/CL2op1bJHVhYmFhAdnZ\n2Wu/V6vVCIVCm96WmZmJ+fn5uMcodVvNsUKhQEFBAQDgpz/9KZaXl3HLLbeIEqdUbTW/ExMT+O53\nv4tvfvObEARBrBAlb6s5np6extmzZ/HYY4/hySefREdHB95++22xQpWkreYXAOrr6/HII4/goYce\nQltbG7KyssQIU/KOHj0KlUr1oXF+1sUG84vYYm4Re8wvYou5Rewxv4i9aOcWSV2YyMrKwuLi4trv\nQ6EQlErl2m0LCwtrty0uLiInJyfuMUrdVnMMhPd/fec738Gbb76J7373u2KEKGlbze+LL76ImZkZ\n/Omf/il+9KMf4bnnnsPvfvc7sUKVrK3mOC8vD1VVVaipqYFarcaRI0c+VJGnrW01vz09PWhvb8fJ\nkydx8uRJTE1N4aWXXhIrVFniZ11sML+ILeYWscf8IraYW8Qe8wvxRPo5l9SFieuuuw6vvvoqAODs\n2bPQ6/Vrt9XW1mJwcBBzc3Pw+Xx499130draKlaokrXVHAPA3/zN38Dv9+P73//+2rJL2rmt5vex\nxx7D008/jZ/85Cf4/Oc/jwcffBAPP/ywWKFK1lZzXFlZiaWlpbWGSjabDXV1daLEKVVbzW92djbS\n09ORkpKy9i3o3NycWKHKwtXfbvKzLjaYX8QWc4vYY34RW8wtYo/5RfxEK7dQxypAKTh69ChOnz69\ntj/u29/+Np577jksLy///3bu2LZCKIYCqGkYgZIFqBAD0LAABSUUjEDHAKzDiqT6bSgSYqGcM4Fl\nIXF1Jb+Ypin2fY91XeO6rpimKaqqSp74fb7bcdM0cZ5ndF0X8zxHURSxLEsMw5A89XvcfcP83N2O\nj+OIbdsiIqJt2+j7PnPc17nb7+dl/bIso67rGMcxeeJ3+9zQ+tc9S754lmzxPPniWbLF8+SLv/Nb\n2aK4HIcBAAAASf71KQcAAACQSzEBAAAApFFMAAAAAGkUEwAAAEAaxQQAAACQRjEBAAAApFFMAAAA\nAGkUEwAAAECaL44a1wDqBaGbAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b1b8d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X, y = make_data()\n",
+    "xfit = np.linspace(-0.1, 1.0, 1000)[:, None]\n",
+    "model1 = PolynomialRegression(1).fit(X, y)\n",
+    "model20 = PolynomialRegression(20).fit(X, y)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
+    "\n",
+    "ax[0].scatter(X.ravel(), y, s=40)\n",
+    "ax[0].plot(xfit.ravel(), model1.predict(xfit), color='gray')\n",
+    "ax[0].axis([-0.1, 1.0, -2, 14])\n",
+    "ax[0].set_title('High-bias model: Underfits the data', size=14)\n",
+    "\n",
+    "ax[1].scatter(X.ravel(), y, s=40)\n",
+    "ax[1].plot(xfit.ravel(), model20.predict(xfit), color='gray')\n",
+    "ax[1].axis([-0.1, 1.0, -2, 14])\n",
+    "ax[1].set_title('High-variance model: Overfits the data', size=14)\n",
+    "\n",
+    "fig.savefig('figures/05.03-bias-variance.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Bias-Variance Tradeoff Metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NyMhI7r7JcvWYkEgk+Q6vlclkBQ691a9LTU2Fn58fLl26BIVCwb2GPj4+cHR0xO7du9G6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\nMAEAAAAAACzz/wHBdPoar16gEwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bd77dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
+    "\n",
+    "X2, y2 = make_data(10, rseed=42)\n",
+    "\n",
+    "ax[0].scatter(X.ravel(), y, s=40, c='blue')\n",
+    "ax[0].plot(xfit.ravel(), model1.predict(xfit), color='gray')\n",
+    "ax[0].axis([-0.1, 1.0, -2, 14])\n",
+    "ax[0].set_title('High-bias model: Underfits the data', size=14)\n",
+    "ax[0].scatter(X2.ravel(), y2, s=40, c='red')\n",
+    "ax[0].text(0.02, 0.98, \"training score: $R^2$ = {0:.2f}\".format(model1.score(X, y)),\n",
+    "           ha='left', va='top', transform=ax[0].transAxes, size=14, color='blue')\n",
+    "ax[0].text(0.02, 0.91, \"validation score: $R^2$ = {0:.2f}\".format(model1.score(X2, y2)),\n",
+    "           ha='left', va='top', transform=ax[0].transAxes, size=14, color='red')\n",
+    "\n",
+    "ax[1].scatter(X.ravel(), y, s=40, c='blue')\n",
+    "ax[1].plot(xfit.ravel(), model20.predict(xfit), color='gray')\n",
+    "ax[1].axis([-0.1, 1.0, -2, 14])\n",
+    "ax[1].set_title('High-variance model: Overfits the data', size=14)\n",
+    "ax[1].scatter(X2.ravel(), y2, s=40, c='red')\n",
+    "ax[1].text(0.02, 0.98, \"training score: $R^2$ = {0:.2g}\".format(model20.score(X, y)),\n",
+    "           ha='left', va='top', transform=ax[1].transAxes, size=14, color='blue')\n",
+    "ax[1].text(0.02, 0.91, \"validation score: $R^2$ = {0:.2g}\".format(model20.score(X2, y2)),\n",
+    "           ha='left', va='top', transform=ax[1].transAxes, size=14, color='red')\n",
+    "\n",
+    "fig.savefig('figures/05.03-bias-variance-2.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Validation Curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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buXJlJkyYwNy5cxk8eDB16tQhNDSUl19+OUNlduzYkc8//5z27dun+hyj0ciCBQuYOnUq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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118d1d2b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0, 1, 1000)\n",
+    "y1 = -(x - 0.5) ** 2\n",
+    "y2 = y1 - 0.33 + np.exp(x - 1)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x, y2, lw=10, alpha=0.5, color='blue')\n",
+    "ax.plot(x, y1, lw=10, alpha=0.5, color='red')\n",
+    "\n",
+    "ax.text(0.15, 0.2, \"training score\", rotation=45, size=16, color='blue')\n",
+    "ax.text(0.2, -0.05, \"validation score\", rotation=20, size=16, color='red')\n",
+    "\n",
+    "ax.text(0.02, 0.1, r'$\\longleftarrow$ High Bias', size=18, rotation=90, va='center')\n",
+    "ax.text(0.98, 0.1, r'$\\longleftarrow$ High Variance $\\longrightarrow$', size=18, rotation=90, ha='right', va='center')\n",
+    "ax.text(0.48, -0.12, 'Best$\\\\longrightarrow$\\nModel', size=18, rotation=90, va='center')\n",
+    "\n",
+    "ax.set_xlim(0, 1)\n",
+    "ax.set_ylim(-0.3, 0.5)\n",
+    "\n",
+    "ax.set_xlabel(r'model complexity $\\longrightarrow$', size=14)\n",
+    "ax.set_ylabel(r'model score $\\longrightarrow$', size=14)\n",
+    "\n",
+    "ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
+    "ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
+    "\n",
+    "ax.set_title(\"Validation Curve Schematic\", size=16)\n",
+    "\n",
+    "fig.savefig('figures/05.03-validation-curve.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Learning Curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JZyGEEMLHSDgLIYQQPkbCWQghhPAxEs5CCCGEj5FwFkIIIXyMzwxC8tZbEBRkIiVFIzpa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\nETkYhjMREZGDYTgTERE5mP8Hlp/kNMiE2u4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11a78a240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "N = np.linspace(0, 1, 1000)\n",
+    "y1 = 0.75 + 0.2 * np.exp(-4 * N)\n",
+    "y2 = 0.7 - 0.6 * np.exp(-4 * N)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x, y1, lw=10, alpha=0.5, color='blue')\n",
+    "ax.plot(x, y2, lw=10, alpha=0.5, color='red')\n",
+    "\n",
+    "ax.text(0.2, 0.88, \"training score\", rotation=-10, size=16, color='blue')\n",
+    "ax.text(0.2, 0.5, \"validation score\", rotation=30, size=16, color='red')\n",
+    "\n",
+    "ax.text(0.98, 0.45, r'Good Fit $\\longrightarrow$', size=18, rotation=90, ha='right', va='center')\n",
+    "ax.text(0.02, 0.57, r'$\\longleftarrow$ High Variance $\\longrightarrow$', size=18, rotation=90, va='center')\n",
+    "\n",
+    "ax.set_xlim(0, 1)\n",
+    "ax.set_ylim(0, 1)\n",
+    "\n",
+    "ax.set_xlabel(r'training set size $\\longrightarrow$', size=14)\n",
+    "ax.set_ylabel(r'model score $\\longrightarrow$', size=14)\n",
+    "\n",
+    "ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
+    "ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
+    "\n",
+    "ax.set_title(\"Learning Curve Schematic\", size=16)\n",
+    "\n",
+    "fig.savefig('figures/05.03-learning-curve.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Gaussian Naive Bayes\n",
+    "\n",
+    "### Gaussian Naive Bayes Example\n",
+    "\n",
+    "[Figure Context](05.05-Naive-Bayes.ipynb#Gaussian-Naive-Bayes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0Tq3fv+PCwHMCFdFdMRUKvLB+o0VnB2zfriLBy/qJdCZLHzJb7PZhfkOZjh2P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\n9HPGrhZOPcuH06lE4MmHhjg0mqn4GAeHozz20O8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Rd/zgZyTNxYmA00UPX7/zR3z8HzdG\nnKVUG+CJCYGmqWTTet78rhZLnC3WHU1TFrPbrYTs0CGN7m551LXQy9VBV8PuXNpsFxVqSueW55gV\nlutxw47tqkvR2JggmRAM9IPDAU3N6kBpt1evc670uPOur3IDw1b9zi5Z/Sjd5K6ceh5cZmynrBJs\nX/ZTPRaPZJW9wsKPr907v5652kZjuVGHVcckLiOEetXnrf7E2oKFSQmRCDwbgrFJ9cQNATUpLhCY\nfY3LbarmXh1NZJe8XTSRWbSGY/n9rZTyTOlkUuBwbK7OXyvFEmeL40Zbm8TtlgwMqDh0Oq2sz81g\nnHk8sGOHJJmEqSlBLKbif+Nj4A9AY1P54LnRK7WoRzIZiEZgOqKSvJoa1PSolpZjr1fe0hFEPjG1\nSNClNOntOP4+5FgMRkchmRQEApItW+Sm6JW9Wk7Al2yxkfj9s3Ho6WlBJiPo7d08u2KfD3w+iWEo\nCycyLUjEIRZTndMaGyHYKPF4NnqlFrVOPq+EKhpR+RqguqK1tkpO3QWJ9NokRd3++lv52e/ez2By\n/o/wpGCBd7zptjV5jpVgmqq3/dSUoLlZJZC2tGz+xK+lsMTZ4rjjcMBJJ5kMD6tY7YEDGp2dm+uH\nqOvQ0qIsm0wGpqaUWIdCKk7ocim3d2MjNTtH+kQml81yx53f5eDgGAGvi9ffegtdXd3r/rxSQiIB\nU2EVS9Y0gShlXjc2Ku+LpqnfEOm1ec4tW3r4wkf+gk998Q72HhhFE3DeKT38/Z+9gabm5rV5kmVQ\nYytVYxGXC045BZLJzXM8OBoscbbYEDRNTbQKBCTDwxojI+pg1LMJO3C53dDVLensUo1MIhFBPAaj\nI4KxUXXgbWranLOk65GxsTH+9O8+zNOTAqHZkFLy/fvfz/ve8Squv/aadXnOdFp5V6IRZTEDeLxq\ncxcMsu6NQ84752y++YWzSafT6JqG2704QWw9kBImJ1XSl5Tq9XZ2qvyUem5gtBZY4myxoQSD4PWq\nbO5EQrB/v6Cnx6SxcaNXtvYIAQ0Nqla6WFQlItPTQrm/IwKnU1lH/gCW23sD+ZfPfomnQ7aZBEEh\nBOG8l0//53e5+orLcazR7jGTgURMEIlCPqcu0zRoalaDYzyexQlh643H41k2SW2tSKVgdFQ1FbHZ\nVOMiv7VBncESZ4sNRw3NkExNqZjT4KBGLKY6AG3WRBCbTbUGbWuTJBJKpKNRGB8XjI+r98QfgEBA\nJfwslxFssTaYpsljfxxEiMVZVoejOj/+yb3cfNPLjvrxMxmIx1TCYDYrEKjPNhhUuQh+/+b/rAuF\n2fJDUN2+urs372/9aLHeDouaoblZ4vNJhoYEsZgglRKliTObI6N7Kbw+8PokXd0q3piIKxf/9BRM\nT6nRTF6fJOBX9dMul5X1vV5IKSkUjcpXajrpTGZVj5fJQDYFqZQqCzLmPHQgoHIOGho2vyCDcmGH\nQsqFbZrqe9zdfWLVLq8GS5wtagqnU5UkhULKihweVj/mtjZVF32sorS6ebMuivnZ21ebI20u02Rb\nl0svvHxfuw6uZmhtVgeyVAqV6R0XZDOCUAZCkyqBzOMGr1fi9igX+FJWR7V6ZACPo3Iwc7lUnOUe\nt+p9j/qe1YdWLbzOu/C1rajOWeesnd388o+JRbfpdGd55cteitu+QElLj2uaSowzGVUGlExCIp7m\nxz/9BfFUmpN3dHLDdZcQCAgaGkDXxfL1yFWuqzbr+XjOVV4JiQSMjKiEL11nps+BtclcGkucLWoO\nNQ1K0tgomZwUTE8LRkbmi/RmtzSEKJdlQUeXJJ9XB7hUSpBOqWSZZFLMDN5wOFQCkcspcbnVJmcl\nPZUtFvPnb3wF+z70BUZSs2+gkyyvu/4FNDSo6RGFAmQzkM5ANifIpNXM5Lk89sRjfO4b32YkayL0\nPNojSX69507+6zMfQdc3+XDwEum02mQnEkqFm5tVX2zLhb08Qh7LFngNCIUW71A3C62tfuv1rQGF\ngnKHTU0pd5iK166/SC98fUdjOfcfOcLex5/k7LPOZPu2bau6b5lK1xoGMyKdTgtSaTAXeGOFAIdD\n4nSBywlOl8TlmnWLtzV5mJiqXI+z3EEhl8vx7bvuZt+hIQJeF6+79Ra6V1hqdDws544WL+Ph1IIb\nrLxD2HPPHeDL3/ou/UNxfO4AV118EZe86HJyWcjlwSjOf1xNB7dLZea7PRKns8gNb/hT9k/P70gn\npeQ1L+ziXz/6vtJdj85ybmxwEY0v3dlroy3nZFJlYZdF2edT4x1XmgR+Ihw7l8Pav1jUPHa7mq7T\n1iYJhQThsGB0VBAKCTo7azOzO5VK8Rfv/QgPPDlKrOggYLuLS87s4jMf/Qf8a5CSqusqYUyVXym5\ny+XUKZtVyUa5bPl/iAHlQ70QSkSyXZDMlgTFvfI4djgU4s3vej97R0yEbkdKyf/84h/5xz99BTdd\n/9Jjfm0bgWnOee8yArttF294xXsplkRYAJFp9R7ZHSqU4CkJcdlTMZfv//Be9oXkzCCJMkIIHnry\nEFLKTdm3PJdTSZ3x+Kwot7VJKwv7KLDE2aJusNmgs1PS2qrc3eGwyuyenISurtoqw3j3Bz/Bjx6P\nIIQXoUNC2vnxEzGcH/wEX/jkR9blOcuu7EAAyoItpXKJly2+TEa5YDMZ1RBlKjorEA4nOB3gdCmL\n21F6PIdjvnB/7N++wN4xDVEqvhVCEC54+dRX7uaaKy4/bjWyq0VK5YUp5CFfgFxWzGxeChXaRdsd\n0BAEj0fidlV+L4CK5u10JApa5T7umXwR0zTR17t4+ThiGMp9PTWlQi1er3JfW8leR48lzhZ1h82m\nLOmWFsn4eGki1GENn08dEDZ64lU0EmH33iMIMf/IJITg/scHmZoK09zcctzW43Cok9q7lEUb/G44\nMizJZGat7ERCneQcxRFCeS9sdrDb4PePJqAYRAoDyidM+mMOvveDe7jt1lcet9cGShiKRTCLUCz9\nr5kwNiFmhFj9rWyp6jp4vZQsYCXELvf8JLvV2rjXXXMV//bNXzJVXPxlPG1bx6YRZsOAcFh5sQxD\nfc86O02CJ0ZIfV2xxNmibnE4VJex1lbJ2JiKbx08KGhoUCK9Uf26x8ZGCSVNRIWErOm0ZGho+LiK\ncyWEUK5Z1Z1xNgpsGCX3bk41xsjnBdkcFAuQSUNaQjbrg0IF61hKDh708tSTGrqukn40XcVzRemk\naUD5vDZrhc4Lu8vZy6RULmfTVNO7pKlOpgmGMb80aS6ZBITnegXsatPmcCiL2G6XOJ0q/m63Lz+V\narW0d3Twiiuex5fvfQZDm/0itDizvO2216ztk20A2awS5UhE5YFo2qxXaxN66zcES5wt6h63WzUx\nSSYlY2MasZiKefn9qkZ6xdOgpFRmV6Ggjv5OiYhGZ5ShYJqIsjLA4gctnd+i2znLV2Qkk8VAYAox\n87c9AH09PfNKtJaj2pjL5V9S9RfuWDj41wZu59JJW8UinPc8yc+enACpzzlpNNnzvOy6F+L3ihnh\nXMXLXBFlgddLsV+bDXSb+jv31NoErSk1btBur5R4Jaqcq/TEq7/qQ+99F9u2fJt7H3iUaDLLts4m\n3nzrjbzgwgtmbnP04xddG5L0lSjNkC4netnts8mZVgb22mK9nRabBp8Pdu401bjGcY14XIm0eFid\nLgAAIABJREFUpkFj0KTRl8NnU2ahyOegWEQUC8oXWigg5qXgAtNe9Eiq8pNVWwfwqpM7ufuhAdDm\n/MRMg5vO7CHYfxip6erIZrchlSmHtNnBYUc6SwHOGjRBbDb4y7fezLPv+yzDc0qN7DLHG152Ds9/\nftOiGuiy9TvXEi7/P/emc19u+f+yGJdP1d6SuQIcbABzg98+IQRvft1reMvrj99kp/WgWIRoVOV4\nlMvFvF4VVmpoqMmv6abAEmeLzYWUNDiyBFvTZGN5IhNFolMG0f1FolLisJs0BYo0+gs4HaX4a0ko\npduF1G1KNHUdWgMY7nRJGWZVQor51qZYaGeaJm9633tIfuHL/OqhZ5iK52jzO3jJuafy1jfehpRm\nKRBaQOSylS0vIZAOJ9LlQgR84HQhXa7VpVWvE+edczbf/Nf38KVv3MXhkTANXhfXv+QiXn7TDRVv\nL8T6D26wWFsKBTWIIxZTtfVSqs8xGFSua6v3+/pj1TmvIydCrd6GvT7DgHxeWb75AiKfQ6RTiHR6\n1u1cwhQ6CdPDdNpNLO/BtCkr1eWzEWjSaWqu3LBjLV5fsVgkGo3S0NCA3V7BPW2apRTignotubwS\n7FJdjzANbPY5e2hNQ3o8SI8HXC5ldZd9txUUsNqvu7HBRSRWuVb2WA4KG3VImWs5NzW4mF7itVW8\n7zHcYLn7ViuZOlq39nr89opFmJpS3qb0nPJ3j0e1Gm1qklT6Cq8HJ8KxcznW1HJ+8YtfTF9fHwDn\nnHMO73rXu9by4S1ONErFp6JUByRyWUQ+ry4rVo63SaerJF5eZWm6XGCz4QW8QJdRGmAfVb2OJ0Mw\nGVIHn9bWtS/9sNlstLRUSf7StJkaqLKkzZO2QgFhFtV7kMkg0mlEMomoNE9P19VrdrqQqutITbvI\nLWqDdLo8HU0ld6nudCpXo6Fh841wrRfWTJwHBwc5/fTT+fznP79WD2lxIiElpNOITGbGAha5Jaw6\nhxPp9yMdTuWOttvB4UC6Pcv6T3UdmprU/GTTlMRiEA7PxqfdbmhpqaHGJnY7OD1IAkBJuE0TUilE\nIa+G/+bziEIR8jnlOUilZqy58l/pcILHg/R6kR6vNZPyBMc01ZjS6elZK9luh44O1TbXSu7aeNbs\nI3j66aeZmJjg9a9/PW63m/e85z1sW6JdoYUFhoFIxBGpkhBn0vN8sFLTkT6fsoQdTnA51d81tAI1\nDRobobHRJJlUpSGxmGBoSGNkBLZvV95zv7/GDE9NA7+fCtVH6j0stQpT1nZW/Z/NQjSCiEbU7YQG\n7Y0IQwevD+n1ctx8lhYbxkIrGcDvV8ldNfc9P8E5KnG+6667+NrXvjbvsg984APcfvvtXHPNNTz6\n6KO8+93v5q677lqTRVpsEgoFRCyGFo8qt2xZjIVAutwzsVTp8XK8i5TVkAlJPi+ZmlIHr6kpiEQ0\ndF259xobVa1sTR/AhKDcQFvSMD/mnFNxeZJJZWEnk2jJnBp1BUivDxkMIhuCWL7MzUMmA4mE2niW\nrWSbDVpbVRzZ+qhrkzVLCMtms+i6PpP0cumll7J79+61eGiLeiaTgWiUUtrn7OUej5owHwioDOQa\nHDOVTEIkok6FUojbZlPWtt+vBL2ujc2Se1zNNiy1Bivj9ZbdCpZQ1xmGoT7KWAzicRX5KNPQAK2t\nrLz232LDWDO39r//+78TDAZ561vfyr59++js7FzR/TZ7Rt4J+fqkRESm0cJh5a4GZR37fJj+BmRD\nw+wBP22q8Uo1gMc+f5/qCwYQxTiNPqVf0aggGhUkIrO3cTrB55X4/BKf9+h0LL1EW8n1prXVTygL\n2HzQ6ANfi/JsxCKI4UkYLlnUHi9mYxOyqakmN1GVOJF+e8Wi2gOn04JEQv0tm1y6rtzW6qQ2k/k8\nhMMbuPgVcCJ8fsuxZuL8tre9jXe/+93s3r0bm83Gxz/+8bV6aIt6oVBAmwojpqZmsqnNQINylQYa\n6rbYVQhlKfv9kp4eSSqlaj8TSUEqBVPTgqlpJbAOh2rQ4HaBy6XqQevGurbbkS0tGC0tqkFLNDoT\ngtDTKeTYKLK5GbOl1bKmNwjDUEJsmjA4KMhkxDzLGJRTqizIHo9lIdcraybOgUCAL37xi2v1cBb1\nQjmxKxZDi0VBSqSmY7a2YTa3VC4grmNUmYmKT7e3S6QsxfRKQp1Mqni1Mq7VUdGmq0EKHrekoaEO\n4tYANtusUBcKaNNTiHAYLTSJFg7Nbrr8gbrddNUDmQwzdcfZ7KwQNzaqJiFly9jjAbdbfbesTOvN\ngfUxWqwew0DEohCbwDYwNpPYJZ0uzJbWunJ/HivlARIez2yVci6nkqSzGUE6o8Y0JpOU6qpnD6iB\nAAT8G9oDaGXY7ZjtHdDWPhOu0GJRiEUBlUhmBhuRwaClDMeIlCoNoNwffq5VrOtqU+jxwJYtkEqZ\nm23vazEH65dksTJMExGPISIRtERcHUUavSrLuqEB0+dnw2c11gjluco0zAq2aaoknXhcEIuLUvwa\nQCDsYsYNWdNWtRDIpmaMpmZIp9EScUQigUgl0VNJGB3G9AeQTU0qjFGzL6R2KItxKiVm/pZLnDRN\ntcsMBOSi5MPGRhVrtti8WOJsUZ1kEm0qjBaPzbTFlE4XsrERTtqCkTj+k3HqEU1TmbINDZItSLJZ\niMXVdJ9wVFnXk5NqSIfbLXG7lTXu8dRoZMDjwfR4oL1DlchFImiRafU9iceQug0ZDGI2NlmbtjkY\nhqo1LotxOi3mdZt1OFS3urIgW/ubExdLnC0qk8mgjY0qK5lSV67GRsyGoCp9AlVPa4nzUeEqJYy1\nt0k6csrtrWKL6qSqztSRWdNKSWZudZ/yHOKaiRzY7ci2Noy2NvW9iUwrsZ4Ko02FkW4PZlcX0rd8\nhupmodwLJpNRseLy38KCn4vLpVzVXq/ymtRN8qDFumOJs8V88nm08TG0yDSg4olGRydr3XTabTOr\nXLtMHLZaaf6yZftLXy8ruAnlvB7exxIfXtoE8trAG4T2oDpvmsq6ymQEqbQgndbIJSGfhNic+zkc\n4HTNCrbToRpKOBxzhHsZ02th+ViZoy7vcrsx3d3Q2aUSBaen0WJR9EMHMRuCmJ1dNeoKWD1SznRP\nJZcT8/7P5RZ/FW02lWvgdjMjxlYuncVSWOJsoSgW0SYn0MIhlXHtcmN2dqrYocVxRdNmM8JbkYDE\nMCCVhlxWkM0JslnI55VbvDz4fi66Dg67xO4UOBwShx1sdrDZJLbSKGmz2v7oWBECGWhABhowUyn0\nsVG0WBQtHlOZ/G3tNatMUir3c6Gg4rpqaJiY+b9YVO/9Qiu4jKYpi9jtlvP+WrlyFqvB+rqc6Jgm\nWmgSMTmJMA2k3YHZ0YFsbLICXjWErkPAD/iVWJcxTGWlZXOCfA7yBWXBFQqQywsyOcFSVrsnALmU\nhs2mhEPXQdclugZ5qbLKNU0JemmUNaI01rp83dzLl8TrxThpp8r0HhtDm5xATE9jdnSqzP41+p6Z\nphLW0jAzslklsqapTup/gWGo2xWL6jLDgGJRlP6ubNNit6vNk92uHAEOx+z/lmvaYi2wxPlERUpE\nNII2Po7I55C6DaOrB9nSYolyHaHri0u55mKYJXdryfoziiVrsChweiFehGJBxUaVG1Z99tni3O/A\nyr4PQiw+zRVuIVpAa0JPTyGmpxD9IXAlVPndEmETKVW3q/JJXTZ7KgvpQkFtbFR90VeKECVvg2N2\nM2K3q5PNBna7LP1V562fiMV6Y4nziUZZlCcm1NQiIZSbsb2jZt2MFksTi8f52fd/iMfr45obr5vp\nbV9G11X+nsrhmz/HytcIqeisqpWtS8OARFbMOT9rbZat0Lmn8uVlIS1fVrZOYW78VUN6W8HZCKEQ\nWiQOkVFVktfcrKZjMV/8ZsV98clmK28A5MxGQAhobgZNkzNWfll8dX3WC6Drsx6Dmkmus7AoYYnz\niUIlUW5qVrG/TZKgc6LxlU99lj1f/S6e0RgGkp986ovc8t6/4Mrrrz2qxyuLlt0O5rx92koT4VaT\nMKcB7ZAJoE2Mo8VCQEg1NOnoOObM7tZWlXRlYVGvWOJ8AiBiUbTRUUQ+Z4lyJcomnvL9qrpdw4B8\nDhEKzwlYqgDmb372Cx6591ckJ6ZwNzVw1pWXcPVN18838crmmDLXQNOR5f/L/lK7/aj9oz/7wT08\n9smv488ZgEBH4Ng3zp3v+Thnnn8O7R0da/f+rCduN2bfNsx0Wol0PIZ+6CDS58NoX/sqAQuLesES\n582MaaKNDKNNT82KcnvHqoYWLFVqU2bJkii5TFaNUeX65e5b7fqlsnnKTbCzGUQ2qzKGCkU1oKNQ\nBCSLal+y7YjQxNwH4Zc/uY89X/ourqyBH+DIJI8/eZD02Bg3v/YVVUq5xOLIbckvKx1OcCixlg4n\npakZs77WCj7X33/vx7hzxqLL/WNx7v7yV3nHe/669BzV/bXSqJxy7NaX8fNWedxM8Sh8xB4P5rbt\nmKmUEulEHFvyAKY/gLml18qysjjhsMR5s5JOow8OIHJZpNuD0btVHfBPBIpFSKWUCGezs2IsAeaK\ntwC7DenxgN0Gug0cdmQ5ENnTixkMzPh7TQHfe/fHcWR1YI7ftwiHH9jLVR94Dx63e7YWp5wqbBhq\nM2KYCNMoWeh5tTko5BHpFKRnVjSL04lUrcLUX7d7ph4nE4lXfOkCseR1dYHXi7l9B2YyiT4xplqE\n7t+H2dtrlfVZnFBY4rwJEZOT6OOjIKVK9urs2tzppaapxDiZhFgckcnMv17XlAC7XEiXc7b59UJr\nbKHV6/NB3D1zdmRkmPRzQzgqZC+LgTCPPfwYL3rxC2czleYVtqr7LLSrZ86X3Olkc2ozkVEWvshF\n1Wsq387pRAb8dHa1MIJELlhLAZPOXduWeqfqB58Pw7cTEQqhj42g9x/GbGnF7Ore3N9lC4sSljhv\nJgoF9KEBRCKBtNmVteEPbPSq1od0WolxIoFIpebV2UivB/x+ZW26XPPd+Mu5zKvg9/kQfjdMZRdd\nV3DaaOtoO7oHFoKZ1l5eL/OKonI5yOfVhiOTQSSTiFCYmy6/mO/c/xAilCaDRgaNAmCcs41b/uTW\no3yFtYdsbaXo86EPHEELhxCp1InlBbI4YbHEeZMg4jG0wUGEUVRxut6tm6slUaEAySQilULEY8yd\npSfdbvD5kH6/cv2uU11MsKGBzovOJPXjPYsiyMHnn8KuXTvX/kmdTnCryV8AsjTGaFtrC9f9wzv5\nzbe+R/7AERrsNprPPIWb//odODOZ2TqhzYDbjbHr5Jn8CduB/aomv7l5o1dmYbFubJJf7wmMlGpA\nRWgShFAHrdbWjV7VsWMYyipOJpWFnMvNXidANgSRwQblep4rQuvakxL+7MN/z8en3kNxzwFcpiCP\nifm8rfzVh/9uXZ93BiHURsTn44xbX8kZL7+ZYiSClkphS6fV+zYwAIB0uSDQoBp81HsjZ03D3NKL\n9PvRhobQhwcx4zGVLLZZNiEWFnOwvtX1jGGgDRxBS8SRThfG1r7ZiVH1iJQQj6vuUYkEcwfbSr+a\nFy19PpXNvEFxx/b2dv71rv/kvnvvY2DfQTq29vDSm65D3yjhcziwtbcDYEqp3P2plNrYpNMQCiFC\nIdXr2udToz4bGuq264YMNmJ4vOiDA2jxGOLAcxjbd1hlgRabDkuc65VsFv1Iv8rG9vsxtm6rX8so\nmUREo4hoFIyiEmmnGlEpfT7Vn3KuGB9D3HgtEEJw9UuvhpdevaHrWIQQagPj9UJbG9I0IZOdjc2X\nTmiaEmmPZ6NXfHQ4HBg7TlLT0yYn0A88h7FtuzU32mJTYYlzHSIScbQjRxCmcczZ2NVHN6IO8NVY\nok4Wc3EN7jwKeUQohIhEVGkRqLKmxiAy4J/vASgsSMAq5FiK+SMeV7FeUC7huXT1IKOTcx78GDYF\n1eqN9eo/Q2GrUuNrW6Zm3elAOoPQHERms4hIFBGJICZGVR18Mo5sakIGg/Oz15dZE2LpjeBxqZEW\nArOzC+lwoo8MYTt8EKN3K7IhuLL7W1jUOJY41xlichJ9bETFl7f0IpvqLCnGMBDhMGJiXNX+6hqy\nqVGJg8+nNhlGhcHKFseOy4Xs7EB2tEMiAQ43TIURo2OIsXEl0u1tddXwQzY3Y9ht6ANH0I/0b56c\nC4sTHkuc6wXTRBseQotMI212jL5tx92N94ff/o6n9zxKW08X197ystXd2TSVKIdCqkmIJpBdnSrj\ntk7jn3WLEBAIQNcWTIdNhRTCYcTUFCISQbY0Izs66ybRSgYaKO7Yid5/GH10GLOQh9ZTNnpZFhbH\nRH38+k50ikUVX04lkR6vEubjaN2kUin+z1veSWL343jzkicw+ennv87f/ten2da7pfqdTRMRnkKE\nJpUo6zqyowPZ1Fi/MfLNhM2GbGlRm6SpaTVreTKEiMSQba3KCq2HzZPHg7FzF/rhQ6py4bADvNbG\nz6J+sb65tU6xiH7oICKVxAw2Yuw46bi7HT/7vg9TvO8xvHnVHsOBhvvJQT7z5+9RdbeVkBLCYbQ/\n7kOMqW5lsr0d89RTke3tljDXGkJASzPmKacguzpBCMT4ONqzf0RMTC6Ox9ciDgfGzl0qiTASQT98\naN1L6yws1gtLnGsZ00TvP4zIZjCbWzC39h13S8AwDPofeAStQsvK/J79/Pb+3YvvFI+j7d+PNjIC\npoFsa1MH/Y5jnxldKBQwrQPu+qFpyNZWzFNLn5eUiPExtH37IBLZ6NUtj65jbD8JGhsRqST6kcNV\nhpFYWNQullu7VpES/chhRDqFGWzE7FnGfbxO5HI5iol0xeuchmR0aGT2gkwGMTqqSndMQyUYdXSs\niaX/4P2/4Z4vf4vJZ/uxuRz0XnA6b/+Hv6S5sfGYH9uiArqObG9HtrSorPpQCG1wEDk1hezuBk8N\nj3IUArZtwwzF0RJxtIEjamNr9eS2qCMsca5RtEHVI3umFecG4Xa7adzZC1P7F12XavVz6dVXqPnH\n4+OI6WkA1eyio33NGqI8sucRvvmuj+EJpVBSnGZ68Ld8aGiUT33r8xvXAOREYCZHoAkxMoqIxxAH\nDiBb2tbEE7JuCIHZtw1x+BBaLArDQ6qbmIVFnWCJcw2ijQyjRSNIrw+zb9sx7/jdtirxwmVcxMIs\n8pI3vpJ7nvlnPInZftZ5JKe/+n/RaRqIp58CU4LLidnVBX4/FPOQXzwgYvYBMkteJbPzLfV7v/IN\nPKHU/HUhMPcc4id33Mn1L71i9orc0o+rrq+ypuKCEq7TzoXRwdnz1eKuy31G1cIR9uq1yrJa9yvX\nMhsgZ5VGI11bIJOsfJ1j8WAJ2d2B9HvQRscQE2OI6RBmZycs9F7IZQ4rWpUa6WXuuqpZ0ZqGsW07\n+qGDaqa5zaZ6AlhY1AGWONcY2sQ4WjiEdLlV16MayDZ96S03Yrfb+fU372b6yAie5gbOveJi/uwN\nryUzMAA2HbOzA5qb1sV1GBmarJgc4URj4MCRNX8+iyoEApg+n2pmMjmJNjikXN1bttRmC01dx9i+\nA/3gAbTJCaRuQ7Yd5fQwC4vjiCXONYQIh9HGx5B2h+oXXEMuw6tuuJarbrhWnYnH0QaHEKkUMtiA\n7OlZ17W6GrzkK1xuIPE01HDscwWYpsnXvvk9nv7dU2TiKZq6W7jyhsu47IUXbPTSlkbTVDy6sVHl\nGMTiiAMHMLdsUX27aw2bbUag9bERDF23JlpZ1DyWONcIIhpBHxlC6rYNKZdaEVIixsZmBinQ24s8\nDt28zr3yBez+3X6cC7zK6W4/L3/F9ev+/OvJJz/3DYZ/+CgOBF4gN5Tgf54eIv83Ba667AXc+f2f\n8cTvnyKbTNO8pY3rX3Etzzvj5I1etsLhQPb1Iaen0UZG0I4MqAYmPb21l3zlcMwK9PAghq4hg1Yy\noUXtYolzLZBOow8OIDW9difsFIuIgQGVie1wYG7dCq2tMD627k99y8tvYGJ4nCe/txtvOE0RibGz\nldf95esJ+Op32MFoKMyh3U/iX1Cm5kkW+cUP7ueZZw5w5LsP4ZACHYg+O8GXHjvIGz54Oxece+bG\nLLoSTU2YbjfawCAiPAW5ArKvr/Y6jLlcGNt3YDt0AH1wgKLDWb/DPyw2PTX26zkBMU30wQGQErNv\na20eLHI5tP5+yOWQgQZk75bj6nIXQvCnf/U2wq9/Bb/4+W6CPg9XXnYxtlo7+K+S3b/fiy9WgAo1\n5OH+USKHxghKdV2cAmHyaKE0//w3/8LzLjuPN93+Grr7Vp7JL6Xk0b1PMjgwzMUXX4D/1LV6JYDb\njblrJ2JoCBFPIg4exNy+HRzLDOY43ng8GFv7VKvPoUGMnbtqIq/DwmIh9X102wRoY6OIXBazpRUZ\nqMF4XSqF1n8EjCKytRXZtXHZri1NTdx6682QXSYju05ob20iJ8BVoUdGFpPOhAlopDGYpsB2vOUr\nif30ST52eIR//uo/411BydrhwwN87kP/RmZvP66C5OdNX+fkV13LX/7N7cdciial5De/foCHf34/\n0pRcdM7pXHL6qWgHDmBu21ZzG04ZaMBsbkGbCqONjWJ292z0kiwsFmGJ8wYiEnGVme101WaJRzKJ\ndlh1WDK7u6GlZaNXtP6YJuTyqqxqchIRnlZTsooGolBQl88tPyvHVufGWHUdadORms6DT/6RJ594\nDkPXOe3807j8kgsRJYv/sgvP4fu77oX94flLQBLY1oHx+BgaME6WbSwWOOdzYe684we86c23Vn1J\nUko+8/5PYn9ssCTvAv90joEv3M2X7IK3v+sdq3+f5jz2P/3dBxj69n14StM47/jmT3nopc/nb//8\nf6MdOqRCIIHAUT/HemB2dSOSSfX78/trc2NscUJjifNGUSyiDQ6q0Y+9W4/JtbbcTOaqtczmEgld\nyRRa/2EoFjF7t0AwWHmU4xL1ynKp+tky6cTS1yWrXLfc9enK3czKGKk5s6BNE/J5RD6PyOWRyTQU\n5sx77j0V4+k5zVcMqYRXm++GFjOWb+kfw0SaJl/9ya+IPD2Cs+Safuznj3L4zF/yppuuQbhcSKeT\nt1x3Cf+R/jm2oQQONNI2ifeMTj5y+6t5/4f+A/tYBg2BqOD61hFM/vEQTFSJ+/v83P+7hyk+fgT7\ngoI0OxrP3Pdb5Ftevfh+Hv/SjwkIt5L5++79OcPf+jkeY3Z9bkMw8KM/8KOLzuNlF5yLduggZl/f\nygR6mUSyanXQq66B3tqH7cB+tMFBjJNPqc0kTIsTFkucNwhteAhRLGB2dNac229GmKXE3Npbm+Ux\nR4OUkM0iUmlEJqss4blXFw3V8MPhQNpssH07Rnubiq/rOqyipfe9e/by9FOTeHGgI7EBOpLxp8Z4\nYPuzXHbGKYh0mrPcDj795hvZ/ewBQqkM23dt4/xzzgRN4xW3Xs13/vtejHBqyedxeJdPHhwZHsdt\nVhauTCSBlBJxlNnVe3/xIG5j8X2daDy+50lueNXL0fr70Y4cWblAHy/cbozObvTRYfShAdWT28Ki\nRrDEeQMQkWm0WFR1AGtr3+jlzGeeMG+FZbKhn3vuAPfd/RNMw+Siq17MBc8/7zgtdIWUBTmZRqTS\nkCxZ+ppAupyzYuxwYJpyvuXW2AiuOZ2yVjFw46lnD2NDJ7foGhsPjkW55OpORD4PuTw2TK4465QZ\nw5uhEaTHzWWn7uDsD/8pn/zODwk/cICAOT82nPRqXHP1i5Zdy/POOoUHXffgyy4Obge7245amAHM\n4tJd08xCEXw+zG3b5gt0Y9NRP99aI1tbMROqB7eYnLQalFjUDJY4H2/yebThYeVW21Jj9aALhbmh\nobIru8S//5+P8/v/+5/4k8oCffar9/Dzmy/h//vEB47XipcmkykJcgqMkqjqGqbfh/R6lOgufO9z\nhcWPc5TIKkJuGibY7Ui7HbxeTLdj1sWeziBSKURSnZo0jY+95gb+u+33/Pa+vXgjeSSQbnVy1S2X\nctqO5bO1zzxlJ80X7iSze/+86WJZt42rXnZFlXsuz0kXnMXY3Q/gWOAyN5D0nXeGOrNQoG121eK1\nRjB7tyL270MfH6Xo89WeJ8vihMQS5+OMNjqCMA2Mnt7aqmfO51W51FxhrsKjDz/KH/75K/jTs+Lt\nzUsm7tzNXWffxStuvna9V1yZVBotElVJXQC6hgz4kT4lyDJdqdfY2rN9Rw97Hlkc5y1gsn1nhexg\nTVPrc7mQTY0laz+lXPDxBK+/4AxufN4ufvbMQQyvh+suuwj/KgaL/MP738lnP/M1Dj+yDyORwdfT\nzP/6y7dz3YWnHdPrvPnWW9hz3wMUfvUUtpLwG0jki07h1W9+3ewNFwr0zp3zvRIbic2G2duLfvgQ\n+vAQxq4aafJicUJjifPxJJ9Hi8eQbk9ttQ+UEjE4CKax4haMu79/L970YqvaIQXP7P7D8RfndBpt\nKgLRGADS50EG/JUt5OPATS96Po8+/RzFZ0LzREs/rY1bXnzR8g9QFurmJtBAJJIEkylefc5pqgmM\nuboZxW6nk79799soFIukszkCXg+BF11D4omHjublzWCz2fjolz7Ft77yDQ7veQLTlGw9/0z+5O1v\nwbVQfH0+zC1b0IZHEENDyJNOqhnPkfQHMAMNaPEYpFLgrd/mNhabA0ucjyPa9JSyTJtrqyRJhEKq\nT3agAZpWFg80sktboMXs2rmHlyWdQUxHEBk1bUp6PcjGIDg3tvmFXdf50O2v5Tu/fpD+gyMgoG9H\nN7de+SIcq2meIgR4XEiPG9nUiIhEEIkk2vgE0u1C9nSvygK122w0+Nb2Z+9wOHjjO94M5YosWxWP\nUDCITKYQ0ShMTiLbayfnQra2QjyGNhXGtMTZYoOxxPl4YZqIqSmkpiMXjthbAR770pZStfhm6QZL\nX5dKIkZHwGZDdneCuSDBp1hZhHecexpD3/r5olijRNKxawuklimHikeWvi4arXpXI5qKsUC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nwpV/imzXt4a1E1N195ad+LLAtXRztISaSkJGW1W5y4eDGlxSW8u/U9SmfOYPt77yHCMar/tpEP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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bd53978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_blobs\n",
+    "X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu')\n",
+    "ax.set_title('Naive Bayes Model', size=14)\n",
+    "\n",
+    "xlim = (-8, 8)\n",
+    "ylim = (-15, 5)\n",
+    "\n",
+    "xg = np.linspace(xlim[0], xlim[1], 60)\n",
+    "yg = np.linspace(ylim[0], ylim[1], 40)\n",
+    "xx, yy = np.meshgrid(xg, yg)\n",
+    "Xgrid = np.vstack([xx.ravel(), yy.ravel()]).T\n",
+    "\n",
+    "for label, color in enumerate(['red', 'blue']):\n",
+    "    mask = (y == label)\n",
+    "    mu, std = X[mask].mean(0), X[mask].std(0)\n",
+    "    P = np.exp(-0.5 * (Xgrid - mu) ** 2 / std ** 2).prod(1)\n",
+    "    Pm = np.ma.masked_array(P, P < 0.03)\n",
+    "    ax.pcolorfast(xg, yg, Pm.reshape(xx.shape), alpha=0.5,\n",
+    "                  cmap=color.title() + 's')\n",
+    "    ax.contour(xx, yy, P.reshape(xx.shape),\n",
+    "               levels=[0.01, 0.1, 0.5, 0.9],\n",
+    "               colors=color, alpha=0.2)\n",
+    "    \n",
+    "ax.set(xlim=xlim, ylim=ylim)\n",
+    "\n",
+    "fig.savefig('figures/05.05-gaussian-NB.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Linear Regression\n",
+    "\n",
+    "### Gaussian Basis Functions\n",
+    "\n",
+    "[Figure Context](05.06-Linear-Regression.ipynb#Gaussian-Basis-Functions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KvLzxGA6XT+uQYsLpCxa+OHKRfrkp3DEir1s/2+fz0thYj9ncRCAQ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\nrm0QwiKjyxaJO6+GQimFoihot0dNgElZlpg5vlwbFKVsg223O6jVxo9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66il861vfghtU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\n/9BBE/JV7MXFAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bb9fef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "from sklearn.base import BaseEstimator, TransformerMixin\n",
+    "\n",
+    "class GaussianFeatures(BaseEstimator, TransformerMixin):\n",
+    "    \"\"\"Uniformly-spaced Gaussian Features for 1D input\"\"\"\n",
+    "    \n",
+    "    def __init__(self, N, width_factor=2.0):\n",
+    "        self.N = N\n",
+    "        self.width_factor = width_factor\n",
+    "    \n",
+    "    @staticmethod\n",
+    "    def _gauss_basis(x, y, width, axis=None):\n",
+    "        arg = (x - y) / width\n",
+    "        return np.exp(-0.5 * np.sum(arg ** 2, axis))\n",
+    "        \n",
+    "    def fit(self, X, y=None):\n",
+    "        # create N centers spread along the data range\n",
+    "        self.centers_ = np.linspace(X.min(), X.max(), self.N)\n",
+    "        self.width_ = self.width_factor * (self.centers_[1] - self.centers_[0])\n",
+    "        return self\n",
+    "        \n",
+    "    def transform(self, X):\n",
+    "        return self._gauss_basis(X[:, :, np.newaxis], self.centers_,\n",
+    "                                 self.width_, axis=1)\n",
+    "\n",
+    "rng = np.random.RandomState(1)\n",
+    "x = 10 * rng.rand(50)\n",
+    "y = np.sin(x) + 0.1 * rng.randn(50)\n",
+    "xfit = np.linspace(0, 10, 1000)\n",
+    "\n",
+    "gauss_model = make_pipeline(GaussianFeatures(10, 1.0),\n",
+    "                            LinearRegression())\n",
+    "gauss_model.fit(x[:, np.newaxis], y)\n",
+    "yfit = gauss_model.predict(xfit[:, np.newaxis])\n",
+    "\n",
+    "gf = gauss_model.named_steps['gaussianfeatures']\n",
+    "lm = gauss_model.named_steps['linearregression']\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "for i in range(10):\n",
+    "    selector = np.zeros(10)\n",
+    "    selector[i] = 1\n",
+    "    Xfit = gf.transform(xfit[:, None]) * selector\n",
+    "    yfit = lm.predict(Xfit)\n",
+    "    ax.fill_between(xfit, yfit.min(), yfit, color='gray', alpha=0.2)\n",
+    "\n",
+    "ax.scatter(x, y)\n",
+    "ax.plot(xfit, gauss_model.predict(xfit[:, np.newaxis]))\n",
+    "ax.set_xlim(0, 10)\n",
+    "ax.set_ylim(yfit.min(), 1.5)\n",
+    "\n",
+    "fig.savefig('figures/05.06-gaussian-basis.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Random Forests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper Code\n",
+    "\n",
+    "The following will create a module ``helpers_05_08.py`` which contains some tools used in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting helpers_05_08.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file helpers_05_08.py\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from ipywidgets import interact\n",
+    "\n",
+    "\n",
+    "def visualize_tree(estimator, X, y, boundaries=True,\n",
+    "                   xlim=None, ylim=None, ax=None):\n",
+    "    ax = ax or plt.gca()\n",
+    "    \n",
+    "    # Plot the training points\n",
+    "    ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap='viridis',\n",
+    "               clim=(y.min(), y.max()), zorder=3)\n",
+    "    ax.axis('tight')\n",
+    "    ax.axis('off')\n",
+    "    if xlim is None:\n",
+    "        xlim = ax.get_xlim()\n",
+    "    if ylim is None:\n",
+    "        ylim = ax.get_ylim()\n",
+    "    \n",
+    "    # fit the estimator\n",
+    "    estimator.fit(X, y)\n",
+    "    xx, yy = np.meshgrid(np.linspace(*xlim, num=200),\n",
+    "                         np.linspace(*ylim, num=200))\n",
+    "    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "    # Put the result into a color plot\n",
+    "    n_classes = len(np.unique(y))\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    contours = ax.contourf(xx, yy, Z, alpha=0.3,\n",
+    "                           levels=np.arange(n_classes + 1) - 0.5,\n",
+    "                           cmap='viridis', clim=(y.min(), y.max()),\n",
+    "                           zorder=1)\n",
+    "\n",
+    "    ax.set(xlim=xlim, ylim=ylim)\n",
+    "    \n",
+    "    # Plot the decision boundaries\n",
+    "    def plot_boundaries(i, xlim, ylim):\n",
+    "        if i >= 0:\n",
+    "            tree = estimator.tree_\n",
+    "        \n",
+    "            if tree.feature[i] == 0:\n",
+    "                ax.plot([tree.threshold[i], tree.threshold[i]], ylim, '-k', zorder=2)\n",
+    "                plot_boundaries(tree.children_left[i],\n",
+    "                                [xlim[0], tree.threshold[i]], ylim)\n",
+    "                plot_boundaries(tree.children_right[i],\n",
+    "                                [tree.threshold[i], xlim[1]], ylim)\n",
+    "        \n",
+    "            elif tree.feature[i] == 1:\n",
+    "                ax.plot(xlim, [tree.threshold[i], tree.threshold[i]], '-k', zorder=2)\n",
+    "                plot_boundaries(tree.children_left[i], xlim,\n",
+    "                                [ylim[0], tree.threshold[i]])\n",
+    "                plot_boundaries(tree.children_right[i], xlim,\n",
+    "                                [tree.threshold[i], ylim[1]])\n",
+    "            \n",
+    "    if boundaries:\n",
+    "        plot_boundaries(0, xlim, ylim)\n",
+    "\n",
+    "\n",
+    "def plot_tree_interactive(X, y):\n",
+    "    def interactive_tree(depth=5):\n",
+    "        clf = DecisionTreeClassifier(max_depth=depth, random_state=0)\n",
+    "        visualize_tree(clf, X, y)\n",
+    "\n",
+    "    return interact(interactive_tree, depth=[1, 5])\n",
+    "\n",
+    "\n",
+    "def randomized_tree_interactive(X, y):\n",
+    "    N = int(0.75 * X.shape[0])\n",
+    "    \n",
+    "    xlim = (X[:, 0].min(), X[:, 0].max())\n",
+    "    ylim = (X[:, 1].min(), X[:, 1].max())\n",
+    "    \n",
+    "    def fit_randomized_tree(random_state=0):\n",
+    "        clf = DecisionTreeClassifier(max_depth=15)\n",
+    "        i = np.arange(len(y))\n",
+    "        rng = np.random.RandomState(random_state)\n",
+    "        rng.shuffle(i)\n",
+    "        visualize_tree(clf, X[i[:N]], y[i[:N]], boundaries=False,\n",
+    "                       xlim=xlim, ylim=ylim)\n",
+    "    \n",
+    "    interact(fit_randomized_tree, random_state=[0, 100]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Decision Tree Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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oUWG4RCJh27dvZzzPMwsLC3b9+nW5ZYhEItanTx+Za11MTAzr1asX43merVq1Si6G+qRB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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bbbfc18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(10, 4))\n",
+    "ax = fig.add_axes([0, 0, 0.8, 1], frameon=False, xticks=[], yticks=[])\n",
+    "ax.set_title('Example Decision Tree: Animal Classification', size=24)\n",
+    "\n",
+    "def text(ax, x, y, t, size=20, **kwargs):\n",
+    "    ax.text(x, y, t,\n",
+    "            ha='center', va='center', size=size,\n",
+    "            bbox=dict(boxstyle='round', ec='k', fc='w'), **kwargs)\n",
+    "\n",
+    "text(ax, 0.5, 0.9, \"How big is\\nthe animal?\", 20)\n",
+    "text(ax, 0.3, 0.6, \"Does the animal\\nhave horns?\", 18)\n",
+    "text(ax, 0.7, 0.6, \"Does the animal\\nhave two legs?\", 18)\n",
+    "text(ax, 0.12, 0.3, \"Are the horns\\nlonger than 10cm?\", 14)\n",
+    "text(ax, 0.38, 0.3, \"Is the animal\\nwearing a collar?\", 14)\n",
+    "text(ax, 0.62, 0.3, \"Does the animal\\nhave wings?\", 14)\n",
+    "text(ax, 0.88, 0.3, \"Does the animal\\nhave a tail?\", 14)\n",
+    "\n",
+    "text(ax, 0.4, 0.75, \"> 1m\", 12, alpha=0.4)\n",
+    "text(ax, 0.6, 0.75, \"< 1m\", 12, alpha=0.4)\n",
+    "\n",
+    "text(ax, 0.21, 0.45, \"yes\", 12, alpha=0.4)\n",
+    "text(ax, 0.34, 0.45, \"no\", 12, alpha=0.4)\n",
+    "\n",
+    "text(ax, 0.66, 0.45, \"yes\", 12, alpha=0.4)\n",
+    "text(ax, 0.79, 0.45, \"no\", 12, alpha=0.4)\n",
+    "\n",
+    "ax.plot([0.3, 0.5, 0.7], [0.6, 0.9, 0.6], '-k')\n",
+    "ax.plot([0.12, 0.3, 0.38], [0.3, 0.6, 0.3], '-k')\n",
+    "ax.plot([0.62, 0.7, 0.88], [0.3, 0.6, 0.3], '-k')\n",
+    "ax.plot([0.0, 0.12, 0.20], [0.0, 0.3, 0.0], '--k')\n",
+    "ax.plot([0.28, 0.38, 0.48], [0.0, 0.3, 0.0], '--k')\n",
+    "ax.plot([0.52, 0.62, 0.72], [0.0, 0.3, 0.0], '--k')\n",
+    "ax.plot([0.8, 0.88, 1.0], [0.0, 0.3, 0.0], '--k')\n",
+    "ax.axis([0, 1, 0, 1])\n",
+    "\n",
+    "fig.savefig('figures/05.08-decision-tree.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Decision Tree Levels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AsUahy7F4lrOxUWAnXev3eYShZ9gkclKOfspra252ve19ZlEbHx/YRUTUhEHN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\nNzE1JBv/UXcsZR5g4MTZDhLWvMLR00b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9nCr3oaHZg/VLmwnPFLHOkXnpVSNPrlITGKCi\nvEJE4VN8T4sJhVKJ1TbersjcvZeVbOnDzc35c4nRVkoam1xCjos7QnJLon+wAW9P+/+RLMsMWePv\nm4gjyzIaxcC49ugIFSsXjfDurvdR5uSx6JEXHP37egfx8NSjUinZ8/4feHJ+KV4eCsCds1UyjSNa\nfKzvkRxnRadzrijlr28mf/5fO33fuUvW8P5L+xkXviUwTrSxSpo7FnGO7d2CevQIEUGDHCv1p+mG\nhhfW3MDfV8Q0U+LnLxt4cYMOrVbgWLkKr4hFd3bTboNCqcJidZ6bvYLf3dsapTg0rgpZZPAIne09\nLiHHxR0xSjxG4w3Hb5bVKmMU788hOMBg/zCB3s65RQVBIC5azcpFg2zc+S6Jaf/Mkse+Bdi97fv7\nhvDydkehULDjrV/wrdWVaDRKQElJuY22ISvJ/vvQRktOQokgCPi7tzBr6V84jVe04il2/vHY+MlN\nYFIk+c6q4QEc2LoRb7GMEL8RDh4L4kaHwLce78DDXWR4msRPf2vgb76tQxQF9h53Izhh8R1feyIE\nUcRmcxaHbDZQKO5+raQRx5eN9/UYxjBixNPrsx+iufjsfGWEnMy8HI7vX0FFfQl6zTB9hlCSC+4s\nY/jnRVu3btzCoq/fxvUWC3PyZd46WkrQirG405sfAnPfKWLyx64VHiJgLDtJQWYX1ZfHL1jau9X4\nJo1XbacWP8OZyl8wJcPuMinLMqeqwokIGiY2yr7pMholqpvTWTLTm70fvIKWWkDArMqkeOWTTuOM\nDBsYbX2Hx5eMAgrSEoew7G0nMdY+d1EU+NqTbvznH4KIjEshJCaHouVp93QffXw9OdGZwGxrLUql\nfS5nqxSExN193oro5CmcrTxAXsZY2+FSFVn5M2//IRcubmJ6YTGHd/SguHAKN5WB3tEo8oqfv69z\nau/WAmMbLFmW6emz0dpmZXbeCCXnLzBt9ljOBG+fMc8ZcfQsQf5jz3hSrMzhUwd4ctkwBydYw/QP\nuxHq7uyOKwgCyfmPUXftVRJj7G2SJNPQHo17dS9z8+2nfQNDEl2jmYiiwO73fou74gqSrEbwmMqc\nRc6lwTvaOvEwf0jRAiugIDK0D72qnfAQuyisVAp8d4OOn7wcRkxCAjEp05me+NnDNwFiE6LZ9loU\naYnXHXbvUKmaxKx5d30tn6A0GptPEB0xdm9LzniQuTDnnubo4uGhcMkatm0eQCddRKU002uIYcaS\nb9y3+QiCQFu3GzDmKSRJMt09Njq6rGQldtN4pZX4pChHf18/+wGRzWbDQ7iIl8eYUJGXZqPk1R2s\nfc7Ch7vGj2exaceFqoqiSFjySm50bCY0yP5sWSwyXSOxnK3qYEq6vYR5R7eM1W0Kg/2DnNjzGp7q\nJiySDje/mRQUOYswddW1xHvvJHsGgAJP9w5iQ2T8fe2n7hqNyHc36PjvN2OIiY8lOXsuSVH3JsZO\nmZ7P9je28sLasSSr2w/pyJ179wKRUhtLb/9ZR5g+wMX6QOY84hKMXdwZ81au5+3No3grqxEEiV5T\nAvNWv3Df5uPr78XxBjWzpo7ZGrPZbmt6em2E+rYzODDsKGEuiqLD1vT3DRHtW41GM/Y8zJlq5j9+\nv4fHvwFXJ0gZaJPHHzar1So8Q+fTP7jfIaaPGCR6DAnUNDQ7HAcam0HlU0BXRxdnD7+Bh/oGZpsH\nXuHzyJ0+2+maZ06eYFbiYaLCBUCB1drCzCwFHu72CAl3vciGx3T8xytJRMVGkjltPoHB91YBb9rc\n+Wzecoinlo+FmW0+4MOcR4ru+lpmIQKjscbpkKq1J5hUl4hz3/jKCDlgT94ny8uxWKyo73OFElmW\nEaReXn9/iLnTtWjUAnsPj9Dda6O718buQwaCMwKcPmOxWBEEKD9xnKH+Zm6VfSXTDeqvKZmV78aW\n3SOsWWp/cEYMEjWtWawodL4eQHJ6GudKn+LdvYdRicMMmcOYu+pZRoYH2bhvNyphCLMYxeLHH+Hg\n1jd4ZPZxh2tz38BB9u5UUbT8Ucf1yk+cZNXsEfjohLp/0EZ4qPPpoCAIxMT4M3fl0/d6Gx3MW/td\n3tr1BjrRvhjzjSwkOyvz0z94C3GJcRyuX0j3sSNkJBg4X+uOSbuYtLuMSXfxcFO0/DFk+VGsVptT\nyev7gcViBamfNz8YpGimDptNZueBEZQKaGmzUnbWzJRlzqGNZrMFURQoPXSA0eFObv0pkMyt1NSr\nyU7XcKDEwPw59kVOd69Ejyl33Mk5QN70mZQdHuJC3QkUwihDlmiWr99AW+t1Nu47gEocxaZOYNEj\nq9jz7ks8s/iiQ5htbd/BiYNaZhaPbWIqyo+xvtDCx3awtd1KUpyzXVcoBOITQihaNXm2Zu7KP+f1\n3W+iV93AInkQkrCIyJjIu75OTkE++zbXcK2ljMRoI6crvdCErLnrmHQXDy+CILBo3QYkSUKSpPvu\nuj40OIKSAd7dOkzRTC0DgxLb9w0THami4ZqFE2dsrPi6s2eiyWTfiB3buxONdQBw9saTjG00NmtI\niFVRembUkWPreqsNm65gwsO4WQuWcnS3ES6cRcDMiJTAo994nobqKt7aV4JCMKNwT2feisXs2vif\nbFhZ78ivd/nqJs6XeZFTUOC4XnN9OU/dpNVeu25lxhRnG6fXicTFRzJv1VP3cAfHEEWRKQv/nDf3\nvIdW2Y7R6kN0xnJ8/O7MM/pmZs5fyJZ3r5IYfJGoEBMnL/oRkPDoffPccvHgoVIpWfr4t7DZbMiy\nfN9tTVtrO26qYTbtMFE0U0dbp5Wtu4eZOU1LZa2J0rMizxQ65z41mczYbBIHt28mM2yUW22NabiV\n/gEtYSFKLlaZyEqzf76mQUbjN/FhbtHyx9i5XUJtqQBkjGIqT3/3aS6ePsWFfacQBAl9QB4ziuey\n640f8cKaGx99coDyio3U1QSQmDKWVHio8xJRGTcdwndamZrt/D1CgkSi42IpXvnIZ7t5t6DVakgs\neJE392xGq+xh1BpA8sw1n6kAzdyl63jjnRYyo2sJ9LNy4kIg0TlPTso8XXw2vlJCDtgXPp8k4kiS\nxNE92xCNl7GiJTxpHklp6Xc9js1mo793CB8/T0RR5EZrJxq1Gr8A+yKm5Xo70zO6yU7z4MIlEyaz\nzLOPebJ93wi5mW7kZGh4fdcpYAb9vQOc3P1/+OuvUVUzxLOPqChrG2V0VOeIyTabZZQKK80tZnLS\n3JmW68bmnUO0tGtwD13E0icev+1cc6fPBeY6tfkF+hEZ65x0UyvXOkQcAB8vEdF0CRgTcoJCQ7l+\nAxJj7a8D/BQcOmagIG9sczI6KmFV3FTWZhLQ6dxY9MjknEQWLXuUgf7FXLpyjZSiePR3mUXfhQuw\n25pPEnEsFitHdn6ARm7CLOmJz1pCdPzde4xYLFYGB4Ydp02tzR3o3XWOHDOXzlewZv4wQQEenLlo\nQiHCt57zYtveEablujE1W8Mb+/YRn/QndLS1c/7IK/jprnOxcohvP6dmX+coVqu7Q1QZHpHw9rRR\nfn6Y9es8iY1SsWn7EI1tboQkrWXhuhW3nWtB0WLA2Q04wTOFhJQUx2tJkvBS1TnGAwgLFrBUXgDG\nhBx3L396+mT8fe39kuPVfLhr2FH5D6CrR0LtEX/X9/ST8PLxZPHjk5OQeOHa5+jpWkXV9evkLku6\n56T7Lh5ORFH8xCTqo6Mmju1+F63YhtHqSeq0FYRFhN/1OGazheEhA75+XsiyTHNTO94+Hg5v4fLj\nJWx4FATBndPnjXi4i/z5N73Zvs/AzHwtU7Ik3i/ZwYI162m6cpXLpzfiq2vhXMUwP/imG1v2GJFl\nlUOc6eq2Eh5sY+vuQb7xjDfXW6x8sH2Qa63uJEx9mqJlt/eEm7tkLbDWqS0tJ4e0nDGPt/6+IaID\nrjiJQUmxMucOlANjQo5C7YXRKDlOmfNz7AL2ysVjp8wNjTJ+YXe/VvwkgkKCWPDoi/d8HVEUWfbk\nt+lo66K2rYOZa5Pv+wGDiweTTxP/BvoGKTv0PjpFF6M2X/Jmr8Uv8O4PQo1GE8ZRM94+HthsNpqb\n2gkI9HGsx6vPHuOZdSosFhWnzhkJ9FfwNy/6suvgCCsWupOTbmPPwb0ULlnB5UuXaL70Pt7adiqr\nh/n+N7V8sMMI+WNCRWOzhfQEidfeG+Rbz3pRU2/h/W1DXLvhRebs5ykomj7hPEVRZP6qpwBnATd3\n+gxgzMv5an0j+anN3JzzamqmjbcOHncScmzokSTZEQqZk66hpNTI3Blje6hTFwTi028KyZgEImKi\niYj5/j1fR6VSsvKZH9By/QZX+/qZ93iSSzC+zzx0ln7fpj+yemapw733SNklzgx+nSnT7/yhKT92\nkNGOPYT69XG+zYumFivF04cYtig52ZrIgkdfxNPLnes1GgTBSk7Gx1UUZGwfhXULgoC7qgWA0r0v\n8/yKywwOSWhVEqHBClYu0rN51zAqJYwYZFpuWMnJ1DCnwI2NmwawqWKJTMhl8XMr8LopTOLemCA3\nhOy8eEzJSGXLKwlEhtbj5iYiy9DUEcCb29yYk9fDjQ6RfSe0xCb3Ul1RSWpmxrhrfhnw8vYgK+/u\nPXpcuLhT9rz3Ek8vrHBsDrYduITF8pckpNy58HBi/zYYOkKg9xCnW7zo6rZQXDBI27Cakz0ZLH7s\nT/ELCOBGm5KQIMjPsdsa2005sERRQK9oBuDcoZd5fmUTjc0Wgn0FfLwVrFyo54Mdw2jUMDwi09Ri\nYU6BloJcN375h348g9IJjshg7arlk+JNIgjChPmtbrU1+bNm8cGrB/n62hsolQKCALXXg3h/j8jM\n7H7qm5SUlLsTm9zIlcv1d52M+IvCL8DbIfC7cPF5sO/dn/HCqqsOcfSNDytRzP8ngkPHe+nejsM7\n3kVrOYmPp4HjjV4YDBYK8we4VqOjfSSPhetewMc/kPYumYhQkRlT7bZgxCDhprGPq9GIqOUmZFmm\npuwPPLeykzMXjDy7ToVOJ7K0WM87W262NWYWzNWTla7h3/63n4jEKXgHpvD4uqWf6bT4VkRRQJJF\nbs2nc6v9mVa4iLfeO8Hzq3sQRQG1SuB8fTCyUiI/Y5DKOiWnK72ISbpIs38AEdGTe1A1WQSFBBAU\ncud/cxcu7gZJkjiy5ad8Y13bR7/jDfxm4yUWPv0TPDzuLBeTLMvs2/wqvqpzuGuN7L/iiRILM/MG\nqa12Z0CexbwVT+Cm92Z4RMbDXWR2gd3WtHda8fW2iwZengowXsFstnCj6hXWLxvk4DED33pGg1ot\nUDRTxztbhlAqYdQg09RqYWmxnoRYgX/8aT+JmTPx8E9h/eMLJsX7SKlUjst5BePz6+XOWsZ7e87x\n+JJBBEHA01PB0fMBGKyj5CSPcOaSiop6byLjjuCmXURQSNA9z+3zIDwylPDI0Ps9DReA4kc/+tGP\nvoiBhi1NX8Qwn4jJZGbg6utkJY/9qEeHw74dx2nvNBGb9OmiQ1trO7Ybv2FZoYHIUIHMJBPDQ31k\npahIihPIiOtmx94B0nKncfZsK7HBLY5Fzp5DBnIzNI4qKxX1fkSnFtJV9xYp8Taut1hx14sE+CkQ\nRYG0JA3J8WpOnx9l9WIP8rLc8PZSMDNfx7WmQRQeeSRnpE1aHqD6+k4ifK+idbNf70aHTKuhiKi4\nJKd+sWnT2HvERvVVLReuJjN/3XdIzF3CgZMa+jvrePFZC1lxNxjqOE1VvYbw6HvLW+Hiq4GHOvoL\nGefLYGu6O/vQGd4iLsr5JPj9t/czOKwkMi75Ez5t53JVDSHi6xRNMxEZBtkpJjra+5g5VU18NCSF\nt7HnsIWcgukcOVJPWnQnSqWALMts2jnMvFk6h4h0sSEY7+BMlIPvER0OlTVmkuLUaLUiKpVAerLd\n1hwtHeXr671ITtDg462gcIaOCxV9eIfPJDZxcoQSQRCoqmomKbzFkXyvvhGGVUsJjYxy9BNFkaik\nAvYcMlFzzZ3KpgyWr3+RyNRF7DhgQWW9wjeftJIZ20prwymaOnwJDvtybrBcfLE8TLamrqaOFL8d\nBAWM2Zr0RBuv/WE3VtmLsKiYT71G+YkT5IVtYlq2lagwyEsz0djYS/EsN+KjJEJ9rnP8nI78WTPZ\nse0i2UkDiKLd1ryzZZjlC/QOEel8QziSIpBEn534+4qcrTAyJdsNQRBwcxPJSNGQnKBm7xED3/8T\nH6Ij1fj7KimereVkWS/hiYWER919KONEaNzUlJ+qIzOuy3ECfqFGgei3lsCQYEc/lUpJYGQ+ew4b\nqW30pPpGLquffZGA2GI27xgmxLuJ59aayYxtpvp8Kf3GKPwDAydlji4ebL4IW/NlsDMAp4+fZEFW\nCfqPPPcFQSA13swffrsTpTaEoNBP9wI8tm8Xi7P3kpViIzoc8jNN1F/po3i2loRoGzrhGtWNoeQU\nTGfLpnJyUkYQBAGbTeaDHcOsXKR37HnON8TQ22dmftZJ3NxEKmtMZKfbD7M83EXSkzUkxanYtm+E\nv/ueL2EhKoIClMyfo+VQSR9xWfMJCvnsFXVvxsvHk8MHK8lJ6nfMr+S0isCk9Xj7juUv1eq0uAfk\nsvfIKDXXfLjSM521z30bj5BC3tvSSWpUK08sN5ER08Tp46eRNCl4+bgOglzc3tY8VELOqMHISNsu\nYiOdy6jcaDeREtlKc288/oGffJpx6vBuVsyucxJPYiNVHDphIDFOjUIhUHdNIiqlkLjUXA6eELl0\nWeSNTQaMoyOYzTYiQpRUNSgx6ZYTGhlNY9Uh0uJN+HiLHC0dJS1pzP2+p1dm+34zj65wVrsjQwW6\nms9z4WIXiel3V4L7dsQkpXOgxERNnZGqK140jxQye+GKcUKRQqEgNimV6OQC4lKycNNqEEWRpku7\neHp5p6N/kD+cv9hJTNr8SZmfiwebh2lz1dneg6d0gCB/59OYzi4T/vpmLJopeHh+cnK4i6W7WZDv\n/F2CAhScrzQTFaFCpRKoaRCJTplJbGo+ew5buVSv4rV3h1ArRhkZsREVruLURTWa4HUEBIfQ1nCI\nhGgbgX4KjpwcJSl+7NS7oVHizAUTi+c5hxq6a23II+e5UDlCbPLkeLHFJGWxc/8w9VfNVFzxZUBY\nTP7s8Yn3VGoVsckZRCcXEJucgUqlRKFQ0HZ5G48sGqvUFR4iUX62h5i0ueOu4eLh42GyNdcarpIQ\ncNqxuQK7J0p3jxHBch2vsELUmk/OGVh3bhezstuc2hSiQG+/hL+vAr1OoKJWSUzKNCKTprH74CiX\n6jW88lYvvl5m+gdsREeoOFiqJShpPe6engy1HSEiBHy9RE6dMxEbNTaHU+dl2trMjpP2jxkdHcVH\ndYGKGu5I7L4TIhJy2bG3j4ZGKxUNgVg9VpI1dbwHtpvWjbiULLutSUpFoVCgUinpvrKJFUUjjn6x\nkTZOnOonNnXGuGu4ePh4mIScuqpKcuNqUSjG9gQqJfT1jTLU10x40rxPDAEFuFa5g6mpzpV0e/ps\neHuKuLmJ+HrDmUo1cal5hMRMZfchAxWXNbzydjfhQRb6ByUiwxRsOeBO4rQNyLKA2lSKr7eAKEBT\ni4WQoDEPm92HZQRsTMlyznnV3T1EsL6C2qu6OxK774SQ6Bx27O3iSpPMxfoQ9OGPkpCaOq6f3l1P\nfGoOMSnTiE5IcoTpDza9x8JZFsAukiXFWjhyfIjY1MnZ47l4sLmdrXmoQqs8PPU0d0cCjY62rm4r\nep1AepLAW4cvkJxuf+iMRhOnS44iKpRMmzPHEW/spvdmaFhyeNXAx0Zo7LVF+riCk0jhkpXsfPs3\n/OCFIQL93TGbZX7xikz63K8zZdYUAMzqPLp6DhHgJ5AUp+a194aYnqehtVNHU38OofFmTKbzThnY\nrzRaSIxRou86T2d79x1lNa+vqaOpoZr41Byi46LGvS8IAvNWPHbnN/QWNIrxZekMQz0c2PoeKo2e\n/Lnz0WpdOSJcfPWJig1jb1kYGckdjrZr1y0EByqYlmvl7ZIyQsLsVZpGhg2cPnYErd6DqTNnjMUb\nCzosFueSke2dVgL9b7I1sl3gVamUFK98lC2v/ZR/eHEUL08PDAaJn/5OZM7q75Geaveq6zRkMjRy\nGg+9SFCAkjc3DTM9T01Dszvdlhm4BzYjSQ1OpWxb261kpKjoryjDYHhkwkTHt1JdcYkbTVdIzckn\nNHz8iZdSqWTh2mfu4o46o56gBOZQfzcHtr6LRufNtLnz7nvCexcuvgiypuRwYJMfTy4bq1p3vtJI\nUpyaiDAD+8+UM6PILnD29w5ytvQoXr6B5BXkOw5dLJLbuEqY3b02kuPtz5Asy1gku63R6dyYt/IJ\ntr36Y378lzZ0Og8GBm3860tqlq3/KyKi7d402w+lkJtWRYC/EoXCzLtbR5iarabmmgcGVTGC/hzg\nLB71D9jIy4CG48eQ5ZWf6m0syzIXTp+lp7OVnGmz8Qv0HddHq9Ww6JGv3eVdHUMtDo5r6+tu4+C2\nd9B6BjFt9hxXjggXDwVTZxWy6+BeVs0fdbQdPjHKtFw3RgxdXKlrJCnVHjre1dFNRflJAkIjfQyN\nmgAAIABJREFUycjJcjzLVmn8+mHEIDsiAaxWGUmw76E8vT2ZveQJDrzzz/z07wTUak/aO6386689\nWP38DwkKCSIkLIRtr0YTHd5EYpyaPYdG2LRzlJx0JRV13oi+SzG07gf6nca0WGSSoi3UHDkEfHpV\nSkmSOFt6isH+bqbMKJwwpYWntwdLPmN+PZPJgqduZFx7V1szB7e9g6dfBFNmzLivlZhdfDl5qDxy\nALwCk3n/g0t0tnVSU2/mequVpcV6BoZkmocKiIyJ5WpdHZeO/Acrp58lxr+SXdvL0Pqk4unlSUhE\nFFs2l5ObOubu9/p7Q6xd5o4gCJw4q0If9igBwXa33dbmG/ja3iEpzv7wKRQCuekyF+pDiEmwnzjF\nJKVTclpB1WWJjoEwAhOfYFg1n15jPKk5M8gumMnWD8+QkTCCQiFwqcbEgWMGVCqR/n4jFyr6yJhS\ncNvvDLDznd8R4/4BC6bU037lGKfO9BGXkjWp97a2+ippUdcdm8BT54wI2Fg3r5H4wBp2by/FPSAT\ndw9XmbqHkYfplFwQBNQecWzedJ7e7l6qLpsZGJQonKnjeiuMqosJDgul+sI5msp/xupZFQTpLrB9\nSzmBkXlodW4EhESxb1cZGYlGBEHAbJbZsmeYxfPsG6q9x7SEpK7Hx8++eam6WElO6E5Cg+yCr0ol\nkBIvUdcWT3iUXbiNS8lhf4mNmnqBbkMUoWnP0G2Zw4iUSHpuAYnpU9m/p5y0eCOiKHDi9CiV1WYs\nVujvG+FKo0RC2u1DUCVJYtsb/0NW8HbmZtdx5dIxLtWaiE4Yfyp1L1RX1JIV3z5WIvz4CL5eFlbO\naSTKt4qtm8sJjp6Km0s4fih5mGyNQiFiUYSzfct5+noHqag2IwgwNceNyssCHhGr8PLx4lzpMXrr\nfsGqWVV4iufY+uF5opILUKmUuHuHUHbsNMmx9tPg4RGJY2WjzJqms4dq7vUgbeYGx2/3qWMlLM4t\nweujkrxuGpGwQBvdphwCguwhRzHJU9h9cJTLVxX0m+IIy9hAm6EAkyKJ9NxphESnU3bsDMmxZgRB\nYM/BYVrbbYyMyvR0D9I94EFEzO3ziZnNFra//h/MTNzHjLQ6Lpw+SlOratJDuasqKslO7HW83rl/\nmPhIM0tmNBGku8jmTReISZ1+36v8uLg/PEweORqNmj6DP3t3n6evd5iLVWa8vURSEjWcr9IQmrwG\nN62G0kO7sd74LStm1aAwnGbHzlri0wtQKERQelNXeYbYCHuKi65uK7X1ZrLT3ZAkmY3bfShY/E1H\njqxj+7bzeNE5x0G2u15ErbKh8J6Lh6cHgiAQmZjPjv3D1DeqGLQmEpH1DZoHcpB1qaTnTMHDL5aa\ni+eIi7QiCALvbx9kcEhmYEiio70foxRCcPjtw7KHB0fY/daPKc48ytSEy5SVHKVrwPsTP3O3qFRK\nKs6eJStxTDh+b+sgBdkmiqc04SmeY/OWGpIyp3+q15OLryau0KqP0LvrSZtSTGnpVZbM7ic30w2T\nSWbjrgjmrXwWURQ5e/D3PLW0HZVKQKMWyE4e5eCRbuLS7IYoOHoq//fb/fT3D1NdZyE6UsnFKjPb\nDghETflrEtPSHOPVVFaRG12O1m3swVMqBS5dCSAm2V5dQRAEouITiU6ZSUzqdLo7btBT9xozEk/Q\n01zC6dMtzF31Iq+8eY2ma41U15n4iz/xJT5GTWqShuigDsoq9EREx074nS9dqCAraAsf51gNDQLL\nSDN9lgy8fScv9jI0OoXNH9YhmXsZHpE4eVZi/Rp7bLxSKZCVbGT/4QHiXG6CDyUP0+YKwMvHm5S8\nBRwrqWHtwhHSkjQMDUtsOpxA4bJHEQSBiqO/5bHFvSgUAjqtQE7KMLsP9hOfmodGo8YjMIdXXt5H\nb6+Buqt2l+GKahNb9qtIL/ohUXFjLsEVZ04zN+uy04mNXidwrjaA2CR7tRVBEIhJTCEqZSaxKdO4\nfqUWY+tGChJP0lJ3lOraIabO/xa/f6WK9tZWmlutfHuDN/ExajJTNWiFRupawgkOnTiuvPToERZn\nHyQiVLQvsEJlbjRfR+U9A61u8spu+4UksmVbLUq5n/ZOG5evwpolmo+qFgpkJ4+w+5CBuJTsSRvT\nxYPDw2Zr/AICiM+aT9mJizy+1EhCrIbuXomjldlMnbMAm81GfdmvWTN/GFEU8NALZCcNsvvgKHEp\n9sMVQZfGm6/upbvHSFOLBR9vBVW1Zjbvc2f6yr93SrpZV1HGtNRGpzn4eEFpZRCxiXbvP4VCQWxy\nBlEps4hNzedyRRmK/nfJjyulvrKEljaRlOnP8fuXL9LW2sGwQeaFJ72Ii1aTk65moPMy/ZY0fG7K\nL3EzR/ds48l5p/DzsduauEiJ6qomAmKKJlVU0fvEsntXDW7KAarrJYZGBBbMsW8ytW4C6XED7D8O\nMYkpn3IlF19FHiYhByAoNIzQ+LlcPHuOx5fZiI5Q09ImUdk6g7TcaRhGRumq+Q2L59oFWm8vgeSo\nbg6VqoiOT8THz4+ukUh2bD5Ee6eZji4rGrVATb2ZD/b5sOCJ/4eXj6djvMaaMrITWpzmoNNYqWqO\nJyzSnpNHpVYRn5pNVPIs4lKncrFsPx6WzeRGlVJZXsLgqC+hKY/yxz+epampC51W5Mk1nsRFq8nL\nVFFfXY3G9/ZrlMM73mbD8io89CKiKJAUY+Ps2RYiU+ZNqoeM6BbO4cOX0akHOVFuw9dXSX623SvS\nXS8QG9pD6UUvIqInJxTMxYOFS8i5haSsAo6f1VNZ705tew5FKzegVtt/nNsubyYlzuzUv75RIDLF\n7n7nptVgNCnJibtCQZ6a6AgVgQFKOiwLmDKz0Olzvn5+nD52zHHSBdDUInOlO4/G2tM0NtTjHxzl\nUJ9tNhs1x3/B40sH8PQQiQiRiQ1p48Q5d1Y++SznL/aSFn3DqQSvh17gQjXEpEzslXOp/Ahzs686\ntYUGQckZL2ITJycOHeyKclL2LIbkKfTapqEynSU5TnLqU9fkRlTy7Ekb08WDw8O2uQK7cJKYOYPD\nZSouXfHiSk8B89c8g0KhQJIkuurfd3pGBEGgvknjeEb07nr6+kwU5jaTm6khNkqFh4cKg9tqMvKc\nBVEPLz+qzx4lJmLseuerBHot+VypOknT1WsEh8c4wkQNBiNtF3/J6gUGPNxFosJkfLTXudwaydJH\n1nPo0BWWzO7H33dsU+TvC6crRGKS8yb8vg0Xj5Cf6rzo8vc2c7YuYtISmAJodW4kZc+ly5jDjaEs\non3PER48tqASBIG6JneikicuKeriq83DaGuUSgUxqbM4cFyg6qovLSNzmLf8MQRBoKuzD3fzNqdn\nRBQFLjfqHM+Ip7cX7Td6WT6ng/RkDfHRakSFBlXwUySmpjmNZZPU9LacJPimlIKHS1VYdVNpqDhG\nc2MrIZHRjpCjro5ubG2/Y9FsM+56kbhIG1bDVYbkPOYuXcuunWd54VGrU/h4eIjM0dPibfNyNdUc\nJjuhw6lNwQhtw1Pw85+8AyoPTw/iM4toHcigpTeWgqSL9oo5H6FUClRd8SImecqkjeniweFhE3LA\nvgcKjZvJ/hKJqmsB9LLQkU/zck0DaUGHnJ4RtVqgssHd8YwEBgdy7Woz6xb0k5ygISFWzYhZS0Dy\nN4mMjXYaq7fPhMZyDi/PMdu1/4Q7kiaV+osl3LjRRVhklMNLpaG2nhDxDWbm2XDXiyRGW2m7fgWv\n8AXkzlrM/l0n+ZP1olP4eFyklQMn1cTeRoy9UXeA9DjnvD69PaOofIsm1evXx8+XmLR5NPak0djm\nzYqZVxxJ5AF0WoHztd7EJE9uNIWLBwNXjpxbUCgUzJq/cML3TDY/wDkHg9HqHHs9o3gJJw9CafUZ\nBFlC0qRRvHLduGvp9FqUgWv4cN+HFGQNcvmahvKaUArStzN7qoTFIvPBrhMkz/oBoeFhNF1rIzO+\nAxhLQurtKSIZryIIAkseeZYLO0+OG+dGa9dtv6tvUBxnLu6mrcNEaJCS3EwNlZdFIuMnN9zhY8Kj\n7CXpDlQGcHMMvCzLGKyu0pguHi7UahVzF68Y1y6KIqNWP8B5M3KrrSla/hgH96gQRiuREVF65DJ7\n4bJx1wsI8qdOXMbOI3vJSx2msl7Hudog5ue/S16GjNEo8fb7J5m+7If4+Hlz6fwFZucNAWMLrsgw\nkZOXaxDFApY+9gJdtX/FzTXrZFmmrbV33Ngfo9KHUXXZxNUmCzGRKtKTNVyo0RCfOXmC8c3ExEcS\nGRPGsU2+TMvuc7RbrTIm6ctZttOFi88LrVZD0bK149r9/L0oO+HNNMbc9iVJxmjzc+q3YO0GtmzX\noZbqsMkq3ANnMHX2nHHXS0hJ5PCOBXSdOEpGgoGz1e5UNPixau7rpObZQ7M2vnmSRU/+PVqthovl\npTw1xwKMbUqyU+Gtw2eJT06gcPnTdHb/t1PuQYtFprdnfM6Ij7HiT02diYZGC8nxahJi1dRd9yZt\nfvBtP/NZEQSBhJQ4wqLCuLBvM5HhBsd7wyMSCjdXtTwXDxcennqKV47PqRkVE0nVMXciw02ONqNR\nApXzc7lg3bd5Z8dGdGIjVkmLT0Qh2dnjBYrcgmns21RNVEs5idFGyi56Ud3oydPJrxCVa0/I/u5r\nZax67m9RKBQ01Z3lqVvqJhRNt/BWSSnzl68gd84aBoc24uM9ZmuGRyTM5tt71oxafblUa+TadStZ\nqRoiw1W093mT4HVnJdfvBlEUSU5LxM/fl5PnDlA03ep470aHjGfA7cNNXTycuALtJiA0eQVbD7hh\ns8mYzTLv7nInIXf1uH4zipdQuPYfmLvuHyla/shtXeymzCwkZ+l/Utn/5wRk/ZiI4FFmT7WfmqtU\nAk8uG6KqbCsAgUG+XO9wTqIlSTIWyd7mptVQU29leGTs1P1Y2SiqT8i1NzLUSWe3zPIFevx8Fbz0\nx0FO1eWQkDw55YRvR1jKWjbv1WMySXT1SPxhczD5RY9+rmO6cPEg4RO9lL3H1EiSzOioxOtbfMic\nucapjyAIFC5Zy9y1/0jh2n9g1oLxIs7HzJy/nOTC/+Bi7/cIyflnEiP7ycuwV+lzcxN5fnUP5Ue3\nABAeFcXlRueEwPYFlz2UwU2rpeysGZNpzNbsPmRAq739gsdo6KGrV7CXI1YI/OLlQZqHZ+Ef6Hfb\nz9wrCoUCr8iV7DiswWqVaW2X+MOHkcxcNH5D68LFw4hSqUTlt4iS0wpkWWZwyMYfNgVSMM95XaNQ\nKJi/+inmrP0RRev+P6ZOUEnuY4qWP0Zk/k/stib775mS0kXqR0sKd73I8ytvUHpwBwAhEdE0NDp/\nvrtXQudl39xpdR7sPjSCzTZWUfTD3cN8UjSm0dDP0AisWOjOiEHmFy8PY3Kbd0fJ2D8rOp0bstcS\nDpxQIkkyV6/LvLk7iRnFiz63MV24eJDw8NQzJBZxpsK+TujulXh1WyQzipc69dNo1Cxat4HZa/6J\nonV/S3b+xBEFgiCw6JEX8M/8V/seKu27LCpoJyrcfn1fb5HHF1yl7OgRAHRewXT3OkcCXGkSCImI\nBkBUati0cxhZttsaWZb5cNcwojyxaCzLMkMDfYDI8gV6Wtut/O/LBnTBSz7XXDUBQf50Wos5fsZu\ns6vqZHadyiJvusvL2IUzD61HzieRnJFJf9iPeff4QURRQf6y+ejddZ/+wU9Aq9WQNSULq9WKTtU3\n7n2Nwp5R3d1DR4+5gMbmI0RHCFitMm/v9GHqkpWOvgnxvhw52Y3NBpIEyfFqAgK9JhzXMDKKangP\nS4vtHj7RESqef8KD3RcnL8zhdqRkZWOI/wkflhxB5+7JsmddSbpcuLiZ7PwZdHcm8faRQ6jc9Mx9\ntBg3t3tz1XX30JE9NZv2tm7CAga4Wa8XBAGNaLc1oeEh7DyeTWzEGYL8RUwmiTd3BDP/iSWOvhmp\nnuw+NIQogtUK2eka+usmtoWd7d2E60qYO81ua5IT1Hh4KLnQ9fnHc+cUzGKgL4v3Tpbg4xfIqg1T\nXNUdXLi4iWmFC2ltTuetw8dx0/uw5JkiR5jlZ8Xb15Ns32wqL1SRHD0CjAnDGo2IYLOHI6RlZbDl\ntWQC/arx8lRgMEh8cDCKVc/bvX0UCpFpue5s3TOCUmm3NbPytRy6NHHlufqaOqbGl5OZbLeV2eka\nFEoVA+6ff56agqLF9HTm8/axEwSFRbJmgyvMwYWLm5m7ZB3XGnLZeOg0eu9gVm2Yfc9rf/9AP/wD\n/Tiydx9rpkjc7N3n6y1iGrJ7/+fPms0Hr5Xw7LImdDqRgUEbB8+lsvo5e5EGlUrF7AItm3cOo1YL\nWCywtFjPvoqJbeG5slOsnlNNWLB9XTN9ihajVcQ/If2evs+dULj0EW60zGTj0XKi4hJZsf7z8Wx2\n8WDjEnJug7evJ8XL13x6x7tEqVQyZA4GWh1tkiQzahsLA5i/+mnOlcZTWn8JCXcKli/D09vukSOK\nIsOksnLOGUc8eUOTgHvQxAmEr9RfIztpkJsXWO56EZuxZcL+k41Or6VoyZIvZCwXLh5E/AP9mL9q\n8j3VAoN8KSkJID97LLbbZJIwM5aoeOnj36K05CijFfWg8KX48WVoP4r59vbxoKU/keeX16JQ2BdN\nF2sVBMXMnHC86ovnWTPFOXwiLFikpPoKMGPSv9+tePl4ULzs9t5KLlw87IRFhBIWMT4c4l6JT4rn\n3B4vIsLGQo76BiSU2ijH6xVP/wX7Dx7AOnodQR3MsqcXOzZ3MfFRbDsRw9fXXHcIsMfKVcRnFE44\nXlNDJetveSsjWeStI5UkpCRO6nebCL9AX+YvHx8u68KFCzsx8THExE/+IU5G7lSOlX9I8cyxkKPG\nZhnvIPtzr1AoWPb0D9lxaA+SqR2lNpIVT8939M0rmMqeN8PYsGYsHcX2Q1pypi+YcLyBzgbC0p1F\nqFl5NjafPk/g4onTc0wmoeEhhIav/PSOLh5aXELOfSAy/RHe2fVHls0ZoLcf9pZFMG/t4059cqcX\nABO7Gi5Y+w3e3eaGTriCTVbh5judgqLCCftGx0Zx6Zg7EWG3xqu68ke4cPFVRhRFfGLXsHnvRhbP\nHqGlXeDwuXiWPDUmUAuCwPS5hUDhhNeYt/a7vLnzdfSKRqyyDq/QQnKn5kzYNyE1jbNVCqbnjLk1\nd/dKaL1d+SNcuPgqo9VqEP1WsvPIhxQXjFLXKFJWm8GK9WObI4VCweyFtw9BmrPie7y++w3cVTcw\nSx4ExS0gMX7iSpyhUUnUXd1NYuyYaNzQCCFRn7+I48KFi/uHX4AP1cJiDp7Yw+ypFiouK6hszmPJ\nY2OH2bfLSwj2dVHu/Bd5Y/d76JTtmCUfwlOX4RfoO2F/rXc4XT0SAX5jYs7ZSwrib0kA78LF/UKQ\nPw4U/JxpGzn6RQwzqVw6V07n1QOoxBGGreHMXvwM7p6Tk9zKZDJz5kQpHt7eZORkfq5hAEd3byLW\ncy9TMmW6eyU+OBDJ0qd/6KiUBXBk91mObaoEYPbaDAqXTlyV5lYsFivv/f4ALTXd+Ed68OR3Fk0Y\no952owuzyUJUTOjkfCkXDxwh+rlfyDgPoq05V1pCf2sJSsHIqBxD4YpnnJ7Pe8FgMHL25EkCgkNI\nTv98Qw/2bnqVvOjjpCUK3OiQ2VqSwOrn/8rJrXrXeyc5s7cOhUpk/pO5TJt7Zy7Ko6Mm3v71Prqu\nDRCa5Mvjf7IAtXp86EXL9XYQBMIjXGL1w4rL1tye0kN7MPadRoEFsyKZohVPOKpL3SuDA8OcP1VG\nWFQ08Umfb1LO7Rt/RXHOeWIiRK41Sxw4l83Kp190vC/LMpteOcSlkiY07iqWbyggI+/O8gIODgzz\n9kv76W8bITY7mHUb5o0LDZFlmWtXWtG7awkK/vxygLn4cvNF2JoH0c7IskzJ3q3IwxcAGVmbQeHS\ndZO21+np6qPyXDlxSSlERH9+h0WSJLHl1f9i1Zw6QoMEqupkzjbOZNG6DU59Nr60h4bydvQ+atZ+\naw7xKXeWvqKnq4+3XzrASK+J1OlRLH9i1rh7JMsyV+qa8fH1xC9g8iryuXiwuJ2tcQk5t6Ghtg6x\n8+dMz7GXDZckmT9ujWPFsz+8zzNzRpZlThzch2WwCpusJjK1mMSU8Zu1q/VXuVZTjs4riPxZs50W\nbjvePs7b3y9FMWzPLGjTG3n8Z/msXD++WsWt/O3639C81YRCUCLJEj5zbPzPthdRKsdKHP/L11/h\n6uF+JLNM2Ax3/vrXTxIacfvqVVcuN3Nk23lCY/xYuKZg0haZ94vXf76L4+9VMzpgITLXj+/99BEC\ngydW/7/KuDZXE3Ox/DShwh9IT7J7spjNMm/uy2TZk392n2fmjCRJHN2zDcF0FaukIzFnEZGx412n\nay9Vc+NaJV7+UeQWTHNalGz85R52/ugSSrNd7JV9R/nG7+YzZ/HEXj4fY7PZ+P7qX9JzWEQURGyy\njdBlCv7zne84rt/T1c+Pv/kGLceHQZCJnOPFP7z8LF7eHre97qVzDZw+VENMajCFSx7svDqyLPPb\nf9nMmR1XsYzaiC0I4Ac/ewKPSTp8eJBw2ZqJOXX0IBkB7xDz0b5nxCCx6dgMFj3ytfs7sVuwWKwc\n3bUZpXQdi+xJRsEKgkNDnPrIskzF2fP0tNXjF5JAZl6O0/P70o8+4MT/XEcpfSSIh4zygzdXkZX/\nyV47BoORv1j2KwzlGgRBwCpbSFrvxT/+7gVHn6arbfzsO+/RdsqA6AYJC/z4+98994n5zcqPV1FR\neoWUvCimF32+h3efNzabjf/94btUHmhGliBpdgjf/68nJu3w4UHCJeRMzJFdmylK2UWg/8dJj2X2\nVyxk3orJD+28FwwGI8f3fIBG7MBo82bq3DX4+juvz2VZ5lzZKQa6mwiNSSc53dkb59//7HUqX+lB\n8VEKCzHawD9tfYaoWGebdSvdnX389YrfY6nSIggCFtFE/p+G8Rf//oSjT/X5q/zqL7fSddaI0hPS\nlgfzw18+e9t9kSzLHN9/nvqKVnJnJ5I9LWnCfg8KRqOJ//7B21w+3o6oEMlaHMmL//LoA78v/Czc\ndyEnImryS0J+nmSnWNn6qvPDfPjEKM/8mRGF8svzDxQXaeGNX3oSFmw3IFt2G/ibfzNiNDtHzdls\nErIkoVAqxi0gNJ0+xJicT8WvaS5hChyflPlmrEaJmK4c9HiOtckWan3LULrbX8t9KpKHnDdz9W7n\nsAVMnCFeGlQQPBiHrxyEWTbS6FaNLWD4Exc9VoOMZlSPTbBhcx9Fof7yJFS2jkBsbzY67JtJWZZp\n0F7AFjB8n2f2xdPc1P6FjPOg2Zo5U6288StnW/Pae0P8/U8lRPHLs9hPS7Dw1kveeHvZ7d/LG4f4\n8a+sSJKzPbTZbMgyKCewk7r2ACItzgn7rrhdxBIw6NTW22t/7e5tFyGkEUjtm45aGNsoGeRhLvuV\no/ioipaqV0eyYarDVsiyTI2+HKuPgYkQB9REDCfhhR9GeYSr2mosvre3NbIsIxsEtGYPrIIFi7sB\nUfnlsTUMKUgeyHfcI1mWqdWeweJ3+xLOX1U6mrs+vdMk8KDZmuXzbLz0Ex+ntv98aYCXXv/y2BmA\n/EwLb/zSBzc3+/P1778c4KXX5XFeMVarDRhva2RZxqMtjHBbnFN7ne4skp+zPejvGwLsOcEAbIMC\nKf3TEYWxa/bTRWtwFYqPyoMqutxJMI6Jz5IscdnzFIK3lYmQe1VEjKTgjhcj8hDX9dXga/pEW2Mb\nEdCY9FhFM5KHCcWXyNZI/UqSBqeiEOzrTEmWqHMvB1/zfZ7ZF88Xsa550OwMwFOrJH7yd87eI3//\n7/288eGX5/9YlmWKCqz84b99USoFZFnmb/61n3e2i+OeTavVhiAwTkCQbBL+bXEEyuFO7VX6Mmze\nRqe24X77b/HH6xqhX0368HSnsTqFZm4E1ztsnb7LjzhzhuN9m2zlnPoomsDx6ytZllH26YgxpKET\n3Bmkn+v6aiTv2z+XsiwjDCvRWPRYFEas7kZExZfnb6To05IyMhVRsM/JKlup8TiF7GW5zzP74rnd\nuuYLy5FjNX15xI87QcA2rk2tAqtZRLZ9Ob6LLMssK1Y5RByA1Ut0vLvVREmZwtFnapaZrz+tISJU\nwevvG/lwF5gtyo/el0hKHCbcu5bmKh+EXns4gmhVf+rfzDYqoJM9bs5tilJQgUmFVWX3LtAbPccZ\nRK3Zi36Ts4ED+2LAfygUX9k+B7XgRrwxm6q+MoRbDpVlWYYREZXRjRhbHHrBLiZ1jLbQ4dmEqP5y\nLEzVIzqHiAP2nCTepiDaRkdcFbw+J8zS+Gf3S80E/wYKBVgkGwJfjv9jSZJ4ep3GIeIAfH29B5v3\ndHO+1v5alv9/9s47MKoy68PPnT6T3nsnIL1D6L1IEZCigth17brf6upa1rq66q67uq6uXWFtIIjS\nIfReU6jplfRkkun9fn8MJgyTBFgloOb5L3duee/N3DPnPe85vyMyeqCLuxdqCPCT8OkyE6u3unCK\n7mME0UVSLx2RmhxKj4Ug1YW6t7ukXv8zi9kKIqh83e+1xC71COIAqPFBtMlwnDV/AbZAD1sjCAIa\nawANDivn43I4STAmEoC7JEIl+NDF3Ifj+gOg8U5rlhnkKC0qEpxdUQs+iKLIGXMRVUFlV00wx88c\n6PGMBEHAzxZMtd38i179v5r55fk1Lq9tEkG4yu7DwQO3a5qDOAB/fMCftelaCkvcL7soupg8xs4d\nC91ZxJ98aWbTDjnCWWdfIrHRdUAj/tIcyrPDkZncwSuJQ47tvHs1Gd32wVfjnnRKbHKPIA6AjxiA\n3SRFVLm3+1k9u4RKBAlKSwAma6PX3ThtTpKMXfHFfYyP4Ee8sTv58mykSk/b4XK6kJjkyC1KUlzd\nUQhKRFGk1JRLU1DtVTPB8jEHNgdxwH3/aksQemvdFRxVJ1cTrbm3V1sShVLu4LnH/JHXSp3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8LK7JXIn5XTb9CvN4unM5DzP+IS7V5lBHIZOJ1XRrXf17f1FrtRUanU1v6V9774DrnUglw1hP79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fSBfyO8/uFWnPY8pD8HVewZbDl6gon5Rbxx8AiaxKyY/SWtCVGti0nxBVCY4ujt7SYlOY0+\ntxOVJbb1pCBJKEM+rpu3mOkTenn81fU0h5QIskyWOswDa+7D5/Wy95VXCduHrmlxtzNr2u0ApBo1\nNJx3TjkSpjAtldUrYoVWRVHEHp9MQJOIpNHjbasZ/MzdXoslJ1bTp8+SybOvvsij94yVFIxx5SiU\nSv7+kW+wZ/cuOuq7mFRQOqqmy98qm7Zsx2fNHXQkwtYstuzYj9Vq5r19JzGkF8fsLxnjEaSRwVVB\nbcDv86HRaunv70eVNew4lQZVaIBpJSUkJCXx7Itv0BFQI8lBcm1K7rprLU2NjZx45UNkU7T8SZZl\nkpQDZJ/TxUjUS3Sdd85IKMDEcfmsuuP2mGuJkkRcUgqSJhVRpcbTcgbhXKcYd1stluxJQ+MWBDqw\nsfH111h5x1+X2P4Yny06g4F/eORb7NixHUdnHyWTbycrN+ejD/wbYfOHewjZsgeXhYK2HN7euosF\nTif7yhux5MVqQIjWNCJ15SPOI0tD5QSeAS9adWzprEpvgbCL7Jwc1qy8nhdf30JXWIMqEmBcsoGb\nbr6To2VlVL5/AsEQXSmX5QiZZgUmswWD0USc0o/rvHOG/R5mTJ/CdTdcF3MthUKJLTkNtT4DQZJw\nN55EUkdtjaezcbCjDUQD2nW9Mgf27GHm3L/t7PMxPh72uDh+uPabfLj9Azw+D7PnLiPlI9pm/y3x\n7o5DRKyZCICgEPFac3nrnQ/IzUyhvLUfW/4wW5OQS7i5bsR55PN8Ha/fj3pYWajGHIfe10JcfAJ3\n3riQ197ZQW9Eh0b2Mz3LxrwF8wmFwzQebkE8Jy0hh8PkJpmRJImJUyajf3cfAe1Q1kxkoI+lSxZQ\nMmNYW3G1GltKOjpTNFPQVXcUtSpq+3x9HTF+jahQcbKxh5qqSvIKRnas+2tiLJBzFREEgdkTZ3Mt\nqlScrj/N7qbDAMzPLKUoq+gzvb7DF0RQxq4O9wVl9hzeixifTaC/M+YzWZaxaVREQkFExXk1k452\n/veNDYQQUITCOBsrsBcNGQd/fxdJcfEIgoDdauefH/kS7WdbkSQFCedKuQx6A7dNLuaFD7fjF5UI\nAS82o4YN729m5qRpPLjiZp5483VawyokOUKOVua+VQ8ynDc/eJdOUxaKc4ZQl5pPf0M5kXCISHjY\nuAFRUrKtphn5lRf54uqPX0c5xt8ukiSxYOGiqz2MUTl+9Chl1SeRBJHFpfNIz8r6TK/v8ARgWFym\nzx3gaNlxVHGZBD39KDTnlcCGw1h0EvIwbQjB18+//Px3IAgIkRD9jRWYsycMfu7paCQrO1o7npaW\nxj9+96u0NTeh1ekHu7Tk5uWzsLiCt7cfICgoIeAhMdXGWxvfYs7s2axZdSPPvfIWXUENCoIUxGtY\nufKhEX/Tmxs302/KHQyEG9LH0Vd3AlmOroIxLOAtqrVs2HECX0jg7ruvjsjxGH8dKJRKli677qN3\nvAoc2L+P8qYqVIKC6+YuITHl4wtNXg4Otx+GZfn3uf2cqa5HZYon5HWj1A9pQ0QCPswaxQjhYDHo\n5R/+7ZdIogB+F+6z9RiShrqzuFqrmTQuOqnJLyzgR9/Pp6WxAZPFgtkS1e+bXlJCRfkZdpYdJIyE\nEHAj56SwaeMmFiyYx+pblrJu4wd0R7RoZD+TUs0su36kKPGGt9/Da8kbDIQbs6fQV3f8nPaXOCLL\nT1BqeebNbXT3ubhplE5fY4xxqag0apYvv/a6b8qyzK6dO6jubEQnqli+6AasdttHH/gJ4vD4QTts\nm9tPQ2sHCo2e8DAdvbDXiVE3MiQQ8br53r/+ErVSJOLuw9fXOaghCtFF6KnnsvOmTJ3CxEkTaWls\nwB4Xj8EUtWWLlyzmzJknKausIoyIFPQQKkxny9ubWbh4Iauum83GD/fhiOjRyl5K85NHBHEANr23\nk8B5i27m/FL66o6jscQzsmoCQoKKXz73Fncu62DhogWXeQf/chgL5Iwxgr0V+3k5tB9mRg3Pycp3\nuPd0P7OKP7rz1CeFXauiIRjrvNhUAuPyinm7fhehARfhgC+qSQNEWir40aNf5revr6dNaUdhsBJo\nPUNIbcBtj64IytYw4cNbcNRGH/ygp59IOMTcwtgVw+RRdF/2nalCWzATjRyht+oIbcYMehwSW9/c\nzNKcZH7y6GO0tbegUqmIsyeMOB6gyz2AIMWKGqtMNoJuB/qkHPqbzmDJGups5WypxJg5nqN9Luqb\n6snOyB5+yjHG+Ivm/Q+3smWgAqnQAoSp2PcaD3pu+EzHYDeq6fGP3JaWloKy1kVfcwNqczyiQhmd\nUHVU8JMf/YD/efxp+jXJSBoDvuYKIvp4fOf0ayL6FHxHtyIDarOdgMsBskzxlKGAuCAIpA7rCiHL\nMkcrG9HnzyQSDuGoLqNZSqWjMcy24y+zYlYR//y9r9Ha1IjBaMRiG708rtftRxBiBR9FlYZI0I8u\nLhV3e100BfkczqYzmHOncLCqnRscvVisn63TOcYYnzZvbnqDXapWFIVGZDlIxfvP86WFd3+mY4gz\namgats1m0hBnM6O2RqKdpwpKEEQJWY6g66/lhz/5AT//9dMMmDMRJRX+ppMI1kz8lqjwsLIwHuep\nXYT9A6iMNvz9PYiSyLjCIX9BEATSs2L9h1AwyOnmboz5MwgHfPTVnaBBTqKlIcT2Y89xx7JSfvz9\nr9Lc2IDVZsNkHtmQAaDXE4DheqPnSi9URisDPW3o7EO6X57ORqx509h1rJrrli1Gpb56wupjjPFp\n8ML6Fzme4EQq0CNH/JS/8Tv+/vZHPtMxxBk1Mdm7sixjN6qx6LXoE7PpqzuBrWB6VOQ7HCY+0s1D\n3/97fvnk8wTtuSCDr/E4ypQigkYbQcA4Tk9PzVF0cWko9Wb8/V2ICiWTxw9lvEiSRGZObsxY3C4X\ndd0+jPmlBL0unE1nqAomUlPj48Oyp7j/1kX85LtfobmxgfjExAvqZ/W6/TBcx/qcrVFo9PidvahN\nQ75LwN2LIX86H+4/wYKF86+Z0vFPmrFiwjFGsK3jMBSc58gX2tl29vBnOobV19+IvbeWsM+NHA6j\n7K7j1pml5GbnMt4AxpRc3GfrcdQeJ3B6Fz+8925SktP4l698k28snM5tKRoyrUaM6UMGRhAl7Inp\nZJt1hL1utBotkxIt3HvjyouOZcDrocUbBqJlCeaMYjSWaBaPYEtjW20rjj4HKclpFwziAMQbdMjh\ncMw21UAviRE3RvdZstQRgrWH6W8op6/uOEqtAYVah2BK4FTV6Y9xN8cY49pkX+MJpPShCYI8Lo5t\nx/d+pmO4/ZYbMPTXEvZ7iYSCqBx13HL9fEpmlJKhdGLOKMbVUoWj9jiR2r388FuPYo9P4F9/9H0e\nvn4SKydYSIy3xKyIiwoVyanpJFvUhLwu9HodU3PiWbny4hpT7S3NdAajwWlXSyXWvKmoDZZoTbo1\ngw8OVBAMBEjLzLpgEAfAZlCf0/YaQo8PS7ATc8RJmur/t3fn8VXVd8LHP+fcPcvNzb6QhUDYwxbC\nHiCAyKYsClVR61K1au2M7dj69Jlp7XSmM8902pmn0z61tWrV1qV1QRAUEVkCCKJsArKEJRshZL3Z\nc5dzz/NHNOFyA2FJcpPwff+Xk/M793t5wY9zvuf3+36baDmzl9qCI9ScOoA1Mh7VYMRtcXDmVODy\naiH6Ms3r5bPKfIzxrQ8IiqKgjY3no91bejSOJQvnYHOexudxo3lchDhPs2zRHObMmU2C9xz2tJHU\nFh6l5tQBDEWf8Y8/eIK4+AT+/Wc/4pszMrh9fCwR0ZHYotu7RxmtoaSkDCA21ITW0ojdHsbU4UnM\nvWnuZWM5sG8f9ZbWYsf1JSeIGpaNKSS8tUB61EA2bGu950tLH3TJJA5AVGjgNlO70UtYQyExFi8J\neiVNp/dSW3CYmpP7CY0fiKIo1GsWqisrOriiEH1XQ109hzylGCJbV/EqqoI7O4ENWz7s0ThumZeD\nqeYMPs2L5mrCXneK25YuYvHi+Tiai7GnDv/q3+Q+QisO8vT3nyA1LY1f/Ox/ccfEAaycmEhodByW\n8PZnQZM9hvQB8ThsKl5XM46ICOaOH0xWdvZlY8nblof3q4YwDaWniRqajdEagmow4o1MZ92mnRiM\nRgYOzrhsEfSosMCkr93gJqS+kIRwI7HuEhpP7Wuba8IHDAGgtqW1xmB/JStyRIAG1R14TAk81p3s\n4XZ+9uh32LV3N9X1dcxZdj+hX3VUeeKu+9j26U5OlxmIs9uZPyPXryBw5rBRZA4bxYHiswHXDQsL\n5+ePPcaZwlPYbCEkxicFnHMxk9GERfHhAXweF0abf0rYGx7HoeNHmDk557LXWTZ3Psf+9DznrPGo\n1lCU6mKWT8xiwcw5bed8fnAvz312DOMFk6dSU8qkObd0GqcQfU2jHjivNPp6dq6JT0jgmae/y87t\nO3C5XMzMfbStu8OTf/coWzZvofS8keSEaGbNzm1rHawoCmOzWuuafXLwZMB17VHR/NP3v83p/Hwc\nkQ5i4jrv3BMaHo7p6z8TXQ/YbllHKGdLikgfPOSy11m2ZBFn/t+L1FgHoJosGJzFrLx1LlOnt2/k\n/fijTaw5WI7R2j6fWV3VDB85oqNLCtFntTS30GLWA254m3RPj8YxaPAgnvnhY2zfmoeqquTMWtp2\n7/KD7z/Opo0fU5FoYlBqAtNntL9BVlWViVOmAPDR7sNcHHV0TBzf/84DnD6ZT0xsLJHRMXQmJiYG\n1f0F2FqTN18XMP2a06PSUF932SQOwLJbbua3L75BfWgKqmrEVFvEPauWMWbs2LZz3nl7NduKNQzG\n9vu0SKOL2PiEji4pRJ9VU1GJy27kwvLuiqJ0eK/TnUaPGc1PMwaTty2P0JAYpuasbLt3+dH3H+PD\njZuoTRzCyKHpftuYDEYj02fORPN6eS/vIN6Lrps4IJlHHryLMydPkjQg+Yq6ScbEROM7dgqDIRzV\nYAhYGVPVcGV/NksWzOL519fTHJ4CgK2+iIcfuZdBGe1Ngl555TX2Oa2oavtsHx1q7Ncr/ySRIwIk\nee2c8OvepJPgtXcyquupqsr0idMCjiuKQu6UHHI7GT916BAKD59BCWt9c+1zt5CZ2LqSZtDAK+8O\nZjKZGT8gjt11TSgGE15XM8YLigsaGysZlTGj0+tYLVZ+8sjjfLLvU85XVZIzd5lfS3WACWOyyDp2\nlAPO8ygRcSg1pcxKiyM+rvfc8LjdbvL27MRsMjE9e2rbfw5CXK0Eg92ve5Ou+Ug0Xf7BoTsYjEZm\nzs4NPG4wcNO8wLoQFxs3PI2NR6owhLTOk76WBsZkDEBVVTKGXXmhvQhHJMMTQjna2Pr2yKd52zpf\nAYTTRGJS5wUdIxwOfvz037Fj+w5qa+uYee/dAdulZs+dw5fHn+dkQwtqaBSKs4SbJg7rNS3IAVqa\nm9m+LQ+HI4LsyZP77dJo0b1Cw8OIabHgvOCY1uIhOaTnO/eZzRbm3hxYQ8hkMrNw8cJOx49MT2B3\naROGr4qX+5qcTMgajMFoZMjwK0/CDhw8mLSwjRRpXnSfFlCHJ9yoXVGL8ISkJJ55+gnytm6jpcXF\n7DnfIiTU/2XXLbcu4uRvnuOsy45qs2NwFrNgdhYGY+95BGmor2dH3nYSExMYM368zDXimgxITyNy\nq0Zzavsxb10z6ZGDLz2om9hCQpi/MLAOldVm63R1sMFoZGiSg8N1btSvks16fQWT547BbLYwbOSo\ny46/0MTJk/lo224qfaHoPi3g9x2t6uvI4IwhPPPUt9m6ZSsAs+c85tdSHeD2226l6LcvUGGIQTGH\nYKkr4pZFs3rVv+ea6ip2fbKL9PR0Roy68j/HS1H0i9dfd5PdW4/1xMeILuCsrea3n7xCSYqGAiQX\nG3ki534iwjvPvPY2W3bvYPfxfDw+H8MSYlg5/9Zrak+o6zrvb/uIoyXnOH7qBGrKGAyhdny158lJ\ntHPPrbd1adynC05z5OQxJo4eT0J8zxZkvJwTp/N57oMN1EekgE8jqqGUJ1d+IyAh1ZEpuT1TMHvL\nueM98jni+pWWnOWlDX+lIk5Hceuk1lp5bNVD3D19PibVyBsbtwU7xCv24YaNfHGiEJ9PJzMjhUWL\nF1zTzYOmaax7bz2nisvIzz+FKXUcBksIem0p88YNZPEtnT/sXY1jX37JmdNnmDx1MlFX8Ca/pxzY\nf4DX123DZU9Bd7cQo5Xz5OMPEm7v/KVC7qieuWmWuabvOHkyn9e2rqE6QcXQqJHRYueRu7/FHZPm\n9qm5Rtd11q5Zx7GCc6iqwoTMDObMmd35wA643S7WrFlHQXE5pwuLMaeNQzVZUJwlLJ0xuksLhOq6\nzhcHDlB27hzTZ8wgLLzzJFFP2bljJ+9u2Ys3IgWtuYFkg5Mnv/vIFb3F74m5RuaZvuXQFwd5e89G\nnElGTLVeRhHLfXfex8oJueCF9W9uvarrLb9zLm5N4+2tO7sl3kvRNI233nyHM2U1mA0qUyeMYuq0\na2vP09TYwOp311N09jzFZRWYU8eiqEYMziLuXDiN7ImX3551NXRdZ++ePdTUOMmZOQNbSEiXXft6\nffjhR3y45zi6Ixm9sYb0UBdPPP7wFb0Qv9RcI4kccUlnz5WgAEmJ0tLvax6PmxdWv8nB4jI8mpfh\nMXa+d98jN8yqlH976UVKbP6JpRG+Sr57172djpVEjuiIruuUnCnEYrUSl9S68mxF1qw+9XDVHZqb\nmvjTK69zrKgCXfMwNmMA33rogV71Zqm76LrOz3/5LNW25AuO+Rgb0cT931zV6XhJ5IiO6LpO0cnT\nhDsiiIptTVrKXAPOmhpefvVNTp6tRPFpTB07hLtW3RnssHqE5vXyzC+epcneXnjep3nJSYIVKzt/\nQSeJHNERTdMoOnmKqNhYIqJaO8WtyJrVpxI53aGstJS//G0tBWXVGNCYO20cS5ZcfnVQf9Hc1MhP\nfvUiWmT7XKO5mrlldBTzOlilebFLzTVS7Fhc0oDEZEniXOSlNW9zwGtHSR6JOW0M+eZEXl+/Jthh\n9ZiKppYOjvXfIsp6Jb4AABqNSURBVGKi+ymKQsqggW1JHNHqxZdfJ98djTFpJKaUsRyutfH++g+C\nHVaP8Ho9VDX6VwJRFJWquuYgRST6A0VRSBsyuC2JI1o9//IbFOjxmAZkYkwZy6clHrbnbQ92WD2i\nqqIcp+a/tUM1GCl3NgYpItEfGAwG0ocNbUviiNZE+h///BalxiTMyZkYksey+Ug5h774Itih9Yj8\nEydoMfv/fTBYbJSUVV7XdXvPBlUhLqG5uYk3NqynrL4Ju8XI0hmzSL6COhEXO3HqBGUV55k0fiJW\ni7XDc3RdZ9+h/Zw5d5aJI0eTljLQ7/cnq2pRHe01PAwmCycqAosq91eRVjNlFx1zWC/uPSpE3+Ss\nqeGdNeupbnARGWJm6S0LiImLvapr6LrO0SNHqHXWMnHyJIymjv996LrOnl27OHe+nKmTJxGflOT3\nu4KKepSo9s9WraEcLTjH4mv7an2K0WjCbjVQf9HxiJAr20svRG93vqyM997/CGeTm5hwK7cvv/WK\ntg1eSNd1vti/H4/bw/iJ2ZdcGaxpGp/s2EFNTS0zZk4n8oKOdw11dZTUaahR7Sv91JAIvjh6mhkz\nO6/919dFxsQQpri5sNyqrvtwhPbf4qjixlJYUMCGTXnUN7tJig5nxe3Lrrr4r6Zp7P98L0ajgbFZ\nWZdcGezxuMnbuo3mFhe5ubP8tlAWFxRw3mPjwv/F1fBYPtt3mNFjxlzLV+tTBg5Mx+zZiU77PO/z\nuolxXN82U0nkiF7vl39+idLwNBRLawG9U6vf5affvBf7FdTs0TSNfYf28d7OHZw3R4M1nNV7X+DO\nnClMHjsh4NxfvfI8p3Q7hlAHm9Z/zOzUWO5YtKTtHGMH9XUMav/f6vC1RZOyeWnbLrToVPDpWKsL\nWHrrjfBoKfo7TdP4v7/7E7X2wSiKnbONOgXP/ZmfPP2EX1e8S/F43Oz9dA8btnxCtSkOjBbWbf2M\ne5bfzIhRI/3Odbtc/OrXv6dMiUW1hrLt8LvMn5jBggU3t51jVBUuLgtovEHmGkVRuGnaON7ZdhAc\nyeial9D6QpasvDvYoQlx3Vqam/n1H1+jJbK16cLZOh/Fv3uBf3r6ySvaOulqaWHnju1s/mQ/ddZE\nUFTWfryLh+5ZRmraQL9z6+rq+O/fPk+1JQnVbGXbb19j+ezx5Mxo7bJpMBoxoHNxjQWj4caYa0wm\nM7OyhrBxfwFKRBKax0VkUzG33vdQsEMT4rpVVVTyuz+vxRM5EBQ4W6FR+rvneep737mi8U2NDWzb\nspUd+45Rb00E3UfMxu08/tDdxMT6v+QqP3+e3/zxVepCU1ANRvIOvMiqW2Yxbvw4AMxWC6oeWOz4\nRnmGsjscTBmWxI78cgz2ODRXE/HeMuYveOy6ritbq0Sv9uWJLylR7SgXJFCao9J5f/vWgHNdbhe7\n9+6msLgAgGMnT/CjPzzLr9Z+QHlkBgZ7LAazFVfMIFbv2oPP5/Mbv2X3dk6p0RhCW1fcqJFJ5BWU\nUVVT1XbOuJQktOaGtp99TbVkp6dyo8jOHMc/37WSXLuXm6J1/vWB+xmclh7ssIS4brt27KTGktj2\nIKUoCnWhKWzdvDXg3OamRnbt2ElZaSkA+/bu45n/eJZn//ohtY6hGMOiMFpDaXYMYvWGwPHvr9/A\neUsKBlsYiqKgRCaz9fPjuFpa2j57RFosPnf7Vka9oYpJY3umzlRvMGNmDk9/azlT473MG2zlx089\nRlx850XVhejtNn30MU32gW0/K4pKpSGWvXv2BJxb53Sya8d2KisqANiet4Of/OdzvLwmj8bIYRhD\nIjDawmmIGMTb720KGL927fs4wwdhsISgKCp6VBobdxxou/+xhYSQER+Kz3vBVsb680ybNK5rv3Qv\ntnDRAp5cNZ8pcR4Wj7Dzjz984qpXRwnRG238eAtuR3tNFsVgoKjRTFFBQcC5VRWV7NqxnVpnDQAb\nNmzkJ//9J/668TOao4ZiDAnHGBpBTVg6b65eHzD+3XUf0ujIwGCyoKgGvJHpvL95V9vvExKTSA3z\noV/w7KXWljB71rUVUO6LVq68jceXT2dyrJtl4+N4+qnvYjZf3+o/WZEjejVnXS26yX8blKKquDxe\nv2O793/OXz/5lMaweNSWowy16dS6PDRED8ZQ3+zXwhegymeisqqCuAu6LRVXVGKw+rfe9YbHcuTE\nUWZObn17tWL+YiybN/JFcSmKAhMGp7Fg5pxr+m7lled544N1nKusZM6ECcyb2Xmb494gOiqGOxYt\nDXYYQnSpuro61IvmGtVooqGxye/Yls1beH/nIVwhcRi2HWJkgpWi83W0ONIxOhtRFP/3IxX1LjSv\n16/VbrmzAdXgv5y2jhBKzxaTPngIAPfcfSehq9eQX1yGyaAyefpwpk2fdk3f7WxxEW+vXkd1dQ2L\nFsxm0pRru05PS0hK4hvfuD3YYQjRpZqaW1BU/y2XismGs7bW79h7761n24EzuENiMW49xPhUB4cL\nK/BEpmOsbQhYvVPu9J+rAKobXCiKf9eWWo9CQ30d9ojWl1YPP3gvf3tzNUXlVVhNRmbMG3/NWx1O\nnjjB2nUfUl9Xy8oVSxiZ2Te2TKSlp5OWLi+lRP/i9mgB84RuMlPndPode+ONt9hzshzNFo1x60Em\npEfz+akqiErD4PTf5KwoC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vXqm4HrFeGIlCSOED0sK3si80YnElpXCJWnSPSW8sCq5SiK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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115d78e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from helpers_05_08 import visualize_tree\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.datasets import make_blobs\n",
+    "\n",
+    "        \n",
+    "fig, ax = plt.subplots(1, 4, figsize=(16, 3))\n",
+    "fig.subplots_adjust(left=0.02, right=0.98, wspace=0.1)\n",
+    "\n",
+    "X, y = make_blobs(n_samples=300, centers=4,\n",
+    "                  random_state=0, cluster_std=1.0)\n",
+    "\n",
+    "for axi, depth in zip(ax, range(1, 5)):\n",
+    "    model = DecisionTreeClassifier(max_depth=depth)\n",
+    "    visualize_tree(model, X, y, ax=axi)\n",
+    "    axi.set_title('depth = {0}'.format(depth))\n",
+    "\n",
+    "fig.savefig('figures/05.08-decision-tree-levels.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Decision Tree Overfitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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j1VdgMZslvH0kCau2fUfmZERET+9x9xYc+TAOlBUdw45Mff9rX28RAS5F0HXrx+20ACIi\norFKFEXMXZgid4wxRalUImPd83LHsCmx53Z/4QEAFAoBXqq7MiYiIrIurvkwDiigH9Lm7d6Dri6d\nDGmIiIiI6Mss0tBngqZh2r7oYXUtck/loK2lw1axiIishsWHcUDlMRWNzZZBbeU1oQgM8pMpERER\nEdGTmc1mmM1muWPYhWfIAty+M7A2UkubBUbVjEcef+yTP6On4qfISngblZd+iHPH99sjJhHRiHHa\nxTiwMGMZju6phbeiAF7uPahsCEVCyityxyIiIiIalk7Xg1N7/we+mjuQJBHt5ngs3/zVMbtlc2dH\nN3p7DPAPHPn6HLNTFuPKRQHXsi9CgBGSSzwy1m8Y9tjigitYGHcBkyIFACKWpxpwMvcImhsXwS+A\na4QQ0djEBSfHkZ6eXnR36cfddl2SJOFOSQU0Wg0iJobJHcdhnc8+CHNHIUTBjF4xDmlrt47r1deJ\nnAEXnKSx6uiH/42dWVegUPSNBNDrLfg0dymWbXxR5mSDGY0mHPv4d4jwuQUXjQllNRGYu+zrCAwO\ntGm/J/e/i+1pg3/+TSYJn+Q9h7SVK2zaNxHR43DBSQIAaLUaaLUauWPYVW1NLYpO/TfmJVRD3yZi\n/9lJyNj0Xbh5uModzaZ03XpcysmGxWzEzAXpo14pPffEYcyL3IuwkL6bwK7uGuzea0DW5leskJaI\niGgwd2VFf+EBAFxcRKgt5TImGl7OoY+wY1kRtNq+mcypc6vw5sE3sXLb92zar4vHBLS2WwYtUHm9\nVETU5Ck27ZeIaDS45gM5HJPJhIo71dB1D11I88uKz72LVzbUYepkJWYliviLDRU4f+w9O6SUz4P7\nlcg78I/YNG8/ti46grsXfoxbRVdGdU1jx9X+wgMAuLuJ0FpujjYqERHRsMyS+qna5KaW7vcXHj7n\npa6ErQcWz1u8BB8ej0JHZ996GA/rJVypmIGJMRNt2i8R0WiM65EPJpMJRqMZLi7jazSAIyu6dAFt\nlbsxbVIjbt32RKe4CGmrH731loeyZtBrURTgqqi2aUZJknBi77vQmIqgEIzoME7C4jWv2W20RUnB\nXuxc3Qmgr1iwJq0X7x89iPgZs0ZxVcuQFlGwy4wtIiIahwSPZFTXHuwvfJfcFeERtFDmVEOZLEO3\nLDdaXCEIwjBHW49CocDal36I7NMnYdTXwd0vBqu3yb89a0dbJy6fOQCF0AGV+yQsTM+0+deCiBzH\nuCw+9L05fAdulkK4aHpR1xGB2elftfn8PBodna4HuuoPsXWlDoAa8bE9uFWejZvFcUhImj7sOb0W\nDwDdg9oMZo8RZyjOL0TzwxtQaPwxP20ZNJqhT2Fyju7Dqjln4Ovd9yTEYrmOtw6+jlXbvjvifp+F\nVtEytE1sHtU1RbckNDRVItC/7waip8eCbiluVNckIiJ6lMVZG3DhlBtyb1yFBBFeIfMxd1Gq3LGG\nmBCbidzCO1g42wAAqKwGBA/7FAGUSiVSl2XZpa+n0dWpQ+6Bf8aLa1ugUAhobM7HwY8qsGrr1+WO\nRkRjxLgsPpw7fgRZM3MQ4Pf5MLl7eHP/H7Byxz/Kmoser+hyPjIWdAIYWOQwfjJQfOrqI4sPnhPS\nceXmB5iV0Pfk/kSuGmFxI1uI6fjut7BwylksTxeh01nw7q48LH/hR0NGzgj6W/2FB6BvtIWnsgKS\nJNml+t9jDgTwcFCbzjy6wtqi5Wtx6rABip6rEGCCTpqCjA0vjeqaREREj5OSvgzAMrljPFbctGko\nv/0d7Mo+A1E0ws0/CYtXLJY7liwunT6InWta+tfqCPATMTnwKhrqmhAY7C9zOiIaC8Zl8cHcffsL\nhYc+ob5V6OrUwd3JFyJ0ZMGhYbhXrUDS1IG2nh4LBJXXI8+Zk5qG0ptB2HXyAiySAnEzMxEeFf7M\nfTc3tCDcIw/REX0/N66uIl5e+xC7Tx1C+upNg461SKoh55slpd2GHU5f+Bze2PMAmzIaoFEL2HfK\nC5HTNz35xMcQBAFpq58D8Jx1QhIRETmJyVPjMHkqRwOKUgeUysH3OhNDe3Gz5iGLD0QEYLwWH6Sh\nazzoe9VQqcfll8NhRE+eiH1vT0VM5E24uYowmyXsOhyAtC2PH8kwJSEeUxLiR9X3g8oqJEX34Iv/\nZTQaEYJp6BQHn7CFuFF6B4lT+kZbtLZb0KucMar+n0VQSBCWb/85Tp3LhdHQi3nrlnBdEyIiIrIp\nd/+pqK69iLCQgQd8F6/5YN66qY85i4jGk3H5bjsiPgO5hbf75+e1tlvQZpo57Px9GltWb/8rHMw+\nAhgqYZK8sWj9eri6aocc9+D+A5QWnQYgIjE5E8ETgkfVb2xCHC4d88T6TF1/W2OzBRrP6CHHzpiX\ngqt5Eq4fy4UomiBp47Bs48ZR9f+slEolUtIevccuERGRozIaTbh/txohoYEcsTqGzElJwfHdZQit\nuIyJYXoU3PKD98TnoVYPHRFKROOTINl6L6DP1Hbn2KObp1Z26yaqbp+EUugBXKZgyYp1XI13BOpr\n61F09j24qx6i1+IJ34hMCKIWTQ8rEBmbhJi4yY8932g04eKpbJh7HkLpEoaUjEwoFIrHnvMk1wou\nQd32FpbMM0KSJBw7r4VrxDcwJTFxVNe9fPYE0LoXS5P1KK0QUVyZhNXbvsWfGyJyWCFujl2kHGv3\nFmR7Vy6eR3fNHkyPacKdag90YDHS1jx61yuyv9aWDtQ9rEPMlGioVOPyOSfRuPa4e4txW3yg0ZMk\nCYff/gm+sqmuv+3UeQMUCglLFmhQfBu4Vr0IyzcNvzChJEnY+8a/YsfKu/BwF9HeYcb7x+Ow4ZW/\nH9Ub+lOf/Azbsh4Manvv6GRkbvneiK/5ua5OHYrz8xE+cSIiJkaM+npERHJi8YEcSXeXDtezf4D1\nmfr+tlvlQL3yrxA/fdozXctiseD0wY+gNN6CJIlQeM7GouVrrR2ZiGjcedy9hfjIjxA9QcnNUiye\nVTOoLT1VjbZ2IwAgaSow2T8X1VU1w52Owot5WL+kr/AAAF6eCqyYV4brV4tHlUsjtg9p0yqHto2E\nu4crFqYvYeGBiIjIzvp2vRq8fXb8ZKDu3tVnvtaJve9h9ZxsPL+8FluzarBg4j6czz5krahERDQM\nFh9oxBQKBUymwW2SJOGLY2nmTjej7Mb1Yc9vb3qAkMDBP4KRYUBDzb1R5eo2hQzJ1GUIecTR40dL\nUyuO734Lp/f+FmePHYHFYpE7EhER0VMLDgtHxYPB9w16vQUKjc8zX0truQ4vj4FrhQYLMHVeGXVG\nIiJ6NKeeiGU0mpB3OhtGfQO8A6dg5vx5nJ9vRZPjYnDw7QhMmVTd/3U9dV6HmYkDOytcKlYibnrS\nsOdHx89FwbUTmDN9oFqRW6hE/Mz5o8qVsGAb3tjzX8hIroPRBJwuDEPKqh2juqaja2/tROGxX2Dn\n2lYIgoDW9qvY82EFVm/7ltzRiIjISdTW1OFG/mkoVFrMW5IFN3frLgY5KTYa+96ZiolhN+HhLsJk\nkrDrcBAyX1g+gqsNnXUsYPiifG+vAWcPfwI1qtFr8ULivLWYEDZhBH0SEY1vTlt8MBpNOPj2v2Dn\n6iq4u4l48PAsjnx0Dau2fk3uaE5DEASkrvku3jn6HtxUD9Fr9sS9+92ICHsIACi8DtxrTUVm2PCj\nDibGTMTp0mVoyz2DpCk6XLnthg4xE4sfcfzTCg0PRcjOn+P61etQKpVY81L8uC865eccwLbVrf1f\nBx8vEbHBxaivbURQSIDM6YiIyNEVXcqFqu1d7Eg3wmgEPj14HlMW/g1Cw8Os2s+a7X+NI9lHIBmq\nYLJ4Y+mm9dBqn307aT3ioNdfgItL3+iHphYJksvw60Yc++A/8PKacqjVfX9DPzpSCpeMf4KPn9fI\nP5FxRK/vRXFBIYInhCJqUqTccYhIRk674OTZY0ewYvon/esJAMClIhHKyJ8gLCLUrlnGm5vF11Bb\ndQeTps7ExJiJTzy+tbkdFeXliJkyBV4+HnZIOLzqyirczj8EjaITPVIolqze4jTbr57a9wdsS788\nqK2q2oiy3r/HtBkJMqV6tKJLF9BafQoqQYcucwRSs16Eu6eb3LFgsVjwsLoBvv7ew27xSuRouOCk\nc7tRdA31VdegcQvC/CVpUCpt98zp9Ec/wgsr6we1vXtsBpY9NzZH2JlMJpzc9w5cpBJIUMCsSULa\nmueHPKyoKL8Hl5Z/wYz4gXazWcIHZzKQuWGbvWM7nOL8S+isfB/p8zpQ+VCBy2VTsXrbX9n0Z5GI\n5PW4ewun/Z9v6mkYVHgAgMRYE47dvMPig40lJE1HQtL0pz7ex88Ls/3m2DDRkzU1NKOy4FfYmdW3\nkJXBUIY3P6jB+pdHv0PGWOAZkICqmkuICB24ebp4zR+pm6fImGp4JTduws/yNrKWmwEAFksD3tjX\nhjUv/kDWXLeKrqCu9CPERTTgZpE7usSFSFuzVdZMRESPkr3nbSRHn0VGmtC3m9RbuVj94j9CrVZZ\nvS9JkqBVtg5pH65trFAqlcja/OoTj2tvbUWUnxlfvGVWKARA0kOv74WLy7OPuhgvzGYzWis+wdZV\n3QAU8PEGYiJv4lD2ESxdyZ1FiMYjp11w0itwCqpqBs/dO1egReLMWTIlorHsau4RbFzW1f9arRYw\nK6YMVfcePOYsxzEnJQXnSpbgyFkNbpQa8cFhXwTEbh+TTx4elp/FnERz/2tRFBAbehdNDfLdxBqN\nJjSUvodtq5oxM1GBNWl6zIk8gaLLBbJlIiJ6lKaGZoS5X0BsdF/B2ctTgRfXPMDFU0dt0p8gCOg2\nBg5qkyQJOlPgI85wHNNmTceZAv9BbUU3zGiozEfJyb/CiQ9/ivLbt2VKN7ZV3a9FwqTGQW3ubiJg\nqJQpERHJbey987CSWfPn4ejH1xDXUIjEWBPOFrig12WVrMP6HUnR5QK01N6AqPbB/LQVI5pPaU0G\ngxF5p0/C0NOJaclLEBRi3RsaUegdMtTS38eM8rZ2RCDcqn3JJXPDi+jq3IzGhhYsfi4ECoVC7khP\nTRQAiyTf7hw3i65j8ewWfPFXZkwUkH+qGIC8o3aIiL7s3p27WBBrADDwe97NVYRkaHz0SaMUkbgZ\nHx7+M9altaOjG3j/oCfc/QzI3vMm4ucsQ2i4Y446VSqVCJv2Mt49+BFCfOpQ16yGQd+Cv/mLz7+2\nNXjvwBuIjv1Xh/q7ag9BwX64dcsDiVN6+tssFglGi7eMqYhIToqf/vSnP7VHR11G+1Y5BUHA5ITZ\n6BZm4UppOOKSX8Tk+ES7ZnBUx3e/hRkhn2LxjGrEBJZg7+5ChMemQGXloZrFBVdwK/8Q7pbchpdf\nOFzdXIY9rrmxBWf3/BwbU/Mxa/Jd3Cw4i+pGD4SER1kti65HCV1jPgK/8HDj4NkAzMvYClF0ngFC\nao0KPr6eY/pz0vWq0dFQiGD/vuVoJElCdv4kzEwZyWrm1mEyW9BWfQ6hwV9oM0m4UTkV0XH8vUKO\ny0MdJXeEUbH3vYWj8PL2QdGlM4iNGhhF1thsQY1+CcKjnrwW00j4BQYiOGYpTl/2wrl8NTKTq7By\nYT2mTazCtYJL0Fkmwdff/8kXeoT83HMoK9yLitv50PdqERgc/OSTrMQvMBDRiUvhGpSB6upmvLyu\nYfDHvbpx414UgkO5rfcXqdQqlN3pgtJUAX9fwGCQ8M4BPyzIeg1aTlchclqPu7dw2uLD5zy9PBEx\nMYq/5J5SS1MbhKa3MCux742fUikgMaYLx3MkRE+Jt1o/OUc+xRTvD7B0Vg0So+4h52Qe1N5J8PAc\nOjLl/NH38PLqMqjVIgRBQHSEBVcKahCZkGG1XSwCg4Nw9aaAGzfqUP3QhLwbExA96yX4BYytnSBM\nJhMKLl5Gc1MzAoODnHIXj8DgYJRWeqCwqAUlFQoUlE3BwpXy3qh4enni3PkKxIbVQa0WIEkSPjjs\ng+RlX4VG5lFBRKPB4oNzUmtUqKwRUFlxB1GhZlwvFXDy6nSkD7OgojUplUpETopGy91PkbFAD6Dv\nYVBMpAlnL3QgOn5kW2mfzz6I6QEfYdHMBkybVI/mh1dQWR+AoAnW3UnjcQRBgEajxr2yEiRG3hv0\ndbz3QICWQux5AAAgAElEQVTosxw+fj52y+MoomITcLcuCnlFKtx+mIjUVa/Bw9Nd7lhEZEOPu7dw\n2mkXNDLVD6oxNUoPYGCUg1otQJRarNaH0WiCSn8eU6L7XouigC0ruvBu9gGEbBq6FapWbBlys+Tn\n1Qpdt96qe4inLlsDi2UVenoMmDEGdzK4f+cuyi/9HlkLm9GtA/a/GYYl6/8XvH2db6uv2SlLAIyt\nVfhXvfBtHDx2EOithFHyxOxl6+DpzWlcRDQ2LUhfiZamBfj4Yh4iJsVg3c4Yu/QrSRJUis4h7eph\n2p6Wuf0Cor9Qt5iTaMIHx88Ac+aN+JojNWvhSuw+no/nVvStE2U0Srh0KwbrX7HNiBJnkDhjOhJn\nPHoh8vq6Zmi1anjxbyqR02PxgVBbU4+mxibET4tD7NTJuHzIG2ETuvs/3tRigdpjktX66+zohr93\n15B2tTj8jUmPFABJKhtUgGhs90fiI6ZpjIYoimN2C8Xywg+xc10rABEBfsBrYTV4+9j7WLHlG3JH\nGxcUCgXSVq2XOwYR0VPz9fdG+qoVdu1TEAR0m0IB3OtvM5kk6C0jH6WggH5omzC0zR58/X0wcd7f\n4t1jB6AW29GLUGS98LwsWRxdbU0trp56HXERD9CgV6KyJQErnv/mmFwMm4isg/+7xzGz2YzDH/wW\nCeE3EeNvRM7HAQhNfBHakI3Ym/0pliZ3oeyeEtcfzMSqF9Ks1q+PrycK64OxCHX9bXq9BSYxctjj\n56dvxuufVGBT+kP4eAk4nOMC/0nrHGLKQUtTKy6ffA9uqocwmD0RMnk54meMbMcVd1XdoNeCIMBd\nVf+Io4mIiOQRv2An3tr3OuYl1KCjS4HiislY9vy2EV+vyxQJSbrR/3e/t9eCHsg30iAsIgxhEd+U\nrX9nUXz2Dby6oRqAAMAMvb4Inx78BMs2vCB3NCKyERYfxrGco/uxLfMa3FxFAEpEhbfivYPvI+OF\nf4Fel4ycwqsIjYjAmkXWnVMpCAIiZ2zDroNvYuGMRtQ1qXDtfgJWvrBh2OM9vT2x7pX/jbwLF9F1\nqxVz09Pg4elm1Uy2IEkSLhz+Nb6yofqzG6ZGnLr4Jzy4H4DwqGffQUNv8ga+9PRHb3S+KRdEROTY\nwiIjEBrxz7hTUgEXP1esWzS6hRjnL38Vb+z/HSL978FgUuBhRxyWP7fdSmlJDmazGd6a6kFtLi4i\n1JZ7jziDiJwBiw/jmMJY+VnhYUBMWD0a6lsQFOyH+YtTbNZ37NR4TIr9V9y6XgLfKF+sWxSCirIy\n3C3eD1dVC3TGAEydtwVhEX2FD1EUkZy60GZ5bOFOyV0snFYFQRjYeit9gQHvnjiJ8KhXnvl6PhHL\ncS7/PaTOMUKSgH0n3DB55lorJiYiIrIOQRAweap1pmx6+3ph9c4foKO9C0qlArNsMO2S7EsURRjM\nLgCMg9qN0th/uEREI8fiwzjWY/aAJEmDpi/UN7sjae7oViE2m81oa+mEt6/HY/e8VigUmDYjAUDf\nOhDVxb/DzhWfrwXRiLf2/QZBW38Blcoxf0wtkgXD72gpjeh6M+en4sH9SOw6nQNBUGHmkuXw9efK\n2kREND54enGXBGchCAIk9xRUVh9F5GcDbE/naRARv0zeYERkU475rk4GZrMZl87mQN9Zi6CIaY9d\ntddRzFy4Fh8euYnnV3RAFAVUVAF6VQq0o9g68MrFHHQ8OISwgBbcaPKF+4RVmL1w6RPPyz+Xjecy\nOtE376/PpoxmHD97FqkZ6SPOI6fYqZNx6J0wxEbX9redL1AiZtrSEV8zPCoc4VE7rZCOiIiISD5L\nVm5Gfm4QLpYWwWxRYdK0TEycbL0Fzolo7GHx4SkYjSYcfOdf8fyy+/D1FlFWcRrHdy/C8k0vyx1t\nkKaGZlwryEf0lDhETYp64vGBwYFQZvwY758+DFHQwTd4Bpaunjvi/psbWiC2foitK43oKyK04vj5\nj9DUMA3+gX6PP1kyDxklIIqAxWIZcR65CYKA5OXfxtuHP1tw0uIJv8jlSIrhdlxERETW1NbSgcIL\nJ6BQqJC8OBOunJrhEOYuTAWQKncMIrITQZKkkY0Bf0a13Tn26MYmzh0/gqxpn8DDfeDd8fkCBTzj\nfoagkEAZkw04e2wv/IVjWJxsxLUSEVfvzcSqF75h1x0hThzYg22LD0EUB/q0WCS8f3YVMtdueuy5\nbS0dKMn5MdZn6vrbdh30QuqmX0CjUdssMxHReBbitkTuCKPiyPcWZD0l14rRXvFHrF6qh8kkYfcJ\nL8TM+2tETBx+Fy0iIrKdx91bDDsjnQYz9dQPKjwAwPQpBtwrL5cp0WDNDS3wwzGkLTBBoRAwM0FC\n5swCXM3Lt2sON09ftLYPrmW1dUhw9fB94rnevp7wmfwadh2diD0nvPHu0cmYNPcvWXggIiKix6op\n2Y91GT1QKARoNCK2re5EacEeuWMREdGXcNrFU3DxjkZd4zkEBwwUIC5cdcHUlLGx7sP1q4XYMNuA\nL9aSIkJF5JaWAki2W47k1FR88tYJvLa5FqIowGKR8HF2CNa+vOipzp+SmIgpiYk2TklERETORKto\nHtLmqmqRIQkRET0Oiw9PYd6iRTi46xrmTi5GYqyEM3kqdKtXwsvHQ+5oAICYuKkoLlEiOWlgfYSW\nNgs0HhPsmkOhUCBj8/fx3snd0Cqa0GP2Q8bmzY/d8YKIiIhoNPTmQAD3BrXpTAHyhCEiokfimg/P\n4G7ZXVTeKUPSnHnwC3zyVAJ7OvLRH7E44SKiI0Q0tVjw8clorH/5B3zjT0REj8Q1H8gZ3CkpQXXx\n77E+vQMGo4RPTwRgZsZ3ERIaInc0IqJx53H3Fiw+OAlJknCt4Aqa60rg6hmKuamLWHggIqLHYvGB\nnIVe34vLZ89AoVRh3uLFUKk4uJeISA6Pu7fgb2YnIQgCkubOBjBb7ihEREREduXiosGSrCy5Y4wL\n11pq5I5ARGNYiNujP8big50ZjSbkHP4UKksVes0eSJi3BqHhYXLHcigGgxFdnTr4+HraZCvRz79H\naqkKBrMH4uauQVgEv0dEREQ0fl1rqUFlUyuaGzrQ1h4rdxwiGqOywh/9MRYf7Ozoh7/Bzqyb0Gr7\ndqb49FgptJofj7k1JMaqM4c+gtZwAb5e3SisD8HEmdsRExdn1T6OfPBrvLTyFjSavu/R7uMl0Gp/\nDP9AP6v2Q0RERORIegyJ8G0F0oKD5I5CRA5IfPIhZC01D+owLfJ2f+EBADYt78TVC4dlTGVfvb0G\n5Bw7jBP7PkB1ZfUznXvlYh6SJx7H+sxuLJoL7FxTi4orb8FisTz55KdU86AO06NK+gsPALBxWReK\nLhyxWh9ERERERETjDYsPdtTW2ooAX9OgNkEQIAp6mRLZV2tzG7Lf/wnWzPgU29NOQF/xc1w6c/yp\nz29vuIaJ4YOnWcxLqMWd0nuPOOPZjffvERERERERkS2w+GBHcQmxuFAcOKittALwCx0fi0Tm5+zB\nVzY1wdVVhCAISJ1jRk9jNsxm81Odb7Jo8eXNWRpa1PDx9bZaxqmJU3ChePBQwpK74+d7RERERERE\nZAtc88GOFAoFJs56Fe8e3IVQv3q0dLrB4rYIi1fMkjuaXWjEliELRE7wb0Nbayf8/J9cQEhasBIf\nH7mM51f1AgC6dRZcvROL59OstxaDKIqInv0q3j20CyHetWjr9oDkvgiLssbH94iIiOhzlRX3UV6c\nDYVggHtAEuYuTJU7EhEROTAWH+wsJi4OMXE/Q0d7F2LdtFAqx8+3wCAFwWy+DYVioABR3eiPSb6e\nT3W+u6cHGpoF7D7UBYUCEEXAVdUCk8lk1a/jpClTMGnK/0ZXpw4urhooFAqrXZuIiMgR3C0tg+7+\nf2JHZg8AoLK6CGcO12HpqudkTkbOqqdHD4PRAE8PL7mjEJGNjJ93vmOMp5e73BHsbmHWc/jjBxVY\nnVqJIH8Bh3Nc4DtxA0Tx6Wb/5J89ia9u0UGrHfjadXQ24sTZc1iYnmb1vO4erla/JhERkSO4f/MY\nti/r6X8dGQbk37wIi2XTU//dJnoaZrMZb/zn67hz8Q7MPRYExgfgpe9+BUFBwXJHIyIrY/GB7MbV\nVYv1r/4YRZcLce5OI5IzlzzTG3yjsRcq1eBpGxq1AKOh5xFnPLuS69dRU54DhWCC1nca5i/JsNq1\niYiIHIVK6B7S5qLWwWQyQ61m8YGsZ9+Hn+DuviqoBFeoAHRc0uPd/3wTf/vzH8gdjYisjMUHsitB\nEDBz3pwRnTs7JQOHc05jbfpAsWH/aU8kr7bOqIebV6/AresP2L6sb7eL6tobOHO4lUNMiYho3DEq\noqHXl8PFZaDQ0NwdBrVaJWMqckaVNyuhFAa/JakvrYfJbIJSwbcqRM6E/6PJYfj4ecE98lW8f/Qg\nXBRN0JkCEJ6wEa6uWqtcv+7uaWzPGthmMyxEgFB8CQCLD0RENL4sWbUJuz6qx0S/W/BwM+BWZRgS\nF70sdyxyQhpXDYDOQW1qNzUU4thec6utrQVH9xyCvlOPhLmJSF6QInckojGPxQdyKPEzZiF+hm12\nnlCKQ6dvKAW9TfoiIiIay5RKJdZs/w7aWjuh0+mxIjXwySeR1RRezEN7ww1Y4IH56Wudeh2qxWuX\nYteV96Bo1QAATAoDZqYnDdkhbSxpbKzHb77/K0j3VBAEASWHynF/+z08//IOuaMRjWksPhB9xihG\nQ6ergKtr3xBTSZLQYYySNxQREZGMvH084O3jIXeMcSV7z9tYFHcWkYkCjEYJ7+0tRMqaH8PrKb4P\n9XXN6NX3ImLiBDsktY7E6Ul48WdKnDtyBsYeI6bMiUPmihVyxxqiq6sTx/YfQk93D5oam/oLDwCg\nNmpx9WgR1r2wGVqNdUbkEjkjFh+IPrN0zRZ88EkbgtxuQKsyobIpEvOzviJ3LCIiIhon2ts6EajN\nQ2RY35talUrAi2tb8MGZ/cjc8Oin6np9L7I//g0SIkrhqrXg8PlwzMr4BoInhNgr+qhMTUjA1IQE\nuWM8Ul3tQ/zXP/4alntKiIKIOkUVgoWIQccYWo3o6GiDNoC7dBA9CosPRJ9RKpVY/cI3odP1wGQ0\nIfFL26Fea6mRKdnTm+4b2v/vR+X94jHDcYTPk4isI8RN7gRE9EX1tY2ImqADMLCwp0IhQIH2x553\n9vAuvLq2FEqlAECBGQkP8fbhdxC89Xu2DTxOHPpwP3BfDfGzmSBKkwomGKEUBr5PXtEeCPAPkikh\nkWNg8YHoS4ZbwPJaSw0qm1pxt3xsV7MrJ99ApL9P37+HyTtpct2Tr+EAnycRWUdWuNwJiOiLoidH\n4Pxuf8RGDxQbWtosULlNeux5LsKDzwoPAzyU1TbJOB51Ng1eENMPwahRVcBfEwx0C3CdpMbG17aO\n6XUqiMYCFh+IntLd8mBsDBy7QwKL6+rRY/AH0Ddy4ct5P/+4Wvn4wkKPoQ7RFiApmNV7IiIie1Iq\nlfCN3oKPj36A1JltuFejRsnDGVi5ddljzzNahg5jMljchzmSRiIgMgCNuW0Qhb51wQRBQEx8LL76\nw6+jubkJsVOmcltQoqfA/yVEVtbe3opzp84gOGwCZs9JtksVXJIkNNTVQPRyBRxnjSkiIiL6kulz\n5sEwfRauFd9EUEwwVi958mjEsLgsnM6rQNp8AwDgZpkArf9iW0cdNza/uBU19/4vmgraIBoVUEYB\n61/djqCgEAQFOca6GkRjAYsPRFaUc+Ikjrx+GMpGF5iUF3Fy9jF892ffh0atsVmfVZX38c5/vIHm\nm+2QXIBry93x0r+tA2C7PomIiMh21GoVZsyd8dTHT0lMxH2Xv8Ouk6cgSCYERCZjftpMGyYcX7Ra\nF3zvFz/C7Vs30NLcjOT5C6BSqeWOReRwWHwgp1F2uxRVd0oQlzQHYRGPX1TRFkxmE07uOgFVkysg\nACqzBh2XenHgo914buc2m/X70f/sQs81C9zgAXQBTZ+acSD8DLwWrLZZn+RcJEnC7l0f4nrOdRh7\njAiNn4CXvvNVuLtxyC4RkaOImjQRUZP+Qu4YTm1qfKLcEZzCvYq7OHf8DABgcVYaoiZGj+p6p49l\nIz/7Mgw6A0LjJ2D7116x6YM/GjkWH8jhSZKEQ+//DvNiryJ1iYS8osM4WZSGjHUv2DVHY2M9dA96\n4IKBN2yiIKLpQZPN+jSZTagvb4R2UJ8KNFxvh9cCm3X7zHJzzuLy8TyYek2Imh6FTTu2QqFQyB2L\nPnP80GEUvHkVSrMaIjR4+KAJb5h+j+/86G/ljkZEREROJP9iHnb/+6dQtvUt8H47+3fY/L3nMGfe\nvBFd7+K5czj2m2yo9H3Fhju3q/DHrv/Gt37wN1bLTNYjyh2AaLSKLuVj2axCJE7pWwBowUwLIjzO\noO7hk3d2sCY/3wBoJwwegidJEryCvWzWp0JUwMVr6O4cWp+xU1e8lJuLg/9+EC0XO9FxRY8rb1zH\n2//zJ7lj0RfcvnwLSvPAz64gCKi+XgOT2SRjKiIiInI2OXtO9RceAEDZqkXO3tMjvt6VnML+wgPQ\n9+Cv6mo1DAbDqHKSbbD4QA6vpaEcEaGDf5RTZplw8+oVu+ZQq9VYuCkVBg8dJEmCSTJCM13A2uc3\n2axPQRAwZ8VcGNW9/W3GCZ1Ifenp54na2uUTl6DUDfxRUApKlF8sh8VikTEVfZFCOXQUiqgUIYBb\nhhEREZH1dLfphrR1tXSP/IKSNLTNIkHCMO0ku7HzeJRohNx9IlDfZEGQ/0ABovCGArEJ0+yeZeX6\ntUicOR15ObnwDfDFkmWZNt96ad2WTQgOC8bpkxeBACXWfG0BwmKCUXnRpt0+NbPJPEybBdJwfyxI\nFvMy52PP5b39RSKzZELs/MmcGkNERERW5R/lj9rypv7d4CRJQuDEgBFfL2nRLFTnHobS0DeC0yJZ\nED4jjGs+jFEsPpDDm7twIfa8nYd1qaWYECSg9K6EWw/nYcXCcFnyhEdEIvzFSLv2mbwgBZqJk9A7\nEQibUGPXvp8kbu5U5OSdh9KsAtD3RyZ8Wijf2I4hySkpMP29CZeO58HYa0TU9Chs3mHfNVOIiIjI\n+W39+g683vxfaL3eCUCCzzRPPP+1HSO+XurSJdB1daPwRMFnC06GYec3XrFaXrIuFh/I4YmiiA0v\n/x2u5F3CmVv3ERKViBVbuBrxWJG1djW6Orpw4+xnOykkhOKlb3M17rEmZclipCzhnvBERERkO/5+\nAfjh//dPuHu3HAKA6EmT+0dBjNTyNauwfM0q6wQkm2LxgZyCIAiYvWA+gPlyR6EvEQQBz+18Ac/t\n5JN0IiIiovFOEATExMTKHYNkwOKDA5MkCbkns2HsKoPR4oYZKasRGBwodywiIiIiInJS50+dwdl9\nOehu6Yb/RH9see0FhIVHyB2LHACLDw7s2Cd/wurki/D3FSFJEj45dg3C4n9AQNDIF20hIiIiIiIa\nzp3yMhz89UGoOl0gQoOWmk78ufV1/Pg3/zzq6RPk/LjVpoNqb+tEqEch/H37voWCIOC5rE5czT0k\nczIiIiIiosczmU04dugQ3vqvP+Lk0aMwm4fujkVjz4WT56DqdBnU1nlbj1s3r8uUiBwJRz44qNaW\nDgT59eCL30JBEKASR7FPLpET6uruwnu/ewN1ZfXQemgwf2UKlmRmyB2LiIho3JIkCf/xT/8HLbmd\nUAoqlOAOrl++hu/+5HtyR6MnEBV9I66/OMpBUAAarVbGVOQoWHxwUBFRITh1KRjT4pr62+qbLFB7\nTpExFdHY8/q//RYtZ7sgCAK6YMDR0mPw8PLErLlz5Y5GRETktO6UleLE7uPQtesQHBOM517cBrVa\nDQDIyz2P5ovtUAkaAIASKtTmNqLoaiFmzJwtZ2x6gow1y3Hz5P+DsqGv2CBJEnxneHEBSXoqnHbh\noERRRETSTrx7wB/Ft0w4elaNY1dSkZLGJ7pEn2vvaENtUf2g6rxSp0HBmcsypiIiInJuD2uq8eef\n/gk1xxvQeqkLN98tw+9++ev+j9dU1kAlaQadozJoUHnnnr2j0jMKCQnFiz96GQFLvOE6TYWo9aH4\n1o++K3cschAc+eDAYuMTMHnqL/Cgsg4xUz0x09NN7khEY87TLH5Udvs2zhw6BYPOgEkzJmHF2rVc\nNImIiGgYZpMJZ06egKgQkbJoMZSKoW8nThw4BrFWDXz2p1QURFRffoj6+loEBYVg5rxZuLwrHxq9\na/85Bk89klMX2OvTsBuT2YQTh4+gvqoeodFhSFu+DAqFQu5YoxKfmIj4xES5Y5ADYvHBwQmCgIio\nELljEI1JXp7eCJkRhOaczv5igsmtF3PT5vUfU3LrJt7+p7ehaO4bClqTU4/mumbs/NqrsmQmIiIa\nq+rvVOLsjz+C8r4SEICTU07gaz/6JkJDwwcdZ+o1DSniSz1At64LADApJhZzt85Gwb5CCC1KwN+M\nlOcWICQk1G6fiz1IkoRf/eSXaL3Q1b+2xY18rm1B4xeLD0RkV926biiVSmjUmicfbAVf//638Z7X\nW6gtrYXWQ4uUlRmYOWdO/8dzDp7uLzwAgFJS4dbZWzB9xQilUmWXjERERI6g4E/Hoa106R/RYCkF\n9r21G3/5D3+N69eKcLekHDPmzsaMhbNQduwuVD0Df+s9p7pjYlRM/+stL21HxtoslN0uwdTEBHh5\netv707G5i+fPoSWvEyqh7z7DWmtbFFy6hBMfHkdbbRt8w3ywaudaJCYlWSu2Q+jp0ePo/oPoaOpA\n3Kx4JM93vlEzzojFByKyi9aWFvz5V79H/c1GiGoRk+ZPxFf+6hs2H3ro6uKG1/7mLx/58V5d75A2\nY7cJBqOBxQcionGouqYBe8/mwyJJWD1/BiZFh8kdSTZV9ypRVnQMKkGHGksAuivb4YLBuxo0P2jG\nf/3yP1B1+iHURi0uvJ2HWZtmYNFXF+LykTzo23oQEO2PLd/YNmQ0hK+PH+anLLTnp2RXD6tqoJLU\ng9o+X9tipMWHltZmfPKrj6FqdIEKruis78X79e/hR7+fDBcX1ydfwAno9N3497/7BQy3JIiCArf2\nlqJsSwl2vsZRq2Mdiw9EZBdv//bPaM3thkboW5vk3v5qfOr3AZ5/aYesuaKnR6P23CUopYFCQ2Cc\nP1xduIYKEdF4U3itDP+amwtDQggEQcCZ06fw7brpSE+ZIXc0u6uufIDGm/8XOzL1AIDWNgvyE93Q\nfG/wrgY9im5UneiB2tJXlNDoXHF1XxG++z//C6s2roPZYh52XYjxYGbybFzeVTBobQuj1+jWtsg5\ndrJvp4kv1nEeKJFz4iRWrF07irSO4/Du/TDcAkSh7wGWyqTB9aM30L61zSlH0DiT8fmbgIjsrrak\nDirBpf+1QlCi6kaVjIn6rN64Ac31zSg5WwKTzoyAOD/s/M4rcscioqdwraVG7gjkZP5wLhfG6RP6\n39eZY4Ow59pN+McFoLKpVdZs9lZ3/hB+tEXf/9rHW8T6dXr8+kIb3Ju8IEGCFGrEhPBQ1N8a/LVR\ndbiguPAKQtaEjtvCAwBMmhyLuS/MRuG+K5CaRQgBFqQ8lzKqtS3UWjUssECBgZGjFsECravLY85y\nLh2NHRCFwZs2mlsk1NQ8YPFhjBu/vw2IyK40rmpYvtzmZp91Hx5HEAS8/M2vwvAXBhgMvXB395A7\nEhE9JbVy5HOmiYbTJZ0b0lZr6EZlUyvulgcj2uIvQyp5uOhODWmLCpbwjX/5Jt777zfQVteGIJ9g\niCrAKPQO2jrT6KnHtJkz7Rl3zNry4nZkrlmB8tISTImPH/Wb44yVK5B38CJQ0Vd8kCQJmqkiUpcu\ntUJaxxAZF4ny/fegxMCoVW2ECjGTpsiYip4Giw9EZBdJ6Um4XFUIlanv5sTk1YuUlakypxqgVquh\nVquffCARETmtQIU7OiRp0NoE/qISPYZERFuApOAgGdPZV1H9PNQ3XUOQ/8DX4lpJFApyz8JcrIKP\nEAxDA1BVVgcxzgxDeQ/UJi0MWj2mrU5EaOj4XSvjy3x8fJE8P8Uq19JqtPj6T7+NQ7v2ob2+HT4T\nfLDhpc3jaoRJ2vLlKL9Rjooz9yF0K6AIBVa9upr3cQ5g/PyUEo3QtZaaz4ZaBssdxaFt2r4V3n4+\nuH35FhRqBVKWp2I6n4oQEdEYsmXFJvzu4z+haaIGUIjwutOIDSsWoFPuYDJImr4WHx+uQmhALvx9\ndbh9JxKBE76FioI/QSEMjHJQGTXw8XLHkl+k4X5ZBabNTkLslKkyJncsp49lo+BkPkw9JkRMj8DW\nV3c+sZAQFhaOr3/v23ZKOPaIoohv/N13ULO9Gg8qKzFj1mxoNdonn0iys1vxgfMyyZF9/sRjLGuq\nvQFFw1HUeDShuTcQvS7PyR1piPSs5UjPWi53DHJinV0dAAAPd0+ZkxCRI/Lx88UPv/53KLlxE0aj\nEbFfMUEURZQ+BFqbGnHg4gXETInF1PgEuaPanCAISE7+Nnp6vwqdXocZs30B9A3zH87sOcmYPSfZ\n5rnaWlvw9n/+GbWldVC7qjEzfSY2bNti835t4dzp0zj2/7Kh6u0r5ty8Xoa3uv+Av/jrb8qczDGE\nTghD6ASOsHEkdis+cF4mObY6uQM8Vm3dPUwL/B3WZBgAABZLE/7x9T8BkXNlTkZkH3q9Dr//t9+i\n5kotAGDCzBB8/fvf4q4lRPTMBEHA1GmJAACDqRAAcPn9o7j3RhFcOtxwXnMB4UtD8Jff+y5EUXzc\npZyCVqMd9FR54swo3K9+2L/gn1FpQEJKot3y/OlXr6P1fBeUggssAC5VFsDL3xtpy5bZLYO1XDlT\n2F94AACFoMDd/ApIX5r6Q+QsnP83JtE4UFN9oL/wAACiKGDjggd4WFspYyoi+3n3f95E05kOaDvd\noe10R3NOB9793ZtyxyIiJ9DR2omK94rh2ukOQRCgNmhRnV2P82fOyB1NFq985zXEbI6AMkaCJl5E\nyr8uYmwAACAASURBVGvJyFqz2i59d+u6UX+jYdAbc6VJjVt5N+3Sv7VJlqGjSCwW6ZGjS4gcHdd8\nIHIComAe0ubmIsFg6JEhDZH91ZbWDroZFQQBtSW1MiYiImdx++JdaJrUwBceRKskNSrLKoF0+XLJ\nRaVS49Vvf90ufXXruqFUKqFR940OUCgUENRDRwQo1IohbY4gYUEiTl463b8Yt0WyIHJG+LgYUUPj\nE4sPRE7A1y8deVdyMH/WwMIUe84HYdmCWBlTPZvOzg4c2XMAunYd/n/27ju+iutM+Phv7tyq3gsq\nIIokQIAQvfdmTDEdY+Pu2E5ip62T3c2mbfJmN9l4k9jruOESdxswxfTeRJWoAgSooIJ615Vun/cP\n2QhZQkjiSlflfD8f/tDRzJzngtCceeac58QmDGLshAmuDknoQvQeeqw0TLbpPFy/lasgCF1fv4Te\n7PK5iLqifls/m2IlOLLn7HzR0YpLinj/lbcpuFyErFXRf3x/nvjBs+h1evqN6Uvm1lxkqS7hYPMy\nM2521xwzzHpgHrXVNVw4dAGr2Urk4DAefeEpV4clCO1GJB8EoRvo02cYu06s4kzmfvwNZRSZAiny\nX95l1guWl5Xyl3/5b5Q0DZIkcXXrdTJXpbPqyUddHZrQRYyZO46dqTtRG7/ZytXNzJi5k10clSAI\nXVlNdTVXjuxASxX+M9WUbjFisLpjlSz4jfMUBZTb0Yd/e4/yxBr0kgcAaZuy+CrgS5atWcWTL32P\n9X6fkp2SjdZNy8QHJjFseIKLI267hSuWsnDFUleHIQgdQiQfBKGbCOkzDXPUNAJ65RIA5B3vOm99\nt63fcjvxAKCx6Ti36zwLVi3B3U0UDBTubcrM6Xh6e3L6wElQYOS0UYwYPcbVYQmC0EUZq6q4ufvH\nvLg4H7VaYnGCwl9CYpGrYwnt3YsJk6eIqfHtxOFwkJeah+6bxAOAWlKTdamujpVaVrPqcfFyQhC6\nIpF8EATB5YxlxkazNKzFNkrLikXyQWixhFGjSBgldngRBOH+XT/2Bd//JvEAEB4iMWdCGirDv+Lj\n7efi6Lo3SZLQumkbtTfV5gp7d+zk1K6TmCpNhESHsOaFx/D28nF1WILQJYiUrdCjlJWUsu3rLZw5\neVJUEu5EImIjsGFr0ObZz41eIc7bu9lisbB35w727tyBxWK59wmCIAhCj6WTym4nHr41sF8thUXZ\nLoqoe8q6mclbf36NV37xJ/75xjpqa2uQJIlh04Zhk+vv1TYfM5PmT3FhpHXOnDzJvtf2U3vBhpKp\n5tauIt7+0+uuDksQugwx80HoMQ4dPsi2rFMwOAh7WTb73zzGjx7/Plp911me0F3NefBBbl7LJP3w\nTZQqCX2UzMKnlyHLzqlenZmRzro/vIU9rW4geWj9IZ78t2eI6tvPKdcXBEEQuheHx2AKig4QHFj/\nnm7fCX9iYge6MKrupayslLd+9Q+k7LoZDaUnK3n15iu8/Mdfsnztw/gE+pJ65ioanYYJcyYRN2yY\niyOG5MNnUNfWjxslSSL/fCFl5aX4+ogZMYJwLyL5IPQINquVfWmnkRLqKlOr/T0oGmlg686tLF28\nzMXRCSqViud+9iIFj+ZTUJDHoEFxqNWae5/YQlv++RWka5C/fYmVDls//IoXf/0zp/UhCIIgdB+D\nxs/l4y0pJIQeYkCkjV2nfblVu4w4TeeY+t8d7Nm6A7I0t7cwlSSJ4uRyrqVeITpmILPmzWPWvHmu\nDfI7mirkrZJVov6HILSQSD4IPUJRXgGVfirunOOg0sgUW6tcFpPQWHBwCMHBIU6/bllOGQ02aAfK\ncsud3o8gCILQPq5cukRS6kX0KjWzp87Cy7d919hLksSIRT8j/9YwruQWYh44ieBckXhwJkutpdHD\nvGSRqCjvvPfnsTPHk37kE9RVdSNKh+IgLCFU1HwQhBYSaTqhR/APDsK9zNGgTbE78FV3TDFDm92G\nw+G494FCu/AO9W7cFtK4TRAEQeh8du7ZwbqMvVzsb+FU72r+tOFN8nNvdUjffkF+9B8Wi1orEg/O\nNnraOKwepgZtuv5qho/svIWDh8THs+hfFuEzxh39IJl+SyL53ss/cHVYgtBliJkPQo+g1WmZEjGM\nPakXkKIDcBjN+FyoYMHaFe3ab7WxmvdeeZOcS7nIGpkB4wbw2PNP356epygKp04kkpmaQd/Y/owc\nM6bJKX3C/Zm3ej4fZLyHlFM3eFTCLcxbvdrFUQmCIAj3YrfbOZZzCTkhCABJVmEdFcKOo3t5YuVa\nF0cn3I/o6FhmPj+DY5uOYiwx4t/Hj8VPrUYtd+7Hk7ETJzB24gRXhyEIXVLn/t8tCE40e8Zs4rIH\nciLpFH7ekUx6ZjKyun3/C3zw97cp2F+GVqqbYXF9QwYbfT5n2ZrVKIrCa//1v+Tuz0dj15GsvsCZ\nmSd5/uWX2jWmrux6air7Nu3GVGUiLDaMh1avuOcg5drVK5w+cpJB0wah1WrQ6QzMfGAO7u4ezZ53\nP2w2K1u+3EhBRgEeAR4sWL4YH19RiEoQBKG1TDW11OgcjQas1Q5Tk8cLXcvMeXOZOW8udrvdaUWm\n78Vmt5GRmUZQQBDe3r4d0qcgCHVE8kHoUXpFRLAkIqLD+stNuYUs6W9/LaMm41wGrIFzyUnkHMhD\na6/7vtamI3N/DpfmnSduiOsrOnc2N29m8N6v1yEX1q2zLDxWRvGtomaTNbu2bGP/2wfQGg04FAdF\n2lz6JkQxZuK4dk0+vPr7Vyg6VIEsyShKITeS/8y//vVX6PWGdutTEAShO3LzcMffrKXijjaH1U6I\nXqyx7046KvGQfPo0m97aSE2aGdlHInZGDI+/8Ey3mXWqKApJp0+ReT2DkMhgwsIj6NOnX7f5fELX\nJ5IPgtCONHoN3630oNHX7eKQcS0NrU3f4Htai57rl1PbLflw48olDh7ZTK1iJULvz7KFS9F0kXWs\nB7bsvZ14AJAlmYzEm1RUlDX55sJut5O45RhaY90Dv0pSEWyNIOPETd6ueYNf/vW3Lb4ZHzt0mFN7\nTmAz24iK78uS1SvuWtn6WuoV8k8UoZXq+pUkCVuqxK6t21m0fGlrP7YgCEKPJkkSi0fP4rPj26nu\n7wGVJiIK1Cxa+4irQxO6GLvdzua3NqKkqTGghnJI3ZjGwei9TJs1y9Xh3TeHw8Grf/gL2YfyKLcX\n444XskrGf6gPT738PUJCe7k6REEQBScFoT3FTY7DqrLc/trqbmLMnHEADB4+BIu+tsHxZkMNw0Yl\ntEssGTmpHK08T06cnpIhniT3rmHdp++1S1/twWqyNmqz1zqoqa1p0HbuTBJ//PHv+MXKn5KbmUOF\nUtrg+xISVSk1pKRcaFG/J44eY+ufvqY0sYrKpFqS153nwzfW3fX4vFu3UJkb5nVVkkx1udhZRRAE\noS0GDY7jV4/9mDWGkbw4aDE/fuZFtLqukTgXmmez27h06TwFBXnt3ld65g2MaebbX5uUGsodxZw+\ncrLd++4IiYcPk3ewmEp7GSFE4iX54q54YTrv4LM3P3Z1eIIAiJkPgtCulj26Gg8fT1JPX0XWyIyZ\nOY7R4+uSDzGxgxi8cCAp266gqdZj9ahl2MIh9O3bv11iuVSUgnpawO2vVVo1aXIxxqpq3D3bbwmC\nswwaM5i0vZlorPWzH/wGehMSXJ/Jr6k18sVfP0O+pUePJ6F4UkIBFsWMVtLhUBwoKKACWdWyKZ6n\n9p5AU3vHjAvUXDtxHeV5pcmZE2PGjWd3+C7IrW+z6GtJmNB5q3cLgiB0dmqNhhFjRrs6DMGJLp49\nx/rXv6AmzQzuClETI/nez37Y7BKMA3v2kJqUitagYfrCWfSJ6tvi/oICgpB9JCiHYiUPNRoCCKXg\neDFvvvIqz/74B116eULWjZtoFC0qVI0+R1FakYuiEoSGRPJBENqRJEnMW7iAeQsXNPn9tc89xc0H\nMriYfI74ESMIj4hst1gcKqVxm1rCZm08o6AzmjB5CgU5BZzdk4yp0kxg/wBWvbCmwQ324J59SLla\nuOOe60cQ+WThpfhSQSlBhOMRpyV24OAW9Wu32hu12aw2FKXp5INeb2Dh84vZ8f42qm4aMQTpmLBw\nHAMHtaw/QRAEQejuFEVh49vrcaTJ6HEDI2TvLGB7/80sWLqkyXM+f/8jzn18EbW9btbLW8ff4Knf\nP0O//gNa1Ke3ty+xM2K4uD4FNRp8pLoXMm52TzJ2ZHN6/AlGjx3nnA/oAlGxfTmvSkGxNx7vufm6\nuSAiQWhMJB8EwcV6R0bROzKq3fuJMvQmryATTbAnUHfjDzXq8fbrOpWelzy8gsWrlmG1WdFpdY2+\n7+HlgUOyo7pjRZkDO5Hjw3FUK/havAnsE8iyp1e1+O1GzKhYjpw8htrxzTadikJEXPhdaz4AjBk/\nnlFjx1JUXICPj1+TsQqCIAhCT1VUXEBVuhED9TMv1ZKanNScJo+32axc3FefeACQC3Xs27SLfj9r\nWfIB4PEXnuHvNX+maHtlg3aNXUd6yo0unXwYO2EiyTPPYNxXQZmtCF8pEKhb8jv9wdkujk4Q6ojk\ngyD0EEOjR1KeV0Vu7i1MipVeKm/WLF7j6rBaTaVS3fVhfsKkKRwYtA9rSv2sBPUAhZ//+jdtTgDM\nW7QAY2U1lw5fwmq2Ej44jMdefLpFcQYHhbapT0EQBEHozry8fND6qSG/vk1RlLu+oTdbLJgrzehp\nWOvDVG1u8vi7kSSJJStX8trhv6M11vdllSz0igpr1bU6G0mS+P4vfsyl+ec5lXgcY1kNHu4ejJ42\nlsFxQ10dniAAIvkgCD3KmPHTGBIZ4uow2o0sy/zwdz9h04frKbtVhlewFwsefui+Zh5IksTytQ+z\nfK0TAxUEQRCEHkyv0xM/ZzhJn5xFa9WjKAr0sfLAsqaXqbq7ueMf7Y8xqb6Itx0bvQf1bnXfEZG9\nGTgvliubr6G16rFKFgIn+jBx6tS2fpxOJW7IMLFlu9BpieSDIAjdip+fP0++9D1XhyEIgiAIQjNW\nPPYwfWL6kHLqEgZPA3Mfmo+Pj99djx8/fwJfZH9CbYkZXy9/YiYNYMGyputD3MvjLzzDxQnnuHI2\nhbB+4YyfOLlLF5tsq4rKco4dPERkVB+RsBA6hEg+CEILpauKG0wPdJVhIcGuDkEQBEEQBOG+jR47\nntFjx9/zuG0bNnH4vaP41oTipdiR/e0sf/zhZusv3cuQYfEMGRbf5vO7uv07d7P73d2oi/Qc0h4l\neNwOXvyPn6GWm348dDgc7N62nawrWbj5GJi/bBG+fndPFglCU0TyQRBa4NulCq1bWeh8uTfrEiAi\nASEIgiAIQmukXb/G/q17sRgt9B3al7kLF3SJt/1Wq4UTW06grTEAIEsyynUVWz//ike/96SLo+ua\nzBYz+z/Zh6bYABJorXqKDlWwc9hWHnzooSbPeeev/yDj62zUaFAUhdRTf+Jnr/wCby+fDo5e6MpE\n8kEQWqjT1ErIcHUAgiAIgiB0JWnXr7Hul+8gF9XVQMo9mE9RXhFrn3vKxZHdW1l5GbUFZgxobrdJ\nkkRVcZULo+rasrIzqc224CbVF/BUS2ry05ue4ltcXMiNQ2nocAfq/v6VNDU7Nmxl1ROPdkjMQvfQ\n9rlKgiAIgiAIgiB0evu37r2deACQFQ1XDl3BbHH1nM57C/APxKN3w10wHIqdoD5BLoqo6+sVGo42\nuOE7aIfiwCek6e3XC4sKcDTcnRRJkqiprGmvEIVuSsx8EARBEARBEIQ77D+4j8SbFzA6qgiW1CQM\nXUgIAa4Oq80sRkujNqvRhtlsuq8doTqCSqVi3tr5bHp9I9ItLXatlcDRvixcvtTVoXVZ7m7ujF40\nihMfnkJrMmBTrLjHa3hw6aImj4+OHohbPx1Ken2bVTYTkxDbQREL3YVIPgiC0G3k5maz/fOtVBdX\n4x/pz7LHVuFmcG/3fisqysjJyab/gJhOP4hrTkFBHpfOXyA+IQH/gEBXhyMIguAS58+eZUf1JVTx\nfoAfecDuI1+zdsjjnM8vcHV4baLtE4hNyket1C9d0PfxJMNoAqOpxddxVc2pMRPGMzQhnsQjRwgN\n60VIr15s+2oTBjcD0+bM7tL3Xld5aPUKBo8YytnEM/gG+TF99izUak2Tx6plNUueW8ZXb2+g+kYN\nGn81cbMGM2HSlA6OWujqRPJBENqRoiicS0rCbDIxauxYZLX4L9dejMZq/vHL15Bu1q1fLDleyd/S\n/4df/Pev2rWg1ifvfMD5HRdwlII2Qs3cx+cyafq0duuvvXzyzgec33oBuVLHHt89jFwykmWPrHJ1\nWIIgCB0u+cZFVNENi+gZw9Tk6vII6BXqoqjuz9BnZlBqLqFgfyZKtR3DQC8m/vQhzL1bfg1XF702\nGNyYMXsOp4+f4M//+UfkAj0O7CRuPcYLv3uRkJBeLomrK4uOjiU6umWzF+JHjmBownBy87Lx8/HH\n3d2jnaMTuiPxJCQI7aSspJTXv1xH6QA30MnseO8Ya6ctoV//Aa4OrVvatWUbSqaab/MMkiRRfq6a\nlEsX2m3v6qRTJzn/5SW0VjeQgBzY/s42Ro4bg8Hgds/zO4urV1I4v/EiWlPd55DLDZz68jTjpk0g\nLCzC1eEJgiB0KBkVYG/QJtkUhkaF4R/cdesMDP3zS1hMZswmE54+3m26hhk4n1Hg0l239ny2C3Vh\n3S4NMmqUG7D1400889MXAMjOvsnNjAwSRo3qkNmPPYlKpSIirBUZK0H4DpF8EIR2snH3FirGBqL+\n5mnYNNrA5sRd/KQHJx9Mplo+efuf5Kfmo/PUMWH+JMZOnOCUa5trzKikhjV0VVaZstJSp1y/KZfP\nXkZr1TdszFOTdPoUEydPbbd+ne1S0kW0JkODNm2lgaQTpwhbKpIPgiD0LJOGj+Vy8maUmLoaD4rd\nQWSlvksnHr6l1evQ6lu/RMFut5N38BQpx6/jrnaj3+Nr8PDwbIcIm6coCuV55eho+Na9oqACRVF4\n85XXSD+YiVytYVvI18x5ci5TZ83s8DgFQWia2O1CENpJsaO60XT/IofRRdE4j81mZe+OnWz45DMK\nCpvekulu3vjvV0nbmEXtZRvlJ41s/vNmLp4/75S4xk6fgNWztkGbOgrGjndOcqMpPgHe2JWGb8fs\nHlYi+/Rptz7bQ3hUBFZVw4rnVp2JftE9N1EmCELPk5mWztdbt6BSqVgTO5PwSyb8LlYy+Lqa51Y/\n7erwXGrdT/7GiV8co3ZLGUUbcvjzy/8Pk6n23ic6mSRJ+IX7NWhTFAWfXj4c3LOXzO056IxuqCUN\n6gIDuz/Yjcnc8poWgiC0L5F8EIR24iU1frPghb6JI7uO8vIy/vCj37DvTwc5+/YlXnnuzxzau69F\n51ZUlJGbnNdgdoK6SseJvcecEltUVD9mPz8b9QAFk08VbsM0rHxpNRpN/R7WOdlZbN6wnispl5zS\n5+wF89HFSTgUBwA2rERNjiQyso9Trt9RxowfT+AEX2xYAbBKFiKm9mLwkKEujkwQBKFjfPj5R7x2\ncRNHIst49fwmzl29yA/XfI+fP/JDHlv5KHo3w70v0k1lX88gd0cOaqmuGKEkSdiuSOzauq1F5+fk\n5/D+oU949/DHXM24et/xzH3kAey9TDgUBzasqAc5WPzoMjKvZqBRtA2OteU6SL2Sct99CoLgHGLZ\nhXCbsaqaY8cOExQYzLCEhHYt0tcTzBk9jXeObMA2LAhUElwtYlrsRFeHdV82f7IBa4p0ewCiLXfj\n4JcHmDR9GipV87lMu8PBN8/oDSgOxWnxTZ87m2lzZmGzWRskHQC+/PATTq9PQltpIFF3kt7Tw3j+\nX166r59znVbHy3/6d3Z8tZXywnJ6x/Zm2uzZ9/sxOpwkSfzoVy9zeP9+bmXk0icmivGTJ7s6LEEQ\nhA5x7cpVzrkVo470B0Du68+5rGLGpabSPybGxdG5Xu6NLFTV6rraRt9QSTKVJZX3PPdSegrvFO7A\nPjoQSZI4e30nSy8UMnVow3tMTa2Rs2fO0KdfP8J6hTd7zfiEEQx4K5aDe/bi4enBhClTUMtqfIJ8\ncCg3G7zkkHwVInr3adXnFQSh/YjkgwDA8RPH2Jx6FHtcII7yDHa/dYiX1r6AztC139S7Ut/+/fm5\n37PsO7Ifm8POxImr6RXe/A21s6vIr2j0sF6VV42xphpPD69mz/Xz9SdkWCDlx2puX8NmMDNiykin\nxihJUqPEQ0FhPqc3JKGrqiuoqLXoubn7FifGH2XcxEn31Z/B4MaSh1fe1zU6A1mWmTZrlqvDEARB\n6HCXr19G3du3QZs60pdLqZdF8gGInzKa3VFfQ2Z9m0lTi1v/iHtuPbo+dR+OaUG38xbSAD++PnQK\n36C6v9dhIcEc2LWH3e/vQsmXcXjYGDCjH0+/9HyzLwfc3dyZv2hRg7Z5ixdw8fgFTBfsyJKMRW1i\nyNw4/Hz92/KxBUFoB2LZhYDdbmfnleMo8SGo1DLqAA+KRvqwZedWV4fW5Xn7+bJk0VJWPLSiyyce\nAHzCfFCUhjMVPMM8cHdr2XZLz7z8Ar3mBKKKsmMYombmD2cwYvSY9gi1gXNnzqCpaJhI0zp0ZFxN\nb/e+BUEQhM6tX2RfbHkVDdpseRUMiOrvoog6F72bgTn/tgBHtJUapQpjYDWha/sTsWAo5iia/VPj\nYW10vVqDBXMUpKuKOZWZyZ5/7kZdYEAjadEZ3UjbepMjBw+0Pk69gZ//+T8Y/8NRRK/sy/LfL+OR\nZ59wxl+BIAhOImY+dHGmmlounT9PeGQkIWFt29+4vLiESi+4812xSi1TbKlyTpBCt7FkzQr+evVP\nVF+woHZosAXV8sDDD91zycW3vL18eOEXL7VzlI3FDYtnn9eBupkP37BKFsL7ip0cBEEQeroh8fEM\neP8k19VVqAM9sRdWEZ2vY/DcIa4OrdMYM38yI2aPIyc9k8Beobh7tuylwxn3AK44FCRV/SyGCJ0v\nQyJDAMjclIz9loR8xyQHjaIl43I6k6dNb3WcOq2OB5c81OrzmlNUVMD6dz+n9GYpHoEezFo+l0Fx\ncU7tQxB6CpF86CDXr6WyL+kotYqFML0fSxYsQa3R3Nc1jyUeZVvqMUz9vFAlHmWg2Y8n1zzR6jXs\n3n6+eFQpWO5oUxwK3iqx5EJoyMPDk3/7y285fvQIpSWlTJk5HS/Ptu0V3pHCwsIZMn8wl766jNZs\nwKIyEzLRn4lTp7o6NEEQBKET+N5jz3IuOYm0zEz6hQ8mfuYIV4fU6ag1GvrEtG4XpOXzl/L6Z++Q\nF6IgaWV8b1pYNn/17e+bentg87Shra4v0m1X7HgHevPl0a+4Yc9D41AzKXQ4Y2JHOe2ztJSiKPzj\nP1/DclFBkiRMVyv48NoH/OzVl/EPCOzweAShqxPJhw6QlZnJujNbUQYHAmpuWaop/ngdLzz+XJuv\naTGZ2XbtGPaEEDQA3m5cKa/hyKGDTJ46rVXXUms0TIoYxu7rF5EHBGA3WfBMKuHBh59vc3xC96VS\nqZgweYqrw2i1R599kisTLnEp+QKR/Xozetx4UVRVEARBAOrqBQ0fMZLhI5xbh6in8/Dy5OVnf8zN\ntHQsZgv958Tcvvd+O/vh2uQgzDtL0Dn02BU7xoEmjuuyqB7ohexeN8PivRtHOHEyi8io2DbF0dcR\nANTVmGiNs8lnMKaY0d6xg5mcr2Pf9t2sWLumTbEIQk8mkg8d4MDpI98kHuqotGrS9SVUlJTh7e/b\nzJl3l3r5CrWR7g2WSsg+bqRdy6EtNepnz5hNbEY0p86fxsstmGlPP4ZGq733iYLQhQwcHMfAwS2f\nKmmz2zifnISnlxfRMQPbMTJBEDoDRVE4cvgQGcU5+GjcmTNzbo/eYlEQnKV3v75Ntg+JDCHurV+Q\ntDeR68ev4hPuy9QVc/jdl68hu9c/8Gv7B2BLKWPupLYtd7iYlY8uo/Xn2WxWpCY25XLYm9i+SxCE\nexLJhw5gw06D/YkAm05FbY2xzcmHsMgI1Ndrwd/zdpvDasdH59nMWc2LjOpDZFSfNp8vCN1J6tUr\nfPLKPzHdsKNoHAQk+PDDX/0Eg8Ht3icLgtAlvfvxe1yJMCP3d8NhKeXCB3/n5cdfEjs/Cd3K6TOn\nOHLlNEbFQqjszaoFy/Hwavv48X5JksTIWRMYOWsCAFaLBZvkaFQV30bHP/CPGDmG7THbsKfeEUeA\nianzZnR4LILQHYjdLjpAXEQM9oKGxRuDSlQEh4e1+Zp+Af4MJRRbcTUADosNzzNFzJ05975iba20\na9e5eulSox0QBOG7cnOzWfe3N3j1t//Lhk8/w2a3uTqkZm16Zz2OG2q06NBZDVSeMPHlB586vZ/9\nO3fzP7/4I3/66R9Y//GnOBzibYoguEJeTi5X9GXIPnUJRpVWTeXIAPbs3+3iyATBea5dvcqXWcco\nGOpB9TA/rg1S8eYX77o6rAY0Wi0ReDcYW9rLa4gJiOzwWGRZ5ql/fZaAyd5Ive14jTSw/GcrCAlp\nW5H3O5WUFrPx08/Z9tUmTKZaJ0QrCJ2fmPnQAcaMHUfRzhJOJV+hBiuhkier5iy/7/Xmj6xYQ8yJ\nE6TeSMdb683stQ932PTQyvIKXv98HQVhKlBL+Cfu4MkHHqbXfSRUhO6rpLiI1//1VVQ5dVMo8w4W\nkZdxix/8209cHFnTHA4HJTdL0VFfzVuSJIoyipzaz4Hde9jz132oLXVLnJLPXsBkNImtwQTBBbJu\n3kQJcW/QptKqKTOJnZ+Eu6ssK+dI4mGCA0MYMWpUp68llHjhFFKM3+2vJZVEjo+Fwtw8gsJCXRhZ\nQ48vXsM/N39Kjq0MjSQz1C+K2Ys69gXbtyIie/PSb37m1GueSjzOxr9uQF2kx4GDE9uO89xvf0BY\nmNiFS+jeRPKhgzw490HmK/Nx2O3Iauf8tUuSxOhx4xjNOKdcrzW+3L6R0jF+aL+5yVaFwvr9LuGs\n3wAAIABJREFUW3hxrShS2d3cuJbK/i17MVWZiBwcycJlS1u8tea3dm3ejpStvb36SJZkshJzKCjI\nIzi48wx2vqVSqXD3d8dW0rDdw79lW4u11NmDybcTDwAyaq6duAbPOrUbQRBaYOjweDZ9cQTH8Pok\nvq24mpheCS6MqnUsJjMVpWX4hwS1+ve00HpHE4+wNS0RZXAQ9vJs9r15lB899kLXW6ajknA47K6O\nogEvH29+8NhzKIrS6RM6bbH3i91oig0ggYwM6TJff7KZ7/3LD1wdmiC0K3Fn6kCSJDkt8eBqBY6q\nRjeDAod4O9TdZKan8+6v1pG9PZ+iI+WceiOZdX9/o9XXMRvNjX5elFqJsrJSZ4XqdBMWTcTqYQLq\nitA5ws3MXf6AU/tw2BovsbDbOtcAUBB6CoObG/P6jUVOzsdaWo39aiHxxd6MGjvW1aG1yKavN/Gb\nz//GH058xH++/wpnzya5OqRuzW6zsTv1BAwNQZJVqP09KB7lw9adW10dWrPGDErAkVl2+2tFUQgt\nURMSEe7CqO6uOyYeACryKxq3FVS6IBJB6Fjd40lY6HAeko7v/tr0vGMbIqF72L91D3LhHdtLoSbt\naBrGZ6pxd2/5LIC40UO5uv06Wkv92yD3ATqiozvvDhIz580lsl8fTh08jlavZdaCefj6+t37xFYY\nMCqaE0mnUSsaAByKg8hhHb+mVRCEOpMnTWHMyDFcTUkhPK43/oEBrg6pRS6cO8cR1U3U8cHogBrg\nw10buJWTy/jxE/H1d+7vLgFKCoqo9JG4c+SjUssUWzr3i5iBcXE8VFXOkfPJGB0WQtXerFqy1tVh\n9Th+4X5UF5pvf60oCn5hbStCLwhdiUg+CG0yM34C/zy3A8fgQCRJQrlRwuSYMa4OS3AyS621UZut\nxk5NbU2rkg+jxo4l57EsknYmYSo149vXmyXPrur004Kjo2OJjm7bnuItsXDZEsw1Ji4fu4zNYqPP\nsN6s/f5T7dafIAj3pjPoGTZyhKvDaJULaZexe0LV8WuoDFqsxVW4xQRzKKyEQzvfYWbwMGbPnOPq\nMLsV30B/PCsULHe0KQ4Fb7nzb806ftxExo+b6OowerQHHnmQzwo/gWw1DsmBfrDM4rXLXB2WILQ7\nkXwQ2mTQ4Dhe8vblwMnDOBSF8cMW0HfAAFeHJThZzIgYbu7LRm2vr0vgF+tLYEBQq6/10OoVLFj+\nEMYaI16e3t12KmVrSJLEisfWwGOujkQQhK4sJ/MmljAHPmMHUHb8Ot6j+qHx/mZb4Lhg9l84z8Tq\nCbh5OLduTU+m0WqZFDGMPdcuIA0IwFFjwftcGQseecHVod3TqdMn2Z9yggrFRKDszqLxc+jXX4zh\nOtKQ+HgGvBXDwb37cPNwY8KkKciy7OqwBKHdieSD0Gah4WE8HL7a1WG0u1s5OSSePo673o0Z02ai\n1Te9vCTzRhoHkxOxSw6GRw0mYeTIDo7U+abNmkVhbgEX9l3AUmUlMNqfVd9/pM3XU6s1eHv5ODFC\nQRCEns1UU0u5v4TnwLo1+5JKqk88fMPS25PLly4xsovUr+gqZs+YzeCsgZxIPomvpw+Tn5mCWqNx\nyrWz0jOorKhg4NAhTnsoLSsu4eaNdL5MP4RqeDAABcAHBzbyq94/cVrsQsvo9QbmPvjg7a+zs26y\nd/MurLVWYkcOZPL06S6Mro7VauH9194hIzkDSZLoO7Ivj33/adSyeIQU2kb85HQzZSUl7Dq0l1q7\nmcERAxg9tuN3wuhODh05yNe3TiPFBKJYKjjx/v/yw6VPERAY2OC4lJRLfHhpF0ps3frgK7mJFO8r\nZfaM2a4I26lWPv4ISx+xYrFacDO43/sEQRAEocPkZWdTG6zj24o6kqzCbrIi6+sfJNW3quk7s/u9\n2S4uKuLT7evJc1SilzSMDhvI3FnzOjSGsMgIlkY6b3tEi8nM/334JtlBNnDX4PnubtZMXkR0TEyb\nr2mzWnnr43WkGaqw6iSMhcV4+uvQhdS9DDAO8uHU8ROMnzzJWR9DaKX0tBus+4+3UeXXzTRN33uT\n/Jx8Vqx92KVxffTW+2RuyUGW6n7DpGVn8Zn+Q7EluNBmnXvBtdAqBXn5/GXTOyRH1XI1WuHzyiTW\nb17v6rC6LIfDwcEbZ1DFBiFJEiqdhtqxIXy9f0ejYw+cS7ydeACQw7w5kXWxI8NtV2q1pkclHq6l\nXuXvv/0ffv/Cr3ntD/9LTk62q0MSBEFoUq/ISNwL6uvzeMX3ofRACraqut16bLnlJGgj8Qvwd1WI\n7ebdzR+TPcyAPSEE43B/9tpvcObUSVeHdV++2raJWwkeaPr4own0wjQ6mI2Jjccdrbrm15vIGKxB\nHRuEISqQgGmDqU69VX+A2Yahq20P2s3s27T7duIBQGPTcX7vOWy2xrW3OlLWhSxUUv3MG1mSybyQ\n6bqAhC5PJB+6kZ2Hd2MZEYykqltLrw72JKksHYvJfI8zhaaYa2up1Dbc9lCSJCqU2kbH1iiWxm2O\nxm1C51ddXcUHf3iXokMVmK86KNhXytv/+brLBwCCIAhN0Rn0TI8cjiOlAEVRUCprifUKY25NH0Zk\nGHgyYjqrlqx0dZhOV1pYRL63tUH9IDnUi7OZV5zaj8Vk5p+ff8h/ffQqf/3oDZKSzjj1+t+VZy5D\npW64zKJYrr2vsVxubSkqbcPJzmp3HQ6LDUVR8L1WQ3w3WCraldVWNh5bWiosmC2uHUuqdY0nyau1\nYuK80Hbip6cbqcGKJDX8JzW5S1RVVOCvb32BwJ5O7+aGr1nDnZtmKQ6FALlxwa4wnS9FNnODAUOo\n2rsDoux8FEVh+6YtpBy7BMDAsQN5cOlDXabA5N7tOyFbA3eEa7mmcOTgQabNnOW6wARBEO5i5vRZ\nDC8cRuKJREJDBjHi+VFd5nduW6k1GiSro1G7fJf3ahlp6ew+dRCjYiZY482yB5ega8Hb/jc/XUfW\nUD0qtRcA7x/bzPaT+1A8dASq3FkyYwHBoSH392Hu4KFqXFfK3aZGrW17PQY3qfG5cqUVvytGAtUe\nLF3+VLf/eensIgZFkHe4GPUd4/iAaH/c3Vw763To5GEcu3ocja3u59KqMRM/RexuJ7SdSD50IxEe\ngaTVFiAb6qdt+VfJ+AUFNnNWz1RRWsb1a6nExA7E06fpJIEkScyLn8z65H3YhgTiqDLhf9XI4kee\nb3Ts8gXLKPnkHW66G7FrVISUqlj1QNcqxnk2OYmjV85QbDISVOHF4DlrUatbP9j5esNXJL558vYO\nGYnnTmK12Fjy8Apnh9wuHHYHEg0HYRISDkfjQa6rGY3V7P56O1aTlclzphES0svVIQmC4CL+QUEs\nWLjY1WF0GC9fH6IsXty02esT/9dLmTC0cc2HooIC3j62HvuwYMCNfLuZwo/f4sdPv9hsH5Vl5dzU\nVSGr6x4ALaXVWPUqqkfUJRuMwFtb/sm/P/Mzp20dPWf8DDL2fYY1PghJJeHIKmd85JD7uv6MUZPI\nPLkJe1zdiyh7QRVzYsax8IGFTolZuH8Lly2lMKeAtGMZ2I0OfGO9WfnCGleHxYJlD6HTa0k5ngLA\n0EkTmTFXbNsrtJ2kKIrSER0dyEvtiG5uUxSFrdu3cr08BzUqRvUdwvhxEzo0ho5mt9t5++N1XNdX\nYPfS4J1tYeX4Bxg0OK5D43A4HNhtNjRa7b0PdoGvtn7FiaobWMM90GRXMcE3ttkbcG1NDUcOH8LP\n148Ro0c3+3agtKgYi9lMSHhYe4TOxax8dBkwLCS40ffO5xdgjoKYXrkA7D+u46GgwY2+PySy8Rua\nyymX+CB1D/TzA8BhsRF9wsaLs59tdYx//NHvqLnQcImCbpCKX77221ZfyxUqKsr4r+f/gDr/jr3a\no6z86o3fodF0np/prJuZvPXrf6DcVCMhYfWrZdFLi0XBMKFVxk6NdXUI96WjxxZC52IxW/hyy3py\nzaUYJC2TBo8iPn54o+M+2/gZyX1NDe7f1owSfjJ8GeG9I5u89rlzZ9l6bBd53lY8h9QdU3b8Gj5j\nBzS4jq2kmke8xjDcicsWykpK2X1oD1bFRkLMMAbFtX4cpygKJxKPkZafhb+bNwOjB3IkORELdoZG\nxjBqTM/a+aS58VNnUllVQU2NkeCgUDEbReiymhtbdNuZD59t/Jyk4Ark8Lps9Vc5SXCcbp2AkGWZ\n59Y+S1F+AaXFJfSfFdPhewZv3LqR5KLrmFUOQhQPHp69lNCwzvM2Nisjg2PWdOTBQWgAvN04fO0a\nI7Oz6RXRdLVqg5sbs+e2rHq2X2DAvQ/qhBIvnYZYv9tfq7RqrnsWY6wxtnrKn8PWeIZAU22dlbe3\nL2t+/ii7P99JRUEFvmG+LFi7uFMlHgC2f7YFKUvLt2MTbZkbB9bvE8kHQRB6DK1Oy5rl994NwOKw\nNXqQUwxqqqsqmzy+orSMz87vRpkYinn/JTwU5a4PgookoTic+x7P19+PlfdZp+PdT94jJbQWTX8P\n7KY8tnywm188/hK9+/Z1UpRCe/Dy9MbLs2cu220Lm83K4f0HMFYbmTZ7Jh4enq4OSbiHbpt8uFyR\nhdy/PrupCvfmdMqlbp18+FZgSDCBLsjsHj50gET3POTIEFRAIfDPnV/w86d+1OGx3E3SxbPI/Rom\nCFQDAjidfIZFd0k+dDcXbuaRc+Y8l6/eQqWSSBjeD5vi4Lv1Z+1q2lRksd+IfpxPuXx73aJdsRGT\nEO2M0DtM3LBhxA0b5uowmlVVVN2orbKwqokjBUEQeraRsfFczNiLKtL3dptvlpnoWYOaPP7g0YM4\nhgQhAT4j+1J66AoqrRp9iRlzcjb6EfWzJXyuVxP/9Ij2/gitcis7mxRNCRr/IGpzSjDllEKEJ//z\nzt/4+x/+Kt6oC91CcVEhr/36r5ivOFAhk7j+GMt/tJKE0aNcHZrQjG6bfKh7mPpum72JIwVnuZyX\ngTywYTHGAk8LZcUl+HaSLb7CgkKxleaj9qt/m28vriYyvPVTGjPT09l1cj9VioVAlQfL5i/B3bNx\nMcrOZKhHOBs2bOJwZi2yLgTskH0wh1h/G3b/WuSguoyxoiiEl+jx9vJpdR/L1z6MzfpPrp+6jqIo\nxIzsx6qnHnX2R+nxfMN9qUiqaTCI9I/0a+YMQRCEnmlQXByzC26RmHyBasVCsMqTpdOW3LWOglat\nRbE7kGQVai83/KcOwm40s8DcH29fX/ZfSKRSMRGg8mDJA2ucVu/BWTLS01FFeFNxJg21jzu+Ywdg\nM5ooTE/mYtJZho5McHWIgnDfNn20AdsVFepvtgJV5RvY+cl2kXzo5Lpt8iFS40fGNzcOAHu1mQE+\n7bMOX6ijkRrffNUW0OkbV252lVFjxnLkrZPkDVEju+uwGU1EZDiIn9G6txZlJaW8dehL7AkhgIZC\nh4P8T97k59/7afsE3kZVVZV8vu5jijKKcPdz5+aUkVxIy0H2qH9ro3LzobLaxPSqCM7cvEotVjzL\ntUyKmcv5/II29Ttk4QMMuaOMRkpRyf1+FOE7Bj4wnRvX3kVJVVA5ZKwRFkYvmN3mfzOhZxpL1675\nIAgtNWvGbGYxG7vdfs8lqdOmTufYR3/DOib0dpvHhTImPj0ZWa1usq5EZxKfMIKP398J7lrc+9fV\neVK76wlZOoaki8ki+SB0C+W3yhvN4im/VY7SzDKpe7Hb7Rw/egRjdTWTp0/HYHBzRqjCHbpt8uHx\nZY/w/vqPyLKVokbFIO8IFiwRVX3b09SECVxP3oISW7eswW6yEIM/bh6dZzaAJEn86KkfsG//Xgry\nSwnxDGH6EzNa/Utq7+F92OKDb++JIKkkCiI1pF5KISZucLPndqT/+/1fqT5lQZIkaign6+IWTBOC\n4Tv/JDa7g/lTFzIf8X+kLdJvXOfc+UtEhPdi5D2KkjpPOAnj/0LSkURqqqoZO2sqWm3nSfQJgiC0\nlaIo5Gfn4u3n4/QxREtqYendDDw3bw1fH91Fhd2Er8rAokWPIau7xrDZ3dODYVIvLoc2nPGr0sjY\n3DrXLA1BaCvvUG8qqGnQ5hPq3eYxWFlpKa/+5hVqUqzIDplDnx1i1Y8fZmhC5042djVd47doGxjc\n3Hh+besr9Qtt12/AAJ6wPcCBc4mYFCu9PYJYuHpRq69TazTy/oaPybKVopFk4nx6s3zxcqc90Mlq\nNbNnz72va1gVG5LqO9sxGtRUG433dV1nKrqZRcm5CvRSfdZWX2LAnleMKdiO9M0AzGE10z9cbMfa\nVl98sYHEG6WovIKxp13jcOIZfvTicx1S7FWSJEZO7v51bARB6Dkup1xi/YmdlPiDvsrBUH0YDy97\nuMPrFIRHRPDc6qc7tE9neuLpZ/m3D/4EverqXBhv5GO6VUaNWsv/e/9/mRU3gVEjR7s4SkFou4Vr\nlvD6tb9hSQUZGXuwidmrl7f5ehs//ALLRdBIWpCAXJltH20VyQcn67bJB8E1YgYOJGbgwPu6xvsb\nPiYjToMkh2IGTlWW4rFzGw/Me9A5QTrBuKGjOXthM6p+9bUsPK5XE/9k5yk6ZbdY4TtlTiRJYnBs\nDDXuFaTlVaBSSQyMDGDZslWuCbILObXvCImf7aempIaA6ECW/mQtiiRx4loBKp9wAGQ3b7JMag4d\nOMj0mTNcHLEgCELX4nA4+PL4DmpGBaH/pu1sWQURhw8xecpUV4bWqZ08eYJTaRewY2dgQB9mz5qL\nVq9jfuwEdlw4ja2PN5bCCvwn143PKoD15w8Q3XcA3n6+zV9cEDqp4OAQfvnqbzmwZy+1xhqmzpnZ\nplpl3yrLLWuU5CzLKbuvZRxCYyL5IHQqiqJw01aCJNdvzyl7Gbialc0DLozru6L69+PBvAQOJydT\npZgIlDx4aOriDt/atDnB/ftSPPA0lpT6NpNHDeMWTqPfwBjXBdYFpV9JZdtvN6At0wMShdeKeK/o\nVUY9Mh27eyB3/qvLenduFYoaF4IgCK2VnZZJaYh8O/EAIPu6cy01k8kui6pzO34ikY0lZ1ANrtue\nMac8k6otG1i2aBnTpkwnoXQ4b73zJkUzBzQ4T4kL4vCxQyxYsNgVYQuCU2g0WmY/4JwnBK9gr0bL\nOLxD2r6MQ2iaSD4I7Sr16lX2JB2ue0BXebB83hK8fZvPSqolme9u8Kim861RnDJpKlMmTe20GVFJ\nknji58+y/q1PKcosxt3PnZgpk1uUeEi9mNMBEXYdez78+pvEQx1JkihLLqdqdg1KeT4E9r79PXtt\nNbJVJ/4OhS5h6uB+rg5BEG7zDfBDc9rWoE1RFNxUWhdF1HmkXr3K16f2UuqowVsyMHvoBOKHJ3Aq\n7TyqOO/bx8k+blxMz2DZN197+/kyImEk20xpqN3r72MOiw03vSimJwjfWrB6Ma9f+Tv2NBkVKmwB\ntcxfvsTVYXU7IvkgtJuiggLeP70Vx9AgQEeZovD65+v4xfd+cteHdUmSGOgRzlljFfI3N0klt4LR\n/UZ2YOSt0xkTD98KD4/gR797+fbXLdkJ4duH5mEhwe0WV1dz3sOdCooatKkkGNm3P3azmQM3c5F8\neuEwltNfNvLY3Kc63dZrgiAInZ2Xrw+DCORylQnZU4+iKGiTCpiz6Kk2XS/jRhrXrl1l1Ogx+AUE\nOCXGyrJyFIeCt3/HLVewmMx8dHQzllEhgCclwKfn99A7sg+WJraWt1LfduniBQ6lJVFamkfgvPjb\nYxaPcyVMfvKxDvoEgtD5hfYK49//77fs274TU42JKXOmExAQ5Oqwuh2RfOgidu7ewYXCNByKgwFe\nvXhowd33p+4s9h89iH1IYP2OEJJEUR8NqSkpxMbF3fW81UtX4b5tMzcybqFBxaioBMaOHd8xQQsA\nDPL35VZeDoEBQWg04o3TpDlTubL3DdTfzH5QFAX/oT706dOXPn36Mi43i9OXztM3bhDD4+JdHK0g\nCELX9cTqx9m7dzcZt/JwU2mZu+AJ/FuZOFAUhbc/eodrXtWoInzYu+c9pvgO5MG5ba8dVVtTw1uf\nvUuW3ggqiTCjgWeXP46Hl2ebr9lSRw4fonaIf4MlfsqQYA4eO0g/zxAKa0pQudXtdqQ4FCLkusSI\nw+Fg4+ndmMaG4FvuRdnRVFBBWLWeHzz+PBqtuL8Lwp30Oj3zHxJLkdqTSD50Abv27GSvOh15SN0N\n7rixBOumL1m1ZKWLI2ueHUfjWQEaGbPZ0ux5KpWKxQseasfIhOacPXiY9TuTqMk14dZLz+QVU5g1\nf16H9W+2mNm8bxfF1TUEe7mzcPoclydAovr2Y/nPV3Bw036qy6oJ7hvMymfW3P5+ZFgkkWGRLoxQ\nEAShe5AkiVmz5tzXNU4eTyQ13Iraz6/umoOCOHzpMhNLxuPj79ema366+Qty491Ry3VjsXyHwidb\nPufZR9p/RwydTgs2O6C53abYHahVauY/8CDVX37MVWMudhQiZV8eW/oIAIW5tyjxAz2g8XHDb1Is\nAL1SHQQE9cw3uufzC8TMTkFwIZF86AIu5N9AHlqfWZfd9VytzHZhRC0zMWEs55I2Ig2of2Phk1bD\nkKfFm+HOqriwkIufJeJW6YEbGsiGPe/sYejIeIKDQ9u9f4fDwR/ffZt8r96oZG8uFFu48t47/Psz\nz7t8eUvC6FEkjB7l0hgEQRCEe0vPz0Ldz71Bm6O/H+fPn2PK9OnNnutwOLCYzOjdDA3ac61lSHJ9\n4kJSSdyylTsv6GaMmzCRve+cpHZcyO027blCZq5eiUqlYu3KR1EUBUVRGsyK9fbzRV/VcFmGoii4\nSa5N6F/Myu/wPod6hMOQuqWlLVmCKghC240l9q7fE8mHLkCRlEZtDqVxW2cTGRXFkoLxHDx3ikrF\nRJDkyZI5Kzr9chFXKiksJCnpDLGxg4iM6tOqc4eFBPPVzRSM5TcB6OtoeqlKc4UQj23bjqHCHe54\nzteWu3HswGGWrGo808bZN/Bz586Qpw9Glut+NanUWrLUfnx+YA8DBw1zal+CIDQ/QBCErirYKwCb\nMb1BgUWyyokZ3/zP+849Oziek0K12kagTc+S8fOIjqkr0myQtFR/53i9pGl8kXYgq9U8v+gxNh/Y\nRonDiI/KjQdmrMTgXp9gkSSpUZLe4O5OvCGCpJJSZH8PFIeCLqmAuYue7JC4mzPUI9wl/cYMcU2/\ngiDUEcmHLiDGJ5IjVfnInnU3UYfFRj9D15gyNnbsOMaOHefqMLqELds2c6TqGlJ0ALvPXWXwcV8e\nX/14q974PxQ0mPP5dTNNmppWeK+phsbofmSprqJR6t+K2FRWAoIDGx37beLBmTfylGunUekbvq2S\n3bxQ6ewdMmAoLixiy/ZdVNZYCPByY+mSBRjc3O99oiAIgtBpTJ06jeR3LlIQpyB7GrDlVRDvCCEk\nrNddz7l47hx7zdeQEwJRA2XAJ4c38x/9f4osy0wYEM9XmaeR+nxTTyGngnFRHZcUDw4N4dmHW194\nc/Wy1UQePULqtQzcZD1zHnoG3zYuPREEQbhf8m9+85vfdERHmdVi3/u2ihkQQ9XZDCoy85DzjMRW\nebFm6WpUsnzvk9vAbreze/dODpw9xo2rqfQOi0Sn17VLX0KdksJCPkndj2pgEJJKQuXrRr5SRVCF\nTGiv+sFSYUU16nII8fC467VCPDya/X5zwiIiOJ2SiOWWHUmScCgO3OM1rHm2cRKkoNro9ISAl6c7\nx04mI+m96hvLcljz0Bzc3Ns3CWA2mfjTq+vIkUKoxJ1bNTIXjh1g4oQxLl/yIQjtpU9Q134I6alj\nC0VR2LdvDztOHeDcpXO4SVoCe+ga/qaoVCrGDh+NIdOIZ76FmWEJzJ45u9lzdh3bS3FUw7FOjcZB\nZLWBoJAQIsIjCTbrMaXm41esMKf3qC5TDDsysjfDBw8jbmAchu8sJ3GFwopqgrVe9z6wh0k+c4a9\n+4+QcSON3r0j0Wg6ZmaNIDhbc2MLMfOhC5AkiWWLl3dYf2999A5pAyTkUB2KvYYrn/6Dn6/9IQY3\nsR90e0lKOoMU3bCatzrYi+sZ6Qwf0XHbjKpUKn7y+1+w5fMNlN4qxTfUl0Url3bYUpmwiEhmxffh\n0LkbGCU3PJUapo2J7ZDCWHv37KPaozeqbxINkkpFgeTH2TNnSBglaj0IgtB5rN+8npO+xcgD6+7L\naVd286jiIC5uiIsj6zxkWWbK1GktPl6LGsVhQ1LVJ5ulagve0fVbag4dFs/QYaJuleB8n3++gcTM\namR3XxwlNpJeeYOXX3waTy+RpBG6F5F8EBrIuZnFDS8jand/ACRZhXFkILv37mLRQrEDRXuJiRnI\n7gtXkaP8b7fZKmoJ8e3d7n3fupXD159upqqwCt9wX5Y+tpIVa9fc+8R2Mv/BecyYUcutnGzCIiLR\n6fX3PskJjLUmJLnhr0RJ505JSWmH9C8IgtASiqJwvjQdOaq++CAD/Dhy8aRIPtyH2VNmcuHrddiH\n1/29Oqx2Ikt0hPcWOxkJ7ctYXcXpa7eQvxnzqWQ11V592b59FytXddzLR0HoCKLyn9DArdxcCGg4\nJU+lkamy1rooop6hd98oBhl9sRVWAWCrrKXXZRMTJ01u135ra2t4/T9eJXtbPuWnjaRvzObVX7+C\n4uKCpnqDgb4Dojss8QAwfuxIqLjVoE1bmc2kKe37byAIgtAaDrsds8rRqN2sWF0QTffhFxDA89NX\nM+CKg9CUWkZmufPCo8+6OiyhByguKKBWajj2llQqKmqa35peELoiMfNBaCA+IYFNnx7GnlC/xMKe\nV8HgPl1jXWNX9sTDj3P2zBmupafTy683E5+d3O7LHfZs24GSrubbkgaSJFF9wcTpk8cZ3UXWsjpL\neGRvFoyP5cDJS1Sawd8gMX/eRPQG16+PFbq/G9eucfzUWdSyipkzJhMY1DWKCgsdT1ar6aV4cudm\nhY4aM30823875O4uIjKSpyOfcHUYQg8T3rsPPpKRO1/z2S0mIvt27Zo8gtAUkXzoYkp2g/5JAAAg\nAElEQVSLSzh+4hihwaEMHznS6YXwtHodiwZPYduZw1SEqNGX2hjtGdWhdQd6KkmSSBg1qkPrC5hq\nTEg0/BlSOdRUVlR0WAydyfTp05g6dQqm2hoMbu6i0GQLVVdVsnnLDsqqTQR4u7F40YMiadMKhw8d\nYdOxK+AVgqIoJL/5Bc+unMOA6GhXhyZ0Uo/OX8EHX3/GLV0NapvEIG0wC1cudHVYgiC0gaxWs3DG\nWDbuOUGtIQjJVEm0v8zsuctcHZogOJ1IPnQhBw7tZ0f2aRgcjL00m31vHeWlx7+PVqe998mtMGb0\nWEYmjCQ3I4uA0CDc2rhzwreqyivYvn8nRruZvgHhTJk6TTzUdRKTZ08jefNZNGX1D4qq3nYmtqJI\nV3ejUqlwc7+/n/mexGa18j9/f4cKr75IkoG0Qjvpf3+Tf335JfH/vIUOnrwAXhFAXRLS7tubXfuP\nieSDcFcBQUH89MkXMVZVo9Fo0IodqYROQFEU9uzdxY2SHPQqDdMSJhLVv5+rw+oSxowdw/Dh8ZxN\nTiIsLJzwSFFrROieRM2HLsJqsbAvIwlpSAiSSkId4EFhghc7dm1zyvXtdjunEo+TdOoUDocDWa0m\nckBfpyQe/vz5P0jqU0NqtMLXqlQ++PxDp8Qs3L+QkF48+MMFaAdJmP2rcR+uZeWPH0av67haC85U\nWJDPpq82cezIERyOxmuiBec7dPAgZYZwJKnudvL/27vv+CjOPF30T1V17pa6WxnlBIgsgQCRgwQG\nExwxNjiNs9eemd2ZPbPns5977p49e+7dc+fM2cn27Hjs8ThHzBgwOUeJnAUCJAESylnqXHX/kEfQ\nZFC3St16vv/ppavqkbFQ1a/e9/cKooRaKRb7i/epnCx0tDtvXKt/szGi65kjLCw8UL/xyVefYoOh\nApXDtDgzFPjj/pUoP3de7VghQ6fXY+KkySw8UFjjzIcQUXOpCu0xEq59JBR1GtS5ej89/mJFBd7b\n8AXacyIAl4Lv/rgNrz/6fEC2N1y7dT2c4+MhSN0PJpLNhJPVNWhpbIItmmvZ+oOpM2dg6swZkGW5\nz7bUDIZNGzdjzb5SwJYM3/lL2LrnN/jHH73K6f9B1tLaDlHr//Aj6syor29UKVHoibMaceWarxVF\nRryNWxsTUehwO1040XUJUuTVXVjk4THYfHAnXuLsByL6Xug+aQwwcYmDYG70+o3JXh+itL2fHv7N\njrVwTIyHxmqCJsqM9oI4fL3x216fFwA6fc6ewsPfeKL1qLtSc4sjSC2hXHjweNzYXHIKgj0FgiBA\nY7Cg0ZiG1avXqh0t7E2aNBFoqfIbk1ouYdq0qSolCj1PPPQAItouwNPZDG97A+Jcl7D08YfUjkU0\nIDU3NmHjurWo4Bv7e+J0OOC6ySpgl+K9cZCIBizOfAgReqMB0xNHYdPZkxAHx0DucCLqeDsWPPdU\nr8/dIHcAuPqWTRCE78d6Lys2FSebT0Gym3vGIi+5kDWba5kpcOpratAu63HtfY8gSqhvGZiNM/tS\nYlISHiwYim0lp9Dm08Cm8eKBWXmItNmCcr221hYYTSZotYHtdaOmtPR0/Mt//RFOHjsGo8mEbPZ6\nIFLFuo1rsaX+OJATi/WnSjF4twWvPPsS+9fchUi7DfEOPZquGfO1O5FlS1EtExH1Pyw+hJB5c+Zj\nZOUIFB8uQbQ1FdNemQFJknp93kjRgIYbxgIzVX3a9Bko//wiTlyphduuhbXKjYdyZ0PS8H89CpzY\nhAREiC64rhlTZB9ibOZbHkOBM2dOIWbPmoGW5ibYoqID8u/S9c6VleGzletR3wUYJBn5Q5OxZMmj\nAb+OWkRRxKjcXLVjEA1Y7a1t2FpzDMLo7mUDUloUyiI7sXvXTkydNl3ldKFhWdGj+GTTCtSaXNC6\ngdH6RDzwxHy1Y9FtuN0u1NdcQdygxLAq6lP/FdJPgLIsQ/m+OeJAkZyWiuS0wDaimT1yEr44sQXK\niDhAAaSjtZhbEJgpv4Ig4Lknn0VrUzPqrtQgs2jwgPr7or6h1epQOGE41uwtBezJ8Lk6Eeerw6KF\nr6odbcCQNBpEx/a+T8zNKIqCj75ai7aIdEhGwANgV3krknbtxuSpU4JyTSIaWE4cOwpvls3vxliy\nm1F5vgqBWkR28NABbD6xF+2yE9GiGY9Om4/UjIwAnV19Kamp+KcX/h6tjc0wmIzQG0OzefVAsX7d\nRmw9UIp2xYBI0YmiiSNQWDRb7VgU5kLyKVCWZXz05cc43VkFn6AgVbTh+UeehiUyQu1oIWnc2Hwk\nD0rG9n3bIQkiZi18CVEx0QG9hjXKDmuUPaDnJLpW0ZxCjB49Anv27ENcbCoKJj8Z0n0s6KrLlRWo\n9xlwbVtLyWTFybMVLD4QUUBkDR4CcUcxMPTqA7PP4UaMOTBF1drqK/iidCswJg5AJK4AeG/jF/hv\nL/5jUGaLqckazfu9/q6muhrr9pdBsKdBD8AFYM3eUxgzZhRiYmPVjkdhLCSLDytXr8TxVAdE0yAI\nAC7KCv6y8mO88exrakcLWfGDEvDEI0vVjkHUK3HxCXj4kYfVjkEBFmm1Qiu7/cYURYFBF1437ESk\nnriEeOQKg3DoSiM0g6zwdTgRe6wDhS8VBeT824t3Qhkei2u7R7TnRGL/3r0omHp1boXP68WXf/0K\nFZ110Agi8tNGYOb0WQHJQPQ3e/eWALYkvzHFlow9u/Zg8SNseEzBE5LFh/L2KxDTru7yIIgCLnma\nVUxEd6PyQjkOnziClEHJGJufzwZORHRXrPYo5CSYcbrTBen7bT11rRcx5/HHVU5GROFk+ZLlGHX4\nCE6Xn0VcZBKmvzwzYEtFJUEAFODa6oPslaGz+G9V/MGXH+FkpheSwQoAWHPlBLS7NZgyZVpAchAB\nQEyMHXJFNST91d5Y3q52NLdwdxIKrpCck6zBjW+7dEJI1lEGjBXfrsBvj3yDvWlt+KStBL9+57fw\n+Xxqx6IQoSgKKi9cQM2VarWjkEpefvFZzM7QI11qwnBTK958ZjESBg1SOxYRhZnReblY+vATmDW7\nKKA9qmZNnY22nWd7vlYUBa3FZfApcs+YLMs421UDyaDtGRMHReJQ5amA5SACgMlTpyLWUwPl+3tx\n2edF26UzOHLFh8MHD6mcjsJZSD6xTxqciy8vFkNM7d7KzdfShVFR6eqGChNulxunjh9HYlIS4gYl\nBOSczY1N2NdxDtKIeACAJsaCy3oHdmzfilmzAzOdsT84cHA/Nh7fgxa5C9GSGQvyZ2PEiJFqxwp5\n1VVVePejr1HrMUJUvEi1KHjztR/AYAzMjiwUHPv27MWhk2WQBGBS/miM7uVODpIk4aGHFwcoHRFR\n3+psawdMGjTtKoWgkaB4vLBNHoLjF89g/MSJPZ+TodxwrO8mY0S9IUkS3nz1Ofzkn/8dgtkORQHs\n2XkQNFrsO3QCeePGqh2RwlRIFh8mTCiAeEBE8cmj8MKHnJh0zH1ontqxQl5x8T58e3I7HBkWiLsc\nyHHZ8eLyF3q9PKL01EnI6Ta/+SpShBHV5+t7F7gfaWpoxJentgB5CQBsaATw8b7V+L8zsmAw8SG5\nNz77eg2azen42wZQVbIPP//Fr5CQlAqLQYP58+bAHhWlakbyt37dRqw7Vg3B1N107MzaA3jS6caE\nggkqJyOicNHe2oYNWzegy+fC6IxhGDO2fz8smS1mmCMs0IyP9xvX1F2dhCyKItK10Sj3yRCk7nFf\nUydGxmX3aVYaGPR6PawJKRCi0/3GfbJ88wOIAiAkl10AQH7+BLyx7GX8eNlreGDufPYP6CWvx4PV\nJ3fAOy4B2igLpCGxOJ3mwbatm3t97pzhIyBWtvqN+dodSLSFTzfd7bu3QRnpf0PhGR2LnTu3q5Qo\nfFxp6fL7WhAllDd5UNoVgf2Nevzirb+gtaVFpXR0M8XHynoKDwCAiDjs3H9MvUBEFFYa6uvx88/f\nQnFqO05ke/Fh7S6sXL3ylp93dHVh2+bNOHNSveULUXGxSO+yQHZfXVMvnqhDYcEMv8+9sORZDDmt\nQH+4HpbDjZjuSEZR0dy+jksDgNFkRopdB+WapT9KVwtyh2epmIrCXUjOfKDAu3CmDO1Jer+t7DSR\nRpSfrUZveyzbo6NQYMnCnvPlkLJi4G3oQMp5H6a/GD7dm/VaPRRvCwTd1R8pxeWBiUsDes2s16Dt\nurG/FRsFQURXZAbWrtuIJ59cAgBwu13oaGuDPTqGRck+dPTIEZwtu4CUxAR0eTw3/LnTwyZWRBQY\na7eth2tCQs+/8VKiFSWHz+JBpws6g38Dx+KSfVh5chs8w2OgVJ5A8p5NePO516DV6W526qB6bflL\nWLHqG1S5mmAStCia8BCSUlL8PqM3GvDS8hfu+xpXLldh9/49MOgMKJpVxNmXdFuv/GAZPvj4K1xq\n7IBBq8GEkZmYOo3NTSl4WHwgAEBCUiJ0x9zANW0eFJ+MSI3l1gfdg0cXPYrxFZU4dOwQUpNGIPfl\ncWH1YDh7ZiH2fvRruCd2N8BTFAWRx1tR8MrUOxxJdzJl7DB8d7ASgiUGANB26Sz0tquzTARRRJez\n+2H3m5XfYt/xCnQpGsToZTw6fwZGjR6lSu6B5N33PsDROhkaSxR2VVyAt7YKusj0np9xxedDSkzk\nfZ27uqoKu3bvhdloRNGcQugNhkBGJ6IQ1C67IFzXaNwRIaK1uQWxg67+fvD5fPju5C7IYwd1L/1M\nsqE61ovV61bjkcWP9m1oAFqdDksfC9625rv27MRfL+6DkBMLxdOG4o9+jTcXPof4RDbnpZuzRETi\n7167/2IX0b0K2WUXFFiRdhvGaJPgbegAAMheH0wldZhXGLheGinpaXho8SPIGxd+22waTEa89uBy\nZJ3yIfp4B4aeVvDGkhchSTfuzEL3Zs6cQrwwbxxGWjowOqIdkaITxqirVTKfow1DMpNx5NAhbD/T\nCI89HdqoZLSaU/HZt5vh8/KNezCVX7iA4zUuaCzdfTckYySE+KEw1p+A3HgRaLqILG0jnnzysXs+\n947tO/GLP/8Ve2u12HjeiX/7xduoq60N9LdARCEmyRgN2eU/w8reCsQkxPmNNVypRavN/1hRp0Gt\nI/yW6imKgi1nSiAOi4MgCBB1GrgnDsLq7evUjkZE1IMzH6jHssefQvbevThzrhwRmgjMe2YpjGbz\nnQ8kAEBScgpeeYrV42AYlTsGo3LHAAAOHzqCFeu2o0k2waC4MD4jBlOmTcNfPvwMojna77hWTRRO\nHD+OMXl5asQeEE6dPAkhwv+GX2eNxZhYGx6cVwRREmG2RNzzeRVFwea9RwFbKgQAgkaLLlsWVn23\nES/+4OkApSeiULTwwYW49MEfUW5vg2I3wHy+HQ/lP3DDiw17TDTMbTKuLUErsoJIMfxmUMk+H9oE\n1w2b0bcpTlXyEBHdDIsPYaT6chXW7FiPVsWJaMmMR+Yugu0edgEQBAETJ0/GREwOYkqi3skbm4vR\nY0bhcmUFomPjYInofrA1GXRQWmQI4tUJXZLXgdiYGLWiDghjx43DpqPfArbEnjFfZzOGTBqJCKv1\nvs/rcbvR6vRBvG7lV0un+77PSUThQdJo8OYLf4dLFZVoqK/DqGdzodFqb/iczqDHpPjh2FZ5DlKa\nHbLbC/OBeix86nUVUgeXpNEgGiZcO6dDkRVESYFZPksUKIqiwOnogt5ghChyEv5Aw+JDmHB0deHt\n7z78vueAGXWKgqrP/4R/fvUf+YNNYUeSJKRl+ndjnje3EEd+9z4c1gwIggDZ60a2TUDidc28KLAG\nJSZi0pBY7DlbBcGaCLm9HiPjNMjt5bZ3Wp0O0WYNmq8ZUxQZsdbwe2NJRPcnJT0NKelpt/3MwnkL\nMeR0KQ6VHkWE3oSi55dDbwzPf0cWjp2Fz/avhXt0LOQOF6JKO/HoslfVjkXU4/DBw1i1eTeaHAoi\n9EDhhFGYOXum2rGoD7H4ECa2bN0MZ15sTxMPQRDQPDwCJXv3omDKFFWzEfWFSJsNf//yU/hu3Sa0\nOz1ISbRj0WIug+kLTzzxGKZWVeHwwUMYOmw6sgcP6fU5BUHAoqIp+HT1drgiU6B4HIjx1uHR518M\nQGIiGkiGDMvBkGE5ascIulGjx2DI4KHYvXMHrFYrxr42Iex6bN0Nn9eLyxcrERsfD5OZMz/U4HI6\n0dbagpi4+J7/B50OBz77bjs89kyIJqATwF/3nEZ2dgaSU29fRKTwweJDmHB6XBC0/iv9RJMOHc0d\nKiUi6ntx8fF4/rnlascYkBKTkpCYlBTQc+bm5SInZyh2bt8Bmy0R+ROXDcgbaaKBqKaqGiUHS5Cc\nmHRPjaplWcZnX3+GMx3VkCEjQx+HZx5fpsrWmmrQGw2YPXeu2jFUs2/fPqzaUoIWnxFGODExJwlL\nlvT9zibh6sK5Mhw9dhJZmekYnZt708989dU3KDl9CQ7oEK1147EHZ2LU6NHYtXMnXBEp/rsdWBOx\ne+9+LGXxYcDgfPwwMa1gGnCqzm9Md6wB06bNUClRcLgcTtReroYsy2pHIaI+YDAaMWfeAxhfUMDC\nA9EAsWbdGvxi18fYldqCj1tL8Mt3fn3XOxd9s2oFDid1wDk2Fu6x8Tg92IePV3wa5MTUH7icTnyz\nsRiOyHTo7fGQ7WnYdb4Nhw8eVDtaWPjkk8/x68+3YWe1iHfXH8Xvfv9HKIri95n9xSXYca4VXns6\ntPZEtFnS8dmqLfB5vYiKioLi7vL7vOLzwmwy9uW3QSpj8SFMxCXE49HMaYg83AT5YBWiDrfiqYkP\nhtW6xhWrVuBfv/gN/r3kE/zb+/+BI4cPqR2JiIiIAqirowM76k5AyuneMlITY0H1aDM2btpwV8ef\na78C0azv+VrUaVDurA9WXOpHjhw6BIfRf/clyRKFE6XnVUoUPqqrqlByoRmSNR4AIJntONtlQvGe\nvX6fO3HmfM/W23/TJtlx+tRJ5I0bhzilCYpy9QWiuaMSRUWzg/8NUL/BZRdhpKBgEgoKJkFRlLB7\nQ3j44EHs1l6GJjceegBdAL7avxEjRowcMFMpiQi4fPkyvvjmO9S2OGAxaDFjwihMnzFN7VhEFCDn\nzpTBnWzGtb/ZJaMONVVNd3W8eJP3apLAd20DQWJSIgTXCcBwdZt42edFxDXFqHAhyzL27NyFiqoa\nJMZFY8asmZCk6zdaDZyjh49CsCb4jWmMkai8fAUF14yZdBooigzhmp85yeNAbEwsBEHAP7z5IlZ8\nsxr1rV2wmfVY9MTTMBg582EgYfEhDIVb4QEATlSUQpPtv21fV3YEThw9irzx41VKRcFytvQMtuwq\nhtPtRUZiDBYtXshdWwiKouC9j75GiyUDsAPNAFbuLkVCfByG5AxVOx4RBUDm4Gzovt0IREf0jPmc\nHsSb7m7b5NxBg7G+vgxSbHejQV+bA8OtqUHJqia304W1G75Dg6sdcUYr5s2dP+BfxqSkpWOwXUCZ\n0wlJZ4Aiy4hsr8ADc19TO1rAvfWHP6Gs0wLJaMH+K/U4dPxt/PTv3wjaM8CYvDHYcHQVYL1mW21H\nG9KSM/0+98C8Ihz93V96dh7zeVwYEqNBfGL3cSazBU8//WRQMlJoYPGBQoJJ0kPxuSFIVx9AhWYn\n4jITbnMUhaLz587hnRVb4Ivsbl5YccGJ+vc/xEsvPKdystBw8vgJbNxRjPYuN+LtJjzx+GLYbHa1\nYwXE2dLTqFMi/N6IIjIee/cfYvGBKExYIiMwxT4UO86dg5QdA19zJ+JLnZjz4t39DphTOBfCFhFH\nj5+BDGCoPQWLHloEoHsXhAPFxTAaTRiVlxuyL2tkWcZ//Pm3aMy3Q9RpUOqqx5k//x4/feXvQ/Z7\nCpTXX30BG9ZtwMWaRljNeix4/iUYTSa1Y/VaR3sbVq1eh5ZOF+DqxNkWCRprd4FN0ptwyWlH8Z69\nKJgyOSjXT0xKwvgMO4rLayFZ4+HtaMJgixsTJ0/y+5zNZsdPXlmG79ZvRrvTg9SUKDy4kA0/6SoW\nHygkzJ01F0c+fwuuCQndlVSnG5ktRiSlpqgdjQJs6459PYUHABB1BpyuqoWjqyssbiCC6Up1Nd7/\n6zb4bKmAEWhyKPj9Hz/EP/+XH4bFDaleb4Ao+/zGFEWBxFkxRGFl8YKHkFtegYPHD2FQTDYmvjr5\nnv4NK5pdhCIU+Y1dOHcO72/5Gp3DrVCavIj+z01444kXYYuKusVZ+q+9u3ejfrgZGl33bbyo16Jm\niB4HS0qQP3GiyunUJUkS5i+Yr3aMgPJ43PjFb/6E1shMCIIBss+MlisHER0Z1/NzIUh6bNmyDaIk\nYPzE3jVolmUZmzdtRkVVHaxmAxY8OBdmSwSWLVuKCWVncfz4KWRmjMGYvLybHh8TF4dnn3nqvq9P\nN9fS3IxPvliJK02dMBu0mDFxNCZdV/wJBbxjo5AQYY3EPzz2MsaUaZFx2ocZ9XF47ZmX1Y5FQeDy\n3riTiUcW4XI6VEgTWrZs2wWv9WpBThAE1PosOHf2rIqpAic9MxNJBpdfsypN6yXMnjlVxVREFAyp\nGel4ZPGjKJg8JSDF05V71sE1MQGaCCO0sRFoLYjF1+u/7X1QFdQ11UGy+q+TF+0mXKm9olIiCqYt\nm7ei2ZTS00dBlDSwJGbD0VAFAOhqqEJr5SnURQzFxzvL8T//16/Q1tZ239f74zvvY/XxRpzujMDe\nGhH/36/fgaOre5eK7MFD8MijD9+y8EDB84d3P0KZOwqdEamo0w7Cl9tP4Mzp02rHumec+UAhIyo6\nGsuXLFc7BgVZTkYSyo7WQDRcXe+bYFZgi4pWMVVgVF26iF17SmA2GlA0pzDgTZZk+cZms4ogweN2\nB/Q6geR2u3C2tBTJycl39Xf85qvP4fOv/oqa7yv/cx+ZjcSkpDseR0QDW73cCeDq7xVBENAgt6sX\nqBcm5E3A7uLPIQ2J7RlTSutRMCu83vhTt5bWdkha/6aZerMNbZdPw2eLhauxCvahE7r/QKNFk2LG\nipWr8fyzy+75WtWXL6O03gPJ1j3TVBAltFnSsX79Rjz8yEO9/l76QnNjI37567dQ2+aCKGkwNCUG\nb7zxCrTa0O2JUn35EqqcOmgN19zjRcRjd/EhDB02TL1g94HFByIKGkVRsHnTZly4VAuLQYP58+bA\nfocprrOLZqOhaQUOlVXA5QUGRWrxzFOP9FHi4Nm2dTu+3X0KijURiuzG3l+8jR+/shxx8fEBu8bU\ngnE4/Pkmv4ZQMUozho0cGbBrBNLuXbuxatsBdGjs0Hl3ITctCs88/eRt33KaLRF44fmn+zAlEYWD\nSMGAluvHxNDcjjwpNQWzTg3DrqMn0RWng7nWjcKkMYgN4O8T6j8mjMvFvs+3+O02oWm7hP/+T3+H\nc2VlWNnsf18lCAIa25z3da2qy5ch6yP8psaLkgatHV33db6+pigK/vX//T9w27Ngzu4uzlX4vHj7\nD+/iRz98XeV0908QBEBRbj4eYlh8IKKgee/PH+JYsxaSPgJKp4yTb/0FP3vzB7DabLc8RhAELF36\nGB73euHxeMJiCyZFUbBl33HAlgoBgCBp4LBnY/XajQF9kM7IzsaSWfXYuu8o2h0eJNiMWPLskn75\ny8npcODbrQfhsWd830DShoPVrcgpLsaEgoI7HE1EdG9mDZ2AFad3ATmxgKxAc7QOD0x7TO1Y923B\nvAWY3TUL1RcvIXlGGvTG0Cyk0J1lZGejKPccdh45i3afATaNEw/OGoeUtHQkp6Zh+4FSXL/IwmrS\n3te1RufmwrixGB6jpWfM19mMERNC4+36scOH0erVwm67OitIlDQovdQMn9cLSROaj76DkpKRbPKi\nRrlmhmtbDaYUTVc32H0Izb8BIur3mhsbcKKqHZK9uweBIIjosmZg3bpNWPrk43c8XtJoQvaXxPVc\nTifa3PINTXZaOgK/HGLS5Ekh0YDo+NGjcBhi/H4JSWYrSs9VsvhARAFXUDAJqUnJ2HlgD7SiBrMX\nPwRbdOg1m7yW0WRCVhjt9NPZ0Y6Gujokp6aFze//QFmwYB7mFM1CU0M9YuMTev77CIKAwsl5+GbH\nUSjWZCg+L8ztlVj0+L0vuQAAvcGAhwsnYNWWErSKkTD4OjExKw75EyYE8tsJGqfLCeDGFy4Kul8E\nhbLXX3oGn37xDa40dcFk0GDm7FwMyclRO9Y94082EQVFQ309PJLJb1tEQRDR4ey//Qfu5MK589i0\nfTccbh/SB0Vj0eKFEO9ipwW9wYBoo4jma8YURUaMLfRnddyvlNRUSO4jwDVvV2SfF1YLdzT5m5Ur\nV+FwaSXcPhmpMRY8/8xSGE1mtWMRhazElBQsTVmqdgy6iU8/+woHz16BQzDALjnwUNEkjJ8wXu1Y\n/YpOr0dCUvIN49OmT8WQ7Ezs2L0PJoMBhUWv92rW6KTJkzB+fD4qzp9HQmIiLJGRvYndp/InTIT5\n82/hbm+CLqK7uKjIPmTGRUCjvb/ZIP1FpNWKV19+Xu0YvcbdLogoKDKzB8Om+E8E9DnaMCTjxl+c\noeBiZSX+8Pk6lDpsqPRFY+sFJ95974O7OlYQBCyaMxXa5nLIXg98Xa2I6arEY48sDHJqf1eqq/HR\nx5/jLx98ijOnT/Xpta+XkJiIYXF6+JwdAADF54O1owIPPFB0hyMHhk0bNmJrWRvaLalwWdNx1hWF\nP73/qdqxiIgCbv++Yuyr7IQclQa9PR5dken4esMeuN0utaOFjPjERCxZ8igWLFoQkOWqGq0W2Tk5\nIVV4ALq3Wv3HH70KQ/N5tJwtQceFQ4jpOI8fvcEd8voLznwgoqCQNBo8Nn8Gvl63Hc2yGQbFiXHp\n0Zg6fZra0e7Lpq3+21iKOgNOV9eho70dloiI2xzZLTcvF8OG5WDXjp2w21ORl5/fp70YSk+fxrsr\ntsAbmQxBEHDkm914qKYeM2fN6LMM13v5peexbetWlF+qhc1iwPz5r4VFj49AOHC2rKwAAByCSURB\nVF52CZIxpudrQRRRUd8R0mtWiYhu5uTZC5DMdr+xTl00Th47hrx8zn4YCJoaG1CyrwRDhg5GZvbg\nXp0rPTMDv/j5vwUoGQUa72CoV44fOYKT5WcQa7Fj5qzZvCkmP3ljczF6zChcrqxAdExsyFXQr+X2\nyjeMeQQNHJ0dd1V8ALqXXxTOnRPoaHdl47Z98FlTelZCCpHx2HHg5B2LD1eqqrFmwxa0d7mREGXB\no48sht4QmMZmgiBg1uzZmBWQs4UXSQTgu25MEIB+2DyUiKg3IkwGKM0+CJLUMya5O5CQmHibo9RV\ndekSzpWVIW/sWETepok23dmaNeuw+dB5KNYkrD26HTnRO/DaKy/0y2bZ1HtcdkH37dOvPsX7V3bg\ncKYTa80V+Pk7v4TbFbrr+Sk4JElCWmZWSBceAGBYVgp8Dv894eP1HsTEJ9ziiKv6Q5OjDpfnhrF2\nx41jfn/e2orfvPcFTnVG4pISg5J6LX7z1rvBikjXmJA7HEpnY8/XsseNocl2SNfcnBMRhYP58+fA\n3F4OReku8vtcDgyN1WFQYpLKyW6kKArefe8D/O+/fIcVRxvxP377Idav26BKFp/Xi727duNAcQlk\n+cYXJKGgraUFWw6dA+wpEEQRUmQcStsM2L1zl9rRKEj4mpruS2N9Aw55L0OTGAcAkMx6NI61Y+Pm\n9Vjw4CKV0xEF3oxZM1FT9zUOnS2H0yciwSJh2ROLbluZv3DuHL5ctQm1LQ5EGDSYlj8cRXMK+zD1\nVQk2E+raZQiCeM3Y7Zc4rN+wGU5r+tXZEqKEy04Dzp05g+yh4dNhvT8qmFQAr9eLfYdPw+OVkZEY\nhSVLlqsdi4go4ExmC372wxex5rv1aHd4kJoZhbnzlqgd66ZK9u7FsQYBkm0QAEC2p2HD/jOYPGki\nIqzWPstRWVGBP328Em36eECRsWbLHrzx4jLExMX1WYZAOHb0GLzmOFxbVpeMESi/dAVTVUtFwcTi\nA92XivPn4Uu0+E2dkQxaNDnbb3kMUahbuvQxPObxwOVywmy5/VILWZbx/hdr0BGZAcQC7QBW77+A\nlORBGDps+C2P8/l82Lp5Cy7XNCLGHoEH5s2BVqu75edvRrl2H+jvPfnEI6h/+31cdmqhiFrECG14\nYtkjtz2P0+2BIPoXKGStEc3Nzbc4ggJp6rSpmDqNt19EFP4irVY89dQTase4o7KKy5BM/kUGryUB\nhw8fxvSZM/ssx4pVG9Fpzex5aG9RLPhy5Xd4/ZXn+yxDIAweOhjizpOA7uosF5/bibjovivkUN9i\n8YHuy7CRI6D/Zjvk0Ve3xfO2OpBiT7nNUUR9r7bmCvaXHEB2diZyho/o9fk0Wu1dbdd06vhxNIs2\nXPtJMTIeJQeP37b48Lu33sF5tx0avQlyowPHfvk2/umnb97VdPvjx45j1cZdaGh3wmbWYc6UsZg0\neRIAwGgy42c/fQMXKyrgcHRhSM6wO66nHD9uDA6s2AkxMr5nLNJVh7xx/fONFBERUTDF2iPhq2mD\npLva+0jsakT24Ml9mqO+3Qlcs5pVEAQ0tDv7NEMgxCcMQl6aDQermiBZouBzdSHBV4vCwtu/HKHQ\nxZ4PdF9MFgtmJeZCOVYD2euDt7IJg8sFTJ8xU+1oRD3++tfV+F/vfIPNlTLe/rYYv3vrj/e9LtLj\nccPjufueJpYICwTZ//OKokCjufU/uyePH8eFTj00+u7ZBqJWh1oxDrt27Ljj9ZwOBz5auQkN+iQg\nJgstxhR8ufkg6mpr/D6Xmp6OocOG31Ujp6E5OZg/Nh2mtgrIDRWwd13EsofnhPxe2URERPejsKgQ\n8Z5q+NzdD/q+zhaMTjQjMalv+1NEGm/8PRxpCL3fzc1NjTAa9Mg0tCNHW4eH8+Lws5++yfuMMMaZ\nD3Tf5hbOxcSm8dhfUozM4dORmZ2tdiSiHs3NTdhxrAIu6OEsPw4IIg5ccWHn9h2YMWvmXZ/H7XLh\nT3/+COfrOiAoQEacBS+/8DR0ev1tj0vPzEKKfh2qZRmC2F1w0LVWYs6TT93ymMqKSojXbTcmGcyo\nrW+6Y85dO3bCFZnqV1FWbCnYsWMPHl/y6B2Pv5UH5s3BnLmFcDkdMJrM930eIiKiUKfRavGzn7yB\nLZu3or6pBUPyhyJ/4oQ+zzFn2nh8snYvZFsKAAXalkrMW/JAn+fojbKzZ/Gnz9fBbUsFpFSI9VXI\nzY1gY+Uwx+ID9Yo1yo6iefPUjkF0gxNHj6PTJ8HnboctczSA7h0Dvl27+Z6KDx9/+iXKXFEQomKh\nADjn8eHDj7/Aiy88c8djf/h3L+CLr1aitrkTFqMWDzy1EDGxsbf8/MSCAmw68hlgT+0Z87XVIbfo\nzuv+LRYzZG8DRM3VtwWK7INBf2/9Im5GFEUWHoiIiNBdgJg7b66qGcblj0NyUiK2bd8FSZJQuPxp\n2KOiVc10r9Zu3gWP/WpTa8WWjE27DmLCxPGq5qLgYvGBiFRx8sRJ7Ck5BJ8MjB05BBMKJgb0/EOH\nDYXrq3Ww50zqGRO1Ojh1NnS0t8MScfuGkX9TWdcGwXR1YaUgSrhY33ZXxxqMRjz7zK1nOlwvOjYG\nc/IHY8uBs3Dq7dC5WjE1ZxCG5OTc8dgJkyZh/Y4StOjNPUsqTG0VKJrz+l1fn4iIiPoXn89309kA\n8YMGYemToduDqa3LDVw3ibTVcffLWyk0sfhARH3uwP4D+HTjISiRCQCA0u2n0dLahrkPzAnYNeLi\nExAbaYT3unFRZ4LT0XXXxQf9TXo06DTBmxI4f/4DmDF9Cs6WnkFGViasNvudD0L37IQfv/Y8Vqxc\n091w0qTDwheWwmC8/XaaRERE1P+cKS3Fiu+2oq7ViUiTFjPGj8Ds2bPUjhUwsVYjGh3+u3PFRRpu\ncwSFAzacJKI+t6P4SE/hAQBEcxT2HSsL+HUWzZsFX2eL39ggow8xcfG3OOJG40cNgdJ5teeC3NWM\n8SOzApbxZkxmC3LHjbvrwsPf2Ox2vPCDp/GzH72EV156ts8bYBEREVHv+bxefPjVOtTrkiDEZqHd\nnIpVe8/iwrnA3yupZckjC2DvLIe3qxU+RwfMrRfw2MLAvYSi/okzH4iozzncXuC6VgRdLk/ArzNt\n+jTU1zfhwOlyOLwKEq16LF92b9s3Fc2ZDaNxFw4eLwOgYGzBYEydPi3gWYmIiIgA4MD+ErTp4/we\n1ATrIOwtOYTM7MGq5QqkqOgY/Ld/+jGOHjoEl9uF/AlL2WxyAGDxgYj6XEpMJBqafRC+/yWjKAqS\nooLT0PDRxx7Cw7IMn88Lrfb+mi9OmToVU6beuekjERERUW9ZI62A1wXA0jOmyDL0YbYFpSAIyB03\nTu0Y1IdYfCCiPvfk0sfQ8s77uNDkgQIBiRbgmaeXBe16oihCFG9eeNiyeQuOnamAKAiYmDcMEwsK\ngpaDiIiI6FYURcH6tetw7lItfLVnIJkLerbr1rdWYO6zz6sbkKiXWHwg6kN1NTX4dP0K1PjaYBS0\nKEgdhbmF6m7XpAadXo8fvfkq2ltb4fN5YVNpe6hV367BptImSKYYAED59tNwuTyYPqP/Lavw+XzY\ntmUrqmubkJIYg+kzZ0IU2baHiIioL8iyjP3FxWhtacW0GdNhNJkCfo2/fPAJDjdKkHRR0KSNQ1vp\nbqSlpSI+2ooHn30EkTZbwK9J1JdYfCDqQ++t+gTNE6IBmNEJYENVKWIORmHsuHy1o6kiwmpV9fqH\nSisgmVJ6vhbM0dh35HS/Kz4oioJf/eYPuCjHQNIbcaC2FkdP/hE//uFrakcjIiIKe21tbfj1799F\ngyYegs6ATQfewVMPTkfeuLyAXcPR1YkTF5sg2dO6BxQFlqx8JEd78cLzywN2nVBTsq8Y+4+ehiAA\n+WOGY8LECQCA9tZWrN+wGQ6XB+NyR2D4yJEqJ6W7wddmRH2k9nI1aqNlvzEpyYrD50+olIjcHvnG\nMe+NY2rbX1yMSq8Vkr5720xJb8aFLiOOHTmicjIiIqLw5na58Ktf/R5NlkxIRgtESQOvPQOrN++G\noigBu46jqwsuWYTsdaPp7EF01V1EV8NlHDxyAp0d7QG7TijZumUrPt1+Cuc9UTjnjsKn205i29bt\nqK2pwb//9s/YfUXE4RYj/rhqH9asWat2XLoLLD4Q9RGtXgfxJg+7En8MVZMSa4EiX/07kb0eZCTc\n2/aWfeFy1RVojJF+Y6LJhvLySpUSERERhb+KC+X47z9/CxUtPgiC4PdnjV0+eNzugF0rKiYW8UYF\nrZWnYM/ORUTyEEQkDYYuayI+/OSrgF0nlOw9cgaC+erSXMEcjb1HTmPNuk1w2jJ7+mGIEXHYfaQM\nPq9Xrah0l/jUQ9RHomJjkNZpguz1XR0824hpeZPUCzXAPff0UmRK9RAayyE2lWOEpQNLn3hU7Vg3\nGD16BOS2Wr8xpfUKxo9nh2giIqJg+XbdFjjtWQBunOFgNUjQ6u5vF61beWbJQmjhgyBe3XJSEERU\nNXYE9Dr9zdHDR/D1V9/g1An/2cBOj++Gz7rcPnQ4biwydHhFdHaG93+ncMCeD0R96LVlL+OLb79E\nlbsZJkGPmaMKkTU4PPZrDkVGkwk/fONleDxuCBCg6adbWGUPHoJJWcdRXHYZPlMMNJ31mD48GYnJ\nyWpHIyIiCluNHS7ABJgTMtFUdhC2jNEQJA2E1ssonDbmhtkQvZWWkY7s9CTUXDdu0PXP+5PeUhQF\nf/jPd3G6RQONJQo7yvZjVPFBvPTicwCAlOgInOqSe2Y4KLIPKbERiLCYceGKF6J09VE2xghERKrb\nS4zujMUHoj6kM+jx9BNPqx2DrqPVBvbNRTAsXfoYiuobUHr6FIaPLIRdpR1CiIiIBooosx7tCqA1\nRcCWMQrt1ecQ4WvFf/3pm4hPTAzKNSePHY4Vu88Clu6duJTOJhSMGxqUa6nt2OEjKG3VQmPpXvIq\nRUTjeF09ykrPYHDOUDy97HH88b0PUdHoAgBkRBux/KmnIUoiLr/9HipbNZB1JkS46vHQghnYX7wP\nVpsNQ3OGqflt0W2w+EBEFCKiY2MwJXa62jGIiIgGhIUPTMc7n66GMzINEETEWPR44Ylng1Z4AICp\n06bCZDSi5PBJKIqCcfnDMKFgYlCupSgK9u7ejcvVtRg5fGif7xhRdqEcktm/15YUEYvT3xcfjCYT\nfvzmq+jq7IAgCDCazD2f+8mPX0fF+fNoaGiAwTAKn67agnZ9HOB1IUm7CT9+4yUYjMY+/X7ozlh8\nICIiIiIiuk5W9mD8y09fxZbNW6AoQGHRy34PwMEyNn8cxuYHt6+Tz+fDr37zNi56oyAZLdh9vgTj\nDh3Ds88uC+p1r5UzJBu7zh2EaLk6m1Nuq8PIkTP8PmcyW256fHpWFtKzsvD//OItOGyZ3z/YWlAj\n2/HVim/x9PKlwQtP94UNJ4mIiIiIiG7CaDJhwaKFWLh4YZ8UHvrKru07cNEXDcnY/WAvRcTg0MU2\nVFdV9VmGkaNHY0SMALm9HgDga6vF2GQTMrPvvh+a2+1CfYf/riOCKKK2uTOgWSkwOPOBiIiIiIho\nAKmqa4RkuK6YEhGH0ydPITEpqc9yvPzS8zhTWoozpWcxYuRMZN1D4QHo7tsVoZdwfakh0hSeTTpD\nHWc+EBERERERDSCZKYnwOtr8xsS2GuTm5fV5lqE5OVj88OJ7LjwAgCAImJ4/HMr3W5Irsgx9ywXM\nnzsr0DEpADjzgYiIiIiIaACZOHkSDh8/jdLmeoiWGCit1Zg+IgnRsTFqR7tnc+YUIiMtBcUHjkCv\n02Dus88j0mZTOxbdBIsPREREREREA4ggCHj9tRdxtrQUZ8+WYXz+Q0HdxSPYsocMQfaQIWrHoDtg\n8YGIiIiIiGgAGpKTgyE5OWrHoAGCPR+IqN9QFAUnjh1D6anuva2JiIiIiCg8cOYDEfULNdXV+M+/\nfIEGwQ4oCuJXbcIbLz8De1RUn+ZQFAWHDxxAfUMDps+YHlbbahERERERqYUzH4ioX/j8m+/QGpEJ\nrcUObUQUGk3p+Pyrb/s0g6OrC//+v3+D97eWYu1ZB/7l//wJ+0v292kGIiIiIqJwxJkPRNQv1LY6\ngMirXwuC0D0WZA319Sg/fx4jRo3Eyr+uQb0hDRqxuy7rtWdgzZZ9yB+fD0EQgp6FiIiIiChYFEXB\nxvUbceFyLUx6DR58oBAxcXF9dn0WH4ioX4g06uC8bizCoA3qNT/86FMcKm+Gz2iHfmMJtD4HhJhh\nfp9pdgnoaGtDhNUa1CxERERERMH07p8/wPFmHSS9FYpDwen//AT/5fWnERXTN1usctkFEfULsyfl\nQWitAtBdlRVbLmLOjIn3fb4zpaexe8cOuF2um/754YMHsf+yC4I9GRqDGT57Omrq6m9odBmhVWCy\nWO47BxERERGR2lpbmnGyqh2SvrufmSAIcNoysG7D5j7LwJkPRNQvTCiYgIRBcdi5uxgCBMx+7FEk\n3Md+026XC79560+46DRC0JmxascfsPTBGcgbm+v3uVNnLkBj8W9maUodDfniYQiJIyFotEBrNWYV\njIQkSb363oiIiIj6O6/Hg40bNqGmsQWJsXYUzSmCpOHjYrhobmyEWzBAf82YIAjocnn7LAP/byKi\nfiM1LR3L09J7dY6VK1ehSkyEJqK7YODWZ+DbjbuQmzfGr29DbJQVvpoWSDpDz5jkc+LNl55C2bkL\n6OjswpSHFyIpJaVXecLZuTNnceDIMURaTCgqKoROr7/zQURERNTvKIqC//j126iSBkHSmnGkoQ0n\nSv+An/z9G+x7FSZSMzIRLX6HjmvGfI42DBnZd/e6XHZBRGGlprkDwnUzFZpcAtpaW/zGZhfOQqy7\nCrLHDQDwOtoxPFaLwUOH4sEF8/HEE4+x8HAbq1d9h99+tQ0l9TpsKOvC//zF79HW2qp2LCIiIroP\nJXv3okqJhqTtfpEg6Qy46Lbi8MEDKiejQBFFEUsXzYalrRyuphqIzZUYP0iDaTOm91kGznwgorAS\nadRCaVP8qvRmSYbFEuH3OY1Wi5/95O+wccNmNDS3IXNUCqZOnxa0XG6XC+9/8CnO17RCFAUMS43F\n08uXQhRDrwbsdruw6+h5SLY0AICo0aE9MhOr16zHsmVPqJyOiIiI7lXVlTpIRv97JdEUiUuXqjE2\nX6VQFHAjRo3Ev44cgZqqy7Da7TCZ+7avWejd9RIR3cbC+XNhar0AxecDAMhttZiWN/imaxZ1Oj0W\nLHwQzz3zJKbNmB7UaYV/+fAznO6ywmPPgMuajkP1Er7+emXQrhdMTQ0N6JD9dyIRBBEtnTdv7klE\nRET9W/64XMgtV/zGlNZqTJg4XqVEFCyCIGBQckqfFx4AFh+IKMzExMXi//rJK5iRKmJclBNvPDod\n8x+cp3YsVNS2+S0HEXUGnLvcoGKi+xcbnwC7xuM3Jvu8SIiKuMURRERE1J+lpqdj5shEiM2V8HS1\nQWyuROHoVAy6j+bfRLfCZRdEFHaMJjMefuQhtWP4kaQbZ1VobjIWCiRJwoMz8/HNpmK4ralQHG1I\n0Xdg0eJX1I5GREQ0YJWfv4DN2/fA5fVhcFoi5swtuqdZnQ8/vAiFs1px4fw5ZA0eAksEXypQYLH4\nQETUB0ZlJWJ3ZSdEQ/feynJXM8blZ6uc6v5NmjwJubmjsWfXbiQmDsGwkSPVjkRERDRglZ87j7e/\nWA9vZDIAoOx4A+obvsDy5Uvv6TwRVivGjB0XjIhELD4QEfWFxx9/BKbVa3GqvAqiIGDc+MGYOWuG\n2rF6xWgyo3DuXLVjEBERDXibduzpKTwAgGgw48iFCixxubgVNvUbLD4QEfUBQRCwYNGDWKB2ECIi\nIgo7TrfvhjG3LMLpdLD4QP0GG04SERERERGFsKzkOPjcXX5jCWYBkVabSomIbsTiAxERERERUQib\nv2A+8qJ9EJsr4W28hGjnJTy9ZKHasYj8cNkFERENOB6PG+2trbBFRUMUWYcnIqLQJggCnn9uOZwO\nBxxdXbBHR6sdqVcURcGFsjJYLBbEc7vPsMHiAxERDShr1qzFriPn0OHTIErnw+KiAozLz1c7FhER\nUa8ZjEYYjEa1Y/RKZXkF3v/8W9TLZog+DzKswBuv/QA6HXtXhDq+7iEiogHjTGkpNh69DJc1Ddqo\nJLRbUvHF2t1wOZ1qRyMiIiIAn/11HVot6dBFxkJjT8RFJR5ffbVS7VgUACw+EBHRgHHw0DGIkfF+\nY07zIJTsK1YpEREREf2Nz+dDbbPDb0yQJFQ1tKuUiAKJxQciIhowzCYDZJ/Xf9DVgbiEOHUCERER\nUQ9RFGHSSzeMWwzsFhAOWHwgIqIBY+7cIljaK6AoCgBA9nmRYnBgaM4wlZMRERGRIAiYkjcUckcD\ngO7Gk5qWSsydPUXlZBQILCEREdGAYTSZ8JPXn8XqNRvQ5vAgIT4Cix96Se1YRERE9L358x/AoITD\nOHz0NDQaEXMeewwJ3PEiLLD4QEREA0pUdAyefXaZ2jGIiIjoFnLz8pCbl6d2DAowLrsgIiIiIiIi\noqBi8YGIiIiIiIiIgorFByIiIiIiIiIKKhYfiIiIiIiIiCioWHwgIiIiIiIioqBi8YGIiIiIiIiI\ngorFByIiIiIiIiIKKhYfiIiIiIiIiCioWHwgIiIiIiIioqBi8YGIiIiIiIiIgkqjdgAiIlKXoijY\nX1yMsgsXER9jx6zZsyBp+OuBiIior3V2tGP9+s3odLgwLncEho8cqXYkooDh3SUR0QD37nsf4Hij\nCMlkhe9KE/Yf+T1+9pM3WIAgIiLqQw319fjlHz5ClzUdgmjEgVXFKLxQgcWLF6odjSgguOyCiGgA\nu1hZgRM1LkgmKwBA0htRIyVg29btKicjIiIaWL5buwldtkwIogQAkCJisedYOTwet8rJiAKDxQci\nogGs7MxZwBLjNybpjahtaFIpERER0cDU5vRAEAS/sU5Zg/bWVpUSEQUWiw9ERAPY2PxxkNqq/ca8\nnS0Ymp2uTiAiIqIBKjE6ArLX4zcWpfPCHh1ziyOIQguLD0REA5g9Khqzx2ZBaL4I2euG3HIFeXEC\nxubnqx2NiIhoQFm8eCHSxDr4WuvgdXRA33wBD8+ddsNsCKJQxW5iREQD3IIF8zF1SjMOHTyEoUML\nkJicrHYkIiKiAUej1eIffvQ6Ks6fR21dLcbmPw6tVqd2LKKAYfGBiIhgtdkxq7BQ7RhEREQDXnpW\nFtKzstSOQRRwXHZBREREREREREHF4gMRERERERERBRWLD0REREREREQUVCw+EBEREREREVFQsfhA\nREREREREREHF4gMRERERERERBRWLD0REREREREQUVCw+EBEREREREVFQsfhAREREREREREHF4gMR\nERERERERBRWLD0REREREREQUVIKiKIraIYiIiIiIiIgofHHmAxEREREREREFFYsPRERERERERBRU\nLD4QERERERERUVCx+EBEREREREREQcXiAxEREREREREFFYsPRERERERERBRULD4QERERERERUVCx\n+EBEREREREREQcXiAxEREREREREFFYsPRERERERERBRULD4QERERERERUVCx+EBEREREREREQcXi\nAxEREREREREFFYsPRERERERERBRULD4QERERERERUVCx+EBEREREREREQcXiAxEREREREREFFYsP\nRERERERERBRULD4QERERERERUVCx+EBEREREREREQcXiAxEREREREREFFYsPRERERERERBRU/z/F\n4A+byZI8PwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118fc00b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = DecisionTreeClassifier()\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
+    "visualize_tree(model, X[::2], y[::2], boundaries=False, ax=ax[0])\n",
+    "visualize_tree(model, X[1::2], y[1::2], boundaries=False, ax=ax[1])\n",
+    "\n",
+    "fig.savefig('figures/05.08-decision-tree-overfitting.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Principal Component Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Principal Components Rotation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def draw_vector(v0, v1, ax=None):\n",
+    "    ax = ax or plt.gca()\n",
+    "    arrowprops=dict(arrowstyle='->',\n",
+    "                    linewidth=2,\n",
+    "                    shrinkA=0, shrinkB=0)\n",
+    "    ax.annotate('', v1, v0, arrowprops=arrowprops)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xEQVCuBWMZJ2IwHL9XsxmFQQhHpcu1SI9PYn9TNQGAxtEREQBcv78OWzYsB46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\nYey4h4Cx7LKeA6PtiJVd/haARDLddq8zZszQww8/rEAgIL/fr+rqak2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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11ba330f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rng = np.random.RandomState(1)\n",
+    "X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T\n",
+    "pca = PCA(n_components=2, whiten=True)\n",
+    "pca.fit(X)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
+    "\n",
+    "# plot data\n",
+    "ax[0].scatter(X[:, 0], X[:, 1], alpha=0.2)\n",
+    "for length, vector in zip(pca.explained_variance_, pca.components_):\n",
+    "    v = vector * 3 * np.sqrt(length)\n",
+    "    draw_vector(pca.mean_, pca.mean_ + v, ax=ax[0])\n",
+    "ax[0].axis('equal');\n",
+    "ax[0].set(xlabel='x', ylabel='y', title='input')\n",
+    "\n",
+    "# plot principal components\n",
+    "X_pca = pca.transform(X)\n",
+    "ax[1].scatter(X_pca[:, 0], X_pca[:, 1], alpha=0.2)\n",
+    "draw_vector([0, 0], [0, 3], ax=ax[1])\n",
+    "draw_vector([0, 0], [3, 0], ax=ax[1])\n",
+    "ax[1].axis('equal')\n",
+    "ax[1].set(xlabel='component 1', ylabel='component 2',\n",
+    "          title='principal components',\n",
+    "          xlim=(-5, 5), ylim=(-3, 3.1))\n",
+    "\n",
+    "fig.savefig('figures/05.09-PCA-rotation.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Digits Pixel Components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_pca_components(x, coefficients=None, mean=0, components=None,\n",
+    "                        imshape=(8, 8), n_components=8, fontsize=12,\n",
+    "                        show_mean=True):\n",
+    "    if coefficients is None:\n",
+    "        coefficients = x\n",
+    "        \n",
+    "    if components is None:\n",
+    "        components = np.eye(len(coefficients), len(x))\n",
+    "        \n",
+    "    mean = np.zeros_like(x) + mean\n",
+    "        \n",
+    "\n",
+    "    fig = plt.figure(figsize=(1.2 * (5 + n_components), 1.2 * 2))\n",
+    "    g = plt.GridSpec(2, 4 + bool(show_mean) + n_components, hspace=0.3)\n",
+    "\n",
+    "    def show(i, j, x, title=None):\n",
+    "        ax = fig.add_subplot(g[i, j], xticks=[], yticks=[])\n",
+    "        ax.imshow(x.reshape(imshape), interpolation='nearest')\n",
+    "        if title:\n",
+    "            ax.set_title(title, fontsize=fontsize)\n",
+    "\n",
+    "    show(slice(2), slice(2), x, \"True\")\n",
+    "    \n",
+    "    approx = mean.copy()\n",
+    "    \n",
+    "    counter = 2\n",
+    "    if show_mean:\n",
+    "        show(0, 2, np.zeros_like(x) + mean, r'$\\mu$')\n",
+    "        show(1, 2, approx, r'$1 \\cdot \\mu$')\n",
+    "        counter += 1\n",
+    "\n",
+    "    for i in range(n_components):\n",
+    "        approx = approx + coefficients[i] * components[i]\n",
+    "        show(0, i + counter, components[i], r'$c_{0}$'.format(i + 1))\n",
+    "        show(1, i + counter, approx,\n",
+    "             r\"${0:.2f} \\cdot c_{1}$\".format(coefficients[i], i + 1))\n",
+    "        if show_mean or i > 0:\n",
+    "            plt.gca().text(0, 1.05, '$+$', ha='right', va='bottom',\n",
+    "                           transform=plt.gca().transAxes, fontsize=fontsize)\n",
+    "\n",
+    "    show(slice(2), slice(-2, None), approx, \"Approx\")\n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ixAk1atRIbdq00bhx4+q8bF3GcCIy8l9+fr7+9a9/ad68ebUuF645rV+/XsXFxYqKilLn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\nAAAHopkDAAAAAAf6P1QpRwQf0IzUAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d01edd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_digits\n",
+    "\n",
+    "digits = load_digits()\n",
+    "sns.set_style('white')\n",
+    "\n",
+    "fig = plot_pca_components(digits.data[10],\n",
+    "                          show_mean=False)\n",
+    "\n",
+    "fig.savefig('figures/05.09-digits-pixel-components.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Digits PCA Components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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89thjAIDXX38d/fr1g7+/v+b7nd3n3MVV+cjNzcXzzz+P+Ph4VKtWDb169UJU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\n4oCOiIiIiIjIoP4/QFJA2Z7Z0loAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11ce84a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pca = PCA(n_components=8)\n",
+    "Xproj = pca.fit_transform(digits.data)\n",
+    "sns.set_style('white')\n",
+    "fig = plot_pca_components(digits.data[10], Xproj[10],\n",
+    "                          pca.mean_, pca.components_)\n",
+    "\n",
+    "fig.savefig('figures/05.09-digits-pca-components.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Manifold Learning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### LLE vs MDS Linkages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_hello(N=1000, rseed=42):\n",
+    "    # Make a plot with \"HELLO\" text; save as png\n",
+    "    fig, ax = plt.subplots(figsize=(4, 1))\n",
+    "    fig.subplots_adjust(left=0, right=1, bottom=0, top=1)\n",
+    "    ax.axis('off')\n",
+    "    ax.text(0.5, 0.4, 'HELLO', va='center', ha='center', weight='bold', size=85)\n",
+    "    fig.savefig('hello.png')\n",
+    "    plt.close(fig)\n",
+    "    \n",
+    "    # Open this PNG and draw random points from it\n",
+    "    from matplotlib.image import imread\n",
+    "    data = imread('hello.png')[::-1, :, 0].T\n",
+    "    rng = np.random.RandomState(rseed)\n",
+    "    X = rng.rand(4 * N, 2)\n",
+    "    i, j = (X * data.shape).astype(int).T\n",
+    "    mask = (data[i, j] < 1)\n",
+    "    X = X[mask]\n",
+    "    X[:, 0] *= (data.shape[0] / data.shape[1])\n",
+    "    X = X[:N]\n",
+    "    return X[np.argsort(X[:, 0])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_hello_s_curve(X):\n",
+    "    t = (X[:, 0] - 2) * 0.75 * np.pi\n",
+    "    x = np.sin(t)\n",
+    "    y = X[:, 1]\n",
+    "    z = np.sign(t) * (np.cos(t) - 1)\n",
+    "    return np.vstack((x, y, z)).T\n",
+    "\n",
+    "X = make_hello(1000)\n",
+    "XS = make_hello_s_curve(X)\n",
+    "colorize = dict(c=X[:, 0], cmap=plt.cm.get_cmap('rainbow', 5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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p527nPX1+6V/2VnjM9Caw2WztvgOampqyvxCKyL5P+act5Z/2lH9kT1P+ka5S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iFr88UXsZaW6Lsul5eX6R88iHT2LHsyGdbPnGFPGNKs10nU69TOniX8hV9AH2fgvKtwy6Fj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trp0iFAKICYr5kkngikajRmXU6XQayyKnG+byxTw3gZW7mYuu/yI0JapQWYVY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\nERERUUJsOCIiIiIiIiIiooT+P2wTBDSvVgX3AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d01b518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mpl_toolkits.mplot3d.art3d import Line3DCollection\n",
+    "from sklearn.neighbors import NearestNeighbors\n",
+    "\n",
+    "# construct lines for MDS\n",
+    "rng = np.random.RandomState(42)\n",
+    "ind = rng.permutation(len(X))\n",
+    "lines_MDS = [(XS[i], XS[j]) for i in ind[:100] for j in ind[100:200]]\n",
+    "\n",
+    "# construct lines for LLE\n",
+    "nbrs = NearestNeighbors(n_neighbors=100).fit(XS).kneighbors(XS[ind[:100]])[1]\n",
+    "lines_LLE = [(XS[ind[i]], XS[j]) for i in range(100) for j in nbrs[i]]\n",
+    "titles = ['MDS Linkages', 'LLE Linkages (100 NN)']\n",
+    "\n",
+    "# plot the results\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(16, 6),\n",
+    "                       subplot_kw=dict(projection='3d', axisbg='none'))\n",
+    "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0, wspace=0)\n",
+    "\n",
+    "for axi, title, lines in zip(ax, titles, [lines_MDS, lines_LLE]):\n",
+    "    axi.scatter3D(XS[:, 0], XS[:, 1], XS[:, 2], **colorize);\n",
+    "    axi.add_collection(Line3DCollection(lines, lw=1, color='black',\n",
+    "                                        alpha=0.05))\n",
+    "    axi.view_init(elev=10, azim=-80)\n",
+    "    axi.set_title(title, size=18)\n",
+    "\n",
+    "fig.savefig('figures/05.10-LLE-vs-MDS.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## K-Means"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Expectation-Maximization\n",
+    "\n",
+    "[Figure Context](05.11-K-Means.ipynb#K-Means-Algorithm:-Expectation-Maximization)\n",
+    "\n",
+    "The following figure shows a visual depiction of the Expectation-Maximization approach to K Means:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9l1dffZWysjJWrFjBkiVLAA4qn8FNN93EVVddxd13382FF17IihUrWL58eW6/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X28d1NtxBLMvqM+63jfPV1VVSqRSnTt1+btDtcCd0AnamY4RCIfL5/EAqRafTIZlM4vf7EUWx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BIKhfY4XRzEg2pK5PN5NjY2Hjmo8Sjn3BUqHTHiXmanJtguXcQWAsOFuuu6aE6P6cnD7chE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qmqYRRQfX//7y84R4EhPDo7Tmuqy0q9iZJL7nozQcLgwdO7RfLW+sYmQOTpK047UPzVPy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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bc67dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "from sklearn.metrics import pairwise_distances_argmin\n",
+    "\n",
+    "X, y_true = make_blobs(n_samples=300, centers=4,\n",
+    "                       cluster_std=0.60, random_state=0)\n",
+    "\n",
+    "rng = np.random.RandomState(42)\n",
+    "centers = [0, 4] + rng.randn(4, 2)\n",
+    "\n",
+    "def draw_points(ax, c, factor=1):\n",
+    "    ax.scatter(X[:, 0], X[:, 1], c=c, cmap='viridis',\n",
+    "               s=50 * factor, alpha=0.3)\n",
+    "    \n",
+    "def draw_centers(ax, centers, factor=1, alpha=1.0):\n",
+    "    ax.scatter(centers[:, 0], centers[:, 1],\n",
+    "               c=np.arange(4), cmap='viridis', s=200 * factor,\n",
+    "               alpha=alpha)\n",
+    "    ax.scatter(centers[:, 0], centers[:, 1],\n",
+    "               c='black', s=50 * factor, alpha=alpha)\n",
+    "\n",
+    "def make_ax(fig, gs):\n",
+    "    ax = fig.add_subplot(gs)\n",
+    "    ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
+    "    ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
+    "    return ax\n",
+    "\n",
+    "fig = plt.figure(figsize=(15, 4))\n",
+    "gs = plt.GridSpec(4, 15, left=0.02, right=0.98, bottom=0.05, top=0.95, wspace=0.2, hspace=0.2)\n",
+    "ax0 = make_ax(fig, gs[:4, :4])\n",
+    "ax0.text(0.98, 0.98, \"Random Initialization\", transform=ax0.transAxes,\n",
+    "         ha='right', va='top', size=16)\n",
+    "draw_points(ax0, 'gray', factor=2)\n",
+    "draw_centers(ax0, centers, factor=2)\n",
+    "\n",
+    "for i in range(3):\n",
+    "    ax1 = make_ax(fig, gs[:2, 4 + 2 * i:6 + 2 * i])\n",
+    "    ax2 = make_ax(fig, gs[2:, 5 + 2 * i:7 + 2 * i])\n",
+    "    \n",
+    "    # E-step\n",
+    "    y_pred = pairwise_distances_argmin(X, centers)\n",
+    "    draw_points(ax1, y_pred)\n",
+    "    draw_centers(ax1, centers)\n",
+    "    \n",
+    "    # M-step\n",
+    "    new_centers = np.array([X[y_pred == i].mean(0) for i in range(4)])\n",
+    "    draw_points(ax2, y_pred)\n",
+    "    draw_centers(ax2, centers, alpha=0.3)\n",
+    "    draw_centers(ax2, new_centers)\n",
+    "    for i in range(4):\n",
+    "        ax2.annotate('', new_centers[i], centers[i],\n",
+    "                     arrowprops=dict(arrowstyle='->', linewidth=1))\n",
+    "        \n",
+    "    \n",
+    "    # Finish iteration\n",
+    "    centers = new_centers\n",
+    "    ax1.text(0.95, 0.95, \"E-Step\", transform=ax1.transAxes, ha='right', va='top', size=14)\n",
+    "    ax2.text(0.95, 0.95, \"M-Step\", transform=ax2.transAxes, ha='right', va='top', size=14)\n",
+    "\n",
+    "\n",
+    "# Final E-step    \n",
+    "y_pred = pairwise_distances_argmin(X, centers)\n",
+    "axf = make_ax(fig, gs[:4, -4:])\n",
+    "draw_points(axf, y_pred, factor=2)\n",
+    "draw_centers(axf, centers, factor=2)\n",
+    "axf.text(0.98, 0.98, \"Final Clustering\", transform=axf.transAxes,\n",
+    "         ha='right', va='top', size=16)\n",
+    "\n",
+    "\n",
+    "fig.savefig('figures/05.11-expectation-maximization.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Interactive K-Means\n",
+    "\n",
+    "The following script uses IPython's interactive widgets to demonstrate the K-means algorithm interactively.\n",
+    "Run this within the IPython notebook to explore the expectation maximization algorithm for computing K Means."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SRxRCr9DwkfqM+HwkgYGBTuvyL4PBwMtvvuL0XJmyZRn0bs4Urn/nMl4+Fub0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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11df6f898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn; seaborn.set()  # for plot styling\n",
+    "import numpy as np\n",
+    "\n",
+    "from ipywidgets import interact\n",
+    "from sklearn.metrics import pairwise_distances_argmin\n",
+    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "\n",
+    "def plot_kmeans_interactive(min_clusters=1, max_clusters=6):\n",
+    "    X, y = make_blobs(n_samples=300, centers=4,\n",
+    "                      random_state=0, cluster_std=0.60)\n",
+    "        \n",
+    "    def plot_points(X, labels, n_clusters):\n",
+    "        plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='viridis',\n",
+    "                    vmin=0, vmax=n_clusters - 1);\n",
+    "            \n",
+    "    def plot_centers(centers):\n",
+    "        plt.scatter(centers[:, 0], centers[:, 1], marker='o',\n",
+    "                    c=np.arange(centers.shape[0]),\n",
+    "                    s=200, cmap='viridis')\n",
+    "        plt.scatter(centers[:, 0], centers[:, 1], marker='o',\n",
+    "                    c='black', s=50)\n",
+    "            \n",
+    "\n",
+    "    def _kmeans_step(frame=0, n_clusters=4):\n",
+    "        rng = np.random.RandomState(2)\n",
+    "        labels = np.zeros(X.shape[0])\n",
+    "        centers = rng.randn(n_clusters, 2)\n",
+    "\n",
+    "        nsteps = frame // 3\n",
+    "\n",
+    "        for i in range(nsteps + 1):\n",
+    "            old_centers = centers\n",
+    "            if i < nsteps or frame % 3 > 0:\n",
+    "                labels = pairwise_distances_argmin(X, centers)\n",
+    "\n",
+    "            if i < nsteps or frame % 3 > 1:\n",
+    "                centers = np.array([X[labels == j].mean(0)\n",
+    "                                    for j in range(n_clusters)])\n",
+    "                nans = np.isnan(centers)\n",
+    "                centers[nans] = old_centers[nans]\n",
+    "\n",
+    "        # plot the data and cluster centers\n",
+    "        plot_points(X, labels, n_clusters)\n",
+    "        plot_centers(old_centers)\n",
+    "\n",
+    "        # plot new centers if third frame\n",
+    "        if frame % 3 == 2:\n",
+    "            for i in range(n_clusters):\n",
+    "                plt.annotate('', centers[i], old_centers[i], \n",
+    "                             arrowprops=dict(arrowstyle='->', linewidth=1))\n",
+    "            plot_centers(centers)\n",
+    "\n",
+    "        plt.xlim(-4, 4)\n",
+    "        plt.ylim(-2, 10)\n",
+    "\n",
+    "        if frame % 3 == 1:\n",
+    "            plt.text(3.8, 9.5, \"1. Reassign points to nearest centroid\",\n",
+    "                     ha='right', va='top', size=14)\n",
+    "        elif frame % 3 == 2:\n",
+    "            plt.text(3.8, 9.5, \"2. Update centroids to cluster means\",\n",
+    "                     ha='right', va='top', size=14)\n",
+    "    \n",
+    "    return interact(_kmeans_step, frame=[0, 50],\n",
+    "                    n_clusters=[min_clusters, max_clusters])\n",
+    "\n",
+    "plot_kmeans_interactive();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Gaussian Mixture Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Covariance Type\n",
+    "\n",
+    "[Figure Context](http://localhost:8888/notebooks/05.12-Gaussian-Mixtures.ipynb#Choosing-the-Covariance-Type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BQ/5fqhwo49urqoqqgK6rhEwTwzDQdb38WmKyI1kzu5HW224Uw6NxAq2+h5n4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\nk4yv0gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11bec04a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.mixture import GMM\n",
+    "\n",
+    "from matplotlib.patches import Ellipse\n",
+    "\n",
+    "def draw_ellipse(position, covariance, ax=None, **kwargs):\n",
+    "    \"\"\"Draw an ellipse with a given position and covariance\"\"\"\n",
+    "    ax = ax or plt.gca()\n",
+    "    \n",
+    "    # Convert covariance to principal axes\n",
+    "    if covariance.shape == (2, 2):\n",
+    "        U, s, Vt = np.linalg.svd(covariance)\n",
+    "        angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\n",
+    "        width, height = 2 * np.sqrt(s)\n",
+    "    else:\n",
+    "        angle = 0\n",
+    "        width, height = 2 * np.sqrt(covariance)\n",
+    "    \n",
+    "    # Draw the Ellipse\n",
+    "    for nsig in range(1, 4):\n",
+    "        ax.add_patch(Ellipse(position, nsig * width, nsig * height,\n",
+    "                             angle, **kwargs))\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 3, figsize=(14, 4), sharex=True, sharey=True)\n",
+    "fig.subplots_adjust(wspace=0.05)\n",
+    "\n",
+    "rng = np.random.RandomState(5)\n",
+    "X = np.dot(rng.randn(500, 2), rng.randn(2, 2))\n",
+    "\n",
+    "for i, cov_type in enumerate(['diag', 'spherical', 'full']):\n",
+    "    model = GMM(1, covariance_type=cov_type).fit(X)\n",
+    "    ax[i].axis('equal')\n",
+    "    ax[i].scatter(X[:, 0], X[:, 1], alpha=0.5)\n",
+    "    ax[i].set_xlim(-3, 3)\n",
+    "    ax[i].set_title('covariance_type=\"{0}\"'.format(cov_type),\n",
+    "                    size=14, family='monospace')\n",
+    "    draw_ellipse(model.means_[0], model.covars_[0], ax[i], alpha=0.2)\n",
+    "    ax[i].xaxis.set_major_formatter(plt.NullFormatter())\n",
+    "    ax[i].yaxis.set_major_formatter(plt.NullFormatter())\n",
+    "\n",
+    "fig.savefig('figures/05.12-covariance-type.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [Further Machine Learning Resources](05.15-Learning-More.ipynb) | [Contents](Index.ipynb) |\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  },
+  "widgets": {
+   "state": {
+    "a65a11f142ca44eebc913788d256adcb": {
+     "views": [
+      {
+       "cell_index": 92
+      }
+     ]
+    }
+   },
+   "version": "1.2.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/Index.ipynb b/notebooks_v1/Index.ipynb
similarity index 100%
rename from notebooks/Index.ipynb
rename to notebooks_v1/Index.ipynb
diff --git a/notebooks_v1/Untitled.ipynb b/notebooks_v1/Untitled.ipynb
new file mode 100644
index 000000000..363fcab7e
--- /dev/null
+++ b/notebooks_v1/Untitled.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/data/BicycleWeather.csv b/notebooks_v1/data/BicycleWeather.csv
similarity index 100%
rename from notebooks/data/BicycleWeather.csv
rename to notebooks_v1/data/BicycleWeather.csv
diff --git a/notebooks/data/Seattle2014.csv b/notebooks_v1/data/Seattle2014.csv
similarity index 100%
rename from notebooks/data/Seattle2014.csv
rename to notebooks_v1/data/Seattle2014.csv
diff --git a/notebooks_v1/data/births.csv b/notebooks_v1/data/births.csv
new file mode 100644
index 000000000..4a5bb7aef
--- /dev/null
+++ b/notebooks_v1/data/births.csv
@@ -0,0 +1,15548 @@
+year,month,day,gender,births
+1969,1,1,F,4046
+1969,1,1,M,4440
+1969,1,2,F,4454
+1969,1,2,M,4548
+1969,1,3,F,4548
+1969,1,3,M,4994
+1969,1,4,F,4440
+1969,1,4,M,4520
+1969,1,5,F,4192
+1969,1,5,M,4198
+1969,1,6,F,4710
+1969,1,6,M,4850
+1969,1,7,F,4646
+1969,1,7,M,5092
+1969,1,8,F,4800
+1969,1,8,M,4934
+1969,1,9,F,4592
+1969,1,9,M,4842
+1969,1,10,F,4852
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diff --git a/notebooks/data/california_cities.csv b/notebooks_v1/data/california_cities.csv
similarity index 100%
rename from notebooks/data/california_cities.csv
rename to notebooks_v1/data/california_cities.csv
diff --git a/notebooks_v1/data/president_heights.csv b/notebooks_v1/data/president_heights.csv
new file mode 100644
index 000000000..ade149d72
--- /dev/null
+++ b/notebooks_v1/data/president_heights.csv
@@ -0,0 +1,43 @@
+order,name,height(cm)
+1,George Washington,189
+2,John Adams,170
+3,Thomas Jefferson,189
+4,James Madison,163
+5,James Monroe,183
+6,John Quincy Adams,171
+7,Andrew Jackson,185
+8,Martin Van Buren,168
+9,William Henry Harrison,173
+10,John Tyler,183
+11,James K. Polk,173
+12,Zachary Taylor,173
+13,Millard Fillmore,175
+14,Franklin Pierce,178
+15,James Buchanan,183
+16,Abraham Lincoln,193
+17,Andrew Johnson,178
+18,Ulysses S. Grant,173
+19,Rutherford B. Hayes,174
+20,James A. Garfield,183
+21,Chester A. Arthur,183
+23,Benjamin Harrison,168
+25,William McKinley,170
+26,Theodore Roosevelt,178
+27,William Howard Taft,182
+28,Woodrow Wilson,180
+29,Warren G. Harding,183
+30,Calvin Coolidge,178
+31,Herbert Hoover,182
+32,Franklin D. Roosevelt,188
+33,Harry S. Truman,175
+34,Dwight D. Eisenhower,179
+35,John F. Kennedy,183
+36,Lyndon B. Johnson,193
+37,Richard Nixon,182
+38,Gerald Ford,183
+39,Jimmy Carter,177
+40,Ronald Reagan,185
+41,George H. W. Bush,188
+42,Bill Clinton,188
+43,George W. Bush,182
+44,Barack Obama,185
diff --git a/notebooks_v1/data/state-abbrevs.csv b/notebooks_v1/data/state-abbrevs.csv
new file mode 100644
index 000000000..6d4db366f
--- /dev/null
+++ b/notebooks_v1/data/state-abbrevs.csv
@@ -0,0 +1,52 @@
+"state","abbreviation"
+"Alabama","AL"
+"Alaska","AK"
+"Arizona","AZ"
+"Arkansas","AR"
+"California","CA"
+"Colorado","CO"
+"Connecticut","CT"
+"Delaware","DE"
+"District of Columbia","DC"
+"Florida","FL"
+"Georgia","GA"
+"Hawaii","HI"
+"Idaho","ID"
+"Illinois","IL"
+"Indiana","IN"
+"Iowa","IA"
+"Kansas","KS"
+"Kentucky","KY"
+"Louisiana","LA"
+"Maine","ME"
+"Montana","MT"
+"Nebraska","NE"
+"Nevada","NV"
+"New Hampshire","NH"
+"New Jersey","NJ"
+"New Mexico","NM"
+"New York","NY"
+"North Carolina","NC"
+"North Dakota","ND"
+"Ohio","OH"
+"Oklahoma","OK"
+"Oregon","OR"
+"Maryland","MD"
+"Massachusetts","MA"
+"Michigan","MI"
+"Minnesota","MN"
+"Mississippi","MS"
+"Missouri","MO"
+"Pennsylvania","PA"
+"Rhode Island","RI"
+"South Carolina","SC"
+"South Dakota","SD"
+"Tennessee","TN"
+"Texas","TX"
+"Utah","UT"
+"Vermont","VT"
+"Virginia","VA"
+"Washington","WA"
+"West Virginia","WV"
+"Wisconsin","WI"
+"Wyoming","WY"
\ No newline at end of file
diff --git a/notebooks_v1/data/state-areas.csv b/notebooks_v1/data/state-areas.csv
new file mode 100644
index 000000000..322345c52
--- /dev/null
+++ b/notebooks_v1/data/state-areas.csv
@@ -0,0 +1,53 @@
+state,area (sq. mi)
+Alabama,52423
+Alaska,656425
+Arizona,114006
+Arkansas,53182
+California,163707
+Colorado,104100
+Connecticut,5544
+Delaware,1954
+Florida,65758
+Georgia,59441
+Hawaii,10932
+Idaho,83574
+Illinois,57918
+Indiana,36420
+Iowa,56276
+Kansas,82282
+Kentucky,40411
+Louisiana,51843
+Maine,35387
+Maryland,12407
+Massachusetts,10555
+Michigan,96810
+Minnesota,86943
+Mississippi,48434
+Missouri,69709
+Montana,147046
+Nebraska,77358
+Nevada,110567
+New Hampshire,9351
+New Jersey,8722
+New Mexico,121593
+New York,54475
+North Carolina,53821
+North Dakota,70704
+Ohio,44828
+Oklahoma,69903
+Oregon,98386
+Pennsylvania,46058
+Rhode Island,1545
+South Carolina,32007
+South Dakota,77121
+Tennessee,42146
+Texas,268601
+Utah,84904
+Vermont,9615
+Virginia,42769
+Washington,71303
+West Virginia,24231
+Wisconsin,65503
+Wyoming,97818
+District of Columbia,68
+Puerto Rico,3515
diff --git a/notebooks_v1/data/state-population.csv b/notebooks_v1/data/state-population.csv
new file mode 100644
index 000000000..c76110ea1
--- /dev/null
+++ b/notebooks_v1/data/state-population.csv
@@ -0,0 +1,2545 @@
+state/region,ages,year,population
+AL,under18,2012,1117489
+AL,total,2012,4817528
+AL,under18,2010,1130966
+AL,total,2010,4785570
+AL,under18,2011,1125763
+AL,total,2011,4801627
+AL,total,2009,4757938
+AL,under18,2009,1134192
+AL,under18,2013,1111481
+AL,total,2013,4833722
+AL,total,2007,4672840
+AL,under18,2007,1132296
+AL,total,2008,4718206
+AL,under18,2008,1134927
+AL,total,2005,4569805
+AL,under18,2005,1117229
+AL,total,2006,4628981
+AL,under18,2006,1126798
+AL,total,2004,4530729
+AL,under18,2004,1113662
+AL,total,2003,4503491
+AL,under18,2003,1113083
+AL,total,2001,4467634
+AL,under18,2001,1120409
+AL,total,2002,4480089
+AL,under18,2002,1116590
+AL,under18,1999,1121287
+AL,total,1999,4430141
+AL,total,2000,4452173
+AL,under18,2000,1122273
+AL,total,1998,4404701
+AL,under18,1998,1118252
+AL,under18,1997,1122893
+AL,total,1997,4367935
+AL,total,1996,4331103
+AL,total,1995,4296800
+AL,under18,1995,1110553
+AL,under18,1996,1112092
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diff --git a/notebooks_v1/helpers_05_08.py b/notebooks_v1/helpers_05_08.py
new file mode 100644
index 000000000..0f3b15aa9
--- /dev/null
+++ b/notebooks_v1/helpers_05_08.py
@@ -0,0 +1,83 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.tree import DecisionTreeClassifier
+from ipywidgets import interact
+
+
+def visualize_tree(estimator, X, y, boundaries=True,
+                   xlim=None, ylim=None, ax=None):
+    ax = ax or plt.gca()
+    
+    # Plot the training points
+    ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap='viridis',
+               clim=(y.min(), y.max()), zorder=3)
+    ax.axis('tight')
+    ax.axis('off')
+    if xlim is None:
+        xlim = ax.get_xlim()
+    if ylim is None:
+        ylim = ax.get_ylim()
+    
+    # fit the estimator
+    estimator.fit(X, y)
+    xx, yy = np.meshgrid(np.linspace(*xlim, num=200),
+                         np.linspace(*ylim, num=200))
+    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])
+
+    # Put the result into a color plot
+    n_classes = len(np.unique(y))
+    Z = Z.reshape(xx.shape)
+    contours = ax.contourf(xx, yy, Z, alpha=0.3,
+                           levels=np.arange(n_classes + 1) - 0.5,
+                           cmap='viridis', clim=(y.min(), y.max()),
+                           zorder=1)
+
+    ax.set(xlim=xlim, ylim=ylim)
+    
+    # Plot the decision boundaries
+    def plot_boundaries(i, xlim, ylim):
+        if i >= 0:
+            tree = estimator.tree_
+        
+            if tree.feature[i] == 0:
+                ax.plot([tree.threshold[i], tree.threshold[i]], ylim, '-k', zorder=2)
+                plot_boundaries(tree.children_left[i],
+                                [xlim[0], tree.threshold[i]], ylim)
+                plot_boundaries(tree.children_right[i],
+                                [tree.threshold[i], xlim[1]], ylim)
+        
+            elif tree.feature[i] == 1:
+                ax.plot(xlim, [tree.threshold[i], tree.threshold[i]], '-k', zorder=2)
+                plot_boundaries(tree.children_left[i], xlim,
+                                [ylim[0], tree.threshold[i]])
+                plot_boundaries(tree.children_right[i], xlim,
+                                [tree.threshold[i], ylim[1]])
+            
+    if boundaries:
+        plot_boundaries(0, xlim, ylim)
+
+
+def plot_tree_interactive(X, y):
+    def interactive_tree(depth=5):
+        clf = DecisionTreeClassifier(max_depth=depth, random_state=0)
+        visualize_tree(clf, X, y)
+
+    return interact(interactive_tree, depth=[1, 5])
+
+
+def randomized_tree_interactive(X, y):
+    N = int(0.75 * X.shape[0])
+    
+    xlim = (X[:, 0].min(), X[:, 0].max())
+    ylim = (X[:, 1].min(), X[:, 1].max())
+    
+    def fit_randomized_tree(random_state=0):
+        clf = DecisionTreeClassifier(max_depth=15)
+        i = np.arange(len(y))
+        rng = np.random.RandomState(random_state)
+        rng.shuffle(i)
+        visualize_tree(clf, X[i[:N]], y[i[:N]], boundaries=False,
+                       xlim=xlim, ylim=ylim)
+    
+    interact(fit_randomized_tree, random_state=[0, 100]);
\ No newline at end of file
diff --git a/notebooks_v2/data.csv b/notebooks_v2/data.csv
new file mode 100644
index 000000000..25cc7c115
--- /dev/null
+++ b/notebooks_v2/data.csv
@@ -0,0 +1,11 @@
+,a,b
+0,one,1385
+1,one,1162
+2,one,2827
+3,one,2138
+4,one,1847
+5,one,1477
+6,one,883
+7,one,9071
+8,one,835
+9,one,2104